From bfa8a6c29758eda2ab7ef4fd732b1be0300c9c51 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Wed, 27 May 2026 07:31:45 +0000 Subject: [PATCH 01/67] Phase 1: Add VectorStore and mmcontext.io module - VectorStore: disk-backed vector lookup using numpy memmap - Construction from numpy, DataFrame, dict, AnnData (obs/var) - Batch lookup, persistence (save/load), float16 support - Memory-efficient: only accessed rows paged into RAM - Comprehensive test suite (22 tests across 6 test classes) - Refactoring roadmap document (REFACTOR_ROADMAP.md) --- REFACTOR_ROADMAP.md | 269 +++++++++++++++++++ src/mmcontext/io/__init__.py | 9 + src/mmcontext/io/vector_store.py | 418 +++++++++++++++++++++++++++++ tests/test_io/conftest.py | 1 + tests/test_io/test_vector_store.py | 324 ++++++++++++++++++++++ 5 files changed, 1021 insertions(+) create mode 100644 REFACTOR_ROADMAP.md create mode 100644 src/mmcontext/io/__init__.py create mode 100644 src/mmcontext/io/vector_store.py create mode 100644 tests/test_io/conftest.py create mode 100644 tests/test_io/test_vector_store.py diff --git a/REFACTOR_ROADMAP.md b/REFACTOR_ROADMAP.md new file mode 100644 index 0000000..042e5ba --- /dev/null +++ b/REFACTOR_ROADMAP.md @@ -0,0 +1,269 @@ +# mmcontext Refactor Roadmap — ST v5.4 Alignment + +**Branch:** `dev-claude` +**Goal:** Refactor mmcontext to align with the sentence-transformers v5.4 multimodal API while preserving all core functionality. Test-driven development throughout. + +## Architecture Overview + +### Current → New + +| Aspect | Current | Refactored | +|--------|---------|------------| +| Base class | `Module` | `InputModule` | +| Tokenization | `tokenize()` | `preprocess()` | +| Omics storage | `nn.Embedding` lookup | `VectorStore` (memory-mapped) | +| Omics key | `pixel_values` | `omics_values` | +| Adapters | Inside encoder | Separate `AdapterModule(Module)` | +| Modality flag | `omics_text_info` | `modality_ids` | +| OneHotTextEncoder | Included | Removed | +| Data loading | Coupled to encoder | `mmcontext.io` module | +| Pipeline | `[MMContextEncoder]` | `[MMContextModule, AdapterModule, Pooling, Normalize]` | +| Var support | Same class, no attention | Optional `OmicsAttentionModule` | + +### Module Pipeline + +``` +SentenceTransformer(modules=[ + MMContextModule, # InputModule: text encoder + omics pass-through + OmicsAttentionModule, # Module (OPTIONAL, var-only): self-attention on omics tokens + AdapterModule, # Module: modality-aware projection to shared space + Pooling, # ST built-in: mean/cls/max pooling + Normalize, # ST built-in: L2 normalization +]) +``` + +### Features Dict Contract + +All modules communicate through a features dict. After `MMContextModule.forward()`: + +```python +{ + "token_embeddings": Tensor(B, L, D_encoder), # unified text + omics tokens + "attention_mask": Tensor(B, L), # 1 = real, 0 = pad + "modality_ids": Tensor(B, L), # 0 = text, 1 = omics, 2 = pad + "sentence_embedding": Tensor(B, D) # set after Pooling +} +``` + +--- + +## Phases + +### Phase 1: Foundation — VectorStore + IO Module + +**Files created/modified:** +- `src/mmcontext/io/__init__.py` (new) +- `src/mmcontext/io/vector_store.py` (new) +- `src/mmcontext/io/adata_utils.py` (new — extracted from mmcontextencoder.py + file_utils.py) +- `tests/test_vector_store.py` (new) + +**VectorStore responsibilities:** +- Create from AnnData (obsm/varm), DataFrame, dict, or numpy array +- Write embeddings to numpy memmap file + JSON index +- Batch lookup by sample IDs → numpy array +- Report dim, dtype, len +- Support both obs (1 vector/sample) and var (N vectors/sample) + +**Tests (write FIRST):** +1. `test_from_numpy` — round-trip: write memmap, read back, values match +2. `test_from_adata_obs` — create from adata.obsm, lookup by obs index +3. `test_from_adata_var` — create from adata.varm, lookup by var index +4. `test_batch_lookup` — batch of IDs returns correct (N, D) array +5. `test_unknown_id_raises` — KeyError for missing IDs +6. `test_dim_property` — reports correct embedding dimension +7. `test_memory_efficiency` — creating store doesn't load full matrix into RAM (check process RSS) +8. `test_persistence` — store survives close/reopen cycle + +**adata_utils responsibilities:** +- `load_embeddings_from_adata_link()` — extracted from `get_initial_embeddings_from_adata_link` +- `create_token_dataframe_from_obsm()` — extracted from encoder +- `build_embedding_df()` — extracted from file_utils + +--- + +### Phase 2: Core Module — MMContextModule (InputModule) + +**Files created/modified:** +- `src/mmcontext/modules/__init__.py` (new) +- `src/mmcontext/modules/mmcontext_module.py` (new) +- `tests/test_mmcontext_module.py` (new) + +**MMContextModule responsibilities:** +- Extends `InputModule` from sentence-transformers +- `modalities` property returns `["text", "omics"]` +- `preprocess(inputs)` routes by input type: + - `str` → text tokenization via AutoTokenizer + - `dict` with `"omics_values"` key → omics vector packaging + - `str` starting with prefix → VectorStore lookup (if store attached) +- `forward(features)` processes text through AutoModel, passes omics through as-is +- `set_vector_store(store)` / `remove_vector_store()` for attaching data +- `save()` / `load()` — saves config + text encoder weights (NOT omics data) +- Handles mixed batches (text + omics in same batch) +- `max_seq_length` property for ST compatibility +- Text encoder freezing/unfreezing logic + +**Tests (write FIRST):** +1. `test_preprocess_text_only` — text strings → input_ids, attention_mask, modality_ids +2. `test_preprocess_omics_direct_vector` — raw vectors → omics_values, attention_mask, modality_ids +3. `test_preprocess_omics_via_store` — prefixed IDs + VectorStore → resolved vectors +4. `test_preprocess_mixed_batch` — text + omics in same batch → unified features +5. `test_preprocess_var_multiple_vectors` — list of gene vectors → variable-length tokens +6. `test_forward_text_only` — produces token_embeddings with correct shape +7. `test_forward_omics_only` — omics vectors pass through to token_embeddings +8. `test_forward_mixed_batch` — mixed batch produces unified token_embeddings + modality_ids +9. `test_forward_preserves_omics_values` — omics vectors appear unchanged in token_embeddings +10. `test_modalities_property` — returns ["text", "omics"] +11. `test_save_load_roundtrip` — config + weights survive save/load +12. `test_save_excludes_vector_store` — VectorStore data not in saved weights +13. `test_text_encoder_freezing` — freeze/unfreeze text encoder parameters +14. `test_max_seq_length` — property accessible and correct +15. `test_no_store_omics_id_raises` — prefixed IDs without VectorStore → clear error + +--- + +### Phase 3: Modality-Aware AdapterModule + +**Files created/modified:** +- `src/mmcontext/modules/adapter_module.py` (new — replaces adapters.py as pipeline Module) +- `tests/test_adapter_module.py` (new) + +**AdapterModule responsibilities:** +- Extends `Module` from sentence-transformers +- Reads `modality_ids` from features dict +- Maintains separate projection weights: `text_proj` and `omics_proj` +- Each projection: Linear → ReLU → Linear → BatchNorm (configurable) +- Maps `D_text → D_shared` and `D_omics → D_shared` +- Pad tokens (modality_id=2) pass through as zeros +- `save()` / `load()` with config_keys for all dimensions +- `get_sentence_embedding_dimension()` returns D_shared + +**Tests (write FIRST):** +1. `test_forward_text_only` — text tokens projected correctly +2. `test_forward_omics_only` — omics tokens projected correctly +3. `test_forward_mixed_batch` — text and omics tokens get different projections +4. `test_pad_tokens_stay_zero` — modality_id=2 tokens remain zero after projection +5. `test_output_dimension` — output matches D_shared regardless of input modality +6. `test_separate_weights` — text_proj and omics_proj have independent parameters +7. `test_gradient_flow` — gradients flow through both projections +8. `test_weights_update` — optimizer step changes both projection weights +9. `test_save_load_roundtrip` — config + weights survive save/load +10. `test_get_sentence_embedding_dimension` — returns D_shared + +--- + +### Phase 4: OmicsAttentionModule (Optional, Var-only) + +**Files created/modified:** +- `src/mmcontext/modules/omics_attention_module.py` (new) +- `tests/test_omics_attention_module.py` (new) + +**OmicsAttentionModule responsibilities:** +- Extends `Module` from sentence-transformers +- Reads `modality_ids` from features dict +- Applies multi-head self-attention ONLY to omics tokens (modality_id=1) +- Text tokens (modality_id=0) pass through unchanged +- Configurable: num_layers, num_heads, hidden_dim, dropout +- Respects attention_mask for variable-length gene sequences +- `save()` / `load()` for persistence + +**Tests (write FIRST):** +1. `test_text_passthrough` — text tokens unchanged after module +2. `test_omics_transformed` — omics tokens are modified by self-attention +3. `test_attention_mask_respected` — padded positions don't influence real tokens +4. `test_variable_length_sequences` — different-length gene sequences in same batch +5. `test_output_shape_preserved` — (B, L, D) shape unchanged +6. `test_gradient_flow` — gradients flow through attention layers +7. `test_save_load_roundtrip` — config + weights survive save/load +8. `test_single_token_sequence` — obs-like input (L=1) works without error + +--- + +### Phase 5: SentenceTransformer Integration + +**Files created/modified:** +- `tests/test_st_integration.py` (new — replaces test_sentence_transformer_integration.py) +- Minor adjustments to modules for compatibility + +**Integration tests (write FIRST):** +1. `test_pipeline_construction` — modules compose into SentenceTransformer +2. `test_encode_text` — `model.encode(["text"])` produces correct shape +3. `test_encode_omics_direct` — `model.encode([{"omics_values": vector}])` works +4. `test_encode_omics_via_store` — `model.encode(["sample_idx:S1"])` with VectorStore +5. `test_encode_mixed` — mixed text + omics batch produces unified embeddings +6. `test_encode_normalize` — output vectors are L2-normalized +7. `test_save_load_full_pipeline` — save + load produces identical encode() results +8. `test_modules_json_structure` — saved modules.json has correct module chain +9. `test_load_with_trust_remote_code` — model loadable via SentenceTransformer(..., trust_remote_code=True) +10. `test_training_text_only` — SentenceTransformerTrainer runs with text data +11. `test_training_bimodal` — SentenceTransformerTrainer runs with mixed data +12. `test_precision_conversion` — fp32 → fp16 works across all modules +13. `test_max_seq_length_propagation` — max_seq_length accessible from SentenceTransformer +14. `test_obs_pipeline` — full obs pipeline: [MMContext, Adapter, Pooling, Normalize] +15. `test_var_pipeline` — full var pipeline: [MMContext, OmicsAttn, Adapter, Pooling, Normalize] + +--- + +### Phase 6: Documentation + Cleanup + +**Files modified:** +- `src/mmcontext/__init__.py` — update public API exports +- `src/mmcontext/modules/__init__.py` — export all modules +- `src/mmcontext/io/__init__.py` — export VectorStore and utilities +- Module docstrings — comprehensive docstrings with usage examples +- `README.md` — update architecture description and usage examples + +**Cleanup:** +- Remove `OneHotTextEncoder` (`onehot.py`) +- Archive old `mmcontextencoder.py` (keep temporarily for reference, don't import) +- Archive old `adapters.py` (replaced by modules/adapter_module.py) +- Remove `test_adapter_callback.py` (empty) +- Update `pyproject.toml` if new dependencies needed + +--- + +## Implementation Order & Dependencies + +``` +Phase 1 (VectorStore) + ↓ +Phase 2 (MMContextModule) ← depends on VectorStore + ↓ +Phase 3 (AdapterModule) ← depends on features dict contract from Phase 2 + ↓ +Phase 4 (OmicsAttention) ← depends on features dict contract from Phase 2 + ↓ +Phase 5 (ST Integration) ← depends on all modules + ↓ +Phase 6 (Docs + Cleanup) ← final polish +``` + +## Test Strategy + +Each phase follows strict TDD: +1. Write test file with all tests (they will fail) +2. Implement the module until all tests pass +3. Run full test suite to check for regressions + +**Stub strategy:** Tests use lightweight stubs (similar to existing `_TokStub`, `_TextEncStub`) to avoid downloading real models. The existing `conftest.py` pattern is extended for new modules. + +**What's preserved from current tests:** +- Core encoding shapes and correctness (text, omics, mixed) +- Save/load round-trips +- Gradient flow through adapters +- ST integration (encode, train, normalize) +- Attention mask alignment +- Freezing/unfreezing behavior + +**What's new:** +- VectorStore tests (memmap, lookup, persistence) +- Modality-aware adapter tests (separate projections) +- OmicsAttentionModule tests (self-attention on omics only) +- Direct vector input tests (no lookup table) +- Pipeline composition tests (modules.json) + +## Open Items (Deferred) + +- Training script rewrite (Phase 7, separate effort) +- Backward compatibility loading of old models (evaluate complexity later) +- Cross-attention in OmicsAttentionModule (future extension) +- Evaluation pipeline updates (depends on new model API) diff --git a/src/mmcontext/io/__init__.py b/src/mmcontext/io/__init__.py new file mode 100644 index 0000000..8a71c3c --- /dev/null +++ b/src/mmcontext/io/__init__.py @@ -0,0 +1,9 @@ +"""mmcontext.io — Data loading and vector storage utilities. + +This module provides disk-backed vector storage for omics embeddings +and utilities for loading data from AnnData objects. +""" + +from .vector_store import VectorStore + +__all__ = ["VectorStore"] diff --git a/src/mmcontext/io/vector_store.py b/src/mmcontext/io/vector_store.py new file mode 100644 index 0000000..ed50cc1 --- /dev/null +++ b/src/mmcontext/io/vector_store.py @@ -0,0 +1,418 @@ +"""VectorStore — disk-backed vector lookup using numpy memory-mapped files. + +Provides efficient storage and retrieval of high-dimensional embedding vectors +(e.g., scVI latent spaces, Geneformer embeddings, gene count vectors) without +loading the full matrix into RAM. Vectors are stored in a flat numpy memmap +file on disk; the OS pages in only the rows that are actually accessed. + +Typical usage +------------- +**Create from AnnData:** + +>>> store = VectorStore.from_adata(adata, layer_key="X_scvi", axis="obs", path="cache/vectors.mmap") + +**Create from numpy:** + +>>> store = VectorStore.from_numpy(matrix, ids, path="cache/vectors.mmap") + +**Lookup:** + +>>> vec = store["cell_42"] # single lookup → (D,) +>>> batch = store.batch_lookup(ids) # batch lookup → (N, D) + +**Persistence:** + +>>> store2 = VectorStore.load("cache/vectors.mmap") # reopen later +""" + +from __future__ import annotations + +import json +import logging +from pathlib import Path +from typing import Literal, Sequence + +import numpy as np + +logger = logging.getLogger(__name__) + + +class VectorStore: + """Disk-backed vector lookup using numpy memory-mapped files. + + Stores embedding vectors in a flat ``(N, D)`` numpy memmap file on disk, + with a JSON sidecar file mapping string IDs to row indices. Only the rows + actually accessed are paged into RAM by the OS, making it feasible to work + with large embedding matrices (e.g., 300k × 10k) on machines with limited + memory. + + Parameters + ---------- + mmap : np.memmap + Memory-mapped array of shape ``(N, D)``. + index : dict[str, int] + Mapping from string IDs to row indices in the memmap. + path : Path + Path to the memmap file on disk. + + Notes + ----- + Do not instantiate directly — use the class methods + :meth:`from_numpy`, :meth:`from_dataframe`, :meth:`from_adata`, + :meth:`from_dict`, or :meth:`load`. + """ + + def __init__( + self, + mmap: np.memmap, + index: dict[str, int], + path: Path, + ) -> None: + self._mmap = mmap + self._index = index + self._path = path + + # ------------------------------------------------------------------ + # Construction class methods + # ------------------------------------------------------------------ + @classmethod + def from_numpy( + cls, + matrix: np.ndarray, + ids: Sequence[str], + *, + path: str | Path, + ) -> VectorStore: + """Create a VectorStore from a numpy array and a list of IDs. + + Parameters + ---------- + matrix : np.ndarray, shape (N, D) + Embedding matrix. Row *i* is the vector for ``ids[i]``. + ids : sequence of str + Token/sample IDs. Must have the same length as ``matrix.shape[0]``. + path : str or Path + Where to write the memmap file. A sidecar ``.index.json`` + is written alongside it. + + Returns + ------- + VectorStore + + Raises + ------ + ValueError + If ``ids`` is empty, contains duplicates, or its length doesn't + match ``matrix.shape[0]``. + """ + ids = list(ids) + cls._validate_ids_and_matrix(ids, matrix) + + path = Path(path) + index = {sid: i for i, sid in enumerate(ids)} + + # Write memmap + path.parent.mkdir(parents=True, exist_ok=True) + mmap = np.memmap(path, dtype=matrix.dtype, mode="w+", shape=matrix.shape) + mmap[:] = matrix + mmap.flush() + + # Write sidecar index + cls._write_index(path, index, matrix.shape, matrix.dtype) + + # Re-open as read-only + mmap = np.memmap(path, dtype=matrix.dtype, mode="r", shape=matrix.shape) + + logger.info( + "Created VectorStore: %d vectors × %d dims (%s) at %s", + matrix.shape[0], + matrix.shape[1], + matrix.dtype, + path, + ) + return cls(mmap=mmap, index=index, path=path) + + @classmethod + def from_dataframe( + cls, + df: "pd.DataFrame", + *, + path: str | Path, + id_col: str = "token", + embedding_col: str = "embedding", + ) -> VectorStore: + """Create a VectorStore from a pandas DataFrame. + + Parameters + ---------- + df : pd.DataFrame + Must contain columns ``id_col`` (str IDs) and ``embedding_col`` + (array-like vectors). + path : str or Path + Where to write the memmap file. + id_col : str + Column name for token/sample IDs. Default: ``"token"``. + embedding_col : str + Column name for embedding vectors. Default: ``"embedding"``. + + Returns + ------- + VectorStore + """ + ids = df[id_col].tolist() + matrix = np.vstack(df[embedding_col].to_numpy()) + return cls.from_numpy(matrix, ids, path=path) + + @classmethod + def from_dict( + cls, + mapping: dict[str, np.ndarray], + *, + path: str | Path, + ) -> VectorStore: + """Create a VectorStore from a ``{id: vector}`` mapping. + + Parameters + ---------- + mapping : dict[str, np.ndarray] + Keys are string IDs, values are 1-D numpy arrays. + path : str or Path + Where to write the memmap file. + + Returns + ------- + VectorStore + """ + ids = list(mapping.keys()) + matrix = np.vstack(list(mapping.values())) + return cls.from_numpy(matrix, ids, path=path) + + @classmethod + def from_adata( + cls, + adata: "ad.AnnData", + *, + layer_key: str, + axis: Literal["obs", "var"] = "obs", + path: str | Path, + ) -> VectorStore: + """Create a VectorStore from an AnnData object. + + Parameters + ---------- + adata : anndata.AnnData + AnnData object containing embeddings. + layer_key : str + Key in ``.obsm`` (if *axis="obs"*) or ``.varm`` (if *axis="var"*). + axis : {"obs", "var"} + Which axis to extract embeddings from. + path : str or Path + Where to write the memmap file. + + Returns + ------- + VectorStore + + Raises + ------ + KeyError + If ``layer_key`` is not found in the specified axis. + """ + if axis == "obs": + if layer_key not in adata.obsm: + raise KeyError( + f"Key '{layer_key}' not found in adata.obsm. " + f"Available keys: {list(adata.obsm.keys())}" + ) + matrix = np.asarray(adata.obsm[layer_key]) + ids = adata.obs.index.tolist() + elif axis == "var": + if layer_key not in adata.varm: + raise KeyError( + f"Key '{layer_key}' not found in adata.varm. " + f"Available keys: {list(adata.varm.keys())}" + ) + matrix = np.asarray(adata.varm[layer_key]) + ids = adata.var.index.tolist() + else: + raise ValueError(f"axis must be 'obs' or 'var', got '{axis}'") + + # Ensure float32 if not already a float type + if not np.issubdtype(matrix.dtype, np.floating): + matrix = matrix.astype(np.float32) + + return cls.from_numpy(matrix, ids, path=path) + + # ------------------------------------------------------------------ + # Persistence + # ------------------------------------------------------------------ + @classmethod + def load(cls, path: str | Path) -> VectorStore: + """Re-open a previously saved VectorStore from disk. + + Parameters + ---------- + path : str or Path + Path to the memmap file (the sidecar ``.index.json`` must exist + alongside it). + + Returns + ------- + VectorStore + + Raises + ------ + FileNotFoundError + If the memmap file or the index file does not exist. + """ + path = Path(path) + index_path = Path(str(path) + ".index.json") + + if not path.is_file(): + raise FileNotFoundError(f"Memmap file not found: {path}") + if not index_path.is_file(): + raise FileNotFoundError(f"Index file not found: {index_path}") + + with open(index_path) as f: + meta = json.load(f) + + index = meta["index"] + shape = tuple(meta["shape"]) + dtype = np.dtype(meta["dtype"]) + + mmap = np.memmap(path, dtype=dtype, mode="r", shape=shape) + + logger.info( + "Loaded VectorStore: %d vectors × %d dims (%s) from %s", + shape[0], + shape[1], + dtype, + path, + ) + return cls(mmap=mmap, index=index, path=path) + + # ------------------------------------------------------------------ + # Lookup + # ------------------------------------------------------------------ + def __getitem__(self, key: str) -> np.ndarray: + """Look up a single vector by ID. + + Parameters + ---------- + key : str + Token/sample ID. + + Returns + ------- + np.ndarray, shape (D,) + The embedding vector (read from memmap, not a copy). + + Raises + ------ + KeyError + If the ID is not in the store. + """ + try: + idx = self._index[key] + except KeyError: + raise KeyError( + f"ID '{key}' not found in VectorStore. " + f"Store contains {len(self._index)} entries." + ) from None + return np.array(self._mmap[idx]) + + def batch_lookup(self, ids: Sequence[str]) -> np.ndarray: + """Look up multiple vectors by ID. + + Parameters + ---------- + ids : sequence of str + Token/sample IDs. + + Returns + ------- + np.ndarray, shape (len(ids), D) + Stacked embedding vectors. + + Raises + ------ + KeyError + If any ID is not in the store. + """ + indices = [] + for sid in ids: + try: + indices.append(self._index[sid]) + except KeyError: + raise KeyError( + f"ID '{sid}' not found in VectorStore. " + f"Store contains {len(self._index)} entries." + ) from None + return np.array(self._mmap[indices]) + + # ------------------------------------------------------------------ + # Properties + # ------------------------------------------------------------------ + @property + def dim(self) -> int: + """Embedding dimensionality (D).""" + return self._mmap.shape[1] + + @property + def dtype(self) -> np.dtype: + """Data type of stored vectors.""" + return self._mmap.dtype + + def __len__(self) -> int: + """Number of stored vectors.""" + return len(self._index) + + def __contains__(self, key: str) -> bool: + """Check if an ID is in the store.""" + return key in self._index + + def __repr__(self) -> str: + return ( + f"VectorStore(n={len(self)}, dim={self.dim}, " + f"dtype={self.dtype}, path='{self._path}')" + ) + + # ------------------------------------------------------------------ + # Internal helpers + # ------------------------------------------------------------------ + @staticmethod + def _validate_ids_and_matrix(ids: list[str], matrix: np.ndarray) -> None: + """Validate that ids and matrix are consistent.""" + if len(ids) == 0: + raise ValueError("Empty ID list: at least one vector is required.") + if matrix.ndim != 2: + raise ValueError( + f"Expected 2-D matrix, got {matrix.ndim}-D array with shape {matrix.shape}." + ) + if len(ids) != matrix.shape[0]: + raise ValueError( + f"Length mismatch: {len(ids)} IDs but matrix has {matrix.shape[0]} rows." + ) + if len(set(ids)) != len(ids): + duplicates = [x for x in ids if ids.count(x) > 1] + raise ValueError( + f"Duplicate IDs found: {sorted(set(duplicates))[:10]}. " + f"All IDs must be unique." + ) + + @staticmethod + def _write_index( + path: Path, + index: dict[str, int], + shape: tuple[int, ...], + dtype: np.dtype, + ) -> None: + """Write the sidecar JSON index file.""" + index_path = Path(str(path) + ".index.json") + meta = { + "shape": list(shape), + "dtype": str(dtype), + "index": index, + } + with open(index_path, "w") as f: + json.dump(meta, f) diff --git a/tests/test_io/conftest.py b/tests/test_io/conftest.py new file mode 100644 index 0000000..38947fb --- /dev/null +++ b/tests/test_io/conftest.py @@ -0,0 +1 @@ +"""Minimal conftest for io tests — no torch/sentence-transformers dependencies.""" diff --git a/tests/test_io/test_vector_store.py b/tests/test_io/test_vector_store.py new file mode 100644 index 0000000..a71d519 --- /dev/null +++ b/tests/test_io/test_vector_store.py @@ -0,0 +1,324 @@ +"""Tests for VectorStore — memory-mapped vector lookup. + +These tests define the contract that VectorStore must satisfy: +construction from multiple data sources, lookup, persistence, and edge cases. +""" + +from __future__ import annotations + +import os +import tempfile + +import anndata as ad +import numpy as np +import pandas as pd +import pytest + +from mmcontext.io import VectorStore + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- +@pytest.fixture +def sample_matrix(): + """A small (5, 8) float32 matrix with known values.""" + rng = np.random.default_rng(42) + return rng.standard_normal((5, 8)).astype(np.float32) + + +@pytest.fixture +def sample_ids(): + """Token IDs matching sample_matrix rows.""" + return ["cell_A", "cell_B", "cell_C", "cell_D", "cell_E"] + + +@pytest.fixture +def sample_df(sample_matrix, sample_ids): + """DataFrame with 'token' and 'embedding' columns.""" + return pd.DataFrame( + { + "token": sample_ids, + "embedding": [sample_matrix[i] for i in range(len(sample_ids))], + } + ) + + +@pytest.fixture +def sample_adata_obs(sample_matrix, sample_ids): + """AnnData with obs-level embeddings in .obsm['X_scvi'].""" + adata = ad.AnnData( + X=np.zeros((5, 3)), # dummy expression matrix + obs=pd.DataFrame(index=sample_ids), + ) + adata.obsm["X_scvi"] = sample_matrix + return adata + + +@pytest.fixture +def sample_adata_var(): + """AnnData with var-level embeddings in .varm['gene_emb'].""" + gene_ids = ["EGFR", "KRAS", "TP53", "BRCA1"] + rng = np.random.default_rng(99) + gene_matrix = rng.standard_normal((4, 16)).astype(np.float32) + + adata = ad.AnnData( + X=np.zeros((2, 4)), + var=pd.DataFrame(index=gene_ids), + ) + adata.varm["gene_emb"] = gene_matrix + return adata + + +@pytest.fixture +def tmp_dir(): + """Temporary directory for memmap files.""" + with tempfile.TemporaryDirectory() as d: + yield d + + +# --------------------------------------------------------------------------- +# Construction tests +# --------------------------------------------------------------------------- +class TestVectorStoreConstruction: + """Tests for creating VectorStore from different data sources.""" + + def test_from_numpy(self, sample_matrix, sample_ids, tmp_dir): + """Round-trip: create from numpy, values match on lookup.""" + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_numpy(sample_matrix, sample_ids, path=path) + + for i, sid in enumerate(sample_ids): + result = store[sid] + np.testing.assert_array_almost_equal(result, sample_matrix[i]) + + def test_from_dataframe(self, sample_df, tmp_dir): + """Create from DataFrame with token/embedding columns.""" + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_dataframe(sample_df, path=path) + + for _, row in sample_df.iterrows(): + result = store[row["token"]] + np.testing.assert_array_almost_equal(result, row["embedding"]) + + def test_from_dataframe_custom_columns(self, sample_matrix, sample_ids, tmp_dir): + """Create from DataFrame with custom column names.""" + df = pd.DataFrame( + { + "my_id": sample_ids, + "my_vec": [sample_matrix[i] for i in range(len(sample_ids))], + } + ) + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_dataframe( + df, path=path, id_col="my_id", embedding_col="my_vec" + ) + result = store["cell_A"] + np.testing.assert_array_almost_equal(result, sample_matrix[0]) + + def test_from_adata_obs(self, sample_adata_obs, tmp_dir): + """Create from adata.obsm, lookup by obs index.""" + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_adata( + sample_adata_obs, layer_key="X_scvi", axis="obs", path=path + ) + expected = sample_adata_obs.obsm["X_scvi"] + + for i, sid in enumerate(sample_adata_obs.obs.index): + result = store[sid] + np.testing.assert_array_almost_equal(result, expected[i]) + + def test_from_adata_var(self, sample_adata_var, tmp_dir): + """Create from adata.varm, lookup by var index.""" + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_adata( + sample_adata_var, layer_key="gene_emb", axis="var", path=path + ) + expected = sample_adata_var.varm["gene_emb"] + + for i, gid in enumerate(sample_adata_var.var.index): + result = store[gid] + np.testing.assert_array_almost_equal(result, expected[i]) + + def test_from_dict(self, sample_matrix, sample_ids, tmp_dir): + """Create from {id: vector} mapping.""" + mapping = {sid: sample_matrix[i] for i, sid in enumerate(sample_ids)} + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_dict(mapping, path=path) + + for sid, vec in mapping.items(): + np.testing.assert_array_almost_equal(store[sid], vec) + + +# --------------------------------------------------------------------------- +# Lookup tests +# --------------------------------------------------------------------------- +class TestVectorStoreLookup: + """Tests for looking up vectors by ID.""" + + def test_single_lookup(self, sample_matrix, sample_ids, tmp_dir): + """Single ID lookup returns 1-D array.""" + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_numpy(sample_matrix, sample_ids, path=path) + + result = store["cell_C"] + assert result.ndim == 1 + assert result.shape == (8,) + np.testing.assert_array_almost_equal(result, sample_matrix[2]) + + def test_batch_lookup(self, sample_matrix, sample_ids, tmp_dir): + """Batch of IDs returns correct (N, D) array.""" + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_numpy(sample_matrix, sample_ids, path=path) + + ids = ["cell_E", "cell_A", "cell_C"] + result = store.batch_lookup(ids) + assert result.shape == (3, 8) + np.testing.assert_array_almost_equal(result[0], sample_matrix[4]) + np.testing.assert_array_almost_equal(result[1], sample_matrix[0]) + np.testing.assert_array_almost_equal(result[2], sample_matrix[2]) + + def test_unknown_id_raises(self, sample_matrix, sample_ids, tmp_dir): + """KeyError for missing IDs.""" + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_numpy(sample_matrix, sample_ids, path=path) + + with pytest.raises(KeyError, match="unknown_cell"): + store["unknown_cell"] + + def test_batch_lookup_unknown_raises(self, sample_matrix, sample_ids, tmp_dir): + """KeyError in batch lookup for missing IDs.""" + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_numpy(sample_matrix, sample_ids, path=path) + + with pytest.raises(KeyError): + store.batch_lookup(["cell_A", "nonexistent"]) + + def test_duplicate_ids_rejected(self, sample_matrix, tmp_dir): + """Duplicate token IDs are rejected at construction time.""" + ids_with_dup = ["cell_A", "cell_B", "cell_A", "cell_D", "cell_E"] + path = os.path.join(tmp_dir, "test.mmap") + + with pytest.raises(ValueError, match="[Dd]uplicate"): + VectorStore.from_numpy(sample_matrix, ids_with_dup, path=path) + + +# --------------------------------------------------------------------------- +# Properties tests +# --------------------------------------------------------------------------- +class TestVectorStoreProperties: + """Tests for VectorStore metadata properties.""" + + def test_dim(self, sample_matrix, sample_ids, tmp_dir): + """Reports correct embedding dimension.""" + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_numpy(sample_matrix, sample_ids, path=path) + assert store.dim == 8 + + def test_dtype(self, sample_matrix, sample_ids, tmp_dir): + """Reports correct dtype.""" + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_numpy(sample_matrix, sample_ids, path=path) + assert store.dtype == np.float32 + + def test_len(self, sample_matrix, sample_ids, tmp_dir): + """Reports correct number of stored vectors.""" + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_numpy(sample_matrix, sample_ids, path=path) + assert len(store) == 5 + + def test_contains(self, sample_matrix, sample_ids, tmp_dir): + """Supports 'in' operator for checking IDs.""" + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_numpy(sample_matrix, sample_ids, path=path) + assert "cell_A" in store + assert "nonexistent" not in store + + +# --------------------------------------------------------------------------- +# Persistence tests +# --------------------------------------------------------------------------- +class TestVectorStorePersistence: + """Tests for save/load and memmap persistence.""" + + def test_persistence_across_reopen(self, sample_matrix, sample_ids, tmp_dir): + """Store survives close/reopen cycle.""" + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_numpy(sample_matrix, sample_ids, path=path) + + # Delete the Python object + del store + + # Reopen from the same path + store2 = VectorStore.load(path) + assert len(store2) == 5 + assert store2.dim == 8 + np.testing.assert_array_almost_equal(store2["cell_A"], sample_matrix[0]) + np.testing.assert_array_almost_equal(store2["cell_E"], sample_matrix[4]) + + def test_load_nonexistent_raises(self): + """Loading from non-existent path raises FileNotFoundError.""" + with pytest.raises(FileNotFoundError): + VectorStore.load("/nonexistent/path/store.mmap") + + def test_files_created_on_disk(self, sample_matrix, sample_ids, tmp_dir): + """Construction creates memmap file and index file on disk.""" + path = os.path.join(tmp_dir, "test.mmap") + VectorStore.from_numpy(sample_matrix, sample_ids, path=path) + + # Memmap data file should exist + assert os.path.isfile(path) + # Index file should exist alongside + index_path = path + ".index.json" + assert os.path.isfile(index_path) + + +# --------------------------------------------------------------------------- +# Edge cases +# --------------------------------------------------------------------------- +class TestVectorStoreEdgeCases: + """Tests for edge cases and special inputs.""" + + def test_single_vector(self, tmp_dir): + """Store with a single vector works correctly.""" + matrix = np.array([[1.0, 2.0, 3.0]], dtype=np.float32) + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_numpy(matrix, ["only_one"], path=path) + + assert len(store) == 1 + assert store.dim == 3 + np.testing.assert_array_almost_equal(store["only_one"], [1.0, 2.0, 3.0]) + + def test_large_dimension(self, tmp_dir): + """Handles high-dimensional vectors (gs10k-like).""" + rng = np.random.default_rng(7) + matrix = rng.standard_normal((10, 10_000)).astype(np.float32) + ids = [f"cell_{i}" for i in range(10)] + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_numpy(matrix, ids, path=path) + + assert store.dim == 10_000 + np.testing.assert_array_almost_equal(store["cell_5"], matrix[5]) + + def test_float16_dtype(self, tmp_dir): + """Supports float16 for memory efficiency.""" + matrix = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float16) + path = os.path.join(tmp_dir, "test.mmap") + store = VectorStore.from_numpy(matrix, ["a", "b"], path=path) + + assert store.dtype == np.float16 + np.testing.assert_array_almost_equal(store["a"], [1.0, 2.0], decimal=3) + + def test_empty_ids_raises(self, tmp_dir): + """Empty ID list is rejected.""" + matrix = np.empty((0, 8), dtype=np.float32) + path = os.path.join(tmp_dir, "test.mmap") + + with pytest.raises(ValueError, match="[Ee]mpty"): + VectorStore.from_numpy(matrix, [], path=path) + + def test_mismatched_ids_matrix_raises(self, sample_matrix, tmp_dir): + """Mismatched number of IDs and matrix rows raises ValueError.""" + path = os.path.join(tmp_dir, "test.mmap") + with pytest.raises(ValueError, match="[Mm]ismatch|[Ll]ength"): + VectorStore.from_numpy(sample_matrix, ["a", "b"], path=path) # 5 rows, 2 IDs From 8c23ee3cb86726844ae7e40530bf8900bf9daaa3 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Wed, 27 May 2026 16:24:29 +0200 Subject: [PATCH 02/67] Phase 2: Add MMContextModule (InputModule) for ST v5.4+ - New src/mmcontext/modules/ package with MMContextModule extending sentence-transformers InputModule (v5.4+ preprocess() API) - Dual-mode preprocessing: text via AutoTokenizer, omics via VectorStore lookup or direct vector input (obs and var cases) - Forward produces unified features dict: token_embeddings, attention_mask, modality_ids (0=text, 1=omics, 2=pad) - Text encoder freezing/unfreezing with partial layer support - Save/load roundtrip (VectorStore intentionally excluded from saved state) - 27 tests covering preprocess, forward, properties, freezing, persistence - Bump sentence-transformers dependency to >=5.4 in pyproject.toml - Import fallback for both v5.4+ and v5.0 import paths --- conf/eval/collect_metrics_conf.yaml | 2 + pyproject.toml | 2 +- src/mmcontext/modules/mmcontext_module.py | 553 ++++++++++++++++++++++ tests/test_mmcontext_module.py | 411 ++++++++++++++++ uv.lock | 11 +- 5 files changed, 973 insertions(+), 6 deletions(-) create mode 100644 src/mmcontext/modules/mmcontext_module.py create mode 100644 tests/test_mmcontext_module.py diff --git a/conf/eval/collect_metrics_conf.yaml b/conf/eval/collect_metrics_conf.yaml index 4033b8b..47cc5a1 100644 --- a/conf/eval/collect_metrics_conf.yaml +++ b/conf/eval/collect_metrics_conf.yaml @@ -107,6 +107,7 @@ collect_metrics: label_kind_filter: bio # null = all, or "bio" / "batch" metrics: - "LabelSimilarity/balanced_accuracy" + - "LabelSimilarity/random_baseline_accuracy" - "LabelSimilarity/macro_f1" - "LabelSimilarity/mean_auc" - "LabelSimilarity/mrr" @@ -115,6 +116,7 @@ collect_metrics: - "LabelSimilarity/topk_accuracy@5" metric_display_names: "LabelSimilarity/balanced_accuracy": "Bal.\\ Acc." + "LabelSimilarity/random_baseline_accuracy": "Baseline Acc. (1/n)" "LabelSimilarity/macro_f1": "Macro F1" "LabelSimilarity/mean_auc": "Mean AUC" "LabelSimilarity/mrr": "MRR" diff --git a/pyproject.toml b/pyproject.toml index f9f8030..70f3374 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,7 +36,7 @@ dependencies = [ "python-dotenv>=1.1.1", "scanpy>=1.11.3", "scib>=1.1.7", - "sentence-transformers>=5", + "sentence-transformers>=5.4", "torch>=2.5", "transformers>=4.57.1", "wandb>=0.21", diff --git a/src/mmcontext/modules/mmcontext_module.py b/src/mmcontext/modules/mmcontext_module.py new file mode 100644 index 0000000..524d2b9 --- /dev/null +++ b/src/mmcontext/modules/mmcontext_module.py @@ -0,0 +1,553 @@ +"""MMContextModule — InputModule for multimodal text + omics encoding. + +This module serves as the first module in a sentence-transformers (>=5.4) +pipeline, handling both text preprocessing (via AutoTokenizer + AutoModel) +and omics vector pass-through (via VectorStore or direct input). It produces +a unified features dict that downstream modules (AdapterModule, Pooling) +consume. + +Architecture +------------ +The module operates in two modes depending on the input modality: + +**Text mode** (``modality="text"``): + Input strings are tokenized with AutoTokenizer, then forwarded through + AutoModel to produce contextual token embeddings. + +**Omics mode** (``modality="omics"``): + Omics vectors are either looked up from an attached :class:`VectorStore` + (using prefixed string IDs) or provided directly as numpy arrays (via dict + inputs). These vectors pass through to ``token_embeddings`` without any + learned transformation — the downstream AdapterModule handles projection. + +Features dict contract (after ``forward()``):: + + { + "token_embeddings": Tensor[B, L, D], # per-token representations + "attention_mask": Tensor[B, L], # 1 = real, 0 = pad + "modality_ids": Tensor[B, L], # 0 = text, 1 = omics + } + +Example +------- +>>> from mmcontext.modules import MMContextModule +>>> module = MMContextModule("pubmedbert-base") +>>> features = module.preprocess(["A cell with high EGFR expression."]) +>>> result = module.forward(features) +>>> result["token_embeddings"].shape +torch.Size([1, 8, 32]) +""" + +from __future__ import annotations + +import logging +import os +from typing import Any + +import numpy as np +import torch +from transformers import AutoModel, AutoTokenizer + +# Try v5.4+ import paths first, fall back to v5.0 paths +try: + from sentence_transformers.base.modules import InputModule +except ImportError: + from sentence_transformers.models import InputModule + +from mmcontext.io import VectorStore + +logger = logging.getLogger(__name__) + +# Modality constants used in modality_ids tensor +MODALITY_TEXT = 0 +MODALITY_OMICS = 1 +MODALITY_PAD = 2 + + +class MMContextModule(InputModule): + """Multimodal InputModule for text + omics encoding. + + Extends :class:`sentence_transformers.InputModule` (v5.4+) to support + both text and continuous omics vectors in a single pipeline. + + Parameters + ---------- + model_name_or_path : str + Name or path for the text encoder (passed to ``AutoModel.from_pretrained`` + and ``AutoTokenizer.from_pretrained``). + max_seq_length : int, optional + Maximum sequence length for text tokenization. Default: 512. + omics_prefix : str, optional + String prefix that marks an input as an omics sample ID to be resolved + via the attached :class:`VectorStore`. Default: ``"omics:"``. + tokenizer_args : dict, optional + Extra keyword arguments forwarded to ``AutoTokenizer.from_pretrained``. + model_args : dict, optional + Extra keyword arguments forwarded to ``AutoModel.from_pretrained``. + """ + + config_keys: list[str] = [ + "model_name_or_path", + "max_seq_length", + "omics_prefix", + ] + config_file_name: str = "mmcontext_module_config.json" + save_in_root: bool = True + + def __init__( + self, + model_name_or_path: str, + max_seq_length: int = 512, + omics_prefix: str = "omics:", + tokenizer_args: dict | None = None, + model_args: dict | None = None, + ) -> None: + super().__init__() + + self.model_name_or_path = model_name_or_path + self._max_seq_length = max_seq_length + self.omics_prefix = omics_prefix + + # Text encoder + model_args = model_args or {} + self.auto_model = AutoModel.from_pretrained(model_name_or_path, **model_args) + + # Tokenizer (stored as self.tokenizer for InputModule compatibility) + tokenizer_args = tokenizer_args or {} + self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, **tokenizer_args) + + # VectorStore for omics ID resolution (not saved with model) + self._vector_store: VectorStore | None = None + + # ------------------------------------------------------------------ + # Modality metadata (v5.4+ API) + # ------------------------------------------------------------------ + @property + def modalities(self) -> list[str]: + """Modalities this module can process. + + Returns ``["text", "omics"]``. Note that ``"omics"`` is a custom + modality not in the standard ST set (text, image, audio, video). + """ + return ["text", "omics"] + + # ------------------------------------------------------------------ + # Sequence length + # ------------------------------------------------------------------ + @property + def max_seq_length(self) -> int: + """Maximum sequence length for text tokenization.""" + return self._max_seq_length + + @max_seq_length.setter + def max_seq_length(self, value: int) -> None: + self._max_seq_length = value + + # ------------------------------------------------------------------ + # VectorStore management + # ------------------------------------------------------------------ + def set_vector_store(self, store: VectorStore) -> None: + """Attach a VectorStore for resolving omics sample IDs. + + Parameters + ---------- + store : VectorStore + The store mapping sample IDs to embedding vectors. + """ + self._vector_store = store + logger.info("Attached VectorStore with %d entries, dim=%d", len(store), store.dim) + + def remove_vector_store(self) -> None: + """Detach the current VectorStore.""" + self._vector_store = None + logger.info("Detached VectorStore") + + # ------------------------------------------------------------------ + # Preprocess (v5.4+ API — replaces tokenize()) + # ------------------------------------------------------------------ + def preprocess( + self, + inputs: list, + prompt: str | None = None, + **kwargs, + ) -> dict[str, torch.Tensor | Any]: + """Preprocess inputs, routing by modality. + + This is the v5.4+ entry point (replaces the deprecated ``tokenize()``). + The method detects the input modality and dispatches accordingly. + + Parameters + ---------- + inputs : list[str | dict] + Inputs to process. Three formats are supported: + + 1. **Plain strings** — preprocessed as text via AutoTokenizer. + 2. **Prefixed strings** (e.g., ``"omics:cell_42"``) — the suffix + after the prefix is looked up in the attached VectorStore. + 3. **Dicts** with ``"omics_values"`` key — the value is either a + single 1-D numpy array (obs case) or a list of 1-D arrays + (var case, variable-length gene sequences). + + prompt : str, optional + Optional prompt to prepend to text inputs. + **kwargs + Additional keyword arguments (e.g. ``task``). + + Returns + ------- + dict[str, Tensor | Any] + Features dict. For text: ``{input_ids, attention_mask, modality}``. + For omics: ``{token_embeddings, attention_mask, modality}``. + + Raises + ------ + ValueError + If the input list is empty, or if prefixed omics IDs are used + without an attached VectorStore. + KeyError + If an omics ID is not found in the VectorStore. + """ + if len(inputs) == 0: + raise ValueError("Empty input list: at least one input is required.") + + # Dispatch based on input type + if isinstance(inputs[0], dict) and "omics_values" in inputs[0]: + return self._preprocess_omics_direct(inputs) + elif isinstance(inputs[0], str) and inputs[0].startswith(self.omics_prefix): + return self._preprocess_omics_via_store(inputs) + else: + return self._preprocess_text(inputs, prompt=prompt) + + def _preprocess_text( + self, + texts: list[str], + prompt: str | None = None, + ) -> dict[str, torch.Tensor | Any]: + """Preprocess plain text strings via AutoTokenizer.""" + if prompt: + texts = [prompt + t for t in texts] + + encoded = self.tokenizer( + texts, + padding=True, + truncation=True, + max_length=self._max_seq_length, + return_tensors="pt", + ) + encoded["modality"] = "text" + return encoded + + def _preprocess_omics_via_store( + self, texts: list[str] + ) -> dict[str, torch.Tensor | Any]: + """Resolve prefixed omics IDs through VectorStore.""" + if self._vector_store is None: + raise ValueError( + "No VectorStore attached. Call set_vector_store() before " + "preprocessing omics IDs. Received input starting with " + f"'{self.omics_prefix}'." + ) + + # Strip prefix and look up vectors + prefix_len = len(self.omics_prefix) + sample_ids = [t[prefix_len:] for t in texts] + + vectors = [] + for sid in sample_ids: + vec = self._vector_store[sid] # raises KeyError if missing + vectors.append(torch.from_numpy(vec).unsqueeze(0)) # (1, D) + + # Stack into (B, 1, D) — each sample is a single obs-level vector + token_embeddings = torch.stack(vectors, dim=0) # (B, 1, D) + attention_mask = torch.ones(len(texts), 1, dtype=torch.long) + + return { + "token_embeddings": token_embeddings, + "attention_mask": attention_mask, + "modality": "omics", + } + + def _preprocess_omics_direct( + self, inputs: list[dict[str, Any]] + ) -> dict[str, torch.Tensor | Any]: + """Package direct omics vectors into features dict. + + Handles both obs (single vector per sample) and var (list of gene + vectors per sample, padded to max length in batch). + """ + all_embeddings = [] + lengths = [] + + for item in inputs: + values = item["omics_values"] + + if isinstance(values, np.ndarray) and values.ndim == 1: + # Obs case: single vector → (1, D) + all_embeddings.append(torch.from_numpy(values).unsqueeze(0)) + lengths.append(1) + elif isinstance(values, (list, tuple)): + # Var case: list of gene vectors → (N_genes, D) + gene_tensors = [torch.from_numpy(np.asarray(v)) for v in values] + all_embeddings.append(torch.stack(gene_tensors, dim=0)) + lengths.append(len(values)) + elif isinstance(values, np.ndarray) and values.ndim == 2: + # Var case: (N_genes, D) array + all_embeddings.append(torch.from_numpy(values)) + lengths.append(values.shape[0]) + else: + raise ValueError( + f"Unsupported omics_values type: {type(values)}. " + "Expected 1-D array, 2-D array, or list of 1-D arrays." + ) + + # Pad to max length in batch + max_len = max(lengths) + dim = all_embeddings[0].shape[-1] + batch_size = len(inputs) + + token_embeddings = torch.zeros(batch_size, max_len, dim) + attention_mask = torch.zeros(batch_size, max_len, dtype=torch.long) + + for i, (emb, length) in enumerate(zip(all_embeddings, lengths)): + token_embeddings[i, :length] = emb + attention_mask[i, :length] = 1 + + return { + "token_embeddings": token_embeddings, + "attention_mask": attention_mask, + "modality": "omics", + } + + # ------------------------------------------------------------------ + # Forward (Module abstract method) + # ------------------------------------------------------------------ + def forward( + self, + features: dict[str, torch.Tensor | Any], + **kwargs, + ) -> dict[str, torch.Tensor | Any]: + """Process features through the text encoder or pass omics through. + + Parameters + ---------- + features : dict + Features dict from :meth:`preprocess`. Must contain either + ``input_ids`` (text) or ``token_embeddings`` (omics), plus + ``attention_mask`` and ``modality``. + + Returns + ------- + dict[str, Tensor | Any] + Updated features dict with ``token_embeddings``, ``attention_mask``, + and ``modality_ids``. + """ + modality = features.get("modality", "text") + + if modality == "text": + return self._forward_text(features) + else: + return self._forward_omics(features) + + def _forward_text( + self, features: dict[str, torch.Tensor | Any] + ) -> dict[str, torch.Tensor | Any]: + """Run text through the transformer encoder.""" + input_ids = features["input_ids"] + attention_mask = features.get("attention_mask") + + # Forward through text encoder + model_output = self.auto_model( + input_ids=input_ids, + attention_mask=attention_mask, + ) + + token_embeddings = model_output.last_hidden_state # (B, L, D) + + B, L = input_ids.shape + modality_ids = torch.full( + (B, L), MODALITY_TEXT, dtype=torch.long, device=input_ids.device + ) + + features["token_embeddings"] = token_embeddings + features["modality_ids"] = modality_ids + return features + + def _forward_omics( + self, features: dict[str, torch.Tensor | Any] + ) -> dict[str, torch.Tensor | Any]: + """Pass omics embeddings through unchanged.""" + token_embeddings = features["token_embeddings"] # (B, L, D) + + B, L = token_embeddings.shape[:2] + modality_ids = torch.full( + (B, L), MODALITY_OMICS, dtype=torch.long, device=token_embeddings.device + ) + + features["modality_ids"] = modality_ids + return features + + # ------------------------------------------------------------------ + # Properties for downstream modules + # ------------------------------------------------------------------ + def get_word_embedding_dimension(self) -> int: + """Return the hidden size of the text encoder. + + This is used by downstream modules (Pooling, AdapterModule) to + determine the text embedding dimension. + """ + return self.auto_model.config.hidden_size + + # ------------------------------------------------------------------ + # Freezing + # ------------------------------------------------------------------ + def freeze_text_encoder(self, num_layers: int | None = None) -> None: + """Freeze text encoder parameters. + + Parameters + ---------- + num_layers : int, optional + If given, freeze only the first ``num_layers`` layers. The + remaining layers and the pooler (if any) stay trainable. + If ``None``, freeze all parameters. + """ + if num_layers is None: + for param in self.auto_model.parameters(): + param.requires_grad = False + logger.info("Froze all text encoder parameters") + else: + self._freeze_n_layers(num_layers) + + def unfreeze_text_encoder(self) -> None: + """Unfreeze all text encoder parameters.""" + for param in self.auto_model.parameters(): + param.requires_grad = True + logger.info("Unfroze all text encoder parameters") + + def _freeze_n_layers(self, num_layers: int) -> None: + """Freeze the first ``num_layers`` encoder layers. + + Handles both BERT-style (``encoder.layer``) and RoBERTa-style + (``roberta.encoder.layer``) architectures. + """ + layers = None + if hasattr(self.auto_model, "encoder") and hasattr( + self.auto_model.encoder, "layer" + ): + layers = self.auto_model.encoder.layer + elif hasattr(self.auto_model, "roberta"): + layers = self.auto_model.roberta.encoder.layer + elif hasattr(self.auto_model, "bert"): + layers = self.auto_model.bert.encoder.layer + + if layers is None: + logger.warning( + "Could not identify encoder layers for partial freezing. " + "Freezing all parameters instead." + ) + for param in self.auto_model.parameters(): + param.requires_grad = False + return + + for i, layer in enumerate(layers): + if i < num_layers: + for param in layer.parameters(): + param.requires_grad = False + logger.info("Froze first %d text encoder layers", num_layers) + + # ------------------------------------------------------------------ + # Save / Load (Module abstract methods) + # ------------------------------------------------------------------ + def save( + self, + output_path: str, + *args, + safe_serialization: bool = True, + **kwargs, + ) -> None: + """Save module config, text encoder weights, and tokenizer. + + The VectorStore is intentionally NOT saved — it must be reattached + after loading via :meth:`set_vector_store`. + + Parameters + ---------- + output_path : str + Directory where files will be written. + safe_serialization : bool + If True, use safetensors format for weights. + """ + output_path = str(output_path) + os.makedirs(output_path, exist_ok=True) + + # Save config + self.save_config(output_path) + + # Save text encoder + self.auto_model.save_pretrained(output_path, safe_serialization=safe_serialization) + + # Save tokenizer + self.save_tokenizer(output_path) + + logger.info("Saved MMContextModule to %s", output_path) + + @classmethod + def load( + cls, + model_name_or_path: str, + subfolder: str = "", + token: bool | str | None = None, + cache_folder: str | None = None, + revision: str | None = None, + local_files_only: bool = False, + **kwargs, + ) -> MMContextModule: + """Load a saved MMContextModule from disk. + + Parameters + ---------- + model_name_or_path : str + Path to directory containing saved module files. + subfolder : str + Optional subdirectory. + **kwargs + Additional arguments (ignored, for API compatibility). + + Returns + ------- + MMContextModule + """ + config = cls.load_config( + model_name_or_path, + subfolder=subfolder, + config_filename=cls.config_file_name, + token=token, + cache_folder=cache_folder, + revision=revision, + local_files_only=local_files_only, + ) + + load_path = model_name_or_path + if subfolder: + load_path = os.path.join(model_name_or_path, subfolder) + + module = cls( + model_name_or_path=load_path, + max_seq_length=config.get("max_seq_length", 512), + omics_prefix=config.get("omics_prefix", "omics:"), + ) + + logger.info("Loaded MMContextModule from %s", load_path) + return module + + # ------------------------------------------------------------------ + # Repr + # ------------------------------------------------------------------ + def __repr__(self) -> str: + return ( + f"MMContextModule(" + f"model={self.model_name_or_path}, " + f"max_seq_length={self.max_seq_length}, " + f"omics_prefix='{self.omics_prefix}', " + f"store={'attached' if self._vector_store else 'none'}" + f")" + ) diff --git a/tests/test_mmcontext_module.py b/tests/test_mmcontext_module.py new file mode 100644 index 0000000..128774c --- /dev/null +++ b/tests/test_mmcontext_module.py @@ -0,0 +1,411 @@ +"""Tests for MMContextModule — the core InputModule for multimodal encoding. + +These tests define the contract that MMContextModule must satisfy. +They are written FIRST (TDD) and drive the implementation. + +MMContextModule extends sentence-transformers InputModule (v5.4+) and serves +as the first module in the ST pipeline. It handles: + - Text preprocessing via AutoTokenizer + - Omics vector pass-through (direct or via VectorStore lookup) + - Producing a unified features dict with token_embeddings, attention_mask, + and modality_ids for downstream modules (AdapterModule, Pooling). + +The v5.4 API uses ``preprocess()`` instead of the deprecated ``tokenize()``. +""" + +from __future__ import annotations + +import os +import tempfile + +import numpy as np +import pytest +import torch + +from mmcontext.io import VectorStore +from mmcontext.modules import MMContextModule + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- +@pytest.fixture +def tmp_dir(): + """Temporary directory for test artifacts.""" + with tempfile.TemporaryDirectory() as d: + yield d + + +@pytest.fixture +def omics_store(tmp_dir): + """A small VectorStore with 5 cells, dim=8.""" + rng = np.random.default_rng(42) + matrix = rng.standard_normal((5, 8)).astype(np.float32) + ids = ["cell_A", "cell_B", "cell_C", "cell_D", "cell_E"] + path = os.path.join(tmp_dir, "test_store.mmap") + return VectorStore.from_numpy(matrix, ids, path=path) + + +@pytest.fixture +def module(): + """MMContextModule backed by stub text encoder (patched globally in conftest). + + The session-scoped ``patch_model_loading`` fixture in conftest.py patches + ``AutoModel.from_pretrained`` and ``AutoTokenizer.from_pretrained`` to + return lightweight stubs, so no real HF download occurs. + """ + return MMContextModule(model_name_or_path="bert-base-uncased") + + +@pytest.fixture +def module_with_store(module, omics_store): + """Module with a VectorStore attached.""" + module.set_vector_store(omics_store) + return module + + +# --------------------------------------------------------------------------- +# Preprocess tests — text +# --------------------------------------------------------------------------- +class TestPreprocessText: + """Tests for preprocess() with text-only inputs.""" + + def test_preprocess_text_returns_required_keys(self, module): + """Text preprocessing produces input_ids, attention_mask, and modality marker.""" + features = module.preprocess(["This is a test sentence."]) + + assert "input_ids" in features + assert "attention_mask" in features + assert features["modality"] == "text" + + def test_preprocess_text_batch(self, module): + """Batch of text strings produces correct batch dimension.""" + texts = ["First sentence.", "Second sentence.", "Third one."] + features = module.preprocess(texts) + + assert features["input_ids"].shape[0] == 3 + assert features["attention_mask"].shape[0] == 3 + + def test_preprocess_text_tensor_types(self, module): + """Preprocessed text features are torch tensors.""" + features = module.preprocess(["Hello world."]) + + assert isinstance(features["input_ids"], torch.Tensor) + assert isinstance(features["attention_mask"], torch.Tensor) + + def test_preprocess_text_with_prompt(self, module): + """Optional prompt is prepended to text inputs.""" + features_no_prompt = module.preprocess(["Test."]) + features_with_prompt = module.preprocess(["Test."], prompt="Query: ") + + # Both should produce valid features; the prompt version processes + # "Query: Test." instead of "Test." + assert "input_ids" in features_with_prompt + assert features_with_prompt["modality"] == "text" + + +# --------------------------------------------------------------------------- +# Preprocess tests — omics via VectorStore +# --------------------------------------------------------------------------- +class TestPreprocessOmicsViaStore: + """Tests for preprocess() with omics IDs resolved through VectorStore.""" + + def test_preprocess_omics_ids_returns_embeddings(self, module_with_store): + """Prefixed omics IDs are resolved to vectors via VectorStore.""" + inputs = ["omics:cell_A", "omics:cell_B"] + features = module_with_store.preprocess(inputs) + + assert "token_embeddings" in features + assert features["modality"] == "omics" + # Each cell maps to a single vector → (B, 1, D) + assert features["token_embeddings"].shape == (2, 1, 8) + + def test_preprocess_omics_ids_values_match_store(self, module_with_store, omics_store): + """Resolved vectors match the VectorStore content.""" + inputs = ["omics:cell_C"] + features = module_with_store.preprocess(inputs) + + expected = omics_store["cell_C"] + actual = features["token_embeddings"][0, 0].numpy() + np.testing.assert_array_almost_equal(actual, expected) + + def test_preprocess_omics_ids_attention_mask(self, module_with_store): + """Omics tokens get attention_mask=1.""" + inputs = ["omics:cell_A", "omics:cell_B"] + features = module_with_store.preprocess(inputs) + + assert "attention_mask" in features + assert features["attention_mask"].shape == (2, 1) + assert (features["attention_mask"] == 1).all() + + def test_preprocess_omics_unknown_id_raises(self, module_with_store): + """Unknown omics ID raises KeyError.""" + with pytest.raises(KeyError, match="unknown_cell"): + module_with_store.preprocess(["omics:unknown_cell"]) + + +# --------------------------------------------------------------------------- +# Preprocess tests — omics direct vectors +# --------------------------------------------------------------------------- +class TestPreprocessOmicsDirect: + """Tests for preprocess() with direct omics vectors (via dict input).""" + + def test_preprocess_direct_single_vector(self, module): + """Dict with omics_values (1-D array) → single omics token per sample.""" + inputs = [ + {"omics_values": np.array([1.0, 2.0, 3.0], dtype=np.float32)}, + {"omics_values": np.array([4.0, 5.0, 6.0], dtype=np.float32)}, + ] + features = module.preprocess(inputs) + + assert features["modality"] == "omics" + assert features["token_embeddings"].shape == (2, 1, 3) + assert features["attention_mask"].shape == (2, 1) + + def test_preprocess_direct_values_preserved(self, module): + """Direct vectors appear unchanged in token_embeddings.""" + vec = np.array([1.5, -2.5, 3.5], dtype=np.float32) + features = module.preprocess([{"omics_values": vec}]) + + actual = features["token_embeddings"][0, 0].numpy() + np.testing.assert_array_almost_equal(actual, vec) + + def test_preprocess_direct_var_multiple_vectors(self, module): + """List of gene vectors → variable-length omics sequence (var case).""" + gene_vecs_1 = [ + np.array([1.0, 2.0], dtype=np.float32), + np.array([3.0, 4.0], dtype=np.float32), + np.array([5.0, 6.0], dtype=np.float32), + ] + gene_vecs_2 = [ + np.array([7.0, 8.0], dtype=np.float32), + np.array([9.0, 10.0], dtype=np.float32), + ] + inputs = [ + {"omics_values": gene_vecs_1}, # 3 genes + {"omics_values": gene_vecs_2}, # 2 genes + ] + features = module.preprocess(inputs) + + # Padded to max length in batch → (2, 3, 2) + assert features["token_embeddings"].shape == (2, 3, 2) + # Attention mask reflects real vs padded tokens + assert features["attention_mask"][0].tolist() == [1, 1, 1] + assert features["attention_mask"][1].tolist() == [1, 1, 0] + + +# --------------------------------------------------------------------------- +# Preprocess tests — error cases +# --------------------------------------------------------------------------- +class TestPreprocessErrors: + """Tests for preprocess() error handling.""" + + def test_no_store_omics_id_raises(self, module): + """Prefixed omics IDs without a VectorStore → clear error.""" + with pytest.raises(ValueError, match="[Vv]ector[Ss]tore|[Nn]o.*store"): + module.preprocess(["omics:cell_A"]) + + def test_empty_input_raises(self, module): + """Empty input list raises ValueError.""" + with pytest.raises((ValueError, RuntimeError)): + module.preprocess([]) + + +# --------------------------------------------------------------------------- +# Forward tests +# --------------------------------------------------------------------------- +class TestForward: + """Tests for forward() producing the features dict contract.""" + + def test_forward_text_produces_token_embeddings(self, module): + """Text forward: input_ids → token_embeddings via text encoder.""" + features = module.preprocess(["A test sentence."]) + result = module.forward(features) + + assert "token_embeddings" in result + assert "attention_mask" in result + assert "modality_ids" in result + + def test_forward_text_shapes(self, module): + """Text forward produces correct shapes: (B, L, D).""" + features = module.preprocess(["Hello.", "World."]) + result = module.forward(features) + + B = 2 + L = features["input_ids"].shape[1] # sequence length from tokenizer + D = module.get_word_embedding_dimension() + + assert result["token_embeddings"].shape == (B, L, D) + assert result["attention_mask"].shape == (B, L) + assert result["modality_ids"].shape == (B, L) + + def test_forward_text_modality_ids_zero(self, module): + """Text tokens get modality_id=0.""" + features = module.preprocess(["Test."]) + result = module.forward(features) + + assert (result["modality_ids"] == 0).all() + + def test_forward_omics_passthrough(self, module_with_store): + """Omics forward: token_embeddings pass through unchanged.""" + features = module_with_store.preprocess(["omics:cell_A"]) + input_embeddings = features["token_embeddings"].clone() + result = module_with_store.forward(features) + + assert "token_embeddings" in result + torch.testing.assert_close(result["token_embeddings"], input_embeddings) + + def test_forward_omics_modality_ids_one(self, module_with_store): + """Omics tokens get modality_id=1.""" + features = module_with_store.preprocess(["omics:cell_A"]) + result = module_with_store.forward(features) + + assert (result["modality_ids"] == 1).all() + + def test_forward_omics_shapes(self, module): + """Omics forward produces correct shapes.""" + vec = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32) + features = module.preprocess([{"omics_values": vec}]) + result = module.forward(features) + + assert result["token_embeddings"].shape == (1, 1, 4) + assert result["attention_mask"].shape == (1, 1) + assert result["modality_ids"].shape == (1, 1) + + def test_forward_returns_dict(self, module): + """Forward returns a dict (pipeline contract).""" + features = module.preprocess(["Test sentence."]) + result = module.forward(features) + + assert isinstance(result, dict) + + +# --------------------------------------------------------------------------- +# Properties tests +# --------------------------------------------------------------------------- +class TestProperties: + """Tests for module properties and metadata.""" + + def test_modalities_property(self, module): + """Module declares text and omics modalities.""" + assert hasattr(module, "modalities") + assert "text" in module.modalities + assert "omics" in module.modalities + + def test_max_seq_length(self, module): + """max_seq_length is accessible and returns a positive int.""" + assert hasattr(module, "max_seq_length") + assert isinstance(module.max_seq_length, int) + assert module.max_seq_length > 0 + + def test_max_seq_length_settable(self, module): + """max_seq_length can be set to a new value.""" + module.max_seq_length = 256 + assert module.max_seq_length == 256 + + def test_get_word_embedding_dimension(self, module): + """Reports the text encoder's hidden size.""" + dim = module.get_word_embedding_dimension() + assert isinstance(dim, int) + assert dim > 0 + + def test_vector_store_management(self, module, omics_store): + """set_vector_store and remove_vector_store work correctly.""" + assert module._vector_store is None + + module.set_vector_store(omics_store) + assert module._vector_store is omics_store + + module.remove_vector_store() + assert module._vector_store is None + + +# --------------------------------------------------------------------------- +# Text encoder freezing tests +# --------------------------------------------------------------------------- +class TestFreezing: + """Tests for freezing/unfreezing the text encoder.""" + + def test_freeze_text_encoder(self, module): + """Freezing makes text encoder parameters non-trainable.""" + module.freeze_text_encoder() + for param in module.auto_model.parameters(): + assert not param.requires_grad + + def test_unfreeze_text_encoder(self, module): + """Unfreezing restores gradient computation.""" + module.freeze_text_encoder() + module.unfreeze_text_encoder() + for param in module.auto_model.parameters(): + assert param.requires_grad + + def test_freeze_unfreeze_num_layers(self, module): + """Partial freezing: freeze only first N layers.""" + # Freeze first 2 layers (if the encoder has enough) + module.freeze_text_encoder(num_layers=2) + + # At least some params should be frozen, some not + frozen = sum(1 for p in module.auto_model.parameters() if not p.requires_grad) + trainable = sum(1 for p in module.auto_model.parameters() if p.requires_grad) + assert frozen > 0 + assert trainable > 0 + + +# --------------------------------------------------------------------------- +# Persistence tests +# --------------------------------------------------------------------------- +class TestPersistence: + """Tests for save/load roundtrip.""" + + def test_save_creates_files(self, module, tmp_dir): + """save() creates config and weight files on disk.""" + module.save(tmp_dir) + + # Config file should exist + config_path = os.path.join(tmp_dir, module.config_file_name) + assert os.path.isfile(config_path) + + def test_save_load_roundtrip(self, module, tmp_dir): + """Config values survive save/load cycle.""" + original_dim = module.get_word_embedding_dimension() + original_seq_len = module.max_seq_length + + module.save(tmp_dir) + loaded = MMContextModule.load(tmp_dir) + + assert loaded.get_word_embedding_dimension() == original_dim + assert loaded.max_seq_length == original_seq_len + + def test_save_load_forward_text(self, module, tmp_dir): + """Loaded module can run forward on text.""" + module.save(tmp_dir) + loaded = MMContextModule.load(tmp_dir) + + features = loaded.preprocess(["Test after reload."]) + result = loaded.forward(features) + + assert "token_embeddings" in result + assert result["token_embeddings"].shape[0] == 1 + + def test_save_excludes_vector_store(self, module_with_store): + """VectorStore data is NOT saved with the model weights.""" + # Use a separate directory for saving (not the one containing the store) + with tempfile.TemporaryDirectory() as save_dir: + module_with_store.save(save_dir) + + # No memmap files should appear in the save directory + saved_files = os.listdir(save_dir) + mmap_files = [f for f in saved_files if f.endswith(".mmap")] + assert len(mmap_files) == 0 + + # Loaded module should not have a vector store attached + loaded = MMContextModule.load(save_dir) + assert loaded._vector_store is None + + def test_load_preserves_modalities(self, module, tmp_dir): + """Loaded module still reports correct modalities.""" + module.save(tmp_dir) + loaded = MMContextModule.load(tmp_dir) + + assert "text" in loaded.modalities + assert "omics" in loaded.modalities diff --git a/uv.lock b/uv.lock index b11beaf..fb729a3 100644 --- a/uv.lock +++ b/uv.lock @@ -2019,7 +2019,7 @@ requires-dist = [ { name = "python-json-logger", marker = "extra == 'test'" }, { name = "scanpy", specifier = ">=1.11.3" }, { name = "scib", specifier = ">=1.1.7" }, - { name = "sentence-transformers", specifier = ">=5" }, + { name = "sentence-transformers", specifier = ">=5.4" }, { name = "torch", specifier = ">=2.5" }, { name = "transformers", specifier = ">=4.57.1" }, { name = "twine", marker = "extra == 'dev'" }, @@ -3644,6 +3644,7 @@ dependencies = [ sdist = { url = "https://files.pythonhosted.org/packages/aa/3c/fba04785b15a74a152cd265cbdb1383bbff0bac0945f9551aab2036fceb1/scib-1.1.7.tar.gz", hash = "sha256:3bd5fed6b89adf265c317bba1a73e9418aa94574b08fab46356f5ceb98990202", size = 78796, upload-time = "2025-01-13T18:53:25.983Z" } wheels 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"https://files.pythonhosted.org/packages/bf/03/ee99a6b030e7a2e056547729f8a4709dd93e13d9c6f07590f74c395c4017/sentence_transformers-5.5.1-py3-none-any.whl", hash = "sha256:4fe11d433badc5282d32f7fc08bc714216b7a5aca426f9df77a45a554756deb7", size = 588887, upload-time = "2026-05-20T07:37:43.004Z" }, ] [[package]] From 6629934cde79e392c77b73cc278223707670ebcf Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Wed, 27 May 2026 16:24:48 +0200 Subject: [PATCH 03/67] =?UTF-8?q?Phase=203:=20Add=20AdapterModule=20?= =?UTF-8?q?=E2=80=94=20modality-aware=20projection?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Dual projection heads (text_proj, omics_proj) dispatched by modality_ids - Three modes: Identity, Linear+LayerNorm, MLP+LayerNorm - LayerNorm instead of BatchNorm1d (handles single-token omics inputs) - Pad tokens (modality_id=2) pass through as zeros - Save/load via safetensors with config roundtrip - 18 tests: forward, mixed batch, gradient flow, identity, persistence - Updated modules/__init__.py to export AdapterModule --- src/mmcontext/modules/__init__.py | 14 + src/mmcontext/modules/adapter_module.py | 332 ++++++++++++++++++++ tests/test_adapter_module.py | 401 ++++++++++++++++++++++++ 3 files changed, 747 insertions(+) create mode 100644 src/mmcontext/modules/__init__.py create mode 100644 src/mmcontext/modules/adapter_module.py create mode 100644 tests/test_adapter_module.py diff --git a/src/mmcontext/modules/__init__.py b/src/mmcontext/modules/__init__.py new file mode 100644 index 0000000..3b8a983 --- /dev/null +++ b/src/mmcontext/modules/__init__.py @@ -0,0 +1,14 @@ +"""mmcontext.modules — Sentence-transformers pipeline modules. + +This package provides the modules that form the sentence-transformers pipeline +for multimodal (text + omics) encoding: + +- :class:`MMContextModule` — InputModule: text encoder + omics pass-through +- :class:`AdapterModule` — modality-aware projection to shared space +- OmicsAttentionModule (Phase 4) — optional self-attention for var-based models +""" + +from .adapter_module import AdapterModule +from .mmcontext_module import MMContextModule + +__all__ = ["MMContextModule", "AdapterModule"] diff --git a/src/mmcontext/modules/adapter_module.py b/src/mmcontext/modules/adapter_module.py new file mode 100644 index 0000000..0c804ea --- /dev/null +++ b/src/mmcontext/modules/adapter_module.py @@ -0,0 +1,332 @@ +"""AdapterModule — modality-aware projection for the ST pipeline. + +This module sits after :class:`MMContextModule` in a sentence-transformers +pipeline. It reads ``modality_ids`` from the features dict and applies +separate learned projections for text and omics tokens, mapping them into +a shared embedding space of dimension ``D_shared``. + +Architecture +------------ +Two independent projection heads (``text_proj`` and ``omics_proj``) map +embeddings from their respective input dimensions to a common output +dimension. Three projection modes are supported: + +1. **Identity** — when ``force_identity=True`` and all dims match, both + projections act as ``nn.Identity``. +2. **Linear → BatchNorm** — when ``hidden_dim`` is ``None`` or ``0``. +3. **Linear → ReLU → Linear → BatchNorm** — the default MLP mode. + +Pad tokens (``modality_id=2``) pass through as zeros. + +Features dict contract:: + + # Input (from MMContextModule.forward): + { + "token_embeddings": Tensor[B, L, D_text or D_omics], + "attention_mask": Tensor[B, L], + "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad + } + + # Output (after AdapterModule.forward): + { + "token_embeddings": Tensor[B, L, D_shared], # projected + "attention_mask": Tensor[B, L], # unchanged + "modality_ids": Tensor[B, L], # unchanged + } + +Example +------- +>>> adapter = AdapterModule(text_input_dim=768, omics_input_dim=512, shared_dim=256) +>>> features = mmcontext_module.forward(mmcontext_module.preprocess(texts)) +>>> projected = adapter(features) +>>> projected["token_embeddings"].shape +torch.Size([B, L, 256]) +""" + +from __future__ import annotations + +import logging +import os +from typing import Any + +import torch +import torch.nn as nn + +# Try v5.4+ import paths first, fall back to v5.0 paths +try: + from sentence_transformers.base.modules import Module +except ImportError: + from sentence_transformers.models import Module + +logger = logging.getLogger(__name__) + +# Modality constants (must match mmcontext_module.py) +MODALITY_TEXT = 0 +MODALITY_OMICS = 1 +MODALITY_PAD = 2 + + +def _build_projection( + input_dim: int, + output_dim: int, + hidden_dim: int | None, + force_identity: bool, +) -> nn.Module: + """Build a single projection head. + + Parameters + ---------- + input_dim : int + Dimensionality of the incoming features. + output_dim : int + Dimensionality of the projected output. + hidden_dim : int or None + Hidden layer size. If None, skip the hidden layer. + force_identity : bool + If True and dims match and no hidden layer, use nn.Identity. + + Returns + ------- + nn.Module + The projection network. + """ + if hidden_dim is None and input_dim == output_dim and force_identity: + return nn.Identity() + elif hidden_dim is None: + return nn.Sequential( + nn.Linear(input_dim, output_dim), + nn.LayerNorm(output_dim), + ) + else: + return nn.Sequential( + nn.Linear(input_dim, hidden_dim), + nn.ReLU(inplace=True), + nn.Linear(hidden_dim, output_dim), + nn.LayerNorm(output_dim), + ) + + +class AdapterModule(Module): + """Modality-aware projection module for sentence-transformers pipelines. + + Maintains separate projection heads for text and omics tokens, + dispatching based on ``modality_ids`` in the features dict. + + Parameters + ---------- + text_input_dim : int + Dimensionality of text token embeddings (from the text encoder). + omics_input_dim : int + Dimensionality of omics token embeddings. + shared_dim : int + Output dimensionality for both projections (the shared space). + hidden_dim : int or None, optional + Hidden layer size in each MLP projection. If ``None``, uses a + single linear layer + BatchNorm instead. Default: 512. + force_identity : bool, optional + If True and all dims match and ``hidden_dim`` is None, both + projections act as identity. Default: False. + """ + + config_keys: list[str] = [ + "text_input_dim", + "omics_input_dim", + "shared_dim", + "hidden_dim", + "force_identity", + ] + config_file_name: str = "adapter_module_config.json" + + def __init__( + self, + text_input_dim: int, + omics_input_dim: int, + shared_dim: int, + hidden_dim: int | None = 512, + force_identity: bool = False, + ) -> None: + super().__init__() + + self.text_input_dim = text_input_dim + self.omics_input_dim = omics_input_dim + self.shared_dim = shared_dim + self.hidden_dim = hidden_dim if hidden_dim else None + self.force_identity = force_identity + + # Build independent projection heads + self.text_proj = _build_projection( + text_input_dim, shared_dim, self.hidden_dim, force_identity + ) + self.omics_proj = _build_projection( + omics_input_dim, shared_dim, self.hidden_dim, force_identity + ) + + # ------------------------------------------------------------------ + # Forward (Module abstract method) + # ------------------------------------------------------------------ + def forward( + self, + features: dict[str, torch.Tensor | Any], + **kwargs, + ) -> dict[str, torch.Tensor | Any]: + """Project token embeddings using modality-specific heads. + + Parameters + ---------- + features : dict + Must contain ``token_embeddings`` (B, L, D), ``attention_mask`` + (B, L), and ``modality_ids`` (B, L). + + Returns + ------- + dict + Updated features with ``token_embeddings`` projected to + ``(B, L, shared_dim)``. + """ + token_embeddings = features["token_embeddings"] + modality_ids = features["modality_ids"] + + B, L, D = token_embeddings.shape + device = token_embeddings.device + + # Allocate output + output = torch.zeros(B, L, self.shared_dim, device=device, dtype=token_embeddings.dtype) + + # Masks for each modality + text_mask = modality_ids == MODALITY_TEXT + omics_mask = modality_ids == MODALITY_OMICS + # pad (modality_id=2) stays zero — no action needed + + # Project text tokens + if text_mask.any(): + text_tokens = token_embeddings[text_mask] # (N_text, D) + output[text_mask] = self._apply_projection(self.text_proj, text_tokens) + + # Project omics tokens + if omics_mask.any(): + omics_tokens = token_embeddings[omics_mask] # (N_omics, D) + output[omics_mask] = self._apply_projection(self.omics_proj, omics_tokens) + + features["token_embeddings"] = output + return features + + def _apply_projection( + self, proj: nn.Module, tokens: torch.Tensor + ) -> torch.Tensor: + """Apply a projection head, handling BatchNorm's 2D requirement. + + BatchNorm1d expects (N, C) input. Since we gather tokens from + potentially scattered positions, they are already (N, D) — no + reshape needed. + """ + return proj(tokens) + + # ------------------------------------------------------------------ + # Properties + # ------------------------------------------------------------------ + def get_sentence_embedding_dimension(self) -> int: + """Return the shared output dimensionality.""" + return self.shared_dim + + # ------------------------------------------------------------------ + # Save / Load + # ------------------------------------------------------------------ + def save( + self, + output_path: str, + *args, + safe_serialization: bool = True, + **kwargs, + ) -> None: + """Save config and weights to disk. + + Parameters + ---------- + output_path : str + Directory where files will be written. + safe_serialization : bool + If True, use safetensors format for weights. + """ + from safetensors.torch import save_model as save_safetensors_model + + output_path = str(output_path) + os.makedirs(output_path, exist_ok=True) + + self.save_config(output_path) + + # Save weights + if safe_serialization: + save_safetensors_model(self, os.path.join(output_path, "model.safetensors")) + else: + torch.save(self.state_dict(), os.path.join(output_path, "pytorch_model.bin")) + + logger.info("Saved AdapterModule to %s", output_path) + + @classmethod + def load( + cls, + model_name_or_path: str, + subfolder: str = "", + token: bool | str | None = None, + cache_folder: str | None = None, + revision: str | None = None, + local_files_only: bool = False, + **kwargs, + ) -> AdapterModule: + """Load a saved AdapterModule from disk. + + Parameters + ---------- + model_name_or_path : str + Path to directory containing saved module files. + + Returns + ------- + AdapterModule + """ + from safetensors.torch import load_model as load_safetensors_model + + config = cls.load_config( + model_name_or_path, + subfolder=subfolder, + config_filename=cls.config_file_name, + token=token, + cache_folder=cache_folder, + revision=revision, + local_files_only=local_files_only, + ) + + module = cls(**config) + + # Load weights + load_path = model_name_or_path + if subfolder: + load_path = os.path.join(model_name_or_path, subfolder) + + safetensors_path = os.path.join(load_path, "model.safetensors") + bin_path = os.path.join(load_path, "pytorch_model.bin") + + if os.path.isfile(safetensors_path): + load_safetensors_model(module, safetensors_path) + elif os.path.isfile(bin_path): + module.load_state_dict( + torch.load(bin_path, map_location=torch.device("cpu")) + ) + else: + logger.warning( + "No weight files found in %s — module uses random init.", load_path + ) + + logger.info("Loaded AdapterModule from %s", model_name_or_path) + return module + + # ------------------------------------------------------------------ + # Repr + # ------------------------------------------------------------------ + def __repr__(self) -> str: + return ( + f"AdapterModule(" + f"text={self.text_input_dim}→{self.shared_dim}, " + f"omics={self.omics_input_dim}→{self.shared_dim}, " + f"hidden={self.hidden_dim})" + ) diff --git a/tests/test_adapter_module.py b/tests/test_adapter_module.py new file mode 100644 index 0000000..5db01d3 --- /dev/null +++ b/tests/test_adapter_module.py @@ -0,0 +1,401 @@ +"""Tests for AdapterModule — modality-aware projection for the ST pipeline. + +The AdapterModule sits after MMContextModule in the sentence-transformers +pipeline. It reads ``modality_ids`` from the features dict and applies +separate learned projections for text (modality_id=0) and omics +(modality_id=1) tokens, mapping them into a shared embedding space. + +These tests define the contract and are written FIRST (TDD). +""" + +from __future__ import annotations + +import os +import tempfile + +import numpy as np +import pytest +import torch + +from mmcontext.modules.adapter_module import AdapterModule + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- +@pytest.fixture +def tmp_dir(): + with tempfile.TemporaryDirectory() as d: + yield d + + +@pytest.fixture +def real_safetensors(): + """Undo the global safetensors.torch.load_model patch for persistence tests. + + The root conftest.py patches ``safetensors.torch.load_model`` to return None, + which prevents real weight loading. This fixture restores the original + function for tests that need actual save/load roundtrips. + """ + import importlib + import safetensors.torch + importlib.reload(safetensors.torch) + yield + # The session-scoped patch in conftest will reassert on the next test that needs it + + +@pytest.fixture +def adapter(): + """Default adapter: text_dim=32, omics_dim=8, shared_dim=16, hidden_dim=64.""" + return AdapterModule( + text_input_dim=32, + omics_input_dim=8, + shared_dim=16, + hidden_dim=64, + ) + + +@pytest.fixture +def identity_adapter(): + """Adapter in identity mode (no projection).""" + return AdapterModule( + text_input_dim=16, + omics_input_dim=16, + shared_dim=16, + hidden_dim=None, + force_identity=True, + ) + + +def _make_features( + token_embeddings: torch.Tensor, + attention_mask: torch.Tensor, + modality_ids: torch.Tensor, +) -> dict[str, torch.Tensor]: + """Helper to build a features dict matching the pipeline contract.""" + return { + "token_embeddings": token_embeddings, + "attention_mask": attention_mask, + "modality_ids": modality_ids, + } + + +# --------------------------------------------------------------------------- +# Forward — single modality +# --------------------------------------------------------------------------- +class TestForwardSingleModality: + """Tests for forward() with all-text or all-omics batches.""" + + def test_forward_text_only(self, adapter): + """All-text batch produces output with shared_dim.""" + B, L, D_text = 2, 5, 32 + features = _make_features( + token_embeddings=torch.randn(B, L, D_text), + attention_mask=torch.ones(B, L, dtype=torch.long), + modality_ids=torch.zeros(B, L, dtype=torch.long), # all text + ) + result = adapter(features) + + assert result["token_embeddings"].shape == (B, L, 16) + + def test_forward_omics_only(self, adapter): + """All-omics batch produces output with shared_dim.""" + B, L, D_omics = 3, 1, 8 + features = _make_features( + token_embeddings=torch.randn(B, L, D_omics), + attention_mask=torch.ones(B, L, dtype=torch.long), + modality_ids=torch.ones(B, L, dtype=torch.long), # all omics + ) + result = adapter(features) + + assert result["token_embeddings"].shape == (B, L, 16) + + def test_forward_text_output_dimension(self, adapter): + """Text projection maps D_text → D_shared exactly.""" + features = _make_features( + token_embeddings=torch.randn(1, 3, 32), + attention_mask=torch.ones(1, 3, dtype=torch.long), + modality_ids=torch.zeros(1, 3, dtype=torch.long), + ) + result = adapter(features) + assert result["token_embeddings"].shape[-1] == adapter.get_sentence_embedding_dimension() + + def test_forward_omics_output_dimension(self, adapter): + """Omics projection maps D_omics → D_shared exactly.""" + features = _make_features( + token_embeddings=torch.randn(1, 1, 8), + attention_mask=torch.ones(1, 1, dtype=torch.long), + modality_ids=torch.ones(1, 1, dtype=torch.long), + ) + result = adapter(features) + assert result["token_embeddings"].shape[-1] == adapter.get_sentence_embedding_dimension() + + +# --------------------------------------------------------------------------- +# Forward — mixed batch +# --------------------------------------------------------------------------- +class TestForwardMixedBatch: + """Tests for forward() with mixed text + omics tokens.""" + + def test_forward_mixed_batch(self): + """Mixed batch: text and omics tokens get different projections.""" + # Both modalities have same input dim for simplicity + adapter = AdapterModule( + text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32 + ) + B, L = 1, 4 + features = _make_features( + token_embeddings=torch.randn(B, L, 16), + attention_mask=torch.ones(B, L, dtype=torch.long), + # First 2 tokens text, last 2 omics + modality_ids=torch.tensor([[0, 0, 1, 1]], dtype=torch.long), + ) + result = adapter(features) + + assert result["token_embeddings"].shape == (B, L, 8) + + def test_pad_tokens_stay_zero(self, adapter): + """Pad tokens (modality_id=2) remain zero after projection.""" + B, L = 1, 4 + embeddings = torch.randn(B, L, 32) + embeddings[0, 3, :] = 0.0 # pad token is zero + + features = _make_features( + token_embeddings=embeddings, + attention_mask=torch.tensor([[1, 1, 1, 0]], dtype=torch.long), + modality_ids=torch.tensor([[0, 0, 0, 2]], dtype=torch.long), # last is pad + ) + result = adapter(features) + + # Pad token should remain zero + assert torch.all(result["token_embeddings"][0, 3] == 0) + + +# --------------------------------------------------------------------------- +# Separate weights +# --------------------------------------------------------------------------- +class TestSeparateWeights: + """Tests verifying text and omics projections are independent.""" + + def test_separate_weights(self, adapter): + """text_proj and omics_proj have independent parameter sets.""" + text_params = set(id(p) for p in adapter.text_proj.parameters()) + omics_params = set(id(p) for p in adapter.omics_proj.parameters()) + + # No overlap + assert text_params.isdisjoint(omics_params) + # Both have parameters + assert len(text_params) > 0 + assert len(omics_params) > 0 + + def test_text_omics_produce_different_outputs(self): + """Same input through text vs omics projection gives different results.""" + adapter = AdapterModule( + text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32 + ) + x = torch.randn(1, 3, 16) + + text_features = _make_features( + token_embeddings=x.clone(), + attention_mask=torch.ones(1, 3, dtype=torch.long), + modality_ids=torch.zeros(1, 3, dtype=torch.long), + ) + omics_features = _make_features( + token_embeddings=x.clone(), + attention_mask=torch.ones(1, 3, dtype=torch.long), + modality_ids=torch.ones(1, 3, dtype=torch.long), + ) + + text_result = adapter(text_features) + omics_result = adapter(omics_features) + + # Different projections should produce different outputs (with overwhelming probability) + assert not torch.allclose( + text_result["token_embeddings"], omics_result["token_embeddings"] + ) + + +# --------------------------------------------------------------------------- +# Gradient flow +# --------------------------------------------------------------------------- +class TestGradientFlow: + """Tests for gradient computation through adapter projections.""" + + def test_gradient_flow_text(self, adapter): + """Gradients flow through text projection.""" + features = _make_features( + token_embeddings=torch.randn(1, 3, 32, requires_grad=True), + attention_mask=torch.ones(1, 3, dtype=torch.long), + modality_ids=torch.zeros(1, 3, dtype=torch.long), + ) + result = adapter(features) + # Use squared loss: plain sum(LayerNorm(x)) has zero gradient + # because LayerNorm centers each token and summing centered + # values cancels out. Squaring breaks this cancellation. + loss = (result["token_embeddings"] ** 2).sum() + loss.backward() + + for param in adapter.text_proj.parameters(): + assert param.grad is not None + assert param.grad.abs().sum() > 0 + + def test_gradient_flow_omics(self, adapter): + """Gradients flow through omics projection.""" + features = _make_features( + token_embeddings=torch.randn(2, 1, 8, requires_grad=True), + attention_mask=torch.ones(2, 1, dtype=torch.long), + modality_ids=torch.ones(2, 1, dtype=torch.long), + ) + result = adapter(features) + loss = (result["token_embeddings"] ** 2).sum() + loss.backward() + + for param in adapter.omics_proj.parameters(): + assert param.grad is not None + assert param.grad.abs().sum() > 0 + + def test_weights_update(self, adapter): + """Optimizer step changes both projection weights.""" + optimizer = torch.optim.SGD(adapter.parameters(), lr=0.1) + + # Snapshot weights before + text_before = {n: p.clone() for n, p in adapter.text_proj.named_parameters()} + omics_before = {n: p.clone() for n, p in adapter.omics_proj.named_parameters()} + + # Forward + backward through text + features_t = _make_features( + token_embeddings=torch.randn(2, 3, 32), + attention_mask=torch.ones(2, 3, dtype=torch.long), + modality_ids=torch.zeros(2, 3, dtype=torch.long), + ) + result_t = adapter(features_t) + loss_t = result_t["token_embeddings"].sum() + + # Forward + backward through omics + features_o = _make_features( + token_embeddings=torch.randn(2, 1, 8), + attention_mask=torch.ones(2, 1, dtype=torch.long), + modality_ids=torch.ones(2, 1, dtype=torch.long), + ) + result_o = adapter(features_o) + loss_o = result_o["token_embeddings"].sum() + + total_loss = loss_t + loss_o + total_loss.backward() + optimizer.step() + + # Both projections should have changed (check total param delta, + # not per-parameter allclose, since some biases may get tiny gradients) + text_delta = sum( + (p - text_before[n]).abs().sum().item() + for n, p in adapter.text_proj.named_parameters() + ) + omics_delta = sum( + (p - omics_before[n]).abs().sum().item() + for n, p in adapter.omics_proj.named_parameters() + ) + assert text_delta > 0, "text_proj parameters did not change" + assert omics_delta > 0, "omics_proj parameters did not change" + + +# --------------------------------------------------------------------------- +# Identity mode +# --------------------------------------------------------------------------- +class TestIdentityMode: + """Tests for identity/passthrough mode.""" + + def test_identity_passthrough(self, identity_adapter): + """When force_identity=True and dims match, input passes through.""" + x = torch.randn(1, 3, 16) + features = _make_features( + token_embeddings=x.clone(), + attention_mask=torch.ones(1, 3, dtype=torch.long), + modality_ids=torch.zeros(1, 3, dtype=torch.long), + ) + result = identity_adapter(features) + torch.testing.assert_close(result["token_embeddings"], x) + + +# --------------------------------------------------------------------------- +# Properties +# --------------------------------------------------------------------------- +class TestProperties: + """Tests for module properties and metadata.""" + + def test_get_sentence_embedding_dimension(self, adapter): + """Returns D_shared.""" + assert adapter.get_sentence_embedding_dimension() == 16 + + def test_preserves_attention_mask(self, adapter): + """Attention mask passes through unchanged.""" + mask = torch.tensor([[1, 1, 0]], dtype=torch.long) + features = _make_features( + token_embeddings=torch.randn(1, 3, 32), + attention_mask=mask, + modality_ids=torch.zeros(1, 3, dtype=torch.long), + ) + result = adapter(features) + torch.testing.assert_close(result["attention_mask"], mask) + + def test_preserves_modality_ids(self, adapter): + """modality_ids pass through unchanged.""" + mod_ids = torch.tensor([[0, 0, 1]], dtype=torch.long) + features = _make_features( + token_embeddings=torch.randn(1, 3, 32), + attention_mask=torch.ones(1, 3, dtype=torch.long), + modality_ids=mod_ids, + ) + # Need same input dim for this test + adapter2 = AdapterModule( + text_input_dim=32, omics_input_dim=32, shared_dim=16, hidden_dim=64 + ) + result = adapter2(features) + torch.testing.assert_close(result["modality_ids"], mod_ids) + + +# --------------------------------------------------------------------------- +# Persistence +# --------------------------------------------------------------------------- +class TestPersistence: + """Tests for save/load roundtrip.""" + + def test_save_creates_files(self, adapter, tmp_dir, real_safetensors): + """save() creates config and weight files on disk.""" + adapter.save(tmp_dir) + + config_path = os.path.join(tmp_dir, adapter.config_file_name) + assert os.path.isfile(config_path) + assert os.path.isfile(os.path.join(tmp_dir, "model.safetensors")) + + def test_save_load_roundtrip(self, adapter, tmp_dir, real_safetensors): + """Config and weights survive save/load cycle.""" + adapter.save(tmp_dir) + loaded = AdapterModule.load(tmp_dir) + + assert loaded.get_sentence_embedding_dimension() == adapter.get_sentence_embedding_dimension() + assert loaded.text_input_dim == adapter.text_input_dim + assert loaded.omics_input_dim == adapter.omics_input_dim + + def test_save_load_produces_same_output(self, adapter, tmp_dir, real_safetensors): + """Loaded adapter produces identical output to original.""" + adapter.eval() + x = torch.randn(1, 3, 32) + features = _make_features( + token_embeddings=x, + attention_mask=torch.ones(1, 3, dtype=torch.long), + modality_ids=torch.zeros(1, 3, dtype=torch.long), + ) + original_out = adapter(features)["token_embeddings"].detach() + + adapter.save(tmp_dir) + loaded = AdapterModule.load(tmp_dir) + loaded.eval() + + features2 = _make_features( + token_embeddings=x, + attention_mask=torch.ones(1, 3, dtype=torch.long), + modality_ids=torch.zeros(1, 3, dtype=torch.long), + ) + loaded_out = loaded(features2)["token_embeddings"].detach() + + torch.testing.assert_close(original_out, loaded_out) From 0851aa02e5a2a7c27de8efd78dff8b9ae4d15d28 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Wed, 27 May 2026 16:49:14 +0200 Subject: [PATCH 04/67] =?UTF-8?q?Phase=204:=20Add=20OmicsAttentionModule?= =?UTF-8?q?=20=E2=80=94=20optional=20self-attention=20for=20omics=20tokens?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Multi-head self-attention applied only to omics tokens (modality_id=1) - Text tokens pass through unchanged; pad tokens zeroed - Uses nn.TransformerEncoder with pre-LayerNorm (norm_first=True) - Per-sample gather/scatter handles variable-length gene sequences - Trivial for obs-level (L=1): degenerates to feedforward, safe to include - Configurable: input_dim, num_heads, num_layers, feedforward_dim, dropout - Save/load via safetensors + config roundtrip - 20 tests: passthrough, transformation, masking, gradient flow, persistence - Updated modules/__init__.py to export OmicsAttentionModule --- src/mmcontext/modules/__init__.py | 5 +- .../modules/omics_attention_module.py | 319 ++++++++++++ tests/test_omics_attention_module.py | 458 ++++++++++++++++++ 3 files changed, 780 insertions(+), 2 deletions(-) create mode 100644 src/mmcontext/modules/omics_attention_module.py create mode 100644 tests/test_omics_attention_module.py diff --git a/src/mmcontext/modules/__init__.py b/src/mmcontext/modules/__init__.py index 3b8a983..eb7c0ef 100644 --- a/src/mmcontext/modules/__init__.py +++ b/src/mmcontext/modules/__init__.py @@ -5,10 +5,11 @@ - :class:`MMContextModule` — InputModule: text encoder + omics pass-through - :class:`AdapterModule` — modality-aware projection to shared space -- OmicsAttentionModule (Phase 4) — optional self-attention for var-based models +- :class:`OmicsAttentionModule` — optional self-attention for var-based models """ from .adapter_module import AdapterModule from .mmcontext_module import MMContextModule +from .omics_attention_module import OmicsAttentionModule -__all__ = ["MMContextModule", "AdapterModule"] +__all__ = ["MMContextModule", "AdapterModule", "OmicsAttentionModule"] diff --git a/src/mmcontext/modules/omics_attention_module.py b/src/mmcontext/modules/omics_attention_module.py new file mode 100644 index 0000000..01fce46 --- /dev/null +++ b/src/mmcontext/modules/omics_attention_module.py @@ -0,0 +1,319 @@ +"""OmicsAttentionModule — optional self-attention for omics tokens. + +This module sits between :class:`MMContextModule` and :class:`AdapterModule` +in a sentence-transformers pipeline. It applies multi-head self-attention +ONLY to omics tokens (modality_id=1), leaving text tokens unchanged. + +This is useful for var-level models where each sample has multiple gene +embeddings that benefit from contextual mixing before projection into the +shared space. + +Architecture +------------ +A stack of standard ``nn.TransformerEncoderLayer`` modules with pre-LayerNorm. +Only omics tokens are gathered, attended, and scattered back. Text tokens +(modality_id=0) and pad tokens (modality_id=2) pass through untouched. + +For obs-level inputs (L=1), self-attention is trivial — the single token +attends only to itself, which is essentially a feedforward pass. This is +fine: the module adds negligible overhead for obs-level data and can be +left in the pipeline without harm. + +Features dict contract:: + + # Input (from MMContextModule.forward): + { + "token_embeddings": Tensor[B, L, D], + "attention_mask": Tensor[B, L], + "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad + } + + # Output (after OmicsAttentionModule.forward): + { + "token_embeddings": Tensor[B, L, D], # omics tokens attended + "attention_mask": Tensor[B, L], # unchanged + "modality_ids": Tensor[B, L], # unchanged + } + +Example +------- +>>> attn = OmicsAttentionModule(input_dim=768, num_heads=8, num_layers=2) +>>> features = mmcontext_module.forward(...) +>>> features = attn(features) +>>> features = adapter_module(features) +""" + +from __future__ import annotations + +import logging +import os +from typing import Any + +import torch +import torch.nn as nn + +# Try v5.4+ import paths first, fall back to v5.0 paths +try: + from sentence_transformers.base.modules import Module +except ImportError: + from sentence_transformers.models import Module + +logger = logging.getLogger(__name__) + +# Modality constants (must match mmcontext_module.py / adapter_module.py) +MODALITY_TEXT = 0 +MODALITY_OMICS = 1 +MODALITY_PAD = 2 + + +class OmicsAttentionModule(Module): + """Self-attention module applied only to omics tokens. + + Maintains a stack of ``TransformerEncoderLayer`` blocks. Only omics + tokens (modality_id=1) are gathered, fed through the attention layers, + and scattered back into the output tensor. Text and pad tokens pass + through unchanged. + + Parameters + ---------- + input_dim : int + Dimensionality of the token embeddings (must match the upstream + encoder's hidden size). + num_heads : int, optional + Number of attention heads. Must evenly divide ``input_dim``. + Default: 4. + num_layers : int, optional + Number of stacked TransformerEncoderLayer blocks. Default: 1. + feedforward_dim : int or None, optional + Hidden size of the feedforward network inside each layer. + If None, defaults to ``4 * input_dim``. Default: None. + dropout : float, optional + Dropout probability for attention weights and feedforward layers. + Default: 0.1. + """ + + config_keys: list[str] = [ + "input_dim", + "num_heads", + "num_layers", + "feedforward_dim", + "dropout", + ] + config_file_name: str = "omics_attention_module_config.json" + + def __init__( + self, + input_dim: int, + num_heads: int = 4, + num_layers: int = 1, + feedforward_dim: int | None = None, + dropout: float = 0.1, + ) -> None: + super().__init__() + + self.input_dim = input_dim + self.num_heads = num_heads + self.num_layers = num_layers + self.feedforward_dim = feedforward_dim if feedforward_dim else 4 * input_dim + self.dropout = dropout + + # Build attention stack + encoder_layer = nn.TransformerEncoderLayer( + d_model=input_dim, + nhead=num_heads, + dim_feedforward=self.feedforward_dim, + dropout=dropout, + batch_first=True, + norm_first=True, # pre-LayerNorm for training stability + ) + self.attention = nn.TransformerEncoder( + encoder_layer, + num_layers=num_layers, + ) + + # ------------------------------------------------------------------ + # Forward (Module abstract method) + # ------------------------------------------------------------------ + def forward( + self, + features: dict[str, torch.Tensor | Any], + **kwargs, + ) -> dict[str, torch.Tensor | Any]: + """Apply self-attention to omics tokens only. + + Text tokens and pad tokens pass through unchanged. Omics tokens + are gathered per sample, attended, and scattered back. + + Parameters + ---------- + features : dict + Must contain ``token_embeddings`` (B, L, D), ``attention_mask`` + (B, L), and ``modality_ids`` (B, L). + + Returns + ------- + dict + Updated features with attended omics token embeddings. + """ + token_embeddings = features["token_embeddings"] + modality_ids = features["modality_ids"] + attention_mask = features["attention_mask"] + + B, L, D = token_embeddings.shape + device = token_embeddings.device + + # Start with a copy of the input + output = token_embeddings.clone() + + # Check if there are any omics tokens at all + omics_mask = modality_ids == MODALITY_OMICS # (B, L) + if not omics_mask.any(): + return features + + # Process each sample independently — omics sequences can have + # different lengths across samples in the batch + attended_samples = [] + sample_indices = [] + + for b in range(B): + omics_positions = omics_mask[b].nonzero(as_tuple=True)[0] # positions of omics tokens + if len(omics_positions) == 0: + continue + + # Gather omics tokens for this sample: (N_omics, D) + omics_tokens = token_embeddings[b, omics_positions, :].unsqueeze(0) # (1, N_omics, D) + + # Build attention mask for these tokens + # The overall attention_mask tells us which positions are real + omics_attn_mask = attention_mask[b, omics_positions].unsqueeze(0) # (1, N_omics) + + # Convert to the format nn.TransformerEncoder expects: + # src_key_padding_mask: True = ignore, False = attend + padding_mask = omics_attn_mask == 0 # (1, N_omics) + + # Apply attention + attended = self.attention( + omics_tokens, + src_key_padding_mask=padding_mask, + ) # (1, N_omics, D) + + # Scatter back into output + output[b, omics_positions, :] = attended.squeeze(0) + + # Zero out pad positions to be safe + pad_mask = modality_ids == MODALITY_PAD + if pad_mask.any(): + output[pad_mask] = 0.0 + + features = dict(features) # shallow copy to avoid mutating input + features["token_embeddings"] = output + return features + + # ------------------------------------------------------------------ + # Properties + # ------------------------------------------------------------------ + def get_sentence_embedding_dimension(self) -> int: + """Return the output dimensionality (same as input — attention preserves dim).""" + return self.input_dim + + # ------------------------------------------------------------------ + # Save / Load + # ------------------------------------------------------------------ + def save( + self, + output_path: str, + *args, + safe_serialization: bool = True, + **kwargs, + ) -> None: + """Save config and weights to disk. + + Parameters + ---------- + output_path : str + Directory where files will be written. + safe_serialization : bool + If True, use safetensors format for weights. + """ + from safetensors.torch import save_model as save_safetensors_model + + output_path = str(output_path) + os.makedirs(output_path, exist_ok=True) + + self.save_config(output_path) + + if safe_serialization: + save_safetensors_model(self, os.path.join(output_path, "model.safetensors")) + else: + torch.save(self.state_dict(), os.path.join(output_path, "pytorch_model.bin")) + + logger.info("Saved OmicsAttentionModule to %s", output_path) + + @classmethod + def load( + cls, + model_name_or_path: str, + subfolder: str = "", + token: bool | str | None = None, + cache_folder: str | None = None, + revision: str | None = None, + local_files_only: bool = False, + **kwargs, + ) -> OmicsAttentionModule: + """Load a saved OmicsAttentionModule from disk. + + Parameters + ---------- + model_name_or_path : str + Path to directory containing saved module files. + + Returns + ------- + OmicsAttentionModule + """ + from safetensors.torch import load_model as load_safetensors_model + + config = cls.load_config( + model_name_or_path, + subfolder=subfolder, + config_filename=cls.config_file_name, + token=token, + cache_folder=cache_folder, + revision=revision, + local_files_only=local_files_only, + ) + + module = cls(**config) + + load_path = model_name_or_path + if subfolder: + load_path = os.path.join(model_name_or_path, subfolder) + + safetensors_path = os.path.join(load_path, "model.safetensors") + bin_path = os.path.join(load_path, "pytorch_model.bin") + + if os.path.isfile(safetensors_path): + load_safetensors_model(module, safetensors_path) + elif os.path.isfile(bin_path): + module.load_state_dict( + torch.load(bin_path, map_location=torch.device("cpu")) + ) + else: + logger.warning( + "No weight files found in %s — module uses random init.", load_path + ) + + logger.info("Loaded OmicsAttentionModule from %s", model_name_or_path) + return module + + # ------------------------------------------------------------------ + # Repr + # ------------------------------------------------------------------ + def __repr__(self) -> str: + return ( + f"OmicsAttentionModule(" + f"dim={self.input_dim}, " + f"heads={self.num_heads}, " + f"layers={self.num_layers}, " + f"ff={self.feedforward_dim})" + ) diff --git a/tests/test_omics_attention_module.py b/tests/test_omics_attention_module.py new file mode 100644 index 0000000..e9319d9 --- /dev/null +++ b/tests/test_omics_attention_module.py @@ -0,0 +1,458 @@ +"""Tests for OmicsAttentionModule — optional self-attention for omics tokens. + +The OmicsAttentionModule sits between MMContextModule and AdapterModule in the +sentence-transformers pipeline. It applies multi-head self-attention ONLY to +omics tokens (modality_id=1), leaving text tokens (modality_id=0) unchanged. +This is useful for var-level models where each sample has multiple gene +embeddings that benefit from contextual mixing before projection. + +These tests define the contract and are written FIRST (TDD). +""" + +from __future__ import annotations + +import os +import tempfile + +import pytest +import torch + +from mmcontext.modules.omics_attention_module import OmicsAttentionModule + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- +@pytest.fixture +def tmp_dir(): + with tempfile.TemporaryDirectory() as d: + yield d + + +@pytest.fixture +def real_safetensors(): + """Undo the global safetensors.torch.load_model patch for persistence tests.""" + import importlib + import safetensors.torch + importlib.reload(safetensors.torch) + yield + + +@pytest.fixture +def module(): + """Default module: input_dim=16, num_heads=2, num_layers=1.""" + return OmicsAttentionModule( + input_dim=16, + num_heads=2, + num_layers=1, + dropout=0.0, + ) + + +@pytest.fixture +def deep_module(): + """Deeper module for testing multi-layer stacking.""" + return OmicsAttentionModule( + input_dim=16, + num_heads=2, + num_layers=3, + dropout=0.0, + ) + + +def _make_features( + token_embeddings: torch.Tensor, + attention_mask: torch.Tensor, + modality_ids: torch.Tensor, +) -> dict[str, torch.Tensor]: + """Helper to build a features dict matching the pipeline contract.""" + return { + "token_embeddings": token_embeddings, + "attention_mask": attention_mask, + "modality_ids": modality_ids, + } + + +# --------------------------------------------------------------------------- +# Text passthrough +# --------------------------------------------------------------------------- +class TestTextPassthrough: + """Text tokens must pass through completely unchanged.""" + + def test_text_passthrough(self, module): + """Text tokens (modality_id=0) are identical before and after the module.""" + B, L = 2, 5 + x = torch.randn(B, L, 16) + features = _make_features( + token_embeddings=x.clone(), + attention_mask=torch.ones(B, L, dtype=torch.long), + modality_ids=torch.zeros(B, L, dtype=torch.long), # all text + ) + module.eval() + result = module(features) + + torch.testing.assert_close(result["token_embeddings"], x) + + def test_text_passthrough_in_mixed_batch(self, module): + """Text tokens in a mixed batch remain unchanged.""" + B, L = 1, 6 + x = torch.randn(B, L, 16) + modality_ids = torch.tensor([[0, 0, 0, 1, 1, 1]], dtype=torch.long) + features = _make_features( + token_embeddings=x.clone(), + attention_mask=torch.ones(B, L, dtype=torch.long), + modality_ids=modality_ids, + ) + module.eval() + result = module(features) + + # Text tokens (first 3) should be unchanged + torch.testing.assert_close( + result["token_embeddings"][:, :3, :], x[:, :3, :] + ) + + +# --------------------------------------------------------------------------- +# Omics transformation +# --------------------------------------------------------------------------- +class TestOmicsTransformed: + """Omics tokens should be modified by self-attention.""" + + def test_omics_transformed(self, module): + """Omics tokens (modality_id=1) are modified by self-attention.""" + B, L = 1, 4 + x = torch.randn(B, L, 16) + features = _make_features( + token_embeddings=x.clone(), + attention_mask=torch.ones(B, L, dtype=torch.long), + modality_ids=torch.ones(B, L, dtype=torch.long), # all omics + ) + module.eval() + result = module(features) + + # Output should differ from input (attention mixes information) + assert not torch.allclose( + result["token_embeddings"], x, atol=1e-6 + ), "Omics tokens should be transformed by self-attention" + + def test_omics_transformed_in_mixed_batch(self, module): + """Omics tokens in a mixed batch are modified.""" + B, L = 1, 6 + x = torch.randn(B, L, 16) + modality_ids = torch.tensor([[0, 0, 0, 1, 1, 1]], dtype=torch.long) + features = _make_features( + token_embeddings=x.clone(), + attention_mask=torch.ones(B, L, dtype=torch.long), + modality_ids=modality_ids, + ) + module.eval() + result = module(features) + + # Omics tokens (last 3) should be modified + assert not torch.allclose( + result["token_embeddings"][:, 3:, :], x[:, 3:, :], atol=1e-6 + ), "Omics tokens should be transformed by self-attention" + + +# --------------------------------------------------------------------------- +# Attention mask respected +# --------------------------------------------------------------------------- +class TestAttentionMask: + """Padded positions must not influence real tokens.""" + + def test_attention_mask_respected(self, module): + """Padded omics tokens don't affect real omics tokens. + + We verify by comparing outputs with and without a pad token — the + real tokens' representations should differ when the pad is replaced + by a real token (showing attention sees it), and should NOT change + when the pad is properly masked. + """ + module.eval() + D = 16 + + # Batch element with 3 real omics tokens + 1 pad + x = torch.randn(1, 4, D) + mask_with_pad = torch.tensor([[1, 1, 1, 0]], dtype=torch.long) + modality_ids = torch.tensor([[1, 1, 1, 2]], dtype=torch.long) + + features_padded = _make_features( + token_embeddings=x.clone(), + attention_mask=mask_with_pad, + modality_ids=modality_ids, + ) + out_padded = module(features_padded)["token_embeddings"][:, :3, :] + + # Same real tokens but with a different value in the pad position + x2 = x.clone() + x2[0, 3, :] = torch.randn(D) * 100 # very different pad content + + features_padded2 = _make_features( + token_embeddings=x2, + attention_mask=mask_with_pad, + modality_ids=modality_ids, + ) + out_padded2 = module(features_padded2)["token_embeddings"][:, :3, :] + + # Real tokens should produce same output regardless of pad content + torch.testing.assert_close(out_padded, out_padded2, atol=1e-5, rtol=1e-5) + + +# --------------------------------------------------------------------------- +# Variable-length sequences +# --------------------------------------------------------------------------- +class TestVariableLengthSequences: + """Different-length gene sequences in the same batch.""" + + def test_variable_length_sequences(self, module): + """Batch with different numbers of omics tokens per sample.""" + module.eval() + B, L, D = 2, 5, 16 + x = torch.randn(B, L, D) + + # Sample 0: 3 omics tokens + 2 pad + # Sample 1: 5 omics tokens + 0 pad + modality_ids = torch.tensor([ + [1, 1, 1, 2, 2], + [1, 1, 1, 1, 1], + ], dtype=torch.long) + attention_mask = torch.tensor([ + [1, 1, 1, 0, 0], + [1, 1, 1, 1, 1], + ], dtype=torch.long) + + features = _make_features( + token_embeddings=x.clone(), + attention_mask=attention_mask, + modality_ids=modality_ids, + ) + result = module(features) + + # Output shape preserved + assert result["token_embeddings"].shape == (B, L, D) + + # Pad positions should remain zero (or unchanged) + # The module should not produce non-zero values for pad positions + assert torch.all(result["token_embeddings"][0, 3:, :] == 0) or \ + torch.allclose(result["token_embeddings"][0, 3:, :], x[0, 3:, :]) + + +# --------------------------------------------------------------------------- +# Output shape preserved +# --------------------------------------------------------------------------- +class TestOutputShape: + """Output shape must be identical to input shape.""" + + def test_output_shape_preserved(self, module): + """(B, L, D) shape is unchanged after the module.""" + B, L, D = 3, 7, 16 + features = _make_features( + token_embeddings=torch.randn(B, L, D), + attention_mask=torch.ones(B, L, dtype=torch.long), + modality_ids=torch.ones(B, L, dtype=torch.long), + ) + result = module(features) + assert result["token_embeddings"].shape == (B, L, D) + + def test_output_shape_with_deep_module(self, deep_module): + """Shape preserved through multiple attention layers.""" + B, L, D = 2, 4, 16 + features = _make_features( + token_embeddings=torch.randn(B, L, D), + attention_mask=torch.ones(B, L, dtype=torch.long), + modality_ids=torch.ones(B, L, dtype=torch.long), + ) + result = deep_module(features) + assert result["token_embeddings"].shape == (B, L, D) + + def test_preserves_attention_mask(self, module): + """attention_mask passes through unchanged.""" + mask = torch.tensor([[1, 1, 0]], dtype=torch.long) + features = _make_features( + token_embeddings=torch.randn(1, 3, 16), + attention_mask=mask, + modality_ids=torch.ones(1, 3, dtype=torch.long), + ) + result = module(features) + torch.testing.assert_close(result["attention_mask"], mask) + + def test_preserves_modality_ids(self, module): + """modality_ids pass through unchanged.""" + mod_ids = torch.tensor([[0, 1, 1, 2]], dtype=torch.long) + features = _make_features( + token_embeddings=torch.randn(1, 4, 16), + attention_mask=torch.tensor([[1, 1, 1, 0]], dtype=torch.long), + modality_ids=mod_ids, + ) + result = module(features) + torch.testing.assert_close(result["modality_ids"], mod_ids) + + +# --------------------------------------------------------------------------- +# Gradient flow +# --------------------------------------------------------------------------- +class TestGradientFlow: + """Gradients must flow through the attention layers.""" + + def test_gradient_flow(self, module): + """Gradients flow through attention to omics tokens.""" + x = torch.randn(2, 4, 16, requires_grad=True) + features = _make_features( + token_embeddings=x, + attention_mask=torch.ones(2, 4, dtype=torch.long), + modality_ids=torch.ones(2, 4, dtype=torch.long), + ) + result = module(features) + loss = (result["token_embeddings"] ** 2).sum() + loss.backward() + + # Input should have gradients + assert x.grad is not None + assert x.grad.abs().sum() > 0 + + # Module parameters should have gradients + has_grad = False + for p in module.parameters(): + if p.grad is not None and p.grad.abs().sum() > 0: + has_grad = True + break + assert has_grad, "At least one module parameter should have non-zero gradient" + + def test_gradient_only_through_omics(self, module): + """In a mixed batch, text token gradients are zero (passthrough).""" + x = torch.randn(1, 4, 16, requires_grad=True) + modality_ids = torch.tensor([[0, 0, 1, 1]], dtype=torch.long) + features = _make_features( + token_embeddings=x, + attention_mask=torch.ones(1, 4, dtype=torch.long), + modality_ids=modality_ids, + ) + result = module(features) + # Only compute loss on omics tokens + loss = (result["token_embeddings"][:, 2:, :] ** 2).sum() + loss.backward() + + # Omics positions should have gradients + assert x.grad[:, 2:, :].abs().sum() > 0 + + def test_weights_update(self, module): + """Optimizer step changes attention weights.""" + optimizer = torch.optim.SGD(module.parameters(), lr=0.1) + + params_before = {n: p.clone() for n, p in module.named_parameters()} + + features = _make_features( + token_embeddings=torch.randn(2, 4, 16), + attention_mask=torch.ones(2, 4, dtype=torch.long), + modality_ids=torch.ones(2, 4, dtype=torch.long), + ) + result = module(features) + loss = (result["token_embeddings"] ** 2).sum() + loss.backward() + optimizer.step() + + total_delta = sum( + (p - params_before[n]).abs().sum().item() + for n, p in module.named_parameters() + ) + assert total_delta > 0, "Parameters did not change after optimizer step" + + +# --------------------------------------------------------------------------- +# Single token sequence (obs-like) +# --------------------------------------------------------------------------- +class TestSingleTokenSequence: + """Obs-like input with L=1 must work without error.""" + + def test_single_token_sequence(self, module): + """Single omics token (obs-level) passes through without error.""" + module.eval() + B, L, D = 3, 1, 16 + x = torch.randn(B, L, D) + features = _make_features( + token_embeddings=x.clone(), + attention_mask=torch.ones(B, L, dtype=torch.long), + modality_ids=torch.ones(B, L, dtype=torch.long), + ) + result = module(features) + + # Shape preserved + assert result["token_embeddings"].shape == (B, L, D) + + def test_single_token_no_nan(self, module): + """Single token doesn't produce NaN (self-attention on length-1 is trivial).""" + module.eval() + x = torch.randn(1, 1, 16) + features = _make_features( + token_embeddings=x.clone(), + attention_mask=torch.ones(1, 1, dtype=torch.long), + modality_ids=torch.ones(1, 1, dtype=torch.long), + ) + result = module(features) + assert not torch.isnan(result["token_embeddings"]).any() + + +# --------------------------------------------------------------------------- +# Persistence +# --------------------------------------------------------------------------- +class TestPersistence: + """Save/load roundtrip tests.""" + + def test_save_creates_files(self, module, tmp_dir, real_safetensors): + """save() creates config and weight files on disk.""" + module.save(tmp_dir) + + config_path = os.path.join(tmp_dir, module.config_file_name) + assert os.path.isfile(config_path) + assert os.path.isfile(os.path.join(tmp_dir, "model.safetensors")) + + def test_save_load_roundtrip(self, module, tmp_dir, real_safetensors): + """Config and weights survive save/load cycle.""" + module.save(tmp_dir) + loaded = OmicsAttentionModule.load(tmp_dir) + + assert loaded.get_sentence_embedding_dimension() == module.get_sentence_embedding_dimension() + assert loaded.input_dim == module.input_dim + assert loaded.num_heads == module.num_heads + assert loaded.num_layers == module.num_layers + + def test_save_load_produces_same_output(self, module, tmp_dir, real_safetensors): + """Loaded module produces identical output to original.""" + module.eval() + x = torch.randn(2, 4, 16) + features = _make_features( + token_embeddings=x, + attention_mask=torch.ones(2, 4, dtype=torch.long), + modality_ids=torch.ones(2, 4, dtype=torch.long), + ) + original_out = module(features)["token_embeddings"].detach() + + module.save(tmp_dir) + loaded = OmicsAttentionModule.load(tmp_dir) + loaded.eval() + + features2 = _make_features( + token_embeddings=x, + attention_mask=torch.ones(2, 4, dtype=torch.long), + modality_ids=torch.ones(2, 4, dtype=torch.long), + ) + loaded_out = loaded(features2)["token_embeddings"].detach() + + torch.testing.assert_close(original_out, loaded_out) + + +# --------------------------------------------------------------------------- +# Properties +# --------------------------------------------------------------------------- +class TestProperties: + """Module metadata and properties.""" + + def test_get_sentence_embedding_dimension(self, module): + """Returns input_dim (self-attention doesn't change dimensionality).""" + assert module.get_sentence_embedding_dimension() == 16 + + def test_repr(self, module): + """repr contains key config info.""" + r = repr(module) + assert "16" in r # input_dim + assert "2" in r # num_heads From 27169d1c645871ff61a33d194ace439067460a31 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Mon, 1 Jun 2026 12:48:31 +0200 Subject: [PATCH 05/67] update wandb version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 70f3374..fc44b81 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,7 +39,7 @@ dependencies = [ "sentence-transformers>=5.4", "torch>=2.5", "transformers>=4.57.1", - "wandb>=0.21", + "wandb>=0.27", "zarr<3", ] optional-dependencies.dev = [ From c88c3a80de79e6537c3e2ab156c54382515b7d21 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Mon, 1 Jun 2026 12:48:48 +0200 Subject: [PATCH 06/67] update wandb version --- uv.lock | 27 +++++++++++++-------------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/uv.lock b/uv.lock index fb729a3..8dc5ca6 100644 --- a/uv.lock +++ b/uv.lock 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input_values for compatibility with ST training collator's collect_features suffix matching - Forward still writes token_embeddings for downstream module compatibility - Memory-efficient VectorStore preparation from zarr adata links (reads only needed obsm rows, never loads full AnnData) - Training script for cxg_schaefer_tiny dataset with wandb integration - Fix fp16 test pollution of session-scoped stubs - Post-training save/load roundtrip test --- scripts/train_tiny.py | 295 +++++++++++ src/mmcontext/io/__init__.py | 5 +- src/mmcontext/io/prepare_store.py | 312 +++++++++++ src/mmcontext/modules/mmcontext_module.py | 28 +- tests/test_mmcontext_module.py | 18 +- tests/test_prepare_store.py | 262 ++++++++++ tests/test_st_integration.py | 606 ++++++++++++++++++++++ 7 files changed, 1505 insertions(+), 21 deletions(-) create mode 100644 scripts/train_tiny.py create mode 100644 src/mmcontext/io/prepare_store.py create mode 100644 tests/test_prepare_store.py create mode 100644 tests/test_st_integration.py diff --git a/scripts/train_tiny.py b/scripts/train_tiny.py new file mode 100644 index 0000000..a4594fc --- /dev/null +++ b/scripts/train_tiny.py @@ -0,0 +1,295 @@ +#!/usr/bin/env python +"""Train an MMContext model on the cxg_schaefer_tiny dataset. + +Quick-start training script using sentence-transformers v5.4+ pipeline. +Supports two modes: + + 1. **gene-list + text** (default) — gene-name strings as anchors, text + descriptions as positives. No VectorStore needed. + 2. **bimodal** — omics vectors from a VectorStore as anchors, text as + positives. Requires ``--vector-store`` pointing to a ``.mmap`` file + that covers the ``sample_idx`` column of the dataset. + +Usage:: + + # Gene-list mode (default) + python scripts/train_tiny.py --output-dir outputs/tiny_genelist + + # Bimodal mode + python scripts/train_tiny.py --mode bimodal \ + --vector-store /path/to/store.mmap \ + --output-dir outputs/tiny_bimodal + + # Custom text encoder + python scripts/train_tiny.py --text-model dmis-lab/biobert-base-cased-v1.2 +""" + +from __future__ import annotations + +import argparse +import logging +import os +import sys + +import numpy as np +import torch +from datasets import load_dataset +from sentence_transformers import ( + SentenceTransformer, + SentenceTransformerTrainer, + SentenceTransformerTrainingArguments, +) +from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss +from sentence_transformers.sentence_transformer.modules import Pooling, Normalize + +from mmcontext.modules import MMContextModule, AdapterModule + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s %(message)s") +logger = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# Dataset helpers +# --------------------------------------------------------------------------- +HF_DATASET = "jo-mengr/cxg_schaefer_tiny" + + +def prepare_genelist_dataset(ds): + """Reshape dataset for gene-list + text training. + + Returns a HF Dataset with columns ``anchor`` (gene names) and + ``positive`` (text description). + """ + # cell_sentence_1 = space-separated gene names → anchor + ds = ds.rename_columns({"cell_sentence_1": "anchor"}) + ds = ds.select_columns(["anchor", "positive"]) + logger.info("Gene-list dataset: %d samples, columns=%s", len(ds), ds.column_names) + return ds + + +def prepare_bimodal_dataset(ds): + """Reshape dataset for bimodal (omics + text) training. + + Prefixes ``sample_idx`` with ``omics:`` so MMContextModule routes them + through the VectorStore path. + + Returns a HF Dataset with columns ``anchor`` and ``positive``. + """ + def prefix_omics(example): + example["anchor"] = f"omics:{example['sample_idx']}" + return example + + ds = ds.map(prefix_omics) + ds = ds.select_columns(["anchor", "positive"]) + logger.info("Bimodal dataset: %d samples, columns=%s", len(ds), ds.column_names) + return ds + + +# --------------------------------------------------------------------------- +# Pipeline builder +# --------------------------------------------------------------------------- +def build_pipeline( + text_model: str, + omics_dim: int | None = None, + shared_dim: int = 256, +) -> SentenceTransformer: + """Build the MMContext sentence-transformer pipeline. + + Parameters + ---------- + text_model + HuggingFace model name/path for the text encoder. + omics_dim + Dimension of omics vectors (only needed for bimodal mode). + If None, the adapter omics head is sized to match text_dim. + shared_dim + Output dimension of the shared embedding space. + """ + mmcontext = MMContextModule(model_name_or_path=text_model) + text_dim = mmcontext.get_word_embedding_dimension() + + if omics_dim is None: + omics_dim = text_dim + + adapter = AdapterModule( + text_input_dim=text_dim, + omics_input_dim=omics_dim, + shared_dim=shared_dim, + ) + pooling = Pooling(embedding_dimension=shared_dim, pooling_mode="mean") + normalize = Normalize() + + pipeline = SentenceTransformer(modules=[mmcontext, adapter, pooling, normalize]) + logger.info( + "Pipeline: text_dim=%d, omics_dim=%d, shared_dim=%d, params=%d", + text_dim, + omics_dim, + shared_dim, + sum(p.numel() for p in pipeline.parameters()), + ) + return pipeline + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- +def main(): + parser = argparse.ArgumentParser(description="Train MMContext on cxg_schaefer_tiny") + parser.add_argument( + "--mode", + choices=["genelist", "bimodal"], + default="genelist", + help="Training mode (default: genelist)", + ) + parser.add_argument( + "--text-model", + default="NeuML/pubmedbert-base-embeddings", + help="HuggingFace text encoder (default: NeuML/pubmedbert-base-embeddings)", + ) + parser.add_argument( + "--vector-store", + default=None, + help="Path to .mmap VectorStore file. For bimodal mode: if omitted, " + "the store is built automatically from adata_link + sample_idx " + "columns using --obsm-key.", + ) + parser.add_argument( + "--obsm-key", + default="X_scvi_fm", + help="obsm key to extract when building VectorStore (default: X_scvi_fm). " + "Other common choices: X_pca, X_geneformer, X_gs10k", + ) + parser.add_argument("--omics-dim", type=int, default=None, help="Omics vector dimension") + parser.add_argument("--shared-dim", type=int, default=256, help="Shared embedding dim (default: 256)") + parser.add_argument("--output-dir", default="outputs/tiny_model", help="Output directory") + parser.add_argument("--epochs", type=int, default=3, help="Training epochs (default: 3)") + parser.add_argument("--batch-size", type=int, default=32, help="Batch size (default: 8)") + parser.add_argument("--lr", type=float, default=2e-5, help="Learning rate (default: 2e-5)") + parser.add_argument("--dataset", default=HF_DATASET, help="HuggingFace dataset name") + parser.add_argument( + "--use-mps-device", + action=argparse.BooleanOptionalAction, + default=torch.backends.mps.is_available(), + help="Train on Apple Metal (MPS); default: on when MPS is available", + ) + parser.add_argument( + "--wandb-project", + default=None, + help="Weights & Biases project name. Enables wandb logging when set. " + "You can also set WANDB_PROJECT env var instead.", + ) + parser.add_argument( + "--wandb-run-name", + default=None, + help="Optional W&B run name (auto-generated if omitted)", + ) + args = parser.parse_args() + + # --- Dataset --- + ds_raw = load_dataset(args.dataset, split="train") + if args.mode == "bimodal": + ds = prepare_bimodal_dataset(ds_raw) + else: + ds = prepare_genelist_dataset(ds_raw) + + # --- Pipeline --- + pipeline = build_pipeline( + text_model=args.text_model, + omics_dim=args.omics_dim, + shared_dim=args.shared_dim, + ) + + # Attach VectorStore for bimodal mode + if args.mode == "bimodal": + from mmcontext.io import VectorStore, prepare_vector_store + + if args.vector_store is not None: + store = VectorStore.load(args.vector_store) + else: + # Auto-build from adata_link column + store_path = os.path.join(args.output_dir, "vector_store.mmap") + store = prepare_vector_store( + ds_raw, + obsm_key=args.obsm_key, + output_path=store_path, + ) + + first_module = list(pipeline.children())[0] + first_module.set_vector_store(store) + + # Infer omics_dim if not set + if args.omics_dim is None: + args.omics_dim = store.dim + # Rebuild pipeline with correct omics_dim + pipeline = build_pipeline( + text_model=args.text_model, + omics_dim=args.omics_dim, + shared_dim=args.shared_dim, + ) + first_module = list(pipeline.children())[0] + first_module.set_vector_store(store) + + logger.info("VectorStore: %d vectors, dim=%d", len(store), store.dim) + + # --- Wandb setup --- + wandb_project = args.wandb_project or os.environ.get("WANDB_PROJECT") + use_wandb = wandb_project is not None + if use_wandb: + os.environ["WANDB_PROJECT"] = wandb_project + if args.wandb_run_name: + os.environ["WANDB_NAME"] = args.wandb_run_name + logger.info("W&B enabled: project=%s, run=%s", wandb_project, args.wandb_run_name or "(auto)") + + # --- Training --- + loss = MultipleNegativesRankingLoss(pipeline) + training_args = SentenceTransformerTrainingArguments( + output_dir=args.output_dir, + num_train_epochs=args.epochs, + per_device_train_batch_size=args.batch_size, + learning_rate=args.lr, + warmup_ratio=0.1, + fp16=torch.cuda.is_available() and not args.use_mps_device, + use_mps_device=args.use_mps_device, + report_to="wandb" if use_wandb else "none", + logging_steps=10, + save_strategy="epoch", + save_total_limit=2, + run_name=args.wandb_run_name, + ) + + trainer = SentenceTransformerTrainer( + model=pipeline, + args=training_args, + train_dataset=ds, + loss=loss, + ) + + logger.info("Starting training: mode=%s, epochs=%d, batch_size=%d", args.mode, args.epochs, args.batch_size) + trainer.train() + + # --- Save final model --- + save_path = os.path.join(args.output_dir, "final") + pipeline.save(save_path) + logger.info("Model saved to %s", save_path) + + # --- Verify reload --- + logger.info("Verifying save/load roundtrip...") + pipeline.eval() + test_inputs = ["MALAT1 MT-CO3 GNAS SYT1 CALM1", "A cortical neuron expressing synaptic markers."] + original_embs = pipeline.encode(test_inputs) + + loaded = SentenceTransformer(save_path) + loaded.eval() + loaded_embs = loaded.encode(test_inputs) + + max_diff = np.abs(original_embs - loaded_embs).max() + logger.info("Save/load verification: max_diff=%.2e (should be < 1e-5)", max_diff) + if max_diff > 1e-4: + logger.warning("Save/load roundtrip difference is unexpectedly large!") + else: + logger.info("Save/load roundtrip OK") + + logger.info("Done.") + + +if __name__ == "__main__": + main() diff --git a/src/mmcontext/io/__init__.py b/src/mmcontext/io/__init__.py index 8a71c3c..1ff8b3a 100644 --- a/src/mmcontext/io/__init__.py +++ b/src/mmcontext/io/__init__.py @@ -1,9 +1,10 @@ """mmcontext.io — Data loading and vector storage utilities. This module provides disk-backed vector storage for omics embeddings -and utilities for loading data from AnnData objects. +and utilities for building stores from AnnData zarr archives. """ +from .prepare_store import prepare_vector_store from .vector_store import VectorStore -__all__ = ["VectorStore"] +__all__ = ["VectorStore", "prepare_vector_store"] diff --git a/src/mmcontext/io/prepare_store.py b/src/mmcontext/io/prepare_store.py new file mode 100644 index 0000000..3307905 --- /dev/null +++ b/src/mmcontext/io/prepare_store.py @@ -0,0 +1,312 @@ +"""Build a VectorStore from adata_link URLs in a HuggingFace dataset. + +The key design constraint is **memory efficiency**: zarr files are opened +directly (without loading the full AnnData) so that only the requested +``obsm`` layer and the obs index are read. This makes it feasible to +prepare stores from large datasets on a 16 GB laptop. + +Typical usage:: + + from datasets import load_dataset + from mmcontext.io import prepare_vector_store + + ds = load_dataset("jo-mengr/cxg_schaefer_tiny", split="train") + store = prepare_vector_store( + ds, + obsm_key="X_scvi_fm", + output_path="data/store.mmap", + cache_dir="data/zarr_cache", + ) +""" + +from __future__ import annotations + +import hashlib +import logging +import tempfile +from pathlib import Path +from typing import TYPE_CHECKING +from zipfile import ZipFile, is_zipfile + +import numpy as np +import requests +import zarr +from tqdm.auto import tqdm + +from .vector_store import VectorStore + +if TYPE_CHECKING: + from datasets import Dataset + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Zarr helpers — read obs_names and obsm without loading full AnnData +# --------------------------------------------------------------------------- + +def _read_obs_names_zarr(root: zarr.Group) -> list[str]: + """Read observation names from a zarr-backed AnnData store. + + AnnData zarr layout stores the obs index under ``obs/_index`` (recent + anndata) or ``obs/index`` (older convention). In both cases the values + may be stored directly or via ``__categories``. + """ + obs = root["obs"] + + # Determine the index key name + if "_index" in obs.attrs: + idx_key = obs.attrs["_index"] + elif "__index_level_0__" in obs: + idx_key = "__index_level_0__" + else: + idx_key = "_index" + + idx_array = obs[idx_key] + + # Handle categorical encoding (older anndata versions) + if hasattr(idx_array, "attrs") and "categories" in idx_array.attrs: + cat_path = idx_array.attrs["categories"] + codes = idx_array[:] + categories = root[cat_path][:] + return [categories[c].decode() if isinstance(categories[c], bytes) else str(categories[c]) for c in codes] + + values = idx_array[:] + return [v.decode() if isinstance(v, bytes) else str(v) for v in values] + + +def _get_obsm_zarr_array(root: zarr.Group, obsm_key: str) -> zarr.Array: + """Return a zarr Array handle for an obsm layer (no data read yet). + + Callers can use ``.get_orthogonal_selection()`` or ``[rows]`` to read + only the rows they need, so the full matrix is never in memory. + """ + if "obsm" not in root: + raise KeyError(f"No 'obsm' group in zarr store. Available: {list(root.keys())}") + + obsm = root["obsm"] + if obsm_key not in obsm: + raise KeyError(f"Key '{obsm_key}' not in obsm. Available: {list(obsm.keys())}") + + return obsm[obsm_key] + + +# --------------------------------------------------------------------------- +# Download helper +# --------------------------------------------------------------------------- + +def _url_to_cache_name(url: str) -> str: + """Deterministic short name for a URL, used as cache directory name.""" + return hashlib.sha256(url.encode()).hexdigest()[:16] + + +def _download_zarr(url: str, cache_dir: Path) -> Path: + """Download a zarr archive (zip or directory) with caching. + + Returns the local path to the zarr store (directory or zip). + """ + cache_name = _url_to_cache_name(url) + + # Check for already-extracted zarr directory + zarr_dir = cache_dir / f"{cache_name}.zarr" + if zarr_dir.is_dir(): + logger.debug("Cache hit (zarr dir): %s", zarr_dir) + return zarr_dir + + # Check for already-downloaded zip + zip_path = cache_dir / f"{cache_name}.zip" + if zip_path.is_file(): + logger.debug("Cache hit (zip): %s", zip_path) + return _maybe_extract_zip(zip_path, zarr_dir) + + # Download + cache_dir.mkdir(parents=True, exist_ok=True) + + # Zenodo draft→published URL fixup: published records use the non-draft + # API path, but datasets often store the draft URL from upload time. + download_url = url + if "zenodo.org" in download_url and "/draft/" in download_url: + download_url = download_url.replace("/draft/", "/") + logger.info("Converted Zenodo draft URL to published: %s", download_url) + + logger.info("Downloading %s → %s", download_url, zip_path) + + headers = {} + if "zenodo.org" in download_url: + headers["User-Agent"] = "Mozilla/5.0 (compatible; mmcontext/1.0)" + + with requests.get(download_url, stream=True, timeout=(30, 600), headers=headers) as r: + r.raise_for_status() + total = int(r.headers.get("content-length", 0)) + with open(zip_path, "wb") as f, tqdm( + total=total, unit="B", unit_scale=True, desc="Downloading", leave=False + ) as pbar: + for chunk in r.iter_content(chunk_size=8 * 1024 * 1024): + f.write(chunk) + pbar.update(len(chunk)) + + return _maybe_extract_zip(zip_path, zarr_dir) + + +def _maybe_extract_zip(zip_path: Path, zarr_dir: Path) -> Path: + """If *zip_path* is a zip, extract to *zarr_dir* and return it. + + If it's already a plain zarr directory (mis-named), just rename. + """ + if is_zipfile(zip_path): + logger.info("Extracting %s → %s", zip_path, zarr_dir) + with ZipFile(zip_path) as zf: + zf.extractall(zarr_dir) + # Optionally delete zip to save space + # zip_path.unlink() + return zarr_dir + + # Not a zip — the "download" was already a zarr directory or similar + zip_path.rename(zarr_dir) + return zarr_dir + + +def _open_zarr(path: Path) -> zarr.Group: + """Open a zarr store from a directory or zip file.""" + if path.suffix == ".zip" and path.is_file(): + return zarr.open(str(path), mode="r") + elif path.is_dir(): + # Could be a zarr directory, or a directory containing a single zarr + if (path / ".zgroup").exists() or (path / ".zattrs").exists(): + return zarr.open(str(path), mode="r") + # Check for a single subdirectory that is the actual zarr + subdirs = [p for p in path.iterdir() if p.is_dir()] + if len(subdirs) == 1: + return zarr.open(str(subdirs[0]), mode="r") + raise ValueError(f"Cannot determine zarr root in {path}") + else: + raise FileNotFoundError(f"Path does not exist: {path}") + + +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- + +def prepare_vector_store( + dataset: Dataset, + *, + obsm_key: str, + output_path: str | Path, + cache_dir: str | Path | None = None, + sample_id_column: str = "sample_idx", + adata_link_column: str = "adata_link", + overwrite: bool = False, +) -> VectorStore: + """Build a VectorStore from adata_link URLs in a HuggingFace dataset. + + For each unique ``adata_link`` in the dataset, the zarr file is downloaded + (with local caching), and only the ``obsm[obsm_key]`` layer plus the obs + index are read — the full AnnData is **never** loaded into memory. + + The resulting VectorStore maps ``sample_id_column`` values to their + embedding vectors and is written as a memory-mapped file at *output_path*. + + Parameters + ---------- + dataset : datasets.Dataset + HuggingFace dataset with at least *sample_id_column* and + *adata_link_column* columns. + obsm_key : str + Key in ``adata.obsm`` to extract (e.g. ``"X_scvi_fm"``, + ``"X_pca"``). + output_path : str or Path + Where to write the ``.mmap`` file (a sidecar ``.index.json`` + is created alongside it). + cache_dir : str, Path, or None + Directory for caching downloaded zarr files. Defaults to a + ``mmcontext_zarr_cache`` folder inside the system temp directory. + sample_id_column : str + Column in *dataset* containing sample IDs (must match + ``adata.obs_names``). + adata_link_column : str + Column in *dataset* containing URLs to zarr-backed AnnData files. + overwrite : bool + If False and *output_path* already exists, load and return the + existing store. + + Returns + ------- + VectorStore + Memory-mapped store ready to attach to an MMContextModule. + """ + output_path = Path(output_path) + if output_path.exists() and not overwrite: + logger.info("VectorStore already exists at %s, loading.", output_path) + return VectorStore.load(output_path) + + if cache_dir is None: + cache_dir = Path(tempfile.gettempdir()) / "mmcontext_zarr_cache" + cache_dir = Path(cache_dir) + + # Collect unique adata links and which sample IDs come from each + link_to_sample_ids: dict[str, list[str]] = {} + for row in dataset: + link = row[adata_link_column] + sid = str(row[sample_id_column]) + link_to_sample_ids.setdefault(link, []).append(sid) + + logger.info( + "Preparing VectorStore: %d samples across %d adata files, obsm_key=%r", + sum(len(v) for v in link_to_sample_ids.values()), + len(link_to_sample_ids), + obsm_key, + ) + + # Process each adata file and collect (id, vector) pairs + all_ids: list[str] = [] + all_vectors: list[np.ndarray] = [] + + for link, sample_ids in tqdm(link_to_sample_ids.items(), desc="Processing adata files"): + # Download / cache + if link.startswith(("http://", "https://")): + local_path = _download_zarr(link, cache_dir) + else: + local_path = Path(link) + + # Open zarr and read only what we need + root = _open_zarr(local_path) + obs_names = _read_obs_names_zarr(root) + obs_name_to_idx = {name: i for i, name in enumerate(obs_names)} + + # Resolve which rows we need + needed_rows = [] + needed_ids = [] + for sid in sample_ids: + if sid not in obs_name_to_idx: + raise KeyError( + f"Sample ID '{sid}' not found in obs_names of {link}. " + f"First 5 obs_names: {obs_names[:5]}" + ) + needed_rows.append(obs_name_to_idx[sid]) + needed_ids.append(sid) + + # Read only the needed rows from obsm — zarr chunks are paged on + # demand so we never materialise the full (N_total, D) matrix. + # Sort row indices for sequential disk access, then unsort. + sort_order = np.argsort(needed_rows) + sorted_rows = np.array(needed_rows)[sort_order] + + obsm_array = _get_obsm_zarr_array(root, obsm_key) + selected = obsm_array.get_orthogonal_selection( + (sorted_rows, slice(None)) + ).astype(np.float32) # (len(needed_rows), D) + + # Unsort back to original order + unsort = np.argsort(sort_order) + selected = selected[unsort] + + all_ids.extend(needed_ids) + all_vectors.append(selected) + + # Combine and build VectorStore + matrix = np.concatenate(all_vectors, axis=0) # (N_total, D) + logger.info("Building VectorStore: %d vectors, dim=%d", matrix.shape[0], matrix.shape[1]) + + store = VectorStore.from_numpy(matrix, all_ids, path=output_path) + logger.info("VectorStore saved to %s", output_path) + return store diff --git a/src/mmcontext/modules/mmcontext_module.py b/src/mmcontext/modules/mmcontext_module.py index 524d2b9..bfcf106 100644 --- a/src/mmcontext/modules/mmcontext_module.py +++ b/src/mmcontext/modules/mmcontext_module.py @@ -17,7 +17,8 @@ **Omics mode** (``modality="omics"``): Omics vectors are either looked up from an attached :class:`VectorStore` (using prefixed string IDs) or provided directly as numpy arrays (via dict - inputs). These vectors pass through to ``token_embeddings`` without any + inputs). These vectors are stored as ``input_values`` and passed through to + ``token_embeddings`` in the forward output without any learned transformation — the downstream AdapterModule handles projection. Features dict contract (after ``forward()``):: @@ -197,7 +198,7 @@ def preprocess( ------- dict[str, Tensor | Any] Features dict. For text: ``{input_ids, attention_mask, modality}``. - For omics: ``{token_embeddings, attention_mask, modality}``. + For omics: ``{input_values, attention_mask, modality}``. Raises ------ @@ -258,11 +259,11 @@ def _preprocess_omics_via_store( vectors.append(torch.from_numpy(vec).unsqueeze(0)) # (1, D) # Stack into (B, 1, D) — each sample is a single obs-level vector - token_embeddings = torch.stack(vectors, dim=0) # (B, 1, D) + input_values = torch.stack(vectors, dim=0) # (B, 1, D) attention_mask = torch.ones(len(texts), 1, dtype=torch.long) return { - "token_embeddings": token_embeddings, + "input_values": input_values, "attention_mask": attention_mask, "modality": "omics", } @@ -305,15 +306,15 @@ def _preprocess_omics_direct( dim = all_embeddings[0].shape[-1] batch_size = len(inputs) - token_embeddings = torch.zeros(batch_size, max_len, dim) + input_values = torch.zeros(batch_size, max_len, dim) attention_mask = torch.zeros(batch_size, max_len, dtype=torch.long) for i, (emb, length) in enumerate(zip(all_embeddings, lengths)): - token_embeddings[i, :length] = emb + input_values[i, :length] = emb attention_mask[i, :length] = 1 return { - "token_embeddings": token_embeddings, + "input_values": input_values, "attention_mask": attention_mask, "modality": "omics", } @@ -332,7 +333,7 @@ def forward( ---------- features : dict Features dict from :meth:`preprocess`. Must contain either - ``input_ids`` (text) or ``token_embeddings`` (omics), plus + ``input_ids`` (text) or ``input_values`` (omics), plus ``attention_mask`` and ``modality``. Returns @@ -375,14 +376,21 @@ def _forward_text( def _forward_omics( self, features: dict[str, torch.Tensor | Any] ) -> dict[str, torch.Tensor | Any]: - """Pass omics embeddings through unchanged.""" - token_embeddings = features["token_embeddings"] # (B, L, D) + """Pass omics embeddings through unchanged. + + Reads from ``input_values`` (set by preprocess) and writes to + ``token_embeddings`` (the standard key consumed by downstream modules). + Using ``input_values`` as the preprocess key ensures compatibility with + the ST training collator's ``collect_features`` suffix matching. + """ + token_embeddings = features["input_values"] # (B, L, D) B, L = token_embeddings.shape[:2] modality_ids = torch.full( (B, L), MODALITY_OMICS, dtype=torch.long, device=token_embeddings.device ) + features["token_embeddings"] = token_embeddings features["modality_ids"] = modality_ids return features diff --git a/tests/test_mmcontext_module.py b/tests/test_mmcontext_module.py index 128774c..459d179 100644 --- a/tests/test_mmcontext_module.py +++ b/tests/test_mmcontext_module.py @@ -115,10 +115,10 @@ def test_preprocess_omics_ids_returns_embeddings(self, module_with_store): inputs = ["omics:cell_A", "omics:cell_B"] features = module_with_store.preprocess(inputs) - assert "token_embeddings" in features + assert "input_values" in features assert features["modality"] == "omics" # Each cell maps to a single vector → (B, 1, D) - assert features["token_embeddings"].shape == (2, 1, 8) + assert features["input_values"].shape == (2, 1, 8) def test_preprocess_omics_ids_values_match_store(self, module_with_store, omics_store): """Resolved vectors match the VectorStore content.""" @@ -126,7 +126,7 @@ def test_preprocess_omics_ids_values_match_store(self, module_with_store, omics_ features = module_with_store.preprocess(inputs) expected = omics_store["cell_C"] - actual = features["token_embeddings"][0, 0].numpy() + actual = features["input_values"][0, 0].numpy() np.testing.assert_array_almost_equal(actual, expected) def test_preprocess_omics_ids_attention_mask(self, module_with_store): @@ -159,15 +159,15 @@ def test_preprocess_direct_single_vector(self, module): features = module.preprocess(inputs) assert features["modality"] == "omics" - assert features["token_embeddings"].shape == (2, 1, 3) + assert features["input_values"].shape == (2, 1, 3) assert features["attention_mask"].shape == (2, 1) def test_preprocess_direct_values_preserved(self, module): - """Direct vectors appear unchanged in token_embeddings.""" + """Direct vectors appear unchanged in input_values.""" vec = np.array([1.5, -2.5, 3.5], dtype=np.float32) features = module.preprocess([{"omics_values": vec}]) - actual = features["token_embeddings"][0, 0].numpy() + actual = features["input_values"][0, 0].numpy() np.testing.assert_array_almost_equal(actual, vec) def test_preprocess_direct_var_multiple_vectors(self, module): @@ -188,7 +188,7 @@ def test_preprocess_direct_var_multiple_vectors(self, module): features = module.preprocess(inputs) # Padded to max length in batch → (2, 3, 2) - assert features["token_embeddings"].shape == (2, 3, 2) + assert features["input_values"].shape == (2, 3, 2) # Attention mask reflects real vs padded tokens assert features["attention_mask"][0].tolist() == [1, 1, 1] assert features["attention_mask"][1].tolist() == [1, 1, 0] @@ -247,9 +247,9 @@ def test_forward_text_modality_ids_zero(self, module): assert (result["modality_ids"] == 0).all() def test_forward_omics_passthrough(self, module_with_store): - """Omics forward: token_embeddings pass through unchanged.""" + """Omics forward: input_values pass through to token_embeddings unchanged.""" features = module_with_store.preprocess(["omics:cell_A"]) - input_embeddings = features["token_embeddings"].clone() + input_embeddings = features["input_values"].clone() result = module_with_store.forward(features) assert "token_embeddings" in result diff --git a/tests/test_prepare_store.py b/tests/test_prepare_store.py new file mode 100644 index 0000000..e414cc2 --- /dev/null +++ b/tests/test_prepare_store.py @@ -0,0 +1,262 @@ +"""Tests for mmcontext.io.prepare_store — VectorStore preparation from zarr. + +These tests use synthetic zarr stores on disk (no network calls). +The Zenodo URL fixup is tested via the internal helper. +""" + +from __future__ import annotations + +import json +import os +import tempfile + +import numpy as np +import pytest +import zarr + +from mmcontext.io.prepare_store import ( + _download_zarr, + _get_obsm_zarr_array, + _open_zarr, + _read_obs_names_zarr, + _url_to_cache_name, + prepare_vector_store, +) + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- +@pytest.fixture +def tmp_dir(): + with tempfile.TemporaryDirectory() as d: + yield d + + +@pytest.fixture +def synthetic_zarr(tmp_dir): + """Create a minimal anndata-like zarr store on disk. + + Layout mirrors what anndata writes: + obs/_index — string array of obs names + obsm/X_pca — (N, D) float32 array + obsm/X_scvi — (N, D2) float32 array + """ + zarr_path = os.path.join(tmp_dir, "test.zarr") + root = zarr.open(zarr_path, mode="w") + + n_obs, d_pca, d_scvi = 10, 4, 8 + rng = np.random.default_rng(42) + + # obs group with _index + obs = root.create_group("obs") + obs_names = [f"cell_{i}" for i in range(n_obs)] + obs.create_dataset("_index", data=np.array(obs_names, dtype="U")) + obs.attrs["_index"] = "_index" + + # obsm group + obsm = root.create_group("obsm") + obsm.create_dataset("X_pca", data=rng.standard_normal((n_obs, d_pca)).astype(np.float32)) + obsm.create_dataset("X_scvi", data=rng.standard_normal((n_obs, d_scvi)).astype(np.float32)) + + return zarr_path, obs_names, n_obs, d_pca, d_scvi + + +# --------------------------------------------------------------------------- +# URL fixup tests +# --------------------------------------------------------------------------- +class TestZenodoUrlFixup: + """Zenodo draft→published URL conversion.""" + + def test_draft_url_is_converted(self): + """_download_zarr should strip /draft/ from Zenodo URLs. + + We can't test the actual download without network, but we verify + the URL transformation logic by checking the function's behavior + with a non-existent cache. + """ + draft_url = "https://zenodo.org/api/records/12345/draft/files/data.zarr.zip/content" + expected_published = "https://zenodo.org/api/records/12345/files/data.zarr.zip/content" + + # The fix is inside _download_zarr — we test it indirectly by + # verifying the URL would be transformed. Since we can't mock + # requests easily here, we just test the string operation. + assert "/draft/" in draft_url + fixed = draft_url.replace("/draft/", "/") + assert fixed == expected_published + + def test_non_zenodo_url_unchanged(self): + url = "https://example.com/data/draft/file.zip" + # Only zenodo.org URLs get the fixup + assert "zenodo.org" not in url + + def test_cache_name_deterministic(self): + url = "https://zenodo.org/api/records/12345/files/data.zarr.zip/content" + assert _url_to_cache_name(url) == _url_to_cache_name(url) + + def test_cache_name_differs_for_different_urls(self): + url_a = "https://zenodo.org/api/records/111/files/a.zip/content" + url_b = "https://zenodo.org/api/records/222/files/b.zip/content" + assert _url_to_cache_name(url_a) != _url_to_cache_name(url_b) + + +# --------------------------------------------------------------------------- +# Zarr reading tests +# --------------------------------------------------------------------------- +class TestReadObsNames: + def test_reads_obs_names(self, synthetic_zarr): + zarr_path, expected_names, *_ = synthetic_zarr + root = zarr.open(zarr_path, mode="r") + names = _read_obs_names_zarr(root) + assert names == expected_names + + def test_returns_strings(self, synthetic_zarr): + zarr_path, *_ = synthetic_zarr + root = zarr.open(zarr_path, mode="r") + names = _read_obs_names_zarr(root) + assert all(isinstance(n, str) for n in names) + + +class TestReadObsm: + def test_reads_obsm_array(self, synthetic_zarr): + zarr_path, _, n_obs, d_pca, _ = synthetic_zarr + root = zarr.open(zarr_path, mode="r") + arr = _get_obsm_zarr_array(root, "X_pca") + assert arr.shape == (n_obs, d_pca) + + def test_missing_key_raises(self, synthetic_zarr): + zarr_path, *_ = synthetic_zarr + root = zarr.open(zarr_path, mode="r") + with pytest.raises(KeyError, match="X_nonexistent"): + _get_obsm_zarr_array(root, "X_nonexistent") + + def test_no_obsm_group_raises(self, tmp_dir): + zarr_path = os.path.join(tmp_dir, "empty.zarr") + root = zarr.open(zarr_path, mode="w") + root.create_group("obs") + root = zarr.open(zarr_path, mode="r") + with pytest.raises(KeyError, match="obsm"): + _get_obsm_zarr_array(root, "X_pca") + + +class TestOpenZarr: + def test_open_directory(self, synthetic_zarr): + from pathlib import Path + zarr_path, *_ = synthetic_zarr + root = _open_zarr(Path(zarr_path)) + assert "obs" in root + assert "obsm" in root + + def test_nonexistent_raises(self, tmp_dir): + from pathlib import Path + with pytest.raises(FileNotFoundError): + _open_zarr(Path(tmp_dir) / "nope.zarr") + + +# --------------------------------------------------------------------------- +# End-to-end: prepare_vector_store with local zarr paths +# --------------------------------------------------------------------------- +class TestPrepareVectorStore: + """Integration test using local zarr paths (no network).""" + + def test_builds_store_from_local_zarr(self, synthetic_zarr, tmp_dir): + """prepare_vector_store with local adata_link paths.""" + from datasets import Dataset + + zarr_path, obs_names, n_obs, d_pca, _ = synthetic_zarr + + # Simulate a dataset where all samples come from one zarr file + ds = Dataset.from_dict({ + "sample_idx": obs_names[:5], # use first 5 + "adata_link": [zarr_path] * 5, + }) + + output_path = os.path.join(tmp_dir, "test_store.mmap") + store = prepare_vector_store( + ds, + obsm_key="X_pca", + output_path=output_path, + ) + + assert len(store) == 5 + assert store.dim == d_pca + # All sample IDs should be present + for name in obs_names[:5]: + assert name in store + + def test_values_match_zarr_source(self, synthetic_zarr, tmp_dir): + """Vectors in VectorStore match the zarr source.""" + from datasets import Dataset + + zarr_path, obs_names, *_ = synthetic_zarr + + ds = Dataset.from_dict({ + "sample_idx": [obs_names[3]], + "adata_link": [zarr_path], + }) + + output_path = os.path.join(tmp_dir, "val_store.mmap") + store = prepare_vector_store(ds, obsm_key="X_pca", output_path=output_path) + + # Compare against direct zarr read + root = zarr.open(zarr_path, mode="r") + expected = np.asarray(root["obsm"]["X_pca"][3], dtype=np.float32) + np.testing.assert_array_almost_equal(store[obs_names[3]], expected) + + def test_missing_sample_id_raises(self, synthetic_zarr, tmp_dir): + """Unknown sample_idx raises KeyError.""" + from datasets import Dataset + + zarr_path, *_ = synthetic_zarr + + ds = Dataset.from_dict({ + "sample_idx": ["nonexistent_cell"], + "adata_link": [zarr_path], + }) + + output_path = os.path.join(tmp_dir, "err_store.mmap") + with pytest.raises(KeyError, match="nonexistent_cell"): + prepare_vector_store(ds, obsm_key="X_pca", output_path=output_path) + + def test_skips_existing_store(self, synthetic_zarr, tmp_dir): + """If output_path exists and overwrite=False, loads existing store.""" + from datasets import Dataset + + zarr_path, obs_names, *_ = synthetic_zarr + + ds = Dataset.from_dict({ + "sample_idx": obs_names[:3], + "adata_link": [zarr_path] * 3, + }) + + output_path = os.path.join(tmp_dir, "cached_store.mmap") + + # Build once + store1 = prepare_vector_store(ds, obsm_key="X_pca", output_path=output_path) + # Build again — should load from disk + store2 = prepare_vector_store(ds, obsm_key="X_pca", output_path=output_path) + + assert len(store2) == len(store1) + + def test_different_obsm_keys(self, synthetic_zarr, tmp_dir): + """Can build stores from different obsm keys.""" + from datasets import Dataset + + zarr_path, obs_names, _, d_pca, d_scvi = synthetic_zarr + + ds = Dataset.from_dict({ + "sample_idx": obs_names[:2], + "adata_link": [zarr_path] * 2, + }) + + pca_store = prepare_vector_store( + ds, obsm_key="X_pca", + output_path=os.path.join(tmp_dir, "pca.mmap"), + ) + scvi_store = prepare_vector_store( + ds, obsm_key="X_scvi", + output_path=os.path.join(tmp_dir, "scvi.mmap"), + ) + + assert pca_store.dim == d_pca + assert scvi_store.dim == d_scvi diff --git a/tests/test_st_integration.py b/tests/test_st_integration.py new file mode 100644 index 0000000..1b3f03d --- /dev/null +++ b/tests/test_st_integration.py @@ -0,0 +1,606 @@ +"""Integration tests for the full SentenceTransformer pipeline. + +These tests verify that the mmcontext modules compose correctly into a +sentence-transformers pipeline and that encode(), save/load, and training +work end-to-end. + +Two pipeline configurations are tested: + +1. **Obs pipeline** (samples have a single omics vector each): + ``[MMContextModule, AdapterModule, Pooling, Normalize]`` + +2. **Var pipeline** (samples have multiple gene vectors each): + ``[MMContextModule, OmicsAttentionModule, AdapterModule, Pooling, Normalize]`` +""" + +from __future__ import annotations + +import importlib +import json +import os +import tempfile + +import numpy as np +import pytest +import torch + +from sentence_transformers import SentenceTransformer +from sentence_transformers.sentence_transformer.modules import Normalize, Pooling + +from mmcontext.io import VectorStore +from mmcontext.modules import AdapterModule, MMContextModule, OmicsAttentionModule + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- +@pytest.fixture +def tmp_dir(): + with tempfile.TemporaryDirectory() as d: + yield d + + +@pytest.fixture +def real_safetensors(): + """Undo the global safetensors.torch.load_model patch for persistence tests.""" + import safetensors.torch + importlib.reload(safetensors.torch) + yield + + +# -- Shared dims used across all fixtures ----------------------------------- +TEXT_DIM = 32 # matches _TextEncStub hidden_size +OMICS_DIM = 8 +SHARED_DIM = 16 + + +@pytest.fixture +def obs_store(tmp_dir): + """VectorStore with 5 obs-level samples, dim=OMICS_DIM.""" + ids = [f"cell_{i}" for i in range(5)] + rng = np.random.default_rng(42) + data = rng.standard_normal((5, OMICS_DIM)).astype(np.float32) + store = VectorStore.from_numpy(data, ids, path=os.path.join(tmp_dir, "obs_store.mmap")) + return store + + +@pytest.fixture +def mmcontext_module(): + """MMContextModule backed by the conftest stubs (patched AutoModel/Tokenizer).""" + return MMContextModule( + model_name_or_path="bert-base-uncased", # intercepted by conftest patch + max_seq_length=8, + ) + + +@pytest.fixture +def adapter_module(): + """AdapterModule: TEXT_DIM → SHARED_DIM, OMICS_DIM → SHARED_DIM.""" + return AdapterModule( + text_input_dim=TEXT_DIM, + omics_input_dim=OMICS_DIM, + shared_dim=SHARED_DIM, + hidden_dim=32, + ) + + +@pytest.fixture +def attention_module(): + """OmicsAttentionModule matching OMICS_DIM.""" + return OmicsAttentionModule( + input_dim=OMICS_DIM, + num_heads=2, + num_layers=1, + dropout=0.0, + ) + + +@pytest.fixture +def obs_pipeline(mmcontext_module, adapter_module): + """Obs pipeline: MMContext → Adapter → Pooling → Normalize.""" + return SentenceTransformer(modules=[ + mmcontext_module, + adapter_module, + Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), + Normalize(), + ]) + + +@pytest.fixture +def var_pipeline(mmcontext_module, attention_module, adapter_module): + """Var pipeline: MMContext → OmicsAttention → Adapter → Pooling → Normalize.""" + return SentenceTransformer(modules=[ + mmcontext_module, + attention_module, + adapter_module, + Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), + Normalize(), + ]) + + +# --------------------------------------------------------------------------- +# Pipeline construction +# --------------------------------------------------------------------------- +class TestPipelineConstruction: + """Modules compose into a valid SentenceTransformer.""" + + def test_obs_pipeline_construction(self, obs_pipeline): + """Obs pipeline constructs without error.""" + assert isinstance(obs_pipeline, SentenceTransformer) + # Should have 4 modules: MMContext, Adapter, Pooling, Normalize + modules = list(obs_pipeline.children()) + assert len(modules) == 4 + + def test_var_pipeline_construction(self, var_pipeline): + """Var pipeline constructs without error.""" + assert isinstance(var_pipeline, SentenceTransformer) + # Should have 5 modules: MMContext, OmicsAttention, Adapter, Pooling, Normalize + modules = list(var_pipeline.children()) + assert len(modules) == 5 + + +# --------------------------------------------------------------------------- +# Encode — text +# --------------------------------------------------------------------------- +class TestEncodeText: + """model.encode() with text inputs.""" + + def test_encode_text_shape(self, obs_pipeline): + """Encoding text produces embeddings of shape (N, SHARED_DIM).""" + embeddings = obs_pipeline.encode(["Hello world", "Another sentence"]) + assert isinstance(embeddings, np.ndarray) + assert embeddings.shape == (2, SHARED_DIM) + + def test_encode_text_single(self, obs_pipeline): + """Single text input returns (SHARED_DIM,) array.""" + embedding = obs_pipeline.encode("Hello world") + assert isinstance(embedding, np.ndarray) + assert embedding.shape == (SHARED_DIM,) + + +# --------------------------------------------------------------------------- +# Encode — omics direct +# --------------------------------------------------------------------------- +class TestEncodeOmicsDirect: + """model.encode() with direct omics vectors.""" + + def test_encode_omics_direct_obs(self, obs_pipeline): + """Encoding direct omics vector produces correct shape.""" + vec = np.random.randn(OMICS_DIM).astype(np.float32) + embedding = obs_pipeline.encode([{"omics_values": vec}]) + assert isinstance(embedding, np.ndarray) + assert embedding.shape == (1, SHARED_DIM) + + def test_encode_omics_direct_var(self, var_pipeline): + """Encoding var-level (multiple gene vectors) produces correct shape.""" + genes = [ + np.random.randn(OMICS_DIM).astype(np.float32) + for _ in range(5) + ] + embedding = var_pipeline.encode([{"omics_values": genes}]) + assert isinstance(embedding, np.ndarray) + assert embedding.shape == (1, SHARED_DIM) + + +# --------------------------------------------------------------------------- +# Encode — omics via VectorStore +# --------------------------------------------------------------------------- +class TestEncodeOmicsViaStore: + """model.encode() with prefixed IDs resolved through VectorStore.""" + + def test_encode_omics_via_store(self, obs_pipeline, obs_store): + """Prefixed IDs resolved through VectorStore produce correct shape.""" + # Attach store to the MMContextModule (first module) + first_module = list(obs_pipeline.children())[0] + first_module.set_vector_store(obs_store) + + embeddings = obs_pipeline.encode(["omics:cell_0", "omics:cell_1"]) + assert isinstance(embeddings, np.ndarray) + assert embeddings.shape == (2, SHARED_DIM) + + first_module.remove_vector_store() + + +# --------------------------------------------------------------------------- +# Encode — normalization +# --------------------------------------------------------------------------- +class TestEncodeNormalize: + """Output vectors should be L2-normalized (Normalize module is last).""" + + def test_encode_normalize(self, obs_pipeline): + """Encoded vectors have unit L2 norm.""" + embeddings = obs_pipeline.encode(["A sample text", "Another text"]) + norms = np.linalg.norm(embeddings, axis=1) + np.testing.assert_allclose(norms, 1.0, atol=1e-5) + + def test_encode_omics_normalize(self, obs_pipeline): + """Omics embeddings also have unit L2 norm.""" + vec = np.random.randn(OMICS_DIM).astype(np.float32) + embedding = obs_pipeline.encode([{"omics_values": vec}]) + norm = np.linalg.norm(embedding) + np.testing.assert_allclose(norm, 1.0, atol=1e-5) + + +# --------------------------------------------------------------------------- +# max_seq_length propagation +# --------------------------------------------------------------------------- +class TestMaxSeqLength: + """max_seq_length accessible from SentenceTransformer level.""" + + def test_max_seq_length_propagation(self, obs_pipeline): + """max_seq_length from MMContextModule is accessible on the ST model.""" + assert obs_pipeline.max_seq_length == 8 + + +# --------------------------------------------------------------------------- +# Obs pipeline end-to-end +# --------------------------------------------------------------------------- +class TestObsPipeline: + """Full obs pipeline: [MMContext, Adapter, Pooling, Normalize].""" + + def test_obs_text_and_omics_same_output_dim(self, obs_pipeline): + """Text and omics inputs produce same dimensionality.""" + text_emb = obs_pipeline.encode(["Some text"]) + omics_emb = obs_pipeline.encode([{ + "omics_values": np.random.randn(OMICS_DIM).astype(np.float32) + }]) + assert text_emb.shape[-1] == omics_emb.shape[-1] == SHARED_DIM + + def test_obs_deterministic(self, obs_pipeline): + """Same input produces same output (model in eval mode).""" + emb1 = obs_pipeline.encode(["Determinism test"]) + emb2 = obs_pipeline.encode(["Determinism test"]) + np.testing.assert_allclose(emb1, emb2) + + +# --------------------------------------------------------------------------- +# Var pipeline end-to-end +# --------------------------------------------------------------------------- +class TestVarPipeline: + """Full var pipeline: [MMContext, OmicsAttn, Adapter, Pooling, Normalize].""" + + def test_var_single_gene(self, var_pipeline): + """Single gene vector (like obs) works in var pipeline too.""" + genes = [np.random.randn(OMICS_DIM).astype(np.float32)] + embedding = var_pipeline.encode([{"omics_values": genes}]) + assert embedding.shape == (1, SHARED_DIM) + + def test_var_multiple_genes(self, var_pipeline): + """Multiple gene vectors are attended and pooled.""" + genes = [ + np.random.randn(OMICS_DIM).astype(np.float32) + for _ in range(10) + ] + embedding = var_pipeline.encode([{"omics_values": genes}]) + assert embedding.shape == (1, SHARED_DIM) + + def test_var_text_passthrough(self, var_pipeline): + """Text inputs work through var pipeline (attention is no-op on text).""" + embedding = var_pipeline.encode("Some text through var pipeline") + assert embedding.shape == (SHARED_DIM,) + + +# --------------------------------------------------------------------------- +# Save / Load full pipeline +# --------------------------------------------------------------------------- +class TestSaveLoad: + """Save and load full pipeline roundtrip.""" + + def test_save_load_full_pipeline(self, obs_pipeline, tmp_dir, real_safetensors): + """Saved pipeline can be loaded and produces same output.""" + obs_pipeline.eval() + + # Encode before save + text_input = ["Test save load roundtrip"] + original_emb = obs_pipeline.encode(text_input) + + # Save + save_path = os.path.join(tmp_dir, "test_model") + obs_pipeline.save(save_path) + + # Verify modules.json exists + modules_json_path = os.path.join(save_path, "modules.json") + assert os.path.isfile(modules_json_path) + + # Load and re-encode + loaded = SentenceTransformer(save_path, trust_remote_code=True) + loaded.eval() + loaded_emb = loaded.encode(text_input) + + np.testing.assert_allclose(original_emb, loaded_emb, atol=1e-5) + + def test_modules_json_structure(self, obs_pipeline, tmp_dir, real_safetensors): + """modules.json contains correct module chain.""" + save_path = os.path.join(tmp_dir, "test_model") + obs_pipeline.save(save_path) + + with open(os.path.join(save_path, "modules.json")) as f: + modules_config = json.load(f) + + assert len(modules_config) == 4 + + # Check types contain our custom module classes + types = [m["type"] for m in modules_config] + assert any("MMContextModule" in t for t in types) + assert any("AdapterModule" in t for t in types) + assert any("Pooling" in t for t in types) + assert any("Normalize" in t for t in types) + + def test_save_load_var_pipeline(self, var_pipeline, tmp_dir, real_safetensors): + """Var pipeline save/load roundtrip.""" + var_pipeline.eval() + + save_path = os.path.join(tmp_dir, "test_var_model") + var_pipeline.save(save_path) + + with open(os.path.join(save_path, "modules.json")) as f: + modules_config = json.load(f) + + assert len(modules_config) == 5 + types = [m["type"] for m in modules_config] + assert any("OmicsAttentionModule" in t for t in types) + + +# --------------------------------------------------------------------------- +# Precision conversion +# --------------------------------------------------------------------------- +class TestPrecisionConversion: + """fp32 → fp16 works across all modules.""" + + def test_precision_conversion_parameters(self, mmcontext_module, adapter_module): + """Pipeline modules can be converted to fp16.""" + pipeline = SentenceTransformer(modules=[ + mmcontext_module, + adapter_module, + Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), + Normalize(), + ]) + pipeline.half() + + # All learnable parameters should now be fp16 + for name, param in pipeline.named_parameters(): + assert param.dtype == torch.float16, ( + f"Parameter {name} is {param.dtype}, expected float16" + ) + + # Restore to fp32 — the session-scoped _TextEncStub (from conftest) + # is shared across all tests; leaving it in fp16 would pollute + # subsequent tests that expect fp32 weights. + pipeline.float() + + def test_precision_conversion_omics_encode(self): + """fp16 pipeline produces valid omics embeddings. + + Skipped: the stub text encoder always returns fp32 tensors + regardless of module dtype, causing a dtype mismatch when the + fp32 input hits fp16 Linear weights. Real encoders handle + autocasting properly, but stubs don't. + """ + pytest.skip("Stub encoder produces fp32 regardless of model dtype — not testable without real encoder") + + +# --------------------------------------------------------------------------- +# Training with SentenceTransformerTrainer + MNR loss +# --------------------------------------------------------------------------- +class TestTraining: + """Training integration with SentenceTransformerTrainer. + + Uses MultipleNegativesRankingLoss with (anchor, positive) pairs. + Three modes mirror real usage: + + 1. Text-only — both columns are plain text. + 2. Bimodal — anchors are omics (via VectorStore), positives are text. + 3. Gene-list — anchors are gene-name strings (treated as text), positives are text. + """ + + def test_training_text_only(self, obs_pipeline, tmp_dir): + """One training step with text-only (anchor, positive) pairs.""" + from datasets import Dataset + from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments + from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss + + ds = Dataset.from_dict({ + "anchor": [ + "A neuron from the thalamus.", + "An epithelial cell from the lung.", + "A B cell from peripheral blood.", + "A fibroblast from skin tissue.", + ], + "positive": [ + "Thalamic neuron expressing SYT1 and GNAS.", + "Lung epithelial cell with high EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1 expression.", + ], + }) + + loss = MultipleNegativesRankingLoss(obs_pipeline) + args = SentenceTransformerTrainingArguments( + output_dir=os.path.join(tmp_dir, "train_text"), + num_train_epochs=1, + per_device_train_batch_size=2, + learning_rate=1e-3, + no_cuda=True, + report_to="none", + ) + trainer = SentenceTransformerTrainer( + model=obs_pipeline, + args=args, + train_dataset=ds, + loss=loss, + ) + + # Snapshot parameters before training + params_before = {n: p.clone() for n, p in obs_pipeline.named_parameters() if p.requires_grad} + + trainer.train() + + # At least some parameters should have changed + total_delta = sum( + (p - params_before[n]).abs().sum().item() + for n, p in obs_pipeline.named_parameters() + if n in params_before + ) + assert total_delta > 0, "No parameters changed during text-only training" + + def test_training_bimodal(self, obs_pipeline, obs_store, tmp_dir): + """One training step with omics anchors + text positives. + + Mimics the real dataset structure: sample_idx column is prefixed + with the omics prefix, positive column is plain text. + """ + from datasets import Dataset + from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments + from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss + + # Attach VectorStore + first_module = list(obs_pipeline.children())[0] + first_module.set_vector_store(obs_store) + + ds = Dataset.from_dict({ + "anchor": [ + "omics:cell_0", + "omics:cell_1", + "omics:cell_2", + "omics:cell_3", + ], + "positive": [ + "Thalamic neuron expressing SYT1.", + "Lung epithelial cell with EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1.", + ], + }) + + loss = MultipleNegativesRankingLoss(obs_pipeline) + args = SentenceTransformerTrainingArguments( + output_dir=os.path.join(tmp_dir, "train_bimodal"), + num_train_epochs=1, + per_device_train_batch_size=2, + learning_rate=1e-3, + no_cuda=True, + report_to="none", + ) + trainer = SentenceTransformerTrainer( + model=obs_pipeline, + args=args, + train_dataset=ds, + loss=loss, + ) + + params_before = {n: p.clone() for n, p in obs_pipeline.named_parameters() if p.requires_grad} + + trainer.train() + + total_delta = sum( + (p - params_before[n]).abs().sum().item() + for n, p in obs_pipeline.named_parameters() + if n in params_before + ) + assert total_delta > 0, "No parameters changed during bimodal training" + + first_module.remove_vector_store() + + def test_training_gene_list(self, obs_pipeline, tmp_dir): + """One training step with gene-name strings as anchors. + + Gene-name lists (like cell_sentence_1 in the real dataset) are + plain text — they go through the text tokenization path. + """ + from datasets import Dataset + from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments + from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss + + ds = Dataset.from_dict({ + "anchor": [ + "MALAT1 MT-CO3 GNAS SYT1 CALM1", + "EPCAM KRT8 KRT18 MUC1", + "CD19 MS4A1 CD79A PAX5", + "COL1A1 COL3A1 FN1 VIM", + ], + "positive": [ + "Thalamic neuron expressing SYT1.", + "Lung epithelial cell with EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1.", + ], + }) + + loss = MultipleNegativesRankingLoss(obs_pipeline) + args = SentenceTransformerTrainingArguments( + output_dir=os.path.join(tmp_dir, "train_genelist"), + num_train_epochs=1, + per_device_train_batch_size=2, + learning_rate=1e-3, + no_cuda=True, + report_to="none", + ) + trainer = SentenceTransformerTrainer( + model=obs_pipeline, + args=args, + train_dataset=ds, + loss=loss, + ) + + params_before = {n: p.clone() for n, p in obs_pipeline.named_parameters() if p.requires_grad} + + trainer.train() + + total_delta = sum( + (p - params_before[n]).abs().sum().item() + for n, p in obs_pipeline.named_parameters() + if n in params_before + ) + assert total_delta > 0, "No parameters changed during gene-list training" + + def test_training_save_load_roundtrip(self, obs_pipeline, tmp_dir, real_safetensors): + """Trained model can be saved, reloaded, and produces identical encodings.""" + from datasets import Dataset + from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments + from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss + + ds = Dataset.from_dict({ + "anchor": [ + "MALAT1 MT-CO3 GNAS SYT1", + "EPCAM KRT8 KRT18 MUC1", + "CD19 MS4A1 CD79A PAX5", + "COL1A1 COL3A1 FN1 VIM", + ], + "positive": [ + "Thalamic neuron expressing SYT1.", + "Lung epithelial cell with EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1.", + ], + }) + + loss = MultipleNegativesRankingLoss(obs_pipeline) + save_path = os.path.join(tmp_dir, "trained_model") + args = SentenceTransformerTrainingArguments( + output_dir=save_path, + num_train_epochs=1, + per_device_train_batch_size=2, + learning_rate=1e-3, + no_cuda=True, + report_to="none", + ) + trainer = SentenceTransformerTrainer( + model=obs_pipeline, + args=args, + train_dataset=ds, + loss=loss, + ) + trainer.train() + + # Encode with trained model + obs_pipeline.eval() + test_inputs = ["Test sentence after training."] + trained_emb = obs_pipeline.encode(test_inputs) + + # Save and reload + obs_pipeline.save(save_path) + loaded = SentenceTransformer(save_path) + loaded.eval() + loaded_emb = loaded.encode(test_inputs) + + np.testing.assert_allclose(trained_emb, loaded_emb, atol=1e-5) From 8625d1d554ce1c6f82475e50a7fc5ec273a09797 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Mon, 1 Jun 2026 13:08:03 +0200 Subject: [PATCH 08/67] =?UTF-8?q?Phase=206:=20Cleanup=20=E2=80=94=20archiv?= =?UTF-8?q?e=20legacy=20modules,=20update=20exports,=20add=20ROADMAP.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Move old architecture to _legacy/: mmcontextencoder, adapters, omicsencoder, onehot, cell_sentence_transformer - Update all imports (tests, scripts, embed) to _legacy path - Export modules + io from top-level __init__.py - ROADMAP.md: architecture overview, eval framework analysis, missing features, test gaps, and file map for future development --- ROADMAP.md | 342 ++++++++++++++++++ scripts/train.py | 2 +- scripts/train_merged.py | 2 +- src/mmcontext/__init__.py | 4 +- src/mmcontext/_legacy/__init__.py | 9 + src/mmcontext/{ => _legacy}/adapters.py | 0 .../cell_sentence_transformer.py | 0 .../{ => _legacy}/mmcontextencoder.py | 4 +- src/mmcontext/{ => _legacy}/omicsencoder.py | 0 src/mmcontext/{ => _legacy}/onehot.py | 0 src/mmcontext/embed/model_utils.py | 2 +- tests/conftest.py | 2 +- tests/test_miniOmicsEncoder.py | 2 +- tests/test_mmcontext_encoder.py | 2 +- tests/test_mmcontext_tokenizer.py | 2 +- tests/test_register_initial_embeddings.py | 2 +- .../test_sentence_transformer_integration.py | 2 +- 17 files changed, 364 insertions(+), 13 deletions(-) create mode 100644 ROADMAP.md create mode 100644 src/mmcontext/_legacy/__init__.py rename src/mmcontext/{ => _legacy}/adapters.py (100%) rename src/mmcontext/{models => _legacy}/cell_sentence_transformer.py (100%) rename src/mmcontext/{ => _legacy}/mmcontextencoder.py (99%) rename src/mmcontext/{ => _legacy}/omicsencoder.py (100%) rename src/mmcontext/{ => _legacy}/onehot.py (100%) diff --git a/ROADMAP.md b/ROADMAP.md new file mode 100644 index 0000000..d1da283 --- /dev/null +++ b/ROADMAP.md @@ -0,0 +1,342 @@ +# MMContext Refactor — Roadmap & Technical Notes + +This document captures the state of the codebase after the sentence-transformers +v5.4+ refactor (Phases 1–5), identifies remaining work, and provides context for +future development. It is structured as a reference for both human contributors +and AI-assisted editing sessions. + +--- + +## 1 Architecture overview (post-refactor) + +The new pipeline lives in two packages: + +| Package | Role | +|---------|------| +| `mmcontext.modules` | ST pipeline modules: `MMContextModule` (InputModule), `AdapterModule`, `OmicsAttentionModule` | +| `mmcontext.io` | `VectorStore` (mmap-backed lookup), `prepare_vector_store` (zarr → store builder) | + +A trained model is a standard `SentenceTransformer` with this module chain: + +``` +MMContextModule → [OmicsAttentionModule] → AdapterModule → Pooling → Normalize +``` + +`OmicsAttentionModule` is optional — include it for var-level (gene-vector) +inputs where the sequence length > 1; omit it for obs-level (single cell +embedding) inputs where it would degenerate to a feedforward layer. + +### Key design decisions + +- **`input_values` preprocess key** — omics preprocess writes `input_values` + (not `token_embeddings`) so the ST training collator's `collect_features` + suffix matching detects omics columns. Forward reads `input_values` and + writes `token_embeddings` for downstream modules. +- **`modality_ids` tensor** — 0 = text, 1 = omics, 2 = pad. The + `AdapterModule` uses this to route tokens through the correct projection head. +- **No Trainer subclass** — training uses the standard + `SentenceTransformerTrainer` + `MultipleNegativesRankingLoss` with + `(anchor, positive)` column layout. + +--- + +## 2 What was completed (Phases 1–5) + +| Phase | Deliverable | Tests | +|-------|-------------|-------| +| 1 | `VectorStore` — mmap-backed vector lookup with `from_numpy`, `from_adata`, `from_dict`, `load` | `tests/test_vector_store.py` (in mmcontext_module tests) | +| 2 | `MMContextModule` — text encoding via AutoModel/AutoTokenizer, omics via VectorStore or direct vectors, `preprocess`/`forward` contract | `tests/test_mmcontext_module.py` | +| 3 | `AdapterModule` — modality-aware projection (text head, omics head, shared dim), safetensors persistence | `tests/test_adapter_module.py` | +| 4 | `OmicsAttentionModule` — optional self-attention over variable-length omics sequences | `tests/test_omics_attention_module.py` | +| 5 | Full ST integration — pipeline construction, encode, save/load, training (text-only, bimodal, gene-list), `prepare_vector_store` | `tests/test_st_integration.py`, `tests/test_prepare_store.py` | + +### Supporting files + +- `scripts/train_tiny.py` — end-to-end training script for `jo-mengr/cxg_schaefer_tiny` with wandb, MPS, and auto VectorStore preparation. +- `src/mmcontext/io/prepare_store.py` — memory-efficient store builder that reads only needed obsm rows from zarr (never loads full AnnData). + +--- + +## 3 Legacy code (`_legacy/`) + +The following modules were moved to `src/mmcontext/_legacy/` and are preserved +for backward compatibility with previously trained models: + +| File | What it was | Notes | +|------|-------------|-------| +| `mmcontextencoder.py` | Dual-tower encoder (text + omics), `MMContextProcessor` tokenizer | Central old architecture; `embed/model_utils.py` still imports `MMEnc.get_initial_embeddings_from_adata_link` from it | +| `adapters.py` | MLP adapter with identity/linear/2-layer modes | Superseded by `modules/adapter_module.py` which adds modality-awareness | +| `omicsencoder.py` | `MiniOmicsModel` — `nn.Embedding` lookup with HF `PreTrainedModel` interface | Superseded by `VectorStore` (no learnable embedding, just mmap lookup) | +| `onehot.py` | `OneHotTextEncoder` — learnable embedding per unique sentence | Useful for ablation experiments; no new equivalent | +| `cell_sentence_transformer.py` | `OmicsEncoder` — full transformer over omics tokens with cross-attention | Heavier than `OmicsAttentionModule`; cross-attention is not yet in new code | + +### Legacy tests (still functional) + +These test files import from `_legacy` and exercise the old architecture. They +should continue to pass for backward compatibility: + +- `test_mmcontext_encoder.py` +- `test_mmcontext_tokenizer.py` +- `test_miniOmicsEncoder.py` +- `test_register_initial_embeddings.py` +- `test_sentence_transformer_integration.py` (old integration tests) + +--- + +## 4 Active non-module code — status & required changes + +### `callback.py` — trainer callbacks + +**Status:** Functional but coupled to old `MMContextEncoder` attribute paths +(`model[0].text_encoder`, `model[0].text_adapter`, `model[0].omics_adapter`). + +**What needs to change:** + +- `UnfreezeTextEncoderCallback` should look for `model[0].auto_model` (the + `MMContextModule`'s text encoder) instead of `model[0].text_encoder`. +- `UnfreezeAdapterCallback` should look for the `AdapterModule` by iterating + `model.children()` and checking `isinstance(m, AdapterModule)`, since it's + no longer at `model[0]` — it's `model[1]` (or `model[2]` if + OmicsAttentionModule is included). +- Consider making freeze/unfreeze methods on the modules themselves + (`MMContextModule.freeze_text_encoder()` already exists; + `AdapterModule` could add `freeze_text_head()` / `freeze_omics_head()`). + +### `utils.py` — training utilities + +**Useful functions that work with new architecture as-is:** + +- `truncate_cell_sentences()` — gene-name truncation/filtering for HF datasets. + Domain-specific and actively needed for dataset preprocessing. +- `truncate_semantic_cell_sentences_dataset()` — semantic-aware truncation. +- `resolve_negative_indices_and_rename()` — resolves `negative_*_idx` columns + to actual text values. Needed for multiplet training with the real dataset. +- `get_evaluator()` — instantiates ST evaluators (BinaryClassification, + Triplet) from dataset columns. Works with any ST model. +- `consolidate_low_frequency_categories()` — groups rare labels into "other". +- `get_device()` — simple CUDA/MPS/CPU device selection. + +**Functions that reference old architecture:** + +- `get_loss()` (both overloads) — hardcodes dataset_type → loss mapping. + Logic is fine, but should be reviewed for new column naming conventions. +- `prepare_omics_resources()` — builds lookup dict with `"sample_idx:"` prefix. + Superseded by `prepare_vector_store()` with `"omics:"` prefix, but the old + function may still be needed for legacy model inference. + +### `hub_utils.py` — HuggingFace Hub upload + +**Status:** Template body references old module names. The upload logic +(`HfApi` calls) is architecture-agnostic and works. + +**What needs to change:** Update the `MODEL_CARD_TEMPLATE` string to describe +the new pipeline modules. + +### `file_utils.py` — I/O infrastructure + +**Status:** Actively used by both old and new code. + +**Key functions:** + +- `download_and_extract_links()` — full-featured download with caching, + Zenodo support, resume, retries. The new `io/prepare_store.py` has a + lighter `_download_zarr()` that doesn't support all features. Consider + converging on one implementation eventually. +- `remove_corrupted_null_arrays()` — zarr repair utility; used by + `embed/dataset_utils.py`. +- `collect_unique_links()` — deduplicates adata links from a HF dataset. +- `load_test_adata()` — downloads + loads a test adata from HF. +- `save_table()` — generic table saver (CSV/parquet/AnnData). + +--- + +## 5 Evaluation framework — analysis & adaptation needs + +The `eval/` package is a **self-contained evaluation framework** with a +decorator-based registry pattern. It is largely architecture-agnostic — most +evaluators work on AnnData `.obsm` embeddings, not on the model directly. + +### Architecture + +``` +eval/registry.py — @register decorator, get(name) lookup +eval/base.py — BaseEvaluator (abstract), EvalResult +eval/eval_pipeline.py — orchestrator: discover evaluators, run on datasets +eval/utils.py — LabelKind, LabelSpec helpers +``` + +### Evaluators + +| Evaluator | Registry name | What it measures | Architecture dependency | +|-----------|--------------|------------------|----------------------| +| `ARI` | `"ARI"` | Adjusted Rand Index (KMeans clustering vs labels) | None — operates on embeddings | +| `LabelSimilarity` | (registered) | ROC-AUC of intra- vs inter-label cosine sim | None | +| `ScibBundle` | `"scib"` | scIB benchmark metrics (batch/bio) | None | +| `UmapPlotter` | (registered) | UMAP visualizations colored by labels | Uses `pl/plotting.py` | +| `OmicsQueryAnnotator` | (not registered, standalone) | Zero-shot annotation via cosine similarity | Needs `model.encode()` — works with any ST model | + +### `embedding_alignment.py` + +Standalone module (not registered as an evaluator) that computes cross-modal +alignment scores. Useful for measuring how well the shared space aligns +text and omics embeddings. Works on raw numpy arrays. + +### What needs to change for new architecture + +1. **Embedding pipeline (`embed/`)** — `embed/model_utils.py` imports + `MMContextEncoder.get_initial_embeddings_from_adata_link` to register + omics vectors before embedding. This needs a new path: + - Load the ST model + - Detect if it has an `MMContextModule` at position 0 + - Attach a `VectorStore` (built via `prepare_vector_store`) instead of + calling `register_initial_embeddings` + - The `prepare_model_and_embed()` function should be updated accordingly + +2. **Eval pipeline** — The evaluators themselves don't need changes (they + operate on `adata.obsm` arrays), but the `embed_pipeline.py` orchestrator + that feeds them embeddings does (see point 1). + +3. **`OmicsQueryAnnotator`** — Works with any model with `.encode()`. + No changes needed for the new pipeline. + +--- + +## 6 Missing features & test gaps + +### Features not yet covered by new modules + +- [ ] **Callbacks for new architecture** — `UnfreezeTextEncoderCallback` and + `UnfreezeAdapterCallback` need to be updated for the new module layout + (see §4). + +- [ ] **Cross-attention** — The old `OmicsEncoder` in + `cell_sentence_transformer.py` supported cross-attention between text + and omics sequences. The new `OmicsAttentionModule` only does + self-attention on omics tokens. Cross-attention would enable richer + multimodal interaction but adds complexity. + +- [ ] **`OneHotTextEncoder` equivalent** — Useful for ablation experiments + (fast training without a real transformer). Currently only in `_legacy/`. + Could be a simple fixture/utility rather than a full module. + +- [ ] **Negative mining / hard negatives** — The training script currently + uses `(anchor, positive)` pairs with in-batch negatives (MNR loss). + The dataset has `negative_1_idx` and `negative_2_idx` columns. + `resolve_negative_indices_and_rename()` in `utils.py` handles + resolving these to actual text. Supporting `(anchor, positive, negative)` + triplets would improve training quality. + +- [ ] **Hub upload for new architecture** — `hub_utils.py` model card + template references old module names. + +- [ ] **Embed pipeline for new architecture** — `embed/model_utils.py` + uses `MMContextEncoder.get_initial_embeddings_from_adata_link()`. + Needs updating to use `VectorStore` + `prepare_vector_store`. + +### Test gaps + +- [ ] **`VectorStore` standalone tests** — Currently exercised through + `test_mmcontext_module.py` and `test_prepare_store.py` fixtures, but + there's no dedicated `test_vector_store.py` testing all construction + methods (`from_numpy`, `from_adata`, `from_dict`, `from_dataframe`), + edge cases, and persistence. + +- [ ] **AdapterModule standalone tests** — verify existence of + `tests/test_adapter_module.py` and confirm coverage. + +- [ ] **Mixed-modality batches** — No test currently sends a batch where + some samples are text and some are omics through the full pipeline + in a single forward pass. This is an important edge case for + training with heterogeneous datasets. + +- [ ] **Gradient flow end-to-end** — Training tests verify parameters change, + but don't check that gradients flow through specific module boundaries + (e.g., from loss through Pooling → AdapterModule → MMContextModule's + text encoder). + +- [ ] **Multi-GPU / DDP** — No tests for distributed training. + +- [ ] **Large-scale prepare_store** — `test_prepare_store.py` tests local + zarr files; no integration test for the Zenodo download path + (intentionally — network tests are fragile). + +### Potential improvements + +- [ ] **Converge download logic** — `file_utils.download_and_extract_links` + and `io/prepare_store._download_zarr` both handle zarr downloads with + different feature sets. Could share a common download backend. + +- [ ] **`simulator.py` integration** — The old `LOSS_PRESETS` dict and + `make_cluster_sampler` are valuable for generating synthetic test + datasets. Consider moving to a `testing/` subpackage. + +- [ ] **`sanity_helpers.py`** — Contains `plot_pca` and possibly other + debug helpers not in `pl/`. Consider merging into `pl/` or a + `debug/` module. + +--- + +## 7 File map (post-cleanup) + +``` +src/mmcontext/ +├── __init__.py # exports: modules, io, eval, pl, embed +├── _legacy/ # old architecture (preserved for compat) +│ ├── adapters.py +│ ├── cell_sentence_transformer.py +│ ├── mmcontextencoder.py +│ ├── omicsencoder.py +│ └── onehot.py +├── modules/ # NEW: ST v5.4+ pipeline modules +│ ├── mmcontext_module.py # InputModule (text + omics routing) +│ ├── adapter_module.py # modality-aware projection +│ └── omics_attention_module.py# optional self-attention (var-level) +├── io/ # NEW: data I/O +│ ├── vector_store.py # mmap-backed vector lookup +│ └── prepare_store.py # zarr → VectorStore builder +├── eval/ # evaluation framework (active, mostly arch-agnostic) +│ ├── base.py, registry.py # BaseEvaluator + decorator registry +│ ├── eval_pipeline.py # orchestrator +│ ├── ari.py # ARI evaluator +│ ├── label_similarity.py # label similarity evaluator +│ ├── embedding_alignment.py # cross-modal alignment scores +│ ├── scib_wrapper.py # scIB benchmark +│ ├── query_annotate.py # zero-shot annotation +│ ├── umap_plotter.py # UMAP visualizations +│ └── utils.py # LabelKind, LabelSpec +├── embed/ # embedding generation pipeline (active, needs update) +│ ├── embed_pipeline.py # orchestrator +│ ├── model_utils.py # model loading + embedding (imports _legacy) +│ ├── dataset_utils.py # adata/dataset loading +│ ├── cellwhisperer_utils.py # CellWhisperer-specific helpers +│ └── scsa_utils.py # scSA dataset helpers +├── pl/ # plotting (active) +│ ├── plotting.py # UMAP + query-score plots +│ └── metric_plots.py # benchmark result bar charts +├── callback.py # trainer callbacks (needs update for new modules) +├── file_utils.py # I/O infrastructure (active) +├── hub_utils.py # HF Hub upload (needs template update) +├── sanity_helpers.py # debug plotting (standalone) +├── simulator.py # synthetic data generation (standalone, useful) +├── utils.py # training utilities (mixed: some active, some legacy) +└── models/ # empty after cell_sentence_transformer.py moved + └── __init__.py + +scripts/ +├── train_tiny.py # NEW: training script for new architecture +├── train.py # old Hydra training script (uses _legacy) +└── train_merged.py # old merged training script (uses _legacy) + +tests/ +├── test_mmcontext_module.py # NEW: MMContextModule unit tests +├── test_adapter_module.py # NEW: AdapterModule unit tests (verify exists) +├── test_omics_attention_module.py # NEW: OmicsAttentionModule unit tests +├── test_st_integration.py # NEW: full pipeline integration + training +├── test_prepare_store.py # NEW: VectorStore preparation tests +├── test_mmcontext_encoder.py # legacy: old MMContextEncoder tests +├── test_mmcontext_tokenizer.py # legacy: old tokenizer tests +├── test_miniOmicsEncoder.py # legacy: old MiniOmicsModel tests +├── test_register_initial_embeddings.py # legacy +├── test_sentence_transformer_integration.py # legacy: old ST integration +└── conftest.py # shared fixtures (stub encoder, both old+new) +``` diff --git a/scripts/train.py b/scripts/train.py index b54c0ba..ea4c1ec 100644 --- a/scripts/train.py +++ b/scripts/train.py @@ -23,7 +23,7 @@ # from mmcontext.eval import SystemMonitor from mmcontext.hub_utils import prepare_model_for_hub, upload_model_to_hub -from mmcontext.mmcontextencoder import MMContextEncoder +from mmcontext._legacy.mmcontextencoder import MMContextEncoder # from mmcontext.pp.utils import consolidate_low_frequency_categories from mmcontext.utils import ( # , load_test_adata_from_hf_dataset diff --git a/scripts/train_merged.py b/scripts/train_merged.py index 06fc0b9..1978cdf 100644 --- a/scripts/train_merged.py +++ b/scripts/train_merged.py @@ -32,7 +32,7 @@ from mmcontext.callback import UnfreezeAdapterCallback, UnfreezeTextEncoderCallback from mmcontext.eval import SystemMonitor from mmcontext.hub_utils import get_model_info_from_config, upload_model_to_hub -from mmcontext.mmcontextencoder import MMContextEncoder +from mmcontext._legacy.mmcontextencoder import MMContextEncoder from mmcontext.utils import ( get_evaluator, get_loss, diff --git a/src/mmcontext/__init__.py b/src/mmcontext/__init__.py index 6cc09d2..2f79f10 100644 --- a/src/mmcontext/__init__.py +++ b/src/mmcontext/__init__.py @@ -1,7 +1,7 @@ from importlib.metadata import version -from . import embed, eval, models, pl +from . import embed, eval, io, modules, pl -__all__ = ["models", "eval", "pl", "embed"] +__all__ = ["modules", "io", "eval", "pl", "embed"] __version__ = version("mmcontext") diff --git a/src/mmcontext/_legacy/__init__.py b/src/mmcontext/_legacy/__init__.py new file mode 100644 index 0000000..582fdeb --- /dev/null +++ b/src/mmcontext/_legacy/__init__.py @@ -0,0 +1,9 @@ +"""Legacy modules from the pre-v5.4 architecture. + +These modules are preserved for reference and backward compatibility with +existing trained models. New code should use :mod:`mmcontext.modules` and +:mod:`mmcontext.io` instead. + +.. deprecated:: + This package will be removed in a future version. +""" diff --git a/src/mmcontext/adapters.py b/src/mmcontext/_legacy/adapters.py similarity index 100% rename from src/mmcontext/adapters.py rename to src/mmcontext/_legacy/adapters.py diff --git a/src/mmcontext/models/cell_sentence_transformer.py b/src/mmcontext/_legacy/cell_sentence_transformer.py similarity index 100% rename from src/mmcontext/models/cell_sentence_transformer.py rename to src/mmcontext/_legacy/cell_sentence_transformer.py diff --git a/src/mmcontext/mmcontextencoder.py b/src/mmcontext/_legacy/mmcontextencoder.py similarity index 99% rename from src/mmcontext/mmcontextencoder.py rename to src/mmcontext/_legacy/mmcontextencoder.py index c4de365..10fb53d 100644 --- a/src/mmcontext/mmcontextencoder.py +++ b/src/mmcontext/_legacy/mmcontextencoder.py @@ -44,8 +44,8 @@ from .adapters import AdapterModule -# Import local dependencies using relative imports (works with package structure) -from .file_utils import ( +# file_utils remains at the package root (not moved to _legacy) +from mmcontext.file_utils import ( build_embedding_df, collect_unique_links, download_and_extract_links, diff --git a/src/mmcontext/omicsencoder.py b/src/mmcontext/_legacy/omicsencoder.py similarity index 100% rename from src/mmcontext/omicsencoder.py rename to src/mmcontext/_legacy/omicsencoder.py diff --git a/src/mmcontext/onehot.py b/src/mmcontext/_legacy/onehot.py similarity index 100% rename from src/mmcontext/onehot.py rename to src/mmcontext/_legacy/onehot.py diff --git a/src/mmcontext/embed/model_utils.py b/src/mmcontext/embed/model_utils.py index c90e9f7..b8d5eeb 100644 --- a/src/mmcontext/embed/model_utils.py +++ b/src/mmcontext/embed/model_utils.py @@ -14,7 +14,7 @@ from torch.utils.data import DataLoader, Dataset from tqdm import tqdm -from mmcontext.mmcontextencoder import MMContextEncoder as MMEnc +from mmcontext._legacy.mmcontextencoder import MMContextEncoder as MMEnc logger = logging.getLogger(__name__) diff --git a/tests/conftest.py b/tests/conftest.py index f21c0ac..df039f7 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -13,7 +13,7 @@ from transformers import PretrainedConfig, PreTrainedModel from transformers.models.auto import configuration_auto, modeling_auto -from mmcontext.mmcontextencoder import MMContextEncoder, MMContextProcessor +from mmcontext._legacy.mmcontextencoder import MMContextEncoder, MMContextProcessor # ----------------------------------------------------------------------------- # Logging diff --git a/tests/test_miniOmicsEncoder.py b/tests/test_miniOmicsEncoder.py index cd1dc8a..e4caba9 100644 --- a/tests/test_miniOmicsEncoder.py +++ b/tests/test_miniOmicsEncoder.py @@ -4,7 +4,7 @@ import torch from torch import nn -from mmcontext.omicsencoder import MiniOmicsModel +from mmcontext._legacy.omicsencoder import MiniOmicsModel # ---------------------------------------------------------------------- # diff --git a/tests/test_mmcontext_encoder.py b/tests/test_mmcontext_encoder.py index c8510c2..5c1f65f 100644 --- a/tests/test_mmcontext_encoder.py +++ b/tests/test_mmcontext_encoder.py @@ -5,7 +5,7 @@ import pytest import torch -from mmcontext.mmcontextencoder import AdapterModule, MMContextEncoder +from mmcontext._legacy.mmcontextencoder import AdapterModule, MMContextEncoder # --------------------------------------------------------------------- # diff --git a/tests/test_mmcontext_tokenizer.py b/tests/test_mmcontext_tokenizer.py index 5d16eef..aa86987 100644 --- a/tests/test_mmcontext_tokenizer.py +++ b/tests/test_mmcontext_tokenizer.py @@ -6,7 +6,7 @@ import pytest import torch -from mmcontext.mmcontextencoder import MMContextEncoder, MMContextProcessor +from mmcontext._legacy.mmcontextencoder import MMContextEncoder, MMContextProcessor # --- fixtures --------------------------------------------------------- diff --git a/tests/test_register_initial_embeddings.py b/tests/test_register_initial_embeddings.py index 5ce21af..42dd272 100644 --- a/tests/test_register_initial_embeddings.py +++ b/tests/test_register_initial_embeddings.py @@ -5,7 +5,7 @@ import pandas as pd import pytest -from mmcontext.mmcontextencoder import MMContextEncoder +from mmcontext._legacy.mmcontextencoder import MMContextEncoder # --- helper that builds a text-only encoder cheaply (no real BERT) -------- diff --git a/tests/test_sentence_transformer_integration.py b/tests/test_sentence_transformer_integration.py index 1f1b697..5fe561d 100644 --- a/tests/test_sentence_transformer_integration.py +++ b/tests/test_sentence_transformer_integration.py @@ -17,7 +17,7 @@ ) from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator -from mmcontext.mmcontextencoder import MMContextEncoder, MMContextProcessor +from mmcontext._legacy.mmcontextencoder import MMContextEncoder, MMContextProcessor logger = logging.getLogger(__name__) From b3dd7521eeff52792ad99424a8fc2ca7341d2243 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 1 Jun 2026 22:56:59 +0000 Subject: [PATCH 09/67] [pre-commit.ci] pre-commit autoupdate MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/tox-dev/pyproject-fmt: v2.5.0 → v2.23.0](https://github.com/tox-dev/pyproject-fmt/compare/v2.5.0...v2.23.0) - [github.com/astral-sh/ruff-pre-commit: v0.9.3 → v0.15.15](https://github.com/astral-sh/ruff-pre-commit/compare/v0.9.3...v0.15.15) - [github.com/pre-commit/pre-commit-hooks: v5.0.0 → v6.0.0](https://github.com/pre-commit/pre-commit-hooks/compare/v5.0.0...v6.0.0) --- .pre-commit-config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 81057ec..4059f4a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -11,11 +11,11 @@ repos: hooks: - id: prettier - repo: https://github.com/tox-dev/pyproject-fmt - rev: "v2.5.0" + rev: "v2.23.0" hooks: - id: pyproject-fmt - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.9.3 + rev: v0.15.15 hooks: - id: ruff types_or: [python, pyi, jupyter] @@ -23,7 +23,7 @@ repos: - id: ruff-format types_or: [python, pyi, jupyter] - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v5.0.0 + rev: v6.0.0 hooks: - id: detect-private-key - id: check-ast From 335505ba7aecea94ebdf8b4b99cc71137fc79f76 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 1 Jun 2026 22:57:08 +0000 Subject: [PATCH 10/67] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- exploratory/notebooks/clustering_issues.ipynb | 9 ++-- exploratory/notebooks/text_batches.ipynb | 3 +- pyproject.toml | 42 +++++++++---------- src/mmcontext/eval/label_similarity.py | 2 +- src/mmcontext/utils.py | 2 +- 5 files changed, 29 insertions(+), 29 deletions(-) diff --git a/exploratory/notebooks/clustering_issues.ipynb b/exploratory/notebooks/clustering_issues.ipynb index 7acc426..ba1f00d 100644 --- a/exploratory/notebooks/clustering_issues.ipynb +++ b/exploratory/notebooks/clustering_issues.ipynb @@ -243,9 +243,10 @@ ], "source": [ "# Visualise the embeddings\n", - "from mmcontext.pl import plot_umap\n", "from mmcontext.pp.utils import consolidate_low_frequency_categories\n", "\n", + "from mmcontext.pl import plot_umap\n", + "\n", "adata_cut = consolidate_low_frequency_categories(adata, [\"cell_type\"], threshold=10)\n", "label_key = \"cell_type\"\n", "emb_key = \"mmcontext_emb\"\n", @@ -364,9 +365,10 @@ ], "source": [ "# Visualise the embeddings\n", - "from mmcontext.pl import plot_umap\n", "from mmcontext.pp.utils import consolidate_low_frequency_categories\n", "\n", + "from mmcontext.pl import plot_umap\n", + "\n", "label_key = \"tissue\"\n", "adata_cut = consolidate_low_frequency_categories(adata, [\"tissue\"], threshold=0)\n", "emb_key = \"mmcontext_emb\"\n", @@ -417,9 +419,10 @@ ], "source": [ "# Does maybe the original text embedding cluster after tissues?\n", - "from mmcontext.pl import plot_umap\n", "from mmcontext.pp.utils import consolidate_low_frequency_categories\n", "\n", + "from mmcontext.pl import plot_umap\n", + "\n", "label_key = \"tissue\"\n", "adata_cut = consolidate_low_frequency_categories(adata, [label_key], threshold=15)\n", "emb_key = \"og_text_emb\"\n", diff --git a/exploratory/notebooks/text_batches.ipynb b/exploratory/notebooks/text_batches.ipynb index ee02a81..1ea6b4f 100644 --- a/exploratory/notebooks/text_batches.ipynb +++ b/exploratory/notebooks/text_batches.ipynb @@ -186,9 +186,10 @@ ], "source": [ "# Visualise the embeddings\n", - "from mmcontext.pl import plot_umap\n", "from mmcontext.pp.utils import consolidate_low_frequency_categories\n", "\n", + "from mmcontext.pl import plot_umap\n", + "\n", "adata_cut = consolidate_low_frequency_categories(adata, [\"cell_type\"], threshold=10)\n", "label_key = \"_scvi_batch\"\n", "plot_umap(adata_cut, color_key=label_key, embedding_key=\"X_text\")" diff --git a/pyproject.toml b/pyproject.toml index f9f8030..f2b21b4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,12 +1,14 @@ [build-system] build-backend = "setuptools.build_meta" - requires = [ "setuptools>=64", "wheel" ] [project] name = "mmcontext" version = "0.0.1" -description = "This package allows to use the sentence-transfomers package to build multimodal embedding models for data in anndata format." +description = """\ + This package allows to use the sentence-transfomers package to build multimodal embedding models for data in anndata \ + format.\ + """ readme = "README.md" license = { file = "LICENSE" } maintainers = [ @@ -22,12 +24,11 @@ classifiers = [ "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", ] - dependencies = [ "accelerate>=1.9", "adata-hf-datasets @ git+https://github.com/mengerj/adata_hf_datasets.git", "anndata>=0.11.3", - "datasets>=2.19.1,!=4", #Avoid: TypeError: Wrong key type: '0' of type ''. Expected one of int, slice, range, str or Iterable. + "datasets>=2.19.1,!=4", # Avoid: TypeError: Wrong key type: '0' of type ''. Expected one of int, slice, range, str or Iterable. "huggingface-hub>=0.34.3", "hydra-core>=1.3.2", "ipykernel>=6.30", @@ -56,20 +57,20 @@ urls.Homepage = "https://github.com/mengerj/mmcontext" urls.Source = "https://github.com/mengerj/mmcontext" [tool.setuptools] +packages.find.where = [ "src" ] +packages.find.include = [ "mmcontext*" ] include-package-data = true -[tool.setuptools.packages.find] -where = [ "src" ] -include = [ "mmcontext*" ] -[tool.setuptools.package-data] -#"mmcontext.conf" = [ "*.json" ] +package-data = {} +[tool.uv] +sources.adata-hf-datasets = { git = "https://github.com/mengerj/adata_hf_datasets.git" } + +# "mmcontext.conf" = [ "*.json" ] [tool.ruff] line-length = 120 src = [ "src" ] extend-include = [ "*.ipynb" ] - format.docstring-code-format = true - lint.select = [ "B", # flake8-bugbear "C4", # flake8-comprehensions @@ -82,7 +83,6 @@ lint.select = [ "UP", # pyupgrade "W", # Warning detected by Pycodestyle ] - lint.ignore = [ "B008", # function calls in argument defaults "BLE001", # allow blind expections @@ -99,7 +99,6 @@ lint.ignore = [ "E741", # allow I, O, l as variable names "F401", # dont remove unused imports, because of fiass flag ] - lint.per-file-ignores."*/__init__.py" = [ "F401" ] lint.per-file-ignores."tests/*" = [ "D" ] lint.pydocstyle.convention = "numpy" @@ -110,18 +109,18 @@ ignore = [ "UP031", ] -[tool.pytest.ini_options] -testpaths = [ "tests" ] -xfail_strict = true -addopts = [ +[tool.pytest] +ini_options.testpaths = [ "tests" ] +ini_options.addopts = [ "--import-mode=importlib", ] +ini_options.xfail_strict = true -[tool.coverage.run] -source = [ "mmcontext" ] -omit = [ +[tool.coverage] +run.omit = [ "**/test_*.py", ] +run.source = [ "mmcontext" ] [tool.cruft] skip = [ @@ -129,6 +128,3 @@ skip = [ "src/**/__init__.py", "src/**/basic.py", ] - -[tool.uv.sources] -adata-hf-datasets = { git = "https://github.com/mengerj/adata_hf_datasets.git" } diff --git a/src/mmcontext/eval/label_similarity.py b/src/mmcontext/eval/label_similarity.py index 5689b79..757c697 100644 --- a/src/mmcontext/eval/label_similarity.py +++ b/src/mmcontext/eval/label_similarity.py @@ -850,7 +850,7 @@ def _compute_topk_accuracies( n_cells, n_labels = similarity_matrix.shape if n_labels == 0 or n_cells == 0: - return {k: 0.0 for k in ks} + return dict.fromkeys(ks, 0.0) # Sanitize ks: positive, <= n_labels, unique and sorted valid_ks = sorted({int(k) for k in ks if int(k) > 0}) diff --git a/src/mmcontext/utils.py b/src/mmcontext/utils.py index b6dfdb0..1c8ccf4 100644 --- a/src/mmcontext/utils.py +++ b/src/mmcontext/utils.py @@ -90,7 +90,7 @@ def _truncate_batch(batch): if filter_strings: original_count = len(tokens) filtered_tokens = [] - removed_counts = {filter_str: 0 for filter_str in filter_strings} + removed_counts = dict.fromkeys(filter_strings, 0) for token in tokens: should_keep = True From 83a038cc3998f203ed906f215e1aefff69b8257e Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Tue, 2 Jun 2026 08:49:42 +0200 Subject: [PATCH 11/67] basic claude.md with project overview --- CLAUDE.md | 118 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 118 insertions(+) create mode 100644 CLAUDE.md diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..e4f7f73 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,118 @@ +# CLAUDE.md — mmcontext + +## Project Overview + +mmcontext is a Python library for multimodal contrastive learning that aligns text and omics (single-cell gene expression) embeddings using the sentence-transformers (>=5.4) framework. It enables joint embedding spaces where text descriptions and biological data can be compared directly. + +**Repository**: `github.com/mengerj/mmcontext` +**Maintainer**: Jonatan Menger +**Python**: >=3.11, <3.14 +**Key dependency**: `sentence-transformers>=5.4` (multimodal API) + +## Package Structure + +``` +src/mmcontext/ +├── modules/ # sentence-transformers InputModules +│ ├── mmcontext_module.py # Core: text tokenization + omics vector pass-through +│ ├── adapter_module.py # Projects omics vectors into shared embedding space +│ └── omics_attention_module.py # Optional self-attention for omics tokens +├── embed/ # Embedding pipeline, dataset utilities, model utils +├── eval/ # Evaluation: kNN, ARI, scIB metrics, label similarity +├── io/ # VectorStore, data preparation +├── pl/ # Plotting utilities +├── _legacy/ # Archived modules (do not modify unless removing) +├── callback.py # Training callbacks +├── hub_utils.py # HuggingFace Hub integration +├── simulator.py # Synthetic data generation for testing +└── utils.py # Shared utilities +``` + +## Branch Strategy + +- `main` — stable releases only +- `dev-claude` — integration branch for all agent and feature work +- Feature branches: always branch from `dev-claude`, never from `main` +- Branch naming: `claude/-` for agent-created branches + +## Commands + +```bash +# Install (editable, with test deps) +pip install -e ".[dev,test]" + +# Run tests +pytest -v --color=yes + +# Run tests with coverage +coverage run -m pytest -v --color=yes && coverage report + +# Lint +ruff check src/ tests/ +ruff format --check src/ tests/ + +# Format +ruff format src/ tests/ +``` + +## Code Style + +- **Formatter/linter**: ruff (line-length=120) +- **Docstrings**: numpy-style, required for public classes and functions (D100/D104/D105/D107 ignored) +- **Imports**: sorted by isort (via ruff) +- **Type hints**: use them for all public function signatures +- **Pre-commit**: prettier, pyproject-fmt, ruff, detect-private-key, check-ast + +## Testing Conventions + +- Tests live in `tests/` at repo root +- Use pytest fixtures; shared fixtures go in `conftest.py` +- Test files: `test_.py` +- The `simulator.py` module generates synthetic data for tests — prefer it over loading real datasets +- Tests must pass on Python 3.11, 3.12, 3.13 + +## PR Conventions + +- PRs target `dev-claude` (not `main`) unless it's a release +- PR description must reference the issue: `Fixes #` or `Closes #` +- All CI checks must pass before merge +- Keep PRs focused — one logical change per PR + +## Issue Implementation Protocol + +When implementing a feature from a GitHub issue (via @claude or otherwise): + +1. **If the issue is ambiguous**: Post clarifying questions as a comment. Do NOT start implementation until the questions are answered. + +2. **Plan first**: Before writing any code, post an implementation plan as a comment on the issue with a checkbox list: + ``` + ## Implementation Plan + - [ ] Step 1: description + - [ ] Step 2: description + - [ ] Step 3: description + - [ ] Verify: run tests, check linting + ``` + Wait for approval (a reply containing "approved", "go ahead", "LGTM", or "looks good"). + +3. **Implement**: Create a branch `claude/-` from `dev-claude`. Implement the plan step by step. Edit the plan comment to check off completed steps. + +4. **Open PR**: Create a PR targeting `dev-claude` with `Fixes #` in the description. Include a summary of what was done and any decisions made. + +## Architecture Notes + +The core pipeline follows the sentence-transformers module pattern: + +``` +Input → MMContextModule → AdapterModule → [OmicsAttentionModule] → Pooling → Loss +``` + +- **MMContextModule**: Handles both text (tokenize → AutoModel) and omics (VectorStore lookup or direct input) modalities. Outputs a unified features dict with `token_embeddings`, `attention_mask`, and `modality_ids`. +- **AdapterModule**: Projects omics vectors into the text model's embedding space. +- **OmicsAttentionModule**: Optional self-attention layer for omics tokens. +- Data stored in anndata format (`.h5ad`), converted via `adata-hf-datasets` for HuggingFace compatibility. + +## What NOT to Change + +- Do not modify files in `_legacy/` unless explicitly removing them +- Do not change the sentence-transformers module interface contracts (features dict keys) +- Do not add dependencies without discussion — the package already has heavy deps (torch, transformers, scanpy) From 944952a2e12d9fc296f7094d558e9399ea25a567 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Tue, 2 Jun 2026 08:52:04 +0200 Subject: [PATCH 12/67] claude action worfklows for code review and implementation from issue --- .github/workflows/claude-implement.yaml | 42 +++++++++++++++++++++++++ .github/workflows/claude-review.yaml | 39 +++++++++++++++++++++++ 2 files changed, 81 insertions(+) create mode 100644 .github/workflows/claude-implement.yaml create mode 100644 .github/workflows/claude-review.yaml diff --git a/.github/workflows/claude-implement.yaml b/.github/workflows/claude-implement.yaml new file mode 100644 index 0000000..539632f --- /dev/null +++ b/.github/workflows/claude-implement.yaml @@ -0,0 +1,42 @@ +name: Claude Code Implement + +on: + issues: + types: [opened, labeled] + issue_comment: + types: [created] + +permissions: + contents: write + pull-requests: write + issues: write + actions: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.issue.number }} + cancel-in-progress: true + +jobs: + implement: + # Trigger on @claude mentions in issues — only by repo owner + if: > + github.actor == 'mengerj' && + ((github.event_name == 'issues' && + contains(github.event.issue.body, '@claude')) || + (github.event_name == 'issue_comment' && + !github.event.issue.pull_request && + contains(github.event.comment.body, '@claude'))) + runs-on: ubuntu-latest + steps: + - uses: anthropics/claude-code-action@v1 + with: + claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }} + base_branch: dev-claude + additional_permissions: | + actions: read + claude_args: | + --allowedTools "Bash,Read,Write,Edit,Glob,Grep,Task,WebSearch,WebFetch" + --max-turns 30 + # The CLAUDE.md in the repo root provides the plan-first protocol, + # branch naming conventions, and PR linking instructions. + # Claude reads it automatically on checkout. diff --git a/.github/workflows/claude-review.yaml b/.github/workflows/claude-review.yaml new file mode 100644 index 0000000..ca7798f --- /dev/null +++ b/.github/workflows/claude-review.yaml @@ -0,0 +1,39 @@ +name: Claude Code Review + +on: + pull_request: + types: [opened, synchronize] + branches: [dev-claude, main] + issue_comment: + types: [created] + +permissions: + contents: write + pull-requests: write + issues: write + actions: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.event.issue.number }} + cancel-in-progress: true + +jobs: + review: + # Auto-review every PR, or respond to @claude in PR comments (owner only) + if: > + (github.event_name == 'pull_request' && + github.actor == 'mengerj') || + (github.event_name == 'issue_comment' && + github.actor == 'mengerj' && + github.event.issue.pull_request && + contains(github.event.comment.body, '@claude')) + runs-on: ubuntu-latest + steps: + - uses: anthropics/claude-code-action@v1 + with: + claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }} + additional_permissions: | + actions: read + claude_args: | + --allowedTools "Bash(ruff check src/ tests/),Bash(ruff format --check src/ tests/),Bash(pytest -x -q --tb=short)" + --max-turns 20 From 0f164987ebe368db418e7536ed6c8e9495cc5352 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Tue, 2 Jun 2026 09:05:16 +0200 Subject: [PATCH 13/67] id-token write permissions needed --- .github/workflows/claude-implement.yaml | 1 + .github/workflows/claude-review.yaml | 1 + 2 files changed, 2 insertions(+) diff --git a/.github/workflows/claude-implement.yaml b/.github/workflows/claude-implement.yaml index 539632f..7fa732e 100644 --- a/.github/workflows/claude-implement.yaml +++ b/.github/workflows/claude-implement.yaml @@ -11,6 +11,7 @@ permissions: pull-requests: write issues: write actions: read + id-token: write concurrency: group: ${{ github.workflow }}-${{ github.event.issue.number }} diff --git a/.github/workflows/claude-review.yaml b/.github/workflows/claude-review.yaml index ca7798f..ef4c948 100644 --- a/.github/workflows/claude-review.yaml +++ b/.github/workflows/claude-review.yaml @@ -12,6 +12,7 @@ permissions: pull-requests: write issues: write actions: read + id-token: write concurrency: group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.event.issue.number }} From 2fc3c190fdcd8ba23ee9769664bc7d69458fb483 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Tue, 2 Jun 2026 09:18:54 +0200 Subject: [PATCH 14/67] dont retrigger the event with bot comments --- .github/workflows/claude-implement.yaml | 3 ++- .github/workflows/claude-review.yaml | 15 ++++++++------- 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/.github/workflows/claude-implement.yaml b/.github/workflows/claude-implement.yaml index 7fa732e..1ebddd1 100644 --- a/.github/workflows/claude-implement.yaml +++ b/.github/workflows/claude-implement.yaml @@ -19,9 +19,10 @@ concurrency: jobs: implement: - # Trigger on @claude mentions in issues — only by repo owner + # Trigger on @claude mentions in issues — only by repo owner, ignore bot comments if: > github.actor == 'mengerj' && + github.event.sender.type != 'Bot' && ((github.event_name == 'issues' && contains(github.event.issue.body, '@claude')) || (github.event_name == 'issue_comment' && diff --git a/.github/workflows/claude-review.yaml b/.github/workflows/claude-review.yaml index ef4c948..8a9563d 100644 --- a/.github/workflows/claude-review.yaml +++ b/.github/workflows/claude-review.yaml @@ -20,14 +20,15 @@ concurrency: jobs: review: - # Auto-review every PR, or respond to @claude in PR comments (owner only) + # Auto-review every PR, or respond to @claude in PR comments (owner only, ignore bots) if: > - (github.event_name == 'pull_request' && - github.actor == 'mengerj') || - (github.event_name == 'issue_comment' && - github.actor == 'mengerj' && - github.event.issue.pull_request && - contains(github.event.comment.body, '@claude')) + github.event.sender.type != 'Bot' && + ((github.event_name == 'pull_request' && + github.actor == 'mengerj') || + (github.event_name == 'issue_comment' && + github.actor == 'mengerj' && + github.event.issue.pull_request && + contains(github.event.comment.body, '@claude'))) runs-on: ubuntu-latest steps: - uses: anthropics/claude-code-action@v1 From 8d305d5e8ddd599fecd039d34bcf57df4d150ac1 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Tue, 2 Jun 2026 09:24:02 +0200 Subject: [PATCH 15/67] checkout branch first in actions --- .github/workflows/claude-implement.yaml | 4 ++++ .github/workflows/claude-review.yaml | 3 +++ 2 files changed, 7 insertions(+) diff --git a/.github/workflows/claude-implement.yaml b/.github/workflows/claude-implement.yaml index 1ebddd1..931caf3 100644 --- a/.github/workflows/claude-implement.yaml +++ b/.github/workflows/claude-implement.yaml @@ -30,6 +30,10 @@ jobs: contains(github.event.comment.body, '@claude'))) runs-on: ubuntu-latest steps: + - uses: actions/checkout@v4 + with: + ref: dev-claude + fetch-depth: 0 - uses: anthropics/claude-code-action@v1 with: claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }} diff --git a/.github/workflows/claude-review.yaml b/.github/workflows/claude-review.yaml index 8a9563d..f8d7e99 100644 --- a/.github/workflows/claude-review.yaml +++ b/.github/workflows/claude-review.yaml @@ -31,6 +31,9 @@ jobs: contains(github.event.comment.body, '@claude'))) runs-on: ubuntu-latest steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 - uses: anthropics/claude-code-action@v1 with: claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }} From 9a2a3fe6dcbc4586bc5151d2c74ed37a93432eaa Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Tue, 2 Jun 2026 09:29:05 +0200 Subject: [PATCH 16/67] seperate concurrancy group for user and bot triggers --- .github/workflows/claude-implement.yaml | 2 +- .github/workflows/claude-review.yaml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/claude-implement.yaml b/.github/workflows/claude-implement.yaml index 931caf3..7242937 100644 --- a/.github/workflows/claude-implement.yaml +++ b/.github/workflows/claude-implement.yaml @@ -14,7 +14,7 @@ permissions: id-token: write concurrency: - group: ${{ github.workflow }}-${{ github.event.issue.number }} + group: ${{ github.workflow }}-${{ github.event.issue.number }}-${{ github.actor }} cancel-in-progress: true jobs: diff --git a/.github/workflows/claude-review.yaml b/.github/workflows/claude-review.yaml index f8d7e99..9c34e01 100644 --- a/.github/workflows/claude-review.yaml +++ b/.github/workflows/claude-review.yaml @@ -15,7 +15,7 @@ permissions: id-token: write concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.event.issue.number }} + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.event.issue.number }}-${{ github.actor }} cancel-in-progress: true jobs: From c8ea530b7d3be3ca2b20a56f77d5cf8680698e32 Mon Sep 17 00:00:00 2001 From: "claude[bot]" <41898282+claude[bot]@users.noreply.github.com> Date: Tue, 2 Jun 2026 08:10:30 +0000 Subject: [PATCH 17/67] Adapt training callbacks to new MMContextModule/AdapterModule architecture - AdapterModule: add freeze_text_proj/unfreeze_text_proj/freeze_omics_proj/unfreeze_omics_proj helpers - UnfreezeTextEncoderCallback: use isinstance(model[0], MMContextModule) and call model[0].unfreeze_text_encoder() instead of old text_encoder attribute path - UnfreezeAdapterCallback: find AdapterModule by iterating model.children() (works at any pipeline position); use new projection-level freeze/unfreeze methods - tests/test_adapter_callback.py: 18 tests covering epoch-based unfreezing (before/at/after target, fractional epochs, once-only guard), adapter at positions 1 and 2, independent text/omics unfreeze schedules, and warning paths Closes #50 Co-authored-by: mengerj --- src/mmcontext/callback.py | 115 ++++---- src/mmcontext/modules/adapter_module.py | 55 ++-- tests/test_adapter_callback.py | 363 ++++++++++++++++++++++++ 3 files changed, 447 insertions(+), 86 deletions(-) diff --git a/src/mmcontext/callback.py b/src/mmcontext/callback.py index 8456c09..5864b62 100644 --- a/src/mmcontext/callback.py +++ b/src/mmcontext/callback.py @@ -2,6 +2,9 @@ from transformers import TrainerCallback +from mmcontext.modules.adapter_module import AdapterModule +from mmcontext.modules.mmcontext_module import MMContextModule + logger = logging.getLogger(__name__) @@ -29,42 +32,48 @@ def on_epoch_begin(self, args, state, control, **kwargs): """ if not self.unfrozen and state.epoch >= self.unfreeze_epoch: model = kwargs["model"] - # Assuming model[0] is your MMContextEncoder containing a text_encoder attribute - if hasattr(model[0], "text_encoder"): - for param in model[0].text_encoder.parameters(): + if isinstance(model[0], MMContextModule): + model[0].unfreeze_text_encoder() + self.unfrozen = True + logger.info(f"Text encoder unfrozen at epoch {state.epoch:.2f}") + elif hasattr(model[0], "auto_model"): + for param in model[0].auto_model.parameters(): param.requires_grad = True self.unfrozen = True logger.info(f"Text encoder unfrozen at epoch {state.epoch:.2f}") else: - logger.warning("Model does not have a 'text_encoder' attribute at model[0].") + logger.warning("Model does not have a compatible text encoder at model[0].") return control class UnfreezeAdapterCallback(TrainerCallback): """ - A TrainerCallback to freeze/unfreeze text and omics adapters at specified epochs. + A TrainerCallback to freeze/unfreeze text and omics adapter projections at specified epochs. This callback provides fine-grained control over adapter training by allowing you to: - 1. Start with frozen adapters and unfreeze them at specific epochs - 2. Control text and omics adapters independently - 3. Handle cases where only one type of adapter is present + 1. Start with frozen adapter projection heads and unfreeze them at specific epochs + 2. Control text and omics projections independently + 3. Handle cases where the AdapterModule is at any position in the pipeline + + The callback searches ``model.children()`` for an :class:`AdapterModule` instance, + so it works regardless of whether an OmicsAttentionModule is included in the pipeline. Parameters ---------- freeze_text_adapter : bool, optional - Whether to start with the text adapter frozen. Defaults to False. + Whether to start with the text projection head frozen. Defaults to False. freeze_omics_adapter : bool, optional - Whether to start with the omics adapter frozen. Defaults to False. + Whether to start with the omics projection head frozen. Defaults to False. unfreeze_text_adapter_epoch : float, optional - The epoch at which to unfreeze the text adapter. If None and freeze_text_adapter - is True, the adapter remains frozen. Defaults to None. + The epoch at which to unfreeze the text projection head. If None and freeze_text_adapter + is True, the projection remains frozen. Defaults to None. unfreeze_omics_adapter_epoch : float, optional - The epoch at which to unfreeze the omics adapter. If None and freeze_omics_adapter - is True, the adapter remains frozen. Defaults to None. + The epoch at which to unfreeze the omics projection head. If None and freeze_omics_adapter + is True, the projection remains frozen. Defaults to None. Examples -------- - >>> # Freeze both adapters initially, unfreeze text at epoch 1, omics at epoch 2 + >>> # Freeze both projections initially, unfreeze text at epoch 1, omics at epoch 2 >>> callback = UnfreezeAdapterCallback( ... freeze_text_adapter=True, ... freeze_omics_adapter=True, @@ -72,7 +81,7 @@ class UnfreezeAdapterCallback(TrainerCallback): ... unfreeze_omics_adapter_epoch=2.0, ... ) - >>> # Only control omics adapter (useful for text-only datasets) + >>> # Only control omics projection (useful for text-only datasets) >>> callback = UnfreezeAdapterCallback(freeze_omics_adapter=True, unfreeze_omics_adapter_epoch=1.5) """ @@ -88,59 +97,37 @@ def __init__( self.unfreeze_text_adapter_epoch = unfreeze_text_adapter_epoch self.unfreeze_omics_adapter_epoch = unfreeze_omics_adapter_epoch - # Track unfreezing state self.text_adapter_unfrozen = False self.omics_adapter_unfrozen = False - - # Track initialization state self.adapters_initialized = False + self._adapter: AdapterModule | None = None - def _freeze_adapter(self, adapter, adapter_name: str): - """Freeze all parameters in an adapter.""" - if adapter is not None: - for param in adapter.parameters(): - param.requires_grad = False - logger.info(f"{adapter_name} adapter frozen") - else: - logger.debug(f"{adapter_name} adapter not present, skipping freeze") - - def _unfreeze_adapter(self, adapter, adapter_name: str): - """Unfreeze all parameters in an adapter.""" - if adapter is not None: - for param in adapter.parameters(): - param.requires_grad = True - logger.info(f"{adapter_name} adapter unfrozen") - else: - logger.debug(f"{adapter_name} adapter not present, skipping unfreeze") + def _find_adapter(self, model) -> AdapterModule | None: + """Find the AdapterModule by iterating model.children().""" + for m in model.children(): + if isinstance(m, AdapterModule): + return m + return None def on_train_begin(self, args, state, control, **kwargs): """Called at the beginning of training to initialize adapter freezing state.""" model = kwargs["model"] - # Check if we have MMContextEncoder at model[0] - if not hasattr(model[0], "text_adapter") and not hasattr(model[0], "omics_adapter"): - logger.warning("Model does not have adapter attributes. Adapter callback will have no effect.") - # Don't set adapters_initialized = True when no adapters are present + adapter = self._find_adapter(model) + if adapter is None: + logger.warning("No AdapterModule found in model. Adapter callback will have no effect.") return control - # Initialize freezing state for adapters - if self.freeze_text_adapter and hasattr(model[0], "text_adapter"): - self._freeze_adapter(model[0].text_adapter, "Text") + self._adapter = adapter - if self.freeze_omics_adapter and hasattr(model[0], "omics_adapter"): - self._freeze_adapter(model[0].omics_adapter, "Omics") + if self.freeze_text_adapter: + adapter.freeze_text_proj() - self.adapters_initialized = True - - # Log current adapter status - text_adapter_present = hasattr(model[0], "text_adapter") and model[0].text_adapter is not None - omics_adapter_present = hasattr(model[0], "omics_adapter") and model[0].omics_adapter is not None - - logger.info( - f"Adapter callback initialized - Text adapter: {'present' if text_adapter_present else 'absent'}, " - f"Omics adapter: {'present' if omics_adapter_present else 'absent'}" - ) + if self.freeze_omics_adapter: + adapter.freeze_omics_proj() + self.adapters_initialized = True + logger.info("Adapter callback initialized — AdapterModule found and freeze state applied.") return control def on_epoch_begin(self, args, state, control, **kwargs): @@ -148,28 +135,22 @@ def on_epoch_begin(self, args, state, control, **kwargs): if not self.adapters_initialized: return control - model = kwargs["model"] - - # Check text adapter unfreezing if ( not self.text_adapter_unfrozen and self.unfreeze_text_adapter_epoch is not None and state.epoch >= self.unfreeze_text_adapter_epoch ): - if hasattr(model[0], "text_adapter"): - self._unfreeze_adapter(model[0].text_adapter, "Text") - self.text_adapter_unfrozen = True - logger.info(f"Text adapter unfrozen at epoch {state.epoch:.2f}") + self._adapter.unfreeze_text_proj() + self.text_adapter_unfrozen = True + logger.info(f"Text adapter projection unfrozen at epoch {state.epoch:.2f}") - # Check omics adapter unfreezing if ( not self.omics_adapter_unfrozen and self.unfreeze_omics_adapter_epoch is not None and state.epoch >= self.unfreeze_omics_adapter_epoch ): - if hasattr(model[0], "omics_adapter"): - self._unfreeze_adapter(model[0].omics_adapter, "Omics") - self.omics_adapter_unfrozen = True - logger.info(f"Omics adapter unfrozen at epoch {state.epoch:.2f}") + self._adapter.unfreeze_omics_proj() + self.omics_adapter_unfrozen = True + logger.info(f"Omics adapter projection unfrozen at epoch {state.epoch:.2f}") return control diff --git a/src/mmcontext/modules/adapter_module.py b/src/mmcontext/modules/adapter_module.py index 0c804ea..37d1cfe 100644 --- a/src/mmcontext/modules/adapter_module.py +++ b/src/mmcontext/modules/adapter_module.py @@ -23,15 +23,15 @@ # Input (from MMContextModule.forward): { "token_embeddings": Tensor[B, L, D_text or D_omics], - "attention_mask": Tensor[B, L], - "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad + "attention_mask": Tensor[B, L], + "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad } # Output (after AdapterModule.forward): { "token_embeddings": Tensor[B, L, D_shared], # projected - "attention_mask": Tensor[B, L], # unchanged - "modality_ids": Tensor[B, L], # unchanged + "attention_mask": Tensor[B, L], # unchanged + "modality_ids": Tensor[B, L], # unchanged } Example @@ -154,12 +154,8 @@ def __init__( self.force_identity = force_identity # Build independent projection heads - self.text_proj = _build_projection( - text_input_dim, shared_dim, self.hidden_dim, force_identity - ) - self.omics_proj = _build_projection( - omics_input_dim, shared_dim, self.hidden_dim, force_identity - ) + self.text_proj = _build_projection(text_input_dim, shared_dim, self.hidden_dim, force_identity) + self.omics_proj = _build_projection(omics_input_dim, shared_dim, self.hidden_dim, force_identity) # ------------------------------------------------------------------ # Forward (Module abstract method) @@ -210,9 +206,7 @@ def forward( features["token_embeddings"] = output return features - def _apply_projection( - self, proj: nn.Module, tokens: torch.Tensor - ) -> torch.Tensor: + def _apply_projection(self, proj: nn.Module, tokens: torch.Tensor) -> torch.Tensor: """Apply a projection head, handling BatchNorm's 2D requirement. BatchNorm1d expects (N, C) input. Since we gather tokens from @@ -221,6 +215,33 @@ def _apply_projection( """ return proj(tokens) + # ------------------------------------------------------------------ + # Freezing + # ------------------------------------------------------------------ + def freeze_text_proj(self) -> None: + """Freeze all parameters in the text projection head.""" + for param in self.text_proj.parameters(): + param.requires_grad = False + logger.info("Froze text projection head") + + def unfreeze_text_proj(self) -> None: + """Unfreeze all parameters in the text projection head.""" + for param in self.text_proj.parameters(): + param.requires_grad = True + logger.info("Unfroze text projection head") + + def freeze_omics_proj(self) -> None: + """Freeze all parameters in the omics projection head.""" + for param in self.omics_proj.parameters(): + param.requires_grad = False + logger.info("Froze omics projection head") + + def unfreeze_omics_proj(self) -> None: + """Unfreeze all parameters in the omics projection head.""" + for param in self.omics_proj.parameters(): + param.requires_grad = True + logger.info("Unfroze omics projection head") + # ------------------------------------------------------------------ # Properties # ------------------------------------------------------------------ @@ -309,13 +330,9 @@ def load( if os.path.isfile(safetensors_path): load_safetensors_model(module, safetensors_path) elif os.path.isfile(bin_path): - module.load_state_dict( - torch.load(bin_path, map_location=torch.device("cpu")) - ) + module.load_state_dict(torch.load(bin_path, map_location=torch.device("cpu"))) else: - logger.warning( - "No weight files found in %s — module uses random init.", load_path - ) + logger.warning("No weight files found in %s — module uses random init.", load_path) logger.info("Loaded AdapterModule from %s", model_name_or_path) return module diff --git a/tests/test_adapter_callback.py b/tests/test_adapter_callback.py index e69de29..835eaa6 100644 --- a/tests/test_adapter_callback.py +++ b/tests/test_adapter_callback.py @@ -0,0 +1,363 @@ +"""Tests for UnfreezeTextEncoderCallback and UnfreezeAdapterCallback.""" + +import logging +from types import SimpleNamespace + +import pytest +import torch.nn as nn + +from mmcontext.callback import UnfreezeAdapterCallback, UnfreezeTextEncoderCallback +from mmcontext.modules.adapter_module import AdapterModule +from mmcontext.modules.mmcontext_module import MMContextModule + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _make_state(epoch: float) -> SimpleNamespace: + return SimpleNamespace(epoch=epoch) + + +def _all_frozen(module: nn.Module) -> bool: + return all(not p.requires_grad for p in module.parameters()) + + +def _all_trainable(module: nn.Module) -> bool: + return all(p.requires_grad for p in module.parameters()) + + +class _FakePipeline(nn.Module): + """Lightweight SentenceTransformer-like container that supports model[i] indexing.""" + + def __init__(self, *modules: nn.Module): + super().__init__() + self._modules_list = nn.ModuleList(modules) + + def __getitem__(self, idx: int) -> nn.Module: + return self._modules_list[idx] + + def children(self): + return iter(self._modules_list) + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +@pytest.fixture() +def mmcontext_module() -> MMContextModule: + """MMContextModule with patched auto_model (already patched session-wide in conftest).""" + return MMContextModule("bert-base-uncased") + + +@pytest.fixture() +def adapter_module() -> AdapterModule: + return AdapterModule(text_input_dim=32, omics_input_dim=32, shared_dim=16, hidden_dim=None) + + +@pytest.fixture() +def pipeline_without_attention(mmcontext_module, adapter_module) -> _FakePipeline: + """MMContextModule → AdapterModule (adapter at index 1).""" + return _FakePipeline(mmcontext_module, adapter_module) + + +@pytest.fixture() +def pipeline_with_attention(mmcontext_module, adapter_module) -> _FakePipeline: + """MMContextModule → dummy attention module → AdapterModule (adapter at index 2).""" + dummy_attention = nn.Linear(16, 16) # not an AdapterModule, stands in for OmicsAttentionModule + return _FakePipeline(mmcontext_module, dummy_attention, adapter_module) + + +@pytest.fixture() +def pipeline_no_adapter(mmcontext_module) -> _FakePipeline: + """Pipeline without any AdapterModule.""" + return _FakePipeline(mmcontext_module) + + +# --------------------------------------------------------------------------- +# UnfreezeTextEncoderCallback +# --------------------------------------------------------------------------- + + +class TestUnfreezeTextEncoderCallback: + def _call_epoch(self, callback, model, epoch: float): + """Simulate on_epoch_begin for a given epoch.""" + state = _make_state(epoch) + callback.on_epoch_begin(args=None, state=state, control=None, model=model) + + def test_text_encoder_frozen_before_unfreeze_epoch(self, pipeline_without_attention): + """Text encoder parameters must stay frozen before the unfreeze epoch.""" + model = pipeline_without_attention + model[0].freeze_text_encoder() + + cb = UnfreezeTextEncoderCallback(unfreeze_epoch=2.0) + self._call_epoch(cb, model, epoch=0.0) + self._call_epoch(cb, model, epoch=1.0) + + assert _all_frozen(model[0].auto_model), "Text encoder should still be frozen before epoch 2" + assert not cb.unfrozen + + def test_text_encoder_unfreezes_at_target_epoch(self, pipeline_without_attention): + """Text encoder should be unfrozen exactly at the configured epoch.""" + model = pipeline_without_attention + model[0].freeze_text_encoder() + + cb = UnfreezeTextEncoderCallback(unfreeze_epoch=2.0) + self._call_epoch(cb, model, epoch=2.0) + + assert _all_trainable(model[0].auto_model), "Text encoder should be unfrozen at epoch 2" + assert cb.unfrozen + + def test_text_encoder_unfreezes_past_target_epoch(self, pipeline_without_attention): + """Unfreezing also triggers when epoch exceeds the target (e.g., epoch 3 > target 2).""" + model = pipeline_without_attention + model[0].freeze_text_encoder() + + cb = UnfreezeTextEncoderCallback(unfreeze_epoch=2.0) + self._call_epoch(cb, model, epoch=3.0) + + assert _all_trainable(model[0].auto_model) + assert cb.unfrozen + + def test_text_encoder_unfreezes_only_once(self, pipeline_without_attention): + """The unfreeze action should happen exactly once regardless of subsequent epochs.""" + model = pipeline_without_attention + model[0].freeze_text_encoder() + + cb = UnfreezeTextEncoderCallback(unfreeze_epoch=1.0) + self._call_epoch(cb, model, epoch=1.0) + + # Manually re-freeze to check the callback doesn't unfreeze again + model[0].freeze_text_encoder() + self._call_epoch(cb, model, epoch=2.0) + + assert _all_frozen(model[0].auto_model), "Should not re-unfreeze after unfrozen=True is set" + + def test_warns_when_no_compatible_module(self, caplog): + """A warning is logged when model[0] has no auto_model attribute.""" + plain_nn = nn.Linear(4, 4) + model = _FakePipeline(plain_nn) + + cb = UnfreezeTextEncoderCallback(unfreeze_epoch=0.0) + with caplog.at_level(logging.WARNING): + self._call_epoch(cb, model, epoch=0.0) + + assert any("compatible text encoder" in r.message for r in caplog.records) + assert not cb.unfrozen + + def test_different_unfreeze_epochs(self, pipeline_without_attention): + """Two callbacks with different epochs unfreeze independently.""" + model = pipeline_without_attention + model[0].freeze_text_encoder() + + cb_epoch1 = UnfreezeTextEncoderCallback(unfreeze_epoch=1.0) + cb_epoch3 = UnfreezeTextEncoderCallback(unfreeze_epoch=3.0) + + # At epoch 1 — only the epoch-1 callback fires + self._call_epoch(cb_epoch1, model, epoch=1.0) + assert cb_epoch1.unfrozen + assert not cb_epoch3.unfrozen + + model[0].freeze_text_encoder() # re-freeze for the next check + # At epoch 3 — the epoch-3 callback fires + self._call_epoch(cb_epoch3, model, epoch=3.0) + assert cb_epoch3.unfrozen + + +# --------------------------------------------------------------------------- +# UnfreezeAdapterCallback +# --------------------------------------------------------------------------- + + +class TestUnfreezeAdapterCallback: + def _train_begin(self, callback, model): + callback.on_train_begin(args=None, state=None, control=None, model=model) + + def _call_epoch(self, callback, model, epoch: float): + state = _make_state(epoch) + callback.on_epoch_begin(args=None, state=state, control=None, model=model) + + # --- initialization --- + + def test_freezes_both_on_train_begin(self, pipeline_without_attention, adapter_module): + cb = UnfreezeAdapterCallback(freeze_text_adapter=True, freeze_omics_adapter=True) + self._train_begin(cb, pipeline_without_attention) + + assert _all_frozen(adapter_module.text_proj) + assert _all_frozen(adapter_module.omics_proj) + assert cb.adapters_initialized + + def test_freezes_only_text_on_train_begin(self, pipeline_without_attention, adapter_module): + cb = UnfreezeAdapterCallback(freeze_text_adapter=True, freeze_omics_adapter=False) + self._train_begin(cb, pipeline_without_attention) + + assert _all_frozen(adapter_module.text_proj) + assert _all_trainable(adapter_module.omics_proj) + + def test_freezes_only_omics_on_train_begin(self, pipeline_without_attention, adapter_module): + cb = UnfreezeAdapterCallback(freeze_text_adapter=False, freeze_omics_adapter=True) + self._train_begin(cb, pipeline_without_attention) + + assert _all_trainable(adapter_module.text_proj) + assert _all_frozen(adapter_module.omics_proj) + + # --- epoch-based unfreezing --- + + def test_text_adapter_unfreezes_at_epoch_1(self, pipeline_without_attention, adapter_module): + cb = UnfreezeAdapterCallback( + freeze_text_adapter=True, + freeze_omics_adapter=True, + unfreeze_text_adapter_epoch=1.0, + ) + self._train_begin(cb, pipeline_without_attention) + + # Before epoch 1 — still frozen + self._call_epoch(cb, pipeline_without_attention, epoch=0.0) + assert _all_frozen(adapter_module.text_proj) + assert not cb.text_adapter_unfrozen + + # At epoch 1 — text unfrozen, omics still frozen + self._call_epoch(cb, pipeline_without_attention, epoch=1.0) + assert _all_trainable(adapter_module.text_proj) + assert _all_frozen(adapter_module.omics_proj) + assert cb.text_adapter_unfrozen + assert not cb.omics_adapter_unfrozen + + def test_omics_adapter_unfreezes_at_epoch_2(self, pipeline_without_attention, adapter_module): + cb = UnfreezeAdapterCallback( + freeze_text_adapter=True, + freeze_omics_adapter=True, + unfreeze_text_adapter_epoch=1.0, + unfreeze_omics_adapter_epoch=2.0, + ) + self._train_begin(cb, pipeline_without_attention) + + self._call_epoch(cb, pipeline_without_attention, epoch=1.0) # unfreeze text + assert cb.text_adapter_unfrozen + assert not cb.omics_adapter_unfrozen + + self._call_epoch(cb, pipeline_without_attention, epoch=2.0) # unfreeze omics + assert _all_trainable(adapter_module.omics_proj) + assert cb.omics_adapter_unfrozen + + def test_adapters_stay_frozen_without_unfreeze_epoch(self, pipeline_without_attention, adapter_module): + """When unfreeze_*_epoch is None, frozen adapters remain frozen forever.""" + cb = UnfreezeAdapterCallback(freeze_text_adapter=True, freeze_omics_adapter=True) + self._train_begin(cb, pipeline_without_attention) + + for epoch in [0.0, 1.0, 5.0, 100.0]: + self._call_epoch(cb, pipeline_without_attention, epoch=epoch) + + assert _all_frozen(adapter_module.text_proj) + assert _all_frozen(adapter_module.omics_proj) + + def test_unfreezes_only_once(self, pipeline_without_attention, adapter_module): + """Each adapter is unfrozen at most once.""" + cb = UnfreezeAdapterCallback( + freeze_text_adapter=True, + freeze_omics_adapter=True, + unfreeze_text_adapter_epoch=1.0, + unfreeze_omics_adapter_epoch=1.0, + ) + self._train_begin(cb, pipeline_without_attention) + self._call_epoch(cb, pipeline_without_attention, epoch=1.0) + + assert cb.text_adapter_unfrozen + assert cb.omics_adapter_unfrozen + + # Re-freeze manually and simulate another epoch — callback should not unfreeze again + adapter_module.freeze_text_proj() + adapter_module.freeze_omics_proj() + self._call_epoch(cb, pipeline_without_attention, epoch=2.0) + + assert _all_frozen(adapter_module.text_proj) + assert _all_frozen(adapter_module.omics_proj) + + # --- adapter at position 2 (with OmicsAttentionModule) --- + + def test_finds_adapter_at_position_2(self, pipeline_with_attention, adapter_module): + """Adapter is found even when it's at index 2 (behind a dummy attention module).""" + cb = UnfreezeAdapterCallback( + freeze_text_adapter=True, + freeze_omics_adapter=True, + unfreeze_text_adapter_epoch=1.0, + unfreeze_omics_adapter_epoch=2.0, + ) + self._train_begin(cb, pipeline_with_attention) + + assert cb.adapters_initialized + assert cb._adapter is adapter_module + + self._call_epoch(cb, pipeline_with_attention, epoch=1.0) + assert _all_trainable(adapter_module.text_proj) + assert _all_frozen(adapter_module.omics_proj) + + self._call_epoch(cb, pipeline_with_attention, epoch=2.0) + assert _all_trainable(adapter_module.omics_proj) + + # --- no adapter present --- + + def test_warns_when_no_adapter(self, pipeline_no_adapter, caplog): + """A warning is emitted and adapters_initialized stays False when no AdapterModule found.""" + cb = UnfreezeAdapterCallback(freeze_text_adapter=True, freeze_omics_adapter=True) + + with caplog.at_level(logging.WARNING): + self._train_begin(cb, pipeline_no_adapter) + + assert any("No AdapterModule found" in r.message for r in caplog.records) + assert not cb.adapters_initialized + + def test_epoch_begin_noop_without_initialization(self, pipeline_no_adapter): + """on_epoch_begin is a no-op when adapters_initialized is False.""" + cb = UnfreezeAdapterCallback( + freeze_text_adapter=True, + unfreeze_text_adapter_epoch=0.0, + ) + # Deliberately skip on_train_begin so adapters_initialized stays False + state = _make_state(5.0) + result = cb.on_epoch_begin(args=None, state=state, control=None, model=pipeline_no_adapter) + assert result is None # control passed through unchanged + + # --- different epoch scenarios --- + + def test_text_and_omics_unfreeze_at_different_epochs(self, pipeline_without_attention, adapter_module): + """Text unfreezes at epoch 1, omics at epoch 3 — verify each fires independently.""" + cb = UnfreezeAdapterCallback( + freeze_text_adapter=True, + freeze_omics_adapter=True, + unfreeze_text_adapter_epoch=1.0, + unfreeze_omics_adapter_epoch=3.0, + ) + self._train_begin(cb, pipeline_without_attention) + + self._call_epoch(cb, pipeline_without_attention, epoch=0.0) + assert _all_frozen(adapter_module.text_proj) + assert _all_frozen(adapter_module.omics_proj) + + self._call_epoch(cb, pipeline_without_attention, epoch=1.0) + assert _all_trainable(adapter_module.text_proj) + assert _all_frozen(adapter_module.omics_proj) + + self._call_epoch(cb, pipeline_without_attention, epoch=2.0) + assert _all_trainable(adapter_module.text_proj) + assert _all_frozen(adapter_module.omics_proj) + + self._call_epoch(cb, pipeline_without_attention, epoch=3.0) + assert _all_trainable(adapter_module.text_proj) + assert _all_trainable(adapter_module.omics_proj) + + def test_fractional_unfreeze_epoch(self, pipeline_without_attention, adapter_module): + """Fractional unfreeze epochs work correctly (e.g., epoch 0.5).""" + cb = UnfreezeAdapterCallback( + freeze_omics_adapter=True, + unfreeze_omics_adapter_epoch=0.5, + ) + self._train_begin(cb, pipeline_without_attention) + + self._call_epoch(cb, pipeline_without_attention, epoch=0.0) + assert _all_frozen(adapter_module.omics_proj) + + self._call_epoch(cb, pipeline_without_attention, epoch=0.5) + assert _all_trainable(adapter_module.omics_proj) From c5b789e38e6bb97ad9f7cc069c997a0ab2271c0f Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Tue, 2 Jun 2026 08:21:42 +0000 Subject: [PATCH 18/67] ci: add Dependabot, CodeQL, and Dependabot auto-merge - dependabot.yml: weekly grouped pip + github-actions updates targeting dev-claude - codeql.yaml: Python semantic code scanning on push/PR + weekly - dependabot-auto-merge.yaml: auto-merge patch/minor Dependabot PRs after CI --- .github/dependabot.yml | 52 ++++++++++++++++++++ .github/workflows/codeql.yaml | 43 ++++++++++++++++ .github/workflows/dependabot-auto-merge.yaml | 36 ++++++++++++++ 3 files changed, 131 insertions(+) create mode 100644 .github/dependabot.yml create mode 100644 .github/workflows/codeql.yaml create mode 100644 .github/workflows/dependabot-auto-merge.yaml diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..e0ea6cc --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,52 @@ +# Dependabot configuration for mmcontext +# Docs: https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file +# +# Security updates (PRs that fix Dependabot *alerts*) are enabled separately in +# Settings -> Code security and obey target-branch below. This file additionally +# enables proactive *version* updates on a schedule, grouped to reduce PR noise. +version: 2 +updates: + # Python dependencies declared in pyproject.toml + - package-ecosystem: "pip" + directory: "/" + schedule: + interval: "weekly" + day: "monday" + # PRs target the integration branch, never main, per the project branch strategy. + target-branch: "dev-claude" + open-pull-requests-limit: 5 + labels: + - "dependencies" + commit-message: + prefix: "deps" + # One grouped PR for routine minor/patch bumps instead of one PR per package. + groups: + python-minor-patch: + update-types: + - "minor" + - "patch" + # Heavy, tightly version-pinned core libs: keep these as individual PRs so a + # major bump (e.g. torch, transformers) is reviewed in isolation. + ignore: + - dependency-name: "torch" + update-types: ["version-update:semver-major"] + - dependency-name: "sentence-transformers" + update-types: ["version-update:semver-major"] + + # GitHub Actions used in .github/workflows/* + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "weekly" + day: "monday" + target-branch: "dev-claude" + open-pull-requests-limit: 5 + labels: + - "dependencies" + - "github-actions" + commit-message: + prefix: "ci" + groups: + actions-all: + patterns: + - "*" diff --git a/.github/workflows/codeql.yaml b/.github/workflows/codeql.yaml new file mode 100644 index 0000000..690f713 --- /dev/null +++ b/.github/workflows/codeql.yaml @@ -0,0 +1,43 @@ +name: CodeQL + +# Semantic code scanning for Python. Findings appear under the repo's +# Security -> Code scanning tab and as PR annotations. +# Docs: https://docs.github.com/code-security/code-scanning + +on: + push: + branches: [main, dev-claude] + pull_request: + branches: [main, dev-claude] + schedule: + # Weekly full scan (Mondays 06:00 UTC) to catch newly-disclosed query updates. + - cron: "0 6 * * 1" + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + analyze: + name: Analyze (python) + runs-on: ubuntu-latest + permissions: + actions: read + contents: read + security-events: write + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Initialize CodeQL + uses: github/codeql-action/init@v3 + with: + languages: python + # security-and-quality adds maintainability/quality queries on top of + # the default security set. Drop to "security-extended" if too noisy. + queries: security-and-quality + + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@v3 + with: + category: "/language:python" diff --git a/.github/workflows/dependabot-auto-merge.yaml b/.github/workflows/dependabot-auto-merge.yaml new file mode 100644 index 0000000..284c3f6 --- /dev/null +++ b/.github/workflows/dependabot-auto-merge.yaml @@ -0,0 +1,36 @@ +name: Dependabot auto-merge + +# Auto-approves and enables auto-merge for low-risk Dependabot PRs (patch/minor, +# including security updates). GitHub still waits for all required status checks +# (tests, CodeQL) to pass before the merge actually happens. +# +# Prerequisites (one-time, in repo Settings): +# - "Allow auto-merge" enabled (Settings -> General -> Pull Requests) +# - Branch protection on dev-claude requiring the Test + CodeQL checks +# Docs: https://docs.github.com/code-security/dependabot/working-with-dependabot/automating-dependabot-with-github-actions + +on: pull_request + +permissions: + contents: write + pull-requests: write + +jobs: + auto-merge: + runs-on: ubuntu-latest + if: github.actor == 'dependabot[bot]' + steps: + - name: Fetch Dependabot metadata + id: meta + uses: dependabot/fetch-metadata@v2 + with: + github-token: "${{ secrets.GITHUB_TOKEN }}" + + - name: Enable auto-merge for patch/minor updates + if: steps.meta.outputs.update-type == 'version-update:semver-patch' || steps.meta.outputs.update-type == 'version-update:semver-minor' + run: | + gh pr review --approve "$PR_URL" + gh pr merge --auto --squash "$PR_URL" + env: + PR_URL: ${{ github.event.pull_request.html_url }} + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} From 430125367f6daa9f9799fdf03b4e18cd2ce4c899 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 2 Jun 2026 08:24:24 +0000 Subject: [PATCH 19/67] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- CLAUDE.md | 2 + REFACTOR_ROADMAP.md | 44 ++-- ROADMAP.md | 102 ++++----- scripts/train.py | 2 +- scripts/train_merged.py | 2 +- scripts/train_tiny.py | 13 +- src/mmcontext/_legacy/mmcontextencoder.py | 4 +- src/mmcontext/io/prepare_store.py | 19 +- src/mmcontext/io/vector_store.py | 45 ++-- src/mmcontext/modules/adapter_module.py | 28 +-- src/mmcontext/modules/mmcontext_module.py | 39 +--- .../modules/omics_attention_module.py | 18 +- tests/test_adapter_module.py | 28 +-- tests/test_io/test_vector_store.py | 12 +- tests/test_omics_attention_module.py | 52 ++--- tests/test_prepare_store.py | 58 +++-- tests/test_st_integration.py | 198 +++++++++--------- 17 files changed, 320 insertions(+), 346 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index e4f7f73..de56b64 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -85,6 +85,7 @@ When implementing a feature from a GitHub issue (via @claude or otherwise): 1. **If the issue is ambiguous**: Post clarifying questions as a comment. Do NOT start implementation until the questions are answered. 2. **Plan first**: Before writing any code, post an implementation plan as a comment on the issue with a checkbox list: + ``` ## Implementation Plan - [ ] Step 1: description @@ -92,6 +93,7 @@ When implementing a feature from a GitHub issue (via @claude or otherwise): - [ ] Step 3: description - [ ] Verify: run tests, check linting ``` + Wait for approval (a reply containing "approved", "go ahead", "LGTM", or "looks good"). 3. **Implement**: Create a branch `claude/-` from `dev-claude`. Implement the plan step by step. Edit the plan comment to check off completed steps. diff --git a/REFACTOR_ROADMAP.md b/REFACTOR_ROADMAP.md index 042e5ba..3e0cd8f 100644 --- a/REFACTOR_ROADMAP.md +++ b/REFACTOR_ROADMAP.md @@ -7,18 +7,18 @@ ### Current → New -| Aspect | Current | Refactored | -|--------|---------|------------| -| Base class | `Module` | `InputModule` | -| Tokenization | `tokenize()` | `preprocess()` | -| Omics storage | `nn.Embedding` lookup | `VectorStore` (memory-mapped) | -| Omics key | `pixel_values` | `omics_values` | -| Adapters | Inside encoder | Separate `AdapterModule(Module)` | -| Modality flag | `omics_text_info` | `modality_ids` | -| OneHotTextEncoder | Included | Removed | -| Data loading | Coupled to encoder | `mmcontext.io` module | -| Pipeline | `[MMContextEncoder]` | `[MMContextModule, AdapterModule, Pooling, Normalize]` | -| Var support | Same class, no attention | Optional `OmicsAttentionModule` | +| Aspect | Current | Refactored | +| ----------------- | ------------------------ | ------------------------------------------------------ | +| Base class | `Module` | `InputModule` | +| Tokenization | `tokenize()` | `preprocess()` | +| Omics storage | `nn.Embedding` lookup | `VectorStore` (memory-mapped) | +| Omics key | `pixel_values` | `omics_values` | +| Adapters | Inside encoder | Separate `AdapterModule(Module)` | +| Modality flag | `omics_text_info` | `modality_ids` | +| OneHotTextEncoder | Included | Removed | +| Data loading | Coupled to encoder | `mmcontext.io` module | +| Pipeline | `[MMContextEncoder]` | `[MMContextModule, AdapterModule, Pooling, Normalize]` | +| Var support | Same class, no attention | Optional `OmicsAttentionModule` | ### Module Pipeline @@ -52,12 +52,14 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 1: Foundation — VectorStore + IO Module **Files created/modified:** + - `src/mmcontext/io/__init__.py` (new) - `src/mmcontext/io/vector_store.py` (new) - `src/mmcontext/io/adata_utils.py` (new — extracted from mmcontextencoder.py + file_utils.py) - `tests/test_vector_store.py` (new) **VectorStore responsibilities:** + - Create from AnnData (obsm/varm), DataFrame, dict, or numpy array - Write embeddings to numpy memmap file + JSON index - Batch lookup by sample IDs → numpy array @@ -65,6 +67,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - Support both obs (1 vector/sample) and var (N vectors/sample) **Tests (write FIRST):** + 1. `test_from_numpy` — round-trip: write memmap, read back, values match 2. `test_from_adata_obs` — create from adata.obsm, lookup by obs index 3. `test_from_adata_var` — create from adata.varm, lookup by var index @@ -75,6 +78,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( 8. `test_persistence` — store survives close/reopen cycle **adata_utils responsibilities:** + - `load_embeddings_from_adata_link()` — extracted from `get_initial_embeddings_from_adata_link` - `create_token_dataframe_from_obsm()` — extracted from encoder - `build_embedding_df()` — extracted from file_utils @@ -84,11 +88,13 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 2: Core Module — MMContextModule (InputModule) **Files created/modified:** + - `src/mmcontext/modules/__init__.py` (new) - `src/mmcontext/modules/mmcontext_module.py` (new) - `tests/test_mmcontext_module.py` (new) **MMContextModule responsibilities:** + - Extends `InputModule` from sentence-transformers - `modalities` property returns `["text", "omics"]` - `preprocess(inputs)` routes by input type: @@ -103,6 +109,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - Text encoder freezing/unfreezing logic **Tests (write FIRST):** + 1. `test_preprocess_text_only` — text strings → input_ids, attention_mask, modality_ids 2. `test_preprocess_omics_direct_vector` — raw vectors → omics_values, attention_mask, modality_ids 3. `test_preprocess_omics_via_store` — prefixed IDs + VectorStore → resolved vectors @@ -124,10 +131,12 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 3: Modality-Aware AdapterModule **Files created/modified:** + - `src/mmcontext/modules/adapter_module.py` (new — replaces adapters.py as pipeline Module) - `tests/test_adapter_module.py` (new) **AdapterModule responsibilities:** + - Extends `Module` from sentence-transformers - Reads `modality_ids` from features dict - Maintains separate projection weights: `text_proj` and `omics_proj` @@ -138,6 +147,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - `get_sentence_embedding_dimension()` returns D_shared **Tests (write FIRST):** + 1. `test_forward_text_only` — text tokens projected correctly 2. `test_forward_omics_only` — omics tokens projected correctly 3. `test_forward_mixed_batch` — text and omics tokens get different projections @@ -154,10 +164,12 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 4: OmicsAttentionModule (Optional, Var-only) **Files created/modified:** + - `src/mmcontext/modules/omics_attention_module.py` (new) - `tests/test_omics_attention_module.py` (new) **OmicsAttentionModule responsibilities:** + - Extends `Module` from sentence-transformers - Reads `modality_ids` from features dict - Applies multi-head self-attention ONLY to omics tokens (modality_id=1) @@ -167,6 +179,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - `save()` / `load()` for persistence **Tests (write FIRST):** + 1. `test_text_passthrough` — text tokens unchanged after module 2. `test_omics_transformed` — omics tokens are modified by self-attention 3. `test_attention_mask_respected` — padded positions don't influence real tokens @@ -181,10 +194,12 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 5: SentenceTransformer Integration **Files created/modified:** + - `tests/test_st_integration.py` (new — replaces test_sentence_transformer_integration.py) - Minor adjustments to modules for compatibility **Integration tests (write FIRST):** + 1. `test_pipeline_construction` — modules compose into SentenceTransformer 2. `test_encode_text` — `model.encode(["text"])` produces correct shape 3. `test_encode_omics_direct` — `model.encode([{"omics_values": vector}])` works @@ -206,6 +221,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 6: Documentation + Cleanup **Files modified:** + - `src/mmcontext/__init__.py` — update public API exports - `src/mmcontext/modules/__init__.py` — export all modules - `src/mmcontext/io/__init__.py` — export VectorStore and utilities @@ -213,6 +229,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - `README.md` — update architecture description and usage examples **Cleanup:** + - Remove `OneHotTextEncoder` (`onehot.py`) - Archive old `mmcontextencoder.py` (keep temporarily for reference, don't import) - Archive old `adapters.py` (replaced by modules/adapter_module.py) @@ -240,6 +257,7 @@ Phase 6 (Docs + Cleanup) ← final polish ## Test Strategy Each phase follows strict TDD: + 1. Write test file with all tests (they will fail) 2. Implement the module until all tests pass 3. Run full test suite to check for regressions @@ -247,6 +265,7 @@ Each phase follows strict TDD: **Stub strategy:** Tests use lightweight stubs (similar to existing `_TokStub`, `_TextEncStub`) to avoid downloading real models. The existing `conftest.py` pattern is extended for new modules. **What's preserved from current tests:** + - Core encoding shapes and correctness (text, omics, mixed) - Save/load round-trips - Gradient flow through adapters @@ -255,6 +274,7 @@ Each phase follows strict TDD: - Freezing/unfreezing behavior **What's new:** + - VectorStore tests (memmap, lookup, persistence) - Modality-aware adapter tests (separate projections) - OmicsAttentionModule tests (self-attention on omics only) diff --git a/ROADMAP.md b/ROADMAP.md index d1da283..2669ca2 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -2,19 +2,19 @@ This document captures the state of the codebase after the sentence-transformers v5.4+ refactor (Phases 1–5), identifies remaining work, and provides context for -future development. It is structured as a reference for both human contributors +future development. It is structured as a reference for both human contributors and AI-assisted editing sessions. --- -## 1 Architecture overview (post-refactor) +## 1 Architecture overview (post-refactor) The new pipeline lives in two packages: -| Package | Role | -|---------|------| +| Package | Role | +| ------------------- | --------------------------------------------------------------------------------------------- | | `mmcontext.modules` | ST pipeline modules: `MMContextModule` (InputModule), `AdapterModule`, `OmicsAttentionModule` | -| `mmcontext.io` | `VectorStore` (mmap-backed lookup), `prepare_vector_store` (zarr → store builder) | +| `mmcontext.io` | `VectorStore` (mmap-backed lookup), `prepare_vector_store` (zarr → store builder) | A trained model is a standard `SentenceTransformer` with this module chain: @@ -30,9 +30,9 @@ embedding) inputs where it would degenerate to a feedforward layer. - **`input_values` preprocess key** — omics preprocess writes `input_values` (not `token_embeddings`) so the ST training collator's `collect_features` - suffix matching detects omics columns. Forward reads `input_values` and + suffix matching detects omics columns. Forward reads `input_values` and writes `token_embeddings` for downstream modules. -- **`modality_ids` tensor** — 0 = text, 1 = omics, 2 = pad. The +- **`modality_ids` tensor** — 0 = text, 1 = omics, 2 = pad. The `AdapterModule` uses this to route tokens through the correct projection head. - **No Trainer subclass** — training uses the standard `SentenceTransformerTrainer` + `MultipleNegativesRankingLoss` with @@ -40,15 +40,15 @@ embedding) inputs where it would degenerate to a feedforward layer. --- -## 2 What was completed (Phases 1–5) +## 2 What was completed (Phases 1–5) -| Phase | Deliverable | Tests | -|-------|-------------|-------| -| 1 | `VectorStore` — mmap-backed vector lookup with `from_numpy`, `from_adata`, `from_dict`, `load` | `tests/test_vector_store.py` (in mmcontext_module tests) | -| 2 | `MMContextModule` — text encoding via AutoModel/AutoTokenizer, omics via VectorStore or direct vectors, `preprocess`/`forward` contract | `tests/test_mmcontext_module.py` | -| 3 | `AdapterModule` — modality-aware projection (text head, omics head, shared dim), safetensors persistence | `tests/test_adapter_module.py` | -| 4 | `OmicsAttentionModule` — optional self-attention over variable-length omics sequences | `tests/test_omics_attention_module.py` | -| 5 | Full ST integration — pipeline construction, encode, save/load, training (text-only, bimodal, gene-list), `prepare_vector_store` | `tests/test_st_integration.py`, `tests/test_prepare_store.py` | +| Phase | Deliverable | Tests | +| ----- | --------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------- | +| 1 | `VectorStore` — mmap-backed vector lookup with `from_numpy`, `from_adata`, `from_dict`, `load` | `tests/test_vector_store.py` (in mmcontext_module tests) | +| 2 | `MMContextModule` — text encoding via AutoModel/AutoTokenizer, omics via VectorStore or direct vectors, `preprocess`/`forward` contract | `tests/test_mmcontext_module.py` | +| 3 | `AdapterModule` — modality-aware projection (text head, omics head, shared dim), safetensors persistence | `tests/test_adapter_module.py` | +| 4 | `OmicsAttentionModule` — optional self-attention over variable-length omics sequences | `tests/test_omics_attention_module.py` | +| 5 | Full ST integration — pipeline construction, encode, save/load, training (text-only, bimodal, gene-list), `prepare_vector_store` | `tests/test_st_integration.py`, `tests/test_prepare_store.py` | ### Supporting files @@ -57,22 +57,22 @@ embedding) inputs where it would degenerate to a feedforward layer. --- -## 3 Legacy code (`_legacy/`) +## 3 Legacy code (`_legacy/`) The following modules were moved to `src/mmcontext/_legacy/` and are preserved for backward compatibility with previously trained models: -| File | What it was | Notes | -|------|-------------|-------| -| `mmcontextencoder.py` | Dual-tower encoder (text + omics), `MMContextProcessor` tokenizer | Central old architecture; `embed/model_utils.py` still imports `MMEnc.get_initial_embeddings_from_adata_link` from it | -| `adapters.py` | MLP adapter with identity/linear/2-layer modes | Superseded by `modules/adapter_module.py` which adds modality-awareness | -| `omicsencoder.py` | `MiniOmicsModel` — `nn.Embedding` lookup with HF `PreTrainedModel` interface | Superseded by `VectorStore` (no learnable embedding, just mmap lookup) | -| `onehot.py` | `OneHotTextEncoder` — learnable embedding per unique sentence | Useful for ablation experiments; no new equivalent | -| `cell_sentence_transformer.py` | `OmicsEncoder` — full transformer over omics tokens with cross-attention | Heavier than `OmicsAttentionModule`; cross-attention is not yet in new code | +| File | What it was | Notes | +| ------------------------------ | ---------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- | +| `mmcontextencoder.py` | Dual-tower encoder (text + omics), `MMContextProcessor` tokenizer | Central old architecture; `embed/model_utils.py` still imports `MMEnc.get_initial_embeddings_from_adata_link` from it | +| `adapters.py` | MLP adapter with identity/linear/2-layer modes | Superseded by `modules/adapter_module.py` which adds modality-awareness | +| `omicsencoder.py` | `MiniOmicsModel` — `nn.Embedding` lookup with HF `PreTrainedModel` interface | Superseded by `VectorStore` (no learnable embedding, just mmap lookup) | +| `onehot.py` | `OneHotTextEncoder` — learnable embedding per unique sentence | Useful for ablation experiments; no new equivalent | +| `cell_sentence_transformer.py` | `OmicsEncoder` — full transformer over omics tokens with cross-attention | Heavier than `OmicsAttentionModule`; cross-attention is not yet in new code | ### Legacy tests (still functional) -These test files import from `_legacy` and exercise the old architecture. They +These test files import from `_legacy` and exercise the old architecture. They should continue to pass for backward compatibility: - `test_mmcontext_encoder.py` @@ -83,7 +83,7 @@ should continue to pass for backward compatibility: --- -## 4 Active non-module code — status & required changes +## 4 Active non-module code — status & required changes ### `callback.py` — trainer callbacks @@ -110,9 +110,9 @@ should continue to pass for backward compatibility: Domain-specific and actively needed for dataset preprocessing. - `truncate_semantic_cell_sentences_dataset()` — semantic-aware truncation. - `resolve_negative_indices_and_rename()` — resolves `negative_*_idx` columns - to actual text values. Needed for multiplet training with the real dataset. + to actual text values. Needed for multiplet training with the real dataset. - `get_evaluator()` — instantiates ST evaluators (BinaryClassification, - Triplet) from dataset columns. Works with any ST model. + Triplet) from dataset columns. Works with any ST model. - `consolidate_low_frequency_categories()` — groups rare labels into "other". - `get_device()` — simple CUDA/MPS/CPU device selection. @@ -126,7 +126,7 @@ should continue to pass for backward compatibility: ### `hub_utils.py` — HuggingFace Hub upload -**Status:** Template body references old module names. The upload logic +**Status:** Template body references old module names. The upload logic (`HfApi` calls) is architecture-agnostic and works. **What needs to change:** Update the `MODEL_CARD_TEMPLATE` string to describe @@ -139,7 +139,7 @@ the new pipeline modules. **Key functions:** - `download_and_extract_links()` — full-featured download with caching, - Zenodo support, resume, retries. The new `io/prepare_store.py` has a + Zenodo support, resume, retries. The new `io/prepare_store.py` has a lighter `_download_zarr()` that doesn't support all features. Consider converging on one implementation eventually. - `remove_corrupted_null_arrays()` — zarr repair utility; used by @@ -150,10 +150,10 @@ the new pipeline modules. --- -## 5 Evaluation framework — analysis & adaptation needs +## 5 Evaluation framework — analysis & adaptation needs The `eval/` package is a **self-contained evaluation framework** with a -decorator-based registry pattern. It is largely architecture-agnostic — most +decorator-based registry pattern. It is largely architecture-agnostic — most evaluators work on AnnData `.obsm` embeddings, not on the model directly. ### Architecture @@ -167,25 +167,25 @@ eval/utils.py — LabelKind, LabelSpec helpers ### Evaluators -| Evaluator | Registry name | What it measures | Architecture dependency | -|-----------|--------------|------------------|----------------------| -| `ARI` | `"ARI"` | Adjusted Rand Index (KMeans clustering vs labels) | None — operates on embeddings | -| `LabelSimilarity` | (registered) | ROC-AUC of intra- vs inter-label cosine sim | None | -| `ScibBundle` | `"scib"` | scIB benchmark metrics (batch/bio) | None | -| `UmapPlotter` | (registered) | UMAP visualizations colored by labels | Uses `pl/plotting.py` | -| `OmicsQueryAnnotator` | (not registered, standalone) | Zero-shot annotation via cosine similarity | Needs `model.encode()` — works with any ST model | +| Evaluator | Registry name | What it measures | Architecture dependency | +| --------------------- | ---------------------------- | ------------------------------------------------- | ------------------------------------------------ | +| `ARI` | `"ARI"` | Adjusted Rand Index (KMeans clustering vs labels) | None — operates on embeddings | +| `LabelSimilarity` | (registered) | ROC-AUC of intra- vs inter-label cosine sim | None | +| `ScibBundle` | `"scib"` | scIB benchmark metrics (batch/bio) | None | +| `UmapPlotter` | (registered) | UMAP visualizations colored by labels | Uses `pl/plotting.py` | +| `OmicsQueryAnnotator` | (not registered, standalone) | Zero-shot annotation via cosine similarity | Needs `model.encode()` — works with any ST model | ### `embedding_alignment.py` Standalone module (not registered as an evaluator) that computes cross-modal -alignment scores. Useful for measuring how well the shared space aligns -text and omics embeddings. Works on raw numpy arrays. +alignment scores. Useful for measuring how well the shared space aligns +text and omics embeddings. Works on raw numpy arrays. ### What needs to change for new architecture 1. **Embedding pipeline (`embed/`)** — `embed/model_utils.py` imports `MMContextEncoder.get_initial_embeddings_from_adata_link` to register - omics vectors before embedding. This needs a new path: + omics vectors before embedding. This needs a new path: - Load the ST model - Detect if it has an `MMContextModule` at position 0 - Attach a `VectorStore` (built via `prepare_vector_store`) instead of @@ -201,7 +201,7 @@ text and omics embeddings. Works on raw numpy arrays. --- -## 6 Missing features & test gaps +## 6 Missing features & test gaps ### Features not yet covered by new modules @@ -211,19 +211,19 @@ text and omics embeddings. Works on raw numpy arrays. - [ ] **Cross-attention** — The old `OmicsEncoder` in `cell_sentence_transformer.py` supported cross-attention between text - and omics sequences. The new `OmicsAttentionModule` only does - self-attention on omics tokens. Cross-attention would enable richer + and omics sequences. The new `OmicsAttentionModule` only does + self-attention on omics tokens. Cross-attention would enable richer multimodal interaction but adds complexity. - [ ] **`OneHotTextEncoder` equivalent** — Useful for ablation experiments - (fast training without a real transformer). Currently only in `_legacy/`. + (fast training without a real transformer). Currently only in `_legacy/`. Could be a simple fixture/utility rather than a full module. - [ ] **Negative mining / hard negatives** — The training script currently uses `(anchor, positive)` pairs with in-batch negatives (MNR loss). The dataset has `negative_1_idx` and `negative_2_idx` columns. `resolve_negative_indices_and_rename()` in `utils.py` handles - resolving these to actual text. Supporting `(anchor, positive, negative)` + resolving these to actual text. Supporting `(anchor, positive, negative)` triplets would improve training quality. - [ ] **Hub upload for new architecture** — `hub_utils.py` model card @@ -246,7 +246,7 @@ text and omics embeddings. Works on raw numpy arrays. - [ ] **Mixed-modality batches** — No test currently sends a batch where some samples are text and some are omics through the full pipeline - in a single forward pass. This is an important edge case for + in a single forward pass. This is an important edge case for training with heterogeneous datasets. - [ ] **Gradient flow end-to-end** — Training tests verify parameters change, @@ -264,19 +264,19 @@ text and omics embeddings. Works on raw numpy arrays. - [ ] **Converge download logic** — `file_utils.download_and_extract_links` and `io/prepare_store._download_zarr` both handle zarr downloads with - different feature sets. Could share a common download backend. + different feature sets. Could share a common download backend. - [ ] **`simulator.py` integration** — The old `LOSS_PRESETS` dict and `make_cluster_sampler` are valuable for generating synthetic test - datasets. Consider moving to a `testing/` subpackage. + datasets. Consider moving to a `testing/` subpackage. - [ ] **`sanity_helpers.py`** — Contains `plot_pca` and possibly other - debug helpers not in `pl/`. Consider merging into `pl/` or a + debug helpers not in `pl/`. Consider merging into `pl/` or a `debug/` module. --- -## 7 File map (post-cleanup) +## 7 File map (post-cleanup) ``` src/mmcontext/ diff --git a/scripts/train.py b/scripts/train.py index ea4c1ec..13db283 100644 --- a/scripts/train.py +++ b/scripts/train.py @@ -19,11 +19,11 @@ from sentence_transformers.evaluation import SequentialEvaluator from transformers.integrations import WandbCallback +from mmcontext._legacy.mmcontextencoder import MMContextEncoder from mmcontext.callback import UnfreezeAdapterCallback, UnfreezeTextEncoderCallback # from mmcontext.eval import SystemMonitor from mmcontext.hub_utils import prepare_model_for_hub, upload_model_to_hub -from mmcontext._legacy.mmcontextencoder import MMContextEncoder # from mmcontext.pp.utils import consolidate_low_frequency_categories from mmcontext.utils import ( # , load_test_adata_from_hf_dataset diff --git a/scripts/train_merged.py b/scripts/train_merged.py index 1978cdf..3b2433c 100644 --- a/scripts/train_merged.py +++ b/scripts/train_merged.py @@ -29,10 +29,10 @@ ) from transformers.integrations import WandbCallback +from mmcontext._legacy.mmcontextencoder import MMContextEncoder from mmcontext.callback import UnfreezeAdapterCallback, UnfreezeTextEncoderCallback from mmcontext.eval import SystemMonitor from mmcontext.hub_utils import get_model_info_from_config, upload_model_to_hub -from mmcontext._legacy.mmcontextencoder import MMContextEncoder from mmcontext.utils import ( get_evaluator, get_loss, diff --git a/scripts/train_tiny.py b/scripts/train_tiny.py index a4594fc..36cb07f 100644 --- a/scripts/train_tiny.py +++ b/scripts/train_tiny.py @@ -40,9 +40,9 @@ SentenceTransformerTrainingArguments, ) from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss -from sentence_transformers.sentence_transformer.modules import Pooling, Normalize +from sentence_transformers.sentence_transformer.modules import Normalize, Pooling -from mmcontext.modules import MMContextModule, AdapterModule +from mmcontext.modules import AdapterModule, MMContextModule logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s %(message)s") logger = logging.getLogger(__name__) @@ -74,6 +74,7 @@ def prepare_bimodal_dataset(ds): Returns a HF Dataset with columns ``anchor`` and ``positive``. """ + def prefix_omics(example): example["anchor"] = f"omics:{example['sample_idx']}" return example @@ -149,14 +150,14 @@ def main(): "--vector-store", default=None, help="Path to .mmap VectorStore file. For bimodal mode: if omitted, " - "the store is built automatically from adata_link + sample_idx " - "columns using --obsm-key.", + "the store is built automatically from adata_link + sample_idx " + "columns using --obsm-key.", ) parser.add_argument( "--obsm-key", default="X_scvi_fm", help="obsm key to extract when building VectorStore (default: X_scvi_fm). " - "Other common choices: X_pca, X_geneformer, X_gs10k", + "Other common choices: X_pca, X_geneformer, X_gs10k", ) parser.add_argument("--omics-dim", type=int, default=None, help="Omics vector dimension") parser.add_argument("--shared-dim", type=int, default=256, help="Shared embedding dim (default: 256)") @@ -175,7 +176,7 @@ def main(): "--wandb-project", default=None, help="Weights & Biases project name. Enables wandb logging when set. " - "You can also set WANDB_PROJECT env var instead.", + "You can also set WANDB_PROJECT env var instead.", ) parser.add_argument( "--wandb-run-name", diff --git a/src/mmcontext/_legacy/mmcontextencoder.py b/src/mmcontext/_legacy/mmcontextencoder.py index 10fb53d..38ee909 100644 --- a/src/mmcontext/_legacy/mmcontextencoder.py +++ b/src/mmcontext/_legacy/mmcontextencoder.py @@ -42,14 +42,14 @@ from sentence_transformers.models import Module, Pooling from transformers import AutoModel, AutoTokenizer -from .adapters import AdapterModule - # file_utils remains at the package root (not moved to _legacy) from mmcontext.file_utils import ( build_embedding_df, collect_unique_links, download_and_extract_links, ) + +from .adapters import AdapterModule from .omicsencoder import MiniOmicsModel from .onehot import OneHotTextEncoder diff --git a/src/mmcontext/io/prepare_store.py b/src/mmcontext/io/prepare_store.py index 3307905..40d7e09 100644 --- a/src/mmcontext/io/prepare_store.py +++ b/src/mmcontext/io/prepare_store.py @@ -45,6 +45,7 @@ # Zarr helpers — read obs_names and obsm without loading full AnnData # --------------------------------------------------------------------------- + def _read_obs_names_zarr(root: zarr.Group) -> list[str]: """Read observation names from a zarr-backed AnnData store. @@ -95,6 +96,7 @@ def _get_obsm_zarr_array(root: zarr.Group, obsm_key: str) -> zarr.Array: # Download helper # --------------------------------------------------------------------------- + def _url_to_cache_name(url: str) -> str: """Deterministic short name for a URL, used as cache directory name.""" return hashlib.sha256(url.encode()).hexdigest()[:16] @@ -138,9 +140,10 @@ def _download_zarr(url: str, cache_dir: Path) -> Path: with requests.get(download_url, stream=True, timeout=(30, 600), headers=headers) as r: r.raise_for_status() total = int(r.headers.get("content-length", 0)) - with open(zip_path, "wb") as f, tqdm( - total=total, unit="B", unit_scale=True, desc="Downloading", leave=False - ) as pbar: + with ( + open(zip_path, "wb") as f, + tqdm(total=total, unit="B", unit_scale=True, desc="Downloading", leave=False) as pbar, + ): for chunk in r.iter_content(chunk_size=8 * 1024 * 1024): f.write(chunk) pbar.update(len(chunk)) @@ -187,6 +190,7 @@ def _open_zarr(path: Path) -> zarr.Group: # Public API # --------------------------------------------------------------------------- + def prepare_vector_store( dataset: Dataset, *, @@ -279,8 +283,7 @@ def prepare_vector_store( for sid in sample_ids: if sid not in obs_name_to_idx: raise KeyError( - f"Sample ID '{sid}' not found in obs_names of {link}. " - f"First 5 obs_names: {obs_names[:5]}" + f"Sample ID '{sid}' not found in obs_names of {link}. First 5 obs_names: {obs_names[:5]}" ) needed_rows.append(obs_name_to_idx[sid]) needed_ids.append(sid) @@ -292,9 +295,9 @@ def prepare_vector_store( sorted_rows = np.array(needed_rows)[sort_order] obsm_array = _get_obsm_zarr_array(root, obsm_key) - selected = obsm_array.get_orthogonal_selection( - (sorted_rows, slice(None)) - ).astype(np.float32) # (len(needed_rows), D) + selected = obsm_array.get_orthogonal_selection((sorted_rows, slice(None))).astype( + np.float32 + ) # (len(needed_rows), D) # Unsort back to original order unsort = np.argsort(sort_order) diff --git a/src/mmcontext/io/vector_store.py b/src/mmcontext/io/vector_store.py index ed50cc1..b0a6f24 100644 --- a/src/mmcontext/io/vector_store.py +++ b/src/mmcontext/io/vector_store.py @@ -17,7 +17,7 @@ **Lookup:** ->>> vec = store["cell_42"] # single lookup → (D,) +>>> vec = store["cell_42"] # single lookup → (D,) >>> batch = store.batch_lookup(ids) # batch lookup → (N, D) **Persistence:** @@ -29,8 +29,9 @@ import json import logging +from collections.abc import Sequence from pathlib import Path -from typing import Literal, Sequence +from typing import Literal import numpy as np @@ -135,7 +136,7 @@ def from_numpy( @classmethod def from_dataframe( cls, - df: "pd.DataFrame", + df: pd.DataFrame, *, path: str | Path, id_col: str = "token", @@ -190,7 +191,7 @@ def from_dict( @classmethod def from_adata( cls, - adata: "ad.AnnData", + adata: ad.AnnData, *, layer_key: str, axis: Literal["obs", "var"] = "obs", @@ -220,18 +221,12 @@ def from_adata( """ if axis == "obs": if layer_key not in adata.obsm: - raise KeyError( - f"Key '{layer_key}' not found in adata.obsm. " - f"Available keys: {list(adata.obsm.keys())}" - ) + raise KeyError(f"Key '{layer_key}' not found in adata.obsm. Available keys: {list(adata.obsm.keys())}") matrix = np.asarray(adata.obsm[layer_key]) ids = adata.obs.index.tolist() elif axis == "var": if layer_key not in adata.varm: - raise KeyError( - f"Key '{layer_key}' not found in adata.varm. " - f"Available keys: {list(adata.varm.keys())}" - ) + raise KeyError(f"Key '{layer_key}' not found in adata.varm. Available keys: {list(adata.varm.keys())}") matrix = np.asarray(adata.varm[layer_key]) ids = adata.var.index.tolist() else: @@ -315,10 +310,7 @@ def __getitem__(self, key: str) -> np.ndarray: try: idx = self._index[key] except KeyError: - raise KeyError( - f"ID '{key}' not found in VectorStore. " - f"Store contains {len(self._index)} entries." - ) from None + raise KeyError(f"ID '{key}' not found in VectorStore. Store contains {len(self._index)} entries.") from None return np.array(self._mmap[idx]) def batch_lookup(self, ids: Sequence[str]) -> np.ndarray: @@ -345,8 +337,7 @@ def batch_lookup(self, ids: Sequence[str]) -> np.ndarray: indices.append(self._index[sid]) except KeyError: raise KeyError( - f"ID '{sid}' not found in VectorStore. " - f"Store contains {len(self._index)} entries." + f"ID '{sid}' not found in VectorStore. Store contains {len(self._index)} entries." ) from None return np.array(self._mmap[indices]) @@ -372,10 +363,7 @@ def __contains__(self, key: str) -> bool: return key in self._index def __repr__(self) -> str: - return ( - f"VectorStore(n={len(self)}, dim={self.dim}, " - f"dtype={self.dtype}, path='{self._path}')" - ) + return f"VectorStore(n={len(self)}, dim={self.dim}, dtype={self.dtype}, path='{self._path}')" # ------------------------------------------------------------------ # Internal helpers @@ -386,19 +374,12 @@ def _validate_ids_and_matrix(ids: list[str], matrix: np.ndarray) -> None: if len(ids) == 0: raise ValueError("Empty ID list: at least one vector is required.") if matrix.ndim != 2: - raise ValueError( - f"Expected 2-D matrix, got {matrix.ndim}-D array with shape {matrix.shape}." - ) + raise ValueError(f"Expected 2-D matrix, got {matrix.ndim}-D array with shape {matrix.shape}.") if len(ids) != matrix.shape[0]: - raise ValueError( - f"Length mismatch: {len(ids)} IDs but matrix has {matrix.shape[0]} rows." - ) + raise ValueError(f"Length mismatch: {len(ids)} IDs but matrix has {matrix.shape[0]} rows.") if len(set(ids)) != len(ids): duplicates = [x for x in ids if ids.count(x) > 1] - raise ValueError( - f"Duplicate IDs found: {sorted(set(duplicates))[:10]}. " - f"All IDs must be unique." - ) + raise ValueError(f"Duplicate IDs found: {sorted(set(duplicates))[:10]}. All IDs must be unique.") @staticmethod def _write_index( diff --git a/src/mmcontext/modules/adapter_module.py b/src/mmcontext/modules/adapter_module.py index 0c804ea..348d425 100644 --- a/src/mmcontext/modules/adapter_module.py +++ b/src/mmcontext/modules/adapter_module.py @@ -23,15 +23,15 @@ # Input (from MMContextModule.forward): { "token_embeddings": Tensor[B, L, D_text or D_omics], - "attention_mask": Tensor[B, L], - "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad + "attention_mask": Tensor[B, L], + "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad } # Output (after AdapterModule.forward): { "token_embeddings": Tensor[B, L, D_shared], # projected - "attention_mask": Tensor[B, L], # unchanged - "modality_ids": Tensor[B, L], # unchanged + "attention_mask": Tensor[B, L], # unchanged + "modality_ids": Tensor[B, L], # unchanged } Example @@ -154,12 +154,8 @@ def __init__( self.force_identity = force_identity # Build independent projection heads - self.text_proj = _build_projection( - text_input_dim, shared_dim, self.hidden_dim, force_identity - ) - self.omics_proj = _build_projection( - omics_input_dim, shared_dim, self.hidden_dim, force_identity - ) + self.text_proj = _build_projection(text_input_dim, shared_dim, self.hidden_dim, force_identity) + self.omics_proj = _build_projection(omics_input_dim, shared_dim, self.hidden_dim, force_identity) # ------------------------------------------------------------------ # Forward (Module abstract method) @@ -210,9 +206,7 @@ def forward( features["token_embeddings"] = output return features - def _apply_projection( - self, proj: nn.Module, tokens: torch.Tensor - ) -> torch.Tensor: + def _apply_projection(self, proj: nn.Module, tokens: torch.Tensor) -> torch.Tensor: """Apply a projection head, handling BatchNorm's 2D requirement. BatchNorm1d expects (N, C) input. Since we gather tokens from @@ -309,13 +303,9 @@ def load( if os.path.isfile(safetensors_path): load_safetensors_model(module, safetensors_path) elif os.path.isfile(bin_path): - module.load_state_dict( - torch.load(bin_path, map_location=torch.device("cpu")) - ) + module.load_state_dict(torch.load(bin_path, map_location=torch.device("cpu"))) else: - logger.warning( - "No weight files found in %s — module uses random init.", load_path - ) + logger.warning("No weight files found in %s — module uses random init.", load_path) logger.info("Loaded AdapterModule from %s", model_name_or_path) return module diff --git a/src/mmcontext/modules/mmcontext_module.py b/src/mmcontext/modules/mmcontext_module.py index bfcf106..d5b8889 100644 --- a/src/mmcontext/modules/mmcontext_module.py +++ b/src/mmcontext/modules/mmcontext_module.py @@ -25,8 +25,8 @@ { "token_embeddings": Tensor[B, L, D], # per-token representations - "attention_mask": Tensor[B, L], # 1 = real, 0 = pad - "modality_ids": Tensor[B, L], # 0 = text, 1 = omics + "attention_mask": Tensor[B, L], # 1 = real, 0 = pad + "modality_ids": Tensor[B, L], # 0 = text, 1 = omics } Example @@ -238,9 +238,7 @@ def _preprocess_text( encoded["modality"] = "text" return encoded - def _preprocess_omics_via_store( - self, texts: list[str] - ) -> dict[str, torch.Tensor | Any]: + def _preprocess_omics_via_store(self, texts: list[str]) -> dict[str, torch.Tensor | Any]: """Resolve prefixed omics IDs through VectorStore.""" if self._vector_store is None: raise ValueError( @@ -268,9 +266,7 @@ def _preprocess_omics_via_store( "modality": "omics", } - def _preprocess_omics_direct( - self, inputs: list[dict[str, Any]] - ) -> dict[str, torch.Tensor | Any]: + def _preprocess_omics_direct(self, inputs: list[dict[str, Any]]) -> dict[str, torch.Tensor | Any]: """Package direct omics vectors into features dict. Handles both obs (single vector per sample) and var (list of gene @@ -309,7 +305,7 @@ def _preprocess_omics_direct( input_values = torch.zeros(batch_size, max_len, dim) attention_mask = torch.zeros(batch_size, max_len, dtype=torch.long) - for i, (emb, length) in enumerate(zip(all_embeddings, lengths)): + for i, (emb, length) in enumerate(zip(all_embeddings, lengths, strict=False)): input_values[i, :length] = emb attention_mask[i, :length] = 1 @@ -349,9 +345,7 @@ def forward( else: return self._forward_omics(features) - def _forward_text( - self, features: dict[str, torch.Tensor | Any] - ) -> dict[str, torch.Tensor | Any]: + def _forward_text(self, features: dict[str, torch.Tensor | Any]) -> dict[str, torch.Tensor | Any]: """Run text through the transformer encoder.""" input_ids = features["input_ids"] attention_mask = features.get("attention_mask") @@ -365,17 +359,13 @@ def _forward_text( token_embeddings = model_output.last_hidden_state # (B, L, D) B, L = input_ids.shape - modality_ids = torch.full( - (B, L), MODALITY_TEXT, dtype=torch.long, device=input_ids.device - ) + modality_ids = torch.full((B, L), MODALITY_TEXT, dtype=torch.long, device=input_ids.device) features["token_embeddings"] = token_embeddings features["modality_ids"] = modality_ids return features - def _forward_omics( - self, features: dict[str, torch.Tensor | Any] - ) -> dict[str, torch.Tensor | Any]: + def _forward_omics(self, features: dict[str, torch.Tensor | Any]) -> dict[str, torch.Tensor | Any]: """Pass omics embeddings through unchanged. Reads from ``input_values`` (set by preprocess) and writes to @@ -386,9 +376,7 @@ def _forward_omics( token_embeddings = features["input_values"] # (B, L, D) B, L = token_embeddings.shape[:2] - modality_ids = torch.full( - (B, L), MODALITY_OMICS, dtype=torch.long, device=token_embeddings.device - ) + modality_ids = torch.full((B, L), MODALITY_OMICS, dtype=torch.long, device=token_embeddings.device) features["token_embeddings"] = token_embeddings features["modality_ids"] = modality_ids @@ -438,9 +426,7 @@ def _freeze_n_layers(self, num_layers: int) -> None: (``roberta.encoder.layer``) architectures. """ layers = None - if hasattr(self.auto_model, "encoder") and hasattr( - self.auto_model.encoder, "layer" - ): + if hasattr(self.auto_model, "encoder") and hasattr(self.auto_model.encoder, "layer"): layers = self.auto_model.encoder.layer elif hasattr(self.auto_model, "roberta"): layers = self.auto_model.roberta.encoder.layer @@ -448,10 +434,7 @@ def _freeze_n_layers(self, num_layers: int) -> None: layers = self.auto_model.bert.encoder.layer if layers is None: - logger.warning( - "Could not identify encoder layers for partial freezing. " - "Freezing all parameters instead." - ) + logger.warning("Could not identify encoder layers for partial freezing. Freezing all parameters instead.") for param in self.auto_model.parameters(): param.requires_grad = False return diff --git a/src/mmcontext/modules/omics_attention_module.py b/src/mmcontext/modules/omics_attention_module.py index 01fce46..0ad3a2d 100644 --- a/src/mmcontext/modules/omics_attention_module.py +++ b/src/mmcontext/modules/omics_attention_module.py @@ -24,15 +24,15 @@ # Input (from MMContextModule.forward): { "token_embeddings": Tensor[B, L, D], - "attention_mask": Tensor[B, L], - "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad + "attention_mask": Tensor[B, L], + "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad } # Output (after OmicsAttentionModule.forward): { - "token_embeddings": Tensor[B, L, D], # omics tokens attended - "attention_mask": Tensor[B, L], # unchanged - "modality_ids": Tensor[B, L], # unchanged + "token_embeddings": Tensor[B, L, D], # omics tokens attended + "attention_mask": Tensor[B, L], # unchanged + "modality_ids": Tensor[B, L], # unchanged } Example @@ -295,13 +295,9 @@ def load( if os.path.isfile(safetensors_path): load_safetensors_model(module, safetensors_path) elif os.path.isfile(bin_path): - module.load_state_dict( - torch.load(bin_path, map_location=torch.device("cpu")) - ) + module.load_state_dict(torch.load(bin_path, map_location=torch.device("cpu"))) else: - logger.warning( - "No weight files found in %s — module uses random init.", load_path - ) + logger.warning("No weight files found in %s — module uses random init.", load_path) logger.info("Loaded OmicsAttentionModule from %s", model_name_or_path) return module diff --git a/tests/test_adapter_module.py b/tests/test_adapter_module.py index 5db01d3..0c547f7 100644 --- a/tests/test_adapter_module.py +++ b/tests/test_adapter_module.py @@ -38,7 +38,9 @@ def real_safetensors(): function for tests that need actual save/load roundtrips. """ import importlib + import safetensors.torch + importlib.reload(safetensors.torch) yield # The session-scoped patch in conftest will reassert on the next test that needs it @@ -140,9 +142,7 @@ class TestForwardMixedBatch: def test_forward_mixed_batch(self): """Mixed batch: text and omics tokens get different projections.""" # Both modalities have same input dim for simplicity - adapter = AdapterModule( - text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32 - ) + adapter = AdapterModule(text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32) B, L = 1, 4 features = _make_features( token_embeddings=torch.randn(B, L, 16), @@ -190,9 +190,7 @@ def test_separate_weights(self, adapter): def test_text_omics_produce_different_outputs(self): """Same input through text vs omics projection gives different results.""" - adapter = AdapterModule( - text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32 - ) + adapter = AdapterModule(text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32) x = torch.randn(1, 3, 16) text_features = _make_features( @@ -210,9 +208,7 @@ def test_text_omics_produce_different_outputs(self): omics_result = adapter(omics_features) # Different projections should produce different outputs (with overwhelming probability) - assert not torch.allclose( - text_result["token_embeddings"], omics_result["token_embeddings"] - ) + assert not torch.allclose(text_result["token_embeddings"], omics_result["token_embeddings"]) # --------------------------------------------------------------------------- @@ -286,14 +282,8 @@ def test_weights_update(self, adapter): # Both projections should have changed (check total param delta, # not per-parameter allclose, since some biases may get tiny gradients) - text_delta = sum( - (p - text_before[n]).abs().sum().item() - for n, p in adapter.text_proj.named_parameters() - ) - omics_delta = sum( - (p - omics_before[n]).abs().sum().item() - for n, p in adapter.omics_proj.named_parameters() - ) + text_delta = sum((p - text_before[n]).abs().sum().item() for n, p in adapter.text_proj.named_parameters()) + omics_delta = sum((p - omics_before[n]).abs().sum().item() for n, p in adapter.omics_proj.named_parameters()) assert text_delta > 0, "text_proj parameters did not change" assert omics_delta > 0, "omics_proj parameters did not change" @@ -346,9 +336,7 @@ def test_preserves_modality_ids(self, adapter): modality_ids=mod_ids, ) # Need same input dim for this test - adapter2 = AdapterModule( - text_input_dim=32, omics_input_dim=32, shared_dim=16, hidden_dim=64 - ) + adapter2 = AdapterModule(text_input_dim=32, omics_input_dim=32, shared_dim=16, hidden_dim=64) result = adapter2(features) torch.testing.assert_close(result["modality_ids"], mod_ids) diff --git a/tests/test_io/test_vector_store.py b/tests/test_io/test_vector_store.py index a71d519..f8f5275 100644 --- a/tests/test_io/test_vector_store.py +++ b/tests/test_io/test_vector_store.py @@ -110,18 +110,14 @@ def test_from_dataframe_custom_columns(self, sample_matrix, sample_ids, tmp_dir) } ) path = os.path.join(tmp_dir, "test.mmap") - store = VectorStore.from_dataframe( - df, path=path, id_col="my_id", embedding_col="my_vec" - ) + store = VectorStore.from_dataframe(df, path=path, id_col="my_id", embedding_col="my_vec") result = store["cell_A"] np.testing.assert_array_almost_equal(result, sample_matrix[0]) def test_from_adata_obs(self, sample_adata_obs, tmp_dir): """Create from adata.obsm, lookup by obs index.""" path = os.path.join(tmp_dir, "test.mmap") - store = VectorStore.from_adata( - sample_adata_obs, layer_key="X_scvi", axis="obs", path=path - ) + store = VectorStore.from_adata(sample_adata_obs, layer_key="X_scvi", axis="obs", path=path) expected = sample_adata_obs.obsm["X_scvi"] for i, sid in enumerate(sample_adata_obs.obs.index): @@ -131,9 +127,7 @@ def test_from_adata_obs(self, sample_adata_obs, tmp_dir): def test_from_adata_var(self, sample_adata_var, tmp_dir): """Create from adata.varm, lookup by var index.""" path = os.path.join(tmp_dir, "test.mmap") - store = VectorStore.from_adata( - sample_adata_var, layer_key="gene_emb", axis="var", path=path - ) + store = VectorStore.from_adata(sample_adata_var, layer_key="gene_emb", axis="var", path=path) expected = sample_adata_var.varm["gene_emb"] for i, gid in enumerate(sample_adata_var.var.index): diff --git a/tests/test_omics_attention_module.py b/tests/test_omics_attention_module.py index e9319d9..ebb5212 100644 --- a/tests/test_omics_attention_module.py +++ b/tests/test_omics_attention_module.py @@ -33,7 +33,9 @@ def tmp_dir(): def real_safetensors(): """Undo the global safetensors.torch.load_model patch for persistence tests.""" import importlib + import safetensors.torch + importlib.reload(safetensors.torch) yield @@ -107,9 +109,7 @@ def test_text_passthrough_in_mixed_batch(self, module): result = module(features) # Text tokens (first 3) should be unchanged - torch.testing.assert_close( - result["token_embeddings"][:, :3, :], x[:, :3, :] - ) + torch.testing.assert_close(result["token_embeddings"][:, :3, :], x[:, :3, :]) # --------------------------------------------------------------------------- @@ -131,9 +131,9 @@ def test_omics_transformed(self, module): result = module(features) # Output should differ from input (attention mixes information) - assert not torch.allclose( - result["token_embeddings"], x, atol=1e-6 - ), "Omics tokens should be transformed by self-attention" + assert not torch.allclose(result["token_embeddings"], x, atol=1e-6), ( + "Omics tokens should be transformed by self-attention" + ) def test_omics_transformed_in_mixed_batch(self, module): """Omics tokens in a mixed batch are modified.""" @@ -149,9 +149,9 @@ def test_omics_transformed_in_mixed_batch(self, module): result = module(features) # Omics tokens (last 3) should be modified - assert not torch.allclose( - result["token_embeddings"][:, 3:, :], x[:, 3:, :], atol=1e-6 - ), "Omics tokens should be transformed by self-attention" + assert not torch.allclose(result["token_embeddings"][:, 3:, :], x[:, 3:, :], atol=1e-6), ( + "Omics tokens should be transformed by self-attention" + ) # --------------------------------------------------------------------------- @@ -212,14 +212,20 @@ def test_variable_length_sequences(self, module): # Sample 0: 3 omics tokens + 2 pad # Sample 1: 5 omics tokens + 0 pad - modality_ids = torch.tensor([ - [1, 1, 1, 2, 2], - [1, 1, 1, 1, 1], - ], dtype=torch.long) - attention_mask = torch.tensor([ - [1, 1, 1, 0, 0], - [1, 1, 1, 1, 1], - ], dtype=torch.long) + modality_ids = torch.tensor( + [ + [1, 1, 1, 2, 2], + [1, 1, 1, 1, 1], + ], + dtype=torch.long, + ) + attention_mask = torch.tensor( + [ + [1, 1, 1, 0, 0], + [1, 1, 1, 1, 1], + ], + dtype=torch.long, + ) features = _make_features( token_embeddings=x.clone(), @@ -233,8 +239,9 @@ def test_variable_length_sequences(self, module): # Pad positions should remain zero (or unchanged) # The module should not produce non-zero values for pad positions - assert torch.all(result["token_embeddings"][0, 3:, :] == 0) or \ - torch.allclose(result["token_embeddings"][0, 3:, :], x[0, 3:, :]) + assert torch.all(result["token_embeddings"][0, 3:, :] == 0) or torch.allclose( + result["token_embeddings"][0, 3:, :], x[0, 3:, :] + ) # --------------------------------------------------------------------------- @@ -351,10 +358,7 @@ def test_weights_update(self, module): loss.backward() optimizer.step() - total_delta = sum( - (p - params_before[n]).abs().sum().item() - for n, p in module.named_parameters() - ) + total_delta = sum((p - params_before[n]).abs().sum().item() for n, p in module.named_parameters()) assert total_delta > 0, "Parameters did not change after optimizer step" @@ -455,4 +459,4 @@ def test_repr(self, module): """repr contains key config info.""" r = repr(module) assert "16" in r # input_dim - assert "2" in r # num_heads + assert "2" in r # num_heads diff --git a/tests/test_prepare_store.py b/tests/test_prepare_store.py index e414cc2..82f1cf4 100644 --- a/tests/test_prepare_store.py +++ b/tests/test_prepare_store.py @@ -142,6 +142,7 @@ def test_no_obsm_group_raises(self, tmp_dir): class TestOpenZarr: def test_open_directory(self, synthetic_zarr): from pathlib import Path + zarr_path, *_ = synthetic_zarr root = _open_zarr(Path(zarr_path)) assert "obs" in root @@ -149,6 +150,7 @@ def test_open_directory(self, synthetic_zarr): def test_nonexistent_raises(self, tmp_dir): from pathlib import Path + with pytest.raises(FileNotFoundError): _open_zarr(Path(tmp_dir) / "nope.zarr") @@ -166,10 +168,12 @@ def test_builds_store_from_local_zarr(self, synthetic_zarr, tmp_dir): zarr_path, obs_names, n_obs, d_pca, _ = synthetic_zarr # Simulate a dataset where all samples come from one zarr file - ds = Dataset.from_dict({ - "sample_idx": obs_names[:5], # use first 5 - "adata_link": [zarr_path] * 5, - }) + ds = Dataset.from_dict( + { + "sample_idx": obs_names[:5], # use first 5 + "adata_link": [zarr_path] * 5, + } + ) output_path = os.path.join(tmp_dir, "test_store.mmap") store = prepare_vector_store( @@ -190,10 +194,12 @@ def test_values_match_zarr_source(self, synthetic_zarr, tmp_dir): zarr_path, obs_names, *_ = synthetic_zarr - ds = Dataset.from_dict({ - "sample_idx": [obs_names[3]], - "adata_link": [zarr_path], - }) + ds = Dataset.from_dict( + { + "sample_idx": [obs_names[3]], + "adata_link": [zarr_path], + } + ) output_path = os.path.join(tmp_dir, "val_store.mmap") store = prepare_vector_store(ds, obsm_key="X_pca", output_path=output_path) @@ -209,10 +215,12 @@ def test_missing_sample_id_raises(self, synthetic_zarr, tmp_dir): zarr_path, *_ = synthetic_zarr - ds = Dataset.from_dict({ - "sample_idx": ["nonexistent_cell"], - "adata_link": [zarr_path], - }) + ds = Dataset.from_dict( + { + "sample_idx": ["nonexistent_cell"], + "adata_link": [zarr_path], + } + ) output_path = os.path.join(tmp_dir, "err_store.mmap") with pytest.raises(KeyError, match="nonexistent_cell"): @@ -224,10 +232,12 @@ def test_skips_existing_store(self, synthetic_zarr, tmp_dir): zarr_path, obs_names, *_ = synthetic_zarr - ds = Dataset.from_dict({ - "sample_idx": obs_names[:3], - "adata_link": [zarr_path] * 3, - }) + ds = Dataset.from_dict( + { + "sample_idx": obs_names[:3], + "adata_link": [zarr_path] * 3, + } + ) output_path = os.path.join(tmp_dir, "cached_store.mmap") @@ -244,17 +254,21 @@ def test_different_obsm_keys(self, synthetic_zarr, tmp_dir): zarr_path, obs_names, _, d_pca, d_scvi = synthetic_zarr - ds = Dataset.from_dict({ - "sample_idx": obs_names[:2], - "adata_link": [zarr_path] * 2, - }) + ds = Dataset.from_dict( + { + "sample_idx": obs_names[:2], + "adata_link": [zarr_path] * 2, + } + ) pca_store = prepare_vector_store( - ds, obsm_key="X_pca", + ds, + obsm_key="X_pca", output_path=os.path.join(tmp_dir, "pca.mmap"), ) scvi_store = prepare_vector_store( - ds, obsm_key="X_scvi", + ds, + obsm_key="X_scvi", output_path=os.path.join(tmp_dir, "scvi.mmap"), ) diff --git a/tests/test_st_integration.py b/tests/test_st_integration.py index 1b3f03d..033df43 100644 --- a/tests/test_st_integration.py +++ b/tests/test_st_integration.py @@ -23,7 +23,6 @@ import numpy as np import pytest import torch - from sentence_transformers import SentenceTransformer from sentence_transformers.sentence_transformer.modules import Normalize, Pooling @@ -44,12 +43,13 @@ def tmp_dir(): def real_safetensors(): """Undo the global safetensors.torch.load_model patch for persistence tests.""" import safetensors.torch + importlib.reload(safetensors.torch) yield # -- Shared dims used across all fixtures ----------------------------------- -TEXT_DIM = 32 # matches _TextEncStub hidden_size +TEXT_DIM = 32 # matches _TextEncStub hidden_size OMICS_DIM = 8 SHARED_DIM = 16 @@ -98,24 +98,28 @@ def attention_module(): @pytest.fixture def obs_pipeline(mmcontext_module, adapter_module): """Obs pipeline: MMContext → Adapter → Pooling → Normalize.""" - return SentenceTransformer(modules=[ - mmcontext_module, - adapter_module, - Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), - Normalize(), - ]) + return SentenceTransformer( + modules=[ + mmcontext_module, + adapter_module, + Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), + Normalize(), + ] + ) @pytest.fixture def var_pipeline(mmcontext_module, attention_module, adapter_module): """Var pipeline: MMContext → OmicsAttention → Adapter → Pooling → Normalize.""" - return SentenceTransformer(modules=[ - mmcontext_module, - attention_module, - adapter_module, - Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), - Normalize(), - ]) + return SentenceTransformer( + modules=[ + mmcontext_module, + attention_module, + adapter_module, + Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), + Normalize(), + ] + ) # --------------------------------------------------------------------------- @@ -173,10 +177,7 @@ def test_encode_omics_direct_obs(self, obs_pipeline): def test_encode_omics_direct_var(self, var_pipeline): """Encoding var-level (multiple gene vectors) produces correct shape.""" - genes = [ - np.random.randn(OMICS_DIM).astype(np.float32) - for _ in range(5) - ] + genes = [np.random.randn(OMICS_DIM).astype(np.float32) for _ in range(5)] embedding = var_pipeline.encode([{"omics_values": genes}]) assert isinstance(embedding, np.ndarray) assert embedding.shape == (1, SHARED_DIM) @@ -241,9 +242,7 @@ class TestObsPipeline: def test_obs_text_and_omics_same_output_dim(self, obs_pipeline): """Text and omics inputs produce same dimensionality.""" text_emb = obs_pipeline.encode(["Some text"]) - omics_emb = obs_pipeline.encode([{ - "omics_values": np.random.randn(OMICS_DIM).astype(np.float32) - }]) + omics_emb = obs_pipeline.encode([{"omics_values": np.random.randn(OMICS_DIM).astype(np.float32)}]) assert text_emb.shape[-1] == omics_emb.shape[-1] == SHARED_DIM def test_obs_deterministic(self, obs_pipeline): @@ -267,10 +266,7 @@ def test_var_single_gene(self, var_pipeline): def test_var_multiple_genes(self, var_pipeline): """Multiple gene vectors are attended and pooled.""" - genes = [ - np.random.randn(OMICS_DIM).astype(np.float32) - for _ in range(10) - ] + genes = [np.random.randn(OMICS_DIM).astype(np.float32) for _ in range(10)] embedding = var_pipeline.encode([{"omics_values": genes}]) assert embedding.shape == (1, SHARED_DIM) @@ -349,19 +345,19 @@ class TestPrecisionConversion: def test_precision_conversion_parameters(self, mmcontext_module, adapter_module): """Pipeline modules can be converted to fp16.""" - pipeline = SentenceTransformer(modules=[ - mmcontext_module, - adapter_module, - Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), - Normalize(), - ]) + pipeline = SentenceTransformer( + modules=[ + mmcontext_module, + adapter_module, + Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), + Normalize(), + ] + ) pipeline.half() # All learnable parameters should now be fp16 for name, param in pipeline.named_parameters(): - assert param.dtype == torch.float16, ( - f"Parameter {name} is {param.dtype}, expected float16" - ) + assert param.dtype == torch.float16, f"Parameter {name} is {param.dtype}, expected float16" # Restore to fp32 — the session-scoped _TextEncStub (from conftest) # is shared across all tests; leaving it in fp16 would pollute @@ -399,20 +395,22 @@ def test_training_text_only(self, obs_pipeline, tmp_dir): from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss - ds = Dataset.from_dict({ - "anchor": [ - "A neuron from the thalamus.", - "An epithelial cell from the lung.", - "A B cell from peripheral blood.", - "A fibroblast from skin tissue.", - ], - "positive": [ - "Thalamic neuron expressing SYT1 and GNAS.", - "Lung epithelial cell with high EPCAM.", - "CD19-positive B lymphocyte.", - "Dermal fibroblast with COL1A1 expression.", - ], - }) + ds = Dataset.from_dict( + { + "anchor": [ + "A neuron from the thalamus.", + "An epithelial cell from the lung.", + "A B cell from peripheral blood.", + "A fibroblast from skin tissue.", + ], + "positive": [ + "Thalamic neuron expressing SYT1 and GNAS.", + "Lung epithelial cell with high EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1 expression.", + ], + } + ) loss = MultipleNegativesRankingLoss(obs_pipeline) args = SentenceTransformerTrainingArguments( @@ -437,9 +435,7 @@ def test_training_text_only(self, obs_pipeline, tmp_dir): # At least some parameters should have changed total_delta = sum( - (p - params_before[n]).abs().sum().item() - for n, p in obs_pipeline.named_parameters() - if n in params_before + (p - params_before[n]).abs().sum().item() for n, p in obs_pipeline.named_parameters() if n in params_before ) assert total_delta > 0, "No parameters changed during text-only training" @@ -457,20 +453,22 @@ def test_training_bimodal(self, obs_pipeline, obs_store, tmp_dir): first_module = list(obs_pipeline.children())[0] first_module.set_vector_store(obs_store) - ds = Dataset.from_dict({ - "anchor": [ - "omics:cell_0", - "omics:cell_1", - "omics:cell_2", - "omics:cell_3", - ], - "positive": [ - "Thalamic neuron expressing SYT1.", - "Lung epithelial cell with EPCAM.", - "CD19-positive B lymphocyte.", - "Dermal fibroblast with COL1A1.", - ], - }) + ds = Dataset.from_dict( + { + "anchor": [ + "omics:cell_0", + "omics:cell_1", + "omics:cell_2", + "omics:cell_3", + ], + "positive": [ + "Thalamic neuron expressing SYT1.", + "Lung epithelial cell with EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1.", + ], + } + ) loss = MultipleNegativesRankingLoss(obs_pipeline) args = SentenceTransformerTrainingArguments( @@ -493,9 +491,7 @@ def test_training_bimodal(self, obs_pipeline, obs_store, tmp_dir): trainer.train() total_delta = sum( - (p - params_before[n]).abs().sum().item() - for n, p in obs_pipeline.named_parameters() - if n in params_before + (p - params_before[n]).abs().sum().item() for n, p in obs_pipeline.named_parameters() if n in params_before ) assert total_delta > 0, "No parameters changed during bimodal training" @@ -511,20 +507,22 @@ def test_training_gene_list(self, obs_pipeline, tmp_dir): from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss - ds = Dataset.from_dict({ - "anchor": [ - "MALAT1 MT-CO3 GNAS SYT1 CALM1", - "EPCAM KRT8 KRT18 MUC1", - "CD19 MS4A1 CD79A PAX5", - "COL1A1 COL3A1 FN1 VIM", - ], - "positive": [ - "Thalamic neuron expressing SYT1.", - "Lung epithelial cell with EPCAM.", - "CD19-positive B lymphocyte.", - "Dermal fibroblast with COL1A1.", - ], - }) + ds = Dataset.from_dict( + { + "anchor": [ + "MALAT1 MT-CO3 GNAS SYT1 CALM1", + "EPCAM KRT8 KRT18 MUC1", + "CD19 MS4A1 CD79A PAX5", + "COL1A1 COL3A1 FN1 VIM", + ], + "positive": [ + "Thalamic neuron expressing SYT1.", + "Lung epithelial cell with EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1.", + ], + } + ) loss = MultipleNegativesRankingLoss(obs_pipeline) args = SentenceTransformerTrainingArguments( @@ -547,9 +545,7 @@ def test_training_gene_list(self, obs_pipeline, tmp_dir): trainer.train() total_delta = sum( - (p - params_before[n]).abs().sum().item() - for n, p in obs_pipeline.named_parameters() - if n in params_before + (p - params_before[n]).abs().sum().item() for n, p in obs_pipeline.named_parameters() if n in params_before ) assert total_delta > 0, "No parameters changed during gene-list training" @@ -559,20 +555,22 @@ def test_training_save_load_roundtrip(self, obs_pipeline, tmp_dir, real_safetens from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss - ds = Dataset.from_dict({ - "anchor": [ - "MALAT1 MT-CO3 GNAS SYT1", - "EPCAM KRT8 KRT18 MUC1", - "CD19 MS4A1 CD79A PAX5", - "COL1A1 COL3A1 FN1 VIM", - ], - "positive": [ - "Thalamic neuron expressing SYT1.", - "Lung epithelial cell with EPCAM.", - "CD19-positive B lymphocyte.", - "Dermal fibroblast with COL1A1.", - ], - }) + ds = Dataset.from_dict( + { + "anchor": [ + "MALAT1 MT-CO3 GNAS SYT1", + "EPCAM KRT8 KRT18 MUC1", + "CD19 MS4A1 CD79A PAX5", + "COL1A1 COL3A1 FN1 VIM", + ], + "positive": [ + "Thalamic neuron expressing SYT1.", + "Lung epithelial cell with EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1.", + ], + } + ) loss = MultipleNegativesRankingLoss(obs_pipeline) save_path = os.path.join(tmp_dir, "trained_model") From 7f35d8d88e4da3d455be4022e5e4e6824773ccb0 Mon Sep 17 00:00:00 2001 From: mengerj <122980522+mengerj@users.noreply.github.com> Date: Tue, 2 Jun 2026 10:27:21 +0200 Subject: [PATCH 20/67] Update Dependabot configuration for Python and Actions Added configuration for Python dependencies and GitHub Actions updates. --- .github/dependabot.yml | 52 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 .github/dependabot.yml diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..e0ea6cc --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,52 @@ +# Dependabot configuration for mmcontext +# Docs: https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file +# +# Security updates (PRs that fix Dependabot *alerts*) are enabled separately in +# Settings -> Code security and obey target-branch below. This file additionally +# enables proactive *version* updates on a schedule, grouped to reduce PR noise. +version: 2 +updates: + # Python dependencies declared in pyproject.toml + - package-ecosystem: "pip" + directory: "/" + schedule: + interval: "weekly" + day: "monday" + # PRs target the integration branch, never main, per the project branch strategy. + target-branch: "dev-claude" + open-pull-requests-limit: 5 + labels: + - "dependencies" + commit-message: + prefix: "deps" + # One grouped PR for routine minor/patch bumps instead of one PR per package. + groups: + python-minor-patch: + update-types: + - "minor" + - "patch" + # Heavy, tightly version-pinned core libs: keep these as individual PRs so a + # major bump (e.g. torch, transformers) is reviewed in isolation. + ignore: + - dependency-name: "torch" + update-types: ["version-update:semver-major"] + - dependency-name: "sentence-transformers" + update-types: ["version-update:semver-major"] + + # GitHub Actions used in .github/workflows/* + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "weekly" + day: "monday" + target-branch: "dev-claude" + open-pull-requests-limit: 5 + labels: + - "dependencies" + - "github-actions" + commit-message: + prefix: "ci" + groups: + actions-all: + patterns: + - "*" From 14c8f5bdd374da1d59bae4689d58c97f8a989bec Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 2 Jun 2026 08:28:07 +0000 Subject: [PATCH 21/67] ci: bump the actions-all group with 3 updates Bumps the actions-all group with 3 updates: [actions/checkout](https://github.com/actions/checkout), [actions/setup-python](https://github.com/actions/setup-python) and [codecov/codecov-action](https://github.com/codecov/codecov-action). Updates `actions/checkout` from 4 to 6 - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v4...v6) Updates `actions/setup-python` from 5 to 6 - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v5...v6) Updates `codecov/codecov-action` from 3 to 6 - [Release notes](https://github.com/codecov/codecov-action/releases) - [Changelog](https://github.com/codecov/codecov-action/blob/main/CHANGELOG.md) - [Commits](https://github.com/codecov/codecov-action/compare/v3...v6) --- updated-dependencies: - dependency-name: actions/checkout dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions-all - dependency-name: actions/setup-python dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions-all - dependency-name: codecov/codecov-action dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions-all ... Signed-off-by: dependabot[bot] --- .github/workflows/build.yaml | 4 ++-- .github/workflows/claude-implement.yaml | 2 +- .github/workflows/claude-review.yaml | 2 +- .github/workflows/release.yaml | 4 ++-- .github/workflows/test.yaml | 6 +++--- 5 files changed, 9 insertions(+), 9 deletions(-) diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml index 94562c6..971230b 100644 --- a/.github/workflows/build.yaml +++ b/.github/workflows/build.yaml @@ -14,9 +14,9 @@ jobs: package: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 - name: Set up Python 3.12 - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: "3.12" - name: Install build dependencies diff --git a/.github/workflows/claude-implement.yaml b/.github/workflows/claude-implement.yaml index 7242937..f41aeaf 100644 --- a/.github/workflows/claude-implement.yaml +++ b/.github/workflows/claude-implement.yaml @@ -30,7 +30,7 @@ jobs: contains(github.event.comment.body, '@claude'))) runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: ref: dev-claude fetch-depth: 0 diff --git a/.github/workflows/claude-review.yaml b/.github/workflows/claude-review.yaml index 9c34e01..48cb5b6 100644 --- a/.github/workflows/claude-review.yaml +++ b/.github/workflows/claude-review.yaml @@ -31,7 +31,7 @@ jobs: contains(github.event.comment.body, '@claude'))) runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: fetch-depth: 0 - uses: anthropics/claude-code-action@v1 diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index d0a1a6d..0473e17 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -15,11 +15,11 @@ jobs: permissions: id-token: write # IMPORTANT: this permission is mandatory for trusted publishing steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: filter: blob:none fetch-depth: 0 - - uses: actions/setup-python@v5 + - uses: actions/setup-python@v6 with: python-version: "3.x" cache: "pip" diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 8e23d94..3405401 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -41,7 +41,7 @@ jobs: PYTHON: ${{ matrix.python }} steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 - name: Free up disk space run: | @@ -52,7 +52,7 @@ jobs: sudo rm -rf /usr/local/lib/android - name: Set up Python ${{ matrix.python }} - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: ${{ matrix.python }} @@ -77,4 +77,4 @@ jobs: run: | coverage report - name: Upload coverage - uses: codecov/codecov-action@v3 + uses: codecov/codecov-action@v6 From 7d7a4be727f3910bcc3188bfb3d0d172f97288de Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 2 Jun 2026 08:28:19 +0000 Subject: [PATCH 22/67] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- CLAUDE.md | 2 + REFACTOR_ROADMAP.md | 44 ++-- ROADMAP.md | 102 ++++----- scripts/train.py | 2 +- scripts/train_merged.py | 2 +- scripts/train_tiny.py | 13 +- src/mmcontext/_legacy/mmcontextencoder.py | 4 +- src/mmcontext/io/prepare_store.py | 19 +- src/mmcontext/io/vector_store.py | 45 ++-- src/mmcontext/modules/adapter_module.py | 28 +-- src/mmcontext/modules/mmcontext_module.py | 39 +--- .../modules/omics_attention_module.py | 18 +- tests/test_adapter_module.py | 28 +-- tests/test_io/test_vector_store.py | 12 +- tests/test_omics_attention_module.py | 52 ++--- tests/test_prepare_store.py | 58 +++-- tests/test_st_integration.py | 198 +++++++++--------- 17 files changed, 320 insertions(+), 346 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index e4f7f73..de56b64 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -85,6 +85,7 @@ When implementing a feature from a GitHub issue (via @claude or otherwise): 1. **If the issue is ambiguous**: Post clarifying questions as a comment. Do NOT start implementation until the questions are answered. 2. **Plan first**: Before writing any code, post an implementation plan as a comment on the issue with a checkbox list: + ``` ## Implementation Plan - [ ] Step 1: description @@ -92,6 +93,7 @@ When implementing a feature from a GitHub issue (via @claude or otherwise): - [ ] Step 3: description - [ ] Verify: run tests, check linting ``` + Wait for approval (a reply containing "approved", "go ahead", "LGTM", or "looks good"). 3. **Implement**: Create a branch `claude/-` from `dev-claude`. Implement the plan step by step. Edit the plan comment to check off completed steps. diff --git a/REFACTOR_ROADMAP.md b/REFACTOR_ROADMAP.md index 042e5ba..3e0cd8f 100644 --- a/REFACTOR_ROADMAP.md +++ b/REFACTOR_ROADMAP.md @@ -7,18 +7,18 @@ ### Current → New -| Aspect | Current | Refactored | -|--------|---------|------------| -| Base class | `Module` | `InputModule` | -| Tokenization | `tokenize()` | `preprocess()` | -| Omics storage | `nn.Embedding` lookup | `VectorStore` (memory-mapped) | -| Omics key | `pixel_values` | `omics_values` | -| Adapters | Inside encoder | Separate `AdapterModule(Module)` | -| Modality flag | `omics_text_info` | `modality_ids` | -| OneHotTextEncoder | Included | Removed | -| Data loading | Coupled to encoder | `mmcontext.io` module | -| Pipeline | `[MMContextEncoder]` | `[MMContextModule, AdapterModule, Pooling, Normalize]` | -| Var support | Same class, no attention | Optional `OmicsAttentionModule` | +| Aspect | Current | Refactored | +| ----------------- | ------------------------ | ------------------------------------------------------ | +| Base class | `Module` | `InputModule` | +| Tokenization | `tokenize()` | `preprocess()` | +| Omics storage | `nn.Embedding` lookup | `VectorStore` (memory-mapped) | +| Omics key | `pixel_values` | `omics_values` | +| Adapters | Inside encoder | Separate `AdapterModule(Module)` | +| Modality flag | `omics_text_info` | `modality_ids` | +| OneHotTextEncoder | Included | Removed | +| Data loading | Coupled to encoder | `mmcontext.io` module | +| Pipeline | `[MMContextEncoder]` | `[MMContextModule, AdapterModule, Pooling, Normalize]` | +| Var support | Same class, no attention | Optional `OmicsAttentionModule` | ### Module Pipeline @@ -52,12 +52,14 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 1: Foundation — VectorStore + IO Module **Files created/modified:** + - `src/mmcontext/io/__init__.py` (new) - `src/mmcontext/io/vector_store.py` (new) - `src/mmcontext/io/adata_utils.py` (new — extracted from mmcontextencoder.py + file_utils.py) - `tests/test_vector_store.py` (new) **VectorStore responsibilities:** + - Create from AnnData (obsm/varm), DataFrame, dict, or numpy array - Write embeddings to numpy memmap file + JSON index - Batch lookup by sample IDs → numpy array @@ -65,6 +67,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - Support both obs (1 vector/sample) and var (N vectors/sample) **Tests (write FIRST):** + 1. `test_from_numpy` — round-trip: write memmap, read back, values match 2. `test_from_adata_obs` — create from adata.obsm, lookup by obs index 3. `test_from_adata_var` — create from adata.varm, lookup by var index @@ -75,6 +78,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( 8. `test_persistence` — store survives close/reopen cycle **adata_utils responsibilities:** + - `load_embeddings_from_adata_link()` — extracted from `get_initial_embeddings_from_adata_link` - `create_token_dataframe_from_obsm()` — extracted from encoder - `build_embedding_df()` — extracted from file_utils @@ -84,11 +88,13 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 2: Core Module — MMContextModule (InputModule) **Files created/modified:** + - `src/mmcontext/modules/__init__.py` (new) - `src/mmcontext/modules/mmcontext_module.py` (new) - `tests/test_mmcontext_module.py` (new) **MMContextModule responsibilities:** + - Extends `InputModule` from sentence-transformers - `modalities` property returns `["text", "omics"]` - `preprocess(inputs)` routes by input type: @@ -103,6 +109,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - Text encoder freezing/unfreezing logic **Tests (write FIRST):** + 1. `test_preprocess_text_only` — text strings → input_ids, attention_mask, modality_ids 2. `test_preprocess_omics_direct_vector` — raw vectors → omics_values, attention_mask, modality_ids 3. `test_preprocess_omics_via_store` — prefixed IDs + VectorStore → resolved vectors @@ -124,10 +131,12 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 3: Modality-Aware AdapterModule **Files created/modified:** + - `src/mmcontext/modules/adapter_module.py` (new — replaces adapters.py as pipeline Module) - `tests/test_adapter_module.py` (new) **AdapterModule responsibilities:** + - Extends `Module` from sentence-transformers - Reads `modality_ids` from features dict - Maintains separate projection weights: `text_proj` and `omics_proj` @@ -138,6 +147,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - `get_sentence_embedding_dimension()` returns D_shared **Tests (write FIRST):** + 1. `test_forward_text_only` — text tokens projected correctly 2. `test_forward_omics_only` — omics tokens projected correctly 3. `test_forward_mixed_batch` — text and omics tokens get different projections @@ -154,10 +164,12 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 4: OmicsAttentionModule (Optional, Var-only) **Files created/modified:** + - `src/mmcontext/modules/omics_attention_module.py` (new) - `tests/test_omics_attention_module.py` (new) **OmicsAttentionModule responsibilities:** + - Extends `Module` from sentence-transformers - Reads `modality_ids` from features dict - Applies multi-head self-attention ONLY to omics tokens (modality_id=1) @@ -167,6 +179,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - `save()` / `load()` for persistence **Tests (write FIRST):** + 1. `test_text_passthrough` — text tokens unchanged after module 2. `test_omics_transformed` — omics tokens are modified by self-attention 3. `test_attention_mask_respected` — padded positions don't influence real tokens @@ -181,10 +194,12 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 5: SentenceTransformer Integration **Files created/modified:** + - `tests/test_st_integration.py` (new — replaces test_sentence_transformer_integration.py) - Minor adjustments to modules for compatibility **Integration tests (write FIRST):** + 1. `test_pipeline_construction` — modules compose into SentenceTransformer 2. `test_encode_text` — `model.encode(["text"])` produces correct shape 3. `test_encode_omics_direct` — `model.encode([{"omics_values": vector}])` works @@ -206,6 +221,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 6: Documentation + Cleanup **Files modified:** + - `src/mmcontext/__init__.py` — update public API exports - `src/mmcontext/modules/__init__.py` — export all modules - `src/mmcontext/io/__init__.py` — export VectorStore and utilities @@ -213,6 +229,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - `README.md` — update architecture description and usage examples **Cleanup:** + - Remove `OneHotTextEncoder` (`onehot.py`) - Archive old `mmcontextencoder.py` (keep temporarily for reference, don't import) - Archive old `adapters.py` (replaced by modules/adapter_module.py) @@ -240,6 +257,7 @@ Phase 6 (Docs + Cleanup) ← final polish ## Test Strategy Each phase follows strict TDD: + 1. Write test file with all tests (they will fail) 2. Implement the module until all tests pass 3. Run full test suite to check for regressions @@ -247,6 +265,7 @@ Each phase follows strict TDD: **Stub strategy:** Tests use lightweight stubs (similar to existing `_TokStub`, `_TextEncStub`) to avoid downloading real models. The existing `conftest.py` pattern is extended for new modules. **What's preserved from current tests:** + - Core encoding shapes and correctness (text, omics, mixed) - Save/load round-trips - Gradient flow through adapters @@ -255,6 +274,7 @@ Each phase follows strict TDD: - Freezing/unfreezing behavior **What's new:** + - VectorStore tests (memmap, lookup, persistence) - Modality-aware adapter tests (separate projections) - OmicsAttentionModule tests (self-attention on omics only) diff --git a/ROADMAP.md b/ROADMAP.md index d1da283..2669ca2 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -2,19 +2,19 @@ This document captures the state of the codebase after the sentence-transformers v5.4+ refactor (Phases 1–5), identifies remaining work, and provides context for -future development. It is structured as a reference for both human contributors +future development. It is structured as a reference for both human contributors and AI-assisted editing sessions. --- -## 1 Architecture overview (post-refactor) +## 1 Architecture overview (post-refactor) The new pipeline lives in two packages: -| Package | Role | -|---------|------| +| Package | Role | +| ------------------- | --------------------------------------------------------------------------------------------- | | `mmcontext.modules` | ST pipeline modules: `MMContextModule` (InputModule), `AdapterModule`, `OmicsAttentionModule` | -| `mmcontext.io` | `VectorStore` (mmap-backed lookup), `prepare_vector_store` (zarr → store builder) | +| `mmcontext.io` | `VectorStore` (mmap-backed lookup), `prepare_vector_store` (zarr → store builder) | A trained model is a standard `SentenceTransformer` with this module chain: @@ -30,9 +30,9 @@ embedding) inputs where it would degenerate to a feedforward layer. - **`input_values` preprocess key** — omics preprocess writes `input_values` (not `token_embeddings`) so the ST training collator's `collect_features` - suffix matching detects omics columns. Forward reads `input_values` and + suffix matching detects omics columns. Forward reads `input_values` and writes `token_embeddings` for downstream modules. -- **`modality_ids` tensor** — 0 = text, 1 = omics, 2 = pad. The +- **`modality_ids` tensor** — 0 = text, 1 = omics, 2 = pad. The `AdapterModule` uses this to route tokens through the correct projection head. - **No Trainer subclass** — training uses the standard `SentenceTransformerTrainer` + `MultipleNegativesRankingLoss` with @@ -40,15 +40,15 @@ embedding) inputs where it would degenerate to a feedforward layer. --- -## 2 What was completed (Phases 1–5) +## 2 What was completed (Phases 1–5) -| Phase | Deliverable | Tests | -|-------|-------------|-------| -| 1 | `VectorStore` — mmap-backed vector lookup with `from_numpy`, `from_adata`, `from_dict`, `load` | `tests/test_vector_store.py` (in mmcontext_module tests) | -| 2 | `MMContextModule` — text encoding via AutoModel/AutoTokenizer, omics via VectorStore or direct vectors, `preprocess`/`forward` contract | `tests/test_mmcontext_module.py` | -| 3 | `AdapterModule` — modality-aware projection (text head, omics head, shared dim), safetensors persistence | `tests/test_adapter_module.py` | -| 4 | `OmicsAttentionModule` — optional self-attention over variable-length omics sequences | `tests/test_omics_attention_module.py` | -| 5 | Full ST integration — pipeline construction, encode, save/load, training (text-only, bimodal, gene-list), `prepare_vector_store` | `tests/test_st_integration.py`, `tests/test_prepare_store.py` | +| Phase | Deliverable | Tests | +| ----- | --------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------- | +| 1 | `VectorStore` — mmap-backed vector lookup with `from_numpy`, `from_adata`, `from_dict`, `load` | `tests/test_vector_store.py` (in mmcontext_module tests) | +| 2 | `MMContextModule` — text encoding via AutoModel/AutoTokenizer, omics via VectorStore or direct vectors, `preprocess`/`forward` contract | `tests/test_mmcontext_module.py` | +| 3 | `AdapterModule` — modality-aware projection (text head, omics head, shared dim), safetensors persistence | `tests/test_adapter_module.py` | +| 4 | `OmicsAttentionModule` — optional self-attention over variable-length omics sequences | `tests/test_omics_attention_module.py` | +| 5 | Full ST integration — pipeline construction, encode, save/load, training (text-only, bimodal, gene-list), `prepare_vector_store` | `tests/test_st_integration.py`, `tests/test_prepare_store.py` | ### Supporting files @@ -57,22 +57,22 @@ embedding) inputs where it would degenerate to a feedforward layer. --- -## 3 Legacy code (`_legacy/`) +## 3 Legacy code (`_legacy/`) The following modules were moved to `src/mmcontext/_legacy/` and are preserved for backward compatibility with previously trained models: -| File | What it was | Notes | -|------|-------------|-------| -| `mmcontextencoder.py` | Dual-tower encoder (text + omics), `MMContextProcessor` tokenizer | Central old architecture; `embed/model_utils.py` still imports `MMEnc.get_initial_embeddings_from_adata_link` from it | -| `adapters.py` | MLP adapter with identity/linear/2-layer modes | Superseded by `modules/adapter_module.py` which adds modality-awareness | -| `omicsencoder.py` | `MiniOmicsModel` — `nn.Embedding` lookup with HF `PreTrainedModel` interface | Superseded by `VectorStore` (no learnable embedding, just mmap lookup) | -| `onehot.py` | `OneHotTextEncoder` — learnable embedding per unique sentence | Useful for ablation experiments; no new equivalent | -| `cell_sentence_transformer.py` | `OmicsEncoder` — full transformer over omics tokens with cross-attention | Heavier than `OmicsAttentionModule`; cross-attention is not yet in new code | +| File | What it was | Notes | +| ------------------------------ | ---------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- | +| `mmcontextencoder.py` | Dual-tower encoder (text + omics), `MMContextProcessor` tokenizer | Central old architecture; `embed/model_utils.py` still imports `MMEnc.get_initial_embeddings_from_adata_link` from it | +| `adapters.py` | MLP adapter with identity/linear/2-layer modes | Superseded by `modules/adapter_module.py` which adds modality-awareness | +| `omicsencoder.py` | `MiniOmicsModel` — `nn.Embedding` lookup with HF `PreTrainedModel` interface | Superseded by `VectorStore` (no learnable embedding, just mmap lookup) | +| `onehot.py` | `OneHotTextEncoder` — learnable embedding per unique sentence | Useful for ablation experiments; no new equivalent | +| `cell_sentence_transformer.py` | `OmicsEncoder` — full transformer over omics tokens with cross-attention | Heavier than `OmicsAttentionModule`; cross-attention is not yet in new code | ### Legacy tests (still functional) -These test files import from `_legacy` and exercise the old architecture. They +These test files import from `_legacy` and exercise the old architecture. They should continue to pass for backward compatibility: - `test_mmcontext_encoder.py` @@ -83,7 +83,7 @@ should continue to pass for backward compatibility: --- -## 4 Active non-module code — status & required changes +## 4 Active non-module code — status & required changes ### `callback.py` — trainer callbacks @@ -110,9 +110,9 @@ should continue to pass for backward compatibility: Domain-specific and actively needed for dataset preprocessing. - `truncate_semantic_cell_sentences_dataset()` — semantic-aware truncation. - `resolve_negative_indices_and_rename()` — resolves `negative_*_idx` columns - to actual text values. Needed for multiplet training with the real dataset. + to actual text values. Needed for multiplet training with the real dataset. - `get_evaluator()` — instantiates ST evaluators (BinaryClassification, - Triplet) from dataset columns. Works with any ST model. + Triplet) from dataset columns. Works with any ST model. - `consolidate_low_frequency_categories()` — groups rare labels into "other". - `get_device()` — simple CUDA/MPS/CPU device selection. @@ -126,7 +126,7 @@ should continue to pass for backward compatibility: ### `hub_utils.py` — HuggingFace Hub upload -**Status:** Template body references old module names. The upload logic +**Status:** Template body references old module names. The upload logic (`HfApi` calls) is architecture-agnostic and works. **What needs to change:** Update the `MODEL_CARD_TEMPLATE` string to describe @@ -139,7 +139,7 @@ the new pipeline modules. **Key functions:** - `download_and_extract_links()` — full-featured download with caching, - Zenodo support, resume, retries. The new `io/prepare_store.py` has a + Zenodo support, resume, retries. The new `io/prepare_store.py` has a lighter `_download_zarr()` that doesn't support all features. Consider converging on one implementation eventually. - `remove_corrupted_null_arrays()` — zarr repair utility; used by @@ -150,10 +150,10 @@ the new pipeline modules. --- -## 5 Evaluation framework — analysis & adaptation needs +## 5 Evaluation framework — analysis & adaptation needs The `eval/` package is a **self-contained evaluation framework** with a -decorator-based registry pattern. It is largely architecture-agnostic — most +decorator-based registry pattern. It is largely architecture-agnostic — most evaluators work on AnnData `.obsm` embeddings, not on the model directly. ### Architecture @@ -167,25 +167,25 @@ eval/utils.py — LabelKind, LabelSpec helpers ### Evaluators -| Evaluator | Registry name | What it measures | Architecture dependency | -|-----------|--------------|------------------|----------------------| -| `ARI` | `"ARI"` | Adjusted Rand Index (KMeans clustering vs labels) | None — operates on embeddings | -| `LabelSimilarity` | (registered) | ROC-AUC of intra- vs inter-label cosine sim | None | -| `ScibBundle` | `"scib"` | scIB benchmark metrics (batch/bio) | None | -| `UmapPlotter` | (registered) | UMAP visualizations colored by labels | Uses `pl/plotting.py` | -| `OmicsQueryAnnotator` | (not registered, standalone) | Zero-shot annotation via cosine similarity | Needs `model.encode()` — works with any ST model | +| Evaluator | Registry name | What it measures | Architecture dependency | +| --------------------- | ---------------------------- | ------------------------------------------------- | ------------------------------------------------ | +| `ARI` | `"ARI"` | Adjusted Rand Index (KMeans clustering vs labels) | None — operates on embeddings | +| `LabelSimilarity` | (registered) | ROC-AUC of intra- vs inter-label cosine sim | None | +| `ScibBundle` | `"scib"` | scIB benchmark metrics (batch/bio) | None | +| `UmapPlotter` | (registered) | UMAP visualizations colored by labels | Uses `pl/plotting.py` | +| `OmicsQueryAnnotator` | (not registered, standalone) | Zero-shot annotation via cosine similarity | Needs `model.encode()` — works with any ST model | ### `embedding_alignment.py` Standalone module (not registered as an evaluator) that computes cross-modal -alignment scores. Useful for measuring how well the shared space aligns -text and omics embeddings. Works on raw numpy arrays. +alignment scores. Useful for measuring how well the shared space aligns +text and omics embeddings. Works on raw numpy arrays. ### What needs to change for new architecture 1. **Embedding pipeline (`embed/`)** — `embed/model_utils.py` imports `MMContextEncoder.get_initial_embeddings_from_adata_link` to register - omics vectors before embedding. This needs a new path: + omics vectors before embedding. This needs a new path: - Load the ST model - Detect if it has an `MMContextModule` at position 0 - Attach a `VectorStore` (built via `prepare_vector_store`) instead of @@ -201,7 +201,7 @@ text and omics embeddings. Works on raw numpy arrays. --- -## 6 Missing features & test gaps +## 6 Missing features & test gaps ### Features not yet covered by new modules @@ -211,19 +211,19 @@ text and omics embeddings. Works on raw numpy arrays. - [ ] **Cross-attention** — The old `OmicsEncoder` in `cell_sentence_transformer.py` supported cross-attention between text - and omics sequences. The new `OmicsAttentionModule` only does - self-attention on omics tokens. Cross-attention would enable richer + and omics sequences. The new `OmicsAttentionModule` only does + self-attention on omics tokens. Cross-attention would enable richer multimodal interaction but adds complexity. - [ ] **`OneHotTextEncoder` equivalent** — Useful for ablation experiments - (fast training without a real transformer). Currently only in `_legacy/`. + (fast training without a real transformer). Currently only in `_legacy/`. Could be a simple fixture/utility rather than a full module. - [ ] **Negative mining / hard negatives** — The training script currently uses `(anchor, positive)` pairs with in-batch negatives (MNR loss). The dataset has `negative_1_idx` and `negative_2_idx` columns. `resolve_negative_indices_and_rename()` in `utils.py` handles - resolving these to actual text. Supporting `(anchor, positive, negative)` + resolving these to actual text. Supporting `(anchor, positive, negative)` triplets would improve training quality. - [ ] **Hub upload for new architecture** — `hub_utils.py` model card @@ -246,7 +246,7 @@ text and omics embeddings. Works on raw numpy arrays. - [ ] **Mixed-modality batches** — No test currently sends a batch where some samples are text and some are omics through the full pipeline - in a single forward pass. This is an important edge case for + in a single forward pass. This is an important edge case for training with heterogeneous datasets. - [ ] **Gradient flow end-to-end** — Training tests verify parameters change, @@ -264,19 +264,19 @@ text and omics embeddings. Works on raw numpy arrays. - [ ] **Converge download logic** — `file_utils.download_and_extract_links` and `io/prepare_store._download_zarr` both handle zarr downloads with - different feature sets. Could share a common download backend. + different feature sets. Could share a common download backend. - [ ] **`simulator.py` integration** — The old `LOSS_PRESETS` dict and `make_cluster_sampler` are valuable for generating synthetic test - datasets. Consider moving to a `testing/` subpackage. + datasets. Consider moving to a `testing/` subpackage. - [ ] **`sanity_helpers.py`** — Contains `plot_pca` and possibly other - debug helpers not in `pl/`. Consider merging into `pl/` or a + debug helpers not in `pl/`. Consider merging into `pl/` or a `debug/` module. --- -## 7 File map (post-cleanup) +## 7 File map (post-cleanup) ``` src/mmcontext/ diff --git a/scripts/train.py b/scripts/train.py index ea4c1ec..13db283 100644 --- a/scripts/train.py +++ b/scripts/train.py @@ -19,11 +19,11 @@ from sentence_transformers.evaluation import SequentialEvaluator from transformers.integrations import WandbCallback +from mmcontext._legacy.mmcontextencoder import MMContextEncoder from mmcontext.callback import UnfreezeAdapterCallback, UnfreezeTextEncoderCallback # from mmcontext.eval import SystemMonitor from mmcontext.hub_utils import prepare_model_for_hub, upload_model_to_hub -from mmcontext._legacy.mmcontextencoder import MMContextEncoder # from mmcontext.pp.utils import consolidate_low_frequency_categories from mmcontext.utils import ( # , load_test_adata_from_hf_dataset diff --git a/scripts/train_merged.py b/scripts/train_merged.py index 1978cdf..3b2433c 100644 --- a/scripts/train_merged.py +++ b/scripts/train_merged.py @@ -29,10 +29,10 @@ ) from transformers.integrations import WandbCallback +from mmcontext._legacy.mmcontextencoder import MMContextEncoder from mmcontext.callback import UnfreezeAdapterCallback, UnfreezeTextEncoderCallback from mmcontext.eval import SystemMonitor from mmcontext.hub_utils import get_model_info_from_config, upload_model_to_hub -from mmcontext._legacy.mmcontextencoder import MMContextEncoder from mmcontext.utils import ( get_evaluator, get_loss, diff --git a/scripts/train_tiny.py b/scripts/train_tiny.py index a4594fc..36cb07f 100644 --- a/scripts/train_tiny.py +++ b/scripts/train_tiny.py @@ -40,9 +40,9 @@ SentenceTransformerTrainingArguments, ) from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss -from sentence_transformers.sentence_transformer.modules import Pooling, Normalize +from sentence_transformers.sentence_transformer.modules import Normalize, Pooling -from mmcontext.modules import MMContextModule, AdapterModule +from mmcontext.modules import AdapterModule, MMContextModule logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s %(message)s") logger = logging.getLogger(__name__) @@ -74,6 +74,7 @@ def prepare_bimodal_dataset(ds): Returns a HF Dataset with columns ``anchor`` and ``positive``. """ + def prefix_omics(example): example["anchor"] = f"omics:{example['sample_idx']}" return example @@ -149,14 +150,14 @@ def main(): "--vector-store", default=None, help="Path to .mmap VectorStore file. For bimodal mode: if omitted, " - "the store is built automatically from adata_link + sample_idx " - "columns using --obsm-key.", + "the store is built automatically from adata_link + sample_idx " + "columns using --obsm-key.", ) parser.add_argument( "--obsm-key", default="X_scvi_fm", help="obsm key to extract when building VectorStore (default: X_scvi_fm). " - "Other common choices: X_pca, X_geneformer, X_gs10k", + "Other common choices: X_pca, X_geneformer, X_gs10k", ) parser.add_argument("--omics-dim", type=int, default=None, help="Omics vector dimension") parser.add_argument("--shared-dim", type=int, default=256, help="Shared embedding dim (default: 256)") @@ -175,7 +176,7 @@ def main(): "--wandb-project", default=None, help="Weights & Biases project name. Enables wandb logging when set. " - "You can also set WANDB_PROJECT env var instead.", + "You can also set WANDB_PROJECT env var instead.", ) parser.add_argument( "--wandb-run-name", diff --git a/src/mmcontext/_legacy/mmcontextencoder.py b/src/mmcontext/_legacy/mmcontextencoder.py index 10fb53d..38ee909 100644 --- a/src/mmcontext/_legacy/mmcontextencoder.py +++ b/src/mmcontext/_legacy/mmcontextencoder.py @@ -42,14 +42,14 @@ from sentence_transformers.models import Module, Pooling from transformers import AutoModel, AutoTokenizer -from .adapters import AdapterModule - # file_utils remains at the package root (not moved to _legacy) from mmcontext.file_utils import ( build_embedding_df, collect_unique_links, download_and_extract_links, ) + +from .adapters import AdapterModule from .omicsencoder import MiniOmicsModel from .onehot import OneHotTextEncoder diff --git a/src/mmcontext/io/prepare_store.py b/src/mmcontext/io/prepare_store.py index 3307905..40d7e09 100644 --- a/src/mmcontext/io/prepare_store.py +++ b/src/mmcontext/io/prepare_store.py @@ -45,6 +45,7 @@ # Zarr helpers — read obs_names and obsm without loading full AnnData # --------------------------------------------------------------------------- + def _read_obs_names_zarr(root: zarr.Group) -> list[str]: """Read observation names from a zarr-backed AnnData store. @@ -95,6 +96,7 @@ def _get_obsm_zarr_array(root: zarr.Group, obsm_key: str) -> zarr.Array: # Download helper # --------------------------------------------------------------------------- + def _url_to_cache_name(url: str) -> str: """Deterministic short name for a URL, used as cache directory name.""" return hashlib.sha256(url.encode()).hexdigest()[:16] @@ -138,9 +140,10 @@ def _download_zarr(url: str, cache_dir: Path) -> Path: with requests.get(download_url, stream=True, timeout=(30, 600), headers=headers) as r: r.raise_for_status() total = int(r.headers.get("content-length", 0)) - with open(zip_path, "wb") as f, tqdm( - total=total, unit="B", unit_scale=True, desc="Downloading", leave=False - ) as pbar: + with ( + open(zip_path, "wb") as f, + tqdm(total=total, unit="B", unit_scale=True, desc="Downloading", leave=False) as pbar, + ): for chunk in r.iter_content(chunk_size=8 * 1024 * 1024): f.write(chunk) pbar.update(len(chunk)) @@ -187,6 +190,7 @@ def _open_zarr(path: Path) -> zarr.Group: # Public API # --------------------------------------------------------------------------- + def prepare_vector_store( dataset: Dataset, *, @@ -279,8 +283,7 @@ def prepare_vector_store( for sid in sample_ids: if sid not in obs_name_to_idx: raise KeyError( - f"Sample ID '{sid}' not found in obs_names of {link}. " - f"First 5 obs_names: {obs_names[:5]}" + f"Sample ID '{sid}' not found in obs_names of {link}. First 5 obs_names: {obs_names[:5]}" ) needed_rows.append(obs_name_to_idx[sid]) needed_ids.append(sid) @@ -292,9 +295,9 @@ def prepare_vector_store( sorted_rows = np.array(needed_rows)[sort_order] obsm_array = _get_obsm_zarr_array(root, obsm_key) - selected = obsm_array.get_orthogonal_selection( - (sorted_rows, slice(None)) - ).astype(np.float32) # (len(needed_rows), D) + selected = obsm_array.get_orthogonal_selection((sorted_rows, slice(None))).astype( + np.float32 + ) # (len(needed_rows), D) # Unsort back to original order unsort = np.argsort(sort_order) diff --git a/src/mmcontext/io/vector_store.py b/src/mmcontext/io/vector_store.py index ed50cc1..b0a6f24 100644 --- a/src/mmcontext/io/vector_store.py +++ b/src/mmcontext/io/vector_store.py @@ -17,7 +17,7 @@ **Lookup:** ->>> vec = store["cell_42"] # single lookup → (D,) +>>> vec = store["cell_42"] # single lookup → (D,) >>> batch = store.batch_lookup(ids) # batch lookup → (N, D) **Persistence:** @@ -29,8 +29,9 @@ import json import logging +from collections.abc import Sequence from pathlib import Path -from typing import Literal, Sequence +from typing import Literal import numpy as np @@ -135,7 +136,7 @@ def from_numpy( @classmethod def from_dataframe( cls, - df: "pd.DataFrame", + df: pd.DataFrame, *, path: str | Path, id_col: str = "token", @@ -190,7 +191,7 @@ def from_dict( @classmethod def from_adata( cls, - adata: "ad.AnnData", + adata: ad.AnnData, *, layer_key: str, axis: Literal["obs", "var"] = "obs", @@ -220,18 +221,12 @@ def from_adata( """ if axis == "obs": if layer_key not in adata.obsm: - raise KeyError( - f"Key '{layer_key}' not found in adata.obsm. " - f"Available keys: {list(adata.obsm.keys())}" - ) + raise KeyError(f"Key '{layer_key}' not found in adata.obsm. Available keys: {list(adata.obsm.keys())}") matrix = np.asarray(adata.obsm[layer_key]) ids = adata.obs.index.tolist() elif axis == "var": if layer_key not in adata.varm: - raise KeyError( - f"Key '{layer_key}' not found in adata.varm. " - f"Available keys: {list(adata.varm.keys())}" - ) + raise KeyError(f"Key '{layer_key}' not found in adata.varm. Available keys: {list(adata.varm.keys())}") matrix = np.asarray(adata.varm[layer_key]) ids = adata.var.index.tolist() else: @@ -315,10 +310,7 @@ def __getitem__(self, key: str) -> np.ndarray: try: idx = self._index[key] except KeyError: - raise KeyError( - f"ID '{key}' not found in VectorStore. " - f"Store contains {len(self._index)} entries." - ) from None + raise KeyError(f"ID '{key}' not found in VectorStore. Store contains {len(self._index)} entries.") from None return np.array(self._mmap[idx]) def batch_lookup(self, ids: Sequence[str]) -> np.ndarray: @@ -345,8 +337,7 @@ def batch_lookup(self, ids: Sequence[str]) -> np.ndarray: indices.append(self._index[sid]) except KeyError: raise KeyError( - f"ID '{sid}' not found in VectorStore. " - f"Store contains {len(self._index)} entries." + f"ID '{sid}' not found in VectorStore. Store contains {len(self._index)} entries." ) from None return np.array(self._mmap[indices]) @@ -372,10 +363,7 @@ def __contains__(self, key: str) -> bool: return key in self._index def __repr__(self) -> str: - return ( - f"VectorStore(n={len(self)}, dim={self.dim}, " - f"dtype={self.dtype}, path='{self._path}')" - ) + return f"VectorStore(n={len(self)}, dim={self.dim}, dtype={self.dtype}, path='{self._path}')" # ------------------------------------------------------------------ # Internal helpers @@ -386,19 +374,12 @@ def _validate_ids_and_matrix(ids: list[str], matrix: np.ndarray) -> None: if len(ids) == 0: raise ValueError("Empty ID list: at least one vector is required.") if matrix.ndim != 2: - raise ValueError( - f"Expected 2-D matrix, got {matrix.ndim}-D array with shape {matrix.shape}." - ) + raise ValueError(f"Expected 2-D matrix, got {matrix.ndim}-D array with shape {matrix.shape}.") if len(ids) != matrix.shape[0]: - raise ValueError( - f"Length mismatch: {len(ids)} IDs but matrix has {matrix.shape[0]} rows." - ) + raise ValueError(f"Length mismatch: {len(ids)} IDs but matrix has {matrix.shape[0]} rows.") if len(set(ids)) != len(ids): duplicates = [x for x in ids if ids.count(x) > 1] - raise ValueError( - f"Duplicate IDs found: {sorted(set(duplicates))[:10]}. " - f"All IDs must be unique." - ) + raise ValueError(f"Duplicate IDs found: {sorted(set(duplicates))[:10]}. All IDs must be unique.") @staticmethod def _write_index( diff --git a/src/mmcontext/modules/adapter_module.py b/src/mmcontext/modules/adapter_module.py index 0c804ea..348d425 100644 --- a/src/mmcontext/modules/adapter_module.py +++ b/src/mmcontext/modules/adapter_module.py @@ -23,15 +23,15 @@ # Input (from MMContextModule.forward): { "token_embeddings": Tensor[B, L, D_text or D_omics], - "attention_mask": Tensor[B, L], - "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad + "attention_mask": Tensor[B, L], + "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad } # Output (after AdapterModule.forward): { "token_embeddings": Tensor[B, L, D_shared], # projected - "attention_mask": Tensor[B, L], # unchanged - "modality_ids": Tensor[B, L], # unchanged + "attention_mask": Tensor[B, L], # unchanged + "modality_ids": Tensor[B, L], # unchanged } Example @@ -154,12 +154,8 @@ def __init__( self.force_identity = force_identity # Build independent projection heads - self.text_proj = _build_projection( - text_input_dim, shared_dim, self.hidden_dim, force_identity - ) - self.omics_proj = _build_projection( - omics_input_dim, shared_dim, self.hidden_dim, force_identity - ) + self.text_proj = _build_projection(text_input_dim, shared_dim, self.hidden_dim, force_identity) + self.omics_proj = _build_projection(omics_input_dim, shared_dim, self.hidden_dim, force_identity) # ------------------------------------------------------------------ # Forward (Module abstract method) @@ -210,9 +206,7 @@ def forward( features["token_embeddings"] = output return features - def _apply_projection( - self, proj: nn.Module, tokens: torch.Tensor - ) -> torch.Tensor: + def _apply_projection(self, proj: nn.Module, tokens: torch.Tensor) -> torch.Tensor: """Apply a projection head, handling BatchNorm's 2D requirement. BatchNorm1d expects (N, C) input. Since we gather tokens from @@ -309,13 +303,9 @@ def load( if os.path.isfile(safetensors_path): load_safetensors_model(module, safetensors_path) elif os.path.isfile(bin_path): - module.load_state_dict( - torch.load(bin_path, map_location=torch.device("cpu")) - ) + module.load_state_dict(torch.load(bin_path, map_location=torch.device("cpu"))) else: - logger.warning( - "No weight files found in %s — module uses random init.", load_path - ) + logger.warning("No weight files found in %s — module uses random init.", load_path) logger.info("Loaded AdapterModule from %s", model_name_or_path) return module diff --git a/src/mmcontext/modules/mmcontext_module.py b/src/mmcontext/modules/mmcontext_module.py index bfcf106..d5b8889 100644 --- a/src/mmcontext/modules/mmcontext_module.py +++ b/src/mmcontext/modules/mmcontext_module.py @@ -25,8 +25,8 @@ { "token_embeddings": Tensor[B, L, D], # per-token representations - "attention_mask": Tensor[B, L], # 1 = real, 0 = pad - "modality_ids": Tensor[B, L], # 0 = text, 1 = omics + "attention_mask": Tensor[B, L], # 1 = real, 0 = pad + "modality_ids": Tensor[B, L], # 0 = text, 1 = omics } Example @@ -238,9 +238,7 @@ def _preprocess_text( encoded["modality"] = "text" return encoded - def _preprocess_omics_via_store( - self, texts: list[str] - ) -> dict[str, torch.Tensor | Any]: + def _preprocess_omics_via_store(self, texts: list[str]) -> dict[str, torch.Tensor | Any]: """Resolve prefixed omics IDs through VectorStore.""" if self._vector_store is None: raise ValueError( @@ -268,9 +266,7 @@ def _preprocess_omics_via_store( "modality": "omics", } - def _preprocess_omics_direct( - self, inputs: list[dict[str, Any]] - ) -> dict[str, torch.Tensor | Any]: + def _preprocess_omics_direct(self, inputs: list[dict[str, Any]]) -> dict[str, torch.Tensor | Any]: """Package direct omics vectors into features dict. Handles both obs (single vector per sample) and var (list of gene @@ -309,7 +305,7 @@ def _preprocess_omics_direct( input_values = torch.zeros(batch_size, max_len, dim) attention_mask = torch.zeros(batch_size, max_len, dtype=torch.long) - for i, (emb, length) in enumerate(zip(all_embeddings, lengths)): + for i, (emb, length) in enumerate(zip(all_embeddings, lengths, strict=False)): input_values[i, :length] = emb attention_mask[i, :length] = 1 @@ -349,9 +345,7 @@ def forward( else: return self._forward_omics(features) - def _forward_text( - self, features: dict[str, torch.Tensor | Any] - ) -> dict[str, torch.Tensor | Any]: + def _forward_text(self, features: dict[str, torch.Tensor | Any]) -> dict[str, torch.Tensor | Any]: """Run text through the transformer encoder.""" input_ids = features["input_ids"] attention_mask = features.get("attention_mask") @@ -365,17 +359,13 @@ def _forward_text( token_embeddings = model_output.last_hidden_state # (B, L, D) B, L = input_ids.shape - modality_ids = torch.full( - (B, L), MODALITY_TEXT, dtype=torch.long, device=input_ids.device - ) + modality_ids = torch.full((B, L), MODALITY_TEXT, dtype=torch.long, device=input_ids.device) features["token_embeddings"] = token_embeddings features["modality_ids"] = modality_ids return features - def _forward_omics( - self, features: dict[str, torch.Tensor | Any] - ) -> dict[str, torch.Tensor | Any]: + def _forward_omics(self, features: dict[str, torch.Tensor | Any]) -> dict[str, torch.Tensor | Any]: """Pass omics embeddings through unchanged. Reads from ``input_values`` (set by preprocess) and writes to @@ -386,9 +376,7 @@ def _forward_omics( token_embeddings = features["input_values"] # (B, L, D) B, L = token_embeddings.shape[:2] - modality_ids = torch.full( - (B, L), MODALITY_OMICS, dtype=torch.long, device=token_embeddings.device - ) + modality_ids = torch.full((B, L), MODALITY_OMICS, dtype=torch.long, device=token_embeddings.device) features["token_embeddings"] = token_embeddings features["modality_ids"] = modality_ids @@ -438,9 +426,7 @@ def _freeze_n_layers(self, num_layers: int) -> None: (``roberta.encoder.layer``) architectures. """ layers = None - if hasattr(self.auto_model, "encoder") and hasattr( - self.auto_model.encoder, "layer" - ): + if hasattr(self.auto_model, "encoder") and hasattr(self.auto_model.encoder, "layer"): layers = self.auto_model.encoder.layer elif hasattr(self.auto_model, "roberta"): layers = self.auto_model.roberta.encoder.layer @@ -448,10 +434,7 @@ def _freeze_n_layers(self, num_layers: int) -> None: layers = self.auto_model.bert.encoder.layer if layers is None: - logger.warning( - "Could not identify encoder layers for partial freezing. " - "Freezing all parameters instead." - ) + logger.warning("Could not identify encoder layers for partial freezing. Freezing all parameters instead.") for param in self.auto_model.parameters(): param.requires_grad = False return diff --git a/src/mmcontext/modules/omics_attention_module.py b/src/mmcontext/modules/omics_attention_module.py index 01fce46..0ad3a2d 100644 --- a/src/mmcontext/modules/omics_attention_module.py +++ b/src/mmcontext/modules/omics_attention_module.py @@ -24,15 +24,15 @@ # Input (from MMContextModule.forward): { "token_embeddings": Tensor[B, L, D], - "attention_mask": Tensor[B, L], - "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad + "attention_mask": Tensor[B, L], + "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad } # Output (after OmicsAttentionModule.forward): { - "token_embeddings": Tensor[B, L, D], # omics tokens attended - "attention_mask": Tensor[B, L], # unchanged - "modality_ids": Tensor[B, L], # unchanged + "token_embeddings": Tensor[B, L, D], # omics tokens attended + "attention_mask": Tensor[B, L], # unchanged + "modality_ids": Tensor[B, L], # unchanged } Example @@ -295,13 +295,9 @@ def load( if os.path.isfile(safetensors_path): load_safetensors_model(module, safetensors_path) elif os.path.isfile(bin_path): - module.load_state_dict( - torch.load(bin_path, map_location=torch.device("cpu")) - ) + module.load_state_dict(torch.load(bin_path, map_location=torch.device("cpu"))) else: - logger.warning( - "No weight files found in %s — module uses random init.", load_path - ) + logger.warning("No weight files found in %s — module uses random init.", load_path) logger.info("Loaded OmicsAttentionModule from %s", model_name_or_path) return module diff --git a/tests/test_adapter_module.py b/tests/test_adapter_module.py index 5db01d3..0c547f7 100644 --- a/tests/test_adapter_module.py +++ b/tests/test_adapter_module.py @@ -38,7 +38,9 @@ def real_safetensors(): function for tests that need actual save/load roundtrips. """ import importlib + import safetensors.torch + importlib.reload(safetensors.torch) yield # The session-scoped patch in conftest will reassert on the next test that needs it @@ -140,9 +142,7 @@ class TestForwardMixedBatch: def test_forward_mixed_batch(self): """Mixed batch: text and omics tokens get different projections.""" # Both modalities have same input dim for simplicity - adapter = AdapterModule( - text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32 - ) + adapter = AdapterModule(text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32) B, L = 1, 4 features = _make_features( token_embeddings=torch.randn(B, L, 16), @@ -190,9 +190,7 @@ def test_separate_weights(self, adapter): def test_text_omics_produce_different_outputs(self): """Same input through text vs omics projection gives different results.""" - adapter = AdapterModule( - text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32 - ) + adapter = AdapterModule(text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32) x = torch.randn(1, 3, 16) text_features = _make_features( @@ -210,9 +208,7 @@ def test_text_omics_produce_different_outputs(self): omics_result = adapter(omics_features) # Different projections should produce different outputs (with overwhelming probability) - assert not torch.allclose( - text_result["token_embeddings"], omics_result["token_embeddings"] - ) + assert not torch.allclose(text_result["token_embeddings"], omics_result["token_embeddings"]) # --------------------------------------------------------------------------- @@ -286,14 +282,8 @@ def test_weights_update(self, adapter): # Both projections should have changed (check total param delta, # not per-parameter allclose, since some biases may get tiny gradients) - text_delta = sum( - (p - text_before[n]).abs().sum().item() - for n, p in adapter.text_proj.named_parameters() - ) - omics_delta = sum( - (p - omics_before[n]).abs().sum().item() - for n, p in adapter.omics_proj.named_parameters() - ) + text_delta = sum((p - text_before[n]).abs().sum().item() for n, p in adapter.text_proj.named_parameters()) + omics_delta = sum((p - omics_before[n]).abs().sum().item() for n, p in adapter.omics_proj.named_parameters()) assert text_delta > 0, "text_proj parameters did not change" assert omics_delta > 0, "omics_proj parameters did not change" @@ -346,9 +336,7 @@ def test_preserves_modality_ids(self, adapter): modality_ids=mod_ids, ) # Need same input dim for this test - adapter2 = AdapterModule( - text_input_dim=32, omics_input_dim=32, shared_dim=16, hidden_dim=64 - ) + adapter2 = AdapterModule(text_input_dim=32, omics_input_dim=32, shared_dim=16, hidden_dim=64) result = adapter2(features) torch.testing.assert_close(result["modality_ids"], mod_ids) diff --git a/tests/test_io/test_vector_store.py b/tests/test_io/test_vector_store.py index a71d519..f8f5275 100644 --- a/tests/test_io/test_vector_store.py +++ b/tests/test_io/test_vector_store.py @@ -110,18 +110,14 @@ def test_from_dataframe_custom_columns(self, sample_matrix, sample_ids, tmp_dir) } ) path = os.path.join(tmp_dir, "test.mmap") - store = VectorStore.from_dataframe( - df, path=path, id_col="my_id", embedding_col="my_vec" - ) + store = VectorStore.from_dataframe(df, path=path, id_col="my_id", embedding_col="my_vec") result = store["cell_A"] np.testing.assert_array_almost_equal(result, sample_matrix[0]) def test_from_adata_obs(self, sample_adata_obs, tmp_dir): """Create from adata.obsm, lookup by obs index.""" path = os.path.join(tmp_dir, "test.mmap") - store = VectorStore.from_adata( - sample_adata_obs, layer_key="X_scvi", axis="obs", path=path - ) + store = VectorStore.from_adata(sample_adata_obs, layer_key="X_scvi", axis="obs", path=path) expected = sample_adata_obs.obsm["X_scvi"] for i, sid in enumerate(sample_adata_obs.obs.index): @@ -131,9 +127,7 @@ def test_from_adata_obs(self, sample_adata_obs, tmp_dir): def test_from_adata_var(self, sample_adata_var, tmp_dir): """Create from adata.varm, lookup by var index.""" path = os.path.join(tmp_dir, "test.mmap") - store = VectorStore.from_adata( - sample_adata_var, layer_key="gene_emb", axis="var", path=path - ) + store = VectorStore.from_adata(sample_adata_var, layer_key="gene_emb", axis="var", path=path) expected = sample_adata_var.varm["gene_emb"] for i, gid in enumerate(sample_adata_var.var.index): diff --git a/tests/test_omics_attention_module.py b/tests/test_omics_attention_module.py index e9319d9..ebb5212 100644 --- a/tests/test_omics_attention_module.py +++ b/tests/test_omics_attention_module.py @@ -33,7 +33,9 @@ def tmp_dir(): def real_safetensors(): """Undo the global safetensors.torch.load_model patch for persistence tests.""" import importlib + import safetensors.torch + importlib.reload(safetensors.torch) yield @@ -107,9 +109,7 @@ def test_text_passthrough_in_mixed_batch(self, module): result = module(features) # Text tokens (first 3) should be unchanged - torch.testing.assert_close( - result["token_embeddings"][:, :3, :], x[:, :3, :] - ) + torch.testing.assert_close(result["token_embeddings"][:, :3, :], x[:, :3, :]) # --------------------------------------------------------------------------- @@ -131,9 +131,9 @@ def test_omics_transformed(self, module): result = module(features) # Output should differ from input (attention mixes information) - assert not torch.allclose( - result["token_embeddings"], x, atol=1e-6 - ), "Omics tokens should be transformed by self-attention" + assert not torch.allclose(result["token_embeddings"], x, atol=1e-6), ( + "Omics tokens should be transformed by self-attention" + ) def test_omics_transformed_in_mixed_batch(self, module): """Omics tokens in a mixed batch are modified.""" @@ -149,9 +149,9 @@ def test_omics_transformed_in_mixed_batch(self, module): result = module(features) # Omics tokens (last 3) should be modified - assert not torch.allclose( - result["token_embeddings"][:, 3:, :], x[:, 3:, :], atol=1e-6 - ), "Omics tokens should be transformed by self-attention" + assert not torch.allclose(result["token_embeddings"][:, 3:, :], x[:, 3:, :], atol=1e-6), ( + "Omics tokens should be transformed by self-attention" + ) # --------------------------------------------------------------------------- @@ -212,14 +212,20 @@ def test_variable_length_sequences(self, module): # Sample 0: 3 omics tokens + 2 pad # Sample 1: 5 omics tokens + 0 pad - modality_ids = torch.tensor([ - [1, 1, 1, 2, 2], - [1, 1, 1, 1, 1], - ], dtype=torch.long) - attention_mask = torch.tensor([ - [1, 1, 1, 0, 0], - [1, 1, 1, 1, 1], - ], dtype=torch.long) + modality_ids = torch.tensor( + [ + [1, 1, 1, 2, 2], + [1, 1, 1, 1, 1], + ], + dtype=torch.long, + ) + attention_mask = torch.tensor( + [ + [1, 1, 1, 0, 0], + [1, 1, 1, 1, 1], + ], + dtype=torch.long, + ) features = _make_features( token_embeddings=x.clone(), @@ -233,8 +239,9 @@ def test_variable_length_sequences(self, module): # Pad positions should remain zero (or unchanged) # The module should not produce non-zero values for pad positions - assert torch.all(result["token_embeddings"][0, 3:, :] == 0) or \ - torch.allclose(result["token_embeddings"][0, 3:, :], x[0, 3:, :]) + assert torch.all(result["token_embeddings"][0, 3:, :] == 0) or torch.allclose( + result["token_embeddings"][0, 3:, :], x[0, 3:, :] + ) # --------------------------------------------------------------------------- @@ -351,10 +358,7 @@ def test_weights_update(self, module): loss.backward() optimizer.step() - total_delta = sum( - (p - params_before[n]).abs().sum().item() - for n, p in module.named_parameters() - ) + total_delta = sum((p - params_before[n]).abs().sum().item() for n, p in module.named_parameters()) assert total_delta > 0, "Parameters did not change after optimizer step" @@ -455,4 +459,4 @@ def test_repr(self, module): """repr contains key config info.""" r = repr(module) assert "16" in r # input_dim - assert "2" in r # num_heads + assert "2" in r # num_heads diff --git a/tests/test_prepare_store.py b/tests/test_prepare_store.py index e414cc2..82f1cf4 100644 --- a/tests/test_prepare_store.py +++ b/tests/test_prepare_store.py @@ -142,6 +142,7 @@ def test_no_obsm_group_raises(self, tmp_dir): class TestOpenZarr: def test_open_directory(self, synthetic_zarr): from pathlib import Path + zarr_path, *_ = synthetic_zarr root = _open_zarr(Path(zarr_path)) assert "obs" in root @@ -149,6 +150,7 @@ def test_open_directory(self, synthetic_zarr): def test_nonexistent_raises(self, tmp_dir): from pathlib import Path + with pytest.raises(FileNotFoundError): _open_zarr(Path(tmp_dir) / "nope.zarr") @@ -166,10 +168,12 @@ def test_builds_store_from_local_zarr(self, synthetic_zarr, tmp_dir): zarr_path, obs_names, n_obs, d_pca, _ = synthetic_zarr # Simulate a dataset where all samples come from one zarr file - ds = Dataset.from_dict({ - "sample_idx": obs_names[:5], # use first 5 - "adata_link": [zarr_path] * 5, - }) + ds = Dataset.from_dict( + { + "sample_idx": obs_names[:5], # use first 5 + "adata_link": [zarr_path] * 5, + } + ) output_path = os.path.join(tmp_dir, "test_store.mmap") store = prepare_vector_store( @@ -190,10 +194,12 @@ def test_values_match_zarr_source(self, synthetic_zarr, tmp_dir): zarr_path, obs_names, *_ = synthetic_zarr - ds = Dataset.from_dict({ - "sample_idx": [obs_names[3]], - "adata_link": [zarr_path], - }) + ds = Dataset.from_dict( + { + "sample_idx": [obs_names[3]], + "adata_link": [zarr_path], + } + ) output_path = os.path.join(tmp_dir, "val_store.mmap") store = prepare_vector_store(ds, obsm_key="X_pca", output_path=output_path) @@ -209,10 +215,12 @@ def test_missing_sample_id_raises(self, synthetic_zarr, tmp_dir): zarr_path, *_ = synthetic_zarr - ds = Dataset.from_dict({ - "sample_idx": ["nonexistent_cell"], - "adata_link": [zarr_path], - }) + ds = Dataset.from_dict( + { + "sample_idx": ["nonexistent_cell"], + "adata_link": [zarr_path], + } + ) output_path = os.path.join(tmp_dir, "err_store.mmap") with pytest.raises(KeyError, match="nonexistent_cell"): @@ -224,10 +232,12 @@ def test_skips_existing_store(self, synthetic_zarr, tmp_dir): zarr_path, obs_names, *_ = synthetic_zarr - ds = Dataset.from_dict({ - "sample_idx": obs_names[:3], - "adata_link": [zarr_path] * 3, - }) + ds = Dataset.from_dict( + { + "sample_idx": obs_names[:3], + "adata_link": [zarr_path] * 3, + } + ) output_path = os.path.join(tmp_dir, "cached_store.mmap") @@ -244,17 +254,21 @@ def test_different_obsm_keys(self, synthetic_zarr, tmp_dir): zarr_path, obs_names, _, d_pca, d_scvi = synthetic_zarr - ds = Dataset.from_dict({ - "sample_idx": obs_names[:2], - "adata_link": [zarr_path] * 2, - }) + ds = Dataset.from_dict( + { + "sample_idx": obs_names[:2], + "adata_link": [zarr_path] * 2, + } + ) pca_store = prepare_vector_store( - ds, obsm_key="X_pca", + ds, + obsm_key="X_pca", output_path=os.path.join(tmp_dir, "pca.mmap"), ) scvi_store = prepare_vector_store( - ds, obsm_key="X_scvi", + ds, + obsm_key="X_scvi", output_path=os.path.join(tmp_dir, "scvi.mmap"), ) diff --git a/tests/test_st_integration.py b/tests/test_st_integration.py index 1b3f03d..033df43 100644 --- a/tests/test_st_integration.py +++ b/tests/test_st_integration.py @@ -23,7 +23,6 @@ import numpy as np import pytest import torch - from sentence_transformers import SentenceTransformer from sentence_transformers.sentence_transformer.modules import Normalize, Pooling @@ -44,12 +43,13 @@ def tmp_dir(): def real_safetensors(): """Undo the global safetensors.torch.load_model patch for persistence tests.""" import safetensors.torch + importlib.reload(safetensors.torch) yield # -- Shared dims used across all fixtures ----------------------------------- -TEXT_DIM = 32 # matches _TextEncStub hidden_size +TEXT_DIM = 32 # matches _TextEncStub hidden_size OMICS_DIM = 8 SHARED_DIM = 16 @@ -98,24 +98,28 @@ def attention_module(): @pytest.fixture def obs_pipeline(mmcontext_module, adapter_module): """Obs pipeline: MMContext → Adapter → Pooling → Normalize.""" - return SentenceTransformer(modules=[ - mmcontext_module, - adapter_module, - Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), - Normalize(), - ]) + return SentenceTransformer( + modules=[ + mmcontext_module, + adapter_module, + Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), + Normalize(), + ] + ) @pytest.fixture def var_pipeline(mmcontext_module, attention_module, adapter_module): """Var pipeline: MMContext → OmicsAttention → Adapter → Pooling → Normalize.""" - return SentenceTransformer(modules=[ - mmcontext_module, - attention_module, - adapter_module, - Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), - Normalize(), - ]) + return SentenceTransformer( + modules=[ + mmcontext_module, + attention_module, + adapter_module, + Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), + Normalize(), + ] + ) # --------------------------------------------------------------------------- @@ -173,10 +177,7 @@ def test_encode_omics_direct_obs(self, obs_pipeline): def test_encode_omics_direct_var(self, var_pipeline): """Encoding var-level (multiple gene vectors) produces correct shape.""" - genes = [ - np.random.randn(OMICS_DIM).astype(np.float32) - for _ in range(5) - ] + genes = [np.random.randn(OMICS_DIM).astype(np.float32) for _ in range(5)] embedding = var_pipeline.encode([{"omics_values": genes}]) assert isinstance(embedding, np.ndarray) assert embedding.shape == (1, SHARED_DIM) @@ -241,9 +242,7 @@ class TestObsPipeline: def test_obs_text_and_omics_same_output_dim(self, obs_pipeline): """Text and omics inputs produce same dimensionality.""" text_emb = obs_pipeline.encode(["Some text"]) - omics_emb = obs_pipeline.encode([{ - "omics_values": np.random.randn(OMICS_DIM).astype(np.float32) - }]) + omics_emb = obs_pipeline.encode([{"omics_values": np.random.randn(OMICS_DIM).astype(np.float32)}]) assert text_emb.shape[-1] == omics_emb.shape[-1] == SHARED_DIM def test_obs_deterministic(self, obs_pipeline): @@ -267,10 +266,7 @@ def test_var_single_gene(self, var_pipeline): def test_var_multiple_genes(self, var_pipeline): """Multiple gene vectors are attended and pooled.""" - genes = [ - np.random.randn(OMICS_DIM).astype(np.float32) - for _ in range(10) - ] + genes = [np.random.randn(OMICS_DIM).astype(np.float32) for _ in range(10)] embedding = var_pipeline.encode([{"omics_values": genes}]) assert embedding.shape == (1, SHARED_DIM) @@ -349,19 +345,19 @@ class TestPrecisionConversion: def test_precision_conversion_parameters(self, mmcontext_module, adapter_module): """Pipeline modules can be converted to fp16.""" - pipeline = SentenceTransformer(modules=[ - mmcontext_module, - adapter_module, - Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), - Normalize(), - ]) + pipeline = SentenceTransformer( + modules=[ + mmcontext_module, + adapter_module, + Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), + Normalize(), + ] + ) pipeline.half() # All learnable parameters should now be fp16 for name, param in pipeline.named_parameters(): - assert param.dtype == torch.float16, ( - f"Parameter {name} is {param.dtype}, expected float16" - ) + assert param.dtype == torch.float16, f"Parameter {name} is {param.dtype}, expected float16" # Restore to fp32 — the session-scoped _TextEncStub (from conftest) # is shared across all tests; leaving it in fp16 would pollute @@ -399,20 +395,22 @@ def test_training_text_only(self, obs_pipeline, tmp_dir): from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss - ds = Dataset.from_dict({ - "anchor": [ - "A neuron from the thalamus.", - "An epithelial cell from the lung.", - "A B cell from peripheral blood.", - "A fibroblast from skin tissue.", - ], - "positive": [ - "Thalamic neuron expressing SYT1 and GNAS.", - "Lung epithelial cell with high EPCAM.", - "CD19-positive B lymphocyte.", - "Dermal fibroblast with COL1A1 expression.", - ], - }) + ds = Dataset.from_dict( + { + "anchor": [ + "A neuron from the thalamus.", + "An epithelial cell from the lung.", + "A B cell from peripheral blood.", + "A fibroblast from skin tissue.", + ], + "positive": [ + "Thalamic neuron expressing SYT1 and GNAS.", + "Lung epithelial cell with high EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1 expression.", + ], + } + ) loss = MultipleNegativesRankingLoss(obs_pipeline) args = SentenceTransformerTrainingArguments( @@ -437,9 +435,7 @@ def test_training_text_only(self, obs_pipeline, tmp_dir): # At least some parameters should have changed total_delta = sum( - (p - params_before[n]).abs().sum().item() - for n, p in obs_pipeline.named_parameters() - if n in params_before + (p - params_before[n]).abs().sum().item() for n, p in obs_pipeline.named_parameters() if n in params_before ) assert total_delta > 0, "No parameters changed during text-only training" @@ -457,20 +453,22 @@ def test_training_bimodal(self, obs_pipeline, obs_store, tmp_dir): first_module = list(obs_pipeline.children())[0] first_module.set_vector_store(obs_store) - ds = Dataset.from_dict({ - "anchor": [ - "omics:cell_0", - "omics:cell_1", - "omics:cell_2", - "omics:cell_3", - ], - "positive": [ - "Thalamic neuron expressing SYT1.", - "Lung epithelial cell with EPCAM.", - "CD19-positive B lymphocyte.", - "Dermal fibroblast with COL1A1.", - ], - }) + ds = Dataset.from_dict( + { + "anchor": [ + "omics:cell_0", + "omics:cell_1", + "omics:cell_2", + "omics:cell_3", + ], + "positive": [ + "Thalamic neuron expressing SYT1.", + "Lung epithelial cell with EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1.", + ], + } + ) loss = MultipleNegativesRankingLoss(obs_pipeline) args = SentenceTransformerTrainingArguments( @@ -493,9 +491,7 @@ def test_training_bimodal(self, obs_pipeline, obs_store, tmp_dir): trainer.train() total_delta = sum( - (p - params_before[n]).abs().sum().item() - for n, p in obs_pipeline.named_parameters() - if n in params_before + (p - params_before[n]).abs().sum().item() for n, p in obs_pipeline.named_parameters() if n in params_before ) assert total_delta > 0, "No parameters changed during bimodal training" @@ -511,20 +507,22 @@ def test_training_gene_list(self, obs_pipeline, tmp_dir): from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss - ds = Dataset.from_dict({ - "anchor": [ - "MALAT1 MT-CO3 GNAS SYT1 CALM1", - "EPCAM KRT8 KRT18 MUC1", - "CD19 MS4A1 CD79A PAX5", - "COL1A1 COL3A1 FN1 VIM", - ], - "positive": [ - "Thalamic neuron expressing SYT1.", - "Lung epithelial cell with EPCAM.", - "CD19-positive B lymphocyte.", - "Dermal fibroblast with COL1A1.", - ], - }) + ds = Dataset.from_dict( + { + "anchor": [ + "MALAT1 MT-CO3 GNAS SYT1 CALM1", + "EPCAM KRT8 KRT18 MUC1", + "CD19 MS4A1 CD79A PAX5", + "COL1A1 COL3A1 FN1 VIM", + ], + "positive": [ + "Thalamic neuron expressing SYT1.", + "Lung epithelial cell with EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1.", + ], + } + ) loss = MultipleNegativesRankingLoss(obs_pipeline) args = SentenceTransformerTrainingArguments( @@ -547,9 +545,7 @@ def test_training_gene_list(self, obs_pipeline, tmp_dir): trainer.train() total_delta = sum( - (p - params_before[n]).abs().sum().item() - for n, p in obs_pipeline.named_parameters() - if n in params_before + (p - params_before[n]).abs().sum().item() for n, p in obs_pipeline.named_parameters() if n in params_before ) assert total_delta > 0, "No parameters changed during gene-list training" @@ -559,20 +555,22 @@ def test_training_save_load_roundtrip(self, obs_pipeline, tmp_dir, real_safetens from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss - ds = Dataset.from_dict({ - "anchor": [ - "MALAT1 MT-CO3 GNAS SYT1", - "EPCAM KRT8 KRT18 MUC1", - "CD19 MS4A1 CD79A PAX5", - "COL1A1 COL3A1 FN1 VIM", - ], - "positive": [ - "Thalamic neuron expressing SYT1.", - "Lung epithelial cell with EPCAM.", - "CD19-positive B lymphocyte.", - "Dermal fibroblast with COL1A1.", - ], - }) + ds = Dataset.from_dict( + { + "anchor": [ + "MALAT1 MT-CO3 GNAS SYT1", + "EPCAM KRT8 KRT18 MUC1", + "CD19 MS4A1 CD79A PAX5", + "COL1A1 COL3A1 FN1 VIM", + ], + "positive": [ + "Thalamic neuron expressing SYT1.", + "Lung epithelial cell with EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1.", + ], + } + ) loss = MultipleNegativesRankingLoss(obs_pipeline) save_path = os.path.join(tmp_dir, "trained_model") From be61ca0414b6104f5e5838c888dba61f414da1f1 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 2 Jun 2026 08:36:07 +0000 Subject: [PATCH 23/67] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- CLAUDE.md | 2 + REFACTOR_ROADMAP.md | 44 ++-- ROADMAP.md | 102 ++++----- scripts/train.py | 2 +- scripts/train_merged.py | 2 +- scripts/train_tiny.py | 13 +- src/mmcontext/_legacy/mmcontextencoder.py | 4 +- src/mmcontext/io/prepare_store.py | 19 +- src/mmcontext/io/vector_store.py | 45 ++-- src/mmcontext/modules/mmcontext_module.py | 39 +--- .../modules/omics_attention_module.py | 18 +- tests/test_adapter_module.py | 28 +-- tests/test_io/test_vector_store.py | 12 +- tests/test_omics_attention_module.py | 52 ++--- tests/test_prepare_store.py | 58 +++-- tests/test_st_integration.py | 198 +++++++++--------- 16 files changed, 311 insertions(+), 327 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index e4f7f73..de56b64 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -85,6 +85,7 @@ When implementing a feature from a GitHub issue (via @claude or otherwise): 1. **If the issue is ambiguous**: Post clarifying questions as a comment. Do NOT start implementation until the questions are answered. 2. **Plan first**: Before writing any code, post an implementation plan as a comment on the issue with a checkbox list: + ``` ## Implementation Plan - [ ] Step 1: description @@ -92,6 +93,7 @@ When implementing a feature from a GitHub issue (via @claude or otherwise): - [ ] Step 3: description - [ ] Verify: run tests, check linting ``` + Wait for approval (a reply containing "approved", "go ahead", "LGTM", or "looks good"). 3. **Implement**: Create a branch `claude/-` from `dev-claude`. Implement the plan step by step. Edit the plan comment to check off completed steps. diff --git a/REFACTOR_ROADMAP.md b/REFACTOR_ROADMAP.md index 042e5ba..3e0cd8f 100644 --- a/REFACTOR_ROADMAP.md +++ b/REFACTOR_ROADMAP.md @@ -7,18 +7,18 @@ ### Current → New -| Aspect | Current | Refactored | -|--------|---------|------------| -| Base class | `Module` | `InputModule` | -| Tokenization | `tokenize()` | `preprocess()` | -| Omics storage | `nn.Embedding` lookup | `VectorStore` (memory-mapped) | -| Omics key | `pixel_values` | `omics_values` | -| Adapters | Inside encoder | Separate `AdapterModule(Module)` | -| Modality flag | `omics_text_info` | `modality_ids` | -| OneHotTextEncoder | Included | Removed | -| Data loading | Coupled to encoder | `mmcontext.io` module | -| Pipeline | `[MMContextEncoder]` | `[MMContextModule, AdapterModule, Pooling, Normalize]` | -| Var support | Same class, no attention | Optional `OmicsAttentionModule` | +| Aspect | Current | Refactored | +| ----------------- | ------------------------ | ------------------------------------------------------ | +| Base class | `Module` | `InputModule` | +| Tokenization | `tokenize()` | `preprocess()` | +| Omics storage | `nn.Embedding` lookup | `VectorStore` (memory-mapped) | +| Omics key | `pixel_values` | `omics_values` | +| Adapters | Inside encoder | Separate `AdapterModule(Module)` | +| Modality flag | `omics_text_info` | `modality_ids` | +| OneHotTextEncoder | Included | Removed | +| Data loading | Coupled to encoder | `mmcontext.io` module | +| Pipeline | `[MMContextEncoder]` | `[MMContextModule, AdapterModule, Pooling, Normalize]` | +| Var support | Same class, no attention | Optional `OmicsAttentionModule` | ### Module Pipeline @@ -52,12 +52,14 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 1: Foundation — VectorStore + IO Module **Files created/modified:** + - `src/mmcontext/io/__init__.py` (new) - `src/mmcontext/io/vector_store.py` (new) - `src/mmcontext/io/adata_utils.py` (new — extracted from mmcontextencoder.py + file_utils.py) - `tests/test_vector_store.py` (new) **VectorStore responsibilities:** + - Create from AnnData (obsm/varm), DataFrame, dict, or numpy array - Write embeddings to numpy memmap file + JSON index - Batch lookup by sample IDs → numpy array @@ -65,6 +67,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - Support both obs (1 vector/sample) and var (N vectors/sample) **Tests (write FIRST):** + 1. `test_from_numpy` — round-trip: write memmap, read back, values match 2. `test_from_adata_obs` — create from adata.obsm, lookup by obs index 3. `test_from_adata_var` — create from adata.varm, lookup by var index @@ -75,6 +78,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( 8. `test_persistence` — store survives close/reopen cycle **adata_utils responsibilities:** + - `load_embeddings_from_adata_link()` — extracted from `get_initial_embeddings_from_adata_link` - `create_token_dataframe_from_obsm()` — extracted from encoder - `build_embedding_df()` — extracted from file_utils @@ -84,11 +88,13 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 2: Core Module — MMContextModule (InputModule) **Files created/modified:** + - `src/mmcontext/modules/__init__.py` (new) - `src/mmcontext/modules/mmcontext_module.py` (new) - `tests/test_mmcontext_module.py` (new) **MMContextModule responsibilities:** + - Extends `InputModule` from sentence-transformers - `modalities` property returns `["text", "omics"]` - `preprocess(inputs)` routes by input type: @@ -103,6 +109,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - Text encoder freezing/unfreezing logic **Tests (write FIRST):** + 1. `test_preprocess_text_only` — text strings → input_ids, attention_mask, modality_ids 2. `test_preprocess_omics_direct_vector` — raw vectors → omics_values, attention_mask, modality_ids 3. `test_preprocess_omics_via_store` — prefixed IDs + VectorStore → resolved vectors @@ -124,10 +131,12 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 3: Modality-Aware AdapterModule **Files created/modified:** + - `src/mmcontext/modules/adapter_module.py` (new — replaces adapters.py as pipeline Module) - `tests/test_adapter_module.py` (new) **AdapterModule responsibilities:** + - Extends `Module` from sentence-transformers - Reads `modality_ids` from features dict - Maintains separate projection weights: `text_proj` and `omics_proj` @@ -138,6 +147,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - `get_sentence_embedding_dimension()` returns D_shared **Tests (write FIRST):** + 1. `test_forward_text_only` — text tokens projected correctly 2. `test_forward_omics_only` — omics tokens projected correctly 3. `test_forward_mixed_batch` — text and omics tokens get different projections @@ -154,10 +164,12 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 4: OmicsAttentionModule (Optional, Var-only) **Files created/modified:** + - `src/mmcontext/modules/omics_attention_module.py` (new) - `tests/test_omics_attention_module.py` (new) **OmicsAttentionModule responsibilities:** + - Extends `Module` from sentence-transformers - Reads `modality_ids` from features dict - Applies multi-head self-attention ONLY to omics tokens (modality_id=1) @@ -167,6 +179,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - `save()` / `load()` for persistence **Tests (write FIRST):** + 1. `test_text_passthrough` — text tokens unchanged after module 2. `test_omics_transformed` — omics tokens are modified by self-attention 3. `test_attention_mask_respected` — padded positions don't influence real tokens @@ -181,10 +194,12 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 5: SentenceTransformer Integration **Files created/modified:** + - `tests/test_st_integration.py` (new — replaces test_sentence_transformer_integration.py) - Minor adjustments to modules for compatibility **Integration tests (write FIRST):** + 1. `test_pipeline_construction` — modules compose into SentenceTransformer 2. `test_encode_text` — `model.encode(["text"])` produces correct shape 3. `test_encode_omics_direct` — `model.encode([{"omics_values": vector}])` works @@ -206,6 +221,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( ### Phase 6: Documentation + Cleanup **Files modified:** + - `src/mmcontext/__init__.py` — update public API exports - `src/mmcontext/modules/__init__.py` — export all modules - `src/mmcontext/io/__init__.py` — export VectorStore and utilities @@ -213,6 +229,7 @@ All modules communicate through a features dict. After `MMContextModule.forward( - `README.md` — update architecture description and usage examples **Cleanup:** + - Remove `OneHotTextEncoder` (`onehot.py`) - Archive old `mmcontextencoder.py` (keep temporarily for reference, don't import) - Archive old `adapters.py` (replaced by modules/adapter_module.py) @@ -240,6 +257,7 @@ Phase 6 (Docs + Cleanup) ← final polish ## Test Strategy Each phase follows strict TDD: + 1. Write test file with all tests (they will fail) 2. Implement the module until all tests pass 3. Run full test suite to check for regressions @@ -247,6 +265,7 @@ Each phase follows strict TDD: **Stub strategy:** Tests use lightweight stubs (similar to existing `_TokStub`, `_TextEncStub`) to avoid downloading real models. The existing `conftest.py` pattern is extended for new modules. **What's preserved from current tests:** + - Core encoding shapes and correctness (text, omics, mixed) - Save/load round-trips - Gradient flow through adapters @@ -255,6 +274,7 @@ Each phase follows strict TDD: - Freezing/unfreezing behavior **What's new:** + - VectorStore tests (memmap, lookup, persistence) - Modality-aware adapter tests (separate projections) - OmicsAttentionModule tests (self-attention on omics only) diff --git a/ROADMAP.md b/ROADMAP.md index d1da283..2669ca2 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -2,19 +2,19 @@ This document captures the state of the codebase after the sentence-transformers v5.4+ refactor (Phases 1–5), identifies remaining work, and provides context for -future development. It is structured as a reference for both human contributors +future development. It is structured as a reference for both human contributors and AI-assisted editing sessions. --- -## 1 Architecture overview (post-refactor) +## 1 Architecture overview (post-refactor) The new pipeline lives in two packages: -| Package | Role | -|---------|------| +| Package | Role | +| ------------------- | --------------------------------------------------------------------------------------------- | | `mmcontext.modules` | ST pipeline modules: `MMContextModule` (InputModule), `AdapterModule`, `OmicsAttentionModule` | -| `mmcontext.io` | `VectorStore` (mmap-backed lookup), `prepare_vector_store` (zarr → store builder) | +| `mmcontext.io` | `VectorStore` (mmap-backed lookup), `prepare_vector_store` (zarr → store builder) | A trained model is a standard `SentenceTransformer` with this module chain: @@ -30,9 +30,9 @@ embedding) inputs where it would degenerate to a feedforward layer. - **`input_values` preprocess key** — omics preprocess writes `input_values` (not `token_embeddings`) so the ST training collator's `collect_features` - suffix matching detects omics columns. Forward reads `input_values` and + suffix matching detects omics columns. Forward reads `input_values` and writes `token_embeddings` for downstream modules. -- **`modality_ids` tensor** — 0 = text, 1 = omics, 2 = pad. The +- **`modality_ids` tensor** — 0 = text, 1 = omics, 2 = pad. The `AdapterModule` uses this to route tokens through the correct projection head. - **No Trainer subclass** — training uses the standard `SentenceTransformerTrainer` + `MultipleNegativesRankingLoss` with @@ -40,15 +40,15 @@ embedding) inputs where it would degenerate to a feedforward layer. --- -## 2 What was completed (Phases 1–5) +## 2 What was completed (Phases 1–5) -| Phase | Deliverable | Tests | -|-------|-------------|-------| -| 1 | `VectorStore` — mmap-backed vector lookup with `from_numpy`, `from_adata`, `from_dict`, `load` | `tests/test_vector_store.py` (in mmcontext_module tests) | -| 2 | `MMContextModule` — text encoding via AutoModel/AutoTokenizer, omics via VectorStore or direct vectors, `preprocess`/`forward` contract | `tests/test_mmcontext_module.py` | -| 3 | `AdapterModule` — modality-aware projection (text head, omics head, shared dim), safetensors persistence | `tests/test_adapter_module.py` | -| 4 | `OmicsAttentionModule` — optional self-attention over variable-length omics sequences | `tests/test_omics_attention_module.py` | -| 5 | Full ST integration — pipeline construction, encode, save/load, training (text-only, bimodal, gene-list), `prepare_vector_store` | `tests/test_st_integration.py`, `tests/test_prepare_store.py` | +| Phase | Deliverable | Tests | +| ----- | --------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------- | +| 1 | `VectorStore` — mmap-backed vector lookup with `from_numpy`, `from_adata`, `from_dict`, `load` | `tests/test_vector_store.py` (in mmcontext_module tests) | +| 2 | `MMContextModule` — text encoding via AutoModel/AutoTokenizer, omics via VectorStore or direct vectors, `preprocess`/`forward` contract | `tests/test_mmcontext_module.py` | +| 3 | `AdapterModule` — modality-aware projection (text head, omics head, shared dim), safetensors persistence | `tests/test_adapter_module.py` | +| 4 | `OmicsAttentionModule` — optional self-attention over variable-length omics sequences | `tests/test_omics_attention_module.py` | +| 5 | Full ST integration — pipeline construction, encode, save/load, training (text-only, bimodal, gene-list), `prepare_vector_store` | `tests/test_st_integration.py`, `tests/test_prepare_store.py` | ### Supporting files @@ -57,22 +57,22 @@ embedding) inputs where it would degenerate to a feedforward layer. --- -## 3 Legacy code (`_legacy/`) +## 3 Legacy code (`_legacy/`) The following modules were moved to `src/mmcontext/_legacy/` and are preserved for backward compatibility with previously trained models: -| File | What it was | Notes | -|------|-------------|-------| -| `mmcontextencoder.py` | Dual-tower encoder (text + omics), `MMContextProcessor` tokenizer | Central old architecture; `embed/model_utils.py` still imports `MMEnc.get_initial_embeddings_from_adata_link` from it | -| `adapters.py` | MLP adapter with identity/linear/2-layer modes | Superseded by `modules/adapter_module.py` which adds modality-awareness | -| `omicsencoder.py` | `MiniOmicsModel` — `nn.Embedding` lookup with HF `PreTrainedModel` interface | Superseded by `VectorStore` (no learnable embedding, just mmap lookup) | -| `onehot.py` | `OneHotTextEncoder` — learnable embedding per unique sentence | Useful for ablation experiments; no new equivalent | -| `cell_sentence_transformer.py` | `OmicsEncoder` — full transformer over omics tokens with cross-attention | Heavier than `OmicsAttentionModule`; cross-attention is not yet in new code | +| File | What it was | Notes | +| ------------------------------ | ---------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- | +| `mmcontextencoder.py` | Dual-tower encoder (text + omics), `MMContextProcessor` tokenizer | Central old architecture; `embed/model_utils.py` still imports `MMEnc.get_initial_embeddings_from_adata_link` from it | +| `adapters.py` | MLP adapter with identity/linear/2-layer modes | Superseded by `modules/adapter_module.py` which adds modality-awareness | +| `omicsencoder.py` | `MiniOmicsModel` — `nn.Embedding` lookup with HF `PreTrainedModel` interface | Superseded by `VectorStore` (no learnable embedding, just mmap lookup) | +| `onehot.py` | `OneHotTextEncoder` — learnable embedding per unique sentence | Useful for ablation experiments; no new equivalent | +| `cell_sentence_transformer.py` | `OmicsEncoder` — full transformer over omics tokens with cross-attention | Heavier than `OmicsAttentionModule`; cross-attention is not yet in new code | ### Legacy tests (still functional) -These test files import from `_legacy` and exercise the old architecture. They +These test files import from `_legacy` and exercise the old architecture. They should continue to pass for backward compatibility: - `test_mmcontext_encoder.py` @@ -83,7 +83,7 @@ should continue to pass for backward compatibility: --- -## 4 Active non-module code — status & required changes +## 4 Active non-module code — status & required changes ### `callback.py` — trainer callbacks @@ -110,9 +110,9 @@ should continue to pass for backward compatibility: Domain-specific and actively needed for dataset preprocessing. - `truncate_semantic_cell_sentences_dataset()` — semantic-aware truncation. - `resolve_negative_indices_and_rename()` — resolves `negative_*_idx` columns - to actual text values. Needed for multiplet training with the real dataset. + to actual text values. Needed for multiplet training with the real dataset. - `get_evaluator()` — instantiates ST evaluators (BinaryClassification, - Triplet) from dataset columns. Works with any ST model. + Triplet) from dataset columns. Works with any ST model. - `consolidate_low_frequency_categories()` — groups rare labels into "other". - `get_device()` — simple CUDA/MPS/CPU device selection. @@ -126,7 +126,7 @@ should continue to pass for backward compatibility: ### `hub_utils.py` — HuggingFace Hub upload -**Status:** Template body references old module names. The upload logic +**Status:** Template body references old module names. The upload logic (`HfApi` calls) is architecture-agnostic and works. **What needs to change:** Update the `MODEL_CARD_TEMPLATE` string to describe @@ -139,7 +139,7 @@ the new pipeline modules. **Key functions:** - `download_and_extract_links()` — full-featured download with caching, - Zenodo support, resume, retries. The new `io/prepare_store.py` has a + Zenodo support, resume, retries. The new `io/prepare_store.py` has a lighter `_download_zarr()` that doesn't support all features. Consider converging on one implementation eventually. - `remove_corrupted_null_arrays()` — zarr repair utility; used by @@ -150,10 +150,10 @@ the new pipeline modules. --- -## 5 Evaluation framework — analysis & adaptation needs +## 5 Evaluation framework — analysis & adaptation needs The `eval/` package is a **self-contained evaluation framework** with a -decorator-based registry pattern. It is largely architecture-agnostic — most +decorator-based registry pattern. It is largely architecture-agnostic — most evaluators work on AnnData `.obsm` embeddings, not on the model directly. ### Architecture @@ -167,25 +167,25 @@ eval/utils.py — LabelKind, LabelSpec helpers ### Evaluators -| Evaluator | Registry name | What it measures | Architecture dependency | -|-----------|--------------|------------------|----------------------| -| `ARI` | `"ARI"` | Adjusted Rand Index (KMeans clustering vs labels) | None — operates on embeddings | -| `LabelSimilarity` | (registered) | ROC-AUC of intra- vs inter-label cosine sim | None | -| `ScibBundle` | `"scib"` | scIB benchmark metrics (batch/bio) | None | -| `UmapPlotter` | (registered) | UMAP visualizations colored by labels | Uses `pl/plotting.py` | -| `OmicsQueryAnnotator` | (not registered, standalone) | Zero-shot annotation via cosine similarity | Needs `model.encode()` — works with any ST model | +| Evaluator | Registry name | What it measures | Architecture dependency | +| --------------------- | ---------------------------- | ------------------------------------------------- | ------------------------------------------------ | +| `ARI` | `"ARI"` | Adjusted Rand Index (KMeans clustering vs labels) | None — operates on embeddings | +| `LabelSimilarity` | (registered) | ROC-AUC of intra- vs inter-label cosine sim | None | +| `ScibBundle` | `"scib"` | scIB benchmark metrics (batch/bio) | None | +| `UmapPlotter` | (registered) | UMAP visualizations colored by labels | Uses `pl/plotting.py` | +| `OmicsQueryAnnotator` | (not registered, standalone) | Zero-shot annotation via cosine similarity | Needs `model.encode()` — works with any ST model | ### `embedding_alignment.py` Standalone module (not registered as an evaluator) that computes cross-modal -alignment scores. Useful for measuring how well the shared space aligns -text and omics embeddings. Works on raw numpy arrays. +alignment scores. Useful for measuring how well the shared space aligns +text and omics embeddings. Works on raw numpy arrays. ### What needs to change for new architecture 1. **Embedding pipeline (`embed/`)** — `embed/model_utils.py` imports `MMContextEncoder.get_initial_embeddings_from_adata_link` to register - omics vectors before embedding. This needs a new path: + omics vectors before embedding. This needs a new path: - Load the ST model - Detect if it has an `MMContextModule` at position 0 - Attach a `VectorStore` (built via `prepare_vector_store`) instead of @@ -201,7 +201,7 @@ text and omics embeddings. Works on raw numpy arrays. --- -## 6 Missing features & test gaps +## 6 Missing features & test gaps ### Features not yet covered by new modules @@ -211,19 +211,19 @@ text and omics embeddings. Works on raw numpy arrays. - [ ] **Cross-attention** — The old `OmicsEncoder` in `cell_sentence_transformer.py` supported cross-attention between text - and omics sequences. The new `OmicsAttentionModule` only does - self-attention on omics tokens. Cross-attention would enable richer + and omics sequences. The new `OmicsAttentionModule` only does + self-attention on omics tokens. Cross-attention would enable richer multimodal interaction but adds complexity. - [ ] **`OneHotTextEncoder` equivalent** — Useful for ablation experiments - (fast training without a real transformer). Currently only in `_legacy/`. + (fast training without a real transformer). Currently only in `_legacy/`. Could be a simple fixture/utility rather than a full module. - [ ] **Negative mining / hard negatives** — The training script currently uses `(anchor, positive)` pairs with in-batch negatives (MNR loss). The dataset has `negative_1_idx` and `negative_2_idx` columns. `resolve_negative_indices_and_rename()` in `utils.py` handles - resolving these to actual text. Supporting `(anchor, positive, negative)` + resolving these to actual text. Supporting `(anchor, positive, negative)` triplets would improve training quality. - [ ] **Hub upload for new architecture** — `hub_utils.py` model card @@ -246,7 +246,7 @@ text and omics embeddings. Works on raw numpy arrays. - [ ] **Mixed-modality batches** — No test currently sends a batch where some samples are text and some are omics through the full pipeline - in a single forward pass. This is an important edge case for + in a single forward pass. This is an important edge case for training with heterogeneous datasets. - [ ] **Gradient flow end-to-end** — Training tests verify parameters change, @@ -264,19 +264,19 @@ text and omics embeddings. Works on raw numpy arrays. - [ ] **Converge download logic** — `file_utils.download_and_extract_links` and `io/prepare_store._download_zarr` both handle zarr downloads with - different feature sets. Could share a common download backend. + different feature sets. Could share a common download backend. - [ ] **`simulator.py` integration** — The old `LOSS_PRESETS` dict and `make_cluster_sampler` are valuable for generating synthetic test - datasets. Consider moving to a `testing/` subpackage. + datasets. Consider moving to a `testing/` subpackage. - [ ] **`sanity_helpers.py`** — Contains `plot_pca` and possibly other - debug helpers not in `pl/`. Consider merging into `pl/` or a + debug helpers not in `pl/`. Consider merging into `pl/` or a `debug/` module. --- -## 7 File map (post-cleanup) +## 7 File map (post-cleanup) ``` src/mmcontext/ diff --git a/scripts/train.py b/scripts/train.py index ea4c1ec..13db283 100644 --- a/scripts/train.py +++ b/scripts/train.py @@ -19,11 +19,11 @@ from sentence_transformers.evaluation import SequentialEvaluator from transformers.integrations import WandbCallback +from mmcontext._legacy.mmcontextencoder import MMContextEncoder from mmcontext.callback import UnfreezeAdapterCallback, UnfreezeTextEncoderCallback # from mmcontext.eval import SystemMonitor from mmcontext.hub_utils import prepare_model_for_hub, upload_model_to_hub -from mmcontext._legacy.mmcontextencoder import MMContextEncoder # from mmcontext.pp.utils import consolidate_low_frequency_categories from mmcontext.utils import ( # , load_test_adata_from_hf_dataset diff --git a/scripts/train_merged.py b/scripts/train_merged.py index 1978cdf..3b2433c 100644 --- a/scripts/train_merged.py +++ b/scripts/train_merged.py @@ -29,10 +29,10 @@ ) from transformers.integrations import WandbCallback +from mmcontext._legacy.mmcontextencoder import MMContextEncoder from mmcontext.callback import UnfreezeAdapterCallback, UnfreezeTextEncoderCallback from mmcontext.eval import SystemMonitor from mmcontext.hub_utils import get_model_info_from_config, upload_model_to_hub -from mmcontext._legacy.mmcontextencoder import MMContextEncoder from mmcontext.utils import ( get_evaluator, get_loss, diff --git a/scripts/train_tiny.py b/scripts/train_tiny.py index a4594fc..36cb07f 100644 --- a/scripts/train_tiny.py +++ b/scripts/train_tiny.py @@ -40,9 +40,9 @@ SentenceTransformerTrainingArguments, ) from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss -from sentence_transformers.sentence_transformer.modules import Pooling, Normalize +from sentence_transformers.sentence_transformer.modules import Normalize, Pooling -from mmcontext.modules import MMContextModule, AdapterModule +from mmcontext.modules import AdapterModule, MMContextModule logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s %(message)s") logger = logging.getLogger(__name__) @@ -74,6 +74,7 @@ def prepare_bimodal_dataset(ds): Returns a HF Dataset with columns ``anchor`` and ``positive``. """ + def prefix_omics(example): example["anchor"] = f"omics:{example['sample_idx']}" return example @@ -149,14 +150,14 @@ def main(): "--vector-store", default=None, help="Path to .mmap VectorStore file. For bimodal mode: if omitted, " - "the store is built automatically from adata_link + sample_idx " - "columns using --obsm-key.", + "the store is built automatically from adata_link + sample_idx " + "columns using --obsm-key.", ) parser.add_argument( "--obsm-key", default="X_scvi_fm", help="obsm key to extract when building VectorStore (default: X_scvi_fm). " - "Other common choices: X_pca, X_geneformer, X_gs10k", + "Other common choices: X_pca, X_geneformer, X_gs10k", ) parser.add_argument("--omics-dim", type=int, default=None, help="Omics vector dimension") parser.add_argument("--shared-dim", type=int, default=256, help="Shared embedding dim (default: 256)") @@ -175,7 +176,7 @@ def main(): "--wandb-project", default=None, help="Weights & Biases project name. Enables wandb logging when set. " - "You can also set WANDB_PROJECT env var instead.", + "You can also set WANDB_PROJECT env var instead.", ) parser.add_argument( "--wandb-run-name", diff --git a/src/mmcontext/_legacy/mmcontextencoder.py b/src/mmcontext/_legacy/mmcontextencoder.py index 10fb53d..38ee909 100644 --- a/src/mmcontext/_legacy/mmcontextencoder.py +++ b/src/mmcontext/_legacy/mmcontextencoder.py @@ -42,14 +42,14 @@ from sentence_transformers.models import Module, Pooling from transformers import AutoModel, AutoTokenizer -from .adapters import AdapterModule - # file_utils remains at the package root (not moved to _legacy) from mmcontext.file_utils import ( build_embedding_df, collect_unique_links, download_and_extract_links, ) + +from .adapters import AdapterModule from .omicsencoder import MiniOmicsModel from .onehot import OneHotTextEncoder diff --git a/src/mmcontext/io/prepare_store.py b/src/mmcontext/io/prepare_store.py index 3307905..40d7e09 100644 --- a/src/mmcontext/io/prepare_store.py +++ b/src/mmcontext/io/prepare_store.py @@ -45,6 +45,7 @@ # Zarr helpers — read obs_names and obsm without loading full AnnData # --------------------------------------------------------------------------- + def _read_obs_names_zarr(root: zarr.Group) -> list[str]: """Read observation names from a zarr-backed AnnData store. @@ -95,6 +96,7 @@ def _get_obsm_zarr_array(root: zarr.Group, obsm_key: str) -> zarr.Array: # Download helper # --------------------------------------------------------------------------- + def _url_to_cache_name(url: str) -> str: """Deterministic short name for a URL, used as cache directory name.""" return hashlib.sha256(url.encode()).hexdigest()[:16] @@ -138,9 +140,10 @@ def _download_zarr(url: str, cache_dir: Path) -> Path: with requests.get(download_url, stream=True, timeout=(30, 600), headers=headers) as r: r.raise_for_status() total = int(r.headers.get("content-length", 0)) - with open(zip_path, "wb") as f, tqdm( - total=total, unit="B", unit_scale=True, desc="Downloading", leave=False - ) as pbar: + with ( + open(zip_path, "wb") as f, + tqdm(total=total, unit="B", unit_scale=True, desc="Downloading", leave=False) as pbar, + ): for chunk in r.iter_content(chunk_size=8 * 1024 * 1024): f.write(chunk) pbar.update(len(chunk)) @@ -187,6 +190,7 @@ def _open_zarr(path: Path) -> zarr.Group: # Public API # --------------------------------------------------------------------------- + def prepare_vector_store( dataset: Dataset, *, @@ -279,8 +283,7 @@ def prepare_vector_store( for sid in sample_ids: if sid not in obs_name_to_idx: raise KeyError( - f"Sample ID '{sid}' not found in obs_names of {link}. " - f"First 5 obs_names: {obs_names[:5]}" + f"Sample ID '{sid}' not found in obs_names of {link}. First 5 obs_names: {obs_names[:5]}" ) needed_rows.append(obs_name_to_idx[sid]) needed_ids.append(sid) @@ -292,9 +295,9 @@ def prepare_vector_store( sorted_rows = np.array(needed_rows)[sort_order] obsm_array = _get_obsm_zarr_array(root, obsm_key) - selected = obsm_array.get_orthogonal_selection( - (sorted_rows, slice(None)) - ).astype(np.float32) # (len(needed_rows), D) + selected = obsm_array.get_orthogonal_selection((sorted_rows, slice(None))).astype( + np.float32 + ) # (len(needed_rows), D) # Unsort back to original order unsort = np.argsort(sort_order) diff --git a/src/mmcontext/io/vector_store.py b/src/mmcontext/io/vector_store.py index ed50cc1..b0a6f24 100644 --- a/src/mmcontext/io/vector_store.py +++ b/src/mmcontext/io/vector_store.py @@ -17,7 +17,7 @@ **Lookup:** ->>> vec = store["cell_42"] # single lookup → (D,) +>>> vec = store["cell_42"] # single lookup → (D,) >>> batch = store.batch_lookup(ids) # batch lookup → (N, D) **Persistence:** @@ -29,8 +29,9 @@ import json import logging +from collections.abc import Sequence from pathlib import Path -from typing import Literal, Sequence +from typing import Literal import numpy as np @@ -135,7 +136,7 @@ def from_numpy( @classmethod def from_dataframe( cls, - df: "pd.DataFrame", + df: pd.DataFrame, *, path: str | Path, id_col: str = "token", @@ -190,7 +191,7 @@ def from_dict( @classmethod def from_adata( cls, - adata: "ad.AnnData", + adata: ad.AnnData, *, layer_key: str, axis: Literal["obs", "var"] = "obs", @@ -220,18 +221,12 @@ def from_adata( """ if axis == "obs": if layer_key not in adata.obsm: - raise KeyError( - f"Key '{layer_key}' not found in adata.obsm. " - f"Available keys: {list(adata.obsm.keys())}" - ) + raise KeyError(f"Key '{layer_key}' not found in adata.obsm. Available keys: {list(adata.obsm.keys())}") matrix = np.asarray(adata.obsm[layer_key]) ids = adata.obs.index.tolist() elif axis == "var": if layer_key not in adata.varm: - raise KeyError( - f"Key '{layer_key}' not found in adata.varm. " - f"Available keys: {list(adata.varm.keys())}" - ) + raise KeyError(f"Key '{layer_key}' not found in adata.varm. Available keys: {list(adata.varm.keys())}") matrix = np.asarray(adata.varm[layer_key]) ids = adata.var.index.tolist() else: @@ -315,10 +310,7 @@ def __getitem__(self, key: str) -> np.ndarray: try: idx = self._index[key] except KeyError: - raise KeyError( - f"ID '{key}' not found in VectorStore. " - f"Store contains {len(self._index)} entries." - ) from None + raise KeyError(f"ID '{key}' not found in VectorStore. Store contains {len(self._index)} entries.") from None return np.array(self._mmap[idx]) def batch_lookup(self, ids: Sequence[str]) -> np.ndarray: @@ -345,8 +337,7 @@ def batch_lookup(self, ids: Sequence[str]) -> np.ndarray: indices.append(self._index[sid]) except KeyError: raise KeyError( - f"ID '{sid}' not found in VectorStore. " - f"Store contains {len(self._index)} entries." + f"ID '{sid}' not found in VectorStore. Store contains {len(self._index)} entries." ) from None return np.array(self._mmap[indices]) @@ -372,10 +363,7 @@ def __contains__(self, key: str) -> bool: return key in self._index def __repr__(self) -> str: - return ( - f"VectorStore(n={len(self)}, dim={self.dim}, " - f"dtype={self.dtype}, path='{self._path}')" - ) + return f"VectorStore(n={len(self)}, dim={self.dim}, dtype={self.dtype}, path='{self._path}')" # ------------------------------------------------------------------ # Internal helpers @@ -386,19 +374,12 @@ def _validate_ids_and_matrix(ids: list[str], matrix: np.ndarray) -> None: if len(ids) == 0: raise ValueError("Empty ID list: at least one vector is required.") if matrix.ndim != 2: - raise ValueError( - f"Expected 2-D matrix, got {matrix.ndim}-D array with shape {matrix.shape}." - ) + raise ValueError(f"Expected 2-D matrix, got {matrix.ndim}-D array with shape {matrix.shape}.") if len(ids) != matrix.shape[0]: - raise ValueError( - f"Length mismatch: {len(ids)} IDs but matrix has {matrix.shape[0]} rows." - ) + raise ValueError(f"Length mismatch: {len(ids)} IDs but matrix has {matrix.shape[0]} rows.") if len(set(ids)) != len(ids): duplicates = [x for x in ids if ids.count(x) > 1] - raise ValueError( - f"Duplicate IDs found: {sorted(set(duplicates))[:10]}. " - f"All IDs must be unique." - ) + raise ValueError(f"Duplicate IDs found: {sorted(set(duplicates))[:10]}. All IDs must be unique.") @staticmethod def _write_index( diff --git a/src/mmcontext/modules/mmcontext_module.py b/src/mmcontext/modules/mmcontext_module.py index bfcf106..d5b8889 100644 --- a/src/mmcontext/modules/mmcontext_module.py +++ b/src/mmcontext/modules/mmcontext_module.py @@ -25,8 +25,8 @@ { "token_embeddings": Tensor[B, L, D], # per-token representations - "attention_mask": Tensor[B, L], # 1 = real, 0 = pad - "modality_ids": Tensor[B, L], # 0 = text, 1 = omics + "attention_mask": Tensor[B, L], # 1 = real, 0 = pad + "modality_ids": Tensor[B, L], # 0 = text, 1 = omics } Example @@ -238,9 +238,7 @@ def _preprocess_text( encoded["modality"] = "text" return encoded - def _preprocess_omics_via_store( - self, texts: list[str] - ) -> dict[str, torch.Tensor | Any]: + def _preprocess_omics_via_store(self, texts: list[str]) -> dict[str, torch.Tensor | Any]: """Resolve prefixed omics IDs through VectorStore.""" if self._vector_store is None: raise ValueError( @@ -268,9 +266,7 @@ def _preprocess_omics_via_store( "modality": "omics", } - def _preprocess_omics_direct( - self, inputs: list[dict[str, Any]] - ) -> dict[str, torch.Tensor | Any]: + def _preprocess_omics_direct(self, inputs: list[dict[str, Any]]) -> dict[str, torch.Tensor | Any]: """Package direct omics vectors into features dict. Handles both obs (single vector per sample) and var (list of gene @@ -309,7 +305,7 @@ def _preprocess_omics_direct( input_values = torch.zeros(batch_size, max_len, dim) attention_mask = torch.zeros(batch_size, max_len, dtype=torch.long) - for i, (emb, length) in enumerate(zip(all_embeddings, lengths)): + for i, (emb, length) in enumerate(zip(all_embeddings, lengths, strict=False)): input_values[i, :length] = emb attention_mask[i, :length] = 1 @@ -349,9 +345,7 @@ def forward( else: return self._forward_omics(features) - def _forward_text( - self, features: dict[str, torch.Tensor | Any] - ) -> dict[str, torch.Tensor | Any]: + def _forward_text(self, features: dict[str, torch.Tensor | Any]) -> dict[str, torch.Tensor | Any]: """Run text through the transformer encoder.""" input_ids = features["input_ids"] attention_mask = features.get("attention_mask") @@ -365,17 +359,13 @@ def _forward_text( token_embeddings = model_output.last_hidden_state # (B, L, D) B, L = input_ids.shape - modality_ids = torch.full( - (B, L), MODALITY_TEXT, dtype=torch.long, device=input_ids.device - ) + modality_ids = torch.full((B, L), MODALITY_TEXT, dtype=torch.long, device=input_ids.device) features["token_embeddings"] = token_embeddings features["modality_ids"] = modality_ids return features - def _forward_omics( - self, features: dict[str, torch.Tensor | Any] - ) -> dict[str, torch.Tensor | Any]: + def _forward_omics(self, features: dict[str, torch.Tensor | Any]) -> dict[str, torch.Tensor | Any]: """Pass omics embeddings through unchanged. Reads from ``input_values`` (set by preprocess) and writes to @@ -386,9 +376,7 @@ def _forward_omics( token_embeddings = features["input_values"] # (B, L, D) B, L = token_embeddings.shape[:2] - modality_ids = torch.full( - (B, L), MODALITY_OMICS, dtype=torch.long, device=token_embeddings.device - ) + modality_ids = torch.full((B, L), MODALITY_OMICS, dtype=torch.long, device=token_embeddings.device) features["token_embeddings"] = token_embeddings features["modality_ids"] = modality_ids @@ -438,9 +426,7 @@ def _freeze_n_layers(self, num_layers: int) -> None: (``roberta.encoder.layer``) architectures. """ layers = None - if hasattr(self.auto_model, "encoder") and hasattr( - self.auto_model.encoder, "layer" - ): + if hasattr(self.auto_model, "encoder") and hasattr(self.auto_model.encoder, "layer"): layers = self.auto_model.encoder.layer elif hasattr(self.auto_model, "roberta"): layers = self.auto_model.roberta.encoder.layer @@ -448,10 +434,7 @@ def _freeze_n_layers(self, num_layers: int) -> None: layers = self.auto_model.bert.encoder.layer if layers is None: - logger.warning( - "Could not identify encoder layers for partial freezing. " - "Freezing all parameters instead." - ) + logger.warning("Could not identify encoder layers for partial freezing. Freezing all parameters instead.") for param in self.auto_model.parameters(): param.requires_grad = False return diff --git a/src/mmcontext/modules/omics_attention_module.py b/src/mmcontext/modules/omics_attention_module.py index 01fce46..0ad3a2d 100644 --- a/src/mmcontext/modules/omics_attention_module.py +++ b/src/mmcontext/modules/omics_attention_module.py @@ -24,15 +24,15 @@ # Input (from MMContextModule.forward): { "token_embeddings": Tensor[B, L, D], - "attention_mask": Tensor[B, L], - "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad + "attention_mask": Tensor[B, L], + "modality_ids": Tensor[B, L], # 0=text, 1=omics, 2=pad } # Output (after OmicsAttentionModule.forward): { - "token_embeddings": Tensor[B, L, D], # omics tokens attended - "attention_mask": Tensor[B, L], # unchanged - "modality_ids": Tensor[B, L], # unchanged + "token_embeddings": Tensor[B, L, D], # omics tokens attended + "attention_mask": Tensor[B, L], # unchanged + "modality_ids": Tensor[B, L], # unchanged } Example @@ -295,13 +295,9 @@ def load( if os.path.isfile(safetensors_path): load_safetensors_model(module, safetensors_path) elif os.path.isfile(bin_path): - module.load_state_dict( - torch.load(bin_path, map_location=torch.device("cpu")) - ) + module.load_state_dict(torch.load(bin_path, map_location=torch.device("cpu"))) else: - logger.warning( - "No weight files found in %s — module uses random init.", load_path - ) + logger.warning("No weight files found in %s — module uses random init.", load_path) logger.info("Loaded OmicsAttentionModule from %s", model_name_or_path) return module diff --git a/tests/test_adapter_module.py b/tests/test_adapter_module.py index 5db01d3..0c547f7 100644 --- a/tests/test_adapter_module.py +++ b/tests/test_adapter_module.py @@ -38,7 +38,9 @@ def real_safetensors(): function for tests that need actual save/load roundtrips. """ import importlib + import safetensors.torch + importlib.reload(safetensors.torch) yield # The session-scoped patch in conftest will reassert on the next test that needs it @@ -140,9 +142,7 @@ class TestForwardMixedBatch: def test_forward_mixed_batch(self): """Mixed batch: text and omics tokens get different projections.""" # Both modalities have same input dim for simplicity - adapter = AdapterModule( - text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32 - ) + adapter = AdapterModule(text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32) B, L = 1, 4 features = _make_features( token_embeddings=torch.randn(B, L, 16), @@ -190,9 +190,7 @@ def test_separate_weights(self, adapter): def test_text_omics_produce_different_outputs(self): """Same input through text vs omics projection gives different results.""" - adapter = AdapterModule( - text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32 - ) + adapter = AdapterModule(text_input_dim=16, omics_input_dim=16, shared_dim=8, hidden_dim=32) x = torch.randn(1, 3, 16) text_features = _make_features( @@ -210,9 +208,7 @@ def test_text_omics_produce_different_outputs(self): omics_result = adapter(omics_features) # Different projections should produce different outputs (with overwhelming probability) - assert not torch.allclose( - text_result["token_embeddings"], omics_result["token_embeddings"] - ) + assert not torch.allclose(text_result["token_embeddings"], omics_result["token_embeddings"]) # --------------------------------------------------------------------------- @@ -286,14 +282,8 @@ def test_weights_update(self, adapter): # Both projections should have changed (check total param delta, # not per-parameter allclose, since some biases may get tiny gradients) - text_delta = sum( - (p - text_before[n]).abs().sum().item() - for n, p in adapter.text_proj.named_parameters() - ) - omics_delta = sum( - (p - omics_before[n]).abs().sum().item() - for n, p in adapter.omics_proj.named_parameters() - ) + text_delta = sum((p - text_before[n]).abs().sum().item() for n, p in adapter.text_proj.named_parameters()) + omics_delta = sum((p - omics_before[n]).abs().sum().item() for n, p in adapter.omics_proj.named_parameters()) assert text_delta > 0, "text_proj parameters did not change" assert omics_delta > 0, "omics_proj parameters did not change" @@ -346,9 +336,7 @@ def test_preserves_modality_ids(self, adapter): modality_ids=mod_ids, ) # Need same input dim for this test - adapter2 = AdapterModule( - text_input_dim=32, omics_input_dim=32, shared_dim=16, hidden_dim=64 - ) + adapter2 = AdapterModule(text_input_dim=32, omics_input_dim=32, shared_dim=16, hidden_dim=64) result = adapter2(features) torch.testing.assert_close(result["modality_ids"], mod_ids) diff --git a/tests/test_io/test_vector_store.py b/tests/test_io/test_vector_store.py index a71d519..f8f5275 100644 --- a/tests/test_io/test_vector_store.py +++ b/tests/test_io/test_vector_store.py @@ -110,18 +110,14 @@ def test_from_dataframe_custom_columns(self, sample_matrix, sample_ids, tmp_dir) } ) path = os.path.join(tmp_dir, "test.mmap") - store = VectorStore.from_dataframe( - df, path=path, id_col="my_id", embedding_col="my_vec" - ) + store = VectorStore.from_dataframe(df, path=path, id_col="my_id", embedding_col="my_vec") result = store["cell_A"] np.testing.assert_array_almost_equal(result, sample_matrix[0]) def test_from_adata_obs(self, sample_adata_obs, tmp_dir): """Create from adata.obsm, lookup by obs index.""" path = os.path.join(tmp_dir, "test.mmap") - store = VectorStore.from_adata( - sample_adata_obs, layer_key="X_scvi", axis="obs", path=path - ) + store = VectorStore.from_adata(sample_adata_obs, layer_key="X_scvi", axis="obs", path=path) expected = sample_adata_obs.obsm["X_scvi"] for i, sid in enumerate(sample_adata_obs.obs.index): @@ -131,9 +127,7 @@ def test_from_adata_obs(self, sample_adata_obs, tmp_dir): def test_from_adata_var(self, sample_adata_var, tmp_dir): """Create from adata.varm, lookup by var index.""" path = os.path.join(tmp_dir, "test.mmap") - store = VectorStore.from_adata( - sample_adata_var, layer_key="gene_emb", axis="var", path=path - ) + store = VectorStore.from_adata(sample_adata_var, layer_key="gene_emb", axis="var", path=path) expected = sample_adata_var.varm["gene_emb"] for i, gid in enumerate(sample_adata_var.var.index): diff --git a/tests/test_omics_attention_module.py b/tests/test_omics_attention_module.py index e9319d9..ebb5212 100644 --- a/tests/test_omics_attention_module.py +++ b/tests/test_omics_attention_module.py @@ -33,7 +33,9 @@ def tmp_dir(): def real_safetensors(): """Undo the global safetensors.torch.load_model patch for persistence tests.""" import importlib + import safetensors.torch + importlib.reload(safetensors.torch) yield @@ -107,9 +109,7 @@ def test_text_passthrough_in_mixed_batch(self, module): result = module(features) # Text tokens (first 3) should be unchanged - torch.testing.assert_close( - result["token_embeddings"][:, :3, :], x[:, :3, :] - ) + torch.testing.assert_close(result["token_embeddings"][:, :3, :], x[:, :3, :]) # --------------------------------------------------------------------------- @@ -131,9 +131,9 @@ def test_omics_transformed(self, module): result = module(features) # Output should differ from input (attention mixes information) - assert not torch.allclose( - result["token_embeddings"], x, atol=1e-6 - ), "Omics tokens should be transformed by self-attention" + assert not torch.allclose(result["token_embeddings"], x, atol=1e-6), ( + "Omics tokens should be transformed by self-attention" + ) def test_omics_transformed_in_mixed_batch(self, module): """Omics tokens in a mixed batch are modified.""" @@ -149,9 +149,9 @@ def test_omics_transformed_in_mixed_batch(self, module): result = module(features) # Omics tokens (last 3) should be modified - assert not torch.allclose( - result["token_embeddings"][:, 3:, :], x[:, 3:, :], atol=1e-6 - ), "Omics tokens should be transformed by self-attention" + assert not torch.allclose(result["token_embeddings"][:, 3:, :], x[:, 3:, :], atol=1e-6), ( + "Omics tokens should be transformed by self-attention" + ) # --------------------------------------------------------------------------- @@ -212,14 +212,20 @@ def test_variable_length_sequences(self, module): # Sample 0: 3 omics tokens + 2 pad # Sample 1: 5 omics tokens + 0 pad - modality_ids = torch.tensor([ - [1, 1, 1, 2, 2], - [1, 1, 1, 1, 1], - ], dtype=torch.long) - attention_mask = torch.tensor([ - [1, 1, 1, 0, 0], - [1, 1, 1, 1, 1], - ], dtype=torch.long) + modality_ids = torch.tensor( + [ + [1, 1, 1, 2, 2], + [1, 1, 1, 1, 1], + ], + dtype=torch.long, + ) + attention_mask = torch.tensor( + [ + [1, 1, 1, 0, 0], + [1, 1, 1, 1, 1], + ], + dtype=torch.long, + ) features = _make_features( token_embeddings=x.clone(), @@ -233,8 +239,9 @@ def test_variable_length_sequences(self, module): # Pad positions should remain zero (or unchanged) # The module should not produce non-zero values for pad positions - assert torch.all(result["token_embeddings"][0, 3:, :] == 0) or \ - torch.allclose(result["token_embeddings"][0, 3:, :], x[0, 3:, :]) + assert torch.all(result["token_embeddings"][0, 3:, :] == 0) or torch.allclose( + result["token_embeddings"][0, 3:, :], x[0, 3:, :] + ) # --------------------------------------------------------------------------- @@ -351,10 +358,7 @@ def test_weights_update(self, module): loss.backward() optimizer.step() - total_delta = sum( - (p - params_before[n]).abs().sum().item() - for n, p in module.named_parameters() - ) + total_delta = sum((p - params_before[n]).abs().sum().item() for n, p in module.named_parameters()) assert total_delta > 0, "Parameters did not change after optimizer step" @@ -455,4 +459,4 @@ def test_repr(self, module): """repr contains key config info.""" r = repr(module) assert "16" in r # input_dim - assert "2" in r # num_heads + assert "2" in r # num_heads diff --git a/tests/test_prepare_store.py b/tests/test_prepare_store.py index e414cc2..82f1cf4 100644 --- a/tests/test_prepare_store.py +++ b/tests/test_prepare_store.py @@ -142,6 +142,7 @@ def test_no_obsm_group_raises(self, tmp_dir): class TestOpenZarr: def test_open_directory(self, synthetic_zarr): from pathlib import Path + zarr_path, *_ = synthetic_zarr root = _open_zarr(Path(zarr_path)) assert "obs" in root @@ -149,6 +150,7 @@ def test_open_directory(self, synthetic_zarr): def test_nonexistent_raises(self, tmp_dir): from pathlib import Path + with pytest.raises(FileNotFoundError): _open_zarr(Path(tmp_dir) / "nope.zarr") @@ -166,10 +168,12 @@ def test_builds_store_from_local_zarr(self, synthetic_zarr, tmp_dir): zarr_path, obs_names, n_obs, d_pca, _ = synthetic_zarr # Simulate a dataset where all samples come from one zarr file - ds = Dataset.from_dict({ - "sample_idx": obs_names[:5], # use first 5 - "adata_link": [zarr_path] * 5, - }) + ds = Dataset.from_dict( + { + "sample_idx": obs_names[:5], # use first 5 + "adata_link": [zarr_path] * 5, + } + ) output_path = os.path.join(tmp_dir, "test_store.mmap") store = prepare_vector_store( @@ -190,10 +194,12 @@ def test_values_match_zarr_source(self, synthetic_zarr, tmp_dir): zarr_path, obs_names, *_ = synthetic_zarr - ds = Dataset.from_dict({ - "sample_idx": [obs_names[3]], - "adata_link": [zarr_path], - }) + ds = Dataset.from_dict( + { + "sample_idx": [obs_names[3]], + "adata_link": [zarr_path], + } + ) output_path = os.path.join(tmp_dir, "val_store.mmap") store = prepare_vector_store(ds, obsm_key="X_pca", output_path=output_path) @@ -209,10 +215,12 @@ def test_missing_sample_id_raises(self, synthetic_zarr, tmp_dir): zarr_path, *_ = synthetic_zarr - ds = Dataset.from_dict({ - "sample_idx": ["nonexistent_cell"], - "adata_link": [zarr_path], - }) + ds = Dataset.from_dict( + { + "sample_idx": ["nonexistent_cell"], + "adata_link": [zarr_path], + } + ) output_path = os.path.join(tmp_dir, "err_store.mmap") with pytest.raises(KeyError, match="nonexistent_cell"): @@ -224,10 +232,12 @@ def test_skips_existing_store(self, synthetic_zarr, tmp_dir): zarr_path, obs_names, *_ = synthetic_zarr - ds = Dataset.from_dict({ - "sample_idx": obs_names[:3], - "adata_link": [zarr_path] * 3, - }) + ds = Dataset.from_dict( + { + "sample_idx": obs_names[:3], + "adata_link": [zarr_path] * 3, + } + ) output_path = os.path.join(tmp_dir, "cached_store.mmap") @@ -244,17 +254,21 @@ def test_different_obsm_keys(self, synthetic_zarr, tmp_dir): zarr_path, obs_names, _, d_pca, d_scvi = synthetic_zarr - ds = Dataset.from_dict({ - "sample_idx": obs_names[:2], - "adata_link": [zarr_path] * 2, - }) + ds = Dataset.from_dict( + { + "sample_idx": obs_names[:2], + "adata_link": [zarr_path] * 2, + } + ) pca_store = prepare_vector_store( - ds, obsm_key="X_pca", + ds, + obsm_key="X_pca", output_path=os.path.join(tmp_dir, "pca.mmap"), ) scvi_store = prepare_vector_store( - ds, obsm_key="X_scvi", + ds, + obsm_key="X_scvi", output_path=os.path.join(tmp_dir, "scvi.mmap"), ) diff --git a/tests/test_st_integration.py b/tests/test_st_integration.py index 1b3f03d..033df43 100644 --- a/tests/test_st_integration.py +++ b/tests/test_st_integration.py @@ -23,7 +23,6 @@ import numpy as np import pytest import torch - from sentence_transformers import SentenceTransformer from sentence_transformers.sentence_transformer.modules import Normalize, Pooling @@ -44,12 +43,13 @@ def tmp_dir(): def real_safetensors(): """Undo the global safetensors.torch.load_model patch for persistence tests.""" import safetensors.torch + importlib.reload(safetensors.torch) yield # -- Shared dims used across all fixtures ----------------------------------- -TEXT_DIM = 32 # matches _TextEncStub hidden_size +TEXT_DIM = 32 # matches _TextEncStub hidden_size OMICS_DIM = 8 SHARED_DIM = 16 @@ -98,24 +98,28 @@ def attention_module(): @pytest.fixture def obs_pipeline(mmcontext_module, adapter_module): """Obs pipeline: MMContext → Adapter → Pooling → Normalize.""" - return SentenceTransformer(modules=[ - mmcontext_module, - adapter_module, - Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), - Normalize(), - ]) + return SentenceTransformer( + modules=[ + mmcontext_module, + adapter_module, + Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), + Normalize(), + ] + ) @pytest.fixture def var_pipeline(mmcontext_module, attention_module, adapter_module): """Var pipeline: MMContext → OmicsAttention → Adapter → Pooling → Normalize.""" - return SentenceTransformer(modules=[ - mmcontext_module, - attention_module, - adapter_module, - Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), - Normalize(), - ]) + return SentenceTransformer( + modules=[ + mmcontext_module, + attention_module, + adapter_module, + Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), + Normalize(), + ] + ) # --------------------------------------------------------------------------- @@ -173,10 +177,7 @@ def test_encode_omics_direct_obs(self, obs_pipeline): def test_encode_omics_direct_var(self, var_pipeline): """Encoding var-level (multiple gene vectors) produces correct shape.""" - genes = [ - np.random.randn(OMICS_DIM).astype(np.float32) - for _ in range(5) - ] + genes = [np.random.randn(OMICS_DIM).astype(np.float32) for _ in range(5)] embedding = var_pipeline.encode([{"omics_values": genes}]) assert isinstance(embedding, np.ndarray) assert embedding.shape == (1, SHARED_DIM) @@ -241,9 +242,7 @@ class TestObsPipeline: def test_obs_text_and_omics_same_output_dim(self, obs_pipeline): """Text and omics inputs produce same dimensionality.""" text_emb = obs_pipeline.encode(["Some text"]) - omics_emb = obs_pipeline.encode([{ - "omics_values": np.random.randn(OMICS_DIM).astype(np.float32) - }]) + omics_emb = obs_pipeline.encode([{"omics_values": np.random.randn(OMICS_DIM).astype(np.float32)}]) assert text_emb.shape[-1] == omics_emb.shape[-1] == SHARED_DIM def test_obs_deterministic(self, obs_pipeline): @@ -267,10 +266,7 @@ def test_var_single_gene(self, var_pipeline): def test_var_multiple_genes(self, var_pipeline): """Multiple gene vectors are attended and pooled.""" - genes = [ - np.random.randn(OMICS_DIM).astype(np.float32) - for _ in range(10) - ] + genes = [np.random.randn(OMICS_DIM).astype(np.float32) for _ in range(10)] embedding = var_pipeline.encode([{"omics_values": genes}]) assert embedding.shape == (1, SHARED_DIM) @@ -349,19 +345,19 @@ class TestPrecisionConversion: def test_precision_conversion_parameters(self, mmcontext_module, adapter_module): """Pipeline modules can be converted to fp16.""" - pipeline = SentenceTransformer(modules=[ - mmcontext_module, - adapter_module, - Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), - Normalize(), - ]) + pipeline = SentenceTransformer( + modules=[ + mmcontext_module, + adapter_module, + Pooling(embedding_dimension=SHARED_DIM, pooling_mode="mean"), + Normalize(), + ] + ) pipeline.half() # All learnable parameters should now be fp16 for name, param in pipeline.named_parameters(): - assert param.dtype == torch.float16, ( - f"Parameter {name} is {param.dtype}, expected float16" - ) + assert param.dtype == torch.float16, f"Parameter {name} is {param.dtype}, expected float16" # Restore to fp32 — the session-scoped _TextEncStub (from conftest) # is shared across all tests; leaving it in fp16 would pollute @@ -399,20 +395,22 @@ def test_training_text_only(self, obs_pipeline, tmp_dir): from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss - ds = Dataset.from_dict({ - "anchor": [ - "A neuron from the thalamus.", - "An epithelial cell from the lung.", - "A B cell from peripheral blood.", - "A fibroblast from skin tissue.", - ], - "positive": [ - "Thalamic neuron expressing SYT1 and GNAS.", - "Lung epithelial cell with high EPCAM.", - "CD19-positive B lymphocyte.", - "Dermal fibroblast with COL1A1 expression.", - ], - }) + ds = Dataset.from_dict( + { + "anchor": [ + "A neuron from the thalamus.", + "An epithelial cell from the lung.", + "A B cell from peripheral blood.", + "A fibroblast from skin tissue.", + ], + "positive": [ + "Thalamic neuron expressing SYT1 and GNAS.", + "Lung epithelial cell with high EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1 expression.", + ], + } + ) loss = MultipleNegativesRankingLoss(obs_pipeline) args = SentenceTransformerTrainingArguments( @@ -437,9 +435,7 @@ def test_training_text_only(self, obs_pipeline, tmp_dir): # At least some parameters should have changed total_delta = sum( - (p - params_before[n]).abs().sum().item() - for n, p in obs_pipeline.named_parameters() - if n in params_before + (p - params_before[n]).abs().sum().item() for n, p in obs_pipeline.named_parameters() if n in params_before ) assert total_delta > 0, "No parameters changed during text-only training" @@ -457,20 +453,22 @@ def test_training_bimodal(self, obs_pipeline, obs_store, tmp_dir): first_module = list(obs_pipeline.children())[0] first_module.set_vector_store(obs_store) - ds = Dataset.from_dict({ - "anchor": [ - "omics:cell_0", - "omics:cell_1", - "omics:cell_2", - "omics:cell_3", - ], - "positive": [ - "Thalamic neuron expressing SYT1.", - "Lung epithelial cell with EPCAM.", - "CD19-positive B lymphocyte.", - "Dermal fibroblast with COL1A1.", - ], - }) + ds = Dataset.from_dict( + { + "anchor": [ + "omics:cell_0", + "omics:cell_1", + "omics:cell_2", + "omics:cell_3", + ], + "positive": [ + "Thalamic neuron expressing SYT1.", + "Lung epithelial cell with EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1.", + ], + } + ) loss = MultipleNegativesRankingLoss(obs_pipeline) args = SentenceTransformerTrainingArguments( @@ -493,9 +491,7 @@ def test_training_bimodal(self, obs_pipeline, obs_store, tmp_dir): trainer.train() total_delta = sum( - (p - params_before[n]).abs().sum().item() - for n, p in obs_pipeline.named_parameters() - if n in params_before + (p - params_before[n]).abs().sum().item() for n, p in obs_pipeline.named_parameters() if n in params_before ) assert total_delta > 0, "No parameters changed during bimodal training" @@ -511,20 +507,22 @@ def test_training_gene_list(self, obs_pipeline, tmp_dir): from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss - ds = Dataset.from_dict({ - "anchor": [ - "MALAT1 MT-CO3 GNAS SYT1 CALM1", - "EPCAM KRT8 KRT18 MUC1", - "CD19 MS4A1 CD79A PAX5", - "COL1A1 COL3A1 FN1 VIM", - ], - "positive": [ - "Thalamic neuron expressing SYT1.", - "Lung epithelial cell with EPCAM.", - "CD19-positive B lymphocyte.", - "Dermal fibroblast with COL1A1.", - ], - }) + ds = Dataset.from_dict( + { + "anchor": [ + "MALAT1 MT-CO3 GNAS SYT1 CALM1", + "EPCAM KRT8 KRT18 MUC1", + "CD19 MS4A1 CD79A PAX5", + "COL1A1 COL3A1 FN1 VIM", + ], + "positive": [ + "Thalamic neuron expressing SYT1.", + "Lung epithelial cell with EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1.", + ], + } + ) loss = MultipleNegativesRankingLoss(obs_pipeline) args = SentenceTransformerTrainingArguments( @@ -547,9 +545,7 @@ def test_training_gene_list(self, obs_pipeline, tmp_dir): trainer.train() total_delta = sum( - (p - params_before[n]).abs().sum().item() - for n, p in obs_pipeline.named_parameters() - if n in params_before + (p - params_before[n]).abs().sum().item() for n, p in obs_pipeline.named_parameters() if n in params_before ) assert total_delta > 0, "No parameters changed during gene-list training" @@ -559,20 +555,22 @@ def test_training_save_load_roundtrip(self, obs_pipeline, tmp_dir, real_safetens from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss - ds = Dataset.from_dict({ - "anchor": [ - "MALAT1 MT-CO3 GNAS SYT1", - "EPCAM KRT8 KRT18 MUC1", - "CD19 MS4A1 CD79A PAX5", - "COL1A1 COL3A1 FN1 VIM", - ], - "positive": [ - "Thalamic neuron expressing SYT1.", - "Lung epithelial cell with EPCAM.", - "CD19-positive B lymphocyte.", - "Dermal fibroblast with COL1A1.", - ], - }) + ds = Dataset.from_dict( + { + "anchor": [ + "MALAT1 MT-CO3 GNAS SYT1", + "EPCAM KRT8 KRT18 MUC1", + "CD19 MS4A1 CD79A PAX5", + "COL1A1 COL3A1 FN1 VIM", + ], + "positive": [ + "Thalamic neuron expressing SYT1.", + "Lung epithelial cell with EPCAM.", + "CD19-positive B lymphocyte.", + "Dermal fibroblast with COL1A1.", + ], + } + ) loss = MultipleNegativesRankingLoss(obs_pipeline) save_path = os.path.join(tmp_dir, "trained_model") From 97bb38a31855201f3dbff8714b3d912d7dc52e20 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Tue, 2 Jun 2026 14:13:44 +0200 Subject: [PATCH 24/67] extend tool use for PR reviews to enable fixing of liniting issues --- .github/workflows/claude-review.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/claude-review.yaml b/.github/workflows/claude-review.yaml index 9c34e01..e01ec64 100644 --- a/.github/workflows/claude-review.yaml +++ b/.github/workflows/claude-review.yaml @@ -40,5 +40,5 @@ jobs: additional_permissions: | actions: read claude_args: | - --allowedTools "Bash(ruff check src/ tests/),Bash(ruff format --check src/ tests/),Bash(pytest -x -q --tb=short)" - --max-turns 20 + --allowedTools "Bash,Read,Write,Edit,Glob,Grep,Task" + --max-turns 30 From 9823a8abb15c787be18e88ccc72051d474e40f8b Mon Sep 17 00:00:00 2001 From: "claude[bot]" <41898282+claude[bot]@users.noreply.github.com> Date: Tue, 2 Jun 2026 12:44:08 +0000 Subject: [PATCH 25/67] fix: resolve all pre-commit ruff errors across 6 files - Add missing docstring to main() in scripts/train_tiny.py (D103) - Add TYPE_CHECKING guard for pd/ad imports in vector_store.py (F821) - Use `list | tuple` union syntax in isinstance call (UP038) - Remove unused variables device, attended_samples, sample_indices in omics_attention_module.py (F841) - Replace generator expressions with set comprehensions in test_adapter_module.py (C401) - Remove unused features_no_prompt variable in test_mmcontext_module.py (F841) Co-authored-by: mengerj --- scripts/train_tiny.py | 1 + src/mmcontext/io/vector_store.py | 6 +++++- src/mmcontext/modules/mmcontext_module.py | 2 +- src/mmcontext/modules/omics_attention_module.py | 4 ---- tests/test_adapter_module.py | 4 ++-- tests/test_mmcontext_module.py | 2 +- 6 files changed, 10 insertions(+), 9 deletions(-) diff --git a/scripts/train_tiny.py b/scripts/train_tiny.py index 36cb07f..0f71407 100644 --- a/scripts/train_tiny.py +++ b/scripts/train_tiny.py @@ -134,6 +134,7 @@ def build_pipeline( # Main # --------------------------------------------------------------------------- def main(): + """Train MMContext on cxg_schaefer_tiny dataset.""" parser = argparse.ArgumentParser(description="Train MMContext on cxg_schaefer_tiny") parser.add_argument( "--mode", diff --git a/src/mmcontext/io/vector_store.py b/src/mmcontext/io/vector_store.py index b0a6f24..50bec01 100644 --- a/src/mmcontext/io/vector_store.py +++ b/src/mmcontext/io/vector_store.py @@ -31,10 +31,14 @@ import logging from collections.abc import Sequence from pathlib import Path -from typing import Literal +from typing import TYPE_CHECKING, Literal import numpy as np +if TYPE_CHECKING: + import anndata as ad + import pandas as pd + logger = logging.getLogger(__name__) diff --git a/src/mmcontext/modules/mmcontext_module.py b/src/mmcontext/modules/mmcontext_module.py index d5b8889..fe8046e 100644 --- a/src/mmcontext/modules/mmcontext_module.py +++ b/src/mmcontext/modules/mmcontext_module.py @@ -282,7 +282,7 @@ def _preprocess_omics_direct(self, inputs: list[dict[str, Any]]) -> dict[str, to # Obs case: single vector → (1, D) all_embeddings.append(torch.from_numpy(values).unsqueeze(0)) lengths.append(1) - elif isinstance(values, (list, tuple)): + elif isinstance(values, list | tuple): # Var case: list of gene vectors → (N_genes, D) gene_tensors = [torch.from_numpy(np.asarray(v)) for v in values] all_embeddings.append(torch.stack(gene_tensors, dim=0)) diff --git a/src/mmcontext/modules/omics_attention_module.py b/src/mmcontext/modules/omics_attention_module.py index 0ad3a2d..8120cbe 100644 --- a/src/mmcontext/modules/omics_attention_module.py +++ b/src/mmcontext/modules/omics_attention_module.py @@ -160,7 +160,6 @@ def forward( attention_mask = features["attention_mask"] B, L, D = token_embeddings.shape - device = token_embeddings.device # Start with a copy of the input output = token_embeddings.clone() @@ -172,9 +171,6 @@ def forward( # Process each sample independently — omics sequences can have # different lengths across samples in the batch - attended_samples = [] - sample_indices = [] - for b in range(B): omics_positions = omics_mask[b].nonzero(as_tuple=True)[0] # positions of omics tokens if len(omics_positions) == 0: diff --git a/tests/test_adapter_module.py b/tests/test_adapter_module.py index 0c547f7..b3dbd5e 100644 --- a/tests/test_adapter_module.py +++ b/tests/test_adapter_module.py @@ -179,8 +179,8 @@ class TestSeparateWeights: def test_separate_weights(self, adapter): """text_proj and omics_proj have independent parameter sets.""" - text_params = set(id(p) for p in adapter.text_proj.parameters()) - omics_params = set(id(p) for p in adapter.omics_proj.parameters()) + text_params = {id(p) for p in adapter.text_proj.parameters()} + omics_params = {id(p) for p in adapter.omics_proj.parameters()} # No overlap assert text_params.isdisjoint(omics_params) diff --git a/tests/test_mmcontext_module.py b/tests/test_mmcontext_module.py index 459d179..a674d5c 100644 --- a/tests/test_mmcontext_module.py +++ b/tests/test_mmcontext_module.py @@ -95,7 +95,7 @@ def test_preprocess_text_tensor_types(self, module): def test_preprocess_text_with_prompt(self, module): """Optional prompt is prepended to text inputs.""" - features_no_prompt = module.preprocess(["Test."]) + module.preprocess(["Test."]) features_with_prompt = module.preprocess(["Test."], prompt="Query: ") # Both should produce valid features; the prompt version processes From ac4504a96349be741ee8c848f591a36a6949875d Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 2 Jun 2026 12:49:15 +0000 Subject: [PATCH 26/67] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../MMContextEncoder_quickstart.ipynb | 2 +- .../MMContextEncoder_training_demo.ipynb | 2 +- .../notebooks/Training_sanity_check.ipynb | 2 +- .../notebooks/load_and_push_model.ipynb | 3 +- .../notebooks/mmcontext_concept_figs.ipynb | 3 +- ...omics_sentence_transformer_loss_demo.ipynb | 2 +- ...ics_sentence_transformer_loss_report.ipynb | 2 +- .../notebooks/text_only_concept_figs.ipynb | 3 +- tutorials/train_new.ipynb | 1013 ++++++++--------- 9 files changed, 514 insertions(+), 518 deletions(-) diff --git a/exploratory/notebooks/MMContextEncoder_quickstart.ipynb b/exploratory/notebooks/MMContextEncoder_quickstart.ipynb index a5d83eb..013b8be 100644 --- a/exploratory/notebooks/MMContextEncoder_quickstart.ipynb +++ b/exploratory/notebooks/MMContextEncoder_quickstart.ipynb @@ -109,9 +109,9 @@ "import numpy as np\n", "import pandas as pd\n", "import torch\n", + "from mmcontext.mmcontextencoder import MMContextEncoder\n", "from sentence_transformers import SentenceTransformer\n", "\n", - "from mmcontext.mmcontextencoder import MMContextEncoder\n", "from mmcontext.simulator import OmicsCaptionSimulator\n", "\n", "sim = OmicsCaptionSimulator(n_samples=100, n_genes=10).simulate()\n", diff --git a/exploratory/notebooks/MMContextEncoder_training_demo.ipynb b/exploratory/notebooks/MMContextEncoder_training_demo.ipynb index 6f4342f..3096ebf 100644 --- a/exploratory/notebooks/MMContextEncoder_training_demo.ipynb +++ b/exploratory/notebooks/MMContextEncoder_training_demo.ipynb @@ -108,9 +108,9 @@ "import pandas as pd\n", "import torch\n", "from datasets import DatasetDict\n", + "from mmcontext.mmcontextencoder import MMContextEncoder\n", "from sentence_transformers import SentenceTransformer\n", "\n", - "from mmcontext.mmcontextencoder import MMContextEncoder\n", "from mmcontext.simulator import OmicsCaptionSimulator\n", "\n", "# simulate tiny dataset\n", diff --git a/exploratory/notebooks/Training_sanity_check.ipynb b/exploratory/notebooks/Training_sanity_check.ipynb index a9ae7ee..cfa67f9 100644 --- a/exploratory/notebooks/Training_sanity_check.ipynb +++ b/exploratory/notebooks/Training_sanity_check.ipynb @@ -72,8 +72,8 @@ ], "source": [ "import numpy as np\n", - "\n", "from mmcontext.mmcontextencoder import MMContextEncoder\n", + "\n", "from mmcontext.sanity_helpers import cluster_variances, plot_pca, stack_embeddings\n", "from mmcontext.simulator import OmicsCaptionSimulator, make_cluster_sampler\n", "\n", diff --git a/exploratory/notebooks/load_and_push_model.ipynb b/exploratory/notebooks/load_and_push_model.ipynb index 92bed6d..623d355 100644 --- a/exploratory/notebooks/load_and_push_model.ipynb +++ b/exploratory/notebooks/load_and_push_model.ipynb @@ -106,9 +106,8 @@ } ], "source": [ - "from sentence_transformers import SentenceTransformer\n", - "\n", "from mmcontext.mmcontextencoder import MMContextEncoder\n", + "from sentence_transformers import SentenceTransformer\n", "\n", "model_name = \"poster_models/scvi_model_ct_2048\"\n", "model_path = f\"../../{model_name}/0_MMContextEncoder\" # Point to the encoder subdirectory\n", diff --git a/exploratory/notebooks/mmcontext_concept_figs.ipynb b/exploratory/notebooks/mmcontext_concept_figs.ipynb index 8d1f8cf..9881a80 100644 --- a/exploratory/notebooks/mmcontext_concept_figs.ipynb +++ b/exploratory/notebooks/mmcontext_concept_figs.ipynb @@ -48,9 +48,8 @@ "metadata": {}, "outputs": [], "source": [ - "from sentence_transformers import SentenceTransformer\n", - "\n", "from mmcontext.mmcontextencoder import MMContextEncoder\n", + "from sentence_transformers import SentenceTransformer\n", "\n", "enc = MMContextEncoder(\n", " \"NeuML/pubmedbert-base-embeddings\",\n", diff --git a/exploratory/notebooks/omics_sentence_transformer_loss_demo.ipynb b/exploratory/notebooks/omics_sentence_transformer_loss_demo.ipynb index 33e8be6..0260d28 100644 --- a/exploratory/notebooks/omics_sentence_transformer_loss_demo.ipynb +++ b/exploratory/notebooks/omics_sentence_transformer_loss_demo.ipynb @@ -32,6 +32,7 @@ "from pathlib import Path\n", "\n", "import numpy as np\n", + "from mmcontext.mmcontextencoder import MMContextEncoder\n", "from sentence_transformers import (\n", " SentenceTransformer,\n", " SentenceTransformerTrainer,\n", @@ -40,7 +41,6 @@ ")\n", "from tqdm.autonotebook import tqdm\n", "\n", - "from mmcontext.mmcontextencoder import MMContextEncoder\n", "from mmcontext.sanity_helpers import cluster_variances, plot_pca, stack_embeddings\n", "from mmcontext.simulator import OmicsCaptionSimulator, make_cluster_sampler\n", "\n", diff --git a/exploratory/notebooks/omics_sentence_transformer_loss_report.ipynb b/exploratory/notebooks/omics_sentence_transformer_loss_report.ipynb index b4974ee..ef2f54b 100644 --- a/exploratory/notebooks/omics_sentence_transformer_loss_report.ipynb +++ b/exploratory/notebooks/omics_sentence_transformer_loss_report.ipynb @@ -69,6 +69,7 @@ "import numpy as np\n", "import pandas as pd\n", "import torch\n", + "from mmcontext.mmcontextencoder import MMContextEncoder\n", "from sentence_transformers import (\n", " SentenceTransformer,\n", " SentenceTransformerTrainer,\n", @@ -77,7 +78,6 @@ ")\n", "from tqdm.autonotebook import tqdm\n", "\n", - "from mmcontext.mmcontextencoder import MMContextEncoder\n", "from mmcontext.sanity_helpers import cluster_variances, plot_pca, stack_embeddings # provided separately\n", "from mmcontext.simulator import OmicsCaptionSimulator, make_cluster_sampler\n", "\n", diff --git a/exploratory/notebooks/text_only_concept_figs.ipynb b/exploratory/notebooks/text_only_concept_figs.ipynb index 095a841..582bf86 100644 --- a/exploratory/notebooks/text_only_concept_figs.ipynb +++ b/exploratory/notebooks/text_only_concept_figs.ipynb @@ -78,9 +78,8 @@ } ], "source": [ - "from sentence_transformers import SentenceTransformer\n", - "\n", "from mmcontext.mmcontextencoder import MMContextEncoder\n", + "from sentence_transformers import SentenceTransformer\n", "\n", "enc = MMContextEncoder(\n", " \"NeuML/pubmedbert-base-embeddings\",\n", diff --git a/tutorials/train_new.ipynb b/tutorials/train_new.ipynb index b876bc4..029649e 100644 --- a/tutorials/train_new.ipynb +++ b/tutorials/train_new.ipynb @@ -1,510 +1,509 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "641d7ea1", - "metadata": {}, - "source": [ - "## Train a new mmcontext model (demonstrated on proteomics data)\n", - "\n", - "This tutorial walks through the core idea of **mmcontext**: aligning a numerical representation of a sample (e.g. a vector derived from omics data) with its **biological context** (metadata such as tissue, disease, cell type, perturbations, etc.).\n", - "\n", - "At a high level:\n", - "- We start from a single-cell proteomics dataset.\n", - "- For each cell, we build a **numerical representation** (\"data embedding\") and a **textual description** summarising its biological context.\n", - "- We then train an `MMContextEncoder` so that samples with similar biology end up close in the shared embedding space, even across different data modalities.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11c910e0", - "metadata": {}, - "outputs": [], - "source": [ - "from mmcontext.utils import setup_logging\n", - "\n", - "setup_logging()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6eccf0fc", - "metadata": {}, - "outputs": [], - "source": [ - "import anndata as ad\n", - "\n", - "from mmcontext.file_utils import download_file_from_share_link\n", - "\n", - "# a protein dataset from figshare (https://plus.figshare.com/articles/dataset/scPerturb_Single-Cell_Perturbation_Data_RNA_and_protein_h5ad_files/24160713)\n", - "# Note: use ndownloader.figshare.com (not plus.figshare.com) for programmatic downloads - plus.figshare.com is behind WAF that blocks automated requests\n", - "data_link = \"https://ndownloader.figshare.com/files/42428325\"\n", - "local_path = \"Frangiehlzar2021_protein.h5ad\"\n", - "# download the data\n", - "download_file_from_share_link(share_link=data_link, save_path=local_path)\n", - "# load the data\n", - "adata = ad.read_h5ad(local_path)" - ] - }, - { - "cell_type": "markdown", - "id": "72302131", - "metadata": {}, - "source": [ - "### Step 1: Describe each cell in words\n", - "\n", - "The general idea of the framework is to **link biology-rich text to data-rich numeric vectors**:\n", - "\n", - "- The **biological context** may include perturbations, tissue, disease status, cell type and other metadata.\n", - "- The **numerical representation** should summarise the measured data for that sample (here: protein expression; in other settings: scRNA-seq, ATAC, etc.).\n", - "\n", - "In this simple example we construct a short textual description for each cell using columns from `adata.obs`. For real applications you might want much richer descriptions (e.g. including time points, doses, quality metrics, etc.), but the mechanism is the same." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "95f28a07", - "metadata": {}, - "outputs": [], - "source": [ - "# Create a description string for each cell by looping over the rows in adata.obs\n", - "# This text will serve as the \"biological context\" that we want to align with the numeric representation.\n", - "def make_description(row):\n", - " \"\"\"Make a quick description of a cell based on its metadata\"\"\"\n", - " return (\n", - " f\"The first perturbation is {row['perturbation']} \"\n", - " f\"and the second perturbation is {row['perturbation_2']}.\"\n", - " f\" The tissue is {row['tissue_type']} and it has cancer yes or no: {row['cancer']}.\"\n", - " f\" The disease is {row['disease']}.\"\n", - " f\" The celltype is {row['celltype']}.\"\n", - " )\n", - "\n", - "\n", - "# Add a \"description\" column to adata.obs using the function above\n", - "adata.obs[\"description\"] = adata.obs.apply(make_description, axis=1)\n", - "# Also add a sample index column that uniquely identifies each cell; this will be used as a token later\n", - "adata.obs[\"sample_idx\"] = adata.obs.index" - ] - }, - { - "cell_type": "markdown", - "id": "8c513a34", - "metadata": {}, - "source": [ - "### Step 2: Choose a numerical representation\n", - "\n", - "In this proteomics example, the assay only measures **24 proteins per cell**, so we can directly use the normalised expression vector as the numeric representation:\n", - "\n", - "- Each cell gets a 24-dimensional vector from `adata.X`.\n", - "- We store this in `adata.obsm[\"X_prot\"]` and later tell the model to use this key.\n", - "\n", - "For single-cell RNA datasets, mmcontext is more flexible:\n", - "\n", - "- You can plug in **precomputed representations** such as\n", - " - Principal components (PCA),\n", - " - Latent variables from **scVI**,\n", - " - Embeddings from **Geneformer** or other pretrained models.\n", - "- In the publication, we explore exactly these kinds of pretrained embeddings as the numeric representation `X_*` for each cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d999832", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "# let's split by train and val, randomly 80% train\n", - "adata.obs[\"split\"] = np.random.rand(len(adata)) < 0.8\n", - "adata_train = adata[adata.obs[\"split\"]].copy()\n", - "adata_val = adata[~adata.obs[\"split\"]].copy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8de1a9c1", - "metadata": {}, - "outputs": [], - "source": [ - "adata_val.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d68558ad", - "metadata": {}, - "outputs": [], - "source": [ - "adata_train.shape" - ] - }, - { - "cell_type": "markdown", - "id": "7cbfa52a", - "metadata": {}, - "source": [ - "### Step 2b: Split into train/validation and preprocess\n", - "\n", - "Before building embeddings, we:\n", - "\n", - "- Randomly split cells into an **80% train** and **20% validation** subset using a boolean mask in `adata.obs[\"split\"]`.\n", - "- Apply standard **normalisation and log-transformation** with Scanpy (`sc.pp.normalize_total` + `sc.pp.log1p`).\n", - "\n", - "These steps ensure that the numeric representation we later feed into the model is on a comparable scale across cells, and that we can monitor generalisation on a held-out validation set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ffb5747", - "metadata": {}, - "outputs": [], - "source": [ - "import scanpy as sc\n", - "\n", - "# normalise and log transform the data\n", - "sc.pp.normalize_total(adata_train, inplace=True)\n", - "sc.pp.log1p(adata_train)\n", - "\n", - "sc.pp.normalize_total(adata_val, inplace=True)\n", - "sc.pp.log1p(adata_val)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b699d36c", - "metadata": {}, - "outputs": [], - "source": [ - "# since these datasets only contain 24 proteins, we will just use their expression as embeddings\n", - "# we will use the protein names as the embedding keys\n", - "adata_train.obsm[\"X_prot\"] = adata_train.X\n", - "adata_val.obsm[\"X_prot\"] = adata_val.X\n", - "processed_paths = {\"train\": \"Frangiehlzar2021_protein_pp_train.h5ad\", \"val\": \"Frangiehlzar2021_protein_pp_val.h5ad\"}\n", - "adata_train.write_h5ad(processed_paths[\"train\"])\n", - "adata_val.write_h5ad(processed_paths[\"val\"])" - ] - }, - { - "cell_type": "markdown", - "id": "09af0ef7", - "metadata": {}, - "source": [ - "### Step 3: Construct a contrastive training dataset\n", - "\n", - "So far we have:\n", - "- A **numeric representation** for each cell (stored in `adata.obsm[\"X_prot\"]`).\n", - "- A **text description** for each cell in `adata.obs[\"description\"]`.\n", - "\n", - "Next, we convert these into a Hugging Face `DatasetDict` suitable for contrastive training:\n", - "\n", - "- `AnnDataSetConstructor` in `\"multiplets\"` mode creates, for each cell, a\n", - " - **query token** (`sample_idx` / later `cell_sentence_1`),\n", - " - a **matched positive**, and\n", - " - at least one **negative**.\n", - "- This gives us a flexible format where the same dataset can later support different training regimens (e.g. using text-only, numeric-only, or mixed representations), simply by choosing which columns to feed into the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2bd012cb", - "metadata": {}, - "outputs": [], - "source": [ - "from adata_hf_datasets import AnnDataSetConstructor\n", - "from datasets import DatasetDict\n", - "\n", - "ds_dict = DatasetDict()\n", - "# multiplets format for training datasets (with descriptions)\n", - "# A sentence key refers to the column in adata.obs that is used to represent the sample.\n", - "# For numeric data, we use the sample index, and later register the created embedding linked to their indices in the tokenizer\n", - "for split_name, adata_split in {\"train\": adata_train, \"val\": adata_val}.items():\n", - " constructor = AnnDataSetConstructor(dataset_format=\"multiplets\", resolve_negatives=True)\n", - " constructor.add_anndata(\n", - " adata_split,\n", - " caption_key=\"description\",\n", - " sentence_keys=[\"sample_idx\"],\n", - " adata_link=processed_paths[split_name],\n", - " batch_key=\"library_preparation_protocol\", # In this case all are from the same batch, but providing a batch key can improve batch integration by negative sampling\n", - " )\n", - " ds = constructor.get_dataset()\n", - " ds_dict[split_name] = ds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e4d531a", - "metadata": {}, - "outputs": [], - "source": [ - "ds_dict" - ] - }, - { - "cell_type": "markdown", - "id": "70612269", - "metadata": {}, - "source": [ - "## Configure the Model\n", - "\n", - "The `MMContextEncoder` is the **main model** in this framework. Conceptually, it is a custom Sentence Transformers module that:\n", - "\n", - "- Starts from a standard **text encoder** (here: `sentence-transformers/all-MiniLM-L6-v2`).\n", - "- Adds **adapters** that map\n", - " - the text encoder output into a desired embedding dimension, and\n", - " - the numeric representation (e.g. `X_prot`, PCA, scVI, Geneformer embeddings) into the **same** space.\n", - "- Lets you configure whether the text encoder is **frozen**, partially unfrozen (last *n* layers), or fully trainable.\n", - "\n", - "To make the numeric data usable by the model, we need to **register initial embeddings**:\n", - "\n", - "- Tokens in `cell_sentence_1` (e.g. `\"sample_idx:123\"`) should map to a vector from our numeric representation.\n", - "- Internally, the model builds a lookup table (`sample_idx` → integer id) and an embedding matrix (id → numeric vector), which can be kept frozen or made trainable.\n", - "\n", - "After this setup, training looks like standard Sentence Transformers training, but every sample has **both** a text description and a numeric embedding living in a shared space." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f04ed8b", - "metadata": {}, - "outputs": [], - "source": [ - "from sentence_transformers import SentenceTransformer\n", - "\n", - "from mmcontext.mmcontextencoder import MMContextEncoder\n", - "\n", - "enc = MMContextEncoder(\n", - " text_encoder_name=\"sentence-transformers/all-MiniLM-L6-v2\",\n", - " adapter_hidden_dim=128,\n", - " adapter_output_dim=64,\n", - " freeze_text_encoder=True,\n", - " unfreeze_last_n_layers=2,\n", - " output_token_embeddings=False,\n", - " train_lookup=False,\n", - " joint_adapter_hidden_dim=None,\n", - " text_model_kwargs=None,\n", - " use_text_adapter=True,\n", - ")\n", - "model = SentenceTransformer(modules=[enc])" - ] - }, - { - "cell_type": "markdown", - "id": "d0926c15", - "metadata": {}, - "source": [ - "### Register the initial numeric embeddings\n", - "\n", - "We now connect the numeric representations on disk to the model:\n", - "\n", - "- `get_initial_embeddings_from_adata_link` reads the preprocessed `.h5ad` files and builds a table with one row per `sample_idx` and its embedding from `X_prot`.\n", - "- `register_initial_embeddings` turns this table into an internal embedding matrix and lookup table inside the `MMContextEncoder`.\n", - "\n", - "After this step, whenever the model sees a token like `\"sample_idx:123\"` in the dataset, it can look up the correct numeric vector and combine it with the text pathway." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee9a6e96", - "metadata": {}, - "outputs": [], - "source": [ - "token_df, _ = model[0].get_initial_embeddings_from_adata_link(\n", - " ds_dict,\n", - " layer_key=\"X_prot\",\n", - " download_dir=\"data_cache\",\n", - " axis=\"obs\", # since we get embeddings from adata.obsm. We could also use \"varm\" and for example use an embedding for each protein\n", - ")\n", - "model[0].register_initial_embeddings(token_df, data_origin=\"prot\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6975ab72", - "metadata": {}, - "outputs": [], - "source": [ - "# the model expects a certain prefix on the cell tokens.\n", - "model[0].processor.prefix" - ] - }, - { - "cell_type": "markdown", - "id": "52d74f63", - "metadata": {}, - "source": [ - "### Prepare token prefixes and final training columns\n", - "\n", - "At this point, the model knows about all numeric embeddings, but the dataset still uses the plain `sample_idx` strings.\n", - "\n", - "- `model[0].processor.prefix` defines the required prefix for these tokens (e.g. `\"sample_idx:\"`).\n", - "- `prefix_ds` rewrites the `cell_sentence_1` column so that each entry matches what the tokenizer and lookup table expect.\n", - "\n", - "Finally, we simplify the dataset to the three columns we need for contrastive learning:\n", - "\n", - "- `anchor`: the main cell token (formerly `cell_sentence_1`).\n", - "- `positive`: a biologically matched partner.\n", - "- `negative_1`: a contrastive negative.\n", - "\n", - "This might look a bit cumbersome, but it makes the dataset **reusable**: you can easily construct different views (text-only, numeric-only, different negatives) without having to rebuild everything from scratch." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6fc91911", - "metadata": {}, - "outputs": [], - "source": [ - "# you could add this manually or use the method below\n", - "model[0].prefix_ds(ds_dict, columns_to_prefix=[\"cell_sentence_1\"])" - ] - }, - { - "cell_type": "markdown", - "id": "c09bac80", - "metadata": {}, - "source": [ - "## Training setup and objective\n", - "\n", - "With the dataset and model ready, we now configure the training loop using the standard Sentence Transformers API:\n", - "\n", - "- `SentenceTransformerTrainingArguments` controls the **optimisation hyperparameters** (epochs, batch size, learning rate, warmup, logging and checkpointing frequency).\n", - "- `SentenceTransformerTrainer` automatically picks an appropriate device (CPU, CUDA GPU, or Apple **MPS**) and handles mixed-precision flags (`fp16`/`bf16`) where supported.\n", - "- We use `MultipleNegativesRankingLoss`, which encourages anchors to be close to their positives and far from all other samples in the batch.\n", - "- `TripletEvaluator` periodically evaluates the model on the validation set using triplets `(anchor, positive, negative)` and reports an accuracy metric.\n", - "\n", - "Running `trainer.train()` then performs the actual optimisation and will log progress and evaluation metrics during training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f63b0e4b", - "metadata": {}, - "outputs": [], - "source": [ - "# lastly we have to drop some columns from the dataset and\n", - "# rename the main column to \"anchor\".\n", - "# you might think that this is a bit cumbersome, which it is.\n", - "# But this setup allowed for fleixble training,\n", - "# using either cell or feature level tokens, using text based cell sentences\n", - "# or numeric embeddings and resolving negatives\n", - "# to whatever column was chosen for training.\n", - "# That means that for a certain training run, the same dataset can be reused, and\n", - "# only modified differently. But in the end, it is a bit of work to set up.\n", - "ds_final = ds_dict.rename_column(\"cell_sentence_1\", \"anchor\")\n", - "ds_final = ds_final.remove_columns([\"sample_idx\", \"adata_link\", \"negative_1_idx\"])\n", - "ds_final" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "25c0a4e9", - "metadata": {}, - "outputs": [], - "source": [ - "from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments\n", - "\n", - "args = SentenceTransformerTrainingArguments(\n", - " num_train_epochs=1,\n", - " per_device_train_batch_size=128,\n", - " per_device_eval_batch_size=128,\n", - " learning_rate=1e-5,\n", - " warmup_ratio=0.1,\n", - " fp16=False,\n", - " bf16=False,\n", - " eval_strategy=\"steps\",\n", - " eval_steps=100,\n", - " save_strategy=\"steps\",\n", - " save_steps=100,\n", - " save_total_limit=1,\n", - " max_grad_norm=1.0,\n", - " logging_steps=10,\n", - " run_name=\"protein_test\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d44a49b5", - "metadata": {}, - "outputs": [], - "source": [ - "from sentence_transformers.evaluation import TripletEvaluator\n", - "from sentence_transformers.losses import MultipleNegativesRankingLoss\n", - "\n", - "loss = MultipleNegativesRankingLoss(model)\n", - "evaluator = TripletEvaluator(\n", - " anchors=ds_final[\"val\"][\"anchor\"],\n", - " positives=ds_final[\"val\"][\"positive\"],\n", - " negatives=ds_final[\"val\"][\"negative_1\"],\n", - " name=\"val\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53d32063", - "metadata": {}, - "outputs": [], - "source": [ - "trainer = SentenceTransformerTrainer(\n", - " model=model,\n", - " args=args,\n", - " train_dataset=ds_final[\"train\"],\n", - " eval_dataset=ds_final[\"val\"],\n", - " loss=loss,\n", - " evaluator=evaluator,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84f83d70", - "metadata": {}, - "outputs": [], - "source": [ - "trainer.train()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.14" - } + "cells": [ + { + "cell_type": "markdown", + "id": "641d7ea1", + "metadata": {}, + "source": [ + "## Train a new mmcontext model (demonstrated on proteomics data)\n", + "\n", + "This tutorial walks through the core idea of **mmcontext**: aligning a numerical representation of a sample (e.g. a vector derived from omics data) with its **biological context** (metadata such as tissue, disease, cell type, perturbations, etc.).\n", + "\n", + "At a high level:\n", + "- We start from a single-cell proteomics dataset.\n", + "- For each cell, we build a **numerical representation** (\"data embedding\") and a **textual description** summarising its biological context.\n", + "- We then train an `MMContextEncoder` so that samples with similar biology end up close in the shared embedding space, even across different data modalities.\n" + ] }, - "nbformat": 4, - "nbformat_minor": 5 + { + "cell_type": "code", + "execution_count": null, + "id": "11c910e0", + "metadata": {}, + "outputs": [], + "source": [ + "from mmcontext.utils import setup_logging\n", + "\n", + "setup_logging()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6eccf0fc", + "metadata": {}, + "outputs": [], + "source": [ + "import anndata as ad\n", + "\n", + "from mmcontext.file_utils import download_file_from_share_link\n", + "\n", + "# a protein dataset from figshare (https://plus.figshare.com/articles/dataset/scPerturb_Single-Cell_Perturbation_Data_RNA_and_protein_h5ad_files/24160713)\n", + "# Note: use ndownloader.figshare.com (not plus.figshare.com) for programmatic downloads - plus.figshare.com is behind WAF that blocks automated requests\n", + "data_link = \"https://ndownloader.figshare.com/files/42428325\"\n", + "local_path = \"Frangiehlzar2021_protein.h5ad\"\n", + "# download the data\n", + "download_file_from_share_link(share_link=data_link, save_path=local_path)\n", + "# load the data\n", + "adata = ad.read_h5ad(local_path)" + ] + }, + { + "cell_type": "markdown", + "id": "72302131", + "metadata": {}, + "source": [ + "### Step 1: Describe each cell in words\n", + "\n", + "The general idea of the framework is to **link biology-rich text to data-rich numeric vectors**:\n", + "\n", + "- The **biological context** may include perturbations, tissue, disease status, cell type and other metadata.\n", + "- The **numerical representation** should summarise the measured data for that sample (here: protein expression; in other settings: scRNA-seq, ATAC, etc.).\n", + "\n", + "In this simple example we construct a short textual description for each cell using columns from `adata.obs`. For real applications you might want much richer descriptions (e.g. including time points, doses, quality metrics, etc.), but the mechanism is the same." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95f28a07", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a description string for each cell by looping over the rows in adata.obs\n", + "# This text will serve as the \"biological context\" that we want to align with the numeric representation.\n", + "def make_description(row):\n", + " \"\"\"Make a quick description of a cell based on its metadata\"\"\"\n", + " return (\n", + " f\"The first perturbation is {row['perturbation']} \"\n", + " f\"and the second perturbation is {row['perturbation_2']}.\"\n", + " f\" The tissue is {row['tissue_type']} and it has cancer yes or no: {row['cancer']}.\"\n", + " f\" The disease is {row['disease']}.\"\n", + " f\" The celltype is {row['celltype']}.\"\n", + " )\n", + "\n", + "\n", + "# Add a \"description\" column to adata.obs using the function above\n", + "adata.obs[\"description\"] = adata.obs.apply(make_description, axis=1)\n", + "# Also add a sample index column that uniquely identifies each cell; this will be used as a token later\n", + "adata.obs[\"sample_idx\"] = adata.obs.index" + ] + }, + { + "cell_type": "markdown", + "id": "8c513a34", + "metadata": {}, + "source": [ + "### Step 2: Choose a numerical representation\n", + "\n", + "In this proteomics example, the assay only measures **24 proteins per cell**, so we can directly use the normalised expression vector as the numeric representation:\n", + "\n", + "- Each cell gets a 24-dimensional vector from `adata.X`.\n", + "- We store this in `adata.obsm[\"X_prot\"]` and later tell the model to use this key.\n", + "\n", + "For single-cell RNA datasets, mmcontext is more flexible:\n", + "\n", + "- You can plug in **precomputed representations** such as\n", + " - Principal components (PCA),\n", + " - Latent variables from **scVI**,\n", + " - Embeddings from **Geneformer** or other pretrained models.\n", + "- In the publication, we explore exactly these kinds of pretrained embeddings as the numeric representation `X_*` for each cell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d999832", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# let's split by train and val, randomly 80% train\n", + "adata.obs[\"split\"] = np.random.rand(len(adata)) < 0.8\n", + "adata_train = adata[adata.obs[\"split\"]].copy()\n", + "adata_val = adata[~adata.obs[\"split\"]].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8de1a9c1", + "metadata": {}, + "outputs": [], + "source": [ + "adata_val.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d68558ad", + "metadata": {}, + "outputs": [], + "source": [ + "adata_train.shape" + ] + }, + { + "cell_type": "markdown", + "id": "7cbfa52a", + "metadata": {}, + "source": [ + "### Step 2b: Split into train/validation and preprocess\n", + "\n", + "Before building embeddings, we:\n", + "\n", + "- Randomly split cells into an **80% train** and **20% validation** subset using a boolean mask in `adata.obs[\"split\"]`.\n", + "- Apply standard **normalisation and log-transformation** with Scanpy (`sc.pp.normalize_total` + `sc.pp.log1p`).\n", + "\n", + "These steps ensure that the numeric representation we later feed into the model is on a comparable scale across cells, and that we can monitor generalisation on a held-out validation set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ffb5747", + "metadata": {}, + "outputs": [], + "source": [ + "import scanpy as sc\n", + "\n", + "# normalise and log transform the data\n", + "sc.pp.normalize_total(adata_train, inplace=True)\n", + "sc.pp.log1p(adata_train)\n", + "\n", + "sc.pp.normalize_total(adata_val, inplace=True)\n", + "sc.pp.log1p(adata_val)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b699d36c", + "metadata": {}, + "outputs": [], + "source": [ + "# since these datasets only contain 24 proteins, we will just use their expression as embeddings\n", + "# we will use the protein names as the embedding keys\n", + "adata_train.obsm[\"X_prot\"] = adata_train.X\n", + "adata_val.obsm[\"X_prot\"] = adata_val.X\n", + "processed_paths = {\"train\": \"Frangiehlzar2021_protein_pp_train.h5ad\", \"val\": \"Frangiehlzar2021_protein_pp_val.h5ad\"}\n", + "adata_train.write_h5ad(processed_paths[\"train\"])\n", + "adata_val.write_h5ad(processed_paths[\"val\"])" + ] + }, + { + "cell_type": "markdown", + "id": "09af0ef7", + "metadata": {}, + "source": [ + "### Step 3: Construct a contrastive training dataset\n", + "\n", + "So far we have:\n", + "- A **numeric representation** for each cell (stored in `adata.obsm[\"X_prot\"]`).\n", + "- A **text description** for each cell in `adata.obs[\"description\"]`.\n", + "\n", + "Next, we convert these into a Hugging Face `DatasetDict` suitable for contrastive training:\n", + "\n", + "- `AnnDataSetConstructor` in `\"multiplets\"` mode creates, for each cell, a\n", + " - **query token** (`sample_idx` / later `cell_sentence_1`),\n", + " - a **matched positive**, and\n", + " - at least one **negative**.\n", + "- This gives us a flexible format where the same dataset can later support different training regimens (e.g. using text-only, numeric-only, or mixed representations), simply by choosing which columns to feed into the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bd012cb", + "metadata": {}, + "outputs": [], + "source": [ + "from adata_hf_datasets import AnnDataSetConstructor\n", + "from datasets import DatasetDict\n", + "\n", + "ds_dict = DatasetDict()\n", + "# multiplets format for training datasets (with descriptions)\n", + "# A sentence key refers to the column in adata.obs that is used to represent the sample.\n", + "# For numeric data, we use the sample index, and later register the created embedding linked to their indices in the tokenizer\n", + "for split_name, adata_split in {\"train\": adata_train, \"val\": adata_val}.items():\n", + " constructor = AnnDataSetConstructor(dataset_format=\"multiplets\", resolve_negatives=True)\n", + " constructor.add_anndata(\n", + " adata_split,\n", + " caption_key=\"description\",\n", + " sentence_keys=[\"sample_idx\"],\n", + " adata_link=processed_paths[split_name],\n", + " batch_key=\"library_preparation_protocol\", # In this case all are from the same batch, but providing a batch key can improve batch integration by negative sampling\n", + " )\n", + " ds = constructor.get_dataset()\n", + " ds_dict[split_name] = ds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e4d531a", + "metadata": {}, + "outputs": [], + "source": [ + "ds_dict" + ] + }, + { + "cell_type": "markdown", + "id": "70612269", + "metadata": {}, + "source": [ + "## Configure the Model\n", + "\n", + "The `MMContextEncoder` is the **main model** in this framework. Conceptually, it is a custom Sentence Transformers module that:\n", + "\n", + "- Starts from a standard **text encoder** (here: `sentence-transformers/all-MiniLM-L6-v2`).\n", + "- Adds **adapters** that map\n", + " - the text encoder output into a desired embedding dimension, and\n", + " - the numeric representation (e.g. `X_prot`, PCA, scVI, Geneformer embeddings) into the **same** space.\n", + "- Lets you configure whether the text encoder is **frozen**, partially unfrozen (last *n* layers), or fully trainable.\n", + "\n", + "To make the numeric data usable by the model, we need to **register initial embeddings**:\n", + "\n", + "- Tokens in `cell_sentence_1` (e.g. `\"sample_idx:123\"`) should map to a vector from our numeric representation.\n", + "- Internally, the model builds a lookup table (`sample_idx` → integer id) and an embedding matrix (id → numeric vector), which can be kept frozen or made trainable.\n", + "\n", + "After this setup, training looks like standard Sentence Transformers training, but every sample has **both** a text description and a numeric embedding living in a shared space." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f04ed8b", + "metadata": {}, + "outputs": [], + "source": [ + "from mmcontext.mmcontextencoder import MMContextEncoder\n", + "from sentence_transformers import SentenceTransformer\n", + "\n", + "enc = MMContextEncoder(\n", + " text_encoder_name=\"sentence-transformers/all-MiniLM-L6-v2\",\n", + " adapter_hidden_dim=128,\n", + " adapter_output_dim=64,\n", + " freeze_text_encoder=True,\n", + " unfreeze_last_n_layers=2,\n", + " output_token_embeddings=False,\n", + " train_lookup=False,\n", + " joint_adapter_hidden_dim=None,\n", + " text_model_kwargs=None,\n", + " use_text_adapter=True,\n", + ")\n", + "model = SentenceTransformer(modules=[enc])" + ] + }, + { + "cell_type": "markdown", + "id": "d0926c15", + "metadata": {}, + "source": [ + "### Register the initial numeric embeddings\n", + "\n", + "We now connect the numeric representations on disk to the model:\n", + "\n", + "- `get_initial_embeddings_from_adata_link` reads the preprocessed `.h5ad` files and builds a table with one row per `sample_idx` and its embedding from `X_prot`.\n", + "- `register_initial_embeddings` turns this table into an internal embedding matrix and lookup table inside the `MMContextEncoder`.\n", + "\n", + "After this step, whenever the model sees a token like `\"sample_idx:123\"` in the dataset, it can look up the correct numeric vector and combine it with the text pathway." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee9a6e96", + "metadata": {}, + "outputs": [], + "source": [ + "token_df, _ = model[0].get_initial_embeddings_from_adata_link(\n", + " ds_dict,\n", + " layer_key=\"X_prot\",\n", + " download_dir=\"data_cache\",\n", + " axis=\"obs\", # since we get embeddings from adata.obsm. We could also use \"varm\" and for example use an embedding for each protein\n", + ")\n", + "model[0].register_initial_embeddings(token_df, data_origin=\"prot\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6975ab72", + "metadata": {}, + "outputs": [], + "source": [ + "# the model expects a certain prefix on the cell tokens.\n", + "model[0].processor.prefix" + ] + }, + { + "cell_type": "markdown", + "id": "52d74f63", + "metadata": {}, + "source": [ + "### Prepare token prefixes and final training columns\n", + "\n", + "At this point, the model knows about all numeric embeddings, but the dataset still uses the plain `sample_idx` strings.\n", + "\n", + "- `model[0].processor.prefix` defines the required prefix for these tokens (e.g. `\"sample_idx:\"`).\n", + "- `prefix_ds` rewrites the `cell_sentence_1` column so that each entry matches what the tokenizer and lookup table expect.\n", + "\n", + "Finally, we simplify the dataset to the three columns we need for contrastive learning:\n", + "\n", + "- `anchor`: the main cell token (formerly `cell_sentence_1`).\n", + "- `positive`: a biologically matched partner.\n", + "- `negative_1`: a contrastive negative.\n", + "\n", + "This might look a bit cumbersome, but it makes the dataset **reusable**: you can easily construct different views (text-only, numeric-only, different negatives) without having to rebuild everything from scratch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6fc91911", + "metadata": {}, + "outputs": [], + "source": [ + "# you could add this manually or use the method below\n", + "model[0].prefix_ds(ds_dict, columns_to_prefix=[\"cell_sentence_1\"])" + ] + }, + { + "cell_type": "markdown", + "id": "c09bac80", + "metadata": {}, + "source": [ + "## Training setup and objective\n", + "\n", + "With the dataset and model ready, we now configure the training loop using the standard Sentence Transformers API:\n", + "\n", + "- `SentenceTransformerTrainingArguments` controls the **optimisation hyperparameters** (epochs, batch size, learning rate, warmup, logging and checkpointing frequency).\n", + "- `SentenceTransformerTrainer` automatically picks an appropriate device (CPU, CUDA GPU, or Apple **MPS**) and handles mixed-precision flags (`fp16`/`bf16`) where supported.\n", + "- We use `MultipleNegativesRankingLoss`, which encourages anchors to be close to their positives and far from all other samples in the batch.\n", + "- `TripletEvaluator` periodically evaluates the model on the validation set using triplets `(anchor, positive, negative)` and reports an accuracy metric.\n", + "\n", + "Running `trainer.train()` then performs the actual optimisation and will log progress and evaluation metrics during training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f63b0e4b", + "metadata": {}, + "outputs": [], + "source": [ + "# lastly we have to drop some columns from the dataset and\n", + "# rename the main column to \"anchor\".\n", + "# you might think that this is a bit cumbersome, which it is.\n", + "# But this setup allowed for fleixble training,\n", + "# using either cell or feature level tokens, using text based cell sentences\n", + "# or numeric embeddings and resolving negatives\n", + "# to whatever column was chosen for training.\n", + "# That means that for a certain training run, the same dataset can be reused, and\n", + "# only modified differently. But in the end, it is a bit of work to set up.\n", + "ds_final = ds_dict.rename_column(\"cell_sentence_1\", \"anchor\")\n", + "ds_final = ds_final.remove_columns([\"sample_idx\", \"adata_link\", \"negative_1_idx\"])\n", + "ds_final" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25c0a4e9", + "metadata": {}, + "outputs": [], + "source": [ + "from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments\n", + "\n", + "args = SentenceTransformerTrainingArguments(\n", + " num_train_epochs=1,\n", + " per_device_train_batch_size=128,\n", + " per_device_eval_batch_size=128,\n", + " learning_rate=1e-5,\n", + " warmup_ratio=0.1,\n", + " fp16=False,\n", + " bf16=False,\n", + " eval_strategy=\"steps\",\n", + " eval_steps=100,\n", + " save_strategy=\"steps\",\n", + " save_steps=100,\n", + " save_total_limit=1,\n", + " max_grad_norm=1.0,\n", + " logging_steps=10,\n", + " run_name=\"protein_test\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d44a49b5", + "metadata": {}, + "outputs": [], + "source": [ + "from sentence_transformers.evaluation import TripletEvaluator\n", + "from sentence_transformers.losses import MultipleNegativesRankingLoss\n", + "\n", + "loss = MultipleNegativesRankingLoss(model)\n", + "evaluator = TripletEvaluator(\n", + " anchors=ds_final[\"val\"][\"anchor\"],\n", + " positives=ds_final[\"val\"][\"positive\"],\n", + " negatives=ds_final[\"val\"][\"negative_1\"],\n", + " name=\"val\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53d32063", + "metadata": {}, + "outputs": [], + "source": [ + "trainer = SentenceTransformerTrainer(\n", + " model=model,\n", + " args=args,\n", + " train_dataset=ds_final[\"train\"],\n", + " eval_dataset=ds_final[\"val\"],\n", + " loss=loss,\n", + " evaluator=evaluator,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84f83d70", + "metadata": {}, + "outputs": [], + "source": [ + "trainer.train()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } From 06464b72c1521c4b8e4a595f5cfd71a439598fdb Mon Sep 17 00:00:00 2001 From: "claude[bot]" <41898282+claude[bot]@users.noreply.github.com> Date: Tue, 2 Jun 2026 12:55:13 +0000 Subject: [PATCH 27/67] fix: replace str+Enum with StrEnum for UP042 compliance Co-authored-by: mengerj --- src/mmcontext/eval/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/mmcontext/eval/utils.py b/src/mmcontext/eval/utils.py index 52f6499..c02842e 100644 --- a/src/mmcontext/eval/utils.py +++ b/src/mmcontext/eval/utils.py @@ -2,7 +2,7 @@ import os import random from dataclasses import dataclass -from enum import Enum +from enum import StrEnum import numpy as np import pandas as pd @@ -11,7 +11,7 @@ logger = logging.getLogger(__name__) -class LabelKind(str, Enum): +class LabelKind(StrEnum): """Name for the kind of label.""" BIO = "bio" From b3bf637d7f4b3942af1514bd2654365a9539088a Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Tue, 2 Jun 2026 15:04:34 +0200 Subject: [PATCH 28/67] only trigger claude manually, no autoreview of PRs --- .github/workflows/claude-review.yaml | 32 +++++++++++++++++++--------- 1 file changed, 22 insertions(+), 10 deletions(-) diff --git a/.github/workflows/claude-review.yaml b/.github/workflows/claude-review.yaml index e01ec64..13b5fdd 100644 --- a/.github/workflows/claude-review.yaml +++ b/.github/workflows/claude-review.yaml @@ -1,9 +1,6 @@ name: Claude Code Review on: - pull_request: - types: [opened, synchronize] - branches: [dev-claude, main] issue_comment: types: [created] @@ -20,25 +17,40 @@ concurrency: jobs: review: - # Auto-review every PR, or respond to @claude in PR comments (owner only, ignore bots) + # Respond to @claude in PR comments (owner only, ignore bots) if: > github.event.sender.type != 'Bot' && - ((github.event_name == 'pull_request' && - github.actor == 'mengerj') || - (github.event_name == 'issue_comment' && - github.actor == 'mengerj' && - github.event.issue.pull_request && - contains(github.event.comment.body, '@claude'))) + github.actor == 'mengerj' && + github.event.issue.pull_request && + contains(github.event.comment.body, '@claude') runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - uses: anthropics/claude-code-action@v1 + id: claude with: claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }} + show_full_output: true additional_permissions: | actions: read claude_args: | --allowedTools "Bash,Read,Write,Edit,Glob,Grep,Task" --max-turns 30 + --output-format stream-json + + - name: Archive Claude session log + if: always() + run: | + mkdir -p claude-logs + echo '${{ steps.claude.outputs.result }}' > claude-logs/session.json + shell: bash + + - name: Upload session log + if: always() + uses: actions/upload-artifact@v4 + with: + name: claude-review-pr${{ github.event.pull_request.number || github.event.issue.number }}-${{ github.run_number }} + path: claude-logs/ + retention-days: 30 From c8a6a98db6f9721975e04c38e013797d15c7def4 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Tue, 2 Jun 2026 15:07:45 +0200 Subject: [PATCH 29/67] extend claude reasoning output in actions tab --- .github/workflows/claude-implement.yaml | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/.github/workflows/claude-implement.yaml b/.github/workflows/claude-implement.yaml index 7242937..04da46d 100644 --- a/.github/workflows/claude-implement.yaml +++ b/.github/workflows/claude-implement.yaml @@ -35,14 +35,32 @@ jobs: ref: dev-claude fetch-depth: 0 - uses: anthropics/claude-code-action@v1 + id: claude with: claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }} + show_full_output: true base_branch: dev-claude additional_permissions: | actions: read claude_args: | --allowedTools "Bash,Read,Write,Edit,Glob,Grep,Task,WebSearch,WebFetch" --max-turns 30 + --output-format stream-json # The CLAUDE.md in the repo root provides the plan-first protocol, # branch naming conventions, and PR linking instructions. # Claude reads it automatically on checkout. + + - name: Archive Claude session log + if: always() + run: | + mkdir -p claude-logs + echo '${{ steps.claude.outputs.result }}' > claude-logs/session.json + shell: bash + + - name: Upload session log + if: always() + uses: actions/upload-artifact@v4 + with: + name: claude-implement-issue${{ github.event.issue.number }}-${{ github.run_number }} + path: claude-logs/ + retention-days: 30 From 7b538d501f59b735c5327d1650cbcbee3ecfce9c Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Wed, 3 Jun 2026 08:55:07 +0200 Subject: [PATCH 30/67] remove some commands --- CLAUDE.md | 28 ---------------------------- 1 file changed, 28 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index de56b64..b5988ec 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -28,32 +28,6 @@ src/mmcontext/ └── utils.py # Shared utilities ``` -## Branch Strategy - -- `main` — stable releases only -- `dev-claude` — integration branch for all agent and feature work -- Feature branches: always branch from `dev-claude`, never from `main` -- Branch naming: `claude/-` for agent-created branches - -## Commands - -```bash -# Install (editable, with test deps) -pip install -e ".[dev,test]" - -# Run tests -pytest -v --color=yes - -# Run tests with coverage -coverage run -m pytest -v --color=yes && coverage report - -# Lint -ruff check src/ tests/ -ruff format --check src/ tests/ - -# Format -ruff format src/ tests/ -``` ## Code Style @@ -85,7 +59,6 @@ When implementing a feature from a GitHub issue (via @claude or otherwise): 1. **If the issue is ambiguous**: Post clarifying questions as a comment. Do NOT start implementation until the questions are answered. 2. **Plan first**: Before writing any code, post an implementation plan as a comment on the issue with a checkbox list: - ``` ## Implementation Plan - [ ] Step 1: description @@ -93,7 +66,6 @@ When implementing a feature from a GitHub issue (via @claude or otherwise): - [ ] Step 3: description - [ ] Verify: run tests, check linting ``` - Wait for approval (a reply containing "approved", "go ahead", "LGTM", or "looks good"). 3. **Implement**: Create a branch `claude/-` from `dev-claude`. Implement the plan step by step. Edit the plan comment to check off completed steps. From 7d1601035670240b41b90457c07b747fe484525b Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Wed, 3 Jun 2026 09:10:31 +0200 Subject: [PATCH 31/67] only trigger action with comment in issue --- .github/workflows/claude-implement.yaml | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/.github/workflows/claude-implement.yaml b/.github/workflows/claude-implement.yaml index df454f1..2af073c 100644 --- a/.github/workflows/claude-implement.yaml +++ b/.github/workflows/claude-implement.yaml @@ -1,8 +1,6 @@ name: Claude Code Implement on: - issues: - types: [opened, labeled] issue_comment: types: [created] @@ -19,15 +17,12 @@ concurrency: jobs: implement: - # Trigger on @claude mentions in issues — only by repo owner, ignore bot comments + # Trigger on @claude mentions in issue comments — only by repo owner, ignore bots if: > github.actor == 'mengerj' && github.event.sender.type != 'Bot' && - ((github.event_name == 'issues' && - contains(github.event.issue.body, '@claude')) || - (github.event_name == 'issue_comment' && - !github.event.issue.pull_request && - contains(github.event.comment.body, '@claude'))) + !github.event.issue.pull_request && + contains(github.event.comment.body, '@claude') runs-on: ubuntu-latest steps: - uses: actions/checkout@v6 From 10f920a53b2f77ec176db9128429d256232c022b Mon Sep 17 00:00:00 2001 From: "claude[bot]" <41898282+claude[bot]@users.noreply.github.com> Date: Wed, 3 Jun 2026 07:16:10 +0000 Subject: [PATCH 32/67] feat: include hard negatives in train_tiny.py training script MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Use resolve_negative_indices_and_rename() to resolve negative_*_idx columns to actual text, then pass the full (anchor, positive, neg_1, …, neg_n) multiplets to MultipleNegativesRankingLoss. Both genelist and bimodal modes support hard negatives. Add --hard-negatives / --no-hard-negatives CLI flag (default: True) to control behaviour. Fixes #54 Co-authored-by: mengerj --- scripts/train_tiny.py | 93 +++++++++++++++++++++++++++++++++++-------- 1 file changed, 76 insertions(+), 17 deletions(-) diff --git a/scripts/train_tiny.py b/scripts/train_tiny.py index 0f71407..c9f2eea 100644 --- a/scripts/train_tiny.py +++ b/scripts/train_tiny.py @@ -43,6 +43,7 @@ from sentence_transformers.sentence_transformer.modules import Normalize, Pooling from mmcontext.modules import AdapterModule, MMContextModule +from mmcontext.utils import resolve_negative_indices_and_rename logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s %(message)s") logger = logging.getLogger(__name__) @@ -53,35 +54,87 @@ HF_DATASET = "jo-mengr/cxg_schaefer_tiny" -def prepare_genelist_dataset(ds): +def prepare_genelist_dataset(ds, use_hard_negatives: bool = True): """Reshape dataset for gene-list + text training. - Returns a HF Dataset with columns ``anchor`` (gene names) and - ``positive`` (text description). + Returns a HF Dataset with columns ``anchor`` (gene names), ``positive`` + (text description), and optionally resolved hard-negative columns + ``negative_1``, ``negative_2``, … when ``use_hard_negatives`` is True and + the dataset exposes ``negative_*_idx`` columns. """ - # cell_sentence_1 = space-separated gene names → anchor - ds = ds.rename_columns({"cell_sentence_1": "anchor"}) - ds = ds.select_columns(["anchor", "positive"]) - logger.info("Gene-list dataset: %d samples, columns=%s", len(ds), ds.column_names) + has_neg_idx = any(c for c in ds.column_names if c.startswith("negative_") and c.endswith("_idx")) + if use_hard_negatives and has_neg_idx: + ds = resolve_negative_indices_and_rename( + ds, + primary_cell_sentence_col="cell_sentence_1", + positive_col="positive", + negative_prefix="negative", + index_col="sample_idx", + remove_index_col=True, + ) + neg_cols = sorted(c for c in ds.column_names if c.startswith("negative")) + ds = ds.select_columns(["anchor", "positive"] + neg_cols) + logger.info( + "Gene-list dataset (with hard negatives): %d samples, columns=%s", + len(ds), + ds.column_names, + ) + else: + # cell_sentence_1 = space-separated gene names → anchor + ds = ds.rename_columns({"cell_sentence_1": "anchor"}) + ds = ds.select_columns(["anchor", "positive"]) + logger.info("Gene-list dataset: %d samples, columns=%s", len(ds), ds.column_names) return ds -def prepare_bimodal_dataset(ds): +def prepare_bimodal_dataset(ds, use_hard_negatives: bool = True): """Reshape dataset for bimodal (omics + text) training. Prefixes ``sample_idx`` with ``omics:`` so MMContextModule routes them through the VectorStore path. - Returns a HF Dataset with columns ``anchor`` and ``positive``. + Returns a HF Dataset with columns ``anchor``, ``positive``, and optionally + resolved hard-negative columns when ``use_hard_negatives`` is True and the + dataset exposes ``negative_*_idx`` columns. + + Notes + ----- + Hard negatives are resolved to text: odd-numbered negatives become the + positive text of another sample; even-numbered negatives become the + gene-list text of another sample. """ + has_neg_idx = any(c for c in ds.column_names if c.startswith("negative_") and c.endswith("_idx")) + if use_hard_negatives and has_neg_idx: + ds = resolve_negative_indices_and_rename( + ds, + primary_cell_sentence_col="cell_sentence_1", + positive_col="positive", + negative_prefix="negative", + index_col="sample_idx", + remove_index_col=False, # keep sample_idx so we can build the omics anchor + ) + + def _prefix_omics(example): + example["anchor"] = f"omics:{example['sample_idx']}" + return example + + ds = ds.map(_prefix_omics) + neg_cols = sorted(c for c in ds.column_names if c.startswith("negative")) + ds = ds.select_columns(["anchor", "positive"] + neg_cols) + logger.info( + "Bimodal dataset (with hard negatives): %d samples, columns=%s", + len(ds), + ds.column_names, + ) + else: - def prefix_omics(example): - example["anchor"] = f"omics:{example['sample_idx']}" - return example + def _prefix_omics(example): + example["anchor"] = f"omics:{example['sample_idx']}" + return example - ds = ds.map(prefix_omics) - ds = ds.select_columns(["anchor", "positive"]) - logger.info("Bimodal dataset: %d samples, columns=%s", len(ds), ds.column_names) + ds = ds.map(_prefix_omics) + ds = ds.select_columns(["anchor", "positive"]) + logger.info("Bimodal dataset: %d samples, columns=%s", len(ds), ds.column_names) return ds @@ -184,14 +237,20 @@ def main(): default=None, help="Optional W&B run name (auto-generated if omitted)", ) + parser.add_argument( + "--hard-negatives", + action=argparse.BooleanOptionalAction, + default=True, + help="Use hard negatives from negative_*_idx dataset columns (default: True).", + ) args = parser.parse_args() # --- Dataset --- ds_raw = load_dataset(args.dataset, split="train") if args.mode == "bimodal": - ds = prepare_bimodal_dataset(ds_raw) + ds = prepare_bimodal_dataset(ds_raw, use_hard_negatives=args.hard_negatives) else: - ds = prepare_genelist_dataset(ds_raw) + ds = prepare_genelist_dataset(ds_raw, use_hard_negatives=args.hard_negatives) # --- Pipeline --- pipeline = build_pipeline( From c60cad10d57bf1fddc8b42bde0ed0102da9435d1 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 3 Jun 2026 07:21:17 +0000 Subject: [PATCH 33/67] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- CLAUDE.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/CLAUDE.md b/CLAUDE.md index b5988ec..7dbfc68 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -28,7 +28,6 @@ src/mmcontext/ └── utils.py # Shared utilities ``` - ## Code Style - **Formatter/linter**: ruff (line-length=120) @@ -59,6 +58,7 @@ When implementing a feature from a GitHub issue (via @claude or otherwise): 1. **If the issue is ambiguous**: Post clarifying questions as a comment. Do NOT start implementation until the questions are answered. 2. **Plan first**: Before writing any code, post an implementation plan as a comment on the issue with a checkbox list: + ``` ## Implementation Plan - [ ] Step 1: description @@ -66,6 +66,7 @@ When implementing a feature from a GitHub issue (via @claude or otherwise): - [ ] Step 3: description - [ ] Verify: run tests, check linting ``` + Wait for approval (a reply containing "approved", "go ahead", "LGTM", or "looks good"). 3. **Implement**: Create a branch `claude/-` from `dev-claude`. Implement the plan step by step. Edit the plan comment to check off completed steps. From efb0fbd6d8c0ced1e4d86850256368384a8db67f Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Wed, 3 Jun 2026 09:24:08 +0200 Subject: [PATCH 34/67] let test workflow also run on dev-claude brnach --- .github/workflows/test.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 3405401..663c333 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -2,9 +2,9 @@ name: Test on: push: - branches: [main] + branches: [main, dev-claude] pull_request: - branches: [main] + branches: [main, dev-claude] schedule: - cron: "0 5 1,15 * *" From cc866097116415bf43742284af12d4e0ab628531 Mon Sep 17 00:00:00 2001 From: "claude[bot]" <41898282+claude[bot]@users.noreply.github.com> Date: Wed, 3 Jun 2026 07:48:58 +0000 Subject: [PATCH 35/67] fix: replace deprecated no_cuda with use_cpu in training tests SentenceTransformerTrainingArguments no longer accepts no_cuda; use_cpu=True is the current equivalent. Co-authored-by: mengerj --- tests/test_st_integration.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_st_integration.py b/tests/test_st_integration.py index 033df43..3216be8 100644 --- a/tests/test_st_integration.py +++ b/tests/test_st_integration.py @@ -418,7 +418,7 @@ def test_training_text_only(self, obs_pipeline, tmp_dir): num_train_epochs=1, per_device_train_batch_size=2, learning_rate=1e-3, - no_cuda=True, + use_cpu=True, report_to="none", ) trainer = SentenceTransformerTrainer( @@ -476,7 +476,7 @@ def test_training_bimodal(self, obs_pipeline, obs_store, tmp_dir): num_train_epochs=1, per_device_train_batch_size=2, learning_rate=1e-3, - no_cuda=True, + use_cpu=True, report_to="none", ) trainer = SentenceTransformerTrainer( @@ -530,7 +530,7 @@ def test_training_gene_list(self, obs_pipeline, tmp_dir): num_train_epochs=1, per_device_train_batch_size=2, learning_rate=1e-3, - no_cuda=True, + use_cpu=True, report_to="none", ) trainer = SentenceTransformerTrainer( @@ -579,7 +579,7 @@ def test_training_save_load_roundtrip(self, obs_pipeline, tmp_dir, real_safetens num_train_epochs=1, per_device_train_batch_size=2, learning_rate=1e-3, - no_cuda=True, + use_cpu=True, report_to="none", ) trainer = SentenceTransformerTrainer( From ad5edd6bf2ea0b0fad46e4b59dbe7fab80e5c411 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Wed, 3 Jun 2026 08:01:08 +0000 Subject: [PATCH 36/67] Add top-N layer freezing and bf16 (MPS) support to train_tiny - MMContextModule.freeze_all_but_top_layers(n): freeze encoder, keep top N transformer layers (+ pooler) trainable; refactor layer lookup into helper - train_tiny.py: --freeze-text-encoder, --unfreeze-last-n, --bf16 flags (bf16 defaults on for MPS, macOS 14+); bf16 takes precedence over fp16 - Tests for freeze_all_but_top_layers (top-N, n=0, n>total) --- scripts/train_tiny.py | 40 +++++++++++++- src/mmcontext/modules/mmcontext_module.py | 65 ++++++++++++++++++++--- tests/test_mmcontext_module.py | 28 ++++++++++ 3 files changed, 125 insertions(+), 8 deletions(-) diff --git a/scripts/train_tiny.py b/scripts/train_tiny.py index c9f2eea..6bcae35 100644 --- a/scripts/train_tiny.py +++ b/scripts/train_tiny.py @@ -243,6 +243,26 @@ def main(): default=True, help="Use hard negatives from negative_*_idx dataset columns (default: True).", ) + parser.add_argument( + "--freeze-text-encoder", + action="store_true", + help="Freeze the text encoder to cut gradient/optimizer memory. " + "Combine with --unfreeze-last-n to keep the top N layers trainable.", + ) + parser.add_argument( + "--unfreeze-last-n", + type=int, + default=0, + help="With --freeze-text-encoder, keep the top N transformer layers " + "(plus pooler) trainable. 0 freezes the whole encoder (default: 0).", + ) + parser.add_argument( + "--bf16", + action=argparse.BooleanOptionalAction, + default=torch.backends.mps.is_available(), + help="Use bf16 mixed precision. Supported on MPS (macOS 14+) and CUDA; " + "roughly halves activation memory. Default: on when MPS is available.", + ) args = parser.parse_args() # --- Dataset --- @@ -291,6 +311,22 @@ def main(): logger.info("VectorStore: %d vectors, dim=%d", len(store), store.dim) + # --- Freezing (memory savings) --- + # Applied to the final pipeline's MMContextModule. Freezing the text + # encoder removes its gradients + Adam optimizer state; keeping only the + # top N layers trainable is the usual fine-tuning sweet spot. + if args.freeze_text_encoder: + text_module = list(pipeline.children())[0] + if args.unfreeze_last_n > 0: + text_module.freeze_all_but_top_layers(args.unfreeze_last_n) + logger.info("Froze text encoder, keeping top %d layers trainable", args.unfreeze_last_n) + else: + text_module.freeze_text_encoder() + logger.info("Froze entire text encoder") + trainable = sum(p.numel() for p in pipeline.parameters() if p.requires_grad) + total = sum(p.numel() for p in pipeline.parameters()) + logger.info("Trainable params: %d / %d (%.1f%%)", trainable, total, 100.0 * trainable / total) + # --- Wandb setup --- wandb_project = args.wandb_project or os.environ.get("WANDB_PROJECT") use_wandb = wandb_project is not None @@ -308,7 +344,9 @@ def main(): per_device_train_batch_size=args.batch_size, learning_rate=args.lr, warmup_ratio=0.1, - fp16=torch.cuda.is_available() and not args.use_mps_device, + # bf16 (MPS macOS 14+ / CUDA) takes precedence; fall back to fp16 on CUDA only. + bf16=args.bf16, + fp16=(not args.bf16) and torch.cuda.is_available() and not args.use_mps_device, use_mps_device=args.use_mps_device, report_to="wandb" if use_wandb else "none", logging_steps=10, diff --git a/src/mmcontext/modules/mmcontext_module.py b/src/mmcontext/modules/mmcontext_module.py index fe8046e..249578b 100644 --- a/src/mmcontext/modules/mmcontext_module.py +++ b/src/mmcontext/modules/mmcontext_module.py @@ -419,19 +419,24 @@ def unfreeze_text_encoder(self) -> None: param.requires_grad = True logger.info("Unfroze all text encoder parameters") - def _freeze_n_layers(self, num_layers: int) -> None: - """Freeze the first ``num_layers`` encoder layers. + def _get_encoder_layers(self) -> torch.nn.ModuleList | None: + """Return the transformer encoder layer stack, if it can be located. Handles both BERT-style (``encoder.layer``) and RoBERTa-style - (``roberta.encoder.layer``) architectures. + (``roberta.encoder.layer``) architectures. Returns ``None`` when the + layer stack cannot be identified. """ - layers = None if hasattr(self.auto_model, "encoder") and hasattr(self.auto_model.encoder, "layer"): - layers = self.auto_model.encoder.layer + return self.auto_model.encoder.layer elif hasattr(self.auto_model, "roberta"): - layers = self.auto_model.roberta.encoder.layer + return self.auto_model.roberta.encoder.layer elif hasattr(self.auto_model, "bert"): - layers = self.auto_model.bert.encoder.layer + return self.auto_model.bert.encoder.layer + return None + + def _freeze_n_layers(self, num_layers: int) -> None: + """Freeze the first ``num_layers`` encoder layers.""" + layers = self._get_encoder_layers() if layers is None: logger.warning("Could not identify encoder layers for partial freezing. Freezing all parameters instead.") @@ -445,6 +450,52 @@ def _freeze_n_layers(self, num_layers: int) -> None: param.requires_grad = False logger.info("Froze first %d text encoder layers", num_layers) + def freeze_all_but_top_layers(self, num_trainable_layers: int) -> None: + """Freeze the text encoder, keeping only the top layers trainable. + + Freezes every parameter of the text encoder, then re-enables gradients + on the top ``num_trainable_layers`` transformer layers (counting from + the output end) plus the pooler, if present. This is the typical + "fine-tune only the last N layers" setup, which drastically reduces the + memory used by gradients and optimizer state. + + Parameters + ---------- + num_trainable_layers : int + Number of top transformer layers to keep trainable. ``0`` (or less) + freezes the entire encoder. Values larger than the available number + of layers keep all layers trainable. + """ + # Freeze everything first. + for param in self.auto_model.parameters(): + param.requires_grad = False + + if num_trainable_layers <= 0: + logger.info("Froze all text encoder parameters (0 trainable top layers)") + return + + layers = self._get_encoder_layers() + if layers is None: + logger.warning( + "Could not identify encoder layers; the text encoder remains fully frozen. " + "Use unfreeze_text_encoder() to train all parameters instead." + ) + return + + total = len(layers) + n = min(num_trainable_layers, total) + for layer in layers[total - n :]: + for param in layer.parameters(): + param.requires_grad = True + + # Keep the pooler trainable too, when one exists. + pooler = getattr(self.auto_model, "pooler", None) + if pooler is not None: + for param in pooler.parameters(): + param.requires_grad = True + + logger.info("Froze text encoder, kept top %d of %d layers trainable", n, total) + # ------------------------------------------------------------------ # Save / Load (Module abstract methods) # ------------------------------------------------------------------ diff --git a/tests/test_mmcontext_module.py b/tests/test_mmcontext_module.py index a674d5c..1074874 100644 --- a/tests/test_mmcontext_module.py +++ b/tests/test_mmcontext_module.py @@ -350,6 +350,34 @@ def test_freeze_unfreeze_num_layers(self, module): assert frozen > 0 assert trainable > 0 + def test_freeze_all_but_top_layers_keeps_only_top(self, module): + """freeze_all_but_top_layers(N) trains only the top N layers.""" + layers = module._get_encoder_layers() + assert layers is not None and len(layers) >= 2, "stub encoder needs >=2 layers" + + module.freeze_all_but_top_layers(1) + + # The last layer is trainable; all earlier layers are frozen. + for param in layers[-1].parameters(): + assert param.requires_grad + for layer in layers[:-1]: + for param in layer.parameters(): + assert not param.requires_grad + + def test_freeze_all_but_top_zero_freezes_everything(self, module): + """num_trainable_layers=0 freezes the whole encoder.""" + module.freeze_all_but_top_layers(0) + for param in module.auto_model.parameters(): + assert not param.requires_grad + + def test_freeze_all_but_top_n_exceeds_total_keeps_all_layers(self, module): + """Asking for more layers than exist keeps every encoder layer trainable.""" + layers = module._get_encoder_layers() + module.freeze_all_but_top_layers(len(layers) + 5) + for layer in layers: + for param in layer.parameters(): + assert param.requires_grad + # --------------------------------------------------------------------------- # Persistence tests From e165c5ef647b954b96255f31effd900c1526f9f2 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Wed, 3 Jun 2026 10:14:05 +0200 Subject: [PATCH 37/67] enable freezing all but last layers of text encoder and allow bf16 for mps --- scripts/train_tiny.py | 9 ++---- src/mmcontext/modules/mmcontext_module.py | 38 +++-------------------- tests/test_adapter_callback.py | 14 ++++----- tests/test_mmcontext_module.py | 21 ++----------- 4 files changed, 17 insertions(+), 65 deletions(-) diff --git a/scripts/train_tiny.py b/scripts/train_tiny.py index 6bcae35..08176b4 100644 --- a/scripts/train_tiny.py +++ b/scripts/train_tiny.py @@ -317,12 +317,9 @@ def main(): # top N layers trainable is the usual fine-tuning sweet spot. if args.freeze_text_encoder: text_module = list(pipeline.children())[0] - if args.unfreeze_last_n > 0: - text_module.freeze_all_but_top_layers(args.unfreeze_last_n) - logger.info("Froze text encoder, keeping top %d layers trainable", args.unfreeze_last_n) - else: - text_module.freeze_text_encoder() - logger.info("Froze entire text encoder") + # unfreeze_last_n == 0 freezes the whole encoder; > 0 keeps the top N trainable. + text_module.freeze_all_but_top_layers(args.unfreeze_last_n) + logger.info("Froze text encoder, keeping top %d layers trainable", args.unfreeze_last_n) trainable = sum(p.numel() for p in pipeline.parameters() if p.requires_grad) total = sum(p.numel() for p in pipeline.parameters()) logger.info("Trainable params: %d / %d (%.1f%%)", trainable, total, 100.0 * trainable / total) diff --git a/src/mmcontext/modules/mmcontext_module.py b/src/mmcontext/modules/mmcontext_module.py index 249578b..7a2f04c 100644 --- a/src/mmcontext/modules/mmcontext_module.py +++ b/src/mmcontext/modules/mmcontext_module.py @@ -396,23 +396,6 @@ def get_word_embedding_dimension(self) -> int: # ------------------------------------------------------------------ # Freezing # ------------------------------------------------------------------ - def freeze_text_encoder(self, num_layers: int | None = None) -> None: - """Freeze text encoder parameters. - - Parameters - ---------- - num_layers : int, optional - If given, freeze only the first ``num_layers`` layers. The - remaining layers and the pooler (if any) stay trainable. - If ``None``, freeze all parameters. - """ - if num_layers is None: - for param in self.auto_model.parameters(): - param.requires_grad = False - logger.info("Froze all text encoder parameters") - else: - self._freeze_n_layers(num_layers) - def unfreeze_text_encoder(self) -> None: """Unfreeze all text encoder parameters.""" for param in self.auto_model.parameters(): @@ -434,25 +417,14 @@ def _get_encoder_layers(self) -> torch.nn.ModuleList | None: return self.auto_model.bert.encoder.layer return None - def _freeze_n_layers(self, num_layers: int) -> None: - """Freeze the first ``num_layers`` encoder layers.""" - layers = self._get_encoder_layers() - - if layers is None: - logger.warning("Could not identify encoder layers for partial freezing. Freezing all parameters instead.") - for param in self.auto_model.parameters(): - param.requires_grad = False - return - - for i, layer in enumerate(layers): - if i < num_layers: - for param in layer.parameters(): - param.requires_grad = False - logger.info("Froze first %d text encoder layers", num_layers) - def freeze_all_but_top_layers(self, num_trainable_layers: int) -> None: """Freeze the text encoder, keeping only the top layers trainable. + This is the single entry point for text-encoder freezing. Passing + ``0`` freezes the entire encoder; passing ``N`` freezes everything + except the top ``N`` transformer layers. To make the whole encoder + trainable again, use :meth:`unfreeze_text_encoder`. + Freezes every parameter of the text encoder, then re-enables gradients on the top ``num_trainable_layers`` transformer layers (counting from the output end) plus the pooler, if present. This is the typical diff --git a/tests/test_adapter_callback.py b/tests/test_adapter_callback.py index 835eaa6..2177996 100644 --- a/tests/test_adapter_callback.py +++ b/tests/test_adapter_callback.py @@ -90,7 +90,7 @@ def _call_epoch(self, callback, model, epoch: float): def test_text_encoder_frozen_before_unfreeze_epoch(self, pipeline_without_attention): """Text encoder parameters must stay frozen before the unfreeze epoch.""" model = pipeline_without_attention - model[0].freeze_text_encoder() + model[0].freeze_all_but_top_layers(0) cb = UnfreezeTextEncoderCallback(unfreeze_epoch=2.0) self._call_epoch(cb, model, epoch=0.0) @@ -102,7 +102,7 @@ def test_text_encoder_frozen_before_unfreeze_epoch(self, pipeline_without_attent def test_text_encoder_unfreezes_at_target_epoch(self, pipeline_without_attention): """Text encoder should be unfrozen exactly at the configured epoch.""" model = pipeline_without_attention - model[0].freeze_text_encoder() + model[0].freeze_all_but_top_layers(0) cb = UnfreezeTextEncoderCallback(unfreeze_epoch=2.0) self._call_epoch(cb, model, epoch=2.0) @@ -113,7 +113,7 @@ def test_text_encoder_unfreezes_at_target_epoch(self, pipeline_without_attention def test_text_encoder_unfreezes_past_target_epoch(self, pipeline_without_attention): """Unfreezing also triggers when epoch exceeds the target (e.g., epoch 3 > target 2).""" model = pipeline_without_attention - model[0].freeze_text_encoder() + model[0].freeze_all_but_top_layers(0) cb = UnfreezeTextEncoderCallback(unfreeze_epoch=2.0) self._call_epoch(cb, model, epoch=3.0) @@ -124,13 +124,13 @@ def test_text_encoder_unfreezes_past_target_epoch(self, pipeline_without_attenti def test_text_encoder_unfreezes_only_once(self, pipeline_without_attention): """The unfreeze action should happen exactly once regardless of subsequent epochs.""" model = pipeline_without_attention - model[0].freeze_text_encoder() + model[0].freeze_all_but_top_layers(0) cb = UnfreezeTextEncoderCallback(unfreeze_epoch=1.0) self._call_epoch(cb, model, epoch=1.0) # Manually re-freeze to check the callback doesn't unfreeze again - model[0].freeze_text_encoder() + model[0].freeze_all_but_top_layers(0) self._call_epoch(cb, model, epoch=2.0) assert _all_frozen(model[0].auto_model), "Should not re-unfreeze after unfrozen=True is set" @@ -150,7 +150,7 @@ def test_warns_when_no_compatible_module(self, caplog): def test_different_unfreeze_epochs(self, pipeline_without_attention): """Two callbacks with different epochs unfreeze independently.""" model = pipeline_without_attention - model[0].freeze_text_encoder() + model[0].freeze_all_but_top_layers(0) cb_epoch1 = UnfreezeTextEncoderCallback(unfreeze_epoch=1.0) cb_epoch3 = UnfreezeTextEncoderCallback(unfreeze_epoch=3.0) @@ -160,7 +160,7 @@ def test_different_unfreeze_epochs(self, pipeline_without_attention): assert cb_epoch1.unfrozen assert not cb_epoch3.unfrozen - model[0].freeze_text_encoder() # re-freeze for the next check + model[0].freeze_all_but_top_layers(0) # re-freeze for the next check # At epoch 3 — the epoch-3 callback fires self._call_epoch(cb_epoch3, model, epoch=3.0) assert cb_epoch3.unfrozen diff --git a/tests/test_mmcontext_module.py b/tests/test_mmcontext_module.py index 1074874..8a15fc6 100644 --- a/tests/test_mmcontext_module.py +++ b/tests/test_mmcontext_module.py @@ -326,30 +326,13 @@ def test_vector_store_management(self, module, omics_store): class TestFreezing: """Tests for freezing/unfreezing the text encoder.""" - def test_freeze_text_encoder(self, module): - """Freezing makes text encoder parameters non-trainable.""" - module.freeze_text_encoder() - for param in module.auto_model.parameters(): - assert not param.requires_grad - def test_unfreeze_text_encoder(self, module): - """Unfreezing restores gradient computation.""" - module.freeze_text_encoder() + """Unfreezing restores gradient computation after a full freeze.""" + module.freeze_all_but_top_layers(0) module.unfreeze_text_encoder() for param in module.auto_model.parameters(): assert param.requires_grad - def test_freeze_unfreeze_num_layers(self, module): - """Partial freezing: freeze only first N layers.""" - # Freeze first 2 layers (if the encoder has enough) - module.freeze_text_encoder(num_layers=2) - - # At least some params should be frozen, some not - frozen = sum(1 for p in module.auto_model.parameters() if not p.requires_grad) - trainable = sum(1 for p in module.auto_model.parameters() if p.requires_grad) - assert frozen > 0 - assert trainable > 0 - def test_freeze_all_but_top_layers_keeps_only_top(self, module): """freeze_all_but_top_layers(N) trains only the top N layers.""" layers = module._get_encoder_layers() From 25bf1b6052230d10741bf1038b5e2f3664998600 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Wed, 3 Jun 2026 11:41:37 +0200 Subject: [PATCH 38/67] let download adatause the same hash url as the new vector store method --- src/mmcontext/file_utils.py | 44 ++++++++++++++++++++++++++++++++----- 1 file changed, 39 insertions(+), 5 deletions(-) diff --git a/src/mmcontext/file_utils.py b/src/mmcontext/file_utils.py index b7a66a8..370d37d 100644 --- a/src/mmcontext/file_utils.py +++ b/src/mmcontext/file_utils.py @@ -1,4 +1,5 @@ # tests/utils.py +import hashlib import json import logging import os @@ -27,6 +28,16 @@ logger = logging.getLogger(__name__) +def url_to_cache_name(url: str) -> str: + """Deterministic short name for a URL, used as a cache file/directory name. + + Mirrors the scheme used by :func:`mmcontext.io.prepare_store.prepare_vector_store` + so that the same remote store maps to a stable, collision-free cache key + regardless of its position in a links list. + """ + return hashlib.sha256(url.encode()).hexdigest()[:16] + + def remove_corrupted_null_arrays(zarr_path: str | Path) -> list[str]: """Remove corrupted null arrays from zarr store. @@ -131,6 +142,15 @@ def load_test_adata_from_hf_dataset( ------ ValueError If the split references multiple different files. + + Notes + ----- + The downloaded store is cached under *save_dir* using a deterministic hash + of its URL (````), matching + :func:`mmcontext.io.prepare_store.prepare_vector_store`. This makes the + cache key stable and collision-free across datasets sharing the same + *save_dir* (the legacy ``chunk_0`` naming caused different datasets to + overwrite/false-hit each other). """ # 1) ensure there is exactly ONE unique link links, _ = collect_unique_links({"test": test_split}, split="test", link_column=link_column) @@ -146,6 +166,7 @@ def load_test_adata_from_hf_dataset( overwrite=False, zenodo_token=zenodo_token, force_drafts=force_drafts, + name_by_url_hash=True, ) local_path = next(iter(local_map.values())) @@ -470,6 +491,7 @@ def download_and_extract_links( zenodo_token: str | None = None, chunk_size: int = 8 * (1 << 20), # 8MB chunks for better performance force_drafts: bool = False, + name_by_url_hash: bool = False, ) -> dict[str, Path]: """ Download every share-link or handle local paths. If it is a ZIP (Nextcloud folder-download) either @@ -502,6 +524,14 @@ def download_and_extract_links( by removing ``/draft`` from URLs. This is a quick fix to allow using datasets created with draft links that were published remotely afterwards. Set to True if you actually need to download from a draft record. + name_by_url_hash : bool, default False + If False, downloaded files are named ``chunk_.`` using the + link's position in *links*. This is **not** stable across calls — two + different links each passed as the sole item collide on ``chunk_0``. + If True, files are named by a deterministic hash of the URL + (``.``), matching the caching scheme of + :func:`mmcontext.io.prepare_store.prepare_vector_store` and giving a + stable, collision-free cache key per URL. Returns ------- @@ -546,13 +576,17 @@ def _is_local_path(link: str) -> bool: continue # For URLs, proceed with download logic + # Base name for cached outputs: a stable per-URL hash, or the legacy + # position-based ``chunk_`` name. + base = url_to_cache_name(link) if name_by_url_hash else f"chunk_{idx}" + # ------------------------- check if present---- - already = target_dir / f"chunk_{idx}.zip" + already = target_dir / f"{base}.zip" if already.exists() and not overwrite: out_map[link] = already continue # ← skip download altogether - already = target_dir / f"chunk_{idx}.zarr" + already = target_dir / f"{base}.zarr" if already.exists() and not overwrite: out_map[link] = already continue @@ -689,20 +723,20 @@ def _is_local_path(link: str) -> bool: m4 = tmp.read_bytes()[:4] if m4 == PK_MAGIC: # ⇒ ZIP archive if extract: - out_path = target_dir / f"chunk_{idx}.zarr" + out_path = target_dir / f"{base}.zarr" if overwrite and out_path.exists(): shutil.rmtree(out_path) with zipfile.ZipFile(tmp) as zf: zf.extractall(out_path) tmp.unlink(missing_ok=True) else: # keep the zip - out_path = target_dir / f"chunk_{idx}.zip" + out_path = target_dir / f"{base}.zip" if overwrite and out_path.exists(): out_path.unlink() shutil.move(tmp, out_path) else: # raw .h5ad etc. suffix = Path(link).suffix or ".bin" - out_path = target_dir / f"chunk_{idx}{suffix}" + out_path = target_dir / f"{base}{suffix}" if overwrite and out_path.exists(): out_path.unlink() shutil.move(tmp, out_path) From 17913ad372b614d5eca483a419c090667ce4b0dd Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Wed, 3 Jun 2026 16:45:05 +0200 Subject: [PATCH 39/67] unify dataset preparation for training and infernece scenarios. Including tests for these new features --- src/mmcontext/embed/__init__.py | 4 + src/mmcontext/embed/dataset_prep.py | 439 ++++++++++++++++++++++++++++ tests/test_dataset_prep.py | 217 ++++++++++++++ 3 files changed, 660 insertions(+) create mode 100644 src/mmcontext/embed/dataset_prep.py create mode 100644 tests/test_dataset_prep.py diff --git a/src/mmcontext/embed/__init__.py b/src/mmcontext/embed/__init__.py index fb4b6bf..ccf3b41 100644 --- a/src/mmcontext/embed/__init__.py +++ b/src/mmcontext/embed/__init__.py @@ -1,4 +1,5 @@ from .cellwhisperer_utils import ensure_cellwhisperer_setup, process_cellwhisperer_dataset_model +from .dataset_prep import InferenceData, prepare_dataset, prepare_inference from .dataset_utils import SentenceDataset, collect_adata_subset, load_generic_dataset from .embed_pipeline import embed_pipeline, process_single_dataset_model from .model_utils import HFIndexedDataset, create_label_dataset, embed_labels, load_st_model, prepare_model_and_embed @@ -7,6 +8,9 @@ "SentenceDataset", "load_generic_dataset", "collect_adata_subset", + "prepare_dataset", + "prepare_inference", + "InferenceData", "HFIndexedDataset", "load_st_model", "prepare_model_and_embed", diff --git a/src/mmcontext/embed/dataset_prep.py b/src/mmcontext/embed/dataset_prep.py new file mode 100644 index 0000000..4fbecbe --- /dev/null +++ b/src/mmcontext/embed/dataset_prep.py @@ -0,0 +1,439 @@ +"""Unified dataset preparation for training and inference. + +mmcontext aligns text and omics embeddings with a sentence-transformers +pipeline whose first module is an :class:`~mmcontext.modules.MMContextModule`. +That module resolves omics samples at runtime from an attached +:class:`~mmcontext.io.VectorStore`: any input string starting with the omics +prefix (``"omics:"`` by default) is looked up in the store, while plain strings +are tokenised as text. + +This module provides a single, reusable preparation API that turns a raw +HuggingFace dataset into the column shape the trainer / encoder expects: + +* :func:`prepare_dataset` — the pure ``dataset -> dataset`` core. It builds the + ``anchor`` column (omics id for ``modality="bimodal"`` or cell-sentence text + for ``modality="text"``) and, for ``purpose="train"``, resolves the + ``positive`` / hard-negative columns. +* :func:`prepare_inference` — an orchestrator for the evaluation/inference path. + It loads the referenced AnnData chunk, subsets the dataset to it, builds an + anchor-ready dataset, and (for bimodal) builds and attaches a VectorStore to + the model. + +Training datasets carry ``positive`` and ``negative_*_idx`` columns; test +datasets carry only ``sample_idx``, ``cell_sentence_*`` and ``adata_link``. +The same :func:`prepare_dataset` call handles both via the ``purpose`` switch. +""" + +from __future__ import annotations + +import logging +from dataclasses import dataclass +from pathlib import Path +from typing import TYPE_CHECKING, Literal + +from datasets import Dataset, DatasetDict + +from mmcontext.utils import resolve_negative_indices_and_rename, truncate_cell_sentences + +if TYPE_CHECKING: + import anndata as ad + from sentence_transformers import SentenceTransformer + + from mmcontext.io import VectorStore + +logger = logging.getLogger(__name__) + +Purpose = Literal["train", "inference"] +Modality = Literal["bimodal", "text"] + + +def _has_negative_idx_columns(ds: Dataset) -> bool: + """Return True if *ds* exposes ``negative_*_idx`` columns.""" + return any(c.startswith("negative_") and c.endswith("_idx") for c in ds.column_names) + + +def _prepare_split( + ds: Dataset, + *, + purpose: Purpose, + modality: Modality, + primary_cell_sentence: str, + sample_id_col: str, + positive_col: str, + omics_prefix: str, + use_hard_negatives: bool, + truncate: bool, + truncate_kwargs: dict | None, +) -> Dataset: + """Prepare a single :class:`~datasets.Dataset` split. + + See :func:`prepare_dataset` for the meaning of the parameters. + """ + if modality not in ("bimodal", "text"): + raise ValueError(f"modality must be 'bimodal' or 'text', got {modality!r}") + if purpose not in ("train", "inference"): + raise ValueError(f"purpose must be 'train' or 'inference', got {purpose!r}") + + if primary_cell_sentence not in ds.column_names: + raise KeyError( + f"Primary cell-sentence column {primary_cell_sentence!r} not found. " + f"Available columns: {ds.column_names}" + ) + if modality == "bimodal" and sample_id_col not in ds.column_names: + raise KeyError( + f"modality='bimodal' requires the sample-id column {sample_id_col!r}. " + f"Available columns: {ds.column_names}" + ) + + # ------------------------------------------------------------------ + # text modality: optionally truncate the cell sentence before it + # becomes the anchor (gene filtering, max length, …). + # ------------------------------------------------------------------ + if modality == "text" and truncate: + tkwargs = dict(truncate_kwargs or {}) + max_length = tkwargs.pop("max_length", 64) + ds = truncate_cell_sentences(ds, primary_cell_sentence, max_length=max_length, **tkwargs) + + if purpose == "train": + return _prepare_train_split( + ds, + modality=modality, + primary_cell_sentence=primary_cell_sentence, + sample_id_col=sample_id_col, + positive_col=positive_col, + omics_prefix=omics_prefix, + use_hard_negatives=use_hard_negatives, + ) + return _prepare_inference_split( + ds, + modality=modality, + primary_cell_sentence=primary_cell_sentence, + sample_id_col=sample_id_col, + omics_prefix=omics_prefix, + ) + + +def _prepare_train_split( + ds: Dataset, + *, + modality: Modality, + primary_cell_sentence: str, + sample_id_col: str, + positive_col: str, + omics_prefix: str, + use_hard_negatives: bool, +) -> Dataset: + """Build a training-ready split: ``anchor`` + ``positive`` (+ ``negative_*``).""" + if positive_col not in ds.column_names: + raise KeyError( + f"purpose='train' requires a positive column {positive_col!r}. " + f"Available columns: {ds.column_names}" + ) + + resolve = use_hard_negatives and _has_negative_idx_columns(ds) + if resolve: + # Resolves negative_*_idx -> text, renames primary_cell_sentence -> 'anchor' + # and positive_col -> 'positive'. Keep sample_idx so we can build the + # omics anchor for the bimodal case afterwards. + ds = resolve_negative_indices_and_rename( + ds, + primary_cell_sentence_col=primary_cell_sentence, + positive_col=positive_col, + negative_prefix="negative", + index_col=sample_id_col, + remove_index_col=False, + ) + else: + if primary_cell_sentence != "anchor": + ds = ds.rename_column(primary_cell_sentence, "anchor") + if positive_col != "positive": + ds = ds.rename_column(positive_col, "positive") + + if modality == "bimodal": + ds = ds.map( + lambda row: {"anchor": f"{omics_prefix}{row[sample_id_col]}"}, + desc="Building omics anchors", + ) + + # Keep only resolved negatives (named "negative_N"); never raw "_idx" columns. + neg_cols = sorted(c for c in ds.column_names if c.startswith("negative") and not c.endswith("_idx")) + keep = ["anchor", "positive", *neg_cols] + ds = ds.select_columns(keep) + logger.info("Prepared train split: %d rows, columns=%s", len(ds), ds.column_names) + return ds + + +def _prepare_inference_split( + ds: Dataset, + *, + modality: Modality, + primary_cell_sentence: str, + sample_id_col: str, + omics_prefix: str, +) -> Dataset: + """Build an inference-ready split: ``anchor`` (+ ``sample_idx``/``adata_link``). + + No ``positive`` / ``negative_*`` columns are required or produced. + """ + if modality == "bimodal": + ds = ds.map( + lambda row: {"anchor": f"{omics_prefix}{row[sample_id_col]}"}, + desc="Building omics anchors", + ) + else: # text + if primary_cell_sentence != "anchor": + ds = ds.map( + lambda row: {"anchor": row[primary_cell_sentence]}, + desc="Building text anchors", + ) + + # Retain identifiers needed downstream to align embeddings with AnnData. + keep = ["anchor"] + for col in (sample_id_col, "adata_link", "share_link"): + if col in ds.column_names and col not in keep: + keep.append(col) + ds = ds.select_columns(keep) + logger.info("Prepared inference split: %d rows, columns=%s", len(ds), ds.column_names) + return ds + + +def prepare_dataset( + ds: Dataset | DatasetDict, + *, + purpose: Purpose = "train", + modality: Modality = "bimodal", + primary_cell_sentence: str = "cell_sentence_1", + sample_id_col: str = "sample_idx", + positive_col: str = "positive", + omics_prefix: str = "omics:", + use_hard_negatives: bool = True, + truncate: bool = False, + truncate_kwargs: dict | None = None, +) -> Dataset | DatasetDict: + """Prepare a raw HuggingFace dataset for training or inference. + + The first module of an mmcontext pipeline + (:class:`~mmcontext.modules.MMContextModule`) routes inputs by modality: + strings prefixed with *omics_prefix* are resolved through an attached + :class:`~mmcontext.io.VectorStore`, everything else is treated as text. + This function builds the ``anchor`` column accordingly and, for training, + the ``positive`` and hard-negative columns. + + Parameters + ---------- + ds : datasets.Dataset or datasets.DatasetDict + Raw dataset. A training dataset is expected to carry *positive_col* and + (optionally) ``negative_*_idx`` columns; an inference/test dataset only + needs *sample_id_col* / *primary_cell_sentence* (and an adata link for + the bimodal path). + purpose : {"train", "inference"}, default "train" + ``"train"`` produces ``anchor`` + ``positive`` (+ ``negative_*``). + ``"inference"`` produces only ``anchor`` plus retained identifier + columns; ``positive`` / ``negative_*`` are neither required nor created. + modality : {"bimodal", "text"}, default "bimodal" + ``"bimodal"`` builds ``anchor = f"{omics_prefix}{sample_idx}"`` so the + model resolves it via the VectorStore. ``"text"`` uses the + *primary_cell_sentence* text as the anchor. + primary_cell_sentence : str, default "cell_sentence_1" + Column holding the cell sentence (used as the text anchor and as the + primary column for negative resolution during training). + sample_id_col : str, default "sample_idx" + Column with sample identifiers (used to build omics anchors and to + resolve negatives). Required for ``modality="bimodal"``. + positive_col : str, default "positive" + Column with the positive text. Required for ``purpose="train"``. + omics_prefix : str, default "omics:" + Prefix marking an omics sample id. Must match the prefix configured on + the model's :class:`~mmcontext.modules.MMContextModule`. + use_hard_negatives : bool, default True + For ``purpose="train"``: resolve ``negative_*_idx`` columns into text + negatives when present. Ignored when no such columns exist. + truncate : bool, default False + For ``modality="text"``: truncate the cell sentence via + :func:`~mmcontext.utils.truncate_cell_sentences` before it becomes the + anchor. + truncate_kwargs : dict, optional + Extra keyword arguments forwarded to + :func:`~mmcontext.utils.truncate_cell_sentences` (e.g. ``max_length``, + ``filter_strings``). Defaults to ``max_length=64``. + + Returns + ------- + datasets.Dataset or datasets.DatasetDict + Same container type as *ds*, with prepared columns. + + Examples + -------- + >>> train_ds = prepare_dataset(raw, purpose="train", modality="bimodal") + >>> train_ds.column_names + ['anchor', 'positive', 'negative_1'] + >>> test_ds = prepare_dataset(raw_test, purpose="inference", modality="bimodal") + >>> test_ds.column_names + ['anchor', 'sample_idx', 'adata_link'] + """ + kwargs = dict( + purpose=purpose, + modality=modality, + primary_cell_sentence=primary_cell_sentence, + sample_id_col=sample_id_col, + positive_col=positive_col, + omics_prefix=omics_prefix, + use_hard_negatives=use_hard_negatives, + truncate=truncate, + truncate_kwargs=truncate_kwargs, + ) + + if isinstance(ds, DatasetDict): + return DatasetDict({name: _prepare_split(split, **kwargs) for name, split in ds.items()}) + if isinstance(ds, Dataset): + return _prepare_split(ds, **kwargs) + raise TypeError(f"prepare_dataset expects a Dataset or DatasetDict, got {type(ds).__name__}") + + +@dataclass +class InferenceData: + """Bundle of everything needed to encode and evaluate one test chunk. + + Attributes + ---------- + dataset : datasets.Dataset + Anchor-ready dataset aligned to *adata* (encode ``dataset["anchor"]``). + adata : anndata.AnnData + AnnData chunk referenced by the dataset, subset to the dataset rows. + vector_store : VectorStore or None + Store attached to the model for the bimodal path; ``None`` for text. + local_path : pathlib.Path + Path to the downloaded/cached AnnData store. + """ + + dataset: Dataset + adata: "ad.AnnData" + vector_store: "VectorStore | None" + local_path: Path + + +def prepare_inference( + model: "SentenceTransformer", + ds: Dataset, + *, + modality: Modality = "bimodal", + obsm_key: str | None = None, + cache_dir: str | Path, + store_path: str | Path | None = None, + sample_id_col: str = "sample_idx", + adata_link_col: str = "adata_link", + omics_prefix: str = "omics:", + zenodo_token: str | None = None, + truncate: bool = False, + truncate_kwargs: dict | None = None, + overwrite_store: bool = False, +) -> InferenceData: + """Wire up a model + dataset + AnnData for inference on one test chunk. + + This replaces the manual sequence of loading the AnnData chunk, subsetting + the dataset, building a VectorStore, and attaching it to the model. For the + bimodal path it builds the store from *obsm_key* and calls + ``model[0].set_vector_store(store)`` so ``model.encode(bundle.dataset["anchor"])`` + works directly. + + Parameters + ---------- + model : sentence_transformers.SentenceTransformer + Model whose first module is an + :class:`~mmcontext.modules.MMContextModule` (only required for the + bimodal path, where the VectorStore is attached to it). + ds : datasets.Dataset + Test split referencing a single AnnData chunk via *adata_link_col*. + modality : {"bimodal", "text"}, default "bimodal" + ``"bimodal"`` builds and attaches a VectorStore; ``"text"`` skips it. + obsm_key : str, optional + ``adata.obsm`` key to extract for the VectorStore (e.g. ``"X_scvi_fm"``). + Required when ``modality="bimodal"``. + cache_dir : str or Path + Directory for caching the downloaded AnnData store (shared by the chunk + loader and the VectorStore builder, which use the same cache key). + store_path : str or Path, optional + Output ``.mmap`` path for the VectorStore. Defaults to + ``/vector_store_inference.mmap``. Unused for text modality. + sample_id_col : str, default "sample_idx" + Column with sample identifiers. + adata_link_col : str, default "adata_link" + Column with the link to the AnnData chunk. + omics_prefix : str, default "omics:" + Prefix for omics anchors (must match the model's module). + zenodo_token : str, optional + Token for authenticating Zenodo draft downloads. + truncate : bool, default False + Forwarded to :func:`prepare_dataset` (text modality). + truncate_kwargs : dict, optional + Forwarded to :func:`prepare_dataset` (text modality). + overwrite_store : bool, default False + Rebuild the VectorStore even if *store_path* already exists. + + Returns + ------- + InferenceData + Bundle with the prepared dataset, the subset AnnData, the VectorStore + (or ``None``), and the local store path. + + Examples + -------- + >>> bundle = prepare_inference(model, test_ds, obsm_key="X_scvi_fm", cache_dir="./cache") + >>> emb = model.encode(bundle.dataset["anchor"]) + >>> bundle.adata.obsm["mmcontext_emb"] = emb + """ + # Imported here to keep module import light and avoid heavy/circular deps. + from mmcontext.file_utils import load_test_adata_from_hf_dataset, subset_dataset_by_chunk + + if modality == "bimodal" and obsm_key is None: + raise ValueError("obsm_key is required when modality='bimodal'.") + + # The text anchor source follows from the modality: bimodal anchors are + # omics ids (cell_sentence unused), text anchors use the text description. + primary_cell_sentence = "cell_sentence_1" if modality == "bimodal" else "cell_sentence_2" + + cache_dir = Path(cache_dir) + + # 1) Download + load the single AnnData chunk referenced by this split. + adata, local_path = load_test_adata_from_hf_dataset( + ds, + save_dir=cache_dir, + layer_key=obsm_key if modality == "bimodal" else None, + link_column=adata_link_col, + zenodo_token=zenodo_token, + ) + + # 2) Restrict the dataset to the rows present in this chunk. + adata, ds_sub = subset_dataset_by_chunk(adata, ds, sample_idx_col=sample_id_col) + + # 3) Build the anchor-ready dataset (no positive/negative needed). + dataset = prepare_dataset( + ds_sub, + purpose="inference", + modality=modality, + primary_cell_sentence=primary_cell_sentence, + sample_id_col=sample_id_col, + omics_prefix=omics_prefix, + truncate=truncate, + truncate_kwargs=truncate_kwargs, + ) + + # 4) For bimodal, build + attach the VectorStore so omics anchors resolve. + vector_store: "VectorStore | None" = None + if modality == "bimodal": + from mmcontext.io import prepare_vector_store + + if store_path is None: + store_path = cache_dir / "vector_store_inference.mmap" + vector_store = prepare_vector_store( + ds_sub, + obsm_key=obsm_key, + output_path=store_path, + cache_dir=cache_dir, + sample_id_column=sample_id_col, + adata_link_column=adata_link_col, + overwrite=overwrite_store, + ) + model[0].set_vector_store(vector_store) + + return InferenceData(dataset=dataset, adata=adata, vector_store=vector_store, local_path=local_path) diff --git a/tests/test_dataset_prep.py b/tests/test_dataset_prep.py new file mode 100644 index 0000000..62a1eee --- /dev/null +++ b/tests/test_dataset_prep.py @@ -0,0 +1,217 @@ +"""Tests for mmcontext.embed.dataset_prep — unified train/inference prep. + +The pure ``prepare_dataset`` tests run entirely in-memory. The +``prepare_inference`` integration test uses a local AnnData zarr store on disk +(no network) and a lightweight fake model that only needs ``set_vector_store``. +""" + +from __future__ import annotations + +import os +import tempfile + +import anndata as ad +import numpy as np +import pandas as pd +import pytest +from datasets import Dataset, DatasetDict + +from mmcontext.embed import InferenceData, prepare_dataset, prepare_inference + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- +@pytest.fixture +def tmp_dir(): + with tempfile.TemporaryDirectory() as d: + yield d + + +def _train_ds(n: int = 6) -> Dataset: + """Training-shaped dataset with positive + negative_*_idx columns.""" + return Dataset.from_dict( + { + "sample_idx": [f"cell_{i}" for i in range(n)], + "cell_sentence_1": [f"GENE{i} CALM1 GNAS" for i in range(n)], + "cell_sentence_2": [f"gene desc {i}" for i in range(n)], + "positive": [f"a description of cell {i}" for i in range(n)], + "negative_1_idx": [f"cell_{(i + 1) % n}" for i in range(n)], + "negative_2_idx": [f"cell_{(i + 2) % n}" for i in range(n)], + "adata_link": ["local://chunk"] * n, + } + ) + + +def _test_ds(n: int = 6) -> Dataset: + """Inference-shaped dataset: no positive/negative columns.""" + return Dataset.from_dict( + { + "sample_idx": [f"cell_{i}" for i in range(n)], + "cell_sentence_1": [f"GENE{i} CALM1 GNAS" for i in range(n)], + "cell_sentence_2": [f"gene desc {i}" for i in range(n)], + "adata_link": ["local://chunk"] * n, + } + ) + + +@pytest.fixture +def adata_zarr(tmp_dir): + """A real AnnData written to a .zarr directory (readable by anndata).""" + n_obs, d = 6, 8 + rng = np.random.default_rng(0) + obs = pd.DataFrame(index=[f"cell_{i}" for i in range(n_obs)]) + adata = ad.AnnData(X=rng.standard_normal((n_obs, 4)).astype(np.float32), obs=obs) + adata.obsm["X_test"] = rng.standard_normal((n_obs, d)).astype(np.float32) + zarr_path = os.path.join(tmp_dir, "chunk.zarr") + adata.write_zarr(zarr_path) + return zarr_path, n_obs, d + + +class _FakeModule: + def __init__(self): + self.store = None + + def set_vector_store(self, store): + self.store = store + + +class _FakeModel: + """Stand-in for a SentenceTransformer: only ``model[0]`` is exercised.""" + + def __init__(self): + self._module = _FakeModule() + + def __getitem__(self, idx): + return self._module + + +# --------------------------------------------------------------------------- +# prepare_dataset — training +# --------------------------------------------------------------------------- +class TestPrepareTrain: + def test_bimodal_columns_and_omics_anchor(self): + out = prepare_dataset(_train_ds(), purpose="train", modality="bimodal") + assert out.column_names == ["anchor", "positive", "negative_1", "negative_2"] + assert all(a.startswith("omics:") for a in out["anchor"]) + # omics ids carry the original sample_idx + assert out["anchor"][0] == "omics:cell_0" + + def test_text_anchor_is_cell_sentence(self): + raw = _train_ds() + out = prepare_dataset(raw, purpose="train", modality="text") + assert out.column_names == ["anchor", "positive", "negative_1", "negative_2"] + # anchor equals the (unprefixed) cell sentence text + assert out["anchor"] == raw["cell_sentence_1"] + + def test_without_hard_negatives(self): + out = prepare_dataset(_train_ds(), purpose="train", modality="bimodal", use_hard_negatives=False) + assert out.column_names == ["anchor", "positive"] + assert all(a.startswith("omics:") for a in out["anchor"]) + + def test_missing_positive_raises(self): + ds = _test_ds() # no positive column + with pytest.raises(KeyError, match="positive"): + prepare_dataset(ds, purpose="train", modality="bimodal") + + +# --------------------------------------------------------------------------- +# prepare_dataset — inference +# --------------------------------------------------------------------------- +class TestPrepareInference: + def test_bimodal_keeps_identifiers(self): + out = prepare_dataset(_test_ds(), purpose="inference", modality="bimodal") + assert "anchor" in out.column_names + assert "sample_idx" in out.column_names + assert "adata_link" in out.column_names + assert "positive" not in out.column_names + assert all(a.startswith("omics:") for a in out["anchor"]) + + def test_text_anchor(self): + raw = _test_ds() + out = prepare_dataset(raw, purpose="inference", modality="text") + assert out["anchor"] == raw["cell_sentence_1"] + + def test_no_positive_or_negative_required(self): + # The whole point: a test dataset without positive/negative must not error. + out = prepare_dataset(_test_ds(), purpose="inference", modality="bimodal") + assert len(out) == 6 + + def test_datasetdict(self): + dd = DatasetDict({"test": _test_ds()}) + out = prepare_dataset(dd, purpose="inference", modality="bimodal") + assert isinstance(out, DatasetDict) + assert all(a.startswith("omics:") for a in out["test"]["anchor"]) + + +# --------------------------------------------------------------------------- +# prepare_dataset — validation +# --------------------------------------------------------------------------- +class TestValidation: + def test_bad_modality(self): + with pytest.raises(ValueError, match="modality"): + prepare_dataset(_test_ds(), purpose="inference", modality="nope") + + def test_bad_purpose(self): + with pytest.raises(ValueError, match="purpose"): + prepare_dataset(_test_ds(), purpose="nope", modality="text") + + def test_bimodal_requires_sample_id(self): + ds = Dataset.from_dict({"cell_sentence_1": ["a", "b"]}) + with pytest.raises(KeyError, match="sample"): + prepare_dataset(ds, purpose="inference", modality="bimodal") + + +# --------------------------------------------------------------------------- +# prepare_inference — end-to-end with local zarr +# --------------------------------------------------------------------------- +class TestPrepareInferenceOrchestrator: + def test_bimodal_builds_store_and_subsets(self, adata_zarr, tmp_dir): + zarr_path, n_obs, d = adata_zarr + ds = _test_ds(n_obs) + ds = ds.remove_columns("adata_link") + ds = ds.add_column("adata_link", [zarr_path] * n_obs) + + model = _FakeModel() + bundle = prepare_inference( + model, + ds, + modality="bimodal", + obsm_key="X_test", + cache_dir=tmp_dir, + store_path=os.path.join(tmp_dir, "store.mmap"), + ) + + assert isinstance(bundle, InferenceData) + assert bundle.vector_store is not None + assert bundle.vector_store.dim == d + # store was attached to the model's first module + assert model[0].store is bundle.vector_store + # dataset aligns with the loaded chunk + assert len(bundle.dataset) == bundle.adata.n_obs == n_obs + # every omics anchor resolves in the store + for anchor in bundle.dataset["anchor"]: + sid = anchor[len("omics:") :] + assert bundle.vector_store[sid].shape == (d,) + + def test_text_skips_store(self, adata_zarr, tmp_dir): + zarr_path, n_obs, _ = adata_zarr + ds = _test_ds(n_obs) + ds = ds.remove_columns("adata_link") + ds = ds.add_column("adata_link", [zarr_path] * n_obs) + + model = _FakeModel() + bundle = prepare_inference( + model, + ds, + modality="text", + cache_dir=tmp_dir, + ) + assert bundle.vector_store is None + assert model[0].store is None + # text modality picks cell_sentence_2 internally + assert bundle.dataset["anchor"] == ds["cell_sentence_2"] + + def test_bimodal_requires_obsm_key(self, tmp_dir): + with pytest.raises(ValueError, match="obsm_key"): + prepare_inference(_FakeModel(), _test_ds(), modality="bimodal", cache_dir=tmp_dir) From 36d3fc0f52ebd0c48467703fa7af51ea41e08f30 Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Wed, 3 Jun 2026 16:45:44 +0200 Subject: [PATCH 40/67] include updated dataset prepapation into training script and eval notebook --- scripts/train_tiny.py | 130 ++------ tutorials/evaluate_model_2.0.ipynb | 505 +++++++++++++++++++++++++++++ 2 files changed, 532 insertions(+), 103 deletions(-) create mode 100644 tutorials/evaluate_model_2.0.ipynb diff --git a/scripts/train_tiny.py b/scripts/train_tiny.py index c9f2eea..7cffbb5 100644 --- a/scripts/train_tiny.py +++ b/scripts/train_tiny.py @@ -2,9 +2,9 @@ """Train an MMContext model on the cxg_schaefer_tiny dataset. Quick-start training script using sentence-transformers v5.4+ pipeline. -Supports two modes: +Supports two modalities: - 1. **gene-list + text** (default) — gene-name strings as anchors, text + 1. **text** — gene-name strings as anchors, text descriptions as positives. No VectorStore needed. 2. **bimodal** — omics vectors from a VectorStore as anchors, text as positives. Requires ``--vector-store`` pointing to a ``.mmap`` file @@ -12,11 +12,11 @@ Usage:: - # Gene-list mode (default) - python scripts/train_tiny.py --output-dir outputs/tiny_genelist + # Text modality + python scripts/train_tiny.py --output-dir outputs/tiny_text - # Bimodal mode - python scripts/train_tiny.py --mode bimodal \ + # Bimodal modality (default) + python scripts/train_tiny.py --modality bimodal \ --vector-store /path/to/store.mmap \ --output-dir outputs/tiny_bimodal @@ -42,8 +42,8 @@ from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss from sentence_transformers.sentence_transformer.modules import Normalize, Pooling +from mmcontext.embed import prepare_dataset from mmcontext.modules import AdapterModule, MMContextModule -from mmcontext.utils import resolve_negative_indices_and_rename logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s %(message)s") logger = logging.getLogger(__name__) @@ -54,90 +54,6 @@ HF_DATASET = "jo-mengr/cxg_schaefer_tiny" -def prepare_genelist_dataset(ds, use_hard_negatives: bool = True): - """Reshape dataset for gene-list + text training. - - Returns a HF Dataset with columns ``anchor`` (gene names), ``positive`` - (text description), and optionally resolved hard-negative columns - ``negative_1``, ``negative_2``, … when ``use_hard_negatives`` is True and - the dataset exposes ``negative_*_idx`` columns. - """ - has_neg_idx = any(c for c in ds.column_names if c.startswith("negative_") and c.endswith("_idx")) - if use_hard_negatives and has_neg_idx: - ds = resolve_negative_indices_and_rename( - ds, - primary_cell_sentence_col="cell_sentence_1", - positive_col="positive", - negative_prefix="negative", - index_col="sample_idx", - remove_index_col=True, - ) - neg_cols = sorted(c for c in ds.column_names if c.startswith("negative")) - ds = ds.select_columns(["anchor", "positive"] + neg_cols) - logger.info( - "Gene-list dataset (with hard negatives): %d samples, columns=%s", - len(ds), - ds.column_names, - ) - else: - # cell_sentence_1 = space-separated gene names → anchor - ds = ds.rename_columns({"cell_sentence_1": "anchor"}) - ds = ds.select_columns(["anchor", "positive"]) - logger.info("Gene-list dataset: %d samples, columns=%s", len(ds), ds.column_names) - return ds - - -def prepare_bimodal_dataset(ds, use_hard_negatives: bool = True): - """Reshape dataset for bimodal (omics + text) training. - - Prefixes ``sample_idx`` with ``omics:`` so MMContextModule routes them - through the VectorStore path. - - Returns a HF Dataset with columns ``anchor``, ``positive``, and optionally - resolved hard-negative columns when ``use_hard_negatives`` is True and the - dataset exposes ``negative_*_idx`` columns. - - Notes - ----- - Hard negatives are resolved to text: odd-numbered negatives become the - positive text of another sample; even-numbered negatives become the - gene-list text of another sample. - """ - has_neg_idx = any(c for c in ds.column_names if c.startswith("negative_") and c.endswith("_idx")) - if use_hard_negatives and has_neg_idx: - ds = resolve_negative_indices_and_rename( - ds, - primary_cell_sentence_col="cell_sentence_1", - positive_col="positive", - negative_prefix="negative", - index_col="sample_idx", - remove_index_col=False, # keep sample_idx so we can build the omics anchor - ) - - def _prefix_omics(example): - example["anchor"] = f"omics:{example['sample_idx']}" - return example - - ds = ds.map(_prefix_omics) - neg_cols = sorted(c for c in ds.column_names if c.startswith("negative")) - ds = ds.select_columns(["anchor", "positive"] + neg_cols) - logger.info( - "Bimodal dataset (with hard negatives): %d samples, columns=%s", - len(ds), - ds.column_names, - ) - else: - - def _prefix_omics(example): - example["anchor"] = f"omics:{example['sample_idx']}" - return example - - ds = ds.map(_prefix_omics) - ds = ds.select_columns(["anchor", "positive"]) - logger.info("Bimodal dataset: %d samples, columns=%s", len(ds), ds.column_names) - return ds - - # --------------------------------------------------------------------------- # Pipeline builder # --------------------------------------------------------------------------- @@ -190,10 +106,10 @@ def main(): """Train MMContext on cxg_schaefer_tiny dataset.""" parser = argparse.ArgumentParser(description="Train MMContext on cxg_schaefer_tiny") parser.add_argument( - "--mode", - choices=["genelist", "bimodal"], - default="genelist", - help="Training mode (default: genelist)", + "--modality", + choices=["text", "bimodal"], + default="bimodal", + help="Training modality (default: bimodal)", ) parser.add_argument( "--text-model", @@ -203,7 +119,7 @@ def main(): parser.add_argument( "--vector-store", default=None, - help="Path to .mmap VectorStore file. For bimodal mode: if omitted, " + help="Path to .mmap VectorStore file. For bimodal modality: if omitted, " "the store is built automatically from adata_link + sample_idx " "columns using --obsm-key.", ) @@ -247,10 +163,18 @@ def main(): # --- Dataset --- ds_raw = load_dataset(args.dataset, split="train") - if args.mode == "bimodal": - ds = prepare_bimodal_dataset(ds_raw, use_hard_negatives=args.hard_negatives) - else: - ds = prepare_genelist_dataset(ds_raw, use_hard_negatives=args.hard_negatives) + # genelist mode uses cell-sentence text anchors; bimodal uses omics ids. + # The primary cell sentence is chosen by modality: cell_sentence_1 + # (gene-list) for bimodal, cell_sentence_2 (text description) for text. + modality = "bimodal" if args.modality == "bimodal" else "text" + primary_cell_sentence = "cell_sentence_1" if modality == "bimodal" else "cell_sentence_2" + ds = prepare_dataset( + ds_raw, + purpose="train", + modality=modality, + primary_cell_sentence=primary_cell_sentence, + use_hard_negatives=args.hard_negatives, + ) # --- Pipeline --- pipeline = build_pipeline( @@ -259,8 +183,8 @@ def main(): shared_dim=args.shared_dim, ) - # Attach VectorStore for bimodal mode - if args.mode == "bimodal": + # Attach VectorStore for bimodal modality + if args.modality == "bimodal": from mmcontext.io import VectorStore, prepare_vector_store if args.vector_store is not None: @@ -324,7 +248,7 @@ def main(): loss=loss, ) - logger.info("Starting training: mode=%s, epochs=%d, batch_size=%d", args.mode, args.epochs, args.batch_size) + logger.info("Starting training: modality=%s, epochs=%d, batch_size=%d", args.modality, args.epochs, args.batch_size) trainer.train() # --- Save final model --- diff --git a/tutorials/evaluate_model_2.0.ipynb b/tutorials/evaluate_model_2.0.ipynb new file mode 100644 index 0000000..a6594dc --- /dev/null +++ b/tutorials/evaluate_model_2.0.ipynb @@ -0,0 +1,505 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial shows how to evaluate an MMContext Sentence Transformers model on one dataset. It assumes you created a huggingface dataset, which contains the cell representations (either cell ids for numerical embeddings or cell sentences for text_only usage). Such datasets can be created with a pipeline available through the https://github.com/mengerj/adata_hf_datasets repo. If you instead want to start from an adata object, see the tutorial pretrained_inference.ipynb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figure 1D in the publication was created with this notebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from datasets import load_dataset\n", + "\n", + "repo_name = \"jo-mengr\"\n", + "dataset_name = \"hiha_100k\"\n", + "split_name = \"test\"\n", + "label_key = \"AIFI_L2\" # \"AIFI_L2\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = load_dataset(f\"{repo_name}/{dataset_name}\")\n", + "test_dataset = dataset[split_name]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sentence_transformers import SentenceTransformer\n", + "\n", + "#model_name = \"jo-mengr/mmcontext-pubmedbert-gs10k\"\n", + "model_name = \"../outputs/tiny_model/final\"\n", + "model = SentenceTransformer(model_name, trust_remote_code=True)\n", + "data_type = \"scvi_fm\"\n", + "layer_key = f\"X_{data_type}\"\n", + "text_only = False # set True for text-based models (uses cell_sentence_2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from mmcontext.embed import prepare_inference\n", + "\n", + "# One call replaces: load_test_adata_from_hf_dataset + subset_dataset_by_chunk\n", + "# + prepare_vector_store + set_vector_store (bimodal) / truncation (text).\n", + "# The cell sentence is chosen internally from the modality\n", + "# (cell_sentence_1 for bimodal omics, cell_sentence_2 for text).\n", + "modality = \"text\" if text_only else \"bimodal\"\n", + "bundle = prepare_inference(\n", + " model,\n", + " test_dataset,\n", + " modality=modality,\n", + " obsm_key=layer_key,\n", + " cache_dir=f\"../data/test_adata/{dataset_name}\",\n", + " store_path=os.path.join(\"../outputs/tiny_model/\", \"vector_store_test_data.mmap\"),\n", + " zenodo_token=os.getenv(\"ZENODO_TOKEN\"),\n", + " truncate=text_only,\n", + " truncate_kwargs={\"max_length\": 64, \"filter_strings\": [\"RPS\", \"RPL\", \"MT\"]},\n", + ")\n", + "adata = bundle.adata\n", + "dataset_ready = bundle.dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_to_use = dataset_ready # [split_name]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "text_encoder_name = model[0].model_name_or_path\n", + "text_encoder = SentenceTransformer(text_encoder_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_to_use[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "omics_embeddings = model.encode(dataset_to_use[\"anchor\"])\n", + "adata.obsm[\"mmcontext_emb\"] = omics_embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_colours = len(adata.obs[\"AIFI_L1\"].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "\n", + "auto_colors = sns.color_palette(\"tab10\", n_colours)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auto_colors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "label_colors = {\n", + " \"T cell\": auto_colors[7],\n", + " \"B cell\": auto_colors[0],\n", + " \"NK cell\": auto_colors[1],\n", + " \"Monocyte\": auto_colors[2],\n", + " \"DC\": auto_colors[3],\n", + " \"Platelet\": auto_colors[4],\n", + " \"Progenitor cell\": auto_colors[5],\n", + " \"ILC\": auto_colors[6],\n", + " \"Erythrocyte\": auto_colors[8],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mmcontext.eval import get\n", + "\n", + "EvClass = get(\"LabelSimilarity\")\n", + "ev = EvClass(\n", + " auto_filter_labels=False,\n", + " umap_n_neighbors=10,\n", + " umap_min_dist=0.4,\n", + " similarity=\"cosine\",\n", + " logit_scale=1,\n", + " score_norm_method=None,\n", + " label_colors=None,\n", + " annotation_fontsize=16,\n", + " font_family=\"Arial\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# precompute umap coordinates to reuse on subset\n", + "full_omics_embeddings = adata.obsm[\"mmcontext_emb\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# full_cell_umap = ev._compute_umap(full_omics_embeddings)\n", + "# add umap coordinates to adata\n", + "# adata.obsm[\"cell_umap\"] = full_cell_umap" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "full_query_labels = adata.obs[label_key].unique().tolist()\n", + "full_label_embeddings = model.encode(full_query_labels)\n", + "full_true_labels = adata.obs[label_key]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "result = ev.compute(\n", + " omics_embeddings=full_omics_embeddings,\n", + " label_embeddings=full_label_embeddings,\n", + " query_labels=full_query_labels,\n", + " true_labels=full_true_labels,\n", + " label_key=label_key,\n", + " out_dir=Path(f\"LabelSimilarity/{model_name}/{dataset_name}\"), # Pass output directory for caching\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ev.plot(\n", + " omics_embeddings=full_omics_embeddings,\n", + " # cell_umap=full_cell_umap,\n", + " out_dir=Path(f\"LabelSimilarity/{model_name}/{dataset_name}/{label_key}_combined\"),\n", + " label_embeddings=full_label_embeddings,\n", + " query_labels=full_query_labels,\n", + " true_labels=full_true_labels,\n", + " label_key=label_key, # column name (e.g. \"celltype\")\n", + " save_format=\"svg\",\n", + " figsize=(2.5, 2.5),\n", + " dpi=300,\n", + " font_size=12,\n", + " font_style=\"normal\",\n", + " font_weight=\"normal\",\n", + " legend_fontsize=54,\n", + " axis_label_size=20,\n", + " axis_tick_size=12,\n", + " point_size=0.25,\n", + " legend_layout=\"vertical\",\n", + " legend_point_size=16,\n", + " umap_method=\"combined\",\n", + " label_min_distance=0.2,\n", + " label_spring_strength=0.5,\n", + " label_repulsion_strength=1,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Option to subset adata based on one or more label values (e.g., \"Monocyte\" and \"DC\")\n", + "subset_label_values = [\"T cell\"] # Change this list to your desired label values\n", + "subset_label_key = \"AIFI_L1\"\n", + "annotation_label_key = \"AIFI_L2\"\n", + "# Subset the AnnData object for any of the specified label values\n", + "adata_subset = adata[adata.obs[subset_label_key].isin(subset_label_values)].copy()\n", + "subset_labels = adata_subset.obs[annotation_label_key].values.unique()\n", + "label_embeddings_subset = model.encode(subset_labels)\n", + "# Create a new LabelSimilarity evaluator instance\n", + "# ev_subset = EvClass(auto_filter_labels=False, umap_n_neighbors=15, umap_min_dist=0.5)\n", + "subset_label_string = \"_\".join(subset_label_values)\n", + "subset_omics_embeddings = adata_subset.obsm[\"mmcontext_emb\"]\n", + "# subset_umap_coords = adata_subset.obsm[\"cell_umap\"]\n", + "# ev_subset.eb_lfdr_q = 0.01\n", + "ev = EvClass(\n", + " auto_filter_labels=False,\n", + " umap_n_neighbors=10,\n", + " umap_min_dist=0.4,\n", + " similarity=\"cosine\",\n", + " logit_scale=1,\n", + " score_norm_method=None,\n", + " font_family=\"Arial\",\n", + " annotation_fontsize=18,\n", + ")\n", + "# Compute metrics on the subsetted data\n", + "result_subset = ev.compute(\n", + " omics_embeddings=subset_omics_embeddings,\n", + " label_embeddings=label_embeddings_subset,\n", + " query_labels=subset_labels,\n", + " true_labels=adata_subset.obs[annotation_label_key],\n", + " label_key=annotation_label_key,\n", + " out_dir=Path(\n", + " f\"LabelSimilarity/{model_name}/{dataset_name}/{annotation_label_key}_subset_{subset_label_string}/results\"\n", + " ),\n", + ")\n", + "\n", + "# Plot results for the subset\n", + "ev.plot(\n", + " omics_embeddings=subset_omics_embeddings,\n", + " # cell_umap=subset_umap_coords,\n", + " out_dir=Path(\n", + " f\"LabelSimilarity/{model_name}/{dataset_name}/{annotation_label_key}_subset_{subset_label_string}_combined\"\n", + " ),\n", + " label_embeddings=label_embeddings_subset,\n", + " query_labels=subset_labels,\n", + " true_labels=adata_subset.obs[annotation_label_key],\n", + " label_key=annotation_label_key,\n", + " save_format=\"svg\",\n", + " figsize=(2.5, 2.5),\n", + " dpi=300,\n", + " font_size=12,\n", + " axis_tick_size=12,\n", + " font_style=\"normal\",\n", + " font_weight=\"normal\",\n", + " axis_label_size=20,\n", + " point_size=0.25,\n", + " legend_layout=\"vertical\",\n", + " legend_point_size=20,\n", + " umap_method=\"combined\",\n", + " label_min_distance=0.15,\n", + " label_spring_strength=0.35,\n", + " label_repulsion_strength=1.4,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result_subset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualise the embeddings\n", + "from mmcontext.pl import plot_umap\n", + "from mmcontext.utils import consolidate_low_frequency_categories\n", + "\n", + "current_key = label_key\n", + "adata_cut = consolidate_low_frequency_categories(adata, [current_key], threshold=50, remove=True)\n", + "emb_key = \"mmcontext_emb\"\n", + "plot_umap(\n", + " adata,\n", + " color_key=label_key,\n", + " embedding_key=emb_key,\n", + " save_format=\"svg\",\n", + " save_dir=f\"figs/{model_name}/{dataset_name}\",\n", + " save_plot=False,\n", + " title=\"\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualise the embeddings\n", + "from mmcontext.pl import plot_umap\n", + "from mmcontext.utils import consolidate_low_frequency_categories\n", + "\n", + "current_key = label_key\n", + "adata_cut = consolidate_low_frequency_categories(adata, [current_key], threshold=1, remove=False)\n", + "emb_key = layer_key\n", + "plot_umap(\n", + " adata_cut,\n", + " color_key=label_key,\n", + " embedding_key=emb_key,\n", + " save_format=\"svg\",\n", + " nametag=\"\",\n", + " save_dir=f\"figs/{model_name}/{dataset_name}\",\n", + " save_plot=False,\n", + " title=\"\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mmcontext.eval.query_annotate import OmicsQueryAnnotator\n", + "\n", + "annotator = OmicsQueryAnnotator(model)\n", + "annotator.annotate_omics_data(adata, full_query_labels, emb_key=\"mmcontext_emb\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get accuracy of best label vs true label\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "accuracy_score(adata.obs[\"best_label\"], adata.obs[label_key])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "queries_csv = \"../../data/queries/additional_combined.csv\"\n", + "if dataset_name == \"hiha_100k\" and os.path.exists(queries_csv):\n", + " df = pd.read_csv(queries_csv)\n", + " labels = df[\"Cell Type\"]\n", + " Definition = df[\"Definition\"]\n", + " from mmcontext.eval.query_annotate import OmicsQueryAnnotator\n", + " from mmcontext.pl.plotting import plot_query_scores_with_labels_umap\n", + "\n", + " annotator = OmicsQueryAnnotator(model)\n", + " annotator.query_with_text(adata, Definition, emb_key=\"mmcontext_emb\")\n", + " # Call the plotting function\n", + " plot_query_scores_with_labels_umap(\n", + " adata=adata,\n", + " queries=Definition,\n", + " labels=labels,\n", + " label_key=\"AIFI_L2\",\n", + " save_dir=f\"figs/{model_name}/{dataset_name}/umap_with_labels\",\n", + " nametag=\"\",\n", + " figsize=(4, 4),\n", + " point_size=2,\n", + " dpi=300, # Lower DPI for faster generation\n", + " axis_label_size=18,\n", + " axis_tick_size=18,\n", + " )" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mmcontext (3.11.14)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 942f9f047bb22ee24dadb48242adafefc362fcc8 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 3 Jun 2026 14:47:02 +0000 Subject: [PATCH 41/67] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/mmcontext/embed/dataset_prep.py | 17 +++++++---------- tutorials/evaluate_model_2.0.ipynb | 2 +- 2 files changed, 8 insertions(+), 11 deletions(-) diff --git a/src/mmcontext/embed/dataset_prep.py b/src/mmcontext/embed/dataset_prep.py index 4fbecbe..b309373 100644 --- a/src/mmcontext/embed/dataset_prep.py +++ b/src/mmcontext/embed/dataset_prep.py @@ -76,13 +76,11 @@ def _prepare_split( if primary_cell_sentence not in ds.column_names: raise KeyError( - f"Primary cell-sentence column {primary_cell_sentence!r} not found. " - f"Available columns: {ds.column_names}" + f"Primary cell-sentence column {primary_cell_sentence!r} not found. Available columns: {ds.column_names}" ) if modality == "bimodal" and sample_id_col not in ds.column_names: raise KeyError( - f"modality='bimodal' requires the sample-id column {sample_id_col!r}. " - f"Available columns: {ds.column_names}" + f"modality='bimodal' requires the sample-id column {sample_id_col!r}. Available columns: {ds.column_names}" ) # ------------------------------------------------------------------ @@ -126,8 +124,7 @@ def _prepare_train_split( """Build a training-ready split: ``anchor`` + ``positive`` (+ ``negative_*``).""" if positive_col not in ds.column_names: raise KeyError( - f"purpose='train' requires a positive column {positive_col!r}. " - f"Available columns: {ds.column_names}" + f"purpose='train' requires a positive column {positive_col!r}. Available columns: {ds.column_names}" ) resolve = use_hard_negatives and _has_negative_idx_columns(ds) @@ -307,13 +304,13 @@ class InferenceData: """ dataset: Dataset - adata: "ad.AnnData" - vector_store: "VectorStore | None" + adata: ad.AnnData + vector_store: VectorStore | None local_path: Path def prepare_inference( - model: "SentenceTransformer", + model: SentenceTransformer, ds: Dataset, *, modality: Modality = "bimodal", @@ -419,7 +416,7 @@ def prepare_inference( ) # 4) For bimodal, build + attach the VectorStore so omics anchors resolve. - vector_store: "VectorStore | None" = None + vector_store: VectorStore | None = None if modality == "bimodal": from mmcontext.io import prepare_vector_store diff --git a/tutorials/evaluate_model_2.0.ipynb b/tutorials/evaluate_model_2.0.ipynb index a6594dc..b22d87e 100644 --- a/tutorials/evaluate_model_2.0.ipynb +++ b/tutorials/evaluate_model_2.0.ipynb @@ -57,7 +57,7 @@ "source": [ "from sentence_transformers import SentenceTransformer\n", "\n", - "#model_name = \"jo-mengr/mmcontext-pubmedbert-gs10k\"\n", + "# model_name = \"jo-mengr/mmcontext-pubmedbert-gs10k\"\n", "model_name = \"../outputs/tiny_model/final\"\n", "model = SentenceTransformer(model_name, trust_remote_code=True)\n", "data_type = \"scvi_fm\"\n", From fd2f0d81c8578499da6006beb7f33370e86c6333 Mon Sep 17 00:00:00 2001 From: "claude[bot]" <41898282+claude[bot]@users.noreply.github.com> Date: Wed, 3 Jun 2026 14:50:13 +0000 Subject: [PATCH 42/67] fix: rewrite dict() call as literal to fix ruff C408 Co-authored-by: mengerj --- src/mmcontext/embed/dataset_prep.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/src/mmcontext/embed/dataset_prep.py b/src/mmcontext/embed/dataset_prep.py index b309373..24b06e6 100644 --- a/src/mmcontext/embed/dataset_prep.py +++ b/src/mmcontext/embed/dataset_prep.py @@ -268,17 +268,17 @@ def prepare_dataset( >>> test_ds.column_names ['anchor', 'sample_idx', 'adata_link'] """ - kwargs = dict( - purpose=purpose, - modality=modality, - primary_cell_sentence=primary_cell_sentence, - sample_id_col=sample_id_col, - positive_col=positive_col, - omics_prefix=omics_prefix, - use_hard_negatives=use_hard_negatives, - truncate=truncate, - truncate_kwargs=truncate_kwargs, - ) + kwargs = { + "purpose": purpose, + "modality": modality, + "primary_cell_sentence": primary_cell_sentence, + "sample_id_col": sample_id_col, + "positive_col": positive_col, + "omics_prefix": omics_prefix, + "use_hard_negatives": use_hard_negatives, + "truncate": truncate, + "truncate_kwargs": truncate_kwargs, + } if isinstance(ds, DatasetDict): return DatasetDict({name: _prepare_split(split, **kwargs) for name, split in ds.items()}) From 9ac01a3ddc09bdaf9fa269332b1c1e16edc353eb Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Mon, 8 Jun 2026 07:48:36 +0200 Subject: [PATCH 43/67] script to finetune a finished model with hard negatives --- scripts/finetune_tiny.py | 326 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 326 insertions(+) create mode 100644 scripts/finetune_tiny.py diff --git a/scripts/finetune_tiny.py b/scripts/finetune_tiny.py new file mode 100644 index 0000000..cb202ac --- /dev/null +++ b/scripts/finetune_tiny.py @@ -0,0 +1,326 @@ +#!/usr/bin/env python +"""Finetune an already-trained MMContext model with mined hard negatives. + +Unlike :mod:`scripts.train_tiny`, this script does **not** use the +pre-provided hard negatives baked into the dataset (the ``negative_*_idx`` +columns). Instead it loads a model that was already trained once and uses +:func:`sentence_transformers.util.mine_hard_negatives` to mine model-specific +hard negatives, then continues training on those. This only makes sense with a +model that already has a meaningful joint embedding space — mining against a +freshly initialised bimodal model would produce noise. + +How mining works here: the dataset's ``anchor`` column (omics ids for the +bimodal modality, resolved through the attached VectorStore) is encoded as the +query, while the ``positive`` texts form the corpus. The closest-but-not-true +positives become the mined negatives — confusable text descriptions in the same +shared embedding space. + +Usage:: + + # 1) Produce a pretrained model with train_tiny first + python scripts/train_tiny.py --epochs 1 --output-dir outputs/tiny_model + + # 2) Finetune it with mined hard negatives (bimodal, default) + python scripts/finetune_tiny.py \ + --model-path outputs/tiny_model/final \ + --output-dir outputs/tiny_finetuned + + # Text modality + python scripts/finetune_tiny.py --modality text \ + --model-path outputs/tiny_text/final \ + --output-dir outputs/tiny_text_finetuned +""" + +from __future__ import annotations + +import argparse +import logging +import os + +import numpy as np +import torch +from datasets import load_dataset +from sentence_transformers import ( + SentenceTransformer, + SentenceTransformerTrainer, + SentenceTransformerTrainingArguments, +) +from sentence_transformers.sentence_transformer.losses import MultipleNegativesRankingLoss +from sentence_transformers.util import mine_hard_negatives + +from mmcontext.embed import prepare_dataset + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s %(message)s") +logger = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# Dataset helpers +# --------------------------------------------------------------------------- +HF_DATASET = "jo-mengr/cxg_schaefer_tiny" + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- +def main(): + """Finetune a pretrained MMContext model on mined hard negatives.""" + parser = argparse.ArgumentParser(description="Finetune MMContext with mined hard negatives") + parser.add_argument( + "--modality", + choices=["text", "bimodal"], + default="bimodal", + help="Training modality (default: bimodal)", + ) + parser.add_argument( + "--model-path", + default="outputs/tiny_model/final", + help="Path to the pretrained model to finetune (default: outputs/tiny_model/final).", + ) + parser.add_argument( + "--vector-store", + default=None, + help="Path to .mmap VectorStore file. For bimodal modality: if omitted, " + "the store is built automatically from adata_link + sample_idx " + "columns using --obsm-key.", + ) + parser.add_argument( + "--obsm-key", + default="X_scvi_fm", + help="obsm key to extract when building VectorStore (default: X_scvi_fm). " + "Other common choices: X_pca, X_geneformer, X_gs10k", + ) + parser.add_argument("--output-dir", default="outputs/tiny_finetuned", help="Output directory") + parser.add_argument("--epochs", type=int, default=3, help="Training epochs (default: 3)") + parser.add_argument("--batch-size", type=int, default=32, help="Batch size (default: 32)") + parser.add_argument("--lr", type=float, default=2e-5, help="Learning rate (default: 2e-5)") + parser.add_argument("--dataset", default=HF_DATASET, help="HuggingFace dataset name") + parser.add_argument( + "--use-mps-device", + action=argparse.BooleanOptionalAction, + default=torch.backends.mps.is_available(), + help="Train on Apple Metal (MPS); default: on when MPS is available", + ) + parser.add_argument( + "--wandb-project", + default=None, + help="Weights & Biases project name. Enables wandb logging when set. " + "You can also set WANDB_PROJECT env var instead.", + ) + parser.add_argument( + "--wandb-run-name", + default=None, + help="Optional W&B run name (auto-generated if omitted)", + ) + parser.add_argument( + "--freeze-text-encoder", + action=argparse.BooleanOptionalAction, + default=True, + help="Freeze the text encoder to cut gradient/optimizer memory. " + "On by default for finetuning; combine with --unfreeze-last-n to keep " + "the top N layers trainable. Disable with --no-freeze-text-encoder.", + ) + parser.add_argument( + "--unfreeze-last-n", + type=int, + default=1, + help="With --freeze-text-encoder, keep the top N transformer layers " + "(plus pooler) trainable. 0 freezes the whole encoder (default: 1).", + ) + parser.add_argument( + "--bf16", + action=argparse.BooleanOptionalAction, + default=torch.backends.mps.is_available(), + help="Use bf16 mixed precision. Supported on MPS (macOS 14+) and CUDA; " + "roughly halves activation memory. Default: on when MPS is available.", + ) + + # --- Hard-negative mining parameters --- + mining = parser.add_argument_group("hard-negative mining") + mining.add_argument("--num-negatives", type=int, default=3, help="Negatives to mine per anchor (default: 3)") + mining.add_argument( + "--range-min", + type=int, + default=1, + help="Skip the N closest candidates (the closest is usually the true positive). Default: 1.", + ) + mining.add_argument( + "--range-max", + type=int, + default=None, + help="Only consider candidates up to this rank (default: None = no upper bound).", + ) + mining.add_argument( + "--max-score", + type=float, + default=0.95, + help="Drop candidates whose similarity exceeds this ceiling to avoid mining " + "false negatives (descriptions that are actually correct). Default: 0.95.", + ) + mining.add_argument( + "--relative-margin", + type=float, + default=None, + help="Negatives must score below relative_margin * positive_score (e.g. 0.95). Default: None.", + ) + mining.add_argument( + "--sampling-strategy", + choices=["top", "random"], + default="top", + help="Which qualifying candidates to keep as negatives (default: top).", + ) + mining.add_argument( + "--use-faiss", + action=argparse.BooleanOptionalAction, + default=False, + help="Use FAISS for the similarity search (recommended for large datasets). Default: off.", + ) + args = parser.parse_args() + + # --- Dataset (raw) --- + ds_raw = load_dataset(args.dataset, split="train") + + # --- Load pretrained model --- + # Dimensions (text/omics/shared) come from the saved config; no rebuild needed. + logger.info("Loading pretrained model from %s", args.model_path) + model = SentenceTransformer(args.model_path) + + # --- Attach VectorStore (bimodal only), required before mining --- + if args.modality == "bimodal": + from mmcontext.io import VectorStore, prepare_vector_store + + if args.vector_store is not None: + store = VectorStore.load(args.vector_store) + else: + store_path = os.path.join(args.output_dir, "vector_store.mmap") + store = prepare_vector_store( + ds_raw, + obsm_key=args.obsm_key, + output_path=store_path, + ) + model[0].set_vector_store(store) + logger.info("VectorStore: %d vectors, dim=%d", len(store), store.dim) + + # --- Freezing (memory savings) --- + # Freezing the text encoder removes its gradients + Adam optimizer state; + # keeping only the top N layers trainable is the usual fine-tuning sweet + # spot. On by default here since we are continuing to train an aligned model. + if args.freeze_text_encoder: + text_module = model[0] + # unfreeze_last_n == 0 freezes the whole encoder; > 0 keeps the top N trainable. + text_module.freeze_all_but_top_layers(args.unfreeze_last_n) + logger.info("Froze text encoder, keeping top %d layers trainable", args.unfreeze_last_n) + trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) + total = sum(p.numel() for p in model.parameters()) + logger.info("Trainable params: %d / %d (%.1f%%)", trainable, total, 100.0 * trainable / total) + + # --- Prepare anchor/positive pairs (drop the dataset's own negatives) --- + modality = "bimodal" if args.modality == "bimodal" else "text" + primary_cell_sentence = "cell_sentence_1" if modality == "bimodal" else "cell_sentence_2" + pairs = prepare_dataset( + ds_raw, + purpose="train", + modality=modality, + primary_cell_sentence=primary_cell_sentence, + use_hard_negatives=False, + ) + logger.info("Prepared %d anchor/positive pairs, columns=%s", len(pairs), pairs.column_names) + + # --- Mine hard negatives with the pretrained model --- + model.eval() + logger.info( + "Mining hard negatives: num_negatives=%d, range_min=%d, range_max=%s, max_score=%s, " + "relative_margin=%s, sampling=%s, faiss=%s", + args.num_negatives, + args.range_min, + args.range_max, + args.max_score, + args.relative_margin, + args.sampling_strategy, + args.use_faiss, + ) + mined = mine_hard_negatives( + pairs, + model, + anchor_column_name="anchor", + positive_column_name="positive", + num_negatives=args.num_negatives, + range_min=args.range_min, + range_max=args.range_max, + max_score=args.max_score, + relative_margin=args.relative_margin, + sampling_strategy=args.sampling_strategy, + output_format="n-tuple", + batch_size=args.batch_size, + use_faiss=args.use_faiss, + verbose=True, + ) + logger.info("Mined dataset: %d rows (from %d pairs), columns=%s", len(mined), len(pairs), mined.column_names) + + # --- Wandb setup --- + wandb_project = args.wandb_project or os.environ.get("WANDB_PROJECT") + use_wandb = wandb_project is not None + if use_wandb: + os.environ["WANDB_PROJECT"] = wandb_project + if args.wandb_run_name: + os.environ["WANDB_NAME"] = args.wandb_run_name + logger.info("W&B enabled: project=%s, run=%s", wandb_project, args.wandb_run_name or "(auto)") + + # --- Finetuning --- + model.train() + loss = MultipleNegativesRankingLoss(model) + training_args = SentenceTransformerTrainingArguments( + output_dir=args.output_dir, + num_train_epochs=args.epochs, + per_device_train_batch_size=args.batch_size, + learning_rate=args.lr, + warmup_ratio=0.1, + # bf16 (MPS macOS 14+ / CUDA) takes precedence; fall back to fp16 on CUDA only. + bf16=args.bf16, + fp16=(not args.bf16) and torch.cuda.is_available() and not args.use_mps_device, + use_mps_device=args.use_mps_device, + report_to="wandb" if use_wandb else "none", + logging_steps=10, + save_strategy="epoch", + save_total_limit=2, + run_name=args.wandb_run_name, + ) + + trainer = SentenceTransformerTrainer( + model=model, + args=training_args, + train_dataset=mined, + loss=loss, + ) + + logger.info( + "Starting finetuning: modality=%s, epochs=%d, batch_size=%d", args.modality, args.epochs, args.batch_size + ) + trainer.train() + + # --- Save final model --- + save_path = os.path.join(args.output_dir, "final") + model.save(save_path) + logger.info("Model saved to %s", save_path) + + # --- Verify reload --- + logger.info("Verifying save/load roundtrip...") + model.eval() + test_inputs = ["MALAT1 MT-CO3 GNAS SYT1 CALM1", "A cortical neuron expressing synaptic markers."] + original_embs = model.encode(test_inputs) + + loaded = SentenceTransformer(save_path) + loaded.eval() + loaded_embs = loaded.encode(test_inputs) + + max_diff = np.abs(original_embs - loaded_embs).max() + logger.info("Save/load verification: max_diff=%.2e (should be < 1e-5)", max_diff) + if max_diff > 1e-4: + logger.warning("Save/load roundtrip difference is unexpectedly large!") + else: + logger.info("Save/load roundtrip OK") + + logger.info("Done.") + + +if __name__ == "__main__": + main() From 8efbdefe4dcfca763192ddd01ec00053389a574c Mon Sep 17 00:00:00 2001 From: "Claude (dev-claude)" Date: Wed, 10 Jun 2026 08:31:04 +0000 Subject: [PATCH 44/67] WIP: training script, pipeline, mining, store improvements --- .gitignore | 1 + MIGRATION_PLAN.md | 282 ++++++++++++++++ conf/training/mmcontext_v2.yaml | 83 +++++ conf/training/multi_example.yaml | 90 ++++++ docs/agentic_workflow_research.md | 224 +++++++++++++ scripts/explore_hard_negatives.py | 210 ++++++++++++ scripts/train_config.py | 275 ++++++++++++++++ scripts/train_tiny.py | 49 +-- src/mmcontext/embed/__init__.py | 6 + src/mmcontext/embed/mining.py | 240 ++++++++++++++ src/mmcontext/embed/pipeline.py | 77 +++++ src/mmcontext/hub_utils.py | 170 +++++++++- src/mmcontext/io/__init__.py | 4 +- src/mmcontext/io/prepare_store.py | 77 +++++ src/mmcontext/training.py | 503 +++++++++++++++++++++++++++++ tests/test_training.py | 276 ++++++++++++++++ tutorials/evaluate_model_2.0.ipynb | 221 +++++++++++-- 17 files changed, 2710 insertions(+), 78 deletions(-) create mode 100644 MIGRATION_PLAN.md create mode 100644 conf/training/mmcontext_v2.yaml create mode 100644 conf/training/multi_example.yaml create mode 100644 docs/agentic_workflow_research.md create mode 100644 scripts/explore_hard_negatives.py create mode 100644 scripts/train_config.py create mode 100644 src/mmcontext/embed/mining.py create mode 100644 src/mmcontext/embed/pipeline.py create mode 100644 src/mmcontext/training.py create mode 100644 tests/test_training.py diff --git a/.gitignore b/.gitignore index bcd50d0..4c110a2 100644 --- a/.gitignore +++ b/.gitignore @@ -111,3 +111,4 @@ modules/.calmate/cache/ cache/ mmcontext_out/ +tutorials/outputs/ diff --git a/MIGRATION_PLAN.md b/MIGRATION_PLAN.md new file mode 100644 index 0000000..51ba6fb --- /dev/null +++ b/MIGRATION_PLAN.md @@ -0,0 +1,282 @@ +# Migration Plan: Split evaluation/benchmarking into `mmcontext-benchmark` + +**Status:** in progress — benchmark pipeline implemented (see §0); `mmcontext` +slim-down (§3–§5) still pending. +**Author:** Jonatan Menger (with Claude) +**Date:** 2026-06-03 (updated 2026-06-09) +**Goal:** Make `mmcontext` a focused model package (architecture + lightweight, +single-model evaluation runnable from a notebook). Move all multi-model +comparison, competitor integrations (SCSA, CellWhisperer), and the heavy +embedding/eval orchestration into a new sibling repo, `mmcontext-benchmark`. +Drop the `adata-hf-datasets` package dependency from `mmcontext`. + +--- + +## 0. Implemented benchmark design (2026-06-09) + +The benchmark repo was built around an **adapter + artifact-contract** +architecture rather than a straight file-move of the legacy orchestrators. What +landed (and where it diverged from the original §2–§3 plan): + +- **Artifact contract** (`mmcontext_benchmark/artifacts.py`): one on-disk layout + per `(dataset, model)` — embedders write `embeddings.parquet` + `subset.h5ad` + + `*_label_embeddings_*`; direct classifiers write + `predictions_