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[4/8] Add the sparse teacher-policy training path - #2638
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Docstring coverage is 70.41% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 98 functions across 24 files. (1 skipped:… | Write docstrings for the functions missing them to satisfy the coverage threshold. |
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Check name Status Explanation Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly and concisely describes the main change: adding the sparse teacher-policy training path. Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request. Security Anti-Patterns No listed security anti-pattern is introduced. The pull-request diff adds no torch.load(..., weights_only=False),allow_pickle=True, hardcodedtrust_remote_code=True,eval()/exec()on extern…Full details: Security Anti-Patterns
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torch.load(..., weights_only=False),allow_pickle=True, hardcodedtrust_remote_code=True,eval()/exec()on external input, or# nosec. The newnp.loadcalls use the safe default forallow_pickle. No dependency-file changes are present.
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Inline comments:
Review comments at @CHANGELOG.rst:
- Line 131: Move the external draft speculative-decoding entry in the changelog
from the 0.47.0 section to the 0.48.0 section under Speculative Decoding,
preserving its existing text.
Review comments at @modelopt/torch/speculative/external/sparse_data.py:
- Around line 254-262: Update the stale-cache handling in SparsePolicyDataset to
use the RANK environment variable when the process group is not initialized, so
only rank zero builds the shared cache. When no process-group barrier is
available, have non-zero ranks wait with a timeout for the published meta.json
before loading it.
Review comments at @modelopt/torch/speculative/plugins/hf_external.py:
- Around line 124-125: Update the sparse accuracy handling in the visible method
so it assigns the detached accuracy to draft_out.train_acc as a one-element
list, matching the dense path and making it available to EagleTrainerWithAccLog.
- Around line 190-208: Update validate_tokenizer_against_base to validate the
full vocabulary by default, so remapped token IDs above 255 cannot pass
unchecked; retain optional sampling only when explicitly requested and make the
mismatch message reflect whether it checked a sample or the full vocabulary.
Review comments at @tests/unit/torch/speculative/test_external_draft_sparse.py:
- Around line 189-192: Move the SPARSE_CAPABLE_LOSSES and DataArguments imports
out of their test functions and to module scope after the existing
pytest.importorskip("transformers") guard. Keep both imports below that guard so
importing SPARSE_CAPABLE_LOSSES does not bypass the optional Transformers
dependency check.
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CHANGELOG.rstexamples/speculative_decoding/README.mdexamples/speculative_decoding/collect_hidden_states/common.pyexamples/speculative_decoding/collect_hidden_states/compute_hidden_states_hf.pyexamples/speculative_decoding/eagle_utils.pyexamples/speculative_decoding/main.pymodelopt/recipe/config.pymodelopt/recipe/loader.pymodelopt/torch/speculative/__init__.pymodelopt/torch/speculative/config.pymodelopt/torch/speculative/eagle/utils.pymodelopt/torch/speculative/external/__init__.pymodelopt/torch/speculative/external/conversion.pymodelopt/torch/speculative/external/external_model.pymodelopt/torch/speculative/external/sparse_data.pymodelopt/torch/speculative/mode.pymodelopt/torch/speculative/plugins/__init__.pymodelopt/torch/speculative/plugins/hf_external.pymodelopt/torch/speculative/plugins/hf_training_args.pymodelopt_recipes/general/speculative_decoding/external_draft.yamltests/gpu/torch/speculative/plugins/test_hf_external.pytests/unit/recipe/test_loader.pytests/unit/torch/speculative/plugins/test_hf_speculative_offline.pytests/unit/torch/speculative/test_external_draft.pytests/unit/torch/speculative/test_external_draft_offline.pytests/unit/torch/speculative/test_external_draft_sparse.py
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/claude review focus on modelopt/torch/speculative/external/sparse_data.py and modelopt/torch/speculative/plugins/hf_external.py. This slice now targets main directly after [3/8] merged. Two findings from earlier rounds look still open and are worth re-checking first: |
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Inline comments:
Review comments at @modelopt/torch/speculative/external/sparse_data.py:
- Around line 159-164: Update the fast-path shape validation near the `tok` and
`prob` construction to require `prob.shape` to match `tok.shape`, in addition to
the existing `tok` dimensions and width checks. Let mismatched records fall
through to the ragged fallback path.
Review comments at @tests/unit/torch/speculative/test_external_draft_sparse.py:
- Around line 34-37: Move the imports of SPARSE_CAPABLE_LOSSES from hf_external
and DataArguments from hf_training_args below the transformers
pytest.importorskip guard so module collection skips cleanly when transformers
is unavailable.
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examples/speculative_decoding/eagle_utils.pyexamples/speculative_decoding/main.pymodelopt/torch/speculative/config.pymodelopt/torch/speculative/external/sparse_data.pymodelopt/torch/speculative/plugins/hf_external.pytests/unit/torch/speculative/test_external_draft_sparse.py
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Reviewed at cdaa1a0. The PR is rebased onto main after #2637 merged and is a single commit, so it is cleanly reviewable now.
The core logic looks right:
- The one-position shift in
compute_sparse_lossmatches how the dump indexes the policy. - The out-of-support term makes
_tvd_sparsean exact TVD. - Zero-probability pad entries are harmless.
Locally, test_external_draft_sparse.py, the GPU test_hf_external.py, the main.py tests, tests/unit/recipe and the external-draft tests all pass (596).
Should fix (inline)
- Cache build vs. the 30-minute rank wait. An hour-long first build times out on multi-GPU, and there is no documented pre-build step (
sparse_data.pyL309). - No
cache_dirin the recipe. A read-only dataset directory can't be used, and the advice in the race error can't be followed (sparse_data.pyL295). - Non-external recipes accept
sparse_data_path. Also, nothing tests thesoft_ce+ sparse gate or the skipped head attach (main.pyL243).
Duplication, which I'd like removed in this PR (inline)
forward: the sparse and dense branches duplicate the draft forward and the loss/accuracy wiring, and defaultloss_maskdifferently.- The draft-side
tvd_deploy/softmax choice and the top-1 accuracy block are each copied betweencompute_lossandcompute_sparse_loss. - The
sample_sizecheck is copied in two branches ofmake_speculative_data_module.
Test bloat (inline)
I mutated the loss one change at a time to see which tests actually guard what:
- Three of the five loss tests are subsumed by
test_sparse_tvd_matches_one_minus_acceptanceonce it is parametrized over both losses. test_export_keeps_the_original_architectureduplicates a [2/8] test.- The two GPU tests repeat the CPU forward/backward test. A CUDA test of the ragged-row dedup, where
stable=Truematters, would be worth more. - A couple of tests can be folded into existing parametrizations.
PR description: it is stale. It still says "Fourth PR of 6", has the stacked-PR note, and says "review only the final commit ... before df59537c". Please refresh it now that this is a single commit on main.
| assert any(q.grad is not None and q.grad.abs().sum() > 0 for q in model.parameters()) | ||
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| # soft_ce needs full teacher logits, so it must NOT be listed | ||
| assert "soft_ce" not in SPARSE_CAPABLE_LOSSES |
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This checks a constant against itself. Please replace it with the main.py gate test suggested on main.py L243, which tests the behaviour that matters.
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Dropped the assertion. The main.py gate test you asked for is in now - test_sparse_requires_a_tvd_objective - so this was just restating the constant next to it.
| ({"topk_lp": [[0.0] * TOPK]}, 2), | ||
| ], | ||
| ) | ||
| def test_malformed_policy_records_never_abort_the_build(tmp_path, bad, kept): |
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Nit: this test and test_damaged_shards both check that the build survives bad input: dropped records, or warn vs raise. Folding the two bad records into test_damaged_shards' parametrization would leave one test for that behaviour.
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I've left these two separate, happy to be overruled.
test_damaged_shards covers a shard that cannot be read or parsed at all - truncated, unreadable, empty - and the thresholds that decide warn vs raise. The malformed-records one covers well-formed shards whose individual records are wrong: a missing topk_lp, an empty policy, a record with no scored positions. Those take different paths through the builder, and the record-level one is what the new drop-rate gate keys on.
Merging them would fold the record cases into a shard parametrization where they don't really fit, and I think we'd lose a case rather than a duplicate.
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Inline comments:
Review comments at
@modelopt_recipes/general/speculative_decoding/external_draft.yaml:
- Around line 31-40: Update the pre-build command in external_draft.yaml at
lines 31-40 and the TimeoutError command in sparse_data.py at lines 111-118 to
discover both *.jsonl.gz and *.jsonl shards recursively in sorted order and pass
the configured cache_dir when sparse_cache_dir is set, matching the shard
discovery and cache directory used by training. Alternatively, add a shared
shard-discovery helper and use it in both commands and eagle_utils.py.
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examples/speculative_decoding/eagle_utils.pyexamples/speculative_decoding/main.pymodelopt/torch/speculative/external/sparse_data.pymodelopt/torch/speculative/plugins/hf_external.pymodelopt/torch/speculative/plugins/hf_training_args.pymodelopt_recipes/general/speculative_decoding/external_draft.yamltests/gpu/torch/speculative/plugins/test_hf_external.pytests/unit/examples/test_speculative_decoding_main.pytests/unit/torch/speculative/test_external_draft_sparse.py
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@kevalmorabia97 everything from your review is in at Should fix
Duplication — all four gone. Unifying Test bloat — 20 tests down to 15. The reference-formula test is parametrized over both TVD objectives, which subsumes the alignment, zero-at-optimum and out-of-support cases. The export duplicate and the fractional-weights case are gone, and the two GPU tests are replaced by a CUDA check of the ragged-row dedup, where Two I did not do, both with reasoning in-thread: folding the malformed-records test into Worth flagging given your last review said the core logic looked right — a later pass found a genuine one I had missed. Locally: 908 passed on this slice alone. |
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Re-reviewed at 4994382. Most of the first review is addressed:
forwardis now a single path, which also unified theloss_maskdefault._draft_probs,_top1_accuracyand the hoistedsample_sizecheck remove the duplication.sparse_cache_diris plumbed through.- Non-external recipes are refused, with tests for the
soft_cegate and the skipped head attach. - The reference-formula test is parametrized over both losses.
- The description is refreshed.
The new width inference and record drop-rate gate look right. I also agree with your reply on outside: _deployment_probs scatters into zeros_like, so q_full sums to 1 over the vocabulary.
Locally, the sparse, main.py, recipe and external-draft unit tests pass (592), and the GPU test_hf_external.py passes on one GPU (4).
Still open (inline)
- The 30-minute limit still applies under
main.py. The process group already exists when the dataset is built, so ranks take theddp_timeout-bound barrier and never reach the new stall-aware wait. I reproduced the barrier failing atddp_timeouton 2 GPUs. There is a code suggestion that routes every non-builder rank through_wait_for_cache.
Tests (inline)
- The GPU dedup test is obsolete now that the dedup no longer sorts. Please drop it.
- The schema-drift test fits as one more row of the malformed-record parametrization.
Nits
- The
top_k=advice in the wider-row error cannot be followed from the recipe (inline). - Your reasoning for keeping the
external/__init__re-export is fine. Theeagle_utils.pycomment that says the import is there "so the other modes do not pay for" the module is still inaccurate, though, and could be reworded to the reason you gave.
| # Under DDP every rank sees the same shards; one builds and the rest wait. | ||
| # The dataset is often constructed before the process group exists, so fall | ||
| # back to the launcher's RANK: without it every worker takes the rank-0 | ||
| # branch and they race to delete and rewrite the same cache directory. | ||
| if torch.distributed.is_initialized(): | ||
| rank, can_barrier = torch.distributed.get_rank(), True | ||
| else: | ||
| rank, can_barrier = int(os.environ.get("RANK", 0)), False | ||
| if rank == 0: | ||
| _build_cache(cache, files, top_k) | ||
| if can_barrier: | ||
| torch.distributed.barrier() | ||
| elif rank != 0: | ||
| _wait_for_cache(cache, files) |
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Still open: in main.py the stall-aware wait is never reached, so the 30-minute limit is unchanged.
HfTrainingArguments(...) initializes the process group itself: TrainingArguments.__post_init__ touches self.device, which builds a PartialState with timeout=ddp_timeout, 1800s by default. So by the time make_speculative_data_module runs, is_initialized() is True. The barrier branch is taken, and _wait_for_cache is bypassed.
I checked both parts:
- On a 2-process
torchrun, constructingTrainingArgumentsalone leavestorch.distributed.is_initialized() == Trueon both ranks, withddp_timeout=1800. - On 2 GPUs with NCCL,
ddp_timeout=5, and rank 0's_build_cachepatched to take 15s, rank 1 fails at this barrier after 5.0s withDistBackendError. With the default timeout, any first build over 30 minutes fails the same way.
_wait_for_cache needs no process group, so the simplest fix is to use it for every non-builder rank and drop the barrier:
| # Under DDP every rank sees the same shards; one builds and the rest wait. | |
| # The dataset is often constructed before the process group exists, so fall | |
| # back to the launcher's RANK: without it every worker takes the rank-0 | |
| # branch and they race to delete and rewrite the same cache directory. | |
| if torch.distributed.is_initialized(): | |
| rank, can_barrier = torch.distributed.get_rank(), True | |
| else: | |
| rank, can_barrier = int(os.environ.get("RANK", 0)), False | |
| if rank == 0: | |
| _build_cache(cache, files, top_k) | |
| if can_barrier: | |
| torch.distributed.barrier() | |
| elif rank != 0: | |
| _wait_for_cache(cache, files) | |
| # Under DDP every rank sees the same shards; one builds and the rest wait on the | |
| # files, not a barrier: a collective is bounded by ddp_timeout, which a first | |
| # build over a large corpus outlasts. | |
| if torch.distributed.is_initialized(): | |
| rank = torch.distributed.get_rank() | |
| else: | |
| rank = int(os.environ.get("RANK", 0)) | |
| if rank == 0: | |
| _build_cache(cache, files, top_k) | |
| else: | |
| _wait_for_cache(cache, files) |
A test that sets RANK=1 with an initialized group (or a mocked is_initialized) and checks that _wait_for_cache is called would pin this.
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You are right, and thanks for actually running it. training_args = HfTrainingArguments(...) is at main.py L311 and make_speculative_data_module at L479, so the group is always up by then and the barrier branch always won. The wait I added was dead code on the real path and the 1800s bound was still there.
Fixed in 29eaeff9: the barrier is gone and every non-builder rank takes _wait_for_cache, which needs no process group. test_a_non_builder_rank_waits_on_the_files_not_a_barrier pins it with is_initialized mocked True and rank 1, asserting the wait ran and barrier was not called.
| assert not any("_base_lm_head" in k for k in draft.state_dict()) | ||
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| class TestExternalDraftSparseGPU: |
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The reason for this test no longer holds. The dedup now masks on topk_prob > 0, and _tvd_sparse no longer sorts, so nothing in it depends on the device. Both docstrings here still describe the old sort(..., stable=True). test_ragged_row_whose_support_includes_id_zero already covers the same computation on CPU, so please drop this class.
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Agreed, the reason went away when I took your topk_prob > 0 suggestion. Dropped the whole class in 29eaeff9. test_ragged_row_whose_support_includes_id_zero had the same stale paragraph about the removed sort, so I corrected that docstring too.
| assert kept == {"alpha", "beta"}, f"sample came from {kept}, not both shards" | ||
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| def test_a_schema_drifted_dump_refuses_rather_than_publishing_a_few_rows(tmp_path): |
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I'm fine with keeping test_malformed_policy_records_never_abort_the_build separate from test_damaged_shards, for the reason you gave. This one, though, is the record-level case: a well-formed shard whose records fail to parse. It fits that test's parametrization as one more (bad, kept) row with a raises expectation, rather than being a third test of damaged input.
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Fair distinction, I had merged the two cases in my head. Folded it into the parametrization in 29eaeff9 as a third (bad, n_bad, kept) row with kept="raises": 40 records with topk_lp renamed to logprobs against one intact record, which puts the drop rate over the gate. n_bad is new because the existing rows need exactly one bad record to stay under it.
| raise ValueError( | ||
| f"{path}: a stored policy row holds {widest} entries " | ||
| f"but the cache was sized at {width} from the first " | ||
| f"record. Pass top_k=<dump's top-k> to size it " |
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Nit: main.py and eagle_utils.py never pass top_k, so a recipe user cannot follow this advice. The producer in [6/8] writes fixed-width rows with -inf padding, so only third-party ragged dumps reach this branch. Either add a data.sparse_top_k field and pass it through, or point the message at the pre-build command instead, where top_k= can be set.
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Good catch, the message named something a recipe user cannot reach. Took the second option in 29eaeff9 rather than adding a field, since the producer in [6/8] writes fixed-width rows and only third-party ragged dumps land here. The message now points at the pre-build call with top_k= and cache_dir=, and the recipe comment documenting that pre-build says the same.
Adds data.sparse_data_path: shards holding the base's top-k deployment policy per token instead of its hidden states. The policy is roughly 120 B against 10 KiB per token, is captured during generation, and needs no base lm_head at training time, so the base model costs nothing on the GPU. Only the TVD objectives can consume it; soft_ce needs full base logits and main.py rejects the combination rather than training against a zero-padded distribution. SparsePolicyDataset backs the shards with a memmapped cache and can oversample by the record's source field, since response lengths differ by an order of magnitude across sources. The sparse objective shifts the draft one position left before comparing: the stored policy at t is what the base sampled input_ids[t] from, while the draft's logits at t predict t + 1. Dropping the shift still yields a falling loss curve against the wrong target. Signed-off-by: Peter Hicks <phicks@nvidia.com> Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Peter Hicks <phicks@nvidia.com>
The module guarded itself with pytest.importorskip("transformers") but
imported plugins.hf_external two lines above the guard. The plugins package
registers that module behind import_plugin("transformers"); importing the
submodule directly bypasses the guard, so on a torch-only install the module
failed to collect instead of skipping, and partial-install (torch) went red.
Move the two plugin imports below the guard.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Signed-off-by: Peter Hicks <phicks@nvidia.com>
The stall-aware wait was unreachable. HfTrainingArguments builds the process group while parsing arguments (TrainingArguments.__post_init__ touches self.device, which constructs a PartialState with ddp_timeout), so by the time make_speculative_data_module runs is_initialized() is already True and the barrier branch always won. Every non-builder rank was still bounded by the 1800s ddp_timeout, which a first build over a large corpus outlasts. Drop the barrier and give every non-builder rank the file wait, which needs no process group and bounds time without progress instead. Also: - Drop TestExternalDraftSparseGPU. It existed to pin sort(..., stable=True) on CUDA; the dedup now masks on topk_prob > 0 and _tvd_sparse does not sort, so nothing in it is device-dependent. Its CPU twin covers the computation. The twin's docstring described the same removed sort and is corrected. - Fold the schema-drift case into test_malformed_policy_records_never_abort_ the_build as one more (bad, n_bad, kept) row with a raises expectation, rather than a third test of damaged input. - Point the top-k width refusal at the single-process pre-build, which accepts top_k, since no recipe field threads one through. Document that in the recipe. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Peter Hicks <phicks@nvidia.com>
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Re-reviewed at 81f4cf6. All four threads from my last pass are addressed. LGTM apart from the one leftover nit inline.
- Rank wait: I reran my 2-GPU NCCL repro (
ddp_timeout=5, rank 0's build delayed by 15s). Before the fix, rank 1 died at the barrier after 5.0s. Now both ranks finish (rank 0 after 15.0s, rank 1 after 14.0s). With the fix reverted, the newtest_a_non_builder_rank_waits_on_the_files_not_a_barrierfails, so it guards the change. - Tests: the obsolete GPU class is gone, and the schema-drift case is now a row of the malformed-records parametrization.
- Wider-row error: the message now points at the pre-build with
top_k=andcache_dir=, and the recipe comment says the same. - Ran: 585 unit tests passed (sparse, external draft,
main.py, recipe), in 12s; the slowest took 2.0s.tests/gpu/torch/speculative/plugins/test_hf_external.pypassed 3 of 3 on one GPU.
| # Imported here, like the streaming dataset above, so the other modes do not | ||
| # pay for a module only this branch uses. |
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Nit, still open from my first pass: since you kept the from .sparse_data import * re-export in external/__init__.py, import modelopt.torch.speculative already loads this module in every mode. So the reason given here does not hold.
| # Imported here, like the streaming dataset above, so the other modes do not | |
| # pay for a module only this branch uses. | |
| # Imported here to keep the sparse-only names next to the one branch that uses them. |
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LGTM. The only open item is the eagle_utils.py comment nit, which can go in here or in a later slice.
What does this PR do?
Fourth PR of 8 to support external draft training for LPU systems with ModelOpt.
Type of change: new feature
Adds
data.sparse_data_path: shards holding the base's top-k deployment policy per tokeninstead of its hidden states. The policy is far smaller than a hidden state per token and
needs no base lm_head at training time, so the base is not placed on the GPU. The producer
is [6/8].
This is essentially an alternative to storing hidden states - can be used if you don't require full logit reconstruction and you only want to train on the sampled distribution.
Only the TVD objectives can consume it;
soft_ceneeds full base logits andmain.pyrejects the combination rather than training against a zero-padded distribution.
SparsePolicyDatasetbacks the shards with a memmapped cache and can oversample by therecord's
sourcefield.The sparse objective shifts the draft one position left before comparing: the stored policy
at
tis what the base sampledinput_ids[t]from, while the draft's logits attpredictt + 1. Dropping the shift still yields a falling loss curve, against the wrong target.Testing
pytest tests/unit/torch/speculativeand the GPU plugin tests.End-to-end validation. The series was exercised by running the whole workflow on real
models, not unit tests alone: a teacher policy was dumped from a 27B target, three drafts were
trained against it concurrently from one dump -- two sharing the target's tokenizer, one needing
the cross-tokenizer vocabulary swap -- each for a full epoch, and each exported a plain HF
checkpoint whose
architecturesresolves to the draft's own class. Acceptance was then measuredfor all three under a matched target policy. That run surfaced four defects this series' unit
tests and several review passes had all missed, two of which corrupted training silently rather
than failing; all four are fixed here.
Before your PR is "Ready for review"
CONTRIBUTING.md: N/A — no copied code, no new dependenciesAdditional Information
OSS provenance. No line added by this PR is copied from another project, with or
without modification. Every new file carries only the NVIDIA Apache-2.0 header, nothing was
added to the
insert-licenseexclude list, and there are no new PIP dependencies. One itemdeclared for completeness:
_deployment_probs(in [2/8]) implements the standardtop-k-then-top-p filter, the same algorithm as
transformers.generation.logits_process, butwas written independently and is textually different. Flagged because the algorithm is
widely shared, not because the code is.
Part of an eight-PR series. Merge order: [1/8] -> [2/8] -> [3/8] -> [4/8] -> [5/8] -> [6/8] -> [7/8] -> [8/8].
Summary by CodeRabbit
tvdandtvd_deployobjectives and does not require the base model’s language-model head.