feat(simulator): expand topology and workload exploration

[ShwStone]

Purpose

Extend the experimental DeepSeek-V4-Flash prefill simulator introduced in #269 so AFD and merged deployments can be compared across equal-die topologies, independent batching policies, chunked FIFO waves, and burst-preserving production-like length traces.

This also checks in the normalized analytic profiles and prompt-free MoonConv arrival/length fixtures so a fresh clone can use the WebUI without installing msModeling or downloading prompt token IDs.

Issue

• Related issue(s): follow-up to feat: add AFD prefill performance simulator #269
• Closing keyword: none

Scope

• In scope:
  • Simulator-only scheduling, workload, profile, HTTP, and WebUI changes.
  • Independent AFD and merged max-batch/chunk controls and DPLB policies.
  • FIFO wave estimators using token sum or token-square work.
  • Equal-die dynamic topology selection and profile composition/reuse.
  • Canvas/LOD timeline rendering for large event sets.
  • QPS-scaled MoonConv V4 traces that preserve zero-gap bursts.
  • Checked-in 64K analytic profiles for 16/24/32/40/48-die comparisons.
• Out of scope:
  • Production AFD runtime behavior changes.
  • Decode, KV-transfer, capacity/eviction, EPLB, or real expert skew.
  • GPU/NPU execution or msModeling calibration against hardware.

Implementation Notes

• The experimental scheduler configuration is intentionally breaking: the shared policy and batch-token fields are replaced by architecture-specific AFD and merged settings.
• Current-runtime policy models AFD as round-robin on the present async engine path and merged as vLLM queue-aware DPLB.
• Chunked requests retain FIFO order; unstarted waves are not arbitrarily reordered.
• AFD DP replication reuses per-DP msModeling points when TP and FFN EP are unchanged. Merged TP/global-EP changes use independently generated profiles.
• Every comparison validates equal physical die budgets from profile topology metadata.
• scaled_trace preserves all relative intervals and simultaneous bursts, rescales the complete cycle to the configured mean QPS, and repeats the cycle across long warmup/measurement windows.
• MoonConv fixtures contain only arrival_time_ms and input_length. Source revision, hashes, ODC-By attribution, and exclusions are documented under simulator/data/README.md.
• No vLLM checkout, compatibility shim, monkey patch, or plugin runtime class is changed.

Test Plan

• python -m ruff check simulator
• python -m unittest discover -s simulator/tests -v
• Load every checked-in profile and validate its AFD/merged die budget.
• Start the HTTP server with all profiles and smoke each newly added topology.
• Replay MoonConv formal_0 at target 4 QPS with 32K chunking.

Test Result

• Ruff: passed.
• Simulator unit tests: 47 passed.
• Profiles: all 9 checked-in JSON bundles loaded; all equal-die checks passed.
• Dynamic topology API: 6 AFD choices and 8 merged choices returned; all three newly requested pairings completed simulation smoke tests.
• MoonConv 4-QPS smoke: 284 generated arrivals, including 255 preserved zero-gap arrivals; 249 requests landed in the 60-second measured window. Finite-window offered QPS was 4.15 because burst boundaries are preserved; each complete repeated cycle averages exactly 4 QPS.
• GPU/NPU E2E: not run because this PR changes only the dependency-free analytic simulator.

Docs Impact

• Files updated: simulator/README.md, simulator/profiles/README.md, and simulator/data/README.md.
• The docs cover topology budgets, device/die terminology, scheduler semantics, scaled trace behavior, profile generation/reuse, dataset provenance, and WebUI operation.

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Essential PR Checklist

• Purpose is clear and linked to public context when possible.
• Scope is bounded.
• Compatibility with vLLM v0.26.0 is considered.
• No changes are made to the vLLM source checkout.
• Plugin-owned classes or explicit dotted class paths are preferred over monkey patches; this PR changes no runtime classes.
• No compat shim or monkey patch is added.
• Imports remain CPU-safe; the simulator uses the standard library at runtime.
• Validation evidence is included, including the GPU/NPU skip reason.
• Documentation impact is stated.

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