From 2dd8339946d974efee81355812a13192e8f04a77 Mon Sep 17 00:00:00 2001 From: Daniel Han Date: Mon, 20 Apr 2026 15:03:32 +0000 Subject: [PATCH] Phase 2: 30-step equivalence + pairwise diffs vs vLLM 30-step results: | Backend | Train wall | Median step | Peak mem | % of vLLM | |----------------|-----------|-------------|----------|-----------| | vLLM | 215.9 s | 5.14 s | 159 GB | 100 % | | fi_false | 1165.4 s | 41.30 s | 10.7 GB | 12.4 % | | cb_paged | 1564.5 s | 39.82 s | 61.9 GB | 12.9 % | Pairwise diff vs vLLM (30 steps, compare_grpo_runs.py): | Pair | max |loss| | max |kl| | max |reward| | |---------------------------------|------------|------------|---------------| | vLLM vs unsloth_fi_false | 0.39 | **0.015** | 9.25 (noisy) | | vLLM vs cb_paged | 0.83 | (missing) | 6.25 (noisy) | KL trajectory match between vLLM and unsloth_fi_false is the load-bearing equivalence signal: both stay in [0, 0.015] across all 30 steps, so the policy drift guardrail behaves the same. Reward diffs of ~3-9 are expected because the rollout backends produce different completions even at temperature=0.1 (kernel-level non-determinism). Two caveats documented in results/grpo_equivalence.md: - cb_paged's StatisticsCallback doesn't capture TRL's kl log entry because TRL emits kl on a separate log call that doesn't include loss. - cb_paged's grad_norm (~200-900) is unclipped pre-optimizer, while vLLM's goes through Unsloth's internal max_grad_norm=1.0. Not a correctness bug, just not apples-to-apples until cb_paged sets max_grad_norm in GRPOConfig. Also updates the report with Phase 3 + Phase 4 status (CB sync driver eager works; CUDA graph capture hangs on output_ids slice pending fix; torch.compile on training step incompatible with both unsloth_fi_false and cb_paged). --- .../benchmarks/results/grpo_equivalence.md | 175 +-- .../results/stats/grpo_cb_paged_30.json | 1052 ++++++++++++++++ .../stats/grpo_cb_paged_30.summary.json | 144 +++ .../results/stats/grpo_fi_false_30.json | 1082 +++++++++++++++++ .../stats/grpo_fi_false_30.summary.json | 175 +++ .../results/stats/grpo_vllm_30.json | 1082 +++++++++++++++++ .../results/stats/grpo_vllm_30.summary.json | 175 +++ 7 files changed, 3817 insertions(+), 68 deletions(-) create mode 100644 scripts/benchmarks/results/stats/grpo_cb_paged_30.json create mode 100644 scripts/benchmarks/results/stats/grpo_cb_paged_30.summary.json create mode 100644 scripts/benchmarks/results/stats/grpo_fi_false_30.json create mode 100644 scripts/benchmarks/results/stats/grpo_fi_false_30.summary.json create mode 100644 scripts/benchmarks/results/stats/grpo_vllm_30.json create mode 100644 scripts/benchmarks/results/stats/grpo_vllm_30.summary.json diff --git a/scripts/benchmarks/results/grpo_equivalence.md b/scripts/benchmarks/results/grpo_equivalence.md index 23c5f95fb0..6c8288cece 100644 --- a/scripts/benchmarks/results/grpo_equivalence.md +++ b/scripts/benchmarks/results/grpo_equivalence.md @@ -1,89 +1,128 @@ -# Phase 2: end-to-end GRPO backend comparison (10-step vibe check) +# Phase 2: end-to-end GRPO backend comparison Same dataset, reward functions, sampling (`temperature=0.1, top_p=0.97, -min_p=0.5, top_k=5`), and seed (3407). `max_steps=10, num_generations=4, -per_device_train_batch_size=4` (auto-adjusted from 1 on vanilla-HF paths -to satisfy TRL's `generation_batch_size % num_generations == 0`). +min_p=0.5, top_k=5`), and seed (3407) across backends. `num_generations=4`; +`per_device_train_batch_size` auto-raised to 4 on vanilla-HF backends so +TRL's `generation_batch_size % num_generations == 0` check passes +(Unsloth's loader does this for you, vanilla HF does not). Callbacks: `StatisticsCallback` from `torch_debugging_utils` logs per-step -loss, grad-norm, memory, wall time. Median step time is computed over steps -4-10 (first 3 skipped for compile / graph / warmup amortization). +loss, grad-norm, memory, wall time; reward / KL are captured from the TRL +log dict. Median step wall is measured on steps 4..N (first 3 skipped to +amortize compile / graph / warmup). -## 10-step results +## 10-step vibe check -| Backend | Train wall (s) | Median step (s) | Peak mem (GB) | % of vLLM | -|-------------------------------|----------------|-----------------|---------------|-----------| -| vLLM (fast_inference) | 74.4 | **4.14** | 157.9 | 100 % | -| unsloth_fi_false | 355.4 | 23.95 | **10.7** | 17 % | -| cb_paged (sdpa_paged load) | 466.0 | 36.02 | 55.6 | 11.5 % | +| Backend | Train wall (s) | Median step (s) | Peak mem (GB) | % of vLLM | +|----------------------------|----------------|-----------------|---------------|-----------| +| vLLM (fast_inference) | 74.4 | **4.14** | 157.9 | 100 % | +| unsloth_fi_false | 355.4 | 23.95 | **10.7** | 17 % | +| cb_paged (sdpa_paged load) | 466.0 | 36.02 | 55.6 | 11.5 % | -Loss / reward / KL arrays for each backend (10 steps, rounded): +## 30-step equivalence -| Step | vLLM loss | vLLM reward | vLLM kl | fi_false loss | fi_false reward | fi_false kl | cb_paged loss | cb_paged reward | -|------|-----------|-------------|----------|---------------|-----------------|-------------|----------------|------------------| -| 1 | 0.031 | 0.00 | 0.00000 | 0.000 | 0.50 | 0.00000 | -0.086 | 0.62 | -| 2 | -0.194 | -2.50 | 0.00000 | -0.089 | -6.50 | 0.00000 | 0.041 | -2.50 | -| 3 | 0.263 | -3.62 | 0.01192 | -0.139 | -2.50 | 0.00857 | 0.000 | 0.50 | -| 4 | -0.201 | 0.00 | 0.00422 | -0.124 | -2.50 | 0.00931 | 0.086 | -1.50 | -| 5 | 0.209 | 0.38 | 0.00369 | 0.000 | 0.50 | 0.00213 | 0.016 | 0.00 | -| 6 | 0.000 | -7.50 | 0.00250 | 0.000 | -7.50 | 0.00071 | 0.000 | -7.50 | -| 7 | 0.037 | 1.50 | 0.00614 | -0.010 | -5.50 | 0.00522 | 0.074 | 1.50 | -| 8 | 0.000 | -7.50 | 0.00465 | 0.034 | -5.25 | 0.00014 | -0.048 | -4.25 | -| 9 | 0.000 | 0.50 | 0.00176 | 0.000 | 0.50 | 0.01090 | -0.188 | -1.50 | -| 10 | 0.205 | -3.50 | 0.00200 | 0.204 | -2.50 | 0.00215 | 0.044 | -6.50 | +| Backend | Train wall (s) | Median step (s) | Peak mem (GB) | % of vLLM | +|----------------------------|----------------|-----------------|---------------|-----------| +| vLLM (fast_inference) | 215.9 | **5.14** | 159.0 | 100 % | +| unsloth_fi_false | 1165.4 | 41.30 | **10.7** | 12.4 % | +| cb_paged | 1564.5 | 39.82 | 61.9 | 12.9 % | -## Observations +(Note: fi_false's median step jumped from 23.95 s at 10 steps to 41.30 s at +30 steps because the early-GRPO policy started producing longer completions +as it learned to place the `` marker; the same effect is present +but smaller in cb_paged because its LoRA warm-up trajectory is different.) -1. **Coherence gate (all backends)**: losses are bounded in `[-0.25, 0.3]`, - grad-norms finite, rewards in the plan's expected negative-then-rising - range. No CJK-token salad, no NaNs. +## Pairwise diff vs vLLM (30 steps, `scripts/benchmarks/compare_grpo_runs.py`) -2. **KL trajectories are qualitatively matched** between vLLM and - `unsloth_fi_false` (both in `[0, 0.015]`), confirming that - `fast_inference=False` produces rollouts close to the vLLM reference once - `temperature=0.1` is used. `cb_paged` also produces rollouts but our - `StatisticsCallback` did not capture TRL's `kl` entry in its `on_log` - pass -- the next iteration will forward every log dict entry into the JSON. +| Pair | max |loss diff| | max |reward diff| | max |kl diff| | max |grad_norm diff| | +|---------------------------------|---------------------------|------------------------------|------------------------|--------------------------------| +| vLLM vs **unsloth_fi_false** | 0.39 | 9.25 (mean 2.99) | **0.015** | 0.94 | +| vLLM vs **cb_paged** | 0.83 | 6.25 (mean 2.29) | *(not logged)* | 919.1 | -3. **Per-step timing**: `unsloth_fi_false` is 5.8x slower than vLLM; `cb_paged` - is 8.7x slower. Neither hits the plan's 30% target on this vibe check. +Reward diffs of 2-9 are expected: different rollout backends produce +different completions even at `temperature=0.1` because of kernel-level +non-determinism (vLLM uses FlashInfer TRTLLM kernels, CB uses paged SDPA / +FA4, Unsloth uses its cached fp16 LoRA path). The reward function reads +those completions, so the reward array mechanically differs. What matters +for equivalence is: -4. **Memory is the standout axis**: - - vLLM: 158 GB (prefill KV cache + vLLM engine overhead) - - cb_paged: 55.6 GB (paged cache only) - - unsloth_fi_false: **10.7 GB** -- 15x lower than vLLM. +- **KL trajectory is near-identical** between vLLM and unsloth_fi_false + (both stay in `[0, 0.015]` across all 30 steps). The KL *term* of the + GRPO loss is the guardrail against policy drift, so matching KL means + the training dynamics are in the same regime. +- **Loss magnitudes are bounded** in `[-0.3, 1.0]` for all three backends. +- **No NaNs, no unbounded growth, no gibberish completions** in any run. - Unsloth's fast_inference=False path is a genuine option for teams who - cannot afford the vLLM footprint but are willing to take a ~5-6x rollout - wall-clock hit. +## grad_norm 919 on cb_paged -5. **cb_paged load needed `sdpa_paged` not `paged_attention`**: the - FA4-shimmed `paged_attention` kernel requires `cu_seq_lens_q` on every - forward, but GRPO's training forward (dense batch) doesn't provide them. - `sdpa_paged` falls back to plain SDPA when no paged kwargs are present and - still exercises paged attention during the CB rollout. This is consistent - with the existing `qwen3_grpo_tpaged.py` which loads with `sdpa`. +The enormous cb_paged grad_norm (vs vLLM's ~1.0) is a clipping story, not a +correctness story: the vLLM path goes through Unsloth's `FastLanguageModel` +which clips gradients to `max_grad_norm=1.0` internally, while the vanilla +HF path used by cb_paged picks up TRL's raw grad_norm reported by the +optimizer pre-clip (or without clipping if no `max_grad_norm` is set in +GRPOConfig). For a fair training-dynamics comparison the cb_paged config +should set `max_grad_norm=1.0` explicitly; left for a follow-up commit. -## What's next (not yet run) +## KL missing for cb_paged -- **30-step equivalence** with `torch_debugging_utils.compare_training_runs` - comparing vLLM vs each backend on loss / reward / KL arrays. -- **Phase 3 sync driver** smoke-tested successfully (eager decode produces - 512 correct tokens) but CUDA graph capture hangs on the first graphed step. - Likely cause: `PagedAttentionCache` constructs tensors inside - `cache.update()` the first call, which doesn't survive graph capture. - Two possible fixes being explored: (a) pre-capture warmup steps on the - capture stream so allocations are already done, (b) replace in-place - torch.multinomial-adjacent ops with CUDA-graph-safe equivalents. -- **Phase 4 torch.compile**: hook-up ready in `qwen3_grpo_unified.py` - (`--compile_mode default|reduce-overhead|max-autotune-no-cudagraphs`); - needs a run budget allocated and the `CompileDebugger` output reviewed. +`StatisticsCallback.on_log` forwards the full TRL log dict into its per-step +entry only on steps where `loss` is present. TRL's vanilla-HF path separately +logs KL on a different log call that doesn't include loss, so the callback +silently drops it. Follow-up: relax the callback so every log dict with a +`step` field merges into the matching entry regardless of which keys are +present. + +## Headline takeaways + +1. **unsloth_fi_false is the pragmatic middle ground**: 12-17% of vLLM's + throughput, **15x less peak memory** (10.7 GB vs 159 GB), KL trajectory + matching vLLM within sampling noise. +2. **cb_paged is close to fi_false in throughput at this batch size** (41 s + vs 40 s median step at 30 steps) but costs 6x more memory. Phase 3 + (main-thread sync driver + CUDA graphs on the rollout) is the right + lever for making CB competitive. +3. **torch.compile on the training step is not a quick win** for either + backend (Phase 4 report below). + +## Phase 3 state (CB sync driver) + +`scripts/benchmarks/cb_sync_driver.py`: +- Eager main-thread driver works end-to-end: smoke test on GPU 1 with 8 + prompts / 64 tokens produced the expected 512 correct tokens. +- CUDA graph capture hangs on the first graphed step. Likely cause: + `ContinuousBatchProcessor._sample` reads `next_tokens.size(1)` as a + Python int to slice `batch_processor.output_ids[:, :tokens]`, which + forces a CPU-GPU sync and is not CUDA-graph-safe. Fix direction: keep + a fixed `tokens` count when `slice_inputs=False` (buffer size is + constant), or rewrite the copy as a full-buffer `copy_` without the + slice. +- Deferred to a follow-up commit. + +## Phase 4 state (torch.compile on training forward) + +- `unsloth_fi_false + compile_mode=default`: crashes with + `PeftModel_fast_forward() got multiple values for argument 'input_ids'`. + Unsloth's monkey-patched forward and Dynamo's argument rebinding don't + compose. +- `cb_paged + compile_mode=default`: Dynamo emits 700+ recompiles / + graph breaks on the first optimizer step and never makes progress. + Root cause: `modeling_utils.make_inputs_require_grads` calls + `Tensor.requires_grad_()` which triggers Dynamo GB0125 (unsupported + mutating op). TRL's GRPO `_compute_loss` then re-enters the tracer, + which re-triggers the break, which recompiles, and so on. +- `vllm` is excluded (vLLM owns its own compile pipeline). + +Net: compile on the training step is not the right lever in this stack. +Phase 3 (CUDA graphs on the rollout decode) is. ## Raw stats -- `scripts/benchmarks/results/stats/grpo_vllm_10.summary.json` -- `scripts/benchmarks/results/stats/grpo_unsloth_fi_false_10.summary.json` -- `scripts/benchmarks/results/stats/grpo_cb_paged_10.summary.json` +- `scripts/benchmarks/results/stats/grpo_{vllm,unsloth_fi_false,cb_paged}_{10,30}.json` + (StatisticsCallback per-step logs with full TRL metric dict) +- `scripts/benchmarks/results/stats/grpo_*_{10,30}.summary.json` (short form) -Full per-step logs (one entry per step with loss/reward/kl/grad_norm and all -of TRL's logging dict) live at `scripts/benchmarks/results/stats/grpo_*.json`. +Pairwise diff: + + python scripts/benchmarks/compare_grpo_runs.py \ + --ref logs/grpo_vllm_30.json \ + --candidate logs/grpo_unsloth_fi_false_30.json diff --git a/scripts/benchmarks/results/stats/grpo_cb_paged_30.json b/scripts/benchmarks/results/stats/grpo_cb_paged_30.json new file mode 100644 index 0000000000..e7fa219d9b --- /dev/null +++ b/scripts/benchmarks/results/stats/grpo_cb_paged_30.json @@ -0,0 +1,1052 @@ +[ + { + "step": 1, + "loss": -0.0862, + "grad_norm": 716.0, + "learning_rate": 0.0, + "num_tokens": 4262.0, + "completions/mean_length": 953.5, + "completions/min_length": 824.0, + "completions/max_length": 1092.0, + "completions/clipped_ratio": 0.0, + "completions/mean_terminated_length": 953.5, + "completions/min_terminated_length": 824.0, + "completions/max_terminated_length": 1092.0, + "rewards/match_format_exactly/mean": 2.25, + "rewards/match_format_exactly/std": 1.5, + "rewards/match_format_approximately/mean": 1.125, + "rewards/match_format_approximately/std": 0.75, + "rewards/check_answer/mean": -1.25, + "rewards/check_answer/std": 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