[https://nvbugs/5440241][fix] Fix 70B GSM8K Accuracy drop #7075
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Signed-off-by: Chenfei Zhang <[email protected]>
📝 WalkthroughWalkthroughUpdates integration accuracy references for Llama-3.3-70B-Instruct on GSM8K and MMLU and adjusts two PyTorch accuracy tests to new FP8/FP4 model paths, introduce KvCacheConfig (free_gpu_memory_fraction=0.5), set max_tokens=256, add max_batch_size=32 in FP4 test, and change GPQADiamond evaluator args. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
actor Tester
participant Test as PyTorch Test
participant LLM as LLM(init with KvCacheConfig)
participant Eval as Task/Evaluator
Tester->>Test: run test_fp8_tp4 / test_nvfp4_tp4
Test->>LLM: init(model_path, kv_cache_config[, max_batch_size])
Note right of LLM: KV cache reserves GPU memory per free_gpu_memory_fraction
Test->>Eval: evaluate(llm, sampling_params(max_tokens=256)[, extra_evaluator_kwargs])
Eval->>LLM: generate()
LLM-->>Eval: outputs
Eval-->>Test: metrics
Test-->>Tester: assert accuracy vs references
Estimated code review effort🎯 2 (Simple) | ⏱️ ~10 minutes Possibly related PRs
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Actionable comments posted: 0
🧹 Nitpick comments (2)
tests/integration/defs/accuracy/test_llm_api_pytorch.py (2)
435-444: NVFP4: memory safety knobs look right; consider aligning max_seq_len with the FP8 test
- model_path updated to the FP4 artifact, kv_cache_config set to 0.5 fraction, max_batch_size=32, and max_tokens=256 — all make sense to address the illegal memory access and stabilize accuracy.
For parity and reproducibility across devices, you may also set max_seq_len=8192 here (as you did in the FP8 test). Proposed local change:
- with LLM(model_path, - tensor_parallel_size=4, - max_batch_size=32, - kv_cache_config=kv_cache_config) as llm: + with LLM(model_path, + tensor_parallel_size=4, + max_seq_len=8192, + max_batch_size=32, + kv_cache_config=kv_cache_config) as llm:
411-421: Residual reference to old FP8 path in Eagle3 test
- tests/integration/defs/accuracy/test_llm_api_pytorch.py:390 still points to
modelopt-hf-model-hub/Llama-3.3-70B-Instruct-fp8.
Update it to the new layout for consistency:- model_path = f"{llm_models_root()}/modelopt-hf-model-hub/Llama-3.3-70B-Instruct-fp8" + model_path = f"{llm_models_root()}/llama-3.3-models/Llama-3.3-70B-Instruct-FP8"
- Verify that
llama-3.3-models/Llama-3.3-70B-Instruct-FP8exists in the CI model store.
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📒 Files selected for processing (3)
tests/integration/defs/accuracy/references/gsm8k.yaml(1 hunks)tests/integration/defs/accuracy/references/mmlu.yaml(1 hunks)tests/integration/defs/accuracy/test_llm_api_pytorch.py(3 hunks)
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**/*.py
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tests/integration/defs/accuracy/test_llm_api_pytorch.py
**/*.{cpp,cxx,cc,cu,h,hpp,hxx,hh,cuh,py}
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tests/integration/defs/accuracy/test_llm_api_pytorch.py
🧠 Learnings (1)
📚 Learning: 2025-07-28T17:06:08.621Z
Learnt from: moraxu
PR: NVIDIA/TensorRT-LLM#6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.
Applied to files:
tests/integration/defs/accuracy/test_llm_api_pytorch.py
🧬 Code Graph Analysis (1)
tests/integration/defs/accuracy/test_llm_api_pytorch.py (4)
tests/integration/defs/conftest.py (1)
llm_models_root(77-83)tensorrt_llm/llmapi/llm_args.py (1)
KvCacheConfig(923-1002)tensorrt_llm/quantization/mode.py (1)
QuantAlgo(23-44)tensorrt_llm/sampling_params.py (1)
SamplingParams(125-483)
🔇 Additional comments (3)
tests/integration/defs/accuracy/references/gsm8k.yaml (1)
16-21: Confirm disambiguation for FP8 entries in GSM8K referencesI ran the verification script but it failed sorting entries missing both
quant_algoandkv_cache_quant_algo. Please manually verify that the harness selection logic:
- Matches on both
quant_algoandkv_cache_quant_algowhen present, so the specific FP8+KV entry wins over the generic FP8 entry.- Never falls back to the generic
quant_algo: FP8row when the model is actually using FP8 KV–cache.File under review (lines 16–21):
tests/integration/defs/accuracy/references/gsm8k.yaml- quant_algo: NVFP4 kv_cache_quant_algo: FP8 accuracy: 87.33 - quant_algo: FP8 kv_cache_quant_algo: FP8 accuracy: 90.30 - quant_algo: FP8 accuracy: 90.30tests/integration/defs/accuracy/references/mmlu.yaml (1)
67-72: MMLU entries include dual FP8 variants; ensure resolver specificity
- File: tests/integration/defs/accuracy/references/mmlu.yaml
Section: meta-llama/Llama-3.3-70B-Instruct- Confirmed entries:
• NVFP4 + kv_cache_FP8 → 78.78
• FP8 + kv_cache_FP8 → 80.40
• FP8 (plain) → 80.40- Verify these values match the source benchmarks and that your matcher will prefer the “FP8 + kv_cache_FP8” entry over the plain “FP8” when both criteria apply.
tests/integration/defs/accuracy/test_llm_api_pytorch.py (1)
429-431: Switching GPQA to extra_evaluator_kwargs is consistent with recent harness changesMoving GPQADiamond to use extra_evaluator_kwargs=dict(apply_chat_template=True) (without sampling_params) matches the pattern used elsewhere in this file.
Also applies to: 452-454
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PR_Github #15881 [ run ] completed with state |
Signed-off-by: Chenfei Zhang <[email protected]>
Signed-off-by: Chenfei Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
Signed-off-by: Chenfei Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
Signed-off-by: Chenfei Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
Signed-off-by: Chenfei Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
Signed-off-by: Chenfei Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
Description
This PR fixes Llama3.3 70B fp8/fp4 gsm8k's accuracy drop by adding
max_tokens=256.This PR fixes Llama3.3 70B fp4's illegal memory access by adding
free_gpu_mem_fraction=0.5, max_batch_size=32.FP8 MMLU on H200:
MMLU weighted average accuracy: 80.51 (4104)
FP8 GSM8K on H200:
FP8 GPQA_Diamond on H200:
FP8 MMLU on B200:
MMLU weighted average accuracy: 80.48 (4104)
FP8 GSM8K on B200:
FP8 GPQA_Diamond on B200:
FP4 MMLU on B200:
MMLU weighted average accuracy: 78.78 (4104)
FP4 GSM8K on B200:
FP4 GPQA_Diamond on B200:
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