[https://nvbugs/5698434][test] add qwen3-4b accuracy test case

[crazydemo]

Summary by CodeRabbit

• New Features

  • Added benchmarking and accuracy metrics for the Qwen3-4B model variant with speculative decoding enabled, achieving 85.823% accuracy on the GSM8K evaluation dataset

• Tests

  • Expanded test suite with comprehensive coverage for Qwen3-4B speculative decoding configuration, including GPU memory management and KV cache optimization strategies

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Description

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📝 Walkthrough

Walkthrough

These changes add testing support for the Qwen3-4B model with Eagle3 speculative decoding. A new model reference with accuracy baseline is added to the GSM8K benchmark, along with a corresponding test class that validates the configuration and evaluates performance on the dataset.

Changes

Cohort / File(s)	Summary
Accuracy Reference Data tests/integration/defs/accuracy/references/gsm8k.yaml	Added Qwen3/Qwen3-4B model entry with Eagle speculative decoding algorithm and 85.823 accuracy baseline.
Test Implementation tests/integration/defs/accuracy/test_llm_api_pytorch.py	Added TestQwen3_4B test class with test_eagle3 method. Configures PyTorch, KvCache, and EagleDecodingConfig (max_draft_len=3), then evaluates GSM8K benchmark.
Test Discovery tests/integration/test_lists/qa/llm_function_core.txt	Added test entries for accuracy/test_llm_api_pytorch.py::TestQwen3_4B::test_eagle3.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

Pre-merge checks and finishing touches

❌ Failed checks (2 warnings)

Check name	Status	Explanation	Resolution
Description check	⚠️ Warning	The PR description is incomplete; it contains only the template with empty required sections (Description and Test Coverage) and lacks actual content explaining the changes or test details.	Fill in the Description section with rationale for adding the Qwen3-4B test case, and the Test Coverage section with details about the test_eagle3 test.
Docstring Coverage	⚠️ Warning	Docstring coverage is 50.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly identifies the PR's main objective: adding a Qwen3-4B accuracy test case with Eagle3 speculative decoding.

✨ Finishing touches

• 📝 Generate docstrings

---

📜 Recent review details

Configuration used: Path: .coderabbit.yaml

Review profile: CHILL

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📥 Commits

Reviewing files that changed from the base of the PR and between 5773a4d and 4d8ebfa.

📒 Files selected for processing (3)

• tests/integration/defs/accuracy/references/gsm8k.yaml
• tests/integration/defs/accuracy/test_llm_api_pytorch.py
• tests/integration/test_lists/qa/llm_function_core.txt

🧰 Additional context used

📓 Path-based instructions (2)

**/*.py

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Files:

• tests/integration/defs/accuracy/test_llm_api_pytorch.py

**/*.{cpp,h,cu,cuh,py}

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All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the year of its latest meaningful modification

Files:

• tests/integration/defs/accuracy/test_llm_api_pytorch.py

🧠 Learnings (8)

📓 Common learnings

Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 9511
File: tests/integration/defs/examples/serve/test_serve.py:136-186
Timestamp: 2025-11-27T09:23:18.742Z
Learning: In TensorRT-LLM testing, when adding test cases based on RCCA commands, the command format should be copied exactly as it appears in the RCCA case, even if it differs from existing tests. For example, some RCCA commands for trtllm-serve may omit the "serve" subcommand while others include it.

📚 Learning: 2025-09-09T09:40:45.658Z

Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.658Z
Learning: In TensorRT-LLM test lists, it's common and intentional for the same test to appear in multiple test list files when they serve different purposes (e.g., llm_function_core.txt for comprehensive core functionality testing and llm_function_core_sanity.txt for quick sanity checks). This duplication allows tests to be run in different testing contexts.

Applied to files:

• tests/integration/test_lists/qa/llm_function_core.txt

📚 Learning: 2025-09-17T02:48:52.732Z

Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 7781
File: tests/integration/test_lists/waives.txt:313-313
Timestamp: 2025-09-17T02:48:52.732Z
Learning: In TensorRT-LLM, `tests/integration/test_lists/waives.txt` is specifically for waiving/skipping tests, while other test list files like those in `test-db/` and `qa/` directories are for different test execution contexts (pre-merge, post-merge, QA tests). The same test appearing in both waives.txt and execution list files is intentional - the test is part of test suites but will be skipped due to the waiver.

Applied to files:

• tests/integration/test_lists/qa/llm_function_core.txt

📚 Learning: 2025-07-28T17:06:08.621Z

Learnt from: moraxu
Repo: NVIDIA/TensorRT-LLM PR: 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/test_lists/qa/llm_function_core.txt
• tests/integration/defs/accuracy/test_llm_api_pytorch.py

📚 Learning: 2025-08-26T09:49:04.956Z

Learnt from: pengbowang-nv
Repo: NVIDIA/TensorRT-LLM PR: 7192
File: tests/integration/test_lists/test-db/l0_dgx_b200.yml:56-72
Timestamp: 2025-08-26T09:49:04.956Z
Learning: In TensorRT-LLM test configuration files, the test scheduling system handles wildcard matching with special rules that prevent duplicate test execution even when the same tests appear in multiple yaml files with overlapping GPU wildcards (e.g., "*b200*" and "*gb200*").

Applied to files:

• tests/integration/test_lists/qa/llm_function_core.txt

📚 Learning: 2025-08-29T14:07:45.863Z

Learnt from: EmmaQiaoCh
Repo: NVIDIA/TensorRT-LLM PR: 7370
File: tests/unittest/trt/model_api/test_model_quantization.py:24-27
Timestamp: 2025-08-29T14:07:45.863Z
Learning: In TensorRT-LLM's CI infrastructure, pytest skip markers (pytest.mark.skip) are properly honored even when test files have __main__ blocks that call test functions directly. The testing system correctly skips tests without requiring modifications to the __main__ block execution pattern.

Applied to files:

• tests/integration/test_lists/qa/llm_function_core.txt

📚 Learning: 2025-08-06T13:58:07.506Z

Learnt from: galagam
Repo: NVIDIA/TensorRT-LLM PR: 6487
File: tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py:1-12
Timestamp: 2025-08-06T13:58:07.506Z
Learning: In TensorRT-LLM, test files (files under tests/ directories) do not require NVIDIA copyright headers, unlike production source code files. Test files typically start directly with imports, docstrings, or code.

Applied to files:

• tests/integration/test_lists/qa/llm_function_core.txt

📚 Learning: 2025-11-27T09:23:18.742Z

Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 9511
File: tests/integration/defs/examples/serve/test_serve.py:136-186
Timestamp: 2025-11-27T09:23:18.742Z
Learning: In TensorRT-LLM testing, when adding test cases based on RCCA commands, the command format should be copied exactly as it appears in the RCCA case, even if it differs from existing tests. For example, some RCCA commands for trtllm-serve may omit the "serve" subcommand while others include it.

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 (5)

tensorrt_llm/llmapi/llm_args.py (4)

• CudaGraphConfig (107-164)
• KvCacheConfig (1598-1742)
• EagleDecodingConfig (843-966)
• speculative_model_dir (2135-2136)

tensorrt_llm/_torch/speculative/eagle3.py (1)

• max_draft_len (367-368)

tensorrt_llm/_torch/speculative/interface.py (1)

• max_draft_len (374-378)

tensorrt_llm/_torch/speculative/mtp.py (1)

• max_draft_len (359-360)

tensorrt_llm/llmapi/llm.py (1)

• LLM (1171-1187)

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)

• GitHub Check: Pre-commit Check

🔇 Additional comments (3)

tests/integration/test_lists/qa/llm_function_core.txt (1)

526-526: No issues found—test entry is properly added without duplicates.

The test has been correctly added once to llm_function_core.txt at line 526 with proper formatting. The entry is correctly positioned among other PyTorch LLM API accuracy tests.

tests/integration/defs/accuracy/references/gsm8k.yaml (1)

109-111: LGTM! New Qwen3-4B accuracy baseline added.

The new model entry is properly formatted and the accuracy baseline (85.823 with Eagle) is reasonable given that Qwen3-8B achieves 87.1114. The placement within the Qwen3 model family is appropriate.

Verify that the corresponding test class TestQwen3_4B::test_eagle3 exists in tests/integration/defs/accuracy/test_llm_api_pytorch.py:

#!/bin/bash
# Verify TestQwen3_4B test class exists with test_eagle3 method
ast-grep --pattern $'class TestQwen3_4B($$$):
  $$$
  def test_eagle3($$$):
    $$$'

tests/integration/defs/accuracy/test_llm_api_pytorch.py (1)

3301-3327: Verify Eagle3 configuration matches RCCA case requirements.

The EagleDecodingConfig doesn't explicitly specify the eagle3_one_model parameter. Based on disable_overlap_scheduler=True, this appears to be configured for two-model Eagle3, but relying on the default value could be fragile if defaults change.

Additionally, consider whether hardware requirements are needed. Similar Eagle3 tests (e.g., TestLlama3_1_8BInstruct.test_eagle3 at lines 256-289) use @skip_pre_hopper decorators since Eagle3 has specific hardware dependencies.

Suggested improvements for clarity and robustness

+    @skip_pre_hopper
     def test_eagle3(self):
         "RCCA: https://nvbugspro.nvidia.com/bug/5698434"
         pytorch_config = dict(
             disable_overlap_scheduler=True,
             cuda_graph_config=CudaGraphConfig(),
         )
         kv_cache_config = KvCacheConfig(
             enable_block_reuse=False,
             free_gpu_memory_fraction=0.6,
         )

         eagle_model_dir = f"{llm_models_root()}/Qwen3/Qwen3-4B_eagle3/"
         target_model_dir = f"{llm_models_root()}/Qwen3/Qwen3-4B"

         draft_len = 3
         spec_config = EagleDecodingConfig(max_draft_len=draft_len,
-                                          speculative_model_dir=eagle_model_dir)
+                                          speculative_model_dir=eagle_model_dir,
+                                          eagle3_one_model=False)

Based on learnings, when adding test cases based on RCCA commands, verify the configuration matches the RCCA case exactly. Please confirm whether the hardware decorator and explicit eagle3_one_model parameter are needed for this test case.

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