[None][chore] Add unit test for Gemma3 lora #6560
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📝 WalkthroughWalkthroughThe changes update the Changes
Sequence Diagram(s)sequenceDiagram
participant Test as test_gemma3_1b_instruct_multi_lora
participant LLM as LLM
participant Model as Gemma3ForCausalLM
participant TextModel as Gemma3TextModel
participant DecoderLayer as Gemma3DecoderLayer
participant Attention as Gemma3Attention
Test->>LLM: generate(prompts, lora_requests)
LLM->>Model: forward(..., **kwargs)
Model->>TextModel: forward(..., **kwargs)
TextModel->>DecoderLayer: forward(..., **kwargs)
DecoderLayer->>Attention: forward(..., **kwargs)
Attention-->>DecoderLayer: output
DecoderLayer-->>TextModel: output
TextModel-->>Model: output
Model-->>LLM: output
LLM-->>Test: outputs
Estimated code review effort🎯 2 (Simple) | ⏱️ ~8 minutes Possibly related PRs
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tensorrt_llm/_torch/models/modeling_gemma3.py(1 hunks)tests/unittest/llmapi/test_llm_pytorch.py(2 hunks)
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**/*.py
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
**/*.py: The code developed for TensorRT-LLM should conform to Python 3.8+.
Indent Python code with 4 spaces. Do not use tabs.
Always maintain the namespace when importing in Python, even if only one class or function from a module is used.
Python filenames should use snake_case (e.g., some_file.py).
Python classes should use PascalCase (e.g., class SomeClass).
Python functions and methods should use snake_case (e.g., def my_awesome_function():).
Python local variables should use snake_case. Prefix k for variable names that start with a number (e.g., k_99th_percentile = ...).
Python global variables should use upper snake_case and prefix G (e.g., G_MY_GLOBAL = ...).
Python constants should use upper snake_case (e.g., MY_CONSTANT = ...).
Avoid shadowing variables declared in an outer scope in Python.
Initialize all externally visible members of a class in the constructor in Python.
For interfaces that may be used outside a file, prefer docstrings over comments in Python.
Comments in Python should be reserved for code within a function, or interfaces that are local to a file.
Use Google style docstrings for classes and functions in Python, which can be parsed by Sphinx.
Attributes and variables in Python can be documented inline; attribute docstrings will be rendered under the docstring for the class.
Avoid using reflection in Python when functionality can be easily achieved without it.
When using try-except blocks in Python, limit the except to the smallest set of errors possible.
When using try-except blocks to handle multiple possible variable types in Python, keep the body of the try as small as possible, using the else block to implement the logic.
Files:
tensorrt_llm/_torch/models/modeling_gemma3.pytests/unittest/llmapi/test_llm_pytorch.py
**/*.{cpp,h,hpp,cc,cxx,cu,py}
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the current year. This includes .cpp, .h, .cu, .py, and any other source files which are compiled or interpreted.
Files:
tensorrt_llm/_torch/models/modeling_gemma3.pytests/unittest/llmapi/test_llm_pytorch.py
🧠 Learnings (4)
📓 Common learnings
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.
Learnt from: amitz-nv
PR: NVIDIA/TensorRT-LLM#5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.402Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks `is_adapter_in_cpu_cache()` and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
📚 Learning: in tensorrt-llm testing, it's common to have both cli flow tests (test_cli_flow.py) and pytorch api ...
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/unittest/llmapi/test_llm_pytorch.py
📚 Learning: applies to **/*.py : the code developed for tensorrt-llm should conform to python 3.8+....
Learnt from: CR
PR: NVIDIA/TensorRT-LLM#0
File: CODING_GUIDELINES.md:0-0
Timestamp: 2025-08-01T07:34:42.734Z
Learning: Applies to **/*.py : The code developed for TensorRT-LLM should conform to Python 3.8+.
Applied to files:
tests/unittest/llmapi/test_llm_pytorch.py
📚 Learning: in tensorrt-llm, examples directory can have different dependency versions than the root requirement...
Learnt from: yibinl-nvidia
PR: NVIDIA/TensorRT-LLM#6506
File: examples/models/core/mixtral/requirements.txt:3-3
Timestamp: 2025-08-01T15:14:45.630Z
Learning: In TensorRT-LLM, examples directory can have different dependency versions than the root requirements.txt file. Version conflicts between root and examples dependencies are acceptable because examples are designed to be standalone and self-contained.
Applied to files:
tests/unittest/llmapi/test_llm_pytorch.py
🔇 Additional comments (2)
tests/unittest/llmapi/test_llm_pytorch.py (2)
4-4: LGTM!The
KvCacheConfigimport is necessary for the new test function and properly placed with other imports.
496-547: LGTM! Well-structured multi-LoRA test for Gemma3.The test follows established patterns in the codebase and properly:
- Creates dummy LoRA adapters with zeroed parameters for testing
- Configures appropriate target modules (
attn_q,attn_k,attn_v)- Disables KV cache reuse to avoid complications during LoRA testing
- Tests generation with multiple LoRA requests
- Validates expected output count
This PyTorch API test appropriately complements the existing test coverage for the Gemma3 model with LoRA functionality.
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🧹 Nitpick comments (1)
tests/unittest/llmapi/test_llm_pytorch.py (1)
527-527: Fix line length violation.The comment exceeds the 120-character limit. Consider breaking it into multiple lines:
- # Disabling kv cache reuse as a WAR to deal with gaps in kernel support for Gemma3's non-inclusive sliding window size. + # Disabling kv cache reuse as a WAR to deal with gaps in kernel support + # for Gemma3's non-inclusive sliding window size.
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tensorrt_llm/_torch/models/modeling_gemma3.py(3 hunks)tests/unittest/llmapi/test_llm_pytorch.py(2 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- tensorrt_llm/_torch/models/modeling_gemma3.py
🧰 Additional context used
📓 Path-based instructions (2)
**/*.py
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
**/*.py: The code developed for TensorRT-LLM should conform to Python 3.8+.
Indent Python code with 4 spaces. Do not use tabs.
Always maintain the namespace when importing in Python, even if only one class or function from a module is used.
Python filenames should use snake_case (e.g., some_file.py).
Python classes should use PascalCase (e.g., class SomeClass).
Python functions and methods should use snake_case (e.g., def my_awesome_function():).
Python local variables should use snake_case. Prefix k for variable names that start with a number (e.g., k_99th_percentile = ...).
Python global variables should use upper snake_case and prefix G (e.g., G_MY_GLOBAL = ...).
Python constants should use upper snake_case (e.g., MY_CONSTANT = ...).
Avoid shadowing variables declared in an outer scope in Python.
Initialize all externally visible members of a class in the constructor in Python.
For interfaces that may be used outside a file, prefer docstrings over comments in Python.
Comments in Python should be reserved for code within a function, or interfaces that are local to a file.
Use Google style docstrings for classes and functions in Python, which can be parsed by Sphinx.
Attributes and variables in Python can be documented inline; attribute docstrings will be rendered under the docstring for the class.
Avoid using reflection in Python when functionality can be easily achieved without it.
When using try-except blocks in Python, limit the except to the smallest set of errors possible.
When using try-except blocks to handle multiple possible variable types in Python, keep the body of the try as small as possible, using the else block to implement the logic.
Files:
tests/unittest/llmapi/test_llm_pytorch.py
**/*.{cpp,h,hpp,cc,cxx,cu,py}
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the current year. This includes .cpp, .h, .cu, .py, and any other source files which are compiled or interpreted.
Files:
tests/unittest/llmapi/test_llm_pytorch.py
🧠 Learnings (5)
📓 Common learnings
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.
📚 Learning: in tensorrt-llm testing, it's common to have both cli flow tests (test_cli_flow.py) and pytorch api ...
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/unittest/llmapi/test_llm_pytorch.py
📚 Learning: applies to **/*.py : the code developed for tensorrt-llm should conform to python 3.8+....
Learnt from: CR
PR: NVIDIA/TensorRT-LLM#0
File: CODING_GUIDELINES.md:0-0
Timestamp: 2025-08-01T07:34:42.734Z
Learning: Applies to **/*.py : The code developed for TensorRT-LLM should conform to Python 3.8+.
Applied to files:
tests/unittest/llmapi/test_llm_pytorch.py
📚 Learning: in tensorrt-llm, examples directory can have different dependency versions than the root requirement...
Learnt from: yibinl-nvidia
PR: NVIDIA/TensorRT-LLM#6506
File: examples/models/core/mixtral/requirements.txt:3-3
Timestamp: 2025-08-01T15:14:45.673Z
Learning: In TensorRT-LLM, examples directory can have different dependency versions than the root requirements.txt file. Version conflicts between root and examples dependencies are acceptable because examples are designed to be standalone and self-contained.
Applied to files:
tests/unittest/llmapi/test_llm_pytorch.py
📚 Learning: in tensorrt_llm/executor/worker.py, the lora adapter cache optimization logic that checks `is_adapte...
Learnt from: amitz-nv
PR: NVIDIA/TensorRT-LLM#5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.402Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks `is_adapter_in_cpu_cache()` and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
Applied to files:
tests/unittest/llmapi/test_llm_pytorch.py
🪛 Ruff (0.12.2)
tests/unittest/llmapi/test_llm_pytorch.py
527-527: Line too long (127 > 120)
(E501)
🔇 Additional comments (2)
tests/unittest/llmapi/test_llm_pytorch.py (2)
4-4: LGTM!The
KvCacheConfigimport is necessary for the new test function and follows the existing import structure.
496-550: LGTM! Comprehensive test implementation for Gemma3 multi-LoRA.The test follows established patterns and thoroughly validates multi-LoRA functionality:
- Creates dummy LoRA adapters with proper PEFT configuration
- Implements necessary KvCache workaround for Gemma3's sliding window limitations
- Tests generation with multiple LoRA requests
- Validates expected output count
The implementation aligns well with similar tests in the codebase and provides good coverage for the Gemma3 model's LoRA support.
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PR_Github #13845 [ run ] completed with state |
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PR_Github #13859 [ run ] triggered by Bot |
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PR_Github #13859 [ run ] completed with state |
Signed-off-by: Balaram Buddharaju <[email protected]>
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Actionable comments posted: 0
🧹 Nitpick comments (1)
tests/unittest/llmapi/test_llm_pytorch.py (1)
496-550: Good test implementation with one formatting fix needed.The test follows the established pattern for multi-LoRA testing and appropriately handles Gemma3-specific requirements with the KV cache workaround. The implementation correctly validates multi-LoRA functionality.
Fix the line length issue on line 527:
- # Disabling kv cache reuse as a WAR to deal with gaps in kernel support for Gemma3's non-inclusive sliding window size. + # Disabling kv cache reuse as a WAR to deal with gaps in kernel support + # for Gemma3's non-inclusive sliding window size.
📜 Review details
Configuration used: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (3)
tensorrt_llm/_torch/models/modeling_gemma3.py(3 hunks)tests/integration/test_lists/qa/examples_test_list.txt(1 hunks)tests/unittest/llmapi/test_llm_pytorch.py(2 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- tensorrt_llm/_torch/models/modeling_gemma3.py
🧰 Additional context used
📓 Path-based instructions (2)
**/*.py
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
**/*.py: The code developed for TensorRT-LLM should conform to Python 3.8+.
Indent Python code with 4 spaces. Do not use tabs.
Always maintain the namespace when importing in Python, even if only one class or function from a module is used.
Python filenames should use snake_case (e.g., some_file.py).
Python classes should use PascalCase (e.g., class SomeClass).
Python functions and methods should use snake_case (e.g., def my_awesome_function():).
Python local variables should use snake_case. Prefix k for variable names that start with a number (e.g., k_99th_percentile = ...).
Python global variables should use upper snake_case and prefix G (e.g., G_MY_GLOBAL = ...).
Python constants should use upper snake_case (e.g., MY_CONSTANT = ...).
Avoid shadowing variables declared in an outer scope in Python.
Initialize all externally visible members of a Python class in the constructor.
For interfaces that may be used outside a file, prefer docstrings over comments in Python.
Comments in Python should be reserved for code within a function, or interfaces that are local to a file.
Use Google style docstrings for classes and functions in Python, which can be parsed by Sphinx.
Attributes and variables in Python can be documented inline; attribute docstrings will be rendered under the docstring for the class.
Avoid using reflection in Python when functionality can be easily achieved without it.
When using try-except blocks in Python, limit the except to the smallest set of errors possible.
When using try-except blocks to handle multiple possible variable types in Python, keep the body of the try as small as possible, using the else block to implement the logic.
Files:
tests/unittest/llmapi/test_llm_pytorch.py
**/*.{cpp,h,hpp,cc,cxx,cu,py}
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the current year. This includes .cpp, .h, .cu, .py, and any other source files which are compiled or interpreted.
Files:
tests/unittest/llmapi/test_llm_pytorch.py
🧠 Learnings (5)
📓 Common learnings
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.
📚 Learning: in tensorrt-llm testing, it's common to have both cli flow tests (test_cli_flow.py) and pytorch api ...
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/test_lists/qa/examples_test_list.txttests/unittest/llmapi/test_llm_pytorch.py
📚 Learning: in tensorrt-llm, examples directory can have different dependency versions than the root requirement...
Learnt from: yibinl-nvidia
PR: NVIDIA/TensorRT-LLM#6506
File: examples/models/core/mixtral/requirements.txt:3-3
Timestamp: 2025-08-01T15:14:45.673Z
Learning: In TensorRT-LLM, examples directory can have different dependency versions than the root requirements.txt file. Version conflicts between root and examples dependencies are acceptable because examples are designed to be standalone and self-contained.
Applied to files:
tests/integration/test_lists/qa/examples_test_list.txttests/unittest/llmapi/test_llm_pytorch.py
📚 Learning: applies to **/*.py : the code developed for tensorrt-llm should conform to python 3.8+....
Learnt from: CR
PR: NVIDIA/TensorRT-LLM#0
File: CODING_GUIDELINES.md:0-0
Timestamp: 2025-08-04T02:12:17.582Z
Learning: Applies to **/*.py : The code developed for TensorRT-LLM should conform to Python 3.8+.
Applied to files:
tests/unittest/llmapi/test_llm_pytorch.py
📚 Learning: in tensorrt_llm/executor/worker.py, the lora adapter cache optimization logic that checks `is_adapte...
Learnt from: amitz-nv
PR: NVIDIA/TensorRT-LLM#5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.402Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks `is_adapter_in_cpu_cache()` and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
Applied to files:
tests/unittest/llmapi/test_llm_pytorch.py
🪛 Ruff (0.12.2)
tests/unittest/llmapi/test_llm_pytorch.py
527-527: Line too long (127 > 120)
(E501)
⏰ 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 (2)
tests/integration/test_lists/qa/examples_test_list.txt (1)
554-554: LGTM! Test correctly added to integration test list.The new Gemma3 LoRA unit test has been properly integrated into the test suite, ensuring it will be executed during automated testing.
tests/unittest/llmapi/test_llm_pytorch.py (1)
4-4: LGTM! Import correctly added for new test.The
KvCacheConfigimport is appropriately added to support the new Gemma3 LoRA test functionality.
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PR_Github #13933 [ run ] triggered by Bot |
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PR_Github #13933 [ run ] completed with state |
Signed-off-by: Balaram Buddharaju <[email protected]> Signed-off-by: Lanyu Liao <[email protected]>
Signed-off-by: Balaram Buddharaju <[email protected]>
3 participants
Description
This MR adds a unit test for Gemma3's lora and also cleans up how kwargs are passed down in
modeling_gemma3.py.Test Coverage
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Summary by CodeRabbit
Summary by CodeRabbit
New Features
Tests
Refactor