[None][fix] fix config loading for DeepSeek-V3.2 in trtllm-bench #8729
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Signed-off-by: Fanrong Li <[email protected]>
Signed-off-by: Fanrong Li <[email protected]>
📝 WalkthroughWalkthroughThe changes implement dynamic model configuration loading using a registry mechanism in two files. Both Changes
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⚠️ Outside diff range comments (2)
tensorrt_llm/bench/build/dataclasses.py (1)
1-1: Missing NVIDIA Apache-2.0 header.Add the required license header with current year.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License.As per coding guidelines.
tensorrt_llm/bench/build/build.py (1)
1-1: Missing NVIDIA Apache-2.0 header.Add the required license header with current year.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License.As per coding guidelines.
🧹 Nitpick comments (3)
tensorrt_llm/bench/build/dataclasses.py (2)
199-211: Avoid duplicate HF config resolution; factor into a shared helper.This registry + fallback logic is duplicated here and in build.py. Extract a single utility (e.g., resolve_pretrained_config(path, trust_remote_code) -> PretrainedConfig) and reuse to prevent drift and double network hits. I can draft this helper in a common module if you want.
195-197: Import style consistency.Prefer explicit import of PretrainedConfig at module level to match existing AutoConfig import and the guidelines.
Proposed top-of-file tweak:
-from transformers import AutoConfig +from transformers import AutoConfig, PretrainedConfigtensorrt_llm/bench/build/build.py (1)
89-105: Deduplicate config loading with dataclasses.py.The same registry + fallback logic appears in two places. Extract a shared helper (e.g., tensorrt_llm/bench/build/config_loading.py: resolve_pretrained_config) to avoid divergence and reduce duplicate network calls. I can provide a patch if desired.
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tensorrt_llm/bench/build/build.py(1 hunks)tensorrt_llm/bench/build/dataclasses.py(1 hunks)
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**/*.{cpp,cxx,cc,h,hpp,hh,hxx,cu,cuh,py}
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tensorrt_llm/bench/build/build.pytensorrt_llm/bench/build/dataclasses.py
🧬 Code graph analysis (2)
tensorrt_llm/bench/build/build.py (2)
tensorrt_llm/_torch/model_config.py (1)
from_pretrained(425-516)tensorrt_llm/_torch/pyexecutor/config_utils.py (1)
is_nemotron_hybrid(1-6)
tensorrt_llm/bench/build/dataclasses.py (2)
tensorrt_llm/models/modeling_utils.py (4)
PretrainedConfig(369-570)to_dict(265-271)to_dict(359-366)to_dict(494-502)tensorrt_llm/_torch/model_config.py (1)
from_pretrained(425-516)
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🔇 Additional comments (1)
tensorrt_llm/bench/build/build.py (1)
274-275: Based on my verification, the review comment identifies a legitimate issue:trust_remote_code=Trueis hardcoded in multiple locations withinget_model_config()andModelConfig.from_hf()methods, rather than being parameterized and threaded through from the calling context.Pass trust_remote_code as a parameter through the call chain.
The original script output confirms that
trust_remote_code=Trueis hardcoded at:
tensorrt_llm/bench/build/build.pylines 95, 101, 104tensorrt_llm/bench/build/dataclasses.pylines 201, 207, 210This flag is security-sensitive—it should only be set to True for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. Rather than hardcoding it, the parameter should be:
- Added to
get_model_config(model_name: str, model_path: Path = None, trust_remote_code: bool = False)- Added to
ModelConfig.from_hf(cls, model_hf_name, hf_model_path, trust_remote_code: bool = False)- Threaded through the call at line 274
If
bench_envor the build command parses this flag, pass it through; otherwise, default toFalsefor security.
Signed-off-by: Fanrong Li <[email protected]>
Signed-off-by: Fanrong Li <[email protected]>
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Signed-off-by: Fanrong Li <[email protected]>
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Summary by CodeRabbit
Description
The transformers cannot support deepseek_v32 by using the
AutoConfig.from_pretrained. As a workaround, we need to load the configuration manually, similar to the approach used inmodel_config.py.Test Coverage
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