Fix prefix caching is currently not supported with sliding window attention when using qwen1.5 #3377
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carrey-feng
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cadedaniel
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Mar 13, 2024
vllm/config.py
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| def get_sliding_window(self) -> Optional[int]: | ||
| return getattr(self.hf_config, "sliding_window", None) | ||
| return (getattr(self.hf_config, "sliding_window", None) | ||
| if self.get_use_sliding_window() else None) | ||
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| def get_use_sliding_window(self) -> bool: | ||
| return getattr(self.hf_config, "use_sliding_window", False) |
Collaborator
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- Can we remove the public function
get_us_sliding_windowunless we need it in the codebase? - Can we add some docstrings here?
- Can we add a unit test for this functionality?
Author
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- docstrings
Of course，I will submit unit tests and some docstrings later.
Author
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@cadedaniel I have submitted unit tests and docstrings |
…emory (OOM) issues.
Collaborator
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Hi @a516072575 , sorry it seems #3373 got in a little faster. |
Author
Collaborator
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Good catch -- created #3437, can you review? |
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My model is trained using qwen1.5. When I use prefix caching to do offline batch inference, the program throws AssertionError: Prefix caching is currently not supported with sliding window attention. I noticed that in vllm/config.py, the get_sliding_window function does not take into account whether the value of use_sliding_window is set to true.
The use_sliding_window is a new configuration option introduced in Qwen 1.5 that controls whether to use a sliding window, and its default value is set to false. So I add a judgment for value of use_sliding_window when calling the get_sliding_window function. This is similar to the implementation in qwen2.py
The AssertionError error message is as follows
File "/test.py", line 45, in outputs = model.generate(prompts, sampling_params, prefix_pos=[prefix_pos] * len(prompts)) File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/llm.py", line 182, in generate return self._run_engine(use_tqdm) File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/llm.py", line 208, in _run_engine step_outputs = self.llm_engine.step() File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 838, in step all_outputs = self._run_workers( File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 1041, in _run_workers driver_worker_output = getattr(self.driver_worker, File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 223, in execute_model output = self.model_runner.execute_model(seq_group_metadata_list, File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 571, in execute_model lora_mapping) = self.prepare_input_tensors(seq_group_metadata_list) File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 490, in prepare_input_tensors lora_requests) = self._prepare_prompt(seq_group_metadata_list) File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 193, in _prepare_prompt assert prefix_len == 0, ( AssertionError: Prefix caching is currently not supported with sliding window attention