Model: Device-id and data-parallel inference in CLI and Torch#452
Model: Device-id and data-parallel inference in CLI and Torch#452michaelfeil merged 5 commits intomainfrom
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PR Summary
Based on my analysis of the pull request, here is a concise summary of the key changes:
Added device-id and data-parallel inference capabilities to enable running models across multiple GPUs/devices:
- Added new
--device-idCLI option that accepts comma-separated device IDs (e.g. "0,1") for model placement across multiple GPUs/devices - Introduced LoadingStrategy class to manage device mapping, dtype configuration, and quantization settings across different hardware
- Modified BatchHandler to support multiple model replicas running in parallel across specified devices
- Updated test suite with retry logic and adjusted tolerance parameters to handle numerical differences from parallel processing
- Added proper error handling for device validation and unavailable hardware configurations
The changes enable better scaling and performance through parallel inference while maintaining the existing API interface.
23 file(s) reviewed, 32 comment(s)
Edit PR Review Bot Settings | Greptile
| │ --device-id TEXT device id defines the model │ | ||
| │ placement. e.g. `0,1` will │ | ||
| │ place the model on │ | ||
| │ MPS/CUDA/GPU 0 and 1 each │ |
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style: The phrase 'each' at the end of this line is ambiguous - does it mean the model is replicated on each device or split across devices?
| def update_loading_stategy(self): | ||
| """Assign a device id to the EngineArgs object.""" | ||
| from infinity_emb.inference import loading_strategy # type: ignore |
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syntax: update_loading_stategy() has a typo in its name (should be 'strategy')
| if self._loading_strategy is None: | ||
| self.update_loading_stategy() | ||
| elif isinstance(self._loading_strategy, dict): | ||
| object.__setattr__(self, "_loading_strategy", LoadingStrategy(**self._loading_strategy)) |
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style: loading strategy initialization should happen before pydantic validation to ensure the complete object is validated
| embedding_dtype: EmbeddingDtype = EmbeddingDtype[MANAGER.embedding_dtype[0]] | ||
| served_model_name: str = MANAGER.served_model_name[0] | ||
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| _loading_strategy: Optional[LoadingStrategy] = None |
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style: _loading_strategy should be documented with a type hint comment explaining its purpose and structure
|
Codecov ReportAttention: Patch coverage is
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## main #452 +/- ##
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- Coverage 79.18% 79.15% -0.04%
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Files 41 42 +1
Lines 3248 3363 +115
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+ Hits 2572 2662 +90
- Misses 676 701 +25 ☔ View full report in Codecov by Sentry. |
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