Updating base.py (parallel calibration and model #1809) #1837
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I've added ThreadPoolExecuter to manage parallel threads and also added New Helper Method (quantize_and_update_module and updated the compress_modules making it simple
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Summary of Changes
Hello @aashvgit, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly improves the efficiency of the GPTQ quantization process by enabling parallel execution. It refactors the core compression logic to utilize a ThreadPoolExecutor, allowing multiple modules to be quantized concurrently across available GPUs. This enhancement aims to reduce the overall time required for model quantization, with a robust fallback to sequential CPU processing if no GPU resources are detected.
Highlights
- Parallel Quantization: Implemented parallel execution for GPTQ quantization using
ThreadPoolExecutorto distribute tasks across available GPUs, significantly speeding up the process. - New Helper Method: Introduced
_quantize_and_update_moduleto encapsulate the logic for quantizing and updating a single module, facilitating its use in parallel threads. - GPU Detection & Fallback: Added logic to detect available CUDA devices and utilize them for parallel quantization; if no GPUs are found, the process gracefully defaults to sequential execution on the CPU.
- Refactored
compress_modules: Thecompress_modulesmethod was simplified and updated to orchestrate the new parallel quantization workflow, making it cleaner and more efficient.
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Code Review
This pull request introduces parallel execution for GPTQ quantization across multiple GPUs using a ThreadPoolExecutor. The implementation refactors the quantization logic into a helper function, which is then called in parallel for different modules. This is a good approach to speed up the quantization process. However, I've found a potential issue with how the original device of a module is handled, which could lead to incorrect behavior in model-parallel scenarios. My feedback includes a specific code suggestion to address this.
| original_device = get_execution_device(modules_to_quantize[0]) | ||
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| with ThreadPoolExecutor(max_workers=len(gpu_ids)) as executor: | ||
| futures = [] | ||
| for i, module in enumerate(modules_to_quantize): | ||
| target_gpu_id = gpu_ids[i % len(gpu_ids)] | ||
| target_device = f"cuda:{target_gpu_id}" | ||
| future = executor.submit( | ||
| self._quantize_and_update_module, | ||
| module, | ||
| target_device, | ||
| original_device | ||
| ) | ||
| comp_logger.set_loss(loss) | ||
| futures.append(future) | ||
| for future in futures: | ||
| future.result() |
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The current implementation assumes that all modules to be quantized reside on the same device initially. It determines the original_device from the first module in the list and uses it for all modules. This can cause problems in a model-parallel setup where modules might be distributed across different devices. If that's the case, after quantization, all modules would be incorrectly moved to the device of the first module.
To make this more robust, you should determine and store the original device for each module individually and use that to move the module back after processing.
| original_device = get_execution_device(modules_to_quantize[0]) | |
| with ThreadPoolExecutor(max_workers=len(gpu_ids)) as executor: | |
| futures = [] | |
| for i, module in enumerate(modules_to_quantize): | |
| target_gpu_id = gpu_ids[i % len(gpu_ids)] | |
| target_device = f"cuda:{target_gpu_id}" | |
| future = executor.submit( | |
| self._quantize_and_update_module, | |
| module, | |
| target_device, | |
| original_device | |
| ) | |
| comp_logger.set_loss(loss) | |
| futures.append(future) | |
| for future in futures: | |
| future.result() | |
| original_devices = {m: get_execution_device(m) for m in modules_to_quantize} | |
| with ThreadPoolExecutor(max_workers=len(gpu_ids)) as executor: | |
| futures = [] | |
| for i, module in enumerate(modules_to_quantize): | |
| target_gpu_id = gpu_ids[i % len(gpu_ids)] | |
| target_device = f"cuda:{target_gpu_id}" | |
| future = executor.submit( | |
| self._quantize_and_update_module, | |
| module, | |
| target_device, | |
| original_devices[module], | |
| ) | |
| futures.append(future) | |
| for future in futures: | |
| future.result() |
There was a problem hiding this comment.
This might make sense, if we are allowing multi-gpu compression, modules could also be spread out across different GPUs.
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| original_device = get_execution_device(modules_to_quantize[0]) | ||
|
|
||
| with ThreadPoolExecutor(max_workers=len(gpu_ids)) as executor: | ||
| futures = [] | ||
| for i, module in enumerate(modules_to_quantize): | ||
| target_gpu_id = gpu_ids[i % len(gpu_ids)] | ||
| target_device = f"cuda:{target_gpu_id}" | ||
| future = executor.submit( | ||
| self._quantize_and_update_module, | ||
| module, | ||
| target_device, | ||
| original_device | ||
| ) | ||
| comp_logger.set_loss(loss) | ||
| futures.append(future) | ||
| for future in futures: | ||
| future.result() |
There was a problem hiding this comment.
This might make sense, if we are allowing multi-gpu compression, modules could also be spread out across different GPUs.
SUMMARY:
I've added ThreadPoolExecuter To manage parallel threads and also added New Helper Method (quantize_and_update_module and updated the compress_modules making it simple and making ThreadPoolExecuter with one worker for each GPU (OPTION B)