Update generate.py: Add parallel processing for token generation #426
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vThis update introduces parallel processing for token generation using torch.multiprocessing.Pool. The new implementation improves inference speed by processing multiple sequences concurrently. - Added the generate_parallel() function for parallel token generation. - Used multiprocessing to distribute the workload across multiple processes, allowing for faster generation of tokens for multiple prompts. - The generate_single_sequence() function was added to handle individual sequence generation logic, which is called by each worker in parallel. - The num_workers parameter is introduced to control the number of worker processes (default is 4). - Model is shared across processes for efficient memory usage. These changes are particularly beneficial for batch processing or multi-prompt generation scenarios where multiple sequences need to be generated simultaneously.
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Do you have any benchmarks for this? |
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This update introduces parallel processing for token generation using torch.multiprocessing.Pool. The new implementation improves inference speed by processing multiple sequences concurrently.
These changes are particularly beneficial for batch processing or multi-prompt generation scenarios where multiple sequences need to be generated simultaneously.