[FRONTEND] Add max_trials_per_task parameter to static_shape_tuning_pipeline#18159
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ConvolutedDog wants to merge 3 commits into
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[FRONTEND] Add max_trials_per_task parameter to static_shape_tuning_pipeline#18159ConvolutedDog wants to merge 3 commits into
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…ipeline This commit introduces the max_trials_per_task parameter to the static_shape_tuning_pipeline, giving users explicit control over the maximum number of trials allocated to each task during MetaSchedule tuning.
Hzfengsy
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Jul 24, 2025
| gpu_out = vm["main"](gpu_data, *gpu_params).numpy() | ||
| gpu_out = vm["main"](gpu_data, *gpu_params) | ||
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| gpu_out = np.array([gpu_out[0].numpy()[0][j] for j in range(1000)]) |
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What's the purpose of this change?
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I have simplified the numpy conversion.
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After the new FFI was introduced, vm["main"](...) returns an object of <class 'tvm.ffi.container.Array'>, so a [0] needs to be added.
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This PR introduces the
max_trials_per_taskparameter to thestatic_shape_tuning_pipeline, giving users explicit control over the maximum number of trials allocated to each task during MetaSchedule tuning.Currently, the number of tuning trials per task per iteration is determined by
min(max_trials_per_iter=64, total_trials). This leads to potential issues when tuning on GPUs:When the user-specified
total_trialsis:(number_of_tasks * 64)but greater thanmax_trials_per_iter=64, ormax_trials_per_iter=64itself,Some TIR functions will not be tuned at all, and fail to be bound to threads properly.