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Moves utility functions into a standalone file.
Summary: X-link: facebookresearch/FBGEMM#749 Moves functions to better modularize code. facebookresearch/FBGEMM#749 #3671 Reviewed By: jianyuh Differential Revision: D69377391
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,127 @@ | ||
| # Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| # All rights reserved. | ||
| # | ||
| # This source code is licensed under the BSD-style license found in the | ||
| # LICENSE file in the root directory of this source tree. | ||
|
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| import sys | ||
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| import torch | ||
| import triton # @manual | ||
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| import triton.language as tl # @manual | ||
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| def map_dtype_to_triton(dtype: torch.dtype) -> tl.dtype: | ||
| """ | ||
| Maps torch dtype to triton dtype. | ||
| Args: | ||
| dtype (torch.dtype): input dtype. | ||
| Returns: | ||
| tl.dtype: triton dtype. | ||
| """ | ||
| if dtype == torch.float16: | ||
| return tl.float16 | ||
| elif dtype == torch.bfloat16: | ||
| return tl.bfloat16 | ||
| elif dtype == torch.float32: | ||
| return tl.float32 | ||
| elif dtype == torch.int32: | ||
| return tl.int32 | ||
| else: | ||
| raise ValueError(f"Unsupported dtype {dtype}") | ||
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| # check if we have the TMA version in Triton PR #4498 (https://github.com/triton-lang/triton/pull/4498). | ||
| HAS_TMA_DESC = "nv_tma_desc_type" in dir(tl) | ||
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| if HAS_TMA_DESC: | ||
| print( | ||
| "TMA benchmarks will be running with experimental grid constant TMA descriptor.", | ||
| file=sys.stderr, | ||
| ) | ||
| else: | ||
| print( | ||
| "TMA benchmarks will be running without grid constant TMA descriptor.", | ||
| file=sys.stderr, | ||
| ) | ||
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| class TmaAutoTuneHelper: | ||
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| # duck typing wrapper to implement the same interface as TmaDescKernelParam in Triton PR #4498 | ||
| class KernelParamWrapper: | ||
| def __init__(self, desc): | ||
| self.desc = desc | ||
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| def tma_desc_cpu_ptr(self): | ||
| return self.desc.data_ptr() | ||
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| TMA_SIZE = 128 | ||
|
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| def __init__(self): | ||
| self.fill_1d_tma_descriptor_inner = ( | ||
| triton.runtime.driver.active.utils.fill_1d_tma_descriptor | ||
| ) | ||
| self.fill_2d_tma_descriptor_inner = ( | ||
| triton.runtime.driver.active.utils.fill_2d_tma_descriptor | ||
| ) | ||
| if HAS_TMA_DESC: | ||
| self.descriptors = {} | ||
| else: | ||
| self.cuda_descriptors = {} | ||
|
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| # Call this method outside of the lambda function for grid size | ||
| def init_tma_descriptor(self, name): | ||
| if HAS_TMA_DESC: | ||
| self.descriptors[name] = torch.empty( | ||
| TmaAutoTuneHelper.TMA_SIZE, device="cpu", dtype=torch.int8 | ||
| ) | ||
| else: | ||
| self.cuda_descriptors[name] = torch.empty( | ||
| TmaAutoTuneHelper.TMA_SIZE, device="cuda", dtype=torch.int8 | ||
| ) | ||
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| # Call this method inside the lambda function for grid size | ||
| def fill_1d_tma_descriptor(self, name, ptr, dim, block_dim, element_size): | ||
| if HAS_TMA_DESC: | ||
| desc_x = self.descriptors[name] | ||
| assert desc_x.data_ptr() % 64 == 0 | ||
| self.fill_1d_tma_descriptor_inner( | ||
| ptr, dim, block_dim, element_size, desc_x.data_ptr() | ||
| ) | ||
| else: | ||
| desc_x = self.cuda_descriptors[name] | ||
| buf_x = torch.empty_like(desc_x, device="cpu", pin_memory=True) | ||
| self.fill_1d_tma_descriptor_inner( | ||
| ptr, dim, block_dim, element_size, buf_x.data_ptr() | ||
| ) | ||
| desc_x.copy_(buf_x, non_blocking=True) | ||
|
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| # Call this method inside the lambda function for grid size | ||
| def fill_2d_tma_descriptor( | ||
| self, name, ptr, dim1, dim0, block_dim1, block_dim0, element_size | ||
| ): | ||
| if HAS_TMA_DESC: | ||
| desc_x = self.descriptors[name] | ||
| assert desc_x.data_ptr() % 64 == 0 | ||
| self.fill_2d_tma_descriptor_inner( | ||
| ptr, dim1, dim0, block_dim1, block_dim0, element_size, desc_x.data_ptr() | ||
| ) | ||
| else: | ||
| desc_x = self.cuda_descriptors[name] | ||
| buf_x = torch.empty_like(desc_x, device="cpu", pin_memory=True) | ||
| self.fill_2d_tma_descriptor_inner( | ||
| ptr, dim1, dim0, block_dim1, block_dim0, element_size, buf_x.data_ptr() | ||
| ) | ||
| desc_x.copy_(buf_x, non_blocking=True) | ||
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| def get_tma_descriptor_kernel_param(self, name): | ||
| if HAS_TMA_DESC: | ||
| assert self.descriptors[name] is not None | ||
| return self.KernelParamWrapper(self.descriptors[name]) | ||
| else: | ||
| assert self.cuda_descriptors[name] is not None | ||
| return self.cuda_descriptors[name] |