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Summary: - Add example triton test to exercise triton integration - Add NCCL integration Differential Revision: D57741140 Pulled By: q10
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| /* | ||
| * 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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| #include <nccl.h> | ||
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| namespace fbgemm_gpu::experimental { | ||
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| void example_nccl_code() { | ||
| ncclComm_t comms[4]; | ||
| int devs[4] = {0, 1, 2, 3}; | ||
| ncclCommInitAll(comms, 4, devs); | ||
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| for (int i = 0; i < 4; i++) { | ||
| ncclCommDestroy(comms[i]); | ||
| } | ||
| } | ||
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| } // namespace fbgemm_gpu::experimental |
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| # 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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| # pyre-strict | ||
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| import unittest | ||
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| import torch | ||
| import triton | ||
| import triton.language as tl | ||
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| @triton.jit | ||
| def triton_add_kernel(x_ptr, y_ptr, z_ptr, n_elements, BLOCK_SIZE: tl.constexpr): | ||
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| # We use a 1D launch grid so axis is 0. | ||
| pid = tl.program_id(axis=0) | ||
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| # Compute the offsets in BLOCK_SIZE chunks. | ||
| offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) | ||
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| # Create a mask to guard memory operations against out-of-bounds accesses. | ||
| mask = offsets < n_elements | ||
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| # Load x and y from DRAM. | ||
| x = tl.load(x_ptr + offsets, mask=mask) | ||
| y = tl.load(y_ptr + offsets, mask=mask) | ||
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| # Sum and write back to DRAM. | ||
| output = x + y | ||
| tl.store(z_ptr + offsets, output, mask=mask) | ||
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| def triton_add(x: torch.Tensor, y: torch.Tensor): | ||
| # Pre-allocate the output. | ||
| output = torch.empty_like(x) | ||
| assert x.is_cuda and y.is_cuda and output.is_cuda | ||
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| # Create the SPMD launch grid. It can be either Tuple[int], or | ||
| # Callable(metaparameters) -> Tuple[int]. In this case, we use a 1D grid | ||
| # where the size is the number of blocks: | ||
| n_elements = output.numel() | ||
| grid = lambda meta: (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) # noqa: E731 | ||
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| # Launch the kernel. | ||
| # | ||
| # Each torch.tensor object is implicitly converted into a pointer to its | ||
| # first element. | ||
| # | ||
| # `triton.jit`'ed functions can be indexed with a launch grid to obtain a | ||
| # callable GPU kernel. | ||
| # | ||
| # Pass meta-parameters as keywords arguments. | ||
| triton_add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024) | ||
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| # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been | ||
| # called, the kernel is still running asynchronously at this point. | ||
| return output | ||
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| @unittest.skipIf( | ||
| not torch.cuda.is_available(), | ||
| "Requires CUDA to run", | ||
| ) | ||
| class TestTriton(unittest.TestCase): | ||
| def test_triton_example(self) -> None: | ||
| size = 98432 | ||
| X = torch.rand(size, device="cuda") | ||
| Y = torch.rand(size, device="cuda") | ||
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| torch.testing.assert_close(triton_add(X, Y).cpu(), (X + Y).cpu()) | ||
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