140 lines
4.9 KiB
Python
140 lines
4.9 KiB
Python
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#################################################################################################
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#
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# Copyright (c) 2023 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# 1. Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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#################################################################################################
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"""
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High-level tests for running batched GEMMs
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"""
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from functools import partial
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from math import prod
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import cutlass
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import logging
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import torch
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import unittest
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from cutlass.backend.test.utils import LayoutCombination, add_test_gemm
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from cutlass.backend.utils.device import device_cc
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cutlass.set_log_level(logging.WARNING)
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torch.manual_seed(2023)
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def pytorch_reference(A, B, C, alpha, beta):
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# Get the batch count. Assume that any of A, B, and C
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# with a batch dimension ahve matching batch count. Thus,
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# we break out of the loop once we have found the first
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# tensor containing a batch dimension.
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batch_count = (1,)
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for tensor in [A, B, C]:
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if len(tensor.shape) > 2:
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batch_count = tensor.shape[:-2]
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break
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int_batch_count = prod(batch_count)
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def add_batch(tensor):
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if len(tensor.shape) == 2:
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return tensor.unsqueeze(0).repeat(int_batch_count, 1, 1)
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else:
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return tensor.reshape(-1, tensor.size(-2), tensor.size(-1))
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# Reshape tensors to have batch dimension
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A = add_batch(A)
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B = add_batch(B)
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C = add_batch(C)
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ret = (torch.bmm(A, B) * alpha) + (C * beta)
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reshape_vals = batch_count + C.shape[-2:]
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return ret.reshape(*reshape_vals)
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def initialize(rows, cols, batch):
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tensor = torch.randint(-3, 3, size=(rows*cols*prod(batch),), device='cuda').half()
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if len(batch) > 0 and prod(batch) > 1:
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reshape_vals = batch + (rows, cols)
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return tensor.reshape(*reshape_vals)
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else:
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return tensor.reshape(rows, cols)
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class GemmF16Batched(unittest.TestCase):
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def run_batched(self, batch_count: tuple, batch_A: bool, batch_B: bool, batch_C: bool):
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M = 512
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N = 256
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K = 128
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alpha = 1.
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beta = 2.
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A = initialize(M, K, batch_count if batch_A else (1,))
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B = initialize(K, N, batch_count if batch_B else (1,))
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C = initialize(M, N, batch_count if batch_C else (1,))
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D = initialize(M, N, batch_count)
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plan = cutlass.op.Gemm(A=A, B=B, C=C, D=D, element_accumulator=cutlass.DataType.f32)
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plan.run(A, B, C, D, alpha, beta)
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reference = pytorch_reference(A, B, C, alpha, beta)
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assert reference.equal(D)
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def test_batched_ABC(self):
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self.run_batched((3,), True, True, True)
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self.run_batched((2, 3), True, True, True)
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def test_batched_AB(self):
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self.run_batched((3,), True, True, False)
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self.run_batched((2, 3), True, True, False)
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def test_batched_AC(self):
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self.run_batched((3,), True, False, True)
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self.run_batched((2, 3), True, False, True)
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def test_batched_BC(self):
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self.run_batched((3,), False, True, True)
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self.run_batched((2, 3), False, True, True)
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def test_batched_A(self):
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self.run_batched((3,), True, False, False)
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self.run_batched((2, 3), True, False, False)
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def test_batched_B(self):
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self.run_batched((3,), False, True, False)
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self.run_batched((2, 3), False, True, False)
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def test_batched_C(self):
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self.run_batched((3,), False, False, True)
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self.run_batched((2, 3), False, False, True)
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if __name__ == '__main__':
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unittest.main()
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