62750a2b75
* v3.8 update x * fix blackwell gg * doc change * doc change * doc change --------- Co-authored-by: yuzhai <yuzhai@nvidia.com> Co-authored-by: Haicheng Wu <haichengw@nvidia.com> Co-authored-by: Haicheng Wu <57973641+hwu36@users.noreply.github.com>
106 lines
4.6 KiB
C++
106 lines
4.6 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2024 - 2025 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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#pragma once
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#include <cute/tensor.hpp> // CuTe tensor implementation
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#include <cute/arch/copy_sm90_desc.hpp>
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template <class AccType,
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class TensorA, class TensorB,
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class TensorC, class TensorD,
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class Alpha, class Beta>
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void
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reference_gemm(TensorA const& tensor_A, TensorB const& tensor_B,
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TensorC const& tensor_C, TensorD & tensor_D,
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Alpha alpha, Beta beta)
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{
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using namespace cute;
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for (int m = 0; m < size<0>(tensor_D); ++m) {
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for (int n = 0; n < size<1>(tensor_D); ++n) {
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AccType c = AccType(0.f);
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for (int k = 0; k < size<1>(tensor_A); ++k) {
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c += tensor_A(m,k) * tensor_B(n,k);
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}
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tensor_D(m,n) = alpha * c + beta * tensor_C(m,n);
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}
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}
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}
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template <class TensorA, class TensorB,
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class TensorC, class TensorD,
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class RefTensorD>
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bool
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compare_results(TensorA const& tensor_A, TensorB const& tensor_B,
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TensorC const& tensor_C, TensorD const& tensor_D,
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RefTensorD const& ref_tensor_D,
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bool print_diff = false)
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{
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using namespace cute;
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auto norm_A = matrix_inf_norm(tensor_A);
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auto norm_B = matrix_inf_norm(tensor_B);
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auto norm_C = matrix_inf_norm(tensor_C);
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auto norm_D = matrix_inf_norm(tensor_D);
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auto norm_ref_D = matrix_inf_norm(ref_tensor_D);
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auto norm_diff = matrix_diff_inf_norm(tensor_D, ref_tensor_D);
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if (print_diff) {
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for (int m = 0; m < size<0>(tensor_D); ++m) {
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for (int n = 0; n < size<1>(tensor_D); ++n) {
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std::cout << m << "," << n << " : " << tensor_D(m,n) << " vs. " << ref_tensor_D(m,n) << std::endl;
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}
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}
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}
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std::cout << "norm (A) : " << norm_A.inf_norm << std::endl;
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std::cout << "norm (B) : " << norm_B.inf_norm << std::endl;
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std::cout << "norm (C) : " << norm_C.inf_norm << std::endl;
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std::cout << "norm (D) : " << norm_D.inf_norm << std::endl;
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std::cout << "norm (ref_D) : " << norm_ref_D.inf_norm << std::endl;
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std::cout << "norm (D-ref_D) : " << norm_diff.inf_norm << std::endl;
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return (!norm_A.found_nan) && (!norm_B.found_nan) &&
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(!norm_C.found_nan) && (!norm_D.found_nan) && (!norm_ref_D.found_nan) && // There are no NaNs
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(norm_A.inf_norm > 0.0) && (norm_B.inf_norm > 0.0) &&
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(norm_C.inf_norm > 0.0) && (norm_D.inf_norm > 0.0) && (norm_ref_D.inf_norm > 0.0) && // Values in tensors aren't zeros
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(norm_diff.inf_norm <= 0.0); // Diff (ref_D-D) == 0
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}
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template <class Tensor>
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void
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initialize_tensor(Tensor& tensor, cute::tuple<int, int> value_range = {-2, 2})
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{
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using DataType = typename Tensor::element_type;
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auto [min, max] = value_range;
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for (int i = 0; i < cute::size(tensor); i++) {
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tensor(i) = DataType(int((max-min)*(rand() / double(RAND_MAX)) + min));
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}
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}
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