Blockwise and Groupwise GEMM for Blackwell and Improvements for Hopper (#2139)
- Blockwise and Groupwise GEMM improvements for Hopper. - Blockwise and Groupwise GEMM for Blackwell. - Blockwise Grouped GEMM for Hopper. - Static ScalePromotionInterval for Hopper FP8 GEMMs. Co-authored-by: dePaul Miller <23461061+depaulmillz@users.noreply.github.com>
This commit is contained in:
dePaul Miller
co-authored by
dePaul Miller
dePaul Miller
parent
eefa171318
commit
ca4fdbea70
+11
-4
@@ -398,6 +398,10 @@ void initialize(const Options<RasterOrderOptions> &options) {
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blockscale_tensor_A.sync_device();
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blockscale_tensor_B.sync_device();
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// Note : This value has to match the KernelSchedule::ScalePromotionInterval
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// Else kernel will fail can_implement() check
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// Deprecation Notice : We plan to remove this params member in an upcoming release
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// Users can safely delete this line from their code, since the default is already 4
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mma_promotion_interval = 4;
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if (options.save_aux) {
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@@ -662,9 +666,11 @@ int run(Options<RasterOrderOptions> &options)
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// Check if output from CUTLASS kernel and reference kernel are equal or not
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Result result;
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result.passed = verify(options);
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if (options.verify) {
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result.passed = verify(options);
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std::cout << " Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
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std::cout << " Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
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}
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// if (!result.passed) {
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// exit(-1);
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@@ -674,8 +680,9 @@ int run(Options<RasterOrderOptions> &options)
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if (options.iterations > 0)
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{
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GpuTimer timer;
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timer.start();
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for (int iter = 0; iter < options.iterations; ++iter) {
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for (int iter = 0; iter < options.warmup + options.iterations; ++iter) {
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if (iter == options.warmup)
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timer.start();
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CUTLASS_CHECK(gemm.run());
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}
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timer.stop();
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+19
-13
@@ -453,6 +453,10 @@ void initialize(const Options<RasterOrderOptions> &options) {
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blockscale_tensor_A.sync_device();
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blockscale_tensor_B.sync_device();
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// Note : This value has to match the KernelSchedule::ScalePromotionInterval
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// Else kernel will fail can_implement() check
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// Deprecation Notice : We plan to remove this params member in an upcoming release
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// Users can safely delete this line from their code, since the default is already 4
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mma_promotion_interval = 4;
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if (options.save_aux) {
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@@ -668,14 +672,14 @@ bool verify(const Options<RasterOrderOptions> &options, const int ScaleMsPerTile
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tensor_D.sync_host();
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bool passed = cutlass::reference::host::TensorEquals(tensor_ref_D.host_view(), tensor_D.host_view());
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if (false) {
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std::cout << "tensor_ref_D.host_view() {" << std::endl
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<< tensor_ref_D.host_view() << std::endl
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<< "}" << std::endl;
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std::cout << "tensor_D.host_view() {" << std::endl
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<< tensor_D.host_view() << std::endl
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<< "}" << std::endl;
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}
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#if 0
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std::cout << "tensor_ref_D.host_view() {" << std::endl
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<< tensor_ref_D.host_view() << std::endl
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<< "}" << std::endl;
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std::cout << "tensor_D.host_view() {" << std::endl
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<< tensor_D.host_view() << std::endl
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<< "}" << std::endl;
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#endif
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if (IsDFp8 && options.save_amax) {
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abs_max_D.sync_host();
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@@ -729,13 +733,15 @@ int run(Options<RasterOrderOptions> &options)
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// Check if output from CUTLASS kernel and reference kernel are equal or not
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Result result;
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result.passed = verify(options, ScaleMsPerTile, ScaleNsPerTile);
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if (options.verify) {
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result.passed = verify(options, ScaleMsPerTile, ScaleNsPerTile);
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std::cout << " Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
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std::cout << " Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
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}
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// if (!result.passed) {
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// exit(-1);
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// }
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if (!result.passed) {
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exit(-1);
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}
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// Run profiling loop
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if (options.iterations > 0)
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+7
-3
@@ -34,6 +34,7 @@ template<typename RasterOrderOptions>
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struct Options {
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bool help = false;
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bool verify = true;
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float alpha = 1.f, beta = 0.f;
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float scale_a = 1.f, scale_b = 1.f, scale_c = 1.f, scale_d = 1.f, scale_aux = 1.f;
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@@ -41,6 +42,7 @@ struct Options {
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bool save_aux = true;
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bool save_amax = true;
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int iterations = 1000;
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int warmup = 1000;
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int m = 1024, n = 512, k = 1024, l = 1;
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RasterOrderOptions raster;
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int swizzle;
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@@ -68,7 +70,9 @@ struct Options {
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cmd.get_cmd_line_argument("device_scale", device_scale, false);
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cmd.get_cmd_line_argument("save_aux", save_aux, true);
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cmd.get_cmd_line_argument("save_amax", save_amax, true);
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cmd.get_cmd_line_argument("warmup", warmup);
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cmd.get_cmd_line_argument("iterations", iterations);
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cmd.get_cmd_line_argument("verify", verify);
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char raster_char;
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cmd.get_cmd_line_argument("raster", raster_char);
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@@ -89,8 +93,8 @@ struct Options {
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/// Prints the usage statement.
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std::ostream & print_usage(std::ostream &out) const {
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out << "54_fp8_hopper_warp_specialized_gemm\n\n"
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<< " Hopper FP8 GEMM using a Warp Specialized kernel.\n\n"
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out << "67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling\n\n"
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<< " Hopper FP8 GEMM using a Warp Specialized kernel with Blockwise Scaling.\n\n"
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<< "Options:\n\n"
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<< " --help If specified, displays this usage statement\n\n"
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<< " --m=<int> Sets the M extent of the GEMM\n"
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@@ -113,7 +117,7 @@ struct Options {
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out
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<< "\n\nExamples:\n\n"
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<< "$ " << "54_fp8_hopper_warp_specialized_gemm" << " --m=1024 --n=512 --k=1024 --alpha=2 --beta=0.707 \n\n";
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<< "$ " << "67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling" << " --m=1024 --n=512 --k=1024 --alpha=2 --beta=0.707 \n\n";
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return out;
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}
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+841
@@ -0,0 +1,841 @@
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/***************************************************************************************************
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* Copyright (c) 2023 - 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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/*! \file
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\brief Grouped scale Hopper FP8 Grouped GEMM example using CUTLASS 3.0 APIs for NVIDIA Hopper architecture
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This example demonstrates a grouped scaled FP8 Grouped GEMM using the new CUTLASS 3.0.
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APIs on NVIDIA Hopper architecture. New features that will be showcased in this example are as follows:
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1. NVIDIA Hopper architecture introduces a new series of tensor core instructions (GMMA)
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which are more efficient than the Ampere tensor core instructions.
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2. NVIDIA Hopper architecture includes new Tensor Memory Accelerator (TMA) unit to transfer large
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blocks of data efficiently between global memory and shared memory. TMA also supports asynchronous
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copies between thread blocks in a cluster. This example also showcases on-the-fly modification of TMA
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descriptors to move between groups/problem_count (represented by groups).
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3. This example uses the Warp Specialized kernel design (see /media/docs/efficient_gemm.md for details).
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4. A simple way to tune the CTA rasterization direction and swizzle pattern of Hopper kernels. Both the
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CTA rasterization direction and swizzle pattern impact cross-CTA locality of accesses. By tuning we can
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improve performance.
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Examples:
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$ ./examples/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling \
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--m=2816 --n=3072 --k=16384 --save_aux=false --save_amax=false \
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--raster=h --swizzle=2 --benchmark=./test_benchmark.txt
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Where the test_benchmark.txt may look as such:
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0 256x512x128
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1 256x512x512
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2 512x256x128
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3 256x256x128
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4 256x512x1024
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5 1024x512x128 and so on
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*/
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#include <iostream>
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#include <optional>
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#include <fstream>
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#include <sstream>
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#include <vector>
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#include <cfloat>
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#include "cutlass/cutlass.h"
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#include "cutlass/numeric_types.h"
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#include "cute/tensor.hpp"
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#include "cutlass/tensor_ref.h"
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#include "cutlass/gemm/dispatch_policy.hpp"
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#include "cutlass/gemm/collective/collective_builder.hpp"
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#include "cutlass/gemm/device/gemm_universal_adapter.h"
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#include "cutlass/gemm/kernel/gemm_universal.hpp"
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#include "cutlass/gemm/kernel/tile_scheduler_params.h"
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#include "cutlass/epilogue/dispatch_policy.hpp"
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#include "cutlass/epilogue/collective/collective_builder.hpp"
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#include "cutlass/util/command_line.h"
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#include "cutlass/util/distribution.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/packed_stride.hpp"
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#include "cutlass/util/tensor_view_io.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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#include "cutlass/util/reference/host/tensor_copy.h"
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#include "cutlass/util/reference/host/tensor_compare.h"
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#include "cutlass/util/reference/host/tensor_norm.h"
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#include "cutlass/util/reference/device/tensor_fill.h"
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// Includes from examples directory
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#include "helper.h"
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#include "hopper_fp8_commandline.hpp"
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#include "reference/host/gemm_with_groupwise_scaling.h"
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using namespace cute;
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using ProblemShape = cutlass::gemm::GroupProblemShape<Shape<int,int,int>>; // <M,N,K> per group
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#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// GEMM kernel configurations
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// A matrix configuration
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using ElementA = cutlass::float_e4m3_t; // Element type for A matrix operand
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using LayoutA = cutlass::layout::RowMajor; // Layout type for A matrix operand
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constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value; // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes)
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// B matrix configuration
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using ElementB = cutlass::float_e4m3_t; // Element type for B matrix operand
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using LayoutB = cutlass::layout::ColumnMajor; // Layout type for B matrix operand
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constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value; // Memory access granularity/alignment of B matrix in units of elements (up to 16 bytes)
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// C matrix configuration
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using ElementC = cutlass::float_e4m3_t; // Element type for C and D matrix operands
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using LayoutC = cutlass::layout::ColumnMajor; // Layout type for C and D matrix operands
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constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value; // Memory access granularity/alignment of C matrix in units of elements (up to 16 bytes)
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// D matrix configuration
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using ElementD = ElementC;
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using LayoutD = LayoutC;
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constexpr int AlignmentD = AlignmentC;
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// Core kernel configurations
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using ElementAccumulator = float; // Element type for internal accumulation
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using ElementBlockScale = float; // Element type for blockscaling during accumulation
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using ElementCompute = float; // Element type for epilogue computation
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using TileShape_ = Shape<_128,_128,_128>; // This one is just to make the compiler happy with verify()...
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// ScaleGranularity{M,N}: number of {rows in A}/{columns in B} that share the same scaling factor
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// Given TileShape = Shape<_128,_128,_128>:
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// ScaleGranularityM == 128 and ScaleGranularityN == 128 --> 2Dx2D (the shape of the scaling factor)
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// ScaleGranularityM == 1 and ScaleGranularityN == 128 --> 1Dx2D scaling
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// ScaleGranularityM == 128 and ScaleGranularityN == 1 --> 2Dx1D scaling
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// ScaleGranularityM == 1 and ScaleGranularityN == 1 --> 1Dx1D scaling
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template <int ScaleGranularityM_, int ScaleGranularityN_>
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struct GroupScaleConfig {
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using ArchTag = cutlass::arch::Sm90; // Tag indicating the minimum SM that supports the intended feature
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using OperatorClass = cutlass::arch::OpClassTensorOp; // Operator class tag
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using TileShape = Shape<_128,_128,_128>; // Threadblock-level tile size
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using ClusterShape = Shape<_1,_2,_1>; // Shape of the threadblocks in a cluster
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static constexpr int ScaleGranularityM = ScaleGranularityM_;
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static constexpr int ScaleGranularityN = ScaleGranularityN_;
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static constexpr int ScaleMsPerTile = size<0>(TileShape{}) / ScaleGranularityM;
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static constexpr int ScaleNsPerTile = size<1>(TileShape{}) / ScaleGranularityN;
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static_assert(size<0>(TileShape{}) == ScaleGranularityM * ScaleMsPerTile,
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"FP8 scaling granularity must evenly divide tile shape along M.");
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static_assert(size<1>(TileShape{}) == ScaleGranularityN * ScaleNsPerTile,
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"FP8 scaling granularity must evenly divide tile shape along N.");
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using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedCooperativeFP8BlockScaledAccum<ScaleGranularityM_, ScaleGranularityN_>;
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using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecializedCooperative;
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using EpilogueTileType = cutlass::epilogue::collective::EpilogueTileAuto;
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using FusionOperation = cutlass::epilogue::fusion::LinearCombination<ElementC, ElementAccumulator>;
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};
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using GroupScale1D1DConfig = GroupScaleConfig< 1, 1>;
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using GroupScale1D2DConfig = GroupScaleConfig< 1, size<1>(TileShape_{})>;
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using GroupScale2D1DConfig = GroupScaleConfig<size<0>(TileShape_{}), 1>;
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using GroupScale2D2DConfig = GroupScaleConfig<size<0>(TileShape_{}), size<1>(TileShape_{})>;
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template <typename ScheduleConfig>
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struct GroupScaleGemm {
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using ArchTag = typename ScheduleConfig::ArchTag;
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using OperatorClass = typename ScheduleConfig::OperatorClass;
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using TileShape = typename ScheduleConfig::TileShape;
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using ClusterShape = typename ScheduleConfig::ClusterShape;
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using KernelSchedule = typename ScheduleConfig::KernelSchedule;
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using EpilogueSchedule = typename ScheduleConfig::EpilogueSchedule;
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using EpilogueTileType = typename ScheduleConfig::EpilogueTileType;
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using FusionOperation = typename ScheduleConfig::FusionOperation;
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using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
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ArchTag, OperatorClass,
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TileShape, ClusterShape,
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EpilogueTileType,
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ElementAccumulator, ElementCompute,
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ElementC, LayoutC *, AlignmentC,
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ElementD, LayoutD *, AlignmentD,
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EpilogueSchedule,
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FusionOperation
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>::CollectiveOp;
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using CollectiveMainloopWithGroupWiseScaling = typename cutlass::gemm::collective::CollectiveBuilder<
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ArchTag, OperatorClass,
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ElementA, LayoutA *, AlignmentA,
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ElementB, LayoutB *, AlignmentB,
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ElementAccumulator,
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TileShape, ClusterShape,
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cutlass::gemm::collective::StageCountAutoCarveout<
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static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))
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>,
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KernelSchedule
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>::CollectiveOp;
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using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
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ProblemShape,
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CollectiveMainloopWithGroupWiseScaling,
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CollectiveEpilogue
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>;
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using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
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};
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using GroupScale1D1DGemm = GroupScaleGemm<GroupScale1D1DConfig>;
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using GroupScale1D2DGemm = GroupScaleGemm<GroupScale1D2DConfig>;
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using GroupScale2D1DGemm = GroupScaleGemm<GroupScale2D1DConfig>;
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using GroupScale2D2DGemm = GroupScaleGemm<GroupScale2D2DConfig>;
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// Extract information from Gemm kernel.
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using EpilogueOutputOp = typename GroupScale1D1DGemm::Gemm::EpilogueOutputOp;
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using ElementScalar = typename EpilogueOutputOp::ElementScalar;
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using ActivationFunctor = typename EpilogueOutputOp::ActivationFn;
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using StrideA = typename GroupScale1D1DGemm::Gemm::GemmKernel::InternalStrideA;
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using StrideB = typename GroupScale1D1DGemm::Gemm::GemmKernel::InternalStrideB;
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using StrideC = typename GroupScale1D1DGemm::Gemm::GemmKernel::InternalStrideC;
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using StrideD = typename GroupScale1D1DGemm::Gemm::GemmKernel::InternalStrideD;
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static_assert(cute::is_same_v<ElementAccumulator, ElementBlockScale>,
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"ElementAccumulator and ElementBlockScale should be same datatype");
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/// Initialization
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cutlass::DeviceAllocation<typename ProblemShape::UnderlyingProblemShape> problem_sizes;
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std::vector<int64_t> offset_A;
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std::vector<int64_t> offset_B;
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std::vector<int64_t> offset_C;
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std::vector<int64_t> offset_D;
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std::vector<int64_t> offset_blockscale_A;
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std::vector<int64_t> offset_blockscale_B;
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std::vector<StrideA> stride_A_host;
|
||||
std::vector<StrideB> stride_B_host;
|
||||
std::vector<StrideC> stride_C_host;
|
||||
std::vector<StrideD> stride_D_host;
|
||||
|
||||
std::vector<ElementAccumulator> alpha_host;
|
||||
std::vector<ElementAccumulator> beta_host;
|
||||
|
||||
uint64_t seed;
|
||||
|
||||
cutlass::DeviceAllocation<ElementA> block_A;
|
||||
cutlass::DeviceAllocation<ElementB> block_B;
|
||||
cutlass::DeviceAllocation<ElementC> block_C;
|
||||
cutlass::DeviceAllocation<ElementD> block_D;
|
||||
cutlass::DeviceAllocation<ElementBlockScale> blockscale_block_A;
|
||||
cutlass::DeviceAllocation<ElementBlockScale> blockscale_block_B;
|
||||
|
||||
cutlass::DeviceAllocation<const ElementA *> ptr_A;
|
||||
cutlass::DeviceAllocation<const ElementB *> ptr_B;
|
||||
cutlass::DeviceAllocation<const ElementC *> ptr_C;
|
||||
cutlass::DeviceAllocation<ElementD *> ptr_D;
|
||||
cutlass::DeviceAllocation<ElementD *> ptr_ref_D;
|
||||
cutlass::DeviceAllocation<const ElementBlockScale *> ptr_blockscale_A;
|
||||
cutlass::DeviceAllocation<const ElementBlockScale *> ptr_blockscale_B;
|
||||
|
||||
cutlass::DeviceAllocation<StrideA> stride_A;
|
||||
cutlass::DeviceAllocation<StrideB> stride_B;
|
||||
cutlass::DeviceAllocation<StrideC> stride_C;
|
||||
cutlass::DeviceAllocation<StrideD> stride_D;
|
||||
|
||||
cutlass::DeviceAllocation<ElementAccumulator*> alpha_device;
|
||||
cutlass::DeviceAllocation<ElementAccumulator*> beta_device;
|
||||
cutlass::DeviceAllocation<ElementAccumulator> block_alpha;
|
||||
cutlass::DeviceAllocation<ElementAccumulator> block_beta;
|
||||
|
||||
#endif // defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/// Testbed utility types
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
using RasterOrderOptions = typename cutlass::gemm::kernel::detail::PersistentTileSchedulerSm90GroupParams<Shape<int,int,int>>::RasterOrderOptions;
|
||||
|
||||
/// Result structure
|
||||
struct Result
|
||||
{
|
||||
double avg_runtime_ms;
|
||||
double gflops;
|
||||
cutlass::Status status;
|
||||
cudaError_t error;
|
||||
bool passed;
|
||||
|
||||
Result(
|
||||
double avg_runtime_ms = 0,
|
||||
double gflops = 0,
|
||||
cutlass::Status status = cutlass::Status::kSuccess,
|
||||
cudaError_t error = cudaSuccess)
|
||||
:
|
||||
avg_runtime_ms(avg_runtime_ms), gflops(gflops), status(status), error(error), passed(false)
|
||||
{}
|
||||
|
||||
};
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/// GEMM setup and evaluation
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Helper to initialize a block of device data
|
||||
template <class Element, class ScopeMin = std::nullopt_t, class ScopeMax = std::nullopt_t>
|
||||
bool initialize_block(
|
||||
cutlass::DeviceAllocation<Element>& block,
|
||||
uint64_t seed=2023,
|
||||
ScopeMin scope_min = std::nullopt, ScopeMax scope_max = std::nullopt) {
|
||||
|
||||
double _scope_max, _scope_min;
|
||||
int bits_input = cutlass::sizeof_bits<Element>::value;
|
||||
if (bits_input == 1) {
|
||||
_scope_max = 2;
|
||||
_scope_min = 0;
|
||||
} else if (bits_input <= 8) {
|
||||
_scope_max = 2;
|
||||
_scope_min = -2;
|
||||
} else if (bits_input == 16) {
|
||||
_scope_max = 5;
|
||||
_scope_min = -5;
|
||||
} else {
|
||||
_scope_max = 8;
|
||||
_scope_min = -8;
|
||||
}
|
||||
if constexpr (!std::is_same_v<ScopeMax, std::nullopt_t>) {
|
||||
_scope_max = scope_max;
|
||||
}
|
||||
if constexpr (!std::is_same_v<ScopeMin, std::nullopt_t>) {
|
||||
_scope_min = scope_min;
|
||||
}
|
||||
cutlass::reference::device::BlockFillRandomUniform(
|
||||
block.get(), block.size(), seed, (Element) _scope_max, (Element) _scope_min, 0);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/// Allocates device-side data
|
||||
template <typename OptionType>
|
||||
void allocate(const OptionType &options) {
|
||||
|
||||
using TileShape = typename OptionType::GroupScaleConfig::TileShape;
|
||||
const int ScaleMsPerTile = OptionType::GroupScaleConfig::ScaleMsPerTile;
|
||||
const int ScaleNsPerTile = OptionType::GroupScaleConfig::ScaleNsPerTile;
|
||||
|
||||
int64_t total_elements_A = 0;
|
||||
int64_t total_elements_B = 0;
|
||||
int64_t total_elements_C = 0;
|
||||
int64_t total_elements_D = 0;
|
||||
int64_t total_elements_blockscale_A = 0;
|
||||
int64_t total_elements_blockscale_B = 0;
|
||||
|
||||
offset_A.clear();
|
||||
offset_B.clear();
|
||||
offset_C.clear();
|
||||
offset_D.clear();
|
||||
offset_blockscale_A.clear();
|
||||
offset_blockscale_B.clear();
|
||||
stride_A_host.clear();
|
||||
stride_B_host.clear();
|
||||
stride_C_host.clear();
|
||||
stride_D_host.clear();
|
||||
|
||||
for (int32_t i = 0; i < options.groups; ++i) {
|
||||
|
||||
auto problem = options.problem_sizes_host.at(i);
|
||||
auto M = get<0>(problem);
|
||||
auto N = get<1>(problem);
|
||||
auto K = get<2>(problem);
|
||||
|
||||
auto blockscale_shape = shape(get<1>(cute::zipped_divide(cute::make_layout(problem), TileShape{})));
|
||||
auto groupscale_m = cute::get<0>(blockscale_shape) * ScaleMsPerTile; // We need to pad along M in scale tensor of A to prevent illegal memory access.
|
||||
auto groupscale_n = cute::get<1>(blockscale_shape) * ScaleNsPerTile; // We need to pad along N in scale tensor of A to prevent illegal memory access.
|
||||
auto blockscale_k = cute::get<2>(blockscale_shape);
|
||||
|
||||
offset_A.push_back(total_elements_A);
|
||||
offset_B.push_back(total_elements_B);
|
||||
offset_C.push_back(total_elements_C);
|
||||
offset_D.push_back(total_elements_D);
|
||||
offset_blockscale_A.push_back(total_elements_blockscale_A);
|
||||
offset_blockscale_B.push_back(total_elements_blockscale_B);
|
||||
|
||||
int64_t elements_A = M * K;
|
||||
int64_t elements_B = K * N;
|
||||
int64_t elements_C = M * N;
|
||||
int64_t elements_D = M * N;
|
||||
int64_t elements_blockscale_A = groupscale_m * blockscale_k;
|
||||
int64_t elements_blockscale_B = groupscale_n * blockscale_k;
|
||||
|
||||
total_elements_A += elements_A;
|
||||
total_elements_B += elements_B;
|
||||
total_elements_C += elements_C;
|
||||
total_elements_D += elements_D;
|
||||
total_elements_blockscale_A += elements_blockscale_A;
|
||||
total_elements_blockscale_B += elements_blockscale_B;
|
||||
|
||||
stride_A_host.push_back(cutlass::make_cute_packed_stride(StrideA{}, {M, K, 1}));
|
||||
stride_B_host.push_back(cutlass::make_cute_packed_stride(StrideB{}, {N, K, 1}));
|
||||
stride_C_host.push_back(cutlass::make_cute_packed_stride(StrideC{}, {M, N, 1}));
|
||||
stride_D_host.push_back(cutlass::make_cute_packed_stride(StrideD{}, {M, N, 1}));
|
||||
|
||||
}
|
||||
|
||||
block_A.reset(total_elements_A);
|
||||
block_B.reset(total_elements_B);
|
||||
block_C.reset(total_elements_C);
|
||||
block_D.reset(total_elements_D);
|
||||
block_alpha.reset(options.groups);
|
||||
block_beta.reset(options.groups);
|
||||
blockscale_block_A.reset(total_elements_blockscale_A);
|
||||
blockscale_block_B.reset(total_elements_blockscale_B);
|
||||
}
|
||||
|
||||
/// Initialize operands to be used in the GEMM and reference GEMM
|
||||
template <typename OptionType>
|
||||
void initialize(const OptionType &options) {
|
||||
|
||||
problem_sizes.reset(options.groups);
|
||||
problem_sizes.copy_from_host(options.problem_sizes_host.data());
|
||||
|
||||
std::vector<ElementA *> ptr_A_host(options.groups);
|
||||
std::vector<ElementB *> ptr_B_host(options.groups);
|
||||
std::vector<ElementC *> ptr_C_host(options.groups);
|
||||
std::vector<ElementD *> ptr_D_host(options.groups);
|
||||
std::vector<ElementAccumulator *> ptr_alpha_host(options.groups);
|
||||
std::vector<ElementAccumulator *> ptr_beta_host(options.groups);
|
||||
std::vector<ElementBlockScale *> ptr_blockscale_A_host(options.groups);
|
||||
std::vector<ElementBlockScale *> ptr_blockscale_B_host(options.groups);
|
||||
|
||||
alpha_host.clear();
|
||||
beta_host.clear();
|
||||
|
||||
for (int i = 0; i < options.groups; i++) {
|
||||
ptr_A_host.at(i) = block_A.get() + offset_A.at(i);
|
||||
ptr_B_host.at(i) = block_B.get() + offset_B.at(i);
|
||||
ptr_C_host.at(i) = block_C.get() + offset_C.at(i);
|
||||
ptr_D_host.at(i) = block_D.get() + offset_D.at(i);
|
||||
ptr_blockscale_A_host.at(i) = blockscale_block_A.get() + offset_blockscale_A.at(i);
|
||||
ptr_blockscale_B_host.at(i) = blockscale_block_B.get() + offset_blockscale_B.at(i);
|
||||
alpha_host.push_back((options.alpha == FLT_MAX) ? static_cast<ElementAccumulator>((rand() % 5) + 1) : options.alpha);
|
||||
beta_host.push_back((options.beta == FLT_MAX) ? static_cast<ElementAccumulator>(rand() % 5) : options.beta);
|
||||
ptr_alpha_host.at(i) = block_alpha.get() + i;
|
||||
ptr_beta_host.at(i) = block_beta.get() + i;
|
||||
}
|
||||
|
||||
ptr_A.reset(options.groups);
|
||||
ptr_A.copy_from_host(ptr_A_host.data());
|
||||
|
||||
ptr_B.reset(options.groups);
|
||||
ptr_B.copy_from_host(ptr_B_host.data());
|
||||
|
||||
ptr_C.reset(options.groups);
|
||||
ptr_C.copy_from_host(ptr_C_host.data());
|
||||
|
||||
ptr_D.reset(options.groups);
|
||||
ptr_D.copy_from_host(ptr_D_host.data());
|
||||
|
||||
ptr_blockscale_A.reset(options.groups);
|
||||
ptr_blockscale_A.copy_from_host(ptr_blockscale_A_host.data());
|
||||
|
||||
ptr_blockscale_B.reset(options.groups);
|
||||
ptr_blockscale_B.copy_from_host(ptr_blockscale_B_host.data());
|
||||
|
||||
stride_A.reset(options.groups);
|
||||
stride_A.copy_from_host(stride_A_host.data());
|
||||
|
||||
stride_B.reset(options.groups);
|
||||
stride_B.copy_from_host(stride_B_host.data());
|
||||
|
||||
stride_C.reset(options.groups);
|
||||
stride_C.copy_from_host(stride_C_host.data());
|
||||
|
||||
stride_D.reset(options.groups);
|
||||
stride_D.copy_from_host(stride_D_host.data());
|
||||
|
||||
alpha_device.reset(options.groups);
|
||||
alpha_device.copy_from_host(ptr_alpha_host.data());
|
||||
beta_device.reset(options.groups);
|
||||
beta_device.copy_from_host(ptr_beta_host.data());
|
||||
|
||||
initialize_block(block_A, seed + 2022);
|
||||
initialize_block(block_B, seed + 2023);
|
||||
initialize_block(block_C, seed + 2024);
|
||||
initialize_block(blockscale_block_A, seed + 2025, -1, 1);
|
||||
initialize_block(blockscale_block_B, seed + 2026, -1, 1);
|
||||
|
||||
block_alpha.copy_from_host(alpha_host.data());
|
||||
block_beta.copy_from_host(beta_host.data());
|
||||
|
||||
}
|
||||
|
||||
/// Populates a Gemm::Arguments structure from the given commandline options
|
||||
template<typename GemmArguments, typename OptionType>
|
||||
GemmArguments args_from_options(const OptionType &options, bool host_problem_shapes_available = true)
|
||||
{
|
||||
// Change device_id to another value if you are running on a machine with multiple GPUs and wish
|
||||
// to use a GPU other than that with device ID 0.
|
||||
int device_id = 0;
|
||||
cutlass::KernelHardwareInfo kernel_hw_info = cutlass::KernelHardwareInfo::make_kernel_hardware_info<typename GroupScale1D1DGemm::Gemm::GemmKernel>(device_id);
|
||||
|
||||
GemmArguments arguments{
|
||||
cutlass::gemm::GemmUniversalMode::kGrouped,
|
||||
{options.groups, problem_sizes.get(), host_problem_shapes_available ? options.problem_sizes_host.data() : (decltype(options.problem_sizes_host.data())) nullptr},
|
||||
{ptr_A.get(), stride_A.get(), ptr_B.get(), stride_B.get(),
|
||||
ptr_blockscale_A.get(),
|
||||
ptr_blockscale_B.get()
|
||||
},
|
||||
{
|
||||
{}, // epilogue.thread
|
||||
ptr_C.get(), stride_C.get(),
|
||||
ptr_D.get(), stride_D.get()
|
||||
},
|
||||
kernel_hw_info
|
||||
};
|
||||
|
||||
auto &fusion_args = arguments.epilogue.thread;
|
||||
if (options.alpha != FLT_MAX && options.beta != FLT_MAX) {
|
||||
// If both alpha/beta are provided (via cmd line args) and are scalar, i.e., same alpha/beta applies to all batches.
|
||||
fusion_args.alpha = options.alpha;
|
||||
fusion_args.beta = options.beta;
|
||||
fusion_args.alpha_ptr = nullptr;
|
||||
fusion_args.beta_ptr = nullptr;
|
||||
fusion_args.alpha_ptr_array = nullptr;
|
||||
fusion_args.beta_ptr_array = nullptr;
|
||||
// Single alpha and beta for all groups
|
||||
fusion_args.dAlpha = {cute::_0{}, cute::_0{}, 0};
|
||||
fusion_args.dBeta = {cute::_0{}, cute::_0{}, 0};
|
||||
}
|
||||
else {
|
||||
// If pointers to alpha/beta are provided, i.e., alpha/beta can differ between batches/groups.
|
||||
fusion_args.alpha = 0;
|
||||
fusion_args.beta = 0;
|
||||
fusion_args.alpha_ptr = nullptr;
|
||||
fusion_args.beta_ptr = nullptr;
|
||||
fusion_args.alpha_ptr_array = alpha_device.get();
|
||||
fusion_args.beta_ptr_array = beta_device.get();
|
||||
// One alpha and beta per each group
|
||||
fusion_args.dAlpha = {cute::_0{}, cute::_0{}, 1};
|
||||
fusion_args.dBeta = {cute::_0{}, cute::_0{}, 1};
|
||||
}
|
||||
|
||||
arguments.scheduler.raster_order = options.raster;
|
||||
// The tile scheduler will swizzle up to 8 and with the nearest multiple of 2 (i.e., 1, 2, 4, and 8)
|
||||
arguments.scheduler.max_swizzle_size = options.swizzle;
|
||||
|
||||
return arguments;
|
||||
}
|
||||
|
||||
template <typename OptionType>
|
||||
bool verify(const OptionType &options) {
|
||||
|
||||
//
|
||||
// Compute reference output
|
||||
//
|
||||
|
||||
std::vector<ElementA> block_A_host(block_A.size());
|
||||
std::vector<ElementB> block_B_host(block_B.size());
|
||||
std::vector<ElementC> block_C_host(block_C.size());
|
||||
std::vector<ElementD> block_D_host_kernel(block_D.size());
|
||||
std::vector<ElementD> block_D_host_ref(block_D.size());
|
||||
std::vector<ElementBlockScale> blockscale_block_A_host(blockscale_block_A.size());
|
||||
std::vector<ElementBlockScale> blockscale_block_B_host(blockscale_block_B.size());
|
||||
|
||||
block_A.copy_to_host(block_A_host.data());
|
||||
block_B.copy_to_host(block_B_host.data());
|
||||
block_C.copy_to_host(block_C_host.data());
|
||||
block_D.copy_to_host(block_D_host_kernel.data());
|
||||
blockscale_block_A.copy_to_host(blockscale_block_A_host.data());
|
||||
blockscale_block_B.copy_to_host(blockscale_block_B_host.data());
|
||||
|
||||
bool passed = true;
|
||||
for (int group_idx = 0; group_idx < options.groups; group_idx++) {
|
||||
// Group scaling tensors shapes based `ScaleGranularityM`, CTA Block (TileShape) and GEMM Problem shape
|
||||
auto [m, n, k] = options.problem_sizes_host.at(group_idx);
|
||||
auto gemm_problem_shape = cute::make_shape(m, n, k);
|
||||
auto blockscale_shape = shape(get<1>(cute::zipped_divide(cute::make_layout(gemm_problem_shape), TileShape_{})));
|
||||
auto blockscale_m = cute::get<0>(blockscale_shape);
|
||||
auto blockscale_n = cute::get<1>(blockscale_shape);
|
||||
auto blockscale_k = cute::get<2>(blockscale_shape);
|
||||
auto groupscale_m = blockscale_m * OptionType::GroupScaleConfig::ScaleMsPerTile;
|
||||
auto groupscale_n = blockscale_n * OptionType::GroupScaleConfig::ScaleNsPerTile;
|
||||
|
||||
// Create instantiation for device reference gemm kernel
|
||||
auto A = cute::make_tensor(block_A_host.data() + offset_A.at(group_idx),
|
||||
cute::make_layout(
|
||||
cute::make_shape(m, k, 1),
|
||||
stride_A_host.at(group_idx)
|
||||
)
|
||||
);
|
||||
auto B = cute::make_tensor(block_B_host.data() + offset_B.at(group_idx),
|
||||
cute::make_layout(
|
||||
cute::make_shape(n, k, 1),
|
||||
stride_B_host.at(group_idx)
|
||||
)
|
||||
);
|
||||
auto C = cute::make_tensor(block_C_host.data() + offset_C.at(group_idx),
|
||||
cute::make_layout(
|
||||
cute::make_shape(m, n, 1),
|
||||
stride_C_host.at(group_idx)
|
||||
)
|
||||
);
|
||||
auto D = cute::make_tensor(block_D_host_ref.data() + offset_D.at(group_idx),
|
||||
cute::make_layout(
|
||||
cute::make_shape(m, n, 1),
|
||||
stride_D_host.at(group_idx)
|
||||
)
|
||||
);
|
||||
|
||||
auto blockscale_A = cute::make_tensor(blockscale_block_A_host.data() + offset_blockscale_A.at(group_idx),
|
||||
cute::make_layout(
|
||||
cute::make_shape(groupscale_m, blockscale_k, 1),
|
||||
cute::make_stride(1, groupscale_m, groupscale_m * blockscale_k)
|
||||
)
|
||||
);
|
||||
auto blockscale_B = cute::make_tensor(blockscale_block_B_host.data() + offset_blockscale_B.at(group_idx),
|
||||
cute::make_layout(
|
||||
cute::make_shape(groupscale_n, blockscale_k, 1),
|
||||
cute::make_stride(1, groupscale_n, groupscale_n * blockscale_k)
|
||||
)
|
||||
);
|
||||
|
||||
using unused_t = decltype(D);
|
||||
|
||||
cutlass::reference::host::GettMainloopParams<
|
||||
ElementAccumulator,
|
||||
decltype(A),
|
||||
decltype(B),
|
||||
decltype(blockscale_A),
|
||||
decltype(blockscale_B),
|
||||
TileShape_
|
||||
> mainloop_params{
|
||||
A, B, // Operand Tensors
|
||||
blockscale_A, blockscale_B // Groupwise scaling Tensors
|
||||
};
|
||||
|
||||
cutlass::reference::host::GettEpilogueParams<
|
||||
ElementScalar,
|
||||
ElementScalar,
|
||||
ElementAccumulator,
|
||||
ElementCompute,
|
||||
decltype(C),
|
||||
decltype(D),
|
||||
unused_t, // bias
|
||||
unused_t, // Aux
|
||||
unused_t, // valpha
|
||||
unused_t, // vbeta
|
||||
ActivationFunctor
|
||||
> epilogue_params;
|
||||
|
||||
epilogue_params.C = C;
|
||||
epilogue_params.D = D;
|
||||
epilogue_params.alpha = alpha_host.at(group_idx);
|
||||
epilogue_params.beta = beta_host.at(group_idx);
|
||||
|
||||
// get reference result
|
||||
cutlass::reference::host::Gemm3x(mainloop_params, epilogue_params);
|
||||
|
||||
// Check if output from CUTLASS kernel and reference kernel are equal or not
|
||||
auto this_group_passed = std::equal(
|
||||
// std::execution::par_unseq,
|
||||
block_D_host_ref.data() + offset_D.at(group_idx),
|
||||
block_D_host_ref.data() + offset_D.at(group_idx) + m * n,
|
||||
block_D_host_kernel.data() + offset_D.at(group_idx)
|
||||
);
|
||||
|
||||
passed &= this_group_passed;
|
||||
|
||||
#if 0
|
||||
std::cout << "Group: " << group_idx << " M: " << m << " N: " << n << " K: " << k << " Status: " << this_group_passed << std::endl;
|
||||
#endif
|
||||
|
||||
}
|
||||
|
||||
return passed;
|
||||
}
|
||||
|
||||
/// Execute a given example GEMM computation
|
||||
template <typename Gemm, typename OptionType>
|
||||
int run(OptionType &options, bool host_problem_shapes_available = true)
|
||||
{
|
||||
using TileShape = typename OptionType::GroupScaleConfig::TileShape;
|
||||
const int ScaleGranularityM = OptionType::GroupScaleConfig::ScaleGranularityM;
|
||||
const int ScaleGranularityN = OptionType::GroupScaleConfig::ScaleGranularityN;
|
||||
const int ScaleMsPerTile = OptionType::GroupScaleConfig::ScaleMsPerTile;
|
||||
const int ScaleNsPerTile = OptionType::GroupScaleConfig::ScaleNsPerTile;
|
||||
|
||||
allocate(options);
|
||||
initialize(options);
|
||||
|
||||
// Instantiate CUTLASS kernel depending on templates
|
||||
Gemm gemm;
|
||||
|
||||
// Create a structure of gemm kernel arguments suitable for invoking an instance of Gemm
|
||||
auto arguments = args_from_options<typename Gemm::Arguments>(options, host_problem_shapes_available);
|
||||
|
||||
// Using the arguments, query for extra workspace required for matrix multiplication computation
|
||||
size_t workspace_size = Gemm::get_workspace_size(arguments);
|
||||
|
||||
// Allocate workspace memory
|
||||
cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
|
||||
|
||||
// Check if the problem size is supported or not
|
||||
CUTLASS_CHECK(gemm.can_implement(arguments));
|
||||
|
||||
// Initialize CUTLASS kernel with arguments and workspace pointer
|
||||
CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
|
||||
|
||||
// Correctness / Warmup iteration
|
||||
CUTLASS_CHECK(gemm.run());
|
||||
|
||||
// Check if output from CUTLASS kernel and reference kernel are equal or not
|
||||
Result result;
|
||||
result.passed = verify(options);
|
||||
|
||||
std::cout << " Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
|
||||
|
||||
if (!result.passed) {
|
||||
exit(-1);
|
||||
}
|
||||
|
||||
// Run profiling loop
|
||||
if (options.iterations > 0)
|
||||
{
|
||||
GpuTimer timer;
|
||||
timer.start();
|
||||
for (int iter = 0; iter < options.iterations; ++iter) {
|
||||
CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
|
||||
CUTLASS_CHECK(gemm.run());
|
||||
}
|
||||
timer.stop();
|
||||
|
||||
// Compute average runtime and GFLOPs.
|
||||
float elapsed_ms = timer.elapsed_millis();
|
||||
result.avg_runtime_ms = double(elapsed_ms) / double(options.iterations);
|
||||
result.gflops = options.gflops(result.avg_runtime_ms / 1000.0);
|
||||
|
||||
std::string raster = "Heuristic";
|
||||
|
||||
if (options.raster == RasterOrderOptions::AlongN) {
|
||||
raster = "Along N";
|
||||
}
|
||||
else if (options.raster == RasterOrderOptions::AlongM) {
|
||||
raster = "Along M";
|
||||
}
|
||||
|
||||
std::cout << " Problem Sizes, Alpha, Beta " << std::endl;
|
||||
for (int32_t i = 0; i < options.groups; ++i) {
|
||||
std::cout << " " << options.problem_sizes_host.at(i);
|
||||
std::cout << ", " << alpha_host.at(i) << ", " << beta_host.at(i) << std::endl;
|
||||
}
|
||||
std::cout << " Groups : " << options.groups << std::endl;
|
||||
std::cout << " Tile shape (M, N, K): " << size<0>(TileShape{}) << ", " << size<1>(TileShape{}) << ", " << size<2>(TileShape{}) << std::endl;
|
||||
std::cout << " ScaleGranularityM: " << ScaleGranularityM << " (ScaleMsPerTile: " << ScaleMsPerTile << ")" << std::endl;
|
||||
std::cout << " ScaleGranularityN: " << ScaleGranularityN << " (ScaleNsPerTile: " << ScaleNsPerTile << ")" << std::endl;
|
||||
std::cout << " Rasterization: " << raster << " with a maximum CTA swizzle of " << options.swizzle << std::endl;
|
||||
std::cout << " Avg runtime: " << result.avg_runtime_ms << " ms" << std::endl;
|
||||
std::cout << " GFLOPS: " << result.gflops << std::endl;
|
||||
fflush(stdout);
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
#endif // defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
int main(int argc, char const **args) {
|
||||
|
||||
// CUTLASS must be compiled with CUDA 12.0 Toolkit to run this example
|
||||
// and must have compute capability at least 90.
|
||||
if (__CUDACC_VER_MAJOR__ < 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ < 3)) {
|
||||
std::cerr << "This example requires CUDA 12.3 or newer.\n";
|
||||
// Returning zero so this test passes on older Toolkits. Its actions are no-op.
|
||||
return 0;
|
||||
}
|
||||
|
||||
cudaDeviceProp props;
|
||||
int current_device_id;
|
||||
CUDA_CHECK(cudaGetDevice(¤t_device_id));
|
||||
CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
|
||||
cudaError_t error = cudaGetDeviceProperties(&props, 0);
|
||||
if (props.major != 9) {
|
||||
std::cerr
|
||||
<< "This example requires a GPU of NVIDIA's Hopper Architecture or "
|
||||
<< "later (compute capability 90 or greater).\n";
|
||||
return 0;
|
||||
}
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
|
||||
|
||||
//
|
||||
// Parse options
|
||||
//
|
||||
|
||||
Options<RasterOrderOptions, ProblemShape, GroupScale1D1DConfig> options_1d1d;
|
||||
Options<RasterOrderOptions, ProblemShape, GroupScale1D2DConfig> options_1d2d;
|
||||
Options<RasterOrderOptions, ProblemShape, GroupScale2D1DConfig> options_2d1d;
|
||||
Options<RasterOrderOptions, ProblemShape, GroupScale2D2DConfig> options_2d2d;
|
||||
|
||||
options_1d1d.parse(argc, args);
|
||||
options_1d2d.parse(argc, args);
|
||||
options_2d1d.parse(argc, args);
|
||||
options_2d2d.parse(argc, args);
|
||||
|
||||
if (options_1d1d.help) {
|
||||
options_1d1d.print_usage(std::cout) << std::endl;
|
||||
return 0;
|
||||
}
|
||||
|
||||
//
|
||||
// Evaluate CUTLASS kernels
|
||||
//
|
||||
|
||||
auto run_tests = [&] (bool host_problem_shapes_available = true) {
|
||||
std::cout << "Grouped GEMM kernel with 1D1D group scale" << std::endl;
|
||||
run<GroupScale1D1DGemm::Gemm>(options_1d1d, host_problem_shapes_available);
|
||||
std::cout << "Grouped GEMM kernel with 1D2D group scale" << std::endl;
|
||||
run<GroupScale1D2DGemm::Gemm>(options_1d2d, host_problem_shapes_available);
|
||||
std::cout << "Grouped GEMM kernel with 2D1D group scale" << std::endl;
|
||||
run<GroupScale2D1DGemm::Gemm>(options_2d1d, host_problem_shapes_available);
|
||||
std::cout << "Grouped GEMM kernel with 2D2D group scale" << std::endl;
|
||||
run<GroupScale2D2DGemm::Gemm>(options_2d2d, host_problem_shapes_available);
|
||||
std::cout << std::endl;
|
||||
};
|
||||
|
||||
std::cout << "Running tests with host problem shapes:" << std::endl;
|
||||
run_tests(true);
|
||||
std::cout << "Running tests without host problem shapes:" << std::endl;
|
||||
run_tests(false);
|
||||
|
||||
#endif
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
+61
@@ -0,0 +1,61 @@
|
||||
# Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
# SPDX-License-Identifier: BSD-3-Clause
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright notice, this
|
||||
# list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# 3. Neither the name of the copyright holder nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
# Note that we set --iterations=0 for all tests below to disable the performance benchmarking.
|
||||
# Only the correctness check will be run by these commands.
|
||||
|
||||
set(TEST_RANDOM --iterations=0) # Random problem sizes
|
||||
set(TEST_RANDOM_LARGE_GROUP --groups=500 --iterations=0) # Random problem sizes
|
||||
|
||||
set(TEST_EPILOGUE --alpha=0.5 --beta=0.5 --iterations=0) # Random problem sizes
|
||||
set(TEST_EPILOGUE_LARGE_GROUP --alpha=1.5 --beta=2.0 --groups=500 --iterations=0) # Random problem sizes
|
||||
|
||||
set(TEST_EPILOGUE_OP --beta=0.5 --iterations=0) # Random problem sizes
|
||||
set(TEST_EPILOGUE_OP_LARGE_GROUP --alpha=1.5 --iterations=0) # Random problem sizes
|
||||
|
||||
set(TEST_FIXED --m=2048 --n=5120 --k=512 --groups=50 --iterations=0) # Fixed problem sizes
|
||||
set(TEST_FIXED_LARGE_GROUP --m=2048 --n=512 --k=512 --groups=512 --iterations=0) # Fixed problem sizes
|
||||
|
||||
set(TEST_SMALL --m=256 --n=128 --iterations=0) # Small problem sizes
|
||||
set(TEST_SMALL_LARGE_GROUP --m=128 --n=128 --groups=500 --iterations=0) # Small problem sizes
|
||||
|
||||
cutlass_example_add_executable(
|
||||
68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling
|
||||
68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling.cu
|
||||
TEST_COMMAND_OPTIONS
|
||||
TEST_RANDOM
|
||||
TEST_RANDOM_LARGE_GROUP
|
||||
TEST_EPILOGUE
|
||||
TEST_EPILOGUE_LARGE_GROUP
|
||||
TEST_EPILOGUE_OP
|
||||
TEST_EPILOGUE_OP_LARGE_GROUP
|
||||
TEST_FIXED
|
||||
TEST_FIXED_LARGE_GROUP
|
||||
TEST_SMALL
|
||||
TEST_SMALL_LARGE_GROUP
|
||||
)
|
||||
+211
@@ -0,0 +1,211 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright notice, this
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
* this list of conditions and the following disclaimer in the documentation
|
||||
* and/or other materials provided with the distribution.
|
||||
*
|
||||
* 3. Neither the name of the copyright holder nor the names of its
|
||||
* contributors may be used to endorse or promote products derived from
|
||||
* this software without specific prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
|
||||
// Command line options parsing
|
||||
template<typename _RasterOrderOptions, typename _ProblemShape, typename _GroupScaleConfig>
|
||||
struct Options {
|
||||
|
||||
using RasterOrderOptions = _RasterOrderOptions;
|
||||
using ProblemShape = _ProblemShape;
|
||||
using GroupScaleConfig = _GroupScaleConfig;
|
||||
|
||||
bool help = false;
|
||||
|
||||
float alpha = 1.f, beta = 0.f;
|
||||
int iterations = 1000;
|
||||
int m = 1024, n = 512, k = 1024, groups = 10;
|
||||
std::string benchmark_path;
|
||||
std::vector<typename ProblemShape::UnderlyingProblemShape> problem_sizes_host;
|
||||
int const tma_alignment_bits = 128;
|
||||
int const alignment = tma_alignment_bits / cutlass::sizeof_bits<cutlass::float_e4m3_t>::value;
|
||||
int const k_alignment = 128;
|
||||
int const m_alignment = 128;
|
||||
int const n_alignment = 128;
|
||||
|
||||
RasterOrderOptions raster;
|
||||
int swizzle;
|
||||
|
||||
// Parses the command line
|
||||
void parse(int argc, char const **args) {
|
||||
cutlass::CommandLine cmd(argc, args);
|
||||
|
||||
if (cmd.check_cmd_line_flag("help")) {
|
||||
help = true;
|
||||
return;
|
||||
}
|
||||
|
||||
cmd.get_cmd_line_argument("m", m);
|
||||
cmd.get_cmd_line_argument("n", n);
|
||||
cmd.get_cmd_line_argument("k", k);
|
||||
cmd.get_cmd_line_argument("groups", groups);
|
||||
cmd.get_cmd_line_argument("alpha", alpha, 1.f);
|
||||
cmd.get_cmd_line_argument("beta", beta, 0.f);
|
||||
cmd.get_cmd_line_argument("iterations", iterations);
|
||||
|
||||
char raster_char;
|
||||
cmd.get_cmd_line_argument("raster", raster_char);
|
||||
|
||||
if (raster_char == 'N' || raster_char == 'n') {
|
||||
raster = RasterOrderOptions::AlongN;
|
||||
}
|
||||
else if (raster_char == 'M' || raster_char == 'm') {
|
||||
raster = RasterOrderOptions::AlongM;
|
||||
}
|
||||
else if (raster_char == 'H' || raster_char == 'h') {
|
||||
raster = RasterOrderOptions::Heuristic;
|
||||
}
|
||||
|
||||
cmd.get_cmd_line_argument("swizzle", swizzle, 1);
|
||||
cmd.get_cmd_line_argument("benchmark", benchmark_path);
|
||||
|
||||
// Decide how to initialize the problems
|
||||
if (!benchmark_path.empty()) {
|
||||
if (!benchmark_problems()) {
|
||||
problem_sizes_host.clear();
|
||||
return;
|
||||
}
|
||||
}
|
||||
else {
|
||||
randomize_problems(cmd);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
void randomize_problems(cutlass::CommandLine &cmd) {
|
||||
int cmd_line_m = -1, cmd_line_n = -1, cmd_line_k = -1;
|
||||
cmd.get_cmd_line_argument("m", cmd_line_m);
|
||||
cmd.get_cmd_line_argument("n", cmd_line_n);
|
||||
cmd.get_cmd_line_argument("k", cmd_line_k);
|
||||
|
||||
problem_sizes_host.reserve(groups);
|
||||
|
||||
for (int i = groups; i > 0; i--) {
|
||||
int m = cmd_line_m;
|
||||
int n = cmd_line_n;
|
||||
int k = cmd_line_k;
|
||||
if (m < 1) {
|
||||
m = m_alignment * ((rand() % (64 * alignment / m_alignment)) + 1);
|
||||
}
|
||||
if (n < 1) {
|
||||
n = n_alignment * ((rand() % (64 * alignment / n_alignment)) + 1);
|
||||
}
|
||||
if (k < 1) {
|
||||
k = k_alignment * ((rand() % (32 * alignment / k_alignment)) + 1);
|
||||
}
|
||||
problem_sizes_host.push_back({m, n, k});
|
||||
}
|
||||
}
|
||||
|
||||
/// Load a benchmark
|
||||
bool benchmark_problems() {
|
||||
std::ifstream file(benchmark_path);
|
||||
if (!file.good()) {
|
||||
return false;
|
||||
}
|
||||
|
||||
while (file.good()) {
|
||||
|
||||
int idx = -1;
|
||||
std::string extent_str;
|
||||
|
||||
file >> idx >> extent_str;
|
||||
|
||||
if (idx < 0 || extent_str.empty()) {
|
||||
break;
|
||||
}
|
||||
|
||||
cutlass::gemm::GemmCoord extent;
|
||||
std::vector<std::string> tokens;
|
||||
|
||||
cutlass::CommandLine::tokenize(tokens, extent_str, 'x');
|
||||
|
||||
for (int i = 0; i < int(tokens.size()); ++i) {
|
||||
int x = std::atoi(tokens.at(i).c_str());
|
||||
|
||||
// round up
|
||||
if (x % alignment) {
|
||||
x += (alignment - (x % alignment));
|
||||
}
|
||||
|
||||
extent.at(i) = x;
|
||||
}
|
||||
|
||||
if (extent.product()) {
|
||||
problem_sizes_host.push_back({extent.m(), extent.n(), extent.k()});
|
||||
}
|
||||
}
|
||||
groups = static_cast<int>(problem_sizes_host.size());
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/// Prints the usage statement.
|
||||
std::ostream & print_usage(std::ostream &out) const {
|
||||
|
||||
out << "68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling\n\n"
|
||||
<< " Hopper FP8 Grouped GEMM using a Warp Specialized kernel with Blockwise Scaling.\n\n"
|
||||
<< "Options:\n\n"
|
||||
<< " --help If specified, displays this usage statement\n\n"
|
||||
<< " --m=<int> Sets the M extent of the GEMM\n"
|
||||
<< " --n=<int> Sets the N extent of the GEMM\n"
|
||||
<< " --k=<int> Sets the K extent of the GEMM\n"
|
||||
<< " --groups=<int> Sets the number of individual GEMM problems for Grouped GEMM\n"
|
||||
<< " --alpha=<f32> Epilogue scalar alpha\n"
|
||||
<< " --beta=<f32> Epilogue scalar beta\n"
|
||||
<< " --raster=<char> CTA Rasterization direction (N for along N, M for along M, and H for heuristic)\n\n"
|
||||
<< " --swizzle=<int> CTA Rasterization swizzle\n\n"
|
||||
<< " --benchmark=<str> Executes a benchmark problem size.\n\n"
|
||||
<< " --iterations=<int> Number of profiling iterations to perform.\n\n";
|
||||
|
||||
out
|
||||
<< "\n\nExamples:\n\n"
|
||||
<< "$ " << "68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling" << " --m=1024 --n=512 --k=1024 --groups=10 --alpha=2 --beta=0.707 \n\n";
|
||||
|
||||
return out;
|
||||
}
|
||||
|
||||
/// Compute performance in GFLOP/s
|
||||
double gflops(double runtime_s) const
|
||||
{
|
||||
// Number of real-valued multiply-adds
|
||||
uint64_t fmas = 0ull;
|
||||
|
||||
for (auto const [m, n, k] : problem_sizes_host) {
|
||||
fmas += static_cast<uint64_t>(m) *
|
||||
static_cast<uint64_t>(n) *
|
||||
static_cast<uint64_t>(k);
|
||||
}
|
||||
// Two flops per multiply-add
|
||||
uint64_t flop = uint64_t(2) * uint64_t(fmas);
|
||||
double gflop = double(flop) / double(1.0e9);
|
||||
return gflop / runtime_s;
|
||||
}
|
||||
};
|
||||
+520
@@ -0,0 +1,520 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright notice, this
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
* this list of conditions and the following disclaimer in the documentation
|
||||
* and/or other materials provided with the distribution.
|
||||
*
|
||||
* 3. Neither the name of the copyright holder nor the names of its
|
||||
* contributors may be used to endorse or promote products derived from
|
||||
* this software without specific prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
/*! \file
|
||||
\brief Reference implementation for GETT in host-side code.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/complex.h"
|
||||
#include "cutlass/numeric_conversion.h"
|
||||
#include "cutlass/epilogue/thread/activation.h"
|
||||
#include "cutlass/relatively_equal.h"
|
||||
#include <iostream>
|
||||
#include "cute/tensor.hpp"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass::reference::host {
|
||||
|
||||
template<class T, class = void>
|
||||
struct ElementTraits {
|
||||
using type = T;
|
||||
};
|
||||
|
||||
template<class T>
|
||||
struct ElementTraits<T, std::enable_if_t<!std::is_same_v<decltype(std::declval<T>().get()), void> > > {
|
||||
using type = decltype(std::declval<T>().get());
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template<
|
||||
class ElementAccumulator_,
|
||||
class TensorA_, // (M, K, L)
|
||||
class TensorB_, // (N, K, L)
|
||||
class TensorScaleA_, // (m, k, L)
|
||||
class TensorScaleB_, // (n, k, L)
|
||||
class TileShape_
|
||||
>
|
||||
struct GettMainloopParams {
|
||||
using ElementAccumulator = ElementAccumulator_;
|
||||
using TensorA = TensorA_;
|
||||
using TensorB = TensorB_;
|
||||
using EngineA = typename TensorA::engine_type;
|
||||
using LayoutA = typename TensorA::layout_type;
|
||||
using EngineB = typename TensorB::engine_type;
|
||||
using LayoutB = typename TensorB::layout_type;
|
||||
|
||||
using TensorScaleA = TensorScaleA_;
|
||||
using TensorScaleB = TensorScaleB_;
|
||||
using TileShape = TileShape_;
|
||||
using EngineScaleA = typename TensorScaleA::engine_type;
|
||||
using EngineScaleB = typename TensorScaleB::engine_type;
|
||||
|
||||
TensorA A{};
|
||||
TensorB B{};
|
||||
TensorScaleA ScaleA{};
|
||||
TensorScaleB ScaleB{};
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
template<
|
||||
class ElementScalar_,
|
||||
class ElementScalingFactor_,
|
||||
class ElementAccumulator_,
|
||||
class ElementCompute_,
|
||||
class TensorC_, // (M, N, L)
|
||||
class TensorD_, // (M, N, L)
|
||||
class VectorBias_ = TensorD_, // (M, 1)
|
||||
class TensorAux_ = TensorD_, // (M, N, L)
|
||||
class VectorAlpha_ = TensorD_, // (M, 1)
|
||||
class VectorBeta_ = VectorAlpha_, // (M, 1)
|
||||
class ActivationFunctor_ = cutlass::epilogue::thread::Identity<ElementCompute_>,
|
||||
class BiasBinaryOp_ = cutlass::plus<ElementCompute_>,
|
||||
bool PerColumnBias_ = false
|
||||
>
|
||||
struct GettEpilogueParams {
|
||||
using ElementScalar = ElementScalar_;
|
||||
using ElementScalingFactor = ElementScalingFactor_;
|
||||
using ElementAccumulator = ElementAccumulator_;
|
||||
using ElementCompute = ElementCompute_;
|
||||
using TensorC = TensorC_;
|
||||
using TensorD = TensorD_;
|
||||
using TensorAux = TensorAux_;
|
||||
using VectorBias = VectorBias_;
|
||||
using VectorAlpha = VectorAlpha_;
|
||||
using VectorBeta = VectorBeta_;
|
||||
using ActivationFunctor = ActivationFunctor_;
|
||||
using BiasBinaryOp = BiasBinaryOp_;
|
||||
|
||||
using EngineC = typename TensorC::engine_type;
|
||||
using LayoutC = typename TensorC::layout_type;
|
||||
using EngineD = typename TensorD::engine_type;
|
||||
using LayoutD = typename TensorD::layout_type;
|
||||
static constexpr bool PerColumnBias = PerColumnBias_;
|
||||
ElementScalar alpha = ElementScalar(1);
|
||||
ElementScalar beta = ElementScalar(0);
|
||||
|
||||
TensorC C{};
|
||||
TensorD D{};
|
||||
VectorBias Bias{};
|
||||
TensorAux Aux{};
|
||||
VectorAlpha Valpha{};
|
||||
VectorBeta Vbeta{};
|
||||
ElementCompute st = ElementCompute(1);
|
||||
|
||||
ElementAccumulator* abs_max_D = nullptr;
|
||||
ElementAccumulator* abs_max_Aux = nullptr;
|
||||
|
||||
ElementScalingFactor scale_a = ElementScalingFactor(1);
|
||||
ElementScalingFactor scale_b = ElementScalingFactor(1);
|
||||
ElementScalingFactor scale_c = ElementScalingFactor(1);
|
||||
ElementScalingFactor scale_d = ElementScalingFactor(1);
|
||||
ElementScalingFactor scale_aux = ElementScalingFactor(1);
|
||||
|
||||
bool beta_per_channel_scaling = false;
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// GETT - General Tensor-Tensor contraction reference kernel with Groupwise scaling
|
||||
template <
|
||||
class MainloopParams,
|
||||
class EpilogueParams
|
||||
>
|
||||
void Gett(
|
||||
MainloopParams const& mainloop_params,
|
||||
EpilogueParams const& epilogue_params)
|
||||
{
|
||||
|
||||
static int constexpr kBlockM = cute::get<0>(typename MainloopParams::TileShape{});
|
||||
static int constexpr kBlockN = cute::get<1>(typename MainloopParams::TileShape{});
|
||||
// printf("mainloop_params.ScaleA.layout()"); cute::print(mainloop_params.ScaleA.layout()); printf("\n");
|
||||
// printf("mainloop_params.ScaleB.layout()"); cute::print(mainloop_params.ScaleB.layout()); printf("\n");
|
||||
|
||||
#if defined(_OPENMP)
|
||||
#pragma omp parallel for collapse(3)
|
||||
#endif
|
||||
for (int64_t l = 0; l < cute::size<2>(mainloop_params.A.layout()); ++l) {
|
||||
for (int64_t m = 0; m < cute::size<0>(mainloop_params.A.layout()); m += kBlockM) {
|
||||
for (int64_t n = 0; n < cute::size<0>(mainloop_params.B.layout()); n += kBlockN) {
|
||||
typename MainloopParams::ElementAccumulator acc[kBlockM][kBlockN];
|
||||
gett_mainloop(mainloop_params, m, n, l, acc);
|
||||
gett_epilogue(epilogue_params, m, n, l, acc);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// GETT - Mainloop
|
||||
template <class MainloopParams, class ElementAccumulator, int kBlockM, int kBlockN>
|
||||
void gett_mainloop(
|
||||
MainloopParams const& mainloop_params,
|
||||
int64_t m,
|
||||
int64_t n,
|
||||
int64_t l,
|
||||
ElementAccumulator (&acc)[kBlockM][kBlockN])
|
||||
{
|
||||
|
||||
static_assert(cute::rank(typename MainloopParams::LayoutA{}) == 3, "M, K, B");
|
||||
static_assert(cute::rank(typename MainloopParams::LayoutB{}) == 3, "N, K, B");
|
||||
|
||||
using cute::raw_pointer_cast;
|
||||
|
||||
using ElementA = typename ElementTraits<typename MainloopParams::EngineA::value_type>::type;
|
||||
using ElementB = typename ElementTraits<typename MainloopParams::EngineB::value_type>::type;
|
||||
using ElementBlockScaleA = typename ElementTraits<typename MainloopParams::EngineScaleA::value_type>::type;
|
||||
using ElementBlockScaleB = typename ElementTraits<typename MainloopParams::EngineScaleB::value_type>::type;
|
||||
|
||||
using RingOp = multiply_add<ElementAccumulator, ElementAccumulator, ElementAccumulator>;
|
||||
RingOp fma_op;
|
||||
|
||||
multiplies<ElementAccumulator> scale_op;
|
||||
|
||||
static int constexpr kBlockK = cute::get<2>(typename MainloopParams::TileShape{});;
|
||||
|
||||
// Tempo accumulators to seperate blockwise accumulation
|
||||
typename MainloopParams::ElementAccumulator acc_temp[kBlockM][kBlockN];
|
||||
|
||||
// Zero out accumulators
|
||||
for (int m_b = 0; m_b < kBlockM; ++m_b) {
|
||||
for (int n_b = 0; n_b < kBlockN; ++n_b) {
|
||||
acc[m_b][n_b] = ElementAccumulator(0); // RingOp::AdditionIdentity
|
||||
acc_temp[m_b][n_b] = ElementAccumulator(0);
|
||||
}
|
||||
}
|
||||
|
||||
const int M = cute::size<0>(mainloop_params.A.layout());
|
||||
const int N = cute::size<0>(mainloop_params.B.layout());
|
||||
|
||||
const int ScaleGranularityM = M / cute::size<0>(mainloop_params.ScaleA.layout());
|
||||
const int ScaleGranularityN = N / cute::size<0>(mainloop_params.ScaleB.layout());
|
||||
|
||||
assert(ScaleGranularityM && M % ScaleGranularityM == 0 && "ScaleGranularityM must divide M");
|
||||
assert(ScaleGranularityN && N % ScaleGranularityN == 0 && "ScaleGranularityN must divide N");
|
||||
|
||||
cute::Tensor blockscale_A = domain_offset(make_coord(m / ScaleGranularityM, _0{}), mainloop_params.ScaleA(_, _, l));
|
||||
cute::Tensor blockscale_B = domain_offset(make_coord(n / ScaleGranularityN, _0{}), mainloop_params.ScaleB(_, _, l));
|
||||
|
||||
// Compute on this k-block
|
||||
for (int64_t k = 0; k < cute::size<1>(mainloop_params.A.layout()); ++k) {
|
||||
|
||||
// Load Blockwise scaling factor from blockscale Tensors for B
|
||||
int64_t block_k = k / kBlockK;
|
||||
cute::Tensor scale_a = blockscale_A(_, block_k);
|
||||
cute::Tensor scale_b = blockscale_B(_, block_k);
|
||||
|
||||
// Load A
|
||||
ElementAccumulator a_frag[kBlockM];
|
||||
for (int m_b = 0; m_b < kBlockM; ++m_b) {
|
||||
if (m + m_b < cute::size<0>(mainloop_params.A.layout())) {
|
||||
// Perform reference GEMM calculations at the accumulator's precision. Cast A value to accumulator type.
|
||||
a_frag[m_b] = static_cast<ElementAccumulator>(ElementA(mainloop_params.A(m + m_b, k, l)));
|
||||
} else {
|
||||
a_frag[m_b] = ElementAccumulator(0); // RingOp::AdditionIdentity
|
||||
}
|
||||
}
|
||||
|
||||
// Load B
|
||||
ElementAccumulator b_frag[kBlockN];
|
||||
for (int n_b = 0; n_b < kBlockN; ++n_b) {
|
||||
if (n + n_b < cute::size<0>(mainloop_params.B.layout())) {
|
||||
// Perform reference GEMM calculations at the accumulator's precision. Cast A value to accumulator type.
|
||||
b_frag[n_b] = static_cast<ElementAccumulator>(ElementB(mainloop_params.B(n + n_b, k, l)));
|
||||
} else {
|
||||
b_frag[n_b] = ElementAccumulator(0); // RingOp::AdditionIdentity
|
||||
}
|
||||
}
|
||||
|
||||
// do compute
|
||||
for (int m_b = 0; m_b < kBlockM; ++m_b) {
|
||||
for (int n_b = 0; n_b < kBlockN; ++n_b) {
|
||||
acc_temp[m_b][n_b] = fma_op(a_frag[m_b], b_frag[n_b], acc_temp[m_b][n_b]);
|
||||
}
|
||||
}
|
||||
|
||||
// Apply Groupwise-scaling at kBlockK boundary
|
||||
// (a) Apply group and block scaling factors on the partial accumulated results (acc_temp) at the kBlocK boundary
|
||||
// (b) Zero-out partial temporary (acc_temp),
|
||||
// (c) Update permanent (accu)
|
||||
if ((k+1) % kBlockK == 0) {
|
||||
for (int m_b = 0; m_b < kBlockM; ++m_b) {
|
||||
auto scale_a_m_b = scale_a[m_b / ScaleGranularityM];
|
||||
for (int n_b = 0; n_b < kBlockN; ++n_b) {
|
||||
auto scale_b_n_b = scale_b[n_b / ScaleGranularityN];
|
||||
ElementAccumulator blockwise_scaled_accum = acc_temp[m_b][n_b] * scale_a_m_b * scale_b_n_b;
|
||||
acc[m_b][n_b] = blockwise_scaled_accum + acc[m_b][n_b];
|
||||
acc_temp[m_b][n_b] = ElementAccumulator(0);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// GETT - Epilogue
|
||||
template <class EpilogueParams, class ElementAccumulator, int kBlockM, int kBlockN>
|
||||
void gett_epilogue(
|
||||
EpilogueParams const& epilogue_params,
|
||||
int64_t m,
|
||||
int64_t n,
|
||||
int64_t l,
|
||||
ElementAccumulator (&acc)[kBlockM][kBlockN])
|
||||
{
|
||||
static_assert(cute::rank(typename EpilogueParams::LayoutC{}) == 3, "M, K, B");
|
||||
static_assert(cute::rank(typename EpilogueParams::LayoutD{}) == 3, "N, K, B");
|
||||
|
||||
using cute::raw_pointer_cast;
|
||||
|
||||
using ElementCompute = typename EpilogueParams::ElementCompute;
|
||||
using ElementC = typename EpilogueParams::TensorC::value_type;
|
||||
using ElementD = typename EpilogueParams::TensorD::value_type;
|
||||
using ElementAux = typename EpilogueParams::TensorAux::value_type;
|
||||
using ElementBias = typename EpilogueParams::VectorBias::value_type;
|
||||
using ElementScalar = typename EpilogueParams::ElementScalar;
|
||||
using ElementScalingFactor = typename EpilogueParams::ElementScalingFactor;
|
||||
using ActivationFunctor = typename EpilogueParams::ActivationFunctor;
|
||||
using BiasBinaryOp = typename EpilogueParams::BiasBinaryOp;
|
||||
|
||||
constexpr bool PerColBias = EpilogueParams::PerColumnBias;
|
||||
constexpr bool IsScalingAndAmaxOutputNeeded =
|
||||
cute::is_same_v<ElementD, cutlass::float_e4m3_t> or
|
||||
cute::is_same_v<ElementD, cutlass::float_e5m2_t>;
|
||||
|
||||
constexpr bool IsScalingAndAmaxAuxOutputNeeded =
|
||||
cute::is_same_v<ElementAux, cutlass::float_e4m3_t> or
|
||||
cute::is_same_v<ElementAux, cutlass::float_e5m2_t>;
|
||||
|
||||
constexpr bool IsReLUAuxNeeded =
|
||||
(cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::ReLu<ElementCompute>> or
|
||||
cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::Clamp<ElementCompute>>) and
|
||||
cute::is_same_v<ElementAux, cutlass::uint1b_t>;
|
||||
constexpr bool IsClamp =
|
||||
cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::Clamp<ElementCompute>>;
|
||||
|
||||
constexpr bool IsBackpropFusion =
|
||||
cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::dGELU<ElementCompute>> or
|
||||
cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::dReLU<ElementCompute>>;
|
||||
|
||||
// Input related converter
|
||||
NumericConverter<ElementCompute, ElementAccumulator> accumulator_converter;
|
||||
NumericConverter<ElementCompute, ElementC> source_converter;
|
||||
NumericConverter<ElementCompute, ElementBias> bias_converter;
|
||||
[[maybe_unused]] NumericConverter<ElementCompute, ElementAux> aux_source_converter;
|
||||
|
||||
// Scale related converter
|
||||
NumericConverter<ElementCompute, ElementScalar> scale_converter;
|
||||
NumericConverter<ElementCompute, ElementScalingFactor> scaling_factor_converter;
|
||||
|
||||
// Abs max converter
|
||||
[[maybe_unused]] NumericConverter<ElementAccumulator, ElementCompute> abs_max_output_converter;
|
||||
|
||||
// Output related converter
|
||||
NumericConverter<ElementD, ElementCompute> destination_converter;
|
||||
[[maybe_unused]] NumericConverter<ElementAux, ElementCompute> aux_destination_converter;
|
||||
NumericConverter<ElementBias, ElementCompute> dBias_converter;
|
||||
|
||||
// Epilogue operations
|
||||
multiply_add<ElementCompute, ElementCompute, ElementCompute> epilogue_fma;
|
||||
multiplies<ElementCompute> mul;
|
||||
plus<ElementCompute> add;
|
||||
|
||||
// Activation operation
|
||||
|
||||
auto activation = [] (ElementCompute x, ElementCompute y = ElementCompute(0)) {
|
||||
if constexpr (std::is_same_v<ActivationFunctor, void>) {
|
||||
return x + y;
|
||||
} else {
|
||||
return ActivationFunctor()(x, y);
|
||||
}
|
||||
};
|
||||
|
||||
// Bias binary operation
|
||||
BiasBinaryOp bias_op;
|
||||
|
||||
// Do conversion
|
||||
ElementCompute converted_alpha = scale_converter(epilogue_params.alpha);
|
||||
ElementCompute converted_beta = scale_converter(epilogue_params.beta);
|
||||
ElementCompute converted_scale_a = scaling_factor_converter(epilogue_params.scale_a);
|
||||
ElementCompute converted_scale_b = scaling_factor_converter(epilogue_params.scale_b);
|
||||
ElementCompute converted_scale_c = scaling_factor_converter(epilogue_params.scale_c);
|
||||
ElementCompute converted_scale_d = scaling_factor_converter(epilogue_params.scale_d);
|
||||
ElementCompute converted_scale_aux = scaling_factor_converter(epilogue_params.scale_aux);
|
||||
|
||||
// Init local var
|
||||
[[maybe_unused]] ElementCompute local_abs_max_output = ElementCompute(0);
|
||||
[[maybe_unused]] ElementCompute local_abs_max_aux_output = ElementCompute(0);
|
||||
|
||||
converted_alpha = mul(converted_alpha, mul(converted_scale_a, converted_scale_b));
|
||||
converted_beta = mul(converted_beta, converted_scale_c);
|
||||
|
||||
ElementCompute inter_accum[kBlockM][kBlockN];
|
||||
|
||||
for (int m_b = 0; m_b < kBlockM; ++m_b) {
|
||||
ElementCompute local_dBias = ElementCompute(0);
|
||||
|
||||
for (int n_b = 0; n_b < kBlockN; ++n_b) {
|
||||
if (m + m_b < cute::size<0>(epilogue_params.D.layout()) && n + n_b < cute::size<1>(epilogue_params.D.layout())) {
|
||||
// Convert every type to ElementCompute first, do compute, convert to output type, write it out
|
||||
ElementCompute converted_acc = accumulator_converter(acc[m_b][n_b]);
|
||||
// per-row alpha
|
||||
if (raw_pointer_cast(epilogue_params.Valpha.data())) {
|
||||
converted_alpha = scale_converter(epilogue_params.Valpha(m + m_b));
|
||||
}
|
||||
ElementCompute output = mul(converted_alpha, converted_acc);
|
||||
|
||||
if (raw_pointer_cast(epilogue_params.Bias.data()) && not IsBackpropFusion) {
|
||||
ElementCompute converted_bias = bias_converter(epilogue_params.Bias(PerColBias ? n + n_b : m + m_b));
|
||||
output = bias_op(output, converted_bias);
|
||||
}
|
||||
|
||||
if (raw_pointer_cast(epilogue_params.C.data())) {
|
||||
ElementCompute converted_src = source_converter(epilogue_params.C(m + m_b, n + n_b, l));
|
||||
// per-row beta
|
||||
if (epilogue_params.Vbeta.data()) {
|
||||
converted_beta = scale_converter(epilogue_params.Vbeta(m + m_b));
|
||||
}
|
||||
output = epilogue_fma(converted_beta, converted_src, output);
|
||||
}
|
||||
|
||||
if constexpr (IsBackpropFusion) {
|
||||
ElementAux aux_input = ElementAux(0);
|
||||
if (raw_pointer_cast(epilogue_params.Aux.data())) {
|
||||
aux_input = epilogue_params.Aux(m + m_b, n + n_b, l);
|
||||
}
|
||||
|
||||
output = activation(output, aux_source_converter(aux_input));
|
||||
local_dBias = add(local_dBias, output);
|
||||
}
|
||||
else {
|
||||
if (raw_pointer_cast(epilogue_params.Aux.data())) {
|
||||
auto aux_output = output;
|
||||
if constexpr (IsScalingAndAmaxAuxOutputNeeded) {
|
||||
maximum_absolute_value_reduction<ElementCompute, true> amax_op;
|
||||
local_abs_max_aux_output = amax_op(local_abs_max_aux_output, aux_output);
|
||||
aux_output = epilogue_fma(converted_scale_aux, aux_output, ElementCompute(0));
|
||||
}
|
||||
|
||||
if constexpr (IsReLUAuxNeeded) {
|
||||
epilogue_params.Aux(m + m_b, n + n_b, l) = not (aux_output < 0) ? uint1b_t(1) : uint1b_t(0);
|
||||
} else {
|
||||
epilogue_params.Aux(m + m_b, n + n_b, l) = aux_destination_converter(aux_output);
|
||||
}
|
||||
}
|
||||
|
||||
if constexpr (IsClamp) { // Treat Clamp as ReLU
|
||||
output = activation(output, {0, std::numeric_limits<ElementCompute>::max()});
|
||||
}
|
||||
else {
|
||||
output = activation(output);
|
||||
}
|
||||
}
|
||||
|
||||
if constexpr (IsScalingAndAmaxOutputNeeded) {
|
||||
maximum_absolute_value_reduction<ElementCompute, true> amax_op;
|
||||
local_abs_max_output = amax_op(local_abs_max_output, output);
|
||||
output = epilogue_fma(converted_scale_d, output, ElementCompute(0));
|
||||
}
|
||||
|
||||
inter_accum[m_b][n_b] = ElementCompute(output);
|
||||
}
|
||||
} // n_b
|
||||
|
||||
if (m + m_b < cute::size<0>(epilogue_params.D.layout()) && n < cute::size<1>(epilogue_params.D.layout())) {
|
||||
if (raw_pointer_cast(epilogue_params.Bias.data()) && IsBackpropFusion) {
|
||||
ElementCompute converted_dBias = bias_converter(epilogue_params.Bias(m + m_b));
|
||||
local_dBias = add(local_dBias, converted_dBias);
|
||||
epilogue_params.Bias(m + m_b) = dBias_converter(local_dBias);
|
||||
}
|
||||
}
|
||||
} // m_b
|
||||
for (int m_b = 0; m_b < kBlockM; ++m_b) {
|
||||
for (int n_b = 0; n_b < kBlockN; ++n_b) {
|
||||
if (m + m_b < cute::size<0>(epilogue_params.D.layout()) && n + n_b < cute::size<1>(epilogue_params.D.layout())) {
|
||||
epilogue_params.D(m + m_b, n + n_b, l) = destination_converter(inter_accum[m_b][n_b]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#if defined(_OPENMP)
|
||||
#pragma omp critical(Abs_Max_Data_Update)
|
||||
#endif
|
||||
{
|
||||
if constexpr (IsScalingAndAmaxOutputNeeded) {
|
||||
if (epilogue_params.abs_max_D) {
|
||||
*epilogue_params.abs_max_D = maximum_with_nan_propogation<ElementAccumulator>{}(
|
||||
*epilogue_params.abs_max_D, abs_max_output_converter(local_abs_max_output));
|
||||
}
|
||||
}
|
||||
|
||||
if constexpr (IsScalingAndAmaxAuxOutputNeeded) {
|
||||
if (epilogue_params.abs_max_Aux) {
|
||||
*epilogue_params.abs_max_Aux = maximum_with_nan_propogation<ElementAccumulator>{}(
|
||||
*epilogue_params.abs_max_Aux, abs_max_output_converter(local_abs_max_aux_output));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// GEMM - General Matrix-Matrix contraction without conjugation options
|
||||
template <
|
||||
class MainloopParams,
|
||||
class EpilogueParams
|
||||
>
|
||||
void Gemm3x(
|
||||
MainloopParams const& mainloop_params,
|
||||
EpilogueParams const& epilogue_params)
|
||||
{
|
||||
using namespace cute;
|
||||
|
||||
static_assert(cute::rank(typename MainloopParams::LayoutA{}) == cute::rank(typename MainloopParams::LayoutB{}));
|
||||
static_assert(cute::rank(typename EpilogueParams::LayoutC{}) == cute::rank(typename EpilogueParams::LayoutD{}));
|
||||
static_assert(cute::rank(typename MainloopParams::LayoutA{}) == cute::rank(typename EpilogueParams::LayoutC{}));
|
||||
static_assert(cute::rank(typename MainloopParams::LayoutA{}) == 3, "Only Rank3 Tensors (M, K, Batch_Count) "
|
||||
"with Batchmode are supported");
|
||||
// Lower the Matrix-Multiplication with Groupwise scaling (Gemm3x) to a Tensor Contraction (Gett).
|
||||
Gett(mainloop_params, epilogue_params);
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // cutlass::reference::host
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
@@ -0,0 +1,585 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2025 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright notice, this
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
* this list of conditions and the following disclaimer in the documentation
|
||||
* and/or other materials provided with the distribution.
|
||||
*
|
||||
* 3. Neither the name of the copyright holder nor the names of its
|
||||
* contributors may be used to endorse or promote products derived from
|
||||
* this software without specific prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
|
||||
/*! \file
|
||||
\brief A FP8 blockwise scaled GEMM example for the NVIDIA Blackwell SM100 architecture using CUTLASS.
|
||||
*/
|
||||
|
||||
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/epilogue/thread/activation.h"
|
||||
#include "cutlass/gemm/dispatch_policy.hpp"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/dispatch_policy.hpp"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cutlass/gemm/device/gemm_universal_adapter.h"
|
||||
#include "cutlass/gemm/kernel/gemm_universal.hpp"
|
||||
#include "cutlass/gemm/kernel/tile_scheduler_params.h"
|
||||
|
||||
#include "cutlass/util/command_line.h"
|
||||
#include "cutlass/util/distribution.h"
|
||||
#include "cutlass/util/host_tensor.h"
|
||||
#include "cutlass/util/packed_stride.hpp"
|
||||
#include "cutlass/util/tensor_view_io.h"
|
||||
#include "cutlass/util/reference/host/tensor_fill.h"
|
||||
#include "cutlass/util/reference/host/tensor_copy.h"
|
||||
#include "cutlass/util/reference/host/tensor_compare.h"
|
||||
#include "cutlass/util/reference/host/tensor_norm.h"
|
||||
|
||||
#include "cutlass/util/reference/host/gett.hpp"
|
||||
|
||||
#include "helper.h"
|
||||
|
||||
using namespace cute;
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/// GEMM kernel configurations
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// A matrix configuration
|
||||
using ElementA = cutlass::float_e4m3_t; // Element type for A matrix operand
|
||||
using LayoutA = cutlass::layout::RowMajor; // Layout type for A matrix operand
|
||||
constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value; // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes)
|
||||
|
||||
// B matrix configuration
|
||||
using ElementB = cutlass::float_e4m3_t; // Element type for B matrix operand
|
||||
using LayoutB = cutlass::layout::ColumnMajor; // Layout type for B matrix operand
|
||||
constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value; // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes)
|
||||
|
||||
// C/D matrix configuration
|
||||
using ElementC = cutlass::float_e4m3_t; // Element type for C and D matrix operands
|
||||
using LayoutC = cutlass::layout::ColumnMajor; // Layout type for C and D matrix operands
|
||||
constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value; // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes)
|
||||
|
||||
using ElementD = ElementC;
|
||||
using LayoutD = LayoutC;
|
||||
constexpr int AlignmentD = AlignmentC;
|
||||
|
||||
// MMA type
|
||||
using ElementAccumulator = float; // Element Accumulator will also be our scale factor type
|
||||
using ElementCompute = float;
|
||||
|
||||
|
||||
// MMA and Cluster Tile Shapes
|
||||
// Shape of the tile computed by tcgen05 MMA, could be across 2 SMs if Cluster Shape %2 == 0
|
||||
using MmaTileShape_MNK = Shape<_128,_128,_128>;
|
||||
// Shape of the threadblocks in a cluster
|
||||
using ClusterShape_MNK = Shape<_1,_1,_1>;
|
||||
|
||||
using ScaleConfig = decltype(cutlass::detail::sm100_trivial_blockwise_scale_config(MmaTileShape_MNK{}));
|
||||
|
||||
using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA()); // Layout type for SFA matrix operand
|
||||
using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB()); // Layout type for SFB matrix operand
|
||||
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape_MNK, ClusterShape_MNK,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
ElementAccumulator, ElementCompute,
|
||||
ElementC, LayoutC, AlignmentC,
|
||||
ElementD, LayoutC, AlignmentD,
|
||||
cutlass::epilogue::TmaWarpSpecialized1Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementA, cute::tuple<LayoutA, LayoutSFA>, AlignmentA,
|
||||
ElementB, cute::tuple<LayoutB, LayoutSFB>, AlignmentB,
|
||||
ElementAccumulator,
|
||||
MmaTileShape_MNK, ClusterShape_MNK,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecializedBlockwise1SmSm100 // Note: Groupwise and Blockwise only support 1 SM MMA at this moment
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue,
|
||||
void>; // Default to ClusterLaunchControl (CLC) based tile scheduler
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
|
||||
using StrideA = typename Gemm::GemmKernel::StrideA;
|
||||
using StrideB = typename Gemm::GemmKernel::StrideB;
|
||||
using StrideC = typename Gemm::GemmKernel::StrideC;
|
||||
using StrideD = typename Gemm::GemmKernel::StrideD;
|
||||
|
||||
/// Initialization
|
||||
StrideA stride_A;
|
||||
StrideB stride_B;
|
||||
StrideC stride_C;
|
||||
StrideD stride_D;
|
||||
// Strides just iterate over scalars and have no zeros
|
||||
LayoutSFA layout_SFA;
|
||||
LayoutSFB layout_SFB;
|
||||
// Layouts are tiled to the problem size and the strides have zeros
|
||||
uint64_t seed;
|
||||
|
||||
cutlass::HostTensor<ElementA , LayoutA> tensor_A;
|
||||
cutlass::HostTensor<ElementAccumulator, cutlass::layout::PackedVectorLayout> tensor_SFA;
|
||||
cutlass::HostTensor<ElementB , LayoutB> tensor_B;
|
||||
cutlass::HostTensor<ElementAccumulator, cutlass::layout::PackedVectorLayout> tensor_SFB;
|
||||
cutlass::HostTensor<ElementC , LayoutC> tensor_C;
|
||||
cutlass::HostTensor<ElementD , LayoutD> tensor_D;
|
||||
cutlass::HostTensor<ElementD , LayoutD> tensor_ref_D;
|
||||
|
||||
#endif // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/// Testbed utility types
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
// Command line options parsing
|
||||
struct Options {
|
||||
|
||||
bool help = false;
|
||||
bool skip_verification = false;
|
||||
|
||||
float alpha = 1.f, beta = 0.f;
|
||||
int iterations = 1000;
|
||||
int m = 1024, n = 512, k = 1024, l = 1;
|
||||
|
||||
// Parses the command line
|
||||
void parse(int argc, char const **args) {
|
||||
cutlass::CommandLine cmd(argc, args);
|
||||
|
||||
if (cmd.check_cmd_line_flag("help")) {
|
||||
help = true;
|
||||
return;
|
||||
}
|
||||
|
||||
if (cmd.check_cmd_line_flag("skip-verification")) {
|
||||
skip_verification = true;
|
||||
}
|
||||
|
||||
cmd.get_cmd_line_argument("m", m);
|
||||
cmd.get_cmd_line_argument("n", n);
|
||||
cmd.get_cmd_line_argument("k", k);
|
||||
cmd.get_cmd_line_argument("l", l);
|
||||
cmd.get_cmd_line_argument("alpha", alpha, 1.f);
|
||||
cmd.get_cmd_line_argument("beta", beta, 0.f);
|
||||
cmd.get_cmd_line_argument("iterations", iterations);
|
||||
}
|
||||
|
||||
/// Prints the usage statement.
|
||||
std::ostream & print_usage(std::ostream &out) const {
|
||||
|
||||
out << "81_blackwell_gemm_blockwise\n\n"
|
||||
<< " Blackwell FP8 GEMM with Blockwise Scaling using a Warp Specialized kernel.\n\n"
|
||||
<< "Options:\n\n"
|
||||
<< " --help If specified, displays this usage statement\n\n"
|
||||
<< " --m=<int> Sets the M extent of the GEMM\n"
|
||||
<< " --n=<int> Sets the N extent of the GEMM\n"
|
||||
<< " --k=<int> Sets the K extent of the GEMM\n"
|
||||
<< " --l=<int> Sets the l extent (batch) of the GEMM\n"
|
||||
<< " --alpha=<f32> Epilogue scalar alpha\n"
|
||||
<< " --beta=<f32> Epilogue scalar beta\n"
|
||||
<< " --iterations=<int> Number of profiling iterations to perform.\n\n"
|
||||
<< " --skip-verification Skip verification.\n\n";
|
||||
|
||||
out
|
||||
<< "\n\nExamples:\n\n"
|
||||
<< "$ " << "81_blackwell_gemm_blockwise" << " --m=1024 --n=512 --k=1024 --alpha=2 --beta=0.707 \n\n";
|
||||
|
||||
return out;
|
||||
}
|
||||
|
||||
/// Compute performance in GFLOP/s
|
||||
double gflops(double runtime_s) const
|
||||
{
|
||||
// Two flops per multiply-add
|
||||
uint64_t flop = uint64_t(2) * m * n * k;
|
||||
double gflop = double(flop) / double(1.0e9);
|
||||
return gflop / runtime_s;
|
||||
}
|
||||
};
|
||||
|
||||
/// Result structure
|
||||
struct Result
|
||||
{
|
||||
double avg_runtime_ms;
|
||||
double gflops;
|
||||
cutlass::Status status;
|
||||
cudaError_t error;
|
||||
bool passed;
|
||||
|
||||
Result(
|
||||
double avg_runtime_ms = 0,
|
||||
double gflops = 0,
|
||||
cutlass::Status status = cutlass::Status::kSuccess,
|
||||
cudaError_t error = cudaSuccess)
|
||||
:
|
||||
avg_runtime_ms(avg_runtime_ms), gflops(gflops), status(status), error(error), passed(false)
|
||||
{}
|
||||
|
||||
};
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/// GEMM setup and evaluation
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Helper to initialize a block of device data
|
||||
template <typename Element, typename Layout>
|
||||
bool initialize_tensor(
|
||||
cutlass::TensorView<Element, Layout> view,
|
||||
cutlass::Distribution::Kind dist_kind,
|
||||
uint64_t seed) {
|
||||
|
||||
if (dist_kind == cutlass::Distribution::Uniform) {
|
||||
|
||||
double scope_max, scope_min;
|
||||
int bits_input = cutlass::sizeof_bits<Element>::value;
|
||||
int bits_output = cutlass::sizeof_bits<Element>::value;
|
||||
|
||||
if (bits_input == 1) {
|
||||
scope_max = 2;
|
||||
scope_min = 0;
|
||||
} else if (bits_input <= 8) {
|
||||
scope_max = 2;
|
||||
scope_min = -2;
|
||||
} else if (bits_output == 16) {
|
||||
scope_max = 5;
|
||||
scope_min = -5;
|
||||
} else {
|
||||
scope_max = 8;
|
||||
scope_min = -8;
|
||||
}
|
||||
|
||||
cutlass::reference::host::TensorFillRandomUniform(
|
||||
view, seed, scope_max, scope_min, 0);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::AllZeros) {
|
||||
cutlass::reference::host::TensorFill(view);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::Identity) {
|
||||
|
||||
cutlass::reference::host::TensorFillIdentity(view);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::Gaussian) {
|
||||
|
||||
cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::Sequential) {
|
||||
cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
|
||||
}
|
||||
else {
|
||||
throw std::runtime_error("Not implementated.");
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/// Helper to initialize a block of device data (scale_tensors)
|
||||
template <typename Element, typename Layout>
|
||||
bool initialize_scale_tensor(
|
||||
cutlass::TensorView<Element, Layout> view,
|
||||
cutlass::Distribution::Kind dist_kind,
|
||||
uint64_t seed) {
|
||||
|
||||
if (dist_kind == cutlass::Distribution::Uniform) {
|
||||
|
||||
double scope_max, scope_min;
|
||||
|
||||
scope_min = -8;
|
||||
scope_max = 8;
|
||||
|
||||
cutlass::reference::host::TensorFillRandomUniform(
|
||||
view, seed, scope_max, scope_min, 0);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::AllZeros) {
|
||||
cutlass::reference::host::TensorFill(view);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::Identity) {
|
||||
|
||||
cutlass::reference::host::TensorFillIdentity(view);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::Gaussian) {
|
||||
|
||||
cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::Sequential) {
|
||||
cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
|
||||
}
|
||||
else {
|
||||
throw std::runtime_error("Not implementated.");
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/// Initialize operands to be used in the GEMM and reference GEMM
|
||||
void initialize(const Options &options) {
|
||||
using namespace cute;
|
||||
|
||||
auto gemm_problem_shape = cute::make_shape(options.m, options.n, options.k);
|
||||
|
||||
stride_A = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(options.m, options.k, options.l));
|
||||
stride_B = cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(options.n, options.k, options.l));
|
||||
stride_C = cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(options.m, options.n, options.l));
|
||||
stride_D = cutlass::make_cute_packed_stride(StrideD{}, cute::make_shape(options.m, options.n, options.l));
|
||||
|
||||
layout_SFA = ScaleConfig::tile_atom_to_shape_SFA(make_shape(options.m, options.n, options.k, options.l));
|
||||
layout_SFB = ScaleConfig::tile_atom_to_shape_SFB(make_shape(options.m, options.n, options.k, options.l));
|
||||
|
||||
auto a_coord = cutlass::make_Coord(options.m * options.l, options.k);
|
||||
auto c_coord = cutlass::make_Coord(options.m * options.l, options.n);
|
||||
auto b_coord = cutlass::make_Coord(options.k, options.n * options.l);
|
||||
auto blockscale_a_coord = cutlass::make_Coord(size(filter_zeros(layout_SFA)));
|
||||
auto blockscale_b_coord = cutlass::make_Coord(size(filter_zeros(layout_SFB)));
|
||||
|
||||
tensor_A.resize(a_coord);
|
||||
tensor_B.resize(b_coord);
|
||||
tensor_C.resize(c_coord);
|
||||
tensor_D.resize(c_coord);
|
||||
tensor_ref_D.resize(c_coord);
|
||||
tensor_SFA.resize(blockscale_a_coord);
|
||||
tensor_SFB.resize(blockscale_b_coord);
|
||||
|
||||
initialize_tensor(tensor_A.host_view(), cutlass::Distribution::Uniform, seed + 2022);
|
||||
initialize_tensor(tensor_B.host_view(), cutlass::Distribution::Uniform, seed + 2023);
|
||||
initialize_tensor(tensor_C.host_view(), cutlass::Distribution::Uniform, seed + 2024);
|
||||
|
||||
initialize_scale_tensor(tensor_SFA.host_view(), cutlass::Distribution::Uniform, seed + 2025);
|
||||
initialize_scale_tensor(tensor_SFB.host_view(), cutlass::Distribution::Uniform, seed + 2026);
|
||||
|
||||
tensor_A.sync_device();
|
||||
tensor_B.sync_device();
|
||||
tensor_C.sync_device();
|
||||
tensor_D.sync_device();
|
||||
|
||||
tensor_SFA.sync_device();
|
||||
tensor_SFB.sync_device();
|
||||
|
||||
}
|
||||
|
||||
/// Populates a Gemm::Arguments structure from the given commandline options
|
||||
typename Gemm::Arguments args_from_options(const Options &options)
|
||||
{
|
||||
typename Gemm::Arguments arguments{
|
||||
cutlass::gemm::GemmUniversalMode::kGemm,
|
||||
{options.m, options.n, options.k, options.l},
|
||||
{tensor_A.device_data(), stride_A,
|
||||
tensor_B.device_data(), stride_B,
|
||||
tensor_SFA.device_data(), layout_SFA,
|
||||
tensor_SFB.device_data(), layout_SFB},
|
||||
{
|
||||
{}, // epilogue.thread
|
||||
tensor_C.device_data(), stride_C,
|
||||
tensor_D.device_data(), stride_D
|
||||
}
|
||||
};
|
||||
|
||||
auto &fusion_args = arguments.epilogue.thread;
|
||||
fusion_args.alpha = options.alpha;
|
||||
fusion_args.beta = options.beta;
|
||||
|
||||
return arguments;
|
||||
}
|
||||
|
||||
bool verify(const Options &options) {
|
||||
//
|
||||
// Compute reference output
|
||||
//
|
||||
|
||||
// Create instantiation for device reference gemm kernel
|
||||
auto A = cute::make_tensor(tensor_A.host_data(),
|
||||
cute::make_layout(cute::make_shape(options.m, options.k, options.l), stride_A));
|
||||
auto B = cute::make_tensor(tensor_B.host_data(),
|
||||
cute::make_layout(cute::make_shape(options.n, options.k, options.l), stride_B));
|
||||
auto C = cute::make_tensor(tensor_C.host_data(),
|
||||
cute::make_layout(cute::make_shape(options.m, options.n, options.l), stride_C));
|
||||
auto D = cute::make_tensor(tensor_ref_D.host_data(),
|
||||
cute::make_layout(cute::make_shape(options.m, options.n, options.l), stride_D));
|
||||
auto SFA = cute::make_tensor(tensor_SFA.host_data(), layout_SFA);
|
||||
auto SFB = cute::make_tensor(tensor_SFB.host_data(), layout_SFB);
|
||||
|
||||
using unused_t = decltype(D);
|
||||
|
||||
cutlass::reference::host::GettBlockScalingMainloopParams<
|
||||
ElementAccumulator,
|
||||
decltype(A),
|
||||
decltype(SFA),
|
||||
decltype(B),
|
||||
decltype(SFB)
|
||||
> mainloop_params{A, SFA, B, SFB};
|
||||
|
||||
cutlass::reference::host::GettEpilogueParams<
|
||||
ElementAccumulator,
|
||||
ElementAccumulator,
|
||||
ElementAccumulator,
|
||||
ElementCompute,
|
||||
decltype(C),
|
||||
decltype(D)
|
||||
> epilogue_params;
|
||||
|
||||
epilogue_params.C = C;
|
||||
epilogue_params.D = D;
|
||||
epilogue_params.alpha = options.alpha;
|
||||
epilogue_params.beta = options.beta;
|
||||
|
||||
// get reference result
|
||||
cutlass::reference::host::Gemm3x(mainloop_params, epilogue_params);
|
||||
|
||||
// compare_reference
|
||||
tensor_D.sync_host();
|
||||
bool passed = cutlass::reference::host::TensorEquals(tensor_ref_D.host_view(), tensor_D.host_view());
|
||||
|
||||
return passed;
|
||||
}
|
||||
|
||||
/// Execute a given example GEMM computation
|
||||
template <typename Gemm>
|
||||
int run(Options &options)
|
||||
{
|
||||
initialize(options);
|
||||
|
||||
|
||||
// Instantiate CUTLASS kernel depending on templates
|
||||
Gemm gemm;
|
||||
|
||||
// Create a structure of gemm kernel arguments suitable for invoking an instance of Gemm
|
||||
auto arguments = args_from_options(options);
|
||||
|
||||
// Using the arguments, query for extra workspace required for matrix multiplication computation
|
||||
size_t workspace_size = Gemm::get_workspace_size(arguments);
|
||||
|
||||
// Allocate workspace memory
|
||||
cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
|
||||
|
||||
|
||||
// Check if the problem size is supported or not
|
||||
CUTLASS_CHECK(gemm.can_implement(arguments));
|
||||
|
||||
|
||||
// Initialize CUTLASS kernel with arguments and workspace pointer
|
||||
CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
|
||||
|
||||
|
||||
// Correctness / Warmup iteration
|
||||
CUTLASS_CHECK(gemm.run());
|
||||
|
||||
Result result;
|
||||
if (!options.skip_verification) {
|
||||
// Check if output from CUTLASS kernel and reference kernel are equal or not
|
||||
result.passed = verify(options);
|
||||
|
||||
std::cout << " Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
|
||||
|
||||
if (!result.passed) {
|
||||
exit(-1);
|
||||
}
|
||||
}
|
||||
|
||||
// Run profiling loop
|
||||
if (options.iterations > 0)
|
||||
{
|
||||
GpuTimer timer;
|
||||
timer.start();
|
||||
for (int iter = 0; iter < options.iterations; ++iter) {
|
||||
CUTLASS_CHECK(gemm.run());
|
||||
}
|
||||
timer.stop();
|
||||
|
||||
// Compute average runtime and GFLOPs.
|
||||
float elapsed_ms = timer.elapsed_millis();
|
||||
result.avg_runtime_ms = double(elapsed_ms) / double(options.iterations);
|
||||
result.gflops = options.gflops(result.avg_runtime_ms / 1000.0);
|
||||
|
||||
std::cout << " Problem Size: " << options.m << 'x' << options.n << 'x' << options.k << 'x' << options.l << std::endl;
|
||||
std::cout << " Avg runtime: " << result.avg_runtime_ms << " ms" << std::endl;
|
||||
std::cout << " GFLOPS: " << result.gflops << std::endl;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
#endif // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
int main(int argc, char const **args) {
|
||||
|
||||
// CUTLASS must be compiled with CUDA 12.0 Toolkit to run this example
|
||||
// and must have compute capability at least sm100a.
|
||||
|
||||
if (__CUDACC_VER_MAJOR__ < 12) {
|
||||
std::cerr << "This example requires CUDA 12 or newer.\n";
|
||||
// Returning zero so this test passes on older Toolkits. Its actions are no-op.
|
||||
return 0;
|
||||
}
|
||||
|
||||
cudaDeviceProp props;
|
||||
int current_device_id;
|
||||
CUDA_CHECK(cudaGetDevice(¤t_device_id));
|
||||
CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
|
||||
cudaError_t error = cudaGetDeviceProperties(&props, 0);
|
||||
if (props.major != 10 || props.minor != 0) {
|
||||
std::cerr << "This example requires a GPU with compute capability 100a)." << std::endl;
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
||||
//
|
||||
// Parse options
|
||||
//
|
||||
|
||||
Options options;
|
||||
|
||||
options.parse(argc, args);
|
||||
|
||||
if (options.help) {
|
||||
options.print_usage(std::cout) << std::endl;
|
||||
return 0;
|
||||
}
|
||||
|
||||
//
|
||||
// Run
|
||||
//
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
run<Gemm>(options);
|
||||
#endif // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
@@ -0,0 +1,589 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2025 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright notice, this
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
* this list of conditions and the following disclaimer in the documentation
|
||||
* and/or other materials provided with the distribution.
|
||||
*
|
||||
* 3. Neither the name of the copyright holder nor the names of its
|
||||
* contributors may be used to endorse or promote products derived from
|
||||
* this software without specific prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
|
||||
/*! \file
|
||||
\brief A FP8 groupwise scaled GEMM example for the NVIDIA Blackwell SM100 architecture using CUTLASS.
|
||||
*/
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/epilogue/thread/activation.h"
|
||||
#include "cutlass/gemm/dispatch_policy.hpp"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/dispatch_policy.hpp"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cutlass/gemm/device/gemm_universal_adapter.h"
|
||||
#include "cutlass/gemm/kernel/gemm_universal.hpp"
|
||||
#include "cutlass/gemm/kernel/tile_scheduler_params.h"
|
||||
|
||||
#include "cutlass/util/command_line.h"
|
||||
#include "cutlass/util/distribution.h"
|
||||
#include "cutlass/util/host_tensor.h"
|
||||
#include "cutlass/util/packed_stride.hpp"
|
||||
#include "cutlass/util/tensor_view_io.h"
|
||||
#include "cutlass/util/reference/host/tensor_fill.h"
|
||||
#include "cutlass/util/reference/host/tensor_copy.h"
|
||||
#include "cutlass/util/reference/host/tensor_compare.h"
|
||||
#include "cutlass/util/reference/host/tensor_norm.h"
|
||||
|
||||
#include "cutlass/util/reference/host/gett.hpp"
|
||||
|
||||
#include "helper.h"
|
||||
|
||||
using namespace cute;
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/// GEMM kernel configurations
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// A matrix configuration
|
||||
using ElementA = cutlass::float_e4m3_t; // Element type for A matrix operand
|
||||
using LayoutA = cutlass::layout::RowMajor; // Layout type for A matrix operand
|
||||
constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value; // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes)
|
||||
|
||||
// B matrix configuration
|
||||
using ElementB = cutlass::float_e4m3_t; // Element type for B matrix operand
|
||||
using LayoutB = cutlass::layout::ColumnMajor; // Layout type for B matrix operand
|
||||
constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value; // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes)
|
||||
|
||||
// C/D matrix configuration
|
||||
using ElementC = cutlass::float_e4m3_t; // Element type for C and D matrix operands
|
||||
using LayoutC = cutlass::layout::ColumnMajor; // Layout type for C and D matrix operands
|
||||
constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value; // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes)
|
||||
|
||||
using ElementD = ElementC;
|
||||
using LayoutD = LayoutC;
|
||||
constexpr int AlignmentD = AlignmentC;
|
||||
|
||||
// MMA type
|
||||
using ElementAccumulator = float; // Element Accumulator will also be our scale factor type
|
||||
using ElementCompute = float;
|
||||
|
||||
|
||||
// MMA and Cluster Tile Shapes
|
||||
// Shape of the tile computed by tcgen05 MMA, could be across 2 SMs if Cluster Shape %2 == 0
|
||||
using MmaTileShape_MNK = Shape<_128,_128,_128>;
|
||||
// Shape of the threadblocks in a cluster
|
||||
using ClusterShape_MNK = Shape<_1,_1,_1>;
|
||||
|
||||
constexpr int ScaleGranularityM = 1;
|
||||
constexpr int ScaleGranularityN = 128;
|
||||
constexpr int ScaleGranularityK = 128;
|
||||
using ScaleConfig = cutlass::detail::Sm100BlockwiseScaleConfig<ScaleGranularityM, ScaleGranularityN, ScaleGranularityK>;
|
||||
|
||||
// Note when we have multiple scale factors per tile (in this case 128 scales in M per tile), we will restrict up to a
|
||||
// 16B alignment if possible (i.e., we have at least 16B of scales in M).
|
||||
// In this case the smallest M that can be executed is 16. To avoid this for smaller M, you can swap A and B
|
||||
// and transpose A, B, C, and scales since B^T A^T = C^T.
|
||||
using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA()); // Layout type for SFA matrix operand
|
||||
using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB()); // Layout type for SFB matrix operand
|
||||
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape_MNK, ClusterShape_MNK,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
ElementAccumulator, ElementCompute,
|
||||
ElementC, LayoutC, AlignmentC,
|
||||
ElementD, LayoutC, AlignmentD,
|
||||
cutlass::epilogue::TmaWarpSpecialized1Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementA, cute::tuple<LayoutA, LayoutSFA>, AlignmentA,
|
||||
ElementB, cute::tuple<LayoutB, LayoutSFB>, AlignmentB,
|
||||
ElementAccumulator,
|
||||
MmaTileShape_MNK, ClusterShape_MNK,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecializedBlockwise1SmSm100 // Note: Groupwise and Blockwise only support 1 SM MMA at this moment
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue,
|
||||
void>; // Default to ClusterLaunchControl (CLC) based tile scheduler
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
|
||||
using StrideA = typename Gemm::GemmKernel::StrideA;
|
||||
using StrideB = typename Gemm::GemmKernel::StrideB;
|
||||
using StrideC = typename Gemm::GemmKernel::StrideC;
|
||||
using StrideD = typename Gemm::GemmKernel::StrideD;
|
||||
|
||||
/// Initialization
|
||||
StrideA stride_A;
|
||||
StrideB stride_B;
|
||||
StrideC stride_C;
|
||||
StrideD stride_D;
|
||||
// Strides just iterate over scalars and have no zeros
|
||||
LayoutSFA layout_SFA;
|
||||
LayoutSFB layout_SFB;
|
||||
// Layouts are tiled to the problem size and the strides have zeros
|
||||
uint64_t seed;
|
||||
|
||||
cutlass::HostTensor<ElementA , LayoutA> tensor_A;
|
||||
cutlass::HostTensor<ElementAccumulator, cutlass::layout::PackedVectorLayout> tensor_SFA;
|
||||
cutlass::HostTensor<ElementB , LayoutB> tensor_B;
|
||||
cutlass::HostTensor<ElementAccumulator, cutlass::layout::PackedVectorLayout> tensor_SFB;
|
||||
cutlass::HostTensor<ElementC , LayoutC> tensor_C;
|
||||
cutlass::HostTensor<ElementD , LayoutD> tensor_D;
|
||||
cutlass::HostTensor<ElementD , LayoutD> tensor_ref_D;
|
||||
|
||||
#endif // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/// Testbed utility types
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
// Command line options parsing
|
||||
struct Options {
|
||||
|
||||
bool help = false;
|
||||
bool skip_verification = false;
|
||||
|
||||
float alpha = 1.f, beta = 0.f;
|
||||
int iterations = 1000;
|
||||
int m = 1024, n = 512, k = 1024, l = 1;
|
||||
|
||||
// Parses the command line
|
||||
void parse(int argc, char const **args) {
|
||||
cutlass::CommandLine cmd(argc, args);
|
||||
|
||||
if (cmd.check_cmd_line_flag("help")) {
|
||||
help = true;
|
||||
return;
|
||||
}
|
||||
|
||||
if (cmd.check_cmd_line_flag("skip-verification")) {
|
||||
skip_verification = true;
|
||||
}
|
||||
|
||||
cmd.get_cmd_line_argument("m", m);
|
||||
cmd.get_cmd_line_argument("n", n);
|
||||
cmd.get_cmd_line_argument("k", k);
|
||||
cmd.get_cmd_line_argument("l", l);
|
||||
cmd.get_cmd_line_argument("alpha", alpha, 1.f);
|
||||
cmd.get_cmd_line_argument("beta", beta, 0.f);
|
||||
cmd.get_cmd_line_argument("iterations", iterations);
|
||||
}
|
||||
|
||||
/// Prints the usage statement.
|
||||
std::ostream & print_usage(std::ostream &out) const {
|
||||
|
||||
out << "81_blackwell_gemm_groupwise\n\n"
|
||||
<< " Blackwell FP8 GEMM with Groupwise Scaling using a Warp Specialized kernel.\n\n"
|
||||
<< "Options:\n\n"
|
||||
<< " --help If specified, displays this usage statement\n\n"
|
||||
<< " --m=<int> Sets the M extent of the GEMM\n"
|
||||
<< " --n=<int> Sets the N extent of the GEMM\n"
|
||||
<< " --k=<int> Sets the K extent of the GEMM\n"
|
||||
<< " --l=<int> Sets the l extent (batch) of the GEMM\n"
|
||||
<< " --alpha=<f32> Epilogue scalar alpha\n"
|
||||
<< " --beta=<f32> Epilogue scalar beta\n"
|
||||
<< " --iterations=<int> Number of profiling iterations to perform.\n\n"
|
||||
<< " --skip-verification Skip verification.\n\n";
|
||||
|
||||
out
|
||||
<< "\n\nExamples:\n\n"
|
||||
<< "$ " << "81_blackwell_gemm_groupwise" << " --m=1024 --n=512 --k=1024 --alpha=2 --beta=0.707 \n\n";
|
||||
|
||||
return out;
|
||||
}
|
||||
|
||||
/// Compute performance in GFLOP/s
|
||||
double gflops(double runtime_s) const
|
||||
{
|
||||
// Two flops per multiply-add
|
||||
uint64_t flop = uint64_t(2) * m * n * k;
|
||||
double gflop = double(flop) / double(1.0e9);
|
||||
return gflop / runtime_s;
|
||||
}
|
||||
};
|
||||
|
||||
/// Result structure
|
||||
struct Result
|
||||
{
|
||||
double avg_runtime_ms;
|
||||
double gflops;
|
||||
cutlass::Status status;
|
||||
cudaError_t error;
|
||||
bool passed;
|
||||
|
||||
Result(
|
||||
double avg_runtime_ms = 0,
|
||||
double gflops = 0,
|
||||
cutlass::Status status = cutlass::Status::kSuccess,
|
||||
cudaError_t error = cudaSuccess)
|
||||
:
|
||||
avg_runtime_ms(avg_runtime_ms), gflops(gflops), status(status), error(error), passed(false)
|
||||
{}
|
||||
|
||||
};
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/// GEMM setup and evaluation
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Helper to initialize a block of device data
|
||||
template <typename Element, typename Layout>
|
||||
bool initialize_tensor(
|
||||
cutlass::TensorView<Element, Layout> view,
|
||||
cutlass::Distribution::Kind dist_kind,
|
||||
uint64_t seed) {
|
||||
|
||||
if (dist_kind == cutlass::Distribution::Uniform) {
|
||||
|
||||
double scope_max, scope_min;
|
||||
int bits_input = cutlass::sizeof_bits<Element>::value;
|
||||
int bits_output = cutlass::sizeof_bits<Element>::value;
|
||||
|
||||
if (bits_input == 1) {
|
||||
scope_max = 2;
|
||||
scope_min = 0;
|
||||
} else if (bits_input <= 8) {
|
||||
scope_max = 2;
|
||||
scope_min = -2;
|
||||
} else if (bits_output == 16) {
|
||||
scope_max = 5;
|
||||
scope_min = -5;
|
||||
} else {
|
||||
scope_max = 8;
|
||||
scope_min = -8;
|
||||
}
|
||||
|
||||
cutlass::reference::host::TensorFillRandomUniform(
|
||||
view, seed, scope_max, scope_min, 0);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::AllZeros) {
|
||||
cutlass::reference::host::TensorFill(view);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::Identity) {
|
||||
|
||||
cutlass::reference::host::TensorFillIdentity(view);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::Gaussian) {
|
||||
|
||||
cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::Sequential) {
|
||||
cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
|
||||
}
|
||||
else {
|
||||
throw std::runtime_error("Not implementated.");
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/// Helper to initialize a block of device data (scale_tensors)
|
||||
template <typename Element, typename Layout>
|
||||
bool initialize_scale_tensor(
|
||||
cutlass::TensorView<Element, Layout> view,
|
||||
cutlass::Distribution::Kind dist_kind,
|
||||
uint64_t seed) {
|
||||
|
||||
if (dist_kind == cutlass::Distribution::Uniform) {
|
||||
|
||||
double scope_max, scope_min;
|
||||
|
||||
scope_min = -8;
|
||||
scope_max = 8;
|
||||
|
||||
cutlass::reference::host::TensorFillRandomUniform(
|
||||
view, seed, scope_max, scope_min, 0);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::AllZeros) {
|
||||
cutlass::reference::host::TensorFill(view);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::Identity) {
|
||||
|
||||
cutlass::reference::host::TensorFillIdentity(view);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::Gaussian) {
|
||||
|
||||
cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::Sequential) {
|
||||
cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
|
||||
}
|
||||
else {
|
||||
throw std::runtime_error("Not implementated.");
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/// Initialize operands to be used in the GEMM and reference GEMM
|
||||
void initialize(const Options &options) {
|
||||
using namespace cute;
|
||||
|
||||
auto gemm_problem_shape = cute::make_shape(options.m, options.n, options.k);
|
||||
|
||||
stride_A = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(options.m, options.k, options.l));
|
||||
stride_B = cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(options.n, options.k, options.l));
|
||||
stride_C = cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(options.m, options.n, options.l));
|
||||
stride_D = cutlass::make_cute_packed_stride(StrideD{}, cute::make_shape(options.m, options.n, options.l));
|
||||
|
||||
layout_SFA = ScaleConfig::tile_atom_to_shape_SFA(make_shape(options.m, options.n, options.k, options.l));
|
||||
layout_SFB = ScaleConfig::tile_atom_to_shape_SFB(make_shape(options.m, options.n, options.k, options.l));
|
||||
|
||||
auto a_coord = cutlass::make_Coord(options.m * options.l, options.k);
|
||||
auto c_coord = cutlass::make_Coord(options.m * options.l, options.n);
|
||||
auto b_coord = cutlass::make_Coord(options.k, options.n * options.l);
|
||||
auto blockscale_a_coord = cutlass::make_Coord(size(filter_zeros(layout_SFA)));
|
||||
auto blockscale_b_coord = cutlass::make_Coord(size(filter_zeros(layout_SFB)));
|
||||
|
||||
tensor_A.resize(a_coord);
|
||||
tensor_B.resize(b_coord);
|
||||
tensor_C.resize(c_coord);
|
||||
tensor_D.resize(c_coord);
|
||||
tensor_ref_D.resize(c_coord);
|
||||
tensor_SFA.resize(blockscale_a_coord);
|
||||
tensor_SFB.resize(blockscale_b_coord);
|
||||
|
||||
initialize_tensor(tensor_A.host_view(), cutlass::Distribution::Uniform, seed + 2022);
|
||||
initialize_tensor(tensor_B.host_view(), cutlass::Distribution::Uniform, seed + 2023);
|
||||
initialize_tensor(tensor_C.host_view(), cutlass::Distribution::Uniform, seed + 2024);
|
||||
|
||||
initialize_scale_tensor(tensor_SFA.host_view(), cutlass::Distribution::Uniform, seed + 2025);
|
||||
initialize_scale_tensor(tensor_SFB.host_view(), cutlass::Distribution::Uniform, seed + 2026);
|
||||
|
||||
tensor_A.sync_device();
|
||||
tensor_B.sync_device();
|
||||
tensor_C.sync_device();
|
||||
tensor_D.sync_device();
|
||||
|
||||
tensor_SFA.sync_device();
|
||||
tensor_SFB.sync_device();
|
||||
|
||||
}
|
||||
|
||||
/// Populates a Gemm::Arguments structure from the given commandline options
|
||||
typename Gemm::Arguments args_from_options(const Options &options)
|
||||
{
|
||||
typename Gemm::Arguments arguments{
|
||||
cutlass::gemm::GemmUniversalMode::kGemm,
|
||||
{options.m, options.n, options.k, options.l},
|
||||
{tensor_A.device_data(), stride_A,
|
||||
tensor_B.device_data(), stride_B,
|
||||
tensor_SFA.device_data(), layout_SFA,
|
||||
tensor_SFB.device_data(), layout_SFB},
|
||||
{
|
||||
{}, // epilogue.thread
|
||||
tensor_C.device_data(), stride_C,
|
||||
tensor_D.device_data(), stride_D
|
||||
}
|
||||
};
|
||||
|
||||
auto &fusion_args = arguments.epilogue.thread;
|
||||
fusion_args.alpha = options.alpha;
|
||||
fusion_args.beta = options.beta;
|
||||
|
||||
return arguments;
|
||||
}
|
||||
|
||||
bool verify(const Options &options) {
|
||||
//
|
||||
// Compute reference output
|
||||
//
|
||||
|
||||
// Create instantiation for device reference gemm kernel
|
||||
auto A = cute::make_tensor(tensor_A.host_data(),
|
||||
cute::make_layout(cute::make_shape(options.m, options.k, options.l), stride_A));
|
||||
auto B = cute::make_tensor(tensor_B.host_data(),
|
||||
cute::make_layout(cute::make_shape(options.n, options.k, options.l), stride_B));
|
||||
auto C = cute::make_tensor(tensor_C.host_data(),
|
||||
cute::make_layout(cute::make_shape(options.m, options.n, options.l), stride_C));
|
||||
auto D = cute::make_tensor(tensor_ref_D.host_data(),
|
||||
cute::make_layout(cute::make_shape(options.m, options.n, options.l), stride_D));
|
||||
auto SFA = cute::make_tensor(tensor_SFA.host_data(), layout_SFA);
|
||||
auto SFB = cute::make_tensor(tensor_SFB.host_data(), layout_SFB);
|
||||
|
||||
using unused_t = decltype(D);
|
||||
|
||||
cutlass::reference::host::GettBlockScalingMainloopParams<
|
||||
ElementAccumulator,
|
||||
decltype(A),
|
||||
decltype(SFA),
|
||||
decltype(B),
|
||||
decltype(SFB)
|
||||
> mainloop_params{A, SFA, B, SFB};
|
||||
|
||||
cutlass::reference::host::GettEpilogueParams<
|
||||
ElementAccumulator,
|
||||
ElementAccumulator,
|
||||
ElementAccumulator,
|
||||
ElementCompute,
|
||||
decltype(C),
|
||||
decltype(D)
|
||||
> epilogue_params;
|
||||
|
||||
epilogue_params.C = C;
|
||||
epilogue_params.D = D;
|
||||
epilogue_params.alpha = options.alpha;
|
||||
epilogue_params.beta = options.beta;
|
||||
|
||||
// get reference result
|
||||
cutlass::reference::host::Gemm3x(mainloop_params, epilogue_params);
|
||||
|
||||
// compare_reference
|
||||
tensor_D.sync_host();
|
||||
bool passed = cutlass::reference::host::TensorEquals(tensor_ref_D.host_view(), tensor_D.host_view());
|
||||
|
||||
return passed;
|
||||
}
|
||||
|
||||
/// Execute a given example GEMM computation
|
||||
template <typename Gemm>
|
||||
int run(Options &options)
|
||||
{
|
||||
initialize(options);
|
||||
|
||||
|
||||
// Instantiate CUTLASS kernel depending on templates
|
||||
Gemm gemm;
|
||||
|
||||
// Create a structure of gemm kernel arguments suitable for invoking an instance of Gemm
|
||||
auto arguments = args_from_options(options);
|
||||
|
||||
// Using the arguments, query for extra workspace required for matrix multiplication computation
|
||||
size_t workspace_size = Gemm::get_workspace_size(arguments);
|
||||
|
||||
// Allocate workspace memory
|
||||
cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
|
||||
|
||||
|
||||
// Check if the problem size is supported or not
|
||||
CUTLASS_CHECK(gemm.can_implement(arguments));
|
||||
|
||||
|
||||
// Initialize CUTLASS kernel with arguments and workspace pointer
|
||||
CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
|
||||
|
||||
|
||||
// Correctness / Warmup iteration
|
||||
CUTLASS_CHECK(gemm.run());
|
||||
|
||||
Result result;
|
||||
if (!options.skip_verification) {
|
||||
// Check if output from CUTLASS kernel and reference kernel are equal or not
|
||||
result.passed = verify(options);
|
||||
|
||||
std::cout << " Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
|
||||
|
||||
if (!result.passed) {
|
||||
exit(-1);
|
||||
}
|
||||
}
|
||||
|
||||
// Run profiling loop
|
||||
if (options.iterations > 0)
|
||||
{
|
||||
GpuTimer timer;
|
||||
timer.start();
|
||||
for (int iter = 0; iter < options.iterations; ++iter) {
|
||||
CUTLASS_CHECK(gemm.run());
|
||||
}
|
||||
timer.stop();
|
||||
|
||||
// Compute average runtime and GFLOPs.
|
||||
float elapsed_ms = timer.elapsed_millis();
|
||||
result.avg_runtime_ms = double(elapsed_ms) / double(options.iterations);
|
||||
result.gflops = options.gflops(result.avg_runtime_ms / 1000.0);
|
||||
|
||||
std::cout << " Problem Size: " << options.m << 'x' << options.n << 'x' << options.k << 'x' << options.l << std::endl;
|
||||
std::cout << " Avg runtime: " << result.avg_runtime_ms << " ms" << std::endl;
|
||||
std::cout << " GFLOPS: " << result.gflops << std::endl;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
#endif // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
int main(int argc, char const **args) {
|
||||
|
||||
// CUTLASS must be compiled with CUDA 12.0 Toolkit to run this example
|
||||
// and must have compute capability at least sm100a.
|
||||
|
||||
if (__CUDACC_VER_MAJOR__ < 12) {
|
||||
std::cerr << "This example requires CUDA 12 or newer.\n";
|
||||
// Returning zero so this test passes on older Toolkits. Its actions are no-op.
|
||||
return 0;
|
||||
}
|
||||
|
||||
cudaDeviceProp props;
|
||||
int current_device_id;
|
||||
CUDA_CHECK(cudaGetDevice(¤t_device_id));
|
||||
CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
|
||||
cudaError_t error = cudaGetDeviceProperties(&props, 0);
|
||||
if (props.major != 10 || props.minor != 0) {
|
||||
std::cerr << "This example requires a GPU with compute capability 100a)." << std::endl;
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
||||
//
|
||||
// Parse options
|
||||
//
|
||||
|
||||
Options options;
|
||||
|
||||
options.parse(argc, args);
|
||||
|
||||
if (options.help) {
|
||||
options.print_usage(std::cout) << std::endl;
|
||||
return 0;
|
||||
}
|
||||
|
||||
//
|
||||
// Run
|
||||
//
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
run<Gemm>(options);
|
||||
#endif // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
@@ -0,0 +1,57 @@
|
||||
|
||||
# Copyright (c) 2025 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
# SPDX-License-Identifier: BSD-3-Clause
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright notice, this
|
||||
# list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# 3. Neither the name of the copyright holder nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
|
||||
if (CUTLASS_NVCC_ARCHS MATCHES 100a)
|
||||
|
||||
set(TEST_RANDOM --iterations=0) # Random problem sizes
|
||||
|
||||
set(TEST_EPILOGUE --alpha=0.5 --beta=0.5 --iterations=0) # Random problem sizes
|
||||
|
||||
set(TEST_SMALL --m=256 --n=128 --k=128 --iterations=0) # Small problem sizes
|
||||
|
||||
cutlass_example_add_executable(
|
||||
81_blackwell_gemm_blockwise
|
||||
81_blackwell_gemm_blockwise.cu
|
||||
TEST_COMMAND_OPTIONS
|
||||
TEST_RANDOM
|
||||
TEST_EPILOGUE
|
||||
TEST_SMALL
|
||||
)
|
||||
|
||||
cutlass_example_add_executable(
|
||||
81_blackwell_gemm_groupwise
|
||||
81_blackwell_gemm_groupwise.cu
|
||||
TEST_COMMAND_OPTIONS
|
||||
TEST_RANDOM
|
||||
TEST_EPILOGUE
|
||||
TEST_SMALL
|
||||
)
|
||||
|
||||
endif()
|
||||
@@ -146,6 +146,7 @@ foreach(EXAMPLE
|
||||
64_ada_fp8_gemm_grouped
|
||||
65_distributed_gemm
|
||||
67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling
|
||||
68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling
|
||||
69_hopper_mixed_dtype_grouped_gemm
|
||||
70_blackwell_gemm
|
||||
71_blackwell_gemm_with_collective_builder
|
||||
@@ -156,6 +157,7 @@ foreach(EXAMPLE
|
||||
76_blackwell_conv
|
||||
77_blackwell_fmha
|
||||
78_blackwell_emulated_bf16x9_gemm
|
||||
81_blackwell_gemm_blockwise
|
||||
)
|
||||
|
||||
add_subdirectory(${EXAMPLE})
|
||||
|
||||
Reference in New Issue
Block a user