mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-20 06:49:15 +00:00
Adding more instances of grouped convolution 3d forward for FP8 with ConvScale+Bias element-wise operation. (#1412)
* Add CMakePresets configurations. * Add binary elementwise ConvScaleAdd and an example. * Numerical verification of results. Observed significant irregularities in F8 to F32 type conversions: ```log ConvScaleAdd: float=145.000000 f8_t=160.000000 e=144.000000 ConvScaleAdd: float=97.000000 f8_t=96.000000 e=104.000000 ConvScaleAdd: float=65.000000 f8_t=64.000000 e=72.000000 ``` * Implemented ConvScaleAdd + Example. * Add ConvScale+Bias Instances * Add Client Example for ConvScale+Bias * Fix number of bytes in an example.. * Cleanup.
This commit is contained in:
Andriy Roshchenko
committed by
GitHub
GitHub
parent
ffabd70a15
commit
4a8a1befd5
@@ -39,6 +39,10 @@ target_link_libraries(client_grouped_convnd_fwd_bilinear_residual_fp16 PRIVATE c
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add_executable(client_conv3d_fwd_convinvscale_fp8
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grouped_convnd_fwd_convinvscale/conv3d_fwd_convinvscale_fp8.cpp)
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target_link_libraries(client_conv3d_fwd_convinvscale_fp8 PRIVATE composable_kernel::device_conv_operations)
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# Fwd convscale + Bias
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add_executable(client_conv3d_fwd_convscale_add_fp8
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grouped_convnd_fwd_convscale_add/conv3d_fwd_convscale_add_fp8.cpp)
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target_link_libraries(client_conv3d_fwd_convscale_add_fp8 PRIVATE composable_kernel::device_conv_operations)
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# Fwd convscale + ReLU
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add_executable(client_conv3d_fwd_convscale_relu_fp8
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grouped_convnd_fwd_convscale_relu/conv3d_fwd_convscale_relu_fp8.cpp)
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@@ -0,0 +1,302 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <cstdlib>
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#include <iomanip>
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#include <iostream>
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#include <iterator>
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#include <numeric>
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#include <string>
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#include <vector>
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#include "ck/ck.hpp"
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#include "ck/utility/type.hpp"
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#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convscale_add.hpp"
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using ConvScaleAdd = ck::tensor_operation::element_wise::ConvScaleAdd;
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using F32 = float;
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using BiasDataType = F32;
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struct SimpleDeviceMem
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{
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SimpleDeviceMem() = delete;
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SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
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{
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(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
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}
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void* GetDeviceBuffer() { return p_mem_; }
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~SimpleDeviceMem() { (void)hipFree(p_mem_); }
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void* p_mem_;
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};
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template <ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
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std::size_t
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GetFlops(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths,
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const std::size_t& ds_size)
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{
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// G * N * C * <output spatial lengths product> * (2 * K * <filter spatial lengths product> +
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// <number of scale factors>)
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ck::index_t G = weights_lengths[0];
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ck::index_t N = output_lengths[1];
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ck::index_t K = weights_lengths[1];
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ck::index_t C = weights_lengths[2];
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return G * N * C *
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std::accumulate(std::next(std::begin(output_lengths), NumNonSpatialDim),
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std::end(output_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<>()) *
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(static_cast<std::size_t>(2) * K *
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std::accumulate(std::next(std::begin(weights_lengths), NumNonSpatialDim),
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std::end(weights_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<>()) +
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ds_size);
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}
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template <typename InDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
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std::size_t
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GetInputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& input_lengths)
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{
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// sizeof(InDataType) * (G * N * C * <input spatial lengths product>) +
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return sizeof(InDataType) * std::accumulate(std::begin(input_lengths),
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std::end(input_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<>());
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}
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template <typename WeiDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
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std::size_t
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GetWeightByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths)
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{
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// sizeof(WeiDataType) * (G * K * C * <filter spatial lengths product>) +
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return sizeof(WeiDataType) * std::accumulate(std::begin(weights_lengths),
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std::end(weights_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<>());
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}
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template <typename OutDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
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std::size_t
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GetOutputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths)
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{
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// sizeof(OutDataType) * (G * N * K * <output spatial lengths product>);
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return sizeof(OutDataType) * std::accumulate(std::begin(output_lengths),
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std::end(output_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<std::size_t>());
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}
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template <ck::index_t NumDimSpatial,
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typename InDataType,
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typename WeiDataType,
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typename OutDataType,
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typename InLayout,
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typename WeiLayout,
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typename OutLayout,
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ck::index_t NumNonSpatialDim = 3,
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typename AComputeType = InDataType,
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typename BComputeType = AComputeType>
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bool run_grouped_conv_fwd_convscale_add(
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std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> in_lengths,
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std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> wei_lengths,
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std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> out_lengths)
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{
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namespace ctc = ck::tensor_layout::convolution;
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static_assert(NumDimSpatial == 3 && ck::is_same_v<InLayout, ctc::NDHWGC> &&
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ck::is_same_v<WeiLayout, ctc::GKZYXC> &&
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ck::is_same_v<OutLayout, ctc::NDHWGK>,
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"Unsupported configuration");
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const ck::index_t G = in_lengths[4];
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const ck::index_t N = in_lengths[0];
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const ck::index_t K = wei_lengths[1];
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const ck::index_t C = in_lengths[5];
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const ck::index_t Z = wei_lengths[2];
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const ck::index_t Y = wei_lengths[3];
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const ck::index_t X = wei_lengths[4];
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const ck::index_t Di = in_lengths[1];
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const ck::index_t Hi = in_lengths[2];
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const ck::index_t Wi = in_lengths[3];
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const ck::index_t Do = out_lengths[1];
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const ck::index_t Ho = out_lengths[2];
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const ck::index_t Wo = out_lengths[3];
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const std::size_t in_mem_size = sizeof(InDataType) * N * Di * Hi * Wi * G * C;
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const std::size_t wei_mem_size = sizeof(WeiDataType) * G * K * Z * Y * X * C;
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const std::size_t out_mem_size = sizeof(OutDataType) * N * Do * Ho * Wo * G * K;
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const std::size_t bias_mem_size = sizeof(BiasDataType) * N * Do * Ho * Wo * G * K;
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SimpleDeviceMem in(in_mem_size);
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SimpleDeviceMem wei(wei_mem_size);
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SimpleDeviceMem out(out_mem_size);
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SimpleDeviceMem bias(bias_mem_size);
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float scale_in = float(std::rand()) / float(RAND_MAX);
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float scale_wei = float(std::rand()) / float(RAND_MAX);
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float scale_out = float(std::rand()) / float(RAND_MAX);
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// We have NDHWGC/GKZYXC/NDHWGK (x, weight, y) in memory space.
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// However, CK's API only accepts lengths and strides with order of GNCDHW/GKCZYX/GNKDHW.
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// Hence, we need to adjust the order of strides.
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const std::array<ck::index_t, NumDimSpatial + 3> input_lengths{G, N, C, Di, Hi, Wi};
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const std::array<ck::index_t, NumDimSpatial + 3> input_strides{
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C, Di * Hi * Wi * G * C, 1, Hi * Wi * G * C, Wi * G * C, G * C};
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const std::array<ck::index_t, NumDimSpatial + 3> weights_lengths{G, K, C, Z, Y, X};
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const std::array<ck::index_t, NumDimSpatial + 3> weights_strides{
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K * Z * Y * X * C, Z * Y * X * C, 1, Y * X * C, X * C, C};
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const std::array<ck::index_t, NumDimSpatial + 3> output_lengths{G, N, K, Do, Ho, Wo};
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const std::array<ck::index_t, NumDimSpatial + 3> output_strides{
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K, Do * Ho * Wo * G * K, 1, Ho * Wo * G * K, Wo * G * K, G * K};
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const std::array<ck::index_t, NumDimSpatial + 3> bias_lengths{G, N, K, Do, Ho, Wo};
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const std::array<ck::index_t, NumDimSpatial + 3> bias_strides{
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K, Do * Ho * Wo * G * K, 1, Ho * Wo * G * K, Wo * G * K, G * K};
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const std::array<ck::index_t, NumDimSpatial> conv_filter_strides{1, 1, 1};
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const std::array<ck::index_t, NumDimSpatial> conv_filter_dilations{1, 1, 1};
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const std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1, 1};
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const std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1, 1};
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std::size_t ds_size = 3 + 1; // 3 element-wise scale multipliers + 1 elementwise Bias
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std::size_t flop = GetFlops<NumDimSpatial>(output_lengths, weights_lengths, ds_size);
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std::size_t num_bytes = in_mem_size + wei_mem_size + sizeof(float) + sizeof(float) +
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sizeof(float) + out_mem_size + bias_mem_size;
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using DeviceOp =
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ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
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InLayout,
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WeiLayout,
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ck::Tuple<OutLayout>,
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OutLayout,
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InDataType,
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WeiDataType,
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ck::Tuple<BiasDataType>,
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OutDataType,
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PassThrough,
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PassThrough,
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ConvScaleAdd,
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AComputeType,
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BComputeType>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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std::string best_op_name;
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int best_op_id = -1;
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float best_avg_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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float best_tflops = 0;
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// profile device operation instances
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std::cout << "Run all instances and do timing" << std::endl;
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for(int i = 0; i < op_ptrs.size(); ++i)
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{
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auto& op_ptr = op_ptrs[i];
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auto argument_ptr = op_ptr->MakeArgumentPointer(
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in.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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std::array<const void*, 1>{bias.GetDeviceBuffer()},
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out.GetDeviceBuffer(),
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input_lengths,
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input_strides,
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weights_lengths,
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weights_strides,
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std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 1>{
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{bias_lengths}},
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std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 1>{
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{bias_strides}},
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output_lengths,
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output_strides,
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conv_filter_strides,
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conv_filter_dilations,
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input_left_pads,
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input_right_pads,
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PassThrough{},
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PassThrough{},
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ConvScaleAdd{scale_in, scale_wei, scale_out});
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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std::string op_name = op_ptr->GetTypeString();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
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float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
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float gb_per_sec = num_bytes / 1.E6 / avg_time;
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std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
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<< gb_per_sec << " GB/s, " << op_name << std::endl;
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if(tflops > best_tflops)
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{
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best_op_id = i;
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best_op_name = op_name;
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best_avg_time = avg_time;
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best_gb_per_sec = gb_per_sec;
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best_tflops = tflops;
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}
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}
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else
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{
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std::cerr << op_name << " does not support this problem" << std::endl;
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}
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}
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if(best_op_id < 0)
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{
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std::cerr << "no suitable instance" << std::endl;
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return false;
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}
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std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
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<< " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
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// run the best intance
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{
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auto& op_ptr = op_ptrs[best_op_id];
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std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
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<< std::endl;
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auto argument_ptr = op_ptr->MakeArgumentPointer(
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in.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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std::array<const void*, 1>{bias.GetDeviceBuffer()},
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out.GetDeviceBuffer(),
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input_lengths,
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input_strides,
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weights_lengths,
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weights_strides,
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std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 1>{
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{bias_lengths}},
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std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 1>{
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{bias_strides}},
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output_lengths,
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output_strides,
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conv_filter_strides,
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conv_filter_dilations,
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input_left_pads,
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input_right_pads,
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PassThrough{},
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PassThrough{},
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ConvScaleAdd{scale_in, scale_wei, scale_out});
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
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}
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std::cout << "Done" << std::endl;
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}
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return true;
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}
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@@ -0,0 +1,50 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#include "common.hpp"
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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using InDataType = ck::f8_t;
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using WeiDataType = ck::f8_t;
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using CShuffleDataType = float;
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using OutDataType = ck::f8_t;
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using AComputeDataType = ck::f8_t;
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using BComputeDataType = ck::f8_t;
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using InLayout = ck::tensor_layout::convolution::NDHWGC;
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using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
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using OutLayout = ck::tensor_layout::convolution::NDHWGK;
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static constexpr ck::index_t NumDimSpatial = 3;
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static constexpr ck::index_t G = 1;
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static constexpr ck::index_t N = 64;
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static constexpr ck::index_t K = 128;
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static constexpr ck::index_t C = 64;
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static constexpr ck::index_t Z = 3;
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static constexpr ck::index_t Y = 3;
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static constexpr ck::index_t X = 3;
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static constexpr ck::index_t Di = 28;
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static constexpr ck::index_t Hi = 28;
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static constexpr ck::index_t Wi = 3;
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static constexpr ck::index_t Do = 28;
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static constexpr ck::index_t Ho = 28;
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static constexpr ck::index_t Wo = 3;
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int main()
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{
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return run_grouped_conv_fwd_convscale_add<NumDimSpatial,
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InDataType,
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WeiDataType,
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OutDataType,
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InLayout,
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WeiLayout,
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OutLayout,
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3,
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AComputeDataType,
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BComputeDataType>(
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{N, Di, Hi, Wi, G, C}, {G, K, Z, Y, X, C}, {N, Do, Ho, Wo, G, K})
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? EXIT_SUCCESS
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: EXIT_FAILURE;
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}
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