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Add GemmAddSoftmaxGemm support for MSFT ORT (instances and client API) (#576)
* add instance for gemm bias softmax gemm
* add client example
* change CGridDesc_G_M_N to CGridDesc_G_M_O
* add gridwise
* change c grid name
* device add d0s data
* fix 08 client_example
* add example 47_fused_attention
* example output correct
* add d0 to example
* add d0 element op
* rechange instance code
* change Acc0ElementwiseOperation to C0DEElementwiseOperation
* change example name
* update instance for cdeelementwiseop
* add bhalf_t ScaleAdd
* add test
* not surport geem1 bias
* remove some ignore
* fix test bug
[ROCm/composable_kernel commit: 332ccc3367]
This commit is contained in:
ltqin
@@ -1,2 +1,5 @@
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add_executable(client_fused_attention fused_attention.cpp)
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target_link_libraries(client_fused_attention PRIVATE composable_kernel::device_operations)
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add_executable(client_fused_attention_bias fused_attention_bias.cpp)
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target_link_libraries(client_fused_attention_bias PRIVATE composable_kernel::device_operations)
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226
client_example/08_fused_attention/fused_attention_bias.cpp
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226
client_example/08_fused_attention/fused_attention_bias.cpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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#include <vector>
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#include "ck/ck.hpp"
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#include "ck/library/tensor_operation_instance/gpu/batched_gemm_bias_softmax_gemm_permute.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_batched_gemm_softmax_gemm_permute.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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using AElementOp = ck::tensor_operation::element_wise::PassThrough;
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using B0ElementOp = ck::tensor_operation::element_wise::PassThrough;
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using Acc0ElementOp = ck::tensor_operation::element_wise::ScaleAdd;
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using B1ElementOp = ck::tensor_operation::element_wise::PassThrough;
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using CElementOp = ck::tensor_operation::element_wise::PassThrough;
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constexpr static auto MaskingSpec =
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ck::tensor_operation::device::MaskingSpecialization::MaskDisabled;
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using ADataType = ck::half_t;
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using B0DataType = ck::half_t;
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using B1DataType = ck::half_t;
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using CDataType = ck::half_t;
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using D0DataType = ck::half_t;
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using AccDataType = float;
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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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int main(int argc, char* argv[])
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{
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int G0 = 48;
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int G1 = 16;
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int M = 1024;
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int N = 1024;
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int K = 64;
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int O = 64;
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// A layout [G0, M, G1, K]
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std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M, K};
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std::vector<ck::index_t> a_gs_ms_ks_strides{M * G1 * K, K, G1 * K, 1};
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// B0 layout [G0, N, G1, K]
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std::vector<ck::index_t> b0_gs_ns_ks_lengths{G0, G1, N, K};
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std::vector<ck::index_t> b0_gs_ns_ks_strides{N * G1 * K, K, G1 * K, 1};
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// B1 layout [G0, N, G1, O]
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std::vector<ck::index_t> b1_gs_os_ns_lengths{G0, G1, O, N};
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std::vector<ck::index_t> b1_gs_os_ns_strides{N * G1 * O, O, 1, G1 * O};
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// C layout [G0, M, G1, O]
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std::vector<ck::index_t> c_gs_ms_os_lengths{G0, G1, M, O};
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std::vector<ck::index_t> c_gs_ms_os_strides{M * G1 * O, O, G1 * O, 1};
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// D layout [G0, M, G1, N]
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std::vector<ck::index_t> d0_gs_ms_ns_lengths{G0, G1, M, N};
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std::vector<ck::index_t> d0_gs_ms_ns_strides{M * G1 * N, N, G1 * N, 1};
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SimpleDeviceMem a_device_buf(sizeof(ADataType) * G0 * G1 * M * K);
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SimpleDeviceMem b0_device_buf(sizeof(B0DataType) * G0 * G1 * N * K);
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SimpleDeviceMem d0_device_buf(sizeof(D0DataType) * G0 * G1 * M * N);
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SimpleDeviceMem b1_device_buf(sizeof(B1DataType) * G0 * G1 * O * N);
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SimpleDeviceMem c_device_buf(sizeof(CDataType) * G0 * G1 * M * O);
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using DeviceOp =
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ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute<2,
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1,
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1,
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1,
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1,
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ADataType,
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B0DataType,
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B1DataType,
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CDataType,
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ck::Tuple<D0DataType>,
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ck::Tuple<>,
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AElementOp,
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B0ElementOp,
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Acc0ElementOp,
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B1ElementOp,
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CElementOp,
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MaskingSpec>;
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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_ave_time = 0;
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float best_tflops = 0;
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float best_gb_per_sec = 0;
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// profile device op 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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a_device_buf.GetDeviceBuffer(),
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b0_device_buf.GetDeviceBuffer(),
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b1_device_buf.GetDeviceBuffer(),
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c_device_buf.GetDeviceBuffer(),
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std::array<void*, 1>{d0_device_buf.GetDeviceBuffer()}, // p_acc0_biases
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{}, // p_acc1_biases
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a_gs_ms_ks_lengths,
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a_gs_ms_ks_strides,
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b0_gs_ns_ks_lengths,
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b0_gs_ns_ks_strides,
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b1_gs_os_ns_lengths,
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b1_gs_os_ns_strides,
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c_gs_ms_os_lengths,
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c_gs_ms_os_strides,
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std::array<std::vector<ck::index_t>, 1>{
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d0_gs_ms_ns_lengths}, // acc0_biases_gs_ms_ns_lengths
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std::array<std::vector<ck::index_t>, 1>{
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d0_gs_ms_ns_strides}, // acc0_biases_gs_ms_ns_strides
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{}, // acc1_biases_gs_ms_os_lengths
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{}, // acc1_biases_gs_ms_os_strides
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AElementOp{},
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B0ElementOp{},
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Acc0ElementOp{1 / sqrtf(K)},
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B1ElementOp{},
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CElementOp{});
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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 ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
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std::size_t flop = (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * G0 * G1;
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std::size_t num_btype = (sizeof(ADataType) * M * K + sizeof(B0DataType) * K * N +
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sizeof(B1DataType) * N * O + sizeof(CDataType) * M * O +
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sizeof(D0DataType) * M * N) *
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G0 * G1;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
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<< " 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_tflops = tflops;
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best_ave_time = ave_time;
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best_gb_per_sec = gb_per_sec;
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}
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}
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else
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{
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std::cout << op_name << " does not support this problem" << std::endl;
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}
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}
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std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
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<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
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// run the best instance
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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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a_device_buf.GetDeviceBuffer(),
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b0_device_buf.GetDeviceBuffer(),
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b1_device_buf.GetDeviceBuffer(),
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c_device_buf.GetDeviceBuffer(),
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std::array<void*, 1>{d0_device_buf.GetDeviceBuffer()}, // p_acc0_biases
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{}, // p_acc1_biases
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a_gs_ms_ks_lengths,
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a_gs_ms_ks_strides,
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b0_gs_ns_ks_lengths,
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b0_gs_ns_ks_strides,
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b1_gs_os_ns_lengths,
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b1_gs_os_ns_strides,
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c_gs_ms_os_lengths,
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c_gs_ms_os_strides,
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std::array<std::vector<ck::index_t>, 1>{
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d0_gs_ms_ns_lengths}, // acc0_biases_gs_ms_ns_lengths
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std::array<std::vector<ck::index_t>, 1>{
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d0_gs_ms_ns_strides}, // acc0_biases_gs_ms_ns_strides
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{}, // acc1_biases_gs_ms_os_lengths
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{}, // acc1_biases_gs_ms_os_strides
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AElementOp{},
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B0ElementOp{},
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Acc0ElementOp{1 / sqrtf(K)},
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B1ElementOp{},
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CElementOp{});
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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 0;
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}
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