bf16A_Int8B with fastgelu/bias (#1264)

* changed the copy function to v7r2

* adding multi_abd

* in-progress

* add post-load oob check

* debugging

* adjust instances

* add run_lds

* add elemntwise_op

* replace multi_abd_device with v3

* clean up

* clean

* clean

* Added LDSType

* profiling

* adjust oobcheck

* add missing file

* refactor

* clean

* add examples

client_example/30_gemm_bf16Aint8B/CMakeLists.txt

@@ -10,4 +10,7 @@ if(GPU_TARGETS MATCHES "gfx9" AND ((DTYPES MATCHES "int8" AND DTYPES MATCHES "bf
 
 	add_executable(client_gemm_bf16_i8_bf16 gemm_xdl_bf16_i8.cpp)
 	target_link_libraries(client_gemm_bf16_i8_bf16 PRIVATE composable_kernel::device_gemm_operations)
+
+	add_executable(client_gemm_multiply_bf16_i8_bf16 gemm_xdl_multiply_bf16_i8.cpp)
+	target_link_libraries(client_gemm_multiply_bf16_i8_bf16 PRIVATE composable_kernel::device_gemm_operations)
 endif()

client_example/30_gemm_bf16Aint8B/gemm_bias_fastgelu_xdl_bf16_i8.cpp

@@ -38,19 +38,19 @@ using EDataType        = BF16;
 
 using A0Layout = Row;
 using AsLayout = ck::Tuple<A0Layout>;
-using B0Layout = Col;
+using B0Layout = Row;
 using B1Layout = B0Layout;
 using BsLayout = ck::Tuple<B0Layout, B1Layout>;
 using D0Layout = Row;
 using DsLayout = ck::Tuple<D0Layout>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = AddFastGelu;
 
 static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;

client_example/30_gemm_bf16Aint8B/gemm_bias_xdl_bf16_i8.cpp

@@ -36,7 +36,7 @@ using D0DataType       = BF16;
 using DsDataType       = ck::Tuple<D0DataType>;
 using EDataType        = BF16;
 
-using A0Layout = Col;
+using A0Layout = Row;
 using AsLayout = ck::Tuple<A0Layout>;
 using B0Layout = Row;
 using B1Layout = B0Layout;
@@ -45,12 +45,12 @@ using D0Layout = Row;
 using DsLayout = ck::Tuple<D0Layout>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using Add         = ck::tensor_operation::element_wise::Add;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = Add;
 
 static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;

client_example/30_gemm_bf16Aint8B/gemm_xdl_bf16_i8.cpp

@@ -37,19 +37,19 @@ using EDataType        = BF16;
 
 using A0Layout = Row;
 using AsLayout = ck::Tuple<A0Layout>;
-using B0Layout = Col;
+using B0Layout = Row;
 using B1Layout = B0Layout;
 using BsLayout = ck::Tuple<B0Layout, B1Layout>;
 using D0Layout = Row;
 using DsLayout = ck::Tuple<>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using Add         = ck::tensor_operation::element_wise::Add;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = PassThrough;
 
 static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
@@ -74,12 +74,12 @@ struct SimpleDeviceMem
 int main(int argc, char* argv[])
 {
     // GEMM shape
-    ck::index_t M = 64;
-    ck::index_t N = 1024;
-    ck::index_t K = 512;
+    ck::index_t M = 4096;
+    ck::index_t N = 768;
+    ck::index_t K = 6144;
 
     ck::index_t StrideA = K;
-    ck::index_t StrideB = N;
+    ck::index_t StrideB = K;
     ck::index_t StrideE = N;
 
     if(argc == 1)

client_example/30_gemm_bf16Aint8B/gemm_xdl_gelu_bf16_i8.cpp

@@ -37,19 +37,19 @@ using EDataType        = BF16;
 
 using A0Layout = Row;
 using AsLayout = ck::Tuple<A0Layout>;
-using B0Layout = Col;
+using B0Layout = Row;
 using B1Layout = B0Layout;
 using BsLayout = ck::Tuple<B0Layout, B1Layout>;
 using D0Layout = Row;
 using DsLayout = ck::Tuple<>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using FastGelu    = ck::tensor_operation::element_wise::FastGelu;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = FastGelu;
 
 static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;

client_example/30_gemm_bf16Aint8B/gemm_xdl_multiply_bf16_i8.cpp

@@ -0,0 +1,220 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <iomanip>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/device_gemm_multiple_abd.hpp"
+#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+
+#include "ck/library/tensor_operation_instance/gpu/gemm_multi_abd.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using BF16 = ck::bhalf_t;
+using I8   = int8_t;
+using F32  = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using A0DataType       = BF16;
+using AsDataType       = ck::Tuple<A0DataType>;
+using B0DataType       = I8;
+using B1DataType       = BF16;
+using BsDataType       = ck::Tuple<B0DataType>;
+using AccDataType      = F32;
+using CShuffleDataType = BF16;
+using DsDataType       = ck::Tuple<B1DataType>;
+using EDataType        = BF16;
+
+using A0Layout = Row;
+using AsLayout = ck::Tuple<A0Layout>;
+using B0Layout = Row;
+using B1Layout = B0Layout;
+using BsLayout = ck::Tuple<B0Layout>;
+using D0Layout = Row;
+using DsLayout = ck::Tuple<B1Layout>;
+using ELayout  = Row;
+
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+using CDEElementOp = Multiply;
+
+static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
+
+struct SimpleDeviceMem
+{
+    SimpleDeviceMem() = delete;
+
+    SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
+    {
+        (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
+    }
+
+    void* GetDeviceBuffer() { return p_mem_; }
+
+    ~SimpleDeviceMem() { (void)hipFree(p_mem_); }
+
+    void* p_mem_;
+};
+
+// clang-format on
+int main(int argc, char* argv[])
+{
+    // GEMM shape
+    ck::index_t M = 4096;
+    ck::index_t N = 768;
+    ck::index_t K = 6144;
+
+    ck::index_t StrideA = K;
+    ck::index_t StrideB = K;
+    ck::index_t StrideE = N;
+
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 7)
+    {
+        M = std::stoi(argv[1]);
+        N = std::stoi(argv[2]);
+        K = std::stoi(argv[3]);
+
+        StrideA = std::stoi(argv[4]);
+        StrideB = std::stoi(argv[5]);
+        StrideE = std::stoi(argv[6]);
+    }
+    else
+    {
+        printf("arg1 to 7: M, N, K, StrideA, StrideB, StrideE\n");
+        exit(0);
+    }
+
+    auto f_matrix_space_size =
+        [](std::size_t nRow, std::size_t nCol, std::size_t stride, auto layout) {
+            using Layout = decltype(layout);
+
+            if constexpr(std::is_same<Layout, Row>::value)
+            {
+                return (nRow - 1) * stride + nCol;
+            }
+            else
+            {
+                return (nCol - 1) * stride + nRow;
+            }
+        };
+
+    SimpleDeviceMem a0_device_buf(sizeof(A0DataType) *
+                                  f_matrix_space_size(M, K, StrideA, A0Layout{}));
+    SimpleDeviceMem b0_device_buf(sizeof(B0DataType) *
+                                  f_matrix_space_size(K, N, StrideB, B0Layout{}));
+    SimpleDeviceMem b1_device_buf(sizeof(B1DataType) * f_matrix_space_size(K, N, 0, B1Layout{}));
+    SimpleDeviceMem e_device_buf(sizeof(EDataType) * f_matrix_space_size(M, N, StrideE, ELayout{}));
+
+    auto a_element_op   = AElementOp{};
+    auto b_element_op   = BElementOp{};
+    auto cde_element_op = CDEElementOp{};
+
+    constexpr ck::index_t NumATensor = 1;
+    constexpr ck::index_t NumBTensor = 1;
+    constexpr ck::index_t NumDTensor = 1;
+
+    using DeviceOp = ck::tensor_operation::device::DeviceGemmMultipleABD<AsLayout,
+                                                                         BsLayout,
+                                                                         DsLayout,
+                                                                         Row,
+                                                                         AsDataType,
+                                                                         BsDataType,
+                                                                         DsDataType,
+                                                                         BF16,
+                                                                         AElementOp,
+                                                                         BElementOp,
+                                                                         CDEElementOp>;
+
+    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
+        DeviceOp>::GetInstances();
+
+    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
+
+    std::string best_op_name;
+    bool found            = false;
+    int best_op_id        = -1;
+    float best_ave_time   = 0;
+    float best_tflops     = 0;
+    float best_gb_per_sec = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr = op_ptrs[i];
+
+        auto argument_ptr = op_ptr->MakeArgumentPointer(
+            std::array<const void*, NumATensor>{a0_device_buf.GetDeviceBuffer()},
+            std::array<const void*, NumBTensor>{b0_device_buf.GetDeviceBuffer()},
+            std::array<const void*, NumDTensor>{b1_device_buf.GetDeviceBuffer()},
+            e_device_buf.GetDeviceBuffer(),
+            M,
+            N,
+            K,
+            std::array<ck::index_t, NumATensor>{StrideA},
+            std::array<ck::index_t, NumBTensor>{StrideB},
+            std::array<ck::index_t, NumDTensor>{0},
+            StrideE,
+            a_element_op,
+            b_element_op,
+            cde_element_op);
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            std::size_t flop = std::size_t(2) * M * N * K;
+
+            std::size_t num_btype =
+                sizeof(A0DataType) * M * K + sizeof(B0DataType) * K * N + sizeof(EDataType) * M * N;
+
+            float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+            float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+            std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                found           = true;
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_tflops     = tflops;
+                best_ave_time   = ave_time;
+                best_gb_per_sec = gb_per_sec;
+            }
+        }
+        else
+        {
+            std::cout << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
+              << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+    return 0;
+}

client_example/31_grouped_gemm_bf16Aint8B/grouped_gemm_bias_fastgelu_xdl_bf16_i8.cpp

@@ -38,19 +38,19 @@ using EDataType        = BF16;
 
 using A0Layout = Row;
 using AsLayout = ck::Tuple<A0Layout>;
-using B0Layout = Col;
+using B0Layout = Row;
 using B1Layout = B0Layout;
 using BsLayout = ck::Tuple<B0Layout, B1Layout>;
 using D0Layout = Row;
 using DsLayout = ck::Tuple<D0Layout>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = AddFastGelu;
 
 static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;

client_example/31_grouped_gemm_bf16Aint8B/grouped_gemm_fastgelu_xdl_bf16_i8.cpp

@@ -36,7 +36,7 @@ using D0DataType       = BF16;
 using DsDataType       = ck::Tuple<>;
 using EDataType        = BF16;
 
-using A0Layout = Col;
+using A0Layout = Row;
 using AsLayout = ck::Tuple<A0Layout>;
 using B0Layout = Row;
 using B1Layout = B0Layout;
@@ -45,12 +45,12 @@ using D0Layout = Row;
 using DsLayout = ck::Tuple<>;
 using ELayout  = Row;
 
-using Scales      = ck::tensor_operation::element_wise::Scales;
+using Multiply    = ck::tensor_operation::element_wise::Multiply;
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using FastGelu    = ck::tensor_operation::element_wise::FastGelu;
 
 using AElementOp   = PassThrough;
-using BElementOp   = Scales;
+using BElementOp   = Multiply;
 using CDEElementOp = FastGelu;
 
 static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;