refactor(megdnn): remove unnessary 1x1 algo

GitOrigin-RevId: 809f9b2bba

dnn/src/cuda/conv_bias/1x1.cpp

@@ -1,113 +0,0 @@
-/**
- * \file dnn/src/cuda/conv_bias/1x1.cpp
- * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
- *
- * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- */
-
-#include "src/common/conv_bias.h"
-#include "src/cuda/conv_bias/algo.h"
-#include "src/cuda/handle.h"
-#include "src/cuda/utils.cuh"
-
-using namespace megdnn;
-using namespace cuda;
-using namespace conv_bias;
-
-bool ConvBiasForwardImpl::Algo1x1::is_available(const SizeArgs& args) const {
-    if (args.z_layout->ndim > 0)
-        return false;
-
-    auto&& fm = args.filter_meta;
-    return fm.format == Param::Format::NCHW &&
-          (fm.dtype.enumv() == DTypeEnum::Float32 ||
-           fm.dtype.enumv() == DTypeEnum::Float16) &&
-          fm.spatial_ndim == 2 && fm.group == 1 && fm.dilation[0] == 1 &&
-          fm.dilation[1] == 1 && fm.spatial[0] == 1 && fm.spatial[1] == 1 &&
-          fm.padding[0] == 0 && fm.padding[1] == 0 && fm.stride[0] == 1 &&
-          fm.stride[1] == 1;
-}
-
-void ConvBiasForwardImpl::Algo1x1::extract_matmul_layouts(const SizeArgs& args,
-                                                          TensorLayout& A,
-                                                          TensorLayout& B,
-                                                          TensorLayout& C) {
-    auto&& fm = args.filter_meta;
-    A = {{fm.ocpg, fm.icpg}, fm.dtype};
-    B.ndim = 2;
-    B.shape[0] = args.src_layout->shape[1];
-    B.shape[1] = args.src_layout->shape[2] * args.src_layout->shape[3];
-    B.stride[0] = args.src_layout->stride[1];
-    B.stride[1] = 1;
-    B.dtype = args.src_layout->dtype;
-    C = {{args.dst_layout->shape[1], B.shape[1]}, args.dst_layout->dtype};
-}
-
-WorkspaceBundle ConvBiasForwardImpl::Algo1x1::get_workspace_bundle(
-        void* ptr, const SizeArgs& args) const {
-    auto dst_layout = *args.dst_layout;
-    SmallVector<size_t> sizes;
-    if (dst_layout.dtype.enumv() != args.bias_layout->dtype.enumv()) {
-        dst_layout.dtype = DType();
-        args.opr->check_or_deduce_dtype_fwd(args.src_layout->dtype,
-                                            args.filter_layout->dtype,
-                                            dst_layout.dtype);
-        sizes.push_back(dst_layout.span().dist_byte());
-    }
-
-    SizeArgs conv_args = args;
-    conv_args.dst_layout = &dst_layout;
-    TensorLayout A, B, C;
-    extract_matmul_layouts(conv_args, A, B, C);
-    sizes.insert(sizes.begin(),
-                 args.handle->matmul_opr()->get_workspace_in_bytes(A, B, C));
-    return {ptr, std::move(sizes)};
-}
-
-size_t ConvBiasForwardImpl::Algo1x1::get_workspace_in_bytes(
-        const SizeArgs& args) const {
-    return get_workspace_bundle(nullptr, args).total_size_in_bytes();
-}
-
-void ConvBiasForwardImpl::Algo1x1::exec(const ExecArgs& args) const {
-    auto bundle = get_workspace_bundle(args.workspace.raw_ptr, args);
-    auto conv_dst_tensor = *args.dst_tensor;
-    if (args.dst_layout->dtype.enumv() != args.bias_layout->dtype.enumv()) {
-        conv_dst_tensor.raw_ptr = bundle.get(1);
-        conv_dst_tensor.layout.dtype = DType();
-        args.opr->check_or_deduce_dtype_fwd(args.src_layout->dtype,
-                                            args.filter_layout->dtype,
-                                            conv_dst_tensor.layout.dtype);
-    }
-
-    ExecArgs conv_args = args;
-    conv_args.dst_tensor = &conv_dst_tensor;
-    conv_args.dst_layout = &conv_dst_tensor.layout;
-    {
-        TensorND A, B, C;
-        extract_matmul_layouts(conv_args, A.layout, B.layout, C.layout);
-        A.raw_ptr = conv_args.filter_tensor->raw_ptr;
-        B.raw_ptr = conv_args.src_tensor->raw_ptr;
-        C.raw_ptr = conv_args.dst_tensor->raw_ptr;
-        size_t batch = conv_args.src_layout->shape[0];
-        auto mm = conv_args.handle->matmul_opr();
-        auto strd_B = conv_args.src_layout->stride[0] *
-                      conv_args.src_layout->dtype.size(),
-             strd_C = conv_args.dst_layout->stride[0] *
-                      conv_args.dst_layout->dtype.size();
-        for (size_t i = 0; i < batch; ++i) {
-            mm->exec(A, B, C, bundle.get_workspace(0));
-            incr_voidp(B.raw_ptr, strd_B);
-            incr_voidp(C.raw_ptr, strd_C);
-        }
-    }
-    handle_bias_and_nonlinear(args.handle, args.nonlinear_mode,
-                              &conv_dst_tensor, args.dst_tensor,
-                              args.bias_tensor);
-}
-
-// vim: syntax=cpp.doxygen

dnn/src/cuda/conv_bias/algo.cpp

@@ -24,7 +24,6 @@ ConvBiasForwardImpl::AlgoPack::AlgoPack() {
     non_cudnn_algos.push_back(&matmul);
     non_cudnn_algos.push_back(&matmul8x8x32);
     non_cudnn_algos.push_back(&batched_matmul);
-    non_cudnn_algos.push_back(&a1x1);
 
     fill_cudnn_algos();
     for (auto&& algo : cudnn_conv_bias_activations) {
@@ -43,7 +42,6 @@ ConvBiasForwardImpl::AlgoPack::AlgoPack() {
     conv_algos.push_back(&matmul);
     conv_algos.push_back(&matmul8x8x32);
     conv_algos.push_back(&batched_matmul);
-    conv_algos.push_back(&a1x1);
 
     conv_algos.reserve(conv_algos.size() * 2);
     //! add gconv algos by AlgoGroupConvGeneral

dnn/src/cuda/conv_bias/algo.h

@@ -51,7 +51,6 @@ public:
         CUDA_INPLACE_MATMUL,
         CUDA_MATMUL,
         CUDA_MATMUL_INT8X8X32,
-        CUDA_1X1,
         CUDA_BATCHED_MATMUL,
         CUDA_GROUP_CONV_GENERAL,
         CUDA_WMMA_UINT4X4X32,
@@ -358,31 +357,6 @@ private:
     mutable std::string m_name;
 };
 
-//! optimized 1x1 conv
-class ConvBiasForwardImpl::Algo1x1 final : public AlgoBase {
-    static void extract_matmul_layouts(const SizeArgs& args, TensorLayout& A,
-                                       TensorLayout& B, TensorLayout& C);
-
-public:
-    bool is_available(const SizeArgs& args) const override;
-    size_t get_workspace_in_bytes(const SizeArgs& args) const override;
-    void exec(const ExecArgs& args) const override;
-
-    const char* name() const override {
-        if (m_name.empty()) {
-            m_name = ConvBiasForward::algo_name<ConvBiasForward::MatmulParam>(
-                    "MATMUL1X1", {});
-        }
-        return m_name.c_str();
-    }
-    bool is_reproducible() const override { return true; }
-    MEGDNN_DECL_ALGO_TYPE(CUDA_1X1)
-
-private:
-    WorkspaceBundle get_workspace_bundle(void* ptr, const SizeArgs& args) const;
-    mutable std::string m_name;
-};
-
 class ConvBiasForwardImpl::AlgoBatchedMatmul final : public AlgoBase {
     static void extract_matmul_layouts(const SizeArgs& args, TensorLayout& A,
                                        TensorLayout& B, TensorLayout& C);
@@ -738,7 +712,6 @@ public:
     AlgoMatmul matmul;
     AlgoMatmul8x8x32 matmul8x8x32;
     AlgoBatchedMatmul batched_matmul;
-    Algo1x1 a1x1;
     std::vector<AlgoInt8NCHW4DotProdImplicitGemm> int8_nchw4_dotprod;
     AlgoInt8CHWN4DotProdImplicitGemm int8_chwn4_dotprod;
 #if CUDA_VERSION >= 10000

dnn/src/cuda/conv_bias/opr_impl.cpp

@@ -118,9 +118,6 @@ ConvBiasForward::Algorithm* ConvBiasForwardImpl::get_algorithm_heuristic(
         if (sm_algo_pack.batched_matmul.is_available_reproducible(
                     size_arg, reproducible, workspace_limit_in_bytes)) {
             return &sm_algo_pack.batched_matmul;
-        } else if (sm_algo_pack.a1x1.is_available_reproducible(
-                           size_arg, reproducible, workspace_limit_in_bytes)) {
-            return &sm_algo_pack.a1x1;
         }
         return nullptr;
     };
@@ -178,12 +175,6 @@ ConvBiasForward::Algorithm* ConvBiasForwardImpl::get_algorithm_heuristic(
             return algo;
     }
 
-    int batch = src[0];
-    if (batch == 1 && sm_algo_pack.a1x1.is_available_reproducible(
-                              args, reproducible, workspace_limit_in_bytes)) {
-        return &sm_algo_pack.a1x1;
-    }
-
     // modify conv_args dst_layout
     conv_args.dst_layout = &dst_layout;
     if (is_cudnn_supported(conv_args)) {

dnn/test/cuda/conv_bias.cpp

@@ -834,12 +834,6 @@ TEST_F(CUDA, CONV_BIAS_FORWARD_MATMUL_1x1) {
             .set_epsilon(1e-3);
     for (auto&& arg : args) {
         checker.set_param(arg.param);
-        checker.set_before_exec_callback(
-                conv_bias::ConvBiasAlgoChecker<ConvBias>(
-                        ConvBiasForward::algo_name<
-                                ConvBiasForward::MatmulParam>("MATMUL1X1", {})
-                                .c_str()));
-        checker.execs({arg.src, arg.filter, arg.bias, {}, {}});
         checker.set_before_exec_callback(conv_bias::ConvBiasAlgoChecker<
                                          ConvBias>(
                 ConvBiasForward::algo_name<ConvBiasForward::MatmulParam>(

dnn/test/cuda/group_conv.cpp

@@ -115,7 +115,7 @@ TEST_F(CUDA, GROUP_CONV_FORWARD_1x1) {
 #if CUDNN_MAJOR <= 6
         std::string conv1x1_name =
                 ConvBiasForward::algo_name<ConvBiasForward::MatmulParam>(
-                        "MATMUL1X1", {});
+                        "BATCHEDMATMUL", {});
         checker.set_before_exec_callback(
                 AlgoChecker<ConvolutionForward>(ExecutionPolicyAlgoName{
                         "DEFAULT",