275 lines
12 KiB
C++
275 lines
12 KiB
C++
/*******************************************************************************
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* Copyright (c) 2019-2020 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author Oleg Semeniv <oleg.semeniv@gmail.com>
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//
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//
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#include <ops/declarable/PlatformHelper.h>
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#include <ops/declarable/OpRegistrator.h>
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#include <system/platform_boilerplate.h>
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#include <helpers/MKLDNNStream.h>
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#include "mkldnnUtils.h"
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using namespace dnnl;
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namespace sd {
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namespace ops {
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namespace platforms {
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//////////////////////////////////////////////////////////////////////
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static void softmaxMKLDNN(const NDArray* x, NDArray* z, const int axis) {
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const auto xRank = x->rankOf();
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dnnl::memory::dims xShape, zShape;
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mkldnnUtils::getDims(x, xRank, xShape);
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mkldnnUtils::getDims(z, xRank, zShape);
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dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank);
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// optimized cases
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if (2 == xRank && 0 == axis) {
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format = dnnl::memory::format_tag::ba;
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}
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else if (4 == xRank && 1 == axis && (x->sizeAt(2) * x->sizeAt(3)) > 1) {
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format = dnnl::memory::format_tag::acdb;
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}
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dnnl::memory::data_type xType = dnnl::memory::data_type::f32;
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dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, xType, format);
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dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, xType, format);
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mkldnnUtils::setBlockStrides(x, x_user_md);
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// z
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dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zShape, xType, format);
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dnnl::memory::desc z_user_md = dnnl::memory::desc(zShape, xType, format);
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mkldnnUtils::setBlockStrides(z, z_user_md);
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auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
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// Create attributes (to handle alpha and beta if necessary)
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dnnl::primitive_attr attr; // it is empty since we have usual values for alpha (=1) and beta (=0)
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// operation primitive description
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dnnl::softmax_forward::desc op_desc(dnnl::prop_kind::forward_inference, x_mkl_md, axis);
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dnnl::softmax_forward::primitive_desc op_prim_desc(op_desc, attr, engine);
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// arguments (memory buffers) necessary for calculations
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std::unordered_map<int, dnnl::memory> args;
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dnnl::stream stream(engine);
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// provide memory buffers and check whether reorder is required
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// input
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mkldnnUtils::loadDataToMklStream(x, engine, stream, x_user_md, op_prim_desc.src_desc(), args[DNNL_ARG_SRC]);
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// z
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auto z_user_mem = dnnl::memory(z_user_md, engine, z->getBuffer());
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const bool zReorder = op_prim_desc.dst_desc() != z_user_mem.get_desc();
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auto z_mkl_mem = zReorder ? dnnl::memory(op_prim_desc.dst_desc(), engine) : z_user_mem;
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args[DNNL_ARG_DST] = z_mkl_mem;
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// run calculations
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dnnl::softmax_forward(op_prim_desc).execute(stream, args);
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// reorder outputs if necessary
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if (zReorder)
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dnnl::reorder(z_mkl_mem, z_user_mem).execute(stream, z_mkl_mem, z_user_mem);
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stream.wait();
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}
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PLATFORM_IMPL(softmax, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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const int rank = input->rankOf();
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int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : rank - 1;
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if (dim < 0) {
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dim += rank;
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}
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REQUIRE_TRUE(dim < rank && dim >= 0, 0, "SOFTMAX_MKLDNN OP: the value of input integer parameter (dimension) must be less than input array rank %i, but got dimension = %i instead !", rank, dim);
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REQUIRE_TRUE(rank <= 6, 0, "SOFTMAX_MKLDNN OP: the rank of input must be less or qual 6, but got rank = %i instead !", rank);
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// mkldnnSoftMax
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softmaxMKLDNN(input, output, dim);
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return Status::OK();
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}
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PLATFORM_CHECK(softmax, ENGINE_CPU) {
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auto x = INPUT_VARIABLE(0);
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auto z = OUTPUT_VARIABLE(0);
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const DataType xType = x->dataType();
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const DataType zType = z->dataType();
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const int xRank = x->rankOf();
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bool bSupportedRanks = (xRank > 2 && xRank < 7);
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/*
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Source Destination
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f32 f32
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*/
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return !x->isEmpty() && block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32);
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}
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//////////////////////////////////////////////////////////////////////
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static void softmaxBpMKLDNN(const NDArray* x, const NDArray* dLdz, NDArray* dLdx, const int axis) {
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const auto xRank = x->rankOf();
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const auto dLdzRank = dLdz->rankOf();
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dnnl::memory::dims xShape, dLdxShape, dLdzShape;
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mkldnnUtils::getDims(x, xRank, xShape);
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mkldnnUtils::getDims(dLdx, xRank, dLdxShape);
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mkldnnUtils::getDims(dLdz, dLdzRank, dLdzShape);
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dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank);
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// x
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dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
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dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
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mkldnnUtils::setBlockStrides(x, x_user_md);
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// dLdx
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dnnl::memory::desc dLdx_mkl_md = dnnl::memory::desc(dLdxShape, dnnl::memory::data_type::f32, format);
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dnnl::memory::desc dLdx_user_md = dnnl::memory::desc(dLdxShape, dnnl::memory::data_type::f32, format);
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mkldnnUtils::setBlockStrides(dLdx, dLdx_user_md);
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// todo if mkl does not support broadcast we can remove this
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format = mkldnnUtils::getFormat(dLdzRank);
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// dLdz
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dnnl::memory::desc dLdz_mkl_md = dnnl::memory::desc(dLdzShape, dnnl::memory::data_type::f32, format);
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dnnl::memory::desc dLdz_user_md = dnnl::memory::desc(dLdzShape, dnnl::memory::data_type::f32, format);
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mkldnnUtils::setBlockStrides(dLdz, dLdz_user_md);
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auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
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// operation primitive description
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// forward description
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dnnl::softmax_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference, x_mkl_md, axis);
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dnnl::softmax_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine);
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// backward description
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dnnl::softmax_backward::desc op_bp_desc(dLdz_mkl_md, dLdx_mkl_md, axis);
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dnnl::softmax_backward::primitive_desc op_bp_prim_desc(op_bp_desc, engine, op_ff_prim_desc);
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// arguments (memory buffers) necessary for calculations
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std::unordered_map<int, dnnl::memory> argsbp, argsff;
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dnnl::stream stream(engine);
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// provide memory buffers and check whether reorder is required for forward
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// input
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mkldnnUtils::loadDataToMklStream(x, engine, stream, x_user_md, op_ff_prim_desc.src_desc(), argsff[DNNL_ARG_SRC]);
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// dLdx
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auto dLdx_user_mem = dnnl::memory(dLdx_user_md, engine, dLdx->getBuffer());
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const bool dLdxReorder = op_ff_prim_desc.dst_desc() != dLdx_user_mem.get_desc();
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auto dLdx_mkl_mem = dLdxReorder ? dnnl::memory(op_ff_prim_desc.dst_desc(), engine) : dLdx_user_mem;
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argsff[DNNL_ARG_DST] = dLdx_mkl_mem;
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// check and arg set for backprob
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argsbp[DNNL_ARG_DIFF_SRC] = dLdx_mkl_mem;
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argsbp[DNNL_ARG_DST] = dLdx_mkl_mem;
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// dLdz
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mkldnnUtils::loadDataToMklStream(dLdz, engine, stream, dLdz_user_md, op_bp_prim_desc.diff_dst_desc(), argsbp[DNNL_ARG_DIFF_DST]);
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// run calculations forward
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dnnl::softmax_forward(op_ff_prim_desc).execute(stream, argsff);
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// run calculations backward
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dnnl::softmax_backward(op_bp_prim_desc).execute(stream, argsbp);
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// reorder outputs if necessary
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if (dLdxReorder)
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dnnl::reorder(dLdx_mkl_mem, dLdx_user_mem).execute(stream, dLdx_mkl_mem, dLdx_user_mem);
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stream.wait();
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}
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PLATFORM_IMPL(softmax_bp, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto dLdz = INPUT_VARIABLE(1);
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auto dLdx = OUTPUT_VARIABLE(0);
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const int rank = input->rankOf();
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const int dLdzRank = dLdz->rankOf();
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int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : rank - 1;
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if (dim < 0) {
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dim += rank;
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}
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REQUIRE_TRUE(dim < rank && dim >= 0, 0, "SOFTMAX_MKLDNN_BP OP: the value of input integer parameter (dimension) must be less than input array rank %i, but got dimension = %i instead !", rank, dim);
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REQUIRE_TRUE(rank <= 6 && dLdzRank <= 6, 0, "SOFTMAX_MKLDNN_BP OP: the rank of input and dLdz must be less or qual 6, but got input rank = %i and dLdz rank rank = %i instead !", rank, dLdzRank);
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// mkldnnSoftMax
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softmaxBpMKLDNN(input, dLdz, dLdx, dim);
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return Status::OK();
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}
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PLATFORM_CHECK(softmax_bp, ENGINE_CPU) {
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auto x = INPUT_VARIABLE(0);
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auto dLdz = INPUT_VARIABLE(1);
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auto dLdx = OUTPUT_VARIABLE(0);
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const DataType xType = x->dataType();
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const DataType dLdzType = dLdz->dataType();
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const DataType dLdxType = dLdx->dataType();
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const int xRank = x->rankOf();
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const int dLdzRank = dLdz->rankOf();
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bool bSupportedRanks = xRank < 7 && dLdzRank == xRank && (!x->isEmpty() && !dLdz->isEmpty());
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if (bSupportedRanks) {
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for (int i = 0; i < xRank; i++) {
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if (x->sizeAt(i) != dLdz->sizeAt(i)) {
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bSupportedRanks = false;
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break;
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}
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}
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}
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//Source Destination
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//f32 f32
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return block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && dLdzType == DataType::FLOAT32 && dLdxType == DataType::FLOAT32);
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}
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}
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}
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}
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