chore: import upstream snapshot with attribution
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wehub-resource-sync
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/*
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* ******************************************************************************
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* *
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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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* * See the NOTICE file distributed with this work for additional
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* * information regarding copyright ownership.
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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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//
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// @author Oleg Semeniv <oleg.semeniv@gmail.com>
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//
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//
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#include <helpers/MKLDNNStream.h>
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#include <ops/declarable/OpRegistrator.h>
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#include <ops/declarable/PlatformHelper.h>
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#include <system/platform_boilerplate.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(NDArray* x, NDArray* z, const sd::LongType axis) {
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dnnl::memory::dims shape = x->getShapeAsFlatVector();
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const sd::LongType xRank = x->rankOf();
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dnnl::memory::format_tag xFormat = onednnUtils::getFormat(*x);
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dnnl::memory::format_tag zFormat = onednnUtils::getFormat(*z);
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// optimized cases
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if (2 == xRank && 0 == axis) {
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xFormat = dnnl::memory::format_tag::ba;
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zFormat = dnnl::memory::format_tag::ba;
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} else if (4 == xRank && 1 == axis && (x->sizeAt(2) * x->sizeAt(3)) > 1) {
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xFormat = dnnl::memory::format_tag::acdb;
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zFormat = 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, x_user_md, z_mkl_md, z_user_md;
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x_user_md = x_mkl_md = dnnl::memory::desc(shape, xType, xFormat);
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onednnUtils::setBlockStrides(*x, x_user_md);
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// z
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z_user_md = z_mkl_md = dnnl::memory::desc(shape, xType, zFormat);
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onednnUtils::setBlockStrides(*z, z_user_md);
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auto engine = onednnUtils::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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onednnUtils::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 =
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onednnUtils::loadDataToMklStream(*z, engine, stream, z_user_md, op_prim_desc.dst_desc(), args[DNNL_ARG_DST]);
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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 (op_prim_desc.dst_desc() != z_user_mem.get_desc())
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dnnl::reorder(args[DNNL_ARG_DST], z_user_mem).execute(stream, args[DNNL_ARG_DST], 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,
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"SOFTMAX_MKLDNN OP: the value of input integer parameter (dimension) must be less than input array rank "
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"%i, but got dimension = %i instead !",
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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 !",
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rank);
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// mkldnnSoftMax
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softmaxMKLDNN(input, output, dim);
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return sd::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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Requirements req("ONEDNN SOFTMAX OP");
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req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) &&
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req.expectFalse(makeInfoVariable(x->isEmpty(), IS_EMPTY_MSG_INPUT), EXPECTED_FALSE) &&
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req.expectLess(makeInfoVariable(x->rankOf(), RANK_MSG_INPUT), 7) &&
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req.expectGreater(makeInfoVariable(x->rankOf(), RANK_MSG_INPUT), 2) &&
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req.expectEq(makeInfoVariable(x->dataType(), TYPE_MSG_INPUT), DataType::FLOAT32) &&
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req.expectEq(makeInfoVariable(z->dataType(), TYPE_MSG_OUTPUT), DataType::FLOAT32);
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req.logTheSuccess();
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return req;
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}
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//////////////////////////////////////////////////////////////////////
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static void softmaxBpMKLDNN(NDArray* x, NDArray* dLdz, NDArray* dLdx, const sd::LongType axis) {
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dnnl::memory::desc x_user_md, x_mkl_md, dLdx_mkl_md, dLdx_user_md, dLdz_mkl_md, dLdz_user_md;
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// x
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x_mkl_md = x_user_md =
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dnnl::memory::desc(x->getShapeAsFlatVector(), dnnl::memory::data_type::f32, onednnUtils::getFormat(*x));
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onednnUtils::setBlockStrides(*x, x_user_md);
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// dLdx
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dLdx_mkl_md = dLdx_user_md =
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dnnl::memory::desc(dLdx->getShapeAsFlatVector(), dnnl::memory::data_type::f32, onednnUtils::getFormat(*dLdx));
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onednnUtils::setBlockStrides(*dLdx, dLdx_user_md);
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// dLdz
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dLdz_mkl_md = dLdz_user_md =
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dnnl::memory::desc(dLdz->getShapeAsFlatVector(), dnnl::memory::data_type::f32, onednnUtils::getFormat(*dLdz));
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onednnUtils::setBlockStrides(*dLdz, dLdz_user_md);
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auto engine = onednnUtils::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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onednnUtils::loadDataToMklStream(*x, engine, stream, x_user_md, op_ff_prim_desc.src_desc(), argsff[DNNL_ARG_SRC]);
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// dLdz
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onednnUtils::loadDataToMklStream(*dLdz, engine, stream, dLdz_user_md, op_bp_prim_desc.diff_dst_desc(),
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argsbp[DNNL_ARG_DIFF_DST]);
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// dLdx
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auto dLdx_user_mem = onednnUtils::loadDataToMklStream(*dLdx, engine, stream, dLdx_user_md, op_ff_prim_desc.src_desc(),
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argsff[DNNL_ARG_DST]);
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// check and arg set for backprob
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argsbp[DNNL_ARG_DIFF_SRC] = argsff[DNNL_ARG_DST];
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argsbp[DNNL_ARG_DST] = argsff[DNNL_ARG_DST];
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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 (op_ff_prim_desc.dst_desc() != dLdx_user_mem.get_desc())
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dnnl::reorder(argsff[DNNL_ARG_DST], dLdx_user_mem).execute(stream, argsff[DNNL_ARG_DST], 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 softmaxOutput = INPUT_VARIABLE(2);
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auto dLdx = OUTPUT_VARIABLE(0);
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dLdx->assign(softmaxOutput);
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const sd::LongType rank = input->rankOf();
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const sd::LongType 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,
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"SOFTMAX_MKLDNN_BP OP: the value of input integer parameter (dimension) must be less than input array "
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"rank %i, but got dimension = %i instead !",
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rank, dim);
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REQUIRE_TRUE(rank <= 6 && dLdzRank <= 6, 0,
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"SOFTMAX_MKLDNN_BP OP: the rank of input and dLdz must be less or qual 6, but got input rank = %i and "
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"dLdz rank rank = %i instead !",
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rank, dLdzRank);
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// mkldnnSoftMax
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softmaxBpMKLDNN(input, dLdz, dLdx, dim);
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return sd::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 softmaxOutput = INPUT_VARIABLE(2);
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auto dLdx = OUTPUT_VARIABLE(0);
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dLdx->assign(softmaxOutput);
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Requirements req("ONEDNN SOFTMAX_BP OP");
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req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) &&
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req.expectFalse(makeInfoVariable(x->isEmpty(), IS_EMPTY_MSG_INPUT0), EXPECTED_FALSE) &&
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req.expectFalse(makeInfoVariable(dLdz->isEmpty(), IS_EMPTY_MSG_INPUT1), EXPECTED_FALSE) &&
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req.expectLess(makeInfoVariable(x->rankOf(), RANK_MSG_INPUT0), 7) &&
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req.expectEq(makeInfoVariable(x->rankOf(), RANK_MSG_INPUT0), makeInfoVariable(dLdz->rankOf(), RANK_MSG_INPUT1)) &&
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req.expectGreater(makeInfoVariable(x->rankOf(), RANK_MSG_INPUT), 0) &&
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req.expectEq(makeInfoVariable(x->dataType(), TYPE_MSG_INPUT0), DataType::FLOAT32) &&
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req.expectEq(makeInfoVariable(dLdz->dataType(), TYPE_MSG_INPUT1), DataType::FLOAT32) &&
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req.expectEq(makeInfoVariable(dLdx->dataType(), TYPE_MSG_OUTPUT), DataType::FLOAT32) &&
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req.expect(
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makeShapeInfoVariable(x, SHAPE_MSG_INPUT0), makeShapeInfoVariable(dLdz, SHAPE_MSG_INPUT1),
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[](const decltype(x)& l, const decltype(dLdz)& r) {
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for (int i = 0; i < l->rankOf(); i++) {
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if (l->sizeAt(i) != r->sizeAt(i)) {
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return false;
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}
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}
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return true;
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},
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EXPECTED_EQ_MSG);
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req.logTheSuccess();
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return req;
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}
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} // namespace platforms
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} // namespace ops
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} // namespace sd
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