libnd4j/include/ops/declarable/platform/mkldnn/softmax.cpp
/*
* ******************************************************************************
* *
* *
* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* * License for the specific language governing permissions and limitations
* * under the License.
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* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
//
// @author Oleg Semeniv <oleg.semeniv@gmail.com>
//
//
#include <helpers/MKLDNNStream.h>
#include <ops/declarable/OpRegistrator.h>
#include <ops/declarable/PlatformHelper.h>
#include <system/platform_boilerplate.h>
#include "mkldnnUtils.h"
using namespace dnnl;
namespace sd {
namespace ops {
namespace platforms {
//////////////////////////////////////////////////////////////////////
static void softmaxMKLDNN(const NDArray* x, NDArray* z, const sd::LongType axis) {
dnnl::memory::dims shape = x->getShapeAsFlatVector();
const sd::LongType xRank = x->rankOf();
dnnl::memory::format_tag xFormat = onednnUtils::getFormat(*x);
dnnl::memory::format_tag zFormat = onednnUtils::getFormat(*z);
// optimized cases
if (2 == xRank && 0 == axis) {
if (x->ews() == 1) xFormat = dnnl::memory::format_tag::ba;
if (z->ews() == 1) zFormat = dnnl::memory::format_tag::ba;
} else if (4 == xRank && 1 == axis && (x->sizeAt(2) * x->sizeAt(3)) > 1) {
if (x->ews() == 1) xFormat = dnnl::memory::format_tag::acdb;
if (z->ews() == 1) zFormat = dnnl::memory::format_tag::acdb;
}
dnnl::memory::data_type xType = dnnl::memory::data_type::f32;
dnnl::memory::desc x_mkl_md, x_user_md, z_mkl_md, z_user_md;
x_user_md = x_mkl_md = dnnl::memory::desc(shape, xType, xFormat);
onednnUtils::setBlockStrides(*x, x_user_md);
// z
z_user_md = z_mkl_md = dnnl::memory::desc(shape, xType, zFormat);
onednnUtils::setBlockStrides(*z, z_user_md);
auto engine = onednnUtils::getEngine(LaunchContext::defaultContext()->engine());
// Create attributes (to handle alpha and beta if necessary)
dnnl::primitive_attr attr; // it is empty since we have usual values for alpha (=1) and beta (=0)
// operation primitive description
dnnl::softmax_forward::desc op_desc(dnnl::prop_kind::forward_inference, x_mkl_md, axis);
dnnl::softmax_forward::primitive_desc op_prim_desc(op_desc, attr, engine);
// arguments (memory buffers) necessary for calculations
std::unordered_map<int, dnnl::memory> args;
dnnl::stream stream(engine);
// provide memory buffers and check whether reorder is required
// input
onednnUtils::loadDataToMklStream(*x, engine, stream, x_user_md, op_prim_desc.src_desc(), args[DNNL_ARG_SRC]);
// z
auto z_user_mem =
onednnUtils::loadDataToMklStream(*z, engine, stream, z_user_md, op_prim_desc.dst_desc(), args[DNNL_ARG_DST]);
// run calculations
dnnl::softmax_forward(op_prim_desc).execute(stream, args);
// reorder outputs if necessary
if (op_prim_desc.dst_desc() != z_user_mem.get_desc())
dnnl::reorder(args[DNNL_ARG_DST], z_user_mem).execute(stream, args[DNNL_ARG_DST], z_user_mem);
stream.wait();
}
PLATFORM_IMPL(softmax, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
const int rank = input->rankOf();
int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : rank - 1;
if (dim < 0) {
dim += rank;
}
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);
REQUIRE_TRUE(rank <= 6, 0, "SOFTMAX_MKLDNN OP: the rank of input must be less or qual 6, but got rank = %i instead !",
rank);
// mkldnnSoftMax
softmaxMKLDNN(input, output, dim);
return sd::Status::OK;
}
PLATFORM_CHECK(softmax, ENGINE_CPU) {
auto x = INPUT_VARIABLE(0);
auto z = OUTPUT_VARIABLE(0);
Requirements req("ONEDNN SOFTMAX OP");
req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) &&
req.expectFalse(makeInfoVariable(x->isEmpty(), IS_EMPTY_MSG_INPUT), EXPECTED_FALSE) &&
req.expectLess(makeInfoVariable(x->rankOf(), RANK_MSG_INPUT), 7) &&
req.expectGreater(makeInfoVariable(x->rankOf(), RANK_MSG_INPUT), 2) &&
req.expectEq(makeInfoVariable(x->dataType(), TYPE_MSG_INPUT), DataType::FLOAT32) &&
req.expectEq(makeInfoVariable(z->dataType(), TYPE_MSG_OUTPUT), DataType::FLOAT32);
req.logTheSuccess();
return req;
}
//////////////////////////////////////////////////////////////////////
static void softmaxBpMKLDNN(const NDArray* x, const NDArray* dLdz, NDArray* dLdx, const sd::LongType axis) {
dnnl::memory::desc x_user_md, x_mkl_md, dLdx_mkl_md, dLdx_user_md, dLdz_mkl_md, dLdz_user_md;
// x
x_mkl_md = x_user_md =
dnnl::memory::desc(x->getShapeAsFlatVector(), dnnl::memory::data_type::f32, onednnUtils::getFormat(*x));
onednnUtils::setBlockStrides(*x, x_user_md);
// dLdx
dLdx_mkl_md = dLdx_user_md =
dnnl::memory::desc(dLdx->getShapeAsFlatVector(), dnnl::memory::data_type::f32, onednnUtils::getFormat(*dLdx));
onednnUtils::setBlockStrides(*dLdx, dLdx_user_md);
// dLdz
dLdz_mkl_md = dLdz_user_md =
dnnl::memory::desc(dLdz->getShapeAsFlatVector(), dnnl::memory::data_type::f32, onednnUtils::getFormat(*dLdz));
onednnUtils::setBlockStrides(*dLdz, dLdz_user_md);
auto engine = onednnUtils::getEngine(LaunchContext::defaultContext()->engine());
// operation primitive description
// forward description
dnnl::softmax_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference, x_mkl_md, axis);
dnnl::softmax_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine);
// backward description
dnnl::softmax_backward::desc op_bp_desc(dLdz_mkl_md, dLdx_mkl_md, axis);
dnnl::softmax_backward::primitive_desc op_bp_prim_desc(op_bp_desc, engine, op_ff_prim_desc);
// arguments (memory buffers) necessary for calculations
std::unordered_map<int, dnnl::memory> argsbp, argsff;
dnnl::stream stream(engine);
// provide memory buffers and check whether reorder is required for forward
// input
onednnUtils::loadDataToMklStream(*x, engine, stream, x_user_md, op_ff_prim_desc.src_desc(), argsff[DNNL_ARG_SRC]);
// dLdz
onednnUtils::loadDataToMklStream(*dLdz, engine, stream, dLdz_user_md, op_bp_prim_desc.diff_dst_desc(),
argsbp[DNNL_ARG_DIFF_DST]);
// dLdx
auto dLdx_user_mem = onednnUtils::loadDataToMklStream(*dLdx, engine, stream, dLdx_user_md, op_ff_prim_desc.src_desc(),
argsff[DNNL_ARG_DST]);
// check and arg set for backprob
argsbp[DNNL_ARG_DIFF_SRC] = argsff[DNNL_ARG_DST];
argsbp[DNNL_ARG_DST] = argsff[DNNL_ARG_DST];
// run calculations backward
dnnl::softmax_backward(op_bp_prim_desc).execute(stream, argsbp);
// reorder outputs if necessary
if (op_ff_prim_desc.dst_desc() != dLdx_user_mem.get_desc())
dnnl::reorder(argsff[DNNL_ARG_DST], dLdx_user_mem).execute(stream, argsff[DNNL_ARG_DST], dLdx_user_mem);
stream.wait();
}
PLATFORM_IMPL(softmax_bp, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0);
auto dLdz = INPUT_VARIABLE(1);
auto softmaxOutput = INPUT_VARIABLE(2);
auto dLdx = OUTPUT_VARIABLE(0);
dLdx->assign(softmaxOutput);
const sd::LongType rank = input->rankOf();
const sd::LongType dLdzRank = dLdz->rankOf();
int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : rank - 1;
if (dim < 0) {
dim += rank;
}
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);
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);
// mkldnnSoftMax
softmaxBpMKLDNN(input, dLdz, dLdx, dim);
return sd::Status::OK;
}
PLATFORM_CHECK(softmax_bp, ENGINE_CPU) {
auto x = INPUT_VARIABLE(0);
auto dLdz = INPUT_VARIABLE(1);
auto softmaxOutput = INPUT_VARIABLE(2);
auto dLdx = OUTPUT_VARIABLE(0);
dLdx->assign(softmaxOutput);
Requirements req("ONEDNN SOFTMAX_BP OP");
req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) &&
req.expectFalse(makeInfoVariable(x->isEmpty(), IS_EMPTY_MSG_INPUT0), EXPECTED_FALSE) &&
req.expectFalse(makeInfoVariable(dLdz->isEmpty(), IS_EMPTY_MSG_INPUT1), EXPECTED_FALSE) &&
req.expectLess(makeInfoVariable(x->rankOf(), RANK_MSG_INPUT0), 7) &&
req.expectEq(makeInfoVariable(x->rankOf(), RANK_MSG_INPUT0), makeInfoVariable(dLdz->rankOf(), RANK_MSG_INPUT1)) &&
req.expectGreater(makeInfoVariable(x->rankOf(), RANK_MSG_INPUT), 0) &&
req.expectEq(makeInfoVariable(x->dataType(), TYPE_MSG_INPUT0), DataType::FLOAT32) &&
req.expectEq(makeInfoVariable(dLdz->dataType(), TYPE_MSG_INPUT1), DataType::FLOAT32) &&
req.expectEq(makeInfoVariable(dLdx->dataType(), TYPE_MSG_OUTPUT), DataType::FLOAT32) &&
req.expect(
makeShapeInfoVariable(x, SHAPE_MSG_INPUT0), makeShapeInfoVariable(dLdz, SHAPE_MSG_INPUT1),
[](const decltype(x)& l, const decltype(dLdz)& r) {
for (int i = 0; i < l->rankOf(); i++) {
if (l->sizeAt(i) != r->sizeAt(i)) {
return false;
}
}
return true;
},
EXPECTED_EQ_MSG);
req.logTheSuccess();
return req;
}
} // namespace platforms
} // namespace ops
} // namespace sd