cavis/libnd4j/include/ops/declarable/platform/mkldnn/softmax.cpp

275 lines
12 KiB
C++

/*******************************************************************************
* Copyright (c) 2019-2020 Konduit K.K.
*
* 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.
*
* 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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Oleg Semeniv <oleg.semeniv@gmail.com>
//
//
#include <ops/declarable/PlatformHelper.h>
#include <ops/declarable/OpRegistrator.h>
#include <system/platform_boilerplate.h>
#include <helpers/MKLDNNStream.h>
#include "mkldnnUtils.h"
using namespace dnnl;
namespace sd {
namespace ops {
namespace platforms {
//////////////////////////////////////////////////////////////////////
static void softmaxMKLDNN(const NDArray* x, NDArray* z, const int axis) {
const auto xRank = x->rankOf();
dnnl::memory::dims xShape, zShape;
mkldnnUtils::getDims(x, xRank, xShape);
mkldnnUtils::getDims(z, xRank, zShape);
dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank);
// optimized cases
if (2 == xRank && 0 == axis) {
format = dnnl::memory::format_tag::ba;
}
else if (4 == xRank && 1 == axis && (x->sizeAt(2) * x->sizeAt(3)) > 1) {
format = dnnl::memory::format_tag::acdb;
}
dnnl::memory::data_type xType = dnnl::memory::data_type::f32;
dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, xType, format);
dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, xType, format);
mkldnnUtils::setBlockStrides(x, x_user_md);
// z
dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zShape, xType, format);
dnnl::memory::desc z_user_md = dnnl::memory::desc(zShape, xType, format);
mkldnnUtils::setBlockStrides(z, z_user_md);
auto engine = mkldnnUtils::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
mkldnnUtils::loadDataToMklStream(x, engine, stream, x_user_md, op_prim_desc.src_desc(), args[DNNL_ARG_SRC]);
// z
auto z_user_mem = dnnl::memory(z_user_md, engine, z->getBuffer());
const bool zReorder = op_prim_desc.dst_desc() != z_user_mem.get_desc();
auto z_mkl_mem = zReorder ? dnnl::memory(op_prim_desc.dst_desc(), engine) : z_user_mem;
args[DNNL_ARG_DST] = z_mkl_mem;
// run calculations
dnnl::softmax_forward(op_prim_desc).execute(stream, args);
// reorder outputs if necessary
if (zReorder)
dnnl::reorder(z_mkl_mem, z_user_mem).execute(stream, z_mkl_mem, 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 Status::OK();
}
PLATFORM_CHECK(softmax, ENGINE_CPU) {
auto x = INPUT_VARIABLE(0);
auto z = OUTPUT_VARIABLE(0);
const DataType xType = x->dataType();
const DataType zType = z->dataType();
const int xRank = x->rankOf();
bool bSupportedRanks = (xRank > 2 && xRank < 7);
/*
Source Destination
f32 f32
*/
return !x->isEmpty() && block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32);
}
//////////////////////////////////////////////////////////////////////
static void softmaxBpMKLDNN(const NDArray* x, const NDArray* dLdz, NDArray* dLdx, const int axis) {
const auto xRank = x->rankOf();
const auto dLdzRank = dLdz->rankOf();
dnnl::memory::dims xShape, dLdxShape, dLdzShape;
mkldnnUtils::getDims(x, xRank, xShape);
mkldnnUtils::getDims(dLdx, xRank, dLdxShape);
mkldnnUtils::getDims(dLdz, dLdzRank, dLdzShape);
dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank);
// x
dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
mkldnnUtils::setBlockStrides(x, x_user_md);
// dLdx
dnnl::memory::desc dLdx_mkl_md = dnnl::memory::desc(dLdxShape, dnnl::memory::data_type::f32, format);
dnnl::memory::desc dLdx_user_md = dnnl::memory::desc(dLdxShape, dnnl::memory::data_type::f32, format);
mkldnnUtils::setBlockStrides(dLdx, dLdx_user_md);
// todo if mkl does not support broadcast we can remove this
format = mkldnnUtils::getFormat(dLdzRank);
// dLdz
dnnl::memory::desc dLdz_mkl_md = dnnl::memory::desc(dLdzShape, dnnl::memory::data_type::f32, format);
dnnl::memory::desc dLdz_user_md = dnnl::memory::desc(dLdzShape, dnnl::memory::data_type::f32, format);
mkldnnUtils::setBlockStrides(dLdz, dLdz_user_md);
auto engine = mkldnnUtils::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
mkldnnUtils::loadDataToMklStream(x, engine, stream, x_user_md, op_ff_prim_desc.src_desc(), argsff[DNNL_ARG_SRC]);
// dLdx
auto dLdx_user_mem = dnnl::memory(dLdx_user_md, engine, dLdx->getBuffer());
const bool dLdxReorder = op_ff_prim_desc.dst_desc() != dLdx_user_mem.get_desc();
auto dLdx_mkl_mem = dLdxReorder ? dnnl::memory(op_ff_prim_desc.dst_desc(), engine) : dLdx_user_mem;
argsff[DNNL_ARG_DST] = dLdx_mkl_mem;
// check and arg set for backprob
argsbp[DNNL_ARG_DIFF_SRC] = dLdx_mkl_mem;
argsbp[DNNL_ARG_DST] = dLdx_mkl_mem;
// dLdz
mkldnnUtils::loadDataToMklStream(dLdz, engine, stream, dLdz_user_md, op_bp_prim_desc.diff_dst_desc(), argsbp[DNNL_ARG_DIFF_DST]);
// run calculations forward
dnnl::softmax_forward(op_ff_prim_desc).execute(stream, argsff);
// run calculations backward
dnnl::softmax_backward(op_bp_prim_desc).execute(stream, argsbp);
// reorder outputs if necessary
if (dLdxReorder)
dnnl::reorder(dLdx_mkl_mem, dLdx_user_mem).execute(stream, dLdx_mkl_mem, dLdx_user_mem);
stream.wait();
}
PLATFORM_IMPL(softmax_bp, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0);
auto dLdz = INPUT_VARIABLE(1);
auto dLdx = OUTPUT_VARIABLE(0);
const int rank = input->rankOf();
const int 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 Status::OK();
}
PLATFORM_CHECK(softmax_bp, ENGINE_CPU) {
auto x = INPUT_VARIABLE(0);
auto dLdz = INPUT_VARIABLE(1);
auto dLdx = OUTPUT_VARIABLE(0);
const DataType xType = x->dataType();
const DataType dLdzType = dLdz->dataType();
const DataType dLdxType = dLdx->dataType();
const int xRank = x->rankOf();
const int dLdzRank = dLdz->rankOf();
bool bSupportedRanks = xRank < 7 && dLdzRank == xRank && (!x->isEmpty() && !dLdz->isEmpty());
if (bSupportedRanks) {
for (int i = 0; i < xRank; i++) {
if (x->sizeAt(i) != dLdz->sizeAt(i)) {
bSupportedRanks = false;
break;
}
}
}
//Source Destination
//f32 f32
return block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && dLdzType == DataType::FLOAT32 && dLdxType == DataType::FLOAT32);
}
}
}
}