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

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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 Yurii Shyrma (iuriish@yahoo.com)
//
#include <ops/declarable/PlatformHelper.h>
#include <ops/declarable/OpRegistrator.h>
#include <system/platform_boilerplate.h>
#include <helpers/MKLDNNStream.h>
#include "mkldnnUtils.h"
#include <numeric>
namespace sd {
namespace ops {
namespace platforms {
dnnl::memory::format_tag get_format_tag(const sd::NDArray &array) {
switch (array.rankOf()) {
case 1:
return dnnl::memory::format_tag::ab;
case 2:
return array.ordering() == 'c' ? dnnl::memory::format_tag::ab : dnnl::memory::format_tag::ba;
case 3:
return array.ordering() == 'c' ? dnnl::memory::format_tag::abc : dnnl::memory::format_tag::cba;
default:
throw std::runtime_error("MKLDNN matmul only supports 2D/3D arrays");
}
}
//////////////////////////////////////////////////////////////////////////
static void matmulMKLDNN(const NDArray* x, const NDArray* y, NDArray* z, const bool transX, const bool transY, float alpha = 1.f, float beta = 0.f) {
// mkl works with following
// [M,K] x [K,N] = [M,N]
// [bS, M,K] x [bS, K,N] = [bS, M,N]
// possible input cases not supported by mkl, however we'll perform permut/reshape procedures in order to fit requirements
// [4] x [4] = [1] --> [1,4] x [4,1] = [1,1]
// [4] x [4,5] = [5] --> [1,4] x [4,5] = [1,5]
// [4,5] x [5] = [4] --> [4,5] x [5,1] = [4,1]
// [2,3, 4,5] x [2,3, 5,4] = [2,3, 4,4] --> [6, 4,5] x [6, 5,4] = [6, 4,4]
// [2,2,3, 4,5] x [2,2,3, 5,4] = [2,2,3, 4,4] --> [12, 4,5] x [12, 5,4] = [12, 4,4]
const auto xRank = x->rankOf();
const auto yRank = y->rankOf();
const auto zRank = z->rankOf();
std::vector<int> permut;
// fill permutation vector appropriately if transposition is required
if((transX && xRank > 1) || (transY && yRank > 1)) {
const int rank = xRank >= yRank ? xRank : yRank;
permut.resize(rank);
std::iota(std::begin(permut), std::end(permut), 0);
permut[rank-2] = rank - 1;
permut[rank-1] = rank - 2;
}
const NDArray* xT = (transX && xRank > 1) ? new NDArray(x->permute(permut)) : x;
const NDArray* yT = (transY && yRank > 1) ? new NDArray(y->permute(permut)) : y;
const NDArray* xTR = xRank <= 3 ? xT : new NDArray(xT->reshape(xT->ordering(), {xT->lengthOf() / (xT->sizeAt(-2) * xT->sizeAt(-1)), xT->sizeAt(-2), xT->sizeAt(-1)}));
const NDArray* yTR = xRank <= 3 ? yT : new NDArray(yT->reshape(yT->ordering(), {yT->lengthOf() / (yT->sizeAt(-2) * yT->sizeAt(-1)), yT->sizeAt(-2), yT->sizeAt(-1)}));
NDArray* zR = xRank <= 3 ? z : new NDArray(z->reshape(z->ordering(), {z->lengthOf() / (z->sizeAt(-2) * z->sizeAt(-1)), z->sizeAt(-2), z->sizeAt(-1)})/*, false*/);
// [M,K] x [K,N] = [M,N]
const int64_t M = (xRank > 1) ? xTR->sizeAt(-2) : 1;
const int64_t K = (xRank > 1) ? xTR->sizeAt(-1) : xTR->lengthOf();
const int64_t N = (yRank > 1) ? yTR->sizeAt(-1) : 1;
const int64_t bS = (xRank > 2) ? xTR->sizeAt(0) : 1; // [bS, M,K] x [bS, K,N] = [bS, M,N]
dnnl::memory::dims xShape = xRank < 3 ? dnnl::memory::dims({M, K}) : dnnl::memory::dims({bS, M, K});
dnnl::memory::dims yShape = xRank < 3 ? dnnl::memory::dims({K, N}) : dnnl::memory::dims({bS, K, N});
dnnl::memory::dims zShape = xRank < 3 ? dnnl::memory::dims({M, N}) : dnnl::memory::dims({bS, M, N});
// x type
dnnl::memory::data_type xType;
if(x->dataType() == DataType::FLOAT32)
xType = dnnl::memory::data_type::f32;
else if(x->dataType() == DataType::HALF)
xType = dnnl::memory::data_type::f16;
else if(x->dataType() == DataType::BFLOAT16)
xType = dnnl::memory::data_type::bf16;
else if(x->dataType() == DataType::UINT8)
xType = dnnl::memory::data_type::u8;
else
xType = dnnl::memory::data_type::s8;
// y type
dnnl::memory::data_type yType = xType;
if(y->dataType() == DataType::UINT8)
yType = dnnl::memory::data_type::u8;
else if(y->dataType() == DataType::INT8)
yType = dnnl::memory::data_type::s8;
// z type
dnnl::memory::data_type zType = xType;
if(z->dataType() == DataType::FLOAT32)
zType = dnnl::memory::data_type::f32;
else if(z->dataType() == DataType::INT32)
zType = dnnl::memory::data_type::s32;
else if(z->dataType() == DataType::UINT8)
zType = dnnl::memory::data_type::u8;
else if(z->dataType() == DataType::INT8)
zType = dnnl::memory::data_type::s8;
// memory descriptors for arrays
// x
dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, xType, get_format_tag(*xTR));
dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, xType, get_format_tag(*xTR));
if(xTR->ews() != 1) {
x_user_md.data.format_kind = dnnl_blocked; // overrides format
x_user_md.data.format_desc.blocking.strides[0] = xRank == 1 ? 1 : xTR->strideAt(0);
x_user_md.data.format_desc.blocking.strides[1] = xRank == 1 ? xTR->strideAt(0) : xTR->strideAt(1);
if(xRank > 2)
x_user_md.data.format_desc.blocking.strides[2] = xTR->strideAt(2);
}
// y
dnnl::memory::desc y_mkl_md = dnnl::memory::desc(yShape, yType, get_format_tag(*yTR));
dnnl::memory::desc y_user_md = dnnl::memory::desc(yShape, yType, get_format_tag(*yTR));
if(yTR->ews() != 1) {
y_user_md.data.format_kind = dnnl_blocked; // overrides format
y_user_md.data.format_desc.blocking.strides[0] = yRank == 1 ? 1 : yTR->strideAt(0);
y_user_md.data.format_desc.blocking.strides[1] = yRank == 1 ? yTR->strideAt(0) : yTR->strideAt(1);
if(yRank > 2)
y_user_md.data.format_desc.blocking.strides[2] = yTR->strideAt(2);
}
// z
dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zShape, zType, get_format_tag(*zR));
dnnl::memory::desc z_user_md = dnnl::memory::desc(zShape, zType, get_format_tag(*zR));
if(zR->ews() != 1) {
z_user_md.data.format_kind = dnnl_blocked; // overrides format
z_user_md.data.format_desc.blocking.strides[0] = zRank == 1 ? 1 : zR->strideAt(0);
z_user_md.data.format_desc.blocking.strides[1] = zRank == 1 ? zR->strideAt(0) : zR->strideAt(1);
if(zRank > 2)
z_user_md.data.format_desc.blocking.strides[2] = zR->strideAt(2);
}
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)
if (alpha != 1.f) attr.set_output_scales(0, {alpha});
if (beta != 0.f) {
dnnl::post_ops po;
po.append_sum(beta);
attr.set_post_ops(po);
}
// operation primitive description
dnnl::matmul::desc op_desc(x_mkl_md, y_mkl_md, z_mkl_md);
dnnl::matmul::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(xTR, engine, stream, x_user_md, op_prim_desc.src_desc(), args[DNNL_ARG_SRC]);
/*
auto x_user_mem = dnnl::memory(x_user_md, engine, xTR->buffer());
const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc();
auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem;
if (xReorder)
dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem);
args[DNNL_ARG_SRC] = x_mkl_mem;
*/
// y
mkldnnUtils::loadDataToMklStream(yTR, engine, stream, y_user_md, op_prim_desc.weights_desc(), args[DNNL_ARG_WEIGHTS]);
/*
auto y_user_mem = dnnl::memory(y_user_md, engine, yTR->buffer());
const bool yReorder = op_prim_desc.weights_desc() != y_user_mem.get_desc();
auto y_mkl_mem = yReorder ? dnnl::memory(op_prim_desc.weights_desc(), engine) : y_user_mem;
if (yReorder)
dnnl::reorder(y_user_mem, y_mkl_mem).execute(stream, y_user_mem, y_mkl_mem);
args[DNNL_ARG_WEIGHTS] = y_mkl_mem;
*/
// z
auto z_user_mem = dnnl::memory(z_user_md, engine, zR->buffer());
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::matmul(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();
if(zR->buffer() != z->buffer())
z->assign(zR);
if(zR != z)
delete zR;
if(xTR != xT)
delete xTR;
if(xT != x)
delete xT;
if(yTR != yT)
delete yTR;
if(yT != y)
delete yT;
// shape::printArray(z_mkl_mem.map_data<float>(),8);
}
//////////////////////////////////////////////////////////////////////////
PLATFORM_IMPL(matmul, ENGINE_CPU) {
auto x = INPUT_VARIABLE(0);
auto y = INPUT_VARIABLE(1);
auto z = OUTPUT_VARIABLE(0);
if(x->isEmpty() || y->isEmpty())
return Status::OK();
int iSize = (int) block.getIArguments()->size();
int transX = iSize > 0 ? INT_ARG(0) : 0;
int transY = iSize > 1 ? INT_ARG(1) : 0;
const int transZ = iSize > 2 ? INT_ARG(2) : 0;
// optional use alpha nad beta
iSize = (int)block.getTArguments()->size();
float alpha = iSize > 0 ? T_ARG(0) : 1.0;
float beta = iSize > 1 ? T_ARG(1) : 0.0;
const int xRank = x->rankOf();
const int yRank = y->rankOf();
const int zRank = z->rankOf();
if (transZ) {
x = INPUT_VARIABLE(1);
y = INPUT_VARIABLE(0);
bool temp = transX;
transX = !transY;
transY = !temp;
}
const int xLastDim = transX ? -2 : -1;
const int yLastDim = transY ? -2 : -1;
const int xLastButOneDim = transX ? -1 : -2;
const int yLastButOneDim = transY ? -1 : -2;
// ******* input validation ******* //
REQUIRE_TRUE(xRank > 0 && yRank > 0, 0, "MATMUL MKLDNN OP: input arrays must have rank bigger than 0 (should not be scalars), but got instead: x rank = %i, y rank = %i !", xRank, yRank);
if (xRank == 1 && yRank == 1) { // dot case, output is scalar (or vector with length = 1)
REQUIRE_TRUE(x->lengthOf() == y->lengthOf(), 0,"MATMUL MKLDNN OP: since input arrays are vectors they must have the same length, but got x length = %i, y length = %i !",x->lengthOf(), y->lengthOf());
} else if (xRank == 1 && yRank == 2) { // vector x matrix, i.e. [4] x [4,5] = [5], output is vector
REQUIRE_TRUE(x->lengthOf() == y->sizeAt(yLastButOneDim), 0, "MATMUL MKLDNN OP: input arrays have inconsistent shapes for vector-matrix product: x %s, y %s !", ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str());
} else if (xRank == 2 && yRank == 1) { // matrix x vector , i.e. [4,5] x [5] = [4], output is vector
REQUIRE_TRUE(x->sizeAt(xLastDim) == y->lengthOf(), 0, "MATMUL MKLDNN OP: input arrays have inconsistent shapes for matrix-vector product: x %s, y %s !", ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str());
} else {
REQUIRE_TRUE(xRank == yRank && yRank == zRank, 0, "MATMUL MKLDNN OP: input and output arrays must have the same rank, but got instead: x rank = %i, y rank = %i, z rank = %i !", xRank, yRank, zRank);
REQUIRE_TRUE(x->sizeAt(xLastDim) == y->sizeAt(yLastButOneDim) && x->sizeAt(xLastButOneDim) == z->sizeAt(-2) && y->sizeAt(yLastDim) == z->sizeAt(-1), 0, "MATMUL MKLDNN OP: input/output arrays have inconsistent shapes for matrix product: x %s, y %s, z %s !", ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str(), ShapeUtils::shapeAsString(z).c_str());
if (xRank > 2) // outer dims must be the same
for (int i = 0; i < xRank - 2; ++i)
REQUIRE_TRUE(x->sizeAt(i) == y->sizeAt(i) && y->sizeAt(i) == z->sizeAt(i), 0, "MATMUL MKLDNN OP: input/output arrays have inconsistent shapes for matrix product: x %s, y %s, z %s !", ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str(), ShapeUtils::shapeAsString(z).c_str());
}
// ******* end of input validation ******* //
matmulMKLDNN(x, y, z, transX, transY, alpha, beta);
return Status::OK();
}
//////////////////////////////////////////////////////////////////////////
PLATFORM_CHECK(matmul, ENGINE_CPU) {
auto x = INPUT_VARIABLE(0);
auto y = INPUT_VARIABLE(1);
auto z = OUTPUT_VARIABLE(0);
const auto xType = x->dataType();
const auto yType = y->dataType();
const auto zType = z->dataType();
float alpha = block.numT() > 0 ? T_ARG(0) : 1.0f;
float beta = block.numT() > 1 ? T_ARG(1) : 0.0f;
// we're skipping if result order is F or arrays are not continuous
bool skip2D = z->rankOf() == 2 && (z->ordering() == 'f' || x->ews() != 1 || y->ews() != 1 || z->ews() != 1);
// we're skipping 3D cases if they are not C continuoys
bool skip3D = z->rankOf() == 3 && (x->ordering() == 'f' || y->ordering() == 'f' || z->ordering() == 'f' || x->ews() != 1 || y->ews() != 1 || z->ews() != 1);
return !skip2D && !skip3D && block.isUseMKLDNN() && x->rankOf() < 3 &&
(
(xType==DataType::FLOAT32 && yType==DataType::FLOAT32 && zType==DataType::FLOAT32) ||
(xType==DataType::HALF && yType==DataType::HALF && zType==DataType::FLOAT32) ||
(xType==DataType::BFLOAT16 && yType==DataType::BFLOAT16 && zType==DataType::BFLOAT16) ||
((xType==DataType::UINT8 || xType==DataType::INT8) && (yType==DataType::UINT8 || yType==DataType::INT8) && (zType==DataType::UINT8 || zType==DataType::INT8 || zType==DataType::INT32 || zType==DataType::FLOAT32))
);
}
}
}
}
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