* initial commit Signed-off-by: raver119 <raver119@gmail.com> * one file Signed-off-by: raver119 <raver119@gmail.com> * few more includes Signed-off-by: raver119 <raver119@gmail.com> * m? Signed-off-by: raver119 <raver119@gmail.com> * const Signed-off-by: raver119 <raver119@gmail.com> * cudnn linkage in tests Signed-off-by: raver119 <raver119@gmail.com> * culibos Signed-off-by: raver119 <raver119@gmail.com> * static reminder Signed-off-by: raver119 <raver119@gmail.com> * platform engine tag Signed-off-by: raver119 <raver119@gmail.com> * HAVE_CUDNN moved to config.h.in Signed-off-by: raver119 <raver119@gmail.com> * include Signed-off-by: raver119 <raver119@gmail.com> * include Signed-off-by: raver119 <raver119@gmail.com> * skip cudnn handle creation if there's not cudnn Signed-off-by: raver119 <raver119@gmail.com> * meh Signed-off-by: raver119 <raver119@gmail.com> * target device in context Signed-off-by: raver119 <raver119@gmail.com> * platform engines Signed-off-by: raver119 <raver119@gmail.com> * platform engines Signed-off-by: raver119 <raver119@gmail.com> * allow multiple -h args Signed-off-by: raver119 <raver119@gmail.com> * allow multiple -h args Signed-off-by: raver119 <raver119@gmail.com> * move mkldnn out of CPU block Signed-off-by: raver119 <raver119@gmail.com> * link to mkldnn on cuda Signed-off-by: raver119 <raver119@gmail.com> * less prints Signed-off-by: raver119 <raver119@gmail.com> * minor tweaks Signed-off-by: raver119 <raver119@gmail.com> * next step Signed-off-by: raver119 <raver119@gmail.com> * conv2d NCHW draft Signed-off-by: raver119 <raver119@gmail.com> * conv2d biasAdd Signed-off-by: raver119 <raver119@gmail.com> * test for MKL/CUDNN combined use Signed-off-by: raver119 <raver119@gmail.com> * - provide additional code for conv2d ff based on cudnn api, not tested yet Signed-off-by: Yurii <iuriish@yahoo.com> * - further work on conv2d helper based on using cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - fixing several cuda bugs which appeared after cudnn lib had been started to use Signed-off-by: Yurii <iuriish@yahoo.com> * - implementation of conv2d backprop op based on cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - implementaion of conv3d and conv3d_bp ops based on cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - bugs fixing in conv3d/conv3d_bp ops (cudnn in use) Signed-off-by: Yurii <iuriish@yahoo.com> * - implementation of depthwiseConv2d (ff/bp) op based on cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - implementation of batchnorm ff op based on cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - disable cudnn batchnorm temporary Signed-off-by: Yurii <iuriish@yahoo.com> * - add minor change in cmake Signed-off-by: Yurii <iuriish@yahoo.com> * engine for depthwise mkldnn Signed-off-by: raver119 <raver119@gmail.com> * couple of includes Signed-off-by: raver119 <raver119@gmail.com> * - provide permutation to cudnn batchnorm ff when format is NHWC Signed-off-by: Yurii <iuriish@yahoo.com> * lgamma fix Signed-off-by: raver119 <raver119@gmail.com> * - eliminate memory leak in two tests Signed-off-by: Yurii <iuriish@yahoo.com> Co-authored-by: Yurii Shyrma <iuriish@yahoo.com>
145 lines
7.0 KiB
C++
145 lines
7.0 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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 saudet
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//
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#ifndef DEV_TESTS_MKLDNNUTILS_H
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#define DEV_TESTS_MKLDNNUTILS_H
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#include <NativeOps.h>
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#include <NDArray.h>
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#include <dnnl.hpp>
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#include <MKLDNNStream.h>
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#include <graph/Context.h>
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#include <ops/declarable/PlatformHelper.h>
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#include <platform_boilerplate.h>
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using namespace samediff;
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namespace nd4j{
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namespace ops {
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namespace platforms {
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/**
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* Here we actually declare our platform helpers
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*/
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DECLARE_PLATFORM(conv2d, ENGINE_CPU);
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DECLARE_PLATFORM(conv2d_bp, ENGINE_CPU);
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DECLARE_PLATFORM(avgpool2d, ENGINE_CPU);
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DECLARE_PLATFORM(avgpool2d_bp, ENGINE_CPU);
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DECLARE_PLATFORM(maxpool2d, ENGINE_CPU);
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DECLARE_PLATFORM(maxpool2d_bp, ENGINE_CPU);
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DECLARE_PLATFORM(conv3dnew, ENGINE_CPU);
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DECLARE_PLATFORM(conv3dnew_bp, ENGINE_CPU);
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DECLARE_PLATFORM(maxpool3dnew, ENGINE_CPU);
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DECLARE_PLATFORM(maxpool3dnew_bp, ENGINE_CPU);
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DECLARE_PLATFORM(avgpool3dnew, ENGINE_CPU);
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DECLARE_PLATFORM(avgpool3dnew_bp, ENGINE_CPU);
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DECLARE_PLATFORM(lrn, ENGINE_CPU);
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DECLARE_PLATFORM(batchnorm, ENGINE_CPU);
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DECLARE_PLATFORM(batchnorm_bp, ENGINE_CPU);
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DECLARE_PLATFORM(lstmLayer, ENGINE_CPU);
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DECLARE_PLATFORM(deconv2d, ENGINE_CPU);
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DECLARE_PLATFORM(deconv2d_tf, ENGINE_CPU);
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DECLARE_PLATFORM(deconv3d, ENGINE_CPU);
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DECLARE_PLATFORM(deconv2d_bp, ENGINE_CPU);
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DECLARE_PLATFORM(deconv3d_bp, ENGINE_CPU);
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DECLARE_PLATFORM(depthwise_conv2d, ENGINE_CPU);
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DECLARE_PLATFORM(depthwise_conv2d_bp, ENGINE_CPU);
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}
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}
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namespace mkldnnUtils {
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/**
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* Utility methods for MKLDNN
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*/
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void getMKLDNNMemoryDescConv2d(
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int kH, int kW, int sH, int sW, int pH, int pW, int dH, int dW, const int paddingMode, bool isNCHW,
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int bS, int iC, int iH, int iW, int oC, int oH, int oW, const NDArray* src, const NDArray* diff_src,
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const NDArray* weights, const NDArray* diff_weights, const NDArray* bias, const NDArray* dst,
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dnnl::memory::desc* conv_src_md, dnnl::memory::desc* conv_diff_src_md, dnnl::memory::desc* conv_weights_md,
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dnnl::memory::desc* conv_diff_weights_md, dnnl::memory::desc* conv_bias_md, dnnl::memory::desc* conv_dst_md,
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dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_weights_md,
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dnnl::memory::desc* user_diff_weights_md, dnnl::memory::desc* user_bias_md, dnnl::memory::desc* user_dst_md,
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dnnl::memory::dims& conv_strides, dnnl::memory::dims& conv_padding, dnnl::memory::dims& conv_padding_r, dnnl::memory::dims& conv_dilation);
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void getMKLDNNMemoryDescConv3d(
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int kD, int kH, int kW, int sD, int sH, int sW, int pD, int pH, int pW, int dD, int dH, int dW, bool isSameMode, bool isNCDHW,
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int bS, int iC, int iD, int iH, int iW, int oC, int oD, int oH, int oW, const NDArray* src, const NDArray* diff_src,
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const NDArray* weights, const NDArray* diff_weights, const NDArray* bias, const NDArray* dst,
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dnnl::memory::desc* conv_src_md, dnnl::memory::desc* conv_diff_src_md, dnnl::memory::desc* conv_weights_md,
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dnnl::memory::desc* conv_diff_weights_md, dnnl::memory::desc* conv_bias_md, dnnl::memory::desc* conv_dst_md,
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dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_weights_md,
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dnnl::memory::desc* user_diff_weights_md, dnnl::memory::desc* user_bias_md, dnnl::memory::desc* user_dst_md,
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dnnl::memory::dims& conv_strides, dnnl::memory::dims& conv_padding, dnnl::memory::dims& conv_padding_r, dnnl::memory::dims& conv_dilation);
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void getMKLDNNMemoryDescPool2d(
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int kH, int kW, int sH, int sW, int pH, int pW, int dH, int dW, int poolingMode, int extraParam0, bool isNCHW,
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int bS, int iC, int iH, int iW, int oC, int oH, int oW,
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const NDArray* src, const NDArray* diff_src, const NDArray* dst, dnnl::algorithm& algorithm,
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dnnl::memory::desc* pool_src_md, dnnl::memory::desc* pool_diff_src_md, dnnl::memory::desc* pool_dst_md,
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dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_dst_md,
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dnnl::memory::dims& pool_strides, dnnl::memory::dims& pool_kernel, dnnl::memory::dims& pool_padding, dnnl::memory::dims& pool_padding_r);
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void getMKLDNNMemoryDescPool3d(
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int kD, int kH, int kW, int sD, int sH, int sW, int pD, int pH, int pW, int dD, int dH, int dW, int poolingMode, int extraParam0, bool isNCDHW,
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int bS, int iC, int iD, int iH, int iW, int oC, int oD, int oH, int oW,
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const NDArray* src, const NDArray* diff_src, const NDArray* dst, dnnl::algorithm& algorithm,
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dnnl::memory::desc* pool_src_md, dnnl::memory::desc* pool_diff_src_md, dnnl::memory::desc* pool_dst_md,
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dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_dst_md,
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dnnl::memory::dims& pool_strides, dnnl::memory::dims& pool_kernel, dnnl::memory::dims& pool_padding, dnnl::memory::dims& pool_padding_r);
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void getMKLDNNMemoryDescBatchNorm(const NDArray* src, const NDArray* diff_src, const NDArray* dst,
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dnnl::memory::desc* batchnorm_src_md, dnnl::memory::desc* batchnorm_diff_src_md, dnnl::memory::desc* batchnorm_dst_md,
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dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_dst_md, int axis);
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void getMKLDNNMemoryDescLrn(const NDArray* src, const NDArray* diff_src, const NDArray* dst,
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dnnl::memory::desc* lrn_src_md, dnnl::memory::desc* lrn_diff_src_md, dnnl::memory::desc* lrn_dst_md,
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dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_dst_md, int axis);
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dnnl::engine& getEngine(void *ptr);
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}
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}
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#endif //DEV_TESTS_MKLDNNUTILS_H
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