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Posted to commits@singa.apache.org by wa...@apache.org on 2018/07/05 03:10:05 UTC
[10/18] incubator-singa git commit: SINGA-371 Implement functional
operations in c++ for autograd
SINGA-371 Implement functional operations in c++ for autograd
- merge cpu and gpu parts for con2d operation in python part(not complete)
- redesign handles(recorder)
- redesign api
- parts of codes have passed unit tests
Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/189958ab
Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/189958ab
Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/189958ab
Branch: refs/heads/master
Commit: 189958ab53bff686f39e599a36f21867cd4a265f
Parents: dfe4478
Author: xuewanqi <xu...@outlook.com>
Authored: Wed Jun 27 14:57:21 2018 +0000
Committer: xuewanqi <xu...@outlook.com>
Committed: Wed Jun 27 14:57:21 2018 +0000
----------------------------------------------------------------------
python/singa/autograd.py | 79 ++--
src/CMakeLists.txt | 1 +
src/api/model_operation.i | 43 +--
src/model/convolution_functions.cc | 457 ------------------------
src/model/convolution_functions.h | 95 -----
src/model/operation/convolution_related.cc | 417 +++++++++++++++++++++
src/model/operation/convolution_related.h | 75 ++++
7 files changed, 561 insertions(+), 606 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/189958ab/python/singa/autograd.py
----------------------------------------------------------------------
diff --git a/python/singa/autograd.py b/python/singa/autograd.py
index 4f45bf1..e898312 100644
--- a/python/singa/autograd.py
+++ b/python/singa/autograd.py
@@ -583,7 +583,7 @@ class Flatten(Operation):
def flatten(x):
return Flatten()(x)[0]
-class Conv2d_GPU(Operation):
+class Conv2D(Operation):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True, **kwargs):
@@ -616,20 +616,14 @@ class Conv2d_GPU(Operation):
self.bias = bias
- inner_params = {'cudnn_prefer': 'fastest', 'workspace_MB_limit': 1024}
+ self.inner_params = {'cudnn_prefer': 'fastest', 'workspace_MB_limit': 1024}
# TODO valid value of inner_params check
for kwarg in kwargs:
- if kwarg not in inner_params:
+ if kwarg not in self.inner_params:
raise TypeError('Keyword argument not understood:', kwarg)
else:
- inner_params[kwarg] = kwargs[kwarg]
-
- self.convhandle = singa.SetupConv(self.kernel_size[0], self.kernel_size[1],
- self.padding[0], self.padding[1], self.stride[0], self.stride[1],
- self.in_channels, self.out_channels, self.bias,
- inner_params['workspace_MB_limit']*1024*1024,
- inner_params['cudnn_prefer'])
+ self.inner_params[kwarg] = kwargs[kwarg]
w_shape = (self.out_channels, self.in_channels, self.kernel_size[0], self.kernel_size[1])
self.W = Tensor(shape=w_shape, requires_grad=True, stores_grad=True)
@@ -639,11 +633,13 @@ class Conv2d_GPU(Operation):
if self.bias:
b_shape = (self.out_channels,)
+ self.b = Tensor(shape=b_shape, requires_grad=True, stores_grad=True)
+ self.b.set_value(0.0)
else:
- b_shape = (1,) #to keep consistency when to do forward.
- self.b = Tensor(shape=b_shape, requires_grad=True, stores_grad=True)
- self.b.set_value(0.0)
-
+ #to keep consistency when to do forward.
+ self.b = Tensor(data=CTensor([]), requires_grad=False, stores_grad=False)
+
+ self.reset = False
def __call__(self, x):
assert x.ndim() == 4, 'The dimensions of input should be 4D.'
@@ -651,10 +647,13 @@ class Conv2d_GPU(Operation):
assert 0 == 0, 'invalid padding.'
# TODO valid padding check.
- if not hasattr (self, 'cudnnconvhandle'):
- self.cudnnconvhandle = singa.InitCudnn(x.data, self.convhandle)
- elif x.shape[0] != self.cudnnconvhandle.batchsize:
- self.cudnnconvhandle = singa.InitCudnn(x.data, self.convhandle)
+ if not hasattr (self, 'recorder'):
+ self.recorder = singa.SetupRecorder(x.data, self.kernel_size, self.stride,
+ self.padding, self.in_channels, self.out_channels, self.bias)
+ elif x.shape[0] != self.recorder.batchsize:
+ self.recorder = singa.SetupRecorder(x.data, self.kernel_size, self.stride,
+ self.padding, self.in_channels, self.out_channels, self.bias)
+ self.reset = True
if training:
self.x = x
@@ -664,26 +663,50 @@ class Conv2d_GPU(Operation):
self.W.to_device(self.dev)
xs = [x, self.W]
- self.b.to_device(self.dev)
+ if self.bias:
+ self.b.to_device(self.dev)
xs.append(self.b)
return self._do_forward(*xs)[0]
def forward(self, *xs):
- return singa.CudnnConvForward(xs[0], xs[1], xs[2], self.convhandle, self.cudnnconvhandle)
+ if gpu:
+
+ if not hasattr(self, 'cudnnconvhandles'):
+ self.cudnnconvhandles=InitCudnnConvHandles(xs[0], self.recorder,
+ self.inner_params['workspace_MB_limit']*1024*1024, self.inner_params['cudnn_prefer'])
+ elif self.reset:
+ self.cudnnconvhandles=InitCudnnConvHandles(xs[0], self.recorder,
+ self.inner_params['workspace_MB_limit']*1024*1024, self.inner_params['cudnn_prefer'])
+
+ return singa.GpuConvForward(xs[0], xs[1], xs[2], self.recorder, self.cudnnconvhandles)
+
+ if cpu:
+
+ return singa.CpuConvForward(xs[0], xs[1], xs[2], self.recorder)
def backward(self, dy):
- assert training is True and hasattr(self, 'x'), 'Please set \'training\' as True before do BP. '
+ assert training is True and hasattr(self, 'x'), 'Please set training as True before do BP. '
# todo check device?
dy.ToDevice(self.dev)
- dx = singa.CudnnConvBackwardx(dy, self.W.data, self.x.data, self.cudnnconvhandle)
- dW = singa.CudnnConvBackwardW(dy, self.x.data, self.W.data, self.cudnnconvhandle)
- if self.bias:
- db = singa.CudnnConvBackwardb(dy, self.b.data, self.cudnnconvhandle)
- return dx, dW, db
- else:
- return dx, dW
+ if gpu:
+ dx = singa.GpuConvBackwardx(dy, self.W.data, self.x.data, self.cudnnconvhandles)
+ dW = singa.GpuConvBackwardW(dy, self.x.data, self.W.data, self.cudnnconvhandles)
+ if self.bias:
+ db = singa.GpuConvBackwardb(dy, self.b.data, self.cudnnconvhandles)
+ return dx, dW, db
+ else:
+ return dx, dW
+
+ if cpu:
+ dx = singa.CpuConvBackwardx(dy, self.W.data, self.x.data, self.recorder)
+ dW = singa.CpuConvBackwardW(dy, self.x.data, self.W.data, self.recorder)
+ if self.bias:
+ db = singa.CpuConvBackwardb(dy, self.b.data, self.recorder)
+ return dx, dW, db
+ else:
+ return dx, dW
def infer_dependency(op):
'''
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/189958ab/src/CMakeLists.txt
----------------------------------------------------------------------
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 709894b..7dd9bf7 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -58,6 +58,7 @@ AUX_SOURCE_DIRECTORY(model/optimizer model_source)
AUX_SOURCE_DIRECTORY(model/loss model_source)
AUX_SOURCE_DIRECTORY(model/metric model_source)
AUX_SOURCE_DIRECTORY(model/updater model_source)
+AUX_SOURCE_DIRECTORY(model/operation model_source)
LIST(APPEND singa_sources ${model_source})
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/189958ab/src/api/model_operation.i
----------------------------------------------------------------------
diff --git a/src/api/model_operation.i b/src/api/model_operation.i
index 20f112e..1d31b9d 100644
--- a/src/api/model_operation.i
+++ b/src/api/model_operation.i
@@ -1,47 +1,38 @@
%module model_operation
%{
-#include "../src/model/convolution_functions.h"
+#include "../src/model/operation/convolution_related.h"
%}
namespace singa{
-struct ConvHandle{};
+struct Recorder{size_t batchsize;};
-struct CudnnConvHandle{size_t batchsize;};
+struct CudnnConvHandles{};
-struct CpuConvHandle{};
-ConvHandle SetupConv(
- const size_t kernel_h_, const size_t kernel_w_,
- const size_t pad_h_, const size_t pad_w_,
- const size_t stride_h_,const size_t stride_w_,
- const size_t channels_, const size_t num_filters_,
- const bool bias_term_ = true, const size_t workspace_byte_limit_ =1024*1024*1024,
- const std::string prefer_="fastest");
+Recorder SetupRecorder(const Tensor &input, const std::vector<size_t> kernel_size,
+ const std::vector<size_t> stride, const std::vector<size_t> padding,
+ const size_t in_channels, const size_t out_channels,
+ const bool bias_term_);
-CudnnConvHandle InitCudnn(const Tensor &input, const ConvHandle ch);
+CudnnConvHandles InitCudnnConvHandles(const Tensor &input, const Recorder r,
+ const size_t workspace_byte_limit_=1024*1024*1024, const std::string prefer_="fastest");
-Tensor CudnnConvForward(const Tensor &x, const Tensor &W, const Tensor &b,
- const ConvHandle ch, const CudnnConvHandle cch);
+Tensor GpuConvForward(const Tensor &x, const Tensor &W, const Tensor &b, const Recorder r, const CudnnConvHandles cch);
-Tensor CudnnConvBackwardW(const Tensor &dy, const Tensor &x, const Tensor &W, const CudnnConvHandle cch);
+Tensor GpuConvBackwardx(const Tensor &dy, const Tensor &W, const Tensor &x, const CudnnConvHandles cch);
-Tensor CudnnConvBackwardb(const Tensor &dy, const Tensor &b, const CudnnConvHandle cch);
+Tensor GpuConvBackwardW(const Tensor &dy, const Tensor &x, const Tensor &W, const CudnnConvHandles cch);
-Tensor CudnnConvBackwardx(const Tensor &dy, const Tensor &W, const Tensor &x, const CudnnConvHandle cch);
+Tensor GpuConvBackwardb(const Tensor &dy, const Tensor &b, const CudnnConvHandles cch);
-CpuConvHandle InitCpuHandle(const Tensor &input, const ConvHandle ch);
+Tensor CpuConvForward(const Tensor &x, Tensor &W, Tensor &b, const Recorder r);
-Tensor CpuConvForward(const Tensor &x, Tensor &W, Tensor &b,
- const ConvHandle ch, const CpuConvHandle cch);
+Tensor CpuConvBackwardx(const Tensor &dy, Tensor &W, const Tensor &x, const Recorder r);
-Tensor CpuConvBackwardx(const Tensor &dy, Tensor &W, const Tensor &x,
- const ConvHandle ch, const CpuConvHandle cch);
+Tensor CpuConvBackwardW(const Tensor &dy, const Tensor &x, const Tensor &W, const Recorder r);
-Tensor CpuConvBackwardW(const Tensor &dy, const Tensor &x, const Tensor &W,
- const ConvHandle ch, const CpuConvHandle cch);
-
-Tensor CpuConvBackwardb(const Tensor &dy, const Tensor &b, const ConvHandle ch, const CpuConvHandle cch);
+Tensor CpuConvBackwardb(const Tensor &dy, const Tensor &b, const Recorder r);
}
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/189958ab/src/model/convolution_functions.cc
----------------------------------------------------------------------
diff --git a/src/model/convolution_functions.cc b/src/model/convolution_functions.cc
deleted file mode 100644
index 6e4b195..0000000
--- a/src/model/convolution_functions.cc
+++ /dev/null
@@ -1,457 +0,0 @@
-//#include <string>
-//#include <cudnn.h>
-//#include "./layer/cudnn_convolution.h"
-//#include "./layer/cudnn_utils.h"
-//#include "singa/utils/logging.h"
-#include "./convolution_functions.h"
-#include "./layer/convolution.h"
-#include<iostream>
-namespace singa{
-
-// Done in conv2d.__init__()
-ConvHandle SetupConv(
- const size_t kernel_h_, const size_t kernel_w_,
- const size_t pad_h_, const size_t pad_w_,
- const size_t stride_h_,const size_t stride_w_,
- const size_t channels_, const size_t num_filters_,
- const bool bias_term_ , const size_t workspace_byte_limit_,
- const std::string prefer_){
- return ConvHandle{
- kernel_w_,
- pad_w_,
- stride_w_,
- kernel_h_,
- pad_h_,
- stride_h_,
-
- channels_,
- num_filters_,
-
- bias_term_,
-
- workspace_byte_limit_,
- prefer_,
- };
-};
-
-
-// Done in conv2d.__call__():
-// if self.cudnnconvhandle is None:
-// self.cudnnconvhandle= InitCudnn(...)
-// elif x.shape(0) != self.cudnnconvhandle.batchsize:
-// self.cudnnconvhandle= InitCudnn(...)
-CudnnConvHandle InitCudnn(const Tensor &input, const ConvHandle ch){
-
- cudnnTensorDescriptor_t x_desc_ = nullptr;
- cudnnTensorDescriptor_t y_desc_ = nullptr;
- cudnnTensorDescriptor_t bias_desc_ = nullptr;
- cudnnFilterDescriptor_t filter_desc_ = nullptr;
- cudnnConvolutionDescriptor_t conv_desc_ = nullptr;
- cudnnConvolutionFwdAlgo_t fp_alg_;
- cudnnConvolutionBwdFilterAlgo_t bp_filter_alg_;
- cudnnConvolutionBwdDataAlgo_t bp_data_alg_;
- size_t workspace_count_;
- Tensor workspace_;
-
- size_t height_;
- size_t width_;
- size_t conv_height_;
- size_t conv_width_;
-
- DataType dtype = input.data_type();
- auto dev = input.device();
- Context *ctx = dev->context(0);
-
- size_t batchsize, channels_;
- batchsize = input.shape(0);
- channels_ = input.shape(1);
- height_ = input.shape(2);
- width_ = input.shape(3);
-
- CHECK(channels_ == ch.channels_)<<"the number of input channels mismatched.";
-
- conv_height_ = 1;
- if (ch.stride_h_ > 0)
- conv_height_ = (height_ + 2 * ch.pad_h_ - ch.kernel_h_) / ch.stride_h_ + 1;
- conv_width_ = (width_ + 2 * ch.pad_w_ - ch.kernel_w_) / ch.stride_w_ + 1;
-
- CUDNN_CHECK(cudnnCreateTensorDescriptor(&x_desc_));
- CUDNN_CHECK(cudnnCreateTensorDescriptor(&y_desc_));
- if (ch.bias_term_)
- CUDNN_CHECK(cudnnCreateTensorDescriptor(&bias_desc_));
- CUDNN_CHECK(cudnnCreateFilterDescriptor(&filter_desc_));
- CUDNN_CHECK(cudnnCreateConvolutionDescriptor(&conv_desc_));
-
-
- CUDNN_CHECK(cudnnSetTensor4dDescriptor(x_desc_, CUDNN_TENSOR_NCHW,
- GetCudnnDataType(dtype), batchsize,
- ch.channels_, height_, width_));
- CUDNN_CHECK(cudnnSetTensor4dDescriptor(
- y_desc_, CUDNN_TENSOR_NCHW, GetCudnnDataType(dtype), batchsize,
- ch.num_filters_, conv_height_, conv_width_));
- if (ch.bias_term_)
- CUDNN_CHECK(cudnnSetTensor4dDescriptor(bias_desc_, CUDNN_TENSOR_NCHW,
- GetCudnnDataType(dtype), 1,
- ch.num_filters_, 1, 1));
- CUDNN_CHECK(cudnnSetConvolution2dDescriptor(conv_desc_, ch.pad_h_, ch.pad_w_,
- ch.stride_h_, ch.stride_w_, 1, 1,
- CUDNN_CROSS_CORRELATION,
- GetCudnnDataType(dtype)));
- CUDNN_CHECK(cudnnSetFilter4dDescriptor(filter_desc_, GetCudnnDataType(dtype),
- CUDNN_TENSOR_NCHW, ch.num_filters_,
- channels_, ch.kernel_h_, ch.kernel_w_));
- if (ch.prefer_ == "fastest" || ch.prefer_ == "limited_workspace" ||
- ch.prefer_ == "no_workspace") {
- cudnnConvolutionFwdPreference_t fwd_pref;
- cudnnConvolutionBwdFilterPreference_t bwd_filt_pref;
- cudnnConvolutionBwdDataPreference_t bwd_data_pref;
- if (ch.prefer_ == "fastest") {
- fwd_pref = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST;
- bwd_filt_pref = CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST;
- bwd_data_pref = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST;
- } else if (ch.prefer_ == "limited_workspace") {
- fwd_pref = CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT;
- bwd_filt_pref = CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT;
- bwd_data_pref = CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT;
- } else {
- fwd_pref = CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
- bwd_filt_pref = CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE;
- bwd_data_pref = CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT;
- }
- CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(
- ctx->cudnn_handle, x_desc_, filter_desc_, conv_desc_, y_desc_, fwd_pref,
- ch.workspace_byte_limit_, &fp_alg_));
- CUDNN_CHECK(cudnnGetConvolutionBackwardFilterAlgorithm(
- ctx->cudnn_handle, x_desc_, y_desc_, conv_desc_, filter_desc_,
- bwd_filt_pref, ch.workspace_byte_limit_, &bp_filter_alg_));
- // deprecated in cudnn v7
- CUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm(
- ctx->cudnn_handle, filter_desc_, y_desc_, conv_desc_, x_desc_,
- bwd_data_pref, ch.workspace_byte_limit_, &bp_data_alg_));
- } else if (ch.prefer_ == "autotune") {
- const int topk = 1;
- int num_fp_alg, num_bp_filt_alg, num_bp_data_alg;
- cudnnConvolutionFwdAlgoPerf_t fp_alg_perf[topk];
- cudnnConvolutionBwdFilterAlgoPerf_t bp_filt_perf[topk];
- cudnnConvolutionBwdDataAlgoPerf_t bp_data_perf[topk];
- CUDNN_CHECK(cudnnFindConvolutionForwardAlgorithm(
- ctx->cudnn_handle, x_desc_, filter_desc_, conv_desc_, y_desc_, topk,
- &num_fp_alg, fp_alg_perf));
- fp_alg_ = fp_alg_perf[0].algo;
- CUDNN_CHECK(cudnnFindConvolutionBackwardFilterAlgorithm(
- ctx->cudnn_handle, x_desc_, y_desc_, conv_desc_, filter_desc_, topk,
- &num_bp_filt_alg, bp_filt_perf));
- bp_filter_alg_ = bp_filt_perf[0].algo;
- CUDNN_CHECK(cudnnFindConvolutionBackwardDataAlgorithm(
- ctx->cudnn_handle, filter_desc_, y_desc_, conv_desc_, x_desc_, topk,
- &num_bp_data_alg, bp_data_perf));
- bp_data_alg_ = bp_data_perf[0].algo;
- } else {
- LOG(FATAL) << "Preferred algorithm is not available!";
- }
-
- size_t fp_byte, bp_data_byte, bp_filter_byte;
- CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(
- ctx->cudnn_handle, x_desc_, filter_desc_, conv_desc_, y_desc_, fp_alg_,
- &fp_byte));
- CUDNN_CHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(
- ctx->cudnn_handle, filter_desc_, y_desc_, conv_desc_, x_desc_,
- bp_data_alg_, &bp_data_byte));
- CUDNN_CHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(
- ctx->cudnn_handle, x_desc_, y_desc_, conv_desc_, filter_desc_,
- bp_filter_alg_, &bp_filter_byte));
- workspace_count_ = std::max(std::max(fp_byte, bp_data_byte), bp_filter_byte) /
- sizeof(float) +
- 1;
- if (workspace_count_ * sizeof(float) > ch.workspace_byte_limit_)
- LOG(WARNING) << "The required memory for workspace ("
- << workspace_count_ * sizeof(float)
- << ") is larger than the expected Bytes ("
- << ch.workspace_byte_limit_ << ")";
- workspace_ = Tensor(Shape{workspace_count_}, dev, dtype);
-
- return CudnnConvHandle{
- x_desc_,
- y_desc_,
- bias_desc_,
- filter_desc_,
- conv_desc_,
- fp_alg_,
- bp_filter_alg_,
- bp_data_alg_,
-
- workspace_count_,
- workspace_,
-
- height_,
- width_,
- conv_height_,
- conv_width_,
- batchsize,
- };
-};
-
-Tensor CudnnConvForward(const Tensor &x, const Tensor &W, const Tensor &b,
- const ConvHandle ch, const CudnnConvHandle cch){
- CHECK_EQ(x.device()->lang(), kCuda);
- CHECK_EQ(x.nDim(), 4u);
- CHECK_EQ(x.shape()[0],cch.batchsize);
- CHECK_EQ(x.shape()[1],ch.channels_);
- CHECK_EQ(x.shape()[2],cch.height_);
- CHECK_EQ(x.shape()[3],cch.width_);
-
- DataType dtype = x.data_type();
- auto dev = x.device();
-
- Shape shape{cch.batchsize, ch.num_filters_, cch.conv_height_, cch.conv_width_};
- Tensor output(shape, dev, dtype);
-
- output.device()->Exec([output, x, W, cch](Context *ctx) {
- Block *inblock = x.block(), *outblock = output.block(),
- *wblock = W.block();
- float alpha = 1.f, beta = 0.f;
- cudnnConvolutionForward(ctx->cudnn_handle, &alpha, cch.x_desc_,
- inblock->data(), cch.filter_desc_, wblock->data(),
- cch.conv_desc_, cch.fp_alg_,
- cch.workspace_.block()->mutable_data(),
- cch.workspace_count_ * sizeof(float), &beta,
- cch.y_desc_, outblock->mutable_data());
- }, {x.block(), W.block()}, {output.block()}, cch.workspace_.block());
-
- if (ch.bias_term_) {
- output.device()->Exec([output, b, cch](Context *ctx) {
- float beta = 1.f, alpha = 1.0f;
- Block *outblock = output.block(), *bblock = b.block();
- cudnnAddTensor(ctx->cudnn_handle, &alpha, cch.bias_desc_,
- bblock->data(), &beta, cch.y_desc_,
- outblock->mutable_data());
- }, {output.block(), b.block()}, {output.block()});
- }
- return output;
-};
-
-// input Tensor W for Reset dW purpose, can avoid this later.
-Tensor CudnnConvBackwardW(const Tensor &dy, const Tensor &x, const Tensor &W, const CudnnConvHandle cch){
- CHECK_EQ(dy.device()->lang(), kCuda);
- CHECK_EQ(dy.nDim(), 4u);
-
- Tensor dW;
- dW.ResetLike(W);
-
- dy.device()->Exec([dW, dy, x, W, cch](Context *ctx) {
- Block *inblock = x.block(), *dyblock = dy.block(),
- *dwblock = dW.block();
- float alpha = 1.f, beta = 0.f;
- cudnnConvolutionBackwardFilter(
- ctx->cudnn_handle, &alpha, cch.x_desc_, inblock->data(),
- cch.y_desc_, dyblock->data(), cch.conv_desc_, cch.bp_filter_alg_,
- cch.workspace_.block()->mutable_data(),
- cch.workspace_count_ * sizeof(float), &beta, cch.filter_desc_,
- dwblock->mutable_data());
- }, {dy.block(), x.block()}, {dW.block(), cch.workspace_.block()});
-
- return dW;
-};
-
-// input Tensor b for Reset db purpose, can avoid this later.
-Tensor CudnnConvBackwardb(const Tensor &dy, const Tensor &b, const CudnnConvHandle cch){
- CHECK_EQ(dy.device()->lang(), kCuda);
- CHECK_EQ(dy.nDim(), 4u);
-
- Tensor db;
- db.ResetLike(b);
-
- dy.device()->Exec([db, dy, b, cch](Context *ctx) {
- Block *dyblock = dy.block(), *dbblock = db.block();
- float alpha = 1.f, beta = 0.f;
- cudnnConvolutionBackwardBias(ctx->cudnn_handle, &alpha, cch.y_desc_,
- dyblock->data(), &beta, cch.bias_desc_,
- dbblock->mutable_data());
- }, {dy.block()}, {db.block()});
- return db;
-};
-
-Tensor CudnnConvBackwardx(const Tensor &dy, const Tensor &W, const Tensor &x, const CudnnConvHandle cch){
- CHECK_EQ(dy.device()->lang(), kCuda);
- CHECK_EQ(dy.nDim(), 4u);
-
- Tensor dx;
- dx.ResetLike(x);
-
- dy.device()->Exec([dx, dy, W, cch](Context *ctx) {
- Block *wblock = W.block(), *dyblock = dy.block(),
- *dxblock = dx.block();
- float alpha = 1.f, beta = 0.f;
- cudnnConvolutionBackwardData(ctx->cudnn_handle, &alpha, cch.filter_desc_,
- wblock->data(), cch.y_desc_, dyblock->data(),
- cch.conv_desc_, cch.bp_data_alg_,
- cch.workspace_.block()->mutable_data(),
- cch.workspace_count_ * sizeof(float), &beta,
- cch.x_desc_, dxblock->mutable_data());
- }, {dy.block(), W.block()}, {dx.block(), cch.workspace_.block()});
-
- return dx;
-};
-
-CpuConvHandle InitCpuHandle(const Tensor &input, const ConvHandle ch){
- size_t height_;
- size_t width_;
- size_t conv_height_;
- size_t conv_width_;
- size_t batchsize;
- size_t channels_;
-
- size_t col_height_;
- size_t col_width_;
-
- batchsize = input.shape(0);
- channels_ = input.shape(1);
- height_ = input.shape(2);
- width_ = input.shape(3);
-
- CHECK(channels_ == ch.channels_)<<"the number of input channels mismatched.";
-
- conv_height_ = 1;
- if (ch.stride_h_ > 0)
- conv_height_ = (height_ + 2 * ch.pad_h_ - ch.kernel_h_) / ch.stride_h_ + 1;
- conv_width_ = (width_ + 2 * ch.pad_w_ - ch.kernel_w_) / ch.stride_w_ + 1;
-
- col_height_ = ch.channels_ * ch.kernel_w_ * ch.kernel_h_;
- col_width_ = conv_height_ * conv_width_;
-
- return CpuConvHandle{
- height_,
- width_,
- conv_height_,
- conv_width_,
- batchsize,
-
- col_height_,
- col_width_
- };
-};
-
-Convolution C;
-
-Tensor CpuConvForward(const Tensor &x, Tensor &W, Tensor &b,
- const ConvHandle ch, const CpuConvHandle cch){
- CHECK_EQ(x.device()->lang(), kCpp);
- CHECK_EQ(x.nDim(), 4u);
- CHECK_EQ(x.shape()[0],cch.batchsize);
- CHECK_EQ(x.shape()[1],ch.channels_);
- CHECK_EQ(x.shape()[2],cch.height_);
- CHECK_EQ(x.shape()[3],cch.width_);
-
- size_t imagesize = x.Size() / cch.batchsize;
-
- Shape w_shape= W.shape();
- Shape b_shape= b.shape();
-
- W.Reshape(Shape{ch.num_filters_, cch.col_height_});
- if (ch.bias_term_)
- b.Reshape(Shape{ch.num_filters_});
-
- DataType dtype = x.data_type();
- auto dev = x.device();
- Shape shape{cch.batchsize, ch.num_filters_, cch.conv_height_, cch.conv_width_};
- Tensor output(shape, dev, dtype);
-
- Tensor col_data(Shape{cch.col_height_, cch.col_width_});//broadcasted image
-
- float *data_col = new float[cch.col_height_ * cch.col_width_];
- auto in_data = x.data<float>();
- for (size_t num = 0; num < cch.batchsize; num++) {
- C.Im2col(in_data + num * imagesize, ch.channels_, cch.height_, cch.width_, ch.kernel_h_,
- ch.kernel_w_, ch.pad_h_, ch.pad_w_, ch.stride_h_, ch.stride_w_, data_col);
- col_data.CopyDataFromHostPtr(data_col, cch.col_height_ * cch.col_width_);
- Tensor each = Mult(W, col_data);
- if (ch.bias_term_) {
- AddColumn(b, &each);
- }
- CopyDataToFrom(&output, each, each.Size(), num * each.Size());
- }
- W.Reshape(w_shape);
- b.Reshape(b_shape);
- return output;
-};
-
-Tensor CpuConvBackwardx(const Tensor &dy, Tensor &W, const Tensor &x,
- const ConvHandle ch, const CpuConvHandle cch){
- CHECK_EQ(dy.device()->lang(), kCpp);
- CHECK_EQ(dy.nDim(), 4u);
-
- Shape w_shape= W.shape();
- W.Reshape(Shape{ch.num_filters_, cch.col_height_});
-
- Tensor dx;
- dx.ResetLike(x);
-
- size_t imagesize = x.Size() / cch.batchsize;
- float *dx_b = new float[imagesize];
-
- for (size_t num = 0; num < cch.batchsize; num++) {
- Tensor grad_b(Shape{ch.num_filters_, cch.conv_height_ * cch.conv_width_});
- CopyDataToFrom(&grad_b, dy, grad_b.Size(), 0, num * grad_b.Size());
- Tensor dcol_b = Mult(W.T(), grad_b);
- auto dcol_data = dcol_b.data<float>();
- C.Col2im(dcol_data, ch.channels_, cch.height_, cch.width_, ch.kernel_h_, ch.kernel_w_, ch.pad_h_,
- ch.pad_w_, ch.stride_h_, ch.stride_w_, dx_b);
- dx.CopyDataFromHostPtr(dx_b, imagesize, num * imagesize);
- }
- W.Reshape(w_shape);
- return dx;
-};
-
-Tensor CpuConvBackwardW(const Tensor &dy, const Tensor &x, const Tensor &W,
- const ConvHandle ch, const CpuConvHandle cch){
- CHECK_EQ(dy.device()->lang(), kCpp);
- CHECK_EQ(dy.nDim(), 4u);
-
- size_t imagesize = x.Size() / cch.batchsize;
-
- Tensor dW;
- dW.ResetLike(W);
- dW.SetValue(0.0f);
-
- Shape w_shape= W.shape();
- dW.Reshape(Shape{ch.num_filters_, cch.col_height_});
-
- Tensor col_data(Shape{cch.col_height_, cch.col_width_});//broadcasted image
-
- float *data_col = new float[cch.col_height_ * cch.col_width_];
- auto in_data = dy.data<float>();
- for (size_t num = 0; num < cch.batchsize; num++) {
- C.Im2col(in_data + num * imagesize, ch.channels_, cch.height_, cch.width_, ch.kernel_h_,
- ch.kernel_w_, ch.pad_h_, ch.pad_w_, ch.stride_h_, ch.stride_w_, data_col);
- col_data.CopyDataFromHostPtr(data_col, cch.col_height_ * cch.col_width_);
- Tensor grad_b(Shape{ch.num_filters_, cch.conv_height_ * cch.conv_width_});
- CopyDataToFrom(&grad_b, dy, grad_b.Size(), 0, num * grad_b.Size());
- dW += Mult(grad_b, col_data.T());
- }
- dW.Reshape(w_shape);
- //dW.Reshape(Shape{ch.num_filters_,ch.channels_ , ch.kernel_w_ , ch.kernel_h_});
- return dW;
-};
-
-Tensor CpuConvBackwardb(const Tensor &dy, const Tensor &b, const ConvHandle ch, const CpuConvHandle cch){
- CHECK_EQ(dy.device()->lang(), kCpp);
- CHECK_EQ(dy.nDim(), 4u);
-
- Tensor db;
- db.ResetLike(b);
-
- auto tmpshp = Shape{cch.batchsize * ch.num_filters_, dy.Size() / (cch.batchsize * ch.num_filters_)};
- Tensor tmp1 = Reshape(dy, tmpshp);
-
- Tensor tmp2(Shape{cch.batchsize * ch.num_filters_});
- SumColumns(tmp1, &tmp2);
- Tensor tmp3 = Reshape(tmp2, Shape{cch.batchsize, ch.num_filters_});
-
- SumRows(tmp3, &db);
-
- return db;
-};
-
-} //namespace_singa
-
-
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/189958ab/src/model/convolution_functions.h
----------------------------------------------------------------------
diff --git a/src/model/convolution_functions.h b/src/model/convolution_functions.h
deleted file mode 100644
index 1b90941..0000000
--- a/src/model/convolution_functions.h
+++ /dev/null
@@ -1,95 +0,0 @@
-#include <string>
-#include <cudnn.h>
-#include "./layer/cudnn_convolution.h"
-#include "./layer/cudnn_utils.h"
-#include "singa/utils/logging.h"
-
-namespace singa{
-
-struct ConvHandle{
- size_t kernel_w_;
- size_t pad_w_;
- size_t stride_w_;
- size_t kernel_h_;
- size_t pad_h_;
- size_t stride_h_;
-
- size_t channels_;
- size_t num_filters_;
-
- bool bias_term_;
-
- size_t workspace_byte_limit_;
- string prefer_;
-};
-
-struct CudnnConvHandle{
- cudnnTensorDescriptor_t x_desc_ ;
- cudnnTensorDescriptor_t y_desc_ ;
- cudnnTensorDescriptor_t bias_desc_ ;
- cudnnFilterDescriptor_t filter_desc_ ;
- cudnnConvolutionDescriptor_t conv_desc_ ;
- cudnnConvolutionFwdAlgo_t fp_alg_;
- cudnnConvolutionBwdFilterAlgo_t bp_filter_alg_;
- cudnnConvolutionBwdDataAlgo_t bp_data_alg_;
-
- size_t workspace_count_;
- Tensor workspace_;
-
- size_t height_;
- size_t width_;
- size_t conv_height_;
- size_t conv_width_;
- size_t batchsize;
-};
-
-struct CpuConvHandle{
- size_t height_;
- size_t width_;
- size_t conv_height_;
- size_t conv_width_;
- size_t batchsize;
-
- size_t col_height_;
- size_t col_width_;
-
-};
-
-
-
-ConvHandle SetupConv(
- const size_t kernel_h_, const size_t kernel_w_,
- const size_t pad_h_, const size_t pad_w_,
- const size_t stride_h_,const size_t stride_w_,
- const size_t channels_, const size_t num_filters_,
- const bool bias_term_ = true ,const size_t workspace_byte_limit_=1024*1024*1024,
- const std::string prefer_="fastest");
-
-void testInitCudnn(const Tensor &input, const ConvHandle ch);
-
-CudnnConvHandle InitCudnn(const Tensor &input, const ConvHandle ch);
-
-Tensor CudnnConvForward(const Tensor &x, const Tensor &W, const Tensor &b,
- const ConvHandle ch, const CudnnConvHandle cch);
-
-Tensor CudnnConvBackwardW(const Tensor &dy, const Tensor &x, const Tensor &W, const CudnnConvHandle cch);
-
-Tensor CudnnConvBackwardb(const Tensor &dy, const Tensor &b, const CudnnConvHandle cch);
-
-Tensor CudnnConvBackwardx(const Tensor &dy, const Tensor &W, const Tensor &x, const CudnnConvHandle cch);
-
-
-CpuConvHandle InitCpuHandle(const Tensor &input, const ConvHandle ch);
-
-Tensor CpuConvForward(const Tensor &x, Tensor &W, Tensor &b,
- const ConvHandle ch, const CpuConvHandle cch);
-
-Tensor CpuConvBackwardx(const Tensor &dy, Tensor &W, const Tensor &x,
- const ConvHandle ch, const CpuConvHandle cch);
-
-Tensor CpuConvBackwardW(const Tensor &dy, const Tensor &x, const Tensor &W,
- const ConvHandle ch, const CpuConvHandle cch);
-
-Tensor CpuConvBackwardb(const Tensor &dy, const Tensor &b, const ConvHandle ch, const CpuConvHandle cch);
-
-}
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/189958ab/src/model/operation/convolution_related.cc
----------------------------------------------------------------------
diff --git a/src/model/operation/convolution_related.cc b/src/model/operation/convolution_related.cc
new file mode 100644
index 0000000..1004074
--- /dev/null
+++ b/src/model/operation/convolution_related.cc
@@ -0,0 +1,417 @@
+#include "./convolution_related.h"
+#include "../layer/convolution.h"
+#include<iostream>
+
+namespace singa{
+
+Recorder SetupRecorder(const Tensor &input, const std::vector<size_t> kernel_size,
+ const std::vector<size_t> stride, const std::vector<size_t> padding,
+ const size_t in_channels, const size_t out_channels,
+ const bool bias_term_){
+ size_t kernel_w_;
+ size_t pad_w_;
+ size_t stride_w_;
+ size_t kernel_h_;
+ size_t pad_h_;
+ size_t stride_h_;
+
+ size_t height_;
+ size_t width_;
+ size_t conv_height_;
+ size_t conv_width_;
+ size_t batchsize;
+
+ size_t col_height_;
+ size_t col_width_;
+ size_t imagesize;
+
+ kernel_h_=kernel_size[0];
+ kernel_w_=kernel_size[1];
+
+ pad_h_=padding[0];
+ pad_w_=padding[1];
+
+ stride_h_=stride[0];
+ stride_w_=stride[1];
+
+ batchsize = input.shape(0);
+ CHECK(input.shape(1) == in_channels)<<"the number of input channels mismatched.";
+ height_ = input.shape(2);
+ width_ = input.shape(3);
+
+ conv_height_ = 1;
+ if (stride_h_ > 0)
+ conv_height_ = (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
+ conv_width_ = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
+
+ col_height_ = in_channels * kernel_w_ * kernel_h_;
+ col_width_ = conv_height_ * conv_width_;
+ imagesize = input.Size() / batchsize;
+
+ return Recorder{
+ kernel_w_,
+ pad_w_,
+ stride_w_,
+ kernel_h_,
+ pad_h_,
+ stride_h_,
+
+ in_channels,
+ out_channels,
+
+ bias_term_,
+
+ height_,
+ width_,
+ conv_height_,
+ conv_width_,
+ batchsize,
+
+ col_height_,
+ col_width_,
+ imagesize
+ };
+};
+
+Convolution C;
+
+Tensor CpuConvForward(const Tensor &x, Tensor &W, Tensor &b, const Recorder r){
+ CHECK_EQ(x.device()->lang(), kCpp);
+
+ CHECK(x.shape(1) == r.channels_ && x.shape(2) == r.height_ &&
+ x.shape(3) == r.width_) << "input sample shape should not change";
+
+ CHECK(W.shape(0) == r.num_filters_ && W.shape(1) == r.channels_ &&
+ W.shape(2) == r.kernel_h_ && W.shape(3) == r.kernel_w_) << "weights shape should not change";
+
+ Shape w_shape= W.shape();
+ Shape b_shape= b.shape();
+
+ W.Reshape(Shape{r.num_filters_, r.col_height_});
+ if (r.bias_term_)
+ b.Reshape(Shape{r.num_filters_});
+
+ DataType dtype = x.data_type();
+ auto dev = x.device();
+ Shape shape{r.batchsize, r.num_filters_, r.conv_height_, r.conv_width_};
+ Tensor output(shape, dev, dtype);
+
+ Tensor col_data(Shape{r.col_height_, r.col_width_});//broadcasted image
+
+ float *data_col = new float[r.col_height_ * r.col_width_];
+ auto in_data = x.data<float>();
+ for (size_t num = 0; num < r.batchsize; num++) {
+ C.Im2col(in_data + num * r.imagesize, r.channels_, r.height_, r.width_, r.kernel_h_,
+ r.kernel_w_, r.pad_h_, r.pad_w_, r.stride_h_, r.stride_w_, data_col);
+
+ col_data.CopyDataFromHostPtr(data_col, r.col_height_ * r.col_width_);
+ Tensor each = Mult(W, col_data);
+ if (r.bias_term_) {
+ AddColumn(b, &each);
+ }
+ CopyDataToFrom(&output, each, each.Size(), num * each.Size());
+ };
+ W.Reshape(w_shape);
+ b.Reshape(b_shape);
+ return output;
+};
+
+Tensor CpuConvBackwardx(const Tensor &dy, Tensor &W, const Tensor &x, const Recorder r){
+ CHECK_EQ(dy.device()->lang(), kCpp);
+
+ CHECK(dy.shape(1) == r.num_filters_ && dy.shape(2) == r.conv_height_ &&
+ dy.shape(3) == r.conv_width_) << "input gradients shape should not change";
+
+ CHECK(W.shape(0) == r.num_filters_ && W.shape(1) == r.channels_ &&
+ W.shape(2) == r.kernel_h_ && W.shape(3) == r.kernel_w_) << "weights shape should not change";
+
+ Shape w_shape= W.shape();
+ W.Reshape(Shape{r.num_filters_, r.col_height_});
+
+ Tensor dx;
+ dx.ResetLike(x);
+
+ float *dx_b = new float[r.imagesize];
+
+ for (size_t num = 0; num < r.batchsize; num++) {
+ Tensor grad_b(Shape{r.num_filters_, r.conv_height_ * r.conv_width_});
+ CopyDataToFrom(&grad_b, dy, grad_b.Size(), 0, num * grad_b.Size());
+ Tensor dcol_b = Mult(W.T(), grad_b);
+ auto dcol_data = dcol_b.data<float>();
+ C.Col2im(dcol_data, r.channels_, r.height_, r.width_, r.kernel_h_, r.kernel_w_, r.pad_h_,
+ r.pad_w_, r.stride_h_, r.stride_w_, dx_b);
+ dx.CopyDataFromHostPtr(dx_b, r.imagesize, num * r.imagesize);
+ }
+ W.Reshape(w_shape);
+ return dx;
+};
+
+Tensor CpuConvBackwardW(const Tensor &dy, const Tensor &x, const Tensor &W, const Recorder r){
+ CHECK_EQ(dy.device()->lang(), kCpp);
+
+ CHECK(dy.shape(1) == r.num_filters_ && dy.shape(2) == r.conv_height_ &&
+ dy.shape(3) == r.conv_width_) << "input gradients shape should not change";
+
+ CHECK(x.shape(1) == r.channels_ && x.shape(2) == r.height_ &&
+ x.shape(3) == r.width_) << "input sample shape should not change";
+
+ Tensor dW;
+ dW.ResetLike(W);
+ dW.SetValue(0.0f);
+
+ Shape w_shape= W.shape();
+ dW.Reshape(Shape{r.num_filters_, r.col_height_});
+
+ Tensor col_data(Shape{r.col_height_, r.col_width_});//broadcasted image
+
+ float *data_col = new float[r.col_height_ * r.col_width_];
+ auto in_data = dy.data<float>();
+ for (size_t num = 0; num < r.batchsize; num++) {
+ C.Im2col(in_data + num * r.imagesize, r.channels_, r.height_, r.width_, r.kernel_h_,
+ r.kernel_w_, r.pad_h_, r.pad_w_, r.stride_h_, r.stride_w_, data_col);
+ col_data.CopyDataFromHostPtr(data_col, r.col_height_ * r.col_width_);
+ Tensor grad_b(Shape{r.num_filters_, r.conv_height_ * r.conv_width_});
+ CopyDataToFrom(&grad_b, dy, grad_b.Size(), 0, num * grad_b.Size());
+ dW += Mult(grad_b, col_data.T());
+ }
+ dW.Reshape(w_shape);
+ return dW;
+};
+
+Tensor CpuConvBackwardb(const Tensor &dy, const Tensor &b, const Recorder r){
+ CHECK_EQ(dy.device()->lang(), kCpp);
+
+ CHECK(dy.shape(1) == r.num_filters_ && dy.shape(2) == r.conv_height_ &&
+ dy.shape(3) == r.conv_width_) << "input gradients shape should not change";
+
+ CHECK(b.shape(0) == r.num_filters_)<< "bias shape should not change";
+
+ Tensor db;
+ db.ResetLike(b);
+
+ auto tmpshp = Shape{r.batchsize * r.num_filters_, dy.Size() / (r.batchsize * r.num_filters_)};
+ Tensor tmp1 = Reshape(dy, tmpshp);
+
+ Tensor tmp2(Shape{r.batchsize * r.num_filters_});
+ SumColumns(tmp1, &tmp2);
+ Tensor tmp3 = Reshape(tmp2, Shape{r.batchsize, r.num_filters_});
+
+ SumRows(tmp3, &db);
+
+ return db;
+};
+
+CudnnConvHandles InitCudnnConvHandles(const Tensor &input, const Recorder r, const size_t workspace_byte_limit_,
+ const std::string prefer_){
+
+ CHECK(input.shape(0) == r.batchsize && input.shape(1) == r.channels_ && input.shape(2) == r.height_ &&
+ input.shape(3) == r.width_) << "input sample shape dismatched";
+
+ cudnnTensorDescriptor_t x_desc_ ;
+ cudnnTensorDescriptor_t y_desc_ ;
+ cudnnTensorDescriptor_t bias_desc_ ;
+ cudnnFilterDescriptor_t filter_desc_ ;
+ cudnnConvolutionDescriptor_t conv_desc_ ;
+ cudnnConvolutionFwdAlgo_t fp_alg_;
+ cudnnConvolutionBwdFilterAlgo_t bp_filter_alg_;
+ cudnnConvolutionBwdDataAlgo_t bp_data_alg_;
+
+ size_t workspace_count_;
+ Tensor workspace_;
+
+ DataType dtype = input.data_type();
+ auto dev = input.device();
+ Context *ctx = dev->context(0);
+
+ CUDNN_CHECK(cudnnCreateTensorDescriptor(&x_desc_));
+ CUDNN_CHECK(cudnnCreateTensorDescriptor(&y_desc_));
+ if (r.bias_term_)
+ CUDNN_CHECK(cudnnCreateTensorDescriptor(&bias_desc_));
+ CUDNN_CHECK(cudnnCreateFilterDescriptor(&filter_desc_));
+ CUDNN_CHECK(cudnnCreateConvolutionDescriptor(&conv_desc_));
+
+
+ CUDNN_CHECK(cudnnSetTensor4dDescriptor(x_desc_, CUDNN_TENSOR_NCHW,
+ GetCudnnDataType(dtype), r.batchsize,
+ r.channels_, r.height_, r.width_));
+ CUDNN_CHECK(cudnnSetTensor4dDescriptor(
+ y_desc_, CUDNN_TENSOR_NCHW, GetCudnnDataType(dtype), r.batchsize,
+ r.num_filters_, r.conv_height_, r.conv_width_));
+ if (r.bias_term_)
+ CUDNN_CHECK(cudnnSetTensor4dDescriptor(bias_desc_, CUDNN_TENSOR_NCHW,
+ GetCudnnDataType(dtype), 1,
+ r.num_filters_, 1, 1));
+ CUDNN_CHECK(cudnnSetConvolution2dDescriptor(conv_desc_, r.pad_h_, r.pad_w_,
+ r.stride_h_, r.stride_w_, 1, 1,
+ CUDNN_CROSS_CORRELATION,
+ GetCudnnDataType(dtype)));
+ CUDNN_CHECK(cudnnSetFilter4dDescriptor(filter_desc_, GetCudnnDataType(dtype),
+ CUDNN_TENSOR_NCHW, r.num_filters_,
+ r.channels_, r.kernel_h_, r.kernel_w_));
+ if (prefer_ == "fastest" || prefer_ == "limited_workspace" ||
+ prefer_ == "no_workspace") {
+ cudnnConvolutionFwdPreference_t fwd_pref;
+ cudnnConvolutionBwdFilterPreference_t bwd_filt_pref;
+ cudnnConvolutionBwdDataPreference_t bwd_data_pref;
+ if (prefer_ == "fastest") {
+ fwd_pref = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST;
+ bwd_filt_pref = CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST;
+ bwd_data_pref = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST;
+ } else if (prefer_ == "limited_workspace") {
+ fwd_pref = CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT;
+ bwd_filt_pref = CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT;
+ bwd_data_pref = CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT;
+ } else {
+ fwd_pref = CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
+ bwd_filt_pref = CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE;
+ bwd_data_pref = CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT;
+ }
+ CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(
+ ctx->cudnn_handle, x_desc_, filter_desc_, conv_desc_, y_desc_, fwd_pref,
+ workspace_byte_limit_, &fp_alg_));
+ CUDNN_CHECK(cudnnGetConvolutionBackwardFilterAlgorithm(
+ ctx->cudnn_handle, x_desc_, y_desc_, conv_desc_, filter_desc_,
+ bwd_filt_pref, workspace_byte_limit_, &bp_filter_alg_));
+ // deprecated in cudnn v7
+ CUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm(
+ ctx->cudnn_handle, filter_desc_, y_desc_, conv_desc_, x_desc_,
+ bwd_data_pref, workspace_byte_limit_, &bp_data_alg_));
+ } else if (prefer_ == "autotune") {
+ const int topk = 1;
+ int num_fp_alg, num_bp_filt_alg, num_bp_data_alg;
+ cudnnConvolutionFwdAlgoPerf_t fp_alg_perf[topk];
+ cudnnConvolutionBwdFilterAlgoPerf_t bp_filt_perf[topk];
+ cudnnConvolutionBwdDataAlgoPerf_t bp_data_perf[topk];
+ CUDNN_CHECK(cudnnFindConvolutionForwardAlgorithm(
+ ctx->cudnn_handle, x_desc_, filter_desc_, conv_desc_, y_desc_, topk,
+ &num_fp_alg, fp_alg_perf));
+ fp_alg_ = fp_alg_perf[0].algo;
+ CUDNN_CHECK(cudnnFindConvolutionBackwardFilterAlgorithm(
+ ctx->cudnn_handle, x_desc_, y_desc_, conv_desc_, filter_desc_, topk,
+ &num_bp_filt_alg, bp_filt_perf));
+ bp_filter_alg_ = bp_filt_perf[0].algo;
+ CUDNN_CHECK(cudnnFindConvolutionBackwardDataAlgorithm(
+ ctx->cudnn_handle, filter_desc_, y_desc_, conv_desc_, x_desc_, topk,
+ &num_bp_data_alg, bp_data_perf));
+ bp_data_alg_ = bp_data_perf[0].algo;
+ } else {
+ LOG(FATAL) << "Preferred algorithm is not available!";
+ }
+
+ size_t fp_byte, bp_data_byte, bp_filter_byte;
+ CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(
+ ctx->cudnn_handle, x_desc_, filter_desc_, conv_desc_, y_desc_, fp_alg_,
+ &fp_byte));
+ CUDNN_CHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(
+ ctx->cudnn_handle, filter_desc_, y_desc_, conv_desc_, x_desc_,
+ bp_data_alg_, &bp_data_byte));
+ CUDNN_CHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(
+ ctx->cudnn_handle, x_desc_, y_desc_, conv_desc_, filter_desc_,
+ bp_filter_alg_, &bp_filter_byte));
+ workspace_count_ = std::max(std::max(fp_byte, bp_data_byte), bp_filter_byte) /
+ sizeof(float) +
+ 1;
+ if (workspace_count_ * sizeof(float) > workspace_byte_limit_)
+ LOG(WARNING) << "The required memory for workspace ("
+ << workspace_count_ * sizeof(float)
+ << ") is larger than the expected Bytes ("
+ << workspace_byte_limit_ << ")";
+ workspace_ = Tensor(Shape{workspace_count_}, dev, dtype);
+
+};
+
+Tensor GpuConvForward(const Tensor &x, const Tensor &W, const Tensor &b, const Recorder r, const CudnnConvHandles cch){
+ CHECK_EQ(x.device()->lang(), kCuda);
+
+ DataType dtype = x.data_type();
+ auto dev = x.device();
+
+ Shape shape{r.batchsize, r.num_filters_, r.conv_height_, r.conv_width_};
+ Tensor output(shape, dev, dtype);
+
+ output.device()->Exec([output, x, W, cch](Context *ctx) {
+ Block *inblock = x.block(), *outblock = output.block(),
+ *wblock = W.block();
+ float alpha = 1.f, beta = 0.f;
+ cudnnConvolutionForward(ctx->cudnn_handle, &alpha, cch.x_desc_,
+ inblock->data(), cch.filter_desc_, wblock->data(),
+ cch.conv_desc_, cch.fp_alg_,
+ cch.workspace_.block()->mutable_data(),
+ cch.workspace_count_ * sizeof(float), &beta,
+ cch.y_desc_, outblock->mutable_data());
+ }, {x.block(), W.block()}, {output.block()}, cch.workspace_.block());
+
+ if (r.bias_term_) {
+ output.device()->Exec([output, b, cch](Context *ctx) {
+ float beta = 1.f, alpha = 1.0f;
+ Block *outblock = output.block(), *bblock = b.block();
+ cudnnAddTensor(ctx->cudnn_handle, &alpha, cch.bias_desc_,
+ bblock->data(), &beta, cch.y_desc_,
+ outblock->mutable_data());
+ }, {output.block(), b.block()}, {output.block()});
+ }
+
+ return output;
+};
+
+Tensor GpuConvBackwardx(const Tensor &dy, const Tensor &W, const Tensor &x, const CudnnConvHandles cch){
+ CHECK_EQ(dy.device()->lang(), kCuda);
+
+ Tensor dx;
+ dx.ResetLike(x);
+
+ dy.device()->Exec([dx, dy, W, cch](Context *ctx) {
+ Block *wblock = W.block(), *dyblock = dy.block(),
+ *dxblock = dx.block();
+ float alpha = 1.f, beta = 0.f;
+ cudnnConvolutionBackwardData(ctx->cudnn_handle, &alpha, cch.filter_desc_,
+ wblock->data(), cch.y_desc_, dyblock->data(),
+ cch.conv_desc_, cch.bp_data_alg_,
+ cch.workspace_.block()->mutable_data(),
+ cch.workspace_count_ * sizeof(float), &beta,
+ cch.x_desc_, dxblock->mutable_data());
+ }, {dy.block(), W.block()}, {dx.block(), cch.workspace_.block()});
+
+ return dx;
+};
+
+Tensor GpuConvBackwardW(const Tensor &dy, const Tensor &x, const Tensor &W, const CudnnConvHandles cch){
+ CHECK_EQ(dy.device()->lang(), kCuda);
+
+ Tensor dW;
+ dW.ResetLike(W);
+
+ dy.device()->Exec([dW, dy, x, W, cch](Context *ctx) {
+ Block *inblock = x.block(), *dyblock = dy.block(),
+ *dwblock = dW.block();
+ float alpha = 1.f, beta = 0.f;
+ cudnnConvolutionBackwardFilter(
+ ctx->cudnn_handle, &alpha, cch.x_desc_, inblock->data(),
+ cch.y_desc_, dyblock->data(), cch.conv_desc_, cch.bp_filter_alg_,
+ cch.workspace_.block()->mutable_data(),
+ cch.workspace_count_ * sizeof(float), &beta, cch.filter_desc_,
+ dwblock->mutable_data());
+ }, {dy.block(), x.block()}, {dW.block(), cch.workspace_.block()});
+
+ return dW;
+};
+
+// input Tensor b for Reset db purpose, can avoid this later.
+Tensor GpuConvBackwardb(const Tensor &dy, const Tensor &b, const CudnnConvHandles cch){
+ CHECK_EQ(dy.device()->lang(), kCuda);
+
+ Tensor db;
+ db.ResetLike(b);
+
+ dy.device()->Exec([db, dy, b, cch](Context *ctx) {
+ Block *dyblock = dy.block(), *dbblock = db.block();
+ float alpha = 1.f, beta = 0.f;
+ cudnnConvolutionBackwardBias(ctx->cudnn_handle, &alpha, cch.y_desc_,
+ dyblock->data(), &beta, cch.bias_desc_,
+ dbblock->mutable_data());
+ }, {dy.block()}, {db.block()});
+
+ return db;
+};
+
+}
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/189958ab/src/model/operation/convolution_related.h
----------------------------------------------------------------------
diff --git a/src/model/operation/convolution_related.h b/src/model/operation/convolution_related.h
new file mode 100644
index 0000000..49aab5b
--- /dev/null
+++ b/src/model/operation/convolution_related.h
@@ -0,0 +1,75 @@
+#include <string>
+#include <vector>
+#include <cudnn.h>
+#include "../layer/cudnn_convolution.h"
+#include "../layer/cudnn_utils.h"
+#include "singa/utils/logging.h"
+
+namespace singa{
+
+struct Recorder{
+ size_t kernel_w_;
+ size_t pad_w_;
+ size_t stride_w_;
+ size_t kernel_h_;
+ size_t pad_h_;
+ size_t stride_h_;
+
+ size_t channels_;
+ size_t num_filters_;
+
+ bool bias_term_;
+
+ size_t height_;
+ size_t width_;
+ size_t conv_height_;
+ size_t conv_width_;
+ size_t batchsize;
+
+ size_t col_height_;
+ size_t col_width_;
+ size_t imagesize;
+};
+
+struct CudnnConvHandles{
+ cudnnTensorDescriptor_t x_desc_ ;
+ cudnnTensorDescriptor_t y_desc_ ;
+ cudnnTensorDescriptor_t bias_desc_ ;
+ cudnnFilterDescriptor_t filter_desc_ ;
+ cudnnConvolutionDescriptor_t conv_desc_ ;
+ cudnnConvolutionFwdAlgo_t fp_alg_;
+ cudnnConvolutionBwdFilterAlgo_t bp_filter_alg_;
+ cudnnConvolutionBwdDataAlgo_t bp_data_alg_;
+
+ size_t workspace_count_;
+ Tensor workspace_;
+};
+
+
+Recorder SetupRecorder(const Tensor &input, const std::vector<size_t> kernel_size,
+ const std::vector<size_t> stride, const std::vector<size_t> padding,
+ const size_t in_channels, const size_t out_channels,
+ const bool bias_term_);
+
+CudnnConvHandles InitCudnnConvHandles(const Tensor &input, const Recorder r, const size_t workspace_byte_limit_=1024*1024*1024,
+ const std::string prefer_="fastest");
+
+Tensor GpuConvForward(const Tensor &x, const Tensor &W, const Tensor &b, const Recorder r, const CudnnConvHandles cch);
+
+Tensor GpuConvBackwardx(const Tensor &dy, const Tensor &W, const Tensor &x, const CudnnConvHandles cch);
+
+Tensor GpuConvBackwardW(const Tensor &dy, const Tensor &x, const Tensor &W, const CudnnConvHandles cch);
+
+Tensor GpuConvBackwardb(const Tensor &dy, const Tensor &b, const CudnnConvHandles cch);
+
+
+
+Tensor CpuConvForward(const Tensor &x, Tensor &W, Tensor &b, const Recorder r);
+
+Tensor CpuConvBackwardx(const Tensor &dy, Tensor &W, const Tensor &x, const Recorder r);
+
+Tensor CpuConvBackwardW(const Tensor &dy, const Tensor &x, const Tensor &W, const Recorder r);
+
+Tensor CpuConvBackwardb(const Tensor &dy, const Tensor &b, const Recorder r);
+
+}
\ No newline at end of file