Skip to content

Revert "[Caffe2] MIOpen dims change check" #230

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Sep 29, 2018
Merged

Revert "[Caffe2] MIOpen dims change check" #230

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
222 changes: 93 additions & 129 deletions caffe2/operators/hip/conv_op_miopen.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,19 @@ class MIOPENConvOpBase : public ConvPoolOpBase<HIPContext> {
dilation_h() == 1 && dilation_w() == 1,
"MIOpen convolution does not support dilation for groups > 1.");
}

MIOPEN_ENFORCE(miopenInitConvolutionDescriptor(
conv_desc_,
mode_,
pad_t(),
pad_l(),
stride_h(),
stride_w(),
dilation_h(),
dilation_w()));

MIOPEN_ENFORCE(miopenSetConvolutionGroupCount(
conv_desc_, group_));
}

~MIOPENConvOpBase() {
Expand All @@ -78,8 +91,6 @@ class MIOPENConvOpBase : public ConvPoolOpBase<HIPContext> {
}

protected:
vector<int64_t> mio_input_dims_;
vector<int64_t> mio_weight_dims_;
MIOPENWrapper miopen_wrapper_;
miopenTensorDescriptor_t bottom_desc_;
miopenTensorDescriptor_t bias_desc_;
Expand Down Expand Up @@ -246,59 +257,35 @@ bool MIOPENConvOp::DoRunWithType() {
"If you set group, the number of output channels should be divisible "
"by group.");

bool input_changed = (X.dims() != mio_input_dims_);
bool weight_changed = (Weight.dims() != mio_weight_dims_);

if (input_changed || weight_changed) {
VLOG(1) << "Changing MIOpen descriptor configurations.";
if (input_changed) {
mio_input_dims_ = X.dims();
MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
bottom_desc_, miopenTypeWrapper<T_X>::type, N, C, H, W));
}
MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
bottom_desc_, miopenTypeWrapper<T_X>::type, N, C, H, W));

if (weight_changed) {
mio_weight_dims_ = Weight.dims();
MIOPEN_ENFORCE(miopenInitConvolutionDescriptor(
conv_desc_,
mode_,
pad_t(),
pad_l(),
stride_h(),
stride_w(),
dilation_h(),
dilation_w()));

MIOPEN_ENFORCE(miopenSetConvolutionGroupCount(
conv_desc_, group_));

MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
weight_desc_,
miopenTypeWrapper<T_W>::type,
M,
C / group_,
kernel_h(),
kernel_w()));
}
MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
weight_desc_,
miopenTypeWrapper<T_W>::type,
M,
C / group_,
kernel_h(),
kernel_w()));

MIOPEN_ENFORCE(miopenGetConvolutionForwardOutputDim(
conv_desc_,
bottom_desc_,
weight_desc_,
&N_out,
&C_out,
&H_out,
&W_out));
MIOPEN_ENFORCE(miopenGetConvolutionForwardOutputDim(
conv_desc_,
bottom_desc_,
weight_desc_,
&N_out,
&C_out,
&H_out,
&W_out));

MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
top_desc_, miopenTypeWrapper<T_X>::type, N_out, C_out, H_out, W_out));
MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
top_desc_, miopenTypeWrapper<T_X>::type, N_out, C_out, H_out, W_out));

if (InputSize() == 3) {
if (InputSize() == 3) {
MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
bias_desc_, miopenTypeWrapper<T_B>::type, 1, M, 1, 1));
}
}

while (!bestAlgoFound_) {
while (!bestAlgoFound_) {
miopenConvAlgoPerf_t perf;

MIOPEN_ENFORCE(miopenConvolutionForwardGetWorkSpaceSize(
Expand Down Expand Up @@ -331,8 +318,8 @@ bool MIOPENConvOp::DoRunWithType() {
});
bestAlgoFound_ = true;
fwdAlgo_ = perf.fwd_algo;
}
}

miopen_wrapper_.with_miopen_state(miopen_state_, [&](MIOPENState* state) {
MIOPEN_ENFORCE(miopenConvolutionForward(
state->miopen_handle(),
Expand Down Expand Up @@ -437,59 +424,36 @@ bool MIOPENConvGradientOp::DoRunWithType() {
"by group.");

bool doBwdDataComputation = (OutputSize() == 3 || (no_bias_ && (OutputSize() == 2)));
bool input_changed = (X.dims() != mio_input_dims_);
bool weight_changed = (Weight.dims() != mio_weight_dims_);

if (input_changed || weight_changed) {
VLOG(1) << "Changing MIOpen descriptor configurations.";
if (input_changed) {
mio_input_dims_ = X.dims();
MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
bottom_desc_, miopenTypeWrapper<T_X>::type, N, C, H, W));
}

if (weight_changed) {
mio_weight_dims_ = Weight.dims();
MIOPEN_ENFORCE(miopenInitConvolutionDescriptor(
conv_desc_,
mode_,
pad_t(),
pad_l(),
stride_h(),
stride_w(),
dilation_h(),
dilation_w()));

MIOPEN_ENFORCE(miopenSetConvolutionGroupCount(
conv_desc_, group_));
MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
bottom_desc_, miopenTypeWrapper<T_X>::type, N, C, H, W));

MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
weight_desc_,
miopenTypeWrapper<T_X>::type,
M,
C / group_,
kernel_h(),
kernel_w()));
}
MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
weight_desc_,
miopenTypeWrapper<T_X>::type,
M,
C / group_,
kernel_h(),
kernel_w()));

MIOPEN_ENFORCE(miopenGetConvolutionForwardOutputDim(
conv_desc_,
bottom_desc_,
weight_desc_,
&N_out,
&C_out,
&H_out,
&W_out));
MIOPEN_ENFORCE(miopenGetConvolutionForwardOutputDim(
conv_desc_,
bottom_desc_,
weight_desc_,
&N_out,
&C_out,
&H_out,
&W_out));

MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
top_desc_, miopenTypeWrapper<T_X>::type, N_out, C_out, H_out, W_out));
MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
top_desc_, miopenTypeWrapper<T_X>::type, N_out, C_out, H_out, W_out));

if (!no_bias_) {
MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
bias_desc_, miopenTypeWrapper<T_B>::type, 1, M, 1, 1));
}
if (!no_bias_) {
MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
bias_desc_, miopenTypeWrapper<T_B>::type, 1, M, 1, 1));
}

while ((!bestDataAlgoFound_) && doBwdDataComputation) {
while ((!bestDataAlgoFound_) && doBwdDataComputation) {
miopenConvAlgoPerf_t perf;

MIOPEN_ENFORCE(miopenConvolutionBackwardDataGetWorkSpaceSize(
Expand Down Expand Up @@ -523,43 +487,43 @@ bool MIOPENConvGradientOp::DoRunWithType() {

bestDataAlgoFound_ = true;
bwdDataAlgo_ = perf.bwd_data_algo;
}
}

while (!bestWeightAlgoFound_) {
miopenConvAlgoPerf_t perf;
while (!bestWeightAlgoFound_) {
miopenConvAlgoPerf_t perf;

MIOPEN_ENFORCE(miopenConvolutionBackwardWeightsGetWorkSpaceSize(
miopen_wrapper_.inline_miopen_handle(),
top_desc_,
bottom_desc_,
conv_desc_,
weight_desc_,
&bwdWeightWsSize_));
if ((bwdWeightWsSize_ > 0) && (bwdWeightWs_ == nullptr)) {
HIP_CHECK(hipMalloc(&bwdWeightWs_, bwdWeightWsSize_));
}
MIOPEN_ENFORCE(miopenConvolutionBackwardWeightsGetWorkSpaceSize(
miopen_wrapper_.inline_miopen_handle(),
top_desc_,
bottom_desc_,
conv_desc_,
weight_desc_,
&bwdWeightWsSize_));
if ((bwdWeightWsSize_ > 0) && (bwdWeightWs_ == nullptr)) {
HIP_CHECK(hipMalloc(&bwdWeightWs_, bwdWeightWsSize_));
}

miopen_wrapper_.with_miopen_state(miopen_state_, [&](MIOPENState* state) {
MIOPEN_ENFORCE(miopenFindConvolutionBackwardWeightsAlgorithm(
state->miopen_handle(),
top_desc_,
dY.template data<T_DY>(),
bottom_desc_,
X.template data<T_X>(),
conv_desc_,
weight_desc_,
dW->template mutable_data<T_DW>(),
requestAlgoCount_,
&returnedAlgoCount_,
&perf,
bwdWeightWs_,
bwdWeightWsSize_,
false));
});
bestWeightAlgoFound_ = true;
bwdWeiAlgo_ = perf.bwd_weights_algo;
}
miopen_wrapper_.with_miopen_state(miopen_state_, [&](MIOPENState* state) {
MIOPEN_ENFORCE(miopenFindConvolutionBackwardWeightsAlgorithm(
state->miopen_handle(),
top_desc_,
dY.template data<T_DY>(),
bottom_desc_,
X.template data<T_X>(),
conv_desc_,
weight_desc_,
dW->template mutable_data<T_DW>(),
requestAlgoCount_,
&returnedAlgoCount_,
&perf,
bwdWeightWs_,
bwdWeightWsSize_,
false));
});
bestWeightAlgoFound_ = true;
bwdWeiAlgo_ = perf.bwd_weights_algo;
}

if (doBwdDataComputation) {
miopen_wrapper_.with_miopen_state(miopen_state_, [&](MIOPENState* state) {
MIOPEN_ENFORCE(miopenConvolutionBackwardData(
Expand Down