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Sarkars/batchnorm update #318

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Sarkars/batchnorm update #318

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caa71d5
Changing batchnorm api and adding tests. One test fails
sayantan-nervana Nov 19, 2018
6cffbcb
Fixed second test
sayantan-nervana Nov 19, 2018
b2e49af
Changing tag to ngraph 0.10 release
sayantan-nervana Nov 30, 2018
acbbfd6
Merge branch 'master' into sarkars/batchnorm_update
sayantan-nervana Nov 30, 2018
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32 changes: 17 additions & 15 deletions src/ngraph_builder.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -1717,34 +1717,36 @@ static Status TranslateFusedBatchNormOp(
std::shared_ptr<ng::Node> ng_batch_norm;

if (tf_is_training) {
ng_batch_norm = make_shared<ng::op::BatchNormTraining>(tf_epsilon, ng_scale,
ng_offset, ng_input);
ng_batch_norm = make_shared<ng::op::BatchNormTraining>(
ng_input, ng_scale, ng_offset, tf_epsilon);

shared_ptr<ngraph::Node> ng_y, ng_mean, ng_variance;
ng_y = make_shared<ng::op::GetOutputElement>(ng_batch_norm, 0);
ng_mean = make_shared<ng::op::GetOutputElement>(ng_batch_norm, 1);
ng_variance = make_shared<ng::op::GetOutputElement>(ng_batch_norm, 2);
shared_ptr<ngraph::Node> ng_y_out, ng_mean_out, ng_variance_out;
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I must misunderstand about the training op orders. In ngraph, shouldn't the output order be {gamma, beta, input}? Could you please explain this a little bit?

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So this PR was to sync with this PR in ngraph, which reorders batch norm: NervanaSystems/ngraph#2046 (comment)

But apparently that has been closed, and will come later, so I suppose we don't have to do anything now.

ng_y_out = make_shared<ng::op::GetOutputElement>(ng_batch_norm, 0);
ng_mean_out = make_shared<ng::op::GetOutputElement>(ng_batch_norm, 1);
ng_variance_out = make_shared<ng::op::GetOutputElement>(ng_batch_norm, 2);

BatchToTensorflow(is_nhwc, ng_y);
BatchToTensorflow(is_nhwc, ng_y_out);

SaveNgOp(ng_op_map, op->name(), ng_y);
SaveNgOp(ng_op_map, op->name(), ng_mean);
SaveNgOp(ng_op_map, op->name(), ng_variance);
SaveNgOp(ng_op_map, op->name(), ng_y_out);
SaveNgOp(ng_op_map, op->name(), ng_mean_out);
SaveNgOp(ng_op_map, op->name(), ng_variance_out);
// Output reserve_space_1: A 1D Tensor for the computed batch mean, to be
// reused in the gradient computation.
SaveNgOp(ng_op_map, op->name(), ng_mean);
SaveNgOp(ng_op_map, op->name(), ng_mean_out);
// Output reserve_space_2: A 1D Tensor for the computed batch variance
//(inverted variance in the cuDNN case), to be reused in the gradient
// computation.
SaveNgOp(ng_op_map, op->name(), ng_variance);
SaveNgOp(ng_op_map, op->name(), ng_variance_out);
} else {
ng_batch_norm = make_shared<ng::op::BatchNormInference>(
tf_epsilon, ng_scale, ng_offset, ng_input, ng_mean, ng_variance);
ng_input, ng_scale, ng_offset, ng_mean, ng_variance, tf_epsilon);
BatchToTensorflow(is_nhwc, ng_batch_norm);
SaveNgOp(ng_op_map, op->name(), ng_batch_norm);
// When train=false, only one output is expected
// Note here: EXPECT_EQ(1, tensors_expected.size());
// https://github.com/tensorflow/tensorflow/blob/a767a02ca976d00b9e8e06042bdc2a2bb33b00eb/tensorflow/core/grappler/optimizers/remapper_test.cc#L47
}

SaveNgOp(ng_op_map, op->name(), ng_batch_norm);
return Status::OK();
}

Expand Down Expand Up @@ -1807,7 +1809,7 @@ static Status TranslateFusedBatchNormGradOp(
std::shared_ptr<ng::Node> ng_batch_norm_backprop;

ng_batch_norm_backprop = make_shared<ng::op::BatchNormTrainingBackprop>(
tf_epsilon, ng_scale, ng_beta, ng_input, ng_mean, ng_variance, ng_delta);
ng_input, ng_scale, ng_beta, ng_mean, ng_variance, ng_delta, tf_epsilon);

shared_ptr<ngraph::Node> ng_input_delta_op =
make_shared<ng::op::GetOutputElement>(ng_batch_norm_backprop, 0);
Expand Down
75 changes: 75 additions & 0 deletions test/test_nn_ops.cpp
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Expand Up @@ -912,6 +912,81 @@ TEST(NNOps, Conv2DBackpropInputNHWCWithDilation) {
}
} // end of op Conv2DBackpropInputNHWCWithDilation

// FusedBatchNorm : Forward pass, training = true
// TODO fix this test
TEST(NNOps, DISABLED_FusedBatchNormNHWCTrainTrue) {
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This test does not pass. Sample output:

[ RUN      ] NNOps.DISABLE_FusedBatchNormNHWCTrainTrue
2018-11-19 01:03:39.177831: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool wit
h default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.   
/localdisk/sarkars/workspace1/tf_ngtf_7_mkl_1_12/ngraph-tf/test/test_utilities.h:126: Failure  
Value of: rt
  Actual: false
Expected: true
 TF output 20.955995559692383
 NG output 20.606725692749023
/localdisk/sarkars/workspace1/tf_ngtf_7_mkl_1_12/ngraph-tf/test/test_utilities.h:126: Failure  
Value of: rt
  Actual: false
Expected: true
 TF output 21.971120834350586
 NG output 21.604936599731445
[  FAILED  ] NNOps.DISABLE_FusedBatchNormNHWCTrainTrue (125 ms)

Scope root = Scope::NewRootScope();

// 4D tensor for the gradient with respect to y
Tensor x(DT_FLOAT, TensorShape({5, 4, 3, 2}));
// 4D tensor for input data
Tensor scale(DT_FLOAT, TensorShape({2}));
// 1D tensor for scaling the normalized x
Tensor offset(DT_FLOAT, TensorShape({2}));
// 1D (empty) tensor for population mean
Tensor mean(DT_FLOAT, TensorShape({0}));
// 1D (empty) tensor for population variance
Tensor variance(DT_FLOAT, TensorShape({0}));

AssignInputValuesRandom<float>(x, -10.0f, 5.0f);
AssignInputValuesRandom<float>(scale, -10.0f, 5.0f);
AssignInputValuesRandom<float>(offset, -2.0f, 2.0f);
// We will not fill mean and variance,
// since it is not used when training=true

auto attrs = ops::FusedBatchNorm::Attrs();
attrs.is_training_ = true;
attrs.epsilon_ = 0.0001f;
attrs.data_format_ = "NHWC";

// test grab all the outputs from the FusedBatchNorm op
vector<int> static_input_indexes = {};
vector<DataType> output_datatypes(5, DT_FLOAT);
auto R = ops::FusedBatchNorm(root, x, scale, offset, mean, variance, attrs);
std::vector<Output> sess_run_fetchoutputs = {
R.y, R.batch_mean, R.batch_variance, R.reserve_space_1,
R.reserve_space_2};
OpExecuter opexecuter(root, "FusedBatchNorm", static_input_indexes,
output_datatypes, sess_run_fetchoutputs);
opexecuter.RunTest();
}

// FusedBatchNorm : Forward pass, training = false
TEST(NNOps, FusedBatchNormNHWCTrainFalse) {
Scope root = Scope::NewRootScope();

// 4D tensor for the gradient with respect to y
Tensor x(DT_FLOAT, TensorShape({5, 4, 3, 2}));
// 4D tensor for input data
Tensor scale(DT_FLOAT, TensorShape({2}));
// 1D tensor for scaling the normalized x
Tensor offset(DT_FLOAT, TensorShape({2}));
// 1D tensor for population mean
Tensor mean(DT_FLOAT, TensorShape({2}));
// 1D tensor for population variance
Tensor variance(DT_FLOAT, TensorShape({2}));

AssignInputValuesRandom<float>(x, -5.0f, 10.0f);
AssignInputValuesRandom<float>(scale, -10.0f, 10.0f);
AssignInputValuesRandom<float>(offset, -1.6f, 1.6f);
AssignInputValuesRandom<float>(mean, 1.1f, 1.5f);
AssignInputValuesRandom<float>(variance, 0.5f, 1.5f);

auto attrs = ops::FusedBatchNorm::Attrs();
attrs.is_training_ = false;
attrs.epsilon_ = 0.0001f;
attrs.data_format_ = "NHWC";

// test grab all the outputs from the FusedBatchNorm op
vector<int> static_input_indexes = {};
vector<DataType> output_datatypes = {DT_FLOAT};
auto R = ops::FusedBatchNorm(root, x, scale, offset, mean, variance, attrs);
std::vector<Output> sess_run_fetchoutputs = {R.y};
OpExecuter opexecuter(root, "FusedBatchNorm", static_input_indexes,
output_datatypes, sess_run_fetchoutputs);
opexecuter.RunTest();
}

// FusedBatchNormGrad : Gradient for batch normalization
// On TF CPU: only supports NHWC
TEST(NNOps, FusedBatchNormGradNHWC) {
Expand Down