Skip to content
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

Jump to bottom

Reorganize and add more validation to MKL requantization #51252

Merged
merged 1 commit into from
Aug 6, 2021
Merged

Reorganize and add more validation to MKL requantization #51252

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.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
40 changes: 25 additions & 15 deletions tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -49,35 +49,45 @@ class MklRequantizePerChannelOp : public OpKernel {
void Compute(OpKernelContext* ctx) override {
try {
const Tensor& input = ctx->input(kInputTensorIndex);
OP_REQUIRES(
ctx, input.dims() == 4,
errors::InvalidArgument("Current RequantizePerChannel operator"
"supports 4D tensors only."));

const Tensor& input_min_vec = ctx->input(kInputMinVecIndex);
size_t depth = input_min_vec.NumElements();
float* input_min_vec_data = (float*)const_cast<void*>(
static_cast<const void*>(input_min_vec.flat<float>().data()));

const Tensor& input_max_vec = ctx->input(kInputMaxVecIndex);
OP_REQUIRES(
ctx, input_max_vec.NumElements() == depth,
errors::InvalidArgument("input_max has incorrect size, expected ",
depth, " was ", input_max_vec.NumElements()));
float* input_max_vec_data = (float*)const_cast<void*>(
static_cast<const void*>(input_max_vec.flat<float>().data()));

const Tensor& input_requested_min = ctx->input(this->kRequestMinIndex);
OP_REQUIRES(
ctx, input_requested_min.NumElements() == 1,
errors::InvalidArgument("requested_output_min must be a scalar"));
const float input_requested_min_float =
input_requested_min.flat<float>()(0);

const Tensor& input_requested_max = ctx->input(this->kRequestMaxIndex);
OP_REQUIRES(
ctx, input_requested_min.NumElements() == 1,
errors::InvalidArgument("requested_output_max must be a scalar"));
const float input_requested_max_float =
input_requested_max.flat<float>()(0);

size_t depth = input_min_vec.NumElements();
OP_REQUIRES(
ctx, input.dims() == 4,
errors::InvalidArgument("Current RequantizePerChannel operator"
"supports 4D tensors only."));
OP_REQUIRES(
ctx, input_min_vec.dim_size(0) == depth,
errors::InvalidArgument("input_min has incorrect size, expected ",
depth, " was ", input_min_vec.dim_size(0)));
OP_REQUIRES(
ctx, input_max_vec.dim_size(0) == depth,
errors::InvalidArgument("input_max has incorrect size, expected ",
depth, " was ", input_max_vec.dim_size(0)));

if (out_type_ == DT_QINT8) DCHECK(input_requested_min_float < 0.0f);
if (out_type_ == DT_QINT8) {
OP_REQUIRES(ctx, input_requested_min_float < 0.0f,
errors::InvalidArgument(
"If out_type is QINT8, requested_output_max must be "
"non negative, got ",
input_requested_min_float));
}

const float factor = (out_type_ == DT_QINT8) ? 127.0f : 255.0f;
const float requested_min_max =
Expand Down