101 lines
4.3 KiB
C++
101 lines
4.3 KiB
C++
/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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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,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/compiler/tf2xla/shape_util.h"
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#include "tensorflow/compiler/tf2xla/xla_compiler.h"
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#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
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#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
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#include "tensorflow/compiler/xla/client/xla_builder.h"
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#include "tensorflow/compiler/xla/xla_data.pb.h"
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#include "tensorflow/core/framework/op_kernel.h"
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namespace tensorflow {
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namespace {
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class XlaConvOp : public XlaOpKernel {
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public:
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explicit XlaConvOp(OpKernelConstruction* context) : XlaOpKernel(context) {
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string dnums_attr;
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OP_REQUIRES_OK(context, context->GetAttr("dimension_numbers", &dnums_attr));
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OP_REQUIRES(
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context, dnums_.ParsePartialFromString(dnums_attr),
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errors::InvalidArgument("Error parsing convolution dimension numbers"));
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string precision_config_attr;
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OP_REQUIRES_OK(
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context, context->GetAttr("precision_config", &precision_config_attr));
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OP_REQUIRES(context,
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precision_config_.ParsePartialFromString(precision_config_attr),
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errors::InvalidArgument("Error parsing precision config."));
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}
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void Compile(XlaOpKernelContext* context) override {
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const TensorShape lhs_shape = context->InputShape(0);
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const TensorShape rhs_shape = context->InputShape(1);
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const TensorShape padding_shape = context->InputShape("padding");
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std::vector<int64> window_strides;
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std::vector<int64> lhs_dilation;
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std::vector<int64> rhs_dilation;
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int64 feature_group_count;
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OP_REQUIRES_OK(context, context->ConstantInputAsIntVector("window_strides",
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&window_strides));
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OP_REQUIRES_OK(context, context->ConstantInputAsIntVector("lhs_dilation",
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&lhs_dilation));
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OP_REQUIRES_OK(context, context->ConstantInputAsIntVector("rhs_dilation",
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&rhs_dilation));
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OP_REQUIRES_OK(context, context->ConstantInputAsIntScalar(
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"feature_group_count", &feature_group_count));
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OP_REQUIRES(context,
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TensorShapeUtils::IsMatrix(padding_shape) &&
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padding_shape.dim_size(1) == 2,
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errors::InvalidArgument(
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"padding must be a matrix with minor dimension 2, got ",
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padding_shape.DebugString()));
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xla::Literal padding_literal;
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OP_REQUIRES_OK(context, context->ConstantInputAsInt64Literal(
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"padding", &padding_literal));
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std::vector<std::pair<int64, int64>> padding(padding_shape.dim_size(0));
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for (int i = 0; i < padding.size(); ++i) {
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padding[i] = {padding_literal.Get<int64>({i, 0}),
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padding_literal.Get<int64>({i, 1})};
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}
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// We do only minimal checking, relying on XLA to check the shape
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// invariants.
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xla::XlaOp output = xla::ConvGeneralDilated(
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context->Input(0), context->Input(1), window_strides, padding,
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lhs_dilation, rhs_dilation, dnums_, feature_group_count,
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/*batch_group_count=*/1, &precision_config_);
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context->SetOutput(0, output);
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}
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private:
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xla::ConvolutionDimensionNumbers dnums_;
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xla::PrecisionConfig precision_config_;
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TF_DISALLOW_COPY_AND_ASSIGN(XlaConvOp);
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};
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REGISTER_XLA_OP(Name("XlaConv")
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.CompileTimeConstantInput("window_strides")
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.CompileTimeConstantInput("lhs_dilation")
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.CompileTimeConstantInput("rhs_dilation")
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.CompileTimeConstantInput("feature_group_count")
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.CompileTimeConstantInput("padding"),
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XlaConvOp);
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} // namespace
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} // namespace tensorflow
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