1 /* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
2
3 Licensed under the Apache License, Version 2.0 (the "License");
4 you may not use this file except in compliance with the License.
5 You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9 Unless required by applicable law or agreed to in writing, software
10 distributed under the License is distributed on an "AS IS" BASIS,
11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 See the License for the specific language governing permissions and
13 limitations under the License.
14 ==============================================================================*/
15
16 #include "tensorflow/core/kernels/cwise_ops_common.h"
17
18 namespace tensorflow {
19
BinaryOpShared(OpKernelConstruction * ctx,DataType out,DataType in)20 BinaryOpShared::BinaryOpShared(OpKernelConstruction* ctx, DataType out,
21 DataType in)
22 : OpKernel(ctx) {
23 #if !defined(INTEL_MKL) || !defined(ENABLE_MKL)
24 OP_REQUIRES_OK(ctx, ctx->MatchSignature({in, in}, {out}));
25 #endif // !INTEL_MKL || !ENABLE_MKL
26 }
27
SetUnimplementedError(OpKernelContext * ctx)28 void BinaryOpShared::SetUnimplementedError(OpKernelContext* ctx) {
29 ctx->SetStatus(errors::Unimplemented(
30 "Broadcast between ", ctx->input(0).shape().DebugString(), " and ",
31 ctx->input(1).shape().DebugString(), " is not supported yet."));
32 }
33
SetComputeError(OpKernelContext * ctx)34 void BinaryOpShared::SetComputeError(OpKernelContext* ctx) {
35 // For speed, errors during compute are caught only via boolean flag, with no
36 // associated information. This is sufficient for now, since the only binary
37 // ops that have compute errors are integer division and mod, and the only
38 // error they produce is zero division.
39 const string& op = ctx->op_kernel().type_string();
40 if ((op == "Div" || op == "Mod" || op == "FloorMod" || op == "FloorDiv") &&
41 DataTypeIsInteger(ctx->op_kernel().input_type(0))) {
42 ctx->CtxFailure(errors::InvalidArgument("Integer division by zero"));
43 } else if ((op == "Pow") &&
44 DataTypeIsInteger(ctx->op_kernel().input_type(0)) &&
45 DataTypeIsSigned(ctx->op_kernel().input_type(1))) {
46 ctx->CtxFailure(errors::InvalidArgument(
47 "Integers to negative integer powers are not allowed"));
48 } else {
49 ctx->CtxFailure(
50 errors::Internal("Unexpected error in binary operator "
51 "(only integer div and mod should have errors)"));
52 }
53 }
54
BinaryOpState(OpKernelContext * ctx)55 BinaryOpShared::BinaryOpState::BinaryOpState(OpKernelContext* ctx)
56 : in0(ctx->input(0)),
57 in1(ctx->input(1)),
58 bcast(BCast::FromShape(in0.shape()), BCast::FromShape(in1.shape())) {
59 if (!bcast.IsValid()) {
60 ctx->SetStatus(errors::InvalidArgument(
61 "Incompatible shapes: ", in0.shape().DebugString(), " vs. ",
62 in1.shape().DebugString()));
63 return;
64 }
65 const TensorShape output_shape = BCast::ToShape(bcast.output_shape());
66 out_num_elements = output_shape.num_elements();
67 in0_num_elements = in0.NumElements();
68 in1_num_elements = in1.NumElements();
69 OP_REQUIRES_OK(ctx, ctx->forward_input_or_allocate_output(
70 {0, 1}, 0, output_shape, &out));
71
72 ndims = static_cast<int>(bcast.x_reshape().size());
73 }
74
75 } // namespace tensorflow
76