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placeholder for internal contrib ops (#219)

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Tang, Cheng 2018-12-19 13:54:12 -08:00 committed by Ke Zhang
parent b9cc134576
commit 94f8f2b05c
3 changed files with 80 additions and 44 deletions
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90
onnxruntime/core/graph/contrib_ops/contrib_defs.cc
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@ -5,6 +5,7 @@
#include "core/graph/contrib_ops/attn_lstm_schema_defs.h"
#include "core/graph/contrib_ops/contrib_defs.h"
#include "core/graph/contrib_ops/range_schema_defs.h"
#include "core/graph/contrib_ops/internal_schema_defs.h"
#include "core/graph/op.h"
#include "onnx/defs/shape_inference.h"
@ -590,49 +591,49 @@ The bounding box coordinates corresponding to the selected indices can then be o
.SetDoc(R"DOC([optional] Step1: Remove elements in X if they match any of the stop words so that the output tensor will not contain any stop words. This operator only accepts [C]- and [1, C]-tensors. If all elements in X are dropped, the output will be the default value of string tensor with shape [1] if input shape is [C] and shape [1, 1] if input shape is [1, C].)DOC");
ONNX_CONTRIB_OPERATOR_SCHEMA(GatherND)
.SetDomain(kMSDomain)
.SinceVersion(1)
.Input (0, "data", "Tensor of rank r >= 1.", "T" )
.Input (1, "indices", "Tensor of rank q >= 1.", "Tind" )
.Output (0, "output", "Tensor of rank q-1+r-indices[-1].", "T" )
.TypeConstraint(
"T",
OpSchema::all_tensor_types(),
"Constrain input and output types to any tensor type.")
.TypeConstraint(
"Tind",
{"tensor(int32)", "tensor(int64)"},
"Constrain indice type to int32 or int64")
.TypeAndShapeInferenceFunction( [] (ONNX_NAMESPACE::InferenceContext& ctx) {
propagateElemTypeFromInputToOutput(ctx, 0, 0);
if (!hasNInputShapes(ctx, 2)) {
fail_shape_inference("GatherND requires two tensor inputs.");
}
auto& data_shape = ctx.getInputType(0)->tensor_type().shape();
auto& indices_shape = ctx.getInputType(1)->tensor_type().shape();
auto data_rank = data_shape.dim_size();
auto indices_rank = indices_shape.dim_size();
if (data_rank < 1 || indices_rank < 1) {
fail_shape_inference("both data and indices tensor need to have rank larger than zero.");
}
auto last_indice_dimension = indices_shape.dim(indices_rank - 1).dim_value();
if (last_indice_dimension > data_rank) {
fail_shape_inference("last dimension of indices must not be larger and rank of data tensor");
}
for (int i = 0; i < indices_rank - 1; ++i) {
*ctx.getOutputType(0)
->mutable_tensor_type()
->mutable_shape()
->add_dim() = indices_shape.dim(i);
}
for (int i = static_cast<int>(last_indice_dimension); i < data_rank; ++i) {
*ctx.getOutputType(0)
->mutable_tensor_type()
->mutable_shape()
->add_dim() = data_shape.dim(i);
}
})
.SetDoc(R"DOC(
.SetDomain(kMSDomain)
.SinceVersion(1)
.Input(0, "data", "Tensor of rank r >= 1.", "T")
.Input(1, "indices", "Tensor of rank q >= 1.", "Tind")
.Output(0, "output", "Tensor of rank q-1+r-indices[-1].", "T")
.TypeConstraint(
"T",
OpSchema::all_tensor_types(),
"Constrain input and output types to any tensor type.")
.TypeConstraint(
"Tind",
{"tensor(int32)", "tensor(int64)"},
"Constrain indice type to int32 or int64")
.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
propagateElemTypeFromInputToOutput(ctx, 0, 0);
if (!hasNInputShapes(ctx, 2)) {
fail_shape_inference("GatherND requires two tensor inputs.");
}
auto& data_shape = ctx.getInputType(0)->tensor_type().shape();
auto& indices_shape = ctx.getInputType(1)->tensor_type().shape();
auto data_rank = data_shape.dim_size();
auto indices_rank = indices_shape.dim_size();
if (data_rank < 1 || indices_rank < 1) {
fail_shape_inference("both data and indices tensor need to have rank larger than zero.");
}
auto last_indice_dimension = indices_shape.dim(indices_rank - 1).dim_value();
if (last_indice_dimension > data_rank) {
fail_shape_inference("last dimension of indices must not be larger and rank of data tensor");
}
for (int i = 0; i < indices_rank - 1; ++i) {
*ctx.getOutputType(0)
->mutable_tensor_type()
->mutable_shape()
->add_dim() = indices_shape.dim(i);
}
for (int i = static_cast<int>(last_indice_dimension); i < data_rank; ++i) {
*ctx.getOutputType(0)
->mutable_tensor_type()
->mutable_shape()
->add_dim() = data_shape.dim(i);
}
})
.SetDoc(R"DOC(
Given `data` tensor of rank r >= 1, and `indices` tensor of rank q >= 1, gather
slices of `data` into an output tensor of rank q - 1 + r - indices[-1].
Example 1:
@ -652,7 +653,8 @@ Example 4:
indices = [[[0,1]],[[1,0]]]
output = [[[2,3]],[[4,5]]]
)DOC");
// register internal ops
RegisterInternalSchemas();
}
} // namespace contrib
} // namespace onnxruntime

12
onnxruntime/core/graph/contrib_ops/internal_schema_defs.cc Normal file
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@ -0,0 +1,12 @@
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#include "range_schema_defs.h"
namespace onnxruntime {
namespace contrib {
void RegisterInternalSchemas() {}
} // namespace contrib
} // namespace onnxruntime

22
onnxruntime/core/graph/contrib_ops/internal_schema_defs.h Normal file
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@ -0,0 +1,22 @@
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#pragma once
#ifdef __GNUC__
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wignored-qualifiers"
#pragma GCC diagnostic ignored "-Wunused-parameter"
#endif
#include "onnx/defs/schema.h"
#ifdef __GNUC__
#pragma GCC diagnostic pop
#endif
namespace onnxruntime {
namespace contrib {
void RegisterInternalSchemas();
} // namespace contrib
} // namespace onnxruntime