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training_ops.cc
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training_ops.cc
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/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
namespace tensorflow {
using shape_inference::DimensionHandle;
using shape_inference::InferenceContext;
using shape_inference::ShapeHandle;
template <bool is_resource>
ShapeHandle ShapeOrHandleShape(InferenceContext* c, int input) {
auto* handle_data = c->input_handle_shapes_and_types(input);
if (handle_data != nullptr && !handle_data->empty() &&
(*handle_data)[0].dtype != DT_INVALID) {
return (*handle_data)[0].shape;
}
return c->input(input);
}
template <>
ShapeHandle ShapeOrHandleShape<true>(InferenceContext* c, int input) {
auto* handle_data = c->input_handle_shapes_and_types(input);
if (handle_data != nullptr && !handle_data->empty() &&
(*handle_data)[0].dtype != DT_INVALID) {
return (*handle_data)[0].shape;
}
// If a resource input is missing shape information, we should return
// UnknownShape rather than the shape of the input, which is a scalar
// resource handle.
return c->UnknownShape();
}
// Handle the gradient and, if <is_sparse>, indices inputs.
// <s> is an input+output parameter, containing the current known input shape to
// the gradient.
template <bool is_sparse, bool is_resource>
static Status HandleGradAndIndicesInputs(InferenceContext* c, int grad_idx,
ShapeHandle* s) {
ShapeHandle grad = ShapeOrHandleShape<is_resource>(c, grad_idx);
if (!is_sparse) {
TF_RETURN_IF_ERROR(c->Merge(*s, grad, s));
return absl::OkStatus();
}
// Indices is a vector where indices.dim[0].rank == grad[0].rank.
ShapeHandle indices;
TF_RETURN_IF_ERROR(c->WithRank(c->input(grad_idx + 1), 1, &indices));
DimensionHandle unused;
const auto rank = c->Rank(grad);
if (!rank) {
return absl::InvalidArgumentError(absl::StrCat(
"Argument grad must not be a scalar. ", "Got grad with rank ", rank));
}
TF_RETURN_IF_ERROR(c->Merge(c->Dim(indices, 0), c->Dim(grad, 0), &unused));
// Trailing part of grad matches trailing part of *s.
ShapeHandle grad_unknown_first;
TF_RETURN_IF_ERROR(
c->ReplaceDim(grad, 0, c->UnknownDim(), &grad_unknown_first));
TF_RETURN_IF_ERROR(c->Merge(*s, grad_unknown_first, s));
return absl::OkStatus();
}
template <bool is_resource>
static Status ApplyGradientDescentShapeFn(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle s = ShapeOrHandleShape<is_resource>(c, 0); // var
TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused)); // alpha
TF_RETURN_IF_ERROR(c->Merge(s, c->input(2), &s)); // delta
if (c->num_outputs() > 0) {
c->set_output(0, s);
}
return absl::OkStatus();
}
REGISTER_OP("ApplyGradientDescent")
.Input("var: Ref(T)")
.Input("alpha: T")
.Input("delta: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyGradientDescentShapeFn<false>);
REGISTER_OP("ResourceApplyGradientDescent")
.Input("var: resource")
.Input("alpha: T")
.Input("delta: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyGradientDescentShapeFn<true>);
template <bool is_sparse, bool is_resource>
Status ApplyProximalGradientDescentShapeFn(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle s = ShapeOrHandleShape<is_resource>(c, 0); // var
TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused)); // alpha
TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused)); // l1
TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused)); // l2
TF_RETURN_IF_ERROR(HandleGradAndIndicesInputs<is_sparse, is_resource>(
c, 4 /* grad_idx */, &s));
if (c->num_outputs() > 0) {
c->set_output(0, s);
}
return absl::OkStatus();
}
REGISTER_OP("ApplyProximalGradientDescent")
.Input("var: Ref(T)")
.Input("alpha: T")
.Input("l1: T")
.Input("l2: T")
.Input("delta: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyProximalGradientDescentShapeFn</*is_sparse=*/false,
/*is_resource=*/false>);
REGISTER_OP("SparseApplyProximalGradientDescent")
.Input("var: Ref(T)")
.Input("alpha: T")
.Input("l1: T")
.Input("l2: T")
.Input("grad: T")
.Input("indices: Tindices")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyProximalGradientDescentShapeFn</*is_sparse=*/true,
/*is_resource=*/false>);
REGISTER_OP("ResourceApplyProximalGradientDescent")
.Input("var: resource")
.Input("alpha: T")
.Input("l1: T")
.Input("l2: T")
.Input("delta: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyProximalGradientDescentShapeFn</*is_sparse=*/false,
/*is_resource=*/true>);
REGISTER_OP("ResourceSparseApplyProximalGradientDescent")
.Input("var: resource")
.Input("alpha: T")
.Input("l1: T")
.Input("l2: T")
.Input("grad: T")
.Input("indices: Tindices")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyProximalGradientDescentShapeFn</*is_sparse=*/true,
/*is_resource=*/true>);
template <bool is_sparse, bool is_resource>
static Status ApplyAdadeltaShapeFn(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle s = ShapeOrHandleShape<is_resource>(c, 0); // var
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 1), &s)); // accum
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 2), &s)); // accum update
TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused)); // lr
TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused)); // rho
TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 0, &unused)); // epsilon
TF_RETURN_IF_ERROR(HandleGradAndIndicesInputs<is_sparse, is_resource>(
c, 6 /* grad_idx */, &s));
if (c->num_outputs() > 0) {
c->set_output(0, s);
}
return absl::OkStatus();
}
REGISTER_OP("ApplyAdadelta")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("accum_update: Ref(T)")
.Input("lr: T")
.Input("rho: T")
.Input("epsilon: T")
.Input("grad: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(
ApplyAdadeltaShapeFn</*is_sparse=*/false, /*is_resource=*/false>);
REGISTER_OP("SparseApplyAdadelta")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("accum_update: Ref(T)")
.Input("lr: T")
.Input("rho: T")
.Input("epsilon: T")
.Input("grad: T")
.Input("indices: Tindices")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.SetShapeFn(
ApplyAdadeltaShapeFn</*is_sparse=*/true, /*is_resource=*/false>);
REGISTER_OP("ResourceApplyAdadelta")
.Input("var: resource")
.Input("accum: resource")
.Input("accum_update: resource")
.Input("lr: T")
.Input("rho: T")
.Input("epsilon: T")
.Input("grad: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(
ApplyAdadeltaShapeFn</*is_sparse=*/false, /*is_resource=*/true>);
REGISTER_OP("ResourceSparseApplyAdadelta")
.Input("var: resource")
.Input("accum: resource")
.Input("accum_update: resource")
.Input("lr: T")
.Input("rho: T")
.Input("epsilon: T")
.Input("grad: T")
.Input("indices: Tindices")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyAdadeltaShapeFn</*is_sparse=*/true, /*is_resource=*/true>);
template <bool is_sparse, bool is_resource>
static Status ApplyAdagradShapeFn(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle s = ShapeOrHandleShape<is_resource>(c, 0); // var
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 1), &s)); // accum
TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused)); // lr
TF_RETURN_IF_ERROR(HandleGradAndIndicesInputs<is_sparse, is_resource>(
c, 3 /* grad_idx */, &s));
if (c->num_outputs() > 0) {
c->set_output(0, s);
}
return absl::OkStatus();
}
REGISTER_OP("ApplyAdagrad")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("lr: T")
.Input("grad: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.Attr("update_slots: bool = true")
.SetShapeFn(
ApplyAdagradShapeFn</*is_sparse=*/false, /*is_resource=*/false>);
REGISTER_OP("ResourceApplyAdagrad")
.Input("var: resource")
.Input("accum: resource")
.Input("lr: T")
.Input("grad: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.Attr("update_slots: bool = true")
.SetShapeFn(ApplyAdagradShapeFn</*is_sparse=*/false, /*is_resource=*/true>);
REGISTER_OP("SparseApplyAdagrad")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("lr: T")
.Input("grad: T")
.Input("indices: Tindices")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.Attr("update_slots: bool = true")
.SetShapeFn(ApplyAdagradShapeFn</*is_sparse=*/true, /*is_resource=*/false>);
REGISTER_OP("ResourceSparseApplyAdagrad")
.Input("var: resource")
.Input("accum: resource")
.Input("lr: T")
.Input("grad: T")
.Input("indices: Tindices")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.Attr("update_slots: bool = true")
.SetShapeFn(ApplyAdagradShapeFn</*is_sparse=*/true, /*is_resource=*/true>);
template <bool is_sparse, bool is_resource>
static Status ApplyAdagradV2ShapeFn(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle s = ShapeOrHandleShape<is_resource>(c, 0); // var
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 1), &s)); // accum
TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused)); // lr
TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused)); // epsilon
TF_RETURN_IF_ERROR(HandleGradAndIndicesInputs<is_sparse, is_resource>(
c, 4 /* grad_idx */, &s));
if (c->num_outputs() > 0) {
c->set_output(0, s);
}
return absl::OkStatus();
}
REGISTER_OP("ApplyAdagradV2")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("lr: T")
.Input("epsilon: T")
.Input("grad: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.Attr("update_slots: bool = true")
.SetShapeFn(
ApplyAdagradV2ShapeFn</*is_sparse=*/false, /*is_resource=*/false>);
REGISTER_OP("ResourceApplyAdagradV2")
.Input("var: resource")
.Input("accum: resource")
.Input("lr: T")
.Input("epsilon: T")
.Input("grad: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.Attr("update_slots: bool = true")
.SetShapeFn(
ApplyAdagradV2ShapeFn</*is_sparse=*/false, /*is_resource=*/true>);
REGISTER_OP("SparseApplyAdagradV2")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("lr: T")
.Input("epsilon: T")
.Input("grad: T")
.Input("indices: Tindices")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.Attr("update_slots: bool = true")
.SetShapeFn(
ApplyAdagradV2ShapeFn</*is_sparse=*/true, /*is_resource=*/false>);
REGISTER_OP("ResourceSparseApplyAdagradV2")
.Input("var: resource")
.Input("accum: resource")
.Input("lr: T")
.Input("epsilon: T")
.Input("grad: T")
.Input("indices: Tindices")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.Attr("update_slots: bool = true")
.SetShapeFn(
ApplyAdagradV2ShapeFn</*is_sparse=*/true, /*is_resource=*/true>);
template <bool is_sparse, bool is_resource>
static Status ApplyProximalAdagradShapeFn(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle s = ShapeOrHandleShape<is_resource>(c, 0); // var
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 1), &s)); // accum
TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused)); // lr
TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused)); // l1
TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused)); // l2
TF_RETURN_IF_ERROR(HandleGradAndIndicesInputs<is_sparse, is_resource>(
c, 5 /* grad_idx */, &s));
if (c->num_outputs() > 0) {
c->set_output(0, s);
}
return absl::OkStatus();
}
REGISTER_OP("ApplyProximalAdagrad")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("grad: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyProximalAdagradShapeFn</*is_sparse=*/false,
/*is_resource=*/false>);
REGISTER_OP("ResourceApplyProximalAdagrad")
.Input("var: resource")
.Input("accum: resource")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("grad: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyProximalAdagradShapeFn</*is_sparse=*/false,
/*is_resource=*/false>);
REGISTER_OP("SparseApplyProximalAdagrad")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("grad: T")
.Input("indices: Tindices")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.SetShapeFn(
ApplyProximalAdagradShapeFn</*is_sparse=*/true, /*is_resource=*/false>);
REGISTER_OP("ResourceSparseApplyProximalAdagrad")
.Input("var: resource")
.Input("accum: resource")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("grad: T")
.Input("indices: Tindices")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.SetShapeFn(
ApplyProximalAdagradShapeFn</*is_sparse=*/true, /*is_resource=*/true>);
template <bool is_sparse, bool is_resource>
static Status ApplyAdagradDAShapeFn(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle s = ShapeOrHandleShape<is_resource>(c, 0); // var
TF_RETURN_IF_ERROR(c->Merge(s, ShapeOrHandleShape<is_resource>(c, 1),
&s)); // grad_accumulator
TF_RETURN_IF_ERROR(c->Merge(s, ShapeOrHandleShape<is_resource>(c, 2),
&s)); // gradient_squared_accumulator
TF_RETURN_IF_ERROR(HandleGradAndIndicesInputs<is_sparse, is_resource>(
c, 3 /* grad_idx */, &s));
int idx = is_sparse ? 5 : 4;
TF_RETURN_IF_ERROR(c->WithRank(c->input(idx++), 0, &unused)); // lr
TF_RETURN_IF_ERROR(c->WithRank(c->input(idx++), 0, &unused)); // l1
TF_RETURN_IF_ERROR(c->WithRank(c->input(idx++), 0, &unused)); // l2
TF_RETURN_IF_ERROR(c->WithRank(c->input(idx++), 0, &unused)); // global step
if (c->num_outputs() > 0) {
c->set_output(0, s);
}
return absl::OkStatus();
}
REGISTER_OP("ApplyAdagradDA")
.Input("var: Ref(T)")
.Input("gradient_accumulator: Ref(T)")
.Input("gradient_squared_accumulator: Ref(T)")
.Input("grad: T")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("global_step: int64")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(
ApplyAdagradDAShapeFn</*is_sparse=*/false, /*is_resource=*/false>);
REGISTER_OP("SparseApplyAdagradDA")
.Input("var: Ref(T)")
.Input("gradient_accumulator: Ref(T)")
.Input("gradient_squared_accumulator: Ref(T)")
.Input("grad: T")
.Input("indices: Tindices")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("global_step: int64")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.SetShapeFn(
ApplyAdagradDAShapeFn</*is_sparse=*/true, /*is_resource=*/false>);
REGISTER_OP("ResourceApplyAdagradDA")
.Input("var: resource")
.Input("gradient_accumulator: resource")
.Input("gradient_squared_accumulator: resource")
.Input("grad: T")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("global_step: int64")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(
ApplyAdagradDAShapeFn</*is_sparse=*/false, /*is_resource=*/true>);
REGISTER_OP("ResourceSparseApplyAdagradDA")
.Input("var: resource")
.Input("gradient_accumulator: resource")
.Input("gradient_squared_accumulator: resource")
.Input("grad: T")
.Input("indices: Tindices")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("global_step: int64")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.SetShapeFn(
ApplyAdagradDAShapeFn</*is_sparse=*/true, /*is_resource=*/true>);
template <bool is_sparse, bool is_resource>
static Status ApplyFtrlShapeFn(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle s = ShapeOrHandleShape<is_resource>(c, 0); // var
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 1), &s)); // accum
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 2), &s)); // linear
TF_RETURN_IF_ERROR(HandleGradAndIndicesInputs<is_sparse, is_resource>(
c, 3 /* grad_idx */, &s));
int idx = is_sparse ? 5 : 4;
TF_RETURN_IF_ERROR(c->WithRank(c->input(idx++), 0, &unused)); // lr
TF_RETURN_IF_ERROR(c->WithRank(c->input(idx++), 0, &unused)); // l1
TF_RETURN_IF_ERROR(c->WithRank(c->input(idx++), 0, &unused)); // l2
TF_RETURN_IF_ERROR(c->WithRank(c->input(idx++), 0, &unused)); // lr_power
if (c->num_outputs() > 0) {
c->set_output(0, s);
}
return absl::OkStatus();
}
REGISTER_OP("ApplyFtrl")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("linear: Ref(T)")
.Input("grad: T")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("lr_power: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.Attr("multiply_linear_by_lr: bool = false")
.SetShapeFn(ApplyFtrlShapeFn</*is_sparse=*/false, /*is_resource=*/false>);
REGISTER_OP("SparseApplyFtrl")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("linear: Ref(T)")
.Input("grad: T")
.Input("indices: Tindices")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("lr_power: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.Attr("multiply_linear_by_lr: bool = false")
.SetShapeFn(ApplyFtrlShapeFn</*is_sparse=*/true, /*is_resource=*/false>);
REGISTER_OP("ResourceApplyFtrl")
.Input("var: resource")
.Input("accum: resource")
.Input("linear: resource")
.Input("grad: T")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("lr_power: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.Attr("multiply_linear_by_lr: bool = false")
.SetShapeFn(ApplyFtrlShapeFn</*is_sparse=*/false, /*is_resource=*/true>);
REGISTER_OP("ResourceSparseApplyFtrl")
.Input("var: resource")
.Input("accum: resource")
.Input("linear: resource")
.Input("grad: T")
.Input("indices: Tindices")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("lr_power: T")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.Attr("multiply_linear_by_lr: bool = false")
.SetShapeFn(ApplyFtrlShapeFn</*is_sparse=*/true, /*is_resource=*/true>);
REGISTER_OP("ApplyFtrlV2")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("linear: Ref(T)")
.Input("grad: T")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("l2_shrinkage: T")
.Input("lr_power: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.Attr("multiply_linear_by_lr: bool = false")
.SetShapeFn(ApplyFtrlShapeFn</*is_sparse=*/false, /*is_resource=*/false>);
REGISTER_OP("SparseApplyFtrlV2")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("linear: Ref(T)")
.Input("grad: T")
.Input("indices: Tindices")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("l2_shrinkage: T")
.Input("lr_power: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.Attr("multiply_linear_by_lr: bool = false")
.SetShapeFn(ApplyFtrlShapeFn</*is_sparse=*/true, /*is_resource=*/false>);
REGISTER_OP("ResourceApplyFtrlV2")
.Input("var: resource")
.Input("accum: resource")
.Input("linear: resource")
.Input("grad: T")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("l2_shrinkage: T")
.Input("lr_power: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.Attr("multiply_linear_by_lr: bool = false")
.SetShapeFn(ApplyFtrlShapeFn</*is_sparse=*/false, /*is_resource=*/true>);
REGISTER_OP("ResourceSparseApplyFtrlV2")
.Input("var: resource")
.Input("accum: resource")
.Input("linear: resource")
.Input("grad: T")
.Input("indices: Tindices")
.Input("lr: T")
.Input("l1: T")
.Input("l2: T")
.Input("l2_shrinkage: T")
.Input("lr_power: T")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.Attr("multiply_linear_by_lr: bool = false")
.SetShapeFn(ApplyFtrlShapeFn</*is_sparse=*/true, /*is_resource=*/true>);
template <bool is_sparse, bool is_resource>
static Status ApplyMomentumShapeFn(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle s = ShapeOrHandleShape<is_resource>(c, 0); // var
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 1), &s)); // accum
TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused)); // lr
TF_RETURN_IF_ERROR(HandleGradAndIndicesInputs<is_sparse, is_resource>(
c, 3 /* grad_idx */, &s));
int idx = is_sparse ? 5 : 4;
TF_RETURN_IF_ERROR(c->WithRank(c->input(idx++), 0, &unused)); // momentum
if (c->num_outputs() > 0) {
c->set_output(0, s);
}
return absl::OkStatus();
}
REGISTER_OP("ApplyMomentum")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("lr: T")
.Input("grad: T")
.Input("momentum: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.Attr("use_nesterov: bool = false")
.SetShapeFn(
ApplyMomentumShapeFn</*is_sparse=*/false, /*is_resource=*/false>);
REGISTER_OP("SparseApplyMomentum")
.Input("var: Ref(T)")
.Input("accum: Ref(T)")
.Input("lr: T")
.Input("grad: T")
.Input("indices: Tindices")
.Input("momentum: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.Attr("use_nesterov: bool = false")
.SetShapeFn(
ApplyMomentumShapeFn</*is_sparse=*/true, /*is_resource=*/false>);
REGISTER_OP("ResourceApplyMomentum")
.Input("var: resource")
.Input("accum: resource")
.Input("lr: T")
.Input("grad: T")
.Input("momentum: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.Attr("use_nesterov: bool = false")
.SetShapeFn(
ApplyMomentumShapeFn</*is_sparse=*/false, /*is_resource=*/true>);
REGISTER_OP("ResourceSparseApplyMomentum")
.Input("var: resource")
.Input("accum: resource")
.Input("lr: T")
.Input("grad: T")
.Input("indices: Tindices")
.Input("momentum: T")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.Attr("use_nesterov: bool = false")
.SetShapeFn(ApplyMomentumShapeFn</*is_sparse=*/true, /*is_resource=*/true>);
REGISTER_OP("ResourceApplyKerasMomentum")
.Input("var: resource")
.Input("accum: resource")
.Input("lr: T")
.Input("grad: T")
.Input("momentum: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.Attr("use_nesterov: bool = false")
.SetShapeFn(
ApplyMomentumShapeFn</*is_sparse=*/false, /*is_resource=*/true>);
REGISTER_OP("ResourceSparseApplyKerasMomentum")
.Input("var: resource")
.Input("accum: resource")
.Input("lr: T")
.Input("grad: T")
.Input("indices: Tindices")
.Input("momentum: T")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.Attr("use_nesterov: bool = false")
.SetShapeFn(ApplyMomentumShapeFn</*is_sparse=*/true, /*is_resource=*/true>);
template <bool is_resource>
static Status ApplyAdamShapeFn(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle s = ShapeOrHandleShape<is_resource>(c, 0); // var
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 1), &s)); // m
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 2), &s)); // v
TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused)); // beta1_power
TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused)); // beta2_power
TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 0, &unused)); // lr
TF_RETURN_IF_ERROR(c->WithRank(c->input(6), 0, &unused)); // beta1
TF_RETURN_IF_ERROR(c->WithRank(c->input(7), 0, &unused)); // beta2
TF_RETURN_IF_ERROR(c->WithRank(c->input(8), 0, &unused)); // epsilon
TF_RETURN_IF_ERROR(
HandleGradAndIndicesInputs</*is_sparse=*/false, is_resource>(
c, 9 /* grad_idx */, &s));
if (c->num_outputs() > 0) {
c->set_output(0, s);
}
return absl::OkStatus();
}
REGISTER_OP("ApplyAdam")
.Input("var: Ref(T)")
.Input("m: Ref(T)")
.Input("v: Ref(T)")
.Input("beta1_power: T")
.Input("beta2_power: T")
.Input("lr: T")
.Input("beta1: T")
.Input("beta2: T")
.Input("epsilon: T")
.Input("grad: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.Attr("use_nesterov: bool = false")
.SetShapeFn(ApplyAdamShapeFn</*is_resource=*/false>);
REGISTER_OP("ResourceApplyAdam")
.Input("var: resource")
.Input("m: resource")
.Input("v: resource")
.Input("beta1_power: T")
.Input("beta2_power: T")
.Input("lr: T")
.Input("beta1: T")
.Input("beta2: T")
.Input("epsilon: T")
.Input("grad: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.Attr("use_nesterov: bool = false")
.SetShapeFn(ApplyAdamShapeFn</*is_resource=*/true>);
template <bool is_resource>
static Status ApplyAdamWithAmsgradShapeFn(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle s = ShapeOrHandleShape<is_resource>(c, 0); // var
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 1), &s)); // m
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 2), &s)); // v
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 3), &s)); // vhat
TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused)); // beta1_power
TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 0, &unused)); // beta2_power
TF_RETURN_IF_ERROR(c->WithRank(c->input(6), 0, &unused)); // lr
TF_RETURN_IF_ERROR(c->WithRank(c->input(7), 0, &unused)); // beta1
TF_RETURN_IF_ERROR(c->WithRank(c->input(8), 0, &unused)); // beta2
TF_RETURN_IF_ERROR(c->WithRank(c->input(9), 0, &unused)); // epsilon
TF_RETURN_IF_ERROR(
HandleGradAndIndicesInputs</*is_sparse=*/false, is_resource>(
c, 10 /* grad_idx */, &s));
if (c->num_outputs() > 0) {
c->set_output(0, s);
}
return absl::OkStatus();
}
REGISTER_OP("ResourceApplyAdamWithAmsgrad")
.Input("var: resource")
.Input("m: resource")
.Input("v: resource")
.Input("vhat: resource")
.Input("beta1_power: T")
.Input("beta2_power: T")
.Input("lr: T")
.Input("beta1: T")
.Input("beta2: T")
.Input("epsilon: T")
.Input("grad: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyAdamWithAmsgradShapeFn</*is_resource=*/true>);
template <bool is_resource>
static Status ApplyAdaMaxShapeFn(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle s = ShapeOrHandleShape<is_resource>(c, 0); // var
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 1), &s)); // m
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 2), &s)); // v
TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused)); // beta1_power
TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused)); // lr
TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 0, &unused)); // beta1
TF_RETURN_IF_ERROR(c->WithRank(c->input(6), 0, &unused)); // beta2
TF_RETURN_IF_ERROR(c->WithRank(c->input(7), 0, &unused)); // epsilon
TF_RETURN_IF_ERROR(
HandleGradAndIndicesInputs</*is_sparse=*/false, is_resource>(
c, 8 /* grad_idx */, &s));
if (c->num_outputs() > 0) {
c->set_output(0, s);
}
return absl::OkStatus();
}
REGISTER_OP("ApplyAdaMax")
.Input("var: Ref(T)")
.Input("m: Ref(T)")
.Input("v: Ref(T)")
.Input("beta1_power: T")
.Input("lr: T")
.Input("beta1: T")
.Input("beta2: T")
.Input("epsilon: T")
.Input("grad: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyAdaMaxShapeFn</*is_resource=*/false>);
REGISTER_OP("ResourceApplyAdaMax")
.Input("var: resource")
.Input("m: resource")
.Input("v: resource")
.Input("beta1_power: T")
.Input("lr: T")
.Input("beta1: T")
.Input("beta2: T")
.Input("epsilon: T")
.Input("grad: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyAdaMaxShapeFn</*is_resource=*/true>);
template <bool is_sparse, bool is_resource>
static Status ApplyRMSPropShapeFn(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle s = ShapeOrHandleShape<is_resource>(c, 0); // var
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 1), &s)); // ms
TF_RETURN_IF_ERROR(
c->Merge(s, ShapeOrHandleShape<is_resource>(c, 2), &s)); // mom
TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused)); // lr
TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused)); // rho
TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 0, &unused)); // momentum
TF_RETURN_IF_ERROR(c->WithRank(c->input(6), 0, &unused)); // epsilon
TF_RETURN_IF_ERROR(HandleGradAndIndicesInputs<is_sparse, is_resource>(
c, 7 /* grad_idx */, &s));
if (c->num_outputs() > 0) {
c->set_output(0, s);
}
return absl::OkStatus();
}
REGISTER_OP("ApplyRMSProp")
.Input("var: Ref(T)")
.Input("ms: Ref(T)")
.Input("mom: Ref(T)")
.Input("lr: T")
.Input("rho: T")
.Input("momentum: T")
.Input("epsilon: T")
.Input("grad: T")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(
ApplyRMSPropShapeFn</*is_sparse=*/false, /*is_resource=*/false>);
REGISTER_OP("SparseApplyRMSProp")
.Input("var: Ref(T)")
.Input("ms: Ref(T)")
.Input("mom: Ref(T)")
.Input("lr: T")
.Input("rho: T")
.Input("momentum: T")
.Input("epsilon: T")
.Input("grad: T")
.Input("indices: Tindices")
.Output("out: Ref(T)")
.Attr("T: numbertype")
.Attr("Tindices: {int32, int64}")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyRMSPropShapeFn</*is_sparse=*/true, /*is_resource=*/false>);
REGISTER_OP("ResourceApplyRMSProp")
.Input("var: resource")
.Input("ms: resource")
.Input("mom: resource")
.Input("lr: T")
.Input("rho: T")
.Input("momentum: T")
.Input("epsilon: T")
.Input("grad: T")
.Attr("T: numbertype")
.Attr("use_locking: bool = false")
.SetShapeFn(ApplyRMSPropShapeFn</*is_sparse=*/false, /*is_resource=*/true>);
REGISTER_OP("ResourceSparseApplyRMSProp")
.Input("var: resource")
.Input("ms: resource")
.Input("mom: resource")
.Input("lr: T")
.Input("rho: T")
.Input("momentum: T")
.Input("epsilon: T")
.Input("grad: T")
.Input("indices: Tindices")
.Attr("T: numbertype")