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Merge branch 'master' into lml/release_memory_of_task_node_on_the_fly
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@levi1993
levi1993 authored Apr 29, 2021
2 parents 72c8e75 + 354652e commit 04b328e
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Showing 41 changed files with 445 additions and 244 deletions.
1 change: 0 additions & 1 deletion .gitignore
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Expand Up @@ -7,7 +7,6 @@
/examples/**/oneflow
/benchmark/**/oneflow
log/
default_physical_env_log/
*plan
core.*
*.pyc
Expand Down
15 changes: 7 additions & 8 deletions ...low/api/python/framework/attr_val_map.cpp → oneflow/api/python/framework/attr_map.cpp
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Expand Up @@ -17,29 +17,28 @@ limitations under the License.
#include <pybind11/stl.h>
#include "oneflow/api/python/of_api_registry.h"

#include "oneflow/core/framework/attr_value_map.h"
#include "oneflow/core/framework/attr_map.h"
#include "oneflow/core/framework/user_op_attr.cfg.h"

namespace py = pybind11;

namespace oneflow {

ONEFLOW_API_PYBIND11_MODULE("", m) {
py::class_<AttrValueMap, std::shared_ptr<AttrValueMap>>(m, "AttrValueMap")
py::class_<MutableCfgAttrMap, std::shared_ptr<MutableCfgAttrMap>>(m, "MutableCfgAttrMap")
.def(py::init<>())
.def("__setitem__",
[](AttrValueMap* m, const std::string& attr_name,
[](MutableCfgAttrMap* m, const std::string& attr_name,
const std::shared_ptr<cfg::AttrValue>& attr_value) {
m->SetAttr(attr_name, attr_value).GetOrThrow();
})
.def("__getitem__",
[](const AttrValueMap& m, const std::string& attr_name) {
m.GetAttr<cfg::AttrValue>(attr_name).GetOrThrow();
})
[](const MutableCfgAttrMap& m, const std::string& attr_name) { return m.at(attr_name); })
.def(
"__iter__", [](const AttrValueMap& m) { return py::make_iterator(m.begin(), m.end()); },
"__iter__",
[](const MutableCfgAttrMap& m) { return py::make_iterator(m.begin(), m.end()); },
py::keep_alive<0, 1>())
.def("__len__", [](const AttrValueMap& m) { return m.size(); });
.def("__len__", [](const MutableCfgAttrMap& m) { return m.size(); });
}

} // namespace oneflow
10 changes: 5 additions & 5 deletions oneflow/api/python/framework/op_expr.cpp
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Expand Up @@ -17,7 +17,7 @@ limitations under the License.
#include <pybind11/stl.h>
#include "oneflow/api/python/of_api_registry.h"
#include "oneflow/core/common/protobuf.h"
#include "oneflow/core/framework/attr_value_map.h"
#include "oneflow/core/framework/attr_map.h"
#include "oneflow/core/framework/op_expr.h"
#include "oneflow/core/framework/op_interpreter.h"
#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h"
Expand All @@ -32,7 +32,7 @@ namespace oneflow {
namespace {

Maybe<one::TensorTuple> Interpret(const one::OpExpr& op, const one::TensorTuple& inputs,
const AttrValueMap& attrs) {
const AttrMap& attrs) {
CHECK_EQ_OR_RETURN(op.input_size(), inputs.size())
<< "The operation requires " << op.input_size() << " inputs, but " << inputs.size()
<< " is given.";
Expand All @@ -44,7 +44,7 @@ Maybe<one::TensorTuple> Interpret(const one::OpExpr& op, const one::TensorTuple&

Maybe<one::TensorTuple> Interpret(const one::OpExpr& op,
const std::vector<std::shared_ptr<one::Tensor>>& inputs,
const AttrValueMap& attrs) {
const AttrMap& attrs) {
one::TensorTuple input_list(inputs.size());
for (int i = 0; i < inputs.size(); ++i) { input_list[i] = inputs[i]; }
return JUST(Interpret(op, input_list, attrs));
Expand Down Expand Up @@ -76,11 +76,11 @@ ONEFLOW_API_PYBIND11_MODULE("one", m) {
.def_property_readonly("output_size", &one::OpExpr::output_size)
.def("apply",
[](const one::OpExpr& op_expr, const std::vector<std::shared_ptr<one::Tensor>>& inputs,
const AttrValueMap& attrs) {
const MutableCfgAttrMap& attrs) {
return Interpret(op_expr, inputs, attrs).GetPtrOrThrow();
})
.def("apply", [](const one::OpExpr& op_expr, const one::TensorTuple& inputs,
const AttrValueMap& attrs) {
const MutableCfgAttrMap& attrs) {
return Interpret(op_expr, inputs, attrs).GetPtrOrThrow();
});

Expand Down
6 changes: 3 additions & 3 deletions oneflow/core/autograd/gradient_funcs/batch_gather.cpp
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Expand Up @@ -31,7 +31,7 @@ class BatchGather : public OpExprGradFunction<BatchGatherInterpState> {
public:
Maybe<void> Init(const OpExpr& op) override;
Maybe<void> Capture(BatchGatherInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const override;
const TensorTuple& outputs, const AttrMap& attrs) const override;
Maybe<void> Apply(const BatchGatherInterpState* ctx, const TensorTuple& out_grads,
TensorTuple* in_grads) const override;

Expand All @@ -49,7 +49,7 @@ Maybe<void> BatchGather::Init(const OpExpr& op) {
}

Maybe<void> BatchGather::Capture(BatchGatherInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const {
const TensorTuple& outputs, const AttrMap& attrs) const {
ctx->requires_grad = inputs.at(0)->requires_grad();
if (!ctx->requires_grad) { return Maybe<void>::Ok(); }
const auto& in_shape = inputs.at(0)->shape();
Expand All @@ -64,7 +64,7 @@ Maybe<void> BatchGather::Apply(const BatchGatherInterpState* ctx, const TensorTu
in_grads->resize(2);
if (!ctx->requires_grad) { return Maybe<void>::Ok(); }
const auto& indices = ctx->SavedTensors().at(0);
AttrValueMap attrs;
MutableAttrMap attrs;
JUST(attrs.SetAttr<int32_t>("num_segments", ctx->num_segments));
in_grads->at(0) = JUST(OpInterpUtil::Dispatch<Tensor>(*bw_unsorted_batch_segment_sum_op_,
{out_grads.at(0), indices}, attrs));
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4 changes: 2 additions & 2 deletions oneflow/core/autograd/gradient_funcs/bias_add.cpp
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Expand Up @@ -43,7 +43,7 @@ class BiasAdd : public OpExprGradFunction<BiasAddInterpState> {
}

Maybe<void> Capture(BiasAddInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const override {
const TensorTuple& outputs, const AttrMap& attrs) const override {
CHECK_EQ_OR_RETURN(inputs.size(), 2);
ctx->input_requires_grad = inputs.at(0)->requires_grad();
ctx->bias_requires_grad = inputs.at(1)->requires_grad();
Expand All @@ -60,7 +60,7 @@ class BiasAdd : public OpExprGradFunction<BiasAddInterpState> {
for (int i = 0; i < num_axes; ++i) {
if (i != axis_) { reduce_axes_vec.push_back(i); }
}
AttrValueMap attrs;
MutableAttrMap attrs;
JUST(attrs.SetAttr<std::vector<int32_t>>("axis", reduce_axes_vec));
in_grads->at(1) =
JUST(OpInterpUtil::Dispatch<Tensor>(*backward_bias_op_, {out_grads.at(0)}, attrs));
Expand Down
4 changes: 2 additions & 2 deletions oneflow/core/autograd/gradient_funcs/broadcast_binary_ops.cpp
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Expand Up @@ -38,7 +38,7 @@ class ReduceSumLikeModule {
const auto& in_shape = *(input->shape());
const auto& like_shape = *(like->shape());
TensorTuple inputs{input};
AttrValueMap attrs;
MutableAttrMap attrs;
std::shared_ptr<OpExpr> op = identity_op_;
if (in_shape != like_shape) {
const Shape& left_extended_shape =
Expand Down Expand Up @@ -77,7 +77,7 @@ class BroadcastBinaryGrad : public OpExprGradFunction<OpExprInterpState> {
}

Maybe<void> Capture(OpExprInterpState* ctx, const TensorTuple& inputs, const TensorTuple& outputs,
const AttrValueMap& attrs) const override {
const AttrMap& attrs) const override {
CHECK_EQ_OR_RETURN(inputs.size(), 2);
CHECK_EQ_OR_RETURN(outputs.size(), 1);
ctx->SaveTensorForBackward(inputs.at(0));
Expand Down
4 changes: 2 additions & 2 deletions oneflow/core/autograd/gradient_funcs/cast.cpp
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Expand Up @@ -35,7 +35,7 @@ class Cast : public OpExprGradFunction<OpExprInterpState> {
}

Maybe<void> Capture(OpExprInterpState* ctx, const TensorTuple& inputs, const TensorTuple& outputs,
const AttrValueMap& attrs) const override {
const AttrMap& attrs) const override {
ctx->SaveTensorForBackward(inputs.at(0));
return Maybe<void>::Ok();
}
Expand All @@ -44,7 +44,7 @@ class Cast : public OpExprGradFunction<OpExprInterpState> {
TensorTuple* in_grads) const override {
const auto& x = ctx->SavedTensors().at(0);
in_grads->resize(1);
AttrValueMap attrs;
MutableAttrMap attrs;
JUST(attrs.SetAttr<DataType>("dtype", x->dtype()->data_type()));
in_grads->at(0) = JUST(OpInterpUtil::Dispatch<Tensor>(*grad_op_, {out_grads.at(0)}, attrs));
return Maybe<void>::Ok();
Expand Down
4 changes: 2 additions & 2 deletions oneflow/core/autograd/gradient_funcs/deconv.cpp
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Expand Up @@ -32,7 +32,7 @@ class DeConvolutionNd : public OpExprGradFunction<DeConvolutionNdInterpState> {
public:
Maybe<void> Init(const OpExpr& op) override;
Maybe<void> Capture(DeConvolutionNdInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const override;
const TensorTuple& outputs, const AttrMap& attrs) const override;
Maybe<void> Apply(const DeConvolutionNdInterpState* ctx, const TensorTuple& out_grads,
TensorTuple* in_grads) const override;

Expand Down Expand Up @@ -72,7 +72,7 @@ Maybe<void> DeConvolutionNd::Init(const OpExpr& op) {
}

Maybe<void> DeConvolutionNd::Capture(DeConvolutionNdInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const {
const TensorTuple& outputs, const AttrMap& attrs) const {
ctx->activation_requires_grad = inputs.at(0)->requires_grad();
ctx->weight_requires_grad = inputs.at(1)->requires_grad();
if (ctx->activation_requires_grad) {
Expand Down
4 changes: 2 additions & 2 deletions oneflow/core/autograd/gradient_funcs/default.cpp
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Expand Up @@ -44,7 +44,7 @@ class DefaultOpExprGradFunction : public OpExprGradFunction<DefaultOpExprInterpS
Maybe<void> Init(const OpExpr& op) override;

Maybe<void> Capture(DefaultOpExprInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const override;
const TensorTuple& outputs, const AttrMap& attrs) const override;

Maybe<void> Apply(const DefaultOpExprInterpState* ctx, const TensorTuple& out_grads,
TensorTuple* in_grads) const override;
Expand Down Expand Up @@ -285,7 +285,7 @@ Maybe<void> DefaultOpExprGradFunction::UpdateRequiresBackward(DefaultOpExprInter
Maybe<void> DefaultOpExprGradFunction::Capture(DefaultOpExprInterpState* ctx,
const TensorTuple& inputs,
const TensorTuple& outputs,
const AttrValueMap& attrs) const {
const AttrMap& attrs) const {
CHECK_OR_RETURN(attrs.empty())
<< "The default op expr gradient func does not support dynamic attributes.";
JUST(UpdateRequiresBackward(ctx, inputs));
Expand Down
6 changes: 3 additions & 3 deletions oneflow/core/autograd/gradient_funcs/layer_norm.cpp
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Expand Up @@ -38,7 +38,7 @@ class LayerNorm : public OpExprGradFunction<LayerNormInterpState> {
Maybe<void> Init(const OpExpr& op) override;

Maybe<void> Capture(LayerNormInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const override;
const TensorTuple& outputs, const AttrMap& attrs) const override;

Maybe<void> Apply(const LayerNormInterpState* ctx, const TensorTuple& out_grads,
TensorTuple* in_grads) const override;
Expand Down Expand Up @@ -68,7 +68,7 @@ Maybe<void> LayerNorm::Init(const OpExpr& op) {
}

Maybe<void> LayerNorm::Capture(LayerNormInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const {
const TensorTuple& outputs, const AttrMap& attrs) const {
CHECK_EQ_OR_RETURN(inputs.size(), center_ + scale_ + 1);
CHECK_EQ_OR_RETURN(inputs.size(), scale_ + 3);
ctx->has_beta_diff = center_ && inputs.at(1)->requires_grad();
Expand Down Expand Up @@ -122,7 +122,7 @@ Maybe<void> LayerNorm::Apply(const LayerNormInterpState* ctx, const TensorTuple&
const auto& x = saved_tensors.at(offset);
const auto& mean = saved_tensors.at(offset + 1);
const auto& inv_variance = saved_tensors.at(offset + 2);
AttrValueMap attrs;
MutableAttrMap attrs;
JUST(attrs.SetAttr<int64_t>("begin_norm_axis", begin_norm_axis));
in_grads->at(0) =
JUST(OpInterpUtil::Dispatch<Tensor>(*x_grad_op_, {x, mean, inv_variance, dy}, attrs));
Expand Down
4 changes: 2 additions & 2 deletions oneflow/core/autograd/gradient_funcs/normalization.cpp
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Expand Up @@ -67,7 +67,7 @@ class NormalizationGrad : public OpExprGradFunction<NormalizationGradInterpState
}

Maybe<void> Capture(NormalizationGradInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const override {
const TensorTuple& outputs, const AttrMap& attrs) const override {
ctx->is_training = JUST(op_trait_->GetAttr<bool>("training", attrs));
ctx->SaveTensorForBackward(inputs.at(0)); // x
ctx->SaveTensorForBackward(inputs.at(3)); // gamma
Expand Down Expand Up @@ -118,7 +118,7 @@ class NormalizationGrad : public OpExprGradFunction<NormalizationGradInterpState
dim_vec.push_back(x->shape()->At(axis_));
}
}
AttrValueMap shape_attr;
MutableAttrMap shape_attr;
shape_attr.SetAttr<Shape>("shape", Shape(dim_vec));
const auto& reshaped_gamma =
JUST(OpInterpUtil::Dispatch<Tensor>(*reshape_gamma_op_, {gamma}, shape_attr));
Expand Down
2 changes: 1 addition & 1 deletion oneflow/core/autograd/gradient_funcs/reshape.cpp
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Expand Up @@ -34,7 +34,7 @@ class ReshapeOpExprGrad : public OpExprGradFunction<OpExprInterpState> {
}

Maybe<void> Capture(OpExprInterpState* ctx, const TensorTuple& inputs, const TensorTuple& outputs,
const AttrValueMap& attrs) const override {
const AttrMap& attrs) const override {
ctx->SaveTensorForBackward(inputs.at(0));
return Maybe<void>::Ok();
}
Expand Down
6 changes: 3 additions & 3 deletions oneflow/core/autograd/gradient_funcs/split_like.cpp
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Expand Up @@ -32,7 +32,7 @@ class SplitLike : public OpExprGradFunction<SplitLikeInterpState> {
public:
Maybe<void> Init(const OpExpr& op) override;
Maybe<void> Capture(SplitLikeInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const override;
const TensorTuple& outputs, const AttrMap& attrs) const override;
Maybe<void> Apply(const SplitLikeInterpState* ctx, const TensorTuple& out_grads,
TensorTuple* in_grads) const override;

Expand Down Expand Up @@ -61,7 +61,7 @@ Maybe<void> SplitLike::Init(const OpExpr& op) {
}

Maybe<void> SplitLike::Capture(SplitLikeInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const {
const TensorTuple& outputs, const AttrMap& attrs) const {
CHECK_EQ_OR_RETURN(inputs.size(), outputs.size() + 1);
ctx->requires_grad = inputs.at(0)->requires_grad();
if (!ctx->requires_grad) { return Maybe<void>::Ok(); }
Expand Down Expand Up @@ -92,7 +92,7 @@ Maybe<void> SplitLike::Apply(const SplitLikeInterpState* ctx, const TensorTuple&
inputs.push_back(zero_grad);
}
}
AttrValueMap concat_attrs;
MutableAttrMap concat_attrs;
concat_attrs.SetAttr<int>("axis", axis_);
concat_attrs.SetAttr<int>("max_dim_size", ctx->max_dim_size);
in_grads->at(0) = JUST(OpInterpUtil::Dispatch<Tensor>(*concat_op_, inputs, concat_attrs));
Expand Down
19 changes: 9 additions & 10 deletions oneflow/core/autograd/gradient_funcs/tensor_scalar_binary.cpp
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Expand Up @@ -34,7 +34,7 @@ class TensorScalarAddOrSub : public OpExprGradFunction<TensorScalarInterpState>

Maybe<void> Init(const OpExpr& op) override;
Maybe<void> Capture(TensorScalarInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const override;
const TensorTuple& outputs, const AttrMap& attrs) const override;

protected:
std::shared_ptr<OpExpr> identity_op_;
Expand All @@ -56,8 +56,7 @@ Maybe<void> TensorScalarAddOrSub::Init(const OpExpr& op) {
}

Maybe<void> TensorScalarAddOrSub::Capture(TensorScalarInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs,
const AttrValueMap& attrs) const {
const TensorTuple& outputs, const AttrMap& attrs) const {
ctx->x_requires_grad = inputs.at(0)->requires_grad();
ctx->scalar_requires_grad = inputs.at(1)->requires_grad();
return Maybe<void>::Ok();
Expand All @@ -75,7 +74,7 @@ class TensorScalarAdd : public TensorScalarAddOrSub {
int32_t num_axes = out_grads.at(0)->shape()->NumAxes();
std::vector<int32_t> axes_vec(num_axes);
std::iota(axes_vec.begin(), axes_vec.end(), 0);
AttrValueMap attrs;
MutableAttrMap attrs;
JUST(attrs.SetAttr<std::vector<int32_t>>("axis", axes_vec));
in_grads->at(1) =
JUST(OpInterpUtil::Dispatch<Tensor>(*reduce_sum_op_, {out_grads.at(0)}, attrs));
Expand All @@ -97,7 +96,7 @@ class TensorScalarSub : public TensorScalarAddOrSub {
int32_t num_axes = out_grads.at(0)->shape()->NumAxes();
std::vector<int32_t> axes_vec(num_axes);
std::iota(axes_vec.begin(), axes_vec.end(), 0);
AttrValueMap attrs;
MutableAttrMap attrs;
JUST(attrs.SetAttr<std::vector<int32_t>>("axis", axes_vec));
const auto& reduce_sum =
JUST(OpInterpUtil::Dispatch<Tensor>(*reduce_sum_op_, {out_grads.at(0)}, attrs));
Expand All @@ -114,7 +113,7 @@ class TensorScalarMul : public OpExprGradFunction<TensorScalarInterpState> {
public:
Maybe<void> Init(const OpExpr& op) override;
Maybe<void> Capture(TensorScalarInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const override;
const TensorTuple& outputs, const AttrMap& attrs) const override;
Maybe<void> Apply(const TensorScalarInterpState* ctx, const TensorTuple& out_grads,
TensorTuple* in_grads) const override;

Expand All @@ -136,7 +135,7 @@ Maybe<void> TensorScalarMul::Init(const OpExpr& op) {
}

Maybe<void> TensorScalarMul::Capture(TensorScalarInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const {
const TensorTuple& outputs, const AttrMap& attrs) const {
ctx->x_requires_grad = inputs.at(0)->requires_grad();
ctx->scalar_requires_grad = inputs.at(1)->requires_grad();
if (ctx->x_requires_grad) { ctx->SaveTensorForBackward(inputs.at(1)); }
Expand All @@ -158,7 +157,7 @@ Maybe<void> TensorScalarMul::Apply(const TensorScalarInterpState* ctx, const Ten
int32_t num_axes = out_grads.at(0)->shape()->NumAxes();
std::vector<int32_t> axes_vec(num_axes);
std::iota(axes_vec.begin(), axes_vec.end(), 0);
AttrValueMap attrs;
MutableAttrMap attrs;
JUST(attrs.SetAttr<std::vector<int32_t>>("axis", axes_vec));
in_grads->at(1) = JUST(OpInterpUtil::Dispatch<Tensor>(*reduce_sum_op_, {y}, attrs));
}
Expand All @@ -171,7 +170,7 @@ class TensorScalarDiv : public OpExprGradFunction<TensorScalarInterpState> {
public:
Maybe<void> Init(const OpExpr& op) override;
Maybe<void> Capture(TensorScalarInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const override;
const TensorTuple& outputs, const AttrMap& attrs) const override;
Maybe<void> Apply(const TensorScalarInterpState* ctx, const TensorTuple& out_grads,
TensorTuple* in_grads) const override;

Expand All @@ -190,7 +189,7 @@ Maybe<void> TensorScalarDiv::Init(const OpExpr& op) {
}

Maybe<void> TensorScalarDiv::Capture(TensorScalarInterpState* ctx, const TensorTuple& inputs,
const TensorTuple& outputs, const AttrValueMap& attrs) const {
const TensorTuple& outputs, const AttrMap& attrs) const {
ctx->x_requires_grad = inputs.at(0)->requires_grad();
ctx->scalar_requires_grad = inputs.at(1)->requires_grad();
if (ctx->x_requires_grad || ctx->scalar_requires_grad) {
Expand Down
6 changes: 6 additions & 0 deletions oneflow/core/common/shape.cpp
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Expand Up @@ -14,6 +14,7 @@ See the License for the specific language governing permissions and
limitations under the License.
*/
#include "oneflow/core/common/shape.h"
#include "oneflow/core/common/shape.cfg.h"
#include "oneflow/core/common/shape_view.h"
#include "oneflow/core/common/protobuf.h"

Expand Down Expand Up @@ -58,6 +59,11 @@ Shape::Shape(const ShapeProto& shape_proto) {
UpdateElemCnt();
}

Shape::Shape(const cfg::ShapeProto& shape_proto) {
dim_vec_.assign(shape_proto.dim().begin(), shape_proto.dim().end());
UpdateElemCnt();
}

Shape& Shape::operator=(const Shape& shape) {
dim_vec_ = shape.dim_vec_;
UpdateElemCnt();
Expand Down
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