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optimize scalar to tensor function

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pull/9654/head
buxue 4 years ago
parent d936779f42
commit 0647b8b7db
8 changed files with 83 additions and 61 deletions
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2
mindspore/ccsrc/frontend/parallel/graph_util/generate_graph.cc
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@ -75,7 +75,7 @@ AnfNodePtr CreateInt32Tensor(int64_t value) {
if (it != int_tensor_map.end()) {
return it->second;
}
mindspore::tensor::TensorPtr tensor_ptr = std::make_shared<tensor::Tensor>(py::int_(value), kInt32);
mindspore::tensor::TensorPtr tensor_ptr = std::make_shared<tensor::Tensor>(value, kInt32);
ValuePtr value_ptr = MakeValue(tensor_ptr);
auto anf_node_ptr = ValuePtrToAnfNodePtr(value_ptr);
int_tensor_map[value] = anf_node_ptr;

2
mindspore/ccsrc/pipeline/pynative/pynative_execute.cc
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@ -382,7 +382,7 @@ void ConvertPyObjectToTensor(const py::object &input_object, const PrimitivePtr
tensor_ptr = std::make_shared<tensor::Tensor>(input_value, kFloat32);
*tensor_mask = kValueNodeTensorMask;
} else if (py::isinstance<py::int_>(input_object)) {
tensor_ptr = std::make_shared<tensor::Tensor>(py::cast<py::int_>(input_object), kInt64);
tensor_ptr = std::make_shared<tensor::Tensor>(py::cast<int64_t>(input_object), kInt64);
*tensor_mask = kValueNodeTensorMask;
} else if (py::isinstance<py::array>(input_object)) {
tensor_ptr = TensorPy::MakeTensor(py::cast<py::array>(input_object), nullptr);

97
mindspore/ccsrc/utils/convert_utils.cc
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@ -20,16 +20,13 @@
#include <string>
#include <memory>
#include <algorithm>
#include <list>
#include <utility>
#include <cfloat>
#include "abstract/abstract_value.h"
#include "ir/value.h"
#include "ir/tensor.h"
#include "ir/param_info.h"
#include "utils/ms_context.h"
#include "utils/shape_utils.h"
namespace mindspore {
bool ValueToBool(const ValuePtr &v, bool *value) {
@ -37,13 +34,13 @@ bool ValueToBool(const ValuePtr &v, bool *value) {
if (v->isa<BoolImm>()) {
*value = v->cast<BoolImmPtr>()->value();
} else if (v->isa<Int32Imm>()) {
*value = v->cast<Int32ImmPtr>()->value() == 0 ? false : true;
*value = v->cast<Int32ImmPtr>()->value() != 0;
} else if (v->isa<UInt32Imm>()) {
*value = v->cast<UInt32ImmPtr>()->value() == 0 ? false : true;
*value = v->cast<UInt32ImmPtr>()->value() != 0;
} else if (v->isa<FP32Imm>()) {
*value = v->cast<FP32ImmPtr>()->value() == 0 ? false : true;
*value = v->cast<FP32ImmPtr>()->value() != 0;
} else if (v->isa<FP64Imm>()) {
*value = v->cast<FP64ImmPtr>()->value() == 0 ? false : true;
*value = v->cast<FP64ImmPtr>()->value() != 0;
} else if (v->isa<tensor::Tensor>()) {
auto tensor = v->cast<tensor::TensorPtr>();
MS_EXCEPTION_IF_NULL(tensor);
@ -65,11 +62,11 @@ bool BaseRefToInt(const ValuePtr &v, int64_t *value) {
auto tensor = v->cast<tensor::TensorPtr>();
(void)tensor->data_sync();
if (tensor->Dtype()->ToString() == "Int32") {
int32_t *tensor_data = static_cast<int32_t *>(tensor->data_c());
auto *tensor_data = static_cast<int32_t *>(tensor->data_c());
auto vb = tensor_data[0];
*value = static_cast<int64_t>(vb);
} else if (tensor->Dtype()->ToString() == "Int64") {
int64_t *tensor_data = static_cast<int64_t *>(tensor->data_c());
auto *tensor_data = static_cast<int64_t *>(tensor->data_c());
auto vb = tensor_data[0];
*value = vb;
} else {
@ -86,39 +83,19 @@ bool BaseRefToBool(const BaseRef &v, bool *value) {
return ValueToBool(utils::cast<ValuePtr>(v), value);
} else if (utils::isa<bool>(v)) {
auto vb = utils::cast<bool>(v);
if (vb == true) {
*value = true;
} else {
*value = false;
}
*value = vb;
} else if (utils::isa<int>(v)) {
auto vb = utils::cast<int>(v);
if (vb == 0) {
*value = false;
} else {
*value = true;
}
*value = vb != 0;
} else if (utils::isa<unsigned int>(v)) {
auto vb = utils::cast<unsigned int>(v);
if (vb == 0) {
*value = false;
} else {
*value = true;
}
*value = vb != 0;
} else if (utils::isa<float>(v)) {
auto vb = utils::cast<float>(v);
if (vb >= -FLT_EPSILON && vb <= FLT_EPSILON) {
*value = false;
} else {
*value = true;
}
*value = !(vb >= -FLT_EPSILON && vb <= FLT_EPSILON);
} else if (utils::isa<double>(v)) {
auto vb = utils::cast<double>(v);
if (vb >= -DBL_EPSILON && vb <= DBL_EPSILON) {
*value = false;
} else {
*value = true;
}
*value = !(vb >= -DBL_EPSILON && vb <= DBL_EPSILON);
} else {
MS_LOG(DEBUG) << "value is not supported to cast to be bool";
return false;
@ -187,13 +164,13 @@ bool SameNode(const AnfNodePtr &node1, const AnfNodePtr &node2, FuncGraphPairMap
return SameNodeShallow(node1, node2, equiv_func_graph, equiv_node);
}
bool SameSubgraph(AnfNodePtr root1, AnfNodePtr root2, FuncGraphPairMapEquiv *equiv_func_graph,
bool SameSubgraph(const AnfNodePtr &root1, const AnfNodePtr &root2, FuncGraphPairMapEquiv *equiv_func_graph,
NodeMapEquiv *const equiv_node) {
std::unordered_set<AnfNodePtr> done;
std::stack<std::pair<AnfNodePtr, AnfNodePtr>> todo;
todo.push(std::make_pair(root1, root2));
while (todo.size() > 0) {
while (!todo.empty()) {
AnfNodePtr node1 = todo.top().first;
if (done.count(node1) > 0) {
todo.pop();
@ -231,7 +208,7 @@ bool SameSubgraph(AnfNodePtr root1, AnfNodePtr root2, FuncGraphPairMapEquiv *equ
}
} // namespace
bool Isomorphic(FuncGraphPtr fg1, FuncGraphPtr fg2, FuncGraphPairMapEquiv *equiv_func_graph,
bool Isomorphic(const FuncGraphPtr &fg1, const FuncGraphPtr &fg2, FuncGraphPairMapEquiv *equiv_func_graph,
NodeMapEquiv *const equiv_node) {
auto fg1_fg2 = std::make_pair(fg1, fg2);
if (equiv_func_graph == nullptr) {
@ -267,23 +244,35 @@ tensor::TensorPtr ScalarToTensor(const ScalarPtr &scalar) {
if (scalar == nullptr) {
MS_EXCEPTION(ArgumentError) << "Nullptr Error!";
}
tensor::TensorPtr tensor = nullptr;
if (scalar->isa<FloatImm>()) {
tensor = std::make_shared<tensor::Tensor>(static_cast<double>(GetValue<float>(scalar)), kFloat32);
} else if (scalar->isa<Int32Imm>()) {
tensor = std::make_shared<tensor::Tensor>(static_cast<int64_t>(GetValue<int>(scalar)), kInt32);
} else if (scalar->isa<Int64Imm>()) {
tensor = std::make_shared<tensor::Tensor>(GetValue<int64_t>(scalar), kInt64);
} else if (scalar->isa<BoolImm>()) {
const int64_t bool_value = GetValue<bool>(scalar) ? 1 : 0;
tensor = std::make_shared<tensor::Tensor>(bool_value, kBool);
} else {
auto type = scalar->type();
auto type_str = (type == nullptr) ? "nullptr" : type->ToString();
MS_LOG(EXCEPTION) << "Invalid scalar type: " << type_str;
TypePtr data_type = scalar->type();
MS_EXCEPTION_IF_NULL(data_type);
TypeId type_id = data_type->type_id();
switch (type_id) {
case kNumberTypeBool:
return std::make_shared<tensor::Tensor>(GetValue<bool>(scalar), data_type);
case kNumberTypeInt8:
return std::make_shared<tensor::Tensor>(static_cast<int64_t>(GetValue<int8_t>(scalar)), data_type);
case kNumberTypeInt16:
return std::make_shared<tensor::Tensor>(static_cast<int64_t>(GetValue<int16_t>(scalar)), data_type);
case kNumberTypeInt32:
return std::make_shared<tensor::Tensor>(static_cast<int64_t>(GetValue<int32_t>(scalar)), data_type);
case kNumberTypeInt64:
return std::make_shared<tensor::Tensor>(GetValue<int64_t>(scalar), data_type);
case kNumberTypeUInt8:
return std::make_shared<tensor::Tensor>(static_cast<uint64_t>(GetValue<uint8_t>(scalar)), data_type);
case kNumberTypeUInt16:
return std::make_shared<tensor::Tensor>(static_cast<uint64_t>(GetValue<uint16_t>(scalar)), data_type);
case kNumberTypeUInt32:
return std::make_shared<tensor::Tensor>(static_cast<uint64_t>(GetValue<uint32_t>(scalar)), data_type);
case kNumberTypeUInt64:
return std::make_shared<tensor::Tensor>(GetValue<uint64_t>(scalar), data_type);
case kNumberTypeFloat32:
return std::make_shared<tensor::Tensor>(GetValue<float>(scalar), data_type);
case kNumberTypeFloat64:
return std::make_shared<tensor::Tensor>(GetValue<double>(scalar), data_type);
default:
MS_LOG(EXCEPTION) << "When convert scalar to tensor, the scalar type: " << data_type << "is valid.";
}
MS_EXCEPTION_IF_NULL(tensor);
return tensor;
}
void TensorValueToTensor(const ValuePtr &value, std::vector<tensor::TensorPtr> *tensors) {
@ -301,7 +290,7 @@ void TensorValueToTensor(const ValuePtr &value, std::vector<tensor::TensorPtr> *
}
}
} else if (value->isa<tensor::Tensor>()) {
tensor::TensorPtr tensor = value->cast<tensor::TensorPtr>();
auto tensor = value->cast<tensor::TensorPtr>();
MS_EXCEPTION_IF_NULL(tensor);
tensors->push_back(tensor);
}

3
mindspore/ccsrc/utils/convert_utils.h
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@ -57,7 +57,8 @@ enum EquivState { kNotEquiv = 0, kEquiv = 1, kPending = 2 };
using FuncGraphPairMapEquiv = std::unordered_map<std::pair<FuncGraphPtr, FuncGraphPtr>, EquivState, PairHasher>;
using NodeMapEquiv = std::unordered_map<AnfNodePtr, AnfNodePtr>;
bool Isomorphic(FuncGraphPtr g1, FuncGraphPtr g2, FuncGraphPairMapEquiv *equiv_func_graph, NodeMapEquiv *equiv_node);
bool Isomorphic(const FuncGraphPtr &g1, const FuncGraphPtr &g2, FuncGraphPairMapEquiv *equiv_func_graph,
NodeMapEquiv *equiv_node);
tensor::TensorPtr ScalarToTensor(const ScalarPtr &scalar);

10
mindspore/core/ir/tensor.cc
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@ -491,6 +491,16 @@ Tensor::Tensor(double input, const TypePtr &data_type)
data_(MakeTensorData(data_type_, {}, input)),
id_(MakeId()) {}
Tensor::Tensor(uint64_t input, const TypePtr &data_type)
: MetaTensor(TypeIdOf(data_type, kNumberTypeUInt64), {}),
data_(MakeTensorData(data_type_, {}, input)),
id_(MakeId()) {}
Tensor::Tensor(bool input, const TypePtr &data_type)
: MetaTensor(TypeIdOf(data_type, kNumberTypeBool), {}),
data_(MakeTensorData(data_type_, {}, input)),
id_(MakeId()) {}
bool Tensor::operator==(const Tensor &tensor) const {
return (&tensor == this || (MetaTensor::operator==(tensor) && data_ == tensor.data_));
}

12
mindspore/core/ir/tensor.h
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@ -172,6 +172,18 @@ class Tensor : public MetaTensor {
// param data_type [TypeId] data type
explicit Tensor(double input, const TypePtr &data_type = nullptr);
// brief Create 0 dimension tensor from a uint scalar.
//
// param input [uint] the data for tensor
// param data_type [TypeId] data type
explicit Tensor(uint64_t input, const TypePtr &data_type = nullptr);
// brief Create 0 dimension tensor from a bool scalar.
//
// param input [bool] the data for tensor
// param data_type [TypeId] data type
explicit Tensor(bool input, const TypePtr &data_type = nullptr);
~Tensor() override = default;
MS_DECLARE_PARENT(Tensor, MetaTensor);

16
mindspore/nn/layer/basic.py
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@ -88,6 +88,7 @@ class L1Regularizer(Cell):
l1_regularization = self.scale * self.reduce_sum(self.abs(weights))
return l1_regularization
class Dropout(Cell):
r"""
Dropout layer for the input.
@ -210,6 +211,7 @@ class Flatten(Cell):
def construct(self, x):
return F.reshape(x, (F.shape(x)[0], -1))
@constexpr
def get_broadcast_weight_bias_shape(x_shape, out_channel, in_channel):
"""get broadcast_weight_bias shape"""
@ -217,6 +219,7 @@ def get_broadcast_weight_bias_shape(x_shape, out_channel, in_channel):
broad_bias_shape = x_shape[:-1] + (out_channel,)
return broad_weight_shape, broad_bias_shape
class Dense(Cell):
r"""
The dense connected layer.
@ -262,6 +265,7 @@ class Dense(Cell):
[[ 2.5246444 2.2738023 0.5711005 -3.9399147 ]
[ 1.0739875 4.0155234 0.94188046 -5.459526 ]]
"""
@cell_attr_register(attrs=['has_bias', 'activation', 'in_channels', 'out_channels'])
def __init__(self,
in_channels,
@ -323,7 +327,6 @@ class Dense(Cell):
x = self.activation(x)
return x
def extend_repr(self):
s = 'input_channels={}, output_channels={}'.format(self.in_channels, self.out_channels)
if self.has_bias:
@ -339,10 +342,12 @@ def _is_equal_one(x):
return False
return bool(x.asnumpy().mean() == 1.0)
@constexpr
def _dtype_check(x_dtype):
if x_dtype not in [mstype.float32, mstype.float16]:
raise TypeError("The input type must be float32 or float16.")
raise TypeError("The input type must be float32 or float16.")
@constexpr
def _is_float_dtype(dtype):
@ -539,7 +544,6 @@ class OneHot(Cell):
return self.onehot(indices, self.depth, F.cast(self.on_value, self.dtype), F.cast(self.off_value, self.dtype))
class Pad(Cell):
"""
Pads the input tensor according to the paddings and mode.
@ -672,6 +676,7 @@ class Interpolate(Cell):
>>> print(result.shape)
(1, 1, 5, 5)
"""
def __init__(self):
super(Interpolate, self).__init__()
@ -767,6 +772,7 @@ class Tril(Cell):
[[1 0]
[3 4]]
"""
def __init__(self):
super(Tril, self).__init__()
self.dtype = P.DType()
@ -809,6 +815,7 @@ class Triu(Cell):
[[1 2]
[0 4]]
"""
def __init__(self):
super(Triu, self).__init__()
self.dtype = P.DType()
@ -859,6 +866,7 @@ class MatrixDiag(Cell):
[[ 1. 0.]
[ 0. -1.]]
"""
def __init__(self):
super(MatrixDiag, self).__init__()
self.matrix_diag = inner.MatrixDiag()
@ -895,6 +903,7 @@ class MatrixDiagPart(Cell):
[-1. 1.]
[-1. 1.]]
"""
def __init__(self):
super(MatrixDiagPart, self).__init__()
self.matrix_diag_part = inner.MatrixDiagPart()
@ -936,6 +945,7 @@ class MatrixSetDiag(Cell):
[[-1. 0.]
[ 0. 1.]]]
"""
def __init__(self):
super(MatrixSetDiag, self).__init__()
self.matrix_set_diag = inner.MatrixSetDiag()

2
mindspore/nn/wrap/cell_wrapper.py
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@ -407,7 +407,7 @@ class ParameterUpdate(Cell):
>>> param = network.parameters_dict()['weight']
>>> update = nn.ParameterUpdate(param)
>>> update.phase = "update_param"
>>> weight = Tensor(0.001, mindspore.float32)
>>> weight = Tensor(np.arrange(12).reshape((4, 3)), mindspore.float32)
>>> update(weight)
"""

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