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Tensor value support (#23491)

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* add support for value tensor support of fill_constant Op
revert-23830-2.0-beta
wangchaochaohu 5 years ago committed by GitHub
parent e8efaee92d
commit 29c4fae112
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GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 170 additions and 81 deletions
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15
paddle/fluid/operators/fill_constant_op.cc
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@ -48,16 +48,6 @@ class FillConstantOp : public framework::OperatorWithKernel {
framework::proto::VarType::Type(ctx.Attr<int>("dtype")),
ctx.GetPlace());
}
framework::OpKernelType GetKernelTypeForVar(
const std::string& var_name, const Tensor& tensor,
const framework::OpKernelType& expected_kernel_type) const override {
if (var_name == "ShapeTensor" || var_name == "ShapeTensorList") {
return expected_kernel_type;
}
return framework::OpKernelType(expected_kernel_type.data_type_,
tensor.place(), tensor.layout());
}
};
class FillConstantOpVarTypeInference : public framework::VarTypeInference {
@ -80,6 +70,11 @@ class FillConstantOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<int64_t>>("shape",
"(vector<int64_t>) The shape of the output")
.SetDefault({});
AddInput("ValueTensor",
"(Tensor, optional) If provided, fill_constant Op will use this "
"as value to set the output Tensor, this has a higher priority "
"than attr(str_value), the shape of this tensor MUST BE [1].")
.AsDispensable();
AddInput("ShapeTensor",
"(Tensor<int>), optional). The shape of the output."
"It has a higher priority than Attr(shape).")

16
paddle/fluid/operators/fill_constant_op.h
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@ -99,6 +99,22 @@ class FillConstantKernel : public framework::OpKernel<T> {
value = static_cast<T>(tmp_value);
}
}
if (ctx.HasInput("ValueTensor")) {
auto *value_tensor = ctx.Input<framework::Tensor>("ValueTensor");
PADDLE_ENFORCE_EQ(
value_tensor->numel(), 1,
platform::errors::InvalidArgument(
"When use Tensor as value to set Tensor value in fill_cosntant, "
"value input(ValueTensor) size must be 1, but get %d",
value_tensor->numel()));
const T *tensor_data = value_tensor->data<T>();
framework::Tensor cpu_tensor;
if (platform::is_gpu_place(value_tensor->place())) {
TensorCopySync(*value_tensor, platform::CPUPlace(), &cpu_tensor);
tensor_data = cpu_tensor.data<T>();
}
value = tensor_data[0];
}
auto shape = GetShape(ctx);
if (out_var->IsType<framework::LoDTensor>()) {

13
paddle/fluid/operators/optimizers/adam_op.cc
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@ -42,19 +42,6 @@ void AdamOp::InferShape(framework::InferShapeContext *ctx) const {
platform::errors::NotFound(
"Input(Beta2Pow) of AdamOp should not be null."));
if (ctx->IsRuntime() && ctx->HasInput("Beta1Tensor")) {
auto beta1 = ctx->Inputs("Beta1Tensor");
PADDLE_ENFORCE_EQ(
beta1.size(), 1,
platform::errors::InvalidArgument("Input(Beta1Tensor) size must be 1"));
}
if (ctx->IsRuntime() && ctx->HasInput("Beta2Tensor")) {
auto beta2 = ctx->Inputs("Beta2Tensor");
PADDLE_ENFORCE_EQ(
beta2.size(), 1,
platform::errors::InvalidArgument("Input(Beta2Tensor) size must be 1"));
}
PADDLE_ENFORCE_EQ(ctx->HasOutput("ParamOut"), true,
platform::errors::NotFound(
"Output(ParamOut) of AdamOp should not be null."));

8
paddle/fluid/operators/optimizers/adam_op.cu
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@ -151,11 +151,19 @@ class AdamOpCUDAKernel : public framework::OpKernel<T> {
T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
if (ctx.HasInput("Beta1Tensor")) {
auto* beta1_tensor = ctx.Input<framework::Tensor>("Beta1Tensor");
PADDLE_ENFORCE_EQ(beta1_tensor->numel(), 1,
platform::errors::InvalidArgument(
"Input(Beta1Tensor) size must be 1, but get %d",
beta1_tensor->numel()));
beta1 = static_cast<T>(GetAttrFromTensor(beta1_tensor));
}
T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
if (ctx.HasInput("Beta2Tensor")) {
auto* beta2_tensor = ctx.Input<framework::Tensor>("Beta2Tensor");
PADDLE_ENFORCE_EQ(beta2_tensor->numel(), 1,
platform::errors::InvalidArgument(
"Input(Beta2Tensor) size must be 1, but get %d",
beta2_tensor->numel()));
beta2 = static_cast<T>(GetAttrFromTensor(beta2_tensor));
}
VLOG(3) << "beta1_pow.numel() : " << beta1_pow->numel()

8
paddle/fluid/operators/optimizers/adam_op.h
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@ -406,11 +406,19 @@ class AdamOpKernel : public framework::OpKernel<T> {
T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
if (ctx.HasInput("Beta1Tensor")) {
auto* beta1_tensor = ctx.Input<framework::Tensor>("Beta1Tensor");
PADDLE_ENFORCE_EQ(beta1_tensor->numel(), 1,
platform::errors::InvalidArgument(
"Input(Beta1Tensor) size must be 1, but get %d",
beta1_tensor->numel()));
beta1 = static_cast<T>(GetAttrFromTensor(beta1_tensor));
}
T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
if (ctx.HasInput("Beta2Tensor")) {
auto* beta2_tensor = ctx.Input<framework::Tensor>("Beta2Tensor");
PADDLE_ENFORCE_EQ(beta2_tensor->numel(), 1,
platform::errors::InvalidArgument(
"Input(Beta2Tensor) size must be 1, but get %d",
beta2_tensor->numel()));
beta2 = static_cast<T>(GetAttrFromTensor(beta2_tensor));
}
VLOG(3) << "beta1_pow.numel() : " << beta1_pow->numel()

85
python/paddle/fluid/layers/tensor.py
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@ -550,8 +550,9 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
If ``shape`` is an Variable, it should be an 1-D Tensor .
dtype(np.dtype|core.VarDesc.VarType|str): Data type of the output tensor which can
be float16, float32, float64, int32, int64.
value(float): The constant value used to initialize the Tensor to be created.
force_cpu(True): data should be on CPU if it's true, default value is False.
value(float16|float32|float64|int32|int64|Variable): The constant value used to initialize
the Tensor to be created. If value is an Variable, it should be an 1-D Tensor.
force_cpu(bool): data should be on CPU if it's true, default value is False.
out(Variable, optional): Optional output which can be any created
Variable that meets the requirements to store the result of operation.
if out is None, a new Varibale will be create to store the result.
@ -579,13 +580,21 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
# attr shape is an Variable Tensor.
shape = fluid.layers.fill_constant([1,2], "int32", 2) # shape=[2,2]
data4 = fluid.layers.fill_constant(shape=shape, dtype='bool', value=True) # data4=[[True,True],[True,True]]
# attr value is an Variable Tensor.
val = fluid.layers.fill_constant([1], "float32", 2.0) # val=[2.0]
data5 = fluid.layers.fill_constant(shape=[2,1], value=val, dtype='float32') #data5=[[2.0],[2.0]]
"""
attrs = {'value': float(value), 'force_cpu': force_cpu}
if convert_dtype(dtype) in ['int64', 'int32']:
attrs['str_value'] = str(int(value))
inputs = {}
attrs = {'force_cpu': force_cpu}
if isinstance(value, Variable):
inputs['ValueTensor'] = value
else:
attrs['str_value'] = str(float(value))
attrs['value'] = float(value)
if convert_dtype(dtype) in ['int64', 'int32']:
attrs['str_value'] = str(int(value))
else:
attrs['str_value'] = str(float(value))
if in_dygraph_mode():
if isinstance(shape, (list, tuple)):
@ -596,6 +605,13 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
shape = list(shape.numpy().astype(int))
if out is None:
out = _varbase_creator(dtype=dtype)
if isinstance(value, Variable):
if convert_dtype(dtype) in ['int64', 'int32']:
attrs['str_value'] = str(int(value.numpy()))
else:
attrs['str_value'] = str(float(value.numpy()))
core.ops.fill_constant(out, 'value',
float(value), 'force_cpu', force_cpu, 'dtype',
out.dtype, 'str_value', attrs['str_value'],
@ -608,55 +624,12 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
'fill_constant')
check_type(shape, 'shape', (Variable, list, tuple), 'fill_constant')
inputs = {}
attrs = {'value': float(value), 'force_cpu': force_cpu}
if convert_dtype(dtype) in ['int64', 'int32']:
attrs['str_value'] = str(int(value))
else:
attrs['str_value'] = str(float(value))
def _get_attr_shape(list_shape):
attr_shape = []
for idx, dim in enumerate(list_shape):
if isinstance(dim, Variable):
attr_shape.append(-1)
else:
attr_shape.append(dim)
return attr_shape
def _get_shape_tensor(list_shape):
new_shape_tensor = []
for idx, dim in enumerate(list_shape):
if isinstance(dim, Variable):
dim.stop_gradient = True
check_dtype(
dim.dtype, 'shape[' + str(idx) + ']', ['int32', 'int64'],
'fill_constant',
'(When type of shape in fill_constant is list or tuple.)')
if convert_dtype(dim.dtype) == 'int64':
dim = cast(x=dim, dtype='int32')
new_shape_tensor.append(dim)
else:
temp_out = helper.create_variable_for_type_inference('int32')
fill_constant([1], 'int32', dim, force_cpu=True, out=temp_out)
new_shape_tensor.append(temp_out)
return new_shape_tensor
if isinstance(shape, Variable):
shape.stop_gradient = True
check_dtype(shape.dtype, 'shape', ['int32', 'int64'], 'fill_constant',
'(When type of shape in fill_constant is Variable.)')
if (convert_dtype(shape.dtype) == 'int64'):
shape = cast(shape, 'int32')
inputs["ShapeTensor"] = shape
elif isinstance(shape, (list, tuple)):
assert len(shape) > 0, (
"The size of 'shape' in fill_constant can't be zero, "
"but received %s." % len(shape))
attrs["shape"] = _get_attr_shape(shape)
if utils._contain_var(shape):
inputs['ShapeTensorList'] = _get_shape_tensor(shape)
inputs = utils._get_shape_tensor_inputs(
inputs=inputs,
helper=helper,
attrs=attrs,
shape=shape,
op_type='fill_constant')
if out is None:
out = helper.create_variable_for_type_inference(dtype=dtype)

49
python/paddle/fluid/layers/utils.py
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@ -18,6 +18,8 @@ import copy
import six
import numpy as np
from ..framework import Variable
from ..data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
from ..layer_helper import LayerHelper
def convert_to_list(value, n, name, dtype=np.int):
@ -274,3 +276,50 @@ def _contain_var(list_or_tuple):
if isinstance(item, Variable):
return True
return False
def _get_shape_tensor_inputs(inputs, helper, attrs, shape, op_type):
from .tensor import fill_constant, cast
def _get_attr_shape(list_shape):
attr_shape = []
for idx, dim in enumerate(list_shape):
if isinstance(dim, Variable):
attr_shape.append(-1)
else:
attr_shape.append(dim)
return attr_shape
def _get_shape_tensor(list_shape):
new_shape_tensor = []
for idx, dim in enumerate(list_shape):
if isinstance(dim, Variable):
dim.stop_gradient = True
check_dtype(
dim.dtype, 'shape[' + str(idx) + ']', ['int32', 'int64'],
op_type,
'(When type of shape in' + op_type + 'is list or tuple.)')
if convert_dtype(dim.dtype) == 'int64':
dim = cast(x=dim, dtype='int32')
new_shape_tensor.append(dim)
else:
temp_out = fill_constant([1], 'int32', dim, force_cpu=True)
new_shape_tensor.append(temp_out)
return new_shape_tensor
if isinstance(shape, Variable):
shape.stop_gradient = True
check_dtype(shape.dtype, 'shape', ['int32', 'int64'], 'fill_constant',
'(When type of shape in' + op_type + ' is Variable.)')
if (convert_dtype(shape.dtype) == 'int64'):
shape = cast(shape, 'int32')
inputs["ShapeTensor"] = shape
elif isinstance(shape, (list, tuple)):
assert len(shape) > 0, (
"The size of 'shape' in" + op_type + " can't be zero, "
"but received %s." % len(shape))
attrs["shape"] = _get_attr_shape(shape)
if _contain_var(shape):
inputs['ShapeTensorList'] = _get_shape_tensor(shape)
return inputs

57
python/paddle/fluid/tests/unittests/test_fill_constant_op.py
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@ -212,6 +212,54 @@ class TestFillConstantOp1_ShapeTensor(OpTest):
self.check_output()
# Situation 4: value is a tensor
class TestFillConstantOp1_ValueTensor(OpTest):
def setUp(self):
'''Test fill_constant op with specified value
'''
self.op_type = "fill_constant"
self.init_data()
self.inputs = {
"ShapeTensor": np.array(self.shape).astype("int32"),
'ValueTensor': np.array([self.value]).astype("float32")
}
self.attrs = {'value': self.value + 1.0}
self.outputs = {'Out': np.full(self.shape, self.value)}
def init_data(self):
self.shape = [123, 92]
self.value = 3.8
self.dtype = np.float32
def test_check_output(self):
self.check_output()
# Situation 5: value is a tensor
class TestFillConstantOp2_ValueTensor(OpTest):
def setUp(self):
'''Test fill_constant op with specified value
'''
self.op_type = "fill_constant"
self.init_data()
self.inputs = {
"ShapeTensor": np.array(self.shape).astype("int32"),
'ValueTensor': np.array([self.value]).astype("int32")
}
self.attrs = {'value': self.value, 'dtype': 2}
self.outputs = {'Out': np.full(self.shape, self.value)}
def init_data(self):
self.shape = [123, 92]
self.value = 3
self.dtype = np.int32
def test_check_output(self):
self.check_output()
# Test python API
class TestFillConstantAPI(unittest.TestCase):
def test_api(self):
@ -242,14 +290,18 @@ class TestFillConstantAPI(unittest.TestCase):
out_6 = fluid.layers.fill_constant(
shape=shape_tensor_int64, dtype=np.float32, value=1.1)
val = fluid.layers.fill_constant(shape=[1], dtype=np.float32, value=1.1)
out_7 = fluid.layers.fill_constant(
shape=shape_tensor_int64, dtype=np.float32, value=val)
exe = fluid.Executor(place=fluid.CPUPlace())
res_1, res_2, res_3, res_4, res_5, res_6 = exe.run(
res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run(
fluid.default_main_program(),
feed={
"shape_tensor_int32": np.array([1, 2]).astype("int32"),
"shape_tensor_int64": np.array([1, 2]).astype("int64"),
},
fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6])
fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7])
assert np.array_equal(res_1, np.full([1, 2], 1.1, dtype="float32"))
assert np.array_equal(res_2, np.full([1, 2], 1.1, dtype="float32"))
@ -257,6 +309,7 @@ class TestFillConstantAPI(unittest.TestCase):
assert np.array_equal(res_4, np.full([1, 2], 1.1, dtype="float32"))
assert np.array_equal(res_5, np.full([1, 2], 1.1, dtype="float32"))
assert np.array_equal(res_6, np.full([1, 2], 1.1, dtype="float32"))
assert np.array_equal(res_7, np.full([1, 2], 1.1, dtype="float32"))
class TestFillConstantOpError(unittest.TestCase):

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