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未验证 提交 ef5d216e 编写于 作者: Z zyfncg 提交者： GitHub
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change the format of api yaml (#39532)

上级 672def6c
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Showing with 58 addition and 52 deletion
python/paddle/utils/code_gen/api.yaml
浏览文件 @ ef5d216e
- api : add
args : (const Tensor& x, const Tensor& y)
args : (Tensor x, Tensor y)
output : Tensor
infer_meta :
func : ElementwiseInferMeta
......@@ -7,7 +7,7 @@
func : add
- api : cast
args : (const Tensor& x, DataType out_dtype)
args : (Tensor x, DataType out_dtype)
output : Tensor
infer_meta :
func : CastInferMeta
......@@ -18,7 +18,7 @@
- api : concat
args : (const std::vector<Tensor>& x, const Scalar& axis)
args : (Tensor[] x, Scalar axis)
output : Tensor
infer_meta :
func : ConcatInferMeta
......@@ -27,7 +27,7 @@
func : concat
- api : conj
args : (const Tensor& x)
args : (Tensor x)
output : Tensor
infer_meta :
func : UnchangedInferMeta
......@@ -35,7 +35,7 @@
func : conj
- api : divide
args : (const Tensor& x, const Tensor& y)
args : (Tensor x, Tensor y)
output : Tensor
infer_meta :
func : ElementwiseInferMeta
......@@ -43,7 +43,7 @@
func : divide
- api : dot
args : (const Tensor& x, const Tensor& y)
args : (Tensor x, Tensor y)
output : Tensor
infer_meta :
func : DotInferMeta
......@@ -51,7 +51,7 @@
func : dot
- api : empty
args : (const ScalarArray& shape, DataType dtype=DataType::FLOAT32, Backend place=Backend::CPU, DataLayout layout=DataLayout::NCHW)
args : (ScalarArray shape, DataType dtype=DataType::FLOAT32, Backend place=Backend::CPU, DataLayout layout=DataLayout::NCHW)
output: Tensor
infer_meta :
func : CreateInferMeta
......@@ -64,7 +64,7 @@
layout : layout
- api : empty_like
args : (const Tensor& x, DataType dtype = DataType::UNDEFINED, Backend place = Backend::UNDEFINED, DataLayout layout = DataLayout::UNDEFINED)
args : (Tensor x, DataType dtype = DataType::UNDEFINED, Backend place = Backend::UNDEFINED, DataLayout layout = DataLayout::UNDEFINED)
output: Tensor
infer_meta :
func : CreateLikeInferMeta
......@@ -77,7 +77,7 @@
layout : layout > x
- api : flatten
args : (const Tensor& x, int start_axis, int stop_axis)
args : (Tensor x, int start_axis, int stop_axis)
output : Tensor
infer_meta :
func : FlattenInferMeta
......@@ -85,7 +85,7 @@
func : flatten
- api : full
args : (const ScalarArray& shape, const Scalar& value, DataType dtype=DataType::FLOAT32, Backend place=Backend::CPU, DataLayout layout=DataLayout::NCHW)
args : (ScalarArray shape, Scalar value, DataType dtype=DataType::FLOAT32, Backend place=Backend::CPU, DataLayout layout=DataLayout::NCHW)
output: Tensor
infer_meta :
func : CreateInferMeta
......@@ -98,7 +98,7 @@
layout : layout
- api : full_like
args : (const Tensor& x, const Scalar& value, DataType dtype = DataType::UNDEFINED, Backend place = Backend::UNDEFINED, DataLayout layout = DataLayout::UNDEFINED)
args : (Tensor x, Scalar value, DataType dtype = DataType::UNDEFINED, Backend place = Backend::UNDEFINED, DataLayout layout = DataLayout::UNDEFINED)
output: Tensor
infer_meta :
func : CreateLikeInferMeta
......@@ -111,7 +111,7 @@
layout : layout > x
- api : matmul
args : (const Tensor& x, const Tensor& y, bool transpose_x = false, bool transpose_y = false)
args : (Tensor x, Tensor y, bool transpose_x = false, bool transpose_y = false)
output : Tensor
infer_meta :
func : MatmulInferMeta
......@@ -120,7 +120,7 @@
backward : matmul_grad
- api : mean
args : (const Tensor& x, const std::vector<int64_t>& axis={}, bool keep_dim=false)
args : (Tensor x, int64_t[] axis={}, bool keep_dim=false)
output : Tensor
infer_meta :
func : ReduceInferMeta
......@@ -128,7 +128,7 @@
func : mean
- api : multiply
args : (const Tensor& x, const Tensor& y)
args : (Tensor x, Tensor y)
output : Tensor
infer_meta :
func : ElementwiseInferMeta
......@@ -136,12 +136,12 @@
func : multiply
- api : ones_like
args : (const Tensor& x, DataType dtype=DataType::UNDEFINED, Backend place=Backend::UNDEFINED, DataLayout layout=DataLayout::UNDEFINED)
args : (Tensor x, DataType dtype=DataType::UNDEFINED, Backend place=Backend::UNDEFINED, DataLayout layout=DataLayout::UNDEFINED)
output : Tensor
invoke : full_like(x, 1, dtype, place, layout)
- api : reshape
args : (const Tensor& x, const ScalarArray& shape)
args : (Tensor x, ScalarArray shape)
output : Tensor
infer_meta :
func : ReshapeInferMeta
......@@ -149,7 +149,7 @@
func : reshape
- api : scale
args : (const Tensor& x, const Scalar& scale, float bias, bool bias_after_scale)
args : (Tensor x, Scalar scale, float bias, bool bias_after_scale)
output : Tensor
infer_meta :
func : UnchangedInferMeta
......@@ -158,7 +158,7 @@
func : scale, scale_sr
- api : sign
args : (const Tensor& x)
args : (Tensor x)
output : Tensor
infer_meta :
func : UnchangedInferMeta
......@@ -166,7 +166,7 @@
func : sign
- api : subtract
args : (const Tensor& x, const Tensor& y)
args : (Tensor x, Tensor y)
output : Tensor
infer_meta :
func : ElementwiseInferMeta
......@@ -174,7 +174,7 @@
func : subtract
- api : sum
args : (const Tensor& x, const std::vector<int64_t>& axis={}, DataType dtype=DataType::UNDEFINED, bool keep_dim=false)
args : (Tensor x, int64_t[] axis={}, DataType dtype=DataType::UNDEFINED, bool keep_dim=false)
output : Tensor
infer_meta :
func : SumInferMeta
......@@ -184,6 +184,6 @@
data_type : x
- api : zeros_like
args : (const Tensor& x, DataType dtype=DataType::UNDEFINED, Backend place=Backend::UNDEFINED, DataLayout layout=DataLayout::UNDEFINED)
args : (Tensor x, DataType dtype=DataType::UNDEFINED, Backend place=Backend::UNDEFINED, DataLayout layout=DataLayout::UNDEFINED)
output : Tensor
invoke : full_like(x, 0, dtype, place, layout)
python/paddle/utils/code_gen/api_base.py
浏览文件 @ ef5d216e
......@@ -71,23 +71,26 @@ class BaseAPI(object):
f"Args declaration should start with '(' and end with ')', please check the args of {api_name} in yaml."
args_str = args_str[1:-1]
args_list = args_str.split(',')
input_types = [
'const Tensor&', 'const Tensor &', 'const std::vector<Tensor>&',
'const std::vector<Tensor> &'
]
attr_types = ['const Scalar&', 'const Scalar &', 'const ScalarArray&', 'const ScalarArray &', \
'int', 'int32_t', 'int64_t', 'size_t', 'float', 'double', 'bool', \
'const std::vector<int64_t>&', 'Backend', 'DataLayout', 'DataType']
input_types_map = {
'Tensor': 'const Tensor&',
'Tensor[]': 'const std::vector<Tensor>&'
}
attr_types_map = {'ScalarArray' : 'const ScalarArray&', 'Scalar' : 'const Scalar&', \
'int' : 'int', 'int32_t' : 'int32_t', 'int64_t' : 'int64_t', 'size_t' : 'size_t', \
'float' : 'float', 'double' : 'double', 'bool' : 'bool', \
'Backend' : 'Backend', 'DataLayout' : 'DataLayout', 'DataType' : 'DataType', \
'int64_t[]' : 'const std::vector<int64_t>&', 'int[]' : 'const std::vector<int>&'}
args_declare_str = ""
args_define_str = ""
for item in args_list:
item = item.strip()
type_and_name = item.split(' ')
# match the input tensor
has_input = False
for in_type in input_types:
if item.startswith(in_type):
input_name = item[len(in_type):].strip()
for in_type_symbol, in_type in input_types_map.items():
if type_and_name[0] == in_type_symbol:
input_name = type_and_name[1].strip()
assert len(input_name) > 0, \
f"The input tensor name should not be empty. Please check the args of {api_name} in yaml."
assert len(attrs['names']) == 0, \
......@@ -103,9 +106,9 @@ class BaseAPI(object):
continue
# match the attribute
for attr_type in attr_types:
if item.startswith(attr_type):
attr_name = item[len(attr_type):].strip()
for attr_type_symbol, attr_type in attr_types_map.items():
if type_and_name[0] == attr_type_symbol:
attr_name = item[len(attr_type_symbol):].strip()
assert len(attr_name) > 0, \
f"The attribute name should not be empty. Please check the args of {api_name} in yaml."
default_value = None
......@@ -128,25 +131,28 @@ class BaseAPI(object):
def parse_output(self, api_name, output_config):
def parse_output_item(output_item):
alllowd_output_types = ['Tensor', 'std::vector<Tensor>']
output_type_map = {
'Tensor': 'Tensor',
'Tensor[]': 'std::vector<Tensor>'
}
if re.search(r'\(\w*\)', output_item):
result = re.search(
r"(?P<out_type>[a-zA-Z0-9_<>]+)\s*\((?P<name>\w+)\)",
r"(?P<out_type>[a-zA-Z0-9_[\]]+)\s*\((?P<name>\w+)\)",
output_item)
out_type = result.group('out_type')
assert out_type in alllowd_output_types, \
f"{api_name} : Output type error: the output type only support Tensor and std::vector<Tensor>, \
assert out_type in output_type_map, \
f"{api_name} : Output type error: the output type only support Tensor and Tensor[], \
but now is {out_type}."
return out_type, result.group('name')
else:
if output_item.strip() in alllowd_output_types:
return output_item.strip(), 'out'
if output_item.strip() in output_type_map:
return output_type_map[output_item.strip()], 'out'
else:
raise ValueError(
"{} : Output type error: the output type only support Tensor and std::vector<Tensor>, \
but now is {}.".format(api_name, out_type))
"{} : Output type error: the output type only support Tensor and Tensor[], \
but now is {}.".format(api_name, output_item.strip()))
temp_list = output_config.split(',')
......
python/paddle/utils/code_gen/backward.yaml
浏览文件 @ ef5d216e
- backward_api : matmul_grad
forward : matmul (const Tensor& x, const Tensor& y, bool transpose_x=false, bool transpose_y=false) -> Tensor(out)
args : (const Tensor& x, const Tensor& y, const Tensor& out_grad, bool transpose_x=false, bool transpose_y=false)
forward : matmul (Tensor x, Tensor y, bool transpose_x=false, bool transpose_y=false) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad, bool transpose_x=false, bool transpose_y=false)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
......@@ -9,26 +9,26 @@
func : matmul_grad
- backward_api : scale_grad
forward : scale (const Tensor& x, const Scalar& scale, float bias, bool bias_after_scale) -> Tensor(out)
args : (const Tensor& out_grad, const Scalar& scale, float bias=0.0, bool bias_after_scale=true)
forward : scale (Tensor x, Scalar scale, float bias, bool bias_after_scale) -> Tensor(out)
args : (Tensor out_grad, Scalar scale, float bias=0.0, bool bias_after_scale=true)
output : Tensor(x_grad)
invoke : scale(out_grad, scale, bias, bias_after_scale)
# TODO(zhangyunfei) The config of double grad and triple grad will be supported in the future.
#
# - backward_api : matmul_double_grad
# forward : matmul_grad (const Tensor& x, const Tensor& y, const Tensor& out_grad, bool transpose_x, bool transpose_y) -> tuple<Tensor, Tensor>(dx, dy)
# args : (const Tensor& x, const Tensor& y, const Tensor& out_grad, const Tensor& dx_grad, const Tensor& dy_grad, bool transpose_x, bool transpose_y)
# output : tuple<Tensor, Tensor, Tensor> // d2x, d2y, dout_grad
# forward : matmul_grad (Tensor x, Tensor y, Tensor out_grad, bool transpose_x, bool transpose_y) -> Tensor(dx), Tensor>(dy)
# args : (Tensor x, Tensor y, Tensor out_grad, Tensor dx_grad, Tensor dy_grad, bool transpose_x, bool transpose_y)
# output : Tensor(d2x), Tensor(d2y), Tensor(dout_grad)
# infer_meta :
# func : MatmulDoubleGradInferMeta
# kernel :
# func : matmul_double_grad
# - backward_api : matmul_triple_grad
# forward : matmul_double_grad (const Tensor& x, const Tensor& y, const Tensor& out_grad, const Tensor& dx_grad, const Tensor& dy_grad, bool transpose_x, bool transpose_y) -> tuple<Tensor, Tensor, Tensor>(d2x, d2y, dout_grad)
# args : (const Tensor& x, const Tensor& y, const Tensor& out_grad, const Tensor& dx_grad, const Tensor& dy_grad, const Tensor& d2x_grad, const Tensor& d2y_grad, const Tensor& dout_grad_grad, bool transpose_x, bool transpose_y)
# output : tuple<Tensor, Tensor, Tensor, Tensor, Tensor> // d3x, d3y, d2out_grad, ddx_grad, ddy_grad
# forward : matmul_double_grad (Tensor x, Tensor y, Tensor out_grad, Tensor dx_grad, Tensor dy_grad, bool transpose_x, bool transpose_y) -> Tensor(d2x), Tensor(d2y), Tensor(dout_grad)
# args : (Tensor x, Tensor y, Tensor out_grad, Tensor dx_grad, Tensor dy_grad, Tensor d2x_grad, Tensor d2y_grad, Tensor dout_grad_grad, bool transpose_x, bool transpose_y)
# output : Tensor(d3x), Tensor(d3y), Tensor(d2out_grad), Tensor(ddx_grad), Tensor(ddy_grad)
# infer_meta :
# func : MatmulTripleGradInferMeta
# kernel :
......
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