diff --git a/python/paddle/common_ops_import.py b/python/paddle/common_ops_import.py index 98974808589467622ec71b0d5277cfd9654c9498..de8056f280a3967c4a7016454a60904568b0c79a 100644 --- a/python/paddle/common_ops_import.py +++ b/python/paddle/common_ops_import.py @@ -22,7 +22,7 @@ from paddle.fluid.core import VarDesc from paddle.fluid import core, dygraph_utils from paddle.fluid.data_feeder import check_type, check_dtype, check_variable_and_dtype, convert_dtype from paddle.fluid.layers import fill_constant, utils, scale -from paddle.fluid.layers.layer_function_generator import templatedoc +from paddle.tensor.layer_function_generator import templatedoc import paddle.fluid as fluid import numpy import warnings diff --git a/python/paddle/fluid/tests/unittests/test_multiplex_op.py b/python/paddle/fluid/tests/unittests/test_multiplex_op.py index a840586d78db08c87078eb9aa7c5479e7492e88a..a26eed12246e4cc6df1298973d5a8a8f5f43e90d 100644 --- a/python/paddle/fluid/tests/unittests/test_multiplex_op.py +++ b/python/paddle/fluid/tests/unittests/test_multiplex_op.py @@ -68,26 +68,26 @@ class TestMultiplexOpError(unittest.TestCase): def test_list(): # the inputs type must be list - fluid.layers.multiplex(inputs=x1, index=index) + paddle.multiplex(inputs=x1, index=index) self.assertRaises(TypeError, test_list) def test_len(): - fluid.layers.multiplex(inputs=[x1], index=index) + paddle.multiplex(inputs=[x1], index=index) self.assertRaises(ValueError, test_len) def test_type(): y1 = fluid.data(name='y1', shape=[None, 2], dtype='int16') y2 = fluid.data(name='y2', shape=[None, 2], dtype='int16') - fluid.layers.multiplex(inputs=[y1, y2], index=index) + paddle.multiplex(inputs=[y1, y2], index=index) self.assertRaises(TypeError, test_type) def test_type2(): index2 = fluid.data( name='index2', shape=[None, 1], dtype='int16') - fluid.layers.multiplex(inputs=[x1, x2], index=index2) + paddle.multiplex(inputs=[x1, x2], index=index2) self.assertRaises(TypeError, test_type2) diff --git a/python/paddle/framework/__init__.py b/python/paddle/framework/__init__.py index 2f8c23187e8d1344529a0fb015d686aad6c17f12..ffd1607fe87b4cf3c8cac497988ff72278de439e 100644 --- a/python/paddle/framework/__init__.py +++ b/python/paddle/framework/__init__.py @@ -53,4 +53,7 @@ from ..fluid.framework import dygraph_only # noqa: F401 from ..fluid.framework import convert_np_dtype_to_dtype_, _varbase_creator, OpProtoHolder # noqa: F401 from ..fluid.framework import _dygraph_tracer # noqa: F401 +from ..fluid.layer_helper import LayerHelper # noqa: F401 +from ..fluid.framework import in_dygraph_mode # noqa: F401 + __all__ = [] diff --git a/python/paddle/nn/functional/extension.py b/python/paddle/nn/functional/extension.py index 6a8686b612e7f3dc1feecc2bcc905bcb40b212a4..2483eab6c053acc9fcf6011f1752535adf8f4fc1 100644 --- a/python/paddle/nn/functional/extension.py +++ b/python/paddle/nn/functional/extension.py @@ -20,7 +20,7 @@ from ...fluid.layer_helper import LayerHelper from ...static import Variable from ...tensor.creation import assign from ...fluid import dygraph_utils -from ...fluid.layers.layer_function_generator import templatedoc +from ...tensor.layer_function_generator import templatedoc from ...fluid.layers.sequence_lod import sequence_mask #noqa: F401 from paddle import in_dynamic_mode diff --git a/python/paddle/tensor/layer_function_generator.py b/python/paddle/tensor/layer_function_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..ecb13613a125e59a04379a84166e47f22cfc4189 --- /dev/null +++ b/python/paddle/tensor/layer_function_generator.py @@ -0,0 +1,382 @@ +# Copyright (c) 2022 PaddlePaddle 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. + +from __future__ import print_function +import re +import functools +import warnings +import string + +from six.moves import cStringIO +from ..static import Variable +from ..fluid.proto import framework_pb2 +from ..framework import OpProtoHolder, core, convert_np_dtype_to_dtype_ +from ..framework import LayerHelper +from ..fluid.data_feeder import check_variable_and_dtype +import paddle +from paddle import _C_ops + +__all__ = [] + + +def _convert_(name): + """ + Formatting. + + Args: + name: The name/alias + + This function takes in a name and converts it to a standard format of + group1_group2. Where as per the regular expression, group1 can have + alphabets and numbers and group2 has capital alphabets. + + """ + s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name) + return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower() + + +def _type_to_str_(tp): + return framework_pb2.AttrType.Name(tp) + + +_two_dollar_pattern_ = re.compile(r"\$\$([^\$]+)\$\$") +_single_dollar_pattern_ = re.compile(r"\$([^\$]+)\$") +_two_bang_pattern_ = re.compile(r"!!([^!]+)!!") + + +def escape_math(text): + #return _two_bang_pattern_.sub( + # r'$$\1$$', + # _single_dollar_pattern_.sub(r':math:\n`\1`', + # _two_dollar_pattern_.sub(r"!!\1!!", text))) + return _two_dollar_pattern_.sub(r':math:`\1`', text) + + +def _generate_doc_string_(op_proto, + additional_args_lines=None, + skip_attrs_set=None): + """ + Generate docstring by OpProto + + Args: + op_proto (framework_pb2.OpProto): a protobuf message typed OpProto + + Returns: + str: the document string + """ + + if not isinstance(op_proto, framework_pb2.OpProto): + raise TypeError("OpProto should be `framework_pb2.OpProto`") + + buf = cStringIO() + buf.write(escape_math(op_proto.comment)) + buf.write('\nArgs:\n') + for each_input in op_proto.inputs: + line_begin = ' {0}'.format(_convert_(each_input.name)) + buf.write(line_begin) + buf.write(" (Tensor): ") + buf.write(escape_math(each_input.comment)) + if each_input.duplicable: + buf.write(" Duplicatable.") + if each_input.dispensable: + buf.write(" Optional.") + buf.write('\n') + + skip_attrs = OpProtoHolder.generated_op_attr_names() + # attr use_mkldnn and is_test also should not be visible to users. + skip_attrs.add("use_mkldnn") + skip_attrs.add("is_test") + skip_attrs.add("use_cudnn") + + if skip_attrs_set: + for t in skip_attrs_set: + skip_attrs.add(t) + + for each_attr in op_proto.attrs: + if each_attr.name in skip_attrs: + continue + buf.write(' ') + buf.write(each_attr.name) + buf.write(' (') + buf.write(_type_to_str_(each_attr.type)) + buf.write('): ') + buf.write(escape_math(each_attr.comment)) + buf.write('\n') + + if additional_args_lines is not None: + for line in additional_args_lines: + line = line.strip() + buf.write(' ') + buf.write(line) + buf.write('\n') + + if len(op_proto.outputs) != 0: + buf.write('\nReturns:\n') + buf.write(' ') + for each_opt in op_proto.outputs: + if not each_opt.intermediate: + break + buf.write(_convert_(each_opt.name)) + buf.write(' (Tensor): ') + buf.write(escape_math(each_opt.comment)) + + return buf.getvalue() + + +def generate_layer_fn(op_type): + """Register the Python layer for an Operator. + + Args: + op_type: The name of the operator to be created. + + This function takes in the operator type (sigmoid, mean , average etc) and + creates the operator functionality. + + """ + op_proto = OpProtoHolder.instance().get_op_proto(op_type) + not_intermediate_outputs = \ + [output for output in op_proto.outputs if not output.intermediate] + intermediate_outputs = \ + [output for output in op_proto.outputs if output.intermediate] + + if len(not_intermediate_outputs) != 1: + raise ValueError("Only one non intermediate output operator can be", + "automatically generated. {0}".format(op_type)) + + if not_intermediate_outputs[0].duplicable: + raise ValueError( + "Only non duplicable op can be automatically generated.") + + for output in intermediate_outputs: + if output.duplicable: + raise ValueError("The op can be automatically generated only when ", + "all intermediate ops are not duplicable.") + + o_name = not_intermediate_outputs[0].name + intermediate_output_names = [output.name for output in intermediate_outputs] + + def infer_and_check_dtype(op_proto, *args, **kwargs): + """ + This function performs the sanity check for dtype and + instance type. + """ + dtype = None + for ipt in op_proto.inputs: + name = _convert_(ipt.name) + val = kwargs.pop(name, []) + if not isinstance(val, list) and not isinstance(val, tuple): + val = [val] + if len(val) == 0: + if len(args) == 0: + continue + val = [args[0]] + args = args[1:] + + for each in val: + if not isinstance(each, Variable): + raise ValueError("input of {0} must be variable".format( + op_type)) + + if dtype is None: + dtype = each.dtype + elif dtype != each.dtype: + raise ValueError( + "operator {0} must input same dtype. {1} vs {2}".format( + op_type, dtype, each.dtype)) + + if dtype is None: + arg_dtype = kwargs.get("dtype") + if arg_dtype: + if not isinstance(arg_dtype, core.VarDesc.VarType): + dtype = convert_np_dtype_to_dtype_(arg_dtype) + else: + dtype = arg_dtype + else: + dtype = core.VarDesc.VarType.FP32 + return dtype + + def func(*args, **kwargs): + helper = LayerHelper(op_type, **kwargs) + + dtype = infer_and_check_dtype(op_proto, *args, **kwargs) + + inputs = dict() + for ipt in op_proto.inputs: + name = _convert_(ipt.name) + val = kwargs.pop(name, []) + if not isinstance(val, list) and not isinstance(val, tuple): + val = [val] + if len(val) == 0 and len(args) != 0: + val = args[0] + args = args[1:] + inputs[ipt.name] = val + + outputs = dict() + out = kwargs.pop(_convert_(o_name), []) + if out: + out_var = out[0] if (isinstance(out, list) or + isinstance(out, tuple)) else out + else: + out_var = helper.create_variable_for_type_inference(dtype=dtype) + outputs[o_name] = [out_var] + for name in intermediate_output_names: + outputs[name] = [ + helper.create_variable_for_type_inference(dtype=dtype) + ] + helper.append_op( + type=op_type, inputs=inputs, outputs=outputs, attrs=kwargs) + return helper.append_activation(out_var) + + func.__name__ = op_type + func.__doc__ = _generate_doc_string_(op_proto) + return func + + +def generate_activation_fn(op_type): + """Register the Python layer for an Operator without Attribute. + + Args: + op_type: The name of the operator to be created. + + This function takes in the operator type (sigmoid, exp , tanh etc) and + creates the operator functionality. + + """ + op_proto = OpProtoHolder.instance().get_op_proto(op_type) + + def func(x, name=None): + if paddle.in_dynamic_mode(): + op = getattr(_C_ops, op_type) + return op(x) + + if op_type not in ["abs", "exp", "square"]: + check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], + op_type) + else: + # abs exp square ops support dtype(int32, int64, float16, float32, float64) + check_variable_and_dtype( + x, 'x', ['int32', 'int64', 'float16', 'float32', 'float64'], + op_type) + + helper = LayerHelper(op_type, **locals()) + + output = helper.create_variable_for_type_inference(dtype=x.dtype) + helper.append_op(type=op_type, inputs={"X": x}, outputs={"Out": output}) + return output + + func.__name__ = op_type + func.__doc__ = _generate_doc_string_( + op_proto, + additional_args_lines=[ + "name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`." + ]) + return func + + +def generate_inplace_fn(inplace_op_type): + """Register the Python layer for an Inplace Operator without Attribute. + + Args: + inplace_op_type: The name of the inplace operator to be created. + + This function takes in the inplace operator type (exp_ , ceil_ etc) and + creates the operator functionality. + """ + origin_op_type = inplace_op_type[:-1] + + def func(x, name=None): + if paddle.in_dynamic_mode(): + op = getattr(_C_ops, inplace_op_type) + return op(x) + warnings.warn( + "In static mode, {}() is the same as {}() and does not perform inplace operation.". + format(inplace_op_type, origin_op_type)) + return generate_activation_fn(origin_op_type)(x, name) + + func.__name__ = inplace_op_type + func.__doc__ = """ +Inplace version of ``{0}`` API, the output Tensor will be inplaced with input ``x``. +Please refer to :ref:`api_fluid_layers_{1}`. +""".format(origin_op_type, origin_op_type) + + return func + + +def templatedoc(op_type=None): + """ + Decorator of layer function. It will use the docstring from the layer + function as the template. The template arguments are: + + * ${comment}: The operator comment written in CPP. + * ${{name}_comment}: The comment of ${name} written with AddAttr, AddOutput, + and AddInput. The ${name} is Python snake style. i.e., xxx_xxx. + * ${{name}_type}: The type of ${name}. + + Returns: + Decorated function. + """ + + def trim_ending_dot(msg): + return msg.rstrip('.') + + def __impl__(func): + if op_type is None: + op_type_name = func.__name__ + else: + op_type_name = op_type + op_proto = OpProtoHolder.instance().get_op_proto(op_type_name) + tmpl = string.Template(func.__doc__) + + comment_lines = op_proto.comment.split("\n") + comment = "" + for line in comment_lines: + line = line.strip() + if len(line) != 0: + comment += escape_math(line) + comment += " " + elif len(comment) != 0: + comment += "\n \n " + + args = {"comment": trim_ending_dot(comment)} + for each_input in op_proto.inputs: + input_name = _convert_(each_input.name) + args["{0}_comment".format(input_name)] = trim_ending_dot( + each_input.comment) + args["{0}_type".format(input_name)] = "Variable" + for each_attr in op_proto.attrs: + input_name = _convert_(each_attr.name) + args["{0}_comment".format(input_name)] = trim_ending_dot( + each_attr.comment) + args["{0}_type".format(input_name)] = _type_to_str_(each_attr.type) + + for each_opt in op_proto.outputs: + output_name = _convert_(each_opt.name) + args["{0}_comment".format(output_name)] = trim_ending_dot( + each_opt.comment) + args["{0}_type".format(output_name)] = "Variable" + func.__doc__ = tmpl.substitute(args) + return func + + return __impl__ + + +def add_sample_code(func, sample_code): + """ + Append sample code for dynamically generated functions. + + Args: + func: The function of the function to be append sample code to. + sample_code: sample code session in rst format. + """ + func.__doc__ = func.__doc__ + sample_code diff --git a/python/paddle/tensor/logic.py b/python/paddle/tensor/logic.py index ffd827b0eb530dc4815603fba00533e6498d2689..27aa333b1a54650de487553dbf534a28f44af4ae 100755 --- a/python/paddle/tensor/logic.py +++ b/python/paddle/tensor/logic.py @@ -14,7 +14,7 @@ from ..fluid.layer_helper import LayerHelper from ..fluid.data_feeder import check_type, check_variable_and_dtype -from ..fluid.layers.layer_function_generator import templatedoc +from .layer_function_generator import templatedoc from ..static import Variable from ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode # TODO: define logic functions of a tensor diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py index 298d7af96ea5738b49076ada0ad42697faca50df..3a2d08af88ff8fab4e9d6f53999c5070b007adc2 100644 --- a/python/paddle/tensor/math.py +++ b/python/paddle/tensor/math.py @@ -23,56 +23,52 @@ from paddle.common_ops_import import OpProtoHolder from paddle.common_ops_import import templatedoc from paddle.common_ops_import import dygraph_utils -from paddle.tensor import cast -from paddle.tensor.attribute import _complex_to_real_dtype +from .manipulation import cast +from .creation import _complex_to_real_dtype +from .layer_function_generator import _generate_doc_string_, generate_activation_fn, generate_layer_fn + import paddle -from paddle.static import Variable -from ..framework import core -from ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode, _non_static_mode +from ..static import Variable +from ..framework import core, in_dygraph_mode, _non_static_mode, LayerHelper +from ..fluid.framework import _in_legacy_dygraph from ..framework import _varbase_creator, convert_np_dtype_to_dtype_ -from ..fluid.layer_helper import LayerHelper from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype -from ..fluid.layers.layer_function_generator import _generate_doc_string_, generate_activation_fn, generate_layer_fn from ..fluid.dygraph.inplace_utils import inplace_apis_in_dygraph_only # TODO: define math functions # yapf: disable -from ..fluid.layers import abs # noqa: F401 -from ..fluid.layers import acos # noqa: F401 -from ..fluid.layers import asin # noqa: F401 -from ..fluid.layers import ceil # noqa: F401 -from ..fluid.layers import ceil_ # noqa: F401 -from ..fluid.layers import cos # noqa: F401 -from ..fluid.layers import tan # noqa: F401 -from ..fluid.layers import sinh # noqa: F401 -from ..fluid.layers import cosh # noqa: F401 -from ..fluid.layers import exp # noqa: F401 -from ..fluid.layers import exp_ # noqa: F401 -from ..fluid.layers import expm1 # noqa: F401 -from ..fluid.layers import floor # noqa: F401 -from ..fluid.layers import floor_ # noqa: F401 -from ..fluid.layers import log # noqa: F401 -from ..fluid.layers import reciprocal # noqa: F401 -from ..fluid.layers import reciprocal_ # noqa: F401 -from ..fluid.layers import round # noqa: F401 -from ..fluid.layers import round_ # noqa: F401 -from ..fluid.layers import rsqrt # noqa: F401 -from ..fluid.layers import rsqrt_ # noqa: F401 -from ..fluid.layers import scale # noqa: F401 -from ..fluid.layers import square # noqa: F401 -from ..fluid.layers import stanh # noqa: F401 -from ..fluid.layers import atan # noqa: F401 -from ..fluid.layers import erf # noqa: F401 -from ..fluid.layers import sqrt # noqa: F401 -from ..fluid.layers import sqrt_ # noqa: F401 -from ..fluid.layers import sin # noqa: F401 -from ..fluid.layers import lgamma # noqa: F401 -from ..fluid.layers import asinh # noqa: F401 -from ..fluid.layers import acosh # noqa: F401 -from ..fluid.layers import atanh # noqa: F401 - -from ..fluid.layers import multiplex # noqa: F401 -from ..fluid.layers import reduce_prod +from .ops import abs # noqa: F401 +from .ops import acos # noqa: F401 +from .ops import asin # noqa: F401 +from .ops import ceil # noqa: F401 +from .ops import ceil_ # noqa: F401 +from .ops import cos # noqa: F401 +from .ops import tan # noqa: F401 +from .ops import sinh # noqa: F401 +from .ops import cosh # noqa: F401 +from .ops import exp # noqa: F401 +from .ops import exp_ # noqa: F401 +from .ops import expm1 # noqa: F401 +from .ops import floor # noqa: F401 +from .ops import floor_ # noqa: F401 +from .ops import reciprocal # noqa: F401 +from .ops import reciprocal_ # noqa: F401 +from .ops import round # noqa: F401 +from .ops import round_ # noqa: F401 +from .ops import rsqrt # noqa: F401 +from .ops import rsqrt_ # noqa: F401 +from .ops import square # noqa: F401 +from .ops import atan # noqa: F401 +from .ops import erf # noqa: F401 +from .ops import sqrt # noqa: F401 +from .ops import sqrt_ # noqa: F401 +from .ops import sin # noqa: F401 +from .ops import lgamma # noqa: F401 +from .ops import asinh # noqa: F401 +from .ops import acosh # noqa: F401 +from .ops import atanh # noqa: F401 + + from ..fluid.layers import elementwise_sub from paddle import _C_ops @@ -92,6 +88,241 @@ _supported_float_dtype_ = [ ] +def log(x, name=None): + r""" + Calculates the natural log of the given input tensor, element-wise. + + .. math:: + + Out = \\ln(x) + + Args: + x (Tensor): Input Tensor. Must be one of the following types: float32, float64. + name (str|None): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` + + + Returns: + Tensor: The natural log of the input Tensor computed element-wise. + + Examples: + + .. code-block:: python + + import paddle + + x = [[2,3,4], [7,8,9]] + x = paddle.to_tensor(x, dtype='float32') + res = paddle.log(x) + # [[0.693147, 1.09861, 1.38629], [1.94591, 2.07944, 2.19722]] + """ + if in_dygraph_mode(): + return _C_ops.final_state_log(x) + if _in_legacy_dygraph(): + return _C_ops.log(x) + + check_variable_and_dtype(x, 'x', ['float32', 'float64'], "log") + inputs = {'X': [x]} + helper = LayerHelper('log', **locals()) + dtype = helper.input_dtype(input_param_name='x') + out = helper.create_variable_for_type_inference(dtype) + helper.append_op(type="log", inputs={"X": x}, outputs={"Out": out}) + return out + + +def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None): + """ + Scale operator. + + Putting scale and bias to the input Tensor as following: + + ``bias_after_scale`` is True: + + .. math:: + Out=scale*X+bias + + ``bias_after_scale`` is False: + + .. math:: + Out=scale*(X+bias) + + Args: + x(Tensor): Input N-D Tensor of scale operator. Data type can be float32, float64, int8, int16, int32, int64, uint8. + scale(float|Tensor): The scale factor of the input, it should be a float number or a Tensor with shape [1] and data type as float32. + bias(float): The bias to be put on the input. + bias_after_scale(bool): Apply bias addition after or before scaling. It is useful for numeric stability in some circumstances. + act(str, optional): Activation applied to the output such as tanh, softmax, sigmoid, relu. + name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` + + Returns: + Tensor: Output tensor of scale operator, with shape and data type same as input. + + Examples: + .. code-block:: python + + # scale as a float32 number + import paddle + + data = paddle.randn(shape=[2,3], dtype='float32') + res = paddle.scale(data, scale=2.0, bias=1.0) + + .. code-block:: python + + # scale with parameter scale as a Tensor + import paddle + + data = paddle.randn(shape=[2, 3], dtype='float32') + factor = paddle.to_tensor([2], dtype='float32') + res = paddle.scale(data, scale=factor, bias=1.0) + + """ + + if in_dygraph_mode(): + out = _C_ops.final_state_scale(x, scale, float(bias), bias_after_scale) + return dygraph_utils._append_activation_in_dygraph(out) + if _non_static_mode(): + _scale = scale.numpy().item(0) if isinstance(scale, Variable) else scale + out = _C_ops.scale(x, 'scale', + float(_scale), 'bias', + float(bias), 'bias_after_scale', bias_after_scale) + return dygraph_utils._append_activation_in_dygraph(out) + + check_variable_and_dtype(x, "x", [ + 'float16', 'uint16', 'float32', 'float64', 'int8', 'int16', 'int32', + 'int64', 'uint8' + ], "scale") + inputs = {'X': [x]} + attrs = { + 'bias': float(bias), + 'bias_after_scale': bias_after_scale, + } + if isinstance(scale, Variable): + inputs['ScaleTensor'] = [scale] + else: + attrs['scale'] = float(scale) + helper = LayerHelper('scale', **locals()) + out = helper.create_variable_for_type_inference(dtype=x.dtype) + + helper.append_op( + type='scale', inputs=inputs, outputs={'Out': out}, attrs=attrs) + return helper.append_activation(out) + + +def stanh(x, scale_a=0.67, scale_b=1.7159, name=None): + """ + stanh activation. + + .. math:: + + out = b * \\frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}} + + Parameters: + x (Tensor): The input Tensor with data type float32, float64. + scale_a (float, optional): The scale factor a of the input. Default is 0.67. + scale_b (float, optional): The scale factor b of the output. Default is 1.7159. + name (str, optional): Name for the operation (optional, default is None). + For more information, please refer to :ref:`api_guide_Name`. + + Returns: + A Tensor with the same data type and shape as ``x`` . + + Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0]) + out = paddle.stanh(x, scale_a=0.67, scale_b=1.72) # [1.00616539, 1.49927628, 1.65933108, 1.70390463] + + """ + + if _non_static_mode(): + return _C_ops.stanh(x, 'scale_a', scale_a, 'scale_b', scale_b) + + check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'stanh') + + helper = LayerHelper('stanh', **locals()) + out = helper.create_variable_for_type_inference(dtype=x.dtype) + helper.append_op( + type='stanh', + inputs={'X': x}, + outputs={'Out': out}, + attrs={'scale_a': scale_a, + 'scale_b': scale_b}) + return out + +def multiplex(inputs, index, name=None): + """ + + Based on the given index parameter, the OP selects a specific row from each input Tensor to construct the output Tensor. + + If the input of this OP contains :math:`m` Tensors, where :math:`I_{i}` means the i-th input Tensor, :math:`i` between :math:`[0,m)` . + + And :math:`O` means the output, where :math:`O[i]` means the i-th row of the output, then the output satisfies that :math:`O[i] = I_{index[i]}[i]` . + + For Example: + + .. code-block:: text + + Given: + + inputs = [[[0,0,3,4], [0,1,3,4], [0,2,4,4], [0,3,3,4]], + [[1,0,3,4], [1,1,7,8], [1,2,4,2], [1,3,3,4]], + [[2,0,3,4], [2,1,7,8], [2,2,4,2], [2,3,3,4]], + [[3,0,3,4], [3,1,7,8], [3,2,4,2], [3,3,3,4]]] + + index = [[3],[0],[1],[2]] + + out = [[3,0,3,4], # out[0] = inputs[index[0]][0] = inputs[3][0] = [3,0,3,4] + [0,1,3,4], # out[1] = inputs[index[1]][1] = inputs[0][1] = [0,1,3,4] + [1,2,4,2], # out[2] = inputs[index[2]][2] = inputs[1][2] = [1,2,4,2] + [2,3,3,4]] # out[3] = inputs[index[3]][3] = inputs[2][3] = [2,3,3,4] + + + Args: + inputs (list): The input Tensor list. The list elements are N-D Tensors of data types float32, float64, int32, int64. All input Tensor shapes should be the same and rank must be at least 2. + index (Tensor): Used to select some rows in the input Tensor to construct an index of the output Tensor. It is a 2-D Tensor with data type int32 or int64 and shape [M, 1], where M is the number of input Tensors. + name(str, optional): The default value is None. Normally there is no + need for user to set this property. For more information, please + refer to :ref:`api_guide_Name`. + Returns: + Tensor: Output of multiplex OP, with data type being float32, float64, int32, int64. + + Examples: + + .. code-block:: python + + import paddle + import numpy as np + img1 = np.array([[1, 2], [3, 4]]).astype(np.float32) + img2 = np.array([[5, 6], [7, 8]]).astype(np.float32) + inputs = [paddle.to_tensor(img1), paddle.to_tensor(img2)] + index = paddle.to_tensor(np.array([[1], [0]]).astype(np.int32)) + res = paddle.multiplex(inputs, index) + print(res) # [array([[5., 6.], [3., 4.]], dtype=float32)] + + """ + if _non_static_mode(): + return _C_ops.multiplex(index, inputs) + helper = LayerHelper('multiplex', **locals()) + + check_type(inputs, 'inputs', (list), 'multiplex') + if len(inputs) < 2: + raise ValueError( + "inputs should be a list object with at least 2 elements.") + for id, x in enumerate(inputs): + check_variable_and_dtype(x, 'input[' + str(id) + ']', + ['float32', 'float64', 'int32', 'int64'], + 'multiplex') + check_variable_and_dtype(index, "index", ['int32', 'int64'], 'multiplex') + + out = helper.create_variable_for_type_inference(inputs[0].dtype) + helper.append_op( + type='multiplex', + inputs={'X': inputs, + 'Ids': index}, + outputs={'Out': [out]}) + return out + @inplace_apis_in_dygraph_only def scale_(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None): """ @@ -2973,7 +3204,38 @@ def prod(x, axis=None, keepdim=False, dtype=None, name=None): if x.dtype != convert_np_dtype_to_dtype_(dtype): x = cast(x, dtype) - return reduce_prod(input=x, dim=axis, keep_dim=keepdim, name=name) + input = x + dim = axis + keep_dim = keepdim + if dim is not None and not isinstance(dim, list): + if isinstance(dim, tuple): + dim = list(dim) + elif isinstance(dim, int): + dim = [dim] + else: + raise TypeError( + "The type of axis must be int, list or tuple, but received {}". + format(type(dim))) + if in_dygraph_mode(): + return _C_ops.final_state_reduce_prod( + input, dim if dim != None and dim != [] else [0], keep_dim, True if + dim == None or dim == [] or len(dim) == len(input.shape) else False) + + helper = LayerHelper('reduce_prod', **locals()) + check_variable_and_dtype( + input, 'input', ['float32', 'float64', 'int32', 'int64'], 'reduce_prod') + out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) + helper.append_op( + type='reduce_prod', + inputs={'X': input}, + outputs={'Out': out}, + attrs={ + 'dim': dim if dim != None and dim != [] else [0], + 'keep_dim': keep_dim, + 'reduce_all': True if dim == None or dim == [] or + len(dim) == len(input.shape) else False + }) + return out def sign(x, name=None): diff --git a/python/paddle/tensor/ops.py b/python/paddle/tensor/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..9ee59c6cfd84310886a41ce22f73fe6946ebdac4 --- /dev/null +++ b/python/paddle/tensor/ops.py @@ -0,0 +1,532 @@ +# Copyright (c) 2022 PaddlePaddle 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. + +from __future__ import print_function +import os +from .layer_function_generator import generate_layer_fn, generate_activation_fn, generate_inplace_fn, add_sample_code +from ..framework import core +from ..framework import convert_np_dtype_to_dtype_ +from ..static import Variable +from ..fluid.data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype + +__deprecated_func_name__ = { + 'tanh_shrink': 'tanhshrink', + 'logsigmoid': 'log_sigmoid' +} + +__activations_noattr__ = [ + 'sigmoid', + 'silu', + 'logsigmoid', + 'tanh_shrink', + 'softplus', + 'softsign', + 'tanh', +] + +__unary_func__ = [ + 'exp', + 'expm1', + 'atan', + 'sqrt', + 'rsqrt', + 'abs', + 'ceil', + 'floor', + 'cos', + 'tan', + 'acos', + 'sin', + 'sinh', + 'asin', + 'cosh', + 'round', + 'reciprocal', + 'square', + 'lgamma', + 'acosh', + 'asinh', + 'atanh', +] + +__inplace_unary_func__ = [ + 'exp_', + 'sqrt_', + 'rsqrt_', + 'ceil_', + 'floor_', + 'round_', + 'reciprocal_', +] + +__all__ = [] + +for _OP in set(__all__): + globals()[_OP] = generate_layer_fn(_OP) + +# It is a hot fix in some unittest using: +# fluid.layers.scale(x=x, scale=10.0, out=out_var) +# e.g.: test_program_code.py, test_dist_train.py +globals()['_scale'] = generate_layer_fn('scale') + +globals()['_elementwise_div'] = generate_layer_fn('elementwise_div') + +__all__ += __activations_noattr__ +__all__ += __unary_func__ +__all__ += __inplace_unary_func__ + +for _OP in set(__activations_noattr__): + _new_OP = _OP + if _OP in __deprecated_func_name__: + _new_OP = __deprecated_func_name__[_OP] + _func = generate_activation_fn(_OP) + globals()[_OP] = _func + +for _OP in set(__unary_func__): + _new_OP = _OP + if _OP in __deprecated_func_name__: + _new_OP = __deprecated_func_name__[_OP] + _func = generate_activation_fn(_OP) + globals()[_OP] = _func + +for _OP in set(__inplace_unary_func__): + _new_OP = _OP + if _OP in __deprecated_func_name__: + _new_OP = __deprecated_func_name__[_OP] + _func = generate_inplace_fn(_OP) + globals()[_OP] = _func + +add_sample_code(globals()["sigmoid"], r""" +Examples: + .. code-block:: python + + import paddle + import paddle.nn.functional as F + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = F.sigmoid(x) + print(out) + # [0.40131234 0.450166 0.52497919 0.57444252] + +""") + +add_sample_code(globals()["silu"], r""" +Examples: + .. code-block:: python + + import paddle + import paddle.nn.functional as F + + x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0]) + out = F.silu(x) + print(out) + # [ 0.7310586 1.7615942 2.8577224, 3.9280552 ] + +""") + +add_sample_code(globals()["logsigmoid"], r""" +Examples: + .. code-block:: python + + import paddle + import paddle.nn.functional as F + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = F.log_sigmoid(x) + print(out) + # [-0.91301525 -0.79813887 -0.64439666 -0.55435524] + +""") + +add_sample_code(globals()["exp"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.exp(x) + print(out) + # [0.67032005 0.81873075 1.10517092 1.34985881] + +""") + +add_sample_code(globals()["expm1"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.expm1(x) + print(out) + # [-0.32967997, -0.18126924, 0.10517092, 0.34985882] + +""") + +add_sample_code(globals()["tanh"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.tanh(x) + print(out) + # [-0.37994896 -0.19737532 0.09966799 0.29131261] + +""") + +add_sample_code(globals()["atan"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.atan(x) + print(out) + # [-0.38050638 -0.19739556 0.09966865 0.29145679] + +""") + +add_sample_code(globals()["tanh_shrink"], r""" +Examples: + .. code-block:: python + + import paddle + import paddle.nn.functional as F + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = F.tanhshrink(x) + print(out) + # [-0.020051, -0.00262468, 0.000332005, 0.00868739] + +""") + +add_sample_code(globals()["sqrt"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([0.1, 0.2, 0.3, 0.4]) + out = paddle.sqrt(x) + print(out) + # [0.31622777 0.4472136 0.54772256 0.63245553] + +""") + +add_sample_code(globals()["rsqrt"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([0.1, 0.2, 0.3, 0.4]) + out = paddle.rsqrt(x) + print(out) + # [3.16227766 2.23606798 1.82574186 1.58113883] + +""") + +add_sample_code(globals()["abs"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.abs(x) + print(out) + # [0.4 0.2 0.1 0.3] + +""") + +add_sample_code(globals()["ceil"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.ceil(x) + print(out) + # [-0. -0. 1. 1.] + +""") + +add_sample_code(globals()["floor"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.floor(x) + print(out) + # [-1. -1. 0. 0.] + +""") + +add_sample_code(globals()["cos"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.cos(x) + print(out) + # [0.92106099 0.98006658 0.99500417 0.95533649] + +""") + +add_sample_code(globals()["tan"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.tan(x) + print(out) + # [-0.42279324, -0.20271005, 0.10033467, 0.30933627] + +""") + +add_sample_code(globals()["acos"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.acos(x) + print(out) + # [1.98231317 1.77215425 1.47062891 1.26610367] + +""") + +add_sample_code(globals()["sin"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.sin(x) + print(out) + # [-0.38941834 -0.19866933 0.09983342 0.29552021] + +""") + +add_sample_code(globals()["asin"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.asin(x) + print(out) + # [-0.41151685 -0.20135792 0.10016742 0.30469265] + +""") + +add_sample_code(globals()["cosh"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.cosh(x) + print(out) + # [1.08107237 1.02006676 1.00500417 1.04533851] + +""") + +add_sample_code(globals()["sinh"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.sinh(x) + print(out) + # [-0.41075233 -0.201336 0.10016675 0.30452029] + +""") + +add_sample_code(globals()["asinh"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.asinh(x) + print(out) + # [-0.39003533, -0.19869010, 0.09983408, 0.29567307] + +""") + +add_sample_code(globals()["acosh"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([1., 3., 4., 5.]) + out = paddle.acosh(x) + print(out) + # [0. , 1.76274729, 2.06343699, 2.29243159] + +""") + +add_sample_code(globals()["atanh"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.atanh(x) + print(out) + # [-0.42364895, -0.20273256, 0.10033535, 0.30951962] + +""") + +add_sample_code(globals()["round"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.5, -0.2, 0.6, 1.5]) + out = paddle.round(x) + print(out) + # [-1. -0. 1. 2.] + +""") + +add_sample_code(globals()["reciprocal"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.reciprocal(x) + print(out) + # [-2.5 -5. 10. 3.33333333] + +""") + +add_sample_code(globals()["square"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.square(x) + print(out) + # [0.16 0.04 0.01 0.09] + +""") + +add_sample_code(globals()["lgamma"], r""" +Examples: + .. code-block:: python + + import paddle + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.lgamma(x) + print(out) + # [1.31452441, 1.76149750, 2.25271273, 1.09579802] + +""") + +add_sample_code(globals()["softplus"], r""" +Examples: + .. code-block:: python + + import paddle + import paddle.nn.functional as F + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = F.softplus(x) + print(out) + # [0.513015, 0.598139, 0.744397, 0.854355] + +""") + +add_sample_code(globals()["softsign"], r""" +Examples: + .. code-block:: python + + import paddle + import paddle.nn.functional as F + + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = F.softsign(x) + print(out) + # [-0.285714, -0.166667, 0.0909091, 0.230769] + +""") + +__all__ += ['erf'] + +_erf_ = generate_layer_fn('erf') + + +def erf(x, name=None): + locals_var = locals().copy() + kwargs = dict() + for name, val in locals_var.items(): + if val is not None: + kwargs[name] = val + return _erf_(**kwargs) + + +erf.__doc__ = r""" +:strong:`Erf Operator` +For more details, see [Error function](https://en.wikipedia.org/wiki/Error_function). + +Equation: + .. math:: + out = \\frac{2}{\\sqrt{\\pi}} \\int_{0}^{x}e^{- \\eta^{2}}d\\eta + +Args: + + x (Tensor): The input tensor, it's data type should be float32, float64. + +Returns: + + Tensor: The output of Erf op, dtype: float32 or float64, the same as the input, shape: the same as the input. + +Examples: + + .. code-block:: python + + import paddle + x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3]) + out = paddle.erf(x) + print(out) + # [-0.42839236 -0.22270259 0.11246292 0.32862676] +"""