function_spec.py 14.8 KB
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# Copyright (c) 2020 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.

import six
import inspect
import numpy as np
import collections
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import paddle
from paddle.fluid import core
from paddle.fluid.dygraph import layers
from paddle.fluid.layers.utils import flatten
from paddle.fluid.layers.utils import pack_sequence_as
from paddle.fluid.dygraph.base import switch_to_static_graph
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from paddle.fluid.dygraph.dygraph_to_static import logging_utils
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from paddle.fluid.dygraph.dygraph_to_static.utils import parse_arg_and_kwargs
from paddle.fluid.dygraph.dygraph_to_static.utils import type_name
from paddle.fluid.dygraph.dygraph_to_static.utils import func_to_source_code


class FunctionSpec(object):
    """
    Wrapper class for a function for class method.
    """

    def __init__(self, function, input_spec=None):
        self._dygraph_function = function
        if input_spec is None:
            self._input_spec = None
            self._flat_input_spec = None
        else:
            self._input_spec = self._verify_input_spec(input_spec)
            self._flat_input_spec = flatten(self._input_spec)

        # parse full argument names list.
        self._arg_names, self._default_kwargs = parse_arg_and_kwargs(function)

    def unified_args_and_kwargs(self, args, kwargs):
        """
        Moves kwargs with default value into arguments list to keep `args` contain the same length
        value as function definition.
        
        For example: 
        
            Given function definition: `def foo(x, a=1, b=2)`, 
            when calling it by `foo(23)`, the args is `[23]`, kwargs is `{a=1, b=2}`.
            In this function, it will return args with `[23, 1, 2]`, kwargs with `{}`

        Args:
            args(tuple): tuple of input arguments value of decorated function.
            kwargs(dict): dict of input keyword arguments value of decorated function.

        Return:
            New arguments tuple containing default kwargs value.
        """
        if len(self._arg_names) < len(args):
            error_msg = "The decorated function `{}` requires {} arguments: {}, but received {} with {}.".format(
                self._dygraph_function.__name__,
                len(self._arg_names), self._arg_names, len(args), args)
            if args and inspect.isclass(args[0]):
                error_msg += "\n\tMaybe the function has more than one decorator, we don't support this for now."
                raise NotImplementedError(error_msg)
            else:
                raise ValueError(error_msg)

        args = list(args)

        for i in six.moves.range(len(args), len(self._arg_names)):
            arg_name = self._arg_names[i]
            if arg_name in kwargs:
                args.append(kwargs[arg_name])
                del kwargs[arg_name]
            else:
                if arg_name not in self._default_kwargs:
                    raise ValueError(
                        "`{}()` requires `{}` arguments, but not found in input `args`: {} and `kwargs`: {}.".
                        format(self._dygraph_function.__name__, arg_name, args,
                               kwargs))
                args.append(self._default_kwargs[arg_name])

        return tuple(args), kwargs

    def args_to_input_spec(self, args, kwargs):
        """
        Converts input arguments into InputSpec.
        
        1. If specific input_spec, use them to construct feed layers.
        2. If input_spec is None, consider all Tensor and Numpy.ndarray as feed layers

        Args:
            args(tuple): tuple of input arguments value of function containing default kwargs value.
            kwargs(dict): kwargs arguments received by **kwargs.

        Return:
            Same nest structure with args by replacing value with InputSpec.
        """
        input_with_spec = []

        if self._input_spec is not None:
            # Note: Because the value type and length of `kwargs` is uncertain.
            # So we don't support to deal this case while specificing `input_spec` currently.
            if kwargs:
                raise ValueError(
                    "{} got unexpected keyword arguments: {}. Cannot trace the function when `input_spec` is specificed.".
                    format(self._dygraph_function.__name__, kwargs))

            # Note: The length of `input_spec` can be greater than `args`,
            # because `args` may contains non-tensor value merged form `kwargs`
            # after `unified_args_and_kwargs`.
            if len(args) < len(self._input_spec):
                raise ValueError(
                    "Requires len(arguments) >= len(input_spec), but received len(args):{} < len(InputSpec): {}".
                    format(len(args), len(self._input_spec)))

            # replace argument with corresponding InputSpec.
            input_with_spec = convert_to_input_spec(args, self._input_spec)
        else:
            for idx, input_var in enumerate(flatten(args)):
                if isinstance(input_var, np.ndarray):
                    input_var = paddle.static.InputSpec.from_numpy(input_var)
                elif isinstance(input_var, core.VarBase):
                    input_var = paddle.static.InputSpec.from_tensor(input_var)

                input_with_spec.append(input_var)

            input_with_spec = pack_sequence_as(args, input_with_spec)

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        # If without specificing name in input_spec, add default name
        # according to argument name from decorated function.
        input_with_spec = replace_spec_empty_name(self._arg_names,
                                                  input_with_spec)

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        return input_with_spec

    @switch_to_static_graph
    def to_static_inputs_with_spec(self, input_with_spec, main_program):
        """
        Constructs feed layer by inputs with InputSpec information for main program.

        Args:
            input_with_spec(tuple): input arguments by replacing argument with InputSpec.
            main_program(Program): main program for inserting feed layer.
        """
        flat_input_spec = flatten(input_with_spec)

        inputs = []
        block = main_program.global_block()
        for i, var_spec in enumerate(flat_input_spec):
            if isinstance(var_spec, paddle.static.InputSpec):
                feed_layer = block.create_var(
                    # TODO(Aurelius84): consider a more elegant way to name this
                    name=var_spec.name or "feed_%s" % i,
                    shape=var_spec.shape,
                    dtype=var_spec.dtype,
                    is_data=True,
                    need_check_feed=False)
            else:
                feed_layer = var_spec
            inputs.append(feed_layer)

        return pack_sequence_as(input_with_spec, inputs)

    def _verify_input_spec(self, input_spec):
        """
        Verifies the `input_spec` and its element type is valid.
        """
        if not isinstance(input_spec, (tuple, list)):
            raise TypeError(
                "The type(input_spec) should be one of (tuple, list), but received {}.".
                format(type_name(input_spec)))
        input_spec = tuple(input_spec)
        for spec in flatten(input_spec):
            if not isinstance(spec, paddle.static.InputSpec):
                raise ValueError(
                    "The type(elem) from input_spec should be `InputSpec`, but received {}.".
                    format(type_name(spec)))

        return input_spec

    def __repr__(self):
        return "function: {}({}), input_spec: {}".format(
            self._dygraph_function.__name__, ','.join(self._arg_names),
            self._input_spec)

    @property
    def dygraph_function(self):
        return self._dygraph_function

    @property
    def args_name(self):
        return self._arg_names

    @property
    def input_spec(self):
        return self._input_spec

    @property
    def flat_input_spec(self):
        return self._flat_input_spec

    @property
    def code(self):
        return func_to_source_code(self._dygraph_function)


def get_parameters(layer_instance, include_sublayer=True):
    """
    Returns parameters of decorated layers. If set `include_sublayer` True,
    the parameters created in sub layers will be added.
    """
    params = collections.OrderedDict()
    if layer_instance is not None:
        if isinstance(layer_instance, layers.Layer):
            if include_sublayer:
                params = layer_instance.parameters()
                names = [p.name for p in params]
                params = collections.OrderedDict(zip(names, params))
            else:
                params = layer_instance._parameters
        else:
            raise TypeError(
                "Type of `layer_instance` should be nn.Layer, but received {}".
                format(type_name(layer_instance)))

    return params


def get_buffers(layer_instance, include_sublayer=True):
    """
    Returns Variable buffers of decorated layers. If set `include_sublayer` True,
    the Variable buffers created in sub layers will be added.
    """
    buffers = collections.OrderedDict()
    if layer_instance is not None:
        if isinstance(layer_instance, layers.Layer):
            if include_sublayer:
                buffers = layer_instance.buffers()
                names = [buffer.name for buffer in buffers]
                buffers = collections.OrderedDict(zip(names, buffers))
            else:
                buffers = layer_instance._buffers
        else:
            raise TypeError(
                "Type of `layer_instance` should be nn.Layer, but received {}".
                format(type_name(layer_instance)))
    return buffers


def convert_to_input_spec(inputs, input_spec):
    """
    Replaces tensor in structured `inputs` by InputSpec in `input_spec`.
    
    Args:
        inputs(list|dict): nested structure list or dict.
        input_spec(list|dict): same nested structure list or dict as inputs. 

    
    Return:
        Same structure with inputs by replacing the element with specified InputSpec.
    """

    def check_type_and_len(input, spec, check_length=False):
        if type(input) is not type(spec):
            raise TypeError('type(input) should be {}, but received {}.'.format(
                type(spec), type(input)))
        if check_length and len(input) < len(spec):
            raise ValueError(
                'Requires len(inputs) >= len(input_spec), but received len(inputs):{} < len(input_spec):{}'.
                format(len(inputs), len(input_spec)))

    if isinstance(input_spec, (tuple, list)):
        input_with_spec = []
        check_type_and_len(inputs, input_spec, True)

        for i, spec in enumerate(input_spec):
            out_spec = convert_to_input_spec(inputs[i], spec)
            input_with_spec.append(out_spec)

        # Note: If the rest inputs contain tensor or numpy.ndarray
        # without specific InputSpec, raise warning.
        if len(inputs) > len(input_spec):
            for rest_input in inputs[len(input_spec):]:
                if isinstance(rest_input, (core.VarBase, np.ndarray)):
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                    logging_utils.warn(
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                        "The inputs constain `{}` without specificing InputSpec, its shape and dtype will be treated immutable. "
                        "Please specific InputSpec information in `@declarative` if you expect them as mutable inputs.".
                        format(type_name(rest_input)))
        input_with_spec.extend(inputs[len(input_spec):])

        return input_with_spec
    elif isinstance(input_spec, dict):
        input_with_spec = {}
        check_type_and_len(inputs, input_spec, True)
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        for name, input in six.iteritems(inputs):
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            if name in input_spec:
                input_with_spec[name] = convert_to_input_spec(input,
                                                              input_spec[name])
            else:
                input_with_spec[name] = input
        return input_with_spec
    elif isinstance(input_spec, paddle.static.InputSpec):
        return input_spec
    else:
        raise TypeError(
            "The type(input_spec) should be a `InputSpec` or dict/list/tuple of it, but received {}.".
            type_name(input_spec))
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def replace_spec_empty_name(args_name, input_with_spec):
    """
    Adds default name according to argument name from decorated function
    if without specificing InputSpec.name

    The naming rule are as followed:
        1. If InputSpec.name is not None, do nothing.
        2. If each argument `x` corresponds to an InputSpec, using the argument name like `x`
        3. If the arguments `inputs` corresponds to a list(InputSpec), using name like `inputs_0`, `inputs_1`
        4. If the arguments `input_dic` corresponds to a dict(InputSpec), using key as name.

    For example:
        
        # case 1: foo(x, y)
        foo = to_static(foo, input_spec=[InputSpec([None, 10]), InputSpec([None])])
        print([in_var.name for in_var in foo.inputs])  # [x, y]

        # case 2: foo(inputs) where inputs is a list
        foo = to_static(foo, input_spec=[[InputSpec([None, 10]), InputSpec([None])]])
        print([in_var.name for in_var in foo.inputs])  # [inputs_0, inputs_1]

        # case 3: foo(inputs) where inputs is a dict
        foo = to_static(foo, input_spec=[{'x': InputSpec([None, 10]), 'y': InputSpec([None])}])
        print([in_var.name for in_var in foo.inputs])  # [x, y]
    """
    input_with_spec = list(input_with_spec)
    candidate_arg_names = args_name[:len(input_with_spec)]

    for i, arg_name in enumerate(candidate_arg_names):
        input_spec = input_with_spec[i]
        input_with_spec[i] = _replace_spec_name(arg_name, input_spec)

    return input_with_spec


def _replace_spec_name(name, input_spec):
    """
    Replaces InputSpec.name with given `name` while not specificing it.
    """
    if isinstance(input_spec, paddle.static.InputSpec):
        if input_spec.name is None:
            input_spec.name = name
        return input_spec
    elif isinstance(input_spec, (list, tuple)):
        processed_specs = []
        for i, spec in enumerate(input_spec):
            new_name = "{}_{}".format(name, i)
            processed_specs.append(_replace_spec_name(new_name, spec))
        return processed_specs
    elif isinstance(input_spec, dict):
        processed_specs = {}
        for key, spec in six.iteritems(input_spec):
            processed_specs[key] = _replace_spec_name(key, spec)
        return processed_specs
    else:
        return input_spec