Merge pull request #1496 from bjjwwang/save

fix save model

python/paddle_serving_client/io/__init__.py

@@ -19,7 +19,7 @@ from paddle.fluid.framework import core
 from paddle.fluid.framework import default_main_program
 from paddle.fluid.framework import Program
 from paddle.fluid import CPUPlace
-from paddle.fluid.io import save_inference_model
+from .paddle_io import save_inference_model, normalize_program
 import paddle.fluid as fluid
 from paddle.fluid.core import CipherUtils
 from paddle.fluid.core import CipherFactory
@@ -191,12 +191,14 @@ def save_model(server_model_folder,
     executor = Executor(place=CPUPlace())
 
     feed_var_names = [feed_var_dict[x].name for x in feed_var_dict]
+    feed_vars = [feed_var_dict[x] for x in feed_var_dict]
     target_vars = []
     target_var_names = []
     for key in sorted(fetch_var_dict.keys()):
         target_vars.append(fetch_var_dict[key])
         target_var_names.append(key)
 
+    main_program = normalize_program(main_program, feed_vars, target_vars)
     if not encryption and not show_proto:
         if not os.path.exists(server_model_folder):
             os.makedirs(server_model_folder)
@@ -209,7 +211,7 @@ def save_model(server_model_folder,
         new_params_path = os.path.join(server_model_folder, params_filename)
 
         with open(new_model_path, "wb") as new_model_file:
-            new_model_file.write(main_program.desc.serialize_to_string())
+            new_model_file.write(main_program._remove_training_info(False).desc.serialize_to_string())
         
         paddle.static.save_vars(
             executor=executor,
@@ -229,7 +231,7 @@ def save_model(server_model_folder,
         key = CipherUtils.gen_key_to_file(128, "key")
         params = fluid.io.save_persistables(
             executor=executor, dirname=None, main_program=main_program)
-        model = main_program.desc.serialize_to_string()
+        model = main_program._remove_training_info(False).desc.serialize_to_string()
         if not os.path.exists(server_model_folder):
             os.makedirs(server_model_folder)
         os.chdir(server_model_folder)

python/paddle_serving_client/io/paddle_io.py

+#   Copyright (c) 2018 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 errno
+import inspect
+import logging
+import os
+import warnings
+import six
+import numpy as np
+
+import paddle
+from paddle.fluid import (
+    core,
+    Variable,
+    CompiledProgram,
+    default_main_program,
+    Program,
+    layers,
+    unique_name,
+    program_guard, )
+from paddle.fluid.io import prepend_feed_ops, append_fetch_ops
+from paddle.fluid.framework import static_only, Parameter
+from paddle.fluid.executor import Executor, global_scope
+from paddle.fluid.log_helper import get_logger
+
+__all__ = []
+
+_logger = get_logger(
+    __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s')
+
+
+def _check_args(caller, args, supported_args=None, deprecated_args=None):
+    supported_args = [] if supported_args is None else supported_args
+    deprecated_args = [] if deprecated_args is None else deprecated_args
+    for arg in args:
+        if arg in deprecated_args:
+            raise ValueError(
+                "argument '{}' in function '{}' is deprecated, only {} are supported.".
+                format(arg, caller, supported_args))
+        elif arg not in supported_args:
+            raise ValueError(
+                "function '{}' doesn't support argument '{}',\n only {} are supported.".
+                format(caller, arg, supported_args))
+
+
+def _check_vars(name, var_list):
+    if not isinstance(var_list, list):
+        var_list = [var_list]
+    if not var_list or not all([isinstance(var, Variable) for var in var_list]):
+        raise ValueError(
+            "'{}' should be a Variable or a list of Variable.".format(name))
+
+
+def _normalize_path_prefix(path_prefix):
+    """
+    convert path_prefix to absolute path.
+    """
+    if not isinstance(path_prefix, six.string_types):
+        raise ValueError("'path_prefix' should be a string.")
+    if path_prefix.endswith("/"):
+        raise ValueError("'path_prefix' should not be a directory")
+    path_prefix = os.path.normpath(path_prefix)
+    path_prefix = os.path.abspath(path_prefix)
+    return path_prefix
+
+
+def _get_valid_program(program=None):
+    """
+    return default main program if program is None.
+    """
+    if program is None:
+        program = default_main_program()
+    elif isinstance(program, CompiledProgram):
+        program = program._program
+        if program is None:
+            raise TypeError(
+                "The type of input program is invalid, expected tyep is Program, but received None"
+            )
+        warnings.warn(
+            "The input is a CompiledProgram, this is not recommended.")
+    if not isinstance(program, Program):
+        raise TypeError(
+            "The type of input program is invalid, expected type is fluid.Program, but received %s"
+            % type(program))
+    return program
+
+
+def _clone_var_in_block(block, var):
+    assert isinstance(var, Variable)
+    if var.desc.type() == core.VarDesc.VarType.LOD_TENSOR:
+        return block.create_var(
+            name=var.name,
+            shape=var.shape,
+            dtype=var.dtype,
+            type=var.type,
+            lod_level=var.lod_level,
+            persistable=True)
+    else:
+        return block.create_var(
+            name=var.name,
+            shape=var.shape,
+            dtype=var.dtype,
+            type=var.type,
+            persistable=True)
+
+
+def normalize_program(program, feed_vars, fetch_vars):
+    """
+    :api_attr: Static Graph
+
+    Normalize/Optimize a program according to feed_vars and fetch_vars.
+
+    Args:
+        program(Program): Specify a program you want to optimize.
+        feed_vars(Variable | list[Variable]): Variables needed by inference.
+        fetch_vars(Variable | list[Variable]): Variables returned by inference.
+
+    Returns:
+        Program: Normalized/Optimized program.
+
+    Raises:
+        TypeError: If `program` is not a Program, an exception is thrown.
+        TypeError: If `feed_vars` is not a Variable or a list of Variable, an exception is thrown.
+        TypeError: If `fetch_vars` is not a Variable or a list of Variable, an exception is thrown.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+
+            paddle.enable_static()
+
+            path_prefix = "./infer_model"
+
+            # User defined network, here a softmax regession example
+            image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
+            label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
+            predict = paddle.static.nn.fc(image, 10, activation='softmax')
+
+            loss = paddle.nn.functional.cross_entropy(predict, label)
+
+            exe = paddle.static.Executor(paddle.CPUPlace())
+            exe.run(paddle.static.default_startup_program())
+
+            # normalize main program.
+            program = paddle.static.default_main_program()
+            normalized_program = paddle.static.normalize_program(program, [image], [predict])
+
+    """
+    if not isinstance(program, Program):
+        raise TypeError(
+            "program type must be `fluid.Program`, but received `%s`" %
+            type(program))
+    if not isinstance(feed_vars, list):
+        feed_vars = [feed_vars]
+    if not all(isinstance(v, Variable) for v in feed_vars):
+        raise TypeError(
+            "feed_vars type must be a Variable or a list of Variable.")
+    if not isinstance(fetch_vars, list):
+        fetch_vars = [fetch_vars]
+    if not all(isinstance(v, Variable) for v in fetch_vars):
+        raise TypeError(
+            "fetch_vars type must be a Variable or a list of Variable.")
+
+    # remind users to set auc_states to 0 if auc op were found.
+    for op in program.global_block().ops:
+        # clear device of Op
+        device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
+        op._set_attr(device_attr_name, "")
+        if op.type == 'auc':
+            warnings.warn("Be sure that you have set auc states to 0 "
+                          "before saving inference model.")
+            break
+
+    # fix the bug that the activation op's output as target will be pruned.
+    # will affect the inference performance.
+    # TODO(Superjomn) add an IR pass to remove 1-scale op.
+    #with program_guard(program):
+    #    uniq_fetch_vars = []
+    #    for i, var in enumerate(fetch_vars):
+    #        if var.dtype != paddle.bool:
+    #            var = layers.scale(
+    #                var, 1., name="save_infer_model/scale_{}".format(i))
+    #        uniq_fetch_vars.append(var)
+    #    fetch_vars = uniq_fetch_vars
+
+    # serialize program
+    copy_program = program.clone()
+    global_block = copy_program.global_block()
+    remove_op_idx = []
+    for i, op in enumerate(global_block.ops):
+        op.desc.set_is_target(False)
+        if op.type == "feed" or op.type == "fetch":
+            remove_op_idx.append(i)
+    for idx in remove_op_idx[::-1]:
+        global_block._remove_op(idx)
+    copy_program.desc.flush()
+
+    feed_var_names = [var.name for var in feed_vars]
+    copy_program = copy_program._prune_with_input(
+        feeded_var_names=feed_var_names, targets=fetch_vars)
+    copy_program = copy_program._inference_optimize(prune_read_op=True)
+    fetch_var_names = [var.name for var in fetch_vars]
+    prepend_feed_ops(copy_program, feed_var_names)
+    append_fetch_ops(copy_program, fetch_var_names)
+    copy_program.desc._set_version()
+    return copy_program
+
+
+def is_persistable(var):
+    """
+    Check whether the given variable is persistable.
+
+    Args:
+        var(Variable): The variable to be checked.
+
+    Returns:
+        bool: True if the given `var` is persistable
+        False if not.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import paddle.fluid as fluid
+
+            paddle.enable_static()
+            param = fluid.default_main_program().global_block().var('fc.b')
+            res = fluid.io.is_persistable(param)
+    """
+    if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
+                    var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
+                    var.desc.type() == core.VarDesc.VarType.READER:
+        return False
+    return var.persistable
+
+
+@static_only
+def serialize_program(feed_vars, fetch_vars, **kwargs):
+    """
+    :api_attr: Static Graph
+
+    Serialize default main program according to feed_vars and fetch_vars.
+
+    Args:
+        feed_vars(Variable | list[Variable]): Variables needed by inference.
+        fetch_vars(Variable | list[Variable]): Variables returned by inference.
+        kwargs: Supported keys including 'program'.Attention please, kwargs is used for backward compatibility mainly.
+          - program(Program): specify a program if you don't want to use default main program.
+
+    Returns:
+        bytes: serialized program.
+
+    Raises:
+        ValueError: If `feed_vars` is not a Variable or a list of Variable, an exception is thrown.
+        ValueError: If `fetch_vars` is not a Variable or a list of Variable, an exception is thrown.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+
+            paddle.enable_static()
+
+            path_prefix = "./infer_model"
+
+            # User defined network, here a softmax regession example
+            image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
+            label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
+            predict = paddle.static.nn.fc(image, 10, activation='softmax')
+
+            loss = paddle.nn.functional.cross_entropy(predict, label)
+
+            exe = paddle.static.Executor(paddle.CPUPlace())
+            exe.run(paddle.static.default_startup_program())
+
+            # serialize the default main program to bytes.
+            serialized_program = paddle.static.serialize_program([image], [predict])
+
+            # deserialize bytes to program
+            deserialized_program = paddle.static.deserialize_program(serialized_program)
+
+    """
+    # verify feed_vars
+    _check_vars('feed_vars', feed_vars)
+    # verify fetch_vars
+    _check_vars('fetch_vars', fetch_vars)
+
+    program = _get_valid_program(kwargs.get('program', None))
+    program = normalize_program(program, feed_vars, fetch_vars)
+    return _serialize_program(program)
+
+
+def _serialize_program(program):
+    """
+    serialize given program to bytes.
+    """
+    return program.desc.serialize_to_string()
+
+
+@static_only
+def serialize_persistables(feed_vars, fetch_vars, executor, **kwargs):
+    """
+    :api_attr: Static Graph
+
+    Serialize parameters using given executor and default main program according to feed_vars and fetch_vars.
+
+    Args:
+        feed_vars(Variable | list[Variable]): Variables needed by inference.
+        fetch_vars(Variable | list[Variable]): Variables returned by inference.
+        kwargs: Supported keys including 'program'.Attention please, kwargs is used for backward compatibility mainly.
+          - program(Program): specify a program if you don't want to use default main program.
+
+    Returns:
+        bytes: serialized program.
+
+    Raises:
+        ValueError: If `feed_vars` is not a Variable or a list of Variable, an exception is thrown.
+        ValueError: If `fetch_vars` is not a Variable or a list of Variable, an exception is thrown.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+
+            paddle.enable_static()
+
+            path_prefix = "./infer_model"
+
+            # User defined network, here a softmax regession example
+            image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
+            label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
+            predict = paddle.static.nn.fc(image, 10, activation='softmax')
+
+            loss = paddle.nn.functional.cross_entropy(predict, label)
+
+            exe = paddle.static.Executor(paddle.CPUPlace())
+            exe.run(paddle.static.default_startup_program())
+
+            # serialize parameters to bytes.
+            serialized_params = paddle.static.serialize_persistables([image], [predict], exe)
+
+            # deserialize bytes to parameters.
+            main_program = paddle.static.default_main_program()
+            deserialized_params = paddle.static.deserialize_persistables(main_program, serialized_params, exe)
+
+    """
+    # verify feed_vars
+    _check_vars('feed_vars', feed_vars)
+    # verify fetch_vars
+    _check_vars('fetch_vars', fetch_vars)
+
+    program = _get_valid_program(kwargs.get('program', None))
+    program = normalize_program(program, feed_vars, fetch_vars)
+    return _serialize_persistables(program, executor)
+
+
+def _serialize_persistables(program, executor):
+    """
+    Serialize parameters using given program and executor.
+    """
+    vars_ = list(filter(is_persistable, program.list_vars()))
+    # warn if no variable found in model
+    if len(vars_) == 0:
+        warnings.warn("no variable in your model, please ensure there are any "
+                      "variables in your model to save")
+        return None
+    # create a new program and clone persitable vars to it
+    save_program = Program()
+    save_block = save_program.global_block()
+    save_var_map = {}
+    for var in vars_:
+        if var.type != core.VarDesc.VarType.RAW:
+            var_copy = _clone_var_in_block(save_block, var)
+            save_var_map[var_copy.name] = var
+
+    # create in_vars and out_var, then append a save_combine op to save_program
+    in_vars = []
+    for name in sorted(save_var_map.keys()):
+        in_vars.append(save_var_map[name])
+
+    out_var_name = unique_name.generate("out_var")
+    out_var = save_block.create_var(
+        type=core.VarDesc.VarType.RAW, name=out_var_name)
+    out_var.desc.set_persistable(True)
+    save_block.append_op(
+        type='save_combine',
+        inputs={'X': in_vars},
+        outputs={'Y': out_var},
+        attrs={'file_path': '',
+               'save_to_memory': True})
+    # run save_program to save vars
+    # NOTE(zhiqiu): save op will add variable kLookupTablePath to save_program.desc,
+    # which leads to diff between save_program and its desc. Call _sync_with_cpp
+    # to keep consistency.
+    save_program._sync_with_cpp()
+    executor.run(save_program)
+    # return serialized bytes in out_var
+    return global_scope().find_var(out_var_name).get_bytes()
+
+
+def save_to_file(path, content):
+    """
+    Save content to given path.
+    Args:
+        path(str): Path to write content to.
+        content(bytes): Content to write.
+    Returns:
+        None
+    """
+
+    if not isinstance(content, bytes):
+        raise ValueError("'content' type should be bytes.")
+    with open(path, "wb") as f:
+        f.write(content)
+
+
+@static_only
+def save_inference_model(path_prefix, feed_vars, fetch_vars, executor,
+                         **kwargs):
+    """
+    :api_attr: Static Graph
+
+    Save current model and its parameters to given path. i.e.
+    Given path_prefix = "/path/to/modelname", after invoking
+    save_inference_model(path_prefix, feed_vars, fetch_vars, executor),
+    you will find two files named modelname.pdmodel and modelname.pdiparams
+    under "/path/to", which represent your model and parameters respectively.
+
+    Args:
+        path_prefix(str): Directory path to save model + model name without suffix.
+        feed_vars(Variable | list[Variable]): Variables needed by inference.
+        fetch_vars(Variable | list[Variable]): Variables returned by inference.
+        executor(Executor): The executor that saves the inference model. You can refer
+                            to :ref:`api_guide_executor_en` for more details.
+        kwargs: Supported keys including 'program' and "clip_extra". Attention please, kwargs is used for backward compatibility mainly.
+          - program(Program): specify a program if you don't want to use default main program.
+          - clip_extra(bool): set to True if you want to clip extra information for every operator.
+    Returns:
+        None
+
+    Raises:
+        ValueError: If `feed_vars` is not a Variable or a list of Variable, an exception is thrown.
+        ValueError: If `fetch_vars` is not a Variable or a list of Variable, an exception is thrown.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+
+            paddle.enable_static()
+
+            path_prefix = "./infer_model"
+
+            # User defined network, here a softmax regession example
+            image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
+            label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
+            predict = paddle.static.nn.fc(image, 10, activation='softmax')
+
+            loss = paddle.nn.functional.cross_entropy(predict, label)
+
+            exe = paddle.static.Executor(paddle.CPUPlace())
+            exe.run(paddle.static.default_startup_program())
+
+            # Feed data and train process
+
+            # Save inference model. Note we don't save label and loss in this example
+            paddle.static.save_inference_model(path_prefix, [image], [predict], exe)
+
+            # In this example, the save_inference_mode inference will prune the default
+            # main program according to the network's input node (img) and output node(predict).
+            # The pruned inference program is going to be saved in file "./infer_model.pdmodel"
+            # and parameters are going to be saved in file "./infer_model.pdiparams".
+
+    """
+
+    # check path_prefix, set model_path and params_path
+    path_prefix = _normalize_path_prefix(path_prefix)
+    try:
+        # mkdir may conflict if pserver and trainer are running on the same machine
+        dirname = os.path.dirname(path_prefix)
+        os.makedirs(dirname)
+    except OSError as e:
+        if e.errno != errno.EEXIST:
+            raise
+    model_path = path_prefix + ".pdmodel"
+    params_path = path_prefix + ".pdiparams"
+    if os.path.isdir(model_path):
+        raise ValueError("'{}' is an existing directory.".format(model_path))
+    if os.path.isdir(params_path):
+        raise ValueError("'{}' is an existing directory.".format(params_path))
+
+    # verify feed_vars
+    _check_vars('feed_vars', feed_vars)
+    # verify fetch_vars
+    _check_vars('fetch_vars', fetch_vars)
+
+    program = _get_valid_program(kwargs.get('program', None))
+    clip_extra = kwargs.get('clip_extra', False)
+    program = normalize_program(program, feed_vars, fetch_vars)
+    # serialize and save program
+    program_bytes = _serialize_program(
+        program._remove_training_info(clip_extra=clip_extra))
+    save_to_file(model_path, program_bytes)
+    # serialize and save params
+    params_bytes = _serialize_persistables(program, executor)
+    save_to_file(params_path, params_bytes)
+