Skip to content

P
Paddle

机器未来 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
未验证 提交 baec7a35 编写于 作者: 石 石晓伟 提交者： GitHub
浏览文件
操作

load inference model from memory buffer, test=release/1.7 (#22562) 

* 1. load model from memory

2. scale is no longer added when saving inference model

test=develop

* raise ci coverage, test=develop

* supports saving weights to memory. test=develop

* raise ci coverage, test=develop

* fix PADDLE_ENFORCE messages, test=develop
上级 4e3c535a
隐藏空白更改
内联 并排
Showing with 176 addition and 85 deletion
paddle/fluid/operators/save_combine_op.cc
浏览文件 @ baec7a35
......@@ -71,6 +71,23 @@ to a file on disk.
"The \"file_path\" where the LoDTensor variables will be saved.")
.AddCustomChecker(
[](const std::string& path) { return !path.empty(); });
AddAttr<bool>("save_to_memory",
"(boolean, default false)"
"If true, the variables will be saved to binary strings.")
.SetDefault(false);
AddOutput("Y",
"(RAW, default empty)."
"This output is used when saving variables to binary strings.")
.AsDispensable();
}
};
class SaveCombineOpInferVarType : public framework::VarTypeInference {
public:
void operator()(framework::InferVarTypeContext* ctx) const override {
for (auto& o : ctx->Output("Y")) {
ctx->SetType(o, framework::proto::VarType::RAW);
}
}
};
......@@ -80,7 +97,7 @@ to a file on disk.
namespace ops = paddle::operators;
REGISTER_OPERATOR(save_combine, ops::SaveCombineOp,
ops::SaveCombineOpProtoMaker);
ops::SaveCombineOpProtoMaker, ops::SaveCombineOpInferVarType);
REGISTER_OP_CPU_KERNEL(
save_combine,
......
paddle/fluid/operators/save_combine_op.h
浏览文件 @ baec7a35
......@@ -38,6 +38,8 @@ class SaveCombineOpKernel : public framework::OpKernel<T> {
auto filename = ctx.Attr<std::string>("file_path");
auto overwrite = ctx.Attr<bool>("overwrite");
auto save_as_fp16 = ctx.Attr<bool>("save_as_fp16");
auto save_to_memory = ctx.Attr<bool>("save_to_memory");
auto output = ctx.Output<std::string>("Y");
bool is_present = FileExists(filename);
if (is_present && !overwrite) {
......@@ -45,11 +47,7 @@ class SaveCombineOpKernel : public framework::OpKernel<T> {
filename, overwrite);
}
MkDirRecursively(DirName(filename).c_str());
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
std::ostringstream ss;
auto inp_var_names = ctx.InputNames("X");
auto &inp_vars = ctx.MultiInputVar("X");
PADDLE_ENFORCE_GT(static_cast<int>(inp_var_names.size()), 0,
......@@ -82,12 +80,25 @@ class SaveCombineOpKernel : public framework::OpKernel<T> {
// copy LoD info to the new tensor
out.set_lod(tensor.lod());
framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
framework::SerializeToStream(fout, out, dev_ctx);
framework::SerializeToStream(ss, out, dev_ctx);
} else {
framework::SerializeToStream(fout, tensor, dev_ctx);
framework::SerializeToStream(ss, tensor, dev_ctx);
}
}
fout.close();
if (save_to_memory) {
PADDLE_ENFORCE_NE(output, nullptr,
platform::errors::InvalidArgument(
"Cannot find variable Y for save_combine_op"));
*output = ss.str();
} else {
MkDirRecursively(DirName(filename).c_str());
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE_EQ(
static_cast<bool>(fout), true,
platform::errors::NotFound("Cannot open %s to write", filename));
fout << ss.str();
fout.close();
}
}
};
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ baec7a35
......@@ -915,6 +915,10 @@ All parameter, weight, gradient are variables in Paddle.
return self.GetMutable<LoDTensor>();
},
py::return_value_policy::reference)
.def("get_bytes",
[](Variable &self) {
return py::bytes(*self.GetMutable<std::string>());
})
.def("get_lod_rank_table",
[](Variable &self) { return self.GetMutable<LoDRankTable>(); },
py::return_value_policy::reference)
......
python/paddle/fluid/io.py
浏览文件 @ baec7a35
......@@ -35,6 +35,7 @@ from paddle.fluid.framework import Program, Parameter, default_main_program, def
from paddle.fluid.compiler import CompiledProgram
from paddle.fluid.log_helper import get_logger
from . import reader
from . import unique_name
from .reader import *
from . import core
from .. import compat as cpt
......@@ -213,7 +214,8 @@ def save_vars(executor,
Args:
executor(Executor): The executor to run for saving variables.
dirname(str): The folder where to save variables.
dirname(str, optional): The folder where to save variables.
When you need to save the parameter to the memory, set it to None.
main_program(Program, optional): The program whose variables will be saved.
If it is None, the default main program will
be used automatically.
......@@ -228,7 +230,8 @@ def save_vars(executor,
Default: None
Returns:
None
str: When saving parameters to a file, returns None.
When saving parameters to memory, returns a binary string containing parameters.
Raises:
TypeError: If `main_program` is not an instance of Program nor None.
......@@ -265,17 +268,21 @@ def save_vars(executor,
fluid.io.save_vars(executor=exe, dirname=param_path, main_program=main_prog, vars=None, predicate = name_has_fc)
# all variables whose names contain "fc " are saved.
"""
save_dirname = os.path.normpath(dirname)
save_to_memory = False
if dirname is None and filename is None:
save_to_memory = True
main_program = _get_valid_program(main_program)
if vars is None:
save_vars(
return save_vars(
executor,
main_program=main_program,
dirname=save_dirname,
dirname=dirname,
vars=list(filter(predicate, main_program.list_vars())),
filename=filename)
else:
params_var_name = unique_name.generate("saved_params")
# give warning when there is no var in model
if len(list(vars)) == 0:
warnings.warn(
......@@ -292,33 +299,45 @@ def save_vars(executor,
if each_var.type == core.VarDesc.VarType.RAW:
continue
new_var = _clone_var_in_block_(save_block, each_var)
if filename is None:
save_file_path = os.path.join(save_dirname, new_var.name)
save_file_path = os.path.normpath(save_file_path)
if filename is None and save_to_memory is False:
save_file_path = os.path.join(
os.path.normpath(dirname), new_var.name)
save_block.append_op(
type='save',
inputs={'X': [new_var]},
outputs={},
attrs={'file_path': save_file_path})
attrs={'file_path': os.path.normpath(save_file_path)})
else:
save_var_map[new_var.name] = new_var
if filename is not None:
if filename is not None or save_to_memory:
save_var_list = []
for name in sorted(save_var_map.keys()):
save_var_list.append(save_var_map[name])
save_path = str()
if save_to_memory is False:
save_path = os.path.join(os.path.normpath(dirname), filename)
saved_params = save_block.create_var(
type=core.VarDesc.VarType.RAW, name=params_var_name)
saved_params.desc.set_persistable(True)
save_block.append_op(
type='save_combine',
inputs={'X': save_var_list},
outputs={},
attrs={'file_path': os.path.join(save_dirname, filename)})
outputs={'Y': saved_params},
attrs={
'file_path': save_path,
'save_to_memory': save_to_memory
})
#NOTE(zhiqiu): save op will add variable kLookupTablePath in save_program.desc,
# which leads to diff on save_program and its desc. Call _sync_with_cpp
# to keep consistency.
save_program._sync_with_cpp()
executor.run(save_program)
if save_to_memory:
return global_scope().find_var(params_var_name).get_bytes()
def save_params(executor, dirname, main_program=None, filename=None):
......@@ -346,7 +365,8 @@ def save_params(executor, dirname, main_program=None, filename=None):
Args:
executor(Executor): The executor to run for saving parameters, You can
refer to :ref:`api_guide_executor_en`.
dirname(str): The saving directory path.
dirname(str, optional): The saving directory path.
When you need to save the parameter to the memory, set it to None.
main_program(Program, optional): The program whose parameters will be
saved. You can refer to
:ref:`api_guide_Program_en` for more
......@@ -359,7 +379,8 @@ def save_params(executor, dirname, main_program=None, filename=None):
Default: None
Returns:
None
str: When saving parameters to a file, returns None.
When saving parameters to memory, returns a binary string containing parameters.
Examples:
.. code-block:: python
......@@ -381,7 +402,7 @@ def save_params(executor, dirname, main_program=None, filename=None):
# The parameters weights and bias of the fc layer in the network are going to
# be saved in different files in the path "./my_paddle_model"
"""
save_vars(
return save_vars(
executor,
dirname=dirname,
main_program=main_program,
......@@ -558,7 +579,8 @@ def save_persistables(executor, dirname, main_program=None, filename=None):
executor(Executor): The executor to run for saving persistable variables.
You can refer to :ref:`api_guide_executor_en` for
more details.
dirname(str): The saving directory path.
dirname(str, optional): The saving directory path.
When you need to save the parameter to the memory, set it to None.
main_program(Program, optional): The program whose persistbale variables will
be saved. You can refer to
:ref:`api_guide_Program_en` for more details.
......@@ -570,7 +592,8 @@ def save_persistables(executor, dirname, main_program=None, filename=None):
Default: None.
Returns:
None
str: When saving parameters to a file, returns None.
When saving parameters to memory, returns a binary string containing parameters.
Examples:
.. code-block:: python
......@@ -594,10 +617,10 @@ def save_persistables(executor, dirname, main_program=None, filename=None):
# "./my_paddle_model"
"""
if main_program and main_program._is_distributed:
_save_distributed_persistables(
return _save_distributed_persistables(
executor, dirname=dirname, main_program=main_program)
else:
save_vars(
return save_vars(
executor,
dirname=dirname,
main_program=main_program,
......@@ -687,7 +710,11 @@ def load_vars(executor,
# And all the variables are supposed to be saved in separate files.
"""
load_dirname = os.path.normpath(dirname)
vars_from_memory = False
if dirname is not None:
dirname = os.path.normpath(dirname)
else:
vars_from_memory = True
if vars is None:
if main_program is None:
......@@ -697,7 +724,7 @@ def load_vars(executor,
load_vars(
executor,
dirname=load_dirname,
dirname=dirname,
main_program=main_program,
vars=list(filter(predicate, main_program.list_vars())),
filename=filename)
......@@ -724,13 +751,15 @@ def load_vars(executor,
))
new_var = _clone_var_in_block_(load_block, each_var)
if filename is None:
if dirname is None:
raise ValueError(
"The directory path and params cannot be None at the same time."
)
load_block.append_op(
type='load',
inputs={},
outputs={'Out': [new_var]},
attrs={
'file_path': os.path.join(load_dirname, new_var.name)
})
attrs={'file_path': os.path.join(dirname, new_var.name)})
else:
load_var_map[new_var.name] = new_var
......@@ -739,11 +768,17 @@ def load_vars(executor,
for name in sorted(load_var_map.keys()):
load_var_list.append(load_var_map[name])
if vars_from_memory is False:
filename = os.path.join(dirname, filename)
load_block.append_op(
type='load_combine',
inputs={},
outputs={"Out": load_var_list},
attrs={'file_path': os.path.join(load_dirname, filename)})
attrs={
'file_path': filename,
'model_from_memory': vars_from_memory
})
executor.run(load_prog)
# check var shape
......@@ -1129,17 +1164,6 @@ def save_inference_model(dirname,
)
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(main_program):
uniq_target_vars = []
for i, var in enumerate(target_vars):
if isinstance(var, Variable):
var = layers.scale(
var, 1., name="save_infer_model/scale_{}".format(i))
uniq_target_vars.append(var)
target_vars = uniq_target_vars
target_var_name_list = [var.name for var in target_vars]
# when a pserver and a trainer running on the same machine, mkdir may conflict
......@@ -1223,19 +1247,22 @@ def load_inference_model(dirname,
You can refer to :ref:`api_guide_model_save_reader_en` for more details.
Args:
dirname(str): The given directory path.
dirname(str): One of the following:
- The given directory path.
- Set to None when reading the model from memory.
executor(Executor): The executor to run for loading inference model.
See :ref:`api_guide_executor_en` for more details about it.
model_filename(str, optional): The name of file to load the inference program.
If it is None, the default filename
``__model__`` will be used.
Default: ``None``.
params_filename(str, optional): The name of file to load all parameters.
It is only used for the case that all
parameters were saved in a single binary
file. If parameters were saved in separate
files, set it as ``None``.
Default: ``None``.
model_filename(str, optional): One of the following:
- The name of file to load the inference program.
- If it is None, the default filename ``__model__`` will be used.
- When ``dirname`` is ``None``, it must be set to a string containing model.
Default: ``None``.
params_filename(str, optional): It is only used for the case that all
parameters were saved in a single binary file. One of the following:
- The name of file to load all parameters.
- When ``dirname`` is ``None``, it must be set to a string containing all the parameters.
- If parameters were saved in separate files, set it as ``None``.
Default: ``None``.
pserver_endpoints(list, optional): It is only needed by the distributed inference.
If using a distributed look up table during the training,
......@@ -1303,21 +1330,32 @@ def load_inference_model(dirname,
# fetch_targets, we can use an executor to run the inference
# program for getting the inference result.
"""
load_dirname = os.path.normpath(dirname)
if not os.path.isdir(load_dirname):
raise ValueError("There is no directory named '%s'", dirname)
load_from_memory = False
if dirname is not None:
load_dirname = os.path.normpath(dirname)
if not os.path.isdir(load_dirname):
raise ValueError("There is no directory named '%s'", dirname)
if model_filename is not None:
model_filename = os.path.basename(model_filename)
else:
model_filename = "__model__"
model_filename = os.path.join(load_dirname, model_filename)
if model_filename is None:
model_filename = '__model__'
if params_filename is not None:
params_filename = os.path.basename(params_filename)
model_filename = os.path.join(load_dirname,
os.path.basename(model_filename))
if params_filename is not None:
params_filename = os.path.basename(params_filename)
with open(model_filename, "rb") as f:
program_desc_str = f.read()
with open(model_filename, "rb") as f:
program_desc_str = f.read()
else:
load_from_memory = True
if params_filename is None:
raise ValueError(
"The path of params cannot be None when the directory path is None."
)
load_dirname = dirname
program_desc_str = model_filename
params_filename = params_filename
program = Program.parse_from_string(program_desc_str)
if not core._is_program_version_supported(program._version()):
......
python/paddle/fluid/tests/unittests/test_inference_model_io.py
浏览文件 @ baec7a35
......@@ -16,6 +16,7 @@ from __future__ import print_function
import unittest
import os
import six
import numpy as np
import paddle.fluid.core as core
......@@ -27,13 +28,20 @@ import paddle.fluid.layers as layers
import paddle.fluid.optimizer as optimizer
from paddle.fluid.compiler import CompiledProgram
from paddle.fluid.framework import Program, program_guard
from paddle.fluid.io import save_inference_model, load_inference_model
from paddle.fluid.io import save_inference_model, load_inference_model, save_persistables
from paddle.fluid.transpiler import memory_optimize
class TestBook(unittest.TestCase):
class InferModel(object):
def __init__(self, list):
self.program = list[0]
self.feed_var_names = list[1]
self.fetch_vars = list[2]
def test_fit_line_inference_model(self):
MODEL_DIR = "./tmp/inference_model"
UNI_MODEL_DIR = "./tmp/inference_model1"
init_program = Program()
program = Program()
......@@ -65,30 +73,43 @@ class TestBook(unittest.TestCase):
'y': tensor_y},
fetch_list=[avg_cost])
# Separated model and unified model
save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
save_inference_model(UNI_MODEL_DIR, ["x", "y"], [avg_cost], exe,
program, 'model', 'params')
main_program = program.clone()._prune_with_input(
feeded_var_names=["x", "y"], targets=[avg_cost])
params_str = save_persistables(exe, None, main_program, None)
expected = exe.run(program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])[0]
six.moves.reload_module(executor) # reload to build a new scope
exe = executor.Executor(place)
[infer_prog, feed_var_names, fetch_vars] = load_inference_model(
MODEL_DIR, exe)
outs = exe.run(
infer_prog,
feed={feed_var_names[0]: tensor_x,
feed_var_names[1]: tensor_y},
fetch_list=fetch_vars)
actual = outs[0]
self.assertEqual(feed_var_names, ["x", "y"])
self.assertEqual(len(fetch_vars), 1)
print("fetch %s" % str(fetch_vars[0]))
self.assertTrue("scale" in str(fetch_vars[0]))
self.assertEqual(expected, actual)
model_0 = self.InferModel(load_inference_model(MODEL_DIR, exe))
with open(os.path.join(UNI_MODEL_DIR, 'model'), "rb") as f:
model_str = f.read()
model_1 = self.InferModel(
load_inference_model(None, exe, model_str, params_str))
for model in [model_0, model_1]:
outs = exe.run(model.program,
feed={
model.feed_var_names[0]: tensor_x,
model.feed_var_names[1]: tensor_y
},
fetch_list=model.fetch_vars)
actual = outs[0]
self.assertEqual(model.feed_var_names, ["x", "y"])
self.assertEqual(len(model.fetch_vars), 1)
print("fetch %s" % str(model.fetch_vars[0]))
self.assertEqual(expected, actual)
self.assertRaises(ValueError, fluid.io.load_inference_model, None, exe,
model_str, None)
class TestSaveInferenceModel(unittest.TestCase):
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或