wehub-resource-sync
512 lines
17 KiB
Python
512 lines
17 KiB
Python
# 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 logging
|
|
import os
|
|
import subprocess
|
|
from collections import OrderedDict
|
|
|
|
import numpy as np
|
|
from google.protobuf import text_format
|
|
|
|
import paddle
|
|
from paddle import base
|
|
from paddle.base import core
|
|
from paddle.base.framework import Program
|
|
from paddle.base.proto import framework_pb2
|
|
from paddle.distributed.fleet.base.util_factory import draw_block_graphviz
|
|
from paddle.framework import io_utils
|
|
|
|
__all__ = [
|
|
"load_program",
|
|
"save_program",
|
|
"program_type_trans",
|
|
"check_saved_vars_try_dump",
|
|
"parse_program",
|
|
"check_pruned_program_vars",
|
|
"graphviz",
|
|
]
|
|
|
|
logger = logging.getLogger(__name__)
|
|
logger.setLevel(logging.INFO)
|
|
formatter = logging.Formatter(fmt='%(asctime)s - %(levelname)s - %(message)s')
|
|
ch = logging.StreamHandler()
|
|
ch.setFormatter(formatter)
|
|
logger.addHandler(ch)
|
|
|
|
persistable_vars_out_fn = "vars_persistable.log"
|
|
all_vars_out_fn = "vars_all.log"
|
|
ops_out_fn = "ops.log"
|
|
|
|
feed_fetch_type_list = [
|
|
core.VarDesc.VarType.FEED_MINIBATCH,
|
|
core.VarDesc.VarType.FETCH_LIST,
|
|
]
|
|
not_expected_op_types = ["lookup_table"]
|
|
|
|
|
|
def load_program(model_filename, is_text=False):
|
|
if is_text:
|
|
return load_program_text(model_filename)
|
|
return load_program_binary(model_filename)
|
|
|
|
|
|
def load_program_binary(model_filename):
|
|
"""load program from binary string file"""
|
|
with open(model_filename, "rb") as f:
|
|
program_desc_str = f.read()
|
|
return Program.parse_from_string(program_desc_str)
|
|
|
|
|
|
def load_program_text(model_filename):
|
|
"""load program from human-readable text file"""
|
|
with open(model_filename, "r") as f:
|
|
program_desc_text = f.read()
|
|
|
|
prog_desc = framework_pb2.ProgramDesc()
|
|
text_format.Merge(program_desc_text, prog_desc)
|
|
return Program.parse_from_string(prog_desc.SerializeToString())
|
|
|
|
|
|
def save_program(program, model_filename='__model__', is_text=False):
|
|
if is_text:
|
|
with open(model_filename, "w") as f:
|
|
f.write(str(program))
|
|
else:
|
|
with open(model_filename, "wb") as f:
|
|
f.write(program.desc.serialize_to_string())
|
|
|
|
|
|
def check_pruned_program_vars(train_prog, pruned_prog):
|
|
is_match = True
|
|
|
|
pruned_vars = [
|
|
(v.name, v)
|
|
for v in pruned_prog.list_vars()
|
|
if io_utils.is_persistable(v)
|
|
]
|
|
pruned_vars = OrderedDict(pruned_vars)
|
|
pruned_vars_name = list(pruned_vars)
|
|
logger.info(f"persistable vars in pruned program: {pruned_vars_name}")
|
|
|
|
for var_name in pruned_vars:
|
|
var = pruned_vars[var_name]
|
|
# feed and fetch op is added in pruned program when pruning, not need to be found in train program
|
|
if var.type in feed_fetch_type_list:
|
|
break
|
|
try:
|
|
train_prog_var = train_prog.global_block().var(var_name)
|
|
except ValueError as e:
|
|
logger.error(
|
|
f"not find variable '{var_name}' in train program. please check pruning."
|
|
)
|
|
logger.error(e)
|
|
continue
|
|
if (
|
|
var.shape != train_prog_var.shape
|
|
or var.dtype != train_prog_var.dtype
|
|
):
|
|
logger.error(
|
|
f"variable: {var_name} not match. in pruned program shape: {var.shape} dtype:{var.dtype}, in train program shape: {train_prog_var.shape} dtype: {train_prog_var.dtype}"
|
|
)
|
|
is_match = False
|
|
return is_match
|
|
|
|
|
|
def graphviz(block, output_dir="", filename='debug'):
|
|
dot_path = os.path.join(output_dir, filename + '.dot')
|
|
pdf_path = os.path.join(output_dir, filename + '.pdf')
|
|
draw_block_graphviz(block, path=dot_path)
|
|
cmd = ["dot", "-Tpdf", dot_path, "-o", pdf_path]
|
|
p = subprocess.Popen(
|
|
cmd,
|
|
stdin=subprocess.PIPE,
|
|
stdout=subprocess.PIPE,
|
|
stderr=subprocess.PIPE,
|
|
)
|
|
p.wait()
|
|
|
|
|
|
def program_type_trans(prog_dir, prog_fn, is_text):
|
|
prog = load_program(os.path.join(prog_dir, prog_fn), is_text)
|
|
prog_out_fn = prog_fn + ".bin" if is_text else prog_fn + ".pbtxt"
|
|
save_program(prog, os.path.join(prog_dir, prog_out_fn), 1 - is_text)
|
|
return prog_out_fn
|
|
|
|
|
|
def append_save_op(block, var, path):
|
|
block.append_op(
|
|
type='save', inputs={'X': [var]}, outputs={}, attrs={'file_path': path}
|
|
)
|
|
|
|
|
|
def append_load_op(block, var, path):
|
|
block.append_op(
|
|
type='load',
|
|
inputs={},
|
|
outputs={'Out': [var]},
|
|
attrs={'file_path': path},
|
|
)
|
|
|
|
|
|
def save_var(np_array, var_name, shape_list, dtype, save_path):
|
|
program = base.Program()
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
shape = list(shape_list)
|
|
with base.program_guard(program):
|
|
d0_data = paddle.static.data(var_name, shape=shape, dtype=dtype)
|
|
append_save_op(program.global_block(), d0_data, save_path)
|
|
exe.run(feed={var_name: np_array}, fetch_list=[])
|
|
|
|
|
|
def load_var(var_name, shape_list, dtype, save_path):
|
|
program = base.Program()
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
with base.program_guard(program):
|
|
d0_data = paddle.static.data(var_name, shape=shape_list, dtype=dtype)
|
|
append_load_op(program.global_block(), d0_data, save_path)
|
|
outs = exe.run(feed={}, fetch_list=[d0_data])
|
|
return outs
|
|
|
|
|
|
def reader(batch_size, fn, dim):
|
|
data = []
|
|
if isinstance(dim, (list, tuple)):
|
|
shape = list(dim)
|
|
_temp = 1
|
|
for x in dim:
|
|
_temp = _temp * x
|
|
dim = _temp
|
|
else:
|
|
shape = [dim]
|
|
|
|
shape = [batch_size, *shape]
|
|
dim = dim * batch_size
|
|
|
|
for line in open(fn, 'r'):
|
|
fields = line.strip().split(' ')
|
|
fields = [float(d) for d in fields]
|
|
while len(fields) >= dim:
|
|
tmp = fields[:dim]
|
|
fields = fields[dim:]
|
|
data.append(np.array(tmp).reshape(shape))
|
|
return data
|
|
|
|
|
|
def feed_gen(batch_size, feeded_vars_dims, feeded_vars_filelist):
|
|
batch_feed = []
|
|
for i, fn in enumerate(feeded_vars_filelist):
|
|
batch_feed.append(reader(batch_size, fn, feeded_vars_dims[i]))
|
|
return batch_feed
|
|
|
|
|
|
def try_load_model_vars(
|
|
dump_dir,
|
|
dump_prog_fn,
|
|
is_text_dump_program,
|
|
batch_size,
|
|
feed_config,
|
|
fetch_config,
|
|
save_filename,
|
|
saved_params,
|
|
):
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
scope = base.core.Scope()
|
|
with base.scope_guard(scope):
|
|
if is_text_dump_program:
|
|
dump_prog_fn = program_type_trans(
|
|
dump_dir, dump_prog_fn, is_text_dump_program
|
|
)
|
|
|
|
[
|
|
inference_program,
|
|
feed_target_names,
|
|
fetch_targets,
|
|
] = paddle.static.io.load_inference_model(
|
|
dump_dir,
|
|
exe,
|
|
model_filename=dump_prog_fn,
|
|
params_filename=save_filename,
|
|
)
|
|
|
|
# check program vars and saved vars shape
|
|
orig_para_shape = {
|
|
each_var.name: tuple(each_var.desc.shape())
|
|
for each_var in saved_params
|
|
}
|
|
for each_var in saved_params:
|
|
var_temp = base.global_scope().find_var(each_var.name)
|
|
assert var_temp is not None, "can't not find var: " + each_var.name
|
|
new_shape = (np.array(var_temp.get_tensor())).shape
|
|
assert each_var.name in orig_para_shape, (
|
|
each_var.name + "MUST in var list"
|
|
)
|
|
orig_shape = orig_para_shape.get(each_var.name)
|
|
if new_shape != orig_shape:
|
|
raise RuntimeError(
|
|
f"Shape not matching: the Program requires a parameter with a shape of ({orig_shape}), "
|
|
f"while the loaded parameter (namely [ {each_var.name} ]) has a shape of ({new_shape})."
|
|
)
|
|
|
|
# check feed/fetch vars in program and config
|
|
fetch_targets_names = [v.name for v in fetch_targets]
|
|
if not feed_target_names:
|
|
logger.warning("no feed targets in program.")
|
|
if not fetch_targets_names:
|
|
logger.warning("no fetch targets in program.")
|
|
fetch_list = fetch_targets
|
|
feed_name_list = feed_target_names
|
|
if (
|
|
feed_config.feeded_vars_names is not None
|
|
and feed_target_names != feed_config.feeded_vars_names
|
|
):
|
|
logger.warning(
|
|
f"feed vars in program and config are diff: feed in program: {feed_target_names}. feed in config {feed_config.feeded_vars_names}."
|
|
)
|
|
feed_name_list = feed_config.feeded_vars_names
|
|
# remove feed op in inference_program. new feed op will be added in exe.run
|
|
global_block = inference_program.global_block()
|
|
need_to_remove_op_index = []
|
|
for i, op in enumerate(global_block.ops):
|
|
op.desc.set_is_target(False)
|
|
if op.type == "feed": # only remove feed op here
|
|
need_to_remove_op_index.append(i)
|
|
for index in need_to_remove_op_index[::-1]:
|
|
global_block._remove_op(index)
|
|
if (
|
|
fetch_config.fetch_vars_names is not None
|
|
and fetch_targets_names != fetch_config.fetch_vars_names
|
|
):
|
|
logger.warning(
|
|
f"fetch vars in program and config are diff: fetch in program: {fetch_targets_names}. fetch in config {fetch_config.fetch_vars_names}."
|
|
)
|
|
fetch_list = [
|
|
inference_program.global_block().var(i)
|
|
for i in fetch_config.fetch_vars_names
|
|
]
|
|
# remove fetch op in inference_program. new fetch op will be added in exe.run
|
|
global_block = inference_program.global_block()
|
|
need_to_remove_op_index = []
|
|
for i, op in enumerate(global_block.ops):
|
|
op.desc.set_is_target(False)
|
|
if op.type == "fetch": # only remove fetch op here
|
|
need_to_remove_op_index.append(i)
|
|
for index in need_to_remove_op_index[::-1]:
|
|
global_block._remove_op(index)
|
|
|
|
# if fetch_list have lod tensor
|
|
return_numpy = all(v.lod_level == 0 for v in fetch_list)
|
|
|
|
# try dump fetch_targets
|
|
feed_tensors = []
|
|
assert (
|
|
len(feed_config.feeded_vars_names)
|
|
== len(feed_config.feeded_vars_dims)
|
|
== len(feed_config.feeded_vars_types)
|
|
)
|
|
# check program vars and feed tensor shape in config
|
|
for i in range(len(feed_config.feeded_vars_names)):
|
|
var = inference_program.global_block().var(
|
|
feed_config.feeded_vars_names[i]
|
|
)
|
|
if not isinstance(feed_config.feeded_vars_dims[i], (list, tuple)):
|
|
tensor_shape = (feed_config.feeded_vars_dims[i],)
|
|
else:
|
|
tensor_shape = tuple(feed_config.feeded_vars_dims[i])
|
|
feed_config.feeded_vars_dims[i] = tensor_shape
|
|
var_shape = var.shape[1:]
|
|
if tensor_shape != var_shape:
|
|
raise RuntimeError(
|
|
f"feed variable '{feed_config.feeded_vars_names[i]}' shape not match. infer program shape: {var_shape}. feed tensor shape: {tensor_shape}"
|
|
)
|
|
|
|
if not feed_config.feeded_vars_filelist:
|
|
logger.info("generate random feed vars.")
|
|
for i in range(len(feed_config.feeded_vars_names)):
|
|
var = inference_program.global_block().var(
|
|
feed_config.feeded_vars_names[i]
|
|
)
|
|
# create fake feed tensor. if lod_level > 1, should create_lod_tensor()
|
|
if var.lod_level == 0:
|
|
feed_tensors.append(
|
|
np.array(
|
|
np.random.random(
|
|
(
|
|
batch_size,
|
|
*list(feed_config.feeded_vars_dims[i]),
|
|
)
|
|
),
|
|
dtype=feed_config.feeded_vars_types[i],
|
|
)
|
|
)
|
|
elif var.lod_level == 1:
|
|
t = np.array(
|
|
np.random.random(
|
|
(
|
|
batch_size,
|
|
*list(feed_config.feeded_vars_dims[i]),
|
|
)
|
|
),
|
|
dtype=feed_config.feeded_vars_types[i],
|
|
)
|
|
feed_tensors.append(
|
|
base.create_lod_tensor(t, [[1] * batch_size], place)
|
|
)
|
|
else:
|
|
raise RuntimeError(
|
|
"vars with lod_level >= 2 is not supported now in this infer program check tool."
|
|
)
|
|
results = exe.run(
|
|
inference_program,
|
|
feed={
|
|
name: feed_tensors[i]
|
|
for i, name in enumerate(feed_name_list)
|
|
},
|
|
fetch_list=fetch_list,
|
|
return_numpy=return_numpy,
|
|
)
|
|
else:
|
|
logger.info(
|
|
f"load feed vars from files: {feed_config.feeded_vars_filelist}."
|
|
)
|
|
feed_vars = [
|
|
inference_program.global_block().var(
|
|
feed_config.feeded_vars_names[i]
|
|
)
|
|
for i in range(len(feed_config.feeded_vars_names))
|
|
]
|
|
feeder = base.DataFeeder(feed_list=feed_vars, place=place)
|
|
batch_feed = feed_gen(
|
|
batch_size,
|
|
feed_config.feeded_vars_dims,
|
|
feed_config.feeded_vars_filelist,
|
|
)
|
|
slots = [batch_feed]
|
|
results = exe.run(
|
|
inference_program,
|
|
feed=feeder.feed(slots),
|
|
fetch_list=fetch_list,
|
|
return_numpy=return_numpy,
|
|
)
|
|
for i, v in enumerate(fetch_list):
|
|
logger.info(f"fetch_targets name: {v.name}")
|
|
logger.info(f"fetch_targets: {results[i]}")
|
|
return results
|
|
|
|
|
|
def check_not_expected_ops(prog):
|
|
op_types_set = set()
|
|
for op in prog.global_block().ops:
|
|
if op.type in not_expected_op_types and op.type not in op_types_set:
|
|
logger.warning(
|
|
f"find op type '{op.type}' in program, please check if your program is pruned correctly !"
|
|
)
|
|
op_types_set.add(op.type)
|
|
|
|
|
|
def check_saved_vars_try_dump(
|
|
dump_dir,
|
|
dump_prog_fn,
|
|
is_text_dump_program,
|
|
feed_config,
|
|
fetch_config,
|
|
batch_size=1,
|
|
save_filename=None,
|
|
):
|
|
dump_prog = load_program(
|
|
os.path.join(dump_dir, dump_prog_fn), is_text_dump_program
|
|
)
|
|
saved_params = [
|
|
v for v in dump_prog.list_vars() if io_utils.is_persistable(v)
|
|
]
|
|
logger.info(
|
|
f"persistable vars in dump program: {[v.name for v in saved_params]}"
|
|
)
|
|
|
|
check_not_expected_ops(dump_prog)
|
|
|
|
return try_load_model_vars(
|
|
dump_dir,
|
|
dump_prog_fn,
|
|
is_text_dump_program,
|
|
batch_size,
|
|
feed_config,
|
|
fetch_config,
|
|
save_filename,
|
|
saved_params,
|
|
)
|
|
|
|
|
|
def parse_program(program, output_dir):
|
|
# persistable vars
|
|
output = {}
|
|
persistable_vars = [
|
|
v for v in program.list_vars() if io_utils.is_persistable(v)
|
|
]
|
|
output["persistable_vars"] = [
|
|
{
|
|
'name': str(v.name),
|
|
'shape': str(v.shape),
|
|
'lod_level': int(v.lod_level),
|
|
'dtype': str(v.dtype),
|
|
'type': str(v.type),
|
|
}
|
|
for v in persistable_vars
|
|
]
|
|
with open(os.path.join(output_dir, persistable_vars_out_fn), 'w') as f:
|
|
f.write("persistable vars:\n")
|
|
for var in output["persistable_vars"]:
|
|
f.write(str(var))
|
|
f.write("\n")
|
|
|
|
# all vars
|
|
all_vars = list(program.list_vars())
|
|
output["all_vars"] = [
|
|
(
|
|
{
|
|
'name': str(v.name),
|
|
'shape': str(v.shape),
|
|
'lod_level': int(v.lod_level),
|
|
'dtype': str(v.dtype),
|
|
}
|
|
if v.type not in feed_fetch_type_list
|
|
else {'name': str(v.name), 'type': str(v.type)}
|
|
)
|
|
for v in all_vars
|
|
]
|
|
with open(os.path.join(output_dir, all_vars_out_fn), 'w') as f:
|
|
f.write("all vars:\n")
|
|
for var in output["all_vars"]:
|
|
f.write(str(var))
|
|
f.write("\n")
|
|
|
|
# ops
|
|
ops = program.global_block().ops
|
|
output["ops"] = [
|
|
{
|
|
'type': op.type,
|
|
'input_arg_names': str(op.input_arg_names),
|
|
'output_arg_names': str(op.output_arg_names),
|
|
}
|
|
for op in ops
|
|
]
|
|
with open(os.path.join(output_dir, ops_out_fn), 'w') as f:
|
|
f.write("ops:\n")
|
|
for op in output["ops"]:
|
|
f.write(str(op))
|
|
f.write("\n")
|