# 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. from __future__ import print_function, absolute_import import os import sys import logging import subprocess import numpy as np from collections import OrderedDict import paddle.fluid as fluid from paddle.fluid import core from paddle.fluid.log_helper import get_logger from google.protobuf import text_format from paddle.fluid import debugger from paddle.fluid.framework import Program from paddle.fluid.proto import framework_pb2 __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 fluid.io.is_persistable(v)] pruned_vars = OrderedDict(pruned_vars) pruned_vars_name = [name for name in pruned_vars] logger.info( "persistable vars in pruned program: {}".format(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( "not find variable '%s' in train program. please check pruning." % var_name) logger.error(e) continue if var.shape != train_prog_var.shape or var.dtype != train_prog_var.dtype: logger.error( "variable: {} not match. in pruned program shape: {} dtype:{}, in train program shape: {} dtype: {}" .format(var_name, var.shape, var.dtype, train_prog_var.shape, 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') debugger.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 = fluid.Program() place = fluid.CPUPlace() exe = fluid.Executor(place) with fluid.program_guard(program): d0_data = fluid.layers.data(var_name, shape=shape_list, 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 = fluid.Program() place = fluid.CPUPlace() exe = fluid.Executor(place) with fluid.program_guard(program): d0_data = fluid.layers.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) or isinstance(dim, 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 = fluid.CPUPlace() exe = fluid.Executor(place) scope = fluid.core.Scope() with fluid.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 = \ fluid.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 = fluid.global_scope().find_var(each_var.name) assert var_temp != 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( "Shape not matching: the Program requires a parameter with a shape of ({}), " "while the loaded parameter (namely [ {} ]) has a shape of ({})." .format(orig_shape, each_var.name, 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( "feed vars in program and config are diff: feed in program: {}. feed in config {}." .format(feed_target_names, 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( "fetch vars in program and config are diff: fetch in program: {}. fetch in config {}." .format(fetch_targets_names, 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( "feed variable '{}' shape not match. infer program shape: {}. feed tensor shape: {}" .format(feed_config.feeded_vars_names[i], var_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( tuple([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( tuple([batch_size] + list(feed_config.feeded_vars_dims[i]))), dtype=feed_config.feeded_vars_types[i]) feed_tensors.append( fluid.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("load feed vars from files: {}.".format( 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 = fluid.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("fetch_targets name: %s" % v.name) logger.info("fetch_targets: {}".format(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( "find op type '{}' in program, please check if your program is pruned correctly !" .format(op.type)) 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 fluid.io.is_persistable(v) ] logger.info("persistable vars in dump program: {}".format( [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 fluid.io.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 = [v for v in 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")