You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
255 lines
11 KiB
255 lines
11 KiB
# Copyright (c) 2019 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 sys
|
|
import numpy as np
|
|
from .... import Executor
|
|
from .... import io
|
|
from .... import core, scope_guard
|
|
from ....compiler import CompiledProgram
|
|
from ....compiler import BuildStrategy
|
|
from ....framework import IrGraph, Variable, Program
|
|
from ....log_helper import get_logger
|
|
from ..core.strategy import Strategy
|
|
from .quantization_pass import *
|
|
|
|
__all__ = ['QuantizationStrategy']
|
|
|
|
_logger = get_logger(
|
|
__name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s')
|
|
|
|
|
|
class QuantizationStrategy(Strategy):
|
|
"""
|
|
The strategy for Quantization.
|
|
"""
|
|
|
|
def __init__(self,
|
|
start_epoch=0,
|
|
end_epoch=0,
|
|
float_model_save_path=None,
|
|
mobile_model_save_path=None,
|
|
int8_model_save_path=None,
|
|
activation_bits=8,
|
|
weight_bits=8,
|
|
activation_quantize_type='abs_max',
|
|
weight_quantize_type='abs_max',
|
|
save_in_nodes=None,
|
|
save_out_nodes=None):
|
|
"""
|
|
Args:
|
|
start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0
|
|
end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 0
|
|
float_model_save_path(str): The path to save model with float weights.
|
|
None means it doesn't save float model. default: None.
|
|
mobile_model_save_path(str): The path to save model for paddle-mobile execution.
|
|
None means it doesn't save mobile model. default: None.
|
|
int8_model_save_path(str): The path to save model with int8_t weight.
|
|
None means it doesn't save int8 model. default: None.
|
|
activation_bits(int): quantization bit number for activation. default: 8.
|
|
weight_bits(int): quantization bit number for weights. The bias is not quantized.
|
|
default: 8.
|
|
activation_quantize_type(str): quantization type for activation,
|
|
now support 'abs_max', 'range_abs_max' and 'moving_average_abs_max'.
|
|
If use 'abs_max' mode, the quantization scale will be calculated
|
|
dynamically each step in both training and testing period. If use
|
|
'range_abs_max', a static quantization scale will be calculated
|
|
during training and used in inference.
|
|
weight_quantize_type (str): quantization type for weights, support 'abs_max' and 'channel_wise_abs_max'.
|
|
The 'range_abs_max' usually is not used for weight, since weights are fixed once the model is well trained.
|
|
save_in_nodes(list<str>): A list of variable names used to prune graph
|
|
for saving inference model.
|
|
save_out_nodes(list<str>): A list of variable names used to prune graph
|
|
for saving inference model.
|
|
|
|
"""
|
|
super(QuantizationStrategy, self).__init__(start_epoch, end_epoch)
|
|
self.start_epoch = start_epoch
|
|
self.end_epoch = end_epoch
|
|
self.float_model_save_path = float_model_save_path
|
|
self.mobile_model_save_path = mobile_model_save_path
|
|
self.int8_model_save_path = int8_model_save_path
|
|
self.activation_bits = activation_bits
|
|
self.weight_bits = weight_bits
|
|
self.activation_quantize_type = activation_quantize_type
|
|
self.weight_quantize_type = weight_quantize_type
|
|
self.save_out_nodes = save_out_nodes
|
|
self.save_in_nodes = save_in_nodes
|
|
|
|
def restore_from_checkpoint(self, context):
|
|
"""
|
|
Restore graph when the compression task is inited from checkpoint.
|
|
"""
|
|
# It is inited from checkpoint and has missed start epoch.
|
|
if context.epoch_id != 0 and context.epoch_id > self.start_epoch:
|
|
_logger.info("Restore quantization task from checkpoint")
|
|
self._modify_graph_for_quantization(context)
|
|
_logger.info("Finish restoring quantization task from checkpoint")
|
|
|
|
def _modify_graph_for_quantization(self, context):
|
|
"""
|
|
Insert fake_quantize_op and fake_dequantize_op before training and testing.
|
|
"""
|
|
train_ir_graph = IrGraph(
|
|
core.Graph(context.optimize_graph.program.clone().desc),
|
|
for_test=False)
|
|
test_ir_graph = IrGraph(
|
|
core.Graph(context.eval_graph.program.clone().desc), for_test=True)
|
|
transform_pass = QuantizationTransformPass(
|
|
scope=context.scope,
|
|
place=context.place,
|
|
weight_bits=self.weight_bits,
|
|
activation_bits=self.activation_bits,
|
|
activation_quantize_type=self.activation_quantize_type,
|
|
weight_quantize_type=self.weight_quantize_type)
|
|
transform_pass.apply(train_ir_graph)
|
|
transform_pass.apply(test_ir_graph)
|
|
# Put persistables created by transform_pass into context.optimize_graph.persistables
|
|
# for saving checkpoint.
|
|
program_persistables = set()
|
|
for var in context.optimize_graph.program.list_vars():
|
|
if var.persistable:
|
|
program_persistables.add(var.name)
|
|
|
|
program = Program()
|
|
for var_node in train_ir_graph.all_persistable_nodes():
|
|
if var_node.name() not in program_persistables:
|
|
var_desc = var_node.var()
|
|
var = program.global_block().create_var(
|
|
name=var_node.name(),
|
|
shape=var_desc.shape(),
|
|
dtype=var_desc.dtype(),
|
|
type=var_desc.type(),
|
|
lod_level=var_desc.lod_level())
|
|
context.optimize_graph.persistables[var.name] = var
|
|
|
|
build_strategy = BuildStrategy()
|
|
build_strategy.enable_inplace = False
|
|
build_strategy.memory_optimize = False
|
|
build_strategy.fuse_all_reduce_ops = False
|
|
# for quantization training
|
|
context.optimize_graph.compiled_graph = CompiledProgram(
|
|
train_ir_graph.graph).with_data_parallel(
|
|
loss_name=context.optimize_graph.out_nodes['loss'],
|
|
build_strategy=build_strategy)
|
|
|
|
context.eval_graph.program = test_ir_graph.to_program()
|
|
|
|
# for saving inference model after training
|
|
context.put('quantization_test_ir_graph_backup', test_ir_graph)
|
|
|
|
def on_epoch_begin(self, context):
|
|
"""
|
|
Insert fake_quantize_op and fake_dequantize_op before training and testing.
|
|
"""
|
|
super(QuantizationStrategy, self).on_epoch_begin(context)
|
|
if self.start_epoch == context.epoch_id:
|
|
_logger.info('QuantizationStrategy::on_epoch_begin')
|
|
self._modify_graph_for_quantization(context)
|
|
_logger.info('Finish QuantizationStrategy::on_epoch_begin')
|
|
|
|
def on_epoch_end(self, context):
|
|
"""
|
|
Free and save inference model.
|
|
"""
|
|
super(QuantizationStrategy, self).on_compression_end(context)
|
|
|
|
if context.epoch_id == self.end_epoch:
|
|
_logger.info('QuantizationStrategy::on_epoch_end')
|
|
test_ir_graph = context.get('quantization_test_ir_graph_backup')
|
|
# freeze the graph after training
|
|
freeze_pass = QuantizationFreezePass(
|
|
scope=context.scope,
|
|
place=context.place,
|
|
weight_bits=self.weight_bits,
|
|
activation_bits=self.activation_bits,
|
|
weight_quantize_type=self.weight_quantize_type)
|
|
freeze_pass.apply(test_ir_graph)
|
|
|
|
# for other strategies
|
|
context.eval_graph.program = test_ir_graph.to_program()
|
|
|
|
if self.save_out_nodes == None:
|
|
out_vars = [
|
|
context.eval_graph.var(var_name)._var
|
|
for var_name in context.eval_graph.out_nodes.values()
|
|
]
|
|
else:
|
|
out_vars = [
|
|
context.eval_graph.var(var_name)._var
|
|
for var_name in self.save_out_nodes
|
|
]
|
|
|
|
if self.save_in_nodes == None:
|
|
in_vars = list(context.eval_graph.in_nodes.values())
|
|
else:
|
|
in_vars = self.save_in_nodes
|
|
|
|
# save float model
|
|
if self.float_model_save_path:
|
|
executor = Executor(context.place)
|
|
with scope_guard(context.scope):
|
|
io.save_inference_model(
|
|
self.float_model_save_path,
|
|
in_vars,
|
|
out_vars,
|
|
executor,
|
|
main_program=test_ir_graph.to_program(),
|
|
model_filename='model',
|
|
params_filename='weights',
|
|
export_for_deployment=True)
|
|
|
|
# save int8 model
|
|
if self.int8_model_save_path:
|
|
convert_int8_pass = ConvertToInt8Pass(
|
|
scope=context.scope, place=context.place)
|
|
convert_int8_pass.apply(test_ir_graph)
|
|
|
|
executor = Executor(context.place)
|
|
|
|
with scope_guard(context.scope):
|
|
io.save_inference_model(
|
|
self.int8_model_save_path,
|
|
in_vars,
|
|
out_vars,
|
|
executor,
|
|
main_program=test_ir_graph.to_program(),
|
|
model_filename='model',
|
|
params_filename='weights',
|
|
export_for_deployment=True)
|
|
|
|
# save mobile model
|
|
if self.mobile_model_save_path:
|
|
if not self.int8_model_save_path:
|
|
# convert the weights as int8_t type
|
|
convert_int8_pass = ConvertToInt8Pass(
|
|
scope=context.scope, place=context.place)
|
|
convert_int8_pass.apply(test_ir_graph)
|
|
# make some changes on the graph for the mobile inference
|
|
mobile_pass = TransformForMobilePass()
|
|
mobile_pass.apply(test_ir_graph)
|
|
executor = Executor(context.place)
|
|
with scope_guard(context.scope):
|
|
io.save_inference_model(
|
|
self.mobile_model_save_path,
|
|
in_vars,
|
|
out_vars,
|
|
executor,
|
|
main_program=test_ir_graph.to_program(),
|
|
model_filename='model',
|
|
params_filename='weights',
|
|
export_for_deployment=True)
|
|
_logger.info('Finish QuantizationStrategy::on_epoch_end')
|