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178 lines
7.5 KiB
178 lines
7.5 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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import logging
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import numpy as np
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from .... import core
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from ....framework import Program, Operator, Variable, program_guard
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from .... import unique_name
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from ....layer_helper import LayerHelper
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from ....param_attr import ParamAttr
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from ....initializer import Constant
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from ....log_helper import get_logger
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_logger = get_logger(
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__name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s')
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class QuantizeTranspilerV2(object):
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def __init__(self,
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weight_bits=8,
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activation_bits=8,
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weight_quantize_type='abs_max',
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activation_quantize_type='abs_max',
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quantizable_op_type=['conv2d', 'depthwise_conv2d', 'mul'],
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skip_pattern=['skip_quant']):
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"""
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Add quant_dequant op before the quantized op to quantize the fluid Program.
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It is a patch for distributed quantization, we will support others module for
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distributed quantization.
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Args:
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weight_bits(int): the bit of quantized weight.
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activation_bits(int): the bit of quantized activation.
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weight_quantize_type(str): the quantization type for weight.
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Only support to be 'abs_max' for now.
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activation_quantize_type(str): the quantization type for activation.
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Only support to be 'abs_max' for now.
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quantizable_op_type(str): set the op type for quantization.
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skip_pattern(str|list): The user-defined quantization skip pattern, which
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will be presented in the name scope of an op. When the skip pattern is
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detected in an op's name scope, the corresponding op will not be quantized.
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"""
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self._weight_bits = weight_bits
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self._activation_bits = activation_bits
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assert activation_quantize_type == "abs_max", \
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"activation_quantize_type should be abs_max for now."
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assert weight_quantize_type == "abs_max", \
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"weight_quantize_type should be abs_max for now."
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self._activation_quantize_type = activation_quantize_type
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self._weight_quantize_type = weight_quantize_type
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self._quantizable_ops = quantizable_op_type
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self._quantizable_grad_ops = [
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'%s_grad' % (op) for op in self._quantizable_ops
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]
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self._skip_pattern = skip_pattern
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self.helper = LayerHelper(self.__class__.__name__)
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def apply(self, program, startup_program):
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"""
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Apply quantization to fluid Program.
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Args:
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program(Program): the train or test program to be quantized.
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startup_program(Program): the corresponding startup_program.
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Returns:
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None
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"""
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assert isinstance(program, Program), \
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"program must be the instance of Program"
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assert isinstance(startup_program, Program), \
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"startup_program must be the instance of Program"
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quant_dequant_vars = [
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collections.OrderedDict() for _ in range(len(program.blocks))
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]
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with program_guard(program, startup_program):
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for block in program.blocks:
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ops = list(block.ops)
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for op in ops:
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if op.type in self._quantizable_ops and \
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(not self._is_skip_quant(op)):
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self._transform_forward(block, op, quant_dequant_vars)
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for block in program.blocks:
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ops = list(block.ops)
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for op in ops:
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if op.type in self._quantizable_grad_ops and \
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(not self._is_skip_quant(op)):
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self._transform_backward(block, op, quant_dequant_vars)
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def _is_skip_quant(self, op):
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"""
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Analyse whether the op should skip quantization or not.
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"""
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user_skipped = False
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if isinstance(self._skip_pattern, list):
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user_skipped = op.has_attr("op_namescope") and \
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any(pattern in op.attr("op_namescope") \
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for pattern in self._skip_pattern)
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elif isinstance(self._skip_pattern, str):
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user_skipped = op.has_attr("op_namescope") and \
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op.attr("op_namescope").find(
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self._skip_pattern) != -1
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return user_skipped
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def _transform_forward(self, block, op, quant_dequant_vars):
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op._set_attr("quantization_type", "qat_with_weight")
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idx = block.ops.index(op)
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block_id = block.idx
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for in_name in op.input_arg_names:
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if in_name in quant_dequant_vars[block_id]:
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quant_dequant_var = quant_dequant_vars[block_id][in_name]
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else:
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in_var = block.var(in_name)
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quant_bits = self._weight_bits if in_var.persistable \
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else self._activation_bits
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quant_type = self._weight_quantize_type if in_var.persistable \
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else self._activation_quantize_type
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if quant_type == "abs_max":
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quant_dequant_var = self._insert_quant_dequant_abs_max_op(
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block, idx, in_var, quant_bits)
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else:
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_logger.error("Quant_type only supported to be abs_max")
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quant_dequant_vars[block_id][in_name] = quant_dequant_var
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op._rename_input(in_name, quant_dequant_var.name)
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def _transform_backward(self, block, op, quant_dequant_vars):
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block_id = block.idx
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no_dequanted_input_vars = True
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for name in op.input_arg_names:
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if name in quant_dequant_vars[block_id]:
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dequant_var = quant_dequant_vars[block_id][name]
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op._rename_input(name, dequant_var.name)
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no_dequanted_input_vars = False
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if no_dequanted_input_vars:
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raise ValueError("There is no dequanted inputs for op %s." %
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(op.type))
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def _insert_quant_dequant_abs_max_op(self, block, idx, in_var, quant_bits):
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quant_dequant_var = block.create_var(
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type=in_var.type,
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name="{}.quant_dequant".format(in_var.name),
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shape=in_var.shape,
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dtype=in_var.dtype)
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scale_var = self.helper.create_parameter(
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attr=ParamAttr(
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name="{}.quant_dequant.scale".format(in_var.name),
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initializer=Constant(0.001),
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trainable=False),
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shape=[1],
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dtype=in_var.dtype)
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scale_var.stop_gradient = True
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inputs = {'X': in_var}
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outputs = {'Out': quant_dequant_var, 'OutScale': scale_var}
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attrs = {'bit_length': quant_bits}
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block._insert_op(
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idx,
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type='fake_quantize_dequantize_abs_max',
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attrs=attrs,
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inputs=inputs,
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outputs=outputs)
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return quant_dequant_var
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