# 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 collections import logging import numpy as np import sys import os import paddle from paddle.fluid import dygraph, core, framework from paddle.fluid.executor import Executor from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX from paddle.nn import Linear, Conv2D, Conv2DTranspose, MaxPool2D, MaxPool1D, BatchNorm1D, BatchNorm2D, BatchNorm3D from paddle.fluid.dygraph.nn import BatchNorm, Pool2D from paddle.fluid.io import load_inference_model, save_inference_model from paddle.nn.layer.activation import ReLU, LeakyReLU, Sigmoid, ReLU6, Tanh, Softmax, PReLU, Swish from paddle.fluid.log_helper import get_logger from . import quant_nn from .. import quantization_pass __all__ = ['ImperativeQuantAware', 'ImperativeCalcOutScale'] _logger = get_logger( __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s') _op_real_in_out_name = { "conv2d": [["Input", "Filter"], ["Output"]], "depthwise_conv2d": [["Input", "Filter"], ["Output"]], "pool2d": [["X"], ["Out"]], "elementwise_add": [["X", "Y"], ["Out"]], "softmax": [["X"], ["Out"]], "relu": [["X"], ["Out"]], "relu6": [["X"], ["Out"]], "leaky_relu": [["X"], ["Out"]], "prelu": [["X"], ["Out"]], "tanh": [["X"], ["Out"]], "batch_norm": [["X"], ["Y"]], "sigmoid": [["X"], ["Out"]], "swish": [["X"], ["Out"]], } class ImperativeQuantAware(object): """ Add the fake quant logic for given quantizable layers, namely add the quant_dequant computational logic both for activation inputs and weight inputs. """ def __init__(self, weight_bits=8, activation_bits=8, weight_quantize_type='abs_max', activation_quantize_type='moving_average_abs_max', moving_rate=0.9, quantizable_layer_type=['Conv2D', 'Linear'], weight_preprocess_layer=None, act_preprocess_layer=None, weight_quantize_layer=None, act_quantize_layer=None): r""" The constructor for ImperativeQuantAware. Args: weight_bits(int): quantization bit number for weights, whereas the bias is not quantized. activation_bits(int): quantization bit number for activations. weight_quantize_type(str): quantization type for weights, which supports 'abs_max' now. The 'moving_average_abs_max' usually is not used for weights, since weights are fixed once the model is well trained. activation_quantize_type(str): quantization type for activations, which supports 'abs_max' and 'moving_average_abs_max' now. If using 'abs_max' mode, the quantization scale will be calculated dynamically each step in both training and testing period. If using 'moving_average_abs_max', the static quantization scale will be calculated during training and used in inference. moving_rate(float): the parameter for 'moving_average_abs_max' quantization. quantizable_layer_type(list[str]): List the type of layers that will be quantized. Default is ['Conv2D', 'Linear']. The quantizable_op_type in QuantizationFreezePass and ConvertToInt8Pass must be the same as this. weight_preprocess_layer(paddle.nn.Layer, optional): A paddle Layer that defines how to preprocess weight before quantization. Using this can quickly test if user's preprocess method works or not. The input is non-quantized weight and function returns processed weight to be quantized. If None, the weight will be quantized directly. Default is None. act_preprocess_layer(paddle.nn.Layer, optional): A paddle Layer that defines how to preprocess activation before quantization. Using this can quickly test if user's preprocess method works or not. The input is non-quantized activation and function returns processed activation to be quantized. If None, the activation will be quantized directly. Default is None. weight_quantize_layer(paddle.nn.Layer, optional): A paddle Layer that defines how to quantize weight. Using this can quickly test if user's quantization method works or not. In this layer, user should both define quantization method and dequantization method, that is, the function's input is non-quantized weight and returns dequantized weight. If None, will use quantization op defined by 'weight_quantize_type'. Default is None. act_quantize_layer(paddle.nn.Layer, optional): A paddle Layer that defines how to quantize activation. Using this can quickly test if user's quantization method works or not. In this layer, user should both define quantization method and dequantization method, that is, the function's input is non-quantized activation and returns dequantized activation. If None, will use quantization op defined by 'activation_quantize_type'. Default is None. Note: If user sets attribute 'skip_quant' to a Layer that support dynamic quantization and sets it to true, the layer would not be quantized during training. If this attribute is not sets or the attribute is false, the Layer would be qunatized in training. Examples 1: .. code-block:: python import paddle from paddle.fluid.contrib.slim.quantization \ import ImperativeQuantAware from paddle.vision.models \ import resnet model = resnet.resnet50(pretrained=True) imperative_qat = ImperativeQuantAware( weight_quantize_type='abs_max', activation_quantize_type='moving_average_abs_max') # Add the fake quant logical. # The original model will be rewrite. # The outscale of outputs in supportted layers would be calculated. imperative_qat.quantize(model) # Fine-tune the quantized model # ... # Save quant model for the inference. imperative_qat.save_quantized_model( layer=model, model_path="./resnet50_qat", input_spec=[ paddle.static.InputSpec( shape=[None, 3, 224, 224], dtype='float32')]) Examples 2: .. code-block:: python import paddle from paddle.fluid.contrib.slim.quantization \ import ImperativeQuantAware class ImperativeModel(paddle.nn.Layer): def __init__(self): super(ImperativeModel, self).__init__() # self.linear_0 would skip the quantization. self.linear_0 = paddle.nn.Linear(784, 400) self.linear_0.skip_quant = True # self.linear_1 would not skip the quantization. self.linear_1 = paddle.nn.Linear(400, 10) self.linear_1.skip_quant = False def forward(self, inputs): x = self.linear_0(inputs) x = self.linear_1(inputs) return x model = ImperativeModel() imperative_qat = ImperativeQuantAware( weight_quantize_type='abs_max', activation_quantize_type='moving_average_abs_max') # Add the fake quant logical. # The original model will be rewrite. # # There is only one Layer(self.linear1) would be added the # fake quant logical. imperative_qat.quantize(model) # Fine-tune the quantized model # ... # Save quant model for the inference. imperative_qat.save_quantized_model( layer=model, model_path="./imperative_model_qat") """ super(ImperativeQuantAware, self).__init__() self._weight_bits = weight_bits self._activation_bits = activation_bits self._moving_rate = moving_rate self._activation_quantize_type = activation_quantize_type self._weight_quantize_type = weight_quantize_type self._weight_pre_layer = weight_preprocess_layer self._act_pre_layer = act_preprocess_layer self._weight_quant_layer = weight_quantize_layer self._act_quant_layer = act_quantize_layer self._out_scale = ImperativeCalcOutScale() t_check = lambda method: method is None or issubclass(method, dygraph.layers.Layer) assert t_check( self._weight_pre_layer), "weight_preprocess should be nn.Layer" assert t_check(self._act_pre_layer), "act_preprocess should be nn.Layer" assert t_check( self._weight_quant_layer), "weight_quantize should be nn.Layer" assert t_check(self._act_quant_layer), "act_quantize should be nn.Layer" quant_type = { 'abs_max', 'moving_average_abs_max', 'channel_wise_abs_max' } assert activation_quantize_type != 'channel_wise_abs_max', \ "The activation quantization type does not support 'channel_wise_abs_max'." if activation_quantize_type not in quant_type: raise ValueError( "Unknown activation_quantize_type : '%s'. It can only be " "'abs_max' or 'moving_average_abs_max' now." % (str(activation_quantize_type))) if weight_quantize_type not in quant_type: raise ValueError( "Unknown weight_quantize_type: '%s'. It can only be " "'abs_max' or 'moving_average_abs_max' or 'channel_wise_abs_max' now." % (str(weight_quantize_type))) self._quant_layers_map = { 'Conv2D': Conv2D, 'Linear': Linear, 'Pool2D': Pool2D, 'ReLU': ReLU, 'LeakyReLU': LeakyReLU, 'ReLU6': ReLU6, 'Softmax': Softmax, 'Tanh': Tanh, 'Swish': Swish } self._quantizable_layer_type = tuple( self._quant_layers_map[layer] if layer in self._quant_layers_map else layer for layer in quantizable_layer_type) for layer in self._quantizable_layer_type: assert not isinstance( layer, str), "{} is unspported to be quantized.".format(layer) def quantize(self, model): """ According to weights' and activations' quantization types, the model will be added some fake quant ops, such as fake_quantize_dequantize_moving_average_abs_max, fake_quantize_dequantize_abs_max and so on. At the same time, the out_scale value of outputs would be calculated. Args: model(fluid.dygraph.Layer): the model to be quantized. Returns: None """ for name, layer in model.named_sublayers(): if not isinstance(layer, self._quantizable_layer_type): continue if hasattr(layer, "skip_quant") and layer.skip_quant == True: continue last_idx = 0 idx = 0 obj = model parent = model while idx < len(name): if (name[idx] == '.'): if hasattr(parent, name[last_idx:idx]): obj = getattr(obj, name[last_idx:idx]) parent = obj last_idx = idx + 1 idx += 1 target = name[last_idx:idx] quant_layer = self._get_quantized_counterpart(layer) setattr(quant_layer, "layer_name", layer.full_name()) setattr(obj, target, quant_layer) self._out_scale.calc_out_scale(model) def _get_quantized_counterpart(self, layer): quant_layers = tuple(self._quant_layers_map.values()) quantized_counterpart = tuple('Quantized' + k for k in self._quant_layers_map.keys()) predicate = lambda value: isinstance(layer, value) index_generator = (i for i, v in enumerate(quant_layers) if predicate(v)) try: index = next(index_generator) except StopIteration: _logger.fatal("The layer {} is unsupported to be quantized.".format( layer.full_name())) sys.exit(-1) layer_with_weight = ['QuantizedConv2D', 'QuantizedLinear'] if quantized_counterpart[index] not in layer_with_weight: quant_layer_class_name = 'QuantizedNoweightLayer' else: quant_layer_class_name = quantized_counterpart[index] quantized_layer = quant_nn.__dict__[quant_layer_class_name]( layer, self._weight_bits, self._activation_bits, self._moving_rate, self._weight_quantize_type, self._activation_quantize_type, self._weight_pre_layer, self._act_pre_layer, self._weight_quant_layer, self._act_quant_layer) return quantized_layer def save_quantized_model(self, layer, path, input_spec=None, **config): self._out_scale.save_quantized_model(layer, path, input_spec, **config) class ImperativeCalcOutScale(object): def __init__(self, moving_rate=0.9): """ Add the logic of calculating and setting output quantization scales of some layers. These output quantization scales may be used by tensorRT or some other inference engines. Args: moving_rate(float): The decay coefficient of moving average. The default value is 0.9. """ super(ImperativeCalcOutScale, self).__init__() self._moving_rate = moving_rate self._out_scale_layer_type_list = ( BatchNorm, BatchNorm1D, BatchNorm2D, BatchNorm3D, Conv2D, LeakyReLU, Linear, PReLU, Pool2D, MaxPool1D, MaxPool2D, ReLU, ReLU6, Sigmoid, Softmax, Tanh, Swish) self._register_hook_handle_list = [] self._out_scale_dict = collections.OrderedDict() def calc_out_scale(self, model): """ Insert the `moving_average_abs_max_scale` op to calculate output scale of Specific layers in model. Args: model(fluid.dygraph.Layer): The target model which would be calculate the output quantization scale. Returns: None """ assert isinstance( model, dygraph.Layer), "model must be the instance of dygraph.Layer" for _, layer in model.named_sublayers(): if not isinstance(layer, self._out_scale_layer_type_list): if 'quantized_' not in layer.full_name(): continue forward_post_hook_handle = layer.register_forward_post_hook( self._forward_post_hook) self._register_hook_handle_list.append(forward_post_hook_handle) def save_quantized_model(self, layer, path, input_spec=None, **config): """ Save the quantized model for the inference. Args: layer (Layer): The Layer to be saved. path (str): The path prefix to save model. The format is ``dirname/file_prefix`` or ``file_prefix``. input_spec (list[InputSpec|Tensor], optional): Describes the input of the saved model's forward method, which can be described by InputSpec or example Tensor. If None, all input variables of the original Layer's forward method would be the inputs of the saved model. Default None. **configs (dict, optional): Other save configuration options for compatibility. We do not recommend using these configurations, they may be removed in the future. If not necessary, DO NOT use them. Default None. The following options are currently supported: (1) output_spec (list[Tensor]): Selects the output targets of the saved model. By default, all return variables of original Layer's forward method are kept as the output of the saved model. If the provided ``output_spec`` list is not all output variables, the saved model will be pruned according to the given ``output_spec`` list. Returns: None """ assert isinstance( layer, dygraph.Layer), "model must be the instance of dygraph.Layer" is_dynamic_mode = False with dygraph.guard(): layer.eval() for handle in self._register_hook_handle_list: handle.remove() for key in self._out_scale_dict: self._out_scale_dict[key] = float(self._out_scale_dict[key] .numpy()) if paddle.in_dynamic_mode(): is_dynamic_mode = True paddle.enable_static() paddle.jit.save(layer=layer, path=path, input_spec=input_spec, **config) if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() exe = Executor(place) file_prefix = os.path.basename(path) dirname = os.path.dirname(path) model_filename = file_prefix + INFER_MODEL_SUFFIX params_filename = file_prefix + INFER_PARAMS_SUFFIX [inference_program, feed_target_names, fetch_targets] = ( load_inference_model( dirname=dirname, executor=exe, model_filename=model_filename, params_filename=params_filename)) # Traverse all ops in the program and find out the op matching # the Layer in the dynamic graph. layer_var_dict = collections.OrderedDict() ops_list = [key for key, _ in self._out_scale_dict.items()] op_count = 0 conv_count = 0 for block in inference_program.blocks: for op in block.ops: if op.type in _op_real_in_out_name: if op.type in ["batch_norm", "pool2d"]: if op.type == "pool2d" and op.attr( "pooling_type") != "max": continue op_count = self.op_match(op, ops_list, op_count) if op_count >= len(ops_list): continue op._set_attr('out_threshold', self._out_scale_dict[ops_list[op_count]]) op_count += 1 else: output_var_names = quantization_pass._get_op_output_var_names( op) for output_var_name in output_var_names: output_var_tensor = block.var(output_var_name) if output_var_tensor.dtype not in [ core.VarDesc.VarType.FP64, core.VarDesc.VarType.FP32 ]: continue # Because the Layer in dygraph may correspond to multiple ops # in static program after being saved. To ensure correctness, # the outscale collected for output of dygraph Layer can only # be set to the last op in the corresponding ops in static program. # # We can judge the execution order of the ops which corresponding # to dygraph Layer by the name of output. And use dict to save # the corresponding relationship between the dygraph Layer and the # static graph op that needs to set the outscale attribute. if '.' not in output_var_name: continue dynamic_layer_name, var_name_suffix = output_var_name.split( ".") if dynamic_layer_name in layer_var_dict: if layer_var_dict[dynamic_layer_name][ 0] < var_name_suffix: layer_var_dict[dynamic_layer_name] = [ var_name_suffix, op ] else: layer_var_dict[dynamic_layer_name] = [ var_name_suffix, op ] # Because the naming styles of static and dynamic graph are different, # in order to avoid mistakes, we unify the name here. for (layer_name, var_name_op_list) in layer_var_dict.items(): if 'prelu' in layer_name: layer_name = layer_name.replace('prelu', 'p_re_lu') if 'relu' in layer_name: layer_name = layer_name.replace('relu', 're_lu') if 'conv2d' in layer_name: layer_name = 'conv2d_' + str(conv_count) conv_count = conv_count + 1 if layer_name not in self._out_scale_dict: continue var_name_op_list[1]._set_attr('out_threshold', self._out_scale_dict[layer_name]) # Save the processed program. save_inference_model( dirname=dirname, feeded_var_names=feed_target_names, target_vars=fetch_targets, executor=exe, main_program=inference_program.clone(), model_filename=model_filename, params_filename=params_filename) if is_dynamic_mode: paddle.disable_static() def op_match(self, op, ops_list, op_count): while op_count < len(ops_list) and op.type not in ops_list[op_count]: op_count += 1 while op_count < len(ops_list) and op.type is "pool2d" and op.attr( "pooling_type") != "max": op_count += 1 return op_count def _forward_post_hook(self, layer, input, output): assert isinstance( output, (core.VarBase, framework.Variable) ), "Multiple outputs are not currently supported in ImperativeOutScale." if output.dtype not in [ core.VarDesc.VarType.FP32, core.VarDesc.VarType.FP64 ]: return if not hasattr(layer, "_out_scale"): layer._out_scale = quant_nn.MovingAverageAbsMaxScale( output.name, self._moving_rate, output.dtype) scale_out = layer._out_scale(output) if hasattr(layer, 'layer_name'): layer_name = layer.layer_name else: layer_name = layer.full_name() self._out_scale_dict[layer_name] = scale_out