chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2022 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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# A dict of operators that contain weights and support quantization,
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# including operator names, actual input and output names.
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SUPPORT_WEIGHT_QUANTIZATION_OP_DICT = {
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"conv2d": [["Input", "Filter"], ["Output"]],
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"depthwise_conv2d": [["Input", "Filter"], ["Output"]],
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"conv2d_transpose": [["Input", "Filter"], ["Output"]],
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"mul": [["X", "Y"], ["Out"]],
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"matmul": [["X", "Y"], ["Out"]],
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"matmul_v2": [["X", "Y"], ["Out"]],
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}
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# A dict of operators that supports quantization and has only activation inputs,
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# including operator names, actual input and output names.
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SUPPORT_ACT_QUANTIZATION_OP_DICT = {
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"mul": [["X", "Y"], ["Out"]],
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"matmul": [["X", "Y"], ["Out"]],
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"matmul_v2": [["X", "Y"], ["Out"]],
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"pool2d": [["X"], ["Out"]],
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"elementwise_add": [["X", "Y"], ["Out"]],
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"concat": [["X"], ["Out"]],
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"softmax": [["X"], ["Out"]],
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"argmax": [["X"], ["Out"]],
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"transpose": [["X"], ["Out"]],
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"equal": [["X", "Y"], ["Out"]],
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"gather": [["X"], ["Out"]],
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"greater_equal": [["X", "Y"], ["Out"]],
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"greater_than": [["X", "Y"], ["Out"]],
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"less_equal": [["X", "Y"], ["Out"]],
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"less_than": [["X", "Y"], ["Out"]],
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"mean": [["X"], ["Out"]],
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"not_equal": [["X", "Y"], ["Out"]],
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"reshape": [["X"], ["Out"]],
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"reshape2": [["X"], ["Out"]],
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"transpose2": [["X"], ["Out"]],
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"nearest_interp": [["X"], ["Out"]],
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"trilinear_interp": [["X"], ["Out"]],
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"slice": [["Input"], ["Out"]],
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"squeeze": [["X"], ["Out"]],
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"elementwise_sub": [["X", "Y"], ["Out"]],
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"relu": [["X"], ["Out"]],
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"relu6": [["X"], ["Out"]],
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"leaky_relu": [["X"], ["Out"]],
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"prelu": [["X", "Alpha"], ["Out"]],
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"tanh": [["X"], ["Out"]],
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"swish": [["X"], ["Out"]],
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"dropout": [["X"], ["Out"]],
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"batch_norm": [["X"], ["Y"]],
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"layer_norm": [["X"], ["Y"]],
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"sigmoid": [["X"], ["Out"]],
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"elementwise_mul": [["X", "Y"], ["Out"]],
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"elementwise_pow": [["X", "Y"], ["Out"]],
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"hard_swish": [["X"], ["Out"]],
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"hard_sigmoid": [["X"], ["Out"]],
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"gru": [["Input", "Weight"], ["Hidden"]],
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"lstm": [["Input", "Weight"], ["Hidden"]],
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"pad2d": [["X"], ["Out"]],
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"pad3d": [["X"], ["Out"]],
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"flatten": [["X"], ["Out"]],
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"flatten2": [["X"], ["Out"]],
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"unsqueeze2": [["X"], ["Out"]],
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"flatten_contiguous_range": [["X"], ["Out"]],
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"split": [["X"], ["Out"]],
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"squeeze2": [["X"], ["Out"]],
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"nearest_interp_v2": [["X"], ["Out"]],
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"bilinear_interp": [["X"], ["Out"]],
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"bilinear_interp_v2": [["X"], ["Out"]],
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"fill_constant_batch_size_like": [["Input"], ["Out"]],
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"arg_max": [["X"], ["Out"]],
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"abs": [["X"], ["Out"]],
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"assign": [["X"], ["Out"]],
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"cast": [["X"], ["Out"]],
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"clip": [["X"], ["Out"]],
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"box_coder": [["PriorBox"], ["OutputBox"]],
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"crop": [["X"], ["Out"]],
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"cumsum": [["X"], ["Out"]],
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"expand_v2": [["X"], ["Out"]],
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"fill_any_like": [["X"], ["Out"]],
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"fill_constant": [[], ["Out"]],
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"gelu": [["X"], ["Out"]],
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"instance_norm": [["X"], ["Y"]],
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"lookup_table": [["W", "Ids"], ["Out"]],
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"lookup_table_v2": [["W", "Ids"], ["Out"]],
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"norm": [["X"], ["Norm"]],
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"p_norm": [["X"], ["Out"]],
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"pow": [["X"], ["Out"]],
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"reduce_mean": [["X"], ["Out"]],
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"stack": [["X"], ["Y"]],
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"top_k_v2": [["X"], ["Out", "Indices"]],
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"logical_and": [["X", "Y"], ["Out"]],
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"logical_not": [["X"], ["Out"]],
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"meshgrid": [["X"], ["Out"]],
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"roi_align": [["X", "ROIs"], ["Out"]],
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"strided_slice": [["Input"], ["Out"]],
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"where": [["Condition", "X", "Y"], ["Out"]],
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"grid_sampler": [["X", "Grid"], ["Output"]],
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"tile": [["X"], ["Out"]],
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"group_norm": [["X"], ["Y", "Mean", "Variance"]],
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"reduce_sum": [["X"], ["Out"]],
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"square": [["X"], ["Out"]],
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"softplus": [["X"], ["Out"]],
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"shuffle_channel": [["X"], ["Out"]],
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"reduce_max": [["X"], ["Out"]],
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"scale": [["X"], ["Out"]],
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}
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# A full dict of operators that supports quantization,
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# including operator names, actual input and output names.
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SUPPORT_QUANTIZATION_OP_DICT = SUPPORT_WEIGHT_QUANTIZATION_OP_DICT.copy()
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SUPPORT_QUANTIZATION_OP_DICT.update(SUPPORT_ACT_QUANTIZATION_OP_DICT)
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class BaseQuantizer:
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"""
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Basic quantization configuration class, which configures some hyperparameters
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required for quantization, including the list of op types to be quantized,
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quantization bit number for weight and activation and the range of quantization values.
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Args:
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quantizable_op_type(list[str], optional): List the type of ops
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that will be quantized. Default is []. If quantizable_op_type is [],
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it will use the default quantization op type of the qunat config in
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the current Quantizer.
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quant_bits(int, optional): Quantization bit number for weight and activation.
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Default is 8.
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"""
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def __init__(
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self,
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quantizable_op_type=[],
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quant_bits=8,
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):
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self._quantizable_op_type = quantizable_op_type
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self._quant_bits = quant_bits
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self._quant_min = -128
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self._quant_max = 127
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@property
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def weight_quant_operation_types(self):
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"""
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Operation type list which should support weight quantization.
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And before these ops, quant dequant nodes will be inserted.
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"""
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base_weight_op_type_list = list(
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SUPPORT_WEIGHT_QUANTIZATION_OP_DICT.keys()
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)
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if self._quantizable_op_type:
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weight_list = []
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for _op_type in self._quantizable_op_type:
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if _op_type in base_weight_op_type_list:
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weight_list.append(_op_type)
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return weight_list
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else:
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return base_weight_op_type_list
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@property
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def activation_quant_operation_types(self):
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"""
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Operation type list which should support activation quantization.
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And before these ops, quant dequant nodes will be inserted.
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"""
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base_act_op_type_list = list(SUPPORT_ACT_QUANTIZATION_OP_DICT.keys())
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act_quant_op_list = []
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if self._quantizable_op_type:
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for _op_type in self._quantizable_op_type:
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if _op_type in base_act_op_type_list:
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act_quant_op_list.append(_op_type)
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else:
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act_quant_op_list = [
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'mul',
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'matmul',
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'matmul_v2',
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]
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return act_quant_op_list
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@property
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def observer_operation_types(self):
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"""
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Operation type list for observer in quantization. These nodes only count the
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calibration boundary scale and do not participate in the fake quantization.
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In order to facilitate the deployment of the prediction engine, quant
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and dequant nodes will be inserted after these ops when exporting the model.
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"""
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return list(SUPPORT_ACT_QUANTIZATION_OP_DICT.keys())
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class TensorRTQuantizer(BaseQuantizer):
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"""
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TensorRT quantization configuration class.
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Args:
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quantizable_op_type(list[str], optional): List the type of ops
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that will be quantized. Default is []. If quantizable_op_type is [],
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it will use the default quantization op type of the qunat config in
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the current Quantizer.
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quant_bits(int, optional): Quantization bit number for weight and activation.
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Default is 8.
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"""
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def __init__(
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self,
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quantizable_op_type=[],
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quant_bits=8,
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):
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super().__init__()
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self._quantizable_op_type = quantizable_op_type
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self._quant_bits = quant_bits
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self._quant_min = -128
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self._quant_max = 127
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@property
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def activation_quant_operation_types(self):
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"""
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Operation type list which should support activation quantization.
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And before these ops, quant dequant nodes will be inserted.
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"""
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return [
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"pool2d",
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"elementwise_add",
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"elementwise_sub",
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"elementwise_mul",
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"elementwise_pow",
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"concat",
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"softmax",
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"argmax",
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"mean",
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"relu",
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"relu6",
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"leaky_relu",
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"tanh",
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"swish",
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"softplus",
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"gelu",
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"hard_sigmoid",
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"hard_swish",
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"sigmoid",
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"layer_norm",
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"matmul_v2",
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"split",
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"bilinear_interp",
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"nearest_interp",
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"trilinear_interp",
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"nearest_interp_v2",
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"bilinear_interp",
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"bilinear_interp_v2",
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"clip",
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"pow",
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"reduce_mean",
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"reduce_sum",
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"reduce_max",
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]
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class ARMCPUQuantizer(BaseQuantizer):
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"""
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ARM CPU with Paddle Lite quantization configuration class.
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Args:
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quantizable_op_type(list[str], optional): List the type of ops
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that will be quantized. Default is []. If quantizable_op_type is [],
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it will use the default quantization op type of the qunat config in
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the current Quantizer.
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quant_bits(int, optional): Quantization bit number for weight and activation.
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Default is 8.
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"""
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def __init__(
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self,
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quantizable_op_type=[],
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quant_bits=8,
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):
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super().__init__()
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self._quantizable_op_type = quantizable_op_type
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self._quant_bits = quant_bits
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self._quant_min = -127
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self._quant_max = 127
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