wehub-resource-sync
464 lines
19 KiB
Python
464 lines
19 KiB
Python
# 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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import logging
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import numpy as np
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import paddle
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from paddle.framework import IrGraph, core
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from paddle.static.quantization import (
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AddQuantDequantForInferencePass,
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AddQuantDequantPassV2,
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OutScaleForTrainingPass,
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QuantizationTransformPassV2,
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quant_config,
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)
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from ..auto_parallel.static.converter import Converter
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from ..auto_parallel.static.dist_attribute import (
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OperatorDistAttr,
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TensorDistAttr,
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)
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from .pass_base import PassBase, register_pass
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TRANSFORM_PASS_OP_TYPES = list(
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quant_config.SUPPORT_WEIGHT_QUANTIZATION_OP_DICT.keys()
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)
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QUANT_DEQUANT_PASS_OP_TYPES = list(
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quant_config.SUPPORT_ACT_QUANTIZATION_OP_DICT.keys()
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)
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def _node_id(node):
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return (node.node.graph_id(), node.node.id())
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@register_pass("auto_parallel_quantization")
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class QuantizationPass(PassBase):
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def __init__(self):
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super().__init__()
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self.set_attr("dist_context", None)
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self.set_attr("params_grads", None)
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self.set_attr("mode", "train")
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self.set_attr("loss", None)
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def _check_self(self):
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if self.get_attr("dist_context") is None:
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return False
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if self.get_attr("params_grads") is None:
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return False
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return True
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def _check_conflict(self, other_pass):
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return True
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def _apply_single_impl(self, main_program, startup_program, context):
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dist_context = self.get_attr("dist_context")
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params_grads = self.get_attr("params_grads")
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mode = self.get_attr("mode")
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loss = self.get_attr("loss")
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# TODO: scope and place will be removed,
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# cause params should be initialized by engine module.
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scope = paddle.static.global_scope()
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place = paddle.framework.CUDAPlace(
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paddle.distributed.ParallelEnv().dev_id
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)
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# 0. record the relation among blocks
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parent_idx_dict = {}
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for block in main_program.blocks:
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parent_idx_dict[block.idx] = block.parent_idx
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is_test = True if mode != "train" else False
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# 1. Program convert to Graph, and this pass is only for train mode
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main_graph = IrGraph(
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core.Graph(main_program.desc), for_test=mode != "train"
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)
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# 2. Prepare inputs
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transform_pass_ops = []
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quant_dequant_ops = []
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quantize_op_types = [
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'conv2d',
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'depthwise_conv2d',
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'mul',
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'matmul',
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'matmul_v2',
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]
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for op_type in quantize_op_types:
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if op_type in TRANSFORM_PASS_OP_TYPES:
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transform_pass_ops.append(op_type)
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elif op_type in QUANT_DEQUANT_PASS_OP_TYPES:
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quant_dequant_ops.append(op_type)
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weight_quantize_type = (
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"channel_wise_abs_max"
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if self.get_attr('channel_wise_abs_max')
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else "abs_max"
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)
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# 3. Add quant op for ops which have parameters
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if len(transform_pass_ops) > 0:
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transform_pass = QuantizationTransformPassV2(
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scope=scope,
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place=place,
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weight_bits=self.get_attr('weight_bits'),
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activation_bits=self.get_attr('activation_bits'),
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skip_pattern=self.get_attr('not_quant_pattern'),
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activation_quantize_type="moving_average_abs_max",
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quantizable_op_type=transform_pass_ops,
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weight_quantize_type=weight_quantize_type,
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weight_quantize_func=None,
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act_quantize_func=None,
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weight_preprocess_func=None,
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act_preprocess_func=None,
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optimizer_func=None,
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executor=None,
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is_test=is_test,
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)
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for sub_graph in main_graph.all_sub_graphs():
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transform_pass.apply(sub_graph)
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# 4. Add quant op for ops which don't have parameter
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if len(quant_dequant_ops) > 0:
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quant_dequant_pass = AddQuantDequantPassV2(
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scope=scope,
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place=place,
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quant_bits=self.get_attr('activation_bits'),
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skip_pattern=self.get_attr('not_quant_pattern'),
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quantizable_op_type=quant_dequant_ops,
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is_test=is_test,
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)
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for sub_graph in main_graph.all_sub_graphs():
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quant_dequant_pass.apply(sub_graph)
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# 5. Gather quantitative information for the output
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out_scale_training_pass = OutScaleForTrainingPass(
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scope=scope, place=place, is_test=is_test
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)
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for sub_graph in main_graph.all_sub_graphs():
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out_scale_training_pass.apply(sub_graph)
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# 6. When export quant model, traverse to find the output of each op, and insert the quant/dequant op after it.
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if mode != "train" and self.get_attr('onnx_format'):
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try:
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out_scale_infer_pass = AddQuantDequantForInferencePass(
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scope=scope,
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place=place,
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quant_bits=self.get_attr('activation_bits'),
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)
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# for sub_graph in main_graph.all_sub_graphs():
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# out_scale_infer_pass.apply(sub_graph)
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except:
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logging.warning(
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"Unable to convert quant model with onnx_format=True, please update PaddlePaddle >= 2.4.0"
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)
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# 7. Convert Graph back to Program
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quant_program = main_graph.to_program()
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quant_program = self.move_persist_var_to_global_block(quant_program)
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# 8.1 get new prams_grads from quant_program
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new_params_grads = []
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for param, grad in params_grads:
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if param.name not in quant_program.global_block().vars:
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continue
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new_param = quant_program.global_block().vars[param.name]
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new_grad = quant_program.global_block().vars[grad.name]
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new_params_grads.append((new_param, new_grad))
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# 8.2 get new loss var
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new_loss = None
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if loss:
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new_loss = quant_program.global_block().vars[loss.name]
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# 8.3 recover the relation among blocks
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for block in quant_program.blocks:
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block.desc._set_forward_block_idx(parent_idx_dict[block.idx])
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# 9. complete distributed attribution
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self.set_dist_attr_for_qat_program(
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quant_program, main_program, dist_context
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)
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# 10. reset scale var value with dist_attr
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self.reset_scope_var(quant_program, dist_context, scope, place)
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context.set_attr("main_program", quant_program)
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context.set_attr("startup_program", startup_program)
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context.set_attr("params_grads", new_params_grads)
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context.set_attr("loss", new_loss)
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def move_persist_var_to_global_block(self, program):
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global_block = program.global_block()
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for _op in global_block.ops:
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if _op.type == "while":
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_block_id = _op.attr("sub_block").id
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_block = program.block(_block_id)
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persistables = []
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for _name, _var in _block.vars.items():
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if _var.persistable:
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global_block._clone_variable(_var)
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persistables.append(_name)
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for _name in persistables:
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_block._remove_var(_name)
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persistables.extend(_op.input('X'))
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_op.desc.set_input("X", persistables)
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return program
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def reset_scope_var(self, quant_program, dist_context, scope, place):
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# The var_value, created by quantization_passes, should has same shape with the value after parallel.
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for var in quant_program.list_vars():
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scope_var = scope.find_var(var.name)
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if not (scope_var and scope_var.get_tensor()._is_initialized()):
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continue
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tensor = scope_var.get_tensor()
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if var.shape == tensor.shape:
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continue
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var_dist_attr = dist_context.get_tensor_dist_attr_for_program(var)
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dist_attr = {
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"dims_mapping": var_dist_attr.dims_mapping,
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"process_shape": var_dist_attr.process_mesh.shape,
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"process_group": var_dist_attr.process_mesh.process_ids,
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}
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# slice tensor_value with dist_attr
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sliced_tensor = Converter.slice_with_dist_attr(
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np.array(tensor), dist_attr
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)
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tensor._clear()
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tensor.set(sliced_tensor, place)
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def set_dist_attr_for_qat_program(
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self, quant_program, main_program, dist_context
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):
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# NOTE: hack implement, upgrading soon
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for ib, block in enumerate(quant_program.blocks):
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# recover origin ops' dist_attr and set quant ops' dist_attr
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qat_offset = 0
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for ip, quant_op in enumerate(block.ops):
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quant_op_dist_attr = OperatorDistAttr()
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if (
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"quantize" in quant_op.type
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or quant_op.type == "moving_average_abs_max_scale"
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):
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# set all quantization ops' dist_attr by quantified op
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input_name = quant_op.desc.input('X')[0]
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if "quantize" in input_name:
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input_name = input_name[
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: input_name.index(".quantized")
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]
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if (
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quant_op.type == "moving_average_abs_max_scale"
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or ip - qat_offset >= len(main_program.blocks[ib].ops)
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):
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consume_op = (
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main_program.blocks[ib]
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._var_recursive(input_name)
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.op
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)
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else:
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consume_op = main_program.blocks[ib].ops[
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ip - qat_offset
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]
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consume_op_dist_attr = dist_context.get_dist_op_for_program(
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consume_op
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).dist_attr
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ref_process_mesh = consume_op_dist_attr.process_mesh
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if input_name in consume_op_dist_attr.outputs_dist_attrs:
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consume_input_dist_attr = (
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consume_op_dist_attr.outputs_dist_attrs[input_name]
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)
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else:
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consume_input_dist_attr = (
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consume_op_dist_attr.inputs_dist_attrs[input_name]
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)
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quant_op_dist_attr.impl_idx = 0
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quant_op_dist_attr.impl_type = "default"
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quant_op_dist_attr.process_mesh = ref_process_mesh
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quant_op_dist_attr.set_input_dist_attr(
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quant_op.desc.input('X')[0], consume_input_dist_attr
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)
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for slot_name in quant_op.desc.input_names():
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in_name = quant_op.desc.input(slot_name)[0]
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input_var = block._var_recursive(in_name)
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ref_dims_mapping = [-1 for i in input_var.shape]
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if slot_name == "X":
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continue
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elif slot_name in ['Scale', 'ZeroPoint']:
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if (
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quant_op.has_attr('quant_axis')
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and quant_op.attr('quant_axis') != -1
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):
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x_name = quant_op.desc.input('X')[0]
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x_var = block._var_recursive(x_name)
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x_dist_attr = (
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quant_op_dist_attr.get_input_dist_attr(
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x_name
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)
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)
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quant_axis = quant_op.attr('quant_axis')
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ref_dims_mapping = [
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x_dist_attr.dims_mapping[quant_axis]
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]
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tensor_dist_attr = TensorDistAttr()
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tensor_dist_attr.process_mesh = ref_process_mesh
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tensor_dist_attr.dims_mapping = ref_dims_mapping
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dist_context.set_tensor_dist_attr_for_program(
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input_var, tensor_dist_attr
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)
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quant_op_dist_attr.set_input_dist_attr(
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in_name, tensor_dist_attr
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)
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for slot_name in quant_op.desc.output_names():
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output_name = quant_op.desc.output(slot_name)[0]
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output_var = block._var_recursive(output_name)
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ref_dims_mapping = [-1 for i in output_var.shape]
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if slot_name == "Y":
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dist_context.set_tensor_dist_attr_for_program(
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output_var, consume_input_dist_attr
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)
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quant_op_dist_attr.set_output_dist_attr(
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output_name, consume_input_dist_attr
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)
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continue
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elif slot_name == "OutScale":
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if (
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quant_op.has_attr('quant_axis')
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and quant_op.attr('quant_axis') != -1
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):
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x_name = quant_op.desc.input('X')[0]
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x_var = block._var_recursive(x_name)
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x_dist_attr = (
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quant_op_dist_attr.get_input_dist_attr(
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x_name
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)
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)
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quant_axis = quant_op.attr('quant_axis')
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ref_dims_mapping = [
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x_dist_attr.dims_mapping[quant_axis]
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]
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tensor_dist_attr = TensorDistAttr()
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tensor_dist_attr.process_mesh = ref_process_mesh
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tensor_dist_attr.dims_mapping = ref_dims_mapping
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dist_context.set_tensor_dist_attr_for_program(
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output_var, tensor_dist_attr
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)
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quant_op_dist_attr.set_output_dist_attr(
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output_name, tensor_dist_attr
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)
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quant_op._set_attr("op_device", "")
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qat_offset += 1
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else:
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# recover origin ops' dist_attr
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origin_op = main_program.blocks[ib].ops[ip - qat_offset]
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quant_op.desc.set_original_id(origin_op.desc.original_id())
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dist_origin_op = dist_context.get_dist_op_for_program(
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origin_op
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)
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assert dist_origin_op is not None, (
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"origin op must have dist attr."
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)
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origin_op_dist_attr = dist_origin_op.dist_attr
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quant_op_dist_attr.impl_idx = origin_op_dist_attr.impl_idx
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quant_op_dist_attr.impl_type = origin_op_dist_attr.impl_type
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quant_op_dist_attr.process_mesh = (
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origin_op_dist_attr.process_mesh
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)
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scale_offset = 0
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for idx, input_name in enumerate(quant_op.input_arg_names):
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if (
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origin_op.type == "while"
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and input_name not in origin_op.input_arg_names
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):
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assert (
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"@scale" in input_name
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or "@zero_point" in input_name
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)
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scale_offset += 1
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continue
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idx -= scale_offset
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origin_input_name = origin_op.input_arg_names[idx]
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origin_input_dist_attr = (
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origin_op_dist_attr.inputs_dist_attrs[
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origin_input_name
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]
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)
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quant_op_dist_attr.set_input_dist_attr(
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input_name, origin_input_dist_attr
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)
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for idx, output_name in enumerate(
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quant_op.output_arg_names
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):
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origin_output_name = origin_op.output_arg_names[idx]
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origin_output_dist_attr = (
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origin_op_dist_attr.outputs_dist_attrs[
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origin_output_name
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]
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)
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quant_op_dist_attr.set_output_dist_attr(
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output_name, origin_output_dist_attr
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)
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if not main_program.blocks[ib]._find_var_recursive(
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output_name
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):
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origin_output_var = main_program.blocks[
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ib
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]._var_recursive(origin_output_name)
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origin_out_tensor_dist_attr = (
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dist_context.get_dist_tensor_for_program(
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origin_output_var
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).dist_attr
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)
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quant_output_var = block._var_recursive(output_name)
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dist_context.set_tensor_dist_attr_for_program(
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quant_output_var, origin_out_tensor_dist_attr
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)
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dist_context.set_op_dist_attr_for_program(
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quant_op, quant_op_dist_attr
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)
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# recover vars' dist_attr
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for name, dst_var in block.vars.items():
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if name in main_program.blocks[ib].vars:
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src_var = main_program.blocks[ib].vars[name]
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dist_tensor = dist_context.get_dist_tensor_for_program(
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src_var
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)
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if not dist_tensor:
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continue
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dist_context.set_tensor_dist_attr_for_program(
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dst_var, dist_tensor.dist_attr
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)
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