wehub-resource-sync
708 lines
25 KiB
Python
708 lines
25 KiB
Python
# Copyright (c) 2021 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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from __future__ import annotations
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import copy
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import enum
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import os
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from typing import TYPE_CHECKING, Any
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import numpy as np
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import paddle
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from paddle.base import core, framework
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from paddle.base.executor import global_scope
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from paddle.base.framework import (
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IrGraph,
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IrNode,
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Operator,
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OpProtoHolder,
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convert_nptype_to_vartype,
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)
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from paddle.static.log_helper import get_logger
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from paddle.static.quantization import (
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QuantizationFreezePass,
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QuantizationTransformPass,
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)
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if TYPE_CHECKING:
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from collections.abc import Callable
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LOGLEVEL = os.environ.get("PADDLE_TEST_LOGLEVEL", "INFO").upper()
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logging = get_logger(
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__name__, LOGLEVEL, fmt='%(asctime)s-%(levelname)s: %(message)s'
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)
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class TensorConfig:
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'''
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A config builder for a input or a weight.
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'''
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def __init__(
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self,
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lod: list[list[int]] | None = None,
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data_gen: Callable[..., np.array] | None = None,
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shape: list[list[int]] | None = None,
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):
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'''
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shape: The shape of the tensor.
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dtype: The data type of the tensor.
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data: The value of WeightVar. for input, it should be None
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'''
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self.lod = lod
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if data_gen is not None:
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self.data_gen = data_gen
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self.data = data_gen()
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self.dtype = self.data.dtype
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self.shape = self.data.shape
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else:
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assert shape is not None, (
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"While data_gen is not defined, shape must not be None"
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)
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self.data = np.random.normal(0.0, 1.0, shape).astype(np.float32)
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self.shape = shape
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self.dtype = self.data.dtype
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def __repr__(self):
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return str({'shape': self.shape, 'lod': self.lod, 'dtype': self.dtype})
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def convert_type_inplace(self, type: np.dtype):
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self.data = self.data.astype(type)
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self.dtype = self.data.dtype
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return self
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class VarType(enum.Enum):
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DENSE_TENSOR = 1
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DENSE_TENSOR_ARRAY = 2
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STEP_SCOPES = 3
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class OpConfig:
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'''A config builder for generating a Op.'''
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def __init__(
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self,
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type: str,
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inputs: dict[str, list[str]],
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outputs: dict[str, list[str]],
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attrs: dict[str, Any] | None = None,
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outputs_var_type: dict[str, VarType] | None = None,
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outputs_dtype: dict[str, np.dtype] | None = None,
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**kwargs,
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):
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self.type = type
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self.inputs = inputs
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self.outputs = outputs
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self.outputs_dtype = outputs_dtype
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self.outputs_var_type = outputs_var_type
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self.attrs = attrs
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if self.attrs is None:
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self.attrs = {}
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self.attrs.update(kwargs)
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def __repr__(self):
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log_str = self.type
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log_str += str(self.attrs)
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return log_str
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_OP_WITHOUT_KERNEL_SET = {
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'feed',
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'fetch',
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'go',
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'conditional_block',
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'static_pylayer',
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'while',
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'send',
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'recv',
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'listen_and_serv',
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'fl_listen_and_serv',
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'select',
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'checkpoint_notify',
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'gen_bkcl_id',
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'c_gen_bkcl_id',
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'gen_nccl_id',
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'c_gen_nccl_id',
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'c_comm_init',
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'c_sync_calc_stream',
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'c_sync_comm_stream',
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'heter_listen_and_serv',
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'c_wait_comm',
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'c_wait_compute',
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}
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class BlockConfig:
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'''A config builder for generating a Block.'''
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def __init__(
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self,
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ops: list[OpConfig],
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vars: list[str],
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vars_dtype: dict[str, np.dtype] | None = None,
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vars_var_type: dict[str, VarType] | None = None,
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vars_lod_level: dict[str, int] | None = None,
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):
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self.ops = ops
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self.vars = vars
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self.vars_dtype = vars_dtype
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self.vars_var_type = vars_var_type
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self.vars_lod_level = vars_lod_level
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def fill_block_desc(self, block_desc):
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for name in self.vars:
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var_desc = block_desc.var(name.encode())
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var_desc.set_type(core.VarDesc.VarType.DENSE_TENSOR)
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if (
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self.vars_lod_level is not None
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and name in self.vars_lod_level.keys()
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):
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var_desc.set_lod_level(self.vars_lod_level[name])
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if (
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self.vars_var_type is not None
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and name in self.vars_var_type.keys()
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):
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if self.vars_var_type[name] == VarType.DENSE_TENSOR_ARRAY:
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var_desc.set_type(core.VarDesc.VarType.DENSE_TENSOR_ARRAY)
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elif self.vars_var_type[name] == VarType.STEP_SCOPES:
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var_desc.set_type(core.VarDesc.VarType.STEP_SCOPES)
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continue
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var_desc.set_dtype(convert_nptype_to_vartype(np.float32))
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if self.vars_dtype is not None and name in self.vars_dtype.keys():
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var_desc.set_dtype(
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convert_nptype_to_vartype(self.vars_dtype[name])
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)
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for op_config in self.ops:
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op_desc = block_desc.append_op()
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op_desc.set_type(op_config.type)
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for name, values in op_config.inputs.items():
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op_desc.set_input(name, values)
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# canonicalize scalar attrs
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if OpProtoHolder.instance().has_op_proto(op_config.type):
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proto = OpProtoHolder.instance().get_op_proto(op_config.type)
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canonicalized_attrs = framework.canonicalize_attrs(
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op_config.attrs, proto
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)
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else:
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canonicalized_attrs = op_config.attrs
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for name, values in canonicalized_attrs.items():
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op_desc._set_attr(name, values)
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for name, values in op_config.outputs.items():
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op_desc.set_output(name, values)
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for v in values:
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if block_desc.has_var_recursive(v.encode()):
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continue
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var_desc = block_desc.var(v.encode())
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var_desc.set_type(core.VarDesc.VarType.DENSE_TENSOR)
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if (
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op_config.outputs_var_type is not None
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and v in op_config.outputs_var_type.keys()
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):
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if (
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op_config.outputs_var_type[v]
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== VarType.DENSE_TENSOR_ARRAY
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):
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var_desc.set_type(
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core.VarDesc.VarType.DENSE_TENSOR_ARRAY
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)
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elif (
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op_config.outputs_var_type[v] == VarType.STEP_SCOPES
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):
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var_desc.set_type(core.VarDesc.VarType.STEP_SCOPES)
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continue
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var_desc.set_dtype(convert_nptype_to_vartype(np.float32))
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if (
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op_config.outputs_dtype is not None
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and v in op_config.outputs_dtype.keys()
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):
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var_desc.set_dtype(
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convert_nptype_to_vartype(
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op_config.outputs_dtype[v]
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)
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)
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if op_config.type not in _OP_WITHOUT_KERNEL_SET:
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op_desc.infer_var_type(block_desc)
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op_desc.infer_shape(block_desc)
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op_desc.check_attrs()
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class ProgramConfig:
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'''A config builder for generating a Program.
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input_type : (np.dtype, default=None), the inputs will be casted to input_type before
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fed into TRT engine. If set to None, no casting will be performed.
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no_cast_list : (list[str], default=None), specify the tensors that will skip the casting
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'''
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def __init__(
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self,
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ops: list[OpConfig],
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weights: dict[str, TensorConfig],
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inputs: dict[str, TensorConfig],
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outputs: list[str],
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input_type: np.dtype | None = None,
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no_cast_list: list[str] | None = None,
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):
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self.ops = ops
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# if no weight need to save, we create a place_holder to help serialize params.
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if not weights:
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def generate_weight():
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return np.array([1]).astype(np.float32)
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self.weights = {
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"place_holder_weight": TensorConfig(data_gen=generate_weight)
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}
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else:
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self.weights = weights
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self.inputs = inputs
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self.outputs = outputs
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self.input_type = input_type
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self.no_cast_list = [] if no_cast_list is None else no_cast_list
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self.supported_cast_type = [np.float32, np.float16]
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def __repr__(self):
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log_str = ''
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for i in range(len(self.ops)):
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if i != len(self.ops) - 1:
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log_str += repr(self.ops[i]) + ' + '
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else:
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log_str += repr(self.ops[i])
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log_str += ' -- '
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for t, v in self.inputs.items():
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log_str += '[' + t + ': ' + str(v) + ']'
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for t, v in self.weights.items():
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log_str += '[' + t + ': ' + str(v) + ']'
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log_str += f"['input_type': {self.input_type}]"
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return log_str
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def set_input_type(self, _type: np.dtype) -> None:
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assert _type in self.supported_cast_type or _type is None, (
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"PaddleTRT only supports FP32 / FP16 IO"
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)
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ver = paddle.inference.get_trt_compile_version()
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trt_version = ver[0] * 1000 + ver[1] * 100 + ver[2] * 10
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if trt_version < 8600:
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logging.info("set_input_type is ignored for TRT version < 8600")
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return
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self.input_type = _type
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def get_feed_data(self) -> dict[str, dict[str, Any]]:
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feed_data = {}
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for name, tensor_config in self.inputs.items():
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data = tensor_config.data
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# Cast to target input_type
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if (
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self.input_type is not None
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and name not in self.no_cast_list
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and data.dtype in self.supported_cast_type
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):
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data = data.astype(self.input_type)
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# Truncate FP32 tensors to FP16 precision for FP16 test stability
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if data.dtype == np.float32 and name not in self.no_cast_list:
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data = data.astype(np.float16).astype(np.float32)
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feed_data[name] = {
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'data': data,
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'lod': tensor_config.lod,
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}
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return feed_data
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def _cast(self) -> None:
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if self.input_type is None:
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return
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for name, inp in self.inputs.items():
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if name in self.no_cast_list:
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continue
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if inp.dtype not in self.supported_cast_type:
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continue
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inp.convert_type_inplace(self.input_type)
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for name, weight in self.weights.items():
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if name in self.no_cast_list:
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continue
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if weight.dtype not in self.supported_cast_type:
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continue
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weight.convert_type_inplace(self.input_type)
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return self
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def convert_to_dynamic_shape(dynamic_shape, name):
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if dynamic_shape.min_input_shape == {}:
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return tuple(dynamic_shape.min_input_shape)
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min_shape = tuple(dynamic_shape.min_input_shape[name])
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opt_shape = tuple(dynamic_shape.opt_input_shape[name])
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max_shape = tuple(dynamic_shape.max_input_shape[name])
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result_shape = []
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for i in range(len(min_shape)):
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if min_shape[i] == opt_shape[i] == max_shape[i]:
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result_shape.append(min_shape[i])
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else:
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result_shape.append(-1)
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return tuple(result_shape)
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def create_fake_model(program_config, run_pir=False, dynamic_shape=None):
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'''Create a Paddle model(in memory) according to the given config.'''
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program_config = copy.deepcopy(program_config)
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program_config._cast()
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paddle.enable_static()
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with paddle.pir_utils.OldIrGuard():
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main_program_desc = core.ProgramDesc()
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# util_program = base.Program()
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util_program = paddle.static.Program()
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main_block_desc = main_program_desc.block(0)
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var_desc = main_block_desc.var(b"feed")
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var_desc.set_type(core.VarDesc.VarType.FEED_MINIBATCH)
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var_desc.set_persistable(True)
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index = 0
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for name, tensor_config in program_config.inputs.items():
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var_desc = main_block_desc.var(name.encode())
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var_desc.set_type(core.VarDesc.VarType.DENSE_TENSOR)
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var_desc.set_dtype(convert_nptype_to_vartype(tensor_config.dtype))
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if dynamic_shape is not None:
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dynamic_shape_copy = convert_to_dynamic_shape(
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dynamic_shape, name
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)
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var_desc.set_shape(dynamic_shape_copy)
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else:
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var_desc.set_shape(tensor_config.shape)
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var_desc.set_need_check_feed(True)
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if tensor_config.lod is not None:
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var_desc.set_lod_level(len(tensor_config.lod))
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op_desc = main_block_desc._prepend_op()
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op_desc.set_type("feed")
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op_desc.set_input('X', ["feed"])
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op_desc.set_output('Out', [name])
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op_desc._set_attr("col", index)
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index = index + 1
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save_var_map = {}
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for name, tensor_config in program_config.weights.items():
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var_desc = main_block_desc.var(name.encode())
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var_desc.set_type(core.VarDesc.VarType.DENSE_TENSOR)
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var_desc.set_dtype(convert_nptype_to_vartype(tensor_config.dtype))
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var_desc.set_shape(tensor_config.shape)
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var_desc.set_persistable(True)
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save_var_map[name] = util_program.global_block().create_parameter(
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dtype=tensor_config.dtype,
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shape=tensor_config.shape,
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type=core.VarDesc.VarType.DENSE_TENSOR,
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name=name,
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initializer=paddle.nn.initializer.Assign(tensor_config.data),
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)
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in_vars = []
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for name in sorted(save_var_map.keys()):
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in_vars.append(save_var_map[name])
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out_var = util_program.global_block().create_var(
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type=core.VarDesc.VarType.RAW, name="out_var_0"
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)
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out_var.desc.set_persistable(True)
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if not run_pir:
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util_program.global_block().append_op(
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type='save_combine',
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inputs={'X': in_vars},
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outputs={'Y': out_var},
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attrs={'file_path': '', 'save_to_memory': True},
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)
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for op_config in program_config.ops:
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op_desc = main_block_desc.append_op()
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op_desc.set_type(op_config.type)
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# canonicalize scalar attrs
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if OpProtoHolder.instance().has_op_proto(op_config.type):
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proto = OpProtoHolder.instance().get_op_proto(op_config.type)
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canonicalized_attrs = framework.canonicalize_attrs(
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op_config.attrs, proto
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)
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else:
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canonicalized_attrs = op_config.attrs
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for name, values in op_config.inputs.items():
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op_desc.set_input(name, values)
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for name, values in canonicalized_attrs.items():
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if name == 'sub_block':
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sub_block_desc = main_program_desc.append_block(
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main_block_desc
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)
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values.fill_block_desc(sub_block_desc)
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op_desc._set_attr(name, sub_block_desc)
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else:
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op_desc._set_attr(name, values)
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for name, values in op_config.outputs.items():
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op_desc.set_output(name, values)
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for v in values:
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if main_block_desc.has_var_recursive(v.encode()):
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continue
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var_desc = main_block_desc.var(v.encode())
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var_desc.set_type(core.VarDesc.VarType.DENSE_TENSOR)
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if (
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op_config.outputs_var_type is not None
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and v in op_config.outputs_var_type.keys()
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):
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if (
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op_config.outputs_var_type[v]
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== VarType.DENSE_TENSOR_ARRAY
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):
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var_desc.set_type(
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core.VarDesc.VarType.DENSE_TENSOR_ARRAY
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)
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elif (
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op_config.outputs_var_type[v] == VarType.STEP_SCOPES
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):
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var_desc.set_type(core.VarDesc.VarType.STEP_SCOPES)
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continue
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if run_pir:
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var_desc.set_dtype(
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convert_nptype_to_vartype(tensor_config.dtype)
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)
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else:
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var_desc.set_dtype(
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convert_nptype_to_vartype(np.float32)
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)
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if (
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op_config.outputs_dtype is not None
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and v in op_config.outputs_dtype.keys()
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):
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var_desc.set_dtype(
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convert_nptype_to_vartype(
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op_config.outputs_dtype[v]
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)
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)
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|
if op_config.type not in _OP_WITHOUT_KERNEL_SET:
|
|
op_desc.infer_var_type(main_block_desc)
|
|
op_desc.infer_shape(main_block_desc)
|
|
op_desc.check_attrs()
|
|
|
|
for index, name in enumerate(program_config.outputs):
|
|
var_desc = main_block_desc.var(b"fetch")
|
|
var_desc.set_type(core.VarDesc.VarType.FETCH_LIST)
|
|
var_desc.set_need_check_feed(True)
|
|
op_desc = main_block_desc.append_op()
|
|
op_desc.set_type("fetch")
|
|
op_desc.set_input('X', [name])
|
|
op_desc.set_output('Out', ["fetch"])
|
|
op_desc._set_attr("col", index)
|
|
util_program._sync_with_cpp()
|
|
|
|
return main_program_desc, util_program
|
|
|
|
|
|
def create_quant_model(
|
|
model,
|
|
params,
|
|
activation_quantize_type='moving_average_abs_max',
|
|
weight_quantize_type='channel_wise_abs_max',
|
|
save=False,
|
|
):
|
|
place = paddle.CUDAPlace(0)
|
|
scope = global_scope()
|
|
exe = paddle.static.Executor(place)
|
|
[
|
|
inference_program,
|
|
feed_target_names,
|
|
fetch_targets,
|
|
] = paddle.static.io.load_inference_model(
|
|
path_prefix=None,
|
|
executor=exe,
|
|
model_filename=model,
|
|
params_filename=params,
|
|
)
|
|
graph = IrGraph(core.Graph(inference_program.desc), for_test=True)
|
|
|
|
out_scale_op_list = [
|
|
"conv2d",
|
|
"depthwise_conv2d",
|
|
"mul",
|
|
"matmul",
|
|
"relu",
|
|
"leaky_relu",
|
|
"relu6",
|
|
"sigmoid",
|
|
"tanh",
|
|
"prelu",
|
|
"swish",
|
|
"softmax",
|
|
"batch_norm",
|
|
"layer_norm",
|
|
"elementwise_add",
|
|
"pool2d",
|
|
"reshape2",
|
|
"transpose2",
|
|
"concat",
|
|
"elementwise_mul",
|
|
"scale",
|
|
"slice",
|
|
"hard_swish",
|
|
"hard_sigmoid",
|
|
"conv2d_transpose",
|
|
"gru",
|
|
"bilinear_interp",
|
|
"nearest_interp",
|
|
"trilinear_interp",
|
|
"flatten",
|
|
"flatten2",
|
|
"transpose",
|
|
"pad2d",
|
|
"reshape",
|
|
"layer_norm",
|
|
"fusion_gru",
|
|
"multi_gru",
|
|
"quantize",
|
|
"dequantize",
|
|
]
|
|
op_real_in_out_name = {
|
|
"conv2d": [["Input", "Filter"], ["Output"]],
|
|
"depthwise_conv2d": [["Input", "Filter"], ["Output"]],
|
|
"conv2d_transpose": [["Input", "Filter"], ["Output"]],
|
|
"mul": [["X", "Y"], ["Out"]],
|
|
"matmul": [["X", "Y"], ["Out"]],
|
|
"pool2d": [["X"], ["Out"]],
|
|
"elementwise_add": [["X", "Y"], ["Out"]],
|
|
"concat": [["X"], ["Out"]],
|
|
"softmax": [["X"], ["Out"]],
|
|
"argmax": [["X"], ["Out"]],
|
|
"transpose": [["X"], ["Out"]],
|
|
"equal": [["X", "Y"], ["Out"]],
|
|
"gather": [["X"], ["Out"]],
|
|
"greater_equal": [["X", "Y"], ["Out"]],
|
|
"greater_than": [["X", "Y"], ["Out"]],
|
|
"less_equal": [["X", "Y"], ["Out"]],
|
|
"less_than": [["X", "Y"], ["Out"]],
|
|
"mean": [["X"], ["Out"]],
|
|
"not_equal": [["X", "Y"], ["Out"]],
|
|
"reshape": [["X"], ["Out"]],
|
|
"reshape2": [["X"], ["Out"]],
|
|
"transpose2": [["X"], ["Out"]],
|
|
"bilinear_interp": [["X"], ["Out"]],
|
|
"nearest_interp": [["X"], ["Out"]],
|
|
"trilinear_interp": [["X"], ["Out"]],
|
|
"slice": [["Input"], ["Out"]],
|
|
"squeeze": [["X"], ["Out"]],
|
|
"elementwise_sub": [["X", "Y"], ["Out"]],
|
|
"relu": [["X"], ["Out"]],
|
|
"relu6": [["X"], ["Out"]],
|
|
"leaky_relu": [["X"], ["Out"]],
|
|
"prelu": [["X"], ["Out"]],
|
|
"tanh": [["X"], ["Out"]],
|
|
"swish": [["X"], ["Out"]],
|
|
"dropout": [["X"], ["Out"]],
|
|
"batch_norm": [["X"], ["Y"]],
|
|
"layer_norm": [["X"], ["Y"]],
|
|
"sigmoid": [["X"], ["Out"]],
|
|
"elementwise_mul": [["X", "Y"], ["Out"]],
|
|
"scale": [["X"], ["Out"]],
|
|
"hard_swish": [["X"], ["Out"]],
|
|
"hard_sigmoid": [["X"], ["Out"]],
|
|
"gru": [["Input", "Weight"], ["Hidden"]],
|
|
"lstm": [["Input", "Weight"], ["Hidden"]],
|
|
"pad2d": [["X"], ["Out"]],
|
|
"flatten": [["X"], ["Out"]],
|
|
"flatten2": [["X"], ["Out"]],
|
|
"fusion_gru": [["X", "WeightX", "WeightH"], ["Hidden", "XX"]],
|
|
"multi_gru": [["X", "WeightX", "WeightH"], ["Hidden"]],
|
|
"quantize": [["Input"], ["Output"]],
|
|
"dequantize": [["Input"], ["Output"]],
|
|
}
|
|
|
|
def _get_op_output_var_names(op):
|
|
""" """
|
|
assert isinstance(op, (IrNode, Operator)), (
|
|
"The input op should be IrNode or Operator."
|
|
)
|
|
var_names = []
|
|
op_name = op.name() if isinstance(op, IrNode) else op.type
|
|
if op_name not in op_real_in_out_name:
|
|
return []
|
|
|
|
name_list = op_real_in_out_name[op_name][1]
|
|
for name in name_list:
|
|
var_name = op.output(name)
|
|
if isinstance(var_name, list):
|
|
var_names.extend(var_name)
|
|
else:
|
|
var_names.append(var_name)
|
|
return var_names
|
|
|
|
transform_pass = QuantizationTransformPass(
|
|
scope=scope,
|
|
place=place,
|
|
activation_quantize_type=activation_quantize_type,
|
|
weight_quantize_type=weight_quantize_type,
|
|
)
|
|
transform_pass.apply(graph)
|
|
|
|
op_nodes = graph.all_op_nodes()
|
|
for op_node in op_nodes:
|
|
if op_node.name() in out_scale_op_list:
|
|
var_names = _get_op_output_var_names(op_node)
|
|
for var_name in var_names:
|
|
in_node = graph._find_node_by_name(op_node.outputs, var_name)
|
|
if in_node.dtype() not in [
|
|
core.VarDesc.VarType.FP64,
|
|
core.VarDesc.VarType.FP32,
|
|
]:
|
|
continue
|
|
|
|
op_node.op()._set_attr("out_threshold", 3.0)
|
|
|
|
# Freeze graph for inference, but the weight of fc/conv is still float type.
|
|
freeze_pass = QuantizationFreezePass(
|
|
scope=scope, place=place, weight_quantize_type=weight_quantize_type
|
|
)
|
|
freeze_pass.apply(graph)
|
|
|
|
main_program = graph.to_program()
|
|
|
|
# modify fake_quantize_moving_average_abs_max(InScale) and fake_channel_wise_dequantize_max_abs(Scales)
|
|
op_nodes = graph.all_op_nodes()
|
|
for op_node in op_nodes:
|
|
if op_node.name() == 'fake_quantize_moving_average_abs_max':
|
|
var_name = op_node.input("InScale")[0]
|
|
tensor = scope.var(var_name).get_tensor()
|
|
tensor.set(np.array([1], dtype=np.float32), place)
|
|
elif op_node.name() == 'fake_channel_wise_dequantize_max_abs':
|
|
var_name = op_node.input("Scales")[0]
|
|
tensor = scope.var(var_name).get_tensor()
|
|
tensor.set(np.ones(tensor.shape(), dtype=np.float32), place)
|
|
|
|
feed_vars = [
|
|
main_program.global_block().var(name) for name in feed_target_names
|
|
]
|
|
|
|
if save:
|
|
paddle.static.io.save_inference_model(
|
|
'test_inference_model',
|
|
feed_vars,
|
|
fetch_targets,
|
|
exe,
|
|
program=main_program,
|
|
)
|
|
|
|
serialized_program = paddle.static.serialize_program(
|
|
feed_vars, fetch_targets, program=main_program
|
|
)
|
|
serialized_params = paddle.static.serialize_persistables(
|
|
feed_vars, fetch_targets, executor=exe, program=main_program
|
|
)
|
|
return serialized_program, serialized_params
|