mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-13 22:18:36 +08:00
1730a587d8
* Update TensorRT-LLM --------- Co-authored-by: tonylek <137782967+tonylek@users.noreply.github.com>
970 lines
37 KiB
Python
970 lines
37 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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import contextlib
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import hashlib
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import inspect
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import weakref
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from collections import OrderedDict, defaultdict
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from dataclasses import dataclass, field
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from typing import (Any, Dict, Iterable, List, Optional, OrderedDict, Set,
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Tuple, Union)
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import numpy as np
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import onnx
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import onnx_graphsurgeon as gs
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import tensorrt as trt
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from tensorrt_llm.module import Module
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from ._common import set_network
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from ._utils import get_extra_attr, has_extra_attr, set_extra_attr
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from .logger import logger
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from .plugin import PluginConfig
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class _UniqueNameGenerator(object):
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def __init__(self, prefix=''):
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self.ids = collections.defaultdict(int)
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self.prefix = prefix
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def __call__(self, key, module_name=''):
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if module_name != '':
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module_name = module_name.replace(".", "/")
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key = module_name + '/' + key
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tmp = self.ids[key]
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self.ids[key] += 1
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return f"{self.prefix}{key}_{tmp}"
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class PluginInfo:
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plugin_creator: trt.IPluginCreator
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plugin_name: str
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pfc: trt.PluginFieldCollection
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def __init__(self, plugin_creator: trt.IPluginCreator, plugin_name: str,
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pfc: trt.PluginFieldCollection):
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self.plugin_creator = plugin_creator
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self.plugin_name = plugin_name
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self.pfc = pfc
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self._parse_pfc(pfc)
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def _parse_pfc(self, pfc: trt.PluginFieldCollection):
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self.pfc_as_ndarray = {}
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self.pfc_as_list = {}
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for i in range(len(pfc)):
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name, data = pfc[i].name, pfc[i].data
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array_data = data
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self.pfc_as_ndarray[name] = array_data.copy()
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list_data = array_data.tolist()
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self.pfc_as_list[name] = list_data
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def get_plugin_info(trt_network: trt.INetworkDefinition,
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layer_name: str) -> PluginInfo:
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if not has_extra_attr(trt_network, "plugin_infos"):
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return None
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plugin_infos = get_extra_attr(trt_network, "plugin_infos")
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if layer_name not in plugin_infos:
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return None
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return plugin_infos[layer_name]
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def set_plugin_info(trt_network: trt.INetworkDefinition, layer_name: str,
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plugin_info: PluginInfo):
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if not has_extra_attr(trt_network, "plugin_infos"):
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set_extra_attr(trt_network, "plugin_infos", {})
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plugin_infos = get_extra_attr(trt_network, "plugin_infos")
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plugin_infos[layer_name] = plugin_info
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def delete_plugin_info(trt_network: trt.INetworkDefinition, layer_name: str):
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if not has_extra_attr(trt_network, "plugin_infos"):
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return
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plugin_infos = get_extra_attr(trt_network, "plugin_infos")
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if layer_name not in plugin_infos:
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return
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del plugin_infos[layer_name]
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# TODO: remove this WAR after https://nvbugs/4359151 fixed.
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def get_np_weight(trt_network: trt.INetworkDefinition,
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layer_name: str) -> np.array:
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if not has_extra_attr(trt_network, "np_weights"):
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return None
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np_weights = get_extra_attr(trt_network, "np_weights")
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if layer_name not in np_weights:
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return None
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return np_weights[layer_name]
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# TODO: remove this WAR after https://nvbugs/4359151 fixed.
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def set_np_weight(trt_network: trt.INetworkDefinition, layer_name: str,
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np_weight: np.array):
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if not has_extra_attr(trt_network, "np_weights"):
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set_extra_attr(trt_network, "np_weights", {})
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np_weights = get_extra_attr(trt_network, "np_weights")
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np_weights[layer_name] = np_weight
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class Network(object):
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def __init__(self, **kwargs):
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# intentionally use **kwargs, user should never call this ctor directly
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# use Builder.create_network() instead
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# Holds the removed layers and disable them in graph rewriting and other phases.
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# This is a hacky way since INetwork python API doesn't provide a way to remove a layer.
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# TODO: remove this when TensorRT provides a better way to remove a layer
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self._removed_layers: Set[str] = set()
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self.is_graph_altered = False
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from .graph_rewriting import FLayerInfoMemo
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self.flayer_memo = FLayerInfoMemo() # holds the functional metadata
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self._parameter_tensors = {} # holds the parameter tensors
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def _init(self, trt_network):
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self._trt_network = trt_network
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self._inputs = {}
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self._named_parameters = None
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# layer precision of a given scope, this is used together with precision(dtype) context manager
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self._dtype = None
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self._name_generator = _UniqueNameGenerator()
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self._plugin_config = PluginConfig()
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self._module_call_stack = _TrtLlmModuleCallStack()
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self._registered_ndarrays = []
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self._strongly_typed = trt.INetworkDefinition.get_flag(
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self._trt_network, trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED)
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self._unfilled_weights: Dict[str, Tuple[np.array, np.array]] = {}
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self._auto_parallel_config: Dict[str, Any] = None
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return self
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def _register_unfilled_weights(self, layer_name: str, weights: np.array,
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values: np.array):
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self._unfilled_weights[layer_name] = (weights, values)
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def _fill_weights(self):
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from tensorrt_llm.parameter import Parameter
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for layer_name in list(self._unfilled_weights.keys()):
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weights, values = self._unfilled_weights.pop(layer_name)
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self.register_ndarray(weights)
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if values is not None:
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np.copyto(weights, values, casting='no')
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else:
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Parameter.xavier_init(weights)
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@property
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def parameter_tensors(self):
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return self._parameter_tensors
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def get_parameter_tensor(self, param):
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return self.parameter_tensors.get(param, None)
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def set_parameter_tensor(self, param, tensor):
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assert param not in self.parameter_tensors
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self.parameter_tensors[param] = tensor
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@property
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def dtype(self) -> trt.DataType:
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return self._dtype
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@dtype.setter
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def dtype(self, dtype: trt.DataType):
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assert isinstance(dtype, trt.DataType) or dtype is None
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self._dtype = dtype
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@property
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def trt_network(self) -> trt.INetworkDefinition:
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return self._trt_network
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@property
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def plugin_config(self) -> PluginConfig:
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return self._plugin_config
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@plugin_config.setter
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def plugin_config(self, cfg: PluginConfig):
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assert isinstance(
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cfg,
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PluginConfig), f"Expecting a PluginConfig object, got {type(cfg)}"
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self._plugin_config = cfg
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@property
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def strongly_typed(self) -> bool:
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return self._strongly_typed
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@property
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def auto_parallel_config(self) -> Dict[str, Any]:
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return self._auto_parallel_config
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def _add_input(self,
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tensor,
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name,
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dtype,
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shape,
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dim_range: OrderedDict = None):
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assert isinstance(dtype, trt.DataType)
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tensor.trt_tensor = self.trt_network.add_input(
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name=name,
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shape=shape,
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dtype=dtype,
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)
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assert tensor.trt_tensor is not None, f"Couldn't create TRT tensor for {name} {dtype} {shape}"
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if dim_range is not None:
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logger.debug(
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f'Add input: {name}, shape: {shape}, dtype: {dtype}, dimension names:{list(dim_range.keys())}'
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)
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for i, dim_name in enumerate(dim_range.keys()):
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tensor.trt_tensor.set_dimension_name(i, str(dim_name))
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else:
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logger.debug(f'Add input: {name}, shape: {shape}, dtype: {dtype}')
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self._inputs[name] = tensor
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def _mark_output(self, tensor, name, dtype):
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from .functional import cast
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# In strongly_typed, if tensor output is not the same, add a cast
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if dtype is not None and self.strongly_typed:
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tensor = cast(tensor, dtype)
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self.trt_network.mark_output(tensor.trt_tensor)
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tensor.trt_tensor.name = name
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if not self.strongly_typed:
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tensor.trt_tensor.dtype = dtype or tensor.trt_tensor.dtype
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logger.debug(f'Mark output: {name}, dtype: {dtype}')
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def set_named_parameters(self, named_parameters):
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self._named_parameters = named_parameters
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@property
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def named_parameters(self):
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return self._named_parameters
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def _set_layer_name(self, layer):
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original_layer_name = layer.name
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layer_name = str(layer.type).split('.')[-1]
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current_module = self._module_call_stack.get_current_module()
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func_stack = []
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frame = inspect.currentframe().f_back.f_back
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while frame:
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func_name = frame.f_code.co_name
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line_num = frame.f_lineno
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if func_name == "forward":
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break
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func_stack.insert(0, f"{func_name}_L{line_num}")
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if len(func_stack) >= 10:
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# NOTE: TRT error messages has a character limit.
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# Limiting to only 10 levels helps retain
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# the true error message from TRT.
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break
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frame = frame.f_back
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current_module = f"{current_module}.{'.'.join(func_stack)}"
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if layer.type == trt.LayerType.PLUGIN_V2:
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layer_name = '_'.join(
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[layer_name,
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str(layer.plugin.plugin_type).split('.')[-1]])
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elif layer.type in [
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trt.LayerType.UNARY, trt.LayerType.REDUCE,
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trt.LayerType.ELEMENTWISE
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]:
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layer_name = '_'.join([layer_name, str(layer.op).split('.')[-1]])
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layer.name = self._name_generator(layer_name, current_module)
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for idx in range(layer.num_outputs):
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# TRT initializes tensor names from the initial layer's name when the layer is created,
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# and does not update tensor names when layer name changed by application, needs to
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# change the tensor name to align with the new layer name for better debugging
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layer.get_output(idx).name = f"{layer.name}_output_{idx}"
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if original_layer_name != layer.name:
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if layer.type == trt.LayerType.PLUGIN_V2:
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plugin_info = get_plugin_info(self.trt_network,
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original_layer_name)
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if plugin_info is not None:
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set_plugin_info(self.trt_network, layer.name, plugin_info)
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delete_plugin_info(self.trt_network, original_layer_name)
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# Set layer metadata to the same as the layer name so that it can show up in NVTX.
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layer.metadata = layer.name
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def register_ndarray(self, ndarray: np.ndarray) -> None:
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''' When the functional APIs need to create local numpy array and use as weights for constant or other layers,
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they need to register the ndarray objects to the TRT-LLM Network to prolong the lifetime of the ndarray, such that weights are
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still valid when functional API returned.
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All the weights referenced by the trt Network are weak referenced, it's TRT-LLM's responsibility to keep the weights alive
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during the TRT network construction and TRT engine building process.
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'''
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self._registered_ndarrays.append(ndarray)
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def _generate_optimization_profiles(self) -> List[trt.IOptimizationProfile]:
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input_tensors = self._inputs
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if len(input_tensors) == 0:
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return []
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num_profiles = len(list(input_tensors.values())[0].profiles)
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profiles = []
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for i in range(num_profiles):
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logger.debug(f'Adding optimization profile {i+1}/{num_profiles}')
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profile = self._trt_network.builder.create_optimization_profile()
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for input_name, input_tensor in input_tensors.items():
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shape_profile = input_tensor.profiles[i]
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min_shape = list(shape_profile.min)
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opt_shape = list(shape_profile.opt)
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max_shape = list(shape_profile.max)
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if input_tensor.trt_tensor.is_shape_tensor:
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profile.set_shape_input(input_name, min_shape, opt_shape,
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max_shape)
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else:
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profile.set_shape(input_name, min_shape, opt_shape,
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max_shape)
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logger.debug(
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f'{input_name}, min: {min_shape}, opt: {opt_shape}, max: {max_shape}'
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)
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profiles.append(profile)
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return profiles
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def get_inputs(self):
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'''
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Get the inputs of the network.
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Returns:
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Iterable[Tensor]
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'''
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return self._inputs.values()
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def get_outputs(self):
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'''
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Get the outputs of the network.
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Returns:
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Iterable[Tensor]
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'''
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from .functional import Tensor
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for i in range(self._trt_network.num_outputs):
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tensor = self._trt_network.get_output(i)
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yield Tensor(trt_tensor=tensor,
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network=self,
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is_network_input=False)
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def is_input(self, tensor) -> bool:
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'''
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Tell if a tensor is a input of the network.
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Parameters:
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tensor: Union[Tensor, str, trt.ITensor]
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'''
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from .functional import Tensor
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if isinstance(tensor, str):
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tensor_name = tensor
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elif isinstance(tensor, (trt.ITensor, Tensor)):
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tensor_name = tensor.name
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else:
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raise ValueError(
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f"tensor should be Tensor, str or ITensor, got {tensor}")
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return self._inputs.get(tensor_name, False)
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def is_output(self, tensor) -> bool:
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'''
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Tell if a tensor is a output of the network.
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Parameters:
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tensor: Tensor
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'''
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for i in range(self._trt_network.num_outputs):
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if tensor.trt_tensor is self._trt_network.get_output(i):
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return True
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return False
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def get_layers(self) -> Iterable["Layer"]:
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'''
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Get all the layers of network.
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Returns:
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Iterable[Layer]
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'''
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from .graph_rewriting import Layer
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for i in range(self._trt_network.num_layers):
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layer = Layer(network=self,
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trt_layer=self._trt_network.get_layer(i))
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yield layer
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def get_layer_by_name(self, name: str) -> Optional["Layer"]:
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state = self._get_graph()
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return state.name_to_layer.get(name, None)
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def get_tensor_users(self, tensor) -> Iterable["Layer"]:
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'''
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Get the layers those consumes this tensor.
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'''
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state = self._get_graph()
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for layer in state.tensor_to_consumers[tensor]:
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yield layer
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def get_tensor_parent(self, tensor) -> Optional["Layer"]:
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'''
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Get the layer that produces this tensor.
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'''
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state = self._get_graph()
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return state.tensor_to_producer.get(tensor, None)
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def mark_removed_layer(self, layer: "Layer"):
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from .graph_rewriting import FLayerInfoMemo
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self._removed_layers.add(layer.name)
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# Try to delete the layer if it is a Plugin
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FLayerInfoMemo.instance().remove(layer.name)
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def is_removed_layer(self, layer: "Layer") -> bool:
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return layer.name in self._removed_layers
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@property
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def removed_layers(self) -> Iterable["Layer"]:
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for layer_name in self._removed_layers:
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layer = self.get_layer_by_name(layer_name)
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assert layer, "Invalid layer name"
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yield layer
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def to_dot(self, path=None) -> Optional[str]:
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'''
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Get a graphviz representation of the network.
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NOTE, the graph might be redundancy since TRT's INetwork won't clean the unused inputs and layers
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automatically.
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TODO: add an flag to hide all the removed layers and their output tensors
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TODO: replace this when TensorRT provides a better way to get the graph of INetworkDefinition
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TODO: a little feature, add blocks in the figure to highlight the subgraphes of Modules
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Parameters:
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path: the path to save the graphviz file, if not provided, will return the graphviz source code
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'''
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format = 'text' if not path else path.split('.')[-1]
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try:
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import graphviz
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except ImportError:
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logger.error(
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"Failed to import graphviz, please install graphviz to enable Network.to_dot()"
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)
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return
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dot = graphviz.Digraph(
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comment=
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f'TensorRT Graph of {self._get_network_hash(lightweight=False)}',
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format=format if format != 'text' else None)
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inputs_names = set([x.name for x in self.get_inputs()])
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output_names = set([x.name for x in self.get_outputs()])
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node_style = dict(
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shape='box',
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style='rounded,filled,bold',
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fontname='Arial',
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fillcolor='#ffffff',
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color='#303A3A',
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width='1.3',
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height='0.84',
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)
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hl_node_style = dict(
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shape='box',
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style='rounded,filled,bold',
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fontname='Arial',
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fillcolor='lightblue',
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color='#303A3A',
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width='1.3',
|
|
height='0.84',
|
|
)
|
|
|
|
state = self._get_graph()
|
|
nodes = set()
|
|
tensor_to_alias = {}
|
|
tensor_id = [0]
|
|
|
|
def get_alias(tensor, tensor_id):
|
|
if tensor not in tensor_to_alias:
|
|
if (not tensor in inputs_names) and (not tensor
|
|
in output_names):
|
|
tensor_to_alias[tensor] = f"t{tensor_id[0]}"
|
|
tensor_id[0] += 1
|
|
else:
|
|
tensor_to_alias[tensor] = tensor
|
|
|
|
return tensor_to_alias[tensor]
|
|
|
|
def create_tensor_node(tensor: str, dtype=None, shape=None):
|
|
tensor_alias = get_alias(tensor, tensor_id)
|
|
if tensor_alias not in nodes:
|
|
dot.node(tensor_alias,
|
|
str(dtype) + "\n" + tensor_alias + "\n" + str(shape),
|
|
**node_style)
|
|
nodes.add(tensor_alias)
|
|
return tensor_alias
|
|
|
|
def create_layer_node(layer: str):
|
|
if layer not in nodes:
|
|
dot.node(layer, layer, **hl_node_style)
|
|
nodes.add(layer)
|
|
|
|
for tensor, layer in state.tensor_to_producer.items():
|
|
tensor_alias = create_tensor_node(tensor.name, tensor.dtype,
|
|
tensor.shape)
|
|
create_layer_node(layer.name)
|
|
dot.edge(layer.name, tensor_alias)
|
|
for tensor, layers in state.tensor_to_consumers.items():
|
|
tensor_alias = create_tensor_node(tensor.name, tensor.dtype,
|
|
tensor.shape)
|
|
for layer in layers:
|
|
create_layer_node(layer.name)
|
|
dot.edge(tensor_alias, layer.name)
|
|
|
|
if format == "text":
|
|
return dot.source
|
|
dot.save(path)
|
|
|
|
def to_onnx(self, path=None) -> None:
|
|
'''
|
|
Export the network into a "ONNX-like" file for visualization.
|
|
|
|
Parameters:
|
|
path: the path to the output file
|
|
'''
|
|
trt_network = self.trt_network
|
|
|
|
def layer_type_to_class(layer: trt.ILayer = None) -> trt.ILayer:
|
|
layer_type_name = str(layer.type)[10:]
|
|
if layer_type_name == "ELEMENTWISE": # Some special cases
|
|
return trt.IElementWiseLayer
|
|
if layer_type_name == "LRN":
|
|
return trt.ILRNLayer
|
|
if layer_type_name == "NMS":
|
|
return trt.INMSLayer
|
|
if layer_type_name == "PARAMETRIC_RELU":
|
|
return trt.IParametricReLULayer
|
|
if layer_type_name == "PLUGIN":
|
|
return None # IPluginLayer is not supported any more
|
|
if layer_type_name == "RAGGED_SOFTMAX":
|
|
return trt.IRaggedSoftMaxLayer
|
|
if layer_type_name == "SOFTMAX":
|
|
return trt.ISoftMaxLayer
|
|
if layer_type_name == "TOPK":
|
|
return trt.ITopKLayer
|
|
|
|
# e.g. MATRIX_MULTIPLY -> MatrixMultiply
|
|
name = "".join(name[0] + name[1:].lower()
|
|
for name in layer_type_name.split("_"))
|
|
return trt.__builtins__["getattr"](trt, f"I{name}Layer")
|
|
|
|
def convert_type_to_onnx(node_type: str = "",
|
|
attribution: OrderedDict = OrderedDict()):
|
|
if node_type == "ACTIVATION":
|
|
convert_list = {
|
|
"RELU": "Relu",
|
|
"SIGMOID": "Sigmoid",
|
|
"TANH": "Tanh",
|
|
"LEAKY_RELU": "LeakyRelu",
|
|
"ELU": "Elu",
|
|
"SELU": "Selu",
|
|
"SOFTSIGN": "Softsign",
|
|
"SOFTPLUS": "Softplus",
|
|
"CLIP": "Clip",
|
|
"HARD_SIGMOID": "HardSigmoid",
|
|
"SCALED_TANH": "ScaledTanh",
|
|
"THRESHOLDED_RELU": "ThresholdedRelu",
|
|
}
|
|
# No corresponding operator for GELU_ERF, GELU_TANH
|
|
if "algo-type" in attribution.keys() and attribution[
|
|
"algo-type"].split(".")[-1] in convert_list.keys():
|
|
return convert_list[attribution["algo-type"].split(".")[-1]]
|
|
return node_type
|
|
if node_type == "CAST":
|
|
return "Cast"
|
|
if node_type == "CONCATENATION":
|
|
return "Concat"
|
|
if node_type == "CONSTANT":
|
|
return "Constant"
|
|
if node_type == "CONVOLUTION":
|
|
return "Conv"
|
|
if node_type == "DECONVOLUTION":
|
|
return "Deconv"
|
|
if node_type == "ELEMENTWISE":
|
|
convert_list = {
|
|
"SUM": "Add",
|
|
"PROD": "Mul",
|
|
"MAX": "Max",
|
|
"MIN": "Min",
|
|
"SUB": "Sub",
|
|
"DIV": "Div",
|
|
"POW": "Pow",
|
|
"AND": "And",
|
|
"OR": "Or",
|
|
"XOR": "Xor",
|
|
"EQUAL": "Equal",
|
|
"GREATER": "Greater",
|
|
"LESS": "Less",
|
|
}
|
|
if "op" in attribution.keys() and attribution["op"].split(
|
|
".")[-1] in convert_list.keys():
|
|
return convert_list[attribution["op"].split(".")[-1]]
|
|
return node_type
|
|
if node_type == "GATHER":
|
|
return "Gather"
|
|
if node_type == "LOOP":
|
|
return "Loop"
|
|
if node_type == "MATRIX_MULTIPLY":
|
|
return "Gemm"
|
|
if node_type == "POOLING":
|
|
convert_list = {"MAX": "MaxPool", "AVERAGE": "AveragePool"}
|
|
if "algo-type" in attribution.keys() and attribution[
|
|
"algo-type"].split(".")[-1] in convert_list.keys():
|
|
return convert_list[attribution["algo-type"].split(".")[-1]]
|
|
return node_type
|
|
if node_type == "REDUCE":
|
|
convert_list = {
|
|
"SUM": "ReduceSum",
|
|
"PROD": "ReduceProd",
|
|
"MAX": "ReduceMax",
|
|
"MIN": "ReduceMin",
|
|
"AVG": "ReduceMean",
|
|
}
|
|
if "op" in attribution.keys() and attribution["op"].split(
|
|
".")[-1] in convert_list.keys():
|
|
return convert_list[attribution["op"].split(".")[-1]]
|
|
return node_type
|
|
if node_type == "SELECT":
|
|
return "Where"
|
|
if node_type == "SHUFFLE":
|
|
return "Reshape"
|
|
if node_type == "SHAPE":
|
|
return "Shape"
|
|
if node_type == "SLICE":
|
|
return "Slice"
|
|
if node_type == "SOFTMAX":
|
|
return "Softmax"
|
|
if node_type == "TOPK":
|
|
return "TopK"
|
|
if node_type == "UNARY":
|
|
convert_list = {"SQRT": "Sqrt", "NOT": "Not"}
|
|
if "op" in attribution.keys() and attribution["op"].split(
|
|
".")[-1] in convert_list.keys():
|
|
return convert_list[attribution["op"].split(".")[-1]]
|
|
return node_type
|
|
return node_type
|
|
|
|
def add_node_for_trt_network(
|
|
graph: gs.Graph = None,
|
|
node_name: str = "",
|
|
node_type: str = "",
|
|
input_list: List[gs.Variable] = [],
|
|
attribution: OrderedDict = OrderedDict(),
|
|
name_list: Union[str, List[str]] = "",
|
|
datatype_list: Union[np.dtype, List[np.dtype]] = [],
|
|
shape_list: Union[list, List[list]] = [],
|
|
number: int = 0,
|
|
b_onnx_type: bool = False,
|
|
) -> Tuple[gs.Variable, int]:
|
|
"""
|
|
Simplify version of function `add_node`, and we do some beautify to it.
|
|
"""
|
|
|
|
if isinstance(name_list, list) or isinstance(
|
|
datatype_list, list) or isinstance(
|
|
shape_list, list): # Case of multi-output
|
|
assert len(name_list) == len(datatype_list)
|
|
assert len(name_list) == len(shape_list)
|
|
else: # Case of single-output
|
|
name_list = [name_list]
|
|
datatype_list = [datatype_list]
|
|
shape_list = [shape_list]
|
|
|
|
n_output = len(name_list)
|
|
output_list = []
|
|
for i in range(n_output):
|
|
tensor = gs.Variable(name_list[i], datatype_list[i],
|
|
shape_list[i])
|
|
output_list.append(tensor)
|
|
|
|
if b_onnx_type:
|
|
node_type = convert_type_to_onnx(node_type, attribution)
|
|
|
|
node = gs.Node(node_type,
|
|
node_name,
|
|
inputs=input_list,
|
|
outputs=output_list,
|
|
attrs=attribution)
|
|
graph.nodes.append(node) # Update graph inside `add_node`
|
|
|
|
if len(output_list) == 1: # Case of single-output
|
|
output_list = output_list[0]
|
|
return output_list, number + 1
|
|
|
|
def export_network_as_onnx(
|
|
network,
|
|
export_onnx_file: Path = None,
|
|
b_onnx_type: bool = False,
|
|
):
|
|
graph = gs.Graph(nodes=[], inputs=[], outputs=[])
|
|
graph.name = "" if network.name == "Unnamed Network 0" else network.name
|
|
n = 0
|
|
|
|
global_tensor_map = {
|
|
} # mapping from TRT tensor (trt.ITensor) to GS tensor (gs.Variable)
|
|
for i in range(network.num_inputs):
|
|
trt_tensor = network.get_input(i)
|
|
gs_tensor = gs.Variable(trt_tensor.name,
|
|
trt.nptype(trt_tensor.dtype),
|
|
trt_tensor.shape)
|
|
global_tensor_map[trt_tensor] = gs_tensor
|
|
if gs_tensor not in graph.inputs:
|
|
graph.inputs.append(gs_tensor)
|
|
|
|
for i in range(network.num_layers):
|
|
layer = network.get_layer(i)
|
|
|
|
input_tensor_list = []
|
|
for j in range(layer.num_inputs):
|
|
trt_tensor = layer.get_input(j)
|
|
if trt_tensor is None: # Useful for constant layer
|
|
gs_tensor = None
|
|
elif trt_tensor in global_tensor_map.keys(
|
|
): # already in the map
|
|
gs_tensor = global_tensor_map[trt_tensor]
|
|
else:
|
|
logger.debug(
|
|
f"[ExportONNX]Layer input tensor not in global_tensor_map: {trt_tensor.name}"
|
|
) # ■
|
|
gs_tensor = gs.Variable(trt_tensor.name,
|
|
trt.nptype(trt_tensor.dtype),
|
|
trt_tensor.shape)
|
|
global_tensor_map[trt_tensor] = gs_tensor
|
|
input_tensor_list.append(gs_tensor)
|
|
|
|
output_name_list = []
|
|
output_datatype_list = []
|
|
output_shape_list = []
|
|
for i in range(layer.num_outputs):
|
|
trt_tensor = layer.get_output(i)
|
|
# Don't do this check because we need this trt_tensor to overwrite the placeholder tensor in ■
|
|
# if trt_tensor in global_tensor_map.keys():
|
|
# gs_tensor = global_tensor_map[trt_tensor]
|
|
output_name_list.append(trt_tensor.name)
|
|
output_datatype_list.append(trt.nptype(trt_tensor.dtype))
|
|
output_shape_list.append(trt_tensor.shape)
|
|
|
|
attr = OrderedDict()
|
|
# Set attribution of ILayer
|
|
for key in dir(layer):
|
|
if not (key.startswith("_")
|
|
or callable(layer.__getattribute__(key))):
|
|
attr[key] = str(layer.__getattribute__(key))
|
|
# Set attribution of exact layer type
|
|
layer.__class__ = layer_type_to_class(layer)
|
|
for key in dir(layer):
|
|
if key in dir(trt.ILayer) and key != "type":
|
|
continue
|
|
if key == "type" and not isinstance(layer.type,
|
|
trt.LayerType):
|
|
attr["algo-type"] = str(layer.type)
|
|
continue
|
|
value = layer.__getattribute__(key)
|
|
if isinstance(
|
|
value, np.ndarray
|
|
): # Convert all attributions into string besides weights
|
|
value = value.astype(np.float32) # In case of overflow
|
|
ss = f"shape={value.shape}, SumAbs={np.sum(abs(value)):.5e}, Var={np.var(value):.5f}, "
|
|
ss += f"Max={np.max(value):.5f}, Min={np.min(value):.5f}, SAD={np.sum(np.abs(np.diff(value.reshape(-1)))):.5f}, "
|
|
ss += f"[:5]={value.reshape(-1)[:5]}, [-5:]={value.reshape(-1)[-5:]}"
|
|
attr[key] = ss
|
|
else:
|
|
attr[key] = str(value)
|
|
|
|
output_tensor_list, n = add_node_for_trt_network(graph, layer.name, attr["type"][10:], input_tensor_list, attr, \
|
|
output_name_list, output_datatype_list, output_shape_list, n, b_onnx_type)
|
|
|
|
if layer.num_outputs == 1:
|
|
global_tensor_map[layer.get_output(0)] = output_tensor_list
|
|
else:
|
|
for i in range(layer.num_outputs):
|
|
global_tensor_map[layer.get_output(
|
|
i)] = output_tensor_list[i]
|
|
|
|
for i in range(network.num_outputs):
|
|
gs_tensor = global_tensor_map[network.get_output(i)]
|
|
if gs_tensor not in graph.outputs:
|
|
graph.outputs.append(gs_tensor)
|
|
|
|
onnx_model = gs.export_onnx(graph)
|
|
onnx.save(
|
|
onnx_model,
|
|
export_onnx_file,
|
|
save_as_external_data=True,
|
|
all_tensors_to_one_file=True,
|
|
location=export_onnx_file.split('/')[-1] + ".weight",
|
|
)
|
|
logger.debug(
|
|
f"Export {export_onnx_file.split('/')[-1]}: {len(graph.nodes):5d} Nodes, {len(graph.tensors().keys()):5d} tensors"
|
|
)
|
|
|
|
export_network_as_onnx(trt_network, path, True)
|
|
return
|
|
|
|
def _get_graph(self) -> "Network._GraphState":
|
|
'''
|
|
Get the graph of the network.
|
|
|
|
Returns:
|
|
Network._GraphState
|
|
'''
|
|
return self._get_graph_impl(self._get_network_hash())
|
|
|
|
#TODO: tali, using one LRU cache here can cause the Network object to be leaked, need a way to speed this function w/o using global lru cache.
|
|
def _get_graph_impl(self, network_hash: bytes) -> "Network._GraphState":
|
|
graph = Network._GraphState()
|
|
graph.build(self)
|
|
return graph
|
|
|
|
@dataclass
|
|
class _GraphState:
|
|
# Tensor to Layers
|
|
tensor_to_consumers: Dict[Any, List["Layer"]] = field(
|
|
default_factory=lambda: defaultdict(list))
|
|
# Tensor to Layer
|
|
tensor_to_producer: Dict[Any, "Layer"] = field(default_factory=dict)
|
|
inputs: Dict[str, Any] = field(default_factory=OrderedDict)
|
|
outputs: Dict[str, Any] = field(default_factory=OrderedDict)
|
|
name_to_layer: Dict[str, "Layer"] = field(default_factory=dict)
|
|
|
|
def build(self, network: "Network") -> None:
|
|
from .graph_rewriting import Layer
|
|
self.inputs = network.get_inputs()
|
|
self.outputs = network.get_outputs()
|
|
|
|
for layer in network.get_layers():
|
|
self.name_to_layer[layer.name] = Layer(
|
|
network=network, trt_layer=layer.trt_layer)
|
|
for i in range(layer.num_inputs):
|
|
input_tensor = layer.get_inputs(i)[0]
|
|
if input_tensor.is_trt_wrapper():
|
|
self.tensor_to_consumers[input_tensor].append(layer)
|
|
for i in range(layer.num_outputs):
|
|
output_tensor = layer.get_outputs(i)[0]
|
|
if output_tensor.is_trt_wrapper():
|
|
self.tensor_to_producer[output_tensor] = layer
|
|
|
|
def _get_network_hash(self, lightweight=True) -> bytes:
|
|
# TODO: Ask TensorRT team to add a hash function for INetworkDefinition instead of using this hacky way
|
|
num_layers = self.trt_network.num_layers
|
|
|
|
# Some special layers, such as slice, may be associated with tensors that do not have the `trt_tensor` member.
|
|
get_tensor_tag = lambda tensor: tensor.trt_tensor.name if tensor.is_trt_wrapper(
|
|
) else 'None'
|
|
|
|
if lightweight and not self.is_graph_altered:
|
|
return num_layers
|
|
self.is_graph_altered = False
|
|
|
|
data = hashlib.sha256()
|
|
# network layer count
|
|
data.update(str(num_layers).encode())
|
|
# network inputs
|
|
data.update(','.join(
|
|
[get_tensor_tag(tensor) for tensor in self.get_inputs()]).encode())
|
|
# network outputs
|
|
data.update(','.join(
|
|
[get_tensor_tag(tensor) for tensor in self.get_outputs()]).encode())
|
|
# layer names
|
|
data.update(','.join(
|
|
[layer.trt_layer.name for layer in self.get_layers()]).encode())
|
|
|
|
# layer -> output
|
|
data.update(','.join([
|
|
f'{layer.trt_layer.name}->{get_tensor_tag(tensor)}'
|
|
for layer in self.get_layers() for tensor in layer.get_outputs()
|
|
]).encode())
|
|
|
|
# input -> layer
|
|
data.update(','.join([
|
|
f'{get_tensor_tag(tensor)}->{layer.trt_layer.name}'
|
|
for layer in self.get_layers() for tensor in layer.get_inputs()
|
|
]).encode())
|
|
|
|
return data.hexdigest()
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def net_guard(network):
|
|
from ._common import net
|
|
assert isinstance(
|
|
network, Network
|
|
), f"Invalid network, can only guard Network instance, got: {network}"
|
|
|
|
old_net = net
|
|
set_network(network)
|
|
yield
|
|
set_network(old_net)
|
|
|
|
|
|
class _TrtLlmModuleCallStack(object):
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.call_stack = []
|
|
self.module_name_map = weakref.WeakKeyDictionary()
|
|
self.module_to_layer_range_map: Dict[str, range] = {}
|
|
self.mod_names_set = False
|
|
|
|
def module_names_set(self):
|
|
return self.mod_names_set
|
|
|
|
def set_module_names(self, top_level_module):
|
|
assert top_level_module, "Expected a top level module"
|
|
for name, mod in top_level_module.named_modules(
|
|
prefix=top_level_module._get_name()):
|
|
if mod not in self.module_name_map:
|
|
self.module_name_map[mod] = name
|
|
self.mod_names_set = True
|
|
return
|
|
|
|
def get_current_module(self):
|
|
mod_name = ''
|
|
if len(self.call_stack):
|
|
mod_name = self.call_stack[-1]
|
|
return mod_name
|
|
|
|
def get_mod_name(self, mod_obj):
|
|
name = ''
|
|
if mod_obj in self.module_name_map:
|
|
name = self.module_name_map[mod_obj]
|
|
return name
|
|
|
|
def set_layer_range(self, mod_obj: Module, layer_range: range):
|
|
if mod_obj in self.module_name_map:
|
|
name = self.module_name_map[mod_obj]
|
|
self.module_to_layer_range_map[name] = layer_range
|
|
|
|
def get_stack(self):
|
|
return self.call_stack
|
|
|
|
@contextlib.contextmanager
|
|
def call_stack_mgr(self):
|
|
call_stack = self.get_stack()
|
|
try:
|
|
yield call_stack
|
|
finally:
|
|
call_stack.pop()
|