mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-13 22:18:36 +08:00
d8b408e6dc
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
502 lines
17 KiB
Python
502 lines
17 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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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from collections import defaultdict
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from dataclasses import dataclass, field
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from functools import lru_cache
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from typing import Any, Dict, Iterable, List, Optional, OrderedDict, Set
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import numpy as np
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import tensorrt as trt
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from ._common import set_network
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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 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 rewritings 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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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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return self
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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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@property
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def strongly_typed(self) -> bool:
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return self._strongly_typed
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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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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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if self.strongly_typed:
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if tensor.trt_tensor.dtype != dtype:
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# If stronglyTyped mode is enabled and inferred output dtype does not match desired dtype, add a cast.
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cast_output = cast(tensor, dtype)
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self.trt_network.mark_output(cast_output.trt_tensor)
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cast_output.trt_tensor.name = name
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else:
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# Otherwise, mark the tensor as network output. We should not set tensor dtype in stronglyTyped mode.
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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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else:
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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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tensor.trt_tensor.dtype = 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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layer_name = str(layer.type).split('.')[-1]
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current_module = self._module_call_stack.get_current_module()
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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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def register_ndarray(self, ndarray: np.ndarray) -> None:
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self._registered_ndarrays.append(ndarray)
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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(comment='TensorRT Graph',
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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',
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height='0.84',
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)
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state = self._get_graph()
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nodes = set()
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tensor_to_alias = {}
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tensor_id = [0]
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def get_alias(tensor, tensor_id):
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if tensor not in tensor_to_alias:
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if (not tensor in inputs_names) and (not tensor
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in output_names):
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tensor_to_alias[tensor] = f"t{tensor_id[0]}"
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tensor_id[0] += 1
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else:
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tensor_to_alias[tensor] = tensor
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return tensor_to_alias[tensor]
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def create_tensor_node(tensor: str):
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tensor_alias = get_alias(tensor, tensor_id)
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if tensor_alias not in nodes:
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dot.node(tensor_alias, tensor_alias, **node_style)
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nodes.add(tensor_alias)
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return tensor_alias
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def create_layer_node(layer: str):
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if layer not in nodes:
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dot.node(layer, layer, **hl_node_style)
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nodes.add(layer)
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for tensor, layer in state.tensor_to_producer.items():
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tensor_alias = create_tensor_node(tensor.name)
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create_layer_node(layer.name)
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dot.edge(layer.name, tensor_alias)
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for tensor, layers in state.tensor_to_consumers.items():
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tensor_alias = create_tensor_node(tensor.name)
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for layer in layers:
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create_layer_node(layer.name)
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dot.edge(tensor_alias, layer.name)
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if format == "text":
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return dot.source
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dot.render(path)
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def _get_graph(self) -> "Network._GraphState":
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'''
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Get the graph of the network.
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Returns:
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Network._GraphState
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'''
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return self._get_graph_impl(self._get_network_hash())
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@lru_cache(maxsize=1)
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def _get_graph_impl(self, network_hash: bytes) -> "Network._GraphState":
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graph = Network._GraphState()
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graph.build(self)
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return graph
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@dataclass
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class _GraphState:
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# Tensor to Layers
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tensor_to_consumers: Dict[Any, List["Layer"]] = field(
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default_factory=lambda: defaultdict(list))
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# Tensor to Layer
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tensor_to_producer: Dict[Any, "Layer"] = field(default_factory=dict)
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inputs: Dict[str, Any] = field(default_factory=OrderedDict)
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outputs: Dict[str, Any] = field(default_factory=OrderedDict)
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name_to_layer: Dict[str, "Layer"] = field(default_factory=dict)
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def build(self, network: "Network") -> None:
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from .graph_rewriting import Layer
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self.inputs = network.get_inputs()
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self.outputs = network.get_outputs()
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for layer in network.get_layers():
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self.name_to_layer[layer.name] = Layer(
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network=network, trt_layer=layer.trt_layer)
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for i in range(layer.num_inputs):
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input_tensor = layer.get_inputs(i)[0]
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if input_tensor.is_trt_wrapper():
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self.tensor_to_consumers[input_tensor].append(layer)
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for i in range(layer.num_outputs):
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output_tensor = layer.get_outputs(i)[0]
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if output_tensor.is_trt_wrapper():
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self.tensor_to_producer[output_tensor] = layer
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def _get_network_hash(self, lightweight=True) -> bytes:
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# TODO: Ask TensorRT team to add a hash function for INetworkDefinition instead of using this hacky way
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num_layers = self.trt_network.num_layers
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# Some special layers, such as slice, may be associated with tensors that do not have the `trt_tensor` member.
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get_tensor_tag = lambda tensor: tensor.trt_tensor.name if tensor.is_trt_wrapper(
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) else 'None'
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if lightweight and not self.is_graph_altered:
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return num_layers
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self.is_graph_altered = False
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data = hashlib.sha256()
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# network layer count
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data.update(str(num_layers).encode())
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# network inputs
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data.update(','.join(
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[get_tensor_tag(tensor) for tensor in self.get_inputs()]).encode())
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# network outputs
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data.update(','.join(
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[get_tensor_tag(tensor) for tensor in self.get_outputs()]).encode())
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# layer names
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data.update(','.join(
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[layer.trt_layer.name for layer in self.get_layers()]).encode())
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# layer -> output
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data.update(','.join([
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f'{layer.trt_layer.name}->{get_tensor_tag(tensor)}'
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for layer in self.get_layers() for tensor in layer.get_outputs()
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]).encode())
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# input -> layer
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data.update(','.join([
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f'{get_tensor_tag(tensor)}->{layer.trt_layer.name}'
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for layer in self.get_layers() for tensor in layer.get_inputs()
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]).encode())
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return data.hexdigest()
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@contextlib.contextmanager
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def net_guard(network):
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from ._common import net
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assert isinstance(
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network, Network
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), f"Invalid network, can only guard Network instance, got: {network}"
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old_net = net
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set_network(network)
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yield
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set_network(old_net)
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class _TrtLlmModuleCallStack(object):
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call_stack = []
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module_name_map = {}
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def __init__(self):
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super().__init__()
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self.mod_names_set = False
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def module_names_set(self):
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return self.mod_names_set
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def set_module_names(self, top_level_module):
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assert top_level_module, "Expected a top level module"
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for name, mod in top_level_module.named_modules(
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prefix=top_level_module._get_name()):
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if mod not in self.module_name_map:
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self.module_name_map[mod] = name
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self.mod_names_set = True
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return
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def get_current_module(self):
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mod_name = ''
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if len(self.call_stack):
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mod_name = self.call_stack[-1]
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return mod_name
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def get_mod_name(self, mod_obj):
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name = ''
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if mod_obj in self.module_name_map:
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name = self.module_name_map[mod_obj]
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return name
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def get_stack(self):
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return self.call_stack
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@contextlib.contextmanager
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def call_stack_mgr(self):
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call_stack = self.get_stack()
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try:
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yield call_stack
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finally:
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call_stack.pop()
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