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https://github.com/NVIDIA/TensorRT-LLM.git
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587d063e6d
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
97 lines
3.7 KiB
Python
97 lines
3.7 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 math
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from typing import Optional, Sequence, Union
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import numpy as np
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import tensorrt as trt
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import torch
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from ._utils import str_dtype_to_trt, torch_to_numpy, trt_dtype_to_torch
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from .functional import Tensor, constant
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from .logger import logger
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class Parameter:
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_DEFAULT_DTYPE = trt.DataType.FLOAT
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def __init__(self,
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value: Optional[Union[np.ndarray, torch.Tensor]] = None,
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shape: Sequence[int] = None,
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dtype: Union[str, trt.DataType] = None):
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if dtype is None:
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logger.warning(
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f'Parameter dtype is None, using default dtype: {self._DEFAULT_DTYPE}, it is recommended to always specify dtype explicitly'
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)
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dtype = self._DEFAULT_DTYPE if dtype is None else dtype
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if isinstance(dtype, str):
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dtype = str_dtype_to_trt(dtype)
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if value is None:
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assert isinstance(shape, (list, tuple))
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if len(shape) == 2:
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# Xavier initialization see https://paperswithcode.com/method/xavier-initialization
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v_range = math.sqrt(6) / math.sqrt(shape[0] + shape[1])
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else:
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v_range = 0.1
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if dtype == trt.DataType.INT8:
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upper = math.ceil(128 * v_range)
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value = torch.randint(-upper,
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upper, (shape),
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dtype=trt_dtype_to_torch(dtype),
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device='cuda')
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# value ~ U[int(-128 * v_range), int(128 * v_range)]
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else:
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value = torch.randn(
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(shape), dtype=trt_dtype_to_torch(dtype),
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device='cuda') * 2 - 1
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# value ~ N[-v_range, v_range]
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value = value * v_range
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self._value = self._regularize_value(value)
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@property
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def value(self) -> Tensor:
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if isinstance(self._value, np.ndarray):
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self._value = constant(self._value)
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return self._value
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@property
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def raw_value(self) -> np.ndarray:
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assert isinstance(
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self._value, np.ndarray
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), "Must be np.ndarray. Proper usage: get parameter.raw_value before getting parameter.value"
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return self._value
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@value.setter
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def value(self, v: Union[np.ndarray, torch.Tensor]):
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v = self._regularize_value(v)
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assert v.shape == self._value.shape, \
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f'The value updated is not the same shape as the original. ' \
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f'Updated: {v.shape}, original: {self._value.shape}'
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self._value = v
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def _get_weights(self) -> trt.Weights:
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return self._value.producer.weights if isinstance(self._value,
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Tensor) else None
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def _regularize_value(self, value):
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if isinstance(value, np.ndarray):
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return value
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elif isinstance(value, torch.Tensor):
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return torch_to_numpy(value)
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raise TypeError(
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f'Expected numpy.ndarray or torch.Tensor, got {type(value)}')
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