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https://github.com/NVIDIA/TensorRT-LLM.git
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a75618df24
* Update TensorRT-LLM --------- Co-authored-by: 0xymoro <jerrymeng100@gmail.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
113 lines
4.1 KiB
Python
113 lines
4.1 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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from typing import List
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from .._common import default_net
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from ..functional import Tensor, lora_plugin
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from ..module import Module
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class LoraRuntimeParams(object):
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def __init__(
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self,
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lora_ranks: List[Tensor] = None,
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lora_weights_pointers: List[Tensor] = None,
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host_request_types: Tensor = None,
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host_context_lengths: Tensor = None,
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max_context_length: Tensor = None,
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):
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self.lora_ranks = lora_ranks
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self.lora_weights_pointers = lora_weights_pointers
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self.host_request_types = host_request_types
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self.host_context_lengths = host_context_lengths
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self.max_context_length = max_context_length
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class Lora(Module):
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def __init__(self,
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in_hidden_size: int = 0,
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out_hidden_sizes: List[int] = [0],
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max_low_rank: int = 0) -> None:
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super().__init__()
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self.in_hidden_size = in_hidden_size
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self.out_hidden_sizes = out_hidden_sizes
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self.max_low_rank = max_low_rank
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def forward(self, x, lora_runtime_params: LoraRuntimeParams = None):
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if default_net().plugin_config.lora_plugin:
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result = lora_plugin(
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x,
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in_hidden_size=self.in_hidden_size,
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out_hidden_sizes=self.out_hidden_sizes,
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host_request_types=lora_runtime_params.host_request_types,
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transb=True,
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host_context_lengths=lora_runtime_params.host_context_lengths,
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max_context_length=lora_runtime_params.max_context_length,
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max_low_rank=self.max_low_rank,
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lora_ranks=lora_runtime_params.lora_ranks,
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lora_weights_pointers=lora_runtime_params.lora_weights_pointers)
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else:
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assert False, "Not support lora without plugin"
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return result
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class LoraParams(object):
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def __init__(
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self,
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lora_ranks=None, # : List[dict[Tensor]]
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lora_weights_pointers=None, # : List[dict[Tensor]]
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host_context_lengths: Tensor = None,
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max_context_length: Tensor = None,
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host_request_types: Tensor = None):
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self.lora_ranks = lora_ranks
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self.lora_weights_pointers = lora_weights_pointers
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self.host_context_lengths = host_context_lengths
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self.max_context_length = max_context_length
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self.host_request_types = host_request_types
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def get_layer_params(self, layer_idx: int):
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return LoraParams(
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lora_ranks=[self.lora_ranks[layer_idx]],
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lora_weights_pointers=[self.lora_weights_pointers[layer_idx]],
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host_context_lengths=self.host_context_lengths,
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max_context_length=self.max_context_length,
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host_request_types=self.host_request_types)
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def get_runtime_params(self, layer_idx: int, lora_module: str):
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if f"{lora_module}_lora_ranks" in self.lora_ranks[layer_idx]:
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return LoraRuntimeParams(
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lora_ranks=[
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self.lora_ranks[layer_idx][f"{lora_module}_lora_ranks"]
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],
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lora_weights_pointers=[
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self.lora_weights_pointers[layer_idx]
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[f"{lora_module}_lora_weights_pointers"]
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],
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host_context_lengths=self.host_context_lengths,
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max_context_length=self.max_context_length,
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host_request_types=self.host_request_types,
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)
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else:
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return None
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