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# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List
from .._common import default_net
from ..functional import Tensor, lora_plugin
from ..module import Module
class LoraRuntimeParams(object):
def __init__(
self,
lora_ranks: List[Tensor] = None,
lora_weights_pointers: List[Tensor] = None,
host_request_types: Tensor = None,
host_context_lengths: Tensor = None,
max_context_length: Tensor = None,
max_encoder_context_length: Tensor = None,
host_encoder_input_lengths: Tensor = None,
weight_index: int = 0,
):
self.lora_ranks = lora_ranks
self.lora_weights_pointers = lora_weights_pointers
self.host_request_types = host_request_types
self.host_context_lengths = host_context_lengths
self.max_context_length = max_context_length
self.max_encoder_context_length = max_encoder_context_length
self.host_encoder_input_lengths = host_encoder_input_lengths
self.weight_index = weight_index
class Lora(Module):
def __init__(self,
in_hidden_size: int = 0,
out_hidden_sizes: List[int] = [0],
max_low_rank: int = 0) -> None:
super().__init__()
self.in_hidden_size = in_hidden_size
self.out_hidden_sizes = out_hidden_sizes
self.max_low_rank = max_low_rank
def forward(self,
x,
lora_runtime_params: LoraRuntimeParams = None,
is_cross_attention: bool = False):
if default_net().plugin_config.lora_plugin:
result = lora_plugin(
x,
in_hidden_size=self.in_hidden_size,
out_hidden_sizes=self.out_hidden_sizes,
host_request_types=lora_runtime_params.host_request_types,
transb=True,
# For cross attention, host_encoder_input_lengths should be used instead of host_context_lengths
host_context_lengths=lora_runtime_params.host_context_lengths
if not is_cross_attention else
lora_runtime_params.host_encoder_input_lengths,
# For cross attention, max_encoder_context_length should be used instead of max_context_length
max_context_length=lora_runtime_params.max_context_length
if not is_cross_attention else
lora_runtime_params.max_encoder_context_length,
max_low_rank=self.max_low_rank,
lora_ranks=lora_runtime_params.lora_ranks,
lora_weights_pointers=lora_runtime_params.lora_weights_pointers,
weight_index=lora_runtime_params.weight_index,
)
else:
assert False, "Not support lora without plugin"
return result
class LoraParams(object):
def __init__(
self,
lora_ranks=None, # : List[dict[Tensor]]
lora_weights_pointers=None, # : List[dict[Tensor]]
host_context_lengths: Tensor = None,
max_context_length: Tensor = None,
max_encoder_context_length: Tensor = None, # For cross attention
host_request_types: Tensor = None,
host_encoder_input_lengths: Tensor = None, # For cross attention
weight_index: int = 0,
):
self.lora_ranks = lora_ranks
self.lora_weights_pointers = lora_weights_pointers
self.host_context_lengths = host_context_lengths
self.max_context_length = max_context_length
self.max_encoder_context_length = max_encoder_context_length
self.host_request_types = host_request_types
self.host_encoder_input_lengths = host_encoder_input_lengths
self.weight_index = weight_index
def get_layer_params(self, layer_idx: int):
return LoraParams(
lora_ranks=[self.lora_ranks[layer_idx]],
lora_weights_pointers=[self.lora_weights_pointers[layer_idx]],
host_context_lengths=self.host_context_lengths,
max_context_length=self.max_context_length,
max_encoder_context_length=self.max_encoder_context_length,
host_request_types=self.host_request_types,
host_encoder_input_lengths=self.host_encoder_input_lengths,
weight_index=self.weight_index,
)
def get_runtime_params(self, layer_idx: int, lora_module: str):
if f"{lora_module}_lora_ranks" in self.lora_ranks[layer_idx]:
return LoraRuntimeParams(
lora_ranks=[
self.lora_ranks[layer_idx][f"{lora_module}_lora_ranks"]
],
lora_weights_pointers=[
self.lora_weights_pointers[layer_idx]
[f"{lora_module}_lora_weights_pointers"]
],
host_context_lengths=self.host_context_lengths,
max_context_length=self.max_context_length,
max_encoder_context_length=self.max_encoder_context_length,
host_request_types=self.host_request_types,
host_encoder_input_lengths=self.host_encoder_input_lengths,
weight_index=self.weight_index,
)
else:
return None
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