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336 lines
12 KiB
Python
336 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# pylint: disable=unused-argument
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from typing import TYPE_CHECKING, List, Optional, Tuple, Union, cast
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import torch
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import torch.nn as nn
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from transformers import PretrainedConfig
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from vllm.config import LoRAConfig
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from vllm.distributed.communication_op import (
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tensor_model_parallel_all_gather, tensor_model_parallel_all_reduce)
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from vllm.distributed.parallel_state import get_tensor_model_parallel_rank
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from vllm.lora.layers import (ColumnParallelLinearWithLoRA,
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MergedColumnParallelLinearWithLoRA,
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MergedQKVParallelLinearWithLoRA,
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QKVParallelLinearWithLoRA,
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RowParallelLinearWithLoRA)
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if TYPE_CHECKING:
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pass
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def _fully_sharded_can_replace(can_replace):
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"""
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decorator which adds the condition of fully sharded loras
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intended to wrap can_replace_layer()
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"""
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def dec(*args, **kwargs):
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return (can_replace(*args, **kwargs)
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and kwargs["lora_config"].fully_sharded_loras)
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return dec
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def _mcp_apply(x, bias, layer: ColumnParallelLinearWithLoRA):
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"""
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For `ColumnParallelLinearWithLoRA` or classes that inherit from
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`ColumnParallelLinearWithLoRA`, they share the same `apply` logic.
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"""
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assert (layer.n_slices == len(layer.lora_a_stacked) == len(
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layer.lora_b_stacked) == len(layer.output_slices))
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if layer.lora_bias_stacked is not None:
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assert layer.n_slices == len(layer.lora_bias_stacked)
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output = layer.base_layer.quant_method.apply(layer.base_layer, x, bias)
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x = x.view(-1, x.shape[-1])
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output, out_orig_shape = output.view(-1, output.shape[-1]), output.shape
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# Since communication is needed, the buffer is directly initialized as a
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# tensor rather than a tuple of tensor.
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buffers = torch.zeros(
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(layer.n_slices, x.shape[0], layer.lora_a_stacked[0].shape[2]),
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dtype=torch.float32,
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device=x.device,
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)
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layer.punica_wrapper.add_shrink(buffers, x, layer.lora_a_stacked, 1.0)
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buffers = tensor_model_parallel_all_gather(buffers)
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layer.punica_wrapper.add_expand(output,
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buffers,
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layer.lora_b_stacked,
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layer.lora_bias_stacked,
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layer.output_slices,
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offset_start=0,
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add_input=True)
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output = output.view(*out_orig_shape)
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# now have column partitioned and packed output
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return output
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# these layers are based on the tensor parallelism strategy given in
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# Y. Sheng et al., S-LoRA: Serving Thousands of Concurrent LoRA Adapters. 2023,
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# https://arxiv.org/abs/2311.03285.
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class ColumnParallelLinearWithShardedLoRA(ColumnParallelLinearWithLoRA):
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"""
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Differs from ColumnParallelLinearWithLoRA by slicing LoRA A also.
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Based on S-LoRA, slicing happens along the rank dim.
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"""
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# For all LoRA layers where the `base_layer` is `ColumnParallelLinear`,
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# their `lora_a` and `lora_b` have different sharding patterns. After
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# completing the `lora_a` GEMM , a gather operation is performed.
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# Therefore, the sharding of `lora_a` only needs to correspond with the
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# gather operation.
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def slice_lora_a(self, lora_a: torch.Tensor) -> torch.Tensor:
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tp_rank = get_tensor_model_parallel_rank()
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shard_size = self.lora_a_stacked[0].shape[2]
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start_idx = tp_rank * shard_size
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lora_a = lora_a[:, start_idx:start_idx + shard_size]
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return lora_a
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def apply(self,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None) -> torch.Tensor:
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return _mcp_apply(x, bias, self)
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@classmethod
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@_fully_sharded_can_replace
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def can_replace_layer(
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cls,
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source_layer: nn.Module,
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lora_config: LoRAConfig,
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packed_modules_list: List,
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model_config: Optional[PretrainedConfig],
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) -> bool:
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# specifying kwargs so they can be easily accessed in decorator
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return super().can_replace_layer(
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source_layer=source_layer,
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lora_config=lora_config,
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packed_modules_list=packed_modules_list,
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model_config=model_config,
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decorate=False,
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)
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class MergedColumnParallelLinearWithShardedLoRA(
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MergedColumnParallelLinearWithLoRA):
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"""
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Differs from MergedColumnParallelLinearWithLoRA by slicing the
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LoRA A's also.
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Based on S-LoRA, slicing happens along the rank dim.
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"""
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def slice_lora_a(
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self, lora_a: List[Union[torch.Tensor, None]]
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) -> List[Union[torch.Tensor, None]]:
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#NOTE: lora_a contains 2 subloras, and each sublora could be None.
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output_shard_size = self.lora_a_stacked[0].shape[2]
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output_start_idx = self.tp_rank * output_shard_size
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lora_a = [
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lora_a[0][:, output_start_idx:output_start_idx +
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output_shard_size] if lora_a[0] is not None else None,
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lora_a[1][:, output_start_idx:output_start_idx +
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output_shard_size] if lora_a[1] is not None else None,
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]
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return lora_a
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def apply(self,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None) -> torch.Tensor:
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return _mcp_apply(x, bias, self)
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@classmethod
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@_fully_sharded_can_replace
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def can_replace_layer(
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cls,
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source_layer: nn.Module,
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lora_config: LoRAConfig,
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packed_modules_list: List,
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model_config: Optional[PretrainedConfig],
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) -> bool:
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# specifying kwargs so they can be easily accessed in decorator
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return super().can_replace_layer(
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source_layer=source_layer,
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lora_config=lora_config,
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packed_modules_list=packed_modules_list,
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model_config=model_config,
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decorate=False,
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)
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class QKVParallelLinearWithShardedLoRA(QKVParallelLinearWithLoRA):
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"""
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Differs from QKVParallelLinearWithLoRA by slicing the
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LoRA A's also.
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Based on S-LoRA, slicing happens along the rank dim.
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"""
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def slice_lora_a(self, lora_a: torch.Tensor) -> torch.Tensor:
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tp_rank = get_tensor_model_parallel_rank()
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shard_size = self.lora_a_stacked[0].shape[2]
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start_idx = tp_rank * shard_size
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lora_a = lora_a[:, start_idx:start_idx + shard_size]
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return lora_a
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def apply(self,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None) -> torch.Tensor:
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return _mcp_apply(x, bias, self)
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@classmethod
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@_fully_sharded_can_replace
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def can_replace_layer(cls, source_layer: nn.Module,
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lora_config: LoRAConfig, packed_modules_list: List,
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model_config: Optional[PretrainedConfig]) -> bool:
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# specifying kwargs so they can be easily accessed in decorator
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return super().can_replace_layer(
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source_layer=source_layer,
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lora_config=lora_config,
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packed_modules_list=packed_modules_list,
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model_config=model_config,
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decorate=False,
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)
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class MergedQKVParallelLinearWithShardedLoRA(MergedQKVParallelLinearWithLoRA):
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"""
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Differs from MergedQKVParallelLinearWithLoRA by slicing the
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LoRA A's also.
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Based on S-LoRA, slicing happens along the rank dim.
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"""
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def slice_lora_a(
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self, lora_a: List[Union[torch.Tensor, None]]
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) -> List[Union[torch.Tensor, None]]:
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# NOTE: lora_a contains 3 subloras, and each sublora could be None.
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shard_size = [self.lora_a_stacked[i].shape[2] for i in range(3)]
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start_idx = [self.tp_rank * shard_size[i] for i in range(3)]
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lora_a = [
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lora_a[0][:, start_idx[0]:start_idx[0] +
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shard_size[0]] if lora_a[0] is not None else None,
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lora_a[1][:, start_idx[1]:start_idx[1] +
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shard_size[1]] if lora_a[1] is not None else None,
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lora_a[2][:, start_idx[2]:start_idx[2] +
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shard_size[2]] if lora_a[2] is not None else None,
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]
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return lora_a
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def apply(self,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None) -> torch.Tensor:
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return _mcp_apply(x, bias, self)
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@classmethod
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@_fully_sharded_can_replace
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def can_replace_layer(
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cls,
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source_layer: nn.Module,
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lora_config: LoRAConfig,
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packed_modules_list: List,
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model_config: Optional[PretrainedConfig],
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) -> bool:
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# specifying kwargs so they can be easily accessed in decorator
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return super().can_replace_layer(
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source_layer=source_layer,
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lora_config=lora_config,
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packed_modules_list=packed_modules_list,
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model_config=model_config,
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decorate=False,
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)
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class RowParallelLinearWithShardedLoRA(RowParallelLinearWithLoRA):
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"""
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Differs from RowParallelLinearWithLoRA by slicing the
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LoRA B's also.
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Based on S-LoRA, slicing happens along the output dim.
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This yields a combined partial sum from the row parallel base
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layer and column partitioned output from the LoRA.
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"""
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def slice_lora_b(self, lora_b: torch.Tensor) -> torch.Tensor:
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shard_size = self.lora_b_stacked[0].shape[2]
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start_idx = self.tp_rank * shard_size
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end_idx = (self.tp_rank + 1) * shard_size
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lora_b = lora_b[:, start_idx:end_idx]
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return lora_b
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def slice_bias(self, bias: torch.Tensor) -> torch.Tensor:
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if bias is None:
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return bias
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self.lora_bias_stacked = cast(Tuple[torch.Tensor, ...],
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self.lora_bias_stacked)
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shard_size = self.lora_bias_stacked[0].shape[2]
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start_idx = self.tp_rank * shard_size
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end_idx = (self.tp_rank + 1) * shard_size
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bias = bias[start_idx:end_idx]
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return bias
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def apply(self,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None) -> torch.Tensor:
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output = self.base_layer.quant_method.apply(self.base_layer, x)
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x = x.view(-1, x.shape[-1])
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output, out_orig_shape = output.view(-1,
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output.shape[-1]), output.shape
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buffer = torch.zeros(
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(self.n_slices, x.shape[0], self.lora_a_stacked[0].shape[2]),
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dtype=torch.float32,
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device=x.device,
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)
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self.punica_wrapper.add_shrink(buffer, x, self.lora_a_stacked, 1.0)
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buffer = tensor_model_parallel_all_reduce(buffer)
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# following S-LoRA, allows the fusing of all_gather and all_reduce
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# by adding the column partitioned lora output to a slice of output
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# tensor, which is a partial sum due to row parallel. All that
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# remains is a standard all_reduce. User should be aware though that
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# the output is not the same as a normal row_parallel, it should be
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# reduced before being used
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# NOTE offset are based on the rank.
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shard_size = self.lora_b_stacked[0].shape[2]
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offset_start = self.tp_rank * shard_size
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self.punica_wrapper.add_expand(
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output,
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buffer,
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self.lora_b_stacked,
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self.lora_bias_stacked,
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self.output_slices,
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offset_start=offset_start,
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add_input=True,
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)
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output = output.view(*out_orig_shape)
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return output
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@classmethod
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@_fully_sharded_can_replace
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def can_replace_layer(
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cls,
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source_layer: nn.Module,
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lora_config: LoRAConfig,
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packed_modules_list: List,
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model_config: Optional[PretrainedConfig],
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) -> bool:
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# specifying kwargs so they can be easily accessed in decorator
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return super().can_replace_layer(
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source_layer=source_layer,
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lora_config=lora_config,
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packed_modules_list=packed_modules_list,
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model_config=model_config,
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decorate=False,
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)
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