375 lines
16 KiB
Python
375 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Code adapted from SGLang https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/lora/layers.py
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import math
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import torch
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from torch import nn
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from torch.distributed._composable.fsdp import (CPUOffloadPolicy, OffloadPolicy, fully_shard)
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from torch.distributed.tensor import DTensor
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from fastvideo.distributed import (get_local_torch_device, get_tp_rank, split_tensor_along_last_dim,
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tensor_model_parallel_all_gather, tensor_model_parallel_all_reduce)
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from fastvideo.layers.linear import (ColumnParallelLinear, LinearBase, MergedColumnParallelLinear, QKVParallelLinear,
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ReplicatedLinear, RowParallelLinear)
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from fastvideo.layers.vocab_parallel_embedding import VocabParallelEmbedding
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from fastvideo.utils import get_mixed_precision_state
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torch._dynamo.config.recompile_limit = 16
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class BaseLayerWithLoRA(nn.Module):
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def __init__(
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self,
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base_layer: nn.Module,
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lora_rank: int | None = None,
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lora_alpha: int | None = None,
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training_mode: bool = False,
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):
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super().__init__()
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self.base_layer: nn.Module = base_layer
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self.merged: bool = False
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self.cpu_weight = base_layer.weight.to("cpu")
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# indicates adapter weights don't contain this layer
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# (which shouldn't normally happen, but we want to separate it from the case of erroneous merging)
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self.disable_lora: bool = False
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self.lora_rank = lora_rank
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self.lora_alpha = lora_alpha
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self.training_mode = training_mode
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self.lora_path: str | None = None
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self.lora_strength: float = 1.0
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if training_mode:
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assert self.lora_rank is not None, "LoRA rank must be set for training mode"
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if self.lora_rank is None or self.lora_alpha is None:
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self.lora_alpha = lora_rank
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self.base_layer.requires_grad_(False)
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in_dim = self.base_layer.weight.shape[1]
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out_dim = self.base_layer.weight.shape[0]
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self.lora_A = nn.Parameter(
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torch.zeros(self.lora_rank,
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in_dim,
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device=self.base_layer.weight.device,
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dtype=self.base_layer.weight.dtype))
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self.lora_B = nn.Parameter(
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torch.zeros(out_dim,
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self.lora_rank,
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device=self.base_layer.weight.device,
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dtype=self.base_layer.weight.dtype))
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torch.nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
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torch.nn.init.zeros_(self.lora_B)
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else:
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self.lora_A = None
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self.lora_B = None
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@torch.compile()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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lora_A = self.lora_A
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lora_B = self.lora_B
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if isinstance(self.lora_B, DTensor):
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lora_B = self.lora_B.to_local()
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lora_A = self.lora_A.to_local()
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if not self.merged and not self.disable_lora:
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lora_A_sliced = self.slice_lora_a_weights(lora_A.to(x, non_blocking=True))
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lora_B_sliced = self.slice_lora_b_weights(lora_B.to(x, non_blocking=True))
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delta = x @ lora_A_sliced.T @ lora_B_sliced.T
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if self.lora_alpha != self.lora_rank:
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delta = delta * (
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self.lora_alpha / self.lora_rank # type: ignore
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) # type: ignore
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out, output_bias = self.base_layer(x)
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return out + delta, output_bias
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else:
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out, output_bias = self.base_layer(x)
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return out.to(x), output_bias
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def slice_lora_a_weights(self, A: torch.Tensor) -> torch.Tensor:
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return A
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def slice_lora_b_weights(self, B: torch.Tensor) -> torch.Tensor:
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return B
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def set_lora_weights(self,
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A: torch.Tensor,
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B: torch.Tensor,
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lora_alpha: float | None = None,
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training_mode: bool = False,
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lora_path: str | None = None,
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strength: float = 1.0,
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accumulate: bool = False) -> None:
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self.lora_A = torch.nn.Parameter(A) # share storage with weights in the pipeline
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self.lora_B = torch.nn.Parameter(B)
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self.disable_lora = False
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# Store rank and alpha directly
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rank = A.shape[0] # rank is the first dimension of A
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self.lora_rank = rank
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self.lora_alpha = int(lora_alpha) if lora_alpha is not None else rank
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self.lora_strength = float(strength)
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if not training_mode:
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self.merge_lora_weights(accumulate=accumulate)
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self.lora_path = lora_path
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@torch.no_grad()
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def merge_lora_weights(self, accumulate: bool = False) -> None:
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if self.disable_lora:
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return
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if self.merged and not accumulate:
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self.unmerge_lora_weights()
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assert self.lora_A is not None and self.lora_B is not None, "LoRA weights not set. Please set them first."
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if isinstance(self.base_layer.weight, DTensor):
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mesh = self.base_layer.weight.data.device_mesh
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unsharded_base_layer = ReplicatedLinear(
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input_size=self.base_layer.input_size,
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output_size=self.base_layer.output_size,
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bias=getattr(self.base_layer, "bias", None) is not None,
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skip_bias_add=self.base_layer.skip_bias_add,
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params_dtype=self.base_layer.params_dtype,
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quant_config=self.base_layer.quant_config,
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prefix=self.base_layer.prefix,
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)
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# Using offload param is on CPU, so current_device is for "CPU -> GPU -> merge -> CPU"
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current_device = self.base_layer.weight.data.device
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data = self.base_layer.weight.data.to(get_local_torch_device()).full_tensor()
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# Apply LoRA with alpha scaling
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lora_delta = (
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self.slice_lora_b_weights(self.lora_B).to(data) @ self.slice_lora_a_weights(self.lora_A).to(data))
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scale = ((self.lora_alpha / self.lora_rank) if (self.lora_alpha and self.lora_rank) else 1.0)
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lora_delta *= scale * self.lora_strength
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data += lora_delta
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unsharded_base_layer.weight = nn.Parameter(data.to(current_device))
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if isinstance(getattr(self.base_layer, "bias", None), DTensor):
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unsharded_base_layer.bias = nn.Parameter(
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self.base_layer.bias.to(get_local_torch_device(),
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non_blocking=True).full_tensor().to(current_device))
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offload_policy = CPUOffloadPolicy() if "cpu" in str(current_device) else OffloadPolicy()
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mp_policy = get_mixed_precision_state().mp_policy
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self.base_layer = fully_shard(unsharded_base_layer,
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mesh=mesh,
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mp_policy=mp_policy,
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offload_policy=offload_policy)
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else:
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current_device = self.base_layer.weight.data.device
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data = self.base_layer.weight.data.to(get_local_torch_device())
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# Apply LoRA with alpha scaling
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lora_delta = (
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self.slice_lora_b_weights(self.lora_B.to(data)) @ self.slice_lora_a_weights(self.lora_A.to(data)))
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scale = ((self.lora_alpha / self.lora_rank) if (self.lora_alpha and self.lora_rank) else 1.0)
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lora_delta *= scale * self.lora_strength
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data += lora_delta
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self.base_layer.weight.data = data.to(current_device, non_blocking=True)
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self.merged = True
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@torch.no_grad()
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# @torch.compile(dynamic=True)
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def unmerge_lora_weights(self, *, if_merged_only: bool = True) -> None:
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if self.disable_lora:
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return
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if not self.merged:
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if if_merged_only:
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return
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raise ValueError("unmerge_lora_weights called but no LoRA is currently merged")
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# avoid precision loss
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if isinstance(self.base_layer.weight, DTensor):
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device = self.base_layer.weight.data.device
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self.base_layer.weight = nn.Parameter(self.cpu_weight.to(device, non_blocking=True))
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else:
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self.base_layer.weight.data = self.cpu_weight.data.to(self.base_layer.weight, non_blocking=True)
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self.merged = False
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class VocabParallelEmbeddingWithLoRA(BaseLayerWithLoRA):
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"""
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Vocab parallel embedding layer with support for LoRA (Low-Rank Adaptation).
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Note: The current version does not yet implement the LoRA functionality.
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This class behaves exactly the same as the base VocabParallelEmbedding.
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Future versions will integrate LoRA functionality to support efficient parameter fine-tuning.
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"""
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def __init__(
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self,
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base_layer: VocabParallelEmbedding,
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) -> None:
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super().__init__(base_layer)
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def forward(self, input_: torch.Tensor) -> torch.Tensor:
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raise NotImplementedError("We don't support VocabParallelEmbeddingWithLoRA yet.")
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class ColumnParallelLinearWithLoRA(BaseLayerWithLoRA):
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def __init__(
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self,
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base_layer: ColumnParallelLinear,
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lora_rank: int | None = None,
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lora_alpha: int | None = None,
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training_mode: bool = False,
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) -> None:
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super().__init__(base_layer, lora_rank, lora_alpha, training_mode)
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def forward(self, input_: torch.Tensor) -> torch.Tensor:
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# duplicate the logic in ColumnParallelLinear
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bias = self.base_layer.bias if not self.base_layer.skip_bias_add else None
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output_parallel = self.base_layer.quant_method.apply(self.base_layer, input_, bias)
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output = tensor_model_parallel_all_gather(output_parallel) if self.base_layer.gather_output else output_parallel
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output_bias = self.base_layer.bias if self.base_layer.skip_bias_add else None
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return output, output_bias
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def slice_lora_a_weights(self, A: torch.Tensor) -> torch.Tensor:
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return A
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def slice_lora_b_weights(self, B: torch.Tensor) -> torch.Tensor:
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tp_rank = get_tp_rank()
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shard_size = self.base_layer.output_partition_sizes[0]
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start_idx = tp_rank * shard_size
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end_idx = (tp_rank + 1) * shard_size
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B = B[start_idx:end_idx, :]
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return B
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class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
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def __init__(
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self,
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base_layer: MergedColumnParallelLinear,
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lora_rank: int | None = None,
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lora_alpha: int | None = None,
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training_mode: bool = False,
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) -> None:
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super().__init__(base_layer, lora_rank, lora_alpha, training_mode)
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def slice_lora_a_weights(self, A: torch.Tensor) -> torch.Tensor:
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return A.to(self.base_layer.weight)
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def slice_lora_b_weights(self, B: torch.Tensor) -> torch.Tensor:
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tp_rank = get_tp_rank()
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# Since the outputs for both gate and up are identical, we use a random one.
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shard_size = self.base_layer.output_partition_sizes[0]
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start_idx = tp_rank * shard_size
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end_idx = (tp_rank + 1) * shard_size
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return B[:, start_idx:end_idx, :]
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class QKVParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
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def __init__(
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self,
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base_layer: QKVParallelLinear,
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lora_rank: int | None = None,
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lora_alpha: int | None = None,
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training_mode: bool = False,
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) -> None:
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super().__init__(base_layer, lora_rank, lora_alpha, training_mode)
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def slice_lora_a_weights(self, A: torch.Tensor) -> torch.Tensor:
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return A
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def slice_lora_b_weights(self, B: list[torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]:
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tp_rank = get_tp_rank()
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B_q, B_kv = B
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base_layer = self.base_layer
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q_proj_shard_size = base_layer.q_proj_shard_size
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kv_proj_shard_size = base_layer.kv_proj_shard_size
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num_kv_head_replicas = base_layer.num_kv_head_replicas
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q_start_idx = q_proj_shard_size * tp_rank
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q_end_idx = q_start_idx + q_proj_shard_size
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kv_shard_id = tp_rank // num_kv_head_replicas
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kv_start_idx = kv_proj_shard_size * kv_shard_id
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kv_end_idx = kv_start_idx + kv_proj_shard_size
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return B_q[q_start_idx:q_end_idx, :], B_kv[:, kv_start_idx:kv_end_idx, :]
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class RowParallelLinearWithLoRA(BaseLayerWithLoRA):
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def __init__(
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self,
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base_layer: RowParallelLinear,
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lora_rank: int | None = None,
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lora_alpha: int | None = None,
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training_mode: bool = False,
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) -> None:
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super().__init__(base_layer, lora_rank, lora_alpha, training_mode)
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def forward(self, input_: torch.Tensor):
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# duplicate the logic in RowParallelLinear
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if self.base_layer.input_is_parallel:
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input_parallel = input_
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else:
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tp_rank = get_tp_rank()
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splitted_input = split_tensor_along_last_dim(input_, num_partitions=self.base_layer.tp_size)
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input_parallel = splitted_input[tp_rank].contiguous()
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output_parallel = self.base_layer.quant_method.apply(self.base_layer, input_parallel)
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if self.set_lora:
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output_parallel = self.apply_lora(output_parallel, input_parallel)
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if self.base_layer.reduce_results and self.base_layer.tp_size > 1:
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output_ = tensor_model_parallel_all_reduce(output_parallel)
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else:
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output_ = output_parallel
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if not self.base_layer.skip_bias_add:
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output = (output_ + self.base_layer.bias if self.base_layer.bias is not None else output_)
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output_bias = None
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else:
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output = output_
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output_bias = self.base_layer.bias
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return output, output_bias
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def slice_lora_a_weights(self, A: torch.Tensor) -> torch.Tensor:
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tp_rank = get_tp_rank()
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shard_size = self.base_layer.input_size_per_partition
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start_idx = tp_rank * shard_size
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end_idx = (tp_rank + 1) * shard_size
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A = A[:, start_idx:end_idx].contiguous()
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return A
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def slice_lora_b_weights(self, B: torch.Tensor) -> torch.Tensor:
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return B
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def get_lora_layer(layer: nn.Module,
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lora_rank: int | None = None,
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lora_alpha: int | None = None,
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training_mode: bool = False) -> BaseLayerWithLoRA | None:
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supported_layer_types: dict[type[LinearBase], type[BaseLayerWithLoRA]] = {
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# the order matters
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# VocabParallelEmbedding: VocabParallelEmbeddingWithLoRA,
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QKVParallelLinear: QKVParallelLinearWithLoRA,
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MergedColumnParallelLinear: MergedColumnParallelLinearWithLoRA,
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ColumnParallelLinear: ColumnParallelLinearWithLoRA,
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RowParallelLinear: RowParallelLinearWithLoRA,
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ReplicatedLinear: BaseLayerWithLoRA,
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}
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for src_layer_type, lora_layer_type in supported_layer_types.items():
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if isinstance(layer, src_layer_type): # pylint: disable=unidiomatic-typecheck
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ret = lora_layer_type(layer, lora_rank=lora_rank, lora_alpha=lora_alpha, training_mode=training_mode)
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return ret
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return None
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# source: https://github.com/vllm-project/vllm/blob/93b38bea5dd03e1b140ca997dfaadef86f8f1855/vllm/lora/utils.py#L9
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def replace_submodule(model: nn.Module, module_name: str, new_module: nn.Module) -> nn.Module:
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"""Replace a submodule in a model with a new module."""
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parent = model.get_submodule(".".join(module_name.split(".")[:-1]))
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target_name = module_name.split(".")[-1]
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setattr(parent, target_name, new_module)
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return new_module
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