[Fix] Fix accuracy bug and refactor codes for lora (#3413)
This commit is contained in:
Baizhou Zhang
@@ -0,0 +1,293 @@
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import torch
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from torch import nn
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from sglang.srt.distributed import (
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get_tensor_model_parallel_rank,
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split_tensor_along_last_dim,
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tensor_model_parallel_all_gather,
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tensor_model_parallel_all_reduce,
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)
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from sglang.srt.layers.linear import (
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ColumnParallelLinear,
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
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from sglang.srt.lora.backend import BaseLoRABackend
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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,
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scaling: float,
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lora_backend: BaseLoRABackend,
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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.lora_rank: int = lora_rank
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self.scaling: float = scaling
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self.set_lora: bool = False
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self.lora_backend: BaseLoRABackend = lora_backend
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def forward(self, x: torch.Tensor):
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return self.base_layer.forward(x)
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def set_lora_info(self, *args):
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pass
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class VocabParallelEmbeddingWithLoRA(BaseLayerWithLoRA):
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def __init__(
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self,
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base_layer: VocabParallelEmbedding,
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lora_rank: int,
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scaling: float,
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lora_backend: BaseLoRABackend,
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) -> None:
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super().__init__(base_layer, lora_rank, scaling, lora_backend)
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self.weight = base_layer.weight
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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,
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scaling: float,
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lora_backend: BaseLoRABackend,
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) -> None:
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super().__init__(base_layer, lora_rank, scaling, lora_backend)
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def set_lora_info(
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self,
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A_buffer: torch.Tensor,
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B_buffer: torch.Tensor,
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):
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self.set_lora = True
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self.A_buffer = A_buffer
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self.B_buffer = B_buffer
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def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
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backend_kwargs = {"base_output": base_output, "scaling": self.scaling}
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lora_a_output = self.lora_backend.run_lora_a_sgemm(x, self.A_buffer)
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lora_output = self.lora_backend.run_lora_b_sgemm(
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lora_a_output,
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self.B_buffer[0],
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**backend_kwargs,
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)
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return (
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lora_output
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if self.lora_backend.fuse_output_scaling_add
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else base_output + lora_output * self.scaling
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)
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def forward(self, input_: 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(
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self.base_layer, input_, bias
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)
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if self.set_lora:
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output_parallel = self.apply_lora(output_parallel, input_)
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if self.base_layer.gather_output:
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output = tensor_model_parallel_all_gather(output_parallel)
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else:
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output = 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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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,
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scaling: float,
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lora_backend: BaseLoRABackend,
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) -> None:
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super().__init__(base_layer, lora_rank, scaling, lora_backend)
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def set_lora_info(
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self,
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A_buffer: torch.Tensor,
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B_buffer: torch.Tensor,
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):
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self.set_lora = True
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self.A_buffer_gate_up = A_buffer
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if self.lora_backend.fuse_stacked_lora_b:
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# B_buffer_gate_up: (num_lora, 2 * output_dim, r)
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self.B_buffer_gate_up = torch.cat(
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(B_buffer[0], B_buffer[1]), dim=-2
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).contiguous()
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else:
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self.B_buffer_gate_up = (B_buffer[0], B_buffer[1])
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def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
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backend_kwargs = {"base_output": base_output, "scaling": self.scaling}
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lora_output = self.lora_backend.run_gate_up_lora(
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x,
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self.A_buffer_gate_up,
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self.B_buffer_gate_up,
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**backend_kwargs,
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)
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return (
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lora_output
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if self.lora_backend.fuse_output_scaling_add
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else base_output + lora_output * self.scaling
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)
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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,
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scaling: float,
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lora_backend: BaseLoRABackend,
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) -> None:
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super().__init__(base_layer, lora_rank, scaling, lora_backend)
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def set_lora_info(
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self,
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A_buffer_qkv: torch.Tensor,
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B_buffer_q: torch.Tensor,
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B_buffer_kv: torch.Tensor,
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):
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self.set_lora = True
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self.A_buffer_qkv = A_buffer_qkv
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if self.lora_backend.fuse_stacked_lora_b:
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assert (
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B_buffer_q.shape[-1] == B_buffer_kv.shape[-1]
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), "The lora rank of q and kv should be the same when enabling fusion of qkv lora_b"
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output_dim_q, output_dim_kv = B_buffer_q.shape[-2], B_buffer_kv.shape[-2]
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# B_buffer_qkv: (num_lora, output_dim_q + 2 * output_dim_kv, r)
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self.B_buffer_qkv = torch.cat(
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(B_buffer_q[0], B_buffer_kv[0], B_buffer_kv[1]), dim=-2
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).contiguous()
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# Offsets of q/k/v in output dimension
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self.output_offset = torch.tensor(
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[
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0,
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output_dim_q,
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output_dim_q + output_dim_kv,
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output_dim_q + 2 * output_dim_kv,
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],
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dtype=torch.int32,
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device=B_buffer_q.device,
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)
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# For computing number of launched blocks
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self.max_qkv_out_dim = max(output_dim_q, output_dim_kv)
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else:
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self.B_buffer_qkv = (
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B_buffer_q,
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B_buffer_kv,
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)
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def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
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backend_kwargs = {"base_output": base_output, "scaling": self.scaling}
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if self.lora_backend.fuse_stacked_lora_b:
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backend_kwargs["output_offset"] = self.output_offset
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backend_kwargs["max_qkv_out_dim"] = self.max_qkv_out_dim
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lora_output = self.lora_backend.run_qkv_lora(
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x,
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self.A_buffer_qkv,
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self.B_buffer_qkv,
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**backend_kwargs,
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)
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return (
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lora_output
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if self.lora_backend.fuse_output_scaling_add
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else base_output + lora_output * self.scaling
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)
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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,
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scaling: float,
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lora_backend: BaseLoRABackend,
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) -> None:
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super().__init__(base_layer, lora_rank, scaling, lora_backend)
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def set_lora_info(self, A_buffer: torch.Tensor, B_buffer: torch.Tensor):
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self.set_lora = True
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self.A_buffer = A_buffer
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self.B_buffer = B_buffer
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def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
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backend_kwargs = {"base_output": base_output, "scaling": self.scaling}
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lora_a_output = self.lora_backend.run_lora_a_sgemm(x, self.A_buffer)
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lora_output = self.lora_backend.run_lora_b_sgemm(
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lora_a_output,
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self.B_buffer[0],
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**backend_kwargs,
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)
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return (
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lora_output
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if self.lora_backend.fuse_output_scaling_add
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else base_output + lora_output * self.scaling
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)
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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_tensor_model_parallel_rank()
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splitted_input = split_tensor_along_last_dim(
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input_, num_partitions=self.base_layer.tp_size
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)
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input_parallel = splitted_input[tp_rank].contiguous()
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output_parallel = self.base_layer.quant_method.apply(
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self.base_layer, input_parallel
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)
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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 = (
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output_ + self.base_layer.bias
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if self.base_layer.bias is not None
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else output_
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)
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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 get_lora_layer(
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layer: nn.Module, lora_rank: int, scaling: int, lora_backend: BaseLoRABackend
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) -> BaseLayerWithLoRA:
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supported_layer_types = {
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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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}
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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, scaling, lora_backend)
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return ret
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raise Exception(f"No corresponding LoRA layer supported for {type(layer)}.")
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