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https://git.datalinker.icu/vllm-project/vllm.git
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403 lines
16 KiB
Python
403 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import math
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from typing import TYPE_CHECKING, Optional, Union
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if TYPE_CHECKING:
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from vllm.attention.backends.abstract import AttentionBackend
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from typing import TYPE_CHECKING
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import torch
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import torch.distributed
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import torch.nn.functional as F
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from einops import rearrange
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from torch import nn
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from vllm.attention import AttentionMetadata
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from vllm.config import CacheConfig, ModelConfig, get_current_vllm_config
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from vllm.distributed.communication_op import tensor_model_parallel_all_reduce
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from vllm.distributed.parallel_state import (
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get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
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from vllm.forward_context import ForwardContext, get_forward_context
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from vllm.model_executor.custom_op import CustomOp
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from vllm.model_executor.layers.lightning_attn import (
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lightning_attention, linear_decode_forward_triton)
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.mamba.abstract import MambaBase
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from vllm.model_executor.layers.mamba.mamba_utils import (
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MambaStateDtypeCalculator, MambaStateShapeCalculator)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.utils import direct_register_custom_op
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from vllm.v1.attention.backends.linear_attn import LinearAttentionMetadata
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if TYPE_CHECKING:
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from vllm.attention.backends.abstract import AttentionBackend
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import torch
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import torch.distributed
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class MiniMaxText01RMSNormTP(CustomOp):
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name = "MiniMaxText01RMSNormTP"
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def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
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super().__init__()
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self.tp_world = get_tensor_model_parallel_world_size()
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self.tp_rank = get_tensor_model_parallel_rank()
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self.weight = nn.Parameter(torch.ones(int(hidden_size /
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self.tp_world)))
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self.weight.weight_loader = self.weight_loader
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self.variance_epsilon = eps
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return
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@staticmethod
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def weight_loader(
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param: nn.Parameter,
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loaded_weight: torch.Tensor,
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) -> None:
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tp_world = get_tensor_model_parallel_world_size()
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tp_rank = get_tensor_model_parallel_rank()
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shard_size = loaded_weight.shape[0] // tp_world
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shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
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param.data.copy_(loaded_weight[shard])
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return
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def _forward(
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self,
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x: torch.Tensor,
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) -> torch.Tensor:
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orig_dtype = x.dtype
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x = x.to(torch.float32)
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variance = x.pow(2).mean(dim=-1, keepdim=True, dtype=torch.float32)
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if self.tp_world > 1:
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variance = tensor_model_parallel_all_reduce(
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variance) / self.tp_world
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x = x * torch.rsqrt(variance + self.variance_epsilon)
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x = x.to(orig_dtype) * self.weight
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return x
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def forward(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
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assert residual is None, "RMSNorm does not support residual connection."
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return self._forward(x)
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class MiniMaxText01LinearKernel:
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@staticmethod
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def jit_linear_forward_prefix(q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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kv_caches: torch.Tensor,
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slope_rate: torch.Tensor,
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block_size: int,
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layer_idx: Optional[int] = None,
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**kwargs) -> torch.Tensor:
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slope_rate = slope_rate.to(torch.float32)
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should_pad_dim = q.dim() == 3
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if should_pad_dim:
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q = q.unsqueeze(0)
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k = k.unsqueeze(0)
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v = v.unsqueeze(0)
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b, h, n, d = q.shape
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e = d
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kv_history = kv_caches.reshape(1, h, d, e).contiguous()
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output, kv_history = lightning_attention(q,
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k,
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v,
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slope_rate,
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block_size=block_size,
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kv_history=kv_history)
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kv_caches.copy_(kv_history[:, :, -1, :, :].reshape(h, d, e))
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assert output.shape[0] == 1, "batch size must be 1"
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return rearrange(output.squeeze(0), "h n d -> n (h d)")
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class MiniMaxText01LinearAttention(nn.Module, MambaBase):
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@property
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def mamba_type(self) -> str:
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return "linear_attention"
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def get_attn_backend(self) -> type["AttentionBackend"]:
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from vllm.v1.attention.backends.linear_attn import (
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LinearAttentionBackend)
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return LinearAttentionBackend
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def get_state_dtype(self) -> tuple[torch.dtype]:
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assert self.model_config is not None
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assert self.cache_config is not None
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return MambaStateDtypeCalculator.linear_attention_state_dtype(
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self.model_config.dtype,
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self.cache_config.mamba_cache_dtype,
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)
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def get_state_shape(self) -> tuple[tuple[int, int, int], ...]:
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return MambaStateShapeCalculator.linear_attention_state_shape(
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num_heads=self.num_heads,
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tp_size=self.tp_size,
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head_dim=self.head_dim)
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def __init__(
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self,
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hidden_size: int,
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hidden_inner_size: int,
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num_heads: int,
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head_dim: int,
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max_position: int,
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block_size: int,
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num_hidden_layer: int,
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model_config: Optional[ModelConfig] = None,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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layer_idx: int = 0,
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linear_layer_idx: int = 0,
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prefix: str = "linear_attn",
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) -> None:
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super().__init__()
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self.layer_idx = layer_idx
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self.BLOCK = block_size
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self.hidden_size = hidden_size
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self.num_heads = num_heads
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self.head_dim = head_dim
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self.total_num_heads = num_heads
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self.hidden_inner_size = hidden_inner_size
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self.tp_size = get_tensor_model_parallel_world_size()
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self.tp_rank = get_tensor_model_parallel_rank()
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assert self.total_num_heads % self.tp_size == 0
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self.tp_heads = self.total_num_heads // self.tp_size
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self.qkv_size = self.num_heads * self.head_dim
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self.tp_hidden = self.head_dim * self.tp_heads
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self.model_config = model_config
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self.cache_config = cache_config
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self.prefix = prefix
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self.qkv_proj = ColumnParallelLinear(
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hidden_size,
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self.hidden_inner_size * 3,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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self.output_gate = ColumnParallelLinear(
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hidden_size,
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self.hidden_inner_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.output_gate",
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)
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self.out_proj = RowParallelLinear(
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self.hidden_inner_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.out_proj",
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)
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self.norm = MiniMaxText01RMSNormTP(
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self.hidden_inner_size,
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eps=1e-5,
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)
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slope_rate = MiniMaxText01LinearAttention._build_slope_tensor(
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self.num_heads)
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if num_hidden_layer <= 1:
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self.slope_rate = slope_rate * (1 + 1e-5)
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else:
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self.slope_rate = slope_rate * (1 - layer_idx /
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(num_hidden_layer - 1) + 1e-5)
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self.tp_slope = self.slope_rate[self.tp_rank *
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self.tp_heads:(self.tp_rank + 1) *
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self.tp_heads].contiguous()
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compilation_config = get_current_vllm_config().compilation_config
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if prefix in compilation_config.static_forward_context:
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raise ValueError(f"Duplicate layer name: {prefix}")
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compilation_config.static_forward_context[prefix] = self
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@staticmethod
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def weight_direct_load(param: torch.Tensor,
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loaded_weight: torch.Tensor) -> None:
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assert param.size() == loaded_weight.size()
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param.data.copy_(loaded_weight)
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return
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@staticmethod
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def _build_slope_tensor(n_attention_heads: int):
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def get_slopes(n):
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def get_slopes_power_of_2(n):
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start = 2**(-(2**-(math.log2(n) - 3)))
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ratio = start
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return [start * ratio**i for i in range(n)]
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if math.log2(n).is_integer():
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return get_slopes_power_of_2(n)
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else:
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closest_power_of_2 = 2**math.floor(math.log2(n))
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return (get_slopes_power_of_2(closest_power_of_2) + get_slopes(
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2 * closest_power_of_2)[0::2][:n - closest_power_of_2])
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slopes = torch.tensor(get_slopes(n_attention_heads),
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dtype=torch.float32).reshape(
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n_attention_heads, 1, 1)
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return slopes
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def _prefill_and_mix_infer(self, q, k, v, kv_cache, state_indices_tensor,
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attn_metadata):
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hidden = []
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for _prefill_idx in range(getattr(attn_metadata, "num_prefills", 0)):
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if _prefill_idx >= len(attn_metadata.query_start_loc):
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break
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if _prefill_idx >= len(state_indices_tensor):
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break
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offset = attn_metadata.num_decode_tokens
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_start = attn_metadata.query_start_loc[offset + _prefill_idx]
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_end = attn_metadata.query_start_loc[offset + _prefill_idx + 1]
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slot_id = state_indices_tensor[offset + _prefill_idx]
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qs = q[_start:_end].transpose(0, 1).contiguous()
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ks = k[_start:_end].transpose(0, 1).contiguous()
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vs = v[_start:_end].transpose(0, 1).contiguous()
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slice_layer_cache = kv_cache[slot_id, ...]
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out_slice = MiniMaxText01LinearKernel.jit_linear_forward_prefix(
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qs,
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ks,
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vs,
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slice_layer_cache,
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self.tp_slope,
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self.BLOCK,
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layer_idx=self.layer_idx)
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hidden.append(out_slice.contiguous())
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if attn_metadata.num_decode_tokens > 0:
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hidden_decode = self._decode_infer(q, k, v, kv_cache,
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state_indices_tensor,
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attn_metadata)
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hidden.insert(0, hidden_decode)
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if not hidden:
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return torch.empty((0, q.size(-1)), device=q.device, dtype=q.dtype)
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hidden = torch.concat(hidden, dim=0).contiguous()
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return hidden
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def _decode_infer(self, q, k, v, kv_cache, state_indices_tensor,
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attn_metadata):
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q = q[:attn_metadata.num_decode_tokens].unsqueeze(2).contiguous()
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k = k[:attn_metadata.num_decode_tokens].unsqueeze(2).contiguous()
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v = v[:attn_metadata.num_decode_tokens].unsqueeze(2).contiguous()
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slot_id = state_indices_tensor[:attn_metadata.num_decodes]
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hidden = linear_decode_forward_triton(q, k, v, kv_cache, self.tp_slope,
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slot_id, 32)
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return hidden
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def forward(self, hidden_states: torch.Tensor, output: torch.Tensor,
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positions: torch.Tensor) -> None:
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torch.ops.vllm.linear_attention(
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hidden_states,
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output,
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positions,
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self.prefix,
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)
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def _forward(self, hidden_states: torch.Tensor, output: torch.Tensor,
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positions: torch.Tensor) -> None:
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forward_context = get_forward_context()
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attn_metadata: AttentionMetadata = forward_context.attn_metadata
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if attn_metadata is not None:
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assert isinstance(attn_metadata, dict)
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attn_metadata = attn_metadata[self.prefix]
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assert isinstance(attn_metadata, LinearAttentionMetadata)
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num_actual_tokens = attn_metadata.num_prefill_tokens + \
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attn_metadata.num_decode_tokens
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else:
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num_actual_tokens = hidden_states.shape[0]
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qkv, _ = self.qkv_proj(hidden_states[:num_actual_tokens])
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qkv32 = qkv.to(torch.float32)
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qkvact = torch.nn.functional.silu(qkv32)
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qkvact = qkvact.view((qkv.shape[0], self.tp_heads, -1))
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q, k, v = torch.split(qkvact, [self.head_dim] * 3, dim=-1)
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if attn_metadata is not None:
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kv_cache = self.kv_cache[forward_context.virtual_engine][0]
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state_indices_tensor = attn_metadata.state_indices_tensor
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num_prefills = getattr(attn_metadata, "num_prefills", 0)
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if num_prefills > 0:
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num_decode_tokens = getattr(attn_metadata, "num_decode_tokens",
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0)
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for prefill_idx in range(num_prefills):
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q_start = attn_metadata.query_start_loc[num_decode_tokens +
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prefill_idx]
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q_end = attn_metadata.query_start_loc[num_decode_tokens +
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prefill_idx + 1]
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query_len = q_end - q_start
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context_len = attn_metadata.seq_lens[
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num_decode_tokens + prefill_idx] - query_len
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if context_len == 0:
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block_to_clear = state_indices_tensor[num_decode_tokens
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+ prefill_idx]
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kv_cache[block_to_clear, ...] = 0
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decode_only = getattr(attn_metadata, "num_prefills", 0) == 0
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if attn_metadata is None:
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hidden = torch.empty((q.shape[0], q.shape[1] * q.shape[2]),
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device=q.device,
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dtype=q.dtype)
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else:
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if not decode_only:
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hidden = self._prefill_and_mix_infer(q, k, v, kv_cache,
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state_indices_tensor,
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attn_metadata)
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else:
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hidden = self._decode_infer(q, k, v, kv_cache,
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state_indices_tensor,
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attn_metadata)
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hidden = self.norm._forward(hidden)
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gate, _ = self.output_gate(hidden_states[:num_actual_tokens])
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hidden = F.sigmoid(gate) * hidden
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hidden = hidden.to(hidden_states.dtype)
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output[:num_actual_tokens], _ = self.out_proj(hidden)
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def linear_attention(
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hidden_states: torch.Tensor,
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output: torch.Tensor,
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positions: torch.Tensor,
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layer_name: str,
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) -> None:
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forward_context: ForwardContext = get_forward_context()
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self = forward_context.no_compile_layers[layer_name]
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self._forward(hidden_states=hidden_states,
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output=output,
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positions=positions)
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def linear_attention_fake(
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hidden_states: torch.Tensor,
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output: torch.Tensor,
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positions: torch.Tensor,
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layer_name: str,
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) -> None:
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return
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direct_register_custom_op(
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op_name="linear_attention",
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op_func=linear_attention,
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mutates_args=["output"],
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fake_impl=linear_attention_fake,
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)
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