1432 lines
56 KiB
Python
1432 lines
56 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Inference-only MiniMaxText01 model."""
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import math
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from collections.abc import Iterable
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from itertools import islice
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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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import regex as re
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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 transformers import MiniMaxConfig
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from vllm import envs
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from vllm.attention import Attention, AttentionMetadata
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import (CacheConfig, ModelConfig, VllmConfig,
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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_pp_group, get_tensor_model_parallel_rank,
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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.activation import SiluAndMul
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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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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MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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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.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.utils import maybe_prefix
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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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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from .interfaces import HasInnerState, IsHybrid
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from .minimax_cache import MinimaxCacheManager, MinimaxCacheParams
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from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
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def replace_weight_name(name: str,
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key: str = None,
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to: str = None,
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count: int = None,
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prefix: str = None) -> str:
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name = name.replace(key, to) if count is None else \
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name.replace(key, to, count)
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return name
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def weight_loader_with_alias(alias: str):
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def wrapper(func: callable):
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def inner_func(param: torch.Tensor,
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loaded_weight: torch.Tensor,
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*args,
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prefix: str = None,
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**kwargs):
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value = func(param, loaded_weight, *args, **kwargs)
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return value
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return inner_func
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return wrapper
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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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weight = self.weight
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if x.size(-1) != self.weight.size(0):
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if self.weight.size(0) < x.size(-1):
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repeat_count = (x.size(-1) + self.weight.size(0)) // x.size(-1)
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full_weight = self.weight.repeat(repeat_count)
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weight = full_weight[:x.size(-1)]
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else:
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weight = self.weight[:x.size(-1)]
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x = x.to(orig_dtype) * 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 MiniMaxText01MLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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quant_config: Optional[QuantizationConfig] = None,
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layer_idx: int = None,
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prefix: str = "mlp",
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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.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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intermediate_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}.down_proj",
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)
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self.act_fn = SiluAndMul()
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return
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class MiniMaxText01MoE(nn.Module):
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def __init__(
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self,
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num_experts: int,
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top_k: int,
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hidden_size: int,
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intermediate_size: int,
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params_dtype: Optional[torch.dtype] = None,
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layer_idx: int = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "moe",
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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.tp_size = get_tensor_model_parallel_world_size()
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self.num_total_experts = num_experts
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self.top_k = top_k
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size // self.tp_size
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self.quant_config = quant_config
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if params_dtype is None:
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params_dtype = torch.get_default_dtype()
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self.params_dtype = params_dtype
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self.gate = ReplicatedLinear(
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self.hidden_size,
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self.num_total_experts,
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bias=False,
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params_dtype=torch.float32,
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quant_config=None,
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prefix=f"{prefix}.gate",
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)
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self.gate.weight.weight_loader = MiniMaxText01MoE.gate_weight_loader
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self.experts = FusedMoE(
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num_experts=self.num_total_experts,
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top_k=self.top_k,
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hidden_size=self.hidden_size,
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intermediate_size=self.intermediate_size * self.tp_size,
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params_dtype=self.params_dtype,
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reduce_results=True,
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renormalize=True,
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quant_config=self.quant_config,
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tp_size=self.tp_size,
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prefix=f"{prefix}.experts",
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)
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return
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@staticmethod
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def gate_weight_loader(param: nn.Parameter,
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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.to(torch.float32))
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return
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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num_tokens, hidden_size = hidden_states.shape
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hidden_states = hidden_states.view(-1, self.hidden_size)
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router_logits_fp32, _ = self.gate(hidden_states.to(torch.float32))
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final_hidden_states = self.experts(
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hidden_states, router_logits_fp32.to(hidden_states.dtype))
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final_hidden = final_hidden_states.view(num_tokens, hidden_size)
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return final_hidden
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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: 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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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, ...], tuple[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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if envs.VLLM_USE_V1:
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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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# prefills are packed at end of batch in V1
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offset = attn_metadata.num_decode_tokens if envs.VLLM_USE_V1 else 0
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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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if envs.VLLM_USE_V1:
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hidden.insert(0, hidden_decode)
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else:
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hidden.append(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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if not envs.VLLM_USE_V1:
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q = q[attn_metadata.num_prefill_tokens:].unsqueeze(2).contiguous()
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k = k[attn_metadata.num_prefill_tokens:].unsqueeze(2).contiguous()
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v = v[attn_metadata.num_prefill_tokens:].unsqueeze(2).contiguous()
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num_prefills = getattr(attn_metadata, "num_prefills", 0)
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slot_id = state_indices_tensor[num_prefills:]
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else:
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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]
|
|
hidden = linear_decode_forward_triton(q, k, v, kv_cache, self.tp_slope,
|
|
slot_id, 32)
|
|
return hidden
|
|
|
|
def forward(self, hidden_states: torch.Tensor, output: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: MinimaxCacheParams) -> None:
|
|
if not envs.VLLM_USE_V1:
|
|
self._forward(hidden_states, output, positions, kv_caches)
|
|
else:
|
|
torch.ops.vllm.linear_attention(
|
|
hidden_states,
|
|
output,
|
|
positions,
|
|
self.prefix,
|
|
)
|
|
|
|
def _forward(self, hidden_states: torch.Tensor, output: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: Optional[MinimaxCacheParams]) -> None:
|
|
forward_context = get_forward_context()
|
|
attn_metadata: AttentionMetadata = forward_context.attn_metadata
|
|
if envs.VLLM_USE_V1 and attn_metadata is not None:
|
|
assert isinstance(attn_metadata, dict)
|
|
attn_metadata = attn_metadata[self.prefix]
|
|
assert isinstance(attn_metadata, LinearAttentionMetadata)
|
|
num_actual_tokens = attn_metadata.num_prefill_tokens + \
|
|
attn_metadata.num_decode_tokens
|
|
else:
|
|
num_actual_tokens = hidden_states.shape[0]
|
|
|
|
qkv, _ = self.qkv_proj(hidden_states[:num_actual_tokens])
|
|
qkv32 = qkv.to(torch.float32)
|
|
qkvact = torch.nn.functional.silu(qkv32)
|
|
qkvact = qkvact.view((qkv.shape[0], self.tp_heads, -1))
|
|
q, k, v = torch.split(qkvact, [self.head_dim] * 3, dim=-1)
|
|
if envs.VLLM_USE_V1:
|
|
if attn_metadata is not None:
|
|
kv_cache = self.kv_cache[forward_context.virtual_engine][0]
|
|
state_indices_tensor = attn_metadata.state_indices_tensor
|
|
|
|
num_prefills = getattr(attn_metadata, "num_prefills", 0)
|
|
if num_prefills > 0:
|
|
num_decode_tokens = getattr(attn_metadata,
|
|
"num_decode_tokens", 0)
|
|
for prefill_idx in range(num_prefills):
|
|
q_start = attn_metadata.query_start_loc[
|
|
num_decode_tokens + prefill_idx]
|
|
q_end = attn_metadata.query_start_loc[num_decode_tokens
|
|
+ prefill_idx +
|
|
1]
|
|
query_len = q_end - q_start
|
|
context_len = attn_metadata.seq_lens[
|
|
num_decode_tokens + prefill_idx] - query_len
|
|
if context_len == 0:
|
|
block_to_clear = state_indices_tensor[
|
|
num_decode_tokens + prefill_idx]
|
|
kv_cache[block_to_clear, ...] = 0
|
|
else:
|
|
kv_cache = kv_caches.minimax_cache
|
|
state_indices_tensor = kv_caches.state_indices_tensor
|
|
|
|
decode_only = getattr(attn_metadata, "num_prefills", 0) == 0
|
|
if attn_metadata is None:
|
|
hidden = torch.empty((q.shape[0], q.shape[1] * q.shape[2]),
|
|
device=q.device,
|
|
dtype=q.dtype)
|
|
else:
|
|
if not decode_only:
|
|
hidden = self._prefill_and_mix_infer(q, k, v, kv_cache,
|
|
state_indices_tensor,
|
|
attn_metadata)
|
|
else:
|
|
hidden = self._decode_infer(q, k, v, kv_cache,
|
|
state_indices_tensor,
|
|
attn_metadata)
|
|
hidden = self.norm._forward(hidden)
|
|
gate, _ = self.output_gate(hidden_states[:num_actual_tokens])
|
|
hidden = F.sigmoid(gate) * hidden
|
|
hidden = hidden.to(hidden_states.dtype)
|
|
output[:num_actual_tokens], _ = self.out_proj(hidden)
|
|
|
|
|
|
class MiniMaxText01Attention(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size: int,
|
|
num_heads: int,
|
|
head_dim: int,
|
|
num_kv_heads: int,
|
|
rotary_dim: int,
|
|
max_position: int = 4096 * 32,
|
|
rope_theta: float = 10000,
|
|
sliding_window: Optional[int] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
layer_idx: int = None,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
prefix: str = "mha",
|
|
) -> None:
|
|
super().__init__()
|
|
self.layer_idx = layer_idx
|
|
|
|
self.hidden_size = hidden_size
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
self.total_num_heads = num_heads
|
|
assert self.total_num_heads % tp_size == 0
|
|
self.num_heads = self.total_num_heads // tp_size
|
|
self.total_num_kv_heads = num_kv_heads
|
|
if self.total_num_kv_heads >= tp_size:
|
|
assert self.total_num_kv_heads % tp_size == 0
|
|
else:
|
|
assert tp_size % self.total_num_kv_heads == 0
|
|
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
|
|
self.head_dim = head_dim
|
|
|
|
self.q_size = self.num_heads * self.head_dim
|
|
self.kv_size = self.num_kv_heads * self.head_dim
|
|
self.scaling = self.head_dim**-0.5
|
|
self.rope_theta = rope_theta
|
|
self.sliding_window = sliding_window
|
|
self.prefix = prefix
|
|
|
|
self.qkv_proj = QKVParallelLinear(
|
|
hidden_size,
|
|
self.head_dim,
|
|
self.total_num_heads,
|
|
self.total_num_kv_heads,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.qkv_proj",
|
|
)
|
|
self.o_proj = RowParallelLinear(
|
|
self.total_num_heads * self.head_dim,
|
|
hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.o_proj",
|
|
)
|
|
self.attn = Attention(
|
|
self.num_heads,
|
|
self.head_dim,
|
|
self.scaling,
|
|
num_kv_heads=self.num_kv_heads,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.attn",
|
|
)
|
|
self.rotary_emb = get_rope(
|
|
head_size=self.head_dim,
|
|
rotary_dim=rotary_dim,
|
|
max_position=max_position,
|
|
base=int(rope_theta),
|
|
is_neox_style=True,
|
|
dtype=torch.float32,
|
|
)
|
|
return
|
|
|
|
def forward(self, hidden_states: torch.Tensor, output: torch.Tensor,
|
|
positions: torch.Tensor, **kwargs) -> None:
|
|
qkv, _ = self.qkv_proj(hidden_states)
|
|
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
|
q, k = self.rotary_emb(positions, q, k)
|
|
attn_output = self.attn(q, k, v)
|
|
output[:], _ = self.o_proj(attn_output)
|
|
|
|
|
|
class MiniMaxText01DecoderLayer(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: MiniMaxConfig,
|
|
model_config: Optional[ModelConfig] = None,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
expert_num: int = 1,
|
|
layer_id: int = None,
|
|
linear_layer_id: Optional[int] = None,
|
|
prefix: str = "decoder",
|
|
) -> None:
|
|
self._ilayer = layer_id
|
|
self._irank = get_tensor_model_parallel_rank()
|
|
self.prefix = prefix
|
|
super().__init__()
|
|
|
|
self.hidden_size = config.hidden_size
|
|
self.expert_num = expert_num
|
|
|
|
rope_theta = getattr(config, "rope_theta", 10000)
|
|
|
|
head_dim = getattr(config, "head_dim", None)
|
|
if head_dim is None:
|
|
head_dim = config.hidden_size // config.num_attention_heads
|
|
if hasattr(config, "max_model_len") and isinstance(
|
|
config.max_model_len, int):
|
|
max_position_embeddings = min(config.max_position_embeddings,
|
|
config.max_model_len)
|
|
if config.attention_type == 0:
|
|
use_headxdim = True
|
|
hidden_inner = (head_dim * config.num_attention_heads
|
|
if use_headxdim else config.hidden_size)
|
|
self.self_attn = MiniMaxText01LinearAttention(
|
|
hidden_size=self.hidden_size,
|
|
hidden_inner_size=hidden_inner,
|
|
num_heads=config.num_attention_heads,
|
|
head_dim=head_dim,
|
|
max_position=max_position_embeddings,
|
|
block_size=config.block if hasattr(config, "block") else 256,
|
|
num_hidden_layer=config.num_hidden_layers,
|
|
model_config=model_config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
layer_idx=self._ilayer,
|
|
linear_layer_idx=linear_layer_id,
|
|
prefix=prefix)
|
|
elif config.attention_type == 1:
|
|
self.self_attn = MiniMaxText01Attention(
|
|
hidden_size=self.hidden_size,
|
|
num_heads=config.num_attention_heads,
|
|
head_dim=head_dim,
|
|
rotary_dim=config.rotary_dim
|
|
if hasattr(config, "rotary_dim") else head_dim,
|
|
num_kv_heads=config.num_key_value_heads,
|
|
max_position=max_position_embeddings,
|
|
rope_theta=rope_theta,
|
|
sliding_window=config.sliding_window,
|
|
quant_config=quant_config,
|
|
layer_idx=self._ilayer,
|
|
cache_config=cache_config,
|
|
prefix=prefix)
|
|
else:
|
|
raise ValueError(
|
|
f"Unsupported attention type: {self.config.attention_type}")
|
|
|
|
if expert_num == 1:
|
|
self.mlp = MiniMaxText01MLP(
|
|
hidden_size=self.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
quant_config=quant_config,
|
|
layer_idx=self._ilayer,
|
|
prefix=prefix)
|
|
else:
|
|
self.block_sparse_moe = MiniMaxText01MoE(
|
|
num_experts=expert_num,
|
|
top_k=config.num_experts_per_tok,
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
layer_idx=self._ilayer,
|
|
quant_config=quant_config,
|
|
prefix=prefix)
|
|
|
|
self.input_layernorm = RMSNorm(config.hidden_size,
|
|
eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = RMSNorm(config.hidden_size,
|
|
eps=config.rms_norm_eps)
|
|
if config.attention_type == 0:
|
|
self.layernorm_attention_alpha = getattr(
|
|
config, 'layernorm_linear_attention_alpha',
|
|
getattr(config, 'linear_attn_alpha_factor', 1))
|
|
self.layernorm_attention_beta = getattr(
|
|
config, 'layernorm_linear_attention_beta',
|
|
getattr(config, 'linear_attn_beta_factor', 1))
|
|
else:
|
|
self.layernorm_attention_alpha = getattr(
|
|
config, 'layernorm_full_attention_alpha',
|
|
getattr(config, 'full_attn_alpha_factor', 1))
|
|
self.layernorm_attention_beta = getattr(
|
|
config, 'layernorm_full_attention_beta',
|
|
getattr(config, 'full_attn_beta_factor', 1))
|
|
self.layernorm_mlp_alpha = getattr(
|
|
config, 'layernorm_mlp_alpha',
|
|
getattr(config, 'mlp_alpha_factor', 1))
|
|
self.layernorm_mlp_beta = getattr(
|
|
config, 'layernorm_mlp_beta', getattr(config, 'mlp_beta_factor',
|
|
1))
|
|
self.postnorm = getattr(config, 'postnorm', False)
|
|
self.shared_moe = False
|
|
|
|
shared_intermediate = getattr(config, 'shared_intermediate_size', 0)
|
|
if isinstance(shared_intermediate, list):
|
|
shared_intermediate = shared_intermediate[
|
|
layer_id] if layer_id < len(shared_intermediate) else 0
|
|
if shared_intermediate > 0:
|
|
self.shared_moe = True
|
|
self.shared_mlp = MiniMaxText01MLP(
|
|
hidden_size=self.hidden_size,
|
|
intermediate_size=shared_intermediate,
|
|
quant_config=quant_config,
|
|
layer_idx=self._ilayer,
|
|
prefix=prefix)
|
|
self.coefficient = ReplicatedLinear(
|
|
self.hidden_size,
|
|
1,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
params_dtype=torch.float32,
|
|
)
|
|
self.coefficient.weight.weight_loader = (
|
|
self.shared_moe_coefficient_loader)
|
|
self.shared_moe_mode = getattr(config, 'shared_moe_mode',
|
|
'softmax')
|
|
return
|
|
|
|
def forward(self,
|
|
hidden_states: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: Union[list[dict], Optional[torch.Tensor]],
|
|
attn_metadata: AttentionMetadata,
|
|
residual: Optional[torch.Tensor],
|
|
is_warmup: bool = False,
|
|
**kwargs) -> tuple[torch.Tensor, torch.Tensor]:
|
|
|
|
layernorm_input = hidden_states
|
|
layernorm_output = self.input_layernorm(layernorm_input)
|
|
residual = layernorm_output if self.postnorm else layernorm_input
|
|
self_attention_output = torch.empty_like(layernorm_output)
|
|
self.self_attn(
|
|
hidden_states=layernorm_output,
|
|
output=self_attention_output,
|
|
positions=positions,
|
|
kv_caches=kv_caches,
|
|
)
|
|
|
|
residual = residual * self.layernorm_attention_alpha
|
|
self_attention_output = (self_attention_output *
|
|
self.layernorm_attention_beta)
|
|
|
|
layernorm_input = residual + self_attention_output
|
|
layernorm_output = self.post_attention_layernorm(layernorm_input)
|
|
residual = layernorm_output if self.postnorm else layernorm_input
|
|
|
|
if self.expert_num == 1:
|
|
hidden_states = self.mlp(layernorm_output)
|
|
else:
|
|
moe_layernorm_output = layernorm_output.clone()
|
|
moe_hidden_states = self.block_sparse_moe(moe_layernorm_output)
|
|
if self.shared_moe:
|
|
before_moe_dtype = layernorm_output.dtype
|
|
moe_hidden_fp32 = moe_hidden_states.to(torch.float32)
|
|
output_mlp = self.shared_mlp(layernorm_output).to(
|
|
torch.float32)
|
|
|
|
coef, _ = self.coefficient(layernorm_output.to(torch.float32))
|
|
|
|
if self.shared_moe_mode == 'softmax':
|
|
coef = torch.nn.functional.softmax(coef, dim=-1)
|
|
hidden_states = moe_hidden_fp32 * (
|
|
1 - coef) + output_mlp * coef
|
|
elif self.shared_moe_mode == 'sigmoid':
|
|
coef = torch.nn.functional.sigmoid(coef)
|
|
hidden_states = moe_hidden_fp32 * (
|
|
1 - coef) + output_mlp * coef
|
|
|
|
hidden_states = hidden_states.to(before_moe_dtype)
|
|
else:
|
|
hidden_states = moe_hidden_states
|
|
|
|
residual = residual * self.layernorm_mlp_alpha
|
|
hidden_states = hidden_states * self.layernorm_mlp_beta
|
|
|
|
hidden_states = residual + hidden_states
|
|
|
|
return hidden_states, None
|
|
|
|
@staticmethod
|
|
def shared_moe_coefficient_loader(param: torch.Tensor,
|
|
loaded_weight: torch.Tensor) -> None:
|
|
assert param.size() == loaded_weight.size()
|
|
|
|
param.data.copy_(loaded_weight.to(torch.float32))
|
|
return
|
|
|
|
|
|
@support_torch_compile
|
|
class MiniMaxText01Model(nn.Module):
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config: MiniMaxConfig = vllm_config.model_config.hf_config
|
|
model_config = vllm_config.model_config
|
|
quant_config = vllm_config.quant_config
|
|
cache_config = vllm_config.cache_config
|
|
scheduler_config = vllm_config.scheduler_config
|
|
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.decoder_attention_types = getattr(
|
|
config, "attn_type_list", False) or getattr(
|
|
config, "decoder_attention_types", False)
|
|
# The HF format uses "layer_types" instead of "attn_type_list"
|
|
# where "linear_attention" is 0 and "full_attention" is 1
|
|
if not self.decoder_attention_types and hasattr(config, "layer_types"):
|
|
self.decoder_attention_types = []
|
|
for layer_type in config.layer_types:
|
|
if layer_type == "linear_attention":
|
|
self.decoder_attention_types.append(0)
|
|
elif layer_type == "full_attention":
|
|
self.decoder_attention_types.append(1)
|
|
else:
|
|
raise ValueError(f"Unsupported layer type: {layer_type}")
|
|
# Default to full attention
|
|
if not self.decoder_attention_types:
|
|
self.decoder_attention_types = [1] * config.num_hidden_layers
|
|
self.num_layers = config.num_hidden_layers
|
|
|
|
self._layer_barrier = False
|
|
if get_pp_group().is_first_rank:
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
self.vocab_size,
|
|
config.hidden_size,
|
|
org_num_embeddings=self.vocab_size,
|
|
)
|
|
else:
|
|
self.embed_tokens = PPMissingLayer()
|
|
|
|
def layer_fn(prefix):
|
|
layer_idx = int(prefix.split('.')[-1])
|
|
layer_config = config
|
|
layer_config.attention_type = self.decoder_attention_types[
|
|
layer_idx]
|
|
layer_config.layer_idx = layer_idx
|
|
|
|
decoder_kwargs = {
|
|
"quant_config": quant_config,
|
|
"layer_id": layer_idx,
|
|
"model_config": model_config,
|
|
"cache_config": cache_config
|
|
}
|
|
|
|
if layer_config.attention_type == 0:
|
|
decoder_kwargs["linear_layer_id"] = sum(
|
|
1 for i in range(layer_idx)
|
|
if self.decoder_attention_types[i] == 0)
|
|
else:
|
|
decoder_kwargs["linear_layer_id"] = None
|
|
|
|
if hasattr(config, "num_local_experts") and isinstance(
|
|
config.num_local_experts, list):
|
|
decoder_kwargs["expert_num"] = config.num_local_experts[
|
|
layer_idx]
|
|
elif hasattr(config, "num_local_experts") and isinstance(
|
|
config.num_local_experts, int):
|
|
decoder_kwargs["expert_num"] = config.num_local_experts
|
|
else:
|
|
decoder_kwargs["expert_num"] = 1
|
|
|
|
return MiniMaxText01DecoderLayer(layer_config,
|
|
**decoder_kwargs,
|
|
prefix=prefix)
|
|
|
|
self.start_layer, self.end_layer, self.layers = make_layers(
|
|
config.num_hidden_layers, layer_fn, prefix=f"{prefix}.layers")
|
|
|
|
linear_layer_nums = sum(1 for i in range(config.num_hidden_layers)
|
|
if self.decoder_attention_types[i] == 0)
|
|
max_slots_number = scheduler_config.max_num_seqs
|
|
self.cache_shape = (linear_layer_nums, max_slots_number,
|
|
config.num_attention_heads //
|
|
get_tensor_model_parallel_world_size(),
|
|
config.head_dim, config.head_dim)
|
|
_dummy = torch.zeros(1)
|
|
self._dtype = _dummy.dtype
|
|
del _dummy
|
|
|
|
if not envs.VLLM_USE_V1:
|
|
self.minimax_cache = MinimaxCacheManager(
|
|
dtype=torch.float32, cache_shape=self.cache_shape)
|
|
|
|
norm_kwargs = {}
|
|
if hasattr(config, "rms_norm_eps"):
|
|
norm_kwargs["eps"] = config.rms_norm_eps
|
|
if get_pp_group().is_last_rank:
|
|
self.norm = RMSNorm(config.hidden_size, **norm_kwargs)
|
|
else:
|
|
self.norm = PPMissingLayer()
|
|
self.embed_scale = 1.0
|
|
return
|
|
|
|
def _clear_prefill_cache(self, attn_metadata,
|
|
minimax_cache_tensors: torch.Tensor, **kwargs):
|
|
seq_to_slot_maps = {}
|
|
seq_id_map = sum(list(kwargs["request_ids_to_seq_ids"].values()), [])
|
|
for _, seq_to_slot_map in (
|
|
self.minimax_cache.cache_indices_mapping.items()):
|
|
seq_to_slot_maps.update(seq_to_slot_map)
|
|
|
|
slots_to_clear = []
|
|
for _prefill_id in range(getattr(attn_metadata, "num_prefills", 0)):
|
|
if _prefill_id >= len(seq_id_map):
|
|
break
|
|
seq_id = seq_id_map[_prefill_id]
|
|
if attn_metadata.context_lens_tensor[
|
|
_prefill_id] == 0 and seq_id in seq_to_slot_maps:
|
|
slots_to_clear.append(seq_to_slot_maps[seq_id])
|
|
|
|
if slots_to_clear:
|
|
slots_tensor = torch.tensor(slots_to_clear,
|
|
device=minimax_cache_tensors.device,
|
|
dtype=torch.long)
|
|
minimax_cache_tensors[:, slots_tensor, ...] = 0
|
|
|
|
def get_input_embeddings(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
return self.embed_tokens(input_ids)
|
|
|
|
def forward(self,
|
|
input_ids: Optional[torch.Tensor],
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs) -> Union[torch.Tensor, IntermediateTensors]:
|
|
forward_context = get_forward_context()
|
|
attn_metadata = forward_context.attn_metadata
|
|
if not envs.VLLM_USE_V1 and attn_metadata is None:
|
|
return None
|
|
if not envs.VLLM_USE_V1:
|
|
if "request_ids_to_seq_ids" not in kwargs:
|
|
kwargs["request_ids_to_seq_ids"] = {}
|
|
if "finished_requests_ids" not in kwargs:
|
|
kwargs["finished_requests_ids"] = []
|
|
(
|
|
minimax_cache_tensors,
|
|
state_indices_tensor,
|
|
) = self.minimax_cache.current_run_tensors(**kwargs)
|
|
if getattr(attn_metadata, "num_prefills", 0) > 0:
|
|
self._clear_prefill_cache(attn_metadata, minimax_cache_tensors,
|
|
**kwargs)
|
|
|
|
minimax_cache_params = MinimaxCacheParams(minimax_cache_tensors,
|
|
state_indices_tensor)
|
|
else:
|
|
minimax_cache_params = None
|
|
|
|
if get_pp_group().is_first_rank:
|
|
if inputs_embeds is None:
|
|
hidden_states = self.embed_scale * self.embed_tokens(input_ids)
|
|
else:
|
|
hidden_states = inputs_embeds
|
|
residual = None
|
|
else:
|
|
assert intermediate_tensors is not None
|
|
hidden_states = intermediate_tensors["hidden_states"]
|
|
residual = intermediate_tensors["residual"]
|
|
|
|
minimax_cache_index = 0
|
|
|
|
for layer in islice(self.layers, self.start_layer, self.end_layer):
|
|
_caches = None
|
|
if not envs.VLLM_USE_V1 and isinstance(
|
|
layer.self_attn, MiniMaxText01LinearAttention):
|
|
current_state_layer = minimax_cache_index
|
|
_caches = minimax_cache_params.at_layer_idx(
|
|
current_state_layer)
|
|
minimax_cache_index += 1
|
|
hidden_states, residual = layer(
|
|
hidden_states=hidden_states,
|
|
positions=positions,
|
|
kv_caches=_caches,
|
|
attn_metadata=attn_metadata,
|
|
residual=residual,
|
|
)
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors({
|
|
"hidden_states": hidden_states,
|
|
"residual": residual
|
|
})
|
|
if residual is not None:
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
else:
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid):
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
|
|
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
lora_config = vllm_config.lora_config
|
|
self.config = config
|
|
self.lora_config = lora_config
|
|
|
|
if not hasattr(config, "sliding_window"):
|
|
config.sliding_window = None
|
|
|
|
self.CONCAT_FFN = True
|
|
|
|
self.unpadded_vocab_size = self.config.vocab_size
|
|
if hasattr(vllm_config.model_config, "max_model_len"):
|
|
self.config.max_model_len = vllm_config.model_config.max_model_len
|
|
self.model = MiniMaxText01Model(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"))
|
|
if get_pp_group().is_last_rank:
|
|
self.lm_head = ParallelLMHead(
|
|
self.unpadded_vocab_size,
|
|
self.config.hidden_size,
|
|
org_num_embeddings=self.config.vocab_size,
|
|
padding_size=DEFAULT_VOCAB_PADDING_SIZE,
|
|
)
|
|
|
|
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
|
self.config.vocab_size)
|
|
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
self.lm_head.float()
|
|
flash_layer_count = sum(
|
|
1 for attn_type in self.model.decoder_attention_types
|
|
if attn_type == 1)
|
|
self.kv_cache = [torch.tensor([]) for _ in range(flash_layer_count)]
|
|
return
|
|
|
|
def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
|
|
return self.model.minimax_cache.copy_inputs_before_cuda_graphs(
|
|
input_buffers, **kwargs)
|
|
|
|
def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
|
|
return self.model.minimax_cache.get_seqlen_agnostic_capture_inputs(
|
|
batch_size)
|
|
|
|
def get_input_embeddings(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
return self.model.get_input_embeddings(input_ids)
|
|
|
|
def forward(self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs) -> torch.Tensor:
|
|
hidden_states = self.model(input_ids, positions, intermediate_tensors,
|
|
inputs_embeds, **kwargs)
|
|
|
|
return hidden_states
|
|
|
|
def compute_logits(self, hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata) -> torch.Tensor:
|
|
logits = self.logits_processor(self.lm_head, hidden_states.float(),
|
|
sampling_metadata)
|
|
|
|
return logits
|
|
|
|
def make_empty_intermediate_tensors(
|
|
self, batch_size: int, dtype: torch.dtype,
|
|
device: torch.device) -> IntermediateTensors:
|
|
return IntermediateTensors({
|
|
"hidden_states":
|
|
torch.zeros((batch_size, self.config.hidden_size),
|
|
dtype=dtype,
|
|
device=device),
|
|
"residual":
|
|
torch.zeros((batch_size, self.config.hidden_size),
|
|
dtype=dtype,
|
|
device=device),
|
|
})
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str,
|
|
torch.Tensor]]) -> set[str]:
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: set[str] = set()
|
|
|
|
def which_layer(name: str) -> int:
|
|
if "layers" in name:
|
|
after_layer = name.split("layers")[-1]
|
|
return int(after_layer.split(".")[1])
|
|
return None
|
|
|
|
def is_linear_attn_layer(layer_idx: int) -> bool:
|
|
if layer_idx is None or layer_idx >= len(
|
|
self.model.decoder_attention_types):
|
|
return False
|
|
return self.model.decoder_attention_types[layer_idx] == 0
|
|
|
|
def is_moe_weight(name: str) -> bool:
|
|
return "block_sparse_moe" in name and not name.endswith(".bias")
|
|
|
|
def get_expert_id(param_name):
|
|
pattern = r'model\.layers\.\d+\.block_sparse_moe\.experts\.(\d+)\.'
|
|
match = re.search(pattern, param_name)
|
|
if match:
|
|
return match.group(1)
|
|
return None
|
|
|
|
def load_sparse_moe_weight(name: str, loaded_weight: torch.Tensor,
|
|
self) -> None:
|
|
if isinstance(self.config.num_local_experts, list):
|
|
expert_params_mapping = [
|
|
("w13_weight"
|
|
if weight_name in ["w1", "w3"] else "w2_weight",
|
|
f"experts.{expert_id}.{weight_name}.weight", expert_id)
|
|
for expert_id in range(max(self.config.num_local_experts))
|
|
for weight_name in ["w1", "w2", "w3"]
|
|
]
|
|
else:
|
|
expert_params_mapping = [
|
|
("w13_scale" if weight_name in ["w1", "w3"] else
|
|
"w2_scale", f"{expert_id}.{weight_name}.weight_scale",
|
|
expert_id, weight_name)
|
|
for expert_id in range(self.config.num_local_experts)
|
|
for weight_name in ["w1", "w2", "w3"]
|
|
] + [("w13_weight" if weight_name in ["w1", "w3"] else
|
|
"w2_weight", f"{expert_id}.{weight_name}.weight",
|
|
expert_id, weight_name)
|
|
for expert_id in range(self.config.num_local_experts)
|
|
for weight_name in ["w1", "w2", "w3"]]
|
|
for (param_name, weight_name, expert_id,
|
|
shard_id) in expert_params_mapping:
|
|
name_expert_id = get_expert_id(name)
|
|
if name_expert_id is not None and int(name_expert_id) != int(
|
|
expert_id):
|
|
continue
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
if is_pp_missing_parameter(name, self):
|
|
return
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader = weight_loader_with_alias(name)(weight_loader)
|
|
weight_loader(param,
|
|
loaded_weight,
|
|
weight_name,
|
|
expert_id=expert_id,
|
|
shard_id=shard_id)
|
|
loaded_params.add(name)
|
|
break
|
|
else:
|
|
if is_pp_missing_parameter(name, self):
|
|
return
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader = weight_loader_with_alias(name)(weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return
|
|
|
|
def is_shared_mlp_weight(name: str) -> bool:
|
|
return "shared_mlp" in name and not name.endswith(".bias")
|
|
|
|
def load_shared_mlp_weight(name: str, loaded_weight: torch.Tensor,
|
|
self) -> None:
|
|
if not self.CONCAT_FFN:
|
|
if "gate_proj" in name:
|
|
name = name.replace("gate_proj", "w1", 1)
|
|
elif "up_proj" in name:
|
|
name = name.replace("up_proj", "w3", 1)
|
|
elif "down_proj" in name:
|
|
name = name.replace("down_proj", "w2", 1)
|
|
else:
|
|
if "gate_proj" in name:
|
|
name = name.replace("gate_proj", "gate_up_proj", 1)
|
|
loaded_shard_id = 0
|
|
elif "up_proj" in name:
|
|
name = name.replace("up_proj", "gate_up_proj", 1)
|
|
loaded_shard_id = 1
|
|
if is_pp_missing_parameter(name, self):
|
|
return
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader = weight_loader_with_alias(name)(weight_loader)
|
|
if not self.CONCAT_FFN:
|
|
weight_loader(param, loaded_weight)
|
|
else:
|
|
if "gate_up_proj" in name:
|
|
weight_loader(param, loaded_weight, loaded_shard_id)
|
|
elif "down_proj" in name:
|
|
weight_loader(param, loaded_weight)
|
|
else:
|
|
raise AssertionError(
|
|
"MLP weight not in [gate_up_proj, down_proj]")
|
|
loaded_params.add(name)
|
|
return
|
|
|
|
def is_mha_weight(name: str) -> bool:
|
|
return "self_attn" in name and not name.endswith(".bias")
|
|
|
|
def load_linear_attn_weight(name: str, loaded_weight: torch.Tensor,
|
|
self) -> None:
|
|
if is_pp_missing_parameter(name, self):
|
|
return
|
|
param = params_dict[name]
|
|
|
|
weight_loader = getattr(
|
|
param, "weight_loader",
|
|
MiniMaxText01LinearAttention.weight_direct_load)
|
|
weight_loader = weight_loader_with_alias(name)(weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return
|
|
|
|
def load_flash_attn_weight(name: str, loaded_weight: torch.Tensor,
|
|
self) -> None:
|
|
|
|
flash_mha_params_mapping = [
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
for (param_name, weight_name,
|
|
shard_id) in flash_mha_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
if is_pp_missing_parameter(name, self):
|
|
return
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader = weight_loader_with_alias(name)(weight_loader)
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
loaded_params.add(name)
|
|
break
|
|
else:
|
|
if is_pp_missing_parameter(name, self):
|
|
return
|
|
param = params_dict[name]
|
|
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader = weight_loader_with_alias(name)(weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return
|
|
|
|
def is_layer_norm_weight(name: str) -> bool:
|
|
return "norm" in name and not name.endswith(
|
|
".bias") and name in params_dict
|
|
|
|
def load_layer_norm_weight(name: str, loaded_weight: torch.Tensor,
|
|
self) -> None:
|
|
if is_pp_missing_parameter(name, self):
|
|
return
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader = weight_loader_with_alias(name)(weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return
|
|
|
|
def load_basic_weight(name: str, loaded_weight: torch.Tensor,
|
|
self) -> None:
|
|
if is_pp_missing_parameter(name, self):
|
|
return
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader = weight_loader_with_alias(name)(weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return
|
|
|
|
for name, loaded_weight in weights:
|
|
weight_at_layer = which_layer(name)
|
|
if weight_at_layer and weight_at_layer >= len(
|
|
self.model.decoder_attention_types):
|
|
continue
|
|
|
|
if is_layer_norm_weight(name):
|
|
load_layer_norm_weight(name, loaded_weight, self)
|
|
continue
|
|
if is_mha_weight(name):
|
|
if is_linear_attn_layer(weight_at_layer):
|
|
load_linear_attn_weight(name, loaded_weight, self)
|
|
else:
|
|
load_flash_attn_weight(name, loaded_weight, self)
|
|
continue
|
|
if is_moe_weight(name):
|
|
load_sparse_moe_weight(name, loaded_weight, self)
|
|
continue
|
|
if is_shared_mlp_weight(name):
|
|
load_shared_mlp_weight(name, loaded_weight, self)
|
|
continue
|
|
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
|
|
load_basic_weight(name, loaded_weight, self)
|
|
return loaded_params
|
|
|
|
@classmethod
|
|
def get_mamba_state_dtype_from_config(
|
|
cls,
|
|
vllm_config: "VllmConfig",
|
|
) -> tuple[torch.dtype, torch.dtype]:
|
|
|
|
return MambaStateDtypeCalculator.linear_attention_state_dtype(
|
|
vllm_config.model_config.dtype,
|
|
vllm_config.cache_config.mamba_cache_dtype,
|
|
)
|
|
|
|
@classmethod
|
|
def get_mamba_state_shape_from_config(
|
|
cls,
|
|
vllm_config: "VllmConfig",
|
|
use_v1: bool = True,
|
|
) -> tuple[tuple[int, ...], ...]:
|
|
"""Calculate shape for MiniMaxText01LinearAttention cache.
|
|
|
|
Args:
|
|
vllm_config: vLLM config
|
|
use_v1: Get shapes for V1 (or V0)
|
|
|
|
Returns:
|
|
Tuple containing:
|
|
- state_shape: Shape of the cache
|
|
"""
|
|
parallel_config = vllm_config.parallel_config
|
|
hf_config = vllm_config.model_config.hf_config
|
|
|
|
return MambaStateShapeCalculator.linear_attention_state_shape(
|
|
num_heads=hf_config.num_attention_heads,
|
|
tp_size=parallel_config.tensor_parallel_size,
|
|
head_dim=hf_config.head_dim,
|
|
)
|
|
|
|
|
|
def linear_attention(
|
|
hidden_states: torch.Tensor,
|
|
output: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
layer_name: str,
|
|
) -> None:
|
|
forward_context: ForwardContext = get_forward_context()
|
|
self = forward_context.no_compile_layers[layer_name]
|
|
self._forward(hidden_states=hidden_states,
|
|
output=output,
|
|
positions=positions,
|
|
kv_caches=None)
|
|
|
|
|
|
def linear_attention_fake(
|
|
hidden_states: torch.Tensor,
|
|
output: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
layer_name: str,
|
|
) -> None:
|
|
return
|
|
|
|
|
|
direct_register_custom_op(
|
|
op_name="linear_attention",
|
|
op_func=linear_attention,
|
|
mutates_args=["output"],
|
|
fake_impl=linear_attention_fake,
|
|
dispatch_key=current_platform.dispatch_key,
|
|
)
|