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https://git.datalinker.icu/vllm-project/vllm.git
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1013 lines
37 KiB
Python
1013 lines
37 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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from collections.abc import Iterable
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from itertools import islice
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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pass
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import regex as re
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import torch
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from torch import nn
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from transformers import MiniMaxConfig
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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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from vllm.distributed.parallel_state import (
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get_pp_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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)
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from vllm.forward_context import get_forward_context
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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.linear import (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.mamba.linear_attn import MiniMaxText01LinearAttention
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from vllm.model_executor.layers.mamba.mamba_utils import (
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MambaStateDtypeCalculator,
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MambaStateShapeCalculator,
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)
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from vllm.model_executor.layers.quantization import 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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ParallelLMHead,
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VocabParallelEmbedding,
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)
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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.sequence import IntermediateTensors
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from .interfaces import HasInnerState, IsHybrid
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from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
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def replace_weight_name(
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name: str, key: str = None, to: str = None, count: int = None, prefix: str = None
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) -> str:
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name = name.replace(key, to) if count is None else 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(
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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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):
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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 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: QuantizationConfig | None = 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: torch.dtype | None = None,
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layer_idx: int = None,
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quant_config: QuantizationConfig | None = 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, 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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)
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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 MiniMaxText01Attention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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head_dim: int,
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num_kv_heads: int,
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rotary_dim: int,
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max_position: int = 4096 * 32,
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rope_theta: float = 10000,
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sliding_window: int | None = None,
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quant_config: QuantizationConfig | None = None,
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layer_idx: int = None,
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cache_config: CacheConfig | None = None,
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prefix: str = "mha",
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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.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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assert self.total_num_kv_heads % tp_size == 0
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else:
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = head_dim
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.sliding_window = sliding_window
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self.prefix = prefix
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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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.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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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}.o_proj",
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)
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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)
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self.rotary_emb = get_rope(
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head_size=self.head_dim,
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rotary_dim=rotary_dim,
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max_position=max_position,
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base=int(rope_theta),
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is_neox_style=True,
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dtype=torch.float32,
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)
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return
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def forward(
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self,
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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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**kwargs,
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) -> None:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v)
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output[:], _ = self.o_proj(attn_output)
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class MiniMaxText01DecoderLayer(nn.Module):
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def __init__(
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self,
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config: MiniMaxConfig,
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model_config: ModelConfig | None = None,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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expert_num: int = 1,
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layer_id: int = None,
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linear_layer_id: int | None = None,
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prefix: str = "decoder",
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) -> None:
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self._ilayer = layer_id
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self._irank = get_tensor_model_parallel_rank()
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self.prefix = prefix
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super().__init__()
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self.hidden_size = config.hidden_size
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self.expert_num = expert_num
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rope_theta = getattr(config, "rope_theta", 10000)
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head_dim = getattr(config, "head_dim", None)
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if head_dim is None:
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head_dim = config.hidden_size // config.num_attention_heads
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if hasattr(config, "max_model_len") and isinstance(config.max_model_len, int):
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max_position_embeddings = min(
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config.max_position_embeddings, config.max_model_len
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)
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if config.attention_type == 0:
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use_headxdim = True
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hidden_inner = (
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head_dim * config.num_attention_heads
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if use_headxdim
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else config.hidden_size
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)
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self.self_attn = MiniMaxText01LinearAttention(
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hidden_size=self.hidden_size,
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hidden_inner_size=hidden_inner,
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num_heads=config.num_attention_heads,
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head_dim=head_dim,
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max_position=max_position_embeddings,
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block_size=config.block if hasattr(config, "block") else 256,
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num_hidden_layer=config.num_hidden_layers,
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model_config=model_config,
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cache_config=cache_config,
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quant_config=quant_config,
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layer_idx=self._ilayer,
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linear_layer_idx=linear_layer_id,
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prefix=prefix,
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)
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elif config.attention_type == 1:
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self.self_attn = MiniMaxText01Attention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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head_dim=head_dim,
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rotary_dim=config.rotary_dim
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if hasattr(config, "rotary_dim")
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else head_dim,
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num_kv_heads=config.num_key_value_heads,
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max_position=max_position_embeddings,
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rope_theta=rope_theta,
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sliding_window=config.sliding_window,
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quant_config=quant_config,
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layer_idx=self._ilayer,
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cache_config=cache_config,
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prefix=prefix,
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)
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else:
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raise ValueError(
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f"Unsupported attention type: {self.config.attention_type}"
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)
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if expert_num == 1:
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self.mlp = MiniMaxText01MLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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quant_config=quant_config,
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layer_idx=self._ilayer,
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prefix=prefix,
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)
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else:
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self.block_sparse_moe = MiniMaxText01MoE(
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num_experts=expert_num,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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layer_idx=self._ilayer,
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quant_config=quant_config,
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prefix=prefix,
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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if config.attention_type == 0:
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self.layernorm_attention_alpha = getattr(
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config,
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"layernorm_linear_attention_alpha",
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getattr(config, "linear_attn_alpha_factor", 1),
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)
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self.layernorm_attention_beta = getattr(
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config,
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"layernorm_linear_attention_beta",
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getattr(config, "linear_attn_beta_factor", 1),
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)
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else:
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self.layernorm_attention_alpha = getattr(
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config,
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"layernorm_full_attention_alpha",
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getattr(config, "full_attn_alpha_factor", 1),
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)
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self.layernorm_attention_beta = getattr(
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config,
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"layernorm_full_attention_beta",
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getattr(config, "full_attn_beta_factor", 1),
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)
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self.layernorm_mlp_alpha = getattr(
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config, "layernorm_mlp_alpha", getattr(config, "mlp_alpha_factor", 1)
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)
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self.layernorm_mlp_beta = getattr(
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config, "layernorm_mlp_beta", getattr(config, "mlp_beta_factor", 1)
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)
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self.postnorm = getattr(config, "postnorm", False)
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self.shared_moe = False
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shared_intermediate = getattr(config, "shared_intermediate_size", 0)
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if isinstance(shared_intermediate, list):
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shared_intermediate = (
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shared_intermediate[layer_id]
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if layer_id < len(shared_intermediate)
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else 0
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)
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if shared_intermediate > 0:
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self.shared_moe = True
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self.shared_mlp = MiniMaxText01MLP(
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hidden_size=self.hidden_size,
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intermediate_size=shared_intermediate,
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quant_config=quant_config,
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layer_idx=self._ilayer,
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prefix=prefix,
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)
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self.coefficient = ReplicatedLinear(
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self.hidden_size,
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1,
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bias=False,
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quant_config=quant_config,
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params_dtype=torch.float32,
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)
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self.coefficient.weight.weight_loader = self.shared_moe_coefficient_loader
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self.shared_moe_mode = getattr(config, "shared_moe_mode", "softmax")
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return
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def forward(
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self,
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hidden_states: torch.Tensor,
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positions: torch.Tensor,
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attn_metadata: AttentionMetadata,
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residual: torch.Tensor | None,
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is_warmup: bool = False,
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**kwargs,
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) -> tuple[torch.Tensor, torch.Tensor]:
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layernorm_input = hidden_states
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layernorm_output = self.input_layernorm(layernorm_input)
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residual = layernorm_output if self.postnorm else layernorm_input
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self_attention_output = torch.empty_like(layernorm_output)
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self.self_attn(
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hidden_states=layernorm_output,
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output=self_attention_output,
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positions=positions,
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)
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residual = residual * self.layernorm_attention_alpha
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self_attention_output = self_attention_output * self.layernorm_attention_beta
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layernorm_input = residual + self_attention_output
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layernorm_output = self.post_attention_layernorm(layernorm_input)
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residual = layernorm_output if self.postnorm else layernorm_input
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if self.expert_num == 1:
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hidden_states = self.mlp(layernorm_output)
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else:
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moe_layernorm_output = layernorm_output.clone()
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moe_hidden_states = self.block_sparse_moe(moe_layernorm_output)
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if self.shared_moe:
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before_moe_dtype = layernorm_output.dtype
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moe_hidden_fp32 = moe_hidden_states.to(torch.float32)
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output_mlp = self.shared_mlp(layernorm_output).to(torch.float32)
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coef, _ = self.coefficient(layernorm_output.to(torch.float32))
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if self.shared_moe_mode == "softmax":
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coef = torch.nn.functional.softmax(coef, dim=-1)
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hidden_states = moe_hidden_fp32 * (1 - coef) + output_mlp * coef
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elif self.shared_moe_mode == "sigmoid":
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coef = torch.nn.functional.sigmoid(coef)
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hidden_states = moe_hidden_fp32 * (1 - coef) + output_mlp * coef
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hidden_states = hidden_states.to(before_moe_dtype)
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else:
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hidden_states = moe_hidden_states
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residual = residual * self.layernorm_mlp_alpha
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hidden_states = hidden_states * self.layernorm_mlp_beta
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hidden_states = residual + hidden_states
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return hidden_states, None
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|
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@staticmethod
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def shared_moe_coefficient_loader(
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param: torch.Tensor, loaded_weight: torch.Tensor
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) -> None:
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assert param.size() == loaded_weight.size()
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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
|
|
|
|
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 embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.embed_tokens(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
**kwargs,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
forward_context = get_forward_context()
|
|
attn_metadata = forward_context.attn_metadata
|
|
|
|
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"]
|
|
|
|
for layer in islice(self.layers, self.start_layer, self.end_layer):
|
|
hidden_states, residual = layer(
|
|
hidden_states=hidden_states,
|
|
positions=positions,
|
|
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
|
|
|
|
self.config = config
|
|
|
|
if not hasattr(config, "sliding_window"):
|
|
config.sliding_window = None
|
|
|
|
self.CONCAT_FFN = True
|
|
|
|
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(
|
|
config.vocab_size,
|
|
self.config.hidden_size,
|
|
prefix=maybe_prefix(prefix, "lm_head"),
|
|
)
|
|
|
|
self.logits_processor = LogitsProcessor(
|
|
config.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 embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.embed_input_ids(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = 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) -> torch.Tensor:
|
|
logits = self.logits_processor(self.lm_head, hidden_states.float())
|
|
|
|
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",
|
|
) -> tuple[tuple[int, ...], ...]:
|
|
"""Calculate shape for MiniMaxText01LinearAttention cache.
|
|
|
|
Args:
|
|
vllm_config: vLLM config
|
|
|
|
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,
|
|
)
|