wehub-resource-sync
822 lines
28 KiB
Python
822 lines
28 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import copy
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from collections.abc import Iterable
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from transformers.configuration_utils import PretrainedConfig
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed 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.logger import init_logger
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from vllm.model_executor.layers.fused_moe import (
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FusedMoE,
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fused_moe_make_expert_params_mapping,
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)
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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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ColumnParallelLinear,
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MergedColumnParallelLinear,
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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.bailing_linear_attn import (
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BailingMoELinearAttention,
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_build_rope_parameters,
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)
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from vllm.model_executor.layers.mamba.mamba_utils import (
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MambaStateCopyFuncCalculator,
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MambaStateDtypeCalculator,
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MambaStateShapeCalculator,
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)
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from vllm.model_executor.layers.mla import MLAModules, MultiHeadLatentAttentionWrapper
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from vllm.model_executor.layers.quantization.base_config 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 (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from vllm.model_executor.models.bailing_moe import BailingMLP
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from vllm.sequence import IntermediateTensors
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from vllm.v1.attention.backend import AttentionMetadata
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from .interfaces import HasInnerState, IsHybrid, SupportsPP
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from .utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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is_pp_missing_parameter,
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make_layers,
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maybe_prefix,
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)
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logger = init_logger(__name__)
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def is_linear_layer(layer_idx, layer_group_size):
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if layer_idx is None:
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return False
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if layer_group_size > 0:
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return (layer_idx + 1) % layer_group_size != 0
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else:
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return False
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class BailingMoeV25MLAAttention(nn.Module):
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"""
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MLA Attention for BailingMoeV2.5 full attention layers.
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"""
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: QuantizationConfig | None = None,
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layer_id: int = 0,
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prefix: str = "attention",
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cache_config: CacheConfig | None = None,
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.layer_id = layer_id
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self.prefix = prefix
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# MLA dimensions
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self.qk_nope_head_dim = getattr(config, "qk_nope_head_dim", 128)
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self.qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 64)
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self.qk_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
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self.v_head_dim = getattr(config, "v_head_dim", 128)
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# LoRA ranks
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self.q_lora_rank = getattr(config, "q_lora_rank", None)
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self.kv_lora_rank = getattr(config, "kv_lora_rank", 512)
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tp_size = get_tensor_model_parallel_world_size()
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assert self.num_heads % tp_size == 0
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self.num_local_heads = self.num_heads // tp_size
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self.scaling = self.qk_head_dim**-0.5
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# KV projections
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self.kv_a_layernorm = RMSNorm(
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self.kv_lora_rank,
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eps=config.rms_norm_eps,
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)
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self.kv_b_proj = ColumnParallelLinear(
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self.kv_lora_rank,
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self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.kv_b_proj",
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)
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# Output projection
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self.o_proj = RowParallelLinear(
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self.num_heads * self.v_head_dim,
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self.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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if self.q_lora_rank is not None:
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# Use fused_qkv_a_proj when q_lora_rank is set
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self.fused_qkv_a_proj = MergedColumnParallelLinear(
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self.hidden_size,
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[self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim],
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.fused_qkv_a_proj",
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disable_tp=True,
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)
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self.q_a_layernorm = RMSNorm(
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self.q_lora_rank,
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eps=config.rms_norm_eps,
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)
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self.q_b_proj = ColumnParallelLinear(
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self.q_lora_rank,
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self.num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.q_b_proj",
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)
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self.q_proj = None
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self.kv_a_proj_with_mqa = None
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else:
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# Direct projections when no q_lora_rank
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self.q_proj = ColumnParallelLinear(
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self.hidden_size,
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self.num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.q_proj",
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)
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self.kv_a_proj_with_mqa = ReplicatedLinear(
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self.hidden_size,
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self.kv_lora_rank + self.qk_rope_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.kv_a_proj_with_mqa",
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)
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self.fused_qkv_a_proj = None
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self.q_a_layernorm = None
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self.q_b_proj = None
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rope_parameters = _build_rope_parameters(config) or {}
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# MLA rotates the full qk_rope_head_dim,
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# partial_rotary_factor is for the linear-attn head only.
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rope_parameters = {
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k: v for k, v in rope_parameters.items() if k != "partial_rotary_factor"
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}
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rope_parameters["rope_dim"] = self.qk_rope_head_dim
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max_position = getattr(config, "max_position_embeddings", 8192)
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self.rotary_emb = get_rope(
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head_size=self.qk_rope_head_dim,
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max_position=max_position,
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is_neox_style=False,
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rope_parameters=rope_parameters,
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)
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# Build MLAModules for MultiHeadLatentAttentionWrapper
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mla_modules = MLAModules(
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kv_a_layernorm=self.kv_a_layernorm,
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kv_b_proj=self.kv_b_proj,
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rotary_emb=self.rotary_emb,
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o_proj=self.o_proj,
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fused_qkv_a_proj=self.fused_qkv_a_proj,
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kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
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q_a_layernorm=self.q_a_layernorm,
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q_b_proj=self.q_b_proj,
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q_proj=self.q_proj,
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indexer=None,
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is_sparse=False,
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topk_indices_buffer=None,
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)
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self.mla_attn = MultiHeadLatentAttentionWrapper(
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self.hidden_size,
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self.num_local_heads,
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self.scaling,
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self.qk_nope_head_dim,
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self.qk_rope_head_dim,
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self.v_head_dim,
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self.q_lora_rank,
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self.kv_lora_rank,
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mla_modules,
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cache_config,
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quant_config,
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prefix,
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)
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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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) -> torch.Tensor:
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"""Forward pass for MLA attention."""
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return self.mla_attn(positions, hidden_states)
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class BailingMoEGate(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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params_dtype: torch.dtype | None = None,
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prefix: str = "",
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):
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super().__init__()
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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.weight = nn.Parameter(
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torch.empty(
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(config.num_experts, config.hidden_size),
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dtype=self.params_dtype,
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),
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)
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if getattr(config, "moe_router_enable_expert_bias", False):
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self.expert_bias = nn.Parameter(
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torch.empty((config.num_experts,), dtype=torch.float32),
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)
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else:
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self.expert_bias = None
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def forward(self, hidden_states):
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logits = F.linear(hidden_states.to(self.weight.dtype), self.weight, None).to(
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hidden_states.dtype
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)
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return logits
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class BailingMoeV25(nn.Module):
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"""Bailing MoE v2.5 - standalone implementation for linear attention model."""
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: QuantizationConfig | None = None,
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layer_id: int = 0,
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prefix: str = "",
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):
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super().__init__()
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self.layer_id = layer_id
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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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self.num_experts = config.num_experts
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self.top_k = config.num_experts_per_tok
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norm_topk_prob = getattr(config, "norm_topk_prob", None)
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# Ring-2.5 reference implementations normalize routing weights by default.
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self.norm_expert_prob = True if norm_topk_prob is None else bool(norm_topk_prob)
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self.hidden_size = config.hidden_size
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self.quant_config = quant_config
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self.num_shared_experts = config.num_shared_experts
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self.score_function: str | None = getattr(config, "score_function", None)
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self.n_group = getattr(config, "n_group", None)
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self.topk_group = getattr(config, "topk_group", None)
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self.use_grouped_topk = self.n_group is not None and self.topk_group is not None
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self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
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router_dtype = getattr(config, "router_dtype", None)
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if router_dtype is None or router_dtype == "fp32":
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self.router_dtype = torch.float32
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else:
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self.router_dtype = torch.bfloat16
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# Gate for routing
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self.gate = BailingMoEGate(
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config=config,
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params_dtype=self.router_dtype,
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prefix=f"{prefix}.gate",
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)
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correction_bias = (
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self.gate.expert_bias if self.gate.expert_bias is not None else None
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)
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if self.score_function is not None:
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assert (self.score_function == "softmax" and correction_bias is None) or (
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self.score_function == "sigmoid" and correction_bias is not None
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), (
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"score_function and correction_bias should be "
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"(softmax, None) or (sigmoid, not None)"
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)
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# Shared experts (using BailingMLP)
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if self.num_shared_experts > 0:
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if hasattr(config, "moe_shared_expert_intermediate_size"):
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intermediate_size = config.moe_shared_expert_intermediate_size
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else:
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intermediate_size = config.moe_intermediate_size
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intermediate_size *= config.num_shared_experts
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self.shared_experts = BailingMLP(
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intermediate_size=intermediate_size,
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config=config,
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quant_config=quant_config,
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reduce_results=False,
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prefix=f"{prefix}.shared_experts",
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)
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else:
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self.shared_experts = None
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# Routed experts using FusedMoE
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self.experts = FusedMoE(
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shared_experts=self.shared_experts,
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num_experts=self.num_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=config.moe_intermediate_size,
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renormalize=self.norm_expert_prob,
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quant_config=quant_config,
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prefix=f"{prefix}.experts",
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scoring_func=self.score_function,
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e_score_correction_bias=correction_bias,
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num_expert_group=self.n_group,
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topk_group=self.topk_group,
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use_grouped_topk=self.use_grouped_topk,
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router_logits_dtype=self.router_dtype,
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routed_scaling_factor=self.routed_scaling_factor,
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apply_routed_scale_to_output=True,
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)
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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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# Ensure contiguous token-major layout before router/projections.
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hidden_states = hidden_states.contiguous().view(-1, hidden_size)
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# router_logits: (num_tokens, n_experts)
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router_logits = self.gate(hidden_states.to(self.router_dtype))
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router_logits = router_logits.to(hidden_states.dtype)
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final_hidden_states = self.experts(
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hidden_states=hidden_states, router_logits=router_logits
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)
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return final_hidden_states.view(num_tokens, hidden_size)
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class BailingMoeV25DecoderLayer(nn.Module):
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"""Decoder layer supporting both linear and full attention."""
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def __init__(
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self,
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config: PretrainedConfig,
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vllm_config: VllmConfig,
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prefix: str = "layer",
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layer_id: int = 0,
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) -> None:
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super().__init__()
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self.layer_id = layer_id
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self.hidden_size = config.hidden_size
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# Determine attention type (0 = linear, 1 = full)
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self.attention_type = getattr(config, "attention_type", 1)
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if self.attention_type == 0: # Linear attention
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self.self_attn = BailingMoELinearAttention(
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config,
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vllm_config,
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prefix=f"{prefix}.self_attn",
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)
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else: # Full attention
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self.self_attn = BailingMoeV25MLAAttention(
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config,
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quant_config=vllm_config.quant_config,
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layer_id=layer_id,
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prefix=f"{prefix}.self_attn",
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cache_config=vllm_config.cache_config,
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)
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# MLP/MoE
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is_moe_layer = config.num_experts > 1 and layer_id >= getattr(
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config, "first_k_dense_replace", 0
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)
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if is_moe_layer:
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self.mlp = BailingMoeV25(
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config,
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quant_config=vllm_config.quant_config,
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layer_id=layer_id,
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prefix=f"{prefix}.mlp",
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)
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else:
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self.mlp = BailingMLP(
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intermediate_size=config.intermediate_size,
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config=config,
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quant_config=vllm_config.quant_config,
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reduce_results=True,
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prefix=f"{prefix}.mlp",
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)
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# Layer norms
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rms_norm_eps = float(getattr(config, "rms_norm_eps", 1e-5))
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self.input_layernorm = RMSNorm(self.hidden_size, eps=rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(self.hidden_size, eps=rms_norm_eps)
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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 | None = None,
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residual: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor | None]:
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# Input layernorm
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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# Self attention
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if self.attention_type == 0:
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# Linear attention uses output tensor
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self_attention_output = torch.zeros_like(hidden_states)
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self.self_attn(
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hidden_states=hidden_states,
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output=self_attention_output,
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positions=positions,
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)
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else:
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# Full attention
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self_attention_output = self.self_attn(hidden_states, positions)
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hidden_states, residual = self.post_attention_layernorm(
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self_attention_output, residual
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)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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@support_torch_compile(
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dynamic_arg_dims={
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"input_ids": 0,
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"positions": -1,
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"intermediate_tensors": 0,
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"inputs_embeds": 0,
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}
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)
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class BailingMoeV25Model(nn.Module):
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"""Bailing MoE v2.5 Model with hybrid attention support."""
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def __init__(
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self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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):
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super().__init__()
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config = vllm_config.model_config.hf_config
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self.config = config
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self.vocab_size = config.vocab_size
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self.embed_dim = config.hidden_size
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# Determine layer types based on layer_group_size
|
|
self.layer_group_size = getattr(config, "layer_group_size", 1)
|
|
self.num_layers = config.num_hidden_layers
|
|
|
|
# decoder_attention_types: 0 = linear, 1 = full
|
|
self.decoder_attention_types = [
|
|
0 if is_linear_layer(i, self.layer_group_size) else 1
|
|
for i in range(self.num_layers)
|
|
]
|
|
|
|
# Embeddings
|
|
if get_pp_group().is_first_rank:
|
|
self.word_embeddings = VocabParallelEmbedding(
|
|
self.vocab_size,
|
|
self.embed_dim,
|
|
org_num_embeddings=self.vocab_size,
|
|
)
|
|
else:
|
|
from vllm.model_executor.models.utils import PPMissingLayer
|
|
|
|
self.word_embeddings = PPMissingLayer()
|
|
|
|
# Layers
|
|
def layer_fn(prefix):
|
|
layer_idx = int(prefix.split(".")[-1])
|
|
layer_config = copy.deepcopy(config)
|
|
layer_config.attention_type = self.decoder_attention_types[layer_idx]
|
|
|
|
return BailingMoeV25DecoderLayer(
|
|
config=layer_config,
|
|
vllm_config=vllm_config,
|
|
prefix=prefix,
|
|
layer_id=layer_idx,
|
|
)
|
|
|
|
self.start_layer, self.end_layer, self.layers = make_layers(
|
|
self.num_layers, layer_fn, prefix=f"{prefix}.layers"
|
|
)
|
|
|
|
# Final norm
|
|
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:
|
|
from vllm.model_executor.models.utils import PPMissingLayer
|
|
|
|
self.norm = PPMissingLayer()
|
|
|
|
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.word_embeddings(input_ids)
|
|
|
|
@property
|
|
def embed_tokens(self) -> nn.Module:
|
|
return self.word_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
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.word_embeddings(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 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}
|
|
)
|
|
else:
|
|
if residual is not None:
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
else:
|
|
hidden_states = self.norm(hidden_states)
|
|
return hidden_states
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
"""Get expert parameter mapping for MoE layers."""
|
|
return fused_moe_make_expert_params_mapping(
|
|
self,
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.num_experts,
|
|
num_redundant_experts=0,
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
"""Load checkpoint weights with simplified mapping."""
|
|
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
loaded_params: set[str] = set()
|
|
|
|
# Stacked parameter mappings (fused projections)
|
|
stacked_mappings = [
|
|
(".fused_qkv_a_proj", ".q_a_proj", 0),
|
|
(".fused_qkv_a_proj", ".kv_a_proj_with_mqa", 1),
|
|
(".gate_up_proj", ".gate_proj", 0),
|
|
(".gate_up_proj", ".up_proj", 1),
|
|
]
|
|
|
|
# Expert parameter mappings from FusedMoE
|
|
expert_mappings = list(self.get_expert_mapping())
|
|
|
|
def load_param(name: str, tensor: torch.Tensor, shard_id=None) -> bool:
|
|
"""Load a single parameter."""
|
|
if name not in params_dict or is_pp_missing_parameter(name, self):
|
|
return False
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
return False
|
|
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
|
|
if shard_id is None:
|
|
weight_loader(param, tensor)
|
|
elif isinstance(shard_id, int):
|
|
weight_loader(param, tensor, shard_id)
|
|
else:
|
|
# Expert param: (expert_id, shard_id)
|
|
weight_loader(
|
|
param, tensor, name, expert_id=shard_id[0], shard_id=shard_id[1]
|
|
)
|
|
|
|
loaded_params.add(name)
|
|
return True
|
|
|
|
def normalize_name(name: str) -> str | None:
|
|
"""Normalize checkpoint name to model parameter name."""
|
|
# Skip special weights
|
|
if name.startswith("model.mtp"):
|
|
return None
|
|
# Remove 'model.' prefix if present
|
|
# (e.g., 'model.layers.0...' -> 'layers.0...')
|
|
name = name.removeprefix("model.")
|
|
# Map attention.dense based on layer type
|
|
if "attention.dense" in name:
|
|
layer_idx = (
|
|
int(name.split("layers.")[1].split(".")[0])
|
|
if "layers." in name
|
|
else 0
|
|
)
|
|
attn_name = (
|
|
"self_attn.dense"
|
|
if is_linear_layer(layer_idx, self.config.layer_group_size)
|
|
else "self_attn.o_proj"
|
|
)
|
|
name = name.replace("attention.dense", attn_name)
|
|
|
|
# Standard mappings
|
|
name = name.replace("attention.", "self_attn.")
|
|
name = name.replace(
|
|
"mlp.gate.e_score_correction_bias", "mlp.gate.expert_bias"
|
|
)
|
|
|
|
return maybe_remap_kv_scale_name(name, params_dict)
|
|
|
|
for orig_name, weight in weights:
|
|
norm_name = normalize_name(orig_name)
|
|
if norm_name is None:
|
|
continue
|
|
|
|
# Try stacked mappings
|
|
loaded = False
|
|
for param_suf, weight_suf, shard_id in stacked_mappings:
|
|
if weight_suf not in norm_name:
|
|
continue
|
|
mapped = norm_name.replace(weight_suf, param_suf).replace(
|
|
"attention.", "self_attn."
|
|
)
|
|
if load_param(mapped, weight, shard_id):
|
|
loaded = True
|
|
break
|
|
if loaded:
|
|
continue
|
|
|
|
# Handle expert weights
|
|
if "mlp.experts" in norm_name:
|
|
# Expert bias
|
|
if (
|
|
"mlp.experts.e_score_correction_bias" in norm_name
|
|
or "mlp.experts.expert_bias" in norm_name
|
|
):
|
|
alt = norm_name.replace(
|
|
"mlp.experts.e_score_correction_bias", "mlp.gate.expert_bias"
|
|
).replace("mlp.experts.expert_bias", "mlp.gate.expert_bias")
|
|
if load_param(alt, weight) or load_param(norm_name, weight):
|
|
continue
|
|
|
|
# Routed experts
|
|
for param_name, weight_name, expert_id, shard_id in expert_mappings:
|
|
if weight_name not in norm_name:
|
|
continue
|
|
mapped = norm_name.replace(weight_name, param_name)
|
|
if load_param(mapped, weight, (expert_id, shard_id)):
|
|
break
|
|
continue
|
|
|
|
# General parameters
|
|
load_param(norm_name, weight)
|
|
|
|
return loaded_params
|
|
|
|
|
|
class BailingMoeV25ForCausalLM(nn.Module, HasInnerState, IsHybrid, SupportsPP):
|
|
"""Bailing MoE v2.5 For CausalLM."""
|
|
|
|
packed_modules_mapping = {
|
|
"gate_up_proj": ["gate_proj", "up_proj"],
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
*,
|
|
vllm_config: VllmConfig,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
|
|
self.model = BailingMoeV25Model(
|
|
vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"),
|
|
)
|
|
|
|
if get_pp_group().is_last_rank:
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "lm_head"),
|
|
)
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
|
|
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 | None,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.model(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
intermediate_tensors=intermediate_tensors,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
return self.logits_processor(self.lm_head, hidden_states)
|
|
|
|
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
|
|
),
|
|
}
|
|
)
|
|
|
|
@classmethod
|
|
def get_mamba_state_shape_from_config(
|
|
cls,
|
|
vllm_config: VllmConfig,
|
|
) -> tuple[tuple[int, ...], ...]:
|
|
"""Calculate shape for linear attention cache."""
|
|
config = vllm_config.model_config.hf_config
|
|
tp_size = vllm_config.parallel_config.tensor_parallel_size
|
|
|
|
head_dim = getattr(
|
|
config, "head_dim", config.hidden_size // config.num_attention_heads
|
|
)
|
|
|
|
# Return base state shape from linear attention (no padding)
|
|
return MambaStateShapeCalculator.linear_attention_state_shape(
|
|
num_heads=config.num_attention_heads,
|
|
tp_size=tp_size,
|
|
head_dim=head_dim,
|
|
)
|
|
|
|
@classmethod
|
|
def get_mamba_state_dtype_from_config(
|
|
cls,
|
|
vllm_config: VllmConfig,
|
|
) -> tuple[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_copy_func(cls) -> tuple:
|
|
return MambaStateCopyFuncCalculator.linear_attention_state_copy_func()
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
return self.model.get_expert_mapping()
|