705 lines
25 KiB
Python
705 lines
25 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Iterable
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from itertools import islice
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import torch
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from torch import nn
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from vllm.attention.backends.abstract import AttentionType
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from vllm_kunlun.ops.attention.layer import Attention
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from vllm.config import (
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CacheConfig,
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VllmConfig,
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get_current_vllm_config,
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)
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from vllm.distributed import (
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get_ep_group,
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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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tensor_model_parallel_all_gather,
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)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.fused_moe.layer 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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RowParallelLinear,
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)
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from vllm_kunlun.ops.linear import QKVParallelLinear
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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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 (
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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.utils import sequence_parallel_chunk
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from vllm.sequence import IntermediateTensors
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from vllm.model_executor.models.interfaces import MixtureOfExperts, SupportsPP
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from vllm.model_executor.models.utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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extract_layer_index,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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from vllm_kunlun.ops.activation import SiluAndMul
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logger = init_logger(__name__)
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class MiMoV2MLP(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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hidden_act: str,
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quant_config: QuantizationConfig | None = None,
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reduce_results: bool = True,
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prefix: str = "",
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) -> None:
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super().__init__()
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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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reduce_results=reduce_results,
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prefix=f"{prefix}.down_proj",
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)
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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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def forward(self, x):
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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 MiMoV2MoE(nn.Module):
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def __init__(
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self,
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vllm_config: VllmConfig,
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prefix: str = "",
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is_nextn: bool = False,
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):
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super().__init__()
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config = vllm_config.model_config.hf_text_config
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parallel_config = vllm_config.parallel_config
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quant_config = vllm_config.quant_config
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self.tp_size = get_tensor_model_parallel_world_size()
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self.ep_group = get_ep_group().device_group
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self.ep_rank = get_ep_group().rank_in_group
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self.ep_size = self.ep_group.size()
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self.n_routed_experts = config.n_routed_experts
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if self.tp_size > config.n_routed_experts:
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raise ValueError(
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f"Tensor parallel size {self.tp_size} is greater than "
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f"the number of experts {config.n_routed_experts}."
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)
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if config.hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {config.hidden_act}. "
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"Only silu is supported for now."
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)
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vllm_config = get_current_vllm_config()
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eplb_config = vllm_config.parallel_config.eplb_config
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self.enable_eplb = parallel_config.enable_eplb
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self.n_logical_experts = self.n_routed_experts
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self.n_redundant_experts = eplb_config.num_redundant_experts
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self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
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self.n_local_physical_experts = self.n_physical_experts // self.ep_size
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self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
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self.physical_expert_end = (
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self.physical_expert_start + self.n_local_physical_experts
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)
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self.gate_dtype = torch.float32
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self.gate = nn.Linear(
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config.hidden_size,
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config.n_routed_experts,
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bias=False,
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dtype=self.gate_dtype,
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)
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self.gate.e_score_correction_bias = nn.Parameter(
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torch.empty(config.n_routed_experts, dtype=self.gate_dtype)
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)
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self.experts = FusedMoE(
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num_experts=self.n_routed_experts,
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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.moe_intermediate_size,
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reduce_results=True,
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renormalize=config.norm_topk_prob,
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quant_config=quant_config,
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prefix=f"{prefix}.experts",
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e_score_correction_bias=self.gate.e_score_correction_bias,
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enable_eplb=self.enable_eplb,
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num_redundant_experts=self.n_redundant_experts,
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use_grouped_topk=True,
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num_expert_group=config.n_group,
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topk_group=config.topk_group,
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scoring_func="sigmoid",
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)
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self.register_buffer("kunlun_linear_weights", torch.zeros(
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config.num_local_experts,config.hidden_size,dtype=torch.float))
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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assert hidden_states.dim() <= 2, "MiMoV2MoE only supports 1D or 2D inputs"
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is_input_1d = hidden_states.dim() == 1
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num_tokens, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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if self.gate_dtype is not None:
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gate_input = hidden_states.to(self.gate_dtype)
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else:
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gate_input = hidden_states
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router_logits = self.gate(gate_input)
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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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return final_hidden_states.squeeze(0) if is_input_1d else final_hidden_states
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class MiMoV2Attention(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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num_kv_heads: int,
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head_dim: int,
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v_head_dim: int | None = None,
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sliding_window_size: int = -1,
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attention_bias: bool = False,
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add_swa_attention_sink_bias: bool = False,
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layer_id: int = 0,
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rope_theta: float = 1000000,
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max_position_embeddings: int = 32768,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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partial_rotary_factor: float = 1.0,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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self.layer_id = layer_id
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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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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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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.v_head_dim = v_head_dim if v_head_dim is not None else head_dim
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self.q_size = self.num_heads * self.head_dim
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self.k_size = self.num_kv_heads * self.head_dim
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self.v_size = self.num_kv_heads * self.v_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.max_position_embeddings = max_position_embeddings
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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=attention_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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v_head_size=self.v_head_dim,
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.v_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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reduce_results=True,
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prefix=f"{prefix}.o_proj",
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=self.rope_theta,
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partial_rotary_factor=partial_rotary_factor
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)
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self.attention_sink_bias = (
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torch.nn.Parameter(torch.empty(self.num_heads), requires_grad=False)
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if add_swa_attention_sink_bias
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else None
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)
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sliding_window = sliding_window_size if sliding_window_size > -1 else None
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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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per_layer_sliding_window=sliding_window,
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attn_type=AttentionType.DECODER,
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prefix=f"{prefix}.attn",
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sinks=self.attention_sink_bias,
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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v = v.view(-1, self.num_kv_heads, self.v_head_dim)
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v = torch.nn.functional.pad(v, [0, self.head_dim - self.v_head_dim], value=0)
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v = v.view(-1, self.num_kv_heads * self.head_dim)
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attn_output = self.attn(q, k, v)
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attn_output = attn_output.view(-1, self.num_heads, self.head_dim)[
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..., : self.v_head_dim
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].reshape(-1, self.num_heads * self.v_head_dim)
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output, _ = self.o_proj(attn_output)
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return output
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class MiMoV2FlashDecoderLayer(nn.Module):
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def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
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super().__init__()
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config = vllm_config.model_config.hf_text_config
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quant_config = vllm_config.quant_config
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layer_id = extract_layer_index(prefix)
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self.hidden_size = config.hidden_size
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self.config = config
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self.layer_id = layer_id
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rope_theta = getattr(config, "rope_theta", 1000000)
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max_position_embeddings = getattr(config, "max_position_embeddings", 32768)
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if self.is_compressed_softmax_layer():
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self.self_attn = MiMoV2Attention(
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hidden_size=self.hidden_size,
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num_heads=config.swa_num_attention_heads,
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num_kv_heads=config.swa_num_key_value_heads,
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head_dim=config.swa_head_dim,
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v_head_dim=getattr(config, "swa_v_head_dim", None),
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sliding_window_size=config.sliding_window_size,
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attention_bias=config.attention_bias,
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add_swa_attention_sink_bias=getattr(
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config, "add_swa_attention_sink_bias", False
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),
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layer_id=layer_id,
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rope_theta=getattr(config, "swa_rope_theta", rope_theta),
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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partial_rotary_factor=getattr(config, "partial_rotary_factor", 1.0),
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prefix=f"{prefix}.self_attn",
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)
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else:
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self.self_attn = MiMoV2Attention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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head_dim=config.head_dim,
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v_head_dim=getattr(config, "v_head_dim", None),
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sliding_window_size=-1, # normal attention
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attention_bias=config.attention_bias,
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layer_id=layer_id,
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rope_theta=rope_theta,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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partial_rotary_factor=getattr(config, "partial_rotary_factor", 1.0),
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prefix=f"{prefix}.self_attn",
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)
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self.is_layer_sparse = self.is_moe_layer(layer_id)
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if self.is_layer_sparse:
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self.mlp = MiMoV2MoE(
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vllm_config=vllm_config,
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prefix=f"{prefix}.mlp",
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)
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else:
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self.mlp = MiMoV2MLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.layernorm_epsilon
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: torch.Tensor | None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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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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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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)
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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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def is_moe_layer(self, layer_idx: int) -> bool:
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return (
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hasattr(self.config, "moe_layer_freq")
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and layer_idx >= 0
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and not isinstance(self.config.moe_layer_freq, int)
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and self.config.moe_layer_freq[layer_idx]
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)
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def is_compressed_softmax_layer(self) -> bool:
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return self.config.hybrid_layer_pattern[self.layer_id] == 1
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class MiMoV2Model(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config.get_text_config()
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quant_config = vllm_config.quant_config
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eplb_config = vllm_config.parallel_config.eplb_config
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self.config = config
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self.quant_config = quant_config
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self.vocab_size = config.vocab_size
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self.num_redundant_experts = eplb_config.num_redundant_experts
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self.v_scale = getattr(config, "attention_value_scale", None)
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if get_pp_group().is_first_rank or (
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config.tie_word_embeddings and get_pp_group().is_last_rank
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):
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=f"{prefix}.embed_tokens",
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)
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else:
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self.embed_tokens = PPMissingLayer()
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: MiMoV2FlashDecoderLayer(
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vllm_config=vllm_config,
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prefix=prefix,
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),
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prefix=f"{prefix}.layers",
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)
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size
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)
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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
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else:
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self.norm = PPMissingLayer()
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.embed_input_ids(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for idx, layer in enumerate(
|
|
islice(self.layers, self.start_layer, self.end_layer)
|
|
):
|
|
hidden_states, residual = layer(positions, hidden_states, residual)
|
|
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors(
|
|
{"hidden_states": hidden_states, "residual": residual}
|
|
)
|
|
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
|
|
return hidden_states
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
return FusedMoE.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.n_routed_experts,
|
|
num_redundant_experts=self.num_redundant_experts,
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("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),
|
|
]
|
|
|
|
tp_rank = get_tensor_model_parallel_rank()
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
loaded_params: set[str] = set()
|
|
expert_params_mapping = self.get_expert_mapping()
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
|
|
continue
|
|
if "mtp" in name:
|
|
continue
|
|
|
|
if self.quant_config is not None:
|
|
cache_scale_name = self.quant_config.get_cache_scale(name)
|
|
if cache_scale_name is not None and cache_scale_name in params_dict:
|
|
param = params_dict[cache_scale_name]
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
|
|
kv_scale = loaded_weight
|
|
if kv_scale.dim() > 0 and kv_scale.numel() > 1:
|
|
kv_scale = kv_scale.view(-1)[0]
|
|
|
|
weight_loader(param, kv_scale)
|
|
loaded_params.add(cache_scale_name)
|
|
continue
|
|
|
|
expert_matched = False
|
|
for param_name, weight_name, expert_id, shard_id in expert_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
|
|
name_rewritten = name.replace(weight_name, param_name)
|
|
|
|
if is_pp_missing_parameter(name_rewritten, self):
|
|
continue
|
|
|
|
if (
|
|
name_rewritten.endswith(".bias") or name_rewritten.endswith("_bias")
|
|
) and name_rewritten not in params_dict:
|
|
continue
|
|
|
|
if name_rewritten not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name_rewritten]
|
|
weight_loader = param.weight_loader
|
|
|
|
weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name_rewritten,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
)
|
|
loaded_params.add(name_rewritten)
|
|
expert_matched = True
|
|
break
|
|
|
|
if expert_matched:
|
|
continue
|
|
|
|
stacked_matched = False
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
name_rewritten = name.replace(weight_name, param_name)
|
|
|
|
if (
|
|
name_rewritten.endswith(".bias")
|
|
and name_rewritten not in params_dict
|
|
):
|
|
continue
|
|
|
|
if is_pp_missing_parameter(name_rewritten, self):
|
|
continue
|
|
|
|
if name_rewritten not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name_rewritten]
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
|
|
if param_name == "qkv_proj" and shard_id == "v":
|
|
v_scale = (
|
|
self.v_scale
|
|
if self.v_scale is not None
|
|
else getattr(self.config, "attention_value_scale", None)
|
|
)
|
|
if v_scale is not None and (
|
|
name.endswith("weight_scale_inv") or name.endswith(".bias")
|
|
):
|
|
loaded_weight *= float(v_scale)
|
|
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
loaded_params.add(name_rewritten)
|
|
|
|
stacked_matched = True
|
|
break
|
|
|
|
if stacked_matched:
|
|
continue
|
|
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
|
|
orig_name = name
|
|
mapped_name = maybe_remap_kv_scale_name(name, params_dict)
|
|
name = mapped_name if mapped_name is not None else orig_name
|
|
|
|
if name not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
|
|
if "attention_sink_bias" in name:
|
|
total_heads = loaded_weight.shape[0]
|
|
heads_per_rank = total_heads // tp_size
|
|
head_start = tp_rank * heads_per_rank
|
|
narrow_weight = loaded_weight.narrow(0, head_start, heads_per_rank)
|
|
|
|
param.data.copy_(narrow_weight)
|
|
loaded_params.add(name)
|
|
else:
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
|
|
return loaded_params
|
|
|
|
|
|
class MiMoV2FlashForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
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 = MiMoV2Model(
|
|
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"),
|
|
)
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors
|
|
)
|
|
|
|
def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
|
|
self.model.aux_hidden_state_layers = layers
|
|
|
|
def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
|
|
num_layers = len(self.model.layers)
|
|
return (2, num_layers // 2, num_layers - 3)
|
|
|
|
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,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
hidden_states = self.model(
|
|
input_ids, positions, intermediate_tensors, inputs_embeds
|
|
)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor | None:
|
|
logits = self.logits_processor(self.lm_head, hidden_states)
|
|
return logits
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
return self.model.get_expert_mapping()
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights) |