346 lines
14 KiB
Python
346 lines
14 KiB
Python
from abc import abstractmethod
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from typing import List, Optional, Tuple
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import torch
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from vllm.distributed import (get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.logger import init_logger
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from vllm.model_executor.custom_op import CustomOp
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig, QuantizeMethodBase)
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from vllm.model_executor.utils import set_weight_attrs
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logger = init_logger(__name__)
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class FusedMoEMethodBase(QuantizeMethodBase):
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@abstractmethod
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def create_weights(self, layer: torch.nn.Module, num_experts: int,
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hidden_size: int, intermediate_size: int,
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params_dtype: torch.dtype, **extra_weight_attrs):
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raise NotImplementedError
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@abstractmethod
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def apply(self, layer: torch.nn.Module, x: torch.Tensor,
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router_logits: torch.Tensor, top_k: int, renormalize: bool,
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use_grouped_topk: bool) -> torch.Tensor:
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raise NotImplementedError
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class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
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"""MoE method without quantization."""
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def create_weights(self, layer: torch.nn.Module, num_experts: int,
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hidden_size: int, intermediate_size: int,
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params_dtype: torch.dtype, **extra_weight_attrs):
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# Fused gate_up_proj (column parallel)
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w13_weight = torch.nn.Parameter(torch.empty(num_experts,
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2 * intermediate_size,
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hidden_size,
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dtype=params_dtype),
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requires_grad=False)
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layer.register_parameter("w13_weight", w13_weight)
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set_weight_attrs(w13_weight, extra_weight_attrs)
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# down_proj (row parallel)
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w2_weight = torch.nn.Parameter(torch.empty(num_experts,
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hidden_size,
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intermediate_size,
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dtype=params_dtype),
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requires_grad=False)
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layer.register_parameter("w2_weight", w2_weight)
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set_weight_attrs(w2_weight, extra_weight_attrs)
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def apply(self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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renormalize: bool,
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use_grouped_topk: bool,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None) -> torch.Tensor:
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return self.forward(x=x,
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layer=layer,
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router_logits=router_logits,
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top_k=top_k,
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renormalize=renormalize,
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use_grouped_topk=use_grouped_topk,
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topk_group=topk_group,
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num_expert_group=num_expert_group)
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def forward_cuda(self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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use_grouped_topk: bool,
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top_k: int,
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router_logits: torch.Tensor,
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renormalize: bool,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None) -> torch.Tensor:
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from vllm.model_executor.layers.fused_moe.fused_moe import (
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fused_experts)
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topk_weights, topk_ids = FusedMoE.select_experts(
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hidden_states=x,
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router_logits=router_logits,
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use_grouped_topk=use_grouped_topk,
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top_k=top_k,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group)
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return fused_experts(hidden_states=x,
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w1=layer.w13_weight,
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w2=layer.w2_weight,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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inplace=True)
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def forward_cpu(self, *args, **kwargs):
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raise NotImplementedError(
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"The CPU backend currently does not support MoE.")
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def forward_tpu(self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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use_grouped_topk: bool,
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top_k: int,
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router_logits: torch.Tensor,
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renormalize: bool,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None) -> torch.Tensor:
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from vllm.model_executor.layers.fused_moe.moe_pallas import fused_moe
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assert not use_grouped_topk
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assert num_expert_group is None
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assert topk_group is None
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return fused_moe(hidden_states=x,
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w1=layer.w13_weight,
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w2=layer.w2_weight,
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topk=top_k,
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gating_output=router_logits,
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renormalize=renormalize)
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class FusedMoE(torch.nn.Module):
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"""FusedMoE layer for MoE models.
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This layer contains both MergedColumnParallel weights (gate_up_proj /
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w13) and RowParallelLinear weights (down_proj/ w2).
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Note: Mixtral uses w1, w2, and w3 for gate, up, and down_proj. We
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copy that naming convention here and handle any remapping in the
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load_weights function in each model implementation.
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Args:
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num_experts: Number of experts in the model
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top_k: Number of experts selected for each token
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hidden_size: Input hidden state size of the transformer
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intermediate_size: Intermediate size of the experts
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params_dtype: Data type for the parameters.
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reduce_results: Whether to all all_reduce on the output of the layer
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renomalize: Whether to renormalize the logits in the fused_moe kernel
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quant_config: Quantization configure.
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"""
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def __init__(
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self,
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num_experts: int,
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top_k: int,
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hidden_size: int,
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intermediate_size: int,
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params_dtype: Optional[torch.dtype] = None,
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reduce_results: bool = False,
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renormalize: bool = True,
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use_grouped_topk: bool = False,
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num_expert_group: Optional[int] = None,
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topk_group: Optional[int] = None,
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quant_config: Optional[QuantizationConfig] = None,
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tp_size: Optional[int] = 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.tp_size = (tp_size if tp_size is not None else
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get_tensor_model_parallel_world_size())
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self.top_k = top_k
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self.num_experts = num_experts
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self.intermediate_size_per_partition = intermediate_size // self.tp_size
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self.reduce_results = reduce_results
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self.renormalize = renormalize
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self.use_grouped_topk = use_grouped_topk
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if self.use_grouped_topk:
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assert num_expert_group is not None and topk_group is not None
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self.num_expert_group = num_expert_group
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self.topk_group = topk_group
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if quant_config is None:
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self.quant_method: Optional[QuantizeMethodBase] = (
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UnquantizedFusedMoEMethod())
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else:
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self.quant_method = quant_config.get_quant_method(self, prefix)
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assert self.quant_method is not None
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self.quant_method.create_weights(
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layer=self,
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num_experts=num_experts,
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hidden_size=hidden_size,
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intermediate_size=self.intermediate_size_per_partition,
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params_dtype=params_dtype,
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weight_loader=self.weight_loader)
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def weight_loader(self, param: torch.nn.Parameter,
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loaded_weight: torch.Tensor, weight_name: str,
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shard_id: str, expert_id: int) -> None:
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if shard_id not in ("w1", "w2", "w3"):
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raise ValueError(f"shard_id must be ['w1','w2','w3'] but "
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f"got {shard_id}.")
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# Special case for fp8 scales.
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if getattr(param, "is_fp8_scale", False):
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self._load_fp8_scale(param.data, loaded_weight, weight_name,
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shard_id, expert_id)
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return
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expert_data = param.data[expert_id]
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tp_rank = get_tensor_model_parallel_rank()
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# If transposed, weight is saved as [input_dim, output_dim]
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# Otherwise, weight is saved as [output_dim, input_dim]
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# Default is not transposed/input dim is dim 1
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input_dim = getattr(param, "input_dim", 1)
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output_dim = getattr(param, "output_dim", 0)
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# Index the loaded weight for tp sharding.
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# down_proj: "RowParallel" so tp sharding on input_dim
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if shard_id == "w2":
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shard_dim = input_dim
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shard_size = expert_data.shape[shard_dim]
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# gate_up_proj: "MergedColumnParallel", so tp sharding on output_dim
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elif shard_id in ("w1", "w3"):
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shard_dim = output_dim
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shard_size = expert_data.shape[output_dim] // 2
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offset = shard_size * tp_rank
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loaded_weight = loaded_weight.narrow(shard_dim, offset, shard_size)
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# Narrow parameter and load.
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# w1, gate_proj: Load into first logical weight of w13.
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if shard_id == "w1":
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expert_data = expert_data.narrow(shard_dim, 0, shard_size)
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expert_data.copy_(loaded_weight)
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# w3, up_proj: Load into second logical weight of w13.
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elif shard_id == "w3":
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expert_data = expert_data.narrow(shard_dim, shard_size, shard_size)
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expert_data.copy_(loaded_weight)
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# w2, down_proj: Load into only logical weight of w2.
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elif shard_id == "w2":
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expert_data.copy_(loaded_weight)
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else:
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raise ValueError(
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f"Expected shard_id w1,w2 or w3 but got {shard_id}")
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@staticmethod
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def select_experts(hidden_states: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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use_grouped_topk: bool,
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renormalize: bool,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None):
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from vllm.model_executor.layers.fused_moe.fused_moe import (
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fused_topk, grouped_topk)
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# DeekSeekv2 uses grouped_top_k
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if use_grouped_topk:
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assert topk_group is not None
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assert num_expert_group is not None
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topk_weights, topk_ids = grouped_topk(
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hidden_states=hidden_states,
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gating_output=router_logits,
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topk=top_k,
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renormalize=renormalize,
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num_expert_group=num_expert_group,
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topk_group=topk_group)
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else:
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topk_weights, topk_ids = fused_topk(hidden_states=hidden_states,
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gating_output=router_logits,
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topk=top_k,
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renormalize=renormalize)
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return topk_weights, topk_ids
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def forward(self, hidden_states: torch.Tensor,
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router_logits: torch.Tensor):
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assert self.quant_method is not None
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# Matrix multiply.
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final_hidden_states = self.quant_method.apply(
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layer=self,
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x=hidden_states,
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router_logits=router_logits,
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top_k=self.top_k,
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renormalize=self.renormalize,
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use_grouped_topk=self.use_grouped_topk,
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topk_group=self.topk_group,
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num_expert_group=self.num_expert_group)
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if self.reduce_results and self.tp_size > 1:
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final_hidden_states = tensor_model_parallel_all_reduce(
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final_hidden_states)
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return final_hidden_states
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@classmethod
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def make_expert_params_mapping(
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cls, ckpt_gate_proj_name: str, ckpt_down_proj_name: str,
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ckpt_up_proj_name: str,
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num_experts: int) -> List[Tuple[str, str, int, str]]:
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return [
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# (param_name, weight_name, expert_id, shard_id)
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("experts.w13_" if weight_name
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in [ckpt_gate_proj_name, ckpt_up_proj_name] else "experts.w2_",
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f"experts.{expert_id}.{weight_name}.", expert_id, shard_id)
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for expert_id in range(num_experts) for shard_id, weight_name in [
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("w1", ckpt_gate_proj_name),
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("w2", ckpt_down_proj_name),
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("w3", ckpt_up_proj_name),
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]
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]
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def _load_fp8_scale(self, param: torch.nn.Parameter,
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loaded_weight: torch.Tensor, weight_name: str,
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shard_id: str, expert_id: int) -> None:
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param_data = param.data
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# Input scales can be loaded directly and should be equal.
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if "input_scale" in weight_name:
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if param_data[expert_id] != 1 and (param_data[expert_id] -
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loaded_weight).abs() > 1e-5:
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raise ValueError(
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"input_scales of w1 and w3 of a layer "
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f"must be equal. But got {param_data[expert_id]} "
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f"vs. {loaded_weight}")
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param_data[expert_id] = loaded_weight
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# Weight scales
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elif "weight_scale" in weight_name:
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# If we are in merged column case (gate_up_proj)
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if shard_id in ("w1", "w3"):
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# We have to keep the weight scales of w1 and w3 because
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# we need to re-quantize w1/w3 weights after weight loading.
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idx = 0 if shard_id == "w1" else 1
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param_data[expert_id][idx] = loaded_weight
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# If we are in the row parallel case (down_proj)
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else:
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param_data[expert_id] = loaded_weight
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