636 lines
23 KiB
Python
636 lines
23 KiB
Python
# Adapted from
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# https://github.com/vllm-project/vllm/tree/v0.5.4/vllm/model_executor/layers/fused_moe
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import os
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from abc import abstractmethod
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from typing import List, Optional, Tuple
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import torch
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import torch.nn.functional as F
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from vllm.distributed import (
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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_reduce,
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)
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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,
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QuantizeMethodBase,
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)
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from vllm.model_executor.layers.quantization.fp8 import Fp8Config
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from vllm.model_executor.utils import set_weight_attrs
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from sglang.srt.layers.fused_moe.fused_moe import padding_size
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from sglang.srt.utils import is_hip
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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(
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self,
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layer: torch.nn.Module,
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num_experts: int,
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hidden_size: int,
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intermediate_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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raise NotImplementedError
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@abstractmethod
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def apply(
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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 = 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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) -> 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(
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self,
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layer: torch.nn.Module,
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num_experts: int,
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hidden_size: int,
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intermediate_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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# Fused gate_up_proj (column parallel)
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w13_weight = torch.nn.Parameter(
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torch.empty(
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num_experts, 2 * intermediate_size, hidden_size, dtype=params_dtype
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),
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requires_grad=False,
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)
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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(
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torch.empty(
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num_experts, hidden_size, intermediate_size, dtype=params_dtype
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),
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requires_grad=False,
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)
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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(
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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 = 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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) -> torch.Tensor:
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return self.forward(
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x,
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layer.w13_weight,
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layer.w2_weight,
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router_logits,
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top_k,
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renormalize,
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use_grouped_topk,
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num_expert_group,
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topk_group,
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)
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def forward_cuda(
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self,
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x: torch.Tensor,
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w1: torch.Tensor,
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w2: 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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num_expert_group: Optional[int],
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topk_group: Optional[int],
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) -> torch.Tensor:
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from sglang.srt.layers.fused_moe.fused_moe import fused_moe
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return fused_moe(
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x,
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w1,
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w2,
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router_logits,
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top_k,
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renormalize=renormalize,
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inplace=True,
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use_grouped_topk=use_grouped_topk,
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num_expert_group=num_expert_group,
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topk_group=topk_group,
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)
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def forward_cpu(self, *args, **kwargs):
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raise NotImplementedError("The CPU backend currently does not support MoE.")
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def forward_tpu(
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self,
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x: torch.Tensor,
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w1: torch.Tensor,
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w2: 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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num_expert_group: Optional[int],
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topk_group: Optional[int],
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) -> 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(x, w1, w2, router_logits, top_k, 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 = (
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tp_size if tp_size is not None else get_tensor_model_parallel_world_size()
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)
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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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)
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else:
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if isinstance(quant_config, Fp8Config):
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self.quant_method = Fp8MoEMethod(quant_config)
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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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)
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def weight_loader(
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self,
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param: torch.nn.Parameter,
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loaded_weight: torch.Tensor,
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weight_name: str,
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shard_id: int,
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expert_id: int,
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use_presharded_weights: bool = False,
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):
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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 (
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param_data[expert_id] != 1
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and (param_data[expert_id] - loaded_weight).abs() > 1e-5
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):
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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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)
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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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# shard_id 0 == gate_proj / w1
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# shard_id 2 == up_proj / w3
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if shard_id == 0 or shard_id == 2:
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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 == 0 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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# shard_id 1 == down_proj / w2
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else:
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param_data[expert_id] = loaded_weight
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# Weights
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else:
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tp_rank = get_tensor_model_parallel_rank()
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shard_size = self.intermediate_size_per_partition
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if use_presharded_weights:
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shard = slice(None)
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else:
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shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
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# w1, gate_proj case: Load into first shard of w13.
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if shard_id == 0:
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param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :]
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# w3, up_proj case: Load into second shard of w13.
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elif shard_id == 2:
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param_data[expert_id, shard_size : 2 * shard_size, :] = loaded_weight[
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shard, :
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]
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# w2, down_proj case: Load into only shard of w2.
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elif shard_id == 1:
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param_data[expert_id, :, :] = loaded_weight[:, shard]
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else:
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raise ValueError(f"Shard id must be in [0,1,2] but got {shard_id}")
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def forward(self, hidden_states: torch.Tensor, 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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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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num_expert_group=self.num_expert_group,
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topk_group=self.topk_group,
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)
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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(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,
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ckpt_gate_proj_name: str,
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ckpt_down_proj_name: str,
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ckpt_up_proj_name: str,
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num_experts: int,
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) -> List[Tuple[str, str, int, int]]:
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gate_up = [ckpt_gate_proj_name, ckpt_up_proj_name]
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gate_down_up = [ckpt_gate_proj_name, ckpt_down_proj_name, ckpt_up_proj_name]
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return (
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[
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# These are the weight scales for the experts
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# (param_name, weight_name, expert_id, shard_id)
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(
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(
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"experts.w13_scale"
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if weight_name in gate_up
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else "experts.w2_scale"
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),
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f"experts.{expert_id}.{weight_name}.weight_scale",
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expert_id,
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shard_id,
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)
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for expert_id in range(num_experts)
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for shard_id, weight_name in enumerate(gate_down_up)
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]
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+ [
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# These are the weights for the experts
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# (param_name, weight_name, expert_id, shard_id)
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(
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(
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"experts.w13_weight"
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if weight_name in gate_up
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else "experts.w2_weight"
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),
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f"experts.{expert_id}.{weight_name}.weight",
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expert_id,
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shard_id,
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)
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for expert_id in range(num_experts)
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for shard_id, weight_name in enumerate(gate_down_up)
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]
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+ [
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# These are the weight scales for the experts
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# (param_name, weight_name, expert_id, shard_id)
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(
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(
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"experts.a13_scale"
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if weight_name in gate_up
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else "experts.a2_scale"
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),
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f"experts.{expert_id}.{weight_name}.input_scale",
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expert_id,
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shard_id,
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)
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for expert_id in range(num_experts)
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for shard_id, weight_name in enumerate(gate_down_up)
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]
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)
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import torch
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from torch.nn import Module
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from vllm import _custom_ops as ops
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from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
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all_close_1d,
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normalize_e4m3fn_to_e4m3fnuz,
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per_tensor_dequantize,
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)
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from vllm.utils import print_warning_once
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class Fp8MoEMethod(FusedMoEMethodBase):
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"""MoE method for FP8.
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Supports loading FP8 checkpoints with static weight scale and
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dynamic/static activation scale.
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Also supports loading quantized FP16/BF16 model checkpoints with dynamic
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activation scaling. The weight scaling factor will be initialized after
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the model weights are loaded.
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Args:
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quant_config: The quantization config.
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"""
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def __init__(self, quant_config: Fp8Config):
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self.quant_config = quant_config
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def create_weights(
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self,
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layer: Module,
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num_experts: int,
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hidden_size: int,
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intermediate_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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if self.quant_config.is_checkpoint_fp8_serialized:
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params_dtype = torch.float8_e4m3fn
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# WEIGHTS
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w13_weight = torch.nn.Parameter(
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torch.empty(
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num_experts, 2 * intermediate_size, hidden_size, dtype=params_dtype
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),
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requires_grad=False,
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)
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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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w2_weight = torch.nn.Parameter(
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torch.empty(
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num_experts, hidden_size, intermediate_size, dtype=params_dtype
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),
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requires_grad=False,
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)
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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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# WEIGHT_SCALES
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# Allocate 2 scales for w1 and w3 respectively.
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# They will be combined to a single scale after weight loading.
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w13_scale = torch.nn.Parameter(
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torch.ones(num_experts, 2, dtype=torch.float32), requires_grad=False
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)
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layer.register_parameter("w13_scale", w13_scale)
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w2_scale = torch.nn.Parameter(
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torch.ones(num_experts, dtype=torch.float32), requires_grad=False
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)
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layer.register_parameter("w2_scale", w2_scale)
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# If loading fp8 checkpoint, pass the weight loaders.
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# If loading an fp16 checkpoint, do not (we will quantize in
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# process_weights_after_loading()
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if self.quant_config.is_checkpoint_fp8_serialized:
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set_weight_attrs(w13_scale, extra_weight_attrs)
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set_weight_attrs(w2_scale, extra_weight_attrs)
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# INPUT_SCALES
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if self.quant_config.activation_scheme == "static":
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if not self.quant_config.is_checkpoint_fp8_serialized:
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raise ValueError(
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"Found static activation scheme for checkpoint that "
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"was not serialized fp8."
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)
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a13_scale = torch.nn.Parameter(
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torch.ones(num_experts, dtype=torch.float32), requires_grad=False
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)
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layer.register_parameter("a13_scale", a13_scale)
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set_weight_attrs(a13_scale, extra_weight_attrs)
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a2_scale = torch.nn.Parameter(
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torch.ones(num_experts, dtype=torch.float32), requires_grad=False
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)
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layer.register_parameter("a2_scale", a2_scale)
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set_weight_attrs(a2_scale, extra_weight_attrs)
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else:
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layer.a13_scale = None
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layer.a2_scale = None
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def process_weights_after_loading(self, layer: Module) -> None:
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# If checkpoint is fp16 or bfloat16, quantize in place.
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if not self.quant_config.is_checkpoint_fp8_serialized:
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# If ROCm, use float8_e4m3fnuz instead (MI300x HW)
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fp8_dtype = torch.float8_e4m3fnuz if is_hip() else torch.float8_e4m3fn
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w13_weight = torch.empty_like(layer.w13_weight.data, dtype=fp8_dtype)
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w2_weight = torch.empty_like(layer.w2_weight.data, dtype=fp8_dtype)
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# Re-initialize w13_scale because we directly quantize
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# merged w13 weights and generate a single scaling factor.
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layer.w13_scale = torch.nn.Parameter(
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torch.ones(
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layer.num_experts, dtype=torch.float32, device=w13_weight.device
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
for expert in range(layer.num_experts):
|
|
w13_weight[expert, :, :], layer.w13_scale[expert] = (
|
|
ops.scaled_fp8_quant(layer.w13_weight.data[expert, :, :])
|
|
)
|
|
w2_weight[expert, :, :], layer.w2_scale[expert] = ops.scaled_fp8_quant(
|
|
layer.w2_weight.data[expert, :, :]
|
|
)
|
|
layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
|
|
layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
|
|
|
|
# If ROCm, apply weight padding (min. Mem channel contention) only if set
|
|
if is_hip() and bool(int(os.getenv("MOE_PADDING", "0"))):
|
|
layer.w13_weight = torch.nn.Parameter(
|
|
F.pad(layer.w13_weight.data, (0, padding_size), "constant", 0),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
layer.w2_weight = torch.nn.Parameter(
|
|
F.pad(layer.w2_weight.data, (0, padding_size), "constant", 0),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
return
|
|
|
|
# If checkpoint is fp8, we need to handle that the
|
|
# MoE kernels require single activation scale and single weight
|
|
# scale for w13 per expert.
|
|
else:
|
|
# Fp8 moe kernels require a single activation scale.
|
|
# We take the max of all the scales in case they differ.
|
|
if self.quant_config.activation_scheme == "static":
|
|
if layer.a13_scale is None or layer.a2_scale is None:
|
|
raise ValueError(
|
|
"QuantConfig has static quantization, but found "
|
|
"activation scales are None."
|
|
)
|
|
if not all_close_1d(layer.a13_scale) or not all_close_1d(
|
|
layer.a2_scale
|
|
):
|
|
print_warning_once(
|
|
"Found input_scales that are not equal for "
|
|
"fp8 MoE layer. Using the maximum across experts "
|
|
"for each layer. "
|
|
)
|
|
layer.a13_scale = torch.nn.Parameter(
|
|
layer.a13_scale.max(), requires_grad=False
|
|
)
|
|
layer.a2_scale = torch.nn.Parameter(
|
|
layer.a2_scale.max(), requires_grad=False
|
|
)
|
|
|
|
# If ROCm, normalize the weights and scales to e4m3fnuz
|
|
if is_hip():
|
|
# Normalize the weights and scales
|
|
w13_weight, w13_scale, a13_scale = normalize_e4m3fn_to_e4m3fnuz(
|
|
layer.w13_weight, layer.w13_scale, layer.a13_scale
|
|
)
|
|
w2_weight, w2_scale, a2_scale = normalize_e4m3fn_to_e4m3fnuz(
|
|
layer.w2_weight, layer.w2_scale, layer.a2_scale
|
|
)
|
|
# Reset the parameters
|
|
layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
|
|
layer.w13_scale = torch.nn.Parameter(w13_scale, requires_grad=False)
|
|
if a13_scale is not None:
|
|
layer.a13_scale = torch.nn.Parameter(a13_scale, requires_grad=False)
|
|
layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
|
|
layer.w2_scale = torch.nn.Parameter(w2_scale, requires_grad=False)
|
|
if a2_scale is not None:
|
|
layer.a2_scale = torch.nn.Parameter(a2_scale, requires_grad=False)
|
|
|
|
# Fp8 moe kernel needs single weight scale for w13 per expert.
|
|
# We take the max then dequant and requant each expert.
|
|
assert layer.w13_scale is not None
|
|
shard_size = layer.intermediate_size_per_partition
|
|
max_w13_scales = layer.w13_scale.max(dim=1).values
|
|
for expert_id in range(layer.num_experts):
|
|
start = 0
|
|
for shard_id in range(2):
|
|
dq_weight = per_tensor_dequantize(
|
|
layer.w13_weight[expert_id][start : start + shard_size, :],
|
|
layer.w13_scale[expert_id][shard_id],
|
|
)
|
|
layer.w13_weight[expert_id][start : start + shard_size, :], _ = (
|
|
ops.scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
|
|
)
|
|
start += shard_size
|
|
|
|
layer.w13_scale = torch.nn.Parameter(max_w13_scales, requires_grad=False)
|
|
# If ROCm, apply weight padding (min. Mem channel contention) only if set
|
|
if is_hip() and bool(int(os.getenv("MOE_PADDING", "0"))):
|
|
layer.w13_weight = torch.nn.Parameter(
|
|
F.pad(layer.w13_weight.data, (0, padding_size), "constant", 0),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
layer.w2_weight = torch.nn.Parameter(
|
|
F.pad(layer.w2_weight.data, (0, padding_size), "constant", 0),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
return
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
renormalize: bool = True,
|
|
use_grouped_topk: bool = False,
|
|
num_expert_group: Optional[int] = None,
|
|
topk_group: Optional[int] = None,
|
|
) -> torch.Tensor:
|
|
|
|
from sglang.srt.layers.fused_moe.fused_moe import fused_moe
|
|
|
|
return fused_moe(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
router_logits,
|
|
top_k,
|
|
renormalize=renormalize,
|
|
inplace=True,
|
|
use_fp8=True,
|
|
w1_scale=layer.w13_scale,
|
|
w2_scale=layer.w2_scale,
|
|
a1_scale=layer.a13_scale,
|
|
a2_scale=layer.a2_scale,
|
|
use_grouped_topk=use_grouped_topk,
|
|
num_expert_group=num_expert_group,
|
|
topk_group=topk_group,
|
|
)
|