883 lines
37 KiB
Python
883 lines
37 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from abc import abstractmethod
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from enum import Enum
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from typing import Callable, List, Optional, Tuple
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import torch
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from torch.nn.parameter import UninitializedParameter
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import vllm.envs as envs
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from vllm.config import get_current_vllm_config
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from vllm.distributed import (get_dp_group, 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.forward_context import ForwardContext, get_forward_context
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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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from vllm.platforms import current_platform
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from vllm.platforms.interface import CpuArchEnum
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from vllm.utils import direct_register_custom_op
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if current_platform.is_cuda_alike():
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from .fused_moe import fused_experts
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else:
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fused_experts = None # type: ignore
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if current_platform.is_tpu():
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# the iterative moe implementation is used until the moe_pallas is fixed
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from .moe_torch_iterative import fused_moe as fused_moe_pallas
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else:
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fused_moe_pallas = None # type: ignore
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logger = init_logger(__name__)
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class FusedMoeWeightScaleSupported(Enum):
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TENSOR = "tensor"
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CHANNEL = "channel"
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GROUP = "group"
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BLOCK = "block"
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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_per_partition: 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(
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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,
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use_grouped_topk: bool = False,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: Optional[torch.Tensor] = None
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) -> torch.Tensor:
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raise NotImplementedError
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@CustomOp.register("unquantized_fused_moe")
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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_per_partition: 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(
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num_experts,
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2 * intermediate_size_per_partition,
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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(
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num_experts,
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hidden_size,
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intermediate_size_per_partition,
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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 process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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super().process_weights_after_loading(layer)
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if current_platform.is_cpu():
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if current_platform.get_cpu_architecture() == CpuArchEnum.X86:
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import intel_extension_for_pytorch as ipex
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layer.ipex_fusion = ipex.llm.modules.GatedMLPMOE(
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layer.w13_weight,
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layer.w2_weight,
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use_prepack=True,
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)
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else:
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raise NotImplementedError("CPU MOE only supports x86 arch.")
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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,
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use_grouped_topk: bool = False,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: Optional[torch.Tensor] = None,
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activation: str = "silu",
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) -> 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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global_num_experts=global_num_experts,
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expert_map=expert_map,
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custom_routing_function=custom_routing_function,
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scoring_func=scoring_func,
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e_score_correction_bias=e_score_correction_bias,
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activation=activation)
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def forward_cuda(
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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,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: Optional[torch.Tensor] = None,
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activation: str = "silu",
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) -> torch.Tensor:
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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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custom_routing_function=custom_routing_function,
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scoring_func=scoring_func,
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e_score_correction_bias=e_score_correction_bias)
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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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activation=activation,
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global_num_experts=global_num_experts,
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expert_map=expert_map)
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def forward_cpu(
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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,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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custom_routing_function: Optional[Callable] = None,
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activation: str = "silu",
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**kwargs,
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):
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assert custom_routing_function is None
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assert activation == "silu", f"{activation} is not supported."
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return layer.ipex_fusion(
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x,
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use_grouped_topk,
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top_k,
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router_logits,
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renormalize,
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topk_group,
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num_expert_group,
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)
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def forward_tpu(
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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,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: Optional[torch.Tensor] = None,
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activation: str = "silu",
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) -> torch.Tensor:
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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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assert custom_routing_function is None
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if scoring_func != "softmax":
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raise NotImplementedError(
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"Only softmax scoring function is supported for TPU.")
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if e_score_correction_bias is not None:
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raise NotImplementedError(
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"Expert score correction bias is not supported for TPU.")
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assert activation == "silu", f"{activation} is not supported for TPU."
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return fused_moe_pallas(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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global_num_experts=global_num_experts,
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expert_map=expert_map,
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renormalize=renormalize)
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forward_native = forward_cuda
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def determine_expert_map(
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ep_size: int, ep_rank: int,
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global_num_experts: int) -> Tuple[int, Optional[torch.Tensor]]:
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"""
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Calculates how many experts should be assigned to each rank for EP and
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creates a mapping from global to local expert index. Experts are
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distributed evenly across ranks. Any remaining are assigned to the
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last rank.
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Args:
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ep_size (int): The size of the expert parallel group
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global_num_experts (int): The total number of experts in the model.
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Returns:
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Tuple[int, Optional[torch.Tensor]]: A tuple containing:
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- local_num_experts (int): The number of experts assigned
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to the current rank.
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- expert_map (Optional[torch.Tensor]): A tensor of shape
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(global_num_experts,) mapping from global to local index.
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Contains -1 for experts not assigned to the current rank.
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Returns None if ep_size is 1.
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"""
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assert ep_size > 0
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if ep_size == 1:
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return (global_num_experts, None)
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local_num_experts = global_num_experts // ep_size
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# Create a tensor of size num_experts filled with -1
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expert_map = torch.full((global_num_experts, ), -1, dtype=torch.int32)
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# Create a expert map for the local experts
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if ep_rank < (ep_size - 1):
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# Each non-last rank gets local_num_experts experts.
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expert_map[ep_rank * local_num_experts:
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(ep_rank + 1) * local_num_experts] = \
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torch.arange(0, local_num_experts, dtype=torch.int32)
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else:
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# All remaining experts are assigned to the last rank.
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local_num_experts = (global_num_experts - ep_rank * local_num_experts)
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expert_map[-local_num_experts:] = \
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torch.arange(0, local_num_experts, dtype=torch.int32)
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return (local_num_experts, expert_map)
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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, # Global number of experts
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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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ep_size: Optional[int] = None,
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dp_size: Optional[int] = None,
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prefix: str = "",
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: Optional[torch.Tensor] = None,
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activation: str = "silu",
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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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# For smuggling this layer into the fused moe custom op
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compilation_config = get_current_vllm_config().compilation_config
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if prefix in compilation_config.static_forward_context:
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raise ValueError("Duplicate layer name: {}".format(prefix))
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compilation_config.static_forward_context[prefix] = self
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self.layer_name = prefix
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self.use_direct_call = not envs.VLLM_TEST_ENABLE_EP
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# Note: here we guard against accessing the TP and DP groups when
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# uninitialized (this happens when testing)
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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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tp_rank = 0 if self.tp_size == 1 else get_tensor_model_parallel_rank()
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self.dp_size = (dp_size
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if dp_size is not None else get_dp_group().world_size)
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self.dp_rank = (0
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if self.dp_size == 1 else get_dp_group().rank_in_group)
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self.global_num_experts = num_experts
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if envs.VLLM_TEST_ENABLE_EP:
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# Set TP size to 1 to adjust for EP and adjust EP size and rank
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# for DP attention.
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self.ep_rank = tp_rank + self.tp_size * self.dp_rank
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self.tp_rank = 0
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self.ep_size = self.tp_size * self.dp_size
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self.tp_size = 1
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self.local_num_experts, self.expert_map = determine_expert_map(
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ep_size=self.ep_size,
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ep_rank=self.ep_rank,
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global_num_experts=self.global_num_experts)
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else:
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# Adjust TP size for DP attention
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self.tp_rank = tp_rank + self.tp_size * self.dp_rank
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self.ep_rank = 0
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self.tp_size = self.tp_size * self.dp_size
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self.ep_size = 1
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self.local_num_experts = self.global_num_experts
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self.expert_map = None
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self.top_k = top_k
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self.global_num_experts = num_experts
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assert intermediate_size % self.tp_size == 0
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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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self.custom_routing_function = custom_routing_function
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self.scoring_func = scoring_func
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self.e_score_correction_bias = e_score_correction_bias
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self.activation = activation
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if self.scoring_func != "softmax" and not self.use_grouped_topk:
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raise ValueError("Only softmax scoring function is supported for "
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"non-grouped topk.")
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# Note: get_quant_method will look at the layer's local_num_experts
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# for heuristic purposes, so it must be initialized first.
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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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moe_quant_params = {
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"num_experts": self.local_num_experts,
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"hidden_size": hidden_size,
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"intermediate_size_per_partition":
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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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# need full intermediate size pre-sharding for WNA16 act order
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if (self.quant_method.__class__.__name__
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in ("GPTQMarlinMoEMethod", "CompressedTensorsWNA16MoEMethod")):
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moe_quant_params["intermediate_size_full"] = intermediate_size
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self.quant_method.create_weights(layer=self, **moe_quant_params)
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def _load_per_tensor_weight_scale(self, shard_id: str,
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param: torch.nn.Parameter,
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loaded_weight: torch.Tensor,
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expert_id: int):
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param_data = param.data
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# for per tensor weight quantization
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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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elif shard_id == "w2":
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param_data[expert_id] = loaded_weight
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def _load_model_weight_or_group_weight_scale(self,
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shard_dim: int,
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expert_data: torch.Tensor,
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shard_id: str,
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loaded_weight: torch.Tensor,
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tp_rank: int,
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load_full_w2: bool = False):
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"""
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Load grouped weight scales for group quantization or model weights
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:param shard_dim: dimension to shard
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:param expert_data: parameter for a particular expert
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:param shard_id: either w1, w2, or w3
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:param loaded_weight: checkpoint weight to load into the param
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:param tp_rank: tensor parallel rank
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:param load_full_w2: whether or not the w2 loaded should be sharded.
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"""
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if shard_id == "w2":
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# In the case where we have actorder/g_idx, we do not partition the
|
|
# w2 scales, as indicated by `load_full` argument, for all tp cases
|
|
self._load_w2(shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=tp_rank,
|
|
load_full=load_full_w2)
|
|
elif shard_id in ("w1", "w3"):
|
|
self._load_w13(shard_id=shard_id,
|
|
shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=tp_rank)
|
|
|
|
def _load_per_channel_weight_scale(self, expert_data: torch.Tensor,
|
|
shard_dim: int, shard_id: str,
|
|
loaded_weight: torch.Tensor,
|
|
tp_rank: int):
|
|
# for per channel weight quantization
|
|
if shard_id == "w2":
|
|
expert_data.copy_(loaded_weight)
|
|
elif shard_id in ("w1", "w3"):
|
|
self._load_w13(shard_id=shard_id,
|
|
shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=tp_rank)
|
|
|
|
def _load_w13(self, expert_data: torch.Tensor, shard_dim: int,
|
|
shard_id: str, loaded_weight: torch.Tensor, tp_rank: int):
|
|
|
|
# Index the loaded weight for tp sharding.
|
|
# gate_up_proj: "MergedColumnParallel", so tp sharding on output_dim
|
|
shard_size = expert_data.shape[shard_dim] // 2
|
|
loaded_weight = loaded_weight.narrow(shard_dim, shard_size * tp_rank,
|
|
shard_size)
|
|
# Narrow parameter and load.
|
|
# w1, gate_proj: Load into first logical weight of w13.
|
|
if shard_id == "w1":
|
|
expert_data = expert_data.narrow(shard_dim, 0, shard_size)
|
|
# w3, up_proj: Load into second logical weight of w13.
|
|
else:
|
|
assert shard_id == "w3"
|
|
expert_data = expert_data.narrow(shard_dim, shard_size, shard_size)
|
|
expert_data.copy_(loaded_weight)
|
|
|
|
def _load_w2(self,
|
|
expert_data: torch.Tensor,
|
|
shard_dim: int,
|
|
loaded_weight: torch.Tensor,
|
|
tp_rank: int,
|
|
load_full: bool = False):
|
|
|
|
# Index the loaded weight for tp sharding.
|
|
# down_proj: "RowParallel" so tp sharding on input_dim
|
|
# Narrow parameter and load.
|
|
shard_size = expert_data.shape[shard_dim]
|
|
if not load_full:
|
|
loaded_weight = loaded_weight.narrow(shard_dim,
|
|
shard_size * tp_rank,
|
|
shard_size)
|
|
# w2, down_proj: Load into only logical weight of w2.
|
|
expert_data.copy_(loaded_weight)
|
|
|
|
def _load_single_value(self, param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor, expert_id: int):
|
|
param_data = param.data
|
|
|
|
# Input scales can be loaded directly and should be equal.
|
|
param_data[expert_id] = loaded_weight
|
|
|
|
def _load_g_idx(self, shard_id: str, expert_data: torch.Tensor,
|
|
shard_dim: int, loaded_weight: torch.Tensor, tp_rank: int):
|
|
|
|
if shard_id == "w2":
|
|
self._load_w2(shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=tp_rank)
|
|
else:
|
|
assert shard_id in ("w1", "w3")
|
|
expert_data.copy_(loaded_weight)
|
|
|
|
def _map_global_expert_id_to_local_expert_id(self, expert_id: int) -> int:
|
|
if self.expert_map is None:
|
|
return expert_id
|
|
return self.expert_map[expert_id].item()
|
|
|
|
def weight_loader(self, param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor, weight_name: str,
|
|
shard_id: str, expert_id: int) -> None:
|
|
|
|
expert_id = self._map_global_expert_id_to_local_expert_id(expert_id)
|
|
if expert_id == -1:
|
|
return
|
|
|
|
# compressed-tensors checkpoints with packed weights are stored flipped
|
|
# TODO (mgoin): check self.quant_method.quant_config.quant_format
|
|
# against known CompressionFormat enum values that have this quality
|
|
loaded_weight = loaded_weight.t().contiguous() if (
|
|
self.quant_method.__class__.__name__
|
|
== "CompressedTensorsWNA16MoEMethod") else loaded_weight
|
|
|
|
if shard_id not in ("w1", "w2", "w3"):
|
|
raise ValueError(f"shard_id must be ['w1','w2','w3'] but "
|
|
f"got {shard_id}.")
|
|
|
|
WEIGHT_SCALE_SUPPORTED = [
|
|
e.value for e in FusedMoeWeightScaleSupported
|
|
]
|
|
# Fetch the dim to shard the parameter/loaded weight
|
|
# based on the shard id. This will be whatever
|
|
# dimension intermediate_size_per_partition is used.
|
|
SHARD_ID_TO_SHARDED_DIM = {"w1": 0, "w2": 1, "w3": 0}
|
|
|
|
is_gguf_weight = getattr(param, "is_gguf_weight", False)
|
|
is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False)
|
|
if is_gguf_weight_type:
|
|
param.weight_type = loaded_weight.item()
|
|
param.data.copy_(loaded_weight)
|
|
return
|
|
|
|
# is_transposed: if the dim to shard the weight
|
|
# should be flipped. Required by GPTQ, compressed-tensors
|
|
# should be whatever dimension intermediate_size_per_partition is
|
|
is_transposed = getattr(param, "is_transposed", False)
|
|
shard_dim = SHARD_ID_TO_SHARDED_DIM[shard_id]
|
|
if is_transposed:
|
|
shard_dim = int(not shard_dim)
|
|
|
|
full_load = len(loaded_weight.shape) == 3
|
|
if full_load:
|
|
shard_dim += 1
|
|
|
|
# Materialize GGUF UninitializedParameter
|
|
if is_gguf_weight and isinstance(param, UninitializedParameter):
|
|
final_shape = list(loaded_weight.shape)
|
|
if shard_id in ["w1", "w3"]:
|
|
final_shape[1] *= 2
|
|
final_shape[shard_dim] = final_shape[shard_dim] // self.tp_size
|
|
param.materialize(final_shape, dtype=loaded_weight.dtype)
|
|
|
|
expert_data = param.data if full_load else param.data[expert_id]
|
|
# Case input scale: input_scale loading is only supported for fp8
|
|
if "input_scale" in weight_name:
|
|
# this is needed for compressed-tensors only
|
|
loaded_weight = loaded_weight.to(param.data.device)
|
|
|
|
if param.data[expert_id] != 1 and (param.data[expert_id] -
|
|
loaded_weight).abs() > 1e-5:
|
|
raise ValueError(
|
|
"input_scales of w1 and w3 of a layer "
|
|
f"must be equal. But got {param.data[expert_id]} "
|
|
f"vs. {loaded_weight}")
|
|
|
|
self._load_single_value(param=param,
|
|
loaded_weight=loaded_weight,
|
|
expert_id=expert_id)
|
|
return
|
|
|
|
# Case g_idx
|
|
if "g_idx" in weight_name:
|
|
self._load_g_idx(shard_dim=0,
|
|
shard_id=shard_id,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=self.tp_rank)
|
|
return
|
|
|
|
# Case weight scales and zero_points
|
|
if ("scale" in weight_name or "zero" in weight_name):
|
|
# load the weight scales and zp based on the quantization scheme
|
|
# supported weight scales/zp can be found in
|
|
# FusedMoeWeightScaleSupported
|
|
# TODO @dsikka: once hardened, refactor to use vLLM Parameters
|
|
# specific to each case
|
|
quant_method = getattr(param, "quant_method", None)
|
|
if quant_method == FusedMoeWeightScaleSupported.CHANNEL.value:
|
|
self._load_per_channel_weight_scale(
|
|
shard_id=shard_id,
|
|
shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=self.tp_rank)
|
|
elif quant_method in [
|
|
FusedMoeWeightScaleSupported.GROUP.value,
|
|
FusedMoeWeightScaleSupported.BLOCK.value,
|
|
]:
|
|
self._load_model_weight_or_group_weight_scale(
|
|
shard_id=shard_id,
|
|
shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=self.tp_rank,
|
|
load_full_w2=getattr(param, "load_full_w2", False))
|
|
elif quant_method == FusedMoeWeightScaleSupported.TENSOR.value:
|
|
self._load_per_tensor_weight_scale(shard_id=shard_id,
|
|
param=param,
|
|
loaded_weight=loaded_weight,
|
|
expert_id=expert_id)
|
|
else:
|
|
raise ValueError(
|
|
f"quant method must be one of {WEIGHT_SCALE_SUPPORTED}")
|
|
return
|
|
|
|
# Case weight_shape
|
|
if "weight_shape" in weight_name:
|
|
# only required by compressed-tensors
|
|
self._load_single_value(param=param,
|
|
loaded_weight=loaded_weight,
|
|
expert_id=expert_id)
|
|
return
|
|
|
|
# Case model weights
|
|
if "weight" in weight_name:
|
|
self._load_model_weight_or_group_weight_scale(
|
|
shard_id=shard_id,
|
|
shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=self.tp_rank)
|
|
return
|
|
|
|
@staticmethod
|
|
def select_experts(hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
use_grouped_topk: bool,
|
|
renormalize: bool,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None):
|
|
from vllm.model_executor.layers.fused_moe.fused_moe import (
|
|
fused_topk, grouped_topk)
|
|
|
|
# DeekSeekv2 uses grouped_top_k
|
|
if use_grouped_topk:
|
|
assert topk_group is not None
|
|
assert num_expert_group is not None
|
|
topk_weights, topk_ids = grouped_topk(
|
|
hidden_states=hidden_states,
|
|
gating_output=router_logits,
|
|
topk=top_k,
|
|
renormalize=renormalize,
|
|
num_expert_group=num_expert_group,
|
|
topk_group=topk_group,
|
|
scoring_func=scoring_func,
|
|
e_score_correction_bias=e_score_correction_bias)
|
|
elif custom_routing_function is None:
|
|
topk_weights, topk_ids = fused_topk(hidden_states=hidden_states,
|
|
gating_output=router_logits,
|
|
topk=top_k,
|
|
renormalize=renormalize)
|
|
else:
|
|
topk_weights, topk_ids = custom_routing_function(
|
|
hidden_states=hidden_states,
|
|
gating_output=router_logits,
|
|
topk=top_k,
|
|
renormalize=renormalize)
|
|
|
|
return topk_weights, topk_ids
|
|
|
|
def naive_multicast(self, x: torch.Tensor,
|
|
cu_tokens_across_dp_cpu: torch.Tensor):
|
|
assert (len(x.shape) == 2)
|
|
buffer = torch.empty((cu_tokens_across_dp_cpu[-1], x.size(1)),
|
|
device=x.device,
|
|
dtype=x.dtype)
|
|
|
|
start = 0 if self.dp_rank == 0 else cu_tokens_across_dp_cpu[
|
|
self.dp_rank - 1]
|
|
end = cu_tokens_across_dp_cpu[self.dp_rank]
|
|
buffer[start:end, :].copy_(x)
|
|
for idx in range(get_dp_group().world_size):
|
|
start = 0 if idx == 0 else cu_tokens_across_dp_cpu[idx - 1]
|
|
end = cu_tokens_across_dp_cpu[idx]
|
|
get_dp_group().broadcast(buffer[start:end, :], idx)
|
|
|
|
return buffer
|
|
|
|
def forward(self, hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor):
|
|
if self.use_direct_call:
|
|
return self.forward_impl(hidden_states, router_logits)
|
|
else:
|
|
return torch.ops.vllm.moe_forward(hidden_states, router_logits,
|
|
self.layer_name)
|
|
|
|
def forward_impl(self, hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor):
|
|
assert self.quant_method is not None
|
|
|
|
if self.dp_size > 1:
|
|
cu_tokens_across_dp_cpu = get_forward_context(
|
|
).dp_metadata.cu_tokens_across_dp_cpu
|
|
|
|
hidden_states = self.naive_multicast(hidden_states,
|
|
cu_tokens_across_dp_cpu)
|
|
router_logits = self.naive_multicast(router_logits,
|
|
cu_tokens_across_dp_cpu)
|
|
|
|
# Matrix multiply.
|
|
final_hidden_states = self.quant_method.apply(
|
|
layer=self,
|
|
x=hidden_states,
|
|
router_logits=router_logits,
|
|
top_k=self.top_k,
|
|
renormalize=self.renormalize,
|
|
use_grouped_topk=self.use_grouped_topk,
|
|
global_num_experts=self.global_num_experts,
|
|
expert_map=self.expert_map,
|
|
topk_group=self.topk_group,
|
|
num_expert_group=self.num_expert_group,
|
|
custom_routing_function=self.custom_routing_function,
|
|
scoring_func=self.scoring_func,
|
|
e_score_correction_bias=self.e_score_correction_bias,
|
|
activation=self.activation,
|
|
)
|
|
|
|
if self.dp_size > 1:
|
|
start = 0 if self.dp_rank == 0 else cu_tokens_across_dp_cpu[
|
|
self.dp_rank - 1]
|
|
end = cu_tokens_across_dp_cpu[self.dp_rank]
|
|
|
|
all_hidden_states = get_dp_group().all_reduce(final_hidden_states)
|
|
final_hidden_states = all_hidden_states[start:end, :]
|
|
|
|
if self.reduce_results and (self.tp_size > 1 or self.ep_size > 1):
|
|
# Default set to False. (May have to add shared expert outputs.)
|
|
final_hidden_states = tensor_model_parallel_all_reduce(
|
|
final_hidden_states)
|
|
|
|
return final_hidden_states
|
|
|
|
@classmethod
|
|
def make_expert_params_mapping(
|
|
cls, ckpt_gate_proj_name: str, ckpt_down_proj_name: str,
|
|
ckpt_up_proj_name: str,
|
|
num_experts: int) -> List[Tuple[str, str, int, str]]:
|
|
|
|
return [
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
("experts.w13_" if weight_name
|
|
in [ckpt_gate_proj_name, ckpt_up_proj_name] else "experts.w2_",
|
|
f"experts.{expert_id}.{weight_name}.", expert_id, shard_id)
|
|
for expert_id in range(num_experts) for shard_id, weight_name in [
|
|
("w1", ckpt_gate_proj_name),
|
|
("w2", ckpt_down_proj_name),
|
|
("w3", ckpt_up_proj_name),
|
|
]
|
|
]
|
|
|
|
def _load_fp8_scale(self, param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor, weight_name: str,
|
|
shard_id: str, expert_id: int) -> None:
|
|
param_data = param.data
|
|
|
|
# Input scales can be loaded directly and should be equal.
|
|
if "input_scale" in weight_name:
|
|
if param_data[expert_id] != 1 and (param_data[expert_id] -
|
|
loaded_weight).abs() > 1e-5:
|
|
raise ValueError(
|
|
"input_scales of w1 and w3 of a layer "
|
|
f"must be equal. But got {param_data[expert_id]} "
|
|
f"vs. {loaded_weight}")
|
|
param_data[expert_id] = loaded_weight
|
|
# Weight scales
|
|
elif "weight_scale" in weight_name:
|
|
# If we are in merged column case (gate_up_proj)
|
|
if shard_id in ("w1", "w3"):
|
|
# We have to keep the weight scales of w1 and w3 because
|
|
# we need to re-quantize w1/w3 weights after weight loading.
|
|
idx = 0 if shard_id == "w1" else 1
|
|
param_data[expert_id][idx] = loaded_weight
|
|
# If we are in the row parallel case (down_proj)
|
|
else:
|
|
param_data[expert_id] = loaded_weight
|
|
|
|
def extra_repr(self) -> str:
|
|
|
|
s = (
|
|
f"global_num_experts={self.global_num_experts}, "
|
|
f"local_num_experts={self.local_num_experts}, "
|
|
f"top_k={self.top_k}, "
|
|
f"intermediate_size_per_partition={self.intermediate_size_per_partition}, " # noqa: E501
|
|
f"tp_size={self.tp_size},\n"
|
|
f"ep_size={self.ep_size}, "
|
|
f"reduce_results={self.reduce_results}, "
|
|
f"renormalize={self.renormalize}, "
|
|
f"use_grouped_topk={self.use_grouped_topk}")
|
|
|
|
if self.use_grouped_topk:
|
|
s += f", num_expert_group={self.num_expert_group}, topk_group={self.topk_group}" # noqa: E501
|
|
|
|
s += f", scoring_func='{self.scoring_func}', activation='{self.activation}'" # noqa: E501
|
|
|
|
return s
|
|
|
|
|
|
def moe_forward(hidden_states: torch.Tensor, router_logits: torch.Tensor,
|
|
layer_name: str) -> torch.Tensor:
|
|
forward_context: ForwardContext = get_forward_context()
|
|
self = forward_context.no_compile_layers[layer_name]
|
|
assert self.quant_method is not None
|
|
|
|
return self.forward_impl(hidden_states, router_logits)
|
|
|
|
|
|
def moe_forward_fake(hidden_states: torch.Tensor, router_logits: torch.Tensor,
|
|
layer_name: str) -> torch.Tensor:
|
|
return torch.empty_like(hidden_states)
|
|
|
|
|
|
direct_register_custom_op(
|
|
op_name="moe_forward",
|
|
op_func=moe_forward,
|
|
mutates_args=[],
|
|
fake_impl=moe_forward_fake,
|
|
dispatch_key=current_platform.dispatch_key,
|
|
)
|