981 lines
35 KiB
Python
981 lines
35 KiB
Python
# Adapted from https://github.com/vllm-project/vllm/blob/a6221a144af772fd1a68fe7e627935dc53e81738/vllm/model_executor/layers/fused_moe/layer.py
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import importlib
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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 sglang.srt.custom_op import CustomOp
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from sglang.srt.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 sglang.srt.layers.amx_utils import _amx_process_weight_after_loading
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from sglang.srt.layers.moe.fused_moe_native import moe_forward_native
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from sglang.srt.layers.moe.topk import select_experts
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from sglang.srt.layers.quantization.base_config import (
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QuantizationConfig,
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QuantizeMethodBase,
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)
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from sglang.srt.managers.schedule_batch import global_server_args_dict
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from sglang.srt.model_loader.weight_utils import narrow_padded_param_and_loaded_weight
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from sglang.srt.utils import (
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cpu_has_amx_support,
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get_bool_env_var,
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is_cpu,
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is_hip,
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set_weight_attrs,
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use_intel_amx_backend,
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)
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has_triton_kernels = importlib.util.find_spec("triton_kernels") is not None
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if torch.cuda.is_available():
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from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
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if has_triton_kernels:
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from sglang.srt.layers.moe.fused_moe_triton.triton_kernels_moe import (
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triton_kernel_moe_forward,
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)
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else:
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fused_experts = None # type: ignore
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import logging
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_is_hip = is_hip()
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_is_cpu_amx_available = cpu_has_amx_support()
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_is_cpu = is_cpu()
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_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
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if _use_aiter:
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from aiter import ActivationType
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from aiter.fused_moe import fused_moe
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from aiter.fused_moe_bf16_asm import ck_moe_2stages
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from aiter.ops.shuffle import shuffle_weight
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logger = logging.getLogger(__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(
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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,
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use_grouped_topk: bool,
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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 __init__(self, use_triton_kernels: bool = False):
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super().__init__()
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self.use_triton_kernels = use_triton_kernels
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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_n, w13_weight_k = 2 * intermediate_size, hidden_size
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if self.use_triton_kernels:
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w13_weight_n, w13_weight_k = w13_weight_k, w13_weight_n
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w13_weight = torch.nn.Parameter(
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torch.empty(num_experts, w13_weight_n, w13_weight_k, dtype=params_dtype),
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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_n, w2_weight_k = (
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hidden_size,
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intermediate_size,
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)
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if self.use_triton_kernels:
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w2_weight_n, w2_weight_k = w2_weight_k, w2_weight_n
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w2_weight = torch.nn.Parameter(
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torch.empty(num_experts, w2_weight_n, w2_weight_k, dtype=params_dtype),
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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 process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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if _use_aiter:
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layer.w13_weight = torch.nn.Parameter(
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shuffle_weight(layer.w13_weight.data, (16, 16)),
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requires_grad=False,
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)
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torch.cuda.empty_cache()
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layer.w2_weight = torch.nn.Parameter(
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shuffle_weight(layer.w2_weight.data, (16, 16)),
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requires_grad=False,
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)
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torch.cuda.empty_cache()
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# Pack weight for get better performance on CPU
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if _is_cpu and _is_cpu_amx_available:
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_amx_process_weight_after_loading(layer, ["w13_weight", "w2_weight"])
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return
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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,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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num_fused_shared_experts: int = 0,
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custom_routing_function: Optional[Callable] = None,
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correction_bias: Optional[torch.Tensor] = None,
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activation: str = "silu",
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apply_router_weight_on_input: bool = False,
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inplace: bool = True,
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no_combine: bool = False,
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routed_scaling_factor: Optional[float] = None,
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) -> torch.Tensor:
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return self.forward(
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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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num_fused_shared_experts=num_fused_shared_experts,
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custom_routing_function=custom_routing_function,
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correction_bias=correction_bias,
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activation=activation,
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apply_router_weight_on_input=apply_router_weight_on_input,
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inplace=inplace,
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no_combine=no_combine,
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routed_scaling_factor=routed_scaling_factor,
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)
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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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num_fused_shared_experts: int = 0,
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custom_routing_function: Optional[Callable] = None,
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correction_bias: Optional[torch.Tensor] = None,
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activation: str = "silu",
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apply_router_weight_on_input: bool = False,
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inplace: bool = True,
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no_combine: bool = False,
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routed_scaling_factor: Optional[float] = None,
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) -> torch.Tensor:
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if self.use_triton_kernels:
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return triton_kernel_moe_forward(
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hidden_states=x,
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w1=layer.w13_weight,
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w2=layer.w2_weight,
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gating_output=router_logits,
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topk=top_k,
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renormalize=renormalize,
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)
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else:
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topk_weights, topk_ids = 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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num_fused_shared_experts=num_fused_shared_experts,
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custom_routing_function=custom_routing_function,
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correction_bias=correction_bias,
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routed_scaling_factor=routed_scaling_factor,
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)
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if _use_aiter:
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assert not no_combine, "unsupported"
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if apply_router_weight_on_input:
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assert (
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topk_weights.dim() == 2
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), "`topk_weights` should be in shape (num_tokens, topk)"
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_, topk = topk_weights.shape
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assert (
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topk == 1
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), "Only support topk=1 when `apply_router_weight_on_input` is True"
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x = x * topk_weights.to(x.dtype)
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topk_weights = torch.ones_like(
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topk_weights, dtype=torch.float32
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) # topk_weights must be FP32 (float32)
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return fused_moe(
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x,
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layer.w13_weight,
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layer.w2_weight,
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topk_weights,
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topk_ids,
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activation=(
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ActivationType.Silu
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if activation == "silu"
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else ActivationType.Gelu
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),
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)
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else:
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return fused_experts(
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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=inplace and not no_combine,
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activation=activation,
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apply_router_weight_on_input=apply_router_weight_on_input,
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no_combine=no_combine,
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routed_scaling_factor=routed_scaling_factor,
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)
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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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num_fused_shared_experts: int = 0,
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custom_routing_function: Optional[Callable] = None,
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correction_bias: Optional[torch.Tensor] = None,
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activation: str = "silu",
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apply_router_weight_on_input: bool = False,
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inplace: bool = True,
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no_combine: bool = False,
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routed_scaling_factor: Optional[float] = None,
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) -> torch.Tensor:
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assert activation == "silu", f"activation = {activation} is not supported."
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if use_intel_amx_backend(layer) and not apply_router_weight_on_input:
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topk_weights, topk_ids = 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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num_fused_shared_experts=num_fused_shared_experts,
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custom_routing_function=custom_routing_function,
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correction_bias=correction_bias,
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routed_scaling_factor=routed_scaling_factor,
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)
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# TODO: support apply_router_weight_on_input in the fused_experts_cpu kernel
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return torch.ops.sgl_kernel.fused_experts_cpu(
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x,
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layer.w13_weight,
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layer.w2_weight,
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topk_weights.to(
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torch.float
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), # TODO: the topk_weights of llama4 is computed via Llama4MoE:custom_routing_function and is bfloat16 while the kernel requires it to be float32
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topk_ids,
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False, # inplace # See [Note] inplace should be False in fused_experts.
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False, # use_int8_w8a8
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False, # use_fp8_w8a16
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None, # w1_scale
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None, # w2_scale
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None, # block_size
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None, # a1_scale
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None, # a2_scale
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True, # is_vnni
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)
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else:
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return moe_forward_native(
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layer,
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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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num_fused_shared_experts,
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custom_routing_function,
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correction_bias,
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activation,
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apply_router_weight_on_input,
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inplace,
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no_combine,
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routed_scaling_factor,
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)
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def forward_npu(
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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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num_fused_shared_experts: int = 0,
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custom_routing_function: Optional[Callable] = None,
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correction_bias: Optional[torch.Tensor] = None,
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activation: str = "silu",
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apply_router_weight_on_input: bool = False,
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inplace: bool = True,
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no_combine: bool = False,
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routed_scaling_factor: Optional[float] = None,
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) -> torch.Tensor:
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return moe_forward_native(
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layer,
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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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num_fused_shared_experts,
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custom_routing_function,
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correction_bias,
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activation,
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apply_router_weight_on_input,
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inplace,
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no_combine,
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routed_scaling_factor,
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)
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def forward_tpu(self, *args, **kwargs) -> torch.Tensor:
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raise NotImplementedError("The TPU backend currently does not support MoE.")
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forward_native = forward_cpu
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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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inplace: suggestion to compute inplace (modify input activation).
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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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layer_id: Optional[int] = None,
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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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num_fused_shared_experts: int = 0,
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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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custom_routing_function: Optional[Callable] = None,
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correction_bias: Optional[torch.Tensor] = None,
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activation: str = "silu",
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apply_router_weight_on_input: bool = False,
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use_presharded_weights: bool = False,
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inplace: bool = True,
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no_combine: bool = False,
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routed_scaling_factor: Optional[float] = None,
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enable_flashinfer_moe: Optional[bool] = False,
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enable_ep_moe: Optional[bool] = False,
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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.hidden_size = hidden_size
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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.tp_rank = get_tensor_model_parallel_rank()
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self.num_experts = num_experts
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self.expert_map = None
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if enable_flashinfer_moe and quant_config is None:
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logger.warning("Disable flashinfer MoE when quantization config is None.")
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enable_flashinfer_moe = False
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enable_ep_moe = False
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self.enable_flashinfer_moe = enable_flashinfer_moe
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if enable_ep_moe:
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assert (
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self.enable_flashinfer_moe
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), "FusedMoE only supports EP with --enable-flashinfer-moe"
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self.ep_size = self.tp_size
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self.ep_rank = self.tp_rank
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self.tp_size = 1
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self.tp_rank = 0
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# Create a tensor of size num_experts filled with -1
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self.expert_map = torch.full((self.num_experts,), -1, dtype=torch.int32)
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# Create a expert map for the local experts
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assert num_experts % self.ep_size == 0
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self.local_num_experts = num_experts // self.ep_size
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self.expert_map[
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self.ep_rank
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* self.local_num_experts : (self.ep_rank + 1)
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* self.local_num_experts
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] = torch.arange(0, self.local_num_experts, dtype=torch.int32, device="cpu")
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else:
|
|
self.ep_size = 1
|
|
self.ep_rank = 0
|
|
self.local_num_experts = num_experts
|
|
self.routed_scaling_factor = routed_scaling_factor
|
|
self.top_k = top_k
|
|
assert intermediate_size % self.tp_size == 0
|
|
self.intermediate_size_per_partition = intermediate_size // self.tp_size
|
|
self.reduce_results = reduce_results
|
|
self.renormalize = renormalize
|
|
self.use_grouped_topk = use_grouped_topk
|
|
if self.use_grouped_topk:
|
|
assert num_expert_group is not None and topk_group is not None
|
|
self.num_expert_group = num_expert_group
|
|
self.num_fused_shared_experts = num_fused_shared_experts
|
|
self.topk_group = topk_group
|
|
self.custom_routing_function = custom_routing_function
|
|
self.correction_bias = correction_bias
|
|
self.activation = activation
|
|
self.apply_router_weight_on_input = apply_router_weight_on_input
|
|
self.use_presharded_weights = use_presharded_weights
|
|
self.inplace = inplace
|
|
self.no_combine = no_combine
|
|
|
|
self.use_triton_kernels = (
|
|
not _is_cpu and global_server_args_dict["enable_triton_kernel_moe"]
|
|
)
|
|
|
|
if quant_config is None:
|
|
self.quant_method: Optional[QuantizeMethodBase] = UnquantizedFusedMoEMethod(
|
|
self.use_triton_kernels
|
|
)
|
|
else:
|
|
self.quant_method = quant_config.get_quant_method(self, prefix)
|
|
if self.quant_method.__class__.__name__ == "ModelOptNvFp4FusedMoEMethod":
|
|
self.quant_method.enable_flashinfer_moe = self.enable_flashinfer_moe
|
|
assert self.quant_method is not None
|
|
|
|
self.quant_method.create_weights(
|
|
layer=self,
|
|
num_experts=self.local_num_experts,
|
|
hidden_size=hidden_size,
|
|
# FIXME: figure out which intermediate_size to use
|
|
intermediate_size=self.intermediate_size_per_partition,
|
|
intermediate_size_per_partition=self.intermediate_size_per_partition,
|
|
params_dtype=params_dtype,
|
|
weight_loader=self.weight_loader,
|
|
)
|
|
|
|
def _load_per_tensor_weight_scale(
|
|
self,
|
|
shard_id: str,
|
|
param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor,
|
|
expert_id: int,
|
|
):
|
|
param_data = param.data
|
|
# for per tensor weight quantization
|
|
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)
|
|
elif shard_id == "w2":
|
|
param_data[expert_id] = loaded_weight
|
|
|
|
def _load_model_weight_or_group_weight_scale(
|
|
self,
|
|
shard_dim: int,
|
|
expert_data: torch.Tensor,
|
|
shard_id: str,
|
|
loaded_weight: torch.tensor,
|
|
tp_rank: int,
|
|
):
|
|
# Load grouped weight scales for group quantization
|
|
# or model weights
|
|
if shard_id == "w2":
|
|
self._load_w2(
|
|
shard_id=shard_id,
|
|
shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=tp_rank,
|
|
)
|
|
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
|
|
|
|
# Narrow parameter and load.
|
|
# w1, gate_proj: Load into first logical weight of w13.
|
|
# w3, up_proj: Load into second logical weight of w13.
|
|
# trtllm cutlass kernel assumes differently
|
|
assert shard_id in ("w1", "w3")
|
|
switch_w13 = getattr(self.quant_method, "load_up_proj_weight_first", False)
|
|
if (switch_w13 and shard_id == "w1") or (not switch_w13 and shard_id == "w3"):
|
|
start = shard_size
|
|
else:
|
|
start = 0
|
|
|
|
if _is_cpu:
|
|
expert_data, loaded_weight = narrow_padded_param_and_loaded_weight(
|
|
expert_data,
|
|
loaded_weight,
|
|
start,
|
|
shard_size * tp_rank,
|
|
shard_dim,
|
|
shard_size,
|
|
not self.use_presharded_weights,
|
|
)
|
|
else:
|
|
if not self.use_presharded_weights:
|
|
if self.use_triton_kernels:
|
|
loaded_weight = loaded_weight.transpose(-2, -1)
|
|
loaded_weight = loaded_weight.narrow(
|
|
shard_dim, shard_size * tp_rank, shard_size
|
|
)
|
|
|
|
expert_data = expert_data.narrow(shard_dim, start, shard_size)
|
|
expert_data.copy_(loaded_weight)
|
|
|
|
def _load_w2(
|
|
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.
|
|
# down_proj: "RowParallel" so tp sharding on input_dim
|
|
# Narrow parameter and load.
|
|
shard_size = expert_data.shape[shard_dim]
|
|
|
|
if _is_cpu:
|
|
expert_data, loaded_weight = narrow_padded_param_and_loaded_weight(
|
|
expert_data,
|
|
loaded_weight,
|
|
0, # param_data_start
|
|
shard_size * tp_rank,
|
|
shard_dim,
|
|
shard_size,
|
|
not self.use_presharded_weights,
|
|
)
|
|
else:
|
|
if not self.use_presharded_weights:
|
|
if self.use_triton_kernels:
|
|
loaded_weight = loaded_weight.transpose(-2, -1)
|
|
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_id=shard_id,
|
|
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
|
|
|
|
# TP rank is set to 0 if EP is enabled
|
|
tp_rank = 0 if self.ep_size > 1 else get_tensor_model_parallel_rank()
|
|
|
|
# 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 is used.
|
|
SHARD_ID_TO_SHARDED_DIM = {"w1": 0, "w2": 1, "w3": 0}
|
|
|
|
expert_data = param.data[expert_id]
|
|
|
|
# is_transposed: if the dim to shard the weight
|
|
# should be flipped. Required by GPTQ, compressed-tensors
|
|
# should be whatever dimension intermediate_size is
|
|
is_transposed = getattr(param, "is_transposed", False)
|
|
shard_dim = SHARD_ID_TO_SHARDED_DIM[shard_id]
|
|
if self.use_triton_kernels:
|
|
is_transposed = True
|
|
if is_transposed:
|
|
shard_dim = int(not shard_dim)
|
|
|
|
# Case input scale: input_scale loading is only supported for fp8
|
|
if "input_scale" in weight_name:
|
|
# INT4-FP8 (INT4 MoE Weight, FP8 Compute): Adjust input_scale for e4m3fnuz (AMD)
|
|
if _is_hip and get_bool_env_var("SGLANG_INT4_WEIGHT"):
|
|
loaded_weight = loaded_weight * 2.0
|
|
|
|
# this is needed for compressed-tensors only
|
|
loaded_weight = loaded_weight.to(param.data.device)
|
|
|
|
if (
|
|
"compressed" in self.quant_method.__class__.__name__.lower()
|
|
and 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=tp_rank,
|
|
)
|
|
return
|
|
if "ModelOpt" in self.quant_method.__class__.__name__:
|
|
if "weight_scale_2" in weight_name or "input_scale" in weight_name:
|
|
self._load_per_tensor_weight_scale(
|
|
shard_id=shard_id,
|
|
param=param,
|
|
loaded_weight=loaded_weight,
|
|
expert_id=expert_id,
|
|
)
|
|
elif "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=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:
|
|
# INT4-FP8 (INT4 MoE Weight, FP8 Compute): Adjust INT4 column-wise scaling number to e4m3fnuz (AMD)
|
|
if _is_hip and get_bool_env_var("SGLANG_INT4_WEIGHT"):
|
|
loaded_weight = loaded_weight * 0.5
|
|
|
|
self._load_per_channel_weight_scale(
|
|
shard_id=shard_id,
|
|
shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=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=tp_rank,
|
|
)
|
|
elif quant_method == FusedMoeWeightScaleSupported.TENSOR.value:
|
|
# INT4-FP8 (INT4 MoE Weight, FP8 Compute): Adjust FP8 per-tensor scaling number for e4m3fnuz (AMD)
|
|
if _is_hip and get_bool_env_var("SGLANG_INT4_WEIGHT"):
|
|
loaded_weight = loaded_weight * 2.0
|
|
|
|
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=tp_rank,
|
|
)
|
|
return
|
|
|
|
def forward(self, hidden_states: torch.Tensor, router_logits: torch.Tensor):
|
|
assert self.quant_method is not None
|
|
|
|
# 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,
|
|
topk_group=self.topk_group,
|
|
num_expert_group=self.num_expert_group,
|
|
num_fused_shared_experts=self.num_fused_shared_experts,
|
|
custom_routing_function=self.custom_routing_function,
|
|
correction_bias=self.correction_bias,
|
|
activation=self.activation,
|
|
apply_router_weight_on_input=self.apply_router_weight_on_input,
|
|
routed_scaling_factor=self.routed_scaling_factor,
|
|
**(
|
|
dict(
|
|
tp_rank=self.tp_rank,
|
|
tp_size=self.tp_size,
|
|
ep_rank=self.ep_rank,
|
|
ep_size=self.ep_size,
|
|
)
|
|
if self.quant_method.__class__.__name__ == "ModelOptNvFp4FusedMoEMethod"
|
|
else {}
|
|
),
|
|
)
|
|
|
|
if self.reduce_results and (self.tp_size > 1 or self.ep_size > 1):
|
|
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
|