690 lines
25 KiB
Python
690 lines
25 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 logging
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from enum import Enum
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from typing import List, Optional, Tuple
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import torch
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from sglang.srt.distributed import (
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get_moe_expert_parallel_rank,
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get_moe_expert_parallel_world_size,
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get_moe_tensor_parallel_rank,
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get_moe_tensor_parallel_world_size,
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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.eplb.expert_location import get_global_expert_location_metadata
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from sglang.srt.layers.moe.topk import StandardTopKOutput
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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.layers.quantization.unquant import UnquantizedFusedMoEMethod
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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 cpu_has_amx_support, get_bool_env_var, is_cpu, is_hip
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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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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 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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hidden_size: int,
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intermediate_size: int,
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layer_id: int,
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top_k: Optional[int] = None,
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num_fused_shared_experts: int = 0,
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params_dtype: Optional[torch.dtype] = None,
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reduce_results: bool = False,
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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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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_cutlass_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.layer_id = layer_id
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self.top_k = top_k
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self.hidden_size = hidden_size
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self.num_experts = num_experts
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self.num_fused_shared_experts = num_fused_shared_experts
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self.expert_map_cpu = None
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self.expert_map_gpu = None
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if enable_flashinfer_cutlass_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_cutlass_moe = False
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enable_ep_moe = False
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self.enable_flashinfer_cutlass_moe = enable_flashinfer_cutlass_moe
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self.moe_ep_size = get_moe_expert_parallel_world_size()
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self.moe_ep_rank = get_moe_expert_parallel_rank()
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self.moe_tp_size = get_moe_tensor_parallel_world_size()
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self.moe_tp_rank = get_moe_tensor_parallel_rank()
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assert num_experts % self.moe_ep_size == 0
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self.num_local_experts = num_experts // self.moe_ep_size
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if enable_ep_moe:
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# TODO(ch-wan): support shared experts fusion
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# Create a tensor of size num_experts filled with -1
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self.expert_map_cpu = 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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self.expert_map_cpu[
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self.moe_ep_rank
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* self.num_local_experts : (self.moe_ep_rank + 1)
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* self.num_local_experts
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] = torch.arange(0, self.num_local_experts, dtype=torch.int32, device="cpu")
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if not self.enable_flashinfer_cutlass_moe:
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self.expert_map_gpu = self.expert_map_cpu.to(device="cuda")
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self.routed_scaling_factor = routed_scaling_factor
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assert intermediate_size % self.moe_tp_size == 0
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self.intermediate_size_per_partition = intermediate_size // self.moe_tp_size
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self.reduce_results = reduce_results
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self.activation = activation
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self.apply_router_weight_on_input = apply_router_weight_on_input
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self.use_presharded_weights = use_presharded_weights
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self.inplace = inplace
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self.no_combine = no_combine
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self.use_triton_kernels = (
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not _is_cpu and global_server_args_dict["enable_triton_kernel_moe"]
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)
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if quant_config is None:
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self.quant_method: Optional[QuantizeMethodBase] = UnquantizedFusedMoEMethod(
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self.use_triton_kernels
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)
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else:
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self.quant_method = quant_config.get_quant_method(self, prefix)
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if self.quant_method.__class__.__name__ == "ModelOptNvFp4FusedMoEMethod":
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self.quant_method.enable_flashinfer_cutlass_moe = (
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self.enable_flashinfer_cutlass_moe
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)
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assert self.quant_method is not None
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self.quant_config = quant_config
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self.quant_method.create_weights(
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layer=self,
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num_experts=self.num_local_experts,
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hidden_size=hidden_size,
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# FIXME: figure out which intermediate_size to use
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intermediate_size=self.intermediate_size_per_partition,
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intermediate_size_per_partition=self.intermediate_size_per_partition,
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params_dtype=params_dtype,
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weight_loader=self.weight_loader,
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)
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def _load_per_tensor_weight_scale(
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self,
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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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):
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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(
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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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):
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# Load grouped weight scales for group quantization
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# or model weights
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if shard_id == "w2":
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self._load_w2(
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shard_id=shard_id,
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shard_dim=shard_dim,
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loaded_weight=loaded_weight,
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expert_data=expert_data,
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tp_rank=tp_rank,
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)
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elif shard_id in ("w1", "w3"):
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self._load_w13(
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shard_id=shard_id,
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shard_dim=shard_dim,
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loaded_weight=loaded_weight,
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expert_data=expert_data,
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tp_rank=tp_rank,
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)
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def _load_per_channel_weight_scale(
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self,
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expert_data: torch.Tensor,
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shard_dim: int,
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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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):
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# for per channel weight quantization
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if shard_id == "w2":
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expert_data.copy_(loaded_weight)
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elif shard_id in ("w1", "w3"):
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self._load_w13(
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shard_id=shard_id,
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shard_dim=shard_dim,
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loaded_weight=loaded_weight,
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expert_data=expert_data,
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tp_rank=tp_rank,
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)
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def _load_w13(
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self,
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expert_data: torch.Tensor,
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shard_dim: int,
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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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):
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# Index the loaded weight for tp sharding.
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# gate_up_proj: "MergedColumnParallel", so tp sharding on output_dim
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shard_size = expert_data.shape[shard_dim] // 2
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# Narrow parameter and load.
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# w1, gate_proj: Load into first logical weight of w13.
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# w3, up_proj: Load into second logical weight of w13.
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# trtllm cutlass kernel assumes differently
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assert shard_id in ("w1", "w3")
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switch_w13 = getattr(self.quant_method, "load_up_proj_weight_first", False)
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if (switch_w13 and shard_id == "w1") or (not switch_w13 and shard_id == "w3"):
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start = shard_size
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else:
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start = 0
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if _is_cpu:
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expert_data, loaded_weight = narrow_padded_param_and_loaded_weight(
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expert_data,
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loaded_weight,
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start,
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shard_size * tp_rank,
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shard_dim,
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shard_size,
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not self.use_presharded_weights,
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)
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else:
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if not self.use_presharded_weights:
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if self.use_triton_kernels:
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loaded_weight = loaded_weight.transpose(-2, -1)
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loaded_weight = loaded_weight.narrow(
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shard_dim, shard_size * tp_rank, shard_size
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)
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expert_data = expert_data.narrow(shard_dim, start, shard_size)
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expert_data.copy_(loaded_weight)
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def _load_w2(
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self,
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expert_data: torch.Tensor,
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shard_dim: int,
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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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):
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"""Load w2 weights for down projection.
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Args:
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expert_data: The expert data tensor to load into
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shard_dim: The dimension to shard along
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shard_id: The shard ID (must be "w2")
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loaded_weight: The weight tensor to load from
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tp_rank: The tensor parallel rank
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"""
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if not isinstance(expert_data, torch.Tensor) or not isinstance(
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loaded_weight, torch.Tensor
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):
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raise ValueError("expert_data and loaded_weight must be torch.Tensor")
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if (
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self.quant_config is not None
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and "modelopt" in self.quant_config.get_name()
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and (expert_data.dim() != 2 or loaded_weight.dim() != 2)
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):
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raise ValueError(
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f"Expected 2D tensors, got expert_data shape {expert_data.shape} and loaded_weight shape {loaded_weight.shape}"
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)
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if shard_id != "w2":
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raise ValueError(f"shard_id must be 'w2', got {shard_id}")
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# Index the loaded weight for tp sharding.
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# down_proj: "RowParallel" so tp sharding on input_dim
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# Narrow parameter and load.
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shard_size = expert_data.shape[shard_dim]
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if _is_cpu:
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expert_data, loaded_weight = narrow_padded_param_and_loaded_weight(
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expert_data,
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loaded_weight,
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0, # param_data_start
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shard_size * tp_rank,
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shard_dim,
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shard_size,
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not self.use_presharded_weights,
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)
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else:
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if not self.use_presharded_weights:
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if self.use_triton_kernels:
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loaded_weight = loaded_weight.transpose(-2, -1)
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if shard_size * tp_rank + shard_size > loaded_weight.shape[shard_dim]:
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raise ValueError(
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f"Shard size {shard_size} at rank {tp_rank} exceeds loaded_weight dimension {loaded_weight.shape[shard_dim]}"
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)
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loaded_weight = loaded_weight.narrow(
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shard_dim, shard_size * tp_rank, shard_size
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)
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# w2, down_proj: Load into only logical weight of w2.
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expert_data.copy_(loaded_weight)
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def _load_single_value(
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self, param: torch.nn.Parameter, loaded_weight: torch.Tensor, expert_id: int
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):
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param_data = param.data
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# Input scales can be loaded directly and should be equal.
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param_data[expert_id] = loaded_weight
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def _load_g_idx(
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self,
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shard_id: str,
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expert_data: torch.Tensor,
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shard_dim: int,
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loaded_weight: torch.Tensor,
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tp_rank: int,
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):
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if shard_id == "w2":
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self._load_w2(
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shard_id=shard_id,
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shard_dim=shard_dim,
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loaded_weight=loaded_weight,
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expert_data=expert_data,
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tp_rank=tp_rank,
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)
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else:
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assert shard_id in ("w1", "w3")
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expert_data.copy_(loaded_weight)
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def _map_global_expert_id_to_local_expert_id(self, expert_id: int) -> int:
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if self.expert_map_cpu is None:
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return expert_id
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return self.expert_map_cpu[expert_id].item()
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def weight_loader(
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self,
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param: torch.nn.Parameter,
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loaded_weight: torch.Tensor,
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weight_name: str,
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shard_id: str,
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expert_id: int,
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) -> None:
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global_expert_location_metadata = get_global_expert_location_metadata()
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if global_expert_location_metadata is None:
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self._weight_loader_impl(
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param=param,
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loaded_weight=loaded_weight,
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weight_name=weight_name,
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shard_id=shard_id,
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expert_id=expert_id,
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)
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return
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if expert_id >= self.num_experts - self.num_fused_shared_experts:
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# This is a shared expert.
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physical_expert_ids = [expert_id]
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else:
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physical_expert_ids = (
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global_expert_location_metadata.logical_to_all_physical(
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self.layer_id, expert_id
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)
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)
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for physical_expert_id in physical_expert_ids:
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self._weight_loader_physical(
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param=param,
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loaded_weight=loaded_weight,
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weight_name=weight_name,
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shard_id=shard_id,
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expert_id=physical_expert_id,
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)
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def _weight_loader_physical(
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self,
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param: torch.nn.Parameter,
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loaded_weight: torch.Tensor,
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weight_name: str,
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shard_id: str,
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expert_id: int,
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) -> None:
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expert_id = self._map_global_expert_id_to_local_expert_id(expert_id)
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if expert_id == -1:
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return
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self._weight_loader_impl(
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param=param,
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loaded_weight=loaded_weight,
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weight_name=weight_name,
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shard_id=shard_id,
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expert_id=expert_id,
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)
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def _weight_loader_impl(
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self,
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param: torch.nn.Parameter,
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loaded_weight: torch.Tensor,
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weight_name: str,
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shard_id: str,
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expert_id: int,
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) -> None:
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tp_rank = self.moe_tp_rank
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# compressed-tensors checkpoints with packed weights are stored flipped
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# TODO (mgoin): check self.quant_method.quant_config.quant_format
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# against known CompressionFormat enum values that have this quality
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loaded_weight = (
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loaded_weight.t().contiguous()
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if (
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self.quant_method.__class__.__name__
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== "CompressedTensorsWNA16MoEMethod"
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)
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else loaded_weight
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)
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if shard_id not in ("w1", "w2", "w3"):
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raise ValueError(
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f"shard_id must be ['w1','w2','w3'] but " f"got {shard_id}."
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)
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# Flashinfer assumes w31 format for w13_weight. Same for the scales.
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if getattr(self, "use_flashinfer_trtllm_moe", False):
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shard_id = {"w1": "w3", "w3": "w1", "w2": "w2"}[shard_id]
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WEIGHT_SCALE_SUPPORTED = [e.value for e in FusedMoeWeightScaleSupported]
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# Fetch the dim to shard the parameter/loaded weight
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# based on the shard id. This will be whatever
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# dimension intermediate_size is used.
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SHARD_ID_TO_SHARDED_DIM = {"w1": 0, "w2": 1, "w3": 0}
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expert_data = param.data[expert_id]
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# is_transposed: if the dim to shard the weight
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# should be flipped. Required by GPTQ, compressed-tensors
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# should be whatever dimension intermediate_size is
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is_transposed = getattr(param, "is_transposed", False)
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shard_dim = SHARD_ID_TO_SHARDED_DIM[shard_id]
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if self.use_triton_kernels:
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is_transposed = True
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if is_transposed:
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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__:
|
|
# Determine per-tensor weight scale patterns based on variant
|
|
is_fp4_variant = (
|
|
"ModelOptNvFp4FusedMoEMethod" in self.quant_method.__class__.__name__
|
|
)
|
|
|
|
# FP4 uses "weight_scale_2" for per-tensor, FP8 uses "weight_scale" for per-tensor
|
|
per_tensor_conditions = (
|
|
"weight_scale_2" in weight_name
|
|
if is_fp4_variant
|
|
else "weight_scale" in weight_name
|
|
) or "input_scale" in weight_name
|
|
|
|
if per_tensor_conditions:
|
|
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 or "offset" 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, topk_output: StandardTopKOutput):
|
|
assert self.quant_method is not None
|
|
|
|
if self.expert_map_gpu is not None:
|
|
topk_output = topk_output._replace(
|
|
topk_ids=self.expert_map_gpu[topk_output.topk_ids]
|
|
)
|
|
|
|
# Matrix multiply.
|
|
final_hidden_states = self.quant_method.apply(
|
|
layer=self,
|
|
x=hidden_states,
|
|
topk_output=topk_output,
|
|
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.moe_tp_rank,
|
|
tp_size=self.moe_tp_size,
|
|
ep_rank=self.moe_ep_rank,
|
|
ep_size=self.moe_ep_size,
|
|
)
|
|
if self.quant_method.__class__.__name__ == "ModelOptNvFp4FusedMoEMethod"
|
|
else {}
|
|
),
|
|
)
|
|
|
|
if self.reduce_results and (self.moe_tp_size > 1 or self.moe_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),
|
|
]
|
|
]
|
|
|
|
@classmethod
|
|
def make_expert_input_scale_params_mapping(
|
|
cls,
|
|
num_experts: int,
|
|
) -> List[Tuple[str, str, int, str]]:
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
return [
|
|
(
|
|
"experts.w13_" if shard_id in ["w1", "w3"] else "experts.w2_",
|
|
f"experts.{expert_id}.{shard_id}.",
|
|
expert_id,
|
|
shard_id,
|
|
)
|
|
for expert_id in range(num_experts)
|
|
for shard_id in ["w1", "w2", "w3"]
|
|
]
|