1258 lines
43 KiB
Python
1258 lines
43 KiB
Python
import logging
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from typing import Callable, List, Optional, Tuple
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import torch
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from torch.nn import Module
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from sglang.srt.layers.quantization.deep_gemm import _ENABLE_JIT_DEEPGEMM
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from sglang.srt.managers.expert_location import get_global_expert_location_metadata
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from sglang.srt.managers.expert_location_dispatch import ExpertLocationDispatchInfo
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from sglang.srt.managers.schedule_batch import global_server_args_dict
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try:
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from deep_gemm import (
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get_col_major_tma_aligned_tensor,
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m_grouped_gemm_fp8_fp8_bf16_nt_contiguous,
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m_grouped_gemm_fp8_fp8_bf16_nt_masked,
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)
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from sgl_kernel import silu_and_mul
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from sglang.srt.layers.quantization.fp8_kernel import (
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sglang_per_token_group_quant_fp8,
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)
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use_deep_gemm = True
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except ImportError:
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use_deep_gemm = False
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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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)
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from sglang.srt.layers.moe.ep_moe.kernels import (
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ep_gather,
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ep_scatter,
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gelu_and_mul_triton_kernel,
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grouped_gemm_triton,
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post_reorder_triton_kernel,
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pre_reorder_triton_kernel,
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run_moe_ep_preproess,
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silu_and_mul_masked_post_quant_fwd,
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silu_and_mul_triton_kernel,
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tma_align_input_scale,
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)
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
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from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE, FusedMoEMethodBase
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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.layers.quantization.fp8 import Fp8Config, Fp8MoEMethod
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from sglang.srt.layers.quantization.fp8_kernel import (
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scaled_fp8_quant,
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sglang_per_token_quant_fp8,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardMode
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from sglang.srt.utils import DeepEPMode, dispose_tensor, is_hip, set_weight_attrs
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_is_hip = is_hip()
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if _is_hip:
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from vllm._custom_ops import scaled_fp8_quant
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logger = logging.getLogger(__name__)
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class GroupedGemmRunner(torch.nn.Module):
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flashinfer_gemm_warpper = None
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def __init__(
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self,
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device,
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use_flashinfer: bool = False,
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use_per_token_if_dynamic: bool = True,
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):
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super().__init__()
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self.device = device
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self.use_flashinfer = use_flashinfer
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self.use_per_token_if_dynamic = use_per_token_if_dynamic
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if self.use_flashinfer and GroupedGemmRunner.flashinfer_gemm_warpper is None:
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GroupedGemmRunner._init_flashinfer_wrapper(device)
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@classmethod
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def _init_flashinfer_wrapper(cls, device):
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from flashinfer import SegmentGEMMWrapper
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workspace_buffer = torch.empty(
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128 * 1024 * 1024, dtype=torch.int8, device=device
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)
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cls.flashinfer_gemm_warpper = SegmentGEMMWrapper(workspace_buffer)
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# c = a * b
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def forward(
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self,
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a: torch.Tensor,
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b: torch.Tensor,
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c: torch.Tensor,
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batch_size: int,
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weight_column_major: bool,
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seg_indptr: Optional[torch.Tensor] = None,
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weight_indices: Optional[torch.Tensor] = None,
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use_fp8_w8a8: bool = False,
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scale_a: torch.Tensor = None,
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scale_b: torch.Tensor = None,
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block_shape: Optional[List[int]] = None,
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c_dtype=None,
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):
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if self.use_flashinfer:
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# TODO: flashinfer
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assert False
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assert GroupedGemmRunner.flashinfer_gemm_warpper is not None
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c = GroupedGemmRunner.flashinfer_gemm_warpper.run(
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x=a,
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weights=b,
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batch_size=batch_size,
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weight_column_major=weight_column_major,
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seg_indptr=seg_indptr,
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weight_indices=weight_indices,
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)
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else:
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assert weight_column_major == True
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c = grouped_gemm_triton(
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a,
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b,
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c,
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batch_size,
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weight_column_major,
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seg_indptr,
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weight_indices,
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use_fp8_w8a8,
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scale_a,
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scale_b,
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block_shape=block_shape,
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c_dtype=c_dtype,
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use_per_token_if_dynamic=self.use_per_token_if_dynamic,
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)
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return c
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class EPMoE(torch.nn.Module):
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"""
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MoE Expert Parallel Impl
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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: int,
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params_dtype: Optional[torch.dtype] = None,
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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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correction_bias: Optional[torch.Tensor] = None,
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custom_routing_function: Optional[Callable] = None,
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activation: str = "silu",
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routed_scaling_factor: Optional[float] = None,
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use_per_token_if_dynamic: bool = True,
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):
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super().__init__()
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if params_dtype is None:
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params_dtype = torch.get_default_dtype()
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self.tp_size = (
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tp_size if tp_size is not None else get_tensor_model_parallel_world_size()
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)
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self.tp_rank = get_tensor_model_parallel_rank()
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self.layer_id = layer_id
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self.num_experts = num_experts
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assert self.num_experts % self.tp_size == 0
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self.num_experts_per_partition = self.num_experts // self.tp_size
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self.start_expert_id = self.tp_rank * self.num_experts_per_partition
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self.end_expert_id = self.start_expert_id + self.num_experts_per_partition - 1
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self.top_k = top_k
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self.intermediate_size = intermediate_size
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self.renormalize = renormalize
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self.use_grouped_topk = use_grouped_topk
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if self.use_grouped_topk:
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assert num_expert_group is not None and topk_group is not None
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self.num_expert_group = num_expert_group
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self.num_fused_shared_experts = num_fused_shared_experts
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self.topk_group = topk_group
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self.correction_bias = correction_bias
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self.custom_routing_function = custom_routing_function
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self.activation = activation
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self.routed_scaling_factor = routed_scaling_factor
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self.use_per_token_if_dynamic = use_per_token_if_dynamic
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if quant_config is None:
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self.quant_method: Optional[QuantizeMethodBase] = UnquantizedEPMoEMethod()
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self.use_fp8_w8a8 = False
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self.use_block_quant = False
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self.block_shape = None
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self.activation_scheme = None
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else:
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self.quant_method: Optional[QuantizeMethodBase] = Fp8EPMoEMethod(
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quant_config
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)
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self.use_fp8_w8a8 = True
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self.use_block_quant = getattr(self.quant_method, "block_quant", False)
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self.block_shape = (
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self.quant_method.quant_config.weight_block_size
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if self.use_block_quant
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else None
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)
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self.fp8_dtype = torch.float8_e4m3fn
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self.activation_scheme = quant_config.activation_scheme
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self.quant_method.create_weights(
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layer=self,
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num_experts_per_partition=self.num_experts_per_partition,
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hidden_size=hidden_size,
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intermediate_size=self.intermediate_size,
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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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self.grouped_gemm_runner = None
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def forward(self, hidden_states: torch.Tensor, router_logits: torch.Tensor):
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hidden_states_shape = hidden_states.shape
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hidden_states_dtype = hidden_states.dtype
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hidden_states_device = hidden_states.device
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assert self.quant_method is not None
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if self.grouped_gemm_runner is None:
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self.grouped_gemm_runner = GroupedGemmRunner(
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hidden_states.device,
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use_flashinfer=False, # TODO: use flashinfer
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use_per_token_if_dynamic=self.use_per_token_if_dynamic,
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)
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topk_weights, topk_ids = select_experts(
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hidden_states=hidden_states,
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router_logits=router_logits,
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top_k=self.top_k,
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use_grouped_topk=self.use_grouped_topk,
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renormalize=self.renormalize,
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topk_group=self.topk_group,
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num_expert_group=self.num_expert_group,
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num_fused_shared_experts=self.num_fused_shared_experts,
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correction_bias=self.correction_bias,
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custom_routing_function=self.custom_routing_function,
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routed_scaling_factor=self.routed_scaling_factor,
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expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
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layer_id=self.layer_id,
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),
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)
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reorder_topk_ids, src2dst, seg_indptr = run_moe_ep_preproess(
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topk_ids, self.num_experts
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)
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gateup_input = torch.empty(
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(int(hidden_states.shape[0] * self.top_k), hidden_states.shape[1]),
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device=hidden_states.device,
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dtype=(
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self.fp8_dtype
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if (self.use_fp8_w8a8 and not self.use_block_quant)
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else hidden_states.dtype
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),
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)
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if self.activation_scheme == "dynamic" and not self.use_block_quant:
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if self.use_per_token_if_dynamic:
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max_value = torch.max(hidden_states, dim=1).values.to(torch.float32)
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self.w13_input_scale = max_value / torch.finfo(self.fp8_dtype).max
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else:
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max_value = (
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torch.max(hidden_states)
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.repeat(self.num_experts_per_partition)
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.to(torch.float32)
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)
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self.w13_input_scale = max_value / torch.finfo(self.fp8_dtype).max
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# PreReorder
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pre_reorder_triton_kernel[(hidden_states.shape[0],)](
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hidden_states,
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gateup_input,
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src2dst,
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topk_ids,
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self.w13_input_scale,
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self.start_expert_id,
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self.end_expert_id,
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self.top_k,
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hidden_states.shape[1],
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BLOCK_SIZE=512,
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use_per_token_if_dynamic=self.use_per_token_if_dynamic,
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)
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dispose_tensor(hidden_states)
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if (
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self.activation_scheme == "dynamic"
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and not self.use_block_quant
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and self.use_per_token_if_dynamic
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):
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scale = torch.empty(
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hidden_states_shape[0] * self.top_k,
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device=hidden_states_device,
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dtype=torch.float32,
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)
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scale[src2dst] = (
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self.w13_input_scale.unsqueeze(1)
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.expand(hidden_states_shape[0], self.top_k)
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.reshape(-1)
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)
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self.w13_input_scale = scale
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seg_indptr_cur_rank = seg_indptr[self.start_expert_id : self.end_expert_id + 2]
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weight_indices_cur_rank = torch.arange(
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0,
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self.num_experts_per_partition,
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device=hidden_states_device,
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dtype=torch.int64,
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)
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# GroupGemm-0
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gateup_output = self.grouped_gemm_runner(
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a=gateup_input,
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b=self.w13_weight,
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c=None,
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c_dtype=hidden_states_dtype,
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batch_size=self.num_experts_per_partition,
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weight_column_major=True,
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seg_indptr=seg_indptr_cur_rank,
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weight_indices=weight_indices_cur_rank,
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use_fp8_w8a8=self.use_fp8_w8a8,
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scale_a=self.w13_input_scale,
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scale_b=(
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self.w13_weight_scale_inv
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if self.use_block_quant
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else self.w13_weight_scale
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),
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block_shape=self.block_shape,
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)
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del gateup_input
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# Act
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if self.activation_scheme == "dynamic" and not self.use_block_quant:
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self.w2_input_scale = None
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down_input = torch.empty(
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gateup_output.shape[0],
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gateup_output.shape[1] // 2,
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device=gateup_output.device,
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dtype=hidden_states_dtype,
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)
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else:
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down_input = torch.empty(
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gateup_output.shape[0],
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gateup_output.shape[1] // 2,
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device=gateup_output.device,
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dtype=(
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self.fp8_dtype
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if (self.use_fp8_w8a8 and not self.use_block_quant)
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else hidden_states_dtype
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),
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)
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if self.activation == "silu":
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silu_and_mul_triton_kernel[(gateup_output.shape[0],)](
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gateup_output,
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down_input,
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gateup_output.shape[1],
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reorder_topk_ids,
|
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self.w2_input_scale,
|
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self.start_expert_id,
|
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self.end_expert_id,
|
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BLOCK_SIZE=512,
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)
|
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elif self.activation == "gelu":
|
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gelu_and_mul_triton_kernel[(gateup_output.shape[0],)](
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gateup_output,
|
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down_input,
|
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gateup_output.shape[1],
|
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reorder_topk_ids,
|
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self.w2_input_scale,
|
|
self.start_expert_id,
|
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self.end_expert_id,
|
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BLOCK_SIZE=512,
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)
|
|
else:
|
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raise ValueError(f"Unsupported activation: {self.activation=}")
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del gateup_output
|
|
|
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if self.activation_scheme == "dynamic" and not self.use_block_quant:
|
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if self.use_per_token_if_dynamic:
|
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down_input, self.w2_input_scale = sglang_per_token_quant_fp8(down_input)
|
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else:
|
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self.w2_input_scale = torch.ones(
|
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self.num_experts_per_partition,
|
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dtype=torch.float32,
|
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device=hidden_states_device,
|
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)
|
|
|
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# GroupGemm-1
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down_output = torch.empty(
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down_input.shape[0],
|
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self.w2_weight.shape[1],
|
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device=hidden_states_device,
|
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dtype=hidden_states_dtype,
|
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)
|
|
down_output = self.grouped_gemm_runner(
|
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a=down_input,
|
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b=self.w2_weight,
|
|
c=down_output,
|
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batch_size=self.num_experts_per_partition,
|
|
weight_column_major=True,
|
|
seg_indptr=seg_indptr_cur_rank,
|
|
weight_indices=weight_indices_cur_rank,
|
|
use_fp8_w8a8=self.use_fp8_w8a8,
|
|
scale_a=self.w2_input_scale,
|
|
scale_b=(
|
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self.w2_weight_scale_inv
|
|
if self.use_block_quant
|
|
else self.w2_weight_scale
|
|
),
|
|
block_shape=self.block_shape,
|
|
)
|
|
del down_input
|
|
|
|
# PostReorder
|
|
output = torch.empty(
|
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hidden_states_shape, dtype=hidden_states_dtype, device=hidden_states_device
|
|
)
|
|
post_reorder_triton_kernel[(hidden_states_shape[0],)](
|
|
down_output,
|
|
output,
|
|
src2dst,
|
|
topk_ids,
|
|
topk_weights,
|
|
self.start_expert_id,
|
|
self.end_expert_id,
|
|
self.top_k,
|
|
hidden_states_shape[1],
|
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BLOCK_SIZE=512,
|
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)
|
|
return output
|
|
|
|
@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 weight_loader(
|
|
self,
|
|
param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor,
|
|
weight_name: str,
|
|
shard_id: str,
|
|
expert_id: int,
|
|
) -> None:
|
|
physical_expert_ids = (
|
|
get_global_expert_location_metadata().logical_to_all_physical(
|
|
self.layer_id, expert_id
|
|
)
|
|
)
|
|
for physical_expert_id in physical_expert_ids:
|
|
self._weight_loader_physical(
|
|
param=param,
|
|
loaded_weight=loaded_weight,
|
|
weight_name=weight_name,
|
|
shard_id=shard_id,
|
|
expert_id=physical_expert_id,
|
|
)
|
|
|
|
def _weight_loader_physical(
|
|
self,
|
|
param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor,
|
|
weight_name: str,
|
|
shard_id: str,
|
|
expert_id: int,
|
|
) -> None:
|
|
if expert_id < self.start_expert_id or expert_id > self.end_expert_id:
|
|
return
|
|
expert_id = expert_id - self.start_expert_id
|
|
|
|
if shard_id not in ("w1", "w2", "w3"):
|
|
raise ValueError(
|
|
f"shard_id must be ['w1','w2','w3'] but " f"got {shard_id}."
|
|
)
|
|
|
|
# Special case for fp8 scales.
|
|
if "scale" in weight_name:
|
|
self._load_fp8_scale(
|
|
param.data,
|
|
loaded_weight,
|
|
weight_name,
|
|
shard_id,
|
|
expert_id,
|
|
)
|
|
return
|
|
|
|
if shard_id == "w2":
|
|
param.data[expert_id] = loaded_weight
|
|
elif shard_id == "w1":
|
|
param.data[expert_id][: self.intermediate_size, :] = loaded_weight
|
|
elif shard_id == "w3":
|
|
param.data[expert_id][self.intermediate_size :, :] = loaded_weight
|
|
else:
|
|
raise ValueError(f"Expected shard_id w1,w2 or w3 but got {shard_id}")
|
|
|
|
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 (
|
|
(shard_id == "w1" or shard_id == "w3")
|
|
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}"
|
|
)
|
|
param_data[expert_id] = loaded_weight
|
|
# Weight scales
|
|
elif "weight_scale" in weight_name:
|
|
if self.use_block_quant:
|
|
block_n, block_k = self.block_shape[0], self.block_shape[1]
|
|
if shard_id == "w1":
|
|
param_data[expert_id][
|
|
: (self.intermediate_size + block_n - 1) // block_n, :
|
|
] = loaded_weight
|
|
elif shard_id == "w3":
|
|
param_data[expert_id][
|
|
(self.intermediate_size + block_n - 1) // block_n :, :
|
|
] = loaded_weight
|
|
else: # w2
|
|
param_data[expert_id] = loaded_weight
|
|
# If we are in merged column case (gate_up_proj)
|
|
else:
|
|
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
|
|
|
|
|
|
class UnquantizedEPMoEMethod(FusedMoEMethodBase, CustomOp):
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
num_experts_per_partition: int,
|
|
hidden_size: int,
|
|
intermediate_size: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
):
|
|
# Fused gate_up_proj (column parallel)
|
|
w13_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts_per_partition,
|
|
2 * intermediate_size,
|
|
hidden_size,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight", w13_weight)
|
|
set_weight_attrs(w13_weight, extra_weight_attrs)
|
|
|
|
# down_proj (row parallel)
|
|
w2_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts_per_partition,
|
|
hidden_size,
|
|
intermediate_size,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_weight", w2_weight)
|
|
set_weight_attrs(w2_weight, extra_weight_attrs)
|
|
|
|
# scale
|
|
layer.register_parameter("w13_input_scale", None)
|
|
layer.register_parameter("w13_weight_scale", None)
|
|
|
|
ones_tensor = torch.ones(num_experts_per_partition, dtype=torch.float32)
|
|
|
|
w2_input_scale = torch.nn.Parameter(
|
|
ones_tensor,
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_input_scale", w2_input_scale)
|
|
set_weight_attrs(w2_input_scale, extra_weight_attrs)
|
|
|
|
w2_weight_scale = torch.nn.Parameter(
|
|
ones_tensor,
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_weight_scale", w2_weight_scale)
|
|
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
renormalize: bool,
|
|
use_grouped_topk: bool,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
) -> torch.Tensor:
|
|
raise NotImplementedError
|
|
|
|
|
|
class Fp8EPMoEMethod(Fp8MoEMethod):
|
|
"""MoE method for FP8.
|
|
Supports loading FP8 checkpoints with static weight scale and
|
|
dynamic/static activation scale.
|
|
|
|
Args:
|
|
quant_config: The quantization config.
|
|
"""
|
|
|
|
def __init__(self, quant_config: Fp8Config):
|
|
self.quant_config = quant_config
|
|
self.block_quant = self.quant_config.weight_block_size is not None
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: Module,
|
|
num_experts_per_partition: int,
|
|
hidden_size: int,
|
|
intermediate_size: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
):
|
|
|
|
if self.quant_config.is_checkpoint_fp8_serialized:
|
|
params_dtype = torch.float8_e4m3fn
|
|
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
if self.block_quant:
|
|
block_n, block_k = (
|
|
self.quant_config.weight_block_size[0],
|
|
self.quant_config.weight_block_size[1],
|
|
)
|
|
# NOTE(HandH1998): To ensure proper alignment of the block-wise quantization scales, the output_size of the weights for both the gate and up layers must be divisible by block_n.
|
|
# Required by column parallel or enabling merged weights
|
|
if intermediate_size % block_n != 0:
|
|
raise ValueError(
|
|
f"The output_size of gate's and up's weight = "
|
|
f"{intermediate_size} is not divisible by "
|
|
f"weight quantization block_n = {block_n}."
|
|
)
|
|
if tp_size > 1:
|
|
# Required by row parallel
|
|
if intermediate_size % block_k != 0:
|
|
raise ValueError(
|
|
f"The input_size of down's weight = "
|
|
f"{intermediate_size} is not divisible by "
|
|
f"weight quantization block_k = {block_k}."
|
|
)
|
|
|
|
# WEIGHTS
|
|
w13_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts_per_partition,
|
|
2 * intermediate_size,
|
|
hidden_size,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight", w13_weight)
|
|
set_weight_attrs(w13_weight, extra_weight_attrs)
|
|
|
|
w2_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts_per_partition,
|
|
hidden_size,
|
|
intermediate_size,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_weight", w2_weight)
|
|
set_weight_attrs(w2_weight, extra_weight_attrs)
|
|
|
|
# WEIGHT_SCALES
|
|
if self.block_quant:
|
|
w13_weight_scale = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts_per_partition,
|
|
2 * ((intermediate_size + block_n - 1) // block_n),
|
|
(hidden_size + block_k - 1) // block_k,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight_scale = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts_per_partition,
|
|
(hidden_size + block_n - 1) // block_n,
|
|
(intermediate_size + block_k - 1) // block_k,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight_scale_inv", w13_weight_scale)
|
|
layer.register_parameter("w2_weight_scale_inv", w2_weight_scale)
|
|
assert self.quant_config.activation_scheme == "dynamic"
|
|
else:
|
|
# WEIGHT_SCALES
|
|
# Allocate 2 scales for w1 and w3 respectively.
|
|
w13_weight_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts_per_partition, 2, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight_scale", w13_weight_scale)
|
|
|
|
w2_weight_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts_per_partition, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_weight_scale", w2_weight_scale)
|
|
# Add the quantization method used (per tensor/grouped/channel)
|
|
# to ensure the weight scales are loaded in properly
|
|
extra_weight_attrs.update(
|
|
{"quant_method": FusedMoeWeightScaleSupported.BLOCK.value}
|
|
if self.block_quant
|
|
else {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
|
|
)
|
|
# If loading fp8 checkpoint, pass the weight loaders.
|
|
# If loading an fp16 checkpoint, do not (we will quantize in
|
|
# process_weights_after_loading()
|
|
if self.quant_config.is_checkpoint_fp8_serialized:
|
|
set_weight_attrs(w13_weight_scale, extra_weight_attrs)
|
|
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
|
|
|
|
# INPUT_SCALES
|
|
if self.quant_config.activation_scheme == "static":
|
|
if not self.quant_config.is_checkpoint_fp8_serialized:
|
|
raise ValueError(
|
|
"Found static activation scheme for checkpoint that "
|
|
"was not serialized fp8."
|
|
)
|
|
|
|
w13_input_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts_per_partition, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_input_scale", w13_input_scale)
|
|
set_weight_attrs(w13_input_scale, extra_weight_attrs)
|
|
|
|
w2_input_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts_per_partition, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_input_scale", w2_input_scale)
|
|
set_weight_attrs(w2_input_scale, extra_weight_attrs)
|
|
|
|
else:
|
|
layer.w13_input_scale = None
|
|
layer.w2_input_scale = None
|
|
|
|
def process_weights_after_loading(self, layer: Module) -> None:
|
|
|
|
# If checkpoint is fp16, quantize in place.
|
|
if not self.quant_config.is_checkpoint_fp8_serialized:
|
|
# If rocm, use float8_e4m3fnuz as dtype
|
|
fp8_dtype = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
|
|
w13_weight = torch.empty_like(layer.w13_weight.data, dtype=fp8_dtype)
|
|
w2_weight = torch.empty_like(layer.w2_weight.data, dtype=fp8_dtype)
|
|
|
|
layer.w13_weight_scale = torch.nn.Parameter(
|
|
torch.ones(
|
|
layer.num_experts_per_partition,
|
|
dtype=torch.float32,
|
|
device=w13_weight.device,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
|
|
for expert in range(layer.num_experts_per_partition):
|
|
w13_weight[expert, :, :], layer.w13_weight_scale[expert] = (
|
|
scaled_fp8_quant(layer.w13_weight.data[expert, :, :])
|
|
)
|
|
w2_weight[expert, :, :], layer.w2_weight_scale[expert] = (
|
|
scaled_fp8_quant(layer.w2_weight.data[expert, :, :])
|
|
)
|
|
layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
|
|
layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
|
|
return
|
|
|
|
# If checkpoint is fp8, we need to handle that the
|
|
# MoE kernels require single activation scale and single weight
|
|
# scale for w13 per expert.
|
|
else:
|
|
if self.quant_config.activation_scheme == "static":
|
|
if layer.w13_input_scale is None or layer.w2_input_scale is None:
|
|
raise ValueError(
|
|
"QuantConfig has static quantization, but found "
|
|
"activation scales are None."
|
|
)
|
|
layer.w13_weight_scale = torch.nn.Parameter(
|
|
torch.max(layer.w13_weight_scale, dim=1).values,
|
|
requires_grad=False,
|
|
)
|
|
return
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
renormalize: bool,
|
|
use_grouped_topk: bool,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
) -> torch.Tensor:
|
|
raise NotImplementedError
|
|
|
|
|
|
class DeepEPMoE(EPMoE):
|
|
"""
|
|
MoE Expert Parallel Impl based on DeepEP (https://github.com/deepseek-ai/DeepEP/tree/main)
|
|
"""
|
|
|
|
_has_printed = False
|
|
|
|
def __init__(
|
|
self,
|
|
num_experts: int,
|
|
top_k: int,
|
|
hidden_size: int,
|
|
intermediate_size: int,
|
|
layer_id: int,
|
|
params_dtype: Optional[torch.dtype] = None,
|
|
renormalize: bool = True,
|
|
use_grouped_topk: bool = False,
|
|
num_expert_group: Optional[int] = None,
|
|
topk_group: Optional[int] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
tp_size: Optional[int] = None,
|
|
prefix: str = "",
|
|
correction_bias: Optional[torch.Tensor] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
activation: str = "silu",
|
|
routed_scaling_factor: Optional[float] = None,
|
|
deepep_mode: DeepEPMode = DeepEPMode.auto,
|
|
):
|
|
super().__init__(
|
|
num_experts,
|
|
top_k,
|
|
hidden_size,
|
|
intermediate_size,
|
|
layer_id,
|
|
params_dtype,
|
|
renormalize,
|
|
use_grouped_topk,
|
|
num_expert_group,
|
|
topk_group,
|
|
quant_config,
|
|
tp_size,
|
|
prefix,
|
|
correction_bias,
|
|
custom_routing_function,
|
|
activation,
|
|
routed_scaling_factor,
|
|
)
|
|
self.deepep_mode = deepep_mode
|
|
if self.deepep_mode.enable_low_latency():
|
|
assert use_deep_gemm, f"DeepEP {self.deepep_mode} mode requires deep_gemm"
|
|
self.w13_weight_fp8 = (
|
|
self.w13_weight,
|
|
(
|
|
self.w13_weight_scale_inv
|
|
if self.use_block_quant
|
|
else self.w13_weight_scale
|
|
),
|
|
)
|
|
self.w2_weight_fp8 = (
|
|
self.w2_weight,
|
|
self.w2_weight_scale_inv if self.use_block_quant else self.w2_weight_scale,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
topk_idx: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
reorder_topk_ids: torch.Tensor,
|
|
seg_indptr: torch.Tensor,
|
|
masked_m: torch.Tensor,
|
|
expected_m: int,
|
|
num_recv_tokens_per_expert: List[int],
|
|
forward_mode: ForwardMode,
|
|
):
|
|
resolved_deepep_mode = self.deepep_mode.resolve(forward_mode)
|
|
if resolved_deepep_mode == DeepEPMode.normal:
|
|
if _ENABLE_JIT_DEEPGEMM:
|
|
return self.forward_deepgemm_contiguous(
|
|
hidden_states, topk_idx, topk_weights, num_recv_tokens_per_expert
|
|
)
|
|
else:
|
|
return self.forward_normal(hidden_states, reorder_topk_ids, seg_indptr)
|
|
elif resolved_deepep_mode == DeepEPMode.low_latency:
|
|
return self.forward_deepgemm_masked(hidden_states, masked_m, expected_m)
|
|
else:
|
|
raise ValueError(f"Invalid deepep_mode: {self.deepep_mode}")
|
|
|
|
def forward_normal(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
reorder_topk_ids: torch.Tensor,
|
|
seg_indptr: torch.Tensor,
|
|
):
|
|
hidden_states_dtype = hidden_states.dtype
|
|
hidden_states_device = hidden_states.device
|
|
|
|
assert self.quant_method is not None
|
|
assert self.activation == "silu"
|
|
if self.grouped_gemm_runner is None:
|
|
self.grouped_gemm_runner = GroupedGemmRunner(
|
|
hidden_states.device, use_flashinfer=False # TODO: use flashinfer
|
|
)
|
|
|
|
if self.activation_scheme == "dynamic" and not self.use_block_quant:
|
|
max_value = (
|
|
torch.max(hidden_states)
|
|
.repeat(self.num_experts_per_partition)
|
|
.to(torch.float32)
|
|
)
|
|
self.w13_input_scale = max_value / torch.finfo(self.fp8_dtype).max
|
|
weight_indices_cur_rank = torch.arange(
|
|
0,
|
|
self.num_experts_per_partition,
|
|
device=hidden_states.device,
|
|
dtype=torch.int64,
|
|
)
|
|
|
|
# GroupGemm-0
|
|
if hidden_states.shape[0] > 0:
|
|
gateup_output = self.grouped_gemm_runner(
|
|
a=hidden_states,
|
|
b=self.w13_weight,
|
|
c=None,
|
|
c_dtype=hidden_states.dtype,
|
|
batch_size=self.num_experts_per_partition,
|
|
weight_column_major=True,
|
|
seg_indptr=seg_indptr,
|
|
weight_indices=weight_indices_cur_rank,
|
|
use_fp8_w8a8=self.use_fp8_w8a8,
|
|
scale_a=self.w13_input_scale,
|
|
scale_b=(
|
|
self.w13_weight_scale_inv
|
|
if self.use_block_quant
|
|
else self.w13_weight_scale
|
|
),
|
|
block_shape=self.block_shape,
|
|
)
|
|
else:
|
|
gateup_output = torch.empty(
|
|
hidden_states.shape[0],
|
|
self.w13_weight.shape[1],
|
|
device=hidden_states.device,
|
|
dtype=hidden_states.dtype,
|
|
)
|
|
|
|
# Act
|
|
down_input = torch.empty(
|
|
gateup_output.shape[0],
|
|
gateup_output.shape[1] // 2,
|
|
device=gateup_output.device,
|
|
dtype=(
|
|
self.fp8_dtype
|
|
if (self.use_fp8_w8a8 and not self.use_block_quant)
|
|
else hidden_states_dtype
|
|
),
|
|
)
|
|
if self.w2_input_scale is None and not self.use_block_quant:
|
|
self.w2_input_scale = torch.ones(
|
|
self.num_experts_per_partition,
|
|
dtype=torch.float32,
|
|
device=hidden_states_device,
|
|
)
|
|
|
|
if self.activation == "silu":
|
|
silu_and_mul_triton_kernel[(gateup_output.shape[0],)](
|
|
gateup_output,
|
|
down_input,
|
|
gateup_output.shape[1],
|
|
reorder_topk_ids,
|
|
self.w2_input_scale,
|
|
0,
|
|
self.num_experts_per_partition - 1,
|
|
BLOCK_SIZE=512,
|
|
)
|
|
else:
|
|
raise ValueError(f"Unsupported activation: {self.activation=}")
|
|
|
|
del gateup_output
|
|
|
|
# GroupGemm-1
|
|
down_output = torch.empty(
|
|
down_input.shape[0],
|
|
self.w2_weight.shape[1],
|
|
device=hidden_states_device,
|
|
dtype=hidden_states_dtype,
|
|
)
|
|
if down_input.shape[0] > 0:
|
|
down_output = self.grouped_gemm_runner(
|
|
a=down_input,
|
|
b=self.w2_weight,
|
|
c=down_output,
|
|
batch_size=self.num_experts_per_partition,
|
|
weight_column_major=True,
|
|
seg_indptr=seg_indptr,
|
|
weight_indices=weight_indices_cur_rank,
|
|
use_fp8_w8a8=self.use_fp8_w8a8,
|
|
scale_a=self.w2_input_scale,
|
|
scale_b=(
|
|
self.w2_weight_scale_inv
|
|
if self.use_block_quant
|
|
else self.w2_weight_scale
|
|
),
|
|
block_shape=self.block_shape,
|
|
)
|
|
return down_output
|
|
|
|
def forward_deepgemm_contiguous(
|
|
self,
|
|
hidden_states_fp8: Tuple[torch.Tensor, torch.Tensor],
|
|
topk_idx,
|
|
topk_weights,
|
|
num_recv_tokens_per_expert: List[int],
|
|
):
|
|
hidden_states_fp8, hidden_states_scale = hidden_states_fp8
|
|
assert self.quant_method is not None
|
|
assert self.activation == "silu"
|
|
if num_recv_tokens_per_expert is None:
|
|
return hidden_states_fp8.bfloat16()
|
|
all_tokens = sum(num_recv_tokens_per_expert)
|
|
if all_tokens <= 0:
|
|
return hidden_states_fp8.bfloat16()
|
|
M, K = hidden_states_fp8.size()
|
|
N = self.w13_weight.size(1)
|
|
scale_block_size = 128
|
|
|
|
hidden_states_fp8_shape = hidden_states_fp8.shape
|
|
hidden_states_fp8_device = hidden_states_fp8.device
|
|
hidden_states_fp8_dtype = hidden_states_fp8.dtype
|
|
|
|
input_tensor = [
|
|
torch.empty(
|
|
(all_tokens, K),
|
|
device=hidden_states_fp8.device,
|
|
dtype=hidden_states_fp8.dtype,
|
|
),
|
|
torch.empty(
|
|
(all_tokens, K // 128),
|
|
device=hidden_states_fp8.device,
|
|
dtype=torch.float32,
|
|
),
|
|
]
|
|
m_indices = torch.empty(
|
|
all_tokens, device=hidden_states_fp8.device, dtype=torch.int32
|
|
)
|
|
output_index = torch.empty_like(topk_idx)
|
|
|
|
num_recv_tokens_per_expert_gpu = torch.tensor(
|
|
num_recv_tokens_per_expert,
|
|
dtype=torch.int32,
|
|
pin_memory=True,
|
|
device="cpu",
|
|
).cuda(non_blocking=True)
|
|
expert_start_loc = torch.empty_like(num_recv_tokens_per_expert_gpu)
|
|
|
|
ep_scatter(
|
|
hidden_states_fp8,
|
|
hidden_states_scale,
|
|
topk_idx,
|
|
num_recv_tokens_per_expert_gpu,
|
|
expert_start_loc,
|
|
input_tensor[0],
|
|
input_tensor[1],
|
|
m_indices,
|
|
output_index,
|
|
)
|
|
dispose_tensor(hidden_states_fp8)
|
|
|
|
gateup_output = torch.empty(
|
|
(all_tokens, N),
|
|
device=hidden_states_fp8_device,
|
|
dtype=torch.bfloat16,
|
|
)
|
|
input_tensor[1] = tma_align_input_scale(input_tensor[1])
|
|
m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(
|
|
input_tensor, self.w13_weight_fp8, gateup_output, m_indices
|
|
)
|
|
del input_tensor
|
|
down_input = torch.empty(
|
|
(
|
|
all_tokens,
|
|
N // 2,
|
|
),
|
|
device=gateup_output.device,
|
|
dtype=torch.bfloat16,
|
|
)
|
|
silu_and_mul(gateup_output.view(-1, N), down_input)
|
|
del gateup_output
|
|
down_output = torch.empty(
|
|
(all_tokens, K),
|
|
device=hidden_states_fp8_device,
|
|
dtype=torch.bfloat16,
|
|
)
|
|
down_input_fp8, down_input_scale = sglang_per_token_group_quant_fp8(
|
|
down_input, scale_block_size
|
|
)
|
|
del down_input
|
|
down_input_scale = tma_align_input_scale(down_input_scale)
|
|
m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(
|
|
(down_input_fp8, down_input_scale),
|
|
self.w2_weight_fp8,
|
|
down_output,
|
|
m_indices,
|
|
)
|
|
del down_input_fp8, down_input_scale
|
|
|
|
gather_out = torch.empty(
|
|
hidden_states_fp8_shape,
|
|
device=hidden_states_fp8_device,
|
|
dtype=torch.bfloat16,
|
|
)
|
|
ep_gather(down_output, topk_idx, topk_weights, output_index, gather_out)
|
|
|
|
return gather_out
|
|
|
|
def forward_deepgemm_masked(
|
|
self,
|
|
hidden_states_fp8: Tuple[torch.Tensor, torch.Tensor],
|
|
masked_m: torch.Tensor,
|
|
expected_m: int,
|
|
):
|
|
assert self.quant_method is not None
|
|
assert self.activation == "silu"
|
|
|
|
# GroupGemm-0
|
|
num_groups, m, k = hidden_states_fp8[0].size()
|
|
n = self.w13_weight.size(1)
|
|
expected_m = min(expected_m, m)
|
|
gateup_output = torch.empty(
|
|
(num_groups, m, n), device=hidden_states_fp8[0].device, dtype=torch.bfloat16
|
|
)
|
|
m_grouped_gemm_fp8_fp8_bf16_nt_masked(
|
|
hidden_states_fp8, self.w13_weight_fp8, gateup_output, masked_m, expected_m
|
|
)
|
|
dispose_tensor(hidden_states_fp8[0])
|
|
|
|
# Act
|
|
down_input = torch.empty(
|
|
(
|
|
gateup_output.shape[0],
|
|
gateup_output.shape[1],
|
|
gateup_output.shape[2] // 2,
|
|
),
|
|
device=gateup_output.device,
|
|
dtype=self.fp8_dtype,
|
|
)
|
|
scale_block_size = 128
|
|
down_input_scale = torch.empty(
|
|
(
|
|
gateup_output.shape[0],
|
|
gateup_output.shape[1],
|
|
gateup_output.shape[2] // 2 // scale_block_size,
|
|
),
|
|
device=gateup_output.device,
|
|
dtype=torch.float32,
|
|
)
|
|
silu_and_mul_masked_post_quant_fwd(
|
|
gateup_output,
|
|
down_input,
|
|
down_input_scale,
|
|
scale_block_size,
|
|
masked_m,
|
|
)
|
|
del gateup_output
|
|
|
|
# GroupGemm-1
|
|
n = self.w2_weight.size(1)
|
|
down_input_fp8 = (
|
|
down_input,
|
|
get_col_major_tma_aligned_tensor(down_input_scale),
|
|
)
|
|
down_output = torch.empty(
|
|
(num_groups, m, n), device=down_input.device, dtype=torch.bfloat16
|
|
)
|
|
m_grouped_gemm_fp8_fp8_bf16_nt_masked(
|
|
down_input_fp8, self.w2_weight_fp8, down_output, masked_m, expected_m
|
|
)
|
|
|
|
return down_output
|
|
|
|
|
|
def get_moe_impl_class():
|
|
if global_server_args_dict["enable_deepep_moe"]:
|
|
return DeepEPMoE
|
|
if global_server_args_dict["enable_ep_moe"]:
|
|
return EPMoE
|
|
return FusedMoE
|