Support Llama4 fp8 inference (#5194)
Co-authored-by: laixinn <xielx@shanghaitech.edu.cn> Co-authored-by: sleepcoo <sleepcoo@gmail.com> Co-authored-by: zhyncs <me@zhyncs.com>
This commit is contained in:
HandH1998
co-authored by
laixinn
sleepcoo
zhyncs
laixinn
sleepcoo
zhyncs
parent
86a876d883
commit
4065248214
@@ -1,4 +1,4 @@
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from typing import Any, Dict, List, Optional
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from typing import Any, Callable, Dict, List, Optional
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import torch
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from torch.nn.parameter import Parameter
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@@ -16,7 +16,7 @@ from sglang.srt.layers.quantization.fp8_utils import (
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input_to_float8,
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normalize_e4m3fn_to_e4m3fnuz,
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)
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from sglang.srt.utils import is_hip
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from sglang.srt.utils import is_hip, set_weight_attrs
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_is_hip = is_hip()
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@@ -62,7 +62,9 @@ class W8A8Fp8Config(QuantizationConfig):
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "W8A8Fp8Config":
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quant_method = cls.get_from_keys(config, ["quant_method"])
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is_checkpoint_fp8_serialized = "compressed-tensors" in quant_method
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is_checkpoint_fp8_serialized = (
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"compressed-tensors" in quant_method or "w8a8_fp8" in quant_method
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)
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return cls(is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized)
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def get_quant_method(
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@@ -71,9 +73,12 @@ class W8A8Fp8Config(QuantizationConfig):
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prefix: str,
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) -> Optional["QuantizeMethodBase"]:
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from sglang.srt.layers.linear import LinearBase
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
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if isinstance(layer, LinearBase):
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return W8A8Fp8LinearMethod(self)
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elif isinstance(layer, FusedMoE):
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return W8A8FP8MoEMethod(self)
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return None
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def get_scaled_act_names(self) -> List[str]:
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@@ -131,7 +136,7 @@ class W8A8Fp8LinearMethod(LinearMethodBase):
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs
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**extra_weight_attrs,
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):
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weight_dtype = (
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torch.float8_e4m3fn
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@@ -177,3 +182,148 @@ class W8A8Fp8LinearMethod(LinearMethodBase):
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bias=bias,
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cutlass_fp8_supported=self.cutlass_fp8_supported,
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)
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class W8A8FP8MoEMethod:
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"""MoE method for FP8.
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Supports loading FP8 checkpoints with static weight scale and
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dynamic/static activation scale.
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Also supports loading quantized FP16/BF16 model checkpoints with dynamic
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activation scaling. The weight scaling factor will be initialized after
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the model weights are loaded.
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Args:
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quant_config: The quantization config.
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"""
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def __new__(cls, *args, **kwargs):
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoEMethodBase
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if not hasattr(cls, "_initialized"):
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original_init = cls.__init__
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new_cls = type(
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cls.__name__,
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(FusedMoEMethodBase,),
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{
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"__init__": original_init,
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**{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
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},
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)
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obj = super(new_cls, new_cls).__new__(new_cls)
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obj.__init__(*args, **kwargs)
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return obj
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return super().__new__(cls)
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def __init__(self, quant_config):
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self.quant_config = quant_config
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def create_weights(
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self,
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layer: torch.nn.Module,
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num_experts: int,
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hidden_size: int,
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intermediate_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
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fp8_dtype = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
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# WEIGHTS
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w13_weight = torch.nn.Parameter(
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torch.empty(
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num_experts, 2 * intermediate_size, hidden_size, dtype=fp8_dtype
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_weight", w13_weight)
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set_weight_attrs(w13_weight, extra_weight_attrs)
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w2_weight = torch.nn.Parameter(
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torch.empty(num_experts, hidden_size, intermediate_size, dtype=fp8_dtype),
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requires_grad=False,
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)
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layer.register_parameter("w2_weight", w2_weight)
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set_weight_attrs(w2_weight, extra_weight_attrs)
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w13_weight_scale = torch.nn.Parameter(
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torch.ones(num_experts, 2 * intermediate_size, 1, dtype=torch.float32),
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requires_grad=False,
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)
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w2_weight_scale = torch.nn.Parameter(
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torch.ones(num_experts, hidden_size, 1, dtype=torch.float32),
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requires_grad=False,
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)
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layer.register_parameter("w13_weight_scale", w13_weight_scale)
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layer.register_parameter("w2_weight_scale", w2_weight_scale)
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extra_weight_attrs.update(
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{"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
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)
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set_weight_attrs(w13_weight_scale, extra_weight_attrs)
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set_weight_attrs(w2_weight_scale, extra_weight_attrs)
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w13_input_scale = None
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layer.register_parameter("w13_input_scale", w13_input_scale)
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w2_input_scale = None
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layer.register_parameter("w2_input_scale", w2_input_scale)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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layer.w13_weight = Parameter(layer.w13_weight, requires_grad=False)
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layer.w2_weight = Parameter(layer.w2_weight, requires_grad=False)
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layer.w13_weight_scale = Parameter(
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layer.w13_weight_scale.data, requires_grad=False
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)
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layer.w2_weight_scale = Parameter(
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layer.w2_weight_scale.data, requires_grad=False
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)
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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renormalize: bool,
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use_grouped_topk: bool,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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custom_routing_function: Optional[Callable] = None,
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correction_bias: Optional[torch.Tensor] = None,
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activation: str = "silu",
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inplace: bool = True,
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no_combine: bool = False,
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) -> torch.Tensor:
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from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
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from sglang.srt.layers.moe.topk import select_experts
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# Expert selection
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topk_weights, topk_ids = select_experts(
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hidden_states=x,
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router_logits=router_logits,
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use_grouped_topk=use_grouped_topk,
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top_k=top_k,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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custom_routing_function=custom_routing_function,
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correction_bias=correction_bias,
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)
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return fused_experts(
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x,
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layer.w13_weight,
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layer.w2_weight,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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inplace=inplace,
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activation=activation,
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use_fp8_w8a8=True,
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per_channel_quant=True,
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w1_scale=(layer.w13_weight_scale),
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w2_scale=(layer.w2_weight_scale),
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a1_scale=layer.w13_input_scale,
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a2_scale=layer.w2_input_scale,
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no_combine=no_combine,
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)
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