Enable ModelOpt Llama4 fp8 checkpoint deployment in SGLang (#7129)
This commit is contained in:
Zhiyu
@@ -26,6 +26,7 @@ from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
|
||||
from sglang.srt.layers.quantization.utils import (
|
||||
convert_to_channelwise,
|
||||
is_layer_skipped,
|
||||
per_tensor_dequantize,
|
||||
requantize_with_max_scale,
|
||||
)
|
||||
from sglang.srt.layers.radix_attention import RadixAttention
|
||||
@@ -110,7 +111,12 @@ class ModelOptFp8Config(QuantizationConfig):
|
||||
self, layer: torch.nn.Module, prefix: str
|
||||
) -> Optional["QuantizeMethodBase"]:
|
||||
if self.exclude_modules and any(
|
||||
module in prefix for module in self.exclude_modules
|
||||
module in prefix
|
||||
or (
|
||||
prefix.startswith("language_model.")
|
||||
and module in prefix.removeprefix("language_model.")
|
||||
)
|
||||
for module in self.exclude_modules
|
||||
):
|
||||
return None
|
||||
|
||||
@@ -119,6 +125,12 @@ class ModelOptFp8Config(QuantizationConfig):
|
||||
if self.kv_cache_quant_method and isinstance(layer, RadixAttention):
|
||||
return ModelOptFp8KVCacheMethod(self)
|
||||
|
||||
# Add MoE support
|
||||
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
|
||||
|
||||
if isinstance(layer, FusedMoE):
|
||||
return ModelOptFp8MoEMethod(self)
|
||||
|
||||
return None
|
||||
|
||||
def get_scaled_act_names(self) -> List[str]:
|
||||
@@ -234,6 +246,237 @@ class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
|
||||
super().__init__(quant_config)
|
||||
|
||||
|
||||
class ModelOptFp8MoEMethod:
|
||||
"""MoE method for ModelOpt FP8.
|
||||
Supports loading FP8 checkpoints with static weight scale and activation scale.
|
||||
|
||||
Args:
|
||||
quant_config: The ModelOpt quantization config.
|
||||
"""
|
||||
|
||||
def __new__(cls, *args, **kwargs):
|
||||
"""
|
||||
Dynamic class composition pattern.
|
||||
|
||||
This allows us to effectively "inject" FusedMoEMethodBase as a parent class
|
||||
at runtime while avoiding circular import issues.
|
||||
"""
|
||||
from sglang.srt.layers.moe.fused_moe_triton import FusedMoEMethodBase
|
||||
|
||||
if not hasattr(cls, "_initialized"):
|
||||
original_init = cls.__init__
|
||||
new_cls = type(
|
||||
cls.__name__,
|
||||
(FusedMoEMethodBase,),
|
||||
{
|
||||
"__init__": original_init,
|
||||
**{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
|
||||
},
|
||||
)
|
||||
obj = super(new_cls, new_cls).__new__(new_cls)
|
||||
obj.__init__(*args, **kwargs)
|
||||
return obj
|
||||
return super().__new__(cls)
|
||||
|
||||
def __init__(self, quant_config: ModelOptFp8Config):
|
||||
self.quant_config = quant_config
|
||||
self.cutlass_fp8_supported = cutlass_fp8_supported()
|
||||
|
||||
def create_weights(
|
||||
self,
|
||||
layer: torch.nn.Module,
|
||||
num_experts: int,
|
||||
hidden_size: int,
|
||||
intermediate_size: int,
|
||||
params_dtype: torch.dtype,
|
||||
**extra_weight_attrs,
|
||||
):
|
||||
from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
|
||||
|
||||
# Use FP8 dtype if checkpoint is serialized, otherwise use the default dtype
|
||||
weight_dtype = (
|
||||
torch.float8_e4m3fn
|
||||
if self.quant_config.is_checkpoint_fp8_serialized
|
||||
else params_dtype
|
||||
)
|
||||
weight_loader = extra_weight_attrs.get("weight_loader")
|
||||
|
||||
w13_weight = ModelWeightParameter(
|
||||
data=torch.empty(
|
||||
num_experts, 2 * intermediate_size, hidden_size, dtype=weight_dtype
|
||||
),
|
||||
input_dim=2,
|
||||
output_dim=1,
|
||||
weight_loader=weight_loader,
|
||||
)
|
||||
layer.register_parameter("w13_weight", w13_weight)
|
||||
|
||||
w2_weight = ModelWeightParameter(
|
||||
data=torch.empty(
|
||||
num_experts, hidden_size, intermediate_size, dtype=weight_dtype
|
||||
),
|
||||
input_dim=2,
|
||||
output_dim=1,
|
||||
weight_loader=weight_loader,
|
||||
)
|
||||
layer.register_parameter("w2_weight", w2_weight)
|
||||
|
||||
if self.quant_config.is_checkpoint_fp8_serialized:
|
||||
# WEIGHT SCALES - Per-tensor scaling for ModelOpts
|
||||
# Allocate 2 scales for w1 and w3 respectively.
|
||||
# They will be combined to a single scale after weight loading.
|
||||
w13_weight_scale = PerTensorScaleParameter(
|
||||
data=torch.full(
|
||||
(num_experts, 2),
|
||||
torch.finfo(torch.float32).min,
|
||||
dtype=torch.float32,
|
||||
),
|
||||
weight_loader=weight_loader,
|
||||
)
|
||||
w2_weight_scale = PerTensorScaleParameter(
|
||||
data=torch.full(
|
||||
(num_experts,), torch.finfo(torch.float32).min, dtype=torch.float32
|
||||
),
|
||||
weight_loader=weight_loader,
|
||||
)
|
||||
layer.register_parameter("w13_weight_scale", w13_weight_scale)
|
||||
layer.register_parameter("w2_weight_scale", w2_weight_scale)
|
||||
|
||||
# Set weight loader attributes for scales
|
||||
extra_weight_attrs.update(
|
||||
{"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
|
||||
)
|
||||
|
||||
# INPUT SCALES - Per-tensor scaling for ModelOpt
|
||||
w13_input_scale = PerTensorScaleParameter(
|
||||
data=torch.full((num_experts,), 1.0, dtype=torch.float32),
|
||||
weight_loader=weight_loader,
|
||||
)
|
||||
w2_input_scale = PerTensorScaleParameter(
|
||||
data=torch.full((num_experts,), 1.0, dtype=torch.float32),
|
||||
weight_loader=weight_loader,
|
||||
)
|
||||
layer.register_parameter("w13_input_scale", w13_input_scale)
|
||||
layer.register_parameter("w2_input_scale", w2_input_scale)
|
||||
|
||||
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
||||
"""Process FP8 MoE weights after loading from serialized checkpoint.
|
||||
|
||||
Only supports pre-quantized checkpoints with FP8 weights and scales.
|
||||
"""
|
||||
|
||||
layer.w13_weight = Parameter(layer.w13_weight.data, requires_grad=False)
|
||||
layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)
|
||||
|
||||
# Handle scale parameters
|
||||
if hasattr(layer, "w13_weight_scale") and layer.w13_weight_scale is not None:
|
||||
# Fp8 moe kernel needs single weight scale for w13 per expert.
|
||||
# We take the max of the w1 and w3 scales then dequant and requant each expert.
|
||||
if layer.w13_weight_scale.dim() == 2: # Shape: (num_experts, 2)
|
||||
from sglang.srt.layers.quantization.fp8_kernel import scaled_fp8_quant
|
||||
|
||||
# Get the maximum scale across w1 and w3 for each expert
|
||||
max_w13_scales = layer.w13_weight_scale.max(dim=1).values
|
||||
|
||||
# Requantize each expert's weights using the combined scale
|
||||
# w13_weight has shape (num_experts, 2 * intermediate_size, hidden_size)
|
||||
# where the first intermediate_size rows are w1, the next are w3
|
||||
intermediate_size = layer.w13_weight.shape[1] // 2
|
||||
for expert_id in range(layer.w13_weight.shape[0]):
|
||||
start = 0
|
||||
for shard_id in range(2): # w1 and w3
|
||||
# Dequantize using the original scale for this shard
|
||||
dq_weight = per_tensor_dequantize(
|
||||
layer.w13_weight[expert_id][
|
||||
start : start + intermediate_size, :
|
||||
],
|
||||
layer.w13_weight_scale[expert_id][shard_id],
|
||||
)
|
||||
# Requantize using the combined max scale
|
||||
(
|
||||
layer.w13_weight[expert_id][
|
||||
start : start + intermediate_size, :
|
||||
],
|
||||
_,
|
||||
) = scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
|
||||
|
||||
start += intermediate_size
|
||||
|
||||
# Update the scale parameter to be per-expert instead of per-shard
|
||||
layer.w13_weight_scale = Parameter(max_w13_scales, requires_grad=False)
|
||||
else:
|
||||
layer.w13_weight_scale = Parameter(
|
||||
layer.w13_weight_scale.data, requires_grad=False
|
||||
)
|
||||
|
||||
if hasattr(layer, "w2_weight_scale") and layer.w2_weight_scale is not None:
|
||||
layer.w2_weight_scale = Parameter(
|
||||
layer.w2_weight_scale.data, requires_grad=False
|
||||
)
|
||||
if hasattr(layer, "w13_input_scale") and layer.w13_input_scale is not None:
|
||||
layer.w13_input_scale = Parameter(
|
||||
layer.w13_input_scale.max(), requires_grad=False
|
||||
)
|
||||
if hasattr(layer, "w2_input_scale") and layer.w2_input_scale is not None:
|
||||
layer.w2_input_scale = Parameter(
|
||||
layer.w2_input_scale.max(), requires_grad=False
|
||||
)
|
||||
|
||||
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,
|
||||
num_fused_shared_experts: Optional[int] = None,
|
||||
custom_routing_function: Optional[Callable] = None,
|
||||
correction_bias: Optional[torch.Tensor] = None,
|
||||
activation: str = "silu",
|
||||
apply_router_weight_on_input: bool = False,
|
||||
inplace: bool = True,
|
||||
no_combine: bool = False,
|
||||
routed_scaling_factor: Optional[float] = None,
|
||||
) -> torch.Tensor:
|
||||
from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
|
||||
from sglang.srt.layers.moe.topk import select_experts
|
||||
|
||||
# Expert selection
|
||||
topk_weights, topk_ids = select_experts(
|
||||
hidden_states=x,
|
||||
router_logits=router_logits,
|
||||
use_grouped_topk=use_grouped_topk,
|
||||
top_k=top_k,
|
||||
renormalize=renormalize,
|
||||
topk_group=topk_group,
|
||||
num_expert_group=num_expert_group,
|
||||
num_fused_shared_experts=num_fused_shared_experts,
|
||||
custom_routing_function=custom_routing_function,
|
||||
correction_bias=correction_bias,
|
||||
routed_scaling_factor=routed_scaling_factor,
|
||||
)
|
||||
|
||||
return fused_experts(
|
||||
x,
|
||||
layer.w13_weight,
|
||||
layer.w2_weight,
|
||||
topk_weights=topk_weights,
|
||||
topk_ids=topk_ids,
|
||||
inplace=inplace,
|
||||
activation=activation,
|
||||
use_fp8_w8a8=True,
|
||||
per_channel_quant=False, # ModelOpt uses per-tensor quantization
|
||||
w1_scale=layer.w13_weight_scale,
|
||||
w2_scale=layer.w2_weight_scale,
|
||||
a1_scale=layer.w13_input_scale,
|
||||
a2_scale=layer.w2_input_scale,
|
||||
no_combine=no_combine,
|
||||
)
|
||||
|
||||
|
||||
class ModelOptFp4Config(QuantizationConfig):
|
||||
"""Config class for FP4."""
|
||||
|
||||
|
||||
Reference in New Issue
Block a user