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Unverified Commit 49b87774 authored by Cheng Wan's avatar Cheng Wan Committed by GitHub
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Refactor: move all quantization-related code to `srt/layer/quantization` (#7989)

parent 02404a1e
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python/sglang/srt/layers/linear.py
View file @ 49b87774
"""Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/layers/linear.py"""
from __future__ import annotations
import itertools
import logging
from abc import abstractmethod
from typing import Dict, List, Optional, Tuple
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter, UninitializedParameter
from sglang.srt.distributed import (
......@@ -17,7 +17,6 @@ from sglang.srt.distributed import (
tensor_model_parallel_all_gather,
tensor_model_parallel_all_reduce,
)
from sglang.srt.layers.amx_utils import _amx_process_weight_after_loading
from sglang.srt.layers.parameter import (
BasevLLMParameter,
BlockQuantScaleParameter,
......@@ -27,17 +26,14 @@ from sglang.srt.layers.parameter import (
RowvLLMParameter,
_ColumnvLLMParameter,
)
from sglang.srt.layers.quantization.base_config import (
QuantizationConfig,
QuantizeMethodBase,
)
from sglang.srt.utils import (
cpu_has_amx_support,
is_cpu,
is_npu,
set_weight_attrs,
use_intel_amx_backend,
)
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
from sglang.srt.utils import is_cpu, is_npu, set_weight_attrs
if TYPE_CHECKING:
from sglang.srt.layers.quantization.base_config import (
QuantizationConfig,
QuantizeMethodBase,
)
logger = logging.getLogger(__name__)
......@@ -59,7 +55,6 @@ WEIGHT_LOADER_V2_SUPPORTED = [
"IPEXAWQLinearMethod",
]
_is_cpu_amx_available = cpu_has_amx_support()
_is_cpu = is_cpu()
_is_npu = is_npu()
......@@ -110,91 +105,6 @@ def adjust_scalar_to_fused_array(param, loaded_weight, shard_id):
return param[shard_id], loaded_weight
class LinearMethodBase(QuantizeMethodBase):
"""Base class for different (maybe quantized) linear methods."""
@abstractmethod
def create_weights(
self,
layer: torch.nn.Module,
input_size_per_partition: int,
output_partition_sizes: List[int],
input_size: int,
output_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
"""Create weights for a linear layer.
The weights will be set as attributes of the layer.
Args:
layer: The layer that is using the LinearMethodBase factory.
input_size_per_partition: Size of the weight input dim on rank X.
output_partition_sizes: Sizes of the output dim of each logical
weight on rank X. E.g., output_partition_sizes for QKVLinear
is a list contains the width of Wq, Wk, Wv on rank X.
input_size: Size of the input dim of the weight across all ranks.
output_size: Size of the output dim of the weight across all ranks.
params_dtype: Datatype of the parameters.
"""
raise NotImplementedError
@abstractmethod
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Apply the weights in layer to the input tensor.
Expects create_weights to have been called before on the layer."""
raise NotImplementedError
class UnquantizedLinearMethod(LinearMethodBase):
"""Linear method without quantization."""
def create_weights(
self,
layer: torch.nn.Module,
input_size_per_partition: int,
output_partition_sizes: List[int],
input_size: int,
output_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
weight = Parameter(
torch.empty(
sum(output_partition_sizes),
input_size_per_partition,
dtype=params_dtype,
),
requires_grad=False,
)
set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
layer.register_parameter("weight", weight)
set_weight_attrs(weight, extra_weight_attrs)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
if _is_cpu and _is_cpu_amx_available:
_amx_process_weight_after_loading(layer, ["weight"])
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if use_intel_amx_backend(layer):
return torch.ops.sgl_kernel.weight_packed_linear(
x, layer.weight, bias, True # is_vnni
)
return F.linear(x, layer.weight, bias)
class LinearBase(torch.nn.Module):
"""Base linear layer.
......@@ -310,7 +220,7 @@ class ReplicatedLinear(LinearBase):
assert param.size() == loaded_weight.size()
param.data.copy_(loaded_weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
bias = self.bias if not self.skip_bias_add else None
assert self.quant_method is not None
output = self.quant_method.apply(self, x, bias)
......@@ -845,7 +755,7 @@ class QKVParallelLinear(ColumnParallelLinear):
bias: bool = True,
skip_bias_add: bool = False,
params_dtype: Optional[torch.dtype] = None,
quant_config: Optional[QuantizationConfig] = None,
quant_config: Optional["QuantizationConfig"] = None,
prefix: str = "",
tp_rank: Optional[int] = None,
tp_size: Optional[int] = None,
......
python/sglang/srt/layers/moe/ep_moe/layer.py
View file @ 49b87774
......@@ -27,22 +27,20 @@ from sglang.srt.layers.moe.ep_moe.kernels import (
silu_and_mul_triton_kernel,
tma_align_input_scale,
)
from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE, FusedMoEMethodBase
from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
from sglang.srt.layers.moe.topk import select_experts
from sglang.srt.layers.quantization import deep_gemm_wrapper
from sglang.srt.layers.quantization.base_config import (
QuantizationConfig,
QuantizeMethodBase,
)
from sglang.srt.layers.quantization.fp8 import Fp8Config, Fp8MoEMethod
from sglang.srt.layers.quantization.fp8 import Fp8EPMoEMethod
from sglang.srt.layers.quantization.fp8_kernel import (
is_fp8_fnuz,
scaled_fp8_quant,
sglang_per_token_group_quant_fp8,
sglang_per_token_quant_fp8,
)
from sglang.srt.layers.quantization.fp8_utils import normalize_e4m3fn_to_e4m3fnuz
from sglang.srt.layers.quantization.unquant import UnquantizedEPMoEMethod
from sglang.srt.layers.quantization.w4afp8 import W4AFp8Config, W4AFp8MoEMethod
from sglang.srt.managers.schedule_batch import global_server_args_dict
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
......@@ -53,7 +51,6 @@ from sglang.srt.utils import (
get_bool_env_var,
is_hip,
is_npu,
set_weight_attrs,
)
_is_hip = is_hip()
......@@ -904,324 +901,6 @@ class EPMoE(torch.nn.Module):
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,
)
if self.block_quant:
# If ROCm, normalize the weights and scales to e4m3fnuz
if _is_fp8_fnuz:
# activation_scheme: dynamic
w13_weight, w13_weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
weight=layer.w13_weight,
weight_scale=layer.w13_weight_scale_inv,
input_scale=None,
)
w2_weight, w2_weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
weight=layer.w2_weight,
weight_scale=layer.w2_weight_scale_inv,
input_scale=None,
)
# Reset the parameter
layer.w13_weight = torch.nn.Parameter(
w13_weight, requires_grad=False
)
layer.w13_weight_scale_inv = torch.nn.Parameter(
w13_weight_scale, requires_grad=False
)
layer.w13_input_scale = None
layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
layer.w2_weight_scale_inv = torch.nn.Parameter(
w2_weight_scale, requires_grad=False
)
layer.w2_input_scale = None
if _use_aiter:
layer.w13_weight = torch.nn.Parameter(
shuffle_weight(layer.w13_weight.data, (16, 16)),
requires_grad=False,
)
layer.w2_weight = torch.nn.Parameter(
shuffle_weight(layer.w2_weight.data, (16, 16)),
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)
......
python/sglang/srt/layers/moe/fused_moe_triton/__init__.py
View file @ 49b87774
......@@ -9,7 +9,6 @@ from sglang.srt.layers.moe.fused_moe_triton.fused_moe import (
)
from sglang.srt.layers.moe.fused_moe_triton.layer import (
FusedMoE,
FusedMoEMethodBase,
FusedMoeWeightScaleSupported,
)
......@@ -31,11 +30,9 @@ def get_config() -> Optional[Dict[str, Any]]:
__all__ = [
"FusedMoE",
"FusedMoEMethodBase",
"FusedMoeWeightScaleSupported",
"override_config",
"get_config",
"fused_moe",
"fused_experts",
"get_config_file_name",
"moe_align_block_size",
......
python/sglang/srt/layers/moe/fused_moe_triton/layer.py
View file @ 49b87774
# Adapted from https://github.com/vllm-project/vllm/blob/a6221a144af772fd1a68fe7e627935dc53e81738/vllm/model_executor/layers/fused_moe/layer.py
import importlib
from abc import abstractmethod
import logging
from enum import Enum
from typing import Callable, List, Optional, Tuple
import torch
from sglang.srt.custom_op import CustomOp
from sglang.srt.distributed import (
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
tensor_model_parallel_all_reduce,
)
from sglang.srt.layers.amx_utils import _amx_process_weight_after_loading
from sglang.srt.layers.moe.fused_moe_native import moe_forward_native
from sglang.srt.layers.moe.topk import select_experts
from sglang.srt.layers.quantization.base_config import (
QuantizationConfig,
QuantizeMethodBase,
)
from sglang.srt.layers.quantization.unquant import UnquantizedFusedMoEMethod
from sglang.srt.managers.schedule_batch import global_server_args_dict
from sglang.srt.model_loader.weight_utils import narrow_padded_param_and_loaded_weight
from sglang.srt.utils import (
cpu_has_amx_support,
get_bool_env_var,
is_cpu,
is_hip,
set_weight_attrs,
use_intel_amx_backend,
)
has_triton_kernels = importlib.util.find_spec("triton_kernels") is not None
if torch.cuda.is_available():
from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
if has_triton_kernels:
from sglang.srt.layers.moe.fused_moe_triton.triton_kernels_moe import (
triton_kernel_moe_forward,
)
else:
fused_experts = None # type: ignore
import logging
from sglang.srt.utils import cpu_has_amx_support, get_bool_env_var, is_cpu, is_hip
_is_hip = is_hip()
_is_cpu_amx_available = cpu_has_amx_support()
_is_cpu = is_cpu()
_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
if _use_aiter:
from aiter import ActivationType
from aiter.fused_moe import fused_moe
from aiter.fused_moe_bf16_asm import ck_moe_2stages
from aiter.ops.shuffle import shuffle_weight
logger = logging.getLogger(__name__)
......@@ -66,333 +34,6 @@ class FusedMoeWeightScaleSupported(Enum):
BLOCK = "block"
class FusedMoEMethodBase(QuantizeMethodBase):
@abstractmethod
def create_weights(
self,
layer: torch.nn.Module,
num_experts: int,
hidden_size: int,
intermediate_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
raise NotImplementedError
@abstractmethod
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
router_logits: torch.Tensor,
top_k: int,
renormalize: bool,
use_grouped_topk: bool,
) -> torch.Tensor:
raise NotImplementedError
class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
"""MoE method without quantization."""
def __init__(self, use_triton_kernels: bool = False):
super().__init__()
self.use_triton_kernels = use_triton_kernels
def create_weights(
self,
layer: torch.nn.Module,
num_experts: int,
hidden_size: int,
intermediate_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
# Fused gate_up_proj (column parallel)
w13_weight_n, w13_weight_k = 2 * intermediate_size, hidden_size
if self.use_triton_kernels:
w13_weight_n, w13_weight_k = w13_weight_k, w13_weight_n
w13_weight = torch.nn.Parameter(
torch.empty(num_experts, w13_weight_n, w13_weight_k, 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_n, w2_weight_k = (
hidden_size,
intermediate_size,
)
if self.use_triton_kernels:
w2_weight_n, w2_weight_k = w2_weight_k, w2_weight_n
w2_weight = torch.nn.Parameter(
torch.empty(num_experts, w2_weight_n, w2_weight_k, dtype=params_dtype),
requires_grad=False,
)
layer.register_parameter("w2_weight", w2_weight)
set_weight_attrs(w2_weight, extra_weight_attrs)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
if _use_aiter:
layer.w13_weight = torch.nn.Parameter(
shuffle_weight(layer.w13_weight.data, (16, 16)),
requires_grad=False,
)
torch.cuda.empty_cache()
layer.w2_weight = torch.nn.Parameter(
shuffle_weight(layer.w2_weight.data, (16, 16)),
requires_grad=False,
)
torch.cuda.empty_cache()
# Pack weight for get better performance on CPU
if _is_cpu and _is_cpu_amx_available:
_amx_process_weight_after_loading(layer, ["w13_weight", "w2_weight"])
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,
num_fused_shared_experts: int = 0,
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:
return self.forward(
x=x,
layer=layer,
router_logits=router_logits,
top_k=top_k,
renormalize=renormalize,
use_grouped_topk=use_grouped_topk,
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,
activation=activation,
apply_router_weight_on_input=apply_router_weight_on_input,
inplace=inplace,
no_combine=no_combine,
routed_scaling_factor=routed_scaling_factor,
)
def forward_cuda(
self,
layer: torch.nn.Module,
x: torch.Tensor,
use_grouped_topk: bool,
top_k: int,
router_logits: torch.Tensor,
renormalize: bool,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
num_fused_shared_experts: int = 0,
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:
if self.use_triton_kernels:
return triton_kernel_moe_forward(
hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
gating_output=router_logits,
topk=top_k,
renormalize=renormalize,
)
else:
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,
)
if _use_aiter:
assert not no_combine, "unsupported"
if apply_router_weight_on_input:
assert (
topk_weights.dim() == 2
), "`topk_weights` should be in shape (num_tokens, topk)"
_, topk = topk_weights.shape
assert (
topk == 1
), "Only support topk=1 when `apply_router_weight_on_input` is True"
x = x * topk_weights.to(x.dtype)
topk_weights = torch.ones_like(
topk_weights, dtype=torch.float32
) # topk_weights must be FP32 (float32)
return fused_moe(
x,
layer.w13_weight,
layer.w2_weight,
topk_weights,
topk_ids,
activation=(
ActivationType.Silu
if activation == "silu"
else ActivationType.Gelu
),
)
else:
return fused_experts(
hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=inplace and not no_combine,
activation=activation,
apply_router_weight_on_input=apply_router_weight_on_input,
no_combine=no_combine,
routed_scaling_factor=routed_scaling_factor,
)
def forward_cpu(
self,
layer: torch.nn.Module,
x: torch.Tensor,
use_grouped_topk: bool,
top_k: int,
router_logits: torch.Tensor,
renormalize: bool,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
num_fused_shared_experts: int = 0,
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:
assert activation == "silu", f"activation = {activation} is not supported."
if use_intel_amx_backend(layer) and not apply_router_weight_on_input:
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,
)
# TODO: support apply_router_weight_on_input in the fused_experts_cpu kernel
return torch.ops.sgl_kernel.fused_experts_cpu(
x,
layer.w13_weight,
layer.w2_weight,
topk_weights,
topk_ids,
False, # inplace # See [Note] inplace should be False in fused_experts.
False, # use_int8_w8a8
False, # use_fp8_w8a16
None, # w1_scale
None, # w2_scale
None, # block_size
None, # a1_scale
None, # a2_scale
True, # is_vnni
)
else:
return moe_forward_native(
layer,
x,
use_grouped_topk,
top_k,
router_logits,
renormalize,
topk_group,
num_expert_group,
num_fused_shared_experts,
custom_routing_function,
correction_bias,
activation,
apply_router_weight_on_input,
inplace,
no_combine,
routed_scaling_factor,
)
def forward_npu(
self,
layer: torch.nn.Module,
x: torch.Tensor,
use_grouped_topk: bool,
top_k: int,
router_logits: torch.Tensor,
renormalize: bool,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
num_fused_shared_experts: int = 0,
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:
return moe_forward_native(
layer,
x,
use_grouped_topk,
top_k,
router_logits,
renormalize,
topk_group,
num_expert_group,
num_fused_shared_experts,
custom_routing_function,
correction_bias,
activation,
apply_router_weight_on_input,
inplace,
no_combine,
routed_scaling_factor,
)
def forward_tpu(self, *args, **kwargs) -> torch.Tensor:
raise NotImplementedError("The TPU backend currently does not support MoE.")
forward_native = forward_cpu
class FusedMoE(torch.nn.Module):
"""FusedMoE layer for MoE models.
......@@ -553,7 +194,7 @@ class FusedMoE(torch.nn.Module):
shard_dim: int,
expert_data: torch.Tensor,
shard_id: str,
loaded_weight: torch.tensor,
loaded_weight: torch.Tensor,
tp_rank: int,
):
# Load grouped weight scales for group quantization
......@@ -580,7 +221,7 @@ class FusedMoE(torch.nn.Module):
expert_data: torch.Tensor,
shard_dim: int,
shard_id: str,
loaded_weight: torch.tensor,
loaded_weight: torch.Tensor,
tp_rank: int,
):
# for per channel weight quantization
......@@ -600,7 +241,7 @@ class FusedMoE(torch.nn.Module):
expert_data: torch.Tensor,
shard_dim: int,
shard_id: str,
loaded_weight: torch.tensor,
loaded_weight: torch.Tensor,
tp_rank: int,
):
......@@ -645,7 +286,7 @@ class FusedMoE(torch.nn.Module):
expert_data: torch.Tensor,
shard_dim: int,
shard_id: str,
loaded_weight: torch.tensor,
loaded_weight: torch.Tensor,
tp_rank: int,
):
"""Load w2 weights for down projection.
......@@ -717,7 +358,7 @@ class FusedMoE(torch.nn.Module):
shard_id: str,
expert_data: torch.Tensor,
shard_dim: int,
loaded_weight: torch.tensor,
loaded_weight: torch.Tensor,
tp_rank: int,
):
......
python/sglang/srt/layers/moe/topk.py
View file @ 49b87774
......@@ -19,15 +19,11 @@ import torch
import torch.nn.functional as F
from sglang.srt.eplb import expert_location_dispatch
from sglang.srt.eplb.expert_distribution import (
ExpertDistributionRecorder,
get_global_expert_distribution_recorder,
)
from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
from sglang.srt.eplb.expert_location_dispatch import (
ExpertLocationDispatchInfo,
topk_ids_logical_to_physical,
)
from sglang.srt.managers.schedule_batch import global_server_args_dict
from sglang.srt.utils import (
cpu_has_amx_support,
get_bool_env_var,
......
python/sglang/srt/layers/quantization/__init__.py
View file @ 49b87774
# Adapted from https://raw.githubusercontent.com/vllm-project/vllm/v0.5.5/vllm/model_executor/layers/quantization/__init__.py
import builtins
import inspect
import re
from copy import deepcopy
from typing import Callable, Dict, Optional, Type, Union
import torch
......@@ -45,7 +43,6 @@ except ImportError:
) = QQQConfig = Int8TpuConfig = DummyConfig
from sglang.srt.layers.linear import LinearBase, UnquantizedLinearMethod
from sglang.srt.layers.quantization.awq import AWQConfig
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.quantization.blockwise_int8 import BlockInt8Config
......@@ -66,6 +63,10 @@ from sglang.srt.layers.quantization.modelopt_quant import (
)
from sglang.srt.layers.quantization.moe_wna16 import MoeWNA16Config
from sglang.srt.layers.quantization.qoq import QoQConfig
from sglang.srt.layers.quantization.utils import (
get_dynamic_override,
get_linear_quant_method,
)
from sglang.srt.layers.quantization.w4afp8 import W4AFp8Config
from sglang.srt.layers.quantization.w8a8_fp8 import W8A8Fp8Config
from sglang.srt.layers.quantization.w8a8_int8 import W8A8Int8Config
......@@ -120,99 +121,6 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
return QUANTIZATION_METHODS[quantization]
# Match dynamic rules with module name (prefix) and override quantize
# config if module (prefix) matches a rule
def override_config(config: QuantizationConfig, prefix: str):
weight_bits = get_dynamic_override(config, prefix, "bits", config.weight_bits)
if isinstance(weight_bits, int):
config.weight_bits = weight_bits
group_size = get_dynamic_override(config, prefix, "group_size", config.group_size)
if isinstance(group_size, int):
config.group_size = group_size
desc_act = get_dynamic_override(config, prefix, "desc_act", config.desc_act)
if isinstance(desc_act, bool):
config.desc_act = desc_act
config.pack_factor = 32 // config.weight_bits # packed into int32
if config.get_name() == "gptq_marlin":
is_sym = get_dynamic_override(config, prefix, "sym", config.is_sym)
if isinstance(is_sym, bool):
config.is_sym = is_sym
if (config.weight_bits, config.is_sym) not in config.TYPE_MAP:
raise ValueError(
"Unsupported quantization config: "
f"bits={config.weight_bits}, sym={config.is_sym}"
)
config.quant_type = config.TYPE_MAP[(config.weight_bits, config.is_sym)]
elif config.get_name() == "gptq":
if config.weight_bits not in [2, 3, 4, 8]:
raise ValueError(
"Currently, only 2/3/4/8-bit weight quantization is "
f"supported for GPTQ, but got {config.weight_bits} bits."
)
def get_dynamic_override(
config: QuantizationConfig,
layer_name: str,
key: Optional[str] = None,
default_value: Union[int, bool, None] = None,
) -> Union[Dict, int, bool, None]:
for pattern, pattern_dict in config.dynamic.items():
# Negative match: matched modules are excluded from quantized init
if pattern.startswith("-:"):
if re.match(pattern.removeprefix("-:"), layer_name):
return False
# Positive match: matched modules have quant properties overrides
# base quant config
elif re.match(pattern.removeprefix("+:"), layer_name):
if key is None:
return pattern_dict
else:
return pattern_dict.get(key, default_value)
return default_value
def get_linear_quant_method(
config: QuantizationConfig,
layer: torch.nn.Module,
prefix: str,
linear_method_cls: type,
):
# Move import here to avoid circular import. This is only used in monkey patching
# of vllm's QuantizationConfig.
from sglang.srt.layers.vocab_parallel_embedding import (
ParallelLMHead,
UnquantizedEmbeddingMethod,
)
cloned_config = deepcopy(config)
parallel_lm_head_quantized = (
isinstance(layer, ParallelLMHead) and cloned_config.lm_head_quantized
)
if isinstance(layer, LinearBase) or parallel_lm_head_quantized:
# False = skip module, None = no override, else = Positive match
if (
get_dynamic_override( # noqa: E712
cloned_config, layer_name=prefix # noqa: E712
)
== False
): # noqa: E712
if parallel_lm_head_quantized:
return UnquantizedEmbeddingMethod()
return UnquantizedLinearMethod()
if prefix:
# Dynamic per module/layer rules may override base config
override_config(cloned_config, prefix=prefix)
return linear_method_cls(cloned_config)
return None
def gptq_get_quant_method(self, layer, prefix):
from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
......
python/sglang/srt/layers/quantization/awq.py
View file @ 49b87774
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional
import torch
from sglang.srt.layers.linear import (
LinearBase,
from sglang.srt.layers.parameter import GroupQuantScaleParameter, PackedvLLMParameter
from sglang.srt.layers.quantization.base_config import (
LinearMethodBase,
UnquantizedLinearMethod,
QuantizationConfig,
)
from sglang.srt.layers.parameter import GroupQuantScaleParameter, PackedvLLMParameter
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
from sglang.srt.utils import is_cuda
_is_cuda = is_cuda()
......@@ -81,7 +82,7 @@ class AWQConfig(QuantizationConfig):
]
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "AWQConfig":
def from_config(cls, config: Dict[str, Any]) -> AWQConfig:
weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
zero_point = cls.get_from_keys(config, ["zero_point"])
......@@ -92,7 +93,8 @@ class AWQConfig(QuantizationConfig):
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> Optional["LinearMethodBase"]:
) -> Optional[LinearMethodBase]:
from sglang.srt.layers.linear import LinearBase
if isinstance(layer, LinearBase):
if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
......
python/sglang/srt/layers/quantization/base_config.py
View file @ 49b87774
......@@ -18,14 +18,14 @@ class QuantizeMethodBase(ABC):
"""Create weights for a layer.
The weights will be set as attributes of the layer."""
raise NotImplementedError
raise NotImplementedError()
@abstractmethod
def apply(self, layer: torch.nn.Module, *args, **kwargs) -> torch.Tensor:
"""Apply the weights in layer to the input tensor.
Expects create_weights to have been called before on the layer."""
raise NotImplementedError
raise NotImplementedError()
def process_weights_after_loading(self, layer: nn.Module) -> None:
"""Process the weight after loading.
......@@ -35,6 +35,74 @@ class QuantizeMethodBase(ABC):
return
class LinearMethodBase(QuantizeMethodBase):
"""Base class for different (maybe quantized) linear methods."""
@abstractmethod
def create_weights(
self,
layer: torch.nn.Module,
input_size_per_partition: int,
output_partition_sizes: List[int],
input_size: int,
output_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
"""Create weights for a linear layer.
The weights will be set as attributes of the layer.
Args:
layer: The layer that is using the LinearMethodBase factory.
input_size_per_partition: Size of the weight input dim on rank X.
output_partition_sizes: Sizes of the output dim of each logical
weight on rank X. E.g., output_partition_sizes for QKVLinear
is a list contains the width of Wq, Wk, Wv on rank X.
input_size: Size of the input dim of the weight across all ranks.
output_size: Size of the output dim of the weight across all ranks.
params_dtype: Datatype of the parameters.
"""
raise NotImplementedError()
@abstractmethod
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Apply the weights in layer to the input tensor.
Expects create_weights to have been called before on the layer."""
raise NotImplementedError()
class FusedMoEMethodBase(QuantizeMethodBase):
@abstractmethod
def create_weights(
self,
layer: torch.nn.Module,
num_experts: int,
hidden_size: int,
intermediate_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
raise NotImplementedError()
@abstractmethod
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
router_logits: torch.Tensor,
top_k: int,
renormalize: bool,
use_grouped_topk: bool,
) -> torch.Tensor:
raise NotImplementedError()
class QuantizationConfig(ABC):
"""Base class for quantization configs."""
......@@ -46,12 +114,12 @@ class QuantizationConfig(ABC):
@abstractmethod
def get_name(self) -> str:
"""Name of the quantization method."""
raise NotImplementedError
raise NotImplementedError()
@abstractmethod
def get_supported_act_dtypes(self) -> List[torch.dtype]:
"""List of supported activation dtypes."""
raise NotImplementedError
raise NotImplementedError()
@classmethod
@abstractmethod
......@@ -62,19 +130,19 @@ class QuantizationConfig(ABC):
This requirement is due to the custom CUDA kernels used by the
quantization method.
"""
raise NotImplementedError
raise NotImplementedError()
@staticmethod
@abstractmethod
def get_config_filenames() -> List[str]:
"""List of filenames to search for in the model directory."""
raise NotImplementedError
raise NotImplementedError()
@classmethod
@abstractmethod
def from_config(cls, config: Dict[str, Any]) -> "QuantizationConfig":
"""Create a config class from the model's quantization config."""
raise NotImplementedError
raise NotImplementedError()
@classmethod
def override_quantization_method(cls, hf_quant_cfg, user_quant) -> Optional[str]:
......@@ -117,7 +185,7 @@ class QuantizationConfig(ABC):
The quantize method. None if the given layer doesn't support quant
method.
"""
raise NotImplementedError
raise NotImplementedError()
@abstractmethod
def get_scaled_act_names(self) -> List[str]:
......@@ -125,7 +193,7 @@ class QuantizationConfig(ABC):
For now, this is only used by AWQ.
"""
raise NotImplementedError
raise NotImplementedError()
def method_has_implemented_embedding(method_class: Type[QuantizeMethodBase]) -> bool:
......
python/sglang/srt/layers/quantization/blockwise_int8.py
View file @ 49b87774
# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/layers/quantization/fp8.py
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
......@@ -7,17 +9,15 @@ import torch
from torch.nn import Module
from sglang.srt.distributed import get_tensor_model_parallel_world_size
from sglang.srt.layers.linear import (
LinearBase,
LinearMethodBase,
UnquantizedLinearMethod,
)
from sglang.srt.layers.parameter import BlockQuantScaleParameter, ModelWeightParameter
from sglang.srt.layers.quantization.base_config import (
FusedMoEMethodBase,
LinearMethodBase,
QuantizationConfig,
QuantizeMethodBase,
)
from sglang.srt.layers.quantization.int8_utils import apply_w8a8_block_int8_linear
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
from sglang.srt.layers.quantization.utils import is_layer_skipped
from sglang.srt.utils import set_weight_attrs
......@@ -78,7 +78,7 @@ class BlockInt8Config(QuantizationConfig):
return []
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "BlockInt8Config":
def from_config(cls, config: Dict[str, Any]) -> BlockInt8Config:
quant_method = cls.get_from_keys(config, ["quant_method"])
is_checkpoint_int8_serialized = "int8" in quant_method
activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
......@@ -93,7 +93,8 @@ class BlockInt8Config(QuantizationConfig):
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> Optional["QuantizeMethodBase"]:
) -> Optional[QuantizeMethodBase]:
from sglang.srt.layers.linear import LinearBase
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
if isinstance(layer, LinearBase):
......@@ -230,7 +231,7 @@ class BlockInt8LinearMethod(LinearMethodBase):
)
class BlockInt8MoEMethod:
class BlockInt8MoEMethod(FusedMoEMethodBase):
"""MoE method for INT8.
Supports loading INT8 checkpoints with static weight scale and
dynamic activation scale.
......@@ -242,25 +243,7 @@ class BlockInt8MoEMethod:
quant_config: The quantization config.
"""
def __new__(cls, *args, **kwargs):
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):
def __init__(self, quant_config: BlockInt8Config):
self.quant_config = quant_config
assert self.quant_config.weight_block_size is not None
assert self.quant_config.is_checkpoint_int8_serialized
......
python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors.py
View file @ 49b87774
# Adapted from https://github.com/vllm-project/vllm/tree/v0.8.2/vllm/model_executor/layers/quantization/compressed_tensors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import logging
from contextlib import suppress
......@@ -18,12 +19,8 @@ from compressed_tensors.quantization import (
)
from pydantic import BaseModel
from sglang.srt.layers.linear import (
LinearBase,
LinearMethodBase,
UnquantizedLinearMethod,
)
from sglang.srt.layers.quantization.base_config import (
LinearMethodBase,
QuantizationConfig,
QuantizeMethodBase,
)
......@@ -40,6 +37,7 @@ from sglang.srt.layers.quantization.compressed_tensors.utils import (
is_activation_quantization_format,
should_ignore_layer,
)
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
try:
import vllm
......@@ -97,7 +95,7 @@ class CompressedTensorsConfig(QuantizationConfig):
self.config = config
self.packed_modules_mapping = packed_modules_mapping
def get_linear_method(self) -> "CompressedTensorsLinearMethod":
def get_linear_method(self) -> CompressedTensorsLinearMethod:
return CompressedTensorsLinearMethod(self)
def get_supported_act_dtypes(cls) -> List[torch.dtype]:
......@@ -117,7 +115,8 @@ class CompressedTensorsConfig(QuantizationConfig):
self,
layer: torch.nn.Module,
prefix: str,
) -> Optional["QuantizeMethodBase"]:
) -> Optional[QuantizeMethodBase]:
from sglang.srt.layers.linear import LinearBase
# Check if the layer is skipped for quantization.
# TODO (@robertgshaw2): support module names
......@@ -138,7 +137,7 @@ class CompressedTensorsConfig(QuantizationConfig):
return None
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "CompressedTensorsConfig":
def from_config(cls, config: Dict[str, Any]) -> CompressedTensorsConfig:
ignore: List[str] = cast(List[str], config.get("ignore", []))
quant_format = cast(str, config.get("format"))
target_scheme_map = cls._quantization_scheme_map_from_config(config=config)
......@@ -357,7 +356,7 @@ class CompressedTensorsConfig(QuantizationConfig):
def _get_scheme_from_parts(
self, weight_quant: BaseModel, input_quant: BaseModel
) -> "CompressedTensorsScheme":
) -> CompressedTensorsScheme:
# Detect If Mixed Precision
if self._is_wNa16_group_channel(weight_quant, input_quant):
......@@ -435,7 +434,7 @@ class CompressedTensorsConfig(QuantizationConfig):
def get_scheme(
self, layer: torch.nn.Module, layer_name: Optional[str] = None
) -> Optional["CompressedTensorsScheme"]:
) -> Optional[CompressedTensorsScheme]:
"""
compressed-tensors supports non uniform in the following way:
......
python/sglang/srt/layers/quantization/fp8.py
View file @ 49b87774
# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/layers/quantization/fp8.py
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional, Union
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
import torch
import torch.nn.functional as F
......@@ -28,17 +30,14 @@ except ImportError:
from sglang.srt.distributed import get_tensor_model_parallel_world_size
from sglang.srt.layers.amx_utils import _amx_process_weight_after_loading
from sglang.srt.layers.linear import (
LinearBase,
LinearMethodBase,
UnquantizedLinearMethod,
)
from sglang.srt.layers.parameter import (
BlockQuantScaleParameter,
ModelWeightParameter,
PerTensorScaleParameter,
)
from sglang.srt.layers.quantization.base_config import (
FusedMoEMethodBase,
LinearMethodBase,
QuantizationConfig,
QuantizeMethodBase,
)
......@@ -56,6 +55,7 @@ from sglang.srt.layers.quantization.fp8_utils import (
normalize_e4m3fn_to_e4m3fnuz,
)
from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
from sglang.srt.layers.quantization.utils import (
all_close_1d,
convert_to_channelwise,
......@@ -77,6 +77,9 @@ from sglang.srt.utils import (
use_intel_amx_backend,
)
if TYPE_CHECKING:
from sglang.srt.layers.quantization.w4afp8 import W4AFp8Config
_is_hip = is_hip()
_is_cuda = is_cuda()
_is_npu = is_npu()
......@@ -152,7 +155,7 @@ class Fp8Config(QuantizationConfig):
return []
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "Fp8Config":
def from_config(cls, config: Dict[str, Any]) -> Fp8Config:
quant_method = cls.get_from_keys(config, ["quant_method"])
is_checkpoint_fp8_serialized = "fp8" in quant_method
activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
......@@ -167,7 +170,8 @@ class Fp8Config(QuantizationConfig):
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> Optional["QuantizeMethodBase"]:
) -> Optional[QuantizeMethodBase]:
from sglang.srt.layers.linear import LinearBase
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
if isinstance(layer, LinearBase):
......@@ -200,7 +204,7 @@ class Fp8LinearMethod(LinearMethodBase):
quant_config: The quantization config.
"""
def __init__(self, quant_config: Union["Fp8Config", "W4AFp8Config"]):
def __init__(self, quant_config: Union[Fp8Config, W4AFp8Config]):
self.quant_config = quant_config
self.cutlass_fp8_supported = cutlass_fp8_supported()
......@@ -486,7 +490,7 @@ class Fp8LinearMethod(LinearMethodBase):
)
class Fp8MoEMethod:
class Fp8MoEMethod(FusedMoEMethodBase):
"""MoE method for FP8.
Supports loading FP8 checkpoints with static weight scale and
dynamic/static activation scale.
......@@ -499,25 +503,7 @@ class Fp8MoEMethod:
quant_config: The quantization config.
"""
def __new__(cls, *args, **kwargs):
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):
def __init__(self, quant_config: Fp8Config):
self.quant_config = quant_config
self.block_quant = self.quant_config.weight_block_size is not None
self.cutlass_fp8_supported = cutlass_fp8_supported()
......@@ -1169,6 +1155,254 @@ class Fp8MoEMethod:
return None
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,
):
from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
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,
)
if self.block_quant:
# If ROCm, normalize the weights and scales to e4m3fnuz
if _is_fp8_fnuz:
# activation_scheme: dynamic
w13_weight, w13_weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
weight=layer.w13_weight,
weight_scale=layer.w13_weight_scale_inv,
input_scale=None,
)
w2_weight, w2_weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
weight=layer.w2_weight,
weight_scale=layer.w2_weight_scale_inv,
input_scale=None,
)
# Reset the parameter
layer.w13_weight = torch.nn.Parameter(
w13_weight, requires_grad=False
)
layer.w13_weight_scale_inv = torch.nn.Parameter(
w13_weight_scale, requires_grad=False
)
layer.w13_input_scale = None
layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
layer.w2_weight_scale_inv = torch.nn.Parameter(
w2_weight_scale, requires_grad=False
)
layer.w2_input_scale = None
if _use_aiter:
layer.w13_weight = torch.nn.Parameter(
shuffle_weight(layer.w13_weight.data, (16, 16)),
requires_grad=False,
)
layer.w2_weight = torch.nn.Parameter(
shuffle_weight(layer.w2_weight.data, (16, 16)),
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 Fp8KVCacheMethod(BaseKVCacheMethod):
"""
Supports loading kv-cache scaling factors from FP8 checkpoints.
......
python/sglang/srt/layers/quantization/gptq.py
View file @ 49b87774
from __future__ import annotations
import logging
from dataclasses import dataclass
from fractions import Fraction
......@@ -5,7 +7,6 @@ from typing import Any, Callable, Dict, List, Optional, Union
import torch
from sglang.srt.layers.linear import LinearBase, LinearMethodBase, set_weight_attrs
from sglang.srt.layers.parameter import (
BasevLLMParameter,
ChannelQuantScaleParameter,
......@@ -16,6 +17,8 @@ from sglang.srt.layers.parameter import (
permute_param_layout_,
)
from sglang.srt.layers.quantization.base_config import (
FusedMoEMethodBase,
LinearMethodBase,
QuantizationConfig,
QuantizeMethodBase,
)
......@@ -34,7 +37,11 @@ from sglang.srt.layers.quantization.marlin_utils import (
verify_marlin_supported,
)
from sglang.srt.layers.quantization.scalar_type import ScalarType, scalar_types
from sglang.srt.layers.quantization.utils import replace_parameter, unpack_cols
from sglang.srt.layers.quantization.utils import (
get_linear_quant_method,
replace_parameter,
unpack_cols,
)
try:
from vllm import _custom_ops as ops
......@@ -49,8 +56,6 @@ if _is_cuda:
from sgl_kernel import fused_marlin_moe
FusedMoEMethodBase = QuantizeMethodBase
logger = logging.getLogger(__name__)
......@@ -179,7 +184,7 @@ class GPTQConfig(QuantizationConfig):
return ["quantize_config.json"]
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "GPTQConfig":
def from_config(cls, config: Dict[str, Any]) -> GPTQConfig:
dynamic = cls.get_from_keys_or(config, ["dynamic"], default={})
dynamic = {} if dynamic is None else dynamic
......@@ -191,10 +196,10 @@ class GPTQConfig(QuantizationConfig):
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> Optional["LinearMethodBase"]:
) -> Optional[LinearMethodBase]:
# Delay the import to avoid circular dependency
from sglang.srt.layers.linear import LinearBase
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
from sglang.srt.layers.quantization import get_linear_quant_method
if isinstance(layer, LinearBase):
return get_linear_quant_method(self, layer, prefix, GPTQLinearMethod)
......@@ -303,7 +308,7 @@ class GPTQMarlinConfig(QuantizationConfig):
return ["quantize_config.json"]
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "GPTQMarlinConfig":
def from_config(cls, config: Dict[str, Any]) -> GPTQMarlinConfig:
dynamic = cls.get_from_keys_or(config, ["dynamic"], default={})
dynamic = {} if dynamic is None else dynamic
......@@ -354,7 +359,6 @@ class GPTQMarlinConfig(QuantizationConfig):
) -> Optional[QuantizeMethodBase]:
# Delay the import to avoid circular dependency
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
from sglang.srt.layers.quantization import get_linear_quant_method
if isinstance(layer, FusedMoE):
return GPTQMarlinMoEMethod(self)
......@@ -832,6 +836,7 @@ class GPTQMarlinMoEMethod(FusedMoEMethodBase):
**extra_weight_attrs,
):
# Delay the import to avoid circular dependency
from sglang.srt.layers.linear import set_weight_attrs
from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
intermediate_size = extra_weight_attrs.pop("intermediate_size")
......
python/sglang/srt/layers/quantization/marlin_utils.py
View file @ 49b87774
# SPDX-License-Identifier: Apache-2.0
# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/utils/marlin_utils.py
from __future__ import annotations
import logging
from typing import Any, Optional
from typing import TYPE_CHECKING, Any, Optional
import numpy
import torch
from sglang.srt.layers.linear import LinearBase, LinearMethodBase
from sglang.srt.layers.parameter import (
BasevLLMParameter,
ChannelQuantScaleParameter,
GroupQuantScaleParameter,
PackedvLLMParameter,
)
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.quantization.base_config import (
LinearMethodBase,
QuantizationConfig,
)
from sglang.srt.layers.quantization.scalar_type import ScalarType, scalar_types
from sglang.srt.layers.quantization.utils import pack_cols, unpack_cols
from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
from sglang.srt.utils import get_device_capability
if TYPE_CHECKING:
from sglang.srt.layers.linear import LinearBase
try:
from vllm import _custom_ops as ops
except ImportError:
......@@ -617,7 +623,10 @@ class MarlinConfig(QuantizationConfig):
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> Optional["MarlinLinearMethod"]:
) -> Optional[MarlinLinearMethod]:
from sglang.srt.layers.linear import LinearBase
from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
if isinstance(layer, LinearBase) or (
isinstance(layer, ParallelLMHead) and self.lm_head_quantized
):
......
python/sglang/srt/layers/quantization/modelopt_quant.py
View file @ 49b87774
# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/modelopt.py
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
......@@ -6,14 +7,11 @@ from typing import Any, Callable, Dict, List, Optional
import torch
from torch.nn.parameter import Parameter
from sglang.srt.layers.linear import (
LinearBase,
LinearMethodBase,
UnquantizedLinearMethod,
)
from sglang.srt.layers.moe.cutlass_moe_params import CutlassMoEParams, CutlassMoEType
from sglang.srt.layers.parameter import ModelWeightParameter, PerTensorScaleParameter
from sglang.srt.layers.quantization.base_config import (
FusedMoEMethodBase,
LinearMethodBase,
QuantizationConfig,
QuantizeMethodBase,
)
......@@ -23,6 +21,7 @@ from sglang.srt.layers.quantization.fp8_utils import (
is_sm100_supported,
)
from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
from sglang.srt.layers.quantization.utils import (
convert_to_channelwise,
is_layer_skipped,
......@@ -86,7 +85,7 @@ class ModelOptFp8Config(QuantizationConfig):
return ["hf_quant_config.json"]
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "ModelOptFp8Config":
def from_config(cls, config: Dict[str, Any]) -> ModelOptFp8Config:
quant_method = cls.get_from_keys(config, ["quantization"]).get("quant_algo")
kv_cache_quant_method = cls.get_from_keys(config, ["quantization"]).get(
"kv_cache_quant_algo"
......@@ -109,7 +108,11 @@ class ModelOptFp8Config(QuantizationConfig):
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> Optional["QuantizeMethodBase"]:
) -> Optional[QuantizeMethodBase]:
from sglang.srt.layers.linear import LinearBase
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
if self.exclude_modules and any(
module in prefix
or (
......@@ -125,9 +128,6 @@ 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)
......@@ -246,7 +246,7 @@ class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
super().__init__(quant_config)
class ModelOptFp8MoEMethod:
class ModelOptFp8MoEMethod(FusedMoEMethodBase):
"""MoE method for ModelOpt FP8.
Supports loading FP8 checkpoints with static weight scale and activation scale.
......@@ -254,30 +254,6 @@ class ModelOptFp8MoEMethod:
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()
......@@ -514,7 +490,7 @@ class ModelOptFp4Config(QuantizationConfig):
return ["hf_quant_config.json"]
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "ModelOptFp4Config":
def from_config(cls, config: Dict[str, Any]) -> ModelOptFp4Config:
quant_config = cls.get_from_keys(config, ["quantization"])
quant_method = quant_config["quant_algo"]
if not quant_method in ["FP8", "NVFP4"]:
......@@ -559,7 +535,8 @@ class ModelOptFp4Config(QuantizationConfig):
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> Optional["QuantizeMethodBase"]:
) -> Optional[QuantizeMethodBase]:
from sglang.srt.layers.linear import LinearBase
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
if isinstance(layer, LinearBase):
......@@ -740,31 +717,13 @@ class ModelOptFp4LinearMethod(LinearMethodBase):
return out.view(*output_shape)
class ModelOptNvFp4FusedMoEMethod:
class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
"""
MoE Method for FP4 Quantization with Blockscales and PerTensorScales
Args:
quant_config: NVFP4 Quant Config
"""
def __new__(cls, *args, **kwargs):
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: ModelOptFp4Config):
self.quant_config = quant_config
if not is_sm100_supported():
......
python/sglang/srt/layers/quantization/moe_wna16.py
View file @ 49b87774
# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/moe_wna16.py
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
......@@ -7,13 +8,14 @@ import torch
from sglang.srt.distributed import get_tensor_model_parallel_rank
from sglang.srt.distributed.parallel_state import get_tp_group
from sglang.srt.layers.linear import LinearBase, UnquantizedLinearMethod
from sglang.srt.layers.quantization.awq import AWQConfig
from sglang.srt.layers.quantization.base_config import (
FusedMoEMethodBase,
QuantizationConfig,
QuantizeMethodBase,
)
from sglang.srt.layers.quantization.gptq import GPTQConfig, GPTQMarlinConfig
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
from sglang.srt.utils import get_device_capability, set_weight_attrs
logger = logging.getLogger(__name__)
......@@ -118,7 +120,7 @@ class MoeWNA16Config(QuantizationConfig):
raise NotImplementedError
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "MoeWNA16Config":
def from_config(cls, config: Dict[str, Any]) -> MoeWNA16Config:
quant_method = cls.get_from_keys(config, ["quant_method"])
weight_bits = cls.get_from_keys(config, ["bits"])
group_size = cls.get_from_keys(config, ["group_size"])
......@@ -177,8 +179,9 @@ class MoeWNA16Config(QuantizationConfig):
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> Optional["QuantizeMethodBase"]:
) -> Optional[QuantizeMethodBase]:
# avoid circular import
from sglang.srt.layers.linear import LinearBase
from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
if is_layer_skipped_quant(prefix, self.modules_to_not_convert):
......@@ -209,32 +212,13 @@ def is_layer_skipped_quant(prefix: str, modules_to_not_convert: List[str]):
return any(module_name in prefix for module_name in modules_to_not_convert)
class MoeWNA16Method:
class MoeWNA16Method(FusedMoEMethodBase):
"""Linear method for MOE WNA16 (W8A16/W4A16) quantization.
Args:
quant_config: The MOE WNA16 (W8A16/W4A16) quantization config.
"""
def __new__(cls, *args, **kwargs):
# avoid circular import
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: MoeWNA16Config):
self.quant_config = quant_config
......
python/sglang/srt/layers/quantization/qoq.py
View file @ 49b87774
from typing import Any, Callable, Dict, List, Optional
from __future__ import annotations
from typing import Any, Dict, List, Optional
import torch
from torch.nn.parameter import Parameter
from sglang.srt.distributed import get_tensor_model_parallel_world_size
from sglang.srt.layers.linear import LinearMethodBase
from sglang.srt.layers.parameter import (
ChannelQuantScaleParameter,
GroupQuantScaleParameter,
ModelWeightParameter,
)
from sglang.srt.layers.quantization.base_config import (
LinearMethodBase,
QuantizationConfig,
QuantizeMethodBase,
)
......@@ -71,7 +72,7 @@ class QoQConfig(QuantizationConfig):
return 80
@classmethod
def get_name(self) -> str:
def get_name(cls) -> str:
return "qoq"
@classmethod
......@@ -83,7 +84,7 @@ class QoQConfig(QuantizationConfig):
]
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "QoQConfig":
def from_config(cls, config: Dict[str, Any]) -> QoQConfig:
weight_bits = cls.get_from_keys(config, ["wbits"])
group_size = cls.get_from_keys(config, ["group_size"])
return cls(weight_bits, group_size)
......@@ -92,7 +93,7 @@ class QoQConfig(QuantizationConfig):
self,
layer: torch.nn.Module,
prefix: str,
) -> Optional["QuantizeMethodBase"]:
) -> Optional[QuantizeMethodBase]:
from sglang.srt.layers.linear import LinearBase
if isinstance(layer, LinearBase):
......
python/sglang/srt/layers/quantization/unquant.py 0 → 100644
View file @ 49b87774
import importlib
from typing import Callable, List, Optional
import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from sglang.srt.custom_op import CustomOp
from sglang.srt.layers.amx_utils import _amx_process_weight_after_loading
from sglang.srt.layers.quantization.base_config import (
FusedMoEMethodBase,
LinearMethodBase,
QuantizeMethodBase,
)
from sglang.srt.utils import (
cpu_has_amx_support,
get_bool_env_var,
is_cpu,
is_hip,
set_weight_attrs,
use_intel_amx_backend,
)
has_triton_kernels = importlib.util.find_spec("triton_kernels") is not None
_is_cpu_amx_available = cpu_has_amx_support()
_is_hip = is_hip()
_is_cpu = is_cpu()
_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
if _use_aiter:
from aiter import ActivationType
from aiter.fused_moe import fused_moe
from aiter.fused_moe_bf16_asm import ck_moe_2stages
from aiter.ops.shuffle import shuffle_weight
class UnquantizedEmbeddingMethod(QuantizeMethodBase):
"""Unquantized method for embeddings."""
def create_weights(
self,
layer: torch.nn.Module,
input_size_per_partition: int,
output_partition_sizes: List[int],
input_size: int,
output_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
"""Create weights for embedding layer."""
weight = Parameter(
torch.empty(
sum(output_partition_sizes),
input_size_per_partition,
dtype=params_dtype,
),
requires_grad=False,
)
set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
layer.register_parameter("weight", weight)
set_weight_attrs(weight, extra_weight_attrs)
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
return F.linear(x, layer.weight, bias)
def embedding(self, layer: torch.nn.Module, input_: torch.Tensor) -> torch.Tensor:
return F.embedding(input_, layer.weight)
class UnquantizedLinearMethod(LinearMethodBase):
"""Linear method without quantization."""
def create_weights(
self,
layer: torch.nn.Module,
input_size_per_partition: int,
output_partition_sizes: List[int],
input_size: int,
output_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
weight = Parameter(
torch.empty(
sum(output_partition_sizes),
input_size_per_partition,
dtype=params_dtype,
),
requires_grad=False,
)
set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
layer.register_parameter("weight", weight)
set_weight_attrs(weight, extra_weight_attrs)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
if _is_cpu and _is_cpu_amx_available:
_amx_process_weight_after_loading(layer, ["weight"])
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if use_intel_amx_backend(layer):
return torch.ops.sgl_kernel.weight_packed_linear(
x, layer.weight, bias, True # is_vnni
)
return F.linear(x, layer.weight, bias)
class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
"""MoE method without quantization."""
def __init__(self, use_triton_kernels: bool = False):
super().__init__()
self.use_triton_kernels = use_triton_kernels
from sglang.srt.layers.moe.fused_moe_native import moe_forward_native
if torch.cuda.is_available():
from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
if has_triton_kernels:
from sglang.srt.layers.moe.fused_moe_triton.triton_kernels_moe import (
triton_kernel_moe_forward,
)
else:
triton_kernel_moe_forward = None
else:
fused_experts = None # type: ignore
triton_kernel_moe_forward = None
self.moe_forward_native = moe_forward_native
self.fused_experts = fused_experts
self.triton_kernel_moe_forward = triton_kernel_moe_forward
def create_weights(
self,
layer: torch.nn.Module,
num_experts: int,
hidden_size: int,
intermediate_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
# Fused gate_up_proj (column parallel)
w13_weight_n, w13_weight_k = 2 * intermediate_size, hidden_size
if self.use_triton_kernels:
w13_weight_n, w13_weight_k = w13_weight_k, w13_weight_n
w13_weight = torch.nn.Parameter(
torch.empty(num_experts, w13_weight_n, w13_weight_k, 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_n, w2_weight_k = (
hidden_size,
intermediate_size,
)
if self.use_triton_kernels:
w2_weight_n, w2_weight_k = w2_weight_k, w2_weight_n
w2_weight = torch.nn.Parameter(
torch.empty(num_experts, w2_weight_n, w2_weight_k, dtype=params_dtype),
requires_grad=False,
)
layer.register_parameter("w2_weight", w2_weight)
set_weight_attrs(w2_weight, extra_weight_attrs)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
if _use_aiter:
layer.w13_weight = torch.nn.Parameter(
shuffle_weight(layer.w13_weight.data, (16, 16)),
requires_grad=False,
)
torch.cuda.empty_cache()
layer.w2_weight = torch.nn.Parameter(
shuffle_weight(layer.w2_weight.data, (16, 16)),
requires_grad=False,
)
torch.cuda.empty_cache()
# Pack weight for get better performance on CPU
if _is_cpu and _is_cpu_amx_available:
_amx_process_weight_after_loading(layer, ["w13_weight", "w2_weight"])
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,
num_fused_shared_experts: int = 0,
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:
return self.forward(
x=x,
layer=layer,
router_logits=router_logits,
top_k=top_k,
renormalize=renormalize,
use_grouped_topk=use_grouped_topk,
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,
activation=activation,
apply_router_weight_on_input=apply_router_weight_on_input,
inplace=inplace,
no_combine=no_combine,
routed_scaling_factor=routed_scaling_factor,
)
def forward_cuda(
self,
layer: torch.nn.Module,
x: torch.Tensor,
use_grouped_topk: bool,
top_k: int,
router_logits: torch.Tensor,
renormalize: bool,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
num_fused_shared_experts: int = 0,
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:
if self.use_triton_kernels:
return self.triton_kernel_moe_forward(
hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
gating_output=router_logits,
topk=top_k,
renormalize=renormalize,
)
else:
from sglang.srt.layers.moe.topk import select_experts
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,
)
if _use_aiter:
assert not no_combine, "unsupported"
if apply_router_weight_on_input:
assert (
topk_weights.dim() == 2
), "`topk_weights` should be in shape (num_tokens, topk)"
_, topk = topk_weights.shape
assert (
topk == 1
), "Only support topk=1 when `apply_router_weight_on_input` is True"
x = x * topk_weights.to(x.dtype)
topk_weights = torch.ones_like(
topk_weights, dtype=torch.float32
) # topk_weights must be FP32 (float32)
return fused_moe(
x,
layer.w13_weight,
layer.w2_weight,
topk_weights,
topk_ids,
activation=(
ActivationType.Silu
if activation == "silu"
else ActivationType.Gelu
),
)
else:
return self.fused_experts(
hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=inplace and not no_combine,
activation=activation,
apply_router_weight_on_input=apply_router_weight_on_input,
no_combine=no_combine,
routed_scaling_factor=routed_scaling_factor,
)
def forward_cpu(
self,
layer: torch.nn.Module,
x: torch.Tensor,
use_grouped_topk: bool,
top_k: int,
router_logits: torch.Tensor,
renormalize: bool,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
num_fused_shared_experts: int = 0,
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:
assert activation == "silu", f"activation = {activation} is not supported."
if use_intel_amx_backend(layer) and not apply_router_weight_on_input:
from sglang.srt.layers.moe.topk import select_experts
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,
)
# TODO: support apply_router_weight_on_input in the fused_experts_cpu kernel
return torch.ops.sgl_kernel.fused_experts_cpu(
x,
layer.w13_weight,
layer.w2_weight,
topk_weights,
topk_ids,
False, # inplace # See [Note] inplace should be False in fused_experts.
False, # use_int8_w8a8
False, # use_fp8_w8a16
None, # w1_scale
None, # w2_scale
None, # block_size
None, # a1_scale
None, # a2_scale
True, # is_vnni
)
else:
return self.moe_forward_native(
layer,
x,
use_grouped_topk,
top_k,
router_logits,
renormalize,
topk_group,
num_expert_group,
num_fused_shared_experts,
custom_routing_function,
correction_bias,
activation,
apply_router_weight_on_input,
inplace,
no_combine,
routed_scaling_factor,
)
def forward_npu(
self,
layer: torch.nn.Module,
x: torch.Tensor,
use_grouped_topk: bool,
top_k: int,
router_logits: torch.Tensor,
renormalize: bool,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
num_fused_shared_experts: int = 0,
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:
return self.moe_forward_native(
layer,
x,
use_grouped_topk,
top_k,
router_logits,
renormalize,
topk_group,
num_expert_group,
num_fused_shared_experts,
custom_routing_function,
correction_bias,
activation,
apply_router_weight_on_input,
inplace,
no_combine,
routed_scaling_factor,
)
def forward_tpu(self, *args, **kwargs) -> torch.Tensor:
raise NotImplementedError("The TPU backend currently does not support MoE.")
forward_native = forward_cpu
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
python/sglang/srt/layers/quantization/utils.py
View file @ 49b87774
# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/utils/quant_utils.py
from __future__ import annotations
import re
from copy import deepcopy
from types import MappingProxyType
from typing import List, Mapping, Optional, Tuple, Union
from typing import TYPE_CHECKING, Dict, List, Mapping, Optional, Tuple, Union
import numpy
import torch
......@@ -10,6 +14,9 @@ from sglang.srt.layers.quantization.fp8_kernel import scaled_fp8_quant
from sglang.srt.layers.quantization.scalar_type import ScalarType
from sglang.srt.utils import cpu_has_amx_support, is_cpu, is_cuda, is_npu
if TYPE_CHECKING:
from sglang.srt.layers.quantization.base_config import QuantizationConfig
_is_cuda = is_cuda()
_is_npu = is_npu()
_is_cpu_amx_available = cpu_has_amx_support()
......@@ -147,6 +154,94 @@ def replace_parameter(
mod.register_parameter(name, torch.nn.Parameter(new, requires_grad=False))
# Match dynamic rules with module name (prefix) and override quantize
# config if module (prefix) matches a rule
def override_config(config: QuantizationConfig, prefix: str):
weight_bits = get_dynamic_override(config, prefix, "bits", config.weight_bits)
if isinstance(weight_bits, int):
config.weight_bits = weight_bits
group_size = get_dynamic_override(config, prefix, "group_size", config.group_size)
if isinstance(group_size, int):
config.group_size = group_size
desc_act = get_dynamic_override(config, prefix, "desc_act", config.desc_act)
if isinstance(desc_act, bool):
config.desc_act = desc_act
config.pack_factor = 32 // config.weight_bits # packed into int32
if config.get_name() == "gptq_marlin":
is_sym = get_dynamic_override(config, prefix, "sym", config.is_sym)
if isinstance(is_sym, bool):
config.is_sym = is_sym
if (config.weight_bits, config.is_sym) not in config.TYPE_MAP:
raise ValueError(
"Unsupported quantization config: "
f"bits={config.weight_bits}, sym={config.is_sym}"
)
config.quant_type = config.TYPE_MAP[(config.weight_bits, config.is_sym)]
elif config.get_name() == "gptq":
if config.weight_bits not in [2, 3, 4, 8]:
raise ValueError(
"Currently, only 2/3/4/8-bit weight quantization is "
f"supported for GPTQ, but got {config.weight_bits} bits."
)
def get_dynamic_override(
config: QuantizationConfig,
layer_name: str,
key: Optional[str] = None,
default_value: Union[int, bool, None] = None,
) -> Union[Dict, int, bool, None]:
for pattern, pattern_dict in config.dynamic.items():
# Negative match: matched modules are excluded from quantized init
if pattern.startswith("-:"):
if re.match(pattern.removeprefix("-:"), layer_name):
return False
# Positive match: matched modules have quant properties overrides
# base quant config
elif re.match(pattern.removeprefix("+:"), layer_name):
if key is None:
return pattern_dict
else:
return pattern_dict.get(key, default_value)
return default_value
def get_linear_quant_method(
config: QuantizationConfig,
layer: torch.nn.Module,
prefix: str,
linear_method_cls: type,
):
from sglang.srt.layers.linear import LinearBase
from sglang.srt.layers.quantization.unquant import (
UnquantizedEmbeddingMethod,
UnquantizedLinearMethod,
)
from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
cloned_config = deepcopy(config)
parallel_lm_head_quantized = (
isinstance(layer, ParallelLMHead) and cloned_config.lm_head_quantized
)
if isinstance(layer, LinearBase) or parallel_lm_head_quantized:
# False = skip module, None = no override, else = Positive match
if get_dynamic_override(cloned_config, layer_name=prefix) is False:
if parallel_lm_head_quantized:
return UnquantizedEmbeddingMethod()
return UnquantizedLinearMethod()
if prefix:
# Dynamic per module/layer rules may override base config
override_config(cloned_config, prefix=prefix)
return linear_method_cls(cloned_config)
return None
def get_pack_factor(num_bits):
assert 32 % num_bits == 0, f"Unsupported num_bits = {num_bits}"
return 32 // num_bits
......
python/sglang/srt/layers/quantization/w4afp8.py
View file @ 49b87774
from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional
......@@ -5,12 +7,13 @@ import torch
from torch.nn import Module
from torch.nn.parameter import Parameter
from sglang.srt.layers.linear import LinearBase, UnquantizedLinearMethod
from sglang.srt.layers.quantization.base_config import (
FusedMoEMethodBase,
QuantizationConfig,
QuantizeMethodBase,
)
from sglang.srt.layers.quantization.fp8 import Fp8LinearMethod
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
from sglang.srt.layers.quantization.utils import is_layer_skipped
from sglang.srt.utils import set_weight_attrs
......@@ -62,7 +65,7 @@ class W4AFp8Config(QuantizationConfig):
return []
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "W4AFp8Config":
def from_config(cls, config: Dict[str, Any]) -> W4AFp8Config:
quant_method = cls.get_from_keys(config, ["quant_method"])
is_checkpoint_fp8_serialized = "fp8" in quant_method
is_checkpoint_w4afp8_serialized = "w4afp8" in quant_method
......@@ -79,7 +82,8 @@ class W4AFp8Config(QuantizationConfig):
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> Optional["QuantizeMethodBase"]:
) -> Optional[QuantizeMethodBase]:
from sglang.srt.layers.linear import LinearBase
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
if isinstance(layer, LinearBase):
......@@ -94,7 +98,7 @@ class W4AFp8Config(QuantizationConfig):
return []
class W4AFp8MoEMethod:
class W4AFp8MoEMethod(FusedMoEMethodBase):
def __init__(self, quant_config: W4AFp8Config):
self.quant_config = quant_config
......
python/sglang/srt/layers/quantization/w8a8_fp8.py
View file @ 49b87774
from __future__ import annotations
from typing import Any, Callable, Dict, List, Optional
import torch
from torch.nn.parameter import Parameter
from sglang.srt.layers.linear import LinearMethodBase
from sglang.srt.layers.parameter import ChannelQuantScaleParameter, ModelWeightParameter
from sglang.srt.layers.quantization.base_config import (
FusedMoEMethodBase,
LinearMethodBase,
QuantizationConfig,
QuantizeMethodBase,
)
......@@ -64,7 +67,7 @@ class W8A8Fp8Config(QuantizationConfig):
return []
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "W8A8Fp8Config":
def from_config(cls, config: Dict[str, Any]) -> W8A8Fp8Config:
quant_method = cls.get_from_keys(config, ["quant_method"])
is_checkpoint_fp8_serialized = (
"compressed-tensors" in quant_method or "w8a8_fp8" in quant_method
......@@ -75,7 +78,7 @@ class W8A8Fp8Config(QuantizationConfig):
self,
layer: torch.nn.Module,
prefix: str,
) -> Optional["QuantizeMethodBase"]:
) -> Optional[QuantizeMethodBase]:
from sglang.srt.layers.linear import LinearBase
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
......@@ -183,7 +186,7 @@ class W8A8Fp8LinearMethod(LinearMethodBase):
)
class W8A8FP8MoEMethod:
class W8A8FP8MoEMethod(FusedMoEMethodBase):
"""MoE method for FP8.
Supports loading FP8 checkpoints with static weight scale and
dynamic/static activation scale.
......@@ -194,25 +197,7 @@ class W8A8FP8MoEMethod:
quant_config: The quantization config.
"""
def __new__(cls, *args, **kwargs):
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):
def __init__(self, quant_config: W8A8Fp8Config):
self.quant_config = quant_config
def create_weights(
......
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