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Commit 53250530 authored by gaoqiong's avatar gaoqiong
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Update w8a8_int8.py

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vllm/model_executor/layers/quantization/w8a8_int8.py
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from typing import Any, Callable, Dict, List, Optional
import torch
from vllm.model_executor.utils import set_weight_attrs
from vllm.distributed import get_tensor_model_parallel_world_size
from torch.nn.parameter import Parameter
from vllm.model_executor.layers.linear import (LinearBase,LinearMethodBase)
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig, QuantizeMethodBase)
from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
FusedMoeWeightScaleSupported)
from vllm.model_executor.parameter import (BasevLLMParameter,
ChannelQuantScaleParameter,
ModelWeightParameter,
PerTensorScaleParameter)
from vllm.model_executor.layers.quantization.utils.int8_utils import (
per_token_group_quant_int8,
per_token_quant_int8)
from vllm import _custom_ops as ops
from vllm.utils import W8a8GetCacheJSON
import os
from vllm import _custom_ops as ops
W8A8_TRITONJSON=W8a8GetCacheJSON()
def baseline_scaled_mm(a: torch.Tensor,
b: torch.Tensor,
scale_a: torch.Tensor,
scale_b: torch.Tensor,
out_dtype: torch.dtype,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
scales= scale_a* scale_b.T
gemmout= torch.mm(
a.to(dtype=torch.float32), b.to(dtype=torch.float32))
output = (scales *gemmout).to(out_dtype)
if bias is not None:
output = output + bias
return output.to(out_dtype)
class W8A8Int8Config(QuantizationConfig):
"""Config class for W8A8 Int8 Quantization.
- Weight: static, per-channel, symmetric
- Activation: dynamic, per-token, symmetric
"""
def __init__(self):
pass
@classmethod
def get_supported_act_dtypes(cls) -> List[torch.dtype]:
return [torch.float16, torch.bfloat16]
@classmethod
def get_min_capability(cls) -> int:
return 75
@classmethod
def get_name(self) -> str:
return "w8a8_int8"
@classmethod
def get_config_filenames(cls) -> List[str]:
return []
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "W8A8Int8Config":
return cls()
def get_quant_method(
self,
layer: torch.nn.Module,
prefix: str,
) -> Optional["QuantizeMethodBase"]:
if isinstance(layer, LinearBase):
return W8A8Int8LinearMethod(self)
elif isinstance(layer, FusedMoE):
return W8A8Int8MoEMethod(self)
return None
def get_scaled_act_names(self) -> List[str]:
return []
class W8A8Int8LinearMethod(LinearMethodBase):
def __init__(self, quantization_config: W8A8Int8Config):
self.quantization_config = quantization_config
self.tritonsingleton= W8a8GetCacheJSON()
self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
n=layer.weight.shape[0]
k=layer.weight.shape[1]
if self.w8a8_strategy==1:
if {n,k} not in self.tritonsingleton.weight_shapes:
self.tritonsingleton.weight_shapes.append({n,k})
json_file=self.tritonsingleton.get_w8a8json_name(n,k)
configs_dict=self.tritonsingleton.get_triton_cache(json_file,n,k)
if configs_dict:
self.tritonsingleton.triton_json_dict.update(configs_dict)
for key, value in configs_dict.items():
m=int(key.split('_')[0])
ops.triton_int8_gemm_helper(m=m,n=n,k=k,per_token_act_quant=True,per_out_channel_weight_quant=True,use_bias=False,device=layer.weight.device,best_config=value)
else:
weight_data=layer.weight.data
_weight=weight_data.T.contiguous().reshape(n,-1)
layer.weight.data=_weight
layer.weight = Parameter(layer.weight.t(), requires_grad=False)
layer.weight_scale = Parameter(layer.weight_scale.data, requires_grad=False)
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_loader = extra_weight_attrs.get("weight_loader")
self.logical_widths = output_partition_sizes
weight = ModelWeightParameter(
data=torch.empty(
sum(output_partition_sizes), input_size_per_partition, dtype=torch.int8
),
input_dim=1,
output_dim=0,
weight_loader=weight_loader,
)
layer.register_parameter("weight", weight)
weight_scale = ChannelQuantScaleParameter(
data=torch.empty((sum(output_partition_sizes), 1), dtype=torch.float32),
output_dim=0,
weight_loader=weight_loader,
)
layer.register_parameter("weight_scale", weight_scale)
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None,
):
x_q, x_scale = per_token_quant_int8(x)
if self.w8a8_strategy==1:
m=x_q.shape[0]
k=x_q.shape[1]
n=layer.weight.shape[1]
if len(W8A8_TRITONJSON.triton_json_dict)==0:
best_config=None
elif f"1_{n}_{k}" in W8A8_TRITONJSON.triton_json_dict:
if m<=16:
m_=m
elif m<=64:
m_= (m + 3) & -4 #取值到最近的4的倍数
elif m<=160:
m_=(m + 7) & -8
elif m<200: #256
m_=160
elif m<480: #512
m_=256
elif m<960: #1024
m_=512
elif m<2048:
m_=1024
elif m<4096:
m_=2048
elif m<6000:
m_=4096
else:
m_=8192
best_config=W8A8_TRITONJSON.triton_json_dict[f"{m_}_{n}_{k}"]
else:
best_config=None
if best_config==None:
print("m:{},n:{},k:{}".format(m,n,k))
print("config not found!")
return ops.triton_scaled_mm(x_q,
layer.weight,
scale_a=x_scale,
scale_b=layer.weight_scale,
out_dtype=x.dtype,
bias=bias,best_config=best_config)
elif self.w8a8_strategy==2:
return ops.cutlass_scaled_mm(x_q,
layer.weight,
scale_a=x_scale,
scale_b=layer.weight_scale,
out_dtype=x.dtype,
bias=bias)
else:
return ops.rocblas_scaled_mm(x_q,
layer.weight,
scale_a=x_scale,
scale_b=layer.weight_scale,
out_dtype=x.dtype,
bias=bias)
class W8A8Int8MoEMethod:
"""MoE method for INT8.
Supports loading INT8 checkpoints with static weight scale and
dynamic/static activation scale.
Also supports loading quantized FP16/BF16 model checkpoints with dynamic
activation scaling. The weight scaling factor will be initialized after
the model weights are loaded.
Args:
quant_config: The quantization config.
"""
def __new__(cls, *args, **kwargs):
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):
self.quant_config = quant_config
def create_weights(
self,
layer: torch.nn.Module,
num_experts: int,
hidden_size: int,
intermediate_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
tp_size = get_tensor_model_parallel_world_size()
# WEIGHTS
w13_weight = torch.nn.Parameter(
torch.empty(
num_experts, 2 * intermediate_size, hidden_size, dtype=torch.int8
),
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, hidden_size, intermediate_size, dtype=torch.int8),
requires_grad=False,
)
layer.register_parameter("w2_weight", w2_weight)
set_weight_attrs(w2_weight, extra_weight_attrs)
w13_weight_scale = torch.nn.Parameter(
torch.ones(num_experts, 2 * intermediate_size, 1, dtype=torch.float32),
requires_grad=False,
)
w2_weight_scale = torch.nn.Parameter(
torch.ones(num_experts, hidden_size, 1, dtype=torch.float32),
requires_grad=False,
)
layer.register_parameter("w13_weight_scale", w13_weight_scale)
layer.register_parameter("w2_weight_scale", w2_weight_scale)
extra_weight_attrs.update(
{"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
)
set_weight_attrs(w13_weight_scale, extra_weight_attrs)
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
w13_input_scale = None
layer.register_parameter("w13_input_scale", w13_input_scale)
w2_input_scale = None
layer.register_parameter("w2_input_scale", w2_input_scale)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
layer.w13_weight = Parameter(layer.w13_weight, requires_grad=False)
layer.w2_weight = Parameter(layer.w2_weight, requires_grad=False)
layer.w13_weight_scale = Parameter(
layer.w13_weight_scale.data, requires_grad=False
)
layer.w2_weight_scale = Parameter(
layer.w2_weight_scale.data, 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 = False,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
custom_routing_function: Optional[Callable] = None,
scoring_func: str = "softmax",
e_score_correction_bias: Optional[torch.Tensor] = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
use_nn_moe: Optional[bool] = False,
routed_scaling_factor: Optional[float] = None,
use_fused_gate: Optional[bool] = False,
) -> torch.Tensor:
from vllm.model_executor.layers.fused_moe import fused_experts
# Expert selection
topk_weights, topk_ids = FusedMoE.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,
custom_routing_function=custom_routing_function,
scoring_func=scoring_func,
e_score_correction_bias=e_score_correction_bias,
routed_scaling_factor=routed_scaling_factor,
use_fused_gate=use_fused_gate
)
return fused_experts(
x,
layer.w13_weight,
layer.w2_weight,
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=True,
use_int8_w8a8=True,
per_channel_quant=True,
activation=activation,
expert_map=expert_map,
apply_router_weight_on_input=apply_router_weight_on_input,
global_num_experts=global_num_experts,
w1_scale=(layer.w13_weight_scale),
w2_scale=(layer.w2_weight_scale),
a1_scale=layer.w13_input_scale,
a2_scale=layer.w2_input_scale,
use_nn_moe=use_nn_moe,
)
from typing import Any, Callable, Dict, List, Optional
import torch
from vllm.model_executor.utils import set_weight_attrs
from vllm.distributed import get_tensor_model_parallel_world_size
from torch.nn.parameter import Parameter
from vllm.model_executor.layers.linear import (LinearBase,LinearMethodBase)
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig, QuantizeMethodBase)
from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
FusedMoeWeightScaleSupported)
from vllm.model_executor.parameter import (BasevLLMParameter,
ChannelQuantScaleParameter,
ModelWeightParameter,
PerTensorScaleParameter)
from vllm.model_executor.layers.quantization.utils.int8_utils import (
per_token_group_quant_int8,
per_token_quant_int8)
from vllm import _custom_ops as ops
from vllm.utils import W8a8GetCacheJSON
import os
from vllm import _custom_ops as ops
W8A8_TRITONJSON=W8a8GetCacheJSON()
def baseline_scaled_mm(a: torch.Tensor,
b: torch.Tensor,
scale_a: torch.Tensor,
scale_b: torch.Tensor,
out_dtype: torch.dtype,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
scales= scale_a* scale_b.T
gemmout= torch.mm(
a.to(dtype=torch.float32), b.to(dtype=torch.float32))
output = (scales *gemmout).to(out_dtype)
if bias is not None:
output = output + bias
return output.to(out_dtype)
class W8A8Int8Config(QuantizationConfig):
"""Config class for W8A8 Int8 Quantization.
- Weight: static, per-channel, symmetric
- Activation: dynamic, per-token, symmetric
"""
def __init__(self):
pass
@classmethod
def get_supported_act_dtypes(cls) -> List[torch.dtype]:
return [torch.float16, torch.bfloat16]
@classmethod
def get_min_capability(cls) -> int:
return 75
@classmethod
def get_name(self) -> str:
return "w8a8_int8"
@classmethod
def get_config_filenames(cls) -> List[str]:
return []
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "W8A8Int8Config":
return cls()
def get_quant_method(
self,
layer: torch.nn.Module,
prefix: str,
) -> Optional["QuantizeMethodBase"]:
if isinstance(layer, LinearBase):
return W8A8Int8LinearMethod(self)
elif isinstance(layer, FusedMoE):
return W8A8Int8MoEMethod(self)
return None
def get_scaled_act_names(self) -> List[str]:
return []
class W8A8Int8LinearMethod(LinearMethodBase):
def __init__(self, quantization_config: W8A8Int8Config):
self.quantization_config = quantization_config
self.tritonsingleton= W8a8GetCacheJSON()
self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
n=layer.weight.shape[0]
k=layer.weight.shape[1]
if self.w8a8_strategy==1:
if {n,k} not in self.tritonsingleton.weight_shapes:
self.tritonsingleton.weight_shapes.append({n,k})
json_file=self.tritonsingleton.get_w8a8json_name(n,k)
configs_dict=self.tritonsingleton.get_triton_cache(json_file,n,k)
if configs_dict:
self.tritonsingleton.triton_json_dict.update(configs_dict)
for key, value in configs_dict.items():
m=int(key.split('_')[0])
ops.triton_int8_gemm_helper(m=m,n=n,k=k,per_token_act_quant=True,per_out_channel_weight_quant=True,use_bias=False,device=layer.weight.device,best_config=value)
else:
weight_data=layer.weight.data
_weight=weight_data.T.contiguous().reshape(n,-1)
layer.weight.data=_weight
layer.weight = Parameter(layer.weight.t(), requires_grad=False)
layer.weight_scale = Parameter(layer.weight_scale.data, requires_grad=False)
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_loader = extra_weight_attrs.get("weight_loader")
self.logical_widths = output_partition_sizes
weight = ModelWeightParameter(
data=torch.empty(
sum(output_partition_sizes), input_size_per_partition, dtype=torch.int8
),
input_dim=1,
output_dim=0,
weight_loader=weight_loader,
)
layer.register_parameter("weight", weight)
weight_scale = ChannelQuantScaleParameter(
data=torch.empty((sum(output_partition_sizes), 1), dtype=torch.float32),
output_dim=0,
weight_loader=weight_loader,
)
layer.register_parameter("weight_scale", weight_scale)
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None,
):
x_q, x_scale = per_token_quant_int8(x)
if self.w8a8_strategy==1:
m=x_q.shape[0]
k=x_q.shape[1]
n=layer.weight.shape[1]
if len(W8A8_TRITONJSON.triton_json_dict)==0:
best_config=None
elif f"1_{n}_{k}" in W8A8_TRITONJSON.triton_json_dict:
if m<=16:
m_=m
elif m<=64:
m_= (m + 3) & -4 #取值到最近的4的倍数
elif m<=160:
m_=(m + 7) & -8
elif m<200: #256
m_=160
elif m<480: #512
m_=256
elif m<960: #1024
m_=512
elif m<2048:
m_=1024
elif m<4096:
m_=2048
elif m<6000:
m_=4096
else:
m_=8192
best_config=W8A8_TRITONJSON.triton_json_dict[f"{m_}_{n}_{k}"]
else:
best_config=None
#if best_config==None:
# print("m:{},n:{},k:{}".format(m,n,k))
# print("config not found!")
return ops.triton_scaled_mm(x_q,
layer.weight,
scale_a=x_scale,
scale_b=layer.weight_scale,
out_dtype=x.dtype,
bias=bias,best_config=best_config)
elif self.w8a8_strategy==2:
return ops.cutlass_scaled_mm(x_q,
layer.weight,
scale_a=x_scale,
scale_b=layer.weight_scale,
out_dtype=x.dtype,
bias=bias)
else:
return ops.rocblas_scaled_mm(x_q,
layer.weight,
scale_a=x_scale,
scale_b=layer.weight_scale,
out_dtype=x.dtype,
bias=bias)
class W8A8Int8MoEMethod:
"""MoE method for INT8.
Supports loading INT8 checkpoints with static weight scale and
dynamic/static activation scale.
Also supports loading quantized FP16/BF16 model checkpoints with dynamic
activation scaling. The weight scaling factor will be initialized after
the model weights are loaded.
Args:
quant_config: The quantization config.
"""
def __new__(cls, *args, **kwargs):
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):
self.quant_config = quant_config
def create_weights(
self,
layer: torch.nn.Module,
num_experts: int,
hidden_size: int,
intermediate_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
tp_size = get_tensor_model_parallel_world_size()
# WEIGHTS
w13_weight = torch.nn.Parameter(
torch.empty(
num_experts, 2 * intermediate_size, hidden_size, dtype=torch.int8
),
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, hidden_size, intermediate_size, dtype=torch.int8),
requires_grad=False,
)
layer.register_parameter("w2_weight", w2_weight)
set_weight_attrs(w2_weight, extra_weight_attrs)
w13_weight_scale = torch.nn.Parameter(
torch.ones(num_experts, 2 * intermediate_size, 1, dtype=torch.float32),
requires_grad=False,
)
w2_weight_scale = torch.nn.Parameter(
torch.ones(num_experts, hidden_size, 1, dtype=torch.float32),
requires_grad=False,
)
layer.register_parameter("w13_weight_scale", w13_weight_scale)
layer.register_parameter("w2_weight_scale", w2_weight_scale)
extra_weight_attrs.update(
{"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
)
set_weight_attrs(w13_weight_scale, extra_weight_attrs)
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
w13_input_scale = None
layer.register_parameter("w13_input_scale", w13_input_scale)
w2_input_scale = None
layer.register_parameter("w2_input_scale", w2_input_scale)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
layer.w13_weight = Parameter(layer.w13_weight, requires_grad=False)
layer.w2_weight = Parameter(layer.w2_weight, requires_grad=False)
layer.w13_weight_scale = Parameter(
layer.w13_weight_scale.data, requires_grad=False
)
layer.w2_weight_scale = Parameter(
layer.w2_weight_scale.data, 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 = False,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
custom_routing_function: Optional[Callable] = None,
scoring_func: str = "softmax",
e_score_correction_bias: Optional[torch.Tensor] = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
use_nn_moe: Optional[bool] = False,
routed_scaling_factor: Optional[float] = None,
use_fused_gate: Optional[bool] = False,
) -> torch.Tensor:
from vllm.model_executor.layers.fused_moe import fused_experts
# Expert selection
topk_weights, topk_ids = FusedMoE.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,
custom_routing_function=custom_routing_function,
scoring_func=scoring_func,
e_score_correction_bias=e_score_correction_bias,
routed_scaling_factor=routed_scaling_factor,
use_fused_gate=use_fused_gate
)
return fused_experts(
x,
layer.w13_weight,
layer.w2_weight,
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=True,
use_int8_w8a8=True,
per_channel_quant=True,
activation=activation,
expert_map=expert_map,
apply_router_weight_on_input=apply_router_weight_on_input,
global_num_experts=global_num_experts,
w1_scale=(layer.w13_weight_scale),
w2_scale=(layer.w2_weight_scale),
a1_scale=layer.w13_input_scale,
a2_scale=layer.w2_input_scale,
use_nn_moe=use_nn_moe,
)
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