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Commit 531c6304 authored by zhuwenwen's avatar zhuwenwen
Browse files

增加w4a8相关支持修改

parent 386e5c47
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Showing with 30 additions and 185 deletions
vllm/model_executor/layers/fused_moe/fused_moe.py
View file @ 531c6304
...@@ -1414,7 +1414,7 @@ def outplace_fused_experts( ...@@ -1414,7 +1414,7 @@ def outplace_fused_experts(
use_int8_w8a8: bool = False, use_int8_w8a8: bool = False,
use_int8_w8a16: bool = False, use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False, use_int4_w4a16: bool = False,
use_int4_w4a8: bool =False, use_int4_w4a8: bool = False,
use_mxfp4_w4a4: bool = False, use_mxfp4_w4a4: bool = False,
per_channel_quant: bool = False, per_channel_quant: bool = False,
global_num_experts: int = -1, global_num_experts: int = -1,
...@@ -1649,11 +1649,11 @@ def fused_experts_impl( ...@@ -1649,11 +1649,11 @@ def fused_experts_impl(
inplace=inplace, inplace=inplace,
activation=activation, activation=activation,
apply_router_weight_on_input=apply_router_weight_on_input, apply_router_weight_on_input=apply_router_weight_on_input,
use_fp8_w8a8= False, use_fp8_w8a8=False,
use_int8_w8a8= False, use_int8_w8a8=False,
use_int8_w8a16= False, use_int8_w8a16=False,
use_int4_w4a16 = False, use_int4_w4a16=False,
use_int4_w4a8 = True, use_int4_w4a8=True,
per_channel_quant=per_channel_quant, per_channel_quant=per_channel_quant,
global_num_experts=global_num_experts, global_num_experts=global_num_experts,
expert_map=expert_map, expert_map=expert_map,
...@@ -1664,7 +1664,7 @@ def fused_experts_impl( ...@@ -1664,7 +1664,7 @@ def fused_experts_impl(
a1_scale=a1_scale, a1_scale=a1_scale,
a2_scale=a2_scale, a2_scale=a2_scale,
block_shape=block_shape, block_shape=block_shape,
use_nn_moe= False use_nn_moe=False
) )
if use_int4_w4a16: if use_int4_w4a16:
......
vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py
View file @ 531c6304
...@@ -488,28 +488,7 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod): ...@@ -488,28 +488,7 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
else: else:
layer.w13_input_scale = None layer.w13_input_scale = None
layer.w2_input_scale = None layer.w2_input_scale = None
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
E=layer.w13_weight.shape[0]
N1=layer.w13_weight.shape[1]
N2=layer.w2_weight.shape[1]
K=layer.w2_weight.shape[2]
if [E,N1,N2,K] not in self.tritonsingleton.moe_weight_shapes:
self.tritonsingleton.moe_weight_shapes.append([E,N1,N2,K])
TOPK= self.tritonsingleton.topk
json_file=self.tritonsingleton.get_moeint8json_name(E,N1,N2,K,TOPK)
configs_dict=self.tritonsingleton.get_moeint8_triton_cache(json_file,E,N1,N2,K,TOPK)
#warmup
if configs_dict:
self.tritonsingleton.triton_moejson_dict.update(configs_dict)
#生成模型配置文件
#self.tritonsingleton.gen_model_json(block_size)
return
def process_weights_after_loading(self, layer: torch.nn.Module) -> None: def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
# Fp8 moe kernels require a single activation scale. # Fp8 moe kernels require a single activation scale.
# We take the max of all the scales in case they differ. # We take the max of all the scales in case they differ.
...@@ -600,6 +579,25 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod): ...@@ -600,6 +579,25 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
else: else:
from vllm.model_executor.layers.fused_moe import fused_experts from vllm.model_executor.layers.fused_moe import fused_experts
self.fused_experts_func = fused_experts self.fused_experts_func = fused_experts
E=layer.w13_weight.shape[0]
N1=layer.w13_weight.shape[1]
N2=layer.w2_weight.shape[1]
K=layer.w2_weight.shape[2]
if [E,N1,N2,K] not in self.tritonsingleton.moe_weight_shapes:
self.tritonsingleton.moe_weight_shapes.append([E,N1,N2,K])
TOPK= self.tritonsingleton.topk
json_file=self.tritonsingleton.get_moeint8json_name(E,N1,N2,K,TOPK)
configs_dict=self.tritonsingleton.get_moeint8_triton_cache(json_file,E,N1,N2,K,TOPK)
#warmup
if configs_dict:
self.tritonsingleton.triton_moejson_dict.update(configs_dict)
pass
def select_gemm_impl( def select_gemm_impl(
self, self,
...@@ -730,7 +728,9 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod): ...@@ -730,7 +728,9 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
w1_scale=layer.w13_weight_scale, w1_scale=layer.w13_weight_scale,
w2_scale=layer.w2_weight_scale, w2_scale=layer.w2_weight_scale,
a1_scale=layer.w13_input_scale, a1_scale=layer.w13_input_scale,
a2_scale=layer.w2_input_scale) a2_scale=layer.w2_input_scale,
use_nn_moe=False,
)
class CompressedTensorsW8A8Fp8MoECutlassMethod(CompressedTensorsMoEMethod): class CompressedTensorsW8A8Fp8MoECutlassMethod(CompressedTensorsMoEMethod):
...@@ -1540,159 +1540,4 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod): ...@@ -1540,159 +1540,4 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
w1_zp=None, w1_zp=None,
w2_zp=None, w2_zp=None,
block_shape=[0, self.group_size]) block_shape=[0, self.group_size])
class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
def __init__(
self,
quant_config: "CompressedTensorsConfig" # type: ignore # noqa E501
):
self.quant_config = quant_config
self.weight_quant = self.quant_config.target_scheme_map["Linear"].get(
"weights")
self.input_quant = self.quant_config.target_scheme_map["Linear"].get(
"input_activations")
if not (self.weight_quant.strategy == QuantizationStrategy.CHANNEL
and self.input_quant.strategy == QuantizationStrategy.TOKEN):
raise ValueError(
"For INT8 Fused MoE layers, only per-channel scales"
"for activations and per-token scales for activations are supported. Found "
f"{self.weight_quant}, {self.input_quant}")
self.static_input_scales = not self.input_quant.dynamic
self.tritonsingleton = W8a8GetCacheJSON()
def create_weights(self, layer: torch.nn.Module, num_experts: int,
hidden_size: int, intermediate_size_per_partition: int,
params_dtype: torch.dtype, **extra_weight_attrs):
params_dtype = torch.int8
# WEIGHTS
w13_weight = torch.nn.Parameter(torch.empty(num_experts,
2 * intermediate_size_per_partition,
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,
hidden_size,
intermediate_size_per_partition,
dtype=params_dtype),
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_per_partition,
1,
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,
hidden_size,
1,
dtype=torch.float32),
requires_grad=False)
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)
# INPUT_SCALES
if self.static_input_scales:
raise ValueError(
"For INT8 Fused MoE layers, only dynamic scales"
"for activations are supported. Found "
f"{self.input_quant}")
else:
layer.w13_input_scale = None
layer.w2_input_scale = None
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
E=layer.w13_weight.shape[0]
N1=layer.w13_weight.shape[1]
N2=layer.w2_weight.shape[1]
K=layer.w2_weight.shape[2]
if [E,N1,N2,K] not in self.tritonsingleton.moe_weight_shapes:
self.tritonsingleton.moe_weight_shapes.append([E,N1,N2,K])
TOPK= self.tritonsingleton.topk
json_file=self.tritonsingleton.get_moeint8json_name(E,N1,N2,K,TOPK)
configs_dict=self.tritonsingleton.get_moeint8_triton_cache(json_file,E,N1,N2,K,TOPK)
#warmup
if configs_dict:
self.tritonsingleton.triton_moejson_dict.update(configs_dict)
#生成模型配置文件
#self.tritonsingleton.gen_model_json(block_size)
return
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
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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