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Commit 2767fc34 authored by gaoqiong's avatar gaoqiong
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增加w4a8相关支持修改

parent 98958aed
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Showing with 97 additions and 148 deletions
vllm/engine/arg_utils.py
View file @ 2767fc34
......@@ -1594,6 +1594,9 @@ class EngineArgs:
# For pooling tasks the default is False
if model_config.runner_type != "pooling":
self.enable_chunked_prefill = True
if model_config.enable_chunked_prefill is not None and \
model_config.enable_chunked_prefill is False:
self.enable_chunked_prefill = False
if self.enable_prefix_caching is None:
self.enable_prefix_caching = True
else:
......
vllm/model_executor/layers/fused_moe/fused_moe.py
View file @ 2767fc34
......@@ -28,7 +28,7 @@ from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
from lmslim.layers.gemm.int8_utils import (
per_token_group_quant_int8, per_token_quant_int8)
from lmslim.layers.fused_moe.fuse_moe_int8 import (fused_experts_impl_int8, get_w8a8moe_json)
from lmslim.layers.fused_moe.fuse_moe_w4a8 import fused_experts_impl_w4a8
from vllm.model_executor.layers.fused_moe.prepare_finalize import (
MoEPrepareAndFinalizeNoEP)
......@@ -653,6 +653,7 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
use_int8_w8a8: bool,
use_int8_w8a16: bool,
use_int4_w4a16: bool,
use_int4_w4a8: bool,
per_channel_quant: bool,
block_shape: Optional[list[int]] = None,
use_nn_moe: Optional[bool]=False) -> None:
......@@ -1205,7 +1206,8 @@ def get_config_dtype_str(
use_int4_w4a16: Optional[bool] = False,
use_int8_w8a16: Optional[bool] = False,
use_fp8_w8a8: Optional[bool] = False,
use_int8_w8a8: Optional[bool] = False) -> Optional[str]:
use_int8_w8a8: Optional[bool] = False,
use_int4_w4a8:Optional[bool] = False) -> Optional[str]:
if use_fp8_w8a8:
return "fp8_w8a8"
elif use_int8_w8a8:
......@@ -1214,6 +1216,8 @@ def get_config_dtype_str(
return "int8_w8a16"
elif use_int4_w4a16:
return "int4_w4a16"
elif use_int4_w4a8:
return "int4_w4a8"
elif dtype == torch.float:
# avoiding cases where kernel fails when float32 MoE
# use fp16/bfloat16 configs
......@@ -1232,6 +1236,7 @@ def inplace_fused_experts(hidden_states: torch.Tensor,
use_int8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
use_int4_w4a8: bool =False,
per_channel_quant: bool = False,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
......@@ -1245,7 +1250,7 @@ def inplace_fused_experts(hidden_states: torch.Tensor,
use_nn_moe: Optional[bool] = False) -> None:
fused_experts_impl(hidden_states, w1, w2, topk_weights, topk_ids, True,
activation, apply_router_weight_on_input, use_fp8_w8a8,
use_int8_w8a8, use_int8_w8a16, use_int4_w4a16,
use_int8_w8a8, use_int8_w8a16, use_int4_w4a16,use_int4_w4a8,
per_channel_quant, global_num_experts, expert_map,
w1_scale, w2_scale, w1_zp, w2_zp, a1_scale, a2_scale,
block_shape, use_nn_moe)
......@@ -1263,6 +1268,7 @@ def inplace_fused_experts_fake(
use_int8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
use_int4_w4a8: bool =False,
per_channel_quant: bool = False,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
......@@ -1298,6 +1304,7 @@ def outplace_fused_experts(
use_int8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
use_int4_w4a8: bool =False,
per_channel_quant: bool = False,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
......@@ -1312,7 +1319,7 @@ def outplace_fused_experts(
return fused_experts_impl(hidden_states, w1, w2, topk_weights, topk_ids,
False, activation, apply_router_weight_on_input,
use_fp8_w8a8, use_int8_w8a8, use_int8_w8a16,
use_int4_w4a16, per_channel_quant,
use_int4_w4a16,use_int4_w4a8, per_channel_quant,
global_num_experts, expert_map, w1_scale,
w2_scale, w1_zp, w2_zp, a1_scale, a2_scale,
block_shape, use_nn_moe)
......@@ -1329,6 +1336,7 @@ def outplace_fused_experts_fake(
use_int8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
use_int4_w4a8: bool =False,
per_channel_quant: bool = False,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
......@@ -1383,6 +1391,7 @@ def fused_experts(
use_int8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
use_int4_w4a8: bool =False,
per_channel_quant: bool = False,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
......@@ -1442,6 +1451,7 @@ def fused_experts(
use_int8_w8a8=use_int8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
use_int4_w4a8=use_int4_w4a8,
per_channel_quant=per_channel_quant,
global_num_experts=global_num_experts,
expert_map=expert_map,
......@@ -1468,6 +1478,7 @@ def fused_experts_impl(
use_int8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
use_int4_w4a8: bool =False,
per_channel_quant: bool = False,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
......@@ -1506,6 +1517,34 @@ def fused_experts_impl(
block_shape=block_shape,
use_nn_moe=False
)
elif use_int4_w4a8 is True:
return fused_experts_impl_w4a8(hidden_states=hidden_states,
w1=w1,
w2=w2,
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=inplace,
activation=activation,
apply_router_weight_on_input=apply_router_weight_on_input,
use_fp8_w8a8= False,
use_int8_w8a8= False,
use_int8_w8a16= False,
use_int4_w4a16 = False,
use_int4_w4a8 = True,
per_channel_quant=per_channel_quant,
global_num_experts=global_num_experts,
expert_map=expert_map,
w1_scale=w1_scale,
w2_scale=w2_scale,
w1_zp=w1_zp,
w2_zp=w2_zp,
a1_scale=a1_scale,
a2_scale=a2_scale,
block_shape=block_shape,
use_nn_moe= False
)
#
if use_int4_w4a16:
assert hidden_states.size(1) // 2 == w1.size(2), (
"Hidden size mismatch")
......@@ -1542,6 +1581,7 @@ def fused_experts_impl(
use_int8_w8a8=use_int8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
use_int4_w4a8=use_int4_w4a8,
dtype=hidden_states.dtype)
qtype = get_config_quant_dtype(use_fp8_w8a8=use_fp8_w8a8,
......@@ -1648,6 +1688,7 @@ def fused_experts_impl(
use_int8_w8a8=use_int8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
use_int4_w4a8=use_int4_w4a8,
per_channel_quant=per_channel_quant,
block_shape=block_shape,
use_nn_moe=use_nn_moe)
......@@ -1687,6 +1728,7 @@ def fused_experts_impl(
use_int8_w8a8=use_int8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
use_int4_w4a8=use_int4_w4a8,
per_channel_quant=per_channel_quant,
block_shape=block_shape,
use_nn_moe=use_nn_moe)
......@@ -1714,6 +1756,7 @@ def fused_moe(
use_int8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
use_int4_w4a8: bool =False,
per_channel_quant: bool = False,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
......@@ -1799,6 +1842,7 @@ def fused_moe(
use_int8_w8a8=use_int8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
use_int4_w4a8=use_int4_w4a8,
per_channel_quant=per_channel_quant,
global_num_experts=global_num_experts,
expert_map=expert_map,
......@@ -1820,6 +1864,7 @@ class TritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
use_int8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
use_int4_w4a8: bool =False,
per_act_token_quant: bool = False,
block_shape: Optional[List[int]] = None,
):
......@@ -1829,6 +1874,7 @@ class TritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
use_int8_w8a8=use_int8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
use_int4_w4a8=use_int4_w4a8,
per_act_token_quant=per_act_token_quant,
block_shape=block_shape,
))
......@@ -1837,6 +1883,7 @@ class TritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
self.use_int4_w4a16 = use_int4_w4a16
self.use_int8_w8a8 = use_int8_w8a8
self.use_int8_w8a16 = use_int8_w8a16
self.use_int4_w4a8= use_int4_w4a8
@property
def activation_formats(
......@@ -1914,6 +1961,7 @@ class TritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
config_dtype = get_config_dtype_str(use_fp8_w8a8=self.use_fp8_w8a8,
use_int8_w8a16=self.use_int8_w8a16,
use_int4_w4a16=self.use_int4_w4a16,
use_int4_w4a8=self.use_int4_w4a8,
dtype=hidden_states.dtype)
config = try_get_optimal_moe_config(
......@@ -1966,6 +2014,7 @@ class TritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
use_int8_w8a8=self.use_int8_w8a8,
use_int8_w8a16=self.use_int8_w8a16,
use_int4_w4a16=self.use_int4_w4a16,
use_int4_w4a8= self.use_int4_w4a8,
per_channel_quant=self.per_act_token_quant,
block_shape=self.block_shape)
......@@ -1996,6 +2045,7 @@ class TritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
use_int8_w8a8=self.use_int8_w8a8,
use_int8_w8a16=self.use_int8_w8a16,
use_int4_w4a16=self.use_int4_w4a16,
use_int4_w4a8= self.use_int4_w4a8,
per_channel_quant=self.per_act_token_quant,
block_shape=self.block_shape)
......@@ -2005,6 +2055,7 @@ def modular_triton_fused_moe(
use_int8_w8a8: bool,
use_int8_w8a16: bool,
use_int4_w4a16: bool,
use_int4_w4a8:bool,
per_act_token_quant: bool,
block_shape: Optional[List[int]] = None,
) -> mk.FusedMoEModularKernel:
......@@ -2015,6 +2066,7 @@ def modular_triton_fused_moe(
use_int8_w8a8=use_int8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
use_int4_w4a8= use_int4_w4a8,
per_act_token_quant=per_act_token_quant,
block_shape=block_shape,
),
......
vllm/model_executor/layers/quantization/blockwise_int8.py
View file @ 2767fc34
......@@ -432,7 +432,7 @@ class BlockInt8MoEMethod:
E=layer.w13_weight.shape[0]
N1=layer.w13_weight.shape[1]
N2=layer.w2_weight.shape[1]
K=layer.w2_weight.shape[2]
K=N//2
if [E,N1,N2,K] not in self.tritonsingleton.moe_weight_shapes:
self.tritonsingleton.moe_weight_shapes.append([E,N1,N2,K])
......@@ -446,6 +446,7 @@ class BlockInt8MoEMethod:
if configs_dict:
self.tritonsingleton.triton_moejson_dict.update(configs_dict)
#print("*************self.tritonsingleton:",self.tritonsingleton)
#生成模型配置文件
self.tritonsingleton.gen_model_json(block_size)
......@@ -477,7 +478,7 @@ class BlockInt8MoEMethod:
from vllm.model_executor.layers.fused_moe import fused_experts
if enable_eplb:
raise NotImplementedError(
"EPLB not supported for `MoeWNA16Method` yet.")
"EPLB not supported for `MoeBlockInt8Method` yet.")
# Expert selection
topk_weights, topk_ids = FusedMoE.select_experts(
hidden_states=x,
......
vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py
View file @ 2767fc34
......@@ -1053,6 +1053,22 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
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)
pass
def apply(
......@@ -1076,6 +1092,7 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
expert_load_view: Optional[torch.Tensor] = None,
logical_to_physical_map: Optional[torch.Tensor] = None,
logical_replica_count: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if enable_eplb:
raise NotImplementedError(
......@@ -1112,7 +1129,8 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
w1_scale=layer.w13_weight_scale,
w2_scale=layer.w2_weight_scale,
a1_scale=layer.w13_input_scale,
a2_scale=layer.w2_input_scale)
a2_scale=layer.w2_input_scale,
use_nn_moe=False)
class CompressedTensorsWNA16MarlinMoEMethod(CompressedTensorsMoEMethod):
......@@ -1637,136 +1655,3 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
w2_zp=None,
block_shape=[0, self.group_size])
\ No newline at end of file
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
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 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,
)
vllm/model_executor/layers/quantization/w8a8_int8.py 100755 → 100644
View file @ 2767fc34
......@@ -264,7 +264,7 @@ class W8A8Int8MoEMethod:
# WEIGHTS
w13_weight = torch.nn.Parameter(
torch.empty(
num_experts, 2 * intermediate_size, hidden_size, dtype=torch.int8
num_experts, 2 * intermediate_size, hidden_size//2, dtype=torch.int8
),
requires_grad=False,
)
......@@ -272,7 +272,7 @@ class W8A8Int8MoEMethod:
set_weight_attrs(w13_weight, extra_weight_attrs)
w2_weight = torch.nn.Parameter(
torch.empty(num_experts, hidden_size, intermediate_size, dtype=torch.int8),
torch.empty(num_experts, hidden_size, intermediate_size//2, dtype=torch.int8),
requires_grad=False,
)
layer.register_parameter("w2_weight", w2_weight)
......@@ -306,7 +306,7 @@ class W8A8Int8MoEMethod:
E=layer.w13_weight.shape[0]
N1=layer.w13_weight.shape[1]
N2=layer.w2_weight.shape[1]
K=layer.w2_weight.shape[2]
K=N1//2
if [E,N1,N2,K] not in self.tritonsingleton.moe_weight_shapes:
self.tritonsingleton.moe_weight_shapes.append([E,N1,N2,K])
......@@ -345,12 +345,16 @@ class W8A8Int8MoEMethod:
e_score_correction_bias: Optional[torch.Tensor] = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
enable_eplb: bool = False,
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
if enable_eplb:
raise NotImplementedError(
"EPLB not supported for `W8A8Int8MoeMethod` yet.")
# Expert selection
topk_weights, topk_ids = FusedMoE.select_experts(
hidden_states=x,
......@@ -374,7 +378,7 @@ class W8A8Int8MoEMethod:
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=True,
use_int8_w8a8=True,
use_int4_w4a8=True,
per_channel_quant=True,
activation=activation,
expert_map=expert_map,
......
vllm/v1/attention/backends/mla/common.py
View file @ 2767fc34
......@@ -912,7 +912,11 @@ class MLACommonImpl(MLAAttentionImpl[M], Generic[M]):
) -> torch.Tensor:
assert attn_metadata.prefill is not None
if envs.VLLM_HAS_CONTEXT_DEFAULT:
has_context = attn_metadata.prefill.chunked_context is not None
else:
has_context = False
kv_nope = self.kv_b_proj(kv_c_normed)[0].view(\
-1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
k_nope, v = kv_nope\
......
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