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Commit 4a943d35 authored by 王敏's avatar 王敏
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[feat]1.w8a8 marlin适配deepep低延迟;2.非naive ep模式，去掉多余的dp padding,避免allreduce耗时

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vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py
View file @ 4a943d35
...@@ -4,7 +4,6 @@ ...@@ -4,7 +4,6 @@
import enum import enum
from enum import Enum from enum import Enum
from typing import Callable, Optional from typing import Callable, Optional
from math import prod
import torch import torch
from compressed_tensors import CompressionFormat from compressed_tensors import CompressionFormat
...@@ -14,8 +13,6 @@ from compressed_tensors.quantization import (ActivationOrdering, ...@@ -14,8 +13,6 @@ from compressed_tensors.quantization import (ActivationOrdering,
import vllm.envs as envs import vllm.envs as envs
from vllm import _custom_ops as ops from vllm import _custom_ops as ops
from vllm.logger import init_logger from vllm.logger import init_logger
from vllm.config import get_current_vllm_config
from vllm.distributed import get_tensor_model_parallel_world_size, get_ep_group
from vllm.model_executor.layers.fused_moe import ( from vllm.model_executor.layers.fused_moe import (
FusedMoE, FusedMoEActivationFormat, FusedMoEConfig, FusedMoEMethodBase, FusedMoE, FusedMoEActivationFormat, FusedMoEConfig, FusedMoEMethodBase,
FusedMoEPermuteExpertsUnpermute, FusedMoEPrepareAndFinalize, FusedMoEPermuteExpertsUnpermute, FusedMoEPrepareAndFinalize,
...@@ -35,16 +32,10 @@ from vllm.model_executor.layers.quantization.utils.nvfp4_emulation_utils import ...@@ -35,16 +32,10 @@ from vllm.model_executor.layers.quantization.utils.nvfp4_emulation_utils import
from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
all_close_1d, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize) all_close_1d, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize)
from vllm.model_executor.utils import set_weight_attrs from vllm.model_executor.utils import set_weight_attrs
from vllm.model_executor.layers.fused_moe.utils import _resize_cache
from vllm.platforms import current_platform from vllm.platforms import current_platform
from vllm.scalar_type import scalar_types from vllm.scalar_type import scalar_types
from vllm.utils import W8a8GetCacheJSON from vllm.utils import W8a8GetCacheJSON
try:
from lightop import m_grouped_w8a8_gemm_nt_masked, fuse_silu_mul_quant_ep
except Exception:
print("INFO: Please install lmslim if you want to infer the quantitative model of moe.\n")
logger = init_logger(__name__) logger = init_logger(__name__)
...@@ -1009,27 +1000,11 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod): ...@@ -1009,27 +1000,11 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
"dynamic per token quantization. Found static input scales.") "dynamic per token quantization. Found static input scales.")
self.tritonsingleton= W8a8GetCacheJSON() self.tritonsingleton= W8a8GetCacheJSON()
self.fused_experts = self.fused_moe_forward
vllm_config = get_current_vllm_config()
parallel_config = vllm_config.parallel_config
self.use_deepep = parallel_config.enable_expert_parallel and \
(envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput" or \
envs.VLLM_ALL2ALL_BACKEND == "deepep_low_latency")
if self.use_deepep:
all2all_manager = get_ep_group().device_communicator.all2all_manager
assert all2all_manager is not None
self.num_dispatchers = all2all_manager.world_size
def create_weights(self, layer: torch.nn.Module, num_experts: int, def create_weights(self, layer: torch.nn.Module, num_experts: int,
hidden_size: int, intermediate_size_per_partition: int, hidden_size: int, intermediate_size_per_partition: int,
params_dtype: torch.dtype, **extra_weight_attrs): params_dtype: torch.dtype, **extra_weight_attrs):
if self.use_deepep:
self.N = 2 * intermediate_size_per_partition
self.K = hidden_size
params_dtype = torch.int8 params_dtype = torch.int8
# WEIGHTS # WEIGHTS
...@@ -1127,6 +1102,8 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod): ...@@ -1127,6 +1102,8 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
"EPLB not supported for " "EPLB not supported for "
"`CompressedTensorsW8A8Int8MoEMethod` yet.") "`CompressedTensorsW8A8Int8MoEMethod` yet.")
from vllm.model_executor.layers.fused_moe import fused_experts
topk_weights, topk_ids = FusedMoE.select_experts( topk_weights, topk_ids = FusedMoE.select_experts(
hidden_states=x, hidden_states=x,
router_logits=router_logits, router_logits=router_logits,
...@@ -1139,53 +1116,12 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod): ...@@ -1139,53 +1116,12 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
scoring_func=scoring_func, scoring_func=scoring_func,
routed_scaling_factor=routed_scaling_factor, routed_scaling_factor=routed_scaling_factor,
use_fused_gate=use_fused_gate, use_fused_gate=use_fused_gate,
e_score_correction_bias=e_score_correction_bias, e_score_correction_bias=e_score_correction_bias)
indices_type=torch.int64 if self.use_deepep else None,)
return self.fused_experts(
x,
layer.w13_weight,
layer.w2_weight,
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=True,
activation=activation,
apply_router_weight_on_input=apply_router_weight_on_input,
global_num_experts=global_num_experts,
expert_map=expert_map,
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,
shared_output=shared_output,
routed_scaling_factor=routed_scaling_factor,
)
def fused_moe_forward(self,
x: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_ids: torch.Tensor,
topk_weights: torch.Tensor,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
a1_scale: Optional[torch.Tensor] = None,
a2_scale: Optional[torch.Tensor] = None,
expert_num_tokens: Optional[torch.Tensor] = None,
use_nn_moe: Optional[bool] = False,
routed_scaling_factor: Optional[float] = None,
shared_output: Optional[torch.Tensor] = None,
**_ ):
from vllm.model_executor.layers.fused_moe import fused_experts
return fused_experts( return fused_experts(
hidden_states=x, hidden_states=x,
w1=w1, w1=layer.w13_weight,
w2=w2, w2=layer.w2_weight,
topk_weights=topk_weights, topk_weights=topk_weights,
topk_ids=topk_ids, topk_ids=topk_ids,
inplace=True, inplace=True,
...@@ -1195,149 +1131,14 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod): ...@@ -1195,149 +1131,14 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
per_channel_quant=True, per_channel_quant=True,
global_num_experts=global_num_experts, global_num_experts=global_num_experts,
expert_map=expert_map, expert_map=expert_map,
w1_scale=w1_scale, w1_scale=layer.w13_weight_scale,
w2_scale=w2_scale, w2_scale=layer.w2_weight_scale,
a1_scale=a1_scale, a1_scale=layer.w13_input_scale,
a2_scale=a2_scale, a2_scale=layer.w2_input_scale,
use_nn_moe=False, use_nn_moe=False,
shared_output=shared_output, shared_output=shared_output,
routed_scaling_factor=routed_scaling_factor) routed_scaling_factor=routed_scaling_factor)
def groupgemm_workspace_shapes(self,
a: torch.Tensor,
aq: torch.Tensor,
M: int,
N: int,
K: int,
topk: int,
global_num_experts: int,
local_num_experts: int,):
assert a.dim() == 2
# FIXME (varun): We should be able to dispatch only from the leader
# DP ranks in the case of TP > 1. At the moment, all the Ranks
# end up sending their tokens. This needs to be fixed.
num_dispatchers = self.num_dispatchers
num_experts = local_num_experts
max_num_tokens = a.size(
0) if self.max_num_tokens_per_rank is None else self.max_num_tokens_per_rank
workspace13 = (num_experts, max_num_tokens * num_dispatchers,
max(K, N))
workspace2 = (num_experts, max_num_tokens * num_dispatchers, (N // 2))
output = (num_experts, max_num_tokens * num_dispatchers, K)
return (workspace13, workspace2, output, a.dtype)
def w8a8_groupgemm_forward(self,
x: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_ids: torch.Tensor,
topk_weights: torch.Tensor,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
a1_scale: Optional[torch.Tensor] = None,
a2_scale: Optional[torch.Tensor] = None,
expert_num_tokens: Optional[torch.Tensor] = None,
use_nn_moe: Optional[bool] = False,
routed_scaling_factor: Optional[float] = None,
shared_output: Optional[torch.Tensor] = None,
q_x: Optional[torch.Tensor] = None,
**_ ):
import vllm.model_executor.layers.fused_moe.modular_kernel as mk
local_num_experts = w1.size(0)
E, max_num_tokens, _, _, top_k = mk._moe_problem_size(
q_x, w1, w2, topk_ids)
N, K = self.N, self.K
(workspace13_shape, workspace2_shape, fused_out_shape,
workspace_dtype) = self.groupgemm_workspace_shapes(
x, q_x, max_num_tokens, N, K, top_k, global_num_experts,
local_num_experts)
workspace13 = torch.empty(prod(workspace13_shape),
device=x.device,
dtype=workspace_dtype)
workspace1 = _resize_cache(workspace13, (E, max_num_tokens, N))
fused_out = _resize_cache(workspace13, fused_out_shape)
# (from deepgemm docs) : A value hint (which is a value on CPU)
# for the M expectation of each batch, correctly setting this value
# may lead to better performance.
expected_m = max_num_tokens // 2
# print("##########################gemm1 workspace1 shape:{} q_x shape:{} " \
# "a1_scale shape:{} w1 shape:{} expert_num_tokens:{} expected_m:{}".format(workspace1.shape,
# q_x.shape,
# a1_scale.shape,
# w1.shape,
# expert_num_tokens,
# expected_m))
m_grouped_w8a8_gemm_nt_masked((q_x, a1_scale),
(w1, w1_scale),
workspace1,
expert_num_tokens,
expected_m,
)
assert expert_num_tokens is not None
a2q, a2q_scale = fuse_silu_mul_quant_ep(workspace1, expert_num_tokens)
# print("##########################gemm2 workspace1 shape:{} a2q shape:{} " \
# "a2q_scale shape:{} w2 shape:{} fused_out shape:{} expert_num_tokens:{} expected_m:{}".format(workspace1.shape,
# a2q.shape,
# a2q_scale.shape,
# w2.shape,
# fused_out.shape,
# expert_num_tokens,
# expected_m))
m_grouped_w8a8_gemm_nt_masked((a2q, a2q_scale),
(w2, w2_scale),
fused_out,
expert_num_tokens,
expected_m)
return fused_out
def select_gemm_impl(
self,
prepare_finalize: FusedMoEPrepareAndFinalize,
moe: FusedMoEConfig,
) -> FusedMoEPermuteExpertsUnpermute:
from vllm.model_executor.layers.fused_moe import (
TritonOrGroupGemmExperts)
if (prepare_finalize.activation_format ==
FusedMoEActivationFormat.BatchedExperts):
max_num_tokens_per_rank = (
prepare_finalize.max_num_tokens_per_rank())
assert max_num_tokens_per_rank is not None
self.max_num_tokens_per_rank = max_num_tokens_per_rank
logger.debug(
"TritonOrGroupGemmExperts(%s): "
"max_tokens_per_rank=%s, block_size=%s, per_act_token=%s",
self.__class__.__name__, max_num_tokens_per_rank,
None, True)
return TritonOrGroupGemmExperts(
use_int8_w8a8=True,
per_act_token_quant=True,
fused_experts=self.w8a8_groupgemm_forward
)
else:
logger.debug(
"TritonOrGroupGemmExperts(%s): block_size=%s, per_act_token=%s",
self.__class__.__name__, None,
False)
return TritonOrGroupGemmExperts(
fused_experts=self.fused_moe_forward
)
class CompressedTensorsWNA16MarlinMoEMethod(CompressedTensorsMoEMethod): class CompressedTensorsWNA16MarlinMoEMethod(CompressedTensorsMoEMethod):
......
vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe_marlin.py
View file @ 4a943d35
...@@ -4,17 +4,27 @@ ...@@ -4,17 +4,27 @@
import enum import enum
from enum import Enum from enum import Enum
from typing import Callable, Optional from typing import Callable, Optional
from math import prod
import torch import torch
from compressed_tensors.quantization import (QuantizationStrategy) from compressed_tensors.quantization import (QuantizationStrategy)
import vllm.envs as envs
from vllm.logger import init_logger from vllm.logger import init_logger
from vllm.config import get_current_vllm_config
from vllm.distributed import get_tensor_model_parallel_world_size, get_ep_group, get_dp_group
from torch.nn.parameter import Parameter from torch.nn.parameter import Parameter
from vllm.model_executor.layers.fused_moe import ( from vllm.model_executor.layers.fused_moe import (
FusedMoE, FusedMoEActivationFormat, FusedMoEMethodBase, FusedMoE, FusedMoEActivationFormat, FusedMoEMethodBase,
FusedMoeWeightScaleSupported) FusedMoEConfig, FusedMoeWeightScaleSupported,
FusedMoEPermuteExpertsUnpermute, FusedMoEPrepareAndFinalize,)
from vllm.model_executor.utils import set_weight_attrs from vllm.model_executor.utils import set_weight_attrs
from vllm.model_executor.layers.fused_moe.utils import _resize_cache
from vllm.model_executor.layers.quantization.utils.w8a8_utils import( from vllm.model_executor.layers.quantization.utils.w8a8_utils import(
get_w8a8_int8_marlin_weights) get_w8a8_int8_marlin_weights, w8a8_nt_kpack2_marlin_weight)
try: try:
from lightop import m_grouped_w8a8_gemm_nt_masked, fuse_silu_mul_quant_ep
from lmslim.layers.fused_moe.fuse_moe_int8_marlin import fused_experts_impl_int8_marlin from lmslim.layers.fused_moe.fuse_moe_int8_marlin import fused_experts_impl_int8_marlin
except Exception: except Exception:
print("INFO: Please install lmslim if you want to infer the quantitative model of moe.\n") print("INFO: Please install lmslim if you want to infer the quantitative model of moe.\n")
...@@ -69,11 +79,28 @@ class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod) ...@@ -69,11 +79,28 @@ class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod)
"For INT8 Fused MoE layers, we require channelwise, " "For INT8 Fused MoE layers, we require channelwise, "
"dynamic per token quantization. Found static input scales.") "dynamic per token quantization. Found static input scales.")
self.fused_experts = self.fused_moe_forward
vllm_config = get_current_vllm_config()
parallel_config = vllm_config.parallel_config
dp_size = get_dp_group().world_size
self.use_deepep = dp_size > 1 and parallel_config.enable_expert_parallel and \
(envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput" or \
envs.VLLM_ALL2ALL_BACKEND == "deepep_low_latency")
if self.use_deepep:
all2all_manager = get_ep_group().device_communicator.all2all_manager
assert all2all_manager is not None
self.num_dispatchers = all2all_manager.world_size
def create_weights(self, layer: torch.nn.Module, num_experts: int, def create_weights(self, layer: torch.nn.Module, num_experts: int,
hidden_size: int, intermediate_size_per_partition: int, hidden_size: int, intermediate_size_per_partition: int,
params_dtype: torch.dtype, **extra_weight_attrs): params_dtype: torch.dtype, **extra_weight_attrs):
if self.use_deepep:
self.N = 2 * intermediate_size_per_partition
self.K = hidden_size
params_dtype = torch.int8 params_dtype = torch.int8
# WEIGHTS # WEIGHTS
...@@ -124,20 +151,152 @@ class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod) ...@@ -124,20 +151,152 @@ class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None: def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
w1_marlin_list = [] w1_marlin_list = []
for ii in range(layer.w13_weight.shape[0]): for ii in range(layer.w13_weight.shape[0]):
if not self.use_deepep:
w1_marlin_in = get_w8a8_int8_marlin_weights(layer.w13_weight[ii]) w1_marlin_in = get_w8a8_int8_marlin_weights(layer.w13_weight[ii])
else:
w1_marlin_in = w8a8_nt_kpack2_marlin_weight(layer.w13_weight[ii])
w1_marlin_list.append(w1_marlin_in) w1_marlin_list.append(w1_marlin_in)
w1_marlin = torch.stack(w1_marlin_list, dim=0) w1_marlin = torch.stack(w1_marlin_list, dim=0)
del w1_marlin_list del w1_marlin_list
w2_marlin_list = [] w2_marlin_list = []
for ii in range(layer.w2_weight.shape[0]): for ii in range(layer.w2_weight.shape[0]):
if not self.use_deepep:
w2_marlin_in = get_w8a8_int8_marlin_weights(layer.w2_weight[ii]) w2_marlin_in = get_w8a8_int8_marlin_weights(layer.w2_weight[ii])
else:
w2_marlin_in = w8a8_nt_kpack2_marlin_weight(layer.w2_weight[ii])
w2_marlin_list.append(w2_marlin_in) w2_marlin_list.append(w2_marlin_in)
w2_marlin = torch.stack(w2_marlin_list, dim=0)
w2_marlin = torch.stack(w2_marlin_list, dim=0)
layer.w13_weight = Parameter(w1_marlin, requires_grad=False) layer.w13_weight = Parameter(w1_marlin, requires_grad=False)
layer.w2_weight = Parameter(w2_marlin, requires_grad=False) layer.w2_weight = Parameter(w2_marlin, requires_grad=False)
def groupgemm_workspace_shapes(self,
a: torch.Tensor,
aq: torch.Tensor,
M: int,
N: int,
K: int,
topk: int,
global_num_experts: int,
local_num_experts: int,):
assert a.dim() == 2
# FIXME (varun): We should be able to dispatch only from the leader
# DP ranks in the case of TP > 1. At the moment, all the Ranks
# end up sending their tokens. This needs to be fixed.
num_dispatchers = self.num_dispatchers
num_experts = local_num_experts
max_num_tokens = a.size(
0) if self.max_num_tokens_per_rank is None else self.max_num_tokens_per_rank
workspace13 = (num_experts, max_num_tokens * num_dispatchers,
max(K, N))
workspace2 = (num_experts, max_num_tokens * num_dispatchers, (N // 2))
output = (num_experts, max_num_tokens * num_dispatchers, K)
return (workspace13, workspace2, output, a.dtype)
def w8a8_groupgemm_forward(self,
x: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_ids: torch.Tensor,
topk_weights: torch.Tensor,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
a1_scale: Optional[torch.Tensor] = None,
a2_scale: Optional[torch.Tensor] = None,
expert_num_tokens: Optional[torch.Tensor] = None,
use_nn_moe: Optional[bool] = False,
routed_scaling_factor: Optional[float] = None,
shared_output: Optional[torch.Tensor] = None,
q_x: Optional[torch.Tensor] = None,
**_ ):
import vllm.model_executor.layers.fused_moe.modular_kernel as mk
local_num_experts = w1.size(0)
E, max_num_tokens, _, _, top_k = mk._moe_problem_size(
q_x, w1, w2, topk_ids)
N, K = self.N, self.K
(workspace13_shape, workspace2_shape, fused_out_shape,
workspace_dtype) = self.groupgemm_workspace_shapes(
x, q_x, max_num_tokens, N, K, top_k, global_num_experts,
local_num_experts)
workspace13 = torch.empty(prod(workspace13_shape),
device=x.device,
dtype=workspace_dtype)
workspace1 = _resize_cache(workspace13, (E, max_num_tokens, N))
fused_out = _resize_cache(workspace13, fused_out_shape)
# (from deepgemm docs) : A value hint (which is a value on CPU)
# for the M expectation of each batch, correctly setting this value
# may lead to better performance.
expected_m = max_num_tokens
m_grouped_w8a8_gemm_nt_masked((q_x, a1_scale),
(w1, w1_scale),
workspace1,
expert_num_tokens,
expected_m,
)
assert expert_num_tokens is not None
a2q, a2q_scale = fuse_silu_mul_quant_ep(workspace1, expert_num_tokens)
m_grouped_w8a8_gemm_nt_masked((a2q, a2q_scale),
(w2, w2_scale),
fused_out,
expert_num_tokens,
expected_m)
return fused_out
def fused_moe_forward(self,
x: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_ids: torch.Tensor,
topk_weights: torch.Tensor,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
a1_scale: Optional[torch.Tensor] = None,
a2_scale: Optional[torch.Tensor] = None,
expert_num_tokens: Optional[torch.Tensor] = None,
use_nn_moe: Optional[bool] = False,
routed_scaling_factor: Optional[float] = None,
shared_output: Optional[torch.Tensor] = None,
**_ ):
return fused_experts_impl_int8_marlin(
hidden_states=x,
w1=w1,
w2=w2,
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=True,
activation=activation,
apply_router_weight_on_input=apply_router_weight_on_input,
use_int8_w8a8=True,
per_channel_quant=True,
global_num_experts=global_num_experts,
expert_map=expert_map,
w1_scale=w1_scale,
w2_scale=w2_scale,
a1_scale=a1_scale,
a2_scale=a2_scale,
use_nn_moe=False,
shared_output=shared_output,
routed_scaling_factor=routed_scaling_factor)
def apply( def apply(
self, self,
...@@ -183,25 +342,59 @@ class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod) ...@@ -183,25 +342,59 @@ class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod)
scoring_func=scoring_func, scoring_func=scoring_func,
routed_scaling_factor=routed_scaling_factor, routed_scaling_factor=routed_scaling_factor,
use_fused_gate=use_fused_gate, use_fused_gate=use_fused_gate,
e_score_correction_bias=e_score_correction_bias) e_score_correction_bias=e_score_correction_bias,
indices_type=torch.int64 if self.use_deepep else None,)
return fused_experts_impl_int8_marlin( return self.fused_experts(
hidden_states=x, x,
w1=layer.w13_weight, layer.w13_weight,
w2=layer.w2_weight, layer.w2_weight,
topk_weights=topk_weights, topk_weights=topk_weights,
topk_ids=topk_ids, topk_ids=topk_ids,
inplace=True, inplace=True,
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_int8_w8a8=True,
per_channel_quant=True,
global_num_experts=global_num_experts, global_num_experts=global_num_experts,
expert_map=expert_map, expert_map=expert_map,
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, use_nn_moe=use_nn_moe,
shared_output=shared_output, shared_output=shared_output,
routed_scaling_factor=routed_scaling_factor) routed_scaling_factor=routed_scaling_factor,
\ No newline at end of file )
def select_gemm_impl(
self,
prepare_finalize: FusedMoEPrepareAndFinalize,
moe: FusedMoEConfig,
) -> FusedMoEPermuteExpertsUnpermute:
from vllm.model_executor.layers.fused_moe import (
TritonOrGroupGemmExperts)
if (prepare_finalize.activation_format ==
FusedMoEActivationFormat.BatchedExperts):
max_num_tokens_per_rank = (
prepare_finalize.max_num_tokens_per_rank())
assert max_num_tokens_per_rank is not None
self.max_num_tokens_per_rank = max_num_tokens_per_rank
logger.debug(
"TritonOrGroupGemmExperts(%s): "
"max_tokens_per_rank=%s, block_size=%s, per_act_token=%s",
self.__class__.__name__, max_num_tokens_per_rank,
None, True)
return TritonOrGroupGemmExperts(
use_int8_w8a8=True,
per_act_token_quant=True,
fused_experts=self.w8a8_groupgemm_forward
)
else:
logger.debug(
"TritonOrGroupGemmExperts(%s): block_size=%s, per_act_token=%s",
self.__class__.__name__, None,
False)
return TritonOrGroupGemmExperts(
fused_experts=self.fused_moe_forward
)
\ No newline at end of file
vllm/model_executor/layers/quantization/slimquant_w4a8_marlin.py
View file @ 4a943d35
...@@ -8,7 +8,7 @@ from torch.nn.parameter import Parameter ...@@ -8,7 +8,7 @@ from torch.nn.parameter import Parameter
import vllm.envs as envs import vllm.envs as envs
from vllm import _custom_ops as ops from vllm import _custom_ops as ops
from vllm.model_executor.utils import set_weight_attrs from vllm.model_executor.utils import set_weight_attrs
from vllm.distributed import get_tensor_model_parallel_world_size, get_ep_group from vllm.distributed import get_tensor_model_parallel_world_size, get_ep_group, get_dp_group
from vllm.logger import init_logger from vllm.logger import init_logger
from vllm.config import get_current_vllm_config from vllm.config import get_current_vllm_config
from vllm.forward_context import ForwardContext, get_forward_context from vllm.forward_context import ForwardContext, get_forward_context
...@@ -163,7 +163,8 @@ class SlimQuantW4A8Int8MarlinMoEMethod: ...@@ -163,7 +163,8 @@ class SlimQuantW4A8Int8MarlinMoEMethod:
self.fused_experts = self.w4a8_fused_moe_marlin_forward self.fused_experts = self.w4a8_fused_moe_marlin_forward
vllm_config = get_current_vllm_config() vllm_config = get_current_vllm_config()
parallel_config = vllm_config.parallel_config parallel_config = vllm_config.parallel_config
self.use_deepep = parallel_config.enable_expert_parallel and \ dp_size = get_dp_group().world_size
self.use_deepep = dp_size > 1 and parallel_config.enable_expert_parallel and \
(envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput" or \ (envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput" or \
envs.VLLM_ALL2ALL_BACKEND == "deepep_low_latency") envs.VLLM_ALL2ALL_BACKEND == "deepep_low_latency")
...@@ -352,16 +353,6 @@ class SlimQuantW4A8Int8MarlinMoEMethod: ...@@ -352,16 +353,6 @@ class SlimQuantW4A8Int8MarlinMoEMethod:
# for the M expectation of each batch, correctly setting this value # for the M expectation of each batch, correctly setting this value
# may lead to better performance. # may lead to better performance.
expected_m = max_num_tokens expected_m = max_num_tokens
# forward_context = get_forward_context()
# expected_m = forward_context.dp_metadata.max_tokens_across_dp_cpu * self.num_dispatchers
# print("##########################gemm1 workspace1 shape:{} q_x shape:{} " \
# "a1_scale shape:{} w1 shape:{} expert_num_tokens:{} expected_m:{}".format(workspace1.shape,
# q_x.shape,
# a1_scale.shape,
# w1.shape,
# expert_num_tokens,
# expected_m))
m_grouped_w4a8_gemm_nt_masked((q_x, a1_scale), m_grouped_w4a8_gemm_nt_masked((q_x, a1_scale),
(w1, w1_scale), (w1, w1_scale),
...@@ -371,17 +362,7 @@ class SlimQuantW4A8Int8MarlinMoEMethod: ...@@ -371,17 +362,7 @@ class SlimQuantW4A8Int8MarlinMoEMethod:
) )
assert expert_num_tokens is not None assert expert_num_tokens is not None
a2q, a2q_scale = fuse_silu_mul_quant_ep(workspace1, expert_num_tokens) a2q, a2q_scale = fuse_silu_mul_quant_ep(workspace1, expert_num_tokens)
# print("##########################gemm2 workspace1 shape:{} a2q shape:{} " \
# "a2q_scale shape:{} w2 shape:{} fused_out shape:{} expert_num_tokens:{} expected_m:{}".format(workspace1.shape,
# a2q.shape,
# a2q_scale.shape,
# w2.shape,
# fused_out.shape,
# expert_num_tokens,
# expected_m))
m_grouped_w4a8_gemm_nt_masked((a2q, a2q_scale), m_grouped_w4a8_gemm_nt_masked((a2q, a2q_scale),
(w2, w2_scale), (w2, w2_scale),
fused_out, fused_out,
...@@ -477,6 +458,7 @@ class SlimQuantW4A8Int8MarlinMoEMethod: ...@@ -477,6 +458,7 @@ class SlimQuantW4A8Int8MarlinMoEMethod:
routed_scaling_factor=routed_scaling_factor, routed_scaling_factor=routed_scaling_factor,
use_fused_gate=use_fused_gate use_fused_gate=use_fused_gate
) )
return self.fused_experts( return self.fused_experts(
x, x,
layer.w13_weight, layer.w13_weight,
......
vllm/model_executor/layers/quantization/utils/w8a8_utils.py
View file @ 4a943d35
...@@ -40,6 +40,19 @@ def get_w8a8_int8_marlin_weights( ...@@ -40,6 +40,19 @@ def get_w8a8_int8_marlin_weights(
return weight return weight
def w8a8_nt_kpack2_marlin_weight(w8a8_w, # [size_n, size_k// 2 ]
k_tile=16,
n_tile=16, ):
assert w8a8_w.dtype == torch.int8, "w8a8_w 必须是 int8 类型"
size_n, size_k = w8a8_w.shape
assert size_n % k_tile == 0 and size_k % n_tile == 0, "k_tile / n_tile 必须能整除对应维度"
w8a8_w = w8a8_w.reshape((size_n // n_tile, n_tile, size_k // k_tile, k_tile))
w8a8_w = w8a8_w.permute((0, 2, 1, 3)).contiguous()
w8a8_w = w8a8_w.reshape((size_n // k_tile, size_k * k_tile))
return w8a8_w
def sparse_cutlass_supported() -> bool: def sparse_cutlass_supported() -> bool:
if not current_platform.is_cuda(): if not current_platform.is_cuda():
return False return False
......
vllm/v1/worker/gpu_model_runner.py
View file @ 4a943d35
...@@ -1235,7 +1235,7 @@ class GPUModelRunner(LoRAModelRunnerMixin): ...@@ -1235,7 +1235,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
# TODO(tms) : There are many cases where padding is enabled for # TODO(tms) : There are many cases where padding is enabled for
# prefills, causing unnecessary and excessive padding of activations. # prefills, causing unnecessary and excessive padding of activations.
if dp_size == 1 or self.vllm_config.model_config.enforce_eager or envs.VLLM_ALL2ALL_BACKEND == 'naive': if dp_size == 1 or self.vllm_config.model_config.enforce_eager or envs.VLLM_ALL2ALL_BACKEND != 'naive':
# Early exit. # Early exit.
return 0, None return 0, None
......
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