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Commit 2444e959 authored by lixh6's avatar lixh6
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[FEATRUE] 接入Aiter MoE W8A8-FP8 量化模型支持

parent aef3c487
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Showing with 178 additions and 66 deletions
vllm/envs.py
View file @ 2444e959
......@@ -167,6 +167,7 @@ if TYPE_CHECKING:
VLLM_MOE_USE_DEEP_GEMM: bool = True
VLLM_USE_DEEP_GEMM_E8M0: bool = True
VLLM_USE_DEEP_GEMM_TMA_ALIGNED_SCALES: bool = True
VLLM_USE_AITER_MOE_W8A8: bool = True
VLLM_DEEP_GEMM_WARMUP: Literal[
"skip",
"full",
......@@ -1290,6 +1291,9 @@ environment_variables: dict[str, Callable[[], Any]] = {
"VLLM_USE_DEEP_GEMM_TMA_ALIGNED_SCALES": lambda: bool(
int(os.getenv("VLLM_USE_DEEP_GEMM_TMA_ALIGNED_SCALES", "1"))
),
"VLLM_USE_AITER_MOE_W8A8": lambda: bool(
int(os.getenv("VLLM_USE_AITER_MOE_W8A8", "1"))
),
# DeepGemm JITs the kernels on-demand. The warmup attempts to make DeepGemm
# JIT all the required kernels before model execution so there is no
# JIT'ing in the hot-path. However, this warmup increases the engine
......
vllm/model_executor/layers/fused_moe/fused_moe.py
View file @ 2444e959
......@@ -6,7 +6,9 @@ import functools
import json
import os
import math
import sys
import aiter
from aiter.moe import get_aiter_moe_config, aiter_moe, MoeQuantType
from collections.abc import Callable
from typing import Any, Callable, Dict, List, Optional
......@@ -1858,35 +1860,74 @@ def fused_experts_impl(
cache13 = torch.empty(M * top_k_num * max(N, K if not use_nn_moe else w2.shape[2]), device=hidden_states.device, dtype=hidden_states.dtype)
if use_int8_w8a8 or use_fp8_w8a8:
return fused_experts_impl_int8(hidden_states=hidden_states,
w1=w1,
w2=w2,
topk_weights=topk_weights,
topk_ids=topk_ids,
cache13=cache13,
inplace=inplace,
activation=activation,
apply_router_weight_on_input=apply_router_weight_on_input,
use_fp8_w8a8=use_fp8_w8a8,
use_int8_w8a8=use_int8_w8a8,
use_int8_w8a16=False,
use_int4_w4a16=False,
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,
routed_scaling_factor=routed_scaling_factor,
shared_output=shared_output,
i_q=i_q,
i_s=i_s
)
if envs.VLLM_USE_AITER_MOE_W8A8==True:
K_input = hidden_states.size(1)
actual_N2 = N // 2
quant_type = MoeQuantType.W8A8
status, moe_config = get_aiter_moe_config(
M=num_tokens,
E=global_num_experts,
N1=N,
N2=actual_N2,
K=K_input,
top_k=top_k_num,
block_size=0,
dtype=hidden_states.dtype,
quant_type=quant_type,
)
output = aiter_moe(
hidden_states=hidden_states,
w1=w1,
w2=w2,
topk_weights=topk_weights,
topk_ids=topk_ids,
moe_config=moe_config,
inplace=inplace,
activation=activation,
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=None,
global_num_experts=global_num_experts,
expert_map=expert_map,
routed_scaling_factor=routed_scaling_factor,
)
return output
else:
return fused_experts_impl_int8(hidden_states=hidden_states,
w1=w1,
w2=w2,
topk_weights=topk_weights,
topk_ids=topk_ids,
cache13=cache13,
inplace=inplace,
activation=activation,
apply_router_weight_on_input=apply_router_weight_on_input,
use_fp8_w8a8=use_fp8_w8a8,
use_int8_w8a8=use_int8_w8a8,
use_int8_w8a16=False,
use_int4_w4a16=False,
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,
routed_scaling_factor=routed_scaling_factor,
shared_output=shared_output,
i_q=i_q,
i_s=i_s
)
elif use_int4_w4a8 is True:
return fused_experts_impl_w4a8(hidden_states=hidden_states,
w1=w1,
......
vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe_marlin.py
View file @ 2444e959
......@@ -26,6 +26,12 @@ from vllm.model_executor.layers.fused_moe import (
FusedMoEPrepareAndFinalize,
FusedMoeWeightScaleSupported,
)
import aiter
from aiter.test_common import checkAllclose, perftest
from aiter.ops.shuffle import moe_layout_shuffle_gemm1, moe_layout_shuffle_gemm2
from aiter.fused_moe import fused_topk, torch_moe
from aiter import dtypes, ActivationType
from aiter.moe import get_aiter_moe_config, aiter_moe, MoeSolutionType, MoeQuantType
try:
from lmslim.layers.fused_moe.fuse_moe_int8_marlin import fused_experts_impl_int8_marlin
from lmslim.layers.fused_moe.fuse_moe_fp8_marlin import fused_experts_impl_fp8_marlin
......@@ -169,23 +175,45 @@ class CompressedTensorsW8A8FP8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod):
layer.w13_input_scale = None
layer.w2_input_scale = None
def shuffle_w8a8_gemm1(self, weight_data):
w_fp8 = weight_data.to(torch.float8_e4m3fn)
shuffled = moe_layout_shuffle_gemm1(w_fp8)
return shuffled.view(torch.int8)
def shuffle_w8a8_gemm2(self, weight_data):
w_fp8 = weight_data.to(torch.float8_e4m3fn)
shuffled = moe_layout_shuffle_gemm2(w_fp8)
return shuffled.view(torch.int8)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
w1_marlin_list = []
for ii in range(layer.w13_weight.shape[0]):
w1_marlin_in = get_w8a8_int8_marlin_weights(layer.w13_weight[ii])
w1_marlin_list.append(w1_marlin_in.float() if w1_marlin_in.dtype == torch.float8_e4m3fn else w1_marlin_in)
w1_marlin = torch.stack(w1_marlin_list, dim=0)
w1_marlin = fp32_to_fp8_e4m3fn(w1_marlin)
if envs.VLLM_USE_AITER_MOE_W8A8==True:
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)
del w1_marlin_list
w2_marlin_list = []
for ii in range(layer.w2_weight.shape[0]):
w2_marlin_in = get_w8a8_int8_marlin_weights(layer.w2_weight[ii])
w2_marlin_list.append(w2_marlin_in.float() if w2_marlin_in.dtype == torch.float8_e4m3fn else w2_marlin_in)
w2_marlin = torch.stack(w2_marlin_list, dim=0)
w2_marlin = fp32_to_fp8_e4m3fn(w2_marlin)
layer.w13_weight = Parameter(w1_marlin, requires_grad=False)
layer.w2_weight = Parameter(w2_marlin, requires_grad=False)
shuffled_w13 = self.shuffle_w8a8_gemm1(layer.w13_weight)
w13_data = shuffled_w13.view(*layer.w13_weight.shape).view(torch.int8)
layer.w13_weight = Parameter(w13_data, requires_grad=False)
shuffled_w2 = self.shuffle_w8a8_gemm2(layer.w2_weight)
w2_data = shuffled_w2.view(*layer.w2_weight.shape).view(torch.int8)
layer.w2_weight = Parameter(w2_data, requires_grad=False)
else:
w1_marlin_list = []
for ii in range(layer.w13_weight.shape[0]):
w1_marlin_in = get_w8a8_int8_marlin_weights(layer.w13_weight[ii])
w1_marlin_list.append(w1_marlin_in.float() if w1_marlin_in.dtype == torch.float8_e4m3fn else w1_marlin_in)
w1_marlin = torch.stack(w1_marlin_list, dim=0)
w1_marlin = fp32_to_fp8_e4m3fn(w1_marlin)
del w1_marlin_list
w2_marlin_list = []
for ii in range(layer.w2_weight.shape[0]):
w2_marlin_in = get_w8a8_int8_marlin_weights(layer.w2_weight[ii])
w2_marlin_list.append(w2_marlin_in.float() if w2_marlin_in.dtype == torch.float8_e4m3fn else w2_marlin_in)
w2_marlin = torch.stack(w2_marlin_list, dim=0)
w2_marlin = fp32_to_fp8_e4m3fn(w2_marlin)
layer.w13_weight = Parameter(w1_marlin, requires_grad=False)
layer.w2_weight = Parameter(w2_marlin, requires_grad=False)
def fused_moe_forward(
self,
......@@ -200,27 +228,66 @@ class CompressedTensorsW8A8FP8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod):
routed_scaling_factor: Optional[float] = None,
shared_output: Optional[torch.Tensor] = None,
):
return fused_experts_impl_fp8_marlin(
hidden_states=x,
w1=layer.w13_weight,
w2=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,
use_fp8_w8a8=True,
per_channel_quant=True,
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=False,
shared_output=shared_output,
routed_scaling_factor=routed_scaling_factor)
if envs.VLLM_USE_AITER_MOE_W8A8==True:
m_flat = x.view(-1, x.shape[-1])
M = m_flat.shape[0]
E = layer.w13_weight.size(0)
K = x.size(-1)
N1 = layer.w13_weight.size(1)
topk = topk_ids.size(1)
w1_input = layer.w13_weight.view(E, N1, K)
w2_input = layer.w2_weight.view(E, K, N1 // 2)
_, moe_cfg = get_aiter_moe_config(
M=M,
E=E,
N1=N1,
N2=N1 // 2,
K=K,
top_k=topk,
block_size=0,
dtype=x.dtype,
quant_type=MoeQuantType.W8A8,
)
output = aiter_moe(
hidden_states=x,
w1=w1_input,
w2=w2_input,
topk_weights=topk_weights,
topk_ids=topk_ids,
moe_config=moe_cfg,
inplace=False,
activation=getattr(layer, "activation", "silu"),
w1_scale=layer.w13_weight_scale,
w2_scale=layer.w2_weight_scale,
a1_scale=getattr(layer, "w13_input_scale", None),
a2_scale=getattr(layer, "w2_input_scale", None),
global_num_experts=E,
expert_map=getattr(layer, "expert_map", None),
routed_scaling_factor=routed_scaling_factor,
)
return output
else:
return fused_experts_impl_fp8_marlin(
hidden_states=x,
w1=layer.w13_weight,
w2=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,
use_fp8_w8a8=True,
per_channel_quant=True,
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=False,
shared_output=shared_output,
routed_scaling_factor=routed_scaling_factor)
def apply(
self,
......
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