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[Kernel] Add expert_map support to Cutlass FP8 MOE (#16861)

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Signed-off-by: varun sundar rabindranath <vsundarr@redhat.com>
Co-authored-by: varun sundar rabindranath <vsundarr@redhat.com>
This commit is contained in:
Varun Sundar Rabindranath
2025-04-21 23:44:32 -04:00
committed by GitHub
parent c9acbf1141
commit 7b8a2ab76f
5 changed files with 331 additions and 171 deletions
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438
tests/kernels/test_cutlass_moe.py
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@@ -1,4 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
import dataclasses
from typing import Optional
import pytest
import torch
@@ -12,32 +15,204 @@ from vllm.platforms import current_platform
NUM_EXPERTS = [40, 64]
TOP_KS = [6, 8]
def run(a: torch.Tensor, a_scale: torch.Tensor, w1_q: torch.Tensor,
w2_q: torch.Tensor, w1_scale: torch.Tensor, w2_scale: torch.Tensor,
topk_weights: torch.Tensor, topk_ids: torch.Tensor,
ab_strides1: torch.Tensor, c_strides1: torch.Tensor,
ab_strides2: torch.Tensor, c_strides2: torch.Tensor):
with set_current_vllm_config(
VllmConfig(parallel_config=ParallelConfig(
pipeline_parallel_size=1))):
return cutlass_moe_fp8(a,
w1_q,
w2_q,
w1_scale,
w2_scale,
topk_weights,
topk_ids,
ab_strides1,
c_strides1,
ab_strides2,
c_strides2,
a1_scale=a_scale)
MNK_FACTORS = [
(2, 1024, 1024),
(2, 1024, 1536),
(2, 3072, 1024),
(2, 3072, 1536),
(64, 1024, 1024),
(64, 1024, 1536),
(64, 3072, 1024),
(64, 3072, 1536),
(224, 1024, 1024),
(224, 1024, 1536),
(224, 3072, 1024),
(224, 3072, 1536),
]
@pytest.mark.parametrize("m", [2, 64, 224])
@pytest.mark.parametrize("n", [1024, 3072])
@pytest.mark.parametrize("k", [1024, 1536])
@dataclasses.dataclass
class MOETensors:
a: torch.Tensor
w1: torch.Tensor
w2: torch.Tensor
ab_strides1: torch.Tensor
c_strides1: torch.Tensor
ab_strides2: torch.Tensor
c_strides2: torch.Tensor
@staticmethod
def make_moe_tensors(m: int, k: int, n: int, e: int,
dtype: torch.dtype) -> "MOETensors":
a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
ab_strides1 = torch.full((e, ), k, device="cuda", dtype=torch.int64)
c_strides1 = torch.full((e, ), 2 * n, device="cuda", dtype=torch.int64)
ab_strides2 = torch.full((e, ), n, device="cuda", dtype=torch.int64)
c_strides2 = torch.full((e, ), k, device="cuda", dtype=torch.int64)
return MOETensors(a=a,
w1=w1,
w2=w2,
ab_strides1=ab_strides1,
c_strides1=c_strides1,
ab_strides2=ab_strides2,
c_strides2=c_strides2)
@dataclasses.dataclass
class MOETensors8Bit(MOETensors):
# quantized
a_q: Optional[torch.Tensor] = None # a -> a_q
w1_q: Optional[torch.Tensor] = None # w1 -> w1_q
w2_q: Optional[torch.Tensor] = None # w2 -> w2_q
a_scale: Optional[torch.Tensor] = None
w1_scale: Optional[torch.Tensor] = None
w2_scale: Optional[torch.Tensor] = None
# dequantized
a_d: Optional[torch.Tensor] = None # a -> a_q -> a_d
w1_d: Optional[torch.Tensor] = None # w1 -> w1_q -> w1_d
w2_d: Optional[torch.Tensor] = None # w2 -> w2_q -> w2_d
@staticmethod
def make_moe_tensors_8bit(m: int, k: int, n: int, e: int,
per_act_token: bool,
per_out_channel: bool) -> "MOETensors8Bit":
dtype = torch.half
q_dtype = torch.float8_e4m3fn
moe_tensors_fp16 = MOETensors.make_moe_tensors(m, k, n, e, dtype)
# a -> a_q, w1 -> w1_q, w2 -> w2_q
n_b_scales = 2 * n if per_out_channel else 1
k_b_scales = k if per_out_channel else 1
# Get the right scale for tests.
_, a_scale = ops.scaled_fp8_quant(
moe_tensors_fp16.a, use_per_token_if_dynamic=per_act_token)
a_q, _ = ops.scaled_fp8_quant(moe_tensors_fp16.a,
a_scale,
use_per_token_if_dynamic=per_act_token)
w1_q = torch.empty((e, 2 * n, k), device="cuda", dtype=q_dtype)
w2_q = torch.empty((e, k, n), device="cuda", dtype=q_dtype)
w1_scale = torch.empty((e, n_b_scales, 1),
device="cuda",
dtype=torch.float32)
w2_scale = torch.empty((e, k_b_scales, 1),
device="cuda",
dtype=torch.float32)
for expert in range(e):
w1_q[expert], w1_scale[expert] = ops.scaled_fp8_quant(
moe_tensors_fp16.w1[expert],
use_per_token_if_dynamic=per_out_channel)
w2_q[expert], w2_scale[expert] = ops.scaled_fp8_quant(
moe_tensors_fp16.w2[expert],
use_per_token_if_dynamic=per_out_channel)
# a_q -> a_d, w1_q -> w1_d, w2_q -> w2_d
a_d = a_q.float().mul(a_scale).to(dtype)
w1_d = torch.empty_like(moe_tensors_fp16.w1)
w2_d = torch.empty_like(moe_tensors_fp16.w2)
for expert in range(e):
w1_d[expert] = (w1_q[expert].float() * w1_scale[expert]).half()
w2_d[expert] = (w2_q[expert].float() * w2_scale[expert]).half()
return MOETensors8Bit(a=moe_tensors_fp16.a,
w1=moe_tensors_fp16.w1,
w2=moe_tensors_fp16.w2,
ab_strides1=moe_tensors_fp16.ab_strides1,
c_strides1=moe_tensors_fp16.c_strides1,
ab_strides2=moe_tensors_fp16.ab_strides2,
c_strides2=moe_tensors_fp16.c_strides2,
a_q=a_q,
w1_q=w1_q,
w2_q=w2_q,
a_scale=a_scale,
w1_scale=w1_scale,
w2_scale=w2_scale,
a_d=a_d,
w1_d=w1_d,
w2_d=w2_d)
def run_with_expert_maps(num_experts: int, num_local_experts: int,
**cutlass_moe_kwargs):
def slice_experts():
slice_params = [
"w1_q", "w2_q", "ab_strides1", "ab_strides2", "c_strides1",
"c_strides2", "w1_scale", "w2_scale"
]
full_tensors = {
k: v
for k, v in cutlass_moe_kwargs.items()
if k in slice_params and k in cutlass_moe_kwargs
}
for i in range(0, num_experts, num_local_experts):
s, e = i, i + num_local_experts
# make expert map
expert_map = [-1] * num_experts
expert_map[s:e] = list(range(num_local_experts))
expert_map = torch.tensor(expert_map,
dtype=torch.int32,
device="cuda")
# update cutlass moe arg with expert_map
cutlass_moe_kwargs["expert_map"] = expert_map
# update cutlass moe arg tensors
for k, t in full_tensors.items():
cutlass_moe_kwargs[k] = t[s:e]
yield cutlass_moe_kwargs
out_tensor = torch.zeros_like(cutlass_moe_kwargs["a"])
for kwargs in slice_experts():
out_tensor = out_tensor + cutlass_moe_fp8(**kwargs)
return out_tensor
def run_8_bit(moe_tensors: MOETensors8Bit,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
num_local_experts: Optional[int] = None) -> torch.Tensor:
assert not any([
t is None for t in [
moe_tensors.w1_q, moe_tensors.w2_q, moe_tensors.w1_scale,
moe_tensors.w2_scale, moe_tensors.a_scale
]
])
kwargs = {
'a': moe_tensors.a,
'w1_q': moe_tensors.w1_q.transpose(1, 2), # type: ignore[union-attr]
'w2_q': moe_tensors.w2_q.transpose(1, 2), # type: ignore[union-attr]
'topk_weights': topk_weights,
'topk_ids_': topk_ids,
'ab_strides1': moe_tensors.ab_strides1,
'c_strides1': moe_tensors.c_strides1,
'ab_strides2': moe_tensors.ab_strides2,
'c_strides2': moe_tensors.c_strides2,
'w1_scale': moe_tensors.w1_scale,
'w2_scale': moe_tensors.w2_scale,
'a1_scale': moe_tensors.a_scale
}
num_experts = moe_tensors.w1.size(0)
with_ep = num_local_experts is not None or num_local_experts == num_experts
if not with_ep:
return cutlass_moe_fp8(**kwargs)
assert num_local_experts is not None
return run_with_expert_maps(
num_experts,
num_local_experts, # type: ignore[arg-type]
**kwargs)
@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
@pytest.mark.parametrize("e", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("per_act_token", [True, False])
@@ -46,7 +221,7 @@ def run(a: torch.Tensor, a_scale: torch.Tensor, w1_q: torch.Tensor,
(lambda x: x is None or not ops.cutlass_group_gemm_supported(x.to_int()))(
current_platform.get_device_capability()),
reason="Grouped gemm is not supported on this GPU type.")
def test_cutlass_moe_no_graph(
def test_cutlass_moe_8_bit_no_graph(
m: int,
n: int,
k: int,
@@ -60,80 +235,21 @@ def test_cutlass_moe_no_graph(
VllmConfig(parallel_config=ParallelConfig(
pipeline_parallel_size=1))):
dtype = torch.half
mt = MOETensors8Bit.make_moe_tensors_8bit(m, k, n, e, per_act_token,
per_out_ch)
a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
score = torch.randn((m, e), device="cuda", dtype=torch.half)
topk_weights, topk_ids = fused_topk(mt.a,
score,
topk,
renormalize=False)
# Get the right scale for tests.
_, a_scale1 = ops.scaled_fp8_quant(
a, use_per_token_if_dynamic=per_act_token)
a_q, _ = ops.scaled_fp8_quant(a,
a_scale1,
use_per_token_if_dynamic=per_act_token)
# Note that we are using the dequantized versions of the tensors.
# Using a, w1 and w2 directly results in minor output differences.
triton_output = fused_experts(mt.a_d, mt.w1_d, mt.w2_d, topk_weights,
topk_ids)
a_d = a_q.float().mul(a_scale1).to(dtype)
n_b_scales = 2 * n if per_out_ch else 1
k_b_scales = k if per_out_ch else 1
w1_q = torch.empty((e, 2 * n, k),
device="cuda",
dtype=torch.float8_e4m3fn)
w2_q = torch.empty((e, k, n), device="cuda", dtype=torch.float8_e4m3fn)
w1_scale = torch.empty((e, n_b_scales, 1),
device="cuda",
dtype=torch.float32)
w2_scale = torch.empty((e, k_b_scales, 1),
device="cuda",
dtype=torch.float32)
ab_strides1 = torch.full((e, ), k, device="cuda", dtype=torch.int64)
c_strides1 = torch.full((e, ), 2 * n, device="cuda", dtype=torch.int64)
ab_strides2 = torch.full((e, ), n, device="cuda", dtype=torch.int64)
c_strides2 = torch.full((e, ), k, device="cuda", dtype=torch.int64)
for expert in range(e):
w1_q[expert], w1_scale[expert] = ops.scaled_fp8_quant(
w1[expert], use_per_token_if_dynamic=per_out_ch)
w2_q[expert], w2_scale[expert] = ops.scaled_fp8_quant(
w2[expert], use_per_token_if_dynamic=per_out_ch)
w1_q = w1_q.transpose(1, 2)
w2_q = w2_q.transpose(1, 2)
ab_strides1 = torch.full((e, ), k, device="cuda", dtype=torch.int64)
c_strides1 = torch.full((e, ), 2 * n, device="cuda", dtype=torch.int64)
ab_strides2 = torch.full((e, ), n, device="cuda", dtype=torch.int64)
c_strides2 = torch.full((e, ), k, device="cuda", dtype=torch.int64)
w1_d = torch.empty_like(w1)
w2_d = torch.empty_like(w2)
for expert in range(e):
w1_d[expert] = (w1_q[expert].t().float() * w1_scale[expert]).half()
w2_d[expert] = (w2_q[expert].t().float() * w2_scale[expert]).half()
score = torch.randn((m, e), device="cuda", dtype=dtype)
topk_weights, topk_ids = fused_topk(a, score, topk, renormalize=False)
triton_output = fused_experts(a_d, w1_d, w2_d, topk_weights, topk_ids)
cutlass_output = cutlass_moe_fp8(a,
w1_q,
w2_q,
w1_scale,
w2_scale,
topk_weights,
topk_ids,
ab_strides1,
c_strides1,
ab_strides2,
c_strides2,
a1_scale=a_scale1)
#print(triton_output)
#print(cutlass_output)
#print("*")
cutlass_output = run_8_bit(mt, topk_weights, topk_ids)
torch.testing.assert_close(triton_output,
cutlass_output,
@@ -141,9 +257,7 @@ def test_cutlass_moe_no_graph(
rtol=1e-2)
@pytest.mark.parametrize("m", [2, 64, 224])
@pytest.mark.parametrize("n", [1024, 3072])
@pytest.mark.parametrize("k", [1024, 1536])
@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
@pytest.mark.parametrize("e", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("per_act_token", [True, False])
@@ -152,7 +266,7 @@ def test_cutlass_moe_no_graph(
(lambda x: x is None or not ops.cutlass_group_gemm_supported(x.to_int()))(
current_platform.get_device_capability()),
reason="Grouped gemm is not supported on this GPU type.")
def test_cutlass_moe_cuda_graph(
def test_cutlass_moe_8_bit_cuda_graph(
m: int,
n: int,
k: int,
@@ -168,77 +282,83 @@ def test_cutlass_moe_cuda_graph(
dtype = torch.half
a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
# Get the right scale for tests.
_, a_scale1 = ops.scaled_fp8_quant(
a, use_per_token_if_dynamic=per_act_token)
a_q, _ = ops.scaled_fp8_quant(a,
a_scale1,
use_per_token_if_dynamic=per_act_token)
a_d = a_q.float().mul(a_scale1).to(dtype)
n_b_scales = 2 * n if per_out_ch else 1
k_b_scales = k if per_out_ch else 1
w1_q = torch.empty((e, 2 * n, k),
device="cuda",
dtype=torch.float8_e4m3fn)
w2_q = torch.empty((e, k, n), device="cuda", dtype=torch.float8_e4m3fn)
w1_scale = torch.empty((e, n_b_scales, 1),
device="cuda",
dtype=torch.float32)
w2_scale = torch.empty((e, k_b_scales, 1),
device="cuda",
dtype=torch.float32)
ab_strides1 = torch.full((e, ), k, device="cuda", dtype=torch.int64)
c_strides1 = torch.full((e, ), 2 * n, device="cuda", dtype=torch.int64)
ab_strides2 = torch.full((e, ), n, device="cuda", dtype=torch.int64)
c_strides2 = torch.full((e, ), k, device="cuda", dtype=torch.int64)
for expert in range(e):
w1_q[expert], w1_scale[expert] = ops.scaled_fp8_quant(
w1[expert], use_per_token_if_dynamic=per_out_ch)
w2_q[expert], w2_scale[expert] = ops.scaled_fp8_quant(
w2[expert], use_per_token_if_dynamic=per_out_ch)
w1_q = w1_q.transpose(1, 2)
w2_q = w2_q.transpose(1, 2)
ab_strides1 = torch.full((e, ), k, device="cuda", dtype=torch.int64)
c_strides1 = torch.full((e, ), 2 * n, device="cuda", dtype=torch.int64)
ab_strides2 = torch.full((e, ), n, device="cuda", dtype=torch.int64)
c_strides2 = torch.full((e, ), k, device="cuda", dtype=torch.int64)
w1_d = torch.empty_like(w1)
w2_d = torch.empty_like(w2)
for expert in range(e):
w1_d[expert] = (w1_q[expert].t().float() * w1_scale[expert]).half()
w2_d[expert] = (w2_q[expert].t().float() * w2_scale[expert]).half()
mt = MOETensors8Bit.make_moe_tensors_8bit(m, k, n, e, per_act_token,
per_out_ch)
score = torch.randn((m, e), device="cuda", dtype=dtype)
topk_weights, topk_ids = fused_topk(a, score, topk, renormalize=False)
topk_weights, topk_ids = fused_topk(mt.a,
score,
topk,
renormalize=False)
triton_output = fused_experts(a_d, w1_d, w2_d, topk_weights, topk_ids)
# Note that we are using the dequantized versions of the tensors.
# Using a, w1 and w2 directly results in minor output differences.
triton_output = fused_experts(mt.a_d, mt.w1_d, mt.w2_d, topk_weights,
topk_ids)
stream = torch.cuda.Stream()
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph, stream=stream):
cutlass_output = run(a, a_scale1, w1_q, w2_q, w1_scale, w2_scale,
topk_weights, topk_ids, ab_strides1,
c_strides1, ab_strides2, c_strides2)
cutlass_output = run_8_bit(mt, topk_weights, topk_ids)
torch.cuda.synchronize()
graph.replay()
torch.cuda.synchronize()
#print(triton_output)
#print(cutlass_output)
#print("*")
torch.testing.assert_close(triton_output,
cutlass_output,
atol=9e-2,
rtol=1e-2)
@pytest.mark.parametrize("m", [64])
@pytest.mark.parametrize("n", [1024])
@pytest.mark.parametrize("k", [4096])
@pytest.mark.parametrize("e", [16])
@pytest.mark.parametrize("topk", [1, 8])
@pytest.mark.parametrize("per_act_token", [True])
@pytest.mark.parametrize("per_out_channel", [True])
@pytest.mark.parametrize("ep_size", [1, 2, 4, 8, 16])
@pytest.mark.skipif(
(lambda x: x is None or not ops.cutlass_group_gemm_supported(x.to_int()))(
current_platform.get_device_capability()),
reason="Grouped gemm is not supported on this GPU type.")
def test_cutlass_moe_8_bit_EP(
m: int,
n: int,
k: int,
e: int,
topk: int,
per_act_token: bool,
per_out_channel: bool,
ep_size: int,
):
current_platform.seed_everything(7)
with set_current_vllm_config(
VllmConfig(parallel_config=ParallelConfig(
pipeline_parallel_size=1))):
mt = MOETensors8Bit.make_moe_tensors_8bit(m, k, n, e, per_act_token,
per_out_channel)
score = torch.randn((m, e), device="cuda", dtype=torch.half)
topk_weights, topk_ids = fused_topk(mt.a,
score,
topk,
renormalize=False)
# Note that we are using the dequantized versions of the tensors.
# Using a, w1 and w2 directly results in minor output differences.
triton_output = fused_experts(mt.a_d, mt.w1_d, mt.w2_d, topk_weights,
topk_ids)
assert e % ep_size == 0, "Cannot distribute experts evenly"
cutlass_output = run_8_bit(mt,
topk_weights,
topk_ids,
num_local_experts=e // ep_size)
torch.testing.assert_close(triton_output,
cutlass_output,
atol=5e-2,
rtol=1e-2)