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[NVIDIA] Fix cutedsl backend of MoE (#12353)

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This commit is contained in:
Kaixi Hou
2025-11-04 18:54:55 -08:00
committed by GitHub
parent 09938e1f82
commit 0711d1509b
8 changed files with 579 additions and 28 deletions
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test/srt/run_suite.py
+3
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@@ -179,7 +179,10 @@ suites = {
TestFile("test_llama31_fp4.py", 300),
],
"per-commit-4-gpu-gb200": [
TestFile("test_cutedsl_moe.py", 300),
TestFile("test_deepseek_v3_fp4_4gpu.py", 3600),
# Disabled temporarily, see https://github.com/sgl-project/sglang/issues/12533
# TestFile("test_deepseek_v3_cutedsl_4gpu.py", 3600),
],
"per-commit-4-gpu-deepep": [
TestFile("ep/test_deepep_small.py", 531),
test/srt/test_cutedsl_moe.py
+482
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@@ -0,0 +1,482 @@
# SPDX-License-Identifier: Apache-2.0
import unittest
from typing import Callable
import torch
from flashinfer import fp4_quantize
from sgl_kernel import scaled_fp4_grouped_quant, scaled_fp4_quant
from torch.nn import functional as F
from sglang.srt.layers.activation import SiluAndMul
from sglang.srt.layers.moe.flashinfer_cutedsl_moe import flashinfer_cutedsl_moe_masked
from sglang.srt.layers.moe.topk import TopKConfig, select_experts
SKIP_TEST = torch.cuda.get_device_capability() < (10, 0)
SKIP_REASON = "Nvfp4 Requires compute capability of 10 or above."
kE2M1ToFloat = torch.tensor(
[0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0], dtype=torch.float32
)
FLOAT8_E4M3_MAX = 448.0
FLOAT4_E2M1_MAX = 6.0
def convert_swizzled_to_linear(a_sf_swizzled: torch.Tensor, m, k, block_size):
m_tiles = (m + 128 - 1) // 128
f = block_size * 4
k_tiles = (k + f - 1) // f
tmp = torch.reshape(a_sf_swizzled, (1, m_tiles, k_tiles, 32, 4, 4))
tmp = torch.permute(tmp, (0, 1, 4, 3, 2, 5))
out = tmp.reshape(m_tiles * 128, k_tiles * f // block_size)
return out[0:m, 0:k]
def dequantize_nvfp4_to_dtype(
tensor_fp4, tensor_sf, global_scale, dtype, device, block_size=16
):
"""Dequantize the fp4 tensor back to high precision."""
# Two fp4 values are packed into one uint8.
assert tensor_fp4.dtype == torch.uint8
m, packed_k = tensor_fp4.shape
k = packed_k * 2
tensor_f32 = break_fp4_bytes(tensor_fp4, dtype)
tensor_f32 = tensor_f32.reshape(m, k // block_size, block_size)
tensor_sf = tensor_sf.view(torch.float8_e4m3fn)
tensor_sf = convert_swizzled_to_linear(tensor_sf, m, k, block_size)
tensor_sf_dtype = tensor_sf.to(torch.float32) / global_scale
# scale the tensor
out = (tensor_f32 * tensor_sf_dtype.unsqueeze(-1)).reshape(m, k)
return out.to(dtype=dtype)
def break_fp4_bytes(a, dtype):
assert a.dtype == torch.uint8
m, n = a.shape
# Vectorized nibble processing
a_flat = a.flatten()
high = (a_flat & 0xF0) >> 4 # Upper nibbles
low = a_flat & 0x0F # Lower nibbles
# Combine nibbles for batch processing
combined = torch.stack((low, high), dim=1).flatten()
# Vectorized sign and magnitude extraction
signs = (combined & 0x08).to(torch.bool) # Sign bits
abs_vals = (combined & 0x07).to(torch.long) # Magnitude indices
# Device-aware lookup and sign application
kE2M1 = kE2M1ToFloat.to(device=a.device)
values = kE2M1[abs_vals] * torch.where(signs, -1.0, 1.0)
# Reshape to final form
return values.reshape(m, n * 2).to(dtype=dtype)
def compute_routing(router_logits: torch.Tensor, top_k: int):
routing_weights = torch.softmax(router_logits, dim=1, dtype=torch.float)
routing_weights, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
routing_weights = routing_weights.float()
return routing_weights, selected_experts
def prepare_inputs(
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
num_experts: int,
topk: int,
):
routing_weights, topk_idx = compute_routing(router_logits, topk)
masked_m = []
for i in range(num_experts):
mask = topk_idx.view(-1) == i
masked_m.append(mask.sum())
masked_m = torch.tensor(masked_m, dtype=torch.int32)
hidden_states_3d = torch.empty(
(num_experts, max(masked_m), hidden_states.shape[1]), dtype=hidden_states.dtype
)
for i in range(num_experts):
hidden_states_3d[i, : masked_m[i], :] = hidden_states[topk_idx.view(-1) == i]
return hidden_states_3d, masked_m, topk_idx, routing_weights
MNK_FACTORS = [
(2, 1024, 1024),
(2, 1024, 1536),
(2, 3072, 1024),
(2, 3072, 1536),
(64, 1024, 1024),
(64, 1024, 1536),
(64, 3072, 1024),
(64, 2048, 1024),
(224, 1024, 1024),
(224, 1024, 1536),
]
# Reference implementation of torch_moe
def torch_moe(a, w1, w2, score, topk, expert_map):
B, D = a.shape
a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
score = torch.softmax(score, dim=-1, dtype=torch.float32)
topk_weight, topk_ids = torch.topk(score, topk)
topk_weight = topk_weight.view(-1)
topk_ids = topk_ids.view(-1)
if expert_map is not None:
topk_ids = expert_map[topk_ids]
for i in range(w1.shape[0]):
mask = topk_ids == i
if mask.sum():
out[mask] = SiluAndMul()(a[mask] @ w1[i].transpose(0, 1)) @ w2[i].transpose(
0, 1
)
return (
out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype)
).sum(dim=1)
def torch_moe_nvfp4(a, w1, w2, topk, topk_weight, topk_ids):
B, D = a.shape
a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
topk_weight = topk_weight.view(-1)
topk_ids = topk_ids.view(-1)
for i in range(w1.shape[0]):
mask = topk_ids == i
if mask.sum():
m = w1[i].shape[0]
assert m % 2 == 0
# Note: w1 and w3 are swapped!
w3_expert, w1_expert = w1[i][m // 2 :, :], w1[i][: m // 2, :]
inter = F.silu(a[mask] @ w1_expert.t()) * (a[mask] @ w3_expert.t())
inter_gs = torch.tensor(1.0).cuda()
inter_q, inter_blockscale = fp4_quantize(inter, inter_gs)
inter = dequantize_nvfp4_to_dtype(
inter_q,
inter_blockscale,
inter_gs,
dtype=inter.dtype,
device=inter.device,
block_size=16,
).cuda()
out[mask] = inter @ w2[i].transpose(0, 1)
return (
out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype)
).sum(dim=1)
def check_moe(
m: int,
n: int,
k: int,
e: int,
topk: int,
dtype: torch.dtype,
moe_impl: Callable,
flip_w13: bool,
):
torch.manual_seed(7)
a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
quant_blocksize = 16
round_up = lambda x, y: (x + y - 1) // y * y
sf_w1_2n = round_up(2 * n, 128)
sf_w1_k = round_up(k // quant_blocksize, 4)
w1_blockscale = torch.empty(
(e, sf_w1_2n, sf_w1_k), device="cuda", dtype=torch.float8_e4m3fn
)
w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
sf_w2_k = round_up(k, 128)
sf_w2_n = round_up(n // quant_blocksize, 4)
w2_blockscale = torch.empty(
(e, sf_w2_k, sf_w2_n), device="cuda", dtype=torch.float8_e4m3fn
)
w1_q = torch.empty((e, 2 * n, k // 2), device="cuda", dtype=torch.uint8)
w2_q = torch.empty((e, k, n // 2), device="cuda", dtype=torch.uint8)
w1_gs = torch.empty((e,), device="cuda", dtype=torch.float32)
w2_gs = torch.empty((e,), device="cuda", dtype=torch.float32)
for expert in range(e):
w1_amax = torch.abs(w1).max().to(torch.float32)
w2_amax = torch.abs(w2).max().to(torch.float32)
w1_gs[expert] = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w1_amax
w2_gs[expert] = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w2_amax
w1_q[expert], w1_blockscale[expert] = scaled_fp4_quant(
w1[expert], w1_gs[expert]
)
w2_q[expert], w2_blockscale[expert] = scaled_fp4_quant(
w2[expert], w2_gs[expert]
)
score = torch.randn((m, e), device="cuda", dtype=dtype)
topk_output = select_experts(
hidden_states=a,
router_logits=score,
topk_config=TopKConfig(top_k=topk, renormalize=False),
)
topk_weights, topk_ids, _ = topk_output
a1_gs = torch.ones((e,), device="cuda", dtype=torch.float32)
a2_gs = torch.ones((e,), device="cuda", dtype=torch.float32)
test_output = moe_impl(
a=a,
topk_weights=topk_weights,
topk_ids=topk_ids,
w1_q=w1_q,
w2_q=w2_q,
a1_gs=a1_gs,
w1_blockscale=w1_blockscale,
w1_alphas=(1 / w1_gs),
a2_gs=a2_gs,
w2_blockscale=w2_blockscale,
w2_alphas=(1 / w2_gs),
)
# Reference check:
a_global_scale = (
(FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.amax(a.flatten(), dim=-1)
).to(torch.float32)
a_fp4, a_scale_interleaved = scaled_fp4_quant(a, a_global_scale)
_, m_k = a_fp4.shape
a_in_dtype = dequantize_nvfp4_to_dtype(
a_fp4,
a_scale_interleaved,
a_global_scale,
dtype=a.dtype,
device=a.device,
block_size=quant_blocksize,
)
w1_d = torch.empty((e, 2 * n, k), device="cuda", dtype=dtype)
w2_d = torch.empty((e, k, n), device="cuda", dtype=dtype)
for idx in range(0, e):
w1_d[idx] = dequantize_nvfp4_to_dtype(
w1_q[idx],
w1_blockscale[idx],
w1_gs[idx],
dtype=w1.dtype,
device=w1.device,
block_size=quant_blocksize,
)
w2_d[idx] = dequantize_nvfp4_to_dtype(
w2_q[idx],
w2_blockscale[idx],
w2_gs[idx],
dtype=w2.dtype,
device=w2.device,
block_size=quant_blocksize,
)
if flip_w13:
dim = -2
size = w1_d.size(dim)
assert size % 2 == 0, f"Expected even size in dim {dim}, got {size}"
half = size // 2
# Reorder weight
w1, w3 = w1_d.split(half, dim=dim)
w1_d = torch.cat([w3, w1], dim=dim).contiguous()
torch_output = torch_moe(a_in_dtype, w1_d, w2_d, score, topk, None)
torch.testing.assert_close(torch_output, test_output, atol=1e-1, rtol=1e-1)
class TestFlashinferCutedslMoe(unittest.TestCase):
@unittest.skipIf(SKIP_TEST, SKIP_REASON)
def test_flashinfer_cutedsl_moe_masked(self):
# Test parameters
test_cases = [
(2, 128, 256, 1),
(2, 128, 256, 2),
(2, 128, 256, 4),
(16, 128, 512, 1),
(16, 128, 512, 2),
(16, 128, 512, 4),
]
for bs, hidden_dim, inter_dim, topk in test_cases:
with self.subTest(
bs=bs, hidden_dim=hidden_dim, inter_dim=inter_dim, topk=topk
):
print(
f"Testing with bs={bs}, hidden_dim={hidden_dim}, inter_dim={inter_dim}, topk={topk}"
)
with torch.inference_mode():
torch.manual_seed(42)
device = "cuda"
dtype = torch.bfloat16
num_experts = 8
hidden_states = (
torch.randn(bs, hidden_dim, dtype=torch.bfloat16, device=device)
/ 5.0
)
w1 = (
torch.randn(
num_experts,
2 * inter_dim,
hidden_dim,
dtype=torch.bfloat16,
device=device,
)
/ 10.0
)
w2 = (
torch.randn(
num_experts,
hidden_dim,
inter_dim,
dtype=torch.bfloat16,
device=device,
)
/ 10.0
)
router_logits = torch.randn(bs, num_experts, dtype=torch.float32)
hidden_states_expanded = (
hidden_states.view(bs, -1, hidden_dim)
.repeat(1, topk, 1)
.reshape(-1, hidden_dim)
)
hidden_states_3d, masked_m, topk_idx, routing_weights = (
prepare_inputs(
hidden_states_expanded, router_logits, num_experts, topk
)
)
w1_amax = w1.abs().amax(dim=(1, 2)).to(torch.float32).to(w1.device)
w2_amax = w2.abs().amax(dim=(1, 2)).to(torch.float32).to(w2.device)
input_global_scale = torch.ones(
(num_experts,), dtype=torch.float32, device=hidden_states.device
)
w1_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w1_amax
w2_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w2_amax
a2_global_scale = torch.ones(
(num_experts,), dtype=torch.float32, device=hidden_states.device
) # assume intermediate scale is 1.0
w1_fp4, w1_blockscale = scaled_fp4_grouped_quant(
w1,
w1_global_scale,
torch.ones(num_experts, dtype=torch.int32, device=w1.device)
* 2
* inter_dim,
)
w2_fp4, w2_blockscale = scaled_fp4_grouped_quant(
w2,
w2_global_scale,
torch.ones(num_experts, dtype=torch.int32, device=w2.device)
* hidden_dim,
)
w1_alpha = 1.0 / (input_global_scale * w1_global_scale)
w2_alpha = 1.0 / (a2_global_scale * w2_global_scale)
out = flashinfer_cutedsl_moe_masked(
(hidden_states_3d.to(hidden_states.device), None),
input_global_scale,
w1_fp4.permute(2, 0, 1),
w1_blockscale,
w1_alpha,
w2_fp4.permute(2, 0, 1),
a2_global_scale,
w2_blockscale,
w2_alpha,
masked_m.to(hidden_states.device),
)
# reference
a_fp4, a_scale_interleaved = fp4_quantize(
hidden_states, input_global_scale
)
a_in_dtype = dequantize_nvfp4_to_dtype(
a_fp4,
a_scale_interleaved,
input_global_scale,
dtype=hidden_states.dtype,
device=hidden_states.device,
block_size=16,
)
w1_d = torch.empty(
(num_experts, 2 * inter_dim, hidden_dim),
device=w1.device,
dtype=w1.dtype,
)
w2_d = torch.empty(
(num_experts, hidden_dim, inter_dim),
device=w2.device,
dtype=w2.dtype,
)
for idx in range(0, num_experts):
w1_fp4_sliced, w1_blockscale_sliced = fp4_quantize(
w1[idx], w1_global_scale[idx]
)
w2_fp4_sliced, w2_blockscale_sliced = fp4_quantize(
w2[idx], w2_global_scale[idx]
)
w1_d[idx] = dequantize_nvfp4_to_dtype(
w1_fp4_sliced,
w1_blockscale_sliced,
w1_global_scale[idx],
dtype=w1.dtype,
device=w1.device,
block_size=16,
)
w2_d[idx] = dequantize_nvfp4_to_dtype(
w2_fp4_sliced,
w2_blockscale_sliced,
w2_global_scale[idx],
dtype=w2.dtype,
device=w2.device,
block_size=16,
)
ref_output = torch_moe_nvfp4(
a_in_dtype,
w1_d,
w2_d,
topk,
routing_weights.to(a_in_dtype.device),
topk_idx.to(a_in_dtype.device),
)
out_weighted = torch.zeros_like(
ref_output, device=out.device, dtype=out.dtype
)
positions = torch.nonzero(masked_m[topk_idx], as_tuple=False)
rows, cols = positions[:, 0], positions[:, 1]
experts = topk_idx[rows, cols]
for i in range(num_experts):
mask = experts == i
if mask.any():
idx = torch.nonzero(mask, as_tuple=False).squeeze(-1)
r, c = rows[idx], cols[idx]
out_weighted[r] += out[i, : len(r), :] * routing_weights[
r, c
].to(out.device).unsqueeze(-1)
torch.testing.assert_close(
out_weighted.cpu(), ref_output.cpu(), atol=5e-2, rtol=5e-2
)
print(
f"Test passed with bs={bs}, hidden_dim={hidden_dim}, inter_dim={inter_dim}, topk={topk}"
)
if __name__ == "__main__":
unittest.main()
test/srt/test_cutedsl_flashinfer_8gpu.py → test/srt/test_deepseek_v3_cutedsl_4gpu.py
+18 -6
View File
@@ -24,20 +24,31 @@ class TestDeepseekR1Nvfp4CuteDSLDeepEP(CustomTestCase):
other_args = [
"--trust-remote-code",
"--disable-radix-cache",
"--mem-fraction-static",
"0.89",
"--max-prefill-tokens",
"16384",
"--max-running-requests",
"256",
"--chunked-prefill-size",
"2048",
"1024",
"--tp",
"8",
"4",
"--dp",
"8",
"4",
"--ep",
"4",
"--moe-dense-tp-size",
"1",
"--enable-dp-attention",
"--enable-ep-moe",
"--quantization",
"modelopt_fp4",
"--enable-flashinfer-cutedsl-moe",
"--enable-deepep-moe",
"--attention-backend",
"trtllm_mla",
"--moe-a2a-backend",
"deepep",
"--moe-runner-backend",
"flashinfer_cutedsl",
"--deepep-mode",
"low_latency",
]
@@ -50,6 +61,7 @@ class TestDeepseekR1Nvfp4CuteDSLDeepEP(CustomTestCase):
**os.environ,
"SGLANG_DEEPEP_BF16_DISPATCH": "1",
"SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256",
"SGLANG_CUTEDSL_MOE_NVFP4_DISPATCH": "0",
},
)