Skip to content

Use cuDNN for row-scaled NVFP4 grouped GEMM #3042

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft
zianglih wants to merge 4 commits into
base: main
Choose a base branch
from
Draft

Use cuDNN for row-scaled NVFP4 grouped GEMM #3042

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
b07e8a0
Use cuDNN for row-scaled NVFP4 grouped GEMM
zianglih May 23, 2026
ea5d283
Require cuDNN for row-scaled NVFP4 grouped GEMM
zianglih May 26, 2026
96ae951
Refactor row-scaled cuDNN grouped GEMM integration
zianglih Jul 13, 2026
6a71ed3
Tighten row-scaled cuDNN grouped GEMM integration
zianglih Jul 13, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
165 changes: 102 additions & 63 deletions tests/pytorch/nvfp4/test_nvfp4_gemm_exact.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -240,8 +240,6 @@ def check_nvfp4_gemm_versus_reference(


def check_nvfp4_row_scaled_grouped_gemm_matches_per_gemm(
x_dtype: torch.dtype,
w_dtype: torch.dtype,
out_dtype: torch.dtype,
m_splits: list[int],
k: int,
Expand All @@ -250,15 +248,15 @@ def check_nvfp4_row_scaled_grouped_gemm_matches_per_gemm(
use_bias: bool,
single_output: bool,
use_4over6: bool = False,
nvfp4_4over6_err_mode: str = "MAE",
monkeypatch=None,
expected_error=None,
):
te_dtype = te.DType.kFloat4E2M1
device = "cuda"
torch.manual_seed(23)
torch.cuda.manual_seed(23)

num_gemms = len(m_splits)

dtype = torch.bfloat16
x_quantizer = NVFP4Quantizer(
fp4_dtype=te_dtype,
rowwise=True,
Expand All @@ -269,7 +267,6 @@ def check_nvfp4_row_scaled_grouped_gemm_matches_per_gemm(
with_post_rht_amax=False,
row_scaled_nvfp4=True,
nvfp4_use_4over6=use_4over6,
nvfp4_4over6_err_mode=nvfp4_4over6_err_mode,
)
w_quantizer = NVFP4Quantizer(
fp4_dtype=te_dtype,
Expand All @@ -280,61 +277,102 @@ def check_nvfp4_row_scaled_grouped_gemm_matches_per_gemm(
with_rht=False,
with_post_rht_amax=False,
nvfp4_use_4over6=use_4over6,
nvfp4_4over6_err_mode=nvfp4_4over6_err_mode,
)

x_nvfp4 = []
w_nvfp4 = []
bias = []
expected = []
for m in m_splits:
x = torch.randn((m, k), dtype=x_dtype, device=device)
w = torch.randn((n, k), dtype=w_dtype, device=device)
x = torch.randn((m, k), dtype=dtype, device=device)
w = torch.randn((n, k), dtype=dtype, device=device)
x_nvfp4.append(
x_quantizer.update_quantized(
x, x_quantizer.make_empty(x.shape, dtype=x_dtype, device=device)
x, x_quantizer.make_empty(x.shape, dtype=dtype, device=device)
)
)
w_nvfp4.append(
w_quantizer.update_quantized(
w, w_quantizer.make_empty(w.shape, dtype=w_dtype, device=device)
w, w_quantizer.make_empty(w.shape, dtype=dtype, device=device)
)
)
bias.append(torch.randn(n, dtype=torch.bfloat16, device=device) if use_bias else None)
expected.append(
general_gemm(
w_nvfp4[-1],
x_nvfp4[-1],
out_dtype=out_dtype,
layout="TN",
bias=bias[-1],
)[0]
)
bias.append(torch.randn(n, dtype=dtype, device=device) if use_bias else None)
if expected_error is None:
expected.append(
general_gemm(
w_nvfp4[-1],
x_nvfp4[-1],
out_dtype=out_dtype,
layout="TN",
bias=bias[-1],
)[0]
)

if single_output:
out = [torch.empty((sum(m_splits), n), dtype=out_dtype, device=device)]
else:
out = [torch.empty((m, n), dtype=out_dtype, device=device) for m in m_splits]

grouped_out, _, _ = general_grouped_gemm(
w_nvfp4,
x_nvfp4,
out,
quantization_params=[None] * num_gemms,
out_dtype=out_dtype,
layout="TN",
m_splits=m_splits,
bias=bias,
use_bias=use_bias,
single_output=single_output,
grouped_gemm_args = (w_nvfp4, x_nvfp4, out)
grouped_gemm_kwargs = {
"quantization_params": [None] * len(m_splits),
"out_dtype": out_dtype,
"layout": "TN",
"m_splits": m_splits,
"bias": bias,
"use_bias": use_bias,
"single_output": single_output,
}
if expected_error is not None:
with pytest.raises(NotImplementedError, match=expected_error):
general_grouped_gemm(*grouped_gemm_args, **grouped_gemm_kwargs)
return

try:
import cudnn
except ImportError as exc:
pytest.skip(f"cudnn frontend unavailable: {exc}")
if not hasattr(cudnn, "grouped_gemm_quant_wrapper_sm100"):
pytest.skip("grouped_gemm_quant_wrapper_sm100 unavailable")

calls = []
original_wrapper = cudnn.grouped_gemm_quant_wrapper_sm100

def traced_wrapper(*args, **kwargs):
calls.append(kwargs)
return original_wrapper(*args, **kwargs)

monkeypatch.setattr(cudnn, "grouped_gemm_quant_wrapper_sm100", traced_wrapper)
grouped_out, _, _ = general_grouped_gemm(*grouped_gemm_args, **grouped_gemm_kwargs)

assert len(calls) == 1
call = calls[0]
global_scale_denom = 448.0 * 6.0
expected_alpha = (
torch.cat([tensor._amax_rowwise.view(-1) for tensor in w_nvfp4]) / global_scale_denom
)
expected_row_scale = (
torch.cat([tensor._amax_rowwise.view(-1) for tensor in x_nvfp4]) / global_scale_denom
)
torch.testing.assert_close(call["alpha_tensor"], expected_alpha, atol=0.0, rtol=0.0)
torch.testing.assert_close(call["row_scale_tensor"], expected_row_scale, atol=0.0, rtol=0.0)
torch.testing.assert_close(
call["padded_offsets"],
torch.tensor(m_splits, dtype=torch.int32, device=device).cumsum(0, dtype=torch.int32),
atol=0,
rtol=0,
)
assert call["sf_vec_size"] == 16
assert call["b_major"] == "k"
assert torch.count_nonzero(call["prob_tensor"] != 1).item() == 0
assert (call["bias_tensor"] is not None) == use_bias

if single_output:
grouped_slices = torch.split(grouped_out, m_splits, dim=0)
else:
grouped_slices = grouped_out
for grouped, ref in zip(grouped_slices, expected):
torch.testing.assert_close(grouped, ref, atol=0.0, rtol=0.0)
torch.testing.assert_close(grouped, ref, atol=0.125, rtol=0.25)


def check_nvfp4_row_scaled_gemm_matches_emulated(
Expand Down Expand Up @@ -489,53 +527,54 @@ def test_nvfp4_gemm_versus_reference(
)


@pytest.mark.skipif(not recipe_available, reason=reason_for_no_recipe)
@pytest.mark.parametrize("use_bias", [False, True], ids=["no_bias", "bias"])
@pytest.mark.parametrize("single_output", [False, True], ids=["list_output", "single_output"])
def test_nvfp4_row_scaled_grouped_gemm_matches_per_gemm(
use_bias: bool,
single_output: bool,
monkeypatch,
):
if torch.cuda.get_device_capability() < (10, 0):
pytest.skip("Requires SM100+ for cuDNN grouped GEMM quant kernel.")
check_nvfp4_row_scaled_grouped_gemm_matches_per_gemm(
out_dtype=torch.bfloat16,
m_splits=[256, 256, 256, 256],
k=512,
n=512,
use_bias=use_bias,
single_output=single_output,
monkeypatch=monkeypatch,
)


@pytest.mark.skipif(not recipe_available, reason=reason_for_no_recipe)
@pytest.mark.parametrize(
"m_splits, k, n",
"m_splits, k, n, out_dtype, use_4over6, expected_error",
[
([32, 48, 48], 128, 128),
([64, 80, 112], 128, 256),
([64, 80, 112], 256, 256),
([64, 80, 112], 1024, 256),
([256, 256, 512], 1024, 1024),
([1024, 1536, 1536], 512, 3072),
([16, 32, 64], 128, 96),
([80, 96, 128], 640, 304),
([320, 336, 352], 3072, 992),
([64, 80, 112], 64, 256),
([32, 48, 48], 128, 112),
([128, 256], 128, 128, torch.bfloat16, False, "M multiples of 256"),
([256, 256], 64, 128, torch.bfloat16, False, "K and N multiples of 128"),
([256, 256], 128, 128, torch.float32, False, "BF16/FP16 outputs"),
([256, 256], 128, 128, torch.bfloat16, True, "does not support 4over6"),
],
)
@pytest.mark.parametrize("x_dtype", [torch.float32, torch.bfloat16], ids=str)
@pytest.mark.parametrize("w_dtype", [torch.float32, torch.bfloat16], ids=str)
@pytest.mark.parametrize("out_dtype", [torch.float32, torch.bfloat16], ids=str)
@pytest.mark.parametrize("use_bias", [False, True], ids=["no_bias", "bias"])
@pytest.mark.parametrize("single_output", [False, True], ids=["list_output", "single_output"])
@pytest.mark.parametrize("use_4over6", [False, True], ids=["default", "4over6"])
@pytest.mark.parametrize("nvfp4_4over6_err_mode", ["MAE", "MSE"], ids=["mae_err", "mse_err"])
def test_nvfp4_row_scaled_grouped_gemm_matches_per_gemm(
def test_nvfp4_row_scaled_grouped_gemm_rejects_unsupported(
m_splits: list[int],
k: int,
n: int,
x_dtype: torch.dtype,
w_dtype: torch.dtype,
out_dtype: torch.dtype,
use_bias: bool,
single_output: bool,
use_4over6: bool,
nvfp4_4over6_err_mode: str,
expected_error: str,
):
check_nvfp4_row_scaled_grouped_gemm_matches_per_gemm(
x_dtype=x_dtype,
w_dtype=w_dtype,
out_dtype=out_dtype,
m_splits=m_splits,
k=k,
n=n,
use_bias=use_bias,
single_output=single_output,
use_bias=False,
single_output=True,
use_4over6=use_4over6,
nvfp4_4over6_err_mode=nvfp4_4over6_err_mode,
expected_error=expected_error,
)


Expand Down
Loading