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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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85
tests/kernels/quantization/test_nvfp4_scaled_mm.py
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@@ -2,15 +2,16 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from nvfp4_utils import (FLOAT4_E2M1_MAX, FLOAT8_E4M3_MAX,
dequantize_nvfp4_to_dtype)
from nvfp4_utils import FLOAT4_E2M1_MAX, FLOAT8_E4M3_MAX, dequantize_nvfp4_to_dtype
from vllm import _custom_ops as ops
from vllm.platforms import current_platform
if not current_platform.has_device_capability(100):
pytest.skip(reason="Nvfp4 Requires compute capability of 10 or above.",
allow_module_level=True)
pytest.skip(
reason="Nvfp4 Requires compute capability of 10 or above.",
allow_module_level=True,
)
DTYPES = [torch.float16, torch.bfloat16]
# m, n, k
@@ -19,26 +20,31 @@ PAD_SHAPES = [(150, 128, 64), (128, 128, 96)]
SHAPES.extend(PAD_SHAPES)
SEEDS = [42]
CUDA_DEVICES = ['cuda:0']
CUDA_DEVICES = ["cuda:0"]
def get_ref_results(a_fp4, b_fp4, a_sf, b_sf, a_global_scale, b_global_scale,
m, n, dtype, block_size, device):
def get_ref_results(
a_fp4,
b_fp4,
a_sf,
b_sf,
a_global_scale,
b_global_scale,
m,
n,
dtype,
block_size,
device,
):
_, m_k = a_fp4.shape
_, n_k = b_fp4.shape
assert (m_k == n_k)
a_in_dtype = dequantize_nvfp4_to_dtype(a_fp4,
a_sf,
a_global_scale,
dtype=dtype,
device=device,
block_size=block_size)
b_in_dtype = dequantize_nvfp4_to_dtype(b_fp4,
b_sf,
b_global_scale,
dtype=dtype,
device=device,
block_size=block_size)
assert m_k == n_k
a_in_dtype = dequantize_nvfp4_to_dtype(
a_fp4, a_sf, a_global_scale, dtype=dtype, device=device, block_size=block_size
)
b_in_dtype = dequantize_nvfp4_to_dtype(
b_fp4, b_sf, b_global_scale, dtype=dtype, device=device, block_size=block_size
)
return torch.matmul(a_in_dtype, b_in_dtype.t())
@@ -60,25 +66,34 @@ def test_nvfp4_gemm(
a_dtype = torch.randn((m, k), dtype=dtype, device=device)
b_dtype = torch.randn((n, k), dtype=dtype, device=device)
a_global_scale = ((FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) /
torch.amax(a_dtype.flatten(), dim=-1)).to(torch.float32)
b_global_scale = ((FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) /
torch.amax(b_dtype.flatten(), dim=-1)).to(torch.float32)
alpha = 1. / (a_global_scale * b_global_scale)
a_global_scale = (
(FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.amax(a_dtype.flatten(), dim=-1)
).to(torch.float32)
b_global_scale = (
(FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.amax(b_dtype.flatten(), dim=-1)
).to(torch.float32)
alpha = 1.0 / (a_global_scale * b_global_scale)
# ops.scaled_fp4_quant returns swizzled scales, while weights
# from checkpoints are in linear scales.
a_fp4, a_scale_interleaved = ops.scaled_fp4_quant(a_dtype, a_global_scale)
b_fp4, b_scale_interleaved = ops.scaled_fp4_quant(b_dtype, b_global_scale)
# get_ref_results unswizzles the scales internally.
expected_out = get_ref_results(a_fp4, b_fp4, a_scale_interleaved,
b_scale_interleaved, a_global_scale,
b_global_scale, m, n, dtype, block_size,
device)
out = ops.cutlass_scaled_fp4_mm(a_fp4, b_fp4, a_scale_interleaved,
b_scale_interleaved, alpha, dtype)
expected_out = get_ref_results(
a_fp4,
b_fp4,
a_scale_interleaved,
b_scale_interleaved,
a_global_scale,
b_global_scale,
m,
n,
dtype,
block_size,
device,
)
out = ops.cutlass_scaled_fp4_mm(
a_fp4, b_fp4, a_scale_interleaved, b_scale_interleaved, alpha, dtype
)
torch.testing.assert_close(out,
expected_out.to(dtype=dtype),
atol=1e-1,
rtol=1e-1)
torch.testing.assert_close(out, expected_out.to(dtype=dtype), atol=1e-1, rtol=1e-1)