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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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94
tests/kernels/quantization/test_fp8_quant_group.py
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@@ -6,8 +6,7 @@ import pytest
import torch
from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
from vllm.model_executor.layers.quantization.utils.quant_utils import (
GroupShape)
from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
from vllm.platforms import current_platform
@@ -18,13 +17,14 @@ from vllm.platforms import current_platform
(64, 1024, 64), # Medium
(128, 2048, 128), # Large
(8, 513, 64), # Non-divisible (native only)
])
],
)
@pytest.mark.parametrize("seed", [42])
@pytest.mark.parametrize("use_ue8m0", [True, False])
@torch.inference_mode()
def test_quantfp8_group_functionality(batch_size: int, hidden_dim: int,
group_size: int, seed: int,
use_ue8m0: bool) -> None:
def test_quantfp8_group_functionality(
batch_size: int, hidden_dim: int, group_size: int, seed: int, use_ue8m0: bool
) -> None:
"""Test QuantFP8 group quantization with various configurations.
Tests both CUDA and native implementations, column-major scales,
@@ -32,16 +32,17 @@ def test_quantfp8_group_functionality(batch_size: int, hidden_dim: int,
"""
current_platform.seed_everything(seed)
x = torch.randn(
(batch_size, hidden_dim), dtype=torch.bfloat16, device="cuda") * 8
x = torch.randn((batch_size, hidden_dim), dtype=torch.bfloat16, device="cuda") * 8
expected_num_groups = (hidden_dim + group_size - 1) // group_size
is_divisible = hidden_dim % group_size == 0
group_shape = GroupShape(1, group_size)
quant_op = QuantFP8(static=False,
group_shape=group_shape,
column_major_scales=False,
use_ue8m0=use_ue8m0)
quant_op = QuantFP8(
static=False,
group_shape=group_shape,
column_major_scales=False,
use_ue8m0=use_ue8m0,
)
# 1. Test native implementation (always available)
x_quant_native, scales_native = quant_op.forward_native(x.clone())
@@ -49,10 +50,12 @@ def test_quantfp8_group_functionality(batch_size: int, hidden_dim: int,
assert scales_native.shape == (batch_size, expected_num_groups)
# 2. Test column-major scales configuration
quant_op_col = QuantFP8(static=False,
group_shape=group_shape,
column_major_scales=True,
use_ue8m0=use_ue8m0)
quant_op_col = QuantFP8(
static=False,
group_shape=group_shape,
column_major_scales=True,
use_ue8m0=use_ue8m0,
)
_, scales_col = quant_op_col.forward_native(x.clone())
assert scales_col.shape == (batch_size, expected_num_groups)
assert scales_col.stride(0) == 1
@@ -86,41 +89,48 @@ def test_quantfp8_group_multidimensional(seed: int, use_ue8m0: bool) -> None:
# Test with 3D input
batch1, batch2, hidden_dim = 4, 8, 1024
x_3d = torch.randn(
(batch1, batch2, hidden_dim), dtype=torch.bfloat16, device="cuda") * 8
x_3d = (
torch.randn((batch1, batch2, hidden_dim), dtype=torch.bfloat16, device="cuda")
* 8
)
group_shape = GroupShape(1, group_size)
quant_op = QuantFP8(static=False,
group_shape=group_shape,
column_major_scales=False,
use_ue8m0=use_ue8m0)
quant_op = QuantFP8(
static=False,
group_shape=group_shape,
column_major_scales=False,
use_ue8m0=use_ue8m0,
)
x_quant, scales = quant_op.forward_native(x_3d.clone())
assert x_quant.shape == x_3d.shape
assert scales.shape == (batch1, batch2, hidden_dim // group_size)
# Test column_major_scales with multi-dim
quant_op_col = QuantFP8(static=False,
group_shape=group_shape,
column_major_scales=True,
use_ue8m0=use_ue8m0)
quant_op_col = QuantFP8(
static=False,
group_shape=group_shape,
column_major_scales=True,
use_ue8m0=use_ue8m0,
)
_, scales_col = quant_op_col.forward_native(x_3d.clone())
assert scales_col.shape == (batch1, batch2, hidden_dim // group_size)
# Test with 4D input
batch1, batch2, batch3, hidden_dim = 2, 3, 4, 256
x_4d = torch.randn((batch1, batch2, batch3, hidden_dim),
dtype=torch.bfloat16,
device="cuda") * 8
x_4d = (
torch.randn(
(batch1, batch2, batch3, hidden_dim), dtype=torch.bfloat16, device="cuda"
)
* 8
)
x_quant_4d, scales_4d = quant_op.forward_native(x_4d.clone())
assert x_quant_4d.shape == x_4d.shape
assert scales_4d.shape == (batch1, batch2, batch3,
hidden_dim // group_size)
assert scales_4d.shape == (batch1, batch2, batch3, hidden_dim // group_size)
_, scales_4d_col = quant_op_col.forward_native(x_4d.clone())
assert scales_4d_col.shape == (batch1, batch2, hidden_dim // group_size,
batch3)
assert scales_4d_col.shape == (batch1, batch2, hidden_dim // group_size, batch3)
@pytest.mark.parametrize("seed", [42])
@@ -132,30 +142,24 @@ def test_quantfp8_group_edge_cases(seed: int) -> None:
group_size = 64
# Test with single group (group_size >= hidden_dim)
x_small = torch.randn(
(batch_size, 32), dtype=torch.bfloat16, device="cuda") * 8
x_small = torch.randn((batch_size, 32), dtype=torch.bfloat16, device="cuda") * 8
group_shape = GroupShape(1, group_size)
quant_op = QuantFP8(static=False,
group_shape=group_shape,
column_major_scales=False)
quant_op = QuantFP8(
static=False, group_shape=group_shape, column_major_scales=False
)
x_quant_small, scales_small = quant_op.forward_native(x_small.clone())
assert x_quant_small.shape == x_small.shape
assert scales_small.shape == (batch_size, 1)
# Test with zero inputs
x_zero = torch.zeros((batch_size, 256),
dtype=torch.bfloat16,
device="cuda")
x_zero = torch.zeros((batch_size, 256), dtype=torch.bfloat16, device="cuda")
x_quant_zero, scales_zero = quant_op.forward_native(x_zero.clone())
assert x_quant_zero.shape == x_zero.shape
assert (scales_zero > 0).all(), "Scales should be clamped to minimum"
# Test very large values
x_large = torch.full((batch_size, 256),
1000.0,
dtype=torch.bfloat16,
device="cuda")
x_large = torch.full((batch_size, 256), 1000.0, dtype=torch.bfloat16, device="cuda")
x_quant_large, scales_large = quant_op.forward_native(x_large.clone())
assert x_quant_large.shape == x_large.shape
# FP8 max is typically 448 or 224, so scales should be > 1