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6e668dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from tests.kernels.quant_utils import ref_dynamic_per_token_quant
from tests.kernels.utils import opcheck
from vllm._custom_ops import scaled_int8_quant
from vllm.utils.torch_utils import set_random_seed
DTYPES = [torch.bfloat16, torch.float]
HIDDEN_SIZES = [17, 1024, 1025, 1026, 5137, 8193]
NUM_TOKENS = [1, 7, 4096]
SEEDS = [0]
SCALE = [0.1, 2.1]
def opcheck_int8_quant_static(output, input, scale, azp=None):
if azp is None:
opcheck(torch.ops._C.static_scaled_int8_quant, (output, input, scale, None))
else:
opcheck(torch.ops._C.static_scaled_int8_quant, (output, input, scale, azp))
def opcheck_int8_quant_dynamic(output, input, symmetric=True):
scale = torch.empty(
(input.numel() // input.shape[-1], 1), device=input.device, dtype=torch.float32
)
if symmetric:
opcheck(torch.ops._C.dynamic_scaled_int8_quant, (output, input, scale, None))
else:
azp = torch.empty(
(input.numel() // input.shape[-1], 1),
device=input.device,
dtype=torch.int32,
)
opcheck(torch.ops._C.dynamic_scaled_int8_quant, (output, input, scale, azp))
@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@torch.inference_mode()
def test_dynamic_scaled_int8_quant(
num_tokens: int, hidden_size: int, dtype: torch.dtype, seed: int
) -> None:
set_random_seed(seed)
x = torch.rand(num_tokens, hidden_size, dtype=dtype, device="cuda") * 1000
# reference
ref_out, ref_scales = ref_dynamic_per_token_quant(x, torch.int8)
# kernel
ops_out, ops_scales, _ = scaled_int8_quant(x)
torch.testing.assert_close(ops_scales, ref_scales)
# big atol to account for rounding errors
torch.testing.assert_close(ops_out, ref_out, atol=1, rtol=0.0)
opcheck_int8_quant_dynamic(ops_out, x)
@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@torch.inference_mode()
def test_dynamic_scaled_int8_azp_quant(
num_tokens: int, hidden_size: int, dtype: torch.dtype, seed: int
) -> None:
set_random_seed(seed)
int8_traits = torch.iinfo(torch.int8)
x = torch.rand(num_tokens, hidden_size, dtype=dtype, device="cuda") * 1000 - 300
x_token_max, _ = x.to(dtype=torch.float32).max(dim=1, keepdim=True)
x_token_min, _ = x.to(dtype=torch.float32).min(dim=1, keepdim=True)
# calculate scale and azp, and adjust the range
scales = (x_token_max - x_token_min) / torch.tensor(255.0)
azps = torch.round(torch.tensor(-128.0) - x_token_min / scales).to(torch.int32)
torch_out = (
((x / scales).round() + azps)
.clamp(int8_traits.min, int8_traits.max)
.to(torch.int8)
)
assert torch_out.min() >= int8_traits.min and torch_out.max() <= int8_traits.max
ops_out, scales_out, azp_out = scaled_int8_quant(x, symmetric=False)
if not torch.allclose(scales_out, scales):
print(torch.argmax(torch.abs(scales_out - scales)))
torch.testing.assert_close(scales_out, scales)
# big atol to account for rounding errors
torch.testing.assert_close(azp_out, azps, atol=1, rtol=0.0)
# if AZP is off by 1, after rounding-to-even, the output may be off by 2
torch.testing.assert_close(ops_out, torch_out, atol=2, rtol=0.0)
opcheck_int8_quant_dynamic(ops_out, x, False)
@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("scale", SCALE)
@torch.inference_mode()
def test_static_scaled_int8_quant(
num_tokens: int, hidden_size: int, dtype: torch.dtype, seed: int, scale: float
) -> None:
set_random_seed(seed)
int8_traits = torch.iinfo(torch.int8)
x = torch.rand(num_tokens, hidden_size, dtype=dtype, device="cuda") * 1000
scale_arg = torch.tensor([scale], dtype=torch.float32, device="cuda")
out1 = (
(x / scale_arg).round().clamp(int8_traits.min, int8_traits.max).to(torch.int8)
)
out2, scale2, _ = scaled_int8_quant(x, scale_arg)
assert scale2 is scale_arg
# big atol to account for rounding errors
torch.testing.assert_close(out1, out2, atol=1, rtol=0.0)
opcheck_int8_quant_static(out2, x, scale_arg)
@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("scale", SCALE)
@pytest.mark.parametrize("azp", [-255, 54])
@torch.inference_mode()
def test_static_scaled_int8_azp_quant(
num_tokens: int,
hidden_size: int,
dtype: torch.dtype,
seed: int,
scale: float,
azp: int,
) -> None:
set_random_seed(seed)
int8_traits = torch.iinfo(torch.int8)
x = torch.rand(num_tokens, hidden_size, dtype=dtype, device="cuda") * 1000 - 300
out1 = (
((x / scale).round() + azp)
.clamp(int8_traits.min, int8_traits.max)
.to(torch.int8)
)
scale_arg = torch.tensor([scale], dtype=torch.float32, device="cuda")
azp_arg = torch.tensor([azp], dtype=torch.int32, device="cuda")
out2, scale2, azp2 = scaled_int8_quant(x, scale_arg, azp_arg, symmetric=False)
assert scale2 is scale_arg
assert azp2 is azp_arg
# big atol to account for rounding errors
torch.testing.assert_close(out1, out2, atol=1, rtol=0.0)
opcheck_int8_quant_static(out2, x, scale_arg, azp_arg)
@pytest.mark.parametrize("is_max", [True, False])
@torch.inference_mode()
def test_static_scaled_int8_azp_quant_saturating_cast(is_max: bool) -> None:
# Test that the saturating cast works correctly for values near i32 max/min
from numpy import inf, nextafter
int32_traits = torch.iinfo(torch.int32)
val = float(int32_traits.max if is_max else int32_traits.min)
x_vals = [[nextafter(val, inf), val + 1, val, val - 1, nextafter(val, -inf)]]
x = torch.tensor(x_vals, dtype=torch.float32, device="cuda")
# The calculation in the kernel is: cast<int8>(cast<int32>(x / scale) + azp)
# where cast<T> is a saturating cast to type T.
# Scale is set to 1.0 so that the input values are the ones that are cast.
# AZP is set to 0 to make sure the int8 saturating cast is tested as well.
scale = torch.scalar_tensor(1.0, dtype=torch.float32, device="cuda")
azp = torch.scalar_tensor(0, dtype=torch.int32, device="cuda")
int8_traits = torch.iinfo(torch.int8)
val_i8 = int8_traits.max if is_max else int8_traits.min
expected = torch.full((1, 5), val_i8, dtype=torch.int8, device="cuda")
out, _, _ = scaled_int8_quant(x, scale, azp, symmetric=False)
torch.testing.assert_close(expected, out, atol=0, rtol=0)
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