e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
193 lines
7.5 KiB
Python
193 lines
7.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import pytest
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import torch
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from tests.kernels.quant_utils import ref_dynamic_per_token_quant
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from tests.kernels.utils import opcheck
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from vllm._custom_ops import scaled_int8_quant
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from vllm.platforms import current_platform
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DTYPES = [torch.half, torch.bfloat16, torch.float]
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HIDDEN_SIZES = [16, 67, 768, 5137, 8193] # Arbitrary values for testing
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NUM_TOKENS = [1, 7, 83, 4096] # Arbitrary values for testing
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SEEDS = [0]
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SCALE = [0.1, 2.1]
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def opcheck_int8_quant_static(output, input, scale, azp=None):
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if azp is None:
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opcheck(torch.ops._C.static_scaled_int8_quant,
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(output, input, scale, None))
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else:
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opcheck(torch.ops._C.static_scaled_int8_quant,
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(output, input, scale, azp))
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def opcheck_int8_quant_dynamic(output, input, symmetric=True):
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scale = torch.empty((input.numel() // input.shape[-1], 1),
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device=input.device,
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dtype=torch.float32)
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if symmetric:
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opcheck(torch.ops._C.dynamic_scaled_int8_quant,
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(output, input, scale, None))
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else:
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azp = torch.empty((input.numel() // input.shape[-1], 1),
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device=input.device,
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dtype=torch.int32)
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opcheck(torch.ops._C.dynamic_scaled_int8_quant,
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(output, input, scale, azp))
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@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
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@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@torch.inference_mode()
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def test_dynamic_scaled_int8_quant(num_tokens: int, hidden_size: int,
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dtype: torch.dtype, seed: int) -> None:
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current_platform.seed_everything(seed)
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x = torch.rand(num_tokens, hidden_size, dtype=dtype, device="cuda") * 1000
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# reference
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ref_out, ref_scales = ref_dynamic_per_token_quant(x, torch.int8)
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# kernel
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ops_out, ops_scales, _ = scaled_int8_quant(x)
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torch.testing.assert_close(ops_scales, ref_scales)
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# big atol to account for rounding errors
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torch.testing.assert_close(ops_out, ref_out, atol=1, rtol=0.0)
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opcheck_int8_quant_dynamic(ops_out, x)
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@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
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@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@torch.inference_mode()
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def test_dynamic_scaled_int8_azp_quant(num_tokens: int, hidden_size: int,
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dtype: torch.dtype, seed: int) -> None:
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current_platform.seed_everything(seed)
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int8_traits = torch.iinfo(torch.int8)
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x = torch.rand(num_tokens, hidden_size, dtype=dtype,
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device="cuda") * 1000 - 300
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x_token_max, _ = x.to(dtype=torch.float32).max(dim=1, keepdim=True)
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x_token_min, _ = x.to(dtype=torch.float32).min(dim=1, keepdim=True)
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# calculate scale and azp, and adjust the range
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scales = (x_token_max - x_token_min) / torch.tensor(255.0)
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azps = torch.round(torch.tensor(-128.0) - x_token_min / scales).to(
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torch.int32)
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torch_out = ((x / scales).round() + azps).clamp(
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int8_traits.min, int8_traits.max).to(torch.int8)
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assert torch_out.min() >= int8_traits.min and torch_out.max(
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) <= int8_traits.max
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ops_out, scales_out, azp_out = scaled_int8_quant(x, symmetric=False)
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if (not torch.allclose(scales_out, scales)):
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print(torch.argmax(torch.abs(scales_out - scales)))
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torch.testing.assert_close(scales_out, scales)
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# big atol to account for rounding errors
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torch.testing.assert_close(azp_out, azps, atol=1, rtol=0.0)
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# if AZP is off by 1, after rounding-to-even, the output may be off by 2
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torch.testing.assert_close(ops_out, torch_out, atol=2, rtol=0.0)
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opcheck_int8_quant_dynamic(ops_out, x, False)
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@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
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@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@pytest.mark.parametrize("scale", SCALE)
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@torch.inference_mode()
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def test_static_scaled_int8_quant(num_tokens: int, hidden_size: int,
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dtype: torch.dtype, seed: int,
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scale: float) -> None:
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current_platform.seed_everything(seed)
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int8_traits = torch.iinfo(torch.int8)
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x = torch.rand(num_tokens, hidden_size, dtype=dtype, device="cuda") * 1000
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scale_arg = torch.tensor([scale], dtype=torch.float32, device="cuda")
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out1 = (x / scale_arg).round().clamp(int8_traits.min,
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int8_traits.max).to(torch.int8)
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out2, scale2, _ = scaled_int8_quant(x, scale_arg)
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assert scale2 is scale_arg
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# big atol to account for rounding errors
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torch.testing.assert_close(out1, out2, atol=1, rtol=0.0)
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opcheck_int8_quant_static(out2, x, scale_arg)
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@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
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@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@pytest.mark.parametrize("scale", SCALE)
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@pytest.mark.parametrize("azp", [-255, 54])
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@torch.inference_mode()
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def test_static_scaled_int8_azp_quant(num_tokens: int, hidden_size: int,
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dtype: torch.dtype, seed: int,
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scale: float, azp: int) -> None:
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current_platform.seed_everything(seed)
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int8_traits = torch.iinfo(torch.int8)
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x = torch.rand(num_tokens, hidden_size, dtype=dtype,
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device="cuda") * 1000 - 300
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out1 = ((x / scale).round() + azp).clamp(int8_traits.min,
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int8_traits.max).to(torch.int8)
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scale_arg = torch.tensor([scale], dtype=torch.float32, device="cuda")
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azp_arg = torch.tensor([azp], dtype=torch.int32, device="cuda")
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out2, scale2, azp2 = scaled_int8_quant(x,
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scale_arg,
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azp_arg,
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symmetric=False)
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assert scale2 is scale_arg
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assert azp2 is azp_arg
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# big atol to account for rounding errors
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torch.testing.assert_close(out1, out2, atol=1, rtol=0.0)
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opcheck_int8_quant_static(out2, x, scale_arg, azp_arg)
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@pytest.mark.parametrize("is_max", [True, False])
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@torch.inference_mode()
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def test_static_scaled_int8_azp_quant_saturating_cast(is_max: bool) -> None:
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# Test that the saturating cast works correctly for values near i32 max/min
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from numpy import inf, nextafter
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int32_traits = torch.iinfo(torch.int32)
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val = float(int32_traits.max if is_max else int32_traits.min)
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x_vals = [[
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nextafter(val, inf), val + 1, val, val - 1,
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nextafter(val, -inf)
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]]
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x = torch.tensor(x_vals, dtype=torch.float32, device="cuda")
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# The calculation in the kernel is: cast<int8>(cast<int32>(x / scale) + azp)
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# where cast<T> is a saturating cast to type T.
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# Scale is set to 1.0 so that the input values are the ones that are cast.
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# AZP is set to 0 to make sure the int8 saturating cast is tested as well.
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scale = torch.scalar_tensor(1.0, dtype=torch.float32, device="cuda")
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azp = torch.scalar_tensor(0, dtype=torch.int32, device="cuda")
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int8_traits = torch.iinfo(torch.int8)
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val_i8 = int8_traits.max if is_max else int8_traits.min
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expected = torch.full((1, 5), val_i8, dtype=torch.int8, device="cuda")
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out, _, _ = scaled_int8_quant(x, scale, azp, symmetric=False)
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torch.testing.assert_close(expected, out, atol=0, rtol=0)
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