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>
91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Optional, Tuple, Union
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import torch
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from vllm.platforms import current_platform
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# Using the default value (240.0) from pytorch will cause accuracy
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# issue on dynamic quantization models. Here use 224.0 for rocm.
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ROCM_FP8_MAX = 224.0
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FP8_DTYPE = torch.float8_e4m3fnuz if current_platform.is_rocm() \
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else torch.float8_e4m3fn
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def as_float32_tensor(x: Union[float, torch.tensor]) -> torch.tensor:
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return torch.as_tensor(x, dtype=torch.float32, device='cuda')
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def ref_dynamic_per_token_quant(x: torch.tensor,
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quant_dtype: torch.dtype,
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scale_ub: Optional[torch.tensor] = None) \
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-> Tuple[torch.tensor, torch.tensor]:
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assert quant_dtype in [torch.int8, FP8_DTYPE]
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if scale_ub is not None:
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assert quant_dtype == FP8_DTYPE
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qtype_traits = torch.iinfo(quant_dtype) if quant_dtype == torch.int8 \
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else torch.finfo(quant_dtype)
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qtype_traits_max = ROCM_FP8_MAX if current_platform.is_rocm() \
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else qtype_traits.max
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qtype_traits_min = -ROCM_FP8_MAX if current_platform.is_rocm() \
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else qtype_traits.min
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qtype_max = as_float32_tensor(qtype_traits_max)
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s_1 = as_float32_tensor(1.0)
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s_512 = as_float32_tensor(512.0)
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# For fp8, in order to match the cuda kernel output, we have to do exactly
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# the same operations as in the corresponding fp8 kernel to prevent
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# rounding errors.
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# Compute scales
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x_token_max, _ = x.abs().max(dim=-1)
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x_token_max = as_float32_tensor(x_token_max)
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if scale_ub is not None:
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x_token_max = x_token_max.clamp(max=scale_ub)
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scales = (x_token_max / qtype_max)[:, None]
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# Quant
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if quant_dtype == torch.int8:
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iscales = as_float32_tensor(s_1 / scales)
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torch_out = as_float32_tensor(x) * iscales
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torch_out = torch_out.round()
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torch_out = torch_out.clamp(qtype_traits_min,
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qtype_traits_max).to(quant_dtype)
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else:
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assert quant_dtype == FP8_DTYPE
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min_scaling_factor = s_1 / (qtype_max * s_512)
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scales = scales.clamp(min=min_scaling_factor)
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torch_out = as_float32_tensor(x) / scales
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torch_out = torch_out.clamp(qtype_traits_min,
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qtype_traits_max).to(quant_dtype)
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return torch_out, scales
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# The int8 version is very similar. Incorporate the int8 version, like in
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# ref_dynamic_per_token_quant, when we have a dynamic_per_tensor int8 quant
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# kernel
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def ref_dynamic_per_tensor_fp8_quant(x: torch.tensor) \
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-> Tuple[torch.tensor, torch.tensor]:
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fp8_traits = torch.finfo(FP8_DTYPE)
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fp8_traits_max = ROCM_FP8_MAX if current_platform.is_rocm() \
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else fp8_traits.max
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fp8_traits_min = -ROCM_FP8_MAX if current_platform.is_rocm() \
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else fp8_traits.min
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fp8_max = as_float32_tensor(fp8_traits_max)
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one = as_float32_tensor(1.0)
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# For fp8, in order to match the cuda kernel output, we have to do exactly
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# the same operations as in the corresponding fp8 kernel to prevent
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# rounding errors.
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x_max = as_float32_tensor(x.abs().max())
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ref_scale = x_max / fp8_max
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ref_iscale = one / ref_scale
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ref_out = (as_float32_tensor(x) * ref_iscale).clamp(
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fp8_traits_min, fp8_traits_max).to(FP8_DTYPE)
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return ref_out, ref_scale.view((1, ))
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