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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>
92 lines
4.0 KiB
Python
92 lines
4.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import torch
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from vllm.logger import init_logger
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig, QuantizeMethodBase)
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from vllm.platforms import current_platform
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logger = init_logger(__name__)
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class BaseKVCacheMethod(QuantizeMethodBase):
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"""
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Quant method that adds `_k_scale` and `_v_scale` attributes to the
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Attention layer to support loading those scaling factors from checkpoints.
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The k/v_scale will be used to:
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- quantize k/v_cache entries before saving them to the cache
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- dequantize k/v_cache entries before fetching them from the cache
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:param quant_config: the appropriate QuantizationConfig
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"""
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def __init__(self, quant_config: QuantizationConfig):
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self.quant_config = quant_config
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def create_weights(self, layer: torch.nn.Module):
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"""
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Create "weight" (aka k_scale and v_scale) for an attention layer.
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"""
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# Initialize the KV cache scales to -1.0, which is an invalid value.
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# If the k/v_scale appears in the checkpoint, it will be
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# overwritten when loading weights.
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layer.k_scale = torch.nn.Parameter(torch.tensor(-1.0),
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requires_grad=False)
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layer.v_scale = torch.nn.Parameter(torch.tensor(-1.0),
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requires_grad=False)
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def apply(self, layer: torch.nn.Module) -> torch.Tensor:
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raise RuntimeError(
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f"{self.__class__.__name__}.apply should not be called.")
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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# If the kv-cache dtype is auto, we enforce the k/v_scale to be 1.0
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# regardless whether the kv-scale is available in the checkpoint.
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# No need to process kv scales after loading if we are going to
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# calculate them on the fly.
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if layer.kv_cache_dtype != "auto" and not layer.calculate_kv_scales:
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if layer.k_scale > 0.0 and layer.v_scale > 0.0:
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# We prefer to use separate k_scale and v_scale if present
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k_scale = layer.k_scale.to("cpu").tolist()
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v_scale = layer.v_scale.to("cpu").tolist()
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if current_platform.is_rocm():
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k_scale *= 2
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v_scale *= 2
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elif layer.k_scale < 0.0 and layer.v_scale < 0.0:
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# If no scales were loaded (both scales are invalid negative
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# values), use the default value of 1.0
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k_scale = 1.0
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v_scale = 1.0
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else:
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# If we find a single kv_scale in the checkpoint, we remap
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# kv_scale to k_scale during weight loading, and duplicate
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# k_scale to v_scale here
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assert layer.k_scale > 0.0
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scale_to_duplicate = max(layer.k_scale, layer.v_scale)
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k_scale = scale_to_duplicate.to("cpu").tolist()
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v_scale = scale_to_duplicate.to("cpu").tolist()
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if current_platform.is_rocm():
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k_scale *= 2
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v_scale *= 2
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if not isinstance(k_scale, float) or not isinstance(
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v_scale, float):
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raise ValueError("Only support per-tensor scaling factor "
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"for fp8 KV cache")
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# These are used in the final Attention.forward()
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layer._k_scale.copy_(k_scale)
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layer._v_scale.copy_(v_scale)
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layer._k_scale_float = k_scale
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layer._v_scale_float = v_scale
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if (k_scale == 1.0 and v_scale == 1.0
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and "e5m2" not in layer.kv_cache_dtype):
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logger.warning_once(
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"Using KV cache scaling factor 1.0 for fp8_e4m3. This "
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"may cause accuracy issues. Please make sure k/v_scale "
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"scaling factors are available in the fp8 checkpoint.")
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del layer.k_scale
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del layer.v_scale
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