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>
119 lines
4.3 KiB
Python
119 lines
4.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Tests register custom quantization config.
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See https://github.com/vllm-project/vllm/issues/11926 for more details.
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Run `pytest tests/quantization/test_register_quantization_config.py`.
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"""
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from typing import Any, Dict, List, Optional
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import pytest
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import torch
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import torch.nn.functional as F
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from vllm.model_executor.layers.linear import LinearBase # noqa: E501
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from vllm.model_executor.layers.linear import UnquantizedLinearMethod
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from vllm.model_executor.layers.quantization import (
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get_quantization_config, register_quantization_config)
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from vllm.model_executor.layers.quantization.base_config import ( # noqa: E501
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QuantizationConfig)
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class FakeQuantLinearMethod(UnquantizedLinearMethod):
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"""Fake quantization linear method for per-token dynamic quantization."""
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def __init__(self, num_bits: int = 8) -> None:
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"""Initialize the quantization method."""
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super().__init__()
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self.num_bits = num_bits
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def apply(self,
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layer: "torch.nn.Module",
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x: "torch.Tensor",
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bias: Optional["torch.Tensor"] = None) -> "torch.Tensor":
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"""Perform fake quantization before the linear layer."""
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# Calculate the scales dynamically
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max_val = torch.amax(x, dim=(0, -1), keepdims=True)
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min_val = torch.amin(x, dim=(0, -1), keepdims=True)
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scales = (max_val - min_val) / (2**self.num_bits - 1)
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# Fake quantize the input
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quant_x = torch.clamp(torch.round(x / scales), -2**(self.num_bits - 1),
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2**(self.num_bits - 1) - 1)
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dequant_x = quant_x * scales
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return F.linear(dequant_x, layer.weight, bias)
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@register_quantization_config("custom_quant")
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class CustomQuantConfig(QuantizationConfig):
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"""Custom quantization config for per-token dynamic fake quantization."""
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def __init__(self, num_bits: int = 8) -> None:
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"""Initialize the quantization config."""
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self.num_bits = num_bits
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def get_name(self) -> str:
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"""Name of the quantization method."""
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return "custom_quant"
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def get_supported_act_dtypes(self) -> List["torch.dtype"]:
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"""List of supported activation dtypes."""
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return [torch.float16, torch.bfloat16]
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@classmethod
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def get_min_capability(cls) -> int:
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"""Minimum GPU capability to support the quantization method."""
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return -1
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@staticmethod
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def get_config_filenames() -> List[str]:
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"""List of filenames to search for in the model directory."""
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return []
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "CustomQuantConfig":
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"""Create a config class from the model's quantization config."""
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return CustomQuantConfig(num_bits=config.get("num_bits", 8))
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def get_quant_method(self, layer: "torch.nn.Module",
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prefix: str) -> Optional["FakeQuantLinearMethod"]:
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"""Get the quantize method to use for the quantized layer."""
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if isinstance(layer, LinearBase):
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return FakeQuantLinearMethod(num_bits=self.num_bits)
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return None
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def test_register_quantization_config():
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"""Test register custom quantization config."""
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# The quantization method `custom_quant` should be registered.
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assert get_quantization_config("custom_quant") == CustomQuantConfig
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# The quantization method `custom_quant` is already exists,
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# should raise an error.
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with pytest.raises(ValueError):
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register_quantization_config("custom_quant")(CustomQuantConfig)
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@pytest.mark.parametrize(argnames="model",
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argvalues=[
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"meta-llama/Meta-Llama-3-8B-Instruct",
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])
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def test_custom_quant(vllm_runner, model):
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"""Test infer with the custom quantization method."""
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with vllm_runner(model_name=model,
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quantization="custom_quant",
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enforce_eager=True) as llm:
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model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501
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layer = model.model.layers[0]
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qkv_proj = layer.self_attn.qkv_proj
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# Check the quantization method is FakeQuantLinearMethod
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assert isinstance(qkv_proj.quant_method, FakeQuantLinearMethod)
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output = llm.generate_greedy("Hello my name is", max_tokens=20)
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assert output
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