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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>
130 lines
4.4 KiB
Python
130 lines
4.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from pathlib import Path
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from typing import List
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import pytest
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import torch
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from gguf import GGMLQuantizationType, GGUFReader, ReaderTensor, dequantize
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from huggingface_hub import snapshot_download
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import vllm._custom_ops as ops
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from vllm.platforms import current_platform
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GGUF_SAMPLE = snapshot_download("Isotr0py/test-gguf-sample")
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def get_gguf_sample_tensors(
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hidden_size: int,
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quant_type: GGMLQuantizationType) -> List[ReaderTensor]:
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sample_dir = GGUF_SAMPLE
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filename = f"Quant_{quant_type.name}_{hidden_size}.gguf"
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sample_file = Path(sample_dir) / filename
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return GGUFReader(sample_file).tensors
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DTYPES = [torch.half]
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# Hidden_size for testing, must match the sample file in HF repo,
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# we have `hidden_size = 256, 1024` for test in HF repo currently.
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HIDDEN_SIZES = [256, 1024]
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NUM_TOKENS = [7, 83, 128, 2048] # Arbitrary values for testing
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SEEDS = [0]
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QUANT_TYPES = [
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# i-matrix
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GGMLQuantizationType.IQ1_M,
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GGMLQuantizationType.IQ1_S,
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GGMLQuantizationType.IQ2_S,
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GGMLQuantizationType.IQ2_XS,
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GGMLQuantizationType.IQ3_S,
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GGMLQuantizationType.IQ3_XXS,
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GGMLQuantizationType.IQ4_NL,
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GGMLQuantizationType.IQ4_XS,
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# k-quants
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GGMLQuantizationType.Q2_K,
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GGMLQuantizationType.Q3_K,
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GGMLQuantizationType.Q4_K,
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GGMLQuantizationType.Q5_K,
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GGMLQuantizationType.Q6_K,
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# standard quantization
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GGMLQuantizationType.Q4_0,
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GGMLQuantizationType.Q5_0,
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GGMLQuantizationType.Q8_0,
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]
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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("quant_type", QUANT_TYPES)
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@torch.inference_mode()
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def test_dequantize(hidden_size: int, dtype: torch.dtype,
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quant_type: GGMLQuantizationType):
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tensors = get_gguf_sample_tensors(hidden_size, quant_type)
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for tensor in tensors:
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shape_str = tensor.name.split("_")[-1]
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shape = map(int, shape_str.split("x"))
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ref_output = torch.tensor(dequantize(tensor.data, quant_type),
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device="cuda").to(dtype)
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output = ops.ggml_dequantize(torch.tensor(tensor.data, device="cuda"),
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quant_type, *list(shape)).to(dtype)
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torch.testing.assert_close(output, ref_output, atol=1e-2, rtol=4e-2)
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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("quant_type", QUANT_TYPES)
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@torch.inference_mode()
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def test_mmvq(hidden_size: int, dtype: torch.dtype,
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quant_type: GGMLQuantizationType):
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current_platform.seed_everything(0)
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tensors = get_gguf_sample_tensors(hidden_size, quant_type)
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x = torch.rand((1, hidden_size), dtype=dtype, device="cuda")
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for tensor in tensors:
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weight = torch.tensor(dequantize(tensor.data, quant_type),
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device="cuda").to(dtype)
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ref_output = x @ weight.T
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qweight = torch.tensor(tensor.data, device="cuda")
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output = ops.ggml_mul_mat_vec_a8(qweight, x, quant_type,
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qweight.shape[0]).to(dtype)
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torch.testing.assert_close(output, ref_output, atol=1, rtol=1e-1)
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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(
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"quant_type",
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[
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# k-quants
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GGMLQuantizationType.Q2_K,
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GGMLQuantizationType.Q3_K,
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GGMLQuantizationType.Q4_K,
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GGMLQuantizationType.Q5_K,
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GGMLQuantizationType.Q6_K,
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# standard quants
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GGMLQuantizationType.Q4_0,
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GGMLQuantizationType.Q5_0,
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GGMLQuantizationType.Q8_0,
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])
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@torch.inference_mode()
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def test_mmq(num_tokens: int, hidden_size: int, dtype: torch.dtype,
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quant_type: GGMLQuantizationType):
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current_platform.seed_everything(0)
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tensors = get_gguf_sample_tensors(hidden_size, quant_type)
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x = torch.rand((num_tokens, hidden_size), dtype=dtype, device="cuda")
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for tensor in tensors:
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weight = torch.tensor(dequantize(tensor.data, quant_type),
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device="cuda").to(dtype)
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ref_output = x @ weight.T
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qweight = torch.tensor(tensor.data, device="cuda")
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output = ops.ggml_mul_mat_a8(qweight, x, quant_type,
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qweight.shape[0]).to(dtype)
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torch.testing.assert_close(output, ref_output, atol=1, rtol=1e-1)
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