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>
71 lines
2.8 KiB
Python
71 lines
2.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Compare the outputs of a AQLM model between vLLM and HF Transformers
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Run `pytest tests/models/test_aqlm.py`.
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"""
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import pytest
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from tests.quantization.utils import is_quant_method_supported
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# These ground truth generations were generated using `transformers==4.38.1
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# aqlm==1.1.0 torch==2.2.0`
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# and the below code:
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# ```python
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# from transformers import AutoTokenizer, AutoModelForCausalLM
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# model_id = "ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf"
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# quantized_model = AutoModelForCausalLM.from_pretrained(model_id,
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# torch_dtype="auto", device_map="cuda").cuda()
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# tokenizer = AutoTokenizer.from_pretrained(model_id)
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# outputs = []
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# for prompt in example_prompts:
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# input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to("cuda")
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# hf_outputs = quantized_model.generate(input_ids, max_new_tokens=32)
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# outputs.append(tokenizer.decode(hf_outputs[0][input_ids.shape[1]:]))
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# print(outputs)
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# ```
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ground_truth_generations = [
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'\n### Features\n\n- **High-throughput**: v',
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'The major milestones in the development of artificial intelligence from '
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'195',
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'Compare and contrast artificial intelligence with human intelligence in '
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'terms of processing information. The',
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'Explain the difference between supervised and unsupervised learning.'
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'\nExplain',
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'Write a short story about a robot that dreams for the first time. The',
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'Analyze the impact of the COVID-19 pandemic on global economic',
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'The Mona Lisa is a painting by Leonardo da Vinci, and it',
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'The early bird catches the worm.\nThe early bird catches the'
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]
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@pytest.mark.quant_model
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@pytest.mark.skipif(not is_quant_method_supported("aqlm"),
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reason="AQLM is not supported on this GPU type.")
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@pytest.mark.parametrize("model", ["ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf"])
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@pytest.mark.parametrize("dtype", ["half"])
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@pytest.mark.parametrize("max_tokens", [16])
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@pytest.mark.parametrize("num_logprobs", [1])
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def test_models(
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vllm_runner,
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example_prompts,
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model: str,
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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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) -> None:
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with vllm_runner(model, dtype=dtype) as vllm_model:
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vllm_outputs = vllm_model.generate_greedy_logprobs(
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example_prompts, max_tokens, num_logprobs)
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# loop through the prompts to compare against the ground truth generations
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for prompt_idx in range(len(example_prompts)):
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vllm_output_ids, vllm_output_str, vllm_logprobs = vllm_outputs[
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prompt_idx]
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print("Prompt: ", repr(example_prompts[prompt_idx]))
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print("Reference output:", repr(ground_truth_generations[prompt_idx]))
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print("Output output: ", repr(vllm_output_str))
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assert vllm_output_str == ground_truth_generations[prompt_idx]
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