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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>
105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from functools import partial
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from typing import List, Optional, Tuple, Type
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import pytest
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from PIL import Image
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from vllm.inputs.data import ExplicitEncoderDecoderPrompt
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from vllm.sequence import SampleLogprobs
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from ....conftest import HfRunner, VllmRunner
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from ...utils import check_logprobs_close
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Florence2Prompt = partial(ExplicitEncoderDecoderPrompt,
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decoder_prompt=None,
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mm_processor_kwargs=None)
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MODELS = ["microsoft/Florence-2-base"]
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# Florence-2 uses BartFastTokenizer which can't be loaded from AutoTokenizer
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# Therefore, we borrow the BartTokenizer from the original Bart model
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TOKENIZER = "facebook/bart-base"
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PROMPTS = [
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Florence2Prompt(encoder_prompt="<CAPTION>"),
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Florence2Prompt(encoder_prompt="<DETAILED_CAPTION>"),
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Florence2Prompt(encoder_prompt="<MORE_DETAILED_CAPTION>"),
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Florence2Prompt(encoder_prompt="<CAPTION_TO_PHRASE_GROUNDING>"),
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Florence2Prompt(encoder_prompt="<DENSE_REGION_CAPTION>"),
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Florence2Prompt(encoder_prompt="<REGION_PROPOSAL>"),
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Florence2Prompt(encoder_prompt="<OCR_WITH_REGION>"),
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Florence2Prompt(encoder_prompt="<OCR>"),
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Florence2Prompt(encoder_prompt="<OD>"),
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]
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def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
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Optional[SampleLogprobs]], ):
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"""Sanitize vllm output to be comparable with hf output."""
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output_ids, output_str, out_logprobs = vllm_output
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hf_output_str = "</s><s>" + output_str + "</s>"
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return output_ids, hf_output_str, out_logprobs
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def run_test(
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hf_runner: Type[HfRunner],
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vllm_runner: Type[VllmRunner],
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prompts: List[ExplicitEncoderDecoderPrompt],
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model: str,
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*,
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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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tensor_parallel_size: int,
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distributed_executor_backend: Optional[str] = None,
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) -> None:
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with vllm_runner(model,
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tokenizer_name=TOKENIZER,
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dtype=dtype,
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tensor_parallel_size=tensor_parallel_size,
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distributed_executor_backend=distributed_executor_backend,
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enforce_eager=True) as vllm_model:
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vllm_outputs = vllm_model.generate_encoder_decoder_greedy_logprobs(
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prompts, max_tokens, num_logprobs)
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# Florence-2 processors require image inputs
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dummy_image = Image.new(mode="RGB", size=(2, 2))
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with hf_runner(model, dtype=dtype, skip_tokenizer_init=True) as hf_model:
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hf_model.model.get_output_embeddings = lambda: \
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hf_model.model.language_model.lm_head
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hf_outputs = (hf_model.generate_encoder_decoder_greedy_logprobs_limit(
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prompts,
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max_tokens,
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num_logprobs,
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images=[dummy_image] * len(prompts),
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))
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check_logprobs_close(
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outputs_0_lst=hf_outputs,
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outputs_1_lst=[
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vllm_to_hf_output(vllm_output) for vllm_output in vllm_outputs
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],
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name_0="hf",
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name_1="vllm",
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)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float", "bfloat16"])
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@pytest.mark.parametrize("max_tokens", [64])
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@pytest.mark.parametrize("num_logprobs", [5])
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def test_models(hf_runner, vllm_runner, model, dtype, max_tokens,
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num_logprobs) -> None:
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run_test(
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hf_runner,
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vllm_runner,
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PROMPTS,
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model,
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dtype=dtype,
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max_tokens=max_tokens,
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num_logprobs=num_logprobs,
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tensor_parallel_size=1,
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)
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