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- **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>
139 lines
4.2 KiB
Python
139 lines
4.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""
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Tests gguf models against unquantized models generations
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Note: To pass the test, quantization higher than Q4 should be used
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"""
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import os
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from typing import List, NamedTuple, Type
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import pytest
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from huggingface_hub import hf_hub_download
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from transformers import AutoTokenizer
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from tests.quantization.utils import is_quant_method_supported
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from ....conftest import VllmRunner
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from ...utils import check_logprobs_close
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os.environ["TOKENIZERS_PARALLELISM"] = "true"
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MAX_MODEL_LEN = 1024
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class GGUFTestConfig(NamedTuple):
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original_model: str
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gguf_repo: str
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gguf_filename: str
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@property
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def gguf_model(self):
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return hf_hub_download(self.gguf_repo, filename=self.gguf_filename)
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LLAMA_CONFIG = GGUFTestConfig(
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original_model="meta-llama/Llama-3.2-1B-Instruct",
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gguf_repo="bartowski/Llama-3.2-1B-Instruct-GGUF",
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gguf_filename="Llama-3.2-1B-Instruct-IQ4_XS.gguf",
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)
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QWEN2_CONFIG = GGUFTestConfig(
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original_model="Qwen/Qwen2.5-1.5B-Instruct",
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gguf_repo="Qwen/Qwen2.5-1.5B-Instruct-GGUF",
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gguf_filename="qwen2.5-1.5b-instruct-q6_k.gguf",
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)
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PHI3_CONFIG = GGUFTestConfig(
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original_model="microsoft/Phi-3.5-mini-instruct",
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gguf_repo="bartowski/Phi-3.5-mini-instruct-GGUF",
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gguf_filename="Phi-3.5-mini-instruct-IQ4_XS.gguf",
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)
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GPT2_CONFIG = GGUFTestConfig(
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original_model="openai-community/gpt2-large",
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gguf_repo="QuantFactory/gpt2-large-GGUF",
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gguf_filename="gpt2-large.Q4_K_M.gguf",
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)
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STABLELM_CONFIG = GGUFTestConfig(
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original_model="stabilityai/stablelm-3b-4e1t",
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gguf_repo="afrideva/stablelm-3b-4e1t-GGUF",
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gguf_filename="stablelm-3b-4e1t.q4_k_m.gguf",
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)
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STARCODER_CONFIG = GGUFTestConfig(
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original_model="bigcode/starcoder2-3b",
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gguf_repo="QuantFactory/starcoder2-3b-GGUF",
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gguf_filename="starcoder2-3b.Q6_K.gguf",
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)
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DOLPHIN_CONFIG = GGUFTestConfig(
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# Test VocabParallelEmbedding sharding issue.
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original_model="cognitivecomputations/TinyDolphin-2.8-1.1b",
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gguf_repo="tsunemoto/TinyDolphin-2.8-1.1b-GGUF",
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gguf_filename="tinydolphin-2.8-1.1b.Q6_K.gguf",
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)
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MODELS = [
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LLAMA_CONFIG, QWEN2_CONFIG, PHI3_CONFIG, GPT2_CONFIG, STABLELM_CONFIG,
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DOLPHIN_CONFIG
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# STARCODER_CONFIG, # broken
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]
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@pytest.mark.skipif(not is_quant_method_supported("gguf"),
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reason="gguf is not supported on this GPU type.")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["half"])
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@pytest.mark.parametrize("max_tokens", [32])
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@pytest.mark.parametrize("num_logprobs", [5])
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@pytest.mark.parametrize("tp_size", [1, 2])
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def test_models(
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num_gpus_available: int,
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vllm_runner: Type[VllmRunner],
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example_prompts: List[str],
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model: GGUFTestConfig,
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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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tp_size: int,
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) -> None:
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if num_gpus_available < tp_size:
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pytest.skip(f"Not enough GPUs for tensor parallelism {tp_size}")
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tokenizer = AutoTokenizer.from_pretrained(model.original_model)
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if tokenizer.chat_template is not None:
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messages = [[{
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'role': 'user',
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'content': prompt
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}] for prompt in example_prompts]
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example_prompts = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True)
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# Run unquantized model.
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with vllm_runner(
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model_name=model.original_model,
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enforce_eager=True, # faster tests
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dtype=dtype,
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max_model_len=MAX_MODEL_LEN,
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tensor_parallel_size=tp_size) as original_model:
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original_outputs = original_model.generate_greedy_logprobs(
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example_prompts[:-1], max_tokens, num_logprobs)
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# Run gguf model.
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with vllm_runner(model_name=model.gguf_model,
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enforce_eager=True,
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tokenizer_name=model.original_model,
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dtype=dtype,
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max_model_len=MAX_MODEL_LEN,
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tensor_parallel_size=tp_size) as gguf_model:
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gguf_outputs = gguf_model.generate_greedy_logprobs(
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example_prompts[:-1], max_tokens, num_logprobs)
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check_logprobs_close(
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outputs_0_lst=original_outputs,
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outputs_1_lst=gguf_outputs,
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name_0="original",
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name_1="gguf",
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)
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