# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from typing import Optional import pytest from transformers import AutoModelForSeq2SeqLM from vllm.sequence import SampleLogprobs from ....conftest import (DecoderPromptType, ExplicitEncoderDecoderPrompt, HfRunner, VllmRunner) from ....utils import multi_gpu_test from ...utils import check_logprobs_close def vllm_to_hf_output( vllm_output: tuple[list[int], str, Optional[SampleLogprobs]], decoder_prompt_type: DecoderPromptType, ): """Sanitize vllm output to be comparable with hf output.""" output_ids, output_str, out_logprobs = vllm_output hf_output_str = output_str + "" if decoder_prompt_type == DecoderPromptType.NONE: hf_output_str = "" + hf_output_str return output_ids, hf_output_str, out_logprobs def run_test( hf_runner: type[HfRunner], vllm_runner: type[VllmRunner], prompts: list[ExplicitEncoderDecoderPrompt[str, str]], decoder_prompt_type: DecoderPromptType, model: str, *, dtype: str, max_tokens: int, num_logprobs: int, tensor_parallel_size: int, distributed_executor_backend: Optional[str] = None, ) -> None: ''' Test the vLLM BART model for a variety of encoder/decoder input prompts, by validating it against HuggingFace (HF) BART. Arguments: * hf_runner: HuggingFace (HF) test model runner * vllm_runner: vLLM test model runner * example_encoder_decoder_prompts: test fixture which provides a dictionary of dummy prompts * model: the HF ID of the specific BART variant under test * dtype: the tensor datatype to employ * max_tokens * num_logprobs * decoder_prompt_type: key into the example_encoder_decoder_prompts dictionary; selects specific encoder/decoder prompt scenarios to test A note on using HF BART as a baseline for validating vLLM BART, specifically when the decoder prompt is None. The HF GenerationMixin's default behavior is to force the first decoded token to be if the prompt does not already contain (this is accomplished using a logit processor setting.) So when we use HF BART as our baseline for comparison, note that when the user provides a request with a None decoder prompt (i.e. a singleton encoder prompt, or else an explicit encoder/ decoder prompt with the decoder sub-prompt set to None), HF and vLLM handle this in different ways: * HF will (1) tokenize the None prompt as an empty token-list, (2) append to the beginning, yielding [], (3) pass this token list to the model, and then (4) after computing logits during prefill, override the model logits & force to be the first generated token. * vLLM will (1) tokenize the None prompt as [], (2) append decoder- start-token to the beginning, yielding [], (3) pass these tokens to the model & proceed with generation. The net effect is that compared to vLLM, the list of HF *decoded* tokens will contain one more initial than the vLLM generated tokens, because vLLM's token is injected into the prompt rather than into the generated output. This is in spite of the fact that overall, the complete sequences (prompt + decoded tokens) produced by vLLM will match HF. So when we use HF decoded token output to validate vLLM's decoded token output, the testing process must account for the difference in decoded token sequences between vLLM and HF specifically in the decoder-prompt-is-None case. One option is to disable the logit processor feature that forces the token to be decoded (forced_bos_token_id = None), eliminating the problem entirely. However this is not "normal" BART usage. The other option is - only in the decoder-prompt-is-None case - to discard the first decoded token from the HF output before comparing it to vLLM. To that end, when testing the scenario where the decoder prompt is None (and only in that one scenario), this test skips the first HF decoded token during the process of validating the vLLM decoded output. ''' # NOTE: take care of the order. run vLLM first, and then run HF. # vLLM needs a fresh new process without cuda initialization. # if we run HF first, the cuda initialization will be done and it # will hurt multiprocessing backend with fork method (the default). # Note: currently encoder/decoder models are only compatible with # enforce_eager=True. Normally this is not a problem because # for encoder/decoder models vLLM will # default to enforce_eager=True if enforce_eager # is left unspecified. However, the # VllmRunner test fixture (which wraps around the LLM class) defaults to # enforce_eager=False (a behavior which a number of already-existing # decoder-only unit tests expect), so when testing an encoder/decoder # model we must explicitly specify enforce_eager=True in the VllmRunner # constructor. with vllm_runner(model, dtype=dtype, tensor_parallel_size=tensor_parallel_size, distributed_executor_backend=distributed_executor_backend, enforce_eager=True) as vllm_model: vllm_outputs = vllm_model.generate_encoder_decoder_greedy_logprobs( prompts, max_tokens, num_logprobs) # Configuration settings for HF baseline hf_kwargs = { "top_k": None, "num_beams": 1, "repetition_penalty": 1.0, "top_p": 1.0, "length_penalty": 1.0, "early_stopping": False, "no_repeat_ngram_size": None, "min_length": 0 } with hf_runner(model, dtype=dtype, auto_cls=AutoModelForSeq2SeqLM) as hf_model: hf_outputs = (hf_model.generate_encoder_decoder_greedy_logprobs_limit( prompts, max_tokens, num_logprobs, **hf_kwargs, )) hf_skip_tokens = (1 if decoder_prompt_type == DecoderPromptType.NONE else 0) check_logprobs_close( outputs_0_lst=hf_outputs, outputs_1_lst=[ vllm_to_hf_output(vllm_output, decoder_prompt_type) for vllm_output in vllm_outputs ], name_0="hf", name_1="vllm", num_outputs_0_skip_tokens=hf_skip_tokens, ) @pytest.mark.parametrize( "model", [ pytest.param("facebook/bart-base", marks=[pytest.mark.core_model, pytest.mark.cpu_model]), pytest.param("facebook/bart-large-cnn"), ], ) @pytest.mark.parametrize("dtype", ["float", "bfloat16"]) @pytest.mark.parametrize("max_tokens", [64]) @pytest.mark.parametrize("num_logprobs", [5]) @pytest.mark.parametrize("decoder_prompt_type", list(DecoderPromptType)) def test_models(hf_runner, vllm_runner, example_encoder_decoder_prompts, model, dtype, max_tokens, num_logprobs, decoder_prompt_type) -> None: run_test( hf_runner, vllm_runner, example_encoder_decoder_prompts[decoder_prompt_type], decoder_prompt_type, model, dtype=dtype, max_tokens=max_tokens, num_logprobs=num_logprobs, tensor_parallel_size=1, ) @multi_gpu_test(num_gpus=2) @pytest.mark.parametrize("distributed_executor_backend", ["ray", "mp"]) @pytest.mark.parametrize("model", ["facebook/bart-large-cnn"]) @pytest.mark.parametrize("dtype", ["float"]) @pytest.mark.parametrize("max_tokens", [64]) @pytest.mark.parametrize("num_logprobs", [5]) @pytest.mark.parametrize("decoder_prompt_type", [DecoderPromptType.CUSTOM]) def test_models_distributed(hf_runner, vllm_runner, example_encoder_decoder_prompts, distributed_executor_backend, model, dtype, max_tokens, num_logprobs, decoder_prompt_type) -> None: run_test( hf_runner, vllm_runner, example_encoder_decoder_prompts[decoder_prompt_type], decoder_prompt_type, model, dtype=dtype, max_tokens=max_tokens, num_logprobs=num_logprobs, tensor_parallel_size=2, distributed_executor_backend=distributed_executor_backend, )