# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import warnings from collections.abc import Sequence from dataclasses import dataclass from typing import Any import torch import torch.nn.functional as F from transformers import PretrainedConfig from vllm.config.model import ModelConfig, ModelDType, RunnerOption from vllm.logprobs import Logprob, PromptLogprobs, SampleLogprobs from vllm.multimodal.processing import InputProcessingContext from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config from .. import ci_envs from .registry import HF_EXAMPLE_MODELS TokensText = tuple[list[int], str] def check_outputs_equal( *, outputs_0_lst: Sequence[TokensText], outputs_1_lst: Sequence[TokensText], name_0: str, name_1: str, ): """ Compare the two sequences generated by different models, which should be equal. """ assert len(outputs_0_lst) == len(outputs_1_lst) for prompt_idx, (outputs_0, outputs_1) in enumerate( zip(outputs_0_lst, outputs_1_lst) ): output_ids_0, output_str_0 = outputs_0 output_ids_1, output_str_1 = outputs_1 # The text and token outputs should exactly match fail_msg = ( f"Test{prompt_idx}:" f"\n{name_0}:\t{output_str_0!r}" f"\n{name_1}:\t{output_str_1!r}" ) assert output_str_0 == output_str_1, fail_msg assert output_ids_0 == output_ids_1, fail_msg # Representation of generated sequence as a tuple of # * Token ID list # * String # * List of top sample logprobs for each sampled token # # Assumes prompt logprobs were not requested. TokensTextLogprobs = tuple[ list[int], str, list[dict[int, float]] | SampleLogprobs | None ] # Allow for tokens to be represented as str's rather than IDs; # tuple of # * Token string representations list # * String # * Optional list of top sample logprobs for each sampled token # # Assumes prompt logprobs were not requested. TextTextLogprobs = tuple[ list[str], str, list[dict[str, float]] | list[dict[str, Logprob]] | None ] # Representation of generated sequence as a tuple of # * Token ID list # * String # * Optional list of top sample logprobs for each sampled token # * Optional list of top prompt logprobs for each prompt token # # Allows prompt logprobs to be requested. TokensTextLogprobsPromptLogprobs = tuple[ list[int], str, list[dict[int, float]] | SampleLogprobs | None, list[dict[int, float] | None] | PromptLogprobs | None, ] def check_logprobs_close( *, outputs_0_lst: Sequence[ TokensTextLogprobs | TokensTextLogprobsPromptLogprobs | TextTextLogprobs ], outputs_1_lst: Sequence[ TokensTextLogprobs | TokensTextLogprobsPromptLogprobs | TextTextLogprobs ], name_0: str, name_1: str, num_outputs_0_skip_tokens: int = 0, warn_on_mismatch: bool = True, always_check_logprobs: bool = False, ) -> None: """Compare the logprobs of two sequences generated by different models, which should be similar but not necessarily equal. How sample logprobs are compared: * `always_check_logprobs == True`: set of highest-logprob token ids must match between seq0 and seq1 at all sampled token offsets * `always_check_logprobs == False`: highest-logprob token ids are only compared at sampled token offsets for which generated token ids don't match Prompt logprobs must be provided either for both input sequences, or for neither. If prompt logprobs are provided, then highest-logprob prompt token ids must match between seq0 and seq1 at all prompt token offsets. Args: outputs_0_lst: First sequence to compare outputs_0_lst: Second sequence to compare name_0: sequence #0 name name_1: sequence #1 name num_outputs_0_skip_tokens: If > 0, specifies the number of initial sequence #0 tokens & logprobs to discard before comparison, i.e. all of sequence #1 will be compared to sequence #0 beginning at index num_outputs_0_skip_tokens warn_on_mismatch: Issue a warning if there is token-wise or text-wise mismatch between the two sequences always_check_logprobs: If true, check logprobs even when tokens match """ assert len(outputs_0_lst) == len(outputs_1_lst) # Loop through responses to each prompt. for prompt_idx, (outputs_0, outputs_1) in enumerate( zip(outputs_0_lst, outputs_1_lst) ): assert len(outputs_0) == len(outputs_1) if len(outputs_0) == 3: assert len(outputs_1) == 3 # Break out tokens, text & sample logprobs # (prompt logprobs were not provided) output_ids_0, output_str_0, logprobs_0 = outputs_0 output_ids_1, output_str_1, logprobs_1 = outputs_1 elif len(outputs_0) == 4: assert len(outputs_1) == 4 # Break out tokens, text, sample logprobs & prompt logprobs ( output_ids_0, output_str_0, logprobs_0, prompt_logprobs_0, ) = outputs_0 ( output_ids_1, output_str_1, logprobs_1, prompt_logprobs_1, ) = outputs_1 # Test prompt logprobs closeness if prompt_logprobs_0 is not None and prompt_logprobs_1 is not None: # Both sequences' prompt logprobs lists are not `None` # (although individual list elements may be `None`); # for each token's logprobs: for idx, (logprobs_elem_0, logprobs_elem_1) in enumerate( zip(prompt_logprobs_0, prompt_logprobs_1) ): fail_msg = ( f"Prompt logprobs test:" f"\n{name_0}:\tPrompt index {idx}\t{logprobs_elem_0}" f"\n{name_1}:\tPrompt index {idx}\t{logprobs_elem_1}" ) if logprobs_elem_0 is None: # If the seq 0 token's logprobs are `None`, # the seq 1 token's logprobs must be `None` assert logprobs_elem_1 is None, fail_msg else: # If the seq 0 token's logprobs are not `None`, # the seq 1 token's logprobs must not be `None` assert logprobs_elem_1 is not None, fail_msg # Logprobs check: top-k token choices must be the same assert set(logprobs_elem_0.keys()) == set( logprobs_elem_1.keys() ), fail_msg else: # Both sequence logprobs lists must be `None` fail_msg = ( f"Prompt logprobs test:" f"\n{name_0}:\tlogprobs\t{prompt_logprobs_0}" f"\n{name_1}:\tlogprobs\t{prompt_logprobs_1}" ) assert prompt_logprobs_0 is None and prompt_logprobs_1 is None, fail_msg else: raise ValueError( f"Outputs tuple must have 3 or 4 elements but " f"{len(outputs_0)} elements were provided: " f"{outputs_0}" ) if logprobs_0 is None: logprobs_0 = [None] * len(output_ids_0) if logprobs_1 is None: logprobs_1 = [None] * len(output_ids_1) # Skip specified number of initial sequence #0 tokens # & logprobs, leaving output text as-is for simplicity # (text mismatches may generate warnings but do not # cause the test to fail.) if num_outputs_0_skip_tokens < 0: raise ValueError("num_outputs_0_skip_tokens must be non-negative") output_ids_0 = output_ids_0[num_outputs_0_skip_tokens:] logprobs_0 = logprobs_0[num_outputs_0_skip_tokens:] # Loop through generated tokens. for idx, (output_id_0, output_id_1) in enumerate( zip(output_ids_0, output_ids_1) ): is_tok_mismatch = output_id_0 != output_id_1 # If generated tokens don't match # or it is desired to always check logprobs, # then if is_tok_mismatch or always_check_logprobs: logprobs_elem_0 = logprobs_0[idx] logprobs_elem_1 = logprobs_1[idx] # Each predicted token must be in top N logprobs of the other fail_msg = ( f"Test{prompt_idx}:" f"\nMatched tokens:\t{output_ids_0[:idx]}" f"\n{name_0}:\t{output_str_0!r}\t{logprobs_elem_0}" f"\n{name_1}:\t{output_str_1!r}\t{logprobs_elem_1}" ) assert logprobs_elem_0 is not None, fail_msg assert logprobs_elem_1 is not None, fail_msg assert output_id_0 in logprobs_elem_1, fail_msg assert output_id_1 in logprobs_elem_0, fail_msg if warn_on_mismatch and is_tok_mismatch: with warnings.catch_warnings(): # This ensures that repeated warnings are shown # in the output, not just the first occurrence warnings.simplefilter("always") warnings.warn(fail_msg, stacklevel=2) # Break out since sequences will now diverge. break else: if output_str_0 != output_str_1 and warn_on_mismatch: # The token outputs exactly match, # so the text outputs should exactly match as well fail_msg = ( f"Test{prompt_idx}:" f"\n{name_0}:\t{output_str_0!r}" f"\n{name_1}:\t{output_str_1!r}" ) with warnings.catch_warnings(): # This ensures that repeated warnings are shown # in the output, not just the first occurrence warnings.simplefilter("always") warnings.warn(fail_msg, stacklevel=2) def build_model_context( model_id: str, runner: RunnerOption = "auto", dtype: ModelDType = "auto", model_config_kwargs: dict[str, Any] | None = None, mm_processor_kwargs: dict[str, Any] | None = None, limit_mm_per_prompt: dict[str, int] | None = None, mm_processor_cache_gb: int = 0, ): """Creates an InputProcessingContext for a given model. Args: model_id: ID of the model being considered. mm_processor_kwargs: optional processor kwargs for to be leveraged in the input processor, mapper, dummy data creation, etc. limit_mm_per_prompt: Multimodal limits. Returns: InputProcessingContext for the model being considered. """ model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id) model_info.check_available_online(on_fail="skip") model_info.check_transformers_version(on_fail="skip") model_config_kwargs = model_config_kwargs or {} limit_mm_per_prompt = limit_mm_per_prompt or {} model_config = ModelConfig( model_id, runner=runner, tokenizer=model_info.tokenizer or model_id, tokenizer_mode=model_info.tokenizer_mode, revision=model_info.revision, trust_remote_code=model_info.trust_remote_code, dtype=dtype, seed=0, mm_processor_kwargs=mm_processor_kwargs, limit_mm_per_prompt=limit_mm_per_prompt, mm_processor_cache_gb=mm_processor_cache_gb, hf_overrides=model_info.hf_overrides, skip_tokenizer_init=model_info.require_embed_inputs, enable_prompt_embeds=model_info.require_embed_inputs, enable_mm_embeds=model_info.require_embed_inputs, enforce_eager=model_info.enforce_eager, **model_config_kwargs, ) return InputProcessingContext( model_config, tokenizer=cached_tokenizer_from_config(model_config), ) def check_embeddings_close( *, embeddings_0_lst: Sequence[list[float]], embeddings_1_lst: Sequence[list[float]], name_0: str, name_1: str, tol: float = 1e-3, ) -> None: assert len(embeddings_0_lst) == len(embeddings_1_lst) for prompt_idx, (embeddings_0, embeddings_1) in enumerate( zip(embeddings_0_lst, embeddings_1_lst) ): assert len(embeddings_0) == len(embeddings_1), ( f"Length mismatch: {len(embeddings_0)} vs. {len(embeddings_1)}" ) sim = F.cosine_similarity( torch.tensor(embeddings_0), torch.tensor(embeddings_1), dim=0 ) fail_msg = ( f"Test{prompt_idx}:" f"\nCosine similarity: \t{sim:.4f}" f"\n{name_0}:\t{embeddings_0[:16]!r}" f"\n{name_1}:\t{embeddings_1[:16]!r}" ) assert sim >= 1 - tol, fail_msg def matryoshka_fy(tensor: torch.Tensor, dimensions: int): tensor = torch.tensor(tensor) tensor = tensor[..., :dimensions] tensor = F.normalize(tensor, p=2, dim=1) return tensor def softmax(data): if data.shape[-1] == 1: return F.sigmoid(data) else: return F.softmax(data, dim=-1) @dataclass class ModelInfo: name: str architecture: str = "" dtype: str = "auto" max_model_len: int | None = None hf_dtype: str = "float32" hf_overrides: dict[str, Any] | None = None default_pooling_type: str = "" enable_test: bool = True @dataclass class EmbedModelInfo(ModelInfo): mteb_score: float | None = None is_matryoshka: bool = False matryoshka_dimensions: list[int] | None = None @dataclass class CLSPoolingEmbedModelInfo(EmbedModelInfo): default_pooling_type: str = "CLS" @dataclass class LASTPoolingEmbedModelInfo(EmbedModelInfo): default_pooling_type: str = "LAST" @dataclass class RerankModelInfo(ModelInfo): mteb_score: float | None = None @dataclass class CLSPoolingRerankModelInfo(RerankModelInfo): default_pooling_type: str = "CLS" @dataclass class LASTPoolingRerankModelInfo(RerankModelInfo): default_pooling_type: str = "LAST" @dataclass class GenerateModelInfo(ModelInfo): hf_dtype: str = "auto" hf_ppl: float | None = None def get_vllm_extra_kwargs(model_info: ModelInfo, vllm_extra_kwargs): # A model family has many models with the same architecture, # and we don't need to test each one. if not ci_envs.VLLM_CI_NO_SKIP and not model_info.enable_test: import pytest pytest.skip("Skipping test.") # Allow vllm to test using the given dtype, such as float32 vllm_extra_kwargs = vllm_extra_kwargs or {} vllm_extra_kwargs["dtype"] = ci_envs.VLLM_CI_DTYPE or model_info.dtype # Allow vllm to test using hf_overrides if model_info.hf_overrides is not None: vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides # Allow changing the head dtype used by vllm in tests if ci_envs.VLLM_CI_HEAD_DTYPE is not None: if "hf_overrides" not in vllm_extra_kwargs: vllm_extra_kwargs["hf_overrides"] = {} vllm_extra_kwargs["hf_overrides"]["head_dtype"] = ci_envs.VLLM_CI_HEAD_DTYPE # Allow control over whether tests use enforce_eager if ci_envs.VLLM_CI_ENFORCE_EAGER is not None: vllm_extra_kwargs["enforce_eager"] = ci_envs.VLLM_CI_ENFORCE_EAGER return vllm_extra_kwargs def dummy_hf_overrides( hf_config: PretrainedConfig, *, model_arch: str = "", exist_overrides: dict[str, Any] | None = None, use_original_num_layers: bool = False, ) -> PretrainedConfig: """ Dummy HF overrides function used to create dummy model with only minimum nums of layer. """ hf_config.update(exist_overrides or {}) text_config = hf_config.get_text_config() # Ensure at least 2 expert per group # Since `grouped_topk` assumes top-2 n_group = getattr(text_config, "n_group", None) num_experts = n_group * 2 if n_group is not None else 2 # we use three layers for Gemma-3n to check # both normal layer and kv_shared_layer if use_original_num_layers: # Use the original number of layers from the config num_layers = getattr(text_config, "num_layers", 1) num_hidden_layers = getattr(text_config, "num_hidden_layers", 1) else: # Use minimal layers for testing num_layers = 1 num_hidden_layers = 3 if model_arch == "Gemma3nForConditionalGeneration" else 1 update_dict = { "num_layers": num_layers, # For Gemma-3n "num_kv_shared_layers": 1, } class DummyConfig: hf_text_config = text_config # Only set MoE related config when the model has MoE layers. # Otherwise all models detected as MoE by _get_transformers_backend_cls. if ModelConfig.get_num_experts(DummyConfig) > 0: update_dict.update( { "num_experts": num_experts, "num_experts_per_tok": 2, "num_local_experts": num_experts, # Otherwise there will not be any expert layers "first_k_dense_replace": 0, # To avoid OOM on DeepSeek-V3 "n_routed_experts": num_experts, } ) # Update num_hidden_layers for non-Longcat architectures if model_arch != "LongcatFlashForCausalLM" and model_arch != "LongCatFlashMTPModel": update_dict["num_hidden_layers"] = num_hidden_layers text_config.update(update_dict) if hasattr(hf_config, "vision_config"): hf_config.vision_config.update( { "num_layers": 1, "num_hidden_layers": 1, } ) # e.g.: ibm-granite/granite-speech-3.3-2b if hasattr(hf_config, "encoder_config"): hf_config.encoder_config.update( { "num_layers": 1, "num_hidden_layers": 1, } ) # e.g.: Qwen/Qwen2-Audio-7B-Instruct if hasattr(hf_config, "audio_config"): hf_config.audio_config.update( { "num_layers": 1, "num_hidden_layers": 1, "encoder_layers": 1, } ) return hf_config def check_transformers_version( model: str, min_transformers_version: str | None = None, max_transformers_version: str | None = None, ): from .registry import _HfExamplesInfo return _HfExamplesInfo( model, min_transformers_version=min_transformers_version, max_transformers_version=max_transformers_version, ).check_transformers_version(on_fail="skip")