utils.py

# 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, Optional, Union

import torch
import torch.nn.functional as F
from transformers import PretrainedConfig

from vllm.config 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 .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, Optional[Union[list[dict[int, float]], SampleLogprobs]]
]

# 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, Optional[Union[list[dict[str, float]], list[dict[str, Logprob]]]]
]

# 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,
    Optional[Union[list[dict[int, float]], SampleLogprobs]],
    Optional[Union[list[Optional[dict[int, float]]], PromptLogprobs]],
]


def check_logprobs_close(
    *,
    outputs_0_lst: Sequence[
        Union[TokensTextLogprobs, TokensTextLogprobsPromptLogprobs, TextTextLogprobs]
    ],
    outputs_1_lst: Sequence[
        Union[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: Optional[dict[str, Any]] = None,
    mm_processor_kwargs: Optional[dict[str, Any]] = None,
    limit_mm_per_prompt: Optional[dict[str, int]] = 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.skip_tokenizer_init,
        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"
    hf_dtype: str = "float32"
    hf_overrides: Optional[dict[str, Any]] = None
    default_pooling_type: str = ""
    enable_test: bool = True


@dataclass
class EmbedModelInfo(ModelInfo):
    mteb_score: Optional[float] = None
    is_matryoshka: bool = False
    matryoshka_dimensions: Optional[list[int]] = 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: Optional[float] = 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: Optional[float] = None


def dummy_hf_overrides(
    hf_config: PretrainedConfig,
    *,
    model_arch: str = "",
    exist_overrides: Optional[dict[str, Any]] = 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: Optional[str] = None,
    max_transformers_version: Optional[str] = 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")