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vllm/tests/models/utils.py
XuruiYang 845adb3ec6 [Model] Add LongCat-Flash (#23991) 
Signed-off-by: yangxurui <yangxurui@meituan.com>
Co-authored-by: yangxurui <yangxurui@meituan.com>
2025-09-24 21:53:40 -07:00

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# 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,
"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,
# For Gemma-3n
"num_kv_shared_layers": 1,
}
# 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")
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