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import math
import os
from typing import Iterable
import torch
import torch.nn as nn
from transformers.modeling_outputs import BaseModelOutput
from vllm.model_executor.layers.pooler import DispatchPooler
from vllm.multimodal import MULTIMODAL_REGISTRY
from .constants import (
DEFAULT_DECODER_PAD_TO_MULTIPLE_OF,
DEFAULT_ENCODER_CHUNK_SIZE,
NO_TOKEN_ID,
TEXT_MODALITY,
YES_TOKEN_ID,
)
from .modeling_score import T5Gemma2ForScoreClassification
from .processing import (
TextEncoderDummyInputsBuilder,
TextEncoderProcessingInfo,
TextEncoderProcessor,
)
def _as_token_rows(value: object) -> list[torch.Tensor]:
if isinstance(value, torch.Tensor):
if value.ndim == 1:
return [value]
if value.ndim == 2:
return [row for row in value]
raise ValueError(f"encoder_input_ids must be 1D/2D, got {value.shape}.")
if isinstance(value, list):
return [
item.flatten()
if isinstance(item, torch.Tensor)
else torch.tensor(item, dtype=torch.long)
for item in value
]
raise TypeError(f"Unsupported encoder_input_ids type: {type(value)!r}")
def _split_by_position_zero(
input_ids: torch.Tensor,
positions: torch.Tensor,
) -> tuple[list[torch.Tensor], list[int]]:
flat_ids = input_ids.flatten()
starts = (positions.flatten() == 0).nonzero(as_tuple=False).flatten().tolist()
if not starts:
return [], []
starts.append(int(flat_ids.numel()))
rows: list[torch.Tensor] = []
last_indices: list[int] = []
for start, end in zip(starts[:-1], starts[1:]):
if end > start:
rows.append(flat_ids[start:end])
last_indices.append(end - 1)
return rows, last_indices
def _debug(message: str) -> None:
if os.environ.get("KALM_VLLM_DEBUG") == "1":
print(f"[kalm-vllm-debug] {message}", flush=True)
@MULTIMODAL_REGISTRY.register_processor(
TextEncoderProcessor,
info=TextEncoderProcessingInfo,
dummy_inputs=TextEncoderDummyInputsBuilder,
)
class T5Gemma2VllmScoreClassification(nn.Module):
is_pooling_model = True
supports_multimodal = True
score_type = "cross-encoder"
attn_type = "encoder_decoder"
default_seq_pooling_type = "LAST"
default_tok_pooling_type = "ALL"
def __init__(self, *, vllm_config, prefix: str = "") -> None:
super().__init__()
self.vllm_config = vllm_config
self.model_config = vllm_config.model_config
self.config = self.model_config.hf_config
self.config.num_labels = 1
self.config.yes_token_id = int(
getattr(self.config, "yes_token_id", YES_TOKEN_ID)
)
self.config.no_token_id = int(
getattr(self.config, "no_token_id", NO_TOKEN_ID)
)
self.config.encoder_chunk_size = getattr(
self.config,
"encoder_chunk_size",
DEFAULT_ENCODER_CHUNK_SIZE,
)
self.encoder_chunk_size = self.config.encoder_chunk_size
self.config.decoder_pad_to_multiple_of = int(
getattr(
self.config,
"decoder_pad_to_multiple_of",
DEFAULT_DECODER_PAD_TO_MULTIPLE_OF,
)
)
self.decoder_pad_to_multiple_of = self.config.decoder_pad_to_multiple_of
self.pad_token_id = int(getattr(self.config, "pad_token_id", 0) or 0)
self.score_model = T5Gemma2ForScoreClassification.from_pretrained(
self.model_config.model,
trust_remote_code=True,
dtype=self.model_config.dtype,
)
self.score_model.config.yes_token_id = self.config.yes_token_id
self.score_model.config.no_token_id = self.config.no_token_id
self.score_model.config.num_labels = 1
self.score_model.config.encoder_chunk_size = self.encoder_chunk_size
self.score_model.yes_token_id = self.config.yes_token_id
self.score_model.no_token_id = self.config.no_token_id
self.score_model.encoder_chunk_size = self.encoder_chunk_size
self.score_model._validate_score_config()
self.score_model.eval()
pooler_config = self.model_config.pooler_config
assert pooler_config is not None
self.pooler = DispatchPooler.for_seq_cls(pooler_config)
def get_language_model(self):
return self
def get_num_mm_encoder_tokens(self, num_tokens: int) -> int:
if self.encoder_chunk_size is None:
return int(num_tokens)
return int(math.ceil(num_tokens / int(self.encoder_chunk_size)))
def embed_input_ids(self, input_ids: torch.Tensor, *args, **kwargs) -> torch.Tensor:
return self.score_model.get_input_embeddings()(input_ids)
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
# The semantic wrapper loads the same checkpoint directly.
for _ in weights:
pass
return set(self.state_dict().keys())
def embed_multimodal(self, **kwargs: object) -> list[torch.Tensor]:
if "encoder_input_ids" not in kwargs:
raise ValueError(f"Missing {TEXT_MODALITY} encoder_input_ids.")
rows = _as_token_rows(kwargs["encoder_input_ids"])
device = next(self.score_model.parameters()).device
outputs: list[torch.Tensor] = []
with torch.inference_mode():
for row in rows:
input_ids = row.to(device=device, dtype=torch.long).unsqueeze(0)
attention_mask = torch.ones_like(input_ids)
raw_encoder_outputs = self.score_model.get_encoder()(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=True,
)
hidden = raw_encoder_outputs.last_hidden_state
if self.encoder_chunk_size is not None:
hidden, _ = self.score_model._pool_encoder_chunks(
hidden,
attention_mask,
int(self.encoder_chunk_size),
)
item = hidden.squeeze(0).contiguous()
_debug(f"encoder output shape={tuple(item.shape)}")
outputs.append(item)
return outputs
def _pad_decoder_rows(
self,
rows: list[torch.Tensor],
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
max_len = max(int(row.numel()) for row in rows)
if self.decoder_pad_to_multiple_of > 1:
multiple = self.decoder_pad_to_multiple_of
max_len = int(math.ceil(max_len / multiple) * multiple)
batch = torch.full(
(len(rows), max_len),
self.pad_token_id,
dtype=torch.long,
device=device,
)
mask = torch.zeros_like(batch)
for index, row in enumerate(rows):
row = row.to(device=device, dtype=torch.long)
length = int(row.numel())
batch[index, :length] = row
mask[index, :length] = 1
return batch, mask
@staticmethod
def _pad_encoder_outputs(
encoder_outputs: list[torch.Tensor],
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
max_len = max(int(item.shape[0]) for item in encoder_outputs)
hidden_size = int(encoder_outputs[0].shape[-1])
batch = torch.zeros(
(len(encoder_outputs), max_len, hidden_size),
dtype=encoder_outputs[0].dtype,
device=device,
)
mask = torch.zeros(
(len(encoder_outputs), max_len),
dtype=torch.long,
device=device,
)
for index, item in enumerate(encoder_outputs):
item = item.to(device=device)
length = int(item.shape[0])
batch[index, :length] = item
mask[index, :length] = 1
return batch, mask
def forward(
self,
input_ids: torch.Tensor | None,
positions: torch.Tensor,
intermediate_tensors=None,
inputs_embeds: torch.Tensor | None = None,
encoder_outputs: list[torch.Tensor] | torch.Tensor | None = None,
**kwargs,
) -> torch.Tensor:
if input_ids is None:
raise ValueError("Decoder input_ids are required.")
decoder_rows, last_indices = _split_by_position_zero(input_ids, positions)
hidden = input_ids.new_zeros((input_ids.numel(), 1), dtype=torch.float32)
if not decoder_rows:
return hidden
if encoder_outputs is None:
return hidden
encoder_list = (
[item for item in encoder_outputs]
if isinstance(encoder_outputs, torch.Tensor)
else list(encoder_outputs)
)
if len(encoder_list) != len(decoder_rows):
raise ValueError(
"Mismatched encoder/decoder batch sizes: "
f"{len(encoder_list)} vs {len(decoder_rows)}."
)
device = next(self.score_model.parameters()).device
decoder_batch, decoder_mask = self._pad_decoder_rows(decoder_rows, device)
encoder_batch, encoder_mask = self._pad_encoder_outputs(encoder_list, device)
with torch.inference_mode():
outputs = self.score_model(
encoder_outputs=BaseModelOutput(last_hidden_state=encoder_batch),
attention_mask=encoder_mask,
decoder_input_ids=decoder_batch,
decoder_attention_mask=decoder_mask,
)
margins = outputs.logits.squeeze(-1).to(hidden.device, dtype=hidden.dtype)
_debug(f"margins={margins.float().tolist()}")
for row_index, last_index in enumerate(last_indices):
hidden[last_index, 0] = margins[row_index]
return hidden
__all__ = ["T5Gemma2VllmScoreClassification"]
|