chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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from typing import cast, Any, List, Union, Optional
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import torch
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import numpy as np
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from .base import BaseLLMEmbedder, last_token_pool
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class PseudoMoELLMEmbedder(BaseLLMEmbedder):
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"""Decoder-only embedder for pseudo MoE checkpoints.
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This class follows the same behavior as :class:`BaseLLMEmbedder`, but supports
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selecting an active domain (e.g. ``general``, ``coding``, ``reasoning``) during
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inference when the underlying model implements domain routing.
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Args:
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model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and
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load a model from HuggingFace Hub with the name.
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normalize_embeddings (bool, optional): If True, normalize the embedding vector. Defaults to :data:`True`.
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use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance
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degradation. Defaults to :data:`True`.
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query_instruction_for_retrieval (Optional[str], optional): Query instruction for retrieval tasks, which will be used with
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with :attr:`query_instruction_format`. Defaults to :data:`None`.
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query_instruction_format (str, optional): The template for :attr:`query_instruction_for_retrieval`. Defaults to :data:`"Instruct: {}\nQuery: {}"`.
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devices (Optional[Union[str, int, List[str], List[int]]], optional): Devices to use for model inference. Defaults to :data:`None`.
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trust_remote_code (bool, optional): trust_remote_code for HF datasets or models. Defaults to :data:`False`.
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cache_dir (Optional[str], optional): Cache directory for the model. Defaults to :data:`None`.
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batch_size (int, optional): Batch size for inference. Defaults to :data:`256`.
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query_max_length (int, optional): Maximum length for query. Defaults to :data:`512`.
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passage_max_length (int, optional): Maximum length for passage. Defaults to :data:`512`.
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convert_to_numpy (bool, optional): If True, the output embedding will be a Numpy array. Otherwise, it will be a Torch Tensor.
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Defaults to :data:`True`.
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domain_for_pseudo_moe (str, optional): Specifies the active domain for the decoder-only pseudo-MoE model (e.g., "general", "coding", or "reasoning").
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Defaults to "general".
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Attributes:
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DEFAULT_POOLING_METHOD: The default pooling method when running the model.
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"""
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DEFAULT_POOLING_METHOD = "last_token"
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def __init__(
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self,
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model_name_or_path: str,
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normalize_embeddings: bool = True,
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use_fp16: bool = False,
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use_bf16: bool = True,
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query_instruction_for_retrieval: Optional[str] = None,
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query_instruction_format: str = "Instruct: {}\nQuery: {}",
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devices: Optional[Union[str, List[str]]] = None,
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trust_remote_code: bool = True,
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cache_dir: Optional[str] = None,
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batch_size: int = 256,
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query_max_length: int = 512,
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passage_max_length: int = 512,
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convert_to_numpy: bool = True,
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truncate_dim: Optional[int] = None,
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domain_for_pseudo_moe: Optional[str] = None,
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**kwargs: Any,
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):
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self.domain_for_pseudo_moe = domain_for_pseudo_moe
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super().__init__(
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model_name_or_path=model_name_or_path,
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normalize_embeddings=normalize_embeddings,
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use_fp16=use_fp16,
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use_bf16=use_bf16,
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query_instruction_for_retrieval=query_instruction_for_retrieval,
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query_instruction_format=query_instruction_format,
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devices=devices,
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trust_remote_code=trust_remote_code,
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cache_dir=cache_dir,
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batch_size=batch_size,
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query_max_length=query_max_length,
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passage_max_length=passage_max_length,
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convert_to_numpy=convert_to_numpy,
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truncate_dim=truncate_dim,
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**kwargs,
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)
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def _resolve_domain(self, kwargs: Any) -> Optional[str]:
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domain = kwargs.pop("domain_for_pseudo_moe", None)
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if domain is None:
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domain = kwargs.pop("domain", None)
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if domain is None:
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domain = self.domain_for_pseudo_moe
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return domain
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@torch.no_grad()
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def encode_single_device(
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self,
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sentences: Union[List[str], str],
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batch_size: int = 256,
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max_length: int = 512,
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convert_to_numpy: bool = True,
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device: Optional[str] = None,
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**kwargs: Any
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):
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if device is None:
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device = self.target_devices[0]
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if device == "cpu":
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self.model.float()
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self.model.to(device)
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self.model.eval()
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input_was_string = False
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if isinstance(sentences, str):
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sentences = [sentences]
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input_was_string = True
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domain = self._resolve_domain(kwargs)
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if domain is not None and hasattr(self.model, "set_domain"):
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self.model.set_domain(domain)
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model_forward_kwargs = {"return_dict": True}
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if domain is not None:
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model_forward_kwargs["domain"] = domain
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# tokenize without padding to get the correct length
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all_inputs = []
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for start_index in range(0, len(sentences), batch_size):
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sentences_batch = sentences[start_index:start_index + batch_size]
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inputs_batch = self.tokenizer(
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sentences_batch,
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truncation=True,
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max_length=max_length,
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**kwargs
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)
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inputs_batch = [{
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k: inputs_batch[k][i] for k in inputs_batch.keys()
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} for i in range(len(sentences_batch))]
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all_inputs.extend(inputs_batch)
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# sort by length for less padding
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length_sorted_idx = np.argsort([-len(x['input_ids']) for x in all_inputs])
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all_inputs_sorted = [all_inputs[i] for i in length_sorted_idx]
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# adjust batch size
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flag = False
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while flag is False:
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try:
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inputs_batch = self.tokenizer.pad(
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all_inputs_sorted[: batch_size],
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padding=True,
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return_tensors='pt',
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**kwargs
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).to(device)
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try:
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last_hidden_state = self.model(**inputs_batch, **model_forward_kwargs).last_hidden_state
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except TypeError:
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last_hidden_state = self.model(**inputs_batch, return_dict=True).last_hidden_state
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_ = last_token_pool(last_hidden_state, inputs_batch['attention_mask'])
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flag = True
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except RuntimeError:
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batch_size = batch_size * 3 // 4
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except torch.cuda.OutOfMemoryError:
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batch_size = batch_size * 3 // 4
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# encode
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all_embeddings = []
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for start_index in range(0, len(sentences), batch_size):
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inputs_batch = all_inputs_sorted[start_index:start_index + batch_size]
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inputs_batch = self.tokenizer.pad(
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inputs_batch,
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padding=True,
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return_tensors='pt',
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**kwargs
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).to(device)
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try:
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last_hidden_state = self.model(**inputs_batch, **model_forward_kwargs).last_hidden_state
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except TypeError:
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last_hidden_state = self.model(**inputs_batch, return_dict=True).last_hidden_state
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embeddings = last_token_pool(last_hidden_state, inputs_batch['attention_mask'])
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embeddings = self._truncate_embeddings(embeddings)
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embeddings = torch.nan_to_num(embeddings, nan=0.0, posinf=1e4, neginf=-1e4)
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if self.normalize_embeddings:
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embeddings = torch.nn.functional.normalize(embeddings.float(), dim=-1)
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embeddings = cast(torch.Tensor, embeddings)
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if convert_to_numpy:
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embeddings = self._convert_to_numpy(embeddings, device=device)
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all_embeddings.append(embeddings)
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if convert_to_numpy:
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all_embeddings = np.concatenate(all_embeddings, axis=0)
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else:
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all_embeddings = torch.cat(all_embeddings, dim=0)
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# adjust the order of embeddings
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all_embeddings = all_embeddings[np.argsort(length_sorted_idx)]
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if input_was_string:
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return all_embeddings[0]
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return all_embeddings
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