Upload LLMEncoder

config.json

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+{
+  "architectures": [
+    "LLMEncoder"
+  ],
+  "auto_map": {
+    "AutoConfig": "llmencoder.LLMEncoderConfig",
+    "AutoModel": "llmencoder.LLMEncoder"
+  },
+  "base_model": "Qwen/Qwen2-7B-Instruct",
+  "doc_max_length": 400,
+  "model_config": {
+    "_name_or_path": "Qwen/Qwen2-7B-Instruct",
+    "add_cross_attention": false,
+    "architectures": [
+      "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": 151643,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 151645,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "silu",
+    "hidden_size": 3584,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 18944,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 32768,
+    "max_window_layers": 28,
+    "min_length": 0,
+    "model_type": "qwen2",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 28,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 25,
+    "num_key_value_heads": 4,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "rms_norm_eps": 1e-06,
+    "rope_theta": 1000000.0,
+    "sep_token_id": null,
+    "sliding_window": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": false,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": "bfloat16",
+    "torchscript": false,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 152064
+  },
+  "pooling_mode": "weighted_mean",
+  "skip_instruction": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.2"
+}

llmencoder.py

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+import json
+import logging
+import os
+from typing import Dict, List, Optional, Union
+
+import numpy as np
+import torch
+import torch.multiprocessing as mp
+from peft import PeftModel
+from torch import Tensor, device, nn
+from tqdm.autonotebook import tqdm, trange
+from transformers import (
+    AutoModel,
+    AutoConfig,
+    PretrainedConfig,
+    PreTrainedModel,
+    AutoTokenizer,
+    LlamaConfig,
+    MistralConfig,
+    GemmaConfig,
+    Qwen2Config,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def batch_to_device(batch, target_device: device):
+    """
+    send a pytorch batch to a device (CPU/GPU)
+    """
+    for key in batch:
+        if isinstance(batch[key], Tensor):
+            batch[key] = batch[key].to(target_device)
+    return batch
+
+
+class LLMEncoderConfig(PretrainedConfig):
+    def __init__(
+        self,
+        pooling_mode: str = "weighted_mean",
+        max_length: int = 512,
+        doc_max_length: int = 400,
+        skip_instruction: bool = True,
+        **kwargs,
+    ):
+        if pooling_mode not in ["mean", "weighted_mean", "eos_token", "bos_token"]:
+            raise ValueError(
+                (f"Pooling mode {pooling_mode} is not supported.",
+                "Please choose one of 'mean', 'weighted_mean', 'eos_token', 'bos_token'.")
+            )
+        self.pooling_mode = pooling_mode
+        self.max_length = max_length
+        self.doc_max_length = doc_max_length
+        self.skip_instruction = skip_instruction
+        self.model_config = None
+        self.base_model = None
+        
+        super().__init__(**kwargs)
+
+class LLMEncoder(PreTrainedModel):
+    config_class = LLMEncoderConfig
+    
+    def __init__(
+        self,
+        model: PreTrainedModel,
+        tokenizer: AutoTokenizer,
+        config: LLMEncoderConfig
+    ):
+        super().__init__(config)
+        self.model = model
+        self.tokenizer = tokenizer
+        self.pooling_mode = config.pooling_mode
+        self.max_length = config.max_length
+        self.doc_max_length = config.doc_max_length
+        self.skip_instruction = config.skip_instruction
+        self.model_config = None
+
+    @classmethod
+    def from_pretrained(
+        self,
+        base_model_name_or_path,
+        peft_model_name_or_path=None,
+        config=None,
+        **kwargs,
+    ):
+        """
+        Load a pretrained model from a model identifier or path.
+        Args:
+            base_model_name_or_path: Model identifier or path to pretrained model.
+            peft_model_name_or_path: Path to any PEFT models to apply.
+        Returns: L3Prune model.
+        """
+        
+        if not config:
+            config = LLMEncoderConfig()
+        
+        if not config.base_model:
+            config.base_model = base_model_name_or_path
+        
+        tokenizer = AutoTokenizer.from_pretrained(base_model_name_or_path)
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.padding_side = "left"
+ 
+        if config.model_config:
+            model_config = AutoConfig.from_pretrained(config.base_model)
+            model_config = model_config.from_dict(config.model_config) 
+        else:
+            model_config = AutoConfig.from_pretrained(base_model_name_or_path)
+            config.model_config = model_config
+            
+        model = AutoModel.from_pretrained(base_model_name_or_path, config=model_config, **kwargs)
+        
+
+        if peft_model_name_or_path is not None:
+            model = PeftModel.from_pretrained(
+                model,
+                peft_model_name_or_path,
+            )
+            model = model.merge_and_unload()
+
+        return self(model=model, tokenizer=tokenizer, config=config)
+
+    def prune(self, percent_prune=0):
+        """
+        Prune a model to a percentage of layers of the base model. If percent_prune is equal to or greater than 1, 
+        it is taken as the specific layer number to prune to. For example, if percent_prune=0.3, 30% of the layers will be pruned. If
+        percent_prune=3, the model will be pruned to 3 layers.
+        """
+        # take it as the specific layer number to prune to
+        if percent_prune >= 1:
+            new_num_layers = int(percent_prune)
+        else:
+            new_num_layers = int(self.model.config.num_hidden_layers * (1 - percent_prune))
+        print(f"Pruning to {new_num_layers} layer.")
+        self.model.layers = self.model.layers[:new_num_layers]
+        self.model.config.num_hidden_layers = new_num_layers
+        self.config.model_config.num_hidden_layers = new_num_layers
+    
+    def prepare_for_tokenization(self, text):
+        if self.model.config._name_or_path == "meta-llama/Meta-Llama-3-8B-Instruct":
+            text = (
+                "<|start_header_id|>user<|end_header_id|>\n\n"
+                + text.strip()
+                + "<|eot_id|>"
+            )
+            return text
+        if self.model.config._name_or_path in [
+            "mistralai/Mistral-7B-Instruct-v0.2",
+            "meta-llama/Llama-2-7b-chat-hf",
+        ]:
+            text = "[INST] " + text.strip() + " [/INST]"
+        if self.model.config._name_or_path in [
+            "google/gemma-2-9b-it",
+        ]:
+            text = "<bos><start_of_turn>user\n" + text.strip() + "<end_of_turn>"
+        if self.model.config._name_or_path in [
+            "Qwen/Qwen2-1.5B-Instruct",
+            "Qwen/Qwen2-7B-Instruct",
+        ]:
+            text = "<|im_start|>user\n" + text.strip() + "<|im_end|>"
+        if self.pooling_mode == "eos_token":
+            if self.model.config._name_or_path == "meta-llama/Meta-Llama-3-8B":
+                text = text.strip() + "<|end_of_text|>"
+            elif isinstance(self.model.config, LlamaConfig) or isinstance(
+                self.model.config, MistralConfig
+            ):
+                text = text.strip() + " </s>"
+            elif isinstance(self.model.config, GemmaConfig):
+                text = text.strip() + "<eos>"
+            elif isinstance(self.model.config, Qwen2Config):
+                text = text.strip() + "<|endoftext|>"
+        return text
+
+    def tokenize(self, texts):
+        texts_2 = []
+        original_texts = []
+        for text in texts:
+            t = text.split("!@#$%^&*()")
+            texts_2.append(t[1] if len(t) > 1 else "")
+            original_texts.append("".join(t))
+
+        original = self.tokenizer(
+            original_texts,
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+            max_length=self.max_length,
+        )
+        embed_mask = None
+        for t_i, t in enumerate(texts_2):
+            ids = self.tokenizer(
+                [t],
+                return_tensors="pt",
+                padding=True,
+                truncation=True,
+                max_length=self.max_length,
+                add_special_tokens=False,
+            )
+            if embed_mask is None:
+                e_m = torch.zeros_like(original["attention_mask"][t_i])
+                if len(ids["input_ids"][0]) > 0:
+                    e_m[-len(ids["input_ids"][0]) :] = torch.ones(
+                        len(ids["input_ids"][0])
+                    )
+                embed_mask = e_m.unsqueeze(0)
+            else:
+                e_m = torch.zeros_like(original["attention_mask"][t_i])
+                if len(ids["input_ids"][0]) > 0:
+                    e_m[-len(ids["input_ids"][0]) :] = torch.ones(
+                        len(ids["input_ids"][0])
+                    )
+                embed_mask = torch.cat((embed_mask, e_m.unsqueeze(0)), dim=0)
+
+        original["embed_mask"] = embed_mask
+        return original
+
+    def _skip_instruction(self, sentence_feature):
+        assert (
+            sentence_feature["attention_mask"].shape
+            == sentence_feature["embed_mask"].shape
+        )
+        sentence_feature["attention_mask"] = sentence_feature["embed_mask"]
+
+    def forward(self, sentence_feature: Dict[str, Tensor]):
+        embed_mask = None
+        if "embed_mask" in sentence_feature:
+            embed_mask = sentence_feature.pop("embed_mask")
+        reps = self.model(**sentence_feature)
+        sentence_feature["embed_mask"] = embed_mask
+
+        return self.get_pooling(sentence_feature, reps.last_hidden_state)
+
+    def get_pooling(self, features, last_hidden_states):  # All models padded from left
+        assert (
+            self.tokenizer.padding_side == "left"
+        ), "Pooling modes are implemented for padding from left."
+        if self.skip_instruction:
+            self._skip_instruction(features)
+        seq_lengths = features["attention_mask"].sum(dim=-1)
+        if self.pooling_mode == "mean":
+            return torch.stack(
+                [
+                    last_hidden_states[i, -length:, :].mean(dim=0)
+                    for i, length in enumerate(seq_lengths)
+                ],
+                dim=0,
+            )
+        elif self.pooling_mode == "weighted_mean":
+            bs, l, _ = last_hidden_states.shape
+            complete_weights = torch.zeros(bs, l, device=last_hidden_states.device)
+            for i, seq_l in enumerate(seq_lengths):
+                if seq_l > 0:
+                    complete_weights[i, -seq_l:] = torch.arange(seq_l) + 1
+                    complete_weights[i] /= torch.clamp(
+                        complete_weights[i].sum(), min=1e-9
+                    )
+            return torch.sum(last_hidden_states * complete_weights.unsqueeze(-1), dim=1)
+        elif self.pooling_mode == "eos_token" or self.pooling_mode == "last_token":
+            return last_hidden_states[:, -1]
+        elif self.pooling_mode == "bos_token":
+            return last_hidden_states[
+                features["input_ids"] == self.tokenizer.bos_token_id
+            ]
+        else:
+            raise ValueError(f"{self.pooling_mode} is not implemented yet.")
+
+    def _convert_to_str(self, instruction, text):
+        tokenized_q = self.tokenizer(
+            text,
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+            max_length=self.max_length,
+            add_special_tokens=False,
+        )
+        tokenized_q_length = len(tokenized_q["input_ids"][0])
+
+        while tokenized_q_length > self.doc_max_length:
+            reduction_ratio = self.doc_max_length / tokenized_q_length
+            reduced_length = int(len(text.split()) * reduction_ratio)
+            text = " ".join(text.split()[:reduced_length])
+            tokenized_q = self.tokenizer(
+                text,
+                return_tensors="pt",
+                padding=True,
+                truncation=True,
+                max_length=self.max_length,
+                add_special_tokens=False,
+            )
+            tokenized_q_length = len(tokenized_q["input_ids"][0])
+
+        return (
+            f"{instruction.strip()} !@#$%^&*(){text}"
+            if instruction
+            else f"!@#$%^&*(){text}"
+        )
+
+    def encode(
+        self,
+        sentences: Union[str, List[str]],
+        batch_size: int = 32,
+        show_progress_bar: bool = True,
+        convert_to_numpy: bool = False,
+        convert_to_tensor: bool = False,
+        device: Optional[str] = None,
+    ):
+        """
+        Encode a list of sentences to their respective embeddings. The sentences can be a list of strings or a string.
+        Args:
+            sentences: sentence or sentences to encode.
+            batch_size: batch size for turning sentence tokens into embeddings.
+            show_progress_bar: whether to show progress bars during encoding steps.
+            convert_to_numpy: If true, return numpy arrays instead of torch tensors.
+            convert_to_tensor: If true, return torch tensors (default).
+            device: torch backend device identifier (e.g., 'cuda', 'cpu','mps' etc.). If not specified,
+            the default is to use cuda when available, otherwise cpu. Note that only the choice of 'cuda' supports
+            multiprocessing as currently implemented.
+
+        Returns: embeddings of the sentences. Embeddings are detached and always on the CPU (see _encode implementation).
+
+        """
+        if isinstance(sentences[0], str) and isinstance(sentences[-1], int):
+            sentences = [sentences]
+        # required for MEDI version of MTEB
+        if isinstance(sentences[0], str):
+            sentences = [[""] + [sentence] for sentence in sentences]
+
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        concatenated_input_texts = []
+        for sentence in sentences:
+            assert isinstance(sentence[0], str)
+            assert isinstance(sentence[1], str)
+            concatenated_input_texts.append(
+                self._convert_to_str(sentence[0], sentence[1])
+            )
+        sentences = concatenated_input_texts
+
+        self.eval()
+
+        if convert_to_tensor:
+            convert_to_numpy = False
+
+        length_sorted_idx = np.argsort([-self._text_length(sen) for sen in sentences])
+        sentences_sorted = [sentences[idx] for idx in length_sorted_idx]
+        all_embeddings = []
+
+        if torch.cuda.device_count() <= 1:
+            # This branch also support mps devices
+            self.to(device)
+            for start_index in trange(
+                0,
+                len(sentences),
+                batch_size,
+                desc="Batches",
+                disable=not show_progress_bar,
+            ):
+                sentences_batch = sentences_sorted[
+                    start_index : start_index + batch_size
+                ]
+                embeddings = self._encode(
+                    sentences_batch, device=device, convert_to_numpy=convert_to_numpy
+                )
+                all_embeddings.append(embeddings)
+        else:
+
+            num_proc = torch.cuda.device_count()
+            cuda_compatible_multiprocess = mp.get_context("spawn")
+            with cuda_compatible_multiprocess.Pool(num_proc) as p:
+                sentences_batches = [
+                    sentences_sorted[start_index : start_index + batch_size]
+                    for start_index in range(0, len(sentences), batch_size)
+                ]
+
+                progress_bar = tqdm(
+                    total=len(sentences_batches),
+                    desc="Batches",
+                    disable=not show_progress_bar,
+                )
+                results = []
+
+                def update(*args):
+                    progress_bar.update()
+
+                for batch in sentences_batches:
+                    results.append(
+                        p.apply_async(
+                            self._encode,
+                            args=(batch, None, convert_to_numpy, True),
+                            callback=update,
+                        )
+                    )
+
+                all_embeddings = [result.get() for result in results]
+                progress_bar.close()
+
+        all_embeddings = torch.cat(all_embeddings, dim=0)
+        all_embeddings = all_embeddings[np.argsort(length_sorted_idx)]
+        all_embeddings = all_embeddings.to(torch.float32)
+        if convert_to_numpy:
+            all_embeddings = np.asarray([emb.numpy() for emb in all_embeddings])
+        return all_embeddings
+
+    def save(self, output_path, merge_before_save=False, save_config=True):
+        if merge_before_save and isinstance(self.model, PeftModel):
+            self.model = self.model.merge_and_unload()
+            if hasattr(self.model, "_hf_peft_config_loaded"):
+                self.model._hf_peft_config_loaded = False
+
+        self.model.save_pretrained(output_path)
+        self.tokenizer.save_pretrained(output_path)
+
+        l3prune_config = {
+            "pooling_mode": self.pooling_mode,
+            "max_length": self.max_length,
+            "doc_max_length": self.doc_max_length,
+            "skip_instruction": self.skip_instruction,
+        }
+
+        if save_config:
+            os.makedirs(output_path, exist_ok=True)
+            with open(f"{output_path}/l3prune_config.json", "w") as fOut:
+                json.dump(l3prune_config, fOut, indent=4)
+
+    def _encode(
+        self,
+        sentences_batch,
+        device: Optional[str] = None,
+        convert_to_numpy: bool = False,
+        multiprocessing=False,
+    ):
+        if multiprocessing:
+            # multiprocessing only supports CUDA devices at this time, so we ignore the value of device
+            # and use cuda:rank for the device
+            rank = mp.current_process()._identity[0]
+            if device is None and torch.cuda.is_available():
+                device = f"cuda:{rank % torch.cuda.device_count()}"
+
+        self.to(device)
+        features = self.tokenize(
+            [self.prepare_for_tokenization(sentence) for sentence in sentences_batch]
+        )
+        features = batch_to_device(features, device)
+
+        with torch.no_grad():
+            embeddings = self.forward(features)
+            embeddings = embeddings.detach()
+            embeddings = embeddings.cpu()
+
+        return embeddings
+
+    def _text_length(self, text: Union[List[int], List[List[int]]]):
+        """
+        Help function to get the length for the input text. Text can be either a string (which means a single text)
+        a list of ints (which means a single tokenized text), or a tuple of list of ints
+        (representing several text inputs to the model).
+        """
+        if (
+            isinstance(text, str)
+            or (isinstance(text, list) and isinstance(text[0], int))
+            or len(text) == 0
+        ):  # Single text, list of ints, or empty
+            return len(text)
+        if isinstance(text, dict):  # {key: value} case
+            return len(next(iter(text.values())))
+        elif not hasattr(text, "__len__"):  # Object has no len() method
+            return 1
+        else:
+            return sum([len(t) for t in text])
+
+    def resize_token_embeddings(
+        self,
+        new_num_tokens: Optional[int] = None,
+        pad_to_multiple_of: Optional[int] = None,
+    ) -> nn.Embedding:
+        return self.model.resize_token_embeddings(
+            new_num_tokens=new_num_tokens, pad_to_multiple_of=pad_to_multiple_of
+        )
+
+    def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
+        self.model.gradient_checkpointing_enable(
+            gradient_checkpointing_kwargs=gradient_checkpointing_kwargs
+        )
+    
+    def save_pretrained(self, save_directory, **kwargs):
+        self.tokenizer.save_pretrained(save_directory, **kwargs)
+        super().save_pretrained(save_directory, **kwargs)
+    
+    def push_to_hub(self, repo_id, **kwargs):
+        self.tokenizer.push_to_hub(repo_id, **kwargs)
+        super().push_to_hub(repo_id, **kwargs)

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