Upload migrated model for transformers 5.0+ compatibility

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 4096,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": false
+}

README.md

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+---
+license: cc-by-nc-4.0
+library_name: transformers
+tags:
+- nv-embed
+- embedding
+- retrieval
+- transformers
+base_model: nvidia/NV-Embed-v2
+---
+
+# NV-Embed-v2 (Migrated for transformers 5.0+ compatibility)
+
+This is a migrated version of [nvidia/NV-Embed-v2](https://huggingface.co/nvidia/NV-Embed-v2) that is compatible with transformers 5.0.0 and later versions.
+
+## Changes from Original
+
+The only change made is adding an `all_tied_weights_keys` property to the `NVEmbedModel` class in `modeling_nvembed.py`. This property provides backward compatibility with the transformers library, which changed from using `_tied_weights_keys` (a class attribute) to `all_tied_weights_keys` (a property that returns a dict) in version 5.0.0.
+
+### The Patch
+
+```python
+@property
+def all_tied_weights_keys(self):
+    """Compatibility property for transformers >= 5.0.0."""
+    if hasattr(self, '_tied_weights_keys') and self._tied_weights_keys:
+        return {key: key for key in self._tied_weights_keys}
+    return {}
+```
+
+## Usage
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+model = AutoModel.from_pretrained("Hyukkyu/nv-embed-v2", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("Hyukkyu/nv-embed-v2", trust_remote_code=True)
+```
+
+## Original Model
+
+This model is based on [nvidia/NV-Embed-v2](https://huggingface.co/nvidia/NV-Embed-v2). Please refer to the original repository for:
+- Model architecture details
+- Training information
+- Benchmarks and evaluation results
+- Citation information
+
+## License
+
+This model inherits the license from the original repository. Please check [nvidia/NV-Embed-v2](https://huggingface.co/nvidia/NV-Embed-v2) for license details.
+
+## Migration Tool
+
+This model was migrated using the GenZ model migration tool. The migration script is available at:
+https://github.com/your-repo/GenZ/tree/main/scripts/preprocess/model
+
+## Compatibility
+
+- **transformers**: >= 5.0.0
+- **torch**: >= 2.0.0
+- **Python**: >= 3.9

config.json

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+{
+  "_name_or_path": "nvidia/NV-Embed-v2",
+  "add_eos": true,
+  "add_pad_token": true,
+  "architectures": [
+    "NVEmbedModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_nvembed.NVEmbedConfig",
+    "AutoModel": "modeling_nvembed.NVEmbedModel"
+  },
+  "hidden_size": 4096,
+  "is_mask_instruction": true,
+  "latent_attention_config": {
+    "model_type": "latent_attention"
+  },
+  "mask_type": "b",
+  "model_type": "nvembed",
+  "padding_side": "right",
+  "text_config": {
+    "_name_or_path": "nvidia/NV-Embed-v2",
+    "add_cross_attention": false,
+    "architectures": [
+      "MistralModel"
+    ],
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": 1,
+    "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": 2,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "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,
+    "min_length": 0,
+    "model_type": "bidir_mistral",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 32,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 8,
+    "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-05,
+    "rope_theta": 10000.0,
+    "sep_token_id": null,
+    "sliding_window": 4096,
+    "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": "float32",
+    "torchscript": false,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 32000
+  },
+  "torch_dtype": "float16",
+  "transformers_version": "4.42.4"
+}

config_sentence_transformers.json

@@ -0,0 +1,9 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.7.0",
+    "transformers": "4.37.2",
+    "pytorch": "2.2.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

configuration_nvembed.py

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+
+from typing import Literal
+from transformers import AutoConfig
+from transformers.configuration_utils import PretrainedConfig
+from transformers.models.auto import CONFIG_MAPPING
+from transformers.models.mistral import MistralConfig
+
+NVEMBED_TYPE = "nvembed"
+LATENT_ATTENTION_TYPE = "latent_attention"
+BIDIR_MISTRAL_TYPE = "bidir_mistral"
+
+class NVEmbedConfig(PretrainedConfig):
+    model_type = "nvembed"
+    is_composition = False
+
+    def __init__(
+        self,
+        latent_attention_config=None,
+        text_config=None,
+        padding_side: Literal["right", "left"]="right",
+        add_pad_token: bool=True,
+        is_mask_instruction: bool = True,
+        add_eos: bool=True,
+        mask_type: str="b",
+        **kwargs,
+    ):
+        if isinstance(latent_attention_config, dict):
+            latent_attention_config["model_type"] = (
+                latent_attention_config["model_type"] if "model_type" in latent_attention_config else LATENT_ATTENTION_TYPE
+            )
+            latent_attention_config = CONFIG_MAPPING[latent_attention_config["model_type"]](**latent_attention_config)
+        elif latent_attention_config is None:
+            latent_attention_config = CONFIG_MAPPING[LATENT_ATTENTION_TYPE]()
+
+        self.latent_attention_config = latent_attention_config
+
+        if isinstance(text_config, dict):
+            text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
+            text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
+        elif text_config is None:
+            text_config = None
+
+        self.text_config = text_config
+        self.padding_side = padding_side
+        self.is_mask_instruction = is_mask_instruction
+        self.add_pad_token = add_pad_token
+        self.add_eos = add_eos
+        self.mask_type = mask_type
+        if "hidden_size" in kwargs:
+            self.hidden_size = kwargs["hidden_size"]
+        else:
+            self.hidden_size = 4096
+
+        super().__init__(**kwargs)
+
+
+class LatentAttentionConfig(PretrainedConfig):
+    model_type = LATENT_ATTENTION_TYPE
+    is_composition = False
+    _name_or_path = "latent_attention"
+
+    def __init__(
+        self,
+        num_latents_value: int=512,
+        num_cross_heads: int=8,
+        output_normalize: bool=True,
+        hidden_dim: int=4096,
+        latent_dim: int=4096,
+        cross_dim_head: int=4096,
+        **kwargs,
+    ):
+        self.num_latents_value = num_latents_value
+        self.num_cross_heads = num_cross_heads
+        self.output_normalize = output_normalize
+        self.hidden_dim = hidden_dim
+        self.latent_dim = latent_dim
+        self.cross_dim_head = cross_dim_head
+
+        super().__init__(**kwargs)
+
+
+class BidirectionalMistralConfig(MistralConfig):
+    model_type = BIDIR_MISTRAL_TYPE
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+AutoConfig.register(NVEMBED_TYPE, NVEmbedConfig)
+AutoConfig.register(LATENT_ATTENTION_TYPE, LatentAttentionConfig)
+AutoConfig.register(BIDIR_MISTRAL_TYPE, BidirectionalMistralConfig)
+
+NVEmbedConfig.register_for_auto_class()
+LatentAttentionConfig.register_for_auto_class()
+BidirectionalMistralConfig.register_for_auto_class()

instructions.json

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+{
+    "ClimateFEVER":
+            {
+                "query": "Given a claim about climate change, retrieve documents that support or refute the claim",
+                "corpus": ""
+            },
+    "HotpotQA":
+        {
+            "query": "Given a multi-hop question, retrieve documents that can help answer the question",
+            "corpus": ""
+        },
+    "FEVER":
+        {
+            "query": "Given a claim, retrieve documents that support or refute the claim",
+            "corpus": ""
+        },
+    "MSMARCO":
+        {
+            "query": "Given a web search query, retrieve relevant passages that answer the query",
+            "corpus": ""
+        },
+    "DBPedia":
+        {
+            "query": "Given a query, retrieve relevant entity descriptions from DBPedia",
+            "corpus": ""
+        },
+    "NQ":
+        {
+            "query": "Given a question, retrieve passages that answer the question",
+            "corpus": ""
+        },
+    "QuoraRetrieval":
+        {
+            "query": "Given a question, retrieve questions that are semantically equivalent to the given question",
+            "corpus": "Given a question, retrieve questions that are semantically equivalent to the given question"
+        },
+    "SCIDOCS":
+        {
+            "query": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper",
+            "corpus": ""
+        },
+    "TRECCOVID":
+        {
+            "query": "Given a query on COVID-19, retrieve documents that answer the query",
+            "corpus": ""
+        },
+    "Touche2020":
+        {
+            "query": "Given a question, retrieve passages that answer the question",
+            "corpus": ""
+        },
+    "SciFact":
+        {
+            "query": "Given a scientific claim, retrieve documents that support or refute the claim",
+            "corpus": ""
+        },
+    "NFCorpus":
+        {
+            "query": "Given a question, retrieve relevant documents that answer the question",
+            "corpus": ""
+        },
+    "ArguAna":
+        {
+            "query": "Given a claim, retrieve documents that support or refute the claim",
+            "corpus": ""
+        },
+    "FiQA2018":
+        {
+            "query": "Given a financial question, retrieve relevant passages that answer the query",
+            "corpus": ""
+        },
+    "STS":
+        {
+            "text": "Retrieve semantically similar text"
+        },
+    "SUMM":
+        {
+            "text": "Given a news summary, retrieve other semantically similar summaries"
+        }
+}

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+    "embedding_model.norm.weight": "model-00004-of-00004.safetensors",
+    "latent_attention_model.cross_attend_blocks.0.fn.to_kv.weight": "model-00001-of-00004.safetensors",
+    "latent_attention_model.cross_attend_blocks.0.fn.to_out.weight": "model-00001-of-00004.safetensors",
+    "latent_attention_model.cross_attend_blocks.0.fn.to_q.weight": "model-00001-of-00004.safetensors",
+    "latent_attention_model.cross_attend_blocks.0.norm.bias": "model-00001-of-00004.safetensors",
+    "latent_attention_model.cross_attend_blocks.0.norm.weight": "model-00001-of-00004.safetensors",
+    "latent_attention_model.cross_attend_blocks.0.norm_context.bias": "model-00001-of-00004.safetensors",
+    "latent_attention_model.cross_attend_blocks.0.norm_context.weight": "model-00001-of-00004.safetensors",
+    "latent_attention_model.cross_attend_blocks.1.fn.net.0.bias": "model-00001-of-00004.safetensors",
+    "latent_attention_model.cross_attend_blocks.1.fn.net.0.weight": "model-00001-of-00004.safetensors",
+    "latent_attention_model.cross_attend_blocks.1.fn.net.2.bias": "model-00001-of-00004.safetensors",
+    "latent_attention_model.cross_attend_blocks.1.fn.net.2.weight": "model-00001-of-00004.safetensors",
+    "latent_attention_model.cross_attend_blocks.1.norm.bias": "model-00001-of-00004.safetensors",
+    "latent_attention_model.cross_attend_blocks.1.norm.weight": "model-00001-of-00004.safetensors",
+    "latent_attention_model.latents": "model-00001-of-00004.safetensors"
+  }
+}

modeling_nvembed.py

@@ -0,0 +1,470 @@
+from typing import List, Union, Dict, Mapping, Optional, Tuple, TypedDict
+import torch
+import os
+import json
+import numpy as np
+from functools import partial
+from contextlib import nullcontext
+from transformers import AutoModel, PreTrainedTokenizerFast, BatchEncoding, DataCollatorWithPadding
+from transformers.modeling_utils import PreTrainedModel
+from transformers.models.auto import AutoTokenizer
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa
+from transformers import MistralModel, MistralConfig
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    logging,
+)
+from einops import rearrange, repeat
+from tqdm.auto import tqdm
+from datasets import Dataset
+from torch.utils.data import DataLoader
+from .configuration_nvembed import NVEmbedConfig, LatentAttentionConfig, BidirectionalMistralConfig
+
+logger = logging.get_logger(__name__)
+
+class NVEmbedFeatures(TypedDict):
+    input_dict: torch.Tensor
+    attention_mask: torch.Tensor
+    pool_mask: torch.Tensor
+
+class BidirectionalMistralModel(MistralModel):
+    config_class = BidirectionalMistralConfig
+    
+    def __init__(self, config: MistralConfig):
+        super().__init__(config)
+        for layer in self.layers:
+            layer.self_attn.is_causal = False
+        self._attn_implementation = "eager"
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        past_key_values_length = 0
+
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
+            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
+            if is_padding_right:
+                raise ValueError(
+                    "You are attempting to perform batched generation with padding_side='right'"
+                    " this may lead to unexpected behaviour for Flash Attention version of Mistral. Make sure to "
+                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                )
+
+        if self._attn_implementation == "flash_attention_2":
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        elif self._attn_implementation == "sdpa" and not output_attentions:
+            # output_attentions=True can not be supported when using SDPA, and we fall back on
+            # the manual implementation that requires a 4D causal mask in all cases.
+            attention_mask = _prepare_4d_attention_mask_for_sdpa(
+                attention_mask, inputs_embeds.dtype
+            )
+        else:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_attention_mask(
+                attention_mask, inputs_embeds.dtype,
+            )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+    
+def _move_to_device(maybe_tensor, device: torch.device):
+    if torch.is_tensor(maybe_tensor):
+        return maybe_tensor.to(device, non_blocking=device.type == "cuda")
+    elif isinstance(maybe_tensor, dict):
+        return {key: _move_to_device(value, device) for key, value in maybe_tensor.items()}
+    elif isinstance(maybe_tensor, list):
+        return [_move_to_device(x, device) for x in maybe_tensor]
+    elif isinstance(maybe_tensor, tuple):
+        return tuple([_move_to_device(x, device) for x in maybe_tensor])
+    elif isinstance(maybe_tensor, Mapping):
+        return type(maybe_tensor)({k: _move_to_device(v, device) for k, v in maybe_tensor.items()})
+    else:
+        return maybe_tensor
+
+def move_to_device(sample, device: torch.device):
+    if device.type == "cpu":
+        return sample
+    
+    if len(sample) == 0:
+        return {}
+    return _move_to_device(sample, device)
+
+
+def input_transform_func(
+    tokenizer: PreTrainedTokenizerFast,
+    examples: Dict[str, List],
+    always_add_eos: bool,
+    max_length: int,
+    instruction: str,
+) -> BatchEncoding:
+    if always_add_eos:
+        examples['input_texts'] = [instruction + input_example + tokenizer.eos_token for input_example in examples['input_texts']]
+    batch_dict = tokenizer(
+        examples['input_texts'],
+        max_length=max_length,
+        padding=True,
+        return_token_type_ids=False,
+        return_tensors="pt",
+        truncation=True)
+    return batch_dict
+
+
+class PreNorm(torch.nn.Module):
+    def __init__(self, dim, fn, context_dim = None):
+        super().__init__()
+        self.fn = fn
+        self.norm = torch.nn.LayerNorm(dim)
+        self.norm_context = torch.nn.LayerNorm(context_dim) if exists(context_dim) else None
+
+    def forward(self, x, **kwargs):
+        x = self.norm(x)
+        if exists(self.norm_context):
+            context = kwargs['context']
+            normed_context = self.norm_context(context)
+            kwargs.update(context = normed_context)
+        return self.fn(x, **kwargs)
+
+class GEGLU(torch.nn.Module):
+    def forward(self, x):
+        x, gates = x.chunk(2, dim = -1)
+        return x * torch.nn.functional.gelu(gates)
+
+class FeedForward(torch.nn.Module):
+    def __init__(self, dim, mult = 4):
+        super().__init__()
+        self.net = torch.nn.Sequential(torch.nn.Linear(dim, dim * mult * 2),
+            GEGLU(),
+            torch.nn.Linear(dim * mult, dim))
+
+    def forward(self, x):
+        return self.net(x)
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+
+class Attention(torch.nn.Module):
+    def __init__(self, query_dim, context_dim = None, heads = 8, dim_head = 64):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+        self.scale = dim_head ** -0.5
+        self.heads = heads
+
+        self.to_q = torch.nn.Linear(query_dim, inner_dim, bias = False)
+        self.to_kv = torch.nn.Linear(context_dim, inner_dim * 2, bias = False)
+        self.to_out = torch.nn.Linear(inner_dim, query_dim, bias = False)
+
+    def forward(self, x, context = None, mask = None):
+        h = self.heads
+        q = self.to_q(x)
+        context = default(context, x)
+        k, v = self.to_kv(context).chunk(2, dim = -1)
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h = h), (q, k, v))
+        with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_mem_efficient=True):
+            out = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+        out = rearrange(out, '(b h) n d -> b n (h d)', h = h)
+        return self.to_out(out)
+
+
+class LatentAttentionModel(PreTrainedModel):
+    config_class = LatentAttentionConfig
+
+    def __init__(self, config: LatentAttentionConfig):
+        super().__init__(config)
+        ## cross-attention block
+        num_latents, latent_dim, cross_heads, cross_dim_head = config.num_latents_value, config.latent_dim, config.num_cross_heads, config.cross_dim_head
+        dim = config.hidden_dim
+        # init latent_attention and latents
+        self.cross_attend_blocks = torch.nn.ModuleList([
+            PreNorm(latent_dim, Attention(latent_dim, dim, heads = cross_heads, dim_head = cross_dim_head),
+                    context_dim = dim),
+            PreNorm(latent_dim, FeedForward(latent_dim)),
+        ])
+        self.output_normalize = config.output_normalize
+        self.register_parameter("latents", torch.nn.Parameter(torch.randn(num_latents, latent_dim)))
+
+    @property
+    def all_tied_weights_keys(self):
+        """Compatibility property for transformers >= 5.0.0.
+
+        The transformers library changed from using _tied_weights_keys (a class attribute)
+        to all_tied_weights_keys (a property that returns a dict). This property provides
+        backward compatibility by converting the old format to the new format.
+
+        Returns:
+            dict: A dictionary mapping tied weight keys to themselves.
+        """
+        if hasattr(self, '_tied_weights_keys') and self._tied_weights_keys:
+            return {key: key for key in self._tied_weights_keys}
+        return {}
+
+
+    def forward(self, hiddens, attention_mask: torch.Tensor=None):
+        ## cross-attention block
+        cross_attn, cross_ff = self.cross_attend_blocks
+        b, *_, device = *hiddens.shape, hiddens.device
+        x = repeat(self.latents, 'n d -> b n d', b = b)
+        hiddens = cross_attn(hiddens, context = x, mask = None) + hiddens
+        hiddens = cross_ff(hiddens) + hiddens
+        if attention_mask !=None:
+            s = torch.sum(hiddens * attention_mask.unsqueeze(-1).float(), dim=1)
+            d = attention_mask.sum(dim=1, keepdim=True).float()
+            hiddens = s / d
+            if self.output_normalize:
+                hiddens = torch.nn.functional.normalize(hiddens, p=2, dim=-1)
+        return hiddens
+    
+class NVEmbedModel(PreTrainedModel):
+    config_class = NVEmbedConfig
+    _no_split_modules = ["MistralDecoderLayer", "LatentAttentionModel"]
+    
+    def __init__(self, config: NVEmbedConfig):
+        super().__init__(config)
+        self.latent_attention_model = AutoModel.from_config(config.latent_attention_config)
+        self.embedding_model = AutoModel.from_config(
+            config.text_config,
+        ) if config.text_config is not None else None
+        self.tokenizer = AutoTokenizer.from_pretrained(config.text_config._name_or_path) if config.text_config is not None else None
+        self.padding_side = config.padding_side
+        self.is_mask_instruction = config.is_mask_instruction
+        self.add_eos = config.add_eos
+        self.mask_type = config.mask_type
+        if config.add_pad_token and self.tokenizer is not None:
+            self.add_pad_token()
+
+    def add_pad_token(self):
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+        self.tokenizer.padding_side = self.padding_side
+
+    @property
+    def all_tied_weights_keys(self):
+        """Compatibility property for transformers >= 5.0.0.
+
+        The transformers library changed from using _tied_weights_keys (a class attribute)
+        to all_tied_weights_keys (a property that returns a dict). This property provides
+        backward compatibility by converting the old format to the new format.
+
+        Returns:
+            dict: A dictionary mapping tied weight keys to themselves.
+        """
+        if hasattr(self, '_tied_weights_keys') and self._tied_weights_keys:
+            return {key: key for key in self._tied_weights_keys}
+        return {}
+    
+    def prepare_kwargs_from_batch(self, batch_dict: dict, instruction_lens: int, device: torch.device):
+        batch_dict = move_to_device(batch_dict, device)
+        attention_mask = batch_dict['attention_mask'].clone() if 'attention_mask' in batch_dict else None
+        if (attention_mask is not None and
+            self.padding_side == "right" and
+            self.is_mask_instruction == True and
+            instruction_lens > 0):
+            # Mask out the instruction tokens for mean-pooling
+            attention_mask[:, :instruction_lens] = 0
+        features: NVEmbedFeatures = {
+            'input_ids': torch.tensor(batch_dict.get('input_ids').to(batch_dict.get('input_ids')).long()),
+            'attention_mask': batch_dict['attention_mask'],
+            'pool_mask': attention_mask,
+        }
+        return features
+
+    @torch.no_grad()
+    def _do_encode(self,
+        prompts: List[str],
+        batch_size: int=1,
+        instruction: str="",
+        max_length: int=4096,
+        num_workers: int=32,
+        **kwargs
+    ) -> Union[np.ndarray, torch.FloatTensor]:
+        dataset: Dataset = Dataset.from_dict({'input_texts': prompts})
+        dataset.set_transform(partial(input_transform_func,
+                                      self.tokenizer,
+                                      always_add_eos=True,
+                                      max_length=max_length,
+                                      instruction=instruction))
+
+        data_collator = DataCollatorWithPadding(self.tokenizer)
+        data_loader = DataLoader(
+            dataset,
+            batch_size=batch_size,
+            shuffle=False,
+            drop_last=False,
+            num_workers=num_workers,
+            collate_fn=data_collator,
+            pin_memory=True)
+
+        if self.padding_side == "right" and self.is_mask_instruction == True and len(instruction) > 0:
+            instruction_lens = len(self.tokenizer.tokenize(instruction))
+        else:
+            instruction_lens = 0
+
+        encoded_embeds = []
+        device = next(self.embedding_model.parameters()).device
+        for batch_dict in tqdm(data_loader, desc='encoding', mininterval=10):
+            features = self.prepare_kwargs_from_batch(batch_dict, instruction_lens, device=device)
+            embeds=self(**features)["sentence_embeddings"].squeeze(1)
+            encoded_embeds.append(embeds)
+        encoded_embeds = torch.cat(encoded_embeds, axis=0)
+        if "return_numpy" in kwargs and  kwargs.get("return_numpy"):
+            encoded_embeds = encoded_embeds.cpu().detach().numpy()
+        return encoded_embeds
+
+    def forward(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, pool_mask: Optional[torch.Tensor]=None, return_dict: bool=True):
+        autocast_ctx = torch.autocast if torch.cuda.is_available() else nullcontext
+        with autocast_ctx("cuda"):
+            ## decoder only layer
+            outputs = self.embedding_model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+            )
+            ## latent attention layer
+            embeds = self.latent_attention_model(
+                outputs.last_hidden_state,
+                pool_mask,
+            )
+        if not return_dict:
+            return (embeds,)
+        return {"sentence_embeddings": embeds}
+        
+    
+    @torch.no_grad()
+    def encode(self, prompts: List[str], instruction: str="", max_length: int=4096, **kwargs):
+        if self.padding_side == "right" and self.is_mask_instruction == True and len(instruction) > 0:
+            instruction_lens = len(self.tokenizer.tokenize(instruction))
+        else:
+            instruction_lens = 0
+        
+        device = next(self.embedding_model.parameters()).device
+        batch_dict = input_transform_func(self.tokenizer,
+                                          {"input_texts": [prompt for prompt in prompts]},
+                                          always_add_eos=True,
+                                          max_length=max_length,
+                                          instruction=instruction)
+
+        features: NVEmbedFeatures = self.prepare_kwargs_from_batch(batch_dict, instruction_lens, device=device)
+        return self(**features)["sentence_embeddings"].squeeze(1)
+
+
+## AutoModel Register
+AutoModel.register(NVEmbedConfig, NVEmbedModel)
+AutoModel.register(LatentAttentionConfig, LatentAttentionModel)
+AutoModel.register(BidirectionalMistralConfig, BidirectionalMistralModel)
+
+## Register for auto class
+NVEmbedModel.register_for_auto_class("AutoModel")
+LatentAttentionModel.register_for_auto_class("AutoModel")
+BidirectionalMistralModel.register_for_auto_class("AutoModel")

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 32768,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
+size 493443

tokenizer_config.json

@@ -0,0 +1,43 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}