njt

README.md

@@ -60,10 +60,9 @@ model_name = "facebook/drama-base"
 device = "cuda" if torch.cuda.is_available() else "cpu"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModel.from_pretrained(model_name, trust_remote_code=True).to(device)
-use_nested = False
-query_embs = model.encode_queries(tokenizer, queries, use_nested=use_nested)
-doc_embs = model.encode_documents(tokenizer, documents, use_nested=use_nested)
 
+query_embs = model.encode_queries(tokenizer, queries)
+doc_embs = model.encode_documents(tokenizer, documents)
 
 scores = query_embs @ doc_embs.T
 print(scores.tolist())
@@ -78,8 +77,8 @@ print(scores.tolist())
 DRAMA models are trained using Matryoshka Representation Learning ([MRL](https://github.com/RAIVNLab/MRL)) to support flexible dimensionality. Both queries and documents can be encoded into smaller dimensions, such as 256, using the following:
 
 ```python
-query_embs = model.encode_queries(tokenizer, queries, dim=256, use_nested=use_nested)
-doc_embs = model.encode_documents(tokenizer, documents, dim=256, use_nested=use_nested)
+query_embs = model.encode_queries(tokenizer, queries, dim=256)
+doc_embs = model.encode_documents(tokenizer, documents, dim=256)
 
 scores = query_embs @ doc_embs.T
 print(scores.tolist())
@@ -102,8 +101,8 @@ documents = [
 
 model = SentenceTransformer("facebook/drama-base", trust_remote_code=True)
 
-query_embs = model.encode(queries, prompt_name="query",  use_nested=use_nested)
-doc_embs = model.encode(documents,  use_nested=use_nested)
+query_embs = model.encode(queries, prompt_name="query")
+doc_embs = model.encode(documents)
 
 scores = model.similarity(query_embs, doc_embs)
 print(scores.tolist())
@@ -129,8 +128,8 @@ documents = [
 
 model = SentenceTransformer("facebook/drama-base", truncate_dim=256, trust_remote_code=True)
 
-query_embs = model.encode(queries, prompt_name="query",  use_nested=use_nested)
-doc_embs = model.encode(documents,  use_nested=use_nested)
+query_embs = model.encode(queries, prompt_name="query")
+doc_embs = model.encode(documents)
 
 scores = model.similarity(query_embs, doc_embs)
 print(scores.tolist())
@@ -166,26 +165,3 @@ If you find our paper or models helpful, please consider cite as follows:
   year={2025}
 }
 ```
-
-## Efficient DRAMA
-### Nested Tensors
-[Nested Tensors](https://docs.pytorch.org/docs/stable/nested.html) provide a way to handle ragged-shaped data within a single tensor, allowing for efficient operations on such data.
-They store data in a compact packed representation while offering a standard PyTorch tensor interface, making it easy to apply various
-operations.
-Nested Tensors are particularly advantageous for model deployments that perform inference on large batches of sequences with varying
-lengths. Traditional tensors require padding all sequences in a batch to the same length, which can be inefficient, especially when
-the batch includesmany short sequences and a single long sequence. Nested Tensors eliminate the need for padding, thus avoiding
-unnecessary computation on extra pad tokens. This results in more efficient processing of batches with varying sequence lengths.
-
-### Performance
-Experiments have demonstrated a 1.7x to 2.3x (base,large and 1B) improvement in queries per second (QPS) for batch inference with sequences of varied lengths.
-
-### Usage
-To enable Nested Tensors, simply set the use_nested variable to true. This will activate the nested jagged tensors and allow you to
-take advantage of efficient inference.
-
-> Prerequisites Package versions as this code have been tested with these versions. Please use these or some latest versions to avoid compatibility issues.
-
->- Python: 3.12
->- Transformers: 4.51.1
->- PyTorch: 2.7.1

config.json

@@ -4,7 +4,7 @@
     "DramaModel"
   ],
   "auto_map": {
-    "AutoModel": "modeling_drama.DramaModelWrapper"
+    "AutoModel": "modeling_drama.DramaModel"
   },
   "attention_bias": false,
   "attention_dropout": 0.0,

modeling_drama.py

@@ -1,125 +1,166 @@
-import sys
-import warnings
+from __future__ import annotations
 
+import torch
+import torch.nn.functional as F
 
-def _check_torch_version():
-    """Check if PyTorch version is >= 2.7.1"""
-    try:
-        import torch
+from transformers import LlamaModel, LlamaConfig, PreTrainedTokenizer
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
 
-        # Simple version comparison
-        version_str = torch.__version__.split("+")[0]  # Remove any suffixes like +cu118
-        version_parts = version_str.split(".")
 
-        # Compare major version
-        if int(version_parts[0]) > 2:
-            return True
-        # Compare minor version
-        elif int(version_parts[0]) == 2 and int(version_parts[1]) > 7:
-            return True
-        # Compare patch version
-        elif (
-            int(version_parts[0]) == 2
-            and int(version_parts[1]) == 7
-            and int(version_parts[2]) >= 1
-        ):
-            return True
-
-        return False
-    except (ImportError, AttributeError, IndexError, ValueError):
-        return False
-
-
-def _check_transformers_version():
-    """Check if Transformers version is >= 4.51.1"""
-    try:
-        import transformers
-
-        # Simple version comparison
-        version_str = transformers.__version__.split("+")[0]  # Remove any suffixes
-        version_parts = version_str.split(".")
-
-        # Compare major version
-        if int(version_parts[0]) > 4:
-            return True
-        # Compare minor version
-        elif int(version_parts[0]) == 4 and int(version_parts[1]) > 51:
-            return True
-        # Compare patch version
-        elif (
-            int(version_parts[0]) == 4
-            and int(version_parts[1]) == 51
-            and int(version_parts[2]) >= 1
-        ):
-            return True
-
-        return False
-    except (ImportError, AttributeError, IndexError, ValueError):
-        return False
-
-
-class DramaModelWrapper:
+class DramaModel(LlamaModel):
     """
-    Factory class for DramaModel that returns the appropriate implementation
-    based on the Python version.
-
-    If Python version >= 3.12, returns an instance of the nested tensor implementation.
-    Otherwise, returns an instance of the non-nested implementation.
+    DramaModel is a modified version of the LlamaModel that supports bi-directional attention
+    and provides query and document encoding functionalities.
     """
 
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+    def __init__(self, config: LlamaConfig):
         """
-        Instantiate a pretrained model from a pre-trained model configuration.
+        Initializes the DramaModel by disabling causal masking in self-attention layers.
+        """
+        super().__init__(config)
+        for layer in self.layers:
+            layer.self_attn.is_causal = False
+        # query prefix
+        self.query_prefix = "Query: "
+        self.max_seq_len = 8192
+        self.hidden_size = config.hidden_size
+    
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_seen_tokens=None,
+        output_attentions=False,
+    ):
+        """
+        Updates the causal mask for attention computations.
+        """
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and (attention_mask == 0.0).any():
+                return attention_mask
+            return None
+        if attention_mask is None or attention_mask.dim() == 4:
+            return attention_mask
+        
+        return AttentionMaskConverter._expand_mask(
+            mask=attention_mask,
+            dtype=input_tensor.dtype,
+        )
 
-        This method is required by the transformers library's auto model loading mechanism.
+    def _average_pool(
+        self, last_hidden_states: torch.Tensor, attention_mask: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Computes the average pooled representation of the last hidden states.
+        """
+        last_hidden = last_hidden_states.masked_fill(
+            ~attention_mask[..., None].bool(), 0.0
+        )
+        return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
 
+    def _tokenize(
+            self,
+            tokenizer: PreTrainedTokenizer,
+            texts: list[str],
+            max_seq_len: int = None,
+        ):
+        """
+        Tokenizes input text sequences with optional sequence length restriction.
+        """
+        if max_seq_len is None:
+            max_seq_len = self.max_seq_len
+        tokenized = tokenizer(
+            texts,
+            padding=True,
+            truncation=True,
+            max_length=max_seq_len,
+            return_tensors='pt',
+        ).to(self.device)
+        return tokenized
+
+    def encode(self, input_ids, attention_mask, dim, *args, **kwargs):
+        """
+        Pass through the model and compute normalized embeddings.
+        
         Args:
-            pretrained_model_name_or_path: Path to the pretrained model or its name
-            *model_args: Additional positional arguments to pass to the implementation
-            **kwargs: Additional keyword arguments to pass to the implementation
-
+            input_ids (torch.Tensor): Input token IDs.
+            attention_mask (torch.Tensor): Attention mask tensor.
+            dim (int): Dimensionality for output embeddings.
+        
         Returns:
-            An instance of the appropriate DramaModel implementation.
+            torch.Tensor: Normalized output embeddings.
         """
-        # Check Python version
-        use_nested = sys.version_info >= (3, 15)
-        if not use_nested:
-            warnings.warn(
-                "Python version < 3.12 detected. Using non-nested implementation."
-            )
-            # For Python versions below 3.12, use the non-nested implementation
-            from .modeling_drama_non_nested import DramaModel as NonNestedDramaModel
-
-            return NonNestedDramaModel.from_pretrained(
-                pretrained_model_name_or_path, *model_args, **kwargs
-            )
-
-        # Check PyTorch version
-        if not _check_torch_version():
-            warnings.warn(
-                "PyTorch version < 2.7.1 detected. Falling back to non-nested implementation."
-            )
-            from .modeling_drama_non_nested import DramaModel as NonNestedDramaModel
-
-            return NonNestedDramaModel.from_pretrained(
-                pretrained_model_name_or_path, *model_args, **kwargs
-            )
-
-        # Check Transformers version
-        if not _check_transformers_version():
-            warnings.warn(
-                "Transformers version < 4.51.1 detected. Falling back to non-nested implementation."
-            )
-            from .modeling_drama_non_nested import DramaModel as NonNestedDramaModel
-
-            return NonNestedDramaModel.from_pretrained(
-                pretrained_model_name_or_path, *model_args, **kwargs
-            )
-
-        # Use the nested tensor implementation if all requirements are met
-        from .modeling_drama_nested import DramaModel as NestedDramaModel
-
-        return NestedDramaModel.from_pretrained(
-            pretrained_model_name_or_path, *model_args, **kwargs
+        outputs = self.forward(
+            input_ids, attention_mask, *args, **kwargs
         )
+        embeddings = self._average_pool(
+            outputs.last_hidden_state[:, :, :dim], attention_mask
+        )
+        # normalize embeddings
+        embeddings = F.normalize(embeddings, p=2, dim=1)
+        return embeddings
+
+    def encode_queries(
+            self,
+            tokenizer: PreTrainedTokenizer,
+            queries: list[str],
+            max_seq_len: int = None,
+            dim: int = None,
+        ):
+        """
+        Encodes a list of queries into embeddings.
+        
+        Args:
+            tokenizer (PreTrainedTokenizer): Tokenizer for text processing.
+            queries (list[str]): List of query texts.
+            max_seq_len (int, optional): Maximum sequence length.
+            dim (int, optional): Dimensionality for output embeddings.
+        
+        Returns:
+            torch.Tensor: Encoded query embeddings in shape (num_queries, dim).
+        """
+        if not queries:
+            raise ValueError("queries must not be empty.")
+        if not isinstance(queries, list) or not all(isinstance(q, str) for q in queries):
+            raise ValueError("queries must be a list of strings.")
+        if tokenizer is None:
+            raise ValueError("tokenizer must not be None.")
+        if dim is not None and (dim < 1 or dim > self.hidden_size):
+            raise ValueError(f"dim must be in range [1, {self.hidden_size}].")
+        queries = [self.query_prefix + query for query in queries]
+        tokenized_queries = self._tokenize(tokenizer, queries, max_seq_len)
+        embeddings = self.encode(**tokenized_queries, dim=dim)
+        return embeddings
+
+    def encode_documents(
+            self,
+            tokenizer: PreTrainedTokenizer,
+            documents: list[str],
+            max_seq_len: int = None,
+            dim: int = None,
+        ):
+        """
+        Encodes a list of documents into embeddings.
+        
+        Args:
+            tokenizer (PreTrainedTokenizer): Tokenizer for text processing.
+            documents (list[str]): List of document texts.
+            max_seq_len (int, optional): Maximum sequence length.
+            dim (int, optional): Dimensionality for output embeddings.
+        
+        Returns:
+            torch.Tensor: Encoded document embeddings in shape (num_documents, dim).
+        """
+        if not documents:
+            raise ValueError("documents must not be empty.")
+        if not isinstance(documents, list) or not all(isinstance(d, str) for d in documents):
+            raise ValueError("documents must be a list of strings.")
+        if tokenizer is None:
+            raise ValueError("tokenizer must not be None.")
+        if dim is not None and (dim < 1 or dim > self.hidden_size):
+            raise ValueError(f"dim must be in range [1, {self.hidden_size}].")
+        tokenized_documents = self._tokenize(tokenizer, documents, max_seq_len)
+        embeddings = self.encode(**tokenized_documents, dim=dim)
+        return embeddings
+

modeling_drama_nested.py

@@ -1,639 +0,0 @@
-from __future__ import annotations
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nested._internal.nested_tensor import nested_from_padded
-
-from transformers import (
-    LlamaConfig,
-    LlamaModel,
-    LlamaPreTrainedModel,
-    PreTrainedTokenizer,
-)
-from transformers.cache_utils import Cache, DynamicCache
-from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-from transformers.modeling_outputs import BaseModelOutputWithPast
-from transformers.models.llama.modeling_llama import (
-    LlamaAttention,
-    LlamaDecoderLayer,
-    LlamaMLP,
-    LlamaRMSNorm,
-    LlamaRotaryEmbedding,
-    rotate_half,
-)
-from transformers.processing_utils import Unpack
-
-
-class ModifiedLlamaAttention(LlamaAttention):
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(*args, **kwargs)
-        self.is_causal = False
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
-        attention_mask: Optional[torch.Tensor],
-        past_key_value: Optional[Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        input_shape = hidden_states.shape[:-1]
-        hidden_shape = (*input_shape, -1, self.head_dim)
-
-        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-
-        cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(
-            query_states, key_states, cos, sin
-        )
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(
-                key_states, value_states, self.layer_idx, cache_kwargs
-            )
-
-        if self.config._attn_implementation != "eager":
-            if self.config._attn_implementation == "sdpa" and kwargs.get(
-                "output_attentions", False
-            ):
-                warnings.warn(
-                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
-                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
-                )
-
-        attn_output, attn_weights = sdpa_attention_forward(
-            self,
-            query_states,
-            key_states,
-            value_states,
-            attention_mask,
-            dropout=0.0,
-            scaling=self.scaling,
-            is_causal=False,
-            **kwargs,
-        )
-
-        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-        return attn_output, attn_weights
-
-
-def sdpa_attention_forward(
-    module: torch.nn.Module,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attention_mask: torch.Tensor,
-    dropout: float = 0.0,
-    scaling: Optional[float] = None,
-    is_causal: Optional[bool] = None,
-    **kwargs: Any,
-) -> Tuple[torch.Tensor, None]:
-    if hasattr(module, "num_key_value_groups"):
-        if key.is_nested:
-            key = repeat_jagged_kv(key, module.num_key_value_groups)
-            value = repeat_jagged_kv(value, module.num_key_value_groups)
-        else:
-            key = repeat_dense_kv(key, module.num_key_value_groups)
-            value = repeat_dense_kv(value, module.num_key_value_groups)
-
-    causal_mask = attention_mask
-    if attention_mask is not None and causal_mask.ndim == 4:
-        causal_mask = causal_mask[:, :, :, : key.shape[-2]]
-
-    # SDPA with memory-efficient backend is bugged with non-contiguous inputs and custom attn_mask for some torch versions
-    # Reference: https://github.com/pytorch/pytorch/issues/112577.
-    query = query.contiguous()
-    key = key.contiguous()
-    value = value.contiguous()
-
-    # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
-    # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
-    # Note that it is important to check first for the shape, otherwise compile will fail with `argument 'is_causal' must be bool, not SymBool`
-    if is_causal is None:
-        is_causal = query.shape[2] > 1 and causal_mask is None
-
-    # Shapes (e.g. query.shape[2]) are tensors during jit tracing, resulting in `is_causal` being a tensor.
-    # We convert it to a bool for the SDPA kernel that only accepts bools.
-    if torch.jit.is_tracing() and isinstance(is_causal, torch.Tensor):
-        is_causal = is_causal.item()
-
-    attn_output = torch.nn.functional.scaled_dot_product_attention(
-        query,
-        key,
-        value,
-        attn_mask=causal_mask,
-        dropout_p=dropout,
-        scale=scaling,
-        is_causal=is_causal,
-    )
-    attn_output = attn_output.transpose(1, 2).contiguous()
-
-    return attn_output, None
-
-
-def repeat_jagged_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    expand_shape = (batch, num_key_value_heads, -1, n_rep, head_dim)
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = (
-        hidden_states.unsqueeze(3)
-        .expand(expand_shape)
-        .transpose(1, 2)
-        .flatten(2, 3)
-        .transpose(1, 2)
-    )
-    return hidden_states
-
-
-def repeat_dense_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(
-        batch, num_key_value_heads, n_rep, slen, head_dim
-    )
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-def apply_rotary_pos_emb(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    cos: torch.Tensor,
-    sin: torch.Tensor,
-    unsqueeze_dim: int = 1,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`, *optional*):
-            Deprecated and unused.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-    if q.is_nested and k.is_nested:
-        if q.layout != torch.jagged:
-            raise NotImplementedError(f"Unsupported layout: {q.layout}")
-        if k.layout != torch.jagged:
-            raise NotImplementedError(f"Unsupported layout: {k.layout}")
-        return _jagged_tensor_forward(q, k, cos, sin)
-    else:
-        return _padded_tensor_forward(q, k, cos, sin)
-
-
-def _jagged_tensor_forward(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    cos: torch.Tensor,
-    sin: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    q_dense = q.to_padded_tensor(0.0)
-    k_dense = k.to_padded_tensor(0.0)
-    q_dense_embed = (q_dense * cos) + (rotate_half(q_dense) * sin)
-    k_dense_embed = (k_dense * cos) + (rotate_half(k_dense) * sin)
-    q_jagged_embed = convert_dense_to_jagged(q, q_dense_embed)
-    k_jagged_embed = convert_dense_to_jagged(k, k_dense_embed)
-    return q_jagged_embed, k_jagged_embed
-
-
-def _padded_tensor_forward(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    cos: torch.Tensor,
-    sin: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-def convert_dense_to_jagged(nested_q: torch.Tensor, q: torch.Tensor) -> torch.Tensor:
-    padded_max_S = nested_q._get_max_seqlen()
-    total_L = nested_q._values.shape[nested_q._ragged_idx - 1]
-    if padded_max_S is None:
-        # use upper bound on max seqlen if it's not present
-        padded_max_S = total_L
-
-    # convert dense tensor -> jagged
-    q = q.expand(
-        [
-            x if i != nested_q._ragged_idx else padded_max_S
-            for i, x in enumerate(q.shape)
-        ]
-    )
-    nested_result = nested_from_padded(
-        q,
-        offsets=nested_q._offsets,
-        ragged_idx=nested_q._ragged_idx,
-        sum_S=total_L,
-        min_seqlen=nested_q._get_min_seqlen(),
-        max_seqlen=padded_max_S,
-    )
-    return nested_result
-
-
-class ModifiedLlamaDecoderLayer(LlamaDecoderLayer):
-    def __init__(self, config: LlamaConfig, layer_idx: int) -> None:
-        nn.Module.__init__(self)
-        self.hidden_size: int = config.hidden_size
-
-        self.self_attn = ModifiedLlamaAttention(config=config, layer_idx=layer_idx)
-
-        self.mlp = LlamaMLP(config)
-        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = LlamaRMSNorm(
-            config.hidden_size, eps=config.rms_norm_eps
-        )
-
-
-class LlamaBiModel(LlamaModel):
-    def __init__(self, config: LlamaConfig) -> None:
-        LlamaPreTrainedModel.__init__(self, config)
-        self.padding_idx: int = config.pad_token_id
-        self.vocab_size: int = config.vocab_size
-
-        self.embed_tokens = nn.Embedding(
-            config.vocab_size, config.hidden_size, self.padding_idx
-        )
-        self.layers = nn.ModuleList(
-            [
-                ModifiedLlamaDecoderLayer(config, layer_idx)
-                for layer_idx in range(config.num_hidden_layers)
-            ]
-        )
-        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.rotary_emb = LlamaRotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def _update_causal_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_tensor: torch.Tensor,
-        cache_position: torch.Tensor,
-        past_seen_tokens=None,
-        output_attentions=False,
-    ):
-        """
-        Updates the causal mask for attention computations.
-        """
-        if self.config._attn_implementation == "flash_attention_2":
-            if attention_mask is not None and (attention_mask == 0.0).any():
-                return attention_mask
-            return None
-        if attention_mask is None or attention_mask.dim() == 4:
-            return attention_mask
-
-        return AttentionMaskConverter._expand_mask(
-            mask=attention_mask,
-            dtype=input_tensor.dtype,
-        )
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, 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,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple[torch.Tensor], 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
-        use_cache = False
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
-        if self.gradient_checkpointing and self.training and use_cache:
-            warnings.warn(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-            use_cache = False
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        return_legacy_cache = False
-        if (
-            use_cache and not isinstance(past_key_values, Cache) and not self.training
-        ):  # kept for BC (non `Cache` `past_key_values` inputs)
-            return_legacy_cache = True
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            warnings.warn(
-                "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
-                "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
-        if cache_position is None:
-            past_seen_tokens = (
-                past_key_values.get_seq_length() if past_key_values is not None else 0
-            )
-            if inputs_embeds.is_nested:
-                seq_len = inputs_embeds._get_max_seqlen()
-            else:
-                seq_len = inputs_embeds.shape[1]
-            cache_position = torch.arange(
-                past_seen_tokens,
-                past_seen_tokens + seq_len,
-                device=inputs_embeds.device,
-            )
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-        if not inputs_embeds.is_nested:
-            causal_mask = self._update_causal_mask(
-                attention_mask,
-                inputs_embeds,
-                cache_position,
-                past_key_values,
-            )
-
-        else:
-            causal_mask = None
-        hidden_states = inputs_embeds
-
-        # create position embeddings to be shared across the decoder layers
-        position_embeddings = self.rotary_emb(hidden_states, position_ids)
-
-        # 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,
-                    causal_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                    cache_position,
-                    position_embeddings,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=causal_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                    cache_position=cache_position,
-                    position_embeddings=position_embeddings,
-                )
-
-            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 = next_decoder_cache if use_cache else None
-        if return_legacy_cache:
-            next_cache = next_cache.to_legacy_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,
-        )
-
-
-class DramaModel(LlamaBiModel):
-    """
-    DramaModel is a modified version of the LlamaModel that supports bi-directional attention
-    and provides query and document encoding functionalities.
-    """
-
-    def __init__(self, config: LlamaConfig):
-        """
-        Initializes the DramaModel by disabling causal masking in self-attention layers.
-        """
-        super().__init__(config)
-        for layer in self.layers:
-            layer.self_attn.is_causal = False
-        # query prefix
-        self.query_prefix = "Query: "
-        self.max_seq_len = 8192
-        self.hidden_size = config.hidden_size
-
-    def _average_pool(
-        self, last_hidden_states: torch.Tensor, attention_mask: torch.Tensor
-    ) -> torch.Tensor:
-        """
-        Computes the average pooled representation of the last hidden states.
-        """
-        last_hidden = last_hidden_states.masked_fill(
-            ~attention_mask[..., None].bool(), 0.0
-        )
-        return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
-
-    def _tokenize(
-        self,
-        tokenizer: PreTrainedTokenizer,
-        texts: list[str],
-        max_seq_len: int = None,
-        use_nested: bool = False,
-    ):
-        """
-        Tokenizes input text sequences with optional sequence length restriction.
-        """
-        if max_seq_len is None:
-            max_seq_len = self.max_seq_len
-        if use_nested:
-            tokenized = tokenizer(
-                texts,
-                truncation=True,
-                max_length=max_seq_len,
-                return_length=True,
-            )
-            tokenized.input_ids = torch.nested.nested_tensor(
-                tokenized.input_ids, layout=torch.jagged
-            ).to(self.device)
-            tokenized.attention_mask = None
-        else:
-            tokenized = tokenizer(
-                texts,
-                padding=True,
-                truncation=True,
-                max_length=max_seq_len,
-                return_tensors="pt",
-            ).to(self.device)
-        tokenizer_ouput = {}
-        tokenizer_ouput["input_ids"] = tokenized.input_ids
-        tokenizer_ouput["attention_mask"] = tokenized.attention_mask
-        return tokenizer_ouput
-
-    def encode(self, input_ids, attention_mask, dim, *args, **kwargs):
-        """
-        Pass through the model and compute normalized embeddings.
-
-        Args:
-            input_ids (torch.Tensor): Input token IDs.
-            attention_mask (torch.Tensor): Attention mask tensor.
-            dim (int): Dimensionality for output embeddings.
-
-        Returns:
-            torch.Tensor: Normalized output embeddings.
-        """
-
-        outputs = self.forward(
-            input_ids, attention_mask, *args, **kwargs
-        ).last_hidden_state
-        if not outputs.is_nested:
-            if dim is not None:
-                outputs = outputs[:, :, :dim]
-            embeddings = self._average_pool(outputs, attention_mask)
-        else:
-            if dim is not None:
-                outputs, _ = outputs.split_with_sizes(
-                    split_sizes=[dim, outputs.shape[-1] - dim], dim=-1
-                )
-            embeddings = outputs.sum(dim=-2)
-        # normalize embeddings
-        embeddings = F.normalize(embeddings, p=2, dim=1)
-        return embeddings
-
-    def encode_queries(
-        self,
-        tokenizer: PreTrainedTokenizer,
-        queries: list[str],
-        max_seq_len: int = None,
-        dim: int = None,
-        use_nested: bool = False,
-    ):
-        """
-        Encodes a list of queries into embeddings.
-
-        Args:
-            tokenizer (PreTrainedTokenizer): Tokenizer for text processing.
-            queries (list[str]): List of query texts.
-            max_seq_len (int, optional): Maximum sequence length.
-            dim (int, optional): Dimensionality for output embeddings.
-
-        Returns:
-            torch.Tensor: Encoded query embeddings in shape (num_queries, dim).
-        """
-        if not queries:
-            raise ValueError("queries must not be empty.")
-        if not isinstance(queries, list) or not all(
-            isinstance(q, str) for q in queries
-        ):
-            raise ValueError("queries must be a list of strings.")
-        if tokenizer is None:
-            raise ValueError("tokenizer must not be None.")
-        if dim is not None and (dim < 1 or dim > self.hidden_size):
-            raise ValueError(f"dim must be in range [1, {self.hidden_size}].")
-        queries = [self.query_prefix + query for query in queries]
-        tokenized_queries = self._tokenize(tokenizer, queries, max_seq_len, use_nested)
-        embeddings = self.encode(**tokenized_queries, dim=dim)
-        return embeddings
-
-    def encode_documents(
-        self,
-        tokenizer: PreTrainedTokenizer,
-        documents: list[str],
-        max_seq_len: int = None,
-        dim: int = None,
-        use_nested: bool = False,
-    ):
-        """
-        Encodes a list of documents into embeddings.
-
-        Args:
-            tokenizer (PreTrainedTokenizer): Tokenizer for text processing.
-            documents (list[str]): List of document texts.
-            max_seq_len (int, optional): Maximum sequence length.
-            dim (int, optional): Dimensionality for output embeddings.
-
-        Returns:
-            torch.Tensor: Encoded document embeddings in shape (num_documents, dim).
-        """
-        if not documents:
-            raise ValueError("documents must not be empty.")
-        if not isinstance(documents, list) or not all(
-            isinstance(d, str) for d in documents
-        ):
-            raise ValueError("documents must be a list of strings.")
-        if tokenizer is None:
-            raise ValueError("tokenizer must not be None.")
-        if dim is not None and (dim < 1 or dim > self.hidden_size):
-            raise ValueError(f"dim must be in range [1, {self.hidden_size}].")
-        tokenized_documents = self._tokenize(
-            tokenizer, documents, max_seq_len, use_nested
-        )
-        embeddings = self.encode(**tokenized_documents, dim=dim)
-        return embeddings

modeling_drama_non_nested.py

@@ -1,184 +0,0 @@
-from __future__ import annotations
-
-import warnings
-
-import torch
-import torch.nn.functional as F
-
-from transformers import LlamaConfig, LlamaModel, PreTrainedTokenizer
-from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-
-
-class DramaModel(LlamaModel):
-    """
-    DramaModel is a modified version of the LlamaModel that supports bi-directional attention
-    and provides query and document encoding functionalities.
-    """
-
-    def __init__(self, config: LlamaConfig):
-        """
-        Initializes the DramaModel by disabling causal masking in self-attention layers.
-        """
-        super().__init__(config)
-        for layer in self.layers:
-            layer.self_attn.is_causal = False
-        # query prefix
-        self.query_prefix = "Query: "
-        self.max_seq_len = 8192
-        self.hidden_size = config.hidden_size
-
-    def _update_causal_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_tensor: torch.Tensor,
-        cache_position: torch.Tensor,
-        past_seen_tokens=None,
-        output_attentions=False,
-    ):
-        """
-        Updates the causal mask for attention computations.
-        """
-        if self.config._attn_implementation == "flash_attention_2":
-            if attention_mask is not None and (attention_mask == 0.0).any():
-                return attention_mask
-            return None
-        if attention_mask is None or attention_mask.dim() == 4:
-            return attention_mask
-
-        return AttentionMaskConverter._expand_mask(
-            mask=attention_mask,
-            dtype=input_tensor.dtype,
-        )
-
-    def _average_pool(
-        self, last_hidden_states: torch.Tensor, attention_mask: torch.Tensor
-    ) -> torch.Tensor:
-        """
-        Computes the average pooled representation of the last hidden states.
-        """
-        last_hidden = last_hidden_states.masked_fill(
-            ~attention_mask[..., None].bool(), 0.0
-        )
-        return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
-
-    def _tokenize(
-        self,
-        tokenizer: PreTrainedTokenizer,
-        texts: list[str],
-        max_seq_len: int = None,
-        use_nested: bool = False,  # Added for API compatibility with nested version
-    ):
-        """
-        Tokenizes input text sequences with optional sequence length restriction.
-        """
-        if max_seq_len is None:
-            max_seq_len = self.max_seq_len
-        tokenized = tokenizer(
-            texts,
-            padding=True,
-            truncation=True,
-            max_length=max_seq_len,
-            return_tensors="pt",
-        ).to(self.device)
-        return tokenized
-
-    def encode(self, input_ids, attention_mask, dim, *args, **kwargs):
-        """
-        Pass through the model and compute normalized embeddings.
-
-        Args:
-            input_ids (torch.Tensor): Input token IDs.
-            attention_mask (torch.Tensor): Attention mask tensor.
-            dim (int): Dimensionality for output embeddings.
-
-        Returns:
-            torch.Tensor: Normalized output embeddings.
-        """
-        outputs = self.forward(input_ids, attention_mask, *args, **kwargs)
-        embeddings = self._average_pool(
-            outputs.last_hidden_state[:, :, :dim], attention_mask
-        )
-        # normalize embeddings
-        embeddings = F.normalize(embeddings, p=2, dim=1)
-        return embeddings
-
-    def encode_queries(
-        self,
-        tokenizer: PreTrainedTokenizer,
-        queries: list[str],
-        max_seq_len: int = None,
-        dim: int = None,
-        use_nested: bool = False,  # Added for API compatibility with nested version
-    ):
-        """
-        Encodes a list of queries into embeddings.
-
-        Args:
-            tokenizer (PreTrainedTokenizer): Tokenizer for text processing.
-            queries (list[str]): List of query texts.
-            max_seq_len (int, optional): Maximum sequence length.
-            dim (int, optional): Dimensionality for output embeddings.
-
-        Returns:
-            torch.Tensor: Encoded query embeddings in shape (num_queries, dim).
-        """
-        if not queries:
-            raise ValueError("queries must not be empty.")
-        if not isinstance(queries, list) or not all(
-            isinstance(q, str) for q in queries
-        ):
-            raise ValueError("queries must be a list of strings.")
-        if tokenizer is None:
-            raise ValueError("tokenizer must not be None.")
-        if dim is not None and (dim < 1 or dim > self.hidden_size):
-            raise ValueError(f"dim must be in range [1, {self.hidden_size}].")
-        if use_nested:
-            warnings.warn(
-                "use_nested is not supported due to package import versions.",
-                UserWarning,
-            )
-        queries = [self.query_prefix + query for query in queries]
-        tokenized_queries = self._tokenize(tokenizer, queries, max_seq_len, use_nested)
-        embeddings = self.encode(**tokenized_queries, dim=dim)
-        return embeddings
-
-    def encode_documents(
-        self,
-        tokenizer: PreTrainedTokenizer,
-        documents: list[str],
-        max_seq_len: int = None,
-        dim: int = None,
-        use_nested: bool = False,  # Added for API compatibility with nested version
-    ):
-        """
-        Encodes a list of documents into embeddings.
-
-        Args:
-            tokenizer (PreTrainedTokenizer): Tokenizer for text processing.
-            documents (list[str]): List of document texts.
-            max_seq_len (int, optional): Maximum sequence length.
-            dim (int, optional): Dimensionality for output embeddings.
-
-        Returns:
-            torch.Tensor: Encoded document embeddings in shape (num_documents, dim).
-        """
-        if not documents:
-            raise ValueError("documents must not be empty.")
-        if not isinstance(documents, list) or not all(
-            isinstance(d, str) for d in documents
-        ):
-            raise ValueError("documents must be a list of strings.")
-        if tokenizer is None:
-            raise ValueError("tokenizer must not be None.")
-        if dim is not None and (dim < 1 or dim > self.hidden_size):
-            raise ValueError(f"dim must be in range [1, {self.hidden_size}].")
-        if use_nested:
-            warnings.warn(
-                "use_nested is not supported due to package import versions.",
-                UserWarning,
-            )
-        tokenized_documents = self._tokenize(
-            tokenizer, documents, max_seq_len, use_nested
-        )
-        embeddings = self.encode(**tokenized_documents, dim=dim)
-        return embeddings