update model

config.json

@@ -0,0 +1,37 @@
+{
+  "_name_or_path": "AlumiK/LingLong-317M-Chat",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "LingLongForCausalLM"
+  ],
+  "attn_c": 8,
+  "attn_mode": "sparse",
+  "attn_pdrop": 0.1,
+  "attn_stride": 128,
+  "auto_map": {
+    "AutoConfig": "configuration_linglong.LingLongConfig",
+    "AutoModelForCausalLM": "modeling_linglong.LingLongForCausalLM"
+  },
+  "backward": false,
+  "bos_token_id": 10,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 8,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-08,
+  "model_type": "linglong",
+  "n_embd": 1024,
+  "n_head": 16,
+  "n_inner": null,
+  "n_layer": 24,
+  "n_position": 1024,
+  "pad_token_id": 0,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.2",
+  "use_cache": true,
+  "use_pinyin": false,
+  "vocab_size": 13312
+}

configuration_linglong.py

@@ -0,0 +1,66 @@
+from transformers import PretrainedConfig
+
+
+class LingLongConfig(PretrainedConfig):
+    model_type = 'linglong'
+
+    def __init__(
+            self,
+            vocab_size: int = 13312,
+            n_position: int = 1024,
+            n_embd: int = 1024,
+            n_layer: int = 24,
+            n_head: int = 16,
+            n_inner: int | None = None,
+            activation_function: str = 'gelu_new',
+            resid_pdrop: float = 0.1,
+            embd_pdrop: float = 0.1,
+            attn_pdrop: float = 0.1,
+            layer_norm_epsilon: float = 1e-8,
+            initializer_range: float = 0.02,
+            scale_attn_weights: bool = True,
+            use_cache: bool = True,
+            bos_token_id: int = 10,
+            eos_token_id: int = 8,
+            pad_token_id: int = 0,
+            scale_attn_by_inverse_layer_idx: bool = False,
+            reorder_and_upcast_attn: bool = False,
+            attn_mode: str = 'sparse',
+            attn_stride: int | None = 128,
+            attn_c: int | None = 8,
+            use_pinyin: bool = False,
+            backward: bool = False,
+            **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.n_position = n_position
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.n_inner = n_inner
+        self.activation_function = activation_function
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attn_pdrop = attn_pdrop
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.scale_attn_weights = scale_attn_weights
+        self.use_cache = use_cache
+        self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
+        self.reorder_and_upcast_attn = reorder_and_upcast_attn
+        self.attn_mode = attn_mode
+        self.attn_stride = attn_stride
+        self.attn_c = attn_c
+        self.use_pinyin = use_pinyin
+        self.backward = backward
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.pad_token_id = pad_token_id
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            **kwargs,
+        )

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "do_sample": true,
+  "max_length": 1024,
+  "top_k": 20,
+  "transformers_version": "4.40.2"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:679ffc087598d63db3aad0e8a0aff82d9c133e8215c918cd9efbb6e204ae1d5b
+size 1267996160

modeling_linglong.py

@@ -0,0 +1,622 @@
+import math
+import torch
+import torch.utils.checkpoint
+
+from torch import nn
+from torch.cuda.amp import autocast
+from transformers.utils import logging
+from transformers.activations import ACT2FN
+from transformers.pytorch_utils import Conv1D
+from transformers.modeling_utils import PreTrainedModel
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutput
+
+from .configuration_linglong import LingLongConfig
+
+logger = logging.get_logger(__name__)
+
+
+class LingLongAttention(nn.Module):
+
+    def __init__(self, config, layer_idx=None):
+        super().__init__()
+
+        n_position = config.n_position
+        self.register_buffer(
+            'bias',
+            torch.tril(torch.ones((n_position, n_position), dtype=torch.bool)).view(
+                1, 1, n_position, n_position
+            ),
+            persistent=False,
+        )
+        self.register_buffer('masked_bias', torch.tensor(-1e4), persistent=False)
+
+        self.n_embd = config.n_embd
+        self.n_head = config.n_head
+        self.head_dim = self.n_embd // self.n_head
+        self.split_size = self.n_embd
+        if self.head_dim * self.n_head != self.n_embd:
+            raise ValueError(
+                f'`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.n_embd} and `num_heads`:'
+                f' {self.n_head}).'
+            )
+
+        self.scale_attn_weights = config.scale_attn_weights
+
+        # Layer-wise attention scaling, reordering, and upcasting
+        self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
+        self.layer_idx = layer_idx
+        self.reorder_and_upcast_attn = config.reorder_and_upcast_attn
+
+        self.c_attn = Conv1D(3 * self.n_embd, self.n_embd)
+        self.c_proj = Conv1D(self.n_embd, self.n_embd)
+
+        self.attn_dropout = nn.Dropout(config.attn_pdrop)
+        self.resid_dropout = nn.Dropout(config.resid_pdrop)
+
+        # LingLong sparse attention.
+        self.mode = config.attn_mode
+        self.stride = config.attn_stride
+        self.c = config.attn_c
+        self.causal_mask = None
+
+    def _causal_mask(self, query_length, key_length):
+        return self.bias[:, :, key_length - query_length: key_length, :key_length]
+
+    def _sparse_causal_mask(self, query_length, key_length):
+        layout = torch.zeros([key_length, key_length], dtype=torch.bool, device=self.bias.device)
+        for idx in range(self.c):
+            layout[:, (self.stride - 1 - idx)::self.stride] = 1
+        for q_idx in range(key_length):
+            row = q_idx // self.stride
+            layout[q_idx, row * self.stride:(row + 1) * self.stride] = 1
+            # Any query cannot attend to keys above it.
+            layout[q_idx, q_idx + 1:] = 0
+        return layout[(key_length - query_length):].view(1, 1, query_length, key_length)
+
+    def _attn(self, query, key, value, attention_mask=None):
+        attn_weights = torch.matmul(query, key.transpose(-1, -2))
+
+        if self.scale_attn_weights:
+            attn_weights = attn_weights / torch.full(
+                [], value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device
+            )
+
+        # Layer-wise attention scaling
+        if self.scale_attn_by_inverse_layer_idx:
+            attn_weights = attn_weights / float(self.layer_idx + 1)
+
+        # if only "normal" attention layer implements causal mask
+        query_length, key_length = query.size(-2), key.size(-2)
+        if self.causal_mask is None or self.causal_mask.size() != torch.Size([1, 1, query_length, key_length]):
+            if self.mode == 'sparse' and self.layer_idx % 2 != 0:
+                self.causal_mask = self._sparse_causal_mask(query_length, key_length)
+            else:
+                self.causal_mask = self._causal_mask(query_length, key_length)
+        mask_value = torch.finfo(attn_weights.dtype).min
+        # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
+        # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
+        mask_value = torch.full([], mask_value, dtype=attn_weights.dtype, device=attn_weights.device)
+        attn_weights = torch.where(self.causal_mask, attn_weights.to(attn_weights.dtype), mask_value)
+
+        if attention_mask is not None:
+            # Apply the attention mask
+            attn_weights = attn_weights + attention_mask
+
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+        # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise
+        attn_weights = attn_weights.type(value.dtype)
+        attn_weights = self.attn_dropout(attn_weights)
+
+        attn_output = torch.matmul(attn_weights, value)
+
+        return attn_output, attn_weights
+
+    def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None):
+        # Use `torch.baddbmm` (a bit more efficient w/ alpha param for scaling -- from Megatron-LM)
+        bsz, num_heads, q_seq_len, dk = query.size()
+        _, _, k_seq_len, _ = key.size()
+
+        # Preallocate attn_weights for `baddbmm`
+        attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device)
+
+        # Compute Scale Factor
+        scale_factor = 1.0
+        if self.scale_attn_weights:
+            scale_factor /= float(value.size(-1)) ** 0.5
+
+        if self.scale_attn_by_inverse_layer_idx:
+            scale_factor /= float(self.layer_idx + 1)
+
+        # Upcast (turn off autocast) and reorder (Scale K by 1 / root(dk))
+        with autocast(enabled=False):
+            q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len)
+            attn_weights = torch.baddbmm(attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor)
+            attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len)
+
+        # if only "normal" attention layer implements causal mask
+        query_length, key_length = query.size(-2), key.size(-2)
+        if self.causal_mask is None or self.causal_mask.size() != torch.Size([1, 1, query_length, key_length]):
+            if self.mode == 'sparse' and self.layer_idx % 2 != 0:
+                self.causal_mask = self._sparse_causal_mask(query_length, key_length)
+            else:
+                self.causal_mask = self._causal_mask(query_length, key_length)
+        mask_value = torch.finfo(attn_weights.dtype).min
+        # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
+        # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
+        mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
+        attn_weights = torch.where(self.causal_mask, attn_weights, mask_value)
+
+        if attention_mask is not None:
+            # Apply the attention mask
+            attn_weights = attn_weights + attention_mask
+
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+        # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op if otherwise
+        if attn_weights.dtype != torch.float32:
+            raise RuntimeError('Error with upcasting, attn_weights does not have dtype torch.float32.')
+        attn_weights = attn_weights.type(value.dtype)
+        attn_weights = self.attn_dropout(attn_weights)
+
+        attn_output = torch.matmul(attn_weights, value)
+
+        return attn_output, attn_weights
+
+    @staticmethod
+    def _split_heads(tensor, num_heads, attn_head_size):
+        """
+        Splits hidden_size dim into attn_head_size and num_heads
+        """
+        new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
+        tensor = tensor.view(new_shape)
+        return tensor.permute(0, 2, 1, 3)  # (batch, head, seq_length, head_features)
+
+    @staticmethod
+    def _merge_heads(tensor, num_heads, attn_head_size):
+        """
+        Merges attn_head_size dim and num_attn_heads dim into hidden_size
+        """
+        tensor = tensor.permute(0, 2, 1, 3).contiguous()
+        new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
+        return tensor.view(new_shape)
+
+    def forward(
+            self,
+            hidden_states,
+            layer_past=None,
+            attention_mask=None,
+            use_cache=False,
+            output_attentions=False,
+    ):
+        query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)
+        query = self._split_heads(query, self.n_head, self.head_dim)
+        key = self._split_heads(key, self.n_head, self.head_dim)
+        value = self._split_heads(value, self.n_head, self.head_dim)
+
+        if layer_past is not None:
+            past_key, past_value = layer_past
+            key = torch.cat((past_key, key), dim=-2)
+            value = torch.cat((past_value, value), dim=-2)
+
+        if use_cache is True:
+            present = (key, value)
+        else:
+            present = None
+
+        if self.reorder_and_upcast_attn:
+            attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask)
+        else:
+            attn_output, attn_weights = self._attn(query, key, value, attention_mask)
+
+        attn_output = self._merge_heads(attn_output, self.n_head, self.head_dim)
+        attn_output = self.c_proj(attn_output)
+        attn_output = self.resid_dropout(attn_output)
+
+        outputs = (attn_output, present)
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        return outputs  # a, present, (attentions)
+
+
+class LingLongMLP(nn.Module):
+
+    def __init__(self, intermediate_size, config):
+        super().__init__()
+        n_embd = config.n_embd
+        self.c_fc = Conv1D(intermediate_size, n_embd)
+        self.c_proj = Conv1D(n_embd, intermediate_size)
+        self.act = ACT2FN[config.activation_function]
+        self.dropout = nn.Dropout(config.resid_pdrop)
+
+    def forward(self, hidden_states):
+        hidden_states = self.c_fc(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.c_proj(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+
+class LingLongBlock(nn.Module):
+
+    def __init__(self, config, layer_idx=None):
+        super().__init__()
+        n_embd = config.n_embd
+        inner_dim = config.n_inner if config.n_inner is not None else 4 * n_embd
+
+        self.ln_1 = nn.LayerNorm(n_embd, eps=config.layer_norm_epsilon)
+        self.attn = LingLongAttention(config, layer_idx=layer_idx)
+        self.ln_2 = nn.LayerNorm(n_embd, eps=config.layer_norm_epsilon)
+
+        self.mlp = LingLongMLP(inner_dim, config)
+
+    def forward(
+            self,
+            hidden_states,
+            layer_past=None,
+            attention_mask=None,
+            use_cache=False,
+            output_attentions=False,
+    ):
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        attn_outputs = self.attn(
+            hidden_states,
+            layer_past=layer_past,
+            attention_mask=attention_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+        attn_output = attn_outputs[0]  # output_attn: a, present, (attentions)
+        outputs = attn_outputs[1:]
+        # residual connection
+        hidden_states = attn_output + residual
+
+        residual = hidden_states
+        hidden_states = self.ln_2(hidden_states)
+        feed_forward_hidden_states = self.mlp(hidden_states)
+        # residual connection
+        hidden_states = residual + feed_forward_hidden_states
+
+        if use_cache:
+            outputs = (hidden_states,) + outputs
+        else:
+            outputs = (hidden_states,) + outputs[1:]
+
+        return outputs  # hidden_states, present, (attentions)
+
+
+class LingLongPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = LingLongConfig
+    base_model_prefix = 'transformer'
+    supports_gradient_checkpointing = True
+    _no_split_modules = ['LingLongBlock']
+    _skip_keys_device_placement = 'past_key_values'
+
+    def __init__(self, *inputs, **kwargs):
+        super().__init__(*inputs, **kwargs)
+
+    def _init_weights(self, module):
+        """Initialize the weights."""
+        if isinstance(module, (nn.Linear, Conv1D)):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+        # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
+        #   > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
+        #   > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
+        #   >   -- GPT-2 :: https://openai.com/blog/better-language-models/
+        #
+        # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
+        for name, p in module.named_parameters():
+            if name == 'c_proj.weight':
+                # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
+                p.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer)))
+
+
+class LingLongModel(LingLongPreTrainedModel):
+
+    def __init__(self, config: LingLongConfig):
+        super().__init__(config)
+        self.n_embd = config.n_embd
+        self.wte = nn.Embedding(config.vocab_size, self.n_embd)
+        self.wpe = nn.Embedding(config.n_position, self.n_embd)
+        self.drop = nn.Dropout(config.embd_pdrop)
+        self.h = nn.ModuleList([LingLongBlock(config, layer_idx=i) for i in range(config.n_layer)])
+        self.ln_f = nn.LayerNorm(self.n_embd, eps=config.layer_norm_epsilon)
+
+        # Model parallel
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.wte
+
+    def set_input_embeddings(self, new_embeddings):
+        self.wte = new_embeddings
+
+    def forward(
+            self,
+            input_ids: torch.LongTensor | None = None,
+            past_key_values: tuple[tuple[torch.Tensor]] | None = None,
+            attention_mask: torch.FloatTensor | None = None,
+            position_ids: torch.LongTensor | None = None,
+            inputs_embeds: torch.FloatTensor | None = None,
+            use_cache: bool | None = None,
+            output_attentions: bool | None = None,
+            output_hidden_states: bool | None = None,
+            return_dict: bool | None = None,
+    ) -> 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
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time.')
+        elif input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+            input_shape = input_ids.size()
+            input_ids = input_ids.view(-1, input_shape[-1])
+            batch_size = input_ids.shape[0]
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            batch_size = inputs_embeds.shape[0]
+        else:
+            raise ValueError('You have to specify either input_ids or inputs_embeds.')
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        if past_key_values is None:
+            past_length = 0
+            past_key_values = tuple([None] * len(self.h))
+        else:
+            past_length = past_key_values[0][0].size(-2)
+        if position_ids is None:
+            position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
+            position_ids = position_ids.unsqueeze(0)
+
+        # LingLongAttention mask.
+        if attention_mask is not None:
+            if batch_size <= 0:
+                raise ValueError('batch_size has to be defined and > 0.')
+            attention_mask = attention_mask.view(batch_size, -1)
+            # We create a 3D attention mask from a 2D tensor mask.
+            # Sizes are [batch_size, 1, 1, to_seq_length]
+            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+            # this attention mask is more simple than the triangular masking of causal attention
+            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+            attention_mask = attention_mask[:, None, None, :]
+
+            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+            # masked positions, this operation will create a tensor which is 0.0 for
+            # positions we want to attend and the dtype's smallest value for masked positions.
+            # Since we are adding it to the raw scores before the softmax, this is
+            # effectively the same as removing these entirely.
+            attention_mask = attention_mask.to(dtype=self.dtype)  # fp16 compatibility
+            attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
+
+        if inputs_embeds is None:
+            inputs_embeds = self.wte(input_ids)
+        position_embeds = self.wpe(position_ids)
+        hidden_states = inputs_embeds + position_embeds
+
+        hidden_states = self.drop(hidden_states)
+
+        output_shape = (-1,) + input_shape[1:] + (hidden_states.size(-1),)
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                # noinspection PyUnresolvedReferences
+                logger.warning_once(
+                    '`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...'
+                )
+                use_cache = False
+
+        presents = () if use_cache else None
+        all_self_attentions = () if output_attentions else None
+        all_hidden_states = () if output_hidden_states else None
+        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                outputs = self._gradient_checkpointing_func(
+                    block.__call__,
+                    hidden_states,
+                    None,
+                    attention_mask,
+                    use_cache,
+                    output_attentions,
+                )
+            else:
+                outputs = block(
+                    hidden_states,
+                    layer_past=layer_past,
+                    attention_mask=attention_mask,
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                )
+
+            hidden_states = outputs[0]
+            if use_cache is True:
+                presents = presents + (outputs[1],)
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
+
+        hidden_states = self.ln_f(hidden_states)
+
+        hidden_states = hidden_states.view(output_shape)
+        # Add last hidden state
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, presents, all_hidden_states, all_self_attentions]
+                if v is not None
+            )
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=presents,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+
+class LingLongForCausalLM(LingLongPreTrainedModel):
+    _tied_weights_keys = ['lm_head.weight']
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = LingLongModel(config)
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
+        # Omit tokens covered by past_key_values
+        if past_key_values:
+            past_length = past_key_values[0][0].shape[2]
+
+            # Some generation methods already pass only the last input ID
+            if input_ids.shape[1] > past_length:
+                remove_prefix_length = past_length
+            else:
+                # Default to old behavior: keep only final ID
+                remove_prefix_length = input_ids.shape[1] - 1
+
+            input_ids = input_ids[:, remove_prefix_length:]
+
+        attention_mask = kwargs.get('attention_mask', None)
+        position_ids = kwargs.get('position_ids', None)
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            # noinspection PyUnresolvedReferences
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1]:]
+        else:
+            position_ids = None
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {'inputs_embeds': inputs_embeds}
+        else:
+            model_inputs = {'input_ids': input_ids}
+
+        model_inputs.update(
+            {
+                'past_key_values': past_key_values,
+                'use_cache': kwargs.get('use_cache'),
+                'position_ids': position_ids,
+                'attention_mask': attention_mask,
+            }
+        )
+
+        return model_inputs
+
+    def forward(
+            self,
+            input_ids: torch.LongTensor | None = None,
+            past_key_values: tuple[tuple[torch.Tensor]] | None = None,
+            attention_mask: torch.FloatTensor | None = None,
+            position_ids: torch.LongTensor | None = None,
+            inputs_embeds: torch.FloatTensor | None = None,
+            labels: torch.LongTensor | None = None,
+            use_cache: bool | None = None,
+            output_attentions: bool | None = None,
+            output_hidden_states: bool | None = None,
+            return_dict: bool | None = None,
+    ) -> tuple | CausalLMOutput:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+
+        lm_logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(lm_logits.device)
+            # Shift so that tokens < n predict n
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+        if not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutput(
+            loss=loss,
+            logits=lm_logits,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+    @staticmethod
+    def _reorder_cache(
+            past_key_values: tuple[tuple[torch.Tensor]],
+            beam_idx: torch.Tensor,
+            **kwargs,
+    ) -> tuple[tuple[torch.Tensor]]:
+        """
+        This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
+        [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
+        beam_idx at every generation step.
+        """
+        # noinspection PyTypeChecker
+        return tuple(
+            tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
+            for layer_past in past_key_values
+        )