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modeling_recast_llama.py

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+# filename: recastmlp_llama_model.py
+from .configuration_recast_llama import RECAST1B_llama
+from transformers import PreTrainedModel
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, Union, List
+from transformers import AutoConfig
+from transformers.utils import logging
+from transformers.cache_utils import Cache, StaticCache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.generation import GenerationMixin
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.models.llama.modeling_llama import (
+    LlamaDecoderLayer,
+    LlamaRotaryEmbedding,
+    LlamaRMSNorm,
+    apply_rotary_pos_emb,
+    repeat_kv,
+)
+from transformers.modeling_outputs import BaseModelOutputWithPast
+
+logger = logging.get_logger(__name__)
+
+
+class MLPTemplateBank(nn.Module):
+    def __init__(self, config, coef_rows, coef_columns):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.coef_shape = (coef_rows, coef_columns)
+
+        assert coef_columns is not None, "coef_columns must not be None"
+        
+        # Ensure divisibility for proper reshaping
+        assert (self.hidden_size * self.intermediate_size) % coef_rows == 0, \
+            f"hidden_size * intermediate_size ({self.hidden_size * self.intermediate_size}) must be divisible by coef_rows ({coef_rows})"
+        
+        template_size = self.hidden_size * self.intermediate_size // coef_rows
+        
+        self.up_templates = nn.Parameter(
+            torch.randn(coef_columns, template_size)
+        )
+        self.gate_templates = nn.Parameter(
+            torch.randn(coef_columns, template_size)
+        )
+        
+        # Better initialization
+        nn.init.xavier_uniform_(self.up_templates)
+        nn.init.xavier_uniform_(self.gate_templates)
+
+    def forward(self, up_coeffs, gate_coeffs):
+        # Compute chunked weights
+        up_chunks = torch.matmul(up_coeffs, self.up_templates)  
+        gate_chunks = torch.matmul(gate_coeffs, self.gate_templates)
+        
+        # Reshape to final weight matrices
+        up_weights = up_chunks.reshape(self.intermediate_size, self.hidden_size)
+        gate_weights = gate_chunks.reshape(self.intermediate_size, self.hidden_size)
+        
+        return up_weights, gate_weights
+
+class SharedLlamaMLP(nn.Module):
+    def __init__(self, config, bank):
+        super().__init__()
+        self.config = config
+        self.bank = bank
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+
+        # Initialize coefficients with proper shapes
+        self.up_coefficients = nn.Parameter(torch.randn(bank.coef_shape))
+        self.gate_coefficients = nn.Parameter(torch.randn(bank.coef_shape))
+
+        # Initialize with small random values instead of ones, then orthogonalize
+        nn.init.orthogonal_(self.up_coefficients)
+        nn.init.orthogonal_(self.gate_coefficients)
+
+        if config.mlp_bias:
+            self.gate_bias = nn.Parameter(torch.zeros(self.intermediate_size))
+            self.up_bias = nn.Parameter(torch.zeros(self.intermediate_size))
+        else:
+            self.register_parameter("gate_bias", None)
+            self.register_parameter("up_bias", None)
+
+        self.act_fn = F.silu
+
+    def forward(self, x):
+        # Generate weights using template bank
+        up_weights, gate_weights = self.bank(
+            self.up_coefficients,
+            self.gate_coefficients # Fixed order
+        )
+        
+        # Apply SwiGLU: SiLU(gate * x) * up * x
+        hidden_states = self.act_fn(F.linear(x, gate_weights, self.gate_bias)) * F.linear(x, up_weights, self.up_bias)
+        output = self.down_proj(hidden_states)
+
+        return output
+
+class AttTemplateBank(nn.Module):
+    def __init__(self, config, coef_rows, coef_columns):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.hidden_size // config.num_attention_heads
+        self.num_key_value_heads = getattr(config, 'num_key_value_heads', config.num_attention_heads)
+        self.kv_dim = self.num_key_value_heads * self.head_dim
+        self.coef_shape = (coef_rows, coef_columns)
+
+        # Ensure divisibility
+        assert (self.hidden_size * self.hidden_size) % coef_rows == 0, \
+            "Q projection size must be divisible by coef_rows"
+        assert (self.kv_dim * self.hidden_size) % coef_rows == 0, \
+            "K/V projection size must be divisible by coef_rows"
+
+        # Create templates for Q, K, V
+        self.q_templates = nn.Parameter(
+            torch.randn(coef_columns, self.hidden_size * self.hidden_size // coef_rows)
+        )
+        self.k_templates = nn.Parameter(
+            torch.randn(coef_columns, self.kv_dim * self.hidden_size // coef_rows)
+        )
+        self.v_templates = nn.Parameter(
+            torch.randn(coef_columns, self.kv_dim * self.hidden_size // coef_rows)
+        )
+
+        # Initialize templates
+        nn.init.xavier_uniform_(self.q_templates)
+        nn.init.xavier_uniform_(self.k_templates)
+        nn.init.xavier_uniform_(self.v_templates)
+
+    def forward(self, q_coeffs, k_coeffs, v_coeffs):
+        # Compute chunked weights
+        q_chunks = torch.matmul(q_coeffs, self.q_templates)
+        k_chunks = torch.matmul(k_coeffs, self.k_templates)
+        v_chunks = torch.matmul(v_coeffs, self.v_templates)
+
+        # Reshape to final weight matrices
+        q_weights = q_chunks.reshape(self.hidden_size, self.hidden_size)
+        k_weights = k_chunks.reshape(self.kv_dim, self.hidden_size)
+        v_weights = v_chunks.reshape(self.kv_dim, self.hidden_size)
+
+        return q_weights, k_weights, v_weights
+    
+class SharedLlamaAttention(nn.Module):
+    def __init__(self, config, layer_idx: Optional[int] = None, bank: Optional[AttTemplateBank] = None):
+        super().__init__()
+        self.config = config
+        self.bank = bank
+        self.layer_idx = layer_idx
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = getattr(config, 'num_key_value_heads', config.num_attention_heads)
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = getattr(config, 'rope_theta', 10000.0)
+        self.is_causal = True
+        
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=getattr(config, 'attention_bias', False))
+        self.rotary_emb = LlamaRotaryEmbedding(config=self.config)
+        
+        # Initialize coefficients with proper shapes
+        self.q_coefficients = nn.Parameter(torch.randn(bank.coef_shape))
+        self.k_coefficients = nn.Parameter(torch.randn(bank.coef_shape))
+        self.v_coefficients = nn.Parameter(torch.randn(bank.coef_shape))
+
+        # Initialize with small random values
+        nn.init.orthogonal_(self.q_coefficients)
+        nn.init.orthogonal_(self.k_coefficients)
+        nn.init.orthogonal_(self.v_coefficients)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        past_key_value=None,
+        cache_position=None,
+        position_embeddings=None,
+        position_ids=None,
+        output_attentions=False,
+        use_cache=False,
+        **kwargs,
+    ):
+        bsz, q_len, _ = hidden_states.size()
+        
+        # Generate weights using template bank
+        q_weights, k_weights, v_weights = self.bank( 
+            self.q_coefficients, 
+            self.k_coefficients, 
+            self.v_coefficients
+        )
+
+        # Apply projections
+        query_states = F.linear(hidden_states, q_weights)
+        key_states = F.linear(hidden_states, k_weights)
+        value_states = F.linear(hidden_states, v_weights)
+        
+        # Reshape for multi-head attention
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        
+        # Apply rotary embeddings
+        if position_embeddings is None:
+            cos, sin = self.rotary_emb(value_states, position_ids)
+        else:
+            cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+        
+        # Handle past key values
+        if past_key_value is not None:
+            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)
+        
+        # Repeat key/value for grouped query attention
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        # Compute attention
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights = attn_weights + causal_mask
+
+        # Apply softmax and dropout
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+        
+        if not output_attentions:
+            attn_weights = None
+            
+        return attn_output, attn_weights, past_key_value
+
+
+def fixed_cross_entropy(
+    source,
+    target,
+    num_items_in_batch: int = None,
+    ignore_index: int = -100,
+    **kwargs,
+):
+    reduction = "sum" if num_items_in_batch is not None else "mean"
+    loss = nn.functional.cross_entropy(
+        source, target, ignore_index=ignore_index, reduction=reduction
+    )
+    if reduction == "sum":
+        loss = loss / num_items_in_batch
+    return loss
+
+
+class RECAST1B_llamaModel(PreTrainedModel):
+    config_class = RECAST1B_llama
+    base_model_prefix = "llama"
+    supports_gradient_checkpointing = True
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(
+            config.vocab_size, config.hidden_size, self.padding_idx
+        )
+
+        original_config = AutoConfig.from_pretrained(
+            "meta-llama/Llama-3.2-1b", trust_remote_code=True
+        )
+        self.rotary_emb = LlamaRotaryEmbedding(
+            config=original_config,
+        )
+
+        # Create template banks first
+        self.mlp_banks = []
+        self.attn_banks = []
+        layers_per_group = config.num_hidden_layers // config.num_groups
+        # Explicitly calculate coef_width if not provided in config
+        if hasattr(config, "coef_width") and config.coef_width is not None:
+            coef_width = config.coef_width
+        else:
+            coef_width = config.coef_height * layers_per_group
+        config.coef_width = coef_width
+        print(
+            f"Model config: num_groups={config.num_groups}, layers_per_group={layers_per_group}"
+        )
+        print(f"Coefficient shape: ({config.coef_height}, {config.coef_width})")
+        mlp_banks = nn.ModuleList(
+            [
+                MLPTemplateBank(
+                    config=config, coef_rows=config.coef_height, coef_columns=coef_width
+                )
+                for _ in range(config.num_groups)
+            ]
+        )
+
+        attn_banks = nn.ModuleList(
+            [
+                AttTemplateBank(
+                    config=config, coef_rows=config.coef_height, coef_columns=coef_width
+                )
+                for _ in range(config.num_groups)
+            ]
+        )
+        self.mlp_banks = mlp_banks
+        self.attn_banks = attn_banks
+        # Create layers using LlamaDecoderLayer but replace MLPs
+        self.layers = nn.ModuleList()
+        for layer_idx in range(config.num_hidden_layers):
+            # Create standard LlamaDecoderLayer
+            decoder_layer = LlamaDecoderLayer(config, layer_idx)
+
+            # Replace its MLP with our SharedLlamaMLP
+            group_idx = layer_idx // layers_per_group
+            decoder_layer.mlp = SharedLlamaMLP(config, self.mlp_banks[group_idx])
+            decoder_layer.self_attn = SharedLlamaAttention(
+                config, layer_idx, self.attn_banks[group_idx]
+            )
+
+            self.layers.append(decoder_layer)
+
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        input_ids: 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,
+        **flash_attn_kwargs,
+    ) -> 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
+        )
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You must specify exactly one of input_ids or inputs_embeds"
+            )
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # Set up cache position if not provided
+        if cache_position is None:
+            past_seen_tokens = (
+                0
+                if past_key_values is None
+                else (
+                    past_key_values.get_seq_length()
+                    if isinstance(past_key_values, Cache)
+                    else past_key_values[0][0].size(-2) if past_key_values else 0
+                )
+            )
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
+            )
+        # Create position embeddings to be shared across the decoder layers
+        # Set up position IDs if not provided
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+        # Get updated causal mask
+        causal_mask = self._update_causal_mask(
+            attention_mask,
+            inputs_embeds,
+            cache_position,
+            past_key_values,
+            output_attentions,
+        )
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # Initialize outputs
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        # Process through layers
+        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,
+                    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,
+                    position_embeddings=position_embeddings,
+                    **flash_attn_kwargs,
+                )
+
+            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],)
+
+        # Final layer norm
+        hidden_states = self.norm(hidden_states)
+
+        # Add last hidden state
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+
+        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,
+        )
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        if isinstance(
+            pretrained_model_name_or_path, str
+        ) and pretrained_model_name_or_path.endswith(".pt"):
+            print("Loading from local checkpoint")
+            # Load from local checkpoint
+            config = kwargs.get("config", None)
+            if config is None:
+                config = AutoConfig.from_pretrained(
+                    pretrained_model_name_or_path, trust_remote_code=True
+                )
+
+            model = cls(config)
+            checkpoint = torch.load(pretrained_model_name_or_path, map_location="cpu")
+            state_dict = checkpoint["model_state_dict"]
+            logger.info(
+                f"Loaded checkpoint from epoch {checkpoint.get('epoch')} with loss {checkpoint.get('loss')}"
+            )
+
+            missing_keys, unexpected_keys = model.load_state_dict(
+                state_dict, strict=False
+            )
+
+            if len(missing_keys) > 0:
+                logger.warning(f"Missing keys: {missing_keys}")
+            if len(unexpected_keys) > 0:
+                logger.warning(f"Unexpected keys: {unexpected_keys}")
+
+            return model
+        else:
+            print("Loading from hub")
+            # Load from hub using parent's from_pretrained
+            return super().from_pretrained(
+                pretrained_model_name_or_path, *model_args, **kwargs
+            )
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool,
+    ):
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = (
+            past_key_values.get_seq_length() if past_key_values is not None else 0
+        )
+        using_static_cache = isinstance(past_key_values, StaticCache)
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if (
+            self.config._attn_implementation == "sdpa"
+            and not using_static_cache
+            and not output_attentions
+        ):
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        sequence_length = input_tensor.shape[1]
+        if using_static_cache:
+            target_length = past_key_values.get_max_cache_shape()
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
+            dtype=dtype,
+            device=device,
+            cache_position=cache_position,
+            batch_size=input_tensor.shape[0],
+        )
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type == "cuda"
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            min_dtype = torch.finfo(dtype).min
+            causal_mask = AttentionMaskConverter._unmask_unattended(
+                causal_mask, min_dtype
+            )
+
+        return causal_mask
+
+    @staticmethod
+    def _prepare_4d_causal_attention_mask_with_cache_position(
+        attention_mask: torch.Tensor,
+        sequence_length: int,
+        target_length: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        cache_position: torch.Tensor,
+        batch_size: int,
+        **kwargs,
+    ):
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+            causal_mask = attention_mask
+        else:
+            min_dtype = torch.finfo(dtype).min
+            causal_mask = torch.full(
+                (sequence_length, target_length),
+                fill_value=min_dtype,
+                dtype=dtype,
+                device=device,
+            )
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(
+                target_length, device=device
+            ) > cache_position.reshape(-1, 1)
+            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = (
+                    causal_mask.clone()
+                )  # copy to contiguous memory for in-place edit
+                mask_length = attention_mask.shape[-1]
+                padding_mask = (
+                    causal_mask[:, :, :, :mask_length]
+                    + attention_mask[:, None, None, :]
+                )
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[
+                    :, :, :, :mask_length
+                ].masked_fill(padding_mask, min_dtype)
+
+        return causal_mask
+
+
+class RECAST1B_LlamaForCausalLM(PreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+    _tp_plan = {"lm_head": "colwise_rep"}
+    config_class = RECAST1B_llama
+    base_model_prefix = "llama"
+    supports_gradient_checkpointing = True
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = RECAST1B_llamaModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def loss_function(
+        self,
+        logits,
+        labels,
+        vocab_size: int,
+        num_items_in_batch: int = None,
+        ignore_index: int = -100,
+        **kwargs,
+    ):
+        # Upcast to float if we need to compute the loss to avoid potential precision issues
+        logits = logits.float()
+        # Shift so that tokens < n predict n
+        shift_logits = logits[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+        # Flatten the tokens
+        shift_logits = shift_logits.view(-1, vocab_size)
+        shift_labels = shift_labels.view(-1)
+        # Enable model parallelism
+        shift_labels = shift_labels.to(shift_logits.device)
+        loss = fixed_cross_entropy(
+            shift_logits, shift_labels, num_items_in_batch, ignore_index, **kwargs
+        )
+        return loss
+
+    def forward(
+        self,
+        input_ids: 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,
+        labels: Optional[torch.LongTensor] = 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,
+        num_logits_to_keep: int = 0,
+        **kwargs,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        """
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should be in
+                `[0, ..., config.vocab_size]` or -100 (masked tokens).
+            num_logits_to_keep (`int`, *optional*):
+                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate all logits.
+        """
+        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
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        # Only compute necessary logits
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
+
+        loss = None
+        if labels is not None:
+            # Calculate batch size for loss function
+            num_items_in_batch = (
+                input_ids.size(0) if input_ids is not None else inputs_embeds.size(0)
+            )
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+                num_items_in_batch=num_items_in_batch,
+                **kwargs,
+            )
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        **kwargs,
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        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
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+
+        # 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(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        if isinstance(
+            pretrained_model_name_or_path, str
+        ) and pretrained_model_name_or_path.endswith(".pt"):
+            print("Loading from local checkpoint")
+            config = kwargs.get("config", None)
+            if config is None:
+                config = AutoConfig.from_pretrained(
+                    pretrained_model_name_or_path, trust_remote_code=True
+                )
+            model = torch.load(pretrained_model_name_or_path, map_location="cpu")
+            # model = cls(config)
+            # checkpoint = torch.load(pretrained_model_name_or_path, map_location="cpu")
+            # state_dict = checkpoint["model_state_dict"]
+
+            # missing_keys, unexpected_keys = model.load_state_dict(
+            #     state_dict, strict=False
+            # )
+
+            # if len(missing_keys) > 0:
+            #     logger.warning(f"Missing keys: {missing_keys}")
+            # if len(unexpected_keys) > 0:
+            #     logger.warning(f"Unexpected keys: {unexpected_keys}")
+
+            return model
+        else:
+            print("Loading from hub")
+            return super().from_pretrained(
+                pretrained_model_name_or_path, *model_args, **kwargs
+            )