Update modeling_diff_llama.py

modeling_diff_llama.py

@@ -0,0 +1,518 @@
+import math
+from typing import Optional, Tuple, Union, List, Dict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from transformers import PreTrainedModel, LlamaConfig
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.models.llama.modeling_llama import (
+    LlamaRMSNorm,
+    LlamaRotaryEmbedding,
+    LlamaLinearScalingRotaryEmbedding,
+    LlamaDynamicNTKScalingRotaryEmbedding,
+    LlamaMLP,
+    apply_rotary_pos_emb,
+    repeat_kv,
+)
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+
+
+class DiffLLaMAConfig(LlamaConfig):
+    """
+    Configuration class for the DiffLLaMA model.
+    Inherits from LlamaConfig and can be extended with additional parameters.
+    """
+    model_type = "diff_llama"
+    
+    def __init__(
+        self,
+        num_kv_heads: int = 8,
+        intermediate_size: int = 3072,
+        rope_scaling: Optional[Dict[str, Union[str, float]]] = None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.num_kv_heads = num_kv_heads
+        self.intermediate_size = intermediate_size
+        self.rope_scaling = rope_scaling or {"type": "linear", "factor": 1.0}
+        # Add any custom configuration parameters here
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        """
+        Load configuration from a pretrained model.
+        """
+        config_dict = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+        return cls(**config_dict)
+
+
+def init_method(tensor):
+    """Initialize tensor with Kaiming uniform initialization."""
+    nn.init.kaiming_uniform_(tensor, a=math.sqrt(5))
+
+def lambda_init_fn(depth):
+    """Compute lambda initialization value based on layer depth."""
+    return 0.8 - 0.6 * math.exp(-0.3 * depth)
+
+class MultiheadDiffAttn(nn.Module):
+    def __init__(self, config: DiffLLaMAConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        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 = config.num_kv_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 = config.rope_theta
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.scaling = self.head_dim ** -0.5
+
+        self.rotary_emb = self._init_rope()
+
+        self.lambda_init = lambda_init_fn(layer_idx if layer_idx is not None else 0)
+        self.lambda_q1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0, std=0.1))
+        self.lambda_k1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0, std=0.1))
+        self.lambda_q2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0, std=0.1))
+        self.lambda_k2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0, std=0.1))
+
+        self.subln = nn.LayerNorm(self.num_heads * self.head_dim, elementwise_affine=False)
+
+        self._init_rope()
+
+    def _init_rope(self):
+        if not hasattr(self.config, 'rope_scaling') or self.config.rope_scaling is None:
+            self.rotary_emb = LlamaRotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling.get("type", "linear")
+            scaling_factor = self.config.rope_scaling.get("factor", 1.0)
+            if scaling_type == "linear":
+                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        batch_size, seq_length, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(batch_size, seq_length, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(batch_size, seq_length, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        # Repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(-1, -2))
+        attn_weights = attn_weights * self.scaling
+
+        lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1))
+        lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2))
+        lambda_full = lambda_1 - lambda_2 + self.lambda_init
+
+        # Apply differential attention
+        attn_weights_diff = attn_weights[:, :, :, :-1] - lambda_full * attn_weights[:, :, :, 1:]
+        attn_weights = torch.cat([attn_weights_diff, attn_weights[:, :, :, -1:]], dim=-1)
+
+        if attention_mask is not None:
+            # Expand attention_mask
+            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+            attention_mask = attention_mask.expand(batch_size, self.num_heads, seq_length, attention_mask.size(-1))
+            attention_mask = attention_mask.to(dtype=attn_weights.dtype)  # Convert to same dtype as attn_weights
+            
+            # Use a large negative number instead of negative infinity
+            attn_weights = attn_weights + (1.0 - attention_mask) * -10000.0
+
+        attn_weights = F.softmax(attn_weights, dim=-1)
+
+        attn_output = torch.matmul(attn_weights, value_states)
+        attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_length, self.num_heads * self.head_dim)
+
+        attn_output = self.subln(attn_output)
+        attn_output = attn_output * (1 - self.lambda_init)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class DiffLLaMALayer(nn.Module):
+    """
+    A single layer of the DiffLLaMA model, consisting of multi-head differential attention and a feed-forward network.
+    Incorporates gradient checkpointing for memory efficiency.
+    """
+    def __init__(self, config: DiffLLaMAConfig, layer_idx: int):
+        super().__init__()
+        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.self_attn = MultiheadDiffAttn(
+            config=config,
+            layer_idx=layer_idx
+        )
+        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.mlp = LlamaMLP(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+class DiffLLaMAModel(PreTrainedModel):
+    """
+    DiffLLaMAModel is a variant of LLaMA with differential attention mechanisms.
+    Incorporates mixed precision training and gradient checkpointing for optimized performance.
+    """
+    config_class = DiffLLaMAConfig
+        
+    def __init__(self, config: DiffLLaMAConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([
+            DiffLLaMALayer(config, layer_idx=i) for i in range(config.num_hidden_layers)
+        ])
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        
+        self.rotary_emb = LlamaRotaryEmbedding(
+            dim=config.hidden_size // config.num_attention_heads,
+            max_position_embeddings=config.max_position_embeddings,
+            base=config.rope_theta,
+        )
+        
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+    
+    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[List[Tuple[torch.FloatTensor, 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, BaseModelOutputWithPast]:
+        
+        """
+        Forward pass for the DiffLLaMAModel with performance optimizations.
+
+        Args:
+            input_ids: Input token IDs.
+            attention_mask: Attention mask.
+            position_ids: Position IDs.
+            past_key_values: Past key and value tensors for caching.
+            inputs_embeds: Input embeddings.
+            use_cache: Whether to return present key and value for caching.
+            output_attentions: Whether to output attention weights.
+            output_hidden_states: Whether to output hidden states.
+            return_dict: Whether to return a dict.
+            cache_position: Position IDs for caching.
+
+        Returns:
+            Model output, either as a tuple or a 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:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
+            position_ids = position_ids.unsqueeze(0).expand(batch_size, -1)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        # Position embeddings are handled within each layer; remove pre-computation
+        # Removed the following lines:
+        # cos, sin = self.rotary_emb(position_ids, seq_len=seq_length)
+        # position_embeddings = (cos, sin)
+
+        hidden_states = inputs_embeds
+
+        # Attention mask
+        if attention_mask is None:
+            attention_mask = torch.ones((batch_size, seq_length), device=hidden_states.device)
+
+        # Initialize lists to store outputs
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_cache = () if use_cache else None
+
+        for idx, layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = layer(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values[idx] if past_key_values is not None else None,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+            )
+
+            # Correctly unpack layer_outputs based on the configuration
+            hidden_states = layer_outputs[0]
+            
+            if use_cache:
+                present_key_value = layer_outputs[-1]
+                next_cache += (present_key_value,)
+
+            if output_attentions:
+                self_attn_weights = layer_outputs[1]
+                all_self_attns += (self_attn_weights,)
+
+        hidden_states = self.norm(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = (
+                next_cache.to_legacy_cache() if isinstance(next_cache, Cache) else next_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 DiffLLaMAForCausalLM(PreTrainedModel):
+    """
+    DiffLLaMA model with a causal language modeling head.
+    Incorporates mixed precision training for optimized performance.
+    """
+    config_class = DiffLLaMAConfig
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: DiffLLaMAConfig):
+        super().__init__(config)
+        self.model = DiffLLaMAModel(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 input embeddings."""
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        """Set input embeddings."""
+        self.model.set_input_embeddings(value)
+
+    def get_output_embeddings(self):
+        """Return output embeddings (language modeling head)."""
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        """Set output embeddings (language modeling head)."""
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        """Set the decoder model."""
+        self.model = decoder
+
+    def get_decoder(self):
+        """Get the decoder model."""
+        return self.model
+
+    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[List[Tuple[torch.FloatTensor, 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,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        """
+        Forward pass for DiffLLaMAForCausalLM with performance optimizations.
+
+        Args:
+            input_ids: Input token IDs.
+            attention_mask: Attention mask.
+            position_ids: Position IDs.
+            past_key_values: Past key and value tensors for caching.
+            inputs_embeds: Input embeddings.
+            labels: Labels for computing the loss.
+            use_cache: Whether to return past key and value tensors.
+            output_attentions: Whether to output attention weights.
+            output_hidden_states: Whether to output hidden states.
+            return_dict: Whether to return a dict.
+            cache_position: Position IDs for caching.
+
+        Returns:
+            CausalLMOutputWithPast or tuple containing loss and 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
+        )
+        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
+
+        # Get outputs from the model
+        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,
+        )
+
+        hidden_states = outputs.last_hidden_state if return_dict else outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Compute loss using mixed precision if enabled
+            if shift_logits.dtype == torch.float16:
+                with torch.cuda.amp.autocast(enabled=False):
+                    loss = loss_fct(shift_logits, shift_labels)
+            else:
+                loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            if use_cache:
+                return ((loss, logits) + outputs[1:]) if loss is not None else (logits,) + outputs[1:]
+            else:
+                return (loss, logits) if loss is not None else (logits,)
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+