llama_diffusion_model.py

import torch.nn as nn
from transformers import AutoModelForCausalLM,  PreTrainedModel, PretrainedConfig
from transformers.models.llama.modeling_llama import LlamaAttention
from torch.amp import autocast
from peft import LoraConfig, get_peft_model
from typing import  Optional, Tuple
import torch
import os



hf_token = os.getenv("HF_TOKEN")

class BidirectionalLlamaAttention(LlamaAttention):
    def __init__(self, original_layer, masking = 'unidirectional'):
        super().__init__(original_layer.config, layer_idx=original_layer.layer_idx)
        self.masking = masking

        # Copy weights from original layer
        self.q_proj.weight = original_layer.q_proj.weight
        self.k_proj.weight = original_layer.k_proj.weight
        self.v_proj.weight = original_layer.v_proj.weight
        self.o_proj.weight = original_layer.o_proj.weight

    def repeat_kv(self, 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 eager_attention_forward(self,
        module: nn.Module,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        attention_mask: Optional[torch.Tensor],
        scaling: float,
        dropout: float = 0.0,
        **kwargs,
        ):
        key_states = self.repeat_kv(key, module.num_key_value_groups)
        value_states = self.repeat_kv(value, module.num_key_value_groups)

        attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
        if attention_mask is not None:
            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
            attn_weights = attn_weights + causal_mask

        attn_weights = nn.functional.softmax(attn_weights, dim=-1).to(query.dtype)
        attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
        attn_output = torch.matmul(attn_weights, value_states)
        attn_output = attn_output.transpose(1, 2).contiguous()

        return attn_output, attn_weights

    def rotate_half(self, x):
        """Rotates half the hidden dims of the input."""
        x1 = x[..., : x.shape[-1] // 2]
        x2 = x[..., x.shape[-1] // 2 :]

        return torch.cat((-x2, x1), dim=-1)


    def apply_rotary_pos_emb(self, q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
        """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)
        q_embed = (q * cos) + (self.rotate_half(q) * sin)
        k_embed = (k * cos) + (self.rotate_half(k) * sin)

        return q_embed, k_embed

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor],
        past_key_value: Optional[torch.Tensor] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ):
        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)

        # Apply rotary embeddings
        cos, sin = position_embeddings
        query_states, key_states = self.apply_rotary_pos_emb(query_states, key_states, cos, sin)

        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)

        # 🔄 **Modify the Attention Mask**
        seq_len = hidden_states.shape[1]
        batch_size = hidden_states.shape[0]
        if self.masking == 'bidirectional':
            base_mask = torch.ones((seq_len, seq_len), device=hidden_states.device, dtype=torch.bool)
            attn_mask = base_mask.unsqueeze(0).unsqueeze(1).expand(batch_size, 1, seq_len, seq_len).clone()  # ✅ Copy for each batch
        elif self.masking == 'bidirectional_masked':
            base_mask = torch.ones((seq_len, seq_len), device=hidden_states.device, dtype=torch.bool)
            base_mask[:, :].fill_diagonal_(False)  # ✅ Apply diagonal masking only in 2D
            attn_mask = base_mask.unsqueeze(0).unsqueeze(1).expand(batch_size, 1, seq_len, seq_len).clone()  # ✅ Copy for each batch
        else: # unidirectional
            # 🚀 Standard autoregressive (causal) mask
            attn_mask = torch.tril(torch.ones(seq_len, seq_len, device=hidden_states.device, dtype=torch.bool))
            attn_mask = base_mask.unsqueeze(0).unsqueeze(1).expand(batch_size, 1, seq_len, seq_len).clone()  # ✅ Copy for each batch


        # Call the default attention function
        attn_output, attn_weights = self.eager_attention_forward(
            self,
            query_states,
            key_states,
            value_states,
            attn_mask,  # ✅ Custom mask is applied here
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)

        return attn_output, attn_weights


    def _split_heads(self, 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)

    def _merge_heads(self, 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)

class CustomTransformerConfig(PretrainedConfig):
    def __init__(self, vocab_size=128256, hidden_size=4096, num_layers=32, num_heads=32, prediction_chunk=256, dropout=0, max_position_embeddings=4096, **kwargs):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.dropout = dropout
        self.prediction_chunk = prediction_chunk
        self.max_position_embeddings = max_position_embeddings
        self.input_size = prediction_chunk

class CustomTransformerModel(PreTrainedModel):
    config_class = CustomTransformerConfig

    def __init__(self, config):
        super().__init__(config)

        # Load pre-trained Llama model (excluding its original lm_head)
        self.llama = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B", torch_dtype=torch.float16, device_map="auto", token = hf_token)

        self.llama.resize_token_embeddings(config.vocab_size)

        for i, layer in enumerate(self.llama.model.layers):
            layer.self_attn = BidirectionalLlamaAttention(layer.self_attn, masking='bidirectional_masked')

        # Freeze Llama to retain pre-trained knowledge
        for param in self.llama.parameters():
            param.requires_grad = False

        for param in self.llama.lm_head.parameters():
            param.requires_grad = True

        lora_config = LoraConfig(
            r=512,
            lora_alpha=512,
            lora_dropout=0.0,
            target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],  # Llama-3 uses these attention modules
            bias="none",
            task_type=None
        )

        self.llama = get_peft_model(self.llama, lora_config)
        self.llama.print_trainable_parameters()  # Print number of trainable parameters
        self.llama = self.llama.to(torch.float16)


    def forward(self, input_ids, labels=None, **kwargs):
        batch_size, seq_length = input_ids.shape
        assert seq_length == 256, f"Expected input length input_size, got {seq_length}"

        with autocast("cuda", dtype=torch.float16):  # ✅ Correct future-proof usage


            outputs = self.llama(input_ids, output_hidden_states=True, **kwargs)

            logits = outputs.logits[:,:,:self.config.vocab_size]

            # Reshape logits to (batch, input_size, vocab_size)
            logits = logits.view(batch_size, self.config.prediction_chunk, self.config.vocab_size)

            loss = None

        if labels is not None:
            assert labels.shape == (batch_size, 256), f"Labels shape mismatch: expected (batch, input_size), got {labels.shape}"

            # Compute loss
            loss_fct = torch.nn.CrossEntropyLoss()

            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))


        return {"loss": loss, "logits": logits} if loss is not None else {"logits": logits}


def disable_dropout(model):
    for name, module in model.named_modules():
        if isinstance(module, nn.Dropout):
            setattr(model, name, nn.Identity())
    return model