import torch import torch.nn as nn from torch.amp import autocast from transformers import AutoModelForCausalLM, PreTrainedModel, PretrainedConfig from transformers.models.llama.modeling_llama import LlamaAttention from peft import LoraConfig, get_peft_model import os from typing import Optional, Tuple 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 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: 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, key, value, attention_mask, scaling, dropout=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: attn_mask = (1.0 - attention_mask) * float('-inf') attn_mask = attn_mask.to(dtype=query.dtype) attn_weights = attn_weights + attn_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).transpose(1, 2).contiguous() return attn_output, attn_weights def rotate_half(self, x): 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, unsqueeze_dim=1): 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, position_embeddings, attention_mask=None, past_key_value=None, cache_position=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) 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) attn_output, attn_weights = self.eager_attention_forward( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, **kwargs ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() return self.o_proj(attn_output), attn_weights 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, masking_type="bidirectional", **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 self.masking_type = masking_type class CustomTransformerModel(PreTrainedModel): config_class = CustomTransformerConfig def __init__(self, config): super().__init__(config) 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=config.masking_type) 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"], bias="none", task_type=None ) self.llama = get_peft_model(self.llama, lora_config) self.llama.print_trainable_parameters() # self.llama = self.llama.to(torch.float16) def forward(self, input_ids, labels=None, **kwargs): batch_size, seq_len = input_ids.shape assert seq_len == self.config.prediction_chunk, f"Expected input length {self.config.prediction_chunk}, got {seq_len}" # Build attention mask device = input_ids.device masking_type = getattr(self.config, "masking_type", "bidirectional") if masking_type == 'bidirectional': base_mask = torch.ones(seq_len, seq_len, dtype=torch.bool, device=device) elif masking_type == 'bidirectional_masked': base_mask = torch.ones(seq_len, seq_len, dtype=torch.bool, device=device) base_mask.fill_diagonal_(False) elif masking_type == 'unidirectional': base_mask = torch.tril(torch.ones(seq_len, seq_len, dtype=torch.bool, device=device)) else: raise ValueError(f"Unknown masking type: {self.config.masking_type}") attention_mask = base_mask.unsqueeze(0).unsqueeze(1).expand(batch_size, 1, seq_len, seq_len).clone() attention_mask = attention_mask.to(dtype=torch.float32) # required for SDPA and Flash attention with autocast("cuda", dtype=torch.float16): outputs = self.llama( input_ids, attention_mask=attention_mask, output_hidden_states=True, use_cache=False, **kwargs ) logits = outputs.logits[:, :, :self.config.vocab_size].view(batch_size, seq_len, self.config.vocab_size) loss = None if labels is not None: assert labels.shape == (batch_size, seq_len), f"Labels shape mismatch: expected ({batch_size}, {seq_len}), got {labels.shape}" loss_fct = 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