Upload modeling_modern_protein.py

modeling_modern_protein.py

@@ -0,0 +1,396 @@
+"""
+ModernProteinLM: A next-generation protein encoder combining:
+- ModernBERT architectural improvements (RoPE, Pre-LN, GeGLU, FlashAttention-compatible)
+- ELECTRA-style discriminative pre-training
+- Deep & narrow design optimal for protein sequences
+- Curriculum masking (30% -> 5%)
+- Span masking for protein structural motifs
+
+Architecture goals (~150M params):
+- 28 layers, hidden 576, heads 9, intermediate 2304 (GeGLU)
+- RoPE position embeddings (no absolute PE)
+- Pre-LayerNorm with extra LN after embedding
+- No dropout (following ESM-2)
+- Tied input/output embeddings
+"""
+
+import math
+from typing import Optional, Tuple, Union
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PreTrainedModel, PretrainedConfig
+from transformers.modeling_outputs import MaskedLMOutput, SequenceClassifierOutput, TokenClassifierOutput
+
+
+class ModernProteinLMConfig(PretrainedConfig):
+    model_type = "modern_protein_lm"
+    
+    def __init__(
+        self,
+        vocab_size=33,
+        hidden_size=576,
+        num_hidden_layers=28,
+        num_attention_heads=9,
+        intermediate_size=2304,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        max_position_embeddings=1026,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        position_embedding_type="rotary",
+        rope_theta=10000.0,
+        use_geglu=True,
+        tie_word_embeddings=True,
+        pad_token_id=1,
+        mask_token_id=32,
+        cls_token_id=0,
+        eos_token_id=2,
+        **kwargs,
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            mask_token_id=mask_token_id,
+            cls_token_id=cls_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.position_embedding_type = position_embedding_type
+        self.rope_theta = rope_theta
+        self.use_geglu = use_geglu
+
+
+class RotaryEmbedding(nn.Module):
+    """RoPE (Rotary Position Embedding) for protein sequences."""
+    
+    def __init__(self, dim, max_seq_len=1026, base=10000.0, device=None):
+        super().__init__()
+        self.dim = dim
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.max_seq_len = max_seq_len
+        
+    def forward(self, seq_len, device):
+        t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+        freqs = torch.outer(t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        return emb.cos().to(torch.float32), emb.sin().to(torch.float32)
+
+
+def rotate_half(x):
+    x1, x2 = x.chunk(2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin):
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class ModernProteinAttention(nn.Module):
+    """Multi-head attention with RoPE and optional FlashAttention."""
+    
+    def __init__(self, config: ModernProteinLMConfig):
+        super().__init__()
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.hidden_size // config.num_attention_heads
+        self.scale = self.head_dim ** -0.5
+        
+        self.qkv_proj = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=False)
+        self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        
+        self.rotary_emb = RotaryEmbedding(self.head_dim, max_seq_len=config.max_position_embeddings, base=config.rope_theta)
+        
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob) if config.attention_probs_dropout_prob > 0 else None
+        
+    def forward(self, hidden_states, attention_mask=None, output_attentions=False):
+        batch_size, seq_len, _ = hidden_states.shape
+        
+        qkv = self.qkv_proj(hidden_states)
+        qkv = qkv.reshape(batch_size, seq_len, 3, self.num_heads, self.head_dim)
+        qkv = qkv.permute(2, 0, 3, 1, 4)  # (3, B, H, T, D)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+        
+        # Apply RoPE
+        cos, sin = self.rotary_emb(seq_len, device=hidden_states.device)
+        cos = cos[None, None, :, :]  # (1, 1, T, D)
+        sin = sin[None, None, :, :]
+        q, k = apply_rotary_pos_emb(q, k, cos, sin)
+        
+        # Try FlashAttention if available
+        try:
+            from flash_attn import flash_attn_func
+            if attention_mask is None and q.dtype in [torch.float16, torch.bfloat16]:
+                attn_output = flash_attn_func(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), 
+                                              dropout_p=self.dropout.p if self.dropout else 0.0,
+                                              causal=False)
+                attn_output = attn_output.transpose(1, 2)
+            else:
+                raise ImportError("Fallback to standard attention")
+        except (ImportError, AttributeError):
+            # Standard scaled dot-product attention
+            attn_scores = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+            
+            if attention_mask is not None:
+                attn_scores = attn_scores + attention_mask
+            
+            attn_probs = F.softmax(attn_scores, dim=-1, dtype=torch.float32).to(q.dtype)
+            if self.dropout is not None:
+                attn_probs = self.dropout(attn_probs)
+            
+            attn_output = torch.matmul(attn_probs, v)
+        
+        attn_output = attn_output.transpose(1, 2).reshape(batch_size, seq_len, -1)
+        attn_output = self.out_proj(attn_output)
+        
+        if output_attentions:
+            return attn_output, attn_probs
+        return attn_output, None
+
+
+class GeGLU(nn.Module):
+    """GeGLU activation: GELU(gate) * value. More expressive than GELU alone."""
+    
+    def __init__(self, config: ModernProteinLMConfig):
+        super().__init__()
+        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+        self.act = nn.GELU()
+        
+    def forward(self, x):
+        gate = self.act(self.gate_proj(x))
+        up = self.up_proj(x)
+        return self.down_proj(gate * up)
+
+
+class ModernProteinMLP(nn.Module):
+    def __init__(self, config: ModernProteinLMConfig):
+        super().__init__()
+        if config.use_geglu:
+            self.mlp = GeGLU(config)
+        else:
+            self.mlp = nn.Sequential(
+                nn.Linear(config.hidden_size, config.intermediate_size, bias=False),
+                nn.GELU(),
+                nn.Linear(config.intermediate_size, config.hidden_size, bias=False),
+            )
+    
+    def forward(self, x):
+        return self.mlp(x)
+
+
+class ModernProteinLayer(nn.Module):
+    """Pre-LN transformer layer with optional parallel formulation."""
+    
+    def __init__(self, config: ModernProteinLMConfig):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.attn = ModernProteinAttention(config)
+        self.ln2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.mlp = ModernProteinMLP(config)
+        
+    def forward(self, hidden_states, attention_mask=None, output_attentions=False):
+        # Pre-LN: LN -> Attn -> Residual
+        attn_out, attn_weights = self.attn(self.ln1(hidden_states), attention_mask, output_attentions)
+        hidden_states = hidden_states + attn_out
+        
+        # Pre-LN: LN -> MLP -> Residual
+        mlp_out = self.mlp(self.ln2(hidden_states))
+        hidden_states = hidden_states + mlp_out
+        
+        return hidden_states, attn_weights
+
+
+class ModernProteinLM(PreTrainedModel):
+    config_class = ModernProteinLMConfig
+    
+    def __init__(self, config: ModernProteinLMConfig):
+        super().__init__(config)
+        self.config = config
+        
+        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.embed_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        
+        self.layers = nn.ModuleList([
+            ModernProteinLayer(config) for _ in range(config.num_hidden_layers)
+        ])
+        
+        self.final_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        
+        # Initialize weights
+        self._init_weights()
+        
+        # Tie embeddings if requested
+        if config.tie_word_embeddings:
+            self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+            self.lm_head.weight = self.embeddings.weight
+        else:
+            self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+    
+    def _init_weights(self):
+        for module in self.modules():
+            if isinstance(module, nn.Linear):
+                nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+            elif isinstance(module, nn.Embedding):
+                nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
+            elif isinstance(module, nn.LayerNorm):
+                nn.init.ones_(module.weight)
+                nn.init.zeros_(module.bias)
+    
+    def get_input_embeddings(self):
+        return self.embeddings
+    
+    def set_input_embeddings(self, value):
+        self.embeddings = value
+    
+    def forward(
+        self,
+        input_ids,
+        attention_mask=None,
+        position_ids=None,
+        labels=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        return_dict=True,
+    ):
+        batch_size, seq_len = input_ids.shape
+        
+        # Embedding
+        hidden_states = self.embeddings(input_ids)
+        hidden_states = self.embed_ln(hidden_states)
+        
+        # Attention mask for padding
+        if attention_mask is not None:
+            # (B, T) -> (B, 1, 1, T) for broadcasting
+            attention_mask = (1.0 - attention_mask[:, None, None, :]) * -10000.0
+        
+        all_hidden_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+        
+        # Transformer layers
+        for layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+            
+            hidden_states, attn_weights = layer(hidden_states, attention_mask, output_attentions)
+            
+            if output_attentions:
+                all_attentions += (attn_weights,)
+        
+        hidden_states = self.final_ln(hidden_states)
+        
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+        
+        # LM head
+        logits = self.lm_head(hidden_states)
+        
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
+        
+        if not return_dict:
+            output = (logits,)
+            if output_hidden_states:
+                output += (all_hidden_states,)
+            if output_attentions:
+                output += (all_attentions,)
+            return ((loss,) + output) if loss is not None else output
+        
+        return MaskedLMOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=all_hidden_states,
+            attentions=all_attentions,
+        )
+    
+    def get_sequence_embedding(self, input_ids, attention_mask=None):
+        """Extract CLS or mean-pooled embedding for downstream tasks."""
+        outputs = self.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+        hidden = outputs.hidden_states[-1]
+        
+        if attention_mask is not None:
+            # Mean pool over non-padded positions
+            mask_expanded = attention_mask.unsqueeze(-1).float()
+            sum_hidden = (hidden * mask_expanded).sum(dim=1)
+            pooled = sum_hidden / mask_expanded.sum(dim=1).clamp(min=1e-9)
+        else:
+            pooled = hidden[:, 0]  # CLS token
+        
+        return pooled
+
+
+class ModernProteinLMForMaskedLM(ModernProteinLM):
+    """Masked Language Model wrapper."""
+    pass
+
+
+class ModernProteinLMForSequenceClassification(PreTrainedModel):
+    config_class = ModernProteinLMConfig
+    
+    def __init__(self, config: ModernProteinLMConfig):
+        super().__init__(config)
+        self.modern_protein = ModernProteinLM(config)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+        
+    def forward(self, input_ids, attention_mask=None, labels=None):
+        pooled = self.modern_protein.get_sequence_embedding(input_ids, attention_mask)
+        logits = self.classifier(pooled)
+        
+        loss = None
+        if labels is not None:
+            if self.config.num_labels == 1:
+                loss_fct = nn.MSELoss()
+                loss = loss_fct(logits.squeeze(-1), labels.float())
+            else:
+                loss_fct = nn.CrossEntropyLoss()
+                loss = loss_fct(logits, labels)
+        
+        return SequenceClassifierOutput(loss=loss, logits=logits)
+
+
+class ModernProteinLMForTokenClassification(PreTrainedModel):
+    config_class = ModernProteinLMConfig
+    
+    def __init__(self, config: ModernProteinLMConfig):
+        super().__init__(config)
+        self.modern_protein = ModernProteinLM(config)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+        
+    def forward(self, input_ids, attention_mask=None, labels=None):
+        outputs = self.modern_protein(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            return_dict=True,
+        )
+        logits = self.classifier(outputs.hidden_states[-1])
+        
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
+        
+        return TokenClassifierOutput(loss=loss, logits=logits)