Create embeddings.py

embeddings.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from typing import Optional, Tuple, List
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model: int, max_seq_len: int = 5000, dropout: float = 0.1):
+        super(PositionalEncoding, self).__init__()
+        self.d_model = d_model
+        self.dropout = nn.Dropout(dropout)        
+        pe = torch.zeros(max_seq_len, d_model)
+        position = torch.arange(0, max_seq_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * 
+                           -(math.log(10000.0) / d_model))        
+        pe[:, 0::2] = torch.sin(position * div_term)
+        if d_model % 2 == 1:
+            pe[:, 1::2] = torch.cos(position * div_term[:-1])
+        else:
+            pe[:, 1::2] = torch.cos(position * div_term)        
+        self.register_buffer('pe', pe.unsqueeze(0))    
+    def forward(self, x):
+        batch_size, seq_len, d_model = x.size()
+        x = x + self.pe[:, :seq_len, :d_model]
+        return self.dropout(x)
+class LearnedPositionalEmbedding(nn.Module):
+    def __init__(self, max_seq_len: int, d_model: int, dropout: float = 0.1):
+        super(LearnedPositionalEmbedding, self).__init__()
+        self.max_seq_len = max_seq_len
+        self.d_model = d_model        
+        self.pos_embedding = nn.Embedding(max_seq_len, d_model)
+        self.dropout = nn.Dropout(dropout)
+        nn.init.normal_(self.pos_embedding.weight, std=0.02)    
+    def forward(self, x):
+        batch_size, seq_len, d_model = x.size()
+        if seq_len > self.max_seq_len:
+            raise ValueError(f"Sequence length {seq_len} exceeds maximum {self.max_seq_len}")        
+        positions = torch.arange(seq_len, device=x.device).unsqueeze(0).expand(batch_size, -1)
+        pos_emb = self.pos_embedding(positions)
+        x = x + pos_emb
+        return self.dropout(x)
+class RotaryPositionalEmbedding(nn.Module):
+    def __init__(self, d_model: int, max_seq_len: int = 2048, base: float = 10000.0):
+        super(RotaryPositionalEmbedding, self).__init__()
+        self.d_model = d_model
+        self.max_seq_len = max_seq_len
+        self.base = base        
+        inv_freq = 1.0 / (base ** (torch.arange(0, d_model, 2).float() / d_model))
+        self.register_buffer('inv_freq', inv_freq)        
+        self._seq_len_cached = 0
+        self._cos_cached = None
+        self._sin_cached = None    
+    def _update_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
+        if seq_len > self._seq_len_cached:
+            self._seq_len_cached = seq_len
+            t = torch.arange(seq_len, device=device, dtype=torch.float32)
+            freqs = torch.outer(t, self.inv_freq)
+            self._cos_cached = freqs.cos().to(dtype)
+            self._sin_cached = freqs.sin().to(dtype)    
+    def forward(self, q: torch.Tensor, k: torch.Tensor, start_pos: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
+        batch_size, seq_len, num_heads, head_dim = q.shape
+        self._update_cos_sin_cache(start_pos + seq_len, q.device, q.dtype)        
+        cos = self._cos_cached[start_pos:start_pos + seq_len, :head_dim // 2]
+        sin = self._sin_cached[start_pos:start_pos + seq_len, :head_dim // 2]
+        cos = cos.view(1, seq_len, 1, -1)
+        sin = sin.view(1, seq_len, 1, -1)        
+        q = q.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
+        k = k.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)        
+        q_rot = self._rotate_half(q, cos, sin)
+        k_rot = self._rotate_half(k, cos, sin)        
+        q_rot = q_rot.reshape(batch_size, num_heads, seq_len, head_dim).transpose(1, 2)
+        k_rot = k_rot.reshape(batch_size, num_heads, seq_len, head_dim).transpose(1, 2)        
+        return q_rot, k_rot    
+    def _rotate_half(self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
+        x1 = x[..., :x.shape[-1] // 2]
+        x2 = x[..., x.shape[-1] // 2:]
+        return torch.cat([x1 * cos - x2 * sin, x1 * sin + x2 * cos], dim=-1)
+class TechEmbeddingLayer(nn.Module):
+    def __init__(self, 
+                 vocab_size: int,
+                 d_model: int,
+                 max_seq_len: int = 512,
+                 dropout: float = 0.1,
+                 padding_idx: int = 0,
+                 pos_encoding: str = "learned",
+                 layer_norm: bool = True):
+        super(TechEmbeddingLayer, self).__init__()        
+        self.d_model = d_model
+        self.vocab_size = vocab_size
+        self.padding_idx = padding_idx        
+        self.token_embedding = nn.Embedding(vocab_size, d_model, padding_idx=padding_idx)        
+        self.pos_encoding_type = pos_encoding
+        if pos_encoding == "sinusoidal":
+            self.pos_encoding = PositionalEncoding(d_model, max_seq_len, dropout)
+        elif pos_encoding == "learned":
+            self.pos_encoding = LearnedPositionalEmbedding(max_seq_len, d_model, dropout)
+        elif pos_encoding == "rope":
+            self.pos_encoding = RotaryPositionalEmbedding(d_model, max_seq_len)
+        else:
+            raise ValueError(f"Unknown positional encoding type: {pos_encoding}")        
+        self.layer_norm = nn.LayerNorm(d_model) if layer_norm else nn.Identity()
+        self.dropout = nn.Dropout(dropout)
+        self._init_weights()    
+    def _init_weights(self):
+        nn.init.normal_(self.token_embedding.weight, mean=0.0, std=0.02)
+        if self.padding_idx is not None:
+            nn.init.constant_(self.token_embedding.weight[self.padding_idx], 0.0)    
+    def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
+        if (input_ids >= self.vocab_size).any():
+            raise ValueError(f"Input IDs contain values >= vocab_size ({self.vocab_size})")        
+        embeddings = self.token_embedding(input_ids)
+        if self.pos_encoding_type != "rope":
+            embeddings = self.pos_encoding(embeddings)        
+        embeddings = self.layer_norm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings    
+    def get_positional_encoding(self):
+        return self.pos_encoding if self.pos_encoding_type == "rope" else None
+class AdaptiveEmbedding(nn.Module):
+    def __init__(self, 
+                 vocab_size: int,
+                 d_model: int,
+                 cutoffs: list = [2000, 10000],
+                 div_val: float = 4.0):
+        super(AdaptiveEmbedding, self).__init__()        
+        self.vocab_size = vocab_size
+        self.d_model = d_model
+        self.cutoffs = [0] + cutoffs + [vocab_size]
+        self.div_val = div_val        
+        self.embeddings = nn.ModuleList()
+        self.projections = nn.ModuleList()        
+        for i in range(len(self.cutoffs) - 1):
+            l_idx = self.cutoffs[i]
+            r_idx = self.cutoffs[i + 1]
+            d_emb = int(d_model / (div_val ** i))            
+            emb = nn.Embedding(r_idx - l_idx, d_emb)
+            nn.init.normal_(emb.weight, mean=0.0, std=0.02)
+            self.embeddings.append(emb)            
+            if d_emb != d_model:
+                proj = nn.Linear(d_emb, d_model, bias=False)
+                nn.init.normal_(proj.weight, mean=0.0, std=0.02)
+                self.projections.append(proj)
+            else:
+                self.projections.append(nn.Identity())    
+    def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
+        if (input_ids >= self.vocab_size).any():
+            raise ValueError(f"Input IDs contain values >= vocab_size ({self.vocab_size})")        
+        batch_size, seq_len = input_ids.shape
+        embeddings = torch.zeros(batch_size, seq_len, self.d_model, 
+                               device=input_ids.device, dtype=torch.float32)        
+        for i in range(len(self.cutoffs) - 1):
+            l_idx = self.cutoffs[i]
+            r_idx = self.cutoffs[i + 1]            
+            mask = (input_ids >= l_idx) & (input_ids < r_idx)
+            if mask.any():
+                indices = input_ids[mask] - l_idx
+                indices = indices.clamp(max=r_idx - l_idx - 1)
+                emb = self.embeddings[i](indices)
+                emb = self.projections[i](emb)
+                embeddings[mask] = emb        
+        return embeddings
+def create_padding_mask(input_ids: torch.Tensor, padding_idx: int = 0) -> torch.Tensor:
+    return input_ids == padding_idx
+def create_causal_mask(seq_len: int, device: torch.device) -> torch.Tensor:
+    return torch.triu(torch.ones(seq_len, seq_len, device=device), diagonal=1).bool()
+def create_attention_mask(input_ids: torch.Tensor, 
+                         padding_idx: int = 0,
+                         causal: bool = True) -> torch.Tensor:
+    batch_size, seq_len = input_ids.shape
+    device = input_ids.device
+    
+    padding_mask = create_padding_mask(input_ids, padding_idx)
+    padding_mask = padding_mask.unsqueeze(1).expand(batch_size, seq_len, seq_len)    
+    if causal:
+        causal_mask = create_causal_mask(seq_len, device)
+        causal_mask = causal_mask.unsqueeze(0).expand(batch_size, seq_len, seq_len)
+        combined_mask = padding_mask | causal_mask
+    else:
+        combined_mask = padding_mask
+    
+    return combined_mask
+class EmbeddingAnalyzer:
+    def __init__(self, embedding_layer: nn.Module):
+        self.embedding_layer = embedding_layer    
+    def get_similarity_matrix(self, tokens: List[int] = None) -> torch.Tensor:
+        if hasattr(self.embedding_layer, 'token_embedding'):
+            embeddings = self.embedding_layer.token_embedding.weight
+        elif hasattr(self.embedding_layer, 'embeddings'):
+            weights = [emb.weight for emb in self.embedding_layer.embeddings]
+            embeddings = []
+            for i, w in enumerate(weights):
+                proj = self.embedding_layer.projections[i]
+                embeddings.append(proj(w))
+            embeddings = torch.cat(embeddings, dim=0)
+        else:
+            embeddings = self.embedding_layer.weight        
+        if tokens is not None and len(tokens) > 0:
+            embeddings = embeddings[tokens]        
+        normalized_embeddings = F.normalize(embeddings, p=2, dim=1)
+        return torch.mm(normalized_embeddings, normalized_embeddings.t())    
+    def find_similar_tokens(self, token_id: int, top_k: int = 10) -> List[Tuple[int, float]]:
+        similarity_matrix = self.get_similarity_matrix()
+        similarities = similarity_matrix[token_id]
+        top_similarities, top_indices = torch.topk(similarities, top_k + 1)
+        mask = top_indices != token_id
+        top_similarities = top_similarities[mask][:top_k]
+        top_indices = top_indices[mask][:top_k]
+        return list(zip(top_indices.tolist(), top_similarities.tolist()))    
+    def analyze_embedding_distribution(self):
+        if hasattr(self.embedding_layer, 'token_embedding'):
+            weights = self.embedding_layer.token_embedding.weight
+        elif hasattr(self.embedding_layer, 'embeddings'):
+            weights = torch.cat([emb.weight for emb in self.embedding_layer.embeddings], dim=0)
+        else:
+            weights = self.embedding_layer.weight        
+        stats = {
+            'mean': weights.mean().item(),
+            'std': weights.std().item(),
+            'min': weights.min().item(),
+            'max': weights.max().item(),
+            'norm_mean': weights.norm(dim=1).mean().item(),
+            'norm_std': weights.norm(dim=1).std().item()
+        }
+        return stats
+def test_embeddings():
+    print("Testing embedding layers...")    
+    vocab_size = 1000
+    d_model = 512
+    max_seq_len = 128
+    batch_size = 4
+    seq_len = 64    
+    input_ids = torch.randint(1, vocab_size, (batch_size, seq_len))    
+    embedding_types = [
+        ("Learned Position", "learned"),
+        ("Sinusoidal Position", "sinusoidal"),
+        ("RoPE", "rope")
+    ]    
+    for name, pos_type in embedding_types:
+        print(f"\nTesting {name} Embedding:")
+        embedding_layer = TechEmbeddingLayer(
+            vocab_size=vocab_size,
+            d_model=d_model,
+            max_seq_len=max_seq_len,
+            pos_encoding=pos_type
+        )        
+        embeddings = embedding_layer(input_ids)
+        print(f"Input shape: {input_ids.shape}")
+        print(f"Output shape: {embeddings.shape}")
+        print(f"Expected shape: ({batch_size}, {seq_len}, {d_model})")        
+        analyzer = EmbeddingAnalyzer(embedding_layer)
+        stats = analyzer.analyze_embedding_distribution()
+        print(f"Embedding statistics:")
+        for key, value in stats.items():
+            print(f"  {key}: {value:.4f}")    
+    print(f"\nTesting Adaptive Embeddings:")
+    adaptive_emb = AdaptiveEmbedding(
+        vocab_size=vocab_size,
+        d_model=d_model,
+        cutoffs=[200, 500],
+        div_val=2.0
+    )    
+    embeddings = adaptive_emb(input_ids)
+    print(f"Adaptive embedding output shape: {embeddings.shape}")    
+    print(f"\nTesting masking functions:")
+    input_ids_padded = input_ids.clone()
+    input_ids_padded[:, -10:] = 0
+    padding_mask = create_padding_mask(input_ids_padded, padding_idx=0)
+    causal_mask = create_causal_mask(seq_len, input_ids.device)
+    attention_mask = create_attention_mask(input_ids_padded, padding_idx=0, causal=True)    
+    print(f"Padding mask shape: {padding_mask.shape}")
+    print(f"Causal mask shape: {causal_mask.shape}")
+    print(f"Attention mask shape: {attention_mask.shape}")
+    print(f"Padding positions: {padding_mask.sum().item()}")
+    print(f"Causal mask positions: {causal_mask.sum().item()}")
+    print(f"Combined mask positions: {attention_mask.sum().item()}")    
+    print("\nAll embedding tests completed successfully!")
+if __name__ == "__main__":
+    test_embeddings()