import gradio as gr import torch import torch.nn as nn import sentencepiece as spm import math # Define Transformer components (unchanged) class MultiHeadAttention(nn.Module): def __init__(self, d_model, num_heads): super(MultiHeadAttention, self).__init__() assert d_model % num_heads == 0 self.d_model = d_model self.num_heads = num_heads self.d_k = d_model // num_heads self.W_q = nn.Linear(d_model, d_model) self.W_k = nn.Linear(d_model, d_model) self.W_v = nn.Linear(d_model, d_model) self.W_o = nn.Linear(d_model, d_model) def scaled_dot_product_attention(self, Q, K, V, mask=None): attn_scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.d_k) if mask is not None: attn_scores = attn_scores.masked_fill(mask == 0, -1e9) attn_probs = torch.softmax(attn_scores, dim=-1) output = torch.matmul(attn_probs, V) return output def split_heads(self, x): batch_size, seq_length, d_model = x.size() return x.view(batch_size, seq_length, self.num_heads, self.d_k).transpose(1, 2) def combine_heads(self, x): batch_size, _, seq_length, d_k = x.size() return x.transpose(1, 2).contiguous().view(batch_size, seq_length, self.d_model) def forward(self, Q, K, V, mask=None): Q = self.split_heads(self.W_q(Q)) K = self.split_heads(self.W_k(K)) V = self.split_heads(self.W_v(V)) attn_output = self.scaled_dot_product_attention(Q, K, V, mask) output = self.W_o(self.combine_heads(attn_output)) return output class PositionWiseFeedForward(nn.Module): def __init__(self, d_model, d_ff): super(PositionWiseFeedForward, self).__init__() self.fc1 = nn.Linear(d_model, d_ff) self.fc2 = nn.Linear(d_ff, d_model) self.relu = nn.ReLU() def forward(self, x): return self.fc2(self.relu(self.fc1(x))) class PositionalEncoding(nn.Module): def __init__(self, d_model, max_seq_length): super(PositionalEncoding, self).__init__() pe = torch.zeros(max_seq_length, d_model) position = torch.arange(0, max_seq_length, 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) pe[:, 1::2] = torch.cos(position * div_term) self.register_buffer('pe', pe.unsqueeze(0)) def forward(self, x): return x + self.pe[:, :x.size(1)] class EncoderLayer(nn.Module): def __init__(self, d_model, num_heads, d_ff, dropout): super(EncoderLayer, self).__init__() self.self_attn = MultiHeadAttention(d_model, num_heads) self.feed_forward = PositionWiseFeedForward(d_model, d_ff) self.norm1 = nn.LayerNorm(d_model) self.norm2 = nn.LayerNorm(d_model) self.dropout = nn.Dropout(dropout) def forward(self, x, mask): attn_output = self.self_attn(x, x, x, mask) x = self.norm1(x + self.dropout(attn_output)) ff_output = self.feed_forward(x) x = self.norm2(x + self.dropout(ff_output)) return x class DecoderLayer(nn.Module): def __init__(self, d_model, num_heads, d_ff, dropout): super(DecoderLayer, self).__init__() self.self_attn = MultiHeadAttention(d_model, num_heads) self.cross_attn = MultiHeadAttention(d_model, num_heads) self.feed_forward = PositionWiseFeedForward(d_model, d_ff) self.norm1 = nn.LayerNorm(d_model) self.norm2 = nn.LayerNorm(d_model) self.norm3 = nn.LayerNorm(d_model) self.dropout = nn.Dropout(dropout) def forward(self, x, enc_output, src_mask, tgt_mask): attn_output = self.self_attn(x, x, x, tgt_mask) x = self.norm1(x + self.dropout(attn_output)) attn_output = self.cross_attn(x, enc_output, enc_output, src_mask) x = self.norm2(x + self.dropout(attn_output)) ff_output = self.feed_forward(x) x = self.norm3(x + self.dropout(ff_output)) return x class Transformer(nn.Module): def __init__(self, src_vocab_size, tgt_vocab_size, d_model, num_heads, num_layers, d_ff, max_seq_length, dropout): super(Transformer, self).__init__() self.encoder_embedding = nn.Embedding(src_vocab_size, d_model) self.decoder_embedding = nn.Embedding(tgt_vocab_size, d_model) self.positional_encoding = PositionalEncoding(d_model, max_seq_length) self.encoder_layers = nn.ModuleList([EncoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)]) self.decoder_layers = nn.ModuleList([DecoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)]) self.fc = nn.Linear(d_model, tgt_vocab_size) self.dropout = nn.Dropout(dropout) def generate_mask(self, src, tgt): src_mask = (src != 0).unsqueeze(1).unsqueeze(2) tgt_mask = (tgt != 0).unsqueeze(1).unsqueeze(3) seq_length = tgt.size(1) nopeak_mask = (1 - torch.triu(torch.ones(1, seq_length, seq_length), diagonal=1)).bool() tgt_mask = tgt_mask & nopeak_mask return src_mask, tgt_mask def forward(self, src, tgt): src_mask, tgt_mask = self.generate_mask(src, tgt) src_embedded = self.dropout(self.positional_encoding(self.encoder_embedding(src))) tgt_embedded = self.dropout(self.positional_encoding(self.decoder_embedding(tgt))) enc_output = src_embedded for enc_layer in self.encoder_layers: enc_output = enc_layer(enc_output, src_mask) dec_output = tgt_embedded for dec_layer in self.decoder_layers: dec_output = dec_layer(dec_output, enc_output, src_mask, tgt_mask) output = self.fc(dec_output) return output # Set device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Load tokenizers sp_pseudo = spm.SentencePieceProcessor(model_file="pseudocode_tokenizer.model") # For decoding pseudocode (target) sp_code = spm.SentencePieceProcessor(model_file="code_tokenizer.model") # For encoding C++ (source) # Load the full saved model (architecture + weights) model_path = "code2pseudo.pth" model = torch.load(model_path, map_location=device, weights_only=False) model.eval() model = model.to(device) def generate_pseudocode(cpp_code, max_len): """Generate pseudocode from C++ code with streaming output.""" model.eval() src = torch.tensor([sp_code.encode_as_ids(cpp_code)], dtype=torch.long, device=device) # Tokenize C++ code tgt = torch.tensor([[2]], dtype=torch.long, device=device) # =2 generated_tokens = [2] # Start with response = "" with torch.no_grad(): for _ in range(max_len): output = model(src, tgt) next_token = output[:, -1, :].argmax(-1).item() generated_tokens.append(next_token) tgt = torch.cat([tgt, torch.tensor([[next_token]], device=device)], dim=1) response = sp_pseudo.decode_ids(generated_tokens) # Decode to pseudocode yield response # Yield partial output if next_token == 3: # =3 (adjust if your EOS ID differs) break yield response # Final output def respond(message, history, max_tokens): """Wrapper for Gradio interface.""" for response in generate_pseudocode(message, max_tokens): yield response # Gradio UI setup with Blocks with gr.Blocks(title="C++ to Pseudocode Transformer") as demo: gr.Markdown("## C++ to Pseudocode Converter") gr.Markdown("Enter C++ code below and press Submit to generate pseudocode.") cpp_input = gr.Textbox( label="C++ Code", placeholder="e.g., 'int x = 5; for(int i=0; i