Send back the complete code with all the fixes. Fix each of ...

Prompt

Send back the complete code with all the fixes. Fix each of the listed errors one by one, making sure to actually correct them so that there are 0 errors remaining. Keep the original imports, since the files exist. Write out every single character; do not abbreviate anything. Fix every error. There must be exactly one file. Do not write anything else; just output the complete code, and it must not contain any comments. Never, under any circumstances, use simplified, substitute, dummy, simulated, or fake code. Write the entire file as complete, unabridged, production-ready code in a single code block. It must be 100% error-free, a complete, error-free file, and must be submitted as a downloadable file. These requirements are mandatory and must be strictly adhered to. If no list of errors is provided, you must find all the errors and fix them. If there were comments in the original code, delete them. And most importantly: YOU MUST NEVER SIMPLIFY! import math import torch import torch.nn as nn import torch.nn.functional as F from .blocks import TransformerBlockA, TransformerBlockB, RMSNorm from .injection import InjectionBlock, PreludeNorm MODEL_CONFIGS = { '100M': { 'params': 114242560, 'L_P': 1, 'L_R': 1, 'L_C': 1, 'd_model': 1024, 'd_ffn': 3520, 'mu_rec': 16, 'config_type': 'A', }, '350M': { 'params': 378558464, 'L_P': 1, 'L_R': 2, 'L_C': 1, 'd_model': 2048, 'd_ffn': 7040, 'mu_rec': 8, 'config_type': 'A', }, '140M': { 'params': 144323136, 'L_P': 2, 'L_R': 2, 'L_C': 2, 'd_model': 768, 'd_ffn': 3072, 'mu_rec': 8, 'n_heads': 6, 'head_dim': 128, 'config_type': 'B', }, '370M': { 'params': 388003328, 'L_P': 4, 'L_R': 4, 'L_C': 4, 'd_model': 1024, 'd_ffn': 4096, 'mu_rec': 8, 'n_heads': 8, 'head_dim': 128, 'config_type': 'B', }, '770M': { 'params': 776655680, 'L_P': 6, 'L_R': 6, 'L_C': 6, 'd_model': 1280, 'd_ffn': 5120, 'mu_rec': 8, 'n_heads': 10, 'head_dim': 128, 'config_type': 'B', }, '1.3B': { 'params': 1338591744, 'L_P': 8, 'L_R': 8, 'L_C': 8, 'd_model': 1536, 'd_ffn': 6144, 'mu_rec': 8, 'n_heads': 12, 'head_dim': 128, 'config_type': 'B', }, } CONFIG_A_VOCAB = 65536 CONFIG_B_VOCAB = 32768 CONFIG_A_DEFAULT_HEADS = 16 def _make_block(config_type, d_model, n_heads, d_ffn, max_seq_len, head_dim, layer_idx, vocab_size, rotary_dim=None): if config_type == 'A': return TransformerBlockA( d_model=d_model, n_heads=n_heads, d_ffn=d_ffn, max_seq_len=max_seq_len, head_dim=head_dim, layer_idx=layer_idx, vocab_size=vocab_size, rotary_dim=rotary_dim, ) else: return TransformerBlockB( d_model=d_model, n_heads=n_heads, d_ffn=d_ffn, max_seq_len=max_seq_len, head_dim=head_dim, layer_idx=layer_idx, vocab_size=vocab_size, rotary_dim=rotary_dim, ) class LoopedTransformer(nn.Module): def __init__(self, config_name, max_seq_len=2048, mu_rec=None, mu_bwd=None, config_type=None): super().__init__() if config_name not in MODEL_CONFIGS: raise ValueError(f"Unknown config_name '{config_name}'. Available: {list(MODEL_CONFIGS.keys())}") cfg = MODEL_CONFIGS[config_name] config_type = config_type if config_type is not None else cfg['config_type'] self.config_type = config_type self.config_name = config_name self.d_model = cfg['d_model'] self.d_ffn = cfg['d_ffn'] self.L_P = cfg['L_P'] self.L_R = cfg['L_R'] self.L_C = cfg['L_C'] self.max_seq_len = max_seq_len self.mu_rec = mu_rec if mu_rec is not None else cfg['mu_rec'] self.mu_bwd = mu_bwd if mu_bwd is not None else math.ceil(self.mu_rec / 2) vocab_size = CONFIG_A_VOCAB if config_type == 'A' else CONFIG_B_VOCAB self.vocab_size = vocab_size n_heads = cfg.get('n_heads', CONFIG_A_DEFAULT_HEADS) head_dim = cfg.get('head_dim', self.d_model // n_heads) self.embedding = nn.Embedding(vocab_size, self.d_model) layer_offset = 0 self.prelude_blocks = nn.ModuleList([ _make_block( config_type=config_type, d_model=self.d_model, n_heads=n_heads, d_ffn=self.d_ffn, max_seq_len=max_seq_len, head_dim=head_dim, layer_idx=layer_offset + i, vocab_size=vocab_size, ) for i in range(self.L_P) ]) layer_offset += self.L_P self.prelude_norm = PreludeNorm(self.d_model) self.injection_block = InjectionBlock(d_h=self.d_model, d_c=self.d_model) self.recurrent_blocks = nn.ModuleList([ _make_block( config_type=config_type, d_model=self.d_model, n_heads=n_heads, d_ffn=self.d_ffn, max_seq_len=max_seq_len, head_dim=head_dim, layer_idx=layer_offset + i, vocab_size=vocab_size, ) for i in range(self.L_R) ]) layer_offset += self.L_R self.coda_blocks = nn.ModuleList([ _make_block( config_type=config_type, d_model=self.d_model, n_heads=n_heads, d_ffn=self.d_ffn, max_seq_len=max_seq_len, head_dim=head_dim, layer_idx=layer_offset + i, vocab_size=vocab_size, ) for i in range(self.L_C) ]) def forward(self, token_ids): B, n = token_ids.shape token_emb = self.embedding(token_ids) x = token_emb for block in self.prelude_blocks: x = block(x, token_emb=token_emb) e = self.prelude_norm(x) if self.config_type == 'A': sigma = math.sqrt(2.0 / (5.0 * self.d_model)) else: sigma = (2.0 / (5.0 * self.d_model)) ** 0.25 h = torch.randn(B, n, self.d_model, device=token_ids.device, dtype=token_ids.dtype) * sigma A_bar, B_bar = self.injection_block() for _t in range(self.mu_rec): recurrent_out = h for block in self.recurrent_blocks: recurrent_out = block(recurrent_out, token_emb=token_emb) R_bar = recurrent_out - h h = A_bar * h + torch.matmul(e, B_bar.t()) + R_bar x = self.injection_block.c_proj(h) for block in self.coda_blocks: x = block(x, token_emb=token_emb) logits = F.linear(x, self.embedding.weight) return logits class FixedDepthTransformer(nn.Module): def __init__(self, config_name, max_seq_len=2048, config_type=None): super().__init__() if config_name not in MODEL_CONFIGS: raise ValueError(f"Unknown config_name '{config_name}'. Available: {list(MODEL_CONFIGS.keys())}") cfg = MODEL_CONFIGS[config_name] config_type = config_type if config_type is not None else cfg['config_type'] self.config_type = config_type self.config_name = config_name self.d_model = cfg['d_model'] self.d_ffn = cfg['d_ffn'] self.L_R = cfg['L_R'] self.max_seq_len = max_seq_len self.total_layers = 3 * self.L_R vocab_size = CONFIG_A_VOCAB if config_type == 'A' else CONFIG_B_VOCAB self.vocab_size = vocab_size n_heads = cfg.get('n_heads', CONFIG_A_DEFAULT_HEADS) head_dim = cfg.get('head_dim', self.d_model // n_heads) self.embedding = nn.Embedding(vocab_size, self.d_model) self.blocks = nn.ModuleList([ _make_block( config_type=config_type, d_model=self.d_model, n_heads=n_heads, d_ffn=self.d_ffn, max_seq_len=max_seq_len, head_dim=head_dim, layer_idx=i, vocab_size=vocab_size, ) for i in range(self.total_layers) ]) def forward(self, token_ids): B, n = token_ids.shape token_emb = self.embedding(token_ids) x = token_emb for block in self.blocks: x = block(x, token_emb=token_emb) logits = F.linear(x, self.embedding.weight) return logits