Potential Bug in RoPE Frequency Computation

There appears to be a mismatch between the RoPE frequency table initialization and its usage during training in gpt.py.

Current Behavior

In Transformer.__init__():
self.freqs_cis = precompute_freqs_cis_2d(
    grid_size, 
    self.config.dim // self.config.n_head, 
    self.config.rope_base, 
    self.cls_token_num + self.condition_token_num  # = 1 + 256 = 257
)

The precompute_freqs_cis_2d function creates a frequency table with 257 zero-frequency positions at the beginning, followed by actual RoPE frequencies for the image tokens.

During training in forward():

token_embeddings = torch.cat((cond_embeddings, token_embeddings), dim=1)
# Shape: (bs, 1 + 255, dim) = (bs, 256, dim)

if self.training:
    freqs_cis = self.freqs_cis[:token_embeddings.shape[1]]  # Takes first 256 positions

Problem
Since token_embeddings.shape[1] = 256 and the first 257 positions of freqs_cis are zeros, the sliced freqs_cis[:256] contains all zeros. This means the entire training sequence effectively has no positional encoding from RoPE.

Start in gpt.py by reading Transformer.init, precompute_freqs_cis_2d, and forward together. Trace the sequence lengths and the freqs_cis slice, then verify that the training sequence receives the intended positional frequencies rather than an all-zero slice.