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 torch import torch.nn as nn import torch.nn.functional as F import math class RMSNorm(nn.Module): def __init__(self, dim, eps=1e-6): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def forward(self, x): rms = torch.sqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps) return self.weight * (x / rms) class RotaryPositionalEmbedding(nn.Module): def __init__(self, dim, max_seq_len=2048, base=50000.0): super().__init__() inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) self.register_buffer('inv_freq', inv_freq) t = torch.arange(max_seq_len).float() freqs = torch.outer(t, inv_freq) emb = torch.cat([freqs, freqs], dim=-1) self.register_buffer('cos_cached', emb.cos()) self.register_buffer('sin_cached', emb.sin()) def forward(self, seq_len): return self.cos_cached[:seq_len], self.sin_cached[:seq_len] def rotate_half(x): half = x.shape[-1] // 2 x1 = x[..., :half] x2 = x[..., half:] return torch.cat([-x2, x1], dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, rotary_dim=None): if rotary_dim is not None and rotary_dim < q.shape[-1]: q_rot = q[..., :rotary_dim] q_pass = q[..., rotary_dim:] k_rot = k[..., :rotary_dim] k_pass = k[..., rotary_dim:] q_rot = q_rot * cos + rotate_half(q_rot) * sin k_rot = k_rot * cos + rotate_half(k_rot) * sin q = torch.cat([q_rot, q_pass], dim=-1) k = torch.cat([k_rot, k_pass], dim=-1) else: q = q * cos + rotate_half(q) * sin k = k * cos + rotate_half(k) * sin return q, k class CausalAttention(nn.Module): def __init__(self, d_model, n_heads, head_dim=None, max_seq_len=2048, qk_norm=False, rotary_dim=None): super().__init__() self.n_heads = n_heads self.head_dim = head_dim if head_dim is not None else d_model // n_heads self.d_model = d_model self.inner_dim = n_heads * self.head_dim self.q_proj = nn.Linear(d_model, self.inner_dim, bias=False) self.k_proj = nn.Linear(d_model, self.inner_dim, bias=False) self.v_proj = nn.Linear(d_model, self.inner_dim, bias=False) self.o_proj = nn.Linear(self.inner_dim, d_model, bias=False) self.qk_norm = qk_norm if qk_norm: self.q_norm = RMSNorm(self.inner_dim) self.k_norm = RMSNorm(self.inner_dim) self.rotary_dim = rotary_dim if rotary_dim is not None else self.head_dim self.rope = RotaryPositionalEmbedding(self.rotary_dim, max_seq_len) def forward(self, x, token_emb=None, gated_value_emb=None, apply_gated_value=False): B, n, _ = x.shape q = self.q_proj(x) k = self.k_proj(x) v = self.v_proj(x) if self.qk_norm: q = self.q_norm(q) k = self.k_norm(k) if apply_gated_value and gated_value_emb is not None and token_emb is not None: v = gated_value_emb(v, token_emb) q = q.view(B, n, self.n_heads, self.head_dim).transpose(1, 2) k = k.view(B, n, self.n_heads, self.head_dim).transpose(1, 2) v = v.view(B, n, self.n_heads, self.head_dim).transpose(1, 2) cos, sin = self.rope(n) cos = cos.unsqueeze(0).unsqueeze(0) sin = sin.unsqueeze(0).unsqueeze(0) q, k = apply_rotary_pos_emb(q, k, cos, sin, self.rotary_dim) attn_out = F.scaled_dot_product_attention(q, k, v, is_causal=True) attn_out = attn_out.transpose(1, 2).contiguous().view(B, n, self.inner_dim) return self.o_proj(attn_out) class SwiGLUMLP(nn.Module): def __init__(self, d_model, d_ffn): super().__init__() self.gate_proj = nn.Linear(d_model, d_ffn, bias=False) self.up_proj = nn.Linear(d_model, d_ffn, bias=False) self.down_proj = nn.Linear(d_ffn, d_model, bias=False) def forward(self, x): return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x)) class ReLU2MLP(nn.Module): def __init__(self, d_model, d_ffn): super().__init__() self.fc = nn.Linear(d_model, d_ffn, bias=False) self.proj = nn.Linear(d_ffn, d_model, bias=False) def forward(self, x): h = F.relu(F.relu(self.fc(x))) return self.proj(h) class GatedValueEmbedding(nn.Module): def __init__(self, d_model): super().__init__() self.gate = nn.Parameter(torch.zeros(d_model)) def forward(self, v_proj_output, token_emb): gate = torch.sigmoid(self.gate) return gate * v_proj_output + (1.0 - gate) * token_emb class TransformerBlockA(nn.Module): def __init__(self, d_model, n_heads, d_ffn, max_seq_len=2048, head_dim=None, layer_idx=0, vocab_size=65536, rotary_dim=None): super().__init__() self.layer_idx = layer_idx self.d_model = d_model self.norm1 = RMSNorm(d_model) self.attn = CausalAttention( d_model=d_model, n_heads=n_heads, head_dim=head_dim, max_seq_len=max_seq_len, qk_norm=False, rotary_dim=rotary_dim, ) self.norm2 = RMSNorm(d_model) self.mlp = SwiGLUMLP(d_model, d_ffn) def forward(self, x, token_emb=None): x = x + self.attn(self.norm1(x)) x = x + self.mlp(self.norm2(x)) return x class TransformerBlockB(nn.Module): def __init__(self, d_model, n_heads, d_ffn, max_seq_len=2048, head_dim=128, layer_idx=0, vocab_size=32768, rotary_dim=None): super().__init__() self.layer_idx = layer_idx self.d_model = d_model self.norm1 = RMSNorm(d_model) self.attn = CausalAttention( d_model=d_model, n_heads=n_heads, head_dim=head_dim, max_seq_len=max_seq_len, qk_norm=True, rotary_dim=rotary_dim, ) self.gated_value_emb = GatedValueEmbedding(d_model) self.use_gated_value = (layer_idx % 2 == 0) self.norm2 = RMSNorm(d_model) self.mlp = ReLU2MLP(d_model, d_ffn) def forward(self, x, token_emb=None): normed = self.norm1(x) attn_out = self.attn( normed, token_emb=token_emb, gated_value_emb=self.gated_value_emb, apply_gated_value=self.use_gated_value, ) x = x + attn_out x = x + self.mlp(self.norm2(x)) return x