Merge branch 'mpcusack/jitsave' of https://github.com/mpcusack/llama2.c into mpcusack-mpcusack/jitsave

model.py

@@ -49,7 +49,7 @@ def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
     assert 0 <= 1 < ndim
     assert freqs_cis.shape == (x.shape[1], x.shape[-1])
     shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
-    return freqs_cis.view(*shape)
+    return freqs_cis.view(shape)
 
 def apply_rotary_emb(
     xq: torch.Tensor,
@@ -59,8 +59,8 @@ def apply_rotary_emb(
 ) -> Tuple[torch.Tensor, torch.Tensor]:
 
     # reshape xq and xk to match the complex representation
-    xq_r, xq_i = xq.float().reshape(*xq.shape[:-1], -1, 2).unbind(-1)
-    xk_r, xk_i = xk.float().reshape(*xk.shape[:-1], -1, 2).unbind(-1)
+    xq_r, xq_i = xq.float().reshape(xq.shape[:-1] + (-1, 2)).unbind(-1)
+    xk_r, xk_i = xk.float().reshape(xk.shape[:-1] + (-1, 2)).unbind(-1)
     
     # reshape freqs_cos and freqs_sin for broadcasting
     freqs_cos = reshape_for_broadcast(freqs_cos, xq_r)
@@ -142,10 +142,11 @@ class Attention(nn.Module):
 
         # flash implementation
         if self.flash:
-            output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True)
+            output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None, dropout_p=self.dropout if self.training else 0.0, is_causal=True)
         else:
             # manual implementation
             scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim)
+            assert hasattr(self, 'mask')
             scores = scores + self.mask[:, :, :seqlen, :seqlen]   # (bs, n_local_heads, seqlen, cache_len + seqlen)
             scores = F.softmax(scores.float(), dim=-1).type_as(xq)
             scores = self.attn_dropout(scores)
@@ -198,6 +199,8 @@ class TransformerBlock(nn.Module):
 
 
 class Transformer(nn.Module):
+    last_loss: Optional[torch.Tensor]
+
     def __init__(self, params: ModelArgs):
         super().__init__()
         self.params = params
@@ -227,6 +230,9 @@ class Transformer(nn.Module):
             if pn.endswith('w3.weight') or pn.endswith('wo.weight'):
                 torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * params.n_layers))
 
+        # Initialize attribute for the loss of the last forward call. This will be set if the forward is called with a targets tensor.
+        self.last_loss = None
+
     def _init_weights(self, module):
         if isinstance(module, nn.Linear):
             torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
@@ -235,7 +241,7 @@ class Transformer(nn.Module):
         elif isinstance(module, nn.Embedding):
             torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
 
-    def forward(self, tokens, targets=None):
+    def forward(self, tokens: torch.Tensor, targets: Optional[torch.Tensor] = None) -> torch.Tensor:
         _bsz, seqlen = tokens.shape
         h = self.tok_embeddings(tokens)
         h = self.dropout(h)
@@ -249,13 +255,13 @@ class Transformer(nn.Module):
         if targets is not None:
             # if we are given some desired targets also calculate the loss
             logits = self.output(h)
-            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
+            self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
         else:
             # inference-time mini-optimization: only forward the output on the very last position
             logits = self.output(h[:, [-1], :]) # note: using list [-1] to preserve the time dim
-            loss = None
+            self.last_loss = None
 
-        return logits, loss
+        return logits
 
     def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
         # start with all of the candidate parameters