Save and use cache['num_cached']

learning-at-home · Dec 21, 2021 · 9febeec · 9febeec
1 parent df89951
commit 9febeec
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Showing 2 changed files with 8 additions and 7 deletions.
diff --git a/dalle_pytorch/attention.py b/dalle_pytorch/attention.py
@@ -72,7 +72,7 @@ def forward(self, x, mask = None, rotary_pos_emb = None, cache = None, cache_key
 
         if exists(rotary_pos_emb):
             if using_cache:
-                rotary_pos_emb = rotary_pos_emb[..., n - 1:, :]  # FIXME: Fix rotary index here
+                rotary_pos_emb = rotary_pos_emb[..., cache['num_cached']:, :]
             q, k, v = apply_pos_emb(rotary_pos_emb, (q, k, v))
 
         q = q * self.scale
@@ -92,7 +92,7 @@ def forward(self, x, mask = None, rotary_pos_emb = None, cache = None, cache_key
             dots.masked_fill_(~mask, mask_value)
             del mask
 
-        if self.causal and not using_cache:  # causality is naturally enforced if we run the cached inference
+        if self.causal and not using_cache:  # causality is naturally enforced for the cached inference
             i, j = dots.shape[-2:]
             mask = torch.ones(i, j, device = device).triu_(j - i + 1).bool()
             dots.masked_fill_(mask, mask_value)

diff --git a/dalle_pytorch/dalle_pytorch.py b/dalle_pytorch/dalle_pytorch.py
@@ -586,7 +586,7 @@ def forward(
             alpha = 0.1
             tokens = tokens * alpha + tokens.detach() * (1 - alpha)
 
-        if cache is not None and 'decoding' in cache:
+        if exists(cache) and cache.get('num_cached'):
             tokens = tokens[:, -1:]
         out = self.transformer(tokens, cache=cache)
 
@@ -598,13 +598,14 @@ def forward(
         # mask logits to make sure text predicts text (except last token), and image predicts image
 
         logits_mask = self.logits_mask[:, :seq_len]
-        if cache is not None:
-            if 'decoding' in cache:
-                logits_mask = logits_mask[:, -1:]
-            cache['decoding'] = True
+        if exists(cache) and cache.get('num_cached'):
+            logits_mask = logits_mask[:, -1:]
         max_neg_value = -torch.finfo(logits.dtype).max
         logits.masked_fill_(logits_mask, max_neg_value)
 
+        if exists(cache):
+            cache['num_cached'] = cache.get('num_cached', 0) + logits.shape[1]
+
         if not return_loss:
             return logits