readd ability to condition fine transformer with text, more efficient…

… now with cross attention key/value caching

meshgpt_pytorch/meshgpt_pytorch.py

@@ -1039,6 +1039,7 @@ def __init__(
         fine_attn_depth = 2,
         fine_attn_dim_head = 32,
         fine_attn_heads = 8,
+        fine_cross_attend_text = False,
         pad_id = -1,
         num_sos_tokens = None,
         condition_on_text = False,
@@ -1137,6 +1138,8 @@ def __init__(
 
         # decoding the vertices, 2-stage hierarchy
 
+        self.fine_cross_attend_text = condition_on_text and fine_cross_attend_text
+
         self.fine_decoder = Decoder(
             dim = dim_fine,
             depth = fine_attn_depth,
@@ -1145,6 +1148,9 @@ def __init__(
             attn_flash = flash_attn,
             attn_dropout = dropout,
             ff_dropout = dropout,
+            cross_attend = self.fine_cross_attend_text,
+            cross_attn_dim_context = cross_attn_dim_context,
+            cross_attn_num_mem_kv = cross_attn_num_mem_kv,
             **attn_kwargs
         )
 
@@ -1512,8 +1518,17 @@ def forward_on_codes(
             if exists(fine_cache):
                 for attn_intermediate in fine_cache.attn_intermediates:
                     ck, cv = attn_intermediate.cached_kv
-                    ck, cv = map(lambda t: rearrange(t, '(b nf) ... -> b nf ...', b = batch), (ck, cv))
-                    ck, cv = map(lambda t: t[:, -1, :, :curr_vertex_pos], (ck, cv))
+                    ck, cv = [rearrange(t, '(b nf) ... -> b nf ...', b = batch) for t in (ck, cv)]
+
+                    # when operating on the cached key / values, treat self attention and cross attention differently
+
+                    layer_type = attn_intermediate.layer_type
+
+                    if layer_type == 'a':
+                        ck, cv = [t[:, -1, :, :curr_vertex_pos] for t in (ck, cv)]
+                    elif layer_type == 'c':
+                        ck, cv = [t[:, -1, ...] for t in (ck, cv)]
+
                     attn_intermediate.cached_kv = (ck, cv)
 
         num_faces = fine_vertex_codes.shape[1]
@@ -1524,9 +1539,25 @@ def forward_on_codes(
         if one_face:
             fine_vertex_codes = fine_vertex_codes[:, :(curr_vertex_pos + 1)]
 
+        # handle maybe cross attention conditioning of fine transformer with text
+
+        fine_attn_context_kwargs = dict()
+
+        if self.fine_cross_attend_text:
+            repeat_batch = fine_vertex_codes.shape[0] // text_embed.shape[0]
+
+            text_embed = repeat(text_embed, 'b ... -> (b r) ...' , r = repeat_batch)
+            text_mask = repeat(text_mask, 'b ... -> (b r) ...', r = repeat_batch)
+
+            fine_attn_context_kwargs = dict(
+                context = text_embed,
+                context_mask = text_mask
+            )
+
         attended_vertex_codes, fine_cache = self.fine_decoder(
             fine_vertex_codes,
             cache = fine_cache,
+            **fine_attn_context_kwargs,
             return_hiddens = True
         )
 

meshgpt_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.2.9'
+__version__ = '1.2.10'

setup.py

@@ -36,7 +36,7 @@
     'torchtyping',
     'tqdm',
     'vector-quantize-pytorch>=1.14.22',
-    'x-transformers>=1.30.4',
+    'x-transformers>=1.30.6',
   ],
   classifiers=[
     'Development Status :: 4 - Beta',