add optional adaptive rmsnorm on text embed conditioning

.github/workflows/test.yml

@@ -0,0 +1,21 @@
+name: Pytest
+on: [push, pull_request]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v5
+      with:
+        python-version: "3.10"
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install -e .[test]
+    - name: Test with pytest
+      run: |
+        python -m pytest tests/

meshgpt_pytorch/meshgpt_pytorch.py

@@ -1075,7 +1075,7 @@ def forward(
         return recon_faces, total_loss, loss_breakdown
 
 @save_load(version = __version__)
-class MeshTransformer(Module,PyTorchModelHubMixin):
+class MeshTransformer(Module, PyTorchModelHubMixin):
     @typecheck
     def __init__(
         self,
@@ -1094,12 +1094,13 @@ def __init__(
         cross_attn_num_mem_kv = 4, # needed for preventing nan when dropping out text condition
         dropout = 0.,
         coarse_pre_gateloop_depth = 2,
+        coarse_adaptive_rmsnorm = False,
         fine_pre_gateloop_depth = 2,
         gateloop_use_heinsen = False,
         fine_attn_depth = 2,
         fine_attn_dim_head = 32,
         fine_attn_heads = 8,
-        fine_cross_attend_text = False,
+        fine_cross_attend_text = False, # additional conditioning - fine transformer cross attention to text tokens
         pad_id = -1,
         num_sos_tokens = None,
         condition_on_text = False,
@@ -1177,6 +1178,8 @@ def __init__(
         # main autoregressive attention network
         # attending to a face token
 
+        self.coarse_adaptive_rmsnorm = coarse_adaptive_rmsnorm
+
         self.decoder = Decoder(
             dim = dim,
             depth = attn_depth,
@@ -1185,6 +1188,8 @@ def __init__(
             attn_flash = flash_attn,
             attn_dropout = dropout,
             ff_dropout = dropout,
+            use_adaptive_rmsnorm = coarse_adaptive_rmsnorm,
+            dim_condition = dim_text,
             cross_attend = condition_on_text,
             cross_attn_dim_context = cross_attn_dim_context,
             cross_attn_num_mem_kv = cross_attn_num_mem_kv,
@@ -1458,6 +1463,11 @@ def forward_on_codes(
                 context_mask = text_mask
             )
 
+            if self.coarse_adaptive_rmsnorm:
+                attn_context_kwargs.update(
+                    condition = pooled_text_embed
+                )
+
         # take care of codes that may be flattened
 
         if codes.ndim > 2:

meshgpt_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.2.22'
+__version__ = '1.4.0'

setup.cfg

@@ -0,0 +1,7 @@
+[aliases]
+test=pytest
+
+[tool:pytest]
+addopts = --verbose -s
+python_files = tests/*.py
+python_paths = "."

setup.py

@@ -38,7 +38,13 @@
     'torch_geometric',
     'tqdm',
     'vector-quantize-pytorch>=1.14.22',
-    'x-transformers>=1.30.6',
+    'x-transformers>=1.30.19',
+  ],
+  setup_requires=[
+    'pytest-runner',
+  ],
+  tests_require=[
+    'pytest'
   ],
   classifiers=[
     'Development Status :: 4 - Beta',

tests/test_meshgpt.py

@@ -0,0 +1,52 @@
+import pytest
+import torch
+
+from meshgpt_pytorch import (
+    MeshAutoencoder,
+    MeshTransformer
+)
+
+@pytest.mark.parametrize('adaptive_rmsnorm', (True, False))
+def test_readme(adaptive_rmsnorm):
+
+    autoencoder = MeshAutoencoder(
+        num_discrete_coors = 128
+    )
+
+    # mock inputs
+
+    vertices = torch.randn((2, 121, 3))            # (batch, num vertices, coor (3))
+    faces = torch.randint(0, 121, (2, 64, 3))      # (batch, num faces, vertices (3))
+
+    # forward in the faces
+
+    loss = autoencoder(
+        vertices = vertices,
+        faces = faces
+    )
+
+    loss.backward()
+
+    # after much training...
+    # you can pass in the raw face data above to train a transformer to model this sequence of face vertices
+
+    transformer = MeshTransformer(
+        autoencoder,
+        dim = 512,
+        max_seq_len = 768,
+        num_sos_tokens = 1,
+        fine_cross_attend_text = True,
+        text_cond_with_film = False,
+        condition_on_text = True,
+        coarse_adaptive_rmsnorm = adaptive_rmsnorm
+    )
+
+    loss = transformer(
+        vertices = vertices,
+        faces = faces,
+        texts = ['a high chair', 'a small teapot']
+    )
+
+    loss.backward()
+
+    faces_coordinates, face_mask = transformer.generate(texts = ['a small chair'], cond_scale = 3.)