[Tests] Test attention.py

tests/test_layers_utils.py

@@ -19,8 +19,10 @@
 import numpy as np
 import torch
 
+from diffusers.models.attention import AttentionBlock, SpatialTransformer
 from diffusers.models.embeddings import get_timestep_embedding
 from diffusers.models.resnet import Downsample2D, Upsample2D
+from diffusers.testing_utils import torch_device
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
@@ -216,3 +218,100 @@ def test_downsample_with_conv_out_dim(self):
         output_slice = downsampled[0, -1, -3:, -3:]
         expected_slice = torch.tensor([-0.6586, 0.5985, 0.0721, 0.1256, -0.1492, 0.4436, -0.2544, 0.5021, 1.1522])
         assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)
+
+
+class AttentionBlockTests(unittest.TestCase):
+    def test_attention_block_default(self):
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
+
+        sample = torch.randn(1, 32, 64, 64).to(torch_device)
+        attentionBlock = AttentionBlock(
+            channels=32,
+            num_head_channels=1,
+            rescale_output_factor=1.0,
+            eps=1e-6,
+            num_groups=32,
+        ).to(torch_device)
+        with torch.no_grad():
+            attention_scores = attentionBlock(sample)
+
+        assert attention_scores.shape == (1, 32, 64, 64)
+        output_slice = attention_scores[0, -1, -3:, -3:]
+
+        expected_slice = torch.tensor([-1.4975, -0.0038, -0.7847, -1.4567, 1.1220, -0.8962, -1.7394, 1.1319, -0.5427])
+        assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)
+
+
+class SpatialTransformerTests(unittest.TestCase):
+    def test_spatial_transformer_default(self):
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
+
+        sample = torch.randn(1, 32, 64, 64).to(torch_device)
+        spatial_transformer_block = SpatialTransformer(
+            in_channels=32,
+            n_heads=1,
+            d_head=32,
+            dropout=0.0,
+            context_dim=None,
+        ).to(torch_device)
+        with torch.no_grad():
+            attention_scores = spatial_transformer_block(sample)
+
+        assert attention_scores.shape == (1, 32, 64, 64)
+        output_slice = attention_scores[0, -1, -3:, -3:]
+
+        expected_slice = torch.tensor([-1.2447, -0.0137, -0.9559, -1.5223, 0.6991, -1.0126, -2.0974, 0.8921, -1.0201])
+        assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)
+
+    def test_spatial_transformer_context_dim(self):
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
+
+        sample = torch.randn(1, 64, 64, 64).to(torch_device)
+        spatial_transformer_block = SpatialTransformer(
+            in_channels=64,
+            n_heads=2,
+            d_head=32,
+            dropout=0.0,
+            context_dim=64,
+        ).to(torch_device)
+        with torch.no_grad():
+            context = torch.randn(1, 4, 64).to(torch_device)
+            attention_scores = spatial_transformer_block(sample, context)
+
+        assert attention_scores.shape == (1, 64, 64, 64)
+        output_slice = attention_scores[0, -1, -3:, -3:]
+
+        expected_slice = torch.tensor([-0.2555, -0.8877, -2.4739, -2.2251, 1.2714, 0.0807, -0.4161, -1.6408, -0.0471])
+        assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)
+
+    def test_spatial_transformer_dropout(self):
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
+
+        sample = torch.randn(1, 32, 64, 64).to(torch_device)
+        spatial_transformer_block = (
+            SpatialTransformer(
+                in_channels=32,
+                n_heads=2,
+                d_head=16,
+                dropout=0.3,
+                context_dim=None,
+            )
+            .to(torch_device)
+            .eval()
+        )
+        with torch.no_grad():
+            attention_scores = spatial_transformer_block(sample)
+
+        assert attention_scores.shape == (1, 32, 64, 64)
+        output_slice = attention_scores[0, -1, -3:, -3:]
+
+        expected_slice = torch.tensor([-1.2448, -0.0190, -0.9471, -1.5140, 0.7069, -1.0144, -2.1077, 0.9099, -1.0091])
+        assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)