modify some foundational tests to also test in float16 and bfloat16

tests/foundationals/clip/test_image_encoder.py

@@ -10,12 +10,16 @@ from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
 
 
 @pytest.fixture(scope="module")
-def our_encoder(test_weights_path: Path, test_device: torch.device) -> CLIPImageEncoderH:
+def our_encoder(
+    test_weights_path: Path,
+    test_device: torch.device,
+    test_dtype_fp32_bf16_fp16: torch.dtype,
+) -> CLIPImageEncoderH:
     weights = test_weights_path / "CLIPImageEncoderH.safetensors"
     if not weights.is_file():
         warn(f"could not find weights at {weights}, skipping")
         pytest.skip(allow_module_level=True)
-    encoder = CLIPImageEncoderH(device=test_device)
+    encoder = CLIPImageEncoderH(device=test_device, dtype=test_dtype_fp32_bf16_fp16)
     tensors = load_from_safetensors(weights)
     encoder.load_state_dict(tensors)
     return encoder
@@ -31,24 +35,31 @@ def stabilityai_unclip_weights_path(test_weights_path: Path):
 
 
 @pytest.fixture(scope="module")
-def ref_encoder(stabilityai_unclip_weights_path: Path, test_device: torch.device) -> CLIPVisionModelWithProjection:
-    return CLIPVisionModelWithProjection.from_pretrained(stabilityai_unclip_weights_path, subfolder="image_encoder").to(  # type: ignore
-        test_device  # type: ignore
-    )
+def ref_encoder(
+    stabilityai_unclip_weights_path: Path,
+    test_device: torch.device,
+    test_dtype_fp32_bf16_fp16: torch.dtype,
+) -> CLIPVisionModelWithProjection:
+    return CLIPVisionModelWithProjection.from_pretrained(  # type: ignore
+        stabilityai_unclip_weights_path,
+        subfolder="image_encoder",
+    ).to(device=test_device, dtype=test_dtype_fp32_bf16_fp16)
 
 
+@no_grad()
+@pytest.mark.flaky(reruns=3)
 def test_encoder(
     ref_encoder: CLIPVisionModelWithProjection,
     our_encoder: CLIPImageEncoderH,
-    test_device: torch.device,
 ):
-    x = torch.randn(1, 3, 224, 224).to(test_device)
+    assert ref_encoder.dtype == our_encoder.dtype
+    assert ref_encoder.device == our_encoder.device
+    x = torch.randn((1, 3, 224, 224), dtype=ref_encoder.dtype, device=ref_encoder.device)
 
-    with no_grad():
-        ref_embeddings = ref_encoder(x).image_embeds
-        our_embeddings = our_encoder(x)
+    ref_embeddings = ref_encoder(x).image_embeds
+    our_embeddings = our_encoder(x)
 
     assert ref_embeddings.shape == (1, 1024)
     assert our_embeddings.shape == (1, 1024)
 
-    assert (our_embeddings - ref_embeddings).abs().max() < 0.01
+    assert torch.allclose(our_embeddings, ref_embeddings, atol=0.05)

tests/foundationals/clip/test_text_encoder.py

@@ -30,13 +30,17 @@ PROMPTS = [
 
 
 @pytest.fixture(scope="module")
-def our_encoder(test_weights_path: Path, test_device: torch.device) -> CLIPTextEncoderL:
+def our_encoder(
+    test_weights_path: Path,
+    test_device: torch.device,
+    test_dtype_fp32_fp16: torch.dtype,
+) -> CLIPTextEncoderL:
     weights = test_weights_path / "CLIPTextEncoderL.safetensors"
     if not weights.is_file():
         warn(f"could not find weights at {weights}, skipping")
         pytest.skip(allow_module_level=True)
-    encoder = CLIPTextEncoderL(device=test_device)
     tensors = load_from_safetensors(weights)
+    encoder = CLIPTextEncoderL(device=test_device, dtype=test_dtype_fp32_fp16)
     encoder.load_state_dict(tensors)
     return encoder
 
@@ -56,8 +60,15 @@ def ref_tokenizer(runwayml_weights_path: Path) -> transformers.CLIPTokenizer:
 
 
 @pytest.fixture(scope="module")
-def ref_encoder(runwayml_weights_path: Path, test_device: torch.device) -> transformers.CLIPTextModel:
-    return transformers.CLIPTextModel.from_pretrained(runwayml_weights_path, subfolder="text_encoder").to(test_device)  # type: ignore
+def ref_encoder(
+    runwayml_weights_path: Path,
+    test_device: torch.device,
+    test_dtype_fp32_fp16: torch.dtype,
+) -> transformers.CLIPTextModel:
+    return transformers.CLIPTextModel.from_pretrained(  # type: ignore
+        runwayml_weights_path,
+        subfolder="text_encoder",
+    ).to(device=test_device, dtype=test_dtype_fp32_fp16)  # type: ignore
 
 
 def test_basics(ref_tokenizer: transformers.CLIPTokenizer, our_encoder: CLIPTextEncoderL):
@@ -70,12 +81,12 @@ def prompt(request: pytest.FixtureRequest):
     return long_prompt if request.param == "<long prompt>" else request.param
 
 
+@no_grad()
 def test_encoder(
     prompt: str,
     ref_tokenizer: transformers.CLIPTokenizer,
     ref_encoder: transformers.CLIPTextModel,
     our_encoder: CLIPTextEncoderL,
-    test_device: torch.device,
 ):
     ref_tokens = ref_tokenizer(  # type: ignore
         prompt,
@@ -89,18 +100,16 @@ def test_encoder(
     our_tokens = tokenizer(prompt)
     assert torch.equal(our_tokens, ref_tokens)
 
-    with no_grad():
-        ref_embeddings = ref_encoder(ref_tokens.to(test_device))[0]
-        our_embeddings = our_encoder(prompt)
+    ref_embeddings = ref_encoder(ref_tokens.to(device=ref_encoder.device))[0]
+    our_embeddings = our_encoder(prompt)
 
     assert ref_embeddings.shape == (1, 77, 768)
     assert our_embeddings.shape == (1, 77, 768)
 
     # FG-336 - Not strictly equal because we do not use the same implementation
     # of self-attention. We use `scaled_dot_product_attention` which can have
-    # numerical differences depending on the backend.
-    # Also we use FP16 weights.
-    assert (our_embeddings - ref_embeddings).abs().max() < 0.01
+    # numerical differences depending on the backend. Also we use FP16 weights.
+    torch.testing.assert_close(our_embeddings, ref_embeddings, atol=0.035, rtol=0.0)
 
 
 def test_list_string_tokenizer(

tests/foundationals/dinov2/test_dinov2.py

@@ -109,7 +109,7 @@ def test_dinov2_facebook_weights(
 ) -> None:
     manual_seed(2)
     input_data = torch.randn(
-        (1, 3, resolution, resolution),
+        size=(1, 3, resolution, resolution),
         device=test_device,
     )
 
@@ -129,27 +129,28 @@ def test_dinov2_facebook_weights(
 
 
 @no_grad()
-def test_dinov2_float16(
+def test_dinov2(
     resolution: int,
+    test_dtype_fp32_bf16_fp16: torch.dtype,
     test_device: torch.device,
 ) -> None:
     if test_device.type == "cpu":
         warn("not running on CPU, skipping")
         pytest.skip()
 
-    model = DINOv2_small(device=test_device, dtype=torch.float16)
+    model = DINOv2_small(device=test_device, dtype=test_dtype_fp32_bf16_fp16)
 
     manual_seed(2)
     input_data = torch.randn(
-        (1, 3, resolution, resolution),
+        size=(1, 3, resolution, resolution),
         device=test_device,
-        dtype=torch.float16,
+        dtype=test_dtype_fp32_bf16_fp16,
     )
 
     output = model(input_data)
     sequence_length = (resolution // model.patch_size) ** 2 + 1
     assert output.shape == (1, sequence_length, model.embedding_dim)
-    assert output.dtype == torch.float16
+    assert output.dtype == test_dtype_fp32_bf16_fp16
 
 
 @no_grad()
@@ -162,7 +163,7 @@ def test_dinov2_batch_size(
     batch_size = 4
     manual_seed(2)
     input_data = torch.randn(
-        (batch_size, 3, resolution, resolution),
+        size=(batch_size, 3, resolution, resolution),
         device=test_device,
     )
 

tests/foundationals/latent_diffusion/test_auto_encoder.py

@@ -6,8 +6,8 @@ import torch
 from PIL import Image
 from tests.utils import ensure_similar_images
 
-from refiners.fluxion.utils import load_from_safetensors, no_grad
-from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
+from refiners.fluxion.utils import no_grad
+from refiners.foundationals.latent_diffusion import LatentDiffusionAutoencoder, SD1Autoencoder, SDXLAutoencoder
 
 
 @pytest.fixture(scope="module")
@@ -15,16 +15,37 @@ def ref_path() -> Path:
     return Path(__file__).parent / "test_auto_encoder_ref"
 
 
-@pytest.fixture(scope="module")
-def lda(test_weights_path: Path, test_device: torch.device) -> LatentDiffusionAutoencoder:
-    lda_weights = test_weights_path / "lda.safetensors"
-    if not lda_weights.is_file():
-        warn(f"could not find weights at {lda_weights}, skipping")
-        pytest.skip(allow_module_level=True)
-    encoder = LatentDiffusionAutoencoder(device=test_device)
-    tensors = load_from_safetensors(lda_weights)
-    encoder.load_state_dict(tensors)
-    return encoder
+@pytest.fixture(scope="module", params=["SD1.5", "SDXL"])
+def lda(
+    request: pytest.FixtureRequest,
+    test_weights_path: Path,
+    test_dtype_fp32_bf16_fp16: torch.dtype,
+    test_device: torch.device,
+) -> LatentDiffusionAutoencoder:
+    model_version = request.param
+    match (model_version, test_dtype_fp32_bf16_fp16):
+        case ("SD1.5", _):
+            weight_path = test_weights_path / "lda.safetensors"
+            if not weight_path.is_file():
+                warn(f"could not find weights at {weight_path}, skipping")
+                pytest.skip(allow_module_level=True)
+            model = SD1Autoencoder().load_from_safetensors(weight_path)
+        case ("SDXL", torch.float16):
+            weight_path = test_weights_path / "sdxl-lda-fp16-fix.safetensors"
+            if not weight_path.is_file():
+                warn(f"could not find weights at {weight_path}, skipping")
+                pytest.skip(allow_module_level=True)
+            model = SDXLAutoencoder().load_from_safetensors(weight_path)
+        case ("SDXL", _):
+            weight_path = test_weights_path / "sdxl-lda.safetensors"
+            if not weight_path.is_file():
+                warn(f"could not find weights at {weight_path}, skipping")
+                pytest.skip(allow_module_level=True)
+            model = SDXLAutoencoder().load_from_safetensors(weight_path)
+        case _:
+            raise ValueError(f"Unknown model version: {model_version}")
+    model = model.to(device=test_device, dtype=test_dtype_fp32_bf16_fp16)
+    return model
 
 
 @pytest.fixture(scope="module")

tests/foundationals/latent_diffusion/test_models.py

@@ -0,0 +1,77 @@
+import torch
+from PIL import Image
+
+from refiners.fluxion.utils import manual_seed, no_grad
+from refiners.foundationals.latent_diffusion import StableDiffusion_1, StableDiffusion_1_Inpainting, StableDiffusion_XL
+from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
+
+
+@no_grad()
+def test_sample_noise_zero_offset(test_device: torch.device, test_dtype_fp32_bf16_fp16: torch.dtype) -> None:
+    manual_seed(2)
+    latents_0 = LatentDiffusionModel.sample_noise(
+        size=(1, 4, 64, 64),
+        device=test_device,
+        dtype=test_dtype_fp32_bf16_fp16,
+    )
+    manual_seed(2)
+    latents_1 = LatentDiffusionModel.sample_noise(
+        size=(1, 4, 64, 64),
+        offset_noise=0.0,  # should be no-op
+        device=test_device,
+        dtype=test_dtype_fp32_bf16_fp16,
+    )
+
+    assert torch.allclose(latents_0, latents_1, atol=1e-6, rtol=0)
+
+
+@no_grad()
+def test_sd15_one_step(test_device: torch.device, test_dtype_fp32_bf16_fp16: torch.dtype) -> None:
+    sd = StableDiffusion_1(device=test_device, dtype=test_dtype_fp32_bf16_fp16)
+
+    # prepare inputs
+    latent_noise = torch.randn(1, 4, 64, 64, device=test_device, dtype=test_dtype_fp32_bf16_fp16)
+    text_embedding = sd.compute_clip_text_embedding("")
+
+    # run the pipeline of models, for a single step
+    output = sd(latent_noise, step=0, clip_text_embedding=text_embedding)
+
+    assert output.shape == (1, 4, 64, 64)
+
+
+@no_grad()
+def test_sd15_inpainting_one_step(test_device: torch.device, test_dtype_fp32_bf16_fp16: torch.dtype) -> None:
+    sd = StableDiffusion_1_Inpainting(device=test_device, dtype=test_dtype_fp32_bf16_fp16)
+
+    # prepare inputs
+    latent_noise = torch.randn(1, 4, 64, 64, device=test_device, dtype=test_dtype_fp32_bf16_fp16)
+    target_image = Image.new("RGB", (512, 512))
+    mask = Image.new("L", (512, 512))
+    sd.set_inpainting_conditions(target_image=target_image, mask=mask)
+    text_embedding = sd.compute_clip_text_embedding("")
+
+    # run the pipeline of models, for a single step
+    output = sd(latent_noise, step=0, clip_text_embedding=text_embedding)
+
+    assert output.shape == (1, 4, 64, 64)
+
+
+@no_grad()
+def test_sdxl_one_step(test_device: torch.device, test_dtype_fp32_bf16_fp16: torch.dtype) -> None:
+    sd = StableDiffusion_XL(device=test_device, dtype=test_dtype_fp32_bf16_fp16)
+
+    # prepare inputs
+    latent_noise = torch.randn(1, 4, 128, 128, device=test_device, dtype=test_dtype_fp32_bf16_fp16)
+    text_embedding, pooled_text_embedding = sd.compute_clip_text_embedding("")
+    time_ids = sd.default_time_ids
+
+    # run the pipeline of models, for a single step
+    output = sd(
+        latent_noise,
+        step=0,
+        clip_text_embedding=text_embedding,
+        pooled_text_embedding=pooled_text_embedding,
+        time_ids=time_ids,
+    )
+
+    assert output.shape == (1, 4, 128, 128)

tests/foundationals/latent_diffusion/test_sd15_unet.py

@@ -7,9 +7,15 @@ from refiners.foundationals.latent_diffusion import SD1UNet
 
 
 @pytest.fixture(scope="module")
-def refiners_sd15_unet(test_device: torch.device) -> SD1UNet:
-    unet = SD1UNet(in_channels=4, device=test_device)
-    return unet
+def refiners_sd15_unet(
+    test_device: torch.device,
+    test_dtype_fp32_bf16_fp16: torch.dtype,
+) -> SD1UNet:
+    return SD1UNet(
+        in_channels=4,
+        device=test_device,
+        dtype=test_dtype_fp32_bf16_fp16,
+    )
 
 
 def test_unet_context_flush(refiners_sd15_unet: SD1UNet):