conditionally check if compute capability is met.

Author: sayakpaul

src/diffusers/utils/torch_utils.py

@@ -149,3 +149,13 @@ def apply_freeu(
         res_hidden_states = fourier_filter(res_hidden_states, threshold=1, scale=freeu_kwargs["s2"])
 
     return hidden_states, res_hidden_states
+
+
+def get_torch_cuda_device_capability():
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+        compute_capability = torch.cuda.get_device_capability(device)
+        compute_capability = f"{compute_capability[0]}.{compute_capability[1]}"
+        return float(compute_capability)
+    else:
+        return None

tests/models/test_modeling_common.py

@@ -68,6 +68,7 @@
     torch_all_close,
     torch_device,
 )
+from diffusers.utils.torch_utils import get_torch_cuda_device_capability
 
 from ..others.test_utils import TOKEN, USER, is_staging_test
 
@@ -1412,10 +1413,12 @@ def get_memory_usage(storage_dtype, compute_dtype):
             torch.float8_e4m3fn, torch.bfloat16
         )
 
+        compute_capability = get_torch_cuda_device_capability()
         self.assertTrue(fp8_e4m3_bf16_memory_footprint < fp8_e4m3_fp32_memory_footprint < fp32_memory_footprint)
         # NOTE: the following assertion will fail on our CI (running Tesla T4) due to bf16 using more memory than fp32.
         # On other devices, such as DGX (Ampere) and Audace (Ada), the test passes.
-        self.assertTrue(fp8_e4m3_bf16_max_memory < fp8_e4m3_fp32_max_memory)
+        if compute_capability < 8.9:
+            self.assertTrue(fp8_e4m3_bf16_max_memory < fp8_e4m3_fp32_max_memory)
         # On this dummy test case with a small model, sometimes fp8_e4m3_fp32 max memory usage is higher than fp32 by a few
         # bytes. This only happens for some models, so we allow a small tolerance.
         # For any real model being tested, the order would be fp8_e4m3_bf16 < fp8_e4m3_fp32 < fp32.