[release/2.6] NAVI32 specific fixes

test/inductor/test_flex_decoding.py

@@ -1332,6 +1332,7 @@ def mask_mod(b, h, q, kv):
         self.assertEqual(query.grad[:, :, M:, :].sum(), 0)
 
     @supported_platform
+    @skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Some archs don't support SDPA")
     def test_windowed_no_mask_vs_sdpa(self):
         score_mod = _generate_windowed(1000)
         attention = functools.partial(flex_attention, score_mod=score_mod)

test/inductor/test_max_autotune.py

@@ -33,6 +33,7 @@
 from torch.testing._internal.common_utils import (
     instantiate_parametrized_tests,
     parametrize,
+    skipIfRocmNotEnoughMemory,
     skipIfRocm,
     TEST_WITH_ROCM,
 )
@@ -717,6 +718,8 @@ def test_conv_backend(self):
 
         self.assertIn("NoValidChoicesError", str(context.exception))
 
+    # Some ROCm GPUs don't have enough VRAM to run all autotune configurations and padding benchmarks
+    @skipIfRocmNotEnoughMemory(30)
     def test_non_contiguous_input_mm(self):
         """
         Make sure the triton template can work with non-contiguous inputs without crash.
@@ -766,6 +769,8 @@ def f(x, y):
         act = f(x, y)
         torch.testing.assert_close(act, ref, atol=2e-2, rtol=1e-2)
 
+    # Some ROCm GPUs don't have enough VRAM to run all autotune configurations and padding benchmarks
+    @skipIfRocmNotEnoughMemory(30)
     def test_non_contiguous_input_mm_plus_mm(self):
         x1 = rand_strided((50257, 32768), (1, 50304), device="cuda")
         y1 = rand_strided((32768, 768), (768, 1), device="cuda")

torch/testing/_internal/common_utils.py

@@ -1872,14 +1872,26 @@ def wrap_fn(self, *args, **kwargs):
         return wrap_fn
     return dec_fn
 
-def skipIfRocmArch(arch: tuple[str, ...]):
+# Checks if current ROCm device has enough VRAM against the required amount in GB
+def skipIfRocmNotEnoughMemory(required_amount):
     def dec_fn(fn):
         @wraps(fn)
         def wrap_fn(self, *args, **kwargs):
             if TEST_WITH_ROCM:
-                prop = torch.cuda.get_device_properties(0)
-                if prop.gcnArchName.split(":")[0] in arch:
-                    reason = f"skipIfRocm: test skipped on {arch}"
+                device = torch.cuda.current_device()
+                props = torch.cuda.get_device_properties(device)
+
+                total = props.total_memory / (1024 ** 3)  # in GB
+                # This will probably return 0 because it only counts tensors
+                # and doesn't take into account any small supporting allocations
+                allocated = torch.cuda.memory_allocated(device) / (1024 ** 3)
+                free_global = total - allocated
+
+                result = free_global > required_amount
+
+                if not result:
+                    reason = f"skipIfRocm: Not enough free VRAM on current ROCm device. " \
+                        f"Available: {free_global:.2f} GB | Required: {required_amount:.2f} GB."
                     raise unittest.SkipTest(reason)
             return fn(self, *args, **kwargs)
         return wrap_fn