[UT] Improve pass rate (#1090)

- Unskip passing tests - Mark expected failures as xfail Pass rate: 98.4%->98.65% Signed-off-by: Whitney Tsang <[email protected]>
intel · May 11, 2024 · e47fd95 · e47fd95
1 parent 2758215
commit e47fd95
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Showing 4 changed files with 14 additions and 15 deletions.
diff --git a/python/test/unit/language/test_conversions.py b/python/test/unit/language/test_conversions.py
@@ -321,7 +321,7 @@ def test_typeconvert_upcast(src_dtype, dst_dtype, device):
         pytest.skip(f"{src_dtype} upcast tests not supported on ROCm")
 
     if src_dtype in ('float8e4b8', 'float8e5b16') and (is_cuda() or not is_on_mi300()):
-        pytest.skip("{src_dtype} upcast tests only supported on AMDGPU MI300")
+        pytest.xfail("{src_dtype} upcast tests only supported on AMDGPU MI300")
 
     # dtype : (exponent_bits, mantissa_bits, exponent_bias, max_repr)
     stuff = {

diff --git a/python/test/unit/language/test_core.py b/python/test/unit/language/test_core.py
@@ -1385,10 +1385,13 @@ def test_atomic_rmw(op, dtype_x_str, mode, sem, device):
     check_type_supported(dtype_x_str, device)
     if is_interpreter():
         if dtype_x_str == 'float16':
-            pytest.skip("Only test atomic float16 ops on GPU")
-    if is_xpu():
-        if dtype_x_str == 'float16' and (mode != "min_neg" or sem != "acquire"):
-            pytest.skip("FIXME: Atomic RMW for float16 not yet supported by IGC")
+            pytest.xfail("Only test atomic float16 ops on GPU")
+
+    if torch.cuda.is_available():
+        capability = torch.cuda.get_device_capability()
+        if capability[0] < 7:
+            if dtype_x_str == 'float16':
+                pytest.skip("Only test atomic float16 ops on devices with sm >= 70")
 
     n_programs = 5
 
@@ -3400,7 +3403,7 @@ def kernel(
 def test_max_num_imprecise_acc(device):
 
     if not hasattr(torch, 'float8_e5m2'):
-        pytest.skip(f"torch {torch.__version__} does not support float8_e5m2")
+        pytest.xfail(f"torch {torch.__version__} does not support float8_e5m2")
 
     if is_cuda():
         capability = torch.cuda.get_device_capability()

diff --git a/python/test/unit/operators/test_flash_attention.py b/python/test/unit/operators/test_flash_attention.py
@@ -26,9 +26,8 @@ def test_op(Z, H, N_CTX, D_HEAD, dtype, causal, seq_par, device):
     if dtype == torch.bfloat16 and os.environ.get("TRITON_INTERPRET", "0") == "1":
         pytest.xfail("Flash attention bfloat16 not supported in interpreter mode")
 
-    if device == "xpu":
-        if D_HEAD > 32:
-            pytest.skip("FIXME: results precision issue")
+    if device == "xpu" and D_HEAD >= 64:
+        pytest.skip("FIXME: results precision issue")
 
     # Pytorch does not support Half data type for matmul operation hence the skip
     if device == 'cpu':

diff --git a/python/test/unit/runtime/test_bindings.py b/python/test/unit/runtime/test_bindings.py
@@ -1,7 +1,6 @@
 import triton
 import triton.language as tl
 
-import pytest
 import torch
 
 
@@ -33,8 +32,6 @@ def test_module_walk():
     Test the MLIR bindings exposed for the out-ot-tree walk.
     """
 
-    pytest.skip("FIXME: Incorrect results on XPU")
-
     def walk_fn(op):
         name = op.get_name()
         for i in range(op.get_num_results()):
@@ -55,10 +52,10 @@ def walk_fn(op):
 
     kernel = add_kernel
     args = [
-        torch.empty((32, 32), device="cuda"),  # in_ptr0
-        torch.empty((32, 32), device="cuda"),  # in_ptr1
+        torch.empty((32, 32), device="xpu"),  # in_ptr0
+        torch.empty((32, 32), device="xpu"),  # in_ptr1
         1024,  # n_elements
-        torch.empty((32, 32), device="cuda"),  # out_ptr
+        torch.empty((32, 32), device="xpu"),  # out_ptr
         16,  # BLOCK_SIZE
     ]
     src = triton.compiler.compiler.ASTSource(