update test_dot to use float 32
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@@ -1067,15 +1067,18 @@ def test_permute(dtype_str, shape, perm, device='cuda'):
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[(epilogue, allow_tf32, dtype)
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for epilogue in ['none', 'trans', 'add-matrix', 'add-rows', 'add-cols', 'softmax', 'chain-dot']
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for allow_tf32 in [True, False]
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for dtype in ['float16']
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for dtype in ['float32', 'float16']
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if not (allow_tf32 and (dtype in ['float16']))])
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def test_dot(epilogue, allow_tf32, dtype, device='cuda'):
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cc = _triton.runtime.cc(_triton.runtime.backend.CUDA, torch.cuda.current_device())
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if cc < 80:
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if dtype == 'int8':
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pytest.skip("Only test int8 on devices with sm >= 80")
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elif dtype == 'float32' and allow_tf32:
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pytest.skip("Only test tf32 on devices with sm >= 80")
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if torch.version.hip is not None:
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pass
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else:
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cc = _triton.runtime.cc(_triton.runtime.backend.CUDA, torch.cuda.current_device())
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if cc < 80:
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if dtype == 'int8':
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pytest.skip("Only test int8 on devices with sm >= 80")
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elif dtype == 'float32' and allow_tf32:
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pytest.skip("Only test tf32 on devices with sm >= 80")
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M, N, K = 128, 128, 64
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num_warps = 8
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@@ -1170,15 +1173,18 @@ def test_dot(epilogue, allow_tf32, dtype, device='cuda'):
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# print(z_ref[:,0], z_tri[:,0])
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np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01)
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# make sure ld/st are vectorized
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ptx = pgm.asm['ptx']
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assert 'ld.global.v4' in ptx
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assert 'st.global.v4' in ptx
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if allow_tf32:
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assert 'mma.sync.aligned.m16n8k8.row.col.f32.tf32.tf32.f32' in ptx
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elif dtype == 'float32':
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assert 'mma.sync.aligned.m16n8k8.row.col.f32.tf32.tf32.f32' not in ptx
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elif dtype == 'int8':
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assert 'mma.sync.aligned.m16n8k32.row.col.satfinite.s32.s8.s8.s32' in ptx
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if torch.version.hip is not None:
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pass
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else:
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ptx = pgm.asm['ptx']
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assert 'ld.global.v4' in ptx
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assert 'st.global.v4' in ptx
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if allow_tf32:
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assert 'mma.sync.aligned.m16n8k8.row.col.f32.tf32.tf32.f32' in ptx
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elif dtype == 'float32':
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assert 'mma.sync.aligned.m16n8k8.row.col.f32.tf32.tf32.f32' not in ptx
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elif dtype == 'int8':
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assert 'mma.sync.aligned.m16n8k32.row.col.satfinite.s32.s8.s8.s32' in ptx
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def test_dot_without_load():
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