Merge pull request #631 from InfiniTensor/issue/630

issue/630 - slightly improved unimplemented messages

test/infinicore/ops/dist.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+
+# Test cases format: (shape, a_strides_or_None, b_strides_or_None, p_or_None)
+# dist computes p-norm distance between two tensors
+
+_TEST_CASES_DATA = [
+    ((8, 8), None, None, None),
+    ((8, 8), (16, 1), (16, 1), 1.0),
+    ((2, 3, 4), None, None, 2.0),
+    ((1, 8), None, (0, 1), None),
+    ((16, 64), (128, 1), (128, 1), 3.0),
+    ((4, 5, 6), (60, 12, 2), (60, 12, 2), 0.5),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-3, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, a_strides, b_strides, p = data
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-3})
+            a_spec = TensorSpec.from_tensor(shape, a_strides, dtype)
+            b_spec = TensorSpec.from_tensor(shape, b_strides, dtype)
+
+            kwargs = {}
+            if p is not None:
+                kwargs["p"] = p
+
+            test_cases.append(
+                TestCase(
+                    inputs=[a_spec, b_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="Dist - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Dist operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Dist")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.dist(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.dist(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/dot.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+
+# Test cases format: (vec1_shape, vec2_shape, vec1_strides_or_None, vec2_strides_or_None)
+# infinicore.dot(a, b) — 1-D vectors; returns scalar
+
+_TEST_CASES_DATA = [
+    ((3,), (3,), None, None),
+    ((8,), (8,), None, None),
+    ((1,), (1,), None, None),
+    ((16,), (16,), None, None),
+    ((5,), (5,), None, None),
+    ((32,), (32,), None, None),
+    ((8,), (8,), (2,), (2,)),
+]
+
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-3, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+    infinicore.bfloat16: {"atol": 1e-2, "rtol": 5e-2},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for s1, s2, st1, st2 in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            a = TensorSpec.from_tensor(s1, st1, dtype)
+            b = TensorSpec.from_tensor(s2, st2, dtype)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[a, b],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="dot - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """dot operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("dot")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.dot(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.dot(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/dropout1d.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# Test cases format: (shape, p, training, in_strides_or_None)
+# infinicore.nn.functional.dropout1d(input, p=0.5, training=True)
+
+_TEST_CASES_DATA = [
+    ((8, 16), 0.1, True, None),
+    ((8, 16), 0.2, False, (128, 1)),
+    ((2, 3, 4), 0.5, True, None),
+    ((16, 64), 0.3, True, None),
+    ((4, 5, 6), 0.5, False, None),
+    ((3, 4, 5), 0.4, True, (60, 20, 4)),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-2, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+    infinicore.bfloat16: {"atol": 1e-2, "rtol": 5e-2},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, p, training = data[0], data[1], data[2]
+        in_strides = data[3] if len(data) > 3 else None
+
+        supports_inplace = not is_broadcast(in_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-2, "rtol": 1e-2})
+            input_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+
+            kwargs = {"p": p, "training": training}
+
+            # Out-of-place
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description=f"dropout1d - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Dropout1d operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Dropout1d")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.dropout1d(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.dropout1d(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/dropout2d.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# Test cases format: (shape, p, training, in_strides_or_None)
+# infinicore.nn.functional.dropout2d(input, p=0.5, training=True)
+
+_TEST_CASES_DATA = [
+    ((8, 16, 8, 8), 0.1, True, None),
+    ((8, 16, 8, 8), 0.2, False, (1024, 64, 8, 1)),
+    ((2, 3, 4, 8), 0.5, True, None),
+    ((16, 64, 4, 4), 0.3, True, None),
+    ((4, 5, 6, 8), 0.5, False, None),
+    ((3, 4, 5, 5), 0.4, True, (60, 20, 4, 1)),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-2, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+    infinicore.bfloat16: {"atol": 1e-2, "rtol": 5e-2},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, p, training = data[0], data[1], data[2]
+        in_strides = data[3] if len(data) > 3 else None
+
+        supports_inplace = not is_broadcast(in_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-2, "rtol": 1e-2})
+            input_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+
+            kwargs = {"p": p, "training": training}
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description=f"dropout2d - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Dropout2d operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Dropout2d")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.dropout2d(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.dropout2d(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/dropout3d.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# Test cases format: (shape, p, training, in_strides_or_None)
+# infinicore.nn.functional.dropout3d(input, p=0.5, training=True)
+
+_TEST_CASES_DATA = [
+    ((8, 16, 8, 8), 0.1, True, None),
+    ((8, 16, 8, 8), 0.2, False, (8192, 1024, 128, 16)),
+    ((2, 3, 4, 4), 0.5, True, None),
+    ((16, 64, 4, 4), 0.3, True, None),
+    ((4, 5, 6, 3), 0.5, False, None),
+    ((3, 4, 5, 2), 0.4, True, (240, 60, 12, 2)),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-2, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+    infinicore.bfloat16: {"atol": 1e-2, "rtol": 5e-2},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, p, training = data[0], data[1], data[2]
+        in_strides = data[3] if len(data) > 3 else None
+
+        supports_inplace = not is_broadcast(in_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-2, "rtol": 1e-2})
+            input_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+
+            kwargs = {"p": p, "training": training}
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description=f"dropout3d - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Dropout3d operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Dropout3d")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.dropout3d(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.dropout3d(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/elu.py

@@ -56,7 +56,6 @@ def parse_test_cases():
     """
     Parse ELU test case data according to format:
     (shape, input_strides, alpha)
-    ELU only supports out-of-place and in-place modes via PyTorch's inplace parameter
     """
     test_cases = []
 
@@ -133,9 +132,10 @@ class OpTest(BaseOperatorTest):
         """PyTorch ELU implementation"""
         return torch.nn.functional.elu(*args, **kwargs)
 
-    # def infinicore_operator(self, x, alpha=1.0, out=None, **kwargs):
+    # def infinicore_operator(self, *args, **kwargs):
     #     """InfiniCore ELU implementation"""
-    #     return None
+    #     return infinicore.nn.functional.elu(*args, **kwargs)
+    #     # return None
 
 
 def main():

test/infinicore/ops/empty.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.datatypes import to_torch_dtype
+from framework.runner import GenericTestRunner
+
+# Test cases format: (shape, dtype)
+# Note: infinicore.empty returns uninitialized memory. Tests will compare shape and dtype via output_spec
+_TEST_CASES_DATA = [
+    ((3, 4), infinicore.float32),
+    ((6, 2), infinicore.float16),
+    ((5, 5), infinicore.float32),
+    ((1, 7), infinicore.bfloat16),
+    ((8, 3), infinicore.float32),
+    ((2, 2, 2), infinicore.float16),
+]
+
+_TOLERANCE_MAP = {infinicore.float32: {"atol": 0, "rtol": 0}}
+
+
+def parse_test_cases():
+    test_cases = []
+    for shape, dtype in _TEST_CASES_DATA:
+        out_spec = TensorSpec.from_tensor(shape, None, dtype)
+
+        test_cases.append(
+            TestCase(
+                inputs=[],
+                kwargs={"size": shape, "dtype": dtype},
+                output_spec=out_spec,
+                comparison_target="out",
+                tolerance=_TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 0}),
+                description=f"empty - OUT_OF_PLACE",
+            )
+        )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Empty operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Empty")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        if "size" not in kwargs:
+            raise TypeError("full test did not provide 'size' parameter")
+        size = kwargs.pop("size")
+
+        if "dtype" not in kwargs:
+            raise TypeError("full test did not provide 'dtype' parameter")
+        dtype_torch = to_torch_dtype(kwargs.pop("dtype"))
+        if dtype_torch is None:
+            raise TypeError("full test provided unsupported 'dtype' parameter")
+
+        # 支持测试框架通过 kwargs 注入 out 参数
+        out = kwargs.pop("out", None)
+
+        if out is not None:
+            return torch.empty(tuple(size), dtype=dtype_torch, out=out)
+        else:
+            return torch.empty(tuple(size), dtype=dtype_torch)
+        # return infinicore.empty(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.empty(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/empty_like.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.datatypes import to_torch_dtype
+from framework.runner import GenericTestRunner
+
+# Test cases format: (base_shape, base_strides_or_None, dtype_or_None)
+# Note: empty_like returns uninitialized memory; we validate shape/dtype via output_spec
+_TEST_CASES_DATA = [
+    ((3, 4), None, None),
+    ((6, 2), (12, 1), infinicore.float16),
+    ((5, 5), None, infinicore.float32),
+    ((1, 7), None, infinicore.bfloat16),
+    ((8, 3), (24, 1), None),
+    ((2, 2, 2), None, infinicore.float32),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float32: {"atol": 1e-6, "rtol": 1e-5},
+    infinicore.float16: {"atol": 1e-3, "rtol": 1e-3},
+    infinicore.bfloat16: {"atol": 1e-2, "rtol": 1e-2},
+}
+
+_TENSOR_DTYPES = [infinicore.float32, infinicore.float16, infinicore.bfloat16]
+
+
+def parse_test_cases():
+    test_cases = []
+    for base_shape, base_strides, dtype in _TEST_CASES_DATA:
+        for input_dtype in _TENSOR_DTYPES:
+            base_spec = TensorSpec.from_tensor(base_shape, base_strides, input_dtype)
+
+            kwargs = {"dtype": dtype} if dtype is not None else {}
+
+            test_cases.append(
+                TestCase(
+                    inputs=[base_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=_TOLERANCE_MAP.get(
+                        input_dtype, {"atol": 1e-5, "rtol": 1e-4}
+                    ),
+                    description="empty_like - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """EmptyLike operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("EmptyLike")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        if "dtype" not in kwargs:
+            dtype_torch = None
+        else:
+            dtype_torch = to_torch_dtype(kwargs.pop("dtype"))
+        return torch.empty_like(*args, dtype=dtype_torch)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.empty_like(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/empty_strided.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.datatypes import to_torch_dtype
+
+# Test cases format: (shape, stride, dtype)
+# empty_strided creates a tensor with given stride; use small shapes to exercise strides.
+_TEST_CASES_DATA = [
+    ((3, 4), (16, 1), infinicore.float32),
+    ((4, 3), (12, 4), infinicore.float16),
+    ((2, 5), (20, 1), infinicore.float32),
+    ((1, 6), (48, 8), infinicore.bfloat16),
+    ((2, 2, 2), (8, 4, 2), infinicore.float32),
+    ((5,), (1,), infinicore.float32),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float32: {"atol": 0, "rtol": 0},
+    infinicore.float16: {"atol": 1e-3, "rtol": 1e-3},
+    infinicore.bfloat16: {"atol": 1e-2, "rtol": 1e-2},
+}
+
+
+def parse_test_cases():
+    test_cases = []
+    for shape, stride, dtype in _TEST_CASES_DATA:
+        kwargs = {"size": shape, "stride": stride, "dtype": dtype}
+
+        test_cases.append(
+            TestCase(
+                inputs=[],
+                kwargs=kwargs,
+                output_spec=None,
+                comparison_target=None,
+                tolerance=_TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 0}),
+                description=f"empty_strided - OUT_OF_PLACE",
+            )
+        )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """EmptyStrided operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("EmptyStrided")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        if "size" not in kwargs:
+            raise TypeError("empty_strided test did not provide 'size' parameter")
+        size = kwargs.pop("size")
+
+        if "stride" not in kwargs:
+            raise TypeError("empty_strided test did not provide 'stride' parameter")
+        stride = kwargs.pop("stride")
+
+        if "dtype" not in kwargs:
+            raise TypeError("empty_strided test did not provide 'dtype' parameter")
+        dtype_torch = to_torch_dtype(kwargs.pop("dtype"))
+        if dtype_torch is None:
+            raise TypeError("empty_strided test provided unsupported 'dtype' parameter")
+
+        return torch.empty_strided(tuple(size), stride=stride, dtype=dtype_torch)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.empty_strided(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/equal.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# Test cases format: (shape, a_strides_or_None, b_strides_or_None, out_strides_or_None)
+# equal compares element-wise and returns boolean tensor
+
+_TEST_CASES_DATA = [
+    ((8, 8), None, None, None),
+    ((8, 8), (16, 1), (16, 1), None),
+    ((8, 8), None, (0, 1), None),
+    ((1, 8), None, None, (8, 1)),
+    ((2, 3, 4), None, None, None),
+    ((16, 128), (256, 1), (256, 1), None),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 0, "rtol": 1e-2},
+    infinicore.float32: {"atol": 0, "rtol": 1e-3},
+    infinicore.int32: {"atol": 0, "rtol": 0},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.float32, infinicore.int32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, a_strides, b_strides, out_strides = data[0], data[1], data[2], data[3]
+
+        a_supports_inplace = not is_broadcast(a_strides)
+        b_supports_inplace = not is_broadcast(b_strides)
+        out_supports_inplace = not is_broadcast(out_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 1e-3})
+            a_spec = TensorSpec.from_tensor(shape, a_strides, dtype)
+            b_spec = TensorSpec.from_tensor(shape, b_strides, dtype)
+            out_spec = TensorSpec.from_tensor(shape, out_strides, infinicore.bool)
+
+            # Out-of-place
+            test_cases.append(
+                TestCase(
+                    inputs=[a_spec, b_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="Equal - OUT_OF_PLACE",
+                )
+            )
+
+            # explicit out
+            if out_supports_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[a_spec, b_spec],
+                        kwargs=None,
+                        output_spec=out_spec,
+                        comparison_target="out",
+                        tolerance=tol,
+                        description="Equal - INPLACE(out)",
+                    )
+                )
+
+            # in-place a
+            if a_supports_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[a_spec, b_spec],
+                        kwargs={"out": 0},
+                        output_spec=None,
+                        comparison_target=0,
+                        tolerance=tol,
+                        description="Equal - INPLACE(a)",
+                    )
+                )
+
+            # in-place b
+            if b_supports_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[a_spec, b_spec],
+                        kwargs={"out": 1},
+                        output_spec=None,
+                        comparison_target=1,
+                        tolerance=tol,
+                        description="Equal - INPLACE(b)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Equal operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Equal")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.eq(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.eq(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/erf.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# =======================================================================
+# Test cases format: (shape, input_strides_or_None)
+# =======================================================================
+
+_TEST_CASES_DATA = [
+    ((13, 4), None),
+    ((13, 4), (10, 1)),
+    ((8, 16), None),
+    ((8, 16), (40, 1)),
+    ((2, 3, 4), None),
+    ((16, 5632), None),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-3, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+    infinicore.bfloat16: {"atol": 1e-2, "rtol": 5e-2},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape = data[0]
+        in_strides = data[1] if len(data) > 1 else None
+
+        supports_inplace = not is_broadcast(in_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            input_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+            out_spec = TensorSpec.from_tensor(shape, None, dtype)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="erf - OUT_OF_PLACE",
+                )
+            )
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=None,
+                    output_spec=out_spec,
+                    comparison_target="out",
+                    tolerance=tol,
+                    description="erf - INPLACE(out)",
+                )
+            )
+
+            if supports_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[input_spec],
+                        kwargs={"out": 0},
+                        output_spec=None,
+                        comparison_target=0,
+                        tolerance=tol,
+                        description="erf - INPLACE(input)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Erf operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Erf")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.erf(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.erf(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/erfc.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# =======================================================================
+# Test cases format: (shape, input_strides_or_None)
+# =======================================================================
+
+_TEST_CASES_DATA = [
+    ((13, 4), None),
+    ((13, 4), (10, 1)),
+    ((8, 16), None),
+    ((8, 16), (40, 1)),
+    ((2, 3, 4), None),
+    ((16, 5632), None),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-3, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+    infinicore.bfloat16: {"atol": 1e-2, "rtol": 5e-2},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape = data[0]
+        in_strides = data[1] if len(data) > 1 else None
+
+        supports_inplace = not is_broadcast(in_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            input_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+            out_spec = TensorSpec.from_tensor(shape, None, dtype)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="erfc - OUT_OF_PLACE",
+                )
+            )
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=None,
+                    output_spec=out_spec,
+                    comparison_target="out",
+                    tolerance=tol,
+                    description="erfc - INPLACE(out)",
+                )
+            )
+
+            if supports_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[input_spec],
+                        kwargs={"out": 0},
+                        output_spec=None,
+                        comparison_target=0,
+                        tolerance=tol,
+                        description="erfc - INPLACE(input)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Erfc operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Erfc")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.erfc(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.erfc(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/erfinv.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# =======================================================================
+# Test cases format: (shape, input_strides_or_None)
+# Note: erfinv domain is (-1, 1); test generation will use values within that range.
+# =======================================================================
+
+_TEST_CASES_DATA = [
+    ((13, 4), None),
+    ((13, 4), (10, 1)),
+    ((8, 16), None),
+    ((8, 16), (40, 1)),
+    ((2, 3, 4), None),
+    ((16, 5632), None),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-2, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+    infinicore.bfloat16: {"atol": 1e-2, "rtol": 5e-2},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape = data[0]
+        in_strides = data[1] if len(data) > 1 else None
+
+        supports_inplace = not is_broadcast(in_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            input_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+            out_spec = TensorSpec.from_tensor(shape, None, dtype)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="erfinv - OUT_OF_PLACE",
+                )
+            )
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=None,
+                    output_spec=out_spec,
+                    comparison_target="out",
+                    tolerance=tol,
+                    description="erfinv - INPLACE(out)",
+                )
+            )
+
+            if supports_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[input_spec],
+                        kwargs={"out": 0},
+                        output_spec=None,
+                        comparison_target=0,
+                        tolerance=tol,
+                        description="erfinv - INPLACE(input)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Erfinv operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Erfinv")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.erfinv(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.erfinv(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/exp2.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# Test cases format: (in_shape, in_strides_or_None)
+# infinicore.exp2(input)
+
+_TEST_CASES_DATA = [
+    ((2, 3), None),
+    ((1, 4, 8), (32, 8, 1)),
+    ((3, 2, 5, 7), None),
+    ((2, 1, 16), None),
+    ((1, 8, 9, 11), (792, 99, 11, 1)),
+    ((2, 6, 10), None),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-3, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+}
+_TENSOR_DTYPES = [infinicore.float16, infinicore.float32]
+
+
+def parse_test_cases():
+    cases = []
+    for shape, strides in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP[dtype]
+            in_spec = TensorSpec.from_tensor(shape, strides, dtype)
+
+            cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="exp2 - OUT_OF_PLACE",
+                )
+            )
+            out_spec = TensorSpec.from_tensor(shape, None, dtype)
+            cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs={},
+                    output_spec=out_spec,
+                    comparison_target="out",
+                    tolerance=tol,
+                    description="exp2 - INPLACE(out)",
+                )
+            )
+            if not is_broadcast(in_spec.strides):
+                cases.append(
+                    TestCase(
+                        inputs=[in_spec],
+                        kwargs={"out": 0},
+                        output_spec=None,
+                        comparison_target=0,
+                        tolerance=tol,
+                        description="exp2 - INPLACE(a)",
+                    )
+                )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """Exp2 operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Exp2")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.exp2(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.exp2(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/expm1.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# Test cases format: (in_shape, in_strides_or_None)
+# infinicore.expm1(input)
+
+_TEST_CASES_DATA = [
+    ((2, 3), None),
+    ((1, 4, 8), (32, 8, 1)),
+    ((3, 2, 5, 7), None),
+    ((2, 1, 16), None),
+    ((1, 8, 9, 11), (792, 99, 11, 1)),
+    ((2, 6, 10), None),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-3, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+}
+_TENSOR_DTYPES = [infinicore.float16, infinicore.float32]
+
+
+def parse_test_cases():
+    cases = []
+    for shape, strides in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP[dtype]
+            in_spec = TensorSpec.from_tensor(shape, strides, dtype)
+
+            cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="expm1 - OUT_OF_PLACE",
+                )
+            )
+            out_spec = TensorSpec.from_tensor(shape, None, dtype)
+            cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs={},
+                    output_spec=out_spec,
+                    comparison_target="out",
+                    tolerance=tol,
+                    description="expm1 - INPLACE(out)",
+                )
+            )
+            if not is_broadcast(in_spec.strides):
+                cases.append(
+                    TestCase(
+                        inputs=[in_spec],
+                        kwargs={"out": 0},
+                        output_spec=None,
+                        comparison_target=0,
+                        tolerance=tol,
+                        description="expm1 - INPLACE(a)",
+                    )
+                )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """Expm1 operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Expm1")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.expm1(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.expm1(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/feature_alpha_dropout.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# Test cases format: (shape, p, training, in_strides_or_None)
+# infinicore.nn.functional.feature_alpha_dropout(input, p=0.5, training=True)
+
+_TEST_CASES_DATA = [
+    ((8, 16), 0.1, True, None),
+    ((8, 16), 0.2, False, (128, 1)),
+    ((2, 3, 4), 0.5, True, None),
+    ((16, 64), 0.3, True, None),
+    ((4, 5, 6), 0.5, False, None),
+    ((3, 4, 5), 0.4, True, (60, 20, 4)),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-2, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+    infinicore.bfloat16: {"atol": 1e-2, "rtol": 5e-2},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, p, training = data[0], data[1], data[2]
+        in_strides = data[3] if len(data) > 3 else None
+
+        supports_inplace = not is_broadcast(in_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-2, "rtol": 1e-2})
+            input_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+
+            kwargs = {"p": p, "training": training}
+
+            # Out-of-place
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description=f"feature_alpha_dropout - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """FeatureAlphaDropout operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("FeatureAlphaDropout")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.feature_alpha_dropout(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.feature_alpha_dropout(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/flip.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# Test cases format: (shape, dims_tuple, input_strides_or_None)
+# infinicore.flip(input, dims)
+
+_TEST_CASES_DATA = [
+    ((13, 4), (0,), None),
+    ((8, 16), (1,), (128, 1)),
+    ((2, 3, 4), (2,), None),
+    ((4, 5, 6), (0, 2), None),
+    ((16, 64), (0, 1), None),
+    ((2, 2, 3, 4), (1, 3), None),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 0, "rtol": 1e-2},
+    infinicore.float32: {"atol": 0, "rtol": 1e-4},
+    infinicore.bfloat16: {"atol": 0, "rtol": 5e-2},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, dims = data[0], data[1]
+        in_strides = data[2] if len(data) > 2 else None
+
+        supports_inplace = not is_broadcast(in_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 1e-4})
+            in_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+
+            kwargs = {"dims": dims}
+            test_cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description=f"flip - OUT_OF_PLACE",
+                )
+            )
+
+            # infinicore.flip has no explicit out or inplace flag; skip in-place/out variants.
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Flip operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Flip")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        # dims = kwargs.pop("dims", None)
+        # if dims is not None:
+        #     return infinicore.flip(*args, dims)
+        return torch.flip(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     dims = kwargs.pop("dims", None)
+    #     if dims is not None:
+    #         return infinicore.flip(*args, dims)
+    #     return infinicore.flip(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/fliplr.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# Test cases format: (shape, input_strides_or_None)
+# infinicore.fliplr(input) flips the left/right (dim=-1 for 2D-like tensors)
+
+_TEST_CASES_DATA = [
+    ((13, 4), None),
+    ((8, 16), (128, 1)),
+    ((2, 3, 4), None),
+    ((4, 5), None),
+    ((16, 64), None),
+    ((3, 4, 5), (60, 20, 4)),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 0, "rtol": 1e-2},
+    infinicore.float32: {"atol": 0, "rtol": 1e-4},
+    infinicore.bfloat16: {"atol": 0, "rtol": 5e-2},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape = data[0]
+        in_strides = data[1] if len(data) > 1 else None
+
+        supports_inplace = not is_broadcast(in_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 1e-4})
+            in_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="fliplr - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """FlipLR operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("FlipLR")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.fliplr(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.fliplr(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/flipud.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# Test cases format: (shape, input_strides_or_None)
+# infinicore.flipud(input) flips up/down (dim=-2 for 2D-like tensors)
+
+_TEST_CASES_DATA = [
+    ((13, 4), None),
+    ((8, 16), (128, 1)),
+    ((2, 3, 4), None),
+    ((4, 5), None),
+    ((16, 64), None),
+    ((3, 4, 5), (60, 20, 4)),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 0, "rtol": 1e-2},
+    infinicore.float32: {"atol": 0, "rtol": 1e-4},
+    infinicore.bfloat16: {"atol": 0, "rtol": 5e-2},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape = data[0]
+        in_strides = data[1] if len(data) > 1 else None
+
+        supports_inplace = not is_broadcast(in_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 1e-4})
+            in_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="flipud - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """FlipUD operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("FlipUD")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.flipud(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.flipud(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/float_power.py

+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+# Test cases format: (in_shape, in_strides_or_None, exponent_scalar_or_None, exponent_tensor_shape_or_None)
+# infinicore.float_power(input, exponent)
+
+_TEST_CASES_DATA = [
+    ((2, 3, 4), None, 2.0, None),
+    ((1, 4, 8), (32, 8, 1), None, (1, 4, 8)),
+    ((3, 2, 5, 7), None, 3.0, None),
+    ((2, 1, 16), None, None, (2, 1, 16)),
+    ((1, 8, 9, 11), (792, 99, 11, 1), 1.5, None),
+    ((2, 6, 10), None, None, (2, 6, 10)),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-3, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+}
+_TENSOR_DTYPES = [infinicore.float16, infinicore.float32]
+
+
+def parse_test_cases():
+    cases = []
+    dtype_map = {
+        infinicore.float16: infinicore.float64,
+        infinicore.float32: infinicore.float64,
+        infinicore.complex64: infinicore.complex128,
+    }
+
+    for shape, strides, exp_scalar, exp_tensor_shape in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP[dtype]
+            input_spec = TensorSpec.from_tensor(shape, strides, dtype)
+            out_dtype = dtype_map.get(dtype, dtype)
+
+            # exponent as scalar
+            if exp_scalar is not None:
+                kwargs = {}
+                cases.append(
+                    TestCase(
+                        inputs=[input_spec, exp_scalar],
+                        kwargs=kwargs,
+                        output_spec=None,
+                        comparison_target=None,
+                        tolerance=tol,
+                        description="float_power_scalar_exp - OUT_OF_PLACE",
+                    )
+                )
+                out_spec = TensorSpec.from_tensor(shape, None, out_dtype)
+                cases.append(
+                    TestCase(
+                        inputs=[input_spec, exp_scalar],
+                        kwargs={},
+                        output_spec=out_spec,
+                        comparison_target="out",
+                        tolerance=tol,
+                        description="float_power_scalar_exp - INPLACE(out)",
+                    )
+                )
+
+            # exponent as tensor
+            if exp_tensor_shape is not None:
+                exp_spec = TensorSpec.from_tensor(exp_tensor_shape, None, dtype)
+                cases.append(
+                    TestCase(
+                        inputs=[input_spec, exp_spec],
+                        kwargs={},
+                        output_spec=None,
+                        comparison_target=None,
+                        tolerance=tol,
+                        description="float_power_tensor_exp - OUT_OF_PLACE",
+                    )
+                )
+                out_spec = TensorSpec.from_tensor(shape, None, out_dtype)
+                cases.append(
+                    TestCase(
+                        inputs=[input_spec, exp_spec],
+                        kwargs={},
+                        output_spec=out_spec,
+                        comparison_target="out",
+                        tolerance=tol,
+                        description="float_power_tensor_exp_explicit_out",
+                    )
+                )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """FloatPower operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("FloatPower")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.float_power(*args, **kwargs)
+
+
+# def infinicore_operator(self, *args, **kwargs):
+# """InfiniCore implementation (operator not yet available)."""
+#     return infinicore.float_power(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()