Merge pull request #589 from InfiniTensor/issue/566

issue/566: 添加 Python 算子测试

test/infinicore/ops/logical_xor.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)
+# logical_xor performs element-wise boolean XOR
+
+_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.bool: {"atol": 0, "rtol": 0}}
+
+_TENSOR_DTYPES = [infinicore.bool, infinicore.int32, infinicore.uint8]
+
+
+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": 0})
+            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)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[a_spec, b_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="Logical XOR - OUT_OF_PLACE",
+                )
+            )
+
+            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="Logical XOR - INPLACE(out)",
+                    )
+                )
+
+            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="Logical XOR - INPLACE(a)",
+                    )
+                )
+
+            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="Logical XOR - INPLACE(b)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """LogicalXor operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("LogicalXor")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.logical_xor(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.logical_xor(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/logsigmoid.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)
+
+_TEST_CASES_DATA = [
+    ((13, 4), None),
+    ((13, 4), (10, 1)),
+    ((8, 8, 8), 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():
+    """logsigmoid(input)"""
+    test_cases = []
+
+    for data in _TEST_CASES_DATA:
+        shape = data[0]
+        in_strides = data[1] if len(data) > 1 else None
+
+        for dtype in _TENSOR_DTYPES:
+            tolerance = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            input_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tolerance,
+                    description=f"LogSigmoid - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """LogSigmoid operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("LogSigmoid")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.logsigmoid(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.logsigmoid(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/logsumexp.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, dim, keepdim_or_None, out_strides_or_None)
+# logsumexp computes log(sum(exp(input), dim=dim)) with numerical stability
+
+_TEST_CASES_DATA = [
+    ((8, 8), None, 1, None, None),
+    ((8, 8), (16, 1), 0, False, None),
+    ((2, 3, 4), None, 2, True, (0, 1, 1)),
+    ((1, 8), None, 0, False, None),
+    ((16, 64), (128, 1), 1, True, None),
+    ((4, 5, 6), (60, 12, 2), 2, True, (12, 4, 1)),
+]
+
+_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 _compute_out_shape(shape, dim, keepdim):
+    if isinstance(dim, tuple):
+        dims = sorted([(d if d >= 0 else len(shape) + d) for d in dim])
+    else:
+        dims = [dim]
+
+    if dim is None:
+        return ()
+    if keepdim:
+        out = list(shape)
+        for d in dims:
+            out[d] = 1
+        return tuple(out)
+    else:
+        return tuple(s for i, s in enumerate(shape) if i not in dims)
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, strides, dim, keepdim, out_strides = data
+        out_supports_inplace = not is_broadcast(out_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-3})
+            in_spec = TensorSpec.from_tensor(shape, strides, dtype)
+
+            kwargs = {"dim": dim}
+            if keepdim is not None:
+                kwargs["keepdim"] = keepdim
+
+            test_cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="LogSumExp - OUT_OF_PLACE",
+                )
+            )
+
+            out_shape = _compute_out_shape(shape, dim, keepdim)
+            out_spec = TensorSpec.from_tensor(out_shape, out_strides, dtype)
+            if out_supports_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[in_spec],
+                        kwargs=kwargs,
+                        output_spec=out_spec,
+                        comparison_target="out",
+                        tolerance=tol,
+                        description="LogSumExp - INPLACE(out)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """LogSumExp operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("LogSumExp")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.logsumexp(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.logsumexp(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/lp_pool1d.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: (in_shape, in_strides_or_None, norm_type, kernel_size, stride_or_None, ceil_mode)
+
+_TEST_CASES_DATA = [
+    ((2, 3, 16), None, 2.0, 3, None, False),
+    ((1, 4, 15), (60, 15, 1), 1.0, 5, 1, True),
+    ((2, 1, 32), None, 3.0, 2, 2, False),
+    ((3, 2, 7), None, 2.0, 3, None, True),
+    ((4, 6, 31), None, 1.5, 4, 2, False),
+    ((2, 8, 9), (72, 9, 1), 2.0, 3, 1, False),
+]
+
+_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():
+    tests = []
+    for in_shape, in_strides, p, k, s, ceil_mode in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP[dtype]
+            in_spec = TensorSpec.from_tensor(in_shape, in_strides, dtype)
+            kwargs = {"norm_type": p, "kernel_size": k, "ceil_mode": ceil_mode}
+            if s is not None:
+                kwargs["stride"] = s
+            tests.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="LpPool1d - OUT_OF_PLACE",
+                )
+            )
+
+    return tests
+
+
+class OpTest(BaseOperatorTest):
+    """LpPool1d operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("LpPool1d")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.lp_pool1d(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.lp_pool1d(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/lp_pool2d.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: (in_shape, in_strides_or_None, norm_type, kernel_size, stride_or_None, ceil_mode)
+
+_TEST_CASES_DATA = [
+    ((2, 3, 16, 16), None, 2.0, (3, 3), None, False),
+    ((1, 4, 15, 17), (1020, 255, 17, 1), 1.0, (5, 4), (2, 2), True),
+    ((2, 1, 32, 32), None, 3.0, (2, 2), (2, 2), False),
+    ((3, 2, 7, 9), None, 2.0, (3, 3), None, True),
+    ((4, 6, 31, 29), None, 1.5, (4, 4), (2, 2), False),
+    ((2, 8, 9, 11), (1584, 198, 11, 1), 2.0, (3, 2), 1, False),
+]
+
+_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():
+    tests = []
+    for in_shape, in_strides, p, k, s, ceil_mode in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP[dtype]
+            in_spec = TensorSpec.from_tensor(in_shape, in_strides, dtype)
+            kwargs = {"norm_type": p, "kernel_size": k, "ceil_mode": ceil_mode}
+            if s is not None:
+                kwargs["stride"] = s
+            tests.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="LpPool2d - OUT_OF_PLACE",
+                )
+            )
+
+    return tests
+
+
+class OpTest(BaseOperatorTest):
+    """LpPool2d operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("LpPool2d")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.lp_pool2d(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.lp_pool2d(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/lp_pool3d.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: (in_shape, in_strides_or_None, norm_type, kernel_size, stride_or_None, ceil_mode)
+
+_TEST_CASES_DATA = [
+    ((1, 2, 8, 8, 8), None, 2.0, (2, 2, 2), None, False),
+    ((2, 3, 7, 9, 5), None, 1.0, (3, 3, 2), (2, 2, 1), True),
+    ((1, 4, 16, 16, 6), None, 3.0, (4, 4, 2), (2, 2, 1), False),
+    ((2, 1, 9, 11, 7), None, 2.0, (3, 2, 3), None, True),
+    ((3, 2, 5, 6, 4), None, 1.5, (2, 2, 2), (1, 1, 1), False),
+    ((2, 6, 10, 9, 8), None, 2.0, (3, 3, 2), (2, 1, 2), False),
+]
+
+_TOLERANCE_MAP = {infinicore.float32: {"atol": 1e-5, "rtol": 1e-4}}
+_TENSOR_DTYPES = [infinicore.float32]
+
+
+def parse_test_cases():
+    tests = []
+    for in_shape, in_strides, p, k, s, ceil_mode in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP[dtype]
+            in_spec = TensorSpec.from_tensor(in_shape, in_strides, dtype)
+            kwargs = {"norm_type": p, "kernel_size": k, "ceil_mode": ceil_mode}
+            if s is not None:
+                kwargs["stride"] = s
+            tests.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="LpPool3d - OUT_OF_PLACE",
+                )
+            )
+
+    return tests
+
+
+class OpTest(BaseOperatorTest):
+    """LpPool3d operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("LpPool3d")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.lp_pool3d(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.lp_pool3d(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/margin_ranking_loss.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: (input1_shape, input2_shape, target_shape, input1_strides_or_None, input2_strides_or_None, target_strides_or_None, margin_or_None, p_or_None)
+# infinicore.nn.functional.margin_ranking_loss(input1, input2, target, margin=0, p=1, reduction='mean')
+
+_TEST_CASES_DATA = [
+    ((4,), (4,), (4,), None, None, None, None),
+    ((8,), (8,), (8,), None, None, None, 1),
+    ((1,), (1,), (1,), None, None, None, None),
+    ((16,), (16,), (16,), (2,), None, None, 2),
+    ((3,), (3,), (3,), None, (1,), None, None),
+    ((2,), (2,), (2,), None, None, None, 1),
+]
+
+_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, st, st1, st2, stt, margin 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)
+            y = TensorSpec.from_tensor(st, stt, dtype)
+
+            kwargs = {}
+            if margin is not None:
+                kwargs["margin"] = margin
+
+            test_cases.append(
+                TestCase(
+                    inputs=[a, b, y],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="margin_ranking_loss - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """margin_ranking_loss operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("margin_ranking_loss")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.margin_ranking_loss(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.margin_ranking_loss(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/masked_select.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: (input_shape, input_strides_or_None, mask_shape)
+_TEST_CASES_DATA = [
+    ((3, 4), None, (3, 4)),
+    ((5,), None, (5,)),
+    ((2, 2, 3), (12, 6, 2), (2, 2, 3)),
+    ((1, 6), None, (1, 6)),
+    ((4, 4), None, (4, 4)),
+    ((2, 3, 2), None, (2, 3, 2)),
+]
+
+_TOLERANCE_MAP = {infinicore.float32: {"atol": 1e-5, "rtol": 1e-4}}
+_TENSOR_DTYPES = [infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for shape, strides, mask_shape in _TEST_CASES_DATA:
+        input_spec = TensorSpec.from_tensor(shape, strides, infinicore.float32)
+        mask_spec = TensorSpec.from_tensor(mask_shape, None, infinicore.bool)
+
+        test_cases.append(
+            TestCase(
+                inputs=[input_spec, mask_spec],
+                kwargs={},
+                output_spec=None,
+                comparison_target=None,
+                tolerance=_TOLERANCE_MAP[infinicore.float32],
+                description=f"masked_select - OUT_OF_PLACE",
+            )
+        )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """MaskedSelect operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("MaskedSelect")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.masked_select(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.masked_select(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/matrix_exp.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: (matrix_shape, strides_or_None)
+# infinicore.matrix_exp(input)
+
+_TEST_CASES_DATA = [
+    ((1, 1), None),
+    ((2, 2), None),
+    ((3, 3), (18, 6)),
+    ((4, 4), None),
+    ((6, 6), None),
+    ((8, 8), (512, 1)),
+]
+
+_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 shape, strides in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            spec = TensorSpec.from_tensor(shape, strides, dtype)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="matrix_exp - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """matrix_exp operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("matrix_exp")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.matrix_exp(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.matrix_exp(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/matrix_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
+
+# Test cases format: (matrix_shape, strides_or_None, n_or_None)
+# infinicore.matrix_power(input, n)
+
+_TEST_CASES_DATA = [
+    ((2, 2), None, 1),
+    ((3, 3), None, 2),
+    ((4, 4), (256, 64), 3),
+    ((1, 1), None, 5),
+    ((6, 6), None, 0),
+    ((8, 8), (512, 1), 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 shape, strides, n in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            spec = TensorSpec.from_tensor(shape, strides, dtype)
+
+            kwargs = {"n": n} if n is not None else {}
+
+            test_cases.append(
+                TestCase(
+                    inputs=[spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="matrix_power - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """matrix_power operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("matrix_power")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.matrix_power(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.matrix_power(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/max.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, dim_or_None, keepdim_or_None, out_strides_or_None)
+# max reduces by taking maximum along dim(s) or overall
+
+_TEST_CASES_DATA = [
+    ((8, 8), None, None, None, None),
+    ((8, 8), (16, 1), 1, False, None),
+    ((2, 3, 4), None, 0, True, (0, 1, 1)),
+    ((1, 8), None, 0, False, None),
+    ((16, 64), (128, 1), None, None, None),
+    ((4, 5, 6), (60, 12, 2), 2, True, (12, 4, 1)),
+]
+
+_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 _compute_out_shape(shape, dim, keepdim):
+    if dim is None:
+        return ()
+    if isinstance(dim, tuple):
+        dims = sorted([(d if d >= 0 else len(shape) + d) for d in dim])
+        if keepdim:
+            out = list(shape)
+            for d in dims:
+                out[d] = 1
+            return tuple(out)
+        else:
+            return tuple(s for i, s in enumerate(shape) if i not in dims)
+    else:
+        d = dim if dim >= 0 else len(shape) + dim
+        if keepdim:
+            out = list(shape)
+            out[d] = 1
+            return tuple(out)
+        else:
+            return tuple(s for i, s in enumerate(shape) if i != d)
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, strides, dim, keepdim, out_strides = data
+        out_supports_inplace = not is_broadcast(out_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-3})
+            in_spec = TensorSpec.from_tensor(shape, strides, dtype)
+
+            kwargs = {}
+            if dim is not None:
+                kwargs["dim"] = dim
+            if keepdim is not None:
+                kwargs["keepdim"] = keepdim
+
+            test_cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="Max - OUT_OF_PLACE",
+                )
+            )
+
+            out_shape = _compute_out_shape(shape, dim, keepdim)
+            out_spec = TensorSpec.from_tensor(out_shape, out_strides, dtype)
+            if out_supports_inplace and dim is None:
+                test_cases.append(
+                    TestCase(
+                        inputs=[in_spec],
+                        kwargs=kwargs,
+                        output_spec=out_spec,
+                        comparison_target="out",
+                        tolerance=tol,
+                        description="Max - INPLACE(out)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Max operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Max")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.max(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.max(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/max_pool1d.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: (in_shape, in_strides_or_None, kernel_size, stride_or_None, padding, dilation, ceil_mode)
+
+_TEST_CASES_DATA = [
+    ((2, 3, 16), None, 3, None, 0, 1, False),
+    ((1, 4, 15), (60, 15, 1), 5, 1, 2, 1, True),
+    ((2, 1, 32), None, 2, 2, 0, 1, False),
+    ((3, 2, 7), (14, 7, 1), 3, None, 1, 1, True),
+    ((4, 6, 31), None, 4, 2, 1, 1, False),
+    ((2, 8, 9), (72, 9, 1), 3, 1, 0, 1, False),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 0.0, "rtol": 0.0},
+    infinicore.float32: {"atol": 0.0, "rtol": 0.0},
+}
+_TENSOR_DTYPES = [infinicore.float16, infinicore.float32]
+
+
+def parse_test_cases():
+    cases = []
+    for in_shape, in_strides, k, s, p, d, ceil_mode in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP[dtype]
+            in_spec = TensorSpec.from_tensor(in_shape, in_strides, dtype)
+            kwargs = {"kernel_size": k, "dilation": d, "ceil_mode": ceil_mode}
+            if s is not None:
+                kwargs["stride"] = s
+            if p is not None:
+                kwargs["padding"] = p
+            cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="MaxPool1d - OUT_OF_PLACE",
+                )
+            )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """MaxPool1d operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("MaxPool1d")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.max_pool1d(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.max_pool1d(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/max_pool2d.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: (in_shape, in_strides_or_None, kernel_size, stride_or_None, padding, dilation, ceil_mode)
+
+_TEST_CASES_DATA = [
+    ((2, 3, 16, 16), None, (3, 3), None, (1, 1), (1, 1), False),
+    ((1, 4, 15, 17), (1020, 255, 17, 1), (5, 4), (2, 2), (2, 1), (1, 1), True),
+    ((2, 1, 32, 32), None, (2, 2), (2, 2), (0, 0), (1, 1), False),
+    ((3, 2, 7, 9), None, (3, 3), None, (1, 1), (1, 1), True),
+    ((4, 6, 31, 29), None, (4, 4), (2, 2), (1, 1), (1, 1), False),
+    ((2, 8, 9, 11), (1584, 198, 11, 1), (3, 2), 1, 0, (1, 1), False),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 0.0, "rtol": 0.0},
+    infinicore.float32: {"atol": 0.0, "rtol": 0.0},
+}
+_TENSOR_DTYPES = [infinicore.float16, infinicore.float32]
+
+
+def parse_test_cases():
+    cases = []
+    for in_shape, in_strides, k, s, p, d, ceil_mode in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP[dtype]
+            in_spec = TensorSpec.from_tensor(in_shape, in_strides, dtype)
+            kwargs = {"kernel_size": k, "dilation": d, "ceil_mode": ceil_mode}
+            if s is not None:
+                kwargs["stride"] = s
+            if p is not None:
+                kwargs["padding"] = p
+            cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="MaxPool2d - OUT_OF_PLACE",
+                )
+            )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """MaxPool2d operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("MaxPool2d")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.max_pool2d(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.max_pool2d(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/max_pool3d.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: (in_shape, in_strides_or_None, kernel_size, stride_or_None, padding, dilation, ceil_mode)
+
+_TEST_CASES_DATA = [
+    ((1, 2, 8, 8, 8), None, (2, 2, 2), None, (0, 0, 0), (1, 1, 1), False),
+    ((2, 3, 7, 9, 5), None, (3, 2, 3), (2, 2, 1), (1, 1, 0), (1, 1, 1), True),
+    ((1, 4, 16, 16, 6), None, (4, 4, 2), (2, 2, 1), (0, 1, 0), (1, 1, 1), False),
+    ((2, 1, 9, 11, 7), None, (3, 3, 3), None, (1, 0, 1), (1, 1, 1), True),
+    ((3, 2, 5, 6, 4), None, (2, 2, 2), (1, 1, 1), 0, (1, 1, 1), False),
+    ((2, 6, 10, 9, 8), None, (3, 3, 2), (2, 1, 2), (1, 0, 1), (1, 1, 1), False),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 0.0, "rtol": 0.0},
+    infinicore.float32: {"atol": 0.0, "rtol": 0.0},
+}
+_TENSOR_DTYPES = [infinicore.float16, infinicore.float32]
+
+
+def parse_test_cases():
+    cases = []
+    for in_shape, in_strides, k, s, p, d, ceil_mode in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP[dtype]
+            in_spec = TensorSpec.from_tensor(in_shape, in_strides, dtype)
+            kwargs = {"kernel_size": k, "dilation": d, "ceil_mode": ceil_mode}
+            if s is not None:
+                kwargs["stride"] = s
+            if p is not None:
+                kwargs["padding"] = p
+            cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="MaxPool3d - OUT_OF_PLACE",
+                )
+            )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """MaxPool3d operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("MaxPool3d")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.max_pool3d(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.max_pool3d(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/maximum.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, b_strides)
+_TEST_CASES_DATA = [
+    ((6, 8), None, None),
+    ((8, 4), (16, 1), None),
+    ((5, 5), None, (10, 1)),
+    ((3, 7), (14, 1), (14, 1)),
+    ((10, 3), None, None),
+    ((2, 16), (32, 1), (32, 1)),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 0, "rtol": 1e-2},
+    infinicore.float32: {"atol": 0, "rtol": 1e-3},
+    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, a_strides, b_strides = data
+
+        a_inplace = not is_broadcast(a_strides)
+        b_inplace = not is_broadcast(b_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-of-place
+            test_cases.append(
+                TestCase(
+                    inputs=[a_spec, b_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="maximum - OUT",
+                )
+            )
+
+            # In-place variations
+            if a_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[a_spec, b_spec],
+                        kwargs={"out": 0},
+                        output_spec=None,
+                        comparison_target=0,
+                        tolerance=tol,
+                        description="maximum - INPLACE(a)",
+                    )
+                )
+
+            if b_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[a_spec, b_spec],
+                        kwargs={"out": 1},
+                        output_spec=None,
+                        comparison_target=1,
+                        tolerance=tol,
+                        description="maximum - INPLACE(b)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Maximum operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Maximum")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.maximum(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.maximum(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/mean.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, dim_or_None, keepdim_or_None, out_strides_or_None)
+# mean computes average along dimension(s) or overall
+
+_TEST_CASES_DATA = [
+    ((8, 8), None, None, None, None),
+    ((8, 8), (16, 1), 1, False, None),
+    ((2, 3, 4), None, 0, True, (0, 1, 1)),
+    ((1, 8), None, (0,), False, None),
+    ((16, 64), (128, 1), None, None, None),
+    ((4, 5, 6), (60, 12, 2), 2, True, (12, 4, 1)),
+]
+
+_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.float32, infinicore.bfloat16]
+
+
+def _compute_out_shape(shape, dim, keepdim):
+    if dim is None:
+        return ()
+    if isinstance(dim, tuple):
+        dims = sorted([(d if d >= 0 else len(shape) + d) for d in dim])
+        if keepdim:
+            out = list(shape)
+            for d in dims:
+                out[d] = 1
+            return tuple(out)
+        else:
+            return tuple(s for i, s in enumerate(shape) if i not in dims)
+    else:
+        d = dim if dim >= 0 else len(shape) + dim
+        if keepdim:
+            out = list(shape)
+            out[d] = 1
+            return tuple(out)
+        else:
+            return tuple(s for i, s in enumerate(shape) if i != d)
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, strides, dim, keepdim, out_strides = data
+        out_supports_inplace = not is_broadcast(out_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-3})
+            in_spec = TensorSpec.from_tensor(shape, strides, dtype)
+
+            kwargs = {}
+            if dim is not None:
+                kwargs["dim"] = dim
+            if keepdim is not None:
+                kwargs["keepdim"] = keepdim
+
+            test_cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="Mean - OUT_OF_PLACE",
+                )
+            )
+
+            out_shape = _compute_out_shape(shape, dim, keepdim)
+            out_spec = TensorSpec.from_tensor(out_shape, out_strides, dtype)
+            if out_supports_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[in_spec],
+                        kwargs=kwargs,
+                        output_spec=out_spec,
+                        comparison_target="out",
+                        tolerance=tol,
+                        description="Mean - INPLACE(out)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Mean operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Mean")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.mean(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.mean(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/median.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, dim_or_None, keepdim_or_None, out_strides_or_None)
+# median can return median values (and indices when dim is provided)
+
+_TEST_CASES_DATA = [
+    ((9,), None, None, None, None),
+    ((8, 8), (16, 1), 1, False, None),
+    ((2, 3, 5), None, 2, True, (0, 1, 1)),
+    ((1, 8), None, 0, False, None),
+    ((16, 63), (128, 1), None, None, None),
+    ((5, 6, 7), (210, 35, 5), 2, True, (35, 5, 1)),
+]
+
+_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 _compute_out_shape(shape, dim, keepdim):
+    if dim is None:
+        return ()
+    if isinstance(dim, tuple):
+        dims = sorted([(d if d >= 0 else len(shape) + d) for d in dim])
+        if keepdim:
+            out = list(shape)
+            for d in dims:
+                out[d] = 1
+            return tuple(out)
+        else:
+            return tuple(s for i, s in enumerate(shape) if i not in dims)
+    else:
+        d = dim if dim >= 0 else len(shape) + dim
+        if keepdim:
+            out = list(shape)
+            out[d] = 1
+            return tuple(out)
+        else:
+            return tuple(s for i, s in enumerate(shape) if i != d)
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, strides, dim, keepdim, out_strides = data
+        out_supports_inplace = not is_broadcast(out_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-3})
+            in_spec = TensorSpec.from_tensor(shape, strides, dtype)
+
+            kwargs = {}
+            if dim is not None:
+                kwargs["dim"] = dim
+            if keepdim is not None:
+                kwargs["keepdim"] = keepdim
+
+            test_cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="Median - OUT_OF_PLACE",
+                )
+            )
+
+            # Note: infinicore.median returns (values, indices) when dim is provided. explicit out param for both outputs is not available in PyTorch.
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Median operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Median")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.median(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.median(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/min.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, dim_or_None, keepdim_or_None, out_strides_or_None)
+# min reduces by taking minimum along dim(s) or overall
+
+_TEST_CASES_DATA = [
+    ((8, 8), None, None, None, None),
+    ((8, 8), (16, 1), 1, False, None),
+    ((2, 3, 4), None, 0, True, (0, 1, 1)),
+    ((1, 8), None, 0, False, None),
+    ((16, 64), (128, 1), None, None, None),
+    ((4, 5, 6), (60, 12, 2), 2, True, (12, 4, 1)),
+]
+
+_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 _compute_out_shape(shape, dim, keepdim):
+    if dim is None:
+        return ()
+    if isinstance(dim, tuple):
+        dims = sorted([(d if d >= 0 else len(shape) + d) for d in dim])
+        if keepdim:
+            out = list(shape)
+            for d in dims:
+                out[d] = 1
+            return tuple(out)
+        else:
+            return tuple(s for i, s in enumerate(shape) if i not in dims)
+    else:
+        d = dim if dim >= 0 else len(shape) + dim
+        if keepdim:
+            out = list(shape)
+            out[d] = 1
+            return tuple(out)
+        else:
+            return tuple(s for i, s in enumerate(shape) if i != d)
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, strides, dim, keepdim, out_strides = data
+        out_supports_inplace = not is_broadcast(out_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-3})
+            in_spec = TensorSpec.from_tensor(shape, strides, dtype)
+
+            kwargs = {}
+            if dim is not None:
+                kwargs["dim"] = dim
+            if keepdim is not None:
+                kwargs["keepdim"] = keepdim
+
+            test_cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="Min - OUT_OF_PLACE",
+                )
+            )
+
+            out_shape = _compute_out_shape(shape, dim, keepdim)
+            out_spec = TensorSpec.from_tensor(out_shape, out_strides, dtype)
+            if out_supports_inplace and dim is None:
+                test_cases.append(
+                    TestCase(
+                        inputs=[in_spec],
+                        kwargs=kwargs,
+                        output_spec=out_spec,
+                        comparison_target="out",
+                        tolerance=tol,
+                        description="Min - INPLACE(out)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Min operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Min")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.min(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.min(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/minimum.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, b_strides)
+_TEST_CASES_DATA = [
+    ((6, 8), None, None),
+    ((8, 4), (16, 1), None),
+    ((5, 5), None, (10, 1)),
+    ((3, 7), (14, 1), (14, 1)),
+    ((10, 3), None, None),
+    ((2, 16), (32, 1), (32, 1)),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 0, "rtol": 1e-2},
+    infinicore.float32: {"atol": 0, "rtol": 1e-3},
+    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, a_strides, b_strides = data
+
+        a_inplace = not is_broadcast(a_strides)
+        b_inplace = not is_broadcast(b_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)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[a_spec, b_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="minimum - OUT_OF_PLACE",
+                )
+            )
+
+            if a_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[a_spec, b_spec],
+                        kwargs={"out": 0},
+                        output_spec=None,
+                        comparison_target=0,
+                        tolerance=tol,
+                        description="minimum - INPLACE(a)",
+                    )
+                )
+
+            if b_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[a_spec, b_spec],
+                        kwargs={"out": 1},
+                        output_spec=None,
+                        comparison_target=1,
+                        tolerance=tol,
+                        description="minimum - INPLACE(b)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Minimum operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Minimum")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.minimum(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.minimum(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/mish.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)
+
+_TEST_CASES_DATA = [
+    ((13, 4), None),
+    ((13, 4), (10, 1)),
+    ((8, 8, 8), 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():
+    """mish(input)"""
+    test_cases = []
+
+    for data in _TEST_CASES_DATA:
+        shape = data[0]
+        in_strides = data[1] if len(data) > 1 else None
+
+        for dtype in _TENSOR_DTYPES:
+            tolerance = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            input_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tolerance,
+                    description=f"Mish - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Mish operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Mish")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.mish(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.mish(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()