Merge pull request #631 from InfiniTensor/issue/630

issue/630 - slightly improved unimplemented messages

test/infinicore/ops/isin.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_elements_strides_or_None, extra_or_None)
+# isin checks membership of each element in provided test_elements (tensor or list)
+
+_TEST_CASES_DATA = [
+    ((8, 8), None, None, None),
+    ((8, 8), (16, 1), None, None),
+    ((8, 8), None, (1,), None),
+    ((2, 3, 4), None, None, None),
+    ((1, 8), None, None, None),
+    ((16, 64), (128, 1), None, 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.int32, infinicore.float32, infinicore.float16]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, in_strides, elements_strides, _ = data[0], data[1], data[2], data[3]
+
+        input_supports_inplace = not is_broadcast(in_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 1e-3})
+            input_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+
+            # Build "test elements" as a tensor of small set of values (same dtype)
+            elements_spec = TensorSpec.from_tensor(
+                (4,), elements_strides if elements_strides else None, dtype
+            )
+
+            # Out-of-place
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec, elements_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="IsIn - OUT_OF_PLACE",
+                )
+            )
+
+            # explicit out
+            out_spec = TensorSpec.from_tensor(shape, None, infinicore.bool)
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec, elements_spec],
+                    kwargs={},
+                    output_spec=out_spec,
+                    comparison_target="out",
+                    tolerance=tol,
+                    description="IsIn - INPLACE(out)",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """IsIn operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("IsIn")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.isin(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.isin(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/isneginf.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, out_strides_or_None)
+# isneginf checks for -inf values
+
+_TEST_CASES_DATA = [
+    ((8, 8), None, None),
+    ((8, 8), (16, 1), None),
+    ((8, 8), None, (0, 1)),
+    ((2, 3, 4), None, None),
+    ((1, 8), None, None),
+    ((16, 128), (256, 1), (256, 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.float32, infinicore.bfloat16]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, in_strides, out_strides = data[0], data[1], data[2]
+
+        input_supports_inplace = not is_broadcast(in_strides)
+        out_supports_inplace = not is_broadcast(out_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 1e-3})
+            input_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+            out_spec = TensorSpec.from_tensor(shape, out_strides, infinicore.bool)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="IsNegInf - OUT_OF_PLACE",
+                )
+            )
+
+            if out_supports_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[input_spec],
+                        kwargs=None,
+                        output_spec=out_spec,
+                        comparison_target="out",
+                        tolerance=tol,
+                        description="IsNegInf - INPLACE(out)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """IsNegInf operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("IsNegInf")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.isneginf(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.isneginf(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/isposinf.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, out_strides_or_None)
+# isposinf checks for +inf values
+
+_TEST_CASES_DATA = [
+    ((8, 8), None, None),
+    ((8, 8), (16, 1), None),
+    ((8, 8), None, (0, 1)),
+    ((2, 3, 4), None, None),
+    ((1, 8), None, None),
+    ((16, 128), (256, 1), (256, 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.float32, infinicore.bfloat16]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, in_strides, out_strides = data[0], data[1], data[2]
+
+        input_supports_inplace = not is_broadcast(in_strides)
+        out_supports_inplace = not is_broadcast(out_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 1e-3})
+            input_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+            out_spec = TensorSpec.from_tensor(shape, out_strides, infinicore.bool)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="IsPosInf - OUT_OF_PLACE",
+                )
+            )
+
+            if out_supports_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[input_spec],
+                        kwargs=None,
+                        output_spec=out_spec,
+                        comparison_target="out",
+                        tolerance=tol,
+                        description="IsPosInf - INPLACE(out)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """IsPosInf operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("IsPosInf")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.isposinf(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.isposinf(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/kl_div.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, reduction_or_None, log_target_bool_or_None)
+# infinicore.nn.functional.kl_div(input, target, reduction='mean', log_target=False)
+
+_TEST_CASES_DATA = [
+    ((4, 5), None, "batchmean", None),
+    ((8, 8), (512, 64), "sum", False),
+    ((1, 10), None, "batchmean", True),
+    ((16, 100), None, "batchmean", False),
+    ((3, 7), None, "batchmean", None),
+    ((2, 2), None, "sum", None),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-2, "rtol": 1e-1},
+    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, reduction, log_target in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            a = TensorSpec.from_tensor(shape, strides, dtype)
+            b = TensorSpec.from_tensor(shape, None, dtype)
+
+            kwargs = {}
+            if reduction is not None:
+                kwargs["reduction"] = reduction
+            if log_target is not None:
+                kwargs["log_target"] = log_target
+
+            test_cases.append(
+                TestCase(
+                    inputs=[a, b],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="kl_div - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """kl_div operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("kl_div")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.kl_div(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.kl_div(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/kron.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: (a_shape, b_shape, a_strides_or_None, b_strides_or_None)
+# infinicore.kron(a, b)
+
+_TEST_CASES_DATA = [
+    ((2, 3), (4, 1), None, None),
+    ((1,), (3,), None, None),
+    ((4, 4), (2, 2), (64, 16), (8, 1)),
+    ((6,), (6,), None, None),
+    ((3, 2), (2, 3), None, (12, 1)),
+    ((8, 1), (1, 8), None, 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 a_shape, b_shape, a_strides, b_strides in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            a = TensorSpec.from_tensor(a_shape, a_strides, dtype)
+            b = TensorSpec.from_tensor(b_shape, b_strides, dtype)
+
+            test_cases.append(
+                TestCase(
+                    inputs=[a, b],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="kron - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """kron operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("kron")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.kron(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.kron(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/kthvalue.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, k, dim, keepdim)
+_TEST_CASES_DATA = [
+    ((6, 8), None, 1, 1, False),
+    ((8, 4), (16, 1), 2, 0, True),
+    ((5, 5), None, 3, -1, False),
+    ((3, 7), (14, 1), 2, 1, True),
+    ((10, 3), None, 1, 1, False),
+    ((2, 16), (32, 1), 5, 1, False),
+]
+
+_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, in_strides, k, dim, keepdim = data
+
+        out_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)
+            # kthvalue returns (values, indices). We'll request out-of-place and explicit out for values.
+            values_spec = TensorSpec.from_tensor(shape, None, dtype)
+            indices_spec = TensorSpec.from_tensor(shape, None, infinicore.int64)
+
+            kwargs = {"k": k, "dim": dim, "keepdim": keepdim}
+
+            # Out-of-place
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description=f"kthvalue - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """KthValue operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("KthValue")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.kthvalue(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.kthvalue(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/l1_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: (input_shape, target_shape, input_strides_or_None, reduction_or_None)
+# infinicore.nn.functional.l1_loss(input, target, reduction='mean')
+
+_TEST_CASES_DATA = [
+    ((4, 5), (4, 5), None, None),
+    ((8, 8), (8, 8), (512, 64), "sum"),
+    ((1, 10), (1, 10), None, "mean"),
+    ((16, 100), (16, 100), None, None),
+    ((3, 7), (3, 7), None, "none"),
+    ((2, 2), (2, 2), None, 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 shape, tgt_shape, strides, reduction in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            inp = TensorSpec.from_tensor(shape, strides, dtype)
+            tgt = TensorSpec.from_tensor(tgt_shape, None, dtype)
+
+            kwargs = {}
+            if reduction is not None:
+                kwargs["reduction"] = reduction
+
+            test_cases.append(
+                TestCase(
+                    inputs=[inp, tgt],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="l1_loss - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """l1_loss operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("l1_loss")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.l1_loss(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.l1_loss(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/ldexp.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
+
+# ldexp(input, other) computes input * (2**other)
+
+_TEST_CASES_DATA = [
+    ((2, 3), (3,)),
+    ((1, 4, 8), None),
+    ((3, 2, 5, 7), (1,)),
+    ((2, 1, 16), None),
+    ((1, 8, 9, 11), None),
+    ((2, 6, 10), (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 parse_test_cases():
+    cases = []
+    for shape, other_shape in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP[dtype]
+            in_spec = TensorSpec.from_tensor(shape, None, dtype)
+
+            if other_shape is None:
+                other_spec = TensorSpec.from_tensor((1,), None, infinicore.int32)
+            else:
+                other_spec = TensorSpec.from_tensor(other_shape, None, infinicore.int32)
+
+            # out-of-place
+            cases.append(
+                TestCase(
+                    inputs=[in_spec, other_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="ldexp_out",
+                )
+            )
+
+            # explicit out
+            out_spec = TensorSpec.from_tensor(shape, None, dtype)
+            cases.append(
+                TestCase(
+                    inputs=[in_spec, other_spec],
+                    kwargs={},
+                    output_spec=out_spec,
+                    comparison_target="out",
+                    tolerance=tol,
+                    description="ldexp_explicit_out",
+                )
+            )
+
+            # in-place
+            cases.append(
+                TestCase(
+                    inputs=[in_spec, other_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=0,
+                    tolerance=tol,
+                    description="ldexp_inplace",
+                )
+            )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """Ldexp operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Ldexp")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.ldexp(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.ldexp(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/leaky_relu.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, negative_slope_or_None)
+
+_TEST_CASES_DATA = [
+    ((13, 4), None, None),
+    ((13, 4), (10, 1), 0.01),
+    ((8, 8, 8), None, 0.2),
+]
+
+_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():
+    """
+    leaky_relu(input, negative_slope=0.01, inplace=False)
+    """
+    test_cases = []
+
+    for data in _TEST_CASES_DATA:
+        shape = data[0]
+        in_strides = data[1] if len(data) > 1 else None
+        slope = data[2] if len(data) > 2 else 0.01
+
+        input_supports_inplace = not is_broadcast(in_strides)
+
+        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)
+
+            kwargs = {}
+            if slope is not None:
+                kwargs["negative_slope"] = slope
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tolerance,
+                    description=f"LeakyReLU - OUT_OF_PLACE",
+                )
+            )
+
+            if input_supports_inplace:
+                inplace_kwargs = {"inplace": True}
+                if slope is not None:
+                    inplace_kwargs["negative_slope"] = slope
+                test_cases.append(
+                    TestCase(
+                        inputs=[input_spec],
+                        kwargs=inplace_kwargs,
+                        output_spec=None,
+                        comparison_target=0,
+                        tolerance=tolerance,
+                        description=f"LeakyReLU - INPLACE",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """LeakyReLU operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("LeakyReLU")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.leaky_relu(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.leaky_relu(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/lerp.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: (start_shape, start_strides_or_None, end_shape_or_None, weight_scalar_or_None, weight_tensor_shape_or_None)
+# infinicore.lerp(start, end, weight)
+
+_TEST_CASES_DATA = [
+    ((2, 3, 4), None, None, 0.5, None),
+    ((1, 4, 8), (32, 8, 1), None, None, (1, 4, 8)),
+    ((3, 2, 5, 7), None, None, 0.25, None),
+    ((2, 1, 16), None, None, None, (2, 1, 16)),
+    ((1, 8, 9, 11), (792, 99, 11, 1), None, 0.75, None),
+    ((2, 6, 10), None, 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 = []
+    for (
+        start_shape,
+        start_strides,
+        end_shape,
+        weight_scalar,
+        weight_tensor_shape,
+    ) in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP[dtype]
+            start_spec = TensorSpec.from_tensor(start_shape, start_strides, dtype)
+            end_spec = TensorSpec.from_tensor(
+                start_shape if end_shape is None else end_shape, None, dtype
+            )
+
+            if weight_scalar is not None:
+                weight = weight_scalar
+                cases.append(
+                    TestCase(
+                        inputs=[start_spec, end_spec, weight],
+                        kwargs={},
+                        output_spec=None,
+                        comparison_target=None,
+                        tolerance=tol,
+                        description="lerp_scalar_weight_out",
+                    )
+                )
+                out_spec = TensorSpec.from_tensor(start_shape, None, dtype)
+                cases.append(
+                    TestCase(
+                        inputs=[start_spec, end_spec, weight],
+                        kwargs={},
+                        output_spec=out_spec,
+                        comparison_target="out",
+                        tolerance=tol,
+                        description="lerp_scalar_weight_explicit_out",
+                    )
+                )
+                if not is_broadcast(start_spec.strides):
+                    cases.append(
+                        TestCase(
+                            inputs=[start_spec, end_spec, weight],
+                            kwargs={"out": 0},
+                            output_spec=None,
+                            comparison_target=0,
+                            tolerance=tol,
+                            description="lerp_scalar_inplace_start",
+                        )
+                    )
+                if not is_broadcast(end_spec.strides):
+                    cases.append(
+                        TestCase(
+                            inputs=[start_spec, end_spec, weight],
+                            kwargs={"out": 1},
+                            output_spec=None,
+                            comparison_target=1,
+                            tolerance=tol,
+                            description="lerp_scalar_inplace_end",
+                        )
+                    )
+
+            if weight_tensor_shape is not None:
+                weight_spec = TensorSpec.from_tensor(weight_tensor_shape, None, dtype)
+                cases.append(
+                    TestCase(
+                        inputs=[start_spec, end_spec, weight_spec],
+                        kwargs={},
+                        output_spec=None,
+                        comparison_target=None,
+                        tolerance=tol,
+                        description="lerp_tensor_weight_out",
+                    )
+                )
+                out_spec = TensorSpec.from_tensor(start_shape, None, dtype)
+                cases.append(
+                    TestCase(
+                        inputs=[start_spec, end_spec, weight_spec],
+                        kwargs={},
+                        output_spec=out_spec,
+                        comparison_target="out",
+                        tolerance=tol,
+                        description="lerp_tensor_weight_explicit_out",
+                    )
+                )
+                if not is_broadcast(weight_spec.strides):
+                    cases.append(
+                        TestCase(
+                            inputs=[start_spec, end_spec, weight_spec],
+                            kwargs={"out": 2},
+                            output_spec=None,
+                            comparison_target=2,
+                            tolerance=tol,
+                            description="lerp_inplace_weight",
+                        )
+                    )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """Lerp operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Lerp")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.lerp(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.lerp(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/local_response_norm.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, size, alpha_or_None, beta_or_None, k_or_None)
+# infinicore.nn.functional.local_response_norm(input, size, alpha=1e-4, beta=0.75, k=1.0)
+
+_TEST_CASES_DATA = [
+    ((4, 3, 8, 8), None, 5, None, None, None),
+    ((2, 6, 4, 4), (384, 96, 1, 1), 3, 1e-4, 0.75, 1.0),
+    ((1, 3, 16, 16), None, 7, None, None, None),
+    ((8, 5, 2, 2), None, 1, 1e-3, 0.5, 0.0),
+    ((6, 4, 7, 7), None, 9, None, None, None),
+    ((3, 2, 9, 9), None, 4, 1e-5, 0.9, 2.0),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+}
+
+_TENSOR_DTYPES = [infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for shape, strides, size, alpha, beta, k in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            inp = TensorSpec.from_tensor(shape, strides, dtype)
+
+            kwargs = {"size": size}
+            if alpha is not None:
+                kwargs["alpha"] = alpha
+            if beta is not None:
+                kwargs["beta"] = beta
+            if k is not None:
+                kwargs["k"] = k
+
+            test_cases.append(
+                TestCase(
+                    inputs=[inp],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="local_response_norm - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """local_response_norm operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("local_response_norm")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.local_response_norm(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.local_response_norm(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/log.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.log(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="log_out",
+                )
+            )
+            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="log_out_explicit",
+                )
+            )
+            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="log_inplace",
+                    )
+                )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """Log operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Log")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.log(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.log(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/log10.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.log10(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="log10_out",
+                )
+            )
+            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="log10_out_explicit",
+                )
+            )
+            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="log10_inplace",
+                    )
+                )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """Log10 operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Log10")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.log10(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.log10(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/log1p.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.log1p(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="log1p_out",
+                )
+            )
+            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="log1p_out_explicit",
+                )
+            )
+            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="log1p_inplace",
+                    )
+                )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """Log1p operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Log1p")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.log1p(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.log1p(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/log2.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.log2(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="log2_out",
+                )
+            )
+            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="log2_out_explicit",
+                )
+            )
+            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="log2_inplace",
+                    )
+                )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """Log2 operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Log2")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.log2(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.log2(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/log_softmax.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)
+
+_TEST_CASES_DATA = [
+    ((4, 10), None, -1),
+    ((2, 5, 8), (40, 8, 1), 1),
+    ((8, 20), 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():
+    """log_softmax(input, dim=None, dtype=None)"""
+    test_cases = []
+
+    for data in _TEST_CASES_DATA:
+        shape = data[0]
+        in_strides = data[1] if len(data) > 1 else None
+        dim = data[2] if len(data) > 2 else -1
+
+        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)
+
+            kwargs = {"dim": dim}
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tolerance,
+                    description=f"LogSoftmax - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """LogSoftmax operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("LogSoftmax")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.log_softmax(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.log_softmax(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/logaddexp.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: (a_shape, a_strides_or_None, b_shape_or_None)
+# infinicore.logaddexp(a, b)
+
+_TEST_CASES_DATA = [
+    ((2, 3, 4), None, None),
+    ((1, 4, 8), (32, 8, 1), None),
+    ((3, 2, 5, 7), None, None),
+    ((2, 1, 16), None, None),
+    ((1, 8, 9, 11), (792, 99, 11, 1), None),
+    ((2, 6, 10), None, 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 a_shape, a_strides, b_shape in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP[dtype]
+            a_spec = TensorSpec.from_tensor(a_shape, a_strides, dtype)
+            b_spec = TensorSpec.from_tensor(
+                a_shape if b_shape is None else b_shape, None, dtype
+            )
+
+            cases.append(
+                TestCase(
+                    inputs=[a_spec, b_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="logaddexp_out",
+                )
+            )
+            out_spec = TensorSpec.from_tensor(a_shape, None, dtype)
+            cases.append(
+                TestCase(
+                    inputs=[a_spec, b_spec],
+                    kwargs={},
+                    output_spec=out_spec,
+                    comparison_target="out",
+                    tolerance=tol,
+                    description="logaddexp_out_explicit",
+                )
+            )
+
+            if not is_broadcast(a_spec.strides):
+                cases.append(
+                    TestCase(
+                        inputs=[a_spec, b_spec],
+                        kwargs={"out": 0},
+                        output_spec=None,
+                        comparison_target=0,
+                        tolerance=tol,
+                        description="logaddexp_inplace_a",
+                    )
+                )
+            if not is_broadcast(b_spec.strides):
+                cases.append(
+                    TestCase(
+                        inputs=[a_spec, b_spec],
+                        kwargs={"out": 1},
+                        output_spec=None,
+                        comparison_target=1,
+                        tolerance=tol,
+                        description="logaddexp_inplace_b",
+                    )
+                )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """LogAddExp operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("LogAddExp")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.logaddexp(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.logaddexp(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/logaddexp2.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: (a_shape, a_strides_or_None, b_shape_or_None)
+# infinicore.logaddexp2(a, b)
+
+_TEST_CASES_DATA = [
+    ((2, 3, 4), None, None),
+    ((1, 4, 8), (32, 8, 1), None),
+    ((3, 2, 5, 7), None, None),
+    ((2, 1, 16), None, None),
+    ((1, 8, 9, 11), (792, 99, 11, 1), None),
+    ((2, 6, 10), None, 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 a_shape, a_strides, b_shape in _TEST_CASES_DATA:
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP[dtype]
+            a_spec = TensorSpec.from_tensor(a_shape, a_strides, dtype)
+            b_spec = TensorSpec.from_tensor(
+                a_shape if b_shape is None else b_shape, None, dtype
+            )
+
+            cases.append(
+                TestCase(
+                    inputs=[a_spec, b_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="logaddexp2_out",
+                )
+            )
+            out_spec = TensorSpec.from_tensor(a_shape, None, dtype)
+            cases.append(
+                TestCase(
+                    inputs=[a_spec, b_spec],
+                    kwargs={},
+                    output_spec=out_spec,
+                    comparison_target="out",
+                    tolerance=tol,
+                    description="logaddexp2_out_explicit",
+                )
+            )
+
+            if not is_broadcast(a_spec.strides):
+                cases.append(
+                    TestCase(
+                        inputs=[a_spec, b_spec],
+                        kwargs={"out": 0},
+                        output_spec=None,
+                        comparison_target=0,
+                        tolerance=tol,
+                        description="logaddexp2_inplace_a",
+                    )
+                )
+            if not is_broadcast(b_spec.strides):
+                cases.append(
+                    TestCase(
+                        inputs=[a_spec, b_spec],
+                        kwargs={"out": 1},
+                        output_spec=None,
+                        comparison_target=1,
+                        tolerance=tol,
+                        description="logaddexp2_inplace_b",
+                    )
+                )
+
+    return cases
+
+
+class OpTest(BaseOperatorTest):
+    """LogAddExp2 operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("LogAddExp2")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.logaddexp2(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.logaddexp2(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/logcumsumexp.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, dim, input_strides_or_None)
+# logcumsumexp computes log of cumulative sum of exponentials along dim.
+
+_TEST_CASES_DATA = [
+    ((13, 4), 1, None),
+    ((13, 4), 0, (10, 1)),
+    ((8, 16), 1, None),
+    ((2, 3, 5), 2, None),
+    ((16, 64), 1, None),
+    ((4, 5, 6), 0, 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, dim = 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": 1e-5, "rtol": 1e-4})
+            input_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+            out_spec = TensorSpec.from_tensor(shape, None, dtype)
+
+            # Out-of-place
+            kwargs = {"dim": dim}
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description=f"logcumsumexp - OUT_OF_PLACE",
+                )
+            )
+
+            # PyTorch does not expose explicit out for logcumsumexp — skip out tests
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """LogCumsumExp operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("LogCumsumExp")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.logcumsumexp(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.logcumsumexp(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/logdet.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)
+# logdet(input) — returns (sign, logabsdet) in PyTorch
+
+_TEST_CASES_DATA = [
+    ((1, 1), None),
+    ((2, 2), None),
+    ((3, 3), (3, 1)),
+    ((4, 4), None),
+    ((8, 8), (512, 1)),
+    ((16, 16), 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.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="logdet - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """logdet operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("logdet")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.logdet(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.logdet(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()