Merge pull request #631 from InfiniTensor/issue/630

issue/630 - slightly improved unimplemented messages

test/infinicore/ops/hardsigmoid.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():
+    """hardsigmoid(input)
+
+    No inplace arg on functional API; only out-of-place tests.
+    """
+    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"Hardsigmoid - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Hardsigmoid operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Hardsigmoid")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.hardsigmoid(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.hardsigmoid(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/hardswish.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),
+    ((8, 8), (64, 1)),
+    ((2, 3, 6), 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():
+    """
+    hardswish(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"Hardswish - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Hardswish operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Hardswish")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.hardswish(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.hardswish(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/hardtanh.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, min_val_or_None, max_val_or_None)
+
+_TEST_CASES_DATA = [
+    ((13, 4), None, -1.0, 1.0),
+    ((13, 4), (10, 1), -0.5, 0.5),
+    ((8, 8, 8), None, -2.0, 2.0),
+]
+
+_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():
+    """hardtanh(input, min_val=-1.0, max_val=1.0, inplace=False)"""
+    test_cases = []
+
+    for data in _TEST_CASES_DATA:
+        shape = data[0]
+        in_strides = data[1] if len(data) > 1 else None
+        minv = data[2] if len(data) > 2 else -1.0
+        maxv = data[3] if len(data) > 3 else 1.0
+
+        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 = {"min_val": minv, "max_val": maxv}
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tolerance,
+                    description=f"Hardtanh - OUT_OF_PLACE",
+                )
+            )
+
+            if input_supports_inplace:
+                inplace_kwargs = {"min_val": minv, "max_val": maxv, "inplace": True}
+                test_cases.append(
+                    TestCase(
+                        inputs=[input_spec],
+                        kwargs=inplace_kwargs,
+                        output_spec=None,
+                        comparison_target=0,
+                        tolerance=tolerance,
+                        description=f"Hardtanh - INPLACE",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Hardtanh operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Hardtanh")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.hardtanh(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.hardtanh(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/heaviside.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)
+# heaviside is binary: heaviside(input, values)
+# =======================================================================
+
+_TEST_CASES_DATA = [
+    ((13, 4), None, None, None),
+    ((13, 4), (10, 1), None, None),
+    ((13, 4), None, (10, 1), None),
+    ((8, 16), (40, 1), (40, 1), None),
+    ((2, 3, 4), None, None, None),
+    ((16, 5632), None, None, None),
+]
+
+_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 = data[0]
+        a_strides = data[1] if len(data) > 1 else None
+        b_strides = data[2] if len(data) > 2 else None
+        out_strides = data[3] if len(data) > 3 else None
+
+        a_supports_inplace = not is_broadcast(a_strides)
+        b_supports_inplace = not is_broadcast(b_strides)
+        out_supports_inplace = not is_broadcast(out_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 1e-3})
+            a_spec = TensorSpec.from_tensor(shape, a_strides, dtype)
+            b_spec = TensorSpec.from_tensor(shape, b_strides, dtype)
+            out_spec = TensorSpec.from_tensor(shape, out_strides, dtype)
+
+            # Out-of-place
+            test_cases.append(
+                TestCase(
+                    inputs=[a_spec, b_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="heaviside - OUT_OF_PLACE",
+                )
+            )
+
+            # Explicit out
+            if out_supports_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[a_spec, b_spec],
+                        kwargs=None,
+                        output_spec=out_spec,
+                        comparison_target="out",
+                        tolerance=tol,
+                        description="heaviside - INPLACE(out)",
+                    )
+                )
+
+            # In-place on first input
+            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="heaviside - INPLACE(a)",
+                    )
+                )
+
+            # In-place on second input
+            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="heaviside - INPLACE(b)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Heaviside operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Heaviside")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.heaviside(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.heaviside(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/hinge_embedding_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, target_strides_or_None, margin_or_None)
+# infinicore.nn.functional.hinge_embedding_loss(input, target, margin=1.0, reduction='mean')
+
+_TEST_CASES_DATA = [
+    ((4,), (4,), None, None, None),
+    ((8,), (8,), None, None, 0.5),
+    ((1,), (1,), None, None, None),
+    ((16,), (16,), (4,), None, 1.0),
+    ((3,), (3,), None, None, 0.2),
+    ((2,), (2,), None, 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, s1, s2, margin 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, s1, dtype)
+            tgt = TensorSpec.from_tensor(tgt_shape, s2, dtype)
+
+            kwargs = {}
+            if margin is not None:
+                kwargs["margin"] = margin
+
+            test_cases.append(
+                TestCase(
+                    inputs=[inp, tgt],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="hinge_embedding_loss - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """hinge_embedding_loss operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("hinge_embedding_loss")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.hinge_embedding_loss(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.hinge_embedding_loss(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/histc.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, bins, min_val, max_val)
+_TEST_CASES_DATA = [
+    ((100,), None, 10, 0.0, 1.0),
+    ((50,), None, 5, -1.0, 1.0),
+    ((20,), None, 8, 0.0, 2.0),
+    ((10,), None, 4, 0.0, 1.0),
+    ((1,), None, 3, 0.0, 1.0),
+    ((200,), None, 20, -2.0, 2.0),
+]
+
+_TOLERANCE_MAP = {infinicore.float32: {"atol": 1e-5, "rtol": 1e-4}}
+_TENSOR_DTYPES = [infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, strides, bins, minv, maxv = data
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype)
+            input_spec = TensorSpec.from_tensor(shape, strides, dtype)
+
+            kwargs = {"bins": bins, "min": minv, "max": maxv}
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description=f"histc - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """HistC operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("HistC")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.histc(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.histc(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/histogram.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, bins_or_sequence, range_or_None)
+_TEST_CASES_DATA = [
+    ((100,), None, 10, (0.0, 1.0)),
+    ((20,), None, 5, (0.0, 2.0)),
+    ((10,), None, 4, (0.0, 1.0)),
+    ((200,), None, 20, (-1.0, 1.0)),
+    ((1,), None, 3, (0.0, 1.0)),
+]
+
+_TOLERANCE_MAP = {infinicore.float32: {"atol": 1e-5, "rtol": 1e-4}}
+_TENSOR_DTYPES = [infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, strides, bins, rng = data
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype)
+            input_spec = TensorSpec.from_tensor(shape, strides, dtype)
+
+            kwargs = {"bins": bins}
+            if rng is not None:
+                kwargs["range"] = rng
+
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description=f"histogram - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Histogram operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Histogram")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.histogram(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.histogram(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/hsplit.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, sections_or_None)
+# infinicore.hsplit(input, sections)
+# Note: PyTorch hsplit is a convenience wrapper around split/reshape. We include both int and list sections.
+
+_TEST_CASES_DATA = [
+    ((4, 8), None, 2),
+    ((4, 9), None, [3, 6]),
+    ((2, 6, 12), None, 3),
+    ((1, 10), (10, 1), 5),
+    ((8, 4), None, [1, 3]),
+    ((6, 12), None, 4),
+]
+
+_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, sections 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)
+            # infinicore.hsplit expects positional second arg (sections) rather than kw in this API;
+            # put sections into inputs when present. Convert list -> tuple.
+            if sections is not None:
+                # wrap sections in a small wrapper so TestCase.__init__ does not
+                # interpret the tuple as a Tensor shape
+                class Sections:
+                    def __init__(self, v):
+                        self.v = v
+
+                    def as_tuple(self):
+                        return tuple(self.v) if isinstance(self.v, list) else self.v
+
+                    def __repr__(self):
+                        return f"sections({self.v})"
+
+                sec = Sections(sections)
+                test_inputs = [inp, sec]
+            else:
+                test_inputs = [inp]
+
+            test_cases.append(
+                TestCase(
+                    inputs=test_inputs,
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="hsplit - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """hsplit operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("hsplit")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        # unwrap Sections wrapper if present
+        args = list(args)
+        if len(args) >= 2 and hasattr(args[1], "as_tuple"):
+            args[1] = args[1].as_tuple()
+        return torch.hsplit(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.hsplit(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/huber_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, target_strides_or_None, delta_or_None)
+# infinicore.nn.functional.huber_loss(input, target, reduction='mean', delta=1.0)
+
+_TEST_CASES_DATA = [
+    ((4, 5), (4, 5), None, None, None),
+    ((8, 8), (8, 8), (512, 64), None, 1.0),
+    ((1, 10), (1, 10), None, None, 0.5),
+    ((16, 100), (16, 100), None, None, 2.0),
+    ((3, 7), (3, 7), None, (21, 7), None),
+    ((2, 2), (2, 2), None, 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, s1, s2, delta 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, s1, dtype)
+            tgt = TensorSpec.from_tensor(tgt_shape, s2, dtype)
+
+            kwargs = {}
+            if delta is not None:
+                kwargs["delta"] = delta
+
+            test_cases.append(
+                TestCase(
+                    inputs=[inp, tgt],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="huber_loss - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """huber_loss operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("huber_loss")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.huber_loss(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.huber_loss(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

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

test/infinicore/ops/index_add.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: (target_shape, target_strides_or_None, dim, index_shape, src_shape)
+_TEST_CASES_DATA = [
+    ((5, 6), None, 1, (5, 2), (5, 2)),
+    ((4, 4), (16, 1), 0, (2, 4), (2, 4)),
+    ((3, 5), None, 1, (3, 3), (3, 3)),
+    ((2, 6), None, 1, (2, 2), (2, 2)),
+    ((6, 3), (18, 1), 0, (3, 3), (3, 3)),
+    ((4, 7), None, 1, (4, 2), (4, 2)),
+]
+
+_TOLERANCE_MAP = {infinicore.float32: {"atol": 1e-5, "rtol": 1e-4}}
+_TENSOR_DTYPES = [infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for target_shape, t_strides, dim, idx_shape, src_shape in _TEST_CASES_DATA:
+        target_spec = TensorSpec.from_tensor(
+            target_shape, t_strides, infinicore.float32
+        )
+        # index for index_add should be 1-D with length equal to source.size(dim)
+        index_len = src_shape[dim]
+        from framework.tensor import TensorInitializer
+
+        index_spec = TensorSpec.from_tensor(
+            (index_len,),
+            None,
+            infinicore.int64,
+            init_mode=TensorInitializer.RANDINT,
+            low=0,
+            high=target_shape[dim],
+        )
+        src_spec = TensorSpec.from_tensor(src_shape, None, infinicore.float32)
+
+        # out parameter can be used (explicit out)
+        out_supports = not is_broadcast(t_strides)
+
+        # Out-of-place (return value)
+        # Use positional dim to match infinicore.index_add(input, dim, index, tensor)
+        test_cases.append(
+            TestCase(
+                inputs=[target_spec, dim, index_spec, src_spec],
+                kwargs={},
+                output_spec=None,
+                comparison_target=None,
+                tolerance=_TOLERANCE_MAP[infinicore.float32],
+                description=f"index_add - OUT_OF_PLACE",
+            )
+        )
+
+        # In-place on target (out=target)
+        if out_supports:
+            test_cases.append(
+                TestCase(
+                    inputs=[target_spec, dim, index_spec, src_spec],
+                    kwargs=None,
+                    output_spec=target_spec,
+                    comparison_target="out",
+                    tolerance=_TOLERANCE_MAP[infinicore.float32],
+                    description=f"index_add - INPLACE(out)",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """IndexAdd operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("IndexAdd")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.index_add(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.index_add(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/index_copy.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.tensor import TensorInitializer
+from framework.utils import is_broadcast
+
+# Test cases format: (target_shape, target_strides_or_None, dim, index_shape, src_shape)
+_TEST_CASES_DATA = [
+    ((5, 6), None, 1, (5, 2), (5, 2)),
+    ((4, 4), (16, 1), 0, (2, 4), (2, 4)),
+    ((3, 5), None, 1, (3, 3), (3, 3)),
+    ((2, 6), None, 1, (2, 2), (2, 2)),
+    ((6, 3), (18, 1), 0, (3, 3), (3, 3)),
+    ((4, 7), None, 1, (4, 2), (4, 2)),
+]
+
+_TOLERANCE_MAP = {infinicore.float32: {"atol": 1e-5, "rtol": 1e-4}}
+_TENSOR_DTYPES = [infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for target_shape, t_strides, dim, idx_shape, src_shape in _TEST_CASES_DATA:
+        target_spec = TensorSpec.from_tensor(
+            target_shape, t_strides, infinicore.float32
+        )
+        # index for index_copy should be 1-D with length equal to source.size(dim)
+        index_len = src_shape[dim]
+
+        index_spec = TensorSpec.from_tensor(
+            (index_len,),
+            None,
+            infinicore.int64,
+            init_mode=TensorInitializer.RANDINT,
+            low=0,
+            high=target_shape[dim],
+        )
+        src_spec = TensorSpec.from_tensor(src_shape, None, infinicore.float32)
+
+        out_supports = not is_broadcast(t_strides)
+
+        # Out-of-place: infinicore.index_copy(input, dim, index, source)
+        test_cases.append(
+            TestCase(
+                inputs=[target_spec, dim, index_spec, src_spec],
+                kwargs={},
+                output_spec=None,
+                comparison_target=None,
+                tolerance=_TOLERANCE_MAP[infinicore.float32],
+                description=f"index_copy - OUT_OF_PLACE",
+            )
+        )
+
+        # Explicit out: same ordering, output will be provided by framework
+        if out_supports:
+            test_cases.append(
+                TestCase(
+                    inputs=[target_spec, dim, index_spec, src_spec],
+                    kwargs=None,
+                    output_spec=target_spec,
+                    comparison_target="out",
+                    tolerance=_TOLERANCE_MAP[infinicore.float32],
+                    description=f"index_copy - INPLACE(out)",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """IndexCopy operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("IndexCopy")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.index_copy(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.index_copy(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/index_reduce.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: (target_shape, target_strides_or_None, dim, index_shape, reduce)
+_TEST_CASES_DATA = [
+    ((5, 6), None, 1, (5, 2), "prod"),
+    ((4, 4), (16, 1), 0, (2, 4), "amax"),
+    ((3, 5), None, 1, (3, 3), "amin"),
+    ((2, 6), None, 1, (2, 2), "prod"),
+    ((2, 6), None, 1, (2, 2), "amin"),
+    ((6, 3), (18, 1), 0, (3, 3), "mean"),
+    ((4, 7), None, 1, (4, 2), "prod"),
+]
+
+_TOLERANCE_MAP = {infinicore.float32: {"atol": 1e-5, "rtol": 1e-4}}
+_TENSOR_DTYPES = [infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for tgt_shape, tgt_strides, dim, idx_shape, reduce in _TEST_CASES_DATA:
+        target_spec = TensorSpec.from_tensor(tgt_shape, tgt_strides, infinicore.float32)
+        # idx_shape here represents the source shape for index_reduce; index itself must be 1-D
+        src_shape = idx_shape
+        # determine index length from source along the reduction dim
+        index_len = src_shape[dim]
+        from framework.tensor import TensorInitializer
+
+        index_spec = TensorSpec.from_tensor(
+            (index_len,),
+            None,
+            infinicore.int64,
+            init_mode=TensorInitializer.RANDINT,
+            low=0,
+            high=tgt_shape[dim],
+        )
+
+        src_spec = TensorSpec.from_tensor(src_shape, None, infinicore.float32)
+
+        out_supports = not is_broadcast(tgt_strides)
+
+        kwargs = {"reduce": reduce}
+
+        test_cases.append(
+            TestCase(
+                inputs=[target_spec, dim, index_spec, src_spec],
+                kwargs=kwargs,
+                output_spec=None,
+                comparison_target=None,
+                tolerance=_TOLERANCE_MAP[infinicore.float32],
+                description=f"index_reduce - OUT_OF_PLACE",
+            )
+        )
+
+        if out_supports:
+            test_cases.append(
+                TestCase(
+                    inputs=[target_spec, dim, index_spec, src_spec],
+                    kwargs=kwargs,
+                    output_spec=target_spec,
+                    comparison_target="out",
+                    tolerance=_TOLERANCE_MAP[infinicore.float32],
+                    description=f"index_reduce - INPLACE(out)",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """IndexReduce operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("IndexReduce")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.index_reduce(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.index_reduce(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/index_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, dim, index_shape)
+_TEST_CASES_DATA = [
+    ((3, 4), None, 1, (2,)),
+    ((5, 6), (30, 1), 0, (3,)),
+    ((2, 3, 4), None, 2, (2,)),
+    ((4, 4), None, -1, (1,)),
+    ((6, 2), (12, 1), 1, (2,)),
+    ((3, 5), None, 0, (1,)),
+]
+
+_TOLERANCE_MAP = {infinicore.float32: {"atol": 1e-5, "rtol": 1e-4}}
+_TENSOR_DTYPES = [infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for shape, strides, dim, idx_shape in _TEST_CASES_DATA:
+        input_spec = TensorSpec.from_tensor(shape, strides, infinicore.float32)
+        # index_select requires a 1-D index tensor
+        index_len = idx_shape[0]
+        from framework.tensor import TensorInitializer
+
+        index_spec = TensorSpec.from_tensor(
+            (index_len,),
+            None,
+            infinicore.int64,
+            init_mode=TensorInitializer.RANDINT,
+            low=0,
+            high=shape[dim],
+        )
+
+        # Use positional dim to match infinicore.index_select(input, dim, index)
+        test_cases.append(
+            TestCase(
+                inputs=[input_spec, dim, index_spec],
+                kwargs={},
+                output_spec=None,
+                comparison_target=None,
+                tolerance=_TOLERANCE_MAP[infinicore.float32],
+                description=f"index_select - OUT_OF_PLACE",
+            )
+        )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """IndexSelect operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("IndexSelect")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.index_select(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.index_select(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/inner.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.inner(a, b)
+
+_TEST_CASES_DATA = [
+    ((3,), (3,), None, None),
+    ((2, 3), (3,), None, None),
+    ((4, 5), (4, 5), (20, 5), (20, 5)),
+    ((6,), (6,), None, (0,)),
+    ((2, 3, 4), (4,), None, None),
+    ((8, 8), (8, 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="inner - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """inner operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("inner")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.inner(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.inner(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/instance_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, running_mean_present_bool, running_var_present_bool, weight_bias_present_bool, use_input_stats_or_None, momentum_or_None, eps_or_None)
+# infinicore.nn.functional.instance_norm(input, running_mean=None, running_var=None, weight=None, bias=None, use_input_stats=False, momentum=0.1, eps=1e-5)
+
+_TEST_CASES_DATA = [
+    ((4, 3, 8, 8), None, True, True, True, False, None, None),
+    ((2, 6, 4, 4), None, True, True, True, True, 0.2, 1e-5),
+    ((1, 3, 16, 16), None, True, True, False, False, None, None),
+    ((8, 5, 2, 2), None, True, True, False, True, 0.1, 1e-3),
+    ((6, 4, 7, 7), None, True, True, True, False, None, 1e-4),
+    ((3, 2, 9, 9), None, True, True, True, True, None, 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,
+        mean_p,
+        var_p,
+        wb_p,
+        use_stats,
+        momentum,
+        eps,
+    ) in _TEST_CASES_DATA:
+        C = shape[1]
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            inp = TensorSpec.from_tensor(shape, strides, dtype)
+
+            running_mean = TensorSpec.from_tensor((C,), None, dtype) if mean_p else None
+            running_var = TensorSpec.from_tensor((C,), None, dtype) if var_p else None
+
+            inputs = [inp]
+            # instance_norm signature expects running_mean, running_var next
+            inputs.append(running_mean)
+            inputs.append(running_var)
+            if wb_p:
+                weight = TensorSpec.from_tensor((C,), None, dtype)
+                bias = TensorSpec.from_tensor((C,), None, dtype)
+                inputs.append(weight)
+                inputs.append(bias)
+            else:
+                inputs.append(None)
+                inputs.append(None)
+
+            kwargs = {}
+            if use_stats is not None:
+                kwargs["use_input_stats"] = use_stats
+            if momentum is not None:
+                kwargs["momentum"] = momentum
+            if eps is not None:
+                kwargs["eps"] = eps
+
+            test_cases.append(
+                TestCase(
+                    inputs=inputs,
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="instance_norm - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """instance_norm operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("instance_norm")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.instance_norm(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.instance_norm(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/interpolate.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, size_or_scale_factor, mode, align_corners_or_None, input_strides_or_None)
+# infinicore.nn.functional.interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None)
+
+_TEST_CASES_DATA = [
+    ((1, 3, 16, 16), (32, 32), "bilinear", True, None),
+    ((2, 3, 8, 8), (16, 16), "nearest", None, (384, 128, 16, 1)),
+    ((1, 1, 10), 20, "linear", False, None),
+    ((2, 3, 6, 6), (12, 12), "area", None, None),
+    ((1, 3, 4, 4, 4), (8, 8, 8), "trilinear", True, None),
+    ((4, 3, 7, 7), 2.0, "bilinear", False, None),
+]
+
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-2, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
+    infinicore.bfloat16: {"atol": 1e-2, "rtol": 5e-2},
+}
+
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, size_or_scale, mode, align, in_strides = data
+
+        supports_inplace = not is_broadcast(in_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
+            in_spec = TensorSpec.from_tensor(shape, in_strides, dtype)
+
+            kwargs = {"mode": mode}
+            if isinstance(size_or_scale, tuple):
+                kwargs["size"] = size_or_scale
+            else:
+                kwargs["scale_factor"] = size_or_scale
+            if align is not None:
+                kwargs["align_corners"] = align
+
+            test_cases.append(
+                TestCase(
+                    inputs=[in_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description=f"interpolate - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """Interpolate operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("Interpolate")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.nn.functional.interpolate(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.nn.functional.interpolate(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/inverse.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)
+# inverse(input) — only out-of-place (no out parameter)
+
+_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.float32: {"atol": 1e-5, "rtol": 1e-4},
+}
+
+_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="inverse - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """inverse operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("inverse")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.inverse(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.inverse(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/isclose.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, extra_kwargs_or_None)
+# isclose compares two tensors with atol/rtol; we include kwargs combinations
+
+_TEST_CASES_DATA = [
+    ((8, 8), None, None, {"rtol": 1e-05, "atol": 1e-08}),
+    ((8, 8), (16, 1), (16, 1), {"rtol": 1e-03, "atol": 1e-05}),
+    ((8, 8), None, (0, 1), {"rtol": 1e-02, "atol": 1e-03}),
+    ((2, 3, 4), None, None, {"rtol": 1e-02, "atol": 1e-03}),
+    ((1, 8), None, None, {"equal_nan": True}),
+    ((16, 64), (128, 1), (128, 1), 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.float32, infinicore.bfloat16]
+
+
+def parse_test_cases():
+    test_cases = []
+    for data in _TEST_CASES_DATA:
+        shape, a_strides, b_strides, extra = data[0], data[1], data[2], data[3]
+
+        a_supports_inplace = not is_broadcast(a_strides)
+        b_supports_inplace = not is_broadcast(b_strides)
+
+        for dtype in _TENSOR_DTYPES:
+            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-3})
+            a_spec = TensorSpec.from_tensor(shape, a_strides, dtype)
+            b_spec = TensorSpec.from_tensor(shape, b_strides, dtype)
+
+            kwargs = {}
+            if extra is not None:
+                kwargs.update(extra)
+
+            # Out-of-place  not support 'out='
+            test_cases.append(
+                TestCase(
+                    inputs=[a_spec, b_spec],
+                    kwargs=kwargs,
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="IsClose - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """IsClose operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("IsClose")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.isclose(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.isclose(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()

test/infinicore/ops/isfinite.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)
+# isfinite checks each element for finiteness and returns boolean tensor
+
+_TEST_CASES_DATA = [
+    ((8, 8), None),
+    ((8, 8), (16, 1)),
+    ((8, 8), None),
+    ((2, 3, 4), None),
+    ((1, 8), None),
+    ((16, 128), (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 = data[0], data[1]
+
+        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-of-place
+            test_cases.append(
+                TestCase(
+                    inputs=[input_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tol,
+                    description="IsFinite - OUT_OF_PLACE",
+                )
+            )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """IsFinite operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("IsFinite")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, *args, **kwargs):
+        return torch.isfinite(*args, **kwargs)
+
+    # def infinicore_operator(self, *args, **kwargs):
+    #     """InfiniCore implementation (operator not yet available)."""
+    #     return infinicore.isfinite(*args, **kwargs)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()