issue/610 - removed deprecated templates

test/infinicore/framework/templates.py

-"""
-Templates for common operator patterns to minimize code duplication
-
-Available configuration methods in BaseOperatorTest:
-
-1. get_test_cases() -> List[TestCase]
-   - Define input/output shapes, strides, and operation modes
-   - Operation modes: TestCase.OUT_OF_PLACE, TestCase.IN_PLACE, TestCase.BOTH
-
-2. get_tensor_dtypes() -> List[infinicore.dtype]
-   - Define supported data types for single-dtype tests
-   - Used when dtype_combinations is None
-
-3. get_tolerance_map() -> Dict[infinicore.dtype, Dict[str, float]]
-   - Set tolerance (atol, rtol) for each data type
-   - Example: {infinicore.float16: {"atol": 1e-3, "rtol": 1e-2}}
-
-4. get_dtype_combinations() -> Optional[List[Dict]]
-   - Define mixed dtype configurations for multi-dtype tests
-   - Return None for single-dtype tests
-
-5. torch_operator(*inputs, out=None, **kwargs) -> torch.Tensor
-   - Implement PyTorch reference implementation
-
-6. infinicore_operator(*inputs, out=None, **kwargs) -> infinicore.Tensor
-   - Implement Infinicore operator implementation
-
-New Tensor Initialization Modes:
-- TensorInitializer.RANDOM (default): Random values using torch.rand
-- TensorInitializer.ZEROS: All zeros using torch.zeros
-- TensorInitializer.ONES: All ones using torch.ones
-- TensorInitializer.RANDINT: Random integers using torch.randint
-- TensorInitializer.MANUAL: Use a pre-existing tensor with shape/strides validation
-- TensorInitializer.BINARY: Use a pre-existing tensor with shape validation only
-
-Usage examples in TestCase creation:
-- Basic: TensorSpec.from_tensor(shape)
-- With initialization: TensorSpec.from_tensor(shape, init_mode=TensorInitializer.ZEROS)
-- Strided with custom init: TensorSpec.from_strided_tensor(shape, strides, init_mode=TensorInitializer.ONES)
-"""
-
-import torch
-import infinicore
-from .base import BaseOperatorTest
-from .tensor import TensorSpec, TensorInitializer
-
-
-class BinaryOperatorTest(BaseOperatorTest):
-    """Template for binary operators (matmul, add, mul, etc.)"""
-
-    def __init__(self, operator_name, test_cases, tensor_dtypes, tolerance_map):
-        self._operator_name = operator_name
-        self._test_cases = test_cases
-        self._tensor_dtypes = tensor_dtypes
-        self._tolerance_map = tolerance_map
-        super().__init__(operator_name)
-
-    def get_test_cases(self):
-        return self._test_cases
-
-    def get_tensor_dtypes(self):
-        return self._tensor_dtypes
-
-    def get_tolerance_map(self):
-        return self._tolerance_map
-
-    def torch_operator(self, *inputs, **kwargs):
-        """Generic torch operator dispatch"""
-        # Support both functional and method calls
-        if hasattr(torch, self._operator_name):
-            op = getattr(torch, self._operator_name)
-        else:
-            # Fallback to common operator mappings
-            op_mapping = {
-                "matmul": torch.matmul,
-                "add": torch.add,
-                "mul": torch.mul,
-                "sub": torch.sub,
-                "div": torch.div,
-            }
-            op = op_mapping.get(self._operator_name)
-            if op is None:
-                raise NotImplementedError(
-                    f"Torch operator {self._operator_name} not implemented"
-                )
-
-        return op(*inputs, **kwargs)
-
-    def infinicore_operator(self, *inputs, **kwargs):
-        """Generic infinicore operator dispatch"""
-        op = getattr(infinicore, self._operator_name)
-        return op(*inputs, **kwargs)
-
-
-class UnaryOperatorTest(BinaryOperatorTest):
-    """Template for unary operators (exp, log, sin, etc.)"""
-
-    def torch_operator(self, *inputs, **kwargs):
-        # For unary operators, we only use the first input
-        if hasattr(torch, self._operator_name):
-            op = getattr(torch, self._operator_name)
-            return op(inputs[0], **kwargs)
-        else:
-            return super().torch_operator(*inputs, **kwargs)
-
-    def infinicore_operator(self, *inputs, **kwargs):
-        op = getattr(infinicore, self._operator_name)
-        return op(inputs[0], **kwargs)