Tests for linear op

forge/test/operators/pytorch/nn/test_convtranspose2d.py

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+# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC
+#
+# SPDX-License-Identifier: Apache-2.0
+from functools import reduce
+import random
+import pytest
+
+from typing import List, Dict, Type, Optional, Any
+from loguru import logger
+
+import torch
+import forge
+import forge.op
+
+from forge.verify.config import VerifyConfig
+from forge.verify.value_checkers import AllCloseValueChecker
+
+from test.operators.utils import InputSourceFlags, VerifyUtils, ValueRanges
+from test.operators.utils import InputSource
+from test.operators.utils import TestVector
+from test.operators.utils import TestPlan
+from test.operators.utils import FailingReasons
+from test.operators.utils.compat import TestDevice
+from test.operators.utils.compat import TestTensorsUtils
+from test.operators.utils import TestCollection
+from test.operators.utils import TestCollectionCommon
+
+
+class ModelFromAnotherOp(torch.nn.Module):
+
+    model_name = "model_op_src_from_another_op"
+
+    def __init__(self, operator, opname, shape, kwargs):
+        super(ModelFromAnotherOp, self).__init__()
+        self.testname = "ConvTranspose2d_pytorch_operator_" + opname + "_test_op_src_from_another_op"
+        self.operator = operator
+        self.opname = opname
+        self.shape = shape
+        self.kwargs = {}
+
+        self.ct1 = self.operator(**self.kwargs)
+
+    def forward(self, x: torch.Tensor):
+        # we use Add operator to create one operands which is input for the ConvTranspose2d operator
+        add = torch.add(x, x)
+        output = self.ct1(add)
+        return output
+
+
+class ModelDirect(torch.nn.Module):
+
+    model_name = "model_op_src_from_host"
+
+    def __init__(self, operator, opname, shape, kwargs):
+        super(ModelDirect, self).__init__()
+        self.testname = "ConvTranspose2d_pytorch_operator_" + opname + "_test_op_src_from_host"
+        self.operator = operator
+        self.opname = opname
+        self.shape = shape
+        self.kwargs = {}
+
+        self.ct1 = self.operator(**self.kwargs)
+
+    def forward(self, x: torch.Tensor):
+        output = self.ct1(x)
+        return output
+
+
+class ModelConstEvalPass(torch.nn.Module):
+
+    model_name = "model_op_src_const_eval_pass"
+
+    def __init__(self, operator, opname, shape, kwargs, dtype):
+        super(ModelConstEvalPass, self).__init__()
+        self.testname = "ConvTranspose2d_pytorch_operator_" + opname + "_test_op_src_const_eval_pass"
+        self.operator = operator
+        self.opname = opname
+        self.shape = shape
+        self.kwargs = {}
+
+        self.constant = torch.rand(self.shape, dtype=dtype)
+        self.ct1 = self.operator(**self.kwargs)
+
+    def forward(self, x: torch.Tensor):
+        v1 = self.ct1(self.constant)
+        # v2 = torch.add(x, x)
+        v2 = self.ct1(x)
+        # add consume inputs
+        add = torch.add(v1, v2)
+        return add
+
+
+class TestVerification:
+
+    MODEL_TYPES = {
+        InputSource.FROM_ANOTHER_OP: ModelFromAnotherOp,
+        InputSource.FROM_HOST: ModelDirect,
+        InputSource.FROM_DRAM_QUEUE: ModelDirect,
+        InputSource.CONST_EVAL_PASS: ModelConstEvalPass,
+    }
+
+    @classmethod
+    def verify(
+        cls,
+        test_device: TestDevice,
+        test_vector: TestVector,
+        input_params: List[Dict] = [],
+        number_of_operands: int = 1,
+        warm_reset: bool = False,
+    ):
+        """Common verification function for all tests"""
+
+        input_source_flag: InputSourceFlags = None
+        if test_vector.input_source in (InputSource.FROM_DRAM_QUEUE,):
+            input_source_flag = InputSourceFlags.FROM_DRAM
+
+        operator = getattr(torch.nn, test_vector.operator)
+
+        kwargs = test_vector.kwargs if test_vector.kwargs else {}
+
+        model_type = cls.MODEL_TYPES[test_vector.input_source]
+        if test_vector.input_source == InputSource.CONST_EVAL_PASS:
+            pytorch_model = model_type(
+                operator=operator,
+                opname=test_vector.operator,
+                shape=test_vector.input_shape,
+                kwargs=kwargs,
+                dtype=TestTensorsUtils.get_dtype_for_df(test_vector.dev_data_format),
+            )
+        else:
+            pytorch_model = model_type(
+                operator=operator,
+                opname=test_vector.operator,
+                shape=test_vector.input_shape,
+                kwargs=kwargs,
+            )
+
+        input_shapes = tuple([test_vector.input_shape for _ in range(number_of_operands)])
+        logger.trace(f"***input_shapes: {input_shapes}")
+
+        VerifyUtils.verify(
+            model=pytorch_model,
+            test_device=test_device,
+            input_shapes=input_shapes,
+            input_params=input_params,
+            input_source_flag=input_source_flag,
+            dev_data_format=test_vector.dev_data_format,
+            math_fidelity=test_vector.math_fidelity,
+            pcc=test_vector.pcc,
+            warm_reset=warm_reset,
+            deprecated_verification=False,
+            verify_config=VerifyConfig(value_checker=AllCloseValueChecker(rtol=1e-2, atol=1e-2)),
+            value_range=ValueRanges.SMALL,
+        )
+
+
+class TestParamsData:
+
+    __test__ = False  # Avoid collecting TestParamsData as a pytest test
+
+    test_plan: TestPlan = None
+
+    @classmethod
+    def generate_kwargs(cls, test_vector: TestVector):
+        kwarg_list = []
+        rng = random.Random(sum(test_vector.input_shape))
+        # if len(test_vector.input_shape) == 4:
+        # N = test_vector.input_shape[-4]
+        N = test_vector.input_shape[0]
+        C_in = test_vector.input_shape[-3]
+        H_in = test_vector.input_shape[-2]
+        W_in = test_vector.input_shape[-1]
+
+        in_channels = C_in
+        out_channels = rng.randint(1, C_in + 100)  # it can be less, equal or greater than in_channels
+
+        dilation = rng.randint(1, 3)
+
+        # Two cases for kernel size
+        k_maxh = (H_in if H_in > 3 else 3) / dilation + 1
+        k_maxw = (W_in if W_in > 3 else 3) / dilation + 1
+        kernel_size_option1 = rng.randint(3, k_maxh)
+        kernel_size_option2 = rng.randint(3, k_maxw)
+        # 1. kernel is equal to integer
+        kernel_size = random.choice(kernel_size_option1, kernel_size_option2)
+        # make it odd number
+        kernel_size = kernel_size if kernel_size % 2 != 0 else kernel_size + 1
+        # 2. kernel is equal to tuple
+        kernel_size = (kernel_size_option1, kernel_size_option2)
+        # assert that kernel value will fit in the input shape
+        # if isinstance(kernel_size, tuple):
+        #     assert dilation * (kernel_size[0] - 1) < H_in, "Invalid kernel height!"
+        #     assert dilation * (kernel_size[1] - 1) < W_in, "Invalid kernel width!"
+        # else:
+        #     assert dilation * (kernel_size - 1) < H_in, "Invalid height"
+        #     assert dilation * (kernel_size - 1) < W_in, "Invalid width"
+
+        kwarg_list.append(
+            {"in_channels": in_channels, "out_channels": out_channels, "kernel_size": kernel_size, "dilation": dilation}
+        )
+        return kwarg_list
+
+
+class TestCollectionData:
+
+    __test__ = False  # Avoid collecting TestCollectionData as a pytest test
+
+    all = TestCollection(
+        operators=[
+            "ConvTranspose2d",  # 00
+        ],
+        input_sources=TestCollectionCommon.all.input_sources,
+        # only 4D input tensors are supported
+        input_shapes=[input_shape for input_shape in TestCollectionCommon.all.input_shapes if len(input_shape) == 4],
+        dev_data_formats=TestCollectionCommon.all.dev_data_formats,
+        math_fidelities=TestCollectionCommon.all.math_fidelities,
+    )
+
+    single = TestCollection(
+        input_sources=TestCollectionCommon.single.input_sources,
+        input_shapes=TestCollectionCommon.single.input_shapes,
+        dev_data_formats=TestCollectionCommon.single.dev_data_formats,
+        math_fidelities=TestCollectionCommon.single.math_fidelities,
+    )
+
+
+TestParamsData.test_plan = TestPlan(
+    verify=lambda test_device, test_vector: TestVerification.verify(
+        test_device,
+        test_vector,
+    ),
+    collections=[
+        # Test plan:
+        # 2. Operand source(s):
+        # 3. Operand shapes type(s):
+        # 4. Operand / output size of dimensions
+        TestCollection(
+            operators=TestCollectionData.all.operators,
+            input_sources=TestCollectionData.single.input_sources,
+            input_shapes=TestCollectionData.all.input_shapes,
+            kwargs=lambda test_vector: TestParamsData.generate_kwargs(test_vector),
+        ),
+        # # Test plan:
+        # # 5. Data format
+        # TestCollection(
+        #     operators=TestCollectionData.all.operators,
+        #     input_sources=TestCollectionData.single.input_sources,
+        #     input_shapes=TestCollectionData.single.input_shapes,
+        #     kwargs=lambda test_vector: TestParamsData.generate_kwargs(test_vector),
+        #     dev_data_formats=TestCollectionCommon.float.dev_data_formats,
+        #     math_fidelities=TestCollectionData.single.math_fidelities,
+        # ),
+        # # Test plan:
+        # # 6. Math fidelity
+        # TestCollection(
+        #     operators=TestCollectionData.all.operators,
+        #     input_sources=TestCollectionData.single.input_sources,
+        #     input_shapes=TestCollectionData.single.input_shapes,
+        #     kwargs=lambda test_vector: TestParamsData.generate_kwargs(test_vector),
+        #     dev_data_formats=TestCollectionData.single.dev_data_formats,
+        #     math_fidelities=TestCollectionData.all.math_fidelities,
+        # ),
+    ],
+    failing_rules=[],
+)
+
+
+def get_test_plans() -> List[TestPlan]:
+    return [
+        TestParamsData.test_plan,
+    ]