Add Max operator test plan

forge/test/operators/pytorch/reduce/test_max.py

@@ -0,0 +1,177 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC
+
+# SPDX-License-Identifier: Apache-2.0
+
+import torch
+import random
+import math
+
+from torch import nn
+
+from typing import List, Dict
+from loguru import logger
+
+from forge.op_repo import TensorShape
+from forge.verify.config import VerifyConfig
+
+from forge.verify.value_checkers import AllCloseValueChecker
+
+from test.operators.utils import InputSourceFlags, VerifyUtils
+from test.operators.utils import InputSource
+from test.operators.utils import TestVector
+from test.operators.utils import TestPlan
+from test.operators.utils.compat import TestDevice
+from test.operators.utils import TestCollection
+from test.operators.utils import TestCollectionCommon
+from test.operators.utils import ValueRanges
+
+from test.operators.pytorch.eltwise_unary import ModelFromAnotherOp, ModelDirect, ModelConstEvalPass
+
+
+class ModelFromAnotherOpMax(nn.Module):
+    def __init__(self, operator, kwargs):
+        super().__init__()
+        self.testname = "Element_wise_unary_operators_test_op_src_from_another_op"
+        self.operator = operator
+        self.kwargs = kwargs
+
+    def forward(self, x):
+        xx = torch.add(x, x)
+        return self.operator(xx, **self.kwargs)[0]
+
+
+class ModelDirectMax(nn.Module):
+    def __init__(self, operator, kwargs):
+        super().__init__()
+        self.testname = "Element_wise_unary_operators_test_op_src_from_host"
+        self.operator = operator
+        self.kwargs = kwargs
+
+    def forward(self, x):
+        return self.operator(x, **self.kwargs)[0]
+
+
+class ModelConstEvalPassMax(nn.Module):
+    def __init__(self, operator, shape: TensorShape, kwargs):
+        super().__init__()
+        self.testname = "Element_wise_unary_operators_test_op_src_const_eval_pass"
+        self.operator = operator
+        self.kwargs = kwargs
+        self.c = (torch.rand(shape, requires_grad=False) - 0.5).detach()
+
+    def forward(self, x):
+        cc = self.operator(self.c, **self.kwargs)[0]
+        xx = self.operator(x, **self.kwargs)[0]
+        return torch.add(xx, cc)
+
+
+class TestVerification:
+
+    MODEL_TYPES = {
+        InputSource.FROM_ANOTHER_OP: ModelFromAnotherOp,
+        InputSource.FROM_HOST: ModelDirect,
+        InputSource.FROM_DRAM_QUEUE: ModelDirect,
+        InputSource.CONST_EVAL_PASS: ModelConstEvalPass,
+    }
+
+    MODEL_TYPES_MAX_SPECIFIC = {
+        InputSource.FROM_ANOTHER_OP: ModelFromAnotherOpMax,
+        InputSource.FROM_HOST: ModelDirectMax,
+        InputSource.FROM_DRAM_QUEUE: ModelDirectMax,
+        InputSource.CONST_EVAL_PASS: ModelConstEvalPassMax,
+    }
+
+    @classmethod
+    def verify(
+        cls,
+        test_device: TestDevice,
+        test_vector: TestVector,
+        input_params: List[Dict] = [],
+        warm_reset: bool = False,
+    ):
+
+        input_source_flag: InputSourceFlags = None
+        if test_vector.input_source in (InputSource.FROM_DRAM_QUEUE,):
+            input_source_flag = InputSourceFlags.FROM_DRAM
+
+        operator = getattr(torch, test_vector.operator)
+        kwargs = test_vector.kwargs if test_vector.kwargs else {}
+
+        if not kwargs:
+            # if kwargs is empty, max operator returns a tensor
+            model_type = cls.MODEL_TYPES[test_vector.input_source]
+        else:
+            # if kwargs is not empty, max operator returns a tuple of tensors so we need a different model
+            model_type = cls.MODEL_TYPES_MAX_SPECIFIC[test_vector.input_source]
+
+        pytorch_model = (
+            model_type(operator, test_vector.input_shape, kwargs)
+            if test_vector.input_source in (InputSource.CONST_EVAL_PASS,)
+            else model_type(operator, kwargs)
+        )
+
+        input_shapes = tuple([test_vector.input_shape])
+
+        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,
+            warm_reset=warm_reset,
+            value_range=ValueRanges.SMALL,
+            deprecated_verification=False,
+            verify_config=VerifyConfig(value_checker=AllCloseValueChecker()),
+        )
+
+
+class TestParamsData:
+
+    __test__ = False
+
+    test_plan: TestPlan = None
+
+    operator = ["max"]
+
+    @classmethod
+    def generate_kwargs(cls, test_vector: TestVector):
+
+        rng = random.Random(math.prod(test_vector.input_shape))
+
+        dim = rng.choice(range(0, len(test_vector.input_shape)))
+
+        for ch in [True, False]:
+            yield {"dim": dim, "keepdim": ch}
+
+
+TestParamsData.test_plan = TestPlan(
+    verify=lambda test_device, test_vector: TestVerification.verify(
+        test_device,
+        test_vector,
+    ),
+    collections=[
+        # torch.max(input)
+        TestCollection(
+            operators=TestParamsData.operator,
+            input_sources=TestCollectionCommon.all.input_sources,
+            # we don't need all shapes to show this way of max usage is not working:
+            input_shapes=TestCollectionCommon.all.input_shapes[26:30],
+        ),
+        # torch.max(input, dim=..., keepdim=...)
+        TestCollection(
+            operators=TestParamsData.operator,
+            input_sources=TestCollectionCommon.all.input_sources,
+            input_shapes=TestCollectionCommon.all.input_shapes,
+            kwargs=lambda test_vector: TestParamsData.generate_kwargs(test_vector),
+        ),
+    ],
+    failing_rules=[],
+)
+
+
+def get_test_plans() -> List[TestPlan]:
+    return [TestParamsData.test_plan]