Add tilize with val padding sweep test (#15815)

New sweep:
- tilize_with_val_padding

.github/workflows/ttnn-run-sweeps.yaml

@@ -12,6 +12,7 @@ on:
           - ALL SWEEPS (Nightly)
           - add
           - tilize
+          - tilize_with_val_padding
           - untilize
           - untilize_with_unpadding
           - ccl.line_all_gather

tests/sweep_framework/sweeps/tilize_with_val_padding.py

@@ -0,0 +1,110 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+
+import torch
+import random
+import ttnn
+import math
+from tests.sweep_framework.sweep_utils.utils import gen_shapes
+from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
+
+from tests.ttnn.utils_for_testing import comp_equal, start_measuring_time, stop_measuring_time
+from models.utility_functions import torch_random
+
+from tt_lib.utils import tilize as tilize_util
+
+# Override the default timeout in seconds for hang detection.
+TIMEOUT = 30
+
+random.seed(0)
+
+
+def tilize_with_val_padding(x, output_tensor_shape, pad_value):
+    pad = torch.nn.functional.pad(
+        x,
+        tuple(j for i in reversed(range(len(x.shape))) for j in (0, output_tensor_shape[i] - x.shape[i])),
+        value=pad_value,
+    )
+    tilized = tilize_util(pad)
+    return tilized
+
+
+def _nearest_32(x):
+    return math.ceil(x / 32) * 32
+
+
+# Parameters provided to the test vector generator are defined here.
+# They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
+# Each suite has a key name (in this case "suite_1" and "suite_2") which will associate the test vectors to this specific suite of inputs.
+# Developers can create their own generator functions and pass them to the parameters as inputs.
+parameters = {
+    "xfail": {
+        "input_shape": gen_shapes([1, 1, 32, 32], [6, 12, 128, 128], [1, 1, 32, 32], 32),
+        "input_a_dtype": [ttnn.bfloat16],
+        "input_a_layout": [ttnn.ROW_MAJOR_LAYOUT],
+        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+    },
+}
+
+
+# Invalidate vector is called during the generation phase where each vector will be passed in.
+# If invalidated, the vector will still be stored but will be skipped.
+# Returns False, None if the vector is valid, and True, str with a reason for invalidation if it is invalid.
+def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
+    if test_vector["input_a_layout"] == ttnn.TILE_LAYOUT:
+        return True, "Tile layout is not supported"
+    return False, None
+
+
+# This is the run instructions for the test, defined by the developer.
+# The run function must take the above-defined parameters as inputs.
+# The runner will call this run function with each test vector, and the returned results from this function will be stored.
+# If you defined a mesh_device_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra.
+def run(
+    input_shape,
+    input_a_dtype,
+    input_a_layout,
+    input_a_memory_config,
+    output_memory_config,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+
+    torch_input_tensor_a = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
+    )(input_shape)
+
+    number_generated = torch.tensor(1, dtype=torch.bfloat16).uniform_(-100, 100).item()
+
+    padded_shape = [
+        input_shape[0],
+        input_shape[1],
+        _nearest_32(input_shape[2]),
+        _nearest_32(input_shape[3]),
+    ]
+
+    torch_output_tensor = tilize_with_val_padding(torch_input_tensor_a, padded_shape, number_generated)
+
+    input_tensor_a = ttnn.from_torch(
+        torch_input_tensor_a,
+        dtype=input_a_dtype,
+        layout=input_a_layout,
+        device=device,
+        memory_config=input_a_memory_config,
+    )
+
+    start_time = start_measuring_time()
+    result = ttnn.tilize_with_val_padding(
+        input_tensor_a, padded_shape, number_generated, memory_config=output_memory_config
+    )
+    output_tensor = ttnn.to_torch(result)
+    e2e_perf = stop_measuring_time(start_time)
+
+    return [comp_equal(torch_output_tensor, output_tensor), e2e_perf]