Add forge sweeps for slice and transpose (#16112)

### Ticket
#16106: add forge slice sweeps
#15945: add forge transpose sweeps

### Problem description
There are new input parameters for forge models. These sweeps sweep all
of them.

### What's changed
So far, Forge is at 99% coverage whereas slice is at 80% coverage.

### Checklist
- [ ] Post commit CI passes
- [ ] Blackhole Post commit (if applicable)
- [ ] Model regression CI testing passes (if applicable)
- [ ] Device performance regression CI testing passes (if applicable)
- [ ] **(For models and ops writers)** Full [new
models](https://github.com/tenstorrent/tt-metal/actions/workflows/full-new-models-suite.yaml)
tests passes
- [ ] New/Existing tests provide coverage for changes

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

@@ -332,10 +332,12 @@ on:
           - data_movement.concat.concat_pytorch2
           - data_movement.slice.slice_pytorch2_rm
           - data_movement.slice.slice_pytorch2_tiled
+          - data_movement.slice.slice_forge
           - data_movement.permute.permute
           - data_movement.permute.permute_pytorch2_tiled
           - data_movement.permute.permute_pytorch2_rm
           - data_movement.transpose.transpose_pytorch2
+          - data_movement.transpose.transpose_forge
           - data_movement.transpose.transpose_interleaved
           - data_movement.transpose.t_pytorch2
           - data_movement.copy.copy

tests/sweep_framework/sweeps/data_movement/slice/slice_forge.py

@@ -0,0 +1,82 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+
+import json
+import os
+import torch
+import random
+import ttnn
+
+from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
+from models.utility_functions import torch_random
+
+TIMEOUT = 10
+random.seed(0)
+
+# Load the processed slice specs from slice_forge_processed.json
+base_dir = os.path.dirname(os.path.abspath(__file__))
+json_path = os.path.join(base_dir, "slice_forge_processed.json")
+with open(json_path, "r") as f:
+    processed_slice_specs = json.load(f)
+
+parameters = {
+    "nightly": {
+        "slice_specs": processed_slice_specs,
+        "dtype": [ttnn.bfloat16],
+        "layout": [ttnn.ROW_MAJOR_LAYOUT, ttnn.TILE_LAYOUT],
+    }
+}
+
+
+# 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["layout"] == ttnn.ROW_MAJOR_LAYOUT:
+        if test_vector["dtype"] == ttnn.bfloat8_b:
+            return True, "bfloat8_b not supported with ROW_MAJOR_LAYOUT"
+    if test_vector["dtype"] == ttnn.bfloat8_b:
+        if len(test_vector["slice_specs"]["dims"]) < 2:
+            return True, "bfloat8_b not supported with dims  < 2"
+
+    return False, None
+
+
+def run(
+    slice_specs,
+    dtype,
+    layout,
+    *,
+    device,
+):
+    device.enable_async(False)
+
+    dims = slice_specs["dims"]
+    begins = slice_specs["begins"]
+    ends = slice_specs["ends"]
+    steps = slice_specs["step"]
+
+    # Create the torch input tensor
+    tensor = torch_random(dims, -0.1, 0.1, dtype=torch.bfloat16)
+
+    # Construct Python slice objects from begins, ends, steps
+    indices = [slice(begins[i], ends[i], steps[i]) for i in range(len(begins))]
+
+    # Apply slicing to the torch tensor
+    torch_output_tensor = tensor[tuple(indices)]
+
+    # Convert the input tensor to TTNN
+    ttnn_tensor = ttnn.from_torch(tensor, device=device, layout=layout, dtype=dtype)
+
+    # Run the slicing on TTNN
+    start_time = start_measuring_time()
+    ttnn_output = ttnn.slice(ttnn_tensor, begins, ends, steps)
+    e2e_perf = stop_measuring_time(start_time)
+
+    # Convert TTNN output back to torch
+    ttnn_output_tensor = ttnn.to_torch(ttnn_output)
+
+    return [check_with_pcc(torch_output_tensor, ttnn_output_tensor, 0.999), e2e_perf]

tests/sweep_framework/sweeps/data_movement/slice/slice_forge_processed.json

@@ -0,0 +1 @@
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