[CCL] Fix ccl sweeps failure (#15304)

### Ticket
#15287 

### Problem description
Sweeps tests generation failing in main

### What's changed
Fix test generation

### 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)
- [ ] New/Existing tests provide coverage for changes

tests/sweep_framework/sweeps/ccl/all_gather_n300.py

@@ -45,6 +45,7 @@
         ],
         "enable_async": [True],
         "num_iters": [1],
+        "tile": [(32, 32)],
     },
 }
 
@@ -58,6 +59,7 @@ def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
         test_vector["num_links"],
         test_vector["input_dtype"],
         test_vector["layout"],
+        test_vector["tile"],
     )
     if is_known_failure:
         return True, f"Skipping unsupported case {message}."
@@ -90,6 +92,7 @@ def run(
     mem_config,
     enable_async,
     num_iters,
+    tile,
     *,
     device,
 ) -> list:
@@ -108,7 +111,9 @@ def run(
     input_tensors = torch.chunk(input_tensor, num_devices, dim)
     tt_input_tensors = []
     for i, t in enumerate(input_tensors):
-        tt_input_tensors.append(ttnn.Tensor(t, input_dtype).to(layout).to(all_devices[i], mem_config))
+        tt_input_tensors.append(
+            ttnn.Tensor(t, input_dtype, {}, ttnn.Tile(tile)).to(layout).to(all_devices[i], mem_config)
+        )
 
     input_tensor_mesh = ttnn.aggregate_as_tensor(tt_input_tensors)
     for i in range(num_iters):

tests/sweep_framework/sweeps/ccl/all_gather_n300_focused.py

@@ -121,6 +121,7 @@
         ],
         "enable_async": [True],
         "num_iters": [1],
+        "tile": [(32, 32)],
     },
     "all_gather_n300_focused_large": {
         "num_devices": [2],
@@ -134,6 +135,7 @@
         ],
         "enable_async": [True],
         "num_iters": [1],
+        "tile": [(32, 32)],
     },
 }
 
@@ -179,6 +181,7 @@ def run(
     mem_config,
     enable_async,
     num_iters,
+    tile,
     *,
     device,
 ) -> list:
@@ -204,7 +207,7 @@ def run(
     input_tensors = torch.chunk(input_tensor, num_devices, dim)
     tt_input_tensors = []
     for i, t in enumerate(input_tensors):
-        t = ttnn.from_torch(t, input_dtype, layout=ttnn.Layout.TILE)
+        t = ttnn.from_torch(t, input_dtype, tile=ttnn.Tile(tile), layout=ttnn.Layout.TILE)
         t = t.to(all_devices[i], mem_config)
         tt_input_tensors.append(t)
 

tests/sweep_framework/sweeps/ccl/line_all_gather.py

@@ -39,6 +39,7 @@
         "mem_config": [ttnn.MemoryConfig(buffer_type=ttnn.BufferType.DRAM)],
         "enable_async": [True, False],
         "num_iters": [1],
+        "tile": [(32, 32)],
     },
 }
 
@@ -52,6 +53,7 @@ def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
         test_vector["num_links"],
         test_vector["input_dtype"],
         test_vector["layout"],
+        test_vector["tile"],
     )
     if is_known_failure:
         return True, f"Skipping unsupported case {message}."
@@ -89,6 +91,7 @@ def run(
     mem_config,
     enable_async,
     num_iters,
+    tile,
     *,
     device,
 ) -> list:
@@ -100,7 +103,9 @@ def run(
 
     input_tensor = torch.rand(input_shape).bfloat16()
 
-    ttnn_tensor = ttnn.from_torch(input_tensor, mesh_mapper=ShardTensorToMesh(t3k_mesh_device, dim=dim))
+    ttnn_tensor = ttnn.from_torch(
+        input_tensor, tile=ttnn.Tile(tile), mesh_mapper=ShardTensorToMesh(t3k_mesh_device, dim=dim)
+    )
     input_tensor_mesh = ttnn.to_device(ttnn_tensor, t3k_mesh_device)
 
     for i in range(num_iters):