From 82714614db1ef1e01b4e9819214f58a14f0a2515 Mon Sep 17 00:00:00 2001
From: VirdhatchaniKN <virdhatchani.narayanamoorthy@multicorewareinc.com>
Date: Tue, 5 Nov 2024 12:06:51 +0000
Subject: [PATCH] #14710: Subtract op  Sweep for failing cases

---
 .github/workflows/ttnn-run-sweeps.yaml        |   1 +
 .../binary/subtract/subtract_tensor_fails.py  | 104 ++++++++++++++++++
 2 files changed, 105 insertions(+)
 create mode 100644 tests/sweep_framework/sweeps/eltwise/binary/subtract/subtract_tensor_fails.py

diff --git a/.github/workflows/ttnn-run-sweeps.yaml b/.github/workflows/ttnn-run-sweeps.yaml
index eb5daaf2471..19ce0783ad1 100644
--- a/.github/workflows/ttnn-run-sweeps.yaml
+++ b/.github/workflows/ttnn-run-sweeps.yaml
@@ -195,6 +195,7 @@ on:
           - eltwise.unary_complex.angle_bw.angle_bw
           - eltwise.binary.subtract.subtract
           - eltwise.binary.subtract.subtract_tensor_pytorch2
+          - eltwise.binary.subtract.subtract_tensor_fails
           - eltwise.binary.multiply.multiply
           - eltwise.binary.multiply.mul_tensor_pytorch2
           - eltwise.binary.multiply.multiply_scalar_pytorch2
diff --git a/tests/sweep_framework/sweeps/eltwise/binary/subtract/subtract_tensor_fails.py b/tests/sweep_framework/sweeps/eltwise/binary/subtract/subtract_tensor_fails.py
new file mode 100644
index 00000000000..f50389c2c38
--- /dev/null
+++ b/tests/sweep_framework/sweeps/eltwise/binary/subtract/subtract_tensor_fails.py
@@ -0,0 +1,104 @@
+# 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
+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 check_with_pcc, start_measuring_time, stop_measuring_time
+from models.utility_functions import torch_random
+
+TIMEOUT = 30
+
+random.seed(0)
+
+
+parameters = {
+    "nightly": {
+        "input_specs": [
+            {"shape": [0, 1], "other": [0, 1]},
+            {"shape": [0], "other": [0]},
+            {"shape": [1, 10], "other": [10, 1]},
+            {"shape": [1, 15], "other": [15, 1]},
+            {"shape": [1, 17], "other": [17, 1]},
+            {"shape": [1, 2], "other": [2, 1]},
+            {"shape": [16, 1, 49], "other": [16, 49, 1]},
+            {"shape": [16, 1, 64], "other": [16, 64, 1]},
+            {"shape": [24, 1], "other": [1, 24]},
+            {"shape": [4, 1, 49], "other": [4, 49, 1]},
+            {"shape": [4, 1, 64], "other": [4, 64, 1]},
+            {"shape": [64, 1, 49], "other": [64, 49, 1]},
+            {"shape": [64, 1, 64], "other": [64, 64, 1]},
+        ],
+        "input_a_dtype": [ttnn.bfloat16],
+        "input_b_dtype": [ttnn.bfloat16],
+        "input_a_layout": [ttnn.TILE_LAYOUT],
+        "input_b_layout": [ttnn.TILE_LAYOUT],
+        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "input_b_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+    },
+}
+
+
+def run(
+    input_specs,
+    input_a_dtype,
+    input_b_dtype,
+    input_a_layout,
+    input_b_layout,
+    input_a_memory_config,
+    input_b_memory_config,
+    output_memory_config,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+
+    input_shape = input_specs["shape"]
+    torch_input_tensor_a = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
+    )(input_shape)
+
+    other = input_specs["other"]
+    if isinstance(other, (int, float)):
+        torch_other_tensor = torch.tensor(other, dtype=torch.float32)
+    else:
+        torch_other_tensor = gen_func_with_cast_tt(
+            partial(torch_random, low=-100, high=100, dtype=torch.float32), input_b_dtype
+        )(other)
+
+    golden_function = ttnn.get_golden_function(ttnn.sub)
+    torch_output_tensor = golden_function(torch_input_tensor_a, torch_other_tensor)
+
+    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,
+    )
+
+    input_tensor_b = ttnn.from_torch(
+        torch_other_tensor,
+        dtype=input_b_dtype,
+        layout=input_b_layout,
+        device=device,
+        memory_config=input_b_memory_config,
+    )
+
+    start_time = start_measuring_time()
+
+    output_tensor = ttnn.subtract(input_tensor_a, input_tensor_b, memory_config=output_memory_config)
+    output_tensor = ttnn.to_torch(output_tensor)
+
+    e2e_perf = stop_measuring_time(start_time)
+
+    return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]