#0: update ref links to eltwise pytorch2 sweeps (#15355)

### Ticket
Link to Github Issue

### Problem description
update eltwise pytorch2 sweeps with reference links to .md files

### What's changed
update eltwise pytorch2 sweeps with reference links to .md files

### Checklist
- [ ] Post commit CI passes
https://github.com/tenstorrent/tt-metal/actions/runs/12028888779
- [ ] Blackhole Post commit (if applicable)
- [ ] Model regression CI testing passes (if applicable)
- [ ] Device performance regression CI testing passes (if applicable)
- [x] New/Existing tests provide coverage for changes

tests/sweep_framework/sweeps/eltwise/binary/add/add_all_pytorch2.py

@@ -6,18 +6,14 @@
 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
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
+# Ref: https://github.com/tenstorrent/pytorch2.0_ttnn/blob/main/docs/operations/aten.add.Tensor.md
 
 # 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.
@@ -548,8 +544,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype

tests/sweep_framework/sweeps/eltwise/binary/add/add_set2_pytorch2.py

@@ -14,6 +14,8 @@
 from models.utility_functions import torch_random
 
 
+# Ref: https://github.com/tenstorrent/pytorch2.0_ttnn/blob/main/docs/operations/aten.add.Tensor.md
+
 # 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") which will associate the test vectors to this specific suite of inputs.

tests/sweep_framework/sweeps/eltwise/binary/div/div_tensor_pytorch2.py

@@ -6,18 +6,14 @@
 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
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
+# Ref: https://github.com/tenstorrent/pytorch2.0_ttnn/blob/main/docs/operations/aten.div.Tensor.md
 
 
 parameters = {
@@ -143,8 +139,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     input_shape = input_specs["shape"]
     if len(input_shape) == 0:

tests/sweep_framework/sweeps/eltwise/binary/eq/eq_scalar_pytorch2.py

@@ -6,18 +6,14 @@
 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
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
+# Ref: https://github.com/tenstorrent/pytorch2.0_ttnn/blob/main/docs/operations/aten.eq.Scalar.md
 
 
 # Parameters provided to the test vector generator are defined here.
@@ -63,8 +59,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype

tests/sweep_framework/sweeps/eltwise/binary/floor_divide/floor_divide_pytorch2.py

@@ -6,19 +6,14 @@
 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
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
+# Ref: https://github.com/tenstorrent/pytorch2.0_ttnn/blob/main/docs/operations/aten.floor_divide.default.md
 
 # 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.
@@ -62,8 +57,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype

tests/sweep_framework/sweeps/eltwise/binary/gt/gt_scalar_pytorch2.py

@@ -6,18 +6,14 @@
 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
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
+# Ref: https://github.com/tenstorrent/pytorch2.0_ttnn/blob/main/docs/operations/aten.gt.Scalar.md
 
 
 # Parameters provided to the test vector generator are defined here.
@@ -54,8 +50,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype

tests/sweep_framework/sweeps/eltwise/binary/le/le_tensor_pytorch2.py

@@ -6,18 +6,14 @@
 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
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
+# Ref: https://github.com/tenstorrent/pytorch2.0_ttnn/blob/main/docs/operations/aten.le.Tensor.md
 
 # 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.
@@ -53,8 +49,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype

tests/sweep_framework/sweeps/eltwise/binary/lt/lt_scalar_pytorch2.py

@@ -6,18 +6,14 @@
 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
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
+# Ref: https://github.com/tenstorrent/pytorch2.0_ttnn/blob/main/docs/operations/aten.lt.Scalar.md
 
 
 parameters = {
@@ -47,8 +43,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype

tests/sweep_framework/sweeps/eltwise/binary/lt/lt_tensor_pytorch2.py

@@ -6,18 +6,14 @@
 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 assert_equal, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
+# Ref: https://github.com/tenstorrent/pytorch2.0_ttnn/blob/main/docs/operations/aten.lt.Tensor.md
 
 parameters = {
     "nightly": {
@@ -45,16 +41,15 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
     )(input_shape_a)
     torch_input_tensor_b = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_b_dtype
     )(input_shape_b)
-    
+
     golden_function = ttnn.get_golden_function(ttnn.lt)
     torch_output_tensor = golden_function(torch_input_tensor_a, torch_input_tensor_b)
 

tests/sweep_framework/sweeps/eltwise/binary/multiply/mul_tensor_pytorch2.py

@@ -6,18 +6,14 @@
 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
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
+# Ref: https://github.com/tenstorrent/pytorch2.0_ttnn/blob/main/docs/operations/aten.mul.Tensor.md
 
 # 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.
@@ -427,8 +423,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype

tests/sweep_framework/sweeps/eltwise/binary/multiply/multiply_scalar_pytorch2.py

@@ -6,18 +6,14 @@
 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
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
+# Ref: https://github.com/tenstorrent/pytorch2.0_ttnn/blob/main/docs/operations/aten.mul.Scalar.md
 
 
 parameters = {
@@ -43,8 +39,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype

tests/sweep_framework/sweeps/eltwise/binary/ne/ne_scalar_pytorch2.py

@@ -6,18 +6,14 @@
 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
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
+# Ref: https://github.com/tenstorrent/pytorch2.0_ttnn/blob/main/docs/operations/aten.ne.Scalar.md
 
 
 parameters = {
@@ -48,8 +44,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype

tests/sweep_framework/sweeps/eltwise/binary/remainder/remainder_scalar_pytorch2.py

@@ -6,18 +6,14 @@
 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
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
+# Ref: https://github.com/tenstorrent/pytorch2.0_ttnn/blob/main/docs/operations/aten.remainder.Scalar.md
 
 
 # Parameters provided to the test vector generator are defined here.
@@ -52,8 +48,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype