#12589: Migrate floor div sweep to new sweep framework

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

@@ -353,6 +353,7 @@ on:
           - eltwise.binary.fmod.fmod_unary
           - eltwise.binary.fmod.fmod_unary_sharded
           - eltwise.binary.floor_divide.floor_divide_pytorch2
+          - eltwise.binary.floor_divide.floor_divide
           - eltwise.binary.logaddexp.logaddexp
           - eltwise.binary.logaddexp2.logaddexp2
           - eltwise.binary.ldexp.ldexp

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

@@ -0,0 +1,108 @@
+# SPDX-FileCopyrightText: © 2025 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, sanitize_shape_rm
+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, is_wormhole_b0
+
+# Override the default timeout in seconds for hang detection.
+TIMEOUT = 30
+
+random.seed(0)
+
+
+# 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 = {
+    "nightly": {
+        "input_shape": [[1, 1, 32, 32], [1, 1, 320, 384], [1, 3, 320, 384]],
+        "input_a_dtype": [ttnn.bfloat16],
+        "input_b_dtype": [ttnn.bfloat16],
+        "input_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_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 mesh_device_fixture():
+    device = ttnn.open_device(device_id=0)
+    assert ttnn.device.is_wormhole_b0(device) or ttnn.device.is_blackhole(device), "This op is not available for GS"
+    yield (device, "Wormhole_B0", "Blackhole")
+    ttnn.close_device(device)
+    del device
+
+
+# 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_layout"] == ttnn.ROW_MAJOR_LAYOUT:
+        return True, "Unary operation requires tensor to be in Tile layout when working with non-sharded input tensor"
+    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_b_dtype,
+    input_layout,
+    input_a_memory_config,
+    input_b_memory_config,
+    output_memory_config,
+    *,
+    device,
+) -> list:
+    if input_layout == ttnn.ROW_MAJOR_LAYOUT:
+        input_shape = sanitize_shape_rm(input_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)
+
+    torch_input_tensor_b = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
+    )(input_shape)
+
+    golden_function = ttnn.get_golden_function(ttnn.floor_div)
+    torch_output_tensor = golden_function(torch_input_tensor_a, torch_input_tensor_b)
+
+    input_tensor_a = ttnn.from_torch(
+        torch_input_tensor_a,
+        dtype=input_a_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=input_a_memory_config,
+    )
+
+    input_tensor_b = ttnn.from_torch(
+        torch_input_tensor_b,
+        dtype=input_b_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=input_b_memory_config,
+    )
+
+    start_time = start_measuring_time()
+    output_tensor = ttnn.floor_div(input_tensor_a, input_tensor_b, memory_config=output_memory_config)
+    e2e_perf = stop_measuring_time(start_time)
+
+    output_tensor = ttnn.to_torch(output_tensor)
+
+    return [check_with_pcc(torch_output_tensor, output_tensor, 0.99), e2e_perf]

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

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+# SPDX-FileCopyrightText: © 2025 Tenstorrent Inc.
 
 # SPDX-License-Identifier: Apache-2.0
 
@@ -39,7 +39,7 @@
 
 def mesh_device_fixture():
     device = ttnn.open_device(device_id=0)
-    assert ttnn.device.is_wormhole_b0(device) and ttnn.device.is_blackhole(device), "This op is not available for GS"
+    assert ttnn.device.is_wormhole_b0(device) or ttnn.device.is_blackhole(device), "This op is not available for GS"
     yield (device, "Wormhole_B0", "Blackhole")
     ttnn.close_device(device)
     del device
@@ -51,8 +51,6 @@ def mesh_device_fixture():
 def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
     if test_vector["input_layout"] == ttnn.ROW_MAJOR_LAYOUT:
         return True, "Unary operation requires tensor to be in Tile layout when working with non-sharded input tensor"
-    if test_vector["input_a_dtype"] == ttnn.bfloat8_b:
-        return True, "Input_tensor_a doesn't support bfloat8_b"
     return False, None