#15246: Add sweep tests for logical_and, logical_or, logical_xor (#16132

)

### Ticket
[Link to Github
Issue](#11512)

### Problem description
We need sweep tests testing how ops behave when input is sharded.

### What's changed
Added sweep tests for multiple ops when input is sharded:

logical_and
logical_or
logical_xor

Also updated unit tests with failing situations.

### Pass rates for new sweeps:
sweeps/eltwise/binary/logical_and/logical_and_sharded.py: 210 fail, 1326
pass (86%)
sweeps/eltwise/binary/logical_or/logical_or_sharded.py: 253 fail, 1283
pass (83%)
sweeps/eltwise/binary/logical_xor/logical_xor_sharded.py: 1207 fail, 329
pass (21%)

### Checklist
- [X] Post commit CI passes
https://github.com/tenstorrent/tt-metal/actions/runs/12431640958
- [X] Sweep tests pass

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

@@ -298,12 +298,15 @@ on:
           - eltwise.binary.logical_or.logical_or
           - eltwise.binary.logical_or.logical_or_output
           - eltwise.binary.logical_or.logical_or_forge
+          - eltwise.binary.logical_or.logical_or_sharded
           - eltwise.binary.logical_xor.logical_xor_
           - eltwise.binary.logical_xor.logical_xor
+          - eltwise.binary.logical_xor.logical_xor_sharded
           - eltwise.binary.logical_and.logical_and_
           - eltwise.binary.logical_and.logical_and
           - eltwise.binary.logical_and.logical_and_output
           - eltwise.binary.logical_and.logical_and_forge
+          - eltwise.binary.logical_and.logical_and_sharded
           - eltwise.binary.polyval.polyval
           - eltwise.binary.remainder.remainder
           - eltwise.binary.remainder.remainder_scalar_pytorch2

tests/sweep_framework/sweeps/eltwise/binary/logical_and/logical_and_sharded.py

@@ -0,0 +1,120 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+
+import math
+import torch
+import random
+import ttnn
+from tests.sweep_framework.sweep_utils.utils import gen_shapes, tensor_to_dtype, sanitize_shape_rm
+from tests.sweep_framework.sweep_utils.sharding_utils import gen_sharded_spec_unary, parse_sharding_spec
+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, 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)
+
+# 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.
+# Developers can create their own generator functions and pass them to the parameters as inputs.
+parameters = {
+    "nightly": {
+        "input_spec": gen_sharded_spec_unary(8, layouts=["TILE_LAYOUT"]),
+        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_b_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "output_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+    },
+}
+
+
+# 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]]:
+    input_shape, X, Y, sharding_strategy, _, _, input_layout = test_vector["input_spec"].values()
+    pre_sharded_height = math.prod(input_shape[:-1])
+    pre_sharded_width = input_shape[-1]
+
+    if input_layout == "ROW_MAJOR_LAYOUT":
+        return True, "Input to eltwise binary must be tilized"
+
+    if input_layout == "ROW_MAJOR_LAYOUT" and test_vector["input_a_dtype"] == ttnn.bfloat8_b:
+        return True, "bfloat8_b is only supported on tiled layout"
+
+    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 device_mesh_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_spec,
+    input_a_dtype,
+    input_b_dtype,
+    output_dtype,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+
+    (
+        input_shape,
+        core_grid,
+        sharding_strategy,
+        shard_orientation,
+        tensor_hw_as_shard_shape,
+        input_layout,
+    ) = parse_sharding_spec(input_spec)
+
+    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_b_dtype
+    )(input_shape)
+
+    torch_output_tensor = tensor_to_dtype(torch.logical_and(torch_input_tensor_a, torch_input_tensor_b), output_dtype)
+
+    sharded_config = ttnn.create_sharded_memory_config_(
+        shape=input_shape,
+        core_grid=core_grid,
+        strategy=sharding_strategy,
+        orientation=shard_orientation,
+        use_height_and_width_as_shard_shape=tensor_hw_as_shard_shape,
+    )
+
+    input_tensor_a = ttnn.from_torch(
+        torch_input_tensor_a,
+        dtype=input_a_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=sharded_config,
+    )
+    input_tensor_b = ttnn.from_torch(
+        torch_input_tensor_b,
+        dtype=input_b_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=sharded_config,
+    )
+    start_time = start_measuring_time()
+    output_tensor = ttnn.logical_and(input_tensor_a, input_tensor_b, memory_config=sharded_config, dtype=output_dtype)
+    output_tensor = ttnn.to_torch(output_tensor)
+    e2e_perf = stop_measuring_time(start_time)
+
+    # pcc = assert_equal(torch_output_tensor, output_tensor)
+    pcc = check_with_pcc(torch_output_tensor, output_tensor, 0.999)
+    return [pcc, e2e_perf]

tests/sweep_framework/sweeps/eltwise/binary/logical_or/logical_or_sharded.py

@@ -0,0 +1,120 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+
+import math
+import torch
+import random
+import ttnn
+from tests.sweep_framework.sweep_utils.utils import gen_shapes, tensor_to_dtype, sanitize_shape_rm
+from tests.sweep_framework.sweep_utils.sharding_utils import gen_sharded_spec_unary, parse_sharding_spec
+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, 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)
+
+# 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.
+# Developers can create their own generator functions and pass them to the parameters as inputs.
+parameters = {
+    "nightly": {
+        "input_spec": gen_sharded_spec_unary(8, layouts=["TILE_LAYOUT"]),
+        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_b_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "output_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+    },
+}
+
+
+# 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]]:
+    input_shape, X, Y, sharding_strategy, _, _, input_layout = test_vector["input_spec"].values()
+    pre_sharded_height = math.prod(input_shape[:-1])
+    pre_sharded_width = input_shape[-1]
+
+    if input_layout == "ROW_MAJOR_LAYOUT":
+        return True, "Input to eltwise binary must be tilized"
+
+    if input_layout == "ROW_MAJOR_LAYOUT" and test_vector["input_a_dtype"] == ttnn.bfloat8_b:
+        return True, "bfloat8_b is only supported on tiled layout"
+
+    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 device_mesh_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_spec,
+    input_a_dtype,
+    input_b_dtype,
+    output_dtype,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+
+    (
+        input_shape,
+        core_grid,
+        sharding_strategy,
+        shard_orientation,
+        tensor_hw_as_shard_shape,
+        input_layout,
+    ) = parse_sharding_spec(input_spec)
+
+    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_b_dtype
+    )(input_shape)
+
+    torch_output_tensor = tensor_to_dtype(torch.logical_or(torch_input_tensor_a, torch_input_tensor_b), output_dtype)
+
+    sharded_config = ttnn.create_sharded_memory_config_(
+        shape=input_shape,
+        core_grid=core_grid,
+        strategy=sharding_strategy,
+        orientation=shard_orientation,
+        use_height_and_width_as_shard_shape=tensor_hw_as_shard_shape,
+    )
+
+    input_tensor_a = ttnn.from_torch(
+        torch_input_tensor_a,
+        dtype=input_a_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=sharded_config,
+    )
+    input_tensor_b = ttnn.from_torch(
+        torch_input_tensor_b,
+        dtype=input_b_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=sharded_config,
+    )
+    start_time = start_measuring_time()
+    output_tensor = ttnn.logical_or(input_tensor_a, input_tensor_b, memory_config=sharded_config, dtype=output_dtype)
+    output_tensor = ttnn.to_torch(output_tensor)
+    e2e_perf = stop_measuring_time(start_time)
+
+    # pcc = assert_equal(torch_output_tensor, output_tensor)
+    pcc = check_with_pcc(torch_output_tensor, output_tensor, 0.999)
+    return [pcc, e2e_perf]

tests/sweep_framework/sweeps/eltwise/binary/logical_xor/logical_xor_sharded.py

@@ -0,0 +1,120 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+
+import math
+import torch
+import random
+import ttnn
+from tests.sweep_framework.sweep_utils.utils import gen_shapes, tensor_to_dtype, sanitize_shape_rm
+from tests.sweep_framework.sweep_utils.sharding_utils import gen_sharded_spec_unary, parse_sharding_spec
+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, 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)
+
+# 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.
+# Developers can create their own generator functions and pass them to the parameters as inputs.
+parameters = {
+    "nightly": {
+        "input_spec": gen_sharded_spec_unary(8, layouts=["TILE_LAYOUT"]),
+        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_b_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "output_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+    },
+}
+
+
+# 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]]:
+    input_shape, X, Y, sharding_strategy, _, _, input_layout = test_vector["input_spec"].values()
+    pre_sharded_height = math.prod(input_shape[:-1])
+    pre_sharded_width = input_shape[-1]
+
+    if input_layout == "ROW_MAJOR_LAYOUT":
+        return True, "Input to eltwise binary must be tilized"
+
+    if input_layout == "ROW_MAJOR_LAYOUT" and test_vector["input_a_dtype"] == ttnn.bfloat8_b:
+        return True, "bfloat8_b is only supported on tiled layout"
+
+    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 device_mesh_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_spec,
+    input_a_dtype,
+    input_b_dtype,
+    output_dtype,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+
+    (
+        input_shape,
+        core_grid,
+        sharding_strategy,
+        shard_orientation,
+        tensor_hw_as_shard_shape,
+        input_layout,
+    ) = parse_sharding_spec(input_spec)
+
+    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_b_dtype
+    )(input_shape)
+
+    torch_output_tensor = tensor_to_dtype(torch.logical_xor(torch_input_tensor_a, torch_input_tensor_b), output_dtype)
+
+    sharded_config = ttnn.create_sharded_memory_config_(
+        shape=input_shape,
+        core_grid=core_grid,
+        strategy=sharding_strategy,
+        orientation=shard_orientation,
+        use_height_and_width_as_shard_shape=tensor_hw_as_shard_shape,
+    )
+
+    input_tensor_a = ttnn.from_torch(
+        torch_input_tensor_a,
+        dtype=input_a_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=sharded_config,
+    )
+    input_tensor_b = ttnn.from_torch(
+        torch_input_tensor_b,
+        dtype=input_b_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=sharded_config,
+    )
+    start_time = start_measuring_time()
+    output_tensor = ttnn.logical_xor(input_tensor_a, input_tensor_b, memory_config=sharded_config, dtype=output_dtype)
+    output_tensor = ttnn.to_torch(output_tensor)
+    e2e_perf = stop_measuring_time(start_time)
+
+    # pcc = assert_equal(torch_output_tensor, output_tensor)
+    pcc = check_with_pcc(torch_output_tensor, output_tensor, 0.999)
+    return [pcc, e2e_perf]