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tests/sweep_framework/sweeps/eltwise/unary/abs/abs_sharded.py
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# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc. | ||
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# SPDX-License-Identifier: Apache-2.0 | ||
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from typing import Optional, Tuple | ||
from functools import partial | ||
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import json | ||
import torch | ||
import random | ||
import ttnn | ||
import math | ||
from tests.sweep_framework.sweep_utils.utils import gen_shapes, sanitize_shape_rm | ||
from tests.sweep_framework.sweep_utils.sharding_utils import ( | ||
gen_sharded_spec_unary, | ||
parse_sharding_spec, | ||
invalidate_vector_sharding, | ||
) | ||
from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt | ||
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from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time | ||
from models.utility_functions import torch_random | ||
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# Override the default timeout in seconds for hang detection. | ||
TIMEOUT = 120 | ||
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random.seed(0) | ||
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# 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 = { | ||
"xfail": { | ||
"input_spec": gen_sharded_spec_unary(16, layouts=["TILE_LAYOUT"]), | ||
"input_a_dtype": [ttnn.bfloat16], | ||
}, | ||
} | ||
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# 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_layout = test_vector["input_spec"]["input_layout"] | ||
sharding_invalidated, output_str = invalidate_vector_sharding(test_vector["input_spec"]) | ||
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if input_layout == "ROW_MAJOR_LAYOUT": | ||
return True, "Inputs to eltwise binary must be tilized" | ||
if sharding_invalidated: | ||
return sharding_invalidated, output_str | ||
return False, None | ||
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# 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_spec, | ||
input_a_dtype, | ||
*, | ||
device, | ||
) -> list: | ||
data_seed = random.randint(0, 20000000) | ||
torch.manual_seed(data_seed) | ||
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( | ||
input_shape, | ||
core_grid, | ||
sharding_strategy, | ||
shard_orientation, | ||
tensor_hw_as_shard_shape, | ||
input_layout, | ||
shard_height_mul_of_32, | ||
) = parse_sharding_spec(input_spec) | ||
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if input_layout == ttnn.ROW_MAJOR_LAYOUT: | ||
input_shape = sanitize_shape_rm(input_shape) | ||
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torch_input_tensor_a = 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.abs) | ||
torch_output_tensor = golden_function(torch_input_tensor_a) | ||
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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, | ||
tile_layout=shard_height_mul_of_32, | ||
) | ||
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input_tensor_a = ttnn.from_torch( | ||
torch_input_tensor_a, | ||
dtype=input_a_dtype, | ||
layout=input_layout, | ||
device=device, | ||
memory_config=sharded_config, | ||
) | ||
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start_time = start_measuring_time() | ||
output_tensor = ttnn.abs(input_tensor_a, memory_config=sharded_config) | ||
e2e_perf = stop_measuring_time(start_time) | ||
output_tensor = ttnn.to_torch(output_tensor) | ||
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pcc = check_with_pcc(torch_output_tensor, output_tensor, 0.999) | ||
return [pcc, e2e_perf] |
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