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#15259 added more ops into unit test for related ticket
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umadevimcw authored and bfilipovicTT committed Dec 24, 2024
1 parent be6ed46 commit 1af1de3
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8 changes: 8 additions & 0 deletions .github/workflows/ttnn-run-sweeps.yaml
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Expand Up @@ -21,6 +21,14 @@ on:
- creation.zeros.zeros
- creation.empty.empty
- creation.zeros_like.zeros_like
- eltwise.unary.cos.cos_sharded
- eltwise.unary.cosh.cosh_sharded
- eltwise.unary.deg2rad.deg2rad_sharded
- eltwise.unary.log1p.log1p_sharded
- eltwise.unary.log10.log10_sharded
- eltwise.unary.log2.log2_sharded
- eltwise.unary.nez.nez_sharded
- eltwise.unary.relu6.relu6_sharded
- eltwise.unary.abs.abs_pytorch2
- eltwise.unary.relu.relu
- eltwise.unary.relu.relu_pytorch2
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110 changes: 110 additions & 0 deletions tests/sweep_framework/sweeps/eltwise/unary/cos/cos_sharded.py
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# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.

# SPDX-License-Identifier: Apache-2.0

from typing import Optional, Tuple
from functools import partial

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
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 = 120

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_spec": gen_sharded_spec_unary(16, layouts=["TILE_LAYOUT"]),
"input_a_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_layout = test_vector["input_spec"]["input_layout"]
sharding_invalidated, output_str = invalidate_vector_sharding(test_vector["input_spec"])
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


# 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)

(
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)

if input_layout == ttnn.ROW_MAJOR_LAYOUT:
input_shape = sanitize_shape_rm(input_shape)

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,
)

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.cos)
torch_output_tensor = golden_function(torch_input_tensor_a)

input_tensor_a = ttnn.from_torch(
torch_input_tensor_a,
dtype=input_a_dtype,
layout=input_layout,
device=device,
memory_config=sharded_config,
)

start_time = start_measuring_time()
output_tensor = ttnn.cos(input_tensor_a, memory_config=sharded_config)
e2e_perf = stop_measuring_time(start_time)

output_tensor = ttnn.to_torch(output_tensor)

return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]
110 changes: 110 additions & 0 deletions tests/sweep_framework/sweeps/eltwise/unary/cosh/cosh_sharded.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,110 @@
# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.

# SPDX-License-Identifier: Apache-2.0

from typing import Optional, Tuple
from functools import partial

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
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 = 120

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_spec": gen_sharded_spec_unary(42, layouts=["TILE_LAYOUT"]),
"input_a_dtype": [ttnn.bfloat16],
},
}


# 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"])
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


# 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)

(
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)

if input_layout == ttnn.ROW_MAJOR_LAYOUT:
input_shape = sanitize_shape_rm(input_shape)

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,
)

torch_input_tensor_a = gen_func_with_cast_tt(
partial(torch_random, low=-9, high=9, dtype=torch.float32), input_a_dtype
)(input_shape)

golden_function = ttnn.get_golden_function(ttnn.cosh)
torch_output_tensor = golden_function(torch_input_tensor_a)

input_tensor_a = ttnn.from_torch(
torch_input_tensor_a,
dtype=input_a_dtype,
layout=input_layout,
device=device,
memory_config=sharded_config,
)

start_time = start_measuring_time()
output_tensor = ttnn.cosh(input_tensor_a, memory_config=sharded_config)
e2e_perf = stop_measuring_time(start_time)

output_tensor = ttnn.to_torch(output_tensor)

return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]
110 changes: 110 additions & 0 deletions tests/sweep_framework/sweeps/eltwise/unary/deg2rad/deg2rad_sharded.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,110 @@
# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.

# SPDX-License-Identifier: Apache-2.0

from typing import Optional, Tuple
from functools import partial

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
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 = 120

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_spec": gen_sharded_spec_unary(40, layouts=["TILE_LAYOUT"]),
"input_a_dtype": [ttnn.bfloat16],
},
}


# 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"])
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


# 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)

(
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)

if input_layout == ttnn.ROW_MAJOR_LAYOUT:
input_shape = sanitize_shape_rm(input_shape)

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,
)

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.deg2rad)
torch_output_tensor = golden_function(torch_input_tensor_a)

input_tensor_a = ttnn.from_torch(
torch_input_tensor_a,
dtype=input_a_dtype,
layout=input_layout,
device=device,
memory_config=sharded_config,
)

start_time = start_measuring_time()
output_tensor = ttnn.deg2rad(input_tensor_a, memory_config=sharded_config)
e2e_perf = stop_measuring_time(start_time)

output_tensor = ttnn.to_torch(output_tensor)

return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]
26 changes: 26 additions & 0 deletions tests/sweep_framework/sweeps/eltwise/unary/log10/log10.py
Original file line number Diff line number Diff line change
Expand Up @@ -73,3 +73,29 @@ def run(
e2e_perf = stop_measuring_time(start_time)

return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]


from tests.sweep_framework.framework.permutations import *

start_time = start_measuring_time()
for suite in parameters.keys():
device_id = 0
device = ttnn.open_device(device_id=device_id)
suite_vectors = list(permutations(parameters[suite]))
print(len(suite_vectors))
failed = 0
crashed = 0
for vector in suite_vectors:
try:
passed, _ = run(**vector, device=device)
if passed[0] != True:
print(passed)
failed += 1
except Exception as e:
print(e)
crashed += 1

passed = len(suite_vectors) - failed - crashed
percent_passed = (passed / len(suite_vectors)) * 100
print(f"{crashed} crash, {failed} fail, {passed} pass ({percent_passed}%)")
ttnn.close_device(device)
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