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UFMT formatting on test/autograd test/ao test/cpp test/backends (#123369)

Browse Source 
Partially addresses #123062

Ran lintrunner on
- test/_test_bazel.py
- test/ao
- test/autograd test/backends test/benchmark_uitls test/conftest.py test/bottleneck_test test/cpp

Pull Request resolved: https://github.com/pytorch/pytorch/pull/123369
Approved by: https://github.com/huydhn
This commit is contained in:
Arun Pa
2024-04-05 18:51:38 +00:00
committed by PyTorch MergeBot
parent de7edeea25
commit f71e368969
23 changed files with 1914 additions and 1035 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
test/cpp/api/init_baseline.py
View File

@ -1,6 +1,7 @@
"""Script to generate baseline values from PyTorch initialization algorithms"""
import sys
import torch
HEADER = """
@ -19,13 +20,13 @@ INITIALIZERS = {
"Xavier_Uniform": lambda w: torch.nn.init.xavier_uniform(w),
"Xavier_Normal": lambda w: torch.nn.init.xavier_normal(w),
"Kaiming_Normal": lambda w: torch.nn.init.kaiming_normal(w),
"Kaiming_Uniform": lambda w: torch.nn.init.kaiming_uniform(w)
"Kaiming_Uniform": lambda w: torch.nn.init.kaiming_uniform(w),
}
def emit(initializer_parameter_map):
# Don't write generated with an @ in front, else this file is recognized as generated.
print("// @{} from {}".format('generated', __file__))
print("// @{} from {}".format("generated", __file__))
print(HEADER)
for initializer_name, weights in initializer_parameter_map.items():
print(PARAMETERS.format(initializer_name))
@ -63,10 +64,11 @@ def run(initializer):
def main():
initializer_parameter_map = {}
for initializer in INITIALIZERS.keys():
sys.stderr.write(f'Evaluating {initializer} ...\n')
sys.stderr.write(f"Evaluating {initializer} ...\n")
initializer_parameter_map[initializer] = run(initializer)
emit(initializer_parameter_map)
if __name__ == "__main__":
main()

48
test/cpp/api/optim_baseline.py
View File

@ -21,27 +21,43 @@ FOOTER = "} // namespace expected_parameters"
PARAMETERS = "inline std::vector<std::vector<torch::Tensor>> {}() {{"
OPTIMIZERS = {
"LBFGS" : lambda p: torch.optim.LBFGS(p, 1.0),
"LBFGS_with_line_search" : lambda p: torch.optim.LBFGS(p, 1.0, line_search_fn="strong_wolfe"),
"LBFGS": lambda p: torch.optim.LBFGS(p, 1.0),
"LBFGS_with_line_search": lambda p: torch.optim.LBFGS(
p, 1.0, line_search_fn="strong_wolfe"
),
"Adam": lambda p: torch.optim.Adam(p, 1.0),
"Adam_with_weight_decay": lambda p: torch.optim.Adam(p, 1.0, weight_decay=1e-2),
"Adam_with_weight_decay_and_amsgrad": lambda p: torch.optim.Adam(p, 1.0, weight_decay=1e-6, amsgrad=True),
"Adam_with_weight_decay_and_amsgrad": lambda p: torch.optim.Adam(
p, 1.0, weight_decay=1e-6, amsgrad=True
),
"AdamW": lambda p: torch.optim.AdamW(p, 1.0),
"AdamW_without_weight_decay": lambda p: torch.optim.AdamW(p, 1.0, weight_decay=0),
"AdamW_with_amsgrad": lambda p: torch.optim.AdamW(p, 1.0, amsgrad=True),
"Adagrad": lambda p: torch.optim.Adagrad(p, 1.0),
"Adagrad_with_weight_decay": lambda p: torch.optim.Adagrad(p, 1.0, weight_decay=1e-2),
"Adagrad_with_weight_decay_and_lr_decay": lambda p: torch.optim.Adagrad(p, 1.0, weight_decay=1e-6, lr_decay=1e-3),
"Adagrad_with_weight_decay": lambda p: torch.optim.Adagrad(
p, 1.0, weight_decay=1e-2
),
"Adagrad_with_weight_decay_and_lr_decay": lambda p: torch.optim.Adagrad(
p, 1.0, weight_decay=1e-6, lr_decay=1e-3
),
"RMSprop": lambda p: torch.optim.RMSprop(p, 0.1),
"RMSprop_with_weight_decay": lambda p: torch.optim.RMSprop(p, 0.1, weight_decay=1e-2),
"RMSprop_with_weight_decay_and_centered": lambda p: torch.optim.RMSprop(p, 0.1, weight_decay=1e-6, centered=True),
"RMSprop_with_weight_decay_and_centered_and_momentum":
lambda p: torch.optim.RMSprop(p, 0.1, weight_decay=1e-6, centered=True, momentum=0.9),
"RMSprop_with_weight_decay": lambda p: torch.optim.RMSprop(
p, 0.1, weight_decay=1e-2
),
"RMSprop_with_weight_decay_and_centered": lambda p: torch.optim.RMSprop(
p, 0.1, weight_decay=1e-6, centered=True
),
"RMSprop_with_weight_decay_and_centered_and_momentum": lambda p: torch.optim.RMSprop(
p, 0.1, weight_decay=1e-6, centered=True, momentum=0.9
),
"SGD": lambda p: torch.optim.SGD(p, 0.1),
"SGD_with_weight_decay": lambda p: torch.optim.SGD(p, 0.1, weight_decay=1e-2),
"SGD_with_weight_decay_and_momentum": lambda p: torch.optim.SGD(p, 0.1, momentum=0.9, weight_decay=1e-2),
"SGD_with_weight_decay_and_nesterov_momentum":
lambda p: torch.optim.SGD(p, 0.1, momentum=0.9, weight_decay=1e-6, nesterov=True),
"SGD_with_weight_decay_and_momentum": lambda p: torch.optim.SGD(
p, 0.1, momentum=0.9, weight_decay=1e-2
),
"SGD_with_weight_decay_and_nesterov_momentum": lambda p: torch.optim.SGD(
p, 0.1, momentum=0.9, weight_decay=1e-6, nesterov=True
),
}
@ -75,11 +91,11 @@ def run(optimizer_name, iterations, sample_every):
loss.backward()
def closure():
return torch.tensor([10.])
return torch.tensor([10.0])
optimizer.step(closure)
if i % sample_every == 0:
values.append(
[p.clone().flatten().data.numpy() for p in model.parameters()]
)
@ -89,7 +105,7 @@ def run(optimizer_name, iterations, sample_every):
def emit(optimizer_parameter_map):
# Don't write generated with an @ in front, else this file is recognized as generated.
print("// @{} from {}".format('generated', __file__))
print("// @{} from {}".format("generated", __file__))
print(HEADER)
for optimizer_name, parameters in optimizer_parameter_map.items():
print(PARAMETERS.format(optimizer_name))
@ -115,7 +131,7 @@ def main():
optimizer_parameter_map = {}
for optimizer in OPTIMIZERS.keys():
sys.stderr.write(f'Evaluating {optimizer} ...\n')
sys.stderr.write(f"Evaluating {optimizer} ...\n")
optimizer_parameter_map[optimizer] = run(
optimizer, options.iterations, options.sample_every
)