mirror of
https://github.com/llvm/llvm-project.git
synced 2025-04-27 02:26:05 +00:00

This is the first commit in a series that will reformat all the python files in the LLVM repository. Reformatting is done with `black`. See more information here: https://discourse.llvm.org/t/rfc-document-and-standardize-python-code-style Reviewed By: jhenderson, JDevlieghere, MatzeB Differential Revision: https://reviews.llvm.org/D150545
73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
"""Generate a mock model for LLVM tests for Register Allocation.
|
|
The generated model is not a neural net - it is just a tf.function with the
|
|
correct input and output parameters. By construction, the mock model will always
|
|
output the first liverange that can be evicted.
|
|
"""
|
|
import os
|
|
import sys
|
|
import tensorflow as tf
|
|
|
|
POLICY_DECISION_LABEL = "index_to_evict"
|
|
POLICY_OUTPUT_SPEC = """
|
|
[
|
|
{
|
|
"logging_name": "index_to_evict",
|
|
"tensor_spec": {
|
|
"name": "StatefulPartitionedCall",
|
|
"port": 0,
|
|
"type": "int64_t",
|
|
"shape": [
|
|
1
|
|
]
|
|
}
|
|
}
|
|
]
|
|
"""
|
|
PER_REGISTER_FEATURE_LIST = ["mask"]
|
|
NUM_REGISTERS = 33
|
|
|
|
|
|
def get_input_signature():
|
|
"""Returns (time_step_spec, action_spec) for LLVM register allocation."""
|
|
inputs = dict(
|
|
(key, tf.TensorSpec(dtype=tf.int64, shape=(NUM_REGISTERS), name=key))
|
|
for key in PER_REGISTER_FEATURE_LIST
|
|
)
|
|
return inputs
|
|
|
|
|
|
def get_output_spec_path(path):
|
|
return os.path.join(path, "output_spec.json")
|
|
|
|
|
|
def build_mock_model(path):
|
|
"""Build and save the mock model with the given signature."""
|
|
module = tf.Module()
|
|
# We have to set this useless variable in order for the TF C API to correctly
|
|
# intake it
|
|
module.var = tf.Variable(0, dtype=tf.int64)
|
|
|
|
def action(*inputs):
|
|
result = (
|
|
tf.math.argmax(tf.cast(inputs[0]["mask"], tf.int32), axis=-1) + module.var
|
|
)
|
|
return {POLICY_DECISION_LABEL: result}
|
|
|
|
module.action = tf.function()(action)
|
|
action = {"action": module.action.get_concrete_function(get_input_signature())}
|
|
tf.saved_model.save(module, path, signatures=action)
|
|
output_spec_path = get_output_spec_path(path)
|
|
with open(output_spec_path, "w") as f:
|
|
print(f"Writing output spec to {output_spec_path}.")
|
|
f.write(POLICY_OUTPUT_SPEC)
|
|
|
|
|
|
def main(argv):
|
|
assert len(argv) == 2
|
|
model_path = argv[1]
|
|
build_mock_model(model_path)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main(sys.argv)
|