llvm-project/llvm/lib/Analysis/models/gen-regalloc-eviction-test-model.py
Tobias Hieta b71edfaa4e
[NFC][Py Reformat] Reformat python files in llvm
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
2023-05-17 10:48:52 +02:00

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)