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"""Utility for testing InteractiveModelRunner.
Use it from pass-specific tests by providing a main .py which calls this library's
`run_interactive` with an appropriate callback to provide advice.
From .ll tests, just call the above-mentioned main as a prefix to the opt/llc
invocation (with the appropriate flags enabling the interactive mode)
Examples:
test/Transforms/Inline/ML/interactive-mode.ll
test/CodeGen/MLRegAlloc/interactive-mode.ll
"""
import ctypes
import log_reader
import io
import math
import os
import subprocess
from typing import Callable, List, Union
def send(f: io.BufferedWriter, value: Union[int, float], spec: log_reader.TensorSpec):
"""Send the `value` - currently just a scalar - formatted as per `spec`."""
# just int64 for now
assert spec.element_type == ctypes.c_int64
to_send = ctypes.c_int64(int(value))
assert f.write(bytes(to_send)) == ctypes.sizeof(spec.element_type) * math.prod(
spec.shape
)
f.flush()
def run_interactive(
temp_rootname: str,
make_response: Callable[[List[log_reader.TensorValue]], Union[int, float]],
process_and_args: List[str],
):
"""Host the compiler.
Args:
temp_rootname: the base file name from which to construct the 2 pipes for
communicating with the compiler.
make_response: a function that, given the current tensor values, provides a
response.
process_and_args: the full commandline for the compiler. It it assumed it
contains a flag poiting to `temp_rootname` so that the InteractiveModeRunner
would attempt communication on the same pair as this function opens.
This function sets up the communication with the compiler - via 2 files named
`temp_rootname`.in and `temp_rootname`.out - prints out the received features,
and sends back to the compiler an advice (which it gets from `make_response`).
It's used for testing, and also to showcase how to set up communication in an
interactive ML ("gym") environment.
"""
to_compiler = temp_rootname + ".in"
from_compiler = temp_rootname + ".out"
try:
os.mkfifo(to_compiler, 0o666)
os.mkfifo(from_compiler, 0o666)
compiler_proc = subprocess.Popen(
process_and_args, stderr=subprocess.PIPE, stdout=subprocess.DEVNULL
)
with io.BufferedWriter(io.FileIO(to_compiler, "wb")) as tc:
with io.BufferedReader(io.FileIO(from_compiler, "rb")) as fc:
tensor_specs, _, advice_spec = log_reader.read_header(fc)
context = None
while compiler_proc.poll() is None:
next_event = fc.readline()
if not next_event:
break
(
last_context,
observation_id,
features,
_,
) = log_reader.read_one_observation(
context, next_event, fc, tensor_specs, None
)
if last_context != context:
print(f"context: {last_context}")
context = last_context
print(f"observation: {observation_id}")
tensor_values = []
for fv in features:
log_reader.pretty_print_tensor_value(fv)
tensor_values.append(fv)
send(tc, make_response(tensor_values), advice_spec)
_, err = compiler_proc.communicate()
print(err.decode("utf-8"))
compiler_proc.wait()
finally:
os.unlink(to_compiler)
os.unlink(from_compiler)
|
