def get_observation(self, observation_space: ObservationSpace) -> Event:
logging.info("Computing observation from space %s", observation_space)
if observation_space.name == "ir":
return Event(string_value="Hello, world!")
elif observation_space.name == "features":
observation = Event(
int64_tensor=Int64Tensor(shape=[3], value=[0, 0, 0]))
return observation
elif observation_space.name == "runtime":
return Event(double_value=0)
else:
raise KeyError(observation_space.name)
def get_observation(self, observation_space: ObservationSpace) -> Event:
if observation_space.name == "action_state":
# cursor, (size, tail)
o = self.order[self.cursor]
return Event(int64_tensor=Int64Tensor(
shape=[3], value=[self.cursor, o[0], o[1]]))
elif observation_space.name == "flops":
return Event(double_value=self.flops())
elif observation_space.name == "loop_tree":
loop_tree, parallel = self.lower()
return Event(string_value=loop_tree.dump(lambda x: "[thread]"
if x in parallel else ""))
else:
raise KeyError(observation_space.name)
def test_message_default_converter():
value = 5
converter = py_converters.make_message_default_converter()
message = Event(int64_value=value)
converted = converter(message)
assert type(converted) == int
assert value == converted
def apply_action(
self, action_proto: Event
) -> Tuple[bool, Optional[ActionSpace], bool]:
"""Apply an action."""
if not action_proto.HasField("int64_value"):
raise ValueError("Invalid action, int64_value expected.")
choice_index = action_proto.int64_value
if choice_index < 0 or choice_index >= self.num_actions:
raise ValueError("Out-of-range")
# Get the action
action = actions[choice_index]
# Apply the action to this session and check if we changed anything
old_choices = self.choices.copy()
action(self)
logger.debug("Applied action %s", action)
# Reset the internal variables if this action has caused a change in the
# choices
if old_choices != self.choices:
self.reset_cached()
# The action has not changed anything yet. That waits until an
# observation is taken
return False, None, False
def test_event_message_default_converter():
message_converter = py_converters.TypeBasedConverter(
conversion_map={FloatTensor: py_converters.convert_tensor_message_to_numpy}
)
event_converter = py_converters.EventMessageDefaultConverter(message_converter)
tensor_message = FloatTensor(shape=[1], value=[1])
event_message = Event(float_tensor=tensor_message)
numpy_array = event_converter(event_message)
assert isinstance(numpy_array, np.ndarray)
def test_to_dict_event_message_converter():
converter = py_converters.TypeBasedConverter(
conversion_map={int: lambda x: Event(int64_value=x)}
)
dict_converter = py_converters.ToDictEventMessageConverter(converter)
original_dict = {"a": 1}
converted_dict = dict_converter(original_dict)
assert isinstance(converted_dict, DictEvent)
assert len(converted_dict.event) == len(original_dict)
assert converted_dict.event["a"].int64_value == original_dict["a"]
def test_type_id_dispatch_converter():
def default_converter(msg):
return msg.string_value + "_default"
conversion_map = {
"type_1": lambda msg: msg.string_value + "_type_1",
"type_2": lambda msg: msg.string_value + "_type_2",
}
type_id_converter = py_converters.TypeIdDispatchConverter(
default_converter=default_converter, conversion_map=conversion_map
)
assert type_id_converter(Event(string_value="msg_val")) == "msg_val_default"
assert (
type_id_converter(Event(string_value="msg_val", type_id="type_1"))
== "msg_val_type_1"
)
assert (
type_id_converter(Event(string_value="msg_val", type_id="type_2"))
== "msg_val_type_2"
)
def test_to_list_event_message_converter():
converter = py_converters.TypeBasedConverter(
conversion_map={int: lambda x: Event(int64_value=x)}
)
list_converter = py_converters.ToListEventMessageConverter(converter)
original_list = [1, 2]
converted_list = list_converter(original_list)
assert isinstance(converted_list, ListEvent)
assert len(converted_list.event) == len(original_list)
assert converted_list.event[0].int64_value == original_list[0]
assert converted_list.event[1].int64_value == original_list[1]
def test_list_event_message_converter():
message_converter = py_converters.TypeBasedConverter(
conversion_map={FloatTensor: py_converters.convert_tensor_message_to_numpy}
)
event_converter = py_converters.EventMessageDefaultConverter(message_converter)
list_converter = py_converters.ListEventMessageConverter(event_converter)
tensor_message = FloatTensor(shape=[1], value=[1])
event_message = Event(float_tensor=tensor_message)
list_message = ListEvent(event=[event_message])
converted_list = list_converter(list_message)
assert isinstance(converted_list, Collection)
assert len(converted_list) == 1
assert isinstance(converted_list[0], np.ndarray)
def test_dict_event_message_converter():
message_converter = py_converters.TypeBasedConverter(
conversion_map={FloatTensor: py_converters.convert_tensor_message_to_numpy}
)
event_converter = py_converters.EventMessageDefaultConverter(message_converter)
dict_converter = py_converters.DictEventMessageConverter(event_converter)
tensor_message = FloatTensor(shape=[1], value=[1])
event_message = Event(float_tensor=tensor_message)
dict_message = DictEvent(event={"event_message_key": event_message})
converted_list = dict_converter(dict_message)
assert isinstance(converted_list, Mapping)
assert len(converted_list) == 1
assert "event_message_key" in converted_list
assert isinstance(converted_list["event_message_key"], np.ndarray)
def apply_action(
self, action: Event) -> Tuple[bool, Optional[ActionSpace], bool]:
if not action.HasField("int64_value"):
raise ValueError("Invalid action. int64_value expected.")
choice_index = action.int64_value
if choice_index < 0 or choice_index >= len(
self.action_space.space.named_discrete.name):
raise ValueError("Out-of-range")
logger.info("Applied action %d", choice_index)
act = self.action_space.space.named_discrete.name[choice_index]
if self.mode not in ["size", "select"]:
raise RuntimeError("Invalid mode set: {}".format(self.mode))
if act == "toggle_mode":
if self.mode == "size":
self.mode = "select"
elif self.mode == "select":
self.mode = "size"
if act == "toggle_thread":
self.thread[self.cursor] = not self.thread[self.cursor]
if act == "down":
# always loop around
if self.mode == "size":
self.resize(-1)
elif self.mode == "select":
next_cursor = (self.cursor - 1) % len(self.order)
self.cursor = next_cursor
if act == "up":
# always loop around
if self.mode == "size":
self.resize(1)
elif self.mode == "select":
next_cursor = (self.cursor + 1) % len(self.order)
self.cursor = next_cursor
return False, None, False
class LoopToolCompilationSession(CompilationSession):
"""Represents an instance of an interactive loop_tool session."""
compiler_version: str = pkg_resources.get_distribution(
"loop-tool-py").version
# keep it simple for now: 1 variable, 1 nest
action_spaces = [
ActionSpace(
name="simple",
space=Space(
# shift around a single pre-split order, changing the size of splits
named_discrete=NamedDiscreteSpace(
name=["toggle_mode", "up", "down", "toggle_thread"], ), ),
),
ActionSpace(
name="split",
space=Space(
# potentially define new splits
named_discrete=NamedDiscreteSpace(name=[
"toggle_mode", "up", "down", "toggle_thread", "split"
], ), ),
),
]
observation_spaces = [
ObservationSpace(
name="flops",
space=Space(double_value=DoubleRange()),
deterministic=False,
platform_dependent=True,
default_observation=Event(double_value=0, ),
),
ObservationSpace(
name="loop_tree",
space=Space(
string_value=StringSpace(length_range=Int64Range(min=0)), ),
deterministic=True,
platform_dependent=False,
default_observation=Event(string_value="", ),
),
ObservationSpace(
name="action_state",
space=Space(int64_box=Int64Box(
low=Int64Tensor(shape=[1], value=[0]),
high=Int64Tensor(shape=[1], value=[2**36]),
), ),
deterministic=True,
platform_dependent=False,
default_observation=Event(int64_tensor=Int64Tensor(shape=[1],
value=[0]), ),
),
]
def __init__(self, working_directory: Path, action_space: ActionSpace,
benchmark: Benchmark):
super().__init__(working_directory, action_space, benchmark)
self.action_space = action_space
if "cuda" in benchmark.uri:
self.backend = "cuda"
lt.set_default_hardware("cuda")
else:
self.backend = "cpu"
if self.backend not in lt.backends():
raise EnvironmentNotSupported(
f"Failed to load {self.backend} dataset for loop_tool. Have you installed all required dependecies? See <https://facebookresearch.github.io/CompilerGym/envs/loop_tool.html#installation> for details. "
)
self.ir = lt.IR()
self.var = self.ir.create_var("a")
r0 = self.ir.create_node("read", [], [self.var])
r1 = self.ir.create_node("read", [], [self.var])
add = self.ir.create_node("add", [r0, r1], [self.var])
w = self.ir.create_node("write", [add], [self.var])
self.ir.set_inputs([r0, r1])
self.ir.set_outputs([w])
self.size = int(benchmark.uri.split("/")[-1])
self.Ap = np.random.randn(self.size)
self.Bp = np.random.randn(self.size)
self.order = [(self.size, 0), (1, 0), (1, 0)]
self.thread = [1, 0, 0]
self.cursor = 0
self.mode = "size"
logger.info("Started a compilation session for %s", benchmark.uri)
def resize(self, increment):
"""
The idea is pull from or add to the parent loop.
Three mutations possible to any size:
A) x, y -> x + 1, 0
remove the tail, increase loop size, shrink parent
B) x, y -> x, 0
only remove the tail, add to parent
C) x, 0 -> x - 1, 0
if no tail, shrink the loop size, increase parent
note: this means tails can never exist on innermost loops. this makes good sense :)
A)
[(a, b), (x, y), ...k] -> [(a', b'), (x + 1, 0), ...k]
a * (x * k + y) + b = a' * (x + 1) * k + b'
a' = (a * (x * k + y) + b) // ((x + 1) * k)
b' = " " % " "
B)
[(a, b), (x, y), ...k] -> [(a', b'), (x, 0), ...k]
a * (x * k + y) + b = a' * (x) * k + b'
a' = (a * (x * k + y) + b) // ((x) * k)
b' = " " % " "
C)
[(a, b), (x, y), ...k] -> [(a', b'), (x - 1, 0), ...k]
a * (x * k + y) + b = a' * (x - 1) * k + b'
a' = (a * (x * k + y) + b) // ((x - 1) * k)
b' = " " % " "
example interaction model:
1. cursor = 1 [1024, 1, 1]
2. up [512, 2, 1]
3. up [(341,1), 3, 1]
4. up [256, 4, 1]
5. cursor = 2, up [256, 2, 2]
6. up [256, (1, 1), 3]
7. cursor = 1, down [(341, 1), 1, 3]
8. cursor = 2, down [(341, 1), (1, 1), 2]
9. cursor = 1, down [512, 1, 2]"""
if self.cursor == 0:
return
parent_size = self.order[self.cursor - 1]
a = parent_size[0]
b = parent_size[1]
size = self.order[self.cursor]
x = size[0]
y = size[1]
def lam(v, x):
return v * x[0] + x[1]
k = reduce(lam, self.order[self.cursor + 1:][::-1], 1)
if increment == -1 and y:
increment = 0
if (x + increment) < 1:
return
if (x + increment) > self.size:
return
n = a * x * k + b
d = (x + increment) * k
a_ = n // d
b_ = n % d
if a_ < 1:
return
if a_ > self.size:
return
self.order[self.cursor - 1] = (a_, b_)
self.order[self.cursor] = (x + increment, 0)
end_size = reduce(lam, self.order[::-1], 1)
assert (
end_size == self.size
), f"{end_size} != {self.size} ({a}, {b}), ({x}, {y}) -> ({a_}, {b_}), ({x + increment}, 0)"
def apply_action(
self, action: Event) -> Tuple[bool, Optional[ActionSpace], bool]:
if not action.HasField("int64_value"):
raise ValueError("Invalid action. int64_value expected.")
choice_index = action.int64_value
if choice_index < 0 or choice_index >= len(
self.action_space.space.named_discrete.name):
raise ValueError("Out-of-range")
logger.info("Applied action %d", choice_index)
act = self.action_space.space.named_discrete.name[choice_index]
if self.mode not in ["size", "select"]:
raise RuntimeError("Invalid mode set: {}".format(self.mode))
if act == "toggle_mode":
if self.mode == "size":
self.mode = "select"
elif self.mode == "select":
self.mode = "size"
if act == "toggle_thread":
self.thread[self.cursor] = not self.thread[self.cursor]
if act == "down":
# always loop around
if self.mode == "size":
self.resize(-1)
elif self.mode == "select":
next_cursor = (self.cursor - 1) % len(self.order)
self.cursor = next_cursor
if act == "up":
# always loop around
if self.mode == "size":
self.resize(1)
elif self.mode == "select":
next_cursor = (self.cursor + 1) % len(self.order)
self.cursor = next_cursor
return False, None, False
def lower(self):
for n in self.ir.nodes:
o = [(self.var, k) for k in self.order]
self.ir.set_order(n, o)
# always disable innermost
self.ir.disable_reuse(n, len(o) - 1)
loop_tree = lt.LoopTree(self.ir)
parallel = set()
t = loop_tree.roots[0]
for b in self.thread:
if b:
parallel.add(t)
if self.backend == "cpu":
loop_tree.annotate(t, "cpu_parallel")
t = loop_tree.children(t)[0]
return loop_tree, parallel
def flops(self):
loop_tree, parallel = self.lower()
if self.backend == "cuda":
c = lt.cuda(loop_tree, parallel)
else:
c = lt.cpu(loop_tree)
A = lt.Tensor(self.size)
B = lt.Tensor(self.size)
C = lt.Tensor(self.size)
A.set(self.Ap)
B.set(self.Bp)
iters = 1000
warmup = 50
for i in range(warmup):
c([A, B, C])
t = time.time()
for i in range(iters - 1):
c([A, B, C], False)
c([A, B, C])
t_ = time.time()
flops = self.size * iters / (t_ - t) / 1e9
return flops
def get_observation(self, observation_space: ObservationSpace) -> Event:
if observation_space.name == "action_state":
# cursor, (size, tail)
o = self.order[self.cursor]
return Event(int64_tensor=Int64Tensor(
shape=[3], value=[self.cursor, o[0], o[1]]))
elif observation_space.name == "flops":
return Event(double_value=self.flops())
elif observation_space.name == "loop_tree":
loop_tree, parallel = self.lower()
return Event(string_value=loop_tree.dump(lambda x: "[thread]"
if x in parallel else ""))
else:
raise KeyError(observation_space.name)
class ExampleCompilationSession(CompilationSession):
"""Represents an instance of an interactive compilation session."""
compiler_version: str = "1.0.0"
# The action spaces supported by this service. Here we will implement a
# single action space, called "default", that represents a command line with
# three options: "a", "b", and "c".
action_spaces = [
ActionSpace(
name="default",
space=Space(named_discrete=NamedDiscreteSpace(name=[
"a",
"b",
"c",
], ), ),
)
]
# A list of observation spaces supported by this service. Each of these
# ObservationSpace protos describes an observation space.
observation_spaces = [
ObservationSpace(
name="ir",
space=Space(
string_value=StringSpace(length_range=Int64Range(min=0)), ),
deterministic=True,
platform_dependent=False,
default_observation=Event(string_value=""),
),
ObservationSpace(
name="features",
space=Space(int64_box=Int64Box(
low=Int64Tensor(shape=[3], value=[-100, -100, -100]),
high=Int64Tensor(shape=[3], value=[100, 100, 100]),
), ),
),
ObservationSpace(
name="runtime",
space=Space(double_value=DoubleRange(min=0), ),
deterministic=False,
platform_dependent=True,
default_observation=Event(double_value=0, ),
),
]
def __init__(self, working_directory: Path, action_space: ActionSpace,
benchmark: Benchmark):
super().__init__(working_directory, action_space, benchmark)
logging.info("Started a compilation session for %s", benchmark.uri)
def apply_action(
self, action: Event) -> Tuple[bool, Optional[ActionSpace], bool]:
num_choices = len(self.action_spaces[0].space.named_discrete.name)
# This is the index into the action space's values ("a", "b", "c") that
# the user selected, e.g. 0 -> "a", 1 -> "b", 2 -> "c".
choice_index = action.int64_value
logging.info("Applying action %d", choice_index)
if choice_index < 0 or choice_index >= num_choices:
raise ValueError("Out-of-range")
# Here is where we would run the actual action to update the environment's
# state.
return False, None, False
def get_observation(self, observation_space: ObservationSpace) -> Event:
logging.info("Computing observation from space %s", observation_space)
if observation_space.name == "ir":
return Event(string_value="Hello, world!")
elif observation_space.name == "features":
observation = Event(
int64_tensor=Int64Tensor(shape=[3], value=[0, 0, 0]))
return observation
elif observation_space.name == "runtime":
return Event(double_value=0)
else:
raise KeyError(observation_space.name)
def get_observation(self, observation_space: ObservationSpace) -> Event:
"""Get one of the observations"""
if observation_space.name == "source":
return Event(string_value=self.source or "")
elif observation_space.name == "rtl":
return Event(string_value=self.rtl or "")
elif observation_space.name == "asm":
return Event(string_value=self.asm or "")
elif observation_space.name == "asm_size":
return Event(int64_value=self.asm_size or -1)
elif observation_space.name == "asm_hash":
return Event(string_value=self.asm_hash or "")
elif observation_space.name == "instruction_counts":
return Event(string_value=self.instruction_counts or "{}")
elif observation_space.name == "obj":
value = self.obj or b""
return Event(byte_tensor=ByteTensor(shape=[len(value)], value=value))
elif observation_space.name == "obj_size":
return Event(int64_value=self.obj_size or -1)
elif observation_space.name == "obj_hash":
return Event(string_value=self.obj_hash or "")
elif observation_space.name == "choices":
observation = Event(
int64_tensor=Int64Tensor(
shape=[len(self.choices)], value=self.choices
)
)
return observation
elif observation_space.name == "command_line":
return Event(
string_value=gcc.bin
+ " "
+ " ".join(map(str, self.obj_command_line("src.c", "obj.o")))
)
else:
raise KeyError(observation_space.name)
def make_gcc_compilation_session(gcc_bin: str):
"""Create a class to represent a GCC compilation service.
:param gcc_bin: Path to the gcc executable. This can a command name, like
"gcc", or it can be path to the executable. Finally, if prefixed with
"docker:" it can be the name of a docker image, e.g. "docker:gcc:11.2.0"
"""
gcc = Gcc(gcc_bin)
# The available actions
actions = []
# Actions that are small will have all their various choices made as
# explicit actions.
# Actions that are not small will have the abbility to increment the choice
# by different amounts.
for i, option in enumerate(gcc.spec.options):
if len(option) < 10:
for j in range(len(option)):
actions.append(SimpleAction(option, i, j))
if len(option) >= 10:
actions.append(IncrAction(option, i, 1))
actions.append(IncrAction(option, i, -1))
if len(option) >= 50:
actions.append(IncrAction(option, i, 10))
actions.append(IncrAction(option, i, -10))
if len(option) >= 500:
actions.append(IncrAction(option, i, 100))
actions.append(IncrAction(option, i, -100))
if len(option) >= 5000:
actions.append(IncrAction(option, i, 1000))
actions.append(IncrAction(option, i, -1000))
action_spaces_ = [
ActionSpace(
name="default",
space=Space(
named_discrete=NamedDiscreteSpace(name=[str(a) for a in actions]),
),
),
]
observation_spaces_ = [
# A string of the source code
ObservationSpace(
name="source",
space=Space(string_value=StringSpace(length_range=Int64Range(min=0))),
deterministic=True,
platform_dependent=False,
default_observation=Event(string_value=""),
),
# A string of the rtl code
ObservationSpace(
name="rtl",
space=Space(string_value=StringSpace(length_range=Int64Range(min=0))),
deterministic=True,
platform_dependent=True,
default_observation=Event(string_value=""),
),
# A string of the assembled code
ObservationSpace(
name="asm",
space=Space(string_value=StringSpace(length_range=Int64Range(min=0))),
deterministic=True,
platform_dependent=True,
default_observation=Event(string_value=""),
),
# The size of the assembled code
ObservationSpace(
name="asm_size",
space=Space(int64_value=Int64Range(min=-1)),
deterministic=True,
platform_dependent=True,
default_observation=Event(
int64_value=-1,
),
),
# The hash of the assembled code
ObservationSpace(
name="asm_hash",
space=Space(
string_value=StringSpace(length_range=Int64Range(min=0, max=200)),
),
deterministic=True,
platform_dependent=True,
default_observation=Event(string_value=""),
),
# Asm instruction counts - Counter as a JSON string
ObservationSpace(
name="instruction_counts",
space=Space(
string_value=StringSpace(length_range=Int64Range(min=0)),
),
deterministic=True,
platform_dependent=True,
default_observation=Event(string_value=""),
),
# A bytes of the object code
ObservationSpace(
name="obj",
space=Space(
byte_sequence=ByteSequenceSpace(length_range=Int64Range(min=0)),
),
deterministic=True,
platform_dependent=False,
default_observation=Event(byte_tensor=ByteTensor(shape=[0], value=b"")),
),
# The size of the object code
ObservationSpace(
name="obj_size",
space=Space(int64_value=Int64Range(min=-1)),
deterministic=True,
platform_dependent=True,
default_observation=Event(
int64_value=-1,
),
),
# The hash of the object code
ObservationSpace(
name="obj_hash",
space=Space(
string_value=StringSpace(length_range=Int64Range(min=0, max=200)),
),
deterministic=True,
platform_dependent=True,
default_observation=Event(string_value=""),
),
# A list of the choices. Each element corresponds to an option in the spec.
# '-1' indicates that this is empty on the command line (e.g. if the choice
# corresponding to the '-O' option is -1, then no -O flag will be emitted.)
# If a nonnegative number if given then that particular choice is used
# (e.g. for the -O flag, 5 means use '-Ofast' on the command line.)
ObservationSpace(
name="choices",
space=Space(
space_list=ListSpace(
space=[
Space(int64_value=Int64Range(min=0, max=len(option) - 1))
for option in gcc.spec.options
]
),
),
),
# The command line for compiling the object file as a string
ObservationSpace(
name="command_line",
space=Space(
string_value=StringSpace(length_range=Int64Range(min=0, max=200)),
),
deterministic=True,
platform_dependent=True,
default_observation=Event(string_value=""),
),
]
class GccCompilationSession(CompilationSession):
"""A GCC interactive compilation session."""
compiler_version: str = gcc.spec.version
action_spaces = action_spaces_
observation_spaces = observation_spaces_
def __init__(
self,
working_directory: Path,
action_space: ActionSpace,
benchmark: Benchmark,
):
super().__init__(working_directory, action_space, benchmark)
# The benchmark being used
self.benchmark = benchmark
# Timeout value for compilation (in seconds)
self._timeout = None
# The source code
self._source = None
# The rtl code
self._rtl = None
# The assembled code
self._asm = None
# Size of the assembled code
self._asm_size = None
# Hash of the assembled code
self._asm_hash = None
# The object binary
self._obj = None
# size of the object binary
self._obj_size = None
# Hash of the object binary
self._obj_hash = None
# Set the path to the GCC executable
self._gcc_bin = "gcc"
# Initially the choices and the spec, etc are empty. They will be
# initialised lazily
self._choices = None
@property
def num_actions(self) -> int:
return len(self.action_spaces[0].space.named_discrete.name)
@property
def choices(self) -> List[int]:
if self._choices is None:
self._choices = [-1] * len(gcc.spec.options)
return self._choices
@choices.setter
def choices(self, value: List[int]):
self._choices = value
@property
def source(self) -> str:
"""Get the benchmark source"""
self.prepare_files()
return self._source
@property
def rtl(self) -> bytes:
"""Get the RTL code"""
self.dump_rtl()
return self._rtl
@property
def asm(self) -> bytes:
"""Get the assembled code"""
self.assemble()
return self._asm
@property
def asm_size(self) -> int:
"""Get the assembled code size"""
self.assemble()
return self._asm_size
@property
def asm_hash(self) -> str:
"""Get the assembled code hash"""
self.assemble()
return self._asm_hash
@property
def instruction_counts(self) -> str:
"""Get the instuction counts as a JSON string"""
self.assemble()
insn_pat = re.compile("\t([a-zA-Z-0-9.-]+).*")
insn_cnts = Counter()
lines = self._asm.split("\n")
for line in lines:
m = insn_pat.fullmatch(line)
if m:
insn_cnts[m.group(1)] += 1
return json.dumps(insn_cnts)
@property
def obj(self) -> bytes:
"""Get the compiled code"""
self.compile()
return self._obj
@property
def obj_size(self) -> int:
"""Get the compiled code size"""
self.compile()
return self._obj_size
@property
def obj_hash(self) -> str:
"""Get the compiled code hash"""
self.compile()
return self._obj_hash
@property
def src_path(self) -> Path:
"""Get the path to the source file"""
return self.working_dir / "src.c"
@property
def obj_path(self) -> Path:
"""Get the path to object file"""
return self.working_dir / "obj.o"
@property
def asm_path(self) -> Path:
"""Get the path to the assembly"""
return self.working_dir / "asm.s"
@property
def rtl_path(self) -> Path:
"""Get the path to the rtl"""
return self.working_dir / "rtl.lsp"
def obj_command_line(
self, src_path: Path = None, obj_path: Path = None
) -> List[str]:
"""Get the command line to create the object file.
The 'src_path' and 'obj_path' give the input and output paths. If not
set, then they are taken from 'self.src_path' and 'self.obj_path'. This
is useful for printing where the actual paths are not important."""
src_path = src_path or self.src_path
obj_path = obj_path or self.obj_path
# Gather the choices as strings
opts = [
option[choice]
for option, choice in zip(gcc.spec.options, self.choices)
if choice >= 0
]
cmd_line = opts + ["-w", "-c", src_path, "-o", obj_path]
return cmd_line
def asm_command_line(
self, src_path: Path = None, asm_path: Path = None
) -> List[str]:
"""Get the command line to create the assembly file.
The 'src_path' and 'asm_path' give the input and output paths. If not
set, then they are taken from 'self.src_path' and 'self.obj_path'. This
is useful for printing where the actual paths are not important."""
src_path = src_path or self.src_path
asm_path = asm_path or self.asm_path
opts = [
option[choice]
for option, choice in zip(gcc.spec.options, self.choices)
if choice >= 0
]
cmd_line = opts + ["-w", "-S", src_path, "-o", asm_path]
return cmd_line
def rtl_command_line(
self, src_path: Path = None, rtl_path: Path = None, asm_path: Path = None
) -> List[str]:
"""Get the command line to create the rtl file - might as well do the
asm at the same time.
The 'src_path', 'rtl_path', 'asm_path' give the input and output paths. If not
set, then they are taken from 'self.src_path' and 'self.obj_path'. This
is useful for printing where the actual paths are not important."""
src_path = src_path or self.src_path
rtl_path = rtl_path or self.rtl_path
asm_path = asm_path or self.asm_path
opts = [
option[choice]
for option, choice in zip(gcc.spec.options, self.choices)
if choice >= 0
]
cmd_line = opts + [
"-w",
"-S",
src_path,
f"-fdump-rtl-dfinish={rtl_path}",
"-o",
asm_path,
]
return cmd_line
def prepare_files(self):
"""Copy the source to the working directory."""
if not self._source:
if self.benchmark.program.contents:
self._source = self.benchmark.program.contents.decode()
else:
with urlopen(self.benchmark.program.uri) as r:
self._source = r.read().decode()
with open(self.src_path, "w") as f:
print(self._source, file=f)
def compile(self) -> Optional[str]:
"""Compile the benchmark"""
if not self._obj:
self.prepare_files()
logger.debug(
"Compiling: %s", " ".join(map(str, self.obj_command_line()))
)
gcc(
*self.obj_command_line(),
cwd=self.working_dir,
timeout=self._timeout,
)
with open(self.obj_path, "rb") as f:
# Set the internal variables
self._obj = f.read()
self._obj_size = os.path.getsize(self.obj_path)
self._obj_hash = hashlib.md5(self._obj).hexdigest()
def assemble(self) -> Optional[str]:
"""Assemble the benchmark"""
if not self._asm:
self.prepare_files()
logger.debug(
"Assembling: %s", " ".join(map(str, self.asm_command_line()))
)
gcc(
*self.asm_command_line(),
cwd=self.working_dir,
timeout=self._timeout,
)
with open(self.asm_path, "rb") as f:
# Set the internal variables
asm_bytes = f.read()
self._asm = asm_bytes.decode()
self._asm_size = os.path.getsize(self.asm_path)
self._asm_hash = hashlib.md5(asm_bytes).hexdigest()
def dump_rtl(self) -> Optional[str]:
"""Dump the RTL (and assemble the benchmark)"""
if not self._rtl:
self.prepare_files()
logger.debug(
"Dumping RTL: %s", " ".join(map(str, self.rtl_command_line()))
)
gcc(
*self.rtl_command_line(),
cwd=self.working_dir,
timeout=self._timeout,
)
with open(self.asm_path, "rb") as f:
# Set the internal variables
asm_bytes = f.read()
self._asm = asm_bytes.decode()
self._asm_size = os.path.getsize(self.asm_path)
self._asm_hash = hashlib.md5(asm_bytes).hexdigest()
with open(self.rtl_path, "rb") as f:
# Set the internal variables
rtl_bytes = f.read()
self._rtl = rtl_bytes.decode()
def reset_cached(self):
"""Reset the cached values"""
self._obj = None
self._obj_size = None
self._obj_hash = None
self._rtl = None
self._asm = None
self._asm_size = None
self._asm_hash = None
def apply_action(
self, action_proto: Event
) -> Tuple[bool, Optional[ActionSpace], bool]:
"""Apply an action."""
if not action_proto.HasField("int64_value"):
raise ValueError("Invalid action, int64_value expected.")
choice_index = action_proto.int64_value
if choice_index < 0 or choice_index >= self.num_actions:
raise ValueError("Out-of-range")
# Get the action
action = actions[choice_index]
# Apply the action to this session and check if we changed anything
old_choices = self.choices.copy()
action(self)
logger.debug("Applied action %s", action)
# Reset the internal variables if this action has caused a change in the
# choices
if old_choices != self.choices:
self.reset_cached()
# The action has not changed anything yet. That waits until an
# observation is taken
return False, None, False
def get_observation(self, observation_space: ObservationSpace) -> Event:
"""Get one of the observations"""
if observation_space.name == "source":
return Event(string_value=self.source or "")
elif observation_space.name == "rtl":
return Event(string_value=self.rtl or "")
elif observation_space.name == "asm":
return Event(string_value=self.asm or "")
elif observation_space.name == "asm_size":
return Event(int64_value=self.asm_size or -1)
elif observation_space.name == "asm_hash":
return Event(string_value=self.asm_hash or "")
elif observation_space.name == "instruction_counts":
return Event(string_value=self.instruction_counts or "{}")
elif observation_space.name == "obj":
value = self.obj or b""
return Event(byte_tensor=ByteTensor(shape=[len(value)], value=value))
elif observation_space.name == "obj_size":
return Event(int64_value=self.obj_size or -1)
elif observation_space.name == "obj_hash":
return Event(string_value=self.obj_hash or "")
elif observation_space.name == "choices":
observation = Event(
int64_tensor=Int64Tensor(
shape=[len(self.choices)], value=self.choices
)
)
return observation
elif observation_space.name == "command_line":
return Event(
string_value=gcc.bin
+ " "
+ " ".join(map(str, self.obj_command_line("src.c", "obj.o")))
)
else:
raise KeyError(observation_space.name)
def handle_session_parameter(self, key: str, value: str) -> Optional[str]:
if key == "gcc_spec":
return codecs.encode(pickle.dumps(gcc.spec), "base64").decode()
elif key == "choices":
choices = list(map(int, value.split(",")))
assert len(choices) == len(gcc.spec.options)
assert all(
-1 <= p <= len(gcc.spec.options[i]) for i, p in enumerate(choices)
)
if choices != self.choices:
self.choices = choices
self.reset_cached()
return ""
elif key == "timeout":
self._timeout = None if value == "" else int(value)
return ""
return None
return GccCompilationSession
class UnrollingCompilationSession(CompilationSession):
"""Represents an instance of an interactive compilation session."""
compiler_version: str = "1.0.0"
# The list of actions that are supported by this service.
action_spaces = [
ActionSpace(
name="unrolling",
space=Space(
named_discrete=NamedDiscreteSpace(
name=[
"-loop-unroll -unroll-count=2",
"-loop-unroll -unroll-count=4",
"-loop-unroll -unroll-count=8",
],
),
),
)
]
# A list of observation spaces supported by this service. Each of these
# ObservationSpace protos describes an observation space.
observation_spaces = [
ObservationSpace(
name="ir",
space=Space(
string_value=StringSpace(length_range=Int64Range(min=0)),
),
deterministic=True,
platform_dependent=False,
default_observation=Event(string_value=""),
),
ObservationSpace(
name="features",
space=Space(
int64_box=Int64Box(
low=Int64Tensor(shape=[3], value=[0, 0, 0]),
high=Int64Tensor(shape=[3], value=[100000, 100000, 100000]),
),
),
),
ObservationSpace(
name="runtime",
space=Space(
double_value=DoubleRange(min=0),
),
deterministic=False,
platform_dependent=True,
default_observation=Event(
double_value=0,
),
),
ObservationSpace(
name="size",
space=Space(
double_value=DoubleRange(min=0),
),
deterministic=True,
platform_dependent=True,
default_observation=Event(
double_value=0,
),
),
]
def __init__(
self,
working_directory: Path,
action_space: ActionSpace,
benchmark: Benchmark,
use_custom_opt: bool = True,
):
super().__init__(working_directory, action_space, benchmark)
logging.info("Started a compilation session for %s", benchmark.uri)
self._benchmark = benchmark
self._action_space = action_space
# Resolve the paths to LLVM binaries once now.
self._clang = str(llvm.clang_path())
self._llc = str(llvm.llc_path())
self._llvm_diff = str(llvm.llvm_diff_path())
self._opt = str(llvm.opt_path())
# LLVM's opt does not always enforce the unrolling options passed as cli arguments. Hence, we created our own exeutable with custom unrolling pass in examples/example_unrolling_service/loop_unroller that enforces the unrolling factors passed in its cli.
# if self._use_custom_opt is true, use our custom exeutable, otherwise use LLVM's opt
self._use_custom_opt = use_custom_opt
# Dump the benchmark source to disk.
self._src_path = str(self.working_dir / "benchmark.c")
with open(self.working_dir / "benchmark.c", "wb") as f:
f.write(benchmark.program.contents)
self._llvm_path = str(self.working_dir / "benchmark.ll")
self._llvm_before_path = str(self.working_dir / "benchmark.previous.ll")
self._obj_path = str(self.working_dir / "benchmark.o")
self._exe_path = str(self.working_dir / "benchmark.exe")
run_command(
[
self._clang,
"-Xclang",
"-disable-O0-optnone",
"-emit-llvm",
"-S",
self._src_path,
"-o",
self._llvm_path,
],
timeout=30,
)
def apply_action(self, action: Event) -> Tuple[bool, Optional[ActionSpace], bool]:
num_choices = len(self._action_space.space.named_discrete.name)
# This is the index into the action space's values ("a", "b", "c") that
# the user selected, e.g. 0 -> "a", 1 -> "b", 2 -> "c".
choice_index = action.int64_value
if choice_index < 0 or choice_index >= num_choices:
raise ValueError("Out-of-range")
args = self._action_space.space.named_discrete.name[choice_index]
logging.info(
"Applying action %d, equivalent command-line arguments: '%s'",
choice_index,
args,
)
args = args.split()
# make a copy of the LLVM file to compare its contents after applying the action
shutil.copyfile(self._llvm_path, self._llvm_before_path)
# apply action
if self._use_custom_opt:
# our custom unroller has an additional `f` at the beginning of each argument
for i, arg in enumerate(args):
# convert -<argument> to -f<argument>
arg = arg[0] + "f" + arg[1:]
args[i] = arg
run_command(
[
"../loop_unroller/loop_unroller",
self._llvm_path,
*args,
"-S",
"-o",
self._llvm_path,
],
timeout=30,
)
else:
run_command(
[
self._opt,
*args,
self._llvm_path,
"-S",
"-o",
self._llvm_path,
],
timeout=30,
)
# compare the IR files to check if the action had an effect
try:
subprocess.check_call(
[self._llvm_diff, self._llvm_before_path, self._llvm_path],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
timeout=60,
)
action_had_no_effect = True
except subprocess.CalledProcessError:
action_had_no_effect = False
end_of_session = False # TODO: this needs investigation: for how long can we apply loop unrolling? e.g., detect if there are no more loops in the IR?
new_action_space = None
return (end_of_session, new_action_space, action_had_no_effect)
@property
def ir(self) -> str:
with open(self._llvm_path) as f:
return f.read()
def get_observation(self, observation_space: ObservationSpace) -> Event:
logging.info("Computing observation from space %s", observation_space.name)
if observation_space.name == "ir":
return Event(string_value=self.ir)
elif observation_space.name == "features":
stats = utils.extract_statistics_from_ir(self.ir)
observation = Event(
int64_tensor=Int64Tensor(
shape=[len(list(stats.values()))], value=list(stats.values())
)
)
return observation
elif observation_space.name == "runtime":
# compile LLVM to object file
run_command(
[
self._llc,
"-filetype=obj",
self._llvm_path,
"-o",
self._obj_path,
],
timeout=30,
)
# build object file to binary
run_command(
[
self._clang,
self._obj_path,
"-O3",
"-o",
self._exe_path,
],
timeout=30,
)
# TODO: add documentation that benchmarks need print out execution time
# Running 5 times and taking the average of middle 3
exec_times = []
for _ in range(5):
stdout = run_command(
[self._exe_path],
timeout=30,
)
try:
exec_times.append(int(stdout))
except ValueError:
raise ValueError(
f"Error in parsing execution time from output of command\n"
f"Please ensure that the source code of the benchmark measures execution time and prints to stdout\n"
f"Stdout of the program: {stdout}"
)
exec_times = np.sort(exec_times)
avg_exec_time = np.mean(exec_times[1:4])
return Event(double_value=avg_exec_time)
elif observation_space.name == "size":
# compile LLVM to object file
run_command(
[
self._llc,
"-filetype=obj",
self._llvm_path,
"-o",
self._obj_path,
],
timeout=30,
)
# build object file to binary
run_command(
[
self._clang,
self._obj_path,
"-Oz",
"-o",
self._exe_path,
],
timeout=30,
)
binary_size = os.path.getsize(self._exe_path)
return Event(double_value=binary_size)
else:
raise KeyError(observation_space.name)
def get_observation(self, observation_space: ObservationSpace) -> Event:
logging.info("Computing observation from space %s", observation_space.name)
if observation_space.name == "ir":
return Event(string_value=self.ir)
elif observation_space.name == "features":
stats = utils.extract_statistics_from_ir(self.ir)
observation = Event(
int64_tensor=Int64Tensor(
shape=[len(list(stats.values()))], value=list(stats.values())
)
)
return observation
elif observation_space.name == "runtime":
# compile LLVM to object file
run_command(
[
self._llc,
"-filetype=obj",
self._llvm_path,
"-o",
self._obj_path,
],
timeout=30,
)
# build object file to binary
run_command(
[
self._clang,
self._obj_path,
"-O3",
"-o",
self._exe_path,
],
timeout=30,
)
# TODO: add documentation that benchmarks need print out execution time
# Running 5 times and taking the average of middle 3
exec_times = []
for _ in range(5):
stdout = run_command(
[self._exe_path],
timeout=30,
)
try:
exec_times.append(int(stdout))
except ValueError:
raise ValueError(
f"Error in parsing execution time from output of command\n"
f"Please ensure that the source code of the benchmark measures execution time and prints to stdout\n"
f"Stdout of the program: {stdout}"
)
exec_times = np.sort(exec_times)
avg_exec_time = np.mean(exec_times[1:4])
return Event(double_value=avg_exec_time)
elif observation_space.name == "size":
# compile LLVM to object file
run_command(
[
self._llc,
"-filetype=obj",
self._llvm_path,
"-o",
self._obj_path,
],
timeout=30,
)
# build object file to binary
run_command(
[
self._clang,
self._obj_path,
"-Oz",
"-o",
self._exe_path,
],
timeout=30,
)
binary_size = os.path.getsize(self._exe_path)
return Event(double_value=binary_size)
else:
raise KeyError(observation_space.name)
def get_observation(self, observation_space: ObservationSpace) -> Event:
logging.info("Computing observation from space %s",
observation_space.name)
if observation_space.name == "ir":
return Event(string_value=self.ir)
elif observation_space.name == "Inst2vec":
Inst2vec_str = self.inst2vec.preprocess(self.ir)
Inst2vec_ids = self.inst2vec.encode(Inst2vec_str)
return Event(int64_tensor=Int64Tensor(shape=[len(Inst2vec_ids)],
value=Inst2vec_ids))
elif observation_space.name == "Autophase":
Autophase_str = run_command(
[
os.path.join(
os.path.dirname(__file__),
"../../../compiler_gym/third_party/autophase/compute_autophase-prelinked",
),
self._llvm_path,
],
timeout=30,
)
Autophase_list = list(map(int, list(Autophase_str.split(" "))))
return Event(int64_tensor=Int64Tensor(shape=[len(Autophase_list)],
value=Autophase_list))
elif observation_space.name == "AutophaseDict":
Autophase_str = run_command(
[
os.path.join(
os.path.dirname(__file__),
"../../../compiler_gym/third_party/autophase/compute_autophase-prelinked",
),
self._llvm_path,
],
timeout=30,
)
Autophase_list = list(map(int, list(Autophase_str.split(" "))))
Autophase_dict = {
name: Event(int64_value=val)
for name, val in zip(AUTOPHASE_FEATURE_NAMES, Autophase_list)
}
return Event(event_dict=DictEvent(event=Autophase_dict))
elif observation_space.name == "Programl":
Programl_str = run_command(
[
os.path.join(
os.path.dirname(__file__),
"../../../compiler_gym/third_party/programl/compute_programl",
),
self._llvm_path,
],
timeout=30,
)
return Event(string_value=Programl_str)
elif observation_space.name == "runtime":
# compile LLVM to object file
run_command(
[
self._llc,
"-filetype=obj",
self._llvm_path,
"-o",
self._obj_path,
],
timeout=30,
)
# build object file to binary
run_command(
[
self._clang,
self._obj_path,
"-O3",
"-o",
self._exe_path,
],
timeout=30,
)
# TODO: add documentation that benchmarks need print out execution time
# Running 5 times and taking the average of middle 3
exec_times = []
for _ in range(5):
stdout = run_command(
[self._exe_path],
timeout=30,
)
try:
exec_times.append(int(stdout))
except ValueError:
raise ValueError(
f"Error in parsing execution time from output of command\n"
f"Please ensure that the source code of the benchmark measures execution time and prints to stdout\n"
f"Stdout of the program: {stdout}")
exec_times = np.sort(exec_times)
avg_exec_time = np.mean(exec_times[1:4])
return Event(double_value=avg_exec_time)
elif observation_space.name == "size":
# compile LLVM to object file
run_command(
[
self._llc,
"-filetype=obj",
self._llvm_path,
"-o",
self._obj_path,
],
timeout=30,
)
# build object file to binary
run_command(
[
self._clang,
self._obj_path,
"-Oz",
"-o",
self._exe_path,
],
timeout=30,
)
binary_size = os.path.getsize(self._exe_path)
return Event(double_value=binary_size)
else:
raise KeyError(observation_space.name)
class LoopsOptCompilationSession(CompilationSession):
"""Represents an instance of an interactive compilation session."""
compiler_version: str = "1.0.0"
# The list of actions that are supported by this service.
action_spaces = [
ActionSpace(
name="loop-opt",
space=Space(named_discrete=NamedDiscreteSpace(name=[
"--loop-unroll --unroll-count=2",
"--loop-unroll --unroll-count=4",
"--loop-unroll --unroll-count=8",
"--loop-unroll --unroll-count=16",
"--loop-unroll --unroll-count=32",
"--loop-vectorize -force-vector-width=2",
"--loop-vectorize -force-vector-width=4",
"--loop-vectorize -force-vector-width=8",
"--loop-vectorize -force-vector-width=16",
"--loop-vectorize -force-vector-width=32",
]), ),
)
]
# A list of observation spaces supported by this service. Each of these
# ObservationSpace protos describes an observation space.
observation_spaces = [
ObservationSpace(
name="ir",
space=Space(string_value=StringSpace(length_range=Int64Range(
min=0))),
deterministic=True,
platform_dependent=False,
default_observation=Event(string_value=""),
),
ObservationSpace(
name="Inst2vec",
space=Space(int64_sequence=Int64SequenceSpace(
length_range=Int64Range(min=0)), ),
),
ObservationSpace(
name="Autophase",
space=Space(
int64_sequence=Int64SequenceSpace(length_range=Int64Range(
min=len(AUTOPHASE_FEATURE_NAMES),
max=len(AUTOPHASE_FEATURE_NAMES),
)), ),
deterministic=True,
platform_dependent=False,
),
ObservationSpace(
name="AutophaseDict",
space=Space(space_dict=DictSpace(
space={
name: Space(int64_value=Int64Range(min=0))
for name in AUTOPHASE_FEATURE_NAMES
})),
deterministic=True,
platform_dependent=False,
),
ObservationSpace(
name="Programl",
space=Space(string_value=StringSpace(length_range=Int64Range(
min=0))),
deterministic=True,
platform_dependent=False,
default_observation=Event(string_value=""),
),
ObservationSpace(
name="runtime",
space=Space(double_value=DoubleRange(min=0)),
deterministic=False,
platform_dependent=True,
default_observation=Event(double_value=0, ),
),
ObservationSpace(
name="size",
space=Space(double_value=DoubleRange(min=0)),
deterministic=True,
platform_dependent=True,
default_observation=Event(double_value=0, ),
),
]
def __init__(
self,
working_directory: Path,
action_space: ActionSpace,
benchmark: Benchmark,
use_custom_opt: bool = True,
):
super().__init__(working_directory, action_space, benchmark)
logging.info("Started a compilation session for %s", benchmark.uri)
self._benchmark = benchmark
self._action_space = action_space
self.inst2vec = _INST2VEC_ENCODER
# Resolve the paths to LLVM binaries once now.
self._clang = str(llvm.clang_path())
self._llc = str(llvm.llc_path())
self._llvm_diff = str(llvm.llvm_diff_path())
self._opt = str(llvm.opt_path())
# LLVM's opt does not always enforce the loop optimization options passed as cli arguments.
# Hence, we created our own exeutable with custom unrolling and vectorization pass in examples/loops_opt_service/opt_loops that enforces the unrolling and vectorization factors passed in its cli.
# if self._use_custom_opt is true, use our custom exeutable, otherwise use LLVM's opt
self._use_custom_opt = use_custom_opt
# Dump the benchmark source to disk.
self._src_path = str(self.working_dir / "benchmark.c")
with open(self.working_dir / "benchmark.c", "wb") as f:
f.write(benchmark.program.contents)
self._llvm_path = str(self.working_dir / "benchmark.ll")
self._llvm_before_path = str(self.working_dir /
"benchmark.previous.ll")
self._obj_path = str(self.working_dir / "benchmark.o")
self._exe_path = str(self.working_dir / "benchmark.exe")
run_command(
[
self._clang,
"-Xclang",
"-disable-O0-optnone",
"-emit-llvm",
"-S",
self._src_path,
"-o",
self._llvm_path,
],
timeout=30,
)
def apply_action(
self, action: Event) -> Tuple[bool, Optional[ActionSpace], bool]:
num_choices = len(self._action_space.space.named_discrete.name)
# This is the index into the action space's values ("a", "b", "c") that
# the user selected, e.g. 0 -> "a", 1 -> "b", 2 -> "c".
choice_index = action.int64_value
if choice_index < 0 or choice_index >= num_choices:
raise ValueError("Out-of-range")
args = self._action_space.space.named_discrete.name[choice_index]
logging.info(
"Applying action %d, equivalent command-line arguments: '%s'",
choice_index,
args,
)
args = args.split()
# make a copy of the LLVM file to compare its contents after applying the action
shutil.copyfile(self._llvm_path, self._llvm_before_path)
# apply action
if self._use_custom_opt:
# our custom opt-loops has an additional `f` at the beginning of each argument
for i, arg in enumerate(args):
# convert --<argument> to --f<argument>
arg = arg[0:2] + "f" + arg[2:]
args[i] = arg
run_command(
[
os.path.join(os.path.dirname(__file__),
"../opt_loops/opt_loops"),
self._llvm_path,
*args,
"-S",
"-o",
self._llvm_path,
],
timeout=30,
)
else:
run_command(
[
self._opt,
*args,
self._llvm_path,
"-S",
"-o",
self._llvm_path,
],
timeout=30,
)
# compare the IR files to check if the action had an effect
try:
subprocess.check_call(
[self._llvm_diff, self._llvm_before_path, self._llvm_path],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
timeout=60,
)
action_had_no_effect = True
except subprocess.CalledProcessError:
action_had_no_effect = False
end_of_session = False # TODO: this needs investigation: for how long can we apply loop optimizations? e.g., detect if there are no more loops in the IR? or look at the metadata?
new_action_space = None
return (end_of_session, new_action_space, action_had_no_effect)
@property
def ir(self) -> str:
with open(self._llvm_path) as f:
return f.read()
def get_observation(self, observation_space: ObservationSpace) -> Event:
logging.info("Computing observation from space %s",
observation_space.name)
if observation_space.name == "ir":
return Event(string_value=self.ir)
elif observation_space.name == "Inst2vec":
Inst2vec_str = self.inst2vec.preprocess(self.ir)
Inst2vec_ids = self.inst2vec.encode(Inst2vec_str)
return Event(int64_tensor=Int64Tensor(shape=[len(Inst2vec_ids)],
value=Inst2vec_ids))
elif observation_space.name == "Autophase":
Autophase_str = run_command(
[
os.path.join(
os.path.dirname(__file__),
"../../../compiler_gym/third_party/autophase/compute_autophase-prelinked",
),
self._llvm_path,
],
timeout=30,
)
Autophase_list = list(map(int, list(Autophase_str.split(" "))))
return Event(int64_tensor=Int64Tensor(shape=[len(Autophase_list)],
value=Autophase_list))
elif observation_space.name == "AutophaseDict":
Autophase_str = run_command(
[
os.path.join(
os.path.dirname(__file__),
"../../../compiler_gym/third_party/autophase/compute_autophase-prelinked",
),
self._llvm_path,
],
timeout=30,
)
Autophase_list = list(map(int, list(Autophase_str.split(" "))))
Autophase_dict = {
name: Event(int64_value=val)
for name, val in zip(AUTOPHASE_FEATURE_NAMES, Autophase_list)
}
return Event(event_dict=DictEvent(event=Autophase_dict))
elif observation_space.name == "Programl":
Programl_str = run_command(
[
os.path.join(
os.path.dirname(__file__),
"../../../compiler_gym/third_party/programl/compute_programl",
),
self._llvm_path,
],
timeout=30,
)
return Event(string_value=Programl_str)
elif observation_space.name == "runtime":
# compile LLVM to object file
run_command(
[
self._llc,
"-filetype=obj",
self._llvm_path,
"-o",
self._obj_path,
],
timeout=30,
)
# build object file to binary
run_command(
[
self._clang,
self._obj_path,
"-O3",
"-o",
self._exe_path,
],
timeout=30,
)
# TODO: add documentation that benchmarks need print out execution time
# Running 5 times and taking the average of middle 3
exec_times = []
for _ in range(5):
stdout = run_command(
[self._exe_path],
timeout=30,
)
try:
exec_times.append(int(stdout))
except ValueError:
raise ValueError(
f"Error in parsing execution time from output of command\n"
f"Please ensure that the source code of the benchmark measures execution time and prints to stdout\n"
f"Stdout of the program: {stdout}")
exec_times = np.sort(exec_times)
avg_exec_time = np.mean(exec_times[1:4])
return Event(double_value=avg_exec_time)
elif observation_space.name == "size":
# compile LLVM to object file
run_command(
[
self._llc,
"-filetype=obj",
self._llvm_path,
"-o",
self._obj_path,
],
timeout=30,
)
# build object file to binary
run_command(
[
self._clang,
self._obj_path,
"-Oz",
"-o",
self._exe_path,
],
timeout=30,
)
binary_size = os.path.getsize(self._exe_path)
return Event(double_value=binary_size)
else:
raise KeyError(observation_space.name)
def compute_observation(
observation_space: ObservationSpaceSpec, bitcode: Path, timeout: float = 300
) -> ObservationType:
"""Compute an LLVM observation.
This is a utility function that uses a standalone C++ binary to compute an
observation from an LLVM bitcode file. It is intended for use cases where
you want to compute an observation without the overhead of initializing a
full environment.
Example usage:
>>> env = compiler_gym.make("llvm-v0")
>>> space = env.observation.spaces["Ir"]
>>> bitcode = Path("bitcode.bc")
>>> observation = llvm.compute_observation(space, bitcode, timeout=30)
.. warning::
This is not part of the core CompilerGym API and may change in a future
release.
:param observation_space: The observation that is to be computed.
:param bitcode: The path of an LLVM bitcode file.
:param timeout: The maximum number of seconds to allow the computation to
run before timing out.
:raises ValueError: If computing the observation fails.
:raises TimeoutError: If computing the observation times out.
:raises FileNotFoundError: If the given bitcode does not exist.
"""
if not Path(bitcode).is_file():
raise FileNotFoundError(bitcode)
observation_space_name = pascal_case_to_enum(observation_space.id)
try:
with Popen(
[str(_COMPUTE_OBSERVATION_BIN), observation_space_name, str(bitcode)],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
) as process:
stdout, stderr = process.communicate(timeout=timeout)
if process.returncode:
try:
stderr = stderr.decode("utf-8")
raise ValueError(
f"Failed to compute {observation_space.id} observation: {stderr}"
)
except UnicodeDecodeError as e:
raise ValueError(
f"Failed to compute {observation_space.id} observation"
) from e
except subprocess.TimeoutExpired as e:
raise TimeoutError(
f"Failed to compute {observation_space.id} observation in "
f"{timeout:.1f} {plural(int(round(timeout)), 'second', 'seconds')}"
) from e
try:
stdout = stdout.decode("utf-8")
except UnicodeDecodeError as e:
raise ValueError(
f"Failed to parse {observation_space.id} observation: {e}"
) from e
observation = Event()
try:
google.protobuf.text_format.Parse(stdout, observation)
except google.protobuf.text_format.ParseError as e:
raise ValueError(f"Failed to parse {observation_space.id} observation") from e
return observation_space.translate(observation)