class LibWwqParseCFFI(LibWwqLyParseBase):
def __init__(self):
super(LibWwqParseCFFI, self).__init__()
from cffi import FFI
self.ffi = FFI()
self.lib = self.ffi.dlopen(self.lib_path)
self.ffi.cdef("""
char * get_uuid();
char * get_name();
int parse(char * c,int length,char **result,int *result_length);
int free_str(char * c);
""")
self.lib.__class__.__repr__ = lambda s: "<%s object at 0x%016X>" % (s.__class__.__name__, id(s))
logging.debug("successful load lib %s" % self.lib)
weakref.finalize(self,
lambda: logging.debug("%s released" % self.lib) if self.ffi.dlclose(self.lib) or 1 else None)
def get_uuid(self) -> bytes:
return self.ffi.string(self.lib.get_uuid())
def get_name(self) -> bytes:
return self.ffi.string(self.lib.get_name())
def lib_parse(self, byte_str: bytes) -> bytes:
length = self.ffi.cast("int", len(byte_str))
result_length = self.ffi.new("int *")
result_p = self.ffi.new("char **")
# p = self.ffi.new("char []", byte_str)
p = self.ffi.from_buffer(byte_str)
self.lib.parse(p, length, result_p, result_length)
result = self.ffi.unpack(result_p[0], result_length[0])
self.lib.free_str(result_p[0])
return result
def test_dlclose(self):
if self.Backend is CTypesBackend:
py.test.skip("not with the ctypes backend")
ffi = FFI(backend=self.Backend())
ffi.cdef("int foobar(void); extern int foobaz;")
lib = ffi.dlopen(lib_m)
ffi.dlclose(lib)
e = py.test.raises(ValueError, getattr, lib, 'foobar')
assert str(e.value).startswith("library '")
assert str(e.value).endswith("' has already been closed")
e = py.test.raises(ValueError, getattr, lib, 'foobaz')
assert str(e.value).startswith("library '")
assert str(e.value).endswith("' has already been closed")
e = py.test.raises(ValueError, setattr, lib, 'foobaz', 42)
assert str(e.value).startswith("library '")
assert str(e.value).endswith("' has already been closed")
ffi.dlclose(lib) # does not raise
def test_dlclose(self):
if self.Backend is CTypesBackend:
py.test.skip("not with the ctypes backend")
ffi = FFI(backend=self.Backend())
ffi.cdef("int foobar(void); int foobaz;")
lib = ffi.dlopen(lib_m)
ffi.dlclose(lib)
e = py.test.raises(ValueError, getattr, lib, 'foobar')
assert str(e.value).startswith("library '")
assert str(e.value).endswith("' has already been closed")
e = py.test.raises(ValueError, getattr, lib, 'foobaz')
assert str(e.value).startswith("library '")
assert str(e.value).endswith("' has already been closed")
e = py.test.raises(ValueError, setattr, lib, 'foobaz', 42)
assert str(e.value).startswith("library '")
assert str(e.value).endswith("' has already been closed")
ffi.dlclose(lib) # does not raise
class LibWwqParseCFFI(LibWwqLyParseBase):
def __init__(self):
super(LibWwqParseCFFI, self).__init__()
from cffi import FFI
self.ffi = FFI()
self.lib = self.ffi.dlopen(self.lib_path)
self.ffi.cdef("""
char * get_uuid();
char * get_name();
int parse(char * c,int length,char **result,int *result_length);
int free_str(char * c);
void* atomic_int64_init();
int atomic_int64_destroy(void* ptr);
int64_t atomic_int64_get(void* ptr);
int64_t atomic_int64_set(void* ptr, int64_t val);
int64_t atomic_int64_add(void* ptr, int64_t val);
int64_t atomic_int64_sub(void* ptr, int64_t val);
int64_t atomic_int64_and(void* ptr, int64_t val);
int64_t atomic_int64_or(void* ptr, int64_t val);
int64_t atomic_int64_xor(void* ptr, int64_t val);
typedef union epoll_data {
void* ptr;
int fd;
uint32_t u32;
uint64_t u64;
uintptr_t sock; /* Windows specific */
void* hnd; /* Windows specific */
} epoll_data_t;
typedef struct {
uint32_t events; /* Epoll events and flags */
epoll_data_t data; /* User data variable */
} epoll_event ;
void* epoll_create(int size);
void* epoll_create1(int flags);
int epoll_close(void* ephnd);
int epoll_ctl(void* ephnd, int op, uintptr_t sock, epoll_event* event);
int epoll_wait(void* ephnd, epoll_event* events, int maxevents, int timeout);
""")
self.lib.__class__.__repr__ = lambda s: "<%s object at 0x%016X>" % (s.__class__.__name__, id(s))
logging.debug("successful load lib %s" % self.lib)
weakref.finalize(self,
lambda: logging.debug("%s released" % self.lib) if self.ffi.dlclose(self.lib) or 1 else None)
def get_uuid(self) -> bytes:
return self.ffi.string(self.lib.get_uuid())
def get_name(self) -> bytes:
return self.ffi.string(self.lib.get_name())
def lib_parse(self, byte_str: bytes) -> bytes:
length = self.ffi.cast("int", len(byte_str))
result_length = self.ffi.new("int *")
result_p = self.ffi.new("char **")
# p = self.ffi.new("char []", byte_str)
p = self.ffi.from_buffer(byte_str)
self.lib.parse(p, length, result_p, result_length)
result = self.ffi.unpack(result_p[0], result_length[0])
self.lib.free_str(result_p[0])
return result
fortmod.methods[func.get_name().string] = args
#%%
ffi.cdef("""
void __mod_index_MOD_number_of_terms(
int *nfterme, int *npterme, int *npoly, int *npar, int *jterme);
void __mod_index_MOD_test(
int *nfterme, int *npterme, int *npoly, int *npar);
""")
#%%
npoly = np.array(3)
nfterme = np.array(1)
npterme = np.array(1)
npar = np.array(2)
jterme = np.zeros(npoly+1, dtype=np.int32)
libuq.__mod_index_MOD_number_of_terms(ffi.cast('int*', nfterme.ctypes.data),
ffi.cast('int*', npterme.ctypes.data),
ffi.cast('int*', npoly.ctypes.data),
ffi.cast('int*', npar.ctypes.data),
ffi.cast('int*', jterme.ctypes.data))
print(jterme)
#%%
ffi.dlclose(libuq)
class CVecEnv:
"""
An environment instance created by an EnvLib, uses the VecEnv interface.
https://github.com/openai/baselines/blob/master/baselines/common/vec_env/__init__.py
Args:
num_envs: number of environments to create
lib_dir: a folder containing either lib{name}.so (Linux), lib{name}.dylib (Mac), or {name}.dll (Windows)
lib_name: name of the library (minus the lib part)
c_func_defs: list of cdefs that are passed to FFI in order to define custom functions that can then be called with env.call_func()
options: options to pass to the libenv_make() call for this environment
debug: if set to True, check array data to make sure it matches the provided spaces
reuse_arrays: reduce allocations by using the same numpy arrays for each reset(), step(), and render() call
"""
def __init__(
self,
num_envs: int,
lib_dir: str,
lib_name: str = "env",
c_func_defs: Optional[List[str]] = None,
options: Optional[Dict] = None,
debug: bool = False,
reuse_arrays: bool = False,
) -> None:
if options is None:
options = {}
options = copy.deepcopy(options)
if platform.system() == "Linux":
lib_filename = f"lib{lib_name}.so"
elif platform.system() == "Darwin":
lib_filename = f"lib{lib_name}.dylib"
elif platform.system() == "Windows":
lib_filename = f"{lib_name}.dll"
else:
raise Exception(f"unrecognized platform {platform.system()}")
if c_func_defs is None:
c_func_defs = []
# load cdef for libenv.h
libenv_cdef = ""
with open(os.path.join(SCRIPT_DIR, "libenv.h")) as f:
inside_cdef = False
for line in f:
if line.startswith("// BEGIN_CDEF"):
inside_cdef = True
elif line.startswith("// END_CDEF"):
inside_cdef = False
elif line.startswith("#if") or line.startswith("#endif"):
continue
if inside_cdef:
line = line.replace("LIBENV_API", "")
libenv_cdef += line
self._ffi = FFI()
self._ffi.cdef(libenv_cdef)
for cdef in c_func_defs:
self._ffi.cdef(cdef)
self._lib_path = os.path.join(lib_dir, lib_filename)
assert os.path.exists(
self._lib_path), f"lib not found at {self._lib_path}"
self._c_lib = self._ffi.dlopen(self._lib_path)
with global_open_count_lock:
global_open_count[self._lib_path] += 1
if global_open_count[self._lib_path] == 1:
self._c_lib.libenv_load()
self._options = options
self._state = STATE_NEEDS_RESET
c_options, self._options_keepalives = self._convert_options(
self._ffi, self._c_lib, options)
self._c_env = self._c_lib.libenv_make(num_envs, c_options[0])
self.reward_range = (float("-inf"), float("inf"))
self.spec = None
self.num_envs = num_envs
self.observation_space = self._get_spaces(
self._c_lib.LIBENV_SPACES_OBSERVATION)
self._action_space = self._get_spaces(self._c_lib.LIBENV_SPACES_ACTION)
self._info_space = self._get_spaces(self._c_lib.LIBENV_SPACES_INFO)
self._render_space = self._get_spaces(self._c_lib.LIBENV_SPACES_RENDER)
# allocate buffers
self._observations, self._observation_buffers = self._allocate_dict_space(
self.num_envs, self.observation_space)
# we only use dict spaces for consistency, but action is always a single space
# the private version is the dict space, while the public version is a single space
assert len(self._action_space.spaces
) == 1, "action space can only be 1 element"
assert list(self._action_space.spaces.keys())[0] == ACTION_KEY
self.action_space = self._action_space.spaces[ACTION_KEY]
dict_actions, self._action_buffers = self._allocate_dict_space(
self.num_envs, self._action_space)
self._actions = dict_actions[ACTION_KEY]
self._renders, self._renders_buffers = self._allocate_dict_space(
self.num_envs, self._render_space)
self.metadata = {
"render.modes": list(self._render_space.spaces.keys())
}
self._infos, self._infos_buffers = self._allocate_dict_space(
self.num_envs, self._info_space)
self._rews, self._rews_buffer = self._allocate_array(
self.num_envs, np.dtype("float32"))
self._dones, self._dones_buffer = self._allocate_array(
self.num_envs, np.dtype("bool"))
assert np.dtype("bool").itemsize == 1
c_step = self._ffi.new("struct libenv_step *")
c_step.obs = self._observation_buffers
# cast the pointer to the buffer to avoid a warning from cffi
c_step.rews = self._ffi.cast(
self._ffi.typeof(c_step.rews).cname, self._rews_buffer)
c_step.dones = self._ffi.cast(
self._ffi.typeof(c_step.dones).cname, self._dones_buffer)
c_step.infos = self._infos_buffers
self._c_step = c_step
self._debug = debug
self._reuse_arrays = reuse_arrays
self.closed = False
self.viewer = None
def __repr__(self):
return f"<CVecEnv lib_path={self._lib_path} options={self._options}>"
@staticmethod
def _numpy_aligned(shape, dtype, align=64):
"""
Allocate an aligned numpy array, based on https://github.com/numpy/numpy/issues/5312#issuecomment-299533915
"""
n_bytes = np.prod(shape) * dtype.itemsize
arr = np.zeros(n_bytes + (align - 1), dtype=np.uint8)
data_align = arr.ctypes.data % align
offset = 0 if data_align == 0 else (align - data_align)
view = arr[offset:offset + n_bytes].view(dtype)
return view.reshape(shape)
def _allocate_dict_space(
self, num_envs: int, dict_space: gym.spaces.Dict
) -> Tuple[collections.OrderedDict, Any]:
"""
Allocate arrays for a space, returns an OrderedDict of numpy arrays along with a backing bytearray
"""
result = collections.OrderedDict() # type: collections.OrderedDict
length = len(dict_space.spaces) * num_envs
buffers = self._ffi.new(f"void *[{length}]")
for space_idx, (name, space) in enumerate(dict_space.spaces.items()):
actual_shape = (num_envs, ) + space.shape
arr = self._numpy_aligned(shape=actual_shape, dtype=space.dtype)
result[name] = arr
for env_idx in range(num_envs):
buffers[space_idx * num_envs +
env_idx] = self._ffi.from_buffer(arr.data[env_idx:])
return result, buffers
def _allocate_array(self, num_envs: int,
dtype: np.dtype) -> Tuple[np.ndarray, Any]:
arr = self._numpy_aligned(shape=(num_envs, ), dtype=dtype)
return arr, self._ffi.from_buffer(arr.data)
@staticmethod
def _convert_options(ffi: Any, c_lib: Any, options: Dict) -> Any:
"""
Convert a dictionary to libenv_options
"""
keepalives = (
[]
) # add variables to here to keep them alive after this function returns
c_options = ffi.new("struct libenv_options *")
c_option_array = ffi.new("struct libenv_option[%d]" % len(options))
for i, (k, v) in enumerate(options.items()):
name = str(k).encode("utf8")
assert (len(name) < c_lib.LIBENV_MAX_NAME_LEN -
1), "length of options key is too long"
if isinstance(v, bytes):
c_data = ffi.new("char[]", v)
dtype = c_lib.LIBENV_DTYPE_UINT8
count = len(v)
elif isinstance(v, str):
c_data = ffi.new("char[]", v.encode("utf8"))
dtype = c_lib.LIBENV_DTYPE_UINT8
count = len(v)
elif isinstance(v, bool):
c_data = ffi.new("uint8_t*", v)
dtype = c_lib.LIBENV_DTYPE_UINT8
count = 1
elif isinstance(v, int):
assert -2**31 < v < 2**31
c_data = ffi.new("int32_t*", v)
dtype = c_lib.LIBENV_DTYPE_INT32
count = 1
elif isinstance(v, float):
c_data = ffi.new("float*", v)
dtype = c_lib.LIBENV_DTYPE_FLOAT32
count = 1
elif isinstance(v, np.ndarray):
c_data = ffi.new("char[]", v.tobytes())
if v.dtype == np.dtype("uint8"):
dtype = c_lib.LIBENV_DTYPE_UINT8
elif v.dtype == np.dtype("int32"):
dtype = c_lib.LIBENV_DTYPE_INT32
elif v.dtype == np.dtype("float32"):
dtype = c_lib.LIBENV_DTYPE_FLOAT32
else:
assert False, f"unsupported type {v.dtype}"
count = v.size
else:
assert False, f"unsupported value {v} for option {k}"
c_option_array[i].name = name
c_option_array[i].dtype = dtype
c_option_array[i].count = count
c_option_array[i].data = c_data
keepalives.append(c_data)
keepalives.append(c_option_array)
c_options.items = c_option_array
c_options.count = len(options)
return c_options, keepalives
@staticmethod
def _check_arrays(arrays: Dict[str, np.ndarray], num_envs: int,
dict_space: gym.spaces.Dict) -> None:
"""
Make sure each array in arrays matches the given dict_space
"""
for name, space in dict_space.spaces.items():
arr = arrays[name]
assert isinstance(arr, np.ndarray)
expected_shape = (num_envs, ) + space.shape
assert (
arr.shape == expected_shape
), f"array is invalid shape expected={expected_shape} received={arr.shape}"
assert (
arr.dtype == space.dtype
), f"array has invalid dtype expected={space.dtype} received={arr.dtype}"
if isinstance(space, gym.spaces.Box):
# we only support single low/high values
assert (len(np.unique(space.low)) == 1
and len(np.unique(space.high)) == 1)
low = np.min(space.low)
high = np.max(space.high)
elif isinstance(space, gym.spaces.Discrete):
low = 0
high = space.n - 1
else:
assert False, "unrecognized space type"
assert np.min(arr) >= low, (
'"%s" has values below space lower bound, %f < %f' %
(name, np.min(arr), low))
assert np.max(arr) <= high, (
'"%s" has values above space upper bound, %f > %f' %
(name, np.max(arr), high))
def _maybe_copy_ndarray(self, obj: np.ndarray) -> np.ndarray:
"""
Copy a single numpy array if reuse_arrays is False,
otherwise just return the object
"""
if self._reuse_arrays:
return obj
else:
return obj.copy()
def _maybe_copy_dict(self, obj: Dict[str, Any]) -> Dict[str, Any]:
"""
Copy a list of dicts of numpy arrays if reuse_arrays is False,
otherwise just return the object
"""
if self._reuse_arrays:
return obj
else:
result = {}
for name, arr in obj.items():
result[name] = arr.copy()
return result
def _get_spaces(self, c_name: Any) -> gym.spaces.Dict:
"""
Get a c space and convert to a gym space
"""
count = self._c_lib.libenv_get_spaces(self._c_env, c_name,
self._ffi.NULL)
if count == 0:
return gym.spaces.Dict([])
c_spaces = self._ffi.new("struct libenv_space[%d]" % count)
self._c_lib.libenv_get_spaces(self._c_env, c_name, c_spaces)
# convert to gym spaces
spaces = []
for i in range(count):
c_space = c_spaces[i]
name = self._ffi.string(c_space.name).decode("utf8")
shape = []
for j in range(c_space.ndim):
shape.append(c_space.shape[j])
if c_space.dtype == self._c_lib.LIBENV_DTYPE_UINT8:
dtype = np.dtype("uint8")
low = c_space.low.uint8
high = c_space.high.uint8
elif c_space.dtype == self._c_lib.LIBENV_DTYPE_INT32:
dtype = np.dtype("int32")
low = c_space.low.int32
high = c_space.high.int32
elif c_space.dtype == self._c_lib.LIBENV_DTYPE_FLOAT32:
dtype = np.dtype("float32")
low = c_space.low.float32
high = c_space.high.float32
else:
assert False, "unknown dtype"
if c_space.type == self._c_lib.LIBENV_SPACE_TYPE_BOX:
space = gym.spaces.Box(shape=shape,
low=low,
high=high,
dtype=dtype)
elif c_space.type == self._c_lib.LIBENV_SPACE_TYPE_DISCRETE:
assert shape == [1], "discrete space must have scalar shape"
assert low == 0 and high > 0, "discrete low/high bounds are incorrect"
space = gym.spaces.Discrete(n=high + 1)
space.dtype = dtype
else:
assert False, "unknown space type"
spaces.append((name, space))
# c spaces are aways a single-layer Dict space
return gym.spaces.Dict(spaces)
def reset(self) -> Dict[str, np.ndarray]:
"""
Reset the environment and return the first observation
"""
self._state = STATE_WAIT_ACT
self._c_lib.libenv_reset(self._c_env, self._c_step)
return self._maybe_copy_dict(self._observations)
def step_async(self, actions: np.ndarray) -> None:
"""
Asynchronously take an action in the environment, doesn't return anything.
"""
assert self._state == STATE_WAIT_ACT
self._state = STATE_WAIT_WAIT
if self._debug:
self._check_arrays({"action": actions}, self.num_envs,
self._action_space)
self._actions[:] = actions
self._c_lib.libenv_step_async(self._c_env, self._action_buffers,
self._c_step)
def step_wait(
self
) -> Tuple[Dict[str, np.ndarray], np.ndarray, np.ndarray, List[Dict[str,
Any]]]:
"""
Step the environment, returns (obs, rews, dones, infos)
"""
assert self._state == STATE_WAIT_WAIT
self._state = STATE_WAIT_ACT
self._c_lib.libenv_step_wait(self._c_env)
if self._debug:
self._check_arrays(self._observations, self.num_envs,
self.observation_space)
infos = [{} for _ in range(self.num_envs)] # type: List[Dict]
for key, values in self._infos.items():
for env_idx in range(self.num_envs):
infos[env_idx][key] = values[env_idx]
return (
self._maybe_copy_dict(self._observations),
self._maybe_copy_ndarray(self._rews),
self._maybe_copy_ndarray(self._dones),
infos,
)
def step(
self, actions: np.ndarray
) -> Tuple[Dict[str, np.ndarray], np.ndarray, np.ndarray, List[Dict[str,
Any]]]:
"""
Step the environment, combines step_async() and step_wait() and makes this interface mostly compatible with the normal gym interface
"""
self.step_async(actions)
return self.step_wait()
def render(self, mode: str = "human") -> Union[bool, np.ndarray]:
"""
Render the environment.
mode='human' tells the environment to render in some human visible way and
returns True if the user user visible window created by the environment is still open
Other modes are up to the environment, but end up returning a numpy array of some kind
that will be tiled as if it were an image by this code. Call get_images() instead if
that is not the behavior you want.
"""
if (mode == "human" and "human" not in self.metadata["render.modes"]
and "rgb_array" in self.metadata["render.modes"]):
# fallback human mode
viewer = self.get_viewer()
self._render(mode="rgb_array")
images = self._renders["rgb_array"]
viewer.imshow(self._tile_images(images))
return viewer.isopen
isopen = self._render(mode=mode)
if mode == "human":
return isopen
else:
images = self._renders[mode]
return self._tile_images(images)
def _render(self, mode) -> Union[bool, np.ndarray]:
"""Internal render method, returns is_open and updates the buffers in self._render"""
assert self._state == STATE_WAIT_ACT
assert mode in self.metadata["render.modes"], "unsupported render mode"
c_mode = self._ffi.new("char[]", mode.encode("utf8"))
# only pass the render buffers for the selected mode
space_idx = list(self._render_space.spaces.keys()).index(mode)
render_buffers = self._renders_buffers[space_idx *
self.num_envs:(space_idx + 1) *
self.num_envs]
return self._c_lib.libenv_render(self._c_env, c_mode, render_buffers)
@staticmethod
def _tile_images(images: np.ndarray) -> np.ndarray:
"""Tile a set of NHWC images"""
num_images, height, width, chans = images.shape
width_images = int(np.ceil(np.sqrt(num_images)))
height_images = int(np.ceil(float(num_images) / width_images))
result = np.zeros(
(height_images * height, width_images * width, chans),
dtype=np.uint8)
for col in range(width_images):
for row in range(height_images):
idx = row * width_images + col
if idx >= len(images):
continue
result[row * height:(row + 1) * height,
col * width:(col + 1) * width, :, ] = images[idx]
return result
def get_images(self) -> np.ndarray:
"""
Get rendered images from the environments, if supported.
The returned array's shape will be (num_envs, height, width, num_colors)
"""
self._render(mode="rgb_array")
return self._renders["rgb_array"]
def get_viewer(self):
"""Get the viewer instance being used by render()"""
if self.viewer is None:
from gym.envs.classic_control import rendering
self.viewer = rendering.SimpleImageViewer()
return self.viewer
def close_extras(self):
"""
Override this to close environment-specific resources without having to override close()'
This method is guaranteed to only be called once, unlike close()
"""
def close(self) -> None:
"""Close the environment and free any resources associated with it"""
if not hasattr(self, "closed") or self.closed:
return
self.closed = True
self._c_lib.libenv_close(self._c_env)
with global_open_count_lock:
global_open_count[self._lib_path] -= 1
if global_open_count[self._lib_path] == 0:
self._c_lib.libenv_unload()
self._ffi.dlclose(self._c_lib)
self._c_lib = None
self._ffi = None
self._options_keepalives = None
if self.viewer is not None:
self.viewer.close()
self.close_extras()
def call_func(self, name: str, *args: Any) -> Any:
"""
Call a function of the libenv declared in c_func_defs
"""
return getattr(self._c_lib, name)(*args)
@property
def unwrapped(self):
if hasattr(self, "venv"):
return self.venv.unwrapped # pylint: disable=no-member
else:
return self
def seed(self, seed=None):
"""
Seed the environment, this isn't used by VecEnvs but is part of the Gym Env API
"""
def __del__(self):
self.close()