def __init__(self, topology):
if not lang.misc.is_extension_supported(impl.current_cfg().arch,
lang.extension.mesh):
raise Exception('Backend ' + str(impl.current_cfg().arch) +
' doesn\'t support MeshTaichi extension')
self.topology = topology
self.verts = MeshElement(MeshElementType.Vertex, self)
self.edges = MeshElement(MeshElementType.Edge, self)
self.faces = MeshElement(MeshElementType.Face, self)
if topology == MeshTopology.Tetrahedron:
self.cells = MeshElement(MeshElementType.Cell, self)
self.elements = set()
self.relations = set()
impl.current_cfg().use_mesh = True
def fill(self, val):
"""Fills ndarray with a specific scalar value.
Args:
val (Union[int, float]): Value to fill.
"""
if impl.current_cfg().arch != _ti_core.Arch.cuda and impl.current_cfg(
).arch != _ti_core.Arch.x64:
self._fill_by_kernel(val)
elif self.dtype == primitive_types.f32:
self.arr.fill_float(val)
elif self.dtype == primitive_types.i32:
self.arr.fill_int(val)
elif self.dtype == primitive_types.u32:
self.arr.fill_uint(val)
else:
self._fill_by_kernel(val)
def bit_struct(self, num_bits: int):
"""Adds a bit_struct SNode as a child component of `self`.
Args:
num_bits: Number of bits to use.
Returns:
The added :class:`~taichi.lang.SNode` instance.
"""
return SNode(self.ptr.bit_struct(num_bits, impl.current_cfg().packed))
def ndarray_fill(self, val):
"""Fills ndarray with a specific scalar value.
Args:
val (Union[int, float]): Value to fill.
"""
if impl.current_cfg().ndarray_use_torch:
self.arr.fill_(val)
else:
taichi.lang.meta.fill_ndarray(self, val)
def fill(self, val):
"""Fills ndarray with a specific scalar value.
Args:
val (Union[int, float]): Value to fill.
"""
if impl.current_cfg().ndarray_use_torch:
self.arr.fill_(val)
else:
from taichi.lang.meta import fill_ndarray # pylint: disable=C0415
fill_ndarray(self, val)
def from_source(cls, source_code, compile_fn=None):
if impl.current_cfg().arch not in [
_ti_core.Arch.x64, _ti_core.Arch.cuda
]:
raise TaichiSyntaxError(
"Unsupported arch for external function call")
_temp_dir = tempfile.mkdtemp()
_temp_source = os.path.join(_temp_dir, '_temp_source.cpp')
with open(_temp_source, 'w') as f:
f.write(source_code)
return SourceBuilder.from_file(_temp_source, compile_fn, _temp_dir)
def materialize(self, key=None, args=None, arg_features=None):
if key is None:
key = (self.func, 0)
self.runtime.materialize()
if key in self.compiled_functions:
return
grad_suffix = ""
if self.is_grad:
grad_suffix = "_grad"
kernel_name = f"{self.func.__name__}_c{self.kernel_counter}_{key[1]}{grad_suffix}"
_logging.trace(f"Compiling kernel {kernel_name}...")
tree, ctx = _get_tree_and_ctx(
self,
args=args,
excluded_parameters=self.template_slot_locations,
arg_features=arg_features)
if self.is_grad:
KernelSimplicityASTChecker(self.func).visit(tree)
if impl.current_cfg().use_mesh:
taichi.lang.Mesh.update_relation(tree, ctx)
# Do not change the name of 'taichi_ast_generator'
# The warning system needs this identifier to remove unnecessary messages
def taichi_ast_generator(kernel_cxx):
if self.runtime.inside_kernel:
raise TaichiSyntaxError(
"Kernels cannot call other kernels. I.e., nested kernels are not allowed. "
"Please check if you have direct/indirect invocation of kernels within kernels. "
"Note that some methods provided by the Taichi standard library may invoke kernels, "
"and please move their invocations to Python-scope.")
self.runtime.inside_kernel = True
self.runtime.current_kernel = self
try:
ctx.ast_builder = kernel_cxx.ast_builder()
transform_tree(tree, ctx)
if not ctx.is_real_function:
if self.return_type and ctx.returned != ReturnStatus.ReturnedValue:
raise TaichiSyntaxError(
"Kernel has a return type but does not have a return statement"
)
finally:
self.runtime.inside_kernel = False
self.runtime.current_kernel = None
taichi_kernel = impl.get_runtime().prog.create_kernel(
taichi_ast_generator, kernel_name, self.is_grad)
self.kernel_cpp = taichi_kernel
assert key not in self.compiled_functions
self.compiled_functions[key] = self.get_function_body(taichi_kernel)
def __init__(self, dtype, shape):
self.host_accessor = None
if impl.current_cfg().ndarray_use_torch:
assert has_pytorch(
), "PyTorch must be available if you want to create a Taichi ndarray with PyTorch as its underlying storage."
# pylint: disable=E1101
self.arr = torch.zeros(shape,
dtype=to_pytorch_type(cook_dtype(dtype)))
if impl.current_cfg().arch == _ti_core.Arch.cuda:
self.arr = self.arr.cuda()
def ndarray_fill(val, fill_func):
self.arr.fill_(val)
else:
self.arr = _ti_core.Ndarray(impl.get_runtime().prog,
cook_dtype(dtype), shape)
def ndarray_fill(val, fill_func):
fill_func(self, val)
self.ndarray_fill = ndarray_fill
def __call__(self, *args, **kwargs):
args = _process_args(self, args, kwargs)
# Transform the primal kernel to forward mode grad kernel
# then recover to primal when exiting the forward mode manager
if self.runtime.fwd_mode_manager:
# TODO: if we would like to compute 2nd-order derivatives by forward-on-reverse in a nested context manager fashion,
# i.e., a `Tape` nested in the `FwdMode`, we can transform the kernels with `mode_original == AutodiffMode.REVERSE` only,
# to avoid duplicate computation for 1st-order derivatives
mode_original = self.autodiff_mode
self.autodiff_mode = AutodiffMode.FORWARD
self.runtime.fwd_mode_manager.insert(self, mode_original)
if self.autodiff_mode != AutodiffMode.NONE and impl.current_cfg(
).opt_level == 0:
_logging.warn(
"""opt_level = 1 is enforced to enable gradient computation."""
)
impl.current_cfg().opt_level = 1
key = self.ensure_compiled(*args)
return self.runtime.compiled_functions[key](*args)
def compile_fn_impl(filename):
if impl.current_cfg().arch == _ti_core.Arch.x64:
subprocess.call(get_clangpp() + ' -flto -c ' +
filename + ' -o ' +
os.path.join(self.td, 'source.bc'),
shell=True)
else:
subprocess.call(get_clangpp() + ' -flto -c ' +
filename + ' -o ' +
os.path.join(self.td, 'source.bc') +
' -target nvptx64-nvidia-cuda',
shell=True)
return os.path.join(self.td, 'source.bc')
def ndarray_to_numpy(self):
"""Converts ndarray to a numpy array.
Returns:
numpy.ndarray: The result numpy array.
"""
if impl.current_cfg().ndarray_use_torch:
return self.arr.cpu().numpy()
arr = np.zeros(shape=self.arr.shape, dtype=to_numpy_type(self.dtype))
taichi.lang.meta.ndarray_to_ext_arr(self, arr)
impl.get_runtime().sync()
return arr
def ndarray_matrix_from_numpy(self, arr, as_vector):
"""Loads all values from a numpy array.
Args:
arr (numpy.ndarray): The source numpy array.
"""
if not isinstance(arr, np.ndarray):
raise TypeError(f"{np.ndarray} expected, but {type(arr)} provided")
if tuple(self.arr.shape) != tuple(arr.shape):
raise ValueError(
f"Mismatch shape: {tuple(self.arr.shape)} expected, but {tuple(arr.shape)} provided"
)
if impl.current_cfg().ndarray_use_torch:
self.arr = torch.from_numpy(arr).to(self.arr.dtype) # pylint: disable=E1101
if impl.current_cfg().arch == _ti_core.Arch.cuda:
self.arr = self.arr.cuda()
else:
if hasattr(arr, 'contiguous'):
arr = arr.contiguous()
taichi.lang.meta.ext_arr_to_ndarray_matrix(arr, self, as_vector)
impl.get_runtime().sync()
def __init__(self, dtype, shape):
if isinstance(shape, numbers.Number):
shape = (shape, )
assert has_pytorch(
), "PyTorch must be available if you want to create a Taichi ndarray."
import torch
if impl.current_cfg().arch == _ti_core.Arch.cuda:
device = 'cuda:0'
else:
device = 'cpu'
self.arr = torch.empty(shape,
dtype=to_pytorch_type(dtype),
device=device)
def bitmasked(self, axes, dimensions):
"""Adds a bitmasked SNode as a child component of `self`.
Args:
axes (List[Axis]): Axes to activate.
dimensions (Union[List[int], int]): Shape of each axis.
Returns:
The added :class:`~taichi.lang.SNode` instance.
"""
if isinstance(dimensions, int):
dimensions = [dimensions] * len(axes)
return SNode(
self.ptr.bitmasked(axes, dimensions,
impl.current_cfg().packed))
def quant_array(self, axes, dimensions, num_bits):
"""Adds a quant_array SNode as a child component of `self`.
Args:
axes (List[Axis]): Axes to activate.
dimensions (Union[List[int], int]): Shape of each axis.
num_bits (int): Number of bits to use.
Returns:
The added :class:`~taichi.lang.SNode` instance.
"""
if isinstance(dimensions, int):
dimensions = [dimensions] * len(axes)
return SNode(
self.ptr.quant_array(axes, dimensions, num_bits,
impl.current_cfg().packed))
def dynamic(self, axis, dimension, chunk_size=None):
"""Adds a dynamic SNode as a child component of `self`.
Args:
axis (List[Axis]): Axis to activate, must be 1.
dimension (int): Shape of the axis.
chunk_size (int): Chunk size.
Returns:
The added :class:`~taichi.lang.SNode` instance.
"""
assert len(axis) == 1
if chunk_size is None:
chunk_size = dimension
return SNode(
self.ptr.dynamic(axis[0], dimension, chunk_size,
impl.current_cfg().packed))
def to_numpy(self):
"""Converts ndarray to a numpy array.
Returns:
numpy.ndarray: The result numpy array.
"""
if impl.current_cfg().ndarray_use_torch:
return self.arr.cpu().numpy()
else:
import numpy as np # pylint: disable=C0415
arr = np.zeros(shape=self.arr.shape,
dtype=to_numpy_type(self.dtype))
from taichi.lang.meta import \
ndarray_to_ext_arr # pylint: disable=C0415
ndarray_to_ext_arr(self, arr)
impl.get_runtime().sync()
return arr
def func__(*args):
assert len(args) == len(
self.arguments
), f'{len(self.arguments)} arguments needed but {len(args)} provided'
tmps = []
callbacks = []
has_external_arrays = False
has_torch = has_pytorch()
has_pp = has_paddle()
actual_argument_slot = 0
launch_ctx = t_kernel.make_launch_context()
for i, v in enumerate(args):
needed = self.arguments[i].annotation
if isinstance(needed, template):
continue
provided = type(v)
# Note: do not use sth like "needed == f32". That would be slow.
if id(needed) in primitive_types.real_type_ids:
if not isinstance(v, (float, int)):
raise TaichiRuntimeTypeError.get(
i, needed.to_string(), provided)
launch_ctx.set_arg_float(actual_argument_slot, float(v))
elif id(needed) in primitive_types.integer_type_ids:
if not isinstance(v, int):
raise TaichiRuntimeTypeError.get(
i, needed.to_string(), provided)
launch_ctx.set_arg_int(actual_argument_slot, int(v))
elif isinstance(needed, sparse_matrix_builder):
# Pass only the base pointer of the ti.types.sparse_matrix_builder() argument
launch_ctx.set_arg_int(actual_argument_slot, v._get_addr())
elif isinstance(needed,
ndarray_type.NdarrayType) and isinstance(
v, taichi.lang._ndarray.Ndarray):
has_external_arrays = True
v = v.arr
launch_ctx.set_arg_ndarray(actual_argument_slot, v)
elif isinstance(needed,
texture_type.TextureType) and isinstance(
v, taichi.lang._texture.Texture):
has_external_arrays = True
v = v.tex
launch_ctx.set_arg_texture(actual_argument_slot, v)
elif isinstance(needed,
texture_type.RWTextureType) and isinstance(
v, taichi.lang._texture.Texture):
has_external_arrays = True
v = v.tex
launch_ctx.set_arg_rw_texture(actual_argument_slot, v)
elif isinstance(
needed,
ndarray_type.NdarrayType) and (self.match_ext_arr(v)):
has_external_arrays = True
is_numpy = isinstance(v, np.ndarray)
is_torch = isinstance(v,
torch.Tensor) if has_torch else False
# Element shapes are already spcialized in Taichi codegen.
# The shape information for element dims are no longer needed.
# Therefore we strip the element shapes from the shape vector,
# so that it only holds "real" array shapes.
is_soa = needed.layout == Layout.SOA
array_shape = v.shape
element_dim = needed.element_dim
if element_dim:
array_shape = v.shape[
element_dim:] if is_soa else v.shape[:-element_dim]
if is_numpy:
tmp = np.ascontiguousarray(v)
# Purpose: DO NOT GC |tmp|!
tmps.append(tmp)
launch_ctx.set_arg_external_array_with_shape(
actual_argument_slot, int(tmp.ctypes.data),
tmp.nbytes, array_shape)
elif is_torch:
is_ndarray = False
tmp, torch_callbacks = self.get_torch_callbacks(
v, has_torch, is_ndarray)
callbacks += torch_callbacks
launch_ctx.set_arg_external_array_with_shape(
actual_argument_slot, int(tmp.data_ptr()),
tmp.element_size() * tmp.nelement(), array_shape)
else:
# For now, paddle.fluid.core.Tensor._ptr() is only available on develop branch
tmp, paddle_callbacks = self.get_paddle_callbacks(
v, has_pp)
callbacks += paddle_callbacks
launch_ctx.set_arg_external_array_with_shape(
actual_argument_slot, int(tmp._ptr()),
v.element_size() * v.size, array_shape)
elif isinstance(needed, MatrixType):
if id(needed.dtype) in primitive_types.real_type_ids:
for a in range(needed.n):
for b in range(needed.m):
if not isinstance(v[a, b], (int, float)):
raise TaichiRuntimeTypeError.get(
i, needed.dtype.to_string(),
type(v[a, b]))
launch_ctx.set_arg_float(
actual_argument_slot, float(v[a, b]))
actual_argument_slot += 1
elif id(needed.dtype) in primitive_types.integer_type_ids:
for a in range(needed.n):
for b in range(needed.m):
if not isinstance(v[a, b], int):
raise TaichiRuntimeTypeError.get(
i, needed.dtype.to_string(),
type(v[a, b]))
launch_ctx.set_arg_int(actual_argument_slot,
int(v[a, b]))
actual_argument_slot += 1
else:
raise ValueError(
f'Matrix dtype {needed.dtype} is not integer type or real type.'
)
continue
else:
raise ValueError(
f'Argument type mismatch. Expecting {needed}, got {type(v)}.'
)
actual_argument_slot += 1
# Both the class kernels and the plain-function kernels are unified now.
# In both cases, |self.grad| is another Kernel instance that computes the
# gradient. For class kernels, args[0] is always the kernel owner.
if self.autodiff_mode == AutodiffMode.NONE and self.runtime.target_tape and not self.runtime.grad_replaced:
self.runtime.target_tape.insert(self, args)
if actual_argument_slot > 8 and (
impl.current_cfg().arch == _ti_core.opengl
or impl.current_cfg().arch == _ti_core.cc):
raise TaichiRuntimeError(
f"The number of elements in kernel arguments is too big! Do not exceed 8 on {_ti_core.arch_name(impl.current_cfg().arch)} backend."
)
if actual_argument_slot > 64 and (
(impl.current_cfg().arch != _ti_core.opengl
and impl.current_cfg().arch != _ti_core.cc)):
raise TaichiRuntimeError(
f"The number of elements in kernel arguments is too big! Do not exceed 64 on {_ti_core.arch_name(impl.current_cfg().arch)} backend."
)
try:
t_kernel(launch_ctx)
except Exception as e:
e = handle_exception_from_cpp(e)
raise e from None
ret = None
ret_dt = self.return_type
has_ret = ret_dt is not None
if has_ret:
runtime_ops.sync()
if has_ret:
if id(ret_dt) in primitive_types.integer_type_ids:
ret = t_kernel.get_ret_int(0)
elif id(ret_dt) in primitive_types.real_type_ids:
ret = t_kernel.get_ret_float(0)
elif id(ret_dt.dtype) in primitive_types.integer_type_ids:
it = iter(t_kernel.get_ret_int_tensor(0))
ret = Matrix([[next(it) for _ in range(ret_dt.m)]
for _ in range(ret_dt.n)])
else:
it = iter(t_kernel.get_ret_float_tensor(0))
ret = Matrix([[next(it) for _ in range(ret_dt.m)]
for _ in range(ret_dt.n)])
if callbacks:
for c in callbacks:
c()
return ret
def func__(*args):
assert len(args) == len(
self.argument_annotations
), f'{len(self.argument_annotations)} arguments needed but {len(args)} provided'
tmps = []
callbacks = []
has_external_arrays = False
has_torch = has_pytorch()
ndarray_use_torch = impl.get_runtime().ndarray_use_torch
actual_argument_slot = 0
launch_ctx = t_kernel.make_launch_context()
for i, v in enumerate(args):
needed = self.argument_annotations[i]
if isinstance(needed, template):
continue
provided = type(v)
# Note: do not use sth like "needed == f32". That would be slow.
if id(needed) in primitive_types.real_type_ids:
if not isinstance(v, (float, int)):
raise TaichiRuntimeTypeError(i, needed.to_string(),
provided)
launch_ctx.set_arg_float(actual_argument_slot, float(v))
elif id(needed) in primitive_types.integer_type_ids:
if not isinstance(v, int):
raise TaichiRuntimeTypeError(i, needed.to_string(),
provided)
launch_ctx.set_arg_int(actual_argument_slot, int(v))
elif isinstance(needed, sparse_matrix_builder):
# Pass only the base pointer of the ti.linalg.sparse_matrix_builder() argument
launch_ctx.set_arg_int(actual_argument_slot, v._get_addr())
elif isinstance(needed, any_arr) and isinstance(
v, taichi.lang._ndarray.Ndarray):
has_external_arrays = True
v = v.arr
if ndarray_use_torch:
is_ndarray = True
tmp, torch_callbacks = self.get_torch_callbacks(
v, has_torch, is_ndarray)
callbacks += torch_callbacks
launch_ctx.set_arg_external_array_with_shape(
actual_argument_slot, int(tmp.data_ptr()),
tmp.element_size() * tmp.nelement(), v.shape)
else:
launch_ctx.set_arg_ndarray(actual_argument_slot, v)
elif isinstance(needed, any_arr) and (self.match_ext_arr(v)):
has_external_arrays = True
is_numpy = isinstance(v, np.ndarray)
if is_numpy:
tmp = np.ascontiguousarray(v)
# Purpose: DO NOT GC |tmp|!
tmps.append(tmp)
launch_ctx.set_arg_external_array_with_shape(
actual_argument_slot, int(tmp.ctypes.data),
tmp.nbytes, v.shape)
else:
is_ndarray = False
tmp, torch_callbacks = self.get_torch_callbacks(
v, has_torch, is_ndarray)
callbacks += torch_callbacks
launch_ctx.set_arg_external_array_with_shape(
actual_argument_slot, int(tmp.data_ptr()),
tmp.element_size() * tmp.nelement(), v.shape)
elif isinstance(needed, MatrixType):
if id(needed.dtype) in primitive_types.real_type_ids:
for a in range(needed.n):
for b in range(needed.m):
if not isinstance(v[a, b], (int, float)):
raise TaichiRuntimeTypeError(
i, needed.dtype.to_string(),
type(v[a, b]))
launch_ctx.set_arg_float(
actual_argument_slot, float(v[a, b]))
actual_argument_slot += 1
elif id(needed.dtype) in primitive_types.integer_type_ids:
for a in range(needed.n):
for b in range(needed.m):
if not isinstance(v[a, b], int):
raise TaichiRuntimeTypeError(
i, needed.dtype.to_string(),
type(v[a, b]))
launch_ctx.set_arg_int(actual_argument_slot,
int(v[a, b]))
actual_argument_slot += 1
else:
raise ValueError(
f'Matrix dtype {needed.dtype} is not integer type or real type.'
)
continue
else:
raise ValueError(
f'Argument type mismatch. Expecting {needed}, got {type(v)}.'
)
actual_argument_slot += 1
# Both the class kernels and the plain-function kernels are unified now.
# In both cases, |self.grad| is another Kernel instance that computes the
# gradient. For class kernels, args[0] is always the kernel owner.
if not self.is_grad and self.runtime.target_tape and not self.runtime.grad_replaced:
self.runtime.target_tape.insert(self, args)
t_kernel(launch_ctx)
ret = None
ret_dt = self.return_type
has_ret = ret_dt is not None
if has_ret or (impl.current_cfg().async_mode
and has_external_arrays):
runtime_ops.sync()
if has_ret:
if id(ret_dt) in primitive_types.integer_type_ids:
ret = t_kernel.get_ret_int(0)
else:
ret = t_kernel.get_ret_float(0)
if callbacks:
for c in callbacks:
c()
return ret
def __getitem__(self, key):
if impl.current_cfg().ndarray_use_torch:
return self.arr.__getitem__(key)
self.initialize_host_accessor()
return self.host_accessor.getter(*self.pad_key(key))
def __setitem__(self, key, value):
if impl.current_cfg().ndarray_use_torch:
self.arr.__setitem__(key, value)
else:
self.initialize_host_accessor()
self.host_accessor.setter(value, *self.pad_key(key))
def from_file(cls, filename, compile_fn=None, _temp_dir=None):
self = cls()
self.td = _temp_dir
if self.td is None:
self.td = tempfile.mkdtemp()
if filename.endswith((".cpp", ".c", ".cc")):
if impl.current_cfg().arch not in [
_ti_core.Arch.x64, _ti_core.Arch.cuda
]:
raise TaichiSyntaxError(
"Unsupported arch for external function call")
if compile_fn is None:
def compile_fn_impl(filename):
if impl.current_cfg().arch == _ti_core.Arch.x64:
subprocess.call(get_clangpp() + ' -flto -c ' +
filename + ' -o ' +
os.path.join(self.td, 'source.bc'),
shell=True)
else:
subprocess.call(get_clangpp() + ' -flto -c ' +
filename + ' -o ' +
os.path.join(self.td, 'source.bc') +
' -target nvptx64-nvidia-cuda',
shell=True)
return os.path.join(self.td, 'source.bc')
compile_fn = compile_fn_impl
self.bc = compile_fn(filename)
self.mode = 'bc'
elif filename.endswith(".cu"):
if impl.current_cfg().arch not in [_ti_core.Arch.cuda]:
raise TaichiSyntaxError(
"Unsupported arch for external function call")
if compile_fn is None:
shutil.copy(filename, os.path.join(self.td, 'source.cu'))
def compile_fn_impl(filename):
# Cannot use -o to specify multiple output files
subprocess.call(
get_clangpp() + ' ' +
os.path.join(self.td, 'source.cu') +
' -c -emit-llvm -std=c++17 --cuda-gpu-arch=sm_50 -nocudalib',
cwd=self.td,
shell=True)
return os.path.join(
self.td, 'source-cuda-nvptx64-nvidia-cuda-sm_50.bc')
compile_fn = compile_fn_impl
self.bc = compile_fn(filename)
self.mode = 'bc'
elif filename.endswith((".so", ".dylib", ".dll")):
if impl.current_cfg().arch not in [_ti_core.Arch.x64]:
raise TaichiSyntaxError(
"Unsupported arch for external function call")
self.so = ctypes.CDLL(filename)
self.mode = 'so'
elif filename.endswith(".ll"):
if impl.current_cfg().arch not in [
_ti_core.Arch.x64, _ti_core.Arch.cuda
]:
raise TaichiSyntaxError(
"Unsupported arch for external function call")
subprocess.call('llvm-as ' + filename + ' -o ' +
os.path.join(self.td, 'source.bc'),
shell=True)
self.bc = os.path.join(self.td, 'source.bc')
self.mode = 'bc'
elif filename.endswith(".bc"):
if impl.current_cfg().arch not in [
_ti_core.Arch.x64, _ti_core.Arch.cuda
]:
raise TaichiSyntaxError(
"Unsupported arch for external function call")
self.bc = filename
self.mode = 'bc'
else:
raise TaichiSyntaxError(
'Unsupported file type for external function call.')
return self