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Commit a6abf55d authored by yan.yan's avatar yan.yan
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Merge branch 'develop'

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spconv/build.py 0 → 100644
View file @ a6abf55d
# Copyright 2021 Yan Yan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
import pccm
from pccm.utils import project_is_editable, project_is_installed
from .constants import PACKAGE_NAME, PACKAGE_ROOT
if project_is_installed(PACKAGE_NAME) and project_is_editable(PACKAGE_NAME):
from spconv.core import SHUFFLE_SIMT_PARAMS, SHUFFLE_VOLTA_PARAMS, SHUFFLE_TURING_PARAMS
from cumm.gemm.main import GemmMainUnitTest
from spconv.csrc.sparse.all import SpconvOps
cu = GemmMainUnitTest(SHUFFLE_SIMT_PARAMS + SHUFFLE_VOLTA_PARAMS + SHUFFLE_TURING_PARAMS)
cu.namespace = "cumm.gemm.main"
pccm.builder.build_pybind([cu, SpconvOps()],
PACKAGE_ROOT / "core_cc",
namespace_root=PACKAGE_ROOT,
objects_folder="objects",
load_library=False)
spconv/constants.py 0 → 100644
View file @ a6abf55d
# Copyright 2021 Yan Yan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from pathlib import Path
from typing import List
from pccm.utils import project_is_editable, project_is_installed
PACKAGE_NAME = "spconv"
PACKAGE_ROOT = Path(__file__).parent.resolve()
EDITABLE_INSTALLED = project_is_installed(PACKAGE_NAME) and project_is_editable(PACKAGE_NAME)
_filter_hwio_env = os.getenv("SPCONV_FILTER_HWIO", "0")
FILTER_HWIO = _filter_hwio_env == "1"
\ No newline at end of file
spconv/core.py 0 → 100644
View file @ a6abf55d
# Copyright 2021 Yan Yan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from enum import Enum
from cumm.gemm.main import gen_shuffle_params, GemmAlgoParams
from cumm.gemm import kernel
from typing import List
from cumm.gemm.algospec.core import TensorOpParams
class ConvAlgo(Enum):
Native = "Native"
MaskImplicitGemm = "MaskImplicitGemm"
MaskSplitImplicitGemm = "MaskSplitImplicitGemm"
class AlgoHint(Enum):
NoHint = 0b000
Fowrard = 0b001
BackwardInput = 0b010
BackwardWeight = 0b100
# we can't add more kernels here because build in github action is very slow.
# TODO two step build: build gemm kernels first, then bind for every python
SHUFFLE_SIMT_PARAMS: List[GemmAlgoParams] = [
*gen_shuffle_params(
(64, 128, 32), (32, 64, 32), ["s8,s8,s32,s32,s32"],
2, kernel.GemmAlgo.SimtDP4A, None),
*gen_shuffle_params(
(128, 64, 32), (64, 32, 32), ["s8,s8,s32,s32,s32"],
2, kernel.GemmAlgo.SimtDP4A, None),
*gen_shuffle_params(
(128, 128, 32),
(32, 64, 32), ["s8,s8,s32,s32,s32"], 2,
kernel.GemmAlgo.SimtDP4A, None),
*gen_shuffle_params(
(128, 128, 32),
(64, 32, 32), ["s8,s8,s8,s32,s32", "s8,s8,s32,s32,s32"], 2,
kernel.GemmAlgo.SimtDP4A, None),
*gen_shuffle_params(
(64, 64, 32), (32, 32, 32), ["s8,s8,s32,s32,s32"],
2, kernel.GemmAlgo.SimtDP4A, None),
*gen_shuffle_params(
(64, 256, 8),
(32, 64, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
# *gen_shuffle_params(
# (64, 256, 8),
# (64, 32, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
*gen_shuffle_params(
(32, 128, 16),
(32, 32, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
*gen_shuffle_params(
(32, 512, 8),
(32, 64, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
# *gen_shuffle_params(
# (128, 128, 8),
# (64, 32, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
*gen_shuffle_params(
(128, 128, 8),
(32, 64, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
*gen_shuffle_params(
(64, 128, 8),
(32, 64, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
# *gen_shuffle_params(
# (64, 128, 8),
# (64, 32, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
# *gen_shuffle_params(
# (128, 64, 8),
# (32, 64, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
*gen_shuffle_params(
(128, 64, 8),
(64, 32, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
*gen_shuffle_params(
(64, 64, 8),
(32, 32, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
*gen_shuffle_params(
(32, 64, 16),
(32, 32, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
*gen_shuffle_params(
(64, 32, 16),
(32, 32, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
*gen_shuffle_params(
(32, 32, 32),
(32, 32, 8), ["f32,f32,f32,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
# fall back kernels if mat is misaligned for half
# *gen_shuffle_params(
# (128, 128, 8),
# (32, 64, 8), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
*gen_shuffle_params(
(32, 64, 32),
(32, 32, 8), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
*gen_shuffle_params(
(32, 32, 32),
(32, 32, 8), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
# *gen_shuffle_params(
# (64, 64, 16),
# (32, 32, 8), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
*gen_shuffle_params(
(64, 128, 16),
(32, 64, 8), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
*gen_shuffle_params(
(64, 64, 8),
(32, 32, 8), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2, kernel.GemmAlgo.Simt, None),
]
SHUFFLE_VOLTA_PARAMS: List[GemmAlgoParams] = [
*gen_shuffle_params(
(64, 64, 32),
(32, 32, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
kernel.GemmAlgo.Volta, TensorOpParams((8, 8, 4))),
# *gen_shuffle_params(
# (128, 128, 32),
# (64, 64, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
# kernel.GemmAlgo.Volta, TensorOpParams((8, 8, 4))),
*gen_shuffle_params(
(128, 256, 32),
(64, 64, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
kernel.GemmAlgo.Volta, TensorOpParams((8, 8, 4))),
*gen_shuffle_params(
(256, 128, 32),
(64, 64, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
kernel.GemmAlgo.Volta, TensorOpParams((8, 8, 4))),
*gen_shuffle_params(
(128, 64, 32),
(64, 32, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
kernel.GemmAlgo.Volta, TensorOpParams((8, 8, 4))),
*gen_shuffle_params(
(64, 128, 32),
(32, 64, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
kernel.GemmAlgo.Volta, TensorOpParams((8, 8, 4))),
]
# SHUFFLE_VOLTA_PARAMS = []
SHUFFLE_TURING_PARAMS: List[GemmAlgoParams] = [
*gen_shuffle_params(
(64, 64, 32),
(32, 32, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
kernel.GemmAlgo.Turing, TensorOpParams((16, 8, 8))),
*gen_shuffle_params(
(128, 128, 32),
(32, 64, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
kernel.GemmAlgo.Turing, TensorOpParams((16, 8, 8))),
# *gen_shuffle_params(
# (128, 128, 32),
# (64, 32, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
# kernel.GemmAlgo.Turing, TensorOpParams((16, 8, 8))),
*gen_shuffle_params(
(64, 64, 64),
(32, 32, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
kernel.GemmAlgo.Turing, TensorOpParams((16, 8, 8))),
*gen_shuffle_params(
(64, 128, 64),
(32, 64, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
kernel.GemmAlgo.Turing, TensorOpParams((16, 8, 8))),
*gen_shuffle_params(
(128, 256, 32),
(64, 64, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
kernel.GemmAlgo.Turing, TensorOpParams((16, 8, 8))),
*gen_shuffle_params(
(256, 128, 32),
(64, 64, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
kernel.GemmAlgo.Turing, TensorOpParams((16, 8, 8))),
*gen_shuffle_params(
(128, 64, 32),
(64, 32, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
kernel.GemmAlgo.Turing, TensorOpParams((16, 8, 8))),
*gen_shuffle_params(
(64, 128, 32),
(32, 64, 32), ["f16,f16,f16,f16,f16", "f16,f16,f16,f32,f32"], 2,
kernel.GemmAlgo.Turing, TensorOpParams((16, 8, 8))),
*gen_shuffle_params(
(64, 64, 32), (32, 32, 32), ["s8,s8,s32,s32,s32"],
2, kernel.GemmAlgo.Turing, TensorOpParams((8, 8, 16))),
*gen_shuffle_params(
(128, 128, 32),
(32, 64, 32), ["s8,s8,s32,s32,s32"], 2,
kernel.GemmAlgo.Turing, TensorOpParams((8, 8, 16))),
# *gen_shuffle_params(
# (128, 128, 32),
# (64, 32, 32), ["s8,s8,s8,s32,s32", "s8,s8,s32,s32,s32"], 2,
# kernel.GemmAlgo.Turing, TensorOpParams((8, 8, 16))),
*gen_shuffle_params(
(128, 256, 32),
(64, 64, 32), ["s8,s8,s32,s32,s32"], 2,
kernel.GemmAlgo.Turing, TensorOpParams((8, 8, 16))),
*gen_shuffle_params(
(256, 128, 32),
(64, 64, 32), ["s8,s8,s32,s32,s32"], 2,
kernel.GemmAlgo.Turing, TensorOpParams((8, 8, 16))),
*gen_shuffle_params(
(128, 64, 32), (64, 32, 32), ["s8,s8,s32,s32,s32"],
2, kernel.GemmAlgo.Turing, TensorOpParams((8, 8, 16))),
*gen_shuffle_params(
(64, 128, 32), (32, 64, 32), ["s8,s8,s32,s32,s32"],
2, kernel.GemmAlgo.Turing, TensorOpParams((8, 8, 16))),
]
spconv/core_cc/__init__.pyi 0 → 100644
View file @ a6abf55d
# Copyright 2021 Yan Yan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
spconv/core_cc/csrc/__init__.pyi 0 → 100644
View file @ a6abf55d
# Copyright 2021 Yan Yan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
spconv/core_cc/csrc/sparse/__init__.pyi 0 → 100644
View file @ a6abf55d
# Copyright 2021 Yan Yan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
spconv/core_cc/csrc/sparse/all/__init__.pyi 0 → 100644
View file @ a6abf55d
from typing import overload, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
from pccm.stubs import EnumValue, EnumClassValue
from cumm.tensorview import Tensor
class SpconvOps:
@staticmethod
def generate_conv_inds_stage1(indices: Tensor, indice_pairs: Tensor, indice_pairs_uniq: Tensor, indice_num_per_loc: Tensor, batch_size: int, output_dims: List[int], input_dims: List[int], ksize: List[int], stride: List[int], padding: List[int], dilation: List[int], transposed: bool = False, stream_int: int = 0) -> None:
"""
Args:
indices:
indice_pairs:
indice_pairs_uniq:
indice_num_per_loc:
batch_size:
output_dims:
input_dims:
ksize:
stride:
padding:
dilation:
transposed:
stream_int:
"""
...
@staticmethod
def generate_conv_inds_stage1_5(indice_pairs_uniq: Tensor, ndim: int, uniq_size: int, stream_int: int = 0) -> int:
"""
Args:
indice_pairs_uniq:
ndim:
uniq_size:
stream_int:
"""
...
@staticmethod
def generate_conv_inds_stage2(indices: Tensor, hashdata: Tensor, indice_pairs: Tensor, indice_pairs_uniq: Tensor, out_inds: Tensor, num_out_act: int, batch_size: int, output_dims: List[int], input_dims: List[int], ksize: List[int], stride: List[int], padding: List[int], dilation: List[int], transposed: bool = False, stream_int: int = 0) -> int:
"""
Args:
indices:
hashdata:
indice_pairs:
indice_pairs_uniq:
out_inds:
num_out_act:
batch_size:
output_dims:
input_dims:
ksize:
stride:
padding:
dilation:
transposed:
stream_int:
"""
...
@staticmethod
def generate_subm_conv_inds(indices: Tensor, hashdata: Tensor, indice_pairs: Tensor, out_inds: Tensor, indice_num_per_loc: Tensor, batch_size: int, input_dims: List[int], ksize: List[int], dilation: List[int], indice_pair_mask: Tensor = Tensor(), backward: bool = False, stream_int: int = 0) -> int:
"""
Args:
indices:
hashdata:
indice_pairs:
out_inds:
indice_num_per_loc:
batch_size:
input_dims:
ksize:
dilation:
indice_pair_mask:
backward:
stream_int:
"""
...
@staticmethod
def maxpool_forward(out: Tensor, inp: Tensor, out_inds: Tensor, in_inds: Tensor, stream: int = 0) -> None:
"""
Args:
out:
inp:
out_inds:
in_inds:
stream:
"""
...
@staticmethod
def maxpool_backward(out: Tensor, inp: Tensor, dout: Tensor, dinp: Tensor, out_inds: Tensor, in_inds: Tensor, stream: int = 0) -> None:
"""
Args:
out:
inp:
dout:
dinp:
out_inds:
in_inds:
stream:
"""
...
@staticmethod
def sort_1d_by_key(data: Tensor) -> Tensor:
"""
Args:
data:
"""
...
spconv/core_cc/csrc/sparse/all/ops1d.pyi 0 → 100644
View file @ a6abf55d
from typing import overload, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
from pccm.stubs import EnumValue, EnumClassValue
from cumm.tensorview import Tensor
class Point2Voxel:
hashdata: Tensor
point_indice_data: Tensor
voxels: Tensor
indices: Tensor
num_per_voxel: Tensor
@property
def grid_size(self) -> List[int]: ...
def __init__(self, vsize_xyz: List[float], coors_range_xyz: List[float], num_point_features: int, max_num_voxels: int, max_num_points_per_voxel: int) -> None:
"""
Args:
vsize_xyz:
coors_range_xyz:
num_point_features:
max_num_voxels:
max_num_points_per_voxel:
"""
...
def point_to_voxel_hash(self, points: Tensor, clear_voxels: bool = True) -> Tuple[Tensor, Tensor, Tensor]:
"""
Args:
points:
clear_voxels:
"""
...
spconv/core_cc/csrc/sparse/all/ops2d.pyi 0 → 100644
View file @ a6abf55d
from typing import overload, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
from pccm.stubs import EnumValue, EnumClassValue
from cumm.tensorview import Tensor
class Point2Voxel:
hashdata: Tensor
point_indice_data: Tensor
voxels: Tensor
indices: Tensor
num_per_voxel: Tensor
@property
def grid_size(self) -> List[int]: ...
def __init__(self, vsize_xyz: List[float], coors_range_xyz: List[float], num_point_features: int, max_num_voxels: int, max_num_points_per_voxel: int) -> None:
"""
Args:
vsize_xyz:
coors_range_xyz:
num_point_features:
max_num_voxels:
max_num_points_per_voxel:
"""
...
def point_to_voxel_hash(self, points: Tensor, clear_voxels: bool = True) -> Tuple[Tensor, Tensor, Tensor]:
"""
Args:
points:
clear_voxels:
"""
...
spconv/core_cc/csrc/sparse/all/ops3d.pyi 0 → 100644
View file @ a6abf55d
from typing import overload, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
from pccm.stubs import EnumValue, EnumClassValue
from cumm.tensorview import Tensor
class Point2Voxel:
hashdata: Tensor
point_indice_data: Tensor
voxels: Tensor
indices: Tensor
num_per_voxel: Tensor
@property
def grid_size(self) -> List[int]: ...
def __init__(self, vsize_xyz: List[float], coors_range_xyz: List[float], num_point_features: int, max_num_voxels: int, max_num_points_per_voxel: int) -> None:
"""
Args:
vsize_xyz:
coors_range_xyz:
num_point_features:
max_num_voxels:
max_num_points_per_voxel:
"""
...
def point_to_voxel_hash(self, points: Tensor, clear_voxels: bool = True) -> Tuple[Tensor, Tensor, Tensor]:
"""
Args:
points:
clear_voxels:
"""
...
spconv/core_cc/csrc/sparse/all/ops4d.pyi 0 → 100644
View file @ a6abf55d
from typing import overload, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
from pccm.stubs import EnumValue, EnumClassValue
from cumm.tensorview import Tensor
class Point2Voxel:
hashdata: Tensor
point_indice_data: Tensor
voxels: Tensor
indices: Tensor
num_per_voxel: Tensor
@property
def grid_size(self) -> List[int]: ...
def __init__(self, vsize_xyz: List[float], coors_range_xyz: List[float], num_point_features: int, max_num_voxels: int, max_num_points_per_voxel: int) -> None:
"""
Args:
vsize_xyz:
coors_range_xyz:
num_point_features:
max_num_voxels:
max_num_points_per_voxel:
"""
...
def point_to_voxel_hash(self, points: Tensor, clear_voxels: bool = True) -> Tuple[Tensor, Tensor, Tensor]:
"""
Args:
points:
clear_voxels:
"""
...
spconv/core_cc/csrc/sparse/all/ops_cpu1d.pyi 0 → 100644
View file @ a6abf55d
from typing import overload, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
from pccm.stubs import EnumValue, EnumClassValue
from cumm.tensorview import Tensor
class Point2VoxelCPU:
densehashdata: Tensor
voxels: Tensor
indices: Tensor
num_per_voxel: Tensor
@property
def grid_size(self) -> List[int]: ...
def __init__(self, vsize_xyz: List[float], coors_range_xyz: List[float], num_point_features: int, max_num_voxels: int, max_num_points_per_voxel: int) -> None:
"""
Args:
vsize_xyz:
coors_range_xyz:
num_point_features:
max_num_voxels:
max_num_points_per_voxel:
"""
...
def point_to_voxel(self, points: Tensor, clear_voxels: bool = True) -> Tuple[Tensor, Tensor, Tensor]:
"""
Args:
points:
clear_voxels:
"""
...
def point_to_voxel_empty_mean(self, points: Tensor, clear_voxels: bool = True) -> Tuple[Tensor, Tensor, Tensor]:
"""
Args:
points:
clear_voxels:
"""
...
spconv/core_cc/csrc/sparse/all/ops_cpu2d.pyi 0 → 100644
View file @ a6abf55d
from typing import overload, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
from pccm.stubs import EnumValue, EnumClassValue
from cumm.tensorview import Tensor
class Point2VoxelCPU:
densehashdata: Tensor
voxels: Tensor
indices: Tensor
num_per_voxel: Tensor
@property
def grid_size(self) -> List[int]: ...
def __init__(self, vsize_xyz: List[float], coors_range_xyz: List[float], num_point_features: int, max_num_voxels: int, max_num_points_per_voxel: int) -> None:
"""
Args:
vsize_xyz:
coors_range_xyz:
num_point_features:
max_num_voxels:
max_num_points_per_voxel:
"""
...
def point_to_voxel(self, points: Tensor, clear_voxels: bool = True) -> Tuple[Tensor, Tensor, Tensor]:
"""
Args:
points:
clear_voxels:
"""
...
def point_to_voxel_empty_mean(self, points: Tensor, clear_voxels: bool = True) -> Tuple[Tensor, Tensor, Tensor]:
"""
Args:
points:
clear_voxels:
"""
...
spconv/core_cc/csrc/sparse/all/ops_cpu3d.pyi 0 → 100644
View file @ a6abf55d
from typing import overload, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
from pccm.stubs import EnumValue, EnumClassValue
from cumm.tensorview import Tensor
class Point2VoxelCPU:
densehashdata: Tensor
voxels: Tensor
indices: Tensor
num_per_voxel: Tensor
@property
def grid_size(self) -> List[int]: ...
def __init__(self, vsize_xyz: List[float], coors_range_xyz: List[float], num_point_features: int, max_num_voxels: int, max_num_points_per_voxel: int) -> None:
"""
Args:
vsize_xyz:
coors_range_xyz:
num_point_features:
max_num_voxels:
max_num_points_per_voxel:
"""
...
def point_to_voxel(self, points: Tensor, clear_voxels: bool = True) -> Tuple[Tensor, Tensor, Tensor]:
"""
Args:
points:
clear_voxels:
"""
...
def point_to_voxel_empty_mean(self, points: Tensor, clear_voxels: bool = True) -> Tuple[Tensor, Tensor, Tensor]:
"""
Args:
points:
clear_voxels:
"""
...
spconv/core_cc/csrc/sparse/all/ops_cpu4d.pyi 0 → 100644
View file @ a6abf55d
from typing import overload, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
from pccm.stubs import EnumValue, EnumClassValue
from cumm.tensorview import Tensor
class Point2VoxelCPU:
densehashdata: Tensor
voxels: Tensor
indices: Tensor
num_per_voxel: Tensor
@property
def grid_size(self) -> List[int]: ...
def __init__(self, vsize_xyz: List[float], coors_range_xyz: List[float], num_point_features: int, max_num_voxels: int, max_num_points_per_voxel: int) -> None:
"""
Args:
vsize_xyz:
coors_range_xyz:
num_point_features:
max_num_voxels:
max_num_points_per_voxel:
"""
...
def point_to_voxel(self, points: Tensor, clear_voxels: bool = True) -> Tuple[Tensor, Tensor, Tensor]:
"""
Args:
points:
clear_voxels:
"""
...
def point_to_voxel_empty_mean(self, points: Tensor, clear_voxels: bool = True) -> Tuple[Tensor, Tensor, Tensor]:
"""
Args:
points:
clear_voxels:
"""
...
spconv/core_cc/cumm/__init__.pyi 0 → 100644
View file @ a6abf55d
# Copyright 2021 Yan Yan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
spconv/core_cc/cumm/gemm/__init__.pyi 0 → 100644
View file @ a6abf55d
# Copyright 2021 Yan Yan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
spconv/core_cc/cumm/gemm/gather.pyi 0 → 100644
View file @ a6abf55d
from typing import overload, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
from pccm.stubs import EnumValue, EnumClassValue
from cumm.tensorview import Tensor
class ScatterAll:
def __init__(self) -> None: ...
@staticmethod
def get_all_scatter_params() -> List[Tuple[int, int, int, int]]: ...
def supported_scatter(self, tile_m: int, tile_k_bytes: int, bytes_per_access: int, num_threads: int, channel_size: int, dtype: int) -> bool:
"""
Args:
tile_m:
tile_k_bytes:
bytes_per_access:
num_threads:
channel_size:
dtype:
"""
...
@staticmethod
def stream_synchronize(stream: int = 0) -> None:
"""
Args:
stream:
"""
...
def scatter(self, output: Tensor, input: Tensor, indices: Tensor, tile_m: int, tile_k_bytes: int, bytes_per_access: int, num_threads: int, stream: int = 0) -> None:
"""
Args:
output:
input:
indices:
tile_m:
tile_k_bytes:
bytes_per_access:
num_threads:
stream:
"""
...
def scatter2(self, output: Tensor, input: Tensor, indices: Tensor, size: int, stream: int = 0) -> None:
"""
Args:
output:
input:
indices:
size:
stream:
"""
...
class GatherAll:
def __init__(self) -> None: ...
@staticmethod
def get_all_gather_params() -> List[Tuple[int, int, int, int]]: ...
@staticmethod
def supported(bytes_per_access: int, channel_size: int, dtype: int) -> bool:
"""
Args:
bytes_per_access:
channel_size:
dtype:
"""
...
@staticmethod
def stream_synchronize(stream: int = 0) -> None:
"""
Args:
stream:
"""
...
def gather(self, output: Tensor, input: Tensor, indices: Tensor, tile_m: int, tile_k_bytes: int, bytes_per_access: int, num_threads: int, stream: int = 0) -> None:
"""
Args:
output:
input:
indices:
tile_m:
tile_k_bytes:
bytes_per_access:
num_threads:
stream:
"""
...
def gather2(self, output: Tensor, input: Tensor, indices: Tensor, size: int, stream: int = 0) -> None:
"""
Args:
output:
input:
indices:
size:
stream:
"""
...
spconv/core_cc/cumm/gemm/main.pyi 0 → 100644
View file @ a6abf55d
from typing import overload, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
from pccm.stubs import EnumValue, EnumClassValue
from cumm.tensorview import Tensor
class GemmAlgoDesp:
dtype_a: int
dtype_b: int
dtype_c: int
tile_shape: Tuple[int, int, int]
warp_tile_shape: Tuple[int, int, int]
num_stage: int
dacc: int
dcomp: int
algo: str
tensorop: List[int]
split_k_serial_: int
split_k_parallel_: int
shuffle_type: str
element_per_access_a: int
element_per_access_b: int
element_per_access_c: int
def __init__(self) -> None: ...
def __repr__(self) -> str: ...
@property
def split_k_serial(self) -> bool: ...
@split_k_serial.setter
def split_k_serial(self, val: bool) -> None:
"""
Args:
val:
"""
...
@property
def split_k_parallel(self) -> bool: ...
@split_k_parallel.setter
def split_k_parallel(self, val: bool) -> None:
"""
Args:
val:
"""
...
def check_valid(self) -> None: ...
@property
def trans_a(self) -> bool: ...
@trans_a.setter
def trans_a(self, val: bool) -> None:
"""
Args:
val:
"""
...
@property
def trans_b(self) -> bool: ...
@trans_b.setter
def trans_b(self, val: bool) -> None:
"""
Args:
val:
"""
...
@property
def trans_c(self) -> bool: ...
@trans_c.setter
def trans_c(self, val: bool) -> None:
"""
Args:
val:
"""
...
def query_workspace_size(self, m: int, n: int, k: int, split_k_slices: int) -> int:
"""
Args:
m:
n:
k:
split_k_slices:
"""
...
def supported(self, m: int, n: int, k: int) -> bool:
"""
Args:
m:
n:
k:
"""
...
def supported_ldx(self, lda: int, ldb: int, ldc: int) -> bool:
"""
Args:
lda:
ldb:
ldc:
"""
...
class GemmParams:
algo_desp: GemmAlgoDesp
split_k_slices: int
workspace: Tensor = Tensor()
a_inds: Tensor = Tensor()
b_inds: Tensor = Tensor()
c_inds: Tensor = Tensor()
alpha: float
beta: float
stream: int
def __init__(self) -> None: ...
def check_valid(self) -> None: ...
@property
def a(self) -> Tensor: ...
@a.setter
def a(self, val: Tensor) -> None:
"""
Args:
val:
"""
...
@property
def b(self) -> Tensor: ...
@b.setter
def b(self, val: Tensor) -> None:
"""
Args:
val:
"""
...
@property
def c(self) -> Tensor: ...
@c.setter
def c(self, val: Tensor) -> None:
"""
Args:
val:
"""
...
class GemmMainUnitTest:
@staticmethod
def get_all_algo_desp() -> List[GemmAlgoDesp]: ...
@staticmethod
def extract_mnk(a_shape: List[int], b_shape: List[int], trans_a: bool, trans_b: bool, trans_c: bool, shuffle_type: str = "NS", a_inds_shape: List[int] = [], b_inds_shape: List[int] = [], c_inds_shape: List[int] = []) -> Tuple[int, int, int]:
"""
Args:
a_shape:
b_shape:
trans_a:
trans_b:
trans_c:
shuffle_type:
a_inds_shape:
b_inds_shape:
c_inds_shape:
"""
...
@staticmethod
def align_to_power2(val: int) -> int:
"""
Args:
val:
"""
...
@staticmethod
def device_synchronize() -> None: ...
@staticmethod
def stream_synchronize(stream: int) -> None:
"""
Args:
stream:
"""
...
@staticmethod
def simple_select_tile_shape(m: int, n: int, k: int, tile_ms: List[int], tile_ns: List[int], tile_ks: List[int], tile_shape_to_algos: Dict[int, List[int]], large_k_first: bool) -> List[int]:
"""
Args:
m:
n:
k:
tile_ms:
tile_ns:
tile_ks:
tile_shape_to_algos:
large_k_first:
"""
...
@staticmethod
def matmul2(params: GemmParams) -> None:
"""
Args:
params:
"""
...
spconv/csrc/__init__.py 0 → 100644
View file @ a6abf55d
# Copyright 2021 Yan Yan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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