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mra

Trusted publisher

danieldk HF Staff commited on 25 days ago

Commit

f70f624

1 Parent(s): 2338868

Remove builds incompatible with kernels >= 0.14

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This view is limited to 50 files because it contains too many changes. See raw diff

Files changed (50) hide show

build/torch210-cu128-x86_64-windows/__init__.py +0 -25
build/torch210-cu128-x86_64-windows/_mra_cuda_6ec000c.pyd +0 -3
build/torch210-cu128-x86_64-windows/_ops.py +0 -9
build/torch210-cu128-x86_64-windows/metadata.json +0 -20
build/torch210-cu128-x86_64-windows/mra/__init__.py +0 -26
build/torch211-cu128-x86_64-windows/__init__.py +0 -25
build/torch211-cu128-x86_64-windows/_mra_cuda_2f395ab.pyd +0 -3
build/torch211-cu128-x86_64-windows/_ops.py +0 -9
build/torch211-cu128-x86_64-windows/metadata.json +0 -20
build/torch211-cu128-x86_64-windows/mra/__init__.py +0 -26
build/torch27-cxx11-cu118-x86_64-linux/mra/__init__.py +0 -25
build/torch27-cxx11-cu118-x86_64-linux/mra/__pycache__/__init__.cpython-313.pyc +0 -0
build/torch27-cxx11-cu118-x86_64-linux/mra/__pycache__/_ops.cpython-313.pyc +0 -0
build/torch27-cxx11-cu118-x86_64-linux/mra/_mra_9e0f4db.abi3.so +0 -3
build/torch27-cxx11-cu118-x86_64-linux/mra/_ops.py +0 -9
build/torch27-cxx11-cu126-x86_64-linux/mra/__init__.py +0 -25
build/torch27-cxx11-cu126-x86_64-linux/mra/__pycache__/__init__.cpython-313.pyc +0 -0
build/torch27-cxx11-cu126-x86_64-linux/mra/__pycache__/_ops.cpython-313.pyc +0 -0
build/torch27-cxx11-cu126-x86_64-linux/mra/_mra_9e0f4db.abi3.so +0 -3
build/torch27-cxx11-cu126-x86_64-linux/mra/_ops.py +0 -9
build/torch27-cxx11-cu128-x86_64-linux/mra/__init__.py +0 -25
build/torch27-cxx11-cu128-x86_64-linux/mra/__pycache__/__init__.cpython-313.pyc +0 -0
build/torch27-cxx11-cu128-x86_64-linux/mra/__pycache__/_ops.cpython-313.pyc +0 -0
build/torch27-cxx11-cu128-x86_64-linux/mra/_mra_9e0f4db.abi3.so +0 -3
build/torch27-cxx11-cu128-x86_64-linux/mra/_ops.py +0 -9
build/torch28-cxx11-cu126-x86_64-linux/__init__.py +0 -25
build/torch28-cxx11-cu126-x86_64-linux/_mra_41ac1dc.abi3.so +0 -3
build/torch28-cxx11-cu126-x86_64-linux/_ops.py +0 -9
build/torch28-cxx11-cu126-x86_64-linux/metadata.json +0 -4
build/torch28-cxx11-cu126-x86_64-linux/mra/__init__.py +0 -26
build/torch28-cxx11-cu128-x86_64-linux/__init__.py +0 -25
build/torch28-cxx11-cu128-x86_64-linux/_mra_41ac1dc.abi3.so +0 -3
build/torch28-cxx11-cu128-x86_64-linux/_ops.py +0 -9
build/torch28-cxx11-cu128-x86_64-linux/metadata.json +0 -4
build/torch28-cxx11-cu128-x86_64-linux/mra/__init__.py +0 -26
build/torch28-cxx11-cu129-x86_64-linux/__init__.py +0 -25
build/torch28-cxx11-cu129-x86_64-linux/_mra_41ac1dc.abi3.so +0 -3
build/torch28-cxx11-cu129-x86_64-linux/_ops.py +0 -9
build/torch28-cxx11-cu129-x86_64-linux/metadata.json +0 -4
build/torch28-cxx11-cu129-x86_64-linux/mra/__init__.py +0 -26
build/torch29-cxx11-cu126-aarch64-linux/__init__.py +0 -25
build/torch29-cxx11-cu126-aarch64-linux/_mra_cuda_8d73b81.abi3.so +0 -3
build/torch29-cxx11-cu126-aarch64-linux/_ops.py +0 -9
build/torch29-cxx11-cu126-aarch64-linux/metadata.json +0 -17
build/torch29-cxx11-cu126-aarch64-linux/mra/__init__.py +0 -26
build/torch29-cxx11-cu126-x86_64-linux/__init__.py +0 -25
build/torch29-cxx11-cu126-x86_64-linux/_mra_cuda_8d73b81.abi3.so +0 -3
build/torch29-cxx11-cu126-x86_64-linux/_ops.py +0 -9
build/torch29-cxx11-cu126-x86_64-linux/metadata.json +0 -17
build/torch29-cxx11-cu126-x86_64-linux/mra/__init__.py +0 -26

build/torch210-cu128-x86_64-windows/__init__.py DELETED Viewed

@@ -1,25 +0,0 @@
-from ._ops import ops
-import torch
-def index_max(index_vals: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.index_max(index_vals, indices, A_num_block, B_num_block)
-def mm_to_sparse(dense_A: torch.Tensor, dense_B: torch.Tensor, indices: torch.Tensor):
-    return ops.mm_to_sparse(dense_A, dense_B, indices)
-def sparse_dense_mm(sparse_A: torch.Tensor, indices: torch.Tensor, dense_B: torch.Tensor, A_num_block: int):
-    return ops.sparse_dense_mm(sparse_A, indices, dense_B, A_num_block)
-def reduce_sum(sparse_A: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.reduce_sum(sparse_A, indices, A_num_block, B_num_block)
-def scatter(dense_A: torch.Tensor, indices: torch.Tensor, B_num_block: int):
-    return ops.scatter(dense_A, indices, B_num_block)
-__all__ = [
-    "index_max",
-    "mm_to_sparse",
-    "sparse_dense_mm",
-    "reduce_sum",
-    "scatter",
-]

build/torch210-cu128-x86_64-windows/_mra_cuda_6ec000c.pyd DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:aa6a072526b11ba258ee3c95711b1582a501a40829c22bbd62b493730faee0ee
-size 795648

build/torch210-cu128-x86_64-windows/_ops.py DELETED Viewed

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_cuda_6ec000c
-ops = torch.ops._mra_cuda_6ec000c
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_cuda_6ec000c::{op_name}"

build/torch210-cu128-x86_64-windows/metadata.json DELETED Viewed

@@ -1,20 +0,0 @@
-{
-  "version": 1,
-  "python-depends": [],
-  "backend": {
-    "type": "cuda",
-    "archs": [
-      "10.0",
-      "10.1",
-      "12.0+PTX",
-      "7.0",
-      "7.2",
-      "7.5",
-      "8.0",
-      "8.6",
-      "8.7",
-      "8.9",
-      "9.0"
-    ]
-  }
-}

build/torch210-cu128-x86_64-windows/mra/__init__.py DELETED Viewed

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-import importlib
-from pathlib import Path
-from types import ModuleType
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch211-cu128-x86_64-windows/__init__.py DELETED Viewed

@@ -1,25 +0,0 @@
-from ._ops import ops
-import torch
-def index_max(index_vals: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.index_max(index_vals, indices, A_num_block, B_num_block)
-def mm_to_sparse(dense_A: torch.Tensor, dense_B: torch.Tensor, indices: torch.Tensor):
-    return ops.mm_to_sparse(dense_A, dense_B, indices)
-def sparse_dense_mm(sparse_A: torch.Tensor, indices: torch.Tensor, dense_B: torch.Tensor, A_num_block: int):
-    return ops.sparse_dense_mm(sparse_A, indices, dense_B, A_num_block)
-def reduce_sum(sparse_A: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.reduce_sum(sparse_A, indices, A_num_block, B_num_block)
-def scatter(dense_A: torch.Tensor, indices: torch.Tensor, B_num_block: int):
-    return ops.scatter(dense_A, indices, B_num_block)
-__all__ = [
-    "index_max",
-    "mm_to_sparse",
-    "sparse_dense_mm",
-    "reduce_sum",
-    "scatter",
-]

build/torch211-cu128-x86_64-windows/_mra_cuda_2f395ab.pyd DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:666160c8ca0575c243f0da231efe234e43ca5dd686c73245a83c83a44e1958b7
-size 796160

build/torch211-cu128-x86_64-windows/_ops.py DELETED Viewed

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_cuda_2f395ab
-ops = torch.ops._mra_cuda_2f395ab
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_cuda_2f395ab::{op_name}"

build/torch211-cu128-x86_64-windows/metadata.json DELETED Viewed

@@ -1,20 +0,0 @@
-{
-  "version": 1,
-  "python-depends": [],
-  "backend": {
-    "type": "cuda",
-    "archs": [
-      "10.0",
-      "10.1",
-      "12.0+PTX",
-      "7.0",
-      "7.2",
-      "7.5",
-      "8.0",
-      "8.6",
-      "8.7",
-      "8.9",
-      "9.0"
-    ]
-  }
-}

build/torch211-cu128-x86_64-windows/mra/__init__.py DELETED Viewed

@@ -1,26 +0,0 @@
-import ctypes
-import importlib.util
-import sys
-from pathlib import Path
-from types import ModuleType
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch27-cxx11-cu118-x86_64-linux/mra/__init__.py DELETED Viewed

@@ -1,25 +0,0 @@
-from ._ops import ops
-import torch
-def index_max(index_vals: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.index_max(index_vals, indices, A_num_block, B_num_block)
-def mm_to_sparse(dense_A: torch.Tensor, dense_B: torch.Tensor, indices: torch.Tensor):
-    return ops.mm_to_sparse(dense_A, dense_B, indices)
-def sparse_dense_mm(sparse_A: torch.Tensor, indices: torch.Tensor, dense_B: torch.Tensor, A_num_block: int):
-    return ops.sparse_dense_mm(sparse_A, indices, dense_B, A_num_block)
-def reduce_sum(sparse_A: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.reduce_sum(sparse_A, indices, A_num_block, B_num_block)
-def scatter(dense_A: torch.Tensor, indices: torch.Tensor, B_num_block: int):
-    return ops.scatter(dense_A, indices, B_num_block)
-__all__ = [
-    "index_max",
-    "mm_to_sparse",
-    "sparse_dense_mm",
-    "reduce_sum",
-    "scatter",
-]

build/torch27-cxx11-cu118-x86_64-linux/mra/__pycache__/__init__.cpython-313.pyc DELETED Viewed

Binary file (1.85 kB)

build/torch27-cxx11-cu118-x86_64-linux/mra/__pycache__/_ops.cpython-313.pyc DELETED Viewed

Binary file (505 Bytes)

build/torch27-cxx11-cu118-x86_64-linux/mra/_mra_9e0f4db.abi3.so DELETED Viewed

@@ -1,3 +0,0 @@
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-size 2289080

build/torch27-cxx11-cu118-x86_64-linux/mra/_ops.py DELETED Viewed

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_9e0f4db
-ops = torch.ops._mra_9e0f4db
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_9e0f4db::{op_name}"

build/torch27-cxx11-cu126-x86_64-linux/mra/__init__.py DELETED Viewed

@@ -1,25 +0,0 @@
-from ._ops import ops
-import torch
-def index_max(index_vals: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.index_max(index_vals, indices, A_num_block, B_num_block)
-def mm_to_sparse(dense_A: torch.Tensor, dense_B: torch.Tensor, indices: torch.Tensor):
-    return ops.mm_to_sparse(dense_A, dense_B, indices)
-def sparse_dense_mm(sparse_A: torch.Tensor, indices: torch.Tensor, dense_B: torch.Tensor, A_num_block: int):
-    return ops.sparse_dense_mm(sparse_A, indices, dense_B, A_num_block)
-def reduce_sum(sparse_A: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.reduce_sum(sparse_A, indices, A_num_block, B_num_block)
-def scatter(dense_A: torch.Tensor, indices: torch.Tensor, B_num_block: int):
-    return ops.scatter(dense_A, indices, B_num_block)
-__all__ = [
-    "index_max",
-    "mm_to_sparse",
-    "sparse_dense_mm",
-    "reduce_sum",
-    "scatter",
-]

build/torch27-cxx11-cu126-x86_64-linux/mra/__pycache__/__init__.cpython-313.pyc DELETED Viewed

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build/torch27-cxx11-cu126-x86_64-linux/mra/__pycache__/_ops.cpython-313.pyc DELETED Viewed

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build/torch27-cxx11-cu126-x86_64-linux/mra/_mra_9e0f4db.abi3.so DELETED Viewed

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build/torch27-cxx11-cu126-x86_64-linux/mra/_ops.py DELETED Viewed

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_9e0f4db
-ops = torch.ops._mra_9e0f4db
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_9e0f4db::{op_name}"

build/torch27-cxx11-cu128-x86_64-linux/mra/__init__.py DELETED Viewed

@@ -1,25 +0,0 @@
-from ._ops import ops
-import torch
-def index_max(index_vals: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.index_max(index_vals, indices, A_num_block, B_num_block)
-def mm_to_sparse(dense_A: torch.Tensor, dense_B: torch.Tensor, indices: torch.Tensor):
-    return ops.mm_to_sparse(dense_A, dense_B, indices)
-def sparse_dense_mm(sparse_A: torch.Tensor, indices: torch.Tensor, dense_B: torch.Tensor, A_num_block: int):
-    return ops.sparse_dense_mm(sparse_A, indices, dense_B, A_num_block)
-def reduce_sum(sparse_A: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.reduce_sum(sparse_A, indices, A_num_block, B_num_block)
-def scatter(dense_A: torch.Tensor, indices: torch.Tensor, B_num_block: int):
-    return ops.scatter(dense_A, indices, B_num_block)
-__all__ = [
-    "index_max",
-    "mm_to_sparse",
-    "sparse_dense_mm",
-    "reduce_sum",
-    "scatter",
-]

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build/torch27-cxx11-cu128-x86_64-linux/mra/_mra_9e0f4db.abi3.so DELETED Viewed

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build/torch27-cxx11-cu128-x86_64-linux/mra/_ops.py DELETED Viewed

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_9e0f4db
-ops = torch.ops._mra_9e0f4db
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_9e0f4db::{op_name}"

build/torch28-cxx11-cu126-x86_64-linux/__init__.py DELETED Viewed

@@ -1,25 +0,0 @@
-from ._ops import ops
-import torch
-def index_max(index_vals: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.index_max(index_vals, indices, A_num_block, B_num_block)
-def mm_to_sparse(dense_A: torch.Tensor, dense_B: torch.Tensor, indices: torch.Tensor):
-    return ops.mm_to_sparse(dense_A, dense_B, indices)
-def sparse_dense_mm(sparse_A: torch.Tensor, indices: torch.Tensor, dense_B: torch.Tensor, A_num_block: int):
-    return ops.sparse_dense_mm(sparse_A, indices, dense_B, A_num_block)
-def reduce_sum(sparse_A: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.reduce_sum(sparse_A, indices, A_num_block, B_num_block)
-def scatter(dense_A: torch.Tensor, indices: torch.Tensor, B_num_block: int):
-    return ops.scatter(dense_A, indices, B_num_block)
-__all__ = [
-    "index_max",
-    "mm_to_sparse",
-    "sparse_dense_mm",
-    "reduce_sum",
-    "scatter",
-]

build/torch28-cxx11-cu126-x86_64-linux/_mra_41ac1dc.abi3.so DELETED Viewed

@@ -1,3 +0,0 @@
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-size 2446064

build/torch28-cxx11-cu126-x86_64-linux/_ops.py DELETED Viewed

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_41ac1dc
-ops = torch.ops._mra_41ac1dc
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_41ac1dc::{op_name}"

build/torch28-cxx11-cu126-x86_64-linux/metadata.json DELETED Viewed

@@ -1,4 +0,0 @@
-{
-  "version": 1,
-  "python-depends": []
-}

build/torch28-cxx11-cu126-x86_64-linux/mra/__init__.py DELETED Viewed

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-import importlib
-from pathlib import Path
-from types import ModuleType
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch28-cxx11-cu128-x86_64-linux/__init__.py DELETED Viewed

@@ -1,25 +0,0 @@
-from ._ops import ops
-import torch
-def index_max(index_vals: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.index_max(index_vals, indices, A_num_block, B_num_block)
-def mm_to_sparse(dense_A: torch.Tensor, dense_B: torch.Tensor, indices: torch.Tensor):
-    return ops.mm_to_sparse(dense_A, dense_B, indices)
-def sparse_dense_mm(sparse_A: torch.Tensor, indices: torch.Tensor, dense_B: torch.Tensor, A_num_block: int):
-    return ops.sparse_dense_mm(sparse_A, indices, dense_B, A_num_block)
-def reduce_sum(sparse_A: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.reduce_sum(sparse_A, indices, A_num_block, B_num_block)
-def scatter(dense_A: torch.Tensor, indices: torch.Tensor, B_num_block: int):
-    return ops.scatter(dense_A, indices, B_num_block)
-__all__ = [
-    "index_max",
-    "mm_to_sparse",
-    "sparse_dense_mm",
-    "reduce_sum",
-    "scatter",
-]

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-size 2714440

build/torch28-cxx11-cu128-x86_64-linux/_ops.py DELETED Viewed

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_41ac1dc
-ops = torch.ops._mra_41ac1dc
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_41ac1dc::{op_name}"

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@@ -1,4 +0,0 @@
-{
-  "version": 1,
-  "python-depends": []
-}

build/torch28-cxx11-cu128-x86_64-linux/mra/__init__.py DELETED Viewed

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-import importlib
-from pathlib import Path
-from types import ModuleType
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch28-cxx11-cu129-x86_64-linux/__init__.py DELETED Viewed

@@ -1,25 +0,0 @@
-from ._ops import ops
-import torch
-def index_max(index_vals: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.index_max(index_vals, indices, A_num_block, B_num_block)
-def mm_to_sparse(dense_A: torch.Tensor, dense_B: torch.Tensor, indices: torch.Tensor):
-    return ops.mm_to_sparse(dense_A, dense_B, indices)
-def sparse_dense_mm(sparse_A: torch.Tensor, indices: torch.Tensor, dense_B: torch.Tensor, A_num_block: int):
-    return ops.sparse_dense_mm(sparse_A, indices, dense_B, A_num_block)
-def reduce_sum(sparse_A: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.reduce_sum(sparse_A, indices, A_num_block, B_num_block)
-def scatter(dense_A: torch.Tensor, indices: torch.Tensor, B_num_block: int):
-    return ops.scatter(dense_A, indices, B_num_block)
-__all__ = [
-    "index_max",
-    "mm_to_sparse",
-    "sparse_dense_mm",
-    "reduce_sum",
-    "scatter",
-]

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build/torch28-cxx11-cu129-x86_64-linux/_ops.py DELETED Viewed

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_41ac1dc
-ops = torch.ops._mra_41ac1dc
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_41ac1dc::{op_name}"

build/torch28-cxx11-cu129-x86_64-linux/metadata.json DELETED Viewed

@@ -1,4 +0,0 @@
-{
-  "version": 1,
-  "python-depends": []
-}

build/torch28-cxx11-cu129-x86_64-linux/mra/__init__.py DELETED Viewed

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-import importlib
-from pathlib import Path
-from types import ModuleType
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch29-cxx11-cu126-aarch64-linux/__init__.py DELETED Viewed

@@ -1,25 +0,0 @@
-from ._ops import ops
-import torch
-def index_max(index_vals: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.index_max(index_vals, indices, A_num_block, B_num_block)
-def mm_to_sparse(dense_A: torch.Tensor, dense_B: torch.Tensor, indices: torch.Tensor):
-    return ops.mm_to_sparse(dense_A, dense_B, indices)
-def sparse_dense_mm(sparse_A: torch.Tensor, indices: torch.Tensor, dense_B: torch.Tensor, A_num_block: int):
-    return ops.sparse_dense_mm(sparse_A, indices, dense_B, A_num_block)
-def reduce_sum(sparse_A: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.reduce_sum(sparse_A, indices, A_num_block, B_num_block)
-def scatter(dense_A: torch.Tensor, indices: torch.Tensor, B_num_block: int):
-    return ops.scatter(dense_A, indices, B_num_block)
-__all__ = [
-    "index_max",
-    "mm_to_sparse",
-    "sparse_dense_mm",
-    "reduce_sum",
-    "scatter",
-]

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build/torch29-cxx11-cu126-aarch64-linux/_ops.py DELETED Viewed

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_cuda_8d73b81
-ops = torch.ops._mra_cuda_8d73b81
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_cuda_8d73b81::{op_name}"

build/torch29-cxx11-cu126-aarch64-linux/metadata.json DELETED Viewed

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-{
-  "version": 1,
-  "python-depends": [],
-  "backend": {
-    "type": "cuda",
-    "archs": [
-      "7.0",
-      "7.2",
-      "7.5",
-      "8.0",
-      "8.6",
-      "8.7",
-      "8.9",
-      "9.0+PTX"
-    ]
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build/torch29-cxx11-cu126-aarch64-linux/mra/__init__.py DELETED Viewed

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-import importlib
-from pathlib import Path
-from types import ModuleType
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch29-cxx11-cu126-x86_64-linux/__init__.py DELETED Viewed

@@ -1,25 +0,0 @@
-from ._ops import ops
-import torch
-def index_max(index_vals: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.index_max(index_vals, indices, A_num_block, B_num_block)
-def mm_to_sparse(dense_A: torch.Tensor, dense_B: torch.Tensor, indices: torch.Tensor):
-    return ops.mm_to_sparse(dense_A, dense_B, indices)
-def sparse_dense_mm(sparse_A: torch.Tensor, indices: torch.Tensor, dense_B: torch.Tensor, A_num_block: int):
-    return ops.sparse_dense_mm(sparse_A, indices, dense_B, A_num_block)
-def reduce_sum(sparse_A: torch.Tensor, indices: torch.Tensor, A_num_block: int, B_num_block: int):
-    return ops.reduce_sum(sparse_A, indices, A_num_block, B_num_block)
-def scatter(dense_A: torch.Tensor, indices: torch.Tensor, B_num_block: int):
-    return ops.scatter(dense_A, indices, B_num_block)
-__all__ = [
-    "index_max",
-    "mm_to_sparse",
-    "sparse_dense_mm",
-    "reduce_sum",
-    "scatter",
-]

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build/torch29-cxx11-cu126-x86_64-linux/_ops.py DELETED Viewed

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_cuda_8d73b81
-ops = torch.ops._mra_cuda_8d73b81
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_cuda_8d73b81::{op_name}"

build/torch29-cxx11-cu126-x86_64-linux/metadata.json DELETED Viewed

@@ -1,17 +0,0 @@
-{
-  "version": 1,
-  "python-depends": [],
-  "backend": {
-    "type": "cuda",
-    "archs": [
-      "7.0",
-      "7.2",
-      "7.5",
-      "8.0",
-      "8.6",
-      "8.7",
-      "8.9",
-      "9.0+PTX"
-    ]
-  }
-}

build/torch29-cxx11-cu126-x86_64-linux/mra/__init__.py DELETED Viewed

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-import importlib
-from pathlib import Path
-from types import ModuleType
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))