Build uploaded using `kernels`.

build/torch210-cxx11-cu126-x86_64-linux/__init__.py

@@ -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-cxx11-cu126-x86_64-linux/_mra_b91b835.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fb5be144697731fba4406b4ed16232dca9b2c05cb7715646efe3679e7b6343b0
-size 2451456

build/torch210-cxx11-cu126-x86_64-linux/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_b91b835
-ops = torch.ops._mra_b91b835
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_b91b835::{op_name}"

build/torch210-cxx11-cu126-x86_64-linux/metadata.json

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

build/torch210-cxx11-cu126-x86_64-linux/mra/__init__.py

@@ -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/torch210-cxx11-cu128-x86_64-linux/__init__.py

@@ -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-cxx11-cu128-x86_64-linux/_mra_b91b835.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2719092133c64747d7527e90b18e4bf88616787205bc49ca513544b8d93b7db3
-size 2719824

build/torch210-cxx11-cu128-x86_64-linux/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_b91b835
-ops = torch.ops._mra_b91b835
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_b91b835::{op_name}"

build/torch210-cxx11-cu128-x86_64-linux/metadata.json

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

build/torch210-cxx11-cu128-x86_64-linux/mra/__init__.py

@@ -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/torch210-cxx11-cu130-x86_64-linux/__init__.py

@@ -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-cxx11-cu130-x86_64-linux/_mra_b91b835.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4e562370de6b1e766e9fa49f731fc4516ec726643cdbb0a57d6a291359ea5954
-size 2641336

build/torch210-cxx11-cu130-x86_64-linux/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_b91b835
-ops = torch.ops._mra_b91b835
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_b91b835::{op_name}"

build/torch210-cxx11-cu130-x86_64-linux/metadata.json

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

build/torch210-cxx11-cu130-x86_64-linux/mra/__init__.py

@@ -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-cu126-x86_64-linux/__init__.py

@@ -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_b91b835.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:66023954635a31836ddef4a3d072796477d2df17611a3b0acadc83e4b9d644ad
-size 2446064

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

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_b91b835
-ops = torch.ops._mra_b91b835
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_b91b835::{op_name}"

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

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

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

@@ -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

@@ -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-cu128-x86_64-linux/_mra_b91b835.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:daaac316543e2c585eb477e8c494ee342f46e292048e6ac0d3637fce1577bf68
-size 2714440

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

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_b91b835
-ops = torch.ops._mra_b91b835
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_b91b835::{op_name}"

build/torch28-cxx11-cu128-x86_64-linux/metadata.json

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

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

@@ -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

@@ -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-cu129-x86_64-linux/_mra_b91b835.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9438df3b497af7e80e214b74bf4b9e789d3cd1f856105dc251c5de6ba750bdb4
-size 2748224

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

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_b91b835
-ops = torch.ops._mra_b91b835
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_b91b835::{op_name}"

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

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

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

@@ -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

@@ -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/torch29-cxx11-cu126-x86_64-linux/_mra_b91b835.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:eafd95e7fa0334563e9b39d8381fa91af8f65d9d654185ffcf63a07e9be1b6e3
-size 2446040

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

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_b91b835
-ops = torch.ops._mra_b91b835
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_b91b835::{op_name}"

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

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

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

@@ -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-cu128-x86_64-linux/__init__.py

@@ -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/torch29-cxx11-cu128-x86_64-linux/_mra_b91b835.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b3c5b1dc0d06c9c9bd4813ef6617a3d61d7fe8785be8b000c69a7ecf800572d6
-size 2714408

build/torch29-cxx11-cu128-x86_64-linux/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_b91b835
-ops = torch.ops._mra_b91b835
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_b91b835::{op_name}"

build/torch29-cxx11-cu128-x86_64-linux/metadata.json

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

build/torch29-cxx11-cu128-x86_64-linux/mra/__init__.py

@@ -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-cu130-x86_64-linux/__init__.py

@@ -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/torch29-cxx11-cu130-x86_64-linux/_mra_b91b835.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:95a3d441fa857456fbadde43ecf651976f4aa76f1e886e2327fe794e6e214897
-size 2640024

build/torch29-cxx11-cu130-x86_64-linux/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _mra_b91b835
-ops = torch.ops._mra_b91b835
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_mra_b91b835::{op_name}"

build/torch29-cxx11-cu130-x86_64-linux/metadata.json

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

build/torch29-cxx11-cu130-x86_64-linux/mra/__init__.py

@@ -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")))