Build uploaded using `kernels`.

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

@@ -1,3 +0,0 @@
-from .custom_ops import gemm_int4_forward
-
-__all__ = ["gemm_int4_forward"]

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

@@ -1,9 +0,0 @@
-import torch
-from . import _quantization_gptq_d11f52b
-ops = torch.ops._quantization_gptq_d11f52b
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_quantization_gptq_d11f52b::{op_name}"

build/torch210-cxx11-cpu-x86_64-linux/_quantization_gptq_d11f52b.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:767d96c0b158a7ec52b2bdf2184ad52b0be314397073f2c20b70b6028bb93481
-size 103168

build/torch210-cxx11-cpu-x86_64-linux/custom_ops.py

@@ -1,19 +0,0 @@
-import torch
-from ._ops import ops
-
-def gemm_int4_forward(
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    zeros: torch.Tensor,
-    absmax: torch.Tensor,
-    blocksize: int,
-) -> torch.Tensor:
-    original_dtype = input.dtype
-    if original_dtype != torch.bfloat16:
-        input = input.to(torch.bfloat16)
-
-    output = ops.gemm_int4_forward(input, weight, zeros, absmax, blocksize)
-    if original_dtype != torch.bfloat16:
-        output = output.to(original_dtype)
-
-    return output

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

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

build/torch210-cxx11-cpu-x86_64-linux/quantization_gptq/__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-cpu-x86_64-linux/__init__.py

@@ -1,3 +0,0 @@
-from .custom_ops import gemm_int4_forward
-
-__all__ = ["gemm_int4_forward"]

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

@@ -1,9 +0,0 @@
-import torch
-from . import _quantization_gptq_d11f52b
-ops = torch.ops._quantization_gptq_d11f52b
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_quantization_gptq_d11f52b::{op_name}"

build/torch28-cxx11-cpu-x86_64-linux/_quantization_gptq_d11f52b.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:267134d9dcfbeba26af7b319ab45ba960d8d754c9ad947cb798b6f100f4b1635
-size 101912

build/torch28-cxx11-cpu-x86_64-linux/custom_ops.py

@@ -1,19 +0,0 @@
-import torch
-from ._ops import ops
-
-def gemm_int4_forward(
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    zeros: torch.Tensor,
-    absmax: torch.Tensor,
-    blocksize: int,
-) -> torch.Tensor:
-    original_dtype = input.dtype
-    if original_dtype != torch.bfloat16:
-        input = input.to(torch.bfloat16)
-
-    output = ops.gemm_int4_forward(input, weight, zeros, absmax, blocksize)
-    if original_dtype != torch.bfloat16:
-        output = output.to(original_dtype)
-
-    return output

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

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

build/torch28-cxx11-cpu-x86_64-linux/quantization_gptq/__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-cpu-x86_64-linux/__init__.py

@@ -1,3 +0,0 @@
-from .custom_ops import gemm_int4_forward
-
-__all__ = ["gemm_int4_forward"]

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

@@ -1,9 +0,0 @@
-import torch
-from . import _quantization_gptq_d11f52b
-ops = torch.ops._quantization_gptq_d11f52b
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_quantization_gptq_d11f52b::{op_name}"

build/torch29-cxx11-cpu-x86_64-linux/_quantization_gptq_d11f52b.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a2fe7fdf542f03337c894db4dcc200647dfbde7d8451b9b81b3be8a15616055f
-size 105960

build/torch29-cxx11-cpu-x86_64-linux/custom_ops.py

@@ -1,19 +0,0 @@
-import torch
-from ._ops import ops
-
-def gemm_int4_forward(
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    zeros: torch.Tensor,
-    absmax: torch.Tensor,
-    blocksize: int,
-) -> torch.Tensor:
-    original_dtype = input.dtype
-    if original_dtype != torch.bfloat16:
-        input = input.to(torch.bfloat16)
-
-    output = ops.gemm_int4_forward(input, weight, zeros, absmax, blocksize)
-    if original_dtype != torch.bfloat16:
-        output = output.to(original_dtype)
-
-    return output

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

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

build/torch29-cxx11-cpu-x86_64-linux/quantization_gptq/__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")))