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import glob
import os
import sys
import sysconfig
import importlib
def is_windows():
return sys.platform.startswith("win")
module_name = ".Release._compiled_module" if is_windows() else "._compiled_module"
_pybind_module = importlib.import_module(module_name, package=__name__)
symbols_to_import = [
"backend_version",
"backend_version_string",
"get_last_error_string",
"destroy_handle",
"norm_forward_phase",
"reduction_mode",
"behavior_note",
"knob_type",
"create_handle",
"create_kernel_cache",
"create_device_properties",
"get_stream",
"numerical_note",
"set_stream",
"build_plan_policy",
"data_type",
"tensor_reordering",
"heur_mode",
"pygraph",
"tensor",
"knob",
"cudnnGraphNotSupportedError",
"diagonal_alignment",
"attention_implementation",
]
for symbol_name in symbols_to_import:
globals()[symbol_name] = getattr(_pybind_module, symbol_name)
from .datatypes import _library_type, _is_torch_tensor
__version__ = "1.19.0"
def _tensor(
self,
dim,
stride,
data_type=data_type.NOT_SET,
is_virtual=False,
is_pass_by_value=False,
ragged_offset=None,
reordering_type=tensor_reordering.NONE,
name="",
uid=-1,
):
"""
Create a tensor.
Args:
dim (List[int]): The dimensions of the tensor.
stride (List[int]): The strides of the tensor.
data_type (cudnn.data_type): The data type of the tensor.
is_virtual (bool): Flag indicating if the tensor is virtual.
is_pass_by_value (bool): Flag indicating if the tensor is passed by value.
ragged_offset (cudnn_tensor): The ragged offset tensor.
reordering_type (cudnn.tensor_reordering): The reordering type of the tensor.
name (str): The name of the tensor.
Returns:
cudnn_tensor: The created tensor.
"""
return self._make_tensor(
dim=dim,
stride=stride,
data_type=_library_type(data_type),
is_virtual=is_virtual,
is_pass_by_value=is_pass_by_value,
ragged_offset=ragged_offset,
reordering_type=reordering_type,
name=name,
uid=uid,
)
def _set_data_type(
self,
data_type=data_type.NOT_SET,
):
return self._set_data_type(_library_type(data_type))
_pybind_module.tensor.set_data_type = _set_data_type
pygraph.tensor = _tensor
def _library_device_pointer(input_tensor):
# either pass in pointers directly
if type(input_tensor) is int:
return input_tensor
# directly extract data pointer for torch tensors
elif _is_torch_tensor(input_tensor):
return input_tensor.data_ptr()
# fall back to dlpack support by library
else:
return _pybind_module._get_data_ptr(input_tensor)
def _execute(
self,
tensor_to_device_buffer,
workspace,
handle=None,
override_uids=None,
override_shapes=None,
override_strides=None,
):
"""
Execute a cudnn graph.
Args:
tensor_to_device_buffer (dict(cudnn_tensor, Union[torch.Tensor, int, __dlpack__])): The dimensions of the tensor.
workspace (Union[torch.Tensor, int, __dlpack__]): The name of the tensor.
handle: cudnn_handle created with cudnn.create_handle()
Returns:
None
"""
uid_to_tensor_pointer = {
x if type(x) is int else x.get_uid(): _library_device_pointer(pointer) for x, pointer in tensor_to_device_buffer.items() if x is not None
}
workspace_pointer = _library_device_pointer(workspace)
self._execute(uid_to_tensor_pointer, workspace_pointer, handle)
def _execute_plan_at_index(
self,
tensor_to_device_buffer,
workspace,
index,
handle=None,
override_uids=None,
override_shapes=None,
override_strides=None,
):
"""
Execute a cudnn graph.
Args:
tensor_to_device_buffer (dict(cudnn_tensor, Union[torch.Tensor, int, __dlpack__])): The dimensions of the tensor.
workspace (Union[torch.Tensor, int, __dlpack__]): The name of the tensor.
index(int): Location of execution plan to use.
handle: cudnn_handle created with cudnn.create_handle()
Returns:
None
"""
uid_to_tensor_pointer = {
x if type(x) is int else x.get_uid(): _library_device_pointer(pointer) for x, pointer in tensor_to_device_buffer.items() if x is not None
}
workspace_pointer = _library_device_pointer(workspace)
self._execute_plan_at_index(
uid_to_tensor_pointer,
workspace_pointer,
index,
handle,
override_uids,
override_shapes,
override_strides,
)
pygraph.execute = _execute
pygraph.execute_plan_at_index = _execute_plan_at_index
def load_cudnn():
# First look at python site packages
lib_path = glob.glob(os.path.join(sysconfig.get_path("purelib"), "nvidia/cudnn/bin/cudnn64_9.dll"))
if lib_path:
assert len(lib_path) == 1, f"Found {len(lib_path)} libcudnn.dll.x in nvidia-cudnn-cuXX."
lib = ctypes.windll.LoadLibrary(lib_path[0])
else: # Fallback
lib = ctypes.windll.LoadLibrary("cudnn64_9.dll")
handle = ctypes.cast(lib._handle, ctypes.c_void_p).value
_pybind_module._set_dlhandle_cudnn(handle)
def _dlopen_cudnn():
# First look at python site packages
lib_path = glob.glob(os.path.join(sysconfig.get_path("purelib"), "nvidia/cudnn/lib/libcudnn.so.*[0-9]"))
if not lib_path:
lib_path = glob.glob(os.path.join(sysconfig.get_path("purelib"), "nvidia/cudnn_jit/lib/libcudnn.so.*[0-9]"))
if lib_path:
assert len(lib_path) == 1, f"Found {len(lib_path)} libcudnn.so.x in nvidia-cudnn-cuXX."
lib = ctypes.CDLL(lib_path[0])
else: # Fallback
try:
lib = ctypes.CDLL("libcudnn.so.9")
except Exception:
try:
lib = ctypes.CDLL("libcudnn.so")
except Exception:
lib = None
if lib is not None:
handle = ctypes.cast(lib._handle, ctypes.c_void_p).value
_pybind_module._set_dlhandle_cudnn(handle)
if is_windows():
load_cudnn()
else:
_dlopen_cudnn()
from .graph import graph, jit, graph_cache
from .wrapper import Graph
from typing import Any
def __getattr__(name: str) -> Any:
if name == "NSA":
try:
from .native_sparse_attention import NSA as _NSA
return _NSA
except Exception as e:
raise ImportError(f"NSA requires optional dependencies. Install with 'pip install nvidia-cudnn-frontend[cutedsl]': {e}") from e
elif name == "GemmSwigluSm100":
try:
from .gemm_swiglu import GemmSwigluSm100 as _GemmSwigluSm100
return _GemmSwigluSm100
except Exception as e:
raise ImportError(f"GemmSwigluSm100 requires optional dependencies. Install with 'pip install nvidia-cudnn-frontend[cutedsl]': {e}") from e
elif name == "gemm_swiglu_wrapper_sm100":
try:
from .gemm_swiglu import (
gemm_swiglu_wrapper_sm100 as _gemm_swiglu_wrapper_sm100,
)
return _gemm_swiglu_wrapper_sm100
except Exception as e:
raise ImportError(
f"gemm_swiglu_wrapper_sm100 requires optional dependencies. Install with 'pip install nvidia-cudnn-frontend[cutedsl]': {e}"
) from e
elif name == "GemmAmaxSm100":
try:
from .gemm_amax import GemmAmaxSm100 as _GemmAmaxSm100
return _GemmAmaxSm100
except Exception as e:
raise ImportError(f"GemmAmaxSm100 requires optional dependencies. Install with 'pip install nvidia-cudnn-frontend[cutedsl]': {e}") from e
elif name == "gemm_amax_wrapper_sm100":
try:
from .gemm_amax import (
gemm_amax_wrapper_sm100 as _gemm_amax_wrapper_sm100,
)
return _gemm_amax_wrapper_sm100
except Exception as e:
raise ImportError(f"gemm_amax_wrapper_sm100 requires optional dependencies. Install with 'pip install nvidia-cudnn-frontend[cutedsl]': {e}") from e
# Grouped GEMM module
elif name == "grouped_gemm":
try:
from . import grouped_gemm as _grouped_gemm
return _grouped_gemm
except Exception as e:
raise ImportError(f"grouped_gemm requires optional dependencies. Install with 'pip install nvidia-cudnn-frontend[cutedsl]': {e}") from e
elif name == "GroupedGemmSwigluSm100":
try:
from .grouped_gemm import GroupedGemmSwigluSm100 as _GroupedGemmSwigluSm100
return _GroupedGemmSwigluSm100
except Exception as e:
raise ImportError(f"GroupedGemmSwigluSm100 requires optional dependencies. Install with 'pip install nvidia-cudnn-frontend[cutedsl]': {e}") from e
elif name == "grouped_gemm_swiglu_wrapper_sm100":
try:
from .grouped_gemm import (
grouped_gemm_swiglu_wrapper_sm100 as _grouped_gemm_swiglu_wrapper_sm100,
)
return _grouped_gemm_swiglu_wrapper_sm100
except Exception as e:
raise ImportError(
f"grouped_gemm_swiglu_wrapper_sm100 requires optional dependencies. Install with 'pip install nvidia-cudnn-frontend[cutedsl]': {e}"
) from e
elif name == "GroupedGemmDswigluSm100":
try:
from .grouped_gemm import GroupedGemmDswigluSm100 as _GroupedGemmDswigluSm100
return _GroupedGemmDswigluSm100
except Exception as e:
raise ImportError(f"GroupedGemmDswigluSm100 requires optional dependencies. Install with 'pip install nvidia-cudnn-frontend[cutedsl]': {e}") from e
elif name == "grouped_gemm_dswiglu_wrapper_sm100":
try:
from .grouped_gemm import (
grouped_gemm_dswiglu_wrapper_sm100 as _grouped_gemm_dswiglu_wrapper_sm100,
)
return _grouped_gemm_dswiglu_wrapper_sm100
except Exception as e:
raise ImportError(
f"grouped_gemm_dswiglu_wrapper_sm100 requires optional dependencies. Install with 'pip install nvidia-cudnn-frontend[cutedsl]': {e}"
) from e
else:
raise AttributeError(name)
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