Source code for nvtripy.frontend.ops.copy
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import mlir_tensorrt.runtime.api as runtime
from nvtripy import export
from nvtripy.backend.mlir.utils import MLIRRuntimeClient
from nvtripy.common import device as tp_device
from nvtripy.common.datatype import DATA_TYPES
from nvtripy.common.exception import raise_error
from nvtripy.frontend.ops._registry import register_tensor_method
from nvtripy.frontend import wrappers
from nvtripy.frontend.constraints import GetInput, GetReturn
[docs]
@register_tensor_method("copy")
@export.public_api(document_under="operations/functions")
@wrappers.interface(
output_guarantees=GetReturn(0).dtype == GetInput("input").dtype,
)
def copy(input: "nvtripy.Tensor", device: tp_device) -> "nvtripy.Tensor":
r"""
Copies the input tensor to the specified device.
.. caution:: This function cannot be used in a compiled function or :class:`nvtripy.Module`
because it depends on evaluating its inputs, which is not allowed during compilation.
Args:
input: Input tensor.
device: The target device to copy the tensor to.
Returns:
A new tensor on the specified device.
Raises:
TripyException: If the input tensor is already on the specified device, as
performing copies within the same device is currently not supported.
.. code-block:: python
:linenos:
:caption: Copying To CPU
input = tp.Tensor([1, 2, 3], device=tp.device("gpu"))
output = tp.copy(input, device=tp.device("cpu"))
.. code-block:: python
:linenos:
:caption: Copying To GPU
input = tp.Tensor([1, 2, 3])
output = tp.copy(input, device=tp.device("gpu"))
"""
from nvtripy.frontend.tensor import Tensor
input._eval_for_internal_methods() # Avoid `eval()` - don't want to inadvertently move the tensor to GPU.
memref = input.trace_tensor.producer.data
runtime_client = MLIRRuntimeClient() # This is a singleton class, so we aren't creating it on each function call.
# TODO (#577): Support copying between different GPUs:
if input.device.kind == "cpu" and device.kind == "gpu":
assert memref.address_space == runtime.PointerType.host
out_memref = runtime_client.copy_to_device(
host_memref=memref,
device=runtime_client.get_devices()[device.index],
)
elif input.device.kind == "gpu" and device.kind == "cpu":
assert memref.address_space == runtime.PointerType.device
out_memref = runtime_client.copy_to_host(device_memref=memref)
else:
raise_error(
"Copying within the same device kind is not currently supported. Please file an issue if you need this functionality!",
[
f"Input tensor has device kind: {input.device.kind}, which is the same kind as the target device: {device}."
],
)
return Tensor(out_memref)