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Support cuda tensor in vineyard. #2019

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Support cuda tensor in vineyard. #2019

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11 changes: 10 additions & 1 deletion python/vineyard/contrib/ml/torch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@
from vineyard.core import context
from vineyard.data.utils import build_buffer
from vineyard.data.utils import from_json
from vineyard.data.utils import normalize_cpptype

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from vineyard.data.utils import to_json

torch = lazy_import.lazy_module("torch")
Expand Down Expand Up @@ -97,11 +97,11 @@
return torch.float16
if dtype == 'torch.bfloat16':
return torch.bfloat16
if dtype in ['torch.complex32', 'torch.chalf']:

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return torch.complex32
if dtype in ['torch.complex64', 'torch.cfloat']:

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return torch.complex64
if dtype in ['torch.complex128', 'torch.cdouble']:

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return torch.complex128
if dtype == 'torch.uint8':
return torch.uint8
Expand All @@ -123,10 +123,10 @@
return torch.bool
if dtype == 'torch.quint8':
return torch.quint8
if dtype == 'torch.qint8':

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return torch.qint8

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if dtype == 'torch.qint32':

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return torch.qint32

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if dtype == 'torch.quint4x2':
return torch.quint4x2
if dtype == 'torch.float8_e4m3fn':
Expand All @@ -145,7 +145,12 @@

meta['typename'] = 'vineyard::Tensor<%s>' % str(value.dtype)
meta['value_type_'] = str(value.dtype)
meta.add_member('buffer_', build_torch_buffer(client, value))
if value.is_cuda:
meta['device_'] = str(value.device)
value_in_cpu = value.to('cpu')
meta.add_member('buffer_', build_torch_buffer(client, value_in_cpu))
else:
meta.add_member('buffer_', build_torch_buffer(client, value))

return client.create_metadata(meta)

Expand All @@ -157,16 +162,20 @@
value_type = normalize_tensor_dtype(value_type_name)
shape = from_json(meta['shape_'])
order = from_json(meta.get('order_', 'C'))
device = meta.get('device_', 'cpu')

if np.prod(shape) == 0:
return torch.zeros(shape, dtype=value_type)

buffer = (ctypes.c_char * int(np.prod(shape)) * value_type.itemsize).from_address(

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(obj.member('buffer_').address)
)

c_tensor = torch.frombuffer(buffer, dtype=value_type).reshape(shape)

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tensor = c_tensor if order == 'C' else c_tensor.contiguous()
if "cuda" in device:
cuda_device = torch.device(device)
tensor = c_tensor.to(cuda_device)

return tensor

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