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TorchFX: Constant value pass without copy (#20380) 

* TorchFX: Constant value pass optimization

* Replace op.Constant with make_constant in fx_decoder

* Using shared memory for constant value passing

Co-authored-by: Jan Iwaszkiewicz <jan.iwaszkiewicz@intel.com>

---------

Co-authored-by: Jan Iwaszkiewicz <jan.iwaszkiewicz@intel.com>
This commit is contained in:
Mustafa Cavus 2023-10-19 08:35:10 -07:00 committed by GitHub
parent f6aa2ab7af
commit 5018be82c3
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 2 additions and 15 deletions
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9
src/bindings/python/src/openvino/frontend/pytorch/fx_decoder.py
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@ -7,10 +7,9 @@
from openvino.frontend.pytorch.py_pytorch_frontend import _FrontEndPytorchDecoder as Decoder
from openvino.frontend.pytorch.py_pytorch_frontend import _Type as DecoderType
from openvino.runtime import op, PartialShape, Type as OVType, OVAny, Shape
from openvino.frontend.pytorch.utils import maybe_convert_max_int, make_constant, fetch_attr, pt_to_ov_type_map, ov_to_c_type_map
from openvino.frontend.pytorch.utils import maybe_convert_max_int, make_constant, fetch_attr, pt_to_ov_type_map
import torch
import ctypes
class TorchFXPythonDecoder (Decoder):
@ -224,11 +223,7 @@ class TorchFXPythonDecoder (Decoder):
if self.pt_module.op == 'get_attr':
# Extract Constant from FX module field
ret = fetch_attr(self.fx_gm, self.pt_module.target)
ovshape = PartialShape(ret.size())
ovtype = pt_to_ov_type_map[str(ret.type())]
c_type = ctypes.POINTER(ov_to_c_type_map[ovtype])
data_c_ptr = ctypes.cast(ret.data_ptr(), c_type)
ov_const = op.Constant(ovtype, ovshape.get_shape(), data_c_ptr[:ret.nelement()])
ov_const = op.Constant(ret.numpy(), shared_memory=True)
return ov_const.outputs()

8
src/bindings/python/src/openvino/frontend/pytorch/utils.py
View File

@ -7,7 +7,6 @@
import torch
import numpy as np
import ctypes
from openvino.runtime import op, Type as OVType, Shape, Tensor
from openvino.runtime import opset11 as ops
@ -132,13 +131,6 @@ pt_to_ov_type_map = {
"torch.qint32": OVType.i32
}
ov_to_c_type_map = {
OVType.f32: ctypes.c_float,
OVType.f64: ctypes.c_double,
OVType.i32: ctypes.c_int,
OVType.i64: ctypes.c_int64,
}
wrapper_template = """
import torch