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fix: Improve input weight handling to acc_ops convolution layers in FX #1886

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merged 1 commit into from
May 10, 2023
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fix: Improve input weight handling to acc_ops convolution layers in FX #1886

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12 changes: 6 additions & 6 deletions py/torch_tensorrt/fx/converters/acc_ops_converters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -116,7 +116,7 @@ def acc_ops_conv1d(
# right now
if kwargs["bias"] is not None and not isinstance(kwargs["bias"], torch.Tensor):
raise RuntimeError(
f"linear {name} has bias of type {type(kwargs['bias'])}, Expect Optional[Tenosr]"
f"linear {name} has bias of type {type(kwargs['bias'])}, Expect Optional[Tensor]"
)
bias = to_numpy(kwargs["bias"]) # type: ignore[arg-type]
if bias is not None:
Expand Down Expand Up @@ -146,7 +146,7 @@ def acc_ops_conv1d(
else:
if not isinstance(kwargs["weight"], torch.Tensor):
raise RuntimeError(
f"linear {name} has weight of type {type(kwargs['weight'])}, Expect Optional[Tenosr]"
f"linear {name} has weight of type {type(kwargs['weight'])}, Expect Optional[Tensor]"
)
weight = to_numpy(weight)
weight = np.expand_dims(weight, -1)
Expand Down Expand Up @@ -202,11 +202,11 @@ def acc_ops_convnd(
# right now
if kwargs["bias"] is not None and not isinstance(kwargs["bias"], torch.Tensor):
raise RuntimeError(
f"linear {name} has bias of type {type(kwargs['bias'])}, Expect Optional[Tenosr]"
f"linear {name} has bias of type {type(kwargs['bias'])}, Expect Optional[Tensor]"
)
bias = to_numpy(kwargs["bias"]) # type: ignore[arg-type]

if network.has_explicit_precision:
if network.has_explicit_precision or isinstance(kwargs["weight"], TRTTensor):
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Mirrors the existing code for acc_ops.conv1d

weight = get_trt_tensor(network, kwargs["weight"], f"{name}_weight")
weight_shape = tuple(kwargs["weight"].shape) # type: ignore[union-attr]
# will need to use uninitialized weight and set it later to support
Expand All @@ -224,7 +224,7 @@ def acc_ops_convnd(
else:
if not isinstance(kwargs["weight"], torch.Tensor):
raise RuntimeError(
f"linear {name} has weight of type {type(kwargs['weight'])}, Expect Optional[Tenosr]"
f"linear {name} has weight of type {type(kwargs['weight'])}, Expect Optional[Tensor]"
)
weight = to_numpy(kwargs["weight"])
layer = network.add_convolution_nd(
Expand Down Expand Up @@ -276,7 +276,7 @@ def acc_ops_conv_transposend(
)
bias = to_numpy(kwargs["bias"]) # type: ignore[arg-type]

if network.has_explicit_precision:
if network.has_explicit_precision or isinstance(kwargs["weight"], TRTTensor):
weight = get_trt_tensor(network, kwargs["weight"], f"{name}_weight")
weight_shape = tuple(kwargs["weight"].shape) # type: ignore[union-attr]
# will need to use uninitialized weight and set it later to support
Expand Down