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Fixed the bug to access tensor stride #150

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Fixed the bug to access tensor stride #150

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23 changes: 22 additions & 1 deletion et_replay/execution_trace.py
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Original file line number Diff line number Diff line change
Expand Up @@ -294,9 +294,30 @@ def get_tensors(self, param_list: Iterable) -> List[tuple]:
tensors.extend(self.get_tensors(zip(elem_type, input, shape)))
return tensors

def get_tensor_strides(
self, input_list: Iterable, stride_list: Iterable
) -> List[tuple]:
strides = []
for (type, input, shape), stride in zip(input_list, stride_list):
if type.startswith("Tensor"):
strides.append(tuple(stride))
# GenericList could have tensor elements
elif type.startswith("GenericList"):
elem_type = type[len("GenericList[") : -1].split(",")
strides.extend(
self.get_tensor_strides(zip(elem_type, input, shape), stride)
)
return strides

def get_input_tensors(self) -> List[tuple]:
return self.get_tensors(self.get_inputs())

def get_input_tensor_strides(self) -> Optional[List[tuple]]:
if self.input_strides is None:
return None
else:
return self.get_tensor_strides(self.get_inputs(), self.input_strides)

def get_output_tensors(self) -> List[tuple]:
return self.get_tensors(self.get_outputs())

Expand Down Expand Up @@ -542,7 +563,7 @@ def get_param(value, type, shape):
if type.startswith("genericlist"):
param = {"type": "genericlist"}
param["value"] = []
type_list = type[12:-1].split(",")
type_list = type[len("GenericList[") : -1].split(",")
param_list = zip(value, type_list, shape)
for v, t, s in param_list:
param["value"].append(get_param(v, t, s))
Expand Down
7 changes: 4 additions & 3 deletions et_replay/tools/et_replay.py
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Original file line number Diff line number Diff line change
Expand Up @@ -517,6 +517,7 @@ def allocate_tensors(self):
self.args.pooling_factor,
self.args.alpha,
)
tensor_strides = node.get_input_tensor_strides()
for idx, (data_type, t_id, shape) in enumerate(get_input_tensors(node)):
device = self.device
if self.tensor_with_device:
Expand Down Expand Up @@ -549,7 +550,7 @@ def allocate_tensors(self):

strides = None
if node.input_strides is not None:
strides = node.input_strides[idx]
strides = tensor_strides[idx]
tensor = self.get_tensor_from_storage(
t_id[1], # storage_id
t_id[2], # offset
Expand Down Expand Up @@ -833,7 +834,7 @@ def _parse_element_type(node, output_type, output_tensors, override):
)
elif output_type.startswith("GenericList"):
outputs += "["
elements_type = output_type[12:-1].split(",")
elements_type = output_type[len("GenericList[") : -1].split(",")
for element_type in elements_type:
outputs += _parse_element_type(
node, element_type, output_tensors, override
Expand Down Expand Up @@ -1022,7 +1023,7 @@ def get_tensor_from_storage(
storage_tensor.untyped_storage(),
storage_offset=data_offset,
size=shape,
stride=tuple(strides),
stride=strides,
)

return x
Expand Down
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