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[Relax][PyTorch][Fix] use_convert_torch_tensor_to_relax() where possible #17335

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merged 2 commits into from
Sep 5, 2024
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[Relax][PyTorch][Fix] use_convert_torch_tensor_to_relax() where possible #17335

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f6ce93e
use `_convert_torch_tensor_to_relax` where possible
mshr-h Sep 5, 2024
3557acc
add type annotation
mshr-h Sep 5, 2024
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9 changes: 4 additions & 5 deletions python/tvm/relax/frontend/torch/fx_translator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,7 +62,7 @@ def _fetch_attr(self, model, target: str):
return attr_itr

@staticmethod
def _convert_data_type(input_type, env: Optional[Dict] = None):
def _convert_data_type(input_type: Union[str, torch.dtype], env: Optional[Dict] = None):
"""converts the PyTorch scalar type input_type to a TVM dtype."""
import torch # type: ignore

Expand Down Expand Up @@ -1206,9 +1206,8 @@ def _batch_norm_2d(self, node: fx.node.Node) -> relax.Var:
module = self.named_modules[node.target]
weight = self.params[module.weight]
bias = self.params[module.bias]
dtype = TorchFXImporter._convert_data_type(str(module.running_mean.dtype))
running_mean = relax.const(module.running_mean.cpu().detach().numpy(), dtype)
running_var = relax.const(module.running_var.cpu().detach().numpy(), dtype)
running_mean = self._convert_torch_tensor_to_relax(module.running_mean)
running_var = self._convert_torch_tensor_to_relax(module.running_var)
eps = module.eps

res_tuple = self.block_builder.emit(
Expand Down Expand Up @@ -1769,7 +1768,7 @@ def from_fx(
dtype = self._convert_data_type(str(param.data.dtype))
if dtype in ("float32", "float16"):
if not keep_params_as_input:
self.params[param] = relax.const(param.data.cpu().numpy(), dtype)
self.params[param] = self._convert_torch_tensor_to_relax(param)
else:
raise ValueError("Unsupported data type for model parameters: %s" % dtype)
# Translate the model.
Expand Down
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