slyubomirsky opened a new issue #9572:
URL: https://github.com/apache/tvm/issues/9572
The naming conventions of many other deep learning frameworks do not play
together nicely with the Relay text format. For example, many PyTorch models
include dots in variable names, which are parsed as index operators in Relay.
What is curious is that the imported programs (when they are referenced as the
directly imported AST objects) will work initially, but will not work once
written to the text format and are attempted to be parsed back (at which point
they fail to type check).
For example, with the PyTorch importer (used in the unit tests):
```python
import torch
import tvm
from tvm import relay
model = torch.nn.Transformer(d_model=256, nhead=8, num_encoder_layers=6,
num_decoder_layers=6)
model = model.eval()
src = torch.rand((10, 32, 256))
tgt = torch.rand((10, 32, 256))
trace = torch.jit.trace(model, [src, tgt])
trace = trace.float().eval().cpu()
input_shapes = [("src", (10, 32, 256)), ("tgt", (10, 32, 256))]
mod, params = relay.frontend.from_pytorch(trace, input_shapes, {})
# writes successfully
with open("transformer.relay", "w") as fp:
fp.write(mod.astext())
# attempt to parse...
with open("transformer.relay", "r") as fin:
tvm.parser.fromtext(fin.read())
```
The parsed model fails to read because many of the model parameters have
dots in the name, e.g., `%decoder.layers.0.self_attn.in_proj_weight`.
I see two approaches for addressing this:
1. Sanitize variable names in the text format pretty-printer
2. Sanitize variable names in the importers
In principle, the first approach would be good on the grounds that it would
not require modifying all of the importers. However, importers typically
provide both a modified Relay module _and_ a parameter dictionary and the
sanitizer would have to also sanitize the names in the parameter dictionary. So
the importers themselves may make more sense as the place for sanitizing names.
I also think it would be a good idea to include text-format roundtripping in
importer tests to ensure this will not be an issue in the future.
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