masahi commented on a change in pull request #4944: [Relay, Torch] Clean up and
refactor PyTorch frontend
URL: https://github.com/apache/incubator-tvm/pull/4944#discussion_r384779098
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File path: python/tvm/relay/frontend/pytorch.py
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@@ -1016,17 +978,58 @@ def from_pytorch(script_module, input_shapes):
TorchScripted PyTorch graph
Note: We currently only support traces (ie: torch.jit.trace(model,
input))
- shape : Dictionary of input dimensions
+ input_shape : Dictionary of input dimensions
Graph level input shape dictionary
+ The keys should be the same one returned by get_graph_input_names(...)
above
Returns
-------
mod : tvm.relay.Module
The module that optimizations will be performed on.
- params : dict of str to tvm.runtime
- Dict of converted parameters stored in tvm.runtime format
+ params : dict of str to tvm.runtime.NDArray
+ Dict of converted parameters stored in tvm.runtime.ndarray format
"""
- g = Graph(script_module, input_shapes)
- mod, params = g.from_pytorch()
- return mod, params
+ graph = script_module.graph.copy()
Review comment:
Because we overwrite the input graph in place. This is not new in this PR.
Currently we apply the torch inlineing pass in `run_jit_passes(...)` below.
I also do some surgery on the input graph in my QNN implementation.
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