MasterJH5574 commented on code in PR #14348:
URL: https://github.com/apache/tvm/pull/14348#discussion_r1142847893
##########
tests/python/relax/test_frontend_from_fx.py:
##########
@@ -19,20 +19,51 @@
import tvm
from tvm import relax
import tvm.testing
-from tvm.script.parser import ir as I, relax as R, tir as T
+from tvm.script import ir as I
+from tvm.script import relax as R
+from tvm.script import tir as T
-def verify_model(torch_model, input_info, binding, expected):
+def verify_model(torch_model, input_info, binding, expected, use_dynamo=False):
+ import torch
+ import torch._dynamo as dynamo
from torch import fx
from tvm.relax.frontend.torch import from_fx
- graph_model = fx.symbolic_trace(torch_model)
- mod = from_fx(graph_model, input_info)
+ if use_dynamo:
+ args = []
+ for info in input_info:
+ args.append(torch.zeros(*info[0],
dtype=_convert_data_type(info[1])))
+ graph_model = dynamo.export(torch_model, *args)[0]
+ else:
+ graph_model = fx.symbolic_trace(torch_model)
+ print(graph_model.code)
+ mod = from_fx(graph_model, input_info, unwrap_unit_return_tuple=True)
Review Comment:
For `unwrap_unit_return_tuple=True` I think it depends. Usually for graph
from `dynamo.export`, you want to use `unwrap_unit_return_tuple=True`. But for
graphs from `fx.symbolic_trace`, it is not necessary to use True. We also want
to test the case of `unwrap_unit_return_tuple=False` in this file. Seems that
with this change it is untested.
##########
tests/python/relax/test_frontend_from_fx.py:
##########
@@ -19,20 +19,51 @@
import tvm
from tvm import relax
import tvm.testing
-from tvm.script.parser import ir as I, relax as R, tir as T
+from tvm.script import ir as I
+from tvm.script import relax as R
+from tvm.script import tir as T
-def verify_model(torch_model, input_info, binding, expected):
+def verify_model(torch_model, input_info, binding, expected, use_dynamo=False):
+ import torch
+ import torch._dynamo as dynamo
from torch import fx
from tvm.relax.frontend.torch import from_fx
- graph_model = fx.symbolic_trace(torch_model)
- mod = from_fx(graph_model, input_info)
+ if use_dynamo:
+ args = []
+ for info in input_info:
+ args.append(torch.zeros(*info[0],
dtype=_convert_data_type(info[1])))
+ graph_model = dynamo.export(torch_model, *args)[0]
+ else:
+ graph_model = fx.symbolic_trace(torch_model)
+ print(graph_model.code)
+ mod = from_fx(graph_model, input_info, unwrap_unit_return_tuple=True)
+ print(mod.script())
Review Comment:
Debugging lines to remove.
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