Lyken17 edited a comment on pull request #10439:
URL: https://github.com/apache/tvm/pull/10439#issuecomment-1056647885
Thanks for the explaination. I agree it is important to annote the return
dtype in gradient calculation. But such information is missing when loading
models using `relay.fronted.from_xxx`. For example,
```python
import numpy as np
import torch
import torch.nn as nn
import tvm
from tvm import relay
from tvm.contrib import graph_executor
net = nn.Sequential(
nn.Conv2d(3, 3, 3, padding=1, groups=3)
)
input_shape = [1, 3, 32, 32]
input_data = torch.randn(input_shape)
input_name = "input0"
shape_list = [(input_name, input_data.shape)]
scripted_model = torch.jit.trace(net, input_data).eval()
fmod, params = relay.frontend.from_pytorch(scripted_model, shape_list,
default_dtype="float32")
mod = relay.transform.InferType()(fmod)
bwd_expr = relay.transform.gradient(mod["main"], mode="first_order")
bwd_mod = tvm.IRModule.from_expr(bwd_expr)
bwd_mod = relay.transform.InferType()(bwd_mod)
```
```bash
data types float32 and void do not match in BroadcastRel
note: run with `TVM_BACKTRACE=1` environment variable to display a backtrace.
```
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