[GitHub] [tvm] jwfromm edited a comment on pull request #10321: [ONNX] only broadcast matmul if the shape has changed

[GitBox <gi...@apache.org>]

jwfromm edited a comment on pull request #10321:
URL: https://github.com/apache/tvm/pull/10321#issuecomment-1045234006


   We might have to poke around a little more to figure out the issue. I wasn't able to replicate a failure using a dedicated script with useless `broadcast_to` calls inserted. This seems to run fine for example:
   
   ```
   import numpy as np
   import tvm
   from tvm import relay
   from tvm.contrib import graph_executor
   
   x = relay.var('x', shape=[10], dtype='float32')
   y = relay.var('y', shape=[10], dtype='float32')
   val = relay.const(5, dtype='float32')
   x2 = relay.broadcast_to(x, relay.shape_of(y))
   out = x2 + val
   mod = tvm.IRModule.from_expr(out)
   
   with relay.build_config(opt_level=3):
       lib = relay.build(mod, target="llvm")
   
   gmod = graph_executor.GraphModule(lib["default"](tvm.cpu()))
   x_np = np.random.normal(size=[10]).astype('float32')
   y_np = np.random.normal(size=[10]).astype('float32')
   gmod.set_input('x', x_np)
   gmod.set_input('y', y_np)
   gmod.run()
   print(gmod.get_output(0))
   ```
   
   Do we expect that to trigger the issue or is it more nuanced?


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