[Relay][ONNX] Batch_matmul to dense optimization#8440
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comaniac merged 2 commits intoapache:mainfrom Jul 13, 2021
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Otherwise LGTM. Do we need a test case for this?
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@comaniac Thanks for the valuable comments. I found the Pytorch implementation https://github.com/apache/tvm/blob/main/python/tvm/relay/frontend/pytorch.py#L1624 cannot handle dynamic shapes. So I added this optimization based on the ONNX implementation. For some reason, the weight matrix of nn.dense must be static for the tvm codegen to work correctly, though using libs like mkl works just fine. I created an issue for this #8441. |
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Thanks @ymwangg |
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* [ONNX]Add batch_matmul to dense optimization * Add extra check to avoid unnecessary reshape Co-authored-by: Ubuntu <ubuntu@ip-172-31-14-16.us-west-2.compute.internal>
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* [ONNX]Add batch_matmul to dense optimization * Add extra check to avoid unnecessary reshape Co-authored-by: Ubuntu <ubuntu@ip-172-31-14-16.us-west-2.compute.internal>
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This PR copied the Pytorch matmul implementation that adopts the batch_matmul to dense optimization, which significantly improves the performance of some matmul ops in bert models such as [4, 128, 768] x [768, 768] when using cublas or tensorrt.