rickzx opened a new pull request, #16982: URL: https://github.com/apache/tvm/pull/16982
For workloads with a mixture of symbolic shape and concrete shape as batch sizes, we cannot directly use `int()` to obtain the batch size. Instead, we can use `arith.Analyzer` to check equality. For example: ``` permute_dims1: R.Tensor((batch_size, 12, seq_len, 64), dtype="float16") = R.permute_dims(split_0, axes=[0, 2, 1, 3]) permute_dims2: R.Tensor((batch_size, 12, seq_len, 64), dtype="float16") = R.permute_dims(split_1, axes=[0, 2, 1, 3]) permute_dims3: R.Tensor((batch_size, 12, seq_len, 64), dtype="float16") = R.permute_dims(split_2, axes=[0, 2, 1, 3]) permute_dims4: R.Tensor((batch_size, 12, 64, seq_len), dtype="float16") = R.permute_dims(permute_dims2, axes=[0, 1, 3, 2]) matmul1: R.Tensor((batch_size, 12, seq_len, seq_len), dtype="float16") = R.matmul(permute_dims1, permute_dims4, out_dtype="float16") ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
