Hi all:
Recently I met infer bound error on split op: ``` TVMError: Check failed: match: iter_var(blockIdx.x, , blockIdx.x) domain already inferred, cannot prove their extents are the same floordiv(((({any_dim|any_dim>=0}*{any_dim|any_dim>=0})*({any_dim|any_dim>=0} - (floordiv({any_dim|any_dim>=0}, 4)*3))) + 511), 512) vs floordiv(((({any_dim|any_dim>=0}*{any_dim|any_dim>=0})*floordiv({any_dim|any_dim>=0}, 4)) + 511), 512) Error during compile function ``` And this error can be reproduced by this code snippet: ``` import tvm from tvm import relay import numpy as np def test_split(): # input var data = relay.var("data", tvm.ir.TensorType(shape = (relay.Any(), relay.Any(), relay.Any()), dtype = "float32")) data_1 = relay.var("data_1", tvm.ir.TensorType(shape = (relay.Any(), relay.Any(), relay.Any()), dtype = "float32")) split_out = relay.op.split(data, 4, 2) out = tvm.relay.op.multiply(split_out[0], data_1) out_1 = tvm.relay.op.multiply(split_out[1], data_1) mod = tvm.IRModule() mod["main"] = relay.Function([data, data_1], tvm.relay.expr.Tuple([out, out_1])) # tvm setting target = tvm.target.Target("cuda -libs=cublas,cudnn") ctx = tvm.gpu(0) ex = relay.create_executor("vm", mod=mod, ctx=ctx, target=target) # inputs data data_np = np.random.randint(256, size=(1,1,256)).astype(np.float32) data_1_np = np.random.randint(64, size=(1,1,64)).astype(np.float32) result_tvm = ex.evaluate()(data=data_np, data_1=data_1_np) print("=======tvm result=========") print(result_tvm) if __name__ == "__main__": test_split() ``` It seems that the itervar was inferred twice, but the range->extent can not be proved the same. Could anyone give me some advices on this issue, why tvm cannot correctly infer bound on this case? --- [Visit Topic](https://discuss.tvm.apache.org/t/inferbound-error-domain-already-inferred-of-split-op/11499/1) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/154338b55152b96f56cd13c0dc5bbc98976c690473d5267438d7f834c34f484c).