I have found below Relay IR when doing some job with ResNet-50, we can see the 2 add operators are can be merged to 1 add, the below log is build with opt_level=3. ``` %21 = nn.conv2d(%20, meta[relay.Constant][2] /* ty=Tensor[(32, 8, 1, 1, 8, 8), int8] */, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c", out_dtype="int32") /* ty=Tensor[(1, 32, 56, 56, 8), int32] */; %22 = add(%21, meta[relay.Constant][3] /* ty=Tensor[(1, 32, 1, 1, 8), int32] */) /* ty=Tensor[(1, 32, 56, 56, 8), int32] */; %23 = add(%22, 32 /* ty=int32 */) /* ty=Tensor[(1, 32, 56, 56, 8), int32] */; %24 = right_shift(%23, 6 /* ty=int32 */) /* ty=Tensor[(1, 32, 56, 56, 8), int32] */; %25 = clip(%24, a_min=-127f, a_max=127f) /* ty=Tensor[(1, 32, 56, 56, 8), int32] */; %26 = cast(%25, dtype="int8") /* ty=Tensor[(1, 32, 56, 56, 8), int8] */; ``` Anyone who know whether Relay can't support these optimization now? https://github.com/apache/tvm/blob/26281792e92ae24ec7a14b11e8df8fbacf9c4882/tests/python/relay/test_pass_fold_constant.py#L67 >From the above test case, it seems the pass transform.FoldConstant can't >support these optimization, anybody know why we won't implement these >optimization, does these optimization shouldn't be done? or we just don't have >time to do them? Thanks.
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