masahi commented on a change in pull request #5272: [BYOC] Add example of Composite + Annotate for DNNL fused op URL: https://github.com/apache/incubator-tvm/pull/5272#discussion_r406487275
########## File path: tests/python/relay/test_pass_partition_graph.py ########## @@ -856,6 +857,128 @@ def expected(): partitioned = transform.PartitionGraph()(mod) assert tvm.ir.structural_equal(partitioned, ref_mod, map_free_vars=True) + +def test_dnnl_fuse(): + def make_pattern(with_bias=True): + data = relay.var("data", relay.TensorType((1, 3, 224, 224), "float32")) + weight = relay.var("weight") + bias = relay.var("bias") + conv = relay.nn.conv2d(data=data, weight=weight, kernel_size=(3, 3), + channels=8, padding=(1, 1)) + if with_bias: + conv_out = relay.add(conv, bias) Review comment: No, since I use this pattern to detect conv + add + relu that come from decomposing the batch norm. `SimplyfyInference` generates `relay.add`, see https://github.com/apache/incubator-tvm/blob/14ba49c60c49474a564f990363de9d114c9b019b/src/relay/transforms/simplify_inference.cc#L54 We can have both conv2d + add + relu and conv2d + bias_add + relu patterns in the table. ---------------------------------------------------------------- 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services