[GitHub] [incubator-tvm] zhiics commented on a change in pull request #5272: [BYOC] Add example of Composite + Annotate for DNNL fused op

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zhiics 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_r405815328
 
 

 ##########
 File path: tests/python/relay/test_pass_partition_graph.py
 ##########
 @@ -856,6 +857,111 @@ def expected():
     partitioned = transform.PartitionGraph()(mod)
     assert tvm.ir.structural_equal(partitioned, ref_mod, map_free_vars=True)
 
+
+def test_partition_conv_bias_relu():
+    def make_pattern():
+        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))
+        add = relay.add(conv, bias)
+        return relay.nn.relu(add)
+
+    def get_blocks(prefix, data, in_channel, out_channel,
+                   include_bn=True, include_sigmoid=False):
+        weight = relay.var(prefix + "weight")
+        bn_gamma = relay.var(prefix + "bn_gamma")
+        bn_beta = relay.var(prefix + "bn_beta")
+        bn_mmean = relay.var(prefix + "bn_mean")
+        bn_mvar = relay.var(prefix + "bn_var")
+
+        layer = relay.nn.conv2d(data=data, weight=weight, kernel_size=(3, 3),
+                                channels=out_channel, padding=(1, 1))
+        if include_bn:
+            bn_output = relay.nn.batch_norm(layer, bn_gamma, bn_beta,
+                                            bn_mmean, bn_mvar)
+            layer = bn_output[0]
+        if include_sigmoid:
+            # dummy layer to prevent pattern detection
+            layer = relay.sigmoid(layer)
+        layer = relay.nn.relu(layer)
+        return layer
+
+    def get_net(include_bn=True, include_sigmoid=False):
+        data = relay.var("data", relay.TensorType((1, 3, 224, 224), "float32"))
+        layer1 = get_blocks("layer1_", data, 3, 8, include_bn, include_sigmoid)
+        layer2 = get_blocks("layer2_", layer1, 8, 8, include_bn, include_sigmoid)
+        return relay.Function(relay.analysis.free_vars(layer2), layer2)
+
+    def get_partitoned_mod(mod, params):
+        # This is required for constant folding
+        mod["main"] = bind_params_by_name(mod["main"], params)
+        pattern_table = [
+            ("dnnl.conv_bias_relu", make_pattern())
 
 Review comment:
   Yes, some common patterns could be moved to dnnl.py. Let's keep it here for now and have a separate PR to do that.

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