[GitHub] [incubator-tvm] comaniac commented on a change in pull request #4741: [External codegen] Add test cases for fused ops with manual annotation

[GitBox <gi...@apache.org>]

comaniac commented on a change in pull request #4741: [External codegen] Add test cases for fused ops with manual annotation
URL: https://github.com/apache/incubator-tvm/pull/4741#discussion_r368360353
 
 

 ##########
 File path: tests/python/relay/test_pass_partition_graph.py
 ##########
 @@ -425,10 +470,120 @@ def test_extern_dnnl_mobilenet():
                  (1, 1000), ref_res.asnumpy(), tol=1e-5, params=params)
 
 
+def test_partition_conv_bias_relu():
+    if not tvm.get_global_func("relay.ext.dnnl", True):
+        print("skip because DNNL codegen is not available")
+        return
+
+    def get_layers(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_layers("layer1_", data, 3, 16, include_bn, include_sigmoid)
+        layer2 = get_layers("layer2_", layer1, 16, 16, include_bn, include_sigmoid)
+        last = layer2
+        return relay.Function(relay.analysis.free_vars(last), last)
+
+    def pre_optimize(mod, params):
+        remove_bn_pass = transform.Sequential([
+            relay.transform.InferType(),
+            relay.transform.SimplifyInference(),
+            relay.transform.FoldConstant(),
+            relay.transform.FoldScaleAxis(),
+        ])
+
+        if params != {}:
 
 Review comment:
   `if params:`

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