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_r405771851
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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())
+ ]
+ remove_bn_pass = transform.Sequential([
+ transform.InferType(),
+ transform.SimplifyInference(),
+ transform.FoldConstant(),
+ transform.FoldScaleAxis(),
+ ])
+ composite_partition = transform.Sequential([
+ remove_bn_pass,
+ transform.MergeComposite(pattern_table),
+ transform.AnnotateTarget("dnnl"),
Review comment:
Thanks. @alexwong So the answer is both approaches are possible, but
composite is more general and it also enables easily detecting fused ops inside
codegen, like I do in this line
https://github.com/apache/incubator-tvm/pull/5272/files#diff-1defdbd8a4c2ab55fb62ad44e9b314a8R256
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