[GitHub] [incubator-tvm] masahi edited a comment on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC

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masahi edited a comment on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610659753
 
 
   Using MergeComposite, AnnotateTarget and PartitionGraph, I get the following graph for conv + bias + relu pattern:
   
   ```
   def @dnnl_0(%dnnl_0_i0: Tensor[(1, 3, 224, 224), float32], Inline=1, Compiler="dnnl", global_symbol=runtime.String(0x55a8d9cddbd0), Primitive=1) -> Tensor[(1, 1, 224, 224), float32] {
     %2 = fn (%data: Tensor[(1, 3, 224, 224), float32], %weight: Tensor[(1, 3, 3, 3), float32], %bias: Tensor[(1, 1, 1), float32], Composite="dnnl.conv_bias_relu") -> Tensor[(1, 1, 224, 224), float32] {
       %0 = nn.conv2d(%data, %weight, padding=[1, 1, 1, 1], channels=1, kernel_size=[3, 3]) /* ty=Tensor[(1, 1, 224, 224), float32] */;
       %1 = add(%0, %bias) /* ty=Tensor[(1, 1, 224, 224), float32] */;
       nn.relu(%1) /* ty=Tensor[(1, 1, 224, 224), float32] */
     };
     %2(%dnnl_0_i0, meta[relay.Constant][0] /* ty=Tensor[(1, 3, 3, 3), float32] */ /* ty=Tensor[(1, 3, 3, 3), float32] */, meta[relay.Constant][1] /* ty=Tensor[(1, 1, 1), float32] */ /* ty=Tensor[(1, 1, 1), float32] */) /* ty=Tensor[(1, 1, 224, 224), float32] */
   }
   
   def @main(%data1: Tensor[(1, 3, 224, 224), float32]) -> Tensor[(1, 1, 224, 224), float32] {
     @dnnl_0(%data1) /* ty=Tensor[(1, 1, 224, 224), float32] */
   }
   ```
   
   Is it possible to inline composite function `%2` there into `dnnl_0`? What I want is this:
   ```
   def @dnnl_0(%dnnl_0_i0: Tensor[(1, 3, 224, 224), float32], Inline=1, Compiler="dnnl", global_symbol=runtime.String(0x5599b307c370), Primitive=1) -> Tensor[(1, 1, 224, 224), float32] {
     %0 = nn.conv2d(%dnnl_0_i0, meta[relay.Constant][0] /* ty=Tensor[(1, 3, 3, 3), float32] */ /* ty=Tensor[(1, 3, 3, 3), float32] */, padding=[1, 1, 1, 1], channels=1, kernel_size=[3, 3]) /* ty=Tensor[(1, 1, 224, 224), float32] */;
     %1 = add(%0, meta[relay.Constant][1] /* ty=Tensor[(1, 1, 1), float32] */ /* ty=Tensor[(1, 1, 1), float32] */) /* ty=Tensor[(1, 1, 224, 224), float32] */;
     nn.relu(%1) /* ty=Tensor[(1, 1, 224, 224), float32] */
   }
   
   def @main(%data: Tensor[(1, 3, 224, 224), float32]) -> Tensor[(1, 1, 224, 224), float32] {
     @dnnl_0(%data) /* ty=Tensor[(1, 1, 224, 224), float32] */
   }
   ```
   
   Otherwise I have to support function call in DNNL codegen. @zhiics @mbaret 
   
   UPDATE: hmm if I inline the composite function, I lose the composite attribute and hence cannot detect fused call. Is supporting functional call in DNNL a better option?

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