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From GitBox <...@apache.org>
Subject [GitHub] [incubator-tvm] comaniac opened a new issue #5662: [PatternLang] Lift constant nodes to partitioned function arguments
Date Sat, 23 May 2020 01:05:22 GMT

comaniac opened a new issue #5662:
URL: https://github.com/apache/incubator-tvm/issues/5662


   In #5656, we found that `pattern.partition` will not lift the bind constant nodes to the
partitioned function arguments. This results in argument mismatch and could be a potential
problem when applying to op fusion.
   
   Here is an illustration example:
   ```python
   import tvm
   from tvm import relay
   from tvm.relay.dataflow_pattern import *
   from tvm.relay.build_module import bind_params_by_name
   import numpy as np
   
   x = relay.var('x', shape=(1, 3, 224, 224))
   w = relay.var('w', shape=(3, 3, 3, 3))
   b = relay.var('b', shape=(3,))
   
   conv2d = relay.op.nn.conv2d(x, w)
   out = relay.op.nn.bias_add(conv2d, b)
   func = relay.Function([x, w, b], out)
   mod = tvm.IRModule.from_expr(func)
   
   mod["main"] = bind_params_by_name(mod["main"],
                                     {'w': tvm.nd.array(np.ones(shape=(3, 3, 3, 3)))})
   print('=== Fuse ====')
   print(relay.transform.FuseOps()(mod))
   
   conv2d = is_op('nn.conv2d')(wildcard(), wildcard())
   pattern = is_op('nn.bias_add')(conv2d, wildcard())
   print('=== Partition ===')
   print(pattern.partition(mod['main'].body, {'Composite': 'aa'}))
   ```
   
   Output:
   ```
   === Fuse ====
   def @main(%x: Tensor[(1, 3, 224, 224), float32], %b: Tensor[(3), float32]) -> Tensor[(1,
3, 222, 222), float32] {
     %1 = fn (%p0: Tensor[(1, 3, 224, 224), float32], %p1: Tensor[(3, 3, 3, 3), float64],
%p2: Tensor[(3), float32], Primitive=1) -> Tensor[(1, 3, 222, 222), float32] {
       %0 = nn.conv2d(%p0, %p1, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 3, 222, 222), float32]
*/;
       nn.bias_add(%0, %p2) /* ty=Tensor[(1, 3, 222, 222), float32] */
     };
     %1(%x, meta[relay.Constant][0] /* ty=Tensor[(3, 3, 3, 3), float64] */ /* ty=Tensor[(3,
3, 3, 3), float64] */, %b) /* ty=Tensor[(1, 3, 222, 222), float32] */
   }
   
   // meta data omitted. you can use show_meta_data=True to include meta data
   === Partition ===
   free_var %x: Tensor[(1, 3, 224, 224), float32]
   free_var %b: Tensor[(3), float32]
   %1 = fn (%FunctionVar_0_0, %FunctionVar_0_1, Composite="aa", PartitionedFromPattern="nn.conv2d_nn.bias_add_")
{
     %0 = nn.conv2d(%FunctionVar_0_0, meta[relay.Constant][0] /* ty=Tensor[(3, 3, 3, 3), float64]
*/ /* ty=Tensor[(3, 3, 3, 3), float64] */, padding=[0, 0, 0, 0]);
     nn.bias_add(%0, %FunctionVar_0_1)
   };
   %1(%x, %b)
   // meta data omitted. you can use show_meta_data=True to include meta data
   ```
   
   We can see that the function generated by the op fusion keeps the original arguments and
refers to the constant node in the function call. However, the partitioned function directly
accesses the constant node from inside of the function body. Ideally, the partitioned should
be same as the fused function.
   
   cc @mbrookhart @masahi @zhiics 


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