[BUG] Relu allocates extra memory for storing broadcasted zero tensor

[mmengjiadai]

Describe the bug
allo.relu allocates an extra memory block for storing the zero tensor to which the input is compared.

To Reproduce
Run mlp.py without enable tensor and print the intermediate_module. The last part of output is

%alloc_50 = memref.alloc() : memref<30x30xf32>
    %c0_51 = arith.constant 0 : index
    %c30_52 = arith.constant 30 : index
    %c1_53 = arith.constant 1 : index
    scf.for %arg1 = %c0_51 to %c30_52 step %c1_53 {
      %c0_57 = arith.constant 0 : index
      %c30_58 = arith.constant 30 : index
      %c1_59 = arith.constant 1 : index
      scf.for %arg2 = %c0_57 to %c30_58 step %c1_59 {
        memref.store %cst, %alloc_50[%arg1, %arg2] : memref<30x30xf32>
      }
    }
    %c0_54 = arith.constant 0 : index
    %c30_55 = arith.constant 30 : index
    %c1_56 = arith.constant 1 : index
    scf.for %arg1 = %c0_54 to %c30_55 step %c1_56 {
      %c0_57 = arith.constant 0 : index
      %c30_58 = arith.constant 30 : index
      %c1_59 = arith.constant 1 : index
      scf.for %arg2 = %c0_57 to %c30_58 step %c1_59 {
        %4 = memref.load %alloc_39[%arg1, %arg2] : memref<30x30xf32>
        %5 = memref.load %alloc_50[%arg1, %arg2] : memref<30x30xf32>
        %6 = arith.maxf %4, %5 : f32
        memref.store %6, %alloc_46[%arg1, %arg2] : memref<30x30xf32>
      }
    }

%cst, a 0 constant, is broadcasted into %alloc_50. This allocation is not needed.

Expected behavior
Currently added feature allo.max to dsl.py:

def max(x, y):
    return np.maximum(x, y)

and added attribute max to builder.py:

 if isinstance(arg_type, (F32Type, IntegerType)):
                opcls = {
                    "exp": math_d.ExpOp,
                    "log": math_d.LogOp,
                    "log2": math_d.Log2Op,
                    "log10": math_d.Log10Op,
                    "sqrt": math_d.SqrtOp,
                    "sin": math_d.SinOp,
                    "cos": math_d.CosOp,
                    "tan": math_d.TanOp,
                    "tanh": math_d.TanhOp,
                    "power": math_d.PowFOp,
                    "max": arith_d.MaxFOp,
                }.get(fn_name)

When implementing relu using for loops, there is no overhead.

for i, j in allo.grid(30, 30):
            C[i, j] = allo.max(C[i, j], 0.0)