[HalideIR] Enhance For

#1

There are some limitations of For now

  1. start must be 0, which is limited by codegen_c
  2. step of iterator always is 1

I’d suggested to enhance For to support this pattern:

__global__
void saxpy(int n, float a, float *x, float *y)
{
    for (int i = blockIdx.x * blockDim.x + threadIdx.x; 
         i < n; 
         i += blockDim.x * gridDim.x) 
      {
          y[i] = a * x[i] + y[i];
      }
}

@tqchen Do you think it is a good idea to support this kind of for expression in HalideIR? It’ll be helpful when we write some kernel with low level API

1 Like
#2

Is this really the case? codegen_c is only one of the many “backends” of TVM.
If I remember correctly, the For loops are normalized in the tvm.schedule.normalize() and it is (AFAIK) a simplification so that InferBound is easier.

#3

Yes, there is an assert there to make sure min starts at 0
https://github.com/dmlc/tvm/blob/b63267b92d942b9c64f814b73567b2fe908e67fb/src/codegen/codegen_c.cc#L826

What I mean is that it is not the place to put start to 0, but this check makes non-zero start not to work.

If ir_builder is used, there seems no normalization for this

#4

Most likely we can use the same normalized loop to represent the same program, and low level program optimizer will detect such loop and rewrites to the strided version

for ( int i = 0; i < extent; i ++) {
   y[i * stride + min] = a * x[i * stride + min]
}
#5

Would you mind giving your insights as to which “low level program optimizers” do this.

  1. So do the LLVM mid-backends generally do this?
  2. What about if we want to design a backend for a non-LLVM compilable HW target?

I am thinking that since the problem is due to normalization (required for other TVM routines), TVM could “denormalize” after.

#6

hi I check tvm source code，I never see blockDim.x,and gridDim.x I think loop fused is ok when loop start，cond and step is same.