Fail to Reorder Reduction Loops

[sqPoseidon]

In packed_conv2d_nchw function, there're four reduction loops: in_channel, kernel_h, kernel_w, and bitwidth. When I try to move the output channel loop into the reduction loops, I get the error message:

heterocl-mlir/hcl-dialect/llvm-project/mlir/lib/Dialect/Affine/Utils/LoopUtils.cpp:1496: 
unsigned int mlir::permuteLoops(llvm::MutableArrayRef<mlir::AffineForOp>, llvm::ArrayRef<unsigned int>): 
Assertion `false && "invalid permutation map"' failed.

Here's the example:

import heterocl as hcl
import heterocl.op.bnn as bnn
import numpy as np

def test_bconv_popcnt():
    packing_factor=8
    out_channel = 64
    strides = (1, 1)
    padding = (1, 1)
    in_channel = 8
    bitwidth = min(in_channel, packing_factor)
    in_dtype = hcl.Float()
    out_dtype = hcl.Float()
    in_shape = (1, in_channel, 3, 3) # n, c, h, w
    weight_shape = (out_channel, in_channel, 3, 3) # o, i, h, w
    out_shape = (1, out_channel, 3, 3)

    def conv(data, weight):
        data = hcl.compute(
            data.shape,
            lambda *args: hcl.select(data[args] > 0, 1, 0),
            name="data",
            dtype=hcl.UInt(1),
        )
        weight = hcl.compute(
            weight.shape,
            lambda *args: hcl.select(weight[args] > 0, 1, 0),
            name="weight",
            dtype=hcl.UInt(1),
        )
        # pack along channel dimension
        packed_data = hcl.pack(
            data,
            axis=1,
            factor=bitwidth,
            name="conv_packed",
            dtype=hcl.UInt(bitwidth),
        )
        packed_weight = hcl.pack(
            weight,
            axis=1,
            factor=bitwidth,
            name="conv_packed",
            dtype=hcl.UInt(bitwidth),
        )
        return bnn.packed_conv2d_nchw(
            packed_data,
            packed_weight,
            strides=strides,
            padding=padding,
            name="conv_conv2d",
            out_dtype=out_dtype,
        )

    data = hcl.placeholder(in_shape, "data", dtype=in_dtype)
    weight = hcl.placeholder(weight_shape, "weight", dtype=in_dtype)
    s = hcl.create_schedule([data, weight], conv)

    B = getattr(conv, "conv_conv2d")
    print("B.axis: ", B.axis) # nn, ff, yy, xx, conv_conv2d_rc, conv_conv2d_rx, conv_conv2d_ry
    # s[B].reorder(B.axis[0], B.axis[2], B.axis[1])
    s[B].reorder(B.axis[0], B.axis[2], B.axis[3], B.axis[4], B.axis[1], B.axis[5], B.axis[6]) # nn, yy, xx, conv_conv2d_rc, conv_conv2d_rx, conv_conv2d_ry, ff,

    f = hcl.build(s)
    print(f.host_src)

    a_np = np.random.randint(0, 10, in_shape)
    b_np = np.random.randint(0, 10, weight_shape)

    hcl_a = hcl.asarray(a_np, dtype=in_dtype)
    hcl_b = hcl.asarray(b_np, dtype=in_dtype)
    hcl_c = hcl.asarray(np.zeros(out_shape), dtype=hcl.Float())

    f(hcl_a, hcl_b, hcl_c)

    n, c, h, w = in_shape
    o, i, kh, kw = weight_shape
    # binarize a_np, b_np
    a_np = np.where(a_np > 0, 1, -1)
    b_np = np.where(b_np > 0, 1, -1)
    # pad a_np
    a_np = np.pad(a_np, ((0, 0), (0, 0), (1, 1), (1, 1)), 'constant')
    # calculate convolution
    baseline_output = np.zeros((n, o, h, w))
    for i in range(n):
        for j in range(o):
            for k in range(h):
                for l in range(w):
                    for m in range(c):
                        for p in range(kh):
                            for q in range(kw):
                                baseline_output[i][j][k][l] += a_np[i][m][k + p][l + q] * b_np[j][m][p][q]

    assert np.allclose(hcl_c.asnumpy(), baseline_output)

test_bconv_popcnt()

[zzzDavid]

This seems like a limitation of milr::permuteLoops, I will look into this and provide more detail

[chhzh123]

It's actually our limitation. Currently we put the reduction variable outside all the reduction loops, causing inner loops imperfect, thus we cannot directly permute those reduction loops with spatial loops.