Python tvm.const函数代码示例

def test_reuse_small_buffer():
    ib = tvm.ir_builder.create()
    n = tvm.var("n")
    with ib.for_range(0, n, name="i") as i:
        with ib.for_range(0, 10, name="j") as j:
            A = ib.allocate("int16", 200, name="A", scope="local.L0A")
            A[j] = tvm.const(1, "int16")
            B = ib.allocate("int16", 200, name="B", scope="local.L0A")
            B[j] = tvm.const(1, "int16")
            B1 = ib.allocate("int16", 200, name="B1", scope="local.L0A")
            B1[j] = A[j] + B[j]
            C = ib.allocate("int16", 400, name="C", scope="local.L0A")
            C[j] = tvm.const(1, "int16")
            D = ib.allocate("int16", 400, name="D", scope="local.L0A")
            D[j] = tvm.const(1, "int16")
            E = ib.allocate("int16", 400, name="E", scope="local.L0A")
            E[j] = C[j]

    body = ib.get()
    body = tvm.ir_pass.StorageRewrite(body)

    num_alloc = [0]

    def verify(n):
        if isinstance(n, tvm.stmt.Allocate):
            num_alloc[0] += 1
            assert n.extents[0].value == 800
    tvm.ir_pass.PostOrderVisit(body, verify)
    assert num_alloc[0] == 1

    def _bitpack(*indices):
        packed_data = [tvm.const(0, pack_type)] * bits
        for k in range(data_width):
            # Translate indices for packed data back to original
            idx = [0] * n
            j = 0
            for i in range(n+1):
                if i == bit_axis:
                    continue
                elif i == pack_axis:
                    idx[j] = indices[i] * data_width + k
                else:
                    idx[j] = indices[i]
                j += 1

            element = data(*idx)
            for b in range(bits):
                extracted_bit = ((element & tvm.const(masks[b], "int32")) >> b).astype(pack_type)
                packed_data[b] = (packed_data[b] | extracted_bit)
                if k < data_width - 1:
                    packed_data[b] = packed_data[b] << 1

            if k == data_width - 1:
                return tuple(packed_data)
        return tuple(packed_data)

def multibox_transform_loc(cls_prob, loc_pred, anchor, clip=True, threshold=0.01,
                           variances=(0.1, 0.1, 0.2, 0.2)):
    """Location transformation for multibox detection

    Parameters
    ----------
    cls_prob : tvm.Tensor
        Class probabilities.

    loc_pred : tvm.Tensor
        Location regression predictions.

    anchor : tvm.Tensor
        Prior anchor boxes.

    clip : boolean
        Whether to clip out-of-boundary boxes.

    threshold : float
        Threshold to be a positive prediction.

    variances : tuple of float
        Variances to be decoded from box regression output.

    Returns
    -------
    ret : tuple of tvm.Tensor
    """
    return hybrid_multibox_transform_loc(cls_prob, loc_pred, anchor,
                                         tvm.const(clip, "bool"),
                                         tvm.const(threshold, "float32"),
                                         tvm.convert(variances))

def my_clip(x, a_min, a_max):
    """Unlike topi's current clip, put min and max into two stages."""
    const_min = tvm.const(a_min, x.dtype)
    const_max = tvm.const(a_max, x.dtype)
    x = tvm.compute(x.shape, lambda *i: tvm.min(x(*i), const_max), name="clipA")
    x = tvm.compute(x.shape, lambda *i: tvm.max(x(*i), const_min), name="clipB")
    return x

    def _sample(i, c, ph, pw):
        roi = rois[i]
        batch_index = roi[0].astype('int32')
        roi_start_w, roi_start_h, roi_end_w, roi_end_h = roi[1], roi[2], roi[3], roi[4]
        roi_start_h *= spatial_scale
        roi_end_h *= spatial_scale
        roi_start_w *= spatial_scale
        roi_end_w *= spatial_scale

        # force malformed ROIs to be 1x1
        roi_h = tvm.max(roi_end_h - roi_start_h, tvm.const(1.0, dtype))
        roi_w = tvm.max(roi_end_w - roi_start_w, tvm.const(1.0, dtype))

        bin_h = roi_h / pooled_size_h
        bin_w = roi_w / pooled_size_w

        if sample_ratio > 0:
            roi_bin_grid_h = roi_bin_grid_w = tvm.const(sample_ratio, 'int32')
        else:
            roi_bin_grid_h = tvm.ceil(roi_h / pooled_size_h).astype('int32')
            roi_bin_grid_w = tvm.ceil(roi_w / pooled_size_w).astype('int32')

        count = roi_bin_grid_h * roi_bin_grid_w
        rh = tvm.reduce_axis((0, roi_bin_grid_h))
        rw = tvm.reduce_axis((0, roi_bin_grid_w))
        roi_start_h += ph * bin_h
        roi_start_w += pw * bin_w
        return tvm.sum(_bilinear(batch_index, c,
                                 roi_start_h + (rh + 0.5) * bin_h / roi_bin_grid_h,
                                 roi_start_w + (rw + 0.5) * bin_w / roi_bin_grid_w) / count,
                       axis=[rh, rw])

        def _instr(index):
            ib = tvm.ir_builder.create()
            if index == 1:
                ib.emit(outs[0].vstore(0, tvm.const(0, 'int32x16')))
                return ib.get()

            a_int8 = ins[0].vload([0], "uint8x4")
            re_int32 = tvm.call_pure_intrin('int32', 'reinterpret', a_int8)
            vec_ai32 = re_int32.astype('int32x16')
            vec_a = tvm.call_pure_intrin('int8x64', 'reinterpret', vec_ai32)
            vec_b = ins[1].vload([0, 0], "int8x64")
            vec_one = tvm.const(1, "int16x32")
            pair_reduction = tvm.call_llvm_intrin('int16x32',
                                                  'llvm.x86.avx512.pmaddubs.w.512',
                                                  tvm.const(0, 'uint32'),
                                                  vec_a, vec_b)
            quad_reduction = tvm.call_llvm_intrin('int32x16',
                                                  'llvm.x86.avx512.pmaddw.d.512',
                                                  tvm.const(0, 'uint32'),
                                                  pair_reduction, vec_one)
            if index == 0:
                ib.emit(outs[0].vstore(0, quad_reduction))
            else:
                ib.emit(outs[0].vstore(0, quad_reduction + outs[0].vload([0], 'int32x16')))
            return ib.get()

def test_alloc_seq_type():
    ib = tvm.ir_builder.create()
    n = tvm.var("n")
    with ib.for_range(0, n, name="i") as i:
        with ib.for_range(0, 10, name="j") as j:
            A = ib.allocate("float32", 200, name="A", scope="local.L0A")
            A1 = ib.allocate("float32", 200, name="A1", scope="local.L0A")
            A[j] = 1.2
            A1[j] = 1.3
            B = ib.allocate("int16", 200, name="B", scope="local.L0A")
            B[j] = tvm.const(1, "int16")
            C = ib.allocate("int16", 200, name="C", scope="local.L0A")
            C[j] = tvm.const(1, "int16")
            D = ib.allocate("int16", 200, name="D", scope="local.L0A")
            D[j] = B[j] + C[j]
            A2 = ib.allocate("float32", 200, name="A2", scope="local.L0A")
            A2[j] = A[j]

    body = ib.get()
    body = tvm.ir_pass.StorageRewrite(body)
    num_alloc = [0]
    def verify(n):
        if isinstance(n, tvm.stmt.Allocate):
            num_alloc[0] += 1
            assert n.extents[0].value == 500
    tvm.ir_pass.PostOrderVisit(body, verify)
    assert num_alloc[0] == 1

 def stmt_generater(dtype_list, length):
     ib = tvm.ir_builder.create()
     base_dtype = dtype_list[0]
     global_a = tvm.placeholder((length,), name = "global_a", dtype = base_dtype)
     assert len(dtype_list) == 4
     with ib.for_range(0, length, name="j") as j:
         dtype = dtype_list[0]
         A = ib.allocate(dtype, length, name="A", scope="local.L0A")
         A[j] = tvm.const(1, dtype = dtype)
     with ib.for_range(0, length, name="j") as j:
         dtype = dtype_list[1]
         B = ib.allocate(dtype, length, name="B", scope="local.L0A")
         B[j] = tvm.const(1, dtype = dtype)
     with ib.for_range(0, length, name="j") as j:
         dtype = dtype_list[2]
         C = ib.allocate(dtype, length, name="C", scope="local.L0A")
         C[j] = tvm.const(1, dtype = dtype)
     with ib.for_range(0, length, name="j") as j:
         dtype = dtype_list[3]
         D = ib.allocate(dtype, length, name="D", scope="local.L0A")
         D[j] = tvm.const(1, dtype = dtype)
     with ib.for_range(0, length, name="j") as j:
         dtype = "int8"
         E = ib.allocate(dtype, length, name="E", scope="local.L0A")
         E[j] = A[j].astype(dtype) + B[j].astype(dtype) + C[j].astype(dtype) + D[j].astype(dtype)
     return ib.get()

 def select_array(i, j):
     now = tvm.const(0.0, dtype)
     for ii in range(row):
         for jj in range(col):
             now = tvm.expr.Select(tvm.all(i % row == ii, j % col == jj),
                                   tvm.const(matrix[ii][jj], dtype),
                                   now)
     return now

def test_const_saveload_json():
    # save load json
    x = tvm.const(1, "int32")
    y = tvm.const(10, "int32")
    z = x + y
    z = z + z
    json_str = tvm.save_json(z)
    zz = tvm.load_json(json_str)
    assert tvm.save_json(zz) == tvm.save_json(z)

def test_make_smap():
    # save load json
    x = tvm.const(1, "int32")
    y = tvm.const(10, "int32")
    z = tvm.expr.Add(x, y)
    smap = tvm.convert({"z": z, "x": x})
    json_str = tvm.save_json(tvm.convert([smap]))
    arr = tvm.load_json(json_str)
    assert len(arr) == 1
    assert arr[0]["z"].a == arr[0]["x"]

    def _intrin_func(ins, outs):
        ww, xx = ins
        zz = outs[0]
        vpadd = "llvm.arm.neon.vpadd.v8u8"
        vpadalu = "llvm.arm.neon.vpadalu.v16u8.v8u16"
        args_1 = tvm.const(1, 'uint32')
        args_2 = tvm.const(2, 'uint32')

        def _instr(index):
            irb = tvm.ir_builder.create()
            if index == 1:
                irb.emit(zz.vstore(0, tvm.const(0, 'uint16x8')))
                return irb.get()

            cnts8 = [None] * 8
            cnts4 = [None] * 4
            cnts2 = [None] * 2
            for bw in range(w_b):
                for bx in range(x_b):
                    if k_i == 16:
                        for i in range(m):
                            ands = ww.vload([bw, i, 0], 'uint8x16') & xx.vload([bx, 0], 'uint8x16')
                            cnts = tvm.popcount(ands)
                            upper_half = tvm.call_pure_intrin('uint8x8', 'vectorhigh', cnts)
                            lower_half = tvm.call_pure_intrin('uint8x8', 'vectorlow', cnts)
                            cnts8[i] = upper_half + lower_half
                        for i in range(m//2):
                            cnts4[i] = tvm.call_llvm_intrin('uint8x8', vpadd,
                                                            args_1, cnts8[i*2], cnts8[i*2+1])
                        for i in range(m//4):
                            cnts2[i] = tvm.call_llvm_intrin('uint8x8', vpadd,
                                                            args_1, cnts4[i*2], cnts4[i*2+1])
                        cnts = tvm.call_pure_intrin('uint8x16', 'vectorcombine', cnts2[0], cnts2[1])
                        shifted_cnts = cnts << tvm.const(bw+bx, dtype)
                        out = tvm.call_llvm_intrin('uint16x8', vpadalu,
                                                   args_2, zz.vload(0, 'uint16x8'), shifted_cnts)
                    else: # ki == 8
                        for i in range(m):
                            ands = ww.vload([bw, i, 0], 'uint8x8') & xx.vload([bx, 0], 'uint8x8')
                            cnts8[i] = tvm.popcount(ands)
                        for i in range(m//2):
                            cnts4[i] = tvm.call_llvm_intrin('uint8x8', vpadd,
                                                            args_1, cnts8[i*2], cnts8[i*2+1])
                        for i in range(m//4):
                            cnts2[i] = tvm.call_llvm_intrin('uint8x8', vpadd,
                                                            args_1, cnts4[i*2], cnts4[i*2+1])
                        cnts = tvm.call_pure_intrin('uint8x16', 'vectorcombine', cnts2[0], cnts2[1])
                        shifted_cnts = cnts << tvm.const(bw+bx, dtype)
                        out = tvm.call_llvm_intrin('uint16x8', vpadalu,
                                                   args_2, zz.vload(0, 'uint16x8'), shifted_cnts)
                    irb.emit(zz.vstore(0, out))
            return irb.get()
        # body, reset, update
        return _instr(0), _instr(1), _instr(2)

def test_bitwise():
    x = tvm.var('x')
    y = tvm.var('y')
    assert str(x << y) == 'shift_left(x, y)'
    assert str(x >> y) == 'shift_right(x, y)'
    assert str(x & y) == 'bitwise_and(x, y)'
    assert str(x | y) == 'bitwise_or(x, y)'
    assert str(x ^ y) == 'bitwise_xor(x, y)'
    assert str(~x) == 'bitwise_not(x)'
    assert(tvm.const(1, "int8x2") >> 1).dtype == "int8x2"
    assert(x >> tvm.const(1, "int32x2")).dtype == "int32x2"
    assert(tvm.var("z", "int8x2") << tvm.const(1, "int8x2")).dtype == "int8x2"

def _decl_im2col(data, kernel, stride, padding, layout='NCHW', out_dtype='float32'):
    """declare the Im2Col method for conv2d"""
    _, CI, IH, IW = [x.value for x in data.shape]
    CO, _, KH, KW = [x.value for x in kernel.shape]
    HPAD, WPAD, _, _ = get_pad_tuple(padding, kernel)

    if isinstance(stride, (tuple, list)):
        HSTR, WSTR = stride
    else:
        HSTR, WSTR = stride, stride

    N = 1
    OH = (IH + 2*HPAD - KH) // HSTR + 1
    OW = (IW + 2*WPAD - KW) // WSTR + 1

    DO_PAD = (HPAD != 0 and WPAD != 0)
    if DO_PAD:
        data_pad = pad(data, (0, 0, HPAD, WPAD), name="data_pad")
    else:
        data_pad = data

    ALIGN = 16
    def upround(x, align):
        return (x + align - 1) // align * align

    # A [CO, CI * KH * KW]
    reduce_len = upround(CI * KH * KW, ALIGN)
    A = tvm.compute((upround(CO, ALIGN), reduce_len), lambda i, j:
                    kernel[i][j // KW // KH][j // KW % KH][j % KW], name='A')

    # B [CI * KH * KW, N * OH * OW]
    B = tvm.compute((reduce_len, upround(N * OH * OW, ALIGN)), lambda i, j:\
            tvm.select(tvm.all(i < CI * KH * KW, j < N * OH * OW),
                       data_pad[j // (OH*OW)][i // (KH*KW)][j // OW % OH*HSTR + i // KW % KH]
                       [j % OW*WSTR + i % KW],
                       tvm.const(0, data_pad.dtype)), name='B')

    gemm_n, gemm_l, gemm_m = A.shape[0], reduce_len, B.shape[1]

    # C [CO, N * OH * OW]
    k = tvm.reduce_axis((0, gemm_l), name='k')
    C = tvm.compute((gemm_n, gemm_m), lambda i, j: tvm.sum(A[i, k] * B[k, j], axis=k), name='C')

    # output
    # the last term C[gemm_n-1, gemm_m-1] is for enabling the alignment,
    # otherwise the alignment above will be eliminated by bound inference
    output = tvm.compute((N, CO, OH, OW), lambda n, co, h, w:\
                 C[co][n * OW * OW + h * OW + w] + tvm.const(0, C.dtype) * C[gemm_n-1, gemm_m-1],
                         name='output', tag='im2col_conv_output')

    return output

def test_const_propagation():
    x1 = tvm.const(4, "int32")
    x2 = x1 + 5
    assert isinstance(x2, tvm.expr.IntImm) and x2.value == 9
    x3 = x2 / 3
    assert isinstance(x3, tvm.expr.IntImm) and x3.value == 3
    x4 = x3 + 0.5
    assert isinstance(x4, tvm.expr.FloatImm) and x4.value == 3.5
    x5 = tvm.ceil(x4)
    assert isinstance(x5, tvm.expr.FloatImm) and x5.value == 4
    x6 = x5.astype('int')
    assert isinstance(x6, tvm.expr.IntImm) and x6.value == 4
    y = (tvm.round((tvm.const(6.5, 'float32') - 1) / 1.5) + 2).astype('int')
    assert isinstance(y, tvm.expr.IntImm) and y.value == 6