# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=invalid-name,unused-variable,unused-argument,no-member
"""Depthwise Conv2D schedule on OptSchedule"""
import tvm
from tvm import autotvm
from tvm.autotvm.task import get_config
from tvm.autotvm.task.space import SplitEntity
from tvm.autotvm.task.topi_integration import deserialize_args
from .. import generic, tag
from ..generic import schedule_depthwise_conv2d_nchw
from ..nn.pad import pad
from ..util import get_const_tuple
from ..nn.util import get_pad_tuple
from ..nn.depthwise_conv2d import depthwise_conv2d_nchw, depthwise_conv2d_NCHWc, \
    _get_workload, depthwise_conv2d_infer_layout

from .util import get_fp32_len

def _fallback_schedule(cfg, wkl):
    """
    Get default schedule for the workload
    Parameters
    ----------
    cfg : tvm.autotvm.task.space.FallbackConfigEntity
        Fallback config to be updated
    wkl : topi.nn.depthwise_conv2d.Workload
        Convolution workload
    """
    simd_width = get_fp32_len()

    HPAD, WPAD = wkl.hpad, wkl.wpad
    HSTR, WSTR = wkl.hstride, wkl.wstride
    out_height = (wkl.height + 2 * HPAD - wkl.hkernel) // HSTR + 1
    out_width = (wkl.width + 2 * WPAD - wkl.wkernel) // WSTR + 1

    oc_bn = 1
    for bn in range(simd_width, 0, -1):
        if wkl.out_filter % bn == 0:
            oc_bn = bn
            break

    ic_bn = 1
    for bn in range(oc_bn, 0, -1):
        if wkl.in_filter % bn == 0:
            ic_bn = bn
            break

    reg_n = 1
    for n in range(31, 0, -1):
        if out_width % n == 0:
            reg_n = n
            break

    cfg["tile_ic"] = SplitEntity([wkl.in_filter // ic_bn, ic_bn])
    cfg["tile_oc"] = SplitEntity([wkl.out_filter // oc_bn, oc_bn])
    cfg["tile_ow"] = SplitEntity([out_width // reg_n, reg_n])


autotvm.register_topi_compute(depthwise_conv2d_nchw, 'cpu', 'direct',
                              depthwise_conv2d_nchw.fdefault)
autotvm.register_topi_schedule(schedule_depthwise_conv2d_nchw, 'cpu', 'direct',
                               schedule_depthwise_conv2d_nchw.fdefault)


@autotvm.register_topi_compute(depthwise_conv2d_NCHWc, 'cpu', 'direct')
def _depthwise_conv2d_NCHWc_cpu(cfg, data, kernel, strides, padding, dilation,
                                layout, out_layout, out_dtype=None):
    out_dtype = data.dtype if out_dtype is None else out_dtype
    batch, in_channel_chunk, in_height, in_width, in_channel_block = get_const_tuple(data.shape)
    out_channel_chunk, _, filter_height, filter_width, __, out_channel_block \
        = get_const_tuple(kernel.shape)

    strides = strides if isinstance(strides, (tuple, list)) else (strides, strides)
    HSTR, WSTR = strides
    pad_top, pad_left, pad_down, pad_right = get_pad_tuple(padding, (filter_height, filter_width))

    dh, dw = dilation if isinstance(dilation, (tuple, list)) else (dilation, dilation)
    assert (dh, dw) == (1, 1), "Does not support dilation"

    in_channel = in_channel_chunk * in_channel_block
    out_channel = out_channel_chunk * out_channel_block
    channel_multiplier = out_channel // in_channel

    out_height = (in_height - filter_height + pad_top + pad_down) // HSTR + 1
    out_width = (in_width - filter_width + pad_left + pad_right) // WSTR + 1

    # get workload and related schedule config
    wkl = _get_workload(tvm.placeholder((batch, in_channel, in_height, in_width), dtype=data.dtype),
                        tvm.placeholder((out_channel, in_channel, filter_height, filter_width),
                                        dtype=kernel.dtype),
                        strides, padding, out_dtype)
    if cfg.is_fallback:
        _fallback_schedule(cfg, wkl)

    # padding stage
    DOPAD = (pad_top != 0 or pad_left != 0 or pad_down != 0 or pad_right != 0)
    if DOPAD:
        pad_before = [0, 0, pad_top, pad_left, 0]
        pad_after = [0, 0, pad_down, pad_right, 0]
        data_pad = pad(data, pad_before, pad_after, name="PaddedInput")
    else:
        data_pad = data

    # depthconv stage
    idxdiv = tvm.indexdiv
    idxmod = tvm.indexmod

    kh = tvm.reduce_axis((0, filter_height), name='kh')
    kw = tvm.reduce_axis((0, filter_width), name='kw')
    Output = tvm.compute(
        (batch, out_channel_chunk, out_height, out_width, out_channel_block),
        lambda b, oco, oh, ow, oci: tvm.sum(
            (data_pad[
                b,
                idxdiv(idxdiv(oco * out_channel_block + oci, channel_multiplier), in_channel_block),
                oh*HSTR+kh, ow*WSTR+kw,
                idxmod(idxdiv(oco * out_channel_block + oci, channel_multiplier), in_channel_block)]
             .astype(out_dtype) *
             kernel[oco, 0, kh, kw, 0, oci].astype(out_dtype)),
            axis=[kh, kw]),
        name='DepthwiseConv2d', tag="depthwise_conv2d_NCHWc")
    return Output


@autotvm.register_topi_schedule(generic.schedule_depthwise_conv2d_NCHWc, 'cpu', ['direct'])
def schedule_depthwise_conv2d_NCHWc(cfg, outs):
    """CPU schedule for depthwise conv2d in NCHW[x]c layout"""
    s = tvm.create_schedule([x.op for x in outs])
    scheduled_ops = []
    def traverse(op):
        """Traverse operators from computation graph"""
        # inline all one-to-one-mapping operators except the last stage (output)
        if tag.is_broadcast(op.tag):
            if op not in s.outputs:
                s[op].compute_inline()
            for tensor in op.input_tensors:
                if isinstance(tensor.op, tvm.tensor.ComputeOp) and tensor.op not in scheduled_ops:
                    traverse(tensor.op)
        if 'depthwise_conv2d_NCHWc' in op.tag:
            conv_out = op.output(0)
            data = conv_out.op.input_tensors[0]
            kernel = conv_out.op.input_tensors[1]
            _schedule_depthwise_conv2d_NCHWc_impl(s, cfg, data, kernel, conv_out, outs[0])
        scheduled_ops.append(op)
    traverse(outs[0].op)
    return s

def _schedule_depthwise_conv2d_NCHWc_impl(s, cfg, data, kernel, conv_out, output):
    tile_ow = cfg["tile_ow"].size[-1]
    # schedule data
    A = data
    if isinstance(s[A].op, tvm.tensor.ComputeOp):
        batch, ic_chunk, ih, iw, ic_block = s[A].op.axis
        p = s[A].fuse(ic_chunk, ih)
        s[A].parallel(p)

    C, O = conv_out, output
    CC = s.cache_write(C, 'global')

    _, ic_chunk, oh, ow, ic_block = s[C].op.axis
    ow_chunk, ow_block = s[C].split(ow, factor=tile_ow)
    s[C].reorder(ic_chunk, oh, ow_chunk, ow_block, ic_block)
    parallel_axis = s[C].fuse(ic_chunk, oh)
    s[C].parallel(parallel_axis)
    s[CC].compute_at(s[C], ow_chunk)

    _, ic_chunk, oh, ow, ic_block = s[CC].op.axis
    kh, kw = s[CC].op.reduce_axis
    ow_chunk, ow_block = s[CC].split(ow, factor=tile_ow)
    s[CC].reorder(ic_chunk, oh, kh, kw, ow_block, ic_block)
    s[CC].vectorize(ic_block)
    s[CC].unroll(ow_block)

    if C != O:
        batch, oc_chunk, oh, ow, oc_block = s[O].op.axis
        ow_chunk, ow_block = s[O].split(ow, factor=tile_ow)
        s[O].reorder(oc_chunk, oh, ow_chunk, ow_block, oc_block)
        parallel_axis = s[O].fuse(oc_chunk, oh)
        s[C].compute_at(s[O], parallel_axis)
        s[O].vectorize(oc_block)
        s[O].parallel(parallel_axis)
    return s


@autotvm.task.register("topi_x86_depthwise_conv2d_NCHWc_from_nchw")
def _topi_nn_depthwise_conv2d_NCHWc(*args, **kwargs):
    assert not kwargs, "Do not support kwargs in template function call"
    data, kernel, strides, padding, dilation, dtype = deserialize_args(args)

    batch, in_channel, height, width = get_const_tuple(data.shape)
    filter_channel, channel_multiplier, kh, kw = get_const_tuple(kernel.shape)
    pt, pl, pb, pr = get_pad_tuple(padding, kernel)
    sh, sw = strides if isinstance(strides, (tuple, list)) else (strides, strides)
    out_height = (height - kh + pt + pb) // sh + 1
    out_width = (width - kw + pl + pr) // sw + 1
    out_channel = filter_channel * channel_multiplier

    # get config here
    cfg = get_config()
    cfg.define_split("tile_ic", in_channel, num_outputs=2)
    cfg.define_split("tile_oc", out_channel, num_outputs=2)
    cfg.define_split("tile_ow", out_width, num_outputs=2, filter=lambda y: y.size[-1] <= 64)

    # change shape with the value in config
    ic_bn, oc_bn = cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1]
    new_data_shape = (batch, in_channel // ic_bn, height, width, ic_bn)
    new_kernel_shape = (out_channel // oc_bn, 1, kh, kw, 1, oc_bn)
    new_data = tvm.placeholder(new_data_shape, data.dtype)
    new_kernel = tvm.placeholder(new_kernel_shape, kernel.dtype)

    data_layout = "NCHW%dc" % ic_bn
    out_layout = "NCHW%dc" % oc_bn

    C = _depthwise_conv2d_NCHWc_cpu(cfg, new_data, new_kernel, strides, padding, dilation,
                                    data_layout, out_layout, dtype)
    s = schedule_depthwise_conv2d_NCHWc(cfg, [C])
    return s, [new_data, new_kernel, C]

@depthwise_conv2d_infer_layout.register("cpu")
def _depthwise_conv2d_infer_layout(workload, cfg):
    _, data, kernel, strides, padding, dilation, dtype = workload
    batch_size, in_channel, in_height, in_width = data[:-1]
    filter_channel, channel_multiplier, k_height, k_width = kernel[:-1]
    out_channel = filter_channel * channel_multiplier
    out_height = (in_height + 2 * padding[0] - k_height) // strides[0] + 1
    out_width = (in_width + 2 * padding[1] - k_width) // strides[1] + 1
    tile_ic, tile_oc = cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1]
    in_shape = (batch_size, in_channel // tile_ic, in_height, in_width, tile_ic)
    in_layout = "NCHW%dc" % tile_ic
    out_shape = (batch_size, out_channel // tile_oc, out_height, out_width, tile_oc)
    out_layout = "NCHW%dc" % tile_oc
    return ((in_shape, in_layout),), ((out_shape, out_layout),)

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=invalid-name,unused-variable,unused-argument,no-member
"""Depthwise Conv2D schedule on OptSchedule"""
import tvm
from tvm import autotvm
from tvm.autotvm.task import get_config
from tvm.autotvm.task.space import SplitEntity
from tvm.autotvm.task.topi_integration import deserialize_args
from .. import generic, tag
from ..generic import schedule_depthwise_conv2d_nchw
from ..nn.pad import pad
from ..util import get_const_tuple
from ..nn.util import get_pad_tuple
from ..nn.depthwise_conv2d import depthwise_conv2d_nchw, depthwise_conv2d_NCHWc, \
    _get_workload, depthwise_conv2d_infer_layout

from .util import get_fp32_len

def _fallback_schedule(cfg, wkl):
    """
    Get default schedule for the workload
    Parameters
    ----------
    cfg : tvm.autotvm.task.space.FallbackConfigEntity
        Fallback config to be updated
    wkl : topi.nn.depthwise_conv2d.Workload
        Convolution workload
    """
    simd_width = get_fp32_len()

    HPAD, WPAD = wkl.hpad, wkl.wpad
    HSTR, WSTR = wkl.hstride, wkl.wstride
    out_height = (wkl.height + 2 * HPAD - wkl.hkernel) // HSTR + 1
    out_width = (wkl.width + 2 * WPAD - wkl.wkernel) // WSTR + 1

    oc_bn = 1
    for bn in range(simd_width, 0, -1):
        if wkl.out_filter % bn == 0:
            oc_bn = bn
            break

    ic_bn = 1
    for bn in range(oc_bn, 0, -1):
        if wkl.in_filter % bn == 0:
            ic_bn = bn
            break

    reg_n = 1
    for n in range(31, 0, -1):
        if out_width % n == 0:
            reg_n = n
            break

    cfg["tile_ic"] = SplitEntity([wkl.in_filter // ic_bn, ic_bn])
    cfg["tile_oc"] = SplitEntity([wkl.out_filter // oc_bn, oc_bn])
    cfg["tile_ow"] = SplitEntity([out_width // reg_n, reg_n])


autotvm.register_topi_compute(depthwise_conv2d_nchw, 'cpu', 'direct',
                              depthwise_conv2d_nchw.fdefault)
autotvm.register_topi_schedule(schedule_depthwise_conv2d_nchw, 'cpu', 'direct',
                               schedule_depthwise_conv2d_nchw.fdefault)


@autotvm.register_topi_compute(depthwise_conv2d_NCHWc, 'cpu', 'direct')
def _depthwise_conv2d_NCHWc_cpu(cfg, data, kernel, strides, padding, dilation,
                                layout, out_layout, out_dtype=None):
    out_dtype = data.dtype if out_dtype is None else out_dtype
    batch, in_channel_chunk, in_height, in_width, in_channel_block = get_const_tuple(data.shape)
    out_channel_chunk, _, filter_height, filter_width, __, out_channel_block \
        = get_const_tuple(kernel.shape)

    strides = strides if isinstance(strides, (tuple, list)) else (strides, strides)
    HSTR, WSTR = strides
    pad_top, pad_left, pad_down, pad_right = get_pad_tuple(padding, (filter_height, filter_width))

    dh, dw = dilation if isinstance(dilation, (tuple, list)) else (dilation, dilation)
    assert (dh, dw) == (1, 1), "Does not support dilation"

    in_channel = in_channel_chunk * in_channel_block
    out_channel = out_channel_chunk * out_channel_block
    channel_multiplier = out_channel // in_channel

    out_height = (in_height - filter_height + pad_top + pad_down) // HSTR + 1
    out_width = (in_width - filter_width + pad_left + pad_right) // WSTR + 1

    # get workload and related schedule config
    wkl = _get_workload(tvm.placeholder((batch, in_channel, in_height, in_width), dtype=data.dtype),
                        tvm.placeholder((out_channel, in_channel, filter_height, filter_width),
                                        dtype=kernel.dtype),
                        strides, padding, out_dtype)
    if cfg.is_fallback:
        _fallback_schedule(cfg, wkl)

    # padding stage
    DOPAD = (pad_top != 0 or pad_left != 0 or pad_down != 0 or pad_right != 0)
    if DOPAD:
        pad_before = [0, 0, pad_top, pad_left, 0]
        pad_after = [0, 0, pad_down, pad_right, 0]
        data_pad = pad(data, pad_before, pad_after, name="PaddedInput")
    else:
        data_pad = data

    # depthconv stage
    idxdiv = tvm.indexdiv
    idxmod = tvm.indexmod

    kh = tvm.reduce_axis((0, filter_height), name='kh')
    kw = tvm.reduce_axis((0, filter_width), name='kw')
    Output = tvm.compute(
        (batch, out_channel_chunk, out_height, out_width, out_channel_block),
        lambda b, oco, oh, ow, oci: tvm.sum(
            (data_pad[
                b,
                idxdiv(idxdiv(oco * out_channel_block + oci, channel_multiplier), in_channel_block),
                oh*HSTR+kh, ow*WSTR+kw,
                idxmod(idxdiv(oco * out_channel_block + oci, channel_multiplier), in_channel_block)]
             .astype(out_dtype) *
             kernel[oco, 0, kh, kw, 0, oci].astype(out_dtype)),
            axis=[kh, kw]),
        name='DepthwiseConv2d', tag="depthwise_conv2d_NCHWc")
    return Output


@autotvm.register_topi_schedule(generic.schedule_depthwise_conv2d_NCHWc, 'cpu', ['direct'])
def schedule_depthwise_conv2d_NCHWc(cfg, outs):
    """CPU schedule for depthwise conv2d in NCHW[x]c layout"""
    s = tvm.create_schedule([x.op for x in outs])
    scheduled_ops = []
    def traverse(op):
        """Traverse operators from computation graph"""
        # inline all one-to-one-mapping operators except the last stage (output)
        if tag.is_broadcast(op.tag):
            if op not in s.outputs:
                s[op].compute_inline()
            for tensor in op.input_tensors:
                if isinstance(tensor.op, tvm.tensor.ComputeOp) and tensor.op not in scheduled_ops:
                    traverse(tensor.op)
        if 'depthwise_conv2d_NCHWc' in op.tag:
            conv_out = op.output(0)
            data = conv_out.op.input_tensors[0]
            kernel = conv_out.op.input_tensors[1]
            _schedule_depthwise_conv2d_NCHWc_impl(s, cfg, data, kernel, conv_out, outs[0])
        scheduled_ops.append(op)
    traverse(outs[0].op)
    return s

def _schedule_depthwise_conv2d_NCHWc_impl(s, cfg, data, kernel, conv_out, output):
    tile_ow = cfg["tile_ow"].size[-1]
    # schedule data
    A = data
    if isinstance(s[A].op, tvm.tensor.ComputeOp):
        batch, ic_chunk, ih, iw, ic_block = s[A].op.axis
        p = s[A].fuse(ic_chunk, ih)
        s[A].parallel(p)

    C, O = conv_out, output
    CC = s.cache_write(C, 'global')

    _, ic_chunk, oh, ow, ic_block = s[C].op.axis
    ow_chunk, ow_block = s[C].split(ow, factor=tile_ow)
    s[C].reorder(ic_chunk, oh, ow_chunk, ow_block, ic_block)
    parallel_axis = s[C].fuse(ic_chunk, oh)
    s[C].parallel(parallel_axis)
    s[CC].compute_at(s[C], ow_chunk)

    _, ic_chunk, oh, ow, ic_block = s[CC].op.axis
    kh, kw = s[CC].op.reduce_axis
    ow_chunk, ow_block = s[CC].split(ow, factor=tile_ow)
    s[CC].reorder(ic_chunk, oh, kh, kw, ow_block, ic_block)
    s[CC].vectorize(ic_block)
    s[CC].unroll(ow_block)

    if C != O:
        batch, oc_chunk, oh, ow, oc_block = s[O].op.axis
        ow_chunk, ow_block = s[O].split(ow, factor=tile_ow)
        s[O].reorder(oc_chunk, oh, ow_chunk, ow_block, oc_block)
        parallel_axis = s[O].fuse(oc_chunk, oh)
        s[C].compute_at(s[O], parallel_axis)
        s[O].vectorize(oc_block)
        s[O].parallel(parallel_axis)
    return s


@autotvm.task.register("topi_x86_depthwise_conv2d_NCHWc_from_nchw")
def _topi_nn_depthwise_conv2d_NCHWc(*args, **kwargs):
    assert not kwargs, "Do not support kwargs in template function call"
    data, kernel, strides, padding, dilation, dtype = deserialize_args(args)

    batch, in_channel, height, width = get_const_tuple(data.shape)
    filter_channel, channel_multiplier, kh, kw = get_const_tuple(kernel.shape)
    pt, pl, pb, pr = get_pad_tuple(padding, kernel)
    sh, sw = strides if isinstance(strides, (tuple, list)) else (strides, strides)
    out_height = (height - kh + pt + pb) // sh + 1
    out_width = (width - kw + pl + pr) // sw + 1
    out_channel = filter_channel * channel_multiplier

    # get config here
    cfg = get_config()
    cfg.define_split("tile_ic", in_channel, num_outputs=2)
    cfg.define_split("tile_oc", out_channel, num_outputs=2)
    cfg.define_split("tile_ow", out_width, num_outputs=2, filter=lambda y: y.size[-1] <= 64)

    # change shape with the value in config
    ic_bn, oc_bn = cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1]
    new_data_shape = (batch, in_channel // ic_bn, height, width, ic_bn)
    new_kernel_shape = (out_channel // oc_bn, 1, kh, kw, 1, oc_bn)
    new_data = tvm.placeholder(new_data_shape, data.dtype)
    new_kernel = tvm.placeholder(new_kernel_shape, kernel.dtype)

    data_layout = "NCHW%dc" % ic_bn
    out_layout = "NCHW%dc" % oc_bn

    C = _depthwise_conv2d_NCHWc_cpu(cfg, new_data, new_kernel, strides, padding, dilation,
                                    data_layout, out_layout, dtype)
    s = schedule_depthwise_conv2d_NCHWc(cfg, [C])
    return s, [new_data, new_kernel, C]

@depthwise_conv2d_infer_layout.register("cpu")
def _depthwise_conv2d_infer_layout(workload, cfg):
    _, data, kernel, strides, padding, dilation, dtype = workload
    batch_size, in_channel, in_height, in_width = data[:-1]
    filter_channel, channel_multiplier, k_height, k_width = kernel[:-1]
    out_channel = filter_channel * channel_multiplier
    out_height = (in_height + 2 * padding[0] - k_height) // strides[0] + 1
    out_width = (in_width + 2 * padding[1] - k_width) // strides[1] + 1
    tile_ic, tile_oc = cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1]
    in_shape = (batch_size, in_channel // tile_ic, in_height, in_width, tile_ic)
    in_layout = "NCHW%dc" % tile_ic
    out_shape = (batch_size, out_channel // tile_oc, out_height, out_width, tile_oc)
    out_layout = "NCHW%dc" % tile_oc
    return ((in_shape, in_layout),), ((out_shape, out_layout),)

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements.  See the NOTICE file # distributed with this work for additional information # regarding copyright ownership.  The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License.  You may obtain a copy of the License at # #   http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied.  See the License for the # specific language governing permissions and limitations # under the License. # pylint: disable=invalid-name,unused-variable,unused-argument,no-member Depthwise Conv2D schedule on OptSchedule Get default schedule for the workload     Parameters     ----------     cfg : tvm.autotvm.task.space.FallbackConfigEntity         Fallback config to be updated     wkl : topi.nn.depthwise_conv2d.Workload         Convolution workload # get workload and related schedule config # padding stage # depthconv stage CPU schedule for depthwise conv2d in NCHW[x]c layout Traverse operators from computation graph # inline all one-to-one-mapping operators except the last stage (output) # schedule data # get config here # change shape with the value in config