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"""Tuning a single group conv2d operator"""

from collections import namedtuple
import logging
import os

import tvm
from tvm import te
from tvm import autotvm
import topi
import vta
import vta.testing

env = vta.get_env()

Workload = namedtuple("GroupConv2DWorkload",
                      ['batch', 'height', 'width', 'in_filter', 'out_filter', 'groups',
                       'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride'])

# Mobilenet (grouped variant) workloads
mobilenet_wkls = [
    ('mobilenet.D1', Workload(env.BATCH, 112, 112,   32,   32,  2, 3, 3, 1, 1, 1, 1)),
    ('mobilenet.D2', Workload(env.BATCH, 112, 112,   64,   64,  4, 3, 3, 1, 1, 2, 2)),
    ('mobilenet.D3', Workload(env.BATCH,  56,  56,  128,  128,  8, 3, 3, 1, 1, 1, 1)),
    ('mobilenet.D4', Workload(env.BATCH,  56,  56,  128,  128,  8, 3, 3, 1, 1, 2, 2)),
    ('mobilenet.D5', Workload(env.BATCH,  28,  28,  256,  256, 16, 3, 3, 1, 1, 1, 1)),
    ('mobilenet.D6', Workload(env.BATCH,  28,  28,  256,  256, 16, 3, 3, 1, 1, 2, 2)),
    ('mobilenet.D7', Workload(env.BATCH,  14,  14,  512,  512, 32, 3, 3, 1, 1, 1, 1)),
    ('mobilenet.D8', Workload(env.BATCH,  14,  14,  512,  512, 32, 3, 3, 1, 1, 2, 2)),
    ('mobilenet.D9', Workload(env.BATCH,   7,  7,  1024, 1024, 64, 3, 3, 1, 1, 1, 1)),
]

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

def group_conv2d(N, CI, H, W, CO, KH, KW, strides, padding, dilation, group):

    CI_G = CI // groups
    data_shape = (N//env.BATCH, CI//env.BLOCK_IN, H, W, env.BATCH, env.BLOCK_IN)
    kernel_shape = (CO//env.BLOCK_OUT, CI_G//env.BLOCK_IN, KH, KW, env.BLOCK_OUT, env.BLOCK_IN)
    bias_shape = (N//env.BATCH, CO//env.BLOCK_OUT, 1, 1, env.BATCH, env.BLOCK_OUT)

    data = te.placeholder(data_shape, name="data", dtype=env.inp_dtype)
    kernel = te.placeholder(kernel_shape, name="kernel", dtype=env.wgt_dtype)
    bias = te.placeholder(bias_shape, name="bias", dtype=env.acc_dtype)

    with tvm.target.vta():
        res = topi.nn.group_conv2d_nchw(
            data,
            kernel,
            strides,
            padding,
            dilation,
            groups,
            env.acc_dtype)
        res = topi.right_shift(res, env.WGT_WIDTH)
        res = topi.add(res, bias)
        res = my_clip(res, 0, (1 << env.OUT_WIDTH - 1) - 1)
        res = topi.cast(res, env.out_dtype)

    if tvm.target.Target.current().device_name == 'vta':
        s = topi.generic.schedule_group_conv2d_nchw([res])
    else:
        s = te.create_schedule([res.op])

    return s, [data, kernel, bias, res]

if __name__ == '__main__':

    # Logging config (for printing tuning log to the screen)
    logging.basicConfig()

    # Tuning log files
    log_file = "%s.group_conv2d.log" % (env.TARGET)
    # create tmp log file
    tmp_log_file = log_file + ".tmp"
    if os.path.exists(log_file):
        os.remove(log_file)

    # Get tracker info from env
    tracker_host = os.environ.get("TVM_TRACKER_HOST", None)
    tracker_port = os.environ.get("TVM_TRACKER_PORT", None)
    if not tracker_host or not tracker_port:
        print("Set your AutoTVM tracker node host and port variables to run the autotuner")
        exit()

    for idx, (wl_name, wl) in enumerate(mobilenet_wkls):
        prefix = "[Task %2d/%2d] " % (idx, len(mobilenet_wkls))

        # Read in workload parameters
        N = wl.batch
        CI = wl.in_filter
        H = wl.height
        W = wl.width
        CO = wl.out_filter
        KH = wl.hkernel
        KW = wl.wkernel
        strides = (wl.hstride, wl.wstride)
        padding = (wl.hpad, wl.wpad)
        dilation = (1, 1)
        groups = wl.groups

        # Create task
        task = autotvm.task.create(
                group_conv2d,
                args=(N, CI, H, W, CO, KH, KW, strides, padding, dilation, groups),
                target=tvm.target.vta(),
                target_host=env.target_host,
                template_key='direct')
        print(task.config_space)

        # Tune
        measure_option = autotvm.measure_option(
                builder=autotvm.LocalBuilder(),
                runner=autotvm.RPCRunner(
                    env.TARGET, host=tracker_host, port=int(tracker_port),
                    number=5, timeout=60,
                    check_correctness=True))

        # Run Tuner
        tuner = autotvm.tuner.RandomTuner(task)
        tuner.tune(
            n_trial=len(task.config_space),
            early_stopping=None,
            measure_option=measure_option,
            callbacks=[
                    autotvm.callback.progress_bar(len(task.config_space), prefix=prefix),
                    autotvm.callback.log_to_file(tmp_log_file)])

    # Pick best records to a cache file
    autotvm.record.pick_best(tmp_log_file, log_file)
    os.remove(tmp_log_file)
