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"""Testing topi gemm operator for VTA"""

import os
import json
from collections import namedtuple

import numpy as np

import tvm
from tvm import autotvm
from tvm.contrib import util
from tvm.contrib.pickle_memoize import memoize
import topi
import topi.testing
import vta
from vta import program_fpga, reconfig_runtime
import vta.testing
from vta.testing import simulator

# FIXME: we need a custom clip operator to circumvent a pattern detection limitation
@tvm.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.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 run_gemm(env, remote, target,
             batch_size, in_feat, out_feat,
             check_correctness=True, print_ir=True,
             samples=4):

    # Perform packing only if we are targeting the accelerator
    if "arm_cpu" in target.keys:
        data_pack = False
    elif "vta" in target.keys:
        data_pack = True

    # Derive shapes depending upon packing
    a_shape = (batch_size, in_feat)
    w_shape = (out_feat, in_feat)
    if data_pack:
        data_shape = (batch_size//env.BATCH, in_feat//env.BLOCK_IN,
                      env.BATCH, env.BLOCK_IN)
        kernel_shape = (out_feat//env.BLOCK_OUT, in_feat//env.BLOCK_IN,
                        env.BLOCK_OUT, env.BLOCK_IN)
        fcompute = vta.top.dense_packed
        fschedule = vta.top.schedule_dense_packed
    else:
        data_shape = a_shape
        kernel_shape = w_shape
        fcompute = topi.x86.dense_nopack
        fschedule = topi.x86.schedule_dense_nopack
    data = tvm.placeholder(data_shape, name="data", dtype=env.inp_dtype)
    kernel = tvm.placeholder(kernel_shape, name="kernel", dtype=env.wgt_dtype)

    # Define base computation schedule
    with target:
        res = fcompute(
            data, kernel, None, env.acc_dtype)
        res = topi.right_shift(res, 8)
        res = my_clip(res, 0, (1 << env.OUT_WIDTH - 1) - 1)
        res = topi.cast(res, env.out_dtype)
        # Derive base schedule
        s = fschedule([res])
        if print_ir:
            print(vta.lower(s, [data, kernel, res], simple_mode=True))

    # Derive number of ops
    num_ops = 2 * batch_size * in_feat * out_feat

    # @memoize("vta.tests.test_benchmark_topi.dense.verify")
    def get_ref_data():
        # derive min max for act, wgt types (max non inclusive)
        a_min, a_max = 0 - (1 << (env.INP_WIDTH - 1)), (1 << (env.INP_WIDTH - 1))
        w_min, w_max = 0 - (1 << (env.WGT_WIDTH - 1)), (1 << (env.WGT_WIDTH - 1))
        a_np = np.random.randint(a_min, a_max, size=a_shape).astype(data.dtype)
        w_np = np.random.randint(w_min, w_max, size=w_shape).astype(kernel.dtype)

        r_np = np.dot(a_np.astype(env.acc_dtype), w_np.T.astype(env.acc_dtype)).astype(env.acc_dtype)
        return a_np, w_np, r_np

    # Data in original format
    data_np, kernel_np, res_ref = get_ref_data()
    if data_pack:
        data_np = data_np.reshape(
            batch_size//env.BATCH, env.BATCH,
            in_feat//env.BLOCK_IN, env.BLOCK_IN).transpose((0, 2, 1, 3))
        kernel_np = kernel_np.reshape(
            out_feat//env.BLOCK_OUT, env.BLOCK_OUT,
            in_feat//env.BLOCK_IN, env.BLOCK_IN).transpose((0, 2, 1, 3))

    # Build
    if "vta" in target.keys:
        mod = vta.build(s, [data, kernel, res],
                        target=target,
                        target_host=env.target_host,
                        name="dense")
    else:
        mod = tvm.build(s, [data, kernel, res],
                        target=target,
                        target_host=env.target_host,
                        name="dense")
    temp = util.tempdir()
    mod.save(temp.relpath("dense.o"))
    remote.upload(temp.relpath("dense.o"))
    f = remote.load_module("dense.o")
    ctx = remote.context(str(target))

    res_np = np.zeros(topi.util.get_const_tuple(res.shape)).astype(res.dtype)
    data_arr = tvm.nd.array(data_np, ctx)
    kernel_arr = tvm.nd.array(kernel_np, ctx)
    res_arr = tvm.nd.array(res_np, ctx)
    time_f = f.time_evaluator("dense", ctx, number=samples)

    # In vta sim mode, collect simulator runtime statistics
    stats = {}
    cost = None
    if env.TARGET in ["sim", "tsim"]:
        # Check if we're in local RPC mode (allows us to rebuild the
        # runtime on the fly when varying the VTA designs)
        local_rpc = int(os.environ.get("VTA_LOCAL_SIM_RPC", "0"))
        if local_rpc:
            if env.TARGET == "sim":
                remote.get_function("vta.simulator.profiler_clear")()
            else:
                remote.get_function("vta.tsim.profiler_clear")()
            cost = time_f(data_arr, kernel_arr, res_arr)
            if env.TARGET == "sim":
                stats = json.loads(remote.get_function("vta.simulator.profiler_status")())
            else:
                stats = json.loads(remote.get_function("vta.tsim.profiler_status")())
        else:
            simulator.clear_stats()
            cost = time_f(data_arr, kernel_arr, res_arr)
            stats = simulator.stats()
    else:
        cost = time_f(data_arr, kernel_arr, res_arr)

    # Check correctness
    correct = False
    if check_correctness:
        res_orig = res_arr.asnumpy()
        if data_pack:
            res_orig = res_orig.reshape(batch_size, out_feat)
        res_ref = res_ref >> 8
        res_ref = np.clip(res_ref, 0, (1 << env.OUT_WIDTH - 1) - 1)
        res_ref = res_ref.astype(env.out_dtype)
        correct = np.allclose(res_orig, res_ref)

    gops = (num_ops / cost.mean) / float(10 ** 9)
    status = "PASSED" if correct else "FAILED"
    if "arm_cpu" in target.keys:
        device = "CPU"
    elif "vta" in target.keys:
        device = "VTA"
    print("%s DENSE TEST %s: Time cost = %g sec/op, %g GOPS" % (device, status, cost.mean, gops))

    return correct, cost, stats

def test_gemm(device="vta", batch=128, in_feat=128, out_feat=128):
    def _run(env, remote):
        if device == "vta":
            target = env.target
            if env.TARGET not in ["sim", "tsim"]:
                assert tvm.runtime.enabled("rpc")
                program_fpga(remote, bitstream=None)
                reconfig_runtime(remote)
        elif device == "arm_cpu":
            target = env.target_vta_cpu
        with autotvm.tophub.context(target): # load pre-tuned schedule parameters
            run_gemm(env, remote, target, batch, in_feat, out_feat)
    vta.testing.run(_run)

if __name__ == "__main__":
    test_gemm("vta", 16, 512, 1008)