cost_model.py

'''
Cost model.
'''
#import numpy as np
from operator import mul
from operator import add
from functools import reduce
import copy
import math

from . import loop_enum as le
from . import buffer_enum as be


def get_comp_cost(layer):
    '''
    Compute the total # of MAC computation, it is independent of other optimizations

    Also it is independent of input size and input/filter stride
    Total # of computation = OX*OY*IC*OC*ON*FX*FY
    '''
    cost = layer.wofm * layer.hofm * layer.nifm * layer.nofm \
           * layer.nimg * layer.wfil * layer.hfil
    return cost


def get_ideal_performance(layer, resource):
    '''
    Compute the ideal runtime in cycles by assuming 100% PE array utilization
    Ideal # of cycles = Total # of MAC computation / Total # of PEs

    #LMEI Need to be modified if later when adding precision-scalable PE.
    # of functional PE will change depending on different precision modes.
    '''
    total_comp = get_comp_cost(layer)
    number_pe = reduce(mul, resource.para_count_list, 1)
    runtime = math.ceil(total_comp *1.0 / number_pe)

    return runtime


def get_layer_size(layer):
    '''
    Get size of ifmap, ofmap, filter of the layer

    #LMEI ifmap_size should be able to calculate based on ofmap_size and input stride(IS) /filter stride(FS)
    IX = IS*(OX-1) + FS*(FX-1) + 1
    wifm = wistd*(wofm-1) + wfstd*(wfil-1) + 1
    '''

    ifmap_size = layer.wifm * layer.hifm * layer.nifm * layer.nimg
    ofmap_size = layer.wofm * layer.hofm * layer.nofm * layer.nimg
    flmap_size = layer.wfil * layer.hfil * layer.nifm * layer.nofm

    return [ifmap_size, ofmap_size, flmap_size]


def get_hinted_para(level, hint):
    '''
    Get the actual total spatial unrolling size from loop schedule
    '''
    assert hint

    hinted_para = 1
    for loop in range(le.NUM):
        if loop in hint:
            hinted_loop_para = hint[loop][level][2]
            hinted_para *= hinted_loop_para

    return hinted_para


def valid_dataflow(resource, hint):
    '''
    Check if the actual spatial unrolling size from loop schedule meets the HW utilization requirement
    by comparing it with real HW parallelism size * utilization threshold.
    '''
    num_levels = resource.buffer_levels()

    for level in range(num_levels):
        if resource.paras[level].count != 1 and \
            get_hinted_para(level, hint) < (resource.paras[level].count * resource.utilization_threshold):
            return False

    return True

def get_if_access(level, point, layer, mac_capacity = 1):
    '''
    Get per element # of access of Input at current level

    Not accurate because [FX, FY] is not totally irrelevant terms for ifmap..
    #LMEI Need to be modified by using the concept of the dataset.
    '''

    if level == 0 and mac_capacity == 0:
        return layer.wfil * layer.hfil * layer.nofm / (layer.wstd * layer.hstd)

    ex_order_index = min(point.loop_orders[le.OX][level],
        point.loop_orders[le.OY][level],
        point.loop_orders[le.IC][level],
        point.loop_orders[le.ON][level])

    fx_exclusive = point.loop_orders[le.FX][level] < ex_order_index
    fy_exclusive = point.loop_orders[le.FY][level] < ex_order_index
    oc_exclusive = point.loop_orders[le.OC][level] < ex_order_index

    fx_acc = reduce(mul, point.loop_blockings[le.FX][level+fx_exclusive:], 1)
    fy_acc = reduce(mul, point.loop_blockings[le.FY][level+fy_exclusive:], 1)
    oc_acc = reduce(mul, point.loop_blockings[le.OC][level+oc_exclusive:], 1)

    # No loop orders among unrolled loops, they have the same order
    fx_par = reduce(mul, point.loop_partitionings[le.FX][level:], 1)
    fy_par = reduce(mul, point.loop_partitionings[le.FY][level:], 1)
    oc_par = reduce(mul, point.loop_partitionings[le.OC][level:], 1)

    return fx_acc * fy_acc * oc_acc * fx_par * fy_par * oc_par / (layer.wstd * layer.hstd)


def get_of_access(level, point, layer, mac_capacity = 1):
    '''
    Get per element # of access of Output at current level

    For output:
    Relevant terms [OX, OY, OC, ON]
    irrelevant terms [FX, FY, IC]

    Calculating rule:
    At lowest mem level (directly talk to MAC), calculate per element access
    by timing all irrelevant terms [FX, FY, IC] together

    For the rest higher mem levels,
    firstly, check if there is stationary possibility
    (irrelevant loops for filter [FX, FY, IC] are at the innermost position of this level)
    if there is, exclude the irrelevant loop(s) from the current level's # of per element access computing
    because they have been taken into account in lower level's # of per element access computing

    secondly, calculate the current level's # of per element access
    by multiplying all the irrelevant terms from current level to the highest level
    including both temporal unrolling part and spatial unrolling part (parallelism).
    '''

    if level == 0 and mac_capacity == 0 :
        return layer.wfil * layer.hfil * layer.nifm

    ex_order_index = min(point.loop_orders[le.OX][level],
        point.loop_orders[le.OY][level],
        point.loop_orders[le.OC][level],
        point.loop_orders[le.ON][level])

    fx_exclusive = point.loop_orders[le.FX][level] < ex_order_index
    fy_exclusive = point.loop_orders[le.FY][level] < ex_order_index
    ic_exclusive = point.loop_orders[le.IC][level] < ex_order_index

    fx_acc = reduce(mul, point.loop_blockings[le.FX][level+fx_exclusive:], 1)
    fy_acc = reduce(mul, point.loop_blockings[le.FY][level+fy_exclusive:], 1)
    ic_acc = reduce(mul, point.loop_blockings[le.IC][level+ic_exclusive:], 1)

    fx_par = reduce(mul, point.loop_partitionings[le.FX][level:], 1)
    fy_par = reduce(mul, point.loop_partitionings[le.FY][level:], 1)
    ic_par = reduce(mul, point.loop_partitionings[le.IC][level:], 1)

    return fx_acc * fy_acc * ic_acc * fx_par * fy_par * ic_par


def get_fl_access(level, point, layer, mac_capacity = 1):
    '''
    Get per element # of access of Weight at current level

    For filter:
    Relevant terms [FX, FY, IC, OC]
    irrelevant terms [OX, OY, ON]

    Calculating rule:
    At lowest mem level (directly talk to MAC), calculate per element access
    by timing all irrelevant terms [OX, OY, ON] together

    For the rest higher mem levels,
    firstly, check if there is stationary possibility
    (irrelevant loops for filter [OX, OY, ON] are at the innermost position of this level)
    if there is, exclude the irrelevant loop(s) from the current level's # of per element access computing
    because they have been taken into account in lower level's # of per element access computing

    secondly, calculate the current level's # of per element access
    by multiplying all the irrelevant terms from current level to the highest level
    including both temporal unrolling part and spatial unrolling part (parallelism).
    '''

    if level == 0 and mac_capacity == 0:
        return layer.wofm * layer.hofm * layer.nimg

    ex_order_index = min(point.loop_orders[le.FX][level],
        point.loop_orders[le.FY][level],
        point.loop_orders[le.IC][level],
        point.loop_orders[le.OC][level])

    ox_exclusive = point.loop_orders[le.OX][level] < ex_order_index
    oy_exclusive = point.loop_orders[le.OY][level] < ex_order_index
    on_exclusive = point.loop_orders[le.ON][level] < ex_order_index

    ox_acc = reduce(mul, point.loop_blockings[le.OX][level+ox_exclusive:], 1)