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import NvRules
from RequestedMetrics import MetricRequest, RequestedMetricsParser

requested_metrics = [
    MetricRequest("sm__maximum_warps_per_active_cycle_pct", "theoretical_occupancy"),
    MetricRequest("sm__warps_active.avg.pct_of_peak_sustained_active", "achieved_occupancy"),
]


def get_identifier():
    return "AchievedOccupancy"

def get_name():
    return "Achieved Occupancy"

def get_description():
    return "Analysis of the Achieved Occupancy"

def get_section_identifier():
    return "Occupancy"

def get_parent_rules_identifiers():
    return ["IssueSlotUtilization"]


def get_estimated_speedup(parent_weights, metrics):
    """Estimate potential speedup from increasing the achieved occupancy.

    The performance improvement is approximated as relative proportion of the difference
    of theoretical and achieved occupancy.
    In case it's available, the performance improvement can be upper-bounded by the
    speedup estimate of IssueSlotUtilization.

    """
    theoretical_occupancy = metrics["theoretical_occupancy"].value()
    achieved_occupancy = metrics["achieved_occupancy"].value()
    improvement_local = (theoretical_occupancy - achieved_occupancy) / theoretical_occupancy

    parent_speedup_name = "issue_slot_util_speedup_normalized"
    if parent_speedup_name in parent_weights:
        speedup_type = NvRules.IFrontend.SpeedupType_GLOBAL
        improvement_global = min(
            parent_weights[parent_speedup_name], improvement_local
        )
        improvement_percent = improvement_global * 100
    else:
        speedup_type = NvRules.IFrontend.SpeedupType_LOCAL
        improvement_percent = improvement_local * 100

    return speedup_type, improvement_percent


def apply(handle):
    ctx = NvRules.get_context(handle)
    action = ctx.range_by_idx(0).action_by_idx(0)
    fe = ctx.frontend()
    metrics = RequestedMetricsParser(handle, action).parse(requested_metrics)
    parent_weights = fe.receive_dict_from_parent("IssueSlotUtilization")

    theoretical_occupancy = metrics["theoretical_occupancy"].value()
    achieved_occupancy = metrics["achieved_occupancy"].value()

    occupancy_difference = theoretical_occupancy - achieved_occupancy
    occupancy_difference_threshold = 10

    if occupancy_difference > occupancy_difference_threshold:
        message = "The difference between calculated theoretical ({:.1f}%) and measured achieved occupancy ({:.1f}%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution.".format(theoretical_occupancy, achieved_occupancy)
        message += " Load imbalances can occur between warps within a block as well as across blocks of the same kernel."
        message += " See the @url:CUDA Best Practices Guide:https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy@ for more details on optimizing occupancy."

        msg_id = fe.message(NvRules.IFrontend.MsgType_MSG_OPTIMIZATION, message)

        speedup_type, speedup_value = get_estimated_speedup(parent_weights, metrics)
        fe.speedup(msg_id, speedup_type, speedup_value)

        fe.focus_metric(msg_id, metrics["achieved_occupancy"].name(), achieved_occupancy, NvRules.IFrontend.Severity_SEVERITY_DEFAULT, "Increase the achieved occupancy towards the theoretical limit ({:.1f}%)".format(theoretical_occupancy))
