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import NvRules
from RequestedMetrics import MetricRequest, RequestedMetricsParser

requested_metrics = [
    MetricRequest("sm__instruction_throughput.avg.pct_of_peak_sustained_elapsed", "instruction_throughput"),
    MetricRequest("sm__inst_issued.avg.pct_of_peak_sustained_elapsed", "inst_issued_avg"),
    MetricRequest("sm__inst_issued.max.pct_of_peak_sustained_elapsed", "inst_issued_max"),
]


def get_identifier():
    return "SlowPipeLimiter"

def get_name():
    return "Slow Pipe Limiter"

def get_description():
    return "Slow pipe limiting compute utilization"

def get_section_identifier():
    return "ComputeWorkloadAnalysis"


def get_parent_rules_identifiers():
    return ["Compute"]


def get_estimated_speedup(parent_weights, metrics):
    """Estimate potential speedup from decreasing the usage of slow pipes.

    The performance improvement is approximated as the relative part of instructions
    not issued.
    In case the compute (SM) throughput was collected,
    the above approximation can be improved by weighing it with the achieved throughput.

    """
    inst_issued_avg = metrics["inst_issued_avg"].value()
    inst_issued_max = metrics["inst_issued_max"].value()
    improvement_local = (inst_issued_max - inst_issued_avg) / inst_issued_max

    compute_throughput_name = "compute_throughput_normalized"
    if compute_throughput_name in parent_weights:
        speedup_type = NvRules.IFrontend.SpeedupType_GLOBAL
        improvement_percent = improvement_local * parent_weights[compute_throughput_name] * 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("Compute")

    sm_busy = metrics["instruction_throughput"].value()
    inst_issued_avg = metrics["inst_issued_avg"].value()
    inst_issued_max = metrics["inst_issued_max"].value()

    no_bound_threshold = 80
    issued_avg_threshold = 20
    diff_threshold = 25

    doc_msg = " See the @url:Profiling Guide:https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-decoder@ for the workloads handled by each pipeline."

    pipe_diff = inst_issued_max - inst_issued_avg
    if sm_busy >= no_bound_threshold and inst_issued_avg < issued_avg_threshold and pipe_diff > diff_threshold:
        msg_id = fe.message(NvRules.MsgType.OPTIMIZATION,
        "It is possible that a slow pipeline is preventing better workload performance."\
        " The average pipeline utilization of {:.1f}% is {:.1f}% lower than the maximum utilization of {:.1f}%."\
        " Try moving compute to other pipelines, e.g. from fp64 to fp32 or int."\
        "{}".format(inst_issued_avg, pipe_diff, inst_issued_max, doc_msg), "Slow Pipeline")

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

        fe.focus_metric(msg_id, metrics["instruction_throughput"].name(), sm_busy, NvRules.IFrontend.Severity_SEVERITY_LOW,
            "The higher the instruction throughput the more likely is the impact of a slow pipeline")
        fe.focus_metric(msg_id, metrics["inst_issued_avg"].name(), inst_issued_avg, NvRules.IFrontend.Severity_SEVERITY_HIGH,
            "Increase the average pipeline utilization towards the maximum utilization ({:.1f}%)".format(inst_issued_max))
