import argparse
import json
import os

import numpy
import psutil
from onnx import TensorProto

"""
This profiler tool could run a transformer model and print out the kernel time spent on each Node of the model.
Example of profiling of longformer model:
    python profiler.py --model longformer-base-4096_fp32.onnx --batch_size 1 --sequence_length 4096 --global_length 8 --samples 1000 --thread_num 8 --dummy_inputs longformer --use_gpu
Example of importing profile result file from onnxruntime_perf_test:
    python profiler.py --input profile_2021-10-25_12-02-41.json
"""

NODES_TYPE_CONTAINING_SUBGRAPH = ["Scan", "Loop", "If"]


def parse_arguments(argv=None):
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "-i",
        "--input",
        required=False,
        type=str,
        help="Set the input file for reading the profile results",
    )

    parser.add_argument(
        "-m",
        "--model",
        required=False,
        type=str,
        help="onnx model path to run profiling. Required when --input is not specified.",
    )

    parser.add_argument(
        "-b",
        "--batch_size",
        required=False,
        type=int,
        default=1,
        help="batch size of input",
    )

    parser.add_argument(
        "-s",
        "--sequence_length",
        required=False,
        type=int,
        default=32,
        help="sequence length of input",
    )

    parser.add_argument(
        "--past_sequence_length",
        required=False,
        type=int,
        default=1,
        help="past sequence length for gpt2",
    )

    parser.add_argument(
        "--global_length",
        required=False,
        type=int,
        default=1,
        help="number of global tokens for longformer",
    )

    parser.add_argument(
        "--samples",
        required=False,
        type=int,
        default=1000,
        help="number of samples to test. Set it large enough to reduce the variance of performance result.",
    )

    parser.add_argument(
        "--threshold",
        required=False,
        type=float,
        default=0.01,
        help="Threshold of run time ratio among all nodes. Nodes with larger ratio will show in top expensive nodes.",
    )

    parser.add_argument(
        "--thread_num",
        required=False,
        type=int,
        default=-1,
        help="number of threads to use",
    )

    parser.add_argument(
        "--input_ids_name",
        required=False,
        type=str,
        default=None,
        help="input name for input IDs, for bert",
    )
    parser.add_argument(
        "--segment_ids_name",
        required=False,
        type=str,
        default=None,
        help="input name for segment IDs, for bert",
    )
    parser.add_argument(
        "--input_mask_name",
        required=False,
        type=str,
        default=None,
        help="input name for attention mask, for bert",
    )

    parser.add_argument(
        "--dummy_inputs",
        required=False,
        default="default",
        choices=["bert", "gpt2", "longformer", "default"],
        help="Type of model inputs. The default will create dummy inputs with ones.",
    )

    parser.add_argument("-g", "--use_gpu", required=False, action="store_true", help="use GPU")
    parser.set_defaults(use_gpu=False)

    parser.add_argument(
        "--provider",
        required=False,
        type=str,
        default="cuda",
        help="Execution provider to use",
    )

    parser.add_argument(
        "--basic_optimization",
        required=False,
        action="store_true",
        help="Enable only basic graph optimizations. By default, all optimizations are enabled in OnnxRuntime",
    )
    parser.set_defaults(basic_optimization=False)

    parser.add_argument(
        "--kernel_time_only",
        required=False,
        action="store_true",
        help="Only include the kernel time and no fence time",
    )
    parser.set_defaults(kernel_time_only=False)

    parser.add_argument("-v", "--verbose", required=False, action="store_true")
    parser.set_defaults(verbose=False)

    return parser.parse_args(argv)


def run_profile(onnx_model_path, use_gpu, provider, basic_optimization, thread_num, all_inputs):
    from benchmark_helper import create_onnxruntime_session

    session = create_onnxruntime_session(
        onnx_model_path,
        use_gpu,
        provider,
        enable_all_optimization=not basic_optimization,
        num_threads=thread_num,
        enable_profiling=True,
    )

    for inputs in all_inputs:
        _ = session.run(None, inputs)

    profile_file = session.end_profiling()
    return profile_file


def load_profile_json(profile_file):
    print(f"loading profile output {profile_file} ...")

    with open(profile_file, "r") as opened_file:
        sess_time = json.load(opened_file)

    assert isinstance(sess_time, list)
    return sess_time


def parse_kernel_results(sess_time, threshold=0):
    """Parse profile data and output nodes in two sections - nodes in the original order, and top expensive nodes.

    Args:
        sess_time (List[Dict]): profile data
        kernel_time_only (bool, optional): Only include items for kernel time. Defaults to False.
        threshold (int, optional): Minimum ratio of duration among all. Defaults to 0.

    Returns:
        List[str]: lines of string for output.
    """
    kernel_name_to_op_name = {}
    kernel_time = {}
    kernel_freq = {}
    total = 0
    session_init = False
    for item in sess_time:
        # Skip all MemcpyHostToDevice before session_initialization
        if item["cat"] == "Session" and item["name"] == "session_initialization":
            session_init = True
        if not session_init:
            continue

        if item["cat"] == "Kernel" and "dur" in item and "args" in item and "op_name" in item["args"]:
            kernel_name = item["name"]

            op_name = item["args"]["op_name"]
            if op_name in NODES_TYPE_CONTAINING_SUBGRAPH:
                continue

            # Handle MemcpyHostToDevice and MemcpyDeviceToHost here
            if not op_name:
                op_name = f"({kernel_name})"

            if kernel_name in kernel_time:
                kernel_time[kernel_name] += item["dur"]
                kernel_freq[kernel_name] += 1
            else:
                kernel_time[kernel_name] = item["dur"]
                kernel_freq[kernel_name] = 1
                kernel_name_to_op_name[kernel_name] = op_name

            total += item["dur"]

    if not kernel_time:
        return ["No kernel record found!"]

    # Output items with run time ratio > thresholds, and sorted by duration in the descending order.
    lines = []
    lines.append(f"\nTop expensive kernels with Time% >= {threshold*100:.2f}:")
    lines.append("-" * 64)
    lines.append("Total(μs)\tTime%\tCalls\tAvg(μs)\tKernel")
    for kernel_name, duration in sorted(kernel_time.items(), key=lambda x: x[1], reverse=True):
        ratio = duration / total
        if ratio < threshold:
            continue

        calls = kernel_freq[kernel_name]
        avg_time = duration / float(calls)
        lines.append(f"{duration:10d}\t{ratio * 100.0:5.2f}\t{calls:5d}\t{avg_time:8.1f}\t{kernel_name}")

    # Group by operator
    op_time = {}
    for kernel_name, op_name in kernel_name_to_op_name.items():
        duration = kernel_time[kernel_name]
        if op_name in op_time:
            op_time[op_name] += duration
        else:
            op_time[op_name] = duration

    lines.append(f"\nGroup kernel time by operator:")
    lines.append("-" * 64)
    lines.append("Total(μs)\tTime%\tOperator")
    for op_name, duration in sorted(op_time.items(), key=lambda x: x[1], reverse=True):
        ratio = duration / total
        lines.append(f"{duration:10d}\t{ratio * 100.0:5.2f}\t{op_name}")

    return lines


def parse_node_results(sess_time, kernel_time_only=False, threshold=0):
    """Parse profile data and output nodes in two sections - nodes in the original order, and top expensive nodes.

    Args:
        sess_time (List[Dict]): profile data
        kernel_time_only (bool, optional): Only include items for kernel time. Defaults to False.
        threshold (int, optional): Minimum ratio of duration among all. Defaults to 0.

    Returns:
        List[str]: lines of string for output.
    """
    node_name_list = []
    node_time = {}
    node_freq = {}
    node_provider = {}
    total = 0
    for item in sess_time:
        if item["cat"] == "Node" and "dur" in item and "args" in item and "op_name" in item["args"]:
            node_name = (
                item["name"].replace("_kernel_time", "").replace("_fence_before", "").replace("_fence_after", "")
            )

            if "provider" in item["args"]:
                if item["args"]["provider"] == "CPUExecutionProvider":
                    device = "CPU"
                elif item["args"]["provider"] == "CUDAExecutionProvider":
                    device = "CUDA"
                elif item["args"]["provider"] == "DmlExecutionProvider":
                    device = "DML"

                if node_name not in node_provider:
                    node_provider[node_name] = device
                else:
                    assert node_provider[node_name] == device
            elif kernel_time_only:
                continue

            op_name = item["args"]["op_name"]
            if op_name in NODES_TYPE_CONTAINING_SUBGRAPH:
                continue

            if node_name in node_time:
                node_time[node_name] += item["dur"]
                node_freq[node_name] += 1
            else:
                node_time[node_name] = item["dur"]
                node_freq[node_name] = 1
                node_name_list.append(node_name)

            total += item["dur"]

    # Output items in the original order.
    lines = [
        "\nNodes in the original order:",
        "-" * 64,
        "Total(μs)\tTime%\tAcc %\tAvg(μs)\tCalls\tProvider\tNode",
    ]
    before_percentage = 0.0
    for node_name in node_name_list:
        duration = node_time[node_name]
        calls = node_freq[node_name]
        avg_time = duration / float(calls)
        percentage = (duration / total) * 100.0
        provider = node_provider[node_name] if node_name in node_provider else ""
        before_percentage += percentage
        lines.append(
            f"{duration:10d}\t{percentage:5.2f}\t{before_percentage:5.2f}\t{avg_time:8.1f}\t{calls:5d}\t{provider:8s}\t{node_name}"
        )

    # Output items with run time ratio > thresholds, and sorted by duration in the descending order.
    lines.append(f"\nTop expensive nodes with Time% >= {threshold*100:.2f}:")
    lines.append("-" * 64)
    lines.append("Total(μs)\tTime%\tAvg(μs)\tCalls\tProvider\tNode")
    for node_name, duration in sorted(node_time.items(), key=lambda x: x[1], reverse=True):
        ratio = duration / total
        if ratio < threshold:
            continue

        calls = node_freq[node_name]
        avg_time = duration / float(calls)
        percentage = (duration / total) * 100.0
        provider = node_provider[node_name] if node_name in node_provider else ""
        lines.append(f"{duration:10d}\t{percentage:5.2f}\t{avg_time:8.1f}\t{calls:5d}\t{provider:8s}\t{node_name}")

    return lines


def group_node_results(sess_time, kernel_time_only, use_gpu):
    """Group results by operator name.

    Args:
        sess_time (List[Dict]): profile data
        kernel_time_only (bool): Only include items for kernel time.
        use_gpu (bool): GPU is used in profiling or not.

    Returns:
        List[str]: lines of string for output.
    """
    op_kernel_time = {}
    op_kernel_records = {}
    total_kernel_time = 0

    provider_op_kernel_time = {}
    provider_op_kernel_records = {}
    provider_kernel_time = {}

    op_fence_time = {}
    total_fence_time = 0

    provider_counter = {}
    for item in sess_time:
        if item["cat"] == "Node" and "dur" in item and "args" in item and "op_name" in item["args"]:
            op_name = item["args"]["op_name"]

            # TODO: shall we have a separated group for nodes with subgraph?
            if op_name in NODES_TYPE_CONTAINING_SUBGRAPH:
                continue

            if "provider" not in item["args"]:
                if "fence" in item["name"]:
                    if op_name in op_fence_time:
                        op_fence_time[op_name] += item["dur"]
                    else:
                        op_fence_time[op_name] = item["dur"]
                    total_fence_time += item["dur"]
                continue

            provider = item["args"]["provider"] if "provider" in item["args"] else ""
            if provider in provider_counter:
                provider_counter[provider] += 1
            else:
                provider_counter[provider] = 1

            key = f"{provider}:{op_name}"
            if key in provider_op_kernel_time:
                provider_op_kernel_time[key] += item["dur"]
                provider_op_kernel_records[key] += 1
            else:
                provider_op_kernel_time[key] = item["dur"]
                provider_op_kernel_records[key] = 1

            if provider in provider_kernel_time:
                provider_kernel_time[provider] += item["dur"]
            else:
                provider_kernel_time[provider] = item["dur"]

            if op_name in op_kernel_time:
                op_kernel_time[op_name] += item["dur"]
                op_kernel_records[op_name] += 1
            else:
                op_kernel_time[op_name] = item["dur"]
                op_kernel_records[op_name] = 1

            total_kernel_time += item["dur"]

    lines = ["", "Grouped by operator"]
    lines.append("-" * 64)
    lines.append("Total(μs)\tTime%\tKernel(μs)\tKernel%\tCalls\tAvgKernel(μs)\tFence(μs)\tOperator")
    for op_name, kernel_time in sorted(op_kernel_time.items(), key=lambda x: x[1], reverse=True):
        fence_time = op_fence_time[op_name] if op_name in op_fence_time else 0
        kernel_time_ratio = kernel_time / total_kernel_time
        total_time = kernel_time + fence_time
        time_ratio = total_time / (total_kernel_time + total_fence_time)
        kernel_calls = op_kernel_records[op_name]
        avg_kernel_time = kernel_time / kernel_calls
        lines.append(
            f"{total_time:10d}\t{time_ratio * 100.0:5.2f}\t{kernel_time:11d}\t{kernel_time_ratio * 100.0:5.2f}\t{kernel_calls:5d}\t{avg_kernel_time:14.1f}\t{fence_time:10d}\t{op_name}"
        )

    lines += ["", "Grouped by provider + operator"]
    lines.append("-" * 64)
    lines.append("Kernel(μs)\tProvider%\tCalls\tAvgKernel(μs)\tProvider\tOperator")
    for key, kernel_time in sorted(provider_op_kernel_time.items(), key=lambda x: x[1], reverse=True):
        parts = key.split(":")
        provider = parts[0]
        op_name = parts[1]
        short_ep = provider.replace("ExecutionProvider", "")
        calls = provider_op_kernel_records[key]
        avg_kernel_time = kernel_time / calls
        provider_time_ratio = kernel_time / provider_kernel_time[provider]
        lines.append(
            f"{kernel_time:10d}\t{provider_time_ratio * 100.0:9.2f}\t{calls:5d}\t{avg_kernel_time:14.1f}\t{short_ep:8s}\t{op_name}"
        )

    return lines


def get_dim_from_type_proto(dim):
    return getattr(dim, dim.WhichOneof("value")) if type(dim.WhichOneof("value")) == str else None


def get_shape_from_type_proto(type_proto):
    return [get_dim_from_type_proto(d) for d in type_proto.tensor_type.shape.dim]


def create_dummy_inputs(onnx_model, batch_size, sequence_length, samples):
    """Create dummy inputs for ONNX model.

    Args:
        onnx_model (OnnxModel): ONNX model
        batch_size (int): batch size
        sequence_length (int): sequence length
        samples (int): number of samples

    Returns:
        List[Dict]: list of inputs
    """
    dummy_inputs = {}
    for graph_input in onnx_model.get_graph_inputs_excluding_initializers():
        shape = get_shape_from_type_proto(graph_input.type)
        symbol_dims = []
        for i, dim in enumerate(shape):
            if isinstance(dim, str):
                symbol_dims.append(i)

        # allowed symbolic dimensions: batch_size and sequence_length
        if len(symbol_dims) > 2:
            return None
        if len(symbol_dims) > 0:
            shape[symbol_dims[0]] = batch_size
        if len(symbol_dims) > 1:
            shape[symbol_dims[1]] = sequence_length

        elem_type = graph_input.type.tensor_type.elem_type
        assert elem_type in [TensorProto.FLOAT, TensorProto.INT32, TensorProto.INT64]
        data_type = (
            numpy.float32
            if elem_type == TensorProto.FLOAT
            else (numpy.int64 if elem_type == TensorProto.INT64 else numpy.int32)
        )
        data = numpy.ones(shape, dtype=data_type)
        dummy_inputs[graph_input.name] = data

    all_inputs = [dummy_inputs for _ in range(samples)]
    return all_inputs


def create_bert_inputs(
    onnx_model,
    batch_size,
    sequence_length,
    samples,
    input_ids_name=None,
    segment_ids_name=None,
    input_mask_name=None,
):
    """Create dummy inputs for BERT model.

    Args:
        onnx_model (OnnxModel): ONNX model
        batch_size (int): batch size
        sequence_length (int): sequence length
        samples (int): number of samples
        input_ids_name (str, optional): Name of graph input for input IDs. Defaults to None.
        segment_ids_name (str, optional): Name of graph input for segment IDs. Defaults to None.
        input_mask_name (str, optional): Name of graph input for attention mask. Defaults to None.

    Returns:
        List[Dict]: list of inputs
    """
    from bert_test_data import find_bert_inputs, generate_test_data

    input_ids, segment_ids, input_mask = find_bert_inputs(onnx_model, input_ids_name, segment_ids_name, input_mask_name)
    all_inputs = generate_test_data(
        batch_size,
        sequence_length,
        test_cases=samples,
        seed=123,
        verbose=False,
        input_ids=input_ids,
        segment_ids=segment_ids,
        input_mask=input_mask,
        random_mask_length=False,
    )

    return all_inputs


def create_gpt2_inputs(onnx_model, batch_size, sequence_length, past_sequence_length, samples):
    """Create dummy inputs for GPT-2 model.

    Args:
        onnx_model (OnnxModel): ONNX model
        batch_size (int): batch size
        sequence_length (int): sequence length
        past_sequence_length (int): past sequence length
        samples (int): number of samples

    Raises:
        RuntimeError: symbolic is not supported. Use the tool convert_to_onnx.py to export ONNX model instead.

    Returns:
        List[Dict]: list of inputs
    """
    # The symbolic names shall be same as those used in Gpt2Helper.export_onnx(...) function.
    symbols = {
        "batch_size": batch_size,
        "seq_len": sequence_length,
        "past_seq_len": past_sequence_length,
        "total_seq_len": sequence_length + past_sequence_length,
    }

    dummy_inputs = {}
    for graph_input in onnx_model.get_graph_inputs_excluding_initializers():
        shape = get_shape_from_type_proto(graph_input.type)
        for i, dim in enumerate(shape):
            if isinstance(dim, str):
                if dim not in symbols.keys():
                    raise RuntimeError(f"symbol is not supported: {dim}")
                else:
                    shape[i] = symbols[dim]

        elem_type = graph_input.type.tensor_type.elem_type
        assert elem_type in [TensorProto.FLOAT, TensorProto.INT32, TensorProto.INT64]
        data_type = (
            numpy.float32
            if elem_type == TensorProto.FLOAT
            else (numpy.int64 if elem_type == TensorProto.INT64 else numpy.int32)
        )
        data = numpy.ones(shape, dtype=data_type)
        dummy_inputs[graph_input.name] = data

    all_inputs = [dummy_inputs for _ in range(samples)]
    return all_inputs


def create_longformer_inputs(onnx_model, batch_size, sequence_length, global_length, samples):
    """Create dummy inputs for Longformer model.

    Args:
        onnx_model (OnnxModel): ONNX model
        batch_size (int): batch size
        sequence_length (int): sequence length
        global_length (int): number of global tokens
        samples (int): number of samples

    Raises:
        RuntimeError: symbolic is not supported. Use the tool convert_longformer_to_onnx.py to export ONNX model instead.

    Returns:
        List[Dict]: list of inputs
    """
    symbols = {"batch_size": batch_size, "sequence_length": sequence_length}

    dummy_inputs = {}
    for graph_input in onnx_model.get_graph_inputs_excluding_initializers():
        shape = get_shape_from_type_proto(graph_input.type)
        for i, dim in enumerate(shape):
            if isinstance(dim, str):
                if dim not in symbols.keys():
                    raise RuntimeError(f"symbol is not supported: {dim}")
                else:
                    shape[i] = symbols[dim]

        elem_type = graph_input.type.tensor_type.elem_type
        assert elem_type in [TensorProto.FLOAT, TensorProto.INT32, TensorProto.INT64]
        data_type = (
            numpy.float32
            if elem_type == TensorProto.FLOAT
            else (numpy.int64 if elem_type == TensorProto.INT64 else numpy.int32)
        )

        if "global" in graph_input.name:
            data = numpy.zeros(shape, dtype=data_type)
            data[:, :global_length] = 1
        else:
            data = numpy.ones(shape, dtype=data_type)
        dummy_inputs[graph_input.name] = data

    all_inputs = [dummy_inputs for _ in range(samples)]
    return all_inputs


def process_results(profile_file, args):
    profile_records = load_profile_json(profile_file)

    lines = parse_kernel_results(profile_records, args.threshold)

    lines += parse_node_results(profile_records, args.kernel_time_only, args.threshold)

    lines += group_node_results(profile_records, args.kernel_time_only, args.use_gpu)

    return lines


def run(args):
    num_threads = args.thread_num if args.thread_num > 0 else psutil.cpu_count(logical=False)

    # Set OMP environment variable before importing onnxruntime. Needed for cpu only, and no impact for onnxruntime-gpu package.
    if "OMP_NUM_THREADS" not in os.environ:
        os.environ["OMP_NUM_THREADS"] = str(num_threads)

    from onnx import load
    from onnx_model import OnnxModel

    onnx_model = OnnxModel(load(args.model))

    all_inputs = None
    if args.dummy_inputs == "bert":
        all_inputs = create_bert_inputs(
            onnx_model,
            args.batch_size,
            args.sequence_length,
            args.samples,
            args.input_ids_name,
            args.segment_ids_name,
            args.input_mask_name,
        )
    elif args.dummy_inputs == "gpt2":
        all_inputs = create_gpt2_inputs(
            onnx_model,
            args.batch_size,
            args.sequence_length,
            args.past_sequence_length,
            args.samples,
        )
    elif args.dummy_inputs == "longformer":
        all_inputs = create_longformer_inputs(
            onnx_model,
            args.batch_size,
            args.sequence_length,
            args.global_length,
            args.samples,
        )
    else:  # default
        all_inputs = create_dummy_inputs(onnx_model, args.batch_size, args.sequence_length, args.samples)

    profile_file = run_profile(
        args.model,
        args.use_gpu,
        args.provider,
        args.basic_optimization,
        args.thread_num,
        all_inputs,
    )

    return profile_file


if __name__ == "__main__":
    arguments = parse_arguments()
    print("Arguments", arguments)

    from benchmark_helper import setup_logger

    setup_logger(arguments.verbose)

    if not arguments.input:
        assert arguments.model, "requires either --model to run profiling or --input to read profiling results"
        profile_file = run(arguments)
    else:
        profile_file = arguments.input

    results = process_results(profile_file, arguments)

    for line in results:
        print(line)

import argparse
import json
import os

import numpy
import psutil
from onnx import TensorProto

"""
This profiler tool could run a transformer model and print out the kernel time spent on each Node of the model.
Example of profiling of longformer model:
    python profiler.py --model longformer-base-4096_fp32.onnx --batch_size 1 --sequence_length 4096 --global_length 8 --samples 1000 --thread_num 8 --dummy_inputs longformer --use_gpu
Example of importing profile result file from onnxruntime_perf_test:
    python profiler.py --input profile_2021-10-25_12-02-41.json
"""

NODES_TYPE_CONTAINING_SUBGRAPH = ["Scan", "Loop", "If"]


def parse_arguments(argv=None):
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "-i",
        "--input",
        required=False,
        type=str,
        help="Set the input file for reading the profile results",
    )

    parser.add_argument(
        "-m",
        "--model",
        required=False,
        type=str,
        help="onnx model path to run profiling. Required when --input is not specified.",
    )

    parser.add_argument(
        "-b",
        "--batch_size",
        required=False,
        type=int,
        default=1,
        help="batch size of input",
    )

    parser.add_argument(
        "-s",
        "--sequence_length",
        required=False,
        type=int,
        default=32,
        help="sequence length of input",
    )

    parser.add_argument(
        "--past_sequence_length",
        required=False,
        type=int,
        default=1,
        help="past sequence length for gpt2",
    )

    parser.add_argument(
        "--global_length",
        required=False,
        type=int,
        default=1,
        help="number of global tokens for longformer",
    )

    parser.add_argument(
        "--samples",
        required=False,
        type=int,
        default=1000,
        help="number of samples to test. Set it large enough to reduce the variance of performance result.",
    )

    parser.add_argument(
        "--threshold",
        required=False,
        type=float,
        default=0.01,
        help="Threshold of run time ratio among all nodes. Nodes with larger ratio will show in top expensive nodes.",
    )

    parser.add_argument(
        "--thread_num",
        required=False,
        type=int,
        default=-1,
        help="number of threads to use",
    )

    parser.add_argument(
        "--input_ids_name",
        required=False,
        type=str,
        default=None,
        help="input name for input IDs, for bert",
    )
    parser.add_argument(
        "--segment_ids_name",
        required=False,
        type=str,
        default=None,
        help="input name for segment IDs, for bert",
    )
    parser.add_argument(
        "--input_mask_name",
        required=False,
        type=str,
        default=None,
        help="input name for attention mask, for bert",
    )

    parser.add_argument(
        "--dummy_inputs",
        required=False,
        default="default",
        choices=["bert", "gpt2", "longformer", "default"],
        help="Type of model inputs. The default will create dummy inputs with ones.",
    )

    parser.add_argument("-g", "--use_gpu", required=False, action="store_true", help="use GPU")
    parser.set_defaults(use_gpu=False)

    parser.add_argument(
        "--provider",
        required=False,
        type=str,
        default="cuda",
        help="Execution provider to use",
    )

    parser.add_argument(
        "--basic_optimization",
        required=False,
        action="store_true",
        help="Enable only basic graph optimizations. By default, all optimizations are enabled in OnnxRuntime",
    )
    parser.set_defaults(basic_optimization=False)

    parser.add_argument(
        "--kernel_time_only",
        required=False,
        action="store_true",
        help="Only include the kernel time and no fence time",
    )
    parser.set_defaults(kernel_time_only=False)

    parser.add_argument("-v", "--verbose", required=False, action="store_true")
    parser.set_defaults(verbose=False)

    return parser.parse_args(argv)


def run_profile(onnx_model_path, use_gpu, provider, basic_optimization, thread_num, all_inputs):
    from benchmark_helper import create_onnxruntime_session

    session = create_onnxruntime_session(
        onnx_model_path,
        use_gpu,
        provider,
        enable_all_optimization=not basic_optimization,
        num_threads=thread_num,
        enable_profiling=True,
    )

    for inputs in all_inputs:
        _ = session.run(None, inputs)

    profile_file = session.end_profiling()
    return profile_file


def load_profile_json(profile_file):
    print(f"loading profile output {profile_file} ...")

    with open(profile_file, "r") as opened_file:
        sess_time = json.load(opened_file)

    assert isinstance(sess_time, list)
    return sess_time


def parse_kernel_results(sess_time, threshold=0):
    """Parse profile data and output nodes in two sections - nodes in the original order, and top expensive nodes.

    Args:
        sess_time (List[Dict]): profile data
        kernel_time_only (bool, optional): Only include items for kernel time. Defaults to False.
        threshold (int, optional): Minimum ratio of duration among all. Defaults to 0.

    Returns:
        List[str]: lines of string for output.
    """
    kernel_name_to_op_name = {}
    kernel_time = {}
    kernel_freq = {}
    total = 0
    session_init = False
    for item in sess_time:
        # Skip all MemcpyHostToDevice before session_initialization
        if item["cat"] == "Session" and item["name"] == "session_initialization":
            session_init = True
        if not session_init:
            continue

        if item["cat"] == "Kernel" and "dur" in item and "args" in item and "op_name" in item["args"]:
            kernel_name = item["name"]

            op_name = item["args"]["op_name"]
            if op_name in NODES_TYPE_CONTAINING_SUBGRAPH:
                continue

            # Handle MemcpyHostToDevice and MemcpyDeviceToHost here
            if not op_name:
                op_name = f"({kernel_name})"

            if kernel_name in kernel_time:
                kernel_time[kernel_name] += item["dur"]
                kernel_freq[kernel_name] += 1
            else:
                kernel_time[kernel_name] = item["dur"]
                kernel_freq[kernel_name] = 1
                kernel_name_to_op_name[kernel_name] = op_name

            total += item["dur"]

    if not kernel_time:
        return ["No kernel record found!"]

    # Output items with run time ratio > thresholds, and sorted by duration in the descending order.
    lines = []
    lines.append(f"\nTop expensive kernels with Time% >= {threshold*100:.2f}:")
    lines.append("-" * 64)
    lines.append("Total(μs)\tTime%\tCalls\tAvg(μs)\tKernel")
    for kernel_name, duration in sorted(kernel_time.items(), key=lambda x: x[1], reverse=True):
        ratio = duration / total
        if ratio < threshold:
            continue

        calls = kernel_freq[kernel_name]
        avg_time = duration / float(calls)
        lines.append(f"{duration:10d}\t{ratio * 100.0:5.2f}\t{calls:5d}\t{avg_time:8.1f}\t{kernel_name}")

    # Group by operator
    op_time = {}
    for kernel_name, op_name in kernel_name_to_op_name.items():
        duration = kernel_time[kernel_name]
        if op_name in op_time:
            op_time[op_name] += duration
        else:
            op_time[op_name] = duration

    lines.append(f"\nGroup kernel time by operator:")
    lines.append("-" * 64)
    lines.append("Total(μs)\tTime%\tOperator")
    for op_name, duration in sorted(op_time.items(), key=lambda x: x[1], reverse=True):
        ratio = duration / total
        lines.append(f"{duration:10d}\t{ratio * 100.0:5.2f}\t{op_name}")

    return lines


def parse_node_results(sess_time, kernel_time_only=False, threshold=0):
    """Parse profile data and output nodes in two sections - nodes in the original order, and top expensive nodes.

    Args:
        sess_time (List[Dict]): profile data
        kernel_time_only (bool, optional): Only include items for kernel time. Defaults to False.
        threshold (int, optional): Minimum ratio of duration among all. Defaults to 0.

    Returns:
        List[str]: lines of string for output.
    """
    node_name_list = []
    node_time = {}
    node_freq = {}
    node_provider = {}
    total = 0
    for item in sess_time:
        if item["cat"] == "Node" and "dur" in item and "args" in item and "op_name" in item["args"]:
            node_name = (
                item["name"].replace("_kernel_time", "").replace("_fence_before", "").replace("_fence_after", "")
            )

            if "provider" in item["args"]:
                if item["args"]["provider"] == "CPUExecutionProvider":
                    device = "CPU"
                elif item["args"]["provider"] == "CUDAExecutionProvider":
                    device = "CUDA"
                elif item["args"]["provider"] == "DmlExecutionProvider":
                    device = "DML"

                if node_name not in node_provider:
                    node_provider[node_name] = device
                else:
                    assert node_provider[node_name] == device
            elif kernel_time_only:
                continue

            op_name = item["args"]["op_name"]
            if op_name in NODES_TYPE_CONTAINING_SUBGRAPH:
                continue

            if node_name in node_time:
                node_time[node_name] += item["dur"]
                node_freq[node_name] += 1
            else:
                node_time[node_name] = item["dur"]
                node_freq[node_name] = 1
                node_name_list.append(node_name)

            total += item["dur"]

    # Output items in the original order.
    lines = [
        "\nNodes in the original order:",
        "-" * 64,
        "Total(μs)\tTime%\tAcc %\tAvg(μs)\tCalls\tProvider\tNode",
    ]
    before_percentage = 0.0
    for node_name in node_name_list:
        duration = node_time[node_name]
        calls = node_freq[node_name]
        avg_time = duration / float(calls)
        percentage = (duration / total) * 100.0
        provider = node_provider[node_name] if node_name in node_provider else ""
        before_percentage += percentage
        lines.append(
            f"{duration:10d}\t{percentage:5.2f}\t{before_percentage:5.2f}\t{avg_time:8.1f}\t{calls:5d}\t{provider:8s}\t{node_name}"
        )

    # Output items with run time ratio > thresholds, and sorted by duration in the descending order.
    lines.append(f"\nTop expensive nodes with Time% >= {threshold*100:.2f}:")
    lines.append("-" * 64)
    lines.append("Total(μs)\tTime%\tAvg(μs)\tCalls\tProvider\tNode")
    for node_name, duration in sorted(node_time.items(), key=lambda x: x[1], reverse=True):
        ratio = duration / total
        if ratio < threshold:
            continue

        calls = node_freq[node_name]
        avg_time = duration / float(calls)
        percentage = (duration / total) * 100.0
        provider = node_provider[node_name] if node_name in node_provider else ""
        lines.append(f"{duration:10d}\t{percentage:5.2f}\t{avg_time:8.1f}\t{calls:5d}\t{provider:8s}\t{node_name}")

    return lines


def group_node_results(sess_time, kernel_time_only, use_gpu):
    """Group results by operator name.

    Args:
        sess_time (List[Dict]): profile data
        kernel_time_only (bool): Only include items for kernel time.
        use_gpu (bool): GPU is used in profiling or not.

    Returns:
        List[str]: lines of string for output.
    """
    op_kernel_time = {}
    op_kernel_records = {}
    total_kernel_time = 0

    provider_op_kernel_time = {}
    provider_op_kernel_records = {}
    provider_kernel_time = {}

    op_fence_time = {}
    total_fence_time = 0

    provider_counter = {}
    for item in sess_time:
        if item["cat"] == "Node" and "dur" in item and "args" in item and "op_name" in item["args"]:
            op_name = item["args"]["op_name"]

            # TODO: shall we have a separated group for nodes with subgraph?
            if op_name in NODES_TYPE_CONTAINING_SUBGRAPH:
                continue

            if "provider" not in item["args"]:
                if "fence" in item["name"]:
                    if op_name in op_fence_time:
                        op_fence_time[op_name] += item["dur"]
                    else:
                        op_fence_time[op_name] = item["dur"]
                    total_fence_time += item["dur"]
                continue

            provider = item["args"]["provider"] if "provider" in item["args"] else ""
            if provider in provider_counter:
                provider_counter[provider] += 1
            else:
                provider_counter[provider] = 1

            key = f"{provider}:{op_name}"
            if key in provider_op_kernel_time:
                provider_op_kernel_time[key] += item["dur"]
                provider_op_kernel_records[key] += 1
            else:
                provider_op_kernel_time[key] = item["dur"]
                provider_op_kernel_records[key] = 1

            if provider in provider_kernel_time:
                provider_kernel_time[provider] += item["dur"]
            else:
                provider_kernel_time[provider] = item["dur"]

            if op_name in op_kernel_time:
                op_kernel_time[op_name] += item["dur"]
                op_kernel_records[op_name] += 1
            else:
                op_kernel_time[op_name] = item["dur"]
                op_kernel_records[op_name] = 1

            total_kernel_time += item["dur"]

    lines = ["", "Grouped by operator"]
    lines.append("-" * 64)
    lines.append("Total(μs)\tTime%\tKernel(μs)\tKernel%\tCalls\tAvgKernel(μs)\tFence(μs)\tOperator")
    for op_name, kernel_time in sorted(op_kernel_time.items(), key=lambda x: x[1], reverse=True):
        fence_time = op_fence_time[op_name] if op_name in op_fence_time else 0
        kernel_time_ratio = kernel_time / total_kernel_time
        total_time = kernel_time + fence_time
        time_ratio = total_time / (total_kernel_time + total_fence_time)
        kernel_calls = op_kernel_records[op_name]
        avg_kernel_time = kernel_time / kernel_calls
        lines.append(
            f"{total_time:10d}\t{time_ratio * 100.0:5.2f}\t{kernel_time:11d}\t{kernel_time_ratio * 100.0:5.2f}\t{kernel_calls:5d}\t{avg_kernel_time:14.1f}\t{fence_time:10d}\t{op_name}"
        )

    lines += ["", "Grouped by provider + operator"]
    lines.append("-" * 64)
    lines.append("Kernel(μs)\tProvider%\tCalls\tAvgKernel(μs)\tProvider\tOperator")
    for key, kernel_time in sorted(provider_op_kernel_time.items(), key=lambda x: x[1], reverse=True):
        parts = key.split(":")
        provider = parts[0]
        op_name = parts[1]
        short_ep = provider.replace("ExecutionProvider", "")
        calls = provider_op_kernel_records[key]
        avg_kernel_time = kernel_time / calls
        provider_time_ratio = kernel_time / provider_kernel_time[provider]
        lines.append(
            f"{kernel_time:10d}\t{provider_time_ratio * 100.0:9.2f}\t{calls:5d}\t{avg_kernel_time:14.1f}\t{short_ep:8s}\t{op_name}"
        )

    return lines


def get_dim_from_type_proto(dim):
    return getattr(dim, dim.WhichOneof("value")) if type(dim.WhichOneof("value")) == str else None


def get_shape_from_type_proto(type_proto):
    return [get_dim_from_type_proto(d) for d in type_proto.tensor_type.shape.dim]


def create_dummy_inputs(onnx_model, batch_size, sequence_length, samples):
    """Create dummy inputs for ONNX model.

    Args:
        onnx_model (OnnxModel): ONNX model
        batch_size (int): batch size
        sequence_length (int): sequence length
        samples (int): number of samples

    Returns:
        List[Dict]: list of inputs
    """
    dummy_inputs = {}
    for graph_input in onnx_model.get_graph_inputs_excluding_initializers():
        shape = get_shape_from_type_proto(graph_input.type)
        symbol_dims = []
        for i, dim in enumerate(shape):
            if isinstance(dim, str):
                symbol_dims.append(i)

        # allowed symbolic dimensions: batch_size and sequence_length
        if len(symbol_dims) > 2:
            return None
        if len(symbol_dims) > 0:
            shape[symbol_dims[0]] = batch_size
        if len(symbol_dims) > 1:
            shape[symbol_dims[1]] = sequence_length

        elem_type = graph_input.type.tensor_type.elem_type
        assert elem_type in [TensorProto.FLOAT, TensorProto.INT32, TensorProto.INT64]
        data_type = (
            numpy.float32
            if elem_type == TensorProto.FLOAT
            else (numpy.int64 if elem_type == TensorProto.INT64 else numpy.int32)
        )
        data = numpy.ones(shape, dtype=data_type)
        dummy_inputs[graph_input.name] = data

    all_inputs = [dummy_inputs for _ in range(samples)]
    return all_inputs


def create_bert_inputs(
    onnx_model,
    batch_size,
    sequence_length,
    samples,
    input_ids_name=None,
    segment_ids_name=None,
    input_mask_name=None,
):
    """Create dummy inputs for BERT model.

    Args:
        onnx_model (OnnxModel): ONNX model
        batch_size (int): batch size
        sequence_length (int): sequence length
        samples (int): number of samples
        input_ids_name (str, optional): Name of graph input for input IDs. Defaults to None.
        segment_ids_name (str, optional): Name of graph input for segment IDs. Defaults to None.
        input_mask_name (str, optional): Name of graph input for attention mask. Defaults to None.

    Returns:
        List[Dict]: list of inputs
    """
    from bert_test_data import find_bert_inputs, generate_test_data

    input_ids, segment_ids, input_mask = find_bert_inputs(onnx_model, input_ids_name, segment_ids_name, input_mask_name)
    all_inputs = generate_test_data(
        batch_size,
        sequence_length,
        test_cases=samples,
        seed=123,
        verbose=False,
        input_ids=input_ids,
        segment_ids=segment_ids,
        input_mask=input_mask,
        random_mask_length=False,
    )

    return all_inputs


def create_gpt2_inputs(onnx_model, batch_size, sequence_length, past_sequence_length, samples):
    """Create dummy inputs for GPT-2 model.

    Args:
        onnx_model (OnnxModel): ONNX model
        batch_size (int): batch size
        sequence_length (int): sequence length
        past_sequence_length (int): past sequence length
        samples (int): number of samples

    Raises:
        RuntimeError: symbolic is not supported. Use the tool convert_to_onnx.py to export ONNX model instead.

    Returns:
        List[Dict]: list of inputs
    """
    # The symbolic names shall be same as those used in Gpt2Helper.export_onnx(...) function.
    symbols = {
        "batch_size": batch_size,
        "seq_len": sequence_length,
        "past_seq_len": past_sequence_length,
        "total_seq_len": sequence_length + past_sequence_length,
    }

    dummy_inputs = {}
    for graph_input in onnx_model.get_graph_inputs_excluding_initializers():
        shape = get_shape_from_type_proto(graph_input.type)
        for i, dim in enumerate(shape):
            if isinstance(dim, str):
                if dim not in symbols.keys():
                    raise RuntimeError(f"symbol is not supported: {dim}")
                else:
                    shape[i] = symbols[dim]

        elem_type = graph_input.type.tensor_type.elem_type
        assert elem_type in [TensorProto.FLOAT, TensorProto.INT32, TensorProto.INT64]
        data_type = (
            numpy.float32
            if elem_type == TensorProto.FLOAT
            else (numpy.int64 if elem_type == TensorProto.INT64 else numpy.int32)
        )
        data = numpy.ones(shape, dtype=data_type)
        dummy_inputs[graph_input.name] = data

    all_inputs = [dummy_inputs for _ in range(samples)]
    return all_inputs


def create_longformer_inputs(onnx_model, batch_size, sequence_length, global_length, samples):
    """Create dummy inputs for Longformer model.

    Args:
        onnx_model (OnnxModel): ONNX model
        batch_size (int): batch size
        sequence_length (int): sequence length
        global_length (int): number of global tokens
        samples (int): number of samples

    Raises:
        RuntimeError: symbolic is not supported. Use the tool convert_longformer_to_onnx.py to export ONNX model instead.

    Returns:
        List[Dict]: list of inputs
    """
    symbols = {"batch_size": batch_size, "sequence_length": sequence_length}

    dummy_inputs = {}
    for graph_input in onnx_model.get_graph_inputs_excluding_initializers():
        shape = get_shape_from_type_proto(graph_input.type)
        for i, dim in enumerate(shape):
            if isinstance(dim, str):
                if dim not in symbols.keys():
                    raise RuntimeError(f"symbol is not supported: {dim}")
                else:
                    shape[i] = symbols[dim]

        elem_type = graph_input.type.tensor_type.elem_type
        assert elem_type in [TensorProto.FLOAT, TensorProto.INT32, TensorProto.INT64]
        data_type = (
            numpy.float32
            if elem_type == TensorProto.FLOAT
            else (numpy.int64 if elem_type == TensorProto.INT64 else numpy.int32)
        )

        if "global" in graph_input.name:
            data = numpy.zeros(shape, dtype=data_type)
            data[:, :global_length] = 1
        else:
            data = numpy.ones(shape, dtype=data_type)
        dummy_inputs[graph_input.name] = data

    all_inputs = [dummy_inputs for _ in range(samples)]
    return all_inputs


def process_results(profile_file, args):
    profile_records = load_profile_json(profile_file)

    lines = parse_kernel_results(profile_records, args.threshold)

    lines += parse_node_results(profile_records, args.kernel_time_only, args.threshold)

    lines += group_node_results(profile_records, args.kernel_time_only, args.use_gpu)

    return lines


def run(args):
    num_threads = args.thread_num if args.thread_num > 0 else psutil.cpu_count(logical=False)

    # Set OMP environment variable before importing onnxruntime. Needed for cpu only, and no impact for onnxruntime-gpu package.
    if "OMP_NUM_THREADS" not in os.environ:
        os.environ["OMP_NUM_THREADS"] = str(num_threads)

    from onnx import load
    from onnx_model import OnnxModel

    onnx_model = OnnxModel(load(args.model))

    all_inputs = None
    if args.dummy_inputs == "bert":
        all_inputs = create_bert_inputs(
            onnx_model,
            args.batch_size,
            args.sequence_length,
            args.samples,
            args.input_ids_name,
            args.segment_ids_name,
            args.input_mask_name,
        )
    elif args.dummy_inputs == "gpt2":
        all_inputs = create_gpt2_inputs(
            onnx_model,
            args.batch_size,
            args.sequence_length,
            args.past_sequence_length,
            args.samples,
        )
    elif args.dummy_inputs == "longformer":
        all_inputs = create_longformer_inputs(
            onnx_model,
            args.batch_size,
            args.sequence_length,
            args.global_length,
            args.samples,
        )
    else:  # default
        all_inputs = create_dummy_inputs(onnx_model, args.batch_size, args.sequence_length, args.samples)

    profile_file = run_profile(
        args.model,
        args.use_gpu,
        args.provider,
        args.basic_optimization,
        args.thread_num,
        all_inputs,
    )

    return profile_file


if __name__ == "__main__":
    arguments = parse_arguments()
    print("Arguments", arguments)

    from benchmark_helper import setup_logger

    setup_logger(arguments.verbose)

    if not arguments.input:
        assert arguments.model, "requires either --model to run profiling or --input to read profiling results"
        profile_file = run(arguments)
    else:
        profile_file = arguments.input

    results = process_results(profile_file, arguments)

    for line in results:
        print(line)

This profiler tool could run a transformer model and print out the kernel time spent on each Node of the model. Example of profiling of longformer model:     python profiler.py --model longformer-base-4096_fp32.onnx --batch_size 1 --sequence_length 4096 --global_length 8 --samples 1000 --thread_num 8 --dummy_inputs longformer --use_gpu Example of importing profile result file from onnxruntime_perf_test:     python profiler.py --input profile_2021-10-25_12-02-41.json Parse profile data and output nodes in two sections - nodes in the original order, and top expensive nodes.      Args:         sess_time (List[Dict]): profile data         kernel_time_only (bool, optional): Only include items for kernel time. Defaults to False.         threshold (int, optional): Minimum ratio of duration among all. Defaults to 0.      Returns:         List[str]: lines of string for output. # Skip all MemcpyHostToDevice before session_initialization # Handle MemcpyHostToDevice and MemcpyDeviceToHost here # Output items with run time ratio > thresholds, and sorted by duration in the descending order. # Group by operator Parse profile data and output nodes in two sections - nodes in the original order, and top expensive nodes.      Args:         sess_time (List[Dict]): profile data         kernel_time_only (bool, optional): Only include items for kernel time. Defaults to False.         threshold (int, optional): Minimum ratio of duration among all. Defaults to 0.      Returns:         List[str]: lines of string for output. # Output items in the original order. # Output items with run time ratio > thresholds, and sorted by duration in the descending order. Group results by operator name.      Args:         sess_time (List[Dict]): profile data         kernel_time_only (bool): Only include items for kernel time.         use_gpu (bool): GPU is used in profiling or not.      Returns:         List[str]: lines of string for output. # TODO: shall we have a separated group for nodes with subgraph? Create dummy inputs for ONNX model.      Args:         onnx_model (OnnxModel): ONNX model         batch_size (int): batch size         sequence_length (int): sequence length         samples (int): number of samples      Returns:         List[Dict]: list of inputs # allowed symbolic dimensions: batch_size and sequence_length Create dummy inputs for BERT model.      Args:         onnx_model (OnnxModel): ONNX model         batch_size (int): batch size         sequence_length (int): sequence length         samples (int): number of samples         input_ids_name (str, optional): Name of graph input for input IDs. Defaults to None.         segment_ids_name (str, optional): Name of graph input for segment IDs. Defaults to None.         input_mask_name (str, optional): Name of graph input for attention mask. Defaults to None.      Returns:         List[Dict]: list of inputs Create dummy inputs for GPT-2 model.      Args:         onnx_model (OnnxModel): ONNX model         batch_size (int): batch size         sequence_length (int): sequence length         past_sequence_length (int): past sequence length         samples (int): number of samples      Raises:         RuntimeError: symbolic is not supported. Use the tool convert_to_onnx.py to export ONNX model instead.      Returns:         List[Dict]: list of inputs # The symbolic names shall be same as those used in Gpt2Helper.export_onnx(...) function. Create dummy inputs for Longformer model.      Args:         onnx_model (OnnxModel): ONNX model         batch_size (int): batch size         sequence_length (int): sequence length         global_length (int): number of global tokens         samples (int): number of samples      Raises:         RuntimeError: symbolic is not supported. Use the tool convert_longformer_to_onnx.py to export ONNX model instead.      Returns:         List[Dict]: list of inputs # Set OMP environment variable before importing onnxruntime. Needed for cpu only, and no impact for onnxruntime-gpu package. # default