MegEngine/imperative/python/megengine/utils/module_stats.py

import collections
from collections import namedtuple
from functools import partial
from typing import Iterable

import numpy as np
import tabulate

from .. import Tensor
from .. import functional as F
from .. import get_logger
from .. import module as M
from ..core.tensor.dtype import get_dtype_bit
from ..logger import MegEngineLogFormatter
from .module_utils import set_module_mode_safe

try:
    MegEngineLogFormatter.max_lines = float("inf")
except AttributeError as e:
    raise ValueError("set logger max lines failed")

logger = get_logger(__name__)
logger.setLevel("INFO")


_calc_flops_dict = {}
_calc_receptive_field_dict = {}


def _receptive_field_fallback(module, inputs, outputs):
    if not _receptive_field_enabled:
        return
    assert not hasattr(module, "_rf")
    assert not hasattr(module, "_stride")
    if len(inputs) == 0:
        # TODO: support other dimension
        module._rf = (1, 1)
        module._stride = (1, 1)
        return module._rf, module._stride
    rf, stride = preprocess_receptive_field(module, inputs, outputs)
    module._rf = rf
    module._stride = stride
    return rf, stride


# key tuple, impl_dict, fallback
_iter_list = [
    ("flops_num", _calc_flops_dict, None),
    (
        ("receptive_field", "stride"),
        _calc_receptive_field_dict,
        _receptive_field_fallback,
    ),
]

_receptive_field_enabled = False


def _register_dict(*modules, dict=None):
    def callback(impl):
        for module in modules:
            dict[module] = impl
        return impl

    return callback


def register_flops(*modules):
    return _register_dict(*modules, dict=_calc_flops_dict)


def register_receptive_field(*modules):
    return _register_dict(*modules, dict=_calc_receptive_field_dict)


def enable_receptive_field():
    global _receptive_field_enabled
    _receptive_field_enabled = True


def disable_receptive_field():
    global _receptive_field_enabled
    _receptive_field_enabled = False


@register_flops(M.Conv1d, M.Conv2d, M.Conv3d, M.LocalConv2d, M.DeformableConv2d)
def flops_convNd(module: M.Conv2d, inputs, outputs):
    bias = 1 if module.bias is not None else 0
    # N x Cout x H x W x  (Cin x Kw x Kh + bias)
    return np.prod(outputs[0].shape) * (
        float(module.in_channels // module.groups) * np.prod(module.kernel_size) + bias
    )


@register_flops(M.ConvTranspose2d)
def flops_convNdTranspose(module: M.Conv2d, inputs, outputs):
    bias = 1 if module.bias is not None else 0
    # N x Cout x H x W x  (Cin x Kw x Kh + bias)
    return (
        np.prod(inputs[0].shape)
        * (module.out_channels // module.groups * np.prod(module.kernel_size))
        + np.prod(outputs[0].shape) * bias
    )


@register_flops(
    M.batchnorm._BatchNorm, M.SyncBatchNorm, M.GroupNorm, M.LayerNorm, M.InstanceNorm,
)
def flops_norm(module: M.Linear, inputs, outputs):
    return np.prod(inputs[0].shape) * 7


@register_flops(M.AvgPool2d, M.MaxPool2d)
def flops_pool(module: M.AvgPool2d, inputs, outputs):
    kernel_sum = 0
    if isinstance(module.kernel_size, tuple) and len(module.kernel_size) == 2:
        kernel_sum = np.prod(module.kernel_size)
    else:
        kernel_sum = module.kernel_size ** 2
    return np.prod(outputs[0].shape) * kernel_sum


@register_flops(M.AdaptiveAvgPool2d, M.AdaptiveMaxPool2d)
def flops_adaptivePool(module: M.AdaptiveAvgPool2d, inputs, outputs):
    stride_h = np.floor(inputs[0].shape[2] / (inputs[0].shape[2] - 1))
    kernel_h = inputs[0].shape[2] - (inputs[0].shape[2] - 1) * stride_h
    stride_w = np.floor(inputs[0].shape[3] / (inputs[0].shape[3] - 1))
    kernel_w = inputs[0].shape[3] - (inputs[0].shape[3] - 1) * stride_w
    return np.prod(outputs[0].shape) * kernel_h * kernel_w


@register_flops(M.Linear)
def flops_linear(module: M.Linear, inputs, outputs):
    bias = module.out_features if module.bias is not None else 0
    return np.prod(outputs[0].shape) * module.in_features + bias


@register_flops(M.BatchMatMulActivation)
def flops_batchmatmul(module: M.BatchMatMulActivation, inputs, outputs):
    bias = 1 if module.bias is not None else 0
    x = inputs[0]
    w = module.weight
    batch_size = x.shape[0]
    n, p = x.shape[1:]
    _, m = w.shape[1:]
    return n * (p + bias) * m * batch_size


# does not need import qat and quantized module since they inherit from float module.
hook_modules = (
    M.conv._ConvNd,
    M.Linear,
    M.BatchMatMulActivation,
    M.batchnorm._BatchNorm,
    M.LayerNorm,
    M.GroupNorm,
    M.InstanceNorm,
    M.pooling._PoolNd,
    M.adaptive_pooling._AdaptivePoolNd,
)


def _mean(inp):
    inp = Tensor(inp).astype(np.float32)
    return F.mean(inp).numpy()


def _std(inp):
    inp = Tensor(inp).astype(np.float32)
    return F.std(inp).numpy()


def dict2table(list_of_dict, header):
    table_data = [header]
    for d in list_of_dict:
        row = []
        for h in header:
            v = ""
            if h in d:
                v = d[h]
            row.append(v)
        table_data.append(row)
    return table_data


def sizeof_fmt(num, suffix="B"):
    if suffix == "B":
        scale = 1024.0
        units = ["", "Ki", "Mi", "Gi", "Ti", "Pi", "Ei", "Zi", "Yi"]
    else:
        scale = 1000.0
        units = ["", "K", "M", "G", "T", "P", "E", "Z", "Y"]
    for unit in units:
        if abs(num) < scale or unit == units[-1]:
            return "{:3.3f} {}{}".format(num, unit, suffix)
        num /= scale


def preprocess_receptive_field(module, inputs, outputs):
    # TODO: support other dimensions
    pre_rf = (
        max(getattr(i.owner, "_rf", (1, 1))[0] for i in inputs),
        max(getattr(i.owner, "_rf", (1, 1))[1] for i in inputs),
    )
    pre_stride = (
        max(getattr(i.owner, "_stride", (1, 1))[0] for i in inputs),
        max(getattr(i.owner, "_stride", (1, 1))[1] for i in inputs),
    )
    return pre_rf, pre_stride


def get_op_stats(module, inputs, outputs):
    if not isinstance(outputs, tuple) and not isinstance(outputs, list):
        outputs = (outputs,)
    rst = {
        "input_shapes": [i.shape for i in inputs],
        "output_shapes": [o.shape for o in outputs],
    }
    valid_flag = False
    for key, _dict, fallback in _iter_list:
        for _type in _dict:
            if isinstance(module, _type):
                value = _dict[_type](module, inputs, outputs)
                valid_flag = True
                break
        else:
            if fallback is not None:
                value = fallback(module, inputs, outputs)
            continue

        if isinstance(key, tuple):
            assert isinstance(value, tuple)
            for k, v in zip(key, value):
                rst[k] = v
        else:
            rst[key] = value

    if valid_flag:
        return rst
    else:
        return None
    return


def sum_op_stats(flops, bar_length_max=20):
    max_flops_num = max([i["flops_num"] for i in flops] + [0])
    total_flops_num = 0
    for d in flops:
        total_flops_num += int(d["flops_num"])
        d["flops_cum"] = sizeof_fmt(total_flops_num, suffix="OPs")

    for d in flops:
        ratio = d["ratio"] = d["flops_num"] / total_flops_num
        d["percentage"] = "{:.2f}%".format(ratio * 100)
        bar_length = int(d["flops_num"] / max_flops_num * bar_length_max)
        d["bar"] = "#" * bar_length
        d["flops"] = sizeof_fmt(d["flops_num"], suffix="OPs")

    total_flops_str = sizeof_fmt(total_flops_num, suffix="OPs")
    total_var_size = sum(
        sum(s[1] if len(s) > 1 else 0 for s in d["output_shapes"]) for d in flops
    )
    flops.append(
        dict(name="total", flops=total_flops_str, output_shapes=total_var_size)
    )

    return total_flops_num, flops


def print_op_stats(flops):
    header = [
        "name",
        "class_name",
        "input_shapes",
        "output_shapes",
        "flops",
        "flops_cum",
        "percentage",
        "bar",
    ]
    if _receptive_field_enabled:
        header.insert(4, "receptive_field")
        header.insert(5, "stride")
    logger.info("flops stats: \n" + tabulate.tabulate(dict2table(flops, header=header)))


def get_param_stats(param: Tensor):
    nbits = get_dtype_bit(np.dtype(param.dtype).name)
    shape = param.shape
    param_dim = np.prod(param.shape)
    param_size = param_dim * nbits // 8
    return {
        "dtype": np.dtype(param.dtype),
        "shape": shape,
        "mean": "{:.3g}".format(_mean(param)),
        "std": "{:.3g}".format(_std(param)),
        "param_dim": param_dim,
        "nbits": nbits,
        "size": param_size,
    }


def sum_param_stats(params, bar_length_max=20):
    max_size = max([d["size"] for d in params] + [0])
    total_param_dims, total_param_size = 0, 0
    for d in params:
        total_param_dims += int(d["param_dim"])
        total_param_size += int(d["size"])
        d["size_cum"] = sizeof_fmt(total_param_size)

    for d in params:
        ratio = d["size"] / total_param_size
        d["ratio"] = ratio
        d["percentage"] = "{:.2f}%".format(ratio * 100)
        bar_length = int(d["size"] / max_size * bar_length_max)
        d["size_bar"] = "#" * bar_length
        d["size"] = sizeof_fmt(d["size"])

    param_size = sizeof_fmt(total_param_size)
    params.append(dict(name="total", param_dim=total_param_dims, size=param_size,))

    return total_param_dims, total_param_size, params


def print_param_stats(params):
    header = [
        "name",
        "dtype",
        "shape",
        "mean",
        "std",
        "param_dim",
        "nbits",
        "size",
        "size_cum",
        "percentage",
        "size_bar",
    ]
    logger.info(
        "param stats: \n" + tabulate.tabulate(dict2table(params, header=header))
    )


def get_activation_stats(output: Tensor, has_input=False):
    out_shape = output.shape
    activations_dtype = np.dtype(output.dtype)
    nbits = get_dtype_bit(activations_dtype.name)
    act_dim = np.prod(out_shape)
    act_size = act_dim * nbits // 8
    activation_stats = {
        "dtype": activations_dtype,
        "shape": out_shape,
        "act_dim": act_dim,
        "nbits": nbits,
        "size": act_size,
    }
    if has_input:
        activation_stats["mean"] = "{:.3g}".format(_mean(output))
        activation_stats["std"] = "{:.3g}".format(_std(output))
    return activation_stats


def sum_activations_stats(activations, bar_length_max=20):
    max_act_size = max([i["size"] for i in activations] + [0])
    total_act_dims, total_act_size = 0, 0
    for d in activations:
        total_act_size += int(d["size"])
        total_act_dims += int(d["act_dim"])
        d["size_cum"] = sizeof_fmt(total_act_size)

    for d in activations:
        ratio = d["ratio"] = d["size"] / total_act_size
        d["percentage"] = "{:.2f}%".format(ratio * 100)
        bar_length = int(d["size"] / max_act_size * bar_length_max)
        d["size_bar"] = "#" * bar_length
        d["size"] = sizeof_fmt(d["size"])

    act_size = sizeof_fmt(total_act_size)
    activations.append(dict(name="total", act_dim=total_act_dims, size=act_size,))

    return total_act_dims, total_act_size, activations


def print_activations_stats(activations, has_input=False):
    header = [
        "name",
        "class_name",
        "dtype",
        "shape",
        "nbits",
        "act_dim",
        "size",
        "size_cum",
        "percentage",
        "size_bar",
    ]
    if has_input:
        header.insert(4, "mean")
        header.insert(5, "std")
    logger.info(
        "activations stats: \n"
        + tabulate.tabulate(dict2table(activations, header=header))
    )


def print_summary(**kwargs):
    data = [["item", "value"]]
    data.extend(list(kwargs.items()))
    logger.info("summary\n" + tabulate.tabulate(data))


def module_stats(
    model: M.Module,
    inputs: Iterable[np.ndarray] = None,
    input_shapes: list = None,
    cal_params: bool = True,
    cal_flops: bool = True,
    cal_activations: bool = True,
    logging_to_stdout: bool = True,
    bar_length_max: int = 20,
):
    r"""Calculate and print ``model``'s statistics by adding hook and record Module's inputs outputs size.

    Args:
        model: model that need to get stats info.
        inputs: user defined input data for running model and calculating stats, alternative with input_shapes.
        input_shapes: shapes to generate random inputs for running model and calculating stats, alternative with inputs.
        cal_params: whether calculate and record params size.
        cal_flops: whether calculate and record op flops.
        cal_activations: whether calculate and record op activations.
        logging_to_stdout: whether print all calculated statistic details.
        bar_length_max: size of bar indicating max flops or parameter size in net stats.
    """
    has_inputs = False
    if inputs is not None:
        has_inputs = True
        if not isinstance(inputs, (tuple, list)):
            inputs = [inputs]

        def load_tensor(x):
            if isinstance(x, np.ndarray):
                return Tensor(x)
            elif isinstance(x, collections.abc.Mapping):
                return {k: load_tensor(v) for k, v in x.items()}
            elif isinstance(x, tuple) and hasattr(x, "_fields"):  # nametuple
                return type(x)(*(load_tensor(value) for value in x))
            elif isinstance(x, collections.abc.Sequence):
                return [load_tensor(v) for v in x]
            else:
                return Tensor(x, dtype=np.float32)

        inputs = load_tensor(inputs)

    else:
        if input_shapes:
            if not isinstance(input_shapes[0], tuple):
                input_shapes = [input_shapes]
            inputs = [F.zeros(in_size, dtype=np.float32) for in_size in input_shapes]
        else:
            logger.error(
                "Inputs or input_shapes is required for running model and calculating stats.",
                exc_info=True,
            )
            return
    if not cal_activations:
        log_activations = False

    disable_receptive_field()
    recorded_parameters = set()

    def module_stats_hook(module, inputs, outputs, name=""):
        class_name = str(module.__class__).split(".")[-1].split("'")[0]
        if cal_flops:
            flops_stats = get_op_stats(module, inputs, outputs)
            if flops_stats is not None:
                flops_stats["name"] = name
                flops_stats["class_name"] = class_name
                flops.append(flops_stats)

        if cal_params:
            if (
                hasattr(module, "weight")
                and (module.weight is not None)
                and module.weight not in recorded_parameters
            ):
                w = module.weight
                param_stats = get_param_stats(w)
                param_stats["name"] = name + "-w"
                params.append(param_stats)
                recorded_parameters.add(w)

            if (
                hasattr(module, "bias")
                and module.bias is not None
                and module.bias not in recorded_parameters
            ):
                b = module.bias
                param_stats = get_param_stats(b)
                param_stats["name"] = name + "-b"
                params.append(param_stats)
                recorded_parameters.add(b)

        if cal_activations:
            if not isinstance(outputs, (tuple, list)):
                output = outputs
            else:
                output = outputs[0]
            activation_stats = get_activation_stats(output, has_inputs)
            activation_stats["name"] = name
            activation_stats["class_name"] = class_name
            activations.append(activation_stats)

    params = []
    flops = []
    hooks = []
    activations = []
    total_stats = namedtuple(
        "total_stats", ["param_size", "param_dims", "flops", "act_size", "act_dims"]
    )
    stats_details = namedtuple("module_stats", ["params", "flops", "activations"])

    for (name, module) in model.named_modules():
        if isinstance(module, hook_modules):
            hooks.append(
                module.register_forward_hook(partial(module_stats_hook, name=name))
            )
    with set_module_mode_safe(model, training=False) as model:
        model(*inputs)

    for h in hooks:
        h.remove()

    extra_info = {
        "#params": len(params),
    }
    (
        total_flops,
        total_param_dims,
        total_param_size,
        total_act_dims,
        total_act_size,
    ) = (0, 0, 0, 0, 0)

    if cal_params:
        total_param_dims, total_param_size, params = sum_param_stats(
            params, bar_length_max
        )
        extra_info["total_param_dims"] = sizeof_fmt(total_param_dims, suffix="")
        extra_info["total_param_size"] = sizeof_fmt(total_param_size)
        if logging_to_stdout:
            print_param_stats(params)

    if cal_flops:
        total_flops, flops = sum_op_stats(flops, bar_length_max)
        extra_info["total_flops"] = sizeof_fmt(total_flops, suffix="OPs")
        if logging_to_stdout:
            print_op_stats(flops)

    if cal_activations:
        total_act_dims, total_act_size, activations = sum_activations_stats(
            activations, bar_length_max
        )
        extra_info["total_act_dims"] = sizeof_fmt(total_act_dims, suffix="")
        extra_info["total_act_size"] = sizeof_fmt(total_act_size)
        if logging_to_stdout:
            print_activations_stats(activations, has_inputs)

    if cal_flops and cal_params and total_param_size != 0:
        extra_info["flops/param_size"] = "{:3.3f}".format(
            total_flops / total_param_size
        )

    print_summary(**extra_info)

    return (
        total_stats(
            param_size=total_param_size,
            param_dims=total_param_dims,
            flops=total_flops,
            act_size=total_act_size,
            act_dims=total_act_dims,
        ),
        stats_details(params=params, flops=flops, activations=activations),
    )