workspace.py

## @package workspace
# Module caffe2.python.workspace
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import collections
import contextlib
from google.protobuf.message import Message
from multiprocessing import Process
import os
from collections import defaultdict
import logging
import numpy as np
from past.builtins import basestring
import shutil
import socket
import tempfile

from caffe2.proto import caffe2_pb2
from caffe2.python import scope, utils

import caffe2.python._import_c_extension as C

logger = logging.getLogger(__name__)

Blobs = C.blobs
CreateBlob = C.create_blob
CurrentWorkspace = C.current_workspace
DeserializeBlob = C.deserialize_blob
GlobalInit = C.global_init
HasBlob = C.has_blob
RegisteredOperators = C.registered_operators
SerializeBlob = C.serialize_blob
SwitchWorkspace = C.switch_workspace
RootFolder = C.root_folder
Workspaces = C.workspaces
BenchmarkNet = C.benchmark_net
GetStats = C.get_stats

operator_tracebacks = defaultdict(dict)

is_asan = C.is_asan
has_cuda_support = C.has_cuda_support
has_hip_support = C.has_hip_support
has_gpu_support = C.has_gpu_support
if has_cuda_support:
    GpuDeviceType = caffe2_pb2.CUDA
    NumCudaDevices = C.num_cuda_devices
    # This is a duplicate of NumCudaDevices. Remove
    # NumCudaDevices once replaced everywhere in the code
    NumGpuDevices = C.num_cuda_devices
    GetCUDAVersion = C.get_cuda_version
    GetCuDNNVersion = C.get_cudnn_version

    def GetGpuPeerAccessPattern():
        return np.asarray(C.get_cuda_peer_access_pattern())

    GetDeviceProperties = C.get_device_properties
else:
    NumCudaDevices = lambda: 0 # noqa
    GetCUDAVersion = lambda: 0 # noqa
    GetCuDNNVersion = lambda: 0 # noqa

if has_hip_support:
    GpuDeviceType = caffe2_pb2.HIP
    NumGpuDevices = C.num_hip_devices

    def GetGpuPeerAccessPattern():
        return np.asarray(C.get_hip_peer_access_pattern())
    GetDeviceProperties = C.get_device_properties

if not has_gpu_support:
    # setting cuda as the default GpuDeviceType as some tests
    # like core, scope tests use GpuDeviceType even without gpu support
    GpuDeviceType = caffe2_pb2.CUDA
    NumGpuDevices = lambda: 0 # noqa
    GetDeviceProperties = lambda x: None # noqa
    GetGpuPeerAccessPattern = lambda: np.array([]) # noqa

IsNUMAEnabled = C.is_numa_enabled
GetNumNUMANodes = C.get_num_numa_nodes
GetBlobNUMANode = C.get_blob_numa_node
GetBlobSizeBytes = C.get_blob_size_bytes

def _GetFreeFlaskPort():
    """Get a free flask port."""
    # We will prefer to use 5000. If not, we will then pick a random port.
    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    result = sock.connect_ex(('127.0.0.1', 5000))
    if result == 0:
        return 5000
    else:
        s = socket.socket()
        s.bind(('', 0))
        port = s.getsockname()[1]
        s.close()
        # Race condition: between the interval we close the socket and actually
        # start a mint process, another process might have occupied the port. We
        # don't do much here as this is mostly for convenience in research
        # rather than 24x7 service.
        return port

def StartMint(root_folder=None, port=None):
    """Start a mint instance.

    TODO(Yangqing): this does not work well under ipython yet. According to
        https://github.com/ipython/ipython/issues/5862
    writing up some fix is a todo item.
    """
    from caffe2.python.mint import app
    if root_folder is None:
        # Get the root folder from the current workspace
        root_folder = C.root_folder()
    if port is None:
        port = _GetFreeFlaskPort()
    process = Process(
        target=app.main,
        args=(
            ['-p', str(port), '-r', root_folder],
        )
    )
    process.start()
    print('Mint running at http://{}:{}'.format(socket.getfqdn(), port))
    return process


def StringifyProto(obj):
    """Stringify a protocol buffer object.

  Inputs:
    obj: a protocol buffer object, or a Pycaffe2 object that has a Proto()
        function.
  Outputs:
    string: the output protobuf string.
  Raises:
    AttributeError: if the passed in object does not have the right attribute.
  """
    if isinstance(obj, basestring):
        return obj
    else:
        if isinstance(obj, Message):
            # First, see if this object is a protocol buffer, which we can
            # simply serialize with the SerializeToString() call.
            return obj.SerializeToString()
        elif hasattr(obj, 'Proto'):
            return obj.Proto().SerializeToString()
        else:
            raise ValueError("Unexpected argument to StringifyProto of type " +
                             type(obj).__name__)


def ResetWorkspace(root_folder=None):
    if root_folder is None:
        # Reset the workspace, but keep the current root folder setting.
        return C.reset_workspace(C.root_folder())
    else:
        if not os.path.exists(root_folder):
            os.makedirs(root_folder)
        return C.reset_workspace(root_folder)


def CreateNet(net, overwrite=False, input_blobs=None):
    if input_blobs is None:
        input_blobs = []
    for input_blob in input_blobs:
        C.create_blob(input_blob)
    return CallWithExceptionIntercept(
        C.create_net,
        C.Workspace.current._last_failed_op_net_position,
        GetNetName(net),
        StringifyProto(net), overwrite,
    )


def Predictor(init_net, predict_net):
    return C.Predictor(StringifyProto(init_net), StringifyProto(predict_net))


def GetOperatorCost(operator, blobs):
    return C.get_operator_cost(StringifyProto(operator), blobs)


def RunOperatorOnce(operator):
    return C.run_operator_once(StringifyProto(operator))


def RunOperatorsOnce(operators):
    for op in operators:
        success = RunOperatorOnce(op)
        if not success:
            return False
    return True


def CallWithExceptionIntercept(func, op_id_fetcher, net_name, *args, **kwargs):
    try:
        return func(*args, **kwargs)
    except Exception:
        op_id = op_id_fetcher()
        net_tracebacks = operator_tracebacks.get(net_name, None)
        logger.warning(
            'Original python traceback for operator `{}` in network '
            '`{}` in exception above (most recent call last):'.format(
                op_id, net_name))
        if net_tracebacks and op_id in net_tracebacks:
            tb = net_tracebacks[op_id]
            for line in reversed(tb):
                logger.warning('  File "{}", line {}, in {}'.format(
                    line[0], line[1], line[2]))
        raise


def RunNetOnce(net):
    return CallWithExceptionIntercept(
        C.run_net_once,
        C.Workspace.current._last_failed_op_net_position,
        GetNetName(net),
        StringifyProto(net),
    )


def RunNet(name, num_iter=1, allow_fail=False):
    """Runs a given net.

    Inputs:
      name: the name of the net, or a reference to the net.
      num_iter: number of iterations to run
      allow_fail: if True, does not assert on net exec failure but returns False
    Returns:
      True or an exception.
    """
    return CallWithExceptionIntercept(
        C.run_net,
        C.Workspace.current._last_failed_op_net_position,
        GetNetName(name),
        StringifyNetName(name), num_iter, allow_fail,
    )


def RunPlan(plan_or_step):
    # TODO(jiayq): refactor core.py/workspace.py to avoid circular deps
    import caffe2.python.core as core
    if isinstance(plan_or_step, core.ExecutionStep):
        plan_or_step = core.Plan(plan_or_step)
    return C.run_plan(StringifyProto(plan_or_step))


def RunPlanInBackground(plan_or_step):
    # TODO(jiayq): refactor core.py/workspace.py to avoid circular deps
    import caffe2.python.core as core
    if isinstance(plan_or_step, core.ExecutionStep):
        plan_or_step = core.Plan(plan_or_step)
    return C.run_plan_in_background(StringifyProto(plan_or_step))


def InferShapesAndTypes(nets, blob_dimensions=None, nets_proto=False,
                        blob_types=None):
    """Infers the shapes and types for the specified nets.

    Inputs:
      nets: the list of nets
      blob_dimensions (optional): a dictionary of blobs and their dimensions.
          If not specified, the workspace blobs are used.
      nets_proto (optional): a boolean flag indicating whether the protobuffer
          representation is passed to the routine.
    Returns:
      A tuple of (shapes, types) dictionaries keyed by blob name.
    """
    if nets_proto:
        net_protos = [StringifyProto(n) for n in nets]
    else:
        net_protos = [StringifyProto(n.Proto()) for n in nets]
    if blob_dimensions is None:
        assert blob_types is None
        blobdesc_prototxt = C.infer_shapes_and_types_from_workspace(net_protos)
    elif blob_types is None:
        blobdesc_prototxt = C.infer_shapes_and_types_from_map(
            net_protos, blob_dimensions
        )
    else:
        blobdesc_prototxt = C.infer_shapes_and_types_from_map(
            net_protos, blob_dimensions, blob_types
        )
    blobdesc_proto = caffe2_pb2.TensorShapes()
    blobdesc_proto.ParseFromString(blobdesc_prototxt)
    shapes = {}
    types = {}
    for ts in blobdesc_proto.shapes:
        if not ts.unknown_shape:
            shapes[ts.name] = list(ts.dims)
            types[ts.name] = ts.data_type

    return (shapes, types)


def _StringifyName(name, expected_type):
    if isinstance(name, basestring):
        return name
    assert type(name).__name__ == expected_type, \
        "Expected a string or %s" % expected_type
    return str(name)


def StringifyBlobName(name):
    return _StringifyName(name, "BlobReference")


def StringifyNetName(name):
    return _StringifyName(name, "Net")


def GetNetName(net):
    if isinstance(net, basestring):
        return net
    if type(net).__name__ == "Net":
        return net.Name()
    if isinstance(net, caffe2_pb2.NetDef):
        return net.name
    raise Exception("Not a Net object: {}".format(str(net)))


def FeedBlob(name, arr, device_option=None):
    """Feeds a blob into the workspace.

    Inputs:
      name: the name of the blob.
      arr: either a TensorProto object or a numpy array object to be fed into
          the workspace.
      device_option (optional): the device option to feed the data with.
    Returns:
      True or False, stating whether the feed is successful.
    """
    ws = C.Workspace.current
    return _Workspace_feed_blob(ws, name, arr, device_option)


def FetchBlobs(names):
    """Fetches a list of blobs from the workspace.

    Inputs:
        names: list of names of blobs - strings or BlobReferences
    Returns:
        list of fetched blobs
    """
    return [FetchBlob(name) for name in names]


def FetchBlob(name):
    """Fetches a blob from the workspace.

    Inputs:
      name: the name of the blob - a string or a BlobReference
    Returns:
      Fetched blob (numpy array or string) if successful
    """
    result = C.fetch_blob(StringifyBlobName(name))
    if isinstance(result, tuple):
        raise TypeError(
            "Use FetchInt8Blob to fetch Int8 Blob {}".format(
                StringifyBlobName(name)
            )
        )
    return result


def FetchTorch(name):
    ws = C.Workspace.current
    return ws.blobs[name].to_torch()


Int8Tensor = collections.namedtuple(
    'Int8Tensor', ['data', 'scale', 'zero_point']
)


def FetchInt8Blob(name):
    """Fetches an Int8 blob from the workspace. It shared backend implementation
    with FetchBlob but it is recommened when fetching Int8 Blobs

    Inputs:
      name: the name of the Int8 blob - a string or a BlobReference
    Returns:
      data: int8 numpy array, data
      scale: float, fake quantization scale
      zero_point: int, fake quantization offset
    """
    result = C.fetch_blob(StringifyBlobName(name))
    assert isinstance(result, tuple), \
        'You are not fetching an Int8Blob {}. Please use FetchBlob'.format(
            StringifyBlobName(name))
    return Int8Tensor(*result)


def FetchInt8BlobRealVal(name):
    """Fetches an Int8 blob from the workspace and return its real value representation.

    Inputs:
      name: the name of the Int8 blob - a string or a BlobReference
    Returns:
      real value representation of int8 numpy array
    """
    result = C.fetch_blob(StringifyBlobName(name))
    assert isinstance(result, tuple), \
        'You are not fetching an Int8Blob {}. Please use FetchBlob'.format(
            StringifyBlobName(name))
    int8_blob = Int8Tensor(*result)
    return (int8_blob.data.astype(np.int32) - int(int8_blob.zero_point)).astype(
        np.float32) * int8_blob.scale


def _Workspace_fetch_int8_blob(ws, name):
    """Fetches an Int8 blob from the workspace. It shared backend implementation
    with FetchBlob but it is recommened when fetching Int8 Blobs

    Inputs:
      name: the name of the Int8 blob - a string or a BlobReference
    Returns:
      data: int8 numpy array, data
      scale: float, fake quantization scale
      zero_point: int, fake quantization offset
    """
    result = ws.fetch_blob(name)
    assert isinstance(result, tuple), \
        'You are not fetching an Int8Blob {}. Please use fetch_blob'.format(
            StringifyBlobName(name))
    return Int8Tensor(*result)


C.Workspace.fetch_int8_blob = _Workspace_fetch_int8_blob


def ApplyTransform(transform_key, net):
    """Apply a Transform to a NetDef protobuf object, and returns the new
    transformed NetDef.

    Inputs:
      transform_key: the name of the transform, as it is stored in the registry
      net: a NetDef protobuf object
    Returns:
      Transformed NetDef protobuf object.
    """
    transformed_net = caffe2_pb2.NetDef()
    transformed_str = C.apply_transform(
        str(transform_key).encode('utf-8'),
        net.SerializeToString(),
    )
    transformed_net.ParseFromString(transformed_str)
    return transformed_net


def ApplyTransformIfFaster(transform_key, net, init_net, **kwargs):
    """Apply a Transform to a NetDef protobuf object, and returns the new
    transformed NetDef, only if it runs faster than the original.

    The runs are performed on the current active workspace (gWorkspace).
    You should initialize that workspace before making a call to this function.

    Inputs:
      transform_key: the name of the transform, as it is stored in the registry
      net: a NetDef protobuf object
      init_net: The net to initialize the workspace.
      warmup_runs (optional):
        Determines how many times the net is run before testing.
        Will be 5 by default.
      main_runs (optional):
        Determines how many times the net is run during testing.
        Will be 10 by default.
      improvement_threshold (optional):
        Determines the factor which the new net needs to be faster
        in order to replace the old. Will be 1.01 by default.

    Returns:
      Either a Transformed NetDef protobuf object, or the original netdef.
    """

    warmup_runs = kwargs['warmup_runs'] if 'warmup_runs' in kwargs else 5
    main_runs = kwargs['main_runs'] if 'main_runs' in kwargs else 10
    improvement_threshold = kwargs['improvement_threshold'] \
        if 'improvement_threshold' in kwargs else 1.01

    transformed_net = caffe2_pb2.NetDef()
    transformed_str = C.apply_transform_if_faster(
        str(transform_key).encode('utf-8'),
        net.SerializeToString(),
        init_net.SerializeToString(),
        warmup_runs,
        main_runs,
        float(improvement_threshold),
    )
    transformed_net.ParseFromString(transformed_str)
    return transformed_net


def GetNameScope():
    """Return the current namescope string. To be used to fetch blobs"""
    return scope.CurrentNameScope()


class _BlobDict(object):
    """Provides python dict compatible way to do fetching and feeding"""

    def __getitem__(self, key):
        return FetchBlob(key)

    def __setitem__(self, key, value):
        return FeedBlob(key, value)

    def __len__(self):
        return len(C.blobs())

    def __iter__(self):
        return C.blobs().__iter__()

    def __contains__(self, item):
        return C.has_blob(item)


blobs = _BlobDict()


################################################################################
# Utilities for immediate mode
#
# Caffe2's immediate mode implements the following behavior: between the two
# function calls StartImmediate() and StopImmediate(), for any operator that is
# called through CreateOperator(), we will also run that operator in a workspace
# that is specific to the immediate mode. The user is explicitly expected to
# make sure that these ops have proper inputs and outputs, i.e. one should not
# run an op where an external input is not created or fed.
#
# Users can use FeedImmediate() and FetchImmediate() to interact with blobs
# in the immediate workspace.
#
# Once StopImmediate() is called, all contents in the immediate workspace is
# freed up so one can continue using normal runs.
#
# The immediate mode is solely for debugging purposes and support will be very
# sparse.
################################################################################

_immediate_mode = False
_immediate_workspace_name = "_CAFFE2_IMMEDIATE"
_immediate_root_folder = ''


def IsImmediate():
    return _immediate_mode


@contextlib.contextmanager
def WorkspaceGuard(workspace_name):
    current = CurrentWorkspace()
    SwitchWorkspace(workspace_name, True)
    yield
    SwitchWorkspace(current)


def StartImmediate(i_know=False):
    global _immediate_mode
    global _immediate_root_folder
    if IsImmediate():
        # already in immediate mode. We will kill the previous one
        # and start from fresh.
        StopImmediate()
    _immediate_mode = True
    with WorkspaceGuard(_immediate_workspace_name):
        _immediate_root_folder = tempfile.mkdtemp()
        ResetWorkspace(_immediate_root_folder)
    if i_know:
        # if the user doesn't want to see the warning message, sure...
        return
    print("""
    Enabling immediate mode in caffe2 python is an EXTREMELY EXPERIMENTAL
    feature and may very easily go wrong. This is because Caffe2 uses a
    declarative way of defining operators and models, which is essentially
    not meant to run things in an interactive way. Read the following carefully
    to make sure that you understand the caveats.

    (1) You need to make sure that the sequences of operators you create are
    actually runnable sequentially. For example, if you create an op that takes
    an input X, somewhere earlier you should have already created X.

    (2) Caffe2 immediate uses one single workspace, so if the set of operators
    you run are intended to be under different workspaces, they will not run.
    To create boundaries between such use cases, you can call FinishImmediate()
    and StartImmediate() manually to flush out everything no longer needed.

    (3) Underlying objects held by the immediate mode may interfere with your
    normal run. For example, if there is a leveldb that you opened in immediate
    mode and did not close, your main run will fail because leveldb does not
    support double opening. Immediate mode may also occupy a lot of memory esp.
    on GPUs. Call FinishImmediate() as soon as possible when you no longer
    need it.

    (4) Immediate is designed to be slow. Every immediate call implicitly
    creates a temp operator object, runs it, and destroys the operator. This
    slow-speed run is by design to discourage abuse. For most use cases other
    than debugging, do NOT turn on immediate mode.

    (5) If there is anything FATAL happening in the underlying C++ code, the
    immediate mode will immediately (pun intended) cause the runtime to crash.

    Thus you should use immediate mode with extra care. If you still would
    like to, have fun [https://xkcd.com/149/].
    """)


def StopImmediate():
    """Stops an immediate mode run."""
    # Phew, that was a dangerous ride.
    global _immediate_mode
    global _immediate_root_folder
    if not IsImmediate():
        return
    with WorkspaceGuard(_immediate_workspace_name):
        ResetWorkspace()
    shutil.rmtree(_immediate_root_folder)
    _immediate_root_folder = ''
    _immediate_mode = False


def ImmediateBlobs():
    with WorkspaceGuard(_immediate_workspace_name):
        return Blobs()


def RunOperatorImmediate(op):
    with WorkspaceGuard(_immediate_workspace_name):
        RunOperatorOnce(op)


def FetchImmediate(*args, **kwargs):
    with WorkspaceGuard(_immediate_workspace_name):
        return FetchBlob(*args, **kwargs)


def FeedImmediate(*args, **kwargs):
    with WorkspaceGuard(_immediate_workspace_name):
        return FeedBlob(*args, **kwargs)


# C.Workspace methods.

def _Workspace_create_net_with_exception_intercept(ws, net, overwrite=False):
    return CallWithExceptionIntercept(
        ws._create_net,
        ws._last_failed_op_net_position,
        GetNetName(net),
        StringifyProto(net), overwrite,
    )


def _Workspace_run(ws, obj):
    if hasattr(obj, 'Proto'):
        obj = obj.Proto()
    if isinstance(obj, caffe2_pb2.PlanDef):
        return ws._run_plan(obj.SerializeToString())
    if isinstance(obj, caffe2_pb2.NetDef):
        return CallWithExceptionIntercept(
            ws._run_net,
            ws._last_failed_op_net_position,
            GetNetName(obj),
            obj.SerializeToString(),
        )
        # return ws._run_net(obj.SerializeToString())
    if isinstance(obj, caffe2_pb2.OperatorDef):
        return ws._run_operator(obj.SerializeToString())
    raise ValueError(
        "Don't know how to do Workspace.run() on {}".format(type(obj)))


def _Workspace_feed_blob(ws, name, arr, device_option=None):
    if type(arr) is caffe2_pb2.TensorProto:
        arr = utils.Caffe2TensorToNumpyArray(arr)
    if type(arr) is np.ndarray and arr.dtype.kind in 'SU':
        # Plain NumPy strings are weird, let's use objects instead
        arr = arr.astype(np.object)

    if device_option is None:
        device_option = scope.CurrentDeviceScope()

    if device_option and device_option.device_type == caffe2_pb2.CUDA:
        if arr.dtype == np.dtype('float64'):
            logger.warning(
                "CUDA operators do not support 64-bit doubles, " +
                "please use arr.astype(np.float32) or np.int32 for ints." +
                " Blob: {}".format(name) +
                " type: {}".format(str(arr.dtype))
            )

    name = StringifyBlobName(name)
    if device_option is not None:
        return ws.create_blob(name).feed(arr, device_option)
    else:
        return ws.create_blob(name).feed(arr)


def _Workspace_remove_blob(ws, blob):
    ws._remove_blob(str(blob))


Workspace = C.Workspace
Workspace.create_net = _Workspace_create_net_with_exception_intercept
Workspace.run = _Workspace_run
Workspace.feed_blob = _Workspace_feed_blob
Workspace.remove_blob = _Workspace_remove_blob

# C.Blob methods.


def _Blob_feed(blob, arg, device_option=None):
    # conservative type check to avoid unnecessary import
    if type(arg).__name__ == 'Tensor' and type(arg).__module__ == 'torch':
        import torch
        if isinstance(arg, torch.Tensor):
            assert device_option is None, \
                "device_option doesn't make sense with PyTorch tensors"
            handle = torch._C._tensor_impl_raw_handle(arg)
            blob._wrap_tensor_impl(handle)
            return True  # _feed() returns True for some reason
    if device_option is not None:
        device_option = StringifyProto(device_option)
    return blob._feed(arg, device_option)


C.Blob.feed = _Blob_feed


def _Tensor_to_torch(tensor):
    """
    PyTorch tensor interop (TensorCPU methods)

    Can be accessed as:
      workspace.Workspace.current.blobs['foo'].tensor().to_torch()
    """
    # avoiding circular dependency
    import torch
    handle = tensor._tensor_impl_raw_handle()
    return torch._C._wrap_tensor_impl(handle)

C.TensorCPU.to_torch = _Tensor_to_torch


def _Blob_to_torch(blob):
    if not blob.is_tensor():
        raise RuntimeError("Blob has to be a tensor")
    return blob.as_tensor().to_torch()

C.Blob.to_torch = _Blob_to_torch