mirror of
https://github.com/pytorch/pytorch.git
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a9aaf698a4
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/20108 Add cpp runs for c2, hooked up via pybinds. Print output to terminal. This is not hooked up with the pep output yet because I'd like to verify the numbers first. Note that this isn't quite the same mechanism as the pytorch cpp hookup, which uses cpp_python_extensions. If I can use the same mechanism to pull all the inputs for c2 through cpp and do FeedBlobs in cpp, then I'll switch to that. Reviewed By: zheng-xq Differential Revision: D15155976 fbshipit-source-id: 708079dacd3e19aacfe43d70c5e5bc54da2cf9e3
765 lines
24 KiB
Python
765 lines
24 KiB
Python
## @package workspace
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# Module caffe2.python.workspace
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import collections
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import contextlib
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from google.protobuf.message import Message
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from multiprocessing import Process
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import os
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from collections import defaultdict
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import logging
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import numpy as np
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from past.builtins import basestring
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import shutil
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import socket
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import tempfile
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from caffe2.proto import caffe2_pb2
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from caffe2.python import scope, utils
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import caffe2.python._import_c_extension as C
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logger = logging.getLogger(__name__)
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Blobs = C.blobs
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CreateBlob = C.create_blob
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CurrentWorkspace = C.current_workspace
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DeserializeBlob = C.deserialize_blob
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GlobalInit = C.global_init
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HasBlob = C.has_blob
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RegisteredOperators = C.registered_operators
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SerializeBlob = C.serialize_blob
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SwitchWorkspace = C.switch_workspace
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RootFolder = C.root_folder
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Workspaces = C.workspaces
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BenchmarkNet = C.benchmark_net
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BenchmarkNetOnce = C.benchmark_net_once
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GetStats = C.get_stats
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operator_tracebacks = defaultdict(dict)
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is_asan = C.is_asan
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has_cuda_support = C.has_cuda_support
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has_hip_support = C.has_hip_support
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has_gpu_support = C.has_gpu_support
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if has_cuda_support:
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GpuDeviceType = caffe2_pb2.CUDA
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NumCudaDevices = C.num_cuda_devices
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# This is a duplicate of NumCudaDevices. Remove
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# NumCudaDevices once replaced everywhere in the code
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NumGpuDevices = C.num_cuda_devices
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GetCUDAVersion = C.get_cuda_version
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GetCuDNNVersion = C.get_cudnn_version
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def GetGpuPeerAccessPattern():
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return np.asarray(C.get_cuda_peer_access_pattern())
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GetDeviceProperties = C.get_device_properties
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else:
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NumCudaDevices = lambda: 0 # noqa
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GetCUDAVersion = lambda: 0 # noqa
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GetCuDNNVersion = lambda: 0 # noqa
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if has_hip_support:
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GpuDeviceType = caffe2_pb2.HIP
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NumGpuDevices = C.num_hip_devices
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def GetGpuPeerAccessPattern():
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return np.asarray(C.get_hip_peer_access_pattern())
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GetDeviceProperties = C.get_device_properties
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if not has_gpu_support:
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# setting cuda as the default GpuDeviceType as some tests
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# like core, scope tests use GpuDeviceType even without gpu support
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GpuDeviceType = caffe2_pb2.CUDA
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NumGpuDevices = lambda: 0 # noqa
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GetDeviceProperties = lambda x: None # noqa
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GetGpuPeerAccessPattern = lambda: np.array([]) # noqa
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IsNUMAEnabled = C.is_numa_enabled
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GetNumNUMANodes = C.get_num_numa_nodes
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GetBlobNUMANode = C.get_blob_numa_node
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GetBlobSizeBytes = C.get_blob_size_bytes
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def FillRandomNetworkInputs(net, input_dims, input_types):
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C.fill_random_network_inputs(net.Proto().SerializeToString(), input_dims, input_types)
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def _GetFreeFlaskPort():
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"""Get a free flask port."""
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# We will prefer to use 5000. If not, we will then pick a random port.
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sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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result = sock.connect_ex(('127.0.0.1', 5000))
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if result == 0:
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return 5000
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else:
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s = socket.socket()
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s.bind(('', 0))
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port = s.getsockname()[1]
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s.close()
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# Race condition: between the interval we close the socket and actually
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# start a mint process, another process might have occupied the port. We
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# don't do much here as this is mostly for convenience in research
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# rather than 24x7 service.
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return port
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def StartMint(root_folder=None, port=None):
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"""Start a mint instance.
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TODO(Yangqing): this does not work well under ipython yet. According to
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https://github.com/ipython/ipython/issues/5862
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writing up some fix is a todo item.
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"""
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from caffe2.python.mint import app
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if root_folder is None:
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# Get the root folder from the current workspace
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root_folder = C.root_folder()
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if port is None:
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port = _GetFreeFlaskPort()
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process = Process(
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target=app.main,
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args=(
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['-p', str(port), '-r', root_folder],
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)
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)
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process.start()
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print('Mint running at http://{}:{}'.format(socket.getfqdn(), port))
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return process
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def StringifyProto(obj):
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"""Stringify a protocol buffer object.
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Inputs:
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obj: a protocol buffer object, or a Pycaffe2 object that has a Proto()
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function.
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Outputs:
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string: the output protobuf string.
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Raises:
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AttributeError: if the passed in object does not have the right attribute.
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"""
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if isinstance(obj, basestring):
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return obj
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else:
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if isinstance(obj, Message):
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# First, see if this object is a protocol buffer, which we can
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# simply serialize with the SerializeToString() call.
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return obj.SerializeToString()
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elif hasattr(obj, 'Proto'):
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return obj.Proto().SerializeToString()
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else:
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raise ValueError("Unexpected argument to StringifyProto of type " +
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type(obj).__name__)
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def ResetWorkspace(root_folder=None):
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if root_folder is None:
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# Reset the workspace, but keep the current root folder setting.
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return C.reset_workspace(C.root_folder())
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else:
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if not os.path.exists(root_folder):
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os.makedirs(root_folder)
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return C.reset_workspace(root_folder)
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def CreateNet(net, overwrite=False, input_blobs=None):
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if input_blobs is None:
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input_blobs = []
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for input_blob in input_blobs:
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C.create_blob(input_blob)
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return CallWithExceptionIntercept(
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C.create_net,
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C.Workspace.current._last_failed_op_net_position,
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GetNetName(net),
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StringifyProto(net), overwrite,
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)
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def Predictor(init_net, predict_net):
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return C.Predictor(StringifyProto(init_net), StringifyProto(predict_net))
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def GetOperatorCost(operator, blobs):
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return C.get_operator_cost(StringifyProto(operator), blobs)
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def RunOperatorOnce(operator):
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return C.run_operator_once(StringifyProto(operator))
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def RunOperatorMultiple(operator, num_runs):
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return C.run_operator_multiple(StringifyProto(operator), num_runs)
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def RunOperatorsOnce(operators):
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for op in operators:
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success = RunOperatorOnce(op)
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if not success:
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return False
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return True
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def ClearGlobalNetObserver():
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return C.clear_global_net_observer()
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def CallWithExceptionIntercept(func, op_id_fetcher, net_name, *args, **kwargs):
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try:
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return func(*args, **kwargs)
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except Exception:
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op_id = op_id_fetcher()
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net_tracebacks = operator_tracebacks.get(net_name, None)
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logger.warning(
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'Original python traceback for operator `{}` in network '
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'`{}` in exception above (most recent call last):'.format(
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op_id, net_name))
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if net_tracebacks and op_id in net_tracebacks:
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tb = net_tracebacks[op_id]
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for line in reversed(tb):
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logger.warning(' File "{}", line {}, in {}'.format(
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line[0], line[1], line[2]))
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raise
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def RunNetOnce(net):
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return CallWithExceptionIntercept(
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C.run_net_once,
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C.Workspace.current._last_failed_op_net_position,
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GetNetName(net),
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StringifyProto(net),
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)
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def RunNet(name, num_iter=1, allow_fail=False):
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"""Runs a given net.
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Inputs:
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name: the name of the net, or a reference to the net.
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num_iter: number of iterations to run
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allow_fail: if True, does not assert on net exec failure but returns False
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Returns:
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True or an exception.
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"""
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return CallWithExceptionIntercept(
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C.run_net,
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C.Workspace.current._last_failed_op_net_position,
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GetNetName(name),
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StringifyNetName(name), num_iter, allow_fail,
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)
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def RunPlan(plan_or_step):
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# TODO(jiayq): refactor core.py/workspace.py to avoid circular deps
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import caffe2.python.core as core
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if isinstance(plan_or_step, core.ExecutionStep):
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plan_or_step = core.Plan(plan_or_step)
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return C.run_plan(StringifyProto(plan_or_step))
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def RunPlanInBackground(plan_or_step):
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# TODO(jiayq): refactor core.py/workspace.py to avoid circular deps
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import caffe2.python.core as core
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if isinstance(plan_or_step, core.ExecutionStep):
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plan_or_step = core.Plan(plan_or_step)
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return C.run_plan_in_background(StringifyProto(plan_or_step))
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def InferShapesAndTypes(nets, blob_dimensions=None, nets_proto=False,
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blob_types=None):
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"""Infers the shapes and types for the specified nets.
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Inputs:
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nets: the list of nets
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blob_dimensions (optional): a dictionary of blobs and their dimensions.
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If not specified, the workspace blobs are used.
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nets_proto (optional): a boolean flag indicating whether the protobuffer
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representation is passed to the routine.
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Returns:
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A tuple of (shapes, types) dictionaries keyed by blob name.
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"""
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if nets_proto:
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net_protos = [StringifyProto(n) for n in nets]
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else:
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net_protos = [StringifyProto(n.Proto()) for n in nets]
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if blob_dimensions is None:
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assert blob_types is None
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blobdesc_prototxt = C.infer_shapes_and_types_from_workspace(net_protos)
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elif blob_types is None:
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blobdesc_prototxt = C.infer_shapes_and_types_from_map(
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net_protos, blob_dimensions
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)
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else:
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blobdesc_prototxt = C.infer_shapes_and_types_from_map(
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net_protos, blob_dimensions, blob_types
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)
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blobdesc_proto = caffe2_pb2.TensorShapes()
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blobdesc_proto.ParseFromString(blobdesc_prototxt)
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shapes = {}
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types = {}
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for ts in blobdesc_proto.shapes:
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if not ts.unknown_shape:
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shapes[ts.name] = list(ts.dims)
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types[ts.name] = ts.data_type
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return (shapes, types)
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def _StringifyName(name, expected_type):
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if isinstance(name, basestring):
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return name
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assert type(name).__name__ == expected_type, \
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"Expected a string or %s" % expected_type
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return str(name)
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def StringifyBlobName(name):
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return _StringifyName(name, "BlobReference")
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def StringifyNetName(name):
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return _StringifyName(name, "Net")
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def GetNetName(net):
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if isinstance(net, basestring):
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return net
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if type(net).__name__ == "Net":
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return net.Name()
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if isinstance(net, caffe2_pb2.NetDef):
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return net.name
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raise Exception("Not a Net object: {}".format(str(net)))
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def FeedBlob(name, arr, device_option=None):
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"""Feeds a blob into the workspace.
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Inputs:
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name: the name of the blob.
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arr: either a TensorProto object or a numpy array object to be fed into
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the workspace.
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device_option (optional): the device option to feed the data with.
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Returns:
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True or False, stating whether the feed is successful.
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"""
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ws = C.Workspace.current
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return _Workspace_feed_blob(ws, name, arr, device_option)
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def FetchBlobs(names):
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"""Fetches a list of blobs from the workspace.
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Inputs:
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names: list of names of blobs - strings or BlobReferences
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Returns:
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list of fetched blobs
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"""
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return [FetchBlob(name) for name in names]
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def FetchBlob(name):
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"""Fetches a blob from the workspace.
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Inputs:
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name: the name of the blob - a string or a BlobReference
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Returns:
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Fetched blob (numpy array or string) if successful
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"""
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result = C.fetch_blob(StringifyBlobName(name))
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if isinstance(result, tuple):
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raise TypeError(
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"Use FetchInt8Blob to fetch Int8 Blob {}".format(
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StringifyBlobName(name)
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)
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)
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return result
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def FetchTorch(name):
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ws = C.Workspace.current
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return ws.blobs[name].to_torch()
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Int8Tensor = collections.namedtuple(
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'Int8Tensor', ['data', 'scale', 'zero_point']
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)
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def FetchInt8Blob(name):
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"""Fetches an Int8 blob from the workspace. It shared backend implementation
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with FetchBlob but it is recommened when fetching Int8 Blobs
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Inputs:
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name: the name of the Int8 blob - a string or a BlobReference
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Returns:
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data: int8 numpy array, data
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scale: float, fake quantization scale
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zero_point: int, fake quantization offset
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"""
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result = C.fetch_blob(StringifyBlobName(name))
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assert isinstance(result, tuple), \
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'You are not fetching an Int8Blob {}. Please use FetchBlob'.format(
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StringifyBlobName(name))
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return Int8Tensor(*result)
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def FetchInt8BlobRealVal(name):
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"""Fetches an Int8 blob from the workspace and return its real value representation.
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Inputs:
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name: the name of the Int8 blob - a string or a BlobReference
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Returns:
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real value representation of int8 numpy array
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"""
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result = C.fetch_blob(StringifyBlobName(name))
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assert isinstance(result, tuple), \
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'You are not fetching an Int8Blob {}. Please use FetchBlob'.format(
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StringifyBlobName(name))
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int8_blob = Int8Tensor(*result)
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return (int8_blob.data.astype(np.int32) - int(int8_blob.zero_point)).astype(
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np.float32) * int8_blob.scale
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def _Workspace_fetch_int8_blob(ws, name):
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"""Fetches an Int8 blob from the workspace. It shared backend implementation
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with FetchBlob but it is recommened when fetching Int8 Blobs
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Inputs:
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name: the name of the Int8 blob - a string or a BlobReference
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Returns:
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data: int8 numpy array, data
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scale: float, fake quantization scale
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zero_point: int, fake quantization offset
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"""
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result = ws.fetch_blob(name)
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assert isinstance(result, tuple), \
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'You are not fetching an Int8Blob {}. Please use fetch_blob'.format(
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StringifyBlobName(name))
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return Int8Tensor(*result)
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C.Workspace.fetch_int8_blob = _Workspace_fetch_int8_blob
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def ApplyTransform(transform_key, net):
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"""Apply a Transform to a NetDef protobuf object, and returns the new
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transformed NetDef.
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Inputs:
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transform_key: the name of the transform, as it is stored in the registry
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net: a NetDef protobuf object
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Returns:
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Transformed NetDef protobuf object.
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"""
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transformed_net = caffe2_pb2.NetDef()
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transformed_str = C.apply_transform(
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str(transform_key).encode('utf-8'),
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net.SerializeToString(),
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)
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transformed_net.ParseFromString(transformed_str)
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return transformed_net
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def ApplyTransformIfFaster(transform_key, net, init_net, **kwargs):
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"""Apply a Transform to a NetDef protobuf object, and returns the new
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transformed NetDef, only if it runs faster than the original.
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The runs are performed on the current active workspace (gWorkspace).
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You should initialize that workspace before making a call to this function.
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Inputs:
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transform_key: the name of the transform, as it is stored in the registry
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net: a NetDef protobuf object
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init_net: The net to initialize the workspace.
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warmup_runs (optional):
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Determines how many times the net is run before testing.
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Will be 5 by default.
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main_runs (optional):
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Determines how many times the net is run during testing.
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Will be 10 by default.
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improvement_threshold (optional):
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Determines the factor which the new net needs to be faster
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in order to replace the old. Will be 1.01 by default.
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Returns:
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Either a Transformed NetDef protobuf object, or the original netdef.
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"""
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warmup_runs = kwargs['warmup_runs'] if 'warmup_runs' in kwargs else 5
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main_runs = kwargs['main_runs'] if 'main_runs' in kwargs else 10
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improvement_threshold = kwargs['improvement_threshold'] \
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if 'improvement_threshold' in kwargs else 1.01
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transformed_net = caffe2_pb2.NetDef()
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transformed_str = C.apply_transform_if_faster(
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str(transform_key).encode('utf-8'),
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net.SerializeToString(),
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init_net.SerializeToString(),
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warmup_runs,
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main_runs,
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float(improvement_threshold),
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)
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transformed_net.ParseFromString(transformed_str)
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return transformed_net
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def GetNameScope():
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"""Return the current namescope string. To be used to fetch blobs"""
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return scope.CurrentNameScope()
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class _BlobDict(object):
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"""Provides python dict compatible way to do fetching and feeding"""
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def __getitem__(self, key):
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return FetchBlob(key)
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def __setitem__(self, key, value):
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return FeedBlob(key, value)
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def __len__(self):
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return len(C.blobs())
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def __iter__(self):
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return C.blobs().__iter__()
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def __contains__(self, item):
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return C.has_blob(item)
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blobs = _BlobDict()
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################################################################################
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# Utilities for immediate mode
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#
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# Caffe2's immediate mode implements the following behavior: between the two
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# function calls StartImmediate() and StopImmediate(), for any operator that is
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# called through CreateOperator(), we will also run that operator in a workspace
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# that is specific to the immediate mode. The user is explicitly expected to
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# make sure that these ops have proper inputs and outputs, i.e. one should not
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# run an op where an external input is not created or fed.
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#
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# Users can use FeedImmediate() and FetchImmediate() to interact with blobs
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# in the immediate workspace.
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#
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# Once StopImmediate() is called, all contents in the immediate workspace is
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# freed up so one can continue using normal runs.
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#
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# The immediate mode is solely for debugging purposes and support will be very
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# sparse.
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################################################################################
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_immediate_mode = False
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_immediate_workspace_name = "_CAFFE2_IMMEDIATE"
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_immediate_root_folder = ''
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def IsImmediate():
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return _immediate_mode
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@contextlib.contextmanager
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def WorkspaceGuard(workspace_name):
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current = CurrentWorkspace()
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SwitchWorkspace(workspace_name, True)
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yield
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SwitchWorkspace(current)
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def StartImmediate(i_know=False):
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global _immediate_mode
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global _immediate_root_folder
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if IsImmediate():
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# already in immediate mode. We will kill the previous one
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# and start from fresh.
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StopImmediate()
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_immediate_mode = True
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with WorkspaceGuard(_immediate_workspace_name):
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_immediate_root_folder = tempfile.mkdtemp()
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ResetWorkspace(_immediate_root_folder)
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if i_know:
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# if the user doesn't want to see the warning message, sure...
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return
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print("""
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Enabling immediate mode in caffe2 python is an EXTREMELY EXPERIMENTAL
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feature and may very easily go wrong. This is because Caffe2 uses a
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declarative way of defining operators and models, which is essentially
|
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not meant to run things in an interactive way. Read the following carefully
|
|
to make sure that you understand the caveats.
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(1) You need to make sure that the sequences of operators you create are
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actually runnable sequentially. For example, if you create an op that takes
|
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an input X, somewhere earlier you should have already created X.
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(2) Caffe2 immediate uses one single workspace, so if the set of operators
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you run are intended to be under different workspaces, they will not run.
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To create boundaries between such use cases, you can call FinishImmediate()
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and StartImmediate() manually to flush out everything no longer needed.
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(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
|
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mode and did not close, your main run will fail because leveldb does not
|
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support double opening. Immediate mode may also occupy a lot of memory esp.
|
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on GPUs. Call FinishImmediate() as soon as possible when you no longer
|
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need it.
|
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(4) Immediate is designed to be slow. Every immediate call implicitly
|
|
creates a temp operator object, runs it, and destroys the operator. This
|
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slow-speed run is by design to discourage abuse. For most use cases other
|
|
than debugging, do NOT turn on immediate mode.
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|
|
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(5) If there is anything FATAL happening in the underlying C++ code, the
|
|
immediate mode will immediately (pun intended) cause the runtime to crash.
|
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|
|
Thus you should use immediate mode with extra care. If you still would
|
|
like to, have fun [https://xkcd.com/149/].
|
|
""")
|
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|
|
|
|
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)
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|
|
|
|
|
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
|