soulitzer

import torch
from torch.fx.passes.utils.source_matcher_utils import get_source_partitions
import pprint

m = torch.nn.Linear(10, 10)

def fn(x):
    return m(x)

soulitzer

(pytorch1) jw3468-mbp:pytorch1 jw3468$ python test/dynamo/test_modules.py -k TestTemp -v
test_dynamo_inline_module_nn_AdaptiveAvgPool1d_cpu_float32 (__main__.TestTempCPU) ...
opcode       name       target     args          kwargs
-----------  ---------  ---------  ------------  --------
placeholder  l_args_0_  L_args_0_  ()            {}
call_module  m          m          (l_args_0_,)  {}
output       output     output     ((m,),)       {}

stats [('calls_captured', 1), ('unique_graphs', 1)]
ok

soulitzer

test_dynamo_inline_module_nn_AdaptiveAvgPool1d_cpu_float32 (__main__.TestTempCPU) ...
opcode         name                 target                                                               args                       kwargs
-------------  -------------------  -------------------------------------------------------------------  -------------------------  --------
placeholder    l_args_0_            L_args_0_                                                            ()                         {}
call_function  adaptive_avg_pool1d  <built-in method adaptive_avg_pool1d of type object at 0x103ccc7e8>  (l_args_0_, 3)             {}
output         output               output                                                               ((adaptive_avg_pool1d,),)  {}

inline_call []
stats [('calls_captured', 1), ('unique_graphs', 1)]
ok

soulitzer

======================================================================
ERROR: test_checkpoint_detects_non_determinism (__main__.TestAutograd)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/jw3468/local/a/pytorch/torch/utils/checkpoint.py", line 1071, in unpack_hook_with_error_cb
    return unpack_hook(holder)
  File "/home/jw3468/local/a/pytorch/torch/utils/checkpoint.py", line 1053, in unpack_hook
    frame.check_recomputed_tensors_match(gid)
  File "/home/jw3468/local/a/pytorch/torch/utils/checkpoint.py", line 826, in check_recomputed_tensors_match
    raise CheckpointError(

soulitzer

torch.utils.checkpoint.CheckpointError: torch.utils.checkpoint: Recomputed values for the following tensors have different metadata than during the forward pass.
tensor at position 1:
saved metadata: {'shape': torch.Size([1]), 'dtype': torch.float32, 'device': device(type='cpu')}
recomputed metadata: {'shape': torch.Size([2]), 'dtype': torch.float32, 'device': device(type='cpu')}


The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/local/pytorch1/test/test_autograd.py", line 5692, in test_checkpoint_detects_non_determinism

soulitzer

import torch
from typing import Callable, Any
import contextlib
from torch.utils._python_dispatch import TorchDispatchMode
from typing import Dict, Tuple, Optional, Set
import weakref

_cloned: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
_original: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
_tid_to_weakhandle: Dict[Tuple[int, int], weakref.ReferenceType] = dict()

soulitzer

# Implements Alban's idea of making available the forward traceback
# corresponding to the execution of the current backwared node as a global

import torch
from torch import autograd
from torch.utils._python_dispatch import TorchDispatchMode

current_metadata = None

# Set up hooks so that during backward the global is properly set/unset

soulitzer

import syft as sy
import numpy as np
from uuid import uuid1
sy.logger.remove()

_syf_owner = None
_syf_user = None

def syf_login():
    global _syf_owner

soulitzer

[... slack complaining message is too long]

File ~/Users/jw3468/local/install/miniconda3/envs/pysyft/lib/python3.9/site-packages/urllib3/connectionpool.py:340, in HTTPConnectionPool._raise_timeout(self, err, url, timeout_value)
    339 if isinstance(err, SocketTimeout):
--> 340     raise ReadTimeoutError(
    341         self, url, "Read timed out. (read timeout=%s)" % timeout_value
    342     )
    344 # See the above comment about EAGAIN in Python 3. In Python 2 we have
    345 # to specifically catch it and throw the timeout error

soulitzer

from functools import wraps
import torch
from torch._decomp import decomposition_table
import torch.nn.functional as F
from torch.utils._pytree import tree_map
from torch.testing._internal.logging_tensor import LoggingTensor, capture_logs

# Goals:
# - we want something reusable that can compose with any subclass