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David Berard davidberard98

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<eval_with_key>.0:5: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at /scratch/dberard/dynamo38/pytorch/aten/src/ATen/native/cudnn/RNN.cpp:982.)
lstm = torch.lstm(permute, (zeros, zeros_1), [self_model_lstm_lstm_flat_weights_0_, self_model_lstm_lstm_flat_weights_1_, self_model_lstm_lstm_flat_weights_2_, self_model_lstm_lstm_flat_weights_3_, self_model_lstm_lstm_flat_weights_4_, self_model_lstm_lstm_flat_weights_5_, self_model_lstm_lstm_flat_weights_6_, self_model_lstm_lstm_flat_weights_7_, self_model_lstm_lstm_flat_weights_8_, self_model_lstm_lstm_flat_weights_9_, self_model_lstm_lstm_flat_weights_10_, self_model_lstm_lstm_flat_weights_11_, self_model_lstm_lstm_flat_weights_12_, self_model_lstm_lstm_flat_weights_13_, self_model_lstm_lstm_flat_weights_14_, self_model_lstm_lstm_flat_weights_15_], True, 2, 0.
from math import inf
import torch
from torch import tensor, device
import torch.fx as fx
import torch._dynamo
from torch._dynamo.testing import rand_strided
from torch._dynamo.debug_utils import run_fwd_maybe_bwd
from torch._dynamo.debug_utils import same_two_models
from math import inf
import torch
from torch import tensor, device
import torch.fx as fx
import torch._dynamo
from torch._dynamo.testing import rand_strided
from torch._dynamo.debug_utils import run_fwd_maybe_bwd
from torch._dynamo.debug_utils import same_two_models
FullyShardedDataParallel(
(_fsdp_wrapped_module): T5ForConditionalGeneration(
(shared): Embedding(32128, 1024)
(encoder): T5Stack(
(embed_tokens): Embedding(32128, 1024)
(block): ModuleList(
(0): FullyShardedDataParallel(
(_fsdp_wrapped_module): T5Block(
(layer): ModuleList(
(0): T5LayerSelfAttention(
We can make this file beautiful and searchable if this error is corrected: No commas found in this CSV file in line 0.
Metric;111fe61602;7bc72f5a2f
nnc-dynamic:autogen-0;0.14310094044776633;0.14308511896524578
nnc-dynamic:autogen-1;0.11164433404337615;0.11165716196410358
nnc-dynamic:autogen-10;0.017939746397314594;0.017773296852828933
nnc-dynamic:autogen-11;0.02166838520206511;0.021501831093337385
nnc-dynamic:autogen-12;0.12938609847333285;0.12939167249714956
nnc-dynamic:autogen-13;1.8119537853635848;1.8118110403884202
nnc-dynamic:autogen-14;7.227453680243343;7.228049130644649
nnc-dynamic:autogen-15;0.023439827701076863;0.02318546730093658
nnc-dynamic:autogen-16;0.24199218105059117;0.2419714879943058
This file has been truncated, but you can view the full file.
WARNING:__main__:Sequence Length not defined for MobileBertForMaskedLM. Choosing 128 arbitrarily
[2022-11-07 20:06:13,575] torch._dynamo.testing: [WARNING] High loss value alert - 10.43. Can result in unstable gradients.
cuda train MobileBertForMaskedLM [2022-11-07 20:06:15,061] torch._dynamo.testing: [WARNING] High loss value alert - 10.43. Can result in unstable gradients.
[2022-11-07 20:06:16,693] torch._dynamo.testing: [WARNING] High loss value alert - 10.43. Can result in unstable gradients.
[2022-11-07 20:06:18,063] torch._dynamo.testing: [WARNING] High loss value alert - 10.43. Can result in unstable gradients.
[2022-11-07 20:06:19,501] torch._dynamo.eval_frame: [DEBUG] skipping __init__ /data/home/dberard/miniconda/envs/dynamo38/lib/python3.8/contextlib.py
[2022-11-07 20:06:19,501] torch._dynamo.eval_frame: [DEBUG] skipping __enter__ /data/home/dberard/miniconda/envs/dynamo38/lib/python3.8/contextlib.py
[2022-11-07 20:06:19,506] torch._dynamo.symbolic_convert: [INFO] Step 1: torchdynamo s
/data/home/dberard/miniconda/envs/bench-fast/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
warnings.warn(
/data/home/dberard/miniconda/envs/bench-fast/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
[W kineto_shim.cpp:330] Profiler is not initialized: skipping step() invocation
STAGE:2022-11-01 01:39:13 3461:3461 ActivityProfilerController.cpp:294] Completed Stage: Warm Up
STAGE:2022-11-01 01:39:14 3461:3461 ActivityProfilerController.cpp:300] Completed Stage: Collection
STAGE:2022-11-01 01:39:16 3461:3461 output_json.cpp:417] C
/data/home/dberard/miniconda/envs/bench-fast/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
warnings.warn(
/data/home/dberard/miniconda/envs/bench-fast/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
/data/home/dberard/miniconda/envs/bench-fast/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
warnings.warn(
/data/home/dberard/miniconda/envs/bench-fast/lib/python3.8/site-packages/torchvisi
/data/home/dberard/miniconda/envs/bench-fast/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.
warnings.warn(
/data/home/dberard/miniconda/envs/bench-fast/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
submitit ERROR (2022-10-03 23:44:18,682) - Submitted job triggered an exception
ERROR > Submitted job triggered an exception
Traceback (most recent call last):
File "/data/home/dberard/miniconda/envs/bench-fast/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/data/home/dberard/
import torch
import torchdynamo
import argparse
import os
import logging
from torch.profiler import profile, ProfilerActivity, tensorboard_trace_handler
# torchdynamo.config.verbose = True