Investigate unusual memory usage in rasa test

Rasa version:
2.3.0
Python version:
3.7.2
Operating system (windows, osx, ...):
Linux
Issue:
rasa test fails due to OOM, even though rasa train completes successfully. This suggests that something about rasa test is inefficient. The OOM error originates from tensorflow code, and research will work to bring this down (https://github.com/RasaHQ/rasa/issues/8025). However, it's still strange that test pushes this over the edge, when train doesn't. The tensor referenced in the error needs to be created in both train and test, but we can increase train well above the additional numbers and still train successfully so something must be off about test.
318 dialogues train, 41 test: train and test successful
400 dialogues train, 51 test: train successful, test OOM
480 dialogues, 60 test: train successful, test OOM
Error (including full traceback):
NOTE I suspect this error message isn't relevant, because it's a side-effect of rasa tests memory usage.
2021-02-04 03:45:01.282803: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at cwise_ops_common.h:134 : Resource exhausted: OOM when allocating tensor with shape[3374,49,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
Traceback (most recent call last):
  File "/opt/conda/bin/rasa", line 5, in <module>
    main()
  File "/home/fkr/rasa/rasa/__main__.py", line 116, in main
    cmdline_arguments.func(cmdline_arguments)
  File "/home/fkr/rasa/rasa/cli/test.py", line 108, in run_core_test
    additional_arguments=vars(args),
  File "/home/fkr/rasa/rasa/test.py", line 135, in test_core
    _agent = Agent.load(unpacked_model)
  File "/home/fkr/rasa/rasa/core/agent.py", line 447, in load
    if core_model
  File "/home/fkr/rasa/rasa/core/policies/ensemble.py", line 374, in load
    policy = policy_cls.load(policy_path, **context)
  File "/home/fkr/rasa/rasa/core/policies/ted_policy.py", line 827, in load
    finetune_mode=should_finetune,
  File "/home/fkr/rasa/rasa/utils/tensorflow/models.py", line 402, in load
    False if finetune_mode else True
  File "/home/fkr/rasa/rasa/utils/tensorflow/models.py", line 242, in fit
    self.train_summary_writer,
  File "/home/fkr/rasa/rasa/utils/tensorflow/models.py", line 438, in _batch_loop
    call_model_function(batch_in)
  File "/home/fkr/rasa/rasa/utils/tensorflow/models.py", line 300, in train_on_batch
    prediction_gradients = tape.gradient(prediction_loss, self.trainable_variables)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/backprop.py", line 1073, in gradient
    unconnected_gradients=unconnected_gradients)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/imperative_grad.py", line 77, in imperative_grad
    compat.as_str(unconnected_gradients.value))
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/backprop.py", line 162, in _gradient_function
    return grad_fn(mock_op, *out_grads)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py", line 1456, in _PowGrad
    return grad * y * math_ops.pow(x, y - 1), None
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py", line 1124, in binary_op_wrapper
    return func(x, y, name=name)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py", line 1456, in _mul_dispatch
    return multiply(x, y, name=name)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper
    return target(*args, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py", line 508, in multiply
    return gen_math_ops.mul(x, y, name)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 6166, in mul
    _ops.raise_from_not_ok_status(e, name)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 6843, in raise_from_not_ok_status
    six.raise_from(core._status_to_exception(e.code, message), None)
  File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[3374,49,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Mul]

Command or request that led to error:
`rasa test core`

Definition of Done (added by Enable squad)

[ ] Investigate and propose next steps

@Ghostvv anything you would add?

@koernerfelicia could you explain that tensorflow OOM is unrelated in this case

@Ghostvv I can try! Reason why it's unrelated is because this isn't a problem when we train on a number of dialogues that is greater than the number of total dialogues (train + test) that first cause a problem for test. If it were a tensorflow problem, we'd expect:

318 dialogues train, 41 test: train and test successful
400 dialogues train, 51 test: train successful, test OOM
480 dialogues, 60 test: train unsuccessful

however, we see:

318 dialogues train, 41 test: train and test successful
400 dialogues train, 51 test: train successful, test OOM
480 dialogues, 60 test: train successful, test OOM

From logging, we know that the tensor mentioned in the error message for setup 2. is smaller than tensors successfully created during train in setup 3.

Just looking over this briefly two things come to mind:

A tensor of shape [3374,49,512] has 3374 * 49 * 512 ~ 85 Million weights - definitely not a small tensor
nevertheless it's probably not this tensor itself but it's just the last drop that pushes it into OOM

Yes, good point. We have an issue related to bringing down these tensors sizes as well (https://github.com/RasaHQ/rasa/issues/8025).
We do know that tensors of this size are also being created during train successfully (and also for larger tensors). I can share those logs if you'd like. Let me know! :)

Hey @koernerfelicia 👋 What dataset are you using where this happens? Would you be able to share it? Has this issue come up on other datasets as well before?

Also couple more questions haha:

How are you calling the test? just rasa test from the command line? And is the model already trained at this point?
Is there by any chance a very long example in the test set? I am thinking the memory usage of tensorflow should not be affected as much by the number of stories. In the end your batch size determines how many stories you are handling at once. However, if you have a really big story in your test cases I could imagine it increasing the sequence length (and thus the size of some of your tensors) a lot; leading to OOM.

Hey! This has happened on MultiWOZ and Advising, specifically for e2e training and testing. I haven't tried other datasets, so I don't know if this is also a problem.
I don't think that there's a very long example in the test set, though I can get actual numbers for you. It seems unlikely for Advising, because those stories are pretty simple.
Here's my run.sh (converted to txt so that I can upload it in this comment)
run.txt

Okay, looks like the max story length for the subset of Advising is 15 turns (bot and user), whereas for the subset of MultiWOZ it's 30 turns

Thanks a lot Felicia for all the info, I have access to these two datasets. Were you just using the top N stories from those dataset splits as in the training data repo to create your crash statistics?
What's the config you are using?

Given this happens in e2e training, potentially some of the message lengths could be the culprit. What do you think?
At least in advising test some bot responses are very long:
- bot: 'Cryptography plays a fundamental role in building secure computing and communication systems. With its fascinating history through centuries and intriguing connections to deep mathematical ("how quickly can we factor an integer?") and philosophical ("what is randomness?") questions, Cryptography is an important and beautiful subject. With increasing concerns over privacy, security, and authenticity of data and communications in our wired (and wireless) society, cryptographic applications are bound to pervade our lives. Cryptography is, and will continue to be, a vast and exciting area of research in Computer Science and Mathematics. This course is an introduction to the art and science of cryptography. At the end of the course, students should be well-prepared to apply the core scientific principles of cryptography to build secure software and communication systems as well as to pursue more advanced courses and state of the art research in cryptography. This course will study fundamental concepts, algorithms, encryption schemes, and protocols in cryptography. Main topics include: symmetric (private key) encryption, public key encryption, hash functions, digital signatures, and key distribution. The course emphasizes a rigorous mathematical study of the various cryptographic schemes and their security in terms of algorithmic complexity. A nontrivial part of the course will be devoted to algorithmic and mathematical background from number theory and algebra needed to gain a solid understanding of cryptography. Popular cryptographic schemes such as AES and RSA will be highlighted and their security will be rigorously investigated. Detailed syllabus is available from the course web site (link below). This is a 4-credit course approved as an upper-level CS technical elective for undergraduate students in CS-ENGR and CS-LSA. This course is also approved as a cognate course for Math Majors. Advanced undergraduate and beginning graduate students in Computer Science and Engineering and Mathematics are invited to take this course. Graduate students in EECS can also take it as a 400-level elective course. Grading will be based on homework assignments, a mid-term, and a final project/term paper.'

Given this happens in e2e training, potentially some of the message lengths could be the culprit. What do you think?

I'm sure that doesn't help, but I'm not sure it explains why test fails when train doesn't. For MultiWOZ, the longest bot utterance is in the training data, not the test data. We're already convinced that these datasets and the tensors are large. What's weird is that this is a problem for test much sooner (in terms of dataset size progression) than it is for train. Do you know what I mean?

I should note that my numbers up there for number of stories are not quite correct. I'm sorry about this, it's an oversight on my part. In order to replicate this for MultiWOZ you'll need to set the flag to:
400
500
600

The script creates a train/val/test split, but some dialogues are ignored because they can't be neatly converted, which is why the numbers come out a bit wonky-looking. The number of dialogues I listed above isn't quite correct. These I think are right, though I don't think it should matter.
400 - 312, 41 test
500 - 393, 51 test
600 - 470 train, 63 test

To me it seems that the number of stories is secondary here, as you are using batched training anyway - please correct me if I am wrong. So if you use a batch size of 32 you always get 32 examples into your memory no matter if your entire dataset has 100 or 10000 examples.
What I found is that the dataset_to_rasa script sorts the examples according to their length (which I think is the conversation length) https://github.com/RasaHQ/training-data/blob/395f75e58e092eedd31cb09fc622baa5de4dbce4/public/Advising/scripts/advising_loader.py#L100
It would be easily imaginable that there are many more short dialogues in the larger training set and than in the smaller test set. So if you take the shortest n stories from train, you might get a max len thats much shorter than when you take some stories from test.
Just thinking out loud here. Will investigate a bit more

so those wonky numbers mean you are trying to take the top 400 but only get 312 train examples and 41 test examples?

I think this could make sense with your observations - taking the shortest 500 examples from train -> max len a, taking the shortest 500 examples from test (which has fewer examples than train so you get into larger stories quicker) -> max len b. and b >> a -> OOM

hmm for multiwoz there doesn't seem to be such sorting

so those wonky numbers mean you are trying to take the top 400 but only get 312 train examples and 41 test examples

I just assumed it was creating an 80/10/10 split, because that's what the numbers roughly look like. But now that I look closer it seems to be cutting off at "num_dialogues" and then splitting these by pulling the dialogue IDs from testListFile and valListFile (these come from MultiWOZ itself) here. I haven't figured out how it works out that there is a nice number of dialogues in test/val

I guess some dialogues are lost here

I think this could make sense with your observations

You'd expect test to succeed if we ran test on train data then, yes?

You'd expect test to succeed if we ran test on train data then, yes?
That would indeed be a great test for my hypothesis haha

rasa test core --debug --stories MultiWOZ/MW.500/data/stories.yml --no-plot --model experiments/MultiWOZ.MW.500.config_1/models/20210204-034433.tar.gz
2021-03-03 10:45:05.529383: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0
2021-03-03 10:45:10 INFO     rasa.model  - Loading model experiments/MultiWOZ.MW.500.config_1/models/20210204-034433.tar.gz...
2021-03-03 10:45:11 DEBUG    rasa.model  - Extracted model to '/tmp/tmpq2yrrgx4'.
/home/fkr/rasa/rasa/utils/train_utils.py:93: FutureWarning: `loss_type=softmax` is deprecated. Please update your configuration file to use`loss_type=cross_entropy` instead. (will be removed in 3.0.0)
  warn_until_version=NEXT_MAJOR_VERSION_FOR_DEPRECATIONS,
2021-03-03 10:45:16 DEBUG    rasa.utils.tensorflow.models  - Loading the model from /tmp/tmpq2yrrgx4/core/policy_0_TEDPolicy/ted_policy.tf_model with finetune_mode=False...
2021-03-03 10:45:16.846010: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2021-03-03 10:45:19.110765: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-03 10:45:19.111635: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:00:04.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2021-03-03 10:45:19.111693: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0
2021-03-03 10:45:19.166114: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.11
2021-03-03 10:45:19.192797: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2021-03-03 10:45:19.204650: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2021-03-03 10:45:19.254108: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2021-03-03 10:45:19.270168: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.11
2021-03-03 10:45:19.276527: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.8
2021-03-03 10:45:19.276703: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-03 10:45:19.277529: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-03 10:45:19.278262: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2021-03-03 10:45:19.300611: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency:<PHONE_NUMBER> Hz
2021-03-03 10:45:19.301738: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x562d78d405c0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-03-03 10:45:19.301768: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2021-03-03 10:45:19.556666: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-03 10:45:19.557584: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x562d7b7a22d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2021-03-03 10:45:19.557622: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7
2021-03-03 10:45:19.557918: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-03 10:45:19.558699: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:00:04.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2021-03-03 10:45:19.558801: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0
2021-03-03 10:45:19.558849: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.11
2021-03-03 10:45:19.558885: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2021-03-03 10:45:19.558924: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2021-03-03 10:45:19.558956: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2021-03-03 10:45:19.558989: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.11
2021-03-03 10:45:19.559033: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.8
2021-03-03 10:45:19.559124: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-03 10:45:19.559942: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-03 10:45:19.560655: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2021-03-03 10:45:19.562542: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0
2021-03-03 10:45:24.291850: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-03-03 10:45:24.291940: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0
2021-03-03 10:45:24.291961: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N
2021-03-03 10:45:24.301735: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-03 10:45:24.302588: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-03-03 10:45:24.303391: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10637 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)
2021-03-03 10:45:25.311424: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.11
TensorShape([3374, 49, 128])
TensorShape([3374, 49, 512])
TensorShape([3374, 49, 512])
TensorShape([3374, 49, 128])
TensorShape([3374, 49, 128])
TensorShape([1, 1, 128])
TensorShape([1, 1, 512])
TensorShape([1, 1, 512])
TensorShape([1, 1, 128])
TensorShape([1, 1, 128])
2021-03-03 10:45:37.729887: W tensorflow/core/common_runtime/bfc_allocator.cc:431] Allocator (GPU_0_bfc) ran out of memory trying to allocate 322.90MiB (rounded to 338587648)requested by op Mul
Current allocation summary follows.
2021-03-03 10:45:37.729972: I tensorflow/core/common_runtime/bfc_allocator.cc:970] BFCAllocator dump for GPU_0_bfc
2021-03-03 10:45:37.729990: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (256): 	Total Chunks: 173, Chunks in use: 173. 43.2KiB allocated for chunks. 43.2KiB in use in bin. 4.2KiB client-requested in use in bin.
2021-03-03 10:45:37.730001: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (512): 	Total Chunks: 85, Chunks in use: 84. 43.8KiB allocated for chunks. 43.2KiB in use in bin. 41.6KiB client-requested in use in bin.
2021-03-03 10:45:37.730011: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (1024): 	Total Chunks: 13, Chunks in use: 12. 18.2KiB allocated for chunks. 17.2KiB in use in bin. 14.4KiB client-requested in use in bin.
2021-03-03 10:45:37.730020: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (2048): 	Total Chunks: 24, Chunks in use: 24. 52.8KiB allocated for chunks. 52.8KiB in use in bin. 49.3KiB client-requested in use in bin.
2021-03-03 10:45:37.730045: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (4096): 	Total Chunks: 8, Chunks in use: 8. 36.0KiB allocated for chunks. 36.0KiB in use in bin. 31.2KiB client-requested in use in bin.
2021-03-03 10:45:37.730065: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (8192): 	Total Chunks: 6, Chunks in use: 6. 75.5KiB allocated for chunks. 75.5KiB in use in bin. 69.7KiB client-requested in use in bin.
2021-03-03 10:45:37.730079: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (16384): 	Total Chunks: 6, Chunks in use: 6. 143.0KiB allocated for chunks. 143.0KiB in use in bin. 139.1KiB client-requested in use in bin.
2021-03-03 10:45:37.730091: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (32768): 	Total Chunks: 5, Chunks in use: 5. 242.0KiB allocated for chunks. 242.0KiB in use in bin. 196.4KiB client-requested in use in bin.
2021-03-03 10:45:37.730101: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (65536): 	Total Chunks: 30, Chunks in use: 30. 2.04MiB allocated for chunks. 2.04MiB in use in bin. 1.87MiB client-requested in use in bin.
2021-03-03 10:45:37.730112: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (131072): 	Total Chunks: 4, Chunks in use: 4. 769.5KiB allocated for chunks. 769.5KiB in use in bin. 719.6KiB client-requested in use in bin.
2021-03-03 10:45:37.730122: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (262144): 	Total Chunks: 16, Chunks in use: 16. 4.18MiB allocated for chunks. 4.18MiB in use in bin. 4.01MiB client-requested in use in bin.
2021-03-03 10:45:37.730131: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (524288): 	Total Chunks: 21, Chunks in use: 21. 13.49MiB allocated for chunks. 13.49MiB in use in bin. 13.48MiB client-requested in use in bin.
2021-03-03 10:45:37.730157: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (1048576): 	Total Chunks: 16, Chunks in use: 16. 20.45MiB allocated for chunks. 20.45MiB in use in bin. 18.61MiB client-requested in use in bin.
2021-03-03 10:45:37.730167: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (2097152): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2021-03-03 10:45:37.730182: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (4194304): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2021-03-03 10:45:37.730193: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (8388608): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2021-03-03 10:45:37.730205: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (16777216): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2021-03-03 10:45:37.730219: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (33554432): 	Total Chunks: 2, Chunks in use: 1. 80.80MiB allocated for chunks. 39.10MiB in use in bin. 30.90MiB client-requested in use in bin.
2021-03-03 10:45:37.730232: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (67108864): 	Total Chunks: 65, Chunks in use: 64. 5.30GiB allocated for chunks. 5.22GiB in use in bin. 5.16GiB client-requested in use in bin.
2021-03-03 10:45:37.730246: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (134217728): 	Total Chunks: 1, Chunks in use: 0. 162.73MiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2021-03-03 10:45:37.730259: I tensorflow/core/common_runtime/bfc_allocator.cc:977] Bin (268435456): 	Total Chunks: 15, Chunks in use: 15. 4.81GiB allocated for chunks. 4.81GiB in use in bin. 4.73GiB client-requested in use in bin.
2021-03-03 10:45:37.730272: I tensorflow/core/common_runtime/bfc_allocator.cc:993] Bin for 322.90MiB was 256.00MiB, Chunk State:
2021-03-03 10:45:37.730283: I tensorflow/core/common_runtime/bfc_allocator.cc:1006] Next region of size<PHONE_NUMBER>0
2021-03-03 10:45:37.730300: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de0000 of size 1280 next 1
2021-03-03 10:45:37.730314: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de0500 of size 256 next 2
2021-03-03 10:45:37.730323: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de0600 of size 256 next 3
2021-03-03 10:45:37.730333: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de0700 of size 256 next 4
2021-03-03 10:45:37.730344: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de0800 of size 256 next 5
2021-03-03 10:45:37.730353: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de0900 of size 256 next 6
2021-03-03 10:45:37.730363: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de0a00 of size 256 next 7
2021-03-03 10:45:37.730373: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de0b00 of size 256 next 8
2021-03-03 10:45:37.730383: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de0c00 of size 256 next 9
2021-03-03 10:45:37.730393: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de0d00 of size 256 next 10
2021-03-03 10:45:37.730403: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de0e00 of size 256 next 11
2021-03-03 10:45:37.730412: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de0f00 of size 256 next 12
2021-03-03 10:45:37.730423: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1000 of size 256 next 13
2021-03-03 10:45:37.730433: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1100 of size 256 next 14
2021-03-03 10:45:37.730443: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1200 of size 256 next 15
2021-03-03 10:45:37.730452: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1300 of size 256 next 16
2021-03-03 10:45:37.730462: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1400 of size 256 next 17
2021-03-03 10:45:37.730472: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1500 of size 256 next 18
2021-03-03 10:45:37.730482: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1600 of size 256 next 19
2021-03-03 10:45:37.730493: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1700 of size 256 next 20
2021-03-03 10:45:37.730503: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1800 of size 256 next 21
2021-03-03 10:45:37.730513: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1900 of size 256 next 22
2021-03-03 10:45:37.730541: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1a00 of size 256 next 23
2021-03-03 10:45:37.730550: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1b00 of size 256 next 24
2021-03-03 10:45:37.730556: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1c00 of size 256 next 25
2021-03-03 10:45:37.730563: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1d00 of size 256 next 26
2021-03-03 10:45:37.730569: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1e00 of size 256 next 27
2021-03-03 10:45:37.730576: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de1f00 of size 256 next 28
2021-03-03 10:45:37.730582: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2000 of size 256 next 29
2021-03-03 10:45:37.730602: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2100 of size 256 next 30
2021-03-03 10:45:37.730609: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2200 of size 256 next 31
2021-03-03 10:45:37.730615: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2300 of size 256 next 32
2021-03-03 10:45:37.730622: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2400 of size 256 next 33
2021-03-03 10:45:37.730629: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2500 of size 256 next 34
2021-03-03 10:45:37.730636: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2600 of size 256 next 35
2021-03-03 10:45:37.730645: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2700 of size 256 next 36
2021-03-03 10:45:37.730653: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2800 of size 256 next 37
2021-03-03 10:45:37.730661: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2900 of size 256 next 40
2021-03-03 10:45:37.730668: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2a00 of size 256 next 43
2021-03-03 10:45:37.730678: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2b00 of size 256 next 44
2021-03-03 10:45:37.730689: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2c00 of size 256 next 38
2021-03-03 10:45:37.730697: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2d00 of size 256 next 39
2021-03-03 10:45:37.730706: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de2e00 of size 1280 next 41
2021-03-03 10:45:37.730716: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de3300 of size 1024 next 42
2021-03-03 10:45:37.730758: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de3700 of size 1024 next 45
2021-03-03 10:45:37.730765: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de3b00 of size 256 next 49
2021-03-03 10:45:37.730779: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de3c00 of size 256 next 52
2021-03-03 10:45:37.730786: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de3d00 of size 3584 next 46
2021-03-03 10:45:37.730801: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de4b00 of size 256 next 47
2021-03-03 10:45:37.730809: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de4c00 of size 256 next 48
2021-03-03 10:45:37.730817: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de4d00 of size 4352 next 50
2021-03-03 10:45:37.730828: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de5e00 of size 4096 next 51
2021-03-03 10:45:37.730843: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de6e00 of size 4096 next 54
2021-03-03 10:45:37.730854: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de7e00 of size 2304 next 53
2021-03-03 10:45:37.730865: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de8700 of size 256 next 55
2021-03-03 10:45:37.730875: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de8800 of size 2304 next 56
2021-03-03 10:45:37.730885: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de9100 of size 256 next 57
2021-03-03 10:45:37.730896: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de9200 of size 256 next 58
2021-03-03 10:45:37.730906: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de9300 of size 256 next 59
2021-03-03 10:45:37.730914: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de9400 of size 256 next 60
2021-03-03 10:45:37.730924: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de9500 of size 2304 next 61
2021-03-03 10:45:37.730937: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de9e00 of size 256 next 62
2021-03-03 10:45:37.730948: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03de9f00 of size 2304 next 63
2021-03-03 10:45:37.730958: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03dea800 of size 2304 next 64
2021-03-03 10:45:37.730968: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03deb100 of size 256 next 67
2021-03-03 10:45:37.730978: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03deb200 of size 256 next 76
2021-03-03 10:45:37.730988: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03deb300 of size 256 next 81
2021-03-03 10:45:37.730998: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03deb400 of size 256 next 86
2021-03-03 10:45:37.731009: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03deb500 of size 256 next 87
2021-03-03 10:45:37.731019: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03deb600 of size 256 next 88
2021-03-03 10:45:37.731029: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03deb700 of size 768 next 65
2021-03-03 10:45:37.731039: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03deba00 of size 2304 next 66
2021-03-03 10:45:37.731049: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03dec300 of size 2304 next 68
2021-03-03 10:45:37.731059: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03decc00 of size 2304 next 69
2021-03-03 10:45:37.731069: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ded500 of size 13568 next 70
2021-03-03 10:45:37.731080: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03df0a00 of size 13568 next 77
2021-03-03 10:45:37.731091: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03df3f00 of size 512 next 103
2021-03-03 10:45:37.731105: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03df4100 of size 256 next 108
2021-03-03 10:45:37.731112: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03df4200 of size 84224 next 79
2021-03-03 10:45:37.731125: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03e08b00 of size 677120 next 74
2021-03-03 10:45:37.731135: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eae000 of size 256 next 101
2021-03-03 10:45:37.731148: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eae100 of size 256 next 114
2021-03-03 10:45:37.731159: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eae200 of size 256 next 115
2021-03-03 10:45:37.731169: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eae300 of size 512 next 116
2021-03-03 10:45:37.731180: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eae500 of size 256 next 121
2021-03-03 10:45:37.731190: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eae600 of size 256 next 122
2021-03-03 10:45:37.731199: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eae700 of size 256 next 123
2021-03-03 10:45:37.731221: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eae800 of size 256 next 125
2021-03-03 10:45:37.731231: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eae900 of size 256 next 126
2021-03-03 10:45:37.731241: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eaea00 of size 256 next 127
2021-03-03 10:45:37.731251: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eaeb00 of size 256 next 128
2021-03-03 10:45:37.731261: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eaec00 of size 512 next 137
2021-03-03 10:45:37.731271: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eaee00 of size 512 next 138
2021-03-03 10:45:37.731280: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eaf000 of size 256 next 163
2021-03-03 10:45:37.731290: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eaf100 of size 512 next 166
2021-03-03 10:45:37.731300: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eaf300 of size 512 next 176
2021-03-03 10:45:37.731310: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eaf500 of size 512 next 177
2021-03-03 10:45:37.731320: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eaf700 of size 256 next 189
2021-03-03 10:45:37.731330: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eaf800 of size 2048 next 188
2021-03-03 10:45:37.731340: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eb0000 of size 256 next 195
2021-03-03 10:45:37.731350: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eb0100 of size 256 next 196
2021-03-03 10:45:37.731361: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eb0200 of size 512 next 204
2021-03-03 10:45:37.731371: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eb0400 of size 512 next 216
2021-03-03 10:45:37.731377: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eb0600 of size 512 next 217
2021-03-03 10:45:37.731383: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eb0800 of size 256 next 228
2021-03-03 10:45:37.731389: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eb0900 of size 27136 next 235
2021-03-03 10:45:37.731400: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eb7300 of size 256 next 243
2021-03-03 10:45:37.731410: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eb7400 of size 256 next 244
2021-03-03 10:45:37.731420: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03eb7500 of size 13568 next 251
2021-03-03 10:45:37.731430: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebaa00 of size 512 next 285
2021-03-03 10:45:37.731440: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebac00 of size 512 next 286
2021-03-03 10:45:37.731450: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebae00 of size 512 next 287
2021-03-03 10:45:37.731460: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebb000 of size 512 next 283
2021-03-03 10:45:37.731470: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebb200 of size 512 next 288
2021-03-03 10:45:37.731480: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebb400 of size 512 next 289
2021-03-03 10:45:37.731488: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebb600 of size 256 next 290
2021-03-03 10:45:37.731497: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebb700 of size 256 next 291
2021-03-03 10:45:37.731507: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebb800 of size 256 next 292
2021-03-03 10:45:37.731517: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebb900 of size 512 next 293
2021-03-03 10:45:37.731534: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebbb00 of size 512 next 294
2021-03-03 10:45:37.731545: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebbd00 of size 512 next 295
2021-03-03 10:45:37.731553: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebbf00 of size 512 next 296
2021-03-03 10:45:37.731562: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebc100 of size 512 next 297
2021-03-03 10:45:37.731572: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebc300 of size 512 next 301
2021-03-03 10:45:37.731583: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebc500 of size 512 next 305
2021-03-03 10:45:37.731593: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebc700 of size 512 next 309
2021-03-03 10:45:37.731601: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebc900 of size 256 next 310
2021-03-03 10:45:37.731620: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebca00 of size 256 next 311
2021-03-03 10:45:37.731642: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebcb00 of size 256 next 312
2021-03-03 10:45:37.731652: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebcc00 of size 256 next 313
2021-03-03 10:45:37.731668: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebcd00 of size 256 next 314
2021-03-03 10:45:37.731679: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebce00 of size 256 next 315
2021-03-03 10:45:37.731686: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebcf00 of size 512 next 316
2021-03-03 10:45:37.731696: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebd100 of size 512 next 319
2021-03-03 10:45:37.731706: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebd300 of size 512 next 320
2021-03-03 10:45:37.731716: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebd500 of size 512 next 322
2021-03-03 10:45:37.731726: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebd700 of size 512 next 323
2021-03-03 10:45:37.731735: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebd900 of size 512 next 324
2021-03-03 10:45:37.731745: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebdb00 of size 512 next 325
2021-03-03 10:45:37.731755: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebdd00 of size 512 next 326
2021-03-03 10:45:37.731765: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebdf00 of size 512 next 327
2021-03-03 10:45:37.731774: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebe100 of size 768 next 117
2021-03-03 10:45:37.731784: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ebe400 of size 95488 next 71
2021-03-03 10:45:37.731794: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03ed5900 of size 647936 next 72
2021-03-03 10:45:37.731805: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b03f73c00 of size 65536 next 119
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2021-03-03 10:45:37.733270: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b4d6c3a00 of size 512 next 361
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2021-03-03 10:45:37.733331: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b4d6c4500 of size 256 next 367
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2021-03-03 10:45:37.734461: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] Free  at b4d825500 of size 1024 next 473
2021-03-03 10:45:37.734472: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b4d825900 of size 38656 next 354
2021-03-03 10:45:37.734489: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b4d82f000 of size 262144 next 355
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2021-03-03 10:45:37.734507: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b4d8af000 of size 1059328 next 439
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2021-03-03 10:45:37.734536: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b4dab4400 of size 4096 next 476
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2021-03-03 10:45:37.734557: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b4daf5400 of size 512 next 464
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2021-03-03 10:45:37.734578: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b4db35600 of size 262144 next 481
2021-03-03 10:45:37.734589: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] Free  at b4db75600 of size 512 next 487
2021-03-03 10:45:37.734599: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b4db75800 of size 512 next 491
2021-03-03 10:45:37.734609: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] Free  at b4db75a00 of size 43721728 next 131
2021-03-03 10:45:37.734620: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b50527e00 of size 41000704 next 129
2021-03-03 10:45:37.734631: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at b52c41d00 of size 84646912 next 130
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2021-03-03 10:45:37.734855: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at bc264f900 of size 84646912 next 172
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2021-03-03 10:45:37.734953: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at bf4d91100 of size 338587648 next 192
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2021-03-03 10:45:37.735213: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] Free  at d46e2e500 of size 84646912 next 461
2021-03-03 10:45:37.735223: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at d4bee8100 of size 84646912 next 480
2021-03-03 10:45:37.735233: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at d50fa1d00 of size 423234560 next 479
2021-03-03 10:45:37.735244: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at d6a342900 of size 338587648 next 486
2021-03-03 10:45:37.735255: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] InUse at d7e629900 of size 338587648 next 489
2021-03-03 10:45:37.735271: I tensorflow/core/common_runtime/bfc_allocator.cc:1026] Free  at d92910900 of size 170636288 next<PHONE_NUMBER>3709551615
2021-03-03 10:45:37.735282: I tensorflow/core/common_runtime/bfc_allocator.cc:1031]      Summary of in-use Chunks by size:
2021-03-03 10:45:37.735302: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 173 Chunks of size 256 totalling 43.2KiB
2021-03-03 10:45:37.735315: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 79 Chunks of size 512 totalling 39.5KiB
2021-03-03 10:45:37.735326: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 5 Chunks of size 768 totalling 3.8KiB
2021-03-03 10:45:37.735338: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 3 Chunks of size 1024 totalling 3.0KiB
2021-03-03 10:45:37.735349: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 3 Chunks of size 1280 totalling 3.8KiB
2021-03-03 10:45:37.735360: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 6 Chunks of size 1792 totalling 10.5KiB
2021-03-03 10:45:37.735368: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 14 Chunks of size 2048 totalling 28.0KiB
2021-03-03 10:45:37.735379: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 8 Chunks of size 2304 totalling 18.0KiB
2021-03-03 10:45:37.735389: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 3328 totalling 3.2KiB
2021-03-03 10:45:37.735398: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 3584 totalling 3.5KiB
2021-03-03 10:45:37.735409: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 5 Chunks of size 4096 totalling 20.0KiB
2021-03-03 10:45:37.735420: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 4352 totalling 4.2KiB
2021-03-03 10:45:37.735431: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 5376 totalling 5.2KiB
2021-03-03 10:45:37.735442: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 6656 totalling 6.5KiB
2021-03-03 10:45:37.735453: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 2 Chunks of size 10240 totalling 20.0KiB
2021-03-03 10:45:37.735464: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 3 Chunks of size 13568 totalling 39.8KiB
2021-03-03 10:45:37.735476: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 16128 totalling 15.8KiB
2021-03-03 10:45:37.735487: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 17408 totalling 17.0KiB
2021-03-03 10:45:37.735498: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 25088 totalling 24.5KiB
2021-03-03 10:45:37.735509: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 3 Chunks of size 25600 totalling 75.0KiB
2021-03-03 10:45:37.735529: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 27136 totalling 26.5KiB
2021-03-03 10:45:37.735541: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 38656 totalling 37.8KiB
2021-03-03 10:45:37.735550: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 40704 totalling 39.8KiB
2021-03-03 10:45:37.735557: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 45568 totalling 44.5KiB
2021-03-03 10:45:37.735565: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 2 Chunks of size 61440 totalling 120.0KiB
2021-03-03 10:45:37.735575: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 23 Chunks of size 65536 totalling 1.44MiB
2021-03-03 10:45:37.735582: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 71680 totalling 70.0KiB
2021-03-03 10:45:37.735590: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 84224 totalling 82.2KiB
2021-03-03 10:45:37.735602: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 85248 totalling 83.2KiB
2021-03-03 10:45:37.735613: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 86528 totalling 84.5KiB
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2021-03-03 10:45:37.735629: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 99840 totalling 97.5KiB
2021-03-03 10:45:37.735636: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 107520 totalling 105.0KiB
2021-03-03 10:45:37.735644: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 175872 totalling 171.8KiB
2021-03-03 10:45:37.735653: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 2 Chunks of size 192768 totalling 376.5KiB
2021-03-03 10:45:37.735660: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 226560 totalling 221.2KiB
2021-03-03 10:45:37.735668: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 14 Chunks of size 262144 totalling 3.50MiB
2021-03-03 10:45:37.735676: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 310784 totalling 303.5KiB
2021-03-03 10:45:37.735687: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 399360 totalling 390.0KiB
2021-03-03 10:45:37.735699: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 647936 totalling 632.8KiB
2021-03-03 10:45:37.735710: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 8 Chunks of size 661504 totalling 5.05MiB
2021-03-03 10:45:37.735721: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 8 Chunks of size 674816 totalling 5.15MiB
2021-03-03 10:45:37.735729: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 3 Chunks of size 677120 totalling 1.94MiB
2021-03-03 10:45:37.735738: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 771072 totalling 753.0KiB
2021-03-03 10:45:37.735745: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 1054464 totalling 1.00MiB
2021-03-03 10:45:37.735752: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 5 Chunks of size 1059328 totalling 5.05MiB
2021-03-03 10:45:37.735766: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 1065984 totalling 1.02MiB
2021-03-03 10:45:37.735777: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 2 Chunks of size 1156352 totalling 2.21MiB
2021-03-03 10:45:37.735788: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 1347584 totalling 1.29MiB
2021-03-03 10:45:37.735799: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 6 Chunks of size 1727488 totalling 9.88MiB
2021-03-03 10:45:37.735812: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 41000704 totalling 39.10MiB
2021-03-03 10:45:37.735823: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 5 Chunks of size 82919424 totalling 395.39MiB
2021-03-03 10:45:37.735838: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 54 Chunks of size 84646912 totalling 4.26GiB
2021-03-03 10:45:37.735849: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 103649280 totalling 98.85MiB
2021-03-03 10:45:37.735861: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 4 Chunks of size 129615616 totalling 494.44MiB
2021-03-03 10:45:37.735872: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 14 Chunks of size 338587648 totalling 4.41GiB
2021-03-03 10:45:37.735884: I tensorflow/core/common_runtime/bfc_allocator.cc:1034] 1 Chunks of size 423234560 totalling 403.63MiB
2021-03-03 10:45:37.735896: I tensorflow/core/common_runtime/bfc_allocator.cc:1038] Sum Total of in-use chunks: 10.11GiB
2021-03-03 10:45:37.735906: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] total_region_allocated_bytes_:<PHONE_NUMBER>0 memory_limit_:<PHONE_NUMBER>8 available bytes: 128 curr_region_allocation_bytes_:<PHONE_NUMBER>2
2021-03-03 10:45:37.735925: I tensorflow/core/common_runtime/bfc_allocator.cc:1046] Stats:
Limit:<PHONE_NUMBER>8
InUse:<PHONE_NUMBER>6
MaxInUse:<PHONE_NUMBER>0
NumAllocs:                        1238
MaxAllocSize:                423234560
Reserved:                            0
PeakReserved:                        0
LargestFreeBlock:                    0

2021-03-03 10:45:37.735974: W tensorflow/core/common_runtime/bfc_allocator.cc:439] ***************************************************************************************************_
2021-03-03 10:45:37.736015: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at cwise_ops_common.h:134 : Resource exhausted: OOM when allocating tensor with shape[3374,49,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
Traceback (most recent call last):
  File "/opt/conda/bin/rasa", line 5, in <module>
    main()
  File "/home/fkr/rasa/rasa/__main__.py", line 116, in main
    cmdline_arguments.func(cmdline_arguments)
  File "/home/fkr/rasa/rasa/cli/test.py", line 108, in run_core_test
    additional_arguments=vars(args),
  File "/home/fkr/rasa/rasa/test.py", line 135, in test_core
    _agent = Agent.load(unpacked_model)
  File "/home/fkr/rasa/rasa/core/agent.py", line 447, in load
    if core_model
  File "/home/fkr/rasa/rasa/core/policies/ensemble.py", line 374, in load
    policy = policy_cls.load(policy_path, **context)
  File "/home/fkr/rasa/rasa/core/policies/ted_policy.py", line 847, in load
    finetune_mode=should_finetune,
  File "/home/fkr/rasa/rasa/utils/tensorflow/models.py", line 404, in load
    False if finetune_mode else True
  File "/home/fkr/rasa/rasa/utils/tensorflow/models.py", line 244, in fit
    self.train_summary_writer,
  File "/home/fkr/rasa/rasa/utils/tensorflow/models.py", line 440, in _batch_loop
    call_model_function(batch_in)
  File "/home/fkr/rasa/rasa/utils/tensorflow/models.py", line 302, in train_on_batch
    prediction_gradients = tape.gradient(prediction_loss, self.trainable_variables)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/backprop.py", line 1073, in gradient
    unconnected_gradients=unconnected_gradients)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/imperative_grad.py", line 77, in imperative_grad
    compat.as_str(unconnected_gradients.value))
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/backprop.py", line 162, in _gradient_function
    return grad_fn(mock_op, *out_grads)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py", line 1456, in _PowGrad
    return grad * y * math_ops.pow(x, y - 1), None
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py", line 1124, in binary_op_wrapper
    return func(x, y, name=name)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py", line 1456, in _mul_dispatch
    return multiply(x, y, name=name)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper
    return target(*args, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py", line 508, in multiply
    return gen_math_ops.mul(x, y, name)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 6166, in mul
    _ops.raise_from_not_ok_status(e, name)
  File "/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 6843, in raise_from_not_ok_status
    six.raise_from(core._status_to_exception(e.code, message), None)
  File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[3374,49,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Mul]

Well that hypothesis is dead then :D

😅 Do you have access to the rasa-research google cloud group? If not, it might make sense to ask Alan for access, then you could ssh into e2e-exp-fkr and try some things out

To me it seems that the number of stories is secondary here

Also, I think this isn't quite true. The tensor in question grows with the number of stories, as each unique bot utterance is added to action_names_or_texts, which influences the size of the tensor. Although I guess we have agreed that tensor is more a symptom than a cause... not sure

To me it seems that the number of stories is secondary here

Also, I think this isn't quite true. The tensor in question grows with the number of stories, as each unique bot utterance is added to action_names_or_texts, which influences the size of the tensor. Although I guess we have agreed that tensor is more a symptom than a cause... not sure

That's good to know! Will think and investigate more

@twerkmeister I did some profiling for 100 dialogues, expecting that test would use more memory than train. As you can see, that's not the case.

I guess there may be something wrong with load/fit, because it seems that OOM is before the stories are featurized.

The OOM crash occurs even if only one story is tested.

I'd say this means we can close the issue and have someone from research dig into whether fit is doing something weird? What do you think?

Hey @koernerfelicia 👋
This is fairly in line with my own findings. I was trying to replicate the issue and noticed that the memory consumption was not higher during test than during train for the Multiwoz subsets I tested with. Also I managed to run test on the 51 stories after training on the 390 stories, but I was using cpu only, and possibly there was no OOM due to swapping or sth. Just used 1 epoch during training to keep time manageable on the cpu.
The thing that was most striking looking at memory was that during training my memory consumption spiked significantly during the first dataset loading here in this line: https://github.com/RasaHQ/rasa/blob/2ef804000087197f070e1e6a6bd0b6f7a3925551/rasa/utils/tensorflow/models.py#L466
Had 16GB+ memory consumption for the 390 stories here. Whereas when I was using 40 stories it was just about 1.4GB. Might be worth investigating too.
Also, my reading of the error log is similar in the sense that the OOM already happens during the loading time. Would be interesting to check whether it also happens in other instances where you load the model, like for interactive training or so? In any case there could be something that happens before loading or during loading that isn't properly cleaned up and then clutters the memory

@koernerfelicia did you use https://pypi.org/project/memory-profiler/ to do the profiling and plots? Does that tool actually capture gpu memory as well?

@koernerfelicia So I looked into the eager execution, and it seems it is indeed active when loading the model:
As far as I can tell, this part just takes the default argument of the load function for should_finetune which is False. https://github.com/RasaHQ/rasa/blob/8b9238f9e0138c1e8594566d043e5d2e4f2e004d/rasa/core/policies/ted_policy.py#L857-L869

which then turns into eager mode here
https://github.com/RasaHQ/rasa/blob/16aca813f5aaac5b3c1a65fa235c5b6ba91d995a/rasa/utils/tensorflow/models.py#L394-L406

which in turn seems to have some influence on the construction of the graph. Note that we are not seeing this debug "Building tensorflow {phase} graph..." output in your logs. So it seems eager mode was indeed activated.
https://github.com/RasaHQ/rasa/blob/16aca813f5aaac5b3c1a65fa235c5b6ba91d995a/rasa/utils/tensorflow/models.py#L449-L470

I also noticed that the second code snippet, the call for model.fit, has changed completely in the main branch compared to 2.3.0 which you seem to be running on. Possibly that will already make a difference aswell!

Currently running train/test on 100 dialogues with nvidia-smi gpu query every second. I'll let you know what I find.

has changed completely in the main branch compared to 2.3.0 which you seem to be running on

That's my bad for not updating this issue, but I have somewhat periodically been pulling from main with no discernible change in this behaviour, so I don't think this has fixed itself in 2.4 :( . I pulled latest before starting the above experiment, so we will see soon whether this is a problem still.

@twerkmeister you were absolutely right about this:

I also noticed that the second code snippet, the call for model.fit, has changed completely in the main branch compared to 2.3.0 which you seem to be running on. Possibly that will already make a difference aswell!

test now runs successfully, so I think we can close this particular issue