* [GanH][Easy]: Add assertion to adaptive weighting layer
0 weight causes numeric instability and exploding ne
* [Easy] Add cast op before computing norm in diagnose options
As LpNorm only takes floats we add a manual casting here.
* Introduce a new caching device allocator
`cudaMalloc` and `cudaFree` calls are slow, and become slower the
more GPUs there are. Essentially, they grab a host-wide (not device-wide) lock
because GPU memory is transparently shared across all GPUs. Normally, this
isn't much of a concern since workloads allocate memory upfront, and reuse it
during later computation.
However, under some computation models (specifically, memory conserving
approaches like checkpoint-and-recompute, see
https://medium.com/@yaroslavvb/fitting-larger-networks-into-memory-583e3c758ff9)
this assumption is no longer true. In these situations, `cudaMalloc` and
`cudaFree` are common and frequent. Furthermore, in data parallel contexts,
these calls happen at nearly the same time from all GPUs worsening lock
contention.
A common solution to this problem is to add a custom allocator. In fact,
nVIDIA provides one out of the box: CUB, which Caffe2 already supports.
Unfortunately, the CUB allocator suffers from very high fragmentation. This is
primarily because it is a "buddy" allocator which neither splits nor merges
free cached blocks. Study
https://github.com/NVlabs/cub/blob/1.8.0/cub/util_allocator.cuh#L357 if you
want to convince yourself.
This diff adapts a caching allocator from the Torch codebase
https://github.com/torch/cutorch/blob/master/lib/THC/THCCachingAllocator.cpp
which does splitting and merging and ends up working really well, at least for
workloads like the checkpoint-and-recompute computation models noted above.
I simplified the implementation a little bit, made it a bit more C++-like. I
also removed a bunch of stream synchronization primitives for this diff. I
plan to add them back in subsequent diffs.
* Report reader progress in fblearner workflows
Integrate with fblearner progress reporting API and add support to report training progress from reader nodes.
If reader is constructed with batch limits, report based on finished batch vs total batch. The finished batch may be more than total batch because we evaludate if we should stop processing everytime we dequeue a split.
If no limit for the reader, report based on finished splits (Hive files) vs total splits. This is fairly accurate.
* [GanH][Diagnose]: fix plotting
1. ganh diagnose needs to set plot options
2. modifier's blob name is used for metric field can need to be fixed before
generating net
* Automatic update of fbcode/onnx to 985af3f5a0f7e7d29bc0ee6b13047e7ead9c90c8
* Make CompositeReader stops as soon as one reader finishes
Previously, CompositeReader calls all readers before stopping. It results in flaky test since the last batch may be read by different threads; resulting in dropped data.
* [dper] make sure loss is not nan
as desc.
* [rosetta2] [mobile-vision] Option to export NHWC order for RoIWarp/RoIAlign
Thanks for finding this @stzpz and @wangyanghan. Looks like NHWC is more
optimized. For OCR though it doesn't yet help since NHWC uses more mem b/w but
will soon become important.
* Intra-op parallel FC operator
Intra-op parallel FC operator
* [C2 Proto] extra info in device option
passing extra information in device option
design doc: https://fb.quip.com/yAiuAXkRXZGx
* Unregister MKL fallbacks for NCHW conversions
* Tracing for more executors
Modified Tracer to work with other executors and add more tracing
* Remove ShiftActivationDevices()
* Check for blob entry iff it is present
When processing the placeholders ops, ignore if the blob is not present in the blob_to_device.
* Internalize use of eigen tensor
Move use of eigen tensor out of the header file so we don't get template partial specialization errors when building other libraries.
* feature importance for transformed features.
* - Fix unused parameter warnings
The changes in this diff comments out unused parameters.
This will allow us to enable -Wunused-parameter as error.
#accept2ship
* add opencv dependencies to caffe2
The video input op requires additional opencv packages. This is to add them to
cmake so that it can build
* Add clip_by_value option in gradient clipping
Add clip_by_value option in gradient clipping
when the value is bigger than max or smaller than min, do the clip
* std::round compat
Summary: Came across this bug in doc when I was figuring out NetBuilder form the code.
Reviewed By: volkhin
Differential Revision: D6341821
fbshipit-source-id: 8818f3d92681366bfe7b90d9d4da9f68ef6e4672
Summary:
Adding backward pass support for If operator:
- Implemented necessary changes to Do operator and generation of gradient Do operator to properly forward gradient blobs in and out of subnet
- Using WorkspaceManager to keep track of workspaces used by Do, in case we need to have access to local blobs to compute gradients (also important for loop's backprop)
- Update to Workspace to handle blob binding from multiple parent workspaces
- Implemented generation of gradient If operator
- Unit test to build and train a net with If control op
Reviewed By: azzolini
Differential Revision: D5745096
fbshipit-source-id: 1023c90a2113716254424d1e50b9e560fe9083e5
Summary:
1) Adds monitoring of CPU utilization in trainers and PS's, and report the utilization to global statistics
2) Adds the plan execution time to global stats
3) Uses CPU utilization and network utilization observed from performance estimation job to calculate the optimal number of parameter servers needed for the actual job. The optimal number of parameter server is the minimum number of servers needed while parameter servers are not the bottleneck in execution.
//Note: The calculation assumes that parameter shards are assigned to PS's in a uniform way and accesses to the shards follow a uniform access pattern. In reality, shards' access pattern may be skewed. As a next step, we should monitor shard access pattern in performance estimation job and distribute the shards in the optimal way.//
Reviewed By: sf-wind
Differential Revision: D5674398
fbshipit-source-id: 67a07cb9ed4e4d61ff5e81a0ecfe519b8feb2352
Summary:
This diff adds control flow operators in Caffe2 (starting with If, While):
- Added If operator that executes then/else subnet
- Branch subnet is executed in a separate isolated workspace, with some of the blobs transparently forwarded from the outer workspace
- Adding a new NetBuilder subclass to construct nets using new operator
- NetBuilder also keeps track of outer blob names and automatically sets blob bindings between outer and inner workspace, implementing generic convention on handling local/global variables in blocks
Reviewed By: volkhin
Differential Revision: D5720644
fbshipit-source-id: a674cde0c789f6a6ffdcd9d80159d1e42e49133f
Summary:
This diff adds control flow operators in Caffe2 (starting with If, While):
- Added If operator that executes then/else subnet
- Branch subnet is executed in a separate isolated workspace, with some of the
blobs transparently forwarded from the outer workspace
- Adding a new NetBuilder subclass to construct nets using new operator
- NetBuilder also keeps track of outer blob names and automatically sets
blob bindings between outer and inner workspace, implementing generic
convention on handling local/global variables in blocks
Reviewed By: azzolini
Differential Revision: D5641588
fbshipit-source-id: f9e04429961c3da7da4ebca3e8163bfcc2a09ec9
Summary:
Advantages of cloning the tasks/execution_steps at runtime:
- Less complexity on the python side: no need to clone nets and add prefixes to blob names
- Faster start-up: we had cases of complex plans that took up to 30min to be created.
- Better isolation: each task cloned at runtime has its own child workspace, preventing false sharing of blobs.
- Opens up possibility for dynamic scheduling: Number of threads per task can be increased on the fly, at runtime.
Reviewed By: dzhulgakov
Differential Revision: D5100730
fbshipit-source-id: 71b83193b135da4e6eaf2536d8fc266528e1fdcc
Summary: These return views in Python 3 which would not do anything in a lot of usages currently present in Caffe2. This diff simply removes (almost) all usages of these two in Caffe2 and sub projects in favor of comprehensions which are also easier to read/understand
Reviewed By: akyrola
Differential Revision: D5142049
fbshipit-source-id: e800631d2df7d0823fed698cae46c486038007dc
Summary:
Previously we had several limitations for a reporter net:
- needed to be a net, not an execution step
- only one allowed per execution step, with a single interval
Now, "reporter nets" become repoter steps and multiple of them can be specified with different timeouts.
Reviewed By: dzhulgakov
Differential Revision: D4583686
fbshipit-source-id: ad7266e16f96e7829fd24dcc1f165f39e9db573d
Summary:
hasattr(x, ops) should always work, regardless whether you're inside or outside a NetBuilder context.
There's no ideal solution here. I think this is sensible enough.
Reviewed By: kennyhorror
Differential Revision: D4557228
fbshipit-source-id: 4b1c1db5c8b11e4ccbf977b3f82c63b2c3e6e7db
Summary:
- NetBuilder now honors its name
- When Nets are created in the context of a NetBuilder, they take NetBuilder's name as prefix
- When a NetBuilder is created in the context of a Task, it takes the Tasks's name.
- pipe() now tries to find a good name based on its processor's, output or input queue's name.
- RPC tries to find a name from its handler's name.
- Better names in DataStream
- net_printer prints the name of Tasks and Steps
- net_printer optionally factors out common prefixes form blob names.
Differential Revision: D4527578
fbshipit-source-id: 5d3d1237c186e9576313c5aa01cc8800a9051217
Summary: stop_if() was not being honored in ProcessingReader.
Reviewed By: dzhulgakov
Differential Revision: D4497784
fbshipit-source-id: 1c967c6252f832149800796e2c26aadf10b74850
Summary: The net was being added to the task body by mistake. Also, adds local_init and local_exit functionality.
Reviewed By: dzhulgakov
Differential Revision: D4497794
fbshipit-source-id: 4d9dfb48a277ccfa204f1e74886abba5d44c61f8
