I don't know how to grant permission on Confluence. If somebody else knows how to do so, please grant Marek the edit permissions.
-Marco On Mon, Jun 11, 2018 at 11:05 AM Marek Kolodziej <[email protected]> wrote: > Hi Rajan, > > I wanted to share on Confluence, but it didn't allow me to create a new > document. If my e-mail address gets permissions to add new Confluence > pages, I'll transfer the contents to Confluence. Please keep me posted when > I get edit permissions. > > Thanks! > > Marek > > > > On Mon, Jun 11, 2018 at 11:02 AM [email protected] < > [email protected]> wrote: > > > HI Marek, > > > > Thanks for sharing the document. It would be great if you could share it > > on confluence wiki or a quip document. The formatting here makes it very > > difficult to read a long document. > > > > Appreciate the help. > > > > Thanks > > Rajan > > > > On 2018/06/11 17:50:26, Marek Kolodziej <[email protected]> wrote: > > > *Hi everyone,This is a quick summary of NVIDIA’s plans for > open-sourcing > > an > > > initial integration of TensorRT as a runtime accelerator of MxNet (PR > for > > > discussion coming in the next few days, ETA of the first draft of the > PR > > is > > > this Friday or even earlier). Feedback is appreciated.Best,Marek > > > KolodziejNeed for runtime MxNet-TensorRT integration 1. TensorRT > provides > > > significant acceleration of model inference on NVIDIA GPUs compared to > > > running the full graph in MxNet using unfused GPU operators. In > addition > > to > > > faster fp32 inference, TensorRT optimizes fp16 inference, and is > capable > > of > > > int8 inference (provided the quantization steps are performed). Besides > > > increasing throughput, TensorRT significantly reduces inference > latency, > > > especially for small batches. See more here > > > <https://developer.nvidia.com/tensorrt>.2. Despite its benefits, using > > > pre-trained models with TensorRT typically requires some effort - > either > > > re-writing the model using TensorRT’s graph building APIs, or > exporting a > > > model to ONNX, followed by an import step. Even if the import is > > simplified > > > using ONNX, the TensorRT user still needs to provide their own data > > > pipeline, which used to exist in the framework, but no longer does in a > > > stand-alone TensorRT deployment with a client application.3. TensorRT > is > > > very performant, but does not have the full set of MxNet’s operators. > > While > > > that could be addressed with TensorRT plugins, it’s much simpler to > reuse > > > already-exisitng MxNet operators. Also, the user shouldn’t care about > > > knowing which operators are supported by TensorRT and which ones > aren’t - > > > runtime integration allows the graph partitioner to extract subgraphs > > > capable of running inside of TensorRT, place the subgraph in a TensorRT > > > operator in MxNet, execute that operator as part of MxNet’s graph > > > execusion, and handle non-TensorRT-compatible nodes as regular MxNet > > > operators remaining after the TensorRT subgraph extraction and node > > > substitution. The goal is to accelerate inference without changing user > > > experience.Design considerations 1. Since TensorRT can only determine > all > > > possible optimizations once the tensor shapes are known, it is > imperative > > > that all the shape information be provided. This means that the best > time > > > to construct the TensorRT graph is bind time. The coming PR can > > selectively > > > apply the TensorRT optimization for inference-only graphs at symbol > bind > > > time. This is in fact consistent with the assumptions about TensorRT > made > > > on the MxNet Wiki here > > > < > > > https://cwiki.apache.org/confluence/display/MXNET/Unified+integration+with+external+acceleration+libraries > > >. > > > 2. Since as mentioned in #1, TensorRT graph building needs shape > > > information only available at bind time, an important goal was not to > > > disrupt any existing APIs. Even though C++ permits default function > > > arguments, the Python bindings for symbol-related methods (e.g. simple > > > bind) are exposed via a C, not C++, API, wired on the Python side using > > > Ctypes (e.g. see here > > > < > > > https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/symbol/symbol.py#L1486:L1521 > > > > > > for the simple bind integration). This precludes the addition of extra > > > arguments without causing breaking changes in the C API. Also, adapting > > the > > > Python code to such changes wouldn’t be enough, since all frontend > > > languages use the C (not C++) API for the FFI. Fortunately, C API > changes > > > could be avoided, by simply letting the user enable or disable the > > TensorRT > > > pass using an environment variable (USE_TENSORRT=1 to enable). This > also > > > does not diminish the flexibility of the integration, since the graph > > pass > > > can read the environment variable each time symbol binding is done, and > > > hence permits turning the graph passes on and off, depending on need. > The > > > ability to enable and disable the TensorRT pass at runtime also makes > > unit > > > testing easier.3. TensorRT requires that the workspace size is provided > > at > > > graph construction time. This value constitutes the upper limit on the > > > amount of memory that TensorRT can use, and does not determine > immediate > > > use. Since this amount can be hard for the user to know, its limit > should > > > be set to a reasonable value that the user need not concern themselves > > > with. Given that TensorRT integration is applied at bind time and that > > > TensorRT engines wrapped in TensorRT nodes are constructed during the > > graph > > > pass rather than the memory allocation pass, MxNet will only allocate > > the > > > amount needed for the nodes remaining after the TensorRT subgraphs have > > > been extracted. This means that no memory will be doubly allocated - > > first > > > for the complete MxNet subgraph and then for TensorRT. However, the > > > question remains whether the memory used per TensorRT engine should be > a > > > configurable parameter, either as a method argument or an environment > > > variable, or whether TensorRT should be able to use the maximum > available > > > GPU memory and then reserve only what it needs. I would like to suggest > > the > > > latter. Since the TensorRT subgraph will typically use less memory than > > the > > > same subgraph in MxNet (due to more layer fusion), it’s extremely > > unlikely > > > that a model which runs purely as an MxNet graph would fail with an ouf > > of > > > memory error when parts or most of the graph run inside TensorRT. Fewer > > > knobs (in this case, not giving the user the ability to tweak the > maximum > > > amount of memory availble to TensorRT would simplify use.4. TensorRT > can > > > accept graphs constructed using two main approaches: (a) via the > TensorRT > > > graph API, (b) using ONNX. Approach (a) seems simple on the surface - > one > > > traverses the NNVM graph, finds subgraphs that TensorRT can execute, > > > converts the subgraphs to TensorRT graphs, and substitutes the > subgraphs > > > with TensorRT nodes, each of which contain the TensorRT engine > > > corresponding to the subgraph. However, the approach taken by NVIDA was > > to > > > use ONNX as tha IR. The reason for this is twofold. First, ONNX is a > very > > > well-known IR, which is supported by the entire deep learning software > > > community. This ensures that the design of the IR gets as much feedback > > as > > > possible as to whether the IR is feature complete, and what the > semantics > > > are. NVIDIA already maintains an ONNX-to-TensorRT converter (link > > > <https://github.com/onnx/onnx-tensorrt>), and will continue to do so. > > > Whatever changes that may apply to the TensorRT APIs or the internal > > > features may be nicely hidden behind the well-established ONNX IR. > > Second, > > > ONNX is growing beyond being merely an IR. As it becomes more of a > > > standard, its adoption will be associated with other benefits, such as > > the > > > ability to verify standard compliance.5. Despite the advantages of > using > > > the ONNX route described in #4, there are some costs. The main one is > > the > > > dependency on Protobuf. This is a valid criticism on the surface, > > however, > > > since the TensorRT integration requires an opt-in during build time, > > adding > > > one more dependency is not a problem if it is not a mandatory > dependency. > > > Moreover, the same Protobuf dependency already exists for the MxNet > ONNX > > > importer, which is now part of the MxNet source tree (link > > > < > > > https://github.com/apache/incubator-mxnet/blob/76417594e56a85ec0cc9412b9dd2c7e2ab581d8b/docs/api/python/contrib/onnx.md > > >), > > > rather than being located in a separate repository. Just like the use > of > > > the ONNX importer is optional and requires ONNX (and hence also > > Protobuf), > > > the TensorRT build is optional. 6. The optional integration of TensorRT > > > will be guarded using a config.mk <http://config.mk> flag > > (USE_TENSORRT), > > > which will function similarly to other flags, such as USE_CUDA, > > USE_CUDNN, > > > etc. Needless to say, USE_TENSORRT will depend on CUDA and cuDNN.7. In > > > order to simplify evaluation of the TensorRT build, usability and to > run > > > unit tests, the PR will come with a Dockerfile, which will allow anyone > > to > > > build MxNet with TensorRT, along with its dependencies, i.e. Protobuf > and > > > ONNX. APIs / user experienceThere is no change in the inference APIs, > > > except for the need to set the MXNET_USE_TENSORRT environment variable > to > > > 1. For example, in Python, we can simply > > > do:os.environ["MXNET_USE_TENSORRT"] = “1”Note that for backward > > > compatibility, if the environment variable is not set, it will default > to > > > 0. Also, unlike some other environment variables that are only checked > > > during MxNet initialization, this one gets checked every time graph > > binding > > > happens. This typically happens only once during the inference > > > application’s life cycle, but since one can re-bind a symbol to say > > compare > > > a TensorRT and a non-TensorRT run, the check will happen during each > > > bind/re-bind to enable that. Since the TensorRT graph pass is enabled > > using > > > an environment variable, no break in the C++, C or any frontend > language > > > API is needed. Note that there is one more change required - in calling > > > simple bind. This doesn’t change the simple bind API, but how it’s > called > > > relative to the “usual” case, by using some of the arguments which are > > > optional. This has to do with the shared_buffer parameter. Before > > > explaining how the call changes, let’s consider why it’s necessary: 1. > > The > > > TensorRT graph needs to be constructed during the simple bind call, but > > > before memory gets allocated for the non-TensorRT part of the graph. 2. > > > TensorRT needs the weights, not just the shapes, to be provided before > > the > > > engine is constructed - it will store them inside the ICudaEngine > object. > > > The engine will then be serialized inside the NNVM TensorRT op, and > > > deserialized when the graph executor takes over. This means that the > > > weights need to be provided to the simple bind call to construct the > > > TensorRT engine.3. The way to provide the weights is to hand them over > to > > > the simple bind call via the “shared buffer” argument. The shared > buffer > > > weights can be provided during the bind call and can be freed by the > > > frontend language once binding is complete (e.g. by exiting the > relevant > > > scope in Python, or calling del).Since we need both arg_params > (weights) > > > and aux_params (e.g. BatchNorm moments), we need to merge arg_params > and > > > aux_params into one dictionary. Here’s a Python example:def > > > merge_dicts(*dict_args): """Merge arg_params and aux_params to > > populate > > > shared_buffer""" result = {} for dictionary in dict_args: > > > result.update(dictionary) return resultNow let’s see a use > > > example:device = mx.gpu(0)sym, arg_params, aux_params = > > > mx.model.load_checkpoint(model_name, num_epochs)executor = > > > sym.simple_bind(ctx=device, data=data_shape, > > > softmax_label=(batch_size,), > shared_buffer=merge_dicts(arg_params, > > > aux_params),, grad_req='null', force_rebind=True)Now we can > simply > > > update data in the executor’s arg dict and run the forward > > > pass:executor.arg_dict["data"][:] = > > > my_data_batchexecutor.forward(is_train=False)predictions = > > > executor.outputs[0].asnumpy()Limitations of initial integration and > > > suggested future work 1. Since the new accelerator API proposal (link > > > < > > > https://cwiki.apache.org/confluence/display/MXNET/Unified+integration+with+external+acceleration+libraries > > >) > > > was only published a few days ago and the implementation is still on an > > > MxNet fork, the current TensorRT integration doesn’t use that API yet, > > but > > > could be refactored in a future commit to use it. There is nothing in > the > > > current design that would prevent making use of that API in the near > > > future.2. Building the TensorRT engine takes a non-trivial amount of > > time, > > > because the compiler evaluates performance and the hardware on the > system > > > before creating the fused layers on demand, and then needs to actually > > > compile them. For ResNet-50 this may be a few seconds, but larger > models > > > also exist which may take longer. TensorRT comes with the ability to > > > serialize the TensorRT engine for a particular hardware platform. This > is > > > called the serialization of a TensorRT plan, which is the engine along > > with > > > the ahead-of-time-compiled fused kernels for a given GPU. The first PR > of > > > the TensorRT integration will not provide for TensorRT plan caching, so > > > using TensorRT might have a small start-up cost, but for long-running > > > inference processes, this shouldn’t be a problem. Caching the TensorRT > > plan > > > will be addressed in a future commit.3. As mentioned before, the > > > reproducibility of the build will be demonstrated using a Docker file > > that > > > will provide an easy way to evaluate the build. The Docker recipe was > > > tested on Linux on x86_64, but not other platforms supported by > TensorRT > > > (Linux on 64-bit ARM (aarch64), Android on aarch64, QNX on aarch64). > > > Supporting other platforms, e.g. Linux on aarch64 (e.g. L4T, i.e. Linux > > for > > > Tegra, on the NVIDIA Jetson platform) is left for subsequent commits. > 4. > > > The current commit supports many, but not all, of TensorRT operators. > For > > > example, this integration can run CNNs such as VGG, or ResNet, but not > > > necessarily everything that TensorRT can support. More operators will > be > > > covered in future commits.5. TensorRT supports plugins, which can be > > > integrated into the graph pass. However, this was not a priority since > > the > > > runtime TensorRT integration can always fall back to existing MxNet > > > operators. Supporting plugins is possible, but will be added in future > > > commits.6. The upcoming PR will support fp16 and fp32, but not int8. > > Since > > > int8 support in MxNet is itself very new, figuring out calibration and > > > other details is left for a future commit.7. TensorRT 4 is going to > have > > a > > > new feature called BYOM (bring your own memory). This means that > instead > > of > > > telling TensorRT how much memory it can use, the data/scratch space > > tensors > > > can be provided by MxNet, and can be re-used by MxNet when not running > > the > > > forward pass. The memory in permanent use will then be limited to > > TensorRT > > > storing weights. Support for this feature will be added in a future > > commit.* > > > > > >
