Hello Marek, this sounds great! Definitely looking forward to it.
It seems like our mailing list destroyed your formatting. You might want to consider putting it into a Google Docs document or uploading it to confluence. Best regards, Marco On Mon, Jun 11, 2018 at 10:50 AM 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.* >
