please add your proposal under design proposals, once the community has reviewed and there is consensus on the approach we can create a ONNX-MXNet sub section and move there.
On Mon, Jun 11, 2018 at 9:54 PM, Naveen Swamy <[email protected]> wrote: > you have access now. > > On Mon, Jun 11, 2018 at 8:34 PM, Naveen Swamy <[email protected]> wrote: > >> I'll add in about an hour >> >> > On Jun 11, 2018, at 8:12 PM, Marco de Abreu < >> [email protected]> wrote: >> > >> > 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/76417594e56a8 >> 5ec0cc9412b9dd2c7e2ab581d8b/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.* >> >>>> >> >>> >> >> >> > >
