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/ > 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.* > >>>> > >>> > >> >
