| --- |
| id: tensor |
| title: Tensor |
| --- |
| |
| <!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> |
| |
| Each Tensor instance is a multi-dimensional array allocated on a specific Device |
| instance. Tensor instances store variables and provide linear algebra operations |
| over different types of hardware devices without user awareness. Note that users |
| need to make sure the tensor operands are allocated on the same device except |
| copy functions. |
| |
| ## Tensor Usage |
| |
| ### Create Tensor |
| |
| ```python |
| >>> import numpy as np |
| >>> from singa import tensor |
| >>> tensor.from_numpy( np.asarray([[1, 0, 0], [0, 1, 0]], dtype=np.float32) ) |
| [[1. 0. 0.] |
| [0. 1. 0.]] |
| ``` |
| |
| ### Convert to numpy |
| |
| ```python |
| >>> a = np.asarray([[1, 0, 0], [0, 1, 0]], dtype=np.float32) |
| >>> tensor.from_numpy(a) |
| [[1. 0. 0.] |
| [0. 1. 0.]] |
| >>> tensor.to_numpy(tensor.from_numpy(a)) |
| array([[1., 0., 0.], |
| [0., 1., 0.]], dtype=float32) |
| ``` |
| |
| ### Tensor Methods |
| |
| ```python |
| >>> t = tensor.from_numpy(a) |
| >>> t.transpose([1,0]) |
| [[1. 0.] |
| [0. 1.] |
| [0. 0.]] |
| ``` |
| |
| `tensor` transformation up to 6 dims |
| |
| ```python |
| >>> a = tensor.random((2,3,4,5,6,7)) |
| >>> a.shape |
| (2, 3, 4, 5, 6, 7) |
| >>> a.reshape((2,3,4,5,7,6)).transpose((3,2,1,0,4,5)).shape |
| (5, 4, 3, 2, 7, 6) |
| ``` |
| |
| ### Tensor Arithmetic Methods |
| |
| `tensor` is evaluated in real time. |
| |
| ```python |
| >>> t + 1 |
| [[2. 1. 1.] |
| [1. 2. 1.]] |
| >>> t / 5 |
| [[0.2 0. 0. ] |
| [0. 0.2 0. ]] |
| ``` |
| |
| `tensor` broadcasting arithmetic: |
| |
| ```python |
| >>> a |
| [[1. 2. 3.] |
| [4. 5. 6.]] |
| >>> b |
| [[1. 2. 3.]] |
| >>> a + b |
| [[2. 4. 6.] |
| [5. 7. 9.]] |
| >>> a * b |
| [[ 1. 4. 9.] |
| [ 4. 10. 18.]] |
| >>> a / b |
| [[1. 1. 1. ] |
| [4. 2.5 2. ]] |
| >>> a/=b # inplace operation |
| >>> a |
| [[1. 1. 1. ] |
| [4. 2.5 2. ]] |
| ``` |
| |
| `tensor` broadcasting on matrix multiplication (GEMM) |
| |
| ```python |
| >>> from singa import tensor |
| >>> a = tensor.random((2,2,2,3)) |
| >>> b = tensor.random((2,3,4)) |
| >>> tensor.mult(a,b).shape |
| (2, 2, 2, 4) |
| ``` |
| |
| ### Tensor Functions |
| |
| Functions in module `singa.tensor` return new `tensor` object after applying the |
| transformation defined in the function. |
| |
| ```python |
| >>> tensor.log(t+1) |
| [[0.6931472 0. 0. ] |
| [0. 0.6931472 0. ]] |
| ``` |
| |
| ### Tensor on Different Devices |
| |
| `tensor` is created on host (CPU) by default; it can also be created on |
| different hardware devices by specifying the `device`. A `tensor` could be moved |
| between `device`s via `to_device()` function. |
| |
| ```python |
| >>> from singa import device |
| >>> x = tensor.Tensor((2, 3), device.create_cuda_gpu()) |
| >>> x.gaussian(1,1) |
| >>> x |
| [[1.531889 1.0128608 0.12691343] |
| [2.1674204 3.083676 2.7421203 ]] |
| >>> # move to host |
| >>> x.to_device(device.get_default_device()) |
| ``` |
| |
| ### use Tensor to train MLP |
| |
| ```python |
| |
| """ |
| code snipet from examples/mlp/module.py |
| """ |
| |
| label = get_label() |
| data = get_data() |
| |
| dev = device.create_cuda_gpu_on(0) |
| sgd = opt.SGD(0.05) |
| |
| # define tensor for input data and label |
| tx = tensor.Tensor((400, 2), dev, tensor.float32) |
| ty = tensor.Tensor((400,), dev, tensor.int32) |
| model = MLP(data_size=2, perceptron_size=3, num_classes=2) |
| |
| # attached model to graph |
| model.set_optimizer(sgd) |
| model.compile([tx], is_train=True, use_graph=True, sequential=False) |
| model.train() |
| |
| for i in range(1001): |
| tx.copy_from_numpy(data) |
| ty.copy_from_numpy(label) |
| out, loss = model(tx, ty, 'fp32', spars=None) |
| |
| if i % 100 == 0: |
| print("training loss = ", tensor.to_numpy(loss)[0]) |
| ``` |
| |
| Output: |
| |
| ```bash |
| $ python3 examples/mlp/module.py |
| training loss = 0.6158037 |
| training loss = 0.52852553 |
| training loss = 0.4571422 |
| training loss = 0.37274635 |
| training loss = 0.30146334 |
| training loss = 0.24906921 |
| training loss = 0.21128304 |
| training loss = 0.18390492 |
| training loss = 0.16362564 |
| training loss = 0.148164 |
| training loss = 0.13589878 |
| ``` |
| |
| ## Tensor Implementation |
| |
| The previous section shows the general usage of `Tensor`, the implementation |
| under the hood will be covered below. First, the design of Python and C++ |
| tensors will be introduced. Later part will talk about how the frontend (Python) |
| and backend (C++) are connected and how to extend them. |
| |
| ### Python Tensor |
| |
| Python class `Tensor`, defined in `python/singa/tensor.py`, provides high level |
| tensor manipulations for implementing deep learning operations (via |
| [autograd](./autograd)), as well as data management by end users. |
| |
| It primarily works by simply wrapping around C++ tensor methods, both arithmetic |
| (e.g. `sum`) and non arithmetic methods (e.g. `reshape`). Some advanced |
| arithmetic operations are later introduced and implemented using pure Python |
| tensor API, e.g. `tensordot`. Python Tensor APIs could be used to implement |
| complex neural network operations easily with the flexible methods available. |
| |
| ### C++ Tensor |
| |
| C++ class `Tensor`, defined in `include/singa/core/tensor.h`, primarily manages |
| the memory that holds the data, and provides low level APIs for tensor |
| manipulation. Also, it provides various arithmetic methods (e.g. `matmul`) by |
| wrapping different backends (CUDA, BLAS, cuBLAS, etc.). |
| |
| #### Execution Context and Memory Block |
| |
| Two important concepts or data structures for `Tensor` are the execution context |
| `device`, and the memory block `Block`. |
| |
| Each `Tensor` is physically stored on and managed by a hardware device, |
| representing the execution context (CPU, GPU). Tensor math calculations are |
| executed on the device. |
| |
| Tensor data in a `Block` instance, defined in `include/singa/core/common.h`. |
| `Block` owns the underlying data, while tensors take ownership on the metadata |
| describing the tensor, like `shape`, `strides`. |
| |
| #### Tensor Math Backends |
| |
| To leverage on the efficient math libraries provided by different backend |
| hardware devices, SINGA has one set of implementations of Tensor functions for |
| each supported backend. |
| |
| - 'tensor_math_cpp.h' implements operations using Cpp (with CBLAS) for CppCPU |
| devices. |
| - 'tensor_math_cuda.h' implements operations using Cuda (with cuBLAS) for |
| CudaGPU devices. |
| - 'tensor_math_opencl.h' implements operations using OpenCL for OpenclGPU |
| devices. |
| |
| ### Exposing C++ APIs to Python |
| |
| SWIG(http://www.swig.org/) is a tool that can automatically convert C++ APIs |
| into Python APIs. SINGA uses SWIG to expose the C++ APIs to Python. Several |
| files are generated by SWIG, including `python/singa/singa_wrap.py`. The Python |
| modules (e.g., `tensor`, `device` and `autograd`) imports this module to call |
| the C++ APIs for implementing the Python classes and functions. |
| |
| ```python |
| import tensor |
| |
| t = tensor.Tensor(shape=(2, 3)) |
| ``` |
| |
| For example, when a Python `Tensor` instance is created as above, the `Tensor` |
| class implementation creates an instance of the `Tensor` class defined in |
| `singa_wrap.py`, which corresponds to the C++ `Tensor` class. For clarity, the |
| `Tensor` class in `singa_wrap.py` is referred as `CTensor` in `tensor.py`. |
| |
| ```python |
| # in tensor.py |
| from . import singa_wrap as singa |
| |
| CTensor = singa.Tensor |
| ``` |
| |
| ### Create New Tensor Functions |
| |
| With the groundwork set by the previous description, extending tensor functions |
| could be done easily in a bottom up manner. For math operations, the steps are: |
| |
| - Declare the new API to `tensor.h` |
| - Generate code using the predefined macro in `tensor.cc`, refer to |
| `GenUnaryTensorFn(Abs);` as an example. |
| - Declare the template method/function in `tensor_math.h` |
| - Do the real implementation at least for CPU (`tensor_math_cpp.h`) and |
| GPU(`tensor_math_cuda.h`) |
| - Expose the API via SWIG by adding it into `src/api/core_tensor.i` |
| - Define the Python Tensor API in `tensor.py` by calling the automatically |
| generated function in `singa_wrap.py` |
| - Write unit tests where appropriate |
| |
| ## Python API |
| |
| _work in progress_ |
| |
| ## CPP API |
| |
| _work in progress_ |