Merge branch 'master' into master

cpp/autograd/CMakeLists.txt

@@ -0,0 +1,21 @@
+cmake_minimum_required(VERSION 2.8)
+
+project(autograd)
+set(CMAKE_CXX_STANDARD 14)
+
+find_package(Torch REQUIRED)
+
+add_executable(${PROJECT_NAME} "autograd.cpp")
+target_link_libraries(${PROJECT_NAME} "${TORCH_LIBRARIES}")
+
+# The following code block is suggested to be used on Windows.
+# According to https://github.com/pytorch/pytorch/issues/25457,
+# the DLLs need to be copied to avoid memory errors.
+if (MSVC)
+  file(GLOB TORCH_DLLS "${TORCH_INSTALL_PREFIX}/lib/*.dll")
+  add_custom_command(TARGET ${PROJECT_NAME}
+                     POST_BUILD
+                     COMMAND ${CMAKE_COMMAND} -E copy_if_different
+                     ${TORCH_DLLS}
+                     $<TARGET_FILE_DIR:${PROJECT_NAME}>)
+endif (MSVC)

cpp/autograd/README.md

@@ -0,0 +1,78 @@
+# C++ autograd example
+
+`autograd.cpp` contains several examples of doing autograd in PyTorch C++ frontend.
+
+To build the code, run the following commands from your terminal:
+
+```shell
+$ cd autograd
+$ mkdir build
+$ cd build
+$ cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch ..
+$ make
+```
+
+where `/path/to/libtorch` should be the path to the unzipped *LibTorch*
+distribution, which you can get from the [PyTorch
+homepage](https://pytorch.org/get-started/locally/).
+
+Execute the compiled binary to run:
+
+```shell
+$ ./autograd
+====== Running: "Basic autograd operations" ======
+ 1  1
+ 1  1
+[ CPUFloatType{2,2} ]
+ 3  3
+ 3  3
+[ CPUFloatType{2,2} ]
+AddBackward1
+ 27  27
+ 27  27
+[ CPUFloatType{2,2} ]
+MulBackward1
+27
+[ CPUFloatType{} ]
+MeanBackward0
+false
+true
+SumBackward0
+ 4.5000  4.5000
+ 4.5000  4.5000
+[ CPUFloatType{2,2} ]
+  813.6625
+ 1015.0142
+ -664.8849
+[ CPUFloatType{3} ]
+MulBackward1
+  204.8000
+ 2048.0000
+    0.2048
+[ CPUFloatType{3} ]
+true
+true
+false
+true
+false
+true
+
+====== Running "Computing higher-order gradients in C++" ======
+ 0.0025  0.0946  0.1474  0.1387
+ 0.0238 -0.0018  0.0259  0.0094
+ 0.0513 -0.0549 -0.0604  0.0210
+[ CPUFloatType{3,4} ]
+
+====== Running "Using custom autograd function in C++" ======
+-3.5513  3.7160  3.6477
+-3.5513  3.7160  3.6477
+[ CPUFloatType{2,3} ]
+ 0.3095  1.4035 -0.0349
+ 0.3095  1.4035 -0.0349
+ 0.3095  1.4035 -0.0349
+ 0.3095  1.4035 -0.0349
+[ CPUFloatType{4,3} ]
+ 5.5000
+ 5.5000
+[ CPUFloatType{2} ]
+```

cpp/autograd/autograd.cpp

@@ -0,0 +1,191 @@
+#include <torch/torch.h>
+#include <iostream>
+
+using namespace torch::autograd;
+
+void basic_autograd_operations_example() {
+  std::cout << "====== Running: \"Basic autograd operations\" ======" << std::endl;
+
+  // Create a tensor and set ``torch::requires_grad()`` to track computation with it
+  auto x = torch::ones({2, 2}, torch::requires_grad());
+  std::cout << x << std::endl;
+
+  // Do a tensor operation:
+  auto y = x + 2;
+  std::cout << y << std::endl;
+
+  // ``y`` was created as a result of an operation, so it has a ``grad_fn``.
+  std::cout << y.grad_fn()->name() << std::endl;
+
+  // Do more operations on ``y``
+  auto z = y * y * 3;
+  auto out = z.mean();
+
+  std::cout << z << std::endl;
+  std::cout << z.grad_fn()->name() << std::endl;
+  std::cout << out << std::endl;
+  std::cout << out.grad_fn()->name() << std::endl;
+
+  // ``.requires_grad_( ... )`` changes an existing tensor's ``requires_grad`` flag in-place.
+  auto a = torch::randn({2, 2});
+  a = ((a * 3) / (a - 1));
+  std::cout << a.requires_grad() << std::endl;
+
+  a.requires_grad_(true);
+  std::cout << a.requires_grad() << std::endl;
+
+  auto b = (a * a).sum();
+  std::cout << b.grad_fn()->name() << std::endl;
+
+  // Let's backprop now. Because ``out`` contains a single scalar, ``out.backward()``
+  // is equivalent to ``out.backward(torch::tensor(1.))``.
+  out.backward();
+
+  // Print gradients d(out)/dx
+  std::cout << x.grad() << std::endl;
+
+  // Now let's take a look at an example of vector-Jacobian product:
+  x = torch::randn(3, torch::requires_grad());
+
+  y = x * 2;
+  while (y.norm().item<double>() < 1000) {
+    y = y * 2;
+  }
+
+  std::cout << y << std::endl;
+  std::cout << y.grad_fn()->name() << std::endl;
+
+  // If we want the vector-Jacobian product, pass the vector to ``backward`` as argument:
+  auto v = torch::tensor({0.1, 1.0, 0.0001}, torch::kFloat);
+  y.backward(v);
+
+  std::cout << x.grad() << std::endl;
+
+  // You can also stop autograd from tracking history on tensors that require gradients
+  // either by putting ``torch::NoGradGuard`` in a code block
+  std::cout << x.requires_grad() << std::endl;
+  std::cout << x.pow(2).requires_grad() << std::endl;
+
+  {
+    torch::NoGradGuard no_grad;
+    std::cout << x.pow(2).requires_grad() << std::endl;
+  }
+
+  // Or by using ``.detach()`` to get a new tensor with the same content but that does
+  // not require gradients:
+  std::cout << x.requires_grad() << std::endl;
+  y = x.detach();
+  std::cout << y.requires_grad() << std::endl;
+  std::cout << x.eq(y).all().item<bool>() << std::endl;
+}
+
+void compute_higher_order_gradients_example() {
+  std::cout << "====== Running \"Computing higher-order gradients in C++\" ======" << std::endl;
+
+  // One of the applications of higher-order gradients is calculating gradient penalty.
+  // Let's see an example of it using ``torch::autograd::grad``:
+
+  auto model = torch::nn::Linear(4, 3);
+
+  auto input = torch::randn({3, 4}).requires_grad_(true);
+  auto output = model(input);
+
+  // Calculate loss
+  auto target = torch::randn({3, 3});
+  auto loss = torch::nn::MSELoss()(output, target);
+
+  // Use norm of gradients as penalty
+  auto grad_output = torch::ones_like(output);
+  auto gradient = torch::autograd::grad({output}, {input}, /*grad_outputs=*/{grad_output}, /*create_graph=*/true)[0];
+  auto gradient_penalty = torch::pow((gradient.norm(2, /*dim=*/1) - 1), 2).mean();
+
+  // Add gradient penalty to loss
+  auto combined_loss = loss + gradient_penalty;
+  combined_loss.backward();
+
+  std::cout << input.grad() << std::endl;
+}
+
+// Inherit from Function
+class LinearFunction : public Function<LinearFunction> {
+ public:
+  // Note that both forward and backward are static functions
+
+  // bias is an optional argument
+  static torch::Tensor forward(
+      AutogradContext *ctx, torch::Tensor input, torch::Tensor weight, torch::Tensor bias = torch::Tensor()) {
+    ctx->save_for_backward({input, weight, bias});
+    auto output = input.mm(weight.t());
+    if (bias.defined()) {
+      output += bias.unsqueeze(0).expand_as(output);
+    }
+    return output;
+  }
+
+  static tensor_list backward(AutogradContext *ctx, tensor_list grad_outputs) {
+    auto saved = ctx->get_saved_variables();
+    auto input = saved[0];
+    auto weight = saved[1];
+    auto bias = saved[2];
+
+    auto grad_output = grad_outputs[0];
+    auto grad_input = grad_output.mm(weight);
+    auto grad_weight = grad_output.t().mm(input);
+    auto grad_bias = torch::Tensor();
+    if (bias.defined()) {
+      grad_bias = grad_output.sum(0);
+    }
+
+    return {grad_input, grad_weight, grad_bias};
+  }
+};
+
+class MulConstant : public Function<MulConstant> {
+ public:
+  static torch::Tensor forward(AutogradContext *ctx, torch::Tensor tensor, double constant) {
+    // ctx is a context object that can be used to stash information
+    // for backward computation
+    ctx->saved_data["constant"] = constant;
+    return tensor * constant;
+  }
+
+  static tensor_list backward(AutogradContext *ctx, tensor_list grad_outputs) {
+    // We return as many input gradients as there were arguments.
+    // Gradients of non-tensor arguments to forward must be `torch::Tensor()`.
+    return {grad_outputs[0] * ctx->saved_data["constant"].toDouble(), torch::Tensor()};
+  }
+};
+
+void custom_autograd_function_example() {
+  std::cout << "====== Running \"Using custom autograd function in C++\" ======" << std::endl;
+  {
+    auto x = torch::randn({2, 3}).requires_grad_();
+    auto weight = torch::randn({4, 3}).requires_grad_();
+    auto y = LinearFunction::apply(x, weight);
+    y.sum().backward();
+
+    std::cout << x.grad() << std::endl;
+    std::cout << weight.grad() << std::endl;
+  }
+  {
+    auto x = torch::randn({2}).requires_grad_();
+    auto y = MulConstant::apply(x, 5.5);
+    y.sum().backward();
+
+    std::cout << x.grad() << std::endl;
+  }
+}
+
+int main() {
+  std::cout << std::boolalpha;
+
+  basic_autograd_operations_example();
+
+  std::cout << "\n";
+
+  compute_higher_order_gradients_example();
+
+  std::cout << "\n";
+
+  custom_autograd_function_example();
+}

cpp/dcgan/dcgan.cpp

@@ -121,9 +121,9 @@ int main(int argc, const char* argv[]) {
       torch::data::DataLoaderOptions().batch_size(kBatchSize).workers(2));
 
   torch::optim::Adam generator_optimizer(
-      generator->parameters(), torch::optim::AdamOptions(2e-4).beta1(0.5));
+      generator->parameters(), torch::optim::AdamOptions(2e-4).betas(std::make_tuple (0.5, 0.5)));
   torch::optim::Adam discriminator_optimizer(
-      discriminator->parameters(), torch::optim::AdamOptions(2e-4).beta1(0.5));
+      discriminator->parameters(), torch::optim::AdamOptions(2e-4).betas(std::make_tuple (0.5, 0.5)));
 
   if (kRestoreFromCheckpoint) {
     torch::load(generator, "generator-checkpoint.pt");

distributed/ddp/README.md

@@ -189,4 +189,4 @@ that in turn produces the following output
 ```
 
 # Conclusions
-As the author of a distributed data parallel application, your code needs to be aware of two types of resources: compute nodes and the GPUs within each node. The process of setting up bookkeeping to track how the set of GPUs is mapped to the processes of your application can be tedious and error-prone. We hope that by structuring your application as shown in this example and using the launcher, the mechanics of setting up distributed training can be significantly simplified.
+As the author of a distributed data parallel application, your code needs to be aware of two types of resources: compute nodes and the GPUs within each node. The process of setting up bookkeeping to track how the set of GPUs is mapped to the processes of your application can be tedious and error-prone. We hope that by structuring your application as shown in this example and using the launcher, the mechanics of setting up distributed training can be significantly simplified.