ext/torch/ext.cpp in torch-rb-0.1.2 vs ext/torch/ext.cpp in torch-rb-0.1.3

@@ -86,12 +86,54 @@
 {
   return IntArrayRef(x);
 }
 
 // for now
-typedef float Scalar;
+class Scalar {
+  torch::Scalar value;
+  public:
+    Scalar(Object o) {
+      // TODO cast based on Ruby type
+      if (o.rb_type() == T_FIXNUM) {
+        value = torch::Scalar(from_ruby<int64_t>(o));
+      } else {
+        value = torch::Scalar(from_ruby<float>(o));
+      }
+    }
+    operator torch::Scalar() {
+      return value;
+    }
+};
 
+template<>
+inline
+Scalar from_ruby<Scalar>(Object x)
+{
+  return Scalar(x);
+}
+
+class TensorList {
+  std::vector<torch::Tensor> vec;
+  public:
+    TensorList(Object o) {
+      Array a = Array(o);
+      for (size_t i = 0; i < a.size(); i++) {
+        vec.push_back(from_ruby<torch::Tensor>(a[i]));
+      }
+    }
+    operator torch::TensorList() {
+      return torch::TensorList(vec);
+    }
+};
+
+template<>
+inline
+TensorList from_ruby<TensorList>(Object x)
+{
+  return TensorList(x);
+}
+
 extern "C"
 void Init_ext()
 {
   Module rb_mTorch = define_module("Torch")
     .define_singleton_method(
@@ -205,10 +247,15 @@
       "_argmax_dim",
       *[](torch::Tensor& input, int64_t dim, bool keepdim) {
         return torch::argmax(input, dim, keepdim);
       })
     .define_singleton_method(
+      "_cat",
+      *[](TensorList tensors, int64_t dim) {
+        return torch::cat(tensors, dim);
+      })
+    .define_singleton_method(
       "_norm",
       *[](torch::Tensor& input) {
         return torch::norm(input);
       })
     .define_singleton_method(
@@ -220,20 +267,36 @@
       "_max",
       *[](torch::Tensor& input) {
         return torch::max(input);
       })
     .define_singleton_method(
+      "_max_out",
+      *[](torch::Tensor &max, torch::Tensor &max_indices, const torch::Tensor &input, int64_t dim, bool keepdim) {
+        // TODO add return value
+        torch::_max_out(max, max_indices, input, dim, keepdim);
+      })
+    .define_singleton_method(
+      "_sqrt",
+      *[](torch::Tensor& input) {
+        return torch::sqrt(input);
+      })
+    .define_singleton_method(
       "_exp",
       *[](torch::Tensor& input) {
         return torch::exp(input);
       })
     .define_singleton_method(
       "_log",
       *[](torch::Tensor& input) {
         return torch::log(input);
       })
     .define_singleton_method(
+      "_sign",
+      *[](torch::Tensor& input) {
+        return torch::sign(input);
+      })
+    .define_singleton_method(
       "_unsqueeze",
       *[](torch::Tensor& input, int64_t dim) {
         return torch::unsqueeze(input, dim);
       })
     .define_singleton_method(
@@ -250,17 +313,29 @@
       "_eq",
       *[](torch::Tensor& input, torch::Tensor& other) {
         return torch::eq(input, other);
       })
     .define_singleton_method(
+      "_gt",
+      // TODO support tensors
+      *[](torch::Tensor& input, Scalar other) {
+        return torch::gt(input, other);
+      })
+    .define_singleton_method(
+      "_lt",
+      // TODO support tensors
+      *[](torch::Tensor& input, Scalar other) {
+        return torch::lt(input, other);
+      })
+    .define_singleton_method(
       "_add",
       *[](torch::Tensor& input, torch::Tensor& other) {
         return torch::add(input, other);
       })
     .define_singleton_method(
       "_add_scalar",
-      *[](torch::Tensor& input, float other) {
+      *[](torch::Tensor& input, Scalar other) {
         return torch::add(input, other);
       })
     .define_singleton_method(
       "_add_out",
       *[](torch::Tensor& out, torch::Tensor& input, torch::Tensor& other) {
@@ -271,77 +346,77 @@
       *[](torch::Tensor& input, torch::Tensor& other) {
         return torch::sub(input, other);
       })
     .define_singleton_method(
       "_sub_scalar",
-      *[](torch::Tensor& input, float other) {
+      *[](torch::Tensor& input, Scalar other) {
         return torch::sub(input, other);
       })
     .define_singleton_method(
       "_mul",
       *[](torch::Tensor& input, torch::Tensor& other) {
         return torch::mul(input, other);
       })
     .define_singleton_method(
       "_mul_scalar",
-      *[](torch::Tensor& input, float other) {
+      *[](torch::Tensor& input, Scalar other) {
         return torch::mul(input, other);
       })
     .define_singleton_method(
       "_div",
       *[](torch::Tensor& input, torch::Tensor& other) {
         return torch::div(input, other);
       })
     .define_singleton_method(
       "_div_scalar",
-      *[](torch::Tensor& input, float other) {
+      *[](torch::Tensor& input, Scalar other) {
         return torch::div(input, other);
       })
     .define_singleton_method(
       "_remainder",
       *[](torch::Tensor& input, torch::Tensor& other) {
         return torch::remainder(input, other);
       })
     .define_singleton_method(
       "_remainder_scalar",
-      *[](torch::Tensor& input, float other) {
+      *[](torch::Tensor& input, Scalar other) {
         return torch::remainder(input, other);
       })
     .define_singleton_method(
       "_pow",
       *[](torch::Tensor& input, Scalar exponent) {
         return torch::pow(input, exponent);
       })
     .define_singleton_method(
+      "_abs",
+      *[](torch::Tensor& input) {
+        return torch::abs(input);
+      })
+    .define_singleton_method(
       "_neg",
       *[](torch::Tensor& input) {
         return torch::neg(input);
       })
     .define_singleton_method(
       "_reshape",
       *[](torch::Tensor& input, IntArrayRef shape) {
         return torch::reshape(input, shape);
       })
     .define_singleton_method(
+      "_flatten",
+      *[](torch::Tensor& input, int64_t start_dim, int64_t end_dim) {
+        return torch::flatten(input, start_dim, end_dim);
+      })
+    .define_singleton_method(
       "relu",
       *[](torch::Tensor& input) {
         return torch::relu(input);
       })
     .define_singleton_method(
-      "prelu",
-      *[](torch::Tensor& input, torch::Tensor& weight) {
-        return torch::prelu(input, weight);
-      })
-    .define_singleton_method(
-      "leaky_relu",
-      *[](torch::Tensor& input, Scalar negative_slope = 0.01) {
-        return torch::leaky_relu(input, negative_slope);
-      })
-    .define_singleton_method(
       "conv2d",
-      *[](torch::Tensor& input, torch::Tensor& weight, torch::Tensor& bias, IntArrayRef stride, IntArrayRef padding) {
-        return torch::conv2d(input, weight, bias, stride, padding);
+      *[](torch::Tensor& input, torch::Tensor& weight, torch::Tensor& bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, int64_t groups) {
+        return torch::conv2d(input, weight, bias, stride, padding, dilation, groups);
       })
     .define_singleton_method(
       "linear",
       *[](torch::Tensor& input, torch::Tensor& weight, torch::Tensor& bias) {
         return torch::linear(input, weight, bias);
@@ -355,21 +430,75 @@
       "avg_pool2d",
       *[](torch::Tensor& input, IntArrayRef kernel_size) {
         return torch::avg_pool2d(input, kernel_size);
       })
     .define_singleton_method(
+      "_dropout",
+      *[](torch::Tensor& input, float p, bool train) {
+        return torch::dropout(input, p, train);
+      })
+    .define_singleton_method(
+      "_dropout!",
+      *[](torch::Tensor& input, float p, bool train) {
+        return torch::dropout_(input, p, train);
+      })
+    .define_singleton_method(
+      "_feature_dropout",
+      *[](torch::Tensor& input, float p, bool train) {
+        return torch::feature_dropout(input, p, train);
+      })
+    .define_singleton_method(
+      "_feature_dropout!",
+      *[](torch::Tensor& input, float p, bool train) {
+        return torch::feature_dropout_(input, p, train);
+      })
+    .define_singleton_method(
+      "_alpha_dropout",
+      *[](torch::Tensor& input, float p, bool train) {
+        return torch::alpha_dropout(input, p, train);
+      })
+    .define_singleton_method(
+      "_alpha_dropout!",
+      *[](torch::Tensor& input, float p, bool train) {
+        return torch::alpha_dropout_(input, p, train);
+      })
+    .define_singleton_method(
+      "_feature_alpha_dropout",
+      *[](torch::Tensor& input, float p, bool train) {
+        return torch::feature_alpha_dropout(input, p, train);
+      })
+    .define_singleton_method(
+      "_feature_alpha_dropout!",
+      *[](torch::Tensor& input, float p, bool train) {
+        return torch::feature_alpha_dropout_(input, p, train);
+      })
+    .define_singleton_method(
+      "_embedding",
+      // weight and indices are swapped from Python interface
+      *[](const torch::Tensor &indices, const torch::Tensor &weight, int64_t padding_idx, bool scale_grad_by_freq, bool sparse) {
+        return torch::embedding(weight, indices, padding_idx, scale_grad_by_freq, sparse);
+      })
+    .define_singleton_method(
       "mse_loss",
       *[](torch::Tensor& input, torch::Tensor& target, std::string reduction) {
         auto red = reduction == "mean" ? Reduction::Mean : Reduction::Sum;
         return torch::mse_loss(input, target, red);
       })
     .define_singleton_method(
       "nll_loss",
-      *[](torch::Tensor& input, torch::Tensor& target) {
-        return torch::nll_loss(input, target);
+      *[](torch::Tensor& input, torch::Tensor& target, std::string reduction) {
+        auto red = reduction == "mean" ? Reduction::Mean : Reduction::Sum;
+        return torch::nll_loss(input, target, {}, red);
       })
+    .define_singleton_method("numel", &torch::numel)
     .define_singleton_method(
+      "_from_blob",
+      *[](String s, IntArrayRef size, const torch::TensorOptions &options) {
+        void *data = const_cast<char *>(s.c_str());
+        return torch::from_blob(data, size, options);
+      })
+    .define_singleton_method(
       "_tensor",
       *[](Object o, IntArrayRef size, const torch::TensorOptions &options) {
         Array a = Array(o);
         std::vector<float> vec;
         for (size_t i = 0; i < a.size(); i++) {
@@ -385,14 +514,24 @@
     .define_method("floating_point?", &torch::Tensor::is_floating_point)
     .define_method("signed?", &torch::Tensor::is_signed)
     .define_method("sparse?", &torch::Tensor::is_sparse)
     .define_method("quantized?", &torch::Tensor::is_quantized)
     .define_method("dim", &torch::Tensor::dim)
-    .define_method("numel", &torch::Tensor::numel)
     .define_method("element_size", &torch::Tensor::element_size)
     .define_method("requires_grad", &torch::Tensor::requires_grad)
+    .define_method("view_as", &torch::Tensor::view_as)
     .define_method(
+      "addcmul!",
+      *[](torch::Tensor& self, Scalar value, const torch::Tensor & tensor1, const torch::Tensor & tensor2) {
+        return self.addcmul_(tensor1, tensor2, value);
+      })
+    .define_method(
+      "addcdiv!",
+      *[](torch::Tensor& self, Scalar value, const torch::Tensor & tensor1, const torch::Tensor & tensor2) {
+        return self.addcdiv_(tensor1, tensor2, value);
+      })
+    .define_method(
       "zero!",
       *[](torch::Tensor& self) {
         return self.zero_();
       })
     .define_method(
@@ -458,30 +597,80 @@
       "_view",
       *[](torch::Tensor& self, IntArrayRef size) {
         return self.view(size);
       })
     .define_method(
-      "add!",
+      "resize_as!",
       *[](torch::Tensor& self, torch::Tensor& other) {
-        self.add_(other);
+        return self.resize_as_(other);
       })
     .define_method(
+      "fill!",
+      *[](torch::Tensor& self, Scalar value) {
+        return self.fill_(value);
+      })
+    .define_method(
+      "_add!",
+      *[](torch::Tensor& self, torch::Tensor& other) {
+        return self.add_(other);
+      })
+    .define_method(
+      "_add_alpha!",
+      *[](torch::Tensor& self, torch::Tensor& other, Scalar alpha) {
+        return self.add_(other, alpha);
+      })
+    .define_method(
+      "_add_scalar!",
+      *[](torch::Tensor& self, Scalar other) {
+        return self.add_(other);
+      })
+    .define_method(
+      "normal!",
+      *[](torch::Tensor& self, double mean, double std) {
+        return self.normal_(mean, std);
+      })
+    .define_method(
       "sub!",
       *[](torch::Tensor& self, torch::Tensor& other) {
-        self.sub_(other);
+        return self.sub_(other);
       })
     .define_method(
-      "mul!",
+      "_mul!",
       *[](torch::Tensor& self, torch::Tensor& other) {
-        self.mul_(other);
+        return self.mul_(other);
       })
     .define_method(
+      "_mul_scalar!",
+      *[](torch::Tensor& self, Scalar other) {
+        return self.mul_(other);
+      })
+    .define_method(
       "div!",
       *[](torch::Tensor& self, torch::Tensor& other) {
-        self.div_(other);
+        return self.div_(other);
       })
     .define_method(
+      "sqrt!",
+      *[](torch::Tensor& self) {
+        return self.sqrt_();
+      })
+    .define_method(
+      "unsqueeze!",
+      *[](torch::Tensor& self, int64_t dim) {
+        return self.unsqueeze_(dim);
+      })
+    .define_method(
+      "copy!",
+      *[](torch::Tensor& self, torch::Tensor& src) {
+        return self.copy_(src);
+      })
+    .define_method(
+      "clone",
+      *[](torch::Tensor& self) {
+        return self.clone();
+      })
+    .define_method(
       "log_softmax",
       *[](torch::Tensor& self, int64_t dim) {
         return self.log_softmax(dim);
       })
     .define_method(
@@ -530,22 +719,29 @@
           double* data = self.data_ptr<double>();
           for (int i = 0; i < self.numel(); i++) {
             a.push(data[i]);
           }
         } else if (dtype == torch::kBool) {
-          // bool
-          throw std::runtime_error("Type not supported yet");
+          bool* data = self.data_ptr<bool>();
+          for (int i = 0; i < self.numel(); i++) {
+            a.push(data[i] ? True : False);
+          }
         } else {
           throw std::runtime_error("Unsupported type");
         }
         return a;
       })
     .define_method(
       "_size",
       *[](torch::Tensor& self, int i) {
         return self.size(i);
       })
+    .define_method(
+      "_to",
+      *[](torch::Tensor& self, torch::Device device, int dtype, bool non_blocking, bool copy) {
+        return self.to(device, (torch::ScalarType) dtype, non_blocking, copy);
+      })
     .define_singleton_method(
       "_make_subclass",
       *[](torch::Tensor& rd, bool requires_grad) {
         auto data = torch::autograd::as_variable_ref(rd).detach();
         data.unsafeGetTensorImpl()->set_allow_tensor_metadata_change(true);
@@ -595,16 +791,21 @@
 
   Module rb_mNN = define_module_under(rb_mTorch, "NN");
 
   Module rb_mInit = define_module_under(rb_mNN, "Init")
     .define_singleton_method(
-      "kaiming_uniform_",
+      "kaiming_uniform!",
       *[](torch::Tensor& input, double a) {
         return torch::nn::init::kaiming_uniform_(input, a);
       })
     .define_singleton_method(
-      "uniform_",
+      "normal!",
+      *[](torch::Tensor& input) {
+        return torch::nn::init::normal_(input);
+      })
+    .define_singleton_method(
+      "uniform!",
       *[](torch::Tensor& input, double to, double from) {
         return torch::nn::init::uniform_(input, to, from);
       });
 
   Class rb_cParameter = define_class_under<torch::autograd::Variable, torch::Tensor>(rb_mNN, "Parameter")
@@ -617,6 +818,22 @@
     .define_method(
       "_grad_defined",
       *[](torch::autograd::Variable& self) {
         return self.grad().defined();
       });
+
+  Class rb_cDevice = define_class_under<torch::Device>(rb_mTorch, "Device")
+    .define_constructor(Constructor<torch::Device, std::string>())
+    .define_method("index", &torch::Device::index)
+    .define_method("index?", &torch::Device::has_index)
+    .define_method(
+      "type",
+      *[](torch::Device& self) {
+        std::stringstream s;
+        s << self.type();
+        return s.str();
+      });
+
+  Module rb_mCUDA = define_module_under(rb_mTorch, "CUDA")
+    .define_singleton_method("available?", &torch::cuda::is_available)
+    .define_singleton_method("device_count", &torch::cuda::device_count);
 }