Module: Torch

Defined in:
lib/torch/optim/sgd.rb,
lib/torch.rb,
lib/torch/nn/rnn.rb,
lib/torch/random.rb,
lib/torch/tensor.rb,
lib/torch/nn/init.rb,
lib/torch/nn/loss.rb,
lib/torch/nn/relu.rb,
lib/torch/version.rb,
lib/torch/nn/prelu.rb,
lib/torch/inspector.rb,
lib/torch/nn/conv2d.rb,
lib/torch/nn/convnd.rb,
lib/torch/nn/linear.rb,
lib/torch/nn/module.rb,
lib/torch/nn/dropout.rb,
lib/torch/nn/l1_loss.rb,
lib/torch/nn/sigmoid.rb,
lib/torch/nn/softmax.rb,
lib/torch/nn/softmin.rb,
lib/torch/optim/adam.rb,
lib/torch/optim/asgd.rb,
lib/torch/nn/bce_loss.rb,
lib/torch/nn/bilinear.rb,
lib/torch/nn/ctc_loss.rb,
lib/torch/nn/identity.rb,
lib/torch/nn/mse_loss.rb,
lib/torch/nn/nll_loss.rb,
lib/torch/nn/rnn_base.rb,
lib/torch/nn/softplus.rb,
lib/torch/optim/adamw.rb,
lib/torch/optim/rprop.rb,
lib/torch/nn/dropout2d.rb,
lib/torch/nn/dropout3d.rb,
lib/torch/nn/dropoutnd.rb,
lib/torch/nn/embedding.rb,
lib/torch/nn/parameter.rb,
lib/torch/nn/softmax2d.rb,
lib/torch/optim/adamax.rb,
lib/torch/native/parser.rb,
lib/torch/nn/avg_pool2d.rb,
lib/torch/nn/avg_poolnd.rb,
lib/torch/nn/functional.rb,
lib/torch/nn/leaky_relu.rb,
lib/torch/nn/max_pool2d.rb,
lib/torch/nn/max_poolnd.rb,
lib/torch/nn/sequential.rb,
lib/torch/optim/adagrad.rb,
lib/torch/optim/rmsprop.rb,
lib/torch/nn/kl_div_loss.rb,
lib/torch/nn/log_softmax.rb,
lib/torch/optim/adadelta.rb,
lib/torch/native/function.rb,
lib/torch/optim/optimizer.rb,
lib/torch/native/generator.rb,
lib/torch/nn/alpha_dropout.rb,
lib/torch/nn/embedding_bag.rb,
lib/torch/nn/weighted_loss.rb,
lib/torch/native/dispatcher.rb,
lib/torch/nn/smooth_l1_loss.rb,
lib/torch/nn/poisson_nll_loss.rb,
lib/torch/nn/soft_margin_loss.rb,
lib/torch/nn/cosine_similarity.rb,
lib/torch/nn/multi_margin_loss.rb,
lib/torch/nn/pairwise_distance.rb,
lib/torch/nn/cross_entropy_loss.rb,
lib/torch/nn/margin_ranking_loss.rb,
lib/torch/nn/triplet_margin_loss.rb,
lib/torch/utils/data/data_loader.rb,
lib/torch/nn/bce_with_logits_loss.rb,
lib/torch/nn/hinge_embedding_loss.rb,
lib/torch/nn/cosine_embedding_loss.rb,
lib/torch/nn/feature_alpha_dropout.rb,
lib/torch/utils/data/tensor_dataset.rb,
lib/torch/nn/multi_label_margin_loss.rb,
lib/torch/optim/lr_scheduler/step_lr.rb,
lib/torch/nn/multi_label_soft_margin_loss.rb,
lib/torch/optim/lr_scheduler/lr_scheduler.rb

Overview

We use a generic interface for methods (*args, **options) and this class to determine the C++ method to call

This is needed since LibTorch uses function overloading, which isn’t available in Ruby or Python

PyTorch uses this approach, but the parser/dispatcher is written in C++

We could generate Ruby methods directly, but an advantage of this approach is arguments and keyword arguments can be used interchangably like in Python, making it easier to port code

Defined Under Namespace

Modules: Inspector, NN, Native, Optim, Random, Utils Classes: Error, NotImplementedYet, Tensor

Constant Summary collapse

DTYPE_TO_ENUM =

keys: pytorch.org/docs/stable/tensor_attributes.html#torch.torch.dtype values: github.com/pytorch/pytorch/blob/master/c10/core/ScalarType.h

{
  uint8: 0,
  int8: 1,
  short: 2,
  int16: 2,
  int: 3,
  int32: 3,
  long: 4,
  int64: 4,
  half: 5,
  float16: 5,
  float: 6,
  float32: 6,
  double: 7,
  float64: 7,
  complex_half: 8,
  complex_float: 9,
  complex_double: 10,
  bool: 11,
  qint8: 12,
  quint8: 13,
  qint32: 14,
  bfloat16: 15
}
ENUM_TO_DTYPE = 
DTYPE_TO_ENUM.map(&:reverse).to_h
FloatTensor = 
_make_tensor_class(:float32)
DoubleTensor = 
_make_tensor_class(:float64)
HalfTensor = 
_make_tensor_class(:float16)
ByteTensor = 
_make_tensor_class(:uint8)
CharTensor = 
_make_tensor_class(:int8)
ShortTensor = 
_make_tensor_class(:int16)
IntTensor = 
_make_tensor_class(:int32)
LongTensor = 
_make_tensor_class(:int64)
BoolTensor = 
_make_tensor_class(:bool)
VERSION = 
"0.1.5"

Class Method Summary collapse

Class Method Details

._dtype_to_numo ⇒ Object

private use method for cases when Numo not available or available after Torch loaded

Raises:



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# File 'lib/torch.rb', line 224

def _dtype_to_numo
  raise Error, "Numo not found" unless defined?(Numo::NArray)

  {
    uint8: Numo::UInt8,
    int8: Numo::Int8,
    int16: Numo::Int16,
    int32: Numo::Int32,
    int64: Numo::Int64,
    float32: Numo::SFloat,
    float64: Numo::DFloat
  }
end

._make_tensor_class(dtype, cuda = false) ⇒ Object 



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# File 'lib/torch.rb', line 154

def self._make_tensor_class(dtype, cuda = false)
  cls = Class.new
  device = cuda ? "cuda" : "cpu"
  cls.define_singleton_method("new") do |*args|
    if args.size == 1 && args.first.is_a?(Tensor)
      args.first.send(dtype).to(device)
    elsif args.size == 1 && args.first.is_a?(Array)
      Torch.tensor(args.first, dtype: dtype, device: device)
    else
      Torch.empty(*args, dtype: dtype, device: device)
    end
  end
  cls
end

.arange(start, finish = nil, step = 1, **options) ⇒ Object

— begin tensor creation: pytorch.org/cppdocs/notes/tensor_creation.html —



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# File 'lib/torch.rb', line 254

def arange(start, finish = nil, step = 1, **options)
  # ruby doesn't support start = 0, finish, step = 1, ...
  if finish.nil?
    finish = start
    start = 0
  end
  _arange(start, finish, step, tensor_options(**options))
end

.device(str) ⇒ Object 



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# File 'lib/torch.rb', line 248

def device(str)
  Device.new(str)
end

.empty(*size, **options) ⇒ Object 



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# File 'lib/torch.rb', line 263

def empty(*size, **options)
  _empty(tensor_size(size), tensor_options(**options))
end

.empty_like(input, **options) ⇒ Object 



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# File 'lib/torch.rb', line 338

def empty_like(input, **options)
  empty(input.size, like_options(input, options))
end

.eye(n, m = nil, **options) ⇒ Object 



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# File 'lib/torch.rb', line 267

def eye(n, m = nil, **options)
  _eye(n, m || n, tensor_options(**options))
end

.from_numo(ndarray) ⇒ Object

Raises:



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# File 'lib/torch.rb', line 207

def from_numo(ndarray)
  dtype = _dtype_to_numo.find { |k, v| ndarray.is_a?(v) }
  raise Error, "Cannot convert #{ndarray.class.name} to tensor" unless dtype
  options = tensor_options(device: "cpu", dtype: dtype[0])
  # TODO pass pointer to array instead of creating string
  str = ndarray.to_string
  tensor = _from_blob(str, ndarray.shape, options)
  # from_blob does not own the data, so we need to keep
  # a reference to it for duration of tensor
  # can remove when passing pointer directly
  tensor.instance_variable_set("@_numo_str", str)
  tensor
end

.full(size, fill_value, **options) ⇒ Object 



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# File 'lib/torch.rb', line 271

def full(size, fill_value, **options)
  _full(size, fill_value, tensor_options(**options))
end

.full_like(input, fill_value, **options) ⇒ Object 



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# File 'lib/torch.rb', line 342

def full_like(input, fill_value, **options)
  full(input.size, fill_value, like_options(input, options))
end

.linspace(start, finish, steps = 100, **options) ⇒ Object 



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# File 'lib/torch.rb', line 275

def linspace(start, finish, steps = 100, **options)
  _linspace(start, finish, steps, tensor_options(**options))
end

.log_softmax(input, dim) ⇒ Object

TODO make dim keyword argument



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# File 'lib/torch.rb', line 401

def log_softmax(input, dim)
  _log_softmax(input, dim)
end

.logspace(start, finish, steps = 100, base = 10.0, **options) ⇒ Object 



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# File 'lib/torch.rb', line 279

def logspace(start, finish, steps = 100, base = 10.0, **options)
  _logspace(start, finish, steps, base, tensor_options(**options))
end

.max(input, dim = nil, keepdim: false, out: nil) ⇒ Object 



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# File 'lib/torch.rb', line 391

def max(input, dim = nil, keepdim: false, out: nil)
  if dim
    raise NotImplementedYet unless out
    _max_out(out[0], out[1], input, dim, keepdim)
  else
    _max(input)
  end
end

.mean(input, dim = nil, keepdim: false) ⇒ Object

TODO support out



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# File 'lib/torch.rb', line 370

def mean(input, dim = nil, keepdim: false)
  if dim
    _mean_dim(input, dim, keepdim)
  else
    _mean(input)
  end
end

.no_grad ⇒ Object 



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# File 'lib/torch.rb', line 238

def no_grad
  previous_value = grad_enabled?
  begin
    _set_grad_enabled(false)
    yield
  ensure
    _set_grad_enabled(previous_value)
  end
end

.ones(*size, **options) ⇒ Object 



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# File 'lib/torch.rb', line 283

def ones(*size, **options)
  _ones(tensor_size(size), tensor_options(**options))
end

.ones_like(input, **options) ⇒ Object

— begin like —



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# File 'lib/torch.rb', line 334

def ones_like(input, **options)
  ones(input.size, like_options(input, options))
end

.rand(*size, **options) ⇒ Object 



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# File 'lib/torch.rb', line 287

def rand(*size, **options)
  _rand(tensor_size(size), tensor_options(**options))
end

.rand_like(input, **options) ⇒ Object 



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# File 'lib/torch.rb', line 346

def rand_like(input, **options)
  rand(input.size, like_options(input, options))
end

.randint(low = 0, high, size, **options) ⇒ Object 



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# File 'lib/torch.rb', line 291

def randint(low = 0, high, size, **options)
  _randint(low, high, size, tensor_options(**options))
end

.randint_like(input, low, high = nil, **options) ⇒ Object 



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# File 'lib/torch.rb', line 350

def randint_like(input, low, high = nil, **options)
  # ruby doesn't support input, low = 0, high, ...
  if high.nil?
    high = low
    low = 0
  end
  randint(low, high, input.size, like_options(input, options))
end

.randn(*size, **options) ⇒ Object 



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# File 'lib/torch.rb', line 295

def randn(*size, **options)
  _randn(tensor_size(size), tensor_options(**options))
end

.randn_like(input, **options) ⇒ Object 



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# File 'lib/torch.rb', line 359

def randn_like(input, **options)
  randn(input.size, like_options(input, options))
end

.randperm(n, **options) ⇒ Object 



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# File 'lib/torch.rb', line 299

def randperm(n, **options)
  _randperm(n, tensor_options(**options))
end

.softmax(input, dim: nil) ⇒ Object 



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# File 'lib/torch.rb', line 405

def softmax(input, dim: nil)
  _softmax(input, dim)
end

.sum(input, dim = nil, keepdim: false) ⇒ Object

TODO support dtype



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# File 'lib/torch.rb', line 379

def sum(input, dim = nil, keepdim: false)
  if dim
    _sum_dim(input, dim, keepdim)
  else
    _sum(input)
  end
end

.tensor(data, **options) ⇒ Object 



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# File 'lib/torch.rb', line 307

def tensor(data, **options)
  size = []
  if data.respond_to?(:to_a)
    data = data.to_a
    d = data
    while d.is_a?(Array)
      size << d.size
      d = d.first
    end
    data = data.flatten
  else
    data = [data].compact
  end

  if options[:dtype].nil?
    if data.all? { |v| v.is_a?(Integer) }
      options[:dtype] = :int64
    elsif data.all? { |v| v == true || v == false }
      options[:dtype] = :bool
    end
  end

  _tensor(data, size, tensor_options(**options))
end

.tensor?(obj) ⇒ Boolean

pytorch.org/docs/stable/torch.html

Returns:

  • (Boolean) 


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# File 'lib/torch.rb', line 203

def tensor?(obj)
  obj.is_a?(Tensor)
end

.topk(input, k) ⇒ Object 



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# File 'lib/torch.rb', line 387

def topk(input, k)
  _topk(input, k)
end

.zeros(*size, **options) ⇒ Object 



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# File 'lib/torch.rb', line 303

def zeros(*size, **options)
  _zeros(tensor_size(size), tensor_options(**options))
end

.zeros_like(input, **options) ⇒ Object 



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# File 'lib/torch.rb', line 363

def zeros_like(input, **options)
  zeros(input.size, like_options(input, options))
end