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Re: why is octave 4.2.0 x86_64-w64-mingw32 so slow?


From: Ray Tayek
Subject: Re: why is octave 4.2.0 x86_64-w64-mingw32 so slow?
Date: Sat, 21 Oct 2017 11:01:36 -0700
User-agent: Mozilla/5.0 (Windows NT 6.3; WOW64; rv:52.0) Gecko/20100101 Thunderbird/52.4.0

On 10/21/2017 3:04 AM, Nicholas Jankowski wrote:
    one epoch is taking a couple of minutes.

    any way to speed this up?



Since we have no idea what you're actually trying to do in octave, it's hard to give you any advice to make it run faster. ...

please see code below. it's just doing some standard nn stuff.

... Since you are running on Windows I assume you've done the usual things ... if you're running on a laptop.
it's a desktop.

That said it's entirely possible you're running an infinite loop inside of octave and you may be waiting a while


the program terminates and seems to behave in a rational way.

it's not doing any i/o. 50% of memory is unused.

i would expect to see at least one thread running near 100%.

so i am puzzled.

thanks

% This function trains a neural network language model.
function [model] = train(epochs)
% Inputs:
%   epochs: Number of epochs to run.
% Output:
% model: A struct containing the learned weights and biases and vocabulary.

if size(ver('Octave'),1)
  OctaveMode = 1;
  warning('error', 'Octave:broadcast');
  start_time = time;
else
  OctaveMode = 0;
  start_time = clock;
end

% SET HYPERPARAMETERS HERE.
batchsize = 100;  % Mini-batch size.
learning_rate = .1;  % Learning rate; default = 0.1.
momentum = 0.9;  % Momentum; default = 0.9.
numhid1 = 50;  % Dimensionality of embedding space; default = 50.
numhid2 = 200;  % Number of units in hidden layer; default = 200.
init_wt = 0.01;  % Standard deviation of the normal distribution
% which is sampled to get the initial weights; default = 0.01

% VARIABLES FOR TRACKING TRAINING PROGRESS.
show_training_CE_after = 100;
show_validation_CE_after = 1000;

% LOAD DATA.
[train_input, train_target, valid_input, valid_target, ...
  test_input, test_target, vocab] = load_data(batchsize);
[numwords, batchsize, numbatches] = size(train_input);
vocab_size = size(vocab, 2);

% INITIALIZE WEIGHTS AND BIASES.
word_embedding_weights = init_wt * randn(vocab_size, numhid1);
embed_to_hid_weights = init_wt * randn(numwords * numhid1, numhid2);
hid_to_output_weights = init_wt * randn(numhid2, vocab_size);
hid_bias = zeros(numhid2, 1);
output_bias = zeros(vocab_size, 1);

word_embedding_weights_delta = zeros(vocab_size, numhid1);
word_embedding_weights_gradient = zeros(vocab_size, numhid1);
embed_to_hid_weights_delta = zeros(numwords * numhid1, numhid2);
hid_to_output_weights_delta = zeros(numhid2, vocab_size);
hid_bias_delta = zeros(numhid2, 1);
output_bias_delta = zeros(vocab_size, 1);
expansion_matrix = eye(vocab_size);
count = 0;
tiny = exp(-30);

% TRAIN.
for epoch = 1:epochs
  fprintf(1, 'Epoch %d\n', epoch);
  this_chunk_CE = 0;
  trainset_CE = 0;
  % LOOP OVER MINI-BATCHES.
  for m = 1:numbatches
    input_batch = train_input(:, :, m);
    target_batch = train_target(:, :, m);

    % FORWARD PROPAGATE.
    % Compute the state of each layer in the network given the input batch
    % and all weights and biases
    [embedding_layer_state, hidden_layer_state, output_layer_state] = ...
      fprop(input_batch, ...
            word_embedding_weights, embed_to_hid_weights, ...
            hid_to_output_weights, hid_bias, output_bias);

    % COMPUTE DERIVATIVE.
    %% Expand the target to a sparse 1-of-K vector.
    expanded_target_batch = expansion_matrix(:, target_batch);
    %% Compute derivative of cross-entropy loss function.
    error_deriv = output_layer_state - expanded_target_batch;

    % MEASURE LOSS FUNCTION.
    CE = -sum(sum(...
expanded_target_batch .* log(output_layer_state + tiny))) / batchsize;
    count =  count + 1;
    this_chunk_CE = this_chunk_CE + (CE - this_chunk_CE) / count;
    trainset_CE = trainset_CE + (CE - trainset_CE) / m;
    fprintf(1, '\rBatch %d Train CE %.3f', m, this_chunk_CE);
    if mod(m, show_training_CE_after) == 0
      fprintf(1, '\n');
      count = 0;
      this_chunk_CE = 0;
    end
    if OctaveMode
      fflush(1);
    end

    % BACK PROPAGATE.
    %% OUTPUT LAYER.
    hid_to_output_weights_gradient =  hidden_layer_state * error_deriv';
    output_bias_gradient = sum(error_deriv, 2);
    back_propagated_deriv_1 = (hid_to_output_weights * error_deriv) ...
      .* hidden_layer_state .* (1 - hidden_layer_state);

    %% HIDDEN LAYER.
    % FILL IN CODE. Replace the line below by one of the options.
embed_to_hid_weights_gradient = embed_to_hid_weights_gradient = embedding_layer_state * back_propagated_deriv_1'; % was zeros(numhid1 * numwords, numhid2);
    % Options:
% (a) embed_to_hid_weights_gradient = back_propagated_deriv_1' * embedding_layer_state; % (b) embed_to_hid_weights_gradient = embedding_layer_state * back_propagated_deriv_1';
    % (c) embed_to_hid_weights_gradient = back_propagated_deriv_1;
    % (d) embed_to_hid_weights_gradient = embedding_layer_state;

    % FILL IN CODE. Replace the line below by one of the options.
hid_bias_gradient = sum(back_propagated_deriv_1, 2); % was zeros(numhid2, 1);
    % Options
    % (a) hid_bias_gradient = sum(back_propagated_deriv_1, 2);
    % (b) hid_bias_gradient = sum(back_propagated_deriv_1, 1);
    % (c) hid_bias_gradient = back_propagated_deriv_1;
    % (d) hid_bias_gradient = back_propagated_deriv_1';

    % FILL IN CODE. Replace the line below by one of the options.
back_propagated_deriv_2 = embed_to_hid_weights * back_propagated_deriv_1; % was zeros(numhid2, batchsize);
    % Options
% (a) back_propagated_deriv_2 = embed_to_hid_weights * back_propagated_deriv_1; % (b) back_propagated_deriv_2 = back_propagated_deriv_1 * embed_to_hid_weights; % (c) back_propagated_deriv_2 = back_propagated_deriv_1' * embed_to_hid_weights; % (d) back_propagated_deriv_2 = back_propagated_deriv_1 * embed_to_hid_weights';

    word_embedding_weights_gradient(:) = 0;
    %% EMBEDDING LAYER.
    for w = 1:numwords
word_embedding_weights_gradient = word_embedding_weights_gradient + ...
         expansion_matrix(:, input_batch(w, :)) * ...
(back_propagated_deriv_2(1 + (w - 1) * numhid1 : w * numhid1, :)');
    end

    % UPDATE WEIGHTS AND BIASES.
    word_embedding_weights_delta = ...
      momentum .* word_embedding_weights_delta + ...
      word_embedding_weights_gradient ./ batchsize;
    word_embedding_weights = word_embedding_weights...
      - learning_rate * word_embedding_weights_delta;

    embed_to_hid_weights_delta = ...
      momentum .* embed_to_hid_weights_delta + ...
      embed_to_hid_weights_gradient ./ batchsize;
    embed_to_hid_weights = embed_to_hid_weights...
      - learning_rate * embed_to_hid_weights_delta;

    hid_to_output_weights_delta = ...
      momentum .* hid_to_output_weights_delta + ...
      hid_to_output_weights_gradient ./ batchsize;
    hid_to_output_weights = hid_to_output_weights...
      - learning_rate * hid_to_output_weights_delta;

    hid_bias_delta = momentum .* hid_bias_delta + ...
      hid_bias_gradient ./ batchsize;
    hid_bias = hid_bias - learning_rate * hid_bias_delta;

    output_bias_delta = momentum .* output_bias_delta + ...
      output_bias_gradient ./ batchsize;
    output_bias = output_bias - learning_rate * output_bias_delta;

    % VALIDATE.
    if mod(m, show_validation_CE_after) == 0
      fprintf(1, '\rRunning validation ...');
      if OctaveMode
        fflush(1);
      end
      [embedding_layer_state, hidden_layer_state, output_layer_state] = ...
        fprop(valid_input, word_embedding_weights, embed_to_hid_weights,...
              hid_to_output_weights, hid_bias, output_bias);
      datasetsize = size(valid_input, 2);
      expanded_valid_target = expansion_matrix(:, valid_target);
      CE = -sum(sum(...
expanded_valid_target .* log(output_layer_state + tiny))) /datasetsize;
      fprintf(1, ' Validation CE %.3f\n', CE);
      if OctaveMode
        fflush(1);
      end
    end
  end
  fprintf(1, '\rAverage Training CE %.3f\n', trainset_CE);
end
fprintf(1, 'Finished Training.\n');
if OctaveMode
  fflush(1);
end
fprintf(1, 'Final Training CE %.3f\n', trainset_CE);

% EVALUATE ON VALIDATION SET.
fprintf(1, '\rRunning validation ...');
if OctaveMode
  fflush(1);
end
[embedding_layer_state, hidden_layer_state, output_layer_state] = ...
  fprop(valid_input, word_embedding_weights, embed_to_hid_weights,...
        hid_to_output_weights, hid_bias, output_bias);
datasetsize = size(valid_input, 2);
expanded_valid_target = expansion_matrix(:, valid_target);
CE = -sum(sum(...
  expanded_valid_target .* log(output_layer_state + tiny))) / datasetsize;
fprintf(1, '\rFinal Validation CE %.3f\n', CE);
if OctaveMode
  fflush(1);
end

% EVALUATE ON TEST SET.
fprintf(1, '\rRunning test ...');
if OctaveMode
  fflush(1);
end
[embedding_layer_state, hidden_layer_state, output_layer_state] = ...
  fprop(test_input, word_embedding_weights, embed_to_hid_weights,...
        hid_to_output_weights, hid_bias, output_bias);
datasetsize = size(test_input, 2);
expanded_test_target = expansion_matrix(:, test_target);
CE = -sum(sum(...
  expanded_test_target .* log(output_layer_state + tiny))) / datasetsize;
fprintf(1, '\rFinal Test CE %.3f\n', CE);
if OctaveMode
  fflush(1);
end

model.word_embedding_weights = word_embedding_weights;
model.embed_to_hid_weights = embed_to_hid_weights;
model.hid_to_output_weights = hid_to_output_weights;
model.hid_bias = hid_bias;
model.output_bias = output_bias;
model.vocab = vocab;

% In MATLAB replace line below with 'end_time = clock;'
if OctaveMode
  end_time = time;
  diff = end_time - start_time;
else
  end_time = clock;
  diff = etime(end_time, start_time);
end
fprintf(1, 'Training took %.2f seconds\n', diff);
end



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