[v0.3] Propagate volatile in zeros_like#3984
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Gradients were becoming non-volatile because at::zeros_like returned a Variable with volatile always set to false. The non-volatile gradients accumulated history in the model which results in continuously increasing memory usage, See pytorch#3983, pytorch#3835, pytorch#3824 In v0.4 this will be more robustly solved by pytorch#3970
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Gradients were becoming non-volatile because at::zeros_like returned a Variable with volatile always set to false. The non-volatile gradients accumulated history in the model which results in continuously increasing memory usage, See pytorch#3983, pytorch#3835, pytorch#3824 In v0.4 this will be more robustly solved by pytorch#3970
soumith
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Gradients were becoming non-volatile because at::zeros_like returned a Variable with volatile always set to false. The non-volatile gradients accumulated history in the model which results in continuously increasing memory usage, See #3983, #3835, #3824 In v0.4 this will be more robustly solved by #3970
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Jan 30, 2020
Gradients were becoming non-volatile because at::zeros_like returned a Variable with volatile always set to false. The non-volatile gradients accumulated history in the model which results in continuously increasing memory usage, See pytorch#3983, pytorch#3835, pytorch#3824 In v0.4 this will be more robustly solved by pytorch#3970
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Gradients were becoming non-volatile because at::zeros_like returned a
Variable with volatile always set to false. The non-volatile gradients
accumulated history in the model which results in continuously
increasing memory usage,
See #3983, #3835, #3824
In v0.4 this will be more robustly solved by #3970