Cdjones Comments - Answer Overflow

Cdjones

•Created by Cdjones on 9/28/2024 in #questions

LLVM ERROR: out of memory

Been trying to get a simple program working that uses Tensors of around size 10,000 x 784 float 32 elements. Whenever the program is run, it returns the LLVM out of memory error. Have tried to check if there are any memory leaks in code and even scaling down the Tensor sizes but it doesn't seem to change the output. Any help on this would be greatly appreciated. This is the github link for full code: https://github.com/mehta302/SimpleNN Program:

from tensor import Tensor, TensorShape
from random import seed
from structured.strd.utils import matmul, load_csv, tensor_transpose, copy, add_bias
from structured.strd.nn import softmax, get_accuracy, get_predictions
alias type: DType = DType.float32
alias n: Int = 784
alias m_test: Int = 10000

fn main() raises -> None:
  var test_data: Tensor[type] = tensor_transpose[type, 0, 1](load_csv(
    "./structured/datasets/MNIST_CSV/mnist_test.csv"
  ))
  var Y: Tensor[type] = copy[type](TensorShape(1, m_test), test_data, 0)
  var X: Tensor[type] = copy[type, 255](TensorShape(n, m_test), test_data, m_test)

  seed()
  var W1: Tensor[type] = Tensor[type].randn(TensorShape(10, n), 0, 0.25).clip(-0.5, 0.5)
  var b1: Tensor[type] = Tensor[type].randn(TensorShape(10, 1), 0, 0.25).clip(-0.5, 0.5)
  var W2: Tensor[type] = Tensor[type].randn(TensorShape(10, 10), 0, 0.25).clip(-0.5, 0.5)
  var b2: Tensor[type] = Tensor[type].randn(TensorShape(10, 1), 0, 0.25).clip(-0.5, 0.5)

  var Z1: Tensor[type] = add_bias[type](matmul[type](W1, X), b1)
  var A1: Tensor[type] = Z1.clip(0, Scalar[type].MAX)
  var A2: Tensor[type] = softmax[type](add_bias[type](matmul[type](W2, A1), b2))

  print(get_predictions(A2))

from tensor import Tensor, TensorShape
from random import seed
from structured.strd.utils import matmul, load_csv, tensor_transpose, copy, add_bias
from structured.strd.nn import softmax, get_accuracy, get_predictions
alias type: DType = DType.float32
alias n: Int = 784
alias m_test: Int = 10000

fn main() raises -> None:
  var test_data: Tensor[type] = tensor_transpose[type, 0, 1](load_csv(
    "./structured/datasets/MNIST_CSV/mnist_test.csv"
  ))
  var Y: Tensor[type] = copy[type](TensorShape(1, m_test), test_data, 0)
  var X: Tensor[type] = copy[type, 255](TensorShape(n, m_test), test_data, m_test)

  seed()
  var W1: Tensor[type] = Tensor[type].randn(TensorShape(10, n), 0, 0.25).clip(-0.5, 0.5)
  var b1: Tensor[type] = Tensor[type].randn(TensorShape(10, 1), 0, 0.25).clip(-0.5, 0.5)
  var W2: Tensor[type] = Tensor[type].randn(TensorShape(10, 10), 0, 0.25).clip(-0.5, 0.5)
  var b2: Tensor[type] = Tensor[type].randn(TensorShape(10, 1), 0, 0.25).clip(-0.5, 0.5)

  var Z1: Tensor[type] = add_bias[type](matmul[type](W1, X), b1)
  var A1: Tensor[type] = Z1.clip(0, Scalar[type].MAX)
  var A2: Tensor[type] = softmax[type](add_bias[type](matmul[type](W2, A1), b2))

  print(get_predictions(A2))

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