Open mratsim opened 3 years ago
So at first I thought it was setZero
used in parallel region and so that couldn't be parallelized or we would have too many threads.
But then for this script
import ../src/arraymancer
# Learning XOR function with a neural network.
proc main() =
# Autograd context / neuralnet graph
let ctx = newContext Tensor[float32]
let bsz = 32 # batch size
let x_train_bool = randomTensor([bsz * 100, 2], 1).astype(bool)
let y_bool = x_train_bool[_,0] xor x_train_bool[_,1]
let x_train = ctx.variable(x_train_bool.astype(float32))
let y = y_bool.astype(float32)
# We will build the following network:
# Input --> Linear(out_features = 3) --> relu --> Linear(out_features = 1) --> Sigmoid --> Cross-Entropy Loss
let layer_3neurons = ctx.variable(
randomTensor(3, 2, 2.0f) -. 1.0f,
requires_grad = true
)
let classifier_layer = ctx.variable(
randomTensor(1, 3, 2.0f) -. 1.0f,
requires_grad = true
)
# Stochastic Gradient Descent
let optim = newSGD[float32](
layer_3neurons, classifier_layer, 0.01f
)
# Learning loop
for epoch in 0..10000:
for batch_id in 0..<100:
# minibatch offset in the Tensor
let offset = batch_id * 32
let x = x_train[offset ..< offset + 32, _]
let target = y[offset ..< offset + 32, _]
# Building the network
let n1 = relu linear(x, layer_3neurons)
let n2 = linear(n1, classifier_layer)
let loss = n2.sigmoid_cross_entropy(target)
We have:
which I suspected might be due to nested parallelism here: https://github.com/mratsim/Arraymancer/blob/bdcdfe13e47d3f6e0c5247e16640e45982b83ecc/src/arraymancer/nn_primitives/nnp_sigmoid_cross_entropy.nim#L49-L51
but even rewritten with map-reduce fusion and ultimately serial we're still stuck in those barriers for seemingly no reason. https://github.com/mratsim/Arraymancer/blob/979f5d5894ec225220f4ec3c02d248a1b1eb7131/src/arraymancer/nn_primitives/nnp_sigmoid_cross_entropy.nim#L44-L57
Then seemed like copyFromRaw
was the biggest culprit left.
I'm unsure if the issue is in misuse of OpenMP parallel sections or tied to OpenMP design but we are clearly reaching its limits. I suspenct Facebook came to the same conclusion in PyTorch and introduced their C10 threadpool or Halide with their custom threadpool.
AKA, we likely need to introduce Weave sooner than later as OpenMP doesn't cut it and is also hard to debug/profile (and a pain to install on Mac).
The following bench, reduced to only call
linear
which is just a thin wrapper around BLAS, takes 1.6s without-d:openmp
and 15s with-d:openmp
It seems like the machine stalls on OpenMP barriers. Also it seems like the more core you have the more problematic it is.