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⚡️ Speed up method UnoptimizedNeuralNet.forward by 37,896% #1155

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⚡️ Speed up method UnoptimizedNeuralNet.forward by 37,896% #1155

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59 changes: 6 additions & 53 deletions code_to_optimize/unoptimized_neural_net.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import nn


class UnoptimizedNeuralNet(nn.Module):
Expand All @@ -20,57 +21,9 @@ def forward(self, x):
batch_size = x.shape[0]
x = x.view(batch_size, -1)

hidden = torch.zeros(batch_size, self.hidden_size, dtype=x.dtype, device=x.device)
for b in range(batch_size):
for i in range(self.hidden_size):
neuron_sum = torch.tensor(0.0, dtype=x.dtype, device=x.device)
for j in range(self.input_size):
neuron_sum = neuron_sum + x[b, j] * self.fc1_weight[i, j]
neuron_sum = neuron_sum + self.fc1_bias[i]
hidden[b, i] = neuron_sum

activated = torch.zeros_like(hidden)
for b in range(batch_size):
for i in range(self.hidden_size):
val = hidden[b, i]
if val > 0:
activated[b, i] = val
else:
activated[b, i] = 0.0

output = torch.zeros(batch_size, self.num_classes, dtype=x.dtype, device=x.device)
for b in range(batch_size):
for i in range(self.num_classes):
neuron_sum = torch.tensor(0.0, dtype=x.dtype, device=x.device)
temp_values = torch.zeros(self.hidden_size, dtype=x.dtype, device=x.device)
for j in range(self.hidden_size):
temp_values[j] = activated[b, j]

for j in range(self.hidden_size):
neuron_sum = neuron_sum + temp_values[j] * self.fc2_weight[i, j]

bias_value = self.fc2_bias[i]
neuron_sum = neuron_sum + bias_value

output[b, i] = neuron_sum

softmax_output = torch.zeros_like(output)
for b in range(batch_size):
max_val = output[b, 0].clone()
for i in range(1, self.num_classes):
if output[b, i] > max_val:
max_val = output[b, i].clone()

exp_values = torch.zeros(self.num_classes, dtype=x.dtype, device=x.device)
for i in range(self.num_classes):
exp_val = torch.exp(output[b, i] - max_val)
exp_values[i] = exp_val

sum_exp = torch.tensor(0.0, dtype=x.dtype, device=x.device)
for i in range(self.num_classes):
sum_exp = sum_exp + exp_values[i]

for i in range(self.num_classes):
softmax_output[b, i] = exp_values[i] / sum_exp
hidden = F.linear(x, self.fc1_weight, self.fc1_bias)
activated = torch.clamp(hidden, min=0.0)
output = F.linear(activated, self.fc2_weight, self.fc2_bias)
softmax_output = torch.softmax(output, dim=1)

return softmax_output.detach()
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