Add solver

solver.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+# coding=utf-8
+import argparse
+from cvxopt.glpk import ilp
+import numpy as np
+from cvxopt import matrix
+import torch
+import pickle
+
+# Set up command line arguments
+parser = argparse.ArgumentParser(description='Optimize neuron activation based on VRAM capacity and other parameters.')
+parser.add_argument('--activation_path', type=str, required=True, help='Path to the directory containing activation data.')
+parser.add_argument('--neuron', type=int, default=8192*4, help='Total number of neurons in the network.')
+parser.add_argument('--capacity', type=int, default=int(8192*4*32*0.1), help='Total VRAM capacity for the model.')
+parser.add_argument('--layer', type=int, default=59, help='Total number of layers in the neural network.')
+parser.add_argument('--batch', type=int, default=32, help='Batch size for processing.')
+parser.add_argument('--threshold', type=int, default=512, help='Threshold for splitting a layer across multiple GPUs.')
+parser.add_argument('--output', type=str, required=True, help='File path for the output pickle file.')
+
+args = parser.parse_args()
+
+# Assigning command line arguments to variables
+activation_path = args.activation_path
+neuron = args.neuron
+layer = args.layer
+batch = args.batch
+output_path = args.output
+
+# Processing activation data
+values = []
+for i in range(layer):
+    # Load and sort activation data for each layer
+    freq = torch.load(f"{activation_path}/activation_{i}.pt")
+    freq, _ = torch.sort(freq, descending=True)
+    freq = freq * -1.0
+    freq = freq.view(-1, batch)
+    freq = freq.sum(dim=1)
+    freq = freq.tolist()
+    values += freq
+
+# Padding zero values for additional constraints
+for i in range(layer):
+    values += [0.0]
+c = np.array(values, dtype=float)
+c = matrix(c)
+
+# Setting capacity and neuron count per batch
+CAP = args.capacity
+CAP = int(CAP / batch)
+neuron = int(neuron / batch)
+coeff = []
+h = []
+
+# Constraint 1: Total neuron activation constraint
+lst = []
+for i in range(neuron * layer):
+    lst.append(1)
+for i in range(layer):
+    lst.append(0)
+coeff.append(lst)
+h.append(CAP)
+
+# Constraint 2: Threshold constraint for GPU split per layer
+for i in range(layer):
+    lst = [0] * (neuron * layer + layer)
+    for j in range(neuron):
+        lst[i * neuron + j] = -1
+    lst[neuron * layer + i] = int(args.threshold / batch)
+    coeff.append(lst)
+    h.append(0)
+
+# Constraint 3: Upper bound on neuron activations
+for i in range(layer):
+    lst = [0] * (neuron * layer + layer)
+    for j in range(neuron):
+        lst[i * neuron + j] = 1
+    lst[neuron * layer + i] = -1000000  # Arbitrary large negative number as an upper bound
+    coeff.append(lst)
+    h.append(0)
+
+# Convert lists to matrix format for ILP solver
+coeff = np.array(coeff, dtype=float)
+G = matrix(coeff)
+h = np.array(h, dtype=float)
+h = matrix(h)
+
+# Define the set of integer and binary variables
+I = set(range(neuron * layer + layer))
+B = set()
+
+# Solving the ILP problem
+(status, x) = ilp(c, G, h, None, None, B, I)
+print(f"ILP Status: {status}")
+ans = list(x)
+print(f"Total Activation Units: {sum(ans)}")
+
+# Serialize the solution
+serialize = []
+for i in range(layer):
+    serialize.append(sum(ans[i * neuron:i * neuron + neuron] * batch))
+
+aligned_lst = serialize
+
+# Save the solution to a pickle file
+with open(output_path, 'wb') as handle:
+    pickle.dump(aligned_lst, handle)