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pruning_utils.py
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pruning_utils.py
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# This file will contain helper functions related to the pruning process, including any specialized pruning functions and the SparseGPT functionality.
# DISCLAIMER: The SparseGPT class is a modified version of the original SparseGPT class. The original SparseGPT class can be found in [SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot].
import math
import time
import torch
import torch.nn as nn
import transformers
from quant import *
# turned this flag to be True
DEBUG = True
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.allow_tf32 = False
def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''):
if type(module) in layers:
return {name: module}
res = {}
for name1, child in module.named_children():
res.update(find_layers(
child, layers=layers, name=name + '.' + name1 if name != '' else name1
))
return res
class SparseGPT_OPT:
def __init__(self, layer):
self.layer = layer
self.dev = self.layer.weight.device
W = layer.weight.data.clone()
if isinstance(self.layer, nn.Conv2d):
W = W.flatten(1)
if isinstance(self.layer, transformers.Conv1D):
W = W.t()
self.rows = W.shape[0]
self.columns = W.shape[1]
self.H = torch.zeros((self.columns, self.columns), device=self.dev)
self.nsamples = 0
self.batch_inp = []
self.batch_out = []
def add_batch(self, inp, out, name, blocksize=1024):
if DEBUG:
self.inp1 = inp
self.out1 = out
if len(inp.shape) == 2:
inp = inp.unsqueeze(0)
###### added code
if name == 'fc1' or name == 'fc2':
self.batch_inp.append(inp[0].clone().detach())
if len(out.shape) == 3:
out = out.squeeze(0)
self.batch_out.append(out.clone().detach())
######
tmp = inp.shape[0]
if isinstance(self.layer, nn.Linear) or isinstance(self.layer, transformers.Conv1D):
if len(inp.shape) == 3:
inp = inp.reshape((-1, inp.shape[-1]))
inp = inp.t()
self.H *= self.nsamples / (self.nsamples + tmp)
self.nsamples += tmp
inp = math.sqrt(2 / self.nsamples) * inp.float()
self.H += inp.matmul(inp.t())
def fasterprune(
self, sparsity, prunen=0, prunem=0, blocksize=128, percdamp=.01
):
W = self.layer.weight.data.clone()
if isinstance(self.layer, nn.Conv2d):
W = W.flatten(1)
if isinstance(self.layer, transformers.Conv1D):
W = W.t()
W = W.float()
if hasattr(self, 'quantizer'):
if not self.quantizer.ready():
self.quantizer.find_params(W, weight=True)
tick = time.time()
H = self.H
# del self.H
dead = torch.diag(H) == 0
H[dead, dead] = 1
W[:, dead] = 0
Losses = torch.zeros(self.rows, device=self.dev)
damp = percdamp * torch.mean(torch.diag(H))
diag = torch.arange(self.columns, device=self.dev)
H[diag, diag] += damp
H = torch.linalg.cholesky(H)
H = torch.cholesky_inverse(H)
H = torch.linalg.cholesky(H, upper=True)
Hinv = H
mask = None
for i1 in range(0, self.columns, blocksize):
i2 = min(i1 + blocksize, self.columns)
count = i2 - i1
W1 = W[:, i1:i2].clone()
Q1 = torch.zeros_like(W1)
Err1 = torch.zeros_like(W1)
Losses1 = torch.zeros_like(W1)
Hinv1 = Hinv[i1:i2, i1:i2]
if prunen == 0:
if mask is not None:
mask1 = mask[:, i1:i2]
else:
tmp = W1 ** 2 / (torch.diag(Hinv1).reshape((1, -1))) ** 2
thresh = torch.sort(tmp.flatten())[0][int(tmp.numel() * sparsity)]
mask1 = tmp <= thresh
else:
mask1 = torch.zeros_like(W1) == 1
for i in range(count):
w = W1[:, i]
d = Hinv1[i, i]
if prunen != 0 and i % prunem == 0:
tmp = W1[:, i:(i + prunem)] ** 2 / (torch.diag(Hinv1)[i:(i + prunem)].reshape((1, -1))) ** 2
mask1.scatter_(1, i + torch.topk(tmp, prunen, dim=1, largest=False)[1], True)
q = w.clone()
q[mask1[:, i]] = 0
if hasattr(self, 'quantizer'):
q = quantize(
q.unsqueeze(1), self.quantizer.scale, self.quantizer.zero, self.quantizer.maxq
).flatten()
Q1[:, i] = q
Losses1[:, i] = (w - q) ** 2 / d ** 2
err1 = (w - q) / d
W1[:, i:] -= err1.unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0))
Err1[:, i] = err1
W[:, i1:i2] = Q1
Losses += torch.sum(Losses1, 1) / 2
W[:, i2:] -= Err1.matmul(Hinv[i1:i2, i2:])
# if DEBUG:
# self.layer.weight.data[:, :i2] = W[:, :i2]
# self.layer.weight.data[:, i2:] = W[:, i2:]
# print(torch.sum((self.layer(self.inp1) - self.out1) ** 2))
# print(torch.sum(Losses))
torch.cuda.synchronize()
print('time %.2f' % (time.time() - tick))
print('error', torch.sum(Losses).item())
if isinstance(self.layer, transformers.Conv1D):
W = W.t()
self.layer.weight.data = W.reshape(self.layer.weight.shape).to(self.layer.weight.data.dtype)
# if DEBUG:
# print(torch.sum((self.layer(self.inp1) - self.out1) ** 2))
def free(self):
if DEBUG:
self.inp1 = None
self.out1 = None
self.H = None
torch.cuda.empty_cache()
class SparseGPT_LlaMA:
def __init__(self, layer):
self.layer = layer
self.dev = self.layer.weight.device
W = layer.weight.data.clone()
if isinstance(self.layer, nn.Conv2d):
W = W.flatten(1)
if isinstance(self.layer, transformers.Conv1D):
W = W.t()
self.rows = W.shape[0]
self.columns = W.shape[1]
self.H = torch.zeros((self.columns, self.columns), device=self.dev)
self.nsamples = 0
self.batch_inp = []
self.batch_out = []
def add_batch(self, inp, out, name, blocksize=1024):
if DEBUG:
self.inp1 = inp
self.out1 = out
if len(inp.shape) == 2:
inp = inp.unsqueeze(0)
###### added code
if name == 'mlp.up_proj' or name == 'mlp.down_proj':
self.batch_inp.append(inp[0].clone().detach())
if len(out.shape) == 3:
out = out.squeeze(0)
self.batch_out.append(out.clone().detach())
if name == 'mlp.gate_proj': # for this layer, we only store the outputs. for inputs, they are shared with 'mlp.up_proj'
if len(out.shape) == 3:
out = out.squeeze(0)
self.batch_out.append(out.clone().detach())
######
tmp = inp.shape[0]
if isinstance(self.layer, nn.Linear) or isinstance(self.layer, transformers.Conv1D):
if len(inp.shape) == 3:
inp = inp.reshape((-1, inp.shape[-1]))
inp = inp.t()
self.H *= self.nsamples / (self.nsamples + tmp)
self.nsamples += tmp
inp = math.sqrt(2 / self.nsamples) * inp.float()
self.H += inp.matmul(inp.t())
def fasterprune(
self, sparsity, prunen=0, prunem=0, blocksize=128, percdamp=.01
):
W = self.layer.weight.data.clone()
if isinstance(self.layer, nn.Conv2d):
W = W.flatten(1)
if isinstance(self.layer, transformers.Conv1D):
W = W.t()
W = W.float()
if hasattr(self, 'quantizer'):
if not self.quantizer.ready():
self.quantizer.find_params(W, weight=True)
tick = time.time()
H = self.H
# del self.H
dead = torch.diag(H) == 0
H[dead, dead] = 1
W[:, dead] = 0
Losses = torch.zeros(self.rows, device=self.dev)
damp = percdamp * torch.mean(torch.diag(H))
diag = torch.arange(self.columns, device=self.dev)
H[diag, diag] += damp
H = torch.linalg.cholesky(H)
H = torch.cholesky_inverse(H)
H = torch.linalg.cholesky(H, upper=True)
Hinv = H
mask = None
for i1 in range(0, self.columns, blocksize):
i2 = min(i1 + blocksize, self.columns)
count = i2 - i1
W1 = W[:, i1:i2].clone()
Q1 = torch.zeros_like(W1)
Err1 = torch.zeros_like(W1)
Losses1 = torch.zeros_like(W1)
Hinv1 = Hinv[i1:i2, i1:i2]
if prunen == 0:
if mask is not None:
mask1 = mask[:, i1:i2]
else:
tmp = W1 ** 2 / (torch.diag(Hinv1).reshape((1, -1))) ** 2
thresh = torch.sort(tmp.flatten())[0][int(tmp.numel() * sparsity)]
mask1 = tmp <= thresh
else:
mask1 = torch.zeros_like(W1) == 1
for i in range(count):
w = W1[:, i]
d = Hinv1[i, i]
if prunen != 0 and i % prunem == 0:
tmp = W1[:, i:(i + prunem)] ** 2 / (torch.diag(Hinv1)[i:(i + prunem)].reshape((1, -1))) ** 2
mask1.scatter_(1, i + torch.topk(tmp, prunen, dim=1, largest=False)[1], True)
q = w.clone()
q[mask1[:, i]] = 0
if hasattr(self, 'quantizer'):
q = quantize(
q.unsqueeze(1), self.quantizer.scale, self.quantizer.zero, self.quantizer.maxq
).flatten()
Q1[:, i] = q
Losses1[:, i] = (w - q) ** 2 / d ** 2
err1 = (w - q) / d
W1[:, i:] -= err1.unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0))
Err1[:, i] = err1
W[:, i1:i2] = Q1
Losses += torch.sum(Losses1, 1) / 2
W[:, i2:] -= Err1.matmul(Hinv[i1:i2, i2:])
# if DEBUG:
# self.layer.weight.data[:, :i2] = W[:, :i2]
# self.layer.weight.data[:, i2:] = W[:, i2:]
# print(torch.sum((self.layer(self.inp1) - self.out1) ** 2))
# print(torch.sum(Losses))
torch.cuda.synchronize()
print('time %.2f' % (time.time() - tick))
print('error', torch.sum(Losses).item())
if isinstance(self.layer, transformers.Conv1D):
W = W.t()
self.layer.weight.data = W.reshape(self.layer.weight.shape).to(self.layer.weight.data.dtype)
# if DEBUG:
# print(torch.sum((self.layer(self.inp1) - self.out1) ** 2))
def free(self):
if DEBUG:
self.inp1 = None
self.out1 = None
self.H = None
torch.cuda.empty_cache()