87 lines
3.2 KiB
Python
87 lines
3.2 KiB
Python
# Copyright 2021 Samsung Electronics Co., Ltd.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# =============================================================================
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torch.autograd as autograd
|
|
|
|
from . import measure
|
|
from ..p_utils import get_layer_metric_array
|
|
|
|
|
|
@measure('grasp', bn=True, mode='param')
|
|
def compute_grasp_per_weight(net, inputs, targets, mode, loss_fn, T=1, num_iters=1, split_data=1):
|
|
|
|
# get all applicable weights
|
|
weights = []
|
|
for layer in net.modules():
|
|
if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear):
|
|
weights.append(layer.weight)
|
|
layer.weight.requires_grad_(True) # TODO isn't this already true?
|
|
|
|
# NOTE original code had some input/target splitting into 2
|
|
# I am guessing this was because of GPU mem limit
|
|
net.zero_grad()
|
|
N = inputs.shape[0]
|
|
for sp in range(split_data):
|
|
st=sp*N//split_data
|
|
en=(sp+1)*N//split_data
|
|
|
|
#forward/grad pass #1
|
|
grad_w = None
|
|
for _ in range(num_iters):
|
|
#TODO get new data, otherwise num_iters is useless!
|
|
outputs = net.forward(inputs[st:en])/T
|
|
loss = loss_fn(outputs, targets[st:en])
|
|
grad_w_p = autograd.grad(loss, weights, allow_unused=True)
|
|
if grad_w is None:
|
|
grad_w = list(grad_w_p)
|
|
else:
|
|
for idx in range(len(grad_w)):
|
|
grad_w[idx] += grad_w_p[idx]
|
|
|
|
|
|
for sp in range(split_data):
|
|
st=sp*N//split_data
|
|
en=(sp+1)*N//split_data
|
|
|
|
# forward/grad pass #2
|
|
outputs = net.forward(inputs[st:en])/T
|
|
loss = loss_fn(outputs, targets[st:en])
|
|
grad_f = autograd.grad(loss, weights, create_graph=True, allow_unused=True)
|
|
|
|
# accumulate gradients computed in previous step and call backwards
|
|
z, count = 0,0
|
|
for layer in net.modules():
|
|
if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear):
|
|
if grad_w[count] is not None:
|
|
z += (grad_w[count].data * grad_f[count]).sum()
|
|
count += 1
|
|
z.backward()
|
|
|
|
# compute final sensitivity metric and put in grads
|
|
def grasp(layer):
|
|
if layer.weight.grad is not None:
|
|
return -layer.weight.data * layer.weight.grad # -theta_q Hg
|
|
#NOTE in the grasp code they take the *bottom* (1-p)% of values
|
|
#but we take the *top* (1-p)%, therefore we remove the -ve sign
|
|
#EDIT accuracy seems to be negatively correlated with this metric, so we add -ve sign here!
|
|
else:
|
|
return torch.zeros_like(layer.weight)
|
|
|
|
grads = get_layer_metric_array(net, grasp, mode)
|
|
|
|
return grads |