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