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torch_deep_neural_classifier_iit.py
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torch_deep_neural_classifier_iit.py
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from collections import defaultdict
import copy
import numpy as np
import torch
import torch.nn as nn
import torch.utils.data
from torch_deep_neural_classifier import TorchDeepNeuralClassifier
__author__ = "Atticus Geiger"
__version__ = "CS224u, Stanford, Spring 2022"
class IITModel(torch.nn.Module):
def __init__(self, model, layers, id_to_coords,device):
super().__init__()
self.model = model
self.layers = layers
self.id_to_coords = defaultdict(lambda: defaultdict(list))
for k, vals in id_to_coords.items():
for d in vals:
layer = d['layer']
self.id_to_coords[k][layer].append(d)
self.device = device
def no_IIT_forward(self, X):
return self.model(X)
def forward(self, X):
base = X[:,0,:].squeeze(1).type(torch.FloatTensor).to(self.device)
coord_ids = X[:,1,:].squeeze(1).type(torch.FloatTensor).to(self.device)
sources = X[:,2:,:].to(self.device)
sources = [sources[:,j,:].squeeze(1).type(torch.FloatTensor).to(self.device)
for j in range(sources.shape[1])]
gets = self.id_to_coords[int(coord_ids.flatten()[0])]
sets = copy.deepcopy(gets)
self.activation = dict()
for layer in gets:
for i, get in enumerate(gets[layer]):
handlers = self._gets_sets(gets ={layer: [get]},sets = None)
source_logits = self.no_IIT_forward(sources[i])
for handler in handlers:
handler.remove()
sets[layer][i]["intervention"] = self.activation[f'{get["layer"]}-{get["start"]}-{get["end"]}']
base_logits = self.no_IIT_forward(base)
handlers = self._gets_sets(gets = None, sets = sets)
counterfactual_logits = self.no_IIT_forward(base)
for handler in handlers:
handler.remove()
return counterfactual_logits, base_logits
def make_hook(self, gets, sets, layer):
def hook(model, input, output):
layer_gets, layer_sets = [], []
if gets is not None and layer in gets:
layer_gets = gets[layer]
if sets is not None and layer in sets:
layer_sets = sets[layer]
for set in layer_sets:
output = torch.cat([output[:,:set["start"]], set["intervention"], output[:,set["end"]:]], dim = 1)
for get in layer_gets:
self.activation[f'{get["layer"]}-{get["start"]}-{get["end"]}'] = output[:,get["start"]: get["end"] ]
return output
return hook
def _gets_sets(self,gets=None, sets = None):
handlers = []
for layer in range(len(self.layers)):
hook = self.make_hook(gets,sets, layer)
both_handler = self.layers[layer].register_forward_hook(hook)
handlers.append(both_handler)
return handlers
def retrieve_activations(self, input, get, sets):
input = input.type(torch.FloatTensor).to(self.device)
self.activation = dict()
get_val = {get["layer"]: [get]} if get is not None else None
set_val = {sets["layer"]: [sets]} if sets is not None else None
handlers = self._gets_sets(get_val, set_val)
logits = self.model(input)
for handler in handlers:
handler.remove()
return self.activation[f'{get["layer"]}-{get["start"]}-{get["end"]}']
class CrossEntropyLossIIT(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.CrossEntropyLoss(reduction="mean")
def forward(self, preds, labels):
return self.loss(preds[0], labels[: , 0]) + self.loss(preds[1], labels[:,1])
class TorchDeepNeuralClassifierIIT(TorchDeepNeuralClassifier):
def __init__(self, id_to_coords=None, **base_kwargs):
super().__init__(**base_kwargs)
self.loss = CrossEntropyLossIIT()
self.id_to_coords = id_to_coords
self.shuffle_train = False
def build_graph(self):
model = super().build_graph()
IITmodel = IITModel(model, self.layers, self.id_to_coords, self.device)
return IITmodel
def batched_indices(self, max_len):
batch_indices = [x for x in range((max_len // self.batch_size))]
output = []
while len(batch_indices) != 0:
batch_index = random.sample(batch_indices, 1)[0]
batch_indices.remove(batch_index)
output.append([batch_index*self.batch_size + x for x in range(self.batch_size)])
return output
def build_dataset(self, base, sources, base_y, IIT_y, coord_ids):
base = torch.FloatTensor(np.array(base))
sources = [torch.FloatTensor(np.array(source)) for source in sources]
self.input_dim = base.shape[1]
coord_ids = torch.FloatTensor(np.array(coord_ids))
base_y = np.array(base_y)
self.classes_ = sorted(set(base_y))
self.n_classes_ = len(self.classes_)
class2index = dict(zip(self.classes_, range(self.n_classes_)))
base_y = [class2index[label] for label in base_y]
base_y = torch.tensor(base_y)
IIT_y = np.array(IIT_y)
IIT_y = [class2index[int(label)] for label in IIT_y]
IIT_y = torch.tensor(IIT_y)
bigX = torch.stack([base, coord_ids.unsqueeze(1).expand(-1, base.shape[1])] + sources, dim=1)
bigy = torch.stack((IIT_y, base_y), dim=1)
dataset = torch.utils.data.TensorDataset(bigX, bigy)
return dataset
def prep_input(self, base, sources, coord_ids):
bigX = torch.stack([base, coord_ids.unsqueeze(1).expand(-1, base.shape[1])] + sources, dim=1)
return bigX
def iit_predict(self, base, sources, coord_ids):
IIT_test = self.prep_input(base, sources, coord_ids)
IIT_preds, base_preds = self.model(IIT_test)
IIT_preds = np.array(IIT_preds.argmax(axis=1).cpu())
base_preds = np.array(base_preds.argmax(axis=1).cpu())
return IIT_preds, base_preds
if __name__ == '__main__':
import iit
from sklearn.metrics import classification_report
import utils
utils.fix_random_seeds()
V1 = 0
data_size = 10000
embedding_dim = 4
id_to_coords = {
V1: [{"layer": 1, "start": 0, "end": embedding_dim}]
}
iit_equality_dataset = iit.get_IIT_equality_dataset(
"V1", embedding_dim, data_size)
X_base_train, X_sources_train, y_base_train, y_IIT_train, interventions = iit_equality_dataset
model = TorchDeepNeuralClassifierIIT(
hidden_dim=embedding_dim*4,
hidden_activation=torch.nn.ReLU(),
num_layers=3,
id_to_coords=id_to_coords)
model.fit(
X_base_train,
X_sources_train,
y_base_train,
y_IIT_train,
interventions)
X_base_test, X_sources_test, y_base_test, y_IIT_test, interventions = iit.get_IIT_equality_dataset(
"V1", embedding_dim, 100)
IIT_preds, base_preds = model.iit_predict(
X_base_test, X_sources_test, interventions)
print("\nStandard evaluation")
print(classification_report(y_base_test, base_preds))
print("V1 counterfactual evaluation")
print(classification_report(y_IIT_test, IIT_preds))