File size: 5,666 Bytes
ce00289 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from llm_transparency_tool.models.tlens_model import TransformerLensTransparentLlm
from llm_transparency_tool.models.transparent_llm import ModelInfo
class TransparentLlmTestCase(unittest.TestCase):
@classmethod
def setUpClass(cls):
# Picking the smallest model possible so that the test runs faster. It's ok to
# change this model, but you'll need to update tokenization specifics in some
# tests.
cls._llm = TransformerLensTransparentLlm(
model_name="facebook/opt-125m",
device="cpu",
)
def setUp(self):
self._llm.run(["test", "test 1"])
self._eps = 1e-5
def test_model_info(self):
info = self._llm.model_info()
self.assertEqual(
info,
ModelInfo(
name="facebook/opt-125m",
n_params_estimate=84934656,
n_layers=12,
n_heads=12,
d_model=768,
d_vocab=50272,
),
)
def test_tokens(self):
tokens = self._llm.tokens()
pad = 1
bos = 2
test = 21959
one = 112
self.assertEqual(tokens.tolist(), [[bos, test, pad], [bos, test, one]])
def test_tokens_to_strings(self):
s = self._llm.tokens_to_strings(torch.Tensor([2, 21959, 112]).to(torch.int))
self.assertEqual(s, ["</s>", "test", " 1"])
def test_manage_state(self):
# One llm.run was called at the setup. Call one more and make sure the object
# returns values for the new state.
self._llm.run(["one", "two", "three", "four"])
self.assertEqual(self._llm.tokens().shape[0], 4)
def test_residual_in_and_out(self):
"""
Test that residual_in is a residual_out for the previous layer.
"""
for layer in range(1, 12):
prev_residual_out = self._llm.residual_out(layer - 1)
residual_in = self._llm.residual_in(layer)
diff = torch.max(torch.abs(residual_in - prev_residual_out)).item()
self.assertLess(diff, self._eps, f"layer {layer}")
def test_residual_plus_block(self):
"""
Make sure that new residual = old residual + block output. Here, block is an ffn
or attention. It's not that obvious because it could be that layer norm is
applied after the block output, but before saving the result to residual.
Luckily, this is not the case in TransformerLens, and we're relying on that.
"""
layer = 3
batch = 0
pos = 0
residual_in = self._llm.residual_in(layer)[batch][pos]
residual_mid = self._llm.residual_after_attn(layer)[batch][pos]
residual_out = self._llm.residual_out(layer)[batch][pos]
ffn_out = self._llm.ffn_out(layer)[batch][pos]
attn_out = self._llm.attention_output(batch, layer, pos)
a = residual_mid
b = residual_in + attn_out
diff = torch.max(torch.abs(a - b)).item()
self.assertLess(diff, self._eps, "attn")
a = residual_out
b = residual_mid + ffn_out
diff = torch.max(torch.abs(a - b)).item()
self.assertLess(diff, self._eps, "ffn")
def test_tensor_shapes(self):
# Not much we can do about the tensors, but at least check their shapes and
# that they don't contain NaNs.
vocab_size = 50272
n_batch = 2
n_tokens = 3
d_model = 768
d_hidden = d_model * 4
n_heads = 12
layer = 5
device = self._llm.residual_in(0).device
for name, tensor, expected_shape in [
("r_in", self._llm.residual_in(layer), [n_batch, n_tokens, d_model]),
(
"r_mid",
self._llm.residual_after_attn(layer),
[n_batch, n_tokens, d_model],
),
("r_out", self._llm.residual_out(layer), [n_batch, n_tokens, d_model]),
("logits", self._llm.logits(), [n_batch, n_tokens, vocab_size]),
("ffn_out", self._llm.ffn_out(layer), [n_batch, n_tokens, d_model]),
(
"decomposed_ffn_out",
self._llm.decomposed_ffn_out(0, 0, 0),
[d_hidden, d_model],
),
("neuron_activations", self._llm.neuron_activations(0, 0, 0), [d_hidden]),
("neuron_output", self._llm.neuron_output(0, 0), [d_model]),
(
"attention_matrix",
self._llm.attention_matrix(0, 0, 0),
[n_tokens, n_tokens],
),
(
"attention_output_per_head",
self._llm.attention_output_per_head(0, 0, 0, 0),
[d_model],
),
(
"attention_output",
self._llm.attention_output(0, 0, 0),
[d_model],
),
(
"decomposed_attn",
self._llm.decomposed_attn(0, layer),
[n_tokens, n_tokens, n_heads, d_model],
),
(
"unembed",
self._llm.unembed(torch.zeros([d_model]).to(device), normalize=True),
[vocab_size],
),
]:
self.assertEqual(list(tensor.shape), expected_shape, name)
self.assertFalse(torch.any(tensor.isnan()), name)
if __name__ == "__main__":
unittest.main()
|