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Added verbose option for activation extraction #48

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Added verbose option for activation extraction #48

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50 changes: 30 additions & 20 deletions neurox/data/extraction/transformers_extractor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -127,6 +127,7 @@ def extract_sentence_representations(
dtype="float32",
include_special_tokens=False,
tokenization_counts={},
verbose=False
):
"""
Get representations for a single sentence
Expand Down Expand Up @@ -245,15 +246,16 @@ def extract_sentence_representations(
]
all_hidden_states = np.array(all_hidden_states, dtype=dtype)

print('Sentence : "%s"' % (sentence))
print("Original (%03d): %s" % (len(original_tokens), original_tokens))
print(
"Tokenized (%03d): %s"
% (
len(tokenizer.convert_ids_to_tokens(ids)),
tokenizer.convert_ids_to_tokens(ids),
if verbose:
print('Sentence : "%s"' % (sentence))
print("Original (%03d): %s" % (len(original_tokens), original_tokens))
print(
"Tokenized (%03d): %s"
% (
len(tokenizer.convert_ids_to_tokens(ids)),
tokenizer.convert_ids_to_tokens(ids),
)
)
)

assert all_hidden_states.shape[1] == len(ids)

Expand All @@ -276,13 +278,14 @@ def extract_sentence_representations(
special_token_ids = []

assert all_hidden_states.shape[1] == len(filtered_ids)
print(
"Filtered (%03d): %s"
% (
len(tokenizer.convert_ids_to_tokens(filtered_ids)),
tokenizer.convert_ids_to_tokens(filtered_ids),
if verbose:
print(
"Filtered (%03d): %s"
% (
len(tokenizer.convert_ids_to_tokens(filtered_ids)),
tokenizer.convert_ids_to_tokens(filtered_ids),
)
)
)

# Get actual tokens for filtered ids in order to do subword
# aggregation
Expand Down Expand Up @@ -397,15 +400,17 @@ def extract_sentence_representations(
last_special_token_pointer += 1
counter += 1

print("Detokenized (%03d): %s" % (len(detokenized), detokenized))
print("Counter: %d" % (counter))
if verbose:
print("Detokenized (%03d): %s" % (len(detokenized), detokenized))
print("Counter: %d" % (counter))

if inputs_truncated:
print("WARNING: Input truncated because of length, skipping check")
else:
assert counter == len(filtered_ids)
assert len(detokenized) == len(original_tokens) + len(special_token_ids)
print("===================================================================")
if verbose:
print("===================================================================")
return final_hidden_states, detokenized


Expand All @@ -422,6 +427,7 @@ def extract_representations(
filter_layers=None,
dtype="float32",
include_special_tokens=False,
verbose=False
):
"""
Extract representations for an entire corpus and save them to disk
Expand Down Expand Up @@ -502,6 +508,8 @@ def corpus_generator(input_corpus_path):
print("Extracting representations from model")
tokenization_counts = {} # Cache for tokenizer rules
for sentence_idx, sentence in enumerate(corpus_generator(input_corpus)):
if sentence_idx % 100 == 0:
print(f"Reached Sentence {sentence_idx}")
hidden_states, extracted_words = extract_sentence_representations(
sentence,
model,
Expand All @@ -512,12 +520,15 @@ def corpus_generator(input_corpus_path):
dtype=dtype,
include_special_tokens=include_special_tokens,
tokenization_counts=tokenization_counts,
verbose=verbose
)

print("Hidden states: ", hidden_states.shape)
print("# Extracted words: ", len(extracted_words))
if verbose:
print("Hidden states: ", hidden_states.shape)
print("# Extracted words: ", len(extracted_words))

writer.write_activations(sentence_idx, extracted_words, hidden_states)
print("[INFO] DONE WITH THE ACTIVATION EXTRACTION...")

writer.close()

Expand Down Expand Up @@ -558,7 +569,6 @@ def main():
action="store_true",
help="Include special tokens like [CLS] and [SEP] in the extracted representations",
)

ActivationsWriter.add_writer_options(parser)

args = parser.parse_args()
Expand Down