Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Language modeling flex heads #210

Merged
merged 14 commits into from
Aug 24, 2021
Merged

Language modeling flex heads #210

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
14 commits
Select commit Hold shift + click to select a range
3125b09
Dependency parsing head
calpt Jul 17, 2021
5c2e7e6
Copied example script from hgiyt
calpt Jul 17, 2021
4bd1909
Updated run_udp
calpt Jul 17, 2021
3409dd0
Documentation & minor fixes
calpt Jul 19, 2021
ea044e4
Some more fixes
calpt Jul 19, 2021
cb84e2e
Style
calpt Jul 19, 2021
ad1eef6
Init flex LM heads.
calpt Jul 22, 2021
c98507c
Finished flex LM head implementations.
calpt Jul 23, 2021
040a5cb
Invertible adapters in flex LM heads.
calpt Jul 26, 2021
035e9a4
Fix output_embedding method implementation for XModelWithHeads
calpt Jul 26, 2021
54d4590
hacked fix for GPT-2 pad_token_id problem
calpt Jul 26, 2021
af61f71
Merge branch 'master' into dev/lm_heads
calpt Aug 16, 2021
0c533bf
Merge branch 'master' into dev/lm_heads
calpt Aug 16, 2021
ad8189a
Merge branch 'master' into dev/lm_heads
calpt Aug 23, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
154 changes: 128 additions & 26 deletions src/transformers/adapters/head_utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,8 @@
logger = logging.getLogger(__name__)


# The "layers" attributes in the configs below map from static head module names to flex head module names.
# In this context, "None" refers to a flex-head layer without weights (e.g. dropout, acts).
STATIC_TO_FLEX_HEAD_MAP = {
# BERT
"BertForSequenceClassification": {
Expand All @@ -15,7 +17,7 @@
"activation_function": None,
"use_pooler": True,
},
"layers": ["classifier"],
"layers": [None, "classifier"],
},
"BertForMultipleChoice": {
"config": {
Expand All @@ -24,23 +26,53 @@
"activation_function": None,
"use_pooler": True,
},
"layers": ["classifier"],
"layers": [None, "classifier"],
},
"BertForTokenClassification": {
"config": {
"head_type": "tagging",
"layers": 1,
"activation_function": None,
},
"layers": ["classifier"],
"layers": [None, "classifier"],
},
"BertForQuestionAnswering": {
"config": {
"head_type": "question_answering",
"layers": 1,
"activation_function": None,
},
"layers": ["qa_outputs"],
"layers": [None, "qa_outputs"],
},
"BertForMaskedLM": {
"config": {
"head_type": "masked_lm",
"layers": 2,
"activation_function": "gelu_orig",
"layer_norm": True,
"bias": True,
},
"layers": [
"cls.predictions.transform.dense",
None,
"cls.predictions.transform.LayerNorm",
"cls.predictions.decoder",
],
},
"BertLMHeadModel": {
"config": {
"head_type": "causal_lm",
"layers": 2,
"activation_function": "gelu_orig",
"layer_norm": True,
"bias": True,
},
"layers": [
"cls.predictions.transform.dense",
None,
"cls.predictions.transform.LayerNorm",
"cls.predictions.decoder",
],
},
# RoBERTa
"RobertaForSequenceClassification": {
Expand All @@ -50,7 +82,7 @@
"activation_function": "tanh",
"use_pooler": False,
},
"layers": ["classifier.dense", "classifier.out_proj"],
"layers": [None, "classifier.dense", None, None, "classifier.out_proj"],
},
"RobertaForMultipleChoice": {
"config": {
Expand All @@ -59,23 +91,43 @@
"activation_function": None,
"use_pooler": True,
},
"layers": ["classifier"],
"layers": [None, "classifier"],
},
"RobertaForTokenClassification": {
"config": {
"head_type": "tagging",
"layers": 1,
"activation_function": None,
},
"layers": ["classifier"],
"layers": [None, "classifier"],
},
"RobertaForQuestionAnswering": {
"config": {
"head_type": "question_answering",
"layers": 1,
"activation_function": None,
},
"layers": ["qa_outputs"],
"layers": [None, "qa_outputs"],
},
"RobertaForMaskedLM": {
"config": {
"head_type": "masked_lm",
"layers": 2,
"activation_function": "gelu_orig",
"layer_norm": True,
"bias": True,
},
"layers": ["lm_head.dense", None, "lm_head.layer_norm", "lm_head.decoder"],
},
"RobertaForCausalLM": {
"config": {
"head_type": "causal_lm",
"layers": 2,
"activation_function": "gelu_orig",
"layer_norm": True,
"bias": True,
},
"layers": ["lm_head.dense", None, "lm_head.layer_norm", "lm_head.decoder"],
},
# XLM-RoBERTa
"XLMRobertaForSequenceClassification": {
Expand All @@ -85,7 +137,7 @@
"activation_function": "tanh",
"use_pooler": False,
},
"layers": ["classifier.dense", "classifier.out_proj"],
"layers": [None, "classifier.dense", None, None, "classifier.out_proj"],
},
"XLMRobertaForMultipleChoice": {
"config": {
Expand All @@ -94,23 +146,43 @@
"activation_function": None,
"use_pooler": True,
},
"layers": ["classifier"],
"layers": [None, "classifier"],
},
"XLMRobertaForTokenClassification": {
"config": {
"head_type": "tagging",
"layers": 1,
"activation_function": None,
},
"layers": ["classifier"],
"layers": [None, "classifier"],
},
"XLMRobertaForQuestionAnswering": {
"config": {
"head_type": "question_answering",
"layers": 1,
"activation_function": None,
},
"layers": ["qa_outputs"],
"layers": [None, "qa_outputs"],
},
"XLMRobertaForMaskedLM": {
"config": {
"head_type": "masked_lm",
"layers": 2,
"activation_function": "gelu_orig",
"layer_norm": True,
"bias": True,
},
"layers": ["lm_head.dense", "lm_head.layer_norm", "lm_head.decoder"],
},
"XLMRobertaForCausalLM": {
"config": {
"head_type": "causal_lm",
"layers": 2,
"activation_function": "gelu_orig",
"layer_norm": True,
"bias": True,
},
"layers": ["lm_head.dense", None, "lm_head.layer_norm", "lm_head.decoder"],
},
# BART
"BartForSequenceClassification": {
Expand All @@ -119,15 +191,21 @@
"layers": 2,
"activation_function": "tanh",
},
"layers": ["classification_head.dense", "classification_head.out_proj"],
"layers": [None, "classification_head.dense", None, None, "classification_head.out_proj"],
},
"BartForQuestionAnswering": {
"config": {
"head_type": "question_answering",
"layers": 1,
"activation_function": None,
},
"layers": ["qa_outputs"],
"layers": [None, "qa_outputs"],
},
"BartForConditionalGeneration": {
"config": {
"head_type": "seq2seq_lm",
},
"layers": ["lm_head"],
},
# MBART
"MBartForSequenceClassification": {
Expand All @@ -136,15 +214,21 @@
"layers": 2,
"activation_function": "tanh",
},
"layers": ["classification_head.dense", "classification_head.out_proj"],
"layers": [None, "classification_head.dense", None, None, "classification_head.out_proj"],
},
"MBartForQuestionAnswering": {
"config": {
"head_type": "question_answering",
"layers": 1,
"activation_function": None,
},
"layers": ["qa_outputs"],
"layers": [None, "qa_outputs"],
},
"MBartForConditionalGeneration": {
"config": {
"head_type": "seq2seq_lm",
},
"layers": ["lm_head"],
},
# DistilBERT
"DistilBertForSequenceClassification": {
Expand All @@ -153,31 +237,41 @@
"layers": 2,
"activation_function": "relu",
},
"layers": ["pre_classifier", "classifier"],
"layers": [None, "pre_classifier", None, None, "classifier"],
},
"DistilBertForMultipleChoice": {
"config": {
"head_type": "multiple_choice",
"layers": 2,
"activation_function": "relu",
},
"layers": ["pre_classifier", "classifier"],
"layers": [None, "pre_classifier", None, None, "classifier"],
},
"DistilBertForTokenClassification": {
"config": {
"head_type": "tagging",
"layers": 1,
"activation_function": None,
},
"layers": ["classifier"],
"layers": [None, "classifier"],
},
"DistilBertForQuestionAnswering": {
"config": {
"head_type": "question_answering",
"layers": 1,
"activation_function": None,
},
"layers": ["qa_outputs"],
"layers": [None, "qa_outputs"],
},
"DistilBertForMaskedLM": {
"config": {
"head_type": "masked_lm",
"layers": 2,
"activation_function": "gelu_orig",
"layer_norm": True,
"bias": True,
},
"layers": ["vocab_transform", None, "vocab_layer_norm", "vocab_projector"],
},
# GPT-2
"GPT2ForSequenceClassification": {
Expand All @@ -187,7 +281,13 @@
"activation_function": None,
"bias": False,
},
"layers": ["score"],
"layers": [None, "score"],
},
"GPT2LMHeadModel": {
"config": {
"head_type": "causal_lm",
},
"layers": ["lm_head"],
},
}

Expand All @@ -213,16 +313,18 @@ def get_head_config_and_rename_list(model_class_name, head_name, label2id, num_l
config = copy.deepcopy(data["config"])
if config["head_type"] == "multiple_choice":
config["num_choices"] = num_labels
else:
config["label2id"] = label2id
elif config["head_type"] not in ["causal_lm", "masked_lm", "seq2seq_lm"]:
config["num_labels"] = num_labels
config["label2id"] = label2id
config["label2id"] = label2id
# rename
rename_list = []
i = 0
for name in data["layers"]:
escaped_name = re.escape(name)
rename_list.append((rf"{escaped_name}\.(\S+)", f"heads.{head_name}.{i+1}.{{0}}"))
i += 3 if config["activation_function"] else 2 # there's always a dropout layer in between
if name is not None:
escaped_name = re.escape(name)
rename_list.append((rf"{escaped_name}\.(\S+)", f"heads.{head_name}.{i}.{{0}}"))
i += 1
rename_func = lambda k, rename_list=rename_list: _regex_list_rename_func(k, rename_list)

return config, rename_func
1 change: 1 addition & 0 deletions src/transformers/adapters/heads/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
# flake8: noqa
from .base import *
from .dependency_parsing import *
from .language_modeling import BertStyleMaskedLMHead, CausalLMHead, Seq2SeqLMHead
Loading