mirror of
https://github.com/facebookresearch/fairseq.git
synced 2024-09-11 17:25:31 +03:00
Summary: Support FFN prune for Fairseq. For example, user can apply pruning on top of Roberta base model by specify the argument "--ffn-blocks-to-remove 1024". Also, user needs to provide a ckpt which is already pruned so that the pruned ckpt can be loaded correctly. The idea of prune can be summarized as Fine tune model (e.g. roberta encoder) on a certain datasets with regularization After the model is trained. User could use _get_fc_rank and _prune_fc_layer functions to get the top X blocks with most importance in each transformer layer. Then user uses the rank to prune a new roberta encoder and save the pruned ckpt manually. User will fine tune the the new roberta encoder via the ckpt saved above Reviewed By: dianaml0 Differential Revision: D33525055 fbshipit-source-id: 5087140ee891d6ec9266726e3a477947c233412c
345 lines
11 KiB
Python
345 lines
11 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
|
|
#
|
|
# This source code is licensed under the MIT license found in the
|
|
# LICENSE file in the root directory of this source tree.
|
|
|
|
import functools
|
|
import unittest
|
|
from typing import Any, Dict, Sequence
|
|
|
|
import fairseq
|
|
import fairseq.options
|
|
import fairseq.tasks
|
|
import torch
|
|
from tests.utils import dummy_dictionary
|
|
|
|
VOCAB_SIZE = 100
|
|
|
|
|
|
@fairseq.tasks.register_task("fake_task")
|
|
class FakeTask(fairseq.tasks.LegacyFairseqTask):
|
|
def __init__(self, args):
|
|
super().__init__(args)
|
|
self.dictionary = dummy_dictionary(VOCAB_SIZE - 4)
|
|
assert len(self.dictionary) == VOCAB_SIZE
|
|
|
|
@property
|
|
def source_dictionary(self):
|
|
return self.dictionary
|
|
|
|
@property
|
|
def target_dictionary(self):
|
|
return self.dictionary
|
|
|
|
|
|
@functools.lru_cache()
|
|
def get_toy_model(
|
|
device: str,
|
|
architecture: str = "roberta_enc_dec",
|
|
**extra_args: Any,
|
|
):
|
|
assert device in ("gpu", "cpu")
|
|
kwargs = {
|
|
"arch": architecture,
|
|
# Use characteristics dimensions
|
|
"encoder_layers": 3,
|
|
"encoder_embed_dim": 12,
|
|
"encoder_ffn_embed_dim": 14,
|
|
"encoder_attention_heads": 4,
|
|
"decoder_layers": 3,
|
|
"decoder_embed_dim": 12,
|
|
"decoder_ffn_embed_dim": 14,
|
|
"decoder_attention_heads": 4,
|
|
# Disable dropout so we have comparable tests.
|
|
"dropout": 0,
|
|
"attention_dropout": 0,
|
|
"activation_dropout": 0,
|
|
"encoder_layerdrop": 0,
|
|
# required args
|
|
"tokens_per_sample": 256,
|
|
"data": "/tmp/test_roberta",
|
|
}
|
|
kwargs.update(extra_args)
|
|
fake_task = FakeTask(kwargs)
|
|
args = fairseq.options.get_args(
|
|
task="online_backtranslation",
|
|
mono_langs="en,ro",
|
|
valid_lang_pairs="en-ro",
|
|
**kwargs,
|
|
)
|
|
torch.manual_seed(0)
|
|
model = fake_task.build_model(args)
|
|
if device == "gpu":
|
|
model.cuda()
|
|
return fake_task, model
|
|
|
|
|
|
def mk_sample(
|
|
lang: str, device: str, tok: Sequence[int] = None, batch_size: int = 2
|
|
) -> Dict[str, Any]:
|
|
assert device in ("gpu", "cpu")
|
|
if not tok:
|
|
if lang == "en":
|
|
tok = [10, 11, 12, 13, 14, 15, 2]
|
|
else:
|
|
tok = [20, 21, 22, 23, 24, 25, 26, 27, 2]
|
|
|
|
batch = torch.stack([torch.tensor(tok, dtype=torch.long)] * batch_size)
|
|
if device == "gpu":
|
|
batch = batch.cuda()
|
|
sample = {
|
|
"net_input": {
|
|
"src_tokens": batch,
|
|
"prev_output_tokens": batch,
|
|
"src_lengths": torch.tensor(
|
|
[len(tok)] * batch_size, dtype=torch.long, device=batch.device
|
|
),
|
|
},
|
|
"target": batch[:, 1:],
|
|
}
|
|
return sample
|
|
|
|
|
|
def cpu_gpu(fn):
|
|
def helper(self):
|
|
fn(self, "cpu")
|
|
if torch.cuda.is_available():
|
|
fn(self, "gpu")
|
|
|
|
return helper
|
|
|
|
|
|
def architectures(fn):
|
|
def helper(self):
|
|
for arch in ["roberta_enc_dec", "transformer"]:
|
|
fn(self, arch)
|
|
|
|
return helper
|
|
|
|
|
|
class RobertaTest(unittest.TestCase):
|
|
def assertTensorEqual(self, t1, t2, delta: float = 1e-6):
|
|
self.assertEqual(t1.size(), t2.size(), "size mismatch")
|
|
if delta == 0.0:
|
|
self.assertEqual(t1.ne(t2).long().sum(), 0)
|
|
else:
|
|
self.assertEqual(((t2 - t1).abs() > delta).long().sum(), 0)
|
|
|
|
def assertSharing(self, model, link_groups: Sequence[Sequence[str]]):
|
|
ids = {}
|
|
for group in link_groups:
|
|
group_ids = {name: id(params(model, name)) for name in group}
|
|
shared_id = group_ids[group[0]]
|
|
self.assertEqual(group_ids, {name: shared_id for name in group})
|
|
self.assertNotIn(shared_id, ids)
|
|
ids[shared_id] = group
|
|
|
|
def test_roberta_shared_params(self):
|
|
_, roberta = get_toy_model("cpu", architecture="roberta")
|
|
self.assertSharing(
|
|
roberta,
|
|
[
|
|
[
|
|
"encoder.sentence_encoder.embed_tokens.weight",
|
|
"encoder.lm_head.weight",
|
|
]
|
|
],
|
|
)
|
|
|
|
_, roberta = get_toy_model(
|
|
"cpu", architecture="roberta", untie_weights_roberta=True
|
|
)
|
|
self.assertSharing(
|
|
roberta,
|
|
[
|
|
["encoder.sentence_encoder.embed_tokens.weight"],
|
|
["encoder.lm_head.weight"],
|
|
],
|
|
)
|
|
|
|
def test_roberta_enc_dec_shared_params(self):
|
|
# 3 distinct embeddings
|
|
_, enc_dec = get_toy_model("cpu", architecture="roberta_enc_dec")
|
|
self.assertSharing(
|
|
enc_dec,
|
|
[
|
|
["encoder.embed_tokens.weight"],
|
|
["decoder.embed_tokens.weight"],
|
|
["decoder.output_projection.weight"],
|
|
],
|
|
)
|
|
|
|
# 2 distinct embeddings, one for encoder, one for decoder
|
|
_, enc_dec = get_toy_model(
|
|
"cpu", architecture="roberta_enc_dec", share_decoder_input_output_embed=True
|
|
)
|
|
self.assertSharing(
|
|
enc_dec,
|
|
[
|
|
["encoder.embed_tokens.weight"],
|
|
[
|
|
"decoder.embed_tokens.weight",
|
|
"decoder.output_projection.weight",
|
|
],
|
|
],
|
|
)
|
|
|
|
# shared embeddings
|
|
_, enc_dec = get_toy_model(
|
|
"cpu", architecture="roberta_enc_dec", share_all_embeddings=True
|
|
)
|
|
self.assertSharing(
|
|
enc_dec,
|
|
[
|
|
[
|
|
"encoder.embed_tokens.weight",
|
|
"decoder.embed_tokens.weight",
|
|
"decoder.output_projection.weight",
|
|
]
|
|
],
|
|
)
|
|
|
|
def test_roberta_max_positions_is_correctly_set(self):
|
|
device = "cpu"
|
|
task, model = get_toy_model(device)
|
|
max_pos = model.max_decoder_positions()
|
|
self.assertEqual(max_pos, 256)
|
|
self.assertEqual(max_pos, model.decoder.max_positions())
|
|
self.assertEqual(max_pos, model.encoder.max_positions())
|
|
self.assertEqual(max_pos, model.encoder.embed_positions.max_positions)
|
|
|
|
sentence = [31 for _ in range(max_pos)]
|
|
sample = mk_sample("en", device, sentence, batch_size=1)
|
|
self.assertEqual(list(sample["net_input"]["src_lengths"]), [max_pos])
|
|
self.assertEqual(len(sample["net_input"]["src_tokens"][0]), max_pos)
|
|
x, _ = model.forward(**sample["net_input"])
|
|
self.assertEqual(x.shape, (1, max_pos, VOCAB_SIZE))
|
|
|
|
@cpu_gpu
|
|
def test_roberta_forward_backward(self, device: str):
|
|
_, model = get_toy_model(device)
|
|
sample = mk_sample("en", device)
|
|
en_tokens = sample["net_input"]["src_tokens"]
|
|
(bs, l) = en_tokens.shape
|
|
# Forward
|
|
logits, _ = model(**sample["net_input"])
|
|
self.assertEqual(logits.shape, (bs, l, VOCAB_SIZE))
|
|
|
|
# Backward
|
|
loss = logits.sum()
|
|
loss.backward()
|
|
|
|
@cpu_gpu
|
|
def test_roberta_forward_backward_bs1(self, device: str):
|
|
_, model = get_toy_model(device)
|
|
sample = mk_sample("en", device, batch_size=1)
|
|
o, _ = model.forward(**sample["net_input"])
|
|
loss = o.sum()
|
|
sample2 = mk_sample("ro", device, batch_size=1)
|
|
o, _ = model.forward(**sample2["net_input"])
|
|
loss += o.sum()
|
|
loss.backward()
|
|
|
|
@cpu_gpu
|
|
def test_roberta_batching(self, device: str):
|
|
"""
|
|
Checks that the batch of size 2 give twice the same results than the batch of size 1.
|
|
"""
|
|
_, model = get_toy_model(device)
|
|
sample = mk_sample("en", device, batch_size=1)
|
|
slen = sample["net_input"]["src_lengths"][0]
|
|
sample2 = mk_sample("en", device, batch_size=2)
|
|
with torch.no_grad():
|
|
z = model.encoder.forward(
|
|
sample["net_input"]["src_tokens"], sample["net_input"]["src_lengths"]
|
|
)
|
|
z = z["encoder_out"][-1]
|
|
logits, _ = model.forward(**sample["net_input"])
|
|
|
|
z2 = model.encoder.forward(
|
|
sample2["net_input"]["src_tokens"], sample["net_input"]["src_lengths"]
|
|
)
|
|
z2 = z2["encoder_out"][-1]
|
|
logits2, _ = model.forward(**sample2["net_input"])
|
|
|
|
self.assertEqual(z.shape, (slen, 1, 12))
|
|
self.assertEqual(z2.shape, (slen, 2, 12))
|
|
self.assertTensorEqual(logits2[0], logits2[1])
|
|
self.assertTensorEqual(logits[0], logits2[0])
|
|
|
|
@cpu_gpu
|
|
def test_roberta_incremental_decoder(self, device: str):
|
|
"""
|
|
Checks that incremental decoding yields the same result than non incremental one.
|
|
"""
|
|
task, model = get_toy_model(device)
|
|
|
|
en_sample = mk_sample("en", device)
|
|
en_tokens = en_sample["net_input"]["src_tokens"]
|
|
ro_sample = mk_sample("ro", device)
|
|
ro_tokens = ro_sample["net_input"]["src_tokens"]
|
|
|
|
en_enc = model.encoder.forward(
|
|
en_tokens, src_lengths=en_sample["net_input"]["src_lengths"]
|
|
)
|
|
(bs, tgt_len) = ro_tokens.shape
|
|
|
|
# Decode without incremental state
|
|
ro_dec, _ = model.decoder.forward(ro_tokens, encoder_out=en_enc)
|
|
self.assertEqual(ro_dec.shape, (bs, tgt_len, VOCAB_SIZE))
|
|
self.assertTensorEqual(ro_dec[0], ro_dec[1])
|
|
|
|
# Decode with incremental state
|
|
inc_state = {}
|
|
ro_dec_inc = []
|
|
for i in range(tgt_len):
|
|
ro, _ = model.decoder.forward(
|
|
ro_tokens[:, : i + 1], encoder_out=en_enc, incremental_state=inc_state
|
|
)
|
|
self.assertEqual(ro.shape, (bs, 1, VOCAB_SIZE))
|
|
ro_dec_inc.append(ro)
|
|
|
|
for i in range(tgt_len):
|
|
# Intra-batch
|
|
self.assertTensorEqual(ro_dec_inc[i][0], ro_dec_inc[i][1])
|
|
# Incremental vs non-incremental
|
|
self.assertTensorEqual(ro_dec_inc[i][:, 0], ro_dec[:, i])
|
|
|
|
@cpu_gpu
|
|
def test_regularize_for_adaprune_in_roberta(self, device: str):
|
|
_, model = get_toy_model(
|
|
device=device,
|
|
architecture="roberta_base",
|
|
mha_reg_scale_factor=0.000375,
|
|
ffn_reg_scale_factor=0.000375,
|
|
)
|
|
sample = mk_sample("en", device, batch_size=1)
|
|
task_loss, _ = model.forward(**sample["net_input"])
|
|
head_loss = model._get_adaptive_head_loss()
|
|
ffn_loss = model._get_adaptive_ffn_loss()
|
|
loss = task_loss.sum() + head_loss + ffn_loss
|
|
loss.backward()
|
|
|
|
@cpu_gpu
|
|
def test_ffn_prune_for_adaprune_in_roberta(self, device: str):
|
|
_, model = get_toy_model(
|
|
device=device,
|
|
architecture="roberta_base",
|
|
)
|
|
sample = mk_sample("en", device, batch_size=1)
|
|
for layer in model.encoder.sentence_encoder.layers:
|
|
fc1_original_size = layer.fc1.out_features
|
|
remove_index = layer._get_fc_rank(remove_num=2)
|
|
layer._prune_fc_layer(remove_index=remove_index)
|
|
self.assertEqual(layer.fc1.out_features, fc1_original_size - 2)
|
|
|
|
task_loss, _ = model.forward(**sample["net_input"])
|
|
|
|
|
|
def params(model, name):
|
|
if "." not in name:
|
|
return getattr(model, name)
|
|
|
|
prefix, name = name.split(".", 1)
|
|
return params(getattr(model, prefix), name)
|