File size: 14,265 Bytes
7934b29 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
import torch
from einops import rearrange
from pytorch_lightning.trainer.trainer import Trainer
from nemo.collections.nlp.modules.common.megatron.attention import ParallelChunkedCrossAttention
from nemo.collections.nlp.modules.common.megatron.layer_type import LayerType
from nemo.collections.nlp.modules.common.megatron.megatron_init import initialize_model_parallel_for_nemo
from nemo.collections.nlp.modules.common.megatron.retrieval_token_level_encoder_decoder import (
MegatronRetrievalTokenLevelEncoderDecoderModule,
)
from nemo.collections.nlp.modules.common.megatron.retrieval_transformer import (
MegatronRetrievalTransformerDecoderModule,
MegatronRetrievalTransformerEncoderModule,
)
from nemo.collections.nlp.modules.common.megatron.rotary_pos_embedding import RotaryEmbedding
from nemo.collections.nlp.modules.common.megatron.utils import (
build_attention_mask_3d,
init_method_normal,
scaled_init_method_normal,
)
from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy
try:
from apex.transformer.enums import AttnMaskType
HAVE_APEX = True
except (ImportError, ModuleNotFoundError):
HAVE_APEX = False
@pytest.mark.run_only_on('GPU')
@pytest.mark.skipif(not HAVE_APEX, reason="apex is not installed")
class TestRetrievalModule:
@classmethod
def setup_class(cls):
if not torch.cuda.is_available():
return
GPUS = 1
TP_SIZE = GPUS
PP_SIZE = 1
MB_SIZE = 4
GB_SIZE = 8
SEED = 1234
trainer = Trainer(strategy=NLPDDPStrategy(), devices=GPUS, accelerator='gpu', num_nodes=1, logger=None,)
initialize_model_parallel_for_nemo(
world_size=trainer.world_size,
global_rank=trainer.global_rank,
local_rank=trainer.local_rank,
tensor_model_parallel_size=TP_SIZE,
pipeline_model_parallel_size=PP_SIZE,
micro_batch_size=MB_SIZE,
global_batch_size=GB_SIZE,
seed=SEED,
apex_transformer_log_level=30,
)
def dummy():
return
if trainer.strategy.launcher is not None:
trainer.strategy.launcher.launch(dummy, trainer=trainer)
trainer.strategy.setup_environment()
torch.distributed.barrier()
@pytest.mark.unit
def test_cross_attn(self):
num_layers = 1
init_method_std = 0.02
batch = 2
neighbors = 2
# rotary pos emb dim
dim = 128
pad_id = 19999
num_attention_heads = 8
chunks = 32
text_chunk_size = 64
context_chunk_size = 2 * text_chunk_size
input_length = chunks * text_chunk_size
vocab_size = 20000
rot_dim = dim // num_attention_heads
rotary_pos_emb = RotaryEmbedding(rot_dim).cuda().half()
hidden = torch.randint(0, vocab_size, (input_length, batch)).cuda() # (seq, batch, dim)
hidden_mask = (hidden != pad_id).cuda()
hidden_emb = torch.rand(input_length, batch, dim).cuda().half() # (seq, batch, dim)
retrieved = torch.randint(0, vocab_size, (chunks, neighbors, context_chunk_size, batch)).cuda()
# retrieved tokens - (num chunks, num retrieved neighbors, retrieved chunk with continuation, batch)
# context attention mask [b, np, sq, sk]
context_mask = (retrieved != pad_id).cuda()
retrieved_emb = torch.rand(chunks, neighbors, context_chunk_size, batch, dim).cuda().half()
# retrieved tokens - (num chunks, num retrieved neighbors, retrieved chunk with continuation, batch, hidden)
# need to add extra chunk size, since it will be shifted
cross_attn_q_pos_emb = rotary_pos_emb(text_chunk_size + text_chunk_size - 1, offset=0)
cross_attn_k_pos_emb = rotary_pos_emb(context_chunk_size)
cross_attn_pos_emb = (cross_attn_q_pos_emb, cross_attn_k_pos_emb)
dec_attn_mask = rearrange(hidden_mask, '(k n) b -> (b k) n', k=chunks)
context_attn_mask = rearrange(context_mask, 'k r n b -> (b k) (r n)')
enc_dec_attn_mask_3d = build_attention_mask_3d(
source_mask=dec_attn_mask, target_mask=context_attn_mask, attn_mask_type=AttnMaskType.padding,
)
enc_dec_attn_mask_3d = enc_dec_attn_mask_3d[:, None, :, :]
init_method = init_method_normal(init_method_std)
scaled_init_method = scaled_init_method_normal(init_method_std, num_layers)
cross_attn = (
ParallelChunkedCrossAttention(
init_method=init_method,
output_layer_init_method=scaled_init_method,
layer_number=1,
num_attention_heads=num_attention_heads,
hidden_size=dim,
precision=16,
chunk_size=text_chunk_size,
)
.cuda()
.half()
)
out, bias = cross_attn(
hidden_emb, enc_dec_attn_mask_3d, encoder_output=retrieved_emb, rotary_pos_emb=cross_attn_pos_emb
)
assert out.shape == torch.Size([input_length, batch, dim])
assert bias.shape == torch.Size([dim])
@pytest.mark.unit
def test_retrieval_encoder(self):
init_method_std = 0.02
batch = 2
neighbors = 2
# rotary pos emb dim
dim = 128
pad_id = 19999
num_attention_heads = 8
chunks = 32
text_chunk_size = 64
input_length = chunks * text_chunk_size
vocab_size = 20000
hidden = torch.randint(0, vocab_size, (batch, input_length)).cuda() # (seq, batch, dim)
hidden_mask = (hidden != pad_id).cuda()
hidden_emb = torch.rand(batch, input_length, dim).cuda().half() # (batch, seq, dim)
retrieved = torch.randint(0, vocab_size, (batch, chunks, neighbors, 2 * text_chunk_size)).cuda()
pad_id = vocab_size - 1
context_mask = (retrieved != pad_id).cuda()
retrieved_emb = torch.rand(batch, chunks, neighbors, 2 * text_chunk_size, dim).cuda().half()
layer_type = [LayerType.encoder, LayerType.retrieval_encoder, LayerType.encoder, LayerType.retrieval_encoder]
num_layers = len(layer_type)
init_method = init_method_normal(init_method_std)
scaled_init_method = scaled_init_method_normal(init_method_std, num_layers)
encoder = (
MegatronRetrievalTransformerEncoderModule(
init_method=init_method,
output_layer_init_method=scaled_init_method,
hidden_size=dim,
ffn_hidden_size=dim * 4,
num_layers=num_layers,
num_attention_heads=num_attention_heads,
precision=16,
chunk_size=text_chunk_size,
layer_type=layer_type,
)
.cuda()
.half()
)
out = encoder(retrieved_emb, context_mask, context_attn_mask=hidden_mask, encoder_output=hidden_emb)
assert out.shape == torch.Size([batch, chunks, neighbors, 2 * text_chunk_size, dim])
@pytest.mark.unit
def test_retrieval_decoder(self):
init_method_std = 0.02
# rotary pos emb dim
batch = 2
neighbors = 2
dim = 128
pad_id = 19999
num_attention_heads = 8
chunks = 32
text_chunk_size = 64
input_length = chunks * text_chunk_size
vocab_size = 20000
# rot_dim = dim // num_attention_heads
# rotary_pos_emb = RotaryEmbedding(rot_dim).cuda().half()
hidden = torch.randint(0, vocab_size, (batch, input_length)).cuda() # (seq, batch, dim)
hidden_mask = (hidden != pad_id).cuda()
hidden_emb = torch.rand(batch, input_length, dim).cuda().half() # (batch, seq, dim)
# context_chunk_size = 128
retrieved = torch.randint(0, vocab_size, (batch, chunks, neighbors, 2 * text_chunk_size)).cuda()
# retrieved tokens - (batch, num chunks, num retrieved neighbors, retrieved chunk with continuation)
# context attention mask [b, np, sq, sk]
pad_id = vocab_size - 1
context_mask = (retrieved != pad_id).cuda()
retrieved_emb = torch.rand(batch, chunks, neighbors, 2 * text_chunk_size, dim).cuda().half()
# retrieved tokens - (batch, num chunks, num retrieved neighbors, retrieved chunk with continuation, hidden)
layer_type = [LayerType.encoder, LayerType.retrieval_decoder, LayerType.encoder, LayerType.retrieval_decoder]
num_layers = len(layer_type)
init_method = init_method_normal(init_method_std)
scaled_init_method = scaled_init_method_normal(init_method_std, num_layers)
decoder = (
MegatronRetrievalTransformerDecoderModule(
init_method=init_method,
output_layer_init_method=scaled_init_method,
hidden_size=dim,
ffn_hidden_size=dim * 4,
num_layers=num_layers,
num_attention_heads=num_attention_heads,
precision=16,
chunk_size=text_chunk_size,
layer_type=layer_type,
)
.cuda()
.half()
)
out = decoder(hidden_emb, hidden_mask, retrieved_attn_mask=context_mask, retrieved_emb=retrieved_emb)
assert out.shape == torch.Size([input_length, batch, dim])
@pytest.mark.unit
def test_encoder_decoder_module(self):
# rotary pos emb dim
batch = 2
neighbors = 2
dim = 128
pad_id = 19999
num_attention_heads = 8
chunks = 32
text_chunk_size = 64
input_length = chunks * text_chunk_size
vocab_size = 20000
enc_num_layers = 4
dec_num_layers = 6
enc_cross_attention = [3] # layer numbers for cross attention
dec_cross_attention = [3, 5] # layer numbers for cross attention
all_tokens = torch.randint(0, vocab_size, (batch, input_length + 1)).cuda() # (seq, batch, dim)
hidden = all_tokens[:, :-1]
labels = all_tokens[:, 1:]
hidden_mask = (hidden != pad_id).cuda()
retrieved = torch.randint(0, vocab_size, (batch, chunks, neighbors, 2 * text_chunk_size)).cuda()
pad_id = vocab_size - 1
context_mask = (retrieved != pad_id).cuda()
class FakeTokenizer:
eos_id = vocab_size - 2
tokenizer = FakeTokenizer()
encoder_decoder = (
MegatronRetrievalTokenLevelEncoderDecoderModule(
vocab_size=vocab_size,
hidden_size=dim,
max_position_embeddings=input_length,
num_attention_heads=num_attention_heads,
ffn_hidden_size=dim * 4,
precision=16,
chunk_size=text_chunk_size,
enc_num_layers=enc_num_layers,
dec_num_layers=dec_num_layers,
enc_cross_attention=enc_cross_attention,
dec_cross_attention=dec_cross_attention,
add_position_embedding=False,
tokenizer=tokenizer,
)
.cuda()
.half()
)
out = encoder_decoder(
hidden, hidden_mask, retrieved_ids=retrieved, retrieved_attn_mask=context_mask, labels=labels
)
assert out.shape == torch.Size([batch, input_length])
# verify the attention mask matrix is correct
all_tokens = torch.tensor([[1, 2, vocab_size - 2, 3, vocab_size - 1, vocab_size - 2, 3, 4, 5]]).cuda()
encoder_decoder = (
MegatronRetrievalTokenLevelEncoderDecoderModule(
vocab_size=vocab_size,
hidden_size=dim,
max_position_embeddings=8,
num_attention_heads=num_attention_heads,
ffn_hidden_size=dim * 4,
precision=16,
chunk_size=4,
enc_num_layers=enc_num_layers,
dec_num_layers=dec_num_layers,
enc_cross_attention=enc_cross_attention,
dec_cross_attention=dec_cross_attention,
add_position_embedding=False,
tokenizer=tokenizer,
)
.cuda()
.half()
)
hidden = all_tokens[:, :-1]
labels = all_tokens[:, 1:]
hidden_mask = (hidden != pad_id).cuda()
retrieved = torch.randint(0, vocab_size, (1, 2, neighbors, 8)).cuda()
pad_id = vocab_size - 1
context_mask = (retrieved != pad_id).cuda()
out = encoder_decoder(
hidden, hidden_mask, retrieved_ids=retrieved, retrieved_attn_mask=context_mask, labels=labels
)
mask3d = encoder_decoder.pre_decoder._calculate_dec_att_mask(
hidden_mask, torch.where(hidden == vocab_size - 2)
)
expected = torch.tensor(
[
[
[
[False, True, True, True, True, True, True, True],
[False, False, True, True, True, True, True, True],
[False, False, False, True, True, True, True, True],
[True, True, True, False, True, True, True, True],
[True, True, True, True, True, True, True, True],
[True, True, True, False, True, False, True, True],
[True, True, True, True, True, True, False, True],
[True, True, True, True, True, True, False, False],
]
]
]
).cuda()
assert (mask3d == expected).all()
|