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Adding unit test for mcore RETRO model #9022
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33dff2a
runnable test_retro_model.py, with spm_tok_ende_4k/tokenizer.model to…
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Merge remote-tracking branch 'origin/main' into huvu/mcore_retro_unit…
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cleaning code
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Merge branch 'main' into huvu/mcore_retro_unittest
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Merge branch 'main' into huvu/mcore_retro_unittest
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Merge branch 'main' into huvu/mcore_retro_unittest
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# 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. | ||
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import json | ||
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import pytest | ||
import torch | ||
from omegaconf import DictConfig | ||
from pytorch_lightning import Trainer | ||
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from nemo.collections.nlp.models.language_modeling.megatron_retro_model import MegatronRetroModel | ||
from nemo.collections.nlp.modules.common.megatron.utils import get_ltor_masks_and_position_ids | ||
from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy | ||
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DEVICE_CAPABILITY = None | ||
if torch.cuda.is_available(): | ||
DEVICE_CAPABILITY = torch.cuda.get_device_capability() | ||
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@pytest.fixture() | ||
def retro_workdir_path(test_data_dir): | ||
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config_file = { | ||
"retro_bert_tokenizer_type": "BertWordPieceLowerCase", | ||
"retro_bert_vocab_file": "", | ||
"retro_block_size": 1000, | ||
"retro_gpt_chunk_length": 64, | ||
"retro_gpt_data_cache_path": None, | ||
"retro_gpt_data_path": "", | ||
"retro_gpt_eval_interval": 2000, | ||
"retro_gpt_eval_iters": 100, | ||
"retro_gpt_global_batch_size": 8, | ||
"retro_gpt_merge_file": None, | ||
"retro_gpt_seed": 1234, | ||
"retro_gpt_seq_length": 2048, | ||
"retro_gpt_split": "98,2,0", | ||
"retro_gpt_tokenizer_model": "spm_tok_ende_4k/tokenizer.model", | ||
"retro_gpt_tokenizer_type": "GPTSentencePieceTokenizer", | ||
"retro_gpt_train_samples": 5000, | ||
"retro_gpt_valid_samples": 5000, | ||
"retro_gpt_vocab_file": None, | ||
"retro_neighbor_dirs": {"test": None, "train": None, "valid": None}, | ||
} | ||
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# save config to json file in retro_workdir_path | ||
retro_workdir_path = test_data_dir + "/nlp" | ||
config_file_path = retro_workdir_path + "/config.json" | ||
out_file = open(config_file_path, 'w') | ||
json.dump(config_file, out_file) | ||
out_file.close() | ||
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return retro_workdir_path | ||
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@pytest.fixture() | ||
def model_cfg(test_data_dir, retro_workdir_path): | ||
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# set model configs | ||
model_cfg = { | ||
'mcore_gpt': True, | ||
'precision': '16', | ||
'micro_batch_size': 4, | ||
'global_batch_size': 8, | ||
'tensor_model_parallel_size': 1, | ||
'pipeline_model_parallel_size': 1, | ||
'resume_from_checkpoint': None, | ||
'encoder_seq_length': 2048, | ||
'max_position_embeddings': 2048, | ||
'num_layers': 12, | ||
'hidden_size': 768, | ||
'ffn_hidden_size': 3072, | ||
'num_attention_heads': 12, | ||
'init_method_std': 0.023, | ||
'hidden_dropout': 0.1, | ||
'kv_channels': 64, | ||
# 'apply_query_key_layer_scaling': False, | ||
'apply_query_key_layer_scaling': True, | ||
'layernorm_epsilon': 1e-5, | ||
'make_vocab_size_divisible_by': 128, | ||
'pre_process': True, | ||
'post_process': True, | ||
'persist_layer_norm': True, | ||
'bias': True, | ||
'activation': 'gelu', | ||
'transformer_block_type': 'pre_ln', | ||
'retro': { | ||
# 'retro_project_dir': os.path.join('tests/.data/test_data/nlp/retro_workdir_dummy'), | ||
# 'retro_project_dir': os.path.join(test_data_dir, 'nlp/retro_workdir_dummy'), | ||
# 'retro_project_dir': '/lustre/fsw/coreai_dlalgo_genai/huvu/data/retro/pretrain_data/micro-wiki-core-unittest', | ||
'retro_project_dir': retro_workdir_path, | ||
'retro_encoder_num_layers': 2, | ||
'retro_encoder_hidden_dropout': 0.1, | ||
'retro_encoder_attention_dropout': 0.1, | ||
'retro_num_neighbors': 2, | ||
'retro_num_retrieved_chunks': 2, | ||
'retro_verify_neighbor_count': True, | ||
}, | ||
'tokenizer': { | ||
'library': 'megatron', | ||
'type': None, | ||
'model': None, | ||
'vocab_file': None, | ||
'merge_file': None, | ||
'delimiter': None, | ||
'sentencepiece_legacy': False, | ||
}, | ||
'native_amp_init_scale': 4294967296, | ||
'native_amp_growth_interval': 1000, | ||
'hysteresis': 2, | ||
'fp32_residual_connection': False, | ||
'fp16_lm_cross_entropy': False, | ||
'megatron_amp_O2': True, | ||
'seed': 1234, | ||
'use_cpu_initialization': False, | ||
'onnx_safe': False, | ||
'apex_transformer_log_level': 30, | ||
'activations_checkpoint_method': None, | ||
'activations_checkpoint_num_layers': None, | ||
'data': { | ||
'data_prefix': 'None', | ||
'index_mapping_dir': None, | ||
'data_impl': 'mmap', | ||
'splits_string': '98,2,0', | ||
'seq_length': 2048, | ||
'skip_warmup': True, | ||
'num_workers': 2, | ||
'dataloader_type': 'single', | ||
'reset_position_ids': False, | ||
'reset_attention_mask': False, | ||
'eod_mask_loss': False, | ||
'shuffle_documents': False, | ||
'retro_data': { | ||
'retro_block_size': 10000, | ||
'retro_chunk_length': 64, | ||
'retro_split_preprocessing': "98,2,0", | ||
'retro_neighbor_dirs': None, | ||
}, | ||
}, | ||
'optim': { | ||
'name': 'distributed_fused_adam', | ||
'lr': 6.0e-4, | ||
'weight_decay': 0.1, | ||
'betas': [0.9, 0.95], | ||
'sched': {'name': 'CosineAnnealing', 'warmup_steps': None, 'constant_steps': None, 'min_lr': '6.0e-5'}, | ||
}, | ||
} | ||
return model_cfg | ||
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@pytest.fixture() | ||
def trainer_cfg(): | ||
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trainer_cfg = { | ||
'devices': 1, | ||
'num_nodes': 1, | ||
'accelerator': 'gpu', | ||
'precision': '16', | ||
'logger': False, | ||
'enable_checkpointing': False, | ||
'use_distributed_sampler': False, | ||
'max_epochs': -1, | ||
'max_steps': 750000, | ||
'log_every_n_steps': 10, | ||
'val_check_interval': 100, | ||
'limit_val_batches': 50, | ||
'limit_test_batches': 500, | ||
'accumulate_grad_batches': 1, | ||
'gradient_clip_val': 1.0, | ||
} | ||
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return trainer_cfg | ||
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@pytest.fixture() | ||
def retro_model(model_cfg, trainer_cfg): | ||
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strategy = NLPDDPStrategy() | ||
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trainer = Trainer(strategy=strategy, **trainer_cfg) | ||
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cfg = DictConfig(model_cfg) | ||
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model = MegatronRetroModel(cfg=cfg, trainer=trainer) | ||
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return model | ||
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@pytest.mark.run_only_on('GPU') | ||
class TestRETROModel: | ||
@pytest.mark.unit | ||
def test_constructor(self, retro_model): | ||
assert isinstance(retro_model, MegatronRetroModel) | ||
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num_weights = retro_model.num_weights | ||
# assert num_weights == 306868224 # using "tokenizer/mt_nlg_plus_multilingual_ja_zh_the_stack_frac_015_256k.model" tokenizer | ||
assert num_weights == 113405952 # using "spm_tok_ende_4k/tokenizer.model" tokenizer | ||
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@pytest.mark.unit | ||
def test_forward(self, retro_model): | ||
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# create dummy input | ||
batch_size = 4 | ||
neighbors = 2 | ||
seq_length = 2048 | ||
chunk_length = 64 | ||
num_chunks = seq_length // chunk_length | ||
retrieved_chunk_size = chunk_length * 2 | ||
vocab_size = 2000 | ||
eos_id = vocab_size - 2 | ||
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# set input for forward | ||
all_tokens = torch.randint(0, vocab_size, (batch_size, seq_length + 1)).cuda() | ||
tokens = all_tokens[:, :-1] | ||
labels = all_tokens[:, 1:] | ||
attention_mask, _, text_position_ids = get_ltor_masks_and_position_ids(tokens, eos_id, False, False, False) | ||
context_input_ids = torch.randint( | ||
0, vocab_size, (batch_size * num_chunks * neighbors, retrieved_chunk_size) | ||
).cuda() | ||
_, _, context_position_ids = get_ltor_masks_and_position_ids( # neighbor_tokens is already a 2D array | ||
context_input_ids, eos_id, False, False, False | ||
) | ||
context_mask = None | ||
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# set model to eval mode | ||
retro_model.eval() | ||
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# forward step | ||
with torch.no_grad(): | ||
out = retro_model( | ||
tokens=tokens.cuda(), | ||
text_position_ids=text_position_ids.cuda(), | ||
attention_mask=attention_mask.cuda(), | ||
labels=labels.cuda(), | ||
context_input_ids=context_input_ids.cuda(), | ||
context_position_ids=context_position_ids.cuda(), | ||
context_mask=context_mask, | ||
) | ||
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assert out.shape == torch.Size([batch_size, seq_length]) |
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