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Implement Qwen-1.5 via megatron-core (#158)
Co-authored-by: 同润 <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,169 @@ | ||
| # Copyright (c) 2023 Alibaba PAI and Nvidia Megatron-LM Team. | ||
| # | ||
| # 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 torch | ||
| from typing import Union | ||
| from megatron.core.enums import ModelType | ||
| import megatron.model | ||
| from megatron import get_args | ||
| from megatron import print_rank_0 | ||
| from megatron.core import parallel_state, tensor_parallel | ||
| from megatron.core.pipeline_parallel.p2p_communication import recv_forward | ||
| from megatron.core.pipeline_parallel.p2p_communication import send_forward | ||
| from megatron.initialize import initialize_megatron | ||
| from megatron.utils import unwrap_model | ||
| from megatron.utils import get_ltor_masks_and_position_ids | ||
| from megatron.arguments import core_transformer_config_from_args | ||
| from megatron.checkpointing import load_checkpoint | ||
|
|
||
| from megatron_patch.training import get_model | ||
| from megatron_patch.data import build_evaluation_dataset | ||
| from megatron_patch.finetune_utils import build_data_loader | ||
| from megatron_patch.model.qwen1_5.layer_specs import get_gpt_layer_with_transformer_engine_spec | ||
| from megatron_patch.model.qwen1_5.model import GPTModel | ||
| from megatron_patch.tokenizer import get_tokenizer, build_tokenizer | ||
| from megatron_patch.arguments import get_patch_args | ||
| from megatron_patch.data.utils import get_batch_on_this_tp_rank_original | ||
| import torch._dynamo | ||
| torch._dynamo.config.suppress_errors = True | ||
|
|
||
|
|
||
| def get_model_provider(): | ||
| def model_provider( | ||
| pre_process=True, post_process=True | ||
| ) -> Union[GPTModel, megatron.model.GPTModel]: | ||
| args = get_args() | ||
| build_tokenizer(args) | ||
| config = core_transformer_config_from_args(get_args()) | ||
|
|
||
| transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(args.num_experts, args.moe_grouped_gemm) | ||
| model = GPTModel( | ||
| config=config, | ||
| transformer_layer_spec=transformer_layer_spec, | ||
| vocab_size=args.padded_vocab_size, | ||
| max_sequence_length=args.max_position_embeddings, | ||
| pre_process=pre_process, | ||
| post_process=post_process, | ||
| fp16_lm_cross_entropy=args.fp16_lm_cross_entropy, | ||
| parallel_output=True, | ||
| share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights, | ||
| position_embedding_type=args.position_embedding_type, | ||
| rotary_percent=args.rotary_percent, | ||
| rotary_base=args.rotary_base, | ||
| seq_len_interpolation_factor=args.rotary_seq_len_interpolation_factor | ||
| ) | ||
|
|
||
| return model | ||
|
|
||
| return model_provider | ||
|
|
||
| def forward_step(batch, model): | ||
| """Forward step.""" | ||
|
|
||
| batch = get_batch_on_this_tp_rank_original(batch) | ||
| tokens = batch['tokens'] | ||
| labels = batch['labels'] | ||
| position_ids = batch["position_ids"] | ||
| attention_mask = batch["attention_mask"] | ||
| loss_mask = batch['loss_mask'] | ||
| # Tell the model what our actual batch size will be | ||
| args = get_args() | ||
| args.micro_batch_size = len(labels) | ||
| config = core_transformer_config_from_args(args) | ||
| tensor_shape = (args.seq_length, args.micro_batch_size, args.hidden_size) | ||
| input_tensor = recv_forward(tensor_shape, config) | ||
|
|
||
| # Forward pass through the model. | ||
| unwrapped_model = unwrap_model(model) | ||
| unwrapped_model.set_input_tensor(input_tensor) | ||
| output = model(tokens, position_ids, attention_mask) | ||
| send_forward(output, config) | ||
| #if parallel_state.is_pipeline_last_stage(): | ||
| if output.shape[-1] != args.hidden_size: | ||
| loss_mask = loss_mask.view(-1).float() | ||
| # For loss, return the unreduced loss. | ||
| losses = tensor_parallel.vocab_parallel_cross_entropy( | ||
| output.contiguous().float(), labels.contiguous()) | ||
| loss = torch.sum( | ||
| losses.view(-1) * loss_mask.contiguous().view(-1).float()) / loss_mask.sum() | ||
| print(loss) | ||
| print_rank_0(loss) | ||
| return loss | ||
|
|
||
| return None | ||
|
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||
|
|
||
| def evaluate(data_loader, model): | ||
| """Evaluation.""" | ||
| args = get_args() | ||
|
|
||
| # Turn on evaluation mode which disables dropout. | ||
| model.eval() | ||
|
|
||
| total_output = 0.0 | ||
| with torch.no_grad(): | ||
| # For all the batches in the dataset. | ||
| for iteration, batch in enumerate(data_loader): | ||
| if iteration % args.log_interval == 0: | ||
| print_rank_0('> working on iteration: {}'.format(iteration)) | ||
| # Forward evaluation. | ||
| output = forward_step(batch, model) | ||
|
|
||
| # Reduce across processes. | ||
| if parallel_state.is_pipeline_last_stage(): | ||
| torch.distributed.all_reduce( | ||
| output, group=parallel_state.get_data_parallel_group()) | ||
|
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| total_output += output | ||
|
|
||
| return total_output | ||
|
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||
|
|
||
| def main(): | ||
| """Main program.""" | ||
| args = get_args() | ||
| if args.num_layers_per_virtual_pipeline_stage is not None: | ||
| print('Interleaved pipeline schedule ' | ||
| 'is not yet supported for text generation.') | ||
| exit() | ||
|
|
||
| # Data stuff. | ||
| dataset = build_evaluation_dataset(args.dataset) | ||
| dataloader = build_data_loader(dataset, | ||
| args.micro_batch_size, | ||
| args.num_workers, | ||
| drop_last=False) | ||
|
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||
|
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| # Set up model and load checkpoint. | ||
| model = get_model(get_model_provider(), | ||
| model_type=ModelType.encoder_or_decoder, | ||
| wrap_with_ddp=False) | ||
|
|
||
| if args.load is not None: | ||
| load_checkpoint(model, None, None) | ||
|
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| assert len(model) == 1, 'Above condition should have caught this' | ||
| model = model[0] | ||
|
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|
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| # Run evaluation. | ||
| evaluate(dataloader, model) | ||
| print_rank_0('done :-)') | ||
|
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|
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||
| if __name__ == '__main__': | ||
| initialize_megatron(extra_args_provider=get_patch_args) | ||
| main() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,126 @@ | ||
| # Copyright (c) 2023 Alibaba PAI and Nvidia Megatron-LM Team. | ||
| # | ||
| # 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. | ||
|
|
||
| from functools import partial | ||
| import torch | ||
| import os | ||
| from typing import Union | ||
| import megatron.model | ||
| from megatron.core.enums import ModelType | ||
| from megatron.arguments import core_transformer_config_from_args | ||
| from megatron import get_args | ||
| from megatron.core import tensor_parallel | ||
| from megatron.utils import average_losses_across_data_parallel_group | ||
| from megatron.training import pretrain | ||
|
|
||
| from megatron_patch.model.qwen1_5.layer_specs import get_gpt_layer_with_transformer_engine_spec | ||
| from megatron_patch.data.utils import get_batch_on_this_tp_rank_original | ||
| from megatron_patch.data import \ | ||
| build_pretrain_dataset_from_original, build_pretrain_dataset_from_idxmap | ||
| from megatron_patch.model.qwen1_5.model import GPTModel | ||
| from megatron_patch.tokenizer import get_tokenizer, build_tokenizer | ||
| from megatron_patch.arguments import get_patch_args | ||
| import torch._dynamo | ||
| torch._dynamo.config.suppress_errors = True | ||
|
|
||
| def model_provider( | ||
| pre_process=True, post_process=True | ||
| ) -> Union[GPTModel, megatron.model.GPTModel]: | ||
|
|
||
| args = get_args() | ||
| build_tokenizer(args) | ||
| config = core_transformer_config_from_args(get_args()) | ||
|
|
||
| transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(args.num_experts, args.moe_grouped_gemm) | ||
| model = GPTModel( | ||
| config=config, | ||
| transformer_layer_spec=transformer_layer_spec, | ||
| vocab_size=args.padded_vocab_size, | ||
| max_sequence_length=args.max_position_embeddings, | ||
| pre_process=pre_process, | ||
| post_process=post_process, | ||
| fp16_lm_cross_entropy=args.fp16_lm_cross_entropy, | ||
| parallel_output=True, | ||
| share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights, | ||
| position_embedding_type=args.position_embedding_type, | ||
| rotary_percent=args.rotary_percent, | ||
| rotary_base=args.rotary_base, | ||
| seq_len_interpolation_factor=args.rotary_seq_len_interpolation_factor | ||
| ) | ||
|
|
||
| return model | ||
|
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||
|
|
||
| def forward_step(data_iterator, model): | ||
| args = get_args() | ||
| batch = get_batch_on_this_tp_rank_original(data_iterator) | ||
| tokens = batch['tokens'] | ||
| labels = batch['labels'] | ||
| position_ids = batch["position_ids"] | ||
| attention_mask = batch["attention_mask"] | ||
| loss_mask = batch['loss_mask'] | ||
| logits = model(input_ids=tokens, | ||
| position_ids=position_ids, | ||
| attention_mask=attention_mask) | ||
|
|
||
| if args.enable_parallel_output: | ||
|
|
||
| def loss_func(loss_mask, logits): | ||
| losses = tensor_parallel.vocab_parallel_cross_entropy( | ||
| logits.contiguous().float(), labels.contiguous()) | ||
| loss_mask = loss_mask.view(-1).float() | ||
| loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum() | ||
| averaged_loss = average_losses_across_data_parallel_group([loss]) | ||
| return loss, {'lm loss': averaged_loss[0]} | ||
| else: | ||
|
|
||
| def loss_func(loss_mask, logits): | ||
| loss_func = torch.nn.CrossEntropyLoss(ignore_index=-100) | ||
| loss = loss_func(torch.squeeze(logits).contiguous().float(), torch.squeeze(labels)) | ||
| averaged_loss = average_losses_across_data_parallel_group([loss]) | ||
| return loss, {'lm loss': averaged_loss[0]} | ||
|
|
||
| return logits, partial(loss_func, loss_mask) | ||
|
|
||
| def train_valid_test_datasets_provider(train_val_test_num_samples): | ||
| """Build train, valid, and test datasets.""" | ||
| args = get_args() | ||
|
|
||
| if os.path.isfile(args.train_data_path[0]): | ||
| train_ds, valid_ds, test_ds = \ | ||
| build_pretrain_dataset_from_original(args.dataset) | ||
| else: | ||
| train_ds, valid_ds, test_ds = \ | ||
| build_pretrain_dataset_from_idxmap( | ||
| data_prefix=args.train_data_path, | ||
| max_padding_length=args.max_padding_length, | ||
| dataset_type=args.dataset, | ||
| splits_string=args.split, | ||
| train_valid_test_num_samples=train_val_test_num_samples, | ||
| seed=args.seed, | ||
| skip_warmup=(not args.mmap_warmup) | ||
| ) | ||
|
|
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| return train_ds, valid_ds, test_ds | ||
|
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|
|
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| if __name__ == "__main__": | ||
| train_valid_test_datasets_provider.is_distributed = True | ||
| pretrain( | ||
| train_valid_test_datasets_provider, | ||
| model_provider, | ||
| ModelType.encoder_or_decoder, | ||
| forward_step, | ||
| extra_args_provider=get_patch_args, | ||
| ) |
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