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* [tutorial] polish README and OPT files * [tutorial] polish README and OPT files * [tutorial] polish README and OPT filespull/1931/head
binmakeswell
2 years ago
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6 changed files with 2 additions and 264 deletions
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# Fine-tuning and Serving for OPT from Hugging Face |
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## Overview |
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This example shows how to use ColossalAI to run huggingface GPT training with Gemini and ZeRO DDP. |
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## GPT |
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We use the huggingface transformers GPT2 model. The input data is randonly generated. |
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## Our Modifications |
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We adapt the OPT training code to ColossalAI by leveraging Gemini and ZeRO DDP. |
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## Quick Start |
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You can launch training by using the following bash script |
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```bash |
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pip install -r requirements.txt |
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bash run.sh |
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``` |
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colossalai >= 0.1.10 |
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torch >= 1.8.1 |
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transformers >= 4.231 |
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env OMP_NUM_THREADS=16 torchrun --standalone --nproc_per_node=4 train_gpt_demo.py --tp_degree=2 --placement='cpu' 2>&1 | tee run.log |
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from functools import partial |
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from time import time |
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import psutil |
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import torch |
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import torch.nn as nn |
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from packaging import version |
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import colossalai |
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from colossalai.logging import disable_existing_loggers, get_dist_logger |
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from colossalai.nn.optimizer import HybridAdam |
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from colossalai.nn.parallel import ZeroDDP |
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from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec |
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from colossalai.utils import get_current_device |
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from colossalai.utils.model.colo_init_context import ColoInitContext |
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from colossalai.zero import ZeroOptimizer |
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from transformers import GPT2Config, GPT2LMHeadModel |
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def parse_args(): |
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parser = colossalai.get_default_parser() |
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parser.add_argument( |
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"--tp_degree", |
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type=int, |
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default=1, |
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help="Tensor Parallelism Degree.", |
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) |
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parser.add_argument( |
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"--placement", |
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type=str, |
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default='cpu', |
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help="Placement Policy for Gemini.", |
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) |
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args = parser.parse_args() |
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return args |
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## Parameter Sharding Strategies for Tensor Parallelism |
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def split_param_single_dim_tp1d(dim: int, param: ColoParameter, pg: ProcessGroup): |
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spec = (ShardSpec([dim], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)) |
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if param.process_group.tp_world_size() == 1: |
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param.set_process_group(pg) |
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param.set_tensor_spec(*spec) |
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def split_param_row_tp1d(param: ColoParameter, pg: ProcessGroup): |
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split_param_single_dim_tp1d(0, param, pg) |
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def split_param_col_tp1d(param: ColoParameter, pg: ProcessGroup): |
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split_param_single_dim_tp1d(-1, param, pg) |
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## Define the Model and Loss Based on Huggingface transformers GPT2LMHeadModel |
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class GPTLMModel(nn.Module): |
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def __init__(self, |
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hidden_size=768, |
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num_layers=12, |
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num_attention_heads=12, |
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max_seq_len=1024, |
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vocab_size=50257, |
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checkpoint=False): |
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super().__init__() |
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self.checkpoint = checkpoint |
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self.model = GPT2LMHeadModel( |
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GPT2Config(n_embd=hidden_size, |
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n_layer=num_layers, |
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n_head=num_attention_heads, |
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n_positions=max_seq_len, |
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n_ctx=max_seq_len, |
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vocab_size=vocab_size)) |
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if checkpoint: |
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self.model.gradient_checkpointing_enable() |
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def forward(self, input_ids, attention_mask): |
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# Only return lm_logits |
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return self.model(input_ids=input_ids, attention_mask=attention_mask, use_cache=not self.checkpoint)[0] |
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class GPTLMLoss(nn.Module): |
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def __init__(self): |
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super().__init__() |
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self.loss_fn = nn.CrossEntropyLoss() |
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def forward(self, logits, labels): |
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shift_logits = logits[..., :-1, :].contiguous() |
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shift_labels = labels[..., 1:].contiguous() |
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# Flatten the tokens |
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return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) |
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## Randomly Generated Data |
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def get_data(batch_size, seq_len, vocab_size): |
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input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device()) |
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attention_mask = torch.ones_like(input_ids) |
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return input_ids, attention_mask |
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def gpt2_medium(checkpoint=False): |
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return GPTLMModel(hidden_size=1024, num_layers=24, num_attention_heads=16, checkpoint=checkpoint) |
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def gpt2_xl(checkpoint=True): |
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return GPTLMModel(hidden_size=1600, num_layers=48, num_attention_heads=32, checkpoint=checkpoint) |
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def gpt2_10b(checkpoint=True): |
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return GPTLMModel(hidden_size=4096, num_layers=50, num_attention_heads=16, checkpoint=checkpoint) |
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def get_cpu_mem(): |
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return psutil.Process().memory_info().rss / 1024**2 |
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def get_gpu_mem(): |
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return torch.cuda.memory_allocated() / 1024**2 |
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def get_mem_info(prefix=''): |
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return f'{prefix}GPU memory usage: {get_gpu_mem():.2f} MB, CPU memory usage: {get_cpu_mem():.2f} MB' |
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def get_tflops(model_numel, batch_size, seq_len, step_time): |
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return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12) |
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# Tensor Parallel |
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def tensor_parallelize(model: torch.nn.Module, pg: ProcessGroup): |
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"""tensor_parallelize |
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Sharding the Model Parameters. |
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Args: |
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model (torch.nn.Module): a torch module to be sharded |
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""" |
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for mn, module in model.named_modules(): |
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for pn, param in module.named_parameters(recurse=False): |
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# set process group for all parameters |
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param.set_process_group(pg) |
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if 'mlp.c_fc' in mn: |
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if 'weight' in pn or 'bias' in pn: |
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split_param_col_tp1d(param, pg) # colmn slice |
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# keep the shape of the output from c_fc |
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param.compute_spec.set_output_replicate(False) |
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elif 'mlp.c_proj' in mn: |
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if 'weight' in pn: |
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split_param_row_tp1d(param, pg) # row slice |
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elif 'wte' in mn or 'wpe' in mn: |
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split_param_col_tp1d(param, pg) # colmn slice |
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elif 'c_attn' in mn or 'c_proj' in mn: |
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split_param_col_tp1d(param, pg) # colmn slice |
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# Gemini + ZeRO DDP |
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def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: str = "auto"): |
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cai_version = colossalai.__version__ |
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if version.parse(cai_version) > version.parse("0.1.10"): |
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from colossalai.nn.parallel import GeminiDDP |
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model = GeminiDDP(model, |
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device=get_current_device(), |
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placement_policy=placememt_policy, |
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pin_memory=True, |
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search_range_mb=32) |
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elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"): |
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from colossalai.gemini import ChunkManager, GeminiManager |
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chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32) |
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gemini_manager = GeminiManager(placememt_policy, chunk_manager) |
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chunk_manager = ChunkManager(chunk_size, |
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pg, |
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enable_distributed_storage=True, |
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init_device=GeminiManager.get_default_device(placememt_policy)) |
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model = ZeroDDP(model, gemini_manager) |
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else: |
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raise NotImplemented(f"CAI version {cai_version} is not supported") |
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return model |
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def main(): |
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args = parse_args() |
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BATCH_SIZE = 8 |
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SEQ_LEN = 1024 |
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VOCAB_SIZE = 50257 |
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NUM_STEPS = 10 |
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disable_existing_loggers() |
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colossalai.launch_from_torch(config={}) |
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pg = ProcessGroup(tp_degree=args.tp_degree) |
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logger = get_dist_logger() |
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logger.info(get_mem_info(), ranks=[0]) |
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# build GPT model |
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with ColoInitContext(device=get_current_device()): |
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model = gpt2_medium(checkpoint=True) |
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numel = sum([p.numel() for p in model.parameters()]) |
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logger.info(f'Model numel: {numel}', ranks=[0]) |
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get_tflops_func = partial(get_tflops, numel, BATCH_SIZE, SEQ_LEN) |
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# Tensor Parallelism (TP) |
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tensor_parallelize(model, pg) |
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# Gemini + ZeRO DP, Note it must be used after TP |
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model = gemini_zero_dpp(model, pg, args.placement) |
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logger.info(get_mem_info(prefix='After init model, '), ranks=[0]) |
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# build criterion |
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criterion = GPTLMLoss() |
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# build optimizer |
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optimizer = HybridAdam(model.parameters(), lr=1e-3) |
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optimizer = ZeroOptimizer(optimizer, model, initial_scale=2**5) |
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logger.info(get_mem_info(prefix='After init optim, '), ranks=[0]) |
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torch.cuda.synchronize() |
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model.train() |
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for n in range(NUM_STEPS): |
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# we just use randomly generated data here |
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input_ids, attn_mask = get_data(BATCH_SIZE, SEQ_LEN, VOCAB_SIZE) |
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optimizer.zero_grad() |
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start = time() |
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outputs = model(input_ids, attn_mask) |
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loss = criterion(outputs, input_ids) |
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logger.info(get_mem_info(prefix=f'[{n+1}/{NUM_STEPS}] Forward '), ranks=[0]) |
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optimizer.backward(loss) |
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logger.info(get_mem_info(prefix=f'[{n+1}/{NUM_STEPS}] Backward '), ranks=[0]) |
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optimizer.step() |
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logger.info(get_mem_info(prefix=f'[{n+1}/{NUM_STEPS}] Optimizer step '), ranks=[0]) |
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step_time = time() - start |
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logger.info( |
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f'[{n+1}/{NUM_STEPS}] Loss:{loss.item():.3f}, Step time: {step_time:.3f}s, TFLOPS: {get_tflops_func(step_time):.3f}', |
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ranks=[0]) |
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torch.cuda.synchronize() |
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if __name__ == '__main__': |
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main() |
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