Add GPT PP Example (#2272)

Co-authored-by: Ziyue Jiang <ziyue.jiang@gmail.com>

examples/language/gpt/README.md

@@ -44,6 +44,11 @@ For simplicity, the input data is randonly generated here.
 bash run.sh
 ```
 
+Pipeline Parallel
+```bash
+bash run_pp.sh
+```
+
 ### Training config
 
 The `train_gpt_demo.py` provides three distributed plans, you can choose the plan you want in `run.sh`. The Colossal-AI leverages Tensor Parallel and Gemini + ZeRO DDP.

examples/language/gpt/run_pp.sh

+export GPUNUM=${GPUNUM:-2}
+export BATCH_SIZE=${BATCH_SIZE:-16}
+export MODEL_TYPE=${MODEL_TYPE:-"gpt2_medium"}
+export NUM_MICROBATCH=${NUM_MICROBATCH:-4}
+
+mkdir -p logs
+python train_gpt_pp_demo.py --device="cuda" --model_type=${MODEL_TYPE} --num_microbatches=${NUM_MICROBATCH} --world_size=${GPUNUM} --batch_size=${BATCH_SIZE} 2>&1 | tee ./logs/${MODEL_TYPE}_gpu_${GPUNUM}_bs_${BATCH_SIZE}_nm_${NUM_MICROBATCH}.log

examples/language/gpt/train_gpt_pp_demo.py

+import argparse
+import time
+from functools import partial
+
+import torch
+from model_zoo import model_builder
+from torch import nn
+from tqdm import tqdm
+
+from colossalai.fx import ColoTracer
+from colossalai.fx.passes.adding_split_node_pass import avgnode_split_pass, split_with_split_nodes_pass
+from colossalai.logging import disable_existing_loggers, get_dist_logger
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.pipeline.middleware.adaptor import get_fx_topology
+from colossalai.pipeline.rpc._pipeline_schedule import OneFOneBPipelineEngine
+from colossalai.pipeline.rpc.utils import rpc_run
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_type', type=str, default="gpt2_medium")
+    parser.add_argument('--world_size', type=int, default=2)
+    parser.add_argument('--batch_size', type=int, default=16)
+    parser.add_argument('--dp_degree', type=int, default=1)
+    parser.add_argument('--tp_degree', type=int, default=1)
+    parser.add_argument('--num_microbatches', type=int, default=2)
+    parser.add_argument('--device', type=str, choices=['cpu', 'cuda'], default='cuda')
+    parser.add_argument('--master_addr', type=str, default='localhost')
+    parser.add_argument('--master_port', type=str, default='29020')
+    parser.add_argument('--num_worker_threads', type=int, default=128)
+    return parser.parse_args()
+
+
+class GPTLMLoss(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.loss_fn = nn.CrossEntropyLoss()
+
+    def forward(self, logits, labels):
+        shift_logits = logits[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+        # Flatten the tokens
+        return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+
+# Randomly Generated Data
+def get_data(batch_size, seq_len, vocab_size):
+    input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device())
+    attention_mask = torch.ones_like(input_ids)
+    return input_ids, attention_mask
+
+
+def create_partition_module(pp_rank: int, stage_num: int, model, data_kwargs):
+    tracer = ColoTracer()
+    meta_args = {k: v.to('meta') for k, v in data_kwargs.items()}
+    graph = tracer.trace(root=model, meta_args=meta_args)
+    gm = torch.fx.GraphModule(model, graph, model.__class__.__name__)
+    annotated_model = avgnode_split_pass(gm, stage_num)
+
+    top_module, split_submodules = split_with_split_nodes_pass(annotated_model, merge_output=True)
+    topo = get_fx_topology(top_module)
+    for submodule in split_submodules:
+        if isinstance(submodule, torch.fx.GraphModule):
+            setattr(submodule, '_topo', topo)
+    return split_submodules[pp_rank + 1]
+
+
+def partition(logger, model_type, data_kwargs, pp_rank: int, chunk: int, stage_num: int):
+    # build model
+    model = model_builder(model_type)(checkpoint=False)
+    module = create_partition_module(pp_rank, stage_num, model, data_kwargs)
+    num_params = sum(param.numel() for param in module.parameters())
+    logger.info(f'{pp_rank=} number of args in this partition:{num_params}')
+    return module
+
+
+def run_master(args):
+    batch_size = args.batch_size
+    device = args.device
+    world_size = args.world_size
+    stage_num = world_size
+    num_microbatches = args.num_microbatches
+    model_type = args.model_type
+    # batch size per DP degree
+    SEQ_LEN = 1024
+    VOCAB_SIZE = 50257
+    NUM_STEPS = 10
+
+    disable_existing_loggers()
+    logger = get_dist_logger()
+    logger.info(f"{args.model_type}, batch size {batch_size}, num stage {stage_num}, num microbatch {num_microbatches}",
+                ranks=[0])
+
+    torch.manual_seed(123)
+
+    # build criterion
+    criterion = GPTLMLoss()
+
+    # warm up pipeline fx partition
+    input_ids, attn_mask = get_data(batch_size, SEQ_LEN, VOCAB_SIZE)
+    warmup_data_kwargs = {'input_ids': input_ids, 'attention_mask': attn_mask}
+
+    # set 1f1b pipeline engine
+    pp_engine = OneFOneBPipelineEngine(partition_fn=partial(partition, logger, model_type, warmup_data_kwargs),
+                                       stage_num=stage_num,
+                                       num_microbatches=num_microbatches,
+                                       device=device,
+                                       chunk=1,
+                                       criterion=criterion,
+                                       metric=None,
+                                       checkpoint=False)
+
+    # build optim
+    pp_engine.initialize_optimizer(HybridAdam, lr=1e-3)
+
+    times = []
+    for n in tqdm(range(NUM_STEPS)):
+        # we just use randomly generated data here
+        input_ids, attn_mask = get_data(batch_size, SEQ_LEN, VOCAB_SIZE)
+        batch = {'input_ids': input_ids, 'attention_mask': attn_mask}
+
+        start = time.time()
+        outputs = pp_engine.forward_backward(batch=batch, labels=input_ids, forward_only=False)
+        cost_time = time.time() - start
+        times.append(cost_time)
+
+    logger.info("avg cost time : {}s".format(sum(times) / len(times)))
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    rpc_run(args, run_master)