mirror of https://github.com/hpcaitech/ColossalAI
polish code
jiaruifang
parent
9cba38b492
commit
e64a05b38b
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@ -1,22 +1,22 @@
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import gzip
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import random
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from time import time
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from functools import partial
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from time import time
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import numpy as np
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import torch
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import torch.optim as optim
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import torch.nn as nn
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import torch.optim as optim
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import tqdm
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from packaging import version
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from palm_pytorch import PaLM
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from palm_pytorch.autoregressive_wrapper import AutoregressiveWrapper
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from torch.nn import functional as F
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from torch.utils.data import DataLoader, Dataset
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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.gemini_optimizer import GeminiAdamOptimizer
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from colossalai.nn.parallel import GeminiDDP, ZeroDDP
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from colossalai.nn.parallel import ZeroDDP
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from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec
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from colossalai.utils import MultiTimer, get_current_device
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from colossalai.utils.model.colo_init_context import ColoInitContext
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@ -69,6 +69,7 @@ def parse_args():
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args = parser.parse_args()
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return args
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# helpers
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def cycle(loader):
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while True:
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@ -79,12 +80,15 @@ def cycle(loader):
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def decode_token(token):
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return str(chr(max(32, token)))
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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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def decode_tokens(tokens):
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return "".join(list(map(decode_token, tokens)))
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def get_model_size(model: nn.Module):
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total_numel = 0
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for module in model.modules():
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@ -92,6 +96,7 @@ def get_model_size(model: nn.Module):
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total_numel += p.numel()
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return total_numel
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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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@ -115,6 +120,7 @@ def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy:
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raise NotImplemented(f"CAI version {cai_version} is not supported")
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return model
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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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@ -128,6 +134,7 @@ def split_param_row_tp1d(param: ColoParameter, pg: ProcessGroup):
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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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# 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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@ -159,7 +166,7 @@ def tensor_parallelize(model: torch.nn.Module, pg: ProcessGroup):
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args = parse_args()
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if args.distplan not in ["colossalai", "pytorch"]:
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raise TypeError(f"{args.distplan} is error")
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raise TypeError(f"{args.distplan} is error")
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disable_existing_loggers()
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colossalai.launch_from_torch(config={})
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logger = get_dist_logger()
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@ -216,7 +223,7 @@ else:
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model.cuda()
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optim = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
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# model is shared after TP
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# model is shared after TP
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numel = get_model_size(model)
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get_tflops_func = partial(get_tflops, numel, args.batch_size, SEQ_LEN)
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@ -251,7 +258,7 @@ for i in tqdm.tqdm(range(NUM_BATCHES), mininterval=10.0, desc="training"):
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)
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if i >= WARMUP_BATCHES:
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tflops_list.append(step_tflops)
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else:
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for __ in range(GRADIENT_ACCUMULATE_EVERY):
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loss = model(next(train_loader))
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@ -261,18 +268,17 @@ for i in tqdm.tqdm(range(NUM_BATCHES), mininterval=10.0, desc="training"):
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torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
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optim.step()
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optim.zero_grad()
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tflops_list.sort()
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median_index = ((NUM_BATCHES - WARMUP_BATCHES) >> 1) + WARMUP_BATCHES
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logger.info(f"Median TFLOPS is {tflops_list[median_index]:.3f}")
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# TODO
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# if i % VALIDATE_EVERY == 0:
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# model.eval()
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# with torch.no_grad():
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# loss = model(next(val_loader))
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# print(f"validation loss: {loss.item()}")
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# TODO
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# if i % VALIDATE_EVERY == 0:
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# model.eval()
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# with torch.no_grad():
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# loss = model(next(val_loader))
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# print(f"validation loss: {loss.item()}")
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# if i % GENERATE_EVERY == 0:
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# model.eval()
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@ -282,4 +288,4 @@ logger.info(f"Median TFLOPS is {tflops_list[median_index]:.3f}")
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# sample = model.generate(inp[None, ...], GENERATE_LENGTH)
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# output_str = decode_tokens(sample[0])
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# print(output_str)
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# print(output_str)
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