From 9e69bff1a892e79e120630f5389f93c4a1202524 Mon Sep 17 00:00:00 2001 From: Xuanlei Zhao <43881818+oahzxl@users.noreply.github.com> Date: Tue, 10 Dec 2024 23:25:44 +0800 Subject: [PATCH] [polish] polish train doc and code (#257) * update * bench * polish scripts --- README.md | 2 +- assets/example_data/art-museum.mp4 | Bin 590014 -> 0 bytes assets/example_data/demo.csv | 54 ------------------ assets/example_data/demo_preprocess.csv | 19 ------ assets/example_data/lagos.mp4 | Bin 653438 -> 0 bytes assets/example_data/man-on-the-cloud.mp4 | Bin 568678 -> 0 bytes docs/dcp.md | 3 + examples/training/open_sora/benchmark.sh | 10 ++-- examples/training/open_sora/preprocess.py | 2 +- examples/training/open_sora/train.py | 5 +- examples/training/open_sora/train.sh | 11 ++++ requirements.txt | 2 +- tests/test_sampler.py | 11 +--- videosys/core/dcp/profiler.py | 14 ++--- videosys/models/modules/attentions.py | 4 +- .../transformers/open_sora_transformer_3d.py | 4 +- .../training/datasets/open_sora/sampler.py | 45 +++++++++------ 17 files changed, 66 insertions(+), 120 deletions(-) delete mode 100644 assets/example_data/art-museum.mp4 delete mode 100644 assets/example_data/demo_preprocess.csv delete mode 100644 assets/example_data/lagos.mp4 delete mode 100644 assets/example_data/man-on-the-cloud.mp4 create mode 100644 examples/training/open_sora/train.sh diff --git a/README.md b/README.md index c9e8ce88..ab309cc9 100644 --- a/README.md +++ b/README.md @@ -118,7 +118,7 @@ You can also find easy demo with HuggingFace Space method

-Data-Centric Parallel (DCP) is a simple but effective approach to accelerate distributed training of any-size videos. Unlike previous methods that fix training settings, DCP dyanmically adjusts parallelism and other configs driven by incoming data during runtime. This method significantly reduces communication overhead and computational inefficiencies, achieving up to 2.1x speedup. As a ease-of-use method, DCP can enpower any video models and parallel methods with minimal code changes. +Data-Centric Parallel (DCP) is a simple but effective approach to accelerate distributed training of variable sequences. Unlike previous methods that fix training settings, DCP dyanmically adjusts parallelism and other configs driven by incoming data during runtime, achieving up to 2.1x speedup. As a ease-of-use method, DCP can enpower any video models and parallel methods with minimal code changes. 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examples/training/open_sora/configs/benchmarks/dcp_intra.yaml # DCP inter torchrun --standalone --nproc_per_node 8 examples/training/open_sora/train.py \ - examples/training/open_sora/configs/benchmarks/dcp_inter.yaml > dcp_inter.log 2>& 1 + examples/training/open_sora/configs/benchmarks/dcp_inter.yaml # DCP inter + ckpt torchrun --standalone --nproc_per_node 8 examples/training/open_sora/train.py \ - examples/training/open_sora/configs/benchmarks/dcp_inter_ckpt.yaml > dcp_inter_ckpt.log 2>& 1 + examples/training/open_sora/configs/benchmarks/dcp_inter_ckpt.yaml diff --git a/examples/training/open_sora/preprocess.py b/examples/training/open_sora/preprocess.py index 766d9c59..1506adcb 100644 --- a/examples/training/open_sora/preprocess.py +++ b/examples/training/open_sora/preprocess.py @@ -107,7 +107,7 @@ def main(args): y = batch["text"] model_args = encode_prompt(text_encoder, tokenizer, y) for i in range(len(y)): - cur_model_args = {k: v[i].cpu() for k, v in model_args.items()} + cur_model_args = {k: v[i].cpu() if isinstance(v, torch.Tensor) else v for k, v in model_args.items()} emb_path = os.path.join( args.output_emb_path, os.path.basename(batch["path"][i]) + f"_{int(batch['index'][i])}_text.pt" ) diff --git a/examples/training/open_sora/train.py b/examples/training/open_sora/train.py index 44f6985b..2b3c1b0d 100644 --- a/examples/training/open_sora/train.py +++ b/examples/training/open_sora/train.py @@ -1,7 +1,6 @@ import argparse import logging import os -import time from copy import deepcopy from datetime import timedelta from pprint import pformat @@ -279,7 +278,7 @@ def main(args): else: cfg_epochs = args.epochs running_loss = 0.0 - logging.info(f"Training for {args.epochs} epochs{' with profiling' if profiler.need_profile() else ''}.") + logging.info(f"Training for {cfg_epochs} epochs{' with profiling' if profiler.need_profile() else ''}.") # ======================================================= # 5. training loop @@ -426,7 +425,7 @@ def main(args): token_counter.fill_(local_token_counter) dist.all_reduce(token_counter) if rank == 0 and not disable: - elapsed_time = pbar.format_dict['elapsed'] + elapsed_time = pbar.format_dict["elapsed"] logging.info( f"Epoch {epoch}: steps: {num_steps_per_epoch} elapsed time: {elapsed_time:.2f} s" f", effective samples: {sampler.effective_samples}" diff --git a/examples/training/open_sora/train.sh b/examples/training/open_sora/train.sh new file mode 100644 index 00000000..3173771b --- /dev/null +++ b/examples/training/open_sora/train.sh @@ -0,0 +1,11 @@ +# =============== program params ================ +export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True +export TOKENIZERS_PARALLELISM=false + +# =============== preprocess =============== +torchrun --standalone --nproc_per_node 8 examples/training/open_sora/preprocess.py examples/training/open_sora/configs/preprocess.yaml + +# =============== train =============== +# If you run this for the first time, you need to run the program twice. +# The first time is to profile the model and save results, and the second time is to run the benchmark. +torchrun --standalone --nproc_per_node 8 examples/training/open_sora/train.py examples/training/open_sora/configs/train.yaml diff --git a/requirements.txt b/requirements.txt index c6b43c71..4325625d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,4 +23,4 @@ sentencepiece timm torch>=1.13 tqdm -transformers==4.39.3 \ No newline at end of file +transformers==4.39.3 diff --git a/tests/test_sampler.py b/tests/test_sampler.py index df5818c6..340bf1a1 100644 --- a/tests/test_sampler.py +++ b/tests/test_sampler.py @@ -24,12 +24,7 @@ from videosys.training.datasets.open_sora.utils import MaskGenerator from videosys.training.lr_schedulers.linear_warmup_open_sora import LinearWarmupLR from videosys.utils.logging import init_logger -from videosys.utils.training import ( - define_experiment_workspace, - format_numel_str, - get_model_numel, - requires_grad, -) +from videosys.utils.training import define_experiment_workspace, format_numel_str, get_model_numel, requires_grad from videosys.utils.utils import merge_args, set_seed, str_to_dtype @@ -95,7 +90,7 @@ def main(args): # == build text-encoder and vae == if not preprocessed_data: text_encoder = T5EncoderModel.from_pretrained("DeepFloyd/t5-v1_1-xxl", torch_dtype=dtype).to(device).eval() - tokenizer = AutoTokenizer.from_pretrained("DeepFloyd/t5-v1_1-xxl") + AutoTokenizer.from_pretrained("DeepFloyd/t5-v1_1-xxl") vae = ( OpenSoraVAE_V1_2( from_pretrained="hpcai-tech/OpenSora-VAE-v1.2", @@ -146,7 +141,7 @@ def main(args): model.enable_parallel(parallel_mgr=parallel_mgr) if args.mask_ratios is not None: - mask_generator = MaskGenerator(args.mask_ratios) + MaskGenerator(args.mask_ratios) # ====================================================== # 3. build dataset and dataloader diff --git a/videosys/core/dcp/profiler.py b/videosys/core/dcp/profiler.py index ae29c265..a91313b3 100644 --- a/videosys/core/dcp/profiler.py +++ b/videosys/core/dcp/profiler.py @@ -636,7 +636,7 @@ def profile(self, batch, model, gas): self.dp_results.append(result_row) else: self.latest_raw_result = result_row - + self.detail_results.append(result_row) if self.logger: @@ -656,18 +656,18 @@ def profile(self, batch, model, gas): else: assert self.latest_raw_result is not None self.dp_results.append(self.latest_raw_result) - + if sp_size < self.max_sp: self.next_sp_size = sp_size * 2 # bs // 2 is the previous successful bs - self.next_bs = 1 if self.dynamic_recompute else max(1, bs//4) + self.next_bs = 1 if self.dynamic_recompute else max(1, bs // 4) self.next_warmup_iter = not self.auto_grad_acc elif len(self.dp_results) == 0: if sp_size < self.world_size: self.next_sp_size = sp_size * 2 - self.next_bs = 1 if self.dynamic_recompute else max(1, bs//4) + self.next_bs = 1 if self.dynamic_recompute else max(1, bs // 4) self.next_warmup_iter = not self.auto_grad_acc - + if self.logger: self.logger.info( f">>> [Profiling] bucket {ar_name} {num_frame} cross nodes, increase sp size to {self.next_sp_size}" @@ -734,7 +734,7 @@ def profile(self, batch, model, gas): sp_size = self.latest_raw_result.sp_size pred_full_time, pred_full_mem = self.estimate_overhead(self.latest_raw_result) - cur_throughput = bs/sp_size/pred_full_time + cur_throughput = bs / sp_size / pred_full_time if len(self.dp_results) > 0: prev_row = self.dp_results[-2] prev_time, prev_mem = self.estimate_overhead(prev_row) @@ -757,7 +757,7 @@ def profile(self, batch, model, gas): memory_consumed=pred_full_mem / GB, ), ) - + self.latest_raw_result = None self.dp_results = [] diff --git a/videosys/models/modules/attentions.py b/videosys/models/modules/attentions.py index 666ff924..e92f16e8 100644 --- a/videosys/models/modules/attentions.py +++ b/videosys/models/modules/attentions.py @@ -127,7 +127,7 @@ def native_attention(self, q, k, v): def reset_cross_attn_mask(): # print(f"rank {torch.distributed.get_rank()} reset cross attention mask") global SPATIAL_Q_SEQINFO, SPATIAL_K_SEQINFO, TEMPORAL_Q_SEQINFO, TEMPORAL_K_SEQINFO - + SPATIAL_Q_SEQINFO, SPATIAL_K_SEQINFO = None, None TEMPORAL_Q_SEQINFO, TEMPORAL_K_SEQINFO = None, None @@ -187,7 +187,7 @@ def forward(self, x, cond, mask=None): def get_qk_mask(self, B, N, mask): global TEMPORAL_Q_SEQINFO, TEMPORAL_K_SEQINFO, SPATIAL_Q_SEQINFO, SPATIAL_K_SEQINFO device = torch.cuda.current_device() - if B*N > 200000 and self.training: + if B * N > 200000 and self.training: if self.temporal: if TEMPORAL_Q_SEQINFO is None: TEMPORAL_Q_SEQINFO = [_SeqLenInfo.from_seqlens([N] * 1) for _ in range(B)] diff --git a/videosys/models/transformers/open_sora_transformer_3d.py b/videosys/models/transformers/open_sora_transformer_3d.py index 56348c81..33a622cb 100644 --- a/videosys/models/transformers/open_sora_transformer_3d.py +++ b/videosys/models/transformers/open_sora_transformer_3d.py @@ -123,7 +123,9 @@ def __init__( rope=rope, enable_flash_attn=enable_flash_attn, ) - self.cross_attn = OpenSoraMultiHeadCrossAttention(hidden_size, num_heads, enable_flash_attn=enable_flash_attn, temporal=temporal) + self.cross_attn = OpenSoraMultiHeadCrossAttention( + hidden_size, num_heads, enable_flash_attn=enable_flash_attn, temporal=temporal + ) self.norm2 = nn.LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False) self.mlp = Mlp( in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0 diff --git a/videosys/training/datasets/open_sora/sampler.py b/videosys/training/datasets/open_sora/sampler.py index c65789f9..93933149 100644 --- a/videosys/training/datasets/open_sora/sampler.py +++ b/videosys/training/datasets/open_sora/sampler.py @@ -1,4 +1,5 @@ import logging +import math import time from collections import OrderedDict, defaultdict from dataclasses import dataclass @@ -8,7 +9,7 @@ import torch import torch.distributed as dist from torch.utils.data import Dataset, DistributedSampler -import math + from videosys.core.dcp.profiler import get_profiler from .bucket import Bucket @@ -671,7 +672,9 @@ def _build_local_bucket_id_access_order_sp_balance(self, bucket_sample_dict): f"iter {len(bucket_id_access_order)}\noriginal buckets: {[(each.bucket_id, each.batch_size, each.sp_size, each.exec_time) for each in cur_batch_bucket_id_list]}" ) - min_time, min_bucket = min([(each.exec_time, each.bucket_id) for each in cur_batch_bucket_id_list], key=lambda x: x[0]) + min_time, min_bucket = min( + [(each.exec_time, each.bucket_id) for each in cur_batch_bucket_id_list], key=lambda x: x[0] + ) skip_bucket_idx = [] if self.keep_last: no_last_batch, last_batch = [], [] @@ -680,13 +683,15 @@ def _build_local_bucket_id_access_order_sp_balance(self, bucket_sample_dict): no_last_batch.append(bucket_plan) else: last_batch.append(bucket_plan) - + if not no_last_batch: assert len(last_batch) == len(cur_batch_bucket_id_list) skip_bucket_idx = list(range(len(cur_batch_bucket_id_list))) min_time = 0 else: - min_time, min_bucket = min([(each.exec_time, each.bucket_id) for each in no_last_batch], key=lambda x: x[0]) + min_time, min_bucket = min( + [(each.exec_time, each.bucket_id) for each in no_last_batch], key=lambda x: x[0] + ) skip_bucket_idx = [] if last_batch: for i, bucket_plan in enumerate(cur_batch_bucket_id_list): @@ -706,7 +711,7 @@ def _build_local_bucket_id_access_order_sp_balance(self, bucket_sample_dict): original_remain_samples = bucket_sample_counts[bucket_plan.bucket_id] - best_diff = float('inf') + best_diff = float("inf") best_exec_time, best_bs, best_sp = original_exec_time, original_batch_size, original_sp_size cur_sp_size = original_sp_size log_str = f"\n>>> bucket {bucket_plan.bucket_id}, bs: {original_batch_size}, sp: {original_sp_size}, time: {original_exec_time}" @@ -720,21 +725,25 @@ def _build_local_bucket_id_access_order_sp_balance(self, bucket_sample_dict): cur_bs = max(1, round(min_time / cur_unit_time)) if cur_bs > max_bs: cur_bs = max_bs - + cur_exec_time = cur_unit_time * cur_bs cur_diff = abs(cur_exec_time - min_time) - log_str += f"\nCHECK sp: {cur_sp_size}, bs: {cur_bs}, time: {cur_exec_time}, diff: {cur_diff}/{best_diff}" + log_str += ( + f"\nCHECK sp: {cur_sp_size}, bs: {cur_bs}, time: {cur_exec_time}, diff: {cur_diff}/{best_diff}" + ) if cur_diff < best_diff: best_diff = cur_diff best_exec_time = cur_exec_time best_bs = cur_bs best_sp = cur_sp_size - + if abs(cur_exec_time / min_time - 1) < 0.1: break cur_sp_size *= 2 logs.append(log_str) - assert best_bs > 0, f"best_bs: {best_bs} - {original_batch_size}, best sp: {best_sp} - {original_sp_size}, best time: {best_exec_time} - {original_exec_time}, min time: {min_time}" + assert ( + best_bs > 0 + ), f"best_bs: {best_bs} - {original_batch_size}, best sp: {best_sp} - {original_sp_size}, best time: {best_exec_time} - {original_exec_time}, min time: {min_time}" # return left samples back to record if best_bs < cur_batch_bucket_id_list[i].batch_size: @@ -753,7 +762,7 @@ def _build_local_bucket_id_access_order_sp_balance(self, bucket_sample_dict): elif best_bs > cur_batch_bucket_id_list[i].batch_size: bucket_id = cur_batch_bucket_id_list[i].bucket_id org_sp = bucket_plan.sp_size - + bucket_sample_counts[bucket_id] -= best_bs - cur_batch_bucket_id_list[i].batch_size if bucket_sample_counts[bucket_id] == 0: sp_bucket_map[org_sp].remove(bucket_id) @@ -790,7 +799,7 @@ def _build_local_bucket_id_access_order_sp_balance(self, bucket_sample_dict): assert total_gpus == wsize # rebalance bs only - min_max_time, min_max_bucket = float('inf'), None + min_max_time, min_max_bucket = float("inf"), None for i, bucket_plan in enumerate(cur_batch_bucket_id_list): bucket_id = bucket_plan.bucket_id ar_name, num_frame = bucket_id[:2] @@ -798,15 +807,14 @@ def _build_local_bucket_id_access_order_sp_balance(self, bucket_sample_dict): cur_sp = bucket_plan.sp_size cur_time = bucket_plan.exec_time unit_time = cur_time / cur_bs - + max_bs = self.profiler.detail_results[ar_name][num_frame][cur_sp]["bs"] if max_bs - cur_bs > bucket_sample_counts[bucket_id]: max_bs = cur_bs + bucket_sample_counts[bucket_id] - + max_tmp_time = unit_time * max_bs if max_tmp_time < min_max_time: min_max_time = max_tmp_time - min_max_bucket = bucket_id for i, bucket_plan in enumerate(cur_batch_bucket_id_list): bucket_id = bucket_plan.bucket_id @@ -826,7 +834,7 @@ def _build_local_bucket_id_access_order_sp_balance(self, bucket_sample_dict): continue increment_bs = int(diff_time // unit_time) - if increment_bs+cur_bs > max_bs: + if increment_bs + cur_bs > max_bs: increment_bs = max_bs - cur_bs increment_bs = min(increment_bs, bucket_sample_counts[bucket_id]) increment_time = unit_time * increment_bs @@ -839,11 +847,12 @@ def _build_local_bucket_id_access_order_sp_balance(self, bucket_sample_dict): if not sp_bucket_map[sp]: sp_size_list.remove(sp) sp_bucket_map.pop(sp) - + bucket_plan.batch_size += increment_bs bucket_plan.exec_time += increment_time - assert bucket_plan.batch_size > 0, \ - f"increment_bs: {increment_bs}, cur_bs: {cur_bs}, max_bs: {max_bs}, increment_time: {increment_time}, cur_time: {cur_exec_time}, min_max_time: {min_max_time}" + assert ( + bucket_plan.batch_size > 0 + ), f"increment_bs: {increment_bs}, cur_bs: {cur_bs}, max_bs: {max_bs}, increment_time: {increment_time}, cur_time: {cur_exec_time}, min_max_time: {min_max_time}" this_bucket_acc_list = [] for bucket_plan in cur_batch_bucket_id_list:

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