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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,105 @@ | ||
| # GPT-2 pretraining setup | ||
| { | ||
| # parallelism settings ( you will want to change these based on your cluster setup, ideally scheduling pipeline stages | ||
| # across the node boundaries ) | ||
| "pipe_parallel_size": 1, | ||
| "model_parallel_size": 1, | ||
|
|
||
| # model settings | ||
| "num_layers": 24, | ||
| "hidden_size": 2048, | ||
| "num_attention_heads": 16, | ||
| "seq_length": 2048, | ||
| "max_position_embeddings": 2048, | ||
| "norm": "layernorm", | ||
| "pos_emb": "rotary", | ||
| "no_weight_tying": true, | ||
| "gpt_j_residual": false, | ||
| "output_layer_parallelism": "column", | ||
|
|
||
| # Transformer Engine settings | ||
| "te_columnparallel": false, | ||
| "te_rowparallel": false, | ||
| "te_layernorm_mlp": true, | ||
| "te_mha": true, | ||
| "te_fp8_format": "hybrid", | ||
| "te_fp8_wgrad": true, | ||
| "te_fp8_amax_history_len": 1, | ||
| "te_fp8_amax_compute_algo": "most_recent", | ||
| "te_fp8_margin": 0, | ||
| "te_fp8_mha": false, | ||
|
|
||
| # these should provide some speedup but takes a while to build, set to true if desired | ||
| "scaled_upper_triang_masked_softmax_fusion": false, | ||
| "bias_gelu_fusion": false, | ||
| "rope_fusion": false, | ||
| "layernorm_fusion": false, | ||
|
|
||
| # init methods | ||
| "init_method": "small_init", | ||
| "output_layer_init_method": "wang_init", | ||
|
|
||
| # optimizer settings | ||
| "optimizer": { | ||
| "type": "Adam", | ||
| "params": { | ||
| "lr": 0.0002, | ||
| "betas": [0.9, 0.95], | ||
| "eps": 1.0e-8, | ||
| } | ||
| }, | ||
| "min_lr": 0.00002, | ||
|
|
||
| # for all zero_optimization options, see https://www.deepspeed.ai/docs/config-json/#zero-optimizations-for-fp16-training | ||
| "zero_optimization": { | ||
| "stage": 1, | ||
| "allgather_partitions": True, | ||
| "allgather_bucket_size": 500000000, | ||
| "overlap_comm": True, | ||
| "reduce_scatter": True, | ||
| "reduce_bucket_size": 500000000, | ||
| "contiguous_gradients": True, | ||
| }, | ||
|
|
||
| # batch / data settings | ||
| "train_micro_batch_size_per_gpu": 4, | ||
| "data_impl": "mmap", | ||
|
|
||
| # activation checkpointing | ||
| "checkpoint_activations": true, | ||
| "checkpoint_num_layers": 1, | ||
| "partition_activations": true, | ||
| "synchronize_each_layer": true, | ||
|
|
||
| # regularization | ||
| "gradient_clipping": 1.0, | ||
| "weight_decay": 0.1, | ||
| "hidden_dropout": 0, | ||
| "attention_dropout": 0, | ||
|
|
||
| # precision settings | ||
| "fp16": { | ||
| "fp16": true, | ||
| "enabled": true, | ||
| "loss_scale": 0, | ||
| "loss_scale_window": 1000, | ||
| "hysteresis": 2, | ||
| "min_loss_scale": 1 | ||
| }, | ||
|
|
||
| # misc. training settings | ||
| "train_iters": 320000, | ||
| "lr_decay_iters": 320000, | ||
| "distributed_backend": "nccl", | ||
| "lr_decay_style": "cosine", | ||
| "warmup": 0.01, | ||
| "checkpoint_factor": 10000, | ||
| "eval_interval": 1000, | ||
| "eval_iters": 10, | ||
|
|
||
| # logging | ||
| "log_interval": 100, | ||
| "steps_per_print": 10, | ||
| "keep_last_n_checkpoints": 4, | ||
| "wall_clock_breakdown": true, | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,128 @@ | ||
| # Copyright (c) 2024, EleutherAI | ||
| # This file is based on code by the authors denoted below and has been modified from its original version. | ||
| # | ||
| # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. | ||
| # | ||
| # 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. | ||
|
|
||
| """Online dataset.""" | ||
| from typing import Union, List | ||
|
|
||
| import numpy as np | ||
| import torch | ||
| import torch.utils.data | ||
| import socket | ||
| import pickle | ||
| from megatron.mpu.initialize import get_data_parallel_rank | ||
|
|
||
|
|
||
| class OnlineDataset(torch.utils.data.Dataset): | ||
| def __init__( | ||
| self, | ||
| num_samples, | ||
| seq_length, | ||
| leave_one_out=False, | ||
| data_split="train", | ||
| dataserver_ips: Union[str, List[str]] = "localhost", | ||
| dataserver_ports: Union[int, List[int]] = 10000, | ||
| ): | ||
| self.num_samples = num_samples | ||
| self.global_rank = get_data_parallel_rank() | ||
| self.leave_one_out = leave_one_out | ||
| self.reward_buffer = [] | ||
| self.online_batching_data = [] | ||
| self.data_split = data_split | ||
| self.seq_length = seq_length | ||
| self.dataserver_ips = dataserver_ips | ||
| self.dataserver_ports = dataserver_ports | ||
|
|
||
| def __len__(self): | ||
| # dummy value since it's decided by the Online Trainer | ||
| return self.num_samples | ||
|
|
||
| def update_online_batches(self): | ||
| s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) | ||
| if isinstance(self.dataserver_ips, str): | ||
| ipaddr = self.dataserver_ips | ||
| else: | ||
| ipaddr = self.dataserver_ips[self.global_rank] | ||
| if isinstance(self.dataserver_ports, int): | ||
| # simply add over the global rank | ||
| port = self.dataserver_ports | ||
| else: | ||
| # in case we want to use different ports for different ranks, e.g. per machine sampling | ||
| port = self.dataserver_ports[self.global_rank] | ||
| print(f"Connecting to {ipaddr}:{port}") | ||
| s.connect((ipaddr, port)) | ||
| s.send(self.data_split.encode()) | ||
| data = b"" | ||
| while True: | ||
| chunk = s.recv(4096) | ||
| if not chunk: | ||
| break | ||
| data += chunk | ||
| batch_data = pickle.loads(data) | ||
| s.close() | ||
| print(f"Received {len(batch_data)} samples from the server.") | ||
| for data in batch_data: | ||
| if self.leave_one_out: | ||
| rewards = list() | ||
| for i in range(len(data["rewards"])): | ||
| rewards.append( | ||
| data["rewards"][i] | ||
| - np.mean( | ||
| [ | ||
| data["rewards"][j] | ||
| for j in range(len(data["rewards"])) | ||
| if j != i | ||
| ] | ||
| ) | ||
| ) | ||
| data["raw_rewards"] = data["rewards"] | ||
| data["rewards"] = rewards | ||
| else: | ||
| moving_average = 0 | ||
| if len(self.reward_buffer) > 0: | ||
| moving_average = np.mean(self.reward_buffer) | ||
| self.reward_buffer.append(np.mean(data["rewards"])) | ||
| if len(self.reward_buffer) > 100: | ||
| self.reward_buffer.pop(0) | ||
| # For metrics... | ||
| data["raw_rewards"] = data["rewards"] | ||
| data["rewards"] = [r - moving_average for r in data["rewards"]] | ||
| for i in range(len(data["completions"])): | ||
| self.online_batching_data.append( | ||
| [ | ||
| data["prefix"], | ||
| data["completions"][i], | ||
| data["rewards"][i], | ||
| data["raw_rewards"][i], | ||
| ] | ||
| ) | ||
|
|
||
| def __getitem__(self, idx): | ||
| if len(self.online_batching_data) == 0: | ||
| self.update_online_batches() | ||
| batch = self.online_batching_data.pop(0) | ||
| text = batch[0] + batch[1] | ||
| label = [-100 for _ in batch[0]] + batch[1] | ||
| # +1 because of causal masking | ||
| if len(text) <= self.seq_length: | ||
| text = text + [0] * ((self.seq_length + 1) - len(text)) | ||
| label = label + [-100] * ((self.seq_length + 1) - len(label)) | ||
| return { | ||
| "text": np.array(text, dtype=np.int64), | ||
| "label": np.array(label, dtype=np.int64), | ||
| "reward": np.array([batch[2]], dtype=np.float32), | ||
| "raw_reward": np.array([batch[3]], dtype=np.float32), | ||
| } |
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