Merge branch 'main' into main

examples/qwen/conf/config.yaml

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+defaults:
+  - _self_
+  - train: train_qwen_2.5_1.5b
+  # - train: train_mixtral_1.8b
+
+experiment:
+  exp_name: train_qwen_2.5_1.5b
+  exp_dir: ./outputs # outputs ## log、checkpoints output path
+  task:
+    type: train
+    backend: megatron
+    entrypoint: ./flagscale/train/train_aquila.py
+  runner:
+    backend: torchrun
+    nnodes: 2 
+    nproc_per_node: 8
+    hostfile: torchrun # Please replace with your actual hostfile path
+  envs:
+    CUDA_VISIBLE_DEVICES: "0,1,2,3,4,5,6,7"
+    CUDA_DEVICE_MAX_CONNECTIONS: 1
+    NCCL_SOCKET_IFNAME: eth0
+    NCCL_IB_DISABLE: 0
+    NCCL_IB_CUDA_SUPPORT: 1
+    NCCL_IB_GID_INDEX: 0
+    NCCL_DEBUG: INFO
+    OMP_NUM_THREADS: 4
+    GLOO_SOCKET_IFNAME: eth0
+    NCCL_IB_HCA: mlx5_2,mlx5_5
+  cmds:
+    before_start: "" # activate environment
+    after_stop: ""
+
+action: run

examples/qwen/conf/config_qwen2.5_1.5b.yaml

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+defaults:
+  - _self_
+  - train: train_qwen_2.5_1.5b
+  # - train: train_mixtral_1.8b
+
+experiment:
+  exp_name: train_qwen_2.5_1.5b
+  exp_dir: ./outputs # outputs ## log、checkpoints output path
+  task:
+    type: train
+    backend: megatron
+    entrypoint: ./flagscale/train/train_aquila.py
+  runner:
+    backend: torchrun
+    nnodes: 2 
+    nproc_per_node: 8
+    hostfile: torchrun # Please replace with your actual hostfile path
+  envs:
+    CUDA_VISIBLE_DEVICES: "0,1,2,3,4,5,6,7"
+    CUDA_DEVICE_MAX_CONNECTIONS: 1
+    NCCL_SOCKET_IFNAME: eth0
+    NCCL_IB_DISABLE: 0
+    NCCL_IB_CUDA_SUPPORT: 1
+    NCCL_IB_GID_INDEX: 0
+    NCCL_DEBUG: INFO
+    OMP_NUM_THREADS: 4
+    GLOO_SOCKET_IFNAME: eth0
+    NCCL_IB_HCA: mlx5_2,mlx5_5
+
+action: run

examples/qwen/conf/train/qwen_2.5_1.5b.yaml

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+system:
+  tensor_model_parallel_size: 1
+  pipeline_model_parallel_size: 1
+  make_vocab_size_divisible_by: 128
+  disable_bias_linear: True
+  sequence_parallel: True
+  use_flash_attn: True
+  use_distributed_optimizer: True
+  distributed-timeout-minutes: 60
+  precision:
+    bf16: True
+    attention_softmax_in_fp32: True 
+    accumulate_allreduce_grads_in_fp32: True 
+  logging: 
+    log_interval: 1 
+    tensorboard_log_interval: 1 
+    wandb_project: "train-qwen2.5-1.5B" 
+    wandb_exp_name: "train-qwen2.5-1.5B"
+  checkpoint:
+    load: ${megatron_model__path:}
+    # If you want to train the model, you need to comment out ckpt_format, ckpt_convert_format, ckpt_convert_save, which are used for converting ckpt.
+    ckpt_format: torch_dist # ${experiment.ckpt_format}
+    ckpt_convert_format: torch # ${experiment.ckpt_convert_format}
+    ckpt_convert_save: ${experiment.ckpt_convert_save}
+    save_interval: 5000000
+    rampup_save_interval: 50000 
+
+model:
+  use_mcore_models: true
+  num_layers: 28
+  hidden_size: 1536
+  num_attention_heads: 12
+  num_query_groups: 2
+  group_query_attention: True
+  ffn_hidden_size: 8960
+  seq_length: 4096
+  max_position_embeddings: 4096
+  norm_epsilon: 1e-6
+  norm_init_weight: 0.02
+  use_rotary_position_embeddings: true
+  rotary_base: 1000000.0  
+  no_position_embedding: true
+  reset_position_ids: true
+  add_qkv_bias: true
+  reset_attention_mask: true
+  swiglu: true
+  normalization: RMSNorm
+  untie_embeddings_and_output_weights: false
+  init_method_std: 0.02
+  attention_dropout: 0.0
+  hidden_dropout: 0.0
+  weight_decay: 0.0
+  clip_grad: 1.0
+  train_samples: 1478125
+  eval_iters: 0
+  eval_interval: 2000000
+  micro_batch_size: 1
+  global_batch_size: 512
+  finetune: true
+  transformer_impl: transformer_engine
+  seed: 42
+    #data_searching_range: [1156,1274]
+  optimizer:
+    weight_decay: 0.0
+    adam_beta1: 0.9
+    adam_beta2: 0.95
+    lr_scheduler:
+      lr: 1e-5
+      min_lr: 0
+      lr_warmup_samples: 21120
+      lr_decay_style: cosine 
+
+data:
+  data_path: ${data_path:??}
+  split: 1
+  apply_sft_dataset_separated_loss_mask_if_existed: true
+  tokenizer:
+    tokenizer_type: HFTokenizerFS
+    tokenizer_path: ${HF_model_path:??}
+    vocab_size: 151665

examples/qwen/utils/convo_dataset.py

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+"""GPT style dataset."""
+
+import copy
+import hashlib
+import os
+import time
+
+import numpy as np
+import torch
+
+from megatron import print_rank_0
+from megatron.core import mpu
+from megatron.data.data_samplers import RandomSeedDataset
+
+class ConversationDatasetCPT(torch.utils.data.Dataset):
+    def __init__(self, conversations, tokenizer, maxlen, seed, num_samples, role_sep="\n\n"):
+        super(ConversationDatasetCPT, self).__init__()
+        self.conversations = conversations
+        self.tokenizer = tokenizer
+        self.maxlen = maxlen+1
+        self.seed = seed
+        self.num_samples = num_samples
+
+        ## TODO convo template
+        self.sep = role_sep
+
+        # rng state
+        np_rng = np.random.RandomState(seed=seed)
+        np_rng.shuffle(self.conversations)
+
+    def __getitem__(self, i):
+        source = self.conversations[i]
+
+        instruction = source['instruction']
+        conversations = source['conversations']
+
+        BOS_TOKEN = self.tokenizer.cls
+        EOS_TOKEN = self.tokenizer.eod
+        example = [BOS_TOKEN]
+
+        # instruction
+        instruction = self.tokenizer.tokenize(f"{instruction}")
+        example += instruction
+
+        labels = [-100] * len(example)
+
+        for conversation in conversations:
+            role = conversation['from']
+            content = conversation['value']
+            content += self.sep
+
+            content = self.tokenizer.tokenize(f"{content}")
+
+            example += content
+            if role == 'gpt':
+                role_labels = copy.deepcopy(content)
+            else:
+                # masking
+                role_labels = [-100] * len(content)
+            labels += role_labels
+
+        example.append(EOS_TOKEN)
+        labels.append(EOS_TOKEN)
+
+        # maxlen
+        example = example[:self.maxlen]
+        labels = labels[:self.maxlen]
+
+        # padding
+        delta = self.maxlen - len(example)
+        if delta > 0:
+            example.extend([self.tokenizer.pad]*delta)
+            labels.extend([-100]*delta)
+
+        output = {
+            "tokens": np.array(example, dtype=np.int64),
+            "labels": np.array(labels, dtype=np.int64),
+        }
+        return output
+
+    def __len__(self):
+        return len(self.conversations)
+
+
+class ConversationDatasetV2(torch.utils.data.Dataset):
+    def __init__(self, conversations, tokenizer, maxlen, seed, num_samples):
+        super(ConversationDatasetV2, self).__init__()
+        self.conversations = conversations
+        self.tokenizer = tokenizer
+        self.maxlen = maxlen+1
+        self.seed = seed
+        self.num_samples = num_samples
+
+        # rng state
+        np_rng = np.random.RandomState(seed=seed)
+        np_rng.shuffle(self.conversations)
+
+
+    def __getitem__(self, i):
+        from examples.aquila.utils.convo_prompt import _add_speaker_and_signal
+        from examples.aquila.utils.convo_prompt import header
+
+        #source = self.conversations[self.sample_idx[i]]
+        source = self.conversations[i]
+        _add_speaker_and_signal(source)
+
+        source["chat_desc"] = header
+        chat_desc = source['chat_desc']
+        instruction = source['instruction']
+        conversations = source['conversations']
+
+        BOS_TOKEN = self.tokenizer.cls
+        EOS_TOKEN = self.tokenizer.eod
+        example = [BOS_TOKEN]
+
+        # chat_desc
+        example += self.tokenizer.tokenize(f"{chat_desc}")
+
+        # instruction
+        instruction = self.tokenizer.tokenize(f"{instruction}")
+        example += instruction
+
+        labels = copy.deepcopy(example)
+        # add zero-out
+        #labels = [-100] * len(example)
+
+        for conversation in conversations:
+            role = conversation['from']
+            content = conversation['value']
+            content = self.tokenizer.tokenize(f"{content}")
+            example += content
+            if role == 'gpt':
+                role_labels = copy.deepcopy(content)
+            else:
+                # masking
+                role_labels = [-100] * len(content)
+            labels += role_labels
+
+        example.append(EOS_TOKEN)
+        labels.append(EOS_TOKEN)
+
+        # maxlen
+        example = example[:self.maxlen]
+        labels = labels[:self.maxlen]
+
+        # padding
+        delta = self.maxlen - len(example)
+        if delta > 0:
+            example.extend([self.tokenizer.pad]*delta)
+            labels.extend([-100]*delta)
+
+        output = {
+            "tokens": np.array(example, dtype=np.int64),
+            "labels": np.array(labels, dtype=np.int64),
+        }
+        return output
+
+    def __len__(self):
+        #return len(self.sample_idx)
+        return len(self.conversations)
+
+
+def build_train_valid_test_datasets(train_valid_test_num_samples,
+                                    seq_length, seed, tokenizer,
+                                    train_data_prefix,
+                                    valid_data_prefix,
+                                    test_data_prefix=None,
+                                    finetune_dataset_type=None):
+    """Build train, valid, and test datasets."""
+    suppored_dataset_types = dict(CPT=ConversationDatasetCPT)
+    dataset_cls = ConversationDatasetV2
+    if finetune_dataset_type in suppored_dataset_types:
+        dataset_cls = suppored_dataset_types[finetune_dataset_type]
+
+    def read_file(jsonl_file):
+        import jsonlines
+        conversations = []
+        with jsonlines.open(jsonl_file) as reader:
+            for line in reader:
+                conversations.append(line)
+        return conversations
+
+    train_dataset, valid_dataset, test_dataset = None, None, None
+    # Single dataset.
+    if train_data_prefix is not None:
+        train_conversations = read_file(train_data_prefix[0])
+        train_dataset = dataset_cls(
+            train_conversations,
+            tokenizer=tokenizer,
+            maxlen=seq_length,
+            seed=seed,
+            num_samples=train_valid_test_num_samples[0])
+        train_dataset = RandomSeedDataset(train_dataset)
+
+    if valid_data_prefix is not None:
+        valid_conversations = read_file(valid_data_prefix[0])
+        valid_dataset = dataset_cls(
+            valid_conversations,
+            tokenizer=tokenizer,
+            maxlen=seq_length,
+            seed=seed,
+            num_samples=train_valid_test_num_samples[1])
+        valid_dataset = RandomSeedDataset(valid_dataset)
+
+    if test_data_prefix is not None:
+        test_conversations = read_file(test_data_prefix[0])
+        test_dataset = dataset_cls(
+            test_conversations,
+            tokenizer=tokenizer,
+            maxlen=seq_length,
+            seed=seed,
+            num_samples=train_valid_test_num_samples[2])
+        test_dataset = RandomSeedDataset(test_dataset)
+
+    return (train_dataset, valid_dataset, test_dataset)
+
+if __name__ == "__main__":
+    train_valid_test_num_samples = [12000,2000,0]
+    seq_length = 2048
+    seed = 1234
+    from megatron.tokenizer.tokenizer import _AquilaTokenizer
+    tokenizer = _AquilaTokenizer(
+        '../examples/aquila/tokenizer/vocab.json',
+        '../examples/aquila/tokenizer/merges.txt')
+    print(f"{dir(tokenizer)}")
+    train_data_prefix = ['path/to/train/set']
+    valid_data_prefix = ['path/to/valid/set']
+    train_dataset, valid_dataset, test_dataset = build_train_valid_test_datasets(
+        train_valid_test_num_samples,
+        seq_length, seed, tokenizer,
+        train_data_prefix,
+        valid_data_prefix,
+        test_data_prefix=None)
+    for idx, sample in enumerate(train_dataset):
+        print(f"idx={idx} sample={type(sample['labels'])}")
+        break
+