feat(*): Add internlm3 config

README-ja-JP.md

@@ -141,8 +141,9 @@ $ torchrun --nnodes=1 --nproc_per_node=8 train.py --config ./configs/7B_sft.py -
       </td>
       <td>
       <ul>
-        <li><a href="configs/_base_/models/internlm/internlm_7B.py">InternLM</a></li>
-        <li><a href="configs/_base_/models/internlm/internlm2_7B.py">InternLM2</a></li>
+        <li><a href="configs/7B_isp_sft.py">InternLM</a></li>
+        <li><a href="configs/7B_internlm2.py">InternLM2</a></li>
+        <li><a href="configs/8B_internlm3.py">InternLM3</a></li>
         <li><a href="configs/7B_llama2.py">Llama2</a></li>
         <li><a href="configs/7B_qwen2.py">Qwen2</a></li>
         <li><a href="configs/7B_baichuan2.py">Baichuan2</a></li>

README-zh-Hans.md

@@ -141,8 +141,9 @@ $ torchrun --nnodes=1 --nproc_per_node=8 train.py --config ./configs/7B_sft.py -
       </td>
       <td>
       <ul>
-        <li><a href="configs/_base_/models/internlm/internlm_7B.py">InternLM</a></li>
-        <li><a href="configs/_base_/models/internlm/internlm2_7B.py">InternLM2</a></li>
+        <li><a href="configs/7B_isp_sft.py">InternLM</a></li>
+        <li><a href="configs/7B_internlm2.py">InternLM2</a></li>
+        <li><a href="configs/8B_internlm3.py">InternLM3</a></li>
         <li><a href="configs/7B_llama2.py">Llama2</a></li>
         <li><a href="configs/7B_qwen2.py">Qwen2</a></li>
         <li><a href="configs/7B_baichuan2.py">Baichuan2</a></li>

README.md

@@ -141,8 +141,9 @@ Please refer to the [System Architecture document](./doc/en/structure.md) for ar
       </td>
       <td>
       <ul>
-        <li><a href="configs/_base_/models/internlm/internlm_7B.py">InternLM</a></li>
-        <li><a href="configs/_base_/models/internlm/internlm2_7B.py">InternLM2</a></li>
+        <li><a href="configs/7B_isp_sft.py">InternLM</a></li>
+        <li><a href="configs/7B_internlm2.py">InternLM2</a></li>
+        <li><a href="configs/8B_internlm3.py">InternLM3</a></li>
         <li><a href="configs/7B_llama2.py">Llama2</a></li>
         <li><a href="configs/7B_qwen2.py">Qwen2</a></li>
         <li><a href="configs/7B_baichuan2.py">Baichuan2</a></li>

configs/7B_internlm2.py

@@ -1,5 +1,5 @@
 JOB_NAME = "7b_internlm2_train"
-model_type = "INTERNLM2_PUBLIC"
+model_type = "INTERNLM2"
 DO_ALERT = False
 
 VOCAB_SIZE = 92544

configs/7B_isp_sft.py

@@ -1,5 +1,5 @@
 JOB_NAME = "7b_train"
-model_type = "INTERNLM2_PUBLIC"
+model_type = "INTERNLM2"
 DO_ALERT = False
 
 VOCAB_SIZE = 103168

configs/8B_internlm3.py

@@ -0,0 +1,270 @@
+# Copyright (c) InternLM. All rights reserved.
+JOB_NAME = "8b_internlm3_train"
+model_type = "INTERNLM3"
+DO_ALERT = False
+
+VOCAB_SIZE = 128512
+SEQ_LEN = 4096
+HIDDEN_SIZE = 4096
+NUM_ATTENTION_HEAD = 32
+NUM_KV_ATTENTION_HEAD = 2
+MLP_RATIO = 2.5
+NUM_LAYER = 48
+
+
+MODEL_ONLY_FOLDER = None  # "local:llm_ckpts/xxxx"
+# Ckpt folder format:
+# fs: 'local:/mnt/nfs/XXX'
+SAVE_CKPT_FOLDER = None  # "local:llm_ckpts"
+# LOAD_CKPT_FOLDER = "local:llm_ckpts/49"
+
+# boto3 Ckpt folder format:
+# import os
+# BOTO3_IP = os.environ["BOTO3_IP"] # boto3 bucket endpoint
+# SAVE_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm"
+# LOAD_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm/snapshot/1/"
+CHECKPOINT_EVERY = 50
+ckpt = dict(
+    enable_save_ckpt=False,  # enable ckpt save.
+    save_ckpt_folder=SAVE_CKPT_FOLDER,  # Path to save training ckpt.
+    # 'load_ckpt_info' setting guide:
+    # 1. the 'path' indicate ckpt path,
+    # 2. the 'content‘ means what states will be loaded, support: "model", "sampler", "optimizer", "scheduler", "all"
+    # 3. the ’ckpt_type‘ means the type of checkpoint to be loaded, support: "internevo", "hf", or other custom-defined
+    # load function such as "llama"
+    load_ckpt_info=dict(path=MODEL_ONLY_FOLDER, content=("model",), ckpt_type="hf"),
+    # 'auto_resume' is designed to automatically load the latest checkpoint from 'save_ckpt_folder' when encountering
+    # training interruptions/hangs caused by hardware failures, using a scheduling system (such as k8s/slurm)
+    # with an automatic restart mechanism upon training reboot.
+    # Please be aware that if `auto_resume` is not set (its default value is True), it will not load the checkpoint
+    # path specified in `load_ckpt_info` by default.
+    # If you want to initialize your model weights from another model, you must set `auto_resume` to False.
+    # If you want to train from scratch, please set `auto_resume` to False and 'load_ckpt_info' to None.
+    auto_resume=False,
+    checkpoint_every=CHECKPOINT_EVERY,
+    async_upload=True,  # async ckpt upload. (only work for boto3 ckpt)
+    async_upload_tmp_folder="/dev/shm/internlm_tmp_ckpt/",  # path for temporarily files during asynchronous upload.
+    oss_snapshot_freq=int(CHECKPOINT_EVERY / 2),  # snapshot ckpt save frequency.
+    # 'enable_internevo2hf_ckpt' is designed to convert the saved model checkpoint in internevo format to the huggingface format.
+    enable_internevo2hf_ckpt=False,
+)
+
+TRAIN_FOLDER = None
+VALID_FOLDER = None  # "/path/to/dataset"
+data = dict(
+    seq_len=SEQ_LEN,
+    # micro_num means the number of micro_batch contained in one gradient update
+    micro_num=1,
+    # packed_length = micro_bsz * SEQ_LEN
+    micro_bsz=2,
+    # defaults to the value of micro_num
+    valid_micro_num=4,
+    # defaults to 0, means disable evaluate
+    valid_every=0,
+    pack_sample_into_one=False,
+    total_steps=20000,
+    skip_batches="",
+    # rampup_batch_size (str): A string with three space-separated integers representing the
+    #       starting batch size, the increment, and the number of steps between
+    #       each increment. For example, "192 24 8" means that the batch size (micro_num)
+    #       starts at 192 and increases by 24 every 8 steps. Defaults to None.
+    #       (IMPORTANT): The interval step size is 'micro_bsz'.
+    rampup_batch_size="",
+    # Datasets with less than 50 rows will be discarded
+    min_length=50,
+    train_folder=TRAIN_FOLDER,
+    valid_folder=VALID_FOLDER,
+    empty_cache_and_diag_interval=200,
+    diag_outlier_ratio=1.1,
+    # use_packed_dataset=False,
+)
+
+grad_scaler = dict(
+    fp16=dict(
+        # the initial loss scale, defaults to 2**16
+        initial_scale=2**16,
+        # the minimum loss scale, defaults to None
+        min_scale=1,
+        # the number of steps to increase loss scale when no overflow occurs
+        growth_interval=1000,
+    ),
+    # the multiplication factor for increasing loss scale, defaults to 2
+    growth_factor=2,
+    # the multiplication factor for decreasing loss scale, defaults to 0.5
+    backoff_factor=0.5,
+    # the maximum loss scale, defaults to None
+    max_scale=2**24,
+    # the number of overflows before decreasing loss scale, defaults to 2
+    hysteresis=2,
+)
+
+hybrid_zero_optimizer = dict(
+    # Enable low_level_optimzer overlap_communication
+    overlap_sync_grad=True,
+    overlap_sync_param=True,
+    # bucket size for nccl communication params
+    reduce_bucket_size=512 * 1024 * 1024,
+    # grad clipping
+    clip_grad_norm=1.0,
+)
+
+
+# loss config (dict):
+#     1. label_smoothing
+#     2. op_type: cross_entropy operator type, we support five types for loss computing,
+#                 including ["torch_naive", "apex_naive", "py_naive", "flash_vocab_parallel", "py_vocab_parallel"]
+#                 default is "py_vocab_parallel".
+#         "torch_naive": cross_entropy imported from torch, i.e. torch.nn.CrossEntropyLoss
+#         "apex_naive": cross_entropy from apex
+#         "py_naive": self-implemented cross_entropy
+#         "flash_vocab_parallel": vocab parallel cross_entropy imported from flash_attn
+#         "py_vocab_parallel": self-implemented vocab parallel cross_entropy
+
+#         * op_types that ends with "naive" only support parallel_output=False;
+#         * if in no-GPU env, only "torch_naive" and "py_vocab_parallel" are supported.
+loss = dict(label_smoothing=0, op_type="py_vocab_parallel")
+
+adam = dict(
+    lr=1e-4,
+    adam_beta1=0.9,
+    adam_beta2=0.95,
+    adam_beta2_c=0,
+    adam_eps=1e-8,
+    weight_decay=0.01,
+)
+
+lr_scheduler = dict(
+    total_steps=data["total_steps"],
+    init_steps=0,  # optimizer_warmup_step
+    warmup_ratio=0.01,
+    eta_min=1e-5,
+    last_epoch=-1,
+)
+
+beta2_scheduler = dict(
+    init_beta2=adam["adam_beta2"],
+    c=adam["adam_beta2_c"],
+    cur_iter=-1,
+)
+
+use_fp32_norm = False
+model = dict(
+    checkpoint=False,
+    num_chunks=1,
+    num_attention_heads=NUM_ATTENTION_HEAD,
+    num_kv_attention_heads=NUM_KV_ATTENTION_HEAD,
+    embed_split_hidden=True,
+    vocab_size=VOCAB_SIZE,
+    embed_grad_scale=1,
+    parallel_output=True,
+    hidden_size=HIDDEN_SIZE,
+    num_layers=NUM_LAYER,
+    no_bias=True,
+    mlp_ratio=MLP_RATIO,
+    apply_post_layer_norm=False,
+    dtype="torch.bfloat16",
+    norm_type="rmsnorm",
+    layer_norm_epsilon=1e-5,
+    use_flash_attn=True,
+    # Whether the odd and even columns of the query and key in the model are normally interleaved.
+    # If it's True, the model's odd and even columns are normally ordered; if it's False,
+    # it means that the model has prematurely concatenated all odd columns and even columns in front
+    # and back, in order to improve the RoPE's computational efficiency.
+    # Example:
+    # qk_interleaved = True: q[-1] = [q1,q2,q3,q4,q5,q6,...], k[-1] = [k1,k2,k3,k4,k5,k6,...]
+    # qk_interleaved = False: q[-1] = [q1,q3,q5,...,q2,q4,q6,...], k[-1] = [k1,k3,k5,...,k2,k4,k6,...]
+    qk_interleaved=False,
+    rope_base=50000000,
+    enable_qkv_fusion=False,
+)
+
+"""
+zero1 parallel (dict):
+    1. size: int
+        * if size <= 0, the size of the zero process group is equal to the size of the dp process group,
+            so parameters will be divided within the range of dp.
+        * if size == 1, zero is not used, and all dp groups retain the full amount of model parameters.
+        * if size > 1 and size <= dp world size, the world size of zero is a subset of dp world size.
+        For smaller models, it is usually a better choice to split the parameters within nodes with a setting <= 8.
+    2. fsdp: bool, enable/disable torch's fully sharded data parallel, defaults to False.
+tensor parallel (dict):
+    1. size: int, the size of tensor parallel.
+    2. mode: str, the tensor parallel mode, should be in ['mtp', 'msp', 'fsp', 'isp'],
+        defaults to 'mtp', means the pure megatron tensor parallel without sequence parallel.
+        msp: megatron tensor parallel with sequence parallel, sequence parallel size = tensor parallel size.
+        fsp: tensor parallel by flash-attn with sequence parallel, sequence parallel size = tensor parallel size.
+        isp: customed intern sequence parallel without tensor parallel, can be used with weight parallel.
+pipeline parallel (dict):
+    1. size: int, the size of pipeline parallel.
+    2. interleaved_overlap: bool, enable/disable communication overlap when using interleaved pipeline scheduler,
+        defaults to False.
+weight parallel (dict):
+    1. size: int, the size of weight parallel.
+    2. overlap: bool, enable/disable all_gather/reduce_scatter communication overlap, defaults to False.
+    3. launch_allgather_before: str, before which module to launch the all gather communication to
+        prefetch next layer's weight, should be in ['wqkv', 'attn', 'wo', 'w1'], defaults to 'wo'.
+        Must be used with forward_overlap_per 'layer'.
+    4. forward_overlap_per: str, all gather prefetch granularity, per 'module' or per 'layer', defaults to 'layer'.
+sequence_2D (dict):
+    1. enable: bool, whether enable the 2D sequence parallel or not.
+    2. head_size: int, the parallel degree of head parallelism (DeepSpeed Ulysses).
+                  head_size * context_size should be equal tensor size.
+    3. context_size: int, the parallel degree of context parallelism.
+                  head_size * context_size should be equal tensor size.
+    4. window_size: int, the sliding window size in context parallelism.
+    5. device_placement_strategy: dict,
+        head_first: bool, if `True`, ranks of the same head parallel group are
+                              given high priority for colocation on the same node;
+                              if `False`, ranks of the same context parallel group are
+                              given high priority for colocation on the same node;
+        interleaved: bool, if `head_first` is `False` and `window_size` > 1, this config could
+                           interleaved the ranks in the same window to make full use of NIC as much as possible.
+"""
+parallel = dict(
+    zero1=dict(size=1),
+    tensor=dict(size=1, mode="isp"),
+    pipeline=dict(size=1, interleaved_overlap=True),
+    weight=dict(size=16, overlap=True, launch_allgather_before="wo", forward_overlap_per="module"),
+    sequence_2D=dict(
+        enable=False,
+        head_size=2,
+        context_size=4,
+        window_size=1,
+        device_placement_strategy=dict(head_first=True, interleaved=False),
+    ),
+)
+
+cudnn_deterministic = False
+cudnn_benchmark = False
+
+monitor = dict(
+    # feishu alert configs
+    alert=dict(
+        enable_feishu_alert=DO_ALERT,
+        feishu_alert_address=None,  # feishu webhook to send alert message
+        light_monitor_address=None,  # light_monitor address to send heartbeat
+        alert_file_path=f"llm_alter/{JOB_NAME}_alert.log",
+    ),
+    tensorboard=dict(
+        queue_max_length=10,
+    ),
+)
+
+# metric_dtype can be "fp32" or other string
+# only when set to "fp32" will use fp32 to calc in metrics
+# metric_dtype = "fp32"
+
+generation = dict(
+    ckpt_folder="/path/to/saved/ckpt",
+    output_folder="/path/to/save/generation",
+    batch_size=1,
+    eos_id=[2, 0],
+    bos_id=1,
+    max_length=100,
+    do_sample=True,
+    temperature=1.0,
+    top_k=50,
+    top_p=1.0,
+    repetition_penalty=1,
+    length_penalty=1.0,
+)

configs/_base_/models/internlm2_1B.py

@@ -1,6 +1,6 @@
 # Copyright (c) InternLM. All rights reserved.
 
-model_type = "INTERNLM2_PUBLIC"
+model_type = "INTERNLM2"
 
 VOCAB_SIZE = 92544
 HIDDEN_SIZE = 2048

configs/_base_/models/internlm2_20B.py

@@ -1,6 +1,6 @@
 # Copyright (c) InternLM. All rights reserved.
 
-model_type = "INTERNLM2_PUBLIC"
+model_type = "INTERNLM2"
 
 VOCAB_SIZE = 92544
 HIDDEN_SIZE = 6144

configs/_base_/models/internlm2_7B.py

@@ -1,6 +1,6 @@
 # Copyright (c) InternLM. All rights reserved.
 
-model_type = "INTERNLM2_PUBLIC"
+model_type = "INTERNLM2"
 
 VOCAB_SIZE = 92544
 HIDDEN_SIZE = 4096

internlm/initialize/launch.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
+# Copyright (c) InternLM. All rights reserved.
 
 import argparse
 import os
@@ -307,6 +308,9 @@ def args_sanity_check():
         logger.info(f"clip_grad_norm: {clip_grad_norm}")
 
     model = gpc.config.model
+    if "enable_qkv_fusion" not in model:
+        model._add_item("enable_qkv_fusion", True)
+
     if "dtype" not in model:
         logger.warning("dtype is not set, use torch.float16 by defalut!")
         model._add_item("dtype", torch.float16)