add gemma

configs/7B_gemma.py

@@ -0,0 +1,226 @@
+JOB_NAME = "7b_gemma_train"
+model_type = "GEMMA"
+DO_ALERT = False
+
+VOCAB_SIZE = 256000
+SEQ_LEN = 2048
+HIDDEN_SIZE = 3072
+NUM_ATTENTION_HEAD = 16
+NUM_KV_ATTENTION_HEAD = 16
+HEAD_DIM = 256
+MLP_RATIO = 8
+NUM_LAYER = 28
+
+
+MODEL_ONLY_FOLDER = "local:llm_ckpts/xxxx"
+# Ckpt folder format:
+# fs: 'local:/mnt/nfs/XXX'
+SAVE_CKPT_FOLDER = "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.
+    # '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.
+)
+
+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=4,
+    # packed_length = micro_bsz * SEQ_LEN
+    micro_bsz=1,
+    # 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=20,
+    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,
+)
+
+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=False,
+    # bucket size for nccl communication params
+    reduce_bucket_size=512 * 1024 * 1024,
+    # grad clipping
+    clip_grad_norm=1.0,
+)
+
+loss = dict(
+    label_smoothing=0,
+)
+
+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",
+    add_unit_offset=True,
+    norm_type="rmsnorm",
+    layer_norm_epsilon=1e-6,
+    head_dim=HEAD_DIM,
+    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,
+    use_swiglu=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. memory_pool: bool, enable/disable memory pool, defaults to False.
+"""
+parallel = dict(
+    zero1=dict(size=-1),
+    tensor=dict(size=1, mode="mtp"),
+    pipeline=dict(size=1, interleaved_overlap=True),
+    weight=dict(size=1, overlap=True, memory_pool=True),
+)
+
+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,
+)

internlm/model/modeling_baichuan2.py

@@ -1,10 +1,14 @@
 # Copyright (c) InternLM. All rights reserved.
 import math
+import os
 from typing import Optional
 
 import torch
+from einops import rearrange
 from torch import nn
+from tqdm import tqdm
 
+from internlm.accelerator import get_accelerator
 from internlm.core.context import ParallelMode
 from internlm.core.context.parallel_context import global_context as gpc
 from internlm.initialize.initialize_tensor import (
@@ -13,6 +17,7 @@
     scaled_init_method_uniform,
     uniform_,
 )
+from internlm.model.base_model import BaseModel
 from internlm.model.modules.embedding import Embedding1D
 from internlm.model.modules.linear import new_linear
 from internlm.model.modules.mha import MHA
@@ -24,7 +29,14 @@
 )
 from internlm.solver.activation_checkpoint import activation_checkpoint
 from internlm.utils.logger import get_logger
+from internlm.utils.storage_manager import get_fns, llm_load, llm_save
+from transformers.modeling_utils import (
+    SAFE_WEIGHTS_INDEX_NAME,
+    SAFE_WEIGHTS_NAME,
+    shard_checkpoint,
+)
 
+internlm_accelerator = get_accelerator()
 logger = get_logger(__file__)
 
 
@@ -136,7 +148,7 @@ def __init__(
             mlp_layer_fusion=mlp_layer_fusion,
             multiple_of=multiple_of,
             # TODO: to support more activation functions
-            activation_type="swiglu" if use_swiglu else "swiglu",
+            activation_type="swiglu" if use_swiglu else "gelu",
         )
 
         self.use_swiglu = use_swiglu
@@ -189,7 +201,7 @@ def forward(self, hidden_states, residual=None, **kwargs):
         else:
             return self._forward(hidden_states, residual, **kwargs)
 
-    def _forward(self, hidden_states=None, residual=None, *args, **kwargs):  # pylint: disable=W1113
+    def _forward(self, hidden_states, residual, *args, **kwargs):
         r"""Pass the input through the encoder layer.
 
         Args:
@@ -258,7 +270,7 @@ def _dropout_and_norm_ffn(_residual, _hidden_states):
             return hidden_states
 
 
-class Baichuan2(nn.Module):
+class Baichuan2(BaseModel):
     """
     1D Packed Flash Llama.
 
@@ -429,3 +441,11 @@ def forward(self, hidden_states=None, input_ids=None, **kwargs):
             hidden_states = self.output(hidden_states)
 
         return hidden_states
+
+    @staticmethod
+    def load_hf_weights(folder: str, model: nn.Module) -> None:
+        raise NotImplementedError
+
+    @staticmethod
+    def convert_internevo2hf_weights(src: str, tgt: str) -> None:
+        raise NotImplementedError