Merge branch 'main' into 1003-rope-casting

README.md

@@ -18,6 +18,8 @@ GPT-NeoX leverages many of the same features and technologies as the popular Meg
 * Easy connections with the open source ecosystem, including Hugging Face's [tokenizers](https://github.com/huggingface/tokenizers) and [transformers](https://github.com/huggingface/transformers/) libraries, logging via [WandB](https://wandb.ai/site), and evaluation via our [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness).
 
 ## News
+**[8/10/2023]** We now support checkpointing with AWS S3! Activate with the `s3_path` config option (for more detail, see [the PR](https://github.com/EleutherAI/gpt-neox/pull/1010))
+
 **[9/20/2023]** As of https://github.com/EleutherAI/gpt-neox/pull/1035, we have deprecated Flash Attention 0.x and 1.x, and migrated support to Flash Attention 2.x. We don't believe this will cause problems, but if you have a specific use-case that requires old flash support using the latest GPT-NeoX, please raise an issue.
 
 **[8/10/2023]** We have experimental support for LLaMA 2 and Flash Attention v2 supported in our [math-lm](https://github.com/EleutherAI/math-lm) project that will be upstreamed later this month.

configs/neox_arguments.md

@@ -111,7 +111,7 @@ Logging Arguments
 
 - **git_hash**: str
 
-    Default = 1d20559
+    Default = 1f832c1
 
     current git hash of repository
 
@@ -1169,6 +1169,22 @@ Training Arguments
 
 
 
+- **s3_path**: str
+
+    Default = None
+
+    Path to s3 bucket for saving checkpoints.
+
+
+
+- **s3_chunk_size**: int
+
+    Default = 104857600
+
+    The number of bytes in each file chunk when uploading to s3. Defaults to 100MiB.
+
+
+
 - **config_files**: dict
 
     Default = None

megatron/checkpointing.py

@@ -18,13 +18,23 @@
 """Input/output checkpointing."""
 
 import json
+import math
 import os
 import re
 import shutil
+import time
 import random
 import sys
 import numpy as np
 
+try:
+    import boto3
+except ModuleNotFoundError:
+    print("For s3 checkpointing, please install boto3 either using requirements/requirements-s3.txt or https://github.com/boto/boto3")
+try:
+    import hf_transfer
+except ModuleNotFoundError:
+    print("For s3 checkpointing, please install hf_transfer either using requirements/requirements-s3.txt or https://github.com/huggingface/hf_transfer")
 import torch
 from glob import glob
 
@@ -137,6 +147,10 @@ def get_checkpoint_name(checkpoints_path, iteration, release=False, mp_rank=None
     )
 
 
+def get_checkpoint_tag(iteration: int) -> str:
+    return f"global_step{iteration}"
+
+
 def delete_old_checkpoints(save_dir, n_to_keep):
     if torch.distributed.get_rank() == 0:
         ckpt_dir_regex = r"global_step[\d]*"
@@ -188,7 +202,7 @@ def save_ds_checkpoint(iteration, model, neox_args):
         sd["checkpoint_validation_logits"] = logits
 
     # checkpoint folder name
-    tag = f"global_step{iteration}"
+    tag = get_checkpoint_tag(iteration)
 
     # save checkpoint
     model.save_checkpoint(neox_args.save, tag=tag, client_state=sd)
@@ -203,6 +217,111 @@ def save_ds_checkpoint(iteration, model, neox_args):
                     f.write(config_data)
                 else:
                     json.dump(config_data, f)
+def multiprocessing_starmap(func, args, num_processes=None):
+    """Wrapper to allow for re-usable multiprocessing pools with `spawn` context handling
+    Args:
+        func (Callable): Function to call
+        args (Iterable): Iterable of arguments to pass to `func`
+        num_processes (int, optional): Number of processes to spawn. Defaults to `multiprocessing.cpu_count() - 1`
+    """
+    import multiprocessing
+    num_processes = num_processes or (multiprocessing.cpu_count() - 1)
+    with multiprocessing.get_context("spawn").Pool(processes=num_processes) as process_pool:
+        process_pool.starmap(func, args)
+        process_pool.terminate()
+        process_pool.join()
+        del process_pool
+
+
+def _upload(
+    file_path: str,
+    s3_key: str,
+    chunk_size: int = 104_857_600,
+    max_files: int = 64,
+    parallel_failures: int = 63,
+    max_retries: int = 5,
+):
+    """Upload local file to S3 using `hf_transfer` library
+    Args:
+        file_path (str): Local filename to upload
+        s3_key (str): S3 key to upload to. E.g. `s3://bucket-name/path/to/file`
+        chunk_size (int, optional): Chunk size to use for multipart upload.
+            Defaults to 100MiB = 104_857_600
+        max_files (int, optional):  Number of open file handles, which determines
+            the maximum number of parallel downloads. Defaults to 64
+        parallel_failures (int, optional): Number of maximum failures of different
+            chunks in parallel (cannot exceed max_files). Defaults to 63
+        max_retries (int, optional): Number of retries for each chunk. Defaults to 5
+    """
+    s3 = boto3.client('s3')
+    bucket = s3_key.split("s3://")[1].split("/")[0]
+    key = s3_key.split(bucket)[1].lstrip("/")
+
+    # 1. Init multipart upload and obtain unique upload identifier
+    upload = s3.create_multipart_upload(
+        ACL="bucket-owner-full-control",
+        Bucket=bucket,
+        Key=key,
+    )
+    upload_id = upload["UploadId"]
+
+    # 2. Generate presigned URLs for each part
+    file_size = os.stat(file_path).st_size
+    urls = []
+    nb_parts = math.ceil(file_size / chunk_size)
+    for part_number in range(1, nb_parts + 1):
+        params = {
+            "Bucket": bucket,
+            "Key": key,
+            "PartNumber": part_number,
+            "UploadId": upload_id,
+        }
+        urls.append(
+            s3.generate_presigned_url(
+                ClientMethod="upload_part", Params=params, ExpiresIn=86400
+            )
+        )
+
+    # 3. Upload parts in parallel
+    responses = hf_transfer.multipart_upload(
+        file_path=file_path,
+        parts_urls=urls,
+        chunk_size=chunk_size,
+        max_files=max_files,
+        parallel_failures=parallel_failures,
+        max_retries=max_retries,
+    )
+
+    # 4. Complete multipart upload request with ETag values
+    etag_with_parts = []
+    for part_number, header in enumerate(responses):
+        etag = header.get("etag")
+        etag_with_parts.append({"ETag": etag, "PartNumber": part_number + 1})
+    parts = {"Parts": etag_with_parts}
+    s3.complete_multipart_upload(
+        Bucket=bucket, Key=key, MultipartUpload=parts, UploadId=upload_id
+    )
+
+
+def upload_checkpoint(iteration, neox_args):
+    local_checkpoint_path = os.path.join(os.path.abspath(neox_args.save), get_checkpoint_tag(iteration))
+    local_checkpoint_list = sorted(filter(
+        lambda x: os.path.isfile(x),
+        [str(p) for p in Path(local_checkpoint_path).rglob("*")],
+    ))
+    remote_checkpoint_path = os.path.join(
+        neox_args.s3_path, os.path.basename(neox_args.save), get_checkpoint_tag(iteration))
+    remote_checkpoint_list = [
+        os.path.join(remote_checkpoint_path, os.path.relpath(local_checkpoint, local_checkpoint_path))
+        for local_checkpoint in local_checkpoint_list
+    ]
+    inputs = zip(local_checkpoint_list, remote_checkpoint_list, [neox_args.s3_chunk_size] * len(local_checkpoint_list))
+
+    print_rank_0(f"[RANK {torch.distributed.get_rank()}] Uploading checkpoint `{local_checkpoint_path}` to `{remote_checkpoint_path}`...")
+    start = time.time()
+    multiprocessing_starmap(_upload, inputs)
+    total_time = time.time() - start
+    print_rank_0(f"[RANK {torch.distributed.get_rank()}] Uploaded checkpoint `{local_checkpoint_path}` to `{remote_checkpoint_path}` in {total_time:.2f}s")
 
 
 def save_checkpoint(neox_args, iteration, model, optimizer, lr_scheduler):
@@ -213,6 +332,11 @@ def save_checkpoint(neox_args, iteration, model, optimizer, lr_scheduler):
     else:
         raise ValueError("Must be using deepspeed to use neox")
 
+    torch.distributed.barrier()
+    upload_to_s3 = torch.distributed.get_rank() == 0 and neox_args.s3_path is not None
+    if upload_to_s3:
+        upload_checkpoint(iteration, neox_args)
+
     # Wait so everyone is done (necessary)
     torch.distributed.barrier()
     if neox_args.keep_last_n_checkpoints is not None:
@@ -233,7 +357,7 @@ def load_checkpoint(
         if neox_args.finetune:
             load_optim_and_scheduler = False
         if iteration is not None:
-            tag = f"global_step{iteration}"
+            tag = get_checkpoint_tag(iteration)
         else:
             tag = None
         checkpoint_name, state_dict = model.load_checkpoint(

megatron/learning_rates.py

@@ -71,7 +71,8 @@ def get_lr(self):
 
         num_iters_ = num_iters_ - self.warmup_iter
         if self.decay_style == "linear":
-            lr = self.start_lr * (self.end_iter - num_iters_) / self.end_iter
+            end_iter_ = self.end_iter - self.warmup_iter
+            lr = self.start_lr * (end_iter_ - num_iters_) / end_iter_
         elif self.decay_style == "cosine":
             end_iter_ = self.end_iter - self.warmup_iter
             lr = self.min_lr + (
@@ -81,7 +82,8 @@ def get_lr(self):
             )
         elif self.decay_style == "exponential":
             # exp(-0.693) = 1/2
-            lr = self.start_lr * math.exp(-0.693 * num_iters_ / self.end_iter)
+            end_iter = self.end_iter - self.warmup_iter
+            lr = self.start_lr * math.exp(-0.693 * num_iters_ / end_iter)
         else:
             lr = self.start_lr
         return max(lr, self.min_lr)

megatron/neox_arguments/neox_args.py

@@ -793,6 +793,16 @@ class NeoXArgsTraining(NeoXArgsTemplate):
     Output directory to save checkpoints to.
     """
 
+    s3_path: str = None
+    """
+    Path to s3 bucket for saving checkpoints.
+    """
+
+    s3_chunk_size: int = 104_857_600
+    """
+    The number of bytes in each file chunk when uploading to s3. Defaults to 100MiB.
+    """ 
+
     config_files: dict = None
     """
     Store of original config files mapping config filename to file contents

requirements/requirements-s3.txt

@@ -0,0 +1,2 @@
+hf-transfer>=0.1.3
+boto3