Merge pull request #96 from huggingface/nouamane/docs

Quick refactos

README.md

@@ -1,4 +1,34 @@
-# ⚡️ Nanotron
+<h1 align="center">⚡️ Nanotron</h1>
+
+<p align="center">
+    <a href="https://github.com/huggingface/nanotron/releases">
+        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/nanotron.svg">
+    </a>
+    <a href="https://arxiv.org/abs/2210.07316">
+        <img alt="GitHub release" src="https://img.shields.io/badge/arXiv-2305.14251-b31b1b.svg">
+    </a>
+    <a href="https://github.com/huggingface/nanotron/blob/master/LICENSE">
+        <img alt="License" src="https://img.shields.io/github/license/huggingface/nanotron.svg?color=green">
+    </a>
+</p>
+
+<h4 align="center">
+    <p>
+        <a href="#Philosophy">Philosophy</a> •
+        <a href="#Core-Features">Core Features</a> •
+        <a href="#Installation">Installation</a> •
+        <a href="#Quick-examples">Usage</a> •
+        <a href="#Development-guidelines">Contributions</a>
+    <p>
+</h4>
+
+<h3 align="center">
+    <a href="https://huggingface.co/nanotron"><img style="float: middle; padding: 10px 10px 10px 10px;" width="60" height="55" src="https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.png" /></a>
+</h3>
+
+
+
+#
 
 The objective of this library is to provide easy distributed primitives in order to train a variety of models efficiently using 3D parallelism. For more information about the internal design of the library or 3D parallelism in general, please check out [[docs.md]](./docs/docs.md) and [[3d_parallelism.md]](./docs/3d_parallelism.md).
 
@@ -28,12 +58,10 @@ To install (in a new env):
 ```bash
 pip install torch
 pip install packaging; pip install "flash-attn>=2.5.0"  --no-build-isolation
-git clone [email protected]:huggingface/nanotron.git
-cd nanotron
-pip install -e .
+pip install nanotron
 ```
 
-Also nice to have `transformers` `datasets` `python-etcd` `tensorboardX`: `pip install transformers datasets python-etcd tensorboardX`
+Also nice to have: `pip install transformers datasets python-etcd tensorboardX`
 
 We also support a set of flavors that you can install using `pip install -e [$FLAVOR]`:
  - `dev`: Used is you are developping in `nanotron`. It installs in particular our linter mechanism. On top of that you have to run `pre-commit install` afterwards.
@@ -68,7 +96,6 @@ pre-commit run --config .pre-commit-config.yaml --all-files
 
 Features we would like to add:
 - [ ] Support `torch.compile`
-- [ ] Support `torch.distributed.rpc`
 - [ ] More optimized kernels
 - [ ] Support Zero3
 - [ ] Other PP schedules (such as Interleaved 1f1b...)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "nanotron"
-version = "0.2"
+version = "0.4"
 description = "Minimalistic Large Language Model Training and Finetuning"
 authors = [
     {name = "Nouamane Tazi", email="[email protected]"},

run_generate.py

@@ -15,9 +15,19 @@
 import torch
 from nanotron import distributed as dist
 from nanotron import logging
-from nanotron.config import GenerationArgs, LoggingArgs, ParallelismArgs, get_config_from_file
-from nanotron.generation.decode import GenerationInput, TokenizerConfig, decode_text, decode_tokenized
-from nanotron.logging import log_rank, set_logger_verbosity_format
+from nanotron.config import (
+    GenerationArgs,
+    LoggingArgs,
+    ParallelismArgs,
+    get_config_from_file,
+)
+from nanotron.generation.decode import (
+    GenerationInput,
+    TokenizerConfig,
+    decode_text,
+    decode_tokenized,
+)
+from nanotron.logging import log_rank, set_ranks_logging_level
 from nanotron.models import build_model
 from nanotron.parallel import ParallelContext
 from nanotron.parallel.parameters import sanity_check
@@ -32,9 +42,7 @@
     get_synced_random_state,
     set_random_seed,
 )
-from nanotron.serialize import (
-    load_weights,
-)
+from nanotron.serialize import load_weights
 from nanotron.trainer import CONFIG_TO_MODEL_CLASS, mark_tied_parameters
 
 try:
@@ -86,12 +94,8 @@ def main():
         log_level_replica="info",
     )
 
-    if dist.get_rank(parallel_context.world_pg) == 0:
-        if logging_config.log_level is not None:
-            set_logger_verbosity_format(logging_config.log_level, parallel_context=parallel_context)
-    else:
-        if logging_config.log_level_replica is not None:
-            set_logger_verbosity_format(logging_config.log_level_replica, parallel_context=parallel_context)
+    # Set log levels
+    set_ranks_logging_level(parallel_context=parallel_context, logging_config=logging_config)
 
     log_rank(f"model_config: {model_config}", logger=logger, level=logging.INFO, rank=0)
     log_rank(f"tokenizer_path: {tokenizer_path}", logger=logger, level=logging.INFO, rank=0)

src/nanotron/__init__.py

@@ -1 +1 @@
-__version__ = "0.2"
+__version__ = "0.4"

src/nanotron/logging.py

@@ -18,13 +18,24 @@
 import sys
 from dataclasses import dataclass
 from functools import lru_cache
-from logging import CRITICAL, DEBUG, ERROR, FATAL, INFO, NOTSET, WARNING, Formatter, Logger
+from logging import (
+    CRITICAL,
+    DEBUG,
+    ERROR,
+    FATAL,
+    INFO,
+    NOTSET,
+    WARNING,
+    Formatter,
+    Logger,
+)
 from typing import List, Optional, Union
 
 import torch
 from torch import distributed as torch_dist
 
 from nanotron import distributed as dist
+from nanotron.config.config import LoggingArgs
 from nanotron.parallel import ParallelContext
 
 log_levels = {
@@ -283,7 +294,6 @@ def set_logger_verbosity_format(logging_level: str, parallel_context: ParallelCo
         f"TP={dist.get_rank(parallel_context.tp_pg)}{expert_parallel_log}{'|' + node_name if node_name else ''}]: %(message)s",
         datefmt="%m/%d/%Y %H:%M:%S",
     )
-    # TODO @thomasw21: `logging.log_levels` returns valid lg log levels
     log_level = log_levels[logging_level]
 
     # main root logger
@@ -299,4 +309,13 @@ def set_logger_verbosity_format(logging_level: str, parallel_context: ParallelCo
     set_formatter(formatter=formatter)
 
 
+def set_ranks_logging_level(parallel_context: ParallelContext, logging_config: LoggingArgs):
+    if dist.get_rank(parallel_context.world_pg) == 0:
+        if logging_config.log_level is not None:
+            set_logger_verbosity_format(logging_config.log_level, parallel_context=parallel_context)
+    else:
+        if logging_config.log_level_replica is not None:
+            set_logger_verbosity_format(logging_config.log_level_replica, parallel_context=parallel_context)
+
+
 _configure_library_root_logger()

src/nanotron/parallel/pipeline_parallel/__init__.py

@@ -0,0 +1,5 @@
+from nanotron.parallel.pipeline_parallel.engine import PipelineEngine
+from nanotron.parallel.pipeline_parallel.tensor_pointer import TensorPointer
+from nanotron.parallel.pipeline_parallel.utils import get_pp_rank_of
+
+__all__ = ["PipelineEngine", "TensorPointer", "get_pp_rank_of"]

src/nanotron/trainer.py

@@ -45,7 +45,7 @@
     human_format,
     log_memory,
     log_rank,
-    set_logger_verbosity_format,
+    set_ranks_logging_level,
 )
 from nanotron.models import NanotronModel, build_model
 from nanotron.models.base import check_model_has_grad
@@ -55,9 +55,11 @@
 from nanotron.parallel import ParallelContext
 from nanotron.parallel.data_parallel.utils import sync_gradients_across_dp
 from nanotron.parallel.parameters import NanotronParameter, sanity_check
-from nanotron.parallel.pipeline_parallel.engine import PipelineEngine
-from nanotron.parallel.pipeline_parallel.tensor_pointer import TensorPointer
-from nanotron.parallel.pipeline_parallel.utils import get_pp_rank_of
+from nanotron.parallel.pipeline_parallel import (
+    PipelineEngine,
+    TensorPointer,
+    get_pp_rank_of,
+)
 from nanotron.parallel.tensor_parallel.nn import (
     TensorParallelLinearMode,
     TensorParallelRowLinear,
@@ -143,14 +145,7 @@ def __init__(
         self.pre_init()
 
         # Set log levels
-        if dist.get_rank(self.parallel_context.world_pg) == 0:
-            if self.config.logging.log_level is not None:
-                set_logger_verbosity_format(self.config.logging.log_level, parallel_context=self.parallel_context)
-        else:
-            if self.config.logging.log_level_replica is not None:
-                set_logger_verbosity_format(
-                    self.config.logging.log_level_replica, parallel_context=self.parallel_context
-                )
+        set_ranks_logging_level(parallel_context=self.parallel_context, logging_config=self.config.logging)
 
         # Log benchmark info
         if os.environ.get("NANOTRON_BENCHMARK", "0") == "1":
@@ -198,8 +193,6 @@ def __init__(
             )
 
         # Define iteration start state
-        self.start_iteration_step: int
-        self.consumed_train_samples: int
         if self.init_checkpoint_path is not None:
             checkpoint_metadata = load_meta(
                 parallel_context=self.parallel_context, root_folder=self.init_checkpoint_path
@@ -266,8 +259,6 @@ def train(
             self.save_checkpoint()
 
         if isinstance(dataloader_or_dls, tuple):
-            dataloader_or_dls[1] if len(dataloader_or_dls) > 1 else None
-            dataloader_or_dls[2] if len(dataloader_or_dls) > 2 else None
             dataloader = dataloader_or_dls[0]
         else:
             dataloader = dataloader_or_dls