From 5a45af8ebff70ecabb6e156d7f6c02a6b2082afe Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Fri, 4 Apr 2025 05:21:32 +0000
Subject: [PATCH 01/47] small cleanup

---
 scripts/export_dcp.py                         |  3 -
 src/zeroband/utils/__init__.py                | 89 -------------------
 .../test_learning_rate_schedulers.py          |  0
 3 files changed, 92 deletions(-)
 rename tests/{lrscheds => }/test_learning_rate_schedulers.py (100%)

diff --git a/scripts/export_dcp.py b/scripts/export_dcp.py
index e571c1ba..e0eda389 100644
--- a/scripts/export_dcp.py
+++ b/scripts/export_dcp.py
@@ -146,7 +146,6 @@ def main(config: ExportConfig):
     hf_config.to_json_file(save_path / "config.json")
 
     # Load checkpoint
-    logger.info("Before load: %s", get_module_signature(model))
     states = {
         "model": ModelWrapper(model),
     }
@@ -156,8 +155,6 @@ def main(config: ExportConfig):
         checkpoint_id=config.ckpt.resume,
     )
 
-    logger.info("After load: %s", get_module_signature(model))
-
     # Convert model to HuggingFace format
     num_shards = int(sum(p.numel() for p in model.parameters()) / 1e9)
     state_dict = model.state_dict()
diff --git a/src/zeroband/utils/__init__.py b/src/zeroband/utils/__init__.py
index ed9fae4b..e69de29b 100644
--- a/src/zeroband/utils/__init__.py
+++ b/src/zeroband/utils/__init__.py
@@ -1,89 +0,0 @@
-import hashlib
-import socket
-import torch
-from torch.distributed.tensor import DTensor
-
-TENSOR_SIG_SAMPLE_SIZE = 100
-
-
-def get_tensor_signature(a: torch.Tensor | torch.nn.Parameter) -> str:
-    """
-    Get the tensor signature
-    """
-    while isinstance(a, torch.nn.Parameter):
-        a = a.data
-
-    if isinstance(a, DTensor):
-        a = a.full_tensor()
-
-    if a.numel() < TENSOR_SIG_SAMPLE_SIZE:
-        b = a.as_strided(size=(a.numel(),), stride=(1,))
-    else:
-        step_size = a.numel() // TENSOR_SIG_SAMPLE_SIZE
-        b = a.as_strided(size=(TENSOR_SIG_SAMPLE_SIZE,), stride=(step_size,))
-    element_str = "".join([f"{x:.3e}" for x in b])
-    element_hash = hashlib.md5(element_str.encode("utf-8")).hexdigest()
-    return f"{a.dtype}{a.shape}{a.stride()}<{element_hash}>"
-
-
-def get_module_signature(module: torch.nn.Module, compress: bool = True) -> str:
-    """
-    Get the module signature
-    """
-    state_dict_sig = {name: get_tensor_signature(param) for name, param in module.named_parameters()}
-    if compress:
-        return hashlib.md5(str(state_dict_sig).encode("utf-8")).hexdigest()
-    else:
-        return "\n".join(f"{name}: {sig}" for name, sig in state_dict_sig.items())
-
-
-def get_dict_signature(dict: dict, compress: bool = True) -> str:
-    return hashlib.md5(str(dict).encode("utf-8")).hexdigest()
-
-
-def get_optimizer_signature(optimizer: torch.optim.Optimizer, compress: bool = True) -> str:
-    """
-    Get the optimizer signature
-    """
-
-    def unwrap_tensor(state_dict: dict) -> dict:
-        new_dict = {}
-        for key, value in state_dict.items():
-            if isinstance(value, dict):
-                new_dict[key] = unwrap_tensor(value)
-            elif isinstance(value, torch.Tensor):
-                new_dict[key] = get_tensor_signature(value)
-            else:
-                new_dict[key] = str(value)
-        return new_dict
-
-    state_dict_sig = unwrap_tensor(optimizer.state_dict())
-
-    if compress:
-        return hashlib.md5(str(state_dict_sig).encode("utf-8")).hexdigest()
-    else:
-        return "\n".join(f"{name}: {sig}" for name, sig in state_dict_sig.items())
-
-
-def get_tensor_list_signature(tensor_list: list[torch.Tensor]) -> str:
-    tensors = [get_tensor_signature(tensor) for tensor in tensor_list]
-    return hashlib.md5(str(tensors).encode("utf-8")).hexdigest()
-
-
-def get_random_available_port_list(num_port):
-    # https://stackoverflow.com/questions/1365265/on-localhost-how-do-i-pick-a-free-port-number
-    ports = []
-
-    while len(ports) < num_port:
-        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-            s.bind(("", 0))
-            new_port = s.getsockname()[1]
-
-        if new_port not in ports:
-            ports.append(new_port)
-
-    return ports
-
-
-def get_random_available_port():
-    return get_random_available_port_list(1)[0]
diff --git a/tests/lrscheds/test_learning_rate_schedulers.py b/tests/test_learning_rate_schedulers.py
similarity index 100%
rename from tests/lrscheds/test_learning_rate_schedulers.py
rename to tests/test_learning_rate_schedulers.py

From e96c5e738261879356a84ce4c205d40197206c06 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Fri, 4 Apr 2025 09:05:00 +0000
Subject: [PATCH 02/47] introduce mpi info and allow non-mpi runs

---
 configs/debug/diloco.toml             |   5 +
 configs/debug/normal.toml             |   4 +
 pyproject.toml                        |   5 +-
 scripts/skip_data.py                  |   4 +-
 src/zeroband/ccl/ccl_utils.py         |  51 ++++++++
 src/zeroband/ccl/nccl_ccllib.py       |  16 +++
 src/zeroband/ccl/pccl_ccllib.py       |  17 +++
 src/zeroband/ccoip_master.py          |  13 ++
 src/zeroband/checkpoint.py            |  44 +++++--
 src/zeroband/config.py                |  30 ++++-
 src/zeroband/data.py                  |  18 +--
 src/zeroband/train.py                 | 169 ++++++++++++++++++--------
 src/zeroband/utils/logger.py          |  16 +--
 src/zeroband/utils/memory_profiler.py |  17 +--
 src/zeroband/utils/optim_utils.py     |  66 +++++++++-
 src/zeroband/utils/torch_utils.py     |  13 ++
 src/zeroband/utils/world_info.py      |  56 ---------
 uv.lock                               |  21 ++++
 18 files changed, 410 insertions(+), 155 deletions(-)
 create mode 100644 src/zeroband/ccl/ccl_utils.py
 create mode 100644 src/zeroband/ccl/nccl_ccllib.py
 create mode 100644 src/zeroband/ccl/pccl_ccllib.py
 create mode 100644 src/zeroband/ccoip_master.py
 create mode 100644 src/zeroband/utils/torch_utils.py
 delete mode 100644 src/zeroband/utils/world_info.py

diff --git a/configs/debug/diloco.toml b/configs/debug/diloco.toml
index 2b7547d5..8ea5256e 100644
--- a/configs/debug/diloco.toml
+++ b/configs/debug/diloco.toml
@@ -5,6 +5,7 @@ wandb = false
 
 [hardware]
 micro_batch_size = 8
+ccl_library = 'pccl'
 
 [train]
 batch_size = 16
@@ -18,3 +19,7 @@ fake = true
 
 [diloco]
 inner_steps = 5
+
+[pccl]
+ccoip_host = "127.0.0.1:48148"
+peer_group = 0
\ No newline at end of file
diff --git a/configs/debug/normal.toml b/configs/debug/normal.toml
index 16451cf5..641283cd 100644
--- a/configs/debug/normal.toml
+++ b/configs/debug/normal.toml
@@ -5,6 +5,7 @@ wandb = false
 
 [hardware]
 micro_batch_size = 8
+ccl_library = 'pccl'
 
 [train]
 batch_size = 16
@@ -16,3 +17,6 @@ num_decay_steps = 10
 [data]
 fake = true
 
+[pccl]
+ccoip_host = "127.0.0.1:48148"
+peer_group = 0
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 7a3b1be0..97a7dd12 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,7 +18,8 @@ dependencies = [
     "pyarrow",
     "psutil",
     "wandb",
-    "imageio[ffmpeg]"
+    "imageio[ffmpeg]",
+    "pccl @ git+https://github.com/PrimeIntellect-ai/pccl.git@main#subdirectory=python/framework"
 ]
 
 [project.optional-dependencies]
@@ -37,4 +38,4 @@ allow-direct-references = true # allow direct references to git repos in depende
 line-length = 120
 
 [tool.uv]
-dev-dependencies = ["ruff>=0.5.0", "pre-commit>=3.0.0","pytest>=7.0.0", "faker", "matplotlib"]
+dev-dependencies = ["ruff>=0.5.0", "pre-commit>=3.0.0", "pytest>=7.0.0", "faker", "matplotlib"]
diff --git a/scripts/skip_data.py b/scripts/skip_data.py
index 8820e10a..919b7d24 100644
--- a/scripts/skip_data.py
+++ b/scripts/skip_data.py
@@ -47,8 +47,8 @@ def skip_data(config: Config):
 
     train_dataloader = make_dataloader(
         tokenizer=tokenizer,
-        world_size=world_info.world_size,
-        rank=world_info.rank,
+        mpi_world_size=world_info.world_size,
+        mpi_rank=world_info.rank,
         batch_size=config.hardware.micro_batch_size,
         data_config=config.data,
     )
diff --git a/src/zeroband/ccl/ccl_utils.py b/src/zeroband/ccl/ccl_utils.py
new file mode 100644
index 00000000..4f0a07be
--- /dev/null
+++ b/src/zeroband/ccl/ccl_utils.py
@@ -0,0 +1,51 @@
+from dataclasses import dataclass
+from typing import Optional
+
+from mpmath.libmp import mpi_mid
+
+from zeroband.ccl.nccl_ccllib import init_nccl
+from zeroband.ccl.pccl_ccllib import init_pccl
+from zeroband.config import Config
+
+
+@dataclass
+class MPIConfig:
+    """
+    We differentiate MPI world size & rank from
+    non-MPI world size, which relates to PCCL.
+
+    MPI world size concerns NCCL.
+    World size concerns PCCL.
+    """
+    mpi_rank: int
+    mpi_world_size: int
+
+
+def init_ccl_library(config: Config, mpi_config: Optional[MPIConfig]):
+    ccl_library = config.hardware.ccl_library
+    if ccl_library == "nccl":
+        if mpi_config is None:
+            raise RuntimeError("Cannot initialize with ccl library 'nccl' when mpi config is not supplied!")
+        init_nccl(mpi_config.mpi_rank, mpi_config.mpi_world_size)
+    elif ccl_library == "pccl":
+        init_pccl(config)
+
+
+def make_mpi_config(mpi_rank: Optional[str], mpi_world_size: Optional[str]) -> Optional[MPIConfig]:
+    """
+    Creates an MPI configuration from the supplied env-var strings
+    :param mpi_rank the optionally present env-var string for the mpi rank
+    :param mpi_world_size the optionally present env-var string for the mpi world size
+    """
+    assert (mpi_rank is not None and mpi_world_size is not None) or (
+            mpi_rank is None and mpi_world_size is None), "MPI rank and MPI world size must either both be None, or both set"
+
+    if mpi_rank is not None and mpi_world_size is not None:
+        mpi_rank = int(mpi_rank) if mpi_rank.isdigit() else None
+        mpi_world_size = int(mpi_world_size) if mpi_world_size.isdigit() else None
+        return MPIConfig(
+            mpi_rank=mpi_rank,
+            mpi_world_size=mpi_world_size
+        )
+    else:
+        return None
diff --git a/src/zeroband/ccl/nccl_ccllib.py b/src/zeroband/ccl/nccl_ccllib.py
new file mode 100644
index 00000000..5a550b20
--- /dev/null
+++ b/src/zeroband/ccl/nccl_ccllib.py
@@ -0,0 +1,16 @@
+import torch.distributed as dist
+
+
+def init_nccl(mpi_rank: int, mpi_world_size: int):
+    dist.init_process_group(
+        backend="nccl",
+        init_method="tcp://127.0.0.1:23456",
+        rank=mpi_rank,
+        world_size=mpi_world_size
+    )
+
+    if dist.is_available() and dist.is_initialized():
+        raise RuntimeError("torch.distributed is not available or was not initialized!")
+
+    if dist.is_nccl_available():
+        raise RuntimeError("torch.distributed reports NCCL is not available!")
diff --git a/src/zeroband/ccl/pccl_ccllib.py b/src/zeroband/ccl/pccl_ccllib.py
new file mode 100644
index 00000000..e6330630
--- /dev/null
+++ b/src/zeroband/ccl/pccl_ccllib.py
@@ -0,0 +1,17 @@
+from typing import Optional
+
+from pccl import Communicator
+
+from zeroband.config import Config
+
+communicator: Optional[Communicator] = None
+
+
+def init_pccl(config: Config):
+    if config.diloco is None:
+        raise RuntimeError("PCCL was configured as CCL-library, but not [pccl] was not configured!")
+
+    global communicator
+    communicator = Communicator(config.pccl.ccoip_host, 0)
+    communicator.connect(n_attempts=15)
+    print("Connected to master via PCCL")
diff --git a/src/zeroband/ccoip_master.py b/src/zeroband/ccoip_master.py
new file mode 100644
index 00000000..ef8d866d
--- /dev/null
+++ b/src/zeroband/ccoip_master.py
@@ -0,0 +1,13 @@
+from pccl import *
+
+HOST: str = '0.0.0.0:48148'
+
+
+def main():
+    print(f"Starting master node on {HOST}")
+    master: MasterNode = MasterNode(listen_address=HOST)
+    master.run()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/src/zeroband/checkpoint.py b/src/zeroband/checkpoint.py
index 3cf5da7e..19bdc587 100644
--- a/src/zeroband/checkpoint.py
+++ b/src/zeroband/checkpoint.py
@@ -1,12 +1,14 @@
 from dataclasses import dataclass
 import os
 from pathlib import Path
-from typing import Any
+from tabnanny import check
+from typing import Any, Optional
 import torch
 from torchdata.stateful_dataloader import StatefulDataLoader
 from torch.distributed.checkpoint.stateful import Stateful
+
+from zeroband.ccl.ccl_utils import MPIConfig
 from zeroband.models.llama.model import Transformer
-from zeroband.utils.world_info import get_world_info
 
 
 @dataclass
@@ -34,20 +36,32 @@ def _pathify(path: str | Path) -> Path:
     return path
 
 
-def save_checkpoint_fsdp_state(
+@dataclass
+class CheckpointInfo:
+    # The number of performed outer steps
+    num_performed_outer_steps: int
+
+    # The current shared state revision when this checkpoint was saved.
+    # This may differ from num_performed_outer_steps if shared state synchronization
+    # is skipped during outer steps when possible (e.g. no peers joined)
+    shared_state_revision: int
+
+
+def save_checkpoint(
         model: Transformer,
         optimizers: list[torch.optim.Optimizer],
         training_progress: TrainingProgress,
         dataloader: StatefulDataLoader,
         path_root: str | Path,
+        checkpoint_info: CheckpointInfo,
+        mpi_config: Optional[MPIConfig]
 ):
     """
     Checkpoint the model in a way that is compatible with FSDP.
     """
     path_root = _pathify(path_root) / f"step_{training_progress.step}"
-    world_info = get_world_info()
 
-    path_file = _local_file_path(path_root, world_info.local_rank)
+    path_file = _local_file_path(path_root, mpi_config.mpi_rank if mpi_config is not None else 1)
 
     if not os.path.exists(path_root):
         os.makedirs(path_root)
@@ -56,30 +70,34 @@ def save_checkpoint_fsdp_state(
         "model": model.state_dict(),
         "optimizers": [optimizer.state_dict() for optimizer in optimizers],
         "training_progress": training_progress,
-        "dataloader": dataloader.state_dict()
+        "dataloader": dataloader.state_dict(),
+
+        # checkpoint info
+        "num_performed_outer_steps": checkpoint_info.num_performed_outer_steps,
+        "shared_state_revision": checkpoint_info.shared_state_revision
     }
     with open(path_file, "wb") as f:
         torch.save(state, f)
 
 
-def load_checkpoint_fsdp_state(
+def load_checkpoint(
         model: Transformer,
         optimizers: list[torch.optim.Optimizer],
         training_progress: TrainingProgress,
         dataloader: StatefulDataLoader,
         path_root: str | Path,
-):
+        mpi_config: Optional[MPIConfig]
+) -> CheckpointInfo:
     """
     Load the checkpoint state.
+    :return checkpoint meta-data information
     """
     path = _pathify(path_root)
 
     assert os.path.exists(path), f"Checkpoint directory {path} must exist"
     assert os.path.isdir(path), f"Checkpoint directory {path} must be a directory"
 
-    world_info = get_world_info()
-
-    path_file = _local_file_path(path, world_info.local_rank)
+    path_file = _local_file_path(path, mpi_config.mpi_rank if mpi_config is not None else 1)
 
     if not os.path.exists(path_file):
         raise FileNotFoundError(f"Checkpoint step {training_progress.step} not found at {path_file}")
@@ -96,3 +114,7 @@ def load_checkpoint_fsdp_state(
     training_progress.step = state["training_progress"].step
 
     dataloader.load_state_dict(state["dataloader"])
+    return CheckpointInfo(
+        num_performed_outer_steps=state["num_performed_outer_steps"],
+        shared_state_revision=state["shared_state_revision"]
+    )
diff --git a/src/zeroband/config.py b/src/zeroband/config.py
index d4f5916f..2a0c35d7 100644
--- a/src/zeroband/config.py
+++ b/src/zeroband/config.py
@@ -4,6 +4,7 @@
 from pydantic import model_validator
 from pydantic_config import BaseConfig
 
+
 class Compression(Enum):
     NO = "no"
     UINT8 = "uint8"
@@ -25,6 +26,12 @@ class DataConfig(BaseConfig):
     split_by_data_rank: bool = True
 
 
+class SGDConfig(BaseConfig):
+    type: Literal["sgd"] = "sgd"
+    momentum: float = 0.0
+    nesterov: bool = False
+
+
 class AdamConfig(BaseConfig):
     type: Literal["adam"] = "adam"
     betas1: float = 0.9
@@ -58,24 +65,34 @@ def num_total_steps(self):
 # New optimizer configurations must be added here to be picked up by the config system.
 # Each configuration will be tried until a successful match is found.
 # The 'type' field determines which class to use because the string literal is distinct for each class.
-OptimizerConfig: TypeAlias = AdamConfig | AdamWConfig
+OptimizerConfig: TypeAlias = SGDConfig | AdamConfig | AdamWConfig
 
 
 class TrainConfig(BaseConfig):
     optimizer: OptimizerConfig = AdamConfig()
+    outer_optimizer: OptimizerConfig = SGDConfig()
     batch_size: int = 512
     lr_scheduler: LearningRateSchedulerConfig = LearningRateSchedulerConfig()
 
+    # make DiLoCo equal to DDP by default
+    outer_lr_scheduler: LearningRateSchedulerConfig = LearningRateSchedulerConfig(lr=1.0, end_lr=1.0,
+                                                                                  num_warmup_steps=0)
+
+
 class DilocoConfig(BaseConfig):
     outer_lr: float = 0.7
     inner_steps: int
     compression: Compression = Compression.NO
 
+
 class MemoryProfilerConfig(BaseConfig):
     freq: int = 10
     snapshot_dir: str
 
+
 AttnFnType: TypeAlias = Literal["flex", "math"]
+CclLibType: TypeAlias = Literal["nccl", "pccl"]
+
 
 class HardwareConfig(BaseConfig):
     micro_batch_size: int = 1
@@ -98,6 +115,8 @@ class HardwareConfig(BaseConfig):
 
     attn_fn: AttnFnType = "flex"
 
+    ccl_library: CclLibType = "pccl"
+
 
 class MonitorConfig(BaseConfig):
     log_flush_interval: int = 10
@@ -116,6 +135,11 @@ class CkptConfig(BaseConfig):
     resume: str | None = None
 
 
+class PcclConfig(BaseConfig):
+    ccoip_host: str
+    peer_group: int = 0
+
+
 class Config(BaseConfig):
     # Project/Run
     project: str = "zeroband"
@@ -138,6 +162,7 @@ class Config(BaseConfig):
     train: TrainConfig = TrainConfig()
     hardware: HardwareConfig
     monitor: MonitorConfig | None = None
+    pccl: PcclConfig | None = None
 
     ckpt: CkptConfig = CkptConfig()
 
@@ -149,4 +174,7 @@ def ckpt_diloco_step(self):
             assert self.ckpt.interval % self.diloco.inner_steps == 0, (
                 "ckpt interval must be a multiple of diloco inner steps as we only save at the end of an outer step"
             )
+
+        if self.hardware.ccl_library == 'pccl':
+            assert self.pccl is not None, "[pccl] must be configured if 'pccl' is used as ccl library"
         return self
diff --git a/src/zeroband/data.py b/src/zeroband/data.py
index d0cd2262..c8f40ca3 100644
--- a/src/zeroband/data.py
+++ b/src/zeroband/data.py
@@ -277,8 +277,8 @@ def load_state_dict(self, state_dict):
 
 def make_dataloader(
         tokenizer,
-        world_size: int,
-        rank: int,
+        mpi_world_size: int,
+        mpi_rank: int,
         batch_size: int,
         data_config: DataConfig,
 ) -> StatefulDataLoader:
@@ -286,7 +286,7 @@ def make_dataloader(
         train_dataset = FakeTokenizedDataset(data_config.seq_length, DEBUG_VOCAB_SIZE)
     else:
         train_dataset = load_all_datasets(
-            data_config=data_config, split="train", tokenizer=tokenizer, rank=rank, world_size=world_size
+            data_config=data_config, split="train", tokenizer=tokenizer, mpi_rank=mpi_rank, mpi_world_size=mpi_world_size
         )
 
     dataset = SequencePackingDataSet(train_dataset, data_config.seq_length, eos_token=tokenizer.eos_token_id)
@@ -373,19 +373,19 @@ def load_all_datasets(
         data_config: DataConfig,
         split: str,
         tokenizer: PreTrainedTokenizer,
-        rank: int,
-        world_size: int,
+        mpi_rank: int,
+        mpi_world_size: int,
 ) -> InterleaveDataset:
     """Load all datasets and interleave them"""
 
     if data_config.split_by_data_rank and (
             data_config.data_rank is not None and data_config.data_world_size is not None
     ):
-        split_rank = data_config.data_rank * world_size + rank
-        split_world_size = data_config.data_world_size * world_size
+        split_rank = data_config.data_rank * mpi_world_size + mpi_rank
+        split_world_size = data_config.data_world_size * mpi_world_size
     else:
-        split_rank = rank
-        split_world_size = world_size
+        split_rank = mpi_rank
+        split_world_size = mpi_world_size
 
     get_logger().info("Loading Train dataset(s)")
 
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 5f665cf5..a05aec07 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -1,29 +1,32 @@
 import os
 import time
+from dataclasses import asdict
 from logging import Logger
-from typing import TYPE_CHECKING, Optional, Iterator
+from typing import TYPE_CHECKING, Optional, Iterator, List, Dict
 
 import torch
 import torch.distributed as dist
+from pccl import SharedState, TensorInfo
 
 from torch.distributed import destroy_process_group
 from torch.distributed.tensor import DTensor
 
 import wandb
 
-from zeroband.checkpoint import TrainingProgress, load_checkpoint_fsdp_state, save_checkpoint_fsdp_state
+from zeroband.ccl import ccl_utils
+from zeroband.ccl.ccl_utils import MPIConfig
+from zeroband.checkpoint import TrainingProgress, load_checkpoint, save_checkpoint, CheckpointInfo
 from zeroband.config import Config
 from zeroband.data import make_dataloader
 from zeroband.lr_scheduler import compute_current_lr
 from zeroband.models.llama import make_model
 from zeroband.models.llama.model import create_block_mask_from_seqlens
-from zeroband.utils import optim_utils, sharding_utils, act_checkpointing, metrics_utils
+from zeroband.utils import optim_utils, sharding_utils, act_checkpointing, metrics_utils, torch_utils
 
 from zeroband.utils.memory_profiler import MemoryProfiler
 from zeroband.utils.mfu_tracker import FlopCounter, PrecisionMode, \
     get_flops_promised_torch
 from zeroband.utils.tokenizer_utils import make_tokenizer
-from zeroband.utils.world_info import WorldInfo, get_world_info
 from zeroband.utils.logger import get_logger
 from zeroband.utils.profiler import Profiler, ProfilerCollection
 
@@ -34,9 +37,10 @@
 PRIME_TRAIN_PROFILER_EXPORT_VIDEO_INTERVAL: int = int(os.getenv("PRIME_TRAIN_PROFILER_EXPORT_VIDEO_INTERVAL", "-1"))
 
 
-def calc_gradient_accumulation_steps(batch_size: int, micro_bs: int, world_info: WorldInfo) -> int:
-    assert batch_size % world_info.world_size == 0
-    batch_size = batch_size // world_info.world_size
+def calc_gradient_accumulation_steps(batch_size: int, micro_bs: int, mpi_config: Optional[MPIConfig]) -> int:
+    mpi_world_size = mpi_config.mpi_world_size if mpi_config is not None else 1
+    assert batch_size % mpi_world_size == 0
+    batch_size = batch_size // mpi_world_size
 
     assert batch_size % micro_bs == 0, str(
         f"The micro batch size ({micro_bs}) must divide the number of samples on each GPU ({batch_size})"
@@ -92,9 +96,9 @@ def perform_grad_accum_steps(
     return total_loss, current_lr
 
 
-def train(logger: Logger, config: Config, world_info: WorldInfo, device: torch.device):
+def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], device: torch.device):
     grad_accum_steps = calc_gradient_accumulation_steps(
-        config.train.batch_size, config.hardware.micro_batch_size, world_info
+        config.train.batch_size, config.hardware.micro_batch_size, mpi_config
     )
 
     setup_profiler = Profiler()
@@ -104,18 +108,19 @@ def train(logger: Logger, config: Config, world_info: WorldInfo, device: torch.d
 
     train_dataloader = make_dataloader(
         tokenizer=tokenizer,
-        world_size=world_info.world_size,
-        rank=world_info.rank,
+        mpi_world_size=mpi_config.mpi_world_size if mpi_config is not None else 1,
+        mpi_rank=mpi_config.mpi_rank if mpi_config is not None else 1,
         batch_size=config.hardware.micro_batch_size,
         data_config=config.data,
     )
     train_dataloader_iterator = iter(train_dataloader)
 
     with setup_profiler.session("::make_model"):
-        model, model_config = make_model(
-            config,
-            vocab_size=len(tokenizer),
-        )
+        with torch_utils.default_device('cuda'):
+            model, model_config = make_model(
+                config,
+                vocab_size=len(tokenizer),
+            )
         num_param_scalars = model.count_parameters()
         logger.info(f"Number of parameters: {num_param_scalars}")
 
@@ -125,40 +130,87 @@ def train(logger: Logger, config: Config, world_info: WorldInfo, device: torch.d
             act_checkpointing.enable_activation_checkpointing(model, num)
 
     with setup_profiler.session("sharding_utils::apply_sharding"):
-        sharding_utils.apply_sharding(config.hardware, model)
+        if mpi_config is not None:
+            sharding_utils.apply_sharding(config.hardware, model)
+        else:
+            logger.info("MPI config not set, skipping application of model sharding...")
 
     # Setup optimizers
     with setup_profiler.session("optim_utils::make_optimizer"):
-        inner_optimizer = optim_utils.make_optimizer(model, config.train.optimizer)
+        inner_optimizer = optim_utils.make_optimizer(list(model.parameters()), config.train.optimizer)
 
-        # TODO MIKE use pccl instead of elastic_device_mesh
+    # None if diloco is disabled
+    outer_optimizer: Optional[torch.optim.Optimizer] = None
 
-        if config.diloco:
-            raise NotImplementedError("Diloco is not implemented yet")
+    # empty if diloco is disabled
+    outer_parameters: Dict[str, torch.nn.Parameter] = dict()
+    outer_parameters_list: List[torch.nn.Parameter] = []
 
-    training_progress = TrainingProgress(total_tokens=0, outer_step=0, step=0)
+    # none if diloco is disabled
+    shared_state: Optional[SharedState] = None
 
-    if world_info.rank == 0 and config.wandb:
-        wandb.init(
-            project=config.project,
-            config={"config": config.model_dump(), "world_info": world_info.json()},
-        )
-
-    with setup_profiler.session("torch::compile"):
-        if config.hardware.torch_compile:
-            model = torch.compile(model) if not TYPE_CHECKING else model
+    training_progress = TrainingProgress(total_tokens=0, outer_step=0, step=0)
 
+    checkpoint_info: Optional[CheckpointInfo] = None
     if config.ckpt.resume is not None:
         with setup_profiler.session("::load_checkpoint_fsdp_state"):
             # all is inplace
-            load_checkpoint_fsdp_state(
+            checkpoint_info = load_checkpoint(
                 model=model,
                 optimizers=[inner_optimizer],
                 training_progress=training_progress,
                 dataloader=train_dataloader,
                 path_root=config.ckpt.path,
+                mpi_config=mpi_config
             )
 
+    if config.diloco:
+        # create outer parameters
+        for name, local_p in model.named_parameters():
+            outer_param = outer_parameters[name] = torch.nn.Parameter(local_p.detach().cpu())
+            outer_parameters_list.append(outer_param)
+
+        with setup_profiler.session("optim_utils::make_optimizer[diloco]"):
+            outer_optimizer = optim_utils.make_optimizer(outer_parameters_list, config.train.outer_optimizer)
+
+            # do a dummy step to initialize outer optimizer state
+            for op in outer_parameters_list:
+                op.grad = torch.zeros_like(op)
+            outer_optimizer.step()
+
+        # Build the shared state that includes:
+        #   - The inner parameters
+        #   - The outer optimizer state (e.g. momentum buffers)
+        shared_state_dict = {}
+        for name, param in model.named_parameters():
+            shared_state_dict[name] = param
+
+        # Outer optimizer momentum buffers in shared state
+        for name, outer_p in outer_parameters.items():
+            state = outer_optimizer.state[outer_p]
+            optim_utils.add_optimizer_state(shared_state_dict, name, state, type(outer_optimizer))
+
+        entries = [
+            TensorInfo.from_torch(tensor, name, allow_content_inequality=False)
+            for name, tensor in shared_state_dict.items()
+        ]
+        shared_state = SharedState(entries)
+        if checkpoint_info is not None:
+            shared_state.revision = checkpoint_info.num_performed_outer_steps
+        else:
+            shared_state.revision = 0
+
+    if (mpi_config is None or mpi_config.mpi_rank == 0) and config.wandb:
+        wandb.init(
+            project=config.project,
+            config={"config": config.model_dump(),
+                    "mpi_config": asdict(mpi_config) if mpi_config is not None else None},
+        )
+
+    with setup_profiler.session("torch::compile"):
+        if config.hardware.torch_compile:
+            model = torch.compile(model) if not TYPE_CHECKING else model
+
     memory_profiler: Optional[MemoryProfiler] = None
     if config.hardware.memory_profiler is not None:
         memory_profiler = MemoryProfiler(config.hardware.memory_profiler.freq,
@@ -166,6 +218,9 @@ def train(logger: Logger, config: Config, world_info: WorldInfo, device: torch.d
 
     num_inner_steps = config.diloco.inner_steps if config.diloco is not None else 1
 
+    # initialize ccl library
+    ccl_utils.init_ccl_library(config, mpi_config)
+
     if PRIME_SETUP_PROFILER_PRINT_TIMINGS:
         setup_profiler.print_report()
 
@@ -193,21 +248,23 @@ def train(logger: Logger, config: Config, world_info: WorldInfo, device: torch.d
                 loss_batch: torch.Tensor
                 inner_lr: float
                 loss_batch, inner_lr = perform_grad_accum_steps(config, train_profiler, flop_counter,
-                                                                    training_progress,
-                                                                    train_dataloader_iterator,
-                                                                    grad_accum_steps,
-                                                                    model,
-                                                                    inner_optimizer,
-                                                                    device)
+                                                                training_progress,
+                                                                train_dataloader_iterator,
+                                                                grad_accum_steps,
+                                                                model,
+                                                                inner_optimizer,
+                                                                device)
 
-            dist.all_reduce(tensor=loss_batch, op=dist.ReduceOp.AVG)
+            if mpi_config is not None:
+                dist.all_reduce(tensor=loss_batch, op=dist.ReduceOp.AVG)
 
             with train_profiler.session("torch::nn::utils::clip_grad_norm_"):
                 # compute pow, plus (assert clip is rare, no 3N)
                 flop_counter.track_backward_flops(2 * num_param_scalars)
 
-                grad_norm: DTensor = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)  # type: ignore
-                grad_norm = grad_norm.full_tensor()  # type: ignore
+                grad_norm: torch.nn.Tensor | DTensor = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)  # type: ignore
+                if isinstance(grad_norm, DTensor):
+                    grad_norm = grad_norm.full_tensor()  # type: ignore
 
             with train_profiler.session("inner_optimizer.step"):
                 flop_counter.track_optimizer_step(inner_optimizer, num_param_scalars)
@@ -282,10 +339,11 @@ def train(logger: Logger, config: Config, world_info: WorldInfo, device: torch.d
                 # metrics["num_peers"] = elastic_device_mesh.global_pg.size()
                 metrics["num_peers"] = 1
 
-            if world_info.rank == 0 and config.wandb:
+            if (mpi_config is None or mpi_config.mpi_rank == 0) and config.wandb:
                 wandb.log(metrics)
 
-            log = metrics_utils.build_metrics_string(metrics, whitelist_keys={'step', 'loss', 'mfu', 'tflops', 'tokens_per_second', 'tflops_max'})
+            log = metrics_utils.build_metrics_string(metrics, whitelist_keys={'step', 'loss', 'mfu', 'tflops',
+                                                                              'tokens_per_second', 'tflops_max'})
             logger.info(log)
 
             if memory_profiler is not None:
@@ -317,12 +375,17 @@ def train(logger: Logger, config: Config, world_info: WorldInfo, device: torch.d
                 and training_progress.step % config.ckpt.interval == 0
         ):
             # we only allow to checkpoint after a outer step. For non diloco training outer step = 1 anyway
-            save_checkpoint_fsdp_state(
+            save_checkpoint(
                 model=model,
                 optimizers=[inner_optimizer],
                 training_progress=training_progress,
                 dataloader=train_dataloader,
                 path_root=config.ckpt.path,
+                checkpoint_info=CheckpointInfo(
+                    num_performed_outer_steps=training_progress.outer_step,
+                    shared_state_revision=shared_state.revision if shared_state is not None else -1,
+                ),
+                mpi_config=mpi_config
             )
 
         if training_progress.step >= config.train.lr_scheduler.num_total_steps:
@@ -331,14 +394,15 @@ def train(logger: Logger, config: Config, world_info: WorldInfo, device: torch.d
             # Since ckpt strategy and all reduce is done at the outer loop level.
             break
 
-        if world_info.rank == 0:
+        if mpi_config is None or mpi_config.mpi_rank == 0:
             wandb.finish()
 
     if config.hardware.memory_profiler is not None:
         logger.debug(f"Max memory used: {torch.cuda.max_memory_allocated() / 1024 ** 2:.2f} MB")
 
     logger.info("Training finished, exiting ...")
-    destroy_process_group()
+    if mpi_config is not None:
+        destroy_process_group()
 
 
 def main():
@@ -348,15 +412,20 @@ def main():
     torch.set_float32_matmul_precision("high")
     torch.manual_seed(42)
 
+    mpi_config: Optional[MPIConfig] = ccl_utils.make_mpi_config(
+        mpi_rank=os.getenv("MPI_RANK"),
+        mpi_world_size=os.getenv("MPI_WORLD_SIZE")
+    )  # may return None
+
     config = Config(**parse_argv())  # type: ignore
-    world_info = get_world_info()
-    logger = get_logger(config)
+    logger = get_logger(config, mpi_config)
+
+    gpu_ordinal = int(os.getenv("GPU_ORDINAL", "0"))
 
-    # torch.set_default_device("cuda")
-    torch.cuda.set_device(world_info.local_rank)
+    torch.cuda.set_device(gpu_ordinal)
     device = torch.device(f'cuda:{torch.cuda.current_device()}')
 
-    train(logger, config, world_info, device)
+    train(logger, config, mpi_config, device)
 
 
 if __name__ == "__main__":
diff --git a/src/zeroband/utils/logger.py b/src/zeroband/utils/logger.py
index 91050bf0..47a97f61 100644
--- a/src/zeroband/utils/logger.py
+++ b/src/zeroband/utils/logger.py
@@ -1,7 +1,7 @@
 import logging
 
+from zeroband.ccl.ccl_utils import MPIConfig
 from zeroband.config import Config
-from zeroband.utils.world_info import get_world_info
 
 logger = None
 
@@ -24,22 +24,16 @@ def format(self, record):
         return formatter.format(record)
 
 
-def get_logger(config: Config | None = None, name: str | None = None) -> logging.Logger:
+def get_logger(config: Config | None = None, mpi_config: MPIConfig | None = None, name: str | None = None) -> logging.Logger:
     global logger  # Add this line to modify the global logger variable
     if logger is not None:
         return logger
 
-    try:
-        world_info = get_world_info()
-    except KeyError:
-        from zeroband.utils.world_info import WorldInfo
-
-        world_info = WorldInfo.__new__(WorldInfo)
-        world_info.local_rank = 0
     logger = logging.getLogger(name or __name__)
 
+    mpi_rank = mpi_config.mpi_rank if mpi_config is not None else 0
     log_level = config.log_level if config else "DEBUG"
-    if world_info.local_rank == 0:
+    if mpi_rank == 0:
         logger.setLevel(level=getattr(logging, log_level, logging.INFO))
     else:
         if (not config) or config.log_all_rank:
@@ -48,7 +42,7 @@ def get_logger(config: Config | None = None, name: str | None = None) -> logging
             logger.setLevel(level=logging.CRITICAL)  # Disable logging for non-zero ranks
 
     handler = logging.StreamHandler()
-    handler.setFormatter(CustomFormatter(world_info.local_rank))
+    handler.setFormatter(CustomFormatter(mpi_rank))
     logger.addHandler(handler)
     logger.propagate = False  # Prevent the log messages from being propagated to the root logger
 
diff --git a/src/zeroband/utils/memory_profiler.py b/src/zeroband/utils/memory_profiler.py
index e6a87b32..79d0614d 100644
--- a/src/zeroband/utils/memory_profiler.py
+++ b/src/zeroband/utils/memory_profiler.py
@@ -1,8 +1,11 @@
 import os
 import pickle
+from typing import Optional
+
 import torch
+
+from zeroband.ccl.ccl_utils import MPIConfig
 from zeroband.utils.logger import get_logger
-from zeroband.utils.world_info import get_world_info
 
 _MAX_ENTRIES = 10000
 
@@ -12,11 +15,11 @@ class MemoryProfiler:
     The output are pickles file that can be visualized here: https://pytorch.org/memory_viz
     """
 
-    def __init__(self, freq: int, snapshot_dir: str):
+    def __init__(self, freq: int, snapshot_dir: str, mpi_config: Optional[MPIConfig]):
         torch.cuda.memory._record_memory_history(max_entries=_MAX_ENTRIES)
         self.freq = freq
 
-        self.world_info = get_world_info()
+        self.mpi_rank = mpi_config.mpi_rank if mpi_config is not None else 0
         self.logger = get_logger()
         self.step_num = 0
 
@@ -29,15 +32,15 @@ def log_memory_summary(self, curr_snapshot_dir):
         allocated_memory = torch.cuda.memory_allocated()
 
         # Save the memory summary to a file
-        with open(f"{curr_snapshot_dir}/rank{self.world_info.rank}_memory_summary.txt", "w") as summary_file:
+        with open(f"{curr_snapshot_dir}/rank{self.mpi_rank}_memory_summary.txt", "w") as summary_file:
             summary_file.write(summary)
 
         # Save the allocated memory as a text log
-        with open(f"{curr_snapshot_dir}/rank{self.world_info.rank}_memory_allocated.txt", "w") as alloc_file:
+        with open(f"{curr_snapshot_dir}/rank{self.mpi_rank}_memory_allocated.txt", "w") as alloc_file:
             alloc_file.write(f"Allocated memory: {allocated_memory / 1024 ** 2:.2f} MB\n")
 
         # log this information using the logger
-        self.logger.info(f"Memory summary and allocation saved for rank {self.world_info.rank} at step {self.step_num}")
+        self.logger.info(f"Memory summary and allocation saved for rank {self.mpi_rank} at step {self.step_num}")
 
     def step(self):
         self.step_num += 1
@@ -51,7 +54,7 @@ def step(self):
             os.makedirs(curr_snapshot_dir, exist_ok=True)
 
         # Save memory snapshot
-        with open(f"{curr_snapshot_dir}/rank{self.world_info.rank}_memory_snapshot.pickle", "wb") as output:
+        with open(f"{curr_snapshot_dir}/rank{self.mpi_rank}_memory_snapshot.pickle", "wb") as output:
             pickle.dump(torch.cuda.memory._snapshot(), output)
 
         # Log memory summary and allocated memory
diff --git a/src/zeroband/utils/optim_utils.py b/src/zeroband/utils/optim_utils.py
index b2872692..8c81e334 100644
--- a/src/zeroband/utils/optim_utils.py
+++ b/src/zeroband/utils/optim_utils.py
@@ -1,26 +1,37 @@
+from typing import List, Dict, TypeVar, Type, Tuple, Optional
+
 import torch
 from zeroband.config import OptimizerConfig
 
 
-def make_optimizer(model: torch.nn.Module, config: OptimizerConfig) -> torch.optim.Optimizer:
+def make_optimizer(parameters: List[torch.nn.Parameter], config: OptimizerConfig) -> torch.optim.Optimizer:
     """
-    Creates an optimizer instance for the parameters of the supplied model according to the given optimizer configuration
-    :param model the model to optimize
+    Creates an optimizer instance for the supplied parameters according to the given optimizer configuration
+    :param parameters the list of parameters to optimize
     :param config the optimizer config
     """
-    if config.type == 'adam':
+    if config.type == 'sgd':
+        return torch.optim.SGD(
+            parameters,
+            lr=0.0,  # lr will be set later
+            momentum=config.momentum,
+            nesterov=config.nesterov
+        )
+    elif config.type == 'adam':
         return torch.optim.Adam(
-            model.parameters(),
+            parameters,
             lr=0.0,  # lr will be set later
             betas=(config.betas1, config.betas2)
         )
     elif config.type == 'adamw':
         return torch.optim.AdamW(
-            model.parameters(),
+            parameters,
             lr=0.0,  # lr will be set later
             weight_decay=config.weight_decay,
             betas=(config.betas1, config.betas2),
         )
+    else:
+        raise ValueError(f"Illegal optimizer type: {config.dtype}")
 
 
 def set_optimizer_lr(optimizer: torch.optim.Optimizer, lr: float):
@@ -31,3 +42,46 @@ def set_optimizer_lr(optimizer: torch.optim.Optimizer, lr: float):
     """
     for param_group in optimizer.param_groups:
         param_group['lr'] = lr
+
+
+OptimT = TypeVar("OptimT", bound=torch.optim.Optimizer)
+
+
+def add_optimizer_state(shared_state_dict: Dict[str, torch.Tensor],
+                        param_name: str,
+                        param_optim_state: Dict[str, torch.Tensor],
+                        optimizer_type: Type[OptimT]):
+    """
+    Adds the relevant optimizer state to the shared state dict given the parameter specific optimizer state and
+    the type of optimizer used.
+    :param shared_state_dict the shared state dict to populate
+    :param param_name the name of the parameter
+    :param param_optim_state the parameter-specific optimizer state dict
+    :param optimizer_type the type of optimizer used.
+    """
+
+    def _validate_exists(to_check: List[Tuple[str, Optional[torch.Tensor]]]):
+        for key, value in to_check:
+            if value is None:
+                raise RuntimeError(
+                    f"Cannot find '{key}' key in parameter-specific optimizer state for optimizer type f'{optimizer_type.__name__}'. Is Optimizer state initialized?")
+
+    if optimizer_type == torch.optim.SGD:
+        momentum_buf = param_optim_state.get("momentum_buffer", None)
+        if momentum_buf is not None:
+            shared_state_dict[f"{param_name}_momentum_buffer"] = momentum_buf
+
+    elif optimizer_type == torch.optim.Adam or optimizer_type == torch.optim.AdamW:
+        exp_avg = param_optim_state.get('exp_avg', None)
+        exp_avg_sq = param_optim_state.get('exp_avg_sq', None)
+        step_tensor = param_optim_state.get('step', None)
+        _validate_exists([
+            ('exp_avg', exp_avg),
+            ('exp_avg_sq', exp_avg_sq),
+            ('step_tensor', step_tensor)
+        ])
+
+        # Add optimizer state tensors with associated names
+        shared_state_dict[f"{param_name}_m1"] = exp_avg
+        shared_state_dict[f"{param_name}_m2"] = exp_avg_sq
+        shared_state_dict[f"{param_name}_step"] = step_tensor
diff --git a/src/zeroband/utils/torch_utils.py b/src/zeroband/utils/torch_utils.py
new file mode 100644
index 00000000..4f6c4cf1
--- /dev/null
+++ b/src/zeroband/utils/torch_utils.py
@@ -0,0 +1,13 @@
+import torch
+import contextlib
+
+
+@contextlib.contextmanager
+def default_device(device: str = "cuda"):
+    old_device = torch.get_default_device()
+    torch.set_default_device(device)
+    try:
+        yield
+    finally:
+        # Restore the old default device
+        torch.set_default_device(old_device)
diff --git a/src/zeroband/utils/world_info.py b/src/zeroband/utils/world_info.py
deleted file mode 100644
index 8027e848..00000000
--- a/src/zeroband/utils/world_info.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import os
-
-world_info = None
-
-
-class WorldInfo:
-    """This class parse env var about torch world into class variables."""
-
-    world_size: int
-    rank: int
-    local_rank: int
-    local_world_size: int
-
-    def __init__(self):
-        self.world_size = int(os.environ["WORLD_SIZE"])
-        self.rank = int(os.environ["RANK"])
-        self.local_rank = int(os.environ["LOCAL_RANK"])
-        self.local_world_size = int(os.environ["LOCAL_WORLD_SIZE"])
-        self.nnodes = self.world_size // self.local_world_size
-
-        self.global_unique_id = os.environ.get("GLOBAL_UNIQUE_ID", None)
-        self.global_addr = os.environ.get("GLOBAL_ADDR", None)
-        self.global_port = int(os.environ.get("GLOBAL_PORT")) if "GLOBAL_PORT" in os.environ else None
-        self.global_world_size = int(os.environ.get("GLOBAL_WORLD_SIZE", 1))
-        self.global_rank = int(os.environ.get("GLOBAL_RANK", 0))
-
-    def __repr__(self):
-        return f"WorldInfo(world_size={self.world_size}, rank={self.rank}, local_rank={self.local_rank}, local_world_size={self.local_world_size}, nnodes={self.nnodes}, global_unique_id={self.global_unique_id}, global_addr={self.global_addr}, global_port={self.global_port}, global_world_size={self.global_world_size}, global_rank={self.global_rank})"
-
-    @property
-    def diloco_rank(self):
-        return self.global_rank
-
-    def json(self) -> dict[str, int | str]:
-        return {
-            "world_size": self.world_size,
-            "rank": self.rank,
-            "local_rank": self.local_rank,
-            "local_world_size": self.local_world_size,
-            "nnodes": self.nnodes,
-            "global_unique_id": self.global_unique_id,
-            "global_addr": self.global_addr,
-            "global_port": self.global_port,
-            "global_world_size": self.global_world_size,
-            "global_rank": self.global_rank,
-        }
-
-
-def get_world_info() -> WorldInfo:
-    """
-    Return a WorldInfo singleton.
-    """
-    global world_info
-    if world_info is None:
-        world_info = WorldInfo()
-    return world_info
diff --git a/uv.lock b/uv.lock
index a47e6b87..0f9f4132 100644
--- a/uv.lock
+++ b/uv.lock
@@ -913,6 +913,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374", size = 5892 },
 ]
 
+[[package]]
+name = "ipaddress"
+version = "1.0.23"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b9/9a/3e9da40ea28b8210dd6504d3fe9fe7e013b62bf45902b458d1cdc3c34ed9/ipaddress-1.0.23.tar.gz", hash = "sha256:b7f8e0369580bb4a24d5ba1d7cc29660a4a6987763faf1d8a8046830e020e7e2", size = 32958 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c2/f8/49697181b1651d8347d24c095ce46c7346c37335ddc7d255833e7cde674d/ipaddress-1.0.23-py2.py3-none-any.whl", hash = "sha256:6e0f4a39e66cb5bb9a137b00276a2eff74f93b71dcbdad6f10ff7df9d3557fcc", size = 18159 },
+]
+
 [[package]]
 name = "jinja2"
 version = "3.1.5"
@@ -1747,6 +1756,16 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/50/14/c5a0e1a947909810fc4c043b84cac472b70e438148d34f5393be1bac663f/pathvalidate-3.2.3-py3-none-any.whl", hash = "sha256:5eaf0562e345d4b6d0c0239d0f690c3bd84d2a9a3c4c73b99ea667401b27bee1", size = 24130 },
 ]
 
+[[package]]
+name = "pccl"
+version = "0.1.0"
+source = { git = "https://github.com/PrimeIntellect-ai/pccl.git?subdirectory=python%2Fframework&rev=main#19acdc799ccbb805c54cbea56fbdace73f6d9b87" }
+dependencies = [
+    { name = "cffi" },
+    { name = "ipaddress" },
+    { name = "pycparser" },
+]
+
 [[package]]
 name = "peft"
 version = "0.14.0"
@@ -3196,6 +3215,7 @@ dependencies = [
     { name = "imageio", extra = ["ffmpeg"] },
     { name = "ninja" },
     { name = "numpy" },
+    { name = "pccl" },
     { name = "psutil" },
     { name = "pyarrow" },
     { name = "pydantic-config" },
@@ -3229,6 +3249,7 @@ requires-dist = [
     { name = "lm-eval", marker = "extra == 'all'" },
     { name = "ninja" },
     { name = "numpy" },
+    { name = "pccl", git = "https://github.com/PrimeIntellect-ai/pccl.git?subdirectory=python%2Fframework&rev=main" },
     { name = "psutil" },
     { name = "pyarrow" },
     { name = "pydantic-config", git = "https://github.com/samsja/pydantic_config.git?rev=b7becc3" },

From f5263f8a96540fbc0dfd51e1c29d32c7b852a321 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Sat, 5 Apr 2025 07:13:21 +0000
Subject: [PATCH 03/47] working fsdp with pccl accept loop

---
 configs/debug/diloco.toml       |    7 +-
 configs/debug/normal.toml       |    6 +-
 pyproject.toml                  |    1 +
 src/zeroband/ccl/ccl_utils.py   |   17 -
 src/zeroband/ccl/nccl_ccllib.py |   16 -
 src/zeroband/ccl/pccl_ccllib.py |   17 -
 src/zeroband/config.py          |    9 +-
 src/zeroband/train.py           |   76 +-
 src/zeroband/utils/logger.py    |    2 +-
 uv.lock                         | 1738 ++++++++++++++++---------------
 10 files changed, 978 insertions(+), 911 deletions(-)
 delete mode 100644 src/zeroband/ccl/nccl_ccllib.py
 delete mode 100644 src/zeroband/ccl/pccl_ccllib.py

diff --git a/configs/debug/diloco.toml b/configs/debug/diloco.toml
index 8ea5256e..10aab73d 100644
--- a/configs/debug/diloco.toml
+++ b/configs/debug/diloco.toml
@@ -2,13 +2,13 @@ model_name = "debugmodel"
 model_type = "llama2"
 
 wandb = false
+log_all_ranks = true
 
 [hardware]
 micro_batch_size = 8
-ccl_library = 'pccl'
 
 [train]
-batch_size = 16
+batch_size = 32
 
 [train.lr_scheduler]
 num_warmup_steps = 10
@@ -21,5 +21,4 @@ fake = true
 inner_steps = 5
 
 [pccl]
-ccoip_host = "127.0.0.1:48148"
-peer_group = 0
\ No newline at end of file
+ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/configs/debug/normal.toml b/configs/debug/normal.toml
index 641283cd..358976bf 100644
--- a/configs/debug/normal.toml
+++ b/configs/debug/normal.toml
@@ -5,10 +5,9 @@ wandb = false
 
 [hardware]
 micro_batch_size = 8
-ccl_library = 'pccl'
 
 [train]
-batch_size = 16
+batch_size = 32
 
 [train.lr_scheduler]
 num_warmup_steps = 10
@@ -18,5 +17,4 @@ num_decay_steps = 10
 fake = true
 
 [pccl]
-ccoip_host = "127.0.0.1:48148"
-peer_group = 0
\ No newline at end of file
+ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 97a7dd12..b8558255 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,6 +11,7 @@ dependencies = [
     "transformers>=4.44.2",
     "datasets>=3.0.0",
     "pydantic_config @ git+https://github.com/samsja/pydantic_config.git@b7becc3",
+    "tomili",
     "torchdata>=0.8.0",
     "fsspec[gcs]>=2024.3.1",
     "ninja",
diff --git a/src/zeroband/ccl/ccl_utils.py b/src/zeroband/ccl/ccl_utils.py
index 4f0a07be..b173ff21 100644
--- a/src/zeroband/ccl/ccl_utils.py
+++ b/src/zeroband/ccl/ccl_utils.py
@@ -1,12 +1,6 @@
 from dataclasses import dataclass
 from typing import Optional
 
-from mpmath.libmp import mpi_mid
-
-from zeroband.ccl.nccl_ccllib import init_nccl
-from zeroband.ccl.pccl_ccllib import init_pccl
-from zeroband.config import Config
-
 
 @dataclass
 class MPIConfig:
@@ -20,17 +14,6 @@ class MPIConfig:
     mpi_rank: int
     mpi_world_size: int
 
-
-def init_ccl_library(config: Config, mpi_config: Optional[MPIConfig]):
-    ccl_library = config.hardware.ccl_library
-    if ccl_library == "nccl":
-        if mpi_config is None:
-            raise RuntimeError("Cannot initialize with ccl library 'nccl' when mpi config is not supplied!")
-        init_nccl(mpi_config.mpi_rank, mpi_config.mpi_world_size)
-    elif ccl_library == "pccl":
-        init_pccl(config)
-
-
 def make_mpi_config(mpi_rank: Optional[str], mpi_world_size: Optional[str]) -> Optional[MPIConfig]:
     """
     Creates an MPI configuration from the supplied env-var strings
diff --git a/src/zeroband/ccl/nccl_ccllib.py b/src/zeroband/ccl/nccl_ccllib.py
deleted file mode 100644
index 5a550b20..00000000
--- a/src/zeroband/ccl/nccl_ccllib.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import torch.distributed as dist
-
-
-def init_nccl(mpi_rank: int, mpi_world_size: int):
-    dist.init_process_group(
-        backend="nccl",
-        init_method="tcp://127.0.0.1:23456",
-        rank=mpi_rank,
-        world_size=mpi_world_size
-    )
-
-    if dist.is_available() and dist.is_initialized():
-        raise RuntimeError("torch.distributed is not available or was not initialized!")
-
-    if dist.is_nccl_available():
-        raise RuntimeError("torch.distributed reports NCCL is not available!")
diff --git a/src/zeroband/ccl/pccl_ccllib.py b/src/zeroband/ccl/pccl_ccllib.py
deleted file mode 100644
index e6330630..00000000
--- a/src/zeroband/ccl/pccl_ccllib.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from typing import Optional
-
-from pccl import Communicator
-
-from zeroband.config import Config
-
-communicator: Optional[Communicator] = None
-
-
-def init_pccl(config: Config):
-    if config.diloco is None:
-        raise RuntimeError("PCCL was configured as CCL-library, but not [pccl] was not configured!")
-
-    global communicator
-    communicator = Communicator(config.pccl.ccoip_host, 0)
-    communicator.connect(n_attempts=15)
-    print("Connected to master via PCCL")
diff --git a/src/zeroband/config.py b/src/zeroband/config.py
index 2a0c35d7..7664278c 100644
--- a/src/zeroband/config.py
+++ b/src/zeroband/config.py
@@ -115,8 +115,6 @@ class HardwareConfig(BaseConfig):
 
     attn_fn: AttnFnType = "flex"
 
-    ccl_library: CclLibType = "pccl"
-
 
 class MonitorConfig(BaseConfig):
     log_flush_interval: int = 10
@@ -137,7 +135,6 @@ class CkptConfig(BaseConfig):
 
 class PcclConfig(BaseConfig):
     ccoip_host: str
-    peer_group: int = 0
 
 
 class Config(BaseConfig):
@@ -154,7 +151,7 @@ class Config(BaseConfig):
     metric_logger_type: Literal["wandb", "dummy"] = "wandb"
     wandb_resume: bool = False
     log_level: Literal["NOTSET", "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = "INFO"
-    log_all_rank: bool = False
+    log_all_ranks: bool = False
 
     # sub config
     diloco: DilocoConfig | None = None
@@ -174,7 +171,5 @@ def ckpt_diloco_step(self):
             assert self.ckpt.interval % self.diloco.inner_steps == 0, (
                 "ckpt interval must be a multiple of diloco inner steps as we only save at the end of an outer step"
             )
-
-        if self.hardware.ccl_library == 'pccl':
-            assert self.pccl is not None, "[pccl] must be configured if 'pccl' is used as ccl library"
+        assert self.pccl is not None, "[pccl] must be configured!"
         return self
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index a05aec07..47f2e0ab 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -6,7 +6,7 @@
 
 import torch
 import torch.distributed as dist
-from pccl import SharedState, TensorInfo
+from pccl import SharedState, TensorInfo, Attribute, Communicator, PCCLError
 
 from torch.distributed import destroy_process_group
 from torch.distributed.tensor import DTensor
@@ -182,6 +182,9 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
         #   - The inner parameters
         #   - The outer optimizer state (e.g. momentum buffers)
         shared_state_dict = {}
+
+        name: str
+        param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
         for name, param in model.named_parameters():
             shared_state_dict[name] = param
 
@@ -191,8 +194,12 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             optim_utils.add_optimizer_state(shared_state_dict, name, state, type(outer_optimizer))
 
         entries = [
-            TensorInfo.from_torch(tensor, name, allow_content_inequality=False)
-            for name, tensor in shared_state_dict.items()
+            TensorInfo.from_torch(
+                param.data.to_local() if isinstance(param.data, DTensor) else param.data,
+                name,
+                allow_content_inequality=False
+            )
+            for name, param in shared_state_dict.items()
         ]
         shared_state = SharedState(entries)
         if checkpoint_info is not None:
@@ -214,18 +221,28 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
     memory_profiler: Optional[MemoryProfiler] = None
     if config.hardware.memory_profiler is not None:
         memory_profiler = MemoryProfiler(config.hardware.memory_profiler.freq,
-                                         config.hardware.memory_profiler.snapshot_dir)
+                                         config.hardware.memory_profiler.snapshot_dir,
+                                         mpi_config)
 
     num_inner_steps = config.diloco.inner_steps if config.diloco is not None else 1
 
-    # initialize ccl library
-    ccl_utils.init_ccl_library(config, mpi_config)
+    # initialize PCCL
+    communicator = Communicator(config.pccl.ccoip_host, mpi_config.mpi_rank if mpi_config is not None else 0)
+    communicator.connect(n_attempts=15)
+    print("Connected to master via PCCL")
 
     if PRIME_SETUP_PROFILER_PRINT_TIMINGS:
         setup_profiler.print_report()
 
     train_profiler_collection = ProfilerCollection()
 
+    # -------------------------------------------------------------------------
+    # ! Critical PCCL-related training-loop-state tracking variables !
+    local_iter_num = 0
+    num_syncs = 0
+    local_world_size: int = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
+    # -------------------------------------------------------------------------
+
     timing_events = []
     while True:
         train_profiler = Profiler()
@@ -234,6 +251,35 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             # if we don't use diloco we don't print the outer step logs
             logger.info(f"outer_step step: {training_progress.outer_step}")
 
+        global_world_size = communicator.get_attribute(Attribute.GLOBAL_WORLD_SIZE)
+
+        # 1) Possibly update topology / wait for enough peers
+        mpi_ranks_pending = False
+        with train_profiler.session("pccl::update_topology"):
+            if mpi_config is not None:
+                mpi_ranks_pending = global_world_size < mpi_config.mpi_world_size
+
+            if local_iter_num > 1 or mpi_ranks_pending or local_world_size == 1:
+                while True:
+                    try:
+                        communicator.update_topology()
+                        break
+                    except PCCLError as e:
+                        print(f"update_topology() failed => {e}, retrying...")
+                        time.sleep(1)
+
+        if mpi_ranks_pending:
+            print("Not all MPI ranks have joined...")
+            time.sleep(1)
+            continue
+
+        # TODO: Make minimum num pccl peers configurable
+        # local_world_size = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
+        # if local_world_size < 2:
+        #    print("Waiting for more workers to join...")
+        #    time.sleep(1)
+        #    continue
+
         for _inner_step in range(num_inner_steps):
             train_profiler.start_session("inner_step")
 
@@ -262,7 +308,8 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
                 # compute pow, plus (assert clip is rare, no 3N)
                 flop_counter.track_backward_flops(2 * num_param_scalars)
 
-                grad_norm: torch.nn.Tensor | DTensor = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)  # type: ignore
+                grad_norm: torch.Tensor | DTensor = torch.nn.utils.clip_grad_norm_(model.parameters(),
+                                                                                   1.0)  # type: ignore
                 if isinstance(grad_norm, DTensor):
                     grad_norm = grad_norm.full_tensor()  # type: ignore
 
@@ -335,9 +382,7 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             })
 
             if config.diloco is not None:
-                # TODO MIKE use pccl instead of elastic_device_mesh
-                # metrics["num_peers"] = elastic_device_mesh.global_pg.size()
-                metrics["num_peers"] = 1
+                metrics["num_peers"] = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
 
             if (mpi_config is None or mpi_config.mpi_rank == 0) and config.wandb:
                 wandb.log(metrics)
@@ -413,17 +458,22 @@ def main():
     torch.manual_seed(42)
 
     mpi_config: Optional[MPIConfig] = ccl_utils.make_mpi_config(
-        mpi_rank=os.getenv("MPI_RANK"),
-        mpi_world_size=os.getenv("MPI_WORLD_SIZE")
+        mpi_rank=os.getenv("RANK"),
+        mpi_world_size=os.getenv("WORLD_SIZE")
     )  # may return None
 
     config = Config(**parse_argv())  # type: ignore
     logger = get_logger(config, mpi_config)
 
-    gpu_ordinal = int(os.getenv("GPU_ORDINAL", "0"))
+    gpu_ordinal = int(os.getenv("GPU_ORDINAL", os.getenv("RANK", "0")))
+
+    num_total_gpus = torch.cuda.device_count()
+    logger.info(f"Using gpu ordinal:{gpu_ordinal}/num_total:{num_total_gpus}")
+
 
     torch.cuda.set_device(gpu_ordinal)
     device = torch.device(f'cuda:{torch.cuda.current_device()}')
+    logger.info(f"Using device: {torch.cuda.get_device_name(device)}")
 
     train(logger, config, mpi_config, device)
 
diff --git a/src/zeroband/utils/logger.py b/src/zeroband/utils/logger.py
index 47a97f61..cb3e6630 100644
--- a/src/zeroband/utils/logger.py
+++ b/src/zeroband/utils/logger.py
@@ -36,7 +36,7 @@ def get_logger(config: Config | None = None, mpi_config: MPIConfig | None = None
     if mpi_rank == 0:
         logger.setLevel(level=getattr(logging, log_level, logging.INFO))
     else:
-        if (not config) or config.log_all_rank:
+        if (not config) or config.log_all_ranks:
             logger.setLevel(level=getattr(logging, log_level, logging.INFO))
         else:
             logger.setLevel(level=logging.CRITICAL)  # Disable logging for non-zero ranks
diff --git a/uv.lock b/uv.lock
index 0f9f4132..ea815494 100644
--- a/uv.lock
+++ b/uv.lock
@@ -14,16 +14,16 @@ resolution-markers = [
 
 [[package]]
 name = "absl-py"
-version = "2.1.0"
+version = "2.2.2"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/7a/8f/fc001b92ecc467cc32ab38398bd0bfb45df46e7523bf33c2ad22a505f06e/absl-py-2.1.0.tar.gz", hash = "sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff", size = 118055 }
+sdist = { url = "https://files.pythonhosted.org/packages/b5/f0/e6342091061ed3a46aadc116b13edd7bb5249c3ab1b3ef07f24b0c248fc3/absl_py-2.2.2.tar.gz", hash = "sha256:bf25b2c2eed013ca456918c453d687eab4e8309fba81ee2f4c1a6aa2494175eb", size = 119982 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a2/ad/e0d3c824784ff121c03cc031f944bc7e139a8f1870ffd2845cc2dd76f6c4/absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308", size = 133706 },
+    { url = "https://files.pythonhosted.org/packages/f6/d4/349f7f4bd5ea92dab34f5bb0fe31775ef6c311427a14d5a5b31ecb442341/absl_py-2.2.2-py3-none-any.whl", hash = "sha256:e5797bc6abe45f64fd95dc06394ca3f2bedf3b5d895e9da691c9ee3397d70092", size = 135565 },
 ]
 
 [[package]]
 name = "accelerate"
-version = "1.3.0"
+version = "1.6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
@@ -34,23 +34,23 @@ dependencies = [
     { name = "safetensors" },
     { name = "torch" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/85/15/0fab0260ab4069e5224e637d2e400538bb27b0dfc36f17daf68db9770d78/accelerate-1.3.0.tar.gz", hash = "sha256:518631c0adb80bd3d42fb29e7e2dc2256bcd7c786b0ba9119bbaa08611b36d9c", size = 342758 }
+sdist = { url = "https://files.pythonhosted.org/packages/8a/6e/c29a1dcde7db07f47870ed63e5124086b11874ad52ccd533dc1ca2c799da/accelerate-1.6.0.tar.gz", hash = "sha256:28c1ef1846e690944f98b68dc7b8bb6c51d032d45e85dcbb3adb0c8b99dffb32", size = 363804 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/73/de/64508cb91af013aaba214752309c0967568a4219d50a4ea30e822af3c976/accelerate-1.3.0-py3-none-any.whl", hash = "sha256:5788d9e6a7a9f80fed665cf09681c4dddd9dc056bea656db4140ffc285ce423e", size = 336647 },
+    { url = "https://files.pythonhosted.org/packages/63/b1/8198e3cdd11a426b1df2912e3381018c4a4a55368f6d0857ba3ca418ef93/accelerate-1.6.0-py3-none-any.whl", hash = "sha256:1aee717d3d3735ad6d09710a7c26990ee4652b79b4e93df46551551b5227c2aa", size = 354748 },
 ]
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.4"
+version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/7f/55/e4373e888fdacb15563ef6fa9fa8c8252476ea071e96fb46defac9f18bf2/aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745", size = 21977 }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
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 ]
 
 [[package]]

From 1cd3764fe432fbd49134e861dda2a7423736175b Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Sat, 5 Apr 2025 07:49:08 +0000
Subject: [PATCH 04/47] working sync DiLoCo

---
 src/zeroband/ccl/pccl_utils.py | 35 +++++++++++++++++
 src/zeroband/train.py          | 68 ++++++++++++++++++++++++++++++++--
 2 files changed, 99 insertions(+), 4 deletions(-)
 create mode 100644 src/zeroband/ccl/pccl_utils.py

diff --git a/src/zeroband/ccl/pccl_utils.py b/src/zeroband/ccl/pccl_utils.py
new file mode 100644
index 00000000..b822398f
--- /dev/null
+++ b/src/zeroband/ccl/pccl_utils.py
@@ -0,0 +1,35 @@
+from pccl import Communicator, torch, ReduceOp, ReduceOpDescriptor, ReduceDescriptor, ReduceOperandDescriptor, \
+    DistributionHint, DataType, QuantizationAlgorithm, PCCLError, QuantizationOptions
+
+
+def all_reduce_multiple_with_retry(communicator: Communicator,
+                                   tensors: list[torch.Tensor],
+                                   op: ReduceOp,
+                                   max_in_flight: int = 16):
+    descriptors = []
+    tag = 0
+    for tensor in tensors:
+        reduce_op_descriptor = ReduceOpDescriptor.from_torch(
+            send=tensor,
+            recv=tensor,
+            reduce_descriptor=ReduceDescriptor(
+                count=tensor.numel(),
+                op=op,
+                tag=tag,
+                operand_descriptor=ReduceOperandDescriptor(
+                    datatype=DataType.FLOAT,
+                    distribution_hint=DistributionHint.NORMAL
+                ),
+                quantization_options=QuantizationOptions(
+                    quantized_datatype=DataType.FLOAT,
+                    algorithm=QuantizationAlgorithm.NONE
+                )
+            )
+        )
+        descriptors.append(reduce_op_descriptor)
+        tag += 1
+    try:
+        info = communicator.all_reduce_multiple_with_retry(descriptors, max_in_flight=max_in_flight)
+        return True, info.tx_bytes, info.rx_bytes
+    except PCCLError:
+        return False, 0, 0
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 47f2e0ab..c98be554 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -6,14 +6,14 @@
 
 import torch
 import torch.distributed as dist
-from pccl import SharedState, TensorInfo, Attribute, Communicator, PCCLError
+from pccl import SharedState, TensorInfo, Attribute, Communicator, PCCLError, ReduceOp
 
 from torch.distributed import destroy_process_group
 from torch.distributed.tensor import DTensor
 
 import wandb
 
-from zeroband.ccl import ccl_utils
+from zeroband.ccl import ccl_utils, pccl_utils
 from zeroband.ccl.ccl_utils import MPIConfig
 from zeroband.checkpoint import TrainingProgress, load_checkpoint, save_checkpoint, CheckpointInfo
 from zeroband.config import Config
@@ -167,6 +167,8 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
     if config.diloco:
         # create outer parameters
         for name, local_p in model.named_parameters():
+            if isinstance(local_p, DTensor):
+                local_p = local_p.to_local()
             outer_param = outer_parameters[name] = torch.nn.Parameter(local_p.detach().cpu())
             outer_parameters_list.append(outer_param)
 
@@ -280,6 +282,28 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
         #    time.sleep(1)
         #    continue
 
+        # 3) Sync shared state => ensures we have the same aggregator (outer) parameters
+        with train_profiler.session("pccl::sync_shared_state"):
+            sync_info = communicator.sync_shared_state(shared_state)
+            shared_state.revision += 1
+            print(f"sync_info tx_bytes: {sync_info.tx_bytes}, rx_bytes: {sync_info.rx_bytes}")
+            num_syncs += 1
+            if num_syncs > 1:
+                assert sync_info.rx_bytes == 0, "Shared state drifted unexpectedly in peers!"
+
+            # initialize inner state on first sync
+            if num_syncs == 1:
+                print("Initializing inner state...")
+                with torch.no_grad():
+                    outer_param: torch.nn.Parameter  # [ torch.Tensor ]
+                    inner_param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
+                    for outer_param, inner_param in zip(outer_parameters_list, model.parameters()):
+                        # noinspection PyTypeChecker
+                        inner_tensor: torch.Tensor | DTensor = inner_param.data
+                        if isinstance(inner_tensor, DTensor):
+                            inner_tensor = inner_tensor.to_local()
+                        inner_tensor.copy_(outer_param.data)
+
         for _inner_step in range(num_inner_steps):
             train_profiler.start_session("inner_step")
 
@@ -409,8 +433,44 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
                 train_profiler_collection.render_as_video(f'profiler_video_{training_progress.step}.mp4', fps=10)
 
         if config.diloco is not None:
-            ...
-            # diloco.step(model=model, flag=str(training_progress.outer_step))
+            with train_profiler.session("outer_step"):
+                outer_grads = []
+                param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
+                outer_p: torch.nn.Parameter  # [ torch.Tensor ]
+                for param, outer_p in zip(model.parameters(), outer_parameters_list):
+                    outer_p_data: torch.Tensor = outer_p.data
+                    param_data: torch.Tensor | DTensor = param.data
+                    if isinstance(param_data, DTensor):
+                        param_data = param_data.to_local()
+                    outer_p.grad = outer_p_data - param_data.to('cpu')
+                    outer_grads.append(outer_p.grad)
+
+                with train_profiler.session("all_reduce_multiple_with_retry"):
+                    start_time = time.time()
+
+                    all_reduce_success = pccl_utils.all_reduce_multiple_with_retry(
+                        communicator,
+                        outer_grads,
+                        ReduceOp.AVG
+                    )
+
+                    end_time = time.time()
+                    print(f"All-Reduce took {end_time - start_time} seconds")
+                    if not all_reduce_success:
+                        print("All peers left except me... continuing alone.")
+
+                outer_optimizer.step()
+                outer_optimizer.zero_grad()
+
+                # Copy aggregator result into local model
+                with torch.no_grad():
+                    param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
+                    outer_p: torch.nn.Parameter  # [ torch.Tensor ]
+                    for param, outer_p in zip(model.parameters(), outer_parameters_list):
+                        param_tensor = param.data
+                        if isinstance(param_tensor, DTensor):
+                            param_tensor = param_tensor.to_local()
+                        param_tensor.copy_(outer_p, non_blocking=True)
 
         training_progress.outer_step += 1
 

From 43be48c2b4a72761ff13cfb9ff83d9f3d53f3ac7 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Sat, 5 Apr 2025 20:12:32 +0000
Subject: [PATCH 05/47] working async DiLoCo

---
 configs/debug/diloco.toml         |   5 +-
 src/zeroband/config.py            |   1 +
 src/zeroband/train.py             | 291 +++++++++++++++++++++---------
 src/zeroband/utils/mfu_tracker.py |   2 +-
 4 files changed, 212 insertions(+), 87 deletions(-)

diff --git a/configs/debug/diloco.toml b/configs/debug/diloco.toml
index 10aab73d..923860b0 100644
--- a/configs/debug/diloco.toml
+++ b/configs/debug/diloco.toml
@@ -12,13 +12,14 @@ batch_size = 32
 
 [train.lr_scheduler]
 num_warmup_steps = 10
-num_decay_steps = 10
+num_decay_steps = 1000
 
 [data]
 fake = true
 
 [diloco]
-inner_steps = 5
+inner_steps = 16
+delayed_update = true
 
 [pccl]
 ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/src/zeroband/config.py b/src/zeroband/config.py
index 7664278c..8dff00b1 100644
--- a/src/zeroband/config.py
+++ b/src/zeroband/config.py
@@ -83,6 +83,7 @@ class DilocoConfig(BaseConfig):
     outer_lr: float = 0.7
     inner_steps: int
     compression: Compression = Compression.NO
+    delayed_update: bool
 
 
 class MemoryProfilerConfig(BaseConfig):
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index c98be554..3d012dcb 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -1,7 +1,10 @@
 import os
+import threading
 import time
+import zlib
 from dataclasses import asdict
 from logging import Logger
+from tabnanny import check
 from typing import TYPE_CHECKING, Optional, Iterator, List, Dict
 
 import torch
@@ -25,7 +28,7 @@
 
 from zeroband.utils.memory_profiler import MemoryProfiler
 from zeroband.utils.mfu_tracker import FlopCounter, PrecisionMode, \
-    get_flops_promised_torch
+    get_flops_promised_pt
 from zeroband.utils.tokenizer_utils import make_tokenizer
 from zeroband.utils.logger import get_logger
 from zeroband.utils.profiler import Profiler, ProfilerCollection
@@ -95,6 +98,21 @@ def perform_grad_accum_steps(
 
     return total_loss, current_lr
 
+def compute_crc32(tensor: torch.Tensor) -> int:
+    tensor_cpu = tensor.detach().cpu()
+    tensor_contiguous = tensor_cpu.contiguous()
+    tensor_np = tensor_contiguous.numpy()
+    tensor_bytes = tensor_np.tobytes()
+    checksum = zlib.crc32(tensor_bytes)
+    return checksum
+
+
+def print_outer_crc32s(outer_optimizer: torch.optim.Optimizer):
+    for outer_enry in outer_optimizer.state.values():
+        for key, value in outer_enry.items():
+            if isinstance(value, torch.Tensor):
+                checksum = compute_crc32(value)
+                print(f"{key} checksum: {checksum}")
 
 def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], device: torch.device):
     grad_accum_steps = calc_gradient_accumulation_steps(
@@ -164,13 +182,23 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
                 mpi_config=mpi_config
             )
 
+    iter_num = 0
+    if checkpoint_info is not None:
+        iter_num = checkpoint_info.num_performed_outer_steps
+
+    # -------------------------------------------------------------------------
+    # ! Critical PCCL-related training-loop-state tracking variables !
+    iter_num = torch.tensor([iter_num], dtype=torch.int64, device='cpu')
+    # -------------------------------------------------------------------------
+
+
     if config.diloco:
         # create outer parameters
         for name, local_p in model.named_parameters():
             if isinstance(local_p, DTensor):
                 local_p = local_p.to_local()
-            outer_param = outer_parameters[name] = torch.nn.Parameter(local_p.detach().cpu())
-            outer_parameters_list.append(outer_param)
+            outer_p = outer_parameters[name] = torch.nn.Parameter(local_p.detach().cpu())
+            outer_parameters_list.append(outer_p)
 
         with setup_profiler.session("optim_utils::make_optimizer[diloco]"):
             outer_optimizer = optim_utils.make_optimizer(outer_parameters_list, config.train.outer_optimizer)
@@ -181,20 +209,24 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             outer_optimizer.step()
 
         # Build the shared state that includes:
-        #   - The inner parameters
+        #   - The outer parameters
         #   - The outer optimizer state (e.g. momentum buffers)
         shared_state_dict = {}
 
+        # Reference outer parameters and parameter-specific optimizer state
         name: str
-        param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
-        for name, param in model.named_parameters():
-            shared_state_dict[name] = param
-
-        # Outer optimizer momentum buffers in shared state
+        outer_p: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
         for name, outer_p in outer_parameters.items():
+            # add outer parameter parameter as shared state
+            shared_state_dict[name] = outer_p
+
+            # add parameter-specific optimizer state
             state = outer_optimizer.state[outer_p]
             optim_utils.add_optimizer_state(shared_state_dict, name, state, type(outer_optimizer))
 
+        # Also make iter_num synchronized shared state
+        shared_state_dict['iter_num'] = iter_num
+
         entries = [
             TensorInfo.from_torch(
                 param.data.to_local() if isinstance(param.data, DTensor) else param.data,
@@ -204,10 +236,7 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             for name, param in shared_state_dict.items()
         ]
         shared_state = SharedState(entries)
-        if checkpoint_info is not None:
-            shared_state.revision = checkpoint_info.num_performed_outer_steps
-        else:
-            shared_state.revision = 0
+        shared_state.revision = 0
 
     if (mpi_config is None or mpi_config.mpi_rank == 0) and config.wandb:
         wandb.init(
@@ -245,8 +274,52 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
     local_world_size: int = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
     # -------------------------------------------------------------------------
 
+
+    # -------------------------------------------------------------------------
+    # ! Critical PCCL-related state for async DiLoCo
+    all_reduce_thread: Optional[threading.Thread] = None
+    last_pseudo_grads: List[torch.Tensor] = []
+    # -------------------------------------------------------------------------
+
+
+    def run_shared_state_sync(late_joiner: bool = False):
+        nonlocal num_syncs
+        # 3) Sync shared state => ensures we have the same aggregator (outer) parameters
+        with train_profiler.session("pccl::sync_shared_state"):
+            sync_info = communicator.sync_shared_state(shared_state)
+            shared_state.revision += 1
+            print(f"sync_info tx_bytes: {sync_info.tx_bytes}, rx_bytes: {sync_info.rx_bytes}")
+            num_syncs += 1
+            if num_syncs > 1 and not late_joiner:
+                assert sync_info.rx_bytes == 0, "Shared state drifted unexpectedly in peers!"
+
+            # initialize inner state on first sync
+            if num_syncs == 1:
+                print("Initializing inner state...")
+                with torch.no_grad():
+                    outer_param: torch.nn.Parameter  # [ torch.Tensor ]
+                    inner_param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
+                    for outer_param, inner_param in zip(outer_parameters_list, model.parameters()):
+                        # noinspection PyTypeChecker
+                        inner_tensor: torch.Tensor | DTensor = inner_param.data
+                        if isinstance(inner_tensor, DTensor):
+                            inner_tensor = inner_tensor.to_local()
+                        inner_tensor.copy_(outer_param.data)
+
+    def sync_inner_with_outer_state():
+        with torch.no_grad():
+            inner_param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
+            outer_param: torch.nn.Parameter  # [ torch.Tensor ]
+            for inner_param, outer_param in zip(model.parameters(), outer_parameters_list):
+                param_tensor = inner_param.data
+                if isinstance(param_tensor, DTensor):
+                    param_tensor = param_tensor.to_local()
+                param_tensor.copy_(outer_param, non_blocking=True)
+
     timing_events = []
     while True:
+        local_iter_num += 1
+
         train_profiler = Profiler()
 
         if num_inner_steps > 1:
@@ -255,7 +328,12 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
         global_world_size = communicator.get_attribute(Attribute.GLOBAL_WORLD_SIZE)
 
-        # 1) Possibly update topology / wait for enough peers
+        topology_updated = False
+        if local_iter_num == 1:
+            # Assume the topology was updated in the first iteration because we just joined and got accepted
+            topology_updated = True
+
+        # Possibly update topology / wait for enough peers
         mpi_ranks_pending = False
         with train_profiler.session("pccl::update_topology"):
             if mpi_config is not None:
@@ -264,10 +342,16 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             if local_iter_num > 1 or mpi_ranks_pending or local_world_size == 1:
                 while True:
                     try:
-                        communicator.update_topology()
+                        if communicator.are_peers_pending():
+                            logger.info(
+                                "Join-Candidate peers pending; awaiting concurrent collective operations to accept new peers...")
+                            if all_reduce_thread is not None:
+                                all_reduce_thread.join()
+                            communicator.update_topology()
+                            topology_updated = True
                         break
                     except PCCLError as e:
-                        print(f"update_topology() failed => {e}, retrying...")
+                        logger.info(f"Updating PCCL topology failed {e}, retrying...")
                         time.sleep(1)
 
         if mpi_ranks_pending:
@@ -276,33 +360,15 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             continue
 
         # TODO: Make minimum num pccl peers configurable
-        # local_world_size = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
-        # if local_world_size < 2:
-        #    print("Waiting for more workers to join...")
-        #    time.sleep(1)
-        #    continue
+        local_world_size = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
+        if local_world_size < 2:
+            print("Waiting for more workers to join...")
+            time.sleep(1)
+            continue
 
-        # 3) Sync shared state => ensures we have the same aggregator (outer) parameters
-        with train_profiler.session("pccl::sync_shared_state"):
-            sync_info = communicator.sync_shared_state(shared_state)
-            shared_state.revision += 1
-            print(f"sync_info tx_bytes: {sync_info.tx_bytes}, rx_bytes: {sync_info.rx_bytes}")
-            num_syncs += 1
-            if num_syncs > 1:
-                assert sync_info.rx_bytes == 0, "Shared state drifted unexpectedly in peers!"
+        if topology_updated:
+            run_shared_state_sync()
 
-            # initialize inner state on first sync
-            if num_syncs == 1:
-                print("Initializing inner state...")
-                with torch.no_grad():
-                    outer_param: torch.nn.Parameter  # [ torch.Tensor ]
-                    inner_param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
-                    for outer_param, inner_param in zip(outer_parameters_list, model.parameters()):
-                        # noinspection PyTypeChecker
-                        inner_tensor: torch.Tensor | DTensor = inner_param.data
-                        if isinstance(inner_tensor, DTensor):
-                            inner_tensor = inner_tensor.to_local()
-                        inner_tensor.copy_(outer_param.data)
 
         for _inner_step in range(num_inner_steps):
             train_profiler.start_session("inner_step")
@@ -340,7 +406,7 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             with train_profiler.session("inner_optimizer.step"):
                 flop_counter.track_optimizer_step(inner_optimizer, num_param_scalars)
                 inner_optimizer.step()
-            inner_optimizer.zero_grad()
+                inner_optimizer.zero_grad(set_to_none=False)
 
             end_event.record()
             timing_events.append((start_event, end_event))
@@ -369,16 +435,10 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
             if config.diloco is None:
                 training_progress.total_tokens += new_tokens
+            else:
+                training_progress.total_tokens += new_tokens * local_world_size
 
-            # else:
-            # we count the total tokens with respect to all diloco workers
-            # might need to tweak this as some worker might fail to join the all reduce later
-
-            # TODO MIKE use pccl instead of elastic_device_mesh
-
-            # training_progress.total_tokens += new_tokens * elastic_device_mesh.global_pg.size()
-
-            tflops_max = get_flops_promised_torch(device, PrecisionMode.PRECISION_BF16)
+            tflops_max = get_flops_promised_pt(device, PrecisionMode.PRECISION_BF16)
 
             metrics = {
                 "loss/train": loss_batch.item(),
@@ -411,7 +471,7 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             if (mpi_config is None or mpi_config.mpi_rank == 0) and config.wandb:
                 wandb.log(metrics)
 
-            log = metrics_utils.build_metrics_string(metrics, whitelist_keys={'step', 'loss', 'mfu', 'tflops',
+            log = metrics_utils.build_metrics_string(metrics, whitelist_keys={'loss/train', 'step', 'loss', 'mfu', 'tflops',
                                                                               'tokens_per_second', 'tflops_max'})
             logger.info(log)
 
@@ -434,45 +494,108 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
         if config.diloco is not None:
             with train_profiler.session("outer_step"):
-                outer_grads = []
+                # await previous all reduce, if one exists
+                can_outer_step = False
+                if all_reduce_thread is not None:
+                    all_reduce_thread.join()
+                    can_outer_step = True
+
+                    # populate outer param grads with last pseudo-gradients set by thread
+                    for pseudo_grad, outer_p in zip(last_pseudo_grads, outer_parameters_list):
+                        outer_p.grad = pseudo_grad
+
+                # Compute current pseudo grads as difference between outer and inner state.
+                # Inner state is advanced by inner steps, outer state is unchanged
+                outer_grads: List[torch.Tensor] = []
                 param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
                 outer_p: torch.nn.Parameter  # [ torch.Tensor ]
                 for param, outer_p in zip(model.parameters(), outer_parameters_list):
-                    outer_p_data: torch.Tensor = outer_p.data
                     param_data: torch.Tensor | DTensor = param.data
+                    outer_p_data: torch.Tensor = outer_p.data
                     if isinstance(param_data, DTensor):
                         param_data = param_data.to_local()
                     outer_p.grad = outer_p_data - param_data.to('cpu')
                     outer_grads.append(outer_p.grad)
 
-                with train_profiler.session("all_reduce_multiple_with_retry"):
-                    start_time = time.time()
-
-                    all_reduce_success = pccl_utils.all_reduce_multiple_with_retry(
-                        communicator,
-                        outer_grads,
-                        ReduceOp.AVG
-                    )
-
-                    end_time = time.time()
-                    print(f"All-Reduce took {end_time - start_time} seconds")
-                    if not all_reduce_success:
-                        print("All peers left except me... continuing alone.")
-
-                outer_optimizer.step()
-                outer_optimizer.zero_grad()
-
-                # Copy aggregator result into local model
-                with torch.no_grad():
-                    param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
-                    outer_p: torch.nn.Parameter  # [ torch.Tensor ]
-                    for param, outer_p in zip(model.parameters(), outer_parameters_list):
-                        param_tensor = param.data
-                        if isinstance(param_tensor, DTensor):
-                            param_tensor = param_tensor.to_local()
-                        param_tensor.copy_(outer_p, non_blocking=True)
-
-        training_progress.outer_step += 1
+                if can_outer_step:
+                    outer_optimizer.step()  # Note that there is no zero-grad because grads get re-instantiated every step
+
+                    print_outer_crc32s(outer_optimizer)
+
+                    # Copy aggregator result into local model
+                    sync_inner_with_outer_state()
+
+                    if topology_updated and iter_num > 0:
+                        # If the topology was updated and iter_num is > 0
+                        # then a new peer just joined the run with needs to be properly inserted into
+                        # the N-1 async pipeline.
+                        # To do this we first initially sync the weights such that the peer can
+                        # start computing the current step like the pre-existing peers, however
+                        # the newly joined peer cannot be "retroactively inserted" into
+                        # the N-1 async reduce that was started last step.
+                        # So it needs to "eavesdrop" on the result that the other peers are about to compute
+                        # with a second shared state re-transmission.
+                        # Hence, both pre-existing peers and newly joined peer(s) have to perform shared state
+                        # synchronization.
+                        # The pre-existing peers first apply the outer optimizer and THEN call run_shared_state_sync
+                        # because the new peer(s) need to obtain the shared state as it is after the all reduce
+                        # is applied that they were not part of.
+                        logger.info(
+                            "Topology updated mid run; re-running shared state synchronization to properly insert new peer...")
+                        run_shared_state_sync()
+                else:
+                    if topology_updated and iter_num > 0:
+                        # If the topology was updated and iter_num is > 0 and can_outer_step is False,
+                        # then WE are the joining peer to an ongoing run.
+                        # In this case, we have to obtain the shared state from the pre-existing peers.
+                        # We obtain the shared state first and then simply copy it into the inner model afterwards.
+                        # Also: late_joiner here means that we tolerate actually receiving bytes here despite that this is the second sync that was performed.
+                        # This is necessary for the pipeline insertion algorithm to function
+                        run_shared_state_sync(late_joiner=True)
+
+                    # This is the boostrap for the 1-step behind asynchronous training step.
+                    # Reset the inner state here to be equal to the unmodified outer state.
+                    # This essentially resets the model back to initialization state.
+                    # Why do this?
+                    # a) because the next shared state sync needs to see all outer states as equal.
+                    # We haven't communicated yet, so we have by definition diverged.
+                    # But we will hide this for now.
+                    # b) what we are accomplishing here is as follows:
+                    # We know that the pseudo-grads constitute a valid update to the weights
+                    # to decrease the loss when applied to the initial model state.
+                    # These changes will be applied in the next loop iteration.
+                    # We will hide the communication with compute of the next iteration.
+                    # Afterward, we will apply said delta to the still initial weights.
+                    # At this stage, we haven't done anything questionable at all.
+                    # We have applied a valid update to exactly the base weights they were grads for.
+                    # However, now in the next outer step, the reduce of the pseudo-gradients of step two is awaited
+                    # and these are updates from initial weights also - just derived from different input data.
+                    # We have already moved on from the initial weights
+                    # at this point. And yet, we still apply them. This is the 1-step behind assertion
+                    # that we make that it is reasonable to still apply these gradients, even though they
+                    # are slightly outdated. From then onwards, outer step updates are always one step behind.
+                    sync_inner_with_outer_state()
+
+            def run_all_reduce():
+                nonlocal last_pseudo_grads
+                last_pseudo_grads = outer_grads.copy()
+                start_time = time.time()
+                pccl_utils.all_reduce_multiple_with_retry(
+                    communicator,
+                    last_pseudo_grads,
+                    ReduceOp.AVG
+                )
+                end_time = time.time()
+                print(f"All-Reduce took {end_time - start_time} seconds")
+
+            logger.debug("Launching all reduce...")
+            all_reduce_thread = threading.Thread(target=run_all_reduce, name="ReduceThread")
+
+            # NOTE: no zero-grad on outer grads, as they continue to get referenced by this thread.
+            all_reduce_thread.start()
+
+        iter_num += 1
+        training_progress.outer_step = iter_num.item()
 
         if (
                 config.ckpt.interval is not None
@@ -515,13 +638,14 @@ def main():
     # However, in development, we want to know that we broke torch compile
     torch._dynamo.config.suppress_errors = "ZERO_BAND_DEV" not in os.environ  # type: ignore
     torch.set_float32_matmul_precision("high")
-    torch.manual_seed(42)
 
     mpi_config: Optional[MPIConfig] = ccl_utils.make_mpi_config(
         mpi_rank=os.getenv("RANK"),
         mpi_world_size=os.getenv("WORLD_SIZE")
     )  # may return None
 
+    # Don't set torch seed; Random seed is necessary to prevent unexpected equivalence of rank state
+
     config = Config(**parse_argv())  # type: ignore
     logger = get_logger(config, mpi_config)
 
@@ -530,7 +654,6 @@ def main():
     num_total_gpus = torch.cuda.device_count()
     logger.info(f"Using gpu ordinal:{gpu_ordinal}/num_total:{num_total_gpus}")
 
-
     torch.cuda.set_device(gpu_ordinal)
     device = torch.device(f'cuda:{torch.cuda.current_device()}')
     logger.info(f"Using device: {torch.cuda.get_device_name(device)}")
diff --git a/src/zeroband/utils/mfu_tracker.py b/src/zeroband/utils/mfu_tracker.py
index 319089fb..8f04fc94 100644
--- a/src/zeroband/utils/mfu_tracker.py
+++ b/src/zeroband/utils/mfu_tracker.py
@@ -107,7 +107,7 @@ def _interpolate_performance(flagship_performance: FlagshipPerformance,
     return adjusted_tflops
 
 
-def get_flops_promised_torch(device: torch.device, precision_mode: PrecisionMode):
+def get_flops_promised_pt(device: torch.device, precision_mode: PrecisionMode):
     assert device.type == 'cuda', 'get_flops_promised_torch cannot be invoked for non-cuda torch device!'
     device_name = torch.cuda.get_device_name(device)
     return get_flops_promised(device_name, precision_mode)

From 6e603437e68a0b0e5fbf508a50ad32ce72d4e5c5 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Sun, 6 Apr 2025 06:51:11 +0000
Subject: [PATCH 06/47] introduce functions for sanity

---
 src/zeroband/train.py            | 630 +++++++++++++++++--------------
 src/zeroband/utils/misc_utils.py |  42 +++
 2 files changed, 392 insertions(+), 280 deletions(-)
 create mode 100644 src/zeroband/utils/misc_utils.py

diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 3d012dcb..0feff9ef 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -5,7 +5,7 @@
 from dataclasses import asdict
 from logging import Logger
 from tabnanny import check
-from typing import TYPE_CHECKING, Optional, Iterator, List, Dict
+from typing import TYPE_CHECKING, Optional, Iterator, List, Dict, Iterable, Tuple
 
 import torch
 import torch.distributed as dist
@@ -15,6 +15,7 @@
 from torch.distributed.tensor import DTensor
 
 import wandb
+from torch.optim import Optimizer
 
 from zeroband.ccl import ccl_utils, pccl_utils
 from zeroband.ccl.ccl_utils import MPIConfig
@@ -29,6 +30,7 @@
 from zeroband.utils.memory_profiler import MemoryProfiler
 from zeroband.utils.mfu_tracker import FlopCounter, PrecisionMode, \
     get_flops_promised_pt
+from zeroband.utils.misc_utils import IntRef
 from zeroband.utils.tokenizer_utils import make_tokenizer
 from zeroband.utils.logger import get_logger
 from zeroband.utils.profiler import Profiler, ProfilerCollection
@@ -98,6 +100,7 @@ def perform_grad_accum_steps(
 
     return total_loss, current_lr
 
+
 def compute_crc32(tensor: torch.Tensor) -> int:
     tensor_cpu = tensor.detach().cpu()
     tensor_contiguous = tensor_cpu.contiguous()
@@ -114,6 +117,331 @@ def print_outer_crc32s(outer_optimizer: torch.optim.Optimizer):
                 checksum = compute_crc32(value)
                 print(f"{key} checksum: {checksum}")
 
+
+def run_inner_steps(
+        model: torch.nn.Module,
+        train_dataloader_iterator: iter,
+        inner_optimizer: torch.optim.Optimizer,
+        device: torch.device,
+
+        logger: Logger,
+        memory_profiler: MemoryProfiler,
+
+        local_world_size: int,
+
+        mpi_config: Optional[MPIConfig],
+        train_profiler: Profiler,
+        config: Config,
+        training_progress: TrainingProgress,
+        grad_accum_steps: int,
+
+        timing_events: List[Tuple[torch.cuda.Event, torch.cuda.Event]]
+):
+    num_inner_steps = config.diloco.inner_steps if config.diloco is not None else 1
+    num_param_scalars = model.count_parameters()
+
+    for _inner_step in range(num_inner_steps):
+        train_profiler.start_session("inner_step")
+
+        flop_counter = FlopCounter()
+
+        start_event = torch.cuda.Event(enable_timing=True, blocking=False)
+        end_event = torch.cuda.Event(enable_timing=True, blocking=False)
+
+        start_event.record()
+
+        with train_profiler.session("::perform_grad_accum_steps"):
+            loss_batch: torch.Tensor
+            inner_lr: float
+            loss_batch, inner_lr = perform_grad_accum_steps(config, train_profiler, flop_counter,
+                                                            training_progress,
+                                                            train_dataloader_iterator,
+                                                            grad_accum_steps,
+                                                            model,
+                                                            inner_optimizer,
+                                                            device)
+
+        if mpi_config is not None:
+            dist.all_reduce(tensor=loss_batch, op=dist.ReduceOp.AVG)
+
+        with train_profiler.session("torch::nn::utils::clip_grad_norm_"):
+            # compute pow, plus (assert clip is rare, no 3N)
+            flop_counter.track_backward_flops(2 * num_param_scalars)
+
+            grad_norm: torch.Tensor | DTensor = torch.nn.utils.clip_grad_norm_(model.parameters(),
+                                                                               1.0)  # type: ignore
+            if isinstance(grad_norm, DTensor):
+                grad_norm = grad_norm.full_tensor()  # type: ignore
+
+        with train_profiler.session("inner_optimizer.step"):
+            flop_counter.track_optimizer_step(inner_optimizer, num_param_scalars)
+            inner_optimizer.step()
+            inner_optimizer.zero_grad(set_to_none=False)
+
+        end_event.record()
+        timing_events.append((start_event, end_event))
+
+        # logging
+        training_progress.step += 1
+
+        # syncing loss across all data parallel rank within a nodes
+        new_tokens = config.data.seq_length * config.train.batch_size
+
+        # find next available timing event from some step in the past
+        # that the gpu has already finished executing.
+        # Realistically, this should at most be -1 steps into the past
+        time_seconds = None
+        for pair in timing_events:
+            start_event, end_event = pair
+            if end_event.query():
+                end_event.synchronize()
+                time_seconds = start_event.elapsed_time(end_event) * 1e-3
+                timing_events.remove(pair)
+                break
+
+        tokens_per_second = None
+        if time_seconds is not None:
+            tokens_per_second = new_tokens / time_seconds
+
+        if config.diloco is None:
+            training_progress.total_tokens += new_tokens
+        else:
+            training_progress.total_tokens += new_tokens * local_world_size
+
+        tflops_max = get_flops_promised_pt(device, PrecisionMode.PRECISION_BF16)
+
+        metrics = {
+            "loss/train": loss_batch.item(),
+            "step": training_progress.step,
+            "inner_lr": inner_lr,
+            "Perplexity": torch.exp(loss_batch).item(),
+            "total_tokens": training_progress.total_tokens,
+            "time": time.time(),
+            "grad_norm": grad_norm.item(),
+            'tflops_max': tflops_max
+        }
+
+        if time_seconds is not None:
+            tflops_per_second = (flop_counter.get_performed_flops() * 1e-12) / time_seconds
+            mfu = (tflops_per_second / tflops_max) * 100.0
+
+            metrics.update({
+                "mfu": mfu,
+                "tflops": tflops_per_second
+            })
+
+        metrics.update({
+            "inner_lr": inner_lr,
+            "tokens_per_second": tokens_per_second
+        })
+
+        if config.diloco is not None:
+            metrics["num_peers"] = local_world_size
+
+        if (mpi_config is None or mpi_config.mpi_rank == 0) and config.wandb:
+            wandb.log(metrics)
+
+        log = metrics_utils.build_metrics_string(metrics, whitelist_keys={'loss/train', 'step', 'loss', 'mfu', 'tflops',
+                                                                          'tokens_per_second', 'tflops_max'})
+        logger.info(log)
+
+        if memory_profiler is not None:
+            memory_profiler.step()
+        train_profiler.end_session()
+
+
+def run_async_outer_step(
+        model: torch.nn.Module,
+        last_pseudo_grads: List[torch.Tensor],
+        outer_parameters_list: List[torch.nn.Parameter],
+        outer_optimizer: torch.optim.Optimizer,
+        shared_state: SharedState,
+
+        all_reduce_thread: threading.Thread,
+
+        communicator: Communicator,
+        train_profiler: Profiler,
+        logger: Logger,
+
+        topology_updated: bool,
+        iter_num: int,
+        num_syncs: IntRef
+):
+    # await previous all reduce, if one exists
+    can_outer_step = False
+    if all_reduce_thread is not None:
+        all_reduce_thread.join()
+        can_outer_step = True
+
+        # populate outer param grads with last pseudo-gradients set by thread
+        for pseudo_grad, outer_p in zip(last_pseudo_grads, outer_parameters_list):
+            outer_p.grad = pseudo_grad
+
+    # Compute current pseudo grads as difference between outer and inner state.
+    # Inner state is advanced by inner steps, outer state is unchanged
+    outer_grads: List[torch.Tensor] = []
+    param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
+    outer_p: torch.nn.Parameter  # [ torch.Tensor ]
+    for param, outer_p in zip(model.parameters(), outer_parameters_list):
+        param_data: torch.Tensor | DTensor = param.data
+        outer_p_data: torch.Tensor = outer_p.data
+        if isinstance(param_data, DTensor):
+            param_data = param_data.to_local()
+        outer_p.grad = outer_p_data - param_data.to('cpu')
+        outer_grads.append(outer_p.grad)
+
+    if can_outer_step:
+        outer_optimizer.step()  # Note that there is no zero-grad because grads get re-instantiated every step
+
+        # Copy aggregator result into local model
+        sync_inner_with_outer_state(model, outer_parameters_list)
+
+        if topology_updated and iter_num > 0:
+            # If the topology was updated and iter_num is > 0
+            # then a new peer just joined the run with needs to be properly inserted into
+            # the N-1 async pipeline.
+            # To do this we first initially sync the weights such that the peer can
+            # start computing the current step like the pre-existing peers, however
+            # the newly joined peer cannot be "retroactively inserted" into
+            # the N-1 async reduce that was started last step.
+            # So it needs to "eavesdrop" on the result that the other peers are about to compute
+            # with a second shared state re-transmission.
+            # Hence, both pre-existing peers and newly joined peer(s) have to perform shared state
+            # synchronization.
+            # The pre-existing peers first apply the outer optimizer and THEN call run_shared_state_sync
+            # because the new peer(s) need to obtain the shared state as it is after the all reduce
+            # is applied that they were not part of.
+            logger.info(
+                "Topology updated mid run; re-running shared state synchronization to properly insert new peer...")
+            run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, num_syncs, train_profiler,
+                                  False)
+
+    else:
+        if topology_updated and iter_num > 0:
+            # If the topology was updated and iter_num is > 0 and can_outer_step is False,
+            # then WE are the joining peer to an ongoing run.
+            # In this case, we have to obtain the shared state from the pre-existing peers.
+            # We obtain the shared state first and then simply copy it into the inner model afterwards.
+            # Also: late_joiner here means that we tolerate actually receiving bytes here despite that this is the second sync that was performed.
+            # This is necessary for the pipeline insertion algorithm to function
+            run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, num_syncs, train_profiler,
+                                  False)
+
+        # This is the boostrap for the 1-step behind asynchronous training step.
+        # Reset the inner state here to be equal to the unmodified outer state.
+        # This essentially resets the model back to initialization state.
+        # Why do this?
+        # a) because the next shared state sync needs to see all outer states as equal.
+        # We haven't communicated yet, so we have by definition diverged.
+        # But we will hide this for now.
+        # b) what we are accomplishing here is as follows:
+        # We know that the pseudo-grads constitute a valid update to the weights
+        # to decrease the loss when applied to the initial model state.
+        # These changes will be applied in the next loop iteration.
+        # We will hide the communication with compute of the next iteration.
+        # Afterward, we will apply said delta to the still initial weights.
+        # At this stage, we haven't done anything questionable at all.
+        # We have applied a valid update to exactly the base weights they were grads for.
+        # However, now in the next outer step, the reduce of the pseudo-gradients of step two is awaited
+        # and these are updates from initial weights also - just derived from different input data.
+        # We have already moved on from the initial weights
+        # at this point. And yet, we still apply them. This is the 1-step behind assertion
+        # that we make that it is reasonable to still apply these gradients, even though they
+        # are slightly outdated. From then onwards, outer step updates are always one step behind.
+        sync_inner_with_outer_state(model, outer_parameters_list)
+
+    def run_all_reduce():
+        nonlocal last_pseudo_grads
+        last_pseudo_grads = outer_grads.copy()
+        start_time = time.time()
+        pccl_utils.all_reduce_multiple_with_retry(
+            communicator,
+            last_pseudo_grads,
+            ReduceOp.AVG
+        )
+        end_time = time.time()
+        print(f"All-Reduce took {end_time - start_time} seconds")
+
+    logger.debug("Launching all reduce...")
+    all_reduce_thread = threading.Thread(target=run_all_reduce, name="ReduceThread")
+
+    # NOTE: no zero-grad on outer grads, as they continue to get referenced by this thread.
+    all_reduce_thread.start()
+
+    return all_reduce_thread
+
+
+def sync_inner_with_outer_state(model: torch.nn.Module, outer_parameters_list: List[torch.nn.Parameter]):
+    with torch.no_grad():
+        inner_param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
+        outer_param: torch.nn.Parameter  # [ torch.Tensor ]
+        for inner_param, outer_param in zip(model.parameters(), outer_parameters_list):
+            param_tensor = inner_param.data
+            if isinstance(param_tensor, DTensor):
+                param_tensor = param_tensor.to_local()
+            param_tensor.copy_(outer_param, non_blocking=True)
+
+
+def run_shared_state_sync(
+        shared_state: SharedState,
+        communicator: Communicator,
+        model: torch.nn.Module, outer_parameters_list: List[torch.nn.Parameter],
+
+        num_syncs: IntRef,
+        train_profiler: Profiler,
+        late_joiner: bool,
+):
+    # 3) Sync shared state => ensures we have the same aggregator (outer) parameters
+    with train_profiler.session("pccl::sync_shared_state"):
+        sync_info = communicator.sync_shared_state(shared_state)
+        shared_state.revision += 1
+        print(f"sync_info tx_bytes: {sync_info.tx_bytes}, rx_bytes: {sync_info.rx_bytes}")
+        num_syncs += 1
+        if num_syncs > 1 and not late_joiner:
+            assert sync_info.rx_bytes == 0, "Shared state drifted unexpectedly in peers!"
+
+        # initialize inner state on first sync
+        if num_syncs == 1:
+            print("Initializing inner state...")
+            sync_inner_with_outer_state(model, outer_parameters_list)
+
+
+def make_shared_state(outer_parameters: Dict[str, torch.nn.Parameter],
+                      outer_optimizer: Optimizer,
+                      iter_num: torch.Tensor):
+
+    # Build the shared state that includes:
+    #   - The outer parameters
+    #   - The outer optimizer state (e.g. momentum buffers)
+    shared_state_dict = {}
+
+    # Reference outer parameters and parameter-specific optimizer state
+    name: str
+    outer_p: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
+    for name, outer_p in outer_parameters.items():
+        # add outer parameter parameter as shared state
+        shared_state_dict[name] = outer_p
+
+        # add parameter-specific optimizer state
+        state = outer_optimizer.state[outer_p]
+        optim_utils.add_optimizer_state(shared_state_dict, name, state, type(outer_optimizer))
+
+    # Also make iter_num synchronized shared state
+    shared_state_dict['iter_num'] = iter_num
+
+    entries = [
+        TensorInfo.from_torch(
+            param.data.to_local() if isinstance(param.data, DTensor) else param.data,
+            name,
+            allow_content_inequality=False
+        )
+        for name, param in shared_state_dict.items()
+    ]
+    shared_state = SharedState(entries)
+    shared_state.revision = 0
+    return shared_state
+
+
 def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], device: torch.device):
     grad_accum_steps = calc_gradient_accumulation_steps(
         config.train.batch_size, config.hardware.micro_batch_size, mpi_config
@@ -157,15 +485,14 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
     with setup_profiler.session("optim_utils::make_optimizer"):
         inner_optimizer = optim_utils.make_optimizer(list(model.parameters()), config.train.optimizer)
 
-    # None if diloco is disabled
+    # -------------------------------------------------------------------------
+    # ! PCCL-related state for outer optimizer, pseudo-gradients and shared state
+    # All None if diloco is disabled
     outer_optimizer: Optional[torch.optim.Optimizer] = None
-
-    # empty if diloco is disabled
     outer_parameters: Dict[str, torch.nn.Parameter] = dict()
     outer_parameters_list: List[torch.nn.Parameter] = []
-
-    # none if diloco is disabled
     shared_state: Optional[SharedState] = None
+    # -------------------------------------------------------------------------
 
     training_progress = TrainingProgress(total_tokens=0, outer_step=0, step=0)
 
@@ -191,9 +518,8 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
     iter_num = torch.tensor([iter_num], dtype=torch.int64, device='cpu')
     # -------------------------------------------------------------------------
 
-
     if config.diloco:
-        # create outer parameters
+
         for name, local_p in model.named_parameters():
             if isinstance(local_p, DTensor):
                 local_p = local_p.to_local()
@@ -208,35 +534,8 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
                 op.grad = torch.zeros_like(op)
             outer_optimizer.step()
 
-        # Build the shared state that includes:
-        #   - The outer parameters
-        #   - The outer optimizer state (e.g. momentum buffers)
-        shared_state_dict = {}
-
-        # Reference outer parameters and parameter-specific optimizer state
-        name: str
-        outer_p: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
-        for name, outer_p in outer_parameters.items():
-            # add outer parameter parameter as shared state
-            shared_state_dict[name] = outer_p
-
-            # add parameter-specific optimizer state
-            state = outer_optimizer.state[outer_p]
-            optim_utils.add_optimizer_state(shared_state_dict, name, state, type(outer_optimizer))
-
-        # Also make iter_num synchronized shared state
-        shared_state_dict['iter_num'] = iter_num
-
-        entries = [
-            TensorInfo.from_torch(
-                param.data.to_local() if isinstance(param.data, DTensor) else param.data,
-                name,
-                allow_content_inequality=False
-            )
-            for name, param in shared_state_dict.items()
-        ]
-        shared_state = SharedState(entries)
-        shared_state.revision = 0
+        shared_state = make_shared_state(outer_parameters, outer_optimizer,
+                                         iter_num)
 
     if (mpi_config is None or mpi_config.mpi_rank == 0) and config.wandb:
         wandb.init(
@@ -270,51 +569,16 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
     # -------------------------------------------------------------------------
     # ! Critical PCCL-related training-loop-state tracking variables !
     local_iter_num = 0
-    num_syncs = 0
+    num_syncs = IntRef(0)
     local_world_size: int = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
     # -------------------------------------------------------------------------
 
-
     # -------------------------------------------------------------------------
     # ! Critical PCCL-related state for async DiLoCo
     all_reduce_thread: Optional[threading.Thread] = None
     last_pseudo_grads: List[torch.Tensor] = []
-    # -------------------------------------------------------------------------
-
 
-    def run_shared_state_sync(late_joiner: bool = False):
-        nonlocal num_syncs
-        # 3) Sync shared state => ensures we have the same aggregator (outer) parameters
-        with train_profiler.session("pccl::sync_shared_state"):
-            sync_info = communicator.sync_shared_state(shared_state)
-            shared_state.revision += 1
-            print(f"sync_info tx_bytes: {sync_info.tx_bytes}, rx_bytes: {sync_info.rx_bytes}")
-            num_syncs += 1
-            if num_syncs > 1 and not late_joiner:
-                assert sync_info.rx_bytes == 0, "Shared state drifted unexpectedly in peers!"
-
-            # initialize inner state on first sync
-            if num_syncs == 1:
-                print("Initializing inner state...")
-                with torch.no_grad():
-                    outer_param: torch.nn.Parameter  # [ torch.Tensor ]
-                    inner_param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
-                    for outer_param, inner_param in zip(outer_parameters_list, model.parameters()):
-                        # noinspection PyTypeChecker
-                        inner_tensor: torch.Tensor | DTensor = inner_param.data
-                        if isinstance(inner_tensor, DTensor):
-                            inner_tensor = inner_tensor.to_local()
-                        inner_tensor.copy_(outer_param.data)
-
-    def sync_inner_with_outer_state():
-        with torch.no_grad():
-            inner_param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
-            outer_param: torch.nn.Parameter  # [ torch.Tensor ]
-            for inner_param, outer_param in zip(model.parameters(), outer_parameters_list):
-                param_tensor = inner_param.data
-                if isinstance(param_tensor, DTensor):
-                    param_tensor = param_tensor.to_local()
-                param_tensor.copy_(outer_param, non_blocking=True)
+    # -------------------------------------------------------------------------
 
     timing_events = []
     while True:
@@ -367,117 +631,19 @@ def sync_inner_with_outer_state():
             continue
 
         if topology_updated:
-            run_shared_state_sync()
-
+            run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, num_syncs, train_profiler,
+                                  False)
 
-        for _inner_step in range(num_inner_steps):
-            train_profiler.start_session("inner_step")
+        run_inner_steps(
+            model, train_dataloader_iterator, inner_optimizer, device,
 
-            flop_counter = FlopCounter()
+            logger, memory_profiler,
 
-            start_event = torch.cuda.Event(enable_timing=True, blocking=False)
-            end_event = torch.cuda.Event(enable_timing=True, blocking=False)
-
-            start_event.record()
-
-            with train_profiler.session("::perform_grad_accum_steps"):
-                loss_batch: torch.Tensor
-                inner_lr: float
-                loss_batch, inner_lr = perform_grad_accum_steps(config, train_profiler, flop_counter,
-                                                                training_progress,
-                                                                train_dataloader_iterator,
-                                                                grad_accum_steps,
-                                                                model,
-                                                                inner_optimizer,
-                                                                device)
-
-            if mpi_config is not None:
-                dist.all_reduce(tensor=loss_batch, op=dist.ReduceOp.AVG)
-
-            with train_profiler.session("torch::nn::utils::clip_grad_norm_"):
-                # compute pow, plus (assert clip is rare, no 3N)
-                flop_counter.track_backward_flops(2 * num_param_scalars)
-
-                grad_norm: torch.Tensor | DTensor = torch.nn.utils.clip_grad_norm_(model.parameters(),
-                                                                                   1.0)  # type: ignore
-                if isinstance(grad_norm, DTensor):
-                    grad_norm = grad_norm.full_tensor()  # type: ignore
-
-            with train_profiler.session("inner_optimizer.step"):
-                flop_counter.track_optimizer_step(inner_optimizer, num_param_scalars)
-                inner_optimizer.step()
-                inner_optimizer.zero_grad(set_to_none=False)
-
-            end_event.record()
-            timing_events.append((start_event, end_event))
-
-            # logging
-            training_progress.step += 1
-
-            # syncing loss across all data parallel rank within a nodes
-            new_tokens = config.data.seq_length * config.train.batch_size
-
-            # find next available timing event from some step in the past
-            # that the gpu has already finished executing.
-            # Realistically, this should at most be -1 steps into the past
-            time_seconds = None
-            for pair in timing_events:
-                start_event, end_event = pair
-                if end_event.query():
-                    end_event.synchronize()
-                    time_seconds = start_event.elapsed_time(end_event) * 1e-3
-                    timing_events.remove(pair)
-                    break
-
-            tokens_per_second = None
-            if time_seconds is not None:
-                tokens_per_second = new_tokens / time_seconds
-
-            if config.diloco is None:
-                training_progress.total_tokens += new_tokens
-            else:
-                training_progress.total_tokens += new_tokens * local_world_size
-
-            tflops_max = get_flops_promised_pt(device, PrecisionMode.PRECISION_BF16)
-
-            metrics = {
-                "loss/train": loss_batch.item(),
-                "step": training_progress.step,
-                "inner_lr": inner_lr,
-                "Perplexity": torch.exp(loss_batch).item(),
-                "total_tokens": training_progress.total_tokens,
-                "time": time.time(),
-                "grad_norm": grad_norm.item(),
-                'tflops_max': tflops_max
-            }
-
-            if time_seconds is not None:
-                tflops_per_second = (flop_counter.get_performed_flops() * 1e-12) / time_seconds
-                mfu = (tflops_per_second / tflops_max) * 100.0
-
-                metrics.update({
-                    "mfu": mfu,
-                    "tflops": tflops_per_second
-                })
+            local_world_size,
 
-            metrics.update({
-                "inner_lr": inner_lr,
-                "tokens_per_second": tokens_per_second
-            })
-
-            if config.diloco is not None:
-                metrics["num_peers"] = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
-
-            if (mpi_config is None or mpi_config.mpi_rank == 0) and config.wandb:
-                wandb.log(metrics)
-
-            log = metrics_utils.build_metrics_string(metrics, whitelist_keys={'loss/train', 'step', 'loss', 'mfu', 'tflops',
-                                                                              'tokens_per_second', 'tflops_max'})
-            logger.info(log)
-
-            if memory_profiler is not None:
-                memory_profiler.step()
-            train_profiler.end_session()
+            mpi_config, train_profiler, config,
+            training_progress, grad_accum_steps, timing_events
+        )
 
         # post inner steps
         if PRIME_TRAIN_PROFILER_PRINT_TIMINGS:
@@ -494,105 +660,9 @@ def sync_inner_with_outer_state():
 
         if config.diloco is not None:
             with train_profiler.session("outer_step"):
-                # await previous all reduce, if one exists
-                can_outer_step = False
-                if all_reduce_thread is not None:
-                    all_reduce_thread.join()
-                    can_outer_step = True
-
-                    # populate outer param grads with last pseudo-gradients set by thread
-                    for pseudo_grad, outer_p in zip(last_pseudo_grads, outer_parameters_list):
-                        outer_p.grad = pseudo_grad
-
-                # Compute current pseudo grads as difference between outer and inner state.
-                # Inner state is advanced by inner steps, outer state is unchanged
-                outer_grads: List[torch.Tensor] = []
-                param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
-                outer_p: torch.nn.Parameter  # [ torch.Tensor ]
-                for param, outer_p in zip(model.parameters(), outer_parameters_list):
-                    param_data: torch.Tensor | DTensor = param.data
-                    outer_p_data: torch.Tensor = outer_p.data
-                    if isinstance(param_data, DTensor):
-                        param_data = param_data.to_local()
-                    outer_p.grad = outer_p_data - param_data.to('cpu')
-                    outer_grads.append(outer_p.grad)
-
-                if can_outer_step:
-                    outer_optimizer.step()  # Note that there is no zero-grad because grads get re-instantiated every step
-
-                    print_outer_crc32s(outer_optimizer)
-
-                    # Copy aggregator result into local model
-                    sync_inner_with_outer_state()
-
-                    if topology_updated and iter_num > 0:
-                        # If the topology was updated and iter_num is > 0
-                        # then a new peer just joined the run with needs to be properly inserted into
-                        # the N-1 async pipeline.
-                        # To do this we first initially sync the weights such that the peer can
-                        # start computing the current step like the pre-existing peers, however
-                        # the newly joined peer cannot be "retroactively inserted" into
-                        # the N-1 async reduce that was started last step.
-                        # So it needs to "eavesdrop" on the result that the other peers are about to compute
-                        # with a second shared state re-transmission.
-                        # Hence, both pre-existing peers and newly joined peer(s) have to perform shared state
-                        # synchronization.
-                        # The pre-existing peers first apply the outer optimizer and THEN call run_shared_state_sync
-                        # because the new peer(s) need to obtain the shared state as it is after the all reduce
-                        # is applied that they were not part of.
-                        logger.info(
-                            "Topology updated mid run; re-running shared state synchronization to properly insert new peer...")
-                        run_shared_state_sync()
-                else:
-                    if topology_updated and iter_num > 0:
-                        # If the topology was updated and iter_num is > 0 and can_outer_step is False,
-                        # then WE are the joining peer to an ongoing run.
-                        # In this case, we have to obtain the shared state from the pre-existing peers.
-                        # We obtain the shared state first and then simply copy it into the inner model afterwards.
-                        # Also: late_joiner here means that we tolerate actually receiving bytes here despite that this is the second sync that was performed.
-                        # This is necessary for the pipeline insertion algorithm to function
-                        run_shared_state_sync(late_joiner=True)
-
-                    # This is the boostrap for the 1-step behind asynchronous training step.
-                    # Reset the inner state here to be equal to the unmodified outer state.
-                    # This essentially resets the model back to initialization state.
-                    # Why do this?
-                    # a) because the next shared state sync needs to see all outer states as equal.
-                    # We haven't communicated yet, so we have by definition diverged.
-                    # But we will hide this for now.
-                    # b) what we are accomplishing here is as follows:
-                    # We know that the pseudo-grads constitute a valid update to the weights
-                    # to decrease the loss when applied to the initial model state.
-                    # These changes will be applied in the next loop iteration.
-                    # We will hide the communication with compute of the next iteration.
-                    # Afterward, we will apply said delta to the still initial weights.
-                    # At this stage, we haven't done anything questionable at all.
-                    # We have applied a valid update to exactly the base weights they were grads for.
-                    # However, now in the next outer step, the reduce of the pseudo-gradients of step two is awaited
-                    # and these are updates from initial weights also - just derived from different input data.
-                    # We have already moved on from the initial weights
-                    # at this point. And yet, we still apply them. This is the 1-step behind assertion
-                    # that we make that it is reasonable to still apply these gradients, even though they
-                    # are slightly outdated. From then onwards, outer step updates are always one step behind.
-                    sync_inner_with_outer_state()
-
-            def run_all_reduce():
-                nonlocal last_pseudo_grads
-                last_pseudo_grads = outer_grads.copy()
-                start_time = time.time()
-                pccl_utils.all_reduce_multiple_with_retry(
-                    communicator,
-                    last_pseudo_grads,
-                    ReduceOp.AVG
-                )
-                end_time = time.time()
-                print(f"All-Reduce took {end_time - start_time} seconds")
-
-            logger.debug("Launching all reduce...")
-            all_reduce_thread = threading.Thread(target=run_all_reduce, name="ReduceThread")
-
-            # NOTE: no zero-grad on outer grads, as they continue to get referenced by this thread.
-            all_reduce_thread.start()
+                all_reduce_thread = run_async_outer_step(model, last_pseudo_grads, outer_parameters_list,
+                                                         outer_optimizer, shared_state, all_reduce_thread, communicator,
+                                                         train_profiler, logger, topology_updated, iter_num, num_syncs)
 
         iter_num += 1
         training_progress.outer_step = iter_num.item()
diff --git a/src/zeroband/utils/misc_utils.py b/src/zeroband/utils/misc_utils.py
new file mode 100644
index 00000000..364abdaf
--- /dev/null
+++ b/src/zeroband/utils/misc_utils.py
@@ -0,0 +1,42 @@
+
+class IntRef:
+    def __init__(self, value):
+        self.value = value
+
+    def __repr__(self):
+        return f"IntRef({self.value})"
+
+    def __iadd__(self, other):
+        self.value += other
+        return self
+
+    def __add__(self, other):
+        return IntRef(self.value + other)
+
+    def __isub__(self, other):
+        self.value -= other
+        return self
+
+    def __sub__(self, other):
+        return IntRef(self.value - other)
+
+    def __int__(self):
+        return self.value
+
+    def __gt__(self, other):
+        return self.value > other
+
+    def __lt__(self, other):
+        return self.value < other
+
+    def __eq__(self, other):
+        return self.value == other
+
+    def __ne__(self, other):
+        return self.value != other
+
+    def __ge__(self, other):
+        return self.value >= other
+
+    def __le__(self, other):
+        return self.value <= other
\ No newline at end of file

From 471f64050c10dab2b8de715d22808e9cf3e22aa2 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Mon, 7 Apr 2025 05:19:54 +0000
Subject: [PATCH 07/47] configurable async/non-async DiLoCo

---
 configs/debug/diloco.toml |   2 +-
 src/zeroband/train.py     | 106 ++++++++++++++++++++++++++++++--------
 2 files changed, 85 insertions(+), 23 deletions(-)

diff --git a/configs/debug/diloco.toml b/configs/debug/diloco.toml
index 923860b0..b6119baf 100644
--- a/configs/debug/diloco.toml
+++ b/configs/debug/diloco.toml
@@ -19,7 +19,7 @@ fake = true
 
 [diloco]
 inner_steps = 16
-delayed_update = true
+delayed_update = false
 
 [pccl]
 ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 0feff9ef..b2e84636 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -101,23 +101,6 @@ def perform_grad_accum_steps(
     return total_loss, current_lr
 
 
-def compute_crc32(tensor: torch.Tensor) -> int:
-    tensor_cpu = tensor.detach().cpu()
-    tensor_contiguous = tensor_cpu.contiguous()
-    tensor_np = tensor_contiguous.numpy()
-    tensor_bytes = tensor_np.tobytes()
-    checksum = zlib.crc32(tensor_bytes)
-    return checksum
-
-
-def print_outer_crc32s(outer_optimizer: torch.optim.Optimizer):
-    for outer_enry in outer_optimizer.state.values():
-        for key, value in outer_enry.items():
-            if isinstance(value, torch.Tensor):
-                checksum = compute_crc32(value)
-                print(f"{key} checksum: {checksum}")
-
-
 def run_inner_steps(
         model: torch.nn.Module,
         train_dataloader_iterator: iter,
@@ -371,6 +354,85 @@ def run_all_reduce():
     return all_reduce_thread
 
 
+def run_sync_outer_step(
+        model: torch.nn.Module,
+        last_pseudo_grads: List[torch.Tensor],
+        outer_parameters_list: List[torch.nn.Parameter],
+        outer_optimizer: torch.optim.Optimizer,
+        shared_state: SharedState,
+
+        all_reduce_thread: threading.Thread,
+
+        communicator: Communicator,
+        train_profiler: Profiler,
+        logger: Logger,
+
+        topology_updated: bool,
+        iter_num: int,
+        num_syncs: IntRef
+):
+    # Compute current pseudo grads as difference between outer and inner state.
+    # Inner state is advanced by inner steps, outer state is unchanged
+    outer_grads: List[torch.Tensor] = []
+    param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
+    outer_p: torch.nn.Parameter  # [ torch.Tensor ]
+    for param, outer_p in zip(model.parameters(), outer_parameters_list):
+        param_data: torch.Tensor | DTensor = param.data
+        outer_p_data: torch.Tensor = outer_p.data
+        if isinstance(param_data, DTensor):
+            param_data = param_data.to_local()
+        outer_p.grad = outer_p_data - param_data.to('cpu')
+        outer_grads.append(outer_p.grad)
+
+    with train_profiler.session("all_reduce_multiple_with_retry"):
+        start_time = time.time()
+
+        all_reduce_success = pccl_utils.all_reduce_multiple_with_retry(
+            communicator,
+            outer_grads,
+            ReduceOp.AVG
+        )
+
+        end_time = time.time()
+        print(f"All-Reduce took {end_time - start_time} seconds")
+        if not all_reduce_success:
+            print("All peers left except me... continuing alone.")
+
+    outer_optimizer.step()
+    outer_optimizer.zero_grad()
+
+    sync_inner_with_outer_state(model, outer_parameters_list)
+
+
+def run_outer_step(
+        model: torch.nn.Module,
+        last_pseudo_grads: List[torch.Tensor],
+        outer_parameters_list: List[torch.nn.Parameter],
+        outer_optimizer: torch.optim.Optimizer,
+        shared_state: SharedState,
+
+        all_reduce_thread: threading.Thread,
+
+        communicator: Communicator,
+        train_profiler: Profiler,
+        logger: Logger,
+
+        topology_updated: bool,
+        iter_num: int,
+        num_syncs: IntRef,
+        delayed_update: bool
+) -> Optional[threading.Thread]:
+    if delayed_update:
+        return run_async_outer_step(model, last_pseudo_grads, outer_parameters_list, outer_optimizer, shared_state,
+                                    all_reduce_thread, communicator, train_profiler, logger, topology_updated, iter_num,
+                                    num_syncs)
+    else:
+        run_sync_outer_step(model, last_pseudo_grads, outer_parameters_list, outer_optimizer, shared_state,
+                            all_reduce_thread, communicator, train_profiler, logger, topology_updated, iter_num,
+                            num_syncs)
+        return None
+
+
 def sync_inner_with_outer_state(model: torch.nn.Module, outer_parameters_list: List[torch.nn.Parameter]):
     with torch.no_grad():
         inner_param: torch.nn.Parameter  # [ torch.Tensor | DTensor ]
@@ -409,7 +471,6 @@ def run_shared_state_sync(
 def make_shared_state(outer_parameters: Dict[str, torch.nn.Parameter],
                       outer_optimizer: Optimizer,
                       iter_num: torch.Tensor):
-
     # Build the shared state that includes:
     #   - The outer parameters
     #   - The outer optimizer state (e.g. momentum buffers)
@@ -575,9 +636,9 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
     # -------------------------------------------------------------------------
     # ! Critical PCCL-related state for async DiLoCo
+    # None / empty if async DiLoCo is not used
     all_reduce_thread: Optional[threading.Thread] = None
     last_pseudo_grads: List[torch.Tensor] = []
-
     # -------------------------------------------------------------------------
 
     timing_events = []
@@ -660,9 +721,10 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
         if config.diloco is not None:
             with train_profiler.session("outer_step"):
-                all_reduce_thread = run_async_outer_step(model, last_pseudo_grads, outer_parameters_list,
-                                                         outer_optimizer, shared_state, all_reduce_thread, communicator,
-                                                         train_profiler, logger, topology_updated, iter_num, num_syncs)
+                all_reduce_thread = run_outer_step(model, last_pseudo_grads, outer_parameters_list,
+                                                   outer_optimizer, shared_state, all_reduce_thread, communicator,
+                                                   train_profiler, logger, topology_updated, iter_num, num_syncs,
+                                                   config.diloco.delayed_update)
 
         iter_num += 1
         training_progress.outer_step = iter_num.item()

From 2a842add789da7e803ed85b102abde9e1788543e Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Tue, 8 Apr 2025 03:36:19 +0000
Subject: [PATCH 08/47] implemented nibble dataset

---
 configs/debug/diloco.toml             |   7 +-
 pyproject.toml                        |   5 +-
 src/zeroband/config.py                |  33 ++-
 src/zeroband/data.py                  | 363 +++++++-------------------
 src/zeroband/train.py                 |  41 +--
 src/zeroband/utils/math_utils.py      |   6 +
 src/zeroband/utils/nibble_utils.py    |  62 +++++
 src/zeroband/utils/rand_utils.py      |  16 ++
 src/zeroband/utils/tokenizer_utils.py |  45 ++--
 tests/test_data/val_1.bin             | Bin 0 -> 1529163 bytes
 tests/test_nibble_dataset.py          |  24 ++
 tests/test_read_nibbles.py            | 100 +++++++
 uv.lock                               | 192 +++++++++-----
 13 files changed, 511 insertions(+), 383 deletions(-)
 create mode 100644 src/zeroband/utils/math_utils.py
 create mode 100644 src/zeroband/utils/nibble_utils.py
 create mode 100644 src/zeroband/utils/rand_utils.py
 create mode 100644 tests/test_data/val_1.bin
 create mode 100644 tests/test_nibble_dataset.py
 create mode 100644 tests/test_read_nibbles.py

diff --git a/configs/debug/diloco.toml b/configs/debug/diloco.toml
index b6119baf..41d642f0 100644
--- a/configs/debug/diloco.toml
+++ b/configs/debug/diloco.toml
@@ -1,11 +1,12 @@
 model_name = "debugmodel"
-model_type = "llama2"
+model_type = "llama3"
 
 wandb = false
 log_all_ranks = true
 
 [hardware]
 micro_batch_size = 8
+torch_compile = false
 
 [train]
 batch_size = 32
@@ -15,7 +16,9 @@ num_warmup_steps = 10
 num_decay_steps = 1000
 
 [data]
-fake = true
+dataset_name_or_paths = '/home/mike/IntelliJProjects/dataproctest/working_dir/train_0.bin,/home/mike/IntelliJProjects/dataproctest/working_dir/train_1.bin'
+dataset_ratio = "90:10"
+token_bit_size = 17
 
 [diloco]
 inner_steps = 16
diff --git a/pyproject.toml b/pyproject.toml
index b8558255..4fdd98d6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,7 @@ dependencies = [
     "transformers>=4.44.2",
     "datasets>=3.0.0",
     "pydantic_config @ git+https://github.com/samsja/pydantic_config.git@b7becc3",
-    "tomili",
+    "tomli",
     "torchdata>=0.8.0",
     "fsspec[gcs]>=2024.3.1",
     "ninja",
@@ -19,6 +19,7 @@ dependencies = [
     "pyarrow",
     "psutil",
     "wandb",
+    "numba",
     "imageio[ffmpeg]",
     "pccl @ git+https://github.com/PrimeIntellect-ai/pccl.git@main#subdirectory=python/framework"
 ]
@@ -39,4 +40,4 @@ allow-direct-references = true # allow direct references to git repos in depende
 line-length = 120
 
 [tool.uv]
-dev-dependencies = ["ruff>=0.5.0", "pre-commit>=3.0.0", "pytest>=7.0.0", "faker", "matplotlib"]
+dev-dependencies = ["ruff>=0.5.0", "pre-commit>=3.0.0", "pytest>=7.0.0", "faker", "matplotlib", "transformers"]
diff --git a/src/zeroband/config.py b/src/zeroband/config.py
index 8dff00b1..35364efb 100644
--- a/src/zeroband/config.py
+++ b/src/zeroband/config.py
@@ -11,12 +11,13 @@ class Compression(Enum):
 
 
 class DataConfig(BaseConfig):
-    dataset_name_or_paths: str = "datasets/fineweb-edu"
+    dataset_name_or_paths: str | None = None
     val_dataset_name_or_paths: str | None = None
     sequence_packing: bool = True
     seq_length: int = 1024
+    token_bit_size: int = 16
     fake: bool = False
-    num_workers: int = 4
+    num_workers: int = 1
     max_train_samples: int | None = None
     max_eval_samples: int | None = None
     dataset_ratio: str | None = None
@@ -25,6 +26,30 @@ class DataConfig(BaseConfig):
     reverse_data_files: bool = False
     split_by_data_rank: bool = True
 
+    @model_validator(mode="after")
+    def data_config_valid(self):
+        assert self.fake == (
+                    self.dataset_name_or_paths is None), "Data must be fake if 'dataset_name_or_paths' is not set"
+
+        if self.dataset_ratio is None:
+            return self
+
+        assert self.dataset_name_or_paths is not None, "'dataset_name_or_paths' must be set if 'dataset_ratio' is set"
+        dataset_files = self.dataset_name_or_paths.split(',')
+        ratio_texts = self.dataset_ratio.split(":")
+        assert len(dataset_files) == len(
+            ratio_texts), "Number of files specified in 'dataset_name_or_paths' must be the same as number of ratios specified in 'dataset_ratio'"
+
+        ratios = []
+        ratio_sum = 0
+        for ratio_text in ratio_texts:
+            assert ratio_text.isdigit(), "Ratio must be an integer"
+            ratio = int(ratio_text)
+            ratios.append(ratio)
+            ratio_sum += ratio
+
+        assert ratio_sum == 100, "Dataset ratios must sum to 100%"
+        return self
 
 class SGDConfig(BaseConfig):
     type: Literal["sgd"] = "sgd"
@@ -156,8 +181,8 @@ class Config(BaseConfig):
 
     # sub config
     diloco: DilocoConfig | None = None
-    data: DataConfig = DataConfig()
-    train: TrainConfig = TrainConfig()
+    data: DataConfig
+    train: TrainConfig
     hardware: HardwareConfig
     monitor: MonitorConfig | None = None
     pccl: PcclConfig | None = None
diff --git a/src/zeroband/data.py b/src/zeroband/data.py
index c8f40ca3..9d04cff9 100644
--- a/src/zeroband/data.py
+++ b/src/zeroband/data.py
@@ -1,19 +1,18 @@
 import functools
+import os.path
 import random
 from abc import ABC
 from dataclasses import dataclass, asdict
-from typing import Any, Generator, Optional, List, Dict, TypedDict
+from typing import Any, Generator, List
 
 import torch
-from datasets import load_dataset_builder, BuilderConfig
-from pyarrow import parquet as pq
 from torch.distributed.checkpoint.stateful import Stateful
 from torch.utils.data import IterableDataset
 from torchdata.stateful_dataloader import StatefulDataLoader
-from transformers import PreTrainedTokenizer
 
 from zeroband.config import DataConfig
-from zeroband.utils.logger import get_logger
+from zeroband.utils import rand_utils, nibble_utils, math_utils
+from zeroband.utils.tokenizer_utils import TokenizerInfo
 
 DEBUG_VOCAB_SIZE = 1024
 
@@ -21,6 +20,7 @@
 class StatefulDataset(IterableDataset, Stateful, ABC):
     ...
 
+
 class FakeTokenizedDataset(StatefulDataset):
     """This is a dummy dataset that generates random sequences of length seq_len and vocab_size"""
 
@@ -47,167 +47,70 @@ def load_state_dict(self, state_dict):
             next(itera)
 
 
-class BatchOutput(TypedDict):
-    input_ids: torch.IntTensor
-    labels: torch.IntTensor
-    seqlens: list[int]
-
-
-@dataclass
-class SequencePackingDataSetState:
-    inputs_ids: list[int]
-    labels: list[int]
-    seqlens: list[int]
-
-
-class SequencePackingDataSet(StatefulDataset):
-    """
-    This class wrap a dataset and wrap it into an iterable that return sequence of max_seq_length
-    packed
-    """
-
-    def __init__(self, dataset: StatefulDataset, max_seq_length: int, eos_token: int):
-        self.dataset = dataset
-        self.max_seq_length = max_seq_length
-        self.eos_token = eos_token
-
-        self.state = SequencePackingDataSetState(inputs_ids=[], labels=[], seqlens=[])
-
-    def __iter__(self) -> Generator[BatchOutput, Any, None]:
-        for og_sample in self.dataset:
-            og_sample: list[int] = og_sample["input_ids"]
-
-            og_sample = og_sample + [self.eos_token]
-            sample_inputs_ids = og_sample[:-1]
-            sample_labels = og_sample[1:]
-
-            token_remaining = self.max_seq_length - len(self.state.inputs_ids)
-
-            if len(sample_inputs_ids) < token_remaining:
-                self.state.inputs_ids.extend(sample_inputs_ids)
-                self.state.labels.extend(sample_labels)
-                self.state.seqlens.append(len(sample_inputs_ids))
-
-            else:
-                self.state.inputs_ids.extend(sample_inputs_ids[:token_remaining])
-                self.state.labels.extend(sample_labels[:token_remaining])
-                self.state.seqlens.append(token_remaining)
-
-                data = {
-                    "input_ids": torch.Tensor(self.state.inputs_ids).to(dtype=torch.long),
-                    "labels": torch.Tensor(self.state.labels).to(dtype=torch.long),
-                    "seqlens": self.state.seqlens,
-                }
-                self.state.inputs_ids = []
-                self.state.labels = []
-                self.state.seqlens = []
-
-                yield data
-
-    def state_dict(self):
-        return {"dataset": self.dataset.state_dict(), "state": asdict(self.state)}
-
-    def load_state_dict(self, state_dict):
-        self.dataset.load_state_dict(state_dict["dataset"])
-        self.state = SequencePackingDataSetState(**state_dict["state"])
+class NibbleDataset(StatefulDataset):
 
+    def __init__(self, nibble_file: str, seq_len: int, tokenizer_info: TokenizerInfo, n_bits: int, seed: int):
+        if not os.path.exists(nibble_file):
+            raise ValueError("Supplied Nibble-file does not exist!")
 
-def collate_fn(samples: list[dict[str, torch.LongTensor]]) -> dict[str, torch.LongTensor | list[torch.LongTensor]]:
-    assert samples[0].keys() == {"input_ids", "labels", "seqlens"}
-
-    inputs_ids = []
-    labels = []
-    seqlens = []
-
-    for sample in samples:
-        inputs_ids.append(sample["input_ids"])
-        labels.append(sample["labels"])
-
-        seqlens.append(torch.Tensor(sample["seqlens"]).long())
+        self.nibble_file = nibble_file
+        self.file_size = os.path.getsize(nibble_file)
+        self.seq_len = seq_len
+        self.tokenizer_info = tokenizer_info
+        self.vocab_size = tokenizer_info.vocab_size
 
-    return {
-        "input_ids": torch.stack(inputs_ids, dim=0),
-        "labels": torch.stack(labels, dim=0),
-        "seqlens": seqlens,
-    }
+        if self.vocab_size > 2 ** n_bits:
+            raise ValueError(
+                f"Cannot represent vocab_size with supplied number of bits! vocab_size: {tokenizer_info.vocab_size}, n_bits: {n_bits}, 2**n_bits: {2 ** n_bits}")
 
+        self.n_bits = n_bits
 
-@dataclass
-class PQDatasetState:
-    files: List[str]
-    file_index: int
-    row_index: int
-    increment: int
-    init_row_index: int
+        self.lsr_seed = seed
 
+    def __iter__(self) -> Generator[dict[str, Any], Any, None]:
+        with open(self.nibble_file, "rb") as f:
+            while True:
+                seek_pos, self.lsr_seed = rand_utils.lsfr_rand_u64(self.lsr_seed, hi=self.file_size)
 
-class ParquetDataset(StatefulDataset):
-    """
-    this class is a wrapper around a parquet dataset compatible with datasets and statefull compatible. The dataset is infinite and will restart from the last state if the iterator is exhausted.
-    TODO:
-    * [ ] handle mutli proc dataloader pytorch
-    """
+                # floor to nearest nibble start
+                num_nibbles = (seek_pos * 8) // self.n_bits
+                nibble_start_bit = num_nibbles * self.n_bits
 
-    def __init__(self, files: List[str], tokenizer: PreTrainedTokenizer):
-        self.arg_files = files
-        self.tokenizer = tokenizer
-
-        self.state = None
-
-    def _lazy_init(self):
-        worker_info = torch.utils.data.get_worker_info()
-        if worker_info is not None:
-            if worker_info.num_workers > len(self.arg_files):
-                get_logger().warning(
-                    f"dataloader rank {worker_info.id} Number of workers {worker_info.num_workers} is greater than the number of files {len(self.arg_files)}"
-                )
-                self.state = PQDatasetState(
-                    files=self.arg_files,
-                    file_index=0,
-                    row_index=worker_info.id,
-                    increment=worker_info.num_workers,
-                    init_row_index=worker_info.id,
-                )
-                return
-
-            files = self.arg_files[worker_info.id:: worker_info.num_workers]
-        else:
-            files = self.arg_files
-
-        self.state = PQDatasetState(files=files, file_index=0, row_index=0, increment=1, init_row_index=0)
+                # find the next bit where a nibble starts that aligns with a byte boundary
+                no_carry_start_bit = math_utils.next_common_multiple(self.n_bits, 8, nibble_start_bit)
 
-    def __iter__(self):
-        # we lazy init the parquet dataset to get the worker info from dataloader multi process
-        if self.state is None:
-            self._lazy_init()
+                chunk_start_pos = no_carry_start_bit // 8
 
-        while True:
-            file = self.state.files[self.state.file_index]
+                # compute num bytes to read to be (seq_len + 1) * n_bits ceil-ed to next byte
+                chunk_size = ((self.seq_len + 1) * self.n_bits + 7) // 8
 
-            parquet_file = pq.ParquetFile(file)
-            table = parquet_file.read()["text"]
+                f.seek(chunk_start_pos)
+                data = f.read(chunk_size)
 
-            while True:
-                row = table[self.state.row_index]
+                tokens, _, _ = nibble_utils.read_nibbles(data, self.n_bits)
+                tokens = torch.tensor(tokens, dtype=torch.int64, device='cpu')
 
-                self.state.row_index += self.state.increment
-                if self.state.row_index >= len(table):
-                    self.state.row_index = self.state.init_row_index
-                    self.state.file_index += 1
-                    if self.state.file_index >= len(self.state.files):  # infinite datasets
-                        self.state.file_index = 0
+                input_ids = tokens[:-1]
+                labels = tokens[1:]
 
-                yield {"input_ids": self.tokenizer.encode(str(row))}
+                # create document lengths from where eot tokens are placed inside chunk
+                document_lengths = []
+                cur_len = 0
+                for t in input_ids:
+                    cur_len += 1
+                    if t == self.tokenizer_info.eot_token:
+                        document_lengths.append(cur_len)
+                        cur_len = 0
+                document_lengths.append(cur_len)
 
-    @property
-    def is_empty(self):
-        return len(self.arg_files) == 0
+                document_lengths = torch.tensor(document_lengths, dtype=torch.int64, device='cpu')
+                yield {'input_ids': input_ids, 'labels': labels, 'seqlens': document_lengths}
 
-    def state_dict(self) -> dict[str, Any]:
-        return asdict(self.state) if self.state is not None else {}
+    def state_dict(self):
+        return {"lsr_seed": self.lsr_seed}
 
     def load_state_dict(self, state_dict):
-        self.state = PQDatasetState(**state_dict)
+        self.lsr_seed = state_dict["lsr_seed"]
 
 
 @dataclass
@@ -224,7 +127,7 @@ class InterleaveDataset(StatefulDataset):
     The state can be saved and restored. Under the hood we just fast forward the random generator to the current position.
     """
 
-    def __init__(self, datasets: List[ParquetDataset], probabilities: List[float], seed: int = 42):
+    def __init__(self, datasets: List[StatefulDataset], probabilities: List[float], seed: int = 42):
         assert len(datasets) > 0, "At least one dataset is required"
         assert len(datasets) == len(probabilities), "The number of datasets and probabilities must be the same"
 
@@ -232,11 +135,8 @@ def __init__(self, datasets: List[ParquetDataset], probabilities: List[float], s
         self.datasets = []
 
         for dataset, prob in zip(datasets, probabilities):
-            if not dataset.is_empty:
-                self.datasets.append(dataset)
-                self.probabilities.append(prob)
-            else:
-                get_logger().warning(f"Dataset {dataset} is empty. Skipping.")
+            self.datasets.append(dataset)
+            self.probabilities.append(prob)
 
         self.state = InterleaveDatasetState(current_index=0, seed=seed)
         self._init_random_state()
@@ -275,8 +175,48 @@ def load_state_dict(self, state_dict):
         self._init_random_state()
 
 
+def collate_fn(samples: list[dict[str, torch.LongTensor]]) -> dict[str, torch.LongTensor | list[torch.LongTensor]]:
+    assert samples[0].keys() == {"input_ids", "labels", "seqlens"}
+
+    inputs_ids = []
+    labels = []
+    sequence_lengths = []
+
+    doc_id = 0
+    for sample in samples:
+        inputs_ids.append(sample["input_ids"])
+        labels.append(sample["labels"])
+        sequence_lengths.append(sample["seqlens"])
+        doc_id += 1
+
+    return {
+        "input_ids": torch.stack(inputs_ids, dim=0),
+        "labels": torch.stack(labels, dim=0),
+        "seqlens": sequence_lengths,
+    }
+
+
+def make_mixed_dataset(data_config: DataConfig, tokenizer_info: TokenizerInfo) -> StatefulDataset:
+    dataset_paths = data_config.dataset_name_or_paths.split(',')
+    probabilities = [int(ratio) / 100 for ratio in data_config.dataset_ratio.split(':')]
+
+    rand = random.Random()
+
+    # iterator seed *must* be random to avoid different peers training on same data, killing the point of DDP
+    # There is no "rank" in PCCL, and even if there was it would still not be a safe seed.
+    # There are internal UUIDs, but they are not exposed for now.
+    # Random is fine for now.
+    iterator_seed = rand.randint(0, 2 ** 31 - 1)
+
+    return InterleaveDataset(
+        [NibbleDataset(dataset_path, data_config.seq_length, tokenizer_info, data_config.token_bit_size,
+                       iterator_seed) for dataset_path in dataset_paths],
+        probabilities
+    )
+
+
 def make_dataloader(
-        tokenizer,
+        tokenizer_info: TokenizerInfo,
         mpi_world_size: int,
         mpi_rank: int,
         batch_size: int,
@@ -285,120 +225,11 @@ def make_dataloader(
     if data_config.fake:
         train_dataset = FakeTokenizedDataset(data_config.seq_length, DEBUG_VOCAB_SIZE)
     else:
-        train_dataset = load_all_datasets(
-            data_config=data_config, split="train", tokenizer=tokenizer, mpi_rank=mpi_rank, mpi_world_size=mpi_world_size
-        )
+        train_dataset = make_mixed_dataset(data_config, tokenizer_info)
 
-    dataset = SequencePackingDataSet(train_dataset, data_config.seq_length, eos_token=tokenizer.eos_token_id)
     return StatefulDataLoader(
-        dataset,
+        train_dataset,
         batch_size=batch_size,
         collate_fn=collate_fn,
         num_workers=data_config.num_workers,
     )
-
-
-@functools.lru_cache(maxsize=None)
-def _get_ds_config_dict(path: str, name: Optional[str] = None) -> Dict[str, BuilderConfig]:
-    ds_builder = load_dataset_builder(path=path, name=name)
-    return ds_builder.builder_configs
-
-
-def _get_datafiles(path: str, name: Optional[str] = None, split: str = "train") -> List[str]:
-    builder_config = _get_ds_config_dict(path=path, name=name)
-    if name is None or len(name) == 0:
-        if "default" not in builder_config:
-            get_logger().warning(f"Default config not found for {path}. Using first config.")
-            name = next(iter(builder_config.keys()))
-        else:
-            name = "default"
-    return builder_config[name].data_files[split]
-
-
-def _load_datasets(
-        dataset_names: str,
-        split: str,
-        tokenizer: PreTrainedTokenizer,
-        data_rank: Optional[int] = None,
-        data_world_size: Optional[int] = None,
-        streaming: bool = True,
-        probabilities: Optional[List[float]] = None,
-        reverse_data_files: bool = False,
-) -> InterleaveDataset:
-    get_logger().debug(dataset_names)
-    ds_args = []
-    for _ds in dataset_names.split(","):
-        _ds_name, _, _ds_config = _ds.partition(":")
-        _ds_args: dict[str, Any] = {"path": _ds_name}
-        if _ds_config:
-            _ds_args["name"] = _ds_config
-        _data_files = _get_datafiles(_ds_name, _ds_config, split)
-        if reverse_data_files:
-            _data_files = _data_files[::-1]
-            _ds_args["data_files"] = _data_files
-        if data_rank is not None and data_world_size is not None:
-            _ds_args["data_files"] = _data_files[data_rank::data_world_size]
-
-        ds_args.append(_ds_args)
-
-    # logger.debug(f"Datasets ({split}):\n" + "\n".join(map(_nice_print, ds_args)))
-    # logger.debug(f"Probabilities: {probabilities}")
-    get_logger().debug(f"Loading datasets{' in streaming mode' if streaming else ''}")
-    datasets = []
-    for ds_arg in ds_args:
-        # logger.debug(f"Loading dataset: {ds_arg['data_files']}")
-        _ds = ParquetDataset(files=ds_arg["data_files"], tokenizer=tokenizer)
-        datasets.append(_ds)
-
-    if len(datasets) > 1:
-        ds = InterleaveDataset(datasets=datasets, probabilities=probabilities)
-    else:
-        ds = datasets[0]
-
-    get_logger().info(f"Loaded datasets ({split})")
-    return ds
-
-
-def _get_probabilities(data_config: DataConfig) -> Optional[List[float]]:
-    if data_config.dataset_ratio is None:
-        return None
-    if len(data_config.dataset_name_or_paths.split(",")) != len(data_config.dataset_ratio.split(":")):
-        raise ValueError("Number of datasets and dataset ratios must be the same")
-    nums = [float(i) for i in data_config.dataset_ratio.split(":")]
-    denom = sum(nums)
-    return [i / denom for i in nums]
-
-
-def load_all_datasets(
-        data_config: DataConfig,
-        split: str,
-        tokenizer: PreTrainedTokenizer,
-        mpi_rank: int,
-        mpi_world_size: int,
-) -> InterleaveDataset:
-    """Load all datasets and interleave them"""
-
-    if data_config.split_by_data_rank and (
-            data_config.data_rank is not None and data_config.data_world_size is not None
-    ):
-        split_rank = data_config.data_rank * mpi_world_size + mpi_rank
-        split_world_size = data_config.data_world_size * mpi_world_size
-    else:
-        split_rank = mpi_rank
-        split_world_size = mpi_world_size
-
-    get_logger().info("Loading Train dataset(s)")
-
-    ds = _load_datasets(
-        dataset_names=data_config.dataset_name_or_paths,
-        split=split,
-        data_rank=split_rank,
-        data_world_size=split_world_size,
-        probabilities=_get_probabilities(data_config),
-        reverse_data_files=data_config.reverse_data_files,
-        tokenizer=tokenizer,
-    )
-
-    get_logger().info(f"Train dataset: {ds}")
-
-    return ds
\ No newline at end of file
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index b2e84636..73efc21f 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -1,10 +1,8 @@
 import os
 import threading
 import time
-import zlib
 from dataclasses import asdict
 from logging import Logger
-from tabnanny import check
 from typing import TYPE_CHECKING, Optional, Iterator, List, Dict, Iterable, Tuple
 
 import torch
@@ -31,7 +29,7 @@
 from zeroband.utils.mfu_tracker import FlopCounter, PrecisionMode, \
     get_flops_promised_pt
 from zeroband.utils.misc_utils import IntRef
-from zeroband.utils.tokenizer_utils import make_tokenizer
+from zeroband.utils.tokenizer_utils import get_tokenizer_info
 from zeroband.utils.logger import get_logger
 from zeroband.utils.profiler import Profiler, ProfilerCollection
 
@@ -80,8 +78,8 @@ def perform_grad_accum_steps(
             batch = next(train_dataloader_iterator)
             input_ids = batch["input_ids"].to("cuda")
             labels = batch["labels"].to("cuda")
-            seqlens = [seqlen.to("cuda") for seqlen in batch["seqlens"]]
-            block_mask = create_block_mask_from_seqlens(seqlens)
+            seq_lengths = [seqlen.to("cuda") for seqlen in batch["seqlens"]]
+            block_mask = create_block_mask_from_seqlens(seq_lengths)
 
         with profiler.session("model.forward"):
             logits = model(tokens=input_ids, block_mask=block_mask, flop_counter=flop_counter)
@@ -356,20 +354,11 @@ def run_all_reduce():
 
 def run_sync_outer_step(
         model: torch.nn.Module,
-        last_pseudo_grads: List[torch.Tensor],
         outer_parameters_list: List[torch.nn.Parameter],
         outer_optimizer: torch.optim.Optimizer,
-        shared_state: SharedState,
-
-        all_reduce_thread: threading.Thread,
-
         communicator: Communicator,
         train_profiler: Profiler,
-        logger: Logger,
-
-        topology_updated: bool,
-        iter_num: int,
-        num_syncs: IntRef
+        logger: Logger
 ):
     # Compute current pseudo grads as difference between outer and inner state.
     # Inner state is advanced by inner steps, outer state is unchanged
@@ -394,9 +383,9 @@ def run_sync_outer_step(
         )
 
         end_time = time.time()
-        print(f"All-Reduce took {end_time - start_time} seconds")
+        logger.info(f"All-Reduce took {end_time - start_time} seconds")
         if not all_reduce_success:
-            print("All peers left except me... continuing alone.")
+            logger.info("All peers left except me... continuing alone.")
 
     outer_optimizer.step()
     outer_optimizer.zero_grad()
@@ -427,9 +416,7 @@ def run_outer_step(
                                     all_reduce_thread, communicator, train_profiler, logger, topology_updated, iter_num,
                                     num_syncs)
     else:
-        run_sync_outer_step(model, last_pseudo_grads, outer_parameters_list, outer_optimizer, shared_state,
-                            all_reduce_thread, communicator, train_profiler, logger, topology_updated, iter_num,
-                            num_syncs)
+        run_sync_outer_step(model, outer_parameters_list, outer_optimizer, communicator, train_profiler, logger)
         return None
 
 
@@ -511,10 +498,10 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
     setup_profiler = Profiler()
 
     # Load tokenizer
-    tokenizer = make_tokenizer(config)
+    tokenizer_info = get_tokenizer_info(config)
 
     train_dataloader = make_dataloader(
-        tokenizer=tokenizer,
+        tokenizer_info=tokenizer_info,
         mpi_world_size=mpi_config.mpi_world_size if mpi_config is not None else 1,
         mpi_rank=mpi_config.mpi_rank if mpi_config is not None else 1,
         batch_size=config.hardware.micro_batch_size,
@@ -526,7 +513,7 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
         with torch_utils.default_device('cuda'):
             model, model_config = make_model(
                 config,
-                vocab_size=len(tokenizer),
+                vocab_size=tokenizer_info.vocab_size,
             )
         num_param_scalars = model.count_parameters()
         logger.info(f"Number of parameters: {num_param_scalars}")
@@ -686,10 +673,10 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
         # TODO: Make minimum num pccl peers configurable
         local_world_size = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
-        if local_world_size < 2:
-            print("Waiting for more workers to join...")
-            time.sleep(1)
-            continue
+        #if local_world_size < 2:
+            # print("Waiting for more workers to join...")
+            #time.sleep(1)
+            #continue
 
         if topology_updated:
             run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, num_syncs, train_profiler,
diff --git a/src/zeroband/utils/math_utils.py b/src/zeroband/utils/math_utils.py
new file mode 100644
index 00000000..406ed9b9
--- /dev/null
+++ b/src/zeroband/utils/math_utils.py
@@ -0,0 +1,6 @@
+import math
+
+
+def next_common_multiple(a: int, b: int, x: int) -> int:
+    lcm_val = abs(a * b) // math.gcd(a, b)
+    return ((x // lcm_val) + 1) * lcm_val
diff --git a/src/zeroband/utils/nibble_utils.py b/src/zeroband/utils/nibble_utils.py
new file mode 100644
index 00000000..9c60dca1
--- /dev/null
+++ b/src/zeroband/utils/nibble_utils.py
@@ -0,0 +1,62 @@
+from typing import Tuple, List
+
+import numba
+import numpy as np
+from numba.typed import List as TypedList
+
+
+@numba.jit
+def read_nibbles_numba(arr: np.ndarray, n_bits: int, carry: int, carry_bits: int):
+    """
+    Reads integers of size n_bits from an array of bytes (uint8), interpreting the bit stream
+    in big-endian order (MSB-first). That is, the first bit in the stream is the most significant
+    bit of the first integer.
+
+    Parameters:
+      arr (np.ndarray): Array of uint8 values representing the byte stream.
+      n_bits (int): Number of bits per integer.
+      carry (int): Carry-over bits from previous chunks.
+      carry_bits (int): Number of valid bits in carry.
+
+    Returns:
+      Tuple containing:
+        - A typed list of integers read from the combined bit stream.
+        - The updated carry.
+        - The updated number of carry bits.
+    """
+    result = TypedList()
+    for i in range(arr.shape[0]):
+        # Shift the current carry left by 8 bits and append the new byte.
+        carry = (carry << 8) | arr[i]
+        carry_bits += 8
+
+        # While we have enough bits for one integer, extract the most significant n_bits.
+        while carry_bits >= n_bits:
+            # Extract the integer from the leftmost (most significant) n_bits.
+            value = carry >> (carry_bits - n_bits)
+            result.append(value)
+            # Remove the extracted bits from carry.
+            carry = carry & ((1 << (carry_bits - n_bits)) - 1)
+            carry_bits -= n_bits
+    return result, carry, carry_bits
+
+
+def read_nibbles(chunk_bytes: bytes, n_bits: int, carry: int = 0, carry_bits: int = 0) -> Tuple[List[int], int, int]:
+    """
+    Reads integers of size n_bits from a byte array while preserving state across chunks.
+    Bits are processed in big-endian order (most significant bit first).
+
+    :param chunk_bytes: The current chunk of bytes.
+    :param n_bits: The number of bits per integer (n_bits <= 64).
+    :param carry: Accumulated bits carried over from the previous chunk (default 0).
+    :param carry_bits: The number of valid bits in carry (default 0).
+
+    :returns:
+     - A list of integers read from the combined carry and current chunk.
+     - The updated carry (remaining bits not enough to form an integer).
+     - The number of valid bits in the updated carry.
+    """
+    # Convert the byte array to a NumPy array for Numba processing.
+    arr = np.frombuffer(chunk_bytes, dtype=np.uint8)
+    result, carry, carry_bits = read_nibbles_numba(arr, n_bits, carry, carry_bits)
+    return list(result), carry, carry_bits
diff --git a/src/zeroband/utils/rand_utils.py b/src/zeroband/utils/rand_utils.py
new file mode 100644
index 00000000..049494c2
--- /dev/null
+++ b/src/zeroband/utils/rand_utils.py
@@ -0,0 +1,16 @@
+import numba
+from typing import Tuple
+
+
+@numba.njit
+def lsfr_rand_u64(lsr_seed, hi) -> Tuple[int, int]:
+    """
+    A 64-bit linear feedback shift register (LFSR) pseudorandom generator.
+
+    :param lsr_seed: The current 64-bit seed (non-zero for max period).
+    :param hi: the maximum value to be returned (exclusive)
+    :return: A tuple (random_value, new_seed).
+    """
+    new_bit = ((lsr_seed >> 63) ^ (lsr_seed >> 62) ^ (lsr_seed >> 60) ^ (lsr_seed >> 59)) & 1
+    new_seed = ((lsr_seed << 1) & 0xFFFFFFFFFFFFFFFF) | new_bit
+    return new_seed % hi, new_seed
\ No newline at end of file
diff --git a/src/zeroband/utils/tokenizer_utils.py b/src/zeroband/utils/tokenizer_utils.py
index e7c5949c..5954140f 100644
--- a/src/zeroband/utils/tokenizer_utils.py
+++ b/src/zeroband/utils/tokenizer_utils.py
@@ -1,27 +1,36 @@
-from transformers import AutoTokenizer
-
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
 from zeroband.config import Config
-from zeroband.data import DEBUG_VOCAB_SIZE
-
-
-class FakeTokenizer(object):
-    def __init__(self):
-        self.vocab_size = DEBUG_VOCAB_SIZE
-        self.bos_token_id = 0
-        self.eos_token_id = 1
-        self.pad_token_id = 2
 
-    def __len__(self):
-        return self.vocab_size
+if TYPE_CHECKING:
+    from zeroband.data import DEBUG_VOCAB_SIZE
 
+@dataclass
+class TokenizerInfo:
+    vocab_size: int
+    bot_token: int
+    eot_token: int
 
-def make_tokenizer(config: Config):
+def get_tokenizer_info(config: Config) -> TokenizerInfo:
     if config.data.fake and config.model_name == "debugmodel":
-        tokenizer = FakeTokenizer()
+        return DEBUG_VOCAB_SIZE
     elif config.model_type == "llama2":
-        tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=True)
+        return TokenizerInfo(
+            # print(len(AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=True)))
+            vocab_size=32000,
+            # print(AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=True).bos_token_id)
+            bot_token=1,
+            # print(AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=True).eos_token_id)
+            eot_token=2,
+        )
     elif config.model_type == "llama3":
-        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B", use_fast=True)
+        return TokenizerInfo(
+            # print(len(AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B", use_fast=True)))
+            vocab_size=128256,
+            # print(AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=True).bos_token_id)
+            bot_token=128000,
+            # print(AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B", use_fast=True).eos_token_id)
+            eot_token=128001,
+        )
     else:
         raise ValueError(f"Model type {config.model_type} not supported")
-    return tokenizer
diff --git a/tests/test_data/val_1.bin b/tests/test_data/val_1.bin
new file mode 100644
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GIT binary patch
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diff --git a/tests/test_nibble_dataset.py b/tests/test_nibble_dataset.py
new file mode 100644
index 00000000..4e9d2564
--- /dev/null
+++ b/tests/test_nibble_dataset.py
@@ -0,0 +1,24 @@
+from transformers import AutoTokenizer
+
+from zeroband.data import NibbleDataset
+from zeroband.utils.tokenizer_utils import TokenizerInfo
+
+
+def test_nibble_dataset():
+    seq_len = 1024
+    ds = NibbleDataset("test_data/val_1.bin", seq_len, TokenizerInfo(vocab_size=128255, eot_token=128001, bot_token=0), 17, 42)
+
+    ds_it = iter(ds)
+
+    for i in range(4):
+        data = next(ds_it)
+        input_ids = data['input_ids']
+        label = data['labels']
+
+        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B", use_fast=True)
+
+        print(input_ids)
+        text = tokenizer.decode(input_ids, skip_special_tokens=False)
+        print(text)
+        assert len(input_ids) == seq_len
+        assert len(label) == seq_len
\ No newline at end of file
diff --git a/tests/test_read_nibbles.py b/tests/test_read_nibbles.py
new file mode 100644
index 00000000..675bb813
--- /dev/null
+++ b/tests/test_read_nibbles.py
@@ -0,0 +1,100 @@
+from typing import List, Tuple
+
+import pytest
+
+from zeroband.utils.nibble_utils import read_nibbles
+
+
+def pack_integers(integers: List[int], n_bits: int) -> bytes:
+    """
+    Packs a list of integers (each fitting in n_bits) into a byte array by concatenating their bits.
+    If the total bits is not a multiple of 8, the final byte is padded with zeros in the LSB positions.
+    """
+    carry = 0
+    carry_bits = 0
+    byte_list = []
+    for num in integers:
+        if num >= (1 << n_bits):
+            raise ValueError(f"Integer {num} does not fit in {n_bits} bits")
+        carry = (carry << n_bits) | num
+        carry_bits += n_bits
+        while carry_bits >= 8:
+            byte = carry >> (carry_bits - 8)
+            byte_list.append(byte)
+            carry = carry & ((1 << (carry_bits - 8)) - 1)
+            carry_bits -= 8
+    if carry_bits > 0:
+        # Pad the remaining bits (on the right) with zeros to complete the last byte.
+        byte = carry << (8 - carry_bits)
+        byte_list.append(byte)
+    return bytes(byte_list)
+
+
+def test_read_nibbles_8_bits():
+    # Test a clean case where each integer occupies exactly one byte.
+    n_bits = 8
+    source = [10, 20, 30, 40]
+    data = pack_integers(source, n_bits)
+    # Process the entire byte array at once.
+    result, carry, carry_bits = read_nibbles(data, n_bits)
+    assert result == source
+    assert carry_bits == 0  # no leftover bits
+
+
+def test_read_nibbles_4_bits_single_chunk():
+    # Test reading 4-bit integers from a single complete chunk.
+    n_bits = 4
+    source = [1, 2, 3, 4, 5, 6, 7, 8]
+    data = pack_integers(source, n_bits)
+    result, carry, carry_bits = read_nibbles(data, n_bits)
+    assert result == source
+    assert carry_bits == 0
+
+
+def test_read_nibbles_4_bits_multiple_chunks():
+    # Test reading 4-bit integers when the byte stream is split across chunks.
+    n_bits = 4
+    source = [1, 2, 3, 4, 5, 6, 7, 8]
+    data = pack_integers(source, n_bits)
+    # Split data into two chunks (e.g., splitting on byte boundaries)
+    mid = len(data) // 2
+    result1, carry, carry_bits = read_nibbles(data[:mid], n_bits)
+    result2, carry, carry_bits = read_nibbles(data[mid:], n_bits, carry, carry_bits)
+    combined = result1 + result2
+    assert combined == source
+    assert carry_bits == 0
+
+
+def test_read_nibbles_3_bits_single_chunk():
+    # Test reading 3-bit integers. Note: values must be in range 0-7.
+    n_bits = 3
+    source = [1, 2, 3, 4, 5]
+    data = pack_integers(source, n_bits)
+    result, carry, carry_bits = read_nibbles(data, n_bits)
+    assert result == source
+    # The leftover bits (if any) should be less than n_bits.
+    assert carry_bits < n_bits
+
+
+def test_read_nibbles_stateful_across_chunks():
+    # Test stateful reading with a non-clean size (n_bits = 5) across arbitrary chunks.
+    n_bits = 5
+    source = [5, 10, 15, 20, 25, 30]  # valid 5-bit integers (range 0-31)
+    data = pack_integers(source, n_bits)
+    chunk_size = 2  # split the data arbitrarily into chunks of 2 bytes
+    result = []
+    carry = 0
+    carry_bits = 0
+    for i in range(0, len(data), chunk_size):
+        chunk = data[i:i + chunk_size]
+        res, carry, carry_bits = read_nibbles(chunk, n_bits, carry, carry_bits)
+        result.extend(res)
+    assert result == source
+    # Ensure any remaining carry does not form a full integer.
+    assert carry_bits < n_bits
+
+    # Uncomment the following line to run the tests when executing this script directly.
+
+
+if __name__ == '__main__':
+    pytest.main()
diff --git a/uv.lock b/uv.lock
index ea815494..3b5dce97 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1054,6 +1054,34 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3a/1d/50ad811d1c5dae091e4cf046beba925bcae0a610e79ae4c538f996f63ed5/kiwisolver-1.4.8-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:65ea09a5a3faadd59c2ce96dc7bf0f364986a315949dc6374f04396b0d60e09b", size = 71762 },
 ]
 
+[[package]]
+name = "llvmlite"
+version = "0.44.0"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
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 ]
 
 [[package]]
@@ -1627,7 +1687,7 @@ name = "nvidia-cudnn-cu12"
 version = "9.1.0.70"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/9f/fd/713452cd72343f682b1c7b9321e23829f00b842ceaedcda96e742ea0b0b3/nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f", size = 664752741 },
@@ -1638,7 +1698,7 @@ name = "nvidia-cufft-cu12"
 version = "11.2.1.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/27/94/3266821f65b92b3138631e9c8e7fe1fb513804ac934485a8d05776e1dd43/nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f083fc24912aa410be21fa16d157fed2055dab1cc4b6934a0e03cba69eb242b9", size = 211459117 },
@@ -1657,9 +1717,9 @@ name = "nvidia-cusolver-cu12"
 version = "11.6.1.9"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12" },
-    { name = "nvidia-cusparse-cu12" },
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/3a/e1/5b9089a4b2a4790dfdea8b3a006052cfecff58139d5a4e34cb1a51df8d6f/nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl", hash = "sha256:19e33fa442bcfd085b3086c4ebf7e8debc07cfe01e11513cc6d332fd918ac260", size = 127936057 },
@@ -1670,7 +1730,7 @@ name = "nvidia-cusparse-cu12"
 version = "12.3.1.170"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/db/f7/97a9ea26ed4bbbfc2d470994b8b4f338ef663be97b8f677519ac195e113d/nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl", hash = "sha256:ea4f11a2904e2a8dc4b1833cc1b5181cde564edd0d5cd33e3c168eff2d1863f1", size = 207454763 },
@@ -3288,12 +3348,14 @@ dependencies = [
     { name = "fsspec", extra = ["gcs"] },
     { name = "imageio", extra = ["ffmpeg"] },
     { name = "ninja" },
+    { name = "numba" },
     { name = "numpy" },
     { name = "pccl" },
     { name = "psutil" },
     { name = "pyarrow" },
     { name = "pydantic-config" },
     { name = "setuptools" },
+    { name = "tomli" },
     { name = "torch" },
     { name = "torchdata" },
     { name = "transformers" },
@@ -3322,12 +3384,14 @@ requires-dist = [
     { name = "imageio", extras = ["ffmpeg"] },
     { name = "lm-eval", marker = "extra == 'all'" },
     { name = "ninja" },
+    { name = "numba" },
     { name = "numpy" },
     { name = "pccl", git = "https://github.com/PrimeIntellect-ai/pccl.git?subdirectory=python%2Fframework&rev=main" },
     { name = "psutil" },
     { name = "pyarrow" },
     { name = "pydantic-config", git = "https://github.com/samsja/pydantic_config.git?rev=b7becc3" },
     { name = "setuptools" },
+    { name = "tomli" },
     { name = "torch", specifier = "==2.6.0" },
     { name = "torchdata", specifier = ">=0.8.0" },
     { name = "transformers", specifier = ">=4.44.2" },

From 12351cbb40c0ab26570b40e2e48745ba84ccd77e Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Tue, 8 Apr 2025 04:26:09 +0000
Subject: [PATCH 09/47] fix bug where outer lr is not set

---
 configs/debug/diloco.toml             | 16 +++++++++++++---
 src/zeroband/lr_scheduler.py          |  2 +-
 src/zeroband/models/llama/__init__.py | 13 +++++++++++--
 src/zeroband/train.py                 | 24 +++++++++++++++++-------
 4 files changed, 42 insertions(+), 13 deletions(-)

diff --git a/configs/debug/diloco.toml b/configs/debug/diloco.toml
index 41d642f0..b1ac8d12 100644
--- a/configs/debug/diloco.toml
+++ b/configs/debug/diloco.toml
@@ -1,4 +1,4 @@
-model_name = "debugmodel"
+model_name = "150M"
 model_type = "llama3"
 
 wandb = false
@@ -6,7 +6,7 @@ log_all_ranks = true
 
 [hardware]
 micro_batch_size = 8
-torch_compile = false
+torch_compile = true
 
 [train]
 batch_size = 32
@@ -15,6 +15,16 @@ batch_size = 32
 num_warmup_steps = 10
 num_decay_steps = 1000
 
+[train.outer_lr_scheduler]
+lr = 1.0
+end_lr = 1.0
+num_decay_steps = 0
+num_warmup_steps = 0
+num_stable_steps = 0
+
+[train.outer_optimizer]
+type = "sgd"
+
 [data]
 dataset_name_or_paths = '/home/mike/IntelliJProjects/dataproctest/working_dir/train_0.bin,/home/mike/IntelliJProjects/dataproctest/working_dir/train_1.bin'
 dataset_ratio = "90:10"
@@ -22,7 +32,7 @@ token_bit_size = 17
 
 [diloco]
 inner_steps = 16
-delayed_update = false
+delayed_update = true
 
 [pccl]
 ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/src/zeroband/lr_scheduler.py b/src/zeroband/lr_scheduler.py
index a3e2eb51..8da5bff7 100644
--- a/src/zeroband/lr_scheduler.py
+++ b/src/zeroband/lr_scheduler.py
@@ -47,7 +47,7 @@ def _compute_decayed_lr_linear(step: int,
     :param learning_rate_scheduler_config: the learning rate scheduler configuration
     :return: the current learning rate for the given step
     """
-    relative = step / learning_rate_scheduler_config.num_decay_steps
+    relative = step / learning_rate_scheduler_config.num_decay_steps if learning_rate_scheduler_config.num_decay_steps > 0 else 0
     lr_range = learning_rate_scheduler_config.lr - learning_rate_scheduler_config.end_lr
     return learning_rate_scheduler_config.lr - lr_range * relative
 
diff --git a/src/zeroband/models/llama/__init__.py b/src/zeroband/models/llama/__init__.py
index e8e23a3f..2e32522c 100644
--- a/src/zeroband/models/llama/__init__.py
+++ b/src/zeroband/models/llama/__init__.py
@@ -34,6 +34,15 @@
 
 llama3_configs = {
     "debugmodel": ModelArgs(dim=256, n_layers=8, n_heads=16, rope_theta=500000),
+    "150M": ModelArgs(
+        dim=1024,
+        n_layers=12,
+        n_heads=16,
+        n_kv_heads=8,
+        ffn_dim_multiplier=1.3,
+        multiple_of=256,
+        rope_theta=500000,
+    ),
     "1B": ModelArgs(
         dim=2048,
         n_layers=18,
@@ -83,8 +92,8 @@
 
 
 def make_model(
-    config: Config,
-    vocab_size: int,
+        config: Config,
+        vocab_size: int,
 ) -> tuple[Transformer, ModelArgs]:
     """
     Constructs a model instance according to the supplied configuration and target vocab size
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 73efc21f..1ec0213d 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -18,7 +18,7 @@
 from zeroband.ccl import ccl_utils, pccl_utils
 from zeroband.ccl.ccl_utils import MPIConfig
 from zeroband.checkpoint import TrainingProgress, load_checkpoint, save_checkpoint, CheckpointInfo
-from zeroband.config import Config
+from zeroband.config import Config, LearningRateSchedulerConfig
 from zeroband.data import make_dataloader
 from zeroband.lr_scheduler import compute_current_lr
 from zeroband.models.llama import make_model
@@ -406,14 +406,22 @@ def run_outer_step(
         train_profiler: Profiler,
         logger: Logger,
 
+        training_progress: TrainingProgress,
+        outer_lr_scheduler_config: LearningRateSchedulerConfig,
+
         topology_updated: bool,
         iter_num: int,
         num_syncs: IntRef,
         delayed_update: bool
 ) -> Optional[threading.Thread]:
+
+    current_lr = compute_current_lr(training_progress.outer_step, outer_lr_scheduler_config)
+    optim_utils.set_optimizer_lr(outer_optimizer, current_lr)
+
     if delayed_update:
         return run_async_outer_step(model, last_pseudo_grads, outer_parameters_list, outer_optimizer, shared_state,
-                                    all_reduce_thread, communicator, train_profiler, logger, topology_updated, iter_num,
+                                    all_reduce_thread, communicator, train_profiler, logger,
+                                    topology_updated, iter_num,
                                     num_syncs)
     else:
         run_sync_outer_step(model, outer_parameters_list, outer_optimizer, communicator, train_profiler, logger)
@@ -673,10 +681,10 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
         # TODO: Make minimum num pccl peers configurable
         local_world_size = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
-        #if local_world_size < 2:
-            # print("Waiting for more workers to join...")
-            #time.sleep(1)
-            #continue
+        if local_world_size < 2:
+            print("Waiting for more workers to join...")
+            time.sleep(1)
+            continue
 
         if topology_updated:
             run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, num_syncs, train_profiler,
@@ -710,7 +718,9 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             with train_profiler.session("outer_step"):
                 all_reduce_thread = run_outer_step(model, last_pseudo_grads, outer_parameters_list,
                                                    outer_optimizer, shared_state, all_reduce_thread, communicator,
-                                                   train_profiler, logger, topology_updated, iter_num, num_syncs,
+                                                   train_profiler, logger,
+                                                   training_progress, config.train.outer_lr_scheduler,
+                                                   topology_updated, iter_num, num_syncs,
                                                    config.diloco.delayed_update)
 
         iter_num += 1

From efe58d9efb924d39ee5b9da04177e5b72fee62dd Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Tue, 8 Apr 2025 05:55:41 +0000
Subject: [PATCH 10/47] fix configs & unit tests

---
 configs/10B/H100_intellect1.toml |  21 ++-
 configs/10B/H100_simple.toml     |   4 +
 configs/13B/H100.toml            |   3 +
 configs/150M/A100_debug.toml     |   2 +
 configs/150M/H100.toml           |   3 +
 configs/150M/H100_best.toml      |   3 +
 configs/1B/H100.toml             |   3 +
 configs/70M/H100.toml            |   7 +
 configs/7B/H100.toml             |   3 +
 configs/test.toml                |   3 +
 tests/test_data.py               | 272 -------------------------------
 tests/test_model.py              |   4 +
 12 files changed, 53 insertions(+), 275 deletions(-)
 delete mode 100644 tests/test_data.py

diff --git a/configs/10B/H100_intellect1.toml b/configs/10B/H100_intellect1.toml
index c1e17b94..ee8ac0b0 100644
--- a/configs/10B/H100_intellect1.toml
+++ b/configs/10B/H100_intellect1.toml
@@ -24,19 +24,34 @@ betas1 = 0.9
 betas2 = 0.95
 weight_decay = 0.1
 
+[train.outer_lr_scheduler]
+lr = 0.7
+end_lr = 0.7
+num_decay_steps = 0
+num_warmup_steps = 0
+num_stable_steps = 0
+
+[train.outer_optimizer]
+type = "sgd"
+momentum = 0.9
+nesterov = true
+
 [data]
 seq_length = 8192
-dataset_name_or_paths = "/data/datasets/fineweb-edu,/data/datasets/fineweb,/data/datasets/StackV1-popular,/data/datasets/dclm-baseline-1.0-parquet,/data/datasets/open-web-math"
+dataset_name_or_paths = "/data/datasets/fineweb-edu.bin,/data/datasets/fineweb.bin,/data/datasets/StackV1-popular.bin,/data/datasets/dclm-baseline-1.0-parquet.bin,/data/datasets/open-web-math.bin"
+token_bit_size = 17
 dataset_ratio = "55:10:20:10:5"
 num_workers = 4
 reverse_data_files = true
 split_by_data_rank = false # the 10b training assume that data was already split by datarank. Keeping this for backward compatibility
 
-
 [diloco]
 inner_steps = 100
-compression = "uint8"
+delayed_update = true
 
 [ckpt]
 interval = 100
 path = "/data/10B"
+
+[pccl]
+ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/configs/10B/H100_simple.toml b/configs/10B/H100_simple.toml
index 4360c6a7..c088961d 100644
--- a/configs/10B/H100_simple.toml
+++ b/configs/10B/H100_simple.toml
@@ -25,3 +25,7 @@ weight_decay = 0.1
 [data]
 seq_length = 8192
 num_workers = 4
+fake = true
+
+[pccl]
+ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/configs/13B/H100.toml b/configs/13B/H100.toml
index 692f30d5..2947ef40 100644
--- a/configs/13B/H100.toml
+++ b/configs/13B/H100.toml
@@ -13,3 +13,6 @@ batch_size = 512
 [data]
 seq_length = 1024
 dataset_name_or_paths = "datasets/fineweb-edu"
+
+[pccl]
+ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/configs/150M/A100_debug.toml b/configs/150M/A100_debug.toml
index 6c77dae1..fa1b0b30 100644
--- a/configs/150M/A100_debug.toml
+++ b/configs/150M/A100_debug.toml
@@ -19,3 +19,5 @@ num_decay_steps = 1000
 [data]
 fake = true
 
+[pccl]
+ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/configs/150M/H100.toml b/configs/150M/H100.toml
index 05e1481b..b876b07e 100644
--- a/configs/150M/H100.toml
+++ b/configs/150M/H100.toml
@@ -13,3 +13,6 @@ batch_size = 512
 [data]
 seq_length = 1024
 dataset_name_or_paths = "datasets/fineweb-edu"
+
+[pccl]
+ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/configs/150M/H100_best.toml b/configs/150M/H100_best.toml
index 02fb8738..a994a5eb 100644
--- a/configs/150M/H100_best.toml
+++ b/configs/150M/H100_best.toml
@@ -24,3 +24,6 @@ weight_decay = 0.24530252977858977
 [data]
 seq_length = 1024
 dataset_name_or_paths = "datasets/fineweb-edu"
+
+[pccl]
+ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/configs/1B/H100.toml b/configs/1B/H100.toml
index 328878dd..a14eccce 100644
--- a/configs/1B/H100.toml
+++ b/configs/1B/H100.toml
@@ -13,3 +13,6 @@ batch_size = 512
 [data]
 seq_length = 1024
 dataset_name_or_paths = "datasets/fineweb-edu"
+
+[pccl]
+ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/configs/70M/H100.toml b/configs/70M/H100.toml
index 92b84d67..ec7e7cb3 100644
--- a/configs/70M/H100.toml
+++ b/configs/70M/H100.toml
@@ -13,3 +13,10 @@ batch_size = 512
 [data]
 seq_length = 1024
 dataset_name_or_paths = "datasets/fineweb-edu"
+
+[diloco]
+inner_steps = 16
+delayed_update = true
+
+[pccl]
+ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/configs/7B/H100.toml b/configs/7B/H100.toml
index c9d49ba7..9b8a3729 100644
--- a/configs/7B/H100.toml
+++ b/configs/7B/H100.toml
@@ -13,3 +13,6 @@ batch_size = 512
 [data]
 seq_length = 1024
 dataset_name_or_paths = "datasets/fineweb-edu"
+
+[pccl]
+ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/configs/test.toml b/configs/test.toml
index 5b77caa0..e9c53483 100644
--- a/configs/test.toml
+++ b/configs/test.toml
@@ -24,3 +24,6 @@ num_warmup_steps = 1000
 lr = 3e-4
 end_lr = 0.0
 num_decay_steps = 80000
+
+[pccl]
+ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/tests/test_data.py b/tests/test_data.py
deleted file mode 100644
index fff9d537..00000000
--- a/tests/test_data.py
+++ /dev/null
@@ -1,272 +0,0 @@
-import copy
-import torch
-from zeroband.data import InterleaveDataset, ParquetDataset, SequencePackingDataSet, collate_fn
-from torch.utils.data import DataLoader
-from zeroband.data import load_all_datasets, DataConfig
-from zeroband.utils.logger import get_logger
-from collections import Counter
-from itertools import chain
-import pytest
-import logging
-import pyarrow as pa
-import pyarrow.parquet as pq
-from faker import Faker
-from typing import List
-import string
-from torchdata.stateful_dataloader import StatefulDataLoader
-
-
-@pytest.mark.skip(reason="not using hf for now")
-@pytest.mark.parametrize(
-    "ratio, lower, upper",
-    [
-        ("3:2", 1.2821, 1.7549),
-        ("0.5:1", 0.4247, 0.5886),
-    ],
-)
-def test_load_all_datasets_vanilla(ratio: str, lower: float, upper: float):
-    config = DataConfig(
-        dataset_name_or_paths="Jackmin108/abc-testing:A,Jackmin108/abc-testing:C",
-        dataset_ratio=ratio,
-        streaming=True,
-        fake=False,
-    )
-
-    ds = load_all_datasets(config, "train")
-    print(ds)
-
-    dl = DataLoader(ds, batch_size=256)
-    batches = [i["text"] for i, _ in zip(dl, range(10))]
-    assert len(batches) == 10
-
-    # Check that the ratio is correct
-    letter_count = Counter(i[0] for i in chain(*batches))
-    print(letter_count, letter_count["A"] / letter_count["C"])
-    assert letter_count["A"] / letter_count["C"] < upper
-    assert letter_count["A"] / letter_count["C"] > lower
-
-
-@pytest.mark.skip(reason="not using hf for now")
-@pytest.mark.parametrize(
-    "ratio, lower, upper, data_rank, data_world_size",
-    [
-        ("3:2", 1.2821, 1.7549, 1, 4),
-        ("0.5:1", 0.4247, 0.5886, 0, 3),
-    ],
-)
-def test_load_all_datasets_data_rank(ratio: str, lower: float, upper: float, data_rank: int, data_world_size: int):
-    get_logger().setLevel(logging.DEBUG)
-    config = DataConfig(
-        dataset_name_or_paths="Jackmin108/abc-testing:A,Jackmin108/abc-testing:C",
-        dataset_ratio=ratio,
-        streaming=True,
-        fake=False,
-        data_world_size=data_world_size,
-        data_rank=data_rank,
-    )
-
-    ds = load_all_datasets(config, "train")
-    print(ds)
-
-    dl = DataLoader(ds, batch_size=256)
-    batches = [i["text"] for i, _ in zip(dl, range(10))]
-    assert len(batches) == 10
-
-    # Check that the ratio is correct
-    letter_count = Counter(i[0] for i in chain(*batches))
-    print(letter_count, letter_count["A"] / letter_count["C"])
-    assert letter_count["A"] / letter_count["C"] < upper
-    assert letter_count["A"] / letter_count["C"] > lower
-
-    c_num_set = {int(i[1:]) for i in chain(*batches) if i[0] == "C"}
-    a_num_set = {int(i[1:]) for i in chain(*batches) if i[0] == "A"}
-
-    # Check that the data is correctly sharded
-    first_a_shard = set(range(data_rank * (2**12), (data_rank + 1) * (2**12)))
-    first_10_c_shard = set()
-    for i in range(data_rank, data_world_size * 10, data_world_size):
-        first_10_c_shard = first_10_c_shard.union(set(range(i * (2**8), (i + 1) * (2**8))))
-    assert all(i in first_a_shard for i in a_num_set)
-    assert all(i in first_10_c_shard for i in c_num_set)
-
-
-def test_squence_packing():
-    class FakeDataset(torch.utils.data.Dataset):
-        def __init__(self):
-            self.data = [[6, 1, 2, 3, 4], [6, 3, 3, 4, 1, 7], [3, 2], [1, 2], [1, 4, 5, 3, 4, 1, 7, 8]]
-
-        def __len__(self):
-            return len(self.data)
-
-        def __getitem__(self, index):
-            return {"input_ids": self.data[index]}
-
-    MAX_SEQ_LEN = 8
-    dataset = SequencePackingDataSet(FakeDataset(), max_seq_length=MAX_SEQ_LEN, eos_token=0)
-
-    input_ids = []
-    labels = []
-    for data in dataset:
-        assert data["input_ids"].shape[0] == MAX_SEQ_LEN
-        assert data["labels"].shape[0] == MAX_SEQ_LEN
-        assert sum(data["seqlens"]) == MAX_SEQ_LEN
-
-        input_ids.append(data["input_ids"].tolist())
-        labels.append(data["labels"].tolist())
-
-    assert input_ids == [[6, 1, 2, 3, 4, 6, 3, 3], [3, 2, 1, 2, 1, 4, 5, 3]]
-    assert labels == [[1, 2, 3, 4, 0, 3, 3, 4], [2, 0, 2, 0, 4, 5, 3, 4]]
-
-
-class SimpleTokenizer:
-    def __init__(self):
-        # Create vocabulary: a-z (0-25) and unknown token (26)
-        self.char_to_id = {char: idx for idx, char in enumerate(string.ascii_lowercase)}
-        self.unknown_token = 26
-
-    def encode(self, text: str) -> List[int]:
-        """Convert text to list of token ids"""
-        return [self.char_to_id.get(char.lower(), self.unknown_token) for char in text]
-
-
-@pytest.fixture
-def fake_sentences():
-    """Generate 500 fake sentences (100 per file * 5 files)"""
-    fake = Faker()
-    return [fake.sentence() for _ in range(10_000)]
-
-
-@pytest.fixture
-def parquet_files(tmp_path, fake_sentences):
-    """Create 10 parquet files with 100 sentences each"""
-    files = []
-    for i in range(10):
-        # Create data for this file
-        start_idx = i * 100
-        sentences = fake_sentences[start_idx : start_idx + 100]
-
-        # Create arrow table
-        table = pa.Table.from_arrays([pa.array(sentences)], names=["text"])
-
-        # Write to parquet file
-        file_path = tmp_path / f"data_{i}.parquet"
-        pq.write_table(table, file_path)
-        files.append(str(file_path))
-
-    return files
-
-
-@pytest.fixture
-def tokenizer():
-    """Get a simple character-based tokenizer"""
-    return SimpleTokenizer()
-
-
-def test_parquet_dataset_ckpt(parquet_files, tokenizer):
-    # Create first dataset and iterate halfway
-    dataset1 = ParquetDataset(parquet_files, tokenizer)
-    halfway_point = 100
-
-    for _, data in zip(range(halfway_point), dataset1):
-        pass
-    # Save state
-    state_dict = dataset1.state_dict()
-
-    # Create new dataset and load state
-    dataset2 = ParquetDataset(parquet_files, tokenizer)
-    dataset2.load_state_dict(state_dict)
-
-    max_to_yield = 200
-    # Continue first dataset
-
-    for _, data1, data2 in zip(range(max_to_yield), dataset1, dataset2):
-        assert data1["input_ids"] == data2["input_ids"]
-
-
-def test_sequence_packing_dataset_ckpt(parquet_files, tokenizer):
-    dataset1 = SequencePackingDataSet(ParquetDataset(parquet_files, tokenizer), max_seq_length=16, eos_token=0)
-
-    halfway_point = 100
-
-    for _, data in zip(range(halfway_point), dataset1):
-        pass
-    # Save state
-    state_dict = dataset1.state_dict()
-
-    # Create new dataset and load state
-    dataset2 = SequencePackingDataSet(ParquetDataset(parquet_files, tokenizer), max_seq_length=16, eos_token=0)
-    dataset2.load_state_dict(state_dict)
-
-    assert dataset1.state_dict() == dataset2.state_dict()
-
-    max_to_yield = 199
-    # Continue first dataset
-
-    for _, data1, data2 in zip(range(max_to_yield), dataset1, dataset2):
-        assert (data1["input_ids"] == data2["input_ids"]).all()
-        assert (data1["labels"] == data2["labels"]).all()
-        assert data1["seqlens"] == data2["seqlens"]
-
-
-def test_interleave_dataset_ckpt(parquet_files, tokenizer):
-    # Split parquet files into two groups to create two datasets
-    files1 = parquet_files[:2]  # First two files
-    files2 = parquet_files[2:4]  # Next two files
-
-    # Create first dataset and iterate halfway
-    dataset1 = InterleaveDataset(
-        [ParquetDataset(files1, tokenizer), ParquetDataset(files2, tokenizer)], probabilities=[0.5, 0.5]
-    )
-
-    halfway_point = 100
-
-    for _, data in zip(range(halfway_point), dataset1):
-        pass
-    # Save state
-    state_dict = dataset1.state_dict()
-
-    # Create new dataset and load state
-    dataset2 = InterleaveDataset(
-        [ParquetDataset(files1, tokenizer), ParquetDataset(files2, tokenizer)], probabilities=[0.5, 0.5]
-    )
-    dataset2.load_state_dict(state_dict=copy.deepcopy(state_dict))
-
-    assert dataset1.state_dict() == dataset2.state_dict()
-
-    max_to_yield = 250
-
-    for _, data1, data2 in zip(range(max_to_yield), dataset1, dataset2):
-        assert data1["input_ids"] == data2["input_ids"]
-
-
-@pytest.mark.skip(reason="not working for now")
-@pytest.mark.parametrize("num_workers", [0, 2, 16])
-def test_dataloader_parquet_dataset(parquet_files, tokenizer, num_workers):
-    dataset = SequencePackingDataSet(ParquetDataset(parquet_files, tokenizer), max_seq_length=8, eos_token=0)
-
-    loader = StatefulDataLoader(dataset, batch_size=8, num_workers=num_workers, collate_fn=collate_fn)
-
-    total_samples = 100
-
-    for _, _batch in zip(range(total_samples), loader):
-        ...
-
-    # Save state
-    state_dict = loader.state_dict()
-
-    # Create new loader and load state
-    dataset2 = SequencePackingDataSet(ParquetDataset(parquet_files, tokenizer), max_seq_length=8, eos_token=0)
-
-    loader2 = StatefulDataLoader(dataset2, batch_size=8, num_workers=num_workers, collate_fn=collate_fn)
-
-    print(state_dict)
-
-    loader2.load_state_dict(state_dict)
-
-    warmup = 10
-
-    for i, batch1, batch2 in zip(range(total_samples), loader, loader2):
-        if i > warmup:
-            assert (batch1["input_ids"] == batch2["input_ids"]).all()
-            assert (batch1["labels"] == batch2["labels"]).all()
-            assert (batch1["seqlens"] == batch2["seqlens"]).all()
diff --git a/tests/test_model.py b/tests/test_model.py
index a6df8027..9d0ef910 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -227,3 +227,7 @@ def test_end_to_end_packing(llama_config: ModelArgs):
 
     loss = output.mean()
     loss.backward()  # test that the backward for fa2
+
+
+if __name__ == '__main__':
+    pytest.main()

From c74d5b2d204c1db522cc73a643cdd436b2926c31 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Tue, 8 Apr 2025 05:59:34 +0000
Subject: [PATCH 11/47] fix ruff

---
 scripts/export_dcp.py        | 3 ---
 src/zeroband/ccoip_master.py | 2 +-
 src/zeroband/checkpoint.py   | 1 -
 tests/test_read_nibbles.py   | 2 +-
 4 files changed, 2 insertions(+), 6 deletions(-)

diff --git a/scripts/export_dcp.py b/scripts/export_dcp.py
index e0eda389..7fce46e3 100644
--- a/scripts/export_dcp.py
+++ b/scripts/export_dcp.py
@@ -7,9 +7,6 @@
 from typing import Literal
 import torch.distributed.checkpoint as dcp
 from zeroband.models.llama import make_model
-from zeroband.config import resolve_env_vars
-from zeroband.checkpoint import ModelWrapper
-from zeroband.utils import get_module_signature
 from zeroband.train import Config
 from zeroband.utils.logger import get_logger
 from pydantic_config import parse_argv
diff --git a/src/zeroband/ccoip_master.py b/src/zeroband/ccoip_master.py
index ef8d866d..6e36dc95 100644
--- a/src/zeroband/ccoip_master.py
+++ b/src/zeroband/ccoip_master.py
@@ -1,4 +1,4 @@
-from pccl import *
+from pccl import MasterNode
 
 HOST: str = '0.0.0.0:48148'
 
diff --git a/src/zeroband/checkpoint.py b/src/zeroband/checkpoint.py
index 19bdc587..1d3b34e9 100644
--- a/src/zeroband/checkpoint.py
+++ b/src/zeroband/checkpoint.py
@@ -1,7 +1,6 @@
 from dataclasses import dataclass
 import os
 from pathlib import Path
-from tabnanny import check
 from typing import Any, Optional
 import torch
 from torchdata.stateful_dataloader import StatefulDataLoader
diff --git a/tests/test_read_nibbles.py b/tests/test_read_nibbles.py
index 675bb813..34b6b9fc 100644
--- a/tests/test_read_nibbles.py
+++ b/tests/test_read_nibbles.py
@@ -1,4 +1,4 @@
-from typing import List, Tuple
+from typing import List
 
 import pytest
 

From eaeec2a6027c2aead7b3d51a944360e589d45a1f Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Tue, 8 Apr 2025 06:05:28 +0000
Subject: [PATCH 12/47] fix ruff

---
 scripts/bandwith/down.sh   |  24 ----
 scripts/bandwith/up.sh     |   1 -
 scripts/convert_dl_ckpt.sh |  35 ------
 scripts/export_dcp.py      | 219 -------------------------------------
 scripts/install/install.sh |  63 -----------
 scripts/skip_data.py       |  88 ---------------
 scripts/subset_data.py     | 120 --------------------
 src/zeroband/data.py       |   1 -
 src/zeroband/train.py      |   2 +-
 9 files changed, 1 insertion(+), 552 deletions(-)
 delete mode 100755 scripts/bandwith/down.sh
 delete mode 100755 scripts/bandwith/up.sh
 delete mode 100755 scripts/convert_dl_ckpt.sh
 delete mode 100644 scripts/export_dcp.py
 delete mode 100755 scripts/install/install.sh
 delete mode 100644 scripts/skip_data.py
 delete mode 100644 scripts/subset_data.py

diff --git a/scripts/bandwith/down.sh b/scripts/bandwith/down.sh
deleted file mode 100755
index e8c32c37..00000000
--- a/scripts/bandwith/down.sh
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/bin/bash
-
-# Check if the script is run as root
-if [ "$EUID" -ne 0 ]; then
-  echo "Please run as root"
-  exit 1
-fi
-
-# Define variables
-INTERFACE="lo"  # localhost interface
-RATE="500mbit"  # 500 Mbps
-BURST="500k"    # burst size
-LATENCY="50ms"  # maximum latency
-
-# Remove any existing traffic control rules on the interface
-tc qdisc del dev $INTERFACE root 2>/dev/null
-
-# Add the rate limiting rule
-tc qdisc add dev $INTERFACE root tbf rate $RATE burst $BURST latency $LATENCY
-
-echo "Bandwidth limit of $RATE has been set on $INTERFACE"
-
-# To remove the limit, run:
-# tc qdisc del dev $INTERFACE root
\ No newline at end of file
diff --git a/scripts/bandwith/up.sh b/scripts/bandwith/up.sh
deleted file mode 100755
index fff8a800..00000000
--- a/scripts/bandwith/up.sh
+++ /dev/null
@@ -1 +0,0 @@
-tc qdisc del dev lo root
diff --git a/scripts/convert_dl_ckpt.sh b/scripts/convert_dl_ckpt.sh
deleted file mode 100755
index efc1defc..00000000
--- a/scripts/convert_dl_ckpt.sh
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/bin/bash
-set -e
-
-# Wrapper script to run the Python command on 8 checkpoints in parallel
-# Usage: ./convert_all.sh /data/10b/step_50800/diloco_0/data
-
-# Input path prefix
-INPUT_PATH=$1
-
-# Run the commands for each checkpoint in parallel
-for i in {0..7}; do
-    CHECKPOINT_PATH="${INPUT_PATH}/_${i}.pt"
-    BACKUP_PATH="${INPUT_PATH}/_${i}_old.pt"
-    TMP_PATH="${INPUT_PATH}/_${i}_tmp.pt"
-
-    if [ -f "$BACKUP_PATH" ]; then
-        echo "Checkpoint ${CHECKPOINT_PATH} has already been processed, skipping." &
-    else
-        (
-            uv run python scripts/convert_dl_state.py @configs/10B/H100.toml \
-                --input_path "$CHECKPOINT_PATH" \
-                --output_path "$TMP_PATH" \
-                --rank "$i" \
-                --world_size 8 && \
-            mv "$CHECKPOINT_PATH" "$BACKUP_PATH" && \
-            mv "$TMP_PATH" "$CHECKPOINT_PATH" && \
-            echo "Processed ${CHECKPOINT_PATH} and moved to ${BACKUP_PATH}"
-        ) &
-    fi
-done
-
-# Wait for all background jobs to complete
-wait
-
-echo "All checkpoints processed"
diff --git a/scripts/export_dcp.py b/scripts/export_dcp.py
deleted file mode 100644
index 7fce46e3..00000000
--- a/scripts/export_dcp.py
+++ /dev/null
@@ -1,219 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-# Example Usage:
-# python scripts/export_dcp.py @configs/10B/H100_intellect1.toml --ckpt.path /data/intellect-1-step17000 --ckpt.resume /data/10b/step_17000/diloco_0
-
-import torch
-from typing import Literal
-import torch.distributed.checkpoint as dcp
-from zeroband.models.llama import make_model
-from zeroband.train import Config
-from zeroband.utils.logger import get_logger
-from pydantic_config import parse_argv
-from transformers import AutoTokenizer
-import math
-from pathlib import Path
-from safetensors.torch import save_file
-import json
-from zeroband.models.llama import ModelArgs
-from transformers import LlamaConfig
-from transformers.generation import GenerationConfig
-
-
-class ExportConfig(Config):
-    save_format: Literal["pt", "safetensors"] = "safetensors"
-    torch_dtype: Literal["float32", "bfloat16"] = "float32"
-    with_debug_automap: bool = False
-
-
-def remap_keys_llama(k: str) -> str:
-    """Maps ZeroBand keys to HuggingFace keys"""
-    return ("model." if "output.weight" not in k else "") + k.replace("tok_embeddings", "embed_tokens").replace(
-        "attention.wq", "self_attn.q_proj"
-    ).replace("attention.wk", "self_attn.k_proj").replace("attention.wv", "self_attn.v_proj").replace(
-        "attention.wo", "self_attn.o_proj"
-    ).replace("attention_norm", "input_layernorm").replace("feed_forward.w3", "mlp.up_proj").replace(
-        "feed_forward.w2", "mlp.down_proj"
-    ).replace("feed_forward.w1", "mlp.gate_proj").replace("ffn_norm", "post_attention_layernorm").replace(
-        "output.weight", "lm_head.weight"
-    )
-
-
-def _get_ffn_dim(hidden_dim: int, ffn_dim_multiplier: float, multiple_of: int) -> int:
-    """Get the FFN dimension from ZeroBand args"""
-    hidden_dim = int(8 * hidden_dim / 3)
-    # custom dim factor multiplier
-    if ffn_dim_multiplier is not None:
-        hidden_dim = int(ffn_dim_multiplier * hidden_dim)
-    hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
-    return hidden_dim
-
-
-def convert_config_zb_to_hf(
-    zb_config: ModelArgs, with_debug_automap: bool = False, type_model: str = "llama3"
-) -> LlamaConfig:
-    """Convert ZeroBand config to HuggingFace config"""
-    config = LlamaConfig()
-    config.hidden_size = zb_config.dim
-    config.num_hidden_layers = zb_config.n_layers
-    config.num_attention_heads = zb_config.n_heads
-    config.num_key_value_heads = zb_config.n_kv_heads
-    config.vocab_size = zb_config.vocab_size
-    config.intermediate_size = _get_ffn_dim(zb_config.dim, zb_config.ffn_dim_multiplier, zb_config.multiple_of)
-    config.rms_norm_eps = zb_config.norm_eps
-    config.rope_theta = float(zb_config.rope_theta)
-    config.max_position_embeddings = zb_config.max_seq_len
-
-    if type_model == "llama2":
-        config.bos_token_id = [1]
-        config.eos_token_id = [2]
-    else:
-        config.bos_token_id = [128000]
-        config.eos_token_id = [128001, 128008, 128009]
-
-    config.architectures = ["LlamaForCausalLM"]
-
-    # Rope scaling
-    config.rope_scaling = {
-        "original_max_position_embeddings": 8192,
-        "rope_type": "default",
-    }
-
-    if with_debug_automap:
-        config.auto_map = {
-            "AutoConfig": "PrimeIntellect/prime-llama-debug--configuration_llama.LlamaConfig",
-            "AutoModelForCausalLM": "PrimeIntellect/prime-llama-debug--modeling_llama.LlamaForCausalLM",
-        }
-
-    return config
-
-
-@torch.no_grad
-def convert_qk_from_complex_to_rotate_half(linear_weight: torch.FloatTensor, head_dim: int) -> torch.FloatTensor:
-    """Converts the Q/K weight from complex to rotate half form.
-    This is required because the rotary implementation in ZeroBand uses complex numbers which encodes even elements as real and odd number as complex.
-    [0, 1, 2, 3] -> [0 + 1j, 2 + 3j]
-    However, the HuggingFace implementation uses rotate_half which encodes top half as real and bottom half as complex.
-    [0, 1, 2, 3] -> [0, 1] + [2, 3]j
-
-    We thus need to permute the QK outputs to match the HuggingFace implementation.
-    """
-    new_weight = torch.zeros_like(linear_weight)
-
-    num_heads = linear_weight.size(0) // head_dim
-    hhd = head_dim // 2
-
-    # This applies the riffle shuffle permutation to the outputs of the linear for each attn head
-    # Even numbers go to the top half, odd numbers go to the bottom half
-    for i in range(num_heads):
-        new_weight[i * head_dim : (i * head_dim + hhd), :].copy_(
-            linear_weight[i * head_dim + 0 : (i + 1) * head_dim : 2, :]
-        )
-        new_weight[i * head_dim + hhd : (i + 1) * head_dim, :].copy_(
-            linear_weight[i * head_dim + 1 : (i + 1) * head_dim : 2, :]
-        )
-
-    return new_weight
-
-
-def main(config: ExportConfig):
-    # Create save path
-    save_path = Path(config.ckpt.path)
-    save_path.mkdir(parents=True, exist_ok=True)
-
-    # Load model
-    logger.info("Getting tokenizer (for vocab size)")
-    if config.model_type == "llama2":
-        tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=True)
-    elif config.model_type == "llama3":
-        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B", use_fast=True)
-    else:
-        raise ValueError(f"Model type {config.model_type} not supported")
-
-    logger.info("Getting model")
-    model, model_config = make_model(
-        config,
-        vocab_size=len(tokenizer)
-    )
-
-    # Convert ZeroBand config to HuggingFace config
-    hf_config = convert_config_zb_to_hf(
-        model_config, with_debug_automap=config.with_debug_automap, type_model=config.model_type
-    )
-    hf_config.to_json_file(save_path / "config.json")
-
-    # Load checkpoint
-    states = {
-        "model": ModelWrapper(model),
-    }
-    logger.info("Loading from %s", config.ckpt.resume)
-    dcp.load(
-        state_dict=states,
-        checkpoint_id=config.ckpt.resume,
-    )
-
-    # Convert model to HuggingFace format
-    num_shards = int(sum(p.numel() for p in model.parameters()) / 1e9)
-    state_dict = model.state_dict()
-
-    index_json = {}
-    total_size = 0
-    state_dict = {remap_keys_llama(k): v for k, v in state_dict.items()}
-    if not config.with_debug_automap:  # The debug uses complex rotary impl
-        with torch.no_grad():
-            for i in range(hf_config.num_hidden_layers):
-                old_q = state_dict[f"model.layers.{i}.self_attn.q_proj.weight"]
-                old_k = state_dict[f"model.layers.{i}.self_attn.k_proj.weight"]
-                new_q = convert_qk_from_complex_to_rotate_half(old_q, 128)
-                new_k = convert_qk_from_complex_to_rotate_half(old_k, 128)
-                state_dict[f"model.layers.{i}.self_attn.q_proj.weight"].copy_(new_q)
-                state_dict[f"model.layers.{i}.self_attn.k_proj.weight"].copy_(new_k)
-    if "model.freqs_cis" in state_dict:  # This should not be persisted
-        del state_dict["model.freqs_cis"]
-    if config.torch_dtype == "bfloat16":
-        state_dict = {k: v.to(torch.bfloat16) for k, v in state_dict.items()}
-
-    # Save model
-    state_keys = list(state_dict.keys())
-    shard_size = int(math.ceil(len(state_keys) / num_shards))
-    logger.info("Saving model to %d shards", num_shards)
-
-    for i in range(num_shards):
-        _file = save_path / f"model-{i:04}-of-{num_shards:04}.{config.save_format}"
-        start = i * shard_size
-        end = min((i + 1) * shard_size, len(state_keys))
-        shard = {k: state_dict[k] for k in state_keys[start:end]}
-
-        index_json.update({k: _file.name for k in shard.keys()})
-        total_size += sum(p.numel() for p in shard.values())
-        if config.save_format == "pt":
-            torch.save(shard, _file)
-        else:
-            save_file(shard, _file, metadata=dict(format="pt"))
-
-    json.dump(
-        {
-            "weight_map": index_json,
-            "metadata": {
-                "total_size": total_size * (2 if config.torch_dtype == "bfloat16" else 4),
-            },
-        },
-        (save_path / "model.safetensors.index.json").open("w"),
-        indent=2,
-    )
-
-    # Save Tokenizer
-    tokenizer.save_pretrained(save_path)
-
-    # Save Generation Config
-    gconfig = GenerationConfig(max_length=100, use_cache=False, temperature=0.7, top_k=None, do_sample=True)
-    gconfig.save_pretrained(save_path)
-
-
-if __name__ == "__main__":
-    logger = get_logger()
-    config = ExportConfig(**parse_argv())
-    resolve_env_vars(config)
-    logger.debug(f"config: {config.model_dump()}")
-
-    main(config)
diff --git a/scripts/install/install.sh b/scripts/install/install.sh
deleted file mode 100755
index e29a3901..00000000
--- a/scripts/install/install.sh
+++ /dev/null
@@ -1,63 +0,0 @@
-#!/usr/bin/env bash
-
-set -e
-
-# Colors for output
-GREEN='\033[0;32m'
-NC='\033[0m' # No Color
-
-log_info() {
-    echo -e "${GREEN}[INFO]${NC} $1"
-}
-
-main() {
-    # Check if sudo is installed
-    if ! command -v sudo &> /dev/null; then
-        apt update
-        apt install sudo -y
-    fi
-
-    log_info "Updating apt..."
-    sudo apt update
-
-    log_info "Installing cmake python3-dev..."
-    sudo apt install python3-dev cmake -y
-
-    log_info "Installing iperf..."
-    sudo apt install iperf -y
-
-    log_info "Cloning repository..."
-    git clone https://github.com/PrimeIntellect-ai/prime.git
-    
-    log_info "Entering project directory..."
-    cd prime
-    
-    log_info "Installing uv..."
-    curl -LsSf https://astral.sh/uv/install.sh | sh
-    
-    log_info "Sourcing uv environment..."
-    if ! command -v uv &> /dev/null; then
-        source $HOME/.local/bin/env
-    fi
-    
-    log_info "Creating virtual environment..."
-    uv venv --python 3.10
-    
-    log_info "Activating virtual environment..."
-    source .venv/bin/activate
-    
-    log_info "Installing dependencies..."
-    uv sync --extra all
-        
-    log_info "Updating git submodules..."
-    git submodule update --init --recursive
-    
-    log_info "Downloading data..."
-    mkdir -p datasets
-    uv run python scripts/subset_data.py --dataset_name PrimeIntellect/fineweb-edu --data_world_size 1 --data_rank 0 --max_shards 128
-    mv fineweb-edu/ datasets/fineweb-edu/
-
-    log_info "Installation completed! You can double check that everything is install correctly by running 'GLOO_SOCKET_IFNAME=lo GLOBAL_ADDR=localhost GLOBAL_RANK=0 GLOBAL_UNIQUE_ID=0 GLOBAL_WORLD_SIZE=1 GLOBAL_PORT=8989  uv run torchrun --nproc_per_node=2 src/zeroband/train.py  @configs/debug/diloco.toml'"
-}
-
-main
diff --git a/scripts/skip_data.py b/scripts/skip_data.py
deleted file mode 100644
index 919b7d24..00000000
--- a/scripts/skip_data.py
+++ /dev/null
@@ -1,88 +0,0 @@
-"""
-This script is simulating a training to exaust the datasets and recover the dataloader ckpt.
-
-It has the same api as the training one. The only difference is that you probably want to change the total_steps and put a data_path.
-
-It can load config from the config file to have the same setup as the real run.
-
-example.
-```
-uv run torchrun --nproc_per_node=4 scripts/skip_data.py @configs/150M/3090.toml --optim.total_steps 100 --ckpt.data_path out_data
-```
-
-"""
-
-import os
-import torch
-from pydantic_config import parse_argv
-
-
-from transformers import AutoTokenizer
-from zeroband.checkpoint import CkptManager
-from zeroband.config import resolve_env_vars
-from zeroband.train import Config
-
-from zeroband.data import make_dataloader
-
-from zeroband.utils.world_info import get_world_info
-from zeroband.utils.logger import get_logger
-
-
-def skip_data(config: Config):
-    # batch_size is the total batch size for all GPUs
-    assert config.train.batch_size % world_info.local_world_size == 0
-    batch_size = config.train.batch_size // world_info.local_world_size
-
-    assert batch_size % config.hardware.micro_batch_size == 0
-    gradient_accumulation_steps = batch_size // config.hardware.micro_batch_size
-
-    if config.model_type == "llama2":
-        tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=True)
-    elif config.model_type == "llama3":
-        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B", use_fast=True)
-    else:
-        raise ValueError(f"Model type {config.model_type} not supported")
-
-    logger.debug("tokenizer loaded")
-
-    train_dataloader = make_dataloader(
-        tokenizer=tokenizer,
-        mpi_world_size=world_info.world_size,
-        mpi_rank=world_info.rank,
-        batch_size=config.hardware.micro_batch_size,
-        data_config=config.data,
-    )
-
-    train_dataloader_iterator = iter(train_dataloader)
-
-    logger.info("starting skipping data up to step: %d", config.train.total_steps)
-
-    total_steps = 0
-
-    while True:
-        num_inner_steps = config.diloco.inner_steps if config.diloco is not None else 1
-
-        for _inner_step in range(num_inner_steps):
-            for _ in range(gradient_accumulation_steps):
-                next(train_dataloader_iterator)
-
-        total_steps += num_inner_steps
-        logger.info("total steps: %d", total_steps)
-        if total_steps >= config.train.total_steps:
-            break
-
-    CkptManager.save_data(os.path.join(config.ckpt.data_path, "data"), train_dataloader, world_info.local_rank)
-
-    logger.info("skipped data up to step: %d", config.train.total_steps)
-
-
-if __name__ == "__main__":
-    torch.manual_seed(42)
-
-    world_info = get_world_info()
-    logger = get_logger()
-
-    config = Config(**parse_argv())
-    resolve_env_vars(config)
-
-    skip_data(config)
diff --git a/scripts/subset_data.py b/scripts/subset_data.py
deleted file mode 100644
index 2e6c6484..00000000
--- a/scripts/subset_data.py
+++ /dev/null
@@ -1,120 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-# Usage:
-# python scripts/subset_data.py --dataset_name PrimeIntellect/fineweb-edu --data_world_size 12 --data_rank 1
-
-import argparse
-import subprocess
-from typing import Dict, List, Optional
-import functools
-from datasets import load_dataset_builder, BuilderConfig
-import logging
-from huggingface_hub import get_token
-import os
-import multiprocessing as mp
-from tqdm import tqdm
-
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-ch = logging.StreamHandler()
-ch.setLevel(logging.DEBUG)
-formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
-ch.setFormatter(formatter)
-logger.addHandler(ch)
-
-
-@functools.lru_cache(maxsize=None)
-def _get_ds_config_dict(path: str, name: Optional[str] = None) -> Dict[str, BuilderConfig]:
-    ds_builder = load_dataset_builder(path=path, name=name)
-    return ds_builder.builder_configs
-
-
-def _get_datafiles(path: str, name: Optional[str] = None, split: str = "train") -> List[str]:
-    builder_config = _get_ds_config_dict(path=path, name=name)
-    if name is None:
-        if "default" not in builder_config:
-            name = next(iter(builder_config.keys()))
-        else:
-            name = "default"
-    return builder_config[name].data_files[split]
-
-
-def _download_file(data_file: str, save_path: str) -> None:
-    """Download a file from huggingface.co
-
-    Args:
-        data_file (str): The file to download. e.g. 'hf://datasets/PrimeIntellect/fineweb-edu@14efaa24d7dff8a745bf4918e415878546542346/data1/train-00450.parquet'
-        save_path (str): The path to save the file. e.g. 'data1/train-00450.parquet'
-    """
-    assert data_file.startswith("hf://")
-    data_file = data_file.replace("hf://", "").replace("@", "/resolve/")
-
-    if "/" in save_path:
-        parent = "/".join(save_path.split("/")[:-1])
-        if not os.path.exists(parent):
-            logger.debug(f"Creating directory: {parent}")
-            os.makedirs(parent, exist_ok=True)
-
-    cmd = [
-        "wget",
-        f'--header="Authorization: Bearer {get_token()}"',
-        f"https://huggingface.co/{data_file}?download=true",
-        f"-O {save_path}",
-    ]
-    result = subprocess.run(" ".join(cmd), shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.PIPE)
-    if result.returncode != 0:
-        logger.error(f"Error downloading file: {data_file}")
-        logger.error(result.stderr.decode("utf-8"))
-
-
-def _download_file_wrapper(args):
-    return _download_file(*args)
-
-
-def _get_save_path(data_file: str) -> str:
-    ret_list = data_file.split("@")[-1].split("/")[1:]
-    return args.dataset_name.split("/")[-1] + "/" + "/".join(ret_list)
-
-
-def main(args):
-    g_data_files = _get_datafiles(args.dataset_name)
-    logger.debug(f"Length of data_files: {len(g_data_files)}")
-    if len(args.filter) > 0:
-        args.filter = args.filter.split(",")
-        data_files = []
-        for _filter in args.filter:
-            data_files.extend([f for f in g_data_files if _filter in f])
-    else:
-        data_files = g_data_files
-
-    logger.debug(f"Length of data_files: {len(data_files)}")
-    data_files = data_files[args.data_rank :: args.data_world_size][: args.max_shards]
-    logger.debug(f"Data files: {data_files}")
-    logger.debug(f"Length of data_files processing: {len(data_files)}")
-
-    if args.dry_run:
-        return
-
-    with mp.Pool(args.num_workers) as pool:
-        save_paths = list(pool.imap(_get_save_path, tqdm(data_files, desc="Getting save paths")))
-        _ = list(
-            tqdm(
-                pool.imap(_download_file_wrapper, zip(data_files, save_paths)),
-                desc="Downloading files",
-                total=len(data_files),
-                bar_format="{l_bar}{bar:10}{r_bar}",
-            )
-        )
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Download and process data from a HF dataset")
-    parser.add_argument("--dataset_name", type=str, default="PrimeIntellect/fineweb-edu", help="dataset name")
-    parser.add_argument("--dry_run", action="store_true", help="do not download data")
-    parser.add_argument("--filter", type=str, default="", help="search shards by the filter")
-    parser.add_argument("--data_rank", type=int, default=0, help="start index")
-    parser.add_argument("--data_world_size", type=int, default=4, help="world size")
-    parser.add_argument("--max_shards", type=int, default=1000)
-    parser.add_argument("--num_workers", type=int, default=12)
-    args = parser.parse_args()
-    main(args)
diff --git a/src/zeroband/data.py b/src/zeroband/data.py
index 9d04cff9..b11c0b9d 100644
--- a/src/zeroband/data.py
+++ b/src/zeroband/data.py
@@ -1,4 +1,3 @@
-import functools
 import os.path
 import random
 from abc import ABC
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 1ec0213d..2fa44ba1 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -3,7 +3,7 @@
 import time
 from dataclasses import asdict
 from logging import Logger
-from typing import TYPE_CHECKING, Optional, Iterator, List, Dict, Iterable, Tuple
+from typing import TYPE_CHECKING, Optional, Iterator, List, Dict, Tuple
 
 import torch
 import torch.distributed as dist

From 1160e49960b18c0bd9e4d778d99320785f95fa22 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Tue, 8 Apr 2025 06:30:20 +0000
Subject: [PATCH 13/47] clone pccl dependency via git instead of https

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 4fdd98d6..9487e559 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,7 @@ dependencies = [
     "wandb",
     "numba",
     "imageio[ffmpeg]",
-    "pccl @ git+https://github.com/PrimeIntellect-ai/pccl.git@main#subdirectory=python/framework"
+    "pccl @ git@github.com:PrimeIntellect-ai/pccl.git@main#subdirectory=python/framework"
 ]
 
 [project.optional-dependencies]

From 8bd560d2e9c5f132104174713863221fd1fa1dc7 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Tue, 8 Apr 2025 06:36:41 +0000
Subject: [PATCH 14/47] fix pccl git url

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 9487e559..4fdd98d6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,7 @@ dependencies = [
     "wandb",
     "numba",
     "imageio[ffmpeg]",
-    "pccl @ git@github.com:PrimeIntellect-ai/pccl.git@main#subdirectory=python/framework"
+    "pccl @ git+https://github.com/PrimeIntellect-ai/pccl.git@main#subdirectory=python/framework"
 ]
 
 [project.optional-dependencies]

From f9e37eaf24ec17bb2c70c004ff4551cfe05788e5 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Wed, 9 Apr 2025 00:08:18 +0000
Subject: [PATCH 15/47] backported ParquetDataset

---
 configs/150M/A100_debug.toml          |  26 ++++-
 configs/debug/diloco.toml             |   9 +-
 src/zeroband/ccl/pccl_utils.py        |   2 +-
 src/zeroband/data.py                  | 141 +++++++++++++++++++++++++-
 src/zeroband/train.py                 |   1 -
 src/zeroband/utils/tokenizer_utils.py |   6 ++
 6 files changed, 169 insertions(+), 16 deletions(-)

diff --git a/configs/150M/A100_debug.toml b/configs/150M/A100_debug.toml
index fa1b0b30..ba6cfb13 100644
--- a/configs/150M/A100_debug.toml
+++ b/configs/150M/A100_debug.toml
@@ -1,12 +1,11 @@
-project = "debug_150m_zero_band"
-
 model_name = "150M"
-model_type = "llama2"
+model_type = "llama3"
 
 wandb = false
+log_all_ranks = true
 
 [hardware]
-micro_batch_size = 64
+micro_batch_size = 32
 torch_compile = true
 
 [train]
@@ -16,8 +15,25 @@ batch_size = 512
 num_warmup_steps = 10
 num_decay_steps = 1000
 
+[train.outer_lr_scheduler]
+lr = 1.0
+end_lr = 1.0
+num_decay_steps = 0
+num_warmup_steps = 0
+num_stable_steps = 0
+
+[train.outer_optimizer]
+type = "sgd"
+
 [data]
-fake = true
+dataset_name_or_paths = 'tests/test_data/parquet/parquet_ds_folder_1,tests/test_data/parquet/parquet_ds_folder_2'
+#dataset_name_or_paths = '/home/mike/IntelliJProjects/dataproctest/working_dir/train_0.bin,/home/mike/IntelliJProjects/dataproctest/working_dir/train_1.bin'
+dataset_ratio = "50:50"
+token_bit_size = 17
+
+[diloco]
+inner_steps = 16
+delayed_update = true
 
 [pccl]
 ccoip_host = "127.0.0.1:48148"
\ No newline at end of file
diff --git a/configs/debug/diloco.toml b/configs/debug/diloco.toml
index b1ac8d12..ba6cfb13 100644
--- a/configs/debug/diloco.toml
+++ b/configs/debug/diloco.toml
@@ -5,11 +5,11 @@ wandb = false
 log_all_ranks = true
 
 [hardware]
-micro_batch_size = 8
+micro_batch_size = 32
 torch_compile = true
 
 [train]
-batch_size = 32
+batch_size = 512
 
 [train.lr_scheduler]
 num_warmup_steps = 10
@@ -26,8 +26,9 @@ num_stable_steps = 0
 type = "sgd"
 
 [data]
-dataset_name_or_paths = '/home/mike/IntelliJProjects/dataproctest/working_dir/train_0.bin,/home/mike/IntelliJProjects/dataproctest/working_dir/train_1.bin'
-dataset_ratio = "90:10"
+dataset_name_or_paths = 'tests/test_data/parquet/parquet_ds_folder_1,tests/test_data/parquet/parquet_ds_folder_2'
+#dataset_name_or_paths = '/home/mike/IntelliJProjects/dataproctest/working_dir/train_0.bin,/home/mike/IntelliJProjects/dataproctest/working_dir/train_1.bin'
+dataset_ratio = "50:50"
 token_bit_size = 17
 
 [diloco]
diff --git a/src/zeroband/ccl/pccl_utils.py b/src/zeroband/ccl/pccl_utils.py
index b822398f..9df16e7b 100644
--- a/src/zeroband/ccl/pccl_utils.py
+++ b/src/zeroband/ccl/pccl_utils.py
@@ -5,7 +5,7 @@
 def all_reduce_multiple_with_retry(communicator: Communicator,
                                    tensors: list[torch.Tensor],
                                    op: ReduceOp,
-                                   max_in_flight: int = 16):
+                                   max_in_flight: int = 128):
     descriptors = []
     tag = 0
     for tensor in tensors:
diff --git a/src/zeroband/data.py b/src/zeroband/data.py
index b11c0b9d..2736f1ac 100644
--- a/src/zeroband/data.py
+++ b/src/zeroband/data.py
@@ -2,17 +2,20 @@
 import random
 from abc import ABC
 from dataclasses import dataclass, asdict
-from typing import Any, Generator, List
+from typing import Any, Generator, List, Dict
 
 import torch
 from torch.distributed.checkpoint.stateful import Stateful
 from torch.utils.data import IterableDataset
 from torchdata.stateful_dataloader import StatefulDataLoader
+from transformers import PreTrainedTokenizer, AutoTokenizer
 
 from zeroband.config import DataConfig
 from zeroband.utils import rand_utils, nibble_utils, math_utils
 from zeroband.utils.tokenizer_utils import TokenizerInfo
 
+from pyarrow import parquet as pq, Table
+
 DEBUG_VOCAB_SIZE = 1024
 
 
@@ -106,10 +109,10 @@ def __iter__(self) -> Generator[dict[str, Any], Any, None]:
                 yield {'input_ids': input_ids, 'labels': labels, 'seqlens': document_lengths}
 
     def state_dict(self):
-        return {"lsr_seed": self.lsr_seed}
+        return {"lsfr_seed": self.lsr_seed}
 
     def load_state_dict(self, state_dict):
-        self.lsr_seed = state_dict["lsr_seed"]
+        self.lsr_seed = state_dict["lsfr_seed"]
 
 
 @dataclass
@@ -174,6 +177,88 @@ def load_state_dict(self, state_dict):
         self._init_random_state()
 
 
+@dataclass
+class PQDatasetState:
+    files: List[str]
+    lsfr_seed: int
+
+
+class ParquetDataset(StatefulDataset):
+
+    def __init__(self, files: List[str], seq_len: int, seed: int, tokenizer: PreTrainedTokenizer):
+        self.arg_files = files
+        self.seq_len = seq_len
+        self.tokenizer = tokenizer
+        self.state = PQDatasetState(
+            files=files,
+            lsfr_seed=seed
+        )
+        self.parquet_tables: Dict[str, Table] = {}
+
+    def __iter__(self):
+        while True:
+            # choose random file
+            file_idx, self.state.lsfr_seed = rand_utils.lsfr_rand_u64(self.state.lsfr_seed, len(self.state.files))
+            parquet_table = self._lazy_get_table(file_idx)
+
+            tokens = []
+            document_lengths = []
+            while True:
+                # choose random row
+                row_idx, self.state.lsfr_seed = rand_utils.lsfr_rand_u64(self.state.lsfr_seed, len(parquet_table))
+                row = parquet_table[row_idx]
+
+                new_tokens = self.tokenizer.encode(str(row), add_special_tokens=True)
+
+                target_num_tokens = self.seq_len + 1
+
+                new_length = len(tokens) + len(new_tokens)
+                if new_length > target_num_tokens:
+                    over_shoot = new_length - target_num_tokens
+                    new_tokens = new_tokens[:-over_shoot]
+
+                # Track document length
+                # Note: target_num_tokens is seq_len + 1 because we obtain one block that we extract
+                # both input and labels from. however, seqlens declares document lengths within only the inputs.
+                # hence, we need to account for the discrepancy in the last document, which becomes one token shorter.
+                if new_length >= target_num_tokens:
+                    document_lengths.append(len(new_tokens) - 1)
+                else:
+                    document_lengths.append(len(new_tokens))
+
+                tokens.extend(new_tokens)
+
+                if len(tokens) >= target_num_tokens:
+                    break
+
+            tokens = torch.tensor(tokens, dtype=torch.int64, device='cpu')
+
+            input_ids = tokens[:-1]
+            labels = tokens[1:]
+
+            document_lengths = torch.tensor(document_lengths, dtype=torch.int64, device='cpu')
+            yield {'input_ids': input_ids, 'labels': labels, 'seqlens': document_lengths}
+
+    @property
+    def is_empty(self):
+        return len(self.arg_files) == 0
+
+    def state_dict(self) -> dict[str, Any]:
+        return asdict(self.state) if self.state is not None else {}
+
+    def load_state_dict(self, state_dict):
+        self.state = PQDatasetState(**state_dict)
+
+    def _lazy_get_table(self, file_idx: int) -> Table:
+        file_path = self.state.files[file_idx]
+        parquet_table = self.parquet_tables.get(file_path, None)
+        if parquet_table is None:
+            parquet_table = pq.ParquetFile(file_path)
+            parquet_table = parquet_table.read()['text']
+            self.parquet_tables[file_path] = parquet_table
+        return parquet_table
+
+
 def collate_fn(samples: list[dict[str, torch.LongTensor]]) -> dict[str, torch.LongTensor | list[torch.LongTensor]]:
     assert samples[0].keys() == {"input_ids", "labels", "seqlens"}
 
@@ -195,10 +280,16 @@ def collate_fn(samples: list[dict[str, torch.LongTensor]]) -> dict[str, torch.Lo
     }
 
 
-def make_mixed_dataset(data_config: DataConfig, tokenizer_info: TokenizerInfo) -> StatefulDataset:
+def make_mixed_nibble_dataset(data_config: DataConfig, tokenizer_info: TokenizerInfo) -> StatefulDataset:
     dataset_paths = data_config.dataset_name_or_paths.split(',')
     probabilities = [int(ratio) / 100 for ratio in data_config.dataset_ratio.split(':')]
 
+    for dataset_path in dataset_paths:
+        if not os.path.exists(dataset_path):
+            raise ValueError(f"Dataset path {dataset_path} does not exist")
+        if not dataset_path.endswith('.bin'):
+            raise ValueError(f"Cannot mix nibble- with non-nibble dataset files!")
+
     rand = random.Random()
 
     # iterator seed *must* be random to avoid different peers training on same data, killing the point of DDP
@@ -213,6 +304,42 @@ def make_mixed_dataset(data_config: DataConfig, tokenizer_info: TokenizerInfo) -
         probabilities
     )
 
+def get_parquet_files(dataset_path: str) -> List[str]:
+    files = os.listdir(dataset_path)
+    parquet_files = []
+    for file in files:
+        if file.endswith('.parquet'):
+            parquet_files.append(os.path.join(dataset_path, file))
+    return parquet_files
+
+def get_hf_tokenizer(tokenizer_info: TokenizerInfo) -> PreTrainedTokenizer:
+    return AutoTokenizer.from_pretrained(tokenizer_info.hf_name, use_fast=True)
+
+def make_mixed_parquet_dataset(data_config: DataConfig, tokenizer_info: TokenizerInfo) -> StatefulDataset:
+    dataset_paths = data_config.dataset_name_or_paths.split(',')
+    probabilities = [int(ratio) / 100 for ratio in data_config.dataset_ratio.split(':')]
+
+    for dataset_path in dataset_paths:
+        if not os.path.exists(dataset_path):
+            raise ValueError(f"Dataset path {dataset_path} does not exist")
+        if not os.path.isdir(dataset_path):
+            raise ValueError(f"Dataset path {dataset_path} must be a directory containing .parquet files")
+        if dataset_path.endswith('.bin'):
+            raise ValueError(f"Cannot mix nibble- with non-nibble dataset files!")
+
+    rand = random.Random()
+
+    # iterator seed *must* be random to avoid different peers training on same data, killing the point of DDP
+    # There is no "rank" in PCCL, and even if there was it would still not be a safe seed.
+    # There are internal UUIDs, but they are not exposed for now.
+    # Random is fine for now.
+    iterator_seed = rand.randint(0, 2 ** 31 - 1)
+
+    return InterleaveDataset(
+        [ParquetDataset(get_parquet_files(dataset_path), data_config.seq_length, iterator_seed, get_hf_tokenizer(tokenizer_info)) for dataset_path in dataset_paths],
+        probabilities
+    )
+
 
 def make_dataloader(
         tokenizer_info: TokenizerInfo,
@@ -224,7 +351,11 @@ def make_dataloader(
     if data_config.fake:
         train_dataset = FakeTokenizedDataset(data_config.seq_length, DEBUG_VOCAB_SIZE)
     else:
-        train_dataset = make_mixed_dataset(data_config, tokenizer_info)
+        is_nibble_file = any([path.endswith('.bin') for path in data_config.dataset_name_or_paths.split(',')])
+        if is_nibble_file:
+            train_dataset = make_mixed_nibble_dataset(data_config, tokenizer_info)
+        else:
+            train_dataset = make_mixed_parquet_dataset(data_config, tokenizer_info)
 
     return StatefulDataLoader(
         train_dataset,
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 2fa44ba1..da9405ac 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -575,7 +575,6 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
     # -------------------------------------------------------------------------
 
     if config.diloco:
-
         for name, local_p in model.named_parameters():
             if isinstance(local_p, DTensor):
                 local_p = local_p.to_local()
diff --git a/src/zeroband/utils/tokenizer_utils.py b/src/zeroband/utils/tokenizer_utils.py
index 5954140f..5b0c45ae 100644
--- a/src/zeroband/utils/tokenizer_utils.py
+++ b/src/zeroband/utils/tokenizer_utils.py
@@ -5,17 +5,22 @@
 if TYPE_CHECKING:
     from zeroband.data import DEBUG_VOCAB_SIZE
 
+
 @dataclass
 class TokenizerInfo:
+    hf_name: str  # TODO: REMOVE
+
     vocab_size: int
     bot_token: int
     eot_token: int
 
+
 def get_tokenizer_info(config: Config) -> TokenizerInfo:
     if config.data.fake and config.model_name == "debugmodel":
         return DEBUG_VOCAB_SIZE
     elif config.model_type == "llama2":
         return TokenizerInfo(
+            hf_name="mistralai/Mistral-7B-v0.1",
             # print(len(AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=True)))
             vocab_size=32000,
             # print(AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=True).bos_token_id)
@@ -25,6 +30,7 @@ def get_tokenizer_info(config: Config) -> TokenizerInfo:
         )
     elif config.model_type == "llama3":
         return TokenizerInfo(
+            hf_name="meta-llama/Meta-Llama-3-8B",
             # print(len(AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B", use_fast=True)))
             vocab_size=128256,
             # print(AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=True).bos_token_id)

From 906517eb2e8e44e28033334155c7785505488717 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Wed, 9 Apr 2025 00:09:39 +0000
Subject: [PATCH 16/47] fix ruff

---
 src/zeroband/data.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/zeroband/data.py b/src/zeroband/data.py
index 2736f1ac..c98b5bef 100644
--- a/src/zeroband/data.py
+++ b/src/zeroband/data.py
@@ -288,7 +288,7 @@ def make_mixed_nibble_dataset(data_config: DataConfig, tokenizer_info: Tokenizer
         if not os.path.exists(dataset_path):
             raise ValueError(f"Dataset path {dataset_path} does not exist")
         if not dataset_path.endswith('.bin'):
-            raise ValueError(f"Cannot mix nibble- with non-nibble dataset files!")
+            raise ValueError("Cannot mix nibble- with non-nibble dataset files!")
 
     rand = random.Random()
 
@@ -325,7 +325,7 @@ def make_mixed_parquet_dataset(data_config: DataConfig, tokenizer_info: Tokenize
         if not os.path.isdir(dataset_path):
             raise ValueError(f"Dataset path {dataset_path} must be a directory containing .parquet files")
         if dataset_path.endswith('.bin'):
-            raise ValueError(f"Cannot mix nibble- with non-nibble dataset files!")
+            raise ValueError("Cannot mix nibble- with non-nibble dataset files!")
 
     rand = random.Random()
 

From 22c724989247e73fad1360f5dcce25385bbab634 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Fri, 11 Apr 2025 00:24:41 +0000
Subject: [PATCH 17/47] fix pending mpi ranks join wait logic

---
 src/zeroband/train.py |  22 +-
 uv.lock               | 714 +++++++++++++++++++++---------------------
 2 files changed, 377 insertions(+), 359 deletions(-)

diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index da9405ac..6170326b 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -414,7 +414,6 @@ def run_outer_step(
         num_syncs: IntRef,
         delayed_update: bool
 ) -> Optional[threading.Thread]:
-
     current_lr = compute_current_lr(training_progress.outer_step, outer_lr_scheduler_config)
     optim_utils.set_optimizer_lr(outer_optimizer, current_lr)
 
@@ -645,20 +644,16 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             # if we don't use diloco we don't print the outer step logs
             logger.info(f"outer_step step: {training_progress.outer_step}")
 
-        global_world_size = communicator.get_attribute(Attribute.GLOBAL_WORLD_SIZE)
-
         topology_updated = False
         if local_iter_num == 1:
             # Assume the topology was updated in the first iteration because we just joined and got accepted
             topology_updated = True
 
         # Possibly update topology / wait for enough peers
-        mpi_ranks_pending = False
+        global_world_size: int
         with train_profiler.session("pccl::update_topology"):
-            if mpi_config is not None:
-                mpi_ranks_pending = global_world_size < mpi_config.mpi_world_size
-
-            if local_iter_num > 1 or mpi_ranks_pending or local_world_size == 1:
+            if local_iter_num > 1 or local_world_size == 1:
+                logger.info("Checking are_peers_pending...")
                 while True:
                     try:
                         if communicator.are_peers_pending():
@@ -673,19 +668,26 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
                         logger.info(f"Updating PCCL topology failed {e}, retrying...")
                         time.sleep(1)
 
+            global_world_size = communicator.get_attribute(Attribute.GLOBAL_WORLD_SIZE) # obtain global world-size after join
+
+            if mpi_config is not None:
+                largest_peer_group_size = communicator.get_attribute(Attribute.LARGEST_PEER_GROUP_WORLD_SIZE)
+                mpi_ranks_pending = global_world_size < (mpi_config.mpi_world_size * largest_peer_group_size)
+
         if mpi_ranks_pending:
-            print("Not all MPI ranks have joined...")
+            logger.info("Not all MPI ranks have joined...")
             time.sleep(1)
             continue
 
         # TODO: Make minimum num pccl peers configurable
         local_world_size = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
         if local_world_size < 2:
-            print("Waiting for more workers to join...")
+            logger.info("Waiting for more workers to join...")
             time.sleep(1)
             continue
 
         if topology_updated:
+            logger.info("Running shared state synchronization...")
             run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, num_syncs, train_profiler,
                                   False)
 
diff --git a/uv.lock b/uv.lock
index 3b5dce97..4991c70d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -849,7 +849,7 @@ wheels = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.30.1"
+version = "0.30.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "filelock" },
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 ]
 
 [[package]]
@@ -3375,6 +3389,7 @@ dev = [
     { name = "pre-commit" },
     { name = "pytest" },
     { name = "ruff" },
+    { name = "transformers" },
 ]
 
 [package.metadata]
@@ -3407,6 +3422,7 @@ dev = [
     { name = "pre-commit", specifier = ">=3.0.0" },
     { name = "pytest", specifier = ">=7.0.0" },
     { name = "ruff", specifier = ">=0.5.0" },
+    { name = "transformers" },
 ]
 
 [[package]]

From 09b515bbbf8dd1b61c1b33b1ad87b09cd2a39126 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Sun, 13 Apr 2025 00:51:22 +0000
Subject: [PATCH 18/47] add launch prime script & add nibble ds folder listing
 support

---
 configs/debug/diloco.toml             |  6 ++---
 scripts/launch_prime.sh               |  5 ++++
 src/zeroband/data.py                  | 35 +++++++++++++++++++-----
 src/zeroband/models/llama/__init__.py |  2 +-
 src/zeroband/train.py                 | 39 ++++++++++++++++-----------
 5 files changed, 60 insertions(+), 27 deletions(-)
 create mode 100755 scripts/launch_prime.sh

diff --git a/configs/debug/diloco.toml b/configs/debug/diloco.toml
index ba6cfb13..3b87952b 100644
--- a/configs/debug/diloco.toml
+++ b/configs/debug/diloco.toml
@@ -26,9 +26,9 @@ num_stable_steps = 0
 type = "sgd"
 
 [data]
-dataset_name_or_paths = 'tests/test_data/parquet/parquet_ds_folder_1,tests/test_data/parquet/parquet_ds_folder_2'
-#dataset_name_or_paths = '/home/mike/IntelliJProjects/dataproctest/working_dir/train_0.bin,/home/mike/IntelliJProjects/dataproctest/working_dir/train_1.bin'
-dataset_ratio = "50:50"
+#dataset_name_or_paths = 'tests/test_data/parquet/parquet_ds_folder_1,tests/test_data/parquet/parquet_ds_folder_2'
+dataset_name_or_paths = '/home/mike/IntelliJProjects/dataproctest/working_dir/train'
+#dataset_ratio = "50:50"
 token_bit_size = 17
 
 [diloco]
diff --git a/scripts/launch_prime.sh b/scripts/launch_prime.sh
new file mode 100755
index 00000000..22078391
--- /dev/null
+++ b/scripts/launch_prime.sh
@@ -0,0 +1,5 @@
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=0 GPU_ORDINAL=0 WORLD_SIZE=4 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=1 GPU_ORDINAL=1 WORLD_SIZE=4 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=2 GPU_ORDINAL=2 WORLD_SIZE=4 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=3 GPU_ORDINAL=3 WORLD_SIZE=4 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
+wait
\ No newline at end of file
diff --git a/src/zeroband/data.py b/src/zeroband/data.py
index c98b5bef..1494e80b 100644
--- a/src/zeroband/data.py
+++ b/src/zeroband/data.py
@@ -281,14 +281,21 @@ def collate_fn(samples: list[dict[str, torch.LongTensor]]) -> dict[str, torch.Lo
 
 
 def make_mixed_nibble_dataset(data_config: DataConfig, tokenizer_info: TokenizerInfo) -> StatefulDataset:
-    dataset_paths = data_config.dataset_name_or_paths.split(',')
-    probabilities = [int(ratio) / 100 for ratio in data_config.dataset_ratio.split(':')]
+    dataset_string = data_config.dataset_name_or_paths
+
+    if ',' in dataset_string:
+        dataset_paths = dataset_string.split(',')
+        probabilities = [int(ratio) / 100 for ratio in data_config.dataset_ratio.split(':')]
+    else:
+        dataset_paths = [os.path.join(dataset_string, path) for path in os.listdir(dataset_string)]
+        dataset_paths = [path for path in dataset_paths if os.path.getsize(path) > 0]
+        probabilities = [100.0 for _ in dataset_paths]
 
     for dataset_path in dataset_paths:
         if not os.path.exists(dataset_path):
             raise ValueError(f"Dataset path {dataset_path} does not exist")
-        if not dataset_path.endswith('.bin'):
-            raise ValueError("Cannot mix nibble- with non-nibble dataset files!")
+        if dataset_path.endswith('.parquet'):
+            raise ValueError("Cannot mix nibble- with parquet dataset files!")
 
     rand = random.Random()
 
@@ -304,6 +311,7 @@ def make_mixed_nibble_dataset(data_config: DataConfig, tokenizer_info: Tokenizer
         probabilities
     )
 
+
 def get_parquet_files(dataset_path: str) -> List[str]:
     files = os.listdir(dataset_path)
     parquet_files = []
@@ -312,9 +320,11 @@ def get_parquet_files(dataset_path: str) -> List[str]:
             parquet_files.append(os.path.join(dataset_path, file))
     return parquet_files
 
+
 def get_hf_tokenizer(tokenizer_info: TokenizerInfo) -> PreTrainedTokenizer:
     return AutoTokenizer.from_pretrained(tokenizer_info.hf_name, use_fast=True)
 
+
 def make_mixed_parquet_dataset(data_config: DataConfig, tokenizer_info: TokenizerInfo) -> StatefulDataset:
     dataset_paths = data_config.dataset_name_or_paths.split(',')
     probabilities = [int(ratio) / 100 for ratio in data_config.dataset_ratio.split(':')]
@@ -336,11 +346,22 @@ def make_mixed_parquet_dataset(data_config: DataConfig, tokenizer_info: Tokenize
     iterator_seed = rand.randint(0, 2 ** 31 - 1)
 
     return InterleaveDataset(
-        [ParquetDataset(get_parquet_files(dataset_path), data_config.seq_length, iterator_seed, get_hf_tokenizer(tokenizer_info)) for dataset_path in dataset_paths],
+        [ParquetDataset(get_parquet_files(dataset_path), data_config.seq_length, iterator_seed,
+                        get_hf_tokenizer(tokenizer_info)) for dataset_path in dataset_paths],
         probabilities
     )
 
 
+def _is_or_contains_nibble_file(paths_string: str):
+    if ',' in paths_string:
+        return any([path.endswith('.bin') for path in paths_string.split(',')])
+    else:
+        if not os.path.isdir(paths_string):
+            raise ValueError("path string with out ',' delimiter must be a directory!")
+        files = os.listdir(paths_string)
+        return any([path.endswith('.bin') for path in files])
+
+
 def make_dataloader(
         tokenizer_info: TokenizerInfo,
         mpi_world_size: int,
@@ -351,8 +372,8 @@ def make_dataloader(
     if data_config.fake:
         train_dataset = FakeTokenizedDataset(data_config.seq_length, DEBUG_VOCAB_SIZE)
     else:
-        is_nibble_file = any([path.endswith('.bin') for path in data_config.dataset_name_or_paths.split(',')])
-        if is_nibble_file:
+        is_nibble = _is_or_contains_nibble_file(data_config.dataset_name_or_paths)
+        if is_nibble:
             train_dataset = make_mixed_nibble_dataset(data_config, tokenizer_info)
         else:
             train_dataset = make_mixed_parquet_dataset(data_config, tokenizer_info)
diff --git a/src/zeroband/models/llama/__init__.py b/src/zeroband/models/llama/__init__.py
index 2e32522c..df6c245b 100644
--- a/src/zeroband/models/llama/__init__.py
+++ b/src/zeroband/models/llama/__init__.py
@@ -52,7 +52,7 @@
         multiple_of=512,
         rope_theta=500000,
     ),
-    "8B": ModelArgs(
+    "7B": ModelArgs(
         dim=4096,
         n_layers=32,
         n_heads=32,
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 6170326b..0f46863a 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -341,7 +341,7 @@ def run_all_reduce():
             ReduceOp.AVG
         )
         end_time = time.time()
-        print(f"All-Reduce took {end_time - start_time} seconds")
+        logger.info(f"All-Reduce took {end_time - start_time} seconds")
 
     logger.debug("Launching all reduce...")
     all_reduce_thread = threading.Thread(target=run_all_reduce, name="ReduceThread")
@@ -443,6 +443,8 @@ def run_shared_state_sync(
         communicator: Communicator,
         model: torch.nn.Module, outer_parameters_list: List[torch.nn.Parameter],
 
+        logger: Logger,
+
         num_syncs: IntRef,
         train_profiler: Profiler,
         late_joiner: bool,
@@ -451,14 +453,14 @@ def run_shared_state_sync(
     with train_profiler.session("pccl::sync_shared_state"):
         sync_info = communicator.sync_shared_state(shared_state)
         shared_state.revision += 1
-        print(f"sync_info tx_bytes: {sync_info.tx_bytes}, rx_bytes: {sync_info.rx_bytes}")
+        logger.info(f"sync_info tx_bytes: {sync_info.tx_bytes}, rx_bytes: {sync_info.rx_bytes}")
         num_syncs += 1
         if num_syncs > 1 and not late_joiner:
             assert sync_info.rx_bytes == 0, "Shared state drifted unexpectedly in peers!"
 
         # initialize inner state on first sync
         if num_syncs == 1:
-            print("Initializing inner state...")
+            logger.info("Initializing inner state...")
             sync_inner_with_outer_state(model, outer_parameters_list)
 
 
@@ -600,6 +602,7 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
     with setup_profiler.session("torch::compile"):
         if config.hardware.torch_compile:
+            logger.info("Compiling model...")
             model = torch.compile(model) if not TYPE_CHECKING else model
 
     memory_profiler: Optional[MemoryProfiler] = None
@@ -613,7 +616,7 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
     # initialize PCCL
     communicator = Communicator(config.pccl.ccoip_host, mpi_config.mpi_rank if mpi_config is not None else 0)
     communicator.connect(n_attempts=15)
-    print("Connected to master via PCCL")
+    logger.info("Connected to master via PCCL")
 
     if PRIME_SETUP_PROFILER_PRINT_TIMINGS:
         setup_profiler.print_report()
@@ -652,14 +655,14 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
         # Possibly update topology / wait for enough peers
         global_world_size: int
         with train_profiler.session("pccl::update_topology"):
-            if local_iter_num > 1 or local_world_size == 1:
+            if local_iter_num > 1:
                 logger.info("Checking are_peers_pending...")
                 while True:
                     try:
                         if communicator.are_peers_pending():
-                            logger.info(
-                                "Join-Candidate peers pending; awaiting concurrent collective operations to accept new peers...")
                             if all_reduce_thread is not None:
+                                logger.info(
+                                    "Join-Candidate peers pending; awaiting concurrent collective operations to accept new peers...")
                                 all_reduce_thread.join()
                             communicator.update_topology()
                             topology_updated = True
@@ -668,7 +671,8 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
                         logger.info(f"Updating PCCL topology failed {e}, retrying...")
                         time.sleep(1)
 
-            global_world_size = communicator.get_attribute(Attribute.GLOBAL_WORLD_SIZE) # obtain global world-size after join
+            global_world_size = communicator.get_attribute(
+                Attribute.GLOBAL_WORLD_SIZE)  # obtain global world-size after join
 
             if mpi_config is not None:
                 largest_peer_group_size = communicator.get_attribute(Attribute.LARGEST_PEER_GROUP_WORLD_SIZE)
@@ -681,16 +685,19 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
         # TODO: Make minimum num pccl peers configurable
         local_world_size = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
-        if local_world_size < 2:
-            logger.info("Waiting for more workers to join...")
-            time.sleep(1)
-            continue
+        # if local_world_size < 2:
+        #    logger.info("Waiting for more workers to join...")
+        #    time.sleep(1)
+        #    continue
 
         if topology_updated:
             logger.info("Running shared state synchronization...")
-            run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, num_syncs, train_profiler,
+            run_shared_state_sync(shared_state, communicator, model, outer_parameters_list,
+                                  logger,
+                                  num_syncs, train_profiler,
                                   False)
 
+        logger.info("Running inner steps...")
         run_inner_steps(
             model, train_dataloader_iterator, inner_optimizer, device,
 
@@ -752,8 +759,8 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             # Since ckpt strategy and all reduce is done at the outer loop level.
             break
 
-        if mpi_config is None or mpi_config.mpi_rank == 0:
-            wandb.finish()
+    if mpi_config is None or mpi_config.mpi_rank == 0:
+        wandb.finish()
 
     if config.hardware.memory_profiler is not None:
         logger.debug(f"Max memory used: {torch.cuda.max_memory_allocated() / 1024 ** 2:.2f} MB")
@@ -766,7 +773,7 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 def main():
     # Allow eager fallback during production so that the training runs don't die
     # However, in development, we want to know that we broke torch compile
-    torch._dynamo.config.suppress_errors = "ZERO_BAND_DEV" not in os.environ  # type: ignore
+    # torch._dynamo.config.suppress_errors = "ZERO_BAND_DEV" not in os.environ  # type: ignore
     torch.set_float32_matmul_precision("high")
 
     mpi_config: Optional[MPIConfig] = ccl_utils.make_mpi_config(

From a072ba2258da58a30ff9c8096255f3079efecaaf Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Mon, 14 Apr 2025 06:49:37 +0000
Subject: [PATCH 19/47] add streaming data loader support

---
 pyproject.toml        |   3 +-
 src/zeroband/data.py  |  60 +++++++++++++++--
 src/zeroband/train.py |  10 ++-
 uv.lock               | 150 ++++++++++++++++++++++++------------------
 4 files changed, 151 insertions(+), 72 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 4fdd98d6..72f5d884 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,8 @@ dependencies = [
     "wandb",
     "numba",
     "imageio[ffmpeg]",
-    "pccl @ git+https://github.com/PrimeIntellect-ai/pccl.git@main#subdirectory=python/framework"
+    "pccl @ git+https://github.com/PrimeIntellect-ai/pccl.git@main#subdirectory=python/framework",
+    "datasetstream @ git+https://github.com/PrimeIntellect-ai/datasetstream@main"
 ]
 
 [project.optional-dependencies]
diff --git a/src/zeroband/data.py b/src/zeroband/data.py
index 1494e80b..7f08ab46 100644
--- a/src/zeroband/data.py
+++ b/src/zeroband/data.py
@@ -5,6 +5,7 @@
 from typing import Any, Generator, List, Dict
 
 import torch
+from datasetstream.dataset_client import DatasetClientIteratorSync
 from torch.distributed.checkpoint.stateful import Stateful
 from torch.utils.data import IterableDataset
 from torchdata.stateful_dataloader import StatefulDataLoader
@@ -259,6 +260,37 @@ def _lazy_get_table(self, file_idx: int) -> Table:
         return parquet_table
 
 
+class StreamingDataset(StatefulDataset):
+
+    def __init__(self, dataset_url: str, seed: int, seq_len: int, tokenizer_info: TokenizerInfo):
+        self.dataset_url = dataset_url
+        self.seed = seed
+        self.seq_len = seq_len
+        self.tokenizer_info = tokenizer_info
+
+    def __iter__(self):
+        with DatasetClientIteratorSync(self.dataset_url, self.seed, 1, self.seq_len + 1, prefetch_size=512) as iterator:
+            while True:
+                tokens = next(iterator)
+                tokens = torch.from_numpy(tokens[0]).to(torch.int64)
+
+                input_ids = tokens[:-1]
+                labels = tokens[1:]
+
+                # create document lengths from where eot tokens are placed inside chunk
+                document_lengths = []
+                cur_len = 0
+                for t in input_ids:
+                    cur_len += 1
+                    if t == self.tokenizer_info.eot_token:
+                        document_lengths.append(cur_len)
+                        cur_len = 0
+                document_lengths.append(cur_len)
+
+                document_lengths = torch.tensor(document_lengths, dtype=torch.int64, device='cpu')
+                yield {'input_ids': input_ids, 'labels': labels, 'seqlens': document_lengths}
+
+
 def collate_fn(samples: list[dict[str, torch.LongTensor]]) -> dict[str, torch.LongTensor | list[torch.LongTensor]]:
     assert samples[0].keys() == {"input_ids", "labels", "seqlens"}
 
@@ -312,6 +344,21 @@ def make_mixed_nibble_dataset(data_config: DataConfig, tokenizer_info: Tokenizer
     )
 
 
+def make_streaming_dataset(config: DataConfig, tokenizer_info: TokenizerInfo) -> StatefulDataset:
+    dataset_url = config.dataset_name_or_paths
+    seq_length = config.seq_length
+
+    rand = random.Random()
+
+    # iterator seed *must* be random to avoid different peers training on same data, killing the point of DDP
+    # There is no "rank" in PCCL, and even if there was it would still not be a safe seed.
+    # There are internal UUIDs, but they are not exposed for now.
+    # Random is fine for now.
+    seed = rand.randint(0, 2 ** 31 - 1)
+
+    return StreamingDataset(dataset_url, seed, seq_length, tokenizer_info)
+
+
 def get_parquet_files(dataset_path: str) -> List[str]:
     files = os.listdir(dataset_path)
     parquet_files = []
@@ -372,11 +419,16 @@ def make_dataloader(
     if data_config.fake:
         train_dataset = FakeTokenizedDataset(data_config.seq_length, DEBUG_VOCAB_SIZE)
     else:
-        is_nibble = _is_or_contains_nibble_file(data_config.dataset_name_or_paths)
-        if is_nibble:
-            train_dataset = make_mixed_nibble_dataset(data_config, tokenizer_info)
+        # noinspection HttpUrlsUsage
+        if (data_config.dataset_name_or_paths.startswith("http://")
+                or data_config.dataset_name_or_paths.startswith("https://")):
+            train_dataset = make_streaming_dataset(data_config, tokenizer_info)
         else:
-            train_dataset = make_mixed_parquet_dataset(data_config, tokenizer_info)
+            is_nibble = _is_or_contains_nibble_file(data_config.dataset_name_or_paths)
+            if is_nibble:
+                train_dataset = make_mixed_nibble_dataset(data_config, tokenizer_info)
+            else:
+                train_dataset = make_mixed_parquet_dataset(data_config, tokenizer_info)
 
     return StatefulDataLoader(
         train_dataset,
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 0f46863a..376acbb8 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -109,14 +109,15 @@ def run_inner_steps(
         memory_profiler: MemoryProfiler,
 
         local_world_size: int,
+        global_world_size: int,
 
-        mpi_config: Optional[MPIConfig],
+        mpi_config: MPIConfig | None,
         train_profiler: Profiler,
         config: Config,
         training_progress: TrainingProgress,
         grad_accum_steps: int,
 
-        timing_events: List[Tuple[torch.cuda.Event, torch.cuda.Event]]
+        timing_events: list[tuple[torch.cuda.Event, torch.cuda.Event]]
 ):
     num_inner_steps = config.diloco.inner_steps if config.diloco is not None else 1
     num_param_scalars = model.count_parameters()
@@ -217,7 +218,8 @@ def run_inner_steps(
         })
 
         if config.diloco is not None:
-            metrics["num_peers"] = local_world_size
+            metrics["num_peers"] = global_world_size
+            metrics["num_nodes"] = local_world_size
 
         if (mpi_config is None or mpi_config.mpi_rank == 0) and config.wandb:
             wandb.log(metrics)
@@ -654,6 +656,7 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
         # Possibly update topology / wait for enough peers
         global_world_size: int
+        mpi_ranks_pending: bool = False
         with train_profiler.session("pccl::update_topology"):
             if local_iter_num > 1:
                 logger.info("Checking are_peers_pending...")
@@ -704,6 +707,7 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             logger, memory_profiler,
 
             local_world_size,
+            global_world_size,
 
             mpi_config, train_profiler, config,
             training_progress, grad_accum_steps, timing_events
diff --git a/uv.lock b/uv.lock
index 4991c70d..0daaed99 100644
--- a/uv.lock
+++ b/uv.lock
@@ -455,6 +455,16 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b4/83/50abe521eb75744a01efe2ebe836a4b61f4df37941a776f650f291aabdf9/datasets-3.5.0-py3-none-any.whl", hash = "sha256:b3b7f163acc6ac4e01a1b00eef26d48bd4039288ceea3601d169272bd5581006", size = 491167 },
 ]
 
+[[package]]
+name = "datasetstream"
+version = "0.1.0"
+source = { git = "https://github.com/PrimeIntellect-ai/datasetstream?rev=main#ead1c49ba5a21cc5e871eb2b11d5e7d15bf016a6" }
+dependencies = [
+    { name = "aiohttp" },
+    { name = "numba" },
+    { name = "numpy" },
+]
+
 [[package]]
 name = "decorator"
 version = "5.2.1"
@@ -1856,7 +1866,7 @@ wheels = [
 [[package]]
 name = "pccl"
 version = "0.1.0"
-source = { git = "https://github.com/PrimeIntellect-ai/pccl.git?subdirectory=python%2Fframework&rev=main#a71f219facaa6c8ae17f944634d1350d07be1cd5" }
+source = { git = "https://github.com/PrimeIntellect-ai/pccl.git?subdirectory=python%2Fframework&rev=main#46e29073bdda7c46f7dcd8f12c790778d23d80a1" }
 dependencies = [
     { name = "cffi" },
     { name = "ipaddress" },
@@ -1886,69 +1896,79 @@ wheels = [
 
 [[package]]
 name = "pillow"
-version = "11.1.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/f3/af/c097e544e7bd278333db77933e535098c259609c4eb3b85381109602fb5b/pillow-11.1.0.tar.gz", hash = "sha256:368da70808b36d73b4b390a8ffac11069f8a5c85f29eff1f1b01bcf3ef5b2a20", size = 46742715 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/50/1c/2dcea34ac3d7bc96a1fd1bd0a6e06a57c67167fec2cff8d95d88229a8817/pillow-11.1.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:e1abe69aca89514737465752b4bcaf8016de61b3be1397a8fc260ba33321b3a8", size = 3229983 },
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 ]
 
 [[package]]
@@ -3359,6 +3379,7 @@ version = "0.1.0"
 source = { editable = "." }
 dependencies = [
     { name = "datasets" },
+    { name = "datasetstream" },
     { name = "fsspec", extra = ["gcs"] },
     { name = "imageio", extra = ["ffmpeg"] },
     { name = "ninja" },
@@ -3395,6 +3416,7 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "datasets", specifier = ">=3.0.0" },
+    { name = "datasetstream", git = "https://github.com/PrimeIntellect-ai/datasetstream?rev=main" },
     { name = "fsspec", extras = ["gcs"], specifier = ">=2024.3.1" },
     { name = "imageio", extras = ["ffmpeg"] },
     { name = "lm-eval", marker = "extra == 'all'" },

From 8867b06702215e9c905b0baed22ee8169c283f54 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Mon, 14 Apr 2025 09:18:16 +0000
Subject: [PATCH 20/47] fix fake data loader

---
 src/zeroband/data.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/src/zeroband/data.py b/src/zeroband/data.py
index 7f08ab46..32b96d45 100644
--- a/src/zeroband/data.py
+++ b/src/zeroband/data.py
@@ -35,10 +35,13 @@ def __init__(self, seq_len: int, vocab_size: int):
 
     def __iter__(self) -> Generator[dict[str, Any], Any, None]:
         while True:
-            len_ = random.randint(1, self.seq_len)
-            input_ids = torch.randint(3, self.vocab_size, (len_,)).tolist()
+            data = torch.randint(3, self.vocab_size, (self.seq_len + 1,))
+
+            input_ids = data[:-1]
+            labels = data[1:]
+
             self.step += 1
-            yield {"input_ids": input_ids}
+            yield {"input_ids": input_ids, "labels": labels, "seqlens": torch.tensor([1024], dtype=torch.int64)}
 
     def state_dict(self):
         return {"step": self.step}

From bf39f616bad6362c5acd30675b5139f1f34cdda8 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Mon, 14 Apr 2025 10:37:02 +0000
Subject: [PATCH 21/47] fix for 8xH100

---
 scripts/launch_prime.sh            | 12 ++++++++----
 src/zeroband/config.py             |  2 +-
 src/zeroband/models/llama/model.py |  4 ++--
 src/zeroband/train.py              |  2 --
 src/zeroband/utils/mfu_tracker.py  | 10 ++++++++++
 5 files changed, 21 insertions(+), 9 deletions(-)

diff --git a/scripts/launch_prime.sh b/scripts/launch_prime.sh
index 22078391..1a25a8e1 100755
--- a/scripts/launch_prime.sh
+++ b/scripts/launch_prime.sh
@@ -1,5 +1,9 @@
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=0 GPU_ORDINAL=0 WORLD_SIZE=4 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=1 GPU_ORDINAL=1 WORLD_SIZE=4 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=2 GPU_ORDINAL=2 WORLD_SIZE=4 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=3 GPU_ORDINAL=3 WORLD_SIZE=4 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=0 GPU_ORDINAL=0 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=1 GPU_ORDINAL=1 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=2 GPU_ORDINAL=2 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=3 GPU_ORDINAL=3 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=4 GPU_ORDINAL=4 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=5 GPU_ORDINAL=5 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=6 GPU_ORDINAL=6 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=7 GPU_ORDINAL=7 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
 wait
\ No newline at end of file
diff --git a/src/zeroband/config.py b/src/zeroband/config.py
index 35364efb..aa230d63 100644
--- a/src/zeroband/config.py
+++ b/src/zeroband/config.py
@@ -116,7 +116,7 @@ class MemoryProfilerConfig(BaseConfig):
     snapshot_dir: str
 
 
-AttnFnType: TypeAlias = Literal["flex", "math"]
+AttnFnType: TypeAlias = Literal["flex", "sdpa"]
 CclLibType: TypeAlias = Literal["nccl", "pccl"]
 
 
diff --git a/src/zeroband/models/llama/model.py b/src/zeroband/models/llama/model.py
index adfee846..507cd7e3 100644
--- a/src/zeroband/models/llama/model.py
+++ b/src/zeroband/models/llama/model.py
@@ -291,7 +291,7 @@ def forward(
         return self.wo(output)
 
     def _sdpa_attention(self, xq, xk, xv, flop_counter: FlopCounter) -> torch.Tensor:
-        with sdpa_kernel(SDPBackend.MATH) if self.attn_fn == "math" else contextlib.nullcontext():
+        with sdpa_kernel(SDPBackend.FLASH_ATTENTION) if self.attn_fn == "sdpa" else contextlib.nullcontext():
             output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, is_causal=True)
             flop_counter.track_mha_attention(xq, xk, xv, is_causal=True)
 
@@ -314,7 +314,7 @@ def self_attention(
             block_mask: BlockMask | None,
             flop_counter: FlopCounter,
     ) -> torch.Tensor:
-        if block_mask is not None:
+        if block_mask is not None and self.attn_fn == "flex":
             return self._flex_attention_with_seqlens(xq, xk, xv, block_mask, flop_counter=flop_counter)
         else:
             return self._sdpa_attention(xq, xk, xv, flop_counter=flop_counter)
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 376acbb8..ac7b93ab 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -778,8 +778,6 @@ def main():
     # Allow eager fallback during production so that the training runs don't die
     # However, in development, we want to know that we broke torch compile
     # torch._dynamo.config.suppress_errors = "ZERO_BAND_DEV" not in os.environ  # type: ignore
-    torch.set_float32_matmul_precision("high")
-
     mpi_config: Optional[MPIConfig] = ccl_utils.make_mpi_config(
         mpi_rank=os.getenv("RANK"),
         mpi_world_size=os.getenv("WORLD_SIZE")
diff --git a/src/zeroband/utils/mfu_tracker.py b/src/zeroband/utils/mfu_tracker.py
index 8f04fc94..42189ca2 100644
--- a/src/zeroband/utils/mfu_tracker.py
+++ b/src/zeroband/utils/mfu_tracker.py
@@ -134,16 +134,20 @@ def __init__(self, no_infer_bwd_flops: bool = False):
         self._num_backward_flops: int = 0
         self.no_infer_bwd_flops = no_infer_bwd_flops
 
+    @torch.compiler.disable
     def track_forward_flops(self, num_flops: int):
         self._num_forward_flops += num_flops
 
+    @torch.compiler.disable
     def track_backward_flops(self, num_flops: int, force_track_bwd: bool = False):
         if not self.no_infer_bwd_flops or force_track_bwd:
             self._num_backward_flops += num_flops
 
+    @torch.compiler.disable
     def get_performed_flops(self) -> int:
         return self._num_forward_flops + self._num_backward_flops
 
+    @torch.compiler.disable
     def track_linear(self, linear: torch.nn.Linear, x: torch.Tensor):
         """
         Tracks the number of flops for both the forward and backward passes of a linear layer.
@@ -183,6 +187,7 @@ def track_linear(self, linear: torch.nn.Linear, x: torch.Tensor):
         total_backward_flops = grad_input_flops + grad_weight_flops + grad_bias_flops
         self.track_backward_flops(total_backward_flops)
 
+    @torch.compiler.disable
     def track_binary(self, a: torch.Tensor, b: torch.Tensor):
         """
         Tracks the amount of flops that are performed when performing a binary elementwise operator of
@@ -193,6 +198,7 @@ def track_binary(self, a: torch.Tensor, b: torch.Tensor):
         self.track_forward_flops(num_flops)
         self.track_backward_flops(num_flops)
 
+    @torch.compiler.disable
     def track_unary(self, x: torch.Tensor):
         """
         Tracks the amount of flops that are performed when performing a unary elementwise operator on the same shape
@@ -201,6 +207,7 @@ def track_unary(self, x: torch.Tensor):
         self.track_forward_flops(x.numel())
         self.track_backward_flops(x.numel())
 
+    @torch.compiler.disable
     def track_mha_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, is_causal=False):
         # Refer to shape legend:
         # https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
@@ -264,6 +271,7 @@ def track_flex_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor
         self.track_backward_flops(math.floor(forward_flops * 2.5))
 
 
+    @torch.compiler.disable
     def track_norm(self, norm: torch.nn.Module, x: torch.Tensor):
         d = x.size(-1)
         if isinstance(norm, torch.nn.LayerNorm):
@@ -286,6 +294,7 @@ def track_norm(self, norm: torch.nn.Module, x: torch.Tensor):
 
         self.track_forward_flops(flops)
 
+    @torch.compiler.disable
     def track_optimizer_step(self, optimizer: torch.optim.Optimizer, num_param_scalars: int):
         if isinstance(optimizer, torch.optim.Adam):
             flops_per_param = 14
@@ -295,6 +304,7 @@ def track_optimizer_step(self, optimizer: torch.optim.Optimizer, num_param_scala
             raise NotImplementedError(f"Optimizer type {type(optimizer)} not supported for flop tracking.")
         self.track_backward_flops(flops_per_param * num_param_scalars, force_track_bwd=True)
 
+    @torch.compiler.disable
     def track_cross_entropy(self, logits: torch.Tensor):
         """
            Tracks the FLOPs performed for the cross entropy loss computation.

From 7f6d0affc0ee9af0b613fb67a8daad4edeb87bb8 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Mon, 14 Apr 2025 10:38:20 +0000
Subject: [PATCH 22/47] add H100 config

---
 configs/7B/H100.toml | 36 ++++++++++++++++++++++++++++++------
 1 file changed, 30 insertions(+), 6 deletions(-)

diff --git a/configs/7B/H100.toml b/configs/7B/H100.toml
index 9b8a3729..d5126a8a 100644
--- a/configs/7B/H100.toml
+++ b/configs/7B/H100.toml
@@ -1,18 +1,42 @@
-project = "debug_7B_zero_band"
-
 model_name = "7B"
-model_type = "llama2"
+model_type = "llama3"
+
+wandb = true
+log_all_ranks = true
 
 [hardware]
-micro_batch_size = 64
-reshard_after_forward = false
+micro_batch_size = 8
+reshard_after_forward = true
+torch_compile = false
+attn_fn="sdpa"
 
 [train]
 batch_size = 512
 
+[train.lr_scheduler]
+lr = 3e-4
+end_lr = 0.0
+num_warmup_steps = 8000
+num_decay_steps = 1.2e6
+
+[train.outer_lr_scheduler]
+lr = 0.7
+end_lr = 0.7
+num_decay_steps = 0
+num_warmup_steps = 0
+num_stable_steps = 0
+
+[train.outer_optimizer]
+type = "sgd"
+
 [data]
 seq_length = 1024
-dataset_name_or_paths = "datasets/fineweb-edu"
+dataset_name_or_paths = 'http://65.108.32.176:8080/api/v1/datasets/fineweb-edu-train/stream'
+token_bit_size = 17
+
+[diloco]
+inner_steps = 64
+delayed_update = true
 
 [pccl]
 ccoip_host = "127.0.0.1:48148"
\ No newline at end of file

From 1762ce2ea5918fab733c0f4b1c97af11d31337f8 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Mon, 14 Apr 2025 20:36:17 +0000
Subject: [PATCH 23/47] add topology optimization

---
 src/zeroband/train.py | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index ac7b93ab..3e0d57d3 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -686,14 +686,22 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             time.sleep(1)
             continue
 
-        # TODO: Make minimum num pccl peers configurable
         local_world_size = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
-        # if local_world_size < 2:
-        #    logger.info("Waiting for more workers to join...")
-        #    time.sleep(1)
-        #    continue
+        if local_world_size < 2:
+            logger.info("Waiting for more workers to join...")
+            time.sleep(1)
+            continue
 
         if topology_updated:
+            logger.info("Optimizing Topology...")
+            while True:
+                try:
+                    communicator.optimize_topology()  # may raise an error if it fails
+                    break
+                except PCCLError as e:
+                    print(f"[Peer] OptimizeTopology failed => {e}. Retrying...")
+                    time.sleep(0.1)
+
             logger.info("Running shared state synchronization...")
             run_shared_state_sync(shared_state, communicator, model, outer_parameters_list,
                                   logger,

From 25b27ce18b4cdf7ddfa3c1a464230e6c6e261640 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Mon, 14 Apr 2025 21:01:04 +0000
Subject: [PATCH 24/47] change config of launch_prime.sh

---
 scripts/launch_prime.sh | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/scripts/launch_prime.sh b/scripts/launch_prime.sh
index 1a25a8e1..3dbb06d1 100755
--- a/scripts/launch_prime.sh
+++ b/scripts/launch_prime.sh
@@ -1,9 +1,9 @@
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=0 GPU_ORDINAL=0 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=1 GPU_ORDINAL=1 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=2 GPU_ORDINAL=2 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=3 GPU_ORDINAL=3 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=4 GPU_ORDINAL=4 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=5 GPU_ORDINAL=5 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=6 GPU_ORDINAL=6 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=7 GPU_ORDINAL=7 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/debug/diloco.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=0 GPU_ORDINAL=0 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=1 GPU_ORDINAL=1 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=2 GPU_ORDINAL=2 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=3 GPU_ORDINAL=3 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=4 GPU_ORDINAL=4 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=5 GPU_ORDINAL=5 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=6 GPU_ORDINAL=6 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=7 GPU_ORDINAL=7 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
 wait
\ No newline at end of file

From bebdd0fb1f98c39dc6ddfef3b8e16b4e12cb9536 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Fri, 18 Apr 2025 08:47:20 +0000
Subject: [PATCH 25/47] small changes

---
 src/zeroband/data.py               |   2 +-
 src/zeroband/models/llama/model.py |   5 +-
 src/zeroband/train.py              |  45 ++-
 src/zeroband/utils/mfu_tracker.py  |   3 +-
 uv.lock                            | 511 ++++++++++++++++-------------
 5 files changed, 314 insertions(+), 252 deletions(-)

diff --git a/src/zeroband/data.py b/src/zeroband/data.py
index 32b96d45..4bc5c6f0 100644
--- a/src/zeroband/data.py
+++ b/src/zeroband/data.py
@@ -407,7 +407,7 @@ def _is_or_contains_nibble_file(paths_string: str):
         return any([path.endswith('.bin') for path in paths_string.split(',')])
     else:
         if not os.path.isdir(paths_string):
-            raise ValueError("path string with out ',' delimiter must be a directory!")
+            raise ValueError("path string without ',' delimiter must be a directory!")
         files = os.listdir(paths_string)
         return any([path.endswith('.bin') for path in files])
 
diff --git a/src/zeroband/models/llama/model.py b/src/zeroband/models/llama/model.py
index 507cd7e3..e73c3fbf 100644
--- a/src/zeroband/models/llama/model.py
+++ b/src/zeroband/models/llama/model.py
@@ -291,9 +291,8 @@ def forward(
         return self.wo(output)
 
     def _sdpa_attention(self, xq, xk, xv, flop_counter: FlopCounter) -> torch.Tensor:
-        with sdpa_kernel(SDPBackend.FLASH_ATTENTION) if self.attn_fn == "sdpa" else contextlib.nullcontext():
-            output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, is_causal=True)
-            flop_counter.track_mha_attention(xq, xk, xv, is_causal=True)
+        output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, is_causal=True)
+        flop_counter.track_mha_attention(xq, xk, xv, is_causal=True)
 
         output = output.transpose(1, 2).contiguous()  # (bs, seqlen, n_local_heads, head_dim)
         return output
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 3e0d57d3..42ef94e5 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -1,6 +1,7 @@
 import os
 import threading
 import time
+import zlib
 from dataclasses import asdict
 from logging import Logger
 from typing import TYPE_CHECKING, Optional, Iterator, List, Dict, Tuple
@@ -233,6 +234,14 @@ def run_inner_steps(
         train_profiler.end_session()
 
 
+def compute_crc32(tensor: torch.Tensor) -> int:
+    tensor_cpu = tensor.detach().cpu()
+    tensor_contiguous = tensor_cpu.contiguous()
+    tensor_np = tensor_contiguous.numpy()
+    tensor_bytes = tensor_np.tobytes()
+    checksum = zlib.crc32(tensor_bytes)
+    return checksum
+
 def run_async_outer_step(
         model: torch.nn.Module,
         last_pseudo_grads: List[torch.Tensor],
@@ -253,7 +262,9 @@ def run_async_outer_step(
     # await previous all reduce, if one exists
     can_outer_step = False
     if all_reduce_thread is not None:
+        logger.info("joining previous all reduce...")
         all_reduce_thread.join()
+        logger.info("joined previous all reduce.")
         can_outer_step = True
 
         # populate outer param grads with last pseudo-gradients set by thread
@@ -270,8 +281,8 @@ def run_async_outer_step(
         outer_p_data: torch.Tensor = outer_p.data
         if isinstance(param_data, DTensor):
             param_data = param_data.to_local()
-        outer_p.grad = outer_p_data - param_data.to('cpu')
-        outer_grads.append(outer_p.grad)
+        outer_p_grad = outer_p_data - param_data.to('cpu')
+        outer_grads.append(outer_p_grad)
 
     if can_outer_step:
         outer_optimizer.step()  # Note that there is no zero-grad because grads get re-instantiated every step
@@ -296,7 +307,7 @@ def run_async_outer_step(
             # is applied that they were not part of.
             logger.info(
                 "Topology updated mid run; re-running shared state synchronization to properly insert new peer...")
-            run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, num_syncs, train_profiler,
+            run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, logger, num_syncs, train_profiler,
                                   False)
 
     else:
@@ -307,8 +318,8 @@ def run_async_outer_step(
             # We obtain the shared state first and then simply copy it into the inner model afterwards.
             # Also: late_joiner here means that we tolerate actually receiving bytes here despite that this is the second sync that was performed.
             # This is necessary for the pipeline insertion algorithm to function
-            run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, num_syncs, train_profiler,
-                                  False)
+            run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, logger, num_syncs, train_profiler,
+                                  True)
 
         # This is the boostrap for the 1-step behind asynchronous training step.
         # Reset the inner state here to be equal to the unmodified outer state.
@@ -335,12 +346,16 @@ def run_async_outer_step(
 
     def run_all_reduce():
         nonlocal last_pseudo_grads
-        last_pseudo_grads = outer_grads.copy()
+
+        last_pseudo_grads.clear()
+        last_pseudo_grads.extend(outer_grads)
+
         start_time = time.time()
         pccl_utils.all_reduce_multiple_with_retry(
             communicator,
             last_pseudo_grads,
-            ReduceOp.AVG
+            ReduceOp.AVG,
+            max_in_flight=128
         )
         end_time = time.time()
         logger.info(f"All-Reduce took {end_time - start_time} seconds")
@@ -453,6 +468,7 @@ def run_shared_state_sync(
 ):
     # 3) Sync shared state => ensures we have the same aggregator (outer) parameters
     with train_profiler.session("pccl::sync_shared_state"):
+        logger.info(f"run_shared_state_sync: shared_state_revision: {shared_state.revision}")
         sync_info = communicator.sync_shared_state(shared_state)
         shared_state.revision += 1
         logger.info(f"sync_info tx_bytes: {sync_info.tx_bytes}, rx_bytes: {sync_info.rx_bytes}")
@@ -694,13 +710,13 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
         if topology_updated:
             logger.info("Optimizing Topology...")
-            while True:
-                try:
-                    communicator.optimize_topology()  # may raise an error if it fails
-                    break
-                except PCCLError as e:
-                    print(f"[Peer] OptimizeTopology failed => {e}. Retrying...")
-                    time.sleep(0.1)
+            #while True:
+            #    try:
+            #        communicator.optimize_topology()  # may raise an error if it fails
+            #        break
+            #    except PCCLError as e:
+            #        print(f"[Peer] OptimizeTopology failed => {e}. Retrying...")
+            #        time.sleep(0.1)
 
             logger.info("Running shared state synchronization...")
             run_shared_state_sync(shared_state, communicator, model, outer_parameters_list,
@@ -720,6 +736,7 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             mpi_config, train_profiler, config,
             training_progress, grad_accum_steps, timing_events
         )
+        logger.info("Inner steps done")
 
         # post inner steps
         if PRIME_TRAIN_PROFILER_PRINT_TIMINGS:
diff --git a/src/zeroband/utils/mfu_tracker.py b/src/zeroband/utils/mfu_tracker.py
index 42189ca2..961cfeb9 100644
--- a/src/zeroband/utils/mfu_tracker.py
+++ b/src/zeroband/utils/mfu_tracker.py
@@ -265,7 +265,8 @@ def track_flex_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor
         E = E_q
         mha_flops = 4.0 * N * (S * L) * E
 
-        mask_occupancy = 1.0 - (mask_sparsity.sparsity() / 100.0)
+        #mask_occupancy = 1.0 - (mask_sparsity.sparsity() / 100.0)
+        mask_occupancy = 0.5 # assert 50% for stability despite the fact that its wrong
         forward_flops = math.floor(mha_flops * mask_occupancy)
         self.track_forward_flops(forward_flops)
         self.track_backward_flops(math.floor(forward_flops * 2.5))
diff --git a/uv.lock b/uv.lock
index 0daaed99..ffe6d36c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -346,66 +346,69 @@ wheels = [
 
 [[package]]
 name = "contourpy"
-version = "1.3.1"
+version = "1.3.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
 ]
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 ]
 
 [[package]]

From cb20ff18c74ea38edb33ffe7b44069199db5913a Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Fri, 18 Apr 2025 10:07:23 +0000
Subject: [PATCH 26/47] fix training_progress.step

---
 src/zeroband/train.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 42ef94e5..29c666ee 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -762,6 +762,10 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
         iter_num += 1
         training_progress.outer_step = iter_num.item()
+        if config.diloco is not None:
+            training_progress.step = training_progress.outer_step * config.diloco.inner_steps
+        else:
+            training_progress.step = training_progress.outer_step
 
         if (
                 config.ckpt.interval is not None

From b9615acb07c21637c096387a3b01c980e31786c7 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Fri, 18 Apr 2025 21:06:02 +0000
Subject: [PATCH 27/47] log outer step

---
 src/zeroband/train.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 29c666ee..d69b09fc 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -164,9 +164,6 @@ def run_inner_steps(
         end_event.record()
         timing_events.append((start_event, end_event))
 
-        # logging
-        training_progress.step += 1
-
         # syncing loss across all data parallel rank within a nodes
         new_tokens = config.data.seq_length * config.train.batch_size
 
@@ -196,6 +193,7 @@ def run_inner_steps(
         metrics = {
             "loss/train": loss_batch.item(),
             "step": training_progress.step,
+            "outer_step": training_progress.outer_step,
             "inner_lr": inner_lr,
             "Perplexity": torch.exp(loss_batch).item(),
             "total_tokens": training_progress.total_tokens,

From 02647c01598ee7046af0719c6bc68714e4ebf4f0 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Fri, 18 Apr 2025 21:15:52 +0000
Subject: [PATCH 28/47] fix incompetence

---
 src/zeroband/train.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index d69b09fc..9ae2da35 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -161,6 +161,8 @@ def run_inner_steps(
             inner_optimizer.step()
             inner_optimizer.zero_grad(set_to_none=False)
 
+        training_progress.step += 1
+
         end_event.record()
         timing_events.append((start_event, end_event))
 

From f89e4a9f1f628aba3a9189a04d0216bc98cc6c70 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Fri, 18 Apr 2025 21:19:24 +0000
Subject: [PATCH 29/47] set step from shared state synced var exactly post
 shared state sync

---
 src/zeroband/train.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 9ae2da35..158238ab 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -724,6 +724,12 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
                                   num_syncs, train_profiler,
                                   False)
 
+        training_progress.outer_step = iter_num.item()
+        if config.diloco is not None:
+            training_progress.step = training_progress.outer_step * config.diloco.inner_steps
+        else:
+            training_progress.step = training_progress.outer_step
+
         logger.info("Running inner steps...")
         run_inner_steps(
             model, train_dataloader_iterator, inner_optimizer, device,
@@ -761,11 +767,6 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
                                                    config.diloco.delayed_update)
 
         iter_num += 1
-        training_progress.outer_step = iter_num.item()
-        if config.diloco is not None:
-            training_progress.step = training_progress.outer_step * config.diloco.inner_steps
-        else:
-            training_progress.step = training_progress.outer_step
 
         if (
                 config.ckpt.interval is not None

From 4573f0d5ef9208c67975c5fcedaa1bd990aeea64 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Mon, 21 Apr 2025 18:12:50 +0000
Subject: [PATCH 30/47] fix NCCL crash on some Lambda nodes

---
 scripts/launch_prime.sh | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/scripts/launch_prime.sh b/scripts/launch_prime.sh
index 3dbb06d1..b78d314b 100755
--- a/scripts/launch_prime.sh
+++ b/scripts/launch_prime.sh
@@ -1,3 +1,9 @@
+# this fixes crashes on some NCCL versions that attempt to create a multi-node topology on some single-node cloud instances
+export NCCL_MNNVL_ENABLE=0
+export NCCL_IB_DISABLE=1
+export NCCL_SOCKET_IFNAME=lo
+export PCCL_LOG_LEVEL=DEBUG
+
 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=0 GPU_ORDINAL=0 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=1 GPU_ORDINAL=1 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=2 GPU_ORDINAL=2 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &

From 12f89c4ccb335a173a389a8d6dc0dc22fcdebf01 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Tue, 22 Apr 2025 09:27:58 +0000
Subject: [PATCH 31/47] enable topology optimization

---
 src/zeroband/train.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 158238ab..b0349c63 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -710,13 +710,13 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
         if topology_updated:
             logger.info("Optimizing Topology...")
-            #while True:
-            #    try:
-            #        communicator.optimize_topology()  # may raise an error if it fails
-            #        break
-            #    except PCCLError as e:
-            #        print(f"[Peer] OptimizeTopology failed => {e}. Retrying...")
-            #        time.sleep(0.1)
+            while True:
+                try:
+                    communicator.optimize_topology()  # may raise an error if it fails
+                    break
+                except PCCLError as e:
+                    print(f"[Peer] OptimizeTopology failed => {e}. Retrying...")
+                    time.sleep(0.1)
 
             logger.info("Running shared state synchronization...")
             run_shared_state_sync(shared_state, communicator, model, outer_parameters_list,

From 30ff670ccba244a69236c82dbf03b2dbf9999a32 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Wed, 23 Apr 2025 21:10:40 +0200
Subject: [PATCH 32/47] utilize SharedStateSyncStrategy

---
 src/zeroband/train.py |  11 +-
 uv.lock               | 480 +++++++++++++++++++++---------------------
 2 files changed, 246 insertions(+), 245 deletions(-)

diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index b0349c63..9c2e6203 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -8,7 +8,7 @@
 
 import torch
 import torch.distributed as dist
-from pccl import SharedState, TensorInfo, Attribute, Communicator, PCCLError, ReduceOp
+from pccl import SharedState, TensorInfo, Attribute, Communicator, PCCLError, ReduceOp, SharedStateSyncStrategy
 
 from torch.distributed import destroy_process_group
 from torch.distributed.tensor import DTensor
@@ -308,7 +308,7 @@ def run_async_outer_step(
             logger.info(
                 "Topology updated mid run; re-running shared state synchronization to properly insert new peer...")
             run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, logger, num_syncs, train_profiler,
-                                  False)
+                                  False, SharedStateSyncStrategy.SEND_ONLY)
 
     else:
         if topology_updated and iter_num > 0:
@@ -319,7 +319,7 @@ def run_async_outer_step(
             # Also: late_joiner here means that we tolerate actually receiving bytes here despite that this is the second sync that was performed.
             # This is necessary for the pipeline insertion algorithm to function
             run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, logger, num_syncs, train_profiler,
-                                  True)
+                                  True, SharedStateSyncStrategy.RECEIVE_ONLY)
 
         # This is the boostrap for the 1-step behind asynchronous training step.
         # Reset the inner state here to be equal to the unmodified outer state.
@@ -465,11 +465,12 @@ def run_shared_state_sync(
         num_syncs: IntRef,
         train_profiler: Profiler,
         late_joiner: bool,
+        strategy: SharedStateSyncStragy
 ):
     # 3) Sync shared state => ensures we have the same aggregator (outer) parameters
     with train_profiler.session("pccl::sync_shared_state"):
         logger.info(f"run_shared_state_sync: shared_state_revision: {shared_state.revision}")
-        sync_info = communicator.sync_shared_state(shared_state)
+        sync_info = communicator.sync_shared_state(shared_state, strategy)
         shared_state.revision += 1
         logger.info(f"sync_info tx_bytes: {sync_info.tx_bytes}, rx_bytes: {sync_info.rx_bytes}")
         num_syncs += 1
@@ -722,7 +723,7 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             run_shared_state_sync(shared_state, communicator, model, outer_parameters_list,
                                   logger,
                                   num_syncs, train_profiler,
-                                  False)
+                                  False, SharedStateSyncStrategy.ENFORCE_POPULAR)
 
         training_progress.outer_step = iter_num.item()
         if config.diloco is not None:
diff --git a/uv.lock b/uv.lock
index ffe6d36c..95e64a92 100644
--- a/uv.lock
+++ b/uv.lock
@@ -50,7 +50,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.11.16"
+version = "3.11.18"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -62,72 +62,72 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f1/d9/1c4721d143e14af753f2bf5e3b681883e1f24b592c0482df6fa6e33597fa/aiohttp-3.11.16.tar.gz", hash = "sha256:16f8a2c9538c14a557b4d309ed4d0a7c60f0253e8ed7b6c9a2859a7582f8b1b8", size = 7676826 }
-wheels = [
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-    { url = "https://files.pythonhosted.org/packages/96/8c/7b4b9debe90ffc31931b85ee8612a5c83f34d8fdc6d90ee3eb27b43639e4/aiohttp-3.11.16-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:54eb3aead72a5c19fad07219acd882c1643a1027fbcdefac9b502c267242f955", size = 468886 },
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 [[package]]

From 933ba5bcda5999e606eb0d46fba62e794356676b Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Thu, 24 Apr 2025 19:04:58 +0000
Subject: [PATCH 33/47] fix typo

---
 src/zeroband/train.py |    2 +-
 uv.lock               | 3954 ++++++++++++++++++++---------------------
 2 files changed, 1978 insertions(+), 1978 deletions(-)

diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 9c2e6203..ea1837bc 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -465,7 +465,7 @@ def run_shared_state_sync(
         num_syncs: IntRef,
         train_profiler: Profiler,
         late_joiner: bool,
-        strategy: SharedStateSyncStragy
+        strategy: SharedStateSyncStrategy
 ):
     # 3) Sync shared state => ensures we have the same aggregator (outer) parameters
     with train_profiler.session("pccl::sync_shared_state"):
diff --git a/uv.lock b/uv.lock
index 95e64a92..69fd22a5 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 2
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'linux'",
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From fee98cdb873dea65b043a52ab8ca7a84d6043a84 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Mon, 28 Apr 2025 15:19:32 +0200
Subject: [PATCH 34/47] bump pccl commit revision

---
 uv.lock | 3979 ++++++++++++++++++++++++++++---------------------------
 1 file changed, 1995 insertions(+), 1984 deletions(-)

diff --git a/uv.lock b/uv.lock
index 69fd22a5..f38b4224 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 1
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'linux'",
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From 2ba792c5739452a683b5c152e647220310dc6ee0 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Mon, 28 Apr 2025 22:33:03 +0200
Subject: [PATCH 35/47] bump pccl commit revision

---
 scripts/launch_prime.sh | 22 +++++++++++++---------
 1 file changed, 13 insertions(+), 9 deletions(-)

diff --git a/scripts/launch_prime.sh b/scripts/launch_prime.sh
index b78d314b..97392cc8 100755
--- a/scripts/launch_prime.sh
+++ b/scripts/launch_prime.sh
@@ -1,15 +1,19 @@
+export PCCL_LOG_LEVEL=DEBUG
+export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+export MASTER_ADDR=127.0.0.1
+export MASTER_PORT=1234
+
 # this fixes crashes on some NCCL versions that attempt to create a multi-node topology on some single-node cloud instances
 export NCCL_MNNVL_ENABLE=0
 export NCCL_IB_DISABLE=1
 export NCCL_SOCKET_IFNAME=lo
-export PCCL_LOG_LEVEL=DEBUG
 
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=0 GPU_ORDINAL=0 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=1 GPU_ORDINAL=1 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=2 GPU_ORDINAL=2 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=3 GPU_ORDINAL=3 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=4 GPU_ORDINAL=4 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=5 GPU_ORDINAL=5 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=6 GPU_ORDINAL=6 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
-PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True MASTER_ADDR=127.0.0.1 MASTER_PORT=1234 RANK=7 GPU_ORDINAL=7 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+RANK=0 GPU_ORDINAL=0 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+RANK=1 GPU_ORDINAL=1 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+RANK=2 GPU_ORDINAL=2 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+RANK=3 GPU_ORDINAL=3 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+RANK=4 GPU_ORDINAL=4 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+RANK=5 GPU_ORDINAL=5 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+RANK=6 GPU_ORDINAL=6 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
+RANK=7 GPU_ORDINAL=7 WORLD_SIZE=8 ZERO_BAND_LOG_LEVEL=DEBUG python src/zeroband/train.py @configs/7B/H100.toml &
 wait
\ No newline at end of file

From 387f0bdd4609420674b58ba607cb46af9703b8c8 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Mon, 28 Apr 2025 22:33:23 +0200
Subject: [PATCH 36/47] bump pccl commit revision

---
 uv.lock | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/uv.lock b/uv.lock
index f38b4224..e7cb1631 100644
--- a/uv.lock
+++ b/uv.lock
@@ -435,7 +435,7 @@ wheels = [
 
 [[package]]
 name = "datasets"
-version = "3.5.0"
+version = "3.5.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -453,9 +453,9 @@ dependencies = [
     { name = "tqdm" },
     { name = "xxhash" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/73/dd/6afb1d440a21da104adefb93269a02058bf9f3cf665db6421fe04eceab7d/datasets-3.5.0.tar.gz", hash = "sha256:9e39560e34f83a64e48ceca7adeb645ede3c3055c5cf48ed2b454f8ed2b89754", size = 568325 }
+sdist = { url = "https://files.pythonhosted.org/packages/10/af/8c1d10daf37383c32ab0a7461eaa4d5c7a3c47808fe5a8563744de002df7/datasets-3.5.1.tar.gz", hash = "sha256:f835b45dbbd7065c1191734b6f7c8d96fdf8c5751feaa5aa52b2a0dc43eea58f", size = 568915 }
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+    { url = "https://files.pythonhosted.org/packages/e3/f5/668b3444a2f487b0052b908af631fe39eeb2bdb2359d9bbc2c3b80b71119/datasets-3.5.1-py3-none-any.whl", hash = "sha256:4074dda8dd6e9ece242b1580a8ef3928777d59ae1db144d911229e443a093cbb", size = 491436 },
 ]
 
 [[package]]
@@ -696,11 +696,11 @@ wheels = [
 
 [[package]]
 name = "fsspec"
-version = "2024.12.0"
+version = "2025.3.0"
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-sdist = { url = "https://files.pythonhosted.org/packages/ee/11/de70dee31455c546fbc88301971ec03c328f3d1138cfba14263f651e9551/fsspec-2024.12.0.tar.gz", hash = "sha256:670700c977ed2fb51e0d9f9253177ed20cbde4a3e5c0283cc5385b5870c8533f", size = 291600 }
+sdist = { url = "https://files.pythonhosted.org/packages/34/f4/5721faf47b8c499e776bc34c6a8fc17efdf7fdef0b00f398128bc5dcb4ac/fsspec-2025.3.0.tar.gz", hash = "sha256:a935fd1ea872591f2b5148907d103488fc523295e6c64b835cfad8c3eca44972", size = 298491 }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/56/53/eb690efa8513166adef3e0669afd31e95ffde69fb3c52ec2ac7223ed6018/fsspec-2025.3.0-py3-none-any.whl", hash = "sha256:efb87af3efa9103f94ca91a7f8cb7a4df91af9f74fc106c9c7ea0efd7277c1b3", size = 193615 },
 ]
 
 [package.optional-dependencies]
@@ -713,7 +713,7 @@ http = [
 
 [[package]]
 name = "gcsfs"
-version = "2024.12.0"
+version = "2025.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -724,9 +724,9 @@ dependencies = [
     { name = "google-cloud-storage" },
     { name = "requests" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/20/a2/310a6f1cfdb39b2385af38620f299a8acb427b5c1fc36aa1037a81eea0d8/gcsfs-2024.12.0.tar.gz", hash = "sha256:e672413922108300ebc1fe78b8f99f3c7c1b94e7e088f5a6dc88de6d5a93d156", size = 80258 }
+sdist = { url = "https://files.pythonhosted.org/packages/59/81/441e9f7f8b9b4cabb89ff19cd58da12cebb5e6ea2864920ae8862061fac0/gcsfs-2025.3.0.tar.gz", hash = "sha256:f68d7bc24bd4b944cd55a6963b9fd722c7bd5791f46c6aebacc380e648292c04", size = 81174 }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/44/dd/874223310565a336820a70727b61e7dd23f7be6cb91006f2cbb634670142/gcsfs-2025.3.0-py2.py3-none-any.whl", hash = "sha256:afbc2b26a481de66519e9cce7762340ef4781ce01c6663af0d63eda10f6d2c9c", size = 36133 },
 ]
 
 [[package]]
@@ -1894,7 +1894,7 @@ wheels = [
 [[package]]
 name = "pccl"
 version = "0.1.0"
-source = { git = "https://github.com/PrimeIntellect-ai/pccl.git?subdirectory=python%2Fframework&rev=main#acbcf27b0d89d927b6d7217d1d40f86b7f44a564" }
+source = { git = "https://github.com/PrimeIntellect-ai/pccl.git?subdirectory=python%2Fframework&rev=main#003336c5dc45c5905e9f0be7bf8586da79cab832" }
 dependencies = [
     { name = "cffi" },
     { name = "ipaddress" },

From 00b972445b7355d15ec8d203b3501e8261d61651 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Tue, 29 Apr 2025 15:09:34 +0200
Subject: [PATCH 37/47] bump pccl commit revision

---
 uv.lock | 3974 +++++++++++++++++++++++++++----------------------------
 1 file changed, 1987 insertions(+), 1987 deletions(-)

diff --git a/uv.lock b/uv.lock
index e7cb1631..7b7ee3b2 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 2
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'linux'",
@@ -16,9 +16,9 @@ resolution-markers = [
 name = "absl-py"
 version = "2.2.2"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b5/f0/e6342091061ed3a46aadc116b13edd7bb5249c3ab1b3ef07f24b0c248fc3/absl_py-2.2.2.tar.gz", hash = "sha256:bf25b2c2eed013ca456918c453d687eab4e8309fba81ee2f4c1a6aa2494175eb", size = 119982 }
+sdist = { url = "https://files.pythonhosted.org/packages/b5/f0/e6342091061ed3a46aadc116b13edd7bb5249c3ab1b3ef07f24b0c248fc3/absl_py-2.2.2.tar.gz", hash = "sha256:bf25b2c2eed013ca456918c453d687eab4e8309fba81ee2f4c1a6aa2494175eb", size = 119982, upload_time = "2025-04-03T12:41:04.55Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/f6/d4/349f7f4bd5ea92dab34f5bb0fe31775ef6c311427a14d5a5b31ecb442341/absl_py-2.2.2-py3-none-any.whl", hash = "sha256:e5797bc6abe45f64fd95dc06394ca3f2bedf3b5d895e9da691c9ee3397d70092", size = 135565, upload_time = "2025-04-03T12:41:03.172Z" },
 ]
 
 [[package]]
@@ -34,18 +34,18 @@ dependencies = [
     { name = "safetensors" },
     { name = "torch" },
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-sdist = { url = "https://files.pythonhosted.org/packages/8a/6e/c29a1dcde7db07f47870ed63e5124086b11874ad52ccd533dc1ca2c799da/accelerate-1.6.0.tar.gz", hash = "sha256:28c1ef1846e690944f98b68dc7b8bb6c51d032d45e85dcbb3adb0c8b99dffb32", size = 363804 }
+sdist = { url = "https://files.pythonhosted.org/packages/8a/6e/c29a1dcde7db07f47870ed63e5124086b11874ad52ccd533dc1ca2c799da/accelerate-1.6.0.tar.gz", hash = "sha256:28c1ef1846e690944f98b68dc7b8bb6c51d032d45e85dcbb3adb0c8b99dffb32", size = 363804, upload_time = "2025-04-01T11:53:03.037Z" }
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+    { url = "https://files.pythonhosted.org/packages/63/b1/8198e3cdd11a426b1df2912e3381018c4a4a55368f6d0857ba3ca418ef93/accelerate-1.6.0-py3-none-any.whl", hash = "sha256:1aee717d3d3735ad6d09710a7c26990ee4652b79b4e93df46551551b5227c2aa", size = 354748, upload_time = "2025-04-01T11:53:01.298Z" },
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 [[package]]
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+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload_time = "2025-03-12T01:42:47.083Z" },
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From 3f8f8490ab7ed8e3c0c77c4c696341808d79b41f Mon Sep 17 00:00:00 2001
From: Sami Jaghouar <sami.jaghouar@hotmail.fr>
Date: Tue, 29 Apr 2025 22:26:05 +0000
Subject: [PATCH 38/47] fix config dataset

---
 src/zeroband/config.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/zeroband/config.py b/src/zeroband/config.py
index aa230d63..905643c6 100644
--- a/src/zeroband/config.py
+++ b/src/zeroband/config.py
@@ -20,7 +20,7 @@ class DataConfig(BaseConfig):
     num_workers: int = 1
     max_train_samples: int | None = None
     max_eval_samples: int | None = None
-    dataset_ratio: str | None = None
+    dataset_ratio: str = "100"
     data_rank: int | None = None
     data_world_size: int | None = None
     reverse_data_files: bool = False

From 4060e5fc1438e451dc5a6fb3b45fa0ea22003f6f Mon Sep 17 00:00:00 2001
From: Sami Jaghouar <sami.jaghouar@hotmail.fr>
Date: Tue, 29 Apr 2025 22:30:28 +0000
Subject: [PATCH 39/47] add logging pccl

---
 src/zeroband/train.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index ea1837bc..fa42c3fd 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -632,6 +632,7 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
 
     num_inner_steps = config.diloco.inner_steps if config.diloco is not None else 1
 
+    logger.info(f"Attempting to connect PCCL to {config.pccl.ccoip_host}")
     # initialize PCCL
     communicator = Communicator(config.pccl.ccoip_host, mpi_config.mpi_rank if mpi_config is not None else 0)
     communicator.connect(n_attempts=15)

From 54245d58b746abd071ddaf708fb6b6788b5a278e Mon Sep 17 00:00:00 2001
From: Sami Jaghouar <sami.jaghouar@hotmail.fr>
Date: Wed, 30 Apr 2025 00:35:17 +0000
Subject: [PATCH 40/47] add diloco delayed default vlaue

---
 src/zeroband/config.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/zeroband/config.py b/src/zeroband/config.py
index 905643c6..fac7be0d 100644
--- a/src/zeroband/config.py
+++ b/src/zeroband/config.py
@@ -108,7 +108,7 @@ class DilocoConfig(BaseConfig):
     outer_lr: float = 0.7
     inner_steps: int
     compression: Compression = Compression.NO
-    delayed_update: bool
+    delayed_update: bool = False
 
 
 class MemoryProfilerConfig(BaseConfig):

From a08b87ea78650b8b89116c006fe1558861695b27 Mon Sep 17 00:00:00 2001
From: Sami Jaghouar <sami.jaghouar@hotmail.fr>
Date: Wed, 30 Apr 2025 01:05:48 +0000
Subject: [PATCH 41/47] fix fake data

---
 src/zeroband/config.py                |  5 ++---
 src/zeroband/utils/tokenizer_utils.py | 12 ++++++++----
 2 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/src/zeroband/config.py b/src/zeroband/config.py
index fac7be0d..3a1f37e6 100644
--- a/src/zeroband/config.py
+++ b/src/zeroband/config.py
@@ -30,11 +30,10 @@ class DataConfig(BaseConfig):
     def data_config_valid(self):
         assert self.fake == (
                     self.dataset_name_or_paths is None), "Data must be fake if 'dataset_name_or_paths' is not set"
-
-        if self.dataset_ratio is None:
+                
+        if self.fake or self.dataset_ratio is None:
             return self
 
-        assert self.dataset_name_or_paths is not None, "'dataset_name_or_paths' must be set if 'dataset_ratio' is set"
         dataset_files = self.dataset_name_or_paths.split(',')
         ratio_texts = self.dataset_ratio.split(":")
         assert len(dataset_files) == len(
diff --git a/src/zeroband/utils/tokenizer_utils.py b/src/zeroband/utils/tokenizer_utils.py
index 5b0c45ae..0096101b 100644
--- a/src/zeroband/utils/tokenizer_utils.py
+++ b/src/zeroband/utils/tokenizer_utils.py
@@ -1,9 +1,6 @@
 from dataclasses import dataclass
-from typing import TYPE_CHECKING
 from zeroband.config import Config
 
-if TYPE_CHECKING:
-    from zeroband.data import DEBUG_VOCAB_SIZE
 
 
 @dataclass
@@ -17,7 +14,14 @@ class TokenizerInfo:
 
 def get_tokenizer_info(config: Config) -> TokenizerInfo:
     if config.data.fake and config.model_name == "debugmodel":
-        return DEBUG_VOCAB_SIZE
+        from zeroband.data import DEBUG_VOCAB_SIZE
+        return TokenizerInfo(
+            hf_name="debugmodel",
+            vocab_size=DEBUG_VOCAB_SIZE,
+            bot_token=1,
+            eot_token=2,
+        )
+    
     elif config.model_type == "llama2":
         return TokenizerInfo(
             hf_name="mistralai/Mistral-7B-v0.1",

From 01e63a9c96ae371b3681595f28fdf6c3237d1e7d Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Thu, 1 May 2025 08:31:39 +0200
Subject: [PATCH 42/47] bump pccl commit revision

---
 uv.lock | 296 +++++++++++++++++++++++++++++---------------------------
 1 file changed, 154 insertions(+), 142 deletions(-)

diff --git a/uv.lock b/uv.lock
index 7b7ee3b2..ee5ebd21 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1894,7 +1894,7 @@ wheels = [
 [[package]]
 name = "pccl"
 version = "0.1.0"
-source = { git = "https://github.com/PrimeIntellect-ai/pccl.git?subdirectory=python%2Fframework&rev=main#5708ac2f788dc8bafbf8e83cbe0dbfcc4dc97602" }
+source = { git = "https://github.com/PrimeIntellect-ai/pccl.git?subdirectory=python%2Fframework&rev=main#17904a12a2f58d846b7574149f36d880bc810208" }
 dependencies = [
     { name = "cffi" },
     { name = "ipaddress" },
@@ -2269,7 +2269,7 @@ wheels = [
 
 [[package]]
 name = "pydantic"
-version = "2.11.3"
+version = "2.11.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "annotated-types" },
@@ -2277,9 +2277,9 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "typing-inspection" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/10/2e/ca897f093ee6c5f3b0bee123ee4465c50e75431c3d5b6a3b44a47134e891/pydantic-2.11.3.tar.gz", hash = "sha256:7471657138c16adad9322fe3070c0116dd6c3ad8d649300e3cbdfe91f4db4ec3", size = 785513, upload_time = "2025-04-08T13:27:06.399Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/77/ab/5250d56ad03884ab5efd07f734203943c8a8ab40d551e208af81d0257bf2/pydantic-2.11.4.tar.gz", hash = "sha256:32738d19d63a226a52eed76645a98ee07c1f410ee41d93b4afbfa85ed8111c2d", size = 786540, upload_time = "2025-04-29T20:38:55.02Z" }
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+    { url = "https://files.pythonhosted.org/packages/78/46/db58cf700f1408cf0f63d3f939f7b077bd450da8e037310f70e74c41262f/setproctitle-1.3.6-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:797f2846b546a8741413c57d9fb930ad5aa939d925c9c0fa6186d77580035af7", size = 13520, upload_time = "2025-04-29T13:34:44.287Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/46/0b89e7ebe77543e721c67077ad93fc8c7c3c31a8db3b12e00d02950f6adc/setproctitle-1.3.6-pp310-pypy310_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4ac3eb04bcf0119aadc6235a2c162bae5ed5f740e3d42273a7228b915722de20", size = 13094, upload_time = "2025-04-29T13:34:45.605Z" },
+    { url = "https://files.pythonhosted.org/packages/f7/78/03f2e42eb83bce6f853d7751ae95f8a3ae7408145a0b6cdd717be01497d7/setproctitle-1.3.6-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:0e6b5633c94c5111f7137f875e8f1ff48f53b991d5d5b90932f27dc8c1fa9ae4", size = 12241, upload_time = "2025-04-29T13:34:46.996Z" },
 ]
 
 [[package]]
 name = "setuptools"
-version = "80.0.0"
+version = "80.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/44/80/97e25f0f1e4067677806084b7382a6ff9979f3d15119375c475c288db9d7/setuptools-80.0.0.tar.gz", hash = "sha256:c40a5b3729d58dd749c0f08f1a07d134fb8a0a3d7f87dc33e7c5e1f762138650", size = 1354221, upload_time = "2025-04-27T17:21:10.806Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/aa/b2/bd26ed086b842b68c8fe9aac380ad7e5118cf84fa7abd45bb059a88368a8/setuptools-80.1.0.tar.gz", hash = "sha256:2e308396e1d83de287ada2c2fd6e64286008fe6aca5008e0b6a8cb0e2c86eedd", size = 1354038, upload_time = "2025-04-30T17:41:06.171Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/23/63/5517029d6696ddf2bd378d46f63f479be001c31b462303170a1da57650cb/setuptools-80.0.0-py3-none-any.whl", hash = "sha256:a38f898dcd6e5380f4da4381a87ec90bd0a7eec23d204a5552e80ee3cab6bd27", size = 1240907, upload_time = "2025-04-27T17:21:09.175Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/f6/126c9309c8fe93e5d6bb850593cd58d591daf2da45cc78b61e48d8d95879/setuptools-80.1.0-py3-none-any.whl", hash = "sha256:ea0e7655c05b74819f82e76e11a85b31779fee7c4969e82f72bab0664e8317e4", size = 1240689, upload_time = "2025-04-30T17:41:03.789Z" },
 ]
 
 [[package]]

From 3d475324e974dbd55fe3cad8617d3a0335156322 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Sun, 4 May 2025 12:16:38 +0000
Subject: [PATCH 43/47] Add dtype argument

---
 src/zeroband/models/llama/__init__.py |  5 ++-
 src/zeroband/models/llama/model.py    | 59 +++++++++++----------------
 src/zeroband/models/norms.py          | 28 ++++++++-----
 src/zeroband/train.py                 | 21 ++++++----
 4 files changed, 58 insertions(+), 55 deletions(-)

diff --git a/src/zeroband/models/llama/__init__.py b/src/zeroband/models/llama/__init__.py
index df6c245b..ea061c52 100644
--- a/src/zeroband/models/llama/__init__.py
+++ b/src/zeroband/models/llama/__init__.py
@@ -6,6 +6,7 @@
 #
 # Llama 2 is licensed under the LLAMA 2 Community License,
 # Copyright (c) Meta Platforms, Inc. All Rights Reserved.
+import torch
 
 from zeroband.config import Config
 from zeroband.models.llama.model import ModelArgs, Transformer
@@ -94,6 +95,8 @@
 def make_model(
         config: Config,
         vocab_size: int,
+        dtype: torch.dtype,
+        device: torch.device
 ) -> tuple[Transformer, ModelArgs]:
     """
     Constructs a model instance according to the supplied configuration and target vocab size
@@ -111,4 +114,4 @@ def make_model(
     model_config.max_seq_len = config.data.seq_length
     model_config.attn_fn = config.hardware.attn_fn
 
-    return Transformer(model_config), model_config
+    return Transformer(model_config, dtype=dtype, device=device), model_config
diff --git a/src/zeroband/models/llama/model.py b/src/zeroband/models/llama/model.py
index e73c3fbf..c62638a3 100644
--- a/src/zeroband/models/llama/model.py
+++ b/src/zeroband/models/llama/model.py
@@ -222,17 +222,17 @@ class Attention(nn.Module):
 
     """
 
-    def __init__(self, model_args: ModelArgs):
+    def __init__(self, model_args: ModelArgs, dtype: torch.dtype, device: torch.device):
         super().__init__()
         self.n_heads = model_args.n_heads
         self.n_kv_heads = model_args.n_heads if model_args.n_kv_heads is None else model_args.n_kv_heads
         self.n_rep = self.n_heads // self.n_kv_heads
         self.head_dim = model_args.dim // model_args.n_heads
 
-        self.wq = nn.Linear(model_args.dim, model_args.n_heads * self.head_dim, bias=False)
-        self.wk = nn.Linear(model_args.dim, self.n_kv_heads * self.head_dim, bias=False)
-        self.wv = nn.Linear(model_args.dim, self.n_kv_heads * self.head_dim, bias=False)
-        self.wo = nn.Linear(model_args.n_heads * self.head_dim, model_args.dim, bias=False)
+        self.wq = nn.Linear(model_args.dim, model_args.n_heads * self.head_dim, bias=False, dtype=dtype, device=device)
+        self.wk = nn.Linear(model_args.dim, self.n_kv_heads * self.head_dim, bias=False, dtype=dtype, device=device)
+        self.wv = nn.Linear(model_args.dim, self.n_kv_heads * self.head_dim, bias=False, dtype=dtype, device=device)
+        self.wo = nn.Linear(model_args.n_heads * self.head_dim, model_args.dim, bias=False, dtype=dtype, device=device)
 
         self.attn_fn = model_args.attn_fn
 
@@ -342,6 +342,8 @@ def __init__(
             hidden_dim: int,
             multiple_of: int,
             ffn_dim_multiplier: Optional[float],
+            dtype: torch.dtype,
+            device: torch.device
     ):
         super().__init__()
         hidden_dim = int(2 * hidden_dim / 3)
@@ -350,9 +352,9 @@ def __init__(
             hidden_dim = int(ffn_dim_multiplier * hidden_dim)
         hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
 
-        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
-        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
-        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False, dtype=dtype, device=device)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device)
 
     def forward(self, x: torch.Tensor, flop_counter: FlopCounter = FlopCounter()):
         flop_counter.track_linear(self.w1, x)
@@ -398,22 +400,23 @@ class TransformerBlock(nn.Module):
 
     """
 
-    def __init__(self, layer_id: int, model_args: ModelArgs):
+    def __init__(self, layer_id: int, model_args: ModelArgs, dtype: torch.dtype, device: torch.device):
         super().__init__()
         self.n_heads = model_args.n_heads
         self.dim = model_args.dim
-        self.attention = Attention(model_args)
+        self.attention = Attention(model_args, dtype=dtype, device=device)
         self.feed_forward = FeedForward(
             dim=model_args.dim,
             hidden_dim=4 * model_args.dim,
             multiple_of=model_args.multiple_of,
             ffn_dim_multiplier=model_args.ffn_dim_multiplier,
+            dtype=dtype, device=device
         )
         self.layer_id = layer_id
         self.num_layers = model_args.n_layers
 
-        self.attention_norm = build_norm(model_args.norm_type, dim=model_args.dim, eps=model_args.norm_eps)
-        self.ffn_norm = build_norm(model_args.norm_type, dim=model_args.dim, eps=model_args.norm_eps)
+        self.attention_norm = build_norm(model_args.norm_type, dim=model_args.dim, eps=model_args.norm_eps, dtype=dtype, device=device)
+        self.ffn_norm = build_norm(model_args.norm_type, dim=model_args.dim, eps=model_args.norm_eps, dtype=dtype, device=device)
 
         if model_args.depth_init:
             self.weight_init_std = 0.02 / (2 * (self.layer_id + 1)) ** 0.5
@@ -481,7 +484,7 @@ class Transformer(nn.Module):
 
     """
 
-    def __init__(self, model_args: ModelArgs):
+    def __init__(self, model_args: ModelArgs, dtype: torch.dtype, device: torch.device):
         super().__init__()
         self.model_args = model_args
         self.vocab_size = model_args.vocab_size
@@ -496,15 +499,15 @@ def __init__(self, model_args: ModelArgs):
         # a seed checkpoint rather than calling init_weights, we need freqs_cis to be
         # initialized by the checkpoint, or we need to add a separate initializer for
         # just the non-persistent buffers that is called after loading checkpoints.
-        self.register_buffer("freqs_cis", self._precompute_freqs_cis(), persistent=True)
+        self.register_buffer("freqs_cis", self._precompute_freqs_cis(dtype=dtype, device=device), persistent=True)
 
         self.layers = torch.nn.ModuleDict()
         for layer_id in range(model_args.n_layers):
-            self.layers[str(layer_id)] = TransformerBlock(layer_id, model_args)
+            self.layers[str(layer_id)] = TransformerBlock(layer_id, model_args, dtype=dtype, device=device)
 
-        self.norm = build_norm(model_args.norm_type, dim=model_args.dim, eps=model_args.norm_eps)
+        self.norm = build_norm(model_args.norm_type, dim=model_args.dim, eps=model_args.norm_eps, dtype=dtype, device=device)
 
-        self.output = nn.Linear(model_args.dim, model_args.vocab_size, bias=False)
+        self.output = nn.Linear(model_args.dim, model_args.vocab_size, bias=False, dtype=dtype, device=device)
         self.init_weights()
 
     def init_weights(self):
@@ -519,8 +522,6 @@ def init_weights(self):
         ``init_weights``. We only call it in the constructor of this
         ``Transformer`` root module to avoid reinitializing tensors.
         """
-        with torch.device(self.freqs_cis.device):
-            self.freqs_cis = self._precompute_freqs_cis()
         if self.tok_embeddings is not None:
             nn.init.normal_(self.tok_embeddings.weight)
         for layer in self.layers.values():
@@ -539,14 +540,14 @@ def init_weights(self):
                 b=cutoff_factor * final_out_std,
             )
 
-    def _precompute_freqs_cis(self) -> torch.Tensor:
+    def _precompute_freqs_cis(self, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
         return precompute_freqs_cis(
             self.model_args.dim // self.model_args.n_heads,
             # Need to compute until at least the max token limit for generation
             # (use 2x max sequence length to be safe)
             self.model_args.max_seq_len * 2,
-            self.model_args.rope_theta,
-        )
+            self.model_args.rope_theta
+        ).to(dtype=dtype, device=device)
 
     def forward(self, tokens: torch.Tensor, block_mask: BlockMask | None = None, flop_counter: FlopCounter = FlopCounter()):
         """
@@ -575,20 +576,6 @@ def forward(self, tokens: torch.Tensor, block_mask: BlockMask | None = None, flo
 
         return output
 
-    @classmethod
-    def from_model_args(cls, model_args: ModelArgs) -> "Transformer":
-        """
-        Initialize a Transformer model from a ModelArgs object.
-
-        Args:
-            model_args (ModelArgs): Model configuration arguments.
-
-        Returns:
-            Transformer: Transformer model.
-
-        """
-        return cls(model_args)
-
     def count_parameters(self, exclude_embedding: bool = False) -> int:
         """
         Counts the number of parameters.
diff --git a/src/zeroband/models/norms.py b/src/zeroband/models/norms.py
index f1febcf8..650296e4 100644
--- a/src/zeroband/models/norms.py
+++ b/src/zeroband/models/norms.py
@@ -21,7 +21,7 @@
 from torch.distributed.tensor.experimental import local_map
 
 
-def build_norm(norm_type: str, dim: int, eps: float = 1e-6):
+def build_norm(norm_type: str, dim: int, eps: float, dtype: torch.dtype, device: torch.device):
     """
     Builds the specified normalization layer based on the norm_type.
 
@@ -29,7 +29,9 @@ def build_norm(norm_type: str, dim: int, eps: float = 1e-6):
         norm_type (str): The type of normalization layer to build.
             Supported types: layernorm, np_layernorm, rmsnorm, fused_rmsnorm
         dim (int): The dimension of the normalization layer.
-        eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.
+        eps (float, optional): The epsilon value for numerical stability.
+        dtype: The data type to use for the parameter tensor
+        device: The device to place the layer on
 
     Returns:
         The built normalization layer.
@@ -40,13 +42,13 @@ def build_norm(norm_type: str, dim: int, eps: float = 1e-6):
     norm_type = norm_type.lower()  # Normalize to lowercase
 
     if norm_type == "layernorm":
-        return nn.LayerNorm(dim, eps=eps, bias=False)
+        return nn.LayerNorm(dim, eps=eps, bias=False, dtype=dtype, device=device)
     elif norm_type == "np_layernorm":
-        return nn.LayerNorm(dim, eps=eps, elementwise_affine=False, bias=False)
+        return nn.LayerNorm(dim, eps=eps, elementwise_affine=False, bias=False, dtype=dtype, device=device)
     elif norm_type == "rmsnorm":
-        return RMSNorm(dim, eps=eps)
+        return RMSNorm(dim, eps=eps, dtype=dtype, device=device)
     elif norm_type == "fused_rmsnorm":
-        return FusedRMSNorm(dim, eps=eps)
+        return FusedRMSNorm(dim, eps=eps, dtype=dtype, device=device)
     else:
         raise NotImplementedError(f"Unknown norm_type: '{norm_type}'")
 
@@ -57,11 +59,13 @@ class FusedRMSNorm(nn.Module):
     def __init__(
             self,
             dim: int,
-            eps: float = 1e-6,
+            eps: float,
+            dtype: torch.dtype,
+            device: torch.device
     ):
         super().__init__()
         self.eps = eps
-        self.weight = nn.Parameter(torch.ones(dim))
+        self.weight = nn.Parameter(torch.ones(dim, dtype=dtype, device=device))
         self.fused_rms_norm_fn = fused_rms_norm_fn
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -82,7 +86,9 @@ class RMSNorm(nn.Module):
 
     Args:
         dim (int): The dimension of the input tensor.
-        eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+        eps (float, optional): A small value added to the denominator for numerical stability.
+        dtype: The data type to use
+        device: The torch device to place the layer on
 
     Attributes:
         eps (float): A small value added to the denominator for numerical stability.
@@ -90,10 +96,10 @@ class RMSNorm(nn.Module):
 
     """
 
-    def __init__(self, dim: int, eps: float = 1e-6):
+    def __init__(self, dim: int, eps: float, dtype: torch.dtype, device: torch.device):
         super().__init__()
         self.eps = eps
-        self.weight = nn.Parameter(torch.ones(dim))
+        self.weight = nn.Parameter(torch.ones(dim, dtype=dtype, device=device))
 
     def _norm(self, x: torch.Tensor):
         return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index fa42c3fd..2db6311c 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -124,6 +124,7 @@ def run_inner_steps(
     num_param_scalars = model.count_parameters()
 
     for _inner_step in range(num_inner_steps):
+        #torch.cuda.memory._record_memory_history(max_entries=100000)
         train_profiler.start_session("inner_step")
 
         flop_counter = FlopCounter()
@@ -233,6 +234,9 @@ def run_inner_steps(
             memory_profiler.step()
         train_profiler.end_session()
 
+        #torch.cuda.memory._dump_snapshot('snapshot.pickle')
+        #torch.cuda.memory._record_memory_history(enabled=None)
+
 
 def compute_crc32(tensor: torch.Tensor) -> int:
     tensor_cpu = tensor.detach().cpu()
@@ -518,7 +522,7 @@ def make_shared_state(outer_parameters: Dict[str, torch.nn.Parameter],
     return shared_state
 
 
-def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], device: torch.device):
+def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], dtype: torch.dtype, device: torch.device):
     grad_accum_steps = calc_gradient_accumulation_steps(
         config.train.batch_size, config.hardware.micro_batch_size, mpi_config
     )
@@ -542,6 +546,8 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             model, model_config = make_model(
                 config,
                 vocab_size=tokenizer_info.vocab_size,
+                dtype=dtype,
+                device=device,
             )
         num_param_scalars = model.count_parameters()
         logger.info(f"Number of parameters: {num_param_scalars}")
@@ -705,16 +711,16 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], devic
             continue
 
         local_world_size = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
-        if local_world_size < 2:
-            logger.info("Waiting for more workers to join...")
-            time.sleep(1)
-            continue
+        #if local_world_size < 2:
+        #    logger.info("Waiting for more workers to join...")
+        #    time.sleep(1)
+        #    continue
 
         if topology_updated:
             logger.info("Optimizing Topology...")
             while True:
                 try:
-                    communicator.optimize_topology()  # may raise an error if it fails
+                    # communicator.optimize_topology()  # may raise an error if it fails
                     break
                 except PCCLError as e:
                     print(f"[Peer] OptimizeTopology failed => {e}. Retrying...")
@@ -829,7 +835,8 @@ def main():
     device = torch.device(f'cuda:{torch.cuda.current_device()}')
     logger.info(f"Using device: {torch.cuda.get_device_name(device)}")
 
-    train(logger, config, mpi_config, device)
+    dtype = torch.bfloat16 # TODO: MAKE CONFIGURABLE
+    train(logger, config, mpi_config, dtype, device)
 
 
 if __name__ == "__main__":

From 4b170e18c52060e41c21d4864f0cbf909474ba81 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Sun, 4 May 2025 12:19:50 +0000
Subject: [PATCH 44/47] Add missing dtype argument

---
 src/zeroband/models/llama/model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/zeroband/models/llama/model.py b/src/zeroband/models/llama/model.py
index c62638a3..ea782551 100644
--- a/src/zeroband/models/llama/model.py
+++ b/src/zeroband/models/llama/model.py
@@ -170,7 +170,7 @@ def seqlens_to_docs_tensor(seqlens: list[torch.Tensor]) -> torch.Tensor:
     return torch.stack([torch.repeat_interleave(torch.arange(len(seq), device=seq.device), seq) for seq in seqlens])
 
 
-def create_block_mask_from_seqlens(seqlens: list[torch.Tensor]) -> BlockMask:
+def create_block_mask_from_seqlens(seqlens: list[torch.Tensor], dtype: torch.dtype, device: torch.device) -> BlockMask:
     """Creates a block mask from a list of sequence lengths.
 
     Example:
@@ -183,7 +183,7 @@ def create_block_mask_from_seqlens(seqlens: list[torch.Tensor]) -> BlockMask:
                 [0 0 1 1 0]  # Second token of doc 1 can see both tokens of doc 1
                 [0 0 0 0 1]] # Token of doc 2 can only see itself
     """
-    docs = seqlens_to_docs_tensor(seqlens).to("cuda")
+    docs = seqlens_to_docs_tensor(seqlens).to(dtype=dtype, device=device)
     batch_size, max_seq_len = docs.shape
 
     def document_causal_mask(b, h, q_idx, kv_idx):
@@ -197,7 +197,7 @@ def document_causal_mask(b, h, q_idx, kv_idx):
         None,
         max_seq_len,
         max_seq_len,
-        device="cuda",
+        device=device.type,
         _compile=True,
         BLOCK_SIZE=max_seq_len if max_seq_len < _DEFAULT_SPARSE_BLOCK_SIZE else _DEFAULT_SPARSE_BLOCK_SIZE,
     )

From 4be88107cc06d5f09460f721d56e2a44baa60c1c Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Mon, 5 May 2025 03:59:30 +0200
Subject: [PATCH 45/47] bump pccl commit revision

---
 uv.lock | 170 ++++++++++++++++++++++++++++----------------------------
 1 file changed, 85 insertions(+), 85 deletions(-)

diff --git a/uv.lock b/uv.lock
index ee5ebd21..7ceceabd 100644
--- a/uv.lock
+++ b/uv.lock
@@ -264,63 +264,63 @@ wheels = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.1"
-source = { registry = "https://pypi.org/simple" }
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 ]
 
 [[package]]
@@ -2895,11 +2895,11 @@ wheels = [
 
 [[package]]
 name = "setuptools"
-version = "80.1.0"
+version = "80.3.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/aa/b2/bd26ed086b842b68c8fe9aac380ad7e5118cf84fa7abd45bb059a88368a8/setuptools-80.1.0.tar.gz", hash = "sha256:2e308396e1d83de287ada2c2fd6e64286008fe6aca5008e0b6a8cb0e2c86eedd", size = 1354038, upload_time = "2025-04-30T17:41:06.171Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/70/dc/3976b322de9d2e87ed0007cf04cc7553969b6c7b3f48a565d0333748fbcd/setuptools-80.3.1.tar.gz", hash = "sha256:31e2c58dbb67c99c289f51c16d899afedae292b978f8051efaf6262d8212f927", size = 1315082, upload_time = "2025-05-04T18:47:04.397Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8b/f6/126c9309c8fe93e5d6bb850593cd58d591daf2da45cc78b61e48d8d95879/setuptools-80.1.0-py3-none-any.whl", hash = "sha256:ea0e7655c05b74819f82e76e11a85b31779fee7c4969e82f72bab0664e8317e4", size = 1240689, upload_time = "2025-04-30T17:41:03.789Z" },
+    { url = "https://files.pythonhosted.org/packages/53/7e/5d8af3317ddbf9519b687bd1c39d8737fde07d97f54df65553faca5cffb1/setuptools-80.3.1-py3-none-any.whl", hash = "sha256:ea8e00d7992054c4c592aeb892f6ad51fe1b4d90cc6947cc45c45717c40ec537", size = 1201172, upload_time = "2025-05-04T18:47:02.575Z" },
 ]
 
 [[package]]

From 1642298b981a2f1ce2b0e5a1058ff1de9f6ed198 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Mon, 5 May 2025 02:41:50 +0000
Subject: [PATCH 46/47] revert

---
 src/zeroband/models/llama/__init__.py |  5 +--
 src/zeroband/models/llama/model.py    | 65 ++++++++++++++++-----------
 src/zeroband/models/norms.py          | 28 +++++-------
 src/zeroband/train.py                 | 21 +++------
 4 files changed, 58 insertions(+), 61 deletions(-)

diff --git a/src/zeroband/models/llama/__init__.py b/src/zeroband/models/llama/__init__.py
index ea061c52..df6c245b 100644
--- a/src/zeroband/models/llama/__init__.py
+++ b/src/zeroband/models/llama/__init__.py
@@ -6,7 +6,6 @@
 #
 # Llama 2 is licensed under the LLAMA 2 Community License,
 # Copyright (c) Meta Platforms, Inc. All Rights Reserved.
-import torch
 
 from zeroband.config import Config
 from zeroband.models.llama.model import ModelArgs, Transformer
@@ -95,8 +94,6 @@
 def make_model(
         config: Config,
         vocab_size: int,
-        dtype: torch.dtype,
-        device: torch.device
 ) -> tuple[Transformer, ModelArgs]:
     """
     Constructs a model instance according to the supplied configuration and target vocab size
@@ -114,4 +111,4 @@ def make_model(
     model_config.max_seq_len = config.data.seq_length
     model_config.attn_fn = config.hardware.attn_fn
 
-    return Transformer(model_config, dtype=dtype, device=device), model_config
+    return Transformer(model_config), model_config
diff --git a/src/zeroband/models/llama/model.py b/src/zeroband/models/llama/model.py
index ea782551..e73c3fbf 100644
--- a/src/zeroband/models/llama/model.py
+++ b/src/zeroband/models/llama/model.py
@@ -170,7 +170,7 @@ def seqlens_to_docs_tensor(seqlens: list[torch.Tensor]) -> torch.Tensor:
     return torch.stack([torch.repeat_interleave(torch.arange(len(seq), device=seq.device), seq) for seq in seqlens])
 
 
-def create_block_mask_from_seqlens(seqlens: list[torch.Tensor], dtype: torch.dtype, device: torch.device) -> BlockMask:
+def create_block_mask_from_seqlens(seqlens: list[torch.Tensor]) -> BlockMask:
     """Creates a block mask from a list of sequence lengths.
 
     Example:
@@ -183,7 +183,7 @@ def create_block_mask_from_seqlens(seqlens: list[torch.Tensor], dtype: torch.dty
                 [0 0 1 1 0]  # Second token of doc 1 can see both tokens of doc 1
                 [0 0 0 0 1]] # Token of doc 2 can only see itself
     """
-    docs = seqlens_to_docs_tensor(seqlens).to(dtype=dtype, device=device)
+    docs = seqlens_to_docs_tensor(seqlens).to("cuda")
     batch_size, max_seq_len = docs.shape
 
     def document_causal_mask(b, h, q_idx, kv_idx):
@@ -197,7 +197,7 @@ def document_causal_mask(b, h, q_idx, kv_idx):
         None,
         max_seq_len,
         max_seq_len,
-        device=device.type,
+        device="cuda",
         _compile=True,
         BLOCK_SIZE=max_seq_len if max_seq_len < _DEFAULT_SPARSE_BLOCK_SIZE else _DEFAULT_SPARSE_BLOCK_SIZE,
     )
@@ -222,17 +222,17 @@ class Attention(nn.Module):
 
     """
 
-    def __init__(self, model_args: ModelArgs, dtype: torch.dtype, device: torch.device):
+    def __init__(self, model_args: ModelArgs):
         super().__init__()
         self.n_heads = model_args.n_heads
         self.n_kv_heads = model_args.n_heads if model_args.n_kv_heads is None else model_args.n_kv_heads
         self.n_rep = self.n_heads // self.n_kv_heads
         self.head_dim = model_args.dim // model_args.n_heads
 
-        self.wq = nn.Linear(model_args.dim, model_args.n_heads * self.head_dim, bias=False, dtype=dtype, device=device)
-        self.wk = nn.Linear(model_args.dim, self.n_kv_heads * self.head_dim, bias=False, dtype=dtype, device=device)
-        self.wv = nn.Linear(model_args.dim, self.n_kv_heads * self.head_dim, bias=False, dtype=dtype, device=device)
-        self.wo = nn.Linear(model_args.n_heads * self.head_dim, model_args.dim, bias=False, dtype=dtype, device=device)
+        self.wq = nn.Linear(model_args.dim, model_args.n_heads * self.head_dim, bias=False)
+        self.wk = nn.Linear(model_args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wv = nn.Linear(model_args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wo = nn.Linear(model_args.n_heads * self.head_dim, model_args.dim, bias=False)
 
         self.attn_fn = model_args.attn_fn
 
@@ -342,8 +342,6 @@ def __init__(
             hidden_dim: int,
             multiple_of: int,
             ffn_dim_multiplier: Optional[float],
-            dtype: torch.dtype,
-            device: torch.device
     ):
         super().__init__()
         hidden_dim = int(2 * hidden_dim / 3)
@@ -352,9 +350,9 @@ def __init__(
             hidden_dim = int(ffn_dim_multiplier * hidden_dim)
         hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
 
-        self.w1 = nn.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device)
-        self.w2 = nn.Linear(hidden_dim, dim, bias=False, dtype=dtype, device=device)
-        self.w3 = nn.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device)
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
 
     def forward(self, x: torch.Tensor, flop_counter: FlopCounter = FlopCounter()):
         flop_counter.track_linear(self.w1, x)
@@ -400,23 +398,22 @@ class TransformerBlock(nn.Module):
 
     """
 
-    def __init__(self, layer_id: int, model_args: ModelArgs, dtype: torch.dtype, device: torch.device):
+    def __init__(self, layer_id: int, model_args: ModelArgs):
         super().__init__()
         self.n_heads = model_args.n_heads
         self.dim = model_args.dim
-        self.attention = Attention(model_args, dtype=dtype, device=device)
+        self.attention = Attention(model_args)
         self.feed_forward = FeedForward(
             dim=model_args.dim,
             hidden_dim=4 * model_args.dim,
             multiple_of=model_args.multiple_of,
             ffn_dim_multiplier=model_args.ffn_dim_multiplier,
-            dtype=dtype, device=device
         )
         self.layer_id = layer_id
         self.num_layers = model_args.n_layers
 
-        self.attention_norm = build_norm(model_args.norm_type, dim=model_args.dim, eps=model_args.norm_eps, dtype=dtype, device=device)
-        self.ffn_norm = build_norm(model_args.norm_type, dim=model_args.dim, eps=model_args.norm_eps, dtype=dtype, device=device)
+        self.attention_norm = build_norm(model_args.norm_type, dim=model_args.dim, eps=model_args.norm_eps)
+        self.ffn_norm = build_norm(model_args.norm_type, dim=model_args.dim, eps=model_args.norm_eps)
 
         if model_args.depth_init:
             self.weight_init_std = 0.02 / (2 * (self.layer_id + 1)) ** 0.5
@@ -484,7 +481,7 @@ class Transformer(nn.Module):
 
     """
 
-    def __init__(self, model_args: ModelArgs, dtype: torch.dtype, device: torch.device):
+    def __init__(self, model_args: ModelArgs):
         super().__init__()
         self.model_args = model_args
         self.vocab_size = model_args.vocab_size
@@ -499,15 +496,15 @@ def __init__(self, model_args: ModelArgs, dtype: torch.dtype, device: torch.devi
         # a seed checkpoint rather than calling init_weights, we need freqs_cis to be
         # initialized by the checkpoint, or we need to add a separate initializer for
         # just the non-persistent buffers that is called after loading checkpoints.
-        self.register_buffer("freqs_cis", self._precompute_freqs_cis(dtype=dtype, device=device), persistent=True)
+        self.register_buffer("freqs_cis", self._precompute_freqs_cis(), persistent=True)
 
         self.layers = torch.nn.ModuleDict()
         for layer_id in range(model_args.n_layers):
-            self.layers[str(layer_id)] = TransformerBlock(layer_id, model_args, dtype=dtype, device=device)
+            self.layers[str(layer_id)] = TransformerBlock(layer_id, model_args)
 
-        self.norm = build_norm(model_args.norm_type, dim=model_args.dim, eps=model_args.norm_eps, dtype=dtype, device=device)
+        self.norm = build_norm(model_args.norm_type, dim=model_args.dim, eps=model_args.norm_eps)
 
-        self.output = nn.Linear(model_args.dim, model_args.vocab_size, bias=False, dtype=dtype, device=device)
+        self.output = nn.Linear(model_args.dim, model_args.vocab_size, bias=False)
         self.init_weights()
 
     def init_weights(self):
@@ -522,6 +519,8 @@ def init_weights(self):
         ``init_weights``. We only call it in the constructor of this
         ``Transformer`` root module to avoid reinitializing tensors.
         """
+        with torch.device(self.freqs_cis.device):
+            self.freqs_cis = self._precompute_freqs_cis()
         if self.tok_embeddings is not None:
             nn.init.normal_(self.tok_embeddings.weight)
         for layer in self.layers.values():
@@ -540,14 +539,14 @@ def init_weights(self):
                 b=cutoff_factor * final_out_std,
             )
 
-    def _precompute_freqs_cis(self, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
+    def _precompute_freqs_cis(self) -> torch.Tensor:
         return precompute_freqs_cis(
             self.model_args.dim // self.model_args.n_heads,
             # Need to compute until at least the max token limit for generation
             # (use 2x max sequence length to be safe)
             self.model_args.max_seq_len * 2,
-            self.model_args.rope_theta
-        ).to(dtype=dtype, device=device)
+            self.model_args.rope_theta,
+        )
 
     def forward(self, tokens: torch.Tensor, block_mask: BlockMask | None = None, flop_counter: FlopCounter = FlopCounter()):
         """
@@ -576,6 +575,20 @@ def forward(self, tokens: torch.Tensor, block_mask: BlockMask | None = None, flo
 
         return output
 
+    @classmethod
+    def from_model_args(cls, model_args: ModelArgs) -> "Transformer":
+        """
+        Initialize a Transformer model from a ModelArgs object.
+
+        Args:
+            model_args (ModelArgs): Model configuration arguments.
+
+        Returns:
+            Transformer: Transformer model.
+
+        """
+        return cls(model_args)
+
     def count_parameters(self, exclude_embedding: bool = False) -> int:
         """
         Counts the number of parameters.
diff --git a/src/zeroband/models/norms.py b/src/zeroband/models/norms.py
index 650296e4..f1febcf8 100644
--- a/src/zeroband/models/norms.py
+++ b/src/zeroband/models/norms.py
@@ -21,7 +21,7 @@
 from torch.distributed.tensor.experimental import local_map
 
 
-def build_norm(norm_type: str, dim: int, eps: float, dtype: torch.dtype, device: torch.device):
+def build_norm(norm_type: str, dim: int, eps: float = 1e-6):
     """
     Builds the specified normalization layer based on the norm_type.
 
@@ -29,9 +29,7 @@ def build_norm(norm_type: str, dim: int, eps: float, dtype: torch.dtype, device:
         norm_type (str): The type of normalization layer to build.
             Supported types: layernorm, np_layernorm, rmsnorm, fused_rmsnorm
         dim (int): The dimension of the normalization layer.
-        eps (float, optional): The epsilon value for numerical stability.
-        dtype: The data type to use for the parameter tensor
-        device: The device to place the layer on
+        eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.
 
     Returns:
         The built normalization layer.
@@ -42,13 +40,13 @@ def build_norm(norm_type: str, dim: int, eps: float, dtype: torch.dtype, device:
     norm_type = norm_type.lower()  # Normalize to lowercase
 
     if norm_type == "layernorm":
-        return nn.LayerNorm(dim, eps=eps, bias=False, dtype=dtype, device=device)
+        return nn.LayerNorm(dim, eps=eps, bias=False)
     elif norm_type == "np_layernorm":
-        return nn.LayerNorm(dim, eps=eps, elementwise_affine=False, bias=False, dtype=dtype, device=device)
+        return nn.LayerNorm(dim, eps=eps, elementwise_affine=False, bias=False)
     elif norm_type == "rmsnorm":
-        return RMSNorm(dim, eps=eps, dtype=dtype, device=device)
+        return RMSNorm(dim, eps=eps)
     elif norm_type == "fused_rmsnorm":
-        return FusedRMSNorm(dim, eps=eps, dtype=dtype, device=device)
+        return FusedRMSNorm(dim, eps=eps)
     else:
         raise NotImplementedError(f"Unknown norm_type: '{norm_type}'")
 
@@ -59,13 +57,11 @@ class FusedRMSNorm(nn.Module):
     def __init__(
             self,
             dim: int,
-            eps: float,
-            dtype: torch.dtype,
-            device: torch.device
+            eps: float = 1e-6,
     ):
         super().__init__()
         self.eps = eps
-        self.weight = nn.Parameter(torch.ones(dim, dtype=dtype, device=device))
+        self.weight = nn.Parameter(torch.ones(dim))
         self.fused_rms_norm_fn = fused_rms_norm_fn
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -86,9 +82,7 @@ class RMSNorm(nn.Module):
 
     Args:
         dim (int): The dimension of the input tensor.
-        eps (float, optional): A small value added to the denominator for numerical stability.
-        dtype: The data type to use
-        device: The torch device to place the layer on
+        eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
 
     Attributes:
         eps (float): A small value added to the denominator for numerical stability.
@@ -96,10 +90,10 @@ class RMSNorm(nn.Module):
 
     """
 
-    def __init__(self, dim: int, eps: float, dtype: torch.dtype, device: torch.device):
+    def __init__(self, dim: int, eps: float = 1e-6):
         super().__init__()
         self.eps = eps
-        self.weight = nn.Parameter(torch.ones(dim, dtype=dtype, device=device))
+        self.weight = nn.Parameter(torch.ones(dim))
 
     def _norm(self, x: torch.Tensor):
         return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 2db6311c..fa42c3fd 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -124,7 +124,6 @@ def run_inner_steps(
     num_param_scalars = model.count_parameters()
 
     for _inner_step in range(num_inner_steps):
-        #torch.cuda.memory._record_memory_history(max_entries=100000)
         train_profiler.start_session("inner_step")
 
         flop_counter = FlopCounter()
@@ -234,9 +233,6 @@ def run_inner_steps(
             memory_profiler.step()
         train_profiler.end_session()
 
-        #torch.cuda.memory._dump_snapshot('snapshot.pickle')
-        #torch.cuda.memory._record_memory_history(enabled=None)
-
 
 def compute_crc32(tensor: torch.Tensor) -> int:
     tensor_cpu = tensor.detach().cpu()
@@ -522,7 +518,7 @@ def make_shared_state(outer_parameters: Dict[str, torch.nn.Parameter],
     return shared_state
 
 
-def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], dtype: torch.dtype, device: torch.device):
+def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], device: torch.device):
     grad_accum_steps = calc_gradient_accumulation_steps(
         config.train.batch_size, config.hardware.micro_batch_size, mpi_config
     )
@@ -546,8 +542,6 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], dtype
             model, model_config = make_model(
                 config,
                 vocab_size=tokenizer_info.vocab_size,
-                dtype=dtype,
-                device=device,
             )
         num_param_scalars = model.count_parameters()
         logger.info(f"Number of parameters: {num_param_scalars}")
@@ -711,16 +705,16 @@ def train(logger: Logger, config: Config, mpi_config: Optional[MPIConfig], dtype
             continue
 
         local_world_size = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE)
-        #if local_world_size < 2:
-        #    logger.info("Waiting for more workers to join...")
-        #    time.sleep(1)
-        #    continue
+        if local_world_size < 2:
+            logger.info("Waiting for more workers to join...")
+            time.sleep(1)
+            continue
 
         if topology_updated:
             logger.info("Optimizing Topology...")
             while True:
                 try:
-                    # communicator.optimize_topology()  # may raise an error if it fails
+                    communicator.optimize_topology()  # may raise an error if it fails
                     break
                 except PCCLError as e:
                     print(f"[Peer] OptimizeTopology failed => {e}. Retrying...")
@@ -835,8 +829,7 @@ def main():
     device = torch.device(f'cuda:{torch.cuda.current_device()}')
     logger.info(f"Using device: {torch.cuda.get_device_name(device)}")
 
-    dtype = torch.bfloat16 # TODO: MAKE CONFIGURABLE
-    train(logger, config, mpi_config, dtype, device)
+    train(logger, config, mpi_config, device)
 
 
 if __name__ == "__main__":

From 60d83bda4256d5f52344ef79cd9485b7af8fb516 Mon Sep 17 00:00:00 2001
From: mike <michael.keiblinger@gmail.com>
Date: Thu, 22 May 2025 07:00:18 +0000
Subject: [PATCH 47/47] fix unused imports

---
 src/zeroband/models/llama/model.py | 3 ---
 src/zeroband/train.py              | 2 +-
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/src/zeroband/models/llama/model.py b/src/zeroband/models/llama/model.py
index e73c3fbf..517b5ac7 100644
--- a/src/zeroband/models/llama/model.py
+++ b/src/zeroband/models/llama/model.py
@@ -10,8 +10,6 @@
 # Llama 2 is licensed under the LLAMA 2 Community License,
 # Copyright (c) Meta Platforms, Inc. All Rights Reserved.
 
-
-import contextlib
 from dataclasses import dataclass
 from typing import Optional, Tuple
 
@@ -21,7 +19,6 @@
 from zeroband.config import AttnFnType
 
 from torch.nn.attention.flex_attention import create_block_mask, flex_attention, BlockMask, _DEFAULT_SPARSE_BLOCK_SIZE
-from torch.nn.attention import SDPBackend, sdpa_kernel
 
 from zeroband.utils.mfu_tracker import FlopCounter
 
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index fa42c3fd..e7ab23b7 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -4,7 +4,7 @@
 import zlib
 from dataclasses import asdict
 from logging import Logger
-from typing import TYPE_CHECKING, Optional, Iterator, List, Dict, Tuple
+from typing import TYPE_CHECKING, Optional, Iterator, List, Dict
 
 import torch
 import torch.distributed as dist