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..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] @@ -16,6 +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/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..d5126a8a 100644 --- a/configs/7B/H100.toml +++ b/configs/7B/H100.toml @@ -1,15 +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 diff --git a/configs/debug/diloco.toml b/configs/debug/diloco.toml index 2b7547d5..3b87952b 100644 --- a/configs/debug/diloco.toml +++ b/configs/debug/diloco.toml @@ -1,20 +1,39 @@ -model_name = "debugmodel" -model_type = "llama2" +model_name = "150M" +model_type = "llama3" wandb = false +log_all_ranks = true [hardware] -micro_batch_size = 8 +micro_batch_size = 32 +torch_compile = true [train] -batch_size = 16 +batch_size = 512 [train.lr_scheduler] num_warmup_steps = 10 -num_decay_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' +#dataset_ratio = "50:50" +token_bit_size = 17 [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/configs/debug/normal.toml b/configs/debug/normal.toml index 16451cf5..358976bf 100644 --- a/configs/debug/normal.toml +++ b/configs/debug/normal.toml @@ -7,7 +7,7 @@ wandb = false micro_batch_size = 8 [train] -batch_size = 16 +batch_size = 32 [train.lr_scheduler] num_warmup_steps = 10 @@ -16,3 +16,5 @@ num_decay_steps = 10 [data] fake = true +[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/pyproject.toml b/pyproject.toml index 7a3b1be0..72f5d884 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", + "tomli", "torchdata>=0.8.0", "fsspec[gcs]>=2024.3.1", "ninja", @@ -18,7 +19,10 @@ dependencies = [ "pyarrow", "psutil", "wandb", - "imageio[ffmpeg]" + "numba", + "imageio[ffmpeg]", + "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] @@ -37,4 +41,4 @@ 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/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 e571c1ba..00000000 --- a/scripts/export_dcp.py +++ /dev/null @@ -1,225 +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.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 -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 - logger.info("Before load: %s", get_module_signature(model)) - states = { - "model": ModelWrapper(model), - } - logger.info("Loading from %s", config.ckpt.resume) - dcp.load( - state_dict=states, - 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() - - 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/launch_prime.sh b/scripts/launch_prime.sh new file mode 100755 index 00000000..97392cc8 --- /dev/null +++ b/scripts/launch_prime.sh @@ -0,0 +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 + +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 diff --git a/scripts/skip_data.py b/scripts/skip_data.py deleted file mode 100644 index 8820e10a..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, - world_size=world_info.world_size, - 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/ccl/ccl_utils.py b/src/zeroband/ccl/ccl_utils.py new file mode 100644 index 00000000..b173ff21 --- /dev/null +++ b/src/zeroband/ccl/ccl_utils.py @@ -0,0 +1,34 @@ +from dataclasses import dataclass +from typing import Optional + + +@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 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/pccl_utils.py b/src/zeroband/ccl/pccl_utils.py new file mode 100644 index 00000000..9df16e7b --- /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 = 128): + 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/ccoip_master.py b/src/zeroband/ccoip_master.py new file mode 100644 index 00000000..6e36dc95 --- /dev/null +++ b/src/zeroband/ccoip_master.py @@ -0,0 +1,13 @@ +from pccl import MasterNode + +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..1d3b34e9 100644 --- a/src/zeroband/checkpoint.py +++ b/src/zeroband/checkpoint.py @@ -1,12 +1,13 @@ from dataclasses import dataclass import os from pathlib import Path -from typing import Any +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 +35,32 @@ 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 +69,34 @@ 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 +113,7 @@ 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..3a1f37e6 100644 --- a/src/zeroband/config.py +++ b/src/zeroband/config.py @@ -4,26 +4,57 @@ from pydantic import model_validator from pydantic_config import BaseConfig + class Compression(Enum): NO = "no" UINT8 = "uint8" 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 + dataset_ratio: str = "100" data_rank: int | None = None data_world_size: int | None = None 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.fake or self.dataset_ratio is None: + return self + + 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" + momentum: float = 0.0 + nesterov: bool = False + class AdamConfig(BaseConfig): type: Literal["adam"] = "adam" @@ -58,24 +89,35 @@ 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 + delayed_update: bool = False + class MemoryProfilerConfig(BaseConfig): freq: int = 10 snapshot_dir: str -AttnFnType: TypeAlias = Literal["flex", "math"] + +AttnFnType: TypeAlias = Literal["flex", "sdpa"] +CclLibType: TypeAlias = Literal["nccl", "pccl"] + class HardwareConfig(BaseConfig): micro_batch_size: int = 1 @@ -116,6 +158,10 @@ class CkptConfig(BaseConfig): resume: str | None = None +class PcclConfig(BaseConfig): + ccoip_host: str + + class Config(BaseConfig): # Project/Run project: str = "zeroband" @@ -130,14 +176,15 @@ 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 - data: DataConfig = DataConfig() - train: TrainConfig = TrainConfig() + data: DataConfig + train: TrainConfig hardware: HardwareConfig monitor: MonitorConfig | None = None + pccl: PcclConfig | None = None ckpt: CkptConfig = CkptConfig() @@ -149,4 +196,5 @@ 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" ) + assert self.pccl is not None, "[pccl] must be configured!" return self diff --git a/src/zeroband/data.py b/src/zeroband/data.py index d0cd2262..4bc5c6f0 100644 --- a/src/zeroband/data.py +++ b/src/zeroband/data.py @@ -1,19 +1,21 @@ -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, Dict import torch -from datasets import load_dataset_builder, BuilderConfig -from pyarrow import parquet as pq +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 -from transformers import PreTrainedTokenizer +from transformers import PreTrainedTokenizer, AutoTokenizer 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 + +from pyarrow import parquet as pq, Table DEBUG_VOCAB_SIZE = 1024 @@ -21,6 +23,7 @@ class StatefulDataset(IterableDataset, Stateful, ABC): ... + class FakeTokenizedDataset(StatefulDataset): """This is a dummy dataset that generates random sequences of length seq_len and vocab_size""" @@ -32,10 +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} @@ -47,167 +53,70 @@ 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 +class NibbleDataset(StatefulDataset): - 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"]) - - -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()) - - return { - "input_ids": torch.stack(inputs_ids, dim=0), - "labels": torch.stack(labels, dim=0), - "seqlens": seqlens, - } + 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!") + 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 -@dataclass -class PQDatasetState: - files: List[str] - file_index: int - row_index: int - increment: int - init_row_index: int + 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 -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 - """ + self.lsr_seed = seed - def __init__(self, files: List[str], tokenizer: PreTrainedTokenizer): - self.arg_files = files - self.tokenizer = tokenizer + 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) - 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 + # floor to nearest nibble start + num_nibbles = (seek_pos * 8) // self.n_bits + nibble_start_bit = num_nibbles * self.n_bits - 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 {"lsfr_seed": self.lsr_seed} def load_state_dict(self, state_dict): - self.state = PQDatasetState(**state_dict) + self.lsr_seed = state_dict["lsfr_seed"] @dataclass @@ -224,7 +133,7 @@ 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 +141,8 @@ 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() @@ -275,130 +181,261 @@ def load_state_dict(self, state_dict): self._init_random_state() -def make_dataloader( - tokenizer, - world_size: int, - rank: int, - batch_size: int, - data_config: DataConfig, -) -> StatefulDataLoader: - 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, rank=rank, world_size=world_size +@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] = {} - dataset = SequencePackingDataSet(train_dataset, data_config.seq_length, eos_token=tokenizer.eos_token_id) - return StatefulDataLoader( - dataset, - batch_size=batch_size, - collate_fn=collate_fn, - num_workers=data_config.num_workers, - ) + 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] -@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 + new_tokens = self.tokenizer.encode(str(row), add_special_tokens=True) + target_num_tokens = self.seq_len + 1 -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] + 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] - get_logger().info(f"Loaded datasets ({split})") - return ds + # 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) -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] + if len(tokens) >= target_num_tokens: + break + tokens = torch.tensor(tokens, dtype=torch.int64, device='cpu') -def load_all_datasets( - data_config: DataConfig, - split: str, - tokenizer: PreTrainedTokenizer, - rank: int, - 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 + 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 + + +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"} + + 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_nibble_dataset(data_config: DataConfig, tokenizer_info: TokenizerInfo) -> StatefulDataset: + 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: - split_rank = rank - split_world_size = 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, + 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 dataset_path.endswith('.parquet'): + raise ValueError("Cannot mix nibble- with parquet 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( + [NibbleDataset(dataset_path, data_config.seq_length, tokenizer_info, data_config.token_bit_size, + iterator_seed) for dataset_path in dataset_paths], + probabilities ) - get_logger().info(f"Train dataset: {ds}") - return ds \ No newline at end of file +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 = [] + 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("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 _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 without ',' 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, + mpi_rank: int, + batch_size: int, + data_config: DataConfig, +) -> StatefulDataLoader: + if data_config.fake: + train_dataset = FakeTokenizedDataset(data_config.seq_length, DEBUG_VOCAB_SIZE) + else: + # 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: + 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, + batch_size=batch_size, + collate_fn=collate_fn, + num_workers=data_config.num_workers, + ) 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..df6c245b 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, @@ -43,7 +52,7 @@ multiple_of=512, rope_theta=500000, ), - "8B": ModelArgs( + "7B": ModelArgs( dim=4096, n_layers=32, n_heads=32, @@ -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/models/llama/model.py b/src/zeroband/models/llama/model.py index adfee846..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 @@ -291,9 +288,8 @@ 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(): - 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 @@ -314,7 +310,7 @@ 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 5f665cf5..e7ab23b7 100644 --- a/src/zeroband/train.py +++ b/src/zeroband/train.py @@ -1,29 +1,36 @@ import os +import threading import time +import zlib +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, Attribute, Communicator, PCCLError, ReduceOp, SharedStateSyncStrategy from torch.distributed import destroy_process_group from torch.distributed.tensor import DTensor import wandb +from torch.optim import Optimizer -from zeroband.checkpoint import TrainingProgress, load_checkpoint_fsdp_state, save_checkpoint_fsdp_state -from zeroband.config import Config +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, LearningRateSchedulerConfig 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 + get_flops_promised_pt +from zeroband.utils.misc_utils import IntRef +from zeroband.utils.tokenizer_utils import get_tokenizer_info from zeroband.utils.logger import get_logger from zeroband.utils.profiler import Profiler, ProfilerCollection @@ -34,9 +41,10 @@ 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})" @@ -71,8 +79,8 @@ 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) @@ -92,30 +100,449 @@ def perform_grad_accum_steps( return total_loss, current_lr -def train(logger: Logger, config: Config, world_info: WorldInfo, device: torch.device): +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, + global_world_size: int, + + 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]] +): + 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) + + training_progress.step += 1 + + end_event.record() + timing_events.append((start_event, end_event)) + + # 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, + "outer_step": training_progress.outer_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"] = 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) + + 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 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], + 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: + 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 + 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, logger, num_syncs, train_profiler, + False, SharedStateSyncStrategy.SEND_ONLY) + + 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, logger, num_syncs, train_profiler, + 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. + # 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.clear() + last_pseudo_grads.extend(outer_grads) + + start_time = time.time() + pccl_utils.all_reduce_multiple_with_retry( + communicator, + last_pseudo_grads, + ReduceOp.AVG, + max_in_flight=128 + ) + end_time = time.time() + 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") + + # 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 run_sync_outer_step( + model: torch.nn.Module, + outer_parameters_list: List[torch.nn.Parameter], + outer_optimizer: torch.optim.Optimizer, + communicator: Communicator, + train_profiler: Profiler, + logger: Logger +): + # 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() + logger.info(f"All-Reduce took {end_time - start_time} seconds") + if not all_reduce_success: + logger.info("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, + + 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, + num_syncs) + else: + run_sync_outer_step(model, outer_parameters_list, outer_optimizer, communicator, train_profiler, logger) + 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 ] + 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], + + logger: Logger, + + num_syncs: IntRef, + train_profiler: Profiler, + late_joiner: bool, + strategy: SharedStateSyncStrategy +): + # 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, 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 + 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: + logger.info("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, world_info + config.train.batch_size, config.hardware.micro_batch_size, mpi_config ) setup_profiler = Profiler() # Load tokenizer - tokenizer = make_tokenizer(config) + tokenizer_info = get_tokenizer_info(config) train_dataloader = make_dataloader( - tokenizer=tokenizer, - world_size=world_info.world_size, - rank=world_info.rank, + 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, 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=tokenizer_info.vocab_size, + ) num_param_scalars = model.count_parameters() logger.info(f"Number of parameters: {num_param_scalars}") @@ -125,172 +552,199 @@ 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 - - if config.diloco: - raise NotImplementedError("Diloco is not implemented yet") + # ------------------------------------------------------------------------- + # ! PCCL-related state for outer optimizer, pseudo-gradients and shared state + # All None if diloco is disabled + outer_optimizer: Optional[torch.optim.Optimizer] = None + outer_parameters: Dict[str, torch.nn.Parameter] = dict() + outer_parameters_list: List[torch.nn.Parameter] = [] + shared_state: Optional[SharedState] = None + # ------------------------------------------------------------------------- training_progress = TrainingProgress(total_tokens=0, outer_step=0, step=0) - 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 - + 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 ) + 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: + for name, local_p in model.named_parameters(): + if isinstance(local_p, DTensor): + local_p = local_p.to_local() + 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) + + # do a dummy step to initialize outer optimizer state + for op in outer_parameters_list: + op.grad = torch.zeros_like(op) + outer_optimizer.step() + + 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( + 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: + logger.info("Compiling model...") + 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, - 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 + 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) + logger.info("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 = IntRef(0) + local_world_size: int = communicator.get_attribute(Attribute.LOCAL_WORLD_SIZE) + # ------------------------------------------------------------------------- + + # ------------------------------------------------------------------------- + # ! 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 = [] while True: + local_iter_num += 1 + train_profiler = Profiler() if num_inner_steps > 1: # if we don't use diloco we don't print the outer step logs logger.info(f"outer_step step: {training_progress.outer_step}") - 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) - - 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 - - with train_profiler.session("inner_optimizer.step"): - flop_counter.track_optimizer_step(inner_optimizer, num_param_scalars) - inner_optimizer.step() - inner_optimizer.zero_grad() - - 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) + 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 + 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...") + while True: + try: + if communicator.are_peers_pending(): + 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 + break + except PCCLError as e: + 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: + logger.info("Not all MPI ranks have joined...") + time.sleep(1) + 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 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) - 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: - # 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 + logger.info("Running shared state synchronization...") + run_shared_state_sync(shared_state, communicator, model, outer_parameters_list, + logger, + num_syncs, train_profiler, + False, SharedStateSyncStrategy.ENFORCE_POPULAR) - # 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) - - 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 - }) + 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.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 + logger.info("Running inner steps...") + run_inner_steps( + model, train_dataloader_iterator, inner_optimizer, device, - if world_info.rank == 0 and config.wandb: - wandb.log(metrics) + logger, memory_profiler, - log = metrics_utils.build_metrics_string(metrics, whitelist_keys={'step', 'loss', 'mfu', 'tflops', 'tokens_per_second', 'tflops_max'}) - logger.info(log) + local_world_size, + global_world_size, - 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 + ) + logger.info("Inner steps done") # post inner steps if PRIME_TRAIN_PROFILER_PRINT_TIMINGS: @@ -306,10 +760,15 @@ def train(logger: Logger, config: Config, world_info: WorldInfo, device: torch.d 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"): + all_reduce_thread = run_outer_step(model, last_pseudo_grads, outer_parameters_list, + outer_optimizer, shared_state, all_reduce_thread, communicator, + train_profiler, logger, + training_progress, config.train.outer_lr_scheduler, + topology_updated, iter_num, num_syncs, + config.diloco.delayed_update) - training_progress.outer_step += 1 + iter_num += 1 if ( config.ckpt.interval is not None @@ -317,12 +776,17 @@ 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: @@ -331,32 +795,41 @@ 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: - 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") logger.info("Training finished, exiting ...") - destroy_process_group() + if mpi_config is not None: + destroy_process_group() 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") - torch.manual_seed(42) + # torch._dynamo.config.suppress_errors = "ZERO_BAND_DEV" not in os.environ # type: ignore + 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 - world_info = get_world_info() - logger = get_logger(config) + logger = get_logger(config, mpi_config) + + 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.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()}') + logger.info(f"Using device: {torch.cuda.get_device_name(device)}") - train(logger, config, world_info, device) + train(logger, config, mpi_config, device) if __name__ == "__main__": 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/src/zeroband/utils/logger.py b/src/zeroband/utils/logger.py index 91050bf0..cb3e6630 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 @@ -24,31 +24,25 @@ 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: + 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 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/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/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/mfu_tracker.py b/src/zeroband/utils/mfu_tracker.py index 319089fb..961cfeb9 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) @@ -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 @@ -258,12 +265,14 @@ 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)) + @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 +295,7 @@ 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 +305,7 @@ 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. 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 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/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/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..0096101b 100644 --- a/src/zeroband/utils/tokenizer_utils.py +++ b/src/zeroband/utils/tokenizer_utils.py @@ -1,27 +1,46 @@ -from transformers import AutoTokenizer - +from dataclasses import dataclass 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 +@dataclass +class TokenizerInfo: + hf_name: str # TODO: REMOVE + + 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() + 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": - tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=True) + 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) + 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( + 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) + 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/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/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_data/val_1.bin b/tests/test_data/val_1.bin new file mode 100644 index 00000000..3c6cd5e5 Binary files /dev/null and b/tests/test_data/val_1.bin differ 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 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() 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..34b6b9fc --- /dev/null +++ b/tests/test_read_nibbles.py @@ -0,0 +1,100 @@ +from typing import List + +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 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