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| -rw-r--r-- | LICENSE | 202 | ||||
| -rw-r--r-- | README.md | 32 | ||||
| -rw-r--r-- | dataset/build_maze_dataset.py | 2 | ||||
| -rw-r--r-- | dataset/build_sudoku_dataset.py | 2 | ||||
| -rw-r--r-- | evaluate.py | 2 | ||||
| -rw-r--r-- | models/layers.py | 26 | ||||
| -rw-r--r-- | pretrain.py | 3 |
7 files changed, 250 insertions, 19 deletions
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\ No newline at end of file @@ -15,16 +15,16 @@ These results underscore HRMās potential as a transformative advancement towar Ensure PyTorch and CUDA are installed. The repo needs CUDA extensions to be built. If not present, run the following commands: ```bash -# Install CUDA 12.4 -CUDA_URL=https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda_12.4.1_550.54.15_linux.run +# Install CUDA 12.6 +CUDA_URL=https://developer.download.nvidia.com/compute/cuda/12.6.3/local_installers/cuda_12.6.3_560.35.05_linux.run wget -q --show-progress --progress=bar:force:noscroll -O cuda_installer.run $CUDA_URL sudo sh cuda_installer.run --silent --toolkit --override -export CUDA_HOME=/usr/local/cuda-12.4 +export CUDA_HOME=/usr/local/cuda-12.6 -# Install PyTorch with CUDA 12.4 -PYTORCH_INDEX_URL=https://download.pytorch.org/whl/cu124 +# Install PyTorch with CUDA 12.6 +PYTORCH_INDEX_URL=https://download.pytorch.org/whl/cu126 pip3 install torch torchvision torchaudio --index-url $PYTORCH_INDEX_URL @@ -32,6 +32,20 @@ pip3 install torch torchvision torchaudio --index-url $PYTORCH_INDEX_URL pip3 install packaging ninja wheel setuptools setuptools-scm ``` +Then install FlashAttention. For Hopper GPUs, install FlashAttention 3 + +```bash +git clone git@github.com:Dao-AILab/flash-attention.git +cd flash-attention/hopper +python setup.py install +``` + +For Ampere or earlier GPUs, install FlashAttenion 2 + +```bash +pip3 install flash-attn +``` + ## Install Python Dependencies š ```bash @@ -62,6 +76,14 @@ OMP_NUM_THREADS=8 python pretrain.py data_path=data/sudoku-extreme-1k-aug-1000 e Runtime: ~10 hours on a RTX 4070 laptop GPU +## Trained Checkpoints š§ + + - [ARC-AGI-2](https://huggingface.co/sapientinc/HRM-checkpoint-ARC-2) + - [Sudoku 9x9 Extreme (1000 examples)](https://huggingface.co/sapientinc/HRM-checkpoint-sudoku-extreme) + - [Maze 30x30 Hard (1000 examples)](https://huggingface.co/sapientinc/HRM-checkpoint-maze-30x30-hard) + +To use the checkpoints, see Evaluation section below. + ## Full-scale Experiments šµ Experiments below assume an 8-GPU setup. diff --git a/dataset/build_maze_dataset.py b/dataset/build_maze_dataset.py index e99baf2..a9367f3 100644 --- a/dataset/build_maze_dataset.py +++ b/dataset/build_maze_dataset.py @@ -20,7 +20,7 @@ cli = ArgParser() class DataProcessConfig(BaseModel): - source_repo: str = "imone/small-sample-challenge-maze-30x30-hard" + source_repo: str = "sapientinc/maze-30x30-hard-1k" output_dir: str = "data/maze-30x30-hard-1k" subsample_size: Optional[int] = None diff --git a/dataset/build_sudoku_dataset.py b/dataset/build_sudoku_dataset.py index 5d5b50c..7924438 100644 --- a/dataset/build_sudoku_dataset.py +++ b/dataset/build_sudoku_dataset.py @@ -16,7 +16,7 @@ cli = ArgParser() class DataProcessConfig(BaseModel): - source_repo: str = "imone/sudoku-hard-v2" + source_repo: str = "sapientinc/sudoku-extreme" output_dir: str = "data/sudoku-extreme-full" subsample_size: Optional[int] = None diff --git a/evaluate.py b/evaluate.py index 9bc6ba0..71ee753 100644 --- a/evaluate.py +++ b/evaluate.py @@ -39,7 +39,7 @@ def launch(): # Dataloader train_loader, train_metadata = create_dataloader(config, "train", test_set_mode=False, epochs_per_iter=1, global_batch_size=config.global_batch_size, rank=RANK, world_size=WORLD_SIZE) - eval_loader, eval_metadata = create_dataloader(config, "test", test_set_mode=True, epochs_per_iter=1, global_batch_size=config.global_batch_size, test_set_limit_examples=LIMIT_EXAMPLES, rank=RANK, world_size=WORLD_SIZE) + eval_loader, eval_metadata = create_dataloader(config, "test", test_set_mode=True, epochs_per_iter=1, global_batch_size=config.global_batch_size, rank=RANK, world_size=WORLD_SIZE) # Models train_state = init_train_state(config, train_metadata, world_size=WORLD_SIZE) diff --git a/models/layers.py b/models/layers.py index 4f7dee4..008a172 100644 --- a/models/layers.py +++ b/models/layers.py @@ -4,6 +4,12 @@ import torch from torch import nn import torch.nn.functional as F +try: + from flash_attn_interface import flash_attn_func # type: ignore[import] +except ImportError: + # Fallback to FlashAttention 2 + from flash_attn import flash_attn_func # type: ignore[import] + from models.common import trunc_normal_init_ @@ -22,14 +28,14 @@ def rotate_half(x: torch.Tensor): def apply_rotary_pos_emb(q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor): - # q, k: [bs, num_heads, seq_len, head_dim] + # q, k: [bs, seq_len, num_heads, head_dim] # cos, sin: [seq_len, head_dim] orig_dtype = q.dtype q = q.to(cos.dtype) k = k.to(cos.dtype) - q_embed = (q * cos) + (rotate_half(q) * sin) - k_embed = (k * cos) + (rotate_half(k) * sin) + q_embed = (q * cos.unsqueeze(-2)) + (rotate_half(q) * sin.unsqueeze(-2)) + k_embed = (k * cos.unsqueeze(-2)) + (rotate_half(k) * sin.unsqueeze(-2)) return q_embed.to(orig_dtype), k_embed.to(orig_dtype) @@ -110,10 +116,10 @@ class Attention(nn.Module): qkv = self.qkv_proj(hidden_states) # Split head - qkv = qkv.view(batch_size, seq_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim).transpose(-2, -3) - query = qkv[:, :self.num_heads] - key = qkv[:, self.num_heads: self.num_heads + self.num_key_value_heads] - value = qkv[:, self.num_heads + self.num_key_value_heads:] + qkv = qkv.view(batch_size, seq_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim) + query = qkv[:, :, :self.num_heads] + key = qkv[:, :, self.num_heads: self.num_heads + self.num_key_value_heads] + value = qkv[:, :, self.num_heads + self.num_key_value_heads:] # RoPE if cos_sin is not None: @@ -121,10 +127,12 @@ class Attention(nn.Module): query, key = apply_rotary_pos_emb(query, key, cos, sin) # flash attn - attn_output = F.scaled_dot_product_attention(query=query, key=key, value=value, is_causal=self.causal) + attn_output = flash_attn_func(q=query, k=key, v=value, causal=self.causal) + if isinstance(attn_output, tuple): # fa2 and fa3 compatibility + attn_output = attn_output[0] # attn_output: [batch_size, num_heads, seq_len, head_dim] - attn_output = attn_output.transpose(-2, -3).view(batch_size, seq_len, self.output_size) # type: ignore + attn_output = attn_output.view(batch_size, seq_len, self.output_size) # type: ignore return self.o_proj(attn_output) diff --git a/pretrain.py b/pretrain.py index b939318..245cb5c 100644 --- a/pretrain.py +++ b/pretrain.py @@ -16,7 +16,6 @@ import coolname import hydra import pydantic from omegaconf import DictConfig -from wandb.util import make_artifact_name_safe from adam_atan2 import AdamATan2 from puzzle_dataset import PuzzleDataset, PuzzleDatasetConfig, PuzzleDatasetMetadata @@ -126,7 +125,7 @@ def create_model(config: PretrainConfig, train_metadata: PuzzleDatasetMetadata, model: nn.Module = model_cls(model_cfg) model = loss_head_cls(model, **config.arch.loss.__pydantic_extra__) # type: ignore if "DISABLE_COMPILE" not in os.environ: - model = torch.compile(model, dynamic=False, fullgraph=True) # type: ignore + model = torch.compile(model, dynamic=False) # type: ignore # Broadcast parameters from rank 0 if world_size > 1: |