WIP

README.md

@@ -1,22 +1,39 @@
-[
-Based on assorted code by shuf(mryab@ younesbelkada@ borzunov@ timdettmers@ dbaranchuk@ greenfatguy@ artek0chumak@)
-]
 
 
-# Install
+# Install (core only)
 ```bash
 
-git clone https://github.com/CompVis/latent-diffusion.git
-git clone https://github.com/CompVis/taming-transformers
-pip install -e ./taming-transformers
-pip install omegaconf>=2.0.0 pytorch-lightning>=1.0.8 torch-fidelity einops
-mkdir -p models/ldm/cin256-v2/
-wget -O models/ldm/cin256-v2/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/cin/model.ckpt 
+conda create -y --name demo-for-laion python=3.8.12 pip
+conda activate demo-for-laion
+conda install -y -c conda-forge cudatoolkit-dev==11.3.1 cudatoolkit==11.3.1 cudnn==8.2.1.32
+pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
+pip install https://github.com/learning-at-home/hivemind/archive/refs/heads/master.zip
+```
+
 
+### Run server
+```python
+python -m run_server --custom_module_path ./your_code_here.py --expert_cls ExampleModule --hidden_dim 512 \
+   --dht_prefix "enter_name_here" --identity server1.id  --host_maddrs "/ip4/0.0.0.0/tcp/31337"
+# connect extra servers via --initial_peers ADDRESS_PRINTED_BY_ONE_OR_MORE_EXISTNG_PEERS # e.g. /ip4/123.123.123.123/rcp/31337
 ```
 
+### Call remote inference
 
 ```python
-hivemind-server --custom_module_path ./your_code_here.py --expert_cls ExampleModule --hidden_dim 512 --num_experts 1 \
-    --expert_pattern "expert.0.[0:9999]" --identity server1.id
-```
+import torch
+import hivemind
+from client import BalancedRemoteExpert
+dht = hivemind.DHT(
+    initial_peers=['TODO_COPY_ADDRESS_FROM_ONE_OR_MODE_SERVERS'], start=True, client_mode=True
+)
+
+self = BalancedRemoteExpert(dht=dht, uid_prefix="enter_name_here.")
+
+self(torch.randn(1, 512))
+```
+
+
+[
+Based on assorted code by shuf(mryab@ younesbelkada@ borzunov@ timdettmers@ dbaranchuk@ greenfatguy@ artek0chumak@ and hivemind contributors)
+]

remote.py → client.py

@@ -14,6 +14,8 @@ from hivemind.utils import get_logger, nested_compare, nested_flatten, nested_pa
 
 logger = get_logger(__name__)
 
+MAX_NODES = 99999
+
 
 class BalancedRemoteExpert(nn.Module):
     """
@@ -26,7 +28,7 @@ class BalancedRemoteExpert(nn.Module):
         *,
         dht: hivemind.DHT,
         uid_prefix: str,
-        grid_size: Tuple[int, ...],
+        grid_size: Tuple[int, ...] = (1, MAX_NODES),
         forward_timeout: Optional[float] = None,
         backward_timeout: Optional[float] = None,
         update_period: float = 30.0,

run_server.py

@@ -0,0 +1,104 @@
+from functools import partial
+from pathlib import Path
+
+import configargparse
+import torch
+
+from hivemind.moe import Server
+from hivemind.moe.server.layers import schedule_name_to_scheduler
+from hivemind.proto.runtime_pb2 import CompressionType
+from hivemind.utils.limits import increase_file_limit
+from hivemind.utils.logging import get_logger, use_hivemind_log_handler
+
+from client import MAX_NODES
+
+use_hivemind_log_handler("in_root_logger")
+logger = get_logger(__name__)
+
+
+def main():
+    # fmt:off
+    parser = configargparse.ArgParser(default_config_files=["config.yml"])
+    parser.add('-c', '--config', required=False, is_config_file=True, help='config file path')
+
+    parser.add_argument('--dht_prefix', type=str, required=True)
+    parser.add_argument('--expert_cls', type=str, default='ffn', required=False,
+                        help="expert type from test_utils.layers, e.g. 'ffn', 'transformer', 'det_dropout' or 'nop'")
+    parser.add_argument('--hidden_dim', type=int, default=1024, required=False, help='main dimension for expert_cls')
+
+    parser.add_argument('--host_maddrs', type=str, nargs='+', default=['/ip4/0.0.0.0/tcp/0'], required=False,
+                        help='Multiaddrs to listen for external connections from other p2p instances; default: all IPv4 and TCP: /ip4/0.0.0.0/tcp/0')
+    parser.add_argument('--announce_maddrs', type=list, nargs='+', default=None, required=False,
+                        help='Visible multiaddrs the host announces for external connections from other p2p instances')
+
+    parser.add_argument('--num_handlers', type=int, default=None, required=False,
+                        help='server will use this many processes to handle incoming requests')
+    parser.add_argument('--min_batch_size', type=int, default=1,
+                        help='Minimum required batch size for all expert operations')
+    parser.add_argument('--max_batch_size', type=int, default=16384,
+                        help='The total number of examples in the same batch will not exceed this value')
+    parser.add_argument('--device', type=str, default=None, required=False,
+                        help='all experts will use this device in torch notation; default: cuda if available else cpu')
+
+    parser.add_argument('--optimizer', type=str, default='adam', required=False, help='adam, sgd or none')
+    parser.add_argument('--scheduler', type=str, choices=schedule_name_to_scheduler.keys(), default='none',
+                        help='LR scheduler type to use')
+    parser.add_argument('--num_warmup_steps', type=int, required=False,
+                        help='The number of warmup steps for LR schedule')
+    parser.add_argument('--update_period', type=float, required=False, default=30,
+                        help='Server will report experts to DHT once in this many seconds')
+    parser.add_argument('--expiration', type=float, required=False, default=None,
+                        help='DHT entries will expire after this many seconds')
+    parser.add_argument('--num_training_steps', type=int, required=False, help='The total number of steps for LR schedule')
+
+    parser.add_argument('--clip_grad_norm', type=float, required=False, help='Maximum gradient norm used for clipping')
+
+    parser.add_argument('--initial_peers', type=str, nargs='*', required=False, default=[],
+                        help='multiaddrs of one or more active DHT peers (if you want to join an existing DHT)')
+    parser.add_argument('--increase_file_limit', action='store_true',
+                        help='On *nix, this will increase the max number of processes '
+                             'a server can spawn before hitting "Too many open files"; Use at your own risk.')
+    parser.add_argument('--compression', type=str, default='NONE', required=False, help='Tensor compression for gRPC')
+    parser.add_argument('--checkpoint_dir', type=Path, required=False, help='Directory to store expert checkpoints')
+    parser.add_argument('--stats_report_interval', type=int, required=False,
+                        help='Interval between two reports of batch processing performance statistics')
+
+    parser.add_argument('--custom_module_path', type=str, required=False,
+                        help='Path of a file with custom nn.modules, wrapped into special decorator')
+    parser.add_argument('--identity_path', type=str, required=False, help='Path to identity file to be used in P2P')
+
+    # fmt:on
+    args = vars(parser.parse_args())
+    args.pop("config", None)
+    optimizer = args.pop("optimizer")
+    if optimizer == "adam":
+        optim_cls = torch.optim.Adam
+    elif optimizer == "sgd":
+        optim_cls = partial(torch.optim.SGD, lr=0.01)
+    elif optimizer == "none":
+        optim_cls = None
+    else:
+        raise ValueError("optim_cls must be adam, sgd or none")
+
+    args['num_experts'] = 1
+    dht_prefix = args.pop("dht_prefix", None)
+    args['expert_pattern'] = f"{dht_prefix}.0.[0:{MAX_NODES}]"
+
+    if args.pop("increase_file_limit"):
+        increase_file_limit()
+
+    compression_type = args.pop("compression")
+    compression = getattr(CompressionType, compression_type)
+
+    server = Server.create(**args, optim_cls=optim_cls, start=True, compression=compression)
+
+    try:
+        server.join()
+    except KeyboardInterrupt:
+        logger.info("Caught KeyboardInterrupt, shutting down")
+    finally:
+        server.shutdown()
+
+
+if __name__ == "__main__":
+    main()