Add meta_init, enable it as default init process (#84)

This PR enables meta_init functionality to avoid OOM'ing on cpu for
larger models.
The core functionality is in meta_init.py, and a few changes in
parallelization and train.py.
Key items:
1 - this is largely the same as the earlier PR I had for meta_init, but
I did a new one b/c faster than reworking it with all the interim
changes.
2 - to address feedback in previous PR:
a - why do we need meta_init.py, can't we just do:
~~~
with torch.device("meta"):
    model = Model.from_args(...)
~~~
Unfortunately this does not work b/c the rope embeddings are treated
differently (buffer) and thus the simple lambda call from param_init_fn
in FSDP (lambda module: module.to_device('cuda') ) will not invoke or
move the rope embeddings and the model will fail on first forward.
This issue relates to the nn.embeddings not being moved, and that the
device is referenced in the forward pass for the current rope class.
Have opened #110 to track
this and investigate while not holding up meta init that is working from
landing.

b - per earlier feedback - meta init is now 'not optional' but simply
the default. This should ensure all models leverage it and ensure we
aren't missing things for future meta_init aspects.

3 - misc change - I switched the model_params to just do the normal all
params count instead of 'unique params' b/c it does not mesh with what
people perceive model size as.

Testing:
tested both debugmodel and 26B model with and without meta init to
confirm same loss curves.
Note for future reference - if you get a bad init (meta init failure)
you will simply not train (loss is same every iter).
If you fail to call reset params after FSDP, then you will train (b/c we
default to torch.randn_like) but your starting loss will be 5x+ higher
(telling you that you have not properly init'ed the model).

torchtrain/meta_init.py

@@ -0,0 +1,48 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+from contextlib import contextmanager
+
+import torch
+from torch import nn
+from torch.distributed.fsdp._common_utils import _is_fsdp_flattened
+
+
+@contextmanager
+def meta_model_init():
+    """init model on meta device"""
+    saved_register_parameter = nn.Module.register_parameter
+    saved_register_buffer = nn.Module.register_buffer
+
+    def register_meta_param(module, name, param):
+        saved_register_parameter(module, name, param)
+        if param is not None:
+            param_cls = type(module._parameters[name])
+            kwargs = module._parameters[name].__dict__
+            module._parameters[name] = param_cls(
+                module._parameters[name].to(torch.device("meta")), **kwargs
+            )
+
+    def register_meta_buffer(module, name, buffer):
+        saved_register_buffer(module, name, buffer)
+        if buffer is not None:
+            module._buffers[name] = module._buffers[name].to(torch.device("meta"))
+
+    try:
+        nn.Module.register_parameter = register_meta_param
+        nn.Module.register_buffer = register_meta_buffer
+        yield
+    finally:
+        nn.Module.register_parameter = saved_register_parameter
+        nn.Module.register_buffer = saved_register_buffer
+
+
+@torch.no_grad()
+def meta_to_real_init_fn(module: nn.Module):
+    for submodule in module.modules():
+        for param_name, param in submodule.named_parameters(recurse=False):
+            if not _is_fsdp_flattened(param) and param.is_meta:
+                materialized_param = nn.Parameter(
+                    torch.randn_like(param, device=torch.device("cuda"))
+                )
+                setattr(submodule, param_name, materialized_param)

torchtrain/metrics.py

@@ -193,8 +193,8 @@ def get_num_params(model: nn.Module, only_trainable: bool = False) -> int:
     param_list = list(model.parameters())
     if only_trainable:
         param_list = [p for p in param_list if p.requires_grad]
-    unique_params = {p.data_ptr(): p for p in param_list}.values()
-    return sum(p.numel() for p in unique_params)
+    # unique_params = {p.data_ptr(): p for p in param_list}.values()
+    return sum(p.numel() for p in param_list)
 
 
 class MetricLogger:

torchtrain/models/llama/__init__.py

@@ -7,10 +7,11 @@
 
 llama_configs = {
     "debugmodel": ModelArgs(dim=256, n_layers=2, n_heads=16),
-    "1B": ModelArgs(dim=1024, n_layers=16, n_heads=8),
+    "271M": ModelArgs(dim=1024, n_layers=16, n_heads=8),
+    "1B": ModelArgs(dim=2048, n_layers=18, n_heads=16),
     "7B": ModelArgs(dim=4096, n_layers=32, n_heads=32),
     "13B": ModelArgs(dim=5120, n_layers=40, n_heads=40),
-    "40B": ModelArgs(dim=5120, n_layers=80, n_heads=40),
+    "26B": ModelArgs(dim=5120, n_layers=80, n_heads=40),
     "70B": ModelArgs(
         dim=8192,
         n_layers=80,

torchtrain/models/llama/model.py

@@ -47,7 +47,9 @@ def __init__(self, dim: int, eps: float = 1e-6):
         super().__init__()
         self.eps = eps
         self.weight = nn.Parameter(torch.empty(dim))
-        self.reset_parameters()
+
+        # re-enable if not using meta-init
+        # self.reset_parameters()
 
     def _norm(self, x: torch.Tensor):
         """
@@ -466,7 +468,14 @@ def __init__(self, model_args: ModelArgs):
         self.output = nn.Linear(model_args.dim, model_args.vocab_size, bias=False)
 
         # init model weights
-        self.reset_parameters()
+
+        # we are doing meta_init, which will call reset_parameters() after
+        # the model is moved to actual device.
+        # If you modify and are not using meta_init, you will need to call
+        # reset_parameters() manually as below:
+
+        # self.reset_parameters()
+
         rank0_log(f"Model built with: {self.model_args}")
 
     def reset_parameters(

torchtrain/parallelisms/parallelize_llama.py

@@ -35,6 +35,7 @@
 )
 from torchtrain.config_manager import JobConfig
 from torchtrain.logging_utils import rank0_log
+from torchtrain.meta_init import meta_to_real_init_fn
 
 logger = logging.getLogger(__name__)
 
@@ -193,6 +194,7 @@ def parallelize_llama(model, world_mesh, parallel_dims, job_config: JobConfig):
     if parallel_dims.dp_enabled:
         dp_mesh = world_mesh["dp"] if world_mesh.ndim > 1 else world_mesh
         assert dp_mesh.mesh_dim_names == ("dp",), dp_mesh.mesh_dim_names
+
         fsdp_config = {
             "mixed_precision": MixedPrecision(
                 param_dtype=torch.bfloat16,
@@ -204,12 +206,11 @@ def parallelize_llama(model, world_mesh, parallel_dims, job_config: JobConfig):
             # When torch.compile is active, it requires us to set use_orig_params=True
             "use_orig_params": True,
             "device_mesh": dp_mesh,
+            "param_init_fn": meta_to_real_init_fn,
         }
 
         with enable_wrap(wrapper_cls=FSDP, **fsdp_config):
             for layer_id, transformer_block in enumerate(model.layers):
-                # before wrapping with FSDP, we need to make sure the layer is on GPU
-                transformer_block = transformer_block.cuda()
 
                 # apply selective AC
                 transformer_block = checkpoint_wrapper(
@@ -220,10 +221,13 @@ def parallelize_llama(model, world_mesh, parallel_dims, job_config: JobConfig):
                 model.layers[layer_id] = wrap(transformer_block)
 
             # wrap the rest layers with FSDP
-            model = wrap(model.cuda())
+            model = wrap(model)
 
         rank0_log("Applied FSDP to the model...")
+    else:
+        model.cuda()
 
-    # redundant if FSDP is enabled, but ensure the model is on device regardless of which parallelisms were used
-    model.cuda()
+    # we have now moved from meta to device,
+    # reset parameters for proper initialization
+    model.reset_parameters()
     return model

train.py

@@ -22,6 +22,7 @@
 from torchtrain.datasets import create_tokenizer, dataloader_fn
 from torchtrain.logging_utils import init_logger, rank0_log
 from torchtrain.lr_scheduling import get_lr_scheduler
+from torchtrain.meta_init import meta_model_init
 from torchtrain.metrics import build_metric_logger, get_num_params, GPUMemoryMonitor
 
 from torchtrain.models import model_name_to_cls, model_name_to_tokenizer, models_config
@@ -115,15 +116,17 @@ def main(job_config: JobConfig):
     )
 
     # build model
-    # TODO: add meta initialization
     model_cls = model_name_to_cls[model_name]
     model_config = models_config[model_name][job_config.model.flavor]
     model_config.vocab_size = tokenizer.n_words
 
-    model = model_cls.from_model_args(model_config)
+    # build model using meta init
+    with meta_model_init():
+        model = model_cls.from_model_args(model_config)
 
     # log model size
     model_param_count = get_num_params(model)
+
     if _is_local_logging:
         rank0_log(
             f"{Color.blue}Model {model_name} {job_config.model.flavor} {Color.red}size: {model_param_count:,}"