Checkpointing and retrieval

src/config/base.py

@@ -45,4 +45,6 @@ def parse_args(base_parser, args, namespace):
     # Distributed args
     parser.add_argument('--distributed_backend', default=None, type=str, required=False,
                         choices=distributed.registered_backends())  # distributed backend type
+    parser.add_argument('--save_checkpoint_freq', default=None, type=int, required=False)
+
     return parser.parse_args(args, namespace)

src/config/sparse.py

@@ -47,4 +47,6 @@ def parse_args(base_parser, args, namespace):
     # Distributed args
     parser.add_argument('--distributed_backend', default=None, type=str, required=False,
                         choices=distributed.registered_backends())  # distributed backend type
+    parser.add_argument('--save_checkpoint_freq', default=None, type=int, required=False)
+
     return parser.parse_args(args, namespace)

src/main.py

@@ -112,6 +112,28 @@ def main(args):
     elif os.path.isfile(os.path.join(ckpt_path, "summary.json")): # the experiment was already completed
         print(f"Already found experiment '{ckpt_path}'.\nSkipping.")
         sys.exit(0)
+    itr = 0
+    rng_state_dict = None
+    distributed_backend.sync()
+    checkpoints = [file for file in os.listdir(ckpt_path) if 'ckpt_' in file]
+    if checkpoints:
+        last_ckpt_path = sorted(checkpoints)[-1]
+        print(f"Training interrupted, resuming from {last_ckpt_path}")
+        checkpoint = torch.load(os.path.join(ckpt_path, last_ckpt_path))
+        model_state_dict = {k.replace("_orig_mod.", ""):v for k,v in checkpoint['model'].items()}
+        # FIXME checkpoints from compiled model have _orig_mod keyword
+
+        optimizer_state_dict = checkpoint['optimizer']
+        rng_state_dict = {
+            module: checkpoint[module] for module in ["cpu_rng_state", "gpu_rng_state", "numpy_rng_state", "py_rng_state"]
+        }
+
+        model.load_state_dict(model_state_dict) 
+        opt.load_state_dict(optimizer_state_dict)
+        itr=checkpoint['itr']
+        if not scheduler is None:
+            scheduler_state_dict = checkpoint['scheduler']
+            scheduler.load_state_dict(scheduler_state_dict)
 
     if args.model == 'base': # all train functions have the same interface
         train = train_base
@@ -125,7 +147,7 @@ def main(args):
     stats = train(model, opt, data, args.data_seed, scheduler, args.iterations, args.acc_steps, args.batch_size, args.sequence_length, 
                   eval_freq=args.eval_freq, 
                   distributed_backend=distributed_backend,
-                  ckpt_path=f"{ckpt_path}/ckpt.pt", extra_args=args)
+                  ckpt_path=f"{ckpt_path}/ckpt.pt", itr=itr, rng_state_dict=rng_state_dict, extra_args=args)
 
     args.device = None
     args.dtype = None

src/optim/base.py

@@ -7,15 +7,17 @@
 import time 
 import itertools
 import copy
-
+import random
+import os
+import numpy as np
 from .utils import eval, get_batch, save_checkpoint
 
 
-def train_base(model, opt, data, data_seed, scheduler, iterations, acc_steps, batch_size, sequence_length, eval_freq, ckpt_path, distributed_backend, extra_args):
+def train_base(model, opt, data, data_seed, scheduler, iterations, acc_steps, batch_size, sequence_length, eval_freq, ckpt_path, distributed_backend,extra_args, itr=0,rng_state_dict=None):
     device_type = 'cuda' if 'cuda' in str(extra_args.device) else 'cpu'
     type_ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(
         device_type=device_type, dtype=torch.bfloat16)  # extra_args.dtype)
-    itr, substep, best_val_loss, text_table = 0, 0, float('inf'), None # best_val_loss not used atm, early stopping not recommended but possible 
+    substep, best_val_loss, text_table = 0, float('inf'), None # best_val_loss not used atm, early stopping not recommended but possible 
 
     data["train"], train_sampler = get_dataloader(
         data["train"],
@@ -49,7 +51,13 @@ def train_base(model, opt, data, data_seed, scheduler, iterations, acc_steps, ba
 
     t0 = time.time()
     train_epochs = 0
+    if not rng_state_dict is None:
+            torch.set_rng_state(rng_state_dict["cpu_rng_state"])
+            torch.cuda.set_rng_state(rng_state_dict["gpu_rng_state"])
+            np.random.set_state(rng_state_dict["numpy_rng_state"])
+            random.setstate(rng_state_dict["py_rng_state"])
     while itr < iterations:
+
         for microstep_idx in range(acc_steps):  # gradient accumulation
             x, y = get_batch(data_train_iter, device=extra_args.device)
             with type_ctx:
@@ -122,7 +130,20 @@ def train_base(model, opt, data, data_seed, scheduler, iterations, acc_steps, ba
 
                 model.train()
                 t0 = time.time()
-
+        if distributed_backend.is_master_process():
+            if extra_args.save_checkpoint_freq is not None and itr % extra_args.save_checkpoint_freq == 0:
+                print(f"saving checkpoint to {ckpt_path}/ckpt_{itr}.pt")
+                save_checkpoint(distributed_backend=distributed_backend,
+                                model=model,
+                                opt=opt,
+                                scheduler=scheduler,
+                                itr=itr,
+                                cpu_rng_state=torch.get_rng_state(),
+                                gpu_rng_state=torch.cuda.get_rng_state(),
+                                numpy_rng_state=np.random.get_state(),
+                                py_rng_state=random.getstate(),
+                                ckpt_path=os.path.join(os.path.dirname(ckpt_path), f"ckpt_{itr}.pt"))
+
     if distributed_backend.is_master_process():
         print(f"saving checkpoint to {ckpt_path}")
         save_checkpoint(distributed_backend=distributed_backend,

src/optim/sparse.py

@@ -5,15 +5,16 @@
 import wandb
 import time 
 import copy
-
+import numpy as np
+import random
 from .utils import eval_sparse, get_batch, eval_sweep_dropk, save_checkpoint
 
 
-def train_sparse(model, opt, data, data_seed, scheduler, iterations, acc_steps, batch_size, sequence_length, eval_freq, ckpt_path, distributed_backend, extra_args):
+def train_sparse(model, opt, data, data_seed, scheduler, iterations, acc_steps, batch_size, sequence_length, eval_freq, ckpt_path, distributed_backend, extra_args, itr=0, rng_state_dict=None):
     device_type = 'cuda' if 'cuda' in str(extra_args.device) else 'cpu'
     type_ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(
         device_type=device_type, dtype=torch.bfloat16)  # extra_args.dtype)
-    itr, substep, best_val_loss, text_table, sparsity_plot = 0, 0, float('inf'), None, None # best_val_loss not used atm, early stopping not recommended but possible 
+    substep, best_val_loss, text_table, sparsity_plot = 0, float('inf'), None, None # best_val_loss not used atm, early stopping not recommended but possible 
     data["train"] = get_dataloader(
         data["train"],
         sequence_length=sequence_length,
@@ -42,7 +43,14 @@ def train_sparse(model, opt, data, data_seed, scheduler, iterations, acc_steps,
     model.train()
 
     t0 = time.time()
+    if not rng_state_dict is None:
+            torch.set_rng_state(rng_state_dict["cpu_rng_state"])
+            torch.cuda.set_rng_state(rng_state_dict["gpu_rng_state"])
+            np.random.set_state(rng_state_dict["numpy_rng_state"])
+            random.setstate(rng_state_dict["py_rng_state"])
+
     while itr < iterations:
+
         for microstep_idx in range(acc_steps):  # gradient accumulation
             x, y = get_batch(data_train_iter, device=extra_args.device)
             with type_ctx:
@@ -129,6 +137,20 @@ def train_sparse(model, opt, data, data_seed, scheduler, iterations, acc_steps,
 
                 model.train()
                 t0 = time.time()
+        if distributed_backend.is_master_process():
+            if extra_args.save_checkpoint_freq is not None and itr % extra_args.save_checkpoint_freq == 0:
+                print(f"saving checkpoint to {ckpt_path}/ckpt_{itr}.pt")
+                save_checkpoint(distributed_backend=distributed_backend,
+                                model=model,
+                                opt=opt,
+                                scheduler=scheduler,
+                                itr=itr,
+                                cpu_rng_state=torch.get_rng_state(),
+                                gpu_rng_state=torch.cuda.get_rng_state(),
+                                numpy_rng_state=np.random.get_state(),
+                                py_rng_state=random.getstate(),
+                                ckpt_path=f"{ckpt_path}/ckpt_{itr}.pt")
+
 
     if distributed_backend.is_master_process():
         print(f"saving checkpoint to {ckpt_path}")