Bug Fixes: Small batch barrier and Broken model loading

src/instructlab/training/multipack_sampler.py

@@ -204,6 +204,10 @@ def find_packing_max_batch_len_and_grad_accum(
     while packing_max_batch_len > max_batch_len_per_gpu:
         grad_accum += 1
         total_micro_batch = (effective_batch_size / grad_accum) / num_gpus
+        if int(avg_sample_len * total_micro_batch) < dataset.get_lengths().max():
+            raise RuntimeError(
+                f"Effective batch size is too low for multipack sampling, max sample length={dataset.get_lengths().max()} and min packing length={int(avg_sample_len * total_micro_batch)}"
+            )
         if is_padding:
             addition = find_padding_max_batch_len_addition(
                 avg_sample_len,

src/instructlab/training/utils.py

@@ -483,26 +483,25 @@ class UniversalCheckpointArgs:
 
 @contextmanager
 def ensure_loadable_granite_checkpoint(model_name_or_path: str):
-    if not dist.is_initialized() or dist.get_rank() == 0:
-        try:
-            GPTDolomiteConfig.from_pretrained(model_name_or_path)
-            yield model_name_or_path
-        except:  # pylint: disable=bare-except
-            log_rank_0(
-                f"\033[93mModel saved in {model_name_or_path} requires conversion \033[0m",
-                to_print=True,
-            )
-            # if the load failed then it must not be a granite
-            # for now just assume its a llama
-            # with TemporaryDirectory("w") as tmpdir:
-            # make a temp directory name, but do not create it
-            tmpdir = mktemp()
+    try:
+        GPTDolomiteConfig.from_pretrained(model_name_or_path)
+        yield model_name_or_path
+    except:  # pylint: disable=bare-except
+        log_rank_0(
+            f"\033[93mModel saved in {model_name_or_path} requires conversion \033[0m",
+            to_print=True,
+        )
+        # if the load failed then it must not be a granite
+        # for now just assume its a llama
+        # with TemporaryDirectory("w") as tmpdir:
+        # make a temp directory name, but do not create it
+        tmpdir = mktemp()
+        if not dist.is_initialized() or dist.get_rank() == 0:
             import_from_huggingface(model_name_or_path, tmpdir)
-            yield tmpdir
-            shutil.rmtree(tmpdir, ignore_errors=True)
-
-    if dist.is_initialized():
-        dist.barrier()
+        if dist.is_initialized():
+            dist.barrier()
+        yield tmpdir
+        shutil.rmtree(tmpdir, ignore_errors=True)
 
 
 # this function is for supporting gradient checkpointing for padding free