[megatron_gpt2] checkpoint v3 (#13508)

* [megatron_gpt2] checkpoint v3

* bug fix

* fixes

* switch to default  from  - which is what the current megatron-lm uses

* cleanup

* back compat

src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py

@@ -80,6 +80,22 @@ def convert_megatron_checkpoint(args, input_state_dict, config):
     # The converted output model.
     output_state_dict = {}
 
+    # old versions did not store training args
+    if "args" in input_state_dict:
+        # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
+        train_args = input_state_dict["args"]
+        # from pprint import pprint
+        # pprint(vars(train_args))
+
+        config.vocab_size = train_args.padded_vocab_size
+        config.n_positions = train_args.max_position_embeddings
+        config.n_ctx = train_args.seq_length
+        config.n_embd = train_args.hidden_size
+        config.n_layer = train_args.num_layers
+        config.n_head = train_args.num_attention_heads
+        config.n_inner = train_args.ffn_hidden_size
+        # pprint(config)
+
     # The number of heads.
     heads = config.n_head
     # The hidden_size per head.
@@ -106,9 +122,11 @@ def convert_megatron_checkpoint(args, input_state_dict, config):
     # The position embeddings.
     pos_embeddings = embeddings["position_embeddings"]["weight"]
     # Read the hidden dimension.
-    n_embed = pos_embeddings.size(0)
+    n_embed = pos_embeddings.size(1)
     # DEBUG.
-    assert n_embed == heads * hidden_size_per_head
+    assert (
+        n_embed == heads * hidden_size_per_head
+    ), f"detected mismatch n_embed={n_embed} != heads={heads}*hidden_size_per_head={hidden_size_per_head}"
     # Store the position embeddings.
     output_state_dict["transformer.wpe.weight"] = pos_embeddings
 
@@ -215,7 +233,7 @@ def main():
     parser.add_argument(
         "path_to_checkpoint",
         type=str,
-        help="Path to the ZIP file containing the checkpoint",
+        help="Path to the checkpoint file (.zip archive or direct .pt file)",
     )
     parser.add_argument(
         "--config_file",
@@ -229,10 +247,14 @@ def main():
     basename = os.path.dirname(args.path_to_checkpoint)
 
     # Load the model.
+    # the .zip is very optional, let's keep it for backward compatibility
     print(f"Extracting PyTorch state dictionary from {args.path_to_checkpoint}")
+    if args.path_to_checkpoint.endswith(".zip"):
         with zipfile.ZipFile(args.path_to_checkpoint, "r") as checkpoint:
             with checkpoint.open("release/mp_rank_00/model_optim_rng.pt") as pytorch_dict:
                 input_state_dict = torch.load(pytorch_dict, map_location="cpu")
+    else:
+        input_state_dict = torch.load(args.path_to_checkpoint, map_location="cpu")
 
     # Read the config, or default to the model released by NVIDIA.
     if args.config_file == "":
@@ -245,7 +267,7 @@ def main():
             n_layer=24,
             n_head=16,
             n_inner=4096,
-            activation_function="gelu_new",
+            activation_function="gelu",  # used to be "gelu_new" in earlier versions
             resid_pdrop=0.1,
             embd_pdrop=0.1,
             attn_pdrop=0.1,