add direct meg-ds to hf format script

tools/convert_checkpoint/deepspeed_to_transformers.py

@@ -0,0 +1,81 @@
+import os
+import torch
+import json
+
+from deepspeed_checkpoint import DeepSpeedCheckpoint
+from deepspeed_to_megatron import _create_rank_checkpoint, parse_arguments
+
+# the import was tested to work with this version
+# https://github.com/huggingface/transformers/commit/0af901e83 if it diverges we may consider
+# copying that version here instead
+from transformers.models.megatron_gpt2.convert_megatron_gpt2_checkpoint import convert_megatron_checkpoint
+from transformers import GPT2Config
+
+def main():
+
+    # this first part comes mainly from deepspeed_to_megatron.main
+    args = parse_arguments()
+    print(f'Converting DeepSpeed checkpoint in {args.input_folder} to HF Transformers checkpoint in {args.output_folder}')
+
+    ds_checkpoint = DeepSpeedCheckpoint(args.input_folder, args.target_tp, args.target_pp)
+    iteration = ds_checkpoint.get_iteration()
+    input_state_dict = _create_rank_checkpoint(ds_checkpoint, 0, 0, args.for_release)
+
+    # the 2nd part comes from transformers.models.megatron_gpt2.convert_megatron_gpt2_checkpoint.main
+    # Spell out all parameters in case the defaults change.
+    config = GPT2Config(
+        vocab_size=50257,
+        n_positions=1024,
+        n_ctx=1024,
+        n_embd=1024,
+        n_layer=24,
+        n_head=16,
+        n_inner=4096,
+        activation_function="gelu",  # used to be "gelu_new" in earlier versions
+        resid_pdrop=0.1,
+        embd_pdrop=0.1,
+        attn_pdrop=0.1,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        summary_type="cls_index",
+        summary_use_proj=True,
+        summary_activation=None,
+        summary_proj_to_labels=True,
+        summary_first_dropout=0.1,
+        scale_attn_weights=True,
+        gradient_checkpointing=False,
+        use_cache=True,
+        bos_token_id=50256,
+        eos_token_id=50256,
+    )
+
+    # Convert.
+    print("Converting to HF Checkpoint")
+    output_state_dict = convert_megatron_checkpoint(args, input_state_dict, config)
+
+    basename = args.output_folder
+    os.makedirs(basename, exist_ok=True)
+
+    # Print the structure of converted state dict.
+    #if args.print_checkpoint_structure:
+    #    recursive_print(None, output_state_dict)
+
+    # Store the config to file.
+    output_config_file = os.path.join(basename, "config.json")
+    output_config = config.to_dict()
+    output_config["architectures"] = ["GPT2LMHeadModel"]
+    output_config["model_type"] = "gpt2"
+    print(f'Saving config to "{output_config_file}"')
+    with open(output_config_file, "w") as f:
+        json.dump(output_config, f)
+
+    # Store the state_dict to file.
+    output_checkpoint_file = os.path.join(basename, "pytorch_model.bin")
+    print(f'Saving checkpoint to "{output_checkpoint_file}"')
+    torch.save(output_state_dict, output_checkpoint_file)
+
+    print("Now add tokenizer files and upload to the hub")
+
+
+if __name__ == "__main__":
+    main()