Merge branch 'trintamaki/yi-support' into 'main'

Experimental Yi conversion support

See merge request ADLR/megatron-lm!1534

docs/llama_mistral.md

@@ -1,4 +1,4 @@
-# Llama and Mistral support in Megatron-LM
+# Llama, Mistral and other Llama-like model support in Megatron-LM
 
 NOTE: Llama-3 and Mistral support in Megatron is currently experimental and we are still evaluting benchmark results to confirm model conversion, training and inference correctness.
 
@@ -386,6 +386,12 @@ If loading for either inference or finetuning, use the following arguments:
 --attention-softmax-in-fp32
 ```
 
-# Benchmark results
+## Benchmark results
 
 Mistral-7B support in Megatron is currently experimental and we are still carrying out benchmark evaluations.
+
+# Other Llama-like model support
+
+*Note: Experimental*
+
+Many models such as Yi-34B use the Llama architecture and may be converted from HuggingFace to Megatron using the commands in [Llama3](#llama-3).

tools/checkpoint/loader_llama_mistral.py

@@ -19,7 +19,7 @@ def add_arguments(parser):
 
     # TODO(jbarker): Need assertion to make sure *exactly* one of these is used
     parser.add_argument('--model-size', type=str, required=True,
-                        choices=['llama2-7B', 'llama2-13B', 'llama2-70B', 'llama2-7Bf', 'llama2-13Bf', 'llama2-70Bf', 'llama3-8B', 'llama3-70B', 'llama3-8Bf', 'llama3-70Bf', 'mistral-7B', 'mistral-7Bf'],
+                        choices=['llama2-7B', 'llama2-13B', 'llama2-70B', 'llama2-7Bf', 'llama2-13Bf', 'llama2-70Bf', 'llama3-8B', 'llama3-70B', 'llama3-8Bf', 'llama3-70Bf', 'mistral-7B', 'mistral-7Bf', 'yi-34B'],
                         help='Model size can be `llama2-7B`, `llama2-13B`, `llama2-70B`, `llama3-8B`, `llama3-70B`, `mistral-7B` (for pretrained models), '
                         'and `llama2-7Bf`, `llama2-13Bf`, `llama2-70Bf`, `llama3-8Bf`, `llama3-70bf` and `mistral-7Bf` (for chat-finetuned models).')
     parser.add_argument('--checkpoint-type', type=str, required=True,
@@ -58,6 +58,7 @@ def verify_transformers_version():
     "llama3-70Bf": 8,
     "mistral-7B": 1,
     "mistral-7Bf": 1,
+    "yi-34B": 8,
 }
 
 
@@ -394,7 +395,7 @@ def load_checkpoint_to_model(args):
     '''Set model params.'''
 
     from pretrain_gpt import model_provider
-    if "llama" in args.model_size:
+    if "llama" in args.model_size or "yi" in args.model_size:
         from transformers import LlamaForCausalLM as ModelForCausalLM
     elif "mistral" in args.model_size:
         from transformers import MistralForCausalLM as ModelForCausalLM
@@ -465,7 +466,7 @@ def _load_checkpoint(queue, args):
     margs.tokenizer_model = args.tokenizer_model
     load_args_from_checkpoint(margs)
 
-    if "llama2" in args.model_size:
+    if "llama2" in args.model_size or "yi" in args.model_size:
         margs.tokenizer_type = "Llama2Tokenizer"
     elif "llama3" in args.model_size:
         margs.tokenizer_type = "Llama3Tokenizer"