Fixes for MoE parameter passing & use of AutoTokenizer/Model for mist…

…ral. (NVIDIA#8272)

Signed-off-by: Alexandros Koumparoulis <[email protected]>

nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py

@@ -1702,7 +1702,7 @@ def build_transformer_config(self) -> TransformerConfig:
             'fp8': fp8,
             'tp_comm_overlap': ub_tp_comm_overlap,
             # MoE related
-            'num_experts': self.cfg.get('num_experts', None),
+            'num_moe_experts': self.cfg.get('num_moe_experts', None),
             'moe_router_load_balancing_type': self.cfg.get('moe_router_load_balancing_type', 'aux_loss'),
             'moe_router_topk': self.cfg.get('moe_router_topk', 2),
             'moe_grouped_gemm': self.cfg.get('moe_grouped_gemm', False),
@@ -1713,11 +1713,11 @@ def build_transformer_config(self) -> TransformerConfig:
             'moe_input_jitter_eps': self.cfg.get('moe_input_jitter_eps', None),
             'moe_token_dropping': self.cfg.get('moe_token_dropping', False),  # TODO: Support token dropping.
         }
-        if model_specific_configs['num_experts'] is not None:
+        if model_specific_configs['num_moe_experts'] is not None:
             assert mcore_supports_moe(), 'Megatron-core >= v0.5.0 is required for MoE'
         elif not mcore_supports_moe():
-            if 'num_experts' in model_specific_configs:
-                del model_specific_configs['num_experts']
+            if 'num_moe_experts' in model_specific_configs:
+                del model_specific_configs['num_moe_experts']
             moe_keys = list(filter(lambda x: x.startswith('moe_'), model_specific_configs.keys()))
             for k in moe_keys:
                 del model_specific_configs[k]

scripts/nlp_language_modeling/convert_hf_mistral_7b_to_nemo.py

@@ -32,7 +32,7 @@
 from omegaconf import OmegaConf
 from pytorch_lightning.core.saving import _load_state as ptl_load_state
 from pytorch_lightning.trainer.trainer import Trainer
-from sentencepiece import SentencePieceProcessor
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
 from nemo.collections.nlp.parts.nlp_overrides import (
@@ -127,23 +127,17 @@ def load_config(mistral_config, tokenizer_path):
     return nemo_config
 
 
-def load_mistral_ckpt(dir):
-    params_file = os.path.join(dir, 'config.json')
+def load_mistral_ckpt(in_dir):
+    params_file = os.path.join(in_dir, 'config.json')
     assert os.path.exists(params_file)
     with open(params_file, 'r') as fp:
         model_args = json.load(fp)
 
-    ckpt = OrderedDict()
-    ckpt['state_dict'] = OrderedDict()
-    for i in range(2):
-        ckpt_file = f'pytorch_model-0000{i+1}-of-00002.bin'
-        ckpt_path = os.path.join(dir, ckpt_file)
-        assert os.path.exists(ckpt_path)
-        ckpt.update(torch.load(ckpt_path))
-    tokenizer_file = os.path.join(dir, 'tokenizer.model')
-    assert os.path.exists(tokenizer_file)
-    tokenizer = SentencePieceProcessor(model_file=tokenizer_file)
-    assert tokenizer.get_piece_size() == model_args['vocab_size']
+    model = AutoModelForCausalLM.from_pretrained(in_dir)
+    ckpt = model.state_dict()
+
+    tokenizer = AutoTokenizer.from_pretrained(in_dir)
+    assert tokenizer.vocab_size == model_args['vocab_size']
     return model_args, ckpt, tokenizer
 
 

scripts/nlp_language_modeling/convert_hf_mixtral_to_nemo.py

@@ -81,6 +81,9 @@ def load_model(cls, checkpoint, strict, **kwargs):
 
             # register the artifacts
             cfg = checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY]
+            assert os.path.exists(
+                cfg.tokenizer.model
+            ), f"Expected cfg.tokenizer.model {cfg.tokenizer.model} to be present"
             if cfg.tokenizer.model is not None:
                 model.register_artifact("tokenizer.tokenizer_model", cfg.tokenizer.model)
             if cfg.tokenizer.vocab_file is not None:
@@ -110,8 +113,8 @@ def load_config(mixtral_config, tokenizer_path):
     if 'num_key_value_heads' in mixtral_config:
         nemo_config.num_query_groups = mixtral_config['num_key_value_heads']
 
-    nemo_config.num_experts = int(mixtral_config['num_local_experts'])
-    assert nemo_config.num_experts > 0, "num_experts must be greater than zero."
+    nemo_config.num_moe_experts = int(mixtral_config['num_local_experts'])
+    assert nemo_config.num_moe_experts > 0, "num_experts must be greater than zero."
     nemo_config.moe_router_topk = int(mixtral_config['num_experts_per_tok'])
     assert nemo_config.moe_router_topk > 0, "moe_router_topk must be greater than zero."
     nemo_config.use_cpu_initialization = True
@@ -266,7 +269,7 @@ def convert(args):
             raise Exception("not implemented")
         checkpoint['state_dict'][moe_gate_name] = param_to_weights(moe_gate)
         # Handle experts
-        for i in range(nemo_config.num_experts):
+        for i in range(nemo_config.num_moe_experts):
             gate_proj = ckpt[f'model.layers.{l}.block_sparse_moe.experts.{i}.w1.weight']
             up_proj = ckpt[f'model.layers.{l}.block_sparse_moe.experts.{i}.w3.weight']
             if mcore_gpt:

scripts/nlp_language_modeling/convert_starcoder_hf_to_nemo.py

@@ -194,6 +194,7 @@ def get_new_key(old_key):
     convert_dict = convert_state_dict(state_dict_hf, amp=omega_cfg.megatron_amp_O2)
 
     logging.info("Creating Megatron model...")
+    omega_cfg.cpu_offloading_num_layers = 0
     model = load_state_dict_helper(MegatronGPTModel, omega_cfg, trainer, convert_dict)
     logging.info(f"Created model:\n{model}")