add nemotron5 conversion

examples/nlp/language_modeling/conf/megatron_mamba_config.yaml

@@ -76,7 +76,8 @@ model:
   post_process: True # add pooler
   megatron_legacy: False
   persist_layer_norm: True
-
+  squared_relu_activation: True
+  params_dtype: bf16
   tokenizer:
     library: 'huggingface'
     type: 'EleutherAI/gpt-neox-20b' 
@@ -87,7 +88,7 @@ model:
     use_fast: True
 
   # Distributed checkpoint setup
-  dist_ckpt_format: 'zarr' # Set to 'torch_dist' to use PyTorch distributed checkpoint format.
+  dist_ckpt_format: 'torch_dist' # Set to 'torch_dist' to use PyTorch distributed checkpoint format.
   dist_ckpt_load_on_device: True # whether to load checkpoint weights directly on GPU or to CPU
   dist_ckpt_parallel_save: False # if true, each worker will write its own part of the dist checkpoint
 

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

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import torch
+import torch.nn.functional as F
 from omegaconf.dictconfig import DictConfig
 from pytorch_lightning.trainer.trainer import Trainer
 
@@ -38,6 +39,8 @@
 
     HAVE_MEGATRON_CORE = False
 
+def squared_relu(x):
+    return torch.pow(F.relu(x), 2)
 
 class MegatronMambaModel(MegatronGPTModel):
     """
@@ -62,6 +65,15 @@ def model_provider_func(self, pre_process, post_process):
         self.transformer_config.add_bias_linear = self.cfg.get('add_bias_linear', False)
         self.transformer_config.gated_linear_unit = self.cfg.get('gated_linear_unit', False)
         self.transformer_config.layernorm_epsilon = self.cfg.get('layernorm_epsilon', 1e-5)
+        if self.cfg.get('params_dtype'):
+            self.transformer_config.params_dtype = torch.bfloat16
+        else:
+            self.transformer_config.params_dtype = torch.float32
+        self.transformer_config.params_dtype=torch.bfloat16
+        if self.cfg.get('kv_channels'):
+            self.transformer_config.kv_channels = self.cfg.get('kv_channels')
+        if self.cfg.get('squared_relu_activation'):
+            self.transformer_config.activation_func = squared_relu
 
         model = MambaModel(
             config=self.transformer_config,