Added OLMoModel Class and config.architecture detection, and temporar…

…y fake layernorm

src/python/py/models/builder.py

@@ -995,7 +995,13 @@ def make_layernorm(self, layer_id, layernorm, skip, simple, location):
         skip_input = self.layernorm_attrs["skip_input"]
 
         weight = f"model.layers.{layer_id}.{location}_layernorm.weight"
+        #ShaneTim
+        if layernorm.weight is None:
+            layernorm.weight = torch.ones(2048)
         self.make_external_tensor(layernorm.weight.detach().numpy().astype(self.to_numpy_dtype[self.io_dtype]) + self.layernorm_attrs["add_offset"], weight)
+        #ShaneTim
+        if layernorm.bias is None:
+            layernorm.bias = torch.ones(2048)
         bias = f"model.layers.{layer_id}.{location}_layernorm.bias"
         if not simple:
             self.make_external_tensor(layernorm.bias.detach().numpy().astype(self.to_numpy_dtype[self.io_dtype]), bias)
@@ -3040,6 +3046,10 @@ def make_layer(self, layer_id, layer):
         layer.self_attn = layer.self_attn if hasattr(layer, 'self_attn') else layer.self_attention
         super().make_layer(layer_id, layer)
 
+class OLMoModel(Model):
+    def __init__(self, config, io_dtype, onnx_dtype, ep, cache_dir, extra_options):
+        super().__init__(config, io_dtype, onnx_dtype, ep, cache_dir, extra_options)
+
 def check_extra_options(kv_pairs):
     if "int4_op_types_to_quantize" in kv_pairs:
         op_types_to_quantize = ()
@@ -3144,6 +3154,8 @@ def create_model(model_name, input_path, output_dir, precision, execution_provid
             # Quantized ChatGLM model has ChatGLMForConditionalGeneration as architecture whereas HF model as the latter
             config.hidden_act = "swiglu"
             onnx_model = ChatGLMModel(config, io_dtype, precision, execution_provider, cache_dir, extra_options)
+        elif config.architectures[0] == "OlmoForCausalLM":
+            onnx_model = OLMoModel(config, io_dtype, precision, execution_provider, cache_dir, extra_options)
         else:
             raise NotImplementedError(f"The {hf_name} model is not currently supported.")