fix: modify model config and add Qwen2RMSNorm

colossalai/shardformer/layer/normalization.py

@@ -276,7 +276,7 @@ class FusedRMSNorm(BaseLayerNorm):
 
         LazyInitContext.materialize(module)
         # to check if it is huggingface LlamaRMSNorm or MistralRMSNorm
-        if module.__class__.__name__ in ["LlamaRMSNorm", "MistralRMSNorm"]:
+        if module.__class__.__name__ in ["LlamaRMSNorm", "Qwen2RMSNorm", "MistralRMSNorm"]:
             normalized_shape = module.weight.shape[0]
             eps = module.variance_epsilon
             elementwise_affine = True

tests/kit/model_zoo/transformers/qwen2.py

@@ -0,0 +1,87 @@
+import torch
+import transformers
+
+from ..registry import ModelAttribute, model_zoo
+
+try:
+    from transformers import Qwen2Config
+
+    HAS_QWEN2 = True
+except ImportError:
+    HAS_QWEN2 = False
+
+if HAS_QWEN2:
+    # ===============================
+    # Register Qwen2
+    # ===============================
+
+    def data_gen():
+        # the input ids are corresponding to the sentence
+        # 'Hello, my dog is cute'
+        #
+        # the code is give below:
+        # -----------------------------------
+        # from transformers import Qwen2TokenizerFast
+        # tokenizer = Qwen2TokenizerFast.from_pretrained("Qwen/Qwen1.5-7B-Chat")
+        # input = 'Hello, my dog is cute'
+        # tokenized_input = tokenizer(input, return_tensors='pt').to('cuda')
+        # -----------------------------------
+
+        input_ids = torch.Tensor([[9707, 11, 847, 5562, 374, 18838], [9707, 11, 847, 5562, 374, 18838]]).long()
+        attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]]).long()
+        return dict(input_ids=input_ids, attention_mask=attention_mask)
+
+    # label is needed for casual lm
+    def data_gen_for_casual_lm():
+        data = data_gen()
+        labels = data["input_ids"].clone()
+        data["labels"] = labels
+        return data
+
+    # transform the output to a dict
+    output_transform_fn = lambda x: x
+
+    # function to get the loss
+    loss_fn = lambda output: output["last_hidden_state"].mean()
+    loss_fn_for_casual_lm = lambda output: output["loss"]
+    loss_fn_for_seq_classification = lambda output: output["logits"].mean()
+
+    config = Qwen2Config(
+        hidden_size=128,
+        intermediate_size=256,
+        max_window_layers=4,
+        num_attention_heads=16,
+        num_hidden_layers=4,
+        num_key_value_heads=16,
+    )
+
+    config.pad_token_id = 0
+
+    # register the following models
+    # transformers.Qwen2Model,
+    # transformers.Qwen2ForCausalLM,
+    # transformers.Qwen2ForSequenceClassification,
+    model_zoo.register(
+        name="transformers_qwen2",
+        model_fn=lambda: transformers.Qwen2Model(config),
+        data_gen_fn=data_gen,
+        output_transform_fn=output_transform_fn,
+        loss_fn=loss_fn,
+        model_attribute=ModelAttribute(has_control_flow=True),
+    )
+    model_zoo.register(
+        name="transformers_qwen2_for_casual_lm",
+        model_fn=lambda: transformers.Qwen2ForCausalLM(config),
+        data_gen_fn=data_gen_for_casual_lm,
+        output_transform_fn=output_transform_fn,
+        loss_fn=loss_fn_for_casual_lm,
+        model_attribute=ModelAttribute(has_control_flow=True),
+    )
+    model_zoo.register(
+        name="transformers_qwen2_for_sequence_classification",
+        model_fn=lambda: transformers.Qwen2ForSequenceClassification(config),
+        data_gen_fn=data_gen,
+        output_transform_fn=output_transform_fn,
+        loss_fn=loss_fn_for_seq_classification,
+        model_attribute=ModelAttribute(has_control_flow=True),
+    )