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Author: NaDongsfd

Season2.step_into_llm/05.LLaMA2/llama_inference_debug.py

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+import sys
+import os
+# Add current directory to Python path for local module imports
+sys.path.insert(0, os.path.abspath("."))  
+from ms_mindnlp.transformers.models.llama.modeling_llama import LlamaModel
+from ms_mindnlp.transformers.models.llama.configuration_llama import LlamaConfig
+import mindspore as ms
+from mindspore import dtype, ops
+import debugpy
+
+debugpy.listen(("0.0.0.0", 5678))
+print("Waiting for debugger to attach...")
+
+debugpy.wait_for_client()
+print("Debugger is attached.")
+
+# import inspect
+# llama_config_file_path = inspect.getfile(LlamaConfig)
+# print(f"{llama_config_file_path}")
+
+ms.set_context(mode=ms.PYNATIVE_MODE)
+
+def run():
+    """Main execution function for LLaMA model inference demo"""
+    config = LlamaConfig(
+        vocab_size=32000,  # Tokenizer vocabulary size
+        hidden_size=4096,  # Hidden layer dimension
+        intermediate_size=11008,  # FFN layer inner dimension
+        num_hidden_layers=2,  # Number of transformer blocks
+        num_attention_heads=32,  # Parallel attention heads
+        num_key_value_heads=2,  # KV heads for grouped-query attention
+        max_position_embeddings=2048,  # Maximum sequence length
+    )
+    model = LlamaModel(config=config)
+    # Generate random input tensor: (batch_size=2, seq_length=16)
+    input_ids = ops.randint(0, config.vocab_size, (2, 16), dtype=dtype.int32)
+    output = model(input_ids=input_ids)
+    print("inference")
+    print(output)
+
+if __name__ == "__main__":
+    run()