Create README.md

README.md

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+---
+tags:
+- text-generation-inference
+- llama
+- llama3
+---
+
+
+# Llama 3.1 8B Instruct with Key-Value-Cache enabled in ONNX fp16 format
+- Model creator: [Meta Llama](https://huggingface.co/meta-llama)
+- Original model: [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)
+
+<!-- description start -->
+## Description
+
+This repo contains the ONNX files for the ONNX conversion of Llama 3.1 8B Instruct done by Esperanto Technologies.
+The model is in the fp16 format and has the KVC enabled.
+
+<!-- description end -->
+
+## How to download ONNX model and weight files
+
+The easiest way to obtain the model is to clone this whole repo.
+Alternatively you can download the files is using the `huggingface-hub` Python library.
+
+```shell
+pip3 install huggingface-hub>=0.17.1
+```
+
+Then you can download any individual model file to the current directory, at high speed, with a command like this:
+
+```shell
+huggingface-cli download Esperanto/llama3.1-8b-Instruct-kvc-fp16-onnx --local-dir llama3.1-8b-Instruct-kvc-fp16-onnx --local-dir-use-symlinks False
+```
+
+For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
+
+## How to run from Python code using ONNXRuntime
+
+This model can easily be ran in a CPU using [ONNXRuntime](https://onnxruntime.ai/).
+
+#### First install the packages
+
+```bash
+pip3 install onnx==1.16.1
+pip3 install onnxruntime==1.17.1
+```
+
+#### Example code: generate text with this model
+
+We define the loop with greedy decoding:
+```python
+import numpy as np
+import onnxruntime
+import onnx
+from transformers import AutoTokenizer
+
+def generate_text(model_path, prompt, tokenizer, max_gen_tokens, total_sequence, window, context):
+    model = onnx.load(model_path)
+
+    #we create the inputs for the first iteration
+    input_tensor = tokenizer(prompt, return_tensors="pt")
+    prompt_size = len(input_tensor['input_ids'][0])
+    actual_input = input_tensor['input_ids']
+    if prompt_size < window:
+        actual_input = np.concatenate((tokenizer.bos_token_id*np.ones([1, window - prompt_size], dtype = 'int64'),
+                                       actual_input), axis=1)
+    if prompt_size + max_gen_tokens > total_sequence:
+        print("ERROR: Longer total sequence is needed!")
+        return
+    first_attention = np.concatenate((np.zeros([1, total_sequence - window], dtype = 'int64'),
+                                      np.ones((1, window), dtype = 'int64')), axis=1)
+    max_gen_tokens += prompt_size #we need to generate on top of parsing the prompt
+    inputs_names =[node.name for node in model.graph.input]
+    output_names =[node.name for node in model.graph.output]
+    n_heads = 8 #gqa-heads of the kvc
+    inputs_dict = {}
+    inputs_dict['input_ids'] = actual_input[:, :window].reshape(1, window).numpy()
+    inputs_dict['attention_mask'] = first_attention
+    for name in inputs_names:
+        if name == 'input_ids' or name == 'attention_mask': continue
+        inputs_dict[name] = np.zeros([1, n_heads, context-window, 128], dtype="float16")
+    index = 0
+    new_token = np.array([10])
+    next_index = window
+    old_j = 0
+    total_input = actual_input.numpy()
+
+    rt_session = onnxruntime.InferenceSession(model_path)
+    ## We run the inferences
+    while next_index < max_gen_tokens:
+        if new_token.any() == tokenizer.eos_token_id:
+            break
+        #inference
+        output = rt_session.run(output_names, inputs_dict)
+        outs_dictionary = {name: content for (name, content) in zip (output_names, output)}
+        #we prepare the inputs for the next inference
+        for name in inputs_names:
+            if name == 'input_ids':
+                old_j = next_index
+                if next_index < prompt_size:
+                    if prompt_size - next_index >= window: next_index += window
+                    else: next_index = prompt_size 
+                    j = next_index - window
+                else:
+                    next_index +=1
+                    j = next_index - window
+                    new_token = outs_dictionary['logits'].argmax(-1).reshape(1, window)
+                    total_input = np.concatenate((total_input, new_token[: , -1:]), axis = 1)
+                inputs_dict['input_ids']= total_input[:, j:next_index].reshape(1, window)
+            elif name == 'attention_mask':
+                inputs_dict['attention_mask'] = np.concatenate((np.zeros((1, total_sequence-next_index), dtype = 'int64'), np.ones((1, next_index), dtype = 'int64')), axis=1)
+            else:
+                old_name = name.replace("past_key_values", "present")
+                inputs_dict[name] = outs_dictionary[old_name][:, :, next_index-old_j:context-window+(next_index - old_j), :]
+
+    answer = tokenizer.decode(total_input[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
+    return answer
+```
+We now run the inferences:
+
+```python
+tokenizer = AutoTokenizer.from_pretrained("Esperanto/llama3.1-8b-Instruct-kvc-fp16-onnx")
+model_path = "llama3.1-8b-Instruct-kvc-fp16-onnx/model.onnx"
+
+max_gen_tokens = 20    #number of tokens we want tog eneral
+total_sequence = 128   #total sequence_length
+context = 1024         #the context to extend the kvc
+window = 16            #number of tokens we want to parse at the time
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+generated = generate_text(model_path, prompt, tokenizer, max_gen_tokens, total_sequence, window, context)
+print(generated)
+```