Integrate EAGLE with ITREX (#1504)

* Add main eagle script modeling_eagle.py

Signed-off-by: Siddhi Velankar <siddhi.velankar@intel.com>

examples/huggingface/pytorch/text-generation/eagle/README.md

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+# Eagle - Speculative Sampling
+
+Intel Extension for Transformers supports the EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency) which is a speculative sampling method that improves text generation speed.
+
+See [here](https://arxiv.org/abs/2401.15077) to view the paper and [here](https://github.com/SafeAILab/EAGLE) for more info on EAGLE code.
+
+
+## Setup and installation 
+
+With pip: (recommended)
+
+```bash
+pip install eagle-llm
+```
+
+From the source:
+
+```bash
+git clone https://github.com/SafeAILab/EAGLE.git
+cd EAGLE
+pip install -e .
+```
+
+
+## Usage 
+
+The script accepts several command-line arguments:
+
+- -d or --device: Target device for text generation (default: "cpu", options: "cpu", "xpu", "cuda").
+- -t or --dtype: Data type for text generation (default: "float32", options: "float32", "float16", "bfloat16").
+- --max_new_tokens: Number of max new tokens for text generation (default: 512).
+- --use_eagle: Use EAGLE model for generation (default: False).
+
+```bash
+python3 eagle_example.py
+python eagle_example.py -d xpu --max_new_tokens 1024 --use_eagle
+
+```
+
+The default base model is set to "meta-llama/Llama-2-7b-chat-hf", you can change it in the script by reassigning the variable "base_model_path" to model of your choice
+
+
+# Features
+- Dynamic Device and Data Type Configuration: The script allows specifying the target device and data type for text generation, supporting CPU, XPU, and CUDA devices, as well as different data types.
+- EAGLE Model Integration: The script demonstrates how to integrate the EAGLE model for enhanced text generation capabilities.
+- Performance Measurement: The script measures the performance of the text generation process, including the tokens per second (TPS) based on the total new tokens and total time.
+
+
+# Results
+
+We conducted benchmarking tests on both CPU and XPU environments.
+
+On GPU, llama2-7b-chat + EAGLE outperforms llama2-7b-chat by 3x speed in generating new tokens.
+On CPU, llama2-7b-chat + EAGLE outperforms llama2-7b-chat by 1.75x speed in generating new tokens.
+
+This highlights the superior capabilities of the EAGLE in handling the text generation task.
+

examples/huggingface/pytorch/text-generation/eagle/eagle_example.py

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+# Import necessary libraries
+import os
+import time
+import argparse
+from tqdm import tqdm
+from pprint import pprint
+from eagle.modeling_eagle import EAGLE
+from transformers import AutoModelForCausalLM,LlamaModel,MistralModel,AutoTokenizer,MixtralForCausalLM,LlamaConfig,LlamaForCausalLM
+import torch
+from fastchat.model import get_conversation_template
+
+# Define a dictionary to map string representations of data types to their corresponding PyTorch data types
+DTYPE_MAP = {
+    "float16": torch.float16,
+    "float32": torch.float32,
+    "bfloat16": torch.bfloat16,
+}
+
+# Function to parse command-line arguments
+def parse_argument():
+    # Construct the argument parser and add the arguments
+    arg_parser = argparse.ArgumentParser(description="Test EAGLE with Llama-2")
+    arg_parser.add_argument("-d", '--device', type=str, default="cpu", choices=["cpu", "xpu", "cuda"],
+                            help="Target device for text generation")
+    arg_parser.add_argument("-t", '--dtype', type=str, default="float32", choices=["float32", "float16", "bfloat16"],
+                            help="Data type for text generation")
+    arg_parser.add_argument('--max_new_tokens', type=int, default=512,
+                            help="Number of max new tokens for text generation.")
+    arg_parser.add_argument('--use_eagle', action='store_true', help='Use EAGLE model for generation')
+    return arg_parser.parse_args()
+
+
+if __name__ == "__main__":
+    # Define paths to the base and eagle models
+    # Note - You can use your choice of base model, we are using llama-2-7b-chat in this example
+    base_model_path = "meta-llama/Llama-2-7b-chat-hf"
+    ea_model_path = "yuhuili/EAGLE-llama2-chat-7B"
+
+    # Parse command-line arguments
+    args = parse_argument()
+
+    # Extract arguments
+    device = args.device
+    dtype = DTYPE_MAP[args.dtype]
+    max_new_tokens = args.max_new_tokens
+    use_eagle = args.use_eagle
+
+    if device == "xpu":
+        import intel_extension_for_pytorch as ipex
+
+    if device == "cpu":
+        assert dtype == torch.float32, f"CPU can only support float32. Got dtype = {args.dtype}"
+
+    tokenizer=AutoTokenizer.from_pretrained(base_model_path)
+
+    # Define a message, conversation template and system message to be processed by the model
+    your_message="Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions."
+    conv = get_conversation_template("llama-2-chat")
+    sys_p = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."
+    conv.system_message = sys_p
+    conv.append_message(conv.roles[0], your_message)
+    conv.append_message(conv.roles[1], None)
+    prompt1 = conv.get_prompt()+" "
+
+    your_message="Draft a professional email seeking your supervisor's feedback on the 'Quarterly Financial Report' you prepared. Ask specifically about the data analysis, presentation style, and the clarity of conclusions drawn. Keep the email short and to the point."
+    conv = get_conversation_template("llama-2-chat")
+    sys_p = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."
+    conv.system_message = sys_p
+    conv.append_message(conv.roles[0], your_message)
+    conv.append_message(conv.roles[1], None)
+    prompt2 = conv.get_prompt()+" "
+
+    your_message = "Write a brief technical documentation for a new software feature, explaining its functionality, benefits, and how to implement it."
+    conv = get_conversation_template("llama-2-chat")
+    sys_p = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."
+    conv.system_message = sys_p
+    conv.append_message(conv.roles[0], your_message)
+    conv.append_message(conv.roles[1], None)
+    prompt3 = conv.get_prompt()+" "
+
+    your_message = "Craft a marketing campaign plan for a new product launch, including the target audience, key messages, and promotional strategies."
+    conv = get_conversation_template("llama-2-chat")
+    sys_p = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."
+    conv.system_message = sys_p
+    conv.append_message(conv.roles[0], your_message)
+    conv.append_message(conv.roles[1], None)
+    prompt4 = conv.get_prompt()+" "
+
+    # Pick the prompt you want to use to run the example or write your own prompt, initialize it as a list
+    text = [prompt1]
+
+    # Load the model and set parameters
+    model = AutoModelForCausalLM.from_pretrained(base_model_path,
+                                                 torch_dtype=dtype,
+                                                 ).eval().to(device)
+    # model = torch.xpu.optimize(model)
+    model.config.pad_token_id = model.config.eos_token_id
+    tokenizer.padding_side = "left"
+    tokenizer.pad_token = tokenizer.eos_token
+
+    eagle = EAGLE(model, ea_model_path)
+
+    # Iterate over each prompt in the text list
+    for prompt in text:
+        # Initialize counters
+        t_total = 0.
+        total_new_tokens = 0
+        for _ in tqdm(range(10)):
+            t_b = time.time()
+
+            # inputs = tokenizer(prompt, return_tensors="pt", padding=True).to(device)
+            inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+            # Generate text using the EAGLE model if the flag is set, otherwise use the base model
+            if use_eagle:
+                outs = eagle.generate(**inputs, max_new_tokens=max_new_tokens,temperature=0.5)
+            else:
+                outs=model.generate(**inputs, max_new_tokens=max_new_tokens,temperature=0.5)
+            output=tokenizer.batch_decode(outs)
+            t_e = time.time()
+
+            # Update counters
+            t_total += t_e - t_b
+
+            # Update tokens per second based on outs type
+            if isinstance(outs, list):
+                total_new_tokens +=  len(outs)
+            else:
+                total_new_tokens += len(outs[0]) - inputs.input_ids.shape[-1]
+            # Print output 
+            pprint(output)
+        print(f"TPS:  {total_new_tokens / t_total}")
+
+    del model
+