[remove pipeline examples (#12626)

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/README.md

@@ -1,99 +0,0 @@
-# Run HuggingFace `transformers` Models with Pipeline Optimization on Intel NPU
-
-In this directory, you will find examples on how to directly run HuggingFace `transformers` models with pipeline optimization on Intel NPUs. See the table blow for verified models.
-
-## Verified Models
-
-| Model      | Model Link                                                    |
-|------------|----------------------------------------------------------------|
-| Llama2 | [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) |
-| Llama3 | [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) |
-| Llama3.2 | [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct), [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) |
-| Qwen2 | [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) |
-| Qwen2.5 | [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct), [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) |
-| Baichuan2 | [baichuan-inc/Baichuan2-7B-Chat](https://huggingface.co/baichuan-inc/Baichuan-7B-Chat) |
-| MiniCPM | [openbmb/MiniCPM-1B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-1B-sft-bf16), [openbmb/MiniCPM-2B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16) |
-
-## 0. Prerequisites
-For `ipex-llm` NPU support, please refer to [Quick Start](../../../../../../../docs/mddocs/Quickstart/npu_quickstart.md#install-prerequisites) for details about the required preparations.
-
-## 1. Install & Runtime Configurations
-### 1.1 Installation on Windows
-We suggest using conda to manage environment:
-```cmd
-conda create -n llm python=3.11
-conda activate llm
-
-:: install ipex-llm with 'npu' option
-pip install --pre --upgrade ipex-llm[npu]
-
-:: [optional] for Llama-3.2-1B-Instruct & Llama-3.2-3B-Instruct
-pip install transformers==4.45.0 accelerate==0.33.0
-```
-
-Please refer to [Quick Start](../../../../../../../docs/mddocs/Quickstart/npu_quickstart.md#install-ipex-llm-with-npu-support) for more details about `ipex-llm` installation on Intel NPU.
-
-### 1.2 Runtime Configurations
-Please refer to [Quick Start](../../../../../../../docs/mddocs/Quickstart/npu_quickstart.md#runtime-configurations) for environment variables setting based on your device.
-
-## 2. Run Optimized Models
-The examples below show how to run the **_optimized HuggingFace model implementations_** on Intel NPU:
-
-```cmd
-:: to run Llama-2-7b-chat-hf
-python llama2.py --repo-id-or-model-path "meta-llama/Llama-2-7b-chat-hf" --save-directory <converted_model_path>
-
-:: to run Meta-Llama-3-8B-Instruct
-python llama3.py --repo-id-or-model-path "meta-llama/Meta-Llama-3-8B-Instruct" --save-directory <converted_model_path>
-
-:: to run Llama-3.2-1B-Instruct
-python llama3.py --repo-id-or-model-path "meta-llama/Llama-3.2-1B-Instruct" --save-directory <converted_model_path>
-
-:: to run Llama-3.2-3B-Instruct
-python llama3.py --repo-id-or-model-path "meta-llama/Llama-3.2-3B-Instruct" --save-directory <converted_model_path>
-
-:: to run Qwen2.5-7B-Instruct
-python qwen.py --repo-id-or-model-path "Qwen/Qwen2.5-7B-Instruct" --save-directory <converted_model_path>
-
-:: to run Qwen2-1.5B-Instruct
-python qwen.py --repo-id-or-model-path "Qwen/Qwen2-1.5B-Instruct" --low-bit sym_int8 --save-directory <converted_model_path>
-
-:: to run Qwen2.5-3B-Instruct
-python qwen.py --repo-id-or-model-path "Qwen/Qwen2.5-3B-Instruct" --low-bit sym_int8 --save-directory <converted_model_path>
-
-:: to run Baichuan2-7B-Chat
-python baichuan2.py --repo-id-or-model-path "baichuan-inc/Baichuan2-7B-Chat" --save-directory <converted_model_path>
-
-:: to run MiniCPM-1B-sft-bf16
-python minicpm.py --repo-id-or-model-path "openbmb/MiniCPM-1B-sft-bf16" --save-directory <converted_model_path>
-
-:: to run MiniCPM-2B-sft-bf16
-python minicpm.py --repo-id-or-model-path "openbmb/MiniCPM-2B-sft-bf16" --save-directory <converted_model_path>
-```
-
-Arguments info:
-- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the model (e.g. `meta-llama/Llama-2-7b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder.
-- `--prompt PROMPT`: argument defining the prompt to be infered. It is default to be `What is AI?`.
-- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
-- `--max-context-len MAX_CONTEXT_LEN`: Defines the maximum sequence length for both input and output tokens. It is default to be `1024`.
-- `--max-prompt-len MAX_PROMPT_LEN`: Defines the maximum number of tokens that the input prompt can contain. It is default to be `512`.
-- `--disable-transpose-value-cache`: Disable the optimization of transposing value cache.
-- `--disable-streaming`: Disable streaming mode of generation.
-- `--save-directory SAVE_DIRECTORY`: argument defining the path to save converted model. If it is a non-existing path, the original pretrained model specified by `REPO_ID_OR_MODEL_PATH` will be loaded, otherwise the lowbit model in `SAVE_DIRECTORY` will be loaded.
-
-### Sample Output of Streaming Mode
-#### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
- 
-```log
--------------------- Input --------------------
-input length: 28
-<s>[INST] <<SYS>>
-
-<</SYS>>
-
-What is AI? [/INST]
--------------------- Output --------------------
- AI (Artificial Intelligence) is a field of computer science and technology that focuses on the development of intelligent machines that can perform
-
-Inference time: xxxx s
-```

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/baichuan2.py

@@ -1,120 +0,0 @@
-#
-# Copyright 2016 The BigDL Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-
-import os
-import torch
-import time
-import argparse
-from ipex_llm.transformers.npu_model import AutoModelForCausalLM
-from transformers import AutoTokenizer, TextStreamer
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Predict Tokens using `generate()` API for npu model"
-    )
-    parser.add_argument(
-        "--repo-id-or-model-path",
-        type=str,
-        default="baichuan-inc/Baichuan2-7B-Chat",
-        help="The huggingface repo id for the Baichuan2 model to be downloaded"
-        ", or the path to the huggingface checkpoint folder",
-    )
-    parser.add_argument('--prompt', type=str, default="What is AI?",
-                        help='Prompt to infer')
-    parser.add_argument("--n-predict", type=int, default=32, help="Max tokens to predict")
-    parser.add_argument("--max-context-len", type=int, default=1024)
-    parser.add_argument("--max-prompt-len", type=int, default=512)
-    parser.add_argument("--quantization_group_size", type=int, default=0)
-    parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
-    parser.add_argument("--disable-streaming", action="store_true", default=False)
-    parser.add_argument("--save-directory", type=str,
-        required=True,
-        help="The path of folder to save converted model, "
-             "If path not exists, lowbit model will be saved there. "
-             "Else, lowbit model will be loaded.",
-    )
-
-    args = parser.parse_args()
-    model_path = args.repo_id_or_model_path
-
-    if not os.path.exists(args.save_directory):
-        model = AutoModelForCausalLM.from_pretrained(model_path,
-                                                     optimize_model=True,
-                                                     pipeline=True,
-                                                     max_context_len=args.max_context_len,
-                                                     max_prompt_len=args.max_prompt_len,
-                                                     quantization_group_size=args.quantization_group_size,
-                                                     torch_dtype=torch.float16,
-                                                     attn_implementation="eager",
-                                                     transpose_value_cache=not args.disable_transpose_value_cache,
-                                                     trust_remote_code=True,
-                                                     save_directory=args.save_directory)
-        tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-        tokenizer.save_pretrained(args.save_directory)
-    else:
-        model = AutoModelForCausalLM.load_low_bit(
-            args.save_directory,
-            attn_implementation="eager",
-            torch_dtype=torch.float16,
-            max_context_len=args.max_context_len,
-            max_prompt_len=args.max_prompt_len,
-            pipeline=True,
-            transpose_value_cache=not args.disable_transpose_value_cache,
-            trust_remote_code=True
-        )
-        tokenizer = AutoTokenizer.from_pretrained(args.save_directory, trust_remote_code=True)        
-
-
-    if args.disable_streaming:
-        streamer = None
-    else:
-        streamer = TextStreamer(tokenizer=tokenizer, skip_special_tokens=True)
-
-    DEFAULT_SYSTEM_PROMPT = """\
-    """
-
-    print("-" * 80)
-    print("done")
-    with torch.inference_mode():
-        print("finish to load")
-        for i in range(3):
-            messages = [{"role": "system", "content": "You are a helpful assistant."},
-                        {"role": "user", "content": args.prompt}]
-            text = tokenizer.apply_chat_template(messages,
-                                                 tokenize=False,
-                                                 add_generation_prompt=True)
-            _input_ids = tokenizer([text], return_tensors="pt").input_ids
-            print("-" * 20, "Input", "-" * 20)
-            print("input length:", len(_input_ids[0]))
-            print(args.prompt)
-            print("-" * 20, "Output", "-" * 20)
-            st = time.time()
-            output = model.generate(
-                _input_ids, max_new_tokens=args.n_predict, streamer=streamer
-            )
-            end = time.time()
-            if args.disable_streaming:
-                output_str = tokenizer.decode(output[0], skip_special_tokens=False)
-                print(output_str)
-            print(f"Inference time: {end-st} s")
-
-    print("-" * 80)
-    print("done")
-    print("success shut down")

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/llama2.py

@@ -1,127 +0,0 @@
-#
-# Copyright 2016 The BigDL Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-
-import os
-import torch
-import time
-import argparse
-from ipex_llm.transformers.npu_model import AutoModelForCausalLM
-from transformers import AutoTokenizer, TextStreamer
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-def get_prompt(message: str, chat_history: list[tuple[str, str]],
-               system_prompt: str) -> str:
-    texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
-    # The first user input is _not_ stripped
-    do_strip = False
-    for user_input, response in chat_history:
-        user_input = user_input.strip() if do_strip else user_input
-        do_strip = True
-        texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ')
-    message = message.strip() if do_strip else message
-    texts.append(f'{message} [/INST]')
-    return ''.join(texts)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Predict Tokens using `generate()` API for npu model"
-    )
-    parser.add_argument(
-        "--repo-id-or-model-path",
-        type=str,
-        default="meta-llama/Llama-2-7b-chat-hf",
-        help="The huggingface repo id for the Llama2 model to be downloaded"
-        ", or the path to the huggingface checkpoint folder",
-    )
-    parser.add_argument('--prompt', type=str, default="What is AI?",
-                        help='Prompt to infer')
-    parser.add_argument("--n-predict", type=int, default=32, help="Max tokens to predict")
-    parser.add_argument("--max-context-len", type=int, default=1024)
-    parser.add_argument("--max-prompt-len", type=int, default=512)
-    parser.add_argument("--quantization_group_size", type=int, default=0)
-    parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
-    parser.add_argument("--disable-streaming", action="store_true", default=False)
-    parser.add_argument("--save-directory", type=str,
-        required=True,
-        help="The path of folder to save converted model, "
-             "If path not exists, lowbit model will be saved there. "
-             "Else, lowbit model will be loaded.",
-    )
-
-    args = parser.parse_args()
-    model_path = args.repo_id_or_model_path
-
-    if not os.path.exists(args.save_directory):
-        model = AutoModelForCausalLM.from_pretrained(model_path,
-                                                     optimize_model=True,
-                                                     pipeline=True,
-                                                     max_context_len=args.max_context_len,
-                                                     max_prompt_len=args.max_prompt_len,
-                                                     quantization_group_size=args.quantization_group_size,
-                                                     torch_dtype=torch.float16,
-                                                     attn_implementation="eager",
-                                                     transpose_value_cache=not args.disable_transpose_value_cache,
-                                                     save_directory=args.save_directory)
-        tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-        tokenizer.save_pretrained(args.save_directory)
-    else:
-        model = AutoModelForCausalLM.load_low_bit(
-            args.save_directory,
-            attn_implementation="eager",
-            torch_dtype=torch.float16,
-            max_context_len=args.max_context_len,
-            max_prompt_len=args.max_prompt_len,
-            pipeline=True,
-            transpose_value_cache=not args.disable_transpose_value_cache,
-        )
-        tokenizer = AutoTokenizer.from_pretrained(args.save_directory, trust_remote_code=True)        
-
-
-    if args.disable_streaming:
-        streamer = None
-    else:
-        streamer = TextStreamer(tokenizer=tokenizer, skip_special_tokens=True)
-
-    DEFAULT_SYSTEM_PROMPT = """\
-    """
-
-    print("-" * 80)
-    print("done")
-    with torch.inference_mode():
-        print("finish to load")
-        for i in range(3):
-            prompt = get_prompt(args.prompt, [], system_prompt=DEFAULT_SYSTEM_PROMPT)
-            _input_ids = tokenizer.encode(prompt, return_tensors="pt")
-            print("-" * 20, "Input", "-" * 20)
-            print("input length:", len(_input_ids[0]))
-            print(prompt)
-            print("-" * 20, "Output", "-" * 20)
-            st = time.time()
-            output = model.generate(
-                _input_ids, max_new_tokens=args.n_predict, streamer=streamer
-            )
-            end = time.time()
-            if args.disable_streaming:
-                output_str = tokenizer.decode(output[0], skip_special_tokens=False)
-                print(output_str)
-            print(f"Inference time: {end-st} s")
-
-    print("-" * 80)
-    print("done")
-    print("success shut down")

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/llama3.py

@@ -1,130 +0,0 @@
-#
-# Copyright 2016 The BigDL Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-
-import os
-import torch
-import time
-import argparse
-from ipex_llm.transformers.npu_model import AutoModelForCausalLM
-from transformers import AutoTokenizer, TextStreamer
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-# you could tune the prompt based on your own model,
-# here the prompt tuning refers to https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3
-DEFAULT_SYSTEM_PROMPT = """\
-"""
-
-def get_prompt(user_input: str, chat_history: list[tuple[str, str]],
-               system_prompt: str) -> str:
-    prompt_texts = [f'<|begin_of_text|>']
-
-    if system_prompt != '':
-        prompt_texts.append(f'<|start_header_id|>system<|end_header_id|>\n\n{system_prompt}<|eot_id|>')
-
-    for history_input, history_response in chat_history:
-        prompt_texts.append(f'<|start_header_id|>user<|end_header_id|>\n\n{history_input.strip()}<|eot_id|>')
-        prompt_texts.append(f'<|start_header_id|>assistant<|end_header_id|>\n\n{history_response.strip()}<|eot_id|>')
-
-    prompt_texts.append(f'<|start_header_id|>user<|end_header_id|>\n\n{user_input.strip()}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n')
-    return ''.join(prompt_texts)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Predict Tokens using `generate()` API for npu model"
-    )
-    parser.add_argument(
-        "--repo-id-or-model-path",
-        type=str,
-        default="meta-llama/Meta-Llama-3-8B-Instruct",
-        help="The huggingface repo id for the Llama3 model to be downloaded"
-        ", or the path to the huggingface checkpoint folder",
-    )
-    parser.add_argument('--prompt', type=str, default="What is AI?",
-                        help='Prompt to infer')
-    parser.add_argument("--n-predict", type=int, default=32, help="Max tokens to predict")
-    parser.add_argument("--max-context-len", type=int, default=1024)
-    parser.add_argument("--max-prompt-len", type=int, default=512)
-    parser.add_argument("--quantization_group_size", type=int, default=0)
-    parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
-    parser.add_argument("--disable-streaming", action="store_true", default=False)
-    parser.add_argument("--save-directory", type=str,
-        required=True,
-        help="The path of folder to save converted model, "
-             "If path not exists, lowbit model will be saved there. "
-             "Else, lowbit model will be loaded.",
-    )
-
-    args = parser.parse_args()
-    model_path = args.repo_id_or_model_path
-
-    if not os.path.exists(args.save_directory):
-        model = AutoModelForCausalLM.from_pretrained(model_path,
-                                                    torch_dtype=torch.float16,
-                                                    optimize_model=True,
-                                                    pipeline=True,
-                                                    max_context_len=args.max_context_len,
-                                                    max_prompt_len=args.max_prompt_len,
-                                                    quantization_group_size=args.quantization_group_size,
-                                                    attn_implementation="eager",
-                                                    transpose_value_cache=not args.disable_transpose_value_cache,
-                                                    save_directory=args.save_directory)
-        tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-        tokenizer.save_pretrained(args.save_directory)
-    else:
-        model = AutoModelForCausalLM.load_low_bit(
-            args.save_directory,
-            attn_implementation="eager",
-            torch_dtype=torch.float16,
-            max_context_len=args.max_context_len,
-            max_prompt_len=args.max_prompt_len,
-            pipeline=True,
-            transpose_value_cache=not args.disable_transpose_value_cache,
-        )
-        tokenizer = AutoTokenizer.from_pretrained(args.save_directory, trust_remote_code=True)        
-
-
-    if args.disable_streaming:
-        streamer = None
-    else:
-        streamer = TextStreamer(tokenizer=tokenizer, skip_special_tokens=True)
-
-    print("-" * 80)
-    print("done")
-    with torch.inference_mode():
-        print("finish to load")
-        for i in range(3):
-            prompt = get_prompt(args.prompt, [], system_prompt=DEFAULT_SYSTEM_PROMPT)
-            _input_ids = tokenizer.encode(prompt, return_tensors="pt")
-            print("-" * 20, "Input", "-" * 20)
-            print("input length:", len(_input_ids[0]))
-            print(prompt)
-            print("-" * 20, "Output", "-" * 20)
-            st = time.time()
-            output = model.generate(
-                _input_ids, max_new_tokens=args.n_predict, streamer=streamer
-            )
-            end = time.time()
-            if args.disable_streaming:
-                output_str = tokenizer.decode(output[0], skip_special_tokens=False)
-                print(output_str)
-            print(f"Inference time: {end-st} s")
-
-    print("-" * 80)
-    print("done")
-    print("success shut down")

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/minicpm.py

@@ -1,113 +0,0 @@
-#
-# Copyright 2016 The BigDL Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-
-import torch
-import time
-import argparse
-from ipex_llm.transformers.npu_model import AutoModelForCausalLM
-from transformers import AutoTokenizer, TextStreamer
-from transformers.utils import logging
-import os
-
-logger = logging.get_logger(__name__)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Predict Tokens using `generate()` API for npu model"
-    )
-    parser.add_argument(
-        "--repo-id-or-model-path",
-        type=str,
-        default="openbmb/MiniCPM-1B-sft-bf16", # or "openbmb/MiniCPM-2B-sft-bf16"
-        help="The huggingface repo id for the MiniCPM model to be downloaded"
-        ", or the path to the huggingface checkpoint folder",
-    )
-    parser.add_argument('--prompt', type=str, default="What is AI?",
-                        help='Prompt to infer')
-    parser.add_argument("--n-predict", type=int, default=32, help="Max tokens to predict")
-    parser.add_argument("--max-context-len", type=int, default=1024)
-    parser.add_argument("--max-prompt-len", type=int, default=512)
-    parser.add_argument("--quantization_group_size", type=int, default=0)
-    parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
-    parser.add_argument("--disable-streaming", action="store_true", default=False)
-    parser.add_argument("--save-directory", type=str,
-        required=True,
-        help="The path of folder to save converted model, "
-             "If path not exists, lowbit model will be saved there. "
-             "Else, lowbit model will be loaded.",
-    )
-
-    args = parser.parse_args()
-    model_path = args.repo_id_or_model_path
-
-    if not os.path.exists(args.save_directory):
-        model = AutoModelForCausalLM.from_pretrained(model_path,
-                                                     optimize_model=True,
-                                                     pipeline=True,
-                                                     max_context_len=args.max_context_len,
-                                                     max_prompt_len=args.max_prompt_len,
-                                                     torch_dtype=torch.float16,
-                                                     attn_implementation="eager",
-                                                     quantization_group_size=args.quantization_group_size,
-                                                     transpose_value_cache=not args.disable_transpose_value_cache,
-                                                     trust_remote_code=True,
-                                                     save_directory=args.save_directory)
-        tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-        tokenizer.save_pretrained(args.save_directory)
-    else:
-        model = AutoModelForCausalLM.load_low_bit(
-            args.save_directory,
-            attn_implementation="eager",
-            torch_dtype=torch.float16,
-            max_context_len=args.max_context_len,
-            max_prompt_len=args.max_prompt_len,
-            pipeline=True,
-            transpose_value_cache=not args.disable_transpose_value_cache,
-            trust_remote_code=True
-        )
-        tokenizer = AutoTokenizer.from_pretrained(args.save_directory, trust_remote_code=True)        
-
-
-    if args.disable_streaming:
-        streamer = None
-    else:
-        streamer = TextStreamer(tokenizer=tokenizer, skip_special_tokens=True)
-
-    print("-" * 80)
-    print("done")
-    with torch.inference_mode():
-        print("finish to load")
-        for i in range(3):
-            prompt = "<用户>{}<AI>".format(args.prompt)
-            _input_ids = tokenizer.encode(prompt, return_tensors="pt")
-            print("-" * 20, "Input", "-" * 20)
-            print("input length:", len(_input_ids[0]))
-            print(prompt)
-            print("-" * 20, "Output", "-" * 20)
-            st = time.time()
-            output = model.generate(
-                _input_ids, max_new_tokens=args.n_predict, streamer=streamer
-            )
-            end = time.time()
-            if args.disable_streaming:
-                output_str = tokenizer.decode(output[0], skip_special_tokens=False)
-                print(output_str)
-            print(f"Inference time: {end-st} s")
-
-    print("-" * 80)
-    print("done")
-    print("success shut down")

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/qwen.py

@@ -1,118 +0,0 @@
-#
-# Copyright 2016 The BigDL Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-
-import os
-import torch
-import time
-import argparse
-from ipex_llm.transformers.npu_model import AutoModelForCausalLM
-from transformers import AutoTokenizer, TextStreamer
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Predict Tokens using `generate()` API for npu model"
-    )
-    parser.add_argument(
-        "--repo-id-or-model-path",
-        type=str,
-        default="Qwen/Qwen2.5-7B-Instruct",  # Or Qwen2-7B-Instruct, Qwen2-1.5B-Instruct
-        help="The huggingface repo id for the Qwen model to be downloaded"
-        ", or the path to the huggingface checkpoint folder",
-    )
-    parser.add_argument('--prompt', type=str, default="AI是什么?",
-                        help='Prompt to infer')
-    parser.add_argument("--n-predict", type=int, default=32, help="Max tokens to predict")
-    parser.add_argument("--max-context-len", type=int, default=1024)
-    parser.add_argument("--max-prompt-len", type=int, default=512)
-    parser.add_argument("--quantization_group_size", type=int, default=0)
-    parser.add_argument('--low-bit', type=str, default="sym_int4",
-                        help='Low bit precision to quantize the model')
-    parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
-    parser.add_argument("--disable-streaming", action="store_true", default=False)
-    parser.add_argument("--save-directory", type=str,
-        required=True,
-        help="The path of folder to save converted model, "
-             "If path not exists, lowbit model will be saved there. "
-             "Else, lowbit model will be loaded.",
-    )
-
-    args = parser.parse_args()
-    model_path = args.repo_id_or_model_path
-
-    if not os.path.exists(args.save_directory):
-        model = AutoModelForCausalLM.from_pretrained(model_path,
-                                                     optimize_model=True,
-                                                     pipeline=True,
-                                                     load_in_low_bit=args.low_bit,
-                                                     max_context_len=args.max_context_len,
-                                                     max_prompt_len=args.max_prompt_len,
-                                                     quantization_group_size=args.quantization_group_size,
-                                                     torch_dtype=torch.float16,
-                                                     attn_implementation="eager",
-                                                     transpose_value_cache=not args.disable_transpose_value_cache,
-                                                     trust_remote_code=True,
-                                                     save_directory=args.save_directory)
-        tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-        tokenizer.save_pretrained(args.save_directory)
-    else:
-        model = AutoModelForCausalLM.load_low_bit(
-            args.save_directory,
-            attn_implementation="eager",
-            torch_dtype=torch.float16,
-            max_context_len=args.max_context_len,
-            max_prompt_len=args.max_prompt_len,
-            pipeline=True,
-            transpose_value_cache=not args.disable_transpose_value_cache)
-        tokenizer = AutoTokenizer.from_pretrained(args.save_directory, trust_remote_code=True)        
-
-
-    if args.disable_streaming:
-        streamer = None
-    else:
-        streamer = TextStreamer(tokenizer=tokenizer, skip_special_tokens=True)
-
-    print("-" * 80)
-    print("done")
-    messages = [{"role": "system", "content": "You are a helpful assistant."},
-                {"role": "user", "content": args.prompt}]
-    text = tokenizer.apply_chat_template(messages,
-                                         tokenize=False,
-                                         add_generation_prompt=True)
-    with torch.inference_mode():
-        print("finish to load")
-        for i in range(3):
-            _input_ids = tokenizer([text], return_tensors="pt").input_ids
-            print("-" * 20, "Input", "-" * 20)
-            print("input length:", len(_input_ids[0]))
-            print(text)
-            print("-" * 20, "Output", "-" * 20)
-            st = time.time()
-            output = model.generate(
-                _input_ids, max_new_tokens=args.n_predict, streamer=streamer
-            )
-            end = time.time()
-            if args.disable_streaming:
-                output_str = tokenizer.decode(output[0], skip_special_tokens=False)
-                print(output_str)
-            print(f"Inference time: {end-st} s")
-
-    print("-" * 80)
-    print("done")
-    print("success shut down")

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md

@@ -48,7 +48,7 @@ Please refer to [Quick Start](../../../../../../docs/mddocs/Quickstart/npu_quick
 ### 1.2 Runtime Configurations
 Please refer to [Quick Start](../../../../../../docs/mddocs/Quickstart/npu_quickstart.md#runtime-configurations) for environment variables setting based on your device.
 
-## 2. Run Optimized Models (Experimental)
+## 2. Run Optimized Models
 The examples below show how to run the **_optimized HuggingFace model implementations_** on Intel NPU, including
 - [Llama2-7B](./llama2.py)
 - [Llama3-8B](./llama3.py)