[LLM] support ipex arc int4 & add basic llama2 example (#8700)

* first support of xpu

* make it works on gpu

update setup

update

add GPU llama2 examples

add use_optimize flag to disbale optimize for gpu

fix style

update gpu exmaple readme

fix

* update example, and update env

* fix setup to add cpp files

* replace jit with aot to avoid data leak

* rename to bigdl-core-xe

* update installation in example readme

python/llm/example/transformers/transformers_int4/GPU/README.md

@@ -0,0 +1,15 @@
+# BigDL-LLM Transformers INT4 Optimization for Large Language Model on Intel® Arc™ A-Series Graphics
+You can use BigDL-LLM to run almost every Huggingface Transformer models with INT4 optimizations on your laptops with Intel® Arc™ A-Series Graphics. This directory contains example scripts to help you quickly get started using BigDL-LLM to run some popular open-source models in the community. Each model has its own dedicated folder, where you can find detailed instructions on how to install and run it.
+
+## Recommended Requirements
+To apply Intel® Arc™ A-Series Graphics acceleration, there’re several steps for tools installation and environment preparation.
+Step 1, only Linux system is supported now, Ubuntu 22.04 is prefered.
+Step 2, please refer to our [drive installation](https://dgpu-docs.intel.com/installation-guides/index.html#intel-arc-gpus) for general purpose GPU capabilities.
+Step 3, you also need to download and install [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html). OneMKL and DPC++ compiler are needed, others are optional.
+
+## Best Known Configuration on Linux
+For better performance, it is recommended to set environment variables on Linux:
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+```

python/llm/example/transformers/transformers_int4/GPU/llama2/README.md

@@ -0,0 +1,78 @@
+# Llama2
+In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Llama2 models on any Intel® Arc™ A-Series Graphics. For illustration purposes, we utilize the [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) and [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) as reference Llama2 models.
+
+## 0. Requirements
+To run these examples with BigDL-LLM on Intel® Arc™ A-Series Graphics, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
+
+## Example: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a Llama2 model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations on Intel® Arc™ A-Series Graphics.
+### 1. Install
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.9
+conda activate llm
+# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
+# you can install specific ipex/torch version for your need
+pip install bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+# download wheel from sourceforge(https://sourceforge.net/projects/analytics-zoo/files/bigdl-llm/bigdl_core_xe-0.0.0-cp39-cp39-linux_x86_64.whl/download), then install it
+pip install bigdl_core_xe-0.0.0-cp39-cp39-linux_x86_64.whl
+```
+### 2. Configures OneAPI environment variables
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+### 3. Run
+
+For optimal performance on Arc, it is recommended to set several environment variables.
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+```
+
+```
+python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
+```
+
+Arguments info:
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama2 model (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'`.
+- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). 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`.
+
+#### Sample Output
+#### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+### HUMAN:
+What is AI?
+
+### RESPONSE:
+
+-------------------- Output --------------------
+### HUMAN:
+What is AI?
+
+### RESPONSE:
+
+AI is a term used to describe the development of computer systems that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing images
+```
+
+#### [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+### HUMAN:
+What is AI?
+
+### RESPONSE:
+
+-------------------- Output --------------------
+### HUMAN:
+What is AI?
+
+### RESPONSE:
+
+AI, or artificial intelligence, refers to the ability of machines to perform tasks that would typically require human intelligence, such as learning, problem-solving,
+```

python/llm/example/transformers/transformers_int4/GPU/llama2/generate.py

@@ -0,0 +1,76 @@
+#
+# 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 bigdl.llm.transformers import AutoModelForCausalLM
+from transformers import LlamaTokenizer
+import intel_extension_for_pytorch as ipex
+
+# you could tune the prompt based on your own model,
+# here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style
+LLAMA2_PROMPT_FORMAT = """### HUMAN:
+{prompt}
+
+### RESPONSE:
+"""
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 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 (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) 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')
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    # Load model in 4 bit,
+    # which convert the relevant layers in the model into INT4 format
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 load_in_4bit=True,
+                                                 optimize_model=False,
+                                                 trust_remote_code=True)
+    model = model.half().to('xpu')
+
+    # Load tokenizer
+    tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+    # Generate predicted tokens
+    with torch.inference_mode():
+        prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
+        st = time.time()
+        # if your selected model is capable of utilizing previous key/value attentions
+        # to enhance decoding speed, but has `"use_cache": false` in its model config,
+        # it is important to set `use_cache=True` explicitly in the `generate` function
+        # to obtain optimal performance with BigDL-LLM INT4 optimizations
+        output = model.generate(input_ids,
+                                max_new_tokens=args.n_predict)
+        torch.xpu.synchronize()
+        end = time.time()
+        output = output.cpu()
+        output_str = tokenizer.decode(output[0], skip_special_tokens=True)
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Prompt', '-'*20)
+        print(prompt)
+        print('-'*20, 'Output', '-'*20)
+        print(output_str)

python/llm/setup.py

@@ -34,6 +34,7 @@ import urllib.request
 import requests
 import re
 import glob
+import copy
 
 from setuptools import setup
 
@@ -247,6 +248,13 @@ def setup_package():
     all_requires = ['py-cpuinfo']
     all_requires += CONVERT_DEP
 
+    # install with -f https://developer.intel.com/ipex-whl-stable-xpu
+    xpu_requires = copy.deepcopy(all_requires)
+    xpu_requires.remove('torch')
+    xpu_requires += ["torch==2.0.1a0",
+                     "torchvision==0.15.2a0",
+                     "intel_extension_for_pytorch==2.0.110+xpu;platform_system=='Linux'"]
+
     metadata = dict(
         name='bigdl-llm',
         version=VERSION,
@@ -267,7 +275,8 @@ def setup_package():
                 'llm-convert=bigdl.llm.convert_model:main'
             ]
         },
-        extras_require={"all": all_requires},
+        extras_require={"all": all_requires,
+                        "xpu": xpu_requires},
         classifiers=[
             'License :: OSI Approved :: Apache Software License',
             'Programming Language :: Python :: 3',

python/llm/src/bigdl/llm/transformers/convert.py

@@ -73,7 +73,6 @@ def _replace_with_quant_linear(model, qtype, modules_to_not_convert=None,
             # Check if the current key is not in the `modules_to_not_convert`
             if not any(key in ".".join(current_key_name) for key in modules_to_not_convert):
                 with init_empty_weights():
-
                     new_linear = LinearQuant(
                         module.in_features,
                         module.out_features,
@@ -112,7 +111,7 @@ def _replace_with_quant_linear(model, qtype, modules_to_not_convert=None,
     return model, has_been_replaced
 
 
-def ggml_convert_quant(model, qtype, convert_shape_only=False):
+def ggml_convert_quant(model, qtype, optimize_model=True, convert_shape_only=False):
     modules_to_not_convert = []  # ["lm_head"]
     model, has_been_replaced = _replace_with_quant_linear(
         model, qtype, modules_to_not_convert, None, convert_shape_only=convert_shape_only
@@ -127,7 +126,8 @@ def ggml_convert_quant(model, qtype, convert_shape_only=False):
     else:
         model.to(torch.float32)
 
-    model = optimize(model)
+    if optimize_model:
+        model = optimize(model)
     return model
 
 

python/llm/src/bigdl/llm/transformers/linear_quant.py

@@ -43,7 +43,7 @@
 
 from typing import Optional, TypeVar, Union, overload
 from bigdl.llm.utils.common import invalidInputError
-
+import os
 import torch
 import torch.nn.functional as F
 from torch import Tensor, device, dtype, nn
@@ -52,8 +52,6 @@ T = TypeVar("T", bound="torch.nn.Module")
 
 import bigdl.llm.ggml.model.llama.llama_cpp as ggml
 from bigdl.llm.utils.isa_checker import is_server
-
-import torch
 import ctypes
 from bigdl.llm.ggml.quantize import ggml_tensor_qtype
 IS_SERVER = is_server()
@@ -152,6 +150,17 @@ class ParamsQuant(torch.nn.Parameter):
 
         if (device is not None and device.type == "cpu" and self.data.device.type == "cpu"):
             return self.quantize(device)
+        elif (device is not None and device.type == "xpu" and self.data.device.type == "cpu"):
+            # enter xpu logic, compile linear_int4 extension at first time
+            q_tensor = self.quantize(device)  # tensor is cpu now
+            new_param = ParamsQuant(super().to(device=device,
+                                               dtype=dtype,
+                                               non_blocking=non_blocking),
+                                    requires_grad=self.requires_grad,
+                                    quantized=self.quantized,
+                                    _shape=self._shape,
+                                    qtype=self.qtype)
+            return new_param
         else:
             new_param = ParamsQuant(super().to(device=device,
                                                dtype=dtype,
@@ -224,15 +233,34 @@ class LinearQuant(nn.Linear):
 
         x0 = self.weight.data
 
-        # todo may need to set a different number on different platforms
-        if IS_SERVER and self.qtype == SYM_INT4 and x_2d.shape[0] >= TORCH_LINEAR_THRESHOLD:
-            x0_fp32 = ggml_int4_convert_fp32(x0, self.weight_shape, self.weight_length)
-            result = F.linear(x, x0_fp32, self.bias)
-        else:
-            result = ggml_matmul_src1_x_src0_t(x0, x_2d, self.weight_shape, self.qtype)
+        if x0.device.type == "xpu":
+            # GPU logic
+            try:
+                import intel_extension_for_pytorch
+                import linear_q4_0
+            except ModuleNotFoundError:
+                invalidInputError(False,
+                                  "Please `pip install bigdl_core_xe` first.")
+
+            if x_2d.is_contiguous() is False:
+                x_2d = x_2d.contiguous()
+            # input format of linear_q4.forward is 1: input, 2: weight
+            result = linear_q4_0.forward(x_2d, x0)
             new_shape = x_shape[:-1] + (self.out_len,)
             result = result.view(new_shape)
             if self.bias is not None:
                 result += self.bias
+        else:
+            # CPU logic
+            # todo may need to set a different number on different platforms
+            if IS_SERVER and self.qtype == SYM_INT4 and x_2d.shape[0] >= TORCH_LINEAR_THRESHOLD:
+                x0_fp32 = ggml_int4_convert_fp32(x0, self.weight_shape, self.weight_length)
+                result = F.linear(x, x0_fp32, self.bias)
+            else:
+                result = ggml_matmul_src1_x_src0_t(x0, x_2d, self.weight_shape, self.qtype)
+                new_shape = x_shape[:-1] + (self.out_len,)
+                result = result.view(new_shape)
+                if self.bias is not None:
+                    result += self.bias
 
         return result.to(x.dtype)

python/llm/src/bigdl/llm/transformers/model.py

@@ -68,6 +68,7 @@ class _BaseAutoModelClass:
         # we can convert the model to quantized later.
         load_in_4bit = kwargs.pop("load_in_4bit", False)
         load_in_low_bit = kwargs.pop("load_in_low_bit", None)
+        optimize_model = kwargs.pop("optimize_model", True)
 
         if load_in_4bit or load_in_low_bit:
             # load int x-bit
@@ -78,7 +79,7 @@ class _BaseAutoModelClass:
             if "pretraining_tp" in config_dict:
                 kwargs["pretraining_tp"] = 1
             q_k = load_in_low_bit if load_in_low_bit else "sym_int4"
-            model = cls.load_convert(q_k, *args, **kwargs)
+            model = cls.load_convert(q_k, optimize_model, *args, **kwargs)
         else:
             # load default
             model = cls.HF_Model.from_pretrained(*args, **kwargs)
@@ -86,7 +87,7 @@ class _BaseAutoModelClass:
         return model
 
     @classmethod
-    def load_convert(cls, q_k, *args, **kwargs):
+    def load_convert(cls, q_k, optimize_model, *args, **kwargs):
         from .convert import ggml_convert_quant
         invalidInputError(q_k in ggml_tensor_qtype,
                           f"Unknown load_in_low_bit value: {q_k}, expected:"
@@ -94,7 +95,7 @@ class _BaseAutoModelClass:
         qtype = ggml_tensor_qtype[q_k]
         model = cls.HF_Model.from_pretrained(*args, **kwargs)
         model = model.to("cpu")
-        model = ggml_convert_quant(model, qtype)
+        model = ggml_convert_quant(model, qtype, optimize_model)
         model.config.update({"bigdl_transformers_low_bit": q_k})
 
         # add save_low_bit to pretrained model dynamically
@@ -128,6 +129,9 @@ class _BaseAutoModelClass:
         # set default torch_dtype='auto'
         kwargs["torch_dtype"] = kwargs.get("torch_dtype", 'auto')
 
+        # set default optimize_model=True
+        optimize_model = kwargs.pop("optimize_model", True)
+
         qtype = ggml_tensor_qtype[bigdl_transformers_low_bit]
         # Note that the int4 linear layers cannot currently
         # be recorded in huggingface Pretrained Model or AutoConfig,
@@ -154,7 +158,7 @@ class _BaseAutoModelClass:
 
         # We forcefully modify the model's definition
         # and the tensor shape of int4 weights without quantization.
-        model = ggml_convert_quant(model, qtype, convert_shape_only=True)
+        model = ggml_convert_quant(model, qtype, optimize_model, convert_shape_only=True)
         # Load the quantized model at last.
         resolved_archive_file, is_sharded = extract_local_archive_file(
             pretrained_model_name_or_path,

python/llm/src/bigdl/llm/transformers/models/llama.py

@@ -83,6 +83,7 @@ def llama_attention_forward_4_31(
     use_cache: bool = False,
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
     bsz, q_len, _ = hidden_states.size()
+    device = hidden_states.device
 
     if self.pretraining_tp > 1:
         key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp
@@ -153,8 +154,10 @@ def llama_attention_forward_4_31(
     past_key_value = (key_states, value_states) if use_cache else None
 
     # repeat k/v heads if n_kv_heads < n_heads
-    key_states = repeat_kv(key_states, self.num_key_value_groups)
-    value_states = repeat_kv(value_states, self.num_key_value_groups)
+    key_states = repeat_kv(key_states, self.num_key_value_groups).to(device,
+                                                                     dtype=hidden_states.dtype)
+    value_states = repeat_kv(value_states, self.num_key_value_groups).to(device,
+                                                                         dtype=hidden_states.dtype)
 
     attn_weights = torch.matmul(query_states,
                                 key_states.transpose(2, 3)) / math.sqrt(self.head_dim)