add langchain gpu example (#10277)

* first draft

* fix

* add readme for transformer_int4_gpu

* fix doc

* check device_map

* add arc ut test

* fix ut test

* fix langchain ut

* Refine README

* fix gpu mem too high

* fix ut test

---------

Co-authored-by: Ariadne <wyn2000330@126.com>

.github/workflows/llm_unit_tests.yml

@@ -346,3 +346,17 @@ jobs:
             source /home/arda/intel/oneapi/setvars.sh
           fi
           bash python/llm/test/run-llm-example-tests-gpu.sh
+
+      - name: Run LLM langchain test
+        shell: bash
+        run: |
+          pip install -U langchain==0.0.184
+          pip install -U chromadb==0.3.25
+          pip install -U pandas==2.0.3
+          # Specific oneapi position on arc ut test machines
+          if [[ '${{ matrix.pytorch-version }}' == '2.1' ]]; then
+            source /opt/intel/oneapi/setvars.sh
+          elif [[ '${{ matrix.pytorch-version }}' == '2.0' ]]; then
+            source /home/arda/intel/oneapi/setvars.sh
+          fi
+          bash python/llm/test/run-llm-langchain-tests-gpu.sh

python/llm/example/GPU/LangChain/transformer_int4_gpu/README.md

@@ -0,0 +1,93 @@
+# Langchain examples
+
+The examples in this folder shows how to use [LangChain](https://www.langchain.com/) with `bigdl-llm` on Intel GPU.
+
+### 1. Install bigdl-llm
+Follow the instructions in [GPU Install Guide](https://bigdl.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html) to install bigdl-llm
+
+### 2. Install Required Dependencies for langchain examples. 
+
+```bash
+pip install langchain==0.0.184
+pip install -U chromadb==0.3.25
+pip install -U pandas==2.0.3
+```
+
+### 3. Configures OneAPI environment variables
+#### 3.1 Configurations for Linux
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+#### 3.2 Configurations for Windows
+```cmd
+call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
+```
+> Note: Please make sure you are using **CMD** (**Anaconda Prompt** if using conda) to run the command as PowerShell is not supported.
+### 4. Runtime Configurations
+For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
+#### 4.1 Configurations for Linux
+<details>
+
+<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Data Center GPU Max Series</summary>
+
+```bash
+export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export ENABLE_SDP_FUSION=1
+```
+> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
+</details>
+
+#### 4.2 Configurations for Windows
+<details>
+
+<summary>For Intel iGPU</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+set BIGDL_LLM_XMX_DISABLED=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Arc™ A300-Series or Pro A60</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+```
+
+</details>
+
+<details>
+
+<summary>For other Intel dGPU Series</summary>
+
+There is no need to set further environment variables.
+
+</details>
+
+> Note: For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
+
+### 5. Run the examples
+
+#### 5.1. Streaming Chat
+
+```bash
+python chat.py -m MODEL_PATH -q QUESTION
+```
+arguments info:
+- `-m MODEL_PATH`: **required**, path to the model
+- `-q QUESTION`: question to ask. Default is `What is AI?`.

python/llm/example/GPU/LangChain/transformer_int4_gpu/chat.py

@@ -0,0 +1,65 @@
+#
+# 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.
+#
+
+# This would makes sure Python is aware there is more than one sub-package within bigdl,
+# physically located elsewhere.
+# Otherwise there would be module not found error in non-pip's setting as Python would
+# only search the first bigdl package and end up finding only one sub-package.
+
+import argparse
+
+from bigdl.llm.langchain.llms import TransformersLLM, TransformersPipelineLLM
+from langchain import PromptTemplate, LLMChain
+from langchain import HuggingFacePipeline
+
+
+def main(args):
+    
+    question = args.question
+    model_path = args.model_path
+    template ="""{question}"""
+
+    prompt = PromptTemplate(template=template, input_variables=["question"])
+
+    # llm = TransformersPipelineLLM.from_model_id(
+    #     model_id=model_path,
+    #     task="text-generation",
+    #     model_kwargs={"temperature": 0, "max_length": 64, "trust_remote_code": True},
+    #     device_map='xpu'
+    # )
+
+    llm = TransformersLLM.from_model_id(
+        model_id=model_path,
+        model_kwargs={"temperature": 0, "max_length": 64, "trust_remote_code": True},
+        device_map='xpu'
+    )
+
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+
+    output = llm_chain.run(question)
+    print("====output=====")
+    print(output)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='TransformersLLM Langchain Chat Example')
+    parser.add_argument('-m','--model-path', type=str, required=True,
+                        help='the path to transformers model')
+    parser.add_argument('-q', '--question', type=str, default='What is AI?',
+                        help='qustion you want to ask.')
+    args = parser.parse_args()
+    
+    main(args)

python/llm/example/GPU/README.md

@@ -7,6 +7,7 @@ This folder contains examples of running BigDL-LLM on Intel GPU:
 - [LLM-Finetuning](LLM-Finetuning): running ***finetuning*** (such as LoRA, QLoRA, QA-LoRA, etc) using BigDL-LLM on Intel GPUs
 - [vLLM-Serving](vLLM-Serving): running ***vLLM*** serving framework on intel GPUs (with BigDL-LLM low-bit optimized models)
 - [Deepspeed-AutoTP](Deepspeed-AutoTP): running distributed inference using ***DeepSpeed AutoTP*** (with BigDL-LLM low-bit optimized models) on Intel GPUs
+- [LangChain](LangChain): running ***LangChain*** applications on BigDL-LLM
 - [PyTorch-Models](PyTorch-Models): running any PyTorch model on BigDL-LLM (with "one-line code change")
 - [Speculative-Decoding](Speculative-Decoding): running any ***Hugging Face Transformers*** model with ***self-speculative decoding*** on Intel GPUs
 - [ModelScope-Models](ModelScope-Models): running ***ModelScope*** model with BigDL-LLM on Intel GPUs

python/llm/src/bigdl/llm/langchain/llms/transformersllm.py

@@ -89,6 +89,7 @@ class TransformersLLM(LLM):
         cls,
         model_id: str,
         model_kwargs: Optional[dict] = None,
+        device_map: str = 'cpu',
         **kwargs: Any,
     ) -> LLM:
         """
@@ -131,6 +132,10 @@ class TransformersLLM(LLM):
         except:
             model = AutoModel.from_pretrained(model_id, load_in_4bit=True, **_model_kwargs)
 
+        # TODO: may refactore this code in the future
+        if 'xpu' in device_map:
+            model = model.to(device_map)
+
         if "trust_remote_code" in _model_kwargs:
             _model_kwargs = {
                 k: v for k, v in _model_kwargs.items() if k != "trust_remote_code"
@@ -149,6 +154,7 @@ class TransformersLLM(LLM):
         cls,
         model_id: str,
         model_kwargs: Optional[dict] = None,
+        device_map: str = 'cpu',
         **kwargs: Any,
     ) -> LLM:
         """
@@ -189,6 +195,10 @@ class TransformersLLM(LLM):
         except:
             model = AutoModel.load_low_bit(model_id, **_model_kwargs)
         
+        # TODO: may refactore this code in the future
+        if 'xpu' in device_map:
+            model = model.to(device_map)
+
         if "trust_remote_code" in _model_kwargs:
             _model_kwargs = {
                 k: v for k, v in _model_kwargs.items() if k != "trust_remote_code"
@@ -224,6 +234,7 @@ class TransformersLLM(LLM):
         if self.streaming:
             from transformers import TextStreamer
             input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
+            input_ids = input_ids.to(self.model.device)
             streamer = TextStreamer(self.tokenizer, skip_prompt=True, skip_special_tokens=True)
             if stop is not None:
                 from transformers.generation.stopping_criteria import StoppingCriteriaList
@@ -240,6 +251,7 @@ class TransformersLLM(LLM):
             return text
         else:
             input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
+            input_ids = input_ids.to(self.model.device)
             if stop is not None:
                 from transformers.generation.stopping_criteria import StoppingCriteriaList
                 from transformers.tools.agents import StopSequenceCriteria

python/llm/test/langchain_gpu/test_transformers_api.py

@@ -0,0 +1,48 @@
+#
+# 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.
+#
+
+from bigdl.llm.langchain.llms import TransformersLLM, TransformersPipelineLLM, \
+    LlamaLLM, BloomLLM
+from bigdl.llm.langchain.embeddings import TransformersEmbeddings, LlamaEmbeddings, \
+    BloomEmbeddings
+
+import pytest
+from unittest import TestCase
+import os
+
+device = os.environ['DEVICE']
+print(f'Running on {device}')
+
+class Test_Langchain_Transformers_API(TestCase):
+    def setUp(self):
+        self.llama_model_path = os.environ.get('LLAMA2_7B_ORIGIN_PATH')
+        thread_num = os.environ.get('THREAD_NUM')
+        if thread_num is not None:
+            self.n_threads = int(thread_num)
+        else:
+            self.n_threads = 2         
+
+
+    def test_bigdl_llm(self):
+        texts = 'What is the capital of France?\n\n'
+        bigdl_llm = TransformersLLM.from_model_id(model_id=self.llama_model_path, model_kwargs={'trust_remote_code': True}, device_map=device)
+        
+        output = bigdl_llm(texts)
+        res = "Paris" in output
+        self.assertTrue(res)
+
+if __name__ == '__main__':
+    pytest.main([__file__])

python/llm/test/run-llm-langchain-tests-gpu.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+
+export ANALYTICS_ZOO_ROOT=${ANALYTICS_ZOO_ROOT}
+export LLM_HOME=${ANALYTICS_ZOO_ROOT}/python/llm/src
+export LLM_INFERENCE_TEST_DIR=${ANALYTICS_ZOO_ROOT}/python/llm/test/langchain_gpu
+
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export DEVICE='xpu'
+
+set -e
+
+echo "# Start testing inference"
+start=$(date "+%s")
+
+python -m pytest -s ${LLM_INFERENCE_TEST_DIR}
+
+now=$(date "+%s")
+time=$((now-start))
+
+echo "Bigdl-llm langchain gpu tests finished"
+echo "Time used:$time seconds"