LLM: update DeepSpeed AutoTP example with GPU memory optimization (#9823)

python/llm/example/GPU/Deepspeed-AutoTP/README.md

@@ -26,15 +26,28 @@ conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc
 > **Important**: IPEX 2.1.10+xpu requires Intel® oneAPI Base Toolkit's version == 2024.0. Please make sure you have installed the correct version.
 
 ### 2. Run tensor parallel inference on multiple GPUs
-Here, we provide example usages on different models and different hardwares. Please refer to the appropriate script based on your model and device:
+Here, we separate inference process into two stages. First, convert to deepspeed model and apply bigdl-llm optimization on CPU. Then, utilize XPU as DeepSpeed accelerator to inference. In this way, a *X*B model saved in 16-bit will requires approximately 0.5*X* GB total GPU memory in the whole process. For example, if you select to use two GPUs, 0.25*X* GB memory is required per GPU.
 
-#### Llama2 series
-<details><summary>Show LLaMA2-70B example</summary>
-Run LLaMA2-70B on four Intel Data Center GPU Max 1550
+Please select the appropriate model size based on the capabilities of your machine.
+
+We provide example usages on different models and different hardwares as following:
+
+- Run LLaMA2-70B on one card of Intel Data Center GPU Max 1550
 
 ```
-bash run_llama2_70b_pvc_1550_4_card.sh
+bash run_llama2_70b_pvc_1550_1_card.sh
 ```
-</details>
 
-> **Note**:If you may want to select only part of GPUs on your machine, please change `ZE_AFFINITY_MASK` and `NUM_GPUS` to your prefer value.
+> **Note**: You could change `ZE_AFFINITY_MASK` and `NUM_GPUS` according to your requirements.
+
+- Run Vicuna-33B on two Intel Arc A770
+
+```
+bash run_vicuna_33b_arc_2_card.sh
+```
+
+> **Note**: You could change `NUM_GPUS` to the number of GPUs you have on your machine.
+
+
+### Known Issue
+- In our example scripts, tcmalloc is enabled through `export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so:${LD_PRELOAD}` which speed up inference, but this may raise `munmap_chunk(): invalid pointer` error after finishing inference.

python/llm/example/GPU/Deepspeed-AutoTP/deepspeed_autotp.py

@@ -29,6 +29,10 @@ def get_int_from_env(env_keys, default):
  
 local_rank = get_int_from_env(["LOCAL_RANK","PMI_RANK"], "0")
 world_size = get_int_from_env(["WORLD_SIZE","PMI_SIZE"], "1")
+os.environ["RANK"] = str(local_rank)
+os.environ["WORLD_SIZE"] = str(world_size)
+os.environ["MASTER_PORT"] = os.environ.get("MASTER_PORT", "29500")
+
 
 from bigdl.llm import optimize_model
 
@@ -38,6 +42,9 @@ import argparse
 
 from transformers import AutoModelForCausalLM  # export AutoModelForCausalLM from transformers so that deepspeed use it
 from transformers import LlamaTokenizer, AutoTokenizer
+from deepspeed.accelerator.cpu_accelerator import CPU_Accelerator
+from deepspeed.accelerator import set_accelerator, get_accelerator
+from intel_extension_for_deepspeed import XPU_Accelerator
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model')
@@ -52,7 +59,12 @@ if __name__ == '__main__':
     args = parser.parse_args()
     model_path = args.repo_id_or_model_path
 
+    # First use CPU as accelerator
+    # Convert to deepspeed model and apply bigdl-llm optimization on CPU to decrease GPU memory usage
+    current_accel = CPU_Accelerator()
+    set_accelerator(current_accel)
     model = AutoModelForCausalLM.from_pretrained(args.repo_id_or_model_path,
+                                                 device_map={"": "cpu"},
                                                  low_cpu_mem_usage=True,
                                                  torch_dtype=torch.float16,
                                                  trust_remote_code=True,
@@ -65,14 +77,26 @@ if __name__ == '__main__':
         replace_method="auto",
     )
 
-    # move model to cpu and use bigdl-llm `optimize_model` to convert the
-    # model into optimized low bit format
-    # convert the rest of the model into float16 to reduce allreduce traffic
+    # Use bigdl-llm `optimize_model` to convert the model into optimized low bit format
+    # Convert the rest of the model into float16 to reduce allreduce traffic
     model = optimize_model(model.module.to(f'cpu'), low_bit='sym_int4').to(torch.float16)
 
-    # move model back to xpu
+    # Next, use XPU as accelerator to speed up inference
+    current_accel = XPU_Accelerator()
+    set_accelerator(current_accel)
+
+    # Move model back to xpu
     model = model.to(f'xpu:{local_rank}')
 
+    # Modify backend related settings 
+    if world_size > 1:
+        get_accelerator().set_device(local_rank)
+    dist_backend = get_accelerator().communication_backend_name()
+    import deepspeed.comm.comm
+    deepspeed.comm.comm.cdb = None
+    from deepspeed.comm.comm import init_distributed
+    init_distributed()
+
     print(model)
 
     # Load tokenizer
@@ -80,9 +104,7 @@ if __name__ == '__main__':
 
     # Generate predicted tokens
     with torch.inference_mode():
-        # prompt = get_prompt(args.prompt, [], system_prompt=DEFAULT_SYSTEM_PROMPT)
         prompt = args.prompt
-        # input_str = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:\n"
         input_ids = tokenizer.encode(prompt, return_tensors="pt").to(f'xpu:{local_rank}')
         # ipex model needs a warmup, then inference time can be accurate
         output = model.generate(input_ids,
@@ -108,3 +130,5 @@ if __name__ == '__main__':
             print(prompt)
             print('-'*20, 'Output', '-'*20)
             print(output_str)
+    deepspeed.comm.destroy_process_group()
+    print("process group destroyed, exiting...")

python/llm/example/GPU/Deepspeed-AutoTP/run_llama2_70b_pvc_1550_1_card.sh

@@ -14,12 +14,14 @@
 # limitations under the License.
 #
 
-export ZE_AFFINITY_MASK="0,1,2,3,4,5,6,7" # specify the used GPU
-NUM_GPUS=8 # number of used GPU
+export ZE_AFFINITY_MASK="0,1" # specify the used GPU
+NUM_GPUS=2 # number of used GPU
 export MASTER_ADDR=127.0.0.1
 export FI_PROVIDER=tcp
-export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so:${LD_PRELOAD}
+export CCL_ATL_TRANSPORT=ofi
+export CCL_ZE_IPC_EXCHANGE=sockets
 
+export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so:${LD_PRELOAD}
 basekit_root=/opt/intel/oneapi
 source $basekit_root/setvars.sh --force
 source $basekit_root/ccl/latest/env/vars.sh --force

python/llm/example/GPU/Deepspeed-AutoTP/run_vicuna_33b_arc_2_card.sh

@@ -0,0 +1,33 @@
+#
+# 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.
+#
+
+export MASTER_ADDR=127.0.0.1
+export FI_PROVIDER=tcp
+export CCL_ATL_TRANSPORT=ofi
+export CCL_ZE_IPC_EXCHANGE=sockets
+
+export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so:${LD_PRELOAD}
+basekit_root=/opt/intel/oneapi
+source $basekit_root/setvars.sh --force
+source $basekit_root/ccl/latest/env/vars.sh --force
+
+NUM_GPUS=2 # number of used GPU
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=2
+export TORCH_LLM_ALLREDUCE=0 # Different from PVC
+
+mpirun -np $NUM_GPUS --prepend-rank \
+    python deepspeed_autotp.py --repo-id-or-model-path 'lmsys/vicuna-33b-v1.3'