R1 Hybrid: Add Benchmark for DeepSeek R1 transformers example (#12854)

* init

* fix

* update

* update

* fix

* fix

python/llm/dev/test/lint-python

@@ -21,7 +21,7 @@ SCRIPT_DIR="$( cd "$( dirname "$0" )" && pwd )"
 PYTHON_ROOT_DIR="$SCRIPT_DIR/.."
 echo $PYTHON_ROOT_DIR
 PATHS_TO_CHECK="$SCRIPT_DIR/../../src"
-PATTERNS_TO_EXCLUDE="__init__.py,log4Error.py,$SCRIPT_DIR/../../src/ipex_llm/langchain/*,$SCRIPT_DIR/../../src/ipex_llm/transformers/gguf/models/model_implement/yuan2/*,benchmark_util_4_29.py,benchmark_util_4_42.py,benchmark_util_4_43.py,benchmark_util_4_44.py,benchmark_util_4_45.py,benchmark_util_4_47.py,tgi_api_server.py,api_server.py"
+PATTERNS_TO_EXCLUDE="__init__.py,log4Error.py,$SCRIPT_DIR/../../src/ipex_llm/langchain/*,$SCRIPT_DIR/../../src/ipex_llm/transformers/gguf/models/model_implement/yuan2/*,benchmark_util_4_29.py,benchmark_util_4_42.py,benchmark_util_4_43.py,benchmark_util_4_44.py,benchmark_util_4_45.py,benchmark_util_4_47.py,benchmark_util_deepseek.py,tgi_api_server.py,api_server.py"
 PEP8_REPORT_PATH="$PYTHON_ROOT_DIR/test/pep8-report.txt"
 PYLINT_REPORT_PATH="$PYTHON_ROOT_DIR/test/pylint-report.txt"
 PYLINT_INSTALL_INFO="$PYTHON_ROOT_DIR/test/pylint-info.txt"

python/llm/example/GPU/DeepSeek-R1/benchmark.py

@@ -0,0 +1,109 @@
+#
+# 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 typing import List, Optional, Tuple, Union
+import warnings
+import os
+
+import torch
+from torch import nn
+import time
+import argparse
+import ipex_llm
+import numpy as np
+
+from ipex_llm.transformers import AutoModelForCausalLM, convert_model_hybrid
+from ipex_llm.utils.benchmark_util_deepseek import BenchmarkWrapper
+
+from transformers import AutoTokenizer, GenerationConfig
+from transformers.cache_utils import Cache, DynamicCache
+
+
+PROMPT_FORMAT = """
+A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>.
+User: {prompt}.
+Assistant: <think>
+"""
+
+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="If \( a > 1 \), then the sum of the real solutions of \( \sqrt{a} - \sqrt{a + x} = x \) is equal to:",
+                        help='Prompt to infer')
+    parser.add_argument('--n-predict', type=int, default=32,
+                        help='Max tokens to predict')
+    parser.add_argument('--load-path', type=str, default=None,
+                        help='The path to load the low-bit model.')
+    parser.add_argument('--warm-up', type=int, default=1,
+                        help='Num of warm-up trials.')
+    parser.add_argument('--num-trials', type=int, default=1,
+                        help='Num of trials to run.')
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    load_path = args.load_path
+    if load_path:
+        model = AutoModelForCausalLM.load_low_bit(load_path, trust_remote_code=True)
+        tokenizer = AutoTokenizer.from_pretrained(load_path,
+                                              trust_remote_code=True)
+    else:
+        model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                    load_in_4bit=True,
+                                                    optimize_model=True,
+                                                    trust_remote_code=True,
+                                                    use_cache=True)
+        tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                              trust_remote_code=True)
+    
+    model = model.bfloat16()
+    model = convert_model_hybrid(model)
+    print(model)
+
+    model = BenchmarkWrapper(model)
+    e2e_time_list = []
+    prefill_time_list = []
+    rest_cost_mean_list = []
+
+    # Generate predicted tokens
+    with torch.inference_mode():
+        prompt = PROMPT_FORMAT.format(prompt=args.prompt)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt")
+        # ipex_llm model needs a warmup, then inference time can be accurate
+        for i in range(args.warm_up):
+            output = model.generate(input_ids,
+                                    max_new_tokens=args.n_predict,
+                                    min_new_tokens=args.n_predict)
+
+        # start inference
+        for i in range(args.num_trials):
+            st = time.time()
+            output = model.generate(input_ids,
+                                    max_new_tokens=args.n_predict,
+                                    min_new_tokens=args.n_predict)
+            torch.xpu.synchronize()
+            end = time.time()
+            output = output.cpu()
+            e2e_time_list.append(end - st)
+            prefill_time_list.append(model.first_cost)
+            rest_cost_mean_list.append(model.rest_cost_mean)
+
+        print('-'*20, 'Performance', '-'*20)
+        print(f"End-to-end time: {np.mean(e2e_time_list)} s")
+        print(f"Prefill time: {np.mean(prefill_time_list)} s")
+        print(f"Rest cost mean: {np.mean(rest_cost_mean_list) * 1000} ms")

python/llm/example/GPU/DeepSeek-R1/generate_hybrid.py

@@ -24,8 +24,8 @@ import time
 import argparse
 import ipex_llm
 
-from ipex_llm.transformers import convert_model_hybrid
+from ipex_llm.transformers import AutoModelForCausalLM, convert_model_hybrid
-from ipex_llm.transformers import AutoModelForCausalLM
+
 from transformers import AutoTokenizer, GenerationConfig
 from transformers.cache_utils import Cache, DynamicCache
 
@@ -65,7 +65,7 @@ if __name__ == '__main__':
         tokenizer = AutoTokenizer.from_pretrained(model_path,
                                               trust_remote_code=True)
     
-    #model = model.bfloat16()
+    model = model.bfloat16()
     model = convert_model_hybrid(model)
     print(model)