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intel-analytics · Nov 1, 2024 · a63cc49 · a63cc49
1 parent eda7649
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diff --git a/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/README.md b/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/README.md
@@ -72,6 +72,7 @@ Arguments info:
 - `--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.
 
 ### Sample Output
 #### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)

diff --git a/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/baichuan2.py b/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/baichuan2.py
@@ -20,7 +20,7 @@
 import time
 import argparse
 from ipex_llm.transformers.npu_model import AutoModelForCausalLM
-from transformers import AutoTokenizer
+from transformers import AutoTokenizer, TextStreamer
 from transformers.utils import logging
 
 logger = logging.get_logger(__name__)
@@ -61,6 +61,7 @@ def get_prompt(message: str, chat_history: list[tuple[str, str]],
     parser.add_argument("--max-context-len", type=int, default=1024)
     parser.add_argument("--max-prompt-len", type=int, default=512)
     parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
+    parser.add_argument("--disable-streaming", action="store_true", default=False)
 
     args = parser.parse_args()
     model_path = args.repo_id_or_model_path
@@ -92,6 +93,11 @@ def get_prompt(message: str, chat_history: list[tuple[str, str]],
     if args.lowbit_path and not os.path.exists(args.lowbit_path):
         model.save_low_bit(args.lowbit_path)
 
+    if args.disable_streaming:
+        streamer = None
+    else:
+        streamer = TextStreamer(tokenizer=tokenizer, skip_special_tokens=True)
+
     DEFAULT_SYSTEM_PROMPT = """\
     """
 
@@ -105,7 +111,7 @@ def get_prompt(message: str, chat_history: list[tuple[str, str]],
             print("input length:", len(_input_ids[0]))
             st = time.time()
             output = model.generate(
-                _input_ids, max_new_tokens=args.n_predict, do_print=True
+                _input_ids, max_new_tokens=args.n_predict, do_print=True, streamer=streamer
             )
             end = time.time()
             print(f"Inference time: {end-st} s")

diff --git a/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/llama2.py b/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/llama2.py
@@ -20,7 +20,7 @@
 import time
 import argparse
 from ipex_llm.transformers.npu_model import AutoModelForCausalLM
-from transformers import AutoTokenizer
+from transformers import AutoTokenizer, TextStreamer
 from transformers.utils import logging
 
 logger = logging.get_logger(__name__)
@@ -62,6 +62,7 @@ def get_prompt(message: str, chat_history: list[tuple[str, str]],
     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)
 
     args = parser.parse_args()
     model_path = args.repo_id_or_model_path
@@ -91,6 +92,11 @@ def get_prompt(message: str, chat_history: list[tuple[str, str]],
 
     if args.lowbit_path and not os.path.exists(args.lowbit_path):
         model.save_low_bit(args.lowbit_path)
+
+    if args.disable_streaming:
+        streamer = None
+    else:
+        streamer = TextStreamer(tokenizer=tokenizer, skip_special_tokens=True)
 
     DEFAULT_SYSTEM_PROMPT = """\
     """
@@ -105,7 +111,7 @@ def get_prompt(message: str, chat_history: list[tuple[str, str]],
             print("input length:", len(_input_ids[0]))
             st = time.time()
             output = model.generate(
-                _input_ids, max_new_tokens=args.n_predict, do_print=True
+                _input_ids, max_new_tokens=args.n_predict, do_print=True, streamer=streamer
             )
             end = time.time()
             print(f"Inference time: {end-st} s")

diff --git a/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/llama3.py b/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/llama3.py
@@ -20,7 +20,7 @@
 import time
 import argparse
 from ipex_llm.transformers.npu_model import AutoModelForCausalLM
-from transformers import AutoTokenizer
+from transformers import AutoTokenizer, TextStreamer
 from transformers.utils import logging
 
 logger = logging.get_logger(__name__)
@@ -68,6 +68,7 @@ def get_prompt(user_input: str, chat_history: list[tuple[str, str]],
     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)
 
     args = parser.parse_args()
     model_path = args.repo_id_or_model_path
@@ -98,6 +99,11 @@ def get_prompt(user_input: str, chat_history: list[tuple[str, str]],
     if args.lowbit_path and not os.path.exists(args.lowbit_path):
         model.save_low_bit(args.lowbit_path)
 
+    if args.disable_streaming:
+        streamer = None
+    else:
+        streamer = TextStreamer(tokenizer=tokenizer, skip_special_tokens=True)
+
     print("-" * 80)
     print("done")
     with torch.inference_mode():
@@ -108,7 +114,7 @@ def get_prompt(user_input: str, chat_history: list[tuple[str, str]],
             print("input length:", len(_input_ids[0]))
             st = time.time()
             output = model.generate(
-                _input_ids, max_new_tokens=args.n_predict, do_print=True
+                _input_ids, max_new_tokens=args.n_predict, do_print=True, streamer=streamer
             )
             end = time.time()
             print(f"Inference time: {end-st} s")

diff --git a/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/minicpm.py b/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/minicpm.py
@@ -19,7 +19,7 @@
 import time
 import argparse
 from ipex_llm.transformers.npu_model import AutoModelForCausalLM
-from transformers import AutoTokenizer
+from transformers import AutoTokenizer, TextStreamer
 from transformers.utils import logging
 import os
 
@@ -48,6 +48,7 @@
     parser.add_argument("--max-context-len", type=int, default=1024)
     parser.add_argument("--max-prompt-len", type=int, default=512)
     parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
+    parser.add_argument("--disable-streaming", action="store_true", default=False)
 
     args = parser.parse_args()
     model_path = args.repo_id_or_model_path
@@ -79,6 +80,11 @@
     if args.lowbit_path and not os.path.exists(args.lowbit_path):
         model.save_low_bit(args.lowbit_path)
 
+    if args.disable_streaming:
+        streamer = None
+    else:
+        streamer = TextStreamer(tokenizer=tokenizer, skip_special_tokens=True)
+
     print("-" * 80)
     print("done")
     with torch.inference_mode():
@@ -89,7 +95,7 @@
             print("input length:", len(_input_ids[0]))
             st = time.time()
             output = model.generate(
-                _input_ids, max_new_tokens=args.n_predict, do_print=True
+                _input_ids, max_new_tokens=args.n_predict, do_print=True, streamer=streamer
             )
             end = time.time()
             print(f"Inference time: {end-st} s")

diff --git a/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/qwen.py b/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/qwen.py
@@ -20,7 +20,7 @@
 import time
 import argparse
 from ipex_llm.transformers.npu_model import AutoModelForCausalLM
-from transformers import AutoTokenizer
+from transformers import AutoTokenizer, TextStreamer
 from transformers.utils import logging
 
 logger = logging.get_logger(__name__)
@@ -50,6 +50,7 @@
     parser.add_argument('--load_in_low_bit', type=str, default="sym_int4",
                         help='Load in low bit to use')
     parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
+    parser.add_argument("--disable-streaming", action="store_true", default=False)
 
     args = parser.parse_args()
     model_path = args.repo_id_or_model_path
@@ -81,6 +82,11 @@
     if args.lowbit_path and not os.path.exists(args.lowbit_path):
         model.save_low_bit(args.lowbit_path)
 
+    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."},
@@ -95,7 +101,7 @@
             print("input length:", len(_input_ids[0]))
             st = time.time()
             output = model.generate(
-                _input_ids, max_new_tokens=args.n_predict, do_print=True
+                _input_ids, max_new_tokens=args.n_predict, do_print=True, streamer=streamer
             )
             end = time.time()
             print(f"Inference time: {end-st} s")