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Add transformers_int4_npu_pipeline_win in all-in-one benchmark (#12325)

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* add transformers_int4_npu_pipeline_win

* bugfix

* bugfix: wrong actual_output_len

* fix format

* bugfix & update `README.md`
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Ch1y0q 2024-11-04 16:00:20 +08:00 committed by GitHub
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4 changed files with 83 additions and 5 deletions
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7
python/llm/dev/benchmark/all-in-one/README.md
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@ -27,6 +27,7 @@ Config YAML file has following format
repo_id:
# - 'THUDM/chatglm2-6b'
- 'meta-llama/Llama-2-7b-chat-hf'
# - 'meta-llama/Meta-Llama-3.1-8B-Instruct'
# - 'liuhaotian/llava-v1.5-7b' # requires a LLAVA_REPO_DIR env variables pointing to the llava dir; added only for gpu win related test_api now
local_model_hub: 'path to your local model hub'
warm_up: 1 # must set >=2 when run "pipeline_parallel_gpu" test_api
@ -36,6 +37,7 @@ low_bit: 'sym_int4' # default to use 'sym_int4' (i.e. symmetric int4)
batch_size: 1 # default to 1
in_out_pairs:
- '32-32'
- '960-64'
- '1024-128'
test_api:
- "transformer_int4_fp16_gpu" # on Intel GPU, transformer-like API, (qtype=int4), (dtype=fp16)
@ -60,10 +62,15 @@ test_api:
# - "speculative_cpu" # on Intel CPU, inference with self-speculative decoding
# - "deepspeed_transformer_int4_cpu" # on Intel CPU, deepspeed autotp inference
# - "transformers_int4_npu_win" # on Intel NPU for Windows, transformer-like API, (qtype=int4)
# - "transformers_int4_loadlowbit_npu_win" # on Intel NPU for Windows, transformer-like API, (qtype=int4), use load_low_bit API. Please make sure you have used the save_npu.py to save the converted low bit model
# - "transformers_int4_npu_pipeline_win" # on Intel NPU for Windows, transformer-like API, (qtype=int4)
cpu_embedding: False # whether put embedding to CPU
streaming: False # whether output in streaming way (only available now for gpu win related test_api)
optimize_model: False # whether apply further optimization on NPU (only available now for transformers_int4_npu_win test_api)
use_fp16_torch_dtype: True # whether use fp16 for non-linear layer (only available now for "pipeline_parallel_gpu" test_api)
task: 'continuation' # task can be 'continuation', 'QA' and 'summarize'
transpose_value_cache: True # whether apply transposed v_cache optimization on NPU (only available now for transformers_int4_npu_win test_api)
npu_group_size: 0 # this can only be either 0 or 128, and only works for `transformers_int4_npu_win` / `transformers_int4_npu_pipeline_win`
```

3
python/llm/dev/benchmark/all-in-one/config.yaml
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@ -37,10 +37,11 @@ test_api:
# - "deepspeed_transformer_int4_cpu" # on Intel CPU, deepspeed autotp inference
# - "transformers_int4_npu_win" # on Intel NPU for Windows, transformer-like API, (qtype=int4)
# - "transformers_int4_loadlowbit_npu_win" # on Intel NPU for Windows, transformer-like API, (qtype=int4), use load_low_bit API. Please make sure you have used the save_npu.py to save the converted low bit model
# - "transformers_int4_npu_pipeline_win" # on Intel NPU for Windows, transformer-like API, (qtype=int4)
cpu_embedding: False # whether put embedding to CPU
streaming: False # whether output in streaming way (only available now for gpu win related test_api)
optimize_model: False # whether apply further optimization on NPU (only available now for transformers_int4_npu_win test_api)
use_fp16_torch_dtype: True # whether use fp16 for non-linear layer (only available now for "pipeline_parallel_gpu" test_api)
task: 'continuation' # task can be 'continuation', 'QA' and 'summarize'
transpose_value_cache: True # whether apply transposed v_cache optimization on NPU (only available now for transformers_int4_npu_win test_api)
npu_group_size: 128 # This can only be either 0 or 128, and only works for `transformers_int4_npu_win` / `transformers_int4_npu_pipline_win`
npu_group_size: 0 # this can only be either 0 or 128, and only works for `transformers_int4_npu_win` / `transformers_int4_npu_pipeline_win`

72
python/llm/dev/benchmark/all-in-one/run.py
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@ -191,6 +191,8 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
result = run_pipeline_parallel_gpu(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, batch_size, cpu_embedding, fp16=use_fp16_torch_dtype)
elif test_api == 'transformers_int4_npu_win':
result = transformers_int4_npu_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, batch_size, optimize_model, transpose_value_cache, group_size)
elif test_api == 'transformers_int4_npu_pipeline_win':
result = transformers_int4_npu_pipeline_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, batch_size, optimize_model, transpose_value_cache, group_size)
elif test_api == 'transformers_int4_loadlowbit_npu_win':
result = run_transformer_int4_loadlowbit_npu_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, batch_size, optimize_model, transpose_value_cache)
elif test_api == 'transformers_openvino':
@ -215,7 +217,7 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
result[in_out_pair][-1][6] if any(keyword in test_api for keyword in ['int4_gpu', 'int4_fp16_gpu_win', 'int4_loadlowbit_gpu', 'int4_fp16_loadlowbit_gpu', 'fp16_gpu', 'deepspeed_optimize_model_gpu']) and not lookahead else 'N/A',
streaming if 'win' in test_api else 'N/A',
use_fp16_torch_dtype if 'pipeline_parallel_gpu' in test_api else 'N/A',
group_size if 'transformers_int4_npu_win' in test_api else 'N/A'],
group_size if any(keyword in test_api for keyword in ['transformers_int4_npu_win', 'transformers_int4_npu_pipeline_win']) else 'N/A'],
)
@ -680,6 +682,70 @@ def transformers_int4_npu_win(repo_id,
gc.collect()
return result
def transformers_int4_npu_pipeline_win(repo_id,
local_model_hub,
in_out_pairs,
warm_up,
num_trials,
num_beams,
low_bit,
batch_size,
optimize_model,
transpose_value_cache,
npu_group_size):
from ipex_llm.transformers.npu_model import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, LlamaTokenizer
model_path = get_model_path(repo_id, local_model_hub)
in_out_len = in_out_pairs[0].split("-")
max_context_len = max(int(in_out_len[0]) + int(in_out_len[1]), 1024)
# Load model in 4 bit,
# which convert the relevant layers in the model into INT4 format
st = time.perf_counter()
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True, pipeline=True, torch_dtype=torch.float16,
optimize_model=optimize_model, max_context_len=max_context_len, max_prompt_len=int(in_out_len[0]),
quantization_group_size=npu_group_size, transpose_value_cache=transpose_value_cache,
use_cache=True, attn_implementation="eager").eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
end = time.perf_counter()
load_time = end - st
print(">> loading of model costs {}s".format(load_time))
result = {}
with torch.inference_mode():
for in_out in in_out_pairs:
in_out_len = in_out.split("-")
in_len = int(in_out_len[0])
out_len = int(in_out_len[1])
input_str = get_continuation_input_str(in_len, tokenizer)
# As different tokenizer has different encodings,
# slice the input_ids to ensure the prompt length is required length.
input_ids = tokenizer.encode(input_str, return_tensors="pt")
input_ids = input_ids[:, :in_len]
true_str = tokenizer.batch_decode(input_ids)[0]
input_list = [true_str] * batch_size
input_ids = tokenizer(input_list, return_tensors="pt").input_ids
input_ids = input_ids[:, :in_len]
actual_in_len = input_ids.shape[1]
result[in_out] = []
for i in range(num_trials + warm_up):
st = time.perf_counter()
output_ids = model.generate(input_ids, do_sample=False, max_new_tokens=out_len,
min_new_tokens=out_len, num_beams=num_beams)
end = time.perf_counter()
print("model generate cost: " + str(end - st))
output = tokenizer.batch_decode(output_ids)
print(output[0])
actual_out_len = output_ids.shape[1] - actual_in_len
if i >= warm_up:
result[in_out].append([model.first_cost, model.rest_cost_mean, model.encoder_time,
actual_in_len, actual_out_len, load_time])
del model
gc.collect()
return result
def run_transformer_int4_loadlowbit_npu_win(repo_id,
local_model_hub,
in_out_pairs,
@ -2186,7 +2252,7 @@ if __name__ == '__main__':
streaming = False
use_fp16_torch_dtype = False
task = 'continuation'
optimize_model = False # only for transformers_int4_npu_win
optimize_model = False # only for transformers_int4_npu_win, transformers_int4_npu_pipeline_win
group_size = 64
if 'streaming' in conf:
streaming = conf['streaming']
@ -2233,7 +2299,7 @@ if __name__ == '__main__':
df = pd.DataFrame(results, columns=['model', '1st token avg latency (ms)', '2+ avg latency (ms/token)', 'encoder time (ms)',
'input/output tokens', 'batch_size', 'actual input/output tokens', 'num_beams', 'low_bit', 'cpu_embedding',
'model loading time (s)', 'peak mem (GB)', 'streaming', 'use_fp16_torch_dtype', 'npu_group_size'])
if "pipeline" in api or "deepspeed" in api:
if ("pipeline" in api or "deepspeed" in api) and api != 'transformers_int4_npu_pipeline_win':
if torch.distributed.get_rank() == 0:
df.index += max(line_counter - 1, 0)
if line_counter == 0:

4
python/llm/src/ipex_llm/transformers/npu_pipeline_model/convert_pipeline.py
View file

@ -172,6 +172,10 @@ def generate(
thread.join()
time_end = time.perf_counter()
self.first_cost = (time_t3 - time_start_all - (time_t2 - time_t1)) # seconds
self.rest_cost_mean = (time_end - time_t3) / (idx - 1) # seconds
self.encoder_time = 0.0
if do_print:
print(f" Start the thread and connect the pipe time: {(time_t2 - time_t1):.2f} s")
print(f" Number of input tokens: {input_length}")