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add npu_group_size for transformers_int4_npu_win in all-in-one benchmark api (#12316)

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* add `npu_group_size` for `transformers_int4_npu_win`
small bugfix

* update
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Ch1y0q 2024-11-01 18:44:27 +08:00 committed by GitHub
parent cd5e22cee5
commit 48123af463
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GPG key ID: B5690EEEBB952194
6 changed files with 21 additions and 15 deletions
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1
python/llm/dev/benchmark/all-in-one/config.yaml
View file

@ -43,3 +43,4 @@ optimize_model: False # whether apply further optimization on NPU (only availabl
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`

27
python/llm/dev/benchmark/all-in-one/run.py
View file

@ -190,7 +190,7 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
elif test_api == 'pipeline_parallel_gpu':
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)
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_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':
@ -214,7 +214,8 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
round(result[in_out_pair][-1][5], 2),
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'],
use_fp16_torch_dtype if 'pipeline_parallel_gpu' in test_api else 'N/A',
group_size],
)
@ -589,9 +590,9 @@ def run_transformer_int4_gpu(repo_id,
file.seek(0, os.SEEK_END)
global line_counter
if file.tell() == 0:
csv_writer.writerow(["", "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"])
csv_writer.writerow(["", "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"])
line_counter +=1
csv_writer.writerow([line_counter-1, repo_id, first_token_latency, rest_token_latency, encoder_time, input_output_tokens, batch_size, actual_input_output_tokens, num_beams, low_bit, '', load_time, peak_mem, streaming, use_fp16_torch_dtype])
csv_writer.writerow([line_counter-1, repo_id, first_token_latency, rest_token_latency, encoder_time, input_output_tokens, batch_size, actual_input_output_tokens, num_beams, low_bit, '', load_time, peak_mem, streaming, use_fp16_torch_dtype, group_size])
line_counter += 1
model.to('cpu')
@ -611,7 +612,8 @@ def transformers_int4_npu_win(repo_id,
low_bit,
batch_size,
optimize_model,
transpose_value_cache):
transpose_value_cache,
npu_group_size):
from ipex_llm.transformers.npu_model import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, LlamaTokenizer
@ -623,17 +625,20 @@ def transformers_int4_npu_win(repo_id,
st = time.perf_counter()
if repo_id in CHATGLM_IDS:
model = AutoModel.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True,
optimize_model=optimize_model, max_context_len=max_context_len, max_prompt_len=int(in_out_len[0]), transpose_value_cache=transpose_value_cache,
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,
torch_dtype=torch.float16, attn_implementation="eager").eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
elif repo_id in LLAMA_IDS:
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True, torch_dtype=torch.float16,
optimize_model=optimize_model, max_context_len=max_context_len, max_prompt_len=int(in_out_len[0]), transpose_value_cache=transpose_value_cache,
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 = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
else:
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True, torch_dtype=torch.float16,
optimize_model=optimize_model, max_context_len=max_context_len, max_prompt_len=int(in_out_len[0]), transpose_value_cache=transpose_value_cache,
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()
@ -2191,8 +2196,8 @@ if __name__ == '__main__':
task = conf['task']
if 'optimize_model' in conf:
optimize_model = conf['optimize_model']
if 'group_size' in conf:
group_size = conf['group_size']
if 'npu_group_size' in conf:
group_size = conf['npu_group_size']
lookahead = False
transpose_value_cache = True
if 'transpose_value_cache' in conf:
@ -2225,7 +2230,7 @@ if __name__ == '__main__':
conf['low_bit'], conf['cpu_embedding'], batch_size, streaming, use_fp16_torch_dtype, lookahead, task, optimize_model, transpose_value_cache, group_size)
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'])
'model loading time (s)', 'peak mem (GB)', 'streaming', 'use_fp16_torch_dtype', 'npu_group_size'])
if "pipeline" in api or "deepspeed" in api:
if torch.distributed.get_rank() == 0:
df.index += max(line_counter - 1, 0)

2
python/llm/src/ipex_llm/transformers/npu_models/baichuan_mp.py
View file

@ -868,7 +868,7 @@ class PrefillRunner:
seq_len <= self.max_prompt_len,
(
f"seq_len: {seq_len} should be less than or equal"
" to max_prompt_len {self.max_prompt_len}"
f" to max_prompt_len {self.max_prompt_len}"
),
)
pad_len = self.max_prompt_len - seq_len

2
python/llm/src/ipex_llm/transformers/npu_models/llama_mp.py
View file

@ -907,7 +907,7 @@ class PrefillRunner:
seq_len <= self.max_prompt_len,
(
f"seq_len: {seq_len} should be less than or equal"
" to max_prompt_len {self.max_prompt_len}"
f" to max_prompt_len {self.max_prompt_len}"
),
)
pad_len = self.max_prompt_len - seq_len

2
python/llm/src/ipex_llm/transformers/npu_models/minicpm_mp.py
View file

@ -829,7 +829,7 @@ class PrefillRunner:
seq_len <= self.max_prompt_len,
(
f"seq_len: {seq_len} should be less than or equal"
" to max_prompt_len {self.max_prompt_len}"
f" to max_prompt_len {self.max_prompt_len}"
),
)
pad_len = self.max_prompt_len - seq_len

2
python/llm/src/ipex_llm/transformers/npu_models/qwen2_mp.py
View file

@ -971,7 +971,7 @@ class PrefillRunner:
seq_len <= self.max_prompt_len,
(
f"seq_len: {seq_len} should be less than or equal"
" to max_prompt_len {self.max_prompt_len}"
f" to max_prompt_len {self.max_prompt_len}"
),
)
pad_len = self.max_prompt_len - seq_len