From 5a5fd5af5b9edde9955654101a1a7294e894336b Mon Sep 17 00:00:00 2001 From: Xiangyu Tian <109123695+xiangyuT@users.noreply.github.com> Date: Thu, 21 Mar 2024 09:51:06 +0800 Subject: [PATCH] LLM: Add speculative benchmark on CPU/XPU (#10464) Add speculative benchmark on CPU/XPU. --- .../llm/dev/benchmark/all-in-one/config.yaml | 2 + python/llm/dev/benchmark/all-in-one/run.py | 159 ++++++++++++++++++ 2 files changed, 161 insertions(+) diff --git a/python/llm/dev/benchmark/all-in-one/config.yaml b/python/llm/dev/benchmark/all-in-one/config.yaml index 501e4e80..0331271b 100644 --- a/python/llm/dev/benchmark/all-in-one/config.yaml +++ b/python/llm/dev/benchmark/all-in-one/config.yaml @@ -30,5 +30,7 @@ test_api: # - "transformer_int4_fp16_gpu_win" # on Intel GPU for Windows, use fp16 for non-linear layer # - "transformer_int4_loadlowbit_gpu_win" # on Intel GPU for Windows using load_low_bit API. Please make sure you have used the save.py to save the converted low bit model # - "deepspeed_optimize_model_gpu" # deepspeed autotp on Intel GPU + # - "speculative_cpu" + # - "speculative_gpu" cpu_embedding: False # whether put embedding to CPU (only avaiable now for gpu win related test_api) streaming: False # whether output in streaming way (only avaiable now for gpu win related test_api) diff --git a/python/llm/dev/benchmark/all-in-one/run.py b/python/llm/dev/benchmark/all-in-one/run.py index 59554495..b88e5311 100644 --- a/python/llm/dev/benchmark/all-in-one/run.py +++ b/python/llm/dev/benchmark/all-in-one/run.py @@ -102,6 +102,10 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1, result = run_bigdl_ipex_int8(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, batch_size) elif test_api == 'deepspeed_optimize_model_gpu': result = run_deepspeed_optimize_model_gpu(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, batch_size) + elif test_api == 'speculative_cpu': + result = run_speculative_cpu(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, batch_size) + elif test_api == 'speculative_gpu': + result = run_speculative_gpu(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, batch_size) for in_out_pair in in_out_pairs: if result and result[in_out_pair]: @@ -1523,6 +1527,161 @@ def run_deepspeed_optimize_model_gpu(repo_id, return result +def run_speculative_cpu(repo_id, + local_model_hub, + in_out_pairs, + warm_up, + num_trials, + num_beams, + batch_size): + from bigdl.llm.transformers import AutoModel, AutoModelForCausalLM + from transformers import AutoTokenizer, LlamaTokenizer + from bigdl.llm.transformers.convert import get_enable_ipex + + _enable_ipex = get_enable_ipex() + + model_path = get_model_path(repo_id, local_model_hub) + + st = time.perf_counter() + if repo_id in CHATGLM_IDS: + model = AutoModel.from_pretrained(model_path, load_in_low_bit='bf16', trust_remote_code=True, torch_dtype=torch.bfloat16, + use_cache=True, torchscript=True, speculative=True) + 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='bf16', trust_remote_code=True, torch_dtype=torch.bfloat16, + use_cache=True, torchscript=True, speculative=True) + tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True) + else: + model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit='bf16', trust_remote_code=True, torch_dtype=torch.bfloat16, + use_cache=True, torchscript=True, speculative=True) + tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + + 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]) + # As different tokenizer has different encodings, + # in_len.txt maybe shorter than we need, + # use much longer context to make sure input length + test_length = min(in_len*2, 8192) + while test_length not in [32, 256, 1024, 2048, 8192]: + test_length = test_length * 2 + input_str = open(f"prompt/{test_length}.txt", 'r').read() + # 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 + inputs = tokenizer(input_list, return_tensors="pt") + input_ids = inputs.input_ids + attention_mask = inputs.attention_mask + actual_in_len = input_ids.shape[1] + result[in_out] = [] + for i in range(num_trials + warm_up): + st = time.perf_counter() + if _enable_ipex: + output_ids = model.generate(input_ids, do_sample=False, max_new_tokens=out_len, + num_beams=num_beams, attention_mask=attention_mask) + else: + output_ids = model.generate(input_ids, do_sample=False, max_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: + e2e_time = end - st + rest_cost_mean = (e2e_time - model.first_token_time)/(model.n_token_generated - 1) + result[in_out].append([model.first_token_time, rest_cost_mean, 0, + actual_in_len, actual_out_len, load_time]) + return result + + +def run_speculative_gpu(repo_id, + local_model_hub, + in_out_pairs, + warm_up, + num_trials, + num_beams, + batch_size): + from bigdl.llm.transformers import AutoModel, AutoModelForCausalLM + from transformers import AutoTokenizer, LlamaTokenizer + + model_path = get_model_path(repo_id, local_model_hub) + + st = time.perf_counter() + if repo_id in CHATGLM_IDS: + model = AutoModel.from_pretrained(model_path, load_in_low_bit='fp16', trust_remote_code=True, torch_dtype=torch.float16, + use_cache=True, speculative=True) + tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + model = model.to('xpu') + elif repo_id in LLAMA_IDS: + model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit='fp16', trust_remote_code=True, torch_dtype=torch.float16, + use_cache=True, speculative=True) + tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True) + model = model.to('xpu') + else: + model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit='fp16', trust_remote_code=True, torch_dtype=torch.float16, + use_cache=True, speculative=True) + tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + model = model.to('xpu') + 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]) + # As different tokenizer has different encodings, + # in_len.txt maybe shorter than we need, + # use much longer context to make sure input length + test_length = min(in_len*2, 8192) + while test_length not in [32, 256, 1024, 2048, 8192]: + test_length = test_length * 2 + input_str = open(f"prompt/{test_length}.txt", 'r').read() + # 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.to(model.device) + 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, + num_beams=num_beams) + torch.xpu.synchronize() + end = time.perf_counter() + output_ids = output_ids.cpu() + print("model generate cost: " + str(end - st)) + output = tokenizer.batch_decode(output_ids) + actual_out_len = output_ids.shape[1] - actual_in_len + print(output[0]) + if i >= warm_up: + e2e_time = end - st + rest_cost_mean = (e2e_time - model.first_token_time)/(model.n_token_generated - 1) + result[in_out].append([model.first_token_time, rest_cost_mean, 0, + actual_in_len, actual_out_len, load_time]) + del model + torch.xpu.empty_cache() + return result + + if __name__ == '__main__': from omegaconf import OmegaConf conf = OmegaConf.load(f'{current_dir}/config.yaml')