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add peak gpu mem stats in transformer_int4_gpu (#9766)

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* add peak gpu mem stats in transformer_int4_gpu

* address weiguang's comments
This commit is contained in:
dingbaorong 2023-12-26 15:38:28 +08:00 committed by GitHub
parent 87b4100054
commit 64d05e581c
1 changed files with 8 additions and 4 deletions
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12
python/llm/dev/benchmark/all-in-one/run.py
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@ -45,13 +45,15 @@ LLAVA_IDS = ['liuhaotian/llava-v1.5-7b']
results = []
excludes = []
def run_model_in_thread(model, in_out, tokenizer, result, warm_up, num_beams, input_ids, out_len, actual_in_len, num_trials):
def run_model_in_thread(model, in_out, tokenizer, result, warm_up, num_beams, input_ids, out_len, actual_in_len, num_trials, reserved_mem_list=[]):
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()
reserved_mem_list.append(torch.xpu.memory.memory_reserved()/(1024**3))
gpu_peak_mem = max(reserved_mem_list) # always keep the peak gpu mem at current stage
output_ids = output_ids.cpu()
print("model generate cost: " + str(end - st))
output = tokenizer.batch_decode(output_ids)
@ -59,7 +61,7 @@ def run_model_in_thread(model, in_out, tokenizer, result, warm_up, num_beams, in
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])
actual_in_len, actual_out_len, gpu_peak_mem])
def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1, num_trials=3, num_beams=1, low_bit='sym_int4', cpu_embedding=False):
# TODO: make a parameter
@ -95,7 +97,7 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
num_beams,
low_bit,
cpu_embedding if 'win' in test_api else 'N/A',
result[in_out_pair][-1][5] if 'win' in test_api else 'N/A']) # currently only peak mem for win gpu is caught here
result[in_out_pair][-1][5] if 'int4_gpu' in test_api else 'N/A']) # currently only peak mem for transformer_int4_gpu is caught here
def get_model_path(repo_id, local_model_hub):
@ -354,6 +356,7 @@ def run_transformer_int4_gpu(repo_id,
from bigdl.llm.transformers import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer
import intel_extension_for_pytorch as ipex
reserved_mem_list = []
model_path = get_model_path(repo_id, local_model_hub)
# Load model in 4 bit,
# which convert the relevant layers in the model into INT4 format
@ -378,6 +381,7 @@ def run_transformer_int4_gpu(repo_id,
model = ipex.optimize(model.eval(), inplace=True)
end = time.perf_counter()
print(">> loading of model costs {}s".format(end - st))
reserved_mem_list.append(torch.xpu.memory.memory_reserved()/(1024**3))
model = BenchmarkWrapper(model)
@ -402,7 +406,7 @@ def run_transformer_int4_gpu(repo_id,
input_ids = tokenizer.encode(true_str, return_tensors="pt").to('xpu')
actual_in_len = input_ids.shape[1]
result[in_out] = []
thread = threading.Thread(target=run_model_in_thread, args=(model, in_out, tokenizer, result, warm_up, num_beams, input_ids, out_len, actual_in_len, num_trials))
thread = threading.Thread(target=run_model_in_thread, args=(model, in_out, tokenizer, result, warm_up, num_beams, input_ids, out_len, actual_in_len, num_trials, reserved_mem_list))
thread.start()
thread.join()
del model