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# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import config
import torch
from torch.utils import benchmark
from transformers import AutoModelForCausalLM, AutoTokenizer
def generate_tokens(model, inputs):
outputs = model.generate(
**inputs,
do_sample=False,
max_new_tokens=64,
)
return outputs
def generate_tokens_with_assistance(model, inputs, assistant_early_exit):
outputs = model.generate(
**inputs,
assistant_early_exit=assistant_early_exit,
do_sample=False,
max_new_tokens=64,
)
return outputs
if __name__ == "__main__":
ckpt = config.hub_model_id
model = AutoModelForCausalLM.from_pretrained(ckpt, device_map="auto", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(ckpt)
prompt = "### Instruction: What are my alarms for the rest of the day?\n ### Response: "
results = []
label = "Generation Times"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
results.append(
benchmark.Timer(
stmt="generate_tokens(model, inputs)",
setup="from __main__ import generate_tokens",
globals={"model": model, "inputs": inputs},
num_threads=torch.get_num_threads(),
label=label,
sub_label="no layer skip",
description="generation",
).blocked_autorange()
)
for i in range(1, model.config.num_hidden_layers):
results.append(
benchmark.Timer(
stmt="generate_tokens_with_assistance(model, inputs, assistant_early_exit)",
setup="from __main__ import generate_assistant_tokens",
globals={"model": model, "assistant_early_exit": i, "inputs": inputs},
num_threads=torch.get_num_threads(),
label=label,
sub_label=f"layer skip {i}",
description="generation",
).blocked_autorange()
)
benchmark.Compare(results).print()
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