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# Copyright 2024 The HuggingFace Inc. 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 time
import torch
from packaging import version
from transformers import AutoModelForSeq2SeqLM
from accelerate import PartialState, prepare_pippy
from accelerate import __version__ as accelerate_version
from accelerate.utils import set_seed
if version.parse(accelerate_version) > version.parse("0.33.0"):
raise RuntimeError(
"Using encoder/decoder models is not supported with the `torch.pipelining` integration or accelerate>=0.34.0. "
"Please use a lower accelerate version and `torchpippy`, which this example uses."
)
# Set the random seed to have reproducable outputs
set_seed(42)
# Create an example model
model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
model.eval()
# Input configs
# Create example inputs for the model
input = torch.randint(
low=0,
high=model.config.vocab_size,
size=(2, 1024), # bs x seq_len
device="cpu",
dtype=torch.int64,
requires_grad=False,
)
example_inputs = {"input_ids": input, "decoder_input_ids": input}
# Create a pipeline stage from the model
# Using `auto` is equivalent to letting `device_map="auto"` figure
# out device mapping and will also split the model according to the
# number of total GPUs available if it fits on one GPU
model = prepare_pippy(
model,
no_split_module_classes=["T5Block"],
example_kwargs=example_inputs,
)
# You can pass `gather_output=True` to have the output from the model
# available on all GPUs
# model = prepare_pippy(
# model,
# no_split_module_classes=["T5Block"],
# example_kwargs=example_inputs,
# gather_outputs=True
# )
# The model expects a tuple during real inference
# with the data on the first device
args = (example_inputs["input_ids"].to("cuda:0"), example_inputs["decoder_input_ids"].to("cuda:0"))
# Take an average of 5 times
# Measure first batch
torch.cuda.synchronize()
start_time = time.time()
with torch.no_grad():
output = model(*args)
torch.cuda.synchronize()
end_time = time.time()
first_batch = end_time - start_time
# Now that CUDA is init, measure after
torch.cuda.synchronize()
start_time = time.time()
for i in range(5):
with torch.no_grad():
output = model(*args)
torch.cuda.synchronize()
end_time = time.time()
# The outputs are only on the final process by default
if PartialState().is_last_process:
output = torch.stack(tuple(output[0]))
print(f"Time of first pass: {first_batch}")
print(f"Average time per batch: {(end_time - start_time) / 5}")
PartialState().destroy_process_group()
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