dags/inference/configs/maxtext_inference_microbenchmark_gce_config.py (90 lines of code) (raw):
# Copyright 2024 Google LLC
#
# 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.
"""Utilities to construct configs for maxtext inference microbenchmarks DAG."""
import datetime
import json
from typing import Dict
from xlml.apis import gcp_config, metric_config, task, test_config
from dags.common import test_owner
from dags.multipod.configs import common
from dags.common.vm_resource import TpuVersion, Project, RuntimeVersion
PROJECT_NAME = Project.CLOUD_ML_AUTO_SOLUTIONS.value
RUNTIME_IMAGE = RuntimeVersion.TPU_UBUNTU2204_BASE.value
GCS_SUBFOLDER_PREFIX = test_owner.Team.INFERENCE.value
def config(
tpu_version: TpuVersion,
tpu_cores: int,
tpu_zone: str,
time_out_in_min: int,
test_name: str,
test_mode: common.SetupMode,
project_name: str = PROJECT_NAME,
runtime_version: str = RUNTIME_IMAGE,
network: str = "default",
subnetwork: str = "default",
is_tpu_reserved: bool = True,
num_slices: int = 1,
model_configs: Dict = {},
maxtext_branch: str = "",
):
job_gcp_config = gcp_config.GCPConfig(
project_name=project_name,
zone=tpu_zone,
dataset_name=metric_config.DatasetOption.BENCHMARK_DATASET,
)
set_up_cmds = (
"pip install --upgrade pip",
# Download maxtext
f"if [ ! -d maxtext ]; then git clone {maxtext_branch} https://github.com/google/maxtext.git; fi",
# Create a python virtual environment
"sudo apt-get -y update",
"sudo apt-get -y install python3.10-venv",
"sudo apt-get -y install jq",
"python -m venv .env",
"source .env/bin/activate",
# Setup MaxText
f"cd maxtext && bash setup.sh MODE={test_mode.value} && cd ..",
"pip install torch --index-url https://download.pytorch.org/whl/cpu",
)
additional_metadata_dict = {
"quant_mode": f"{model_configs['quant_mode']}",
"two_axis_order_product_id_list": f"{model_configs['two_axis_order_product_id_list']}",
"prefill_cache_axis_order_list": f"{model_configs['prefill_cache_axis_order_list']}",
"ar_cache_axis_order_list": f"{model_configs['ar_cache_axis_order_list']}",
"accelerator": f"v{tpu_version.value}-{tpu_cores}",
"flatten_microbenchmark_results": "true",
}
run_model_cmds = (
# Start virtual environment
"source .env/bin/activate",
# Get commit hash of the maxtext and jetstream repos
"cd maxtext",
f"export METADATA_DICT='{json.dumps(additional_metadata_dict)}'",
'export MAXTEXT_COMMIT_HASH=$(git log -1 --format="%H")',
# 'export METADATA_DICT=$(jq -c \'. + { "maxtext_commit_hash": $newVal}\' --arg newVal ${MAXTEXT_COMMIT_HASH} <<<"$METADATA_DICT")',
# "echo ${METADATA_DICT}",
'jq \'. + { "maxtext_commit_hash": $newVal}\' --arg newVal ${MAXTEXT_COMMIT_HASH} <<<"$METADATA_DICT" > MaxText/metadata.json',
"cat MaxText/metadata.json",
### Benchmark
# Configure flags
"export XLA_FLAGS='--xla_disable_hlo_passes=rematerialization'",
f"""python3 -m MaxText.inference_microbenchmark_sweep \
MaxText/configs/base.yml \
model_name={model_configs['model_name']} \
tokenizer_path=assets/{model_configs['tokenizer']} \
weight_dtype={model_configs['weight_dtype']} \
scan_layers={model_configs['scan_layers']} \
max_prefill_predict_length={model_configs['max_prefill_predict_length']} \
max_target_length={model_configs['max_target_length']} \
attention={model_configs['attention']} \
ici_fsdp_parallelism={model_configs['ici_fsdp_parallelism']} \
ici_autoregressive_parallelism={model_configs['ici_autoregressive_parallelism']} \
ici_tensor_parallelism={model_configs['ici_tensor_parallelism']} \
quantization={model_configs['quantization']} \
quantize_kvcache={model_configs['quantize_kvcache']} \
per_device_batch_size={model_configs['per_device_batch_size']} \
inference_microbenchmark_prefill_lengths={model_configs['inference_microbenchmark_prefill_lengths']} \
inference_microbenchmark_stages={model_configs['inference_microbenchmark_stages']} \
inference_microbenchmark_loop_iters={model_configs['inference_microbenchmark_loop_iters']} \
base_output_directory={model_configs['base_output_directory']} \
run_name={model_configs['run_name']} \
profiler={model_configs['profiler']} \
save_config_to_gcs={model_configs['save_config_to_gcs']} \
reshape_q={model_configs['reshape_q']} \
kv_quant_axis={model_configs['kv_quant_axis']} \
compute_axis_order={model_configs['compute_axis_order']} \
inference_metadata_file=MaxText/metadata.json""",
"cat inference_microbenchmark_sweep_results.jsonl",
"mv inference_microbenchmark_sweep_results.jsonl metric_report.jsonl",
f"gsutil cp metric_report.jsonl {metric_config.SshEnvVars.GCS_OUTPUT.value}",
)
job_test_config = test_config.TpuVmTest(
test_config.Tpu(
version=tpu_version,
cores=tpu_cores,
runtime_version=runtime_version,
reserved=is_tpu_reserved,
network=network,
subnetwork=subnetwork,
),
test_name=test_name,
set_up_cmds=set_up_cmds,
run_model_cmds=run_model_cmds,
timeout=datetime.timedelta(minutes=time_out_in_min),
task_owner=test_owner.MORGAN_D,
num_slices=num_slices,
gcs_subfolder=f"{GCS_SUBFOLDER_PREFIX}/maxtext",
)
job_metric_config = metric_config.MetricConfig(
json_lines=metric_config.JSONLinesConfig("metric_report.jsonl"),
use_runtime_generated_gcs_folder=True,
)
return task.run_queued_resource_test(
task_test_config=job_test_config,
task_gcp_config=job_gcp_config,
task_metric_config=job_metric_config,
)