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Add Pathways Support to Benchmark Runner #1094

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Add Pathways Support to Benchmark Runner #1094

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7e619b7
Add Pathways Support to Benchmark Runner
SujeethJinesh Dec 10, 2024
25a6841
Merge branch 'main' of github.com:AI-Hypercomputer/maxtext into sujin…
SujeethJinesh Dec 11, 2024
988b0a1
Minor fixes
SujeethJinesh Dec 13, 2024
9a086ea
Update PW Long Running Config
SujeethJinesh Dec 13, 2024
ac4e740
Resolve Comments
SujeethJinesh Dec 13, 2024
eac72e3
Remove default for num_steps
SujeethJinesh Dec 13, 2024
6a9efdb
Minor change
SujeethJinesh Dec 13, 2024
ad82031
More code
SujeethJinesh Dec 13, 2024
4f7df81
fix logic
SujeethJinesh Dec 13, 2024
28c2daa
fix logic
SujeethJinesh Dec 13, 2024
386139f
Added a new config for synthetic run.
RoshaniN Dec 13, 2024
0aba7a8
Allow nightly JAX VERSION also.
RoshaniN Dec 13, 2024
a216153
Update mode name.
RoshaniN Dec 13, 2024
77c8c04
Fix model name config
SujeethJinesh Dec 13, 2024
cd1999e
Shortening model names to be under 40 chars.
RoshaniN Dec 16, 2024
90107c2
Added TFDS config.
RoshaniN Dec 16, 2024
f7dd4cc
remove trailing space
Dec 16, 2024
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70 changes: 69 additions & 1 deletion benchmarks/benchmark_runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,7 @@
from maxtext_xpk_runner import BenchmarkRunner
from maxtext_xpk_runner import HWConfig
from maxtext_xpk_runner import SWconfig
from maxtext_xpk_runner import PathwaysConfig
from maxtext_xpk_runner import xpk_benchmark_runner
from maxtext_xpk_runner import XpkConfig

Expand Down Expand Up @@ -86,6 +87,10 @@ def add_shared_arguments(custom_parser: argparse.ArgumentParser):
'llama2_7b_4096',
'llama2_70b_4096',
'llama2_70b_4096_real_data',
'llama2_70b_4096_pw_long_run',
'llama2_70b_4096_real_data_pw_long_run',
'llama2_70b_4096_sc_synthetic_pw_lr',
'llama2_70b_4096_sc_synthetic',
'llama3_70b_8192',
'llama3_1_405b_8192_fsdp_dcn',
'mixtral_8x7b_dropped',
Expand All @@ -101,6 +106,10 @@ def add_shared_arguments(custom_parser: argparse.ArgumentParser):
'llama2_7b_4096 '
'llama2_70b_4096 '
'llama2_70b_4096_real_data '
'llama2_70b_4096_pw_long_run '
'llama2_70b_4096_real_data_pw_long_run '
'llama2_70b_4096_sc_synthetic_pw_lr '
'llama2_70b_4096_sc_synthetic '
'llama3_1_405b_8192_fsdp_dcn '
'mixtral_8x7b_dropped '
'mixtral_8x7b_dropped_int8 '
Expand All @@ -122,6 +131,56 @@ def add_shared_arguments(custom_parser: argparse.ArgumentParser):
default='maxtext_base_image',
help='version of base docker image to be benchmarked command.',
)
custom_parser.add_argument(
'--pathways_server_image',
type=str,
default=(
'us-docker.pkg.dev/cloud-tpu-v2-images-dev/pathways/server:latest'
),
help='version of pathways server image to be benchmarked command.',
)
custom_parser.add_argument(
'--pathways_proxy_image',
type=str,
default='us-docker.pkg.dev/cloud-tpu-v2-images-dev/pathways/proxy_server:latest',
help='version of pathways proxy image to be benchmarked command.',
)
custom_parser.add_argument(
'--pathways_runner_image',
type=str,
default='us-docker.pkg.dev/cloud-tpu-v2-images-dev/pathways/maxtext_jax_stable:latest',
help='version of pathways runner image to be benchmarked command.',
)
custom_parser.add_argument(
'--use_pathways',
type=bool,
default=False,
help='whether to use pathways or not.',
)
custom_parser.add_argument(
'--xpk_path',
type=str,
default='~/xpk',
help='path to xpk dir.',
)
custom_parser.add_argument(
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num_steps is defined in model configs. Adding extra parser for steps will caused non-intentional replacement.

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@SujeethJinesh SujeethJinesh Dec 11, 2024 •
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Sorry I don't see it in the model configs, I'm looking in this file (benchmarks/maxtext_trillium_model_configs.py), and they don't appear to be defined? Are you referring to the base.yml?

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it would be like steps = 100 in tuning_params

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From the other comments, we can leave this here so the user can override it if they want, but if they don't specify it, then fallback to what's in tuning_params.

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Also in the other configs, none seem to have num_steps defined, so people are just using the default at the moment. I've edited the code to make it the default still.

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We don't want to make it num_steps mandatory as it is already defined in some of the workload. can we make it optional ? Only used if needed by user otherwise, it will use either tuning_params or default 20 num of steps.

'--num_steps',
type=int,
help='Number of steps to run benchmark for.',
)
custom_parser.add_argument(
'--priority',
type=str,
default='medium',
help='Priority the XPK workload should run with.',
)
custom_parser.add_argument(
'--max_restarts',
type=int,
default=0,
help='Number of restarts to attempt.',
)


def main() -> None:
parser = argparse.ArgumentParser(
Expand All @@ -137,11 +196,19 @@ def main() -> None:
num_slices=options.num_slices,
device_type=options.device_type,
base_output_directory=options.base_output_directory,
priority=options.priority,
max_restarts=options.max_restarts,
)

v6e_env_configs = SWconfig(
base_docker_image=options.base_docker_image,
libtpu_version=options.libtpu_version,
pathways_config=PathwaysConfig(
use_pathways=options.use_pathways,
server_image=options.pathways_server_image,
proxy_image=options.pathways_proxy_image,
runner_image=options.pathways_runner_image,
),
)

v6e_256_configs = HWConfig(
Expand All @@ -155,9 +222,10 @@ def main() -> None:
model_name=benchmark_model,
software_config=v6e_env_configs,
hardware_config=v6e_256_configs,
num_steps=options.num_steps,
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@suexu1025 suexu1025 Dec 13, 2024 •
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num_steps is benchmark specific, can we change it to xpk_benchmark_runner optional argument, like exp_name xpk_benchmark_runner(cluster_config: XpkConfig, benchmarks: list[BenchmarkRunner], exp_name=None)
instead of dataclass definition, which is non-optional.

)

xpk_benchmark_runner(cluster_config, [model_runner])
xpk_benchmark_runner(cluster_config, [model_runner], options.xpk_path)
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if __name__ == '__main__':
Expand Down
146 changes: 146 additions & 0 deletions benchmarks/maxtext_trillium_model_configs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -291,6 +291,45 @@ class MaxTextModel:
),
)


llama2_70b_4096_real_data_pw_long_run = MaxTextModel(
model_name="llama2-70b-4096-rd-pw-lr",
model_type="llama2-70b",
tuning_params={
"per_device_batch_size": 4,
"ici_fsdp_parallelism": -1,
"remat_policy": "full",
"max_target_length": 4096,
"attention": "flash",
"gcs_metrics": True,
"use_iota_embed": True,
"reuse_example_batch": 0,
"profiler": "xplane",
"dataset_path": "gs://max-datasets-rogue",
"dataset_type": "tfds",
"tokenizer_path": "assets/tokenizer.llama2",
"sa_block_q": 1024,
"sa_block_q_dkv": 2048,
"sa_block_q_dq": 2048,

# Additional tuning params for pathways long running test.
"enable_checkpointing": True,
"async_checkpointing": True,
"checkpoint_period": 100,
"checkpoint_storage_use_ocdbt": False,
"checkpoint_storage_use_zarr3": False,
"metrics_file": "metrics.txt",
"goodput_upload_interval_seconds": 30,
"enable_pathways_goodput": True,
"enable_checkpoint_cloud_logger": True,
"enable_single_controller": True,
},
xla_flags=(
xla_flags_library.DENSE_VMEM_LIMIT_FLAG
+ xla_flags_library.CF_FOR_ALL_GATHER
),
)

# ici_fsdp_transpose_parallelism gives one TFLOP better performance.
llama2_70b_4096 = MaxTextModel(
model_name="llama2-70b-4096",
Expand Down Expand Up @@ -319,6 +358,109 @@ class MaxTextModel:
+ xla_flags_library.CF_FOR_ALL_GATHER
),
)
llama2_70b_4096_sc_synthetic = MaxTextModel(
model_name="llama2_70b_4096_sc_synthetic",
model_type="llama2-70b",
tuning_params={
"per_device_batch_size": 2,
"ici_fsdp_parallelism": 1,
"ici_fsdp_transpose_parallelism": -1,
"ici_tensor_parallelism": 1,
"remat_policy": "qkv_proj_offloaded",
"max_target_length": 4096,
"attention": "flash",
"gcs_metrics": True,
"use_iota_embed": True,
"dataset_path": "gs://max-datasets-rogue",
"dataset_type": "synthetic",
"enable_checkpointing": False,
"profiler": "xplane",
"sa_block_q": 1024,
"sa_block_q_dkv": 2048,
"sa_block_q_dq": 2048,
},
xla_flags=(
xla_flags_library.DENSE_VMEM_LIMIT_FLAG
+ xla_flags_library.CF_FOR_ALL_GATHER
),
)

llama2_70b_4096_sc_synthetic_pw_lr = MaxTextModel(
model_name="llama2_70b_4096_sc_synthetic_pw_lr",
model_type="llama2-70b",
tuning_params={
"per_device_batch_size": 2,
"ici_fsdp_parallelism": 1,
"ici_fsdp_transpose_parallelism": -1,
"ici_tensor_parallelism": 1,
"remat_policy": "qkv_proj_offloaded",
"max_target_length": 4096,
"attention": "flash",
"gcs_metrics": True,
"use_iota_embed": True,
"dataset_path": "gs://max-datasets-rogue",
"dataset_type": "synthetic",
# "enable_checkpointing": False,
"profiler": "xplane",
"sa_block_q": 1024,
"sa_block_q_dkv": 2048,
"sa_block_q_dq": 2048,
# Additional tuning params for pathways long running test.
"enable_checkpointing": True,
"async_checkpointing": True,
"checkpoint_period": 100,
"checkpoint_storage_use_ocdbt": False,
"checkpoint_storage_use_zarr3": False,
"metrics_file": "metrics.txt",
"goodput_upload_interval_seconds": 30,
"enable_pathways_goodput": True,
"enable_checkpoint_cloud_logger": True,
"enable_single_controller": True,
},
xla_flags=(
xla_flags_library.DENSE_VMEM_LIMIT_FLAG
+ xla_flags_library.CF_FOR_ALL_GATHER
),
)

llama2_70b_4096_pw_long_run = MaxTextModel(
model_name="llama2-70b-4096-pw-lr",
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benchmark name can be llama2-70b-4096-checkpoint?

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We don't want the name to be too long because it may overrun the 40 character limit for XPK :(

model_type="llama2-70b",
tuning_params={
"per_device_batch_size": 4,
"ici_fsdp_parallelism": 1,
"ici_fsdp_transpose_parallelism": -1,
"ici_tensor_parallelism": 1,
"remat_policy": "full",
"max_target_length": 4096,
"attention": "flash",
"gcs_metrics": True,
"use_iota_embed": True,
"dataset_path": "gs://max-datasets-rogue",
"dataset_type": "synthetic",
"reuse_example_batch": 1,
"profiler": "xplane",
"sa_block_q": 1024,
"sa_block_q_dkv": 2048,
"sa_block_q_dq": 2048,

# Additional tuning params for pathways long running test.
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can we move pathways long running test related configurations like "metrics_file": "metrics.txt", "goodput_upload_interval_seconds": 30, "enable_pathways_goodput": True, "enable_checkpoint_cloud_logger": True, "enable_single_controller": True, in maxtext_xpk_runner? like new function xpk-command-for-pathway, maxtext_trillium_model_configs.py is supposed to have different benchmarks. In that case, it will be easy for pathway to run different benchmarks. What do you think?

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These configs are actually for the specific test we're running (long running tests). I think keeping it as this config would make more sense because we may have different tests that are just for perf, or non goodput related, etc. I wouldn't want to bifurcate the code too much, it's much easier for us to just add a specialized config for Pathways and flip on the specific flags for that test I think.

"enable_checkpointing": True,
"async_checkpointing": True,
"checkpoint_period": 100,
"checkpoint_storage_use_ocdbt": False,
"checkpoint_storage_use_zarr3": False,
"metrics_file": "metrics.txt",
"goodput_upload_interval_seconds": 30,
"enable_pathways_goodput": True,
"enable_checkpoint_cloud_logger": True,
"enable_single_controller": True,
},
xla_flags=(
xla_flags_library.DENSE_VMEM_LIMIT_FLAG
+ xla_flags_library.CF_FOR_ALL_GATHER
),
)

llama3_8b_8192 = MaxTextModel(
model_name="llama3-8b-8192",
Expand Down Expand Up @@ -615,9 +757,13 @@ class MaxTextModel:
gpt_3_175b,
llama2_7b_4096,
llama2_70b_4096,
llama2_70b_4096_pw_long_run,
llama2_70b_4096_real_data,
llama2_70b_4096_real_data_pw_long_run,
llama3_8b_8192, # Not Optimizied yet
llama3_70b_8192, # Not Optimizied yet
llama2_70b_4096_sc_synthetic_pw_lr,
llama2_70b_4096_sc_synthetic,
llama3_1_405b_8192_fsdp_dcn,
llama3_1_70b_129024,
mixtral_8x7b_dropped,
Expand Down
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