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Neuron support in Axlearn #566

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Neuron support in Axlearn #566

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c8d0a7d
General Neuron Support
apoorvtintin Jul 1, 2024
6669a41
add 'data' axis to fsdp axis
apoorvtintin Jul 1, 2024
7baa4d9
fix import
apoorvtintin Jul 1, 2024
49f9efa
Address PR comments
apoorvtintin Jul 24, 2024
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4 changes: 4 additions & 0 deletions axlearn/common/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1193,6 +1193,10 @@ def create_device_mesh(
logging.warning("Falling back to ICI-only mesh on GPU, performance may be reduced.")
return build_standard_mesh(mesh_shape, devices=devices)

# Neuron also only uses standard mesh
if device_platform == "neuron":
return build_standard_mesh(mesh_shape, devices=devices)
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# We only break the first device axis (the least communication intensive) across granules.
assert (
ici_mesh_shape[0] % num_granules == 0
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9 changes: 8 additions & 1 deletion axlearn/experiments/text/gpt/common.py
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Expand Up @@ -11,8 +11,10 @@
"""

import math
import numpy as np
from typing import Dict, List, Optional, Sequence, Tuple, Union

import jax
import jax.numpy as jnp
import tensorflow as tf
from jax.sharding import PartitionSpec
Expand Down Expand Up @@ -267,12 +269,17 @@ def model_config(
batch_axis_names=batch_axis_names,
seq_axis_names="seq",
)

device_platform = np.asarray(jax.devices())[0].platform
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jax.devices() during config building may be an unexpected dependency on global state -- should we take a platform arg or similar?

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@apoorvtintin apoorvtintin Jul 24, 2024 •
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We could change it, but I followed the pattern already used here

axlearn/axlearn/common/utils.py

Line 1231 in 89c6f75

devices = jax.devices()

Please let me know if the platform flag is necessary, I can add it. Thanks!

# neuron uses Zero 3
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fsdp_axis_names = ("expert", "fsdp", "seq") if device_platform != 'neuron' else ("data", "expert", "fsdp", "seq")
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cfg.dtype = jnp.float32
# Shard some FFN and attention weights over multiple axes.
set_double_shard_weights_config(
cfg.decoder.transformer.layer,
batch_axis_names=batch_axis_names,
fsdp_axis_names=("expert", "fsdp", "seq"),
fsdp_axis_names=fsdp_axis_names,
tp_axis_names="model",
seq_axis_names=("seq",),
)
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6 changes: 5 additions & 1 deletion axlearn/experiments/text/gpt/fuji.py
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Expand Up @@ -83,6 +83,7 @@ class Version(enum.Enum):
},
}

TRN_MODEL_AXIS_SIZE=8
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def get_trainer_kwargs(
model_size: str,
Expand All @@ -103,7 +104,6 @@ def get_trainer_kwargs(
num_kv_heads = 8

rope_theta = ROPE_THETA[version]

# dict() is more readable here.
# pylint: disable=use-dict-literal
if model_size == "test":
Expand Down Expand Up @@ -167,6 +167,10 @@ def get_trainer_kwargs(
"gpu-(p5.48xlarge|p4de.24xlarge)-(256|512|1024)",
mesh_shape_from_axes(data=-1, fsdp=8),
),
(
"neuron-(trn1.32xlarge|trn1n.32xlarge)-(32|64|256|512|1024|2048)",
mesh_shape_from_axes(data=-1, model=TRN_MODEL_AXIS_SIZE),
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How does model=8 compare to fsdp=8? Usually we find fsdp to be more efficient.

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Might also be worth listing the step times for different configurations, similar to the other mesh rules.

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How does model=8 compare to fsdp=8? Usually we find fsdp to be more efficient.

I am launching a fsdp=8 job with 8 nodes. The job is blocked due to AWS capacity. Hope to get some data to share by Friday

The previous response from AWS was that FSDP is slower due to higher communication overhead.

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Tensor parallel (model) is more performant on trn1 arch

),
),
)
elif model_size == "70B":
Expand Down