re-formatting it to be more readable run.py

It looks like there were some formatting issues in the code. I've taken the liberty of re-formatting it to be more readable.
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Michael G. Inso 2024-03-26 12:24:32 +03:00 committed by GitHub
parent f57a3e2619
commit 6ed2d78bea
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103
run.py
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@ -1,7 +1,7 @@
# Copyright 2024 X.AI Corp.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with 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
@ -30,54 +30,59 @@ def validate_checkpoint(path, expected_hash):
def main():
# Validate checkpoint integrity
# Validate checkpoint integrity
validate_checkpoint(CKPT_PATH, CKPT_HASH)
grok_1_model = LanguageModelConfig(
vocab_size=128 * 1024,
pad_token=0,
eos_token=2,
sequence_len=8192,
embedding_init_scale=1.0,
output_multiplier_scale=0.5773502691896257,
embedding_multiplier_scale=78.38367176906169,
model=TransformerConfig(
emb_size=48 * 128,
widening_factor=8,
key_size=128,
num_q_heads=48,
num_kv_heads=8,
num_layers=64,
attn_output_multiplier=0.08838834764831845,
shard_activations=True,
# MoE.
num_experts=8,
num_selected_experts=2,
# Activation sharding.
data_axis="data",
model_axis="model",
),
)
inference_runner = InferenceRunner(
pad_sizes=(1024,),
runner=ModelRunner(
model=grok_1_model,
bs_per_device=0.125,
checkpoint_path=CKPT_PATH,
# Limit inference rate
inference_runner.rate_limit = 100
),
name="local",
load=CKPT_PATH,
tokenizer_path="./tokenizer.model",
local_mesh_config=(1, 8),
between_hosts_config=(1, 1),
)
inference_runner.initialize()
gen = inference_runner.run()
grok_1_model = LanguageModelConfig(
vocab_size=128 * 1024,
pad_token=0,
eos_token=2,
sequence_len=8192,
embedding_init_scale=1.0,
output_multiplier_scale=0.5773502691896257,
embedding_multiplier_scale=78.38367176906169,
model=TransformerConfig(
emb_size=48 * 128,
widening_factor=8,
key_size=128,
num_q_heads=48,
num_kv_heads=8,
num_layers=64,
attn_output_multiplier=0.08838834764831845,
shard_activations=True,
# MoE.
num_experts=8,
num_selected_experts=2,
# Activation sharding.
data_axis="data",
model_axis="model",
),
)
inference_runner = InferenceRunner(
pad_sizes=(1024,),
runner=ModelRunner(
model=grok_1_model,
bs_per_device=0.125,
checkpoint_path=CKPT_PATH,
# Limit inference rate
inference_runner.rate_limit = 100
),
name="local",
load=CKPT_PATH,
tokenizer_path="./tokenizer.model",
local_mesh_config=(1, 8),
between_hosts_config=(1, 1),
)
inference_runner.initialize()
gen = inference_runner.run()
inp = "The answer to life the universe and everything is of course"
print(f"Output for prompt: {inp}", sample_from_model(gen, inp, max_len=100, temperature=0.01))
inp = "The answer to life the universe and everything is of course"
print(f"Output for prompt: {inp}", sample_from_model(gen, inp, max_len=100, temperature=0.01))
# Add authentication
@app.route("/inference")
@auth.login_required
@ -87,7 +92,7 @@ def main():
gen = inference_runner.run()
# Rest of inference code
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
main()
main()