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# 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 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 logging
from model import LanguageModelConfig, TransformerConfig, QuantizedWeight8bit as QW8Bit
from runners import InferenceRunner, ModelRunner, sample_from_model
CKPT_PATH = "./checkpoints/"
def main():
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,
),
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))
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
main()
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