#!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
CHECKPOINT="your model directory path"
TOKENIZER_PATH="your tokenizer directory path"
MASTER_ADDR=localhost
MASTER_PORT=6000
NNODES=1
NODE_RANK=0
NPUS_PER_NODE=8
WORLD_SIZE=$(($NPUS_PER_NODE*$NNODES))
TP=4
PP=2
SEQ_LENGTH=32768
DISTRIBUTED_ARGS="
--nproc_per_node $NPUS_PER_NODE \
--nnodes $NNODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
"
torchrun $DISTRIBUTED_ARGS inference.py \
--task chat \
--prompt-type qwen \
--use-mcore-models \
--tensor-model-parallel-size ${TP} \
--pipeline-model-parallel-size ${PP} \
--num-layers 28 \
--hidden-size 3584 \
--num-attention-heads 28 \
--ffn-hidden-size 18944 \
--max-position-embeddings ${SEQ_LENGTH} \
--seq-length ${SEQ_LENGTH} \
--disable-bias-linear \
--add-qkv-bias \
--group-query-attention \
--num-query-groups 4 \
--swiglu \
--use-fused-swiglu \
--normalization RMSNorm \
--norm-epsilon 1e-6 \
--use-fused-rmsnorm \
--position-embedding-type rope \
--rotary-base 1000000 \
--use-fused-rotary-pos-emb \
--make-vocab-size-divisible-by 1 \
--padded-vocab-size 152064 \
--micro-batch-size 1 \
--max-new-tokens 256 \
--tokenizer-type PretrainedFromHF \
--tokenizer-name-or-path ${TOKENIZER_PATH} \
--tokenizer-not-use-fast \
--hidden-dropout 0 \
--attention-dropout 0 \
--untie-embeddings-and-output-weights \
--no-gradient-accumulation-fusion \
--attention-softmax-in-fp32 \
--seed 42 \
--load ${CHECKPOINT} \
--exit-on-missing-checkpoint \
| tee logs/generate_mcore_qwen25_7b.log