Throughput and Latency degradation with a single LoRA adapter on A100 40 GB

[kaushikmitr]

System Info

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Setup Summary for LoRAX Benchmarking with Llama-2 Model:

• Hardware: A100 40 GB (a2-highgpu-2g) on Google Kubernetes Engine (GKE)
• Image: ghcr.io/predibase/lorax:latest
• Model: meta-llama/Llama-2-7b-hf
• GPU Count: 1
• Experiments:å
  • Experiment 1: Requests using the base model meta-llama/Llama-2-7b-hf.
  • Experiment 2: lorax deployed with LoRA adapter vineetsharma/qlora-adapter-Llama-2-7b-hf-TweetSumm (size 160 MB).
  • Experiment 3: lorax deployed with LoRA adapter xtuner/Llama-2-7b-qlora-moss-003-sft (size 640 MB).

Each experiment ran for about 100 seconds for each QPS value. For all three experiments, we used the same input prompt (ShareGPT) and observed a similar output length.

Benchmark Metrics: We measured:

• Latency per output token
• Throughput (output tokens per second)

You can view detailed results in the benchmark document: Benchmark 1 server - LoRAX.pdf

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Observations and Questions:

• Using LoRA adapters led to a notable degradation in throughput and latency compared to the base model. Specifically, we observed up to a 50% drop in maximum throughput with LoRA compared to the base model.
• Is this performance degradation expected with LoRA adapters?
• Are there parameters or tuning options that could improve LoRA performance?

Information

• [X] Docker
• [ ] The CLI directly

Tasks

• [X] An officially supported command
• [ ] My own modifications

Reproduction

Sample Query:

curl -i ${IP}:${PORT}//generate     -X POST     -d '{
        "inputs": "[INST] Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May? [/INST]",
        "parameters": {
            "max_new_tokens": 10, 
           "adapter_ids" : "vineetsharma/qlora-adapter-Llama-2-7b-hf-TweetSumm"
        }
    }'     -H 'Content-Type: application/json'

Deployment YAML Configuration:

apiVersion: v1
kind: Service
metadata:
  name: lorax-llama2-7b-pool
spec:
  selector:
    app: lorax-llama2-7b-pool
  ports:
  - protocol: TCP
    port: 8000
    targetPort: 8000
  type: LoadBalancer

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: lorax-llama2-7b-pool
spec:
  replicas: 1
  selector:
    matchLabels:
      app: lorax-llama2-7b-pool
  template:
    metadata:
      labels:
        app: lorax-llama2-7b-pool
    spec:
      containers:
        - name: lora
          image: "ghcr.io/predibase/lorax:latest"
          imagePullPolicy: Always
          #command: ["python3", "-m", "lorax.entrypoints.openai.api_server"]
          args:
          - "--model-id"
          - "meta-llama/Llama-2-7b-hf"
          env:
            - name: PORT
              value: "8000"
            - name: HUGGING_FACE_HUB_TOKEN
              valueFrom:
                secretKeyRef:
                  name: hf-token
                  key: token
          ports:
            - containerPort: 8000
              name: http
              protocol: TCP
          livenessProbe:
            failureThreshold: 240
            httpGet:
              path: /health
              port: http
              scheme: HTTP
            initialDelaySeconds: 5
            periodSeconds: 5
            successThreshold: 1
            timeoutSeconds: 1
          readinessProbe:
            failureThreshold: 600
            httpGet:
              path: /health
              port: http
              scheme: HTTP
            initialDelaySeconds: 5
            periodSeconds: 5
            successThreshold: 1
            timeoutSeconds: 1
          resources:
            limits:
              nvidia.com/gpu: 1
            requests:
              nvidia.com/gpu: 1
          volumeMounts:
            - mountPath: /data
              name: data
            - mountPath: /dev/shm
              name: shm
      restartPolicy: Always
      schedulerName: default-scheduler
      terminationGracePeriodSeconds: 30
      volumes:
        - name: data
          emptyDir: {}
        - name: shm
          emptyDir:
            medium: Memory
        - name: adapters
          emptyDir: {}




---

Expected Behavior

After reviewing the LoRAX blog, particularly the statement:

"Processing 1M tokens spread evenly across 32 different fine-tuned models takes just about as much time as processing the same number of tokens for 1 fine-tuned model due to the near-optimal multi-adapter batching throughput associated with LoRAX."

I anticipated a smaller decrease in latency and throughput when using LoRA adapters. Could you please clarify how the savings in Figure 1 were calculated? It would be helpful to understand if this level of performance degradation is typical and if there are any specific tuning options that might help mitigate this. Thank you for your guidance.