Add lm-eval comparison script

.github/actions/nm-lm-eval-accuracy/action.yml

@@ -0,0 +1,26 @@
+name: run lm-eval accuracy test
+description: 'run lm-eval accuracy test'
+inputs:
+  python:
+    description: 'python version, e.g. 3.10.12'
+    required: true
+  venv:
+    description: 'name for python virtual environment'
+    required: true
+runs:
+  using: composite
+  steps:
+  - id: lm-eval
+    run: |
+      COMMIT=${{ github.sha }}
+      VENV="${{ inputs.venv }}-${COMMIT:0:7}"
+      source $(pyenv root)/versions/${{ inputs.python }}/envs/${VENV}/bin/activate
+
+      pip3 install git+https://github.com/EleutherAI/lm-evaluation-harness.git
+      pip3 install optimum auto-gptq
+
+      SUCCESS=0
+      python .github/scripts/lm_eval_compare_hf_vs_vllm.py --hf_pretrained nm-testing/zephyr-beta-7b-gptq-g128 --vllm_pretrained nm-testing/zephyr-beta-7b-marlin-g128 || SUCCESS=$?
+      echo "test=${SUCCESS}" >> "$GITHUB_OUTPUT"
+      exit ${SUCCESS}
+    shell: bash

.github/scripts/lm_eval_compare_hf_vs_vllm.py

@@ -0,0 +1,126 @@
+import argparse
+import os
+from typing import Dict, List, Tuple
+
+import numpy as np
+import scipy.stats
+
+import lm_eval
+import lm_eval.models.utils
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+def calculate_z_value(res1: Dict, res2: Dict) -> Tuple[float, float]:
+    acc1, acc2 = res1["acc,none"], res2["acc,none"]
+    st_err1, st_err2 = res1["acc_stderr,none"], res2["acc_stderr,none"]
+    Z = (acc1 - acc2) / np.sqrt((st_err1**2) + (st_err2**2))
+    # Determining the p-value
+    p_value = 2 * scipy.stats.norm.sf(abs(Z))  # two-tailed test
+    return Z, p_value
+
+
+def print_results(data_to_print: List = None,
+                  results_dict: Dict = None,
+                  alpha: float = None):
+    model1_data, model2_data = data_to_print
+    for task in model1_data:
+        print(f"Task: {task}")
+        print(f"HF Accuracy: {model1_data[task]['acc,none']}")
+        print(f"vLLM Accuracy: {model2_data[task]['acc,none']}")
+        print(f"HF StdErr: {model1_data[task]['acc_stderr,none']}")
+        print(f"vLLM StdErr: {model2_data[task]['acc_stderr,none']}")
+        z = results_dict[task]["z"]
+        p_value = results_dict[task]["p_value"]
+        result = "PASS" if p_value > alpha else "FAIL"
+        print(f"Z-Score: {z}, P-Value: {p_value}, p > {alpha}: {result}\n")
+
+
+def check_passing_score(results_dict: Dict = None,
+                        alpha: float = None) -> bool:
+    for task in results_dict:
+        p_value = task["p_value"]
+        if p_value <= alpha:
+            return False
+    return True
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--hf_pretrained",
+                        default="EleutherAI/pythia-70m",
+                        help="name of model to compare as baseline")
+    parser.add_argument("--vllm_pretrained",
+                        default="EleutherAI/pythia-70m",
+                        help="name of model to compare as difference")
+    parser.add_argument("--hf_args",
+                        help="huggingface model args <arg>=<value>",
+                        default="")
+    parser.add_argument("--vllm_args",
+                        help="vllm model args <arg>=<value>",
+                        default="")
+    parser.add_argument("--tasks", type=str, default="arc_easy,hellaswag")
+    parser.add_argument(
+        "--limit",
+        type=float,
+        default=100,
+    )
+    parser.add_argument(
+        "--alpha",
+        type=float,
+        default=0.05,
+        help="Significance level for two-tailed z-test",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cuda",
+    )
+    parser.add_argument(
+        "--batch",
+        type=str,
+        default=4,
+    )
+    parser.add_argument(
+        "--verbosity",
+        type=str,
+        default="INFO",
+        help="Logging verbosity",
+    )
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    tasks = args.tasks.split(",")
+    print("Tasks:", tasks)
+    hf_args, vllm_args = "," + args.hf_args, "," + args.vllm_args
+    results_hf = lm_eval.simple_evaluate(
+        model="hf",
+        model_args=f"pretrained={args.hf_pretrained}" + hf_args,
+        tasks=tasks,
+        limit=args.limit,
+        device=args.device,
+        batch_size=args.batch,
+    )
+    lm_eval.models.utils.clear_torch_cache()
+    print("Memory stats cleared")
+    results_vllm = lm_eval.simple_evaluate(
+        model="vllm",
+        model_args=f"pretrained={args.vllm_pretrained}" + vllm_args,
+        tasks=tasks,
+        limit=args.limit,
+        device=args.device,
+        batch_size=args.batch,
+    )
+    all_res = {}
+    for task1, task2 in zip(results_hf["results"].items(),
+                            results_vllm["results"].items()):
+        assert task1[0] == task2[0]
+        z, p_value = calculate_z_value(task1[1], task2[1])
+        all_res[task1[0]] = {"z": z, "p_value": p_value}
+    print_results([results_hf["results"], results_vllm["results"]], all_res,
+                  args.alpha)
+    if not check_passing_score:
+        print("Accuracy test failed!")
+        exit(1)

.github/workflows/nightly.yml

@@ -32,3 +32,14 @@ jobs:
             Gi_per_thread: 12
             python: "3.10.12"
         secrets: inherit
+
+    # single gpu
+    AWS-AVX2-32G-A10G-24G-Accuracy:
+        uses: ./.github/workflows/nm-lm-eval-accuracy.yml
+        with:
+            label: aws-avx2-32G-a10g-24G
+            timeout: 60
+            gitref: '${{ github.ref }}'
+            Gi_per_thread: 12
+            python: "3.10.12"
+        secrets: inherit

.github/workflows/nm-lm-eval-accuracy.yml

@@ -0,0 +1,98 @@
+name: nm-lm-eval-accuracy
+on:
+  # makes workflow reusable
+  workflow_call:
+    inputs:
+      label:
+        description: "requested runner label (specifies instance)"
+        type: string
+        required: true
+      timeout:
+        description: "maximum time runner will be up"
+        type: string
+        required: true
+      gitref:
+        description: "git commit hash or branch name"
+        type: string
+        required: true
+      Gi_per_thread:
+        description: 'requested GiB to reserve per thread'
+        type: string
+        required: true
+      python:
+        description: "python version, e.g. 3.10.12"
+        type: string
+        required: true
+
+  # makes workflow manually callable
+  workflow_dispatch:
+    inputs:
+      label:
+        description: "requested runner label (specifies instance)"
+        type: string
+        required: true
+      timeout:
+        description: "maximum time runner will be up"
+        type: string
+        required: true
+      gitref:
+        description: "git commit hash or branch name"
+        type: string
+        required: true
+      Gi_per_thread:
+        description: 'requested GiB to reserve per thread'
+        type: string
+        required: true
+      python:
+        description: "python version, e.g. 3.10.12"
+        type: string
+        required: true
+
+jobs:
+  LM-EVAL:
+
+    runs-on: ${{ inputs.label }}
+    timeout-minutes: ${{ fromJSON(inputs.timeout) }}
+
+    steps:
+      - name: checkout repository code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ inputs.gitref }}
+          submodules: recursive
+
+      - name: setenv
+        id: setenv
+        uses: ./.github/actions/nm-set-env/
+        with:
+          hf_token: ${{ secrets.NM_HF_TOKEN }}
+          Gi_per_thread: ${{ inputs.Gi_per_thread }}
+
+      - name: set python
+        id: set_python
+        uses: ./.github/actions/nm-set-python/
+        with:
+          python: ${{ inputs.python }}
+          venv: TEST
+
+      - name: hf cache
+        id: hf_cache
+        uses: ./.github/actions/nm-hf-cache/
+        with:
+          fs_cache: ${{ secrets.HF_FS_CACHE }}
+
+      - name: build
+        id: build
+        uses: ./.github/actions/nm-build-vllm/
+        with:
+          Gi_per_thread: ${{ inputs.Gi_per_thread }}
+          python: ${{ inputs.python }}
+          venv: TEST
+          pypi: ${{ secrets.NM_PRIVATE_PYPI_LOCATION }}
+
+      - name: run lm-eval-accuracy
+        uses: ./.github/actions/nm-lm-eval-accuracy/
+        with:
+          python: ${{ inputs.python }}
+          venv: TEST