add qwen3

vllm-v0.6.2/tests/weight_loading/models-large.txt

@@ -0,0 +1,5 @@
+compressed-tensors, nm-testing/Mixtral-8x7B-Instruct-v0.1-W4A16-quantized, main
+compressed-tensors, nm-testing/Mixtral-8x7B-Instruct-v0.1-W4A16-channel-quantized, main
+compressed-tensors, nm-testing/Mixtral-8x7B-Instruct-v0.1-W8A16-quantized, main
+gptq_marlin, TheBloke/Mixtral-8x7B-v0.1-GPTQ, main
+awq_marlin, casperhansen/deepseek-coder-v2-instruct-awq, main

vllm-v0.6.2/tests/weight_loading/models.txt

@@ -0,0 +1,30 @@
+gptq_marlin, robertgshaw2/zephyr-7b-beta-channelwise-gptq, main
+gptq_marlin, TheBloke/Llama-2-7B-GPTQ, main
+gptq_marlin, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, main
+gptq_marlin, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, gptq-8bit--1g-actorder_True
+gptq_marlin, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, gptq-8bit-32g-actorder_True
+gptq_marlin, TechxGenus/gemma-1.1-2b-it-GPTQ, main
+gptq, robertgshaw2/zephyr-7b-beta-channelwise-gptq, main
+gptq, TheBloke/Llama-2-7B-GPTQ, main
+gptq, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, main
+gptq, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, gptq-8bit--1g-actorder_True
+gptq, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, gptq-8bit-32g-actorder_True
+gptq, TechxGenus/gemma-1.1-2b-it-GPTQ, main
+compressed-tensors, nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change, main
+compressed-tensors, nm-testing/tinyllama-oneshot-w8-channel-a8-tensor, main
+compressed-tensors, nm-testing/tinyllama-oneshot-w8a8-dynamic-token-v2, main
+compressed-tensors, nm-testing/tinyllama-oneshot-w8a8-channel-dynamic-token-v2, main
+compressed-tensors, nm-testing/tinyllama-oneshot-w4a16-group128-v2, main
+compressed-tensors, nm-testing/tinyllama-oneshot-w8a16-per-channel, main
+compressed-tensors, nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test, main
+compressed-tensors, nm-testing/Phi-3-mini-128k-instruct-FP8, main
+compressed-tensors, neuralmagic/Phi-3-medium-128k-instruct-quantized.w4a16, main
+compressed-tensors, nm-testing/TinyLlama-1.1B-Chat-v1.0-actorder-group, main
+compressed-tensors, mgoin/DeepSeek-Coder-V2-Lite-Instruct-FP8, main
+awq, casperhansen/mixtral-instruct-awq, main
+awq_marlin, casperhansen/mixtral-instruct-awq, main
+fp8, neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV, main
+marlin, nm-testing/zephyr-beta-7b-marlin-g128, main
+marlin, robertgshaw2/zephyr-7b-beta-channelwise-marlin, main
+qqq, HandH1998/QQQ-Llama-3-8b-g128, main
+qqq, HandH1998/QQQ-Llama-3-8b, main

vllm-v0.6.2/tests/weight_loading/run_model_weight_loading_test.sh

@@ -0,0 +1,45 @@
+#!/bin/bash
+SUCCESS=0
+
+while getopts "c:" OPT; do
+  case ${OPT} in
+    c ) 
+        CONFIG="$OPTARG"
+        ;;
+    \? )
+        usage
+        exit 1
+        ;;
+  esac
+done
+
+
+IFS=$'\n' read -d '' -r -a MODEL_CONFIGS < "$CONFIG"
+
+for MODEL_CONFIG in "${MODEL_CONFIGS[@]}"
+do
+    LOCAL_SUCCESS=0
+    IFS=', ' read -r -a array <<< "$MODEL_CONFIG"
+    
+    echo "=== RUNNING MODEL: $MODEL_CONFIG ==="
+
+    export QUANTIZATION=${array[0]}
+    export MODEL_NAME=${array[1]}
+    export REVISION=${array[2]}
+    pytest -s weight_loading/test_weight_loading.py || LOCAL_SUCCESS=$?
+
+    if [[ $LOCAL_SUCCESS == 0 ]]; then
+        echo "=== PASSED MODEL: ${MODEL_CONFIG} ==="
+    else
+        echo "=== FAILED MODEL: ${MODEL_CONFIG} ==="
+    fi
+
+    SUCCESS=$((SUCCESS + LOCAL_SUCCESS))
+
+done
+
+if [ "${SUCCESS}" -eq "0" ]; then
+    exit 0
+else
+    exit 1
+fi

vllm-v0.6.2/tests/weight_loading/test_weight_loading.py

@@ -0,0 +1,34 @@
+import os
+
+import torch
+
+MAX_MODEL_LEN = 1024
+MODEL_NAME = os.environ.get("MODEL_NAME",
+                            "robertgshaw2/zephyr-7b-beta-channelwise-gptq")
+REVISION = os.environ.get("REVISION", "main")
+QUANTIZATION = os.environ.get("QUANTIZATION", "gptq_marlin")
+
+'''
+=============================
+Modify by vllm_mlu
+=============================
+@brief(MODEL_NAME,REVERSION,QUANTIZATION): Just test weight loading. 
+''' 
+MODEL_NAME = "facebook/opt-125m"
+REVISION = None
+QUANTIZATION = None
+
+def test_weight_loading(vllm_runner):
+    """
+    Test parameter weight loading with tp>1.
+    """
+    with vllm_runner(model_name=MODEL_NAME,
+                     revision=REVISION,
+                     dtype=torch.half if QUANTIZATION == "gptq" else "auto",
+                     quantization=QUANTIZATION,
+                     max_model_len=MAX_MODEL_LEN,
+                     tensor_parallel_size=2) as model:
+
+        output = model.generate_greedy("Hello world!", max_tokens=20)
+        print(output)
+        assert output