Sync from v0.13

tests/quantization/test_gptq_dynamic.py

@@ -0,0 +1,81 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests whether gptq models with dynamic quantized can be loaded.
+
+Run `pytest tests/quantization/test_gptq_dynamic.py --forked`.
+"""
+
+import pytest
+import torch
+
+from vllm.model_executor.layers.linear import UnquantizedLinearMethod
+from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod
+from vllm.model_executor.layers.quantization.gptq_marlin import GPTQMarlinLinearMethod
+from vllm.model_executor.layers.quantization.utils.gptq_utils import (
+    get_dynamic_override,
+)
+
+PROMPT = "On the surface of Mars, we found"
+
+# The first layer is quantized using bits=4, group_size=128
+# The second layer is quantized using bits=8, group_size=32
+# All other layers (layer index >= 2) are not quantized
+MODEL_QUANT = [
+    ("ModelCloud/Qwen1.5-1.8B-Chat-GPTQ-4bits-dynamic-cfg-with-lm_head-symTrue", True),
+    (
+        "ModelCloud/Qwen1.5-1.8B-Chat-GPTQ-4bits-dynamic-cfg-with-lm_head-symFalse",
+        False,
+    ),
+]
+
+
+@pytest.mark.parametrize("model_id, use_marlin_kernel", MODEL_QUANT)
+def test_gptq_with_dynamic(
+    vllm_runner, model_id: str, use_marlin_kernel: bool, monkeypatch
+):
+    # `LLM.apply_model` requires pickling a function.
+    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
+
+    linear_method_cls = (
+        GPTQMarlinLinearMethod if use_marlin_kernel else (GPTQLinearMethod)
+    )
+
+    with vllm_runner(
+        model_id, dtype=torch.float16, max_model_len=2048, enforce_eager=True
+    ) as llm:
+
+        def check_model(model):
+            for name, submodule in model.named_modules():
+                if name == "lm_head":
+                    assert isinstance(submodule.quant_method, linear_method_cls)
+                elif name == "model.layers.0.self_attn.qkv_proj":
+                    # The first layer is quantized using bits=4, group_size=128
+                    # desc_act=True
+                    assert isinstance(submodule.quant_method, linear_method_cls)
+                    config = submodule.quant_method.quant_config
+                    assert config.weight_bits == 4
+                    assert config.group_size == 128
+                    assert config.desc_act
+                elif name == "model.layers.1.self_attn.qkv_proj":
+                    # The second layer is quantized using bits=8, group_size=32
+                    # desc_act=False
+                    assert isinstance(submodule.quant_method, linear_method_cls)
+                    config = submodule.quant_method.quant_config
+                    assert (
+                        get_dynamic_override(config, layer_name=name, key="bits") == 8
+                    )
+                    assert (
+                        get_dynamic_override(config, layer_name=name, key="group_size")
+                        == 32
+                    )
+                    assert not get_dynamic_override(
+                        config, layer_name=name, key="desc_act"
+                    )
+                elif (
+                    name == "model.layers.2.self_attn.qkv_proj"
+                    or name == "model.layers.2.mlp.gate_up_proj"
+                ):
+                    # All other layers (layer index >= 2) are not quantized
+                    assert isinstance(submodule.quant_method, UnquantizedLinearMethod)
+
+        llm.apply_model(check_model)