[Quantization] AutoGPTQ refactor and matmul combination support

This PR refactors the AutoGPTQ integration to better align with the
framework design. The PR, meanwhile, supports the AutoGPTQ quantization
in MLC LLM with matmul combination.

With this PR, you will be able to compile Llama2 using the following
command:
```python
python -m mlc_llm.build --model=Llama-2-7b-chat-hf --quantization autogptq_llama_q4f16_1 --target cuda
```
to use the AutoGPTQ quantization. **Note that the first run may take
around 10 min for AutoGPTQ quantization computation, and the following
runs will be much quicker.** The AutoGPTQ quantization requires the
Python `auto_gptq` package to have version at least 0.2.0.

Co-authored-by: Lei Wang <LeiWang1999@users.noreply.github.com>

mlc_llm/quantization/__init__.py

@@ -3,33 +3,33 @@
 from .quantization import QuantizationSpec, NoQuantizationSpec, ParamQuantKind
 from .quantization import QuantSpecUpdater
 from .group_quantization import GroupQuantizationSpec
-from .autogptq_quantization import AutogptqQuantizationSpec, load_autogptq_params
+from .autogptq_quantization import AutogptqQuantizationSpec
 from .ft_rowwise_quantization import FTRowwiseQuantizationSpec, FTQuantizeUpdater
 
 
 # The predefined quantization schemes.
 quantization_schemes = {
     "autogptq_llama_q4f16_0": QuantizationScheme(
-        pre_quantized=True,
         name="autogptq_llama_q4f16_0",
         linear_weight=AutogptqQuantizationSpec(
             dtype="float16",
             mode="int4",
             sym=False,
-            storage_nbit=32,
+            group_size=128,
+        ),
+        embedding_table=NoQuantizationSpec("float16"),
+        final_fc_weight=NoQuantizationSpec("float16"),
+    ),
+    "autogptq_llama_q4f16_1": QuantizationScheme(
+        name="autogptq_llama_q4f16_1",
+        linear_weight=AutogptqQuantizationSpec(
+            dtype="float16",
+            mode="int4",
+            sym=False,
             group_size=-1,
-            transpose=True,
         ),
         embedding_table=NoQuantizationSpec("float16"),
         final_fc_weight=NoQuantizationSpec("float16"),
-        _base_model_prefix="model",
-        _layers_block_name="model.layers",
-        _inside_layer_modules=[
-            ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"],
-            ["self_attn.o_proj"],
-            ["mlp.gate_proj", "mlp.down_proj", "mlp.up_proj"],
-        ],
-        _load_quantized_params_func=load_autogptq_params,
     ),
     "q0f16": QuantizationScheme("q0f16", NoQuantizationSpec("float16")),
     "q0f32": QuantizationScheme("q0f32", NoQuantizationSpec("float32")),