[Model] Add AWQ quantization support for InternVL2 model (#7187)

tests/models/test_internvl.py

@@ -1,5 +1,5 @@
 import types
-from typing import List, Optional, Type
+from typing import List, Optional, Tuple, Type
 
 import pytest
 import torch
@@ -178,6 +178,74 @@ def run_test(
         )
 
 
+def run_awq_test(
+    vllm_runner: Type[VllmRunner],
+    image_assets: _ImageAssets,
+    models: Tuple[str, str],
+    *,
+    size_factors: List[float],
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+    tensor_parallel_size: int,
+    distributed_executor_backend: Optional[str] = None,
+):
+    source_model, quant_model = models
+
+    images = [asset.pil_image for asset in image_assets]
+
+    inputs_per_image = [(
+        [prompt for _ in size_factors],
+        [rescale_image_size(image, factor) for factor in size_factors],
+    ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
+
+    # NOTE: take care of the order. run vLLM first, and then run HF.
+    # vLLM needs a fresh new process without cuda initialization.
+    # if we run HF first, the cuda initialization will be done and it
+    # will hurt multiprocessing backend with fork method (the default method).
+
+    # max_model_len should be greater than image_feature_size
+    with vllm_runner(source_model,
+                     max_model_len=4096,
+                     dtype=dtype,
+                     tensor_parallel_size=tensor_parallel_size,
+                     distributed_executor_backend=distributed_executor_backend,
+                     enforce_eager=True) as vllm_model:
+        source_outputs_per_image = [
+            vllm_model.generate_greedy_logprobs(prompts,
+                                                max_tokens,
+                                                num_logprobs=num_logprobs,
+                                                images=images)
+            for prompts, images in inputs_per_image
+        ]
+
+    with vllm_runner(quant_model,
+                     quantization="awq",
+                     max_model_len=4096,
+                     dtype=dtype,
+                     tensor_parallel_size=tensor_parallel_size,
+                     distributed_executor_backend=distributed_executor_backend,
+                     enforce_eager=True) as vllm_model:
+        quant_outputs_per_image = [
+            vllm_model.generate_greedy_logprobs(prompts,
+                                                max_tokens,
+                                                num_logprobs=num_logprobs,
+                                                images=images)
+            for prompts, images in inputs_per_image
+        ]
+
+    for source_outputs, quant_outputs in zip(source_outputs_per_image,
+                                             quant_outputs_per_image):
+        # TODO: Check whether using original CLIPVisionModel can improve
+        # consistency against HF
+        check_logprobs_close(
+            outputs_0_lst=source_outputs,
+            outputs_1_lst=quant_outputs,
+            name_0="source",
+            name_1="awq",
+        )
+
+
 target_dtype = "half"
 if is_cpu():
     target_dtype = "bfloat16"
@@ -214,3 +282,36 @@ def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
         num_logprobs=num_logprobs,
         tensor_parallel_size=1,
     )
+
+
+@pytest.mark.parametrize(
+    "models", [("OpenGVLab/InternVL2-2B", "OpenGVLab/InternVL2-2B-AWQ")])
+@pytest.mark.parametrize(
+    "size_factors",
+    [
+        # No image
+        [],
+        # Single-scale
+        [1.0],
+        # Single-scale, batched
+        [1.0, 1.0, 1.0],
+        # Multi-scale
+        [0.25, 0.5, 1.0],
+    ],
+)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [5])
+@torch.inference_mode()
+def test_awq_models(vllm_runner, image_assets, models, size_factors,
+                    dtype: str, max_tokens: int, num_logprobs: int) -> None:
+    run_awq_test(
+        vllm_runner,
+        image_assets,
+        models,
+        size_factors=size_factors,
+        dtype=dtype,
+        max_tokens=max_tokens,
+        num_logprobs=num_logprobs,
+        tensor_parallel_size=1,
+    )

vllm/model_executor/layers/linear.py

@@ -570,7 +570,8 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
             # for the packing.
             if isinstance(param, PackedvLLMParameter
                           ) and param.packed_dim == param.output_dim:
-                param.adjust_shard_indexes_for_packing(
+                shard_size, shard_offset = \
+                    param.adjust_shard_indexes_for_packing(
                     shard_size=shard_size, shard_offset=shard_offset)
 
             loaded_weight_shard = loaded_weight.narrow(param.output_dim,
@@ -719,7 +720,8 @@ class QKVParallelLinear(ColumnParallelLinear):
             # for the packing.
             if isinstance(param, PackedvLLMParameter
                           ) and param.packed_dim == param.output_dim:
-                param.adjust_shard_indexes_for_packing(
+                shard_size, shard_offset = \
+                    param.adjust_shard_indexes_for_packing(
                     shard_size=shard_size, shard_offset=shard_offset)
 
             loaded_weight_shard = loaded_weight.narrow(param.output_dim,

vllm/model_executor/model_loader/weight_utils.py

@@ -131,6 +131,10 @@ def get_quant_config(model_config: ModelConfig,
     # Read the quantization config from the HF model config, if available.
     hf_quant_config = getattr(model_config.hf_config, "quantization_config",
                               None)
+    # some vision model may keep quantization_config in their text_config
+    hf_text_config = getattr(model_config.hf_config, "text_config", None)
+    if hf_quant_config is None and hf_text_config is not None:
+        hf_quant_config = getattr(hf_text_config, "quantization_config", None)
     if hf_quant_config is None:
         # compressed-tensors uses a compressions_config
         hf_quant_config = getattr(model_config.hf_config, "compression_config",

vllm/model_executor/models/internlm2.py

@@ -87,6 +87,7 @@ class InternLM2Attention(nn.Module):
         self.head_dim = hidden_size // self.total_num_heads
         self.q_size = self.num_heads * self.head_dim
         self.kv_size = self.num_kv_heads * self.head_dim
+        self.key_value_groups = int(self.num_heads / self.num_kv_heads)
         self.scaling = self.head_dim**-0.5
         self.rope_theta = rope_theta
         self.max_position_embeddings = max_position_embeddings
@@ -120,6 +121,14 @@ class InternLM2Attention(nn.Module):
                               cache_config=cache_config,
                               quant_config=quant_config)
 
+    def split_qkv(self, qkv: torch.Tensor):
+        qkv = qkv.view(-1, self.num_kv_heads, self.key_value_groups + 2, 128)
+        q, k, v = torch.split(qkv, [self.key_value_groups, 1, 1], dim=2)
+        q = q.reshape(-1, self.q_size)
+        k = k.reshape(-1, self.kv_size)
+        v = v.reshape(-1, self.kv_size)
+        return q, k, v
+
     def forward(
         self,
         positions: torch.Tensor,
@@ -128,7 +137,7 @@ class InternLM2Attention(nn.Module):
         attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         qkv, _ = self.wqkv(hidden_states)
-        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k, v = self.split_qkv(qkv)
         q, k = self.rotary_emb(positions, q, k)
         attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
         output, _ = self.wo(attn_output)
@@ -324,24 +333,6 @@ class InternLM2ForCausalLM(nn.Module):
                 if name.endswith(".bias") and name not in params_dict:
                     continue
                 param = params_dict[name]
-                if "wqkv" in name:
+                weight_loader = getattr(param, "weight_loader",
-                    config = self.config
+                                        default_weight_loader)
-                    kv_groups = (config.num_attention_heads //
+                weight_loader(param, loaded_weight)
-                                 config.num_key_value_heads)
-                    head_dim = config.hidden_size // config.num_attention_heads
-                    loaded_weight = loaded_weight.view(-1, 2 + kv_groups,
-                                                       head_dim,
-                                                       loaded_weight.shape[-1])
-                    wq, wk, wv = torch.split(loaded_weight, [kv_groups, 1, 1],
-                                             dim=1)
-                    wq = wq.reshape(-1, wq.shape[-1])
-                    wk = wk.reshape(-1, wk.shape[-1])
-                    wv = wv.reshape(-1, wv.shape[-1])
-                    weight_loader = param.weight_loader
-                    weight_loader(param, wq, 'q')
-                    weight_loader(param, wk, 'k')
-                    weight_loader(param, wv, 'v')
-                else:
-                    weight_loader = getattr(param, "weight_loader",
-                                            default_weight_loader)
-                    weight_loader(param, loaded_weight)