[Bugfix] Fix InternVL2 vision embeddings process with pipeline parall…

…el (vllm-project#8299)

Signed-off-by: Alvant <[email protected]>

tests/distributed/test_pipeline_parallel.py

@@ -32,7 +32,9 @@
         (1, 4, 1, 0, 0, "meta-llama/Meta-Llama-3-8B", "ray"),
         (2, 2, 1, 0, 0, "meta-llama/Meta-Llama-3-8B", "ray"),
         (2, 2, 0, 1, 0, "meta-llama/Meta-Llama-3-8B", "ray"),
-        (2, 2, 1, 1, 1, "internlm/internlm2_5-7b-chat", "ray"),
+        (1, 2, 1, 1, 1, "OpenGVLab/InternVL2-1B", "ray"),
+        (1, 2, 1, 1, 1, "OpenGVLab/InternVL2-2B", "ray"),
+        (1, 2, 1, 0, 1, "OpenGVLab/InternVL2-4B", "ray"),
     ],
 )
 @fork_new_process_for_each_test
@@ -46,6 +48,8 @@ def test_compare_tp(TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL,
         # use half precision for speed and memory savings in CI environment
         "--dtype",
         "float16",
+        "--max-model-len",
+        "8192",
         "--pipeline-parallel-size",
         str(PP_SIZE),
         "--tensor-parallel-size",
@@ -62,7 +66,9 @@ def test_compare_tp(TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL,
     tp_args = [
         # use half precision for speed and memory savings in CI environment
         "--dtype",
-        "bfloat16",
+        "float16",
+        "--max-model-len",
+        "8192",
         "--tensor-parallel-size",
         str(max(TP_SIZE, 2)),  # We only use 2 GPUs in the CI.
         "--distributed-executor-backend",

vllm/model_executor/models/internvl.py

@@ -17,6 +17,7 @@
 
 from vllm.attention import AttentionMetadata
 from vllm.config import CacheConfig, MultiModalConfig
+from vllm.distributed import get_pp_group
 from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.sampler import SamplerOutput
@@ -480,7 +481,7 @@ def forward(
         **kwargs: object,
     ) -> SamplerOutput:
         image_input = self._parse_and_validate_image_input(**kwargs)
-        if image_input is not None:
+        if image_input is not None and get_pp_group().is_first_rank:
             inputs_embeds = self.language_model.model.get_input_embeddings(
                 input_ids)
             vision_embeddings = self._process_image_input(image_input)