[Misc] Consolidate ModelConfig code related to HF config (vllm-projec…

…t#10104)

Signed-off-by: DarkLight1337 <[email protected]>

docs/source/serving/compatibility_matrix.rst

@@ -359,7 +359,7 @@ Feature x Hardware
      - ✅
      - ✅
      - ✅
-     - `✗ <https://github.com/vllm-project/vllm/blob/a84e598e2125960d3b4f716b78863f24ac562947/vllm/worker/cpu_model_runner.py#L125>`__ 
+     - ✅
      - ✗
    * - :abbr:`logP (Logprobs)`
      - ✅

tests/test_config.py

@@ -165,3 +165,41 @@ def test_rope_customization():
     assert getattr(longchat_model_config.hf_config, "rope_scaling",
                    None) == TEST_ROPE_SCALING
     assert longchat_model_config.max_model_len == 4096
+
+
+@pytest.mark.parametrize(("model_id", "is_encoder_decoder"), [
+    ("facebook/opt-125m", False),
+    ("facebook/bart-base", True),
+    ("meta-llama/Llama-3.2-1B", False),
+    ("meta-llama/Llama-3.2-11B-Vision", True),
+])
+def test_is_encoder_decoder(model_id, is_encoder_decoder):
+    config = ModelConfig(
+        model_id,
+        task="auto",
+        tokenizer=model_id,
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        dtype="float16",
+        seed=0,
+    )
+
+    assert config.is_encoder_decoder == is_encoder_decoder
+
+
+@pytest.mark.parametrize(("model_id", "uses_mrope"), [
+    ("facebook/opt-125m", False),
+    ("Qwen/Qwen2-VL-2B-Instruct", True),
+])
+def test_uses_mrope(model_id, uses_mrope):
+    config = ModelConfig(
+        model_id,
+        task="auto",
+        tokenizer=model_id,
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        dtype="float16",
+        seed=0,
+    )
+
+    assert config.uses_mrope == uses_mrope

vllm/config.py

@@ -15,7 +15,8 @@
 from vllm.tracing import is_otel_available, otel_import_error_traceback
 from vllm.transformers_utils.config import (ConfigFormat, get_config,
                                             get_hf_image_processor_config,
-                                            get_hf_text_config)
+                                            get_hf_text_config,
+                                            is_encoder_decoder, uses_mrope)
 from vllm.utils import (GiB_bytes, cuda_device_count_stateless, get_cpu_memory,
                         print_warning_once)
 
@@ -667,12 +668,13 @@ def get_multimodal_config(self) -> "MultiModalConfig":
         return self.multimodal_config
 
     @property
-    def is_encoder_decoder_model(self) -> bool:
+    def is_encoder_decoder(self) -> bool:
         """Extract the HF encoder/decoder model flag."""
-        return getattr(
-            self.hf_config, "is_encoder_decoder",
-            False) or (hasattr(self.hf_config, "text_config") and getattr(
-                self.hf_config.text_config, "is_encoder_decoder", False))
+        return is_encoder_decoder(self.hf_config)
+
+    @property
+    def uses_mrope(self) -> bool:
+        return uses_mrope(self.hf_config)
 
     @property
     def is_multimodal_model(self) -> bool:

vllm/inputs/preprocess.py

@@ -580,4 +580,4 @@ async def preprocess_async(
         )
 
     def is_encoder_decoder_model(self):
-        return self.model_config.is_encoder_decoder_model
+        return self.model_config.is_encoder_decoder

vllm/transformers_utils/config.py

@@ -129,6 +129,15 @@ def uses_mrope(config: PretrainedConfig) -> bool:
     return "mrope_section" in rope_scaling
 
 
+def is_encoder_decoder(config: PretrainedConfig) -> bool:
+    """Detect if the model with this config is used as an encoder/decoder."""
+    text_config = getattr(config, "text_config", None)
+    if text_config is not None:
+        return is_encoder_decoder(text_config)
+
+    return getattr(config, "is_encoder_decoder", False)
+
+
 def get_config(
     model: Union[str, Path],
     trust_remote_code: bool,

vllm/utils.py

@@ -88,9 +88,6 @@
                                        "currently supported with encoder/"
                                        "decoder models.")
 
-STR_NOT_IMPL_ENC_DEC_CPU = ("CPU is not currently supported with "
-                            "encoder/decoder models.")
-
 # Efficiently import all enc/dec error strings
 # rather than having to import all of the above
 STR_NOT_IMPL_ENC_DEC_ERR_STRS = {
@@ -105,7 +102,6 @@
     "STR_NOT_IMPL_ENC_DEC_SPEC_DEC": STR_NOT_IMPL_ENC_DEC_SPEC_DEC,
     "STR_NOT_IMPL_ENC_DEC_BACKEND": STR_NOT_IMPL_ENC_DEC_BACKEND,
     "STR_NOT_IMPL_ENC_DEC_PROMPT_ADAPTER": STR_NOT_IMPL_ENC_DEC_PROMPT_ADAPTER,
-    "STR_NOT_IMPL_ENC_DEC_CPU": STR_NOT_IMPL_ENC_DEC_CPU
 }
 
 # Constants related to forcing the attention backend selection

vllm/worker/cpu_model_runner.py

@@ -18,7 +18,6 @@
                              MultiModalInputs, MultiModalPlaceholderMap)
 from vllm.sequence import (IntermediateTensors, SequenceData,
                            SequenceGroupMetadata)
-from vllm.transformers_utils.config import uses_mrope
 from vllm.utils import make_tensor_with_pad
 from vllm.worker.model_runner_base import (
     ModelRunnerBase, ModelRunnerInputBase, ModelRunnerInputBuilderBase,
@@ -163,7 +162,7 @@ def _compute_multi_modal_input(self, seq_group: SequenceGroupMetadata,
 
         # special processing for mrope position deltas.
         mrope_positions = None
-        if self.runner.model_is_mrope:
+        if self.runner.model_config.uses_mrope:
             image_grid_thw = mm_kwargs.get("image_grid_thw", None)
             video_grid_thw = mm_kwargs.get("video_grid_thw", None)
             assert image_grid_thw is not None or video_grid_thw is not None, (
@@ -446,12 +445,6 @@ def __init__(
         # Lazy initialization.
         self.model: nn.Module  # Set after init_Model
 
-    @property
-    def model_is_mrope(self) -> bool:
-        """Detect if the model has "mrope" rope_scaling type.
-        mrope requires keep "rope_deltas" between prompt and decoding phases."""
-        return uses_mrope(self.model_config.hf_config)
-
     def load_model(self) -> None:
         self.model = get_model(vllm_config=self.vllm_config)
 

vllm/worker/cpu_worker.py

@@ -151,7 +151,7 @@ def __init__(
             self.local_omp_cpuid = omp_cpuids.split("|")[rank]
 
         ModelRunnerClass: Type[CPUModelRunner] = CPUModelRunner
-        if self._is_encoder_decoder_model():
+        if self.model_config.is_encoder_decoder:
             ModelRunnerClass = CPUEncoderDecoderModelRunner
         self.model_runner: CPUModelRunner = ModelRunnerClass(
             vllm_config=vllm_config,
@@ -188,9 +188,6 @@ def stop_profile(self):
             raise RuntimeError("Profiler is not enabled.")
         self.profiler.stop()
 
-    def _is_encoder_decoder_model(self):
-        return self.model_config.is_encoder_decoder_model
-
     def init_device(self) -> None:
         if self.local_omp_cpuid != "all":
             ret = torch.ops._C_utils.init_cpu_threads_env(self.local_omp_cpuid)

vllm/worker/model_runner.py

@@ -47,7 +47,6 @@
     LRUCacheWorkerPromptAdapterManager)
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import IntermediateTensors, SequenceGroupMetadata
-from vllm.transformers_utils.config import uses_mrope
 from vllm.utils import (DeviceMemoryProfiler, GiB_bytes, PyObjectCache,
                         async_tensor_h2d, flatten_2d_lists,
                         is_pin_memory_available, supports_dynamo,
@@ -493,7 +492,7 @@ def _compute_lens(self, inter_data: InterDataForSeqGroup, seq_idx: int,
             context_len = seq_data.get_num_computed_tokens()
             seq_len = min(seq_len, context_len + token_chunk_size)
         elif self.runner.scheduler_config.is_multi_step or \
-            self.runner.model_config.is_encoder_decoder_model:
+            self.runner.model_config.is_encoder_decoder:
             context_len = seq_len - 1
         else:
             context_len = seq_data.get_num_computed_tokens()
@@ -666,7 +665,7 @@ def _compute_multi_modal_input(self, inter_data: InterDataForSeqGroup,
         inter_data.multi_modal_placeholder_maps = placeholder_maps
 
         # special processing for mrope position deltas.
-        if self.runner.model_is_mrope:
+        if self.runner.model_config.uses_mrope:
             image_grid_thw = mm_kwargs.get("image_grid_thw", None)
             video_grid_thw = mm_kwargs.get("video_grid_thw", None)
             assert image_grid_thw is not None or video_grid_thw is not None, (
@@ -711,7 +710,7 @@ def add_seq_group(self, seq_group_metadata: SequenceGroupMetadata):
 
         encoder_seq_len = 0
 
-        if self.runner.model_config.is_encoder_decoder_model:
+        if self.runner.model_config.is_encoder_decoder:
             encoder_seq_len = seq_group_metadata.encoder_seq_data.get_len()
 
         inter_data = self.init_cached_inter_data(
@@ -837,7 +836,7 @@ def build(self) -> ModelInputForGPU:
             if not inter_data.is_prompt:
                 max_decode_seq_len = max(max_decode_seq_len,
                                          max(inter_data.seq_lens))
-                if self.runner.model_config.is_encoder_decoder_model:
+                if self.runner.model_config.is_encoder_decoder:
                     max_encoder_seq_len = max(max_encoder_seq_len,
                                               inter_data.encoder_seq_len)
 
@@ -1375,12 +1374,6 @@ def list_prompt_adapters(self) -> Set[int]:
             raise RuntimeError("PromptAdapter is not enabled.")
         return self.prompt_adapter_manager.list_adapters()
 
-    @property
-    def model_is_mrope(self) -> bool:
-        """Detect if the model has "mrope" rope_scaling type.
-        mrope requires keep "rope_deltas" between prompt and decoding phases."""
-        return uses_mrope(self.model_config.hf_config)
-
     @torch.inference_mode()
     def capture_model(self, kv_caches: List[List[torch.Tensor]]) -> None:
         """Cuda graph capture a model.
@@ -1411,7 +1404,7 @@ def capture_model(self, kv_caches: List[List[torch.Tensor]]) -> None:
         max_batch_size = self.max_batchsize_to_capture
         input_tokens = torch.zeros(max_batch_size, dtype=torch.long).cuda()
         input_positions = torch.zeros(max_batch_size, dtype=torch.long).cuda()
-        if self.model_is_mrope:
+        if self.model_config.uses_mrope:
             input_positions = torch.tile(input_positions, (3, 1))
         # Prepare dummy previous_hidden_states only if needed by the model.
         # This is used by draft models such as EAGLE.
@@ -1447,7 +1440,7 @@ def capture_model(self, kv_caches: List[List[torch.Tensor]]) -> None:
                         self.attn_state.graph_capture_get_metadata_for_batch(
                             batch_size,
                             is_encoder_decoder_model=self.model_config.
-                            is_encoder_decoder_model))
+                            is_encoder_decoder))
 
                     if self.lora_config:
                         lora_mapping = LoRAMapping(
@@ -1466,7 +1459,7 @@ def capture_model(self, kv_caches: List[List[torch.Tensor]]) -> None:
                     graph_runner = CUDAGraphRunner(
                         self.model, self.attn_backend.get_name(),
                         self.attn_state.graph_clone(batch_size),
-                        self.model_config.is_encoder_decoder_model)
+                        self.model_config.is_encoder_decoder)
 
                     capture_inputs = {
                         "input_ids":
@@ -1497,7 +1490,7 @@ def capture_model(self, kv_caches: List[List[torch.Tensor]]) -> None:
                             self.model.get_seqlen_agnostic_capture_inputs(
                                 batch_size)
                         })
-                    if self.model_config.is_encoder_decoder_model:
+                    if self.model_config.is_encoder_decoder:
                         # add the additional inputs to capture for
                         # encoder-decoder models.
                         self._update_inputs_to_capture_for_enc_dec_model(

vllm/worker/worker.py

@@ -77,7 +77,7 @@ def __init__(
             ModelRunnerClass = model_runner_cls
         elif model_config.task == "embedding":
             ModelRunnerClass = EmbeddingModelRunner
-        elif self._is_encoder_decoder_model():
+        elif self.model_config.is_encoder_decoder:
             ModelRunnerClass = EncoderDecoderModelRunner
         self.model_runner: GPUModelRunnerBase = ModelRunnerClass(
             vllm_config=self.vllm_config,
@@ -119,9 +119,6 @@ def stop_profile(self):
             raise RuntimeError("Profiler is not enabled.")
         self.profiler.stop()
 
-    def _is_encoder_decoder_model(self):
-        return self.model_config.is_encoder_decoder_model
-
     def init_device(self) -> None:
         if self.device_config.device.type == "cuda":
             # torch.distributed.all_reduce does not free the input tensor until