[Core] Whisper support `torch.compile` (#30385)

Signed-off-by: NickLucche <nlucches@redhat.com>

tests/entrypoints/openai/correctness/test_transcription_api_correctness.py

@@ -156,7 +156,9 @@ def test_wer_correctness(
     model_name, dataset_repo, expected_wer, n_examples=-1, max_concurrent_request=None
 ):
     # TODO refactor to use `ASRDataset`
-    with RemoteOpenAIServer(model_name, ["--enforce-eager"]) as remote_server:
+    with RemoteOpenAIServer(
+        model_name, ["--enforce-eager"], max_wait_seconds=480
+    ) as remote_server:
         dataset = load_hf_dataset(dataset_repo)
 
         if not max_concurrent_request:

vllm/compilation/decorators.py

@@ -25,6 +25,7 @@ from vllm.config import (
     set_current_vllm_config,
 )
 from vllm.config.compilation import DynamicShapesType
+from vllm.forward_context import get_forward_context, is_forward_context_available
 from vllm.logger import init_logger
 from vllm.sequence import IntermediateTensors
 from vllm.utils.import_utils import resolve_obj_by_qualname
@@ -388,6 +389,12 @@ def _support_torch_compile(
         if self.do_not_compile or torch.compiler.is_compiling():
             return self.forward(*args, **kwargs)
 
+        # If skip_compiled is set, bypass compiled model call. This is used e.g. for
+        # enc-dec models where tensor shapes/types vary across invocations, preventing
+        # the capture of a single computational graph.
+        if is_forward_context_available() and get_forward_context().skip_compiled:
+            return self.forward(*args, **kwargs)
+
         # if aot_compiled_fn is set, call it with partition wrapper context.
         # The partition wrapper must be active at runtime for CUDA graph
         # capture to work correctly with inductor graph partitioning.

vllm/forward_context.py

@@ -207,6 +207,9 @@ class ForwardContext:
 
     ubatch_slices: UBatchSlices | None = None
 
+    # If True, bypass the compiled model call, e.g. by using .forward() directly
+    skip_compiled: bool = False
+
     additional_kwargs: dict[str, Any] = field(default_factory=dict)
 
     def __post_init__(self):
@@ -240,6 +243,7 @@ def create_forward_context(
     batch_descriptor: BatchDescriptor | None = None,
     ubatch_slices: UBatchSlices | None = None,
     additional_kwargs: dict[str, Any] | None = None,
+    skip_compiled: bool = False,
 ):
     return ForwardContext(
         no_compile_layers=vllm_config.compilation_config.static_forward_context,
@@ -249,6 +253,7 @@ def create_forward_context(
         cudagraph_runtime_mode=cudagraph_runtime_mode,
         batch_descriptor=batch_descriptor,
         ubatch_slices=ubatch_slices,
+        skip_compiled=skip_compiled,
         additional_kwargs=additional_kwargs or {},
     )
 
@@ -278,6 +283,7 @@ def set_forward_context(
     cudagraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
     batch_descriptor: BatchDescriptor | None = None,
     ubatch_slices: UBatchSlices | None = None,
+    skip_compiled: bool = False,
 ):
     """A context manager that stores the current forward context,
     can be attention metadata, etc.
@@ -336,6 +342,7 @@ def set_forward_context(
         batch_descriptor,
         ubatch_slices,
         additional_kwargs,
+        skip_compiled,
     )
 
     try:

vllm/model_executor/models/whisper.py

@@ -19,6 +19,7 @@ from transformers import (
 from transformers.models.whisper.modeling_whisper import sinusoids
 
 from vllm.attention.layer import Attention
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
 from vllm.distributed import get_tensor_model_parallel_world_size
@@ -561,6 +562,7 @@ class WhisperEncoder(nn.Module):
         return self.forward_layers(hidden_states)
 
 
+@support_torch_compile(dynamic_arg_dims={"input_ids": 0, "positions": -1})
 class WhisperDecoder(nn.Module):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()

vllm/v1/worker/gpu_model_runner.py

@@ -3268,6 +3268,13 @@ class GPUModelRunner(
             # Mark KV scales as calculated after the first forward pass
             self.calculate_kv_scales = False
 
+        # Encoder-decoder models can only compile the pure decode steps where no
+        # encoder inputs are present. Use eager for the first pass.
+        num_encoder_reqs = len(scheduler_output.scheduled_encoder_inputs)
+        has_encoder_input = (
+            self.model_config.is_encoder_decoder and num_encoder_reqs > 0
+        )
+
         # Run the model.
         # Use persistent buffers for CUDA graphs.
         with (
@@ -3279,6 +3286,7 @@ class GPUModelRunner(
                 cudagraph_runtime_mode=cudagraph_mode,
                 batch_descriptor=batch_desc,
                 ubatch_slices=ubatch_slices_padded,
+                skip_compiled=has_encoder_input,
             ),
             record_function_or_nullcontext("gpu_model_runner: forward"),
             self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,