refactor: Turn GPUModelRunner.inputs_embeds to a CpuGpuBuffer (vllm-project#24345)

Signed-off-by: Andrew Sansom <[email protected]>

Author: qthequartermasterman

vllm/v1/utils.py

@@ -19,6 +19,8 @@
                         kill_process_tree)
 
 if TYPE_CHECKING:
+    import numpy as np
+
     from vllm.v1.engine.coordinator import DPCoordinator
     from vllm.v1.engine.utils import (CoreEngineActorManager,
                                       CoreEngineProcManager)
@@ -97,20 +99,31 @@ def __repr__(self):
 
 
 class CpuGpuBuffer:
+    """Buffer to easily copy tensors between CPU and GPU."""
 
     def __init__(
         self,
-        *args,
+        *size: Union[int, torch.SymInt],
         dtype: torch.dtype,
         device: torch.device,
         pin_memory: bool,
-    ):
-        self.cpu = torch.zeros(*args,
+        with_numpy: bool = True,
+    ) -> None:
+        self.cpu = torch.zeros(*size,
                                dtype=dtype,
                                device="cpu",
                                pin_memory=pin_memory)
-        self.np = self.cpu.numpy()
         self.gpu = self.cpu.to(device)
+        self.np: np.ndarray
+        # To keep type hints simple (avoiding generics and subclasses), we
+        # only conditionally create the numpy array attribute. This can cause
+        # AttributeError if `self.np` is accessed when `with_numpy=False`.
+        if with_numpy:
+            if dtype == torch.bfloat16:
+                raise ValueError(
+                    "Bfloat16 torch tensors cannot be directly cast to a "
+                    "numpy array, so call CpuGpuBuffer with with_numpy=False")
+            self.np = self.cpu.numpy()
 
     def copy_to_gpu(self, n: Optional[int] = None) -> torch.Tensor:
         if n is None:

vllm/v1/worker/gpu_model_runner.py

@@ -303,10 +303,13 @@ def __init__(
         self.query_start_loc = self._make_buffer(self.max_num_reqs + 1,
                                                  dtype=torch.int32)
         self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
-        self.inputs_embeds = torch.zeros(
-            (self.max_num_tokens, self.hidden_size),
-            dtype=self.dtype,
-            device=self.device)
+        # Because inputs_embeds may be bfloat16 and we don't need a numpy
+        # version of this tensor, avoid a RuntimeError by not creating a
+        # numpy buffer.
+        self.inputs_embeds = self._make_buffer(self.max_num_tokens,
+                                               self.hidden_size,
+                                               dtype=self.dtype,
+                                               numpy=False)
 
         # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
         if self.uses_mrope:
@@ -374,11 +377,18 @@ def __init__(
             device="cpu",
             pin_memory=self.pin_memory)
 
-    def _make_buffer(self, *args, dtype: torch.dtype) -> CpuGpuBuffer:
-        return CpuGpuBuffer(*args,
+    def _make_buffer(self,
+                     *size: Union[int, torch.SymInt],
+                     dtype: torch.dtype,
+                     numpy: bool = True) -> CpuGpuBuffer:
+        # Bfloat16 torch tensors cannot be directly cast to a numpy array, so
+        # if a bfloat16 buffer is needed without a corresponding numpy array,
+        # don't bother instantiating the numpy array.
+        return CpuGpuBuffer(*size,
                             dtype=dtype,
                             device=self.device,
-                            pin_memory=self.pin_memory)
+                            pin_memory=self.pin_memory,
+                            with_numpy=numpy)
 
     def _init_model_kwargs(self, num_tokens: int):
         model_kwargs = dict[str, Any]()
@@ -1645,11 +1655,11 @@ def execute_model(
             )
 
             # TODO(woosuk): Avoid the copy. Optimize.
-            self.inputs_embeds[:num_scheduled_tokens].copy_(
+            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(
                 inputs_embeds_scheduled)
 
             input_ids = None
-            inputs_embeds = self.inputs_embeds[:num_input_tokens]
+            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
             model_kwargs = {
                 **self._init_model_kwargs(num_scheduled_tokens),
                 **self._extract_mm_kwargs(scheduler_output),
@@ -2484,7 +2494,7 @@ def _dummy_run(
                                             num_scheduled_tokens, remove_lora):
             if self.supports_mm_inputs:
                 input_ids = None
-                inputs_embeds = self.inputs_embeds[:num_tokens]
+                inputs_embeds = self.inputs_embeds.gpu[:num_tokens]
                 model_kwargs = {
                     **self._init_model_kwargs(num_tokens),
                     **self._dummy_mm_kwargs(num_reqs),