to make repetition penalty faster

vllm/model_executor/layers/sampler.py

@@ -519,7 +519,7 @@ def _apply_penalties(logits: torch.Tensor, prompt_tokens_tensor: torch.Tensor,
         output_tokens_tensor, vocab_size, num_seqs)
 
     repetition_penalties = repetition_penalties[:, None].repeat(1, vocab_size)
-    repetition_penalties[~(prompt_mask | output_mask)] = 1.0
+    repetition_penalties.masked_fill_(~(prompt_mask | output_mask), 1.0)
     logits = torch.where(logits > 0, logits / repetition_penalties,
                          logits * repetition_penalties)
 

vllm/model_executor/sampling_metadata.py

@@ -9,7 +9,9 @@
 from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, SequenceData,
                            SequenceGroupMetadata)
 from vllm.utils import (PyObjectCache, async_tensor_h2d,
-                        is_pin_memory_available, make_tensor_with_pad)
+                        is_pin_memory_available, 
+                        make_tensor_with_pad,
+                        make_tensor_with_pad_align)
 
 _SAMPLING_EPS = 1e-5
 
@@ -522,20 +524,38 @@ def from_lists(
         do_penalties = prompt_tokens or output_tokens
 
         if do_penalties:
-            prompt_t = make_tensor_with_pad(
-                prompt_tokens,
-                vocab_size,
-                device="cpu",
-                dtype=torch.int64,
-                pin_memory=pin_memory,
-            )
-            output_t = make_tensor_with_pad(
-                output_tokens,
-                vocab_size,
-                device="cpu",
-                dtype=torch.int64,
-                pin_memory=pin_memory,
-            )
+            if current_platform.is_hpu():
+                prompt_t = make_tensor_with_pad_align(
+                    prompt_tokens,
+                    vocab_size,
+                    device="cpu",
+                    dtype=torch.int64,
+                    pin_memory=pin_memory,
+                    max_len_align=1024,
+                )
+                output_t = make_tensor_with_pad_align(
+                    output_tokens,
+                    vocab_size,
+                    device="cpu",
+                    dtype=torch.int64,
+                    pin_memory=pin_memory,
+                    max_len_align=1024,
+                )
+            else:
+                prompt_t = make_tensor_with_pad(
+                    prompt_tokens,
+                    vocab_size,
+                    device="cpu",
+                    dtype=torch.int64,
+                    pin_memory=pin_memory,
+                )
+                output_t = make_tensor_with_pad(
+                    output_tokens,
+                    vocab_size,
+                    device="cpu",
+                    dtype=torch.int64,
+                    pin_memory=pin_memory,
+                )
         else:
             empty_tensor = torch.empty(0, device=device, dtype=torch.long)
             prompt_t = empty_tensor
@@ -545,47 +565,58 @@ def from_lists(
             temperatures,
             device="cpu",
             dtype=dtype,
-            pin_memory=pin_memory,
         )
         top_ps_t = torch.tensor(
             top_ps,
             device="cpu",
             dtype=dtype,
-            pin_memory=pin_memory,
         )
         min_ps_t = torch.tensor(
             min_ps,
             device="cpu",
             dtype=dtype,
-            pin_memory=pin_memory,
         )
         presence_penalties_t = torch.tensor(
             presence_penalties,
             device="cpu",
             dtype=dtype,
-            pin_memory=pin_memory,
         )
         frequency_penalties_t = torch.tensor(
             frequency_penalties,
             device="cpu",
             dtype=dtype,
-            pin_memory=pin_memory,
         )
         repetition_penalties_t = torch.tensor(
             repetition_penalties,
             device="cpu",
             dtype=dtype,
-            pin_memory=pin_memory,
         )
         top_ks_t = torch.tensor(
             top_ks,
             device="cpu",
             dtype=torch.int,
-            pin_memory=pin_memory,
         )
         # Because the memory is pinned, we can do non-blocking
         # transfer to device.
 
+        if pin_memory:
+            if not current_platform.is_hpu():
+                temperatures_t.pin_memory()
+                top_ps_t.pin_memory()
+                min_ps_t.pin_memory()
+                frequency_penalties_t.pin_memory()
+                presence_penalties_t.pin_memory()
+                repetition_penalties_t.pin_memory()
+                top_ks_t.pin_memory()
+            else:
+                temperatures_t.pin_memory(device="hpu")
+                top_ps_t.pin_memory(device="hpu")
+                min_ps_t.pin_memory(device="hpu")
+                frequency_penalties_t.pin_memory(device="hpu")
+                presence_penalties_t.pin_memory(device="hpu")
+                repetition_penalties_t.pin_memory(device="hpu")
+                top_ks_t.pin_memory(device="hpu")
+
         return cls(
             temperatures=temperatures_t.to(device=device, non_blocking=True),
             top_ps=top_ps_t.to(device=device, non_blocking=True),

vllm/utils.py

@@ -6,6 +6,7 @@
 import gc
 import inspect
 import ipaddress
+import math
 import os
 import socket
 import subprocess
@@ -752,9 +753,6 @@
     elif current_platform.is_neuron():
         print_warning_once("Pin memory is not supported on Neuron.")
         return False
-    elif current_platform.is_hpu():
-        print_warning_once("Pin memory is not supported on HPU.")
-        return False
     elif current_platform.is_cpu() or current_platform.is_openvino():
         return False
     return True
@@ -812,6 +810,29 @@
 
     return padded_x
 
+def make_ndarray_with_pad_align(
+    x: List[List[T]],
+    pad: T,
+    dtype: npt.DTypeLike,
+    *,
+    max_len_align: Optional[int] = None,
+) -> npt.NDArray:
+    """
+    Make a padded array from 2D inputs.
+
+    The padding is applied to the end of each inner list until it reaches
+    `max_len`.
+    """
+    # Unlike for most functions, map is faster than a genexpr over `len`
+    max_len = max(map(len, x), default=0)
+    max_len_aligned =  math.ceil(max_len / max_len_align) * max_len_align
+    padded_x = np.full((len(x), max_len_aligned), pad, dtype=dtype)
+
+    for ind, blocktb in enumerate(x):
+        assert len(blocktb) <= max_len_aligned
+        padded_x[ind, :len(blocktb)] = blocktb
+
+    return padded_x
 
 def make_tensor_with_pad(
     x: List[List[T]],
@@ -833,10 +854,37 @@
 
     tensor = torch.from_numpy(padded_x).to(device)
     if pin_memory:
-        tensor = tensor.pin_memory()
+        if not current_platform.is_hpu():
+            tensor = tensor.pin_memory()
+        else:
+            tensor = tensor.pin_memory("hpu")
 
     return tensor
 
+def make_tensor_with_pad_align(
+    x: List[List[T]],
+    pad: T,
+    dtype: torch.dtype,
+    *,
+    max_len_align: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    pin_memory: bool = False,
+) -> torch.Tensor:
+    """
+    Make a padded tensor from 2D inputs.
+
+    The padding is applied to the end of each inner list until it reaches
+    `max_len`.
+    """
+    np_dtype = TORCH_DTYPE_TO_NUMPY_DTYPE[dtype]
+    padded_x = make_ndarray_with_pad_align(x, pad, np_dtype, 
+                max_len_align=max_len_align)
+
+    tensor = torch.from_numpy(padded_x).to(device)
+    if pin_memory:
+        tensor = tensor.pin_memory("hpu")
+
+    return tensor
 
 def async_tensor_h2d(
     data: list,
@@ -845,7 +893,13 @@
     pin_memory: bool,
 ) -> torch.Tensor:
     """Asynchronously create a tensor and copy it from host to device."""
-    t = torch.tensor(data, dtype=dtype, pin_memory=pin_memory, device="cpu")
+    t = torch.tensor(data, dtype=dtype, device="cpu")
+    if pin_memory:
+        if not current_platform.is_hpu():
+            t.pin_memory()
+        else:
+            t.pin_memory(device="hpu")
+
     return t.to(device=target_device, non_blocking=True)