[Chore] Remove unused batched RoPE op & kernel (#24789)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

csrc/ops.h

@@ -122,12 +122,6 @@ void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
                       std::optional<torch::Tensor> key, int64_t head_size,
                       torch::Tensor& cos_sin_cache, bool is_neox);
 
-void batched_rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
-                              std::optional<torch::Tensor> key,
-                              int64_t head_size, torch::Tensor& cos_sin_cache,
-                              bool is_neox, int64_t rot_dim,
-                              torch::Tensor& cos_sin_cache_offsets);
-
 void silu_and_mul(torch::Tensor& out, torch::Tensor& input);
 
 void silu_and_mul_quant(torch::Tensor& out, torch::Tensor& input,

csrc/pos_encoding_kernels.cu

@@ -99,35 +99,6 @@ __global__ void rotary_embedding_kernel(
       token_idx, query_stride, key_stride, head_stride);
 }
 
-template <typename scalar_t, bool IS_NEOX>
-__global__ void batched_rotary_embedding_kernel(
-    const int64_t* __restrict__ positions,  // [batch_size, seq_len] or
-                                            // [num_tokens]
-    scalar_t* __restrict__ query,           // [batch_size, seq_len, num_heads,
-                                   // head_size] or [num_tokens, num_heads,
-                                   // head_size]
-    scalar_t* __restrict__ key,  // nullptr or
-                                 // [batch_size, seq_len, num_kv_heads,
-                                 // head_size] or [num_tokens, num_kv_heads,
-                                 // head_size]
-    const scalar_t* __restrict__ cos_sin_cache,  // [max_position, 2, rot_dim //
-                                                 // 2]
-    const int64_t* __restrict__ cos_sin_cache_offsets,  // [batch_size, seq_len]
-    const int rot_dim, const int64_t query_stride, const int64_t key_stride,
-    const int64_t head_stride, const int num_heads, const int num_kv_heads,
-    const int head_size) {
-  // Each thread block is responsible for one token.
-  const int token_idx = blockIdx.x;
-  int64_t pos = positions[token_idx];
-  int64_t cos_sin_cache_offset = cos_sin_cache_offsets[token_idx];
-  const scalar_t* cache_ptr =
-      cos_sin_cache + (cos_sin_cache_offset + pos) * rot_dim;
-
-  apply_rotary_embedding<scalar_t, IS_NEOX>(
-      query, key, cache_ptr, head_size, num_heads, num_kv_heads, rot_dim,
-      token_idx, query_stride, key_stride, head_stride);
-}
-
 }  // namespace vllm
 
 void rotary_embedding(
@@ -211,96 +182,3 @@ void rotary_embedding(
     }
   });
 }
-
-/*
-Batched version of rotary embedding, pack multiple LoRAs together
-and process in batched manner.
-*/
-void batched_rotary_embedding(
-    torch::Tensor& positions,  // [batch_size, seq_len] or [num_tokens]
-    torch::Tensor& query,  // [batch_size, seq_len, num_heads * head_size] or
-                           // [num_tokens, num_heads * head_size] or
-                           // [batch_size, seq_len, num_heads, head_size] or
-                           // [num_tokens, num_heads, head_size]
-    std::optional<torch::Tensor>
-        key,  // null or
-              // [batch_size, seq_len, num_kv_heads * head_size] or
-              // [num_tokens, num_kv_heads * head_size] or
-              // [batch_size, seq_len, num_heads, head_size] or
-              // [num_tokens, num_heads, head_size]
-    int64_t head_size,
-    torch::Tensor& cos_sin_cache,  // [max_position, rot_dim]
-    bool is_neox, int64_t rot_dim,
-    torch::Tensor& cos_sin_cache_offsets  // [num_tokens] or [batch_size]
-) {
-  // num_tokens = batch_size * seq_len
-  int64_t num_tokens = cos_sin_cache_offsets.size(0);
-  TORCH_CHECK(
-      positions.size(0) == num_tokens || positions.numel() == num_tokens,
-      "positions must have the same num_tokens or batch_size as "
-      "cos_sin_cache_offsets");
-
-  int positions_ndim = positions.dim();
-  // Make sure num_tokens dim is consistent across positions, query, and key
-  TORCH_CHECK(
-      positions_ndim == 1 || positions_ndim == 2,
-      "positions must have shape [num_tokens] or [batch_size, seq_len]");
-  if (positions_ndim == 1) {
-    TORCH_CHECK(query.size(0) == positions.size(0) &&
-                    (!key.has_value() || key->size(0) == positions.size(0)),
-                "query, key and positions must have the same number of tokens");
-  }
-  if (positions_ndim == 2) {
-    TORCH_CHECK(
-        query.size(0) == positions.size(0) &&
-            (!key.has_value() || key->size(0) == positions.size(0)) &&
-            query.size(1) == positions.size(1) &&
-            (!key.has_value() || key->size(1) == positions.size(1)),
-        "query, key and positions must have the same batch_size and seq_len");
-  }
-
-  // Make sure head_size is valid for query and key
-  int query_hidden_size = query.numel() / num_tokens;
-  int key_hidden_size = key.has_value() ? key->numel() / num_tokens : 0;
-  TORCH_CHECK(query_hidden_size % head_size == 0);
-  TORCH_CHECK(key_hidden_size % head_size == 0);
-
-  // Make sure query and key have concistent number of heads
-  int num_heads = query_hidden_size / head_size;
-  int num_kv_heads = key.has_value() ? key_hidden_size / head_size : num_heads;
-  TORCH_CHECK(num_heads % num_kv_heads == 0);
-
-  int seq_dim_idx = positions_ndim - 1;
-  int64_t query_stride = query.stride(seq_dim_idx);
-  int64_t key_stride = key.has_value() ? key->stride(seq_dim_idx) : 0;
-  // Determine head stride: for [*, heads, head_size] use stride of last dim;
-  // for flat [*, heads*head_size], heads blocks are contiguous of size
-  // head_size
-  int query_ndim = query.dim();
-  int64_t head_stride =
-      (query_ndim == positions_ndim + 2) ? query.stride(-2) : head_size;
-
-  dim3 grid(num_tokens);
-  dim3 block(std::min<int64_t>(num_heads * rot_dim / 2, 512));
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(query));
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_TYPES(query.scalar_type(), "rotary_embedding", [&] {
-    if (is_neox) {
-      vllm::batched_rotary_embedding_kernel<scalar_t, true>
-          <<<grid, block, 0, stream>>>(
-              positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
-              key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
-              cos_sin_cache.data_ptr<scalar_t>(),
-              cos_sin_cache_offsets.data_ptr<int64_t>(), rot_dim, query_stride,
-              key_stride, head_stride, num_heads, num_kv_heads, head_size);
-    } else {
-      vllm::batched_rotary_embedding_kernel<scalar_t, false>
-          <<<grid, block, 0, stream>>>(
-              positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
-              key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
-              cos_sin_cache.data_ptr<scalar_t>(),
-              cos_sin_cache_offsets.data_ptr<int64_t>(), rot_dim, query_stride,
-              key_stride, head_stride, num_heads, num_kv_heads, head_size);
-    }
-  });
-}

csrc/torch_bindings.cpp

@@ -214,16 +214,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "                 Tensor cos_sin_cache, bool is_neox) -> ()");
   ops.impl("rotary_embedding", torch::kCUDA, &rotary_embedding);
 
-  // Apply GPT-NeoX or GPT-J style rotary embedding to query and key
-  // (supports multiple loras).
-  ops.def(
-      "batched_rotary_embedding(Tensor positions, Tensor! query,"
-      "                         Tensor!? key, int head_size,"
-      "                         Tensor cos_sin_cache, bool is_neox,"
-      "                         int rot_dim,"
-      "                         Tensor cos_sin_cache_offsets) -> ()");
-  ops.impl("batched_rotary_embedding", torch::kCUDA, &batched_rotary_embedding);
-
   // Quantization ops
 #ifndef USE_ROCM
   // Quantized GEMM for AWQ.

tests/kernels/core/test_pos_encoding.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from itertools import accumulate, product
+from itertools import product
 from typing import Callable, Optional
 
 import pytest
@@ -111,151 +111,6 @@ def test_rotary_embedding(
             "expected returned key to be None"
 
 
-@pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
-@pytest.mark.parametrize("tensor_shape_fn", TENSORS_SHAPES_FN)
-@pytest.mark.parametrize("batch_size", BATCH_SIZES)
-@pytest.mark.parametrize("seq_len", SEQ_LENS)
-@pytest.mark.parametrize("num_heads", NUM_HEADS)
-@pytest.mark.parametrize("head_size", HEAD_SIZES)
-@pytest.mark.parametrize("rotary_dim", ROTARY_DIMS)
-@pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("seed", SEEDS)
-@pytest.mark.parametrize("device", CUDA_DEVICES)
-@pytest.mark.parametrize("use_key", USE_KEY)
-@torch.inference_mode()
-def test_batched_rotary_embedding(
-    is_neox_style: bool,
-    tensor_shape_fn: Callable[[int, int, int, int], tuple[int]],
-    batch_size: int,
-    seq_len: int,
-    num_heads: int,
-    head_size: int,
-    rotary_dim: Optional[int],
-    dtype: torch.dtype,
-    seed: int,
-    device: str,
-    use_key: bool,
-    max_position: int = 8192,
-    base: float = 10000,
-) -> None:
-    current_platform.seed_everything(seed)
-    torch.set_default_device(device)
-    if rotary_dim is None:
-        rotary_dim = head_size
-    rope = get_rope(head_size, rotary_dim, max_position, base, is_neox_style, {
-        "rope_type": "linear",
-        "factor": (1, )
-    })
-    rope = rope.to(dtype=dtype, device=torch.get_default_device())
-
-    positions = torch.randint(0, max_position, (batch_size, seq_len))
-    query_shape = tensor_shape_fn(batch_size, seq_len, num_heads, head_size)
-    query = torch.randn(query_shape, dtype=dtype)
-    key = torch.randn_like(query) if use_key else None
-
-    # slice tensor if required, noop otherwise
-    query = query[..., :head_size]
-    key = key[..., :head_size] if use_key else None
-
-    # NOTE(woosuk): The reference implementation should be executed first
-    # because the custom kernel is in-place.
-    ref_query, ref_key = rope.forward_native(positions, query, key)
-    out_query, out_key = rope.forward(positions,
-                                      query,
-                                      key,
-                                      offsets=torch.zeros(batch_size * seq_len,
-                                                          dtype=torch.long,
-                                                          device=device))
-    # Compare the results.
-    torch.testing.assert_close(out_query,
-                               ref_query,
-                               atol=get_default_atol(out_query),
-                               rtol=get_default_rtol(out_query))
-    if use_key:
-        torch.testing.assert_close(out_key,
-                                   ref_key,
-                                   atol=get_default_atol(out_key),
-                                   rtol=get_default_rtol(out_key))
-    else:
-        assert ref_key is None and out_key is None, \
-            "expected returned key to be None"
-
-
-@pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
-@pytest.mark.parametrize("batch_size", BATCH_SIZES)
-@pytest.mark.parametrize("seq_len", SEQ_LENS)
-@pytest.mark.parametrize("num_heads", NUM_HEADS)
-@pytest.mark.parametrize("head_size", HEAD_SIZES)
-@pytest.mark.parametrize("rotary_dim", ROTARY_DIMS)
-@pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("seed", SEEDS)
-@pytest.mark.parametrize("device", CUDA_DEVICES)
-@pytest.mark.parametrize("use_key", USE_KEY)
-@torch.inference_mode()
-def test_batched_rotary_embedding_multi_lora(
-    is_neox_style: bool,
-    batch_size: int,
-    seq_len: int,
-    num_heads: int,
-    head_size: int,
-    rotary_dim: Optional[int],
-    dtype: torch.dtype,
-    seed: int,
-    device: str,
-    use_key: bool,
-    max_position: int = 8192,
-    base: float = 10000,
-) -> None:
-    current_platform.seed_everything(seed)
-    torch.set_default_device(device)
-    if rotary_dim is None:
-        rotary_dim = head_size
-    scaling_factors: list[int] = [1, 2, 4]
-    rope = get_rope(head_size, rotary_dim, max_position, base, is_neox_style, {
-        "rope_type": "linear",
-        "factor": tuple(scaling_factors)
-    })
-    rope = rope.to(dtype=dtype, device=torch.get_default_device())
-
-    positions = torch.randint(0, max_position, (batch_size, seq_len))
-    query = torch.randn(batch_size,
-                        seq_len,
-                        num_heads * head_size,
-                        dtype=dtype)
-    key = torch.randn_like(query) if use_key else None
-
-    offset_map = torch.tensor(
-        list(
-            accumulate([0] + [
-                max_position * scaling_factor * 2
-                for scaling_factor in scaling_factors[:-1]
-            ])))
-    query_types = torch.randint(0,
-                                len(scaling_factors), (batch_size, seq_len),
-                                device=device)
-    query_offsets = offset_map[query_types]
-
-    # NOTE(woosuk): The reference implementation should be executed first
-    # because the custom kernel is in-place.
-    ref_query, ref_key = rope.forward_native(positions, query, key,
-                                             query_offsets)
-    out_query, out_key = rope.forward(positions, query, key,
-                                      query_offsets.flatten())
-    # Compare the results.
-    torch.testing.assert_close(out_query,
-                               ref_query,
-                               atol=get_default_atol(out_query),
-                               rtol=get_default_rtol(out_query))
-    if use_key:
-        torch.testing.assert_close(out_key,
-                                   ref_key,
-                                   atol=get_default_atol(out_key),
-                                   rtol=get_default_rtol(out_key))
-    else:
-        assert ref_key is None and out_key is None, \
-            "expected returned key to be None"
-
-
 @torch.inference_mode()
 def test_rope_module_cache():
     MAX_POSITIONS = [123, 1234]

tests/kernels/core/test_rotary_embedding.py

@@ -16,20 +16,14 @@ from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
 def rotary_embedding_opcheck(rot,
                              positions: torch.Tensor,
                              query: torch.Tensor,
-                             key: Optional[torch.Tensor] = None,
-                             offsets: Optional[torch.Tensor] = None):
+                             key: Optional[torch.Tensor] = None):
     cos_sin_cache = rot.cos_sin_cache.to(query.device, dtype=query.dtype)
 
-    # ops.rotary_embedding()/batched_rotary_embedding()
-    # are in-place operations that update the query and key tensors.
-    if offsets is not None:
-        opcheck(torch.ops._C.batched_rotary_embedding,
-                (positions, query, key, rot.head_size, cos_sin_cache,
-                 rot.is_neox_style, rot.rotary_dim, offsets))
-    else:
-        opcheck(torch.ops._C.rotary_embedding,
-                (positions, query, key, rot.head_size, cos_sin_cache,
-                 rot.is_neox_style))
+    # ops.rotary_embedding() is a in-place operation
+    # that updates the query and key tensors.
+    opcheck(torch.ops._C.rotary_embedding,
+            (positions, query, key, rot.head_size, cos_sin_cache,
+             rot.is_neox_style))
 
 
 @pytest.mark.parametrize("device", ["cuda"])
@@ -65,10 +59,6 @@ def test_rotary_embedding_opcheck(dist_init, device, max_position,
     key = key[..., :head_size] if use_key else None
 
     rotary_embedding_opcheck(rot, positions, query, key)
-    offsets = torch.zeros(batch_size * seq_len,
-                          device=device,
-                          dtype=torch.long)
-    rotary_embedding_opcheck(rot, positions, query, key, offsets)
 
     # if we have a contiguous head stride, test the alternate
     # [..., num_heads * head_dim] shape/layout

vllm/_custom_ops.py

@@ -257,16 +257,6 @@ def rotary_embedding(
                                   cos_sin_cache, is_neox)
 
 
-def batched_rotary_embedding(positions: torch.Tensor, query: torch.Tensor,
-                             key: Optional[torch.Tensor], head_size: int,
-                             cos_sin_cache: torch.Tensor, is_neox: bool,
-                             rot_dim: int,
-                             cos_sin_cache_offsets: torch.Tensor) -> None:
-    torch.ops._C.batched_rotary_embedding(positions, query, key, head_size,
-                                          cos_sin_cache, is_neox, rot_dim,
-                                          cos_sin_cache_offsets)
-
-
 # layer norm ops
 def rms_norm(out: torch.Tensor, input: torch.Tensor, weight: torch.Tensor,
              epsilon: float) -> None:

vllm/_ipex_ops.py

@@ -148,17 +148,6 @@ class ipex_ops:
                                                      head_size, cos_sin_cache,
                                                      is_neox, rot_dim)
 
-    @staticmethod
-    def batched_rotary_embedding(positions: torch.Tensor, query: torch.Tensor,
-                                 key: torch.Tensor, head_size: int,
-                                 cos_sin_cache: torch.Tensor, is_neox: bool,
-                                 rot_dim: int,
-                                 cos_sin_cache_offsets: torch.Tensor) -> None:
-        ipex.llm.functional.rotary_embedding_batched(positions, query, key,
-                                                     head_size, cos_sin_cache,
-                                                     is_neox, rot_dim,
-                                                     cos_sin_cache_offsets)
-
     @staticmethod
     def rms_norm(input: torch.Tensor, weight: torch.Tensor,
                  epsilon: float) -> torch.Tensor:

vllm/model_executor/layers/rotary_embedding/base.py

@@ -62,11 +62,8 @@ class RotaryEmbedding(CustomOp):
         positions: torch.Tensor,
         query: torch.Tensor,
         key: Optional[torch.Tensor] = None,
-        offsets: Optional[torch.Tensor] = None,
     ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         """A PyTorch-native implementation of forward()."""
-        if offsets is not None:
-            positions = positions + offsets
         positions = positions.flatten()
         num_tokens = positions.shape[0]
         cos_sin = self.cos_sin_cache.index_select(0, positions)
@@ -96,7 +93,6 @@ class RotaryEmbedding(CustomOp):
         positions: torch.Tensor,
         query: torch.Tensor,
         key: Optional[torch.Tensor] = None,
-        offsets: Optional[torch.Tensor] = None,
     ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         from vllm import _custom_ops as ops
 
@@ -107,16 +103,10 @@ class RotaryEmbedding(CustomOp):
             self.cos_sin_cache = self.cos_sin_cache.to(query.device,
                                                        dtype=query.dtype)
 
-        # ops.rotary_embedding()/batched_rotary_embedding()
-        # are in-place operations that update the query and key tensors.
-        if offsets is not None:
-            ops.batched_rotary_embedding(positions, query, key, self.head_size,
-                                         self.cos_sin_cache,
-                                         self.is_neox_style, self.rotary_dim,
-                                         offsets)
-        else:
-            ops.rotary_embedding(positions, query, key, self.head_size,
-                                 self.cos_sin_cache, self.is_neox_style)
+        # ops.rotary_embedding() is an in-place operation
+        # that updates the query and key tensors.
+        ops.rotary_embedding(positions, query, key, self.head_size,
+                             self.cos_sin_cache, self.is_neox_style)
         return query, key
 
     def forward_xpu(
@@ -124,29 +114,21 @@ class RotaryEmbedding(CustomOp):
         positions: torch.Tensor,
         query: torch.Tensor,
         key: Optional[torch.Tensor] = None,
-        offsets: Optional[torch.Tensor] = None,
     ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         from vllm._ipex_ops import ipex_ops as ops
 
         self.cos_sin_cache = self.cos_sin_cache.to(positions.device,
                                                    dtype=query.dtype)
-        # ops.rotary_embedding()/batched_rotary_embedding()
-        # are in-place operations that update the query and key tensors.
+        # ops.rotary_embedding() is an in-place operation
+        # that updates the query and key tensors.
         if key is None:
             # XPU kernel doesn't support key=None so fall back to native impl
             # TODO(sarckk): add support for optional key in
             # ipex.llm.functional.rotary_embedding_batched
-            return self.forward_native(positions, query, key, offsets)
+            return self.forward_native(positions, query, key)
         else:
-            if offsets is not None:
-                ops.batched_rotary_embedding(positions, query, key,
-                                             self.head_size,
-                                             self.cos_sin_cache,
-                                             self.is_neox_style,
-                                             self.rotary_dim, offsets)
-            else:
-                ops.rotary_embedding(positions, query, key, self.head_size,
-                                     self.cos_sin_cache, self.is_neox_style)
+            ops.rotary_embedding(positions, query, key, self.head_size,
+                                 self.cos_sin_cache, self.is_neox_style)
         return query, key
 
     def extra_repr(self) -> str: