255 lines
8.5 KiB
Python
255 lines
8.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Rotary Positional Embeddings Base Class."""
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import torch
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from vllm._aiter_ops import rocm_aiter_ops
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from vllm.model_executor.custom_op import CustomOp
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from .common import ApplyRotaryEmb
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@CustomOp.register("rotary_embedding")
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class RotaryEmbeddingBase(CustomOp):
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"""Original rotary positional embedding."""
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def __init__(
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self,
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head_size: int,
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rotary_dim: int,
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max_position_embeddings: int,
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base: float,
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is_neox_style: bool,
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dtype: torch.dtype,
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) -> None:
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super().__init__()
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self.head_size = head_size
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self.rotary_dim = rotary_dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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self.is_neox_style = is_neox_style
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self.dtype = dtype
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# TODO(mgoin): disabled for now due to failures
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# Flashinfer only supports head_size=64, 128, 256, 512.
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# https://github.com/flashinfer-ai/flashinfer/blob/ebfd655efe830048dba5d582aaa61d61d1cf9a87/include/flashinfer/utils.cuh#L174-L202
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# self.use_flashinfer = (self.enabled()
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# and dtype in (torch.float16, torch.bfloat16)
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# and current_platform.is_cuda()
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# and has_flashinfer()
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# and self.head_size in [64, 128, 256, 512])
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self.use_flashinfer = False
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cache = self._compute_cos_sin_cache()
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if not self.use_flashinfer:
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cache = cache.to(dtype)
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self.cos_sin_cache: torch.Tensor
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self.register_buffer("cos_sin_cache", cache, persistent=False)
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self.is_rocm_triton_rotary_embed_enabled = (
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rocm_aiter_ops.is_triton_rotary_embed_enabled()
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)
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self.apply_rotary_emb = ApplyRotaryEmb(
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is_neox_style=self.is_neox_style,
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)
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def _compute_inv_freq(self, base: float) -> torch.Tensor:
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"""Compute the inverse frequency."""
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# NOTE(woosuk): To exactly match the HF implementation, we need to
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# use CPU to compute the cache and then move it to GPU. However, we
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# create the cache on GPU for faster initialization. This may cause
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# a slight numerical difference between the HF implementation and ours.
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inv_freq = 1.0 / (
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base
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** (
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torch.arange(0, self.rotary_dim, 2, dtype=torch.float) / self.rotary_dim
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)
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)
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return inv_freq
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def _compute_cos_sin_cache(self) -> torch.Tensor:
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"""Compute the cos and sin cache."""
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inv_freq = self._compute_inv_freq(self.base)
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t = torch.arange(self.max_position_embeddings, dtype=torch.float)
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freqs = torch.einsum("i,j -> ij", t, inv_freq)
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cos = freqs.cos()
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sin = freqs.sin()
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cache = torch.cat((cos, sin), dim=-1)
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return cache
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def _match_cos_sin_cache_dtype(self, query: torch.Tensor) -> None:
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# __setattr__ in nn.Module (called by `self.cos_sin_cache = ...`)
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# is expensive, so avoid calling it if possible
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if (
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self.cos_sin_cache.device != query.device
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or self.cos_sin_cache.dtype != query.dtype
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):
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self.cos_sin_cache = self.cos_sin_cache.to(query.device, dtype=query.dtype)
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def get_cos_sin(self, seqlen: int) -> tuple[torch.Tensor, torch.Tensor]:
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cos_sin = self.cos_sin_cache[:seqlen]
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cos, sin = cos_sin.chunk(2, dim=-1)
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return cos, sin
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class RotaryEmbedding(RotaryEmbeddingBase):
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def __init__(
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self,
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head_size: int,
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rotary_dim: int,
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max_position_embeddings: int,
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base: float,
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is_neox_style: bool,
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dtype: torch.dtype,
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) -> None:
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super().__init__(
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head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
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)
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@staticmethod
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def forward_static(
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor | None,
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head_size: int,
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rotary_dim: int,
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cos_sin_cache: torch.Tensor,
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is_neox_style: bool,
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) -> tuple[torch.Tensor, torch.Tensor | None]:
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"""A PyTorch-native implementation of forward()."""
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positions = positions.flatten()
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num_tokens = positions.shape[0]
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cos_sin = cos_sin_cache.index_select(0, positions)
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cos, sin = cos_sin.chunk(2, dim=-1)
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query_shape = query.shape
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query = query.view(num_tokens, -1, head_size)
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query_rot = query[..., :rotary_dim]
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query_pass = query[..., rotary_dim:]
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query_rot = ApplyRotaryEmb.forward_static(
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query_rot,
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cos,
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sin,
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is_neox_style,
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)
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query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
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# key may be None in some cases, e.g. cross-layer KV sharing
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if key is not None:
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key_shape = key.shape
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key = key.view(num_tokens, -1, head_size)
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key_rot = key[..., :rotary_dim]
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key_pass = key[..., rotary_dim:]
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key_rot = ApplyRotaryEmb.forward_static(
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key_rot,
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cos,
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sin,
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is_neox_style,
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)
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key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
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return query, key
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def forward_native(
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self,
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor | None]:
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"""A PyTorch-native implementation of forward()."""
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return self.forward_static(
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positions,
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query,
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key,
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self.head_size,
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self.rotary_dim,
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self.cos_sin_cache,
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self.is_neox_style,
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)
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def forward_cuda(
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self,
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor | None]:
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if self.use_flashinfer:
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torch.ops.vllm.flashinfer_rotary_embedding(
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positions,
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query,
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key,
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self.head_size,
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self.cos_sin_cache,
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self.is_neox_style,
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)
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return query, key
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from vllm import _custom_ops as ops
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self._match_cos_sin_cache_dtype(query)
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# ops.rotary_embedding() is an in-place operation
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# that updates the query and key tensors.
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ops.rotary_embedding(
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positions,
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query,
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key,
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self.head_size,
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self.cos_sin_cache,
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self.is_neox_style,
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)
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return query, key
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def forward_hip(
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self,
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor | None]:
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if self.is_rocm_triton_rotary_embed_enabled:
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self._match_cos_sin_cache_dtype(query)
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rocm_aiter_ops.triton_rotary_embed(
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positions,
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query,
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key,
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self.cos_sin_cache,
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self.head_size,
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self.rotary_dim,
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self.is_neox_style,
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)
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return query, key
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return self.forward_cuda(positions, query, key)
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def forward_xpu(
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self,
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor | None]:
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from vllm._ipex_ops import ipex_ops as ops
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self._match_cos_sin_cache_dtype(query)
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# ops.rotary_embedding() is an in-place operation
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# that updates the query and key tensors.
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if key is None:
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# XPU kernel doesn't support key=None so fall back to native impl
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# TODO(sarckk): add support for optional key in
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# ipex.llm.functional.rotary_embedding_batched
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return self.forward_native(positions, query, key)
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else:
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ops.rotary_embedding(
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positions,
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query,
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key,
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self.head_size,
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self.cos_sin_cache,
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self.is_neox_style,
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)
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return query, key
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def extra_repr(self) -> str:
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s = f"head_size={self.head_size}, rotary_dim={self.rotary_dim}"
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s += f", max_position_embeddings={self.max_position_embeddings}"
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s += f", base={self.base}, is_neox_style={self.is_neox_style}"
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return s
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