1015 lines
39 KiB
Python
1015 lines
39 KiB
Python
# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.33.2/src/transformers/models/llama/modeling_llama.py
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Rotary Positional Embeddings."""
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import math
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from typing import Any, Dict, List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from vllm.model_executor.custom_op import CustomOp
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def _rotate_neox(x: torch.Tensor) -> torch.Tensor:
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x1 = x[..., :x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2:]
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return torch.cat((-x2, x1), dim=-1)
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def _rotate_gptj(x: torch.Tensor) -> torch.Tensor:
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x1 = x[..., ::2]
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x2 = x[..., 1::2]
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x = torch.stack((-x2, x1), dim=-1)
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return x.flatten(-2)
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def _apply_rotary_emb(
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x: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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is_neox_style: bool,
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) -> torch.Tensor:
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"""
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Args:
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x: [num_tokens, num_heads, head_size]
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cos: [num_tokens, head_size // 2]
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sin: [num_tokens, head_size // 2]
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is_neox_style: Whether to use the Neox-style or GPT-J-style rotary
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positional embeddings.
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"""
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cos = cos.unsqueeze(-2).to(x.dtype)
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sin = sin.unsqueeze(-2).to(x.dtype)
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if is_neox_style:
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x1, x2 = torch.chunk(x, 2, dim=-1)
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else:
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x1 = x[..., ::2]
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x2 = x[..., 1::2]
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o1 = x1 * cos - x2 * sin
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o2 = x2 * cos + x1 * sin
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if is_neox_style:
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return torch.cat((o1, o2), dim=-1)
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else:
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return torch.stack((o1, o2), dim=-1).flatten(-2)
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@CustomOp.register("rotary_embedding")
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class RotaryEmbedding(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: int,
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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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cache = self._compute_cos_sin_cache()
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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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def _compute_inv_freq(self, base: Union[int, 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 / (base**(torch.arange(
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0, self.rotary_dim, 2, dtype=torch.float) / self.rotary_dim))
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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 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,
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offsets: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""A PyTorch-native implementation of forward()."""
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if offsets is not None:
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positions = positions + offsets
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positions = positions.flatten()
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num_tokens = positions.shape[0]
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cos_sin = self.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, self.head_size)
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query_rot = query[..., :self.rotary_dim]
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query_pass = query[..., self.rotary_dim:]
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query_rot = _apply_rotary_emb(query_rot, cos, sin, self.is_neox_style)
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query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
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key_shape = key.shape
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key = key.view(num_tokens, -1, self.head_size)
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key_rot = key[..., :self.rotary_dim]
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key_pass = key[..., self.rotary_dim:]
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key_rot = _apply_rotary_emb(key_rot, cos, sin, self.is_neox_style)
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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_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,
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offsets: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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from vllm import _custom_ops as ops
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self.cos_sin_cache = self.cos_sin_cache.to(query.device,
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dtype=query.dtype)
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# ops.rotary_embedding()/batched_rotary_embedding()
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# are in-place operations that update the query and key tensors.
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if offsets is not None:
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ops.batched_rotary_embedding(positions, query, key, self.head_size,
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self.cos_sin_cache,
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self.is_neox_style, self.rotary_dim,
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offsets)
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else:
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ops.rotary_embedding(positions, query, key, self.head_size,
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self.cos_sin_cache, self.is_neox_style)
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return 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,
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offsets: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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from vllm._ipex_ops import ipex_ops as ops
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self.cos_sin_cache = self.cos_sin_cache.to(positions.device,
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dtype=query.dtype)
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# ops.rotary_embedding()/batched_rotary_embedding()
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# are in-place operations that update the query and key tensors.
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if offsets is not None:
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ops.batched_rotary_embedding(positions, query, key, self.head_size,
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self.cos_sin_cache,
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self.is_neox_style, self.rotary_dim,
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offsets)
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else:
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ops.rotary_embedding(positions, query, key, self.head_size,
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self.cos_sin_cache, self.is_neox_style)
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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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class LinearScalingRotaryEmbedding(RotaryEmbedding):
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"""RotaryEmbedding extended with linear scaling.
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It supports multiple scaling factors. Since multiple LoRA adapters may have
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different scaling factors, we need multiple cos/sin caches. In this way,
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instead of running rotary embedding kernel per lora, we can run multiple
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lora in a batched way.
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In addition to that, we also keep the cos/sin cache for the scaling factor
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of 1 (default) at all times.
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Exemplary for two scaling factors x=1, y and z with embeddings
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[[x11, x12, ... x1m], ..., [xn1, xn2, ..., xnm]] and
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[[y11, y12, ... y1o], ..., [yn1, yn2, ..., yno]], and
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[[z11, z12, ... z1p], ..., [zn1, zn2, ..., znp]],
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we construct the cos/sin cache as follows:
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[[x11, x12, ... x1m, y11, y12, ... y1o, z11, z12, ... z1p],
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...
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[xn1, xn2, ... xnm, yn1, yn2, ... yno, zn1, zn2, ... znp]]
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We then use offsets to index into the cos/sin cache for
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the respective scaling factors.
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The offset to cache can be accessed via `scaling_factor_to_offset` API.
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Credits to the Reddit user /u/kaiokendev
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"""
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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: int,
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is_neox_style: bool,
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scaling_factors: Union[List[float], float],
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dtype: torch.dtype,
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) -> None:
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if isinstance(scaling_factors, float):
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scaling_factors = [scaling_factors]
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self.scaling_factors: List[float] = scaling_factors # noqa
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super().__init__(head_size, rotary_dim, max_position_embeddings, base,
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is_neox_style, dtype)
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# Lazy initialized.
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self._scaling_factor_to_offset: Dict[float, int]
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def _compute_cos_sin_cache(self) -> torch.Tensor:
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inv_freq = self._compute_inv_freq(self.base)
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cache_list: List[torch.Tensor] = []
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# offsets to the next cache in a tensor.
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# Each offset corresponds to the same index in scaling_factors.
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offsets: List[int] = []
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for scaling_factor in self.scaling_factors:
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# NOTE(woosuk): self.max_position_embeddings is the original
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# maximum length before applying the rope scaling.
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# Thus, the maximum length after applying the rope scaling is
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# self.max_position_embeddings * self.scaling_factor.
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max_len = self.max_position_embeddings * scaling_factor
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t = torch.arange(max_len, dtype=torch.float)
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t = t / scaling_factor
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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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if not cache_list:
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offset = 0
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else:
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last_offset = offsets[-1]
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next_max_len = cache_list[-1].shape[0]
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offset = last_offset + next_max_len
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offsets.append(offset)
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cache_list.append(cache)
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self._scaling_factor_to_offset = {
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float(scaling_factor): offsets[i]
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for i, scaling_factor in enumerate(self.scaling_factors)
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}
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assert len(self.scaling_factors) == len(offsets)
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return torch.cat(cache_list, dim=0)
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@property
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def scaling_factor_to_offset(self) -> Dict[float, int]:
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return self._scaling_factor_to_offset
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class DynamicNTKScalingRotaryEmbedding(RotaryEmbedding):
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"""RotaryEmbedding extended with Dynamic NTK scaling.
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Credits to the Reddit users /u/bloc97 and /u/emozilla
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"""
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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: int,
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is_neox_style: bool,
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scaling_factor: float,
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dtype: torch.dtype,
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) -> None:
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self.scaling_factor = scaling_factor
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super().__init__(head_size, rotary_dim, max_position_embeddings, base,
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is_neox_style, dtype)
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def _compute_cos_sin_cache(self) -> torch.Tensor:
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# NOTE(woosuk): self.max_position_embeddings is the original
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# maximum length before applying the rope scaling.
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# Thus, the maximum length after applying the rope scaling is
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# self.max_position_embeddings * self.scaling_factor.
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max_len = self.max_position_embeddings * self.scaling_factor
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base = self.base * (
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(self.scaling_factor * max_len / self.max_position_embeddings) -
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(self.scaling_factor - 1))**(self.rotary_dim /
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(self.rotary_dim - 2))
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inv_freq = self._compute_inv_freq(base)
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t = torch.arange(max_len, 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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# Inverse dim formula to find dim based on number of rotations
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def _yarn_find_correction_dim(num_rotations: int,
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dim: int,
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base: float = 10000,
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max_position_embeddings: int = 2048) -> float:
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return (dim * math.log(max_position_embeddings /
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(num_rotations * 2 * math.pi))) / (2 *
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math.log(base))
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# Find dim range bounds based on rotations
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def _yarn_find_correction_range(
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low_rot: int,
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high_rot: int,
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dim: int,
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base: float = 10000,
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max_position_embeddings: int = 2048) -> Tuple[int, int]:
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low = math.floor(
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_yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings))
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high = math.ceil(
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_yarn_find_correction_dim(high_rot, dim, base,
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max_position_embeddings))
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return max(low, 0), min(high, dim - 1) # Clamp values just in case
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def _yarn_linear_ramp_mask(low: float, high: float, dim: int,
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dtype: torch.dtype) -> torch.Tensor:
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if low == high:
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high += 0.001 # Prevent singularity
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linear_func = (torch.arange(dim, dtype=dtype) - low) / (high - low)
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ramp_func = torch.clamp(linear_func, 0, 1)
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return ramp_func
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def _yarn_get_mscale(scale: float = 1) -> float:
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if scale <= 1:
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return 1.0
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return 0.1 * math.log(scale) + 1.0
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class YaRNScalingRotaryEmbedding(RotaryEmbedding):
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"""RotaryEmbedding extended with YaRN method.
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Credits to Peng et al. github.com/jquesnelle/yarn
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"""
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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: int,
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is_neox_style: bool,
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scaling_factor: float,
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dtype: torch.dtype,
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*,
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extrapolation_factor: float = 1,
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attn_factor: float = 1,
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beta_fast: int = 32,
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beta_slow: int = 1,
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) -> None:
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self.scaling_factor = scaling_factor
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self.extrapolation_factor = extrapolation_factor
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self.attn_factor = attn_factor
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self.beta_fast = beta_fast
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self.beta_slow = beta_slow
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# Get n-d magnitude scaling corrected for interpolation
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self.mscale = float(
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_yarn_get_mscale(self.scaling_factor) * attn_factor)
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super().__init__(head_size, rotary_dim, max_position_embeddings, base,
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is_neox_style, dtype)
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def _compute_inv_freq(self, scaling_factor: float) -> torch.Tensor:
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pos_freqs = self.base**(
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torch.arange(0, self.rotary_dim, 2, dtype=torch.float) /
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self.rotary_dim)
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inv_freq_extrapolation = 1.0 / pos_freqs
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inv_freq_interpolation = 1.0 / (scaling_factor * pos_freqs)
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low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow,
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self.rotary_dim, self.base,
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self.max_position_embeddings)
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# Get n-d rotational scaling corrected for extrapolation
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inv_freq_mask = (1 - _yarn_linear_ramp_mask(
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low, high, self.rotary_dim // 2,
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dtype=torch.float)) * self.extrapolation_factor
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inv_freq = inv_freq_interpolation * (
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1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
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return inv_freq
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def _compute_cos_sin_cache(self) -> torch.Tensor:
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inv_freq = self._compute_inv_freq(self.scaling_factor)
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t = torch.arange(self.max_position_embeddings * self.scaling_factor,
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dtype=torch.float32)
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freqs = torch.einsum("i,j -> ij", t, inv_freq)
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cos = (freqs.cos() * self.mscale)
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sin = (freqs.sin() * self.mscale)
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cache = torch.cat((cos, sin), dim=-1)
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return cache
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class Phi3LongRoPEScaledRotaryEmbedding(nn.Module):
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"""Phi3 family of models scaled rotary embedding.
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Based on the original RotaryEmbedding implementation.
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"""
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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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original_max_position_embeddings: int,
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base: int,
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is_neox_style: bool,
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dtype: torch.dtype,
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short_factor: List[float],
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long_factor: List[float],
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short_mscale: Optional[float] = None,
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long_mscale: Optional[float] = None,
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):
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super().__init__()
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if rotary_dim != head_size:
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raise ValueError(
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f"`Phi3LongRoPEScaledRotaryEmbedding` does not support \
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rotary_dim != head_size ({rotary_dim}!={head_size}).")
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if is_neox_style is False:
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raise ValueError(
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"`Phi3LongRoPEScaledRotaryEmbedding` only supports neox_style."
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)
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self.head_size = head_size
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self.max_position_embeddings = max_position_embeddings
|
|
self.original_max_position_embeddings = original_max_position_embeddings
|
|
self.base = base
|
|
self.short_factor = short_factor
|
|
self.long_factor = long_factor
|
|
|
|
scale = self.max_position_embeddings / \
|
|
self.original_max_position_embeddings
|
|
if scale <= 1.0:
|
|
scaling_factor = 1.0
|
|
else:
|
|
scaling_factor = math.sqrt(
|
|
1 + math.log(scale) /
|
|
math.log(self.original_max_position_embeddings))
|
|
if short_mscale is None:
|
|
short_mscale = scaling_factor
|
|
if long_mscale is None:
|
|
long_mscale = scaling_factor
|
|
|
|
self.short_mscale = short_mscale
|
|
self.long_mscale = long_mscale
|
|
|
|
short_cache = self._compute_cos_sin_cache(
|
|
original_max_position_embeddings, short_factor, short_mscale)
|
|
short_cache = short_cache.to(dtype)
|
|
self.register_buffer("short_cos_sin_cache",
|
|
short_cache,
|
|
persistent=False)
|
|
|
|
long_cache = self._compute_cos_sin_cache(max_position_embeddings,
|
|
long_factor, long_mscale)
|
|
long_cache = long_cache.to(dtype)
|
|
self.register_buffer("long_cos_sin_cache",
|
|
long_cache,
|
|
persistent=False)
|
|
|
|
long_short_cache = torch.cat(
|
|
[self.short_cos_sin_cache, self.long_cos_sin_cache], dim=0)
|
|
self.register_buffer("long_short_cos_sin_cache",
|
|
long_short_cache,
|
|
persistent=False)
|
|
|
|
def _compute_inv_freq(self, rescale_factors: List[float]) -> torch.Tensor:
|
|
rescale_factors = torch.tensor(rescale_factors, dtype=torch.float32)
|
|
inv_freq = 1.0 / (rescale_factors * (self.base**(torch.arange(
|
|
0, self.head_size, 2, dtype=torch.float) / self.head_size)))
|
|
return inv_freq
|
|
|
|
def _compute_cos_sin_cache(
|
|
self,
|
|
max_position_embeddings: int,
|
|
rescale_factors: List[float],
|
|
mscale: float,
|
|
) -> torch.Tensor:
|
|
inv_freq = self._compute_inv_freq(rescale_factors)
|
|
t = torch.arange(max_position_embeddings, dtype=torch.float)
|
|
freqs = torch.einsum("i,j -> ij", t, inv_freq)
|
|
cos = freqs.cos() * mscale
|
|
sin = freqs.sin() * mscale
|
|
cache = torch.cat((cos, sin), dim=-1)
|
|
return cache
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
offsets: Optional[torch.Tensor] = None,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
query = query.view(*query.shape[:-1], -1, self.head_size)
|
|
key = key.view(*key.shape[:-1], -1, self.head_size)
|
|
|
|
k = self.original_max_position_embeddings
|
|
long_prompt_offset = (torch.any(positions > k).float() *
|
|
torch.full_like(positions, k)).long()
|
|
idx = (torch.add(positions, long_prompt_offset)
|
|
if long_prompt_offset is not None else positions)
|
|
self.long_short_cos_sin_cache: torch.Tensor = (
|
|
self.long_short_cos_sin_cache.to(idx.device))
|
|
idx = torch.add(idx, offsets) if offsets is not None else idx
|
|
cos_sin = torch.index_select(self.long_short_cos_sin_cache, 0, idx)
|
|
|
|
cos, sin = cos_sin.chunk(2, dim=-1)
|
|
cos = cos.repeat(1, 2).unsqueeze(-2)
|
|
sin = sin.repeat(1, 2).unsqueeze(-2)
|
|
|
|
query = query * cos + _rotate_neox(query) * sin
|
|
key = key * cos + _rotate_neox(key) * sin
|
|
|
|
return query.flatten(-2), key.flatten(-2)
|
|
|
|
|
|
def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
|
|
if scale <= 1:
|
|
return 1.0
|
|
return 0.1 * mscale * math.log(scale) + 1.0
|
|
|
|
|
|
class DeepseekScalingRotaryEmbedding(RotaryEmbedding):
|
|
"""RotaryEmbedding extended with YaRN method.
|
|
|
|
Credits to Peng et al. github.com/jquesnelle/yarn
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
head_size: int,
|
|
rotary_dim: int,
|
|
max_position_embeddings: int,
|
|
base: int,
|
|
is_neox_style: bool,
|
|
scaling_factor: float,
|
|
dtype: torch.dtype,
|
|
*,
|
|
extrapolation_factor: float = 1,
|
|
attn_factor: float = 1,
|
|
beta_fast: int = 32,
|
|
beta_slow: int = 1,
|
|
mscale: float = 1,
|
|
mscale_all_dim: float = 0,
|
|
) -> None:
|
|
self.scaling_factor = scaling_factor
|
|
self.extrapolation_factor = extrapolation_factor
|
|
self.attn_factor = attn_factor
|
|
self.beta_fast = beta_fast
|
|
self.beta_slow = beta_slow
|
|
# Get n-d magnitude scaling corrected for interpolation.
|
|
self.mscale = float(
|
|
yarn_get_mscale(self.scaling_factor, float(mscale)) /
|
|
yarn_get_mscale(self.scaling_factor, float(mscale_all_dim)) *
|
|
attn_factor)
|
|
super().__init__(head_size, rotary_dim, max_position_embeddings, base,
|
|
is_neox_style, dtype)
|
|
|
|
def _compute_inv_freq(self, scaling_factor: float) -> torch.Tensor:
|
|
pos_freqs = self.base**(torch.arange(
|
|
0, self.rotary_dim, 2, dtype=torch.float, device="cuda") /
|
|
self.rotary_dim)
|
|
inv_freq_extrapolation = 1.0 / pos_freqs
|
|
inv_freq_interpolation = 1.0 / (scaling_factor * pos_freqs)
|
|
|
|
low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow,
|
|
self.rotary_dim, self.base,
|
|
self.max_position_embeddings)
|
|
# Get n-d rotational scaling corrected for extrapolation
|
|
inv_freq_mask = (1 - _yarn_linear_ramp_mask(
|
|
low, high, self.rotary_dim // 2,
|
|
dtype=torch.float)) * self.extrapolation_factor
|
|
inv_freq = inv_freq_interpolation * (
|
|
1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
|
|
return inv_freq
|
|
|
|
def _compute_cos_sin_cache(self) -> torch.Tensor:
|
|
inv_freq = self._compute_inv_freq(self.scaling_factor)
|
|
t = torch.arange(self.max_position_embeddings * self.scaling_factor,
|
|
device="cuda",
|
|
dtype=torch.float32)
|
|
freqs = torch.einsum("i,j -> ij", t, inv_freq)
|
|
cos = (freqs.cos() * self.mscale)
|
|
sin = (freqs.sin() * self.mscale)
|
|
cache = torch.cat((cos, sin), dim=-1)
|
|
print("Cache shape", cache.shape)
|
|
return cache
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
offsets: Optional[torch.Tensor] = None,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""PyTorch-native implementation equivalent to forward()."""
|
|
query_rot = query[..., :self.rotary_dim]
|
|
key_rot = key[..., :self.rotary_dim]
|
|
if self.rotary_dim < self.head_size:
|
|
query_pass = query[..., self.rotary_dim:]
|
|
key_pass = key[..., self.rotary_dim:]
|
|
|
|
self.cos_sin_cache: torch.Tensor = self.cos_sin_cache.to(
|
|
positions.device)
|
|
cos_sin = self.cos_sin_cache[torch.add(positions, offsets)
|
|
if offsets is not None else positions]
|
|
cos, sin = cos_sin.chunk(2, dim=-1)
|
|
if self.is_neox_style:
|
|
# NOTE(woosuk): Here we assume that the positions tensor has the
|
|
# shape [batch_size, seq_len].
|
|
cos = cos.repeat(1, 1, 2).unsqueeze(-2)
|
|
sin = sin.repeat(1, 1, 2).unsqueeze(-2)
|
|
else:
|
|
cos = cos.repeat_interleave(2, dim=-1).unsqueeze(-2)
|
|
sin = sin.repeat_interleave(2, dim=-1).unsqueeze(-2)
|
|
|
|
rotate_fn = _rotate_neox if self.is_neox_style else _rotate_gptj
|
|
query_rot = query_rot * cos + rotate_fn(query_rot) * sin
|
|
key_rot = key_rot * cos + rotate_fn(key_rot) * sin
|
|
|
|
if self.rotary_dim < self.head_size:
|
|
query = torch.cat((query_rot, query_pass), dim=-1)
|
|
key = torch.cat((key_rot, key_pass), dim=-1)
|
|
else:
|
|
query = query_rot
|
|
key = key_rot
|
|
return query, key
|
|
|
|
|
|
class Llama3RotaryEmbedding(RotaryEmbedding):
|
|
|
|
def __init__(
|
|
self,
|
|
head_size: int,
|
|
rotary_dim: int,
|
|
max_position_embeddings: int,
|
|
base: int,
|
|
is_neox_style: bool,
|
|
dtype: torch.dtype,
|
|
scaling_factor: float,
|
|
low_freq_factor: float,
|
|
high_freq_factor: float,
|
|
orig_max_position: int,
|
|
) -> None:
|
|
self.scaling_factor = scaling_factor
|
|
self.low_freq_factor = low_freq_factor
|
|
self.high_freq_factor = high_freq_factor
|
|
self.orig_max_position = orig_max_position
|
|
super().__init__(head_size, rotary_dim, max_position_embeddings, base,
|
|
is_neox_style, dtype)
|
|
|
|
def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
|
|
inv_freqs = super()._compute_inv_freq(base)
|
|
low_freq_wavelen = self.orig_max_position / self.low_freq_factor
|
|
high_freq_wavelen = self.orig_max_position / self.high_freq_factor
|
|
|
|
wave_len = 2 * math.pi / inv_freqs
|
|
if self.low_freq_factor != self.high_freq_factor:
|
|
smooth = (self.orig_max_position / wave_len - self.low_freq_factor
|
|
) / (self.high_freq_factor - self.low_freq_factor)
|
|
else:
|
|
smooth = 0
|
|
new_freqs = torch.where(
|
|
wave_len < high_freq_wavelen,
|
|
inv_freqs,
|
|
torch.where(
|
|
wave_len > low_freq_wavelen,
|
|
inv_freqs / self.scaling_factor,
|
|
(1 - smooth) * inv_freqs / self.scaling_factor +
|
|
smooth * inv_freqs,
|
|
),
|
|
)
|
|
return new_freqs
|
|
|
|
|
|
class MRotaryEmbedding(RotaryEmbedding):
|
|
"""Rotary Embedding with Multimodal Sections."""
|
|
|
|
def __init__(
|
|
self,
|
|
head_size: int,
|
|
rotary_dim: int,
|
|
max_position_embeddings: int,
|
|
base: int,
|
|
is_neox_style: bool,
|
|
dtype: torch.dtype,
|
|
mrope_section: Optional[List[int]] = None,
|
|
) -> None:
|
|
super().__init__(head_size, rotary_dim, max_position_embeddings, base,
|
|
is_neox_style, dtype)
|
|
|
|
self.mrope_section = mrope_section
|
|
if self.mrope_section:
|
|
assert sum(self.mrope_section) == rotary_dim // 2
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""PyTorch-native implementation equivalent to forward().
|
|
|
|
Args:
|
|
positions:
|
|
[num_tokens,] (text only) or
|
|
[3, num_tokens] (T/H/W positions with multimodal inputs)
|
|
query: [num_tokens, num_heads * head_size]
|
|
key: [num_tokens, num_kv_heads * head_size]
|
|
"""
|
|
assert positions.ndim == 1 or positions.ndim == 2
|
|
|
|
num_tokens = positions.shape[-1]
|
|
cos_sin = self.cos_sin_cache[positions]
|
|
cos, sin = cos_sin.chunk(2, dim=-1)
|
|
if positions.ndim == 2:
|
|
assert self.mrope_section
|
|
|
|
cos = torch.cat([
|
|
m[i]
|
|
for i, m in enumerate(cos.split(self.mrope_section, dim=-1))
|
|
],
|
|
dim=-1)
|
|
sin = torch.cat([
|
|
m[i]
|
|
for i, m in enumerate(sin.split(self.mrope_section, dim=-1))
|
|
],
|
|
dim=-1)
|
|
|
|
query_shape = query.shape
|
|
query = query.view(num_tokens, -1, self.head_size)
|
|
query_rot = query[..., :self.rotary_dim]
|
|
query_pass = query[..., self.rotary_dim:]
|
|
query_rot = _apply_rotary_emb(query_rot, cos, sin, self.is_neox_style)
|
|
query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
|
|
|
|
key_shape = key.shape
|
|
key = key.view(num_tokens, -1, self.head_size)
|
|
key_rot = key[..., :self.rotary_dim]
|
|
key_pass = key[..., self.rotary_dim:]
|
|
key_rot = _apply_rotary_emb(key_rot, cos, sin, self.is_neox_style)
|
|
key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
|
|
return query, key
|
|
|
|
@staticmethod
|
|
def get_input_positions(
|
|
input_tokens: List[int],
|
|
image_grid_thw: Union[List[List[int]], torch.Tensor],
|
|
video_grid_thw: Union[List[List[int]], torch.Tensor],
|
|
image_token_id: int,
|
|
video_token_id: int,
|
|
vision_start_token_id: int,
|
|
vision_end_token_id: int,
|
|
spatial_merge_size: int,
|
|
context_len: int = 0,
|
|
) -> Tuple[List[List[int]], int]:
|
|
"""Get mrope input positions and delta value."""
|
|
|
|
if isinstance(image_grid_thw, torch.Tensor):
|
|
image_grid_thw = image_grid_thw.tolist()
|
|
if isinstance(video_grid_thw, torch.Tensor):
|
|
video_grid_thw = video_grid_thw.tolist()
|
|
|
|
input_tokens_tensor = torch.tensor(input_tokens)
|
|
vision_start_indices = torch.argwhere(
|
|
input_tokens_tensor == vision_start_token_id).squeeze(1)
|
|
vision_tokens = input_tokens_tensor[vision_start_indices + 1]
|
|
image_nums = (vision_tokens == image_token_id).sum()
|
|
video_nums = (vision_tokens == video_token_id).sum()
|
|
llm_pos_ids_list: list = []
|
|
|
|
st = 0
|
|
remain_images, remain_videos = image_nums, video_nums
|
|
|
|
image_index, video_index = 0, 0
|
|
for _ in range(image_nums + video_nums):
|
|
if image_token_id in input_tokens and remain_images > 0:
|
|
ed_image = input_tokens.index(image_token_id, st)
|
|
else:
|
|
ed_image = len(input_tokens) + 1
|
|
if video_token_id in input_tokens and remain_videos > 0:
|
|
ed_video = input_tokens.index(video_token_id, st)
|
|
else:
|
|
ed_video = len(input_tokens) + 1
|
|
if ed_image < ed_video:
|
|
t, h, w = (
|
|
image_grid_thw[image_index][0],
|
|
image_grid_thw[image_index][1],
|
|
image_grid_thw[image_index][2],
|
|
)
|
|
image_index += 1
|
|
remain_images -= 1
|
|
ed = ed_image
|
|
else:
|
|
t, h, w = (
|
|
video_grid_thw[video_index][0],
|
|
video_grid_thw[video_index][1],
|
|
video_grid_thw[video_index][2],
|
|
)
|
|
video_index += 1
|
|
remain_videos -= 1
|
|
ed = ed_video
|
|
llm_grid_t, llm_grid_h, llm_grid_w = \
|
|
t, h // spatial_merge_size, w // spatial_merge_size
|
|
text_len = ed - st
|
|
|
|
st_idx = llm_pos_ids_list[-1].max() + 1 if len(
|
|
llm_pos_ids_list) > 0 else 0
|
|
llm_pos_ids_list.append(
|
|
torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
|
|
|
|
t_index = torch.arange(llm_grid_t).view(-1, 1).expand(
|
|
-1, llm_grid_h * llm_grid_w).flatten()
|
|
h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(
|
|
llm_grid_t, -1, llm_grid_w).flatten()
|
|
w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(
|
|
llm_grid_t, llm_grid_h, -1).flatten()
|
|
llm_pos_ids_list.append(
|
|
torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
|
|
st = ed + llm_grid_t * llm_grid_h * llm_grid_w
|
|
|
|
if st < len(input_tokens):
|
|
st_idx = llm_pos_ids_list[-1].max() + 1 if len(
|
|
llm_pos_ids_list) > 0 else 0
|
|
text_len = len(input_tokens) - st
|
|
llm_pos_ids_list.append(
|
|
torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
|
|
|
|
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
|
|
llm_positions = llm_positions[:, context_len:]
|
|
mrope_position_delta = (llm_positions.max() + 1 -
|
|
len(input_tokens)).item()
|
|
|
|
return llm_positions.tolist(), mrope_position_delta
|
|
|
|
@staticmethod
|
|
def get_next_input_positions(
|
|
mrope_position_delta: int,
|
|
context_len: int,
|
|
seq_len: int,
|
|
) -> List[List[int]]:
|
|
return [
|
|
list(
|
|
range(context_len + mrope_position_delta,
|
|
seq_len + mrope_position_delta)) for _ in range(3)
|
|
]
|
|
|
|
|
|
_ROPE_DICT: Dict[Tuple, RotaryEmbedding] = {}
|
|
|
|
|
|
def get_rope(
|
|
head_size: int,
|
|
rotary_dim: int,
|
|
max_position: int,
|
|
base: int,
|
|
is_neox_style: bool = True,
|
|
rope_scaling: Optional[Dict[str, Any]] = None,
|
|
dtype: Optional[torch.dtype] = None,
|
|
partial_rotary_factor: float = 1.0,
|
|
) -> RotaryEmbedding:
|
|
if dtype is None:
|
|
dtype = torch.get_default_dtype()
|
|
if rope_scaling is not None:
|
|
# Transforms every value that is a list into a tuple for caching calls
|
|
rope_scaling_tuple = {
|
|
k: tuple(v) if isinstance(v, list) else v
|
|
for k, v in rope_scaling.items()
|
|
}
|
|
rope_scaling_args = tuple(rope_scaling_tuple.items())
|
|
else:
|
|
rope_scaling_args = None
|
|
if partial_rotary_factor < 1.0:
|
|
rotary_dim = int(rotary_dim * partial_rotary_factor)
|
|
key = (head_size, rotary_dim, max_position, base, is_neox_style,
|
|
rope_scaling_args, dtype)
|
|
if key in _ROPE_DICT:
|
|
return _ROPE_DICT[key]
|
|
|
|
if rope_scaling is None:
|
|
rotary_emb = RotaryEmbedding(head_size, rotary_dim, max_position, base,
|
|
is_neox_style, dtype)
|
|
else:
|
|
scaling_type = rope_scaling["rope_type"]
|
|
|
|
if scaling_type == "llama3":
|
|
scaling_factor = rope_scaling["factor"]
|
|
low_freq_factor = rope_scaling["low_freq_factor"]
|
|
high_freq_factor = rope_scaling["high_freq_factor"]
|
|
original_max_position = rope_scaling[
|
|
"original_max_position_embeddings"]
|
|
rotary_emb = Llama3RotaryEmbedding(head_size, rotary_dim,
|
|
max_position, base,
|
|
is_neox_style, dtype,
|
|
scaling_factor, low_freq_factor,
|
|
high_freq_factor,
|
|
original_max_position)
|
|
elif scaling_type == "default":
|
|
if "mrope_section" in rope_scaling:
|
|
rotary_emb = MRotaryEmbedding(
|
|
head_size,
|
|
rotary_dim,
|
|
max_position,
|
|
base,
|
|
is_neox_style,
|
|
dtype,
|
|
mrope_section=rope_scaling["mrope_section"],
|
|
)
|
|
else:
|
|
rotary_emb = RotaryEmbedding(
|
|
head_size,
|
|
rotary_dim,
|
|
max_position,
|
|
base,
|
|
is_neox_style,
|
|
dtype,
|
|
)
|
|
elif scaling_type == "linear":
|
|
scaling_factor = rope_scaling["factor"]
|
|
rotary_emb = LinearScalingRotaryEmbedding(head_size, rotary_dim,
|
|
max_position, base,
|
|
is_neox_style,
|
|
scaling_factor, dtype)
|
|
elif scaling_type == "dynamic":
|
|
scaling_factor = rope_scaling["factor"]
|
|
rotary_emb = DynamicNTKScalingRotaryEmbedding(
|
|
head_size, rotary_dim, max_position, base, is_neox_style,
|
|
scaling_factor, dtype)
|
|
elif scaling_type == "yarn":
|
|
scaling_factor = rope_scaling["factor"]
|
|
original_max_position = rope_scaling[
|
|
"original_max_position_embeddings"]
|
|
extra_kwargs = {
|
|
k: v
|
|
for k, v in rope_scaling.items()
|
|
if k in ("extrapolation_factor", "attn_factor", "beta_fast",
|
|
"beta_slow")
|
|
}
|
|
rotary_emb = YaRNScalingRotaryEmbedding(head_size, rotary_dim,
|
|
original_max_position,
|
|
base, is_neox_style,
|
|
scaling_factor, dtype,
|
|
**extra_kwargs)
|
|
elif scaling_type == "deepseek_yarn":
|
|
scaling_factor = rope_scaling["factor"]
|
|
original_max_position = rope_scaling[
|
|
"original_max_position_embeddings"]
|
|
# assert max_position == original_max_position * scaling_factor
|
|
extra_kwargs = {
|
|
k: v
|
|
for k, v in rope_scaling.items()
|
|
if k in ("extrapolation_factor", "attn_factor", "beta_fast",
|
|
"beta_slow", "mscale", "mscale_all_dim")
|
|
}
|
|
rotary_emb = DeepseekScalingRotaryEmbedding(
|
|
head_size, rotary_dim, original_max_position, base,
|
|
is_neox_style, scaling_factor, dtype, **extra_kwargs)
|
|
elif scaling_type == "longrope":
|
|
short_factor = rope_scaling["short_factor"]
|
|
long_factor = rope_scaling["long_factor"]
|
|
original_max_position = rope_scaling[
|
|
"original_max_position_embeddings"]
|
|
extra_kwargs = {
|
|
k: v
|
|
for k, v in rope_scaling.items()
|
|
if k in ("short_mscale", "long_mscale")
|
|
}
|
|
rotary_emb = Phi3LongRoPEScaledRotaryEmbedding(
|
|
head_size, rotary_dim, max_position, original_max_position,
|
|
base, is_neox_style, dtype, short_factor, long_factor,
|
|
**extra_kwargs)
|
|
else:
|
|
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
|
_ROPE_DICT[key] = rotary_emb
|
|
return rotary_emb
|