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# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Callable, Optional, Union
import torch
from .elementwise_fusion import enabled as _elementwise_enabled, rms, rope
from torch import nn
from transformers.integrations import use_kernel_forward_from_hub
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from transformers.models.qwen3.configuration_qwen3 import Qwen3Config
@use_kernel_forward_from_hub("RMSNorm")
class Qwen3RMSNorm(nn.Module):
def __init__(self, hidden_size, eps: float = 1e-6) -> None:
"""
Qwen3RMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
if hidden_states.shape[-1] == 128 and _elementwise_enabled(hidden_states) and hidden_states.is_contiguous():
return rms(hidden_states, self.weight, self.variance_epsilon)
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
if unsqueeze_dim == 1 and q.ndim == k.ndim == 4 and _elementwise_enabled(q) and _elementwise_enabled(k) and cos.dtype == sin.dtype == q.dtype == k.dtype and cos.is_contiguous() and sin.is_contiguous():
return rope(q, k, cos, sin)
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
class Qwen3RotaryEmbedding(nn.Module):
inv_freq: torch.Tensor # fix linting for `register_buffer`
def __init__(self, config: Qwen3Config, device=None):
super().__init__()
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
rope_parameters = getattr(self.config, "rope_parameters", None)
self.rope_type = (
rope_parameters.get("rope_type", "default")
if rope_parameters is not None
else "default"
)
rope_init_fn: Callable = self.compute_default_rope_parameters
if self.rope_type != "default":
rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.original_inv_freq = inv_freq
self._rope_init_device = inv_freq.device
@staticmethod
def compute_default_rope_parameters(
config: Optional[Qwen3Config] = None,
device: Optional["torch.device"] = None,
seq_len: Optional[int] = None,
) -> tuple["torch.Tensor", float]:
"""
Computes the inverse frequencies according to the original RoPE implementation
Args:
config ([`~transformers.PreTrainedConfig`]):
The model configuration.
device (`torch.device`):
The device to use for initialization of the inverse frequencies.
seq_len (`int`, *optional*):
The current sequence length. Unused for this type of RoPE.
Returns:
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
"""
rope_parameters = getattr(config, "rope_parameters", None)
base = (
rope_parameters["rope_theta"]
if rope_parameters is not None
else config.rope_theta
)
dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
attention_factor = 1.0 # Unused in this type of RoPE
# Compute the inverse frequencies
inv_freq = 1.0 / (
base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
)
return inv_freq, attention_factor
@torch.no_grad()
def _ensure_default_rope_device(self, device):
# Training builds these FP32 frequencies on its execution device.
# CPU pow followed by a device copy, or casting the buffer to BF16,
# changes long-context phases and can change sparse routes.
if self.rope_type != "default":
return
if self._rope_init_device == device and self.inv_freq.dtype == torch.float32:
return
self.inv_freq, self.attention_scaling = self.compute_default_rope_parameters(
self.config, device
)
self.original_inv_freq = self.inv_freq
self._rope_init_device = device
@torch.no_grad()
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
def forward(self, x, position_ids):
self._ensure_default_rope_device(x.device)
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False): # Force float32
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos() * self.attention_scaling
sin = emb.sin() * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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