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1287 lines
55 KiB
Python
1287 lines
55 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/qwen3_next/modular_qwen3_next.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_qwen3_next.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
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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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from typing import Any, Callable, Optional, Union
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import torch
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import torch.nn.functional as F
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from torch import nn
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from transformers.activations import ACT2FN
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from transformers.cache_utils import Cache
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from transformers.generation import GenerationMixin
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from transformers.masking_utils import create_causal_mask
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from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
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from transformers.modeling_layers import (
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GenericForQuestionAnswering,
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GenericForSequenceClassification,
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GenericForTokenClassification,
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GradientCheckpointingLayer,
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)
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from transformers.modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
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from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from transformers.processing_utils import Unpack
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from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
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from transformers.utils.deprecation import deprecate_kwarg
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from transformers.utils.generic import OutputRecorder, check_model_inputs
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try:
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from transformers.utils.import_utils import (
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is_causal_conv1d_available,
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is_flash_linear_attention_available,
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)
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except ImportError:
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is_causal_conv1d_available = lambda: False
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try:
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from transformers.utils.import_utils import (
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is_flash_linear_attention_available,
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)
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except ImportError:
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is_flash_linear_attention_available = lambda: False
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from .configuration_qwen3_next import Qwen3NextConfig
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if is_causal_conv1d_available():
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from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
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else:
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causal_conv1d_update, causal_conv1d_fn = None, None
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if is_flash_linear_attention_available():
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from fla.modules import FusedRMSNormGated
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from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
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else:
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chunk_gated_delta_rule, fused_recurrent_gated_delta_rule = None, None
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FusedRMSNormGated = None
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logger = logging.get_logger(__name__)
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class Qwen3NextRMSNormGated(nn.Module):
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def __init__(self, hidden_size, eps=1e-6, **kwargs):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states, gate=None):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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# Norm before gate
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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hidden_states = self.weight * hidden_states.to(input_dtype)
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hidden_states = hidden_states * F.silu(gate.to(torch.float32))
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return hidden_states.to(input_dtype)
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class Qwen3NextDynamicCache:
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"""
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A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the linear attention
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cache (which has a constant shape regardless of seq_len).
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This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
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and `ssm_states` for gated deltanet cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
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For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
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while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
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For linear attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
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while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
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and `recurrent_states` represents the recurrent state and has a shape of `(batch_size, d_inner, d_state)`.
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"""
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is_compileable = False
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def __init__(self, config: Qwen3NextConfig):
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super().__init__()
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self.layer_types = config.layer_types
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self.transformer_layers = [
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i for i in range(config.num_hidden_layers) if self.layer_types[i] == "full_attention"
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]
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self.last_linear_layer = len(self.layer_types) - 1 - self.layer_types[::-1].index("linear_attention")
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# Initialize everything to None -> will be lazy initialized to allow multi-gpu (device_map) inference
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self.conv_states = [None for _ in range(config.num_hidden_layers)]
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self.recurrent_states = [None for _ in range(config.num_hidden_layers)]
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self.key_cache = [None for _ in range(config.num_hidden_layers)]
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self.value_cache = [None for _ in range(config.num_hidden_layers)]
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def __len__(self):
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return len(self.layer_types)
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def __getitem__(self, layer_idx: int) -> tuple[torch.Tensor, torch.Tensor]:
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return self.key_cache[layer_idx], self.value_cache[layer_idx]
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def update(
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self,
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key_states: torch.Tensor,
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value_states: torch.Tensor,
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layer_idx: int,
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cache_kwargs: Optional[dict[str, Any]] = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if self.key_cache[layer_idx] is None:
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self.key_cache[layer_idx] = key_states
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self.value_cache[layer_idx] = value_states
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else:
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self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
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self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
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return self.key_cache[layer_idx], self.value_cache[layer_idx]
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def reorder_cache(self, beam_idx: torch.LongTensor):
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"""Reorders the cache for beam search, given the selected beam indices."""
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for layer_idx in range(len(self.key_cache)):
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if self.key_cache[layer_idx] is not None:
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device = self.key_cache[layer_idx].device
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beam_idx = beam_idx.to(device)
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self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx)
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self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx)
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if self.conv_states[layer_idx] is not None:
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device = self.conv_states[layer_idx].device
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beam_idx = beam_idx.to(device)
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self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx)
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self.recurrent_states[layer_idx] = self.recurrent_states[layer_idx].index_select(0, beam_idx)
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def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
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"""Returns the sequence length of the cached states. A layer index can be optionally passed."""
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# take any layer that contains cache and not empty tensor
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layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
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if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx] is None:
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return 0
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return self.key_cache[layer_idx].shape[-2]
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def get_mask_sizes(self, cache_position: torch.Tensor, layer_idx: int) -> tuple[int, int]:
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"""
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Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
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the given layer at `layer_idx`.
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The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns for each layer.
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"""
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kv_offset = 0
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query_length = cache_position.shape[0]
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past_seen_tokens = self.get_seq_length(layer_idx)
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kv_length = query_length + past_seen_tokens
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return kv_length, kv_offset
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@property
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def has_previous_state(self):
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"""We have a previous state if the last linear (conv) layer was already updated."""
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return self.conv_states[self.last_linear_layer] is not None
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class Qwen3NextRotaryEmbedding(nn.Module):
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inv_freq: torch.Tensor # fix linting for `register_buffer`
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def __init__(self, config: Qwen3NextConfig, device=None):
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super().__init__()
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# BC: "rope_type" was originally "type"
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if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
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self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
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else:
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self.rope_type = "default"
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self.max_seq_len_cached = config.max_position_embeddings
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self.original_max_seq_len = config.max_position_embeddings
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self.config = config
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self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.original_inv_freq = self.inv_freq
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@torch.no_grad()
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@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
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def forward(self, x, position_ids):
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
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position_ids_expanded = position_ids[:, None, :].float()
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device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
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with torch.autocast(device_type=device_type, enabled=False): # Force float32
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos() * self.attention_scaling
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sin = emb.sin() * self.attention_scaling
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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class Qwen3NextRMSNorm(nn.Module):
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def __init__(self, dim: int, eps: float = 1e-6):
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super().__init__()
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self.hidden_size = dim
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self.variance_epsilon = eps
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self.eps = eps
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self.weight = nn.Parameter(torch.zeros(dim))
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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output = self._norm(x.float())
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# Llama does x.to(float16) * w whilst Qwen3Next is (x * w).to(float16)
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# See https://github.com/huggingface/transformers/pull/29402
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output = output * (1.0 + self.weight.float())
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return output.type_as(x)
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.eps}"
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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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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# Adapted from transformers.models.glm.modular_glm.apply_rotary_pos_emb
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Removes the interleaving of cos and sin from GLM
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`, *optional*):
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Deprecated and unused.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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# Keep half or full tensor for later concatenation
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rotary_dim = cos.shape[-1]
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q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
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k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
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# Apply rotary embeddings on the first half or full tensor
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q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
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k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
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# Concatenate back to full shape
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q_embed = torch.cat([q_embed, q_pass], dim=-1)
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k_embed = torch.cat([k_embed, k_pass], dim=-1)
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return q_embed, k_embed
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class Qwen3NextAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: Qwen3NextConfig, layer_idx: int):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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self.num_attention_heads = config.num_attention_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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self.scaling = self.head_dim**-0.5
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self.attention_dropout = config.attention_dropout
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self.is_causal = True
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self.q_proj = nn.Linear(
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config.hidden_size, config.num_attention_heads * self.head_dim * 2, bias=config.attention_bias
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)
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self.k_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.v_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.o_proj = nn.Linear(
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config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
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)
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self.q_norm = Qwen3NextRMSNorm(self.head_dim, eps=config.rms_norm_eps) # unlike olmo, only on the head dim!
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self.k_norm = Qwen3NextRMSNorm(
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self.head_dim, eps=config.rms_norm_eps
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) # thus post q_norm does not need reshape
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@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_values: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
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input_shape = hidden_states.shape[:-1]
|
|
hidden_shape = (*input_shape, -1, self.head_dim)
|
|
|
|
query_states, gate = torch.chunk(
|
|
self.q_proj(hidden_states).view(*input_shape, -1, self.head_dim * 2), 2, dim=-1
|
|
)
|
|
gate = gate.reshape(*input_shape, -1)
|
|
|
|
query_states = self.q_norm(query_states.view(hidden_shape)).transpose(1, 2)
|
|
key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
|
|
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
|
|
cos, sin = position_embeddings
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
|
|
|
if past_key_values is not None:
|
|
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
|
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
|
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
attention_interface: Callable = eager_attention_forward
|
|
if self.config._attn_implementation != "eager":
|
|
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
|
|
|
attn_output, attn_weights = attention_interface(
|
|
self,
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attention_mask,
|
|
dropout=0.0 if not self.training else self.attention_dropout,
|
|
scaling=self.scaling,
|
|
**kwargs,
|
|
)
|
|
|
|
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
|
attn_output = attn_output * torch.sigmoid(gate)
|
|
|
|
attn_output = self.o_proj(attn_output)
|
|
return attn_output, attn_weights
|
|
|
|
|
|
def apply_mask_to_padding_states(hidden_states, attention_mask):
|
|
"""
|
|
Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66
|
|
"""
|
|
if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1:
|
|
dtype = hidden_states.dtype
|
|
hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
|
|
|
|
return hidden_states
|
|
|
|
|
|
is_fast_path_available = all(
|
|
(causal_conv1d_fn, causal_conv1d_update, chunk_gated_delta_rule, fused_recurrent_gated_delta_rule)
|
|
)
|
|
|
|
|
|
def torch_causal_conv1d_update(
|
|
hidden_states,
|
|
conv_state,
|
|
weight,
|
|
bias=None,
|
|
activation=None,
|
|
):
|
|
_, hidden_size, seq_len = hidden_states.shape
|
|
state_len = conv_state.shape[-1]
|
|
|
|
hidden_states_new = torch.cat([conv_state, hidden_states], dim=-1).to(weight.dtype)
|
|
conv_state.copy_(hidden_states_new[:, :, -state_len:])
|
|
out = F.conv1d(hidden_states_new, weight.unsqueeze(1), bias, padding=0, groups=hidden_size)
|
|
out = F.silu(out[:, :, -seq_len:])
|
|
out = out.to(hidden_states.dtype)
|
|
return out
|
|
|
|
|
|
def torch_chunk_gated_delta_rule(
|
|
query,
|
|
key,
|
|
value,
|
|
g,
|
|
beta,
|
|
chunk_size=64,
|
|
initial_state=None,
|
|
output_final_state=False,
|
|
use_qk_l2norm_in_kernel=False,
|
|
):
|
|
initial_dtype = query.dtype
|
|
if use_qk_l2norm_in_kernel:
|
|
query = F.normalize(query, p=2, dim=-1)
|
|
key = F.normalize(key, p=2, dim=-1)
|
|
query, key, value, beta, g = [
|
|
x.transpose(1, 2).contiguous().to(torch.float32) for x in (query, key, value, beta, g)
|
|
]
|
|
|
|
batch_size, sequence_length, num_heads, k_head_dim = key.shape
|
|
v_head_dim = value.shape[-1]
|
|
pad_size = (chunk_size - num_heads % chunk_size) % chunk_size
|
|
query = F.pad(query, (0, 0, 0, pad_size))
|
|
key = F.pad(key, (0, 0, 0, pad_size))
|
|
value = F.pad(value, (0, 0, 0, pad_size))
|
|
beta = F.pad(beta, (0, pad_size))
|
|
g = F.pad(g, (0, pad_size))
|
|
tot_heads = num_heads + pad_size
|
|
scale = 1 / (query.shape[-1] ** 0.5)
|
|
query = query * scale
|
|
|
|
v_beta = value * beta.unsqueeze(-1)
|
|
k_beta = key * beta.unsqueeze(-1)
|
|
# reshape to chunks
|
|
query, key, value, k_beta, v_beta = [
|
|
x.reshape(x.shape[0], x.shape[1], -1, chunk_size, x.shape[-1]) for x in (query, key, value, k_beta, v_beta)
|
|
]
|
|
g = g.reshape(g.shape[0], g.shape[1], -1, chunk_size)
|
|
mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=0)
|
|
|
|
# chunk decay
|
|
g = g.cumsum(dim=-1)
|
|
decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril()
|
|
attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask, 0)
|
|
for i in range(1, chunk_size):
|
|
row = attn[..., i, :i].clone()
|
|
sub = attn[..., :i, :i].clone()
|
|
attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
|
|
attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
|
|
value = attn @ v_beta
|
|
k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
|
|
last_recurrent_state = (
|
|
torch.zeros(batch_size, sequence_length, k_head_dim, v_head_dim).to(value)
|
|
if initial_state is None
|
|
else initial_state.to(value)
|
|
)
|
|
core_attn_out = torch.zeros_like(value)
|
|
mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=1)
|
|
|
|
# for each chunk
|
|
for i in range(0, tot_heads // chunk_size):
|
|
q_i, k_i, v_i = query[:, :, i], key[:, :, i], value[:, :, i]
|
|
attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
|
|
v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
|
|
v_new = v_i - v_prime
|
|
attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
|
|
core_attn_out[:, :, i] = attn_inter + attn @ v_new
|
|
last_recurrent_state = (
|
|
last_recurrent_state * g[:, :, i, -1, None, None].exp()
|
|
+ (k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]).transpose(-1, -2) @ v_new
|
|
)
|
|
|
|
if not output_final_state:
|
|
last_recurrent_state = None
|
|
core_attn_out = core_attn_out.reshape(core_attn_out.shape[0], core_attn_out.shape[1], -1, core_attn_out.shape[-1])
|
|
core_attn_out = core_attn_out[:, :, :num_heads]
|
|
core_attn_out = core_attn_out.transpose(1, 2).contiguous().to(initial_dtype)
|
|
return core_attn_out, last_recurrent_state
|
|
|
|
|
|
def torch_recurrent_gated_delta_rule(
|
|
query, key, value, g, beta, initial_state, output_final_state, use_qk_l2norm_in_kernel=False
|
|
):
|
|
initial_dtype = query.dtype
|
|
if use_qk_l2norm_in_kernel:
|
|
query = F.normalize(query, p=2, dim=-1)
|
|
key = F.normalize(key, p=2, dim=-1)
|
|
query, key, value, beta, g = [
|
|
x.transpose(1, 2).contiguous().to(torch.float32) for x in (query, key, value, beta, g)
|
|
]
|
|
|
|
batch_size, sequence_length, num_heads, k_head_dim = key.shape
|
|
v_head_dim = value.shape[-1]
|
|
scale = 1 / (query.shape[-1] ** 0.5)
|
|
query = query * scale
|
|
|
|
core_attn_out = torch.zeros(batch_size, sequence_length, num_heads, v_head_dim).to(value)
|
|
last_recurrent_state = (
|
|
torch.zeros(batch_size, sequence_length, k_head_dim, v_head_dim).to(value)
|
|
if initial_state is None
|
|
else initial_state.to(value)
|
|
)
|
|
|
|
for i in range(num_heads):
|
|
q_t = query[:, :, i]
|
|
k_t = key[:, :, i]
|
|
v_t = value[:, :, i]
|
|
g_t = g[:, :, i].exp().unsqueeze(-1).unsqueeze(-1)
|
|
beta_t = beta[:, :, i].unsqueeze(-1)
|
|
|
|
last_recurrent_state = last_recurrent_state * g_t
|
|
kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
|
|
delta = (v_t - kv_mem) * beta_t
|
|
last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta.unsqueeze(-2)
|
|
core_attn_out[:, :, i] = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
|
|
|
|
if not output_final_state:
|
|
last_recurrent_state = None
|
|
core_attn_out = core_attn_out.transpose(1, 2).contiguous().to(initial_dtype)
|
|
return core_attn_out, last_recurrent_state
|
|
|
|
|
|
class Qwen3NextGatedDeltaNet(nn.Module):
|
|
def __init__(self, config: Qwen3NextConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
self.num_v_heads = config.linear_num_value_heads
|
|
self.num_k_heads = config.linear_num_key_heads
|
|
self.head_k_dim = config.linear_key_head_dim
|
|
self.head_v_dim = config.linear_value_head_dim
|
|
self.key_dim = self.head_k_dim * self.num_k_heads
|
|
self.value_dim = self.head_v_dim * self.num_v_heads
|
|
|
|
self.conv_kernel_size = config.linear_conv_kernel_dim
|
|
self.layer_idx = layer_idx
|
|
self.activation = config.hidden_act
|
|
self.act = ACT2FN[config.hidden_act]
|
|
self.layer_norm_epsilon = config.rms_norm_eps
|
|
|
|
self.config = config
|
|
|
|
# QKV
|
|
self.conv_dim = self.key_dim * 2 + self.value_dim
|
|
self.conv1d = nn.Conv1d(
|
|
in_channels=self.conv_dim,
|
|
out_channels=self.conv_dim,
|
|
bias=False,
|
|
kernel_size=self.conv_kernel_size,
|
|
groups=self.conv_dim,
|
|
padding=self.conv_kernel_size - 1,
|
|
)
|
|
|
|
# projection of the input hidden states
|
|
projection_size_qkvz = self.key_dim * 2 + self.value_dim * 2
|
|
projection_size_ba = self.num_v_heads * 2
|
|
self.in_proj_qkvz = nn.Linear(self.hidden_size, projection_size_qkvz, bias=False)
|
|
self.in_proj_ba = nn.Linear(self.hidden_size, projection_size_ba, bias=False)
|
|
|
|
# time step projection (discretization)
|
|
# instantiate once and copy inv_dt in init_weights of PretrainedModel
|
|
self.dt_bias = nn.Parameter(torch.ones(self.num_v_heads))
|
|
|
|
A = torch.empty(self.num_v_heads).uniform_(0, 16)
|
|
self.A_log = nn.Parameter(torch.log(A))
|
|
|
|
self.norm = (
|
|
Qwen3NextRMSNormGated(self.head_v_dim, eps=self.layer_norm_epsilon)
|
|
if FusedRMSNormGated is None
|
|
else FusedRMSNormGated(
|
|
self.head_v_dim,
|
|
eps=self.layer_norm_epsilon,
|
|
activation=self.activation,
|
|
device=torch.cuda.current_device(),
|
|
dtype=config.dtype if config.dtype is not None else torch.get_current_dtype(),
|
|
)
|
|
)
|
|
|
|
self.out_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False)
|
|
|
|
self.causal_conv1d_fn = causal_conv1d_fn
|
|
self.causal_conv1d_update = causal_conv1d_update or torch_causal_conv1d_update
|
|
self.chunk_gated_delta_rule = chunk_gated_delta_rule or torch_chunk_gated_delta_rule
|
|
self.recurrent_gated_delta_rule = fused_recurrent_gated_delta_rule or torch_recurrent_gated_delta_rule
|
|
|
|
if not is_fast_path_available:
|
|
logger.warning_once(
|
|
"The fast path is not available because one of the required library is not installed. Falling back to "
|
|
"torch implementation. To install follow https://github.com/fla-org/flash-linear-attention#installation and"
|
|
" https://github.com/Dao-AILab/causal-conv1d"
|
|
)
|
|
|
|
def fix_query_key_value_ordering(self, mixed_qkvz, mixed_ba):
|
|
"""
|
|
Derives `query`, `key` and `value` tensors from `mixed_qkvz` and `mixed_ba`.
|
|
"""
|
|
|
|
new_tensor_shape_qkvz = mixed_qkvz.size()[:-1] + (
|
|
self.num_k_heads,
|
|
2 * self.head_k_dim + 2 * self.head_v_dim * self.num_v_heads // self.num_k_heads,
|
|
)
|
|
new_tensor_shape_ba = mixed_ba.size()[:-1] + (self.num_k_heads, 2 * self.num_v_heads // self.num_k_heads)
|
|
|
|
mixed_qkvz = mixed_qkvz.view(*new_tensor_shape_qkvz)
|
|
mixed_ba = mixed_ba.view(*new_tensor_shape_ba)
|
|
split_arg_list_qkvz = [
|
|
self.head_k_dim,
|
|
self.head_k_dim,
|
|
(self.num_v_heads // self.num_k_heads * self.head_v_dim),
|
|
(self.num_v_heads // self.num_k_heads * self.head_v_dim),
|
|
]
|
|
split_arg_list_ba = [self.num_v_heads // self.num_k_heads, self.num_v_heads // self.num_k_heads]
|
|
query, key, value, z = torch.split(mixed_qkvz, split_arg_list_qkvz, dim=3)
|
|
b, a = torch.split(mixed_ba, split_arg_list_ba, dim=3)
|
|
# [b, sq, ng, np/ng * hn] -> [b, sq, np, hn]
|
|
value = value.reshape(value.size(0), value.size(1), -1, self.head_v_dim)
|
|
z = z.reshape(z.size(0), z.size(1), -1, self.head_v_dim)
|
|
b = b.reshape(b.size(0), b.size(1), self.num_v_heads)
|
|
a = a.reshape(a.size(0), a.size(1), self.num_v_heads)
|
|
return query, key, value, z, b, a
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
cache_params: Optional[Qwen3NextDynamicCache] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
):
|
|
hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
|
|
|
|
# Set up dimensions for reshapes later
|
|
batch_size, seq_len, _ = hidden_states.shape
|
|
|
|
use_precomputed_states = (
|
|
cache_params is not None
|
|
and cache_params.has_previous_state
|
|
and seq_len == 1
|
|
and cache_position is not None
|
|
)
|
|
|
|
# getting projected states from cache if it exists
|
|
if cache_params is not None:
|
|
conv_state = cache_params.conv_states[self.layer_idx]
|
|
recurrent_state = cache_params.recurrent_states[self.layer_idx]
|
|
|
|
projected_states_qkvz = self.in_proj_qkvz(hidden_states)
|
|
projected_states_ba = self.in_proj_ba(hidden_states)
|
|
query, key, value, z, b, a = self.fix_query_key_value_ordering(projected_states_qkvz, projected_states_ba)
|
|
query, key, value = (x.reshape(x.shape[0], x.shape[1], -1) for x in (query, key, value))
|
|
|
|
mixed_qkv = torch.cat((query, key, value), dim=-1)
|
|
mixed_qkv = mixed_qkv.transpose(1, 2)
|
|
|
|
if use_precomputed_states:
|
|
# 2. Convolution sequence transformation
|
|
# NOTE: the conv state is updated in `causal_conv1d_update`
|
|
mixed_qkv = self.causal_conv1d_update(
|
|
mixed_qkv,
|
|
conv_state,
|
|
self.conv1d.weight.squeeze(1),
|
|
self.conv1d.bias,
|
|
self.activation,
|
|
)
|
|
else:
|
|
if cache_params is not None:
|
|
conv_state = F.pad(mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0))
|
|
cache_params.conv_states[self.layer_idx] = conv_state
|
|
if self.causal_conv1d_fn is not None:
|
|
mixed_qkv = self.causal_conv1d_fn(
|
|
x=mixed_qkv,
|
|
weight=self.conv1d.weight.squeeze(1),
|
|
bias=self.conv1d.bias,
|
|
activation=self.activation,
|
|
seq_idx=None,
|
|
)
|
|
else:
|
|
mixed_qkv = F.silu(self.conv1d(mixed_qkv)[:, :, :seq_len])
|
|
|
|
mixed_qkv = mixed_qkv.transpose(1, 2)
|
|
query, key, value = torch.split(
|
|
mixed_qkv,
|
|
[
|
|
self.key_dim,
|
|
self.key_dim,
|
|
self.value_dim,
|
|
],
|
|
dim=-1,
|
|
)
|
|
query = query.reshape(query.shape[0], query.shape[1], -1, self.head_k_dim)
|
|
key = key.reshape(key.shape[0], key.shape[1], -1, self.head_k_dim)
|
|
value = value.reshape(value.shape[0], value.shape[1], -1, self.head_v_dim)
|
|
|
|
beta = b.sigmoid()
|
|
# If the model is loaded in fp16, without the .float() here, A might be -inf
|
|
g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias)
|
|
if self.num_v_heads // self.num_k_heads > 1:
|
|
query = query.repeat_interleave(self.num_v_heads // self.num_k_heads, dim=2)
|
|
key = key.repeat_interleave(self.num_v_heads // self.num_k_heads, dim=2)
|
|
|
|
if not use_precomputed_states:
|
|
core_attn_out, last_recurrent_state = self.chunk_gated_delta_rule(
|
|
query,
|
|
key,
|
|
value,
|
|
g=g,
|
|
beta=beta,
|
|
initial_state=None,
|
|
output_final_state=cache_params is not None,
|
|
use_qk_l2norm_in_kernel=True,
|
|
)
|
|
|
|
else:
|
|
core_attn_out, last_recurrent_state = self.recurrent_gated_delta_rule(
|
|
query,
|
|
key,
|
|
value,
|
|
g=g,
|
|
beta=beta,
|
|
initial_state=recurrent_state,
|
|
output_final_state=cache_params is not None,
|
|
use_qk_l2norm_in_kernel=True,
|
|
)
|
|
|
|
# Update cache
|
|
if cache_params is not None:
|
|
cache_params.recurrent_states[self.layer_idx] = last_recurrent_state
|
|
|
|
z_shape_og = z.shape
|
|
# reshape input data into 2D tensor
|
|
core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
|
|
z = z.reshape(-1, z.shape[-1])
|
|
core_attn_out = self.norm(core_attn_out, z)
|
|
core_attn_out = core_attn_out.reshape(z_shape_og)
|
|
core_attn_out = core_attn_out.reshape(core_attn_out.shape[0], core_attn_out.shape[1], -1)
|
|
|
|
output = self.out_proj(core_attn_out)
|
|
return output
|
|
|
|
|
|
class Qwen3NextMLP(nn.Module):
|
|
def __init__(self, config, intermediate_size=None):
|
|
super().__init__()
|
|
self.config = config
|
|
self.hidden_size = config.hidden_size
|
|
self.intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size
|
|
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
|
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
|
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
|
self.act_fn = ACT2FN[config.hidden_act]
|
|
|
|
def forward(self, x):
|
|
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
|
return down_proj
|
|
|
|
|
|
class Qwen3NextSparseMoeBlock(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.num_experts = config.num_experts
|
|
self.top_k = config.num_experts_per_tok
|
|
self.norm_topk_prob = config.norm_topk_prob
|
|
|
|
# gating
|
|
self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)
|
|
self.experts = nn.ModuleList(
|
|
[Qwen3NextMLP(config, intermediate_size=config.moe_intermediate_size) for _ in range(self.num_experts)]
|
|
)
|
|
|
|
self.shared_expert = Qwen3NextMLP(config, intermediate_size=config.shared_expert_intermediate_size)
|
|
self.shared_expert_gate = torch.nn.Linear(config.hidden_size, 1, bias=False)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
""" """
|
|
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
|
hidden_states = hidden_states.view(-1, hidden_dim)
|
|
# router_logits: (batch * sequence_length, n_experts)
|
|
router_logits = self.gate(hidden_states)
|
|
|
|
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
|
|
routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
|
|
if self.norm_topk_prob:
|
|
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
|
|
# we cast back to the input dtype
|
|
routing_weights = routing_weights.to(hidden_states.dtype)
|
|
|
|
final_hidden_states = torch.zeros(
|
|
(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
|
|
)
|
|
|
|
# One hot encode the selected experts to create an expert mask
|
|
# this will be used to easily index which expert is going to be sollicitated
|
|
expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
|
|
|
|
# Loop over all available experts in the model and perform the computation on each expert
|
|
expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
|
|
for expert_idx in expert_hit:
|
|
expert_layer = self.experts[expert_idx]
|
|
idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
|
|
|
|
# Index the correct hidden states and compute the expert hidden state for
|
|
# the current expert. We need to make sure to multiply the output hidden
|
|
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
|
|
current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
|
|
current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
|
|
|
|
# However `index_add_` only support torch tensors for indexing so we'll use
|
|
# the `top_x` tensor here.
|
|
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
|
|
|
|
shared_expert_output = self.shared_expert(hidden_states)
|
|
shared_expert_output = F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_expert_output
|
|
|
|
final_hidden_states = final_hidden_states + shared_expert_output
|
|
|
|
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
|
|
return final_hidden_states, router_logits
|
|
|
|
|
|
class Qwen3NextDecoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: Qwen3NextConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
|
|
# token mixer
|
|
self.layer_type = config.layer_types[layer_idx]
|
|
if self.layer_type == "linear_attention":
|
|
self.linear_attn = Qwen3NextGatedDeltaNet(config, layer_idx)
|
|
elif self.layer_type == "full_attention":
|
|
self.self_attn = Qwen3NextAttention(config, layer_idx)
|
|
|
|
if (layer_idx not in config.mlp_only_layers) and (
|
|
config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0
|
|
):
|
|
self.mlp = Qwen3NextSparseMoeBlock(config)
|
|
else:
|
|
self.mlp = Qwen3NextMLP(config, intermediate_size=config.intermediate_size)
|
|
|
|
self.input_layernorm = Qwen3NextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = Qwen3NextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[tuple[torch.Tensor]] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[FlashAttentionKwargs],
|
|
) -> torch.FloatTensor:
|
|
"""
|
|
Args:
|
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
|
`(batch, sequence_length)` where padding elements are indicated by 0.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
output_router_logits (`bool`, *optional*):
|
|
Whether or not to return the logits of all the routers. They are useful for computing the router loss,
|
|
and should not be returned during inference.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
|
(see `past_key_values`).
|
|
past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
|
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
|
position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
|
|
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
|
|
with `head_dim` being the embedding dimension of each attention head.
|
|
kwargs (`dict`, *optional*):
|
|
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
|
|
into the model
|
|
"""
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
# Token Mixer
|
|
if self.layer_type == "linear_attention":
|
|
hidden_states = self.linear_attn(
|
|
hidden_states=hidden_states,
|
|
cache_params=past_key_values,
|
|
cache_position=cache_position,
|
|
attention_mask=attention_mask,
|
|
)
|
|
elif self.layer_type == "full_attention":
|
|
# Self Attention
|
|
hidden_states, _ = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
# For the MoE layers, we need to unpack
|
|
if isinstance(hidden_states, tuple):
|
|
hidden_states, _ = hidden_states
|
|
hidden_states = residual + hidden_states
|
|
|
|
return hidden_states
|
|
|
|
|
|
class Qwen3NextPreTrainedModel(PreTrainedModel):
|
|
config: Qwen3NextConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["Qwen3NextDecoderLayer"]
|
|
_skip_keys_device_placement = "past_key_values"
|
|
_supports_flash_attn_2 = True
|
|
_supports_sdpa = True
|
|
_keys_to_ignore_on_load_unexpected = [r"^mtp.*"]
|
|
_can_record_outputs = {
|
|
"router_logits": OutputRecorder(Qwen3NextSparseMoeBlock, index=1),
|
|
"hidden_states": Qwen3NextDecoderLayer,
|
|
"attentions": Qwen3NextAttention,
|
|
}
|
|
_is_stateful = True
|
|
|
|
def _init_weights(self, module):
|
|
super()._init_weights(module)
|
|
if isinstance(module, Qwen3NextGatedDeltaNet):
|
|
module.dt_bias.data.fill_(1.0)
|
|
module.A_log.data.uniform_(0, 16).log_()
|
|
|
|
|
|
class Qwen3NextModel(Qwen3NextPreTrainedModel):
|
|
def __init__(self, config: Qwen3NextConfig):
|
|
super().__init__(config)
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
|
|
self.layers = nn.ModuleList(
|
|
[Qwen3NextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.norm = Qwen3NextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.rotary_emb = Qwen3NextRotaryEmbedding(config=config)
|
|
self.gradient_checkpointing = False
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@check_model_inputs
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> MoeModelOutputWithPast:
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = Qwen3NextDynamicCache(config=self.config)
|
|
|
|
if cache_position is None:
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
cache_position = torch.arange(
|
|
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
|
)
|
|
if position_ids is None:
|
|
position_ids = cache_position.unsqueeze(0)
|
|
|
|
causal_mask = create_causal_mask(
|
|
config=self.config,
|
|
input_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
cache_position=cache_position,
|
|
past_key_values=past_key_values,
|
|
position_ids=position_ids,
|
|
)
|
|
linear_attn_mask = self._update_linear_attn_mask(attention_mask, cache_position)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
# create position embeddings to be shared across the decoder layers
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
|
layer_mask = linear_attn_mask if decoder_layer.layer_type == "linear_attention" else causal_mask
|
|
|
|
hidden_states = decoder_layer(
|
|
hidden_states,
|
|
position_embeddings=position_embeddings,
|
|
attention_mask=layer_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
return MoeModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
def _update_linear_attn_mask(self, attention_mask, cache_position):
|
|
"""
|
|
NOTE: Left-padding is used for linear attention mask.
|
|
No need for zeroing states when
|
|
1. Cached forward
|
|
2. Attending to all inputs
|
|
"""
|
|
linear_attn_mask = attention_mask
|
|
if cache_position[0] > 0 or (attention_mask is not None and torch.all(attention_mask == 1)):
|
|
linear_attn_mask = None
|
|
return linear_attn_mask
|
|
|
|
|
|
def load_balancing_loss_func(
|
|
gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
|
|
num_experts: Optional[int] = None,
|
|
top_k=2,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, int]:
|
|
r"""
|
|
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
|
|
|
|
See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
|
|
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
|
|
experts is too unbalanced.
|
|
|
|
Args:
|
|
gate_logits:
|
|
Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
|
|
shape [batch_size X sequence_length, num_experts].
|
|
num_experts:
|
|
Number of experts
|
|
top_k:
|
|
The number of experts to route per-token, can be also interpreted as the `top-k` routing
|
|
parameter.
|
|
attention_mask (`torch.Tensor`, *optional*):
|
|
The attention_mask used in forward function
|
|
shape [batch_size X sequence_length] if not None.
|
|
|
|
Returns:
|
|
The auxiliary loss.
|
|
"""
|
|
if gate_logits is None or not isinstance(gate_logits, tuple):
|
|
return 0
|
|
|
|
if isinstance(gate_logits, tuple):
|
|
compute_device = gate_logits[0].device
|
|
concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
|
|
|
|
routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
|
|
|
|
_, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
|
|
|
|
expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
|
|
|
|
if attention_mask is None:
|
|
# Compute the percentage of tokens routed to each experts
|
|
tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
|
|
|
|
# Compute the average probability of routing to these experts
|
|
router_prob_per_expert = torch.mean(routing_weights, dim=0)
|
|
else:
|
|
batch_size, sequence_length = attention_mask.shape
|
|
num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
|
|
|
|
# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
|
|
expert_attention_mask = (
|
|
attention_mask[None, :, :, None, None]
|
|
.expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
|
|
.reshape(-1, top_k, num_experts)
|
|
.to(compute_device)
|
|
)
|
|
|
|
# Compute the percentage of tokens routed to each experts
|
|
tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
|
|
expert_attention_mask, dim=0
|
|
)
|
|
|
|
# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
|
|
router_per_expert_attention_mask = (
|
|
attention_mask[None, :, :, None]
|
|
.expand((num_hidden_layers, batch_size, sequence_length, num_experts))
|
|
.reshape(-1, num_experts)
|
|
.to(compute_device)
|
|
)
|
|
|
|
# Compute the average probability of routing to these experts
|
|
router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
|
|
router_per_expert_attention_mask, dim=0
|
|
)
|
|
|
|
overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
|
|
return overall_loss * num_experts
|
|
|
|
|
|
@auto_docstring
|
|
class Qwen3NextForCausalLM(Qwen3NextPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
_tp_plan = {"lm_head": "colwise_rep"}
|
|
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = Qwen3NextModel(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
self.router_aux_loss_coef = config.router_aux_loss_coef
|
|
self.num_experts = config.num_experts
|
|
self.num_experts_per_tok = config.num_experts_per_tok
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Qwen3NextDynamicCache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_router_logits: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> MoeCausalLMOutputWithPast:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, Qwen3NextForCausalLM
|
|
|
|
>>> model = Qwen3NextForCausalLM.from_pretrained("Qwen/Qwen3-Next-80B-A3B-Instruct")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Next-80B-A3B-Instruct")
|
|
|
|
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
|
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
|
```"""
|
|
|
|
output_router_logits = (
|
|
output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
|
)
|
|
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs: MoeModelOutputWithPast = self.model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_router_logits=output_router_logits,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = outputs.last_hidden_state
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
|
|
|
|
aux_loss = None
|
|
if output_router_logits:
|
|
aux_loss = load_balancing_loss_func(
|
|
outputs.router_logits,
|
|
self.num_experts,
|
|
self.num_experts_per_tok,
|
|
attention_mask,
|
|
)
|
|
if labels is not None:
|
|
loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
|
|
|
|
return MoeCausalLMOutputWithPast(
|
|
loss=loss,
|
|
aux_loss=aux_loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
router_logits=outputs.router_logits,
|
|
)
|
|
|
|
|
|
class Qwen3NextForSequenceClassification(GenericForSequenceClassification, Qwen3NextPreTrainedModel):
|
|
pass
|
|
|
|
|
|
class Qwen3NextForTokenClassification(GenericForTokenClassification, Qwen3NextPreTrainedModel):
|
|
pass
|
|
|
|
|
|
class Qwen3NextForQuestionAnswering(GenericForQuestionAnswering, Qwen3NextPreTrainedModel):
|
|
base_model_prefix = "transformer" # For BC, where `transformer` was used instead of `model`
|
|
|
|
|
|
__all__ = [
|
|
"Qwen3NextForCausalLM",
|
|
"Qwen3NextForQuestionAnswering",
|
|
"Qwen3NextModel",
|
|
"Qwen3NextPreTrainedModel",
|
|
"Qwen3NextForSequenceClassification",
|
|
"Qwen3NextForTokenClassification",
|
|
] |