backbone_custom_modeling_qwen3.py

from typing import Callable, Optional, Tuple

import torch
from torch import nn
from transformers.models.qwen3.modeling_qwen3 import (
    ALL_ATTENTION_FUNCTIONS,
    Cache,
    FlashAttentionKwargs,
    Qwen3Attention,
    Qwen3Config,
    Qwen3DecoderLayer,
    Qwen3ForCausalLM,
    Qwen3Model,
    eager_attention_forward,
    rotate_half,
)
from transformers.processing_utils import Unpack
from transformers.utils import logging

logger = logging.get_logger(__name__)


def custom_apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1, q_start_idx=0):
    """Applies Rotary Position Embedding to the query and key tensors."""
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos[..., q_start_idx:, :]) + (
        rotate_half(q) * sin[..., q_start_idx:, :]
    )
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


class CustomQwen3Attention(Qwen3Attention):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: Qwen3Config, layer_idx: int):
        super().__init__(config, layer_idx=layer_idx)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor],
        past_key_value: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        q_start_idx: int = 0,  # > 0: decoder pass w/encoder inputs in hidden_states
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)
        sa_hidden_sates = hidden_states[:, q_start_idx:, :]
        query_input_shape = sa_hidden_sates.shape[:-1]
        query_hidden_shape = (*query_input_shape, -1, self.head_dim)

        query_states = self.q_norm(
            self.q_proj(sa_hidden_sates).reshape(query_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 = custom_apply_rotary_pos_emb(
            query_states, key_states, cos, sin, q_start_idx=q_start_idx
        )

        if past_key_value 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_value.update(
                key_states, value_states, self.layer_idx, cache_kwargs
            )

        # NOTE: downcast for flex-attention compatibility
        query_states, key_states = (
            query_states.to(value_states.dtype),
            key_states.to(value_states.dtype),
        )

        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,
            sliding_window=self.sliding_window,  # diff with Llama
            **kwargs,
        )

        attn_output = attn_output.reshape(*query_input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


class CustomQwen3DecoderLayer(Qwen3DecoderLayer):
    def __init__(self, config: Qwen3Config, layer_idx: int):
        super().__init__(config, layer_idx=layer_idx)
        self.self_attn = CustomQwen3Attention(config=config, layer_idx=layer_idx)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        q_start_idx: int = 0,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> Tuple[
        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
    ]:
        residual = hidden_states[:, q_start_idx:, ...]

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            q_start_idx=q_start_idx,
            **kwargs,
        )
        hidden_states = residual + hidden_states
        # return hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)
        if output_attentions:
            outputs += (self_attn_weights,)

        return outputs


class CustomQwen3Model(Qwen3Model):
    def __init__(self, config: Qwen3Config):
        super().__init__(config)
        self.layers = nn.ModuleList(
            [
                CustomQwen3DecoderLayer(config, layer_idx)
                for layer_idx in range(config.num_hidden_layers)
            ]
        )
        # Initialize weights and apply final processing
        self.post_init()


class CustomQwen3ForCausalLM(Qwen3ForCausalLM):
    def __init__(self, config: Qwen3Config):
        super().__init__(config)
        # Initialize a new model with custom layers
        self.model = CustomQwen3Model(config)

        # Initialize weights and apply final processing
        self.post_init()