183 lines
6.5 KiB
Python
183 lines
6.5 KiB
Python
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates and/or its affiliates
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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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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.modeling_flash_attention_utils import (
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is_flash_attn_available,
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is_torch_npu_available,
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)
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from ..mammothmoda2_qwen2_5_vl.modeling_mammothmoda2_qwen2_5_vl import Qwen2RMSNorm
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def _swiglu(x, y):
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return F.silu(x.float(), inplace=False).to(x.dtype) * y
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if is_flash_attn_available() and not is_torch_npu_available():
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from flash_attn.ops.activations import swiglu
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else:
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swiglu = _swiglu
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class LuminaFeedForward(nn.Module):
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r"""
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A feed-forward layer.
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Parameters:
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hidden_size (`int`):
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The dimensionality of the hidden layers in the model. This parameter determines the width of the model's
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hidden representations.
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intermediate_size (`int`): The intermediate dimension of the feedforward layer.
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multiple_of (`int`, *optional*): Value to ensure hidden dimension is a multiple
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of this value.
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ffn_dim_multiplier (float, *optional*): Custom multiplier for hidden
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dimension. Defaults to None.
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"""
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def __init__(
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self,
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dim: int,
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inner_dim: int,
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multiple_of: int | None = 256,
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ffn_dim_multiplier: float | None = None,
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):
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super().__init__()
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self.swiglu = swiglu
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# custom hidden_size factor multiplier
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if ffn_dim_multiplier is not None:
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inner_dim = int(ffn_dim_multiplier * inner_dim)
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inner_dim = multiple_of * ((inner_dim + multiple_of - 1) // multiple_of)
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self.linear_1 = nn.Linear(
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dim,
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inner_dim,
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bias=False,
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)
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self.linear_2 = nn.Linear(
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inner_dim,
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dim,
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bias=False,
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)
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self.linear_3 = nn.Linear(
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dim,
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inner_dim,
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bias=False,
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)
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def forward(self, x):
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h1, h2 = self.linear_1(x), self.linear_3(x)
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return self.linear_2(self.swiglu(h1, h2))
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class SimpleQFormerImageRefiner(nn.Module):
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"""
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A lightweight Q-Former-like module that maps a variable-length sequence of
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input features to a fixed number of query tokens (default 128).
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Inputs are first projected to `hidden_size`, then a stack of decoder blocks
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performs self-attention over learnable queries and cross-attention to inputs.
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Output shape: (batch, num_queries, hidden_size)
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"""
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def __init__(
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self,
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hidden_size: int,
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num_queries: int = 128,
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num_layers: int = 2,
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num_heads: int | None = None,
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dropout: float = 0.0,
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norm_eps: float = 1e-5,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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self.num_queries = num_queries
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# ensure num_heads divides hidden_size
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if num_heads is None:
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num_heads = max(1, hidden_size // 128)
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self.num_heads = self._choose_valid_num_heads(hidden_size, num_heads)
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self.input_proj = nn.Sequential(
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Qwen2RMSNorm(hidden_size, eps=norm_eps),
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nn.Linear(hidden_size, hidden_size, bias=True),
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)
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# Learnable query embeddings
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scale = hidden_size**-0.5
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self.query = nn.Parameter(scale * torch.randn(1, num_queries, hidden_size))
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# Decoder layers
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self.layers = nn.ModuleList()
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for _ in range(num_layers):
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self.layers.append(
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nn.ModuleDict(
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dict(
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ln_q1=Qwen2RMSNorm(hidden_size, eps=norm_eps),
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self_attn=nn.MultiheadAttention(
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embed_dim=hidden_size, num_heads=self.num_heads, dropout=dropout, batch_first=True
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),
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ln_q2=Qwen2RMSNorm(hidden_size, eps=norm_eps),
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cross_attn=nn.MultiheadAttention(
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embed_dim=hidden_size, num_heads=self.num_heads, dropout=dropout, batch_first=True
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),
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ln_ffn=Qwen2RMSNorm(hidden_size, eps=norm_eps),
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ffn=LuminaFeedForward(dim=hidden_size, inner_dim=4 * hidden_size),
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)
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)
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)
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@staticmethod
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def _choose_valid_num_heads(hidden_size: int, proposed_heads: int, preferred_head_dim: int = 128) -> int:
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"""Pick a number of heads that divides hidden_size, close to proposed or preferred."""
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# If proposed is valid, use it
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if proposed_heads > 0 and hidden_size % proposed_heads == 0:
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return proposed_heads
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# target based on preferred head dim
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target = max(1, round(hidden_size / preferred_head_dim))
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# collect divisors up to 128 heads (more than enough)
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max_heads_cap = min(128, hidden_size)
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divisors = [d for d in range(1, max_heads_cap + 1) if hidden_size % d == 0]
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# choose closest to target
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best = min(divisors, key=lambda d: (abs(d - target), -d))
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return best
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def forward(self, x: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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x: Tensor of shape (batch, seq_len, input_dim)
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Returns:
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Tensor of shape (batch, num_queries, hidden_size)
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"""
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batch, _, _ = x.shape
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kv = self.input_proj(x)
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q = self.query.repeat(batch, 1, 1).to(kv.dtype)
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for layer in self.layers:
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# Self-attention on queries
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q_norm = layer["ln_q1"](q)
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attn_out, _ = layer["self_attn"](q_norm, q_norm, q_norm, need_weights=False)
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q = q + attn_out
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# Cross-attention: queries attend to inputs
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q_norm = layer["ln_q2"](q)
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cross_out, _ = layer["cross_attn"](q_norm, kv, kv, need_weights=False, key_padding_mask=attention_mask)
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q = q + cross_out
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# Feed-forward
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q = q + layer["ffn"](layer["ln_ffn"](q))
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return q
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