modeling_videollama3_encoder.py

# Adopted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py.
# Below is the original copyright:
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
"""PyTorch VideoLLaMA3 vision encoder model."""

import importlib.util
import os.path as osp
import math
import warnings

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.nn.init import _calculate_fan_in_and_fan_out

from transformers.activations import ACT2FN
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import is_flash_attn_2_available

if is_flash_attn_2_available():
    from flash_attn import flash_attn_varlen_func
else:
    flash_attn_varlen_func = None

try:
    from .configuration_videollama3_encoder import Videollama3VisionEncoderConfig
except ImportError:
    spec = importlib.util.spec_from_file_location(
        "configuration_videollama3_encoder",
        osp.join(osp.dirname(__file__), "configuration_videollama3_encoder.py"),
    )
    configuration_videollama3_encoder = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(configuration_videollama3_encoder)
    Videollama3VisionEncoderConfig = getattr(
        configuration_videollama3_encoder,
        "Videollama3VisionEncoderConfig",
    )


def _trunc_normal_(tensor, mean, std, a, b):
    # Cut & paste from PyTorch official master until it's in a few official releases - RW
    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
    def norm_cdf(x):
        # Computes standard normal cumulative distribution function
        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0

    if (mean < a - 2 * std) or (mean > b + 2 * std):
        warnings.warn(
            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
            "The distribution of values may be incorrect.",
            stacklevel=2,
        )

    # Values are generated by using a truncated uniform distribution and
    # then using the inverse CDF for the normal distribution.
    # Get upper and lower cdf values
    l = norm_cdf((a - mean) / std)
    u = norm_cdf((b - mean) / std)

    # Uniformly fill tensor with values from [l, u], then translate to
    # [2l-1, 2u-1].
    tensor.uniform_(2 * l - 1, 2 * u - 1)

    # Use inverse cdf transform for normal distribution to get truncated
    # standard normal
    tensor.erfinv_()

    # Transform to proper mean, std
    tensor.mul_(std * math.sqrt(2.0))
    tensor.add_(mean)

    # Clamp to ensure it's in the proper range
    tensor.clamp_(min=a, max=b)


def trunc_normal_tf_(
    tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0
) -> torch.Tensor:
    """Fills the input Tensor with values drawn from a truncated
    normal distribution. The values are effectively drawn from the
    normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)`
    with values outside :math:`[a, b]` redrawn until they are within
    the bounds. The method used for generating the random values works
    best when :math:`a \\leq \text{mean} \\leq b`.

    NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
    bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
    and the result is subsequently scaled and shifted by the mean and std args.

    Args:
        tensor: an n-dimensional `torch.Tensor`
        mean: the mean of the normal distribution
        std: the standard deviation of the normal distribution
        a: the minimum cutoff value
        b: the maximum cutoff value
    """
    with torch.no_grad():
        _trunc_normal_(tensor, 0, 1.0, a, b)
        tensor.mul_(std).add_(mean)


def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
    if mode == "fan_in":
        denom = fan_in
    elif mode == "fan_out":
        denom = fan_out
    elif mode == "fan_avg":
        denom = (fan_in + fan_out) / 2

    variance = scale / denom

    if distribution == "truncated_normal":
        # constant is stddev of standard normal truncated to (-2, 2)
        trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
    elif distribution == "normal":
        with torch.no_grad():
            tensor.normal_(std=math.sqrt(variance))
    elif distribution == "uniform":
        bound = math.sqrt(3 * variance)
        with torch.no_grad():
            tensor.uniform_(-bound, bound)
    else:
        raise ValueError(f"invalid distribution {distribution}")


def lecun_normal_(tensor):
    variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")


def default_flax_embed_init(tensor):
    variance_scaling_(tensor, mode="fan_in", distribution="normal")


# Copied from transformers.models.llama.modeling_llama.rotate_half
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_vision(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
    orig_dtype = tensor.dtype
    tensor = tensor.float()
    cos = freqs.cos()
    sin = freqs.sin()
    cos = cos.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
    sin = sin.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
    output = (tensor * cos) + (rotate_half(tensor) * sin)
    output = output.to(orig_dtype)
    return output


class VisionRotaryEmbedding(nn.Module):

    def __init__(self, dim: int, theta: float = 10000.0) -> None:
        super().__init__()
        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

    def forward(self, seqlen: int) -> torch.Tensor:
        seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
        freqs = torch.outer(seq, self.inv_freq)
        return freqs
    

class Videollama3VisionEmbeddings(nn.Module):

    def __init__(self, config: Videollama3VisionEncoderConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.patch_size = config.patch_size

        self.patch_embedding = nn.Conv2d(
            in_channels=config.num_channels,
            out_channels=self.embed_dim,
            kernel_size=self.patch_size,
            stride=self.patch_size,
            padding="valid",
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = hidden_states.view(
            -1, self.config.num_channels, self.patch_size, self.patch_size
        )
        patch_embeds = self.patch_embedding(hidden_states)  # shape = [*, width, grid, grid]
        # embeddings = patch_embeds.flatten(2).transpose(1, 2)
        embeddings = patch_embeds.view(-1, self.embed_dim)

        return embeddings


class VisionAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    # Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
                f" {self.num_heads})."
            )
        self.scale = self.head_dim**-0.5
        self.dropout = config.attention_dropout

        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        cu_seqlens: torch.Tensor,
        rotary_pos_emb: torch.Tensor = None,
    ) -> torch.Tensor:
        """Input shape: Time x Channel"""

        q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(q_len, self.num_heads, self.head_dim)
        key_states = key_states.view(q_len, self.num_heads, self.head_dim)
        value_states = value_states.view(q_len, self.num_heads, self.head_dim)

        query_states = apply_rotary_pos_emb_vision(query_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
        key_states = apply_rotary_pos_emb_vision(key_states.unsqueeze(0), rotary_pos_emb).squeeze(0)

        attention_mask = torch.zeros([1, q_len, q_len], device=query_states.device, dtype=torch.bool)
        for i in range(1, len(cu_seqlens)):
            attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True

        query_states = query_states.transpose(0, 1)
        key_states = key_states.transpose(0, 1)
        value_states = value_states.transpose(0, 1)

        attn_weights = torch.matmul(query_states, key_states.transpose(1, 2)) / math.sqrt(self.head_dim)
        attn_weights = attn_weights + attention_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
        attn_output = torch.matmul(attn_weights, value_states)

        attn_output = attn_output.transpose(0, 1)
        attn_output = attn_output.reshape(q_len, -1)
        attn_output = self.out_proj(attn_output)

        return attn_output


class VisionFlashAttention2(VisionAttention):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    # Adapted from transformers.models.llama.modeling_llama.LlamaFlashAttention2.forward
    def forward(
        self,
        hidden_states: torch.Tensor,
        cu_seqlens: torch.Tensor,
        rotary_pos_emb: torch.Tensor = None,
    ) -> torch.Tensor:
        q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        # Flash attention requires the input to have the shape
        # batch_size x seq_length x head_dim x hidden_dim
        # therefore we just need to keep the original shape
        query_states = query_states.view(q_len, self.num_heads, self.head_dim)
        key_states = key_states.view(q_len, self.num_heads, self.head_dim)
        value_states = value_states.view(q_len, self.num_heads, self.head_dim)
        query_states = apply_rotary_pos_emb_vision(query_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
        key_states = apply_rotary_pos_emb_vision(key_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
        
        max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
        attn_output = flash_attn_varlen_func(query_states, key_states, value_states, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen).reshape(
            q_len, -1
        )
        attn_output = self.out_proj(attn_output)
        
        return attn_output


class VisionSdpaAttention(VisionAttention):

    def forward(
        self,
        hidden_states: torch.Tensor,
        cu_seqlens: torch.Tensor,
        rotary_pos_emb: torch.Tensor = None,
    ) -> torch.Tensor:
        seq_length = hidden_states.shape[0]
        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(seq_length, self.num_heads, self.head_dim)
        key_states = key_states.view(seq_length, self.num_heads, self.head_dim)
        value_states = value_states.view(seq_length, self.num_heads, self.head_dim)

        query_states = apply_rotary_pos_emb_vision(query_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
        key_states = apply_rotary_pos_emb_vision(key_states.unsqueeze(0), rotary_pos_emb).squeeze(0)

        attention_mask = torch.zeros([1, seq_length, seq_length], device=query_states.device, dtype=torch.bool)
        for i in range(1, len(cu_seqlens)):
            attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True

        query_states = query_states.transpose(0, 1)
        key_states = key_states.transpose(0, 1)
        value_states = value_states.transpose(0, 1)
        attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attention_mask, dropout_p=0.0)
        attn_output = attn_output.transpose(0, 1)
        attn_output = attn_output.reshape(seq_length, -1)
        attn_output = self.proj(attn_output)
        return attn_output


VISION_ATTENTION_CLASSES = {
    "eager": VisionAttention,
    "flash_attention_2": VisionFlashAttention2,
    "sdpa": VisionSdpaAttention,
}


# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Videollama3
class Videollama3VisionMLP(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.activation_fn = ACT2FN[config.hidden_act]
        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)
        hidden_states = self.fc2(hidden_states)
        return hidden_states


class Videollama3VisionEncoderLayer(nn.Module):

    def __init__(self, config: Videollama3VisionEncoderConfig):
        super().__init__()
        self.embed_dim = config.hidden_size
        self.self_attn = VISION_ATTENTION_CLASSES[config._attn_implementation](config=config)
        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
        self.mlp = Videollama3VisionMLP(config)
        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)

    # Ignore copy
    def forward(self, hidden_states, cu_seqlens, rotary_pos_emb) -> torch.Tensor:
        hidden_states = hidden_states + self.self_attn(
            self.layer_norm1(hidden_states), cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb
        )
        hidden_states = hidden_states + self.mlp(self.layer_norm2(hidden_states))
        return hidden_states


class Videollama3VisionTransformerEncoder(nn.Module):

    def __init__(self, config: Videollama3VisionEncoderConfig):
        super().__init__()
        self.config = config
        head_dim = config.hidden_size // config.num_attention_heads
        self.rotary_pos_emb = VisionRotaryEmbedding(head_dim // 2)
        self.layers = nn.ModuleList([Videollama3VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

    def rot_pos_emb(self, grid_sizes, merge_sizes):
        pos_ids = []
        for (t, h, w), merge_size in zip(grid_sizes, merge_sizes):
            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
            hpos_ids = hpos_ids.reshape(
                h // merge_size,
                merge_size,
                w // merge_size,
                merge_size,
            )
            hpos_ids = hpos_ids.permute(0, 2, 1, 3)
            hpos_ids = hpos_ids.flatten()

            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
            wpos_ids = wpos_ids.reshape(
                h // merge_size,
                merge_size,
                w // merge_size,
                merge_size,
            )
            wpos_ids = wpos_ids.permute(0, 2, 1, 3)
            wpos_ids = wpos_ids.flatten()
            pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))

        pos_ids = torch.cat(pos_ids, dim=0)
        max_grid_size = grid_sizes[:, 1:].max()
        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)

        return rotary_pos_emb

    def forward(self, hidden_states, grid_sizes, merge_sizes) -> torch.Tensor:
        rotary_pos_emb = self.rot_pos_emb(grid_sizes, merge_sizes)

        cu_seqlens = torch.repeat_interleave(grid_sizes[:, 1] * grid_sizes[:, 2], grid_sizes[:, 0]).cumsum(dim=0, dtype=torch.int32)
        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)

        for blk in self.layers:
            if self.gradient_checkpointing and self.training:
                hidden_states = self._gradient_checkpointing_func(
                    blk.__call__,
                    hidden_states,
                    cu_seqlens,
                    rotary_pos_emb
                )
            else:
                hidden_states = blk(hidden_states, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb)

        return hidden_states


class Videollama3VisionEncoderModel(PreTrainedModel):

    config_class = Videollama3VisionEncoderConfig
    base_model_prefix = "videollama3"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True
    _no_split_modules = [
        "Videollama3VisionEncoderLayer",
        "Videollama3VisionEmbeddings",
    ]
    _supports_flash_attn_2 = True
    _supports_sdpa = True

    def __init__(self, config: Videollama3VisionEncoderConfig):
        super().__init__(config=config)
        embed_dim = config.hidden_size

        self.embeddings = Videollama3VisionEmbeddings(config)
        self.encoder = Videollama3VisionTransformerEncoder(config)
        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)

        self.post_init()

    def forward(self, pixel_values, grid_sizes, merge_sizes=None) -> torch.Tensor:
        hidden_states = self.embeddings(pixel_values)
        hidden_states = self.encoder(hidden_states, grid_sizes, merge_sizes)
        hidden_states = self.post_layernorm(hidden_states)

        hidden_states_chunks = hidden_states.split(grid_sizes.prod(dim=1).tolist(), dim=0)
        outputs = []

        for hidden_states, grid_size, merge_size in zip(hidden_states_chunks, grid_sizes, merge_sizes):
            # NOTE: previous implementation, which supports downsampling with any factor
            c = hidden_states.shape[-1]
            hidden_states = hidden_states.view(
                grid_size[0], grid_size[1] // merge_size, grid_size[2] // merge_size, merge_size, merge_size,  c
            ).permute(0, 1, 3, 2, 4, 5)
            hidden_states = hidden_states.reshape(
                grid_size[0], grid_size[1], grid_size[2], c
            ).permute(0, 3, 1, 2)
            hidden_states = torch.nn.functional.interpolate(
                hidden_states,
                size=(grid_size[1] // merge_size, grid_size[2] // merge_size),
                mode='bilinear'
            )
            hidden_states = hidden_states.permute(0, 2, 3, 1).view(-1, c)

            # NOTE: simplified implementation, which only supports downsampling with integer factor
            # NOTE: this implementation is mathematically equivalent to the previous one when merge_size is 1 or 2 but may cause slightly different results
            # hidden_states = hidden_states.view(-1, merge_size * merge_size, hidden_states.size(-1))
            # hidden_states = hidden_states.mean(dim=1)

            outputs.append(hidden_states)

        return torch.cat(outputs, dim=0)

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, nn.Embedding):
            default_flax_embed_init(module.weight)
        elif isinstance(module, VisionAttention):
            nn.init.xavier_uniform_(module.q_proj.weight)
            nn.init.xavier_uniform_(module.k_proj.weight)
            nn.init.xavier_uniform_(module.v_proj.weight)
            nn.init.xavier_uniform_(module.out_proj.weight)
            nn.init.zeros_(module.q_proj.bias)
            nn.init.zeros_(module.k_proj.bias)
            nn.init.zeros_(module.v_proj.bias)
            nn.init.zeros_(module.out_proj.bias)
        elif isinstance(module, Videollama3VisionMLP):
            nn.init.xavier_uniform_(module.fc1.weight)
            nn.init.xavier_uniform_(module.fc2.weight)
            nn.init.normal_(module.fc1.bias, std=1e-6)
            nn.init.normal_(module.fc2.bias, std=1e-6)
        elif isinstance(module, (nn.Linear, nn.Conv2d)):
            lecun_normal_(module.weight)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)