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pix2struct-docmatix / pix2struct /modeling.py
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98159fd about 2 years ago
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import dataclasses
import json
import math
import os
from typing import Optional, Literal, Union

import safetensors.torch
import torch
from torch import nn

from flash_attn import flash_attn_varlen_func, flash_attn_with_kvcache

try:
    from liger_kernel.transformers.rms_norm import LigerRMSNorm as RMSNorm
    from liger_kernel.ops.geglu import LigerGELUMulFunction as GELUMulFunction
except ModuleNotFoundError:
    from torch.nn import RMSNorm

    class GELUMulFunction:
        @staticmethod
        def apply(hidden_gelu, hidden_linear):
            return torch.nn.functional.gelu(hidden_gelu, approximate='tanh') * hidden_linear


@dataclasses.dataclass
class Pix2StructConfig:
    vision_patch_size: int = 768
    vision_hidden_size: int = 768
    vision_mlp_ff_size: int = 2048
    vision_layers: int = 12
    vision_attention_kv_size: int = 64
    vision_attention_heads: int = 12
    vision_max_rows: int = 4096
    vision_max_columns: int = 4096
    vision_page_mode: Literal['concat', 'index'] = 'index'
    vision_max_pages: int = 4096

    text_vocab_size: int = 50265
    text_layers: int = 12
    text_hidden_size: int = 768
    text_dense_act_ff_size: int = 2048
    text_attention_kv_size: int = 64
    text_attention_heads: int = 12

    rms_norm_eps: float = 1e-6
    dropout_rate: float = 0.1

    @staticmethod
    def from_transformers(config):
        return Pix2StructConfig(
            vision_patch_size=config.vision_config.patch_embed_hidden_size,
            vision_hidden_size=config.vision_config.hidden_size,
            vision_mlp_ff_size=config.vision_config.d_ff,
            vision_layers=config.vision_config.num_hidden_layers,
            vision_attention_kv_size=config.vision_config.d_kv,
            vision_attention_heads=config.vision_config.num_attention_heads,
            vision_max_rows=config.vision_config.seq_len,
            vision_max_columns=config.vision_config.seq_len,
            vision_page_mode='index',
            vision_max_pages=4096,

            text_vocab_size=config.text_config.vocab_size,
            text_layers=config.text_config.num_layers,
            text_hidden_size=config.text_config.hidden_size,
            text_dense_act_ff_size=config.text_config.d_ff,
            text_attention_kv_size=config.text_config.d_kv,
            text_attention_heads=config.text_config.num_heads,

            rms_norm_eps=config.vision_config.layer_norm_eps,
            dropout_rate=config.vision_config.dropout_rate,
        )


class Pix2StructModel(nn.Module):
    def __init__(self, config: Pix2StructConfig):
        super().__init__()
        self.config = config
        self.encoder = Pix2StructVisionEncoder(config)
        self.decoder = Pix2StructTextDecoder(config)

    def forward(
            self,
            flattened_patches: torch.Tensor,
            flattened_patches_cu_seq_lens: torch.LongTensor,
            flattened_patches_max_seq_len: int,
            decoder_input_ids: Optional[torch.LongTensor] = None,
            labels: Optional[torch.LongTensor] = None,
            decoder_cu_seq_lens: Optional[torch.LongTensor] = None,
            decoder_max_seq_len: Optional[int] = None,
            decoder_cross_cu_seq_lens: Optional[torch.LongTensor] = None,
            decoder_cross_max_seq_len: Optional[int] = None,
    ):
        encoder_hidden_states = self.encoder(
            flattened_patches=flattened_patches,
            cu_seq_lens=flattened_patches_cu_seq_lens,
            max_seq_len=flattened_patches_max_seq_len,
        )

        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            labels=labels,
            cu_seq_lens=decoder_cu_seq_lens,
            max_seq_len=decoder_max_seq_len,

            encoder_hidden_states=encoder_hidden_states,
            encoder_cu_seq_lens=flattened_patches_cu_seq_lens,
            encoder_max_seq_len=flattened_patches_max_seq_len,

            cross_cu_seq_lens=decoder_cross_cu_seq_lens,
            cross_max_seq_len=decoder_cross_max_seq_len,
        )

        return decoder_outputs

    def get_encoder_kv_cache(
            self,
            flattened_patches: torch.Tensor,
            flattened_patches_cu_seq_lens: torch.LongTensor,
            flattened_patches_max_seq_len: int,
    ):
        encoder_hidden_states = self.encoder(
            flattened_patches=flattened_patches,
            cu_seq_lens=flattened_patches_cu_seq_lens,
            max_seq_len=flattened_patches_max_seq_len,
        )
        encoder_hidden_states_batch = []
        # print('flattened_patches', flattened_patches.shape)
        # print('flattened_patches_cu_seq_lens', flattened_patches_cu_seq_lens)
        for i in range(1, flattened_patches_cu_seq_lens.shape[0]):
            sub_seq_len = flattened_patches_cu_seq_lens[i] - flattened_patches_cu_seq_lens[i - 1]
            encoder_hidden_states_batch.append(
                torch.nn.functional.pad(
                    encoder_hidden_states[flattened_patches_cu_seq_lens[i - 1]:flattened_patches_cu_seq_lens[i]],
                    (0, 0, 0, flattened_patches_max_seq_len - sub_seq_len),
                )
            )
            # print('encoder_hidden_states_batch', i, encoder_hidden_states_batch[-1].shape, sub_seq_len, flattened_patches_max_seq_len)
        encoder_hidden_states_batch = torch.stack(encoder_hidden_states_batch)
        encoder_k_cache, encoder_v_cache = self.decoder.get_encoder_kv_cache(encoder_hidden_states_batch)
        return {
            'encoder_k_cache': encoder_k_cache,
            'encoder_v_cache': encoder_v_cache,
            'encoder_cache_seqlens': flattened_patches_cu_seq_lens[1:] - flattened_patches_cu_seq_lens[:-1],
        }

    def save(self, model_folder):
        os.makedirs(model_folder, exist_ok=True)
        safetensors.torch.save_file(self.state_dict(), model_folder + '/model.safetensors')
        with open(model_folder + '/config.json', 'w') as f:
            json.dump(dataclasses.asdict(self.config), f)

    @staticmethod
    def load(model_folder):
        if model_folder.startswith('transformers/'):
            return Pix2StructModel.from_transformers(model_folder.replace('transformers/', ''))
        with open(model_folder + '/config.json', 'r') as f:
            config = Pix2StructConfig(**json.load(f))
        model = Pix2StructModel(config)
        model.load_state_dict(safetensors.torch.load_file(model_folder + '/model.safetensors'))
        return model

    @staticmethod
    def from_transformers(model_id):
        import transformers
        donor_config = transformers.Pix2StructConfig.from_pretrained(model_id)
        donor_model = transformers.Pix2StructForConditionalGeneration.from_pretrained(model_id)

        config = Pix2StructConfig.from_transformers(donor_config)
        model = Pix2StructModel(config)
        weights = donor_model.state_dict()

        mapping = {
            'encoder.embeddings.patch_projection.weight': 'encoder.embeddings.patch_projection.weight',
            'encoder.embeddings.patch_projection.bias': 'encoder.embeddings.patch_projection.bias',
            'encoder.embeddings.row_embedder.weight': 'encoder.embeddings.row_embedder.weight',
            'encoder.embeddings.column_embedder.weight': 'encoder.embeddings.column_embedder.weight',
            'encoder.layer_norm.weight': 'encoder.layernorm.weight',
            'decoder.embed_tokens.weight': 'decoder.embed_tokens.weight',
            'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
            'decoder.lm_head.weight': 'decoder.lm_head.weight'
        }

        for vision_layer_idx in range(config.vision_layers):
            prefix = f'encoder.layers.{vision_layer_idx}'
            s_prefix = f'encoder.encoder.layer.{vision_layer_idx}'
            mapping[f'{prefix}.attention.pre_layer_norm.weight'] = f'{s_prefix}.pre_attention_layer_norm.weight'
            mapping[f'{prefix}.attention.query.weight'] = f'{s_prefix}.attention.query.weight'
            mapping[f'{prefix}.attention.key.weight'] = f'{s_prefix}.attention.key.weight'
            mapping[f'{prefix}.attention.value.weight'] = f'{s_prefix}.attention.value.weight'
            mapping[f'{prefix}.attention.output.weight'] = f'{s_prefix}.attention.output.weight'
            mapping[f'{prefix}.mlp.pre_layer_norm.weight'] = f'{s_prefix}.pre_mlp_layer_norm.weight'
            mapping[f'{prefix}.mlp.wi_0.weight'] = f'{s_prefix}.mlp.wi_0.weight'
            mapping[f'{prefix}.mlp.wi_1.weight'] = f'{s_prefix}.mlp.wi_1.weight'
            mapping[f'{prefix}.mlp.wo.weight'] = f'{s_prefix}.mlp.wo.weight'
        for vision_layer_idx in range(config.text_layers):
            prefix = f'decoder.layers.{vision_layer_idx}'
            s_prefix = f'decoder.layer.{vision_layer_idx}'
            mapping[f'{prefix}.encoder_decoder_attention.pre_layer_norm.weight'] = f'{s_prefix}.encoder_decoder_attention.layer_norm.weight'
            mapping[f'{prefix}.encoder_decoder_attention.query.weight'] = f'{s_prefix}.encoder_decoder_attention.attention.query.weight'
            mapping[f'{prefix}.encoder_decoder_attention.key.weight'] = f'{s_prefix}.encoder_decoder_attention.attention.key.weight'
            mapping[f'{prefix}.encoder_decoder_attention.value.weight'] = f'{s_prefix}.encoder_decoder_attention.attention.value.weight'
            mapping[f'{prefix}.encoder_decoder_attention.output.weight'] = f'{s_prefix}.encoder_decoder_attention.attention.output.weight'

            mapping[f'{prefix}.self_attention.pre_layer_norm.weight'] = f'{s_prefix}.self_attention.layer_norm.weight'
            mapping[f'{prefix}.self_attention.query.weight'] = f'{s_prefix}.self_attention.attention.query.weight'
            mapping[f'{prefix}.self_attention.key.weight'] = f'{s_prefix}.self_attention.attention.key.weight'
            mapping[f'{prefix}.self_attention.value.weight'] = f'{s_prefix}.self_attention.attention.value.weight'
            mapping[f'{prefix}.self_attention.output.weight'] = f'{s_prefix}.self_attention.attention.output.weight'

            mapping[f'{prefix}.mlp.pre_layer_norm.weight'] = f'{s_prefix}.mlp.layer_norm.weight'
            mapping[f'{prefix}.mlp.wi_0.weight'] = f'{s_prefix}.mlp.DenseReluDense.wi_0.weight'
            mapping[f'{prefix}.mlp.wi_1.weight'] = f'{s_prefix}.mlp.DenseReluDense.wi_1.weight'
            mapping[f'{prefix}.mlp.wo.weight'] = f'{s_prefix}.mlp.DenseReluDense.wo.weight'
        state_dict = {}
        for target_key, source_key in mapping.items():
            state_dict[target_key] = weights[source_key]
            del weights[source_key]

        load_info = model.load_state_dict(state_dict, strict=False)
        if len(load_info.missing_keys) > 0:
            print('The following keys are missing', load_info.missing_keys)
            if 'encoder.embeddings.page_embedder.weight' in load_info.missing_keys:
                model.encoder.embeddings.page_embedder.weight.data.normal_(mean=0.0, std=0.02)

        return model


# VISION


class Pix2StructVisionEncoder(nn.Module):
    def __init__(self, config: Pix2StructConfig):
        super().__init__()
        self.config = config
        self.embeddings = Pix2StructVisionEmbeddings(config)
        self.layers = nn.ModuleList([Pix2StructVisionLayer(config) for _ in range(config.vision_layers)])
        self.layer_norm = RMSNorm(config.vision_hidden_size, eps=config.rms_norm_eps)

    def forward(
            self,
            flattened_patches: torch.Tensor,  # (sum_seq_lens, vision_patch_size)
            cu_seq_lens: torch.Tensor,  # (seq_count)
            max_seq_len: int,
    ) -> torch.Tensor:
        hidden_states = self.embeddings(flattened_patches)
        for i, layer_module in enumerate(self.layers):
            hidden_states = layer_module(hidden_states, cu_seq_lens, max_seq_len)
        hidden_states = self.layer_norm(hidden_states)
        return hidden_states


class Pix2StructVisionLayer(nn.Module):
    def __init__(self, config: Pix2StructConfig) -> None:
        super().__init__()
        self.attention = Pix2StructVisionAttention(config)
        self.mlp = Pix2StructVisionMLP(config)

    def forward(
            self,
            hidden_states: torch.Tensor,  # (sum_seq_lens, vision_hidden_size)
            cu_seq_lens: torch.Tensor,  # (seq_count)
            max_seq_len: int,
    ) -> torch.Tensor:  # (sum_seq_lens, vision_hidden_size)
        hidden_states = hidden_states + self.attention(
            hidden_states=hidden_states,
            cu_seq_lens=cu_seq_lens,
            max_seq_len=max_seq_len,
        )
        hidden_states = hidden_states + self.mlp(hidden_states)
        return hidden_states


class Pix2StructVisionAttention(nn.Module):
    def __init__(self, config: Pix2StructConfig):
        super().__init__()
        self.config = config
        self.inner_dim = config.vision_attention_kv_size * config.vision_attention_heads
        self.pre_layer_norm = RMSNorm(config.vision_hidden_size, eps=config.rms_norm_eps)
        self.query = nn.Linear(config.vision_hidden_size, self.inner_dim, bias=False)
        self.key = nn.Linear(config.vision_hidden_size, self.inner_dim, bias=False)
        self.value = nn.Linear(config.vision_hidden_size, self.inner_dim, bias=False)
        self.output = nn.Linear(self.inner_dim, config.vision_hidden_size, bias=False)

    def forward(self, hidden_states, cu_seq_lens, max_seq_len):
        sum_seq_lens = hidden_states.size(0)

        def to_projection_shape(states: torch.Tensor) -> torch.Tensor:  # (sum_seq_lens, n_heads, dim_per_head)
            return states.contiguous().view(
                sum_seq_lens,
                self.config.vision_attention_heads,
                self.config.vision_attention_kv_size
            )

        hidden_states = self.pre_layer_norm(hidden_states)
        query_states = to_projection_shape(self.query(hidden_states))
        key_states = to_projection_shape(self.key(hidden_states))
        value_states = to_projection_shape(self.value(hidden_states))

        attn_output = flash_attn_varlen_func(
            q=query_states,
            k=key_states,
            v=value_states,
            cu_seqlens_q=cu_seq_lens,
            cu_seqlens_k=cu_seq_lens,
            max_seqlen_q=max_seq_len,
            max_seqlen_k=max_seq_len,
            dropout_p=self.config.dropout_rate if self.training else 0.0,
            causal=False,
        )

        attn_output = attn_output.contiguous().view(sum_seq_lens, self.inner_dim)
        attn_output = self.output(attn_output)
        return attn_output


class Pix2StructVisionMLP(nn.Module):
    def __init__(self, config: Pix2StructConfig):
        super().__init__()
        self.pre_layer_norm = RMSNorm(config.vision_hidden_size, eps=config.rms_norm_eps)
        self.wi_0 = nn.Linear(config.vision_hidden_size, config.vision_mlp_ff_size, bias=False)
        self.wi_1 = nn.Linear(config.vision_hidden_size, config.vision_mlp_ff_size, bias=False)
        self.wo = nn.Linear(config.vision_mlp_ff_size, config.vision_hidden_size, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.act = nn.GELU(approximate='tanh')

    def forward(self, hidden_states):
        hidden_states = self.pre_layer_norm(hidden_states)

        hidden_gelu = self.wi_0(hidden_states)
        hidden_linear = self.wi_1(hidden_states)
        hidden_states = GELUMulFunction.apply(hidden_gelu, hidden_linear)
        # hidden_gelu = self.act(self.wi_0(hidden_states))
        # hidden_linear = self.wi_1(hidden_states)
        # hidden_states = hidden_gelu * hidden_linear

        if self.training:
            hidden_states = self.dropout(hidden_states)
        hidden_states = self.wo(hidden_states)
        return hidden_states


class Pix2StructVisionEmbeddings(nn.Module):
    def __init__(self, config: Pix2StructConfig) -> None:
        super().__init__()
        self.config = config
        self.patch_projection = nn.Linear(config.vision_patch_size, config.vision_hidden_size)
        if config.vision_page_mode == 'index':
            self.page_embedder = nn.Embedding(config.vision_max_pages, config.vision_hidden_size)
        else:
            self.page_embedder = None
        self.row_embedder = nn.Embedding(config.vision_max_rows, config.vision_hidden_size)
        self.column_embedder = nn.Embedding(config.vision_max_columns, config.vision_hidden_size)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
            self,
            flattened_patches: torch.Tensor,  # (sum_seq_lens, vision_patch_size)
    ) -> torch.Tensor:  # (sum_seq_lens, vision_hidden_size)
        if self.config.vision_page_mode == 'index':
            page_indices = flattened_patches[:, 0].long()
            row_indices = flattened_patches[:, 1].long()
            col_indices = flattened_patches[:, 2].long()
            flattened_patches = flattened_patches[:, 3:]
        else:
            page_indices = None
            row_indices = flattened_patches[:, 0].long()
            col_indices = flattened_patches[:, 1].long()
            flattened_patches = flattened_patches[:, 2:]

        embeddings = self.patch_projection(flattened_patches)
        row_embeddings = self.row_embedder(row_indices)
        col_embeddings = self.column_embedder(col_indices)

        if self.config.vision_page_mode == 'index':
            page_embeddings = self.page_embedder(page_indices)
            embeddings = embeddings + page_embeddings + row_embeddings + col_embeddings
        else:
            embeddings = embeddings + row_embeddings + col_embeddings
        if self.training:
            embeddings = self.dropout(embeddings)
        return embeddings


# TEXT


class Pix2StructTextDecoder(nn.Module):
    def __init__(self, config: Pix2StructConfig):
        super().__init__()
        self.config = config

        self.embed_tokens = nn.Embedding(config.text_vocab_size, config.text_hidden_size)

        self.layers = nn.ModuleList(
            [Pix2StructTextLayer(config, alibi=bool(i == 0)) for i in range(config.text_layers)]
        )
        self.layer_norm = RMSNorm(config.text_hidden_size, eps=config.rms_norm_eps)
        self.lm_head = nn.Linear(config.text_hidden_size, config.text_vocab_size, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
            self,
            input_ids: torch.LongTensor,
            cu_seq_lens: torch.LongTensor,
            max_seq_len: int,
            encoder_hidden_states: torch.Tensor,
            encoder_cu_seq_lens: torch.LongTensor,
            encoder_max_seq_len: int,
            cross_cu_seq_lens: torch.LongTensor,
            cross_max_seq_len: int,
            labels: Optional[torch.LongTensor] = None,
    ):
        hidden_states = self.embed_tokens(input_ids)
        hidden_states = self.dropout(hidden_states) if self.training else hidden_states
        for i, layer_module in enumerate(self.layers):
            hidden_states = layer_module(
                hidden_states,
                cu_seq_lens,
                max_seq_len,
                encoder_hidden_states,
                encoder_cu_seq_lens,
                encoder_max_seq_len,
                cross_cu_seq_lens,
                cross_max_seq_len,
            )

        hidden_states = self.layer_norm(hidden_states)
        if self.training:
            hidden_states = self.dropout(hidden_states)

        if labels is not None:
            logits = self.lm_head(hidden_states)
            loss_fct = nn.CrossEntropyLoss(ignore_index=-100, reduction="mean")
            loss = loss_fct(logits, labels)
            return loss
        else:
            logits = self.lm_head(hidden_states)
            return logits

    def predict(
            self,
            input_ids: torch.LongTensor,  # (batch_size, max_seq_len),
            decoder_k_cache: torch.Tensor,  # [(batch_size_cache, seqlen_cache, nheads_k, headdim)]
            decoder_v_cache: torch.Tensor,  # [(batch_size_cache, seqlen_cache, nheads_k, headdim)]
            decoder_cache_seqlens: torch.IntTensor,  # (batch_size_cache)
            encoder_k_cache: torch.Tensor,  # [(batch_size_cache, seqlen_cache, nheads_k, headdim)]
            encoder_v_cache: torch.Tensor,  # [(batch_size_cache, seqlen_cache, nheads_k, headdim)]
            encoder_cache_seqlens: torch.IntTensor,  # (batch_size_cache)
            encoder_cache_batch_idx: torch.IntTensor,  # (batch_size_cache)
    ):
        hidden_states = self.embed_tokens(input_ids)
        for i, layer_module in enumerate(self.layers):
            hidden_states = layer_module.predict(
                hidden_states,
                decoder_k_cache[i],
                decoder_v_cache[i],
                decoder_cache_seqlens,
                encoder_k_cache[i],
                encoder_v_cache[i],
                encoder_cache_seqlens,
                encoder_cache_batch_idx,
            )
        hidden_states = self.layer_norm(hidden_states)
        logits = self.lm_head(hidden_states)
        return logits

    def get_decoder_kv_cache(self, device, batch_size, seq_len, dtype=torch.float32):
        k_cache_layers = []
        v_cache_layers = []
        for _ in self.layers:
            k_cache_layers.append(
                torch.empty(
                    (batch_size, seq_len, self.config.text_attention_heads, self.config.text_attention_kv_size),
                    device=device, dtype=dtype
                )
            )
            v_cache_layers.append(
                torch.empty(
                    (batch_size, seq_len, self.config.text_attention_heads, self.config.text_attention_kv_size),
                    device=device, dtype=dtype
                )
            )
        return k_cache_layers, v_cache_layers

    def get_encoder_kv_cache(self, encoder_hidden_states: torch.Tensor):
        k_cache_layers = []
        v_cache_layers = []
        for layer in self.layers:
            k, v = layer.get_encoder_kv_cache(encoder_hidden_states)
            k_cache_layers.append(k)
            v_cache_layers.append(v)
        return k_cache_layers, v_cache_layers


class Pix2StructTextLayer(nn.Module):
    def __init__(self, config, alibi=False):
        super().__init__()
        self.self_attention = Pix2StructTextSelfAttention(config, alibi=alibi)
        self.encoder_decoder_attention = Pix2StructTextLayerCrossAttention(config)
        self.mlp = Pix2StructTextMLP(config)

    def forward(
            self,
            hidden_states,
            cu_seq_lens,
            max_seq_len,
            encoder_hidden_states,
            encoder_cu_seq_lens,
            encoder_max_seq_len,
            cross_cu_seq_lens,
            cross_max_seq_len,
    ):
        hidden_states = self.self_attention(
            hidden_states=hidden_states,
            cu_seq_lens=cu_seq_lens,
            max_seq_len=max_seq_len,
        )

        do_cross_attention = encoder_hidden_states is not None
        if do_cross_attention:
            hidden_states = self.encoder_decoder_attention(
                hidden_states=hidden_states,
                cu_seq_lens=cross_cu_seq_lens,
                max_seq_len=cross_max_seq_len,
                kv_hidden_states=encoder_hidden_states,
                kv_cu_seq_lens=encoder_cu_seq_lens,
                kv_max_seq_len=encoder_max_seq_len,
            )

        hidden_states = self.mlp(hidden_states)
        return hidden_states

    def predict(
        self,
        hidden_states,
        decoder_k_cache,
        decoder_v_cache,
        decoder_cache_seqlens,
        encoder_k_cache,
        encoder_v_cache,
        encoder_cache_seqlens,
        encoder_cache_batch_idx,
    ):
        hidden_states = self.self_attention.predict(
            hidden_states,
            decoder_k_cache,
            decoder_v_cache,
            decoder_cache_seqlens,
        )
        do_cross_attention = encoder_k_cache is not None
        if do_cross_attention:
            hidden_states = self.encoder_decoder_attention.predict(
                hidden_states,
                encoder_k_cache,
                encoder_v_cache,
                encoder_cache_seqlens,
                encoder_cache_batch_idx,
            )
        hidden_states = self.mlp(hidden_states)
        return hidden_states

    def get_encoder_kv_cache(self, encoder_hidden_states):
        return self.encoder_decoder_attention.get_kv_cache(encoder_hidden_states)


class Pix2StructTextSelfAttention(nn.Module):
    def __init__(self, config: Pix2StructConfig, alibi=False):
        super().__init__()
        self.config = config
        self.alibi = alibi
        if alibi:
            self.alibi_slopes = generate_alibi_slopes(config.text_attention_heads)
        self.inner_dim = config.text_attention_kv_size * config.text_attention_heads
        self.dropout = config.dropout_rate

        self.pre_layer_norm = RMSNorm(config.text_hidden_size, eps=config.rms_norm_eps)
        self.query = nn.Linear(config.text_hidden_size, self.inner_dim, bias=False)
        self.key = nn.Linear(config.text_hidden_size, self.inner_dim, bias=False)
        self.value = nn.Linear(config.text_hidden_size, self.inner_dim, bias=False)
        self.output = nn.Linear(self.inner_dim, config.text_hidden_size, bias=False)

        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
            self,
            hidden_states,
            cu_seq_lens,
            max_seq_len,
    ):
        normed_hidden_states = self.pre_layer_norm(hidden_states)
        query_states = self.to_projection_shape(self.query(normed_hidden_states))
        key_states = self.to_projection_shape(self.key(normed_hidden_states))
        value_states = self.to_projection_shape(self.value(normed_hidden_states))

        attention_output = flash_attn_varlen_func(
            q=query_states,
            k=key_states,
            v=value_states,
            cu_seqlens_q=cu_seq_lens,
            cu_seqlens_k=cu_seq_lens,
            max_seqlen_q=max_seq_len,
            max_seqlen_k=max_seq_len,
            dropout_p=self.config.dropout_rate if self.training else 0.0,
            causal=True,
            alibi_slopes=self.alibi_slopes.to(query_states.device) if self.alibi else None,
        )
        attention_output = attention_output.contiguous().view(-1, self.inner_dim)
        attention_output = self.output(attention_output)

        return hidden_states + (self.dropout(attention_output) if self.training else attention_output)

    def predict(
        self,
        hidden_states,
        decoder_k_cache,
        decoder_v_cache,
        decoder_cache_seqlens,
    ):
        normed_hidden_states = self.pre_layer_norm(hidden_states)
        query_states = self.to_projection_shape(self.query(normed_hidden_states))
        key_states = self.to_projection_shape(self.key(normed_hidden_states))
        value_states = self.to_projection_shape(self.value(normed_hidden_states))

        attention_output = flash_attn_with_kvcache(
            q=query_states,
            k_cache=decoder_k_cache,
            v_cache=decoder_v_cache,
            k=key_states,
            v=value_states,
            cache_seqlens=decoder_cache_seqlens,
            causal=True,
            alibi_slopes=self.alibi_slopes.to(query_states.device) if self.alibi else None,
        )
        attention_output = attention_output.contiguous().view(*hidden_states.shape)
        attention_output = self.output(attention_output)

        return hidden_states + attention_output


    def to_projection_shape(self, states):
        return states.contiguous().view(
            *states.shape[:-1], self.config.text_attention_heads, self.config.text_attention_kv_size
        )


class Pix2StructTextLayerCrossAttention(nn.Module):
    def __init__(self, config: Pix2StructConfig):
        super().__init__()
        self.config = config
        self.inner_dim = config.text_attention_kv_size * config.text_attention_heads
        self.dropout = config.dropout_rate

        self.pre_layer_norm = RMSNorm(config.text_hidden_size, eps=config.rms_norm_eps)
        self.query = nn.Linear(config.text_hidden_size, self.inner_dim, bias=False)
        self.key = nn.Linear(config.vision_hidden_size, self.inner_dim, bias=False)
        self.value = nn.Linear(config.vision_hidden_size, self.inner_dim, bias=False)
        self.output = nn.Linear(self.inner_dim, config.text_hidden_size, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
            self,
            hidden_states,
            cu_seq_lens,
            max_seq_len,
            kv_hidden_states,
            kv_cu_seq_lens,
            kv_max_seq_len,
    ):
        normed_hidden_states = self.pre_layer_norm(hidden_states)
        query_states = self.to_projection_shape(self.query(normed_hidden_states))
        key_states = self.to_projection_shape(self.key(kv_hidden_states))
        value_states = self.to_projection_shape(self.value(kv_hidden_states))

        attention_output = flash_attn_varlen_func(
            q=query_states,
            k=key_states,
            v=value_states,
            cu_seqlens_q=cu_seq_lens,
            cu_seqlens_k=kv_cu_seq_lens,
            max_seqlen_q=max_seq_len,
            max_seqlen_k=kv_max_seq_len,
            dropout_p=self.config.dropout_rate if self.training else 0.0,
            causal=False,
        )

        attention_output = attention_output.contiguous().view(hidden_states.shape[0], self.inner_dim)
        attention_output = self.output(attention_output)
        hidden_states = hidden_states + (self.dropout(attention_output) if self.training else attention_output)
        return hidden_states

    def predict(
        self,
        hidden_states,
        encoder_k_cache,
        encoder_v_cache,
        encoder_cache_seqlens,
        encoder_cache_batch_idx,
    ):
        normed_hidden_states = self.pre_layer_norm(hidden_states)
        query_states = self.to_projection_shape(self.query(normed_hidden_states))

        attention_output = flash_attn_with_kvcache(
            q=query_states,
            k_cache=encoder_k_cache,
            v_cache=encoder_v_cache,
            cache_seqlens=encoder_cache_seqlens,
            cache_batch_idx=encoder_cache_batch_idx,
            causal=False,
        )
        attention_output = attention_output.contiguous().view(*hidden_states.shape)
        attention_output = self.output(attention_output)
        return hidden_states + attention_output

    def get_kv_cache(self, states):
        return self.to_projection_shape(self.key(states)), self.to_projection_shape(self.value(states))

    def to_projection_shape(self, states: torch.Tensor) -> torch.Tensor:
        return states.contiguous().view(
            *states.shape[:-1], self.config.text_attention_heads, self.config.text_attention_kv_size
        )


class Pix2StructTextMLP(nn.Module):
    def __init__(self, config: Pix2StructConfig):
        super().__init__()
        self.pre_layer_norm = RMSNorm(config.text_hidden_size, eps=config.rms_norm_eps)
        self.wi_0 = nn.Linear(config.text_hidden_size, config.text_dense_act_ff_size, bias=False)
        self.wi_1 = nn.Linear(config.text_hidden_size, config.text_dense_act_ff_size, bias=False)
        self.wo = nn.Linear(config.text_dense_act_ff_size, config.text_hidden_size, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.act = nn.GELU(approximate='tanh')

    def forward(self, hidden_states):
        residual = hidden_states
        hidden_states = self.pre_layer_norm(hidden_states)
        hidden_gelu = self.wi_0(hidden_states)
        hidden_linear = self.wi_1(hidden_states)
        hidden_states = GELUMulFunction.apply(hidden_gelu, hidden_linear)
        # hidden_gelu = self.act(self.wi_0(hidden_states))
        # hidden_linear = self.wi_1(hidden_states)
        # hidden_states = hidden_gelu * hidden_linear
        if self.training:
            hidden_states = self.dropout(hidden_states)
        hidden_states = self.wo(hidden_states)
        hidden_states = residual + (self.dropout(hidden_states) if self.training else hidden_states)
        return hidden_states


def generate_alibi_slopes(num_heads):
    def get_slopes_power_of_two(n):
        start = (2 ** (-2 ** -(math.log2(n) - 3)))
        ratio = start
        return [start * ratio ** i for i in range(n)]

    if num_heads <= 8:
        slopes = get_slopes_power_of_two(num_heads)
    else:
        slopes = get_slopes_power_of_two(8)
        for i in range(8, num_heads):
            slopes.append(slopes[-1] * slopes[0])

    return torch.tensor(slopes, dtype=torch.float32)