infinity/models/basic.py

"""
Definitions of blocks of VAR transformer model.
"""

import math
import os
from functools import partial
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.layers import DropPath, drop_path
from torch.utils.checkpoint import checkpoint

# Import flash_attn's attention
from flash_attn import flash_attn_func                  # q, k, or v: BLHc, ret: BLHc
from flash_attn import flash_attn_varlen_kvpacked_func  # qkv: N3Hc, ret: NHc

from torch.nn.functional import scaled_dot_product_attention as slow_attn    # q, k, v: BHLc

# Import flash_attn's fused ops
try:
    from flash_attn.ops.layer_norm import dropout_add_layer_norm
    from flash_attn.ops.rms_norm import dropout_add_rms_norm
    from flash_attn.ops.rms_norm import rms_norm as rms_norm_impl
    from flash_attn.ops.fused_dense import fused_mlp_func
    flash_fused_op_installed = True
except ImportError:
    dropout_add_layer_norm = dropout_add_rms_norm = fused_mlp_func = None
    flash_fused_op_installed = False
    
    def rms_norm_impl(x, weight, epsilon):
        return (x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True).add_(epsilon))) * weight


def precompute_rope2d_freqs_grid(dim, dynamic_resolution_h_w, rope2d_normalized_by_hw, pad_to_multiplier=1, max_height=2048 // 16, max_width=2048 // 16, base=10000.0, device=None, scaling_factor=1.0):
    # split the dimension into half, one for x and one for y
    half_dim = dim // 2
    inv_freq = 1.0 / (base ** (torch.arange(0, half_dim, 2, dtype=torch.int64).float().to(device) / half_dim)) # namely theta, 1 / (10000^(i/half_dim)), i=0,2,..., half_dim-2
    t_height = torch.arange(max_height, device=device, dtype=torch.int64).type_as(inv_freq)
    t_width = torch.arange(max_width, device=device, dtype=torch.int64).type_as(inv_freq)
    t_height = t_height / scaling_factor
    freqs_height = torch.outer(t_height, inv_freq)  # (max_height, dim / (1 for 1d, 2 for 2d, 3 for 3d) / 2), namely y*theta
    t_width = t_width / scaling_factor
    freqs_width = torch.outer(t_width, inv_freq)  # (max_width, dim / (1 for 1d, 2 for 2d, 3 for 3d) / 2), namely x*theta
    freqs_grid_map = torch.concat([
        freqs_height[:, None, :].expand(-1, max_width, -1), # (max_height, max_width, dim / (1 for 1d, 2 for 2d, 3 for 3d) / 2)
        freqs_width[None, :, :].expand(max_height, -1, -1), # (max_height, max_width, dim / (1 for 1d, 2 for 2d, 3 for 3d) / 2)
    ], dim=-1)  # (max_height, max_width, dim / (1 for 1d, 2 for 2d, 3 for 3d))
    freqs_grid_map = torch.stack([torch.cos(freqs_grid_map), torch.sin(freqs_grid_map)], dim=0)
    # (2, max_height, max_width, dim / (1 for 1d, 2 for 2d, 3 for 3d))

    rope2d_freqs_grid = {}
    for h_div_w in dynamic_resolution_h_w:
        scale_schedule = dynamic_resolution_h_w[h_div_w]['1M']['scales']
        _, ph, pw = scale_schedule[-1]
        max_edge_length = freqs_grid_map.shape[1]
        if ph >= pw:
            uph, upw = max_edge_length, int(max_edge_length / ph * pw)
        else:
            uph, upw = int(max_edge_length / pw * ph), max_edge_length
        rope_cache_list = []
        for (_, ph, pw) in scale_schedule:
            ph_mul_pw = ph * pw
            if rope2d_normalized_by_hw == 1: # downsample
                rope_cache = F.interpolate(freqs_grid_map[:, :uph, :upw, :].permute([0,3,1,2]), size=(ph, pw), mode='bilinear', align_corners=True)
                rope_cache = rope_cache.permute([0,2,3,1]) # (2, ph, pw, half_head_dim)
            elif rope2d_normalized_by_hw == 2: # star stylee
                _, uph, upw = scale_schedule[-1]
                indices = torch.stack([
                    (torch.arange(ph) * (uph / ph)).reshape(ph, 1).expand(ph, pw),
                    (torch.arange(pw) * (upw / pw)).reshape(1, pw).expand(ph, pw),
                ], dim=-1).round().int() # (ph, pw, 2)
                indices = indices.reshape(-1, 2) # (ph*pw, 2)
                rope_cache = freqs_grid_map[:, indices[:,0], indices[:,1], :] # (2, ph*pw, half_head_dim)
                rope_cache = rope_cache.reshape(2, ph, pw, -1)
            elif rope2d_normalized_by_hw == 0:
                rope_cache = freqs_grid_map[:, :ph, :pw, :] # (2, ph, pw, half_head_dim)
            else:
                raise ValueError(f'Unknown rope2d_normalized_by_hw: {rope2d_normalized_by_hw}')
            rope_cache_list.append(rope_cache.reshape(2, ph_mul_pw, -1))
        cat_rope_cache = torch.cat(rope_cache_list, 1) # (2, seq_len, half_head_dim)
        if cat_rope_cache.shape[1] % pad_to_multiplier:
            pad = torch.zeros(2, pad_to_multiplier - cat_rope_cache.shape[1] % pad_to_multiplier, half_dim)
            cat_rope_cache = torch.cat([cat_rope_cache, pad], dim=1)
        cat_rope_cache = cat_rope_cache[:,None,None,None] # (2, 1, 1, 1, seq_len, half_dim)
        for pn in dynamic_resolution_h_w[h_div_w]:
            scale_schedule = dynamic_resolution_h_w[h_div_w][pn]['scales']
            tmp_scale_schedule = [(1, h, w) for _, h, w in scale_schedule]
            rope2d_freqs_grid[str(tuple(tmp_scale_schedule))] = cat_rope_cache
    return rope2d_freqs_grid


def apply_rotary_emb(q, k, scale_schedule, rope2d_freqs_grid, pad_to_multiplier, rope2d_normalized_by_hw, scale_ind):
    qk = torch.stack((q, k), dim=0)  #(2, batch_size, heads, seq_len, head_dim)
    device_type = qk.device.type
    device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
    with torch.autocast(device_type=device_type, enabled=False):
        seq_len = qk.shape[3]
        start = 0
        if scale_ind >= 1:
            assert len(scale_schedule[0]) == 3
            start = np.sum([item[0] * item[1] * item[2] for item in scale_schedule[:scale_ind]])
        rope2d_freqs_grid[str(tuple(scale_schedule))] = rope2d_freqs_grid[str(tuple(scale_schedule))].to(qk.device)
        assert start+seq_len <= rope2d_freqs_grid[str(tuple(scale_schedule))].shape[4]
        rope_cache = rope2d_freqs_grid[str(tuple(scale_schedule))][:, :, :, :, start:start+seq_len] # rope_cache shape: [2, 1, 1, 1, seq_len, half_head_dim]
        qk = qk.reshape(*qk.shape[:-1], -1, 2) #(2, batch_size, heads, seq_len, half_head_dim, 2)
        qk = torch.stack([
            rope_cache[0] * qk[...,0] - rope_cache[1] * qk[...,1],
            rope_cache[1] * qk[...,0] + rope_cache[0] * qk[...,1],
        ], dim=-1) # (2, batch_size, heads, seq_len, half_head_dim, 2), here stack + reshape should not be concate
        qk = qk.reshape(*qk.shape[:-2], -1) #(2, batch_size, heads, seq_len, head_dim)
        q, k = qk.unbind(dim=0) # (batch_size, heads, seq_len, head_dim)
    return q, k


class FastRMSNorm(nn.Module):
    def __init__(self, C, eps=1e-6, elementwise_affine=True):
        super().__init__()
        self.C = C
        self.eps = eps
        self.elementwise_affine = elementwise_affine
        if self.elementwise_affine:
            self.weight = nn.Parameter(torch.ones(C))
        else:
            self.register_buffer('weight', torch.ones(C))
    
    def forward(self, x):
        src_type = x.dtype
        return rms_norm_impl(x.float(), self.weight, epsilon=self.eps).to(src_type)
    
    def extra_repr(self) -> str:
        return f'C={self.C}, eps={self.eps:g}, elementwise_affine={self.elementwise_affine}'


def get_dropout_layer(p):
    return nn.Dropout(p, inplace=True) if p > 0 else nn.Identity()


class FFN(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, drop=0., fused_mlp=False):
        super().__init__()
        self.fused_mlp_func = fused_mlp_func if fused_mlp else None
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = nn.GELU(approximate='tanh')
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = get_dropout_layer(drop)
        self.heuristic = -1
    
    def forward(self, x):
        if self.fused_mlp_func is not None:
            return self.drop(self.fused_mlp_func(
                x=x,
                weight1=self.fc1.weight,
                weight2=self.fc2.weight,
                bias1=self.fc1.bias,
                bias2=self.fc2.bias,
                activation='gelu_approx',
                save_pre_act=self.training,
                return_residual=False,
                checkpoint_lvl=0,
                heuristic=self.heuristic,
                process_group=None,
            ))
        else:
            return self.drop(self.fc2( self.act(self.fc1(x)) ))
    
    def extra_repr(self) -> str:
        return f'fused_mlp={self.fused_mlp_func is not None}'


class FFNSwiGLU(nn.Module):
    def __init__(self, in_features, hidden_features, out_features=None, drop=0., fused_mlp=False):
        super().__init__()
        self.fused_mlp_func = None
        hidden_features = round(2 * hidden_features / 3 / 256) * 256
        
        out_features = out_features or in_features
        self.fcg = nn.Linear(in_features, hidden_features, bias=False)
        self.fc1 = nn.Linear(in_features, hidden_features, bias=False)
        self.fc2 = nn.Linear(hidden_features, out_features, bias=False)
        self.drop = get_dropout_layer(drop)
    
    def forward(self, x):
        return self.drop(self.fc2( F.silu(self.fcg(x), inplace=True).mul_(self.fc1(x)) ))
    
    def extra_repr(self) -> str:
        return f'fused_mlp={self.fused_mlp_func is not None}'


class SelfAttention(nn.Module):
    def __init__(
        self, embed_dim=768, num_heads=12,
        proj_drop=0., tau=1, cos_attn=False, customized_flash_attn=True, use_flex_attn=False, 
        batch_size=2, pad_to_multiplier=1, rope2d_normalized_by_hw=0,
    ):
        """
        :param embed_dim: model's width
        :param num_heads: num heads of multi-head attention
        :param proj_drop: always 0 for testing
        :param tau: always 1
        :param cos_attn: always True: during attention, q and k will be L2-normalized and scaled by a head-wise learnable parameter self.scale_mul_1H11
        :param customized_flash_attn:
        """
        super().__init__()
        assert embed_dim % num_heads == 0
        self.using_flash = customized_flash_attn
        
        self.num_heads, self.head_dim = num_heads, embed_dim // num_heads
        self.tau, self.cos_attn = tau, cos_attn
        if self.cos_attn:
            self.scale = 1
            size = (1, 1, self.num_heads, 1) if self.using_flash else (1, self.num_heads, 1, 1)
            # size: 11H1 or 1H11
            self.scale_mul_1H11 = nn.Parameter(torch.full(size=size, fill_value=4.0).log(), requires_grad=True)
            self.max_scale_mul = torch.log(torch.tensor(100)).item()
        else:
            self.scale = 1 / math.sqrt(self.head_dim) / self.tau
        
        self.mat_qkv = nn.Linear(embed_dim, embed_dim * 3, bias=False)
        self.q_bias, self.v_bias = nn.Parameter(torch.zeros(embed_dim)), nn.Parameter(torch.zeros(embed_dim))
        self.register_buffer('zero_k_bias', torch.zeros(embed_dim))
        
        self.proj = nn.Linear(embed_dim, embed_dim)
        self.proj_drop = get_dropout_layer(proj_drop)
        
        self.caching = False    # kv caching: only used during inference
        self.cached_k = None    # kv caching: only used during inference
        self.cached_v = None    # kv caching: only used during inference

        self.batch_size = batch_size
        self.use_flex_attn = use_flex_attn
        self.pad_to_multiplier = pad_to_multiplier

        self.rope2d_normalized_by_hw = rope2d_normalized_by_hw

    
    def kv_caching(self, enable: bool): # kv caching: only used during inference
        self.caching = enable
        self.cached_k = None
        self.cached_v = None
    
    # NOTE: attn_bias_or_two_vector is None during inference
    def forward(self, x, attn_bias_or_two_vector: Union[torch.Tensor, Tuple[torch.IntTensor, torch.IntTensor]], attn_fn=None, scale_schedule=None, rope2d_freqs_grid=None, scale_ind=0):
        """
        :param (fp32) x: shaped (B or batch_size, L or seq_length, C or hidden_dim); if seq-parallel is used, the `L` dim would be shared
        :param (fp32) attn_bias_or_two_vector:
                if not using_flash:
                    a block-wise, lower-triangle matrix, like:
                    [[[[0, -, -, -, -, -, -, -, -, -, -, -, -, -],
                    [0, 0, 0, 0, 0, -, -, -, -, -, -, -, -, -],
                    [0, 0, 0, 0, 0, -, -, -, -, -, -, -, -, -],
                    [0, 0, 0, 0, 0, -, -, -, -, -, -, -, -, -],
                    [0, 0, 0, 0, 0, -, -, -, -, -, -, -, -, -],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]]
                    where 0 means visible and - means invisible (-inf)
                else:
                    a tuple of two 1-dim int vector (VAR_visible_kvlen, VAR_invisible_qlen)
        :return: shaped (B or batch_size, L or seq_length, C or hidden_dim); if seq-parallel is used, the `L` dim would be shared
        """
        # x: fp32
        B, L, C = x.shape
        
        # qkv: amp, bf16
        ### my code
        # qkv = F.linear(input=x, weight=self.mat_qkv.weight, bias=torch.cat((self.q_bias, self.zero_k_bias, self.v_bias))).view(B, L, 3, self.num_heads, self.head_dim)  # BL3Hc

        # Use the module call rather than accessing .weight/.bias directly so that
        # LoRA-wrapped nn.Linear (LoraInjectedLinear) works correctly.
        # Produce the same concatenated bias as original implementation.
        bias_qkv = torch.cat((self.q_bias, self.zero_k_bias, self.v_bias))
        # If mat_qkv is a plain nn.Linear, calling it is equivalent to F.linear with its weight and bias.
        qkv = self.mat_qkv(x)
        # If the linear had no bias (but original code expects the concatenated bias), add it.
        if (not hasattr(self.mat_qkv, 'bias')) or (self.mat_qkv.bias is None):
            qkv = qkv + bias_qkv.unsqueeze(0).unsqueeze(0)
        qkv = qkv.view(B, L, 3, self.num_heads, self.head_dim)  # BL3Hc

        # #my code
        # #important
        # from lora_diffusion import LoraInjectedLinear
        # if isinstance(self.mat_kv, LoraInjectedLinear):
        #     kv_compact = self.mat_kv(kv_compact).view(N, 2, self.num_heads, self.head_dim)
        # else:     
        #     kv_compact = F.linear(kv_compact, weight=self.mat_kv.weight, bias=torch.cat((self.zero_k_bias, self.v_bias))).view(N, 2, self.num_heads, self.head_dim) # NC => N2Hc

        if self.using_flash: q, k, v = qkv.unbind(dim=2); L_dim = 1           # q or k or v: all are shaped in (B:batch_size, L:seq_len, H:heads, c:head_dim)
        else: q, k, v = qkv.permute(2, 0, 3, 1, 4).unbind(dim=0); L_dim = 2   # q or k or v: all are shaped in (B:batch_size, H:heads, L:seq_len, c:head_dim)
        
        if self.cos_attn:   # always True
            scale_mul = self.scale_mul_1H11.clamp_max(self.max_scale_mul).exp() # 11H1 (flash), or 1H11 (not flash)
            q = F.normalize(q, dim=-1, eps=1e-12).mul(scale_mul).contiguous()   # fp32
            k = F.normalize(k, dim=-1, eps=1e-12).contiguous()                  # fp32
            v = v.contiguous()                                                  # bf16
        else:   # be contiguous, to make kernel happy
            q = q.contiguous()      # bf16
            k = k.contiguous()      # bf16
            v = v.contiguous()      # bf16
        if rope2d_freqs_grid is not None:
            q, k = apply_rotary_emb(q, k, scale_schedule, rope2d_freqs_grid, self.pad_to_multiplier, self.rope2d_normalized_by_hw, scale_ind) #, freqs_cis=freqs_cis)
        if self.caching:    # kv caching: only used during inference
            if self.cached_k is None: self.cached_k = k; self.cached_v = v
            else: k = self.cached_k = torch.cat((self.cached_k, k), dim=L_dim); v = self.cached_v = torch.cat((self.cached_v, v), dim=L_dim)
        
        if self.using_flash:
            if attn_bias_or_two_vector is not None: # training
                kw = dict(VAR_visible_kvlen=attn_bias_or_two_vector[0], VAR_invisible_qlen=attn_bias_or_two_vector[1])
            else:                                   # inference (autoregressive sampling)
                kw = dict()
            oup = flash_attn_func(q.to(v.dtype), k.to(v.dtype), v, dropout_p=0, softmax_scale=self.scale, **kw).view(B, L, C)
        else:
            # if self.cos_attn: q, k are in fp32; v is in bf16
            # else: q, k, v are in bf16
            if self.use_flex_attn and attn_fn is not None:
                oup = attn_fn(q, k, v, scale=self.scale).transpose(1, 2).reshape(B, L, C)
            else:
                oup = slow_attn(query=q, key=k, value=v, scale=self.scale, attn_mask=attn_bias_or_two_vector, dropout_p=0).transpose(1, 2).reshape(B, L, C)
            # oup: bf16
        
        return self.proj_drop(self.proj(oup))
    
    def extra_repr(self) -> str:
        tail = ''
        return f'using_flash={self.using_flash}, tau={self.tau}, cos_attn={self.cos_attn}{tail}'


class CrossAttention(nn.Module):
    def __init__(
        self, for_attn_pool=False, embed_dim=768, kv_dim=4096, num_heads=12,
        proj_drop=0., cos_attn=False,
    ):
        """
        :param for_attn_pool: only used in VAR.text_proj_for_sos
        :param embed_dim: Q's dim
        :param kv_dim: K's and V's dim
        :param num_heads: num heads of multi-head attention
        :param proj_drop: proj drop out
        :param cos_attn: during attention, q and k will be L2-normalized and scaled by a head-wise learnable parameter self.scale_mul_1H11
        """
        cos_attn = False    # TODO: never use cos attn in cross attention with T5 kv
        super().__init__()
        self.for_attn_pool = for_attn_pool
        self.embed_dim = embed_dim
        self.kv_dim = kv_dim
        assert embed_dim % num_heads == 0
        self.num_heads, self.head_dim = num_heads, embed_dim // num_heads  # =64
        self.cos_attn = cos_attn
        if self.cos_attn:
            self.scale = 1
            self.scale_mul_1H1 = nn.Parameter(torch.full(size=(1, self.num_heads, 1, 1), fill_value=4.0).log(), requires_grad=True)
            self.max_scale_mul = torch.log(torch.tensor(100)).item()
        else:
            self.scale = 1 / math.sqrt(self.head_dim)
        
        if for_attn_pool:
            q = torch.empty(1, self.num_heads, self.head_dim)
            nn.init.trunc_normal_(q, mean=0, std=math.sqrt(1 / embed_dim / 3))
            self.mat_q = nn.Parameter(q)
        else:
            self.mat_q = nn.Linear(embed_dim, embed_dim, bias=True)
        self.mat_kv = nn.Linear(kv_dim, embed_dim*2, bias=False)
        self.v_bias = nn.Parameter(torch.zeros(embed_dim))
        self.register_buffer('zero_k_bias', torch.zeros(embed_dim))
        
        self.proj = nn.Linear(embed_dim, embed_dim)
        self.proj_drop = get_dropout_layer(proj_drop)
    
    def forward(self, q, ca_kv):
        """
        :param q: shaped as (batch, seq_len, Q_dim)
        :param ca_kv: contains several vectors, each of which is shaped as (len_i, KV_dim). We have [len_1xKV_dim, len_2xKV_dim, len_3xKV_dim, ...] and lens == [len_1, len_2, len_3, ...]
            - kv_compact: shaped as (sum(lens), KV_dim)
            - cu_seqlens_k: cumulated sum of lens
            - max_seqlen_k: int, max(lens)
        NOTE: seq_len (num of Qs) can reach 10k;  but len_i (num of KVs) must <= 256
        
        :return: shaped as (batch, seq_len, Q_dim)
        """
        kv_compact, cu_seqlens_k, max_seqlen_k = ca_kv
        N = kv_compact.shape[0]
        
        #my code
        #important
        from lora_diffusion import LoraInjectedLinear
        if isinstance(self.mat_kv, LoraInjectedLinear):
            kv_compact = self.mat_kv(kv_compact).view(N, 2, self.num_heads, self.head_dim)
        else:     
            kv_compact = F.linear(kv_compact, weight=self.mat_kv.weight, bias=torch.cat((self.zero_k_bias, self.v_bias))).view(N, 2, self.num_heads, self.head_dim) # NC => N2Hc
        # attn_bias = xformers.ops.fmha.BlockDiagonalMask.from_seqlens
        
        if not self.for_attn_pool:
            B, Lq = q.shape[:2]
            q_compact = self.mat_q(q).view(-1, self.num_heads, self.head_dim)
        else:
            B = cu_seqlens_k.shape[0] - 1
            Lq = 1
            q_compact = self.mat_q.repeat(B, 1, 1).to(dtype=kv_compact.dtype)
        
        if self.cos_attn:   # always False
            scale_mul = self.scale_mul_1H1.clamp_max(self.max_scale_mul).exp()
            k, v = kv_compact.unbind(dim=1)
            q_compact = F.normalize(q_compact, dim=-1).mul(scale_mul)
            k = F.normalize(k, dim=-1)
            kv_compact = torch.stack((k, v), dim=1)
        
        q_compact = q_compact.contiguous()
        kv_compact = kv_compact.contiguous()
        
        cu_seqlens_q = torch.arange(0, Lq * (B+1), Lq, dtype=torch.int32, device=q_compact.device)
        if q_compact.dtype == torch.float32:    # todo: fp16 or bf16?
            oup = flash_attn_varlen_kvpacked_func(q=q_compact.to(dtype=torch.bfloat16), kv=kv_compact.to(dtype=torch.bfloat16), cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=Lq, max_seqlen_k=max_seqlen_k, dropout_p=0, softmax_scale=self.scale).reshape(B, Lq, -1)
            oup = oup.float()
        else:
            oup = flash_attn_varlen_kvpacked_func(q=q_compact, kv=kv_compact, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=Lq, max_seqlen_k=max_seqlen_k, dropout_p=0, softmax_scale=self.scale).reshape(B, Lq, -1)
        
        return self.proj_drop(self.proj(oup))
    
    def extra_repr(self) -> str:
        return f'Cq={self.embed_dim}, Ckv={self.kv_dim}, cos_attn={self.cos_attn}'


class SelfAttnBlock(nn.Module):
    def __init__(
        self, embed_dim, kv_dim, cross_attn_layer_scale, cond_dim, act: bool, shared_aln: bool, norm_layer: partial,
        num_heads, mlp_ratio=4., drop=0., drop_path=0., tau=1, cos_attn=False,
        swiglu=False, customized_flash_attn=False, fused_mlp=False, fused_norm_func=None, checkpointing_sa_only=False,
    ):
        super(SelfAttnBlock, self).__init__()
        self.C, self.D = embed_dim, cond_dim
        self.drop_path_rate = drop_path
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.attn = SelfAttention(
            embed_dim=embed_dim, num_heads=num_heads, proj_drop=drop, tau=tau, cos_attn=cos_attn, customized_flash_attn=customized_flash_attn, attn_fn = attn_fn
        )
        self.using_swiglu = swiglu
        self.ffn = (FFNSwiGLU if swiglu else FFN)(in_features=embed_dim, hidden_features=round(embed_dim * mlp_ratio / 256) * 256, drop=drop, fused_mlp=fused_mlp)
        
        self.ln_wo_grad = norm_layer(embed_dim, elementwise_affine=False)
        self.fused_norm_func = fused_norm_func
        self.norm_eps = norm_layer.keywords.get('eps', 1e-6)
        
        self.shared_aln = shared_aln
        if self.shared_aln:
            self.ada_gss = nn.Parameter(torch.randn(1, 1, 6, embed_dim) / embed_dim**0.5)
        else:
            lin = nn.Linear(cond_dim, 6*embed_dim)
            self.ada_lin = nn.Sequential(nn.SiLU(inplace=False), lin) if act else nn.Sequential(lin)
        
    # NOTE: attn_bias_or_two_vector is None during inference
    def forward(self, x, cond_BD, ca_kv, attn_bias_or_two_vector):  # todo: minGPT and vqgan also uses pre-norm, just like this, while MaskGiT uses post-norm
        with torch.cuda.amp.autocast(enabled=False):
            if self.shared_aln: # always True;                   (1, 1, 6, C)  + (B, 1, 6, C)
                gamma1, gamma2, scale1, scale2, shift1, shift2 = (self.ada_gss + cond_BD).unbind(2) # 116C + B16C =unbind(2)=> 6 B1C
            else:
                gamma1, gamma2, scale1, scale2, shift1, shift2 = self.ada_lin(cond_BD).view(-1, 1, 6, self.C).unbind(2)
        
        if self.fused_ada_norm is None:
            x = x + self.drop_path(self.attn( self.ln_wo_grad(x.float()).mul(scale1.add(1)).add_(shift1), attn_bias_or_two_vector=attn_bias_or_two_vector ).mul_(gamma1))
            x = x + self.drop_path(self.ffn( self.ln_wo_grad(x.float()).mul(scale2.add(1)).add_(shift2) ).mul(gamma2)) # this mul(gamma2) cannot be in-placed cuz we possibly use FusedMLP
        else:
            x = x + self.drop_path(self.attn(self.fused_ada_norm(C=self.C, eps=self.norm_eps, x=x, scale=scale1, shift=shift1), attn_bias_or_two_vector=attn_bias_or_two_vector).mul_(gamma1))
            x = x + self.drop_path(self.ffn(self.fused_ada_norm(C=self.C, eps=self.norm_eps, x=x, scale=scale2, shift=shift2)).mul(gamma2)) # this mul(gamma2) cannot be in-placed cuz we possibly use FusedMLP
        return x
    
    def extra_repr(self) -> str:
        return f'shared_aln={self.shared_aln}, fused_norm={self.fused_norm_func is not None}'


class CrossAttnBlock(nn.Module):
    def __init__(
        self,
        embed_dim, kv_dim, cross_attn_layer_scale, cond_dim, act: bool, shared_aln: bool, norm_layer: partial,
        num_heads, mlp_ratio=4., drop=0., drop_path=0., tau=1, cos_attn=False,
        swiglu=False, customized_flash_attn=False, fused_mlp=False, fused_norm_func=None, checkpointing_sa_only=False,
        use_flex_attn=False, batch_size=2, pad_to_multiplier=1, apply_rope2d=False, rope2d_normalized_by_hw=False,
    ):
        super(CrossAttnBlock, self).__init__()
        self.C, self.D = embed_dim, cond_dim
        self.drop_path_rate = drop_path
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.sa = SelfAttention(
            embed_dim=embed_dim, num_heads=num_heads, proj_drop=drop, tau=tau, cos_attn=cos_attn, customized_flash_attn=customized_flash_attn,
            use_flex_attn=use_flex_attn, batch_size=batch_size, pad_to_multiplier=pad_to_multiplier, rope2d_normalized_by_hw=rope2d_normalized_by_hw,
        )
        self.ca = CrossAttention(embed_dim=embed_dim, kv_dim=kv_dim, num_heads=num_heads, proj_drop=drop, cos_attn=cos_attn)
        self.using_swiglu = swiglu
        self.ffn = (FFNSwiGLU if swiglu else FFN)(in_features=embed_dim, hidden_features=round(embed_dim * mlp_ratio / 256) * 256, drop=drop, fused_mlp=fused_mlp)
        
        self.ln_wo_grad = norm_layer(embed_dim, elementwise_affine=False)
        self.fused_norm_func = fused_norm_func
        self.norm_eps = norm_layer.keywords.get('eps', 1e-6)
        self.ca_norm = norm_layer(embed_dim, elementwise_affine=True)
        
        self.shared_aln = shared_aln
        if self.shared_aln: # always True
            self.ada_gss = nn.Parameter(torch.randn(1, 1, 6, embed_dim) / embed_dim**0.5)
        else:
            lin = nn.Linear(cond_dim, 6*embed_dim)
            self.ada_lin = nn.Sequential(nn.SiLU(inplace=False), lin) if act else nn.Sequential(lin)
        
        if cross_attn_layer_scale >= 0:
            self.ca_gamma = nn.Parameter(cross_attn_layer_scale * torch.ones(embed_dim), requires_grad=True)
        else:
            self.ca_gamma = 1
        
        self.checkpointing_sa_only = checkpointing_sa_only
    
    # NOTE: attn_bias_or_two_vector is None during inference
    def forward(self, x, cond_BD, ca_kv, attn_bias_or_two_vector, attn_fn=None, scale_schedule=None, rope2d_freqs_grid=None, scale_ind=0):    # todo: minGPT and vqgan also uses pre-norm, just like this, while MaskGiT uses post-norm
        with torch.cuda.amp.autocast(enabled=False):    # disable half precision
            if self.shared_aln: # always True;                   (1, 1, 6, C)  + (B, 1, 6, C)
                gamma1, gamma2, scale1, scale2, shift1, shift2 = (self.ada_gss + cond_BD).unbind(2) # 116C + B16C =unbind(2)=> 6 B1C
            else:
                gamma1, gamma2, scale1, scale2, shift1, shift2 = self.ada_lin(cond_BD).view(-1, 1, 6, self.C).unbind(2)
        
        if self.fused_norm_func is None:
            x_sa = self.ln_wo_grad(x.float()).mul(scale1.add(1)).add_(shift1)
            if self.checkpointing_sa_only and self.training:
                x_sa = checkpoint(self.sa, x_sa, attn_bias_or_two_vector, attn_fn, scale_schedule, rope2d_freqs_grid, use_reentrant=False)
            else:
                x_sa = self.sa(x_sa, attn_bias_or_two_vector, attn_fn, scale_schedule, rope2d_freqs_grid)
            x = x + self.drop_path(x_sa.mul_(gamma1))
            x = x + self.ca(self.ca_norm(x), ca_kv).float().mul_(self.ca_gamma)
            x = x + self.drop_path(self.ffn( self.ln_wo_grad(x.float()).mul(scale2.add(1)).add_(shift2) ).mul(gamma2)) # this mul(gamma2) cannot be in-placed cuz we possibly use FusedMLP
        else:
            x_sa = self.fused_norm_func(C=self.C, eps=self.norm_eps, x=x, scale=scale1, shift=shift1)
            if self.checkpointing_sa_only and self.training:
                x_sa = checkpoint(self.sa, x_sa, attn_bias_or_two_vector, attn_fn, scale_schedule, rope2d_freqs_grid, use_reentrant=False)
            else:
                x_sa = self.sa(x_sa, attn_bias_or_two_vector, attn_fn, scale_schedule, rope2d_freqs_grid, scale_ind=scale_ind)
            x = x + self.drop_path(x_sa.mul_(gamma1))
            x = x + self.ca(self.ca_norm(x), ca_kv).float().mul_(self.ca_gamma)
            x = x + self.drop_path(self.ffn(self.fused_norm_func(C=self.C, eps=self.norm_eps, x=x, scale=scale2, shift=shift2)).mul(gamma2)) # this mul(gamma2) cannot be in-placed cuz we possibly use FusedMLP
        return x
    
    def extra_repr(self) -> str:
        return f'shared_aln={self.shared_aln}, fused_norm={self.fused_norm_func is not None}, ca_gamma={"<learnable>" if isinstance(self.ca_gamma, nn.Parameter) else self.ca_gamma}'


class AdaLNBeforeHead(nn.Module):
    def __init__(self, C, D, act: bool, norm_layer: partial, fused_norm_func=None):   # C: embed_dim, D: cond_dim
        super().__init__()
        self.C, self.D = C, D
        self.ln_wo_grad = norm_layer(C, elementwise_affine=False)
        self.fused_norm_func = fused_norm_func
        self.norm_eps = norm_layer.keywords.get('eps', 1e-6)
        lin = nn.Linear(D, 2*C)
        self.ada_lin = nn.Sequential(nn.SiLU(inplace=False), lin) if act else nn.Sequential(lin)
    
    def forward(self, x_BLC: torch.Tensor, cond_BD: Optional[torch.Tensor]):
        scale, shift = self.ada_lin(cond_BD).view(-1, 1, 2, self.C).unbind(2)
        if self.fused_norm_func is None:
            return self.ln_wo_grad(x_BLC).mul(scale.add(1)).add_(shift)
        else:
            return self.fused_norm_func(C=self.C, eps=self.norm_eps, x=x_BLC, scale=scale, shift=shift)


def main():
    dev = 'cpu' # 'cuda' if torch.cuda.is_available() else 'cpu'
    rng = torch.Generator(device=dev)
    # for Li in ([1, 3, 5], [1, 3]):
    rng.manual_seed(0)
    B, H, cq, ckv = 4, 8, 64, 96
    Cq = H*cq
    Ckv = H*ckv
    
    Li = [5, 4, 7, 6]
    Lq = 10
    L = max(Li)
    attn_bias = torch.zeros(B, 1, Lq, L, device=dev)
    for i, x in enumerate(Li):
        attn_bias[i, 0, :, x:] = -torch.inf
    
    q = torch.randn(B, Lq, H, cq, generator=rng, device=dev)
    k = torch.randn(B, L, H, ckv, generator=rng, device=dev)
    v = torch.randn(B, L, H, ckv, generator=rng, device=dev)
    tq, tk, tv = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)    # BHLc
    
    seqlen_k = torch.tensor(Li, dtype=torch.int32, device=dev)
    cu_seqlens_k = F.pad(torch.cumsum(seqlen_k, dim=0, dtype=torch.torch.int32), (1, 0))
    kv = torch.stack([k, v], dim=2)
    kv_compact = torch.cat([kv[i, :Li[i]] for i in range(B)], dim=0)
    
    ca = CrossAttention(for_attn_pool=False, embed_dim=Cq, kv_dim=Ckv, num_heads=H)
    CrossAttention.forward
    ca(q, (kv_compact, cu_seqlens_k, max(Li))).mean().backward()


if __name__ == '__main__':
    main()