from contextlib import nullcontext import math from typing import Optional, Tuple # from megatron.model import LayerNorm from easydict import EasyDict as adict import torch from torch.nn import functional as F from torch import nn from flash_attn import flash_attn_qkvpacked_func, flash_attn_func # from optimus import flash_attn_func # from megatron.core import tensor_parallel # from megatron.core import parallel_state as mpu # from megatron.core.utils import make_viewless_tensor, divide # from megatron.model.fused_rms_norm import RMSNorm # from megatron.model.transformer import ( # FlashSelfAttention, # NoopTransformerLayer, # _cfg_to_kwargs, # ) # from megatron.model.enums import AttnMaskType, AttnType # from megatron.model.fused_softmax import FusedScaleMaskSoftmax # from megatron.model.utils import attention_mask_func # from megatron.model.module import MegatronModule # try: # from einops import rearrange # except ImportError: # rearrange = None # from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func # try: # # flash attention 2.x # from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func # except ImportError: # try: # # flash attention 1.x # from flash_attn.flash_attn_interface import flash_attn_unpadded_func # except ImportError: # flash_attn_unpadded_func = None # try: # from flash_attn.flash_attn_interface import flash_attn_unpadded_relative_attention_bias_func # except ImportError: # flash_attn_unpadded_relative_attention_bias_func = None # try: # from flash_attn.flash_attn_interface import mask_flash_attn_unpadded_func # except ImportError: # mask_flash_attn_unpadded_func = None class LayerNormfp32(torch.nn.LayerNorm): """Subclass torch's LayerNorm to handle fp16.""" def forward(self, x: torch.Tensor): orig_type = x.dtype ret = super().forward(x.type(torch.float32)) return ret.type(orig_type) def get_abs_pos(abs_pos, tgt_size): # abs_pos: L, C # tgt_size: M # return: M, C # print(tgt_size) # print(abs_pos.shape) # exit() dim = abs_pos.size(-1) # print(dim) abs_pos_new = abs_pos.squeeze(0) cls_token, old_pos_embed = abs_pos_new[:1], abs_pos_new[1:] src_size = int(math.sqrt(abs_pos_new.shape[0] - 1)) tgt_size = int(math.sqrt(tgt_size)) dtype = abs_pos.dtype if src_size != tgt_size: old_pos_embed = old_pos_embed.view(1, src_size, src_size, dim).permute(0, 3, 1, 2).contiguous() old_pos_embed = old_pos_embed.to(torch.float32) new_pos_embed = F.interpolate( old_pos_embed, size=(tgt_size, tgt_size), mode='bicubic', antialias=True, align_corners=False, ).to(dtype) new_pos_embed = new_pos_embed.permute(0, 2, 3, 1) new_pos_embed = new_pos_embed.view(tgt_size * tgt_size, dim) vision_pos_embed = torch.cat([cls_token, new_pos_embed], dim=0) vision_pos_embed = vision_pos_embed.view(1, tgt_size * tgt_size + 1, dim) return vision_pos_embed else: return abs_pos @torch.jit.script def quick_gelu(x): return x * torch.sigmoid(1.702 * x) class CLIPVisionEmbeddings(nn.Module): def __init__(self, hidden_size=1024, image_size=224, patch_size=14, num_channels=3): super().__init__() self.embed_dim = hidden_size self.image_size = image_size self.patch_size = patch_size self.class_embedding = torch.nn.Parameter(torch.randn(self.embed_dim)) self.patch_embedding = torch.nn.Conv2d( in_channels=num_channels, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False, ) self.num_patches = (self.image_size // self.patch_size) ** 2 self.num_positions = self.num_patches + 1 self.position_embedding = torch.nn.Embedding(self.num_positions, self.embed_dim) self.register_buffer( "position_ids", torch.arange(self.num_positions).expand((1, -1)) ) def forward(self, pixel_values, patch_embeds): batch_size = pixel_values.shape[0] # patch_embeds = self.patch_embedding( # pixel_values # ) # shape = [*, width, grid, grid] if patch_embeds is not None: patch_embeds = patch_embeds # print(patch_embeds.shape) else: patch_embeds = self.patch_embedding(pixel_values) # print(111111) # shape = [*, width, grid, grid] # patch_embeds = patch_embeds.flatten(2).transpose(1, 2) patch_embeds = patch_embeds.flatten(2).transpose(1, 2) class_embeds = self.class_embedding.expand(batch_size, 1, -1) embeddings = torch.cat([class_embeds, patch_embeds], dim=1) # x = torch.cat([cls_token, x], dim=1) embeddings = embeddings + get_abs_pos(self.position_embedding(self.position_ids), embeddings.size(1)) # embeddings = embeddings + self.position_embedding(self.position_ids) return embeddings class NoTPFeedForward(nn.Module): def __init__( self, cfg, dim: int, hidden_dim: int, ): super().__init__() self.fc1 = torch.nn.Linear(dim, hidden_dim, bias=True) self.fc2 = torch.nn.Linear(hidden_dim, dim, bias=True) def forward(self, x): output = self.fc2(quick_gelu(self.fc1(x))) return output # from optimus.flash_attn_interface import flash_attn_qkvpacked_func # class NoTPAttention(nn.Module): # def __init__(self, cfg): # super().__init__() # self.num_heads = cfg.num_attention_heads # self.n_local_heads = cfg.num_attention_heads # self.head_dim = cfg.hidden_size // cfg.num_attention_heads # self.max_seq_len = cfg.seq_length # self.use_flash_attention = cfg.use_flash_attn # self.qkv_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size * 3, bias=True) # self.out_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size, bias=True) # # self.core_attention = CoreAttention(cfg, AttnType.self_attn) # self.attn_drop = cfg.attention_dropout # def forward( # self, # x: torch.Tensor, # ): # bsz, seqlen, _ = x.shape # xqkv = self.qkv_proj(x) # xqkv = xqkv.view(bsz, seqlen, 3, self.num_heads, self.head_dim) # if self.use_flash_attention: # output = flash_attn_qkvpacked_func(xqkv) # output = output.view(bsz, seqlen, -1) # else: # xq, xk, xv = torch.split(xqkv, 1, dim=2) # xq = xq.squeeze(2) # xk = xk.squeeze(2) # xv = xv.squeeze(2) # # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...] # # （B, num_head, S, head_size) # xq = xq.permute(0, 2, 1, 3) # xk = xk.permute(0, 2, 1, 3) # xv = xv.permute(0, 2, 1, 3) # output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None) # utput = output.permute(0, 2, 1, 3).view(bsz, seqlen, -1) # output = self.out_proj(output) # return output # from optimus.flash_attn_interface import flash_attn_qkvpacked_func class NoTPAttention(torch.nn.Module): def __init__(self, cfg): super().__init__() self.num_heads = cfg.num_attention_heads self.n_local_heads = cfg.num_attention_heads self.head_dim = cfg.hidden_size // cfg.num_attention_heads self.max_seq_len = cfg.seq_length self.use_flash_attention = cfg.use_flash_attn self.qkv_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size * 3, bias=True) self.out_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size, bias=True) # self.core_attention = CoreAttention(cfg, AttnType.self_attn) self.attn_drop = cfg.attention_dropout def forward( self, x: torch.Tensor, ): bsz, seqlen, _ = x.shape xqkv = self.qkv_proj(x) xqkv = xqkv.view(bsz, seqlen, 3, self.num_heads, self.head_dim) if self.use_flash_attention: output = flash_attn_qkvpacked_func(xqkv) output = output.view(bsz, seqlen, -1) # xq, xk, xv = torch.split(xqkv, 1, dim=2) # xq = xq.squeeze(2) # xk = xk.squeeze(2) # xv = xv.squeeze(2) # # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...] # # （B, num_head, S, head_size) # xq = xq.permute(0, 2, 1, 3) # xk = xk.permute(0, 2, 1, 3) # xv = xv.permute(0, 2, 1, 3) # # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False): # output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None) # output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1) # output = output.permute(0, 2, 1, 3).contiguous().view(bsz, seqlen, -1) else: # output = flash_attn_qkvpacked_func(xqkv) xq, xk, xv = torch.split(xqkv, 1, dim=2) xq = xq.squeeze(2) xk = xk.squeeze(2) xv = xv.squeeze(2) # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...] # （B, num_head, S, head_size) xq = xq.permute(0, 2, 1, 3) xk = xk.permute(0, 2, 1, 3) xv = xv.permute(0, 2, 1, 3) # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False): output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None) output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1) output = self.out_proj(output) return output class NoTPTransformerBlock(nn.Module): def __init__(self, cfg, layer_id: int, multiple_of=256): super().__init__() self.n_heads = cfg.num_attention_heads self.dim = cfg.hidden_size self.head_dim = cfg.hidden_size // cfg.num_attention_heads self.self_attn = NoTPAttention(cfg) self.mlp = NoTPFeedForward( cfg, dim=cfg.hidden_size, hidden_dim=cfg.ffn_hidden_size ) self.layer_id = layer_id self.layer_norm1 = torch.nn.LayerNorm( cfg.hidden_size, eps=cfg.layernorm_epsilon ) self.layer_norm2 = torch.nn.LayerNorm( cfg.hidden_size, eps=cfg.layernorm_epsilon ) def forward(self, x: torch.Tensor): residual = self.self_attn.forward(self.layer_norm1(x)) h = x + residual out = h + self.mlp.forward(self.layer_norm2(h)) return out class NoTPTransformer(nn.Module): def __init__(self, cfg): super().__init__() self.cfg = cfg # self.recompute_list = self.cfg.get("recompute_list", []) self.num_layers = cfg.num_layers # _get_num_layers(cfg) self.layers = torch.nn.ModuleList() for layer_id in range(self.num_layers): self.layers.append( NoTPTransformerBlock( cfg, layer_id + 1, ) ) def forward( self, hidden_states, ): for lid, layer in enumerate(self.layers): # if lid in self.recompute_list: # def custom(layer_id): # def custom_forward(*args, **kwargs): # x_ = self.layers[layer_id](*args, **kwargs) # return x_ # return custom_forward # assert hidden_states.requires_grad == True, logger.warning( # "When using recalculation, the input must have grad fn" # ) # hidden_states = tensor_parallel.checkpoint( # custom(lid), # False, # hidden_states.contiguous() # ) # else: hidden_states = layer(hidden_states) return hidden_states # from megatron.core.tensor_parallel.layers import non_tensor_paralleled, local_dp_reduce, local_dp_scatter class VitModel(nn.Module): def __init__( self, cfg, freeze_embed=False, freeze_pre_norm=False ) -> None: super().__init__() self.embeddings = CLIPVisionEmbeddings(hidden_size=cfg.hidden_size, image_size=cfg.image_size, patch_size=cfg.patch_size) if freeze_embed: for name, param in self.embeddings.named_parameters(): param.requires_grad = False self.transformer = NoTPTransformer(cfg=cfg) if cfg.get("fp32norm", False): logger.info("Load fp32 layernorm for ViT.") self.pre_layrnorm = LayerNormfp32( cfg.hidden_size, eps=cfg.get("pre_layernorm_epsilon", 1e-5), ) else: self.pre_layrnorm = torch.nn.LayerNorm( cfg.hidden_size, eps=cfg.get("pre_layernorm_epsilon", 1e-5), ) # self.pre_layrnorm = RMSNorm( # cfg.hidden_size, # eps=cfg.get("pre_layernorm_epsilon", 1e-5), # sequence_parallel=False, # use_fp32=True, # use_optimus=True, # ) if freeze_pre_norm: for name, param in self.pre_layrnorm.named_parameters(): param.requires_grad = False for p in self.parameters(): p.micro_dp = True def set_input_tensor(self, input_tensor): if not isinstance(input_tensor, list): input_tensor = [input_tensor] self.transformer.set_input_tensor(input_tensor[0]) def __str__(self) -> str: return "open_clip" def forward( self, x, patch_embeds ): x = self.embeddings(x, patch_embeds) hidden_states = self.pre_layrnorm(x) # hidden_states, dis = local_dp_scatter(hidden_states) output = self.transformer(hidden_states) # output = local_dp_reduce(output, dis) return output vit_model_cfg = adict( num_layers=24, hidden_size=1024, num_heads = 16, num_attention_heads=16, ffn_hidden_size=4096, seq_length=256, max_position_embeddings=256, use_flash_attn=False, understand_projector_stride=2, hidden_dropout = 0.0, attention_dropout = 0.0, no_persist_layer_norm = False, layernorm_epsilon = 1e-5, pre_layernorm_epsilon = 1e-5, image_size = 224, patch_size = 14, recompute_list = [] ) def build_clip_l(): return VitModel( cfg=vit_model_cfg, freeze_embed=False, freeze_pre_norm=False, ) if __name__ == '__main__': from mmgpt.model.vision_encoder.sam_b import build_sam_vit_b vit_model_cfg = adict( num_layers=24, hidden_size=1024, num_attention_heads=16, ffn_hidden_size=4096, seq_length=256, max_position_embeddings=256, use_flash_attn=False, understand_projector_stride=2, hidden_dropout = 0.0, attention_dropout = 0.0, no_persist_layer_norm = False, layernorm_epsilon = 1e-5, pre_layernorm_epsilon = 1e-5, image_size = 224, patch_size = 14, recompute_list = [] ) sam_model = build_sam_vit_b() vision_model = VitModel( cfg=vit_model_cfg, freeze_embed=False, freeze_pre_norm=False, ) # model = VitModel(1344) # x = torch.zeros(2, 3, 224, 224) x = torch.zeros(2, 3, 1024, 1024) with torch.no_grad(): # y = vision_model(x) patch_embed = sam_model(x) print(patch_embed.shape) y = vision_model(x, patch_embed) print(y.shape) image_feature = torch.add(y[:, 1:], patch_embed.flatten(2).permute(0, 2, 1)) print(image_feature.shape)