from typing import Optional import torch from torch import nn from torch.nn import functional as F from megatron.core.models.common.vision_module.vision_module import VisionModule from megatron.core.transformer.enums import ModelType from megatron.core.transformer.spec_utils import ModuleSpec from .transformer_block import TransformerBlock from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.packed_seq_params import PackedSeqParams from megatron.core import InferenceParams from megatron.core.models.vision.multimodal_projector import MultimodalProjector # copied from https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py class PatchEmbed(nn.Module): def __init__( self, patch_size: int = 14, temporal_patch_size: int = 2, in_channels: int = 3, embed_dim: int = 1152, ) -> None: super().__init__() self.patch_size = patch_size self.temporal_patch_size = temporal_patch_size self.in_channels = in_channels self.embed_dim = embed_dim kernel_size = [temporal_patch_size, patch_size, patch_size] self.proj = nn.Conv3d(in_channels, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: target_dtype = self.proj.weight.dtype hidden_states = hidden_states.view( -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size ) hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(-1, self.embed_dim) return hidden_states # copied from https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py 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.float() class Qwen2_5VisionModel(VisionModule): """Qwen2.5 ViT vision model. Args: transformer_config (TransformerConfig): Transformer config. transformer_layer_spec (ModuleSpec): Specifies module to use for transformer layers. ln_pre_impl (ModuleSpec or type): Specifies the layer norm type to use for ln_pre. add_class_token (bool, optional): Include a class token. Defaults to True. class_token_len (int): Class token length. Defaults to 1 but 8 may be faster. patch_dim (int): Image patch size. img_h (int): Input image height. img_w (int): Input image width. """ def __init__( self, transformer_config: TransformerConfig, transformer_layer_spec: ModuleSpec, projection_config: TransformerConfig, projection_layer_spec: ModuleSpec, projection_type: str = "mlp", pre_process: bool = True, post_process: bool = False ) -> None: super().__init__(config=transformer_config) self.spatial_merge_size = transformer_config.spatial_merge_size embed_dim = transformer_config.hidden_size num_heads = transformer_config.num_attention_heads temporal_patch_size = transformer_config.temporal_patch_size patch_size = transformer_config.patch_size in_channels = transformer_config.in_channels self.patch_size = transformer_config.patch_size self.fullatt_block_indexes = transformer_config.fullatt_block_indexes self.window_size = transformer_config._qwen2_5_vl_window_size self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size self.max_sequence_length = transformer_config.seq_length self.patch_embed = PatchEmbed( patch_size=patch_size, temporal_patch_size=temporal_patch_size, in_channels=in_channels, embed_dim=embed_dim, ) head_dim = embed_dim // num_heads self.rotary_pos_emb = VisionRotaryEmbedding(head_dim // 2) self.model_type = ModelType.encoder_or_decoder self.pre_process = pre_process self.post_process = post_process # Transformer layers. # TODO: Follow-up changes will make pre and post_process configurable. They are needed for supporting pipeline parallelism. # NOTE: a final layer norm and/or linear layer present in some implementations are omitted here. self.decoder = TransformerBlock( config=transformer_config, spec=transformer_layer_spec, pre_process=self.pre_process, post_process=self.post_process, post_layer_norm=True ) self.merge_hidden_size = projection_config.ffn_hidden_size self.square_merge_size = self.merge_hidden_size // embed_dim if self.post_process: self.projection = MultimodalProjector( projection_config, projection_layer_spec, projection_type, projection_config.ffn_hidden_size ) else: self.projection = None self.input_tensor = None def set_input_tensor(self, input_tensor: torch.Tensor) -> None: """Sets input tensor to the model. Args: input_tensor (Tensor): Sets the input tensor for the model. """ if self.pre_process: # always True self.input_tensor = input_tensor else: raise NotImplementedError() def rot_pos_emb(self, grid_thw): pos_ids = [] for t, h, w in grid_thw: hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) hpos_ids = hpos_ids.reshape( h // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_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 // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_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).to(grid_thw.device) max_grid_size = grid_thw[:, 1:].max() rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size).to(grid_thw.device) rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1) return rotary_pos_emb def get_window_index(self, grid_thw): window_index: list = [] cu_window_seqlens: list = [0] window_index_id = 0 vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size for grid_t, grid_h, grid_w in grid_thw: llm_grid_h, llm_grid_w = ( grid_h // self.spatial_merge_size, grid_w // self.spatial_merge_size, ) index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w) pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100) index_padded = index_padded.reshape( grid_t, num_windows_h, vit_merger_window_size, num_windows_w, vit_merger_window_size, ) index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape( grid_t, num_windows_h * num_windows_w, vit_merger_window_size, vit_merger_window_size, ) seqlens = (index_padded != -100).sum([2, 3]).reshape(-1) index_padded = index_padded.reshape(-1) index_new = index_padded[index_padded != -100] window_index.append(index_new + window_index_id) cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1] cu_window_seqlens.extend(cu_seqlens_tmp.tolist()) window_index_id += (grid_t * llm_grid_h * llm_grid_w).item() window_index = torch.cat(window_index, dim=0) return window_index, cu_window_seqlens def forward( self, vision_data: Optional[torch.Tensor], grid_thw: torch.Tensor, inference_params: Optional[InferenceParams] = None, extra_block_kwargs: dict = None, ) -> torch.Tensor: """Forward function of the Qwen2 Vision Model. This function passes the input tensors through the embedding layer and then the transformer. Args: x (torch.Tensor): input image/video data of shape [n_tokens, n_dims] grid_thw (torch.Tensor): the size tensor indicates grid size of each image/frame packed_seq_params (PackedSeqParams): parameters to build attention mask in the backend Returns: x (torch.Tensor): output after final transformer block of shape [b, s, h]. """ assert grid_thw is not None assert self.input_tensor is None assert inference_params is None # Rotary positional embeddings (embedding is None for PP intermediate devices) vision_data = self.patch_embed(vision_data) window_index, cu_window_seqlens = self.get_window_index(grid_thw) cu_window_seqlens = torch.tensor( cu_window_seqlens, device=vision_data.device, dtype=torch.int32, ) cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) seq_len, _ = vision_data.size() vision_data = vision_data.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) vision_data = vision_data[window_index, :, :] vision_data = vision_data.reshape(seq_len, 1, -1) rotary_pos_emb = self.rot_pos_emb(grid_thw) rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) rotary_pos_emb = rotary_pos_emb[window_index, :, :] rotary_pos_emb = rotary_pos_emb.reshape(seq_len, 1, 1, -1).repeat(1, 1, 1, 2) hidden_states = self.decoder( hidden_states = vision_data, attention_mask = None, inference_params = inference_params, rotary_pos_emb=rotary_pos_emb, packed_seq_params=self.build_packed_seq_params(None, cu_window_seqlens), packed_seq_params_full=self.build_packed_seq_params(grid_thw), fullatt_block_indexes=self.fullatt_block_indexes, **(extra_block_kwargs or {}), ) hidden_states = self.projection(hidden_states.view(-1, self.merge_hidden_size)) reverse_indices = torch.argsort(window_index) return hidden_states[reverse_indices, :] def build_packed_seq_params( self, grid_thw: Optional[torch.Tensor], cu_seqlens: Optional[torch.Tensor] = None, ) -> PackedSeqParams: # NOTE: each frame is a sequence (rather than each grid) if grid_thw is not None: seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]) cu_seqlens = seqlens.cumsum(dim=0) cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0).int() else: seqlens = cu_seqlens[1:] - cu_seqlens[:-1] max_seqlen_q = seqlens.max() return PackedSeqParams( cu_seqlens_q=cu_seqlens, cu_seqlens_kv=cu_seqlens, qkv_format='thd', max_seqlen_q=max_seqlen_q, max_seqlen_kv=max_seqlen_q )