1178 lines
50 KiB
Python
1178 lines
50 KiB
Python
# Copyright 2025 Bytedance Ltd. and/or its affiliates.
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# Copyright (c) 2024 The Qwen Team and The HuggingFace Inc. team.
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# SPDX-License-Identifier: Apache-2.0
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#
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# This file has been modified by ByteDance Ltd. and/or its affiliates.
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#
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# Original file was released under Apache-2.0, with the full license text
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# available at https://github.com/huggingface/transformers/blob/main/LICENSE.
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import math
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from dataclasses import dataclass
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from typing import Any
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import numpy as np
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import torch
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from torch import nn
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from torch.nn.attention.flex_attention import flex_attention
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from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
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from transformers.models.qwen2.modeling_qwen2 import (
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Qwen2Attention,
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Qwen2MLP,
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Qwen2PreTrainedModel,
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Qwen2RMSNorm,
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Qwen2RotaryEmbedding,
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)
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from transformers.utils import ModelOutput
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from vllm.transformers_utils.configs.bagel import BagelConfig
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from vllm.vllm_flash_attn import flash_attn_varlen_func
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from vllm_omni.diffusion.layers.rope import RotaryEmbedding
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def patchify(imgs, p):
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"""
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imgs: (N, 3, H, W) or (3, H, W)
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x: (N, L, patch_size**2 *3) or (L, patch_size**2 *3)
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"""
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is_batch = imgs.ndim == 4
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if not is_batch:
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imgs = imgs.unsqueeze(0)
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# n: batch, c: channel, h: grid_h, p: patch_h, w: grid_w, q: patch_w
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x = imgs.reshape(imgs.shape[0], 3, imgs.shape[2] // p, p, imgs.shape[3] // p, p)
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# Permute to (n, grid_h, grid_w, c, patch_h, patch_w) to match Conv2d (c, h, w) flattening
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x = torch.einsum("nchpwq->nhwcpq", x)
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x = x.reshape(imgs.shape[0], -1, 3 * p**2)
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if not is_batch:
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x = x.squeeze(0)
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return x
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class MLPconnector(nn.Module):
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def __init__(self, input_dim, output_dim, activation="gelu_pytorch_tanh"):
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super().__init__()
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self.fc1 = nn.Linear(input_dim, output_dim)
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if activation == "gelu":
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self.act = nn.GELU()
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elif activation == "gelu_pytorch_tanh":
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self.act = nn.GELU(approximate="tanh")
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else:
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self.act = nn.ReLU()
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self.fc2 = nn.Linear(output_dim, output_dim)
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def forward(self, x):
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return self.fc2(self.act(self.fc1(x)))
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torch._dynamo.config.cache_size_limit = 512
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torch._dynamo.config.accumulated_cache_size_limit = 4096
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flex_attention = torch.compile(flex_attention)
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class Qwen2MoTConfig(Qwen2Config):
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"""Configuration for Qwen2MoT (Mixture of Tokens) model.
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This is fundamentally different from Qwen2, hence the distinct name.
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"""
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model_type = "qwen2_mot"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=151936,
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hidden_size=4096,
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intermediate_size=22016,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=32,
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hidden_act="silu",
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max_position_embeddings=32768,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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use_sliding_window=False,
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sliding_window=4096,
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max_window_layers=28,
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attention_dropout=0.0,
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is_causal=True,
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_attn_implementation="eager",
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qk_norm=True,
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layer_module="Qwen2MoTDecoderLayer",
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freeze_und=False,
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**kwargs,
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):
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super().__init__(
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vocab_size=vocab_size,
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hidden_size=hidden_size,
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intermediate_size=intermediate_size,
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num_hidden_layers=num_hidden_layers,
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num_attention_heads=num_attention_heads,
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num_key_value_heads=num_key_value_heads,
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hidden_act=hidden_act,
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max_position_embeddings=max_position_embeddings,
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initializer_range=initializer_range,
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rms_norm_eps=rms_norm_eps,
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use_cache=use_cache,
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tie_word_embeddings=tie_word_embeddings,
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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use_sliding_window=use_sliding_window,
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sliding_window=sliding_window,
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max_window_layers=max_window_layers,
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attention_dropout=attention_dropout,
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is_causal=is_causal,
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_attn_implementation=_attn_implementation,
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**kwargs,
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)
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self.qk_norm = qk_norm
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self.layer_module = layer_module
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class NaiveCache:
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def __init__(self, num_layers):
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self.key_cache = {k: None for k in range(num_layers)}
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self.value_cache = {k: None for k in range(num_layers)}
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@property
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def num_layers(self):
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return len(self.key_cache)
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@property
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def seq_lens(self):
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if self.key_cache[0] is not None:
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return self.key_cache[0].shape[0]
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else:
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return 0
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@dataclass
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class BaseNavitOutputWithPast(ModelOutput):
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packed_query_sequence: torch.FloatTensor = None
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past_key_values: NaiveCache | None = None
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class PackedAttentionMoT(Qwen2Attention):
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def __init__(self, config, layer_idx: int | None = None):
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super().__init__(config, layer_idx)
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self.q_norm = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.q_norm_moe_gen = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm_moe_gen = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.head_dim = config.hidden_size // config.num_attention_heads
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head_dim = self.head_dim
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self.q_proj_moe_gen = nn.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=True)
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self.k_proj_moe_gen = nn.Linear(config.hidden_size, config.num_key_value_heads * head_dim, bias=True)
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self.v_proj_moe_gen = nn.Linear(config.hidden_size, config.num_key_value_heads * head_dim, bias=True)
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self.o_proj_moe_gen = nn.Linear(config.num_attention_heads * head_dim, config.hidden_size, bias=False)
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self.rotary_op = RotaryEmbedding(is_neox_style=True)
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def forward(
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self,
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packed_query_sequence: torch.Tensor,
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query_lens: torch.Tensor,
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packed_query_position_embeddings: torch.Tensor,
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packed_query_indexes: torch.Tensor,
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past_key_values: NaiveCache | None = None,
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key_values_lens: torch.Tensor | None = None,
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packed_key_value_indexes: torch.Tensor | None = None,
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update_past_key_values=True,
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is_causal=True,
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mode="und",
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packed_vae_token_indexes=None,
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packed_text_indexes=None,
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):
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if mode == "und":
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packed_query_states = self.q_proj(packed_query_sequence).view(-1, self.num_heads, self.head_dim)
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packed_key_states = self.k_proj(packed_query_sequence).view(-1, self.num_key_value_heads, self.head_dim)
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packed_value_states = self.v_proj(packed_query_sequence).view(-1, self.num_key_value_heads, self.head_dim)
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packed_query_states = self.q_norm(packed_query_states)
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packed_key_states = self.k_norm(packed_key_states)
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elif mode == "gen":
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packed_query_sequence = packed_query_sequence.to(torch.bfloat16)
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packed_query_states = packed_query_sequence.new_zeros(
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(packed_query_sequence.shape[0], self.num_heads * self.head_dim)
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)
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packed_key_states = packed_query_sequence.new_zeros(
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(packed_query_sequence.shape[0], self.num_key_value_heads * self.head_dim)
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)
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packed_value_states = packed_query_sequence.new_zeros(
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(packed_query_sequence.shape[0], self.num_key_value_heads * self.head_dim)
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)
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packed_text_query_sequence = packed_query_sequence[packed_text_indexes]
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packed_vae_query_sequence = packed_query_sequence[packed_vae_token_indexes]
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packed_query_states[packed_text_indexes] = self.q_proj(packed_text_query_sequence)
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packed_query_states[packed_vae_token_indexes] = self.q_proj_moe_gen(packed_vae_query_sequence)
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packed_key_states[packed_text_indexes] = self.k_proj(packed_text_query_sequence)
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packed_key_states[packed_vae_token_indexes] = self.k_proj_moe_gen(packed_vae_query_sequence)
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packed_value_states[packed_text_indexes] = self.v_proj(packed_text_query_sequence)
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packed_value_states[packed_vae_token_indexes] = self.v_proj_moe_gen(packed_vae_query_sequence)
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packed_query_states = packed_query_states.view(-1, self.num_heads, self.head_dim)
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packed_key_states = packed_key_states.view(-1, self.num_key_value_heads, self.head_dim)
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packed_value_states = packed_value_states.view(-1, self.num_key_value_heads, self.head_dim)
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packed_query_states = packed_query_states.to(torch.float32)
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packed_query_states[packed_text_indexes] = self.q_norm(packed_query_states[packed_text_indexes])
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packed_query_states[packed_vae_token_indexes] = self.q_norm_moe_gen(
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packed_query_states[packed_vae_token_indexes]
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)
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packed_key_states = packed_key_states.to(torch.float32)
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packed_key_states[packed_text_indexes] = self.k_norm(packed_key_states[packed_text_indexes])
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packed_key_states[packed_vae_token_indexes] = self.k_norm_moe_gen(
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packed_key_states[packed_vae_token_indexes]
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)
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cos, sin = [x[..., : self.head_dim // 2] for x in packed_query_position_embeddings]
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packed_query_states = self.rotary_op(packed_query_states.to(cos.dtype).unsqueeze(0), cos, sin).squeeze(0)
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packed_key_states = self.rotary_op(packed_key_states.to(cos.dtype).unsqueeze(0), cos, sin).squeeze(0)
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packed_query_states = packed_query_states.to(torch.bfloat16)
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packed_key_states = packed_key_states.to(torch.bfloat16)
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packed_value_states = packed_value_states.to(torch.bfloat16)
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if past_key_values is not None and past_key_values.key_cache[self.layer_idx] is not None:
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past_key_states = past_key_values.key_cache[self.layer_idx]
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past_value_states = past_key_values.value_cache[self.layer_idx]
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seqlens = sum(query_lens) + sum(key_values_lens)
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merged_key_states = past_key_states.new_zeros(size=[seqlens, self.num_key_value_heads, self.head_dim])
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merged_value_states = past_key_states.new_zeros(size=[seqlens, self.num_key_value_heads, self.head_dim])
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merged_key_states[packed_query_indexes] = packed_key_states
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merged_key_states[packed_key_value_indexes] = past_key_states
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merged_value_states[packed_query_indexes] = packed_value_states
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merged_value_states[packed_key_value_indexes] = past_value_states
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key_values_lens = key_values_lens + query_lens
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else:
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merged_key_states = packed_key_states
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merged_value_states = packed_value_states
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key_values_lens = query_lens
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cu_seqlens_q = torch.nn.functional.pad(torch.cumsum(query_lens, dim=0), (1, 0))
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cu_seqlens_k = torch.nn.functional.pad(torch.cumsum(key_values_lens, dim=0), (1, 0))
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packed_attn_output = flash_attn_varlen_func(
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q=packed_query_states,
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k=merged_key_states,
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v=merged_value_states,
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cu_seqlens_q=cu_seqlens_q.to(torch.int32),
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cu_seqlens_k=cu_seqlens_k.to(torch.int32),
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max_seqlen_q=max(query_lens).item(),
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max_seqlen_k=max(key_values_lens).item(),
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causal=is_causal,
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)
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packed_attn_output = packed_attn_output.reshape(-1, self.hidden_size)
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if mode == "und":
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packed_attn_output = self.o_proj(packed_attn_output)
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elif mode == "gen":
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packed_attn_output[packed_text_indexes] = self.o_proj(packed_attn_output[packed_text_indexes])
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packed_attn_output[packed_vae_token_indexes] = self.o_proj_moe_gen(
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packed_attn_output[packed_vae_token_indexes]
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)
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if update_past_key_values:
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past_key_values.key_cache[self.layer_idx] = merged_key_states
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past_key_values.value_cache[self.layer_idx] = merged_value_states
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return packed_attn_output, past_key_values
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class Qwen2MoTDecoderLayer(nn.Module):
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def __init__(
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self,
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config,
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layer_idx: int | None = None,
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attn_module: Qwen2Attention | None = PackedAttentionMoT,
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):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = attn_module(config, layer_idx)
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self.mlp = Qwen2MLP(config)
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self.mlp_moe_gen = Qwen2MLP(config)
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self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.input_layernorm_moe_gen = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm_moe_gen = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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encoder_hidden_states: torch.Tensor | None = None,
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packed_query_sequence: torch.Tensor | None = None,
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query_lens: torch.Tensor = None,
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packed_query_position_embeddings: torch.Tensor = None,
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packed_query_indexes: torch.Tensor = None,
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past_key_values: NaiveCache | None = None,
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key_values_lens: torch.Tensor | None = None,
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packed_key_value_indexes: torch.Tensor | None = None,
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update_past_key_values=True,
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is_causal=True,
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mode="und",
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packed_vae_token_indexes=None,
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packed_text_indexes=None,
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) -> BaseNavitOutputWithPast:
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if packed_query_sequence is None:
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packed_query_sequence = hidden_states
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residual = packed_query_sequence
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if mode == "und":
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packed_query_sequence = self.input_layernorm(packed_query_sequence)
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elif mode == "gen":
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packed_query_sequence_ = torch.zeros_like(packed_query_sequence)
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packed_query_sequence_[packed_text_indexes] = self.input_layernorm(
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packed_query_sequence[packed_text_indexes]
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)
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packed_query_sequence_[packed_vae_token_indexes] = self.input_layernorm_moe_gen(
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packed_query_sequence[packed_vae_token_indexes]
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)
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packed_query_sequence = packed_query_sequence_
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# Self Attention
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packed_query_sequence, past_key_values = self.self_attn(
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packed_query_sequence=packed_query_sequence,
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query_lens=query_lens,
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packed_query_position_embeddings=packed_query_position_embeddings,
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packed_query_indexes=packed_query_indexes,
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past_key_values=past_key_values,
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key_values_lens=key_values_lens,
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packed_key_value_indexes=packed_key_value_indexes,
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update_past_key_values=update_past_key_values,
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is_causal=is_causal,
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mode=mode,
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packed_vae_token_indexes=packed_vae_token_indexes,
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packed_text_indexes=packed_text_indexes,
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)
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packed_query_sequence = residual + packed_query_sequence
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# Fully Connected
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residual = packed_query_sequence
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if mode == "und":
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packed_query_sequence = self.post_attention_layernorm(packed_query_sequence)
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packed_query_sequence = self.mlp(packed_query_sequence)
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elif mode == "gen":
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packed_text_query_sequence = packed_query_sequence[packed_text_indexes]
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packed_vae_query_sequence = packed_query_sequence[packed_vae_token_indexes]
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packed_text_query_sequence = self.post_attention_layernorm(packed_text_query_sequence).to(torch.bfloat16)
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packed_vae_query_sequence = self.post_attention_layernorm_moe_gen(packed_vae_query_sequence).to(
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torch.bfloat16
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)
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packed_query_sequence_ = torch.zeros_like(packed_query_sequence).to(torch.bfloat16)
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packed_query_sequence_[packed_text_indexes] = self.mlp(packed_text_query_sequence)
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packed_query_sequence_[packed_vae_token_indexes] = self.mlp_moe_gen(packed_vae_query_sequence)
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packed_query_sequence = packed_query_sequence_
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packed_query_sequence = residual + packed_query_sequence
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return packed_query_sequence, past_key_values
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class Qwen2MoTModel(Qwen2PreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
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self.use_moe = "Mo" in config.layer_module
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
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self.layers = nn.ModuleList(
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[
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Qwen2MoTDecoderLayer(config, layer_idx, attn_module=PackedAttentionMoT)
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for layer_idx in range(config.num_hidden_layers)
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]
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)
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self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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if self.use_moe:
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self.norm_moe_gen = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.rotary_emb = Qwen2RotaryEmbedding(config=config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def forward(
|
|
self,
|
|
packed_query_sequence: torch.Tensor,
|
|
query_lens: torch.Tensor,
|
|
packed_query_position_ids: torch.Tensor,
|
|
packed_query_indexes: torch.Tensor,
|
|
past_key_values: NaiveCache | None = None,
|
|
key_values_lens: torch.Tensor | None = None,
|
|
packed_key_value_indexes: torch.Tensor | None = None,
|
|
update_past_key_values=True,
|
|
is_causal=True,
|
|
mode="und",
|
|
packed_vae_token_indexes=None,
|
|
packed_text_indexes=None,
|
|
) -> BaseNavitOutputWithPast:
|
|
# create position embeddings to be shared across the decoder layers
|
|
cos, sin = self.rotary_emb(packed_query_sequence, packed_query_position_ids.unsqueeze(0))
|
|
cos = cos.squeeze(0)
|
|
sin = sin.squeeze(0)
|
|
packed_query_position_embeddings = (cos, sin)
|
|
|
|
extra_inputs = {}
|
|
if self.use_moe:
|
|
extra_inputs.update(mode=mode)
|
|
if mode == "gen":
|
|
assert packed_vae_token_indexes is not None
|
|
assert packed_text_indexes is not None
|
|
extra_inputs.update(
|
|
packed_vae_token_indexes=packed_vae_token_indexes,
|
|
packed_text_indexes=packed_text_indexes,
|
|
)
|
|
|
|
for layer_idx, decoder_layer in enumerate(self.layers):
|
|
packed_query_sequence, past_key_values = decoder_layer(
|
|
hidden_states=packed_query_sequence,
|
|
encoder_hidden_states=None,
|
|
query_lens=query_lens,
|
|
packed_query_position_embeddings=packed_query_position_embeddings,
|
|
packed_query_indexes=packed_query_indexes,
|
|
past_key_values=past_key_values,
|
|
key_values_lens=key_values_lens,
|
|
packed_key_value_indexes=packed_key_value_indexes,
|
|
update_past_key_values=update_past_key_values,
|
|
is_causal=is_causal,
|
|
**extra_inputs,
|
|
)
|
|
|
|
if self.use_moe:
|
|
if mode == "und":
|
|
packed_query_sequence = self.norm(packed_query_sequence)
|
|
elif mode == "gen":
|
|
packed_query_sequence_ = torch.zeros_like(packed_query_sequence)
|
|
packed_query_sequence_[packed_text_indexes] = self.norm(packed_query_sequence[packed_text_indexes])
|
|
packed_query_sequence_[packed_vae_token_indexes] = self.norm_moe_gen(
|
|
packed_query_sequence[packed_vae_token_indexes]
|
|
)
|
|
packed_query_sequence = packed_query_sequence_
|
|
else:
|
|
packed_query_sequence = self.norm(packed_query_sequence)
|
|
|
|
return BaseNavitOutputWithPast(
|
|
packed_query_sequence=packed_query_sequence,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
|
|
class Qwen2MoTForCausalLM(Qwen2PreTrainedModel):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = Qwen2MoTModel(config)
|
|
self.vocab_size = config.vocab_size
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.embed_tokens = value
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
def forward(
|
|
self,
|
|
packed_query_sequence: torch.Tensor,
|
|
query_lens: torch.Tensor,
|
|
packed_query_position_ids: torch.Tensor,
|
|
packed_query_indexes: torch.Tensor,
|
|
past_key_values: NaiveCache | None = None,
|
|
key_values_lens: torch.Tensor | None = None,
|
|
packed_key_value_indexes: torch.Tensor | None = None,
|
|
update_past_key_values=True,
|
|
is_causal=True,
|
|
mode="und",
|
|
packed_vae_token_indexes=None,
|
|
packed_text_indexes=None,
|
|
) -> BaseNavitOutputWithPast:
|
|
outputs = self.model(
|
|
packed_query_sequence=packed_query_sequence,
|
|
query_lens=query_lens,
|
|
packed_query_position_ids=packed_query_position_ids,
|
|
packed_query_indexes=packed_query_indexes,
|
|
past_key_values=past_key_values,
|
|
key_values_lens=key_values_lens,
|
|
packed_key_value_indexes=packed_key_value_indexes,
|
|
update_past_key_values=update_past_key_values,
|
|
is_causal=is_causal,
|
|
mode=mode,
|
|
packed_vae_token_indexes=packed_vae_token_indexes,
|
|
packed_text_indexes=packed_text_indexes,
|
|
)
|
|
|
|
return outputs
|
|
|
|
|
|
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
|
|
grid_h = np.arange(grid_size, dtype=np.float32)
|
|
grid_w = np.arange(grid_size, dtype=np.float32)
|
|
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
|
grid = np.stack(grid, axis=0)
|
|
|
|
grid = grid.reshape([2, 1, grid_size, grid_size])
|
|
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
|
|
if cls_token and extra_tokens > 0:
|
|
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
|
|
return pos_embed
|
|
|
|
|
|
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
|
assert embed_dim % 2 == 0
|
|
|
|
# use half of dimensions to encode grid_h
|
|
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
|
|
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
|
|
|
|
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
|
|
return emb
|
|
|
|
|
|
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
|
|
"""
|
|
embed_dim: output dimension for each position
|
|
pos: a list of positions to be encoded: size (M,)
|
|
out: (M, D)
|
|
"""
|
|
assert embed_dim % 2 == 0
|
|
omega = np.arange(embed_dim // 2, dtype=np.float64)
|
|
omega /= embed_dim / 2.0
|
|
omega = 1.0 / 10000**omega # (D/2,)
|
|
|
|
pos = pos.reshape(-1) # (M,)
|
|
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
|
|
|
|
emb_sin = np.sin(out) # (M, D/2)
|
|
emb_cos = np.cos(out) # (M, D/2)
|
|
|
|
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
|
|
return emb
|
|
|
|
|
|
class TimestepEmbedder(nn.Module):
|
|
"""
|
|
Embeds scalar timesteps into vector representations.
|
|
"""
|
|
|
|
def __init__(self, hidden_size, frequency_embedding_size=256):
|
|
super().__init__()
|
|
self.mlp = nn.Sequential(
|
|
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
|
|
nn.SiLU(),
|
|
nn.Linear(hidden_size, hidden_size, bias=True),
|
|
)
|
|
self.frequency_embedding_size = frequency_embedding_size
|
|
|
|
@staticmethod
|
|
def timestep_embedding(t, dim, max_period=10000):
|
|
"""
|
|
Create sinusoidal timestep embeddings.
|
|
:param t: a 1-D Tensor of N indices, one per batch element.
|
|
These may be fractional.
|
|
:param dim: the dimension of the output.
|
|
:param max_period: controls the minimum frequency of the embeddings.
|
|
:return: an (N, D) Tensor of positional embeddings.
|
|
"""
|
|
half = dim // 2
|
|
freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
|
|
device=t.device
|
|
)
|
|
args = t[:, None].float() * freqs[None]
|
|
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
|
|
if dim % 2:
|
|
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
|
return embedding
|
|
|
|
def forward(self, t):
|
|
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
|
|
t_emb = self.mlp(t_freq)
|
|
return t_emb
|
|
|
|
|
|
class PositionEmbedding(nn.Module):
|
|
def __init__(self, max_num_patch_per_side, hidden_size):
|
|
super().__init__()
|
|
self.max_num_patch_per_side = max_num_patch_per_side
|
|
self.hidden_size = hidden_size
|
|
self.pos_embed = nn.Parameter(torch.zeros(max_num_patch_per_side**2, hidden_size), requires_grad=False)
|
|
self._init_weights()
|
|
|
|
def _init_weights(self):
|
|
# Initialize (and freeze) pos_embed by sin-cos embedding:
|
|
pos_embed = get_2d_sincos_pos_embed(self.hidden_size, self.max_num_patch_per_side)
|
|
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float())
|
|
|
|
def forward(self, position_ids):
|
|
return self.pos_embed[position_ids]
|
|
|
|
|
|
def get_flattened_position_ids_extrapolate(img_h, img_w, patch_size, max_num_patches_per_side):
|
|
num_patches_h, num_patches_w = img_h // patch_size, img_w // patch_size
|
|
coords_h = torch.arange(0, num_patches_h)
|
|
coords_w = torch.arange(0, num_patches_w)
|
|
pos_ids = (coords_h[:, None] * max_num_patches_per_side + coords_w).flatten()
|
|
return pos_ids
|
|
|
|
|
|
class Bagel(torch.nn.Module):
|
|
config_class = BagelConfig
|
|
base_model_prefix = "bagel"
|
|
|
|
def __init__(self, language_model, vit_model, config: BagelConfig):
|
|
super().__init__()
|
|
self.language_model = language_model
|
|
self.hidden_size = config.llm_config.hidden_size
|
|
self.use_moe = "Mo" in config.llm_config.layer_module
|
|
self.num_heads = config.llm_config.num_attention_heads
|
|
|
|
if config.visual_gen:
|
|
self.latent_patch_size = config.latent_patch_size
|
|
self.timestep_shift = config.timestep_shift
|
|
self.latent_downsample = config.vae_config.downsample * config.latent_patch_size
|
|
self.max_latent_size = config.max_latent_size
|
|
self.latent_channel = config.vae_config.z_channels
|
|
self.patch_latent_dim = self.latent_patch_size**2 * self.latent_channel
|
|
self.time_embedder = TimestepEmbedder(self.hidden_size)
|
|
self.vae2llm = nn.Linear(self.patch_latent_dim, self.hidden_size)
|
|
self.llm2vae = nn.Linear(self.hidden_size, self.patch_latent_dim)
|
|
self.latent_pos_embed = PositionEmbedding(self.max_latent_size, self.hidden_size)
|
|
|
|
if config.visual_und:
|
|
self.vit_model = vit_model
|
|
self.vit_patch_size = config.vit_config.patch_size
|
|
self.vit_max_num_patch_per_side = config.vit_max_num_patch_per_side
|
|
self.vit_hidden_size = config.vit_config.hidden_size
|
|
self.connector = MLPconnector(self.vit_hidden_size, self.hidden_size, config.connector_act)
|
|
self.vit_pos_embed = PositionEmbedding(self.vit_max_num_patch_per_side, self.hidden_size)
|
|
|
|
self.get_flattened_position_ids = get_flattened_position_ids_extrapolate
|
|
|
|
self.config = config
|
|
self._init_weights()
|
|
|
|
def _init_weights(self):
|
|
if self.config.visual_gen:
|
|
nn.init.constant_(self.llm2vae.weight, 0)
|
|
nn.init.constant_(self.llm2vae.bias, 0)
|
|
|
|
def prepare_prompts(self, curr_kvlens, curr_rope, prompts, tokenizer, new_token_ids):
|
|
packed_text_ids = list()
|
|
packed_text_position_ids = list()
|
|
text_token_lens = list()
|
|
packed_text_indexes = list()
|
|
packed_key_value_indexes = list()
|
|
|
|
curr = 0
|
|
newlens, new_rope = list(), list()
|
|
for prompt, curr_kvlen, curr_position_id in zip(prompts, curr_kvlens, curr_rope):
|
|
packed_key_value_indexes.extend(range(curr, curr + curr_kvlen))
|
|
curr += curr_kvlen
|
|
|
|
text_ids = tokenizer.encode(prompt)
|
|
text_ids = [new_token_ids["bos_token_id"]] + text_ids + [new_token_ids["eos_token_id"]]
|
|
text_token_lens.append(len(text_ids))
|
|
packed_text_ids.extend(text_ids)
|
|
packed_text_position_ids.extend(range(curr_position_id, curr_position_id + len(text_ids)))
|
|
packed_text_indexes.extend(range(curr, curr + len(text_ids)))
|
|
newlens.append(curr_kvlen + len(text_ids))
|
|
new_rope.append(curr_position_id + len(text_ids))
|
|
curr += len(text_ids)
|
|
|
|
generation_input = {
|
|
"text_token_lens": torch.tensor(text_token_lens, dtype=torch.int),
|
|
"packed_text_ids": torch.tensor(packed_text_ids, dtype=torch.long),
|
|
"packed_text_position_ids": torch.tensor(packed_text_position_ids, dtype=torch.long),
|
|
"packed_text_indexes": torch.tensor(packed_text_indexes, dtype=torch.long),
|
|
"packed_key_value_indexes": torch.tensor(packed_key_value_indexes, dtype=torch.long),
|
|
"key_values_lens": torch.tensor(curr_kvlens, dtype=torch.int),
|
|
}
|
|
|
|
return generation_input, newlens, new_rope
|
|
|
|
def forward_cache_update_text(
|
|
self,
|
|
past_key_values: NaiveCache,
|
|
packed_text_ids: torch.IntTensor,
|
|
packed_text_position_ids: torch.LongTensor,
|
|
text_token_lens: torch.LongTensor,
|
|
packed_text_indexes: torch.LongTensor,
|
|
packed_key_value_indexes: torch.LongTensor,
|
|
key_values_lens: torch.IntTensor,
|
|
):
|
|
packed_text_embedding = self.language_model.model.embed_tokens(packed_text_ids)
|
|
|
|
extra_inputs = {}
|
|
if self.use_moe:
|
|
extra_inputs = {"mode": "und"}
|
|
|
|
output = self.language_model.forward(
|
|
packed_query_sequence=packed_text_embedding,
|
|
query_lens=text_token_lens,
|
|
packed_query_position_ids=packed_text_position_ids,
|
|
packed_query_indexes=packed_text_indexes,
|
|
past_key_values=past_key_values,
|
|
packed_key_value_indexes=packed_key_value_indexes,
|
|
key_values_lens=key_values_lens,
|
|
update_past_key_values=True,
|
|
is_causal=True,
|
|
**extra_inputs,
|
|
)
|
|
past_key_values = output.past_key_values
|
|
|
|
return past_key_values
|
|
|
|
def prepare_vae_images(self, curr_kvlens, curr_rope, images, transforms, new_token_ids, timestep=0):
|
|
patchified_vae_latent_shapes, packed_vae_position_ids = list(), list()
|
|
packed_vae_token_indexes = list()
|
|
packed_text_ids, packed_text_indexes = list(), list()
|
|
packed_seqlens, packed_position_ids, packed_indexes = list(), list(), list()
|
|
packed_key_value_indexes = list()
|
|
|
|
_curr = curr = 0
|
|
vae_image_tensors = list()
|
|
newlens, new_rope = list(), list()
|
|
for image, curr_kvlen, curr_position_id in zip(images, curr_kvlens, curr_rope):
|
|
packed_key_value_indexes.extend(range(curr, curr + curr_kvlen))
|
|
curr += curr_kvlen
|
|
|
|
packed_text_ids.append(new_token_ids["start_of_image"])
|
|
packed_text_indexes.append(_curr)
|
|
packed_indexes.append(curr)
|
|
curr += 1
|
|
_curr += 1
|
|
|
|
image_tensor = transforms(image)
|
|
vae_image_tensors.append(image_tensor)
|
|
vae_position_ids = self.get_flattened_position_ids(
|
|
image_tensor.size(1),
|
|
image_tensor.size(2),
|
|
self.latent_downsample,
|
|
max_num_patches_per_side=self.max_latent_size,
|
|
)
|
|
packed_vae_position_ids.append(vae_position_ids)
|
|
H, W = image_tensor.shape[1:]
|
|
h = H // self.latent_downsample
|
|
w = W // self.latent_downsample
|
|
patchified_vae_latent_shapes.append((h, w))
|
|
|
|
num_img_tokens = w * h
|
|
packed_vae_token_indexes.extend(range(_curr, _curr + num_img_tokens))
|
|
packed_indexes.extend(range(curr, curr + num_img_tokens))
|
|
curr += num_img_tokens
|
|
_curr += num_img_tokens
|
|
|
|
packed_text_ids.append(new_token_ids["end_of_image"])
|
|
packed_text_indexes.append(_curr)
|
|
packed_indexes.append(curr)
|
|
curr += 1
|
|
_curr += 1
|
|
|
|
packed_position_ids.extend([curr_position_id] * (num_img_tokens + 2))
|
|
packed_seqlens.append(num_img_tokens + 2)
|
|
newlens.append(curr_kvlen + num_img_tokens + 2)
|
|
new_rope.append(curr_position_id + 1)
|
|
|
|
image_sizes = [item.shape for item in vae_image_tensors]
|
|
max_image_size = [max(item) for item in list(zip(*image_sizes))]
|
|
padded_images = torch.zeros(size=(len(vae_image_tensors), *max_image_size))
|
|
for i, image_tensor in enumerate(vae_image_tensors):
|
|
padded_images[i, :, : image_tensor.shape[1], : image_tensor.shape[2]] = image_tensor
|
|
|
|
generation_input = {
|
|
"padded_images": padded_images,
|
|
"patchified_vae_latent_shapes": patchified_vae_latent_shapes,
|
|
"packed_vae_position_ids": torch.cat(packed_vae_position_ids, dim=0),
|
|
"packed_timesteps": torch.tensor([timestep]),
|
|
"packed_vae_token_indexes": torch.tensor(packed_vae_token_indexes, dtype=torch.long),
|
|
"packed_text_ids": torch.tensor(packed_text_ids, dtype=torch.long),
|
|
"packed_text_indexes": torch.tensor(packed_text_indexes, dtype=torch.long),
|
|
"packed_position_ids": torch.tensor(packed_position_ids, dtype=torch.long),
|
|
"packed_seqlens": torch.tensor(packed_seqlens, dtype=torch.int),
|
|
"packed_indexes": torch.tensor(packed_indexes, dtype=torch.long),
|
|
"packed_key_value_indexes": torch.tensor(packed_key_value_indexes, dtype=torch.long),
|
|
"key_values_lens": torch.tensor(curr_kvlens, dtype=torch.int),
|
|
}
|
|
|
|
return generation_input, newlens, new_rope
|
|
|
|
def forward_cache_update_vae(
|
|
self,
|
|
vae_model,
|
|
past_key_values: NaiveCache,
|
|
padded_images: torch.Tensor,
|
|
patchified_vae_latent_shapes: list,
|
|
packed_vae_position_ids: torch.LongTensor,
|
|
packed_timesteps: torch.Tensor,
|
|
packed_vae_token_indexes: torch.LongTensor,
|
|
packed_text_ids: torch.LongTensor,
|
|
packed_text_indexes: torch.LongTensor,
|
|
packed_position_ids: torch.LongTensor,
|
|
packed_seqlens: torch.IntTensor,
|
|
packed_indexes: torch.LongTensor,
|
|
key_values_lens: torch.IntTensor,
|
|
packed_key_value_indexes: torch.Tensor,
|
|
):
|
|
packed_text_embedding = self.language_model.model.embed_tokens(packed_text_ids)
|
|
packed_sequence = packed_text_embedding.new_zeros((sum(packed_seqlens), self.hidden_size))
|
|
packed_sequence[packed_text_indexes] = packed_text_embedding
|
|
|
|
padded_latent = vae_model.encode(padded_images)
|
|
|
|
p = self.latent_patch_size
|
|
packed_latent = list()
|
|
for latent, (h, w) in zip(padded_latent, patchified_vae_latent_shapes):
|
|
latent = latent[:, : h * p, : w * p].reshape(self.latent_channel, h, p, w, p)
|
|
latent = torch.einsum("chpwq->hwpqc", latent).reshape(-1, p * p * self.latent_channel)
|
|
packed_latent.append(latent)
|
|
packed_latent = torch.cat(packed_latent, dim=0)
|
|
packed_pos_embed = self.latent_pos_embed(packed_vae_position_ids)
|
|
packed_timestep_embeds = self.time_embedder(packed_timesteps)
|
|
packed_latent = self.vae2llm(packed_latent) + packed_timestep_embeds + packed_pos_embed
|
|
if packed_latent.dtype != packed_sequence.dtype:
|
|
packed_latent = packed_latent.to(packed_sequence.dtype)
|
|
packed_sequence[packed_vae_token_indexes] = packed_latent
|
|
|
|
extra_inputs = {}
|
|
if self.use_moe:
|
|
extra_inputs = {
|
|
"mode": "gen",
|
|
"packed_vae_token_indexes": packed_vae_token_indexes,
|
|
"packed_text_indexes": packed_text_indexes,
|
|
}
|
|
|
|
output = self.language_model.forward(
|
|
packed_query_sequence=packed_sequence,
|
|
query_lens=packed_seqlens,
|
|
packed_query_position_ids=packed_position_ids,
|
|
packed_query_indexes=packed_indexes,
|
|
past_key_values=past_key_values,
|
|
key_values_lens=key_values_lens,
|
|
packed_key_value_indexes=packed_key_value_indexes,
|
|
update_past_key_values=True,
|
|
is_causal=False,
|
|
**extra_inputs,
|
|
)
|
|
past_key_values = output.past_key_values
|
|
|
|
return past_key_values
|
|
|
|
def prepare_vit_images(self, curr_kvlens, curr_rope, images, transforms, new_token_ids):
|
|
packed_vit_token_indexes = list()
|
|
vit_token_seqlens, packed_vit_tokens, packed_vit_position_ids = list(), list(), list()
|
|
packed_text_ids, packed_text_indexes = list(), list()
|
|
packed_seqlens, packed_position_ids, packed_indexes = list(), list(), list()
|
|
packed_key_value_indexes = list()
|
|
|
|
_curr = curr = 0
|
|
newlens, new_rope = list(), list()
|
|
for image, curr_kvlen, curr_position_id in zip(images, curr_kvlens, curr_rope):
|
|
packed_key_value_indexes.extend(range(curr, curr + curr_kvlen))
|
|
curr += curr_kvlen
|
|
|
|
packed_text_ids.append(new_token_ids["start_of_image"])
|
|
packed_text_indexes.append(_curr)
|
|
packed_indexes.append(curr)
|
|
curr += 1
|
|
_curr += 1
|
|
|
|
image_tensor = transforms(image)
|
|
vit_position_ids = self.get_flattened_position_ids(
|
|
image_tensor.size(1),
|
|
image_tensor.size(2),
|
|
self.vit_patch_size,
|
|
max_num_patches_per_side=self.vit_max_num_patch_per_side,
|
|
)
|
|
vit_tokens = patchify(image_tensor, self.vit_patch_size)
|
|
packed_vit_tokens.append(vit_tokens)
|
|
num_img_tokens = vit_tokens.shape[0]
|
|
packed_vit_position_ids.append(vit_position_ids)
|
|
vit_token_seqlens.append(num_img_tokens)
|
|
packed_vit_token_indexes.extend(range(_curr, _curr + num_img_tokens))
|
|
packed_indexes.extend(range(curr, curr + num_img_tokens))
|
|
curr += num_img_tokens
|
|
_curr += num_img_tokens
|
|
|
|
packed_text_ids.append(new_token_ids["end_of_image"])
|
|
packed_text_indexes.append(_curr)
|
|
packed_indexes.append(curr)
|
|
curr += 1
|
|
_curr += 1
|
|
|
|
packed_position_ids.extend([curr_position_id] * (num_img_tokens + 2))
|
|
packed_seqlens.append(num_img_tokens + 2)
|
|
newlens.append(curr_kvlen + num_img_tokens + 2)
|
|
new_rope.append(curr_position_id + 1)
|
|
|
|
generation_input = {
|
|
"packed_text_ids": torch.tensor(packed_text_ids, dtype=torch.long),
|
|
"packed_text_indexes": torch.tensor(packed_text_indexes, dtype=torch.long),
|
|
"vit_token_seqlens": torch.tensor(vit_token_seqlens, dtype=torch.int),
|
|
"packed_vit_tokens": torch.cat(packed_vit_tokens, dim=0),
|
|
"packed_vit_position_ids": torch.cat(packed_vit_position_ids, dim=0),
|
|
"packed_vit_token_indexes": torch.tensor(packed_vit_token_indexes, dtype=torch.long),
|
|
"packed_position_ids": torch.tensor(packed_position_ids, dtype=torch.long),
|
|
"packed_seqlens": torch.tensor(packed_seqlens, dtype=torch.int),
|
|
"packed_indexes": torch.tensor(packed_indexes, dtype=torch.long),
|
|
"packed_key_value_indexes": torch.tensor(packed_key_value_indexes, dtype=torch.long),
|
|
"key_values_lens": torch.tensor(curr_kvlens, dtype=torch.int),
|
|
}
|
|
|
|
return generation_input, newlens, new_rope
|
|
|
|
def forward_cache_update_vit(
|
|
self,
|
|
past_key_values: NaiveCache,
|
|
packed_text_ids: torch.LongTensor,
|
|
packed_text_indexes: torch.LongTensor,
|
|
packed_vit_tokens: torch.Tensor,
|
|
packed_vit_token_indexes: torch.LongTensor,
|
|
packed_vit_position_ids: torch.LongTensor,
|
|
vit_token_seqlens: torch.IntTensor,
|
|
packed_position_ids: torch.LongTensor,
|
|
packed_seqlens: torch.IntTensor,
|
|
packed_indexes: torch.LongTensor,
|
|
packed_key_value_indexes: torch.LongTensor,
|
|
key_values_lens: torch.IntTensor,
|
|
):
|
|
packed_text_embedding = self.language_model.model.embed_tokens(packed_text_ids)
|
|
packed_sequence = packed_text_embedding.new_zeros((sum(packed_seqlens), self.hidden_size))
|
|
packed_sequence[packed_text_indexes] = packed_text_embedding
|
|
|
|
cu_seqlens = torch.nn.functional.pad(torch.cumsum(vit_token_seqlens, dim=0), (1, 0))
|
|
cu_seqlens = cu_seqlens.to(torch.int32)
|
|
max_seqlen = torch.max(vit_token_seqlens).item()
|
|
packed_vit_token_embed = self.vit_model(
|
|
packed_pixel_values=packed_vit_tokens,
|
|
packed_flattened_position_ids=packed_vit_position_ids,
|
|
cu_seqlens=cu_seqlens,
|
|
max_seqlen=max_seqlen,
|
|
)
|
|
packed_vit_token_embed = self.connector(packed_vit_token_embed)
|
|
pos_emb = self.vit_pos_embed(packed_vit_position_ids)
|
|
packed_vit_token_embed = packed_vit_token_embed + pos_emb
|
|
if packed_vit_token_embed.dtype != packed_sequence.dtype:
|
|
packed_vit_token_embed = packed_vit_token_embed.to(packed_sequence.dtype)
|
|
packed_sequence[packed_vit_token_indexes] = packed_vit_token_embed
|
|
|
|
extra_inputs = {}
|
|
if self.use_moe:
|
|
extra_inputs = {"mode": "und"}
|
|
|
|
output = self.language_model.forward(
|
|
packed_query_sequence=packed_sequence,
|
|
query_lens=packed_seqlens,
|
|
packed_query_position_ids=packed_position_ids,
|
|
packed_query_indexes=packed_indexes,
|
|
past_key_values=past_key_values,
|
|
packed_key_value_indexes=packed_key_value_indexes,
|
|
key_values_lens=key_values_lens,
|
|
update_past_key_values=True,
|
|
is_causal=False,
|
|
**extra_inputs,
|
|
)
|
|
past_key_values = output.past_key_values
|
|
|
|
return past_key_values
|
|
|
|
def prepare_input(self, curr_kvlens, curr_rope, image_sizes, new_token_ids=None):
|
|
packed_text_ids, packed_text_indexes = list(), list()
|
|
packed_vae_position_ids, packed_vae_token_indexes, packed_init_noises = list(), list(), list()
|
|
packed_position_ids, packed_seqlens, packed_indexes = list(), list(), list()
|
|
packed_key_value_indexes = list()
|
|
|
|
query_curr = curr = 0
|
|
for (H, W), curr_kvlen, curr_position_id in zip(image_sizes, curr_kvlens, curr_rope):
|
|
packed_key_value_indexes.extend(range(curr, curr + curr_kvlen))
|
|
curr += curr_kvlen
|
|
|
|
packed_text_ids.append(new_token_ids["start_of_image"])
|
|
packed_text_indexes.append(query_curr)
|
|
|
|
packed_indexes.append(curr)
|
|
curr += 1
|
|
query_curr += 1
|
|
|
|
vae_position_ids = self.get_flattened_position_ids(
|
|
H, W, self.latent_downsample, max_num_patches_per_side=self.max_latent_size
|
|
)
|
|
packed_vae_position_ids.append(vae_position_ids)
|
|
|
|
h, w = H // self.latent_downsample, W // self.latent_downsample
|
|
num_image_tokens = h * w
|
|
|
|
packed_init_noises.append(torch.randn(num_image_tokens, self.latent_channel * self.latent_patch_size**2))
|
|
packed_vae_token_indexes.extend(range(query_curr, query_curr + num_image_tokens))
|
|
packed_seqlens.append(num_image_tokens + 2)
|
|
|
|
packed_indexes.extend(range(curr, curr + num_image_tokens))
|
|
curr += num_image_tokens
|
|
query_curr += num_image_tokens
|
|
|
|
packed_text_ids.append(new_token_ids["end_of_image"])
|
|
packed_text_indexes.append(query_curr)
|
|
|
|
packed_indexes.append(curr)
|
|
curr += 1
|
|
query_curr += 1
|
|
|
|
packed_position_ids.extend([curr_position_id] * (num_image_tokens + 2))
|
|
|
|
# Construct Output
|
|
generation_input = {
|
|
"packed_text_ids": torch.tensor(packed_text_ids, dtype=torch.long),
|
|
"packed_text_indexes": torch.tensor(packed_text_indexes, dtype=torch.long),
|
|
"packed_init_noises": torch.cat(packed_init_noises, dim=0),
|
|
"packed_vae_position_ids": torch.cat(packed_vae_position_ids, dim=0),
|
|
"packed_vae_token_indexes": torch.tensor(packed_vae_token_indexes, dtype=torch.long),
|
|
"packed_seqlens": torch.tensor(packed_seqlens, dtype=torch.int),
|
|
"packed_position_ids": torch.tensor(packed_position_ids, dtype=torch.long),
|
|
"key_values_lens": torch.tensor(curr_kvlens, dtype=torch.int),
|
|
"packed_indexes": torch.tensor(packed_indexes, dtype=torch.long),
|
|
"packed_key_value_indexes": torch.tensor(packed_key_value_indexes, dtype=torch.long),
|
|
}
|
|
|
|
return generation_input
|
|
|
|
def prepare_vae_latent(self, curr_kvlens, curr_rope, image_sizes, new_token_ids):
|
|
return self.prepare_input(curr_kvlens, curr_rope, image_sizes, new_token_ids)
|
|
|
|
def generate_image(
|
|
self,
|
|
packed_text_ids: torch.LongTensor,
|
|
packed_text_indexes: torch.LongTensor,
|
|
packed_init_noises: torch.Tensor,
|
|
packed_vae_position_ids: torch.LongTensor,
|
|
packed_vae_token_indexes: torch.LongTensor,
|
|
packed_seqlens: torch.IntTensor,
|
|
packed_position_ids: torch.LongTensor,
|
|
packed_indexes: torch.LongTensor,
|
|
past_key_values: NaiveCache,
|
|
key_values_lens: torch.IntTensor,
|
|
packed_key_value_indexes: torch.LongTensor,
|
|
num_timesteps: int = 24,
|
|
timestep_shift: float = 1.0,
|
|
):
|
|
model_pred_cache_dic, model_pred_current = None, None
|
|
model_pred_text_cache_dic, model_pred_text_current = None, None
|
|
model_pred_img_cache_dic, model_pred_img_current = None, None
|
|
|
|
x_t = packed_init_noises
|
|
|
|
timesteps = torch.linspace(1, 0, num_timesteps, device=x_t.device)
|
|
timesteps = timestep_shift * timesteps / (1 + (timestep_shift - 1) * timesteps)
|
|
dts = timesteps[:-1] - timesteps[1:]
|
|
timesteps = timesteps[:-1]
|
|
|
|
for i, t in enumerate(timesteps):
|
|
timestep = torch.tensor([t] * x_t.shape[0], device=x_t.device)
|
|
v_t = self._forward_flow(
|
|
x_t=x_t,
|
|
timestep=timestep,
|
|
packed_vae_token_indexes=packed_vae_token_indexes,
|
|
packed_vae_position_ids=packed_vae_position_ids,
|
|
packed_text_ids=packed_text_ids,
|
|
packed_text_indexes=packed_text_indexes,
|
|
packed_position_ids=packed_position_ids,
|
|
packed_indexes=packed_indexes,
|
|
packed_seqlens=packed_seqlens,
|
|
key_values_lens=key_values_lens,
|
|
past_key_values=past_key_values,
|
|
packed_key_value_indexes=packed_key_value_indexes,
|
|
# cache
|
|
model_pred_cache_dic=model_pred_cache_dic,
|
|
model_pred_current=model_pred_current,
|
|
model_pred_text_cache_dic=model_pred_text_cache_dic,
|
|
model_pred_text_current=model_pred_text_current,
|
|
model_pred_img_cache_dic=model_pred_img_cache_dic,
|
|
model_pred_img_current=model_pred_img_current,
|
|
)
|
|
|
|
x_t = x_t - v_t.to(x_t.device) * dts[i] # velocity pointing from data to noise
|
|
|
|
unpacked_latent = x_t.split((packed_seqlens - 2).tolist())
|
|
return unpacked_latent
|
|
|
|
def _forward_flow(
|
|
self,
|
|
x_t: torch.Tensor,
|
|
timestep: torch.LongTensor,
|
|
packed_vae_token_indexes: torch.LongTensor,
|
|
packed_vae_position_ids: torch.LongTensor,
|
|
packed_text_ids: torch.LongTensor,
|
|
packed_text_indexes: torch.LongTensor,
|
|
packed_indexes: torch.LongTensor,
|
|
packed_position_ids: torch.LongTensor,
|
|
packed_seqlens: torch.IntTensor,
|
|
key_values_lens: torch.IntTensor,
|
|
past_key_values: NaiveCache,
|
|
packed_key_value_indexes: torch.LongTensor,
|
|
# cache
|
|
model_pred_cache_dic: dict[str, Any] | None = None,
|
|
model_pred_current: int | None = None,
|
|
model_pred_text_cache_dic: dict[str, Any] | None = None,
|
|
model_pred_text_current: int | None = None,
|
|
model_pred_img_cache_dic: dict[str, Any] | None = None,
|
|
model_pred_img_current: int | None = None,
|
|
):
|
|
packed_text_embedding = self.language_model.model.embed_tokens(packed_text_ids)
|
|
packed_sequence = packed_text_embedding.new_zeros((sum(packed_seqlens), self.hidden_size))
|
|
packed_sequence[packed_text_indexes] = packed_text_embedding
|
|
|
|
assert timestep.unique().shape[0] == 1
|
|
packed_pos_embed = self.latent_pos_embed(packed_vae_position_ids)
|
|
packed_timestep_embeds = self.time_embedder(timestep)
|
|
x_t = self.vae2llm(x_t) + packed_timestep_embeds + packed_pos_embed
|
|
if x_t.dtype != packed_sequence.dtype:
|
|
x_t = x_t.to(packed_sequence.dtype)
|
|
packed_sequence[packed_vae_token_indexes] = x_t
|
|
|
|
extra_inputs = {}
|
|
if self.use_moe:
|
|
extra_inputs = {
|
|
"mode": "gen",
|
|
"packed_vae_token_indexes": packed_vae_token_indexes,
|
|
"packed_text_indexes": packed_text_indexes,
|
|
}
|
|
|
|
output = self.language_model.forward(
|
|
packed_query_sequence=packed_sequence,
|
|
query_lens=packed_seqlens,
|
|
packed_query_position_ids=packed_position_ids,
|
|
packed_query_indexes=packed_indexes,
|
|
past_key_values=past_key_values,
|
|
key_values_lens=key_values_lens,
|
|
packed_key_value_indexes=packed_key_value_indexes,
|
|
update_past_key_values=False,
|
|
is_causal=False,
|
|
**extra_inputs,
|
|
)
|
|
v_t = self.llm2vae(output.packed_query_sequence)
|
|
v_t = v_t[packed_vae_token_indexes]
|
|
|
|
return v_t
|