support stepvideo

diffsynth/models/stepvideo_text_encoder.py

@@ -0,0 +1,553 @@
+# Copyright 2025 StepFun Inc. All Rights Reserved.
+# 
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+# ==============================================================================
+import os
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .stepvideo_dit import RMSNorm
+from safetensors.torch import load_file
+from transformers.modeling_utils import PretrainedConfig, PreTrainedModel
+from einops import rearrange
+import json
+from typing import List
+from functools import wraps
+import warnings
+
+
+
+class EmptyInitOnDevice(torch.overrides.TorchFunctionMode):
+    def __init__(self, device=None):
+        self.device = device
+
+    def __torch_function__(self, func, types, args=(), kwargs=None):
+        kwargs = kwargs or {}
+        if getattr(func, '__module__', None) == 'torch.nn.init':
+            if 'tensor' in kwargs:
+                return kwargs['tensor']
+            else:
+                return args[0]
+        if self.device is not None and func in torch.utils._device._device_constructors() and kwargs.get('device') is None:
+            kwargs['device'] = self.device
+        return func(*args, **kwargs)
+    
+
+def with_empty_init(func):
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        with EmptyInitOnDevice('cpu'):
+            return func(*args, **kwargs)
+    return wrapper
+
+
+
+class LLaMaEmbedding(nn.Module):
+    """Language model embeddings.
+
+    Arguments:
+        hidden_size: hidden size
+        vocab_size: vocabulary size
+        max_sequence_length: maximum size of sequence. This
+                             is used for positional embedding
+        embedding_dropout_prob: dropout probability for embeddings
+        init_method: weight initialization method
+        num_tokentypes: size of the token-type embeddings. 0 value
+                        will ignore this embedding
+    """
+
+    def __init__(self,
+                 cfg,
+                 ):
+        super().__init__()
+        self.hidden_size = cfg.hidden_size
+        self.params_dtype = cfg.params_dtype
+        self.fp32_residual_connection = cfg.fp32_residual_connection 
+        self.embedding_weights_in_fp32 = cfg.embedding_weights_in_fp32
+        self.word_embeddings = torch.nn.Embedding(
+            cfg.padded_vocab_size, self.hidden_size,
+        )
+        self.embedding_dropout = torch.nn.Dropout(cfg.hidden_dropout)
+
+    def forward(self, input_ids):
+        # Embeddings.
+        if self.embedding_weights_in_fp32:
+            self.word_embeddings = self.word_embeddings.to(torch.float32)
+        embeddings = self.word_embeddings(input_ids)
+        if self.embedding_weights_in_fp32:
+            embeddings = embeddings.to(self.params_dtype)
+            self.word_embeddings = self.word_embeddings.to(self.params_dtype)
+
+        # Data format change to avoid explicit tranposes : [b s h] --> [s b h].
+        embeddings = embeddings.transpose(0, 1).contiguous()
+
+        # If the input flag for fp32 residual connection is set, convert for float.
+        if self.fp32_residual_connection:
+            embeddings = embeddings.float()
+
+        # Dropout.
+        embeddings = self.embedding_dropout(embeddings)
+
+        return embeddings
+
+
+
+class StepChatTokenizer:
+    """Step Chat Tokenizer"""
+
+    def __init__(
+        self, model_file, name="StepChatTokenizer",
+        bot_token="<|BOT|>",  # Begin of Turn
+        eot_token="<|EOT|>",  # End of Turn
+        call_start_token="<|CALL_START|>",      # Call Start
+        call_end_token="<|CALL_END|>",          # Call End
+        think_start_token="<|THINK_START|>",    # Think Start
+        think_end_token="<|THINK_END|>",        # Think End
+        mask_start_token="<|MASK_1e69f|>",      # Mask start
+        mask_end_token="<|UNMASK_1e69f|>",      # Mask end
+    ):
+        import sentencepiece
+
+        self._tokenizer = sentencepiece.SentencePieceProcessor(model_file=model_file)
+
+        self._vocab = {}
+        self._inv_vocab = {}
+
+        self._special_tokens = {}
+        self._inv_special_tokens = {}
+
+        self._t5_tokens = []
+
+        for idx in range(self._tokenizer.get_piece_size()):
+            text = self._tokenizer.id_to_piece(idx)
+            self._inv_vocab[idx] = text
+            self._vocab[text] = idx
+
+            if self._tokenizer.is_control(idx) or self._tokenizer.is_unknown(idx):
+                self._special_tokens[text] = idx
+                self._inv_special_tokens[idx] = text
+
+        self._unk_id = self._tokenizer.unk_id()
+        self._bos_id = self._tokenizer.bos_id()
+        self._eos_id = self._tokenizer.eos_id()
+
+        for token in [
+            bot_token, eot_token, call_start_token, call_end_token,
+            think_start_token, think_end_token
+        ]:
+            assert token in self._vocab, f"Token '{token}' not found in tokenizer"
+            assert token in self._special_tokens, f"Token '{token}' is not a special token"
+
+        for token in [mask_start_token, mask_end_token]:
+            assert token in self._vocab, f"Token '{token}' not found in tokenizer"
+
+        self._bot_id = self._tokenizer.piece_to_id(bot_token)
+        self._eot_id = self._tokenizer.piece_to_id(eot_token)
+        self._call_start_id = self._tokenizer.piece_to_id(call_start_token)
+        self._call_end_id = self._tokenizer.piece_to_id(call_end_token)
+        self._think_start_id = self._tokenizer.piece_to_id(think_start_token)
+        self._think_end_id = self._tokenizer.piece_to_id(think_end_token)
+        self._mask_start_id = self._tokenizer.piece_to_id(mask_start_token)
+        self._mask_end_id = self._tokenizer.piece_to_id(mask_end_token)
+
+        self._underline_id = self._tokenizer.piece_to_id("\u2581")
+        
+    @property
+    def vocab(self):
+        return self._vocab
+
+    @property
+    def inv_vocab(self):
+        return self._inv_vocab
+
+    @property
+    def vocab_size(self):
+        return self._tokenizer.vocab_size()
+
+    def tokenize(self, text: str) -> List[int]:
+        return self._tokenizer.encode_as_ids(text)
+
+    def detokenize(self, token_ids: List[int]) -> str:
+        return self._tokenizer.decode_ids(token_ids)
+
+    
+class Tokens:
+    def __init__(self, input_ids, cu_input_ids, attention_mask, cu_seqlens, max_seq_len) -> None:
+        self.input_ids = input_ids
+        self.attention_mask = attention_mask
+        self.cu_input_ids = cu_input_ids
+        self.cu_seqlens = cu_seqlens
+        self.max_seq_len = max_seq_len
+    def to(self, device):
+        self.input_ids = self.input_ids.to(device)
+        self.attention_mask = self.attention_mask.to(device)
+        self.cu_input_ids = self.cu_input_ids.to(device)
+        self.cu_seqlens = self.cu_seqlens.to(device)
+        return self
+    
+class Wrapped_StepChatTokenizer(StepChatTokenizer):
+    def __call__(self, text, max_length=320, padding="max_length", truncation=True, return_tensors="pt"):
+        # [bos, ..., eos, pad, pad, ..., pad]
+        self.BOS = 1
+        self.EOS = 2
+        self.PAD = 2
+        out_tokens = []
+        attn_mask = []
+        if len(text) == 0:
+            part_tokens = [self.BOS] + [self.EOS]
+            valid_size = len(part_tokens)
+            if len(part_tokens) < max_length:
+                part_tokens += [self.PAD] * (max_length - valid_size)
+            out_tokens.append(part_tokens)
+            attn_mask.append([1]*valid_size+[0]*(max_length-valid_size))
+        else:
+            for part in text:
+                part_tokens = self.tokenize(part)
+                part_tokens = part_tokens[:(max_length - 2)] # leave 2 space for bos and eos
+                part_tokens = [self.BOS] + part_tokens + [self.EOS]
+                valid_size = len(part_tokens)
+                if len(part_tokens) < max_length:
+                    part_tokens += [self.PAD] * (max_length - valid_size)
+                out_tokens.append(part_tokens)
+                attn_mask.append([1]*valid_size+[0]*(max_length-valid_size))
+
+        out_tokens = torch.tensor(out_tokens, dtype=torch.long)
+        attn_mask = torch.tensor(attn_mask, dtype=torch.long)
+
+        # padding y based on tp size
+        padded_len = 0
+        padded_flag = True if padded_len > 0 else False
+        if padded_flag:
+            pad_tokens = torch.tensor([[self.PAD] * max_length], device=out_tokens.device)
+            pad_attn_mask = torch.tensor([[1]*padded_len+[0]*(max_length-padded_len)], device=attn_mask.device)
+            out_tokens = torch.cat([out_tokens, pad_tokens], dim=0)
+            attn_mask = torch.cat([attn_mask, pad_attn_mask], dim=0)
+        
+        # cu_seqlens
+        cu_out_tokens = out_tokens.masked_select(attn_mask != 0).unsqueeze(0)
+        seqlen = attn_mask.sum(dim=1).tolist()
+        cu_seqlens = torch.cumsum(torch.tensor([0]+seqlen), 0).to(device=out_tokens.device,dtype=torch.int32)
+        max_seq_len = max(seqlen)
+        return Tokens(out_tokens, cu_out_tokens, attn_mask, cu_seqlens, max_seq_len)
+
+
+
+def flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=True,
+                    return_attn_probs=False, tp_group_rank=0, tp_group_size=1):
+    softmax_scale = q.size(-1) ** (-0.5) if softmax_scale is None else softmax_scale
+    if hasattr(torch.ops.Optimus, "fwd"):
+        results = torch.ops.Optimus.fwd(q, k, v, None, dropout_p, softmax_scale, causal, return_attn_probs, None, tp_group_rank, tp_group_size)[0]
+    else:
+        warnings.warn("Cannot load `torch.ops.Optimus.fwd`. Using `torch.nn.functional.scaled_dot_product_attention` instead.")
+        results = torch.nn.functional.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=True, scale=softmax_scale).transpose(1, 2)
+    return results
+
+
+class FlashSelfAttention(torch.nn.Module):
+    def __init__(
+        self,
+        attention_dropout=0.0,
+    ):
+        super().__init__()
+        self.dropout_p = attention_dropout
+
+
+    def forward(self, q, k, v, cu_seqlens=None, max_seq_len=None):
+        if cu_seqlens is None:
+            output = flash_attn_func(q, k, v, dropout_p=self.dropout_p)
+        else:
+            raise ValueError('cu_seqlens is not supported!')
+
+        return output
+
+
+    
+def safediv(n, d):
+    q, r = divmod(n, d)
+    assert r == 0
+    return q
+
+
+class MultiQueryAttention(nn.Module):
+    def __init__(self, cfg, layer_id=None):
+        super().__init__()
+
+        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
+        assert self.use_flash_attention, 'FlashAttention is required!'
+
+        self.n_groups = cfg.num_attention_groups
+        self.tp_size = 1
+        self.n_local_heads = cfg.num_attention_heads
+        self.n_local_groups = self.n_groups
+
+        self.wqkv = nn.Linear(
+            cfg.hidden_size,
+            cfg.hidden_size + self.head_dim * 2 * self.n_groups,
+            bias=False,
+        )
+        self.wo = nn.Linear(
+            cfg.hidden_size,
+            cfg.hidden_size,
+            bias=False,
+        )
+
+        assert self.use_flash_attention, 'non-Flash attention not supported yet.'
+        self.core_attention = FlashSelfAttention(attention_dropout=cfg.attention_dropout)
+        
+        self.layer_id = layer_id
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: Optional[torch.Tensor],
+        cu_seqlens: Optional[torch.Tensor],
+        max_seq_len: Optional[torch.Tensor],
+    ):
+        seqlen, bsz, dim = x.shape
+        xqkv = self.wqkv(x)
+
+        xq, xkv = torch.split(
+            xqkv,
+            (dim // self.tp_size,
+             self.head_dim*2*self.n_groups // self.tp_size
+            ),
+            dim=-1,
+        )
+
+        # gather on 1st dimention
+        xq = xq.view(seqlen, bsz, self.n_local_heads, self.head_dim)
+        xkv = xkv.view(seqlen, bsz, self.n_local_groups, 2 * self.head_dim)
+        xk, xv = xkv.chunk(2, -1)
+
+        # rotary embedding + flash attn
+        xq = rearrange(xq, "s b h d -> b s h d")
+        xk = rearrange(xk, "s b h d -> b s h d")
+        xv = rearrange(xv, "s b h d -> b s h d")
+
+        q_per_kv = self.n_local_heads // self.n_local_groups
+        if q_per_kv > 1:
+            b, s, h, d = xk.size()
+            if h == 1:
+                xk = xk.expand(b, s, q_per_kv, d)
+                xv = xv.expand(b, s, q_per_kv, d)
+            else:
+                ''' To cover the cases where h > 1, we have
+                    the following implementation, which is equivalent to:
+                        xk = xk.repeat_interleave(q_per_kv, dim=-2)
+                        xv = xv.repeat_interleave(q_per_kv, dim=-2)
+                    but can avoid calling aten::item() that involves cpu.
+                '''
+                idx = torch.arange(q_per_kv * h, device=xk.device).reshape(q_per_kv, -1).permute(1, 0).flatten()
+                xk = torch.index_select(xk.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous()
+                xv = torch.index_select(xv.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous()
+
+        if self.use_flash_attention:
+            output = self.core_attention(xq, xk, xv,
+                                      cu_seqlens=cu_seqlens,
+                                      max_seq_len=max_seq_len)
+            # reduce-scatter only support first dimention now
+            output = rearrange(output, "b s h d -> s b (h d)").contiguous()
+        else:
+            xq, xk, xv = [
+                rearrange(x, "b s ... -> s b ...").contiguous()
+                for x in (xq, xk, xv)
+            ]
+            output = self.core_attention(xq, xk, xv, mask)
+        output = self.wo(output)
+        return output
+
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        cfg,
+        dim: int,
+        hidden_dim: int,
+        layer_id: int,
+        multiple_of: int=256,
+    ):
+        super().__init__()
+
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+        def swiglu(x):
+            x = torch.chunk(x, 2, dim=-1)
+            return F.silu(x[0]) * x[1]
+        self.swiglu = swiglu
+            
+        self.w1 = nn.Linear(
+            dim,
+            2 * hidden_dim,
+            bias=False,
+        )
+        self.w2 = nn.Linear(
+            hidden_dim,
+            dim,
+            bias=False,
+        )
+
+    def forward(self, x):
+        x = self.swiglu(self.w1(x))
+        output = self.w2(x)
+        return output
+
+
+
+class TransformerBlock(nn.Module):
+    def __init__(
+        self, cfg, layer_id: int
+    ):
+        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.attention = MultiQueryAttention(
+            cfg,
+            layer_id=layer_id,
+        )
+
+        self.feed_forward = FeedForward(
+            cfg,
+            dim=cfg.hidden_size,
+            hidden_dim=cfg.ffn_hidden_size,
+            layer_id=layer_id,
+        )
+        self.layer_id = layer_id
+        self.attention_norm = RMSNorm(
+            cfg.hidden_size,
+            eps=cfg.layernorm_epsilon,
+        )
+        self.ffn_norm = RMSNorm(
+            cfg.hidden_size,
+            eps=cfg.layernorm_epsilon,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: Optional[torch.Tensor],
+        cu_seqlens: Optional[torch.Tensor],
+        max_seq_len: Optional[torch.Tensor],
+    ):
+        residual = self.attention.forward(
+            self.attention_norm(x), mask,
+            cu_seqlens, max_seq_len
+        )
+        h = x + residual
+        ffn_res = self.feed_forward.forward(self.ffn_norm(h))
+        out = h + ffn_res
+        return out
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        config,
+        max_seq_size=8192,
+    ):
+        super().__init__()
+        self.num_layers = config.num_layers
+        self.layers = self._build_layers(config)
+
+    def _build_layers(self, config):
+        layers = torch.nn.ModuleList()
+        for layer_id in range(self.num_layers):
+            layers.append(
+                TransformerBlock(
+                    config,
+                    layer_id=layer_id + 1 ,
+                )
+            )
+        return layers
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        cu_seqlens=None,
+        max_seq_len=None,
+    ):
+
+        if max_seq_len is not None and not isinstance(max_seq_len, torch.Tensor):
+            max_seq_len = torch.tensor(max_seq_len, dtype=torch.int32, device="cpu")
+
+        for lid, layer in enumerate(self.layers):
+            hidden_states = layer(
+                                    hidden_states,
+                                    attention_mask,
+                                    cu_seqlens,
+                                    max_seq_len,
+                                )
+        return hidden_states
+
+
+class Step1Model(PreTrainedModel):
+    config_class=PretrainedConfig
+    @with_empty_init
+    def __init__(
+        self,
+        config,
+    ):
+        super().__init__(config)
+        self.tok_embeddings = LLaMaEmbedding(config)
+        self.transformer = Transformer(config)
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+    ):
+
+        hidden_states = self.tok_embeddings(input_ids)
+
+        hidden_states = self.transformer(
+            hidden_states,
+            attention_mask,
+        )
+        return hidden_states
+    
+    
+
+class STEP1TextEncoder(torch.nn.Module):
+    def __init__(self, model_dir, max_length=320):
+        super(STEP1TextEncoder, self).__init__()
+        self.max_length = max_length
+        self.text_tokenizer = Wrapped_StepChatTokenizer(os.path.join(model_dir, 'step1_chat_tokenizer.model'))
+        text_encoder = Step1Model.from_pretrained(model_dir)
+        self.text_encoder = text_encoder.eval().to(torch.bfloat16)
+
+    @staticmethod
+    def from_pretrained(path, torch_dtype=torch.bfloat16):
+        model = STEP1TextEncoder(path).to(torch_dtype)
+        return model
+        
+    @torch.no_grad
+    def forward(self, prompts, with_mask=True, max_length=None, device="cuda"):
+        self.device = device
+        with torch.no_grad(), torch.amp.autocast(dtype=torch.bfloat16, device_type=device):
+            if type(prompts) is str:
+                prompts = [prompts]
+            
+            txt_tokens = self.text_tokenizer(
+                prompts, max_length=max_length or self.max_length, padding="max_length", truncation=True, return_tensors="pt"
+            )
+            y = self.text_encoder(
+                txt_tokens.input_ids.to(self.device), 
+                attention_mask=txt_tokens.attention_mask.to(self.device) if with_mask else None
+            )
+            y_mask = txt_tokens.attention_mask
+        return y.transpose(0,1), y_mask
+