0a7a2017a0
Co-authored-by: Brain97 <Brain97@users.noreply.github.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: Mick <mickjagger19@icloud.com> Co-authored-by: blahblah <blahblah>
110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
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# SPDX-License-Identifier: Apache-2.0
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from abc import ABC, abstractmethod
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from typing import Any
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import torch
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from torch import nn
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from sglang.multimodal_gen.configs.models import DiTConfig
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# NOTE: TeaCacheContext and TeaCacheMixin have been moved to
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# sglang.multimodal_gen.runtime.cache.teacache
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# For backwards compatibility, re-export from the new location
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from sglang.multimodal_gen.runtime.cache.teacache import TeaCacheContext # noqa: F401
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from sglang.multimodal_gen.runtime.cache.teacache import TeaCacheMixin
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from sglang.multimodal_gen.runtime.platforms import AttentionBackendEnum
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# TODO
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class BaseDiT(nn.Module, ABC):
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_fsdp_shard_conditions: list = []
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_compile_conditions: list = []
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param_names_mapping: dict
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reverse_param_names_mapping: dict
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hidden_size: int
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num_attention_heads: int
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num_channels_latents: int
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# always supports torch_sdpa
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_supported_attention_backends: set[AttentionBackendEnum] = (
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DiTConfig()._supported_attention_backends
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)
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def __init_subclass__(cls) -> None:
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required_class_attrs = [
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"_fsdp_shard_conditions",
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"param_names_mapping",
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"_compile_conditions",
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]
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super().__init_subclass__()
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for attr in required_class_attrs:
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if not hasattr(cls, attr):
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raise AttributeError(
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f"Subclasses of BaseDiT must define '{attr}' class variable"
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)
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def __init__(self, config: DiTConfig, hf_config: dict[str, Any], **kwargs) -> None:
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super().__init__()
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self.config = config
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self.hf_config = hf_config
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if not self.supported_attention_backends:
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raise ValueError(
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f"Subclass {self.__class__.__name__} must define _supported_attention_backends"
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)
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@abstractmethod
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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 | list[torch.Tensor],
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timestep: torch.LongTensor,
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encoder_hidden_states_image: torch.Tensor | list[torch.Tensor] | None = None,
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guidance=None,
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**kwargs,
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) -> torch.Tensor:
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pass
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def __post_init__(self) -> None:
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required_attrs = ["hidden_size", "num_attention_heads", "num_channels_latents"]
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for attr in required_attrs:
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if not hasattr(self, attr):
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raise AttributeError(
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f"Subclasses of BaseDiT must define '{attr}' instance variable"
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)
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@property
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def supported_attention_backends(self) -> set[AttentionBackendEnum]:
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return self._supported_attention_backends
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@property
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def device(self) -> torch.device:
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"""Get the device of the model."""
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return next(self.parameters()).device
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class CachableDiT(TeaCacheMixin, BaseDiT):
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"""
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An intermediate base class that adds TeaCache optimization functionality to DiT models.
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Inherits TeaCacheMixin for cache logic and BaseDiT for core DiT functionality.
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"""
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# These are required class attributes that should be overridden by concrete implementations
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_fsdp_shard_conditions = []
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param_names_mapping = {}
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reverse_param_names_mapping = {}
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lora_param_names_mapping: dict = {}
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# Ensure these instance attributes are properly defined in subclasses
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hidden_size: int
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num_attention_heads: int
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num_channels_latents: int
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# always supports torch_sdpa
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_supported_attention_backends: set[AttentionBackendEnum] = (
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DiTConfig()._supported_attention_backends
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)
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def __init__(self, config: DiTConfig, **kwargs) -> None:
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super().__init__(config, **kwargs)
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self._init_teacache_state()
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