[Feat] Distrifusion Acceleration Support for Diffusion Inference

colossalai/inference/README.md

@@ -18,7 +18,7 @@
 
 
 ## 📌 Introduction
-ColossalAI-Inference is a module which offers acceleration to the inference execution of Transformers models, especially LLMs. In ColossalAI-Inference, we leverage high-performance kernels, KV cache, paged attention, continous batching and other techniques to accelerate the inference of LLMs. We also provide simple and unified APIs for the sake of user-friendliness. [[blog]](https://hpc-ai.com/blog/colossal-inference)
+ColossalAI-Inference is a module which offers acceleration to the inference execution of Transformers models, especially LLMs and DiT Diffusion Models. In ColossalAI-Inference, we leverage high-performance kernels, KV cache, paged attention, continous batching and other techniques to accelerate the inference of LLMs. We also provide simple and unified APIs for the sake of user-friendliness. [[blog]](https://hpc-ai.com/blog/colossal-inference)
 
 <p align="center">
 <img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference/colossal-inference-v1-1.png" width=1000/>
@@ -310,4 +310,14 @@ If you wish to cite relevant research papars, you can find the reference below.
   journal={arXiv},
   year={2023}
 }
+
+# Distrifusion
+@InProceedings{Li_2024_CVPR,
+    author={Li, Muyang and Cai, Tianle and Cao, Jiaxin and Zhang, Qinsheng and Cai, Han and Bai, Junjie and Jia, Yangqing and Li, Kai and Han, Song},
+    title={DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models},
+    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    month={June},
+    year={2024},
+    pages={7183-7193}
+}
 ```

colossalai/inference/config.py

@@ -186,6 +186,7 @@ class InferenceConfig(RPC_PARAM):
         enable_streamingllm(bool): Whether to use StreamingLLM, the relevant algorithms refer to the paper at https://arxiv.org/pdf/2309.17453 for implementation.
         start_token_size(int): The size of the start tokens, when using StreamingLLM.
         generated_token_size(int): The size of the generated tokens, When using StreamingLLM.
+        patched_parallelism_size(int): Patched Parallelism Size, When using Distrifusion
     """
 
     # NOTE: arrange configs according to their importance and frequency of usage
@@ -245,6 +246,11 @@ class InferenceConfig(RPC_PARAM):
     start_token_size: int = 4
     generated_token_size: int = 512
 
+    # Acceleration for Diffusion Model(PipeFusion or Distrifusion)
+    patched_parallelism_size: int = 1  # for distrifusion
+    # pipeFusion_m_size: int = 1  # for pipefusion
+    # pipeFusion_n_size: int = 1  # for pipefusion
+
     def __post_init__(self):
         self.max_context_len_to_capture = self.max_input_len + self.max_output_len
         self._verify_config()
@@ -288,6 +294,14 @@ def _verify_config(self) -> None:
         # Thereafter, we swap out tokens in units of blocks, and always swapping out the second block when the generated tokens exceeded the limit.
         self.start_token_size = self.block_size
 
+        # check Distrifusion
+        # TODO(@lry89757) need more detailed check
+        if self.patched_parallelism_size > 1:
+            # self.use_patched_parallelism = True
+            self.tp_size = (
+                self.patched_parallelism_size
+            )  # this is not a real tp, because some annoying check, so we have to set this to patched_parallelism_size
+
         # check prompt template
         if self.prompt_template is None:
             return
@@ -324,6 +338,7 @@ def to_model_shard_inference_config(self) -> "ModelShardInferenceConfig":
             use_cuda_kernel=self.use_cuda_kernel,
             use_spec_dec=self.use_spec_dec,
             use_flash_attn=use_flash_attn,
+            patched_parallelism_size=self.patched_parallelism_size,
         )
         return model_inference_config
 
@@ -396,6 +411,7 @@ class ModelShardInferenceConfig:
     use_cuda_kernel: bool = False
     use_spec_dec: bool = False
     use_flash_attn: bool = False
+    patched_parallelism_size: int = 1  # for diffusion model, Distrifusion Technique
 
 
 @dataclass

colossalai/inference/core/diffusion_engine.py

@@ -11,7 +11,7 @@
 from colossalai.accelerator import get_accelerator
 from colossalai.cluster import ProcessGroupMesh
 from colossalai.inference.config import DiffusionGenerationConfig, InferenceConfig, ModelShardInferenceConfig
-from colossalai.inference.modeling.models.diffusion import DiffusionPipe
+from colossalai.inference.modeling.layers.diffusion import DiffusionPipe
 from colossalai.inference.modeling.policy import model_policy_map
 from colossalai.inference.struct import DiffusionSequence
 from colossalai.inference.utils import get_model_size, get_model_type