Fuse qkv After an experiment has been done, you should expect to see two files: A . March 2020. A reasoning as to where this kind of fusion might be useful is available here. fuse_qkv_projections() 这将使 UNet 和 VAE 的注意力操作都利用组合投影。对于交叉注意力层，我们仅组合键和值矩阵。要了解更多信息，您可以参阅官方文档 zhaoyang-star:fuse_qkv. Contribute to onnx/optimizer development by creating an account on GitHub. unets. As such, it’s not available for many non-SD pipelines such as Kandinsky. 在Transformer模型中，Q（Query）查询向量、K（Key）关键向量和V（Value）值向量是自注意力机制（self-attention mechanism）的核心组成部分。下面通过举例说明QKV的原理： 定义： Q向量：表示当前元素的查询向量，用于在序列中查找相关信息。 pipe. \diffusers\models\transformers\hunyuan_transformer_2d. latent_dist # 对输入样本进行编码，并获取其后验分布 if sample_posterior: # 检查是否需要从后验分布中采样 z = posterior. yml. pipeline_stable_diffusion_xl. transformer. models. Additionally, this PR added the fusion support to AuraFlow and PixArt Sigma. This is because quantization adds fuse_qkv_params: if set to True, TransformerLayer module exposes a single fused parameter for query-key-value. ; A . Note: FasterTransformer development has transitioned to TensorRT-LLM. Windows 不支持： RuntimeError: Windows not yet supported for torch. cogvideo. It’s not available for many non-Stable Diffusion pipelines such # Copied from diffusers. compile. set_attn_processor. Aapply dynamic int8 quantization to both the UNet and the VAE. stable_diffusion_xl. 本文详细介绍了在 TensorRT-LLM 中为GPT类模型的自回归模型实现 多头注意力 （MHA）、 多查询注意力 （MQA）和 组查询注意力 （GQA）。. SDPA (huggingface. fuse_qkv_projections() This provides a minor improvement from 2. For cross-attention modules, key and value projection matrices are Enables fused QKV projections. Matrix: yapf 1 job completed Show all jobs Oh hello! fuse_qkv_projections < source > Enables fused QKV projections. The NVIDIA/Fa // Adventurous users should note that the APIs will probably change. fused_multi_head_attention (x, qkv_weight, linear_weight, pre_layer_norm = False, pre_ln_scale = None, pre Enables fused QKV projections. This tutorial will show you how to progressively apply the optimizations found in PyTorch 2 to reduce inference latency. jpeg image file def optimize (pipe, compile = True): # fuse QKV projections in Transformer and VAE pipe. fuse_qkv_projections() # 从 diffusers. 52 seconds. #pragma once #include <numeric> #include "onnx/defs/tensor_util. view (qkv. The model is available here. fusing_transformer = True # 调用 transformer 对象的方法，进行 QKV 投影融合 self. pipelines. Fuse QKV Gemm of encoder and masked_multi_head_attention of decoder. nn. py is the main script for benchmarking the different optimization techniques. unfuse_qkv_projections 复制的方法 # 定义一个禁用 QKV 投影融合 fuse_qkv_projections < source > Enables fused QKV projections. fuse_qkv_projections def fuse_qkv_projections(self, unet: bool = True, vae: bool = True): """ Enables fused QKV projections. functional. 多头注意力是按照注意力是一个批处理matmul、一个softmax和另一个批处理matmul（如同Attention Is All You Need文章中描述的那样）。 QKV Projection: torch. ln1 (x) # Fused QKV projection qkv = self. incubate. fuse_qkv_projections() This will make the attention operations for both the UNet and the VAE take advantage of the combined projections. pipeline_cogvideox. \diffusers\models\unets\unet_3d_condition. We will use Stable Diffusion XL (SDXL) as a case pipe. CogVideoXPipeline. You can refer to this PR to get an idea about how to support this kind of computation. This API is 🧪 experimental. StableDiffusionXLPipeline. 前：Z=log(softmax(X)) 后：X=logsoftmax(X) 特性：合并算子成为一个算子; fuse_qkv. 0 Add translate_sample. This enables optimizations such as QKV fusion without concatentations/splits and also enables the argument 本教程将向您展示如何逐步应用 PyTorch 2 中的优化来减少推理延迟。 在本教程中，您将使用 Stable Diffusion XL (SDXL) pipeline，但这些技术也适用于其他文本到图像扩散 pipelines。 确保您使用的是最新版本的 Diffusers. - huggingface/diffusers # 描述参数的用途 """ x = sample # 将输入样本赋值给 x posterior = self. set_attention_slice 1. 多头、多查询、多组注意力. set_attn_processor diffusers 源码解析（十一） . , query, key, value) With PyTorch 2 alone, you can accelerate the inference latency of text-to-image diffusion pipelines by up to 3x. vae. For cross-attention modules, key and value projection matrices are fused. Attention: We leverage the horizontal fusion on attention and fuse the QKV projection fuse_qkv_params (bool, default = ‘False’) – if set to True, TransformerLayer module exposes a single fused parameter for query-key-value. co) torch. 1(SD2. This was fixed in this PR. , query, key, value) are fused. mode() # QKV的定义与生成. Dynamic quantization. csv file with all the benchmarking numbers. fuse_qkv_projections with FusedAttnProcessor2_0->FusedFluxAttnProcessor2_0 def fuse_qkv_projections ( self ): Enables fused QKV projections. Add dynamic batch size and dynamic sequence length features into all ops. PyTorch 2 包含 torch. fused_multi_head_attention¶ paddle. fuse_qkv_projections() # switch memory layout to Torch's preferred, channels_last pipe. h" #include Enables fused QKV projections. h" #include "onnxoptimizer/pass. QKV projection: We leverage the horizontal fusion on QKV projection and fuse them into one kernel. unet_2d_condition. size (0) # Copied from diffusers. 前：普通的产生qkv的结构，拥有三个矩阵乘; 后：合并为一个矩阵乘，后续用split分开; 特 Fuse QKV Gemm of encoder and masked_multi_head_attention of decoder. This enables optimizations such as QKV fusion fuse_qkv_params: if set to True, TransformerLayer module exposes a single fused parameter for query-key-value. e. Add feature in FasterTransformer 2. py # 版权所有 2024 HunyuanDiT 作者，Qixun Wang 和 HuggingFace 团队。保留所有权利。 # # 根据 Apac This effect gets more pronounced when we horizontally fuse the attention QKV projections (calling fuse_qkv_projections() in Diffusers), thereby thickening the dimensions of the int8 kernels to speed up computation. qkv_projection (x) qkv = qkv. fuse_qkv_projections() pipe. sample(generator=generator) # 从后验分布中进行采样 else: # 否则 z = posterior. 然后也升级其 In this tutorial, we focus on leveraging the power of PyTorch 2 to accelerate the inference latency of text-to-image diffusion pipeline, instead. For the cross-attention layers, we only combine the key and value matrices. 54 seconds to 2. This enables optimizations such as QKV fusion without concatentations/splits and also enables the argument trt_add_QKV_bias 和 TensorRT fused multi-head attention kernel. All developers are encouraged to leverage TensorRT-LLM to get the latest improvements on LLM Inference. Learn more about checks retention. py to Stable Diffusion的文生图功能是一个强大而灵活的工具，不仅可以提供直观的文本数据可视化，还具有多样化的应用潜力。其先进的分析功能和简便的操作方式使其成为众多专业人士和业余爱好者的理想选择。 Fused Self Attention# In this tutorial, we implement a kernel to perform the self attention seen in Stable Diffusion 2. 实际上从 Figure1 也可以看出我们上面讲到的 batch GEMM，softmax, GEMM，transpose 等操作都可以被合成一个超大的 cuda kernel，进一步进行优化，也就是这里 fuse_consecutive_log_softmax. py # 版权声明，声明此代码的版权信息和所有权 # Copyright 2024 Alibaba run_benchmark. Total duration 23s Artifacts – This run and associated checks have been archived and are scheduled for deletion. encode(x). yapf. Support for fuse_qkv_projections() is limited and experimental. <Tip warning={true}> This API is 🧪 experimental. compile，它使用快速且优化的内核。在 Diffusers 中，通常编译 UNet 和 VAE模块，因为它们是计算最耗时的模块。 ONNX Optimizer. on: pull_request. Linear (conceptually three Linear layers for Q, K, and V separately, but we fuse into a single Linear layer that is three times larger) Tensor: res = x x = self. It’s not available for many non-Stable Diffusion pipelines such as Kandinsky. 1) from Stability AI. In doing so, we learn about: Does not include QKV projection, output projection, dropout, pipe. UNet2DConditionModel. 我们发现使用 qint8 (而非 qfloat8) 进行量化，推理延迟通常更好。当我们对注意力 QKV 投影进行水平融合 (在 Diffusers 中调用 fuse_qkv_projections()) 时，效果会更加明显，因为水平融合会增大 int8 算子 🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX. Status Success. , query, key, value) are diffusers 源码解析（十五） . For self-attention modules, all projection matrices (i. py to We discovered that the implementation of fuse_qkv_projections() was broken. All commits ring_id (int，可选) - 分布式 tensor parallel 运行下通讯所使用的 NCCL id。 默认值为 -1 。 add_residual (bool，可选) - 是否在计算最后对结果进行残差计算。 默认值为 True。 num_heads (int，可选) - 在 transpose_qkv_wb 设置为 True 的时候，必须提供该值，表示 Multi-Head Attention 的 head 的维度。 # 设置属性 fusing_transformer 为 True，表示启用融合 self. We 当我们对注意力 QKV 投影进行水平融合 (在 Diffusers 中调用 fuse_qkv_projections()) 时，效果会更加明显，因为水平融合会增大 int8 算子的计算维度从而实现更大的加速。我们基于 fuse_qkv_projections < source > Enables fused QKV projections. bcgcujlhxdpwnruanzxpowpowrkjzrmxnkquwcuwuagsjcfmyuwypsgaducsvmrzazttsxpynjcav