Clarifies mask/bias shapes; updates gradient docs

Corrects tensor shape examples to use 1-sized broadcastable dims for batch, heads, query, and key, improving clarity and avoiding invalid 0-length notation.
Removes “dbias” jargon from the gradient description for clearer wording.
Syncs English and Chinese documentation on shape semantics.

Author: algo-home

README.md

@@ -18,7 +18,7 @@ Flash-DMA is a high-performance attention implementation that integrates Flash A
 ## Key Features
 
 ### 🎯 Core Kernel Advantages
-- **Mask & Bias Support**: Native support for `(batch_size, {1|num_kv_heads|num_heads}, {0|query_len}, key_len)` shaped attention mask and attention bias tensors
+- **Mask & Bias Support**: Native support for `({1|batch_size}, {1|num_kv_heads|num_heads}, {1|query_len}, {1|key_len})` shaped attention mask and attention bias tensors
 - **Intelligent Computation Skipping**: Block-level automatic skipping mechanism based on masks, completely bypassing computation and memory access for zero-mask blocks
 - **Complete Gradient Support**: Built-in full gradient computation path for attention bias, supporting end-to-end training
 
@@ -236,9 +236,9 @@ Flash-DMA integrates the efficient memory access patterns of Flash Attention wit
 
 ### Core Technology Integration
 
-- **🎯 Native Mask & Bias Support**: Kernels directly process `(batch_size, {1|num_kv_heads|num_heads}, {0|query_len}, key_len)` shaped tensors
+- **🎯 Native Mask & Bias Support**: Kernels directly process `({1|batch_size}, {1|num_kv_heads|num_heads}, {1|query_len}, {1|key_len})` shaped tensors
 - **⚡ Block-level Intelligent Skipping**: Unified OR-reduction skipping logic based on masks, completely avoiding computation and memory access for zero blocks
-- **🔄 Complete Gradient Chain**: Built-in attention bias gradient computation (dbias) supporting end-to-end differentiable training
+- **🔄 Complete Gradient Chain**: Built-in attention bias gradient computation supporting end-to-end differentiable training
 
 ### Key Optimization Strategies
 

README_zh.md

@@ -18,7 +18,7 @@ Flash-DMA 是一个高性能的注意力实现，将 Flash Attention 的内存
 ## 主要特性
 
 ### 🎯 核心内核优势
-- **Mask & Bias 支持**: 原生支持 `(batch_size, {1|num_kv_heads|num_heads}, {0|query_len}, key_len)` 形状的 attention_mask 和 attention_bias 张量
+- **Mask & Bias 支持**: 原生支持 `({1|batch_size}, {1|num_kv_heads|num_heads}, {1|query_len}, {1|key_len})` 形状的 attention_mask 和 attention_bias 张量
 - **智能计算跳过**: 基于 attention_mask 的 block-level 自动跳过机制，完全跳过全零 mask 区块的计算和内存访问
 - **完整梯度支持**: 内置 attention_bias 的完整梯度计算路径，支持端到端训练
 
@@ -236,7 +236,7 @@ Flash-DMA 通过将 Flash Attention 的高效内存访问模式与动态掩码
 
 ### 核心技术融合
 
-- **🎯 Mask & Bias 原生支持**: 内核直接处理 `(batch_size, {1|num_kv_heads|num_heads}, {0|query_len}, key_len)` 形状的张量
+- **🎯 Mask & Bias 原生支持**: 内核直接处理 `({1|batch_size}, {1|num_kv_heads|num_heads}, {1|query_len}, {1|key_len})` 形状的张量
 - **⚡ Block-level 智能跳过**: 基于 mask 的统一 OR-reduction 跳过逻辑，完全避免全零区块的计算和内存访问
 - **🔄 完整梯度链路**: 内置 attention bias 梯度计算，支持端到端可微分训练