[Doc] Fix installation scripts and docs for dequantize gemm

README.md

@@ -14,7 +14,7 @@ Tile Language (**tile-lang**) is a concise domain-specific language designed to
 - 01/20/2025 ✨: We are excited to announce that tile-lang, a dsl for high performance AI workloads, is now open source and available to the public!
 
 ## Tested Devices
-Although tile-lang aims to be portable across a range of Devices, it has been specifically tested and validated on the following devices: for NVIDIA GPUs, this includes the H100 (with Auto TMA/WGMMA support), A100, V100, RTX 4090, RTX 3090, and RTX A6000 (Ada); for AMD GPUs, it includes the MI250 (with Auto MatrixCore support) and the MI300X (with Async Copy support).
+Although tile-lang aims to be portable across a range of Devices, it has been specifically tested and validated on the following devices: for NVIDIA GPUs, this includes the H100 (with Auto TMA/WGMMA support), A100, V100, RTX 4090, RTX 3090, and RTX A6000; for AMD GPUs, it includes the MI250 (with Auto MatrixCore support) and the MI300X (with Async Copy support).
 
 ## OP Implementation Examples
 **tile-lang** provides the building blocks to implement a wide variety of operators. Some examples include:
@@ -24,7 +24,8 @@ Although tile-lang aims to be portable across a range of Devices, it has been sp
 - [Flash Attention](./examples/flash_attention/)
 - [Flash Linear Attention](./examples/linear_attention/)
 
-Within the `examples` repository, you will also find additional complex kernels—such as convolutions, forward/backward passes for FlashAttention.
+Within the `examples` directory, you will also find additional complex kernels—such as convolutions, forward/backward passes for FlashAttention, more operators will continuously be added.
+
 
 ## Benchmark Summary
 
@@ -84,8 +85,6 @@ In this section, you’ll learn how to write and execute a straightforward GEMM
 Below is an example that demonstrates more advanced features: layout annotation, parallelized copy, and swizzle for improved L2 cache locality. This snippet shows how to adapt your kernel to maximize performance on complex hardware.
 
 ```python
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
 import tilelang
 import tilelang.language as T
 # `make_mma_swizzle_layout` is a python defined layout function
@@ -103,7 +102,7 @@ def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="flo
         B: T.Buffer((K, N), dtype),
         C: T.Buffer((M, N), dtype),
     ):
-        # Kernel configuration remains similar
+        # Initialize Kernel Context
         with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
             A_shared = T.alloc_shared((block_M, block_K), dtype)
             B_shared = T.alloc_shared((block_K, block_N), dtype)
@@ -122,14 +121,14 @@ def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="flo
             # Clear local accumulation
             T.clear(C_local)
 
-            for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
+            for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
                 # Copy tile of A
                 # This is a sugar syntax for parallelized copy
-                T.copy(A[by * block_M, k * block_K], A_shared)
+                T.copy(A[by * block_M, ko * block_K], A_shared)
 
                 # Demonstrate parallelized copy from global to shared for B
-                for ko, j in T.Parallel(block_K, block_N):
-                    B_shared[ko, j] = B[k * block_K + ko, bx * block_N + j]
+                for k, j in T.Parallel(block_K, block_N):
+                    B_shared[k, j] = B[ko * block_K + k, bx * block_N + j]
 
                 # Perform a tile-level GEMM on the shared buffers
                 # Currently we dispatch to the cute/hip on Nvidia/AMD GPUs
@@ -141,7 +140,7 @@ def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="flo
     return main
 
 
-# 1. Define the kernel (matmul) and compile/lower it into an executable module
+# 1. Define the kernel (matmul) with the desired dimensions
 func = matmul(1024, 1024, 1024, 128, 128, 32)
 
 # 2. Compile the kernel into a torch function
@@ -158,7 +157,7 @@ a = torch.randn(1024, 1024, device="cuda", dtype=torch.float16)
 b = torch.randn(1024, 1024, device="cuda", dtype=torch.float16)
 
 
-# Run the kernel through the Profiler
+# Run the kernel through the JIT-compiled function
 c = jit_kernel(a, b)
 
 # Reference multiplication using PyTorch
@@ -172,7 +171,7 @@ print("Kernel output matches PyTorch reference.")
 cuda_source = jit_kernel.get_kernel_source()
 print("Generated CUDA kernel:\n", cuda_source)
 
-# 5.Pofile latency with kernel
+# 5.Pofile latency with the profiler
 profiler = jit_kernel.get_profiler()
 
 latency = profiler.do_bench()
@@ -189,8 +188,6 @@ In addition to GEMM, we provide a variety of examples to showcase the versatilit
 - [LinearAttention](./examples/linear_attention/): Examples include RetNet and Mamba implementations.
 - [Convolution](./examples/convolution/): Implementations of Convolution with IM2Col.
 
-More operators will continuously be added.
-
 ---
 
 TileLang has now been used in project [BitBLAS](https://github.com/microsoft/BitBLAS).

examples/dequantize_gemm/README.md

@@ -35,3 +35,5 @@ def dequant_matmul(
             T.gemm(B_dequantize_local, A_shared, Ct_local, transpose_B=True)
         T.copy(Ct_local, Ct[bx * block_N, by * block_M])
 ```
+
+**Notes:** Dequantize GEMM with magic layout transformations to get optimal performance can be found at project [BitBLAS](https://github.com/microsoft/BitBLAS), example kernels can be found at `testing/python/kernel/test_tilelang_dequantize_gemm.py`, detailed explanation and examples is coming soon.