Add README.md file with project description and installation instructions

README.md

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+
+Tile Language (tile-lang)
+==============================================
+Tile Language (tile-lang) is an extension of the Apache tvm designed to facilitate the development of simple yet high-performance GPU kernels. The project tile-lang currently supports CUDA devices with architectures including Ampere (sm_80+), Turing (sm_75), and Volta (sm_70).
+
+This project is co-authored by [nox-410](https://github.com/nox-410) and [chengyupku](https://github.com/chengyupku) and [LeiWang1999](https://github.com/LeiWang1999).
+
+Let's get started with a simple GEMM example.
+
+```python
+import tvm.tl.language as T
+def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype = "float"):
+    @T.prim_func
+    def main(
+        A: T.Buffer((M, K), dtype),
+        B: T.Buffer((K, N), dtype),
+        C: T.Buffer((M, N), dtype),
+        bias: T.Buffer([N], dtype),
+    ):
+        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)
+            C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
+            bias_local = T.alloc_fragment((block_N,), dtype)
+            T.clear(C_local)
+            for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
+                T.copy(A[by * block_M, k * block_K], A_shared)
+                T.copy(B[k * block_K, bx * block_N], B_shared)
+                T.gemm(A_shared, B_shared, C_local)
+            T.copy(bias[bx * block_N], bias_local)
+            for i, j in T.Parallel(block_M, block_N):
+                C_local[i, j] += bias_local[j]
+            T.copy(C_local, C[by * block_M, bx * block_N])
+
+    return main
+```
+Despite this simple examples, tvm.tl can be used to write more complicated examples including convolutions, flash-attention-v2 (fwd & bwd), normalizations, these examples can be found under folder tl_scripts.
+
+ The performance of our flash-attention is comparable to the manually implementation. (see [Link](https://github.com/nox-410/tvm.tl/blob/tl/tl_doc/flash_perf.md)).
+
+## Install
+
+Install is similar to tvm. First, fill in USE_CUDA and USE_LLVM in cmake/config.cmake, like this:
+```bash
+set(USE_LLVM "/path/to/llvm-config --link-static")
+set(HIDE_PRIVATE_SYMBOLS ON)
+set(USE_CUDA /usr/local/cuda)
+```
+Then build tvm
+```bash
+mkdir -p build && cd build && cp ../cmake/config.cmake . && cmake .. && make -j && cd -
+export PYTHONPATH="$PYTHONPATH:$PWD/python"
+# some python package required by tvm
+pip install torch attrs cloudpickle decorator psutil synr tornado xgboost
+```
+We also need to prepare the cutlass headers, the default version of cutlass in TVM does not work correctly
+```bash
+git clone https://github.com/NVIDIA/cutlass.git -b v3.2.2
+export TL_CUTLASS_PATH=/path/to/cutlass/include
+```
+Note 1: It is recommeneded to use the latest cuda toolkit, because we requires nvcc to jit compile the generated CUDA code.
+
+Note 2: Don't forget to clone the submodules.
+
+## Language reference
+Still in progress.
+
+See tl_doc/language_ref.md