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_sources/deeplearning_operators/matmul.md.txt

@@ -109,7 +109,7 @@ def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="flo
 func = matmul(1024, 1024, 1024, 128, 128, 32)
 
 # 2. JIT-compile the kernel for NVIDIA GPU
-jit_kernel = tilelang.JITKernel(func, out_idx=[2], target="cuda")
+jit_kernel = tilelang.compile(func, out_idx=[2], target="cuda")
 
 import torch
 

deeplearning_operators/matmul.html

@@ -326,7 +326,7 @@ <h3>Basic Structure<a class="headerlink" href="#basic-structure" title="Permalin
 <span class="n">func</span> <span class="o">=</span> <span class="n">matmul</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span>
 
 <span class="c1"># 2. JIT-compile the kernel for NVIDIA GPU</span>
-<span class="n">jit_kernel</span> <span class="o">=</span> <span class="n">tilelang</span><span class="o">.</span><span class="n">JITKernel</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">out_idx</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">target</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">jit_kernel</span> <span class="o">=</span> <span class="n">tilelang</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">out_idx</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">target</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>