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	<article role="main">
	<section id="Example-of-using-elementwise-activation-functions-in-the-CUTLASS-Python-interface">
	<h1>Example of using elementwise activation functions in the CUTLASS Python interface<a class="headerlink" href="#Example-of-using-elementwise-activation-functions-in-the-CUTLASS-Python-interface" title="Permalink to this heading">#</a></h1>
	<p>This notebook walks through a basic example of using the CUTLASS Python interface to declare, compile, and run GEMMs with different epilogues.</p>
	<p><a class="reference external" href="https://colab.research.google.com/github/NVIDIA/cutlass/tree/master/examples/00_basic_gemm.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
	<p>We first import various packages needed for the example and construct the input and output tensors that will be used in our example.</p>
	<div class="nbinput docutils container">
	<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
	</pre></div>
	</div>
	<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

	<span class="kn">import</span> <span class="nn">cutlass</span>

	<span class="c1"># This controls whether ther C++ GEMM declaration will be printed at each step. Set to `false` to</span>
	<span class="c1"># omit this information.</span>
	<span class="n">print_module</span> <span class="o">=</span> <span class="kc">True</span>

	<span class="n">m</span> <span class="o">=</span> <span class="mi">256</span>
	<span class="n">n</span> <span class="o">=</span> <span class="n">m</span>
	<span class="n">k</span> <span class="o">=</span> <span class="n">m</span>

	<span class="n">type_A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float16</span>
	<span class="n">type_B</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float16</span>
	<span class="n">type_C</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float16</span>
	<span class="n">type_D</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float16</span>

	<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1234</span><span class="p">)</span>
	<span class="n">scope_min</span> <span class="o">=</span> <span class="o">-</span><span class="mi">4</span>
	<span class="n">scope_max</span> <span class="o">=</span> <span class="mi">4</span>
	<span class="n">tensor_A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="n">scope_min</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">scope_max</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">k</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">type_A</span><span class="p">))</span>
	<span class="n">tensor_B</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="n">scope_min</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">scope_max</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">type_B</span><span class="p">))</span>
	<span class="n">tensor_C</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="n">scope_min</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">scope_max</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">type_C</span><span class="p">))</span>

	<span class="n">alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float16</span><span class="p">(</span><span class="mf">1.</span><span class="p">)</span>
	<span class="n">beta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float16</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>

	<span class="n">tensor_D</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">tensor_C</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">type_D</span><span class="p">)</span>
	</pre></div>
	</div>
	</div>
	<div class="nboutput nblast docutils container">
	<div class="prompt empty docutils container">
	</div>
	<div class="output_area stderr docutils container">
	<div class="highlight"><pre>
	/usr/local/lib/python3.8/dist-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
	from .autonotebook import tqdm as notebook_tqdm
	</pre></div></div>
	</div>
	<section id="Run-a-GEMM-with-an-identity-activation-function">
	<h2>Run a GEMM with an identity activation function<a class="headerlink" href="#Run-a-GEMM-with-an-identity-activation-function" title="Permalink to this heading">#</a></h2>
	<p>To begin, we simply run a default GEMM with an identity activation function. This performs the well-known operation <code class="docutils literal notranslate"><span class="pre">D</span> <span class="pre">=</span> <span class="pre">alpha</span> <span class="pre"></span> <span class="pre">(A</span> <span class="pre">@</span> <span class="pre">B)</span> <span class="pre">+</span> <span class="pre">beta</span> <span class="pre"></span> <span class="pre">C</span></code>. This is the default activation function used, and does not need to be specified.</p>
	<div class="nbinput docutils container">
	<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
	</pre></div>
	</div>
	<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plan</span> <span class="o">=</span> <span class="n">cutlass</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">Gemm</span><span class="p">(</span><span class="n">element</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="n">cutlass</span><span class="o">.</span><span class="n">LayoutType</span><span class="o">.</span><span class="n">RowMajor</span><span class="p">)</span>
	<span class="n">plan</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tensor_A</span><span class="p">,</span> <span class="n">tensor_B</span><span class="p">,</span> <span class="n">tensor_C</span><span class="p">,</span> <span class="n">tensor_D</span><span class="p">,</span> <span class="n">print_module</span><span class="o">=</span><span class="n">print_module</span><span class="p">)</span>
	</pre></div>
	</div>
	</div>
	<div class="nboutput docutils container">
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	</div>
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	<div class="highlight"><pre>

	// Gemm operator cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8
	using cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base =
	typename cutlass::gemm::kernel::DefaultGemmUniversal<
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor,
	cutlass::half_t,
	cutlass::arch::OpClassTensorOp,
	cutlass::arch::Sm80,
	cutlass::gemm::GemmShape<256, 128, 64>,
	cutlass::gemm::GemmShape<64, 64, 64>,
	cutlass::gemm::GemmShape<16, 8, 16>,
	cutlass::epilogue::thread::LinearCombination<cutlass::half_t, 8, cutlass::half_t, cutlass::half_t>,
	cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
	3,
	cutlass::arch::OpMultiplyAdd
	>::GemmKernel;

	// Define named type
	struct cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_type :
	public cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base { };

	</pre></div></div>
	</div>
	<div class="nboutput nblast docutils container">
	<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
	</pre></div>
	</div>
	<div class="output_area docutils container">
	<div class="highlight"><pre>
	<cutlass.backend.gemm_operation.GemmArguments2x at 0x7fed907287c0>
	</pre></div></div>
	</div>
	</section>
	<section id="Run-a-GEMM-with-a-ReLU-element-wise-activation-function">
	<h2>Run a GEMM with a ReLU element-wise activation function<a class="headerlink" href="#Run-a-GEMM-with-a-ReLU-element-wise-activation-function" title="Permalink to this heading">#</a></h2>
	<p>CUTLASS makes it easy to support other element-wise activation functions. This results in performing an element-wise after the generic linear combination performed in a GEMM. If we call such an activation function <code class="docutils literal notranslate"><span class="pre">act</span></code>, the resulting formulation is:</p>
	<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>D = alpha * (A @ B) + beta * C
	D = act(D)
	</pre></div>
	</div>
	<p>Here, we will add a ReLU activation function. Given an input <code class="docutils literal notranslate"><span class="pre">x</span></code>, ReLU returns <code class="docutils literal notranslate"><span class="pre">max(x,</span> <span class="pre">0)</span></code>.</p>
	<p>This is easy to do in CUTLASS. One only needs to set the plan’s <code class="docutils literal notranslate"><span class="pre">activation</span></code> field.</p>
	<div class="nbinput docutils container">
	<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
	</pre></div>
	</div>
	<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tensor_D_relu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">tensor_C</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">type_D</span><span class="p">)</span>
	<span class="n">plan</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">cutlass</span><span class="o">.</span><span class="n">epilogue</span><span class="o">.</span><span class="n">relu</span>
	<span class="n">plan</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tensor_A</span><span class="p">,</span> <span class="n">tensor_B</span><span class="p">,</span> <span class="n">tensor_C</span><span class="p">,</span> <span class="n">tensor_D_relu</span><span class="p">,</span> <span class="n">print_module</span><span class="o">=</span><span class="n">print_module</span><span class="p">)</span>
	</pre></div>
	</div>
	</div>
	<div class="nboutput docutils container">
	<div class="prompt empty docutils container">
	</div>
	<div class="output_area docutils container">
	<div class="highlight"><pre>

	// Gemm operator cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8
	using cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base =
	typename cutlass::gemm::kernel::DefaultGemmUniversal<
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor,
	cutlass::half_t,
	cutlass::arch::OpClassTensorOp,
	cutlass::arch::Sm80,
	cutlass::gemm::GemmShape<256, 128, 64>,
	cutlass::gemm::GemmShape<64, 64, 64>,
	cutlass::gemm::GemmShape<16, 8, 16>,
	cutlass::epilogue::thread::LinearCombinationGeneric<cutlass::epilogue::thread::ReLu, cutlass::half_t, 8, cutlass::half_t, cutlass::half_t>,
	cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
	3,
	cutlass::arch::OpMultiplyAdd
	>::GemmKernel;

	// Define named type
	struct cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_type :
	public cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base { };

	</pre></div></div>
	</div>
	<div class="nboutput nblast docutils container">
	<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
	</pre></div>
	</div>
	<div class="output_area docutils container">
	<div class="highlight"><pre>
	<cutlass.backend.gemm_operation.GemmArguments2x at 0x7fed906f2460>
	</pre></div></div>
	</div>
	<p>We can now verify that the result of the GEMM that used a ReLU activation function:</p>
	<div class="nbinput nblast docutils container">
	<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
	</pre></div>
	</div>
	<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">relu_ref</span> <span class="o">=</span> <span class="p">(</span><span class="n">tensor_D</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">type_D</span><span class="p">)</span> <span class="o">*</span> <span class="n">tensor_D</span>
	<span class="n">np</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_array_equal</span><span class="p">(</span><span class="n">relu_ref</span><span class="p">,</span> <span class="n">tensor_D_relu</span><span class="p">)</span>
	</pre></div>
	</div>
	</div>
	</section>
	<section id="Other-element-wise-activation-functions">
	<h2>Other element-wise activation functions<a class="headerlink" href="#Other-element-wise-activation-functions" title="Permalink to this heading">#</a></h2>
	<p>CUTLASS supports a variety of widely-used element-wise activation functions. We can obtain a list of these functions via the <code class="docutils literal notranslate"><span class="pre">get_activations()</span></code> method.</p>
	<div class="nbinput docutils container">
	<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
	</pre></div>
	</div>
	<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">activations</span> <span class="o">=</span> <span class="n">plan</span><span class="o">.</span><span class="n">activations</span><span class="p">()</span>
	<span class="k">for</span> <span class="n">activation</span> <span class="ow">in</span> <span class="n">activations</span><span class="p">:</span>
	<span class="nb">print</span><span class="p">(</span><span class="n">activation</span><span class="p">)</span>
	</pre></div>
	</div>
	</div>
	<div class="nboutput nblast docutils container">
	<div class="prompt empty docutils container">
	</div>
	<div class="output_area docutils container">
	<div class="highlight"><pre>
	<class 'cutlass.backend.epilogue.gelu'>
	<class 'cutlass.backend.epilogue.hardswish'>
	<class 'cutlass.backend.epilogue.identity'>
	<class 'cutlass.backend.epilogue.leaky_relu'>
	<class 'cutlass.backend.epilogue.relu'>
	<class 'cutlass.backend.epilogue.sigmoid'>
	<class 'cutlass.backend.epilogue.silu'>
	<class 'cutlass.backend.epilogue.tanh'>
	</pre></div></div>
	</div>
	<p>We can then run each of them:</p>
	<div class="nbinput docutils container">
	<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
	</pre></div>
	</div>
	<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">activation</span> <span class="ow">in</span> <span class="n">activations</span><span class="p">:</span>
	<span class="nb">print</span><span class="p">(</span><span class="s1">'============================================================================================='</span><span class="p">)</span>
	<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Compiling and running activation </span><span class="si">{</span><span class="n">activation</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
	<span class="nb">print</span><span class="p">(</span><span class="s1">'============================================================================================='</span><span class="p">)</span>
	<span class="n">plan</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">activation</span>
	<span class="n">plan</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tensor_A</span><span class="p">,</span> <span class="n">tensor_B</span><span class="p">,</span> <span class="n">tensor_C</span><span class="p">,</span> <span class="n">tensor_D</span><span class="p">,</span> <span class="n">print_module</span><span class="o">=</span><span class="n">print_module</span><span class="p">)</span>
	</pre></div>
	</div>
	</div>
	<div class="nboutput nblast docutils container">
	<div class="prompt empty docutils container">
	</div>
	<div class="output_area docutils container">
	<div class="highlight"><pre>
	=============================================================================================
	Compiling and running activation <class 'cutlass.backend.epilogue.gelu'>
	=============================================================================================

	// Gemm operator cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8
	using cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base =
	typename cutlass::gemm::kernel::DefaultGemmUniversal<
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor,
	cutlass::half_t,
	cutlass::arch::OpClassTensorOp,
	cutlass::arch::Sm80,
	cutlass::gemm::GemmShape<256, 128, 64>,
	cutlass::gemm::GemmShape<64, 64, 64>,
	cutlass::gemm::GemmShape<16, 8, 16>,
	cutlass::epilogue::thread::LinearCombinationGeneric<cutlass::epilogue::thread::GELU, cutlass::half_t, 8, cutlass::half_t, cutlass::half_t>,
	cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
	3,
	cutlass::arch::OpMultiplyAdd
	>::GemmKernel;

	// Define named type
	struct cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_type :
	public cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base { };

	=============================================================================================
	Compiling and running activation <class 'cutlass.backend.epilogue.hardswish'>
	=============================================================================================

	// Gemm operator cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8
	using cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base =
	typename cutlass::gemm::kernel::DefaultGemmUniversal<
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor,
	cutlass::half_t,
	cutlass::arch::OpClassTensorOp,
	cutlass::arch::Sm80,
	cutlass::gemm::GemmShape<256, 128, 64>,
	cutlass::gemm::GemmShape<64, 64, 64>,
	cutlass::gemm::GemmShape<16, 8, 16>,
	cutlass::epilogue::thread::LinearCombinationGeneric<cutlass::epilogue::thread::HardSwish, cutlass::half_t, 8, cutlass::half_t, cutlass::half_t>,
	cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
	3,
	cutlass::arch::OpMultiplyAdd
	>::GemmKernel;

	// Define named type
	struct cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_type :
	public cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base { };

	=============================================================================================
	Compiling and running activation <class 'cutlass.backend.epilogue.identity'>
	=============================================================================================

	// Gemm operator cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8
	using cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base =
	typename cutlass::gemm::kernel::DefaultGemmUniversal<
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor,
	cutlass::half_t,
	cutlass::arch::OpClassTensorOp,
	cutlass::arch::Sm80,
	cutlass::gemm::GemmShape<256, 128, 64>,
	cutlass::gemm::GemmShape<64, 64, 64>,
	cutlass::gemm::GemmShape<16, 8, 16>,
	cutlass::epilogue::thread::LinearCombination<cutlass::half_t, 8, cutlass::half_t, cutlass::half_t>,
	cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
	3,
	cutlass::arch::OpMultiplyAdd
	>::GemmKernel;

	// Define named type
	struct cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_type :
	public cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base { };

	=============================================================================================
	Compiling and running activation <class 'cutlass.backend.epilogue.leaky_relu'>
	=============================================================================================

	// Gemm operator cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8
	using cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base =
	typename cutlass::gemm::kernel::DefaultGemmUniversal<
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor,
	cutlass::half_t,
	cutlass::arch::OpClassTensorOp,
	cutlass::arch::Sm80,
	cutlass::gemm::GemmShape<256, 128, 64>,
	cutlass::gemm::GemmShape<64, 64, 64>,
	cutlass::gemm::GemmShape<16, 8, 16>,
	cutlass::epilogue::thread::LinearCombinationGeneric<cutlass::epilogue::thread::LeakyReLU, cutlass::half_t, 8, cutlass::half_t, cutlass::half_t>,
	cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
	3,
	cutlass::arch::OpMultiplyAdd
	>::GemmKernel;

	// Define named type
	struct cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_type :
	public cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base { };

	=============================================================================================
	Compiling and running activation <class 'cutlass.backend.epilogue.relu'>
	=============================================================================================

	// Gemm operator cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8
	using cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base =
	typename cutlass::gemm::kernel::DefaultGemmUniversal<
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor,
	cutlass::half_t,
	cutlass::arch::OpClassTensorOp,
	cutlass::arch::Sm80,
	cutlass::gemm::GemmShape<256, 128, 64>,
	cutlass::gemm::GemmShape<64, 64, 64>,
	cutlass::gemm::GemmShape<16, 8, 16>,
	cutlass::epilogue::thread::LinearCombinationGeneric<cutlass::epilogue::thread::ReLu, cutlass::half_t, 8, cutlass::half_t, cutlass::half_t>,
	cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
	3,
	cutlass::arch::OpMultiplyAdd
	>::GemmKernel;

	// Define named type
	struct cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_type :
	public cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base { };

	=============================================================================================
	Compiling and running activation <class 'cutlass.backend.epilogue.sigmoid'>
	=============================================================================================

	// Gemm operator cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8
	using cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base =
	typename cutlass::gemm::kernel::DefaultGemmUniversal<
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor,
	cutlass::half_t,
	cutlass::arch::OpClassTensorOp,
	cutlass::arch::Sm80,
	cutlass::gemm::GemmShape<256, 128, 64>,
	cutlass::gemm::GemmShape<64, 64, 64>,
	cutlass::gemm::GemmShape<16, 8, 16>,
	cutlass::epilogue::thread::LinearCombinationGeneric<cutlass::epilogue::thread::Sigmoid, cutlass::half_t, 8, cutlass::half_t, cutlass::half_t>,
	cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
	3,
	cutlass::arch::OpMultiplyAdd
	>::GemmKernel;

	// Define named type
	struct cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_type :
	public cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base { };

	=============================================================================================
	Compiling and running activation <class 'cutlass.backend.epilogue.silu'>
	=============================================================================================

	// Gemm operator cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8
	using cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base =
	typename cutlass::gemm::kernel::DefaultGemmUniversal<
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor,
	cutlass::half_t,
	cutlass::arch::OpClassTensorOp,
	cutlass::arch::Sm80,
	cutlass::gemm::GemmShape<256, 128, 64>,
	cutlass::gemm::GemmShape<64, 64, 64>,
	cutlass::gemm::GemmShape<16, 8, 16>,
	cutlass::epilogue::thread::LinearCombinationGeneric<cutlass::epilogue::thread::SiLu, cutlass::half_t, 8, cutlass::half_t, cutlass::half_t>,
	cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
	3,
	cutlass::arch::OpMultiplyAdd
	>::GemmKernel;

	// Define named type
	struct cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_type :
	public cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base { };

	=============================================================================================
	Compiling and running activation <class 'cutlass.backend.epilogue.tanh'>
	=============================================================================================

	// Gemm operator cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8
	using cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base =
	typename cutlass::gemm::kernel::DefaultGemmUniversal<
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor, cutlass::ComplexTransform::kNone, 8,
	cutlass::half_t, cutlass::layout::RowMajor,
	cutlass::half_t,
	cutlass::arch::OpClassTensorOp,
	cutlass::arch::Sm80,
	cutlass::gemm::GemmShape<256, 128, 64>,
	cutlass::gemm::GemmShape<64, 64, 64>,
	cutlass::gemm::GemmShape<16, 8, 16>,
	cutlass::epilogue::thread::LinearCombinationGeneric<cutlass::epilogue::thread::Tanh, cutlass::half_t, 8, cutlass::half_t, cutlass::half_t>,
	cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
	3,
	cutlass::arch::OpMultiplyAdd
	>::GemmKernel;

	// Define named type
	struct cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_type :
	public cutlass_sm80_tensorop_h16x8x16gemm_1x1x1_256x128_64x3_tt_align8_base { };

	</pre></div></div>
	</div>
	<div class="nbinput nblast docutils container">
	<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
	</pre></div>
	</div>
	<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
	</pre></div>
	</div>
	</div>
	</section>
	</section>

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	<li><a class="reference internal" href="#">Example of using elementwise activation functions in the CUTLASS Python interface</a><ul>
	<li><a class="reference internal" href="#Run-a-GEMM-with-an-identity-activation-function">Run a GEMM with an identity activation function</a></li>
	<li><a class="reference internal" href="#Run-a-GEMM-with-a-ReLU-element-wise-activation-function">Run a GEMM with a ReLU element-wise activation function</a></li>
	<li><a class="reference internal" href="#Other-element-wise-activation-functions">Other element-wise activation functions</a></li>
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