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| <a href="#pub-methods">Public Member Functions</a> </div> |
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| <div class="title">NEGEMMInterleavedWrapper Class Reference</div> </div> |
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| |
| <p>Equivalent to <a class="el" href="classarm__gemm_1_1_gemm_interleaved.xhtml">arm_gemm::GemmInterleaved</a> but using Compute Library types. |
| <a href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml#details">More...</a></p> |
| |
| <p><code>#include <<a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8h_source.xhtml">NEGEMMInterleavedWrapper.h</a>></code></p> |
| <div class="dynheader"> |
| Collaboration diagram for NEGEMMInterleavedWrapper:</div> |
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| <table class="memberdecls"> |
| <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a> |
| Public Member Functions</h2></td></tr> |
| <tr class="memitem:a088148055d3bdf42760f8dddcc7ba662"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml#a088148055d3bdf42760f8dddcc7ba662">NEGEMMInterleavedWrapper</a> (std::shared_ptr< <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> > memory_manager=nullptr)</td></tr> |
| <tr class="separator:a088148055d3bdf42760f8dddcc7ba662"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a17fb5ffe8b9afd93d3edbee2b65d0318"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml#a17fb5ffe8b9afd93d3edbee2b65d0318">~NEGEMMInterleavedWrapper</a> ()=default</td></tr> |
| <tr class="separator:a17fb5ffe8b9afd93d3edbee2b65d0318"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a0d1a7b2eca4504d463fde5bed41c43df"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml#a0d1a7b2eca4504d463fde5bed41c43df">NEGEMMInterleavedWrapper</a> (const <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml">NEGEMMInterleavedWrapper</a> &)=delete</td></tr> |
| <tr class="separator:a0d1a7b2eca4504d463fde5bed41c43df"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:ac959141838870719b552af8918577e72"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml">NEGEMMInterleavedWrapper</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml#ac959141838870719b552af8918577e72">operator=</a> (const <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml">NEGEMMInterleavedWrapper</a> &)=delete</td></tr> |
| <tr class="separator:ac959141838870719b552af8918577e72"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a61207828a26247c4a8ba4fadf08a7f92"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml#a61207828a26247c4a8ba4fadf08a7f92">configure</a> (const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *a, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *b, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *c, float alpha, float beta, const <a class="el" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a> &gemm_info)</td></tr> |
| <tr class="memdesc:a61207828a26247c4a8ba4fadf08a7f92"><td class="mdescLeft"> </td><td class="mdescRight">Initialise the kernel's input and output. <a href="#a61207828a26247c4a8ba4fadf08a7f92">More...</a><br /></td></tr> |
| <tr class="separator:a61207828a26247c4a8ba4fadf08a7f92"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:ad1717410afd0be936c6213a63c8005fb"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a> () override</td></tr> |
| <tr class="memdesc:ad1717410afd0be936c6213a63c8005fb"><td class="mdescLeft"> </td><td class="mdescRight">Run the kernels contained in the function. <a href="#ad1717410afd0be936c6213a63c8005fb">More...</a><br /></td></tr> |
| <tr class="separator:ad1717410afd0be936c6213a63c8005fb"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a> () override</td></tr> |
| <tr class="memdesc:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="mdescLeft"> </td><td class="mdescRight">Prepare the function for executing. <a href="#aa9b93ef660fc3c5b4b19d3fc7b891b77">More...</a><br /></td></tr> |
| <tr class="separator:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="inherit_header pub_methods_classarm__compute_1_1_i_function"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarm__compute_1_1_i_function')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classarm__compute_1_1_i_function.xhtml">IFunction</a></td></tr> |
| <tr class="memitem:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">~IFunction</a> ()=default</td></tr> |
| <tr class="memdesc:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="mdescLeft"> </td><td class="mdescRight">Destructor. <a href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">More...</a><br /></td></tr> |
| <tr class="separator:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memSeparator" colspan="2"> </td></tr> |
| </table> |
| <a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2> |
| <div class="textblock"><p>Equivalent to <a class="el" href="classarm__gemm_1_1_gemm_interleaved.xhtml">arm_gemm::GemmInterleaved</a> but using Compute Library types. </p> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8h_source.xhtml#l00094">94</a> of file <a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8h_source.xhtml">NEGEMMInterleavedWrapper.h</a>.</p> |
| </div><h2 class="groupheader">Constructor & Destructor Documentation</h2> |
| <a id="a088148055d3bdf42760f8dddcc7ba662"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a088148055d3bdf42760f8dddcc7ba662">◆ </a></span>NEGEMMInterleavedWrapper() <span class="overload">[1/2]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml">NEGEMMInterleavedWrapper</a> </td> |
| <td>(</td> |
| <td class="paramtype">std::shared_ptr< <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> > </td> |
| <td class="paramname"><em>memory_manager</em> = <code>nullptr</code></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8cpp_source.xhtml#l00183">183</a> of file <a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8cpp_source.xhtml">NEGEMMInterleavedWrapper.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  : _memory_group(std::move(memory_manager))</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> {</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> }</div></div><!-- fragment --> |
| </div> |
| </div> |
| <a id="a17fb5ffe8b9afd93d3edbee2b65d0318"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a17fb5ffe8b9afd93d3edbee2b65d0318">◆ </a></span>~NEGEMMInterleavedWrapper()</h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname">~<a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml">NEGEMMInterleavedWrapper</a> </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">default</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| </div> |
| </div> |
| <a id="a0d1a7b2eca4504d463fde5bed41c43df"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a0d1a7b2eca4504d463fde5bed41c43df">◆ </a></span>NEGEMMInterleavedWrapper() <span class="overload">[2/2]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml">NEGEMMInterleavedWrapper</a> </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml">NEGEMMInterleavedWrapper</a> & </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">delete</span></span> </td> |
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| </table> |
| </div><div class="memdoc"> |
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| </div> |
| </div> |
| <h2 class="groupheader">Member Function Documentation</h2> |
| <a id="a61207828a26247c4a8ba4fadf08a7f92"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a61207828a26247c4a8ba4fadf08a7f92">◆ </a></span>configure()</h2> |
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| <td class="memname">void configure </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>a</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>b</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>c</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">float </td> |
| <td class="paramname"><em>alpha</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">float </td> |
| <td class="paramname"><em>beta</em>, </td> |
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| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a> & </td> |
| <td class="paramname"><em>gemm_info</em> </td> |
| </tr> |
| <tr> |
| <td></td> |
| <td>)</td> |
| <td></td><td></td> |
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| <p>Initialise the kernel's input and output. </p> |
| <dl class="section note"><dt>Note</dt><dd>The input and output tensor must have the same dimensions</dd></dl> |
| <dl class="params"><dt>Parameters</dt><dd> |
| <table class="params"> |
| <tr><td class="paramdir">[in]</td><td class="paramname">a</td><td>Input tensor (Matrix A) </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">b</td><td>Input tensor (Matrix B) </td></tr> |
| <tr><td class="paramdir">[out]</td><td class="paramname">c</td><td>Output tensor to store the result of matrix multiplication. Data type supported: same as <code>input0</code>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">alpha</td><td>Scalar multiplier to apply to AB matrix product. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">beta</td><td>Scalar multiplier to apply to input C matrix before adding product. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">gemm_info</td><td>GEMM meta-data </td></tr> |
| </table> |
| </dd> |
| </dl> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8cpp_source.xhtml#l00342">342</a> of file <a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8cpp_source.xhtml">NEGEMMInterleavedWrapper.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00343"></a><span class="lineno"> 343</span> {</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  _params = INEGEMMWrapperKernel::extract_parameters(a, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, c, gemm_info);</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  _a = a;</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  _b = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  _c = c;</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  _pretranspose_b = gemm_info.pretranpose_B();</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> </div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> input_type = a->info()->data_type();</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keyword">const</span> CPUInfo &ci = <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#ab0fd9e7b4f6240c55b4fe6a54d4418b5">cpu_info</a>();</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_threads = <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#ac24584a63e484123e3756d1b2a1c9e2f">num_threads</a>();</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span> </div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keyword">const</span> arm_gemm::KernelDescription gemm_kernel_info = get_gemm_info(input_type, ci, num_threads, _params, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">alpha</a>, beta, _pretranspose_b);</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(gemm_kernel_info.method != arm_gemm::GemmMethod::GEMM_INTERLEAVED);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> </div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="comment">// Forcing 128-byte alignment (required by 32-bit kernels)</span></div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> alignment = 128;</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  _transformed_b.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(TensorInfo{}, alignment);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  _tmp_c.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(TensorInfo{}, alignment);</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  _tag = <span class="stringliteral">"NEGEMMInterleaved_"</span> + gemm_kernel_info.name;</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span> </div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="comment">// Get strategy</span></div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  std::unique_ptr<detail::IInterleavedStrategy> strategy = detail::create_strategy(gemm_kernel_info.name);</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  _num_windows = <a class="code" href="utils_8hpp.xhtml#aa4508679e1d089c2bdcf000f72357683">iceildiv</a>(_params.M, strategy->out_height()) * _params.batches;</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(strategy == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span> </div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keywordflow">if</span>(!_pretranspose_b)</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  {</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  _block_sizes = strategy->calculate_block_sizes_for_strategy(ci, _params);</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  _batch_window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, Window::Dimension(0, <a class="code" href="namespacearm__compute.xhtml#ab237a0a375cf382d52b61653248d3d4a">ceil_to_multiple</a>(_block_sizes.m_round, _block_sizes.strategy_out_height), _block_sizes.strategy_out_height));</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  _batch_window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>, Window::Dimension(0, _params.batches));</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="comment">// If the execution is single threaded or has only one window then the buffer manager only needs 1 buffer else we will use NUM_BUFFERS buffers and ping pong between them:</span></div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_iterations = _batch_window.<a class="code" href="classarm__compute_1_1_window.xhtml#a0654fca130488c15b4b66a9b7131c142">num_iterations_total</a>();</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="keywordflow">if</span>(<a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().num_threads() == 1 || num_iterations == 1)</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  {</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  _buffer_manager = support::cpp14::make_unique<BufferManagerSingleThread>();</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  }</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  {</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span> <span class="preprocessor">#ifdef NO_MULTI_THREADING</span></div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Can't have more than 1 buffer without multiple threads"</span>);</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> <span class="preprocessor">#else </span><span class="comment">/* NO_MULTI_THREADING */</span><span class="preprocessor"></span></div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  _buffer_manager = support::cpp14::make_unique<BufferManagerMultipleThreads>(<a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().num_threads());</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> <span class="preprocessor">#endif </span><span class="comment">/* NO_MULTI_THREADING */</span><span class="preprocessor"></span></div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  }</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="comment">// If B is transposed at every iteration then transformed_B can be managed:</span></div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_transformed_b);</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*_transformed_b.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>(), _b-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_tensor_shape(TensorShape(_block_sizes.x_block * _block_sizes.k_block, _buffer_manager->num_buffers())));</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  }</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  {</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  _tag += <span class="stringliteral">"_preB"</span>;</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  }</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span> </div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  _prepare_b = strategy->instantiate_prepareB(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, &_transformed_b, _params, ci);</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_prepare_b == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span> </div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keywordflow">if</span>(_pretranspose_b)</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  {</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  _block_sizes = _prepare_b->block_sizes();</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  _batch_window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, Window::Dimension(0, <a class="code" href="namespacearm__compute.xhtml#ab237a0a375cf382d52b61653248d3d4a">ceil_to_multiple</a>(_block_sizes.m_round, _block_sizes.strategy_out_height), _block_sizes.strategy_out_height));</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  _batch_window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>, Window::Dimension(0, _params.batches));</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  }</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span> </div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  _block_walker.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, Window::Dimension(0, <a class="code" href="namespacearm__compute.xhtml#ab237a0a375cf382d52b61653248d3d4a">ceil_to_multiple</a>(_params.N, _block_sizes.x_block), _block_sizes.x_block));</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  _block_walker.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>, Window::Dimension(0, <a class="code" href="namespacearm__compute.xhtml#ab237a0a375cf382d52b61653248d3d4a">ceil_to_multiple</a>(_params.K, _block_sizes.k_block), _block_sizes.k_block));</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  _block_walker.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#a893d17b56b9abc4423ce26e9a24ac5dc">Window::DimZ</a>, Window::Dimension(0, _params.multis));</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span> </div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  _transformed_a.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(TensorInfo(TensorShape{ _block_sizes.k_block, _block_sizes.m_round, _params.batches }, 1, input_type), alignment);</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_transformed_a);</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_tmp_c);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> </div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  _transform_a = strategy->instantiate_transformA(_a, &_transformed_a, _block_walker, _params, gemm_info);</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  _matrix_multiply = strategy->instantiate_matrix_multiply(&_transformed_a, &_transformed_b, &_tmp_c, c, _block_walker, _block_sizes, _params, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">alpha</a>, beta, gemm_info, num_threads);</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_transform_a == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_matrix_multiply == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> </div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  _transformed_a.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  _tmp_c.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordflow">if</span>(!_pretranspose_b)</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  {</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  _transformed_b.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  }</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> }</div><div class="ttc" id="_error_8h_xhtml_a05b19c75afe9c24200a62b9724734bbd"><div class="ttname"><a href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(...)</div><div class="ttdoc">Print the given message then throw an std::runtime_error.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00261">Error.h:261</a></div></div> |
| <div class="ttc" id="utils_8hpp_xhtml_aa4508679e1d089c2bdcf000f72357683"><div class="ttname"><a href="utils_8hpp.xhtml#aa4508679e1d089c2bdcf000f72357683">iceildiv</a></div><div class="ttdeci">T iceildiv(const T a, const T b)</div><div class="ttdef"><b>Definition:</b> <a href="utils_8hpp_source.xhtml#l00036">utils.hpp:36</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a3fc6adad84b23f10d54d5a7b6928f872"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">arm_compute::TensorAllocator::init</a></div><div class="ttdeci">void init(const TensorAllocator &allocator, const Coordinates &coords, TensorInfo &sub_info)</div><div class="ttdoc">Shares the same backing memory with another tensor allocator, while the tensor info might be differen...</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator.cpp:108</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aa76b4a6e74940dabc5b7fc6b2dab3545"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">arm_compute::test::validation::b</a></div><div class="ttdeci">SimpleTensor< float > b</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_2_d_f_t_8cpp_source.xhtml#l00157">DFT.cpp:157</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_ab0fd9e7b4f6240c55b4fe6a54d4418b5"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#ab0fd9e7b4f6240c55b4fe6a54d4418b5">arm_compute::IScheduler::cpu_info</a></div><div class="ttdeci">CPUInfo & cpu_info()</div><div class="ttdoc">Get CPU info.</div><div class="ttdef"><b>Definition:</b> <a href="_i_scheduler_8cpp_source.xhtml#l00039">IScheduler.cpp:39</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a47be6fa38308d0003c25b60b7dbc45ce"><div class="ttname"><a href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">arm_compute::auto_init_if_empty</a></div><div class="ttdeci">bool auto_init_if_empty(ITensorInfo &info, const TensorShape &shape, int num_channels, DataType data_type, QuantizationInfo quantization_info=QuantizationInfo())</div><div class="ttdoc">Auto initialize the tensor info (shape, number of channels and data type) if the current assignment i...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00201">Helpers.inl:201</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_a47d74e4e51f9b1a636c4831bd747a97c"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">arm_compute::Tensor::info</a></div><div class="ttdeci">ITensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00033">Tensor.cpp:33</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_base_xhtml_ac1f67376afb7822f262a0174ef4a3104"><div class="ttname"><a href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">arm_compute::MemoryGroupBase::manage</a></div><div class="ttdeci">void manage(TensorType *obj)</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_base_8h_source.xhtml#l00102">MemoryGroupBase.h:102</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab237a0a375cf382d52b61653248d3d4a"><div class="ttname"><a href="namespacearm__compute.xhtml#ab237a0a375cf382d52b61653248d3d4a">arm_compute::ceil_to_multiple</a></div><div class="ttdeci">auto ceil_to_multiple(S value, T divisor) -> decltype(((value+divisor - 1)/divisor) *divisor)</div><div class="ttdoc">Computes the smallest number larger or equal to value that is a multiple of divisor.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l00066">Utils.h:66</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1misc_1_1_i_cloneable_xhtml_a4d10e5012a872e7f78f2b539b673049d"><div class="ttname"><a href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">arm_compute::misc::ICloneable::clone</a></div><div class="ttdeci">virtual std::unique_ptr< T > clone() const =0</div><div class="ttdoc">Provide a clone of the current object of class T.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a3161c2c93c655dd30953372064ec627b"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">arm_compute::test::validation::alpha</a></div><div class="ttdeci">const float alpha</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_accumulate_8cpp_source.xhtml#l00103">Accumulate.cpp:103</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_acd3d2bba51cb84d34dd7656ad2375a6e"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">arm_compute::Window::set</a></div><div class="ttdeci">void set(size_t dimension, const Dimension &dim)</div><div class="ttdoc">Set the values of a given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00048">Window.inl:48</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_ad2d402364fa822b0b7775081291eeca9"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">arm_compute::Window::DimY</a></div><div class="ttdeci">static constexpr size_t DimY</div><div class="ttdoc">Alias for dimension 1 also known as Y dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00045">Window.h:45</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a893d17b56b9abc4423ce26e9a24ac5dc"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a893d17b56b9abc4423ce26e9a24ac5dc">arm_compute::Window::DimZ</a></div><div class="ttdeci">static constexpr size_t DimZ</div><div class="ttdoc">Alias for dimension 2 also known as Z dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00047">Window.h:47</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_ac24584a63e484123e3756d1b2a1c9e2f"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#ac24584a63e484123e3756d1b2a1c9e2f">arm_compute::IScheduler::num_threads</a></div><div class="ttdeci">virtual unsigned int num_threads() const =0</div><div class="ttdoc">Returns the number of threads that the SingleThreadScheduler has in his pool.</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00074">Types.h:74</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a0654fca130488c15b4b66a9b7131c142"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a0654fca130488c15b4b66a9b7131c142">arm_compute::Window::num_iterations_total</a></div><div class="ttdeci">size_t num_iterations_total() const</div><div class="ttdoc">Return the total number of iterations needed to iterate through the entire window.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00268">Window.inl:268</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00096">Scheduler.cpp:96</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator::allocate()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="_n_e_o_n_2_accumulate_8cpp_source.xhtml#l00103">arm_compute::test::validation::alpha</a>, <a class="el" href="_error_8h_source.xhtml#l00261">ARM_COMPUTE_ERROR</a>, <a class="el" href="_error_8h_source.xhtml#l00337">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00201">arm_compute::auto_init_if_empty()</a>, <a class="el" href="_c_p_p_2_d_f_t_8cpp_source.xhtml#l00157">arm_compute::test::validation::b</a>, <a class="el" href="arm__compute_2core_2_utils_8h_source.xhtml#l00066">arm_compute::ceil_to_multiple()</a>, <a class="el" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">ICloneable< T >::clone()</a>, <a class="el" href="_i_scheduler_8cpp_source.xhtml#l00039">IScheduler::cpu_info()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">ITensorInfo::data_type()</a>, <a class="el" href="_window_8h_source.xhtml#l00043">Window::DimX</a>, <a class="el" href="_window_8h_source.xhtml#l00045">Window::DimY</a>, <a class="el" href="_window_8h_source.xhtml#l00047">Window::DimZ</a>, <a class="el" href="_scheduler_8cpp_source.xhtml#l00096">Scheduler::get()</a>, <a class="el" href="utils_8hpp_source.xhtml#l00036">iceildiv()</a>, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">ITensor::info()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00033">Tensor::info()</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator::init()</a>, <a class="el" href="_memory_group_base_8h_source.xhtml#l00102">MemoryGroupBase< TensorType >::manage()</a>, <a class="el" href="_window_8inl_source.xhtml#l00268">Window::num_iterations_total()</a>, <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml#ac24584a63e484123e3756d1b2a1c9e2f">IScheduler::num_threads()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l02007">GEMMInfo::pretranpose_B()</a>, and <a class="el" href="_window_8inl_source.xhtml#l00048">Window::set()</a>.</p> |
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| <h2 class="memtitle"><span class="permalink"><a href="#ac959141838870719b552af8918577e72">◆ </a></span>operator=()</h2> |
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| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml">NEGEMMInterleavedWrapper</a>& operator= </td> |
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| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml">NEGEMMInterleavedWrapper</a> & </td> |
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| <span class="mlabels"><span class="mlabel">delete</span></span> </td> |
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| <h2 class="memtitle"><span class="permalink"><a href="#aa9b93ef660fc3c5b4b19d3fc7b891b77">◆ </a></span>prepare()</h2> |
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| <td class="memname">void prepare </td> |
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| <p>Prepare the function for executing. </p> |
| <p>Any one off pre-processing step required by the function is handled here</p> |
| <dl class="section note"><dt>Note</dt><dd>Prepare stage might not need all the function's buffers' backing memory to be available in order to execute </dd></dl> |
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| <p>Reimplemented from <a class="el" href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26">IFunction</a>.</p> |
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| <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8cpp_source.xhtml#l00196">196</a> of file <a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8cpp_source.xhtml">NEGEMMInterleavedWrapper.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> {</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordflow">if</span>(_pretranspose_b)</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  {</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  _transformed_b.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(_prepare_b.get(), <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  _b-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  }</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  {</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  _prepare_b->create_workloads(_b_workloads);</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  }</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  _transform_a->create_workloads(_a_workloads);</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  _matrix_multiply->create_workloads(_mm_workloads);</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="comment">//Maximum number of workloads to create:</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_threads = <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#ac24584a63e484123e3756d1b2a1c9e2f">num_threads</a>();</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_iterations = std::max(num_threads, _num_windows);</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="comment">//Maximum number of iterations the parameters allow:</span></div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_iterations = _batch_window.<a class="code" href="classarm__compute_1_1_window.xhtml#a0654fca130488c15b4b66a9b7131c142">num_iterations_total</a>();</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="comment">// Keep the smallest of the two:</span></div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_windows = std::min(num_iterations, max_iterations);</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keyword">const</span> TensorShape window_shape = _batch_window.<a class="code" href="classarm__compute_1_1_window.xhtml#abfa164a379c0c401a662792d7d0ccfba">shape</a>();</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_x_blocks = _block_walker.<a class="code" href="classarm__compute_1_1_window.xhtml#a2fb4b3d6b185c9d225073f923f1827c3">num_iterations</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> </div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="comment">// Create a 1D window to dynamically split the batch window:</span></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  Window win_1D;</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  win_1D.set(0, Window::Dimension(0, num_iterations));</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> </div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="comment">// Create one workload for each sub-window:</span></div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a> = 0; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a> < num_windows; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>++)</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  {</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  Window win = win_1D.split_window(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>, num_windows);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keyword">const</span> Coordinates start_offset = <a class="code" href="namespacearm__compute.xhtml#ad34f2150f1c9f8a3ecb7298162124e5d">index2coords</a>(window_shape, win.x().start());</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keyword">const</span> Coordinates end_offset = <a class="code" href="namespacearm__compute.xhtml#ad34f2150f1c9f8a3ecb7298162124e5d">index2coords</a>(window_shape, win.x().end() - 1);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> </div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">if</span>(_pretranspose_b)</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  {</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keyword">auto</span> workload = [start_offset, end_offset, num_x_blocks, <span class="keyword">this</span>](<span class="keyword">const</span> ThreadInfo & <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>)</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  {</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="comment">//For each block of rows in "M"</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keyword">auto</span> workload_mm = this->_mm_workloads.begin();</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &workload_a : this->_a_workloads)</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  {</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="comment">// Transform one k_block from A:</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  this->_transform_a->transform(workload_a, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>, this->_batch_window, start_offset, end_offset);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="comment">// Then perform the matrix multiplication for each x block along N:</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < num_x_blocks; i++)</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  {</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(workload_mm == this->_mm_workloads.end());</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  this->_matrix_multiply->transform(*workload_mm++, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>, this->_batch_window, start_offset, end_offset);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  }</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  }</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  };</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  _workloads.emplace_back(workload);</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  }</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  {</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keyword">auto</span> workload = [num_threads, start_offset, end_offset, num_x_blocks, <span class="keyword">this</span>](<span class="keyword">const</span> ThreadInfo & <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>)</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  {</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="comment">//For each block of rows in "M"</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keyword">auto</span> workload_mm = this->_mm_workloads.begin();</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> workload_b = 0;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="comment">//If there is only one thread then only reshape the B blocks as you need them:</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> workload_b_next = num_threads == 1 ? this->_b_workloads.size() : 1;</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> </div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &workload_a : this->_a_workloads)</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  {</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="comment">// Transform one k_block from A:</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  this->_transform_a->transform(workload_a, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>, this->_batch_window, start_offset, end_offset);</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="comment">// Then perform the matrix multiplication for each x block along N:</span></div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < num_x_blocks; i++)</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  {</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(workload_mm == this->_mm_workloads.end());</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keywordflow">if</span>(workload_b_next < this->_b_workloads.size())</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  {</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="comment">//Lock on BufferManager: need to run it ?</span></div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordflow">if</span>(this->_buffer_manager->lock_to_reshape_if_needed(workload_b_next))</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  {</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  this->_prepare_b->transform(this->_b_workloads[workload_b_next], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>);</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  this->_buffer_manager->mark_as_reshaped(workload_b_next);</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  }</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  workload_b_next++;</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  }</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(workload_b >= this->_b_workloads.size());</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="comment">// Run if needed or wait</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keywordflow">if</span>(this->_buffer_manager->lock_to_reshape_if_needed(workload_b))</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  {</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  this->_prepare_b->transform(this->_b_workloads[workload_b], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  this->_buffer_manager->mark_as_reshaped(workload_b);</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  }</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  this->_buffer_manager->wait_for_reshaping(workload_b);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  this->_matrix_multiply->transform(*workload_mm++, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>, this->_batch_window, start_offset, end_offset);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  this->_buffer_manager->mark_as_unused(workload_b);</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  workload_b++;</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  }</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  }</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  };</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  _workloads.emplace_back(workload);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  }</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  }</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keywordflow">if</span>(!_pretranspose_b && num_windows > 1 && num_windows % num_threads != 0)</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  {</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="comment">//Make sure the number of workloads is a multiple of the number of threads to avoid dead locks:</span></div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> leftover = num_windows % num_threads; leftover != num_threads; leftover++)</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  {</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keyword">auto</span> workload = [<span class="keyword">this</span>](<span class="keyword">const</span> ThreadInfo & <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>)</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  {</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> workload_b = 0;</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="comment">//If there is only one thread then only reshape the B blocks as you need them:</span></div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> workload_b_next = 1;</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span> </div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> iteration = 0; iteration < this->_mm_workloads.size(); iteration++)</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  {</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keywordflow">if</span>(workload_b_next < this->_b_workloads.size())</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  {</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="comment">//Lock on BufferManager: need to run it ?</span></div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="keywordflow">if</span>(this->_buffer_manager->lock_to_reshape_if_needed(workload_b_next))</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  {</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  this->_prepare_b->transform(this->_b_workloads[workload_b_next], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>);</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  this->_buffer_manager->mark_as_reshaped(workload_b_next);</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  }</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  workload_b_next++;</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  }</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(workload_b >= this->_b_workloads.size());</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <span class="comment">// Run if needed or wait</span></div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordflow">if</span>(this->_buffer_manager->lock_to_reshape_if_needed(workload_b))</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  {</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  this->_prepare_b->transform(this->_b_workloads[workload_b], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>);</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  this->_buffer_manager->mark_as_reshaped(workload_b);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  }</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  this->_buffer_manager->wait_for_reshaping(workload_b);</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  this->_buffer_manager->mark_as_unused(workload_b);</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  workload_b++;</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  }</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  };</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  _workloads.emplace_back(workload);</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  }</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  }</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> </div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  }</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> }</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1a367830ae09bf6138df822888ec1d71"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">arm_compute::test::validation::w</a></div><div class="ttdeci">SimpleTensor< float > w</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_2_d_f_t_8cpp_source.xhtml#l00156">DFT.cpp:156</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad34f2150f1c9f8a3ecb7298162124e5d"><div class="ttname"><a href="namespacearm__compute.xhtml#ad34f2150f1c9f8a3ecb7298162124e5d">arm_compute::index2coords</a></div><div class="ttdeci">Coordinates index2coords(const TensorShape &shape, int index)</div><div class="ttdoc">Convert a linear index into n-dimensional coordinates.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00289">Helpers.inl:289</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a2fb4b3d6b185c9d225073f923f1827c3"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a2fb4b3d6b185c9d225073f923f1827c3">arm_compute::Window::num_iterations</a></div><div class="ttdeci">constexpr size_t num_iterations(size_t dimension) const</div><div class="ttdoc">Return the number of iterations needed to iterate through a given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00168">Window.inl:168</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a9bc00234de9adf8c99a21eb1d7d494c2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">arm_compute::ITensor::mark_as_unused</a></div><div class="ttdeci">void mark_as_unused() const</div><div class="ttdoc">Marks a tensor as unused.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00167">ITensor.cpp:167</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_a4e58f95544bd5ac6559a421671bd9842"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">arm_compute::IScheduler::schedule</a></div><div class="ttdeci">virtual void schedule(ICPPKernel *kernel, const Hints &hints)=0</div><div class="ttdoc">Runs the kernel in the same thread as the caller synchronously.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_abfa164a379c0c401a662792d7d0ccfba"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#abfa164a379c0c401a662792d7d0ccfba">arm_compute::Window::shape</a></div><div class="ttdeci">TensorShape shape() const</div><div class="ttdoc">Return the shape of the window in number of steps.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00258">Window.inl:258</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_ac24584a63e484123e3756d1b2a1c9e2f"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#ac24584a63e484123e3756d1b2a1c9e2f">arm_compute::IScheduler::num_threads</a></div><div class="ttdeci">virtual unsigned int num_threads() const =0</div><div class="ttdoc">Returns the number of threads that the SingleThreadScheduler has in his pool.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a4f4125dba5283887b34f889b1c615c0c"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">arm_compute::test::validation::info</a></div><div class="ttdeci">info</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a0654fca130488c15b4b66a9b7131c142"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a0654fca130488c15b4b66a9b7131c142">arm_compute::Window::num_iterations_total</a></div><div class="ttdeci">size_t num_iterations_total() const</div><div class="ttdoc">Return the total number of iterations needed to iterate through the entire window.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00268">Window.inl:268</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00096">Scheduler.cpp:96</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator::allocate()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="_error_8h_source.xhtml#l00337">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="_window_8h_source.xhtml#l00043">Window::DimX</a>, <a class="el" href="_window_8h_source.xhtml#l00097">Window::Dimension::end()</a>, <a class="el" href="_scheduler_8cpp_source.xhtml#l00096">Scheduler::get()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00289">arm_compute::index2coords()</a>, <a class="el" href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">arm_compute::test::validation::info</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00167">ITensor::mark_as_unused()</a>, <a class="el" href="_window_8inl_source.xhtml#l00168">Window::num_iterations()</a>, <a class="el" href="_window_8inl_source.xhtml#l00268">Window::num_iterations_total()</a>, <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml#ac24584a63e484123e3756d1b2a1c9e2f">IScheduler::num_threads()</a>, <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">IScheduler::schedule()</a>, <a class="el" href="_window_8inl_source.xhtml#l00048">Window::set()</a>, <a class="el" href="_window_8inl_source.xhtml#l00258">Window::shape()</a>, <a class="el" href="_window_8inl_source.xhtml#l00175">Window::split_window()</a>, <a class="el" href="_window_8h_source.xhtml#l00092">Window::Dimension::start()</a>, <a class="el" href="_c_p_p_2_d_f_t_8cpp_source.xhtml#l00156">arm_compute::test::validation::w</a>, and <a class="el" href="_window_8h_source.xhtml#l00143">Window::x()</a>.</p> |
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| <p class="reference">Referenced by <a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8cpp_source.xhtml#l00188">NEGEMMInterleavedWrapper::run()</a>.</p> |
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| </div> |
| </div> |
| <a id="ad1717410afd0be936c6213a63c8005fb"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#ad1717410afd0be936c6213a63c8005fb">◆ </a></span>run()</h2> |
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| <div class="memitem"> |
| <div class="memproto"> |
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| <td class="memname">void run </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td> |
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| </div><div class="memdoc"> |
| |
| <p>Run the kernels contained in the function. </p> |
| <p>For NEON kernels:</p><ul> |
| <li>Multi-threading is used for the kernels which are parallelisable.</li> |
| <li>By default std::thread::hardware_concurrency() threads are used.</li> |
| </ul> |
| <dl class="section note"><dt>Note</dt><dd><a class="el" href="classarm__compute_1_1_c_p_p_scheduler.xhtml#ae64eebaa07f4d2da6cc2ba538c3cb095">CPPScheduler::set_num_threads()</a> can be used to manually set the number of threads</dd></dl> |
| <p>For OpenCL kernels:</p><ul> |
| <li>All the kernels are enqueued on the queue associated with <a class="el" href="classarm__compute_1_1_c_l_scheduler.xhtml" title="Provides global access to a CL context and command queue.">CLScheduler</a>.</li> |
| <li>The queue is then flushed.</li> |
| </ul> |
| <dl class="section note"><dt>Note</dt><dd>The function will not block until the kernels are executed. It is the user's responsibility to wait. </dd> |
| <dd> |
| Will call <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77" title="Prepare the function for executing.">prepare()</a> on first run if hasn't been done </dd></dl> |
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| <p>Implements <a class="el" href="classarm__compute_1_1_i_function.xhtml#a18954417d3124a8095783ea13dc6d00b">IFunction</a>.</p> |
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| <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8cpp_source.xhtml#l00188">188</a> of file <a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8cpp_source.xhtml">NEGEMMInterleavedWrapper.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> {</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  MemoryGroupResourceScope scope_mg(_memory_group);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a3becf955da8cad5036399bcb90f1408d">run_tagged_workloads</a>(_workloads, _tag.c_str());</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> }</div><div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_a3becf955da8cad5036399bcb90f1408d"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#a3becf955da8cad5036399bcb90f1408d">arm_compute::IScheduler::run_tagged_workloads</a></div><div class="ttdeci">virtual void run_tagged_workloads(std::vector< Workload > &workloads, const char *tag)</div><div class="ttdoc">Execute all the passed workloads.</div><div class="ttdef"><b>Definition:</b> <a href="_i_scheduler_8cpp_source.xhtml#l00048">IScheduler.cpp:48</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_interleaved_wrapper.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEGEMMInterleavedWrapper::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_interleaved_wrapper_8cpp_source.xhtml#l00196">NEGEMMInterleavedWrapper.cpp:196</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00096">Scheduler.cpp:96</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_scheduler_8cpp_source.xhtml#l00096">Scheduler::get()</a>, <a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8cpp_source.xhtml#l00196">NEGEMMInterleavedWrapper::prepare()</a>, and <a class="el" href="_i_scheduler_8cpp_source.xhtml#l00048">IScheduler::run_tagged_workloads()</a>.</p> |
| |
| </div> |
| </div> |
| <hr/>The documentation for this class was generated from the following files:<ul> |
| <li>arm_compute/runtime/NEON/functions/assembly/<a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8h_source.xhtml">NEGEMMInterleavedWrapper.h</a></li> |
| <li>src/runtime/NEON/functions/assembly/<a class="el" href="_n_e_g_e_m_m_interleaved_wrapper_8cpp_source.xhtml">NEGEMMInterleavedWrapper.cpp</a></li> |
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