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<div class="title">dnnl_memory_desc_t Struct Reference<div class="ingroups"><a class="el" href="group__dnnl__api.html">Dnnl_api</a> &raquo; <a class="el" href="group__dnnl__api__memory.html">Dnnl_api_memory</a></div></div> </div>
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<p><code>#include &lt;<a class="el" href="oneapi_2dnnl_2dnnl__types_8h_source.html">dnnl_types.h</a>&gt;</code></p>
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Public Attributes</h2></td></tr>
<tr class="memitem:a72ed50c98f23f513550255ca8917b473"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structdnnl__memory__desc__t.html#a72ed50c98f23f513550255ca8917b473">ndims</a></td></tr>
<tr class="memdesc:a72ed50c98f23f513550255ca8917b473"><td class="mdescLeft">&#160;</td><td class="mdescRight">Number of dimensions. <a href="structdnnl__memory__desc__t.html#a72ed50c98f23f513550255ca8917b473">More...</a><br /></td></tr>
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<tr class="memdesc:a3b691a662b154a03f4e81a7c68b1b175"><td class="mdescLeft">&#160;</td><td class="mdescRight">Data type of the tensor elements. <a href="structdnnl__memory__desc__t.html#a3b691a662b154a03f4e81a7c68b1b175">More...</a><br /></td></tr>
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<tr class="memdesc:abd9c8bd4bc17b924e21b21ed919a52f9"><td class="mdescLeft">&#160;</td><td class="mdescRight">Size of the data including padding in each dimension. <a href="structdnnl__memory__desc__t.html#abd9c8bd4bc17b924e21b21ed919a52f9">More...</a><br /></td></tr>
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<tr class="memitem:a958606bbc6b074d99aa4fe4f4f98acc2"><td class="memItemLeft" >union {</td></tr>
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<tr class="memitem:ae7e477bd98824fb5bd4291c2e4449d45"><td class="memItemLeft" >&#160;&#160;&#160;<a class="el" href="structdnnl__wino__desc__t.html">dnnl_wino_desc_t</a>&#160;&#160;&#160;<a class="el" href="structdnnl__memory__desc__t.html#a17cbfada7a6188b0531ed563f44e6976">wino_desc</a></td></tr>
<tr class="memdesc:ae7e477bd98824fb5bd4291c2e4449d45"><td class="mdescLeft">&#160;</td><td class="mdescRight">Tensor of weights for integer 8bit winograd convolution. <a href="uniondnnl__memory__desc__t_1_1_0d1.html#ae7e477bd98824fb5bd4291c2e4449d45">More...</a><br /></td></tr>
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<tr class="memitem:a3b2d268032c7f00d36492425c0e1dc79"><td class="memItemLeft" >&#160;&#160;&#160;<a class="el" href="structdnnl__rnn__packed__desc__t.html">dnnl_rnn_packed_desc_t</a>&#160;&#160;&#160;<a class="el" href="structdnnl__memory__desc__t.html#ac8e3fced428d7d7a762cddcbfadadbbf">rnn_packed_desc</a></td></tr>
<tr class="memdesc:a3b2d268032c7f00d36492425c0e1dc79"><td class="mdescLeft">&#160;</td><td class="mdescRight">Tensor of packed weights for RNN. <a href="uniondnnl__memory__desc__t_1_1_0d1.html#a3b2d268032c7f00d36492425c0e1dc79">More...</a><br /></td></tr>
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<tr class="memitem:a958606bbc6b074d99aa4fe4f4f98acc2"><td class="memItemLeft" valign="top">}&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structdnnl__memory__desc__t.html#a958606bbc6b074d99aa4fe4f4f98acc2">format_desc</a></td></tr>
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<tr class="memitem:ac60269949e318245e74713a7a0dae135"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structdnnl__memory__extra__desc__t.html">dnnl_memory_extra_desc_t</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structdnnl__memory__desc__t.html#ac60269949e318245e74713a7a0dae135">extra</a></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Memory descriptor. The description is based on a number of dimensions, dimensions themselves, plus information about elements type and memory format. Additionally, contains format-specific descriptions of the data layout. </p>
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<h2 class="memtitle"><span class="permalink"><a href="#a376a38353672131bfc67fe92bceb6c9e">&#9670;&nbsp;</a></span>blocking</h2>
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<td class="memname"><a class="el" href="structdnnl__blocking__desc__t.html">dnnl_blocking_desc_t</a> dnnl_memory_desc_t::blocking</td>
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<p>Description of the data layout for memory formats that use blocking. </p>
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<td class="memname"><a class="el" href="group__dnnl__api__memory.html#ga012ba1c84ff24bdd068f9d2f9b26a130">dnnl_data_type_t</a> dnnl_memory_desc_t::data_type</td>
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<p>Data type of the tensor elements. </p>
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<h2 class="memtitle"><span class="permalink"><a href="#a47052ab197c58af737ac2865796402ce">&#9670;&nbsp;</a></span>dims</h2>
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<td class="memname"><a class="el" href="group__dnnl__api__memory.html#ga8331e1160e52a5d4babe96736464095a">dnnl_dims_t</a> dnnl_memory_desc_t::dims</td>
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<p>Dimensions in the following order:</p><ul>
<li>CNN data tensors: mini-batch, channel, spatial (<code>{N, C, [[D,] H,] W}</code>)</li>
<li>CNN weight tensors: group (optional), output channel, input channel, spatial (<code>{[G,] O, I, [[D,] H,] W}</code>)</li>
<li>RNN data tensors: time, mini-batch, channels (<code>{T, N, C}</code>) or layers, directions, states, mini-batch, channels (<code>{L, D, S, N, C}</code>)</li>
<li>RNN weight tensor: layers, directions, input channel, gates, output channels (<code>{L, D, I, G, O}</code>).</li>
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<dl class="section note"><dt>Note</dt><dd>The order of dimensions does not depend on the memory format, so whether the data is laid out in <a class="el" href="group__dnnl__api__memory.html#gga395e42b594683adb25ed2d842bb3091da83a751aedeb59613312339d0f8b90f54" title="4D CNN activations tensor, an alias to dnnl_abcd">dnnl_nchw</a> or <a class="el" href="group__dnnl__api__memory.html#gga395e42b594683adb25ed2d842bb3091dae50c534446b3c18cc018b3946b3cebd7" title="4D CNN activations tensor, an alias to dnnl_acdb">dnnl_nhwc</a> the dims for 4D CN data tensor would be <code>{N, C, H, W}</code>. </dd></dl>
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<td class="memname"><a class="el" href="structdnnl__memory__extra__desc__t.html">dnnl_memory_extra_desc_t</a> dnnl_memory_desc_t::extra</td>
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<td class="memname">union { ... } dnnl_memory_desc_t::format_desc</td>
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<td class="memname"><a class="el" href="group__dnnl__api__memory.html#gaa75cad747fa467d9dc527d943ba3367d">dnnl_format_kind_t</a> dnnl_memory_desc_t::format_kind</td>
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<p>Memory format kind. </p>
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<td class="memname">int dnnl_memory_desc_t::ndims</td>
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<p>Number of dimensions. </p>
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<p>Offset from memory origin to the current block, non-zero only in a description of a memory sub-block. </p>
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<p>Size of the data including padding in each dimension. </p>
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<p>Per-dimension offset from the padding to actual data, the top-level tensor with offsets applied must lie within the padding area. </p>
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<p>Tensor of packed weights for RNN. </p>
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<p>Tensor of weights for integer 8bit winograd convolution. </p>
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<hr/>The documentation for this struct was generated from the following file:<ul>
<li>/work/mxnet/include/onednn/oneapi/dnnl/<a class="el" href="oneapi_2dnnl_2dnnl__types_8h_source.html">dnnl_types.h</a></li>
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