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| Public Attributes</h2></td></tr> |
| <tr class="memitem:a72ed50c98f23f513550255ca8917b473"><td class="memItemLeft" align="right" valign="top">int </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"> </td><td class="mdescRight">Number of dimensions. <a href="#a72ed50c98f23f513550255ca8917b473">More...</a><br /></td></tr> |
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| <tr class="memdesc:a3b691a662b154a03f4e81a7c68b1b175"><td class="mdescLeft"> </td><td class="mdescRight">Data type of the tensor elements. <a href="#a3b691a662b154a03f4e81a7c68b1b175">More...</a><br /></td></tr> |
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| <tr class="memdesc:abd9c8bd4bc17b924e21b21ed919a52f9"><td class="mdescLeft"> </td><td class="mdescRight">Size of the data including padding in each dimension. <a href="#abd9c8bd4bc17b924e21b21ed919a52f9">More...</a><br /></td></tr> |
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| <tr class="memitem:ae6f8322c5cff0068a97e7fba2f123e2c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="group__dnnl__api__memory.html#gaa75cad747fa467d9dc527d943ba3367d">dnnl_format_kind_t</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="structdnnl__memory__desc__t.html#ae6f8322c5cff0068a97e7fba2f123e2c">format_kind</a></td></tr> |
| <tr class="memdesc:ae6f8322c5cff0068a97e7fba2f123e2c"><td class="mdescLeft"> </td><td class="mdescRight">Memory format kind. <a href="#ae6f8322c5cff0068a97e7fba2f123e2c">More...</a><br /></td></tr> |
| <tr class="separator:ae6f8322c5cff0068a97e7fba2f123e2c"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:af4d55e681d75413385a316c2719485ab"><td class="memItemLeft" >union {</td></tr> |
| <tr class="memitem:a25f6463c3cd86a035c317b48415f0d43"><td class="memItemLeft" >   <a class="el" href="structdnnl__blocking__desc__t.html">dnnl_blocking_desc_t</a>   <a class="el" href="structdnnl__memory__desc__t.html#a376a38353672131bfc67fe92bceb6c9e">blocking</a></td></tr> |
| <tr class="separator:a25f6463c3cd86a035c317b48415f0d43"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a53f07f993b7706fcce82e90be07ce69d"><td class="memItemLeft" >   <a class="el" href="structdnnl__wino__desc__t.html">dnnl_wino_desc_t</a>   <a class="el" href="structdnnl__memory__desc__t.html#a17cbfada7a6188b0531ed563f44e6976">wino_desc</a></td></tr> |
| <tr class="memdesc:a53f07f993b7706fcce82e90be07ce69d"><td class="mdescLeft"> </td><td class="mdescRight">Tensor of weights for integer 8bit winograd convolution. <a href="#a53f07f993b7706fcce82e90be07ce69d">More...</a><br /></td></tr> |
| <tr class="separator:a53f07f993b7706fcce82e90be07ce69d"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a903cd2617e3d7788af03e1849ed3da49"><td class="memItemLeft" >   <a class="el" href="structdnnl__rnn__packed__desc__t.html">dnnl_rnn_packed_desc_t</a>   <a class="el" href="structdnnl__memory__desc__t.html#ac8e3fced428d7d7a762cddcbfadadbbf">rnn_packed_desc</a></td></tr> |
| <tr class="memdesc:a903cd2617e3d7788af03e1849ed3da49"><td class="mdescLeft"> </td><td class="mdescRight">Tensor of packed weights for RNN. <a href="#a903cd2617e3d7788af03e1849ed3da49">More...</a><br /></td></tr> |
| <tr class="separator:a903cd2617e3d7788af03e1849ed3da49"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:af4d55e681d75413385a316c2719485ab"><td class="memItemLeft" valign="top">} </td><td class="memItemRight" valign="bottom"><a class="el" href="structdnnl__memory__desc__t.html#af4d55e681d75413385a316c2719485ab">format_desc</a></td></tr> |
| <tr class="separator:af4d55e681d75413385a316c2719485ab"><td class="memSeparator" colspan="2"> </td></tr> |
| <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> </td><td class="memItemRight" valign="bottom"><a class="el" href="structdnnl__memory__desc__t.html#ac60269949e318245e74713a7a0dae135">extra</a></td></tr> |
| <tr class="separator:ac60269949e318245e74713a7a0dae135"><td class="memSeparator" colspan="2"> </td></tr> |
| </table> |
| <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> |
| </div><h2 class="groupheader">Member Data Documentation</h2> |
| <a id="a376a38353672131bfc67fe92bceb6c9e"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a376a38353672131bfc67fe92bceb6c9e">◆ </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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| </table> |
| </div><div class="memdoc"> |
| <p>Description of the data layout for memory formats that use blocking. </p> |
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| </div> |
| <a id="a3b691a662b154a03f4e81a7c68b1b175"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a3b691a662b154a03f4e81a7c68b1b175">◆ </a></span>data_type</h2> |
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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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| </div> |
| </div> |
| <a id="a47052ab197c58af737ac2865796402ce"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a47052ab197c58af737ac2865796402ce">◆ </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> |
| </ul> |
| <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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| <a id="ac60269949e318245e74713a7a0dae135"></a> |
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| <td class="memname">union { ... } dnnl_memory_desc_t::format_desc</td> |
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| <h2 class="memtitle"><span class="permalink"><a href="#ae6f8322c5cff0068a97e7fba2f123e2c">◆ </a></span>format_kind</h2> |
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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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| <a id="a72ed50c98f23f513550255ca8917b473"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a72ed50c98f23f513550255ca8917b473">◆ </a></span>ndims</h2> |
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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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| <h2 class="memtitle"><span class="permalink"><a href="#a20a5edce338892ffdba47f8f11ddfe91">◆ </a></span>offset0</h2> |
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| <td class="memname"><a class="el" href="group__dnnl__api__memory.html#ga872631b12a112bf43fba985cba24dd20">dnnl_dim_t</a> dnnl_memory_desc_t::offset0</td> |
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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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| <a id="a7061897266dca53429b1ea3b8672218f"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a7061897266dca53429b1ea3b8672218f">◆ </a></span>padded_offsets</h2> |
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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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| <h2 class="memtitle"><span class="permalink"><a href="#ac8e3fced428d7d7a762cddcbfadadbbf">◆ </a></span>rnn_packed_desc</h2> |
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| <p>Tensor of packed weights for RNN. </p> |
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| <h2 class="memtitle"><span class="permalink"><a href="#a17cbfada7a6188b0531ed563f44e6976">◆ </a></span>wino_desc</h2> |
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| <p>Tensor of weights for integer 8bit winograd convolution. </p> |
| |
| </div> |
| </div> |
| <hr/>The documentation for this struct was generated from the following file:<ul> |
| <li>/work/mxnet/include/mkldnn/oneapi/dnnl/<a class="el" href="oneapi_2dnnl_2dnnl__types_8h_source.html">dnnl_types.h</a></li> |
| </ul> |
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| </a> 1.8.13 |
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