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<a href="#pub-attribs">Public Attributes</a> &#124;
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<div class="title">mkldnn_memory_desc_t Struct Reference<div class="ingroups"><a class="el" href="group__c__api.html">C API</a> &raquo; <a class="el" href="group__c__api__types.html">Types</a> &raquo; <a class="el" href="group__c__api__types__memory.html">Memory</a></div></div> </div>
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<p><code>#include &lt;<a class="el" href="mkldnn__types_8h_source.html">mkldnn_types.h</a>&gt;</code></p>
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Public Attributes</h2></td></tr>
<tr class="memitem:a8fc71fc8a04e665573f86442bda4dc4c"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structmkldnn__memory__desc__t.html#a8fc71fc8a04e665573f86442bda4dc4c">ndims</a></td></tr>
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<tr class="memdesc:a658027f9bfd06e73b9ecfcee6bd13192"><td class="mdescLeft">&#160;</td><td class="mdescRight">Size of the data including padding in each dimension. <a href="#a658027f9bfd06e73b9ecfcee6bd13192">More...</a><br /></td></tr>
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<tr class="memitem:aeeaca6c6890e9486b9f49d514ef67573"><td class="memItemLeft" align="right" valign="top"><a class="el" href="group__c__api__types__generic.html#ga1f6c390306d4c8a438c0efbff08c0539">mkldnn_format_kind_t</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structmkldnn__memory__desc__t.html#aeeaca6c6890e9486b9f49d514ef67573">format_kind</a></td></tr>
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<tr class="memitem:afe8d693d4e42980c6f09b1c48a96d547"><td class="memItemLeft" >union {</td></tr>
<tr class="memitem:a7b68c56e101608bf84f48fd56592621b"><td class="memItemLeft" >&#160;&#160;&#160;<a class="el" href="structmkldnn__blocking__desc__t.html">mkldnn_blocking_desc_t</a>&#160;&#160;&#160;<a class="el" href="structmkldnn__memory__desc__t.html#ab93d81dc3502465caad206777389c747">blocking</a></td></tr>
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<tr class="memitem:a3638656dc280b6200585557a410c2f3f"><td class="memItemLeft" >&#160;&#160;&#160;<a class="el" href="structmkldnn__wino__desc__t.html">mkldnn_wino_desc_t</a>&#160;&#160;&#160;<a class="el" href="structmkldnn__memory__desc__t.html#aa219a8379e78fc4a553e6af998759c78">wino_desc</a></td></tr>
<tr class="memdesc:a3638656dc280b6200585557a410c2f3f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Tensor of weights for integer 8bit winograd convolution. <a href="#a3638656dc280b6200585557a410c2f3f">More...</a><br /></td></tr>
<tr class="separator:a3638656dc280b6200585557a410c2f3f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae9544113957bf56aed1f201144e9cdfb"><td class="memItemLeft" >&#160;&#160;&#160;<a class="el" href="structmkldnn__rnn__packed__desc__t.html">mkldnn_rnn_packed_desc_t</a>&#160;&#160;&#160;<a class="el" href="structmkldnn__memory__desc__t.html#a3d718050430b441becacab903ae6a0aa">rnn_packed_desc</a></td></tr>
<tr class="memdesc:ae9544113957bf56aed1f201144e9cdfb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Tensor of packed weights for RNN. <a href="#ae9544113957bf56aed1f201144e9cdfb">More...</a><br /></td></tr>
<tr class="separator:ae9544113957bf56aed1f201144e9cdfb"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afe8d693d4e42980c6f09b1c48a96d547"><td class="memItemLeft" valign="top">}&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structmkldnn__memory__desc__t.html#afe8d693d4e42980c6f09b1c48a96d547">format_desc</a></td></tr>
<tr class="separator:afe8d693d4e42980c6f09b1c48a96d547"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aca773206a808b23fee49211829ef5fc9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmkldnn__memory__extra__desc__t.html">mkldnn_memory_extra_desc_t</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structmkldnn__memory__desc__t.html#aca773206a808b23fee49211829ef5fc9">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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<td class="memname"><a class="el" href="structmkldnn__blocking__desc__t.html">mkldnn_blocking_desc_t</a> mkldnn_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__c__api__types__generic.html#ga826b2a9be4d94ac17f99bacac6d0cb29">mkldnn_data_type_t</a> mkldnn_memory_desc_t::data_type</td>
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<p>Data type of the tensor elements. </p>
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<td class="memname"><a class="el" href="group__c__api__types__memory.html#ga172d58716d5d4d104a05ed79a21dfec8">mkldnn_dims_t</a> mkldnn_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__c__api__types__generic.html#ggacc2844e341ab1c4f5b7ae1c6068f2a2baed4a51a8db128a648b942882369a06d6" title="4D CNN activations tensor, an alias to mkldnn_abcd ">mkldnn_nchw</a> or <a class="el" href="group__c__api__types__generic.html#ggacc2844e341ab1c4f5b7ae1c6068f2a2badf1fb06f73cf8dd23ae620eea6516526" title="4D CNN activations tensor, an alias to mkldnn_acdb ">mkldnn_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="structmkldnn__memory__extra__desc__t.html">mkldnn_memory_extra_desc_t</a> mkldnn_memory_desc_t::extra</td>
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<td class="memname">union { ... } mkldnn_memory_desc_t::format_desc</td>
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<td class="memname"><a class="el" href="group__c__api__types__generic.html#ga1f6c390306d4c8a438c0efbff08c0539">mkldnn_format_kind_t</a> mkldnn_memory_desc_t::format_kind</td>
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<p>Memory format kind. </p>
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<td class="memname">int mkldnn_memory_desc_t::ndims</td>
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<p>Number of dimensions. </p>
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<td class="memname"><a class="el" href="group__c__api__types__memory.html#ga15898db3725cddbf807b85ef351ca81f">mkldnn_dim_t</a> mkldnn_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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<td class="memname"><a class="el" href="group__c__api__types__memory.html#ga172d58716d5d4d104a05ed79a21dfec8">mkldnn_dims_t</a> mkldnn_memory_desc_t::padded_dims</td>
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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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<td class="memname"><a class="el" href="structmkldnn__rnn__packed__desc__t.html">mkldnn_rnn_packed_desc_t</a> mkldnn_memory_desc_t::rnn_packed_desc</td>
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<p>Tensor of packed weights for RNN. </p>
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<td class="memname"><a class="el" href="structmkldnn__wino__desc__t.html">mkldnn_wino_desc_t</a> mkldnn_memory_desc_t::wino_desc</td>
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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/mkldnn/<a class="el" href="mkldnn__types_8h_source.html">mkldnn_types.h</a></li>
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