mirror of
https://github.com/ml-explore/mlx.git
synced 2025-06-25 09:51:17 +08:00
1653 lines
247 KiB
HTML
1653 lines
247 KiB
HTML
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "https://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
|
|
<html xmlns="http://www.w3.org/1999/xhtml" lang="en-US">
|
|
<head>
|
|
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
|
|
<meta http-equiv="X-UA-Compatible" content="IE=11"/>
|
|
<meta name="generator" content="Doxygen 1.10.0"/>
|
|
<meta name="viewport" content="width=device-width, initial-scale=1"/>
|
|
<title>MLX: mlx/backend/metal/kernels/quantized.h Source File</title>
|
|
<link href="tabs.css" rel="stylesheet" type="text/css"/>
|
|
<script type="text/javascript" src="jquery.js"></script>
|
|
<script type="text/javascript" src="dynsections.js"></script>
|
|
<script type="text/javascript" src="clipboard.js"></script>
|
|
<script type="text/javascript" src="cookie.js"></script>
|
|
<link href="search/search.css" rel="stylesheet" type="text/css"/>
|
|
<script type="text/javascript" src="search/searchdata.js"></script>
|
|
<script type="text/javascript" src="search/search.js"></script>
|
|
<link href="doxygen.css" rel="stylesheet" type="text/css" />
|
|
</head>
|
|
<body>
|
|
<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
|
|
<div id="titlearea">
|
|
<table cellspacing="0" cellpadding="0">
|
|
<tbody>
|
|
<tr id="projectrow">
|
|
<td id="projectalign">
|
|
<div id="projectname">MLX
|
|
</div>
|
|
</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
</div>
|
|
<!-- end header part -->
|
|
<!-- Generated by Doxygen 1.10.0 -->
|
|
<script type="text/javascript">
|
|
/* @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt MIT */
|
|
var searchBox = new SearchBox("searchBox", "search/",'.html');
|
|
/* @license-end */
|
|
</script>
|
|
<script type="text/javascript" src="menudata.js"></script>
|
|
<script type="text/javascript" src="menu.js"></script>
|
|
<script type="text/javascript">
|
|
/* @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt MIT */
|
|
$(function() {
|
|
initMenu('',true,false,'search.php','Search');
|
|
$(function() { init_search(); });
|
|
});
|
|
/* @license-end */
|
|
</script>
|
|
<div id="main-nav"></div>
|
|
<script type="text/javascript">
|
|
/* @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt MIT */
|
|
$(function() { codefold.init(0); });
|
|
/* @license-end */
|
|
</script>
|
|
<!-- window showing the filter options -->
|
|
<div id="MSearchSelectWindow"
|
|
onmouseover="return searchBox.OnSearchSelectShow()"
|
|
onmouseout="return searchBox.OnSearchSelectHide()"
|
|
onkeydown="return searchBox.OnSearchSelectKey(event)">
|
|
</div>
|
|
|
|
<!-- iframe showing the search results (closed by default) -->
|
|
<div id="MSearchResultsWindow">
|
|
<div id="MSearchResults">
|
|
<div class="SRPage">
|
|
<div id="SRIndex">
|
|
<div id="SRResults"></div>
|
|
<div class="SRStatus" id="Loading">Loading...</div>
|
|
<div class="SRStatus" id="Searching">Searching...</div>
|
|
<div class="SRStatus" id="NoMatches">No Matches</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div id="nav-path" class="navpath">
|
|
<ul>
|
|
<li class="navelem"><a class="el" href="dir_938ab0ecf10b8b860ff766c820f665fd.html">mlx</a></li><li class="navelem"><a class="el" href="dir_1d446c9bd3c99228254c9484e0bc5c06.html">backend</a></li><li class="navelem"><a class="el" href="dir_d0c977ea65824390717cdb7efc36c157.html">metal</a></li><li class="navelem"><a class="el" href="dir_70a37effa88bcbd6b791977fa1e64356.html">kernels</a></li> </ul>
|
|
</div>
|
|
</div><!-- top -->
|
|
<div class="header">
|
|
<div class="headertitle"><div class="title">quantized.h</div></div>
|
|
</div><!--header-->
|
|
<div class="contents">
|
|
<a href="quantized_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span><span class="comment">// Copyright © 2023-2024 Apple Inc.</span></div>
|
|
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno"> 2</span> </div>
|
|
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno"> 3</span><span class="preprocessor">#include <metal_simdgroup></span></div>
|
|
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno"> 4</span><span class="preprocessor">#include <metal_stdlib></span></div>
|
|
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno"> 5</span> </div>
|
|
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno"> 6</span><span class="keyword">using namespace </span><a class="code hl_namespace" href="namespacemetal.html">metal</a>;</div>
|
|
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno"> 7</span> </div>
|
|
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"><a class="line" href="quantized_8h.html#a0386011c52d03e60885a31e6fbd903dd"> 8</a></span><span class="preprocessor">#define MLX_MTL_CONST static constant constexpr const</span></div>
|
|
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span> </div>
|
|
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"><a class="line" href="quantized_8h.html#a62969a218d93680f5e35d0c61b160b99"> 10</a></span><a class="code hl_define" href="quantized_8h.html#a0386011c52d03e60885a31e6fbd903dd">MLX_MTL_CONST</a> <span class="keywordtype">int</span> <a class="code hl_variable" href="quantized_8h.html#a62969a218d93680f5e35d0c61b160b99">SIMD_SIZE</a> = 32;</div>
|
|
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span> </div>
|
|
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span><span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> U, <span class="keywordtype">int</span> values_per_thread, <span class="keywordtype">int</span> bits></div>
|
|
<div class="foldopen" id="foldopen00013" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"><a class="line" href="quantized_8h.html#a8dbace41de9e1e21dd59d016db11b3e9"> 13</a></span><span class="keyword">inline</span> U <a class="code hl_function" href="quantized_8h.html#a8dbace41de9e1e21dd59d016db11b3e9">load_vector</a>(<span class="keyword">const</span> device T* x, thread U* x_thread) {</div>
|
|
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span> <span class="keyword">static_assert</span>(</div>
|
|
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span> bits == 2 || bits == 4 || bits == 8,</div>
|
|
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno"> 16</span> <span class="stringliteral">"Template undefined for bits not in {2, 4, 8}"</span>);</div>
|
|
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"> 17</span> </div>
|
|
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</span> U sum = 0;</div>
|
|
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno"> 19</span> </div>
|
|
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"> 20</span> <span class="keywordflow">if</span> (bits == 2) {</div>
|
|
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < values_per_thread; i += 4) {</div>
|
|
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno"> 22</span> sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];</div>
|
|
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span> x_thread[i] = x[i];</div>
|
|
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> x_thread[i + 1] = x[i + 1] / 4.0f;</div>
|
|
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"> 25</span> x_thread[i + 2] = x[i + 2] / 16.0f;</div>
|
|
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span> x_thread[i + 3] = x[i + 3] / 64.0f;</div>
|
|
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"> 27</span> }</div>
|
|
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span> }</div>
|
|
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span> </div>
|
|
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span> <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bits == 4) {</div>
|
|
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < values_per_thread; i += 4) {</div>
|
|
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span> sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];</div>
|
|
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> x_thread[i] = x[i];</div>
|
|
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span> x_thread[i + 1] = x[i + 1] / 16.0f;</div>
|
|
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span> x_thread[i + 2] = x[i + 2] / 256.0f;</div>
|
|
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span> x_thread[i + 3] = x[i + 3] / 4096.0f;</div>
|
|
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span> }</div>
|
|
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span> }</div>
|
|
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span> </div>
|
|
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span> <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bits == 8) {</div>
|
|
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < values_per_thread; i++) {</div>
|
|
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span> sum += x[i];</div>
|
|
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span> x_thread[i] = x[i];</div>
|
|
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"> 44</span> }</div>
|
|
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span> }</div>
|
|
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span> </div>
|
|
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> <span class="keywordflow">return</span> sum;</div>
|
|
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span> </div>
|
|
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span><span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> U, <span class="keywordtype">int</span> values_per_thread, <span class="keywordtype">int</span> bits></div>
|
|
<div class="foldopen" id="foldopen00051" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"><a class="line" href="quantized_8h.html#aa69e143d646fad332c1a53e8c9b337b7"> 51</a></span><span class="keyword">inline</span> U <a class="code hl_function" href="quantized_8h.html#aa69e143d646fad332c1a53e8c9b337b7">load_vector_safe</a>(<span class="keyword">const</span> device T* x, thread U* x_thread, <span class="keywordtype">int</span> N) {</div>
|
|
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span> <span class="keyword">static_assert</span>(</div>
|
|
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span> bits == 2 || bits == 4 || bits == 8,</div>
|
|
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> <span class="stringliteral">"Template undefined for bits not in {2, 4, 8}"</span>);</div>
|
|
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"> 55</span> </div>
|
|
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> U sum = 0;</div>
|
|
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> </div>
|
|
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span> <span class="keywordflow">if</span> (bits == 2) {</div>
|
|
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < N; i += 4) {</div>
|
|
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"> 60</span> sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];</div>
|
|
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> x_thread[i] = x[i];</div>
|
|
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"> 62</span> x_thread[i + 1] = x[i + 1] / 4.0f;</div>
|
|
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"> 63</span> x_thread[i + 2] = x[i + 2] / 16.0f;</div>
|
|
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span> x_thread[i + 3] = x[i + 3] / 64.0f;</div>
|
|
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> }</div>
|
|
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = N; i < values_per_thread; i++) {</div>
|
|
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> x_thread[i] = 0;</div>
|
|
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> }</div>
|
|
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> }</div>
|
|
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"> 70</span> </div>
|
|
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"> 71</span> <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bits == 4) {</div>
|
|
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < N; i += 4) {</div>
|
|
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span> sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];</div>
|
|
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span> x_thread[i] = x[i];</div>
|
|
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> x_thread[i + 1] = x[i + 1] / 16.0f;</div>
|
|
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> x_thread[i + 2] = x[i + 2] / 256.0f;</div>
|
|
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"> 77</span> x_thread[i + 3] = x[i + 3] / 4096.0f;</div>
|
|
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> }</div>
|
|
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"> 79</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = N; i < values_per_thread; i++) {</div>
|
|
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"> 80</span> x_thread[i] = 0;</div>
|
|
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"> 81</span> }</div>
|
|
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> }</div>
|
|
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> </div>
|
|
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bits == 8) {</div>
|
|
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < N; i++) {</div>
|
|
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> sum += x[i];</div>
|
|
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> x_thread[i] = x[i];</div>
|
|
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"> 88</span> }</div>
|
|
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"> 89</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = N; i < values_per_thread; i++) {</div>
|
|
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"> 90</span> x_thread[i] = 0;</div>
|
|
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"> 91</span> }</div>
|
|
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span> }</div>
|
|
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"> 93</span> </div>
|
|
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span> <span class="keywordflow">return</span> sum;</div>
|
|
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"> 96</span> </div>
|
|
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"> 97</span><span class="keyword">template</span> <<span class="keyword">typename</span> U, <span class="keywordtype">int</span> values_per_thread, <span class="keywordtype">int</span> bits></div>
|
|
<div class="foldopen" id="foldopen00098" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"><a class="line" href="quantized_8h.html#ab364d58ab652e3ad87a8f80910556071"> 98</a></span><span class="keyword">inline</span> U <a class="code hl_function" href="quantized_8h.html#ab364d58ab652e3ad87a8f80910556071">qdot</a>(</div>
|
|
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> <span class="keyword">const</span> device uint8_t* w,</div>
|
|
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span> <span class="keyword">const</span> thread U* x_thread,</div>
|
|
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> U scale,</div>
|
|
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"> 102</span> U bias,</div>
|
|
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> U sum) {</div>
|
|
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span> <span class="keyword">static_assert</span>(</div>
|
|
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> bits == 2 || bits == 4 || bits == 8,</div>
|
|
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> <span class="stringliteral">"Template undefined for bits not in {2, 4, 8}"</span>);</div>
|
|
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> </div>
|
|
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> U accum = 0;</div>
|
|
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> </div>
|
|
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"> 110</span> <span class="keywordflow">if</span> (bits == 2) {</div>
|
|
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < (values_per_thread / 4); i++) {</div>
|
|
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"> 112</span> accum +=</div>
|
|
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span> (x_thread[4 * i] * (w[i] & 0x03) +</div>
|
|
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno"> 114</span> x_thread[4 * i + 1] * (w[i] & 0x0c) +</div>
|
|
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span> x_thread[4 * i + 2] * (w[i] & 0x30) +</div>
|
|
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"> 116</span> x_thread[4 * i + 3] * (w[i] & 0xc0));</div>
|
|
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"> 117</span> }</div>
|
|
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span> }</div>
|
|
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"> 119</span> </div>
|
|
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"> 120</span> <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bits == 4) {</div>
|
|
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"> 121</span> <span class="keyword">const</span> device uint16_t* ws = (<span class="keyword">const</span> device uint16_t*)w;</div>
|
|
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno"> 122</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < (values_per_thread / 4); i++) {</div>
|
|
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> accum +=</div>
|
|
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> (x_thread[4 * i] * (ws[i] & 0x000f) +</div>
|
|
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span> x_thread[4 * i + 1] * (ws[i] & 0x00f0) +</div>
|
|
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> x_thread[4 * i + 2] * (ws[i] & 0x0f00) +</div>
|
|
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> x_thread[4 * i + 3] * (ws[i] & 0xf000));</div>
|
|
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> }</div>
|
|
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> }</div>
|
|
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> </div>
|
|
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bits == 8) {</div>
|
|
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < values_per_thread; i++) {</div>
|
|
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> accum += x_thread[i] * w[i];</div>
|
|
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> }</div>
|
|
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> }</div>
|
|
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> </div>
|
|
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> <span class="keywordflow">return</span> scale * accum + sum * bias;</div>
|
|
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"> 139</span> </div>
|
|
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"> 140</span><span class="keyword">template</span> <<span class="keyword">typename</span> U, <span class="keywordtype">int</span> values_per_thread, <span class="keywordtype">int</span> bits></div>
|
|
<div class="foldopen" id="foldopen00141" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"><a class="line" href="quantized_8h.html#a07b26d2d0b0d65dfe925c452c453fa42"> 141</a></span><span class="keyword">inline</span> U <a class="code hl_function" href="quantized_8h.html#a07b26d2d0b0d65dfe925c452c453fa42">qdot_safe</a>(</div>
|
|
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> <span class="keyword">const</span> device uint8_t* w,</div>
|
|
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span> <span class="keyword">const</span> thread U* x_thread,</div>
|
|
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span> U scale,</div>
|
|
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"> 145</span> U bias,</div>
|
|
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> U sum,</div>
|
|
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span> <span class="keywordtype">int</span> N) {</div>
|
|
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"> 148</span> <span class="keyword">static_assert</span>(</div>
|
|
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> bits == 2 || bits == 4 || bits == 8,</div>
|
|
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> <span class="stringliteral">"Template undefined for bits not in {2, 4, 8}"</span>);</div>
|
|
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno"> 151</span> </div>
|
|
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"> 152</span> U accum = 0;</div>
|
|
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"> 153</span> </div>
|
|
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno"> 154</span> <span class="keywordflow">if</span> (bits == 2) {</div>
|
|
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"> 155</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < (N / 4); i++) {</div>
|
|
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> accum +=</div>
|
|
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> (x_thread[4 * i] * (w[i] & 0x03) +</div>
|
|
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> x_thread[4 * i + 1] * (w[i] & 0x0c) +</div>
|
|
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno"> 159</span> x_thread[4 * i + 2] * (w[i] & 0x30) +</div>
|
|
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"> 160</span> x_thread[4 * i + 3] * (w[i] & 0xc0));</div>
|
|
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"> 161</span> }</div>
|
|
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> }</div>
|
|
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</span> </div>
|
|
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span> <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bits == 4) {</div>
|
|
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> <span class="keyword">const</span> device uint16_t* ws = (<span class="keyword">const</span> device uint16_t*)w;</div>
|
|
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"> 166</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < (N / 4); i++) {</div>
|
|
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno"> 167</span> accum +=</div>
|
|
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"> 168</span> (x_thread[4 * i] * (ws[i] & 0x000f) +</div>
|
|
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"> 169</span> x_thread[4 * i + 1] * (ws[i] & 0x00f0) +</div>
|
|
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> x_thread[4 * i + 2] * (ws[i] & 0x0f00) +</div>
|
|
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"> 171</span> x_thread[4 * i + 3] * (ws[i] & 0xf000));</div>
|
|
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"> 172</span> }</div>
|
|
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span> }</div>
|
|
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno"> 174</span> </div>
|
|
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno"> 175</span> <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bits == 8) {</div>
|
|
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"> 176</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < N; i++) {</div>
|
|
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span> accum += x_thread[i] * w[i];</div>
|
|
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"> 178</span> }</div>
|
|
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno"> 179</span> }</div>
|
|
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno"> 180</span> </div>
|
|
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno"> 181</span> <span class="keywordflow">return</span> scale * accum + sum * bias;</div>
|
|
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"> 182</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"> 183</span> </div>
|
|
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span><span class="keyword">template</span> <<span class="keyword">typename</span> U, <span class="keywordtype">int</span> values_per_thread, <span class="keywordtype">int</span> bits></div>
|
|
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span><span class="keyword">inline</span> <span class="keywordtype">void</span></div>
|
|
<div class="foldopen" id="foldopen00186" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"><a class="line" href="quantized_8h.html#ae756f6817b584c60f5dcdd1d9c6b4f58"> 186</a></span><a class="code hl_function" href="quantized_8h.html#ae756f6817b584c60f5dcdd1d9c6b4f58">qouter</a>(<span class="keyword">const</span> thread uint8_t* w, U x, U scale, U bias, thread U* result) {</div>
|
|
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno"> 187</span> <span class="keyword">static_assert</span>(</div>
|
|
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"> 188</span> bits == 2 || bits == 4 || bits == 8,</div>
|
|
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"> 189</span> <span class="stringliteral">"Template undefined for bits not in {2, 4, 8}"</span>);</div>
|
|
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno"> 190</span> </div>
|
|
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno"> 191</span> <span class="keywordflow">if</span> (bits == 2) {</div>
|
|
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span> U s[4] = {scale, scale / 4.0f, scale / 16.0f, scale / 64.0f};</div>
|
|
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < (values_per_thread / 4); i++) {</div>
|
|
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span> result[4 * i] += x * (s[0] * (w[i] & 0x03) + bias);</div>
|
|
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span> result[4 * i + 1] += x * (s[1] * (w[i] & 0x0c) + bias);</div>
|
|
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"> 196</span> result[4 * i + 2] += x * (s[2] * (w[i] & 0x30) + bias);</div>
|
|
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span> result[4 * i + 3] += x * (s[3] * (w[i] & 0xc0) + bias);</div>
|
|
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"> 198</span> }</div>
|
|
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span> }</div>
|
|
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> </div>
|
|
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bits == 4) {</div>
|
|
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> U s[2] = {scale, scale / 16.0f};</div>
|
|
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < (values_per_thread / 2); i++) {</div>
|
|
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span> result[2 * i] += x * (s[0] * (w[i] & 0x0f) + bias);</div>
|
|
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span> result[2 * i + 1] += x * (s[1] * (w[i] & 0xf0) + bias);</div>
|
|
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> }</div>
|
|
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"> 207</span> }</div>
|
|
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"> 208</span> </div>
|
|
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"> 209</span> <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bits == 8) {</div>
|
|
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"> 210</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < values_per_thread; i++) {</div>
|
|
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"> 211</span> result[i] += x * (scale * w[i] + bias);</div>
|
|
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span> }</div>
|
|
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"> 213</span> }</div>
|
|
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"> 214</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"> 215</span> </div>
|
|
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno"> 216</span><span class="keyword">template</span> <<span class="keyword">typename</span> U, <span class="keywordtype">int</span> N, <span class="keywordtype">int</span> bits></div>
|
|
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno"> 217</span><span class="keyword">inline</span> <span class="keywordtype">void</span></div>
|
|
<div class="foldopen" id="foldopen00218" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno"><a class="line" href="quantized_8h.html#aecff265b63566d0d5689cfc4e5b037d2"> 218</a></span><a class="code hl_function" href="quantized_8h.html#aecff265b63566d0d5689cfc4e5b037d2">dequantize</a>(<span class="keyword">const</span> device uint8_t* w, U scale, U bias, threadgroup U* w_local) {</div>
|
|
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno"> 219</span> <span class="keyword">static_assert</span>(</div>
|
|
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"> 220</span> bits == 2 || bits == 4 || bits == 8,</div>
|
|
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno"> 221</span> <span class="stringliteral">"Template undefined for bits not in {2, 4, 8}"</span>);</div>
|
|
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno"> 222</span> </div>
|
|
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno"> 223</span> <span class="keywordflow">if</span> (bits == 2) {</div>
|
|
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"> 224</span> U s[4] = {</div>
|
|
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno"> 225</span> scale,</div>
|
|
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno"> 226</span> scale / <span class="keyword">static_cast<</span>U<span class="keyword">></span>(4.0f),</div>
|
|
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno"> 227</span> scale / <span class="keyword">static_cast<</span>U<span class="keyword">></span>(16.0f),</div>
|
|
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno"> 228</span> scale / <span class="keyword">static_cast<</span>U<span class="keyword">></span>(64.0f)};</div>
|
|
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno"> 229</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < (N / 4); i++) {</div>
|
|
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno"> 230</span> w_local[4 * i] = s[0] * (w[i] & 0x03) + bias;</div>
|
|
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno"> 231</span> w_local[4 * i + 1] = s[1] * (w[i] & 0x0c) + bias;</div>
|
|
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno"> 232</span> w_local[4 * i + 2] = s[2] * (w[i] & 0x30) + bias;</div>
|
|
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno"> 233</span> w_local[4 * i + 3] = s[3] * (w[i] & 0xc0) + bias;</div>
|
|
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"> 234</span> }</div>
|
|
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno"> 235</span> }</div>
|
|
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno"> 236</span> </div>
|
|
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno"> 237</span> <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bits == 4) {</div>
|
|
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno"> 238</span> U s[2] = {scale, scale / <span class="keyword">static_cast<</span>U<span class="keyword">></span>(16.0f)};</div>
|
|
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno"> 239</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < (N / 2); i++) {</div>
|
|
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno"> 240</span> w_local[2 * i] = s[0] * (w[i] & 0x0f) + bias;</div>
|
|
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno"> 241</span> w_local[2 * i + 1] = s[1] * (w[i] & 0xf0) + bias;</div>
|
|
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno"> 242</span> }</div>
|
|
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno"> 243</span> }</div>
|
|
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno"> 244</span> </div>
|
|
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno"> 245</span> <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bits == 8) {</div>
|
|
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno"> 246</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < N; i++) {</div>
|
|
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno"> 247</span> w_local[i] = scale * w[i] + bias;</div>
|
|
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno"> 248</span> }</div>
|
|
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno"> 249</span> }</div>
|
|
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno"> 250</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno"> 251</span> </div>
|
|
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno"> 252</span><span class="keyword">template</span> <</div>
|
|
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno"> 253</span> <span class="keyword">typename</span> T,</div>
|
|
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno"> 254</span> <span class="keywordtype">short</span> BROWS,</div>
|
|
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno"> 255</span> <span class="keywordtype">short</span> BCOLS,</div>
|
|
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno"> 256</span> <span class="keywordtype">short</span> dst_ld,</div>
|
|
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno"> 257</span> <span class="keywordtype">short</span> reduction_dim,</div>
|
|
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno"> 258</span> <span class="keywordtype">short</span> tgp_size,</div>
|
|
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"> 259</span> <span class="keywordtype">short</span> group_size,</div>
|
|
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno"> 260</span> <span class="keywordtype">short</span> <a class="code hl_function" href="namespacemlx_1_1core_1_1random.html#abb895baa477f5a06b5f88e69245f1825">bits</a>></div>
|
|
<div class="foldopen" id="foldopen00261" data-start="{" data-end="};">
|
|
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html"> 261</a></span><span class="keyword">struct </span><a class="code hl_struct" href="struct_quantized_block_loader.html">QuantizedBlockLoader</a> {</div>
|
|
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"> 262</span> <span class="keyword">static_assert</span>(</div>
|
|
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"> 263</span> BCOLS <= group_size,</div>
|
|
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno"> 264</span> <span class="stringliteral">"The group size should be larger than the columns"</span>);</div>
|
|
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno"> 265</span> <span class="keyword">static_assert</span>(</div>
|
|
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno"> 266</span> group_size % BCOLS == 0,</div>
|
|
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno"> 267</span> <span class="stringliteral">"The group size should be divisible by the columns"</span>);</div>
|
|
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno"> 268</span> <span class="keyword">static_assert</span>(</div>
|
|
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno"> 269</span> bits == 2 || bits == 4 || bits == 8,</div>
|
|
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno"> 270</span> <span class="stringliteral">"Template undefined for bits not in {2, 4, 8}"</span>);</div>
|
|
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno"> 271</span> </div>
|
|
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a8eae73a0c04bf1e41fb96131f6aa500d"> 272</a></span> <a class="code hl_define" href="quantized_8h.html#a0386011c52d03e60885a31e6fbd903dd">MLX_MTL_CONST</a> <span class="keywordtype">short</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a8eae73a0c04bf1e41fb96131f6aa500d">pack_factor</a> = 32 / bits;</div>
|
|
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a1392a5278cf6e090ea80ebe7c4ac5fbb"> 273</a></span> <a class="code hl_define" href="quantized_8h.html#a0386011c52d03e60885a31e6fbd903dd">MLX_MTL_CONST</a> <span class="keywordtype">short</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a1392a5278cf6e090ea80ebe7c4ac5fbb">BCOLS_PACKED</a> = BCOLS / <a class="code hl_variable" href="struct_quantized_block_loader.html#a8eae73a0c04bf1e41fb96131f6aa500d">pack_factor</a>;</div>
|
|
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a6213479f7a6d9314d8879f8856b0b6fb"> 274</a></span> <a class="code hl_define" href="quantized_8h.html#a0386011c52d03e60885a31e6fbd903dd">MLX_MTL_CONST</a> <span class="keywordtype">short</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a6213479f7a6d9314d8879f8856b0b6fb">n_reads</a> =</div>
|
|
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno"> 275</span> (<a class="code hl_variable" href="struct_quantized_block_loader.html#a1392a5278cf6e090ea80ebe7c4ac5fbb">BCOLS_PACKED</a> * BROWS < tgp_size) ? 1 : (<a class="code hl_variable" href="struct_quantized_block_loader.html#a1392a5278cf6e090ea80ebe7c4ac5fbb">BCOLS_PACKED</a> * BROWS) / tgp_size;</div>
|
|
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a31e14175f3d4902d9fe5ab5a219f61ba"> 276</a></span> <a class="code hl_define" href="quantized_8h.html#a0386011c52d03e60885a31e6fbd903dd">MLX_MTL_CONST</a> <span class="keywordtype">short</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a31e14175f3d4902d9fe5ab5a219f61ba">group_steps</a> = group_size / BCOLS;</div>
|
|
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno"> 277</span> </div>
|
|
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a8050977d473d1a24fae5c833e609839e"> 278</a></span> <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a8050977d473d1a24fae5c833e609839e">src_ld</a>;</div>
|
|
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#ac3f651c1a645291d1037a2cc8ded2320"> 279</a></span> <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#ac3f651c1a645291d1037a2cc8ded2320">tile_stride</a>;</div>
|
|
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a234feacde36a4afc0d740332a3769fb6"> 280</a></span> <span class="keywordtype">short</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a234feacde36a4afc0d740332a3769fb6">group_step_cnt</a>;</div>
|
|
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a0ace7e3762ecfa5a4106e7dee7e1b6ab"> 281</a></span> <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a0ace7e3762ecfa5a4106e7dee7e1b6ab">group_stride</a>;</div>
|
|
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno"> 282</span> </div>
|
|
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a50821537ea747bc03295a09bb0eef475"> 283</a></span> <span class="keyword">const</span> <span class="keywordtype">short</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a50821537ea747bc03295a09bb0eef475">thread_idx</a>;</div>
|
|
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a85041d72225a2095659c70509291a906"> 284</a></span> <span class="keyword">const</span> <span class="keywordtype">short</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a85041d72225a2095659c70509291a906">bi</a>;</div>
|
|
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#ae2add92b2aaf3414e91f0470b9b0cc00"> 285</a></span> <span class="keyword">const</span> <span class="keywordtype">short</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#ae2add92b2aaf3414e91f0470b9b0cc00">bj</a>;</div>
|
|
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno"> 286</span> </div>
|
|
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a9857214690fe6abad0e19d1045152f83"> 287</a></span> threadgroup T* <a class="code hl_variable" href="struct_quantized_block_loader.html#a9857214690fe6abad0e19d1045152f83">dst</a>;</div>
|
|
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#ad85c6b7e07c81307b3b91eb4dd7be30b"> 288</a></span> <span class="keyword">const</span> device uint32_t* <a class="code hl_variable" href="struct_quantized_block_loader.html#ad85c6b7e07c81307b3b91eb4dd7be30b">src</a>;</div>
|
|
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a6123e4a9209d6eacb58b2c2344ed1ecf"> 289</a></span> <span class="keyword">const</span> device T* <a class="code hl_variable" href="struct_quantized_block_loader.html#a6123e4a9209d6eacb58b2c2344ed1ecf">scales</a>;</div>
|
|
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a17d01a6aba0833b073586ef2c09d0fbd"> 290</a></span> <span class="keyword">const</span> device T* <a class="code hl_variable" href="struct_quantized_block_loader.html#a17d01a6aba0833b073586ef2c09d0fbd">biases</a>;</div>
|
|
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"> 291</span> </div>
|
|
<div class="foldopen" id="foldopen00292" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#af59b054750a65e7e79c1cd05c4acac93"> 292</a></span> <a class="code hl_function" href="struct_quantized_block_loader.html#af59b054750a65e7e79c1cd05c4acac93">QuantizedBlockLoader</a>(</div>
|
|
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno"> 293</span> <span class="keyword">const</span> device uint32_t* src_,</div>
|
|
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno"> 294</span> <span class="keyword">const</span> device T* scales_,</div>
|
|
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno"> 295</span> <span class="keyword">const</span> device T* biases_,</div>
|
|
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno"> 296</span> <span class="keyword">const</span> <span class="keywordtype">int</span> src_ld_,</div>
|
|
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno"> 297</span> threadgroup T* dst_,</div>
|
|
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno"> 298</span> ushort simd_group_id [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno"> 299</span> ushort simd_lane_id [[thread_index_in_simdgroup]])</div>
|
|
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno"> 300</span> : <a class="code hl_variable" href="struct_quantized_block_loader.html#a8050977d473d1a24fae5c833e609839e">src_ld</a>(src_ld_),</div>
|
|
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno"> 301</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#ac3f651c1a645291d1037a2cc8ded2320">tile_stride</a>(</div>
|
|
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno"> 302</span> reduction_dim ? <a class="code hl_variable" href="struct_quantized_block_loader.html#a1392a5278cf6e090ea80ebe7c4ac5fbb">BCOLS_PACKED</a> : BROWS * <a class="code hl_variable" href="struct_quantized_block_loader.html#a8050977d473d1a24fae5c833e609839e">src_ld</a> / <a class="code hl_variable" href="struct_quantized_block_loader.html#a8eae73a0c04bf1e41fb96131f6aa500d">pack_factor</a>),</div>
|
|
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno"> 303</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a234feacde36a4afc0d740332a3769fb6">group_step_cnt</a>(0),</div>
|
|
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno"> 304</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a0ace7e3762ecfa5a4106e7dee7e1b6ab">group_stride</a>(BROWS * <a class="code hl_variable" href="struct_quantized_block_loader.html#a8050977d473d1a24fae5c833e609839e">src_ld</a> / group_size),</div>
|
|
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno"> 305</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a50821537ea747bc03295a09bb0eef475">thread_idx</a>(simd_group_id * 32 + simd_lane_id),</div>
|
|
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno"> 306</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a85041d72225a2095659c70509291a906">bi</a>(<a class="code hl_variable" href="struct_quantized_block_loader.html#a6213479f7a6d9314d8879f8856b0b6fb">n_reads</a> * <a class="code hl_variable" href="struct_quantized_block_loader.html#a50821537ea747bc03295a09bb0eef475">thread_idx</a> / <a class="code hl_variable" href="struct_quantized_block_loader.html#a1392a5278cf6e090ea80ebe7c4ac5fbb">BCOLS_PACKED</a>),</div>
|
|
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno"> 307</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#ae2add92b2aaf3414e91f0470b9b0cc00">bj</a>((<a class="code hl_variable" href="struct_quantized_block_loader.html#a6213479f7a6d9314d8879f8856b0b6fb">n_reads</a> * <a class="code hl_variable" href="struct_quantized_block_loader.html#a50821537ea747bc03295a09bb0eef475">thread_idx</a>) % <a class="code hl_variable" href="struct_quantized_block_loader.html#a1392a5278cf6e090ea80ebe7c4ac5fbb">BCOLS_PACKED</a>),</div>
|
|
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno"> 308</span> dst(dst_ + <a class="code hl_variable" href="struct_quantized_block_loader.html#a85041d72225a2095659c70509291a906">bi</a> * dst_ld + <a class="code hl_variable" href="struct_quantized_block_loader.html#ae2add92b2aaf3414e91f0470b9b0cc00">bj</a> * <a class="code hl_variable" href="struct_quantized_block_loader.html#a8eae73a0c04bf1e41fb96131f6aa500d">pack_factor</a>),</div>
|
|
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno"> 309</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#ad85c6b7e07c81307b3b91eb4dd7be30b">src</a>(src_ + <a class="code hl_variable" href="struct_quantized_block_loader.html#a85041d72225a2095659c70509291a906">bi</a> * <a class="code hl_variable" href="struct_quantized_block_loader.html#a8050977d473d1a24fae5c833e609839e">src_ld</a> / <a class="code hl_variable" href="struct_quantized_block_loader.html#a8eae73a0c04bf1e41fb96131f6aa500d">pack_factor</a> + <a class="code hl_variable" href="struct_quantized_block_loader.html#ae2add92b2aaf3414e91f0470b9b0cc00">bj</a>),</div>
|
|
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno"> 310</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a6123e4a9209d6eacb58b2c2344ed1ecf">scales</a>(scales_ + <a class="code hl_variable" href="struct_quantized_block_loader.html#a85041d72225a2095659c70509291a906">bi</a> * <a class="code hl_variable" href="struct_quantized_block_loader.html#a8050977d473d1a24fae5c833e609839e">src_ld</a> / group_size),</div>
|
|
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno"> 311</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a17d01a6aba0833b073586ef2c09d0fbd">biases</a>(biases_ + <a class="code hl_variable" href="struct_quantized_block_loader.html#a85041d72225a2095659c70509291a906">bi</a> * <a class="code hl_variable" href="struct_quantized_block_loader.html#a8050977d473d1a24fae5c833e609839e">src_ld</a> / group_size) {}</div>
|
|
</div>
|
|
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno"> 312</span> </div>
|
|
<div class="foldopen" id="foldopen00313" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a86009527cb4b53e4c21fd6b1f78cfefc"> 313</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="struct_quantized_block_loader.html#a86009527cb4b53e4c21fd6b1f78cfefc">load_unsafe</a>()<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno"> 314</span> <span class="keywordflow">if</span> (<a class="code hl_variable" href="struct_quantized_block_loader.html#a1392a5278cf6e090ea80ebe7c4ac5fbb">BCOLS_PACKED</a> * BROWS < tgp_size && bi >= BROWS) {</div>
|
|
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno"> 315</span> <span class="keywordflow">return</span>;</div>
|
|
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno"> 316</span> }</div>
|
|
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno"> 317</span> </div>
|
|
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno"> 318</span> T scale = *<a class="code hl_variable" href="struct_quantized_block_loader.html#a6123e4a9209d6eacb58b2c2344ed1ecf">scales</a>;</div>
|
|
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno"> 319</span> T bias = *<a class="code hl_variable" href="struct_quantized_block_loader.html#a17d01a6aba0833b073586ef2c09d0fbd">biases</a>;</div>
|
|
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno"> 320</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < <a class="code hl_variable" href="struct_quantized_block_loader.html#a6213479f7a6d9314d8879f8856b0b6fb">n_reads</a>; i++) {</div>
|
|
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno"> 321</span> dequantize<T, pack_factor, bits>(</div>
|
|
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno"> 322</span> (device uint8_t*)(<a class="code hl_variable" href="struct_quantized_block_loader.html#ad85c6b7e07c81307b3b91eb4dd7be30b">src</a> + i), scale, bias, dst + i * <a class="code hl_variable" href="struct_quantized_block_loader.html#a8eae73a0c04bf1e41fb96131f6aa500d">pack_factor</a>);</div>
|
|
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno"> 323</span> }</div>
|
|
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno"> 324</span> }</div>
|
|
</div>
|
|
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno"> 325</span> </div>
|
|
<div class="foldopen" id="foldopen00326" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a699dc9aa284b8fbf870310bbb224465b"> 326</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="struct_quantized_block_loader.html#a699dc9aa284b8fbf870310bbb224465b">load_safe</a>(short2 src_tile_dim)<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno"> 327</span> <span class="keywordflow">if</span> (<a class="code hl_variable" href="struct_quantized_block_loader.html#a1392a5278cf6e090ea80ebe7c4ac5fbb">BCOLS_PACKED</a> * BROWS < tgp_size && bi >= BROWS) {</div>
|
|
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"> 328</span> <span class="keywordflow">return</span>;</div>
|
|
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno"> 329</span> }</div>
|
|
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno"> 330</span> </div>
|
|
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno"> 331</span> <span class="keywordflow">if</span> (reduction_dim == 1 && <a class="code hl_variable" href="struct_quantized_block_loader.html#a85041d72225a2095659c70509291a906">bi</a> >= src_tile_dim.y) {</div>
|
|
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno"> 332</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < <a class="code hl_variable" href="struct_quantized_block_loader.html#a6213479f7a6d9314d8879f8856b0b6fb">n_reads</a> * <a class="code hl_variable" href="struct_quantized_block_loader.html#a8eae73a0c04bf1e41fb96131f6aa500d">pack_factor</a>; i++) {</div>
|
|
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno"> 333</span> dst[i] = T(0);</div>
|
|
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno"> 334</span> }</div>
|
|
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno"> 335</span> <span class="keywordflow">return</span>;</div>
|
|
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno"> 336</span> }</div>
|
|
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno"> 337</span> </div>
|
|
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno"> 338</span> <span class="keywordflow">if</span> (reduction_dim == 0 && <a class="code hl_variable" href="struct_quantized_block_loader.html#a85041d72225a2095659c70509291a906">bi</a> >= src_tile_dim.x) {</div>
|
|
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno"> 339</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < <a class="code hl_variable" href="struct_quantized_block_loader.html#a6213479f7a6d9314d8879f8856b0b6fb">n_reads</a> * <a class="code hl_variable" href="struct_quantized_block_loader.html#a8eae73a0c04bf1e41fb96131f6aa500d">pack_factor</a>; i++) {</div>
|
|
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno"> 340</span> dst[i] = T(0);</div>
|
|
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno"> 341</span> }</div>
|
|
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno"> 342</span> <span class="keywordflow">return</span>;</div>
|
|
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"> 343</span> }</div>
|
|
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno"> 344</span> </div>
|
|
<div class="line"><a id="l00345" name="l00345"></a><span class="lineno"> 345</span> T scale = *<a class="code hl_variable" href="struct_quantized_block_loader.html#a6123e4a9209d6eacb58b2c2344ed1ecf">scales</a>;</div>
|
|
<div class="line"><a id="l00346" name="l00346"></a><span class="lineno"> 346</span> T bias = *<a class="code hl_variable" href="struct_quantized_block_loader.html#a17d01a6aba0833b073586ef2c09d0fbd">biases</a>;</div>
|
|
<div class="line"><a id="l00347" name="l00347"></a><span class="lineno"> 347</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < <a class="code hl_variable" href="struct_quantized_block_loader.html#a6213479f7a6d9314d8879f8856b0b6fb">n_reads</a>; i++) {</div>
|
|
<div class="line"><a id="l00348" name="l00348"></a><span class="lineno"> 348</span> dequantize<T, pack_factor, bits>(</div>
|
|
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno"> 349</span> (device uint8_t*)(<a class="code hl_variable" href="struct_quantized_block_loader.html#ad85c6b7e07c81307b3b91eb4dd7be30b">src</a> + i), scale, bias, dst + i * <a class="code hl_variable" href="struct_quantized_block_loader.html#a8eae73a0c04bf1e41fb96131f6aa500d">pack_factor</a>);</div>
|
|
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno"> 350</span> }</div>
|
|
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno"> 351</span> }</div>
|
|
</div>
|
|
<div class="line"><a id="l00352" name="l00352"></a><span class="lineno"> 352</span> </div>
|
|
<div class="foldopen" id="foldopen00353" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno"><a class="line" href="struct_quantized_block_loader.html#a674138ef7c43cc45586ea9f8fd6f6bd9"> 353</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="struct_quantized_block_loader.html#a674138ef7c43cc45586ea9f8fd6f6bd9">next</a>() {</div>
|
|
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno"> 354</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#ad85c6b7e07c81307b3b91eb4dd7be30b">src</a> += <a class="code hl_variable" href="struct_quantized_block_loader.html#ac3f651c1a645291d1037a2cc8ded2320">tile_stride</a>;</div>
|
|
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno"> 355</span> <span class="keywordflow">if</span> (reduction_dim == 1) {</div>
|
|
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno"> 356</span> <span class="keywordflow">if</span> (<a class="code hl_variable" href="struct_quantized_block_loader.html#a31e14175f3d4902d9fe5ab5a219f61ba">group_steps</a> > 1) {</div>
|
|
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno"> 357</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a234feacde36a4afc0d740332a3769fb6">group_step_cnt</a>++;</div>
|
|
<div class="line"><a id="l00358" name="l00358"></a><span class="lineno"> 358</span> <span class="keywordflow">if</span> (<a class="code hl_variable" href="struct_quantized_block_loader.html#a234feacde36a4afc0d740332a3769fb6">group_step_cnt</a> == <a class="code hl_variable" href="struct_quantized_block_loader.html#a31e14175f3d4902d9fe5ab5a219f61ba">group_steps</a>) {</div>
|
|
<div class="line"><a id="l00359" name="l00359"></a><span class="lineno"> 359</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a234feacde36a4afc0d740332a3769fb6">group_step_cnt</a> = 0;</div>
|
|
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno"> 360</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a6123e4a9209d6eacb58b2c2344ed1ecf">scales</a>++;</div>
|
|
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno"> 361</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a17d01a6aba0833b073586ef2c09d0fbd">biases</a>++;</div>
|
|
<div class="line"><a id="l00362" name="l00362"></a><span class="lineno"> 362</span> }</div>
|
|
<div class="line"><a id="l00363" name="l00363"></a><span class="lineno"> 363</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00364" name="l00364"></a><span class="lineno"> 364</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a6123e4a9209d6eacb58b2c2344ed1ecf">scales</a>++;</div>
|
|
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno"> 365</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a17d01a6aba0833b073586ef2c09d0fbd">biases</a>++;</div>
|
|
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno"> 366</span> }</div>
|
|
<div class="line"><a id="l00367" name="l00367"></a><span class="lineno"> 367</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00368" name="l00368"></a><span class="lineno"> 368</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a6123e4a9209d6eacb58b2c2344ed1ecf">scales</a> += <a class="code hl_variable" href="struct_quantized_block_loader.html#a0ace7e3762ecfa5a4106e7dee7e1b6ab">group_stride</a>;</div>
|
|
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno"> 369</span> <a class="code hl_variable" href="struct_quantized_block_loader.html#a17d01a6aba0833b073586ef2c09d0fbd">biases</a> += <a class="code hl_variable" href="struct_quantized_block_loader.html#a0ace7e3762ecfa5a4106e7dee7e1b6ab">group_stride</a>;</div>
|
|
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno"> 370</span> }</div>
|
|
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno"> 371</span> }</div>
|
|
</div>
|
|
<div class="line"><a id="l00372" name="l00372"></a><span class="lineno"> 372</span>};</div>
|
|
</div>
|
|
<div class="line"><a id="l00373" name="l00373"></a><span class="lineno"> 373</span> </div>
|
|
<div class="line"><a id="l00374" name="l00374"></a><span class="lineno"> 374</span><span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> group_size, <span class="keywordtype">int</span> bits></div>
|
|
<div class="foldopen" id="foldopen00375" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00375" name="l00375"></a><span class="lineno"><a class="line" href="quantized_8h.html#aba7687e6f8f1d29c0a1b2a3db150bd81"> 375</a></span>METAL_FUNC <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#aba7687e6f8f1d29c0a1b2a3db150bd81">qmv_fast_impl</a>(</div>
|
|
<div class="line"><a id="l00376" name="l00376"></a><span class="lineno"> 376</span> <span class="keyword">const</span> device uint32_t* w,</div>
|
|
<div class="line"><a id="l00377" name="l00377"></a><span class="lineno"> 377</span> <span class="keyword">const</span> device T* scales,</div>
|
|
<div class="line"><a id="l00378" name="l00378"></a><span class="lineno"> 378</span> <span class="keyword">const</span> device T* biases,</div>
|
|
<div class="line"><a id="l00379" name="l00379"></a><span class="lineno"> 379</span> <span class="keyword">const</span> device T* x,</div>
|
|
<div class="line"><a id="l00380" name="l00380"></a><span class="lineno"> 380</span> device T* y,</div>
|
|
<div class="line"><a id="l00381" name="l00381"></a><span class="lineno"> 381</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& in_vec_size,</div>
|
|
<div class="line"><a id="l00382" name="l00382"></a><span class="lineno"> 382</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& out_vec_size,</div>
|
|
<div class="line"><a id="l00383" name="l00383"></a><span class="lineno"> 383</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l00384" name="l00384"></a><span class="lineno"> 384</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l00385" name="l00385"></a><span class="lineno"> 385</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l00386" name="l00386"></a><span class="lineno"> 386</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> packs_per_thread = bits > 2 ? 2 : 1;</div>
|
|
<div class="line"><a id="l00387" name="l00387"></a><span class="lineno"> 387</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> num_simdgroups = 2;</div>
|
|
<div class="line"><a id="l00388" name="l00388"></a><span class="lineno"> 388</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> results_per_simdgroup = 4;</div>
|
|
<div class="line"><a id="l00389" name="l00389"></a><span class="lineno"> 389</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> pack_factor = 32 / bits;</div>
|
|
<div class="line"><a id="l00390" name="l00390"></a><span class="lineno"> 390</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> values_per_thread = pack_factor * packs_per_thread;</div>
|
|
<div class="line"><a id="l00391" name="l00391"></a><span class="lineno"> 391</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> block_size = values_per_thread * <a class="code hl_variable" href="quantized_8h.html#a62969a218d93680f5e35d0c61b160b99">SIMD_SIZE</a>;</div>
|
|
<div class="line"><a id="l00392" name="l00392"></a><span class="lineno"> 392</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> scale_step_per_thread = group_size / values_per_thread;</div>
|
|
<div class="line"><a id="l00393" name="l00393"></a><span class="lineno"> 393</span> </div>
|
|
<div class="line"><a id="l00394" name="l00394"></a><span class="lineno"> 394</span> <span class="keyword">typedef</span> <span class="keywordtype">float</span> U;</div>
|
|
<div class="line"><a id="l00395" name="l00395"></a><span class="lineno"> 395</span> </div>
|
|
<div class="line"><a id="l00396" name="l00396"></a><span class="lineno"> 396</span> thread U x_thread[values_per_thread];</div>
|
|
<div class="line"><a id="l00397" name="l00397"></a><span class="lineno"> 397</span> thread U result[results_per_simdgroup] = {0};</div>
|
|
<div class="line"><a id="l00398" name="l00398"></a><span class="lineno"> 398</span> </div>
|
|
<div class="line"><a id="l00399" name="l00399"></a><span class="lineno"> 399</span> <span class="comment">// Adjust positions</span></div>
|
|
<div class="line"><a id="l00400" name="l00400"></a><span class="lineno"> 400</span> <span class="keyword">const</span> <span class="keywordtype">int</span> in_vec_size_w = in_vec_size / pack_factor;</div>
|
|
<div class="line"><a id="l00401" name="l00401"></a><span class="lineno"> 401</span> <span class="keyword">const</span> <span class="keywordtype">int</span> in_vec_size_g = in_vec_size / group_size;</div>
|
|
<div class="line"><a id="l00402" name="l00402"></a><span class="lineno"> 402</span> <span class="keyword">const</span> <span class="keywordtype">int</span> out_row = tid.x * (num_simdgroups * results_per_simdgroup) +</div>
|
|
<div class="line"><a id="l00403" name="l00403"></a><span class="lineno"> 403</span> simd_gid * results_per_simdgroup;</div>
|
|
<div class="line"><a id="l00404" name="l00404"></a><span class="lineno"> 404</span> w += out_row * in_vec_size_w + simd_lid * packs_per_thread;</div>
|
|
<div class="line"><a id="l00405" name="l00405"></a><span class="lineno"> 405</span> scales += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;</div>
|
|
<div class="line"><a id="l00406" name="l00406"></a><span class="lineno"> 406</span> biases += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;</div>
|
|
<div class="line"><a id="l00407" name="l00407"></a><span class="lineno"> 407</span> x += tid.y * in_vec_size + simd_lid * values_per_thread;</div>
|
|
<div class="line"><a id="l00408" name="l00408"></a><span class="lineno"> 408</span> y += tid.y * out_vec_size + out_row;</div>
|
|
<div class="line"><a id="l00409" name="l00409"></a><span class="lineno"> 409</span> </div>
|
|
<div class="line"><a id="l00410" name="l00410"></a><span class="lineno"> 410</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k < in_vec_size; k += block_size) {</div>
|
|
<div class="line"><a id="l00411" name="l00411"></a><span class="lineno"> 411</span> U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);</div>
|
|
<div class="line"><a id="l00412" name="l00412"></a><span class="lineno"> 412</span> </div>
|
|
<div class="line"><a id="l00413" name="l00413"></a><span class="lineno"> 413</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row < results_per_simdgroup; row++) {</div>
|
|
<div class="line"><a id="l00414" name="l00414"></a><span class="lineno"> 414</span> <span class="keyword">const</span> device uint8_t* wl =</div>
|
|
<div class="line"><a id="l00415" name="l00415"></a><span class="lineno"> 415</span> (<span class="keyword">const</span> device uint8_t*)(w + row * in_vec_size_w);</div>
|
|
<div class="line"><a id="l00416" name="l00416"></a><span class="lineno"> 416</span> <span class="keyword">const</span> device T* sl = scales + row * in_vec_size_g;</div>
|
|
<div class="line"><a id="l00417" name="l00417"></a><span class="lineno"> 417</span> <span class="keyword">const</span> device T* bl = biases + row * in_vec_size_g;</div>
|
|
<div class="line"><a id="l00418" name="l00418"></a><span class="lineno"> 418</span> </div>
|
|
<div class="line"><a id="l00419" name="l00419"></a><span class="lineno"> 419</span> U s = sl[0];</div>
|
|
<div class="line"><a id="l00420" name="l00420"></a><span class="lineno"> 420</span> U b = bl[0];</div>
|
|
<div class="line"><a id="l00421" name="l00421"></a><span class="lineno"> 421</span> result[row] += qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);</div>
|
|
<div class="line"><a id="l00422" name="l00422"></a><span class="lineno"> 422</span> }</div>
|
|
<div class="line"><a id="l00423" name="l00423"></a><span class="lineno"> 423</span> </div>
|
|
<div class="line"><a id="l00424" name="l00424"></a><span class="lineno"> 424</span> w += block_size / pack_factor;</div>
|
|
<div class="line"><a id="l00425" name="l00425"></a><span class="lineno"> 425</span> scales += block_size / group_size;</div>
|
|
<div class="line"><a id="l00426" name="l00426"></a><span class="lineno"> 426</span> biases += block_size / group_size;</div>
|
|
<div class="line"><a id="l00427" name="l00427"></a><span class="lineno"> 427</span> x += block_size;</div>
|
|
<div class="line"><a id="l00428" name="l00428"></a><span class="lineno"> 428</span> }</div>
|
|
<div class="line"><a id="l00429" name="l00429"></a><span class="lineno"> 429</span> </div>
|
|
<div class="line"><a id="l00430" name="l00430"></a><span class="lineno"> 430</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row < results_per_simdgroup; row++) {</div>
|
|
<div class="line"><a id="l00431" name="l00431"></a><span class="lineno"> 431</span> result[row] = <a class="code hl_function" href="namespacemetal.html#a85181e37a00cb4a4217f1bb25389bce5">simd_sum</a>(result[row]);</div>
|
|
<div class="line"><a id="l00432" name="l00432"></a><span class="lineno"> 432</span> <span class="keywordflow">if</span> (simd_lid == 0) {</div>
|
|
<div class="line"><a id="l00433" name="l00433"></a><span class="lineno"> 433</span> y[row] = <span class="keyword">static_cast<</span>T<span class="keyword">></span>(result[row]);</div>
|
|
<div class="line"><a id="l00434" name="l00434"></a><span class="lineno"> 434</span> }</div>
|
|
<div class="line"><a id="l00435" name="l00435"></a><span class="lineno"> 435</span> }</div>
|
|
<div class="line"><a id="l00436" name="l00436"></a><span class="lineno"> 436</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l00437" name="l00437"></a><span class="lineno"> 437</span> </div>
|
|
<div class="line"><a id="l00438" name="l00438"></a><span class="lineno"> 438</span><span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> group_size, <span class="keywordtype">int</span> bits></div>
|
|
<div class="foldopen" id="foldopen00439" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00439" name="l00439"></a><span class="lineno"><a class="line" href="quantized_8h.html#a8e13c7d895624f738d2a6d9893b687fd"> 439</a></span>METAL_FUNC <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#a8e13c7d895624f738d2a6d9893b687fd">qmv_impl</a>(</div>
|
|
<div class="line"><a id="l00440" name="l00440"></a><span class="lineno"> 440</span> <span class="keyword">const</span> device uint32_t* w,</div>
|
|
<div class="line"><a id="l00441" name="l00441"></a><span class="lineno"> 441</span> <span class="keyword">const</span> device T* scales,</div>
|
|
<div class="line"><a id="l00442" name="l00442"></a><span class="lineno"> 442</span> <span class="keyword">const</span> device T* biases,</div>
|
|
<div class="line"><a id="l00443" name="l00443"></a><span class="lineno"> 443</span> <span class="keyword">const</span> device T* x,</div>
|
|
<div class="line"><a id="l00444" name="l00444"></a><span class="lineno"> 444</span> device T* y,</div>
|
|
<div class="line"><a id="l00445" name="l00445"></a><span class="lineno"> 445</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& in_vec_size,</div>
|
|
<div class="line"><a id="l00446" name="l00446"></a><span class="lineno"> 446</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& out_vec_size,</div>
|
|
<div class="line"><a id="l00447" name="l00447"></a><span class="lineno"> 447</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l00448" name="l00448"></a><span class="lineno"> 448</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l00449" name="l00449"></a><span class="lineno"> 449</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l00450" name="l00450"></a><span class="lineno"> 450</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> num_simdgroups = 2;</div>
|
|
<div class="line"><a id="l00451" name="l00451"></a><span class="lineno"> 451</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> results_per_simdgroup = 4;</div>
|
|
<div class="line"><a id="l00452" name="l00452"></a><span class="lineno"> 452</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> packs_per_thread = 1;</div>
|
|
<div class="line"><a id="l00453" name="l00453"></a><span class="lineno"> 453</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> pack_factor = 32 / bits;</div>
|
|
<div class="line"><a id="l00454" name="l00454"></a><span class="lineno"> 454</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> values_per_thread = pack_factor * packs_per_thread;</div>
|
|
<div class="line"><a id="l00455" name="l00455"></a><span class="lineno"> 455</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> block_size = values_per_thread * <a class="code hl_variable" href="quantized_8h.html#a62969a218d93680f5e35d0c61b160b99">SIMD_SIZE</a>;</div>
|
|
<div class="line"><a id="l00456" name="l00456"></a><span class="lineno"> 456</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> scale_step_per_thread = group_size / values_per_thread;</div>
|
|
<div class="line"><a id="l00457" name="l00457"></a><span class="lineno"> 457</span> </div>
|
|
<div class="line"><a id="l00458" name="l00458"></a><span class="lineno"> 458</span> <span class="keyword">typedef</span> <span class="keywordtype">float</span> U;</div>
|
|
<div class="line"><a id="l00459" name="l00459"></a><span class="lineno"> 459</span> </div>
|
|
<div class="line"><a id="l00460" name="l00460"></a><span class="lineno"> 460</span> thread U x_thread[values_per_thread];</div>
|
|
<div class="line"><a id="l00461" name="l00461"></a><span class="lineno"> 461</span> thread U result[results_per_simdgroup] = {0};</div>
|
|
<div class="line"><a id="l00462" name="l00462"></a><span class="lineno"> 462</span> </div>
|
|
<div class="line"><a id="l00463" name="l00463"></a><span class="lineno"> 463</span> <span class="comment">// Adjust positions</span></div>
|
|
<div class="line"><a id="l00464" name="l00464"></a><span class="lineno"> 464</span> <span class="keyword">const</span> <span class="keywordtype">int</span> in_vec_size_w = in_vec_size / pack_factor;</div>
|
|
<div class="line"><a id="l00465" name="l00465"></a><span class="lineno"> 465</span> <span class="keyword">const</span> <span class="keywordtype">int</span> in_vec_size_g = in_vec_size / group_size;</div>
|
|
<div class="line"><a id="l00466" name="l00466"></a><span class="lineno"> 466</span> <span class="keyword">const</span> <span class="keywordtype">int</span> out_row = tid.x * (num_simdgroups * results_per_simdgroup) +</div>
|
|
<div class="line"><a id="l00467" name="l00467"></a><span class="lineno"> 467</span> simd_gid * results_per_simdgroup;</div>
|
|
<div class="line"><a id="l00468" name="l00468"></a><span class="lineno"> 468</span> <span class="keyword">const</span> <span class="keywordtype">int</span> used_out_row = <a class="code hl_function" href="namespacemetal.html#a6653b28c9473087141eddce39878d4d3">min</a>(out_vec_size - results_per_simdgroup, out_row);</div>
|
|
<div class="line"><a id="l00469" name="l00469"></a><span class="lineno"> 469</span> </div>
|
|
<div class="line"><a id="l00470" name="l00470"></a><span class="lineno"> 470</span> <span class="keywordflow">if</span> (out_row >= out_vec_size) {</div>
|
|
<div class="line"><a id="l00471" name="l00471"></a><span class="lineno"> 471</span> <span class="keywordflow">return</span>;</div>
|
|
<div class="line"><a id="l00472" name="l00472"></a><span class="lineno"> 472</span> }</div>
|
|
<div class="line"><a id="l00473" name="l00473"></a><span class="lineno"> 473</span> </div>
|
|
<div class="line"><a id="l00474" name="l00474"></a><span class="lineno"> 474</span> <span class="comment">// In this case we need to properly guard all our reads because there isn't</span></div>
|
|
<div class="line"><a id="l00475" name="l00475"></a><span class="lineno"> 475</span> <span class="comment">// even 1 tile in the matrix</span></div>
|
|
<div class="line"><a id="l00476" name="l00476"></a><span class="lineno"> 476</span> <span class="keywordflow">if</span> (out_vec_size < (num_simdgroups * results_per_simdgroup)) {</div>
|
|
<div class="line"><a id="l00477" name="l00477"></a><span class="lineno"> 477</span> w += out_row * in_vec_size_w + simd_lid * packs_per_thread;</div>
|
|
<div class="line"><a id="l00478" name="l00478"></a><span class="lineno"> 478</span> scales += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;</div>
|
|
<div class="line"><a id="l00479" name="l00479"></a><span class="lineno"> 479</span> biases += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;</div>
|
|
<div class="line"><a id="l00480" name="l00480"></a><span class="lineno"> 480</span> x += tid.y * in_vec_size + simd_lid * values_per_thread;</div>
|
|
<div class="line"><a id="l00481" name="l00481"></a><span class="lineno"> 481</span> y += tid.y * out_vec_size + out_row;</div>
|
|
<div class="line"><a id="l00482" name="l00482"></a><span class="lineno"> 482</span> </div>
|
|
<div class="line"><a id="l00483" name="l00483"></a><span class="lineno"> 483</span> <span class="keywordtype">int</span> k = 0;</div>
|
|
<div class="line"><a id="l00484" name="l00484"></a><span class="lineno"> 484</span> <span class="keywordflow">for</span> (; k < in_vec_size - block_size; k += block_size) {</div>
|
|
<div class="line"><a id="l00485" name="l00485"></a><span class="lineno"> 485</span> U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);</div>
|
|
<div class="line"><a id="l00486" name="l00486"></a><span class="lineno"> 486</span> </div>
|
|
<div class="line"><a id="l00487" name="l00487"></a><span class="lineno"> 487</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; out_row + row < out_vec_size; row++) {</div>
|
|
<div class="line"><a id="l00488" name="l00488"></a><span class="lineno"> 488</span> <span class="keyword">const</span> device uint8_t* wl =</div>
|
|
<div class="line"><a id="l00489" name="l00489"></a><span class="lineno"> 489</span> (<span class="keyword">const</span> device uint8_t*)(w + row * in_vec_size_w);</div>
|
|
<div class="line"><a id="l00490" name="l00490"></a><span class="lineno"> 490</span> <span class="keyword">const</span> device T* sl = scales + row * in_vec_size_g;</div>
|
|
<div class="line"><a id="l00491" name="l00491"></a><span class="lineno"> 491</span> <span class="keyword">const</span> device T* bl = biases + row * in_vec_size_g;</div>
|
|
<div class="line"><a id="l00492" name="l00492"></a><span class="lineno"> 492</span> </div>
|
|
<div class="line"><a id="l00493" name="l00493"></a><span class="lineno"> 493</span> U s = sl[0];</div>
|
|
<div class="line"><a id="l00494" name="l00494"></a><span class="lineno"> 494</span> U b = bl[0];</div>
|
|
<div class="line"><a id="l00495" name="l00495"></a><span class="lineno"> 495</span> result[row] +=</div>
|
|
<div class="line"><a id="l00496" name="l00496"></a><span class="lineno"> 496</span> qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);</div>
|
|
<div class="line"><a id="l00497" name="l00497"></a><span class="lineno"> 497</span> }</div>
|
|
<div class="line"><a id="l00498" name="l00498"></a><span class="lineno"> 498</span> </div>
|
|
<div class="line"><a id="l00499" name="l00499"></a><span class="lineno"> 499</span> w += block_size / pack_factor;</div>
|
|
<div class="line"><a id="l00500" name="l00500"></a><span class="lineno"> 500</span> scales += block_size / group_size;</div>
|
|
<div class="line"><a id="l00501" name="l00501"></a><span class="lineno"> 501</span> biases += block_size / group_size;</div>
|
|
<div class="line"><a id="l00502" name="l00502"></a><span class="lineno"> 502</span> x += block_size;</div>
|
|
<div class="line"><a id="l00503" name="l00503"></a><span class="lineno"> 503</span> }</div>
|
|
<div class="line"><a id="l00504" name="l00504"></a><span class="lineno"> 504</span> <span class="keyword">const</span> <span class="keywordtype">int</span> remaining = clamp(</div>
|
|
<div class="line"><a id="l00505" name="l00505"></a><span class="lineno"> 505</span> <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(in_vec_size - k - simd_lid * values_per_thread),</div>
|
|
<div class="line"><a id="l00506" name="l00506"></a><span class="lineno"> 506</span> 0,</div>
|
|
<div class="line"><a id="l00507" name="l00507"></a><span class="lineno"> 507</span> values_per_thread);</div>
|
|
<div class="line"><a id="l00508" name="l00508"></a><span class="lineno"> 508</span> U sum =</div>
|
|
<div class="line"><a id="l00509" name="l00509"></a><span class="lineno"> 509</span> load_vector_safe<T, U, values_per_thread, bits>(x, x_thread, remaining);</div>
|
|
<div class="line"><a id="l00510" name="l00510"></a><span class="lineno"> 510</span> </div>
|
|
<div class="line"><a id="l00511" name="l00511"></a><span class="lineno"> 511</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; out_row + row < out_vec_size; row++) {</div>
|
|
<div class="line"><a id="l00512" name="l00512"></a><span class="lineno"> 512</span> <span class="keyword">const</span> device uint8_t* wl =</div>
|
|
<div class="line"><a id="l00513" name="l00513"></a><span class="lineno"> 513</span> (<span class="keyword">const</span> device uint8_t*)(w + row * in_vec_size_w);</div>
|
|
<div class="line"><a id="l00514" name="l00514"></a><span class="lineno"> 514</span> <span class="keyword">const</span> device T* sl = scales + row * in_vec_size_g;</div>
|
|
<div class="line"><a id="l00515" name="l00515"></a><span class="lineno"> 515</span> <span class="keyword">const</span> device T* bl = biases + row * in_vec_size_g;</div>
|
|
<div class="line"><a id="l00516" name="l00516"></a><span class="lineno"> 516</span> </div>
|
|
<div class="line"><a id="l00517" name="l00517"></a><span class="lineno"> 517</span> U s = sl[0];</div>
|
|
<div class="line"><a id="l00518" name="l00518"></a><span class="lineno"> 518</span> U b = bl[0];</div>
|
|
<div class="line"><a id="l00519" name="l00519"></a><span class="lineno"> 519</span> result[row] += qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);</div>
|
|
<div class="line"><a id="l00520" name="l00520"></a><span class="lineno"> 520</span> }</div>
|
|
<div class="line"><a id="l00521" name="l00521"></a><span class="lineno"> 521</span> </div>
|
|
<div class="line"><a id="l00522" name="l00522"></a><span class="lineno"> 522</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; out_row + row < out_vec_size; row++) {</div>
|
|
<div class="line"><a id="l00523" name="l00523"></a><span class="lineno"> 523</span> result[row] = <a class="code hl_function" href="namespacemetal.html#a85181e37a00cb4a4217f1bb25389bce5">simd_sum</a>(result[row]);</div>
|
|
<div class="line"><a id="l00524" name="l00524"></a><span class="lineno"> 524</span> <span class="keywordflow">if</span> (simd_lid == 0) {</div>
|
|
<div class="line"><a id="l00525" name="l00525"></a><span class="lineno"> 525</span> y[row] = <span class="keyword">static_cast<</span>T<span class="keyword">></span>(result[row]);</div>
|
|
<div class="line"><a id="l00526" name="l00526"></a><span class="lineno"> 526</span> }</div>
|
|
<div class="line"><a id="l00527" name="l00527"></a><span class="lineno"> 527</span> }</div>
|
|
<div class="line"><a id="l00528" name="l00528"></a><span class="lineno"> 528</span> }</div>
|
|
<div class="line"><a id="l00529" name="l00529"></a><span class="lineno"> 529</span> </div>
|
|
<div class="line"><a id="l00530" name="l00530"></a><span class="lineno"> 530</span> <span class="comment">// In this case the last tile is moved back to redo some output values</span></div>
|
|
<div class="line"><a id="l00531" name="l00531"></a><span class="lineno"> 531</span> <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00532" name="l00532"></a><span class="lineno"> 532</span> w += used_out_row * in_vec_size_w + simd_lid * packs_per_thread;</div>
|
|
<div class="line"><a id="l00533" name="l00533"></a><span class="lineno"> 533</span> scales += used_out_row * in_vec_size_g + simd_lid / scale_step_per_thread;</div>
|
|
<div class="line"><a id="l00534" name="l00534"></a><span class="lineno"> 534</span> biases += used_out_row * in_vec_size_g + simd_lid / scale_step_per_thread;</div>
|
|
<div class="line"><a id="l00535" name="l00535"></a><span class="lineno"> 535</span> x += tid.y * in_vec_size + simd_lid * values_per_thread;</div>
|
|
<div class="line"><a id="l00536" name="l00536"></a><span class="lineno"> 536</span> y += tid.y * out_vec_size + used_out_row;</div>
|
|
<div class="line"><a id="l00537" name="l00537"></a><span class="lineno"> 537</span> </div>
|
|
<div class="line"><a id="l00538" name="l00538"></a><span class="lineno"> 538</span> <span class="keywordtype">int</span> k = 0;</div>
|
|
<div class="line"><a id="l00539" name="l00539"></a><span class="lineno"> 539</span> <span class="keywordflow">for</span> (; k < in_vec_size - block_size; k += block_size) {</div>
|
|
<div class="line"><a id="l00540" name="l00540"></a><span class="lineno"> 540</span> U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);</div>
|
|
<div class="line"><a id="l00541" name="l00541"></a><span class="lineno"> 541</span> </div>
|
|
<div class="line"><a id="l00542" name="l00542"></a><span class="lineno"> 542</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row < results_per_simdgroup; row++) {</div>
|
|
<div class="line"><a id="l00543" name="l00543"></a><span class="lineno"> 543</span> <span class="keyword">const</span> device uint8_t* wl =</div>
|
|
<div class="line"><a id="l00544" name="l00544"></a><span class="lineno"> 544</span> (<span class="keyword">const</span> device uint8_t*)(w + row * in_vec_size_w);</div>
|
|
<div class="line"><a id="l00545" name="l00545"></a><span class="lineno"> 545</span> <span class="keyword">const</span> device T* sl = scales + row * in_vec_size_g;</div>
|
|
<div class="line"><a id="l00546" name="l00546"></a><span class="lineno"> 546</span> <span class="keyword">const</span> device T* bl = biases + row * in_vec_size_g;</div>
|
|
<div class="line"><a id="l00547" name="l00547"></a><span class="lineno"> 547</span> </div>
|
|
<div class="line"><a id="l00548" name="l00548"></a><span class="lineno"> 548</span> U s = sl[0];</div>
|
|
<div class="line"><a id="l00549" name="l00549"></a><span class="lineno"> 549</span> U b = bl[0];</div>
|
|
<div class="line"><a id="l00550" name="l00550"></a><span class="lineno"> 550</span> result[row] +=</div>
|
|
<div class="line"><a id="l00551" name="l00551"></a><span class="lineno"> 551</span> qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);</div>
|
|
<div class="line"><a id="l00552" name="l00552"></a><span class="lineno"> 552</span> }</div>
|
|
<div class="line"><a id="l00553" name="l00553"></a><span class="lineno"> 553</span> </div>
|
|
<div class="line"><a id="l00554" name="l00554"></a><span class="lineno"> 554</span> w += block_size / pack_factor;</div>
|
|
<div class="line"><a id="l00555" name="l00555"></a><span class="lineno"> 555</span> scales += block_size / group_size;</div>
|
|
<div class="line"><a id="l00556" name="l00556"></a><span class="lineno"> 556</span> biases += block_size / group_size;</div>
|
|
<div class="line"><a id="l00557" name="l00557"></a><span class="lineno"> 557</span> x += block_size;</div>
|
|
<div class="line"><a id="l00558" name="l00558"></a><span class="lineno"> 558</span> }</div>
|
|
<div class="line"><a id="l00559" name="l00559"></a><span class="lineno"> 559</span> <span class="keyword">const</span> <span class="keywordtype">int</span> remaining = clamp(</div>
|
|
<div class="line"><a id="l00560" name="l00560"></a><span class="lineno"> 560</span> <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(in_vec_size - k - simd_lid * values_per_thread),</div>
|
|
<div class="line"><a id="l00561" name="l00561"></a><span class="lineno"> 561</span> 0,</div>
|
|
<div class="line"><a id="l00562" name="l00562"></a><span class="lineno"> 562</span> values_per_thread);</div>
|
|
<div class="line"><a id="l00563" name="l00563"></a><span class="lineno"> 563</span> U sum =</div>
|
|
<div class="line"><a id="l00564" name="l00564"></a><span class="lineno"> 564</span> load_vector_safe<T, U, values_per_thread, bits>(x, x_thread, remaining);</div>
|
|
<div class="line"><a id="l00565" name="l00565"></a><span class="lineno"> 565</span> </div>
|
|
<div class="line"><a id="l00566" name="l00566"></a><span class="lineno"> 566</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row < results_per_simdgroup; row++) {</div>
|
|
<div class="line"><a id="l00567" name="l00567"></a><span class="lineno"> 567</span> <span class="keyword">const</span> device uint8_t* wl =</div>
|
|
<div class="line"><a id="l00568" name="l00568"></a><span class="lineno"> 568</span> (<span class="keyword">const</span> device uint8_t*)(w + row * in_vec_size_w);</div>
|
|
<div class="line"><a id="l00569" name="l00569"></a><span class="lineno"> 569</span> <span class="keyword">const</span> device T* sl = scales + row * in_vec_size_g;</div>
|
|
<div class="line"><a id="l00570" name="l00570"></a><span class="lineno"> 570</span> <span class="keyword">const</span> device T* bl = biases + row * in_vec_size_g;</div>
|
|
<div class="line"><a id="l00571" name="l00571"></a><span class="lineno"> 571</span> </div>
|
|
<div class="line"><a id="l00572" name="l00572"></a><span class="lineno"> 572</span> U s = sl[0];</div>
|
|
<div class="line"><a id="l00573" name="l00573"></a><span class="lineno"> 573</span> U b = bl[0];</div>
|
|
<div class="line"><a id="l00574" name="l00574"></a><span class="lineno"> 574</span> result[row] += qdot_safe<U, values_per_thread, bits>(</div>
|
|
<div class="line"><a id="l00575" name="l00575"></a><span class="lineno"> 575</span> wl, x_thread, s, b, sum, remaining);</div>
|
|
<div class="line"><a id="l00576" name="l00576"></a><span class="lineno"> 576</span> }</div>
|
|
<div class="line"><a id="l00577" name="l00577"></a><span class="lineno"> 577</span> </div>
|
|
<div class="line"><a id="l00578" name="l00578"></a><span class="lineno"> 578</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row < results_per_simdgroup; row++) {</div>
|
|
<div class="line"><a id="l00579" name="l00579"></a><span class="lineno"> 579</span> result[row] = <a class="code hl_function" href="namespacemetal.html#a85181e37a00cb4a4217f1bb25389bce5">simd_sum</a>(result[row]);</div>
|
|
<div class="line"><a id="l00580" name="l00580"></a><span class="lineno"> 580</span> <span class="keywordflow">if</span> (simd_lid == 0) {</div>
|
|
<div class="line"><a id="l00581" name="l00581"></a><span class="lineno"> 581</span> y[row] = <span class="keyword">static_cast<</span>T<span class="keyword">></span>(result[row]);</div>
|
|
<div class="line"><a id="l00582" name="l00582"></a><span class="lineno"> 582</span> }</div>
|
|
<div class="line"><a id="l00583" name="l00583"></a><span class="lineno"> 583</span> }</div>
|
|
<div class="line"><a id="l00584" name="l00584"></a><span class="lineno"> 584</span> }</div>
|
|
<div class="line"><a id="l00585" name="l00585"></a><span class="lineno"> 585</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l00586" name="l00586"></a><span class="lineno"> 586</span> </div>
|
|
<div class="line"><a id="l00587" name="l00587"></a><span class="lineno"> 587</span><span class="keyword">template</span> <<span class="keyword">typename</span> T, const <span class="keywordtype">int</span> group_size, const <span class="keywordtype">int</span> bits></div>
|
|
<div class="foldopen" id="foldopen00588" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00588" name="l00588"></a><span class="lineno"><a class="line" href="quantized_8h.html#a9d466e3e3af0d75cfc4eddcf46cb13e4"> 588</a></span>METAL_FUNC <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#a9d466e3e3af0d75cfc4eddcf46cb13e4">qvm_impl</a>(</div>
|
|
<div class="line"><a id="l00589" name="l00589"></a><span class="lineno"> 589</span> <span class="keyword">const</span> device T* x,</div>
|
|
<div class="line"><a id="l00590" name="l00590"></a><span class="lineno"> 590</span> <span class="keyword">const</span> device uint32_t* w,</div>
|
|
<div class="line"><a id="l00591" name="l00591"></a><span class="lineno"> 591</span> <span class="keyword">const</span> device T* scales,</div>
|
|
<div class="line"><a id="l00592" name="l00592"></a><span class="lineno"> 592</span> <span class="keyword">const</span> device T* biases,</div>
|
|
<div class="line"><a id="l00593" name="l00593"></a><span class="lineno"> 593</span> device T* y,</div>
|
|
<div class="line"><a id="l00594" name="l00594"></a><span class="lineno"> 594</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& in_vec_size,</div>
|
|
<div class="line"><a id="l00595" name="l00595"></a><span class="lineno"> 595</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& out_vec_size,</div>
|
|
<div class="line"><a id="l00596" name="l00596"></a><span class="lineno"> 596</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l00597" name="l00597"></a><span class="lineno"> 597</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l00598" name="l00598"></a><span class="lineno"> 598</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l00599" name="l00599"></a><span class="lineno"> 599</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> num_simdgroups = 2;</div>
|
|
<div class="line"><a id="l00600" name="l00600"></a><span class="lineno"> 600</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> pack_factor = 32 / bits;</div>
|
|
<div class="line"><a id="l00601" name="l00601"></a><span class="lineno"> 601</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> tn = 32 / pack_factor;</div>
|
|
<div class="line"><a id="l00602" name="l00602"></a><span class="lineno"> 602</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> blocksize = <a class="code hl_variable" href="quantized_8h.html#a62969a218d93680f5e35d0c61b160b99">SIMD_SIZE</a>;</div>
|
|
<div class="line"><a id="l00603" name="l00603"></a><span class="lineno"> 603</span> </div>
|
|
<div class="line"><a id="l00604" name="l00604"></a><span class="lineno"> 604</span> <span class="keyword">typedef</span> <span class="keywordtype">float</span> U;</div>
|
|
<div class="line"><a id="l00605" name="l00605"></a><span class="lineno"> 605</span> <span class="keyword">typedef</span> <span class="keyword">struct </span>{</div>
|
|
<div class="line"><a id="l00606" name="l00606"></a><span class="lineno"> 606</span> uint32_t wi[tn];</div>
|
|
<div class="line"><a id="l00607" name="l00607"></a><span class="lineno"> 607</span> } vec_w;</div>
|
|
<div class="line"><a id="l00608" name="l00608"></a><span class="lineno"> 608</span> </div>
|
|
<div class="line"><a id="l00609" name="l00609"></a><span class="lineno"> 609</span> thread vec_w w_local;</div>
|
|
<div class="line"><a id="l00610" name="l00610"></a><span class="lineno"> 610</span> thread U result[tn * pack_factor] = {0};</div>
|
|
<div class="line"><a id="l00611" name="l00611"></a><span class="lineno"> 611</span> thread U scale = 1;</div>
|
|
<div class="line"><a id="l00612" name="l00612"></a><span class="lineno"> 612</span> thread U bias = 0;</div>
|
|
<div class="line"><a id="l00613" name="l00613"></a><span class="lineno"> 613</span> thread U x_local = 0;</div>
|
|
<div class="line"><a id="l00614" name="l00614"></a><span class="lineno"> 614</span> </div>
|
|
<div class="line"><a id="l00615" name="l00615"></a><span class="lineno"> 615</span> <span class="comment">// Adjust positions</span></div>
|
|
<div class="line"><a id="l00616" name="l00616"></a><span class="lineno"> 616</span> <span class="keyword">const</span> <span class="keywordtype">int</span> out_vec_size_w = out_vec_size / pack_factor;</div>
|
|
<div class="line"><a id="l00617" name="l00617"></a><span class="lineno"> 617</span> <span class="keyword">const</span> <span class="keywordtype">int</span> out_vec_size_g = out_vec_size / group_size;</div>
|
|
<div class="line"><a id="l00618" name="l00618"></a><span class="lineno"> 618</span> <span class="keywordtype">int</span> out_col =</div>
|
|
<div class="line"><a id="l00619" name="l00619"></a><span class="lineno"> 619</span> tid.x * (num_simdgroups * pack_factor * tn) + simd_gid * pack_factor * tn;</div>
|
|
<div class="line"><a id="l00620" name="l00620"></a><span class="lineno"> 620</span> w += out_col / pack_factor + simd_lid * out_vec_size_w;</div>
|
|
<div class="line"><a id="l00621" name="l00621"></a><span class="lineno"> 621</span> scales += out_col / group_size + simd_lid * out_vec_size_g;</div>
|
|
<div class="line"><a id="l00622" name="l00622"></a><span class="lineno"> 622</span> biases += out_col / group_size + simd_lid * out_vec_size_g;</div>
|
|
<div class="line"><a id="l00623" name="l00623"></a><span class="lineno"> 623</span> x += tid.y * in_vec_size + simd_lid;</div>
|
|
<div class="line"><a id="l00624" name="l00624"></a><span class="lineno"> 624</span> y += tid.y * out_vec_size + out_col;</div>
|
|
<div class="line"><a id="l00625" name="l00625"></a><span class="lineno"> 625</span> </div>
|
|
<div class="line"><a id="l00626" name="l00626"></a><span class="lineno"> 626</span> <span class="keywordflow">if</span> (out_col >= out_vec_size) {</div>
|
|
<div class="line"><a id="l00627" name="l00627"></a><span class="lineno"> 627</span> <span class="keywordflow">return</span>;</div>
|
|
<div class="line"><a id="l00628" name="l00628"></a><span class="lineno"> 628</span> }</div>
|
|
<div class="line"><a id="l00629" name="l00629"></a><span class="lineno"> 629</span> </div>
|
|
<div class="line"><a id="l00630" name="l00630"></a><span class="lineno"> 630</span> <span class="comment">// Loop over in_vec in blocks of blocksize</span></div>
|
|
<div class="line"><a id="l00631" name="l00631"></a><span class="lineno"> 631</span> <span class="keywordtype">int</span> remaining = in_vec_size % blocksize;</div>
|
|
<div class="line"><a id="l00632" name="l00632"></a><span class="lineno"> 632</span> <span class="keywordflow">if</span> (remaining == 0) {</div>
|
|
<div class="line"><a id="l00633" name="l00633"></a><span class="lineno"> 633</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < in_vec_size; i += blocksize) {</div>
|
|
<div class="line"><a id="l00634" name="l00634"></a><span class="lineno"> 634</span> x_local = *x;</div>
|
|
<div class="line"><a id="l00635" name="l00635"></a><span class="lineno"> 635</span> scale = *scales;</div>
|
|
<div class="line"><a id="l00636" name="l00636"></a><span class="lineno"> 636</span> bias = *biases;</div>
|
|
<div class="line"><a id="l00637" name="l00637"></a><span class="lineno"> 637</span> w_local = *((device vec_w*)w);</div>
|
|
<div class="line"><a id="l00638" name="l00638"></a><span class="lineno"> 638</span> </div>
|
|
<div class="line"><a id="l00639" name="l00639"></a><span class="lineno"> 639</span> qouter<U, tn * pack_factor, bits>(</div>
|
|
<div class="line"><a id="l00640" name="l00640"></a><span class="lineno"> 640</span> (thread uint8_t*)&w_local, x_local, scale, bias, result);</div>
|
|
<div class="line"><a id="l00641" name="l00641"></a><span class="lineno"> 641</span> </div>
|
|
<div class="line"><a id="l00642" name="l00642"></a><span class="lineno"> 642</span> x += blocksize;</div>
|
|
<div class="line"><a id="l00643" name="l00643"></a><span class="lineno"> 643</span> scales += blocksize * out_vec_size_g;</div>
|
|
<div class="line"><a id="l00644" name="l00644"></a><span class="lineno"> 644</span> biases += blocksize * out_vec_size_g;</div>
|
|
<div class="line"><a id="l00645" name="l00645"></a><span class="lineno"> 645</span> w += blocksize * out_vec_size_w;</div>
|
|
<div class="line"><a id="l00646" name="l00646"></a><span class="lineno"> 646</span> }</div>
|
|
<div class="line"><a id="l00647" name="l00647"></a><span class="lineno"> 647</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00648" name="l00648"></a><span class="lineno"> 648</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = blocksize; i < in_vec_size; i += blocksize) {</div>
|
|
<div class="line"><a id="l00649" name="l00649"></a><span class="lineno"> 649</span> x_local = *x;</div>
|
|
<div class="line"><a id="l00650" name="l00650"></a><span class="lineno"> 650</span> scale = *scales;</div>
|
|
<div class="line"><a id="l00651" name="l00651"></a><span class="lineno"> 651</span> bias = *biases;</div>
|
|
<div class="line"><a id="l00652" name="l00652"></a><span class="lineno"> 652</span> w_local = *((device vec_w*)w);</div>
|
|
<div class="line"><a id="l00653" name="l00653"></a><span class="lineno"> 653</span> </div>
|
|
<div class="line"><a id="l00654" name="l00654"></a><span class="lineno"> 654</span> qouter<U, tn * pack_factor, bits>(</div>
|
|
<div class="line"><a id="l00655" name="l00655"></a><span class="lineno"> 655</span> (thread uint8_t*)&w_local, x_local, scale, bias, result);</div>
|
|
<div class="line"><a id="l00656" name="l00656"></a><span class="lineno"> 656</span> </div>
|
|
<div class="line"><a id="l00657" name="l00657"></a><span class="lineno"> 657</span> x += blocksize;</div>
|
|
<div class="line"><a id="l00658" name="l00658"></a><span class="lineno"> 658</span> scales += blocksize * out_vec_size_g;</div>
|
|
<div class="line"><a id="l00659" name="l00659"></a><span class="lineno"> 659</span> biases += blocksize * out_vec_size_g;</div>
|
|
<div class="line"><a id="l00660" name="l00660"></a><span class="lineno"> 660</span> w += blocksize * out_vec_size_w;</div>
|
|
<div class="line"><a id="l00661" name="l00661"></a><span class="lineno"> 661</span> }</div>
|
|
<div class="line"><a id="l00662" name="l00662"></a><span class="lineno"> 662</span> <span class="keywordflow">if</span> (<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(simd_lid) < remaining) {</div>
|
|
<div class="line"><a id="l00663" name="l00663"></a><span class="lineno"> 663</span> x_local = *x;</div>
|
|
<div class="line"><a id="l00664" name="l00664"></a><span class="lineno"> 664</span> scale = *scales;</div>
|
|
<div class="line"><a id="l00665" name="l00665"></a><span class="lineno"> 665</span> bias = *biases;</div>
|
|
<div class="line"><a id="l00666" name="l00666"></a><span class="lineno"> 666</span> w_local = *((device vec_w*)w);</div>
|
|
<div class="line"><a id="l00667" name="l00667"></a><span class="lineno"> 667</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00668" name="l00668"></a><span class="lineno"> 668</span> x_local = 0;</div>
|
|
<div class="line"><a id="l00669" name="l00669"></a><span class="lineno"> 669</span> scale = 0;</div>
|
|
<div class="line"><a id="l00670" name="l00670"></a><span class="lineno"> 670</span> bias = 0;</div>
|
|
<div class="line"><a id="l00671" name="l00671"></a><span class="lineno"> 671</span> }</div>
|
|
<div class="line"><a id="l00672" name="l00672"></a><span class="lineno"> 672</span> qouter<U, tn * pack_factor, bits>(</div>
|
|
<div class="line"><a id="l00673" name="l00673"></a><span class="lineno"> 673</span> (thread uint8_t*)&w_local, x_local, scale, bias, result);</div>
|
|
<div class="line"><a id="l00674" name="l00674"></a><span class="lineno"> 674</span> }</div>
|
|
<div class="line"><a id="l00675" name="l00675"></a><span class="lineno"> 675</span> </div>
|
|
<div class="line"><a id="l00676" name="l00676"></a><span class="lineno"> 676</span><span class="comment">// Accumulate in the simdgroup</span></div>
|
|
<div class="line"><a id="l00677" name="l00677"></a><span class="lineno"> 677</span><span class="preprocessor">#pragma clang loop unroll(full)</span></div>
|
|
<div class="line"><a id="l00678" name="l00678"></a><span class="lineno"> 678</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k < tn * pack_factor; k++) {</div>
|
|
<div class="line"><a id="l00679" name="l00679"></a><span class="lineno"> 679</span> result[k] = <a class="code hl_function" href="namespacemetal.html#a85181e37a00cb4a4217f1bb25389bce5">simd_sum</a>(result[k]);</div>
|
|
<div class="line"><a id="l00680" name="l00680"></a><span class="lineno"> 680</span> }</div>
|
|
<div class="line"><a id="l00681" name="l00681"></a><span class="lineno"> 681</span> </div>
|
|
<div class="line"><a id="l00682" name="l00682"></a><span class="lineno"> 682</span> <span class="comment">// Store the result</span></div>
|
|
<div class="line"><a id="l00683" name="l00683"></a><span class="lineno"> 683</span> <span class="keywordflow">if</span> (simd_lid == 0) {</div>
|
|
<div class="line"><a id="l00684" name="l00684"></a><span class="lineno"> 684</span><span class="preprocessor">#pragma clang loop unroll(full)</span></div>
|
|
<div class="line"><a id="l00685" name="l00685"></a><span class="lineno"> 685</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k < tn * pack_factor; k++) {</div>
|
|
<div class="line"><a id="l00686" name="l00686"></a><span class="lineno"> 686</span> y[k] = <span class="keyword">static_cast<</span>T<span class="keyword">></span>(result[k]);</div>
|
|
<div class="line"><a id="l00687" name="l00687"></a><span class="lineno"> 687</span> }</div>
|
|
<div class="line"><a id="l00688" name="l00688"></a><span class="lineno"> 688</span> }</div>
|
|
<div class="line"><a id="l00689" name="l00689"></a><span class="lineno"> 689</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l00690" name="l00690"></a><span class="lineno"> 690</span> </div>
|
|
<div class="line"><a id="l00691" name="l00691"></a><span class="lineno"> 691</span><span class="keyword">template</span> <</div>
|
|
<div class="line"><a id="l00692" name="l00692"></a><span class="lineno"> 692</span> <span class="keyword">typename</span> T,</div>
|
|
<div class="line"><a id="l00693" name="l00693"></a><span class="lineno"> 693</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BM,</div>
|
|
<div class="line"><a id="l00694" name="l00694"></a><span class="lineno"> 694</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BK,</div>
|
|
<div class="line"><a id="l00695" name="l00695"></a><span class="lineno"> 695</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BN,</div>
|
|
<div class="line"><a id="l00696" name="l00696"></a><span class="lineno"> 696</span> <span class="keyword">const</span> <span class="keywordtype">int</span> group_size,</div>
|
|
<div class="line"><a id="l00697" name="l00697"></a><span class="lineno"> 697</span> <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code hl_function" href="namespacemlx_1_1core_1_1random.html#abb895baa477f5a06b5f88e69245f1825">bits</a>,</div>
|
|
<div class="line"><a id="l00698" name="l00698"></a><span class="lineno"> 698</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> aligned_N></div>
|
|
<div class="foldopen" id="foldopen00699" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00699" name="l00699"></a><span class="lineno"><a class="line" href="quantized_8h.html#ac7b6accc41b026ef8efc312987aa9efe"> 699</a></span>METAL_FUNC <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#ac7b6accc41b026ef8efc312987aa9efe">qmm_t_impl</a>(</div>
|
|
<div class="line"><a id="l00700" name="l00700"></a><span class="lineno"> 700</span> <span class="keyword">const</span> device T* x,</div>
|
|
<div class="line"><a id="l00701" name="l00701"></a><span class="lineno"> 701</span> <span class="keyword">const</span> device uint32_t* w,</div>
|
|
<div class="line"><a id="l00702" name="l00702"></a><span class="lineno"> 702</span> <span class="keyword">const</span> device T* scales,</div>
|
|
<div class="line"><a id="l00703" name="l00703"></a><span class="lineno"> 703</span> <span class="keyword">const</span> device T* biases,</div>
|
|
<div class="line"><a id="l00704" name="l00704"></a><span class="lineno"> 704</span> device T* y,</div>
|
|
<div class="line"><a id="l00705" name="l00705"></a><span class="lineno"> 705</span> threadgroup T* Xs,</div>
|
|
<div class="line"><a id="l00706" name="l00706"></a><span class="lineno"> 706</span> threadgroup T* Ws,</div>
|
|
<div class="line"><a id="l00707" name="l00707"></a><span class="lineno"> 707</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& M,</div>
|
|
<div class="line"><a id="l00708" name="l00708"></a><span class="lineno"> 708</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& N,</div>
|
|
<div class="line"><a id="l00709" name="l00709"></a><span class="lineno"> 709</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& K,</div>
|
|
<div class="line"><a id="l00710" name="l00710"></a><span class="lineno"> 710</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l00711" name="l00711"></a><span class="lineno"> 711</span> uint lid [[thread_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l00712" name="l00712"></a><span class="lineno"> 712</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l00713" name="l00713"></a><span class="lineno"> 713</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l00714" name="l00714"></a><span class="lineno"> 714</span> <span class="keyword">static_assert</span>(BK >= <a class="code hl_variable" href="quantized_8h.html#a62969a218d93680f5e35d0c61b160b99">SIMD_SIZE</a>, <span class="stringliteral">"BK should be larger than SIMD_SIZE"</span>);</div>
|
|
<div class="line"><a id="l00715" name="l00715"></a><span class="lineno"> 715</span> <span class="keyword">static_assert</span>(BK % <a class="code hl_variable" href="quantized_8h.html#a62969a218d93680f5e35d0c61b160b99">SIMD_SIZE</a> == 0, <span class="stringliteral">"BK should be divisible by SIMD_SIZE"</span>);</div>
|
|
<div class="line"><a id="l00716" name="l00716"></a><span class="lineno"> 716</span> </div>
|
|
<div class="line"><a id="l00717" name="l00717"></a><span class="lineno"> 717</span> (void)lid;</div>
|
|
<div class="line"><a id="l00718" name="l00718"></a><span class="lineno"> 718</span> </div>
|
|
<div class="line"><a id="l00719" name="l00719"></a><span class="lineno"> 719</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> WM = 2;</div>
|
|
<div class="line"><a id="l00720" name="l00720"></a><span class="lineno"> 720</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> WN = 2;</div>
|
|
<div class="line"><a id="l00721" name="l00721"></a><span class="lineno"> 721</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> pack_factor = 32 / bits;</div>
|
|
<div class="line"><a id="l00722" name="l00722"></a><span class="lineno"> 722</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> BK_padded = (BK + 16 / <span class="keyword">sizeof</span>(T));</div>
|
|
<div class="line"><a id="l00723" name="l00723"></a><span class="lineno"> 723</span> </div>
|
|
<div class="line"><a id="l00724" name="l00724"></a><span class="lineno"> 724</span> <span class="comment">// Instantiate the appropriate BlockMMA and Loader</span></div>
|
|
<div class="line"><a id="l00725" name="l00725"></a><span class="lineno"> 725</span> <span class="keyword">using </span>mma_t = mlx::steel::</div>
|
|
<div class="line"><a id="l00726" name="l00726"></a><span class="lineno"> 726</span> BlockMMA<T, T, BM, BN, BK, WM, WN, false, true, BK_padded, BK_padded>;</div>
|
|
<div class="line"><a id="l00727" name="l00727"></a><span class="lineno"> 727</span> <span class="keyword">using </span>loader_x_t =</div>
|
|
<div class="line"><a id="l00728" name="l00728"></a><span class="lineno"> 728</span> <a class="code hl_struct" href="structmlx_1_1steel_1_1_block_loader.html">mlx::steel::BlockLoader<T, BM, BK, BK_padded, 1, WM * WN * SIMD_SIZE></a>;</div>
|
|
<div class="line"><a id="l00729" name="l00729"></a><span class="lineno"> 729</span> <span class="keyword">using </span>loader_w_t = <a class="code hl_struct" href="struct_quantized_block_loader.html">QuantizedBlockLoader</a><</div>
|
|
<div class="line"><a id="l00730" name="l00730"></a><span class="lineno"> 730</span> T,</div>
|
|
<div class="line"><a id="l00731" name="l00731"></a><span class="lineno"> 731</span> BN,</div>
|
|
<div class="line"><a id="l00732" name="l00732"></a><span class="lineno"> 732</span> BK,</div>
|
|
<div class="line"><a id="l00733" name="l00733"></a><span class="lineno"> 733</span> BK_padded,</div>
|
|
<div class="line"><a id="l00734" name="l00734"></a><span class="lineno"> 734</span> 1,</div>
|
|
<div class="line"><a id="l00735" name="l00735"></a><span class="lineno"> 735</span> WM * WN * <a class="code hl_variable" href="quantized_8h.html#a62969a218d93680f5e35d0c61b160b99">SIMD_SIZE</a>,</div>
|
|
<div class="line"><a id="l00736" name="l00736"></a><span class="lineno"> 736</span> group_size,</div>
|
|
<div class="line"><a id="l00737" name="l00737"></a><span class="lineno"> 737</span> bits>;</div>
|
|
<div class="line"><a id="l00738" name="l00738"></a><span class="lineno"> 738</span> </div>
|
|
<div class="line"><a id="l00739" name="l00739"></a><span class="lineno"> 739</span> <span class="comment">// Set the block</span></div>
|
|
<div class="line"><a id="l00740" name="l00740"></a><span class="lineno"> 740</span> <span class="keyword">const</span> <span class="keywordtype">int</span> K_w = K / pack_factor;</div>
|
|
<div class="line"><a id="l00741" name="l00741"></a><span class="lineno"> 741</span> <span class="keyword">const</span> <span class="keywordtype">int</span> K_g = K / group_size;</div>
|
|
<div class="line"><a id="l00742" name="l00742"></a><span class="lineno"> 742</span> <span class="keyword">const</span> <span class="keywordtype">int</span> y_row = tid.y * BM;</div>
|
|
<div class="line"><a id="l00743" name="l00743"></a><span class="lineno"> 743</span> <span class="keyword">const</span> <span class="keywordtype">int</span> y_col = tid.x * BN;</div>
|
|
<div class="line"><a id="l00744" name="l00744"></a><span class="lineno"> 744</span> </div>
|
|
<div class="line"><a id="l00745" name="l00745"></a><span class="lineno"> 745</span> x += y_row * K;</div>
|
|
<div class="line"><a id="l00746" name="l00746"></a><span class="lineno"> 746</span> w += y_col * K_w;</div>
|
|
<div class="line"><a id="l00747" name="l00747"></a><span class="lineno"> 747</span> scales += y_col * K_g;</div>
|
|
<div class="line"><a id="l00748" name="l00748"></a><span class="lineno"> 748</span> biases += y_col * K_g;</div>
|
|
<div class="line"><a id="l00749" name="l00749"></a><span class="lineno"> 749</span> y += y_row * N + y_col;</div>
|
|
<div class="line"><a id="l00750" name="l00750"></a><span class="lineno"> 750</span> </div>
|
|
<div class="line"><a id="l00751" name="l00751"></a><span class="lineno"> 751</span> <span class="comment">// Make the x loader and mma operation</span></div>
|
|
<div class="line"><a id="l00752" name="l00752"></a><span class="lineno"> 752</span> <span class="keyword">const</span> <span class="keywordtype">short</span> num_els = <a class="code hl_function" href="namespacemetal.html#a6653b28c9473087141eddce39878d4d3">min</a>(BM, M - y_row);</div>
|
|
<div class="line"><a id="l00753" name="l00753"></a><span class="lineno"> 753</span> <span class="keyword">const</span> <span class="keywordtype">short</span> num_outs = <a class="code hl_function" href="namespacemetal.html#a6653b28c9473087141eddce39878d4d3">min</a>(BN, N - y_col);</div>
|
|
<div class="line"><a id="l00754" name="l00754"></a><span class="lineno"> 754</span> loader_x_t loader_x(x, K, Xs, simd_gid, simd_lid);</div>
|
|
<div class="line"><a id="l00755" name="l00755"></a><span class="lineno"> 755</span> loader_w_t loader_w(w, scales, biases, K, Ws, simd_gid, simd_lid);</div>
|
|
<div class="line"><a id="l00756" name="l00756"></a><span class="lineno"> 756</span> mma_t mma_op(simd_gid, simd_lid);</div>
|
|
<div class="line"><a id="l00757" name="l00757"></a><span class="lineno"> 757</span> </div>
|
|
<div class="line"><a id="l00758" name="l00758"></a><span class="lineno"> 758</span> <span class="keywordflow">if</span> (num_els < BM) {</div>
|
|
<div class="line"><a id="l00759" name="l00759"></a><span class="lineno"> 759</span> <span class="keywordflow">if</span> (!aligned_N && num_outs < BN) {</div>
|
|
<div class="line"><a id="l00760" name="l00760"></a><span class="lineno"> 760</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k < K; k += BK) {</div>
|
|
<div class="line"><a id="l00761" name="l00761"></a><span class="lineno"> 761</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00762" name="l00762"></a><span class="lineno"> 762</span> loader_x.load_safe(short2(BK, num_els));</div>
|
|
<div class="line"><a id="l00763" name="l00763"></a><span class="lineno"> 763</span> loader_w.load_safe(short2(BK, num_outs));</div>
|
|
<div class="line"><a id="l00764" name="l00764"></a><span class="lineno"> 764</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00765" name="l00765"></a><span class="lineno"> 765</span> mma_op.mma(Xs, Ws);</div>
|
|
<div class="line"><a id="l00766" name="l00766"></a><span class="lineno"> 766</span> loader_x.next();</div>
|
|
<div class="line"><a id="l00767" name="l00767"></a><span class="lineno"> 767</span> loader_w.next();</div>
|
|
<div class="line"><a id="l00768" name="l00768"></a><span class="lineno"> 768</span> }</div>
|
|
<div class="line"><a id="l00769" name="l00769"></a><span class="lineno"> 769</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00770" name="l00770"></a><span class="lineno"> 770</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k < K; k += BK) {</div>
|
|
<div class="line"><a id="l00771" name="l00771"></a><span class="lineno"> 771</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00772" name="l00772"></a><span class="lineno"> 772</span> loader_x.load_safe(short2(BK, num_els));</div>
|
|
<div class="line"><a id="l00773" name="l00773"></a><span class="lineno"> 773</span> loader_w.load_unsafe();</div>
|
|
<div class="line"><a id="l00774" name="l00774"></a><span class="lineno"> 774</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00775" name="l00775"></a><span class="lineno"> 775</span> mma_op.mma(Xs, Ws);</div>
|
|
<div class="line"><a id="l00776" name="l00776"></a><span class="lineno"> 776</span> loader_x.next();</div>
|
|
<div class="line"><a id="l00777" name="l00777"></a><span class="lineno"> 777</span> loader_w.next();</div>
|
|
<div class="line"><a id="l00778" name="l00778"></a><span class="lineno"> 778</span> }</div>
|
|
<div class="line"><a id="l00779" name="l00779"></a><span class="lineno"> 779</span> }</div>
|
|
<div class="line"><a id="l00780" name="l00780"></a><span class="lineno"> 780</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00781" name="l00781"></a><span class="lineno"> 781</span> <span class="keywordflow">if</span> (!aligned_N && num_outs < BN) {</div>
|
|
<div class="line"><a id="l00782" name="l00782"></a><span class="lineno"> 782</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k < K; k += BK) {</div>
|
|
<div class="line"><a id="l00783" name="l00783"></a><span class="lineno"> 783</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00784" name="l00784"></a><span class="lineno"> 784</span> loader_x.load_unsafe();</div>
|
|
<div class="line"><a id="l00785" name="l00785"></a><span class="lineno"> 785</span> loader_w.load_safe(short2(BK, num_outs));</div>
|
|
<div class="line"><a id="l00786" name="l00786"></a><span class="lineno"> 786</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00787" name="l00787"></a><span class="lineno"> 787</span> mma_op.mma(Xs, Ws);</div>
|
|
<div class="line"><a id="l00788" name="l00788"></a><span class="lineno"> 788</span> loader_x.next();</div>
|
|
<div class="line"><a id="l00789" name="l00789"></a><span class="lineno"> 789</span> loader_w.next();</div>
|
|
<div class="line"><a id="l00790" name="l00790"></a><span class="lineno"> 790</span> }</div>
|
|
<div class="line"><a id="l00791" name="l00791"></a><span class="lineno"> 791</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00792" name="l00792"></a><span class="lineno"> 792</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k < K; k += BK) {</div>
|
|
<div class="line"><a id="l00793" name="l00793"></a><span class="lineno"> 793</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00794" name="l00794"></a><span class="lineno"> 794</span> loader_x.load_unsafe();</div>
|
|
<div class="line"><a id="l00795" name="l00795"></a><span class="lineno"> 795</span> loader_w.load_unsafe();</div>
|
|
<div class="line"><a id="l00796" name="l00796"></a><span class="lineno"> 796</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00797" name="l00797"></a><span class="lineno"> 797</span> mma_op.mma(Xs, Ws);</div>
|
|
<div class="line"><a id="l00798" name="l00798"></a><span class="lineno"> 798</span> loader_x.next();</div>
|
|
<div class="line"><a id="l00799" name="l00799"></a><span class="lineno"> 799</span> loader_w.next();</div>
|
|
<div class="line"><a id="l00800" name="l00800"></a><span class="lineno"> 800</span> }</div>
|
|
<div class="line"><a id="l00801" name="l00801"></a><span class="lineno"> 801</span> }</div>
|
|
<div class="line"><a id="l00802" name="l00802"></a><span class="lineno"> 802</span> }</div>
|
|
<div class="line"><a id="l00803" name="l00803"></a><span class="lineno"> 803</span> </div>
|
|
<div class="line"><a id="l00804" name="l00804"></a><span class="lineno"> 804</span> <span class="comment">// Store results to device memory</span></div>
|
|
<div class="line"><a id="l00805" name="l00805"></a><span class="lineno"> 805</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00806" name="l00806"></a><span class="lineno"> 806</span> <span class="keywordflow">if</span> (num_els < BM || num_outs < BN) {</div>
|
|
<div class="line"><a id="l00807" name="l00807"></a><span class="lineno"> 807</span> mma_op.store_result_safe(y, N, short2(num_outs, num_els));</div>
|
|
<div class="line"><a id="l00808" name="l00808"></a><span class="lineno"> 808</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00809" name="l00809"></a><span class="lineno"> 809</span> mma_op.store_result(y, N);</div>
|
|
<div class="line"><a id="l00810" name="l00810"></a><span class="lineno"> 810</span> }</div>
|
|
<div class="line"><a id="l00811" name="l00811"></a><span class="lineno"> 811</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l00812" name="l00812"></a><span class="lineno"> 812</span> </div>
|
|
<div class="line"><a id="l00813" name="l00813"></a><span class="lineno"> 813</span><span class="keyword">template</span> <</div>
|
|
<div class="line"><a id="l00814" name="l00814"></a><span class="lineno"> 814</span> <span class="keyword">typename</span> T,</div>
|
|
<div class="line"><a id="l00815" name="l00815"></a><span class="lineno"> 815</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BM,</div>
|
|
<div class="line"><a id="l00816" name="l00816"></a><span class="lineno"> 816</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BK,</div>
|
|
<div class="line"><a id="l00817" name="l00817"></a><span class="lineno"> 817</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BN,</div>
|
|
<div class="line"><a id="l00818" name="l00818"></a><span class="lineno"> 818</span> <span class="keyword">const</span> <span class="keywordtype">int</span> group_size,</div>
|
|
<div class="line"><a id="l00819" name="l00819"></a><span class="lineno"> 819</span> <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code hl_function" href="namespacemlx_1_1core_1_1random.html#abb895baa477f5a06b5f88e69245f1825">bits</a>></div>
|
|
<div class="foldopen" id="foldopen00820" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00820" name="l00820"></a><span class="lineno"><a class="line" href="quantized_8h.html#a5f7b71eef2e328af3225d7c777ffb339"> 820</a></span>METAL_FUNC <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#a5f7b71eef2e328af3225d7c777ffb339">qmm_n_impl</a>(</div>
|
|
<div class="line"><a id="l00821" name="l00821"></a><span class="lineno"> 821</span> <span class="keyword">const</span> device T* x,</div>
|
|
<div class="line"><a id="l00822" name="l00822"></a><span class="lineno"> 822</span> <span class="keyword">const</span> device uint32_t* w,</div>
|
|
<div class="line"><a id="l00823" name="l00823"></a><span class="lineno"> 823</span> <span class="keyword">const</span> device T* scales,</div>
|
|
<div class="line"><a id="l00824" name="l00824"></a><span class="lineno"> 824</span> <span class="keyword">const</span> device T* biases,</div>
|
|
<div class="line"><a id="l00825" name="l00825"></a><span class="lineno"> 825</span> device T* y,</div>
|
|
<div class="line"><a id="l00826" name="l00826"></a><span class="lineno"> 826</span> threadgroup T* Xs,</div>
|
|
<div class="line"><a id="l00827" name="l00827"></a><span class="lineno"> 827</span> threadgroup T* Ws,</div>
|
|
<div class="line"><a id="l00828" name="l00828"></a><span class="lineno"> 828</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& M,</div>
|
|
<div class="line"><a id="l00829" name="l00829"></a><span class="lineno"> 829</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& N,</div>
|
|
<div class="line"><a id="l00830" name="l00830"></a><span class="lineno"> 830</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& K,</div>
|
|
<div class="line"><a id="l00831" name="l00831"></a><span class="lineno"> 831</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l00832" name="l00832"></a><span class="lineno"> 832</span> uint lid [[thread_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l00833" name="l00833"></a><span class="lineno"> 833</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l00834" name="l00834"></a><span class="lineno"> 834</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l00835" name="l00835"></a><span class="lineno"> 835</span> <span class="keyword">static_assert</span>(BK >= <a class="code hl_variable" href="quantized_8h.html#a62969a218d93680f5e35d0c61b160b99">SIMD_SIZE</a>, <span class="stringliteral">"BK should be larger than SIMD_SIZE"</span>);</div>
|
|
<div class="line"><a id="l00836" name="l00836"></a><span class="lineno"> 836</span> <span class="keyword">static_assert</span>(BK % <a class="code hl_variable" href="quantized_8h.html#a62969a218d93680f5e35d0c61b160b99">SIMD_SIZE</a> == 0, <span class="stringliteral">"BK should be divisible by SIMD_SIZE"</span>);</div>
|
|
<div class="line"><a id="l00837" name="l00837"></a><span class="lineno"> 837</span> </div>
|
|
<div class="line"><a id="l00838" name="l00838"></a><span class="lineno"> 838</span> (void)lid;</div>
|
|
<div class="line"><a id="l00839" name="l00839"></a><span class="lineno"> 839</span> </div>
|
|
<div class="line"><a id="l00840" name="l00840"></a><span class="lineno"> 840</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> WM = 2;</div>
|
|
<div class="line"><a id="l00841" name="l00841"></a><span class="lineno"> 841</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> WN = 2;</div>
|
|
<div class="line"><a id="l00842" name="l00842"></a><span class="lineno"> 842</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> pack_factor = 32 / bits;</div>
|
|
<div class="line"><a id="l00843" name="l00843"></a><span class="lineno"> 843</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> BK_padded = (BK + 16 / <span class="keyword">sizeof</span>(T));</div>
|
|
<div class="line"><a id="l00844" name="l00844"></a><span class="lineno"> 844</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> BN_padded = (BN + 16 / <span class="keyword">sizeof</span>(T));</div>
|
|
<div class="line"><a id="l00845" name="l00845"></a><span class="lineno"> 845</span> </div>
|
|
<div class="line"><a id="l00846" name="l00846"></a><span class="lineno"> 846</span> <span class="comment">// Instantiate the appropriate BlockMMA and Loader</span></div>
|
|
<div class="line"><a id="l00847" name="l00847"></a><span class="lineno"> 847</span> <span class="keyword">using </span>mma_t = mlx::steel::</div>
|
|
<div class="line"><a id="l00848" name="l00848"></a><span class="lineno"> 848</span> BlockMMA<T, T, BM, BN, BK, WM, WN, false, false, BK_padded, BN_padded>;</div>
|
|
<div class="line"><a id="l00849" name="l00849"></a><span class="lineno"> 849</span> <span class="keyword">using </span>loader_x_t = mlx::steel::</div>
|
|
<div class="line"><a id="l00850" name="l00850"></a><span class="lineno"> 850</span> BlockLoader<T, BM, BK, BK_padded, 1, WM * WN * SIMD_SIZE, 1, 4>;</div>
|
|
<div class="line"><a id="l00851" name="l00851"></a><span class="lineno"> 851</span> <span class="keyword">using </span>loader_w_t = <a class="code hl_struct" href="struct_quantized_block_loader.html">QuantizedBlockLoader</a><</div>
|
|
<div class="line"><a id="l00852" name="l00852"></a><span class="lineno"> 852</span> T,</div>
|
|
<div class="line"><a id="l00853" name="l00853"></a><span class="lineno"> 853</span> BK,</div>
|
|
<div class="line"><a id="l00854" name="l00854"></a><span class="lineno"> 854</span> BN,</div>
|
|
<div class="line"><a id="l00855" name="l00855"></a><span class="lineno"> 855</span> BN_padded,</div>
|
|
<div class="line"><a id="l00856" name="l00856"></a><span class="lineno"> 856</span> 0,</div>
|
|
<div class="line"><a id="l00857" name="l00857"></a><span class="lineno"> 857</span> WM * WN * <a class="code hl_variable" href="quantized_8h.html#a62969a218d93680f5e35d0c61b160b99">SIMD_SIZE</a>,</div>
|
|
<div class="line"><a id="l00858" name="l00858"></a><span class="lineno"> 858</span> group_size,</div>
|
|
<div class="line"><a id="l00859" name="l00859"></a><span class="lineno"> 859</span> bits>;</div>
|
|
<div class="line"><a id="l00860" name="l00860"></a><span class="lineno"> 860</span> </div>
|
|
<div class="line"><a id="l00861" name="l00861"></a><span class="lineno"> 861</span> <span class="comment">// Set the block</span></div>
|
|
<div class="line"><a id="l00862" name="l00862"></a><span class="lineno"> 862</span> <span class="keyword">const</span> <span class="keywordtype">int</span> y_row = tid.y * BM;</div>
|
|
<div class="line"><a id="l00863" name="l00863"></a><span class="lineno"> 863</span> <span class="keyword">const</span> <span class="keywordtype">int</span> y_col = tid.x * BN;</div>
|
|
<div class="line"><a id="l00864" name="l00864"></a><span class="lineno"> 864</span> x += y_row * K;</div>
|
|
<div class="line"><a id="l00865" name="l00865"></a><span class="lineno"> 865</span> w += y_col / pack_factor;</div>
|
|
<div class="line"><a id="l00866" name="l00866"></a><span class="lineno"> 866</span> scales += y_col / group_size;</div>
|
|
<div class="line"><a id="l00867" name="l00867"></a><span class="lineno"> 867</span> biases += y_col / group_size;</div>
|
|
<div class="line"><a id="l00868" name="l00868"></a><span class="lineno"> 868</span> y += y_row * N + y_col;</div>
|
|
<div class="line"><a id="l00869" name="l00869"></a><span class="lineno"> 869</span> </div>
|
|
<div class="line"><a id="l00870" name="l00870"></a><span class="lineno"> 870</span> <span class="comment">// Make the x loader and mma operation</span></div>
|
|
<div class="line"><a id="l00871" name="l00871"></a><span class="lineno"> 871</span> <span class="keyword">const</span> <span class="keywordtype">short</span> num_els = <a class="code hl_function" href="namespacemetal.html#a6653b28c9473087141eddce39878d4d3">min</a>(BM, M - y_row);</div>
|
|
<div class="line"><a id="l00872" name="l00872"></a><span class="lineno"> 872</span> loader_x_t loader_x(x, K, Xs, simd_gid, simd_lid);</div>
|
|
<div class="line"><a id="l00873" name="l00873"></a><span class="lineno"> 873</span> loader_w_t loader_w(w, scales, biases, N, Ws, simd_gid, simd_lid);</div>
|
|
<div class="line"><a id="l00874" name="l00874"></a><span class="lineno"> 874</span> mma_t mma_op(simd_gid, simd_lid);</div>
|
|
<div class="line"><a id="l00875" name="l00875"></a><span class="lineno"> 875</span> </div>
|
|
<div class="line"><a id="l00876" name="l00876"></a><span class="lineno"> 876</span> <span class="keywordflow">if</span> (num_els < BM) {</div>
|
|
<div class="line"><a id="l00877" name="l00877"></a><span class="lineno"> 877</span> <span class="keywordflow">if</span> ((K % BK) != 0) {</div>
|
|
<div class="line"><a id="l00878" name="l00878"></a><span class="lineno"> 878</span> <span class="keyword">const</span> <span class="keywordtype">int</span> k_blocks = K / BK;</div>
|
|
<div class="line"><a id="l00879" name="l00879"></a><span class="lineno"> 879</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k < k_blocks; k++) {</div>
|
|
<div class="line"><a id="l00880" name="l00880"></a><span class="lineno"> 880</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00881" name="l00881"></a><span class="lineno"> 881</span> loader_x.load_safe(short2(BK, num_els));</div>
|
|
<div class="line"><a id="l00882" name="l00882"></a><span class="lineno"> 882</span> loader_w.load_unsafe();</div>
|
|
<div class="line"><a id="l00883" name="l00883"></a><span class="lineno"> 883</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00884" name="l00884"></a><span class="lineno"> 884</span> mma_op.mma(Xs, Ws);</div>
|
|
<div class="line"><a id="l00885" name="l00885"></a><span class="lineno"> 885</span> loader_x.next();</div>
|
|
<div class="line"><a id="l00886" name="l00886"></a><span class="lineno"> 886</span> loader_w.next();</div>
|
|
<div class="line"><a id="l00887" name="l00887"></a><span class="lineno"> 887</span> }</div>
|
|
<div class="line"><a id="l00888" name="l00888"></a><span class="lineno"> 888</span> <span class="keyword">const</span> <span class="keywordtype">short</span> num_k = K - k_blocks * BK;</div>
|
|
<div class="line"><a id="l00889" name="l00889"></a><span class="lineno"> 889</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00890" name="l00890"></a><span class="lineno"> 890</span> loader_x.load_safe(short2(num_k, num_els));</div>
|
|
<div class="line"><a id="l00891" name="l00891"></a><span class="lineno"> 891</span> loader_w.load_safe(short2(BN, num_k));</div>
|
|
<div class="line"><a id="l00892" name="l00892"></a><span class="lineno"> 892</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00893" name="l00893"></a><span class="lineno"> 893</span> mma_op.mma(Xs, Ws);</div>
|
|
<div class="line"><a id="l00894" name="l00894"></a><span class="lineno"> 894</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00895" name="l00895"></a><span class="lineno"> 895</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k < K; k += BK) {</div>
|
|
<div class="line"><a id="l00896" name="l00896"></a><span class="lineno"> 896</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00897" name="l00897"></a><span class="lineno"> 897</span> loader_x.load_safe(short2(BK, num_els));</div>
|
|
<div class="line"><a id="l00898" name="l00898"></a><span class="lineno"> 898</span> loader_w.load_unsafe();</div>
|
|
<div class="line"><a id="l00899" name="l00899"></a><span class="lineno"> 899</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00900" name="l00900"></a><span class="lineno"> 900</span> mma_op.mma(Xs, Ws);</div>
|
|
<div class="line"><a id="l00901" name="l00901"></a><span class="lineno"> 901</span> loader_x.next();</div>
|
|
<div class="line"><a id="l00902" name="l00902"></a><span class="lineno"> 902</span> loader_w.next();</div>
|
|
<div class="line"><a id="l00903" name="l00903"></a><span class="lineno"> 903</span> }</div>
|
|
<div class="line"><a id="l00904" name="l00904"></a><span class="lineno"> 904</span> }</div>
|
|
<div class="line"><a id="l00905" name="l00905"></a><span class="lineno"> 905</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00906" name="l00906"></a><span class="lineno"> 906</span> <span class="keywordflow">if</span> ((K % BK) != 0) {</div>
|
|
<div class="line"><a id="l00907" name="l00907"></a><span class="lineno"> 907</span> <span class="keyword">const</span> <span class="keywordtype">int</span> k_blocks = K / BK;</div>
|
|
<div class="line"><a id="l00908" name="l00908"></a><span class="lineno"> 908</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k < k_blocks; k++) {</div>
|
|
<div class="line"><a id="l00909" name="l00909"></a><span class="lineno"> 909</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00910" name="l00910"></a><span class="lineno"> 910</span> loader_x.load_unsafe();</div>
|
|
<div class="line"><a id="l00911" name="l00911"></a><span class="lineno"> 911</span> loader_w.load_unsafe();</div>
|
|
<div class="line"><a id="l00912" name="l00912"></a><span class="lineno"> 912</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00913" name="l00913"></a><span class="lineno"> 913</span> mma_op.mma(Xs, Ws);</div>
|
|
<div class="line"><a id="l00914" name="l00914"></a><span class="lineno"> 914</span> loader_x.next();</div>
|
|
<div class="line"><a id="l00915" name="l00915"></a><span class="lineno"> 915</span> loader_w.next();</div>
|
|
<div class="line"><a id="l00916" name="l00916"></a><span class="lineno"> 916</span> }</div>
|
|
<div class="line"><a id="l00917" name="l00917"></a><span class="lineno"> 917</span> <span class="keyword">const</span> <span class="keywordtype">short</span> num_k = K - k_blocks * BK;</div>
|
|
<div class="line"><a id="l00918" name="l00918"></a><span class="lineno"> 918</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00919" name="l00919"></a><span class="lineno"> 919</span> loader_x.load_safe(short2(num_k, BM));</div>
|
|
<div class="line"><a id="l00920" name="l00920"></a><span class="lineno"> 920</span> loader_w.load_safe(short2(BN, num_k));</div>
|
|
<div class="line"><a id="l00921" name="l00921"></a><span class="lineno"> 921</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00922" name="l00922"></a><span class="lineno"> 922</span> mma_op.mma(Xs, Ws);</div>
|
|
<div class="line"><a id="l00923" name="l00923"></a><span class="lineno"> 923</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00924" name="l00924"></a><span class="lineno"> 924</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k < K; k += BK) {</div>
|
|
<div class="line"><a id="l00925" name="l00925"></a><span class="lineno"> 925</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00926" name="l00926"></a><span class="lineno"> 926</span> loader_x.load_unsafe();</div>
|
|
<div class="line"><a id="l00927" name="l00927"></a><span class="lineno"> 927</span> loader_w.load_unsafe();</div>
|
|
<div class="line"><a id="l00928" name="l00928"></a><span class="lineno"> 928</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00929" name="l00929"></a><span class="lineno"> 929</span> mma_op.mma(Xs, Ws);</div>
|
|
<div class="line"><a id="l00930" name="l00930"></a><span class="lineno"> 930</span> loader_x.next();</div>
|
|
<div class="line"><a id="l00931" name="l00931"></a><span class="lineno"> 931</span> loader_w.next();</div>
|
|
<div class="line"><a id="l00932" name="l00932"></a><span class="lineno"> 932</span> }</div>
|
|
<div class="line"><a id="l00933" name="l00933"></a><span class="lineno"> 933</span> }</div>
|
|
<div class="line"><a id="l00934" name="l00934"></a><span class="lineno"> 934</span> }</div>
|
|
<div class="line"><a id="l00935" name="l00935"></a><span class="lineno"> 935</span> </div>
|
|
<div class="line"><a id="l00936" name="l00936"></a><span class="lineno"> 936</span> <span class="comment">// Store results to device memory</span></div>
|
|
<div class="line"><a id="l00937" name="l00937"></a><span class="lineno"> 937</span> threadgroup_barrier(mem_flags::mem_threadgroup);</div>
|
|
<div class="line"><a id="l00938" name="l00938"></a><span class="lineno"> 938</span> <span class="keywordflow">if</span> (num_els < BM) {</div>
|
|
<div class="line"><a id="l00939" name="l00939"></a><span class="lineno"> 939</span> mma_op.store_result_safe(y, N, short2(BN, num_els));</div>
|
|
<div class="line"><a id="l00940" name="l00940"></a><span class="lineno"> 940</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00941" name="l00941"></a><span class="lineno"> 941</span> mma_op.store_result(y, N);</div>
|
|
<div class="line"><a id="l00942" name="l00942"></a><span class="lineno"> 942</span> }</div>
|
|
<div class="line"><a id="l00943" name="l00943"></a><span class="lineno"> 943</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l00944" name="l00944"></a><span class="lineno"> 944</span> </div>
|
|
<div class="line"><a id="l00945" name="l00945"></a><span class="lineno"> 945</span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
|
|
<div class="foldopen" id="foldopen00946" data-start="{" data-end="}">
|
|
<div class="line"><a id="l00946" name="l00946"></a><span class="lineno"><a class="line" href="quantized_8h.html#a3ab400746ad77be89c30d25638e01698"> 946</a></span>METAL_FUNC <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#a3ab400746ad77be89c30d25638e01698">adjust_matrix_offsets</a>(</div>
|
|
<div class="line"><a id="l00947" name="l00947"></a><span class="lineno"> 947</span> <span class="keyword">const</span> device T*& x,</div>
|
|
<div class="line"><a id="l00948" name="l00948"></a><span class="lineno"> 948</span> <span class="keyword">const</span> device uint32_t*& w,</div>
|
|
<div class="line"><a id="l00949" name="l00949"></a><span class="lineno"> 949</span> <span class="keyword">const</span> device T*& scales,</div>
|
|
<div class="line"><a id="l00950" name="l00950"></a><span class="lineno"> 950</span> <span class="keyword">const</span> device T*& biases,</div>
|
|
<div class="line"><a id="l00951" name="l00951"></a><span class="lineno"> 951</span> <span class="keyword">const</span> device uint32_t* lhs_indices,</div>
|
|
<div class="line"><a id="l00952" name="l00952"></a><span class="lineno"> 952</span> <span class="keyword">const</span> device uint32_t* rhs_indices,</div>
|
|
<div class="line"><a id="l00953" name="l00953"></a><span class="lineno"> 953</span> device T*& y,</div>
|
|
<div class="line"><a id="l00954" name="l00954"></a><span class="lineno"> 954</span> <span class="keywordtype">int</span> output_stride,</div>
|
|
<div class="line"><a id="l00955" name="l00955"></a><span class="lineno"> 955</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& batch_ndims,</div>
|
|
<div class="line"><a id="l00956" name="l00956"></a><span class="lineno"> 956</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* batch_shape,</div>
|
|
<div class="line"><a id="l00957" name="l00957"></a><span class="lineno"> 957</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* lhs_strides,</div>
|
|
<div class="line"><a id="l00958" name="l00958"></a><span class="lineno"> 958</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* rhs_strides,</div>
|
|
<div class="line"><a id="l00959" name="l00959"></a><span class="lineno"> 959</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& x_batch_ndims,</div>
|
|
<div class="line"><a id="l00960" name="l00960"></a><span class="lineno"> 960</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* x_shape,</div>
|
|
<div class="line"><a id="l00961" name="l00961"></a><span class="lineno"> 961</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* x_strides,</div>
|
|
<div class="line"><a id="l00962" name="l00962"></a><span class="lineno"> 962</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& w_batch_ndims,</div>
|
|
<div class="line"><a id="l00963" name="l00963"></a><span class="lineno"> 963</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* w_shape,</div>
|
|
<div class="line"><a id="l00964" name="l00964"></a><span class="lineno"> 964</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* w_strides,</div>
|
|
<div class="line"><a id="l00965" name="l00965"></a><span class="lineno"> 965</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* s_strides,</div>
|
|
<div class="line"><a id="l00966" name="l00966"></a><span class="lineno"> 966</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* b_strides,</div>
|
|
<div class="line"><a id="l00967" name="l00967"></a><span class="lineno"> 967</span> uint3 tid [[threadgroup_position_in_grid]]) {</div>
|
|
<div class="line"><a id="l00968" name="l00968"></a><span class="lineno"> 968</span> <span class="comment">// Set the input/output matrices</span></div>
|
|
<div class="line"><a id="l00969" name="l00969"></a><span class="lineno"> 969</span> uint32_t x_idx;</div>
|
|
<div class="line"><a id="l00970" name="l00970"></a><span class="lineno"> 970</span> uint32_t w_idx;</div>
|
|
<div class="line"><a id="l00971" name="l00971"></a><span class="lineno"> 971</span> <span class="keywordflow">if</span> (batch_ndims == 1) {</div>
|
|
<div class="line"><a id="l00972" name="l00972"></a><span class="lineno"> 972</span> x_idx = lhs_indices[tid.z * lhs_strides[0]];</div>
|
|
<div class="line"><a id="l00973" name="l00973"></a><span class="lineno"> 973</span> w_idx = rhs_indices[tid.z * rhs_strides[0]];</div>
|
|
<div class="line"><a id="l00974" name="l00974"></a><span class="lineno"> 974</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00975" name="l00975"></a><span class="lineno"> 975</span> ulong2 idx = <a class="code hl_function" href="backend_2metal_2kernels_2steel_2utils_8h.html#aaf4974425147d6f26d031691e321637f">elem_to_loc_broadcast</a>(</div>
|
|
<div class="line"><a id="l00976" name="l00976"></a><span class="lineno"> 976</span> tid.z, batch_shape, lhs_strides, rhs_strides, batch_ndims);</div>
|
|
<div class="line"><a id="l00977" name="l00977"></a><span class="lineno"> 977</span> x_idx = lhs_indices[idx.x];</div>
|
|
<div class="line"><a id="l00978" name="l00978"></a><span class="lineno"> 978</span> w_idx = rhs_indices[idx.y];</div>
|
|
<div class="line"><a id="l00979" name="l00979"></a><span class="lineno"> 979</span> }</div>
|
|
<div class="line"><a id="l00980" name="l00980"></a><span class="lineno"> 980</span> <span class="keywordflow">if</span> (x_batch_ndims == 1) {</div>
|
|
<div class="line"><a id="l00981" name="l00981"></a><span class="lineno"> 981</span> x += x_idx * x_strides[0];</div>
|
|
<div class="line"><a id="l00982" name="l00982"></a><span class="lineno"> 982</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00983" name="l00983"></a><span class="lineno"> 983</span> x += <a class="code hl_function" href="backend_2metal_2kernels_2utils_8h.html#a2e49fa7ab8f6348543455c6c45d7e2a9">elem_to_loc</a>(x_idx, x_shape, x_strides, x_batch_ndims);</div>
|
|
<div class="line"><a id="l00984" name="l00984"></a><span class="lineno"> 984</span> }</div>
|
|
<div class="line"><a id="l00985" name="l00985"></a><span class="lineno"> 985</span> <span class="keywordflow">if</span> (w_batch_ndims == 1) {</div>
|
|
<div class="line"><a id="l00986" name="l00986"></a><span class="lineno"> 986</span> w += w_idx * w_strides[0];</div>
|
|
<div class="line"><a id="l00987" name="l00987"></a><span class="lineno"> 987</span> scales += w_idx * s_strides[0];</div>
|
|
<div class="line"><a id="l00988" name="l00988"></a><span class="lineno"> 988</span> biases += w_idx * b_strides[0];</div>
|
|
<div class="line"><a id="l00989" name="l00989"></a><span class="lineno"> 989</span> } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a id="l00990" name="l00990"></a><span class="lineno"> 990</span> ulong3 idx = <a class="code hl_function" href="backend_2metal_2kernels_2steel_2utils_8h.html#aaf4974425147d6f26d031691e321637f">elem_to_loc_broadcast</a>(</div>
|
|
<div class="line"><a id="l00991" name="l00991"></a><span class="lineno"> 991</span> w_idx, w_shape, w_strides, s_strides, b_strides, w_batch_ndims);</div>
|
|
<div class="line"><a id="l00992" name="l00992"></a><span class="lineno"> 992</span> w += idx.x;</div>
|
|
<div class="line"><a id="l00993" name="l00993"></a><span class="lineno"> 993</span> scales += idx.y;</div>
|
|
<div class="line"><a id="l00994" name="l00994"></a><span class="lineno"> 994</span> biases += idx.z;</div>
|
|
<div class="line"><a id="l00995" name="l00995"></a><span class="lineno"> 995</span> }</div>
|
|
<div class="line"><a id="l00996" name="l00996"></a><span class="lineno"> 996</span> y += tid.z * output_stride;</div>
|
|
<div class="line"><a id="l00997" name="l00997"></a><span class="lineno"> 997</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l00998" name="l00998"></a><span class="lineno"> 998</span> </div>
|
|
<div class="line"><a id="l00999" name="l00999"></a><span class="lineno"> 999</span><span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> group_size, <span class="keywordtype">int</span> bits></div>
|
|
<div class="foldopen" id="foldopen01000" data-start="{" data-end="}">
|
|
<div class="line"><a id="l01000" name="l01000"></a><span class="lineno"><a class="line" href="quantized_8h.html#ad6d0aa9b080358581844d6583aa2f4ea"> 1000</a></span>[[kernel]] <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#ad6d0aa9b080358581844d6583aa2f4ea">qmv_fast</a>(</div>
|
|
<div class="line"><a id="l01001" name="l01001"></a><span class="lineno"> 1001</span> <span class="keyword">const</span> device uint32_t* w [[buffer(0)]],</div>
|
|
<div class="line"><a id="l01002" name="l01002"></a><span class="lineno"> 1002</span> <span class="keyword">const</span> device T* scales [[buffer(1)]],</div>
|
|
<div class="line"><a id="l01003" name="l01003"></a><span class="lineno"> 1003</span> <span class="keyword">const</span> device T* biases [[buffer(2)]],</div>
|
|
<div class="line"><a id="l01004" name="l01004"></a><span class="lineno"> 1004</span> <span class="keyword">const</span> device T* x [[buffer(3)]],</div>
|
|
<div class="line"><a id="l01005" name="l01005"></a><span class="lineno"> 1005</span> device T* y [[buffer(4)]],</div>
|
|
<div class="line"><a id="l01006" name="l01006"></a><span class="lineno"> 1006</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& in_vec_size [[buffer(5)]],</div>
|
|
<div class="line"><a id="l01007" name="l01007"></a><span class="lineno"> 1007</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& out_vec_size [[buffer(6)]],</div>
|
|
<div class="line"><a id="l01008" name="l01008"></a><span class="lineno"> 1008</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l01009" name="l01009"></a><span class="lineno"> 1009</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01010" name="l01010"></a><span class="lineno"> 1010</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l01011" name="l01011"></a><span class="lineno"> 1011</span> qmv_fast_impl<T, group_size, bits>(</div>
|
|
<div class="line"><a id="l01012" name="l01012"></a><span class="lineno"> 1012</span> w,</div>
|
|
<div class="line"><a id="l01013" name="l01013"></a><span class="lineno"> 1013</span> scales,</div>
|
|
<div class="line"><a id="l01014" name="l01014"></a><span class="lineno"> 1014</span> biases,</div>
|
|
<div class="line"><a id="l01015" name="l01015"></a><span class="lineno"> 1015</span> x,</div>
|
|
<div class="line"><a id="l01016" name="l01016"></a><span class="lineno"> 1016</span> y,</div>
|
|
<div class="line"><a id="l01017" name="l01017"></a><span class="lineno"> 1017</span> in_vec_size,</div>
|
|
<div class="line"><a id="l01018" name="l01018"></a><span class="lineno"> 1018</span> out_vec_size,</div>
|
|
<div class="line"><a id="l01019" name="l01019"></a><span class="lineno"> 1019</span> tid,</div>
|
|
<div class="line"><a id="l01020" name="l01020"></a><span class="lineno"> 1020</span> simd_gid,</div>
|
|
<div class="line"><a id="l01021" name="l01021"></a><span class="lineno"> 1021</span> simd_lid);</div>
|
|
<div class="line"><a id="l01022" name="l01022"></a><span class="lineno"> 1022</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l01023" name="l01023"></a><span class="lineno"> 1023</span> </div>
|
|
<div class="line"><a id="l01024" name="l01024"></a><span class="lineno"> 1024</span><span class="keyword">template</span> <<span class="keyword">typename</span> T, const <span class="keywordtype">int</span> group_size, const <span class="keywordtype">int</span> bits></div>
|
|
<div class="foldopen" id="foldopen01025" data-start="{" data-end="}">
|
|
<div class="line"><a id="l01025" name="l01025"></a><span class="lineno"><a class="line" href="quantized_8h.html#ae024e11c3e38dda71fef772ff0a82fe2"> 1025</a></span>[[kernel]] <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#ae024e11c3e38dda71fef772ff0a82fe2">qmv</a>(</div>
|
|
<div class="line"><a id="l01026" name="l01026"></a><span class="lineno"> 1026</span> <span class="keyword">const</span> device uint32_t* w [[buffer(0)]],</div>
|
|
<div class="line"><a id="l01027" name="l01027"></a><span class="lineno"> 1027</span> <span class="keyword">const</span> device T* scales [[buffer(1)]],</div>
|
|
<div class="line"><a id="l01028" name="l01028"></a><span class="lineno"> 1028</span> <span class="keyword">const</span> device T* biases [[buffer(2)]],</div>
|
|
<div class="line"><a id="l01029" name="l01029"></a><span class="lineno"> 1029</span> <span class="keyword">const</span> device T* x [[buffer(3)]],</div>
|
|
<div class="line"><a id="l01030" name="l01030"></a><span class="lineno"> 1030</span> device T* y [[buffer(4)]],</div>
|
|
<div class="line"><a id="l01031" name="l01031"></a><span class="lineno"> 1031</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& in_vec_size [[buffer(5)]],</div>
|
|
<div class="line"><a id="l01032" name="l01032"></a><span class="lineno"> 1032</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& out_vec_size [[buffer(6)]],</div>
|
|
<div class="line"><a id="l01033" name="l01033"></a><span class="lineno"> 1033</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l01034" name="l01034"></a><span class="lineno"> 1034</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01035" name="l01035"></a><span class="lineno"> 1035</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l01036" name="l01036"></a><span class="lineno"> 1036</span> qmv_impl<T, group_size, bits>(</div>
|
|
<div class="line"><a id="l01037" name="l01037"></a><span class="lineno"> 1037</span> w,</div>
|
|
<div class="line"><a id="l01038" name="l01038"></a><span class="lineno"> 1038</span> scales,</div>
|
|
<div class="line"><a id="l01039" name="l01039"></a><span class="lineno"> 1039</span> biases,</div>
|
|
<div class="line"><a id="l01040" name="l01040"></a><span class="lineno"> 1040</span> x,</div>
|
|
<div class="line"><a id="l01041" name="l01041"></a><span class="lineno"> 1041</span> y,</div>
|
|
<div class="line"><a id="l01042" name="l01042"></a><span class="lineno"> 1042</span> in_vec_size,</div>
|
|
<div class="line"><a id="l01043" name="l01043"></a><span class="lineno"> 1043</span> out_vec_size,</div>
|
|
<div class="line"><a id="l01044" name="l01044"></a><span class="lineno"> 1044</span> tid,</div>
|
|
<div class="line"><a id="l01045" name="l01045"></a><span class="lineno"> 1045</span> simd_gid,</div>
|
|
<div class="line"><a id="l01046" name="l01046"></a><span class="lineno"> 1046</span> simd_lid);</div>
|
|
<div class="line"><a id="l01047" name="l01047"></a><span class="lineno"> 1047</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l01048" name="l01048"></a><span class="lineno"> 1048</span> </div>
|
|
<div class="line"><a id="l01049" name="l01049"></a><span class="lineno"> 1049</span><span class="keyword">template</span> <<span class="keyword">typename</span> T, const <span class="keywordtype">int</span> group_size, const <span class="keywordtype">int</span> bits></div>
|
|
<div class="foldopen" id="foldopen01050" data-start="{" data-end="}">
|
|
<div class="line"><a id="l01050" name="l01050"></a><span class="lineno"><a class="line" href="quantized_8h.html#a2cce78e6692cfcbab8f6aa89afa61580"> 1050</a></span>[[kernel]] <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#a2cce78e6692cfcbab8f6aa89afa61580">qvm</a>(</div>
|
|
<div class="line"><a id="l01051" name="l01051"></a><span class="lineno"> 1051</span> <span class="keyword">const</span> device T* x [[buffer(0)]],</div>
|
|
<div class="line"><a id="l01052" name="l01052"></a><span class="lineno"> 1052</span> <span class="keyword">const</span> device uint32_t* w [[buffer(1)]],</div>
|
|
<div class="line"><a id="l01053" name="l01053"></a><span class="lineno"> 1053</span> <span class="keyword">const</span> device T* scales [[buffer(2)]],</div>
|
|
<div class="line"><a id="l01054" name="l01054"></a><span class="lineno"> 1054</span> <span class="keyword">const</span> device T* biases [[buffer(3)]],</div>
|
|
<div class="line"><a id="l01055" name="l01055"></a><span class="lineno"> 1055</span> device T* y [[buffer(4)]],</div>
|
|
<div class="line"><a id="l01056" name="l01056"></a><span class="lineno"> 1056</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& in_vec_size [[buffer(5)]],</div>
|
|
<div class="line"><a id="l01057" name="l01057"></a><span class="lineno"> 1057</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& out_vec_size [[buffer(6)]],</div>
|
|
<div class="line"><a id="l01058" name="l01058"></a><span class="lineno"> 1058</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l01059" name="l01059"></a><span class="lineno"> 1059</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01060" name="l01060"></a><span class="lineno"> 1060</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l01061" name="l01061"></a><span class="lineno"> 1061</span> qvm_impl<T, group_size, bits>(</div>
|
|
<div class="line"><a id="l01062" name="l01062"></a><span class="lineno"> 1062</span> x,</div>
|
|
<div class="line"><a id="l01063" name="l01063"></a><span class="lineno"> 1063</span> w,</div>
|
|
<div class="line"><a id="l01064" name="l01064"></a><span class="lineno"> 1064</span> scales,</div>
|
|
<div class="line"><a id="l01065" name="l01065"></a><span class="lineno"> 1065</span> biases,</div>
|
|
<div class="line"><a id="l01066" name="l01066"></a><span class="lineno"> 1066</span> y,</div>
|
|
<div class="line"><a id="l01067" name="l01067"></a><span class="lineno"> 1067</span> in_vec_size,</div>
|
|
<div class="line"><a id="l01068" name="l01068"></a><span class="lineno"> 1068</span> out_vec_size,</div>
|
|
<div class="line"><a id="l01069" name="l01069"></a><span class="lineno"> 1069</span> tid,</div>
|
|
<div class="line"><a id="l01070" name="l01070"></a><span class="lineno"> 1070</span> simd_gid,</div>
|
|
<div class="line"><a id="l01071" name="l01071"></a><span class="lineno"> 1071</span> simd_lid);</div>
|
|
<div class="line"><a id="l01072" name="l01072"></a><span class="lineno"> 1072</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l01073" name="l01073"></a><span class="lineno"> 1073</span> </div>
|
|
<div class="line"><a id="l01074" name="l01074"></a><span class="lineno"> 1074</span><span class="keyword">template</span> <</div>
|
|
<div class="line"><a id="l01075" name="l01075"></a><span class="lineno"> 1075</span> <span class="keyword">typename</span> T,</div>
|
|
<div class="line"><a id="l01076" name="l01076"></a><span class="lineno"> 1076</span> <span class="keyword">const</span> <span class="keywordtype">int</span> group_size,</div>
|
|
<div class="line"><a id="l01077" name="l01077"></a><span class="lineno"> 1077</span> <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code hl_function" href="namespacemlx_1_1core_1_1random.html#abb895baa477f5a06b5f88e69245f1825">bits</a>,</div>
|
|
<div class="line"><a id="l01078" name="l01078"></a><span class="lineno"> 1078</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> aligned_N,</div>
|
|
<div class="line"><a id="l01079" name="l01079"></a><span class="lineno"> 1079</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BM = 32,</div>
|
|
<div class="line"><a id="l01080" name="l01080"></a><span class="lineno"> 1080</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BK = 32,</div>
|
|
<div class="line"><a id="l01081" name="l01081"></a><span class="lineno"> 1081</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BN = 32></div>
|
|
<div class="foldopen" id="foldopen01082" data-start="{" data-end="}">
|
|
<div class="line"><a id="l01082" name="l01082"></a><span class="lineno"><a class="line" href="quantized_8h.html#af9b25269ab95632e343631eeef79dc8d"> 1082</a></span>[[kernel]] <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#af9b25269ab95632e343631eeef79dc8d">qmm_t</a>(</div>
|
|
<div class="line"><a id="l01083" name="l01083"></a><span class="lineno"> 1083</span> <span class="keyword">const</span> device T* x [[buffer(0)]],</div>
|
|
<div class="line"><a id="l01084" name="l01084"></a><span class="lineno"> 1084</span> <span class="keyword">const</span> device uint32_t* w [[buffer(1)]],</div>
|
|
<div class="line"><a id="l01085" name="l01085"></a><span class="lineno"> 1085</span> <span class="keyword">const</span> device T* scales [[buffer(2)]],</div>
|
|
<div class="line"><a id="l01086" name="l01086"></a><span class="lineno"> 1086</span> <span class="keyword">const</span> device T* biases [[buffer(3)]],</div>
|
|
<div class="line"><a id="l01087" name="l01087"></a><span class="lineno"> 1087</span> device T* y [[buffer(4)]],</div>
|
|
<div class="line"><a id="l01088" name="l01088"></a><span class="lineno"> 1088</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& M [[buffer(5)]],</div>
|
|
<div class="line"><a id="l01089" name="l01089"></a><span class="lineno"> 1089</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& N [[buffer(6)]],</div>
|
|
<div class="line"><a id="l01090" name="l01090"></a><span class="lineno"> 1090</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& K [[buffer(7)]],</div>
|
|
<div class="line"><a id="l01091" name="l01091"></a><span class="lineno"> 1091</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l01092" name="l01092"></a><span class="lineno"> 1092</span> uint lid [[thread_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01093" name="l01093"></a><span class="lineno"> 1093</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01094" name="l01094"></a><span class="lineno"> 1094</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l01095" name="l01095"></a><span class="lineno"> 1095</span> (void)lid;</div>
|
|
<div class="line"><a id="l01096" name="l01096"></a><span class="lineno"> 1096</span> </div>
|
|
<div class="line"><a id="l01097" name="l01097"></a><span class="lineno"> 1097</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> BK_padded = (BK + 16 / <span class="keyword">sizeof</span>(T));</div>
|
|
<div class="line"><a id="l01098" name="l01098"></a><span class="lineno"> 1098</span> </div>
|
|
<div class="line"><a id="l01099" name="l01099"></a><span class="lineno"> 1099</span> threadgroup T Xs[BM * BK_padded];</div>
|
|
<div class="line"><a id="l01100" name="l01100"></a><span class="lineno"> 1100</span> threadgroup T Ws[BN * BK_padded];</div>
|
|
<div class="line"><a id="l01101" name="l01101"></a><span class="lineno"> 1101</span> </div>
|
|
<div class="line"><a id="l01102" name="l01102"></a><span class="lineno"> 1102</span> qmm_t_impl<T, BM, BK, BN, group_size, bits, aligned_N>(</div>
|
|
<div class="line"><a id="l01103" name="l01103"></a><span class="lineno"> 1103</span> x, w, scales, biases, y, Xs, Ws, M, N, K, tid, lid, simd_gid, simd_lid);</div>
|
|
<div class="line"><a id="l01104" name="l01104"></a><span class="lineno"> 1104</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l01105" name="l01105"></a><span class="lineno"> 1105</span> </div>
|
|
<div class="line"><a id="l01106" name="l01106"></a><span class="lineno"> 1106</span><span class="keyword">template</span> <</div>
|
|
<div class="line"><a id="l01107" name="l01107"></a><span class="lineno"> 1107</span> <span class="keyword">typename</span> T,</div>
|
|
<div class="line"><a id="l01108" name="l01108"></a><span class="lineno"> 1108</span> <span class="keyword">const</span> <span class="keywordtype">int</span> group_size,</div>
|
|
<div class="line"><a id="l01109" name="l01109"></a><span class="lineno"> 1109</span> <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code hl_function" href="namespacemlx_1_1core_1_1random.html#abb895baa477f5a06b5f88e69245f1825">bits</a>,</div>
|
|
<div class="line"><a id="l01110" name="l01110"></a><span class="lineno"> 1110</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BM = 32,</div>
|
|
<div class="line"><a id="l01111" name="l01111"></a><span class="lineno"> 1111</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BK = 32,</div>
|
|
<div class="line"><a id="l01112" name="l01112"></a><span class="lineno"> 1112</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BN = 32></div>
|
|
<div class="foldopen" id="foldopen01113" data-start="{" data-end="}">
|
|
<div class="line"><a id="l01113" name="l01113"></a><span class="lineno"><a class="line" href="quantized_8h.html#ab5c4cfe531d7a0e1c19353060e3d3a6c"> 1113</a></span>[[kernel]] <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#ab5c4cfe531d7a0e1c19353060e3d3a6c">qmm_n</a>(</div>
|
|
<div class="line"><a id="l01114" name="l01114"></a><span class="lineno"> 1114</span> <span class="keyword">const</span> device T* x [[buffer(0)]],</div>
|
|
<div class="line"><a id="l01115" name="l01115"></a><span class="lineno"> 1115</span> <span class="keyword">const</span> device uint32_t* w [[buffer(1)]],</div>
|
|
<div class="line"><a id="l01116" name="l01116"></a><span class="lineno"> 1116</span> <span class="keyword">const</span> device T* scales [[buffer(2)]],</div>
|
|
<div class="line"><a id="l01117" name="l01117"></a><span class="lineno"> 1117</span> <span class="keyword">const</span> device T* biases [[buffer(3)]],</div>
|
|
<div class="line"><a id="l01118" name="l01118"></a><span class="lineno"> 1118</span> device T* y [[buffer(4)]],</div>
|
|
<div class="line"><a id="l01119" name="l01119"></a><span class="lineno"> 1119</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& M [[buffer(5)]],</div>
|
|
<div class="line"><a id="l01120" name="l01120"></a><span class="lineno"> 1120</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& N [[buffer(6)]],</div>
|
|
<div class="line"><a id="l01121" name="l01121"></a><span class="lineno"> 1121</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& K [[buffer(7)]],</div>
|
|
<div class="line"><a id="l01122" name="l01122"></a><span class="lineno"> 1122</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l01123" name="l01123"></a><span class="lineno"> 1123</span> uint lid [[thread_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01124" name="l01124"></a><span class="lineno"> 1124</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01125" name="l01125"></a><span class="lineno"> 1125</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l01126" name="l01126"></a><span class="lineno"> 1126</span> (void)lid;</div>
|
|
<div class="line"><a id="l01127" name="l01127"></a><span class="lineno"> 1127</span> </div>
|
|
<div class="line"><a id="l01128" name="l01128"></a><span class="lineno"> 1128</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> BK_padded = (BK + 16 / <span class="keyword">sizeof</span>(T));</div>
|
|
<div class="line"><a id="l01129" name="l01129"></a><span class="lineno"> 1129</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> BN_padded = (BN + 16 / <span class="keyword">sizeof</span>(T));</div>
|
|
<div class="line"><a id="l01130" name="l01130"></a><span class="lineno"> 1130</span> </div>
|
|
<div class="line"><a id="l01131" name="l01131"></a><span class="lineno"> 1131</span> threadgroup T Xs[BM * BK_padded];</div>
|
|
<div class="line"><a id="l01132" name="l01132"></a><span class="lineno"> 1132</span> threadgroup T Ws[BK * BN_padded];</div>
|
|
<div class="line"><a id="l01133" name="l01133"></a><span class="lineno"> 1133</span> </div>
|
|
<div class="line"><a id="l01134" name="l01134"></a><span class="lineno"> 1134</span> qmm_n_impl<T, BM, BK, BN, group_size, bits>(</div>
|
|
<div class="line"><a id="l01135" name="l01135"></a><span class="lineno"> 1135</span> x, w, scales, biases, y, Xs, Ws, M, N, K, tid, lid, simd_gid, simd_lid);</div>
|
|
<div class="line"><a id="l01136" name="l01136"></a><span class="lineno"> 1136</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l01137" name="l01137"></a><span class="lineno"> 1137</span> </div>
|
|
<div class="line"><a id="l01138" name="l01138"></a><span class="lineno"> 1138</span><span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> group_size, <span class="keywordtype">int</span> bits></div>
|
|
<div class="foldopen" id="foldopen01139" data-start="{" data-end="}">
|
|
<div class="line"><a id="l01139" name="l01139"></a><span class="lineno"><a class="line" href="quantized_8h.html#a6f92bb9f3d29d707bfc680bebe1c80f7"> 1139</a></span>[[kernel]] <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#a6f92bb9f3d29d707bfc680bebe1c80f7">bs_qmv_fast</a>(</div>
|
|
<div class="line"><a id="l01140" name="l01140"></a><span class="lineno"> 1140</span> <span class="keyword">const</span> device uint32_t* w [[buffer(0)]],</div>
|
|
<div class="line"><a id="l01141" name="l01141"></a><span class="lineno"> 1141</span> <span class="keyword">const</span> device T* scales [[buffer(1)]],</div>
|
|
<div class="line"><a id="l01142" name="l01142"></a><span class="lineno"> 1142</span> <span class="keyword">const</span> device T* biases [[buffer(2)]],</div>
|
|
<div class="line"><a id="l01143" name="l01143"></a><span class="lineno"> 1143</span> <span class="keyword">const</span> device T* x [[buffer(3)]],</div>
|
|
<div class="line"><a id="l01144" name="l01144"></a><span class="lineno"> 1144</span> <span class="keyword">const</span> device uint32_t* lhs_indices [[buffer(4)]],</div>
|
|
<div class="line"><a id="l01145" name="l01145"></a><span class="lineno"> 1145</span> <span class="keyword">const</span> device uint32_t* rhs_indices [[buffer(5)]],</div>
|
|
<div class="line"><a id="l01146" name="l01146"></a><span class="lineno"> 1146</span> device T* y [[buffer(6)]],</div>
|
|
<div class="line"><a id="l01147" name="l01147"></a><span class="lineno"> 1147</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& in_vec_size [[buffer(7)]],</div>
|
|
<div class="line"><a id="l01148" name="l01148"></a><span class="lineno"> 1148</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& out_vec_size [[buffer(8)]],</div>
|
|
<div class="line"><a id="l01149" name="l01149"></a><span class="lineno"> 1149</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& batch_ndims [[buffer(9)]],</div>
|
|
<div class="line"><a id="l01150" name="l01150"></a><span class="lineno"> 1150</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* batch_shape [[buffer(10)]],</div>
|
|
<div class="line"><a id="l01151" name="l01151"></a><span class="lineno"> 1151</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* lhs_strides [[buffer(11)]],</div>
|
|
<div class="line"><a id="l01152" name="l01152"></a><span class="lineno"> 1152</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* rhs_strides [[buffer(12)]],</div>
|
|
<div class="line"><a id="l01153" name="l01153"></a><span class="lineno"> 1153</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& x_batch_ndims [[buffer(13)]],</div>
|
|
<div class="line"><a id="l01154" name="l01154"></a><span class="lineno"> 1154</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* x_shape [[buffer(14)]],</div>
|
|
<div class="line"><a id="l01155" name="l01155"></a><span class="lineno"> 1155</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* x_strides [[buffer(15)]],</div>
|
|
<div class="line"><a id="l01156" name="l01156"></a><span class="lineno"> 1156</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& w_batch_ndims [[buffer(16)]],</div>
|
|
<div class="line"><a id="l01157" name="l01157"></a><span class="lineno"> 1157</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* w_shape [[buffer(17)]],</div>
|
|
<div class="line"><a id="l01158" name="l01158"></a><span class="lineno"> 1158</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* w_strides [[buffer(18)]],</div>
|
|
<div class="line"><a id="l01159" name="l01159"></a><span class="lineno"> 1159</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* s_strides [[buffer(19)]],</div>
|
|
<div class="line"><a id="l01160" name="l01160"></a><span class="lineno"> 1160</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* b_strides [[buffer(20)]],</div>
|
|
<div class="line"><a id="l01161" name="l01161"></a><span class="lineno"> 1161</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l01162" name="l01162"></a><span class="lineno"> 1162</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01163" name="l01163"></a><span class="lineno"> 1163</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l01164" name="l01164"></a><span class="lineno"> 1164</span> adjust_matrix_offsets<T>(</div>
|
|
<div class="line"><a id="l01165" name="l01165"></a><span class="lineno"> 1165</span> x,</div>
|
|
<div class="line"><a id="l01166" name="l01166"></a><span class="lineno"> 1166</span> w,</div>
|
|
<div class="line"><a id="l01167" name="l01167"></a><span class="lineno"> 1167</span> scales,</div>
|
|
<div class="line"><a id="l01168" name="l01168"></a><span class="lineno"> 1168</span> biases,</div>
|
|
<div class="line"><a id="l01169" name="l01169"></a><span class="lineno"> 1169</span> lhs_indices,</div>
|
|
<div class="line"><a id="l01170" name="l01170"></a><span class="lineno"> 1170</span> rhs_indices,</div>
|
|
<div class="line"><a id="l01171" name="l01171"></a><span class="lineno"> 1171</span> y,</div>
|
|
<div class="line"><a id="l01172" name="l01172"></a><span class="lineno"> 1172</span> out_vec_size,</div>
|
|
<div class="line"><a id="l01173" name="l01173"></a><span class="lineno"> 1173</span> batch_ndims,</div>
|
|
<div class="line"><a id="l01174" name="l01174"></a><span class="lineno"> 1174</span> batch_shape,</div>
|
|
<div class="line"><a id="l01175" name="l01175"></a><span class="lineno"> 1175</span> lhs_strides,</div>
|
|
<div class="line"><a id="l01176" name="l01176"></a><span class="lineno"> 1176</span> rhs_strides,</div>
|
|
<div class="line"><a id="l01177" name="l01177"></a><span class="lineno"> 1177</span> x_batch_ndims,</div>
|
|
<div class="line"><a id="l01178" name="l01178"></a><span class="lineno"> 1178</span> x_shape,</div>
|
|
<div class="line"><a id="l01179" name="l01179"></a><span class="lineno"> 1179</span> x_strides,</div>
|
|
<div class="line"><a id="l01180" name="l01180"></a><span class="lineno"> 1180</span> w_batch_ndims,</div>
|
|
<div class="line"><a id="l01181" name="l01181"></a><span class="lineno"> 1181</span> w_shape,</div>
|
|
<div class="line"><a id="l01182" name="l01182"></a><span class="lineno"> 1182</span> w_strides,</div>
|
|
<div class="line"><a id="l01183" name="l01183"></a><span class="lineno"> 1183</span> s_strides,</div>
|
|
<div class="line"><a id="l01184" name="l01184"></a><span class="lineno"> 1184</span> b_strides,</div>
|
|
<div class="line"><a id="l01185" name="l01185"></a><span class="lineno"> 1185</span> tid);</div>
|
|
<div class="line"><a id="l01186" name="l01186"></a><span class="lineno"> 1186</span> qmv_fast_impl<T, group_size, bits>(</div>
|
|
<div class="line"><a id="l01187" name="l01187"></a><span class="lineno"> 1187</span> w,</div>
|
|
<div class="line"><a id="l01188" name="l01188"></a><span class="lineno"> 1188</span> scales,</div>
|
|
<div class="line"><a id="l01189" name="l01189"></a><span class="lineno"> 1189</span> biases,</div>
|
|
<div class="line"><a id="l01190" name="l01190"></a><span class="lineno"> 1190</span> x,</div>
|
|
<div class="line"><a id="l01191" name="l01191"></a><span class="lineno"> 1191</span> y,</div>
|
|
<div class="line"><a id="l01192" name="l01192"></a><span class="lineno"> 1192</span> in_vec_size,</div>
|
|
<div class="line"><a id="l01193" name="l01193"></a><span class="lineno"> 1193</span> out_vec_size,</div>
|
|
<div class="line"><a id="l01194" name="l01194"></a><span class="lineno"> 1194</span> tid,</div>
|
|
<div class="line"><a id="l01195" name="l01195"></a><span class="lineno"> 1195</span> simd_gid,</div>
|
|
<div class="line"><a id="l01196" name="l01196"></a><span class="lineno"> 1196</span> simd_lid);</div>
|
|
<div class="line"><a id="l01197" name="l01197"></a><span class="lineno"> 1197</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l01198" name="l01198"></a><span class="lineno"> 1198</span> </div>
|
|
<div class="line"><a id="l01199" name="l01199"></a><span class="lineno"> 1199</span><span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> group_size, <span class="keywordtype">int</span> bits></div>
|
|
<div class="foldopen" id="foldopen01200" data-start="{" data-end="}">
|
|
<div class="line"><a id="l01200" name="l01200"></a><span class="lineno"><a class="line" href="quantized_8h.html#a03ba4a4a5fe9955319b0aa477d2d7d98"> 1200</a></span>[[kernel]] <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#a03ba4a4a5fe9955319b0aa477d2d7d98">bs_qmv</a>(</div>
|
|
<div class="line"><a id="l01201" name="l01201"></a><span class="lineno"> 1201</span> <span class="keyword">const</span> device uint32_t* w [[buffer(0)]],</div>
|
|
<div class="line"><a id="l01202" name="l01202"></a><span class="lineno"> 1202</span> <span class="keyword">const</span> device T* scales [[buffer(1)]],</div>
|
|
<div class="line"><a id="l01203" name="l01203"></a><span class="lineno"> 1203</span> <span class="keyword">const</span> device T* biases [[buffer(2)]],</div>
|
|
<div class="line"><a id="l01204" name="l01204"></a><span class="lineno"> 1204</span> <span class="keyword">const</span> device T* x [[buffer(3)]],</div>
|
|
<div class="line"><a id="l01205" name="l01205"></a><span class="lineno"> 1205</span> <span class="keyword">const</span> device uint32_t* lhs_indices [[buffer(4)]],</div>
|
|
<div class="line"><a id="l01206" name="l01206"></a><span class="lineno"> 1206</span> <span class="keyword">const</span> device uint32_t* rhs_indices [[buffer(5)]],</div>
|
|
<div class="line"><a id="l01207" name="l01207"></a><span class="lineno"> 1207</span> device T* y [[buffer(6)]],</div>
|
|
<div class="line"><a id="l01208" name="l01208"></a><span class="lineno"> 1208</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& in_vec_size [[buffer(7)]],</div>
|
|
<div class="line"><a id="l01209" name="l01209"></a><span class="lineno"> 1209</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& out_vec_size [[buffer(8)]],</div>
|
|
<div class="line"><a id="l01210" name="l01210"></a><span class="lineno"> 1210</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& batch_ndims [[buffer(9)]],</div>
|
|
<div class="line"><a id="l01211" name="l01211"></a><span class="lineno"> 1211</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* batch_shape [[buffer(10)]],</div>
|
|
<div class="line"><a id="l01212" name="l01212"></a><span class="lineno"> 1212</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* lhs_strides [[buffer(11)]],</div>
|
|
<div class="line"><a id="l01213" name="l01213"></a><span class="lineno"> 1213</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* rhs_strides [[buffer(12)]],</div>
|
|
<div class="line"><a id="l01214" name="l01214"></a><span class="lineno"> 1214</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& x_batch_ndims [[buffer(13)]],</div>
|
|
<div class="line"><a id="l01215" name="l01215"></a><span class="lineno"> 1215</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* x_shape [[buffer(14)]],</div>
|
|
<div class="line"><a id="l01216" name="l01216"></a><span class="lineno"> 1216</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* x_strides [[buffer(15)]],</div>
|
|
<div class="line"><a id="l01217" name="l01217"></a><span class="lineno"> 1217</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& w_batch_ndims [[buffer(16)]],</div>
|
|
<div class="line"><a id="l01218" name="l01218"></a><span class="lineno"> 1218</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* w_shape [[buffer(17)]],</div>
|
|
<div class="line"><a id="l01219" name="l01219"></a><span class="lineno"> 1219</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* w_strides [[buffer(18)]],</div>
|
|
<div class="line"><a id="l01220" name="l01220"></a><span class="lineno"> 1220</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* s_strides [[buffer(19)]],</div>
|
|
<div class="line"><a id="l01221" name="l01221"></a><span class="lineno"> 1221</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* b_strides [[buffer(20)]],</div>
|
|
<div class="line"><a id="l01222" name="l01222"></a><span class="lineno"> 1222</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l01223" name="l01223"></a><span class="lineno"> 1223</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01224" name="l01224"></a><span class="lineno"> 1224</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l01225" name="l01225"></a><span class="lineno"> 1225</span> adjust_matrix_offsets<T>(</div>
|
|
<div class="line"><a id="l01226" name="l01226"></a><span class="lineno"> 1226</span> x,</div>
|
|
<div class="line"><a id="l01227" name="l01227"></a><span class="lineno"> 1227</span> w,</div>
|
|
<div class="line"><a id="l01228" name="l01228"></a><span class="lineno"> 1228</span> scales,</div>
|
|
<div class="line"><a id="l01229" name="l01229"></a><span class="lineno"> 1229</span> biases,</div>
|
|
<div class="line"><a id="l01230" name="l01230"></a><span class="lineno"> 1230</span> lhs_indices,</div>
|
|
<div class="line"><a id="l01231" name="l01231"></a><span class="lineno"> 1231</span> rhs_indices,</div>
|
|
<div class="line"><a id="l01232" name="l01232"></a><span class="lineno"> 1232</span> y,</div>
|
|
<div class="line"><a id="l01233" name="l01233"></a><span class="lineno"> 1233</span> out_vec_size,</div>
|
|
<div class="line"><a id="l01234" name="l01234"></a><span class="lineno"> 1234</span> batch_ndims,</div>
|
|
<div class="line"><a id="l01235" name="l01235"></a><span class="lineno"> 1235</span> batch_shape,</div>
|
|
<div class="line"><a id="l01236" name="l01236"></a><span class="lineno"> 1236</span> lhs_strides,</div>
|
|
<div class="line"><a id="l01237" name="l01237"></a><span class="lineno"> 1237</span> rhs_strides,</div>
|
|
<div class="line"><a id="l01238" name="l01238"></a><span class="lineno"> 1238</span> x_batch_ndims,</div>
|
|
<div class="line"><a id="l01239" name="l01239"></a><span class="lineno"> 1239</span> x_shape,</div>
|
|
<div class="line"><a id="l01240" name="l01240"></a><span class="lineno"> 1240</span> x_strides,</div>
|
|
<div class="line"><a id="l01241" name="l01241"></a><span class="lineno"> 1241</span> w_batch_ndims,</div>
|
|
<div class="line"><a id="l01242" name="l01242"></a><span class="lineno"> 1242</span> w_shape,</div>
|
|
<div class="line"><a id="l01243" name="l01243"></a><span class="lineno"> 1243</span> w_strides,</div>
|
|
<div class="line"><a id="l01244" name="l01244"></a><span class="lineno"> 1244</span> s_strides,</div>
|
|
<div class="line"><a id="l01245" name="l01245"></a><span class="lineno"> 1245</span> b_strides,</div>
|
|
<div class="line"><a id="l01246" name="l01246"></a><span class="lineno"> 1246</span> tid);</div>
|
|
<div class="line"><a id="l01247" name="l01247"></a><span class="lineno"> 1247</span> qmv_impl<T, group_size, bits>(</div>
|
|
<div class="line"><a id="l01248" name="l01248"></a><span class="lineno"> 1248</span> w,</div>
|
|
<div class="line"><a id="l01249" name="l01249"></a><span class="lineno"> 1249</span> scales,</div>
|
|
<div class="line"><a id="l01250" name="l01250"></a><span class="lineno"> 1250</span> biases,</div>
|
|
<div class="line"><a id="l01251" name="l01251"></a><span class="lineno"> 1251</span> x,</div>
|
|
<div class="line"><a id="l01252" name="l01252"></a><span class="lineno"> 1252</span> y,</div>
|
|
<div class="line"><a id="l01253" name="l01253"></a><span class="lineno"> 1253</span> in_vec_size,</div>
|
|
<div class="line"><a id="l01254" name="l01254"></a><span class="lineno"> 1254</span> out_vec_size,</div>
|
|
<div class="line"><a id="l01255" name="l01255"></a><span class="lineno"> 1255</span> tid,</div>
|
|
<div class="line"><a id="l01256" name="l01256"></a><span class="lineno"> 1256</span> simd_gid,</div>
|
|
<div class="line"><a id="l01257" name="l01257"></a><span class="lineno"> 1257</span> simd_lid);</div>
|
|
<div class="line"><a id="l01258" name="l01258"></a><span class="lineno"> 1258</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l01259" name="l01259"></a><span class="lineno"> 1259</span> </div>
|
|
<div class="line"><a id="l01260" name="l01260"></a><span class="lineno"> 1260</span><span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> group_size, <span class="keywordtype">int</span> bits></div>
|
|
<div class="foldopen" id="foldopen01261" data-start="{" data-end="}">
|
|
<div class="line"><a id="l01261" name="l01261"></a><span class="lineno"><a class="line" href="quantized_8h.html#a3af1c89416632c9275b8218a33cb8a04"> 1261</a></span>[[kernel]] <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#a3af1c89416632c9275b8218a33cb8a04">bs_qvm</a>(</div>
|
|
<div class="line"><a id="l01262" name="l01262"></a><span class="lineno"> 1262</span> <span class="keyword">const</span> device T* x [[buffer(0)]],</div>
|
|
<div class="line"><a id="l01263" name="l01263"></a><span class="lineno"> 1263</span> <span class="keyword">const</span> device uint32_t* w [[buffer(1)]],</div>
|
|
<div class="line"><a id="l01264" name="l01264"></a><span class="lineno"> 1264</span> <span class="keyword">const</span> device T* scales [[buffer(2)]],</div>
|
|
<div class="line"><a id="l01265" name="l01265"></a><span class="lineno"> 1265</span> <span class="keyword">const</span> device T* biases [[buffer(3)]],</div>
|
|
<div class="line"><a id="l01266" name="l01266"></a><span class="lineno"> 1266</span> <span class="keyword">const</span> device uint32_t* lhs_indices [[buffer(4)]],</div>
|
|
<div class="line"><a id="l01267" name="l01267"></a><span class="lineno"> 1267</span> <span class="keyword">const</span> device uint32_t* rhs_indices [[buffer(5)]],</div>
|
|
<div class="line"><a id="l01268" name="l01268"></a><span class="lineno"> 1268</span> device T* y [[buffer(6)]],</div>
|
|
<div class="line"><a id="l01269" name="l01269"></a><span class="lineno"> 1269</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& in_vec_size [[buffer(7)]],</div>
|
|
<div class="line"><a id="l01270" name="l01270"></a><span class="lineno"> 1270</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& out_vec_size [[buffer(8)]],</div>
|
|
<div class="line"><a id="l01271" name="l01271"></a><span class="lineno"> 1271</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& batch_ndims [[buffer(9)]],</div>
|
|
<div class="line"><a id="l01272" name="l01272"></a><span class="lineno"> 1272</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* batch_shape [[buffer(10)]],</div>
|
|
<div class="line"><a id="l01273" name="l01273"></a><span class="lineno"> 1273</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* lhs_strides [[buffer(11)]],</div>
|
|
<div class="line"><a id="l01274" name="l01274"></a><span class="lineno"> 1274</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* rhs_strides [[buffer(12)]],</div>
|
|
<div class="line"><a id="l01275" name="l01275"></a><span class="lineno"> 1275</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& x_batch_ndims [[buffer(13)]],</div>
|
|
<div class="line"><a id="l01276" name="l01276"></a><span class="lineno"> 1276</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* x_shape [[buffer(14)]],</div>
|
|
<div class="line"><a id="l01277" name="l01277"></a><span class="lineno"> 1277</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* x_strides [[buffer(15)]],</div>
|
|
<div class="line"><a id="l01278" name="l01278"></a><span class="lineno"> 1278</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& w_batch_ndims [[buffer(16)]],</div>
|
|
<div class="line"><a id="l01279" name="l01279"></a><span class="lineno"> 1279</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* w_shape [[buffer(17)]],</div>
|
|
<div class="line"><a id="l01280" name="l01280"></a><span class="lineno"> 1280</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* w_strides [[buffer(18)]],</div>
|
|
<div class="line"><a id="l01281" name="l01281"></a><span class="lineno"> 1281</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* s_strides [[buffer(19)]],</div>
|
|
<div class="line"><a id="l01282" name="l01282"></a><span class="lineno"> 1282</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* b_strides [[buffer(20)]],</div>
|
|
<div class="line"><a id="l01283" name="l01283"></a><span class="lineno"> 1283</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l01284" name="l01284"></a><span class="lineno"> 1284</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01285" name="l01285"></a><span class="lineno"> 1285</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l01286" name="l01286"></a><span class="lineno"> 1286</span> adjust_matrix_offsets<T>(</div>
|
|
<div class="line"><a id="l01287" name="l01287"></a><span class="lineno"> 1287</span> x,</div>
|
|
<div class="line"><a id="l01288" name="l01288"></a><span class="lineno"> 1288</span> w,</div>
|
|
<div class="line"><a id="l01289" name="l01289"></a><span class="lineno"> 1289</span> scales,</div>
|
|
<div class="line"><a id="l01290" name="l01290"></a><span class="lineno"> 1290</span> biases,</div>
|
|
<div class="line"><a id="l01291" name="l01291"></a><span class="lineno"> 1291</span> lhs_indices,</div>
|
|
<div class="line"><a id="l01292" name="l01292"></a><span class="lineno"> 1292</span> rhs_indices,</div>
|
|
<div class="line"><a id="l01293" name="l01293"></a><span class="lineno"> 1293</span> y,</div>
|
|
<div class="line"><a id="l01294" name="l01294"></a><span class="lineno"> 1294</span> out_vec_size,</div>
|
|
<div class="line"><a id="l01295" name="l01295"></a><span class="lineno"> 1295</span> batch_ndims,</div>
|
|
<div class="line"><a id="l01296" name="l01296"></a><span class="lineno"> 1296</span> batch_shape,</div>
|
|
<div class="line"><a id="l01297" name="l01297"></a><span class="lineno"> 1297</span> lhs_strides,</div>
|
|
<div class="line"><a id="l01298" name="l01298"></a><span class="lineno"> 1298</span> rhs_strides,</div>
|
|
<div class="line"><a id="l01299" name="l01299"></a><span class="lineno"> 1299</span> x_batch_ndims,</div>
|
|
<div class="line"><a id="l01300" name="l01300"></a><span class="lineno"> 1300</span> x_shape,</div>
|
|
<div class="line"><a id="l01301" name="l01301"></a><span class="lineno"> 1301</span> x_strides,</div>
|
|
<div class="line"><a id="l01302" name="l01302"></a><span class="lineno"> 1302</span> w_batch_ndims,</div>
|
|
<div class="line"><a id="l01303" name="l01303"></a><span class="lineno"> 1303</span> w_shape,</div>
|
|
<div class="line"><a id="l01304" name="l01304"></a><span class="lineno"> 1304</span> w_strides,</div>
|
|
<div class="line"><a id="l01305" name="l01305"></a><span class="lineno"> 1305</span> s_strides,</div>
|
|
<div class="line"><a id="l01306" name="l01306"></a><span class="lineno"> 1306</span> b_strides,</div>
|
|
<div class="line"><a id="l01307" name="l01307"></a><span class="lineno"> 1307</span> tid);</div>
|
|
<div class="line"><a id="l01308" name="l01308"></a><span class="lineno"> 1308</span> qvm_impl<T, group_size, bits>(</div>
|
|
<div class="line"><a id="l01309" name="l01309"></a><span class="lineno"> 1309</span> x,</div>
|
|
<div class="line"><a id="l01310" name="l01310"></a><span class="lineno"> 1310</span> w,</div>
|
|
<div class="line"><a id="l01311" name="l01311"></a><span class="lineno"> 1311</span> scales,</div>
|
|
<div class="line"><a id="l01312" name="l01312"></a><span class="lineno"> 1312</span> biases,</div>
|
|
<div class="line"><a id="l01313" name="l01313"></a><span class="lineno"> 1313</span> y,</div>
|
|
<div class="line"><a id="l01314" name="l01314"></a><span class="lineno"> 1314</span> in_vec_size,</div>
|
|
<div class="line"><a id="l01315" name="l01315"></a><span class="lineno"> 1315</span> out_vec_size,</div>
|
|
<div class="line"><a id="l01316" name="l01316"></a><span class="lineno"> 1316</span> tid,</div>
|
|
<div class="line"><a id="l01317" name="l01317"></a><span class="lineno"> 1317</span> simd_gid,</div>
|
|
<div class="line"><a id="l01318" name="l01318"></a><span class="lineno"> 1318</span> simd_lid);</div>
|
|
<div class="line"><a id="l01319" name="l01319"></a><span class="lineno"> 1319</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l01320" name="l01320"></a><span class="lineno"> 1320</span> </div>
|
|
<div class="line"><a id="l01321" name="l01321"></a><span class="lineno"> 1321</span><span class="keyword">template</span> <</div>
|
|
<div class="line"><a id="l01322" name="l01322"></a><span class="lineno"> 1322</span> <span class="keyword">typename</span> T,</div>
|
|
<div class="line"><a id="l01323" name="l01323"></a><span class="lineno"> 1323</span> <span class="keyword">const</span> <span class="keywordtype">int</span> group_size,</div>
|
|
<div class="line"><a id="l01324" name="l01324"></a><span class="lineno"> 1324</span> <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code hl_function" href="namespacemlx_1_1core_1_1random.html#abb895baa477f5a06b5f88e69245f1825">bits</a>,</div>
|
|
<div class="line"><a id="l01325" name="l01325"></a><span class="lineno"> 1325</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> aligned_N,</div>
|
|
<div class="line"><a id="l01326" name="l01326"></a><span class="lineno"> 1326</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BM = 32,</div>
|
|
<div class="line"><a id="l01327" name="l01327"></a><span class="lineno"> 1327</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BK = 32,</div>
|
|
<div class="line"><a id="l01328" name="l01328"></a><span class="lineno"> 1328</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BN = 32></div>
|
|
<div class="foldopen" id="foldopen01329" data-start="{" data-end="}">
|
|
<div class="line"><a id="l01329" name="l01329"></a><span class="lineno"><a class="line" href="quantized_8h.html#a3acc2ace766cb855f13da2d1834e7dc7"> 1329</a></span>[[kernel]] <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#a3acc2ace766cb855f13da2d1834e7dc7">bs_qmm_t</a>(</div>
|
|
<div class="line"><a id="l01330" name="l01330"></a><span class="lineno"> 1330</span> <span class="keyword">const</span> device T* x [[buffer(0)]],</div>
|
|
<div class="line"><a id="l01331" name="l01331"></a><span class="lineno"> 1331</span> <span class="keyword">const</span> device uint32_t* w [[buffer(1)]],</div>
|
|
<div class="line"><a id="l01332" name="l01332"></a><span class="lineno"> 1332</span> <span class="keyword">const</span> device T* scales [[buffer(2)]],</div>
|
|
<div class="line"><a id="l01333" name="l01333"></a><span class="lineno"> 1333</span> <span class="keyword">const</span> device T* biases [[buffer(3)]],</div>
|
|
<div class="line"><a id="l01334" name="l01334"></a><span class="lineno"> 1334</span> <span class="keyword">const</span> device uint32_t* lhs_indices [[buffer(4)]],</div>
|
|
<div class="line"><a id="l01335" name="l01335"></a><span class="lineno"> 1335</span> <span class="keyword">const</span> device uint32_t* rhs_indices [[buffer(5)]],</div>
|
|
<div class="line"><a id="l01336" name="l01336"></a><span class="lineno"> 1336</span> device T* y [[buffer(6)]],</div>
|
|
<div class="line"><a id="l01337" name="l01337"></a><span class="lineno"> 1337</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& M [[buffer(7)]],</div>
|
|
<div class="line"><a id="l01338" name="l01338"></a><span class="lineno"> 1338</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& N [[buffer(8)]],</div>
|
|
<div class="line"><a id="l01339" name="l01339"></a><span class="lineno"> 1339</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& K [[buffer(9)]],</div>
|
|
<div class="line"><a id="l01340" name="l01340"></a><span class="lineno"> 1340</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& batch_ndims [[buffer(10)]],</div>
|
|
<div class="line"><a id="l01341" name="l01341"></a><span class="lineno"> 1341</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* batch_shape [[buffer(11)]],</div>
|
|
<div class="line"><a id="l01342" name="l01342"></a><span class="lineno"> 1342</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* lhs_strides [[buffer(12)]],</div>
|
|
<div class="line"><a id="l01343" name="l01343"></a><span class="lineno"> 1343</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* rhs_strides [[buffer(13)]],</div>
|
|
<div class="line"><a id="l01344" name="l01344"></a><span class="lineno"> 1344</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& x_batch_ndims [[buffer(14)]],</div>
|
|
<div class="line"><a id="l01345" name="l01345"></a><span class="lineno"> 1345</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* x_shape [[buffer(15)]],</div>
|
|
<div class="line"><a id="l01346" name="l01346"></a><span class="lineno"> 1346</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* x_strides [[buffer(16)]],</div>
|
|
<div class="line"><a id="l01347" name="l01347"></a><span class="lineno"> 1347</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& w_batch_ndims [[buffer(17)]],</div>
|
|
<div class="line"><a id="l01348" name="l01348"></a><span class="lineno"> 1348</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* w_shape [[buffer(18)]],</div>
|
|
<div class="line"><a id="l01349" name="l01349"></a><span class="lineno"> 1349</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* w_strides [[buffer(19)]],</div>
|
|
<div class="line"><a id="l01350" name="l01350"></a><span class="lineno"> 1350</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* s_strides [[buffer(20)]],</div>
|
|
<div class="line"><a id="l01351" name="l01351"></a><span class="lineno"> 1351</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* b_strides [[buffer(21)]],</div>
|
|
<div class="line"><a id="l01352" name="l01352"></a><span class="lineno"> 1352</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l01353" name="l01353"></a><span class="lineno"> 1353</span> uint lid [[thread_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01354" name="l01354"></a><span class="lineno"> 1354</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01355" name="l01355"></a><span class="lineno"> 1355</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l01356" name="l01356"></a><span class="lineno"> 1356</span> (void)lid;</div>
|
|
<div class="line"><a id="l01357" name="l01357"></a><span class="lineno"> 1357</span> </div>
|
|
<div class="line"><a id="l01358" name="l01358"></a><span class="lineno"> 1358</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> BK_padded = (BK + 16 / <span class="keyword">sizeof</span>(T));</div>
|
|
<div class="line"><a id="l01359" name="l01359"></a><span class="lineno"> 1359</span> </div>
|
|
<div class="line"><a id="l01360" name="l01360"></a><span class="lineno"> 1360</span> threadgroup T Xs[BM * BK_padded];</div>
|
|
<div class="line"><a id="l01361" name="l01361"></a><span class="lineno"> 1361</span> threadgroup T Ws[BN * BK_padded];</div>
|
|
<div class="line"><a id="l01362" name="l01362"></a><span class="lineno"> 1362</span> </div>
|
|
<div class="line"><a id="l01363" name="l01363"></a><span class="lineno"> 1363</span> adjust_matrix_offsets<T>(</div>
|
|
<div class="line"><a id="l01364" name="l01364"></a><span class="lineno"> 1364</span> x,</div>
|
|
<div class="line"><a id="l01365" name="l01365"></a><span class="lineno"> 1365</span> w,</div>
|
|
<div class="line"><a id="l01366" name="l01366"></a><span class="lineno"> 1366</span> scales,</div>
|
|
<div class="line"><a id="l01367" name="l01367"></a><span class="lineno"> 1367</span> biases,</div>
|
|
<div class="line"><a id="l01368" name="l01368"></a><span class="lineno"> 1368</span> lhs_indices,</div>
|
|
<div class="line"><a id="l01369" name="l01369"></a><span class="lineno"> 1369</span> rhs_indices,</div>
|
|
<div class="line"><a id="l01370" name="l01370"></a><span class="lineno"> 1370</span> y,</div>
|
|
<div class="line"><a id="l01371" name="l01371"></a><span class="lineno"> 1371</span> M * N,</div>
|
|
<div class="line"><a id="l01372" name="l01372"></a><span class="lineno"> 1372</span> batch_ndims,</div>
|
|
<div class="line"><a id="l01373" name="l01373"></a><span class="lineno"> 1373</span> batch_shape,</div>
|
|
<div class="line"><a id="l01374" name="l01374"></a><span class="lineno"> 1374</span> lhs_strides,</div>
|
|
<div class="line"><a id="l01375" name="l01375"></a><span class="lineno"> 1375</span> rhs_strides,</div>
|
|
<div class="line"><a id="l01376" name="l01376"></a><span class="lineno"> 1376</span> x_batch_ndims,</div>
|
|
<div class="line"><a id="l01377" name="l01377"></a><span class="lineno"> 1377</span> x_shape,</div>
|
|
<div class="line"><a id="l01378" name="l01378"></a><span class="lineno"> 1378</span> x_strides,</div>
|
|
<div class="line"><a id="l01379" name="l01379"></a><span class="lineno"> 1379</span> w_batch_ndims,</div>
|
|
<div class="line"><a id="l01380" name="l01380"></a><span class="lineno"> 1380</span> w_shape,</div>
|
|
<div class="line"><a id="l01381" name="l01381"></a><span class="lineno"> 1381</span> w_strides,</div>
|
|
<div class="line"><a id="l01382" name="l01382"></a><span class="lineno"> 1382</span> s_strides,</div>
|
|
<div class="line"><a id="l01383" name="l01383"></a><span class="lineno"> 1383</span> b_strides,</div>
|
|
<div class="line"><a id="l01384" name="l01384"></a><span class="lineno"> 1384</span> tid);</div>
|
|
<div class="line"><a id="l01385" name="l01385"></a><span class="lineno"> 1385</span> qmm_t_impl<T, BM, BK, BN, group_size, bits, aligned_N>(</div>
|
|
<div class="line"><a id="l01386" name="l01386"></a><span class="lineno"> 1386</span> x, w, scales, biases, y, Xs, Ws, M, N, K, tid, lid, simd_gid, simd_lid);</div>
|
|
<div class="line"><a id="l01387" name="l01387"></a><span class="lineno"> 1387</span>}</div>
|
|
</div>
|
|
<div class="line"><a id="l01388" name="l01388"></a><span class="lineno"> 1388</span> </div>
|
|
<div class="line"><a id="l01389" name="l01389"></a><span class="lineno"> 1389</span><span class="keyword">template</span> <</div>
|
|
<div class="line"><a id="l01390" name="l01390"></a><span class="lineno"> 1390</span> <span class="keyword">typename</span> T,</div>
|
|
<div class="line"><a id="l01391" name="l01391"></a><span class="lineno"> 1391</span> <span class="keyword">const</span> <span class="keywordtype">int</span> group_size,</div>
|
|
<div class="line"><a id="l01392" name="l01392"></a><span class="lineno"> 1392</span> <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code hl_function" href="namespacemlx_1_1core_1_1random.html#abb895baa477f5a06b5f88e69245f1825">bits</a>,</div>
|
|
<div class="line"><a id="l01393" name="l01393"></a><span class="lineno"> 1393</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BM = 32,</div>
|
|
<div class="line"><a id="l01394" name="l01394"></a><span class="lineno"> 1394</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BK = 32,</div>
|
|
<div class="line"><a id="l01395" name="l01395"></a><span class="lineno"> 1395</span> <span class="keyword">const</span> <span class="keywordtype">int</span> BN = 32></div>
|
|
<div class="foldopen" id="foldopen01396" data-start="{" data-end="}">
|
|
<div class="line"><a id="l01396" name="l01396"></a><span class="lineno"><a class="line" href="quantized_8h.html#acfc43edcf0c742ddc090d22cb7229284"> 1396</a></span>[[kernel]] <span class="keywordtype">void</span> <a class="code hl_function" href="quantized_8h.html#acfc43edcf0c742ddc090d22cb7229284">bs_qmm_n</a>(</div>
|
|
<div class="line"><a id="l01397" name="l01397"></a><span class="lineno"> 1397</span> <span class="keyword">const</span> device T* x [[buffer(0)]],</div>
|
|
<div class="line"><a id="l01398" name="l01398"></a><span class="lineno"> 1398</span> <span class="keyword">const</span> device uint32_t* w [[buffer(1)]],</div>
|
|
<div class="line"><a id="l01399" name="l01399"></a><span class="lineno"> 1399</span> <span class="keyword">const</span> device T* scales [[buffer(2)]],</div>
|
|
<div class="line"><a id="l01400" name="l01400"></a><span class="lineno"> 1400</span> <span class="keyword">const</span> device T* biases [[buffer(3)]],</div>
|
|
<div class="line"><a id="l01401" name="l01401"></a><span class="lineno"> 1401</span> <span class="keyword">const</span> device uint32_t* lhs_indices [[buffer(4)]],</div>
|
|
<div class="line"><a id="l01402" name="l01402"></a><span class="lineno"> 1402</span> <span class="keyword">const</span> device uint32_t* rhs_indices [[buffer(5)]],</div>
|
|
<div class="line"><a id="l01403" name="l01403"></a><span class="lineno"> 1403</span> device T* y [[buffer(6)]],</div>
|
|
<div class="line"><a id="l01404" name="l01404"></a><span class="lineno"> 1404</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& M [[buffer(7)]],</div>
|
|
<div class="line"><a id="l01405" name="l01405"></a><span class="lineno"> 1405</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& N [[buffer(8)]],</div>
|
|
<div class="line"><a id="l01406" name="l01406"></a><span class="lineno"> 1406</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& K [[buffer(9)]],</div>
|
|
<div class="line"><a id="l01407" name="l01407"></a><span class="lineno"> 1407</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& batch_ndims [[buffer(10)]],</div>
|
|
<div class="line"><a id="l01408" name="l01408"></a><span class="lineno"> 1408</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* batch_shape [[buffer(11)]],</div>
|
|
<div class="line"><a id="l01409" name="l01409"></a><span class="lineno"> 1409</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* lhs_strides [[buffer(12)]],</div>
|
|
<div class="line"><a id="l01410" name="l01410"></a><span class="lineno"> 1410</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* rhs_strides [[buffer(13)]],</div>
|
|
<div class="line"><a id="l01411" name="l01411"></a><span class="lineno"> 1411</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& x_batch_ndims [[buffer(14)]],</div>
|
|
<div class="line"><a id="l01412" name="l01412"></a><span class="lineno"> 1412</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* x_shape [[buffer(15)]],</div>
|
|
<div class="line"><a id="l01413" name="l01413"></a><span class="lineno"> 1413</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* x_strides [[buffer(16)]],</div>
|
|
<div class="line"><a id="l01414" name="l01414"></a><span class="lineno"> 1414</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>& w_batch_ndims [[buffer(17)]],</div>
|
|
<div class="line"><a id="l01415" name="l01415"></a><span class="lineno"> 1415</span> <span class="keyword">const</span> constant <span class="keywordtype">int</span>* w_shape [[buffer(18)]],</div>
|
|
<div class="line"><a id="l01416" name="l01416"></a><span class="lineno"> 1416</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* w_strides [[buffer(19)]],</div>
|
|
<div class="line"><a id="l01417" name="l01417"></a><span class="lineno"> 1417</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* s_strides [[buffer(20)]],</div>
|
|
<div class="line"><a id="l01418" name="l01418"></a><span class="lineno"> 1418</span> <span class="keyword">const</span> constant <span class="keywordtype">size_t</span>* b_strides [[buffer(21)]],</div>
|
|
<div class="line"><a id="l01419" name="l01419"></a><span class="lineno"> 1419</span> uint3 tid [[threadgroup_position_in_grid]],</div>
|
|
<div class="line"><a id="l01420" name="l01420"></a><span class="lineno"> 1420</span> uint lid [[thread_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01421" name="l01421"></a><span class="lineno"> 1421</span> uint simd_gid [[simdgroup_index_in_threadgroup]],</div>
|
|
<div class="line"><a id="l01422" name="l01422"></a><span class="lineno"> 1422</span> uint simd_lid [[thread_index_in_simdgroup]]) {</div>
|
|
<div class="line"><a id="l01423" name="l01423"></a><span class="lineno"> 1423</span> (void)lid;</div>
|
|
<div class="line"><a id="l01424" name="l01424"></a><span class="lineno"> 1424</span> </div>
|
|
<div class="line"><a id="l01425" name="l01425"></a><span class="lineno"> 1425</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> BK_padded = (BK + 16 / <span class="keyword">sizeof</span>(T));</div>
|
|
<div class="line"><a id="l01426" name="l01426"></a><span class="lineno"> 1426</span> <span class="keyword">constexpr</span> <span class="keywordtype">int</span> BN_padded = (BN + 16 / <span class="keyword">sizeof</span>(T));</div>
|
|
<div class="line"><a id="l01427" name="l01427"></a><span class="lineno"> 1427</span> </div>
|
|
<div class="line"><a id="l01428" name="l01428"></a><span class="lineno"> 1428</span> threadgroup T Xs[BM * BK_padded];</div>
|
|
<div class="line"><a id="l01429" name="l01429"></a><span class="lineno"> 1429</span> threadgroup T Ws[BK * BN_padded];</div>
|
|
<div class="line"><a id="l01430" name="l01430"></a><span class="lineno"> 1430</span> </div>
|
|
<div class="line"><a id="l01431" name="l01431"></a><span class="lineno"> 1431</span> adjust_matrix_offsets<T>(</div>
|
|
<div class="line"><a id="l01432" name="l01432"></a><span class="lineno"> 1432</span> x,</div>
|
|
<div class="line"><a id="l01433" name="l01433"></a><span class="lineno"> 1433</span> w,</div>
|
|
<div class="line"><a id="l01434" name="l01434"></a><span class="lineno"> 1434</span> scales,</div>
|
|
<div class="line"><a id="l01435" name="l01435"></a><span class="lineno"> 1435</span> biases,</div>
|
|
<div class="line"><a id="l01436" name="l01436"></a><span class="lineno"> 1436</span> lhs_indices,</div>
|
|
<div class="line"><a id="l01437" name="l01437"></a><span class="lineno"> 1437</span> rhs_indices,</div>
|
|
<div class="line"><a id="l01438" name="l01438"></a><span class="lineno"> 1438</span> y,</div>
|
|
<div class="line"><a id="l01439" name="l01439"></a><span class="lineno"> 1439</span> M * N,</div>
|
|
<div class="line"><a id="l01440" name="l01440"></a><span class="lineno"> 1440</span> batch_ndims,</div>
|
|
<div class="line"><a id="l01441" name="l01441"></a><span class="lineno"> 1441</span> batch_shape,</div>
|
|
<div class="line"><a id="l01442" name="l01442"></a><span class="lineno"> 1442</span> lhs_strides,</div>
|
|
<div class="line"><a id="l01443" name="l01443"></a><span class="lineno"> 1443</span> rhs_strides,</div>
|
|
<div class="line"><a id="l01444" name="l01444"></a><span class="lineno"> 1444</span> x_batch_ndims,</div>
|
|
<div class="line"><a id="l01445" name="l01445"></a><span class="lineno"> 1445</span> x_shape,</div>
|
|
<div class="line"><a id="l01446" name="l01446"></a><span class="lineno"> 1446</span> x_strides,</div>
|
|
<div class="line"><a id="l01447" name="l01447"></a><span class="lineno"> 1447</span> w_batch_ndims,</div>
|
|
<div class="line"><a id="l01448" name="l01448"></a><span class="lineno"> 1448</span> w_shape,</div>
|
|
<div class="line"><a id="l01449" name="l01449"></a><span class="lineno"> 1449</span> w_strides,</div>
|
|
<div class="line"><a id="l01450" name="l01450"></a><span class="lineno"> 1450</span> s_strides,</div>
|
|
<div class="line"><a id="l01451" name="l01451"></a><span class="lineno"> 1451</span> b_strides,</div>
|
|
<div class="line"><a id="l01452" name="l01452"></a><span class="lineno"> 1452</span> tid);</div>
|
|
<div class="line"><a id="l01453" name="l01453"></a><span class="lineno"> 1453</span> qmm_n_impl<T, BM, BK, BN, group_size, bits>(</div>
|
|
<div class="line"><a id="l01454" name="l01454"></a><span class="lineno"> 1454</span> x, w, scales, biases, y, Xs, Ws, M, N, K, tid, lid, simd_gid, simd_lid);</div>
|
|
<div class="line"><a id="l01455" name="l01455"></a><span class="lineno"> 1455</span>}</div>
|
|
</div>
|
|
<div class="ttc" id="abackend_2metal_2kernels_2steel_2utils_8h_html_aaf4974425147d6f26d031691e321637f"><div class="ttname"><a href="backend_2metal_2kernels_2steel_2utils_8h.html#aaf4974425147d6f26d031691e321637f">elem_to_loc_broadcast</a></div><div class="ttdeci">METAL_FUNC ulong2 elem_to_loc_broadcast(uint elem, constant const int *shape, constant const size_t *a_strides, constant const size_t *b_strides, int ndim)</div><div class="ttdef"><b>Definition</b> utils.h:7</div></div>
|
|
<div class="ttc" id="abackend_2metal_2kernels_2utils_8h_html_a2e49fa7ab8f6348543455c6c45d7e2a9"><div class="ttname"><a href="backend_2metal_2kernels_2utils_8h.html#a2e49fa7ab8f6348543455c6c45d7e2a9">elem_to_loc</a></div><div class="ttdeci">METAL_FUNC stride_t elem_to_loc(uint elem, device const int *shape, device const stride_t *strides, int ndim)</div><div class="ttdef"><b>Definition</b> utils.h:77</div></div>
|
|
<div class="ttc" id="anamespacemetal_html"><div class="ttname"><a href="namespacemetal.html">metal</a></div><div class="ttdef"><b>Definition</b> bf16.h:265</div></div>
|
|
<div class="ttc" id="anamespacemetal_html_a6653b28c9473087141eddce39878d4d3"><div class="ttname"><a href="namespacemetal.html#a6653b28c9473087141eddce39878d4d3">metal::min</a></div><div class="ttdeci">METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)</div><div class="ttdef"><b>Definition</b> bf16_math.h:234</div></div>
|
|
<div class="ttc" id="anamespacemetal_html_a85181e37a00cb4a4217f1bb25389bce5"><div class="ttname"><a href="namespacemetal.html#a85181e37a00cb4a4217f1bb25389bce5">metal::simd_sum</a></div><div class="ttdeci">METAL_FUNC bfloat16_t simd_sum(bfloat16_t data)</div><div class="ttdef"><b>Definition</b> bf16_math.h:392</div></div>
|
|
<div class="ttc" id="anamespacemlx_1_1core_1_1random_html_abb895baa477f5a06b5f88e69245f1825"><div class="ttname"><a href="namespacemlx_1_1core_1_1random.html#abb895baa477f5a06b5f88e69245f1825">mlx::core::random::bits</a></div><div class="ttdeci">array bits(const std::vector< int > &shape, int width, const std::optional< array > &key=std::nullopt, StreamOrDevice s={})</div><div class="ttdoc">Generate an array with type uint32 filled with random bits.</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_a0386011c52d03e60885a31e6fbd903dd"><div class="ttname"><a href="quantized_8h.html#a0386011c52d03e60885a31e6fbd903dd">MLX_MTL_CONST</a></div><div class="ttdeci">#define MLX_MTL_CONST</div><div class="ttdef"><b>Definition</b> quantized.h:8</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_a03ba4a4a5fe9955319b0aa477d2d7d98"><div class="ttname"><a href="quantized_8h.html#a03ba4a4a5fe9955319b0aa477d2d7d98">bs_qmv</a></div><div class="ttdeci">void bs_qmv(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &batch_ndims, const constant int *batch_shape, const constant size_t *lhs_strides, const constant size_t *rhs_strides, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:1200</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_a07b26d2d0b0d65dfe925c452c453fa42"><div class="ttname"><a href="quantized_8h.html#a07b26d2d0b0d65dfe925c452c453fa42">qdot_safe</a></div><div class="ttdeci">U qdot_safe(const device uint8_t *w, const thread U *x_thread, U scale, U bias, U sum, int N)</div><div class="ttdef"><b>Definition</b> quantized.h:141</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_a2cce78e6692cfcbab8f6aa89afa61580"><div class="ttname"><a href="quantized_8h.html#a2cce78e6692cfcbab8f6aa89afa61580">qvm</a></div><div class="ttdeci">void qvm(const device T *x, const device uint32_t *w, const device T *scales, const device T *biases, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, uint3 tid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:1050</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_a3ab400746ad77be89c30d25638e01698"><div class="ttname"><a href="quantized_8h.html#a3ab400746ad77be89c30d25638e01698">adjust_matrix_offsets</a></div><div class="ttdeci">METAL_FUNC void adjust_matrix_offsets(const device T *&x, const device uint32_t *&w, const device T *&scales, const device T *&biases, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, device T *&y, int output_stride, const constant int &batch_ndims, const constant int *batch_shape, const constant size_t *lhs_strides, const constant size_t *rhs_strides, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid)</div><div class="ttdef"><b>Definition</b> quantized.h:946</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_a3acc2ace766cb855f13da2d1834e7dc7"><div class="ttname"><a href="quantized_8h.html#a3acc2ace766cb855f13da2d1834e7dc7">bs_qmm_t</a></div><div class="ttdeci">void bs_qmm_t(const device T *x, const device uint32_t *w, const device T *scales, const device T *biases, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, device T *y, const constant int &M, const constant int &N, const constant int &K, const constant int &batch_ndims, const constant int *batch_shape, const constant size_t *lhs_strides, const constant size_t *rhs_strides, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint lid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:1329</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_a3af1c89416632c9275b8218a33cb8a04"><div class="ttname"><a href="quantized_8h.html#a3af1c89416632c9275b8218a33cb8a04">bs_qvm</a></div><div class="ttdeci">void bs_qvm(const device T *x, const device uint32_t *w, const device T *scales, const device T *biases, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &batch_ndims, const constant int *batch_shape, const constant size_t *lhs_strides, const constant size_t *rhs_strides, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:1261</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_a5f7b71eef2e328af3225d7c777ffb339"><div class="ttname"><a href="quantized_8h.html#a5f7b71eef2e328af3225d7c777ffb339">qmm_n_impl</a></div><div class="ttdeci">METAL_FUNC void qmm_n_impl(const device T *x, const device uint32_t *w, const device T *scales, const device T *biases, device T *y, threadgroup T *Xs, threadgroup T *Ws, const constant int &M, const constant int &N, const constant int &K, uint3 tid, uint lid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:820</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_a62969a218d93680f5e35d0c61b160b99"><div class="ttname"><a href="quantized_8h.html#a62969a218d93680f5e35d0c61b160b99">SIMD_SIZE</a></div><div class="ttdeci">static constant constexpr const int SIMD_SIZE</div><div class="ttdef"><b>Definition</b> quantized.h:10</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_a6f92bb9f3d29d707bfc680bebe1c80f7"><div class="ttname"><a href="quantized_8h.html#a6f92bb9f3d29d707bfc680bebe1c80f7">bs_qmv_fast</a></div><div class="ttdeci">void bs_qmv_fast(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &batch_ndims, const constant int *batch_shape, const constant size_t *lhs_strides, const constant size_t *rhs_strides, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:1139</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_a8dbace41de9e1e21dd59d016db11b3e9"><div class="ttname"><a href="quantized_8h.html#a8dbace41de9e1e21dd59d016db11b3e9">load_vector</a></div><div class="ttdeci">U load_vector(const device T *x, thread U *x_thread)</div><div class="ttdef"><b>Definition</b> quantized.h:13</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_a8e13c7d895624f738d2a6d9893b687fd"><div class="ttname"><a href="quantized_8h.html#a8e13c7d895624f738d2a6d9893b687fd">qmv_impl</a></div><div class="ttdeci">METAL_FUNC void qmv_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, uint3 tid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:439</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_a9d466e3e3af0d75cfc4eddcf46cb13e4"><div class="ttname"><a href="quantized_8h.html#a9d466e3e3af0d75cfc4eddcf46cb13e4">qvm_impl</a></div><div class="ttdeci">METAL_FUNC void qvm_impl(const device T *x, const device uint32_t *w, const device T *scales, const device T *biases, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, uint3 tid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:588</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_aa69e143d646fad332c1a53e8c9b337b7"><div class="ttname"><a href="quantized_8h.html#aa69e143d646fad332c1a53e8c9b337b7">load_vector_safe</a></div><div class="ttdeci">U load_vector_safe(const device T *x, thread U *x_thread, int N)</div><div class="ttdef"><b>Definition</b> quantized.h:51</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_ab364d58ab652e3ad87a8f80910556071"><div class="ttname"><a href="quantized_8h.html#ab364d58ab652e3ad87a8f80910556071">qdot</a></div><div class="ttdeci">U qdot(const device uint8_t *w, const thread U *x_thread, U scale, U bias, U sum)</div><div class="ttdef"><b>Definition</b> quantized.h:98</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_ab5c4cfe531d7a0e1c19353060e3d3a6c"><div class="ttname"><a href="quantized_8h.html#ab5c4cfe531d7a0e1c19353060e3d3a6c">qmm_n</a></div><div class="ttdeci">void qmm_n(const device T *x, const device uint32_t *w, const device T *scales, const device T *biases, device T *y, const constant int &M, const constant int &N, const constant int &K, uint3 tid, uint lid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:1113</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_aba7687e6f8f1d29c0a1b2a3db150bd81"><div class="ttname"><a href="quantized_8h.html#aba7687e6f8f1d29c0a1b2a3db150bd81">qmv_fast_impl</a></div><div class="ttdeci">METAL_FUNC void qmv_fast_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, uint3 tid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:375</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_ac7b6accc41b026ef8efc312987aa9efe"><div class="ttname"><a href="quantized_8h.html#ac7b6accc41b026ef8efc312987aa9efe">qmm_t_impl</a></div><div class="ttdeci">METAL_FUNC void qmm_t_impl(const device T *x, const device uint32_t *w, const device T *scales, const device T *biases, device T *y, threadgroup T *Xs, threadgroup T *Ws, const constant int &M, const constant int &N, const constant int &K, uint3 tid, uint lid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:699</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_acfc43edcf0c742ddc090d22cb7229284"><div class="ttname"><a href="quantized_8h.html#acfc43edcf0c742ddc090d22cb7229284">bs_qmm_n</a></div><div class="ttdeci">void bs_qmm_n(const device T *x, const device uint32_t *w, const device T *scales, const device T *biases, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, device T *y, const constant int &M, const constant int &N, const constant int &K, const constant int &batch_ndims, const constant int *batch_shape, const constant size_t *lhs_strides, const constant size_t *rhs_strides, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint lid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:1396</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_ad6d0aa9b080358581844d6583aa2f4ea"><div class="ttname"><a href="quantized_8h.html#ad6d0aa9b080358581844d6583aa2f4ea">qmv_fast</a></div><div class="ttdeci">void qmv_fast(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, uint3 tid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:1000</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_ae024e11c3e38dda71fef772ff0a82fe2"><div class="ttname"><a href="quantized_8h.html#ae024e11c3e38dda71fef772ff0a82fe2">qmv</a></div><div class="ttdeci">void qmv(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, uint3 tid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:1025</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_ae756f6817b584c60f5dcdd1d9c6b4f58"><div class="ttname"><a href="quantized_8h.html#ae756f6817b584c60f5dcdd1d9c6b4f58">qouter</a></div><div class="ttdeci">void qouter(const thread uint8_t *w, U x, U scale, U bias, thread U *result)</div><div class="ttdef"><b>Definition</b> quantized.h:186</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_aecff265b63566d0d5689cfc4e5b037d2"><div class="ttname"><a href="quantized_8h.html#aecff265b63566d0d5689cfc4e5b037d2">dequantize</a></div><div class="ttdeci">void dequantize(const device uint8_t *w, U scale, U bias, threadgroup U *w_local)</div><div class="ttdef"><b>Definition</b> quantized.h:218</div></div>
|
|
<div class="ttc" id="aquantized_8h_html_af9b25269ab95632e343631eeef79dc8d"><div class="ttname"><a href="quantized_8h.html#af9b25269ab95632e343631eeef79dc8d">qmm_t</a></div><div class="ttdeci">void qmm_t(const device T *x, const device uint32_t *w, const device T *scales, const device T *biases, device T *y, const constant int &M, const constant int &N, const constant int &K, uint3 tid, uint lid, uint simd_gid, uint simd_lid)</div><div class="ttdef"><b>Definition</b> quantized.h:1082</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html"><div class="ttname"><a href="struct_quantized_block_loader.html">QuantizedBlockLoader</a></div><div class="ttdef"><b>Definition</b> quantized.h:261</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a0ace7e3762ecfa5a4106e7dee7e1b6ab"><div class="ttname"><a href="struct_quantized_block_loader.html#a0ace7e3762ecfa5a4106e7dee7e1b6ab">QuantizedBlockLoader::group_stride</a></div><div class="ttdeci">const int group_stride</div><div class="ttdef"><b>Definition</b> quantized.h:281</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a1392a5278cf6e090ea80ebe7c4ac5fbb"><div class="ttname"><a href="struct_quantized_block_loader.html#a1392a5278cf6e090ea80ebe7c4ac5fbb">QuantizedBlockLoader::BCOLS_PACKED</a></div><div class="ttdeci">static constant constexpr const short BCOLS_PACKED</div><div class="ttdef"><b>Definition</b> quantized.h:273</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a17d01a6aba0833b073586ef2c09d0fbd"><div class="ttname"><a href="struct_quantized_block_loader.html#a17d01a6aba0833b073586ef2c09d0fbd">QuantizedBlockLoader::biases</a></div><div class="ttdeci">const device T * biases</div><div class="ttdef"><b>Definition</b> quantized.h:290</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a234feacde36a4afc0d740332a3769fb6"><div class="ttname"><a href="struct_quantized_block_loader.html#a234feacde36a4afc0d740332a3769fb6">QuantizedBlockLoader::group_step_cnt</a></div><div class="ttdeci">short group_step_cnt</div><div class="ttdef"><b>Definition</b> quantized.h:280</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a31e14175f3d4902d9fe5ab5a219f61ba"><div class="ttname"><a href="struct_quantized_block_loader.html#a31e14175f3d4902d9fe5ab5a219f61ba">QuantizedBlockLoader::group_steps</a></div><div class="ttdeci">static constant constexpr const short group_steps</div><div class="ttdef"><b>Definition</b> quantized.h:276</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a50821537ea747bc03295a09bb0eef475"><div class="ttname"><a href="struct_quantized_block_loader.html#a50821537ea747bc03295a09bb0eef475">QuantizedBlockLoader::thread_idx</a></div><div class="ttdeci">const short thread_idx</div><div class="ttdef"><b>Definition</b> quantized.h:283</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a6123e4a9209d6eacb58b2c2344ed1ecf"><div class="ttname"><a href="struct_quantized_block_loader.html#a6123e4a9209d6eacb58b2c2344ed1ecf">QuantizedBlockLoader::scales</a></div><div class="ttdeci">const device T * scales</div><div class="ttdef"><b>Definition</b> quantized.h:289</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a6213479f7a6d9314d8879f8856b0b6fb"><div class="ttname"><a href="struct_quantized_block_loader.html#a6213479f7a6d9314d8879f8856b0b6fb">QuantizedBlockLoader::n_reads</a></div><div class="ttdeci">static constant constexpr const short n_reads</div><div class="ttdef"><b>Definition</b> quantized.h:274</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a674138ef7c43cc45586ea9f8fd6f6bd9"><div class="ttname"><a href="struct_quantized_block_loader.html#a674138ef7c43cc45586ea9f8fd6f6bd9">QuantizedBlockLoader::next</a></div><div class="ttdeci">void next()</div><div class="ttdef"><b>Definition</b> quantized.h:353</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a699dc9aa284b8fbf870310bbb224465b"><div class="ttname"><a href="struct_quantized_block_loader.html#a699dc9aa284b8fbf870310bbb224465b">QuantizedBlockLoader::load_safe</a></div><div class="ttdeci">void load_safe(short2 src_tile_dim) const</div><div class="ttdef"><b>Definition</b> quantized.h:326</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a8050977d473d1a24fae5c833e609839e"><div class="ttname"><a href="struct_quantized_block_loader.html#a8050977d473d1a24fae5c833e609839e">QuantizedBlockLoader::src_ld</a></div><div class="ttdeci">const int src_ld</div><div class="ttdef"><b>Definition</b> quantized.h:278</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a85041d72225a2095659c70509291a906"><div class="ttname"><a href="struct_quantized_block_loader.html#a85041d72225a2095659c70509291a906">QuantizedBlockLoader::bi</a></div><div class="ttdeci">const short bi</div><div class="ttdef"><b>Definition</b> quantized.h:284</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a86009527cb4b53e4c21fd6b1f78cfefc"><div class="ttname"><a href="struct_quantized_block_loader.html#a86009527cb4b53e4c21fd6b1f78cfefc">QuantizedBlockLoader::load_unsafe</a></div><div class="ttdeci">void load_unsafe() const</div><div class="ttdef"><b>Definition</b> quantized.h:313</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a8eae73a0c04bf1e41fb96131f6aa500d"><div class="ttname"><a href="struct_quantized_block_loader.html#a8eae73a0c04bf1e41fb96131f6aa500d">QuantizedBlockLoader::pack_factor</a></div><div class="ttdeci">static constant constexpr const short pack_factor</div><div class="ttdef"><b>Definition</b> quantized.h:272</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_a9857214690fe6abad0e19d1045152f83"><div class="ttname"><a href="struct_quantized_block_loader.html#a9857214690fe6abad0e19d1045152f83">QuantizedBlockLoader::dst</a></div><div class="ttdeci">threadgroup T * dst</div><div class="ttdef"><b>Definition</b> quantized.h:287</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_ac3f651c1a645291d1037a2cc8ded2320"><div class="ttname"><a href="struct_quantized_block_loader.html#ac3f651c1a645291d1037a2cc8ded2320">QuantizedBlockLoader::tile_stride</a></div><div class="ttdeci">const int tile_stride</div><div class="ttdef"><b>Definition</b> quantized.h:279</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_ad85c6b7e07c81307b3b91eb4dd7be30b"><div class="ttname"><a href="struct_quantized_block_loader.html#ad85c6b7e07c81307b3b91eb4dd7be30b">QuantizedBlockLoader::src</a></div><div class="ttdeci">const device uint32_t * src</div><div class="ttdef"><b>Definition</b> quantized.h:288</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_ae2add92b2aaf3414e91f0470b9b0cc00"><div class="ttname"><a href="struct_quantized_block_loader.html#ae2add92b2aaf3414e91f0470b9b0cc00">QuantizedBlockLoader::bj</a></div><div class="ttdeci">const short bj</div><div class="ttdef"><b>Definition</b> quantized.h:285</div></div>
|
|
<div class="ttc" id="astruct_quantized_block_loader_html_af59b054750a65e7e79c1cd05c4acac93"><div class="ttname"><a href="struct_quantized_block_loader.html#af59b054750a65e7e79c1cd05c4acac93">QuantizedBlockLoader::QuantizedBlockLoader</a></div><div class="ttdeci">QuantizedBlockLoader(const device uint32_t *src_, const device T *scales_, const device T *biases_, const int src_ld_, threadgroup T *dst_, ushort simd_group_id, ushort simd_lane_id)</div><div class="ttdef"><b>Definition</b> quantized.h:292</div></div>
|
|
<div class="ttc" id="astructmlx_1_1steel_1_1_block_loader_html"><div class="ttname"><a href="structmlx_1_1steel_1_1_block_loader.html">mlx::steel::BlockLoader</a></div><div class="ttdef"><b>Definition</b> loader.h:25</div></div>
|
|
</div><!-- fragment --></div><!-- contents -->
|
|
<!-- start footer part -->
|
|
<hr class="footer"/><address class="footer"><small>
|
|
Generated by <a href="https://www.doxygen.org/index.html"><img class="footer" src="doxygen.svg" width="104" height="31" alt="doxygen"/></a> 1.10.0
|
|
</small></address>
|
|
</body>
|
|
</html>
|