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create mode 100644 docs/_static/theme_variables.jinja create mode 100644 docs/advanced.html create mode 100644 docs/expert.html create mode 100644 docs/genindex.html create mode 100644 docs/index.html create mode 100644 docs/objects.inv create mode 100644 docs/overview.html create mode 100644 docs/readme.html create mode 100644 docs/search.html create mode 100644 docs/searchindex.js create mode 100644 docs/versioning.html diff --git a/docs/.buildinfo b/docs/.buildinfo new file mode 100644 index 0000000..bb6b3aa --- /dev/null +++ b/docs/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: b1d4f130b437b34669c086e08878ee12 +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/.nojekyll b/docs/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/docs/_sources/advanced.rst.txt b/docs/_sources/advanced.rst.txt new file mode 100644 index 0000000..0cd1d4d --- /dev/null +++ b/docs/_sources/advanced.rst.txt @@ -0,0 +1,97 @@ +.. _hivemind_intermediate: + +Training on unreliable mixed GPUs across the internet (Advanced) +================================================================ + +Reducing Communication By Overlapping Communication +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +We can reduce the impact of communication across all machines by overlapping communication with our training iterations. In short, we enable communication to happen +in the background of training. + +Overlap Gradient and State Averaging +"""""""""""""""""""""""""""""""""""" + +When the target batch size is reached, all processes that are included in the step send gradients and model states to each other. By enabling some flags through +the strategy, communication can happen in the background. This allows training to continue (with slightly outdated weights) but provides us the means +to overlap communication with computation. + +.. warning:: + Enabling overlapping communication means convergence will slightly be affected. + +.. note:: + Enabling these flags means that you must pass in a ``scheduler_fn`` to the ``HivemindStrategy`` instead of relying on a scheduler from ``configure_optimizers``. + The optimizer is re-created by Hivemind, and as a result, the scheduler has to be re-created. + +.. code-block:: python + + import torch + from functools import partial + from pytorch_lightning import Trainer + from lightning_hivemind.strategy import HivemindStrategy + + trainer = Trainer( + strategy=HivemindStrategy( + target_batch_size=8192, + delay_state_averaging=True, + delay_grad_averaging=True, + delay_optimizer_step=True, + offload_optimizer=True, # required to delay averaging + scheduler_fn=partial(torch.optim.lr_scheduler.ExponentialLR, gamma=...), + ), + accelerator="gpu", + devices=1, + ) + + +Reducing GPU Memory requirements by re-using buffers & CPU offloading +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +We can also offload the optimizer state to the CPU whilst re-using gradient buffers to reduce the memory requirement for machines. + +Offloading Optimizer State to the CPU +""""""""""""""""""""""""""""""""""""" + +Offloading the Optimizer state to the CPU works the same as Deepspeed Zero-stage-2-offload, where we save GPU memory by keeping all optimizer states on the CPU. + +.. note:: + Enabling these flags means that you must pass in a ``scheduler_fn`` to the ``HivemindStrategy`` instead of relying on a scheduler from ``configure_optimizers``. + The optimizer is re-created by Hivemind, and as a result, the scheduler has to be re-created. + + We suggest enabling offloading and overlapping communication to hide the additional overhead from having to communicate with the CPU. + +.. code-block:: python + + import torch + from functools import partial + from pytorch_lightning import Trainer + from lightning_hivemind.strategy import HivemindStrategy + + trainer = Trainer( + strategy=HivemindStrategy( + target_batch_size=8192, + offload_optimizer=True, + scheduler_fn=partial(torch.optim.lr_scheduler.ExponentialLR, gamma=...), + ), + accelerator="gpu", + devices=1, + ) + + +Re-using Gradient Buffers +""""""""""""""""""""""""" + +By default, Hivemind accumulates gradients in a separate buffer. This means additional GPU memory is required to store gradients. You can enable re-using the model parameter gradient buffers by passing ``reuse_grad_buffers=True`` to the ``HivemindStrategy``. + +.. warning:: + The ``HivemindStrategy`` will override ``zero_grad`` in your ``LightningModule`` to have no effect. This is because gradients are accumulated in the model + and Hivemind manages when they need to be cleared. + +.. code-block:: python + + from pytorch_lightning import Trainer + from lightning_hivemind.strategy import HivemindStrategy + + trainer = Trainer( + strategy=HivemindStrategy(target_batch_size=8192, reuse_grad_buffers=True), accelerator="gpu", devices=1 + ) diff --git a/docs/_sources/expert.rst.txt b/docs/_sources/expert.rst.txt new file mode 100644 index 0000000..8466777 --- /dev/null +++ b/docs/_sources/expert.rst.txt @@ -0,0 +1,85 @@ +.. _hivemind_expert: + +Training on unreliable mixed GPUs across the internet (Expert) +============================================================== + +Using Compression to Optimize Communications +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Below are some ways to reduce communication when training collaboratively. As the size of your model increase, bottlenecks in communication become more apparent. + +Compress Gradients & State +"""""""""""""""""""""""""" + +Hivemind allows you to compress gradients and states before sending them to other machines. This helps reduce the communication overhead substantially when training across the internet. + +Below, we enable Float16 compression, which compresses gradients and states to Float16 before sending it to other machines. + +.. note:: + Compressing gradients can affect convergence if you're lowering the precision (i.e training in Float32, but compressing gradients to FP16). + +.. code-block:: python + + from hivemind import Float16Compression + from pytorch_lightning import Trainer + from lightning_hivemind.strategy import HivemindStrategy + + trainer = Trainer( + strategy=HivemindStrategy( + target_batch_size=target_batch_size, + grad_compression=Float16Compression(), + state_averaging_compression=Float16Compression(), + ), + accelerator="gpu", + devices=1, + ) + +A slightly more advanced scheme is dynamic compression based on value size. Below, we enable 8-bit quantization for large numbers, and Float16 compression for small values, reducing communication bottlenecks even further. + +Size Adaptive Compression has been used in a variety of Hivemind applications and has shown success, but does quantize gradients further, meaning we lose precision when compressing. + +.. code-block:: python + + from hivemind import Float16Compression, Uniform8BitQuantization + from pytorch_lightning import Trainer + from lightning_hivemind.strategy import HivemindStrategy + + # compresses values above threshold with 8bit Quantization, lower with Float16 + compression = SizeAdaptiveCompression( + threshold=2 ** 16 + 1, less=Float16Compression(), greater_equal=Uniform8BitQuantization() + ) + trainer = Trainer( + strategy=HivemindStrategy( + target_batch_size=target_batch_size, + grad_compression=compression, + state_averaging_compression=compression, + ), + accelerator="gpu", + devices=1, + ) + + +PowerSGD +"""""""" + +`PowerSGD `_ is a technique to reduce distributed communication of gradients across processes. +In short, PowerSGD uses a low-rank approximation to compress gradients before running an `all-reduce` step to sync gradients across all processes. + +.. note:: + Though PowerSGD can impact convergence, it can also substantially reduce communication between processes. + +.. code-block:: python + + from pytorch_lightning import Trainer + from lightning_hivemind.strategy import HivemindStrategy + from functools import partial + from hivemind.optim.power_sgd_averager import PowerSGDGradientAverager + + trainer = Trainer( + strategy=HivemindStrategy( + target_batch_size=8192, + grad_averager_factory=partial(PowerSGDGradientAverager, averager_rank=32, min_compression_ratio=0.5), + ), + accelerator="gpu", + devices=1, + ) diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt new file mode 100644 index 0000000..fa4b325 --- /dev/null +++ b/docs/_sources/index.rst.txt @@ -0,0 +1,15 @@ +.. Lightning-AI-Sandbox documentation master file, created by + sphinx-quickstart on Wed Mar 25 21:34:07 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +.. include:: readme.rst + + +.. toctree:: + :maxdepth: 1 + :name: start + :caption: Start here + + advanced + expert diff --git a/docs/_sources/overview.rst.txt b/docs/_sources/overview.rst.txt new file mode 100644 index 0000000..9f3fb32 --- /dev/null +++ b/docs/_sources/overview.rst.txt @@ -0,0 +1,62 @@ +:orphan: + +################################################################ +Hivemind - training on unreliable mixed GPUs across the internet +################################################################ + +Collaborative Training tries to solve the need for top-tier multi-GPU servers by allowing you to train across unreliable machines, +such as local machines or even preemptible cloud compute across the internet. + +Under the hood, we use `Hivemind `__ which provides de-centralized training across the internet. + +.. warning:: This is an :ref:`experimental ` feature. + + +To use Collaborative Training, you need to first this extension. + +.. code-block:: bash + + pip install lightning-hivemind + +This will install both the `Hivemind `__ package as well as the ``HivemindStrategy`` for the Lightning Trainer: + +Reducing Communication By Overlapping Communication +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +We can reduce the impact of communication across all machines by overlapping communication with our training iterations. In short, we enable communication to happen in the background of training. + +Overlap Gradient and State Averaging +"""""""""""""""""""""""""""""""""""" + +When the target batch size is reached, all processes that are included in the step send gradients and model states to each other. By enabling some flags through +the strategy, communication can happen in the background. This allows training to continue (with slightly outdated weights) but provides us the means +to overlap communication with computation. + +.. warning:: + Enabling overlapping communication means convergence will slightly be affected. + +.. note:: + Enabling these flags means that you must pass in a ``scheduler_fn`` to the ``HivemindStrategy`` instead of relying on a scheduler from ``configure_optimizers``. + The optimizer is re-created by Hivemind, and as a result, the scheduler has to be re-created. + +.. code-block:: python + + import torch + from functools import partial + from lightning import Trainer + from lightning_hivemind.strategy import HivemindStrategy + + trainer = Trainer( + strategy=HivemindStrategy( + target_batch_size=8192, + delay_state_averaging=True, + delay_grad_averaging=True, + delay_optimizer_step=True, + offload_optimizer=True, # required to delay averaging + scheduler_fn=partial(torch.optim.lr_scheduler.ExponentialLR, gamma=...), + ), + accelerator="gpu", + devices=1, + ) + +For more information on the strategy capabilities, see the `lightning-hivemind `__ repo. diff --git a/docs/_sources/readme.rst.txt b/docs/_sources/readme.rst.txt new file mode 100644 index 0000000..61db1ee --- /dev/null +++ b/docs/_sources/readme.rst.txt @@ -0,0 +1,47 @@ +Lightning + Hivemind +==================== + +Collaborative Training tries to solve the need for top-tier multi-GPU +servers by allowing you to train across unreliable machines, such as +local machines or even preemptible cloud computing across the internet. + +Under the hood, we use +`Hivemind `__, which +provides de-centralized training across the internet. + +To use Collaborative Training, you need first to have this extension. + +.. code:: bash + + pip install -U lightning-Hivemind + +The ``HivemindStrategy`` accumulates gradients from all collaborating +processes until they reach a ``target_batch_size``. By default, we use +the batch size of the first batch to determine what each local machine +batch contributes towards the ``target_batch_size``. Once the +``target_batch_size`` is reached, an optimizer step is made on all +processes. + +When using ``HivemindStrategy``, note that you cannot use gradient +accumulation (``accumulate_grad_batches``). This is because Hivemind +manages accumulation internally. + +.. code:: py + + from lightning import Trainer + from lightning_hivemind.strategy import HivemindStrategy + + trainer = Trainer(strategy=HivemindStrategy(target_batch_size=8192), accelerator="gpu", devices=1) + +Followed by: + +.. code:: bash + + python train.py + # Other machines can connect by running the same command: + # INITIAL_PEERS=... python train.py + # or passing the peers to the strategy:" + # HivemindStrategy(initial_peers=...)" + +A helper message is printed once your training begins, showing you how +to train on other machines using the same code. diff --git a/docs/_sources/versioning.rst.txt b/docs/_sources/versioning.rst.txt new file mode 100644 index 0000000..b85b370 --- /dev/null +++ b/docs/_sources/versioning.rst.txt @@ -0,0 +1,26 @@ +:orphan: + +.. warning:: + + This file is a placeholder for the real versioning policy page in the main + documentation. It is here so we can cross link to it from the changelog. + +.. _versioning: + +Versioning Policy +################# + +API Stability +************* + +Stable API +---------- + +Experimental API +---------------- + +API Evolution +************* + +Compatibility matrix +******************** diff --git a/docs/_static/basic.css b/docs/_static/basic.css new file mode 100644 index 0000000..7577acb --- /dev/null +++ b/docs/_static/basic.css @@ -0,0 +1,903 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + 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z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/docs/_static/copybutton.js b/docs/_static/copybutton.js new file mode 100644 index 0000000..453363c --- /dev/null +++ b/docs/_static/copybutton.js @@ -0,0 +1,64 @@ +/* Copied from the official Python docs: https://docs.python.org/3/_static/copybutton.js */ +$(document).ready(function() { + /* Add a [>>>] button on the top-right corner of code samples to hide + * the >>> and ... prompts and the output and thus make the code + * copyable. */ + var div = $('.highlight-python .highlight,' + + '.highlight-python3 .highlight,' + + '.highlight-pycon .highlight,' + + '.highlight-default .highlight'); + var pre = div.find('pre'); + + // get the styles from the current theme + pre.parent().parent().css('position', 'relative'); + var hide_text = 'Hide the prompts and output'; + var show_text = 'Show the prompts and output'; + var border_width = pre.css('border-top-width'); + var border_style = pre.css('border-top-style'); + var border_color = pre.css('border-top-color'); + var button_styles = { + 'cursor':'pointer', 'position': 'absolute', 'top': '0', 'right': '0', + 'border-color': border_color, 'border-style': border_style, + 'border-width': border_width, 'color': border_color, 'text-size': '75%', + 'font-family': 'monospace', 'padding-left': '0.2em', 'padding-right': '0.2em', + 'border-radius': '0 3px 0 0' + } + + // create and add the button to all the code blocks that contain >>> + div.each(function(index) { + var jthis = $(this); + if (jthis.find('.gp').length > 0) { + var button = $('>>>'); + button.css(button_styles) + button.attr('title', hide_text); + button.data('hidden', 'false'); + jthis.prepend(button); + } + // tracebacks (.gt) contain bare text elements that need to be + // wrapped in a span to work with .nextUntil() (see later) + jthis.find('pre:has(.gt)').contents().filter(function() { + return ((this.nodeType == 3) && (this.data.trim().length > 0)); + }).wrap(''); + }); + + // define the behavior of the button when it's clicked + $('.copybutton').click(function(e){ + e.preventDefault(); + var button = $(this); + if (button.data('hidden') === 'false') { + // hide the code output + button.parent().find('.go, .gp, .gt').hide(); + button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden'); + button.css('text-decoration', 'line-through'); + button.attr('title', show_text); + button.data('hidden', 'true'); + } else { + // show the code output + button.parent().find('.go, .gp, .gt').show(); + button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible'); + button.css('text-decoration', 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s=this.getTipElement(),l=d.getUID(this.constructor.NAME);s.setAttribute("id",l),this.element.setAttribute("aria-describedby",l),this.setContent(),this.config.animation&&o.default(s).addClass(Lt);var r="function"==typeof this.config.placement?this.config.placement.call(this,s,this.element):this.config.placement,u=this._getAttachment(r);this.addAttachmentClass(u);var f=this._getContainer();o.default(s).data(this.constructor.DATA_KEY,this),o.default.contains(this.element.ownerDocument.documentElement,this.tip)||o.default(s).appendTo(f),o.default(this.element).trigger(this.constructor.Event.INSERTED),this._popper=new a.default(this.element,s,this._getPopperConfig(u)),o.default(s).addClass(Rt),o.default(s).addClass(this.config.customClass),"ontouchstart"in document.documentElement&&o.default(document.body).children().on("mouseover",null,o.default.noop);var c=function(){t.config.animation&&t._fixTransition();var e=t._hoverState;t._hoverState=null,o.default(t.element).trigger(t.constructor.Event.SHOWN),e===qt&&t._leave(null,t)};if(o.default(this.tip).hasClass(Lt)){var h=d.getTransitionDurationFromElement(this.tip);o.default(this.tip).one(d.TRANSITION_END,c).emulateTransitionEnd(h)}else c()}},e.hide=function(t){var e=this,n=this.getTipElement(),i=o.default.Event(this.constructor.Event.HIDE),a=function(){e._hoverState!==xt&&n.parentNode&&n.parentNode.removeChild(n),e._cleanTipClass(),e.element.removeAttribute("aria-describedby"),o.default(e.element).trigger(e.constructor.Event.HIDDEN),null!==e._popper&&e._popper.destroy(),t&&t()};if(o.default(this.element).trigger(i),!i.isDefaultPrevented()){if(o.default(n).removeClass(Rt),"ontouchstart"in document.documentElement&&o.default(document.body).children().off("mouseover",null,o.default.noop),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1,o.default(this.tip).hasClass(Lt)){var 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t=this.element.getAttribute("data-original-title");return t||(t="function"==typeof this.config.title?this.config.title.call(this.element):this.config.title),t},e._getPopperConfig=function(t){var e=this;return r({},{placement:t,modifiers:{offset:this._getOffset(),flip:{behavior:this.config.fallbackPlacement},arrow:{element:".arrow"},preventOverflow:{boundariesElement:this.config.boundary}},onCreate:function(t){t.originalPlacement!==t.placement&&e._handlePopperPlacementChange(t)},onUpdate:function(t){return e._handlePopperPlacementChange(t)}},this.config.popperConfig)},e._getOffset=function(){var t=this,e={};return"function"==typeof this.config.offset?e.fn=function(e){return 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s="UL"===i.nodeName||"OL"===i.nodeName?he:ce;n=(n=o.default.makeArray(o.default(i).find(s)))[n.length-1]}var l=o.default.Event("hide.bs.tab",{relatedTarget:this._element}),r=o.default.Event("show.bs.tab",{relatedTarget:n});if(n&&o.default(n).trigger(l),o.default(this._element).trigger(r),!r.isDefaultPrevented()&&!l.isDefaultPrevented()){a&&(e=document.querySelector(a)),this._activate(this._element,i);var u=function(){var e=o.default.Event("hidden.bs.tab",{relatedTarget:t._element}),i=o.default.Event("shown.bs.tab",{relatedTarget:n});o.default(n).trigger(e),o.default(t._element).trigger(i)};e?this._activate(e,e.parentNode,u):u()}}},e.dispose=function(){o.default.removeData(this._element,le),this._element=null},e._activate=function(t,e,n){var i=this,a=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?o.default(e).children(ce):o.default(e).find(he))[0],s=n&&a&&o.default(a).hasClass(fe),l=function(){return i._transitionComplete(t,a,n)};if(a&&s){var 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t=this,e=o.default.Event("show.bs.toast");if(o.default(this._element).trigger(e),!e.isDefaultPrevented()){this._clearTimeout(),this._config.animation&&this._element.classList.add("fade");var n=function(){t._element.classList.remove(ye),t._element.classList.add(ve),o.default(t._element).trigger("shown.bs.toast"),t._config.autohide&&(t._timeout=setTimeout((function(){t.hide()}),t._config.delay))};if(this._element.classList.remove(_e),d.reflow(this._element),this._element.classList.add(ye),this._config.animation){var i=d.getTransitionDurationFromElement(this._element);o.default(this._element).one(d.TRANSITION_END,n).emulateTransitionEnd(i)}else n()}},e.hide=function(){if(this._element.classList.contains(ve)){var t=o.default.Event("hide.bs.toast");o.default(this._element).trigger(t),t.isDefaultPrevented()||this._close()}},e.dispose=function(){this._clearTimeout(),this._element.classList.contains(ve)&&this._element.classList.remove(ve),o.default(this._element).off(be),o.default.removeData(this._element,me),this._element=null,this._config=null},e._getConfig=function(t){return t=r({},Ee,o.default(this._element).data(),"object"==typeof t&&t?t:{}),d.typeCheckConfig("toast",t,this.constructor.DefaultType),t},e._setListeners=function(){var t=this;o.default(this._element).on(be,'[data-dismiss="toast"]',(function(){return t.hide()}))},e._close=function(){var t=this,e=function(){t._element.classList.add(_e),o.default(t._element).trigger("hidden.bs.toast")};if(this._element.classList.remove(ve),this._config.animation){var n=d.getTransitionDurationFromElement(this._element);o.default(this._element).one(d.TRANSITION_END,e).emulateTransitionEnd(n)}else e()},e._clearTimeout=function(){clearTimeout(this._timeout),this._timeout=null},t._jQueryInterface=function(e){return this.each((function(){var n=o.default(this),i=n.data(me);if(i||(i=new t(this,"object"==typeof e&&e),n.data(me,i)),"string"==typeof e){if("undefined"==typeof i[e])throw new TypeError('No method named "'+e+'"');i[e](this)}}))},l(t,null,[{key:"VERSION",get:function(){return"4.6.1"}},{key:"DefaultType",get:function(){return Te}},{key:"Default",get:function(){return Ee}}]),t}();o.default.fn.toast=we._jQueryInterface,o.default.fn.toast.Constructor=we,o.default.fn.toast.noConflict=function(){return o.default.fn.toast=pe,we._jQueryInterface},t.Alert=g,t.Button=E,t.Carousel=P,t.Collapse=V,t.Dropdown=lt,t.Modal=Ct,t.Popover=Jt,t.Scrollspy=se,t.Tab=ge,t.Toast=we,t.Tooltip=Wt,t.Util=d,Object.defineProperty(t,"__esModule",{value:!0})})); +//# sourceMappingURL=bootstrap.min.js.map diff --git a/docs/_static/js/vendor/popper.min.js b/docs/_static/js/vendor/popper.min.js new file mode 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of stopwords, stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, is available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // 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further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = docUrlRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = docUrlRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/docs/_static/sphinx_highlight.js b/docs/_static/sphinx_highlight.js new file mode 100644 index 0000000..aae669d --- /dev/null +++ b/docs/_static/sphinx_highlight.js @@ -0,0 +1,144 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + parent.insertBefore( + span, + parent.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

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Training on unreliable mixed GPUs across the internet (Advanced)

+
+

Reducing Communication By Overlapping Communication

+

We can reduce the impact of communication across all machines by overlapping communication with our training iterations. In short, we enable communication to happen +in the background of training.

+
+

Overlap Gradient and State Averaging

+

When the target batch size is reached, all processes that are included in the step send gradients and model states to each other. By enabling some flags through +the strategy, communication can happen in the background. This allows training to continue (with slightly outdated weights) but provides us the means +to overlap communication with computation.

+
+

Warning

+

Enabling overlapping communication means convergence will slightly be affected.

+
+
+

Note

+

Enabling these flags means that you must pass in a scheduler_fn to the HivemindStrategy instead of relying on a scheduler from configure_optimizers. +The optimizer is re-created by Hivemind, and as a result, the scheduler has to be re-created.

+
+
import torch
+from functools import partial
+from pytorch_lightning import Trainer
+from lightning_hivemind.strategy import HivemindStrategy
+
+trainer = Trainer(
+    strategy=HivemindStrategy(
+        target_batch_size=8192,
+        delay_state_averaging=True,
+        delay_grad_averaging=True,
+        delay_optimizer_step=True,
+        offload_optimizer=True,  # required to delay averaging
+        scheduler_fn=partial(torch.optim.lr_scheduler.ExponentialLR, gamma=...),
+    ),
+    accelerator="gpu",
+    devices=1,
+)
+
+
+
+
+
+

Reducing GPU Memory requirements by re-using buffers & CPU offloading

+

We can also offload the optimizer state to the CPU whilst re-using gradient buffers to reduce the memory requirement for machines.

+
+

Offloading Optimizer State to the CPU

+

Offloading the Optimizer state to the CPU works the same as Deepspeed Zero-stage-2-offload, where we save GPU memory by keeping all optimizer states on the CPU.

+
+

Note

+

Enabling these flags means that you must pass in a scheduler_fn to the HivemindStrategy instead of relying on a scheduler from configure_optimizers. +The optimizer is re-created by Hivemind, and as a result, the scheduler has to be re-created.

+

We suggest enabling offloading and overlapping communication to hide the additional overhead from having to communicate with the CPU.

+
+
import torch
+from functools import partial
+from pytorch_lightning import Trainer
+from lightning_hivemind.strategy import HivemindStrategy
+
+trainer = Trainer(
+    strategy=HivemindStrategy(
+        target_batch_size=8192,
+        offload_optimizer=True,
+        scheduler_fn=partial(torch.optim.lr_scheduler.ExponentialLR, gamma=...),
+    ),
+    accelerator="gpu",
+    devices=1,
+)
+
+
+
+
+

Re-using Gradient Buffers

+

By default, Hivemind accumulates gradients in a separate buffer. This means additional GPU memory is required to store gradients. You can enable re-using the model parameter gradient buffers by passing reuse_grad_buffers=True to the HivemindStrategy.

+
+

Warning

+

The HivemindStrategy will override zero_grad in your LightningModule to have no effect. This is because gradients are accumulated in the model +and Hivemind manages when they need to be cleared.

+
+
from pytorch_lightning import Trainer
+from lightning_hivemind.strategy import HivemindStrategy
+
+trainer = Trainer(
+    strategy=HivemindStrategy(target_batch_size=8192, reuse_grad_buffers=True), accelerator="gpu", devices=1
+)
+
+
+
+
+
+ + +
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Training on unreliable mixed GPUs across the internet (Expert)

+
+

Using Compression to Optimize Communications

+

Below are some ways to reduce communication when training collaboratively. As the size of your model increase, bottlenecks in communication become more apparent.

+
+

Compress Gradients & State

+

Hivemind allows you to compress gradients and states before sending them to other machines. This helps reduce the communication overhead substantially when training across the internet.

+

Below, we enable Float16 compression, which compresses gradients and states to Float16 before sending it to other machines.

+
+

Note

+

Compressing gradients can affect convergence if you’re lowering the precision (i.e training in Float32, but compressing gradients to FP16).

+
+
from hivemind import Float16Compression
+from pytorch_lightning import Trainer
+from lightning_hivemind.strategy import HivemindStrategy
+
+trainer = Trainer(
+    strategy=HivemindStrategy(
+        target_batch_size=target_batch_size,
+        grad_compression=Float16Compression(),
+        state_averaging_compression=Float16Compression(),
+    ),
+    accelerator="gpu",
+    devices=1,
+)
+
+
+

A slightly more advanced scheme is dynamic compression based on value size. Below, we enable 8-bit quantization for large numbers, and Float16 compression for small values, reducing communication bottlenecks even further.

+

Size Adaptive Compression has been used in a variety of Hivemind applications and has shown success, but does quantize gradients further, meaning we lose precision when compressing.

+
from hivemind import Float16Compression, Uniform8BitQuantization
+from pytorch_lightning import Trainer
+from lightning_hivemind.strategy import HivemindStrategy
+
+# compresses values above threshold with 8bit Quantization, lower with Float16
+compression = SizeAdaptiveCompression(
+    threshold=2 ** 16 + 1, less=Float16Compression(), greater_equal=Uniform8BitQuantization()
+)
+trainer = Trainer(
+    strategy=HivemindStrategy(
+        target_batch_size=target_batch_size,
+        grad_compression=compression,
+        state_averaging_compression=compression,
+    ),
+    accelerator="gpu",
+    devices=1,
+)
+
+
+
+
+

PowerSGD

+

PowerSGD is a technique to reduce distributed communication of gradients across processes. +In short, PowerSGD uses a low-rank approximation to compress gradients before running an all-reduce step to sync gradients across all processes.

+
+

Note

+

Though PowerSGD can impact convergence, it can also substantially reduce communication between processes.

+
+
from pytorch_lightning import Trainer
+from lightning_hivemind.strategy import HivemindStrategy
+from functools import partial
+from hivemind.optim.power_sgd_averager import PowerSGDGradientAverager
+
+trainer = Trainer(
+    strategy=HivemindStrategy(
+        target_batch_size=8192,
+        grad_averager_factory=partial(PowerSGDGradientAverager, averager_rank=32, min_compression_ratio=0.5),
+    ),
+    accelerator="gpu",
+    devices=1,
+)
+
+
+
+
+
+ + +
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Lightning + Hivemind

+

Collaborative Training tries to solve the need for top-tier multi-GPU +servers by allowing you to train across unreliable machines, such as +local machines or even preemptible cloud computing across the internet.

+

Under the hood, we use +Hivemind, which +provides de-centralized training across the internet.

+

To use Collaborative Training, you need first to have this extension.

+
pip install -U lightning-Hivemind
+
+
+

The HivemindStrategy accumulates gradients from all collaborating +processes until they reach a target_batch_size. By default, we use +the batch size of the first batch to determine what each local machine +batch contributes towards the target_batch_size. Once the +target_batch_size is reached, an optimizer step is made on all +processes.

+

When using HivemindStrategy, note that you cannot use gradient +accumulation (accumulate_grad_batches). This is because Hivemind +manages accumulation internally.

+
from lightning import Trainer
+from lightning_hivemind.strategy import HivemindStrategy
+
+trainer = Trainer(strategy=HivemindStrategy(target_batch_size=8192), accelerator="gpu", devices=1)
+
+
+

Followed by:

+
python train.py
+# Other machines can connect by running the same command:
+# INITIAL_PEERS=... python train.py
+# or passing the peers to the strategy:"
+# HivemindStrategy(initial_peers=...)"
+
+
+

A helper message is printed once your training begins, showing you how +to train on other machines using the same code.

+ +
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+ + + + + + + + + + + + \ No newline at end of file diff --git a/docs/objects.inv b/docs/objects.inv new file mode 100644 index 0000000000000000000000000000000000000000..b30bc6e752039782b0dd8141f5928e5c34a225b8 GIT binary patch literal 1202 zcmV;j1Wo%RAX9K?X>NERX>N99Zgg*Qc_4OWa&u{KZXhxWBOp+6Z)#;@bUGkxX=iA3 zZfS03El6p0Wo>D0WC|l7R%LQ?X>V>iATTa5E-(rsAXI2&AaZ4GVQFq;WpW^IW*~HE zX>%ZEX>4U6X>%ZBZ*6dLWpi_7WFU2OX>MmAdTeQ8E(&%alMypGXW>^ZN>L#EPl{z9K+#PFPMj ze7w*gXD8>br%CbfkQO)=fT$=r&FEjkP{lDPh!;p;ps1{S;yDoD)@s46(Sn840$wzs z&gS>qp{)r^afuQp3`x!r&SQ;?g$O4StuL}e>~ED4Txlf`N;9hB4nc$=hXCb(%Zz{P;2{MKh? z4)KGmUuaRb(&oBpk=Wt3X;IU(GS&*IrfvGgG%Yd^eHjl#9zIRTQ%M*Pr&-;)Ij9+k@4Z%O8uYZ=U^eC!_$n zeGWn$@jO&3nLU;hL`;2i5%}Yy>dxj_DEF0~GDcZmA?8-`r;nJSG>^$sSe|ORsm>oX zS@C8&KEZdoCYg{qc9Yb|(Mn6ZpmwS5Mwc^oP03>zn3XiK84Ezbk2y5o)5JuDjw+v? zK!+laoaghO4t_QxJ{`Qc2H)X+n6Gw+GlJucgvF}Yd)lbA+}lSE*x`*NVwNL7EQirK zvL?b`_FGKqlt!Cv^9e2~BI_cp!=2v(+fu!lU&E#O+6gPFXGv4aH~V2*RSCN=UjeL~ zm*Rkw6lIt*`qYoU(_-c>f?5oGHPf2P2M|t?2(sT#pnd5q$gBcUL-nv!yvJBvL@&V14BXUtjap6xmCKf1k(9Mj>3Ns9^G-$e%6!a*WZ&U7 zrWc}Ojz>K{{i!NAfirU9-LbGResL8;b}+k<X%I zRpodsf1a2Z-S2%I*K&$+Hq!cSL>Y-`P{Dq0TmPQH;FfK-Nq6i%F51YasUsSVh-O8+ z%sBC*rtS=U-ftQ+d)t zz9NjAv~0&sbw63&vg>41r@gjQN!>=^*jtDr&}#=V+ChoOV%ClumnOpqq}qX2JQSIyqf57Opv$uOgO#lD@ literal 0 HcmV?d00001 diff --git a/docs/overview.html b/docs/overview.html new file mode 100644 index 0000000..be99711 --- /dev/null +++ b/docs/overview.html @@ -0,0 +1,649 @@ + + + + + + + + + + + + + + + + + + Hivemind - training on unreliable mixed GPUs across the internet — lightning-Hivemind 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Hivemind - training on unreliable mixed GPUs across the internet

+

Collaborative Training tries to solve the need for top-tier multi-GPU servers by allowing you to train across unreliable machines, +such as local machines or even preemptible cloud compute across the internet.

+

Under the hood, we use Hivemind which provides de-centralized training across the internet.

+
+

Warning

+

This is an experimental feature.

+
+

To use Collaborative Training, you need to first this extension.

+
pip install lightning-hivemind
+
+
+

This will install both the Hivemind package as well as the HivemindStrategy for the Lightning Trainer:

+
+

Reducing Communication By Overlapping Communication

+

We can reduce the impact of communication across all machines by overlapping communication with our training iterations. In short, we enable communication to happen in the background of training.

+
+

Overlap Gradient and State Averaging

+

When the target batch size is reached, all processes that are included in the step send gradients and model states to each other. By enabling some flags through +the strategy, communication can happen in the background. This allows training to continue (with slightly outdated weights) but provides us the means +to overlap communication with computation.

+
+

Warning

+

Enabling overlapping communication means convergence will slightly be affected.

+
+
+

Note

+

Enabling these flags means that you must pass in a scheduler_fn to the HivemindStrategy instead of relying on a scheduler from configure_optimizers. +The optimizer is re-created by Hivemind, and as a result, the scheduler has to be re-created.

+
+
import torch
+from functools import partial
+from lightning import Trainer
+from lightning_hivemind.strategy import HivemindStrategy
+
+trainer = Trainer(
+    strategy=HivemindStrategy(
+        target_batch_size=8192,
+        delay_state_averaging=True,
+        delay_grad_averaging=True,
+        delay_optimizer_step=True,
+        offload_optimizer=True,  # required to delay averaging
+        scheduler_fn=partial(torch.optim.lr_scheduler.ExponentialLR, gamma=...),
+    ),
+    accelerator="gpu",
+    devices=1,
+)
+
+
+

For more information on the strategy capabilities, see the lightning-hivemind repo.

+
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Lightning + Hivemind

+

Collaborative Training tries to solve the need for top-tier multi-GPU +servers by allowing you to train across unreliable machines, such as +local machines or even preemptible cloud computing across the internet.

+

Under the hood, we use +Hivemind, which +provides de-centralized training across the internet.

+

To use Collaborative Training, you need first to have this extension.

+
pip install -U lightning-Hivemind
+
+
+

The HivemindStrategy accumulates gradients from all collaborating +processes until they reach a target_batch_size. By default, we use +the batch size of the first batch to determine what each local machine +batch contributes towards the target_batch_size. Once the +target_batch_size is reached, an optimizer step is made on all +processes.

+

When using HivemindStrategy, note that you cannot use gradient +accumulation (accumulate_grad_batches). This is because Hivemind +manages accumulation internally.

+
from lightning import Trainer
+from lightning_hivemind.strategy import HivemindStrategy
+
+trainer = Trainer(strategy=HivemindStrategy(target_batch_size=8192), accelerator="gpu", devices=1)
+
+
+

Followed by:

+
python train.py
+# Other machines can connect by running the same command:
+# INITIAL_PEERS=... python train.py
+# or passing the peers to the strategy:"
+# HivemindStrategy(initial_peers=...)"
+
+
+

A helper message is printed once your training begins, showing you how +to train on other machines using the same code.

+
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Optimizer State to the CPU": [[0, "offloading-optimizer-state-to-the-cpu"]], "Re-using Gradient Buffers": [[0, "re-using-gradient-buffers"]], "Training on unreliable mixed GPUs across the internet (Expert)": [[1, "training-on-unreliable-mixed-gpus-across-the-internet-expert"]], "Using Compression to Optimize Communications": [[1, "using-compression-to-optimize-communications"]], "Compress Gradients & State": [[1, "compress-gradients-state"]], "PowerSGD": [[1, "powersgd"]], "Lightning + Hivemind": [[2, "lightning-hivemind"], [4, "lightning-hivemind"]], "Start here": [[2, null]], "Hivemind - training on unreliable mixed GPUs across the internet": [[3, "hivemind-training-on-unreliable-mixed-gpus-across-the-internet"]], "Versioning Policy": [[5, "versioning-policy"]], "API Stability": [[5, "api-stability"]], "Stable API": [[5, "stable-api"]], "Experimental API": [[5, "experimental-api"]], "API Evolution": [[5, "api-evolution"]], "Compatibility matrix": [[5, "compatibility-matrix"]]}, "indexentries": {}}) \ No newline at end of file diff --git a/docs/versioning.html b/docs/versioning.html new file mode 100644 index 0000000..9737b90 --- /dev/null +++ b/docs/versioning.html @@ -0,0 +1,621 @@ + + + + + + + + + + + + + + + + + + Versioning Policy — lightning-Hivemind 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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This file is a placeholder for the real versioning policy page in the main +documentation. It is here so we can cross link to it from the changelog.

+
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Versioning Policy

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API Stability

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+

Stable API

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Experimental API

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API Evolution

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Compatibility matrix

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