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<div class="section" id="fsdp-notes">
<span id="id1"></span><h1>FSDP Notes<a class="headerlink" href="#fsdp-notes" title="Permalink to this heading">¶</a></h1>
<div class="section" id="fsdp-prefetch-nuances">
<span id="fsdp-prefetch"></span><h2>FSDP Prefetch Nuances<a class="headerlink" href="#fsdp-prefetch-nuances" title="Permalink to this heading">¶</a></h2>
<p>For overlapping <code class="docutils literal notranslate"><span class="pre">forward</span></code> all-gathers with <code class="docutils literal notranslate"><span class="pre">forward</span></code> compute, there are two possible mechanisms:</p>
<ol class="arabic simple">
<li><p>Implicit forward prefetching (always enabled)</p></li>
<li><p>Explicit forward prefetching (<code class="docutils literal notranslate"><span class="pre">forward_prefetch=True</span></code>)</p></li>
</ol>
<p>Implicit <code class="docutils literal notranslate"><span class="pre">forward</span></code> prefetching refers to relying on issuing the all-gathers from a separate CUDA
stream to allow for overlapping an all-gather with <code class="docutils literal notranslate"><span class="pre">forward</span></code> compute issued before it (from the CPU
perspective). For example, if we have layer 0 all-gather -> layer 0 <code class="docutils literal notranslate"><span class="pre">forward</span></code> compute -> layer 1
all-gather -> …, then layer 1 all-gather can overlap with layer 0 <code class="docutils literal notranslate"><span class="pre">forward</span></code> compute even though the
CPU thread issued it afterwards. (The 1st all-gather will not be able to overlap with anything.)</p>
<p>Explicit <code class="docutils literal notranslate"><span class="pre">forward</span></code> prefetching refers to changing the CPU thread’s issue order: e.g. layer 0
all-gather -> layer 1 all-gather -> layer 0 <code class="docutils literal notranslate"><span class="pre">forward</span></code> compute -> …. In eager mode, there is no way to
know in general which layer is the next layer (e.g. layer 1 in the example) when still executing on
layer 0. Therefore, explicit <code class="docutils literal notranslate"><span class="pre">forward</span></code> prefetching should only be used for models whose execution
order is fixed from iteration to iteration (which we sometimes call “static graph”). An example of a
model that does not satisfy this constraint is <a class="reference external" href="https://pytorch.org/blog/scaling-multimodal-foundation-models-in-torchmultimodal-with-pytorch-distributed/">FLAVA</a>).</p>
<p>Explicit <code class="docutils literal notranslate"><span class="pre">forward</span></code> prefetching only saves the time taken to issue a layer’s <code class="docutils literal notranslate"><span class="pre">forward</span></code> compute kernels at
the cost that the next all-gather’s output tensor must be allocated while the current one is still
in use. By issuing the next all- gather before the current <code class="docutils literal notranslate"><span class="pre">forward</span></code> compute kernels, the next
all-gather can start sooner on GPU. For most LLM workloads, this is not the case, so there is no
motivation for enabling <code class="docutils literal notranslate"><span class="pre">forward_prefetch=True</span></code>.</p>
<p>In contrast, for <code class="docutils literal notranslate"><span class="pre">backward</span></code>, we must use explicit <code class="docutils literal notranslate"><span class="pre">backward</span></code> prefetching or else there will be 0 overlap
of communication and computation. The reason is because we use a single NCCL process group for both
all-gather and reduce-scatter (partially because in earlier NCCL versions, it was not safe to use
multiple concurrently on the same device over the same ranks). A single NCCL process group means a
single internal NCCL stream on which reduce-scatters and all-gathers run serially. As such, unless
we explicitly reorder the CPU issue order to be next all-gather -> current reduce-scatter, then the
current reduce-scatter would block the next all-gather and hence the next <code class="docutils literal notranslate"><span class="pre">backward</span></code> computation,
preventing the current reduce-scatter from overlapping.</p>
</div>
<div class="section" id="communication-payload-size">
<span id="fsdp-comms-payload-size"></span><h2>Communication payload size<a class="headerlink" href="#communication-payload-size" title="Permalink to this heading">¶</a></h2>
<p>In FSDP the communications are:</p>
<ol class="arabic simple">
<li><p>all-gather on parameters in <code class="docutils literal notranslate"><span class="pre">forward</span></code></p></li>
<li><p>all-gather on parameters in <code class="docutils literal notranslate"><span class="pre">backward</span></code></p></li>
<li><p>reduce-scatter on gradients in <code class="docutils literal notranslate"><span class="pre">backward</span></code></p></li>
</ol>
<p>If activation checkpointing (<a class="reference internal" href="../checkpoint.html#torch.utils.checkpoint.checkpoint" title="torch.utils.checkpoint.checkpoint"><code class="xref py py-func docutils literal notranslate"><span class="pre">checkpoint()</span></code></a>) is used there is no
additional communication since the parameters are prefetched anyway during <code class="docutils literal notranslate"><span class="pre">backward</span></code>.</p>
<p>In the FSDP design, the communication payload per rank is determined as follows: Each call to
<code class="xref py py-class docutils literal notranslate"><span class="pre">FullyShardedDataParallel</span></code> creates one communication group consisting of the parameters in
<code class="docutils literal notranslate"><span class="pre">module.parameters()</span></code> except any already assigned to a nested <code class="xref py py-class docutils literal notranslate"><span class="pre">FullyShardedDataParallel</span></code>
instance. For example, for Llama, if you apply <code class="xref py py-class docutils literal notranslate"><span class="pre">FullyShardedDataParallel</span></code> to every
transformer block and also to the root module, then there is one communication group for each
transformer block and finally one communication group with the initial embedding and final linear.
Each communication group corresponds to a single all-gather call and single reduce-scatter call. In
that way, how you apply <code class="xref py py-class docutils literal notranslate"><span class="pre">FullyShardedDataParallel</span></code> determines the communication size. In
general, applying FSDP to each transformer block is a good heuristic for LLMs, and it is hard to do
better than that given the current design.</p>
<p>Let’s consider an example where we have a Transformer-based model sharded over 8 GPUs, where the
sharding happens at the transformer block-level only, and each transformer block contains 1.6B
parameters and the parameters are in fp32 (4 bytes each). Which means that once sharded, each
transformer block will contain 0.2B parameters on each rank.</p>
<ul class="simple">
<li><p>The <code class="docutils literal notranslate"><span class="pre">forward</span></code> pass will communicate in chunks of <code class="docutils literal notranslate"><span class="pre">0.2*4</span> <span class="pre">=</span> <span class="pre">0.8GB</span></code> in all-gather</p></li>
<li><p>The <code class="docutils literal notranslate"><span class="pre">backward</span></code> pass will communicate 2 times <code class="docutils literal notranslate"><span class="pre">0.8GB</span></code> each (1x all-gather and 1x reduce-scatter)</p></li>
</ul>
<p>In other words there will be 3 communications with a payload of <code class="docutils literal notranslate"><span class="pre">0.8GB</span></code> each. If the model was
comprised of 10 transformer blocks there would be a total of 30 communications for a total of
<code class="docutils literal notranslate"><span class="pre">30*0.8=24GB</span></code>.</p>
<p>To formalize the payload size per communication per rank is
<code class="docutils literal notranslate"><span class="pre">total_transformer_block_params_in_B*dtype_bytes/num_gpus</span></code> (GBs).</p>
<p>Please note that in this example we didn’t include the additional communications required for the
embedding, which should be accounted for as well. And the math would depend on whether the input and
output embeddings are tied or not. If they aren’t tied there will be 2x more communications.</p>
</div>
<div class="section" id="fsdp-buffers-sizes">
<span id="id2"></span><h2>FSDP buffers sizes<a class="headerlink" href="#fsdp-buffers-sizes" title="Permalink to this heading">¶</a></h2>
<p>First, let’s cover the buffers allocated for communications:</p>
<p><code class="docutils literal notranslate"><span class="pre">forward</span></code> currently requires 2x all-gather buffer size. Here is why:</p>
<p>As explained in <a class="reference internal" href="#fsdp-prefetch"><span class="std std-ref">FSDP Prefetch Nuances</span></a> in the case of explicit <code class="docutils literal notranslate"><span class="pre">forward</span></code> prefetching
(<code class="docutils literal notranslate"><span class="pre">forward_prefetch=True`)</span> <span class="pre">case</span> <span class="pre">of</span> <span class="pre">layer</span> <span class="pre">0</span> <span class="pre">all-gather</span> <span class="pre">-></span> <span class="pre">layer</span> <span class="pre">0</span> <span class="pre">forward</span> <span class="pre">compute</span> <span class="pre">-></span> <span class="pre">layer</span> <span class="pre">1</span>
<span class="pre">all-gather</span> <span class="pre">there</span> <span class="pre">is</span> <span class="pre">a</span> <span class="pre">need</span> <span class="pre">for</span> <span class="pre">2</span> <span class="pre">all-gather-sized</span> <span class="pre">buffers,</span> <span class="pre">because</span> <span class="pre">one</span> <span class="pre">buffer</span> <span class="pre">is</span> <span class="pre">used</span> <span class="pre">in</span> <span class="pre">the</span> <span class="pre">current</span> <span class="pre">``forward</span></code> while the other is used to do the prefetching.</p>
<p>While the implicit <code class="docutils literal notranslate"><span class="pre">forward</span></code> prefetching (<code class="docutils literal notranslate"><span class="pre">forward_prefetch=False</span></code>, default) case of the same sequence in theory should need only 1 buffer, in reality it’s still 2x all-gather-sized buffers. The reason is that in the flat-parameter FSDP design, we do not copy-out of the all-gather buffer. The parameters used for compute are directly viewed into the all-gather buffer (in fact, the main benefit of the “flat parameter” is exactly this reason). In that case, while ‘layer 1 all-gather’ is overlapping with ‘layer 0 forward compute’, the ‘layer 0 forward compute’ is using the parameters viewed into the ‘layer 0 all-gather’ buffer.</p>
<p>A natural question then is, when would you want <code class="docutils literal notranslate"><span class="pre">forward_prefetch=False</span></code>? For static-graph models (like most LLMs), there is a major technical reason. It is more that, practically, we added this option quickly for some CPU-bound internal models and have not tested every code path with it in unit testing, so we are less confident in it. <code class="docutils literal notranslate"><span class="pre">forward_prefetching=False</span></code> can be slightly easier to reason about since we do not have to check the recorded forward order as a possible ‘failure mode’; a module’s all-gather can always be found under its own <code class="docutils literal notranslate"><span class="pre">record_function</span></code> label in its profiler trace.</p>
<p><code class="docutils literal notranslate"><span class="pre">backward</span></code> currently requires at least 2x all-gather buffer size and potentially a bit more. Here is why:</p>
<p>The current FSDP design uses <code class="docutils literal notranslate"><span class="pre">recordStream</span></code> to manage allocations produced in one stream consumed in another, which can lead to more memory usage than expected. How much more can be “non-deterministic” in that it depends on GPU kernel timing relative to the CPU. The <code class="docutils literal notranslate"><span class="pre">limit_all_gathers=True</span></code> argument is a mitigation to that - for more details refer to this discussion is <a class="reference external" href="https://dev-discuss.pytorch.org/t/fsdp-cudacachingallocator-an-outsider-newb-perspective/1486/1">FSDP & CUDACachingAllocator</a>.</p>
<p>The way existing FSDP works with autograd:</p>
<ul class="simple">
<li><p>Existing FSDP all-gathers the <code class="docutils literal notranslate"><span class="pre">flat_param</span></code>, which is the autograd leaf.</p></li>
<li><p>It calls <code class="docutils literal notranslate"><span class="pre">torch.split</span></code> to get 1D views into the <code class="docutils literal notranslate"><span class="pre">flat_param</span></code> corresponding to its constituent original parameters.</p></li>
<li><p>It calls <code class="docutils literal notranslate"><span class="pre">torch.view</span></code> on each 1D split to view back to ND.</p></li>
<li><p>This means that in <code class="docutils literal notranslate"><span class="pre">backward</span></code>, we end up with <code class="docutils literal notranslate"><span class="pre">ViewBackward</span></code> (ND -> 1D) and <code class="docutils literal notranslate"><span class="pre">SplitWithSizesBackward</span></code> (which is a concat). In particular, each individual gradient is computed as a separate allocation, and an explicit concat happens to construct the reduce-scatter input buffer. This implies actually a 2x buffer size for reduce-scatter at that peak memory point.</p></li>
</ul>
<p>In summary, for <code class="docutils literal notranslate"><span class="pre">backward</span></code>, it is about 2x buffer size for reduce-scatter plus any <code class="docutils literal notranslate"><span class="pre">recordStream</span></code> effects.</p>
<p>Second, let’s discuss the additional buffers:</p>
<p>Once the sharded parameters are gathered from all ranks, they require an additional buffer of <cite>total_transformer_block_params_in_B*dtype_bytes</cite> for the full parameters - so continuing the example from earlier if each transformer block is 1.6B parameters and the parameters are in fp32, then it’d be <cite>1.6*4=6.4GB</cite> buffer.</p>
<p>And there is a need for 2 of those buffers, since there is one currently being used and another being prefetched.</p>
<p>To summarize, we have:</p>
<ol class="arabic simple">
<li><p>2 times communication buffers of <code class="docutils literal notranslate"><span class="pre">total_transformer_block_params_in_B*dtype_bytes/num_gpus</span></code></p></li>
<li><p>2 times unsharded transformer block parameters buffer <code class="docutils literal notranslate"><span class="pre">``total_transformer_block_params_in_B*dtype_bytes</span></code></p></li>
</ol>
<p>or if you have been following the example:</p>
<ol class="arabic simple">
<li><p><code class="docutils literal notranslate"><span class="pre">2*1.6*4/8=1.6GB</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">2**1.6*4=12.8GB</span></code></p></li>
</ol>
<p>and the total of <code class="docutils literal notranslate"><span class="pre">14.4GB</span></code>.</p>
<p>Now let’s briefly discuss what happens to the embeddings as we have left those out from the calculations:</p>
<p>Given the rule we discussed that you included in the note starting with “the communication buffer
size is determined as follows”, we can analyze as follows:</p>
<ul class="simple">
<li><p>Suppose we apply FSDP to the root module (e.g. the <code class="docutils literal notranslate"><span class="pre">Transformer</span></code> class). Suppose we further apply FSDP to each transformer block (e.g. the <code class="docutils literal notranslate"><span class="pre">TransformerBlock</span></code> class).</p></li>
<li><p>Most commonly, the embedding and final linear projection are direct children of the root <code class="docutils literal notranslate"><span class="pre">Transformer</span></code> class.</p></li>
<li><p>Following our rule, that means that the embedding and final linear projection are assigned to the root <code class="docutils literal notranslate"><span class="pre">Transformer</span></code>’s flat parameter.</p></li>
<li><p>We have _another_ special rule, which is that the root does not free its parameters after forward because they will be anyways immediately all-gathered in backward.</p></li>
<li><p>Putting this together, this means that the root’s flat parameter including the embedding and final projection are all-gathered to begin forward and kept in GPU memory until the end of backward.</p></li>
<li><p>If the embedding and final linear are not weight-tied, then we _could_ further apply FSDP to the embedding and to the final linear. For weight-tied parameters, we require them to be part of the same flat parameter (or else it would get double-counted). That would allow the embedding to be freed after its usage in forward and only all-gathered toward the end of backward.</p></li>
<li><p>Hopefully, this gives a better sense – each FSDP module gets assigned parameters in its <code class="docutils literal notranslate"><span class="pre">module.parameters</span></code> except those already assigned to another nested FSDP module, and the FSDP module’s <code class="docutils literal notranslate"><span class="pre">forward</span></code> defines the ‘live’ interval for its parameters. Hence, the nested <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> structure can affect the all-gather/free schedule and hence the memory/throughput performance.</p></li>
</ul>
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