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<h1>Source code for tensorrt_llm.layers.embedding</h1><div class="highlight"><pre>
<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION &amp; AFFILIATES. All rights reserved.</span>
<span class="c1"># SPDX-License-Identifier: Apache-2.0</span>
<span class="c1">#</span>
<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
<span class="c1"># you may not use this file except in compliance with the License.</span>
<span class="c1"># You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
<span class="kn">from</span> <span class="nn">..functional</span> <span class="kn">import</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">unsqueeze</span><span class="p">,</span> <span class="n">where</span>
<span class="kn">from</span> <span class="nn">..module</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="kn">from</span> <span class="nn">..parameter</span> <span class="kn">import</span> <span class="n">Parameter</span>
<div class="viewcode-block" id="Embedding">
<a class="viewcode-back" href="../../../python-api/tensorrt_llm.layers.html#tensorrt_llm.layers.embedding.Embedding">[docs]</a>
<span class="k">class</span> <span class="nc">Embedding</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The embedding layer takes input indices (x) and the embedding lookup table (weight) as input.</span>
<span class="sd"> And output the corresponding embeddings according to input indices.</span>
<span class="sd"> The size of weight is [num_embeddings, embedding_dim]</span>
<span class="sd"> Four parameters (tp_size, tp_group, sharding_dim, tp_rank) are involved in tensor parallelism.</span>
<span class="sd"> Only when &quot;tp_size &gt; 1 and tp_group is not None&quot;, tensor parallelism is enabled.</span>
<span class="sd"> When &quot;sharding_dim == 0&quot;, the weight is shared in the vocabulary dimension.</span>
<span class="sd"> tp_rank must be set when sharding_dim == 0.</span>
<span class="sd"> When &quot;sharding_dim == 1&quot;, the weight is shard in the hidden dimension.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">num_embeddings</span><span class="p">,</span>
<span class="n">embedding_dim</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">tp_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">tp_group</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">sharding_dim</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">tp_rank</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">instance_id</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="c1"># num_embeddings records the total vocab size no matter using TP or not</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_embeddings</span> <span class="o">=</span> <span class="n">num_embeddings</span>
<span class="bp">self</span><span class="o">.</span><span class="n">embedding_dim</span> <span class="o">=</span> <span class="n">embedding_dim</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tp_size</span> <span class="o">=</span> <span class="n">tp_size</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tp_group</span> <span class="o">=</span> <span class="n">tp_group</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sharding_dim</span> <span class="o">=</span> <span class="n">sharding_dim</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tp_rank</span> <span class="o">=</span> <span class="n">tp_rank</span>
<span class="bp">self</span><span class="o">.</span><span class="n">instance_id</span> <span class="o">=</span> <span class="n">instance_id</span>
<span class="k">if</span> <span class="n">sharding_dim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_embeddings</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">embedding_dim</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">tp_size</span><span class="p">),</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">sharding_dim</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_embeddings</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">tp_size</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">embedding_dim</span><span class="p">),</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<div class="viewcode-block" id="Embedding.forward">
<a class="viewcode-back" href="../../../python-api/tensorrt_llm.layers.html#tensorrt_llm.layers.embedding.Embedding.forward">[docs]</a>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">workspace</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
<span class="k">return</span> <span class="n">embedding</span><span class="p">(</span><span class="n">x</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
<span class="n">tp_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tp_size</span><span class="p">,</span>
<span class="n">tp_group</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tp_group</span><span class="p">,</span>
<span class="n">sharding_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sharding_dim</span><span class="p">,</span>
<span class="n">tp_rank</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tp_rank</span><span class="p">,</span>
<span class="n">workspace</span><span class="o">=</span><span class="n">workspace</span><span class="p">,</span>
<span class="n">instance_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">instance_id</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="PromptTuningEmbedding">
<a class="viewcode-back" href="../../../python-api/tensorrt_llm.layers.html#tensorrt_llm.layers.embedding.PromptTuningEmbedding">[docs]</a>
<span class="k">class</span> <span class="nc">PromptTuningEmbedding</span><span class="p">(</span><span class="n">Embedding</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> PromptTuningEmbedding handles fine-tuned prompts with virtual tokens. At runtime,</span>
<span class="sd"> a supplementary embedding dictionary is passed. Tokens whose ids are &gt;= vocab_size are embedded</span>
<span class="sd"> with that additional dictionary.</span>
<span class="sd"> The prompt tuning dictionary holds multiple tasks, and each sequence is assigned a given task.</span>
<span class="sd"> Prompt-tuned tokens from a given sequence use the adequate task dictionary, as defined by the `tasks` input.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">num_embeddings</span><span class="p">,</span>
<span class="n">embedding_dim</span><span class="p">,</span>
<span class="n">vocab_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">tp_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">tp_group</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">sharding_dim</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">tp_rank</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">instance_id</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">num_embeddings</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">tp_size</span><span class="p">,</span>
<span class="n">tp_group</span><span class="p">,</span> <span class="n">sharding_dim</span><span class="p">,</span> <span class="n">tp_rank</span><span class="p">,</span> <span class="n">instance_id</span><span class="p">)</span>
<span class="k">if</span> <span class="n">vocab_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">vocab_size</span> <span class="o">=</span> <span class="n">num_embeddings</span>
<span class="bp">self</span><span class="o">.</span><span class="n">vocab_size</span> <span class="o">=</span> <span class="n">vocab_size</span>
<div class="viewcode-block" id="PromptTuningEmbedding.forward">
<a class="viewcode-back" href="../../../python-api/tensorrt_llm.layers.html#tensorrt_llm.layers.embedding.PromptTuningEmbedding.forward">[docs]</a>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">tokens</span><span class="p">,</span>
<span class="n">prompt_embedding_table</span><span class="p">,</span>
<span class="n">tasks</span><span class="p">,</span>
<span class="n">task_vocab_size</span><span class="p">,</span>
<span class="n">workspace</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Pass all tokens through both normal and prompt embedding tables.</span>
<span class="sd"> Tokens are masked so that &quot;normal&quot; embedding only see &quot;normal&quot; tokens. Same logic for &quot;prompt&quot; embedding.</span>
<span class="sd"> After those two embedding, combine results based on whether the token was &quot;normal&quot; or &quot;prompt-tuned&quot;.</span>
<span class="sd"> Parameters:</span>
<span class="sd"> tokens : Tensor</span>
<span class="sd"> the ids to embbed, size [batch_size, seq_len]</span>
<span class="sd"> prompt_embedding_table : Tensor</span>
<span class="sd"> the additional embedding table for prompt-tuned tokens, size [num_tasks * num_tokens_per_task, hidden_size]</span>
<span class="sd"> tasks: Tensor</span>
<span class="sd"> the task required by each token, size [batch_size, seq_len]</span>
<span class="sd"> task_vocab_size: Tensor</span>
<span class="sd"> the number of tokens used for each task, should be equal to prompt_embedding_table&#39;s num_tokens_per_task, size [1]</span>
<span class="sd"> Returns:</span>
<span class="sd"> Tokens&#39; embedding</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># do not use &quot;&gt;=&quot; because internally the layer works with floating points</span>
<span class="n">prompt_tokens_mask</span> <span class="o">=</span> <span class="n">tokens</span> <span class="o">&gt;</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vocab_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># clip tokens in the [0, vocab_size) range</span>
<span class="n">normal_tokens</span> <span class="o">=</span> <span class="n">where</span><span class="p">(</span><span class="n">prompt_tokens_mask</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocab_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">tokens</span><span class="p">)</span>
<span class="n">normal_embeddings</span> <span class="o">=</span> <span class="n">embedding</span><span class="p">(</span><span class="n">normal_tokens</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tp_size</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tp_group</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sharding_dim</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tp_rank</span><span class="p">,</span>
<span class="n">workspace</span><span class="o">=</span><span class="n">workspace</span><span class="p">)</span>
<span class="c1"># put virtual tokens in the [0, max_prompt_vocab_size) range</span>
<span class="n">prompt_tokens</span> <span class="o">=</span> <span class="n">where</span><span class="p">(</span><span class="n">prompt_tokens_mask</span><span class="p">,</span> <span class="n">tokens</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="c1"># add offsets to match the concatenated embedding tables</span>
<span class="n">tasks</span> <span class="o">=</span> <span class="n">tasks</span> <span class="o">*</span> <span class="n">task_vocab_size</span>
<span class="c1"># tasks: [batch_size, seq_len]</span>
<span class="c1"># prompt_tokens: [batch_size, seq_len]</span>
<span class="n">prompt_tokens</span> <span class="o">=</span> <span class="n">prompt_tokens</span> <span class="o">+</span> <span class="n">tasks</span>
<span class="n">prompt_embeddings</span> <span class="o">=</span> <span class="n">embedding</span><span class="p">(</span><span class="n">prompt_tokens</span><span class="p">,</span> <span class="n">prompt_embedding_table</span><span class="p">)</span>
<span class="c1"># prompt_tokens_mask: [batch_size, seq_len] -&gt; [batch_size, seq_len, 1]</span>
<span class="c1"># combine the correct sources of embedding: normal/prompt</span>
<span class="k">return</span> <span class="n">where</span><span class="p">(</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">prompt_tokens_mask</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">prompt_embeddings</span><span class="p">,</span>
<span class="n">normal_embeddings</span><span class="p">)</span></div>
</div>
</pre></div>
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