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119 lines
4.8 KiB
Python
119 lines
4.8 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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from ..functional import embedding, unsqueeze, where
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from ..module import Module
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from ..parameter import Parameter
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class Embedding(Module):
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"""
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The embedding layer takes input indices (x) and the embedding lookup table (weight) as input.
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And output the corresponding embeddings according to input indices.
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The size of weight is [num_embeddings, embedding_dim]
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Four parameters (tp_size, tp_group, sharding_dim, tp_rank) are involved in tensor parallelism.
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Only when "tp_size > 1 and tp_group is not None", tensor parallelism is enabled.
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When "sharding_dim == 0", the weight is shared in the vocabulary dimension.
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tp_rank must be set when sharding_dim == 0.
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When "sharding_dim == 1", the weight is shard in the hidden dimension.
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"""
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def __init__(self,
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num_embeddings,
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embedding_dim,
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dtype=None,
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tp_size=1,
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tp_group=None,
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sharding_dim=0,
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tp_rank=None):
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super().__init__()
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# num_embeddings records the total vocab size no matter using TP or not
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self.num_embeddings = num_embeddings
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self.embedding_dim = embedding_dim
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self.tp_size = tp_size
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self.tp_group = tp_group
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self.sharding_dim = sharding_dim
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self.tp_rank = tp_rank
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if sharding_dim == 1:
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self.weight = Parameter(shape=(self.num_embeddings,
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self.embedding_dim // self.tp_size),
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dtype=dtype)
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elif sharding_dim == 0:
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self.weight = Parameter(shape=(math.ceil(
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self.num_embeddings / self.tp_size), self.embedding_dim),
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dtype=dtype)
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self.tp_size = tp_size
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self.tp_group = tp_group
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def forward(self, x):
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return embedding(x,
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self.weight.value,
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tp_size=self.tp_size,
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tp_group=self.tp_group,
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sharding_dim=self.sharding_dim,
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tp_rank=self.tp_rank)
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class PromptTuningEmbedding(Embedding):
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"""
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Pass all tokens though both normal and prompt embedding tables.
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Then, combine results based on whether the token was "normal" or "prompt/virtual".
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"""
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def __init__(self,
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num_embeddings,
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embedding_dim,
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vocab_size=None,
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dtype=None,
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tp_size=1,
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tp_group=None,
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sharding_dim=0,
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tp_rank=0):
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super().__init__(num_embeddings, embedding_dim, dtype, tp_size,
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tp_group, sharding_dim, tp_rank)
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if vocab_size is None:
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vocab_size = num_embeddings
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self.vocab_size = vocab_size
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def forward(self, tokens, prompt_embedding_table, tasks, task_vocab_size):
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# do not use ">=" because internally the layer works with floating points
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prompt_tokens_mask = tokens > (self.vocab_size - 1)
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# clip tokens in the [0, vocab_size) range
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normal_tokens = where(prompt_tokens_mask, self.vocab_size - 1, tokens)
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normal_embeddings = embedding(normal_tokens, self.weight.value,
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self.tp_size, self.tp_group,
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self.sharding_dim, self.tp_rank)
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# put virtual tokens in the [0, max_prompt_vocab_size) range
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prompt_tokens = where(prompt_tokens_mask, tokens - self.vocab_size, 0)
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# add offsets to match the concatenated embedding tables
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tasks = tasks * task_vocab_size
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# tasks: [batch_size, seq_len]
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# prompt_tokens: [batch_size, seq_len]
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prompt_tokens = prompt_tokens + tasks
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prompt_embeddings = embedding(prompt_tokens, prompt_embedding_table)
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# prompt_tokens_mask: [batch_size, seq_len] -> [batch_size, seq_len, 1]
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# combine the correct sources of embedding: normal/prompt
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return where(unsqueeze(prompt_tokens_mask, -1), prompt_embeddings,
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normal_embeddings)
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