mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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250d9c293d
* Update TensorRT-LLM --------- Co-authored-by: Bhuvanesh Sridharan <bhuvan.sridharan@gmail.com> Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com> Co-authored-by: Eddie-Wang1120 <wangjinheng1120@163.com> Co-authored-by: meghagarwal <16129366+megha95@users.noreply.github.com>
411 lines
16 KiB
Python
411 lines
16 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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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import numpy as np
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from ..._common import default_net
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from ...functional import (ACT2FN, bert_attention, cast, concat, constant,
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expand, expand_mask, matmul, select, shape, slice,
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softmax, split, unsqueeze)
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from ...layers import MLP, ColumnLinear, Embedding, LayerNorm, Linear, RowLinear
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from ...mapping import Mapping
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from ...module import Module, ModuleList
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class BertEmbedding(Module):
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def __init__(self,
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vocab_size,
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hidden_size,
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max_position_embeddings,
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type_vocab_size,
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dtype=None):
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super().__init__()
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self.vocab_embedding = Embedding(vocab_size, hidden_size, dtype=dtype)
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self.position_embedding = Embedding(max_position_embeddings,
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hidden_size,
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dtype=dtype)
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self.token_embedding = Embedding(type_vocab_size,
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hidden_size,
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dtype=dtype)
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self.max_position_embeddings = max_position_embeddings
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self.embedding_ln = LayerNorm(normalized_shape=hidden_size, dtype=dtype)
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def forward(self, input_ids, position_ids, token_type_ids):
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x = self.vocab_embedding(input_ids)
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x = x + self.position_embedding(position_ids)
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x = x + self.token_embedding(token_type_ids)
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x = self.embedding_ln(x)
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return x
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class BertAttention(Module):
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def __init__(self,
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hidden_size,
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num_attention_heads,
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max_position_embeddings,
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dtype=None,
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tp_group=None,
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tp_size=1):
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super().__init__()
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self.attention_head_size = hidden_size // num_attention_heads
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self.num_attention_heads = num_attention_heads // tp_size
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self.hidden_size = hidden_size // tp_size
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self.max_position_embeddings = max_position_embeddings
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self.norm_factor = math.sqrt(self.attention_head_size)
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self.qkv = ColumnLinear(hidden_size,
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hidden_size * 3,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size,
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gather_output=False)
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self.dense = RowLinear(hidden_size,
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hidden_size,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size)
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def forward(self, hidden_states, attention_mask=None, input_lengths=None):
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qkv = self.qkv(hidden_states)
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# attention
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if default_net().plugin_config.bert_attention_plugin:
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assert input_lengths is not None
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context = bert_attention(qkv, input_lengths,
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self.num_attention_heads,
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self.attention_head_size, 1.0)
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else:
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def transpose_for_scores(x):
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new_x_shape = concat([
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shape(x, 0),
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shape(x, 1), self.num_attention_heads,
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self.attention_head_size
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])
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return x.view(new_x_shape).permute([0, 2, 1, 3])
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query, key, value = split(qkv, self.hidden_size, dim=2)
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query = transpose_for_scores(query)
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key = transpose_for_scores(key)
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value = transpose_for_scores(value)
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key = key.permute([0, 1, 3, 2])
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attention_scores = matmul(query, key)
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attention_scores = attention_scores / self.norm_factor
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if attention_mask is not None:
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attention_mask = cast(attention_mask, attention_scores.dtype)
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attention_scores = attention_scores + attention_mask
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attention_probs = softmax(attention_scores, dim=-1)
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context = matmul(attention_probs, value,
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use_fp32_acc=False).permute([0, 2, 1, 3])
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context = context.view(
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concat([shape(context, 0),
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shape(context, 1), self.hidden_size]))
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context = self.dense(context)
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return context
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class BertEncoderLayer(Module):
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def __init__(self,
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hidden_size,
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num_attention_heads,
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max_position_embeddings,
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hidden_act='relu',
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tp_group=None,
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tp_size=1,
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dtype=None):
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super().__init__()
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self.input_layernorm = LayerNorm(normalized_shape=hidden_size,
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dtype=dtype)
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self.attention = BertAttention(hidden_size,
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num_attention_heads,
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max_position_embeddings,
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tp_group=tp_group,
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tp_size=tp_size,
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dtype=dtype)
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self.mlp = MLP(hidden_size=hidden_size,
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ffn_hidden_size=hidden_size * 4,
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hidden_act=hidden_act,
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tp_group=tp_group,
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tp_size=tp_size,
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dtype=dtype)
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self.post_layernorm = LayerNorm(normalized_shape=hidden_size,
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dtype=dtype)
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def forward(self, hidden_states, attention_mask=None, input_lengths=None):
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residual = hidden_states
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attention_output = self.attention(hidden_states,
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attention_mask=attention_mask,
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input_lengths=input_lengths)
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hidden_states = residual + attention_output
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hidden_states = self.input_layernorm(hidden_states)
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residual = hidden_states
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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hidden_states = self.post_layernorm(hidden_states)
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return hidden_states
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class BertModel(Module):
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def __init__(self,
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num_layers,
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num_heads,
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hidden_size,
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vocab_size,
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hidden_act,
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max_position_embeddings,
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type_vocab_size,
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pad_token_id=None,
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is_roberta=False,
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mapping=Mapping(),
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dtype=None):
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super().__init__()
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self.max_position_embeddings = max_position_embeddings
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self.padding_idx = pad_token_id
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self.is_roberta = is_roberta
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self.embedding = BertEmbedding(
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vocab_size=vocab_size,
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hidden_size=hidden_size,
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max_position_embeddings=max_position_embeddings,
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type_vocab_size=type_vocab_size,
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dtype=dtype)
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self.layers = ModuleList([
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BertEncoderLayer(hidden_size=hidden_size,
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num_attention_heads=num_heads,
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max_position_embeddings=max_position_embeddings,
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hidden_act=hidden_act,
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tp_group=mapping.tp_group,
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tp_size=mapping.tp_size,
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dtype=dtype) for _ in range(num_layers)
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])
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def forward(self,
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input_ids=None,
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input_lengths=None,
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position_ids=None,
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token_type_ids=None,
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hidden_states=None):
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seq_len_2d = concat([1, shape(input_ids, 1)])
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# create position ids
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position_ids_buffer = constant(
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np.expand_dims(
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np.arange(self.max_position_embeddings).astype(np.int32), 0))
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tmp_position_ids = slice(position_ids_buffer,
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starts=[0, 0],
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sizes=seq_len_2d)
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tmp_position_ids = expand(tmp_position_ids, shape(input_ids)) #BxL
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tmp_input_lengths = unsqueeze(input_lengths, 1) #Bx1
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tmp_input_lengths = expand(tmp_input_lengths, shape(input_ids)) #BxL
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mask = tmp_position_ids < tmp_input_lengths # BxL
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mask = mask.cast('int32')
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if position_ids is None:
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if self.is_roberta:
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# see create_position_ids_from_input_ids() in https://github.com/huggingface/transformers/blob/main/src/transformers/models/roberta/modeling_roberta.py
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position_ids = (tmp_position_ids + 1) * mask
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position_ids = position_ids + self.padding_idx
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else:
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position_ids = slice(position_ids_buffer,
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starts=[0, 0],
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sizes=seq_len_2d)
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position_ids = expand(position_ids, shape(input_ids))
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# create extended_attention_mask as https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py
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extended_attention_mask = expand_mask(mask, tgt_len=1) # BxL -> Bx1x1xL
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# create token_type_ids
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if token_type_ids is None:
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token_type_ids_buffer = constant(
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np.expand_dims(
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np.zeros(self.max_position_embeddings).astype(np.int32), 0))
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token_type_ids = slice(token_type_ids_buffer,
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starts=[0, 0],
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sizes=seq_len_2d)
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token_type_ids = expand(token_type_ids, shape(input_ids))
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hidden_states = self.embedding(input_ids, position_ids, token_type_ids)
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for layer in self.layers:
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hidden_states = layer(hidden_states=hidden_states,
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input_lengths=input_lengths,
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attention_mask=extended_attention_mask)
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return hidden_states
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class BertForQuestionAnswering(Module):
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def __init__(self,
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num_layers,
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num_heads,
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hidden_size,
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vocab_size,
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hidden_act,
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max_position_embeddings,
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type_vocab_size,
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pad_token_id=None,
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is_roberta=False,
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num_labels=2,
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mapping=Mapping(),
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dtype=None):
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super().__init__()
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self.bert = BertModel(num_layers=num_layers,
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num_heads=num_heads,
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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hidden_act=hidden_act,
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max_position_embeddings=max_position_embeddings,
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type_vocab_size=type_vocab_size,
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pad_token_id=pad_token_id,
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is_roberta=is_roberta,
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mapping=mapping,
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dtype=dtype)
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self.num_labels = num_labels
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self.qa_outputs = Linear(hidden_size, num_labels, dtype=dtype)
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def forward(self,
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input_ids=None,
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input_lengths=None,
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token_type_ids=None,
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position_ids=None,
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hidden_states=None):
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hidden_states = self.bert.forward(input_ids=input_ids,
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input_lengths=input_lengths,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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hidden_states=hidden_states)
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logits = self.qa_outputs(hidden_states)
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return logits
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class BertPooler(Module):
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def __init__(self, hidden_size, dtype):
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super().__init__()
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self.dense = Linear(hidden_size, hidden_size, dtype=dtype)
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self.activation = ACT2FN['tanh']
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def forward(self, hidden_states):
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# We "pool" the model by simply taking the hidden state corresponding
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# to the first token.
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first_token_tensor = select(hidden_states, 1, 0)
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pooled_output = self.dense(first_token_tensor)
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pooled_output = self.activation(pooled_output)
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return pooled_output
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class RobertaClassificationHead(Module):
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"""Head for sentence-level classification tasks."""
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def __init__(self, hidden_size, dtype, num_labels):
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super().__init__()
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self.dense = Linear(hidden_size, hidden_size, dtype=dtype)
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self.out_proj = Linear(hidden_size, num_labels)
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def forward(self, features, **kwargs):
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x = select(features, 1, 0)
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x = self.dense(x)
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x = ACT2FN['tanh'](x)
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x = self.out_proj(x)
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return x
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class BertForSequenceClassification(Module):
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def __init__(self,
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num_layers,
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num_heads,
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hidden_size,
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vocab_size,
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hidden_act,
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max_position_embeddings,
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type_vocab_size,
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pad_token_id=None,
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is_roberta=False,
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num_labels=2,
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mapping=Mapping(),
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dtype=None):
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super().__init__()
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self.is_roberta = is_roberta
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self.bert = BertModel(num_layers=num_layers,
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num_heads=num_heads,
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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hidden_act=hidden_act,
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max_position_embeddings=max_position_embeddings,
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type_vocab_size=type_vocab_size,
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pad_token_id=pad_token_id,
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is_roberta=is_roberta,
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mapping=mapping,
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dtype=dtype)
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self.num_labels = num_labels
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if not is_roberta:
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self.pooler = BertPooler(hidden_size=hidden_size, dtype=dtype)
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self.classifier = Linear(hidden_size, num_labels, dtype=dtype)
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else:
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self.classifier = RobertaClassificationHead(hidden_size=hidden_size,
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num_labels=num_labels,
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dtype=dtype)
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def forward(self,
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input_ids=None,
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input_lengths=None,
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token_type_ids=None,
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position_ids=None,
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hidden_states=None):
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hidden_states = self.bert.forward(input_ids=input_ids,
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input_lengths=input_lengths,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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hidden_states=hidden_states)
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if not self.is_roberta:
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pooled_output = self.pooler(hidden_states)
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logits = self.classifier(pooled_output)
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else:
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logits = self.classifier(hidden_states)
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return logits
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