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	# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
	# SPDX-License-Identifier: Apache-2.0
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	import math
	from collections import OrderedDict
	from typing import List, Optional

	import tensorrt as trt
	import torch

	from tensorrt_llm._common import default_net
	from tensorrt_llm._utils import numpy_to_torch, str_dtype_to_torch
	from tensorrt_llm.functional import (LayerNormPositionType, LayerNormType,
	MLPType, PositionEmbeddingType, Tensor,
	assertion, cast, gather_last_token_logits,
	gelu, maximum, minimum, recv, send, shape,
	slice, transpose)
	from tensorrt_llm.layers import (MLP, Attention, AttentionMaskType,
	AttentionParams, BertAttention, ColumnLinear,
	Conv1d, Embedding, FusedGatedMLP, GatedMLP,
	GroupNorm, KeyValueCacheParams, LayerNorm,
	LoraParams, PromptTuningEmbedding, RmsNorm)
	from tensorrt_llm.lora_manager import (LoraConfig,
	get_default_trtllm_modules_to_hf_modules,
	use_lora)
	from tensorrt_llm.mapping import Mapping
	from tensorrt_llm.models.modeling_utils import PretrainedConfig, PretrainedModel
	from tensorrt_llm.module import Module, ModuleList
	from tensorrt_llm.parameter import Parameter
	from tensorrt_llm.plugin.plugin import current_all_reduce_helper

	layernorm_map = {
	LayerNormType.LayerNorm: LayerNorm,
	LayerNormType.RmsNorm: RmsNorm,
	LayerNormType.GroupNorm: GroupNorm,
	}

	mlp_map = {
	MLPType.MLP: MLP,
	MLPType.GatedMLP: GatedMLP,
	MLPType.FusedGatedMLP: FusedGatedMLP,
	}


	class EncDecEmbedding(Module):

	def __init__(self,
	vocab_size,
	hidden_size,
	max_position_embeddings=None,
	has_position_embedding=False,
	type_vocab_size=None,
	has_embedding_layernorm=False,
	has_embedding_scale=False,
	layernorm_eps=1e-5,
	layernorm_type=LayerNormType.LayerNorm,
	dtype=None,
	use_parallel_embedding=False,
	embedding_sharding_dim=0,
	mapping=Mapping()):
	super().__init__()

	self.layernorm_type = layernorm_type
	ln_type = layernorm_map[layernorm_type]

	self.vocab_embedding = Embedding(
	vocab_size,
	hidden_size,
	dtype=dtype,
	tp_size=mapping.tp_size if use_parallel_embedding else 1,
	tp_group=mapping.tp_group if use_parallel_embedding else None,
	sharding_dim=embedding_sharding_dim,
	tp_rank=mapping.tp_rank)

	self.position_embedding = None
	self.max_position_embeddings = max_position_embeddings
	if has_position_embedding:
	self.position_embedding = Embedding(
	max_position_embeddings,
	hidden_size,
	dtype=dtype,
	tp_size=mapping.tp_size if use_parallel_embedding else 1,
	tp_group=mapping.tp_group if use_parallel_embedding else None,
	sharding_dim=embedding_sharding_dim,
	tp_rank=mapping.tp_rank)

	self.token_type_embedding = None
	if type_vocab_size:
	self.token_type_embedding = Embedding(
	type_vocab_size,
	hidden_size,
	dtype=dtype,
	tp_size=mapping.tp_size if use_parallel_embedding else 1,
	tp_group=mapping.tp_group if use_parallel_embedding else None,
	sharding_dim=embedding_sharding_dim,
	tp_rank=mapping.tp_rank)

	# e.g. BART true, T5 false
	self.embedding_layernorm = None
	if has_embedding_layernorm:
	self.embedding_layernorm = ln_type(normalized_shape=hidden_size,
	eps=layernorm_eps,
	dtype=dtype)

	# e.g. BART true, T5 false
	self.embedding_scale = 1.0
	if has_embedding_scale:
	self.embedding_scale = math.sqrt(hidden_size)

	# Note: embedding offset in BART is not considered as a standard. For the specific case,
	# we just need to shrink its position embedding table by [offset:] during weight loading

	def forward(self,
	input_ids,
	position_ids=None,
	token_type_ids=None,
	prompt_embedding_table=None,
	prompt_tasks=None,
	prompt_vocab_size=None):
	# position_ids and token_type_ids are provided inputs
	# and should not be formulated deterministically

	args = [prompt_embedding_table, prompt_tasks, prompt_vocab_size
	] if prompt_embedding_table is not None else []

	x = self.vocab_embedding(input_ids, args) self.embedding_scale
	self.register_network_output('word_embeddings', x)

	if self.position_embedding:
	pos_emb = self.position_embedding(position_ids)
	self.register_network_output('position_embeddings', pos_emb)
	x = x + pos_emb
	if self.token_type_embedding:
	x = x + self.token_type_embedding(token_type_ids)

	if self.embedding_layernorm:
	x = self.embedding_layernorm(x)

	return x


	class EncoderLayer(Module):

	def __init__(self,
	hidden_size,
	ffn_hidden_size,
	num_attention_heads,
	num_kv_heads,
	head_size,
	max_position_embeddings=None,
	q_scaling=1.0,
	has_attention_qkvo_bias=False,
	has_mlp_bias=False,
	layernorm_position=LayerNormPositionType.pre_layernorm,
	layernorm_type=LayerNormType.LayerNorm,
	layernorm_eps=1e-5,
	hidden_act="relu",
	mlp_type=MLPType.MLP,
	mapping=Mapping(),
	dtype=None,
	residual_scaling=1.0,
	relative_attention=False,
	max_distance=0,
	num_buckets=0,
	fp16_clamping=False):
	super().__init__()

	# e.g. BART regular, T5 RMS
	self.layernorm_type = layernorm_type
	ln_type = layernorm_map[layernorm_type]

	# e.g. BART post, T5 pre
	self.layernorm_position = layernorm_position

	# e.g. BART q_scaling = 1.f, T5 q_scaling = 1.f/sqrt(head_size)
	self.attention = BertAttention(
	hidden_size,
	num_attention_heads,
	attention_head_size=head_size,
	num_kv_heads=num_kv_heads,
	max_position_embeddings=max_position_embeddings,
	q_scaling=q_scaling,
	bias=has_attention_qkvo_bias,
	tp_group=mapping.tp_group,
	tp_size=mapping.tp_size,
	tp_rank=mapping.tp_rank,
	dtype=dtype,
	relative_attention=relative_attention,
	max_distance=max_distance,
	num_buckets=num_buckets)

	self.attention_layernorm = ln_type(normalized_shape=hidden_size,
	eps=layernorm_eps,
	dtype=dtype)

	# T5/BART MLP, Flan-T5 GatedMLP
	self.mlp_type = mlp_type
	mlp_f = mlp_map[mlp_type]
	self.mlp = mlp_f(
	hidden_size=hidden_size,
	ffn_hidden_size=ffn_hidden_size,
	hidden_act=hidden_act,
	bias=has_mlp_bias,
	tp_group=mapping.tp_group,
	tp_size=mapping.tp_size,
	dtype=dtype,
	)

	self.mlp_layernorm = ln_type(normalized_shape=hidden_size,
	eps=layernorm_eps,
	dtype=dtype)

	self.residual_scaling = residual_scaling

	# T5-series model(e.g. t5-large, t5-3b, flan-t5-small) has accuracy issue due to fp16 overflow
	# after residual add. We add workaround for clamping fp16 range [-64000, 64000] after every
	# residual add to avoid accuracy drop.
	self.fp16_clamping = fp16_clamping

	def forward(self,
	hidden_states: Tensor,
	attention_mask=None,
	input_lengths=None,
	max_input_length=None,
	lora_layer_params=None):
	assert isinstance(hidden_states, Tensor)

	# self attention
	residual = hidden_states * self.residual_scaling

	if self.layernorm_position == LayerNormPositionType.pre_layernorm:
	hidden_states = self.attention_layernorm(hidden_states)

	attention_output = self.attention(hidden_states,
	attention_mask=attention_mask,
	input_lengths=input_lengths,
	max_input_length=max_input_length,
	lora_layer_params=lora_layer_params)

	self.register_network_output('attention_output', attention_output)

	hidden_states = residual + attention_output

	if self.fp16_clamping:
	hidden_states = maximum(-64000.0, hidden_states)
	hidden_states = minimum(64000.0, hidden_states)

	if self.layernorm_position == LayerNormPositionType.post_layernorm:
	hidden_states = self.attention_layernorm(hidden_states)

	# MLP
	residual = hidden_states * self.residual_scaling

	if self.layernorm_position == LayerNormPositionType.pre_layernorm:
	hidden_states = self.mlp_layernorm(hidden_states)

	hidden_states = self.mlp(hidden_states,
	lora_layer_params=lora_layer_params)

	self.register_network_output('mlp_output', hidden_states)

	hidden_states = residual + hidden_states

	if self.fp16_clamping:
	hidden_states = maximum(-64000.0, hidden_states)
	hidden_states = minimum(64000.0, hidden_states)

	if self.layernorm_position == LayerNormPositionType.post_layernorm:
	hidden_states = self.mlp_layernorm(hidden_states)

	return hidden_states


	class DecoderLayer(Module):

	def __init__(self,
	*,
	local_layer_idx,
	hidden_size,
	ffn_hidden_size,
	num_attention_heads,
	num_kv_heads,
	head_size,
	max_position_embeddings=None,
	q_scaling=1.0,
	has_attention_qkvo_bias=False,
	has_mlp_bias=False,
	layernorm_position=LayerNormPositionType.pre_layernorm,
	layernorm_type=LayerNormType.LayerNorm,
	layernorm_eps=1e-5,
	hidden_act="relu",
	mlp_type=MLPType.MLP,
	mapping=Mapping(),
	dtype=None,
	residual_scaling=1.0,
	relative_attention=False,
	max_distance=0,
	num_buckets=0,
	fp16_clamping=False,
	skip_cross_qkv=False,
	use_implicit_relative_attention=False):
	super().__init__()

	# e.g. BART regular, T5 RMS
	self.layernorm_type = layernorm_type
	ln_type = layernorm_map[layernorm_type]

	# e.g. BART post, T5 pre
	self.layernorm_position = layernorm_position

	# e.g. BART q_scaling = 1.f, T5 q_scaling = 1.f/sqrt(head_size)
	self.self_attention = Attention(
	local_layer_idx=local_layer_idx,
	hidden_size=hidden_size,
	num_attention_heads=num_attention_heads,
	attention_head_size=head_size,
	num_kv_heads=num_kv_heads,
	max_position_embeddings=max_position_embeddings,
	q_scaling=q_scaling,
	bias=has_attention_qkvo_bias,
	attention_mask_type=AttentionMaskType.causal,
	tp_group=mapping.tp_group,
	tp_size=mapping.tp_size,
	tp_rank=mapping.tp_rank,
	dtype=dtype,
	cross_attention=False,
	relative_attention=relative_attention,
	max_distance=max_distance if use_implicit_relative_attention else 0,
	num_buckets=num_buckets,
	position_embedding_type=PositionEmbeddingType.relative
	if relative_attention else PositionEmbeddingType.learned_absolute,
	use_implicit_relative_attention=use_implicit_relative_attention)

	self.self_attention_layernorm = ln_type(normalized_shape=hidden_size,
	eps=layernorm_eps,
	dtype=dtype)

	# Note: self attn uses MMHA, mask is always causal triangular
	# cross attn has two scenarios:
	# - in context phase, all ones mask, same as padding type
	# - in generation phase, same causal triangular mask as MMHA
	# - context phase special handling is done in plugin by resetting mask type
	#
	# e.g. BART q_scaling = 1.f, T5 q_scaling = 1.f/sqrt(head_size)
	self.cross_attention = Attention(
	local_layer_idx=local_layer_idx,
	hidden_size=hidden_size,
	num_attention_heads=num_attention_heads,
	attention_head_size=head_size,
	num_kv_heads=num_kv_heads,
	max_position_embeddings=max_position_embeddings,
	q_scaling=q_scaling,
	bias=has_attention_qkvo_bias,
	attention_mask_type=AttentionMaskType.causal,
	tp_group=mapping.tp_group,
	tp_size=mapping.tp_size,
	tp_rank=mapping.tp_rank,
	dtype=dtype,
	cross_attention=True,
	relative_attention=
	False, # Cross attention has no relative attention bias
	max_distance=max_distance,
	num_buckets=num_buckets,
	position_embedding_type=PositionEmbeddingType.learned_absolute,
	skip_cross_qkv=skip_cross_qkv)

	self.cross_attention_layernorm = ln_type(normalized_shape=hidden_size,
	eps=layernorm_eps,
	dtype=dtype)

	# T5/BART MLP, Flan-T5 GatedMLP
	self.mlp_type = mlp_type
	mlp_f = mlp_map[mlp_type]
	self.mlp = mlp_f(
	hidden_size=hidden_size,
	ffn_hidden_size=ffn_hidden_size,
	hidden_act=hidden_act,
	bias=has_mlp_bias,
	tp_group=mapping.tp_group,
	tp_size=mapping.tp_size,
	dtype=dtype,
	)

	self.mlp_layernorm = ln_type(normalized_shape=hidden_size,
	eps=layernorm_eps,
	dtype=dtype)

	self.residual_scaling = residual_scaling

	# T5-series model(e.g. t5-large, t5-3b, flan-t5-small) has accuracy issue due to fp16 overflow
	# after residual add. We add workaround for clamping fp16 range [-64000, 64000] after every
	# residual add to avoid accuracy drop.
	self.fp16_clamping = fp16_clamping

	def forward(self,
	hidden_states: Tensor,
	encoder_output: Optional[Tensor] = None,
	attention_mask=None,
	cross_attention_mask=None,
	use_cache=False,
	kv_cache_params=None,
	attention_params=None,
	lora_layer_params=None,
	cross_kv_cache_gen: Optional[Tensor] = None,
	cross_qkv_reuse: Optional[Tensor] = None):
	assert isinstance(hidden_states, Tensor)

	if encoder_output:
	assert isinstance(encoder_output, Tensor)

	# self-attention
	residual = hidden_states * self.residual_scaling

	if self.layernorm_position == LayerNormPositionType.pre_layernorm:
	hidden_states = self.self_attention_layernorm(hidden_states)

	attention_output = self.self_attention(
	hidden_states=hidden_states,
	attention_mask=attention_mask,
	use_cache=use_cache,
	kv_cache_params=kv_cache_params,
	attention_params=attention_params,
	lora_layer_params=lora_layer_params)

	if use_cache:
	attention_output, presents_self = attention_output

	self.register_network_output('self_attention_output', attention_output)

	hidden_states = residual + attention_output

	if self.fp16_clamping:
	hidden_states = maximum(-64000.0, hidden_states)
	hidden_states = minimum(64000.0, hidden_states)

	if self.layernorm_position == LayerNormPositionType.post_layernorm:
	hidden_states = self.self_attention_layernorm(hidden_states)

	# cross attention
	residual = hidden_states * self.residual_scaling

	if self.layernorm_position == LayerNormPositionType.pre_layernorm:
	hidden_states = self.cross_attention_layernorm(hidden_states)

	attention_output = self.cross_attention(
	hidden_states=hidden_states,
	attention_mask=cross_attention_mask,
	encoder_output=encoder_output,
	use_cache=use_cache,
	kv_cache_params=kv_cache_params,
	attention_params=attention_params,
	lora_layer_params=lora_layer_params,
	cross_kv_cache_gen=cross_kv_cache_gen,
	cross_qkv_reuse=cross_qkv_reuse)

	if use_cache:
	attention_output, presents_cross = attention_output

	self.register_network_output('cross_attention_output', attention_output)

	hidden_states = residual + attention_output

	if self.fp16_clamping:
	hidden_states = maximum(-64000.0, hidden_states)
	hidden_states = minimum(64000.0, hidden_states)

	if self.layernorm_position == LayerNormPositionType.post_layernorm:
	hidden_states = self.cross_attention_layernorm(hidden_states)

	# MLP
	residual = hidden_states * self.residual_scaling

	if self.layernorm_position == LayerNormPositionType.pre_layernorm:
	hidden_states = self.mlp_layernorm(hidden_states)

	hidden_states = self.mlp(hidden_states,
	lora_layer_params=lora_layer_params)
	self.register_network_output('mlp_output', hidden_states)

	hidden_states = residual + hidden_states

	if self.fp16_clamping:
	hidden_states = maximum(-64000.0, hidden_states)
	hidden_states = minimum(64000.0, hidden_states)

	if self.layernorm_position == LayerNormPositionType.post_layernorm:
	hidden_states = self.mlp_layernorm(hidden_states)

	if use_cache:
	return (hidden_states, presents_self, presents_cross)
	return hidden_states


	class EncoderModel(PretrainedModel):

	def __init__(self, config: PretrainedConfig):
	self.check_config(config)
	super().__init__(config)
	self.mapping = self.config.mapping

	self.has_position_embedding = self.config.has_position_embedding
	type_vocab_size = self.config.type_vocab_size
	self.has_token_type_embedding = False if type_vocab_size is None else True

	# e.g. BART regular, T5 RMS
	self.layernorm_type = self.config.layernorm_type
	ln_type = layernorm_map[self.layernorm_type]

	# e.g. BART true, T5 false
	self.has_attention_qkvo_bias = self.config.has_attention_qkvo_bias
	self.has_mlp_bias = self.config.has_mlp_bias

	# e.g. BART false, T5 true
	self.has_model_final_layernorm = self.config.has_model_final_layernorm

	self._dtype = self.config.dtype

	self.total_num_layers = self.config.num_hidden_layers
	self.num_layers = self.config.num_hidden_layers // self.mapping.pp_size

	self.hidden_size = self.config.hidden_size
	self.num_heads = self.config.num_attention_heads
	num_kv_heads = self.num_heads
	if num_kv_heads is None or num_kv_heads <= 0:
	num_kv_heads = self.config.num_attention_heads
	self.num_kv_heads = num_kv_heads
	self.head_size = self.hidden_size // self.num_heads if self.config.head_size is None else self.config.head_size

	self.fp16_clamping = (self.config.dtype
	== 'float16') and (self.config.model_type == 't5')
	self.mlp_type = MLPType.MLP if not hasattr(
	self.config, "mlp_type") else self.config.mlp_type

	if self.mapping.is_first_pp_rank():
	self.embedding = EncDecEmbedding(
	self.config.vocab_size,
	self.config.hidden_size,
	max_position_embeddings=self.config.max_position_embeddings,
	has_position_embedding=self.has_position_embedding,
	type_vocab_size=type_vocab_size,
	has_embedding_layernorm=self.config.has_embedding_layernorm,
	has_embedding_scale=self.config.has_embedding_scale,
	layernorm_eps=self.config.norm_epsilon,
	layernorm_type=self.layernorm_type,
	dtype=self.config.dtype,
	use_parallel_embedding=self.config.use_parallel_embedding,
	embedding_sharding_dim=self.config.embedding_sharding_dim,
	mapping=self.mapping)

	self.encoder_layers = ModuleList([
	EncoderLayer(
	hidden_size=self.hidden_size,
	ffn_hidden_size=self.config.intermediate_size,
	num_attention_heads=self.num_heads,
	num_kv_heads=num_kv_heads,
	head_size=self.head_size,
	max_position_embeddings=self.config.max_position_embeddings,
	q_scaling=self.config.q_scaling,
	has_attention_qkvo_bias=self.has_attention_qkvo_bias,
	has_mlp_bias=self.has_mlp_bias,
	layernorm_position=self.config.layernorm_position,
	layernorm_eps=self.config.norm_epsilon,
	layernorm_type=self.layernorm_type,
	hidden_act=self.config.hidden_act,
	mlp_type=self.mlp_type,
	mapping=self.mapping,
	dtype=self.config.dtype,
	residual_scaling=1.0
	if not hasattr(self.config, "residual_scaling") else
	self.config.residual_scaling,
	relative_attention=self.config.relative_attention,
	max_distance=self.config.max_distance,
	num_buckets=self.config.num_buckets,
	fp16_clamping=self.fp16_clamping)
	for _ in self.mapping.pp_layers(self.total_num_layers)
	])

	if self.mapping.is_last_pp_rank():
	if self.has_model_final_layernorm:
	self.final_layernorm = ln_type(
	normalized_shape=self.config.hidden_size,
	eps=self.config.norm_epsilon,
	dtype=self.config.dtype)

	def check_config(self, config: PretrainedConfig):
	config.set_if_not_exist('has_position_embedding', False)
	config.set_if_not_exist('type_vocab_size', None)
	config.set_if_not_exist('rescale_before_lm_head', False)
	config.set_if_not_exist('layernorm_type', LayerNormType.LayerNorm)
	config.set_if_not_exist('layernorm_position',
	LayerNormPositionType.pre_layernorm)
	config.set_if_not_exist('has_attention_qkvo_bias', False)
	config.set_if_not_exist('has_mlp_bias', False)
	config.set_if_not_exist('has_model_final_layernorm', False)
	config.set_if_not_exist('encoder_hidden_size', None)
	config.set_if_not_exist('encoder_num_heads', None)
	config.set_if_not_exist('encoder_num_kv_heads', None)
	config.set_if_not_exist('encoder_head_size', None)
	config.set_if_not_exist('model_type', 't5')
	config.set_if_not_exist('skip_cross_qkv', False)
	config.set_if_not_exist('mlp_type', MLPType.MLP)
	config.set_if_not_exist('has_embedding_scale', False)
	config.set_if_not_exist('residual_scaling', 1.0)
	config.set_if_not_exist('has_lm_head_bias', False)
	config.set_if_not_exist('num_buckets', None)
	config.set_if_not_exist('max_distance', None)
	config.set_if_not_exist('relative_attention', False)
	config.set_if_not_exist('residual_scaling', 1.0)

	def forward(self,
	input_ids: Tensor,
	input_lengths=None,
	position_ids=None,
	token_type_ids=None,
	hidden_states=None,
	max_input_length=None,
	prompt_embedding_table=None,
	prompt_tasks=None,
	prompt_vocab_size=None,
	attention_mask=None,
	lora_params: LoraParams = None):

	# In PP, layer 0 has ids as inputs, all other layers have hidden_states as inputs
	if self.mapping.is_first_pp_rank():
	ptuning_args = [
	prompt_embedding_table, prompt_tasks, prompt_vocab_size
	] if prompt_embedding_table is not None else []

	hidden_states = self.embedding(input_ids, position_ids,
	token_type_ids, *ptuning_args)
	self.register_network_output('embedding_layer_output',
	hidden_states)
	else:
	hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())

	for layer_idx, encoder_layer in enumerate(self.encoder_layers):
	lora_layer_params = None
	if lora_params is not None and lora_params.lora_ranks is not None:
	lora_layer_params = lora_params.get_layer_params(layer_idx)
	hidden_states = encoder_layer(hidden_states=hidden_states,
	attention_mask=attention_mask,
	input_lengths=input_lengths,
	max_input_length=max_input_length,
	lora_layer_params=lora_layer_params)

	if self.mapping.is_last_pp_rank():
	if self.has_model_final_layernorm:
	hidden_states = self.final_layernorm(hidden_states)
	hidden_states.mark_output('encoder_output', self._dtype)
	else:
	hidden_states = send(hidden_states, self.mapping.next_pp_rank())
	hidden_states.mark_output('hidden_states_output', self._dtype)

	return hidden_states

	def prepare_inputs(self,
	max_batch_size,
	max_input_len,
	prompt_embedding_table_size: int = 0,
	lora_target_modules: List[str] = None,
	*args,
	**kwargs):
	'''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
	ranges of the dimensions of when using TRT dynamic shapes.

	@return: a list contains values which can be fed into the self.forward()
	'''

	hidden_size = self.hidden_size

	bs_range = [1, (max_batch_size + 1) // 2, max_batch_size]
	inlen_range = [1, (max_input_len + 1) // 2, max_input_len]
	num_tokens_range = [
	1,
	(max_input_len * max_batch_size + 1) // 2,
	max_input_len * max_batch_size,
	]

	input_ids, position_ids, token_type_ids, hidden_states = None, None, None, None
	remove_input_padding = default_net().plugin_config.remove_input_padding
	use_lora_plugin = default_net().plugin_config.lora_plugin

	attention_mask = None
	if remove_input_padding:
	if self.mapping.is_first_pp_rank():
	input_ids = Tensor(
	name="input_ids",
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([("num_tokens", [num_tokens_range])]),
	)
	if self.has_position_embedding:
	position_ids = Tensor(
	name='position_ids',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([('num_tokens',
	[num_tokens_range])]),
	)
	if self.has_token_type_embedding:
	token_type_ids = Tensor(
	name='token_type_ids',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([('num_tokens',
	[num_tokens_range])]),
	)
	else:
	hidden_states = Tensor(name='hidden_states_input',
	dtype=self._dtype,
	shape=[-1, hidden_size],
	dim_range=OrderedDict([
	('num_tokens', [num_tokens_range]),
	('hidden_size', [hidden_size]),
	]))
	else:
	if self.mapping.is_first_pp_rank():
	input_ids = Tensor(
	name="input_ids",
	dtype=trt.int32,
	shape=[-1, -1],
	dim_range=OrderedDict([("batch_size", [bs_range]),
	("input_len", [inlen_range])]),
	)
	if self.has_position_embedding:
	position_ids = Tensor(
	name='position_ids',
	dtype=trt.int32,
	shape=[-1, -1],
	dim_range=OrderedDict([('batch_size', [bs_range]),
	('input_len', [inlen_range])]),
	)
	if self.has_token_type_embedding:
	token_type_ids = Tensor(
	name='token_type_ids',
	dtype=trt.int32,
	shape=[-1, -1],
	dim_range=OrderedDict([('batch_size', [bs_range]),
	('input_len', [inlen_range])]),
	)
	else:
	hidden_states = Tensor(name='hidden_states_input',
	dtype=self._dtype,
	shape=[-1, -1, hidden_size],
	dim_range=OrderedDict([
	('batch_size', [bs_range]),
	('input_len', [inlen_range]),
	('hidden_size', [hidden_size]),
	]))

	if not default_net().plugin_config.bert_attention_plugin:
	attention_mask = Tensor(
	name='attention_mask',
	dtype=trt.int32,
	shape=[-1, -1],
	dim_range=OrderedDict([
	('batch_size', [bs_range]),
	('input_len', [inlen_range]),
	]),
	)

	# if self.mapping.tp_size > 1:
	# current_all_reduce_helper().set_workspace_tensor(self.mapping, 1)
	# FIXME(TRTLLM-996): Support custom allreduce for encoder models on C++ runtime

	input_lengths = Tensor(
	name="input_lengths",
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([("batch_size", [bs_range])]),
	)
	max_input_length = Tensor(
	name="max_input_length",
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([("max_input_length", [inlen_range])]),
	)

	prompt_embedding_table = None
	tasks = None
	prompt_vocab_size = None

	if self.mapping.is_first_pp_rank() and prompt_embedding_table_size > 0:
	p_embedding_range = [[
	1, prompt_embedding_table_size // 2, prompt_embedding_table_size
	]]

	prompt_embedding_table = Tensor(name='prompt_embedding_table',
	dtype=self._dtype,
	shape=[-1, hidden_size],
	dim_range=OrderedDict([
	('prompt_embedding_table_size',
	p_embedding_range),
	('hidden_size', [hidden_size]),
	]))
	if remove_input_padding:
	tasks = Tensor(name='tasks',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([('input_len_task',
	[num_tokens_range])]))
	else:
	tasks = Tensor(name='tasks',
	dtype=trt.int32,
	shape=[-1, 1],
	dim_range=OrderedDict([
	('batch_size', bs_range),
	('broadcast_dim', [1]),
	]))
	prompt_vocab_size = Tensor(name='prompt_vocab_size',
	dtype=trt.int32,
	shape=[1],
	dim_range=OrderedDict([('size', [1])]))
	'''
	LoRA plugin related inputs:
	lora_target_modules for BART-encoder:
	['attn_q', 'attn_v']
	For BART-decoder, the lora_target_modules is different.
	See comments in the DecoderModel.prepare_inputs() for more details.
	'''
	lora_weights_pointers = None
	lora_ranks = None
	lora_params = None
	if use_lora_plugin:
	lora_weights_pointers = []
	lora_ranks = []
	# In current design, q_lora_params, k_lora_params and v_lora_params should be all enabled or all disabled at the same time.
	# However, BART lora modules only contain two of them, so we use zero tensor to fill the missing ones.
	missing_qkv_modules = []
	if any(x in lora_target_modules
	for x in ["attn_q", "attn_k", "attn_v"]):
	for lora_module in ["attn_q", "attn_k", "attn_v"]:
	if lora_module not in lora_target_modules:
	missing_qkv_modules.append(lora_module)

	layers_range = self.mapping.pp_layers(self.total_num_layers)
	for i in layers_range:
	lora_weight_pointer_dict = {}
	lora_rank_dict = {}
	for lora_module in (lora_target_modules + missing_qkv_modules):
	lora_weight_pointer = Tensor(
	name=f'{lora_module}_lora_weights_pointers_{i}',
	dtype=trt.int64,
	shape=[-1, 2],
	dim_range=OrderedDict([('batch_size', [bs_range]),
	('in_out', [2])]))
	lora_weight_pointer_dict.update({
	f'{lora_module}_lora_weights_pointers':
	lora_weight_pointer
	})

	lora_rank = Tensor(name=f'{lora_module}_lora_ranks_{i}',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([('batch_size',
	[bs_range])]))
	lora_rank_dict.update(
	{f'{lora_module}_lora_ranks': lora_rank})

	lora_weights_pointers.append(lora_weight_pointer_dict)
	lora_ranks.append(lora_rank_dict)

	host_request_types = Tensor(name='host_request_types',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([('batch_size',
	[bs_range])]))

	host_context_lengths = None
	if remove_input_padding:
	host_context_lengths = Tensor(name='host_context_lengths',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([
	('batch_size', [bs_range])
	]))

	lora_params = LoraParams(
	lora_ranks=lora_ranks,
	lora_weights_pointers=lora_weights_pointers,
	max_context_length=max_input_len,
	host_request_types=host_request_types,
	host_context_lengths=host_context_lengths,
	)

	result = {
	'input_ids': input_ids,
	'input_lengths': input_lengths,
	'position_ids': position_ids,
	'token_type_ids': token_type_ids,
	'hidden_states': hidden_states,
	'max_input_length': max_input_length,
	'prompt_embedding_table': prompt_embedding_table,
	'prompt_tasks': tasks,
	'prompt_vocab_size': prompt_vocab_size,
	'attention_mask': attention_mask,
	'lora_params': lora_params,
	}

	return result

	def use_lora(self, lora_config: LoraConfig):
	use_lora(self, lora_config)

	def use_prompt_tuning(self):
	embedding = self.embedding.vocab_embedding
	self.embedding.vocab_embedding = PromptTuningEmbedding(
	num_embeddings=embedding.num_embeddings,
	embedding_dim=embedding.embedding_dim,
	dtype=embedding.dtype,
	tp_size=embedding.tp_size,
	tp_group=embedding.tp_group,
	sharding_dim=embedding.sharding_dim,
	tp_rank=embedding.tp_rank)

	self.embedding.vocab_embedding.weight.value = embedding.weight.raw_value

	def precompute_relative_attention_bias(self, build_config):
	pass


	class DecoderModel(PretrainedModel):

	def __init__(self, config: PretrainedConfig):
	self.check_config(config)
	super().__init__(config)

	self.mapping = self.config.mapping

	self.has_position_embedding = self.config.has_position_embedding
	type_vocab_size = self.config.type_vocab_size
	self.has_token_type_embedding = (type_vocab_size is not None)
	self.rescale_before_lm_head = self.config.rescale_before_lm_head

	# e.g. BART regular, T5 RMS
	self.layernorm_type = self.config.layernorm_type
	ln_type = layernorm_map[self.layernorm_type]

	# e.g. BART true, T5 false
	self.has_attention_qkvo_bias = self.config.has_attention_qkvo_bias
	self.has_mlp_bias = self.config.has_mlp_bias

	# e.g. BART false, T5 true
	self.has_model_final_layernorm = self.config.has_model_final_layernorm
	self._dtype = self.config.dtype
	# no quantization considered for now
	self._kv_dtype = self._dtype
	self._logits_dtype = self.config.logits_dtype

	self.total_num_layers = self.config.num_hidden_layers
	self.num_layers = self.config.num_hidden_layers // self.mapping.pp_size

	self.hidden_size = self.config.hidden_size
	self.num_heads = self.config.num_attention_heads
	num_kv_heads = self.num_heads
	if num_kv_heads is None or num_kv_heads <= 0:
	num_kv_heads = self.num_heads
	self.num_kv_heads = num_kv_heads
	self.head_size = self.hidden_size // self.num_heads if self.config.head_size is None else self.config.head_size

	self.encoder_hidden_size = self.config.encoder_hidden_size
	self.encoder_num_heads = self.config.encoder_num_heads
	encoder_num_kv_heads = None if not hasattr(
	self.config,
	"encoder_num_kv_heads") else self.config.encoder_num_kv_heads
	if encoder_num_kv_heads is None or encoder_num_kv_heads <= 0:
	encoder_num_kv_heads = self.encoder_num_heads
	self.encoder_num_kv_heads = encoder_num_kv_heads
	self.encoder_head_size = self.encoder_hidden_size // self.num_heads if self.config.encoder_head_size is None else self.config.encoder_head_size

	self.has_position_embedding = self.config.has_position_embedding
	self.has_token_type_embedding = type_vocab_size is not None

	self.fp16_clamping = (self.config.dtype
	== 'float16') and (self.config.model_type
	in ['t5', 'pix2struct'])

	self.skip_cross_qkv = self.config.skip_cross_qkv
	self.mlp_type = MLPType.MLP if not hasattr(
	self.config, "mlp_type") else self.config.mlp_type
	self.use_implicit_relative_attention = self.config.use_implicit_relative_attention if hasattr(
	self.config, "use_implicit_relative_attention") else False

	if self.mapping.is_first_pp_rank():
	self.embedding = EncDecEmbedding(
	self.config.vocab_size,
	self.config.hidden_size,
	max_position_embeddings=self.config.max_position_embeddings,
	has_position_embedding=self.config.has_position_embedding,
	type_vocab_size=type_vocab_size,
	has_embedding_layernorm=self.config.has_embedding_layernorm,
	has_embedding_scale=self.config.has_embedding_scale,
	layernorm_eps=self.config.norm_epsilon,
	layernorm_type=self.config.layernorm_type,
	dtype=self._dtype,
	use_parallel_embedding=self.config.use_parallel_embedding,
	embedding_sharding_dim=self.config.embedding_sharding_dim,
	mapping=self.mapping)

	layers_range = self.mapping.pp_layers(self.total_num_layers)
	self.decoder_layers = ModuleList([
	DecoderLayer(
	local_layer_idx=layer_idx - layers_range[0],
	hidden_size=self.config.hidden_size,
	ffn_hidden_size=self.config.intermediate_size,
	num_attention_heads=self.num_heads,
	num_kv_heads=self.num_kv_heads,
	head_size=self.head_size,
	max_position_embeddings=self.config.max_position_embeddings,
	q_scaling=self.config.q_scaling,
	has_attention_qkvo_bias=self.config.has_attention_qkvo_bias,
	has_mlp_bias=self.config.has_mlp_bias,
	layernorm_position=self.config.layernorm_position,
	layernorm_eps=self.config.norm_epsilon,
	layernorm_type=self.config.layernorm_type,
	hidden_act=self.config.hidden_act,
	mlp_type=self.mlp_type,
	mapping=self.mapping,
	dtype=self._dtype,
	residual_scaling=self.config.residual_scaling,
	relative_attention=self.config.relative_attention,
	max_distance=self.config.max_distance,
	num_buckets=self.config.num_buckets,
	fp16_clamping=self.fp16_clamping,
	skip_cross_qkv=self.skip_cross_qkv,
	use_implicit_relative_attention=self.
	use_implicit_relative_attention) for layer_idx in layers_range
	])

	if self.mapping.is_last_pp_rank():
	if self.has_model_final_layernorm:
	self.final_layernorm = ln_type(
	normalized_shape=self.config.hidden_size,
	eps=self.config.norm_epsilon,
	dtype=self.config.dtype)

	self.lm_head = ColumnLinear(
	self.config.hidden_size,
	self.config.vocab_size,
	bias=False if not hasattr(self.config, "has_lm_head_bias") else
	self.config.has_lm_head_bias,
	dtype=self.config.dtype,
	tp_group=self.config.mapping.tp_group,
	tp_size=self.config.mapping.tp_size,
	gather_output=True,
	)

	self.trtllm_modules_to_hf_modules = {
	**get_default_trtllm_modules_to_hf_modules(),
	"attn_q": "self_attn.q_proj",
	"attn_k": "self_attn.k_proj",
	"attn_v": "self_attn.v_proj",
	"attn_dense": "self_attn.o_proj",
	"cross_attn_q": "encoder_attn.q_proj",
	"cross_attn_k": "encoder_attn.k_proj",
	"cross_attn_v": "encoder_attn.v_proj",
	"cross_attn_dense": "encoder_attn.o_proj",
	}

	if self.config.relative_attention and not self.use_implicit_relative_attention:
	self.rel_attn_table = Parameter(
	shape=(self.config.num_attention_heads // self.mapping.tp_size,
	self.config.num_buckets),
	dtype=self._dtype)

	def check_config(self, config: PretrainedConfig):
	config.set_if_not_exist('has_position_embedding', False)
	config.set_if_not_exist('type_vocab_size', None)
	config.set_if_not_exist('rescale_before_lm_head', False)
	config.set_if_not_exist('layernorm_type', LayerNormType.LayerNorm)
	config.set_if_not_exist('layernorm_position',
	LayerNormPositionType.pre_layernorm)
	config.set_if_not_exist('has_attention_qkvo_bias', False)
	config.set_if_not_exist('has_mlp_bias', False)
	config.set_if_not_exist('has_model_final_layernorm', False)
	config.set_if_not_exist('encoder_hidden_size', None)
	config.set_if_not_exist('encoder_num_heads', None)
	config.set_if_not_exist('encoder_num_kv_heads', None)
	config.set_if_not_exist('encoder_head_size', None)
	config.set_if_not_exist('model_type', 't5')
	config.set_if_not_exist('skip_cross_qkv', False)
	config.set_if_not_exist('mlp_type', MLPType.MLP)
	config.set_if_not_exist('has_embedding_scale', False)
	config.set_if_not_exist('residual_scaling', 1.0)
	config.set_if_not_exist('has_lm_head_bias', False)
	config.set_if_not_exist('num_buckets', None)
	config.set_if_not_exist('max_distance', None)
	config.set_if_not_exist('relative_attention', False)
	config.set_if_not_exist('residual_scaling', 1.0)

	def forward(self,
	decoder_input_ids: Tensor,
	encoder_output: Tensor,
	position_ids=None,
	token_type_ids=None,
	use_cache=False,
	attention_mask=None,
	cross_attention_mask=None,
	last_token_ids=None,
	kv_cache_params=None,
	attention_params=None,
	hidden_states=None,
	lora_params: LoraParams = None,
	cross_kv_cache_gen: Optional[Tensor] = None,
	cross_qkv_reuse: Optional[Tensor] = None):
	if self.mapping.is_first_pp_rank():
	assert isinstance(decoder_input_ids, Tensor)
	else:
	assert isinstance(hidden_states, Tensor)

	# In PP, layer 0 has ids as inputs, all other layers have hidden_states as inputs
	if self.mapping.is_first_pp_rank():
	hidden_states = self.embedding(decoder_input_ids, position_ids,
	token_type_ids)
	self.register_network_output('embedding_layer_output',
	hidden_states)
	else:
	hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())

	kv_cache_params.fill_none_tensor_list(len(self.decoder_layers))

	if use_cache:
	presents = []

	for i, (decoder_layer, past) in enumerate(
	zip(self.decoder_layers, kv_cache_params.past_key_value)):

	lora_layer_params = None
	if lora_params is not None and lora_params.lora_ranks is not None:
	lora_layer_params = lora_params.get_layer_params(i)

	hidden_states = decoder_layer(
	hidden_states,
	encoder_output=encoder_output,
	attention_mask=attention_mask,
	cross_attention_mask=cross_attention_mask,
	use_cache=use_cache,
	kv_cache_params=KeyValueCacheParams(
	past_key_value=past,
	host_past_key_value_lengths=kv_cache_params.
	host_past_key_value_lengths,
	host_max_attention_window_sizes=kv_cache_params.
	host_max_attention_window_sizes,
	host_sink_token_length=kv_cache_params.
	host_sink_token_length,
	cache_indirection=kv_cache_params.cache_indirection,
	kv_cache_block_offsets=kv_cache_params.
	kv_cache_block_offsets,
	host_kv_cache_block_offsets=kv_cache_params.
	host_cross_kv_cache_block_offsets,
	host_kv_cache_pool_pointers=kv_cache_params.
	host_kv_cache_pool_pointers,
	cross_kv_cache_block_offsets=kv_cache_params.
	cross_kv_cache_block_offsets,
	host_cross_kv_cache_block_offsets=kv_cache_params.
	host_cross_kv_cache_block_offsets,
	host_cross_kv_cache_pool_pointers=kv_cache_params.
	host_cross_kv_cache_pool_pointers),
	attention_params=attention_params,
	lora_layer_params=lora_layer_params,
	cross_kv_cache_gen=cross_kv_cache_gen,
	cross_qkv_reuse=cross_qkv_reuse)

	if use_cache:
	presents_self, presents_cross = hidden_states[1], hidden_states[
	2]
	presents.append((presents_self, presents_cross))
	hidden_states = hidden_states[0]
	self.register_network_output(f'decoder_layer_{i}_output',
	hidden_states)

	if self.mapping.is_last_pp_rank():
	if self.has_model_final_layernorm:
	hidden_states = self.final_layernorm(hidden_states)

	# [bs, seq, hidden_size] or [num_tokens, hidden_size] -> [bs, hidden_size]
	hidden_states = gather_last_token_logits(
	hidden_states, last_token_ids,
	default_net().plugin_config.remove_input_padding)
	self.register_network_output('logits_before_lmhead', hidden_states)

	# Rescale output before projecting on vocab (for T5)
	# See https://github.com/huggingface/transformers/blob/0b192de1f353b0e04dad4813e02e2c672de077be/src/transformers/models/t5/modeling_t5.py#L1769-L1772
	# Note: this is specific for T5, to make it more generic, one can pass in a config:
	# self.config.tie_word_embeddings - default to be True for T5
	# openai whisper model didn't use this rescale
	if self.rescale_before_lm_head:
	hidden_states = hidden_states * (self.hidden_size**-0.5)

	# [bs, hidden_size] -> [bs, vocab_size]
	lm_logits = self.lm_head(hidden_states)
	lm_logits.mark_output('logits', self._logits_dtype)
	else:
	hidden_states = send(hidden_states, self.mapping.next_pp_rank())
	hidden_states.mark_output('hidden_states_output', self._dtype)

	if use_cache and default_net().plugin_config.paged_kv_cache == False:
	for i, present in zip(self.mapping.pp_layers(self.total_num_layers),
	presents):
	present[0].mark_output(f'present_key_value_{i}', self._kv_dtype)
	if default_net().plugin_config.gpt_attention_plugin:
	present[1].mark_output(f'cross_present_key_value_{i}',
	self._kv_dtype)
	if self.mapping.is_last_pp_rank():
	return (lm_logits, tuple(presents))
	return (hidden_states, tuple(presents))
	else:
	if self.mapping.is_last_pp_rank():
	return lm_logits
	return hidden_states

	def prepare_inputs(self,
	max_batch_size,
	max_beam_width,
	max_decoder_input_len,
	max_seq_len,
	max_encoder_input_len,
	gather_context_logits: bool = False,
	gather_generation_logits: bool = False,
	lora_target_modules: List[str] = None,
	use_cache=True,
	*args,
	**kwargs):
	'''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
	ranges of the dimensions of when using TRT dynamic shapes.

	@return: a list contains values which can be fed into the self.forward()
	'''

	# Prepare inputs
	max_output_len = max_decoder_input_len + max_seq_len

	head_size = self.head_size
	num_kv_heads = (self.num_kv_heads + self.mapping.tp_size -
	1) // self.mapping.tp_size

	encoder_head_size = self.encoder_head_size
	encoder_num_kv_heads = (self.encoder_num_kv_heads + self.mapping.tp_size
	- 1) // self.mapping.tp_size

	bb_range = [
	1, (max_batch_size * max_beam_width + 1) // 2,
	max_batch_size * max_beam_width
	]
	bs_range = [1, (max_batch_size + 1) // 2, max_batch_size]
	beam_width_range = [1, (max_beam_width + 1) // 2, max_beam_width]
	inlen_range = [
	1, 1, max_decoder_input_len
	] # context phase >= 1 (if forced_input_ids), generation phase = 1
	encoder_inlen_range = [
	1, (max_encoder_input_len + 1) // 2, max_encoder_input_len
	]
	mask_len_range = [1, (max_output_len + 1) // 2 + 1, max_output_len + 1]
	max_output_len_range = [0, (max_output_len + 1) // 2, max_output_len]

	encoder_num_tokens_range = [
	0, # 0 for generation phase, >0 for context phase
	(max_encoder_input_len * max_batch_size + 1) // 2,
	max_encoder_input_len * max_batch_size,
	]
	decoder_num_tokens_range = [
	1,
	max_batch_size * max_beam_width,
	max(max_decoder_input_len * max_batch_size,
	max_beam_width * max_batch_size),
	]

	# No enable_two_optimization_profiles support yet

	encoder_input_len_range = [
	0, # 0 for generation phase, >0 for context phase
	(max_encoder_input_len + 1) // 2,
	max_encoder_input_len
	]
	past_key_value = []
	sequence_length = None
	host_past_key_value_lengths = None
	runtime_perf_knobs = None
	attention_mask = None
	cross_attention_mask = None
	use_gpt_attention_plugin = default_net(
	).plugin_config.gpt_attention_plugin
	remove_input_padding = default_net().plugin_config.remove_input_padding
	paged_kv_cache = default_net().plugin_config.paged_kv_cache
	tokens_per_block = default_net().plugin_config.tokens_per_block
	use_lora_plugin = default_net().plugin_config.lora_plugin

	input_ids, position_ids, token_type_ids, hidden_states = None, None, None, None
	if remove_input_padding:
	if self.mapping.is_first_pp_rank():
	input_ids = Tensor(name='input_ids',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([
	('decoder_num_tokens',
	[decoder_num_tokens_range]),
	]))
	if self.has_position_embedding:
	position_ids = Tensor(name='position_ids',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([
	('decoder_num_tokens',
	[decoder_num_tokens_range]),
	]))
	if self.has_token_type_embedding:
	token_type_ids = Tensor(
	name='token_type_ids',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([('decoder_num_tokens',
	[decoder_num_tokens_range])]),
	)
	else:
	hidden_states = Tensor(name='hidden_states_input',
	dtype=self._dtype,
	shape=[-1, self.hidden_size],
	dim_range=OrderedDict([
	('decoder_num_tokens',
	[decoder_num_tokens_range]),
	('hidden_size', [self.hidden_size]),
	]))
	else:
	if self.mapping.is_first_pp_rank():
	input_ids = Tensor(name='input_ids',
	dtype=trt.int32,
	shape=[-1, -1],
	dim_range=OrderedDict([
	('batch_size_beam_width', [bb_range]),
	('input_len', [inlen_range]),
	]))
	if self.has_position_embedding:
	position_ids = Tensor(name='position_ids',
	dtype=trt.int32,
	shape=[-1, -1],
	dim_range=OrderedDict([
	('batch_size_beam_width',
	[bb_range]),
	('input_len', [inlen_range]),
	]))
	if self.has_token_type_embedding:
	token_type_ids = Tensor(
	name='token_type_ids',
	dtype=trt.int32,
	shape=[-1, -1],
	dim_range=OrderedDict([('batch_size_beam_width',
	[bb_range]),
	('input_len', [inlen_range])]),
	)
	else:
	hidden_states = Tensor(name='hidden_states_input',
	dtype=self._dtype,
	shape=[-1, -1, self.hidden_size],
	dim_range=OrderedDict([
	('batch_size_beam_width', [bb_range
	]),
	('input_len', [inlen_range]),
	('hidden_size', [self.hidden_size]),
	]))

	encoder_input_lengths = Tensor(
	name="encoder_input_lengths",
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([("batch_size_beam_width", [bb_range])]),
	)
	encoder_max_input_length = Tensor(
	name="encoder_max_input_length",
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([("encoder_max_input_length",
	[encoder_inlen_range])]),
	)
	encoder_output = None
	if remove_input_padding:
	encoder_output = Tensor(
	name="encoder_output",
	dtype=self._dtype,
	shape=[-1, self.encoder_hidden_size],
	dim_range=OrderedDict([
	("encoder_num_tokens", [encoder_num_tokens_range]),
	("encoder_hidden_size", [self.encoder_hidden_size]),
	]),
	)
	else:
	encoder_output = Tensor(
	name="encoder_output",
	dtype=self._dtype,
	shape=[-1, -1, self.encoder_hidden_size],
	dim_range=OrderedDict([
	("batch_size_beam_width_encoder", [bb_range]),
	("encoder_input_len", [encoder_input_len_range]),
	("encoder_hidden_size", [self.encoder_hidden_size]),
	]),
	)

	if use_gpt_attention_plugin:
	host_past_key_value_lengths = Tensor(
	name='host_past_key_value_lengths',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([('batch_size_beam_width', [bb_range])]),
	)

	context_lengths = None
	host_context_lengths = None
	host_request_types = None
	if use_gpt_attention_plugin and remove_input_padding:
	host_context_lengths = Tensor(name='host_context_lengths',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([
	('batch_size_beam_width',
	[bb_range])
	]))

	if use_gpt_attention_plugin:
	sequence_length = Tensor(
	name='sequence_length',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([('batch_size_beam_width', [bb_range])]),
	)

	context_lengths = Tensor(name='context_lengths',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([
	('batch_size_beam_width', [bb_range])
	]))
	host_request_types = Tensor(name='host_request_types',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([
	('batch_size_beam_width',
	[bb_range])
	]))
	runtime_perf_knobs = Tensor(name='host_runtime_perf_knobs',
	dtype=trt.int64,
	shape=[16],
	dim_range=OrderedDict([
	('perf_knob_size', [16])
	]))

	last_token_ids = None
	if self.mapping.is_last_pp_rank() and not gather_context_logits:
	last_token_ids = Tensor(
	name="last_token_ids",
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([("batch_size_last_token_ids", [bb_range])
	]),
	)

	if not use_gpt_attention_plugin:
	attention_mask = Tensor(
	name='attention_mask',
	dtype=trt.int32,
	shape=[-1, -1],
	dim_range=OrderedDict([
	('batch_size_beam_width', [bb_range]),
	('mask_len', [mask_len_range]),
	]),
	)

	cross_attention_mask = Tensor(
	name='cross_attention_mask',
	dtype=trt.int32,
	shape=[-1, -1, -1],
	dim_range=OrderedDict([
	('batch_size_beam_width', [bb_range]),
	('query_len', [1]),
	('encoder_input_len', [encoder_input_len_range]),
	]),
	)

	cache_indirection = Tensor(
	name='cache_indirection',
	dtype=trt.int32,
	shape=[-1, -1, -1],
	dim_range=OrderedDict([
	('batch_size_cache', [bs_range]),
	('beam_width', [beam_width_range]),
	('max_seq_len', [max_output_len_range]),
	]),
	)

	if self.mapping.tp_size > 1:
	current_all_reduce_helper().set_workspace_tensor(self.mapping, 1)

	layers_range = self.mapping.pp_layers(self.total_num_layers)
	num_pp_layers = len(layers_range)

	host_max_attention_window_sizes = None
	host_sink_token_length = None
	if use_gpt_attention_plugin:
	host_max_attention_window_sizes = Tensor(
	name=f'host_max_attention_window_sizes',
	dtype=trt.int32,
	shape=[num_pp_layers],
	dim_range=OrderedDict([('num_layers', [num_pp_layers])]))
	host_sink_token_length = Tensor(name='host_sink_token_length',
	dtype=trt.int32,
	shape=[1],
	dim_range=OrderedDict([('scalar',
	[1])]))
	'''
	LoRA plugin related inputs:
	lora_target_modules for BART-decoder:
	['attn_q', 'cross_attn_q',
	'attn_v', 'cross_attn_v']
	This is NOT directly loaded from the adapter-config file
	We make it this way because BART has LoRA weights for both self-attention and cross-attention in decoder
	'''
	lora_weights_pointers = None
	lora_ranks = None
	lora_params = None
	if use_lora_plugin:
	lora_weights_pointers = []
	lora_ranks = []
	# In current design, q_lora_params, k_lora_params and v_lora_params should be all enabled or all disabled at the same time.
	# However, BART lora modules only contain two of them, so we use zero tensor to fill the missing ones.
	missing_qkv_modules = []
	if any(x in lora_target_modules
	for x in ["attn_q", "attn_k", "attn_v"]):
	for lora_module in [
	"attn_q",
	"attn_k",
	"attn_v",
	]:
	if lora_module not in lora_target_modules:
	missing_qkv_modules.append(lora_module)
	if any(x in lora_target_modules
	for x in ["cross_attn_q", "cross_attn_k", "cross_attn_v"]):
	for lora_module in [
	"cross_attn_q", "cross_attn_k", "cross_attn_v"
	]:
	if lora_module not in lora_target_modules:
	missing_qkv_modules.append(lora_module)

	for i in layers_range:
	lora_weight_pointer_dict = {}
	lora_rank_dict = {}
	for lora_module in (lora_target_modules + missing_qkv_modules):
	lora_weight_pointer = Tensor(
	name=f'{lora_module}_lora_weights_pointers_{i}',
	dtype=trt.int64,
	shape=[-1, 2],
	dim_range=OrderedDict([('batch_size_beam_width',
	[bb_range]), ('in_out', [2])]))
	lora_weight_pointer_dict.update({
	f'{lora_module}_lora_weights_pointers':
	lora_weight_pointer
	})

	lora_rank = Tensor(name=f'{lora_module}_lora_ranks_{i}',
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([
	('batch_size_beam_width', [bb_range])
	]))
	lora_rank_dict.update(
	{f'{lora_module}_lora_ranks': lora_rank})

	lora_weights_pointers.append(lora_weight_pointer_dict)
	lora_ranks.append(lora_rank_dict)

	# For cross attention, we need to use encoder_input_lengths (in CPU) to pass
	# as the host_context_lengths to the lora_plugin. But for self attention, we
	# should keep using the original host_context_lengths. Therefore, we keep both
	# of them in the lora_params.
	host_encoder_input_lengths = None
	if remove_input_padding:
	host_encoder_input_lengths = Tensor(
	name="host_encoder_input_lengths",
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([("batch_size_beam_width", [bb_range])
	]),
	)

	lora_params = LoraParams(
	lora_ranks=lora_ranks,
	lora_weights_pointers=lora_weights_pointers,
	host_context_lengths=host_context_lengths,
	max_context_length=max_decoder_input_len,
	max_encoder_context_length=max_encoder_input_len,
	host_request_types=host_request_types,
	host_encoder_input_lengths=host_encoder_input_lengths,
	)

	kv_cache_block_offsets = None
	host_kv_cache_block_offsets = None
	host_kv_cache_pool_pointers = None

	cross_kv_cache_block_offsets = None
	host_cross_kv_cache_block_offsets = None
	host_cross_kv_cache_pool_pointers = None

	if use_cache:
	if not paged_kv_cache:
	for i in layers_range:
	kv_dim_range = OrderedDict([
	('batch_size_beam_width', [bb_range]),
	('kv', [2]),
	('num_heads', [num_kv_heads]),
	('past_key_len', [max_output_len_range]),
	('head_size', [head_size]),
	])
	kv = Tensor(name=f'past_key_value_{i}',
	dtype=self._kv_dtype,
	shape=[-1, 2, num_kv_heads, -1, head_size],
	dim_range=kv_dim_range)

	if use_gpt_attention_plugin:
	cross_kv_dim_range = OrderedDict([
	('batch_size_beam_width', [bb_range]),
	('kv', [2]),
	('cross_num_heads', [encoder_num_kv_heads]),
	('cross_past_key_len', [encoder_input_len_range]),
	('cross_head_size', [encoder_head_size]),
	])
	cross_kv = Tensor(name=f'cross_past_key_value_{i}',
	dtype=self._kv_dtype,
	shape=[
	-1, 2, encoder_num_kv_heads, -1,
	encoder_head_size
	],
	dim_range=cross_kv_dim_range)
	past_key_value.append((kv, cross_kv))
	else:
	# use encoder_output directly, no need to save cross_past_key_value
	past_key_value.append((kv, ))

	# TODO: Remove this when TRT fix the named dimension
	if not remove_input_padding:
	assertion(
	shape(
	input_ids if self.mapping.is_first_pp_rank() else
	hidden_states, 0) == shape(kv, 0), 'batch size')

	else: # paged_kv_cache == True
	# PagedKV setup for KV cache of self-attention
	max_blocks_per_seq_range = [[
	math.ceil(max_output_len_range[0] / tokens_per_block),
	math.ceil(max_output_len_range[1] / tokens_per_block),
	math.ceil(max_output_len_range[2] / tokens_per_block)
	]]
	max_blocks_per_seq_range = [[
	x for x in max_blocks_per_seq_range[0]
	]]

	# PagedKV setup for KV cache of cross-attention
	max_cross_blocks_per_seq_range = [[
	math.ceil(encoder_input_len_range[0] / tokens_per_block),
	math.ceil(encoder_input_len_range[1] / tokens_per_block),
	math.ceil(encoder_input_len_range[2] / tokens_per_block)
	]]
	max_cross_blocks_per_seq_range = [[
	x for x in max_cross_blocks_per_seq_range[0]
	]]

	kv_cache_block_offsets = Tensor(name=f'kv_cache_block_offsets',
	dtype=trt.int32,
	shape=[-1, 2, -1],
	dim_range=OrderedDict([
	('batch_size_beam_width',
	[bb_range]),
	('kv', [2]),
	('max_blocks_per_seq',
	max_blocks_per_seq_range),
	]))
	host_kv_cache_block_offsets = Tensor(
	name=f'host_kv_cache_block_offsets',
	dtype=trt.int32,
	shape=[-1, 2, -1],
	dim_range=OrderedDict([
	('batch_size_beam_width', [bb_range]),
	('kv', [2]),
	('max_blocks_per_seq', max_blocks_per_seq_range),
	]))
	host_kv_cache_pool_pointers = Tensor(
	name=f'host_kv_cache_pool_pointers',
	dtype=trt.int64,
	shape=[2],
	dim_range=OrderedDict([
	('num_pools', [2]),
	]))

	# paged blocks for cross kv
	cross_kv_cache_block_offsets = Tensor(
	name=f'cross_kv_cache_block_offsets',
	dtype=trt.int32,
	shape=[-1, 2, -1],
	dim_range=OrderedDict([
	('batch_size_beam_width', [bb_range]),
	('kv', [2]),
	('max_cross_blocks_per_seq',
	max_cross_blocks_per_seq_range),
	]))
	host_cross_kv_cache_block_offsets = Tensor(
	name=f'host_cross_kv_cache_block_offsets',
	dtype=trt.int32,
	shape=[-1, 2, -1],
	dim_range=OrderedDict([
	('batch_size_beam_width', [bb_range]),
	('kv', [2]),
	('max_cross_blocks_per_seq',
	max_cross_blocks_per_seq_range),
	]))
	host_cross_kv_cache_pool_pointers = Tensor(
	name=f'host_cross_kv_cache_pool_pointers',
	dtype=trt.int64,
	shape=[2],
	dim_range=OrderedDict([
	('num_pools', [2]),
	]))

	for i in layers_range:
	past_key_value.append(None)

	kv_cache_params = KeyValueCacheParams(
	past_key_value=past_key_value,
	host_past_key_value_lengths=host_past_key_value_lengths,
	host_max_attention_window_sizes=host_max_attention_window_sizes,
	host_sink_token_length=host_sink_token_length,
	cache_indirection=cache_indirection,
	kv_cache_block_offsets=kv_cache_block_offsets,
	host_kv_cache_block_offsets=host_kv_cache_block_offsets,
	host_kv_cache_pool_pointers=host_kv_cache_pool_pointers,
	cross_kv_cache_block_offsets=cross_kv_cache_block_offsets,
	host_cross_kv_cache_block_offsets=
	host_cross_kv_cache_block_offsets,
	host_cross_kv_cache_pool_pointers=
	host_cross_kv_cache_pool_pointers,
	)

	attention_params = AttentionParams(
	sequence_length=sequence_length,
	context_lengths=context_lengths,
	host_context_lengths=host_context_lengths,
	max_context_length=max_decoder_input_len,
	host_request_types=host_request_types,
	encoder_input_lengths=encoder_input_lengths,
	encoder_max_input_length=encoder_max_input_length,
	host_runtime_perf_knobs=runtime_perf_knobs)

	cross_kv_cache_gen = Tensor(name='cross_kv_cache_gen',
	dtype=trt.bool,
	shape=[1],
	dim_range=OrderedDict([
	('boolean', [1]),
	]))
	cross_qkv_reuse = None
	num_heads = (self.num_heads + self.mapping.tp_size -
	1) // self.mapping.tp_size
	cross_qkv_out_dim = num_heads * self.head_size + 2 * num_kv_heads * self.head_size
	if self.skip_cross_qkv:
	if remove_input_padding:
	cross_qkv_reuse = Tensor(
	name="cross_qkv_reuse",
	dtype=self._dtype,
	shape=[-1, cross_qkv_out_dim],
	dim_range=OrderedDict([
	("encoder_num_tokens", [encoder_num_tokens_range]),
	("encoder_qkv_size", [cross_qkv_out_dim]),
	]),
	)
	else:
	cross_qkv_reuse = Tensor(
	name="cross_qkv_reuse",
	dtype=self._dtype,
	shape=[-1, -1, cross_qkv_out_dim],
	dim_range=OrderedDict([
	("batch_size_beam_width_encoder", [bb_range]),
	("encoder_input_len", [encoder_input_len_range]),
	("encoder_qkv_size", [cross_qkv_out_dim]),
	]),
	)

	result = {
	'decoder_input_ids': input_ids,
	'encoder_output': encoder_output,
	'position_ids': position_ids,
	'token_type_ids': token_type_ids,
	'use_cache': True,
	'attention_mask': attention_mask,
	'cross_attention_mask': cross_attention_mask,
	'last_token_ids': last_token_ids,
	'kv_cache_params': kv_cache_params,
	'attention_params': attention_params,
	'hidden_states': hidden_states,
	'lora_params': lora_params,
	'cross_kv_cache_gen': cross_kv_cache_gen,
	'cross_qkv_reuse': cross_qkv_reuse,
	}

	return result

	def use_lora(self, lora_config: LoraConfig):
	use_lora(self, lora_config, self.trtllm_modules_to_hf_modules)

	def precompute_relative_attention_bias(self, build_config):
	if self.config.relative_attention and not self.use_implicit_relative_attention:
	relative_attention_bias_builder = torch.ops.tensorrt_llm.relative_attention_bias
	rel_attn_precomputed = torch.zeros(
	(self.config.num_attention_heads // self.mapping.tp_size,
	build_config.max_seq_len + 1, build_config.max_seq_len + 1),
	dtype=str_dtype_to_torch(self.config.dtype),
	device='cuda')
	rel_attn_table = numpy_to_torch(
	self.rel_attn_table.raw_value).to('cuda')
	relative_attention_bias_builder(
	rel_attn_precomputed,
	rel_attn_table,
	self.config.num_attention_heads // self.mapping.tp_size,
	build_config.max_seq_len,
	self.config.num_buckets,
	False,
	self.config.max_distance,
	)
	for layer_idx in range(self.num_layers):
	self.decoder_layers[
	layer_idx].self_attention.set_rel_attn_table(
	build_config.max_seq_len, rel_attn_precomputed)


	class WhisperEncoder(PretrainedModel):

	def __init__(self, config: PretrainedConfig):
	super().__init__(config)
	self._dtype = self.config.dtype
	# Encoder conv needs to run in fp32 on Volta/Turing
	major, minor = torch.cuda.get_device_capability()
	if major >= 8:
	self._conv_dtype = self._dtype
	else:
	self._conv_dtype = "float32"
	self.conv1 = Conv1d(config.n_mels,
	config.hidden_size,
	kernel_size=3,
	padding=1,
	dtype=self._conv_dtype)
	self.conv2 = Conv1d(config.hidden_size,
	config.hidden_size,
	kernel_size=3,
	stride=2,
	padding=1,
	dtype=self._conv_dtype)

	self.positional_embedding = Parameter(shape=(config.n_audio_ctx,
	config.hidden_size),
	dtype=self._dtype)
	self.encoder_layers = ModuleList([
	EncoderLayer(
	hidden_size=config.hidden_size,
	ffn_hidden_size=config.hidden_size * 4,
	num_attention_heads=config.num_attention_heads,
	num_kv_heads=config.num_attention_heads,
	head_size=config.hidden_size // config.num_attention_heads,
	max_position_embeddings=3000,
	q_scaling=1.0,
	has_attention_qkvo_bias=True,
	has_mlp_bias=True,
	hidden_act='gelu',
	dtype=self._dtype) for _ in range(config.num_hidden_layers)
	])

	self.ln_post = LayerNorm(config.hidden_size, dtype=self._dtype)
	self.max_audio_feature_seq_len = 3000

	def forward(self, input_features: Tensor, input_lengths=None):
	if default_net().plugin_config.remove_input_padding:
	# BXT,D -> B,T,D -> B,D,T
	input_features = input_features.view([
	input_lengths.shape[0], self.max_audio_feature_seq_len,
	self.config.n_mels
	])
	input_features = transpose(input_features, 1, 2)
	# Encoder conv needs to run in fp32 on Volta/Turing
	x_type = input_features.dtype
	input_features = cast(input_features, self._conv_dtype)
	x = self.conv1(input_features)
	x = gelu(x)
	x = self.conv2(x)
	x = cast(x, x_type)
	x = gelu(x)
	x = transpose(x, 2, 1)
	x = x + cast(
	slice(input=self.positional_embedding.value,
	starts=[0, 0],
	sizes=[
	self.max_audio_feature_seq_len // 2,
	self.positional_embedding.shape[1]
	],
	strides=[1, 1]), x.dtype)
	if default_net().plugin_config.remove_input_padding:
	#B,T,D -> BxT,D
	x = x.view([-1, self.config.hidden_size])
	hidden_states = x
	for encoder_layer in self.encoder_layers:
	hidden_states = encoder_layer(hidden_states,
	input_lengths=input_lengths)

	x = hidden_states
	x = self.ln_post(x)
	x.mark_output('encoder_output', self._dtype)
	return x

	def prepare_inputs(self, max_batch_size=16):

	bs_range = [1, (max_batch_size + 1) // 2, max_batch_size]
	# You may change max_audio_feature_seq_len here for distill-whisper models.
	max_audio_feature_seq_len = self.max_audio_feature_seq_len
	if not default_net().plugin_config.remove_input_padding:
	x = Tensor(
	name="input_features",
	dtype=self._dtype,
	shape=[-1, self.config.n_mels, max_audio_feature_seq_len],
	dim_range=OrderedDict([
	("batch_size", [bs_range]),
	("feature_dim", [self.config.n_mels]),
	("feature_len_range", [max_audio_feature_seq_len]),
	]))
	else:
	batch_seqlen_range = [
	1,
	(max_audio_feature_seq_len * max_batch_size + 1) // 2,
	max_audio_feature_seq_len * max_batch_size,
	]
	x = Tensor(name="input_features",
	dtype=self._dtype,
	shape=[-1, self.config.n_mels],
	dim_range=OrderedDict([
	("batch_seqlen_range", [batch_seqlen_range]),
	("feature_dim", [self.config.n_mels]),
	]))
	input_lengths = Tensor(
	name="input_lengths",
	dtype=trt.int32,
	shape=[-1],
	dim_range=OrderedDict([("batch_size", [bs_range])]),
	)

	return {'input_features': x, 'input_lengths': input_lengths}

	def precompute_relative_attention_bias(self, build_config):
	pass