# Source code for tensorrt_llm.models.falcon.model

    # SPDX-FileCopyrightText: Copyright (c) 2022-2023 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.
    from typing import Optional, Union

    import tensorrt as trt

    from ..._common import default_net
    from ..._utils import pad_vocab_size, str_dtype_to_trt
    from ...functional import Tensor, gather_last_token_logits, recv, send
    from ...layers import (MLP, Attention, AttentionMaskType, AttentionParams,
                           ColumnLinear, Embedding, KeyValueCacheParams, LayerNorm,
                           PositionEmbeddingType)
    from ...mapping import Mapping
    from ...module import Module, ModuleList
    from ...quantization import QuantMode
    from ..generation_mixin import GenerationMixin

    class FalconDecoderLayer(Module):

        def __init__(
            self,
            hidden_size,
            num_attention_heads,
            max_position_embeddings,
            num_attention_kv_heads=None,
            dtype=None,
            hidden_act='gelu',
            quant_mode=QuantMode(0),
            mlp_hidden_size=None,
            bias=True,
            use_alibi=True,
            new_decoder_architecture=False,
            parallel_attention=False,
            layernorm_epsilon=1e-5,
            tp_group=None,
            tp_size=1,
            tp_rank=0,
            layer_id=None,
        ):
            super().__init__()
            self._layer_id = layer_id  # useful for debugging
            self.hidden_size = hidden_size
            self.num_attention_heads = num_attention_heads
            self.num_attention_kv_heads = num_attention_kv_heads
            self.max_position_embeddings = max_position_embeddings
            self.dtype = dtype
            self.hidden_act = hidden_act
            self.tp_group = tp_group
            self.tp_size = tp_size
            self.input_layernorm = LayerNorm(normalized_shape=hidden_size,
                                             eps=layernorm_epsilon,
                                             dtype=dtype)
            if use_alibi:
                # Note falcon models will also scale alibi with inv_sqrt_Dh
                position_embedding_type = PositionEmbeddingType.alibi_with_scale
            else:
                position_embedding_type = PositionEmbeddingType.rope_gpt_neox

            self.attention = Attention(
                hidden_size=hidden_size,
                num_attention_heads=num_attention_heads,
                num_kv_heads=num_attention_kv_heads,
                max_position_embeddings=max_position_embeddings,
                dtype=dtype,
                attention_mask_type=AttentionMaskType.causal,
                bias=bias,
                position_embedding_type=position_embedding_type,
                tp_group=tp_group,
                tp_size=tp_size,
                tp_rank=tp_rank,
                use_int8_kv_cache=quant_mode.has_int8_kv_cache(),
                quant_mode=quant_mode,
                instance_id=2 * layer_id,
            )

            if mlp_hidden_size is None:
                mlp_hidden_size = hidden_size * 4

            self.new_decoder_architecture = new_decoder_architecture
            self.parallel_attn = parallel_attention

            if self.new_decoder_architecture:
                # Layernorm before MLP.
                self.mlp_layernorm = LayerNorm(normalized_shape=hidden_size,
                                               eps=layernorm_epsilon,
                                               dtype=dtype)
            else:
                self.mlp_layernorm = None
            self.mlp = MLP(
                hidden_size=hidden_size,
                ffn_hidden_size=mlp_hidden_size,
                hidden_act=hidden_act,
                dtype=dtype,
                bias=bias,
                tp_group=tp_group,
                tp_size=tp_size,
                quant_mode=quant_mode,
                instance_id=2 * layer_id + 1,
            )
            if self.new_decoder_architecture or self.parallel_attn:
                self.post_layernorm = None
            else:
                self.post_layernorm = LayerNorm(normalized_shape=hidden_size,
                                                dtype=dtype)

        def forward(self,
                    hidden_states: Tensor,
                    attention_mask=None,
                    use_cache=False,
                    kv_cache_params=None,
                    attention_params=None,
                    all_reduce_workspace=None):
            assert isinstance(hidden_states, Tensor)

            residual = hidden_states

            if self.new_decoder_architecture:
                mlp_ln_output = self.mlp_layernorm(hidden_states)
            hidden_states = self.input_layernorm(hidden_states)
            input_ln_output = hidden_states
            attention_output = self.attention(hidden_states,
                                              attention_mask=attention_mask,
                                              use_cache=use_cache,
                                              kv_cache_params=kv_cache_params,
                                              attention_params=attention_params,
                                              workspace=all_reduce_workspace)

            if use_cache:
                attention_output, presents = attention_output

            if not self.new_decoder_architecture:
                if self.parallel_attn:
                    hidden_states = input_ln_output
                else:
                    hidden_states = residual + attention_output
                    residual = hidden_states
                    hidden_states = self.post_layernorm(hidden_states)
            else:
                hidden_states = mlp_ln_output

            hidden_states = self.mlp(hidden_states, all_reduce_workspace)

            if self.new_decoder_architecture or self.parallel_attn:
                hidden_states = hidden_states + attention_output

            hidden_states = residual + hidden_states
            if use_cache:
                return hidden_states, presents
            return hidden_states

    class FalconModel(Module):

        def __init__(
            self,
            num_layers: int,
            num_heads: int,
            hidden_size: int,
            vocab_size: int,
            hidden_act: int,
            max_position_embeddings: int,
            dtype: Optional[Union[str, trt.DataType]] = None,
            mapping: Mapping = Mapping(),
            num_kv_heads: Optional[int] = None,
            mlp_hidden_size: Optional[int] = None,
            bias: bool = True,
            quant_mode: QuantMode = QuantMode(0),
            use_alibi: bool = True,
            parallel_attention: bool = False,
            new_decoder_architecture: bool = False,
        ):
            super().__init__()
            self.num_layers = num_layers
            self.num_heads = num_heads
            self.num_kv_heads = num_kv_heads or num_heads
            self.hidden_size = hidden_size
            self.vocab_size = vocab_size
            self.mapping = mapping

            # Falcon variants
            self.parallel_attention = parallel_attention
            self.new_decoder_architecture = new_decoder_architecture

            self.quant_mode = quant_mode
            assert isinstance(dtype, (str, trt.DataType))
            if isinstance(dtype, str):
                self.dtype = str_dtype_to_trt(dtype)
            else:
                self.dtype = dtype
            if self.quant_mode.has_int8_kv_cache():
                self.kv_dtype = str_dtype_to_trt('int8')
            elif quant_mode.has_fp8_kv_cache():
                self.kv_dtype = str_dtype_to_trt('fp8')
            else:
                self.kv_dtype = self.dtype

            if self.mapping.is_first_pp_rank():
                self.embedding = Embedding(vocab_size, hidden_size, dtype=dtype)

            self.layers = ModuleList([
                FalconDecoderLayer(
                    hidden_size=hidden_size,
                    num_attention_heads=num_heads,
                    max_position_embeddings=max_position_embeddings,
                    dtype=dtype,
                    bias=bias,
                    quant_mode=self.quant_mode,
                    hidden_act=hidden_act,
                    num_attention_kv_heads=self.num_kv_heads,
                    mlp_hidden_size=mlp_hidden_size,
                    use_alibi=use_alibi,
                    parallel_attention=parallel_attention,
                    new_decoder_architecture=new_decoder_architecture,
                    tp_group=mapping.tp_group,
                    tp_size=mapping.tp_size,
                    tp_rank=mapping.tp_rank,
                    layer_id=i,
                ) for i in self.get_transformer_layers(self.mapping, num_layers)
            ])

            if self.mapping.is_last_pp_rank():
                self.ln_f = LayerNorm(normalized_shape=hidden_size, dtype=dtype)

        def forward(self,
                    input_ids: Tensor,
                    position_ids=None,
                    use_cache=False,
                    attention_mask=None,
                    kv_cache_params=None,
                    attention_params=None,
                    hidden_states=None,
                    all_reduce_workspace=None):

            kv_cache_params.fill_none_tensor_list(len(self.layers))

            if use_cache:
                presents = []

            if self.mapping.is_first_pp_rank():
                hidden_states = self.embedding(input_ids)
            else:
                hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())

            for layer, past, pointer, max_kv_cache_length in zip(
                    self.layers, kv_cache_params.past_key_value,
                    kv_cache_params.kv_cache_block_pointers,
                    kv_cache_params.host_max_kv_cache_lengths):
                hidden_states = layer(
                    hidden_states,
                    use_cache=use_cache,
                    attention_mask=attention_mask,
                    kv_cache_params=KeyValueCacheParams(
                        past_key_value=[past],
                        host_past_key_value_lengths=kv_cache_params.
                        host_past_key_value_lengths,
                        host_max_kv_cache_lengths=max_kv_cache_length,
                        kv_cache_block_pointers=[pointer],
                        cache_indirection=kv_cache_params.cache_indirection),
                    attention_params=attention_params,
                    all_reduce_workspace=all_reduce_workspace)

                if use_cache:
                    presents.append(hidden_states[1])
                    hidden_states = hidden_states[0]

            if self.mapping.is_last_pp_rank():
                hidden_states = self.ln_f(hidden_states)
            else:
                hidden_states = send(hidden_states, self.mapping.next_pp_rank())

            if use_cache:
                return (hidden_states, tuple(presents))
            return hidden_states

    class FalconForCausalLM(FalconModel, GenerationMixin):

        def __init__(self,
                     num_layers: int,
                     num_heads: int,
                     hidden_size: int,
                     vocab_size: int,
                     max_position_embeddings: int,
                     hidden_act: str = 'gelu',
                     dtype: Optional[Union[str, trt.DataType]] = None,
                     num_kv_heads: Optional[int] = None,
                     mlp_hidden_size: Optional[int] = None,
                     bias: bool = True,
                     quant_mode: QuantMode = QuantMode(0),
                     use_alibi: bool = True,
                     parallel_attention: bool = False,
                     new_decoder_architecture: bool = False,
                     logits_dtype: Union[str, trt.DataType] = 'float32',
                     mapping=Mapping()):
            super().__init__(num_layers=num_layers,
                             num_heads=num_heads,
                             hidden_size=hidden_size,
                             vocab_size=vocab_size,
                             hidden_act=hidden_act,
                             max_position_embeddings=max_position_embeddings,
                             dtype=dtype,
                             num_kv_heads=num_kv_heads,
                             mlp_hidden_size=mlp_hidden_size,
                             bias=bias,
                             quant_mode=quant_mode,
                             mapping=mapping,
                             use_alibi=use_alibi,
                             parallel_attention=parallel_attention,
                             new_decoder_architecture=new_decoder_architecture)

            # TODO: For compatibility to quantization modules. Remove it later.
            self._num_layers = num_layers

            vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
            if self.mapping.is_last_pp_rank():
                self.lm_head = ColumnLinear(
                    hidden_size,
                    vocab_size_padded,
                    bias=False,
                    dtype=dtype,
                    tp_group=mapping.tp_group,
                    tp_size=mapping.tp_size,
                    gather_output=True,
                )
            if isinstance(logits_dtype, str):
                self.logits_dtype = str_dtype_to_trt(logits_dtype)
            else:
                assert isinstance(logits_dtype, trt.DataType)
                self.logits_dtype = logits_dtype

        def forward(self,
                    input_ids: Tensor,
                    position_ids=None,
                    use_cache=False,
                    last_token_ids=None,
                    attention_mask=None,
                    kv_cache_params=None,
                    attention_params=None,
                    hidden_states=None,
                    all_reduce_workspace=None):

            hidden_states = super().forward(
                input_ids=input_ids,
                position_ids=position_ids,
                use_cache=use_cache,
                attention_mask=attention_mask,
                kv_cache_params=kv_cache_params,
                attention_params=attention_params,
                hidden_states=hidden_states,
                all_reduce_workspace=all_reduce_workspace)

            if use_cache:
                hidden_states, presents = hidden_states

            if self.mapping.is_last_pp_rank():
                hidden_states = gather_last_token_logits(
                    hidden_states,
                    last_token_ids,
                    default_net().plugin_config.remove_input_padding,
                )

                # [batch_size, hidden_size] -> [batch_size, vocab_size]
                lm_logits = self.lm_head(hidden_states)
                lm_logits.mark_output('logits', self.logits_dtype)
            else:
                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.get_transformer_layers(self.mapping, self.num_layers),
                        presents):
                    present.mark_output(f'present_key_value_{i}', self.kv_dtype)
                if self.mapping.is_last_pp_rank():
                    return lm_logits, presents
                else:
                    return hidden_states, presents
            else:
                if self.mapping.is_last_pp_rank():
                    return lm_logits
                else:
                    return hidden_states

        def prepare_inputs(self,
                           max_batch_size: int,
                           max_input_len: int,
                           max_new_tokens: int,
                           use_cache: bool,
                           max_beam_width: int = 1,
                           max_num_tokens: int = None):
            '''

            @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
            head_size = self.hidden_size // self.num_heads

            plugin_config = default_net().plugin_config
            use_gpt_attention_plugin = plugin_config.gpt_attention_plugin
            remove_input_padding = plugin_config.remove_input_padding
            use_gemm_plugin = plugin_config.gemm_plugin
            paged_kv_cache = plugin_config.paged_kv_cache
            tokens_per_block = plugin_config.tokens_per_block
            use_custom_all_reduce = plugin_config.use_custom_all_reduce

            model_inputs = self.prepare_basic_inputs(
                max_batch_size=max_batch_size,
                max_beam_width=max_beam_width,
                max_input_len=max_input_len,
                max_new_tokens=max_new_tokens,
                num_kv_heads=self.num_kv_heads,
                head_size=head_size,
                num_layers=self.num_layers,
                kv_dtype=self.kv_dtype,
                remove_input_padding=remove_input_padding,
                use_gpt_attention_plugin=use_gpt_attention_plugin,
                use_gemm_plugin=use_gemm_plugin,
                use_custom_all_reduce=use_custom_all_reduce,
                paged_kv_cache=paged_kv_cache,
                tokens_per_block=tokens_per_block,
                dtype=self.dtype,
                num_heads=self.num_heads,
                mapping=self.mapping,
                max_num_tokens=max_num_tokens)

            return (
                model_inputs['input_ids'],
                model_inputs['position_ids'],
                use_cache,
                model_inputs['last_token_ids'],
                model_inputs['attention_mask'],
                KeyValueCacheParams(
                    past_key_value=model_inputs['past_key_value'],
                    host_past_key_value_lengths=model_inputs[
                        'host_past_key_value_lengths'],
                    host_max_kv_cache_lengths=model_inputs[
                        'host_max_kv_cache_lengths'],
                    kv_cache_block_pointers=model_inputs[
                        'kv_cache_block_pointers_list'],
                    cache_indirection=model_inputs['cache_indirection']),
                AttentionParams(
                    sequence_length=model_inputs['sequence_length'],
                    context_lengths=model_inputs['context_lengths'],
                    host_context_lengths=model_inputs['host_context_lengths'],
                    max_context_length=max_input_len,
                    host_request_types=model_inputs['host_request_types']),
                model_inputs['hidden_states_input'],
                model_inputs['all_reduce_workspace'],
            )