# Source code for tensorrt_llm.models.gptneox.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.
    import tensorrt as trt

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

    class GPTNeoXDecoderLayer(Module):

        def __init__(self,
                     hidden_size,
                     num_attention_heads,
                     max_position_embeddings,
                     num_layers,
                     rotary_dim,
                     dtype=None,
                     apply_query_key_layer_scaling=False,
                     attention_mask_type=AttentionMaskType.causal,
                     hidden_act='relu',
                     position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
                     tp_group=None,
                     tp_size=1):
            super().__init__()
            self.input_layernorm = LayerNorm(normalized_shape=hidden_size,
                                             dtype=dtype)

            self.post_attention_layernorm = LayerNorm(normalized_shape=hidden_size,
                                                      dtype=dtype)

            self.attention = Attention(
                hidden_size=hidden_size,
                num_attention_heads=num_attention_heads,
                rotary_embedding_percentage=rotary_dim /
                (hidden_size // num_attention_heads),
                position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
                max_position_embeddings=max_position_embeddings,
                dtype=dtype,
                attention_mask_type=AttentionMaskType.causal,
                bias=True,
                tp_group=tp_group,
                tp_size=tp_size)

            self.mlp = MLP(hidden_size=hidden_size,
                           ffn_hidden_size=hidden_size * 4,
                           hidden_act=hidden_act,
                           dtype=dtype,
                           tp_group=tp_group,
                           tp_size=tp_size)

        def forward(self,
                    hidden_states: Tensor,
                    attention_mask=None,
                    use_cache=False,
                    kv_cache_params=None,
                    attention_params=None):
            if not default_net(
            ).plugin_config.layernorm_plugin and trt.__version__[:3] == '8.6':
                raise AssertionError(
                    "You need to enable the LayerNorm plugin for GPT-NeoX with TensorRT 8.6. Please set plugin_config.layernorm_plugin"
                )
            residual = hidden_states

            input_layernorm_output = self.input_layernorm(hidden_states)
            post_attention_layernorm_output = self.post_attention_layernorm(
                hidden_states)

            attention_output = self.attention(input_layernorm_output,
                                              attention_mask=attention_mask,
                                              use_cache=use_cache,
                                              kv_cache_params=kv_cache_params,
                                              attention_params=attention_params,
                                              norm_before_bmm1=True)

            if use_cache:
                attention_output, presents = attention_output

            feed_forward_hidden_states = self.mlp(post_attention_layernorm_output)
            hidden_states = attention_output + feed_forward_hidden_states + residual
            if use_cache:
                return (hidden_states, presents)
            return hidden_states

    class GPTNeoXModel(Module):

        def __init__(self,
                     num_layers,
                     num_heads,
                     hidden_size,
                     vocab_size,
                     hidden_act,
                     max_position_embeddings,
                     rotary_dim,
                     dtype=None,
                     position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
                     mapping=Mapping(),
                     apply_query_key_layer_scaling=False,
                     use_parallel_embedding=False,
                     embedding_sharding_dim=0):
            super().__init__()
            self.embedding = Embedding(
                num_embeddings=vocab_size,
                embedding_dim=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.rank)

            self.layers = ModuleList([
                GPTNeoXDecoderLayer(
                    hidden_size=hidden_size,
                    num_attention_heads=num_heads,
                    max_position_embeddings=max_position_embeddings,
                    num_layers=num_layers,
                    rotary_dim=rotary_dim,
                    dtype=dtype,
                    apply_query_key_layer_scaling=apply_query_key_layer_scaling,
                    attention_mask_type=AttentionMaskType.causal,
                    hidden_act=hidden_act,
                    position_embedding_type=position_embedding_type,
                    tp_group=mapping.tp_group,
                    tp_size=mapping.tp_size) for _ in range(num_layers)
            ])

            self.ln_f = LayerNorm(normalized_shape=hidden_size, dtype=dtype)

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

            kv_cache_params.fill_none_tensor_list(len(self.layers))

            if use_cache:
                presents = []

            for layer, past, max_kv_cache_length in zip(
                    self.layers, kv_cache_params.past_key_value,
                    kv_cache_params.host_max_kv_cache_lengths):
                hidden_states = layer(
                    hidden_states,
                    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_kv_cache_lengths=max_kv_cache_length,
                        cache_indirection=kv_cache_params.cache_indirection),
                    attention_params=attention_params)

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

            hidden_states = self.ln_f(hidden_states)

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

    class GPTNeoXForCausalLM(GPTNeoXModel, GenerationMixin):

        def __init__(self,
                     num_layers,
                     num_heads,
                     hidden_size,
                     vocab_size,
                     hidden_act,
                     max_position_embeddings,
                     rotary_dim,
                     dtype,
                     position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
                     mapping=Mapping(),
                     apply_query_key_layer_scaling=False,
                     use_parallel_embedding=False,
                     embedding_sharding_dim=0):
            if isinstance(dtype, str):
                self._kv_dtype = str_dtype_to_trt(dtype)
            else:
                assert isinstance(dtype, trt.DataType)
                self._kv_dtype = dtype
            self._num_layers = num_layers
            self._num_heads = num_heads
            self._hidden_size = hidden_size
            self._vocab_size = vocab_size
            self._tp_size = mapping.tp_size
            self._use_parallel_embedding = use_parallel_embedding
            self._embedding_sharding_dim = embedding_sharding_dim

            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,
                rotary_dim=rotary_dim,
                dtype=dtype,
                position_embedding_type=position_embedding_type,
                mapping=mapping,
                apply_query_key_layer_scaling=apply_query_key_layer_scaling,
                use_parallel_embedding=use_parallel_embedding,
                embedding_sharding_dim=embedding_sharding_dim)

            vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
            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)

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

            if use_cache:
                hidden_states, presents = hidden_states

            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._kv_dtype)

            if use_cache and default_net().plugin_config.paged_kv_cache == False:
                for i, present in enumerate(presents):
                    present.mark_output(f'present_key_value_{i}', self._kv_dtype)
                return (lm_logits, presents)

            return lm_logits

        def prepare_inputs(self, max_batch_size, max_input_len, max_new_tokens,
                           use_cache, max_beam_width):
            '''@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
            num_heads = self._num_heads // self._tp_size
            remove_input_padding = default_net().plugin_config.remove_input_padding
            use_gpt_attention_plugin = default_net(
            ).plugin_config.gpt_attention_plugin
            use_gemm_plugin = default_net().plugin_config.gemm_plugin

            model_inputs = self.prepare_basic_inputs(
                max_batch_size,
                max_beam_width,
                max_input_len,
                max_new_tokens,
                num_heads,
                head_size,
                self._num_layers,
                self._kv_dtype,
                remove_input_padding,
                use_gpt_attention_plugin,
                use_gemm_plugin=use_gemm_plugin)

            return (model_inputs['input_ids'], model_inputs['position_ids'], True,
                    model_inputs['last_token_ids'],
                    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'],
                        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']))