# Source code for tensorrt_llm.models.llama.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

    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 (Attention, AttentionMaskType, AttentionParams,
                           ColumnLinear, Embedding, FusedGatedMLP, GatedMLP,
                           KeyValueCacheParams, PositionEmbeddingType,
                           PromptTuningEmbedding, RmsNorm)
    from ...mapping import Mapping
    from ...module import Module, ModuleList
    from ...quantization import QuantMode
    from ..generation_mixin import GenerationMixin

    class LLaMADecoderLayer(Module):

        def __init__(self,
                     layer_id,
                     hidden_size,
                     num_attention_heads,
                     num_kv_heads=None,
                     max_position_embeddings=2048,
                     dtype=None,
                     attention_mask_type=AttentionMaskType.causal,
                     hidden_act='silu',
                     position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
                     rotary_base=10000.0,
                     rotary_scaling=None,
                     mlp_hidden_size=None,
                     tp_group=None,
                     tp_size=1,
                     quant_mode=QuantMode(0),
                     rms_norm_eps=1e-06,
                     attn_bias=False,
                     mlp_bias=False,
                     use_fused_mlp=False):
            super().__init__()
            self._layer_id = layer_id  # useful for debugging
            # used for quantizing model
            self.hidden_size = hidden_size
            self.num_attention_heads = num_attention_heads
            self.num_kv_heads = num_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.mlp_hidden_size = mlp_hidden_size
            self.attention_mask_type = attention_mask_type
            self.position_embedding_type = position_embedding_type
            self.input_layernorm = RmsNorm(normalized_shape=hidden_size,
                                           eps=rms_norm_eps,
                                           dtype=dtype)

            self.attention = Attention(
                hidden_size,
                num_attention_heads,
                num_kv_heads,
                max_position_embeddings,
                dtype=dtype,
                attention_mask_type=AttentionMaskType.causal,
                bias=attn_bias,
                position_embedding_type=position_embedding_type,
                rotary_embedding_base=rotary_base,
                rotary_embedding_scaling=rotary_scaling,
                tp_group=tp_group,
                tp_size=tp_size,
                use_int8_kv_cache=quant_mode.has_int8_kv_cache(),
                quant_mode=quant_mode,
                instance_id=2 * layer_id,
            )
            if not mlp_hidden_size:
                self.mlp_hidden_size = hidden_size * 4
            ClsMLP = FusedGatedMLP if use_fused_mlp is True else GatedMLP
            self.mlp = ClsMLP(hidden_size=hidden_size,
                              ffn_hidden_size=self.mlp_hidden_size,
                              hidden_act=hidden_act,
                              dtype=dtype,
                              bias=mlp_bias,
                              tp_group=tp_group,
                              tp_size=tp_size,
                              quant_mode=quant_mode,
                              instance_id=2 * layer_id + 1)
            self.post_layernorm = RmsNorm(normalized_shape=hidden_size,
                                          eps=rms_norm_eps,
                                          dtype=dtype)

        def forward(self,
                    hidden_states,
                    attention_mask=None,
                    use_cache=False,
                    kv_cache_params=None,
                    attention_params=None,
                    all_reduce_workspace=None):
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
            if self._layer_id == 0:
                self.register_network_output(f"norm0", 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 self._layer_id == 0:
                self.register_network_output(f"attn", attention_output)

            hidden_states = residual + attention_output

            residual = hidden_states
            hidden_states = self.post_layernorm(hidden_states)
            if self._layer_id == 0:
                self.register_network_output(f"norm1", hidden_states)

            hidden_states = self.mlp(hidden_states, all_reduce_workspace)
            if self._layer_id == 0:
                self.register_network_output(f"mlp", hidden_states)

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

    class LLaMAModel(Module):

        def __init__(self,
                     num_layers,
                     num_heads,
                     num_kv_heads,
                     hidden_size,
                     vocab_size,
                     hidden_act,
                     max_position_embeddings,
                     dtype,
                     mlp_hidden_size=None,
                     position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
                     rotary_base=10000.0,
                     rotary_scaling=None,
                     mapping=Mapping(),
                     quant_mode=QuantMode(0),
                     use_parallel_embedding=False,
                     embedding_sharding_dim=0,
                     rms_norm_eps=1e-06,
                     use_fused_mlp=False,
                     attn_bias=False,
                     mlp_bias=False,
                     use_prompt_tuning: bool = False):
            super().__init__()
            self.mapping = mapping
            self.use_prompt_tuning = use_prompt_tuning

            EmbeddingCls = PromptTuningEmbedding if use_prompt_tuning else Embedding
            if self.mapping.is_first_pp_rank():
                self.vocab_embedding = EmbeddingCls(
                    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.tp_rank,
                    instance_id=2 *
                    num_layers,  # ids in [0, 2 * (num_layers - 1) + 1] already used
                )

            self.layers = ModuleList([
                LLaMADecoderLayer(layer_id=i,
                                  hidden_size=hidden_size,
                                  num_attention_heads=num_heads,
                                  num_kv_heads=num_kv_heads,
                                  max_position_embeddings=max_position_embeddings,
                                  dtype=dtype,
                                  hidden_act=hidden_act,
                                  mlp_hidden_size=mlp_hidden_size,
                                  position_embedding_type=position_embedding_type,
                                  rotary_base=rotary_base,
                                  rotary_scaling=rotary_scaling,
                                  tp_group=mapping.tp_group,
                                  tp_size=mapping.tp_size,
                                  quant_mode=quant_mode,
                                  rms_norm_eps=rms_norm_eps,
                                  attn_bias=attn_bias,
                                  mlp_bias=mlp_bias,
                                  use_fused_mlp=use_fused_mlp)
                for i in self.get_transformer_layers(self.mapping, num_layers)
            ])

            if self.mapping.is_last_pp_rank():
                self.ln_f = RmsNorm(normalized_shape=hidden_size,
                                    eps=rms_norm_eps,
                                    dtype=dtype)

        def forward(
            self,
            input_ids,
            position_ids=None,
            use_cache=False,
            attention_mask=None,
            kv_cache_params=None,
            attention_params=None,
            hidden_states=None,
            all_reduce_workspace=None,
            prompt_embedding_table: Optional[Tensor] = None,
            prompt_tasks: Optional[Tensor] = None,
            prompt_vocab_size: Optional[Tensor] = None,
        ):

            kv_cache_params.fill_none_tensor_list(len(self.layers))

            if use_cache:
                presents = []

            ptuning_args = []
            if self.use_prompt_tuning:
                ptuning_args = [
                    prompt_embedding_table, prompt_tasks, prompt_vocab_size
                ]
            if self.mapping.is_first_pp_rank():
                hidden_states = self.vocab_embedding(input_ids, *ptuning_args,
                                                     all_reduce_workspace)
            else:
                hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())
            self.register_network_output(f"embd", hidden_states)

            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 LLaMAForCausalLM(LLaMAModel, GenerationMixin):

        def __init__(self,
                     num_layers,
                     num_heads,
                     num_kv_heads,
                     hidden_size,
                     vocab_size,
                     hidden_act,
                     max_position_embeddings,
                     dtype,
                     logits_dtype="float32",
                     mlp_hidden_size=None,
                     position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
                     rotary_base=10000.0,
                     rotary_scaling=None,
                     mapping=Mapping(),
                     quant_mode=QuantMode(0),
                     use_parallel_embedding=False,
                     embedding_sharding_dim=0,
                     rms_norm_eps=1e-06,
                     use_fused_mlp=False,
                     attn_bias=False,
                     mlp_bias=False,
                     use_prompt_tuning: bool = False):

            if isinstance(dtype, str):
                self.dtype = str_dtype_to_trt(dtype)
            else:
                assert isinstance(dtype, trt.DataType)
                self.dtype = dtype

            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

            self.num_layers = num_layers
            self.num_heads = num_heads
            if num_kv_heads is None or num_kv_heads <= 0:
                num_kv_heads = num_heads
            self.num_kv_heads = num_kv_heads
            self.hidden_size = hidden_size
            self.vocab_size = vocab_size
            self.tp_size = mapping.tp_size

            self.kv_dtype = self.dtype
            if 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')

            self.quant_mode = quant_mode
            self.use_parallel_embedding = use_parallel_embedding
            self.embedding_sharding_dim = embedding_sharding_dim

            super().__init__(num_layers, num_heads, num_kv_heads, hidden_size,
                             vocab_size, hidden_act, max_position_embeddings, dtype,
                             mlp_hidden_size, position_embedding_type, rotary_base,
                             rotary_scaling, mapping, quant_mode,
                             use_parallel_embedding, embedding_sharding_dim,
                             rms_norm_eps, use_fused_mlp, attn_bias, mlp_bias,
                             use_prompt_tuning)

            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)

        def forward(
            self,
            input_ids,
            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,
            prompt_embedding_table: Optional[Tensor] = None,
            prompt_tasks: Optional[Tensor] = None,
            prompt_vocab_size: Optional[Tensor] = None,
        ):
            hidden_states = super().forward(input_ids, position_ids, use_cache,
                                            attention_mask, kv_cache_params,
                                            attention_params, hidden_states,
                                            all_reduce_workspace,
                                            prompt_embedding_table, prompt_tasks,
                                            prompt_vocab_size)

            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)
                return (hidden_states, presents)
            else:
                if self.mapping.is_last_pp_rank():
                    return lm_logits
                return hidden_states

        def prepare_inputs(self,
                           max_batch_size,
                           max_input_len,
                           max_new_tokens,
                           use_cache,
                           max_beam_width,
                           max_num_tokens: int = None,
                           prompt_embedding_table_size: int = 0,
                           gather_all_token_logits: bool = False):
            '''@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
            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
            paged_kv_cache = default_net().plugin_config.paged_kv_cache
            tokens_per_block = default_net().plugin_config.tokens_per_block
            use_custom_all_reduce = default_net(
            ).plugin_config.use_custom_all_reduce

            model_inputs = self.prepare_basic_inputs(
                max_batch_size,
                max_beam_width,
                max_input_len,
                max_new_tokens,
                self.num_kv_heads,
                head_size,
                self.num_layers,
                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,
                prompt_embedding_table_size=prompt_embedding_table_size,
                gather_all_token_logits=gather_all_token_logits,
            )

            return (
                model_inputs['input_ids'],
                model_inputs['position_ids'],
                True,
                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'],
                model_inputs['prompt_embedding_table'],
                model_inputs['tasks'],
                model_inputs['prompt_vocab_size'],
            )