# Source code for tensorrt_llm.models.chatglm.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 copy import deepcopy
    from types import SimpleNamespace

    import tensorrt as trt

    from tensorrt_llm.mapping import Mapping
    from tensorrt_llm.quantization import QuantMode

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

    class ChatGLMParams:
        apply_query_key_layer_scaling: bool = None
        apply_residual_connection_post_layernorm: bool = None
        dtype: str = None
        enable_debug_output: bool = None
        ffn_hidden_size: int = None
        hidden_act: str = None
        hidden_size: int = None
        linear_bias: bool = None
        logits_dtype: str = None
        mapping: Mapping = None
        max_input_len: int = None
        max_output_len: int = None
        max_seq_length: int = None
        model_name: str = None
        norm_epsilon: float = None
        num_heads: int = None
        num_kv_heads: int = None
        num_layers: int = None
        qkv_bias: bool = None
        quant_mode: QuantMode = None
        rmsnorm: bool = None
        rotary_embedding_scaling: float = None
        tokens_per_block: int = None
        use_cache: bool = None
        vocab_size: int = None

        # default values
        default_config = {}
        default_config["chatglm_6b"] = SimpleNamespace(
            apply_query_key_layer_scaling=False,
            apply_residual_connection_post_layernorm=False,
            dtype="float16",
            ffn_hidden_size=16384,
            hidden_act='gelu',
            hidden_size=4096,
            linear_bias=True,
            logits_dtype="float16",
            mapping=Mapping(),
            max_input_len=1024,
            max_output_len=1024,
            max_seq_length=2048,
            norm_epsilon=1.0e-5,
            num_heads=32,
            num_kv_heads=32,
            num_layers=28,
            qkv_bias=True,
            quant_mode=QuantMode(0),
            rmsnorm=False,
            rotary_embedding_scaling=1.0,
            use_cache=True,
            vocab_size=130528,
        )
        default_config["chatglm2_6b"] = SimpleNamespace(
            apply_query_key_layer_scaling=False,
            apply_residual_connection_post_layernorm=False,
            dtype="float16",
            ffn_hidden_size=13696,
            hidden_act='swiglu',
            hidden_size=4096,
            linear_bias=False,
            logits_dtype="float16",
            mapping=Mapping(),
            max_input_len=1024,
            max_output_len=1024,
            max_seq_length=32768,
            norm_epsilon=1.0e-5,
            num_heads=32,
            num_kv_heads=2,
            num_layers=28,
            qkv_bias=True,
            quant_mode=QuantMode(0),
            rmsnorm=True,
            rotary_embedding_scaling=1.0,
            use_cache=True,
            vocab_size=65024,
        )
        default_config["chatglm3_6b"] = default_config["chatglm2_6b"]
        default_config["chatglm2_6b_32k"] = deepcopy(default_config["chatglm2_6b"])
        default_config["chatglm2_6b_32k"].rotary_embedding_scaling = 50.0
        default_config["chatglm3_6b_32k"] = default_config["chatglm2_6b_32k"]
        default_config["glm_10b"] = SimpleNamespace(
            apply_query_key_layer_scaling=False,
            apply_residual_connection_post_layernorm=False,
            dtype="float16",
            ffn_hidden_size=16384,
            hidden_act='gelu',
            hidden_size=4096,
            linear_bias=True,
            logits_dtype="float16",
            mapping=Mapping(),
            max_input_len=1024,
            max_output_len=1024,
            max_seq_length=2048,
            norm_epsilon=1.0e-5,
            num_heads=32,
            num_kv_heads=32,
            num_layers=28,
            qkv_bias=True,
            quant_mode=QuantMode(0),
            rmsnorm=False,
            rotary_embedding_scaling=1.0,
            use_cache=True,
            vocab_size=50304,
        )

        def __init__(self, **args):

            for key, value in args.items():
                assert key in dir(
                    self), f"{key} is not in configuration of ChatGLMHeadModel"
                if value is not None:
                    self.__setattr__(key, value)
            assert self.model_name is not None, "model_name must be set for ChatGLMHeadModel"

            # fill other parameters as default values
            for key, value in self.default_config[self.model_name].__dict__.items():
                if self.__getattribute__(key) is None:
                    self.__setattr__(key, value)

        def report(self):
            for key, value in self.__dict__.items():
                print(f"{key} = {value}")

    class ChatGLMDecoderLayer(Module):

        def __init__(self, layer_id, config):

            super().__init__()

            self.model_name = config.model_name
            self.use_cache = config.use_cache
            self.rotary_embedding_base = 10000.0
            rotary_embedding_scaling = None

            if self.model_name in ["chatglm_6b"]:
                self.alpha = (2 * config.num_layers)**0.5
                self.norm = LayerNorm
                attention_mask_type = AttentionMaskType.bidirectional
                position_embedding_type = PositionEmbeddingType.chatglm
            elif config.model_name in [
                    "chatglm2_6b", "chatglm2_6b_32k", "chatglm3_6b",
                    "chatglm3_6b_base", "chatglm3_6b_32k"
            ]:
                self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
                self.norm = RmsNorm if config.rmsnorm else LayerNorm
                attention_mask_type = AttentionMaskType.causal
                position_embedding_type = PositionEmbeddingType.rope_gptj
                if config.model_name in ["chatglm2_6b_32k"]:
                    rotary_embedding_scaling = {
                        "type": "linear",
                        "factor": config.rotary_embedding_scaling
                    }
                elif config.model_name in ["chatglm3_6b_32k"]:
                    self.rotary_embedding_base *= config.rotary_embedding_scaling
            elif config.model_name in ["glm_10b"]:
                self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
                self.norm = LayerNorm
                attention_mask_type = AttentionMaskType.bidirectionalglm
                position_embedding_type = PositionEmbeddingType.learned_absolute

            self.pre_norm = self.norm(
                normalized_shape=config.hidden_size,
                eps=config.norm_epsilon,
                elementwise_affine=True,
                dtype=config.dtype,
            )

            self.attention = Attention(
                hidden_size=config.hidden_size,
                num_attention_heads=config.num_heads,
                num_kv_heads=config.num_kv_heads,
                max_position_embeddings=config.max_seq_length,
                num_layers=config.num_layers,
                apply_query_key_layer_scaling=config.apply_query_key_layer_scaling,
                attention_mask_type=attention_mask_type,
                bias=config.qkv_bias,
                dtype=config.dtype,
                position_embedding_type=position_embedding_type,
                rotary_embedding_base=self.rotary_embedding_base,
                rotary_embedding_scaling=rotary_embedding_scaling,
                use_int8_kv_cache=config.quant_mode.has_int8_kv_cache(),
                rotary_embedding_percentage=0.5,
                tp_group=config.mapping.tp_group,
                tp_size=config.mapping.tp_size,
                tp_rank=config.mapping.rank,
                quant_mode=config.quant_mode,
                q_scaling=1.0,
                cross_attention=False,
                relative_attention=False,
                max_distance=0,
                num_buckets=0,
                instance_id=layer_id * 2,
                dense_bias=config.linear_bias,
            )

            self.mlp = MLP(
                hidden_size=config.hidden_size,
                ffn_hidden_size=config.ffn_hidden_size,
                hidden_act=config.hidden_act,
                bias=config.linear_bias,
                dtype=config.dtype,
                tp_group=config.mapping.tp_group,
                tp_size=config.mapping.tp_size,
                quant_mode=config.quant_mode,
                instance_id=layer_id * 2 + 1,
            )

            self.post_norm = self.norm(
                normalized_shape=config.hidden_size,
                eps=config.norm_epsilon,
                elementwise_affine=True,
                dtype=config.dtype,
            )

        def forward(
            self,
            hidden_states: Tensor,
            position_ids: Tensor = None,  # only used in ChatGLM-6B
            kv_cache_params: KeyValueCacheParams = None,
            attention_params: AttentionParams = None,
        ):

            norm_output = self.pre_norm(hidden_states)

            attention_output = self.attention(
                hidden_states=norm_output,
                attention_mask=None,
                use_cache=self.use_cache,
                kv_cache_params=kv_cache_params,
                attention_params=attention_params,
                encoder_output=None,
                workspace=None,
                position_embedding=position_ids,
            )

            if self.use_cache:
                attention_output, presents = attention_output

            if self.model_name in ["chatglm_6b"]:
                residual = norm_output

                norm_input = residual * self.alpha + attention_output

                norm_output = self.post_norm(norm_input)

                mlp_output = self.mlp(norm_output)

                residual = norm_output

                output = residual * self.alpha + mlp_output

            elif self.model_name in [
                    "chatglm2_6b", "chatglm2_6b_32k", "chatglm3_6b",
                    "chatglm3_6b_base", "chatglm3_6b_32k", "glm_10b"
            ]:
                residual = norm_output if self.apply_residual_connection_post_layernorm else hidden_states

                norm_input = residual + attention_output

                norm_output = self.post_norm(norm_input)

                mlp_output = self.mlp(norm_output)

                residual = norm_output if self.apply_residual_connection_post_layernorm else norm_input

                output = residual + mlp_output

            return (output, presents) if self.use_cache else output

    class ChatGLMModel(Module):

        def __init__(self, config):

            super().__init__()

            self.model_name = config.model_name

            if config.model_name in ["chatglm_6b", "glm_10b"]:
                self.norm = LayerNorm
            elif config.model_name in [
                    "chatglm2_6b", "chatglm2_6b_32k", "chatglm3_6b",
                    "chatglm3_6b_base", "chatglm3_6b_32k"
            ]:
                self.norm = RmsNorm
            self.use_cache = config.use_cache

            self.embedding = Embedding(
                num_embeddings=config.vocab_size,
                embedding_dim=config.hidden_size,
                dtype=config.dtype,
                tp_size=1,  #config.mapping.tp_size,
                tp_group=None,  #config.mapping.tp_group,
                sharding_dim=0,
                tp_rank=0,  #config.mapping.rank,
                instance_id=config.num_layers * 2,
            )

            if config.model_name in ["glm_10b"]:
                self.position_embeddings = Embedding(
                    config.max_seq_length + 1,
                    config.hidden_size,
                    dtype=config.dtype,
                    tp_size=1,  #config.mapping.tp_size,
                    tp_group=None,  #config.mapping.tp_group,
                    sharding_dim=0,
                    tp_rank=0,  #config.mapping.rank,
                    instance_id=config.num_layers * 2,
                )
                self.block_embeddings = Embedding(
                    config.max_seq_length + 1,
                    config.hidden_size,
                    dtype=config.dtype,
                    tp_size=1,  #config.mapping.tp_size,
                    tp_group=None,  #config.mapping.tp_group,
                    sharding_dim=0,
                    tp_rank=0,  #config.mapping.rank,
                    instance_id=config.num_layers * 2,
                )

            self.layers = ModuleList(
                ChatGLMDecoderLayer(i, config) for i in range(config.num_layers))

            self.final_norm = self.norm(
                normalized_shape=config.hidden_size,
                eps=config.norm_epsilon,
                elementwise_affine=True,
                dtype=config.dtype,
            )

        def forward(
            self,
            input_ids: Tensor = None,
            position_ids: Tensor = None,  # only used in ChatGLM-6B
            kv_cache_params: KeyValueCacheParams = None,
            attention_params: AttentionParams = None,
        ):

            hidden_states = self.embedding(input_ids)

            if self.model_name in ["glm_10b"]:
                position_ids_list = position_ids.split(1, dim=1)
                position_embedding = self.position_embeddings(position_ids_list[0])
                block_embedding = self.block_embeddings(position_ids_list[1])
                position_embedding = position_embedding + block_embedding

                position_embedding = position_embedding.view(
                    concat([
                        shape(input_ids, 0),
                        shape(input_ids, 1),
                        4096,
                    ]))

                hidden_states = hidden_states + position_embedding

            kv_cache_params.fill_none_tensor_list(len(self.layers))

            if self.use_cache:
                presents = []

            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):
                layer_output = layer(
                    hidden_states,
                    position_ids,
                    kv_cache_params=KeyValueCacheParams(
                        past_key_value=[past],
                        kv_cache_block_pointers=[pointer],
                        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 self.use_cache:
                    hidden_states = layer_output[0]
                    presents.append(layer_output[1])

            hidden_states = self.final_norm(hidden_states)

            return (hidden_states,
                    tuple(presents)) if self.use_cache else hidden_states

    class ChatGLMHeadModel(ChatGLMModel, GenerationMixin):

        def __init__(
            self,
            apply_query_key_layer_scaling: bool = None,
            apply_residual_connection_post_layernorm: bool = None,
            dtype: str = None,
            enable_debug_output: bool = None,
            ffn_hidden_size: int = None,
            hidden_act: str = None,
            hidden_size: int = None,
            linear_bias: bool = None,
            logits_dtype: str = None,
            mapping: Mapping = None,
            max_input_len: int = None,
            max_output_len: int = None,
            max_seq_length: int = None,
            model_name: str = None,
            norm_epsilon: float = None,
            num_heads: int = None,
            num_kv_heads: int = None,
            num_layers: int = None,
            qkv_bias: bool = None,
            quant_mode: QuantMode = None,
            rmsnorm: bool = None,
            rotary_embedding_scaling: float = None,
            tokens_per_block: int = None,
            use_cache: bool = None,
            vocab_size: int = None,
            max_position_embeddings: int = None,
        ):

            # for benchmark scripts
            if max_seq_length is None and max_position_embeddings is not None:
                max_seq_length = max_position_embeddings

            config = ChatGLMParams(
                apply_query_key_layer_scaling=apply_query_key_layer_scaling,
                apply_residual_connection_post_layernorm=
                apply_residual_connection_post_layernorm,
                dtype=dtype,
                enable_debug_output=enable_debug_output,
                ffn_hidden_size=ffn_hidden_size,
                hidden_act=hidden_act,
                hidden_size=hidden_size,
                linear_bias=linear_bias,
                logits_dtype=logits_dtype,
                mapping=mapping,
                max_input_len=max_input_len,
                max_output_len=max_output_len,
                max_seq_length=max_seq_length,
                model_name=model_name,
                norm_epsilon=norm_epsilon,
                num_heads=num_heads,
                num_kv_heads=num_kv_heads,
                num_layers=num_layers,
                qkv_bias=qkv_bias,
                quant_mode=quant_mode,
                rmsnorm=rmsnorm,
                rotary_embedding_scaling=rotary_embedding_scaling,
                tokens_per_block=tokens_per_block,
                use_cache=use_cache,
                vocab_size=vocab_size,
            )

            super().__init__(config)

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

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

            if config.quant_mode.has_int8_kv_cache():
                self.kv_dtype = str_dtype_to_trt('int8')
            elif config.quant_mode.has_fp8_kv_cache():
                self.kv_dtype = str_dtype_to_trt('fp8')

            self.hidden_size = config.hidden_size
            self.mapping = config.mapping
            self.max_num_tokens = config.max_output_len + config.max_input_len
            self.model_name = config.model_name
            self.num_heads = config.num_heads
            self.num_kv_heads = config.num_kv_heads
            self.num_layers = config.num_layers
            self.tokens_per_block = config.tokens_per_block
            self.use_cache = config.use_cache

            self.lm_head = ColumnLinear(
                in_features=self.hidden_size,
                out_features=pad_vocab_size(config.vocab_size,
                                            self.mapping.tp_size),
                bias=False,
                dtype=self.dtype,
                tp_group=self.mapping.tp_group,
                tp_size=self.mapping.tp_size,
                gather_output=True,
            )

        def forward(
            self,
            input_ids: Tensor = None,
            position_ids: Tensor = None,  # only used in ChatGLM-6B
            last_token_ids: Tensor = None,
            kv_cache_params: KeyValueCacheParams = None,
            attention_params: AttentionParams = None,
        ):

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

            if self.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)

            lm_logits = self.lm_head(hidden_states)
            lm_logits.mark_output('logits', self.logits_dtype)

            if self.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: int = 0,
            max_input_len: int = 0,
            max_new_tokens: int = 0,
            use_cache: bool = True,
            max_beam_width: int = 1,
        ):
            '''@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()
            '''

            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=self.hidden_size // self.num_heads,
                num_layers=self.num_layers,
                kv_dtype=self.kv_dtype,
                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,
                use_custom_all_reduce=False,
                paged_kv_cache=default_net().plugin_config.paged_kv_cache,
                tokens_per_block=self.tokens_per_block,
                gather_all_token_logits=False,
                dtype=self.kv_dtype,
                num_heads=self.num_heads,
                mapping=self.mapping,
                max_num_tokens=self.max_num_tokens,
                prompt_embedding_table_size=0,
                position_encoding_2d=(self.model_name in ["chatglm_6b", "glm_10b"]),
            )

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