# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.

from collections import OrderedDict
from typing import Dict, Literal, Optional

import torch
from torch import Tensor

from megatron.core import InferenceParams, tensor_parallel
from megatron.core.config_logger import has_config_logger_enabled, log_config_to_disk
from megatron.core.dist_checkpointing.mapping import ShardedStateDict
from megatron.core.models.common.embeddings.language_model_embedding import LanguageModelEmbedding
from megatron.core.models.common.embeddings.rotary_pos_embedding import RotaryEmbedding
from megatron.core.models.common.language_module.language_module import LanguageModule
from megatron.core.packed_seq_params import PackedSeqParams
from megatron.core.transformer.enums import ModelType
from megatron.core.transformer.spec_utils import ModuleSpec
from megatron.core.transformer.transformer_block import TransformerBlock
from megatron.core.transformer.transformer_config import TransformerConfig


class GPTModel(LanguageModule):
    """GPT Transformer language model.

    Args:
        config (TransformerConfig):
            Transformer config
        transformer_layer_spec (ModuleSpec):
            Specifies module to use for transformer layers
        vocab_size (int):
            Vocabulary size
        max_sequence_length (int):
            maximum size of sequence. This is used for positional embedding
        pre_process (bool, optional):
            Include embedding layer (used with pipeline parallelism). Defaults to True.
        post_process (bool, optional):
            Include an output layer (used with pipeline parallelism). Defaults to True.
        fp16_lm_cross_entropy (bool, optional):
            Defaults to False.
        parallel_output (bool, optional):
            Do not gather the outputs, keep them split across tensor
            parallel ranks. Defaults to True.
        share_embeddings_and_output_weights (bool, optional):
            When True, input embeddings and output logit weights are shared. Defaults to False.
        position_embedding_type (Literal[learned_absolute,rope], optional):
            Position embedding type.. Defaults to 'learned_absolute'.
        rotary_percent (float, optional):
            Percent of rotary dimension to use for rotary position embeddings.
            Ignored unless position_embedding_type is 'rope'. Defaults to 1.0.
        rotary_base (int, optional):
            Base period for rotary position embeddings. Ignored unless
            position_embedding_type is 'rope'.
            Defaults to 10000.
        rope_scaling (bool, optional): Toggle RoPE scaling.
        rope_scaling_factor (float): RoPE scaling factor. Default 8.
        scatter_embedding_sequence_parallel (bool, optional):
            Whether embeddings should be scattered across sequence parallel
            region or not. Defaults to True.
        seq_len_interpolation_factor (Optional[float], optional):
            scale of linearly interpolating RoPE for longer sequences.
            The value must be a float larger than 1.0. Defaults to None.
    """

    def __init__(
        self,
        config: TransformerConfig,
        transformer_layer_spec: ModuleSpec,
        vocab_size: int,
        max_sequence_length: int,
        pre_process: bool = True,
        post_process: bool = True,
        fp16_lm_cross_entropy: bool = False,
        parallel_output: bool = True,
        share_embeddings_and_output_weights: bool = False,
        position_embedding_type: Literal['learned_absolute', 'rope', 'none'] = 'learned_absolute',
        rotary_percent: float = 1.0,
        rotary_base: int = 10000,
        rope_scaling: bool = False,
        rope_scaling_factor: float = 8.0,
        scatter_embedding_sequence_parallel: bool = True,
        seq_len_interpolation_factor: Optional[float] = None,
    ) -> None:
        super().__init__(config=config)

        if has_config_logger_enabled(config):
            log_config_to_disk(config, locals(), prefix=type(self).__name__)

        self.transformer_layer_spec: ModuleSpec = transformer_layer_spec
        self.vocab_size = vocab_size
        self.max_sequence_length = max_sequence_length
        self.pre_process = pre_process
        self.post_process = post_process
        self.fp16_lm_cross_entropy = fp16_lm_cross_entropy
        self.parallel_output = parallel_output
        self.share_embeddings_and_output_weights = share_embeddings_and_output_weights
        self.position_embedding_type = position_embedding_type

        # megatron core pipelining currently depends on model type
        # TODO: remove this dependency ?
        self.model_type = ModelType.encoder_or_decoder

        # These 4 attributes are needed for TensorRT-LLM export.
        self.max_position_embeddings = max_sequence_length
        self.rotary_percent = rotary_percent
        self.rotary_base = rotary_base
        self.rotary_scaling = rope_scaling

        if self.pre_process:
            self.embedding = LanguageModelEmbedding(
                config=self.config,
                vocab_size=self.vocab_size,
                max_sequence_length=self.max_sequence_length,
                position_embedding_type=position_embedding_type,
                scatter_to_sequence_parallel=scatter_embedding_sequence_parallel,
            )

        if self.position_embedding_type == 'rope' and not self.config.multi_latent_attention:
            self.rotary_pos_emb = RotaryEmbedding(
                kv_channels=self.config.kv_channels,
                rotary_percent=rotary_percent,
                rotary_interleaved=self.config.rotary_interleaved,
                seq_len_interpolation_factor=seq_len_interpolation_factor,
                rotary_base=rotary_base,
                rope_scaling=rope_scaling,
                rope_scaling_factor=rope_scaling_factor,
                use_cpu_initialization=self.config.use_cpu_initialization,
            )

        # Cache for RoPE tensors which do not change between iterations.
        self.rotary_pos_emb_cache = {}

        # Transformer.
        self.decoder = TransformerBlock(
            config=self.config,
            spec=transformer_layer_spec,
            pre_process=self.pre_process,
            post_process=self.post_process,
        )

        # Output
        if post_process:
            if self.config.defer_embedding_wgrad_compute:
                # The embedding activation buffer preserves a reference to the input activations
                # of the final embedding projection layer GEMM. It will hold the activations for
                # all the micro-batches of a global batch for the last pipeline stage. Once we are
                # done with all the back props for all the microbatches for the last pipeline stage,
                # it will be in the pipeline flush stage. During this pipeline flush we use the
                # input activations stored in embedding activation buffer and gradient outputs
                # stored in gradient buffer to calculate the weight gradients for the embedding
                # final linear layer.
                self.embedding_activation_buffer = []
                self.grad_output_buffer = []
            else:
                self.embedding_activation_buffer = None
                self.grad_output_buffer = None

            self.output_layer = tensor_parallel.ColumnParallelLinear(
                config.hidden_size,
                self.vocab_size,
                config=config,
                init_method=config.init_method,
                bias=False,
                skip_bias_add=False,
                gather_output=not self.parallel_output,
                skip_weight_param_allocation=self.pre_process
                and self.share_embeddings_and_output_weights,
                embedding_activation_buffer=self.embedding_activation_buffer,
                grad_output_buffer=self.grad_output_buffer,
            )

        if self.pre_process or self.post_process:
            self.setup_embeddings_and_output_layer()

        if has_config_logger_enabled(self.config):
            log_config_to_disk(
                self.config, self.state_dict(), prefix=f'{type(self).__name__}_init_ckpt'
            )

    def set_input_tensor(self, input_tensor: Tensor) -> None:
        """Sets input tensor to the model.

        See megatron.model.transformer.set_input_tensor()

        Args:
            input_tensor (Tensor): Sets the input tensor for the model.
        """
        # This is usually handled in schedules.py but some inference code still
        # gives us non-lists or None
        if not isinstance(input_tensor, list):
            input_tensor = [input_tensor]

        assert len(input_tensor) == 1, 'input_tensor should only be length 1 for gpt/bert'
        self.decoder.set_input_tensor(input_tensor[0])

    def forward(
        self,
        input_ids: Tensor,
        position_ids: Tensor,
        attention_mask: Tensor,
        decoder_input: Tensor = None,
        labels: Tensor = None,
        inference_params: InferenceParams = None,
        packed_seq_params: PackedSeqParams = None,
        extra_block_kwargs: dict = None,
        runtime_gather_output: Optional[bool] = None,
    ) -> Tensor:
        """Forward function of the GPT Model This function passes the input tensors
        through the embedding layer, and then the decoeder and finally into the post
        processing layer (optional).

        It either returns the Loss values if labels are given  or the final hidden units

        Args:
            runtime_gather_output (bool): Gather output at runtime. Default None means
                `parallel_output` arg in the constructor will be used.
        """
        # If decoder_input is provided (not None), then input_ids and position_ids are ignored.
        # Otherwise, apply embedding layer on input_ids and position_ids to get decoder_input.

        # Decoder embedding.
        if decoder_input is not None:
            pass
        elif self.pre_process:
            decoder_input = self.embedding(input_ids=input_ids, position_ids=position_ids)
        else:
            # intermediate stage of pipeline
            # decoder will get hidden_states from encoder.input_tensor
            decoder_input = None

        # Rotary positional embeddings (embedding is None for PP intermediate devices)
        rotary_pos_emb = None
        rotary_pos_cos = None
        rotary_pos_sin = None
        if self.position_embedding_type == 'rope' and not self.config.multi_latent_attention:
            if not self.training and self.config.flash_decode and inference_params:
                # Flash decoding uses precomputed cos and sin for RoPE
                rotary_pos_cos, rotary_pos_sin = self.rotary_pos_emb_cache.setdefault(
                    inference_params.max_sequence_length,
                    self.rotary_pos_emb.get_cos_sin(inference_params.max_sequence_length),
                )
            else:
                rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len(
                    inference_params, self.decoder, decoder_input, self.config, packed_seq_params
                )
                rotary_pos_emb = self.rotary_pos_emb(
                    rotary_seq_len,
                    packed_seq=packed_seq_params is not None
                    and packed_seq_params.qkv_format == 'thd',
                )
        if (
            (self.config.enable_cuda_graph or self.config.flash_decode)
            and rotary_pos_cos is not None
            and inference_params
        ):
            sequence_len_offset = torch.tensor(
                [inference_params.sequence_len_offset] * inference_params.current_batch_size,
                dtype=torch.int32,
                device=rotary_pos_cos.device,  # Co-locate this with the rotary tensors
            )
        else:
            sequence_len_offset = None

        # Run decoder.
        hidden_states = self.decoder(
            hidden_states=decoder_input,
            attention_mask=attention_mask,
            inference_params=inference_params,
            rotary_pos_emb=rotary_pos_emb,
            rotary_pos_cos=rotary_pos_cos,
            rotary_pos_sin=rotary_pos_sin,
            packed_seq_params=packed_seq_params,
            sequence_len_offset=sequence_len_offset,
            **(extra_block_kwargs or {}),
        )

        if not self.post_process:
            return hidden_states

        # logits and loss
        output_weight = None
        if self.share_embeddings_and_output_weights:
            output_weight = self.shared_embedding_or_output_weight()
        logits, _ = self.output_layer(
            hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output
        )

        if has_config_logger_enabled(self.config):
            payload = OrderedDict(
                {
                    'input_ids': input_ids,
                    'position_ids': position_ids,
                    'attention_mask': attention_mask,
                    'decoder_input': decoder_input,
                    'logits': logits,
                }
            )
            log_config_to_disk(self.config, payload, prefix='input_and_logits')

        if labels is None:
            # [s b h] => [b s h]
            return logits.transpose(0, 1).contiguous()

        loss = self.compute_language_model_loss(labels, logits)

        return loss

    def sharded_state_dict(
        self, prefix: str = '', sharded_offsets: tuple = (), metadata: Optional[Dict] = None
    ) -> ShardedStateDict:
        """Sharded state dict implementation for GPTModel backward-compatibility
        (removing extra state).

        Args:
            prefix (str): Module name prefix.
            sharded_offsets (tuple): PP related offsets, expected to be empty at this module level.
            metadata (Optional[Dict]): metadata controlling sharded state dict creation.

        Returns:
            ShardedStateDict: sharded state dict for the GPTModel
        """
        sharded_state_dict = super().sharded_state_dict(prefix, sharded_offsets, metadata)
        output_layer_extra_state_key = f'{prefix}output_layer._extra_state'

        # Old GPT checkpoints only stored the output layer weight key. So we remove the
        # _extra_state key but check that it doesn't contain any data anyway
        output_extra_state = sharded_state_dict.pop(output_layer_extra_state_key, None)
        assert not (
            output_extra_state and output_extra_state.data
        ), f'Expected output layer extra state to be empty, got: {output_extra_state}'

        return sharded_state_dict
