# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.

import types
from dataclasses import dataclass
from typing import Callable, Optional, Tuple

import torch
import torch.nn.functional as F

from ..model_parallel_config import ModelParallelConfig
from ..utils import init_method_normal, scaled_init_method_normal


@dataclass
class TransformerConfig(ModelParallelConfig):
    """Configuration object for megatron-core transformers.

    The initialization function has an argument for each parameter, including those in ModelParallelConfig.
    """

    ####################
    # model architecture
    ####################
    num_layers: int = 0
    """Number of transformer layers in a transformer block."""

    hidden_size: int = 0
    """Transformer hidden size."""

    num_attention_heads: int = 0
    """Number of transformer attention heads."""

    num_query_groups: int = None
    """Number of query groups for group query attention. If None, normal attention is used."""

    ffn_hidden_size: int = None
    """Transformer Feed-Forward Network hidden size. This is set to 4*hidden_size if not provided."""

    kv_channels: int = None
    """Projection weights dimension in multi-head attention. This is set to hidden_size //
    num_attention_heads if not provided."""

    hidden_dropout: float = 0.1
    """Dropout probability for transformer hidden state."""

    attention_dropout: float = 0.1
    """Post attention dropout probability."""

    fp32_residual_connection: bool = False
    """If true, move residual connections to fp32."""

    # @jcasper should we keep this option?
    apply_residual_connection_post_layernorm: bool = False
    """If True, uses the original BERT residule connection ordering."""

    layernorm_epsilon: float = 1e-5
    """Epsilon value for any LayerNorm operations."""

    layernorm_zero_centered_gamma: bool = False
    """If set to True, the LayerNorm is adjusted to center the gamma values around 0. This improves
    numerical stability."""

    add_bias_linear: bool = True
    """Include a bias term in all linear layers (QKV projections, after core attention, and two in
    MLP layer)."""

    add_qkv_bias: bool = False
    """Add a bias term only for QKV projections."""

    gated_linear_unit: bool = False
    """Use a gated linear unit for the first linear layer in the MLP."""

    activation_func: Callable = F.gelu
    """Activation function to use for the non-linearity in the MLP."""

    activation_func_fp8_input_store: bool = False
    """Store the input of MLP activation function in FP8 for backprop to save memory.
    The stored input is casted back to the original precision before backprop compuatation."""

    num_moe_experts: int = None
    """Number of experts to use for MoE layer. When set, it replaces MLP with MoE layer. Set to None
    for no MoE."""

    rotary_interleaved: bool = False
    """True is rotate pairs of even and odd dimensions (RoFormer style), False is rotate pairs of
    first half and second half (LLaMa style). Default to False."""

    window_size: Optional[Tuple[int, int]] = None
    """If not None, then will use sliding window attention. The size of the window is specified by
    the numbers inside the tuple; -1 is special value meaning "infinite window size"."""

    normalization: bool = "LayerNorm"
    """Which norm to use for normalization layers, valid options are `LayerNorm` and `RMSNorm`."""

    qk_layernorm: bool = False
    """Whether to apply LayerNorm to the query and key embeddings."""

    test_mode: bool = False
    """Whether to run real-time tests."""

    ####################
    # initialization
    ####################
    init_method: Callable = None
    """Method to initialize weights. Note that bias is always set to zero. Should be a function that
    takes a single Tensor and initializes it. If None, will be set to
    megatron.core.utils.init_method_normal(init_method_std) which is torch nn init normal with
    mean=0.0 and std=init_method_std."""

    output_layer_init_method: Callable = None
    """Method to initialize weights of the output layer of both attention and MLP blocks. If None,
    will be set to megatron.core.utils.scaled_init_method_normal(init_method_std) which is torch nn
    init normal with mean=0.0 and std=init_method_std / math.sqrt(2.0 * num_layers)."""

    init_method_std: float = 0.02
    """Standard deviation of the zero mean normal for the default initialization method, not used if
    init_method and output_layer_init_method are provided."""

    ####################
    # mixed-precision
    ####################
    apply_query_key_layer_scaling: bool = False
    """If true, scale Q * K^T by 1 / layer-number. This improve numeric stability when training with
    fp16."""

    attention_softmax_in_fp32: bool = True
    """If True, run attention masking and softmax in fp32. This should be True if
    apply_query_key_layer_scaling is True."""

    ####################
    # fusion
    ####################
    bias_activation_fusion: bool = False
    """If True, fuses bias addition and the activation function when possible."""

    masked_softmax_fusion: bool = False
    """If True, uses softmax fusion."""

    persist_layer_norm: bool = False
    """If True, uses the persistent fused layer norm kernel. This kernel only supports a fixed set
    of hidden sizes."""

    memory_efficient_layer_norm: bool = False
    """If True, and using local layers (not from TransformerEngine), tells Apex to use the memory
    efficient fused LayerNorm kernel. Ignored if not using LayerNorm."""

    bias_dropout_fusion: bool = False  # TODO: this should be bias_dropout_add_fusion?
    """If True, uses bias dropout fusion."""

    apply_rope_fusion: bool = False
    """If True, use fused RoPE kernel."""

    ####################
    # activation recomputation
    ####################
    recompute_granularity: str = None
    recompute_granularity: str = None
    """Determines which type of activation recompute to use.  Megatron-core supports 'selective'
    activation checkpointing where only the memory intensive part of attention is checkpointed.
    These memory intensive activations are also less compute intensive which makes activation
    checkpointing more efficient for LLMs (20B+).  See Reducing Activation Recomputation in Large
    Transformer Models (https://arxiv.org/abs/2205.05198) for more details.  'full' will checkpoint
    the entire transformer layer.  If None, no recompute is performed and all activations are saved.
    If set, must be 'selective' or 'full'. 'selective' always uses all layers.
    """

    recompute_method: str = None
    """Determines which transformer layers will be recomputed. uniform will uniformly divide the
    total number of transformer layers in a transformer block and recompute the input activation of
    each divided chunk at the specified granularity.  block will recompute the input activations for
    only a set number of transformer layers per pipeline stage.  The rest of the layers in the
    pipeline stage will not have any activations recomputed.  If None, and recompute is enabled, all
    layers will do recomputation. If set, must be 'uniform' or 'block'."""

    recompute_num_layers: int = None
    """When recompute_method is uniform, recompute_num_layers is the number of transformer layers in
    each uniformly divided recompute unit.  When recompute_method is block, recompute_num_layers is
    the number of transformer layers to recompute within each pipeline stage.  Must be None for
    'selective' activation checkpointing."""

    distribute_saved_activations: bool = None
    """If True, distribute recomputed activations across the model parallel group."""

    ####################
    # fp8 related
    ####################
    fp8: str = None
    """If set, enables the use of FP8 precision through Transformer Engine. There are 2 predefined
    choices (1) 'e4m3' uniformly uses e4m3 for all FP8 tensors, (2) 'hybrid' uses e4m3 for all FP8
    activation and weight tensors and e5m2 for all FP8 output activation gradient tensors."""

    fp8_margin: int = 0
    """Margin for the scaling factor computation."""

    fp8_interval: int = 1
    """Controls how often the scaling factor is recomputed."""

    fp8_amax_history_len: int = 1
    """The length of the amax history window used for scaling factor computation."""

    fp8_amax_compute_algo: str = "most_recent"
    """Algorithm used for choosing the `amax` value for the scaling factor computation. There are 2
    predefined choices: `max` chooses the largest `amax` in the history window, while `most_recent`
    always chooses the most recently seen value.

    """

    fp8_wgrad: bool = True
    """When set to False, override FP8 config options and do the wgrad computation in higher precision."""

    fp8_dot_product_attention: bool = False
    """When set to True, use the FP8 implementation of Dot Product Attention."""

    fp8_multi_head_attention: bool = False
    """When set to True, use the FP8 implementation of Multi Head Attention."""

    ####################
    # MoE related
    ####################
    moe_router_load_balancing_type: str = "aux_loss"
    """Determines the load balancing strategy for the router. "aux_loss" corresponds to the load
    balancing loss used in GShard and SwitchTransformer, "sinkhorn" corresponds to the balancing
    algorithm used in S-BASE, and "none" implies no load balancing."""

    moe_router_topk: int = 2
    """Number of experts to route to for each token."""

    moe_grouped_gemm: bool = False
    """When there are multiple experts per rank, compress multiple local (potentially small) gemms
    in a single kernel launch to improve the utilization and performance by leveraging the Grouped
    GEMM feature introduced since CUTLASS 2.8 (https://github.com/fanshiqing/grouped_gemm).

    """

    moe_aux_loss_coeff: float = 0  # 1e-2 would be a good start value for load balance loss.
    """Scaling coefficient for the aux loss. A starting value of 1e-2 is recommended."""

    moe_z_loss_coeff: float = None  # 1e-3 would be a good start value for z-loss
    """Scaling coefficient for the z-loss. A starting value of 1e-3 is recommended."""

    moe_input_jitter_eps: float = None
    """Add noise to the input tensor by applying jitter with a specified epsilon value."""

    moe_token_dropping: bool = False  # TODO: Support token dropping.
    """This feature involves selectively dropping and padding tokens for each expert to achieve a
    specified capacity, similar to GShard, Switch-Transformer, and DeepSpeed-MoE. Note that this is
    currently unsupported so should remain False."""

    moe_token_dispatcher_type: str = "allgather"
    """The type of token dispatcher to use. The default is 'allgather'. Options are 'allgather' and 'alltoall'."""
    moe_per_layer_logging: bool = False
    """Enable per-layer logging for MoE, currently supports auxiliary loss and z loss."""

    ####################
    # miscellaneous
    ####################
    clone_scatter_output_in_embedding: bool = True
    """When set to True, clone the output of scatter_to_sequence_parallel_region in embedding layer
    to facilitate garbage collection of input."""

    disable_parameter_transpose_cache: bool = False
    """When set to true, the parameter transposes are not cached for subsequent iterations."""

    enable_cuda_graph: bool = False
    """When set to true, TransformerLayer blocks are wrapped with CUDA graph."""

    # These 2 attributes are WAR for TRTLLM export. DO NOT USE!! WILL BE DEPRECATED SOON!!
    max_position_embeddings: int = 0
    """Deprecated. Do not use."""

    rotary_percent: float = 0
    """Deprecated. Do not use."""

    def __post_init__(self):
        """ Python dataclass method that is used to modify attributes after initialization.
            See https://docs.python.org/3/library/dataclasses.html#post-init-processing for more details.
        """
        super().__post_init__()
        if self.fp16 and self.bf16:
            raise ValueError(
                f'Only one of self.fp16: {self.fp16} and self.bf16 {self.bf16} should be True.'
            )

        if self.num_attention_heads % self.tensor_model_parallel_size != 0:
            raise ValueError(
                f"num_attention_heads ({self.num_attention_heads}) must be a multiple of "
                f"tensor_model_parallel_size ({self.tensor_model_parallel_size})."
            )

        if self.ffn_hidden_size is None:
            self.ffn_hidden_size = 4 * self.hidden_size

        if self.kv_channels is None:
            self.kv_channels = self.hidden_size // self.num_attention_heads

        if self.num_query_groups is None:
            self.num_query_groups = self.num_attention_heads

        if self.num_query_groups % self.tensor_model_parallel_size != 0:
            raise ValueError(
                f"num_query_groups ({self.num_query_groups}) must be a multiple of "
                f"tensor_model_parallel_size ({self.tensor_model_parallel_size})."
            )

        if self.apply_query_key_layer_scaling:
            self.attention_softmax_in_fp32 = True

        if self.expert_model_parallel_size > 1 and self.num_moe_experts is None:
            raise ValueError(f'num_moe_experts must be non None to use expert-parallel.')

        if self.num_moe_experts is not None and self.num_moe_experts <= 0:
            raise ValueError(f'num_moe_experts must be non-negative.')

        if self.cpu_offloading and (
            self.cpu_offloading_num_layers < 0 or self.cpu_offloading_num_layers >= self.num_layers
        ):
            raise ValueError(
                f'CPU offloading can be done only for layers less than {self.num_layers}'
            )

        if self.cpu_offloading and self.pipeline_model_parallel_size > 1:
            raise ValueError(
                f'Currently there is no support for Pipeline parallelism with CPU offloading'
            )

        if self.cpu_offloading and self.recompute_granularity is not None:
            raise ValueError(
                f'CPU offloading does not work when activation recomputation is enabled'
            )

        if self.recompute_granularity is not None:
            if not self.recompute_granularity in ['full', 'selective']:
                raise ValueError(
                    f'When using recompute_granuarlity: {self.recompute_granularity} must be "full" or "selective".'
                )

            if self.recompute_method is not None:
                if not self.recompute_method in ['block', 'uniform']:
                    raise ValueError(
                        f'recompute_method: {self.recompute_method} must be "block" or "uniform".'
                    )
            elif self.recompute_granularity != 'selective':
                raise ValueError(
                    f'Using recompute_granularity: {self.recompute_granularity} so recompute_method must be "block" or "uniform"'
                )

            if self.recompute_granularity != 'selective' and self.recompute_num_layers is None:
                raise ValueError(
                    f'When using recompute_granularity: {self.recompute_granularity} recompute_num_layers must be between '
                    f'1 and num_layers_per_pipeline_rank: {self.num_layers // self.pipeline_model_parallel_size}'
                )
            elif (
                self.recompute_granularity == 'selective' and self.recompute_num_layers is not None
            ):
                raise ValueError(
                    f'When using recompute_granularity: {self.recompute_granularity} recompute_num_layers must be None.'
                )

            if self.distribute_saved_activations and self.sequence_parallel:
                raise ValueError(
                    f'distribute_saved_activations: {self.distribute_saved_activations} must be false when sequence parallel is enabled: {self.sequence_parallel}'
                )

            if self.virtual_pipeline_model_parallel_size is not None:
                if not self.num_layers % self.virtual_pipeline_model_parallel_size == 0:
                    raise ValueError(
                        f'num_layers: {self.num_layers} must be divisible by virtual_model_parallel_size {self.virtual_pipeline_model_parallel_size}'
                    )

        if self.apply_query_key_layer_scaling:
            self.attention_softmax_in_fp32 = True

        if self.bias_activation_fusion:
            if self.activation_func not in [F.gelu, F.silu]:
                raise ValueError(
                    "When bias_activation_fusion is True, activation function should be either gelu or swiglu"
                )
            if (
                self.activation_func == F.gelu
                and not self.gated_linear_unit
                and not self.add_bias_linear
            ):
                raise ValueError(
                    "When bias_activation_fusion is True, gated_linear_unit is False, "
                    "and activation function is gelu, add_bias_linear must also be True."
                )
        if self.activation_func_fp8_input_store:
            if self.activation_func != F.silu or not self.gated_linear_unit:
                raise ValueError("Storing activation input in FP8 is supported only for SwiGLU.")
        if self.apply_rope_fusion and self.rotary_interleaved:
            raise ValueError(f'rotary_interleaved does not work with apply_rope_fusion.')

        if self.init_method is None:
            self.init_method = init_method_normal(self.init_method_std)

        if self.output_layer_init_method is None:
            self.output_layer_init_method = scaled_init_method_normal(
                self.init_method_std, self.num_layers
            )
