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

from typing import List, Optional

import torch
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors

from .. import parallel_state
from ..transformer.transformer_config import TransformerConfig
from ..utils import get_attr_wrapped_model, get_model_config


def _allreduce_word_embedding_grads(model: List[torch.nn.Module], config: TransformerConfig):
    """
    All-reduce word embedding grads.

    Reduce grads across first and last stages to ensure that word_embeddings parameters stay in
    sync.
    """

    if (
        parallel_state.is_rank_in_embedding_group(ignore_virtual=True)
        and torch.distributed.get_world_size(parallel_state.get_embedding_group()) > 1
    ):
        if parallel_state.is_pipeline_first_stage(ignore_virtual=True):
            model_module = model[0]
        elif parallel_state.is_pipeline_last_stage(ignore_virtual=True):
            model_module = model[-1]
        else:  # We do not support an interleaved schedule for models with encoders yet.
            model_module = model[0]

        model_module = get_attr_wrapped_model(model_module, 'pre_process', return_model_obj=True)
        if model_module.share_embeddings_and_output_weights:
            weight = model_module.shared_embedding_or_output_weight()
            grad = weight.main_grad
            torch.distributed.all_reduce(grad, group=parallel_state.get_embedding_group())


def _allreduce_position_embedding_grads(model: List[torch.nn.Module], config: TransformerConfig):
    """
    All-reduce position_embeddings grad across encoder and decoder stages to ensure that position
    embeddings parameters stay in sync.
    """
    if (
        parallel_state.is_rank_in_position_embedding_group()
        and torch.distributed.get_world_size(parallel_state.get_position_embedding_group()) > 1
    ):
        if parallel_state.is_pipeline_first_stage(ignore_virtual=True):
            model_module = model[0]
        elif parallel_state.is_pipeline_last_stage(ignore_virtual=True):
            model_module = model[-1]
        else:  # We do not support an interleaved schedule for models with encoders yet.
            model_module = model[0]

        model_module = get_attr_wrapped_model(model_module, 'pre_process', return_model_obj=True)
        assert hasattr(model_module, 'position_embeddings')
        grad = model_module.position_embeddings.weight.main_grad
        torch.distributed.all_reduce(grad, group=parallel_state.get_position_embedding_group())


def _allreduce_embedding_grads(model: List[torch.nn.Module], config: TransformerConfig):
    """
    All-reduce both word and position embeddings.
    """
    _allreduce_word_embedding_grads(model, config)
    _allreduce_position_embedding_grads(model, config)


def _allreduce_layernorm_grads(model: List[torch.nn.Module], config: TransformerConfig):
    """
    All-reduce layernorm grads (for sequence parallelism).
    """

    # All-reduce layernorm parameters across model parallel nodes
    # when sequence parallelism is used
    if parallel_state.get_tensor_model_parallel_world_size() > 1 and (
        config.sequence_parallel or config.qk_layernorm
    ):
        grads = []
        for model_chunk in model:
            for name, param in get_attr_wrapped_model(model_chunk, 'named_parameters')():
                if (
                    param.requires_grad
                    and getattr(param, 'sequence_parallel', False)
                    or 'q_layernorm' in name
                    or 'k_layernorm' in name
                ):
                    grad = param.main_grad
                    grads.append(grad.data)
        if grads:
            coalesced = _flatten_dense_tensors(grads)
            torch.distributed.all_reduce(
                coalesced, group=parallel_state.get_tensor_model_parallel_group()
            )
            for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
                buf.copy_(synced)


def finalize_model_grads(model: List[torch.nn.Module], num_tokens: Optional[torch.Tensor] = None):
    """
    All-reduce all model grads across DP replicas, layernorm grads for sequence parallelism,
    embedding grads across first and last pipeline stages (if not tied),
    scale gradients by `num_tokens`.
    """

    config = get_model_config(model[0])

    # All-reduce / reduce-scatter across DP replicas.
    if config.timers is not None:
        config.timers('all-grads-sync', log_level=1).start(barrier=config.barrier_with_L1_time)
    for model_chunk in model:
        model_chunk.finish_grad_sync()
    if config.timers is not None:
        config.timers('all-grads-sync').stop()

    # All-reduce layer-norm grads (for sequence parallelism).
    if config.timers is not None:
        config.timers('layernorm-grads-all-reduce', log_level=1).start(
            barrier=config.barrier_with_L1_time
        )
    _allreduce_layernorm_grads(model, config)
    if config.timers is not None:
        config.timers('layernorm-grads-all-reduce').stop()

    # All-reduce embedding grads (for pipeline parallelism).
    if config.timers is not None:
        config.timers('embedding-grads-all-reduce', log_level=1).start(
            barrier=config.barrier_with_L1_time
        )
    _allreduce_embedding_grads(model, config)
    if config.timers is not None:
        config.timers('embedding-grads-all-reduce').stop()

    # normalize gradients for per-token loss normalization.
    # if we are using by the number of tokens, then we use that as a divisor. this number
    # will be the total number of non-padded tokens in the global batch.
    if num_tokens is not None:
        # the number of tokens is only present on the last stage, so broadcast it
        # to the other ranks in the pipeline parallel group.
        torch.distributed.broadcast(
            num_tokens,
            src=parallel_state.get_pipeline_model_parallel_last_rank(),
            group=parallel_state.get_pipeline_model_parallel_group(),
        )
        # all-reduce across DP ranks.
        torch.distributed.all_reduce(num_tokens, group=parallel_state.get_data_parallel_group())
        for model_chunk in model:
            if num_tokens > 0:
                scaling = 1.0 / num_tokens
                model_chunk.scale_gradients(scaling)
