# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

# This file applies the PT-D parallelisms (except pipeline parallelism) and various
# training techniques (e.g. activation checkpointing and compile) to the Llama model.

import torch
import torch.nn as nn
from torch.distributed._composable.replicate import replicate

from torch.distributed.device_mesh import DeviceMesh
from torch.distributed.fsdp import CPUOffloadPolicy, fully_shard, MixedPrecisionPolicy
from torch.distributed.tensor import Replicate, Shard
from torch.distributed.tensor.parallel import (
    ColwiseParallel,
    parallelize_module,
    PrepareModuleInput,
    RowwiseParallel,
    SequenceParallel,
)

from torchtitan.config import JobConfig, TORCH_DTYPE_MAP
from torchtitan.config.job_config import Compile as CompileConfig
from torchtitan.distributed import ParallelDims
from torchtitan.distributed.activation_checkpoint import apply_ac
from torchtitan.distributed.tensor_parallel import maybe_enable_async_tp
from torchtitan.tools.logging import logger


# for selective op activation checkpointing
_op_sac_save_list = {
    torch.ops.aten.mm.default,
    torch.ops.aten._scaled_dot_product_efficient_attention.default,
    torch.ops.aten._scaled_dot_product_flash_attention.default,
    torch.ops.aten._scaled_dot_product_cudnn_attention.default,
    torch.ops.aten._scaled_dot_product_attention_math.default,
    torch.ops.aten._scaled_dot_product_fused_attention_overrideable.default,
    torch.ops._c10d_functional.reduce_scatter_tensor.default,
    # for low precision training, it's useful to always save
    # the result of max, since the absolute maximum is
    # used to compute the scaling factor for quantization.
    torch.ops.aten.max.default,
    torch._higher_order_ops.flex_attention,
    torch.ops.torch_attn._varlen_attn.default,
    torch._higher_order_ops.inductor_compiled_code,
}


def parallelize_llama(
    model: nn.Module,
    parallel_dims: ParallelDims,
    job_config: JobConfig,
):
    """
    Apply tensor parallelism, activation checkpointing, torch.compile, and data
    parallelism to the model.

    NOTE: The passed-in model preferably should be on meta device. Otherwise,
    the model must fit on GPU or CPU memory.
    """
    # TODO: TP currently cannot handle uneven seq_len because we set
    #       `use_local_output=True` to use plain Tensors for legacy reasons.
    #       Need to revisit this.
    assert (
        job_config.training.seq_len % parallel_dims.seq_len_divisor == 0
    ), f"""
        Sequence length {job_config.training.seq_len} must be divisible by the product of TP degree
        ({parallel_dims.tp}) and 2 * CP degree ({parallel_dims.cp}).
        """

    if parallel_dims.tp_enabled:
        enable_float8_linear = "float8" in job_config.model.converters
        float8_is_rowwise = job_config.quantize.linear.float8.recipe_name in (
            "rowwise",
            "rowwise_with_gw_hp",
        )

        # For now, float8 all-gather with TP is only supported for tensorwise
        # float8 scaling recipes. For rowwise recipes, we use regular TP and
        # all-gather happens in high precision.
        enable_float8_tensorwise_tp = enable_float8_linear and not float8_is_rowwise

        tp_mesh = parallel_dims.get_mesh("tp")
        apply_tp(
            model,
            tp_mesh,
            loss_parallel=not job_config.parallelism.disable_loss_parallel,
            enable_float8_tensorwise_tp=enable_float8_tensorwise_tp,
        )
        maybe_enable_async_tp(job_config, tp_mesh)

    model_compile_enabled = (
        job_config.compile.enable and "model" in job_config.compile.components
    )

    if job_config.activation_checkpoint.mode != "none":
        apply_ac(
            model,
            job_config.activation_checkpoint,
            model_compile_enabled=model_compile_enabled,
            # pyrefly: ignore [bad-argument-type]
            op_sac_save_list=_op_sac_save_list,
            base_folder=job_config.job.dump_folder,
        )

    # turn on per-TransformerBlock compile after AC wrapping and before FSDP
    if model_compile_enabled:
        apply_compile(model, job_config.compile)

    if parallel_dims.fsdp_enabled:
        # dp_mesh is the mesh for FSDP/HSDP
        names = (
            ["dp_replicate", "fsdp"] if parallel_dims.dp_replicate_enabled else ["fsdp"]
        )
        dp_mesh = parallel_dims.get_mesh(names)
        apply_fsdp(
            model,
            dp_mesh,
            param_dtype=TORCH_DTYPE_MAP[job_config.training.mixed_precision_param],
            reduce_dtype=TORCH_DTYPE_MAP[job_config.training.mixed_precision_reduce],
            pp_enabled=parallel_dims.pp_enabled,
            cpu_offload=job_config.training.enable_cpu_offload,
            reshard_after_forward_policy=job_config.parallelism.fsdp_reshard_after_forward,
        )

        if parallel_dims.dp_replicate_enabled:
            logger.info("Applied HSDP to the model")
        else:
            logger.info("Applied FSDP to the model")

        if parallel_dims.cp_enabled:
            logger.info("Applied Context Parallel to the model")

        if job_config.training.enable_cpu_offload:
            logger.info("Applied CPU Offloading to the model")
    elif parallel_dims.dp_replicate_enabled:
        dp_replicate_mesh = parallel_dims.get_mesh("dp_replicate")
        if parallel_dims.world_size != dp_replicate_mesh.size():
            raise RuntimeError("DDP has not supported > 1D parallelism")
        apply_ddp(
            model,
            dp_replicate_mesh,
            enable_compile=model_compile_enabled,
        )

    return model


def apply_tp(
    model: nn.Module,
    tp_mesh: DeviceMesh,
    loss_parallel: bool,
    enable_float8_tensorwise_tp: bool,
):
    """Apply tensor parallelism."""
    # 1. Parallelize the embedding and shard its outputs (which are the first
    # transformer block's inputs)
    # 2. Parallelize the root norm layer over the sequence dim
    # 3. Parallelize the final linear output layer
    parallelize_module(
        model,
        tp_mesh,
        {
            "tok_embeddings": RowwiseParallel(
                input_layouts=Replicate(),
                output_layouts=Shard(1),
            ),
            "norm": SequenceParallel(),
            "output": ColwiseParallel(
                input_layouts=Shard(1),
                output_layouts=Shard(-1) if loss_parallel else Replicate(),
                use_local_output=not loss_parallel,
            ),
        },
    )

    # Parallel styles used for transformer block linear weights and their
    # inputs may be different for float8 linears with tensorwise scaling.
    if enable_float8_tensorwise_tp:
        # TODO(vkuzo): add the items below to __init__.py of torchao.float8 and import from there
        from torchao.float8.float8_tensor_parallel import (
            Float8ColwiseParallel,
            Float8RowwiseParallel,
            PrepareFloat8ModuleInput,
        )

        rowwise_parallel, colwise_parallel, prepare_module_input = (
            Float8RowwiseParallel,
            Float8ColwiseParallel,
            PrepareFloat8ModuleInput,
        )
    else:
        rowwise_parallel, colwise_parallel, prepare_module_input = (
            RowwiseParallel,
            ColwiseParallel,
            PrepareModuleInput,
        )

    # Apply tensor + sequence parallelism to every transformer block
    # NOTE: At the cost of model code change, we can accelerate Sequence Parallel
    #       by folding (and unfolding) the batch dimension and the sequence dimension.
    #       Examples can be found at https://github.com/pytorch/torchtitan/pull/437
    # pyrefly: ignore [not-callable]
    for transformer_block in model.layers.values():
        layer_plan = {
            "attention_norm": SequenceParallel(),
            # NOTE: when the fourth argument (positions) is not None, its input layout
            # and desired input layout should be Replicate()
            "attention": prepare_module_input(
                input_layouts=(Shard(1), None, None, None),
                desired_input_layouts=(Replicate(), None, None, None),
            ),
            "attention.wq": colwise_parallel(),
            "attention.wk": colwise_parallel(),
            "attention.wv": colwise_parallel(),
            "attention.wo": rowwise_parallel(output_layouts=Shard(1)),
            "ffn_norm": SequenceParallel(),
            "feed_forward": prepare_module_input(
                input_layouts=(Shard(1),),
                desired_input_layouts=(Replicate(),),
            ),
            "feed_forward.w1": colwise_parallel(),
            "feed_forward.w2": rowwise_parallel(output_layouts=Shard(1)),
            "feed_forward.w3": colwise_parallel(),
        }

        parallelize_module(
            # pyrefly: ignore [bad-argument-type]
            module=transformer_block,
            device_mesh=tp_mesh,
            # pyrefly: ignore [bad-argument-type]
            parallelize_plan=layer_plan,
        )

    logger.info(
        f"Applied {'Float8 tensorwise ' if enable_float8_tensorwise_tp else ''}"
        "Tensor Parallelism to the model"
    )


def apply_compile(model: nn.Module, compile_config: CompileConfig):
    """
    Apply torch.compile to each TransformerBlock, which makes compilation efficient due to
    repeated structure. Alternatively one can compile the whole model (after applying DP).
    """
    # pyrefly: ignore [missing-attribute]
    for layer_id, transformer_block in model.layers.named_children():
        transformer_block = torch.compile(
            transformer_block, backend=compile_config.backend, fullgraph=True
        )
        # pyrefly: ignore [missing-attribute]
        model.layers.register_module(layer_id, transformer_block)

    logger.info("Compiling each TransformerBlock with torch.compile")


def apply_fsdp(
    model: nn.Module,
    dp_mesh: DeviceMesh,
    param_dtype: torch.dtype,
    reduce_dtype: torch.dtype,
    pp_enabled: bool,
    cpu_offload: bool = False,
    reshard_after_forward_policy: str = "default",
):
    """
    Apply data parallelism (via FSDP2) to the model.

    Args:
        model (nn.Module): The model to apply data parallelism to.
        dp_mesh (DeviceMesh): The device mesh to use for data parallelism.
        param_dtype (torch.dtype): The data type to use for model parameters.
        reduce_dtype (torch.dtype): The data type to use for reduction operations.
        pp_enabled (bool): Whether pipeline parallelism is enabled.
        cpu_offload (bool, optional): Whether to offload model parameters to CPU. Defaults to False.
        reshard_after_forward_policy (str, optional): The policy to use for resharding after forward pass. Defaults to "default".
            Other options: "never", "always".
            - "default" applies default resharding behavior, implementing "smart defaults" for known optimal scenarios.
            - "always" will enable `reshard_after_forward` for all forward passes.
            - "never" will disable `reshard_after_forward` for all forward passes.

    """
    mp_policy = MixedPrecisionPolicy(param_dtype=param_dtype, reduce_dtype=reduce_dtype)
    fsdp_config = {"mesh": dp_mesh, "mp_policy": mp_policy}
    if cpu_offload:
        # pyrefly: ignore [bad-typed-dict-key]
        fsdp_config["offload_policy"] = CPUOffloadPolicy()

    match reshard_after_forward_policy:
        case "always":
            reshard_after_forward = True
        case "never":
            reshard_after_forward = False
        case "default":
            # For PP, by default do not reshard after forward to avoid per-microbatch
            # all-gathers, which can be expensive and non-overlapped
            reshard_after_forward = not pp_enabled
        case _:
            raise ValueError(
                f"Invalid reshard_after_forward_policy: {reshard_after_forward_policy}."
            )

    if model.tok_embeddings is not None:
        # pyrefly: ignore [no-matching-overload]
        fully_shard(
            model.tok_embeddings,
            **fsdp_config,
            reshard_after_forward=reshard_after_forward,
        )
    # pyrefly: ignore [missing-attribute]
    for layer_id, transformer_block in model.layers.items():
        fully_shard(
            transformer_block,
            **fsdp_config,
            reshard_after_forward=reshard_after_forward,
        )
    # As an optimization, do not reshard_after_forward the last layers by default
    # since FSDP would prefetch them immediately after the forward pass
    if model.norm is not None and model.output is not None:
        # pyrefly: ignore [no-matching-overload]
        fully_shard(
            [model.norm, model.output],
            **fsdp_config,
            reshard_after_forward=reshard_after_forward_policy == "always",
        )
    fully_shard(model, **fsdp_config)


def apply_ddp(
    model: nn.Module,
    dp_mesh: DeviceMesh,
    enable_compile: bool,
):
    if enable_compile:
        torch._dynamo.config.optimize_ddp = "ddp_optimizer"

    # pyrefly: ignore [invalid-param-spec]
    replicate(model, device_mesh=dp_mesh, bucket_cap_mb=100)

    logger.info("Applied DDP to the model")
