# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.

import os
import argparse

from functools import partial

import torch
import torch.distributed as dist
from torch import nn
from torch.distributed.fsdp import FullyShardedDataParallel, MixedPrecision
from torch.distributed.fsdp.wrap import always_wrap_policy, transformer_auto_wrap_policy
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
    apply_activation_checkpointing,
    checkpoint_wrapper,
)

import transformer_engine.pytorch as te
from transformer_engine.common.recipe import Format, DelayedScaling
from transformer_engine.pytorch.distributed import prepare_te_modules_for_fsdp

LOCAL_RANK = int(os.getenv("LOCAL_RANK", "0"))
WORLD_SIZE = int(os.getenv("WORLD_SIZE", "1"))

# RNG state tracker for checkpointing
rng_seed = 1234
torch.manual_seed(rng_seed)
torch.cuda.manual_seed(rng_seed)
CUDA_RNG_STATES_TRACKER = te.distributed.CudaRNGStatesTracker()
CUDA_RNG_STATES_TRACKER.add("model-parallel-rng", rng_seed)


def get_cuda_rng_tracker():
    return CUDA_RNG_STATES_TRACKER


def apply_fsdp_checkpointing(model, blocks):
    """apply activation checkpointing to model
    returns None as model is updated directly
    """
    wrapper = lambda m: checkpoint_wrapper(
        m,
        checkpoint_fn=te.distributed.checkpoint,
        use_reentrant=False,
        get_rng_state_tracker=get_cuda_rng_tracker,
    )
    check_fn = lambda submodule: isinstance(submodule, blocks)
    apply_activation_checkpointing(model, checkpoint_wrapper_fn=wrapper, check_fn=check_fn)


def lowercase(s):
    return str(s).lower()


def torch_dtype(d):
    typemap = {
        "fp32": torch.float32,
        "float32": torch.float32,
        "fp16": torch.float16,
        "float16": torch.float16,
        "bf16": torch.bfloat16,
        "bfloat16": torch.bfloat16,
    }
    if lowercase(d) not in typemap.keys():
        raise TypeError
    return typemap[lowercase(d)]


te_layer_map = {
    "linear": te.Linear,
    "layernorm": te.LayerNorm,
    "rmsnorm": te.RMSNorm,
    "layernormlinear": te.LayerNormLinear,
    "layernormmlp": te.LayerNormMLP,
    "multiheadattention": te.MultiheadAttention,
    "transformerlayer": te.TransformerLayer,
}


def te_layer(l):
    if l is not None:
        if lowercase(l) not in te_layer_map.keys():
            raise TypeError
        return te_layer_map[lowercase(l)]
    return None


def get_layer_args(opts):
    hidden_size = opts.num_heads * opts.head_dim
    layer_args = (hidden_size,)
    layer_kwargs = {
        "params_dtype": opts.dtype,
        "device": "cuda" if opts.no_defer_init else "meta",
        "get_rng_state_tracker": get_cuda_rng_tracker,
    }
    if opts.layer_type in [te.Linear, te.LayerNormLinear, te.LayerNormMLP]:
        ffn_hidden_size = 3 * hidden_size if opts.num_layers == 1 else hidden_size
        layer_args += (ffn_hidden_size,)
        layer_kwargs["bias"] = True
        if opts.layer_type == te.LayerNormMLP:
            layer_kwargs["seq_length"] = opts.seq_length
    elif opts.layer_type == te.MultiheadAttention:
        layer_args += (opts.num_heads,)
        layer_kwargs["fuse_qkv_params"] = True
        layer_kwargs["input_layernorm"] = True
    elif opts.layer_type == te.TransformerLayer:
        layer_args += (3 * hidden_size, opts.num_heads)
        layer_kwargs["fuse_qkv_params"] = True
        layer_kwargs["seq_length"] = opts.seq_length
    return layer_args, layer_kwargs


def parse_fsdp_args():
    parser = argparse.ArgumentParser(
        description="Run Transformer Engine modules with the "
        + "torch.distributed.fsdp.FullyShardedDataParallel strategy."
    )
    parser.add_argument(
        "-v",
        "--verbose",
        action="store_true",
        default=False,
        help="Print out information from all GPUs instead of only the root GPU-0.",
    )
    parser.add_argument("-b", "--batch-size", type=int, default=32, help="Input batch size.")
    parser.add_argument("-s", "--seq-length", type=int, default=1048, help="Input sequence length.")
    parser.add_argument(
        "-n", "--num-heads", type=int, default=16, help="Number of attention heads."
    )
    parser.add_argument(
        "-d",
        "--head-dim",
        type=int,
        default=128,
        help="Dimension of each attention head (number of KV channels).",
    )
    parser.add_argument(
        "-i", "--num-iters", type=int, default=5, help="Number of dummy 'training' iterations."
    )
    parser.add_argument(
        "-k",
        "--num-layers",
        type=int,
        default=3,
        help="Number of modules chained together with nn.Sequential.",
    )
    parser.add_argument(
        "--layer-type",
        type=te_layer,
        default=te.TransformerLayer,
        choices=list(te_layer_map.values()),
        help="TE module type used to construct the test model.",
    )
    parser.add_argument("--seed", type=int, default=1234, help="PyTorch RNG seed.")
    parser.add_argument(
        "--profile-memory",
        action="store_true",
        help="Enable memory profiling via torch.profiler.profile().",
    )
    parser.add_argument(
        "--profile-name", type=str, default=None, help="File path for memory profiling."
    )
    parser.add_argument(
        "--checkpoint-layer",
        type=te_layer,
        default=None,
        help="Recompute activations of the selected layer during the backward "
        + "pass instead of saving.",
    )
    parser.add_argument(
        "--no-fp8",
        action="store_true",
        default=False,
        help="Disables the te.autocast() context.",
    )
    parser.add_argument(
        "--no-defer-init",
        action="store_true",
        help="Defer module parameter initialization until after FSDP sharding.",
    )
    parser.add_argument(
        "--no-te-fsdp",
        action="store_true",
        help="Disable sharding of intermediate/activation tensors in TE modules.",
    )
    parser.add_argument(
        "--dtype",
        type=torch_dtype,
        default=torch.bfloat16,
        help="Data type for input tensor and Transformer Engine module parameters.",
    )
    return parser.parse_args()


def dist_print(text, all_ranks=False, no_new_line=False):
    if LOCAL_RANK == 0 or all_ranks:
        end = "" if no_new_line else "\n"
        print(f"[GPU-{LOCAL_RANK}] " + text, end=end)


def train(opts):
    # Initialize torch.distributed global process group
    dist.init_process_group(backend="nccl")
    torch.cuda.set_device(LOCAL_RANK)
    dist_print(f"WORLD_SIZE = {WORLD_SIZE}")
    torch.manual_seed(opts.seed)

    # Construct a simple homogeneous model (only one layer type) with NO PARALLELISM
    layer_args, layer_kwargs = get_layer_args(opts)
    if opts.num_layers > 1:
        te_layer_list = []
        for i in range(opts.num_layers):
            if opts.layer_type in [te.MultiheadAttention, te.TransformerLayer]:
                layer_kwargs["layer_number"] = i + 1
            te_layer_list.append(opts.layer_type(*layer_args, **layer_kwargs))
        te_model = nn.Sequential(*te_layer_list)
    else:
        # Single layer model
        te_model = opts.layer_type(*layer_args, **layer_kwargs)

    # Print out allocated device memory before the model parameters are sharded by FSDP
    pre_mem_use = torch.cuda.memory_allocated(device=f"cuda:{LOCAL_RANK}") * 1e-6
    dist_print(f"Pre-FSDP memory use = {pre_mem_use}MiB")

    # Wrap the model with FSDP
    # NOTE: The TE model itself has no inherent parallelism. FSDP shards model parameters and
    #       controls all communication.
    all_gpus = dist.new_group(backend="nccl")
    fsdp_wrap_policy = always_wrap_policy
    if opts.layer_type == te.TransformerLayer:
        # NOTE: FSDP causes illegal memory access without this special policy for Transformers
        fsdp_wrap_policy = partial(
            transformer_auto_wrap_policy, transformer_layer_cls={te.TransformerLayer}
        )
    te_model = FullyShardedDataParallel(
        te_model,
        process_group=all_gpus,
        use_orig_params=True,
        mixed_precision=MixedPrecision(
            param_dtype=opts.dtype,
            reduce_dtype=torch.float32,
        ),
        auto_wrap_policy=fsdp_wrap_policy,
    )

    if opts.checkpoint_layer is not None:
        # Recompute the activations of the selected layer during the backward pass instead of
        # saving them during the forward pass
        apply_fsdp_checkpointing(te_model, blocks=opts.checkpoint_layer)
    elif not opts.no_te_fsdp:
        # Prepare TE modules to shard internal buffers that FSDP cannot shard on its own
        prepare_te_modules_for_fsdp(te_model)

    # Print out allocated device memory after the model parameters are sharded
    post_mem_use = torch.cuda.memory_allocated(device=f"cuda:{LOCAL_RANK}") * 1e-6
    dist_print(f"Post-FSDP memory use = {post_mem_use}MiB")
    dist_print(f"FSDP-Wrapped + Checkpointed TE Model:\n{te_model}")

    # Fp8 setup for TE
    fp8_format = Format.HYBRID
    fp8_recipe = DelayedScaling(fp8_format=fp8_format, amax_history_len=32, amax_compute_algo="max")

    # Optimizer must be created after the model is wrapped in FSDP and the parameters are sharded
    optim = torch.optim.Adam(te_model.parameters(), lr=0.0001)

    # Profile memory use
    if opts.profile_memory:
        torch.cuda.memory._record_memory_history(max_entries=100000)
    else:
        torch.cuda.reset_peak_memory_stats()
        start = torch.cuda.Event(enable_timing=True)
        end = torch.cuda.Event(enable_timing=True)
        torch.cuda.synchronize()
        start.record()

    for i in range(opts.num_iters):
        # Generate a random input batch
        x = torch.rand(
            opts.seq_length,
            opts.batch_size,
            opts.num_heads * opts.head_dim,
            dtype=opts.dtype,
            device="cuda",
        )
        # autocast needs to be given the FSDP process group for amax reductions
        with te.autocast(enabled=not opts.no_fp8, recipe=fp8_recipe, amax_reduction_group=all_gpus):
            y = te_model(x)
            loss = y.sum()
        # calculate gradient and take training step outside the autocast context
        loss.backward()
        optim.step()
        optim.zero_grad(set_to_none=True)
        del x

    if opts.profile_memory:
        torch.cuda.memory._dump_snapshot(f"gpu{LOCAL_RANK}_{opts.profile_name}.pickle")
        torch.cuda.memory._record_memory_history(enabled=None)
    else:
        end.record()
        torch.cuda.synchronize()
        peak_mem = torch.cuda.max_memory_allocated()
        train_time = start.elapsed_time(end) / 1000.0
        dist_print(f"Training Time: {train_time}s")
        dist_print(f"Avg. Iter. Time: {train_time / opts.num_iters}s")
        dist_print(f"Peak Memory Use: {peak_mem * 1e-6}MBs")


# Run with:
#   torchrun --nnodes=1 --nproc-per-node=$(nvidia-smi -L | wc -l) test_fsdp.py --defer-init
if __name__ == "__main__":
    args = parse_fsdp_args()
    train(args)
