# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
    Utilities for exporting a model to our custom format.
"""

import numpy as np
import torch
from utils import torch_to_numpy


def cpu_map_location(storage, loc):
    return storage.cpu()


def gpu_map_location(storage, loc):
    if loc.startswith("cuda"):
        training_gpu_idx = int(loc.split(":")[1])
        inference_gpu_idx = training_gpu_idx % torch.cuda.device_count()
        return storage.cuda(inference_gpu_idx)
    elif loc.startswith("cpu"):
        return storage.cpu()
    else:
        raise ValueError(f"Not handled {loc}")


def save_val(val, dir, key, tp_num=None):
    dir[f"{key}"] = torch.from_numpy(np.asarray(val))


# def save_val(val, dir, key, tp_num=None):
#     suffix = "bin" if tp_num is None else f"{tp_num}.bin"
#     val.tofile(dir / f"{key}.{suffix}")


def save_split(split_vals, dir, key, i, split_factor):
    for j, val in enumerate(split_vals):
        save_val(val, dir, key, i * split_factor + j)


def generate_int8(weights, act_range, is_qkv=False, multi_query_mode=False):
    """
    This function has two purposes:
     - compute quantized weights, scaled either per-tensor or per-column
     - compute scaling factors

     Depending on the GEMM API (CUTLASS/CUBLAS) the required scaling factors differ.
     CUTLASS uses two sets of scaling factors. One for the activation X, one for the weight W.
     CUBLAS only has one (we can't do per-row scaling). So we must provide pre-multiplied scaling factor.

     Here is the list of what we need (T means per-tensor, C per-column):
       # - scale_x_orig_quant puts fp activation into the quantized range (i.e. [-128, 127], for int8). Used before the GEMM. (T)
       # - scale_y_quant_orig puts quantized activation into the fp range. Used if the GEMM outputs int8. (T)
       - scale_w_quant_orig puts weights from quant range to fp range (used with CUTLASS) (T, C)
       # - scale_y_accum_quant puts the GEMM result (XW) from accumulation range (int32)
       #   to quant range (int8) (used for CUBLAS) (T, C)

     Note that we don't do anything special about row-parallel GEMM. Theoretically, we could have per-GPU scaling factors too,
     but then the model would change depending on the number of GPUs used.

     For QKV projection, the behavior is special. Even if we have a single matrix to perform QKV projection, we consider it
     as three different matrices: Q, K, and V. So per-tensor actually means one scaling factor for each Q, K and V.
    """

    # compute weight scaling factors for fp->int8 and int8->fp
    if is_qkv and not multi_query_mode:
        scale_w_orig_quant_t = 127.0 / torch_to_numpy(
            act_range["w"].reshape(3, -1).max(dim=-1, keepdims=True)[0].cpu()
        ).astype(np.float32)
        scale_w_orig_quant_c = 127.0 / torch_to_numpy(
            act_range["w"].reshape(3, -1).cpu()
        ).astype(np.float32)
    elif is_qkv and multi_query_mode:
        raise ValueError(f"Multi-query w/ int8 quant has not been supported yet")
    else:
        scale_w_orig_quant_t = 127.0 / torch_to_numpy(
            act_range["w"].max().cpu()
        ).astype(np.float32)
        scale_w_orig_quant_c = 127.0 / torch_to_numpy(act_range["w"].cpu()).astype(
            np.float32
        )
    scale_w_quant_orig_t = 1.0 / scale_w_orig_quant_t
    scale_w_quant_orig_c = 1.0 / scale_w_orig_quant_c

    # compute the rest of needed scaling factors
    scale_x_orig_quant_t = np.array(127.0 / act_range["x"].max().item())
    scale_y_orig_quant_t = np.array(127.0 / act_range["y"].max().item())
    scale_y_quant_orig_t = np.array(act_range["y"].max().item() / 127.0)
    scale_y_accum_quant_t = scale_y_orig_quant_t / (
        scale_x_orig_quant_t * scale_w_orig_quant_t
    )
    scale_y_accum_quant_c = scale_y_orig_quant_t / (
        scale_x_orig_quant_t * scale_w_orig_quant_c
    )
    if is_qkv:
        scale_y_accum_quant_t = np.broadcast_to(
            scale_y_accum_quant_t, scale_w_orig_quant_c.shape
        )
        scale_w_quant_orig_t = np.broadcast_to(
            scale_w_quant_orig_t, scale_w_orig_quant_c.shape
        )

    to_i8 = lambda x: x.round().clip(-127, 127).astype(np.int8)
    return {
        "weight.int8": to_i8(weights * scale_w_orig_quant_t),
        "weight.int8.col": to_i8(weights * scale_w_orig_quant_c),
        "scale_x_orig_quant": scale_x_orig_quant_t.astype(np.float32),
        "scale_w_quant_orig": scale_w_quant_orig_t.astype(np.float32),
        "scale_w_quant_orig.col": scale_w_quant_orig_c.astype(np.float32),
        "scale_y_accum_quant": scale_y_accum_quant_t.astype(np.float32),
        "scale_y_accum_quant.col": scale_y_accum_quant_c.astype(np.float32),
        "scale_y_quant_orig": scale_y_quant_orig_t.astype(np.float32),
    }


def write_int8(
    vals, dir, base_key, split_dim, tp_rank, split_factor, kv_cache_only=False
):
    if not kv_cache_only:
        # save_split(np.split(vals["weight.int8"], split_factor, axis=split_dim),
        #            dir, f"{base_key}.weight.int8", tp_rank, split_factor)
        save_split(
            np.split(vals["weight.int8.col"], split_factor, axis=split_dim),
            dir,
            f"{base_key}.qweight",
            tp_rank,
            split_factor,
        )

    # saved_keys_once = ["scale_y_quant_orig"]
    saved_keys_once = []
    # if not kv_cache_only:
    # saved_keys_once += [
    # "scale_x_orig_quant",
    # "scale_w_quant_orig",
    # "scale_y_accum_quant"
    # ]
    # per-column scaling factors are loaded per-gpu for ColumnParallel GEMMs (QKV, FC1)
    if not kv_cache_only:
        if split_dim == -1:
            save_split(
                np.split(vals["scale_w_quant_orig.col"], split_factor, axis=split_dim),
                dir,
                f"{base_key}.scales",
                tp_rank,
                split_factor,
            )
            # save_split(
            #     np.split(vals["scale_y_accum_quant.col"],
            #              split_factor,
            #              axis=split_dim), dir,
            #     f"{base_key}.scale_y_accum_quant.col", tp_rank, split_factor)
        else:
            saved_keys_once += [
                "scale_w_quant_orig.col"
                # "scale_y_accum_quant.col"
            ]

    if tp_rank == 0:
        for save_key in saved_keys_once:
            save_val(vals[save_key], dir, f"{base_key}.scales")


# Note: in multi_query_mode, only query heads are split between multiple GPUs, while key/value head
# are not split as there is only one head per key/value.
@torch.no_grad()
def split_and_save_weight(
    tp_rank, saved_dir, split_factor, key, vals, storage_type, act_range, config
):
    saved_dir = {}
    use_attention_nemo_shape = config.get("use_attention_nemo_shape", False)
    split_gated_activation = config.get("split_gated_activation", False)
    num_attention_heads = config.get("num_attention_heads", 0)
    tp_size = config.get("tp_size", 1)
    int8_outputs = config.get("int8_outputs", None)
    multi_query_mode = config.get("multi_query_mode", False)
    local_dim = config.get("local_dim", None)

    save_int8 = int8_outputs == "all" or int8_outputs == "kv_cache_only"

    if not key.endswith(".smoother"):
        if not isinstance(vals, list):
            vals = [vals]

        if config.get("transpose_weights", False) and vals[0].ndim == 2:
            vals = [val.T for val in vals]
        if "layernorm.weight" in key and config.get("apply_layernorm_1p", False):
            vals = [val + 1.0 for val in vals]
        vals = [torch_to_numpy(val.cpu().to(storage_type)) for val in vals]
    else:
        vals = torch_to_numpy(vals.cpu())

    if (
        "ln_1.weight" in key
        or "ln_1.bias" in key
        or "attn.c_attn.bias" in key
        or "ln_2.weight" in key
        or "ln_2.bias" in key
        or "mlp.c_proj.bias" in key
        or "ln_f.weight" in key
    ):
        # "final_layernorm.weight" in key or "final_layernorm.bias" in key:

        # shared weights, only need to convert the weights of rank 0
        if tp_rank == 0:
            save_val(vals[0], saved_dir, key)

    elif "attn.c_proj.weight" in key or "mlp.c_proj.weight" in key:
        cat_dim = 0
        val = np.concatenate(vals, axis=cat_dim)
        split_vals = np.split(val, split_factor, axis=cat_dim)
        # save_split(split_vals, saved_dir, key, tp_rank, split_factor)
        if act_range is not None and int8_outputs == "all":
            base_key = key.replace(".weight", "")
            vals_i8 = generate_int8(val, act_range, multi_query_mode=multi_query_mode)
            write_int8(vals_i8, saved_dir, base_key, cat_dim, tp_rank, split_factor)

    elif (
        "mlp.w1.weight" in key
        or "mlp.w2.weight" in key
        or "mlp.w1.bias" in key
        or "mlp.w2.bias" in key
    ):
        if split_gated_activation:
            splits = [np.split(val, 2, axis=-1) for val in vals]
            vals, gates = list(zip(*splits))
        cat_dim = -1
        val = np.concatenate(vals, axis=cat_dim)
        split_vals = np.split(val, split_factor, axis=cat_dim)
        # save_split(split_vals, saved_dir, key, tp_rank, split_factor)
        if act_range is not None and int8_outputs == "all":
            base_key = key.replace(".weight", "")
            vals_i8 = generate_int8(val, act_range, multi_query_mode=multi_query_mode)
            write_int8(vals_i8, saved_dir, base_key, cat_dim, tp_rank, split_factor)

        if split_gated_activation:
            assert not save_int8
            prefix, dot, suffix = key.rpartition(".")
            key = prefix + ".gate" + dot + suffix

            gate = np.concatenate(gates, axis=cat_dim)
            split_vals = np.split(gate, split_factor, axis=cat_dim)
            save_split(split_vals, saved_dir, key, tp_rank, split_factor)

    elif "attn.c_attn.bias" in key:
        if local_dim is None:
            local_dim = vals[0].shape[-1] // 3

        if multi_query_mode:
            val = vals[0]
            # out_feature = local_dim + 2 * head_size; assumes local_dim equals to hidden_dim
            b_q, b_kv = np.split(val, [local_dim], axis=-1)
            b_q_split = np.split(b_q, split_factor, axis=-1)
            split_vals = [np.concatenate((i, b_kv), axis=-1) for i in b_q_split]
        else:
            if use_attention_nemo_shape:
                head_num = num_attention_heads // tp_size
                size_per_head = local_dim // num_attention_heads
                nemo_shape = (head_num, 3, size_per_head)
                vals = [val.reshape(nemo_shape) for val in vals]
                vals = [val.transpose(1, 0, 2) for val in vals]

            vals = [val.reshape(3, local_dim) for val in vals]
            val = np.concatenate(vals, axis=-1)
            split_vals = np.split(val, split_factor, axis=-1)
        save_split(split_vals, saved_dir, key, tp_rank, split_factor)

    elif "attn.c_attn.weight" in key:
        hidden_dim = vals[0].shape[0]
        if local_dim is None:
            local_dim = vals[0].shape[-1] // 3
        if multi_query_mode:
            val = vals[0]
            # out_feature = local_dim + 2 * head_size; assumes local_dim equals to hidden_dim
            head_size = (val.shape[-1] - local_dim) // 2
            val = val.reshape(hidden_dim, local_dim + 2 * head_size)
            w_q, w_kv = np.split(val, [local_dim], axis=-1)
            w_q_split = np.split(w_q, split_factor, axis=-1)
            split_vals = [np.concatenate((i, w_kv), axis=-1) for i in w_q_split]
        else:
            if use_attention_nemo_shape:
                head_num = num_attention_heads // tp_size
                size_per_head = hidden_dim // num_attention_heads
                vals = [
                    val.reshape(hidden_dim, head_num, 3, size_per_head) for val in vals
                ]
                vals = [val.transpose(0, 2, 1, 3) for val in vals]

            vals = [val.reshape(hidden_dim, 3, local_dim) for val in vals]
            cat_dim = -1
            val = np.concatenate(vals, axis=cat_dim)
            split_vals = np.split(val, split_factor, axis=cat_dim)
        # save_split(split_vals, saved_dir, key, tp_rank, split_factor)
        if save_int8:
            base_key = key.replace(".weight", "")
            vals_i8 = generate_int8(
                val, act_range, is_qkv=True, multi_query_mode=multi_query_mode
            )
            write_int8(
                vals_i8,
                saved_dir,
                base_key,
                cat_dim,
                tp_rank,
                split_factor,
                kv_cache_only=int8_outputs == "kv_cache_only",
            )

    elif "attn.c_proj.smoother" in key or "mlp.c_proj.smoother" in key:
        split_vals = np.split(vals, split_factor, axis=0)
        save_split(split_vals, saved_dir, key, tp_rank, split_factor)
    else:
        print(f"[WARNING] {key} not handled by converter")
    return saved_dir
