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

import torch
from torch import Tensor
from torch.library import impl

# Define meta ops.  To support dynamic shapes, some meta ops need to
# be defined in python instead of C++.
torchao_lib = torch.library.Library("torchao", "IMPL")
for weight_nbit in range(1, 9):

    @impl(torchao_lib, f"_linear_8bit_act_{weight_nbit}bit_weight", "Meta")
    def _(
        activations: Tensor,
        packed_weights: Tensor,
        group_size: int,
        n: int,
        k: int,
    ):
        assert activations.dim() == 2
        m, k_ = activations.shape
        assert k_ == k
        return torch.empty(m, n, dtype=activations.dtype, device="meta")

    @impl(torchao_lib, f"_embedding_{weight_nbit}bit", "Meta")
    def _(
        packed_weight_qvals: Tensor,
        num_embeddings: int,
        embedding_dim: int,
        weight_scales: Tensor,
        weight_zeros: Tensor,
        indices: Tensor,
    ):
        assert indices.dim() == 1
        num_out = indices.shape[0]
        return torch.empty(num_out, embedding_dim, dtype=torch.float32, device="meta")

    @impl(torchao_lib, f"_shared_embedding_{weight_nbit}bit", "Meta")
    def _(packed_weights: Tensor, group_size: int, n: int, k: int, indices: Tensor):
        assert indices.dim() == 1
        num_out = indices.shape[0]
        return torch.empty(num_out, k, dtype=torch.float32, device="meta")


for weight_nbit in range(1, 5):

    @impl(torchao_lib, f"_linear_groupwise_{weight_nbit}bit_weight_with_lut", "Meta")
    def _(
        activations: Tensor,
        packed_weights: Tensor,
        scale_group_size: int,
        lut_group_size: int,
        n: int,
        k: int,
    ):
        assert activations.dim() == 2
        m, k_ = activations.shape
        assert k_ == k
        return torch.empty(m, n, dtype=activations.dtype, device="meta")