# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.

import pytest
import torch

import transformer_engine.pytorch as te

batch_size = 32
seq_length = 2048
num_heads = 16
head_dim = 64
dtype = torch.bfloat16
num_attn_head = 16
ffn_hidden_size = 1024


@pytest.mark.parametrize("kv_channels", [128, 256])
@pytest.mark.parametrize("hidden_size", [128, 256])
@pytest.mark.parametrize("num_gqa_groups", [1, 2, 4, 8, 16])
def test_gqa(kv_channels, hidden_size, num_gqa_groups) -> None:

    model = te.TransformerLayer(
        hidden_size, ffn_hidden_size, num_attn_head, num_gqa_groups, kv_channels=kv_channels
    )

    # Run forward pass
    x = torch.randn((batch_size, 1, hidden_size)).cuda()
    model(x)

    # Check shapes of weights.
    assert model.self_attention.layernorm_qkv.key_weight.shape[0] == kv_channels * num_gqa_groups
    assert model.self_attention.layernorm_qkv.key_weight.shape[1] == hidden_size

    assert model.self_attention.layernorm_qkv.query_weight.shape[0] == kv_channels * num_attn_head
    assert model.self_attention.layernorm_qkv.query_weight.shape[1] == hidden_size

    assert model.self_attention.layernorm_qkv.value_weight.shape[0] == kv_channels * num_gqa_groups
    assert model.self_attention.layernorm_qkv.value_weight.shape[1] == hidden_size

    assert model.self_attention.proj.weight.shape[0] == hidden_size
    assert model.self_attention.proj.weight.shape[1] == kv_channels * num_attn_head
