# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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.
import os
import sys
import unittest

import numpy as np
import pytest
import torch
from parameterized import parameterized
from polygraphy.backend.trt import CreateConfig, EngineFromNetwork, TrtRunner

import tensorrt_llm
from tensorrt_llm import Parameter, Tensor
from tensorrt_llm._utils import torch_to_numpy

sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from utils.util import getSMVersion


class TestFunctional(unittest.TestCase):

    def setUp(self):
        tensorrt_llm.logger.set_level('error')
        torch.manual_seed(42)

    @parameterized.expand([['float16'], ['float32'], ['bfloat16']])
    def test_layer_norm_plugin(self, dtype):

        # Skip tests that are not supported in pre-ampere architecture
        if getSMVersion() < 80:
            if dtype == 'bfloat16':
                pytest.skip(
                    "bfloat16 is not supported in pre-ampere architecture")

        # test data
        hidden_size = 1024
        x_data = torch.randn((8, 128, hidden_size),
                             dtype=torch.float64,
                             device="cuda")
        weight = torch.randn((hidden_size), dtype=torch.float64, device="cuda")
        bias = torch.randn((hidden_size), dtype=torch.float64, device="cuda")
        eps = 1e-5

        m = torch.nn.LayerNorm(hidden_size,
                               eps=eps,
                               dtype=torch.float64,
                               device="cuda")
        m.weight = torch.nn.Parameter(weight)
        m.bias = torch.nn.Parameter(bias)

        # pytorch run
        with torch.no_grad():
            ref = m(x_data)

        m.to(tensorrt_llm._utils.str_dtype_to_torch(dtype))
        x_data = x_data.to(tensorrt_llm._utils.str_dtype_to_torch(dtype))

        gamma_data = m.weight.detach().cpu()
        beta_data = m.bias.detach().cpu()

        # construct trt network
        builder = tensorrt_llm.Builder()
        net = builder.create_network()
        net.plugin_config.set_layernorm_plugin(dtype)
        with tensorrt_llm.net_guard(net):
            network = tensorrt_llm.default_trtnet()
            x = Tensor(name='x',
                       shape=x_data.shape,
                       dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            weight = Parameter(torch_to_numpy(gamma_data.cpu())).value
            bias = Parameter(torch_to_numpy(beta_data.cpu())).value

            output = tensorrt_llm.functional.layer_norm(x, hidden_size, weight,
                                                        bias, eps).trt_tensor
            output.name = 'output'
            network.mark_output(output)
            output.dtype = tensorrt_llm.str_dtype_to_trt(dtype)

        # trt run
        build_engine = EngineFromNetwork(
            (builder.trt_builder, net.trt_network),
            config=CreateConfig(fp16=(dtype == 'float16'),
                                bf16=(dtype == 'bfloat16')))
        assert build_engine is not None, "Build engine failed"
        with TrtRunner(build_engine) as runner:
            outputs = runner.infer(feed_dict={'x': x_data.cpu()})

        # compare diff
        dtype_atol = {"float16": 2e-2, "float32": 2e-6, "bfloat16": 8e-2}
        np.testing.assert_allclose(ref.cpu().numpy(),
                                   outputs['output'].to(torch.float32),
                                   atol=dtype_atol[dtype])