# 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 tensorrt as trt
import torch
from parameterized import parameterized
from polygraphy.backend.trt import CreateConfig, EngineFromNetwork, TrtRunner

import tensorrt_llm
from tensorrt_llm import Tensor

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


class TestMatmul(unittest.TestCase):

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

    def _matmul(self, m, n, k, dtype, ta, tb):
        shape1 = (k, m) if ta else (m, k)
        mat1 = torch.randn(
            shape1, dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype)) * 1e-1
        shape2 = (n, k) if tb else (k, n)
        mat2 = torch.randn(
            shape2, dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype)) * 1e-1
        builder = tensorrt_llm.Builder()
        net = builder.create_network()
        with tensorrt_llm.net_guard(net):
            network = tensorrt_llm.default_trtnet()
            x = Tensor(name='x',
                       shape=mat1.shape,
                       dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            y = Tensor(name='y',
                       shape=mat2.shape,
                       dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            output = tensorrt_llm.functional.matmul(x, y, transa=ta,
                                                    transb=tb).trt_tensor
            output.name = 'output'
            network.mark_output(output)
            output.dtype = tensorrt_llm.str_dtype_to_trt(dtype)

        build_engine = EngineFromNetwork(
            (builder.trt_builder, net.trt_network),
            config=CreateConfig(
                fp16=(dtype == 'float16'),
                bf16=(dtype == 'bfloat16'),
                precision_constraints='obey',
                memory_pool_limits={trt.MemoryPoolType.WORKSPACE: 33554432}))
        with TrtRunner(build_engine) as runner:
            outputs = runner.infer(feed_dict={'x': mat1, 'y': mat2})

        if ta:
            mat1 = mat1.cuda().transpose(0, 1)
        if tb:
            mat2 = mat2.cuda().transpose(0, 1)

        tols = {
            "float32": {
                "rtol": 1e-05,
                "atol": 1e-05
            },
            "float16": {
                "rtol": 1e-02,
                "atol": 1e-02
            },
            "bfloat16": {
                "rtol": 1e-02,
                "atol": 1e-02
            },
        }

        if dtype != "float32":
            mat1 = mat1.cuda()
            mat2 = mat2.cuda()
        else:
            mat1 = mat1.cpu()
            mat2 = mat2.cpu()

        ref = torch.matmul(mat1, mat2).to(torch.float32)
        np.testing.assert_allclose(ref.cpu().numpy(),
                                   outputs['output'].to(torch.float32),
                                   **tols[dtype])

    @parameterized.expand([('float16', False, False), ('float16', False, True),
                           ('float16', True, False), ('float16', True, True),
                           ('bfloat16', True, False), ('bfloat16', True, True),
                           ('float32', False, False), ('float32', False, True),
                           ('float32', True, False), ('float32', True, True)])
    def test_matmul(self, dtype, transa, transb):
        bs = 2
        inseq = 16
        hidden_size = 768
        tp = 1

        # 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")

        # qkv_gemm
        self._matmul(bs * inseq, 3 * hidden_size // tp, hidden_size, dtype,
                     transa, transb)

        # mlp_gemm_1
        self._matmul(bs * inseq, 4 * hidden_size // tp, hidden_size, dtype,
                     transa, transb)

    def test_matmul_broadcast(self):
        dtype = 'float32'
        x_data = torch.randn(16, 4, 4, 5)
        y_data = torch.randn(16, 1, 5, 4)

        builder = tensorrt_llm.Builder()
        net = builder.create_network()
        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))
            y = Tensor(name='y',
                       shape=y_data.shape,
                       dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            output = tensorrt_llm.functional.matmul(x, y).trt_tensor
            output.name = 'output'
            network.mark_output(output)

        build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network))
        with TrtRunner(build_engine) as runner:
            outputs = runner.infer(feed_dict={
                'x': x_data.numpy(),
                'y': y_data.numpy(),
            })

        ref = torch.matmul(x_data, y_data)
        np.testing.assert_allclose(ref.cpu().numpy(),
                                   outputs['output'],
                                   atol=1e-5)