# 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.
import os
import sys
import unittest

import _utils
import numpy as np
import pytest

# isort: off
import torch
import tensorrt as trt
# isort: on
from parameterized import parameterized
from polygraphy.backend.trt import CreateConfig, EngineFromNetwork, TrtRunner

import tensorrt_llm
from tensorrt_llm import Tensor
from tensorrt_llm.quantization.functional import smooth_quant_gemm

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


class TestSmoothQuantGemm(unittest.TestCase):

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

    def _sq_gemm(self, m, n, k, dtype, per_token_scaling, per_channel_scaling):
        # Init operands for multiplication in int32
        shape1 = (m, k)
        mat1 = torch.randint(-128, 128, shape1, dtype=torch.int8)
        shape2 = (n, k)
        mat2 = torch.randint(-128, 128, shape2, dtype=torch.int8)

        # Init scales in fp32
        shape_scale_a = (m, 1) if per_token_scaling else (1, 1)
        scale_a_torch = torch.ones(shape_scale_a, dtype=torch.float32) * 1e-2
        scale_a_torch *= torch.randint(1,
                                       10,
                                       shape_scale_a,
                                       dtype=torch.float32)
        shape_scale_b = (1, n) if per_channel_scaling else (1, 1)
        scale_b_torch = torch.ones(shape_scale_b, dtype=torch.float32) * 1e-2
        scale_b_torch *= torch.randint(1,
                                       10,
                                       shape_scale_b,
                                       dtype=torch.float32)

        # Create builder
        builder = tensorrt_llm.Builder()
        # Create empty network
        net = builder.create_network()
        # Allow SQ plugin of dtype type
        net.plugin_config.set_smooth_quant_gemm_plugin(dtype)
        with tensorrt_llm.net_guard(net):
            network = tensorrt_llm.default_trtnet()
            # Init TensorRT-LLM tensor for mat1
            x = Tensor(name='x',
                       shape=mat1.shape,
                       dtype=tensorrt_llm._utils.str_dtype_to_trt("int8"))
            # Init TensorRT-LLM tensor for mat2
            y = Tensor(name='y',
                       shape=mat2.shape,
                       dtype=tensorrt_llm._utils.str_dtype_to_trt("int8"))
            # Init TensorRT-LLM tensor for per token scaling
            scale_a = Tensor(
                name='scale_a',
                shape=scale_a_torch.shape,
                dtype=tensorrt_llm._utils.str_dtype_to_trt("float32"))
            # Init TensorRT-LLM tensor for per channel scaling
            scale_b = Tensor(
                name='scale_b',
                shape=scale_b_torch.shape,
                dtype=tensorrt_llm._utils.str_dtype_to_trt("float32"))
            # Get output tensor for SQ gemm
            output = smooth_quant_gemm(x, y, scale_a, scale_b,
                                       per_token_scaling,
                                       per_channel_scaling).trt_tensor
            output.name = 'output'
            network.mark_output(output)
            output.dtype = tensorrt_llm._utils.str_dtype_to_trt(dtype)

        # Build engine consisting of only SQ Gemm
        build_engine = EngineFromNetwork(
            (builder.trt_builder, net.trt_network),
            config=CreateConfig(
                int8=True,
                fp16=(dtype == "float16"),
                memory_pool_limits={trt.MemoryPoolType.WORKSPACE: 33554432}))

        # Infer engine
        with TrtRunner(build_engine) as runner:
            outputs = runner.infer(
                feed_dict={
                    'x': mat1.numpy(),
                    'y': mat2.numpy(),
                    'scale_a': scale_a_torch.numpy(),
                    'scale_b': scale_b_torch.numpy()
                })

        ref = _utils.gt_matmul_smooth_quant(mat1,
                                            mat2,
                                            scale_a_torch,
                                            scale_b_torch,
                                            dtype,
                                            bias=None)

        np.testing.assert_allclose(ref.cpu().numpy(), outputs['output'])

    @parameterized.expand([('float16', False, False), ('float16', False, True),
                           ('float16', True, False), ('float16', True, True),
                           ('float32', False, False), ('float32', False, True),
                           ('float32', True, False), ('float32', True, True),
                           ('int32', False, False), ('int32', False, True),
                           ('int32', True, False), ('int32', True, True)])
    @pytest.mark.skipif(
        getSMVersion() < 80,
        reason="Smooth quant is not supported in pre-ampere architecture"
    )  # Skip tests that are not supported in pre-ampere architecture
    def test_matmul(self, dtype, per_token_scaling, per_channel_scaling):
        bs = 2
        inseq = 16
        hidden_size = 768

        # qkv_gemm
        self._sq_gemm(bs * inseq, 3 * hidden_size, hidden_size, dtype,
                      per_token_scaling, per_channel_scaling)

        # mlp_gemm_1
        self._sq_gemm(bs * inseq, 4 * hidden_size, hidden_size, dtype,
                      per_channel_scaling, per_token_scaling)

    def test_sq_matmul_no_plugin(self):
        # Create builder
        builder = tensorrt_llm.Builder()
        # Create empty network
        net = builder.create_network()
        with tensorrt_llm.net_guard(net):
            tensorrt_llm.default_trtnet()
            # Get output tensor for SQ gemm
            with self.assertRaisesRegex(
                    TypeError,
                    "Smooth Quant GEMM is only supported with plugin"):
                smooth_quant_gemm(None, None, None, None, False, False)


if __name__ == '__main__':
    unittest.main()