# 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 unittest
from itertools import chain, product

import numpy as np
import tensorrt as trt
import torch
from parameterized import parameterized
from polygraphy.backend.trt import CreateConfig, EngineFromNetwork, TrtRunner
from utils.util import unittest_name_func

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

from . import _utils


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,
                 use_plugin):
        # 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
        network = builder.create_network()
        # Allow SQ plugin of dtype type
        if use_plugin:
            network.plugin_config.smooth_quant_gemm_plugin = dtype
        with tensorrt_llm.net_guard(network):
            # 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 = tensorrt_llm.functional.constant(scale_a_torch.numpy())
            # Init TensorRT-LLM tensor for per channel scaling
            scale_b = tensorrt_llm.functional.constant(scale_b_torch.numpy())
            # Get output tensor for SQ gemm
            output = smooth_quant_gemm(x, y, scale_a, scale_b,
                                       per_token_scaling, per_channel_scaling,
                                       dtype)
            output.mark_output('output', dtype)

        # TODO: When dtype=int32, per_token_scaling=False, per_channel_scaling=False,
        # This test will break using new API on A30, only when running with all other unit tests.
        # This is a weird issue, so skip changing this file.
        engine = EngineFromNetwork(
            (builder.trt_builder, network.trt_network),
            config=CreateConfig(
                memory_pool_limits={trt.MemoryPoolType.WORKSPACE: 33554432}))

        # Infer engine
        with TrtRunner(engine) as runner:
            outputs = runner.infer(feed_dict={
                'x': mat1.numpy(),
                'y': mat2.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(chain(
        product(["float16", "float32", "int32"], [True, False], [True, False],
                [True]),
        product(["float16", "float32"], [True, False], [True, False], [False])),
                          name_func=unittest_name_func)
    def test_matmul(self, dtype, per_token_scaling, per_channel_scaling,
                    use_plugin):
        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, use_plugin)

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


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