# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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

os.environ['NVIDIA_TF32_OVERRIDE'] = "0"
os.environ['FLAGS_new_einsum'] = "0"

import unittest

import numpy as np
from eager_op_test import (
    OpTest,
    convert_float_to_uint16,
    convert_uint16_to_float,
)
from test_sparse_attention_op import get_cuda_version

import paddle
import paddle.incubate.nn.functional as F
from paddle import _legacy_C_ops, nn
from paddle.fluid import core


@unittest.skipIf(
    not core.is_compiled_with_cuda(), "Paddle is not compiled with CUDA"
)
class TestFusedGateAttentionOp(OpTest):
    def setUp(self):
        self.__class__.op_type = "fused_gate_attention"
        # use autograd to check grad in this unittest.
        self.__class__.no_need_check_grad = True
        self.config()
        self.merge_qkv = self.q_dim == self.kv_dim
        self.generate_input_data()

    def config(self):
        self.dtype = "float32"
        self.has_gating = True
        self.batch_size = 1
        self.msa_len = 3
        self.res_len = 5
        self.q_dim = 6
        self.num_heads = 2
        self.head_dim = 4
        self.m_size = self.res_len
        self.kv_dim = self.q_dim
        self.out_dim = self.q_dim
        self.bias_attr = True

    def generate_input_data(self):
        def _random(shape):
            if self.dtype == "bfloat16":
                data = np.random.random(shape).astype("float32")
                return convert_float_to_uint16(data)
            else:
                return np.random.random(shape).astype(self.dtype)

        np.random.seed(123)
        self.query = _random(
            (self.batch_size, self.msa_len, self.res_len, self.q_dim)
        )
        self.q_weight = _random((self.q_dim, self.num_heads, self.head_dim))
        self.k_weight = _random((self.kv_dim, self.num_heads, self.head_dim))
        self.v_weight = _random((self.kv_dim, self.num_heads, self.head_dim))
        if self.merge_qkv:
            self.key = None
            # (3, self.num_heads, self.head_dim, self.q_dim)
            q_weight_t = np.transpose(self.q_weight, axes=[1, 2, 0])
            k_weight_t = np.transpose(self.k_weight, axes=[1, 2, 0])
            v_weight_t = np.transpose(self.v_weight, axes=[1, 2, 0])
            self.qkv_weight = np.stack([q_weight_t, k_weight_t, v_weight_t])
        else:
            self.key = _random(
                (self.batch_size, self.msa_len, self.m_size, self.kv_dim)
            )
            self.qkv_weight = None

        self.attn_mask = _random(
            (self.batch_size, self.msa_len, 1, 1, self.m_size)
        )

        if self.bias_attr:
            self.nonbatched_bias = _random(
                (self.batch_size, 1, self.num_heads, self.res_len, self.m_size)
            )

        if self.has_gating:
            self.gating_w = _random((self.q_dim, self.num_heads, self.head_dim))
            self.gating_b = _random((self.num_heads, self.head_dim))

        self.output_w = _random((self.num_heads, self.head_dim, self.out_dim))
        self.output_b = _random(self.out_dim)

        self.dout = _random(
            (self.batch_size, self.msa_len, self.res_len, self.q_dim)
        )

    def collect_outputs(self, query, key, softmax_out, fmha_out, gate_out, out):
        outputs = [
            softmax_out,
            fmha_out,
            gate_out if self.has_gating else None,
            out,
            query.grad,
            None if self.merge_qkv else key.grad,
        ]
        return outputs

    def get_reference_out(self):
        paddle.disable_static(place=paddle.CUDAPlace(0))

        query = paddle.to_tensor(self.query, stop_gradient=False)
        key = (
            query
            if self.merge_qkv
            else paddle.to_tensor(self.key, stop_gradient=False)
        )
        q_weight = paddle.to_tensor(self.q_weight, stop_gradient=False)
        k_weight = paddle.to_tensor(self.k_weight, stop_gradient=False)
        v_weight = paddle.to_tensor(self.v_weight, stop_gradient=False)
        src_mask = paddle.to_tensor(self.attn_mask, stop_gradient=True)

        c = self.head_dim ** (-0.5)
        # [batch_size, msa_len, res_len, q_dim], [q_dim, num_heads, head_dim]
        #   -> [batch_size, msa_len, res_len, num_heads, head_dim]
        q = paddle.einsum('nbqa,ahc->nbqhc', query, q_weight) * c
        # [batch_size, msa_len, m_size, kv_dim], [kv_dim, num_heads, head_dim]
        #   -> [batch_size, msa_len, m_size, num_heads, head_dim]
        k = paddle.einsum('nbka,ahc->nbkhc', key, k_weight)
        # [batch_size, msa_len, m_size, kv_dim], [kv_dim, num_heads, head_dim]
        #   -> [batch_size, msa_len, m_size, num_heads, head_dim]
        v = paddle.einsum('nbka,ahc->nbkhc', key, v_weight)

        # [batch_size, msa_len, res_len, num_heads, head_dim], [batch_size, msa_len, m_size, num_heads, head_dim]
        #   -> [batch_size, msa_len, num_heads, res_len, m_size]
        logits = paddle.einsum('nbqhc,nbkhc->nbhqk', q, k)  # qk_out
        # [batch_size, msa_len, num_heads, res_len, m_size], [batch_size, mas_len, 1, 1, m_size]
        #   -> [batch_size, msa_len, num_heads, res_len, m_size]
        logits = logits + src_mask
        if self.bias_attr:
            nonbatched_bias = paddle.to_tensor(
                self.nonbatched_bias, stop_gradient=False
            )
            # [batch_size, msa_len, num_heads, res_len, m_size], [batch_size, 1, num_heads, res_len, m_size]
            #   -> [batch_size, msa_len, num_heads, res_len, m_size]
            logits = logits + nonbatched_bias

        # [batch_size, msa_len, num_heads, res_len, m_size]
        softmax_out = nn.functional.softmax(logits)
        # [batch_size, msa_len, num_heads, res_len, m_size], [batch_size, msa_len, m_size, num_heads, head_dim]
        #   -> [batch_size, msa_len, res_len, num_heads, head_dim]
        # fmha_out = paddle.einsum('nbhqk,nbkhc->nbqhc', softmax_out, v)
        v_trans = paddle.transpose(v, perm=[0, 1, 3, 2, 4])
        qktv_out = paddle.matmul(softmax_out, v_trans)
        fmha_out = paddle.transpose(qktv_out, perm=[0, 1, 3, 2, 4])

        if self.has_gating:
            gating_w = paddle.to_tensor(self.gating_w, stop_gradient=False)
            gating_b = paddle.to_tensor(self.gating_b, stop_gradient=False)
            # [batch_size, msa_len, res_len, q_dim], [q_dim, num_heads, head_dim]
            #   -> [batch_size, msa_len, res_len, num_heads, head_dim]
            # gate_values = paddle.einsum('nbqc,chv->nbqhv', query,
            #                             gating_w) + gating_b
            gating_w_2d = paddle.reshape(
                gating_w, shape=[self.q_dim, self.num_heads * self.head_dim]
            )
            gate_values_4d = paddle.matmul(query, gating_w_2d)
            gate_values = (
                paddle.reshape(
                    gate_values_4d,
                    shape=[
                        self.batch_size,
                        self.msa_len,
                        self.res_len,
                        self.num_heads,
                        self.head_dim,
                    ],
                )
                + gating_b
            )
            gate_values = nn.functional.sigmoid(gate_values)
            gate_out = fmha_out * gate_values
        else:
            gate_out = fmha_out

        output_b = paddle.to_tensor(self.output_b, stop_gradient=False)
        output_w = paddle.to_tensor(self.output_w, stop_gradient=False)

        # [batch_size, msa_len, res_len, num_heads, head_dim], [num_heads, head_dim, out_dim]
        #   -> [batch_size, msa_len, res_len, out_dim]
        # out = paddle.einsum('nbqhc,hco->nbqo', gate_out,
        #                     output_w) + output_b
        gate_out_2d = paddle.reshape(
            gate_out,
            shape=[
                self.batch_size * self.msa_len * self.res_len,
                self.num_heads * self.head_dim,
            ],
        )
        output_w_2d = paddle.reshape(
            output_w, shape=[self.num_heads * self.head_dim, self.out_dim]
        )
        out_2d = paddle.matmul(gate_out_2d, output_w_2d)
        out = (
            paddle.reshape(
                out_2d,
                shape=[
                    self.batch_size,
                    self.msa_len,
                    self.res_len,
                    self.out_dim,
                ],
            )
            + output_b
        )

        paddle.autograd.backward(
            [out], [paddle.to_tensor(self.dout)], retain_graph=True
        )
        return self.collect_outputs(
            query, key, softmax_out, fmha_out, gate_out, out
        )

    def get_fused_gate_attention_out(self):
        paddle.disable_static(place=paddle.CUDAPlace(0))

        query = paddle.to_tensor(self.query, stop_gradient=False)
        if self.merge_qkv:
            key = None
            q_weight = None
            k_weight = None
            v_weight = None
            qkv_weight = paddle.to_tensor(self.qkv_weight, stop_gradient=False)
        else:
            key = paddle.to_tensor(self.key, stop_gradient=False)
            q_weight = paddle.to_tensor(self.q_weight, stop_gradient=False)
            k_weight = paddle.to_tensor(self.k_weight, stop_gradient=False)
            v_weight = paddle.to_tensor(self.v_weight, stop_gradient=False)
            qkv_weight = None

        src_mask = paddle.to_tensor(self.attn_mask, stop_gradient=True)

        if self.bias_attr:
            nonbatched_bias = paddle.to_tensor(
                self.nonbatched_bias, stop_gradient=False
            )
        else:
            nonbatched_bias = None
        if self.has_gating:
            gating_w = paddle.to_tensor(self.gating_w, stop_gradient=False)
            gating_b = paddle.to_tensor(self.gating_b, stop_gradient=False)
        else:
            gating_w = None
            gating_b = None

        output_w = paddle.to_tensor(self.output_w, stop_gradient=False)
        output_b = paddle.to_tensor(self.output_b, stop_gradient=False)

        (
            _,
            _,
            _,
            _,
            softmax_out,
            _,
            fmha_out,
            gate_out,
            out,
        ) = _legacy_C_ops.fused_gate_attention(
            query,
            key,
            q_weight,
            k_weight,
            v_weight,
            qkv_weight,
            nonbatched_bias,
            src_mask,
            gating_w,
            gating_b,
            output_w,
            output_b,
            'has_gating',
            self.has_gating,
            'merge_qkv',
            self.merge_qkv,
        )

        paddle.autograd.backward(
            [out], [paddle.to_tensor(self.dout)], retain_graph=True
        )
        return self.collect_outputs(
            query, key, softmax_out, fmha_out, gate_out, out
        )

    def check(self, ref, out, atol, rtol, check_equal, name):
        def _convert(value):
            if self.dtype == "bfloat16":
                return convert_uint16_to_float(value)
            return value

        if check_equal:
            self.assertTrue(
                np.equal(_convert(ref), _convert(out)).all(),
                f"Checking < {name} > failed!",
            )
        else:
            np.testing.assert_allclose(
                _convert(ref),
                _convert(out),
                atol=atol,
                rtol=rtol,
                err_msg=f"Checking < {name} > failed!",
            )

    def check_output_and_grad(self, atol, rtol):
        output_names = [
            "softmax_out",
            "fmha_out",
            "gate_out",
            "out",
            "query_grad",
            "key_grad",
        ]
        outputs_ref = self.get_reference_out()
        outputs_fused = self.get_fused_gate_attention_out()
        for i in range(len(output_names)):
            ref_res = outputs_ref[i]
            fused_res = outputs_fused[i]
            if ref_res is not None and fused_res is not None:
                # The python implementation of einsum is likely to call
                # matmul(x, y, transpose_x=False, transpose_y=True). With different
                # transpose_x and transpose_y, cublas will launch different kernels
                # and the result cannot be exactly equal.
                # Because the arguments of matmul in einsum are the same as
                # that in fused ops, check_equal is set to False and we use allclose
                # to check the correctness.
                check_equal = False
                self.check(
                    ref_res.numpy(),
                    fused_res.numpy(),
                    atol,
                    rtol,
                    check_equal,
                    output_names[i],
                )

    def test_output_and_grad(self):
        self.check_output_and_grad(atol=1e-5, rtol=1e-6)


class TestMergeQKVLargeBatchSizeCase(TestFusedGateAttentionOp):
    def config(self):
        super().config()
        self.batch_size = 2


class TestSeparatedQKVCase(TestFusedGateAttentionOp):
    def config(self):
        self.dtype = "float32"
        self.has_gating = False
        self.batch_size = 1
        self.msa_len = 3
        self.res_len = 5
        self.q_dim = 6
        self.num_heads = 2
        self.head_dim = 4
        self.m_size = 4
        self.kv_dim = 2
        self.out_dim = self.q_dim
        self.bias_attr = False


class TestMergeQKVNoBiasGatingCase(TestFusedGateAttentionOp):
    def config(self):
        super().config()
        self.has_gating = False
        self.bias_attr = False


class TestMergeQKVFp16Case(TestFusedGateAttentionOp):
    def config(self):
        super().config()
        self.dtype = "float16"

    def test_output_and_grad(self):
        place = core.CUDAPlace(0)
        if core.is_float16_supported(place):
            self.check_output_and_grad(atol=1e-1, rtol=1e-5)


class TestMergeQKVLargeBatchSizeFp16Case(TestMergeQKVFp16Case):
    def config(self):
        super().config()
        self.batch_size = 2


@unittest.skipIf(
    not core.is_compiled_with_cuda()
    or get_cuda_version() < 11000
    or paddle.device.cuda.get_device_capability()[0] < 8,
    "core is not compiled with CUDA and cuda version need larger than or equal to 11.3",
)
class TestMergeQKVBF16Case(TestFusedGateAttentionOp):
    def config(self):
        super().config()
        self.dtype = "bfloat16"

    def test_output_and_grad(self):
        self.check_output_and_grad(atol=1e-1, rtol=1e-2)


class TestMergeQKVLargeBatchSizeBF16Case(TestMergeQKVBF16Case):
    def config(self):
        super().config()
        self.batch_size = 2


class TestFusedGateAttentionApi(unittest.TestCase):
    def setUp(self):
        self.has_gating = True
        self.batch_size = 2
        self.msa_len = 3
        self.res_len = 2
        self.q_dim = 4
        self.num_heads = 2
        self.head_dim = 4
        self.m_size = self.res_len
        self.kv_dim = self.q_dim
        self.out_dim = self.q_dim
        self.merge_qkv = self.q_dim == self.kv_dim

        self.query_shape = [
            self.batch_size,
            self.msa_len,
            self.res_len,
            self.q_dim,
        ]
        self.qkv_weight_shape = [3, self.num_heads, self.head_dim, self.q_dim]

        self.attn_mask_shape = [
            self.batch_size,
            self.msa_len,
            1,
            1,
            self.m_size,
        ]
        self.nonbatched_bias_shape = [
            self.batch_size,
            1,
            self.num_heads,
            self.res_len,
            self.m_size,
        ]

        self.gating_w_shape = [self.q_dim, self.num_heads, self.head_dim]
        self.gating_b_shape = [self.num_heads, self.head_dim]

        self.output_w_shape = [self.num_heads, self.head_dim, self.out_dim]
        self.output_b_shape = [self.out_dim]

        self.out_shape = [
            self.batch_size,
            self.msa_len,
            self.res_len,
            self.out_dim,
        ]

    def test_api(self):
        if not core.is_compiled_with_cuda():
            pass

        query = paddle.rand(shape=self.query_shape, dtype="float32")
        qkv_weight = paddle.rand(shape=self.qkv_weight_shape, dtype="float32")

        attn_mask = paddle.rand(shape=self.attn_mask_shape, dtype="float32")
        nonbatched_bias = paddle.rand(
            shape=self.nonbatched_bias_shape, dtype="float32"
        )

        gate_linear_weight = paddle.rand(
            shape=self.gating_w_shape, dtype="float32"
        )
        gate_linear_bias = paddle.rand(
            shape=self.gating_b_shape, dtype="float32"
        )

        out_linear_weight = paddle.rand(
            shape=self.output_w_shape, dtype="float32"
        )
        out_linear_bias = paddle.rand(
            shape=self.output_b_shape, dtype="float32"
        )

        output = F.fused_gate_attention(
            query=query,
            qkv_weight=qkv_weight,
            gate_linear_weight=gate_linear_weight,
            gate_linear_bias=gate_linear_bias,
            out_linear_weight=out_linear_weight,
            out_linear_bias=out_linear_bias,
            nonbatched_bias=nonbatched_bias,
            attn_mask=attn_mask,
            has_gating=True,
            merge_qkv=True,
        )
        print(f"output.shape={output.shape}")
        self.assertEqual(output.shape, self.out_shape)


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