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# regarding copyright ownership.  The ASF licenses this file
# to you 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
#
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# software distributed under the License is distributed on an
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# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
import numpy as np

from tvm import topi
import tvm.topi.testing
import tvm
from tvm import te
from tvm import relay
from tvm.relay.testing import check_grad, run_infer_type
from tvm.relay.transform import gradient
import tvm.testing


def verify_max_pool2d_grad(x_shape, pool_size, strides, padding, ceil_mode):
    x = relay.var("x", relay.TensorType(x_shape, "float32"))
    y = tvm.relay.nn.max_pool2d(
        x, pool_size=pool_size, strides=strides, padding=padding, ceil_mode=ceil_mode
    )

    fwd_func = relay.Function([x], y)
    fwd_func = run_infer_type(fwd_func)
    bwd_func = run_infer_type(gradient(fwd_func))

    data = np.random.rand(*x_shape).astype("float32")
    ph, pw = padding
    y_shape = topi.utils.get_const_tuple(fwd_func.ret_type.shape)
    out_grad = np.ones(shape=y_shape)
    ref_grad = tvm.topi.testing.pool_grad_nchw(
        data,
        out_grad,
        pool_size=pool_size,
        strides=strides,
        padding=[ph, pw, ph, pw],
        pool_type="max",
        ceil_mode=ceil_mode,
    )

    for target, dev in tvm.testing.enabled_targets():
        op_res, (op_grad,) = relay.create_executor(device=dev, target=target).evaluate(bwd_func)(
            data
        )
        np.testing.assert_allclose(op_grad.numpy(), ref_grad, rtol=0.01)


@tvm.testing.uses_gpu
def test_max_pool2d_grad():
    verify_max_pool2d_grad(
        (1, 4, 16, 16), pool_size=(2, 2), strides=(2, 2), padding=(0, 0), ceil_mode=False
    )
    verify_max_pool2d_grad(
        (1, 4, 16, 16), pool_size=(1, 1), strides=(1, 1), padding=(1, 1), ceil_mode=False
    )


def verify_avg_pool2d_grad(
    x_shape, pool_size, strides, padding, ceil_mode, count_include_pad, dtype="float32"
):

    for shape_dtype in ["int32", "int64"]:
        x = relay.var("x", shape=[tvm.tir.IntImm(shape_dtype, x) for x in x_shape], dtype=dtype)
        y = tvm.relay.nn.avg_pool2d(
            x,
            pool_size=pool_size,
            strides=strides,
            padding=padding,
            ceil_mode=ceil_mode,
            count_include_pad=count_include_pad,
        )

        fwd_func = relay.Function([x], y)
        fwd_func = run_infer_type(fwd_func)
        bwd_func = run_infer_type(gradient(fwd_func))

        data = np.random.rand(*x_shape).astype(dtype)
        ph, pw = padding
        y_shape = topi.utils.get_const_tuple(fwd_func.ret_type.shape)
        out_grad = np.ones(shape=y_shape)
        ref_grad = tvm.topi.testing.pool_grad_nchw(
            data,
            out_grad,
            pool_size=pool_size,
            strides=strides,
            padding=[ph, pw, ph, pw],
            pool_type="avg",
            ceil_mode=ceil_mode,
        )

        for target, dev in tvm.testing.enabled_targets():
            op_res, (op_grad,) = relay.create_executor(device=dev, target=target).evaluate(
                bwd_func
            )(data)
            np.testing.assert_allclose(op_grad.numpy(), ref_grad, rtol=0.01)


@tvm.testing.uses_gpu
def test_avg_pool2d_grad():
    verify_avg_pool2d_grad(
        (1, 4, 16, 16),
        pool_size=(2, 2),
        strides=(2, 2),
        padding=(0, 0),
        ceil_mode=False,
        count_include_pad=True,
    )
    verify_avg_pool2d_grad(
        (1, 4, 16, 16),
        pool_size=(1, 1),
        strides=(1, 1),
        padding=(1, 1),
        ceil_mode=False,
        count_include_pad=False,
    )
    verify_avg_pool2d_grad(
        (1, 4, 16, 16),
        pool_size=(1, 1),
        strides=(1, 1),
        padding=(1, 1),
        ceil_mode=False,
        count_include_pad=False,
        dtype="int32",
    )


def verify_global_avg_pool2d_grad(x_shape):
    x = relay.var("x", relay.TensorType(x_shape, "float32"))
    y = tvm.relay.nn.global_avg_pool2d(x)

    fwd_func = relay.Function([x], y)
    fwd_func = run_infer_type(fwd_func)
    bwd_func = run_infer_type(gradient(fwd_func))

    data = np.random.rand(*x_shape).astype("float32")
    y_shape = topi.utils.get_const_tuple(fwd_func.ret_type.shape)
    out_grad = np.ones(shape=y_shape)
    ref_grad = tvm.topi.testing.pool_grad_nchw(
        data,
        out_grad,
        pool_size=(x_shape[2], x_shape[3]),
        strides=(1, 1),
        padding=[0, 0, 0, 0],
        pool_type="avg",
        ceil_mode=False,
    )

    for target, dev in tvm.testing.enabled_targets():
        op_res, (op_grad,) = relay.create_executor(device=dev, target=target).evaluate(bwd_func)(
            data
        )
        np.testing.assert_allclose(op_grad.numpy(), ref_grad, rtol=0.01)


@tvm.testing.uses_gpu
def test_global_avg_pool2d_grad():
    verify_global_avg_pool2d_grad((1, 4, 16, 16))
    verify_global_avg_pool2d_grad((1, 8, 8, 24))


def verify_conv2d_grad(dshape, wshape, strides, padding, dilation, groups=1, mode="higher_order"):
    dtype = "float32"
    data = relay.var("data", shape=dshape, dtype=dtype)
    weight = relay.var("weight", shape=wshape, dtype=dtype)
    conv = relay.nn.conv2d(
        data, weight, strides=strides, padding=padding, dilation=dilation, groups=groups
    )
    fwd_func = relay.Function([data, weight], conv)
    check_grad(fwd_func, mode=mode)


@tvm.testing.uses_gpu
def test_conv2d_grad():
    verify_conv2d_grad((1, 4, 16, 16), (16, 4, 3, 3), [1, 1], [1, 1], [1, 1])
    verify_conv2d_grad((1, 4, 16, 16), (16, 4, 1, 1), [1, 1], [0, 0], [1, 1])
    verify_conv2d_grad((1, 4, 16, 16), (16, 4, 1, 1), [2, 2], [0, 0], [1, 1])
    verify_conv2d_grad((1, 4, 16, 16), (16, 4, 3, 3), [1, 1], [1, 1], [1, 1], mode="first_order")


def verify_dense_grad(d_shape, w_shape):
    data = relay.var("data", relay.TensorType(d_shape, "float32"))
    weight = relay.var("weight", relay.TensorType(w_shape, "float32"))
    fwd_func = relay.Function([data, weight], relay.nn.dense(data, weight))
    check_grad(fwd_func)


def test_dense_grad():
    verify_dense_grad((1, 8), (16, 8))
    verify_dense_grad((1, 4), (3, 4))
    verify_dense_grad((5, 4), (3, 4))


def verify_matmul_grad(a_shape, b_shape, transpose_a, transpose_b):
    tensor_a = relay.var("tensor_a", relay.TensorType(a_shape, "float32"))
    tensor_b = relay.var("tensor_b", relay.TensorType(b_shape, "float32"))
    fwd_func = relay.Function(
        [tensor_a, tensor_b],
        relay.nn.matmul(tensor_a, tensor_b, transpose_a=transpose_a, transpose_b=transpose_b),
    )
    check_grad(fwd_func)


def test_matmul_grad():
    verify_matmul_grad((1, 8), (8, 16), False, False)
    verify_matmul_grad((4, 1), (4, 3), True, False)
    verify_matmul_grad((4, 5), (3, 4), True, True)


def verify_batch_flatten_grad(d_shape):
    data = relay.var("data", relay.TensorType(d_shape, "float32"))
    fwd_func = relay.Function([data], relay.nn.batch_flatten(data))
    check_grad(fwd_func)


def test_batch_flatten_grad():
    verify_batch_flatten_grad((1, 2, 3, 4))
    verify_batch_flatten_grad((1, 8))


if __name__ == "__main__":
    test_max_pool2d_grad()
    test_avg_pool2d_grad()
    test_global_avg_pool2d_grad()
    test_conv2d_grad()
    test_dense_grad()
    test_matmul_grad()
    test_batch_flatten_grad()