import unittest

import torch
import torch.backends.xnnpack
from torch.nn import functional as F
from torch.utils.mobile_optimizer import optimize_for_mobile
from torch.testing import FileCheck
import torch.testing._internal.hypothesis_utils as hu
from torch.testing._internal.common_utils import TestCase, run_tests
from hypothesis import given, assume
from hypothesis import strategies as st
import io
import itertools


@unittest.skipUnless(torch.backends.xnnpack.enabled,
                     " XNNPACK must be enabled for these tests."
                     " Please build with USE_XNNPACK=1.")
class TestXNNPACKOps(TestCase):
    @given(batch_size=st.integers(0, 3),
           data_shape=hu.array_shapes(1, 3, 2, 64),
           weight_output_dim=st.integers(2, 64),
           use_bias=st.booleans())
    def test_linear(self, batch_size, data_shape, weight_output_dim, use_bias):
        data_shape = [batch_size] + list(data_shape)
        input_data = torch.rand(data_shape)
        weight = torch.rand((weight_output_dim, data_shape[-1]))
        if use_bias:
            bias = torch.rand((weight_output_dim))
        else:
            bias = None
        ref_result = F.linear(input_data, weight, bias)
        packed_weight_bias = torch.ops.prepacked.linear_clamp_prepack(weight, bias)
        output_linearprepacked = torch.ops.prepacked.linear_clamp_run(input_data, packed_weight_bias)
        torch.testing.assert_allclose(ref_result, output_linearprepacked, rtol=1e-2, atol=1e-3)

    @given(batch_size=st.integers(0, 3),
           input_channels_per_group=st.integers(1, 32),
           height=st.integers(5, 64),
           width=st.integers(5, 64),
           output_channels_per_group=st.integers(1, 32),
           groups=st.integers(1, 16),
           kernel_h=st.integers(1, 7),
           kernel_w=st.integers(1, 7),
           stride_h=st.integers(1, 2),
           stride_w=st.integers(1, 2),
           pad_h=st.integers(0, 2),
           pad_w=st.integers(0, 2),
           dilation=st.integers(1, 2),
           use_bias=st.booleans(),
           format=st.sampled_from([None, torch.preserve_format, torch.contiguous_format, torch.channels_last]))
    def test_conv2d(self,
                    batch_size,
                    input_channels_per_group,
                    height,
                    width,
                    output_channels_per_group,
                    groups,
                    kernel_h,
                    kernel_w,
                    stride_h,
                    stride_w,
                    pad_h,
                    pad_w,
                    dilation,
                    use_bias,
                    format):
        input_channels = input_channels_per_group * groups
        output_channels = output_channels_per_group * groups
        kernels = (kernel_h, kernel_w)
        strides = (stride_h, stride_w)
        paddings = (pad_h, pad_w)
        dilations = (dilation, dilation)
        assume(height + 2 * paddings[0]
               >= dilations[0] * (kernels[0] - 1) + 1)
        assume(width + 2 * paddings[1]
               >= dilations[1] * (kernels[1] - 1) + 1)

        input_data = torch.rand((batch_size, input_channels, height, width))
        if (format is not None):
            input_data = input_data.contiguous(memory_format=format)
        weight = torch.rand((output_channels, input_channels_per_group, kernel_h, kernel_w))
        bias = None
        if use_bias:
            bias = torch.rand((output_channels))

        ref_result = F.conv2d(input_data, weight, bias,
                              strides, paddings, dilations, groups)
        packed_weight_bias = torch.ops.prepacked.conv2d_clamp_prepack(weight, bias,
                                                                      strides, paddings, dilations, groups)
        xnnpack_result = torch.ops.prepacked.conv2d_clamp_run(input_data, packed_weight_bias)
        torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)

    @given(batch_size=st.integers(1, 3),
           input_channels_per_group=st.integers(1, 32),
           height=st.integers(5, 64),
           width=st.integers(5, 64),
           output_channels_per_group=st.integers(1, 32),
           groups=st.integers(1, 16),
           kernel_h=st.integers(1, 7),
           kernel_w=st.integers(1, 7),
           stride_h=st.integers(1, 2),
           stride_w=st.integers(1, 2),
           pad_h=st.integers(0, 2),
           pad_w=st.integers(0, 2),
           output_pad_h=st.integers(0, 2),
           output_pad_w=st.integers(0, 2),
           dilation=st.integers(1, 2),
           use_bias=st.booleans(),
           format=st.sampled_from([None, torch.preserve_format, torch.contiguous_format, torch.channels_last]))
    def test_conv2d_transpose(self,
                              batch_size,
                              input_channels_per_group,
                              height,
                              width,
                              output_channels_per_group,
                              groups,
                              kernel_h,
                              kernel_w,
                              stride_h,
                              stride_w,
                              pad_h,
                              pad_w,
                              output_pad_h,
                              output_pad_w,
                              dilation,
                              use_bias,
                              format):
        input_channels = input_channels_per_group * groups
        output_channels = output_channels_per_group * groups
        kernels = (kernel_h, kernel_w)
        strides = (stride_h, stride_w)
        paddings = (pad_h, pad_w)
        output_paddings = (output_pad_h, output_pad_w)
        dilations = (dilation, dilation)
        assume(height + 2 * paddings[0]
               >= dilations[0] * (kernels[0] - 1) + 1)
        assume(width + 2 * paddings[1]
               >= dilations[1] * (kernels[1] - 1) + 1)
        assume((output_pad_h < stride_h) and (output_pad_h < dilation))
        assume((output_pad_w < stride_w) and (output_pad_w < dilation))

        input_data = torch.rand((batch_size, input_channels, height, width))
        if (format is not None):
            input_data = input_data.contiguous(memory_format=format)
        weight = torch.rand((input_channels, output_channels_per_group, kernel_h, kernel_w))
        bias = None
        if use_bias:
            bias = torch.rand((output_channels))

        # Note that groups/dilation is in reverse order from conv2d
        ref_result = F.conv_transpose2d(input_data, weight, bias,
                                        strides, paddings, output_paddings, groups, dilation)
        packed_weight_bias = torch.ops.prepacked.conv2d_transpose_clamp_prepack(weight, bias,
                                                                                strides, paddings,
                                                                                output_paddings, dilations,
                                                                                groups)
        xnnpack_result = torch.ops.prepacked.conv2d_transpose_clamp_run(input_data, packed_weight_bias)
        torch.testing.assert_allclose(ref_result.contiguous(), xnnpack_result.contiguous(), rtol=1e-2, atol=1e-3)

@unittest.skipUnless(torch.backends.xnnpack.enabled,
                     " XNNPACK must be enabled for these tests."
                     " Please build with USE_XNNPACK=1.")
class TestXNNPACKSerDes(TestCase):
    @given(batch_size=st.integers(0, 3),
           data_shape=hu.array_shapes(1, 3, 2, 64),
           weight_output_dim=st.integers(2, 64),
           use_bias=st.booleans())
    def test_linear(self, batch_size, data_shape, weight_output_dim, use_bias):
        class Linear(torch.nn.Module):
            def __init__(self, weight, bias=None):
                super(Linear, self).__init__()
                self.weight = weight
                self.bias = bias

            def forward(self, x):
                return F.linear(x, self.weight, self.bias)

        class LinearPrePacked(torch.nn.Module):
            def __init__(self, weight, bias=None):
                super(LinearPrePacked, self).__init__()
                self.packed_weight_bias = torch.ops.prepacked.linear_clamp_prepack(weight, bias)

            def forward(self, x):
                return torch.ops.prepacked.linear_clamp_run(x, self.packed_weight_bias)

        data_shape = [batch_size] + list(data_shape)
        weight = torch.rand((weight_output_dim, data_shape[-1]))
        if use_bias:
            bias = torch.rand((weight_output_dim))
        else:
            bias = None
        scripted_linear = torch.jit.script(Linear(weight, bias))
        scripted_linear_clamp_prepacked = torch.jit.script(LinearPrePacked(weight, bias))
        input_data = torch.rand(data_shape)
        ref_result = scripted_linear(input_data)
        output_linearprepacked = scripted_linear_clamp_prepacked(input_data)
        torch.testing.assert_allclose(ref_result, output_linearprepacked, rtol=1e-2, atol=1e-3)

        # Serialize the modules and then deserialize
        input_data = torch.rand(data_shape)
        buffer = io.BytesIO()
        torch.jit.save(scripted_linear, buffer)
        buffer.seek(0)
        deserialized_linear = torch.jit.load(buffer)
        buffer = io.BytesIO()
        torch.jit.save(scripted_linear_clamp_prepacked, buffer)
        buffer.seek(0)
        deserialized_linear_clamp_prepacked = torch.jit.load(buffer)
        ref_result = deserialized_linear(input_data)
        output_linearprepacked = deserialized_linear_clamp_prepacked(input_data)
        torch.testing.assert_allclose(ref_result, output_linearprepacked, rtol=1e-2, atol=1e-3)

    @given(batch_size=st.integers(0, 3),
           input_channels_per_group=st.integers(1, 32),
           height=st.integers(5, 64),
           width=st.integers(5, 64),
           output_channels_per_group=st.integers(1, 32),
           groups=st.integers(1, 16),
           kernel_h=st.integers(1, 7),
           kernel_w=st.integers(1, 7),
           stride_h=st.integers(1, 2),
           stride_w=st.integers(1, 2),
           pad_h=st.integers(0, 2),
           pad_w=st.integers(0, 2),
           dilation=st.integers(1, 2),
           use_bias=st.booleans(),
           format=st.sampled_from([None, torch.preserve_format, torch.contiguous_format, torch.channels_last]))
    def test_conv2d(self,
                    batch_size,
                    input_channels_per_group,
                    height,
                    width,
                    output_channels_per_group,
                    groups,
                    kernel_h,
                    kernel_w,
                    stride_h,
                    stride_w,
                    pad_h,
                    pad_w,
                    dilation,
                    use_bias,
                    format):
        class Conv2D(torch.nn.Module):
            def __init__(self, weight, bias, strides, paddings, dilations, groups):
                super(Conv2D, self).__init__()
                self.weight = weight
                self.bias = bias
                self.strides = strides
                self.paddings = paddings
                self.dilations = dilations
                self.groups = groups

            def forward(self, x):
                return F.conv2d(x, self.weight, self.bias,
                                self.strides, self.paddings, self.dilations, self.groups)

        class Conv2DPrePacked(torch.nn.Module):
            def __init__(self, weight, bias, strides, paddings, dilations, groups):
                super(Conv2DPrePacked, self).__init__()
                self.packed_weight_bias = torch.ops.prepacked.conv2d_clamp_prepack(weight, bias,
                                                                                   strides, paddings, dilations, groups)

            def forward(self, x):
                return torch.ops.prepacked.conv2d_clamp_run(x, self.packed_weight_bias)

        input_channels = input_channels_per_group * groups
        output_channels = output_channels_per_group * groups
        kernels = (kernel_h, kernel_w)
        strides = (stride_h, stride_w)
        paddings = (pad_h, pad_w)
        dilations = (dilation, dilation)
        assume(height + 2 * paddings[0] >=
               dilations[0] * (kernels[0] - 1) + 1)
        assume(width + 2 * paddings[1] >=
               dilations[1] * (kernels[1] - 1) + 1)

        input_data = torch.rand((batch_size, input_channels, height, width))
        if (format is not None):
            input_data = input_data.contiguous(memory_format=format)
        weight = torch.rand((output_channels, input_channels_per_group, kernel_h, kernel_w))
        bias = None
        if use_bias:
            bias = torch.rand((output_channels))

        scripted_conv2d = torch.jit.script(Conv2D(weight, bias,
                                                  strides, paddings, dilations, groups))
        scripted_conv2d_clamp_prepacked = torch.jit.script(Conv2DPrePacked(
            weight, bias, strides, paddings, dilations, groups))
        ref_result = scripted_conv2d(input_data)
        xnnpack_result = scripted_conv2d_clamp_prepacked(input_data)
        torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)

        # Serialize the modules and then deserialize
        input_data = torch.rand((batch_size, input_channels, height, width))
        if (format is not None):
            input_data = input_data.contiguous(memory_format=format)
        buffer = io.BytesIO()
        torch.jit.save(scripted_conv2d, buffer)
        buffer.seek(0)
        deserialized_conv2d = torch.jit.load(buffer)
        buffer = io.BytesIO()
        torch.jit.save(scripted_conv2d_clamp_prepacked, buffer)
        buffer.seek(0)
        deserialized_conv2d_clamp_prepacked = torch.jit.load(buffer)
        ref_result = deserialized_conv2d(input_data)
        xnnpack_result = deserialized_conv2d_clamp_prepacked(input_data)
        torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)

    @given(batch_size=st.integers(0, 3),
           input_channels_per_group=st.integers(1, 32),
           height=st.integers(5, 64),
           width=st.integers(5, 64),
           output_channels_per_group=st.integers(1, 32),
           groups=st.integers(1, 16),
           kernel_h=st.integers(1, 7),
           kernel_w=st.integers(1, 7),
           stride_h=st.integers(1, 2),
           stride_w=st.integers(1, 2),
           pad_h=st.integers(0, 2),
           pad_w=st.integers(0, 2),
           output_pad_h=st.integers(0, 2),
           output_pad_w=st.integers(0, 2),
           dilation=st.integers(1, 2),
           use_bias=st.booleans(),
           format=st.sampled_from([None, torch.preserve_format, torch.contiguous_format, torch.channels_last]))
    def test_conv2d_transpose(self,
                              batch_size,
                              input_channels_per_group,
                              height,
                              width,
                              output_channels_per_group,
                              groups,
                              kernel_h,
                              kernel_w,
                              stride_h,
                              stride_w,
                              pad_h,
                              pad_w,
                              output_pad_h,
                              output_pad_w,
                              dilation,
                              use_bias,
                              format):
        class Conv2DT(torch.nn.Module):
            def __init__(self, weight, bias, strides, paddings, output_paddings, dilations, groups):
                super(Conv2DT, self).__init__()
                self.weight = weight
                self.bias = bias
                self.strides = strides
                self.paddings = paddings
                self.output_paddings = output_paddings
                self.dilations = dilations
                self.groups = groups

            def forward(self, x):
                return F.conv_transpose2d(x, self.weight, self.bias,
                                          self.strides, self.paddings, self.output_paddings, self.groups, self.dilations)

        class Conv2DTPrePacked(torch.nn.Module):
            def __init__(self, weight, bias, strides, paddings, output_paddings, dilations, groups):
                super(Conv2DTPrePacked, self).__init__()
                self.packed_weight_bias = torch.ops.prepacked.conv2d_transpose_clamp_prepack(weight, bias,
                                                                                             strides, paddings,
                                                                                             output_paddings,
                                                                                             dilations, groups)

            def forward(self, x):
                return torch.ops.prepacked.conv2d_transpose_clamp_run(x, self.packed_weight_bias)

        input_channels = input_channels_per_group * groups
        output_channels = output_channels_per_group * groups
        kernels = (kernel_h, kernel_w)
        strides = (stride_h, stride_w)
        paddings = (pad_h, pad_w)
        output_paddings = (output_pad_h, output_pad_w)
        dilations = (dilation, dilation)
        assume(height + 2 * paddings[0] >=
               dilations[0] * (kernels[0] - 1) + 1)
        assume(width + 2 * paddings[1] >=
               dilations[1] * (kernels[1] - 1) + 1)
        assume((output_pad_h < stride_h) and (output_pad_h < dilation))
        assume((output_pad_w < stride_w) and (output_pad_w < dilation))

        input_data = torch.rand((batch_size, input_channels, height, width))
        if (format is not None):
            input_data = input_data.contiguous(memory_format=format)
        weight = torch.rand((input_channels, output_channels_per_group, kernel_h, kernel_w))
        bias = None
        if use_bias:
            bias = torch.rand((output_channels))

        scripted_conv2d = torch.jit.script(Conv2DT(weight, bias,
                                                   strides, paddings,
                                                   output_paddings, dilations, groups))
        scripted_conv2d_clamp_prepacked = torch.jit.script(Conv2DTPrePacked(
            weight, bias, strides, paddings, output_paddings, dilations, groups))
        ref_result = scripted_conv2d(input_data)
        xnnpack_result = scripted_conv2d_clamp_prepacked(input_data)
        torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)

        # Serialize the modules and then deserialize
        input_data = torch.rand((batch_size, input_channels, height, width))
        if (format is not None):
            input_data = input_data.contiguous(memory_format=format)
        buffer = io.BytesIO()
        torch.jit.save(scripted_conv2d, buffer)
        buffer.seek(0)
        deserialized_conv2d = torch.jit.load(buffer)
        buffer = io.BytesIO()
        torch.jit.save(scripted_conv2d_clamp_prepacked, buffer)
        buffer.seek(0)
        deserialized_conv2d_clamp_prepacked = torch.jit.load(buffer)
        ref_result = deserialized_conv2d(input_data)
        xnnpack_result = deserialized_conv2d_clamp_prepacked(input_data)
        torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)

    @given(batch_size=st.integers(0, 3),
           input_channels_per_group=st.integers(1, 32),
           height=st.integers(5, 64),
           width=st.integers(5, 64),
           output_channels_per_group=st.integers(1, 32),
           groups=st.integers(1, 16),
           kernel_h=st.integers(1, 7),
           kernel_w=st.integers(1, 7),
           stride_h=st.integers(1, 2),
           stride_w=st.integers(1, 2),
           pad_h=st.integers(0, 2),
           pad_w=st.integers(0, 2),
           dilation=st.integers(1, 2),
           linear_weight_output_dim=st.integers(2, 64),
           use_bias=st.booleans(),
           format=st.sampled_from([None, torch.preserve_format, torch.contiguous_format, torch.channels_last]))
    def test_combined_model(self,
                            batch_size,
                            input_channels_per_group,
                            height,
                            width,
                            output_channels_per_group,
                            groups,
                            kernel_h,
                            kernel_w,
                            stride_h,
                            stride_w,
                            pad_h,
                            pad_w,
                            dilation,
                            linear_weight_output_dim,
                            use_bias,
                            format):
        class M(torch.nn.Module):
            def __init__(self, conv_weight, conv_bias, linear_weight, linear_bias,
                         strides, paddings, dilations, groups):
                super(M, self).__init__()
                self.conv_weight = conv_weight
                self.conv_bias = conv_bias
                self.linear_weight = linear_weight
                self.linear_bias = linear_bias
                self.strides = strides
                self.paddings = paddings
                self.dilations = dilations
                self.groups = groups

            def forward(self, x):
                o = F.conv2d(x, self.conv_weight, self.conv_bias,
                             self.strides, self.paddings, self.dilations, self.groups)
                o = o.permute([0, 2, 3, 1])
                o = F.linear(o, self.linear_weight, self.linear_bias)
                return F.relu(o)

        class MPrePacked(torch.nn.Module):
            def __init__(self, conv_weight, conv_bias, linear_weight, linear_bias,
                         strides, paddings, dilations, groups):
                super(MPrePacked, self).__init__()
                self.conv2d_clamp_run_weight_bias = \
                    torch.ops.prepacked.conv2d_clamp_prepack(conv_weight, conv_bias,
                                                             strides, paddings, dilations, groups)
                self.linear_clamp_run_weight_bias = \
                    torch.ops.prepacked.linear_clamp_prepack(linear_weight, linear_bias)

            def forward(self, x):
                o = torch.ops.prepacked.conv2d_clamp_run(x, self.conv2d_clamp_run_weight_bias)
                o = o.permute([0, 2, 3, 1])
                o = torch.ops.prepacked.linear_clamp_run(o, self.linear_clamp_run_weight_bias)
                return F.relu(o)

        input_channels = input_channels_per_group * groups
        output_channels = output_channels_per_group * groups
        kernels = (kernel_h, kernel_w)
        strides = (stride_h, stride_w)
        paddings = (pad_h, pad_w)
        dilations = (dilation, dilation)
        assume(height + 2 * paddings[0]
               >= dilations[0] * (kernels[0] - 1) + 1)
        assume(width + 2 * paddings[1]
               >= dilations[1] * (kernels[1] - 1) + 1)

        input_data = torch.rand((batch_size, input_channels, height, width))
        if (format is not None):
            input_data = input_data.contiguous(memory_format=format)
        conv_weight = torch.rand((output_channels, input_channels_per_group, kernel_h, kernel_w))
        conv_bias = None
        if use_bias:
            conv_bias = torch.rand((output_channels))

        # This is done just to find the output shape of the result
        # so that the shape of weight for the following linear layer
        # can be determined.
        result = F.conv2d(input_data, conv_weight, conv_bias,
                          strides, paddings, dilations, groups)
        linear_input_shape = result.shape[1]

        linear_weight = torch.rand((linear_weight_output_dim, linear_input_shape))
        linear_bias = None
        if use_bias:
            linear_bias = torch.rand((linear_weight_output_dim))

        scripted_m = torch.jit.script(M(conv_weight, conv_bias, linear_weight,
                                        linear_bias, strides, paddings, dilations, groups))
        scripted_m_prepacked = torch.jit.script(
            MPrePacked(
                conv_weight,
                conv_bias,
                linear_weight,
                linear_bias,
                strides,
                paddings,
                dilations,
                groups))
        ref_result = scripted_m(input_data)
        xnnpack_result = scripted_m_prepacked(input_data)
        torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)

        # Serialize the modules and then deserialize
        input_data = torch.rand((batch_size, input_channels, height, width))
        input_data = input_data.contiguous(memory_format=torch.channels_last)
        buffer = io.BytesIO()
        torch.jit.save(scripted_m, buffer)
        buffer.seek(0)
        deserialized_m = torch.jit.load(buffer)
        buffer = io.BytesIO()
        torch.jit.save(scripted_m_prepacked, buffer)
        buffer.seek(0)
        deserialized_m_prepacked = torch.jit.load(buffer)
        ref_result = deserialized_m(input_data)
        xnnpack_result = deserialized_m_prepacked(input_data)
        torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)


@unittest.skipUnless(torch.backends.xnnpack.enabled,
                     " XNNPACK must be enabled for these tests."
                     " Please build with USE_XNNPACK=1.")
class TestXNNPACKRewritePass(TestCase):
    @staticmethod
    def validate_transformed_module(
            # To please flake
            self,
            pattern_count_map,
            data_shape,
            prepack_removal=False,
            fuse_clamping_ops=False):
        input_data = torch.normal(1, 20, size=data_shape)

        for jit_method in ["script", "trace"]:
            module_instance = self
            if jit_method == "script":
                scripted_model = torch.jit.script(module_instance)
            else:
                scripted_model = torch.jit.trace(module_instance, input_data)
            scripted_model.eval()
            ref_result = scripted_model(input_data)
            torch._C._jit_pass_insert_prepacked_ops(scripted_model._c)
            if fuse_clamping_ops or prepack_removal:
                scripted_model._c = torch._C._freeze_module(scripted_model._c)
            if fuse_clamping_ops:
                torch._C._jit_pass_fuse_clamp_w_prepacked_linear_conv(scripted_model._c)
            if (prepack_removal):
                torch._C._jit_pass_fold_prepacking_ops(scripted_model._c)

            buffer = io.BytesIO()
            torch.jit.save(scripted_model, buffer)
            buffer.seek(0)
            deserialized_scripted_model = torch.jit.load(buffer)
            for pattern, v in pattern_count_map.items():
                if (v == 0):
                    FileCheck().check(pattern).run(deserialized_scripted_model.graph)
                elif (v == -1):
                    FileCheck().check_not(pattern).run(deserialized_scripted_model.graph)
                else:
                    FileCheck().check_count(pattern, v, exactly=True).run(deserialized_scripted_model.graph)
            xnnpack_result = deserialized_scripted_model(input_data)
            torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)

    def test_linear(self):
        data_shape = [2, 3, 32]
        weight_output_dim = 24
        weight_shape = (weight_output_dim, data_shape[-1])

        class Linear(torch.nn.Module):
            def __init__(self):
                super(Linear, self).__init__()
                self.weight = torch.nn.Parameter(torch.Tensor(torch.rand(weight_shape)), requires_grad=False)
                self.bias = torch.nn.Parameter(torch.Tensor(torch.rand((weight_output_dim))), requires_grad=False)

            def forward(self, x):
                return F.linear(x, self.weight, self.bias)

        class LinearNoBias(torch.nn.Module):
            def __init__(self):
                super(LinearNoBias, self).__init__()
                self.weight = torch.nn.Parameter(torch.Tensor(torch.rand(weight_shape)), requires_grad=False)

            def forward(self, x):
                return F.linear(x, self.weight, None)

        # Linear with bias pattern.
        pattern_count_map = {"Tensor = prim::CallFunction": -1,
                             "prepacked::linear_clamp_prepack": 1,
                             "prepacked::linear_clamp_run": 1}
        TestXNNPACKRewritePass.validate_transformed_module(Linear(), pattern_count_map, data_shape)
        TestXNNPACKRewritePass.validate_transformed_module(LinearNoBias(), pattern_count_map, data_shape)

        # Conv params
        batch_size = 2
        input_channels_per_group = 6
        height = 16
        width = 16
        output_channels_per_group = 6
        groups = 4
        kernel_h = kernel_w = 3
        stride_h = stride_w = 1
        pad_h = pad_w = 1
        output_pad_h = output_pad_w = 0
        dilation = 1
        input_channels = input_channels_per_group * groups
        output_channels = output_channels_per_group * groups
        kernels = (kernel_h, kernel_w)
        strides = (stride_h, stride_w)
        paddings = (pad_h, pad_w)
        output_paddings = (output_pad_h, output_pad_w)
        dilations = (dilation, dilation)
        conv_weight_shape = (output_channels, input_channels_per_group, kernel_h, kernel_w)
        conv_transpose_weight_shape = (input_channels, output_channels_per_group, kernel_h, kernel_w)
        conv_bias_shape = (output_channels)

        class Conv2D(torch.nn.Module):
            def __init__(self):
                super(Conv2D, self).__init__()
                self.weight = torch.nn.Parameter(torch.Tensor(torch.rand(conv_weight_shape)), requires_grad=False)
                self.bias = torch.nn.Parameter(torch.Tensor(torch.rand(conv_bias_shape)), requires_grad=False)
                self.strides = strides
                self.paddings = paddings
                self.dilations = dilations
                self.groups = groups

            def forward(self, x):
                return F.conv2d(x, self.weight, self.bias,
                                self.strides, self.paddings, self.dilations, self.groups)

        class Conv2DT(torch.nn.Module):
            def __init__(self):
                super(Conv2DT, self).__init__()
                self.weight = torch.nn.Parameter(torch.Tensor(torch.rand(conv_transpose_weight_shape)), requires_grad=False)
                self.bias = torch.nn.Parameter(torch.Tensor(torch.rand(conv_bias_shape)), requires_grad=False)
                self.strides = strides
                self.paddings = paddings
                self.output_paddings = output_paddings
                self.dilations = dilations
                self.groups = groups

            def forward(self, x):
                return F.conv_transpose2d(x, self.weight, self.bias,
                                          self.strides, self.paddings, self.output_paddings, self.groups, self.dilations)


        data_shape = (batch_size, input_channels, height, width)
        pattern_count_map = {"Tensor = aten::conv2d": -1,
                             "prepacked::conv2d_clamp_prepack": 1,
                             "prepacked::conv2d_clamp_run": 1}
        TestXNNPACKRewritePass.validate_transformed_module(Conv2D(), pattern_count_map, data_shape)

        transpose_data_shape = (batch_size, input_channels, height, width)
        transpose_pattern_count_map = {"Tensor = aten::conv_transpose2d": -1,
                                       "prepacked::conv2d_transpose_clamp_prepack": 1,
                                       "prepacked::conv2d_transpose_clamp_run": 1}
        TestXNNPACKRewritePass.validate_transformed_module(Conv2DT(), transpose_pattern_count_map, data_shape)

        input_data = torch.rand((batch_size, input_channels, height, width))
        conv_weight = torch.rand((output_channels, input_channels_per_group, kernel_h, kernel_w))
        conv_bias = torch.rand((output_channels))
        result = F.conv2d(input_data, conv_weight, conv_bias,
                          strides, paddings, dilations, groups)
        linear_input_shape = result.shape[1]
        linear_weight_shape = (weight_output_dim, linear_input_shape)

        class M(torch.nn.Module):
            def __init__(self, activation_fn=F.relu):
                super(M, self).__init__()
                self.conv_weight = torch.nn.Parameter(torch.Tensor(torch.rand(conv_weight_shape)), requires_grad=False)
                self.conv_bias = torch.nn.Parameter(torch.Tensor(torch.rand((conv_bias_shape))), requires_grad=False)
                self.linear_weight = torch.nn.Parameter(torch.Tensor(torch.rand(linear_weight_shape)), requires_grad=False)
                self.linear_bias = torch.nn.Parameter(torch.Tensor(torch.rand((weight_output_dim))), requires_grad=False)
                self.strides = strides
                self.paddings = paddings
                self.dilations = dilations
                self.groups = groups
                self.activation_fn = activation_fn

            def forward(self, x):
                o = F.conv2d(x, self.conv_weight, self.conv_bias,
                             self.strides, self.paddings, self.dilations, self.groups)
                o = self.activation_fn(o)
                o = o.permute([0, 2, 3, 1])
                o = F.linear(o, self.linear_weight, self.linear_bias)
                return self.activation_fn(o)

        pattern_count_map = {"Tensor = aten::conv2d": -1,
                             "prepacked::conv2d_clamp_prepack": 1,
                             "prepacked::conv2d_clamp_run": 1,
                             "prepacked::linear_clamp_prepack": 1,
                             "prepacked::linear_clamp_run": 1}
        TestXNNPACKRewritePass.validate_transformed_module(M(), pattern_count_map, data_shape)
        pattern_count_map["prepacked::conv2d_clamp_prepack"] = -1
        pattern_count_map["Tensor = prim::CallFunction"] = -1
        pattern_count_map["prepacked::linear_clamp_prepack"] = -1
        TestXNNPACKRewritePass.validate_transformed_module(M(), pattern_count_map, data_shape, prepack_removal=True)

        # Not inplace relu fusion test.
        pattern_count_map = {"aten::relu": 2,
                             "prepacked::conv2d_clamp_prepack": -1,
                             "prepacked::conv2d_clamp_run": 1,
                             "prepacked::linear_clamp_prepack": -1,
                             "prepacked::linear_clamp_run": 1}
        TestXNNPACKRewritePass.validate_transformed_module(M(), pattern_count_map, data_shape, prepack_removal=True)
        pattern_count_map["prepacked::conv2d_clamp_prepack"] = -1
        pattern_count_map["prepacked::linear_clamp_prepack"] = -1
        pattern_count_map["aten::relu"] = -1
        TestXNNPACKRewritePass.validate_transformed_module(
            M(),
            pattern_count_map,
            data_shape,
            prepack_removal=True,
            fuse_clamping_ops=True)

        # Inplace relu fusion test.
        pattern_count_map = {"aten::relu": 2,
                             "prepacked::conv2d_clamp_prepack": -1,
                             "prepacked::conv2d_clamp_run": 1,
                             "prepacked::linear_clamp_prepack": -1,
                             "prepacked::linear_clamp_run": 1}
        TestXNNPACKRewritePass.validate_transformed_module(
            M(F.relu_),
            pattern_count_map,
            data_shape,
            prepack_removal=True)
        pattern_count_map["prepacked::conv2d_clamp_prepack"] = -1
        pattern_count_map["prepacked::linear_clamp_prepack"] = -1
        pattern_count_map["aten::relu"] = -1
        TestXNNPACKRewritePass.validate_transformed_module(
            M(F.relu_),
            pattern_count_map,
            data_shape,
            prepack_removal=True,
            fuse_clamping_ops=True)

        # Not inplace hardtanh fusion test.
        pattern_count_map = {"aten::hardtanh": 2,
                             "prepacked::conv2d_clamp_prepack": -1,
                             "prepacked::conv2d_clamp_run": 1,
                             "prepacked::linear_clamp_prepack": -1,
                             "prepacked::linear_clamp_run": 1}
        TestXNNPACKRewritePass.validate_transformed_module(
            M(F.hardtanh),
            pattern_count_map,
            data_shape,
            prepack_removal=True)
        pattern_count_map["prepacked::conv2d_clamp_prepack"] = -1
        pattern_count_map["prepacked::linear_clamp_prepack"] = -1
        pattern_count_map["aten::hardtanh"] = -1
        TestXNNPACKRewritePass.validate_transformed_module(
            M(F.hardtanh),
            pattern_count_map,
            data_shape,
            prepack_removal=True,
            fuse_clamping_ops=True)

        # Inplace hardtanh fusion test.
        pattern_count_map = {"aten::hardtanh_": 2,
                             "prepacked::conv2d_clamp_prepack": -1,
                             "prepacked::conv2d_clamp_run": 1,
                             "prepacked::linear_clamp_prepack": -1,
                             "prepacked::linear_clamp_run": 1}
        TestXNNPACKRewritePass.validate_transformed_module(
            M(F.hardtanh_),
            pattern_count_map,
            data_shape,
            prepack_removal=True)
        pattern_count_map["prepacked::conv2d_clamp_prepack"] = -1
        pattern_count_map["prepacked::linear_clamp_prepack"] = -1
        pattern_count_map["aten::hardtanh_"] = -1
        TestXNNPACKRewritePass.validate_transformed_module(
            M(F.hardtanh_),
            pattern_count_map,
            data_shape,
            prepack_removal=True,
            fuse_clamping_ops=True)

        class MFusionAntiPattern(torch.nn.Module):
            def __init__(self):
                super(MFusionAntiPattern, self).__init__()
                self.linear_weight = torch.nn.Parameter(torch.Tensor(torch.rand(linear_weight_shape)), requires_grad=False)
                self.linear_bias = torch.nn.Parameter(torch.Tensor(torch.rand((weight_output_dim))), requires_grad=False)
                self.strides = strides
                self.paddings = paddings
                self.dilations = dilations
                self.groups = groups

            def forward(self, x):
                o = F.linear(x, self.linear_weight, self.linear_bias)
                o = F.relu(o)
                o = F.hardtanh(o)
                return o

        # Unfusable hardtanh.
        pattern_count_map = {"aten::hardtanh": 1,  # hardtanh cannot be.
                             "aten::relu": -1,  # relu is fused.
                             "prepacked::linear_clamp_prepack": -1,
                             "prepacked::linear_clamp_run": 1}
        TestXNNPACKRewritePass.validate_transformed_module(
            MFusionAntiPattern(),
            pattern_count_map,
            (16, linear_weight_shape[1]),
            prepack_removal=True,
            fuse_clamping_ops=True)

        class MFusionAntiPatternParamMinMax(torch.nn.Module):
            def __init__(self):
                super(MFusionAntiPatternParamMinMax, self).__init__()
                self.linear_weight = torch.nn.Parameter(torch.Tensor(torch.rand(linear_weight_shape)), requires_grad=False)
                self.linear_bias = torch.nn.Parameter(torch.Tensor(torch.rand((weight_output_dim))), requires_grad=False)
                self.strides = strides
                self.paddings = paddings
                self.dilations = dilations
                self.groups = groups

            def forward(self, x):
                min = x[0, 0]
                max = min + 10
                o = F.linear(x, self.linear_weight, self.linear_bias)
                o = F.hardtanh(o, min, max)
                return o

        # Unfusable hardtanh.
        pattern_count_map = {"aten::hardtanh": 1,  # hardtanh cannot be.
                             "prepacked::linear_clamp_prepack": -1,
                             "prepacked::linear_clamp_run": 1}
        TestXNNPACKRewritePass.validate_transformed_module(
            MFusionAntiPatternParamMinMax(),
            pattern_count_map,
            (16, linear_weight_shape[1]),
            prepack_removal=True,
            fuse_clamping_ops=True)

    def test_decomposed_linear(self):
        data_shape = [2, 32]
        weight_output_dim = 24
        weight_shape = (weight_output_dim, data_shape[-1])

        class DecomposedLinearAddmm(torch.nn.Module):
            def __init__(self):
                super(DecomposedLinearAddmm, self).__init__()
                self.weight = torch.nn.Parameter(torch.Tensor(torch.rand(weight_shape)), requires_grad=False)
                self.bias = torch.nn.Parameter(torch.Tensor(torch.rand((weight_output_dim))), requires_grad=False)

            def forward(self, x):
                weight_t = self.weight.t()
                return torch.addmm(self.bias, x, weight_t)

        class DecomposedLinearMatmulAdd(torch.nn.Module):
            def __init__(self):
                super(DecomposedLinearMatmulAdd, self).__init__()
                self.weight = torch.nn.Parameter(torch.Tensor(torch.rand(weight_shape)), requires_grad=False)
                self.bias = torch.nn.Parameter(torch.Tensor(torch.rand((weight_output_dim))), requires_grad=False)

            def forward(self, x):
                weight_t = self.weight.t()
                y = torch.matmul(x, weight_t)
                res = y.add_(self.bias)
                return res

        class DecomposedLinearMatmul(torch.nn.Module):
            def __init__(self):
                super(DecomposedLinearMatmul, self).__init__()
                self.weight = torch.nn.Parameter(torch.Tensor(torch.rand(weight_shape)), requires_grad=False)
                self.bias = torch.nn.Parameter(torch.Tensor(torch.rand((weight_output_dim))), requires_grad=False)

            def forward(self, x):
                weight_t = self.weight.t()
                res = torch.matmul(x, weight_t)
                return res

        # Linear with bias pattern.
        pattern_count_map = {"Tensor = prim::CallFunction": -1,
                             "prepacked::linear_clamp_prepack": 1,
                             "prepacked::linear_clamp_run": 1}
        TestXNNPACKRewritePass.validate_transformed_module(DecomposedLinearAddmm(), pattern_count_map, data_shape)
        TestXNNPACKRewritePass.validate_transformed_module(DecomposedLinearMatmulAdd(), pattern_count_map, data_shape)
        TestXNNPACKRewritePass.validate_transformed_module(DecomposedLinearMatmul(), pattern_count_map, data_shape)

@unittest.skipUnless(torch.backends.xnnpack.enabled,
                     " XNNPACK must be enabled for these tests."
                     " Please build with USE_XNNPACK=1.")
class TestXNNPACKConv1dTransformPass(TestCase):
    @staticmethod
    def validate_transform_conv1d_to_conv2d(
            self,
            pattern_count_transformed_map,
            pattern_count_optimized_map,
            data_shape):
        input_data = torch.normal(1, 20, size=data_shape)

        for jit_method in ["script", "trace"]:
            module_instance = self
            if jit_method == "script":
                scripted_model = torch.jit.script(module_instance)
            else:
                scripted_model = torch.jit.trace(module_instance, input_data)
            scripted_model.eval()
            ref_result = scripted_model(input_data)
            torch._C._jit_pass_transform_conv1d_to_conv2d(scripted_model._c)
            optimized_scripted_model = optimize_for_mobile(scripted_model)

            buffer = io.BytesIO()
            torch.jit.save(scripted_model, buffer)
            buffer.seek(0)
            deserialized_scripted_model = torch.jit.load(buffer)

            for pattern, v in pattern_count_transformed_map.items():
                if (v == 0):
                    FileCheck().check(pattern).run(deserialized_scripted_model.graph)
                elif (v == -1):
                    FileCheck().check_not(pattern).run(deserialized_scripted_model.graph)
                else:
                    FileCheck().check_count(pattern, v, exactly=True).run(deserialized_scripted_model.graph)
            transformed_result = deserialized_scripted_model(input_data)
            torch.testing.assert_allclose(ref_result, transformed_result, rtol=1e-2, atol=1e-3)

            optimized_buffer = io.BytesIO()
            torch.jit.save(optimized_scripted_model, optimized_buffer)
            optimized_buffer.seek(0)
            deserialized_optimized_scripted_model = torch.jit.load(optimized_buffer)

            for pattern, v in pattern_count_optimized_map.items():
                if (v == 0):
                    FileCheck().check(pattern).run(deserialized_optimized_scripted_model.graph)
                elif (v == -1):
                    FileCheck().check_not(pattern).run(deserialized_optimized_scripted_model.graph)
                else:
                    FileCheck().check_count(pattern, v, exactly=True).run(deserialized_optimized_scripted_model.graph)
            xnnpack_result = deserialized_optimized_scripted_model(input_data)
            torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)


    def test_conv1d_basic(self):
        batch_size_list = range(1, 3)
        input_channels_per_group_list = range(10, 12)
        width_list = range(10, 12)
        output_channels_per_group_list = range(10, 12)
        groups_list = range(1, 3)
        kernel_list = range(1, 4)
        stride_list = range(1, 3)
        padding_list = range(0, 3)
        dilation_list = range(1, 3)

        for hparams in itertools.product(batch_size_list,
                                         input_channels_per_group_list,
                                         width_list,
                                         output_channels_per_group_list,
                                         groups_list,
                                         kernel_list,
                                         stride_list,
                                         padding_list,
                                         dilation_list):
            batch_size, input_channels_per_group, width, output_channels_per_group, \
                groups, kernel, stride, padding, dilation = hparams

            input_channels = input_channels_per_group * groups
            output_channels = output_channels_per_group * groups
            conv_weight_shape = (output_channels, input_channels_per_group, kernel)
            conv_bias_shape = (output_channels)

            class Conv1D(torch.nn.Module):
                def __init__(self):
                    super(Conv1D, self).__init__()
                    self.weight = torch.nn.Parameter(torch.Tensor(torch.rand(conv_weight_shape)), requires_grad=False)
                    self.bias = torch.nn.Parameter(torch.Tensor(torch.rand(conv_bias_shape)), requires_grad=False)
                    self.stride = stride
                    self.padding = padding
                    self.dilation = dilation
                    self.groups = groups

                def forward(self, x):
                    return F.conv1d(x, self.weight, self.bias,
                                    self.stride, self.padding, self.dilation, self.groups)

            data_shape = (batch_size, input_channels, width)
            pattern_count_transformed_map = {"Tensor = aten::conv1d": -1,
                                             "Tensor = aten::conv2d": 1}
            pattern_count_optimized_map = {"Tensor = aten::conv1d": -1,
                                           "Tensor = aten::conv2d": -1,
                                           "prepacked::conv2d_clamp_prepack" : -1,
                                           "prepacked::conv2d_clamp_run": 1}

            TestXNNPACKConv1dTransformPass.validate_transform_conv1d_to_conv2d(Conv1D(),
                                                                               pattern_count_transformed_map,
                                                                               pattern_count_optimized_map,
                                                                               data_shape)

    def test_conv1d_with_relu_fc(self):
        batch_size_list = range(1, 3)
        input_channels_per_group_list = range(10, 12)
        width_list = range(10, 12)
        output_channels_per_group_list = range(10, 12)
        groups_list = range(1, 3)
        kernel_list = range(1, 4)
        stride_list = range(1, 3)
        padding_list = range(0, 3)
        dilation_list = range(1, 3)
        output_features_list = range(1, 3)

        for hparams in itertools.product(batch_size_list,
                                         input_channels_per_group_list,
                                         width_list,
                                         output_channels_per_group_list,
                                         groups_list,
                                         kernel_list,
                                         stride_list,
                                         padding_list,
                                         dilation_list,
                                         output_features_list):
            batch_size, input_channels_per_group, width, output_channels_per_group, \
                groups, kernel, stride, padding, dilation, output_features = hparams

            input_channels = input_channels_per_group * groups
            output_channels = output_channels_per_group * groups
            conv_weight_shape = (output_channels, input_channels_per_group, kernel)
            conv_bias_shape = (output_channels)
            conv_output_width = int((width + 2 * padding - dilation * (kernel - 1) - 1) / stride) + 1
            fc_weight_shape = (output_features, output_channels * conv_output_width)
            fc_bias_shape = (output_features)

            class Net(torch.nn.Module):
                def __init__(self):
                    super(Net, self).__init__()
                    self.conv_weight = torch.nn.Parameter(torch.Tensor(torch.rand(conv_weight_shape)), requires_grad=False)
                    self.conv_bias = torch.nn.Parameter(torch.Tensor(torch.rand(conv_bias_shape)), requires_grad=False)
                    self.stride = stride
                    self.padding = padding
                    self.dilation = dilation
                    self.groups = groups

                    self.fc_weight = torch.nn.Parameter(torch.Tensor(torch.rand(fc_weight_shape)), requires_grad=False)
                    self.fc_bias = torch.nn.Parameter(torch.Tensor(torch.rand(fc_bias_shape)), requires_grad=False)

                def forward(self, x):
                    x = F.conv1d(x, self.conv_weight, self.conv_bias,
                                 self.stride, self.padding, self.dilation, self.groups)
                    x = F.relu(x)
                    x = x.view(x.size(0), -1)
                    x = F.linear(x, self.fc_weight, self.fc_bias)
                    return x

            data_shape = (batch_size, input_channels, width)
            pattern_count_transformed_map = {"Tensor = aten::conv1d": -1,
                                             "Tensor = aten::conv2d": 1}
            pattern_count_optimized_map = {"Tensor = aten::conv1d": -1,
                                           "Tensor = aten::conv2d": -1,
                                           "prepacked::conv2d_clamp_prepack" : -1,
                                           "prepacked::conv2d_clamp_run": 1}
            TestXNNPACKConv1dTransformPass.validate_transform_conv1d_to_conv2d(Net(),
                                                                               pattern_count_transformed_map,
                                                                               pattern_count_optimized_map,
                                                                               data_shape)

if __name__ == "__main__":
    run_tests()
