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4c30fc7238
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/34047 This PR integrates the added xnnpack conv2d and linear op via custom class registration for packed weights. The packed struct is serializable. Test Plan: python test test/test_xnnpack_integration.py Imported from OSS Differential Revision: D20185657 fbshipit-source-id: fc7e692d8f913e493b293b02d92f4e78536d7698
365 lines
16 KiB
Python
365 lines
16 KiB
Python
from __future__ import division
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import unittest
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import torch
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import torch.backends.xnnpack
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from torch.nn import functional as F
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import torch.testing._internal.hypothesis_utils as hu
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from torch.testing._internal.common_utils import TestCase, run_tests
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from hypothesis import given, assume
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from hypothesis import strategies as st
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import io
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@unittest.skipUnless(torch.backends.xnnpack.enabled,
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" XNNPACK must be enabled for these tests."
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" Please build with USE_XNNPACK=1.")
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class TestXNNPACKOps(TestCase):
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@given(batch_size=st.integers(0, 3),
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data_shape=hu.array_shapes(1, 3, 2, 64),
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weight_output_dim=st.integers(2, 64),
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use_bias=st.booleans())
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def test_linear(self, batch_size, data_shape, weight_output_dim, use_bias):
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data_shape = [batch_size] + list(data_shape)
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input_data = torch.rand(data_shape)
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weight = torch.rand((weight_output_dim, data_shape[-1]))
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if use_bias:
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bias = torch.rand((weight_output_dim))
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else:
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bias = None
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ref_result = F.linear(input_data, weight, bias)
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packed_weight_bias = torch.ops._xnnpack.linear_prepack(weight, bias)
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output_linear_xnnpack = torch.ops._xnnpack.linear_packed(input_data, packed_weight_bias)
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torch.testing.assert_allclose(ref_result, output_linear_xnnpack, rtol=1e-2, atol=1e-3)
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@given(batch_size=st.integers(0, 3),
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input_channels_per_group=st.integers(1, 32),
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height=st.integers(5, 64),
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width=st.integers(5, 64),
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output_channels_per_group=st.integers(1, 32),
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groups=st.integers(1, 16),
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kernel_h=st.integers(1, 7),
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kernel_w=st.integers(1, 7),
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stride_h=st.integers(1, 2),
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stride_w=st.integers(1, 2),
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pad_h=st.integers(0, 2),
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pad_w=st.integers(0, 2),
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dilation=st.integers(1, 2),
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use_bias=st.booleans())
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def test_conv2d(self,
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batch_size,
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input_channels_per_group,
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height,
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width,
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output_channels_per_group,
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groups,
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kernel_h,
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kernel_w,
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stride_h,
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stride_w,
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pad_h,
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pad_w,
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dilation,
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use_bias):
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input_channels = input_channels_per_group * groups
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output_channels = output_channels_per_group * groups
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kernels = (kernel_h, kernel_w)
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strides = (stride_h, stride_w)
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paddings = (pad_h, pad_w)
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dilations = (dilation, dilation)
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assume(height + 2 * paddings[0] >=
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dilations[0] * (kernels[0] - 1) + 1)
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assume(width + 2 * paddings[1] >=
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dilations[1] * (kernels[1] - 1) + 1)
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input_data = torch.rand((batch_size, input_channels, height, width))
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weight = torch.rand((output_channels, input_channels_per_group, kernel_h, kernel_w))
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bias = None
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if use_bias:
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bias = torch.rand((output_channels))
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ref_result = F.conv2d(input_data, weight, bias,
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strides, paddings, dilations, groups)
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packed_weight_bias = torch.ops._xnnpack.conv2d_prepack(weight, bias,
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strides, paddings, dilations, groups)
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xnnpack_result = torch.ops._xnnpack.conv2d_packed(input_data, packed_weight_bias)
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torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)
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@unittest.skipUnless(torch.backends.xnnpack.enabled,
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" XNNPACK must be enabled for these tests."
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" Please build with USE_XNNPACK=1.")
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class TestXNNPACKSerDes(TestCase):
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@given(batch_size=st.integers(0, 3),
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data_shape=hu.array_shapes(1, 3, 2, 64),
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weight_output_dim=st.integers(2, 64),
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use_bias=st.booleans())
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def test_linear(self, batch_size, data_shape, weight_output_dim, use_bias):
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class Linear(torch.nn.Module):
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def __init__(self, weight, bias=None):
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super(Linear, self).__init__()
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self.weight = weight
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self.bias = bias
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def forward(self, x):
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return F.linear(x, self.weight, self.bias)
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class LinearPrePacked(torch.nn.Module):
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def __init__(self, weight, bias=None):
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super(LinearPrePacked, self).__init__()
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self.packed_weight_bias = torch.ops._xnnpack.linear_prepack(weight, bias)
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def forward(self, x):
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return torch.ops._xnnpack.linear_packed(x, self.packed_weight_bias)
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data_shape = [batch_size] + list(data_shape)
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weight = torch.rand((weight_output_dim, data_shape[-1]))
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if use_bias:
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bias = torch.rand((weight_output_dim))
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else:
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bias = None
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scripted_linear = torch.jit.script(Linear(weight, bias))
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scripted_linear_prepacked = torch.jit.script(LinearPrePacked(weight, bias))
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input_data = torch.rand(data_shape)
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ref_result = scripted_linear(input_data)
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output_linear_xnnpack = scripted_linear_prepacked(input_data)
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torch.testing.assert_allclose(ref_result, output_linear_xnnpack, rtol=1e-2, atol=1e-3)
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# Serialize the modules and then deserialize
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input_data = torch.rand(data_shape)
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buffer = io.BytesIO()
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torch.jit.save(scripted_linear, buffer)
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buffer.seek(0)
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deserialized_linear = torch.jit.load(buffer)
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buffer = io.BytesIO()
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torch.jit.save(scripted_linear_prepacked, buffer)
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buffer.seek(0)
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deserialized_linear_prepacked = torch.jit.load(buffer)
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ref_result = deserialized_linear(input_data)
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output_linear_xnnpack = deserialized_linear_prepacked(input_data)
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torch.testing.assert_allclose(ref_result, output_linear_xnnpack, rtol=1e-2, atol=1e-3)
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@given(batch_size=st.integers(0, 3),
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input_channels_per_group=st.integers(1, 32),
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height=st.integers(5, 64),
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width=st.integers(5, 64),
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output_channels_per_group=st.integers(1, 32),
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groups=st.integers(1, 16),
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kernel_h=st.integers(1, 7),
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kernel_w=st.integers(1, 7),
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stride_h=st.integers(1, 2),
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stride_w=st.integers(1, 2),
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pad_h=st.integers(0, 2),
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pad_w=st.integers(0, 2),
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dilation=st.integers(1, 2),
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use_bias=st.booleans())
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def test_conv2d(self,
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batch_size,
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input_channels_per_group,
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height,
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width,
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output_channels_per_group,
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groups,
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kernel_h,
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kernel_w,
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stride_h,
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stride_w,
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pad_h,
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pad_w,
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dilation,
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use_bias):
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class Conv2D(torch.nn.Module):
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def __init__(self, weight, bias, strides, paddings, dilations, groups):
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super(Conv2D, self).__init__()
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self.weight = weight
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self.bias = bias
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self.strides = strides
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self.paddings = paddings
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self.dilations = dilations
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self.groups = groups
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def forward(self, x):
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return F.conv2d(x, self.weight, self.bias,
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self.strides, self.paddings, self.dilations, self.groups)
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class Conv2DPrePacked(torch.nn.Module):
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def __init__(self, weight, bias, strides, paddings, dilations, groups):
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super(Conv2DPrePacked, self).__init__()
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self.packed_weight_bias = torch.ops._xnnpack.conv2d_prepack(weight, bias,
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strides, paddings, dilations, groups)
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def forward(self, x):
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return torch.ops._xnnpack.conv2d_packed(x, self.packed_weight_bias)
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input_channels = input_channels_per_group * groups
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output_channels = output_channels_per_group * groups
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kernels = (kernel_h, kernel_w)
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strides = (stride_h, stride_w)
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paddings = (pad_h, pad_w)
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dilations = (dilation, dilation)
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assume(height + 2 * paddings[0] >=
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dilations[0] * (kernels[0] - 1) + 1)
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assume(width + 2 * paddings[1] >=
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dilations[1] * (kernels[1] - 1) + 1)
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input_data = torch.rand((batch_size, input_channels, height, width))
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weight = torch.rand((output_channels, input_channels_per_group, kernel_h, kernel_w))
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bias = None
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if use_bias:
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bias = torch.rand((output_channels))
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scripted_conv2d = torch.jit.script(Conv2D(weight, bias,
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strides, paddings, dilations, groups))
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scripted_conv2d_prepacked = torch.jit.script(Conv2DPrePacked(
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weight, bias, strides, paddings, dilations, groups))
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ref_result = scripted_conv2d(input_data)
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xnnpack_result = scripted_conv2d_prepacked(input_data)
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torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)
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# Serialize the modules and then deserialize
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input_data = torch.rand((batch_size, input_channels, height, width))
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buffer = io.BytesIO()
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torch.jit.save(scripted_conv2d, buffer)
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buffer.seek(0)
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deserialized_conv2d = torch.jit.load(buffer)
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buffer = io.BytesIO()
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torch.jit.save(scripted_conv2d_prepacked, buffer)
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buffer.seek(0)
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deserialized_conv2d_prepacked = torch.jit.load(buffer)
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ref_result = deserialized_conv2d(input_data)
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xnnpack_result = deserialized_conv2d_prepacked(input_data)
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torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)
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@given(batch_size=st.integers(0, 3),
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input_channels_per_group=st.integers(1, 32),
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height=st.integers(5, 64),
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width=st.integers(5, 64),
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output_channels_per_group=st.integers(1, 32),
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groups=st.integers(1, 16),
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kernel_h=st.integers(1, 7),
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kernel_w=st.integers(1, 7),
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stride_h=st.integers(1, 2),
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stride_w=st.integers(1, 2),
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pad_h=st.integers(0, 2),
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pad_w=st.integers(0, 2),
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dilation=st.integers(1, 2),
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linear_weight_output_dim=st.integers(2, 64),
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use_bias=st.booleans())
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def test_combined_model(self,
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batch_size,
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input_channels_per_group,
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height,
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width,
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output_channels_per_group,
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groups,
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kernel_h,
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kernel_w,
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stride_h,
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stride_w,
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pad_h,
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pad_w,
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dilation,
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linear_weight_output_dim,
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use_bias):
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class M(torch.nn.Module):
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def __init__(self, conv_weight, conv_bias, linear_weight, linear_bias,
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strides, paddings, dilations, groups):
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super(M, self).__init__()
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self.conv_weight = conv_weight
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self.conv_bias = conv_bias
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self.linear_weight = linear_weight
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self.linear_bias = linear_bias
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self.strides = strides
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self.paddings = paddings
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self.dilations = dilations
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self.groups = groups
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def forward(self, x):
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o = F.conv2d(x, self.conv_weight, self.conv_bias,
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self.strides, self.paddings, self.dilations, self.groups)
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o = o.permute([0, 2, 3, 1])
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o = F.linear(o, self.linear_weight, self.linear_bias)
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return F.relu(o)
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class MPrePacked(torch.nn.Module):
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def __init__(self, conv_weight, conv_bias, linear_weight, linear_bias,
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strides, paddings, dilations, groups):
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super(MPrePacked, self).__init__()
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self.conv2d_packed_weight_bias = \
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torch.ops._xnnpack.conv2d_prepack(conv_weight, conv_bias,
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strides, paddings, dilations, groups)
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self.linear_packed_weight_bias = \
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torch.ops._xnnpack.linear_prepack(linear_weight, linear_bias)
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def forward(self, x):
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o = torch.ops._xnnpack.conv2d_packed(x, self.conv2d_packed_weight_bias)
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o = o.permute([0, 2, 3, 1])
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o = torch.ops._xnnpack.linear_packed(o, self.linear_packed_weight_bias)
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return F.relu(o)
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input_channels = input_channels_per_group * groups
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output_channels = output_channels_per_group * groups
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kernels = (kernel_h, kernel_w)
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strides = (stride_h, stride_w)
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paddings = (pad_h, pad_w)
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dilations = (dilation, dilation)
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assume(height + 2 * paddings[0] >=
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dilations[0] * (kernels[0] - 1) + 1)
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assume(width + 2 * paddings[1] >=
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dilations[1] * (kernels[1] - 1) + 1)
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input_data = torch.rand((batch_size, input_channels, height, width))
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conv_weight = torch.rand((output_channels, input_channels_per_group, kernel_h, kernel_w))
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conv_bias = None
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if use_bias:
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conv_bias = torch.rand((output_channels))
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# This is done just to find the output shape of the result
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# so that the shape of weight for the following linear layer
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# can be determined.
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result = F.conv2d(input_data, conv_weight, conv_bias,
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strides, paddings, dilations, groups)
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linear_input_shape = result.shape[1]
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input_data = input_data.contiguous(memory_format=torch.channels_last)
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linear_weight = torch.rand((linear_weight_output_dim, linear_input_shape))
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linear_bias = None
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if use_bias:
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linear_bias = torch.rand((linear_weight_output_dim))
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scripted_m = torch.jit.script(M(conv_weight, conv_bias, linear_weight,
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linear_bias, strides, paddings, dilations, groups))
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scripted_m_prepacked = torch.jit.script(
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MPrePacked(
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conv_weight,
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conv_bias,
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linear_weight,
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linear_bias,
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strides,
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paddings,
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dilations,
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groups))
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ref_result = scripted_m(input_data)
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xnnpack_result = scripted_m_prepacked(input_data)
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torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)
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# Serialize the modules and then deserialize
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input_data = torch.rand((batch_size, input_channels, height, width))
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input_data = input_data.contiguous(memory_format=torch.channels_last)
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buffer = io.BytesIO()
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torch.jit.save(scripted_m, buffer)
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buffer.seek(0)
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deserialized_m = torch.jit.load(buffer)
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buffer = io.BytesIO()
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torch.jit.save(scripted_m_prepacked, buffer)
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buffer.seek(0)
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deserialized_m_prepacked = torch.jit.load(buffer)
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ref_result = deserialized_m(input_data)
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xnnpack_result = deserialized_m_prepacked(input_data)
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torch.testing.assert_allclose(ref_result, xnnpack_result, rtol=1e-2, atol=1e-3)
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if __name__ == "__main__":
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run_tests()
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