from __future__ import division 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) @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), 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): module_instance = self scripted_model = torch.jit.script(module_instance) scripted_model.eval() input_data = torch.normal(1, 20, size=data_shape) 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 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) dilations = (dilation, dilation) conv_weight_shape = (output_channels, input_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) 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) 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): module_instance = self scripted_model = torch.jit.script(module_instance) scripted_model.eval() input_data = torch.normal(1, 20, size=data_shape) 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()