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# Owner(s): ["oncall: mobile"]
import torch
import torch._C
import torch.nn.functional as F
from torch.testing._internal.jit_utils import JitTestCase
from torch.testing._internal.common_utils import skipIfNoXNNPACK
class TestOptimizeForMobilePreserveDebugInfo(JitTestCase):
def check_replacement(
self,
model,
replacements,
jit_pass,
):
"""
model: Model which optimization is performed on
replacements: Dict mapping from nodes' kinds in the optimized model
to the kinds of nodes they replaced in the original model
jit_pass: Function to perform optimization
"""
original_kinds = set(replacements.values())
original_source_ranges = {
node.kind(): node.sourceRange()
for node in model.graph.nodes()
if node.kind() in original_kinds
}
jit_pass(model._c)
for node in model.graph.nodes():
if node.kind() in replacements:
self.assertEqual(
node.sourceRange(),
original_source_ranges[replacements[node.kind()]],
)
@skipIfNoXNNPACK
def test_replace_conv1d_with_conv2d(self):
class TestConv1d(torch.nn.Module):
def __init__(self, weight, bias):
super(TestConv1d, self).__init__()
self.weight = weight
self.bias = bias
def forward(self, x):
return F.conv1d(x, self.weight, self.bias)
self.check_replacement(
model=torch.jit.script(
TestConv1d(
weight=torch.rand(3, 3, 3),
bias=torch.rand(3),
),
),
replacements={
"prim::ListUnpack": "aten::conv1d",
"prim::ListConstruct": "aten::conv1d",
"aten::unsqueeze": "aten::conv1d",
"aten::conv2d": "aten::conv1d",
"aten::squeeze": "aten::conv1d",
},
jit_pass=torch._C._jit_pass_transform_conv1d_to_conv2d,
)
@skipIfNoXNNPACK
def test_insert_pre_packed_linear_before_inline_and_conv_2d_op(self):
class TestPrepackedLinearBeforeInlineAndConv2dOp(torch.nn.Module):
def __init__(
self,
linear_weight,
linear_bias,
conv2d_weight,
conv2d_bias,
conv_transpose2d_weight,
conv_transpose2d_bias,
):
super(
TestPrepackedLinearBeforeInlineAndConv2dOp,
self,
).__init__()
self.linear_weight = linear_weight.float()
self.linear_bias = linear_bias.float()
self.conv2d_weight = conv2d_weight.float()
self.conv2d_bias = conv2d_bias.float()
self.conv_transpose2d_weight = conv_transpose2d_weight.float()
self.conv_transpose2d_bias = conv_transpose2d_bias.float()
def forward(self, x):
linear_res = F.linear(
x.float(),
self.linear_weight,
self.linear_bias,
)
conv2d_res = F.conv2d(
input=linear_res.unsqueeze(dim=0).float(),
weight=self.conv2d_weight,
bias=self.conv2d_bias,
)
return F.conv_transpose2d(
input=conv2d_res,
weight=self.conv_transpose2d_weight,
bias=self.conv_transpose2d_bias,
)
minibatch = 1
in_channels = 6
iH = 4
iW = 5
out_channels = 6
kH = 2
kW = 3
self.check_replacement(
model=torch.jit.script(
TestPrepackedLinearBeforeInlineAndConv2dOp(
linear_weight=torch.rand(iW, 3),
linear_bias=torch.rand(iW),
conv2d_weight=torch.rand(out_channels, in_channels, kH, kW),
conv2d_bias=torch.rand(out_channels),
conv_transpose2d_weight=torch.rand(
out_channels,
in_channels,
kH,
kW,
),
conv_transpose2d_bias=torch.rand(out_channels),
),
),
replacements={
"prepacked::linear_clamp_prepack": "aten::linear",
"prepacked::linear_clamp_run": "aten::linear",
"prepacked::conv2d_clamp_prepack": "aten::conv2d",
"prepacked::conv2d_clamp_run": "aten::conv2d",
"prepacked::conv2d_transpose_clamp_prepack":
"aten::conv_transpose2d",
"prepacked::conv2d_transpose_clamp_run":
"aten::conv_transpose2d",
},
jit_pass=torch._C._jit_pass_insert_prepacked_ops,
)
@skipIfNoXNNPACK
def test_insert_pre_packed_linear_op(self):
self.check_replacement(
model=torch.jit.trace(torch.nn.Linear(5, 4), torch.rand(3, 2, 5)),
replacements={
"prepacked::linear_clamp_prepack": "aten::linear",
"prepacked::linear_clamp_run": "aten::linear"
},
jit_pass=torch._C._jit_pass_insert_prepacked_ops,
)
def run_test_fuse_activation_with_pack_ops_linear_conv2d(
self,
linear_activation,
linear_activation_kind,
conv2d_activation,
conv2d_activation_kind,
):
class TestFuseActivationLinearConv2d(torch.nn.Module):
def __init__(
self,
linear_weight,
linear_bias,
conv2d_weight,
conv2d_bias,
):
super(TestFuseActivationLinearConv2d, self).__init__()
self.linear_weight = linear_weight
self.linear_bias = linear_bias
self.conv2d_weight = conv2d_weight
self.conv2d_bias = conv2d_bias
def forward(self, x):
x = F.linear(
input=x,
weight=self.linear_weight,
bias=self.linear_bias,
)
x = linear_activation(x)
x = F.conv2d(
input=x.unsqueeze(dim=0),
weight=self.conv2d_weight,
bias=self.conv2d_bias,
)
return conv2d_activation(x)
linear_in_features = 5
linear_out_features = 4
conv2d_in_channels = 3
conv2d_out_channels = 4
conv2d_kernel = 2
x_shape = (3, 2, 5)
model = torch.jit.trace(
TestFuseActivationLinearConv2d(
linear_weight=torch.nn.Parameter(
data=torch.rand(
linear_out_features,
linear_in_features,
),
requires_grad=False,
),
linear_bias=torch.nn.Parameter(
data=torch.rand(linear_out_features),
requires_grad=False,
),
conv2d_weight=torch.rand(
conv2d_out_channels,
conv2d_in_channels,
conv2d_kernel,
conv2d_kernel,
),
conv2d_bias=torch.rand(conv2d_out_channels),
),
torch.rand(x_shape),
)
torch._C._jit_pass_insert_prepacked_ops(model._c)
self.check_replacement(
model=model,
replacements={
"prepacked::linear_clamp_prepack":
"prepacked::linear_clamp_prepack",
"prepacked::linear_clamp_run": linear_activation_kind,
"prepacked::conv2d_clamp_prepack":
"prepacked::conv2d_clamp_prepack",
"prepacked::conv2d_clamp_run": conv2d_activation_kind,
},
jit_pass=torch._C._jit_pass_fuse_clamp_w_prepacked_linear_conv,
)
@skipIfNoXNNPACK
def test_fuse_activation_with_pack_ops_linear_conv2d_1(self):
self.run_test_fuse_activation_with_pack_ops_linear_conv2d(
linear_activation=F.hardtanh,
linear_activation_kind="aten::hardtanh",
conv2d_activation=F.hardtanh_,
conv2d_activation_kind="aten::hardtanh_"
)
@skipIfNoXNNPACK
def test_fuse_activation_with_pack_ops_linear_conv2d_2(self):
self.run_test_fuse_activation_with_pack_ops_linear_conv2d(
linear_activation=F.hardtanh_,
linear_activation_kind="aten::hardtanh_",
conv2d_activation=F.hardtanh,
conv2d_activation_kind="aten::hardtanh"
)
@skipIfNoXNNPACK
def test_fuse_activation_with_pack_ops_linear_conv2d_3(self):
self.run_test_fuse_activation_with_pack_ops_linear_conv2d(
linear_activation=F.relu,
linear_activation_kind="aten::relu",
conv2d_activation=F.relu_,
conv2d_activation_kind="aten::relu_"
)
@skipIfNoXNNPACK
def test_fuse_activation_with_pack_ops_linear_conv2d_4(self):
self.run_test_fuse_activation_with_pack_ops_linear_conv2d(
linear_activation=F.relu_,
linear_activation_kind="aten::relu_",
conv2d_activation=F.relu,
conv2d_activation_kind="aten::relu"
)
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