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Revert D30279364: [codemod][lint][fbcode/c*] Enable BLACK by default
Test Plan: revert-hammer
Differential Revision:
D30279364 (b004307252)
Original commit changeset: c1ed77dfe43a
fbshipit-source-id: eab50857675c51e0088391af06ec0ecb14e2347e
This commit is contained in:
Shen Li
committed by
Facebook GitHub Bot
Facebook GitHub Bot
parent
ed0b8a3e83
commit
1022443168
@ -1,21 +1,19 @@
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import io
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import torch
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from torch.nn import functional as F
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from torch.testing import FileCheck
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from torch.testing._internal.common_utils import TestCase, run_tests
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from torch.testing._internal.common_utils import TestCase, run_tests
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from torch.testing import FileCheck
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import io
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class TestMetalRewritePass(TestCase):
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@staticmethod
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def validate_transformed_module(
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# To please flake
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self,
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pattern_count_map,
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data_shape,
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prepack_removal=False,
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fuse_clamping_ops=False,
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):
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# To please flake
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self,
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pattern_count_map,
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data_shape,
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prepack_removal=False,
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fuse_clamping_ops=False):
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module_instance = self
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scripted_model = torch.jit.script(module_instance)
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scripted_model.eval()
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@ -34,14 +32,12 @@ class TestMetalRewritePass(TestCase):
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buffer.seek(0)
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deserialized_scripted_model = torch.jit.load(buffer)
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for pattern, v in pattern_count_map.items():
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if v == 0:
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if (v == 0):
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FileCheck().check(pattern).run(deserialized_scripted_model.graph)
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elif v == -1:
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elif (v == -1):
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FileCheck().check_not(pattern).run(deserialized_scripted_model.graph)
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else:
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FileCheck().check_count(pattern, v, exactly=True).run(
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deserialized_scripted_model.graph
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)
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FileCheck().check_count(pattern, v, exactly=True).run(deserialized_scripted_model.graph)
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def test_conv(self):
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# Conv params
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@ -61,150 +57,103 @@ class TestMetalRewritePass(TestCase):
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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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conv_weight_shape = (
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output_channels,
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input_channels_per_group,
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kernel_h,
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kernel_w,
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)
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conv_bias_shape = output_channels
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conv_weight_shape = (output_channels, input_channels_per_group, kernel_h, kernel_w)
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conv_bias_shape = (output_channels)
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class Conv2D(torch.nn.Module):
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def __init__(self):
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super(Conv2D, self).__init__()
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self.weight = torch.nn.Parameter(
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torch.rand(conv_weight_shape), requires_grad=False
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)
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self.bias = torch.nn.Parameter(
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torch.rand(conv_bias_shape), requires_grad=False
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)
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self.weight = torch.nn.Parameter(torch.rand(conv_weight_shape), requires_grad=False)
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self.bias = torch.nn.Parameter(torch.rand(conv_bias_shape), requires_grad=False)
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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(
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x,
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self.weight,
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self.bias,
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self.strides,
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self.paddings,
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self.dilations,
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self.groups,
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)
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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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data_shape = (batch_size, input_channels, height, width)
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pattern_count_map = {
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"Tensor = aten::conv2d": -1,
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"metal_prepack::conv2d_prepack": 1,
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"metal_prepack::conv2d_run": 1,
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}
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TestMetalRewritePass.validate_transformed_module(
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Conv2D(), pattern_count_map, data_shape
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)
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pattern_count_map = {"Tensor = aten::conv2d": -1,
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"metal_prepack::conv2d_prepack": 1,
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"metal_prepack::conv2d_run": 1}
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TestMetalRewritePass.validate_transformed_module(Conv2D(), pattern_count_map, data_shape)
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class Conv2DRelu(torch.nn.Module):
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def __init__(self):
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super(Conv2DRelu, self).__init__()
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self.weight = torch.nn.Parameter(
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torch.rand(conv_weight_shape), requires_grad=False
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)
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self.bias = torch.nn.Parameter(
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torch.rand(conv_bias_shape), requires_grad=False
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)
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self.weight = torch.nn.Parameter(torch.rand(conv_weight_shape), requires_grad=False)
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self.bias = torch.nn.Parameter(torch.rand(conv_bias_shape), requires_grad=False)
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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(
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x,
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self.weight,
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self.bias,
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self.strides,
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self.paddings,
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self.dilations,
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self.groups,
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)
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o = F.conv2d(x, self.weight, self.bias,
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self.strides, self.paddings, self.dilations, self.groups)
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o = F.relu(o)
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return o
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data_shape = (batch_size, input_channels, height, width)
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pattern_count_map = {
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"Tensor = aten::conv2d": -1,
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"metal_prepack::conv2d_prepack": 1,
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"metal_prepack::conv2d_run": 1,
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}
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pattern_count_map = {"Tensor = aten::conv2d": -1,
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"metal_prepack::conv2d_prepack": 1,
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"metal_prepack::conv2d_run": 1}
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TestMetalRewritePass.validate_transformed_module(
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Conv2DRelu(), pattern_count_map, data_shape
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)
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Conv2DRelu(), pattern_count_map, data_shape)
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pattern_count_map["aten::relu"] = 1
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pattern_count_map["metal_prepack::conv2d_prepack"] = -1
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TestMetalRewritePass.validate_transformed_module(
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Conv2DRelu(), pattern_count_map, data_shape, prepack_removal=True
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)
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Conv2DRelu(),
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pattern_count_map,
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data_shape,
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prepack_removal=True)
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pattern_count_map["aten::relu"] = -1
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TestMetalRewritePass.validate_transformed_module(
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Conv2DRelu(),
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pattern_count_map,
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data_shape,
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prepack_removal=True,
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fuse_clamping_ops=True,
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)
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fuse_clamping_ops=True)
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class Conv2DHardtanh(torch.nn.Module):
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def __init__(self):
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super(Conv2DHardtanh, self).__init__()
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self.weight = torch.nn.Parameter(
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torch.rand(conv_weight_shape), requires_grad=False
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)
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self.bias = torch.nn.Parameter(
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torch.rand(conv_bias_shape), requires_grad=False
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)
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self.weight = torch.nn.Parameter(torch.rand(conv_weight_shape), requires_grad=False)
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self.bias = torch.nn.Parameter(torch.rand(conv_bias_shape), requires_grad=False)
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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(
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x,
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self.weight,
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self.bias,
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self.strides,
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self.paddings,
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self.dilations,
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self.groups,
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)
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o = F.conv2d(x, self.weight, self.bias,
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self.strides, self.paddings, self.dilations, self.groups)
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o = F.hardtanh(o)
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return o
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data_shape = (batch_size, input_channels, height, width)
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pattern_count_map = {
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"Tensor = aten::conv2d": -1,
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"metal_prepack::conv2d_prepack": 1,
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"metal_prepack::conv2d_run": 1,
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}
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TestMetalRewritePass.validate_transformed_module(
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Conv2DHardtanh(), pattern_count_map, data_shape
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)
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pattern_count_map = {"Tensor = aten::conv2d": -1,
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"metal_prepack::conv2d_prepack": 1,
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"metal_prepack::conv2d_run": 1}
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TestMetalRewritePass.validate_transformed_module(Conv2DHardtanh(), pattern_count_map, data_shape)
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pattern_count_map["aten::hardtanh"] = 1
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pattern_count_map["metal_prepack::conv2d_prepack"] = -1
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TestMetalRewritePass.validate_transformed_module(
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Conv2DHardtanh(), pattern_count_map, data_shape, prepack_removal=True
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)
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Conv2DHardtanh(),
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pattern_count_map,
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data_shape,
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prepack_removal=True)
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pattern_count_map["aten::hardtanh"] = -1
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TestMetalRewritePass.validate_transformed_module(
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Conv2DRelu(),
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pattern_count_map,
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data_shape,
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prepack_removal=True,
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fuse_clamping_ops=True,
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)
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fuse_clamping_ops=True)
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if __name__ == "__main__":
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run_tests()
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