| import torch |
| import torch.nn.functional as F |
| import torch.nn as nn |
| import torch.nn.quantized as nnq |
| import torch.nn.quantized.dynamic as nnqd |
| import torch.nn.intrinsic.quantized as nniq |
| import torch.multiprocessing as mp |
| |
| # symbolic trace |
| from torch.fx import symbolic_trace |
| |
| # graph mode quantization based on fx |
| from torch.quantization import ( |
| QuantType, |
| fuse_fx, |
| prepare_fx, |
| convert_fx, |
| ) |
| |
| from torch.quantization import ( |
| default_qconfig, |
| default_qat_qconfig, |
| prepare, |
| prepare_qat, |
| convert, |
| ) |
| |
| # test utils |
| from torch.testing._internal.common_quantization import ( |
| QuantizationTestCase, |
| skipIfNoFBGEMM, |
| skip_if_no_torchvision, |
| train_one_epoch, |
| run_ddp, |
| ) |
| |
| from torch.testing._internal.common_distributed import skip_if_not_multigpu |
| |
| from torch.testing._internal.common_quantization import NodeSpec as ns |
| |
| import itertools |
| import operator |
| import unittest |
| |
| class TestQuantizeFx(QuantizationTestCase): |
| """ Unit tests for functionalities |
| """ |
| @skipIfNoFBGEMM |
| def test_functional(self): |
| """ Test quantizing functional conv and linear |
| """ |
| class Conv(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.stride = (1, 1) |
| self.padding = (0, 0) |
| self.dilation = (1, 1) |
| self.groups = 1 |
| |
| def forward(self, x, weight): |
| return F.conv2d(x, weight, None, self.stride, self.padding, self.dilation, self.groups) |
| |
| conv_input = torch.rand(1, 3, 224, 224) |
| conv_weight = torch.rand(3, 3, 3, 3) |
| |
| class Linear(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| |
| def forward(self, x, weight): |
| return F.linear(x, weight) |
| |
| linear_input = torch.rand(8, 5) |
| linear_weight = torch.rand(10, 5) |
| |
| class LinearModule(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.linear = torch.nn.Linear(5, 10) |
| |
| def forward(self, x): |
| return self.linear(x) |
| |
| linear_module_input = torch.rand(8, 5) |
| |
| tests = [ |
| (False, Conv, (conv_input, conv_weight), ns.call_function(torch.ops.quantized.conv2d)), |
| (True, Linear, (linear_input, linear_weight), ns.call_function(torch.ops.quantized.linear_dynamic)), |
| (False, Linear, (linear_input, linear_weight), ns.call_function(torch.ops.quantized.linear)), |
| (True, LinearModule, (linear_module_input,), ns.call_module(nnqd.Linear)), |
| (False, LinearModule, (linear_module_input,), ns.call_module(nnq.Linear)), |
| ] |
| |
| for is_dynamic, M, inputs, quantized_node in tests: |
| quant_type = QuantType.DYNAMIC if is_dynamic else QuantType.STATIC |
| self.checkGraphModeFxOp( |
| M(), inputs, quant_type, quantized_node) |
| |
| class TestQuantizeFxOps(QuantizationTestCase): |
| """Unit tests for individual ops |
| """ |
| @skipIfNoFBGEMM |
| def test_linear(self): |
| class ModuleLinear(torch.nn.Module): |
| def __init__(self, has_relu=False, f_relu=False): |
| super(ModuleLinear, self).__init__() |
| self.linear = torch.nn.Linear(30, 4).float() |
| if has_relu: |
| if f_relu: |
| self.relu = F.relu |
| else: |
| self.relu = torch.nn.ReLU() |
| else: |
| self.relu = torch.nn.Identity() |
| |
| def forward(self, x): |
| return self.relu(self.linear(x)) |
| |
| class FuncLinear(torch.nn.Module): |
| def __init__(self, has_relu=False, f_relu=False): |
| super(FuncLinear, self).__init__() |
| self.w = torch.randn(4, 30) |
| self.b = torch.randn(4) |
| if has_relu: |
| if f_relu: |
| self.relu = F.relu |
| else: |
| self.relu = torch.nn.ReLU() |
| else: |
| self.relu = torch.nn.Identity() |
| |
| def forward(self, x): |
| return self.relu(F.linear(x, self.w, self.b)) |
| |
| data = (torch.rand((1, 30), dtype=torch.float),) |
| options = itertools.product( |
| [(ModuleLinear(has_relu=False), True)], |
| # TODO: enable after raw `tensor` is supported in fx |
| # (FuncLinear(has_relu=False), False)], |
| self.all_quant_types) |
| quantized_nodes = { |
| # is_module |
| True: { |
| # quant_type: |
| QuantType.DYNAMIC: ns.call_module(nnqd.Linear), |
| QuantType.STATIC: ns.call_module(nnq.Linear), |
| # note that we are checking the final result |
| QuantType.QAT: ns.call_module(nnq.Linear), |
| }, |
| False: { |
| # quant_type: |
| QuantType.DYNAMIC: ns.call_function(torch.ops.quantized.linear_dynamic), |
| QuantType.STATIC: ns.call_function(torch.ops.quantized.linear), |
| QuantType.QAT: ns.call_function(torch.ops.quantized.linear), |
| } |
| } |
| for (model, is_module), quant_type in options: |
| self.checkGraphModeFxOp( |
| model, data, quant_type, quantized_nodes[is_module][quant_type]) |
| |
| for f_relu, quant_type in itertools.product([True, False], [QuantType.STATIC, QuantType.QAT]): |
| for model, quantized_node in [ |
| (ModuleLinear(has_relu=True, f_relu=f_relu), ns.call_module(nniq.LinearReLU))]: |
| # TODO: support functional linear + relu fusion |
| # (FuncLinear(has_relu=True, f_relu=f_relu), ns.call_function(torch.ops.quantized.linear_relu))]: |
| self.checkGraphModeFxOp(model, data, quant_type, quantized_node) |
| |
| @skipIfNoFBGEMM |
| def test_quantized_conv(self): |
| conv_module = {1 : torch.nn.Conv1d, 2 : torch.nn.Conv2d, 3 : torch.nn.Conv3d} |
| |
| class Conv(torch.nn.Module): |
| def __init__(self, dim): |
| super(Conv, self).__init__() |
| self.conv = conv_module[dim](3, 3, 3).float() |
| |
| def forward(self, x): |
| return self.conv(x) |
| |
| options = itertools.product([1, 2, 3], self.static_quant_types) |
| quantized_nodes = { |
| # dim |
| 1: ns.call_module(nnq.Conv1d), |
| 2: ns.call_module(nnq.Conv2d), |
| 3: ns.call_module(nnq.Conv3d), |
| } |
| for dim, quant_type in options: |
| model = self.checkGraphModeFxOp( |
| Conv(dim), self.img_data_dict[dim], quant_type, |
| quantized_nodes[dim]) |
| |
| @skipIfNoFBGEMM |
| def test_quantized_conv_relu(self): |
| """tests for conv1d_relu/conv2d_relu/conv3d_relu""" |
| conv_module = {1 : torch.nn.Conv1d, 2 : torch.nn.Conv2d, 3 : torch.nn.Conv3d} |
| |
| class ConvNdRelu(torch.nn.Module): |
| def __init__(self, dim, inplace): |
| super(ConvNdRelu, self).__init__() |
| self.conv = conv_module[dim](3, 3, 3).float() |
| self.relu = torch.nn.ReLU(inplace) |
| |
| def forward(self, x): |
| return self.relu(self.conv(x)) |
| |
| class ConvNdFunctionalRelu(torch.nn.Module): |
| def __init__(self, dim): |
| super(ConvNdFunctionalRelu, self).__init__() |
| self.conv = conv_module[dim](3, 3, 3).float() |
| |
| def forward(self, x): |
| return F.relu(self.conv(x)) |
| |
| class ConvNdInplaceFunctionalRelu(torch.nn.Module): |
| def __init__(self, dim): |
| super(ConvNdInplaceFunctionalRelu, self).__init__() |
| self.conv = conv_module[dim](3, 3, 3).float() |
| |
| def forward(self, x): |
| return F.relu(self.conv(x), True) |
| |
| options = itertools.product([1, 2, 3], self.static_quant_types) |
| quantized_nodes = { |
| # dim |
| 1: ns.call_module(nniq.ConvReLU1d), |
| 2: ns.call_module(nniq.ConvReLU2d), |
| 3: ns.call_module(nniq.ConvReLU3d), |
| } |
| for dim, quant_type in options: |
| for orig_m in [ConvNdRelu(dim, True), |
| ConvNdRelu(dim, False), |
| ConvNdFunctionalRelu(dim), |
| ConvNdInplaceFunctionalRelu(dim)]: |
| conv_name = "conv{}d".format(dim) |
| m = self.checkGraphModeFxOp( |
| orig_m, self.img_data_dict[dim], quant_type, |
| quantized_nodes[dim]) |
| |
| |
| def _test_quantized_binary_op_impl(self, binary_op, ibinary_op, quantized_op): |
| class Op(torch.nn.Module): |
| def __init__(self, is_inplace, is_scalar): |
| super(Op, self).__init__() |
| self.conv1 = torch.nn.Conv2d(1, 1, 1).float() |
| self.conv2 = torch.nn.Conv2d(1, 1, 1).float() |
| self.is_scalar = is_scalar |
| self.op = ibinary_op if is_inplace else binary_op |
| |
| def forward(self, x, y): |
| x = self.conv1(x) |
| y = 3 if self.is_scalar else self.conv2(y) |
| x = self.op(x, y) |
| return x |
| |
| # TODO: decide whether we want to quantize or not |
| # in this case |
| # class NonQuantizedOp(torch.nn.Module): |
| # def __init__(self, is_inplace, is_scalar): |
| # super(NonQuantizedOp, self).__init__() |
| # self.is_scalar = is_scalar |
| # self.op = ibinary_op if is_inplace else binary_op |
| |
| # def forward(self, x, y): |
| # y = 3 if self.is_scalar else y |
| # x = self.op(x, y) |
| # return x |
| |
| data = (torch.randn(1, 1, 1, 1, dtype=torch.float), |
| torch.randn(1, 1, 1, 1, dtype=torch.float)) |
| quantized_node = ns.call_function(quantized_op) |
| options = itertools.product([True, False], [True, False]) |
| quant_type = QuantType.STATIC |
| for is_inplace, is_scalar in options: |
| self.checkGraphModeFxOp( |
| Op(is_inplace, is_scalar), data, quant_type, quantized_node) |
| |
| def _test_quantized_binary_op_relu_impl(self, binary_op, ibinary_op, quantized_op): |
| class OpRelu(torch.nn.Module): |
| def __init__(self, is_inplace, is_functional_relu, |
| is_scalar): |
| super(OpRelu, self).__init__() |
| self.conv1 = torch.nn.Conv2d(1, 1, 1).float() |
| self.conv2 = torch.nn.Conv2d(1, 1, 1).float() |
| self.op = ibinary_op if is_inplace else binary_op |
| self.is_functional_relu = is_functional_relu |
| self.is_scalar = is_scalar |
| self.relu = F.relu if self.is_functional_relu \ |
| else torch.nn.ReLU() |
| |
| def forward(self, x, y): |
| x = self.conv1(x) |
| y = 3 if self.is_scalar else self.conv2(y) |
| x = self.op(x, y) |
| x = self.relu(x) |
| return x |
| |
| data = (torch.rand((1, 1, 1, 1), dtype=torch.float), |
| torch.rand((1, 1, 1, 1), dtype=torch.float)) |
| quant_type = QuantType.STATIC |
| quantized_node = ns.call_function(quantized_op) |
| options = itertools.product( |
| [True, False], [True, False], [True, False]) |
| for is_inplace_op, is_functional_relu, is_scalar in options: |
| self.checkGraphModeFxOp( |
| OpRelu(is_inplace_op, is_functional_relu, is_scalar), |
| data, quant_type, quantized_node) |
| |
| @skipIfNoFBGEMM |
| def test_quantized_add(self): |
| self._test_quantized_binary_op_impl( |
| operator.add, operator.iadd, torch.ops.quantized.add) |
| |
| @skipIfNoFBGEMM |
| def test_quantized_mul(self): |
| self._test_quantized_binary_op_impl( |
| operator.mul, operator.imul, torch.ops.quantized.mul) |
| |
| @skipIfNoFBGEMM |
| def test_quantized_add_relu(self): |
| self._test_quantized_binary_op_relu_impl( |
| operator.add, operator.iadd, torch.ops.quantized.add_relu) |
| |
| @skipIfNoFBGEMM |
| def test_quantized_mul_relu(self): |
| self._test_quantized_binary_op_relu_impl( |
| operator.mul, operator.imul, torch.ops.quantized.mul_relu) |
| |
| @skipIfNoFBGEMM |
| def test_quantized_cat(self): |
| """ quantization of the output of cat will be depend on the |
| input of cat. we only quantize the output of cat when its inputs are quantized. |
| """ |
| class QuantizedCat(torch.nn.Module): |
| def __init__(self): |
| super(QuantizedCat, self).__init__() |
| self.conv1 = torch.nn.Conv2d(2, 2, 2).float() |
| self.conv2 = torch.nn.Conv2d(2, 2, 2).float() |
| |
| def forward(self, x, y): |
| x = self.conv1(x) |
| y = self.conv2(y) |
| return torch.cat([x, y], 1) |
| |
| # TODO: decide whether to quantize in this case |
| # class NonQuantizedCat(torch.nn.Module): |
| # def __init__(self): |
| # super(NonQuantizedCat, self).__init__() |
| |
| # def forward(self, x, y): |
| # return torch.cat([x, y], 1) |
| |
| data = (torch.randn(1, 2, 5, 5, dtype=torch.float), |
| torch.randn(1, 2, 5, 5, dtype=torch.float)) |
| quantized_node = ns.call_function(torch.ops.quantized.cat) |
| for quant_type in self.static_quant_types: |
| self.checkGraphModeFxOp(QuantizedCat(), data, quant_type, quantized_node) |
| |
| |
| @skipIfNoFBGEMM |
| def test_qbatch_norm(self): |
| bn_module = { |
| # TODO: quantized batchnorm 1d module is missing |
| # 1 : torch.nn.BatchNorm1d, |
| 2 : torch.nn.BatchNorm2d, |
| 3 : torch.nn.BatchNorm3d, |
| } |
| |
| class M(torch.nn.Module): |
| def __init__(self, dim): |
| super(M, self).__init__() |
| self.bn = bn_module[dim](3).to(torch.float) |
| |
| def forward(self, x): |
| return self.bn(x) |
| |
| options = itertools.product(self.static_quant_types, [2, 3]) |
| quantized_nodes = { |
| # 1: ns.call_module(nnq.BatchNorm1d), |
| 2: ns.call_module(nnq.BatchNorm2d), |
| 3: ns.call_module(nnq.BatchNorm3d), |
| } |
| for quant_type, dim in options: |
| model = self.checkGraphModeFxOp( |
| M(dim), self.img_data_dict[dim], quant_type, quantized_nodes[dim]) |
| |
| @skipIfNoFBGEMM |
| def test_qbatch_norm_relu(self): |
| bn_module = {2 : torch.nn.BatchNorm2d, 3 : torch.nn.BatchNorm3d} |
| |
| class BNRelu(torch.nn.Module): |
| def __init__(self, dim, inplace): |
| super(BNRelu, self).__init__() |
| self.bn = bn_module[dim](3).to(torch.float) |
| self.relu = torch.nn.ReLU(inplace=inplace) |
| |
| def forward(self, x): |
| return self.relu(self.bn(x)) |
| |
| class BNFuncRelu(torch.nn.Module): |
| def __init__(self, dim): |
| super(BNFuncRelu, self).__init__() |
| self.bn = bn_module[dim](3).to(torch.float) |
| |
| def forward(self, x): |
| return F.relu(self.bn(x), False) |
| |
| class BNFuncInplaceRelu(torch.nn.Module): |
| def __init__(self, dim): |
| super(BNFuncInplaceRelu, self).__init__() |
| self.bn = bn_module[dim](3).to(torch.float) |
| |
| def forward(self, x): |
| return F.relu(self.bn(x), True) |
| |
| options = itertools.product(self.static_quant_types, [2, 3]) |
| quantized_nodes = { |
| 2: ns.call_module(nniq.BNReLU2d), |
| 3: ns.call_module(nniq.BNReLU3d), |
| } |
| for quant_type, dim in options: |
| for instance in [BNRelu(dim, True), BNRelu(dim, False), |
| BNFuncRelu(dim), BNFuncInplaceRelu(dim)]: |
| self.checkGraphModeFxOp( |
| instance, self.img_data_dict[dim], quant_type, |
| quantized_nodes[dim]) |
| |
| def _test_activation_impl( |
| self, float_module, float_op, quantized_module, quantized_op): |
| ''' Test for activation op(with inplace options), float_op can be |
| torch op or functional op |
| ''' |
| class M(torch.nn.Module): |
| def __init__(self, is_module, inplace): |
| super(M, self).__init__() |
| self.is_module = is_module |
| self.inplace = inplace |
| if self.is_module: |
| self.op = float_module(self.inplace) |
| else: |
| self.op = float_op |
| |
| def forward(self, input): |
| if self.is_module: |
| return self.op(input) |
| else: |
| return self.op(input, self.inplace) |
| |
| options = itertools.product([True, False], [True, False], self.static_quant_types) |
| quantized_nodes = { |
| # is_module |
| True: ns.call_module(quantized_module), |
| False: ns.call_function(quantized_op), |
| } |
| |
| for is_module, is_inplace, quant_type in options: |
| self.checkGraphModeFxOp( |
| M(is_module, is_inplace), self.img_data_2d, |
| quant_type, quantized_nodes[is_module]) |
| |
| def test_hardswish(self): |
| self._test_activation_impl(nn.Hardswish, F.hardswish, nnq.Hardswish, torch.ops.quantized.hardswish) |
| |
| def test_elu(self): |
| self._test_activation_impl(nn.ELU, F.elu, nnq.ELU, torch.ops.quantized.elu) |
| |
| def _test_norm_impl( |
| self, float_module, float_op, op_args, data, quantized_module, quantized_op, |
| skip_op_arg_for_functional=False): |
| ''' Test for normalization op, float_op can be torch op or functional op, |
| op_args is a list of positional argument for the module/op |
| ''' |
| class M(torch.nn.Module): |
| def __init__(self, is_module): |
| super(M, self).__init__() |
| self.is_module = is_module |
| if self.is_module: |
| self.op = float_module(*op_args) |
| else: |
| self.op = float_op |
| |
| def forward(self, input): |
| if self.is_module: |
| return self.op(input) |
| else: |
| args = [input] |
| if not skip_op_arg_for_functional: |
| args += op_args |
| return self.op(*args) |
| |
| options = itertools.product([True, False], self.static_quant_types) |
| quantized_nodes = { |
| # is_module |
| True: ns.call_module(quantized_module), |
| False: ns.call_function(quantized_op), |
| } |
| |
| for is_module, quant_type in options: |
| self.checkGraphModeFxOp( |
| M(is_module), data, quant_type, quantized_nodes[is_module]) |
| |
| def test_layer_norm(self): |
| data = (torch.rand((1, 2, 5, 5), dtype=torch.float),) |
| self._test_norm_impl( |
| nn.LayerNorm, F.layer_norm, [[2, 5, 5]], data, nnq.LayerNorm, torch.ops.quantized.layer_norm) |
| |
| def test_instance_norm(self): |
| data_1d = (torch.rand((1, 4, 5), dtype=torch.float),) |
| data_2d = (torch.rand((1, 4, 5, 1), dtype=torch.float),) |
| data_3d = (torch.rand((1, 4, 5, 1, 1), dtype=torch.float),) |
| data_dict = {1 : data_1d, 2 : data_2d, 3 : data_3d} |
| instance_norm_modules = {1 : nn.InstanceNorm1d, |
| 2 : nn.InstanceNorm2d, |
| 3 : nn.InstanceNorm3d} |
| quantized_instance_norm_modules = { |
| 1 : nnq.InstanceNorm1d, |
| 2 : nnq.InstanceNorm2d, |
| 3 : nnq.InstanceNorm3d |
| } |
| for dim in [1, 2, 3]: |
| data = data_dict[dim] |
| module = instance_norm_modules[dim] |
| quantized_module = quantized_instance_norm_modules[dim] |
| self._test_norm_impl( |
| module, F.instance_norm, [4], data, |
| quantized_module, torch.ops.quantized.instance_norm, |
| skip_op_arg_for_functional=True) |
| |
| @skipIfNoFBGEMM |
| def test_clamp(self): |
| class M(torch.nn.Module): |
| def __init__(self): |
| super(M, self).__init__() |
| self.conv = torch.nn.Conv2d(2, 2, 2).float() |
| self.relu6 = torch.nn.ReLU6() |
| self.relu6_ = torch.nn.ReLU6(True) |
| self.hardtanh = torch.nn.Hardtanh() |
| self.hardtanh_ = torch.nn.Hardtanh(inplace=True) |
| |
| def forward(self, x): |
| x = self.conv(x) |
| x = self.relu6(x) |
| self.relu6_(x) |
| x = F.relu6(x) |
| x = torch.clamp(x, -3, 3) |
| x = x.clamp(-2.5, 2.5) |
| # x = x.clamp_(-2, 2) # Enable when quantized `clamp_` is ready |
| x = self.hardtanh(x) |
| self.hardtanh_(x) |
| x = F.hardtanh(x) |
| F.hardtanh_(x) |
| return x |
| |
| data = (torch.rand((1, 2, 5, 5), dtype=torch.float),) |
| # list of node that should occur in order |
| node_list = [ |
| ns.call_function(torch.quantize_per_tensor), |
| ns.call_module(nnq.Conv2d), |
| ns.call_function(F.hardtanh_), |
| ns.call_method('dequantize') |
| ] |
| for quant_type in self.static_quant_types: |
| m = self.checkGraphModeFxOp( |
| M(), data, quant_type, expected_node_list=node_list) |
| |
| @skipIfNoFBGEMM |
| def test_general_shape_ops(self): |
| """ A test that checks dequantize will be swapped for |
| all supported general shape ops like aten::flatten |
| without actually checking for execution of these ops |
| """ |
| class M(torch.nn.Module): |
| def __init__(self): |
| super(M, self).__init__() |
| self.maxpool1d = torch.nn.MaxPool1d(kernel_size=3) |
| self.maxpool2d = torch.nn.MaxPool2d(kernel_size=3) |
| self.maxpool3d = torch.nn.MaxPool3d(kernel_size=3) |
| self.dropout = torch.nn.Dropout() |
| self.conv1 = torch.nn.Conv2d(3, 3, 3) |
| self.conv2 = torch.nn.Conv2d(3, 3, 3) |
| self.relu = torch.nn.ReLU() |
| |
| def forward(self, x): |
| x = self.conv1(x) |
| # add_scalar |
| x = x + 3 |
| # mul_scalar |
| x = x * 3 |
| # add_scalar_out |
| x += 3 |
| # mul_scalar_out |
| x *= 3 |
| # add_scalar_relu |
| x = x + 3 |
| x = F.relu(x) |
| # add_scalar_relu_out |
| x += 3 |
| x = F.relu(x) |
| # mul_scalar_relu |
| x = x * 3 |
| x = F.relu(x) |
| # mul_scalar_relu_out |
| x *= 3 |
| x = F.relu(x) |
| x = self.maxpool1d(x) |
| x = self.maxpool2d(x) |
| x = self.maxpool3d(x) |
| x = torch.flatten(x) |
| x = torch.max(x) |
| x = torch.min(x) |
| x = x.reshape([-1]) |
| x = x.resize_(1, 1, x.numel()) |
| x = x.view(-1) |
| # prim::ListConstruct |
| xs = [x, x] |
| # prim::ListUnpack |
| x, y = xs |
| # prim::TupleConstruct |
| xs = (x, x) |
| # prim::TupleUnpack |
| x, y = xs |
| x = x.transpose(1, 2) |
| x = x.contiguous() |
| x, y = torch.chunk(x, 2) |
| x = F.dropout(x) |
| x = self.dropout(x) |
| x, _ = torch.sort(x) |
| x = x.permute(0, 2, 3, 1) |
| x = x.repeat_interleave(3, 1) |
| x = torch.repeat_interleave(x, 3, 1) |
| x = self.relu(x) |
| x = F.relu(x) |
| x = F.relu(x, inplace=True) |
| x = x.relu() |
| x.relu_() |
| x = x.squeeze(0) |
| x.squeeze_(0) |
| x = torch.squeeze(x, 0) |
| x = x.unsqueeze(0) |
| x.unsqueeze_(0) |
| x = torch.unsqueeze(x, 0) |
| x = x.detach() |
| x.detach_() |
| x = x.repeat(4, 2) |
| y = [] |
| y.append(x) |
| z = torch.stack(y, 0) |
| z = [z, z] |
| x, _ = z |
| x = self.conv2(x) |
| return x |
| |
| data = torch.rand(1, 3, 10, 10) |
| # This model is not executable since we just put all ops |
| # in the same forward |
| m = M() |
| original = symbolic_trace(m) |
| # nothing to fuse so skipping the fuse step |
| qconfig_dict = {'': default_qconfig} |
| prepared = prepare_fx(original, qconfig_dict) |
| # not runnable |
| quantized = convert_fx(prepared) |
| |
| # This checks that the dequantize from the output of first conv |
| # is being propagated to the end, so that we don't insert extra |
| # observers and also successfully fused two quantized::conv2d |
| # patterns |
| # one quantize_per_tensor for input |
| # check exact counts of quantize and dequantize |
| count_check = { |
| ns.call_function(torch.quantize_per_tensor) : 1, |
| ns.call_method('dequantize') : 1 |
| } |
| order_check = [ |
| ns.call_function(torch.quantize_per_tensor), |
| ns.call_module(nnq.Conv2d), |
| ns.call_module(nnq.Conv2d), |
| ns.call_method('dequantize'), |
| ] |
| self.checkGraphModuleNodes( |
| quantized, |
| expected_node_occurrence=count_check, |
| expected_node_list=order_check) |
| |
| @skipIfNoFBGEMM |
| def test_general_value_ops(self): |
| """ A test that checks correct patterns are produced for |
| all supported general value ops like aten::avg_pool2d \ |
| without actually checking for execution of these ops |
| """ |
| class M(torch.nn.Module): |
| def __init__(self): |
| super(M, self).__init__() |
| self.conv = torch.nn.Conv2d(3, 3, 3) |
| self.avg_pool1d = torch.nn.AvgPool1d(3) |
| self.avg_pool2d = torch.nn.AvgPool2d(3) |
| self.avg_pool3d = torch.nn.AvgPool3d(3) |
| self.adaptive_avg_pool1d = torch.nn.AdaptiveAvgPool1d((1)) |
| self.adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d((1, 1)) |
| self.adaptive_avg_pool3d = torch.nn.AdaptiveAvgPool3d((1, 1, 1)) |
| self.leaky_relu = torch.nn.LeakyReLU() |
| self.hardsigmoid = torch.nn.Hardsigmoid() |
| self.sigmoid = torch.nn.Sigmoid() |
| self.tanh = torch.nn.Tanh() |
| |
| def forward(self, x): |
| x = self.conv(x) |
| x = self.avg_pool1d(x) |
| x = self.avg_pool2d(x) |
| x = self.avg_pool3d(x) |
| x = self.adaptive_avg_pool1d(x) |
| x = self.adaptive_avg_pool2d(x) |
| x = self.adaptive_avg_pool3d(x) |
| x = F.avg_pool1d(x, 3) |
| x = F.avg_pool2d(x, 3) |
| x = F.avg_pool3d(x, 3) |
| x = F.adaptive_avg_pool1d(x, (1)) |
| x = F.adaptive_avg_pool2d(x, (1, 1)) |
| x = F.adaptive_avg_pool3d(x, (1, 1, 1)) |
| x = torch.mean(x) |
| x = torch.mean(x, [2, 3], False) |
| x = x.mean() |
| x = x.mean([2, 3], True) |
| x = F.interpolate(x, 4, mode='nearest') |
| x = F.interpolate(x, 4, mode='linear') |
| x = self.leaky_relu(x) |
| x = F.leaky_relu(x) |
| x = F.leaky_relu(x, inplace=True) |
| x = x.leaky_relu() |
| x.leaky_relu_() |
| x = self.hardsigmoid(x) |
| x = F.hardsigmoid(x) |
| x = F.hardsigmoid(x, inplace=True) |
| x = x.hardsigmoid() |
| x.hardsigmoid_() |
| x = self.sigmoid(x) |
| x = torch.sigmoid(x) |
| # F.sigmoid is deprecated |
| x = x.sigmoid() |
| x.sigmoid_() |
| x = self.tanh(x) |
| # F.tanh is deprecated |
| x = torch.tanh(x) |
| x = x.tanh() |
| x.tanh_() |
| x = self.conv(x) |
| return x |
| |
| # This model is not executable since we just put all ops |
| # in the same forward |
| m = M() |
| original = symbolic_trace(m) |
| # nothing to fuse so skipping the fuse step |
| qconfig_dict = {'': default_qconfig} |
| prepared = prepare_fx(original, qconfig_dict) |
| # not runnable |
| quantized = convert_fx(prepared) |
| |
| # This checks that the dequantize from the output of first conv |
| # is being propagated to the end, so that we don't insert extra |
| # observers |
| # check exact counts of quantize and dequantize |
| count_check = { |
| ns.call_function(torch.quantize_per_tensor) : 1, |
| ns.call_method('dequantize') : 1 |
| } |
| order_check = [ |
| ns.call_function(torch.quantize_per_tensor), |
| ns.call_module(nnq.Conv2d), |
| ns.call_module(nnq.Conv2d), |
| ns.call_method('dequantize'), |
| ] |
| self.checkGraphModuleNodes( |
| quantized, |
| expected_node_occurrence=count_check, |
| expected_node_list=order_check) |
| |
| class TestQuantizeFxModels(QuantizationTestCase): |
| def _test_model_impl( |
| self, mode, name, model, eager_quantizable_model, |
| check_with_eager=True, |
| diff_of_quant=None, |
| diff_from_eager=None): |
| if diff_of_quant is None or diff_from_eager is None: |
| diff_of_quant = {} |
| diff_from_eager = {} |
| |
| if mode not in diff_of_quant or mode not in diff_from_eager: |
| diff_of_quant[mode] = {} |
| diff_from_eager[mode] = {} |
| |
| input_tensor = torch.rand(1, 3, 224, 224) |
| input_tensor_inception = torch.rand(1, 3, 299, 299) |
| output_value = torch.randint(0, 1, (1,)) |
| |
| # print('quantizing:', name, ' mode:', mode) |
| if name == 'inception_v3': |
| input_value = input_tensor_inception |
| else: |
| input_value = input_tensor |
| |
| qconfig = default_qconfig if mode == 'static' else default_qat_qconfig |
| qconfig_dict = {'': qconfig} |
| graph_module = symbolic_trace(model) |
| # print('graph module:', graph_module.src) |
| script = torch.jit.script(graph_module) |
| |
| # make sure graph module and script module are both runanble |
| original_out = graph_module(input_value) |
| is_not_tuple_out = not isinstance(original_out, tuple) |
| script_out = script(input_value) |
| self.assertEqual( |
| (original_out - script_out).abs().max(), 0, |
| 'Reslut of original graph module and script module does not match') |
| |
| # set to train just before quantization |
| if mode != 'static': |
| model.train() |
| |
| graph_module = fuse_fx(graph_module) |
| prepared = prepare_fx(graph_module, qconfig_dict) |
| |
| if mode == 'ddp': |
| mp.spawn(run_ddp, |
| args=(world_size, prepared), |
| nprocs=world_size, |
| join=True) |
| elif mode == 'qat': |
| assert prepared.training, 'prepared must be in training mode for qat' |
| optimizer = torch.optim.SGD(prepared.parameters(), lr=0.0001) |
| criterion = nn.CrossEntropyLoss() |
| train_one_epoch(prepared, criterion, optimizer, [(input_value, output_value)], torch.device('cpu'), 1) |
| else: |
| for i in range(10): |
| prepared(input_value) |
| |
| # print('after observation root:', prepared.root) |
| |
| qgraph = convert_fx(prepared) |
| # print('after quantization root:', qgraph.root) |
| # print('after quantization code:', qgraph.src) |
| qgraph.eval() |
| qgraph_script = torch.jit.script(qgraph) |
| # print('quantized and scripted:', qgraph_script.graph) |
| |
| qgraph_out = qgraph(input_value) |
| qgraph_script = qgraph_script(input_value) |
| |
| if is_not_tuple_out: |
| diff_of_quant[mode][name] = (original_out - qgraph_out).abs().max() |
| assert torch.allclose(qgraph_out, qgraph_script), 'graph, scripted graph' |
| else: |
| print('tuple output') |
| |
| if eager_quantizable_model is not None: |
| # comparing to eager mode quantization |
| qeager = eager_quantizable_model |
| ref_out = qeager(input_value) |
| qeager.qconfig = qconfig |
| if mode == 'static': |
| qeager.fuse_model() |
| prepare(qeager, inplace=True) |
| else: |
| qeager.train() |
| qeager.fuse_model() |
| prepare_qat(qeager, inplace=True) |
| |
| # calibration |
| if mode == 'ddp': |
| mp.spawn(run_ddp, |
| args=(world_size, qeager), |
| nprocs=world_size, |
| join=True) |
| elif mode == 'qat': |
| assert qeager.training, 'qeager should be in training mode for qat' |
| optimizer = torch.optim.SGD(qeager.parameters(), lr=0.0001) |
| train_one_epoch(qeager, criterion, optimizer, [(input_value, output_value)], torch.device('cpu'), 1) |
| else: |
| for i in range(10): |
| qeager(input_value) |
| |
| # print('ref after observation:', qeager) |
| |
| convert(qeager, inplace=True) |
| qeager.eval() |
| |
| # print('ref after quantization:', qeager) |
| qeager_out = qeager(input_value) |
| qeager_script = torch.jit.script(qeager) |
| qscript_out = qeager_script(input_value) |
| if is_not_tuple_out: |
| diff_from_eager[mode][name] = (qeager_out - qgraph_out).abs().max() |
| if check_with_eager: |
| self.assertEqual(diff_from_eager[mode][name], 0, |
| 'Result of graph mode quantization and ' + |
| 'eager mode quantization on model: ' + name + |
| ' should match. Mode: ' + mode + |
| ' diff:' + str(diff_from_eager[mode][name])) |
| |
| @skip_if_no_torchvision |
| @skipIfNoFBGEMM |
| @unittest.skip("skip for now since tbb failed") |
| def test_torchvision(self): |
| from torchvision import models |
| from torchvision.models import quantization as quantized_models |
| |
| def get_available_classification_models(models): |
| return [k for k, v in models.__dict__.items() if callable(v) and k[0].lower() == k[0] and k[0] != "_"] |
| |
| model_list = get_available_classification_models(models) |
| quantized_model_list = get_available_classification_models(quantized_models) |
| |
| no_pretrained_model = set(['shufflenet_v2_x0_5', 'shufflenet_v2_x1_5', 'shufflenet_v2_x2_0']) |
| quantized_model_list = set(quantized_model_list) - no_pretrained_model |
| # test eager and graph consistency |
| model_list = quantized_model_list |
| # slice need to be fixed in symbolic tracing(https://github.com/pytorch/pytorch/issues/43511) |
| model_list = set(model_list) - {'googlenet', 'inception_v3'} |
| # getattr should not be used as node name(https://github.com/pytorch/pytorch/issues/43522) |
| model_list -= {'shufflenet_v2_x1_0', 'mobilenet_v2'} |
| |
| # mobilenet: dropout error RuntimeError: "bernoulli_scalar_cpu_" not implemented for 'QUInt8' |
| # incpetion_v3: looks like there is some problem with AuxLogits |
| quantized_not_working = [('qat', 'mobilenet_v2'), |
| ('qat', 'inception_v3'), |
| ('static', 'inception_v3')] |
| |
| fx_eager_not_matching = ['googlenet', # because _transform_input is not quantized in eager |
| 'mobilenet_v2'] # because relu6 is replaced as relu in mobilenetv2 |
| |
| diff_of_quant = {} |
| diff_from_eager = {} |
| modes = ['static', 'qat'] |
| options = itertools.product(modes, model_list) |
| for mode, name in options: |
| pretrained = name in quantized_model_list # load pretrained model to compare with quantized model |
| if name in quantized_model_list: |
| if (mode, name) in quantized_not_working: |
| eager_quantizable_model = None |
| else: |
| eager_quantizable_model = quantized_models.__dict__[name](pretrained=True, quantize=False).eval().float() |
| # compare with eager mode quantized model when it is available |
| pretrained = eager_quantizable_model is not None |
| model = models.__dict__[name](pretrained=pretrained).eval().float() |
| check_with_eager = name not in fx_eager_not_matching |
| self._test_model_impl( |
| mode, name, model, eager_quantizable_model, |
| check_with_eager, |
| diff_of_quant, diff_from_eager) |
| |
| def print_diffs(diffs): |
| for mode, diffs_for_mode in diffs.items(): |
| print('mode:', mode) |
| for name, diff in diffs_for_mode.items(): |
| print(name, ':', diff) |
| |
| # print('differences between float and quantized') |
| # print_diffs(diff_of_quant) |
| # print('----------------------') |
| # print('differences between graph mode and eager mode') |
| # print_diffs(diff_from_eager) |
| # print('----------------------') |
| |
| @skip_if_no_torchvision |
| @skip_if_not_multigpu |
| @skipIfNoFBGEMM |
| @unittest.skip('TODO: not working yet due to https://github.com/pytorch/pytorch/issues/43513') |
| def test_resnet18_ddp(self): |
| from torchvision import models |
| from torchvision.models import quantization as quantized_models |
| eager_quantizable_model = quantized_models.__dict__[name](pretrained=True, quantize=False).eval().float() |
| model = models.__dict__[name](pretrained=True).eval().float() |
| self._test_model_impl( |
| 'ddp', 'resnet18', model, eager_quantizable_model) |
