def test_two_matmul_transpose(): class Net(nn.Cell): def __init__(self): super().__init__() self.matmul1 = P.MatMul() self.matmul2 = P.MatMul() self.transpose1 = P.Transpose() self.transpose2 = P.Transpose() def construct(self, x, y, b): out = self.matmul1(x, y) out = self.matmul2(out, b) out = self.transpose1(out, (1, 0)) out = self.transpose2(out, (1, 0)) return out size = 16 context.set_auto_parallel_context(device_num=size, global_rank=0) x = Tensor(np.ones([128, 32]), dtype=ms.float32) y = Tensor(np.ones([32, 64]), dtype=ms.float32) b = Tensor(np.ones([64, 64]), dtype=ms.float32) net = NetWithLoss(Net()) context.set_auto_parallel_context(parallel_mode="auto_parallel") net.set_auto_parallel() reset_op_id() _executor.compile(net, x, y, b, phase='train') strategies = _executor._get_strategy(net) expected_strategies = {'Default/network-Net/Transpose-op0': [[1, 16]], 'Default/network-Net/Transpose-op1': [[16, 1]], 'Default/network-Net/MatMul-op2': [[16, 1], [1, 1]], 'Default/network-Net/MatMul-op3': [[16, 1], [1, 1]]} assert strategies == expected_strategies
def test_train_64k_8p(epoch_size=3, batch_size=32, num_classes=65536): #1048576 #131072 #32768 #8192 dev_num = 8 context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, device_num=dev_num) cost_model_context.set_cost_model_context(costmodel_gamma=0.001, costmodel_beta=400.0) set_algo_parameters(elementwise_op_strategy_follow=True) resset_op_id() np.random.seed(6) input_np = np.ones([batch_size, 3, 224, 224]).astype(np.float32) label_np = np.zeros([batch_size]).astype(np.int32) for i in range(0, batch_size): label_np[i] = i % num_classes dataset = DatasetLenet(Tensor(input_np), Tensor(label_np), 1) net = resnet50(num_classes) loss = SoftmaxCrossEntropyExpand(sparse=True) opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9) model = Model(net, loss_fn=loss, optimizer=opt) model.train(5, dataset, dataset_sink_mode=False) strategies = _executor._get_strategy(model._train_network) for (k, v) in strategies.items(): if re.search('Conv2D-op', k) is not None: assert v[0][0] == dev_num elif re.search('MatMul-op', k) is not None: assert v == [[1, 1], [dev_num, 1]] elif re.search('ReduceSum-op', k) is not None: assert v == [[1, dev_num]]
def test_auto_parallel_arithmetic_broadcast_both(): class Net(nn.Cell): def __init__(self): super().__init__() self.matmul = P.MatMul() self.floordiv = P.FloorDiv() def construct(self, x, y, b): out = self.matmul(x, y) out = self.floordiv(out, b) return out context.set_auto_parallel_context(device_num=8, global_rank=0) net = NetWithLoss(Net()) context.set_auto_parallel_context(parallel_mode="auto_parallel") reset_op_id() x = Tensor(np.ones([64, 32]), dtype=ms.float32) y = Tensor(np.ones([32, 1]), dtype=ms.float32) b = Tensor(np.ones([1, 64]), dtype=ms.float32) compile_net(net, x, y, b, phase='train') strategies = _executor._get_strategy(net) expected_strategies = { 'Default/network-Net/FloorDiv-op0': [[8, 1], [1, 1]], 'Default/network-Net/MatMul-op1': [[8, 1], [1, 1]] } assert strategies == expected_strategies
def test_matmul_prelu(): class Net(nn.Cell): def __init__(self): super().__init__() self.mul1 = P.Mul() self.prelu = P.PReLU() def construct(self, x, y, b): out = self.mul1(x, y) out = self.prelu(out, b) return out size = 16 context.set_auto_parallel_context(device_num=size, global_rank=0) x = Tensor(np.ones([16, 3, 128, 32]), dtype=ms.float32) y = Tensor(np.ones([16, 3, 128, 32]), dtype=ms.float32) b = Tensor(np.array([0.01, 0.02, 0.03]), dtype=ms.float32) net = NetWithLoss(Net()) context.set_auto_parallel_context(parallel_mode="auto_parallel") net.set_auto_parallel() reset_op_id() _executor.compile(net, x, y, b, phase='train') strategies = _executor._get_strategy(net) for (k, v) in strategies.items(): if re.search('PReLU-op', k) is not None: assert v == [[16, 1, 1, 1], [1]] elif re.search('Mul-op', k) is not None: assert v == [[16, 1, 1, 1], [16, 1, 1, 1]]
def test_matmul_prelu(): class Net(nn.Cell): def __init__(self): super().__init__() self.mul1 = P.Mul() self.prelu = P.PReLU() def construct(self, x, y, b): out = self.mul1(x, y) out = self.prelu(out, b) return out size = 16 context.set_auto_parallel_context(device_num=size, global_rank=0) x = Tensor(np.ones([16, 3, 128, 32]), dtype=ms.float32) y = Tensor(np.ones([16, 3, 128, 32]), dtype=ms.float32) b = Tensor(np.array([0.01, 0.02, 0.03]), dtype=ms.float32) net = NetWithLoss(Net()) context.set_auto_parallel_context(parallel_mode="auto_parallel") reset_op_id() _executor.compile(net, x, y, b, phase='train') strategies = _executor._get_strategy(net) assert strategies['Default/network-Net/PReLU-op2'] == [[16, 1, 1, 1], [1]] assert strategies['Default/network-Net/Mul-op3'] == [[16, 1, 1, 1], [16, 1, 1, 1]]
def test_common_parameter(): class Net(nn.Cell): def __init__(self): super().__init__() self.matmul1 = P.MatMul() self.matmul2 = P.MatMul() self.matmul3 = P.MatMul() self.weight1 = Parameter(Tensor( np.ones([64, 64]).astype(np.float16) * 0.01), "w", requires_grad=True) self.cast1 = P.Cast() self.cast2 = P.Cast() def construct(self, x, y, z, w): m1_result = self.matmul1(x, self.cast1(self.weight1, mstype.float32)) m2_result = self.matmul2(z, self.cast2(self.weight1, mstype.float32)) m3_result = self.matmul3(m2_result, m1_result) return m3_result size = 8 context.set_auto_parallel_context(device_num=size, global_rank=0) set_algo_parameters(elementwise_op_strategy_follow=True) x = Tensor(np.ones([64, 64]), dtype=ms.float32) y = Tensor(np.ones([64, 64]), dtype=ms.float32) z = Tensor(np.ones([64, 64]), dtype=ms.float32) w = Tensor(np.ones([64, 64]), dtype=ms.float32) net = NetWithLoss(Net()) context.set_auto_parallel_context(parallel_mode="auto_parallel") net.set_auto_parallel() reset_op_id() _executor.compile(net, x, y, z, w, phase='train') strategies = _executor._get_strategy(net) expected_strategies = { 'Default/network-Net/MatMul-op1': [[8, 1], [1, 1]], 'Default/network-Net/MatMul-op3': [[8, 1], [1, 1]], 'Default/network-Net/Cast-op2': [[1, 1]], 'Default/network-Net/MatMul-op0': [[8, 1], [1, 1]], 'Default/network-Net/Cast-op4': [[1, 1]] } assert strategies == expected_strategies
def test_double_subgraphs(): cost_model_context.set_cost_model_context(multi_subgraphs=True) context.set_context(save_graphs=True) context.set_auto_parallel_context(device_num=8, global_rank=0) net = TrainStepWarp(NetWithLoss(Net())) context.set_auto_parallel_context(parallel_mode="auto_parallel") x = Tensor(np.ones([8, 8, 8, 8]), dtype=ms.float32) reset_op_id() _executor.compile(net, x, phase='train') strategies = _executor._get_strategy(net) expected_strategies = {'Default/network-NetWithLoss/ReduceMean-op0': [[8, 1, 1, 1]], 'Default/network-NetWithLoss/net-Net/ReLU-op1': [[8, 1, 1, 1]], 'Default/network-NetWithLoss/net-Net/Mul-op2': [[8, 1, 1, 1], [8, 1, 1, 1]], 'Default/network-NetWithLoss/net-Net/Mul-op3': [[8, 1, 1, 1], [8, 1, 1, 1]], 'Default/network-NetWithLoss/ReduceSum-op4': [[8, 1, 1, 1]]} assert strategies == expected_strategies
def test_auto_parallel_assign_sub_with_ref_key(): size = 8 context.set_auto_parallel_context(device_num=size, global_rank=0) x = Tensor(np.random.rand(4, 4, 32, 64), dtype=ms.float32) net = NetWithLoss(nn.PReLU(4)) context.set_auto_parallel_context(parallel_mode="auto_parallel") reset_op_id() _executor.compile(net, x, phase="train") strategies = _executor._get_strategy(net) for (k, v) in strategies.items(): if re.search('PReLU-op', k) is not None: assert v == [[1, 1, 1, 8], [1]] elif re.search('ReLU-op', k) is not None: assert v == [[1]]
def test_auto_parallel_assign_sub_with_ref_key(): size = 8 context.set_auto_parallel_context(device_num=size, global_rank=0) x = Tensor(np.random.rand(4, 4, 32, 64), dtype=ms.float32) net = NetWithLoss(nn.PReLU(4)) context.set_auto_parallel_context(parallel_mode="auto_parallel") reset_op_id() _executor.compile(net, x, phase="train") strategies = _executor._get_strategy(net) expected_strategies = { 'Default/network-PReLU/PReLU-op2': [[1, 1, 1, 8], [1]], 'Default/network-PReLU/ReLU-op3': [[1]] } assert strategies == expected_strategies
def all_to_all_common(): learning_rate = 0.1 momentum = 0.9 epoch_size = 2 context.reset_auto_parallel_context() context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, device_num=1, global_rank=0) predict = Tensor(np.ones([32, 128]), dtype=ms.float32) label = Tensor(np.ones([32]), dtype=ms.int32) dataset = Dataset(predict, label, 2) net = all_to_all_net() loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) opt = Momentum(net.trainable_params(), learning_rate, momentum) model = Model(net, loss, opt) model.train(epoch_size, dataset, dataset_sink_mode=False) strategys = _executor._get_strategy(model._train_network) return strategys
def test_double_star_graph(): class Net(nn.Cell): def __init__(self): super().__init__() self.matmul1 = P.MatMul() self.matmul2 = P.MatMul() self.matmul3 = P.MatMul() self.cast1 = P.Cast() self.cast2 = P.Cast() def construct(self, x, y, z, w): m1_result = self.matmul1(x, y) m2_result = self.matmul2(z, w) m3_result = self.matmul3(self.cast1(m2_result, mstype.float16), self.cast2(m1_result, mstype.float16)) return m3_result size = 8 context.set_auto_parallel_context(device_num=size, global_rank=0) x = Tensor(np.ones([32, 8]), dtype=ms.float32) y = Tensor(np.ones([8, 16]), dtype=ms.float32) z = Tensor(np.ones([8, 16]), dtype=ms.float32) w = Tensor(np.ones([16, 32]), dtype=ms.float32) net = NetWithLoss(Net()) context.set_auto_parallel_context(parallel_mode="auto_parallel") net.set_auto_parallel() reset_op_id() _executor.compile(net, x, y, z, w, phase='train') strategies = _executor._get_strategy(net) expected_strategies = { 'Default/network-Net/Cast-op0': [[8, 1]], 'Default/network-Net/Cast-op1': [[1, 8]], 'Default/network-Net/MatMul-op3': [[8, 1], [1, 1]], 'Default/network-Net/MatMul-op4': [[1, 1], [1, 8]], 'Default/network-Net/MatMul-op2': [[1, 8], [8, 1]] } assert strategies == expected_strategies
def test_two_bn(): class Net(nn.Cell): def __init__(self): super().__init__() self.block1 = getBlock() self.block2 = getBlock() self.relu = P.ReLU() self.add = P.TensorAdd() self.bias = Tensor(np.ones([64, 64]), dtype=ms.float32) def construct(self, x): out = self.block1(x) out = self.relu(out) out = self.add(out, self.bias) out = self.block2(out) return out net = NetWithLoss(Net()) x = Tensor(np.ones([64, 64]), dtype=ms.float32) context.set_context(save_graphs=True) context.set_auto_parallel_context(device_num=8, global_rank=0) context.set_auto_parallel_context(parallel_mode="auto_parallel") net.set_auto_parallel() set_algo_parameters(elementwise_op_strategy_follow=True) reset_op_id() _executor.compile(net, x, phase='train') strategies = _executor._get_strategy(net) assert len(strategies) == 4 for (k, v) in strategies.items(): if re.search('BatchNorm-op', k) is not None: assert v == [[8, 1], [1], [1], [1], [1]] elif re.search('TensorAdd-op', k) is not None: assert v == [[8, 1], [8, 1]] elif re.search('ReLU-op', k) is not None: assert v == [[8, 1]]
def test_common_parameter(): class Net(nn.Cell): def __init__(self): super().__init__() self.matmul1 = P.MatMul() self.matmul2 = P.MatMul() self.matmul3 = P.MatMul() self.weight1 = Parameter(Tensor(np.ones([64, 64]).astype(np.float16) * 0.01), "w", requires_grad=True) self.cast1 = P.Cast() self.cast2 = P.Cast() def construct(self, x, y): m1_result = self.matmul1(x, self.cast1(self.weight1, mstype.float32)) m2_result = self.matmul2(y, self.cast2(self.weight1, mstype.float32)) m3_result = self.matmul3(m2_result, m1_result) return m3_result size = 8 context.set_auto_parallel_context(device_num=size, global_rank=0) set_algo_parameters(elementwise_op_strategy_follow=True) x = Tensor(np.ones([64, 64]), dtype=ms.float32) y = Tensor(np.ones([64, 64]), dtype=ms.float32) net = NetWithLoss(Net()) context.set_auto_parallel_context(parallel_mode="auto_parallel") net.set_auto_parallel() reset_op_id() _executor.compile(net, x, y, phase='train') strategies = _executor._get_strategy(net) for (k, v) in strategies.items(): if re.search('MatMul-op', k) is not None: assert v == [[8, 1], [1, 1]] elif re.search('Cast-op', k) is not None: assert v == [[1, 1]]
def test_two_matmul(): class Net(nn.Cell): def __init__(self): super().__init__() self.matmul1 = P.MatMul() self.matmul2 = P.MatMul() def construct(self, x, y, b): out = self.matmul1(x, y) out = self.matmul2(out, b) return out size = 16 context.set_auto_parallel_context(device_num=size, global_rank=0) cost_model_context.set_cost_model_context( device_memory_capacity=32.0 * 1024.0 * 1024.0 * 1024.0, costmodel_alpha=1.0, costmodel_beta=60.0, costmodel_gamma=0.1, costmodel_communi_threshold=1024.0, costmodel_communi_const=2222.0, costmodel_communi_bias=1111.0) dev_mem_cap = cost_model_context.get_cost_model_context( "device_memory_capacity") assert dev_mem_cap == 32.0 * 1024.0 * 1024.0 * 1024.0 costmodel_alpha = cost_model_context.get_cost_model_context( "costmodel_alpha") assert costmodel_alpha == 1.0 costmodel_beta = cost_model_context.get_cost_model_context( "costmodel_beta") assert costmodel_beta == 60.0 costmodel_gamma = cost_model_context.get_cost_model_context( "costmodel_gamma") assert costmodel_gamma == 0.1 costmodel_communi_threshold = cost_model_context.get_cost_model_context( "costmodel_communi_threshold") assert costmodel_communi_threshold == 1024.0 costmodel_communi_const = cost_model_context.get_cost_model_context( "costmodel_communi_const") assert costmodel_communi_const == 2222.0 costmodel_communi_bias = cost_model_context.get_cost_model_context( "costmodel_communi_bias") assert costmodel_communi_bias == 1111.0 cost_model_context.reset_cost_model_context() dev_mem_cap = cost_model_context.get_cost_model_context( "device_memory_capacity") assert dev_mem_cap == 16.0 * 1024.0 * 1024.0 * 1024.0 costmodel_alpha = cost_model_context.get_cost_model_context( "costmodel_alpha") assert costmodel_alpha == 1.0 costmodel_beta = cost_model_context.get_cost_model_context( "costmodel_beta") assert costmodel_beta == 260.0 costmodel_gamma = cost_model_context.get_cost_model_context( "costmodel_gamma") assert costmodel_gamma == 0.001 costmodel_communi_threshold = cost_model_context.get_cost_model_context( "costmodel_communi_threshold") assert costmodel_communi_threshold == 2048.0 costmodel_communi_const = cost_model_context.get_cost_model_context( "costmodel_communi_const") assert costmodel_communi_const == 3072.0 costmodel_communi_bias = cost_model_context.get_cost_model_context( "costmodel_communi_bias") assert costmodel_communi_bias == 1024.0 set_algo_parameters(simplify_cal=True, tensor_slice_align_enable=False, tensor_slice_align_size=32, fully_use_devices=False, elementwise_op_strategy_follow=False) para_simplify_cal = get_algo_parameters("simplify_cal") assert para_simplify_cal == True para_slice_align_enable = get_algo_parameters("tensor_slice_align_enable") assert para_slice_align_enable == False para_slice_align_size = get_algo_parameters("tensor_slice_align_size") assert para_slice_align_size == 32 fully_use_devices = get_algo_parameters("fully_use_devices") assert fully_use_devices == False elementwise_op_strategy_follow = get_algo_parameters( "elementwise_op_strategy_follow") assert elementwise_op_strategy_follow == False reset_algo_parameters() para_simplify_cal = get_algo_parameters("simplify_cal") assert para_simplify_cal == True para_slice_align_enable = get_algo_parameters("tensor_slice_align_enable") assert para_slice_align_enable == False para_slice_align_size = get_algo_parameters("tensor_slice_align_size") assert para_slice_align_size == 16 fully_use_devices = get_algo_parameters("fully_use_devices") assert fully_use_devices == True elementwise_op_strategy_follow = get_algo_parameters( "elementwise_op_strategy_follow") assert elementwise_op_strategy_follow == False x = Tensor(np.ones([128, 32]), dtype=ms.float32) y = Tensor(np.ones([32, 64]), dtype=ms.float32) b = Tensor(np.ones([64, 64]), dtype=ms.float32) net = NetWithLoss(Net()) context.set_auto_parallel_context(parallel_mode="auto_parallel") reset_op_id() _executor.compile(net, x, y, b, phase='train') strategies = _executor._get_strategy(net) expected_strategies = { 'Default/network-Net/MatMul-op0': [[16, 1], [1, 1]], 'Default/network-Net/MatMul-op1': [[16, 1], [1, 1]] } assert strategies == expected_strategies
def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): #1048576 #131072 #32768 #8192 dev_num = 8 context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, device_num=dev_num) cost_model_context.set_cost_model_context(costmodel_gamma=0.001, costmodel_beta=260.0) cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_algorithm=1) cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_times=2) cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_tail_percent=0.5) set_algo_parameters(elementwise_op_strategy_follow=True) resset_op_id() np.random.seed(6) input_np = np.ones([batch_size, 3, 224, 224]).astype(np.float32) label_np = np.zeros([batch_size]).astype(np.int32) for i in range(0, batch_size): label_np[i] = i % num_classes dataset = DatasetLenet(Tensor(input_np), Tensor(label_np), 1) net = resnet50(num_classes) loss = SoftmaxCrossEntropyExpand(sparse=True) opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9) model = Model(net, loss_fn=loss, optimizer=opt) model.train(5, dataset, dataset_sink_mode=False) strategies = _executor._get_strategy(model._train_network) for (k, v) in strategies.items(): if re.search('Conv2D-op', k) is not None: assert v[0][0] == dev_num elif re.search('MatMul-op', k) is not None: assert v == [[dev_num, 1], [1, 1]] elif re.search('ReduceSum-op', k) is not None: assert v == [[dev_num, 1]] allreduce_fusion_dict = _executor._get_allreduce_fusion(model._train_network) print(allreduce_fusion_dict) expect_dict = {'end_point.bias': 2, 'end_point.weight': 2, 'layer4.2.bn3.beta': 2, 'layer4.2.bn3.gamma': 2, 'layer4.2.conv3.weight': 2, 'layer4.2.bn2.beta': 2, 'layer4.2.bn2.gamma': 2, 'layer4.2.conv2.weight': 2, 'layer4.2.bn1.beta': 2, 'layer4.2.bn1.gamma': 2, 'layer4.2.conv1.weight': 2, 'layer4.1.bn3.beta': 2, 'layer4.1.bn3.gamma': 2, 'layer4.1.conv3.weight': 2, 'layer4.1.bn2.beta': 2, 'layer4.1.bn2.gamma': 2, 'layer4.1.conv2.weight': 2, 'layer4.1.bn1.beta': 2, 'layer4.1.bn1.gamma': 2, 'layer4.1.conv1.weight': 2, 'layer4.0.bn_down_sample.beta': 2, 'layer4.0.bn_down_sample.gamma': 2, 'layer4.0.conv_down_sample.weight': 2, 'layer4.0.bn3.beta': 2, 'layer4.0.bn3.gamma': 2, 'layer4.0.conv3.weight': 2, 'layer4.0.bn2.beta': 2, 'layer4.0.bn2.gamma': 2, 'layer4.0.conv2.weight': 2, 'layer4.0.bn1.beta': 2, 'layer4.0.bn1.gamma': 2, 'layer4.0.conv1.weight': 2, 'layer3.5.bn3.beta': 2, 'layer3.5.bn3.gamma': 2, 'layer3.5.conv3.weight': 2, 'layer3.5.bn2.beta': 2, 'layer3.5.bn2.gamma': 2, 'layer3.5.conv2.weight': 2, 'layer3.5.bn1.beta': 2, 'layer3.5.bn1.gamma': 2, 'layer3.5.conv1.weight': 2, 'layer3.4.bn3.beta': 2, 'layer3.4.bn3.gamma': 2, 'layer3.4.conv3.weight': 2, 'layer3.4.bn2.beta': 2, 'layer3.4.bn2.gamma': 2, 'layer3.4.conv2.weight': 2, 'layer3.4.bn1.beta': 2, 'layer3.4.bn1.gamma': 2, 'layer3.4.conv1.weight': 2, 'layer3.3.bn3.beta': 2, 'layer3.3.bn3.gamma': 2, 'layer3.3.conv3.weight': 2, 'layer3.3.bn2.beta': 2, 'layer3.3.bn2.gamma': 2, 'layer3.3.conv2.weight': 2, 'layer3.3.bn1.beta': 2, 'layer3.3.bn1.gamma': 2, 'layer3.3.conv1.weight': 2, 'layer3.2.bn3.beta': 2, 'layer3.2.bn3.gamma': 2, 'layer3.2.conv3.weight': 2, 'layer3.2.bn2.beta': 2, 'layer3.2.bn2.gamma': 2, 'layer3.2.conv2.weight': 2, 'layer3.2.bn1.beta': 2, 'layer3.2.bn1.gamma': 2, 'layer3.2.conv1.weight': 2, 'layer3.1.bn3.beta': 2, 'layer3.1.bn3.gamma': 2, 'layer3.1.conv3.weight': 2, 'layer3.1.bn2.beta': 2, 'layer3.1.bn2.gamma': 2, 'layer3.1.conv2.weight': 2, 'layer3.1.bn1.beta': 2, 'layer3.1.bn1.gamma': 2, 'layer3.1.conv1.weight': 2, 'layer3.0.bn_down_sample.beta': 1, 'layer3.0.bn_down_sample.gamma': 1, 'layer3.0.conv_down_sample.weight': 2, 'layer3.0.bn3.beta': 1, 'layer3.0.bn3.gamma': 1, 'layer3.0.conv3.weight': 2, 'layer3.0.bn2.beta': 2, 'layer3.0.bn2.gamma': 2, 'layer3.0.conv2.weight': 2, 'layer3.0.bn1.beta': 2, 'layer3.0.bn1.gamma': 2, 'layer3.0.conv1.weight': 2, 'layer2.3.bn3.beta': 2, 'layer2.3.bn3.gamma': 2, 'layer2.3.conv3.weight': 2, 'layer2.3.bn2.beta': 2, 'layer2.3.bn2.gamma': 2, 'layer2.3.conv2.weight': 2, 'layer2.3.bn1.beta': 2, 'layer2.3.bn1.gamma': 2, 'layer2.3.conv1.weight': 2, 'layer2.2.bn3.beta': 2, 'layer2.2.bn3.gamma': 2, 'layer2.2.conv3.weight': 2, 'layer2.2.bn2.beta': 2, 'layer2.2.bn2.gamma': 2, 'layer2.2.conv2.weight': 2, 'layer2.2.bn1.beta': 2, 'layer2.2.bn1.gamma': 2, 'layer2.2.conv1.weight': 2, 'layer2.1.bn3.beta': 1, 'layer2.1.bn3.gamma': 1, 'layer2.1.conv3.weight': 2, 'layer2.1.bn2.beta': 2, 'layer2.1.bn2.gamma': 2, 'layer2.1.conv2.weight': 2, 'layer2.1.bn1.beta': 2, 'layer2.1.bn1.gamma': 2, 'layer2.1.conv1.weight': 2, 'layer2.0.bn_down_sample.beta': 1, 'layer2.0.bn_down_sample.gamma': 1, 'layer2.0.conv_down_sample.weight': 2, 'layer2.0.bn3.beta': 1, 'layer2.0.bn3.gamma': 1, 'layer2.0.conv3.weight': 2, 'layer2.0.bn2.beta': 2, 'layer2.0.bn2.gamma': 2, 'layer2.0.conv2.weight': 2, 'layer2.0.bn1.beta': 2, 'layer2.0.bn1.gamma': 2, 'layer2.0.conv1.weight': 2, 'layer1.2.bn3.beta': 2, 'layer1.2.bn3.gamma': 2, 'layer1.2.conv3.weight': 2, 'layer1.2.bn2.beta': 2, 'layer1.2.bn2.gamma': 2, 'layer1.2.conv2.weight': 2, 'layer1.2.bn1.beta': 2, 'layer1.2.bn1.gamma': 2, 'layer1.2.conv1.weight': 2, 'layer1.1.bn3.beta': 1, 'layer1.1.bn3.gamma': 1, 'layer1.1.conv3.weight': 2, 'layer1.1.bn2.beta': 2, 'layer1.1.bn2.gamma': 2, 'layer1.1.conv2.weight': 2, 'layer1.1.bn1.beta': 2, 'layer1.1.bn1.gamma': 2, 'layer1.1.conv1.weight': 2, 'layer1.0.bn_down_sample.beta': 1, 'layer1.0.bn_down_sample.gamma': 1, 'layer1.0.conv_down_sample.weight': 2, 'layer1.0.bn3.beta': 1, 'layer1.0.bn3.gamma': 1, 'layer1.0.conv3.weight': 2, 'layer1.0.bn2.beta': 2, 'layer1.0.bn2.gamma': 2, 'layer1.0.conv2.weight': 2, 'layer1.0.bn1.beta': 2, 'layer1.0.bn1.gamma': 2, 'layer1.0.conv1.weight': 2, 'bn1.beta': 1, 'bn1.gamma': 1, 'conv1.weight': 2} assert (allreduce_fusion_dict == expect_dict) cost_model_context.reset_cost_model_context()