def test_allreduce_fusion_parameters(): cost_model_context.reset_cost_model_context() cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_algorithm=2) algorithm = cost_model_context.get_cost_model_context('costmodel_allreduce_fusion_algorithm') assert algorithm == 2 cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_algorithm=1) algorithm = cost_model_context.get_cost_model_context('costmodel_allreduce_fusion_algorithm') assert algorithm == 1 cost_model_context.reset_cost_model_context() algorithm = cost_model_context.get_cost_model_context('costmodel_allreduce_fusion_algorithm') assert algorithm == 0 cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_times=2) fusion_times = cost_model_context.get_cost_model_context('costmodel_allreduce_fusion_times') assert fusion_times == 2 cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_tail_percent=0.2) tail_percent = cost_model_context.get_cost_model_context('costmodel_allreduce_fusion_tail_percent') assert tail_percent == 0.2 cost_model_context.reset_cost_model_context() tail_percent = cost_model_context.get_cost_model_context('costmodel_allreduce_fusion_tail_percent') assert tail_percent == 0.1 cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_tail_time=0.2) tail_time = cost_model_context.get_cost_model_context('costmodel_allreduce_fusion_tail_time') assert tail_time == 0.2 cost_model_context.reset_cost_model_context() tail_time = cost_model_context.get_cost_model_context('costmodel_allreduce_fusion_tail_time') assert tail_time == 0.1 cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_allreduce_inherent_time=0.2) allreduce_inherent_time = cost_model_context.get_cost_model_context( 'costmodel_allreduce_fusion_allreduce_inherent_time') assert allreduce_inherent_time == 0.2 cost_model_context.reset_cost_model_context() allreduce_inherent_time = cost_model_context.get_cost_model_context( 'costmodel_allreduce_fusion_allreduce_inherent_time') assert allreduce_inherent_time == 0.1 cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_allreduce_bandwidth=0.2) allreduce_bandwidth = cost_model_context.get_cost_model_context('costmodel_allreduce_fusion_allreduce_bandwidth') assert allreduce_bandwidth == 0.2 cost_model_context.reset_cost_model_context() allreduce_bandwidth = cost_model_context.get_cost_model_context('costmodel_allreduce_fusion_allreduce_bandwidth') assert allreduce_bandwidth == 0.1 cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_computation_time_parameter=0.2) computation_time_parameter = cost_model_context.get_cost_model_context( 'costmodel_allreduce_fusion_computation_time_parameter') assert computation_time_parameter == 0.2 cost_model_context.reset_cost_model_context() computation_time_parameter = cost_model_context.get_cost_model_context( 'costmodel_allreduce_fusion_computation_time_parameter') assert computation_time_parameter == 0.1
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 == 400.0 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(tensor_slice_align_enable=False, tensor_slice_align_size=32, fully_use_devices=False, elementwise_op_strategy_follow=False) para_slice_align_enable = get_algo_parameters("tensor_slice_align_enable") assert not para_slice_align_enable 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 not fully_use_devices elementwise_op_strategy_follow = get_algo_parameters( "elementwise_op_strategy_follow") assert not elementwise_op_strategy_follow reset_algo_parameters() para_slice_align_enable = get_algo_parameters("tensor_slice_align_enable") assert not para_slice_align_enable 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 elementwise_op_strategy_follow = get_algo_parameters( "elementwise_op_strategy_follow") assert not elementwise_op_strategy_follow 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_shard_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_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 == 400.0 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(tensor_slice_align_enable=False, tensor_slice_align_size=32, fully_use_devices=False, elementwise_op_strategy_follow=False, enable_algo_approxi=True, algo_approxi_epsilon=0.001) para_slice_align_enable = get_algo_parameters("tensor_slice_align_enable") assert not para_slice_align_enable 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 not fully_use_devices elementwise_op_strategy_follow = get_algo_parameters( "elementwise_op_strategy_follow") assert not elementwise_op_strategy_follow enable_approxi = get_algo_parameters("enable_algo_approxi") assert enable_approxi algo_epsilon = get_algo_parameters("algo_approxi_epsilon") assert algo_epsilon == 0.001 expecte_single_loop = True signle_loop = _get_algo_single_loop() assert expecte_single_loop == signle_loop expecte_single_loop = False _set_algo_single_loop(expecte_single_loop) signle_loop = _get_algo_single_loop() assert expecte_single_loop == signle_loop reset_algo_parameters() para_slice_align_enable = get_algo_parameters("tensor_slice_align_enable") assert not para_slice_align_enable 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 elementwise_op_strategy_follow = get_algo_parameters( "elementwise_op_strategy_follow") assert not elementwise_op_strategy_follow enable_approxi = get_algo_parameters("enable_algo_approxi") assert not enable_approxi algo_epsilon = get_algo_parameters("algo_approxi_epsilon") assert algo_epsilon == 0.1 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() net.set_train() _executor.compile(net, x, y, b, phase='train') strategies = _executor._get_shard_strategy(net) for (k, v) in strategies.items(): if re.search('MatMul-op', k) is not None: assert v == [[16, 1], [1, 1]]