mirror of https://github.com/hpcaitech/ColossalAI
385 lines
21 KiB
Python
385 lines
21 KiB
Python
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import operator
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from functools import reduce
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import torch
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from colossalai.tensor.sharding_spec import ShardingSpec
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from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
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class ConvHandler:
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'''
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The ConvHandler is used to generate every possible strategies for a Conv node.
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Argument:
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input_node(Node): the input node in conv node argument list.
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input_index(int): the index of input node in the conv node argument list.
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weight(torch.Tensor): Weight of the conv node.
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output_node(Node): Output_node is the output of the conv node.
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device_mesh(DeviceMesh): A logical view of a physical mesh.
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strategies_vector(StrategiesVector): all the strategies generated in this handler will be recorded into the strategies_vector.
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shape_consistency_manager(ShapeConsistencyManager): ShapeConsistencyManager will give the resharding costs of the different sharding specs.
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'''
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def __init__(self, input_node, input_index, weight, output_node, device_mesh, strategies_vector,
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shape_consistency_manager):
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self.input_node = input_node
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self.input_data = self.input_node._meta_data
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self.weight = weight
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self.input_index = input_index
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self.output_node = output_node
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self.output = self.output_node._meta_data
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self.device_mesh = device_mesh
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self.strategies_vector = strategies_vector
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self.shape_consistency_manager = shape_consistency_manager
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self._sanity_check()
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def _sanity_check(self):
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'''
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In sanity check, we need make sure the input data having correct dimension size.
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For Conv1d, the dim of input data should be 3([N, C, L]).
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For Conv2d, the dim of input data should be 4([N, C, H, W]).
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For Conv3d, the dim of input data should be 5([N, C, H, W, D]).
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'''
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assert self.input_data.dim() in (3, 4,
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5), f'We suppose the dim of input fed into conv op should in range of [3, 5].'
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def _generate_sharding_spec_for_input(self, dim_partition_dict_for_input):
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'''
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Generate sharding spec for the input node.
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'''
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entire_shape_for_input = self.input_data.shape
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sharding_spec_for_input = ShardingSpec(device_mesh=self.device_mesh,
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entire_shape=entire_shape_for_input,
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dim_partition_dict=dim_partition_dict_for_input)
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return sharding_spec_for_input
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def _generate_sharding_spec_for_weight(self, dim_partition_dict_for_weight):
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'''
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Generate sharding spec for the weight.
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'''
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entire_shape_for_weight = self.weight.shape
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sharding_spec_for_weight = ShardingSpec(device_mesh=self.device_mesh,
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entire_shape=entire_shape_for_weight,
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dim_partition_dict=dim_partition_dict_for_weight)
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return sharding_spec_for_weight
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def _generate_sharding_spec_for_output(self, dim_partition_dict_for_output):
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'''
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Generate sharding spec for the output node.
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'''
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entire_shape_for_output = self.output.shape
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sharding_spec_for_output = ShardingSpec(device_mesh=self.device_mesh,
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entire_shape=entire_shape_for_output,
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dim_partition_dict=dim_partition_dict_for_output)
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return sharding_spec_for_output
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def _generate_resharding_costs(self, resharding_costs, sharding_spec_for_input):
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'''
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Compute the resharding costs with this specific strategy.
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Note: The resharding_cost of weight is NOT counted.
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Argument:
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resharding_costs(Dict[int, List[float]]): The resharding cost generated in this method will be appended into this dictionary.
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Resharding_cost[i][j] means the cost of i-th argument in the output node argument list
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with j-th strategy in its strategies_vector transforms to sharding spec wanted in this
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strategy.
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sharding_spec_for_input(ShardingSpec): ShardingSpec of the input node.
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'''
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# The resharding_cost of weight is counted due to sharing weight cases.
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resharding_costs[self.input_index] = []
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for stategy in self.input_node.strategies_vector.strategies:
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_, _, resharding_cost = self.shape_consistency_manager.shape_consistency(stategy, sharding_spec_for_input)
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resharding_costs[self.input_index].append(resharding_cost)
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def _generate_compute_cost(self, bs, channel_in, channel_out):
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'''
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Compute the computation cost per device with this specific strategy.
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Note: compute_cost need to be devided by TFLOPS, now it just shows the computation size.
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Argument:
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bs(int): Batch size of the input data.
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channel_in(int): The channel dimension of input data.
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channel_out(int): The out channel of the conv weight.
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Return:
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compute_cost(float): Computation cost per device with this specific strategy
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'''
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# TODO: compute_cost need to be devided by TFLOPS, now it just shows the computation size.
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# 1D: (L) * N * Cout * Cin * kernel
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# 2D: (H * W) * N * Cout * Cin * kernel
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# 3D: (H * W * D) * N * Cout * Cin * kernel
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output_size = self.output.shape[2:]
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output_size_product = reduce(operator.mul, output_size, 1)
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kernel_size = self.weight.shape[2:]
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kernel_size_product = reduce(operator.mul, kernel_size, 1)
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compute_cost = output_size_product * bs * channel_in * channel_out * kernel_size_product
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return compute_cost
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def split_input_batch_weight_out_channel(self, mesh_dim_0, mesh_dim_1):
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name = f'S{mesh_dim_0}S{mesh_dim_1} = S{mesh_dim_0}R x RS{mesh_dim_1}'
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dim_partition_dict_for_input = {0: [mesh_dim_0]}
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sharding_spec_for_input = self._generate_sharding_spec_for_input(dim_partition_dict_for_input)
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dim_partition_dict_for_weight = {1: [mesh_dim_1]}
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sharding_spec_for_weight = self._generate_sharding_spec_for_weight(dim_partition_dict_for_weight)
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dim_partition_dict_for_output = {0: [mesh_dim_0], 1: [mesh_dim_1]}
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sharding_spec_for_ouput = self._generate_sharding_spec_for_output(dim_partition_dict_for_output)
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# generate resharding cost for this strategy
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resharding_costs = {}
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self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
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# compute the computation cost of this strategy
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bs = self.input_data.shape[0] // self.device_mesh.shape[mesh_dim_0]
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channel_in = self.input_data.shape[1]
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channel_out = self.weight.shape[1] // self.device_mesh.shape[mesh_dim_1]
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compute_cost = self._generate_compute_cost(bs, channel_in, channel_out)
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# compute the memory cost of this strategy
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dtype = self.input_data.dtype
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numel = self.output.numel()
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size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
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sharding_size = self.device_mesh.shape[mesh_dim_0] * self.device_mesh.shape[mesh_dim_1]
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memory_cost = numel * size_per_elem_bytes / sharding_size
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# This strategy do not need to do all_reduce operation
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communication_cost = 0
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sharding_strategies = ShardingStrategy(name,
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output_sharding_spec=sharding_spec_for_ouput,
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compute_cost=compute_cost,
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communication_cost=communication_cost,
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memory_cost=memory_cost,
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resharding_costs=resharding_costs,
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input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
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self.strategies_vector.strategies.append(sharding_strategies)
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def split_input_both_dim_weight_in_channel(self, mesh_dim_0, mesh_dim_1):
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name = f'S{mesh_dim_0}R = S{mesh_dim_0}S{mesh_dim_1} x S{mesh_dim_1}R'
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dim_partition_dict_for_input = {0: [mesh_dim_0], 1: [mesh_dim_1]}
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sharding_spec_for_input = self._generate_sharding_spec_for_input(dim_partition_dict_for_input)
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dim_partition_dict_for_weight = {0: [mesh_dim_0]}
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sharding_spec_for_weight = self._generate_sharding_spec_for_weight(dim_partition_dict_for_weight)
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dim_partition_dict_for_output = {0: [mesh_dim_0]}
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sharding_spec_for_ouput = self._generate_sharding_spec_for_output(dim_partition_dict_for_output)
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# generate resharding cost for this strategy
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resharding_costs = {}
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self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
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# compute the computation cost of this strategy
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bs = self.input_data.shape[0] // self.device_mesh.shape[mesh_dim_0]
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channel_in = self.input_data.shape[1] // self.device_mesh.shape[mesh_dim_1]
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channel_out = self.weight.shape[1]
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compute_cost = self._generate_compute_cost(bs, channel_in, channel_out)
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# compute the memory cost of this strategy
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dtype = self.input_data.dtype
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numel = self.output.numel()
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size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
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sharding_size = self.device_mesh.shape[mesh_dim_0]
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memory_cost = numel * size_per_elem_bytes / sharding_size
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# compute the communication cost of this strategy
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communication_cost = self.device_mesh.all_reduce_cost(memory_cost, mesh_dim_1)
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sharding_strategies = ShardingStrategy(name,
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output_sharding_spec=sharding_spec_for_ouput,
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compute_cost=compute_cost,
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communication_cost=communication_cost,
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memory_cost=memory_cost,
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resharding_costs=resharding_costs,
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input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
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self.strategies_vector.strategies.append(sharding_strategies)
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def split_input_in_channel_weight_both_channel(self, mesh_dim_0, mesh_dim_1):
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name = f'RS{mesh_dim_1} = RS{mesh_dim_0} x S{mesh_dim_0}S{mesh_dim_1}'
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dim_partition_dict_for_input = {1: [mesh_dim_0]}
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sharding_spec_for_input = self._generate_sharding_spec_for_input(dim_partition_dict_for_input)
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dim_partition_dict_for_weight = {0: [mesh_dim_0], 1: [mesh_dim_1]}
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sharding_spec_for_weight = self._generate_sharding_spec_for_weight(dim_partition_dict_for_weight)
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dim_partition_dict_for_output = {1: [mesh_dim_1]}
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sharding_spec_for_ouput = self._generate_sharding_spec_for_output(dim_partition_dict_for_output)
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# generate resharding cost for this strategy
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resharding_costs = {}
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self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
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# compute the computation cost of this strategy
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bs = self.input_data.shape[0]
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channel_in = self.input_data.shape[1] // self.device_mesh.shape[mesh_dim_0]
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channel_out = self.weight.shape[1] // self.device_mesh.shape[mesh_dim_1]
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compute_cost = self._generate_compute_cost(bs, channel_in, channel_out)
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# compute the memory cost of this strategy
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dtype = self.input_data.dtype
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numel = self.output.numel()
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size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
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sharding_size = self.device_mesh.shape[mesh_dim_0]
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memory_cost = numel * size_per_elem_bytes / sharding_size
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# compute the communication cost of this strategy
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communication_cost = self.device_mesh.all_reduce_cost(memory_cost, mesh_dim_1)
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sharding_strategies = ShardingStrategy(name,
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output_sharding_spec=sharding_spec_for_ouput,
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compute_cost=compute_cost,
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communication_cost=communication_cost,
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memory_cost=memory_cost,
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resharding_costs=resharding_costs,
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input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
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self.strategies_vector.strategies.append(sharding_strategies)
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def split_weight_out_channel(self, mesh_dim_0):
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name = f'RS{mesh_dim_0} = RR x RS{mesh_dim_0}'
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dim_partition_dict_for_input = {}
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sharding_spec_for_input = self._generate_sharding_spec_for_input(dim_partition_dict_for_input)
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dim_partition_dict_for_weight = {1: [mesh_dim_0]}
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sharding_spec_for_weight = self._generate_sharding_spec_for_weight(dim_partition_dict_for_weight)
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dim_partition_dict_for_output = {1: [mesh_dim_0]}
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sharding_spec_for_ouput = self._generate_sharding_spec_for_output(dim_partition_dict_for_output)
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# generate resharding cost for this strategy
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resharding_costs = {}
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self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
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# compute the computation cost of this strategy
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bs = self.input_data.shape[0]
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channel_in = self.input_data.shape[1]
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channel_out = self.weight.shape[1] // self.device_mesh.shape[mesh_dim_0]
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compute_cost = self._generate_compute_cost(bs, channel_in, channel_out)
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# compute the memory cost of this strategy
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dtype = self.input_data.dtype
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numel = self.output.numel()
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size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
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sharding_size = self.device_mesh.shape[mesh_dim_0]
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memory_cost = numel * size_per_elem_bytes / sharding_size
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# This strategy do not need to do all_reduce operation
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communication_cost = 0
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sharding_strategies = ShardingStrategy(name,
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output_sharding_spec=sharding_spec_for_ouput,
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compute_cost=compute_cost,
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communication_cost=communication_cost,
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memory_cost=memory_cost,
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resharding_costs=resharding_costs,
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input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
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self.strategies_vector.strategies.append(sharding_strategies)
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def non_split(self):
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name = f'RR = RR x RR'
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dim_partition_dict_for_input = {}
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sharding_spec_for_input = self._generate_sharding_spec_for_input(dim_partition_dict_for_input)
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dim_partition_dict_for_weight = {}
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sharding_spec_for_weight = self._generate_sharding_spec_for_weight(dim_partition_dict_for_weight)
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dim_partition_dict_for_output = {}
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sharding_spec_for_ouput = self._generate_sharding_spec_for_output(dim_partition_dict_for_output)
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# generate resharding cost for this strategy
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resharding_costs = {}
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self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
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# compute the computation cost of this strategy
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bs = self.input_data.shape[0]
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channel_in = self.input_data.shape[1]
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channel_out = self.weight.shape[1]
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compute_cost = self._generate_compute_cost(bs, channel_in, channel_out)
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# compute the memory cost of this strategy
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dtype = self.input_data.dtype
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numel = self.output.numel()
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size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
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memory_cost = numel * size_per_elem_bytes
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# This strategy do not need to do all_reduce operation
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communication_cost = 0
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sharding_strategies = ShardingStrategy(name,
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output_sharding_spec=sharding_spec_for_ouput,
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compute_cost=compute_cost,
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communication_cost=communication_cost,
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memory_cost=memory_cost,
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resharding_costs=resharding_costs,
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input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
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self.strategies_vector.strategies.append(sharding_strategies)
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def register_strategy_into_strategies_vector(self):
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'''
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Generate every possible strategies for a Conv node, and record all strategies into the strategies_vector.
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Example:
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physical_mesh_id = torch.arange(0, 4)
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mesh_shape = (2, 2)
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# [[0, 1]
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# [2, 3]]
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device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
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shape_consistency_manager = ShapeConsistencyManager()
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model = ConvModel(16, 32)
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input_sample = {'x': torch.rand(4, 16, 64, 64).to('meta')}
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# graph():
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# %x : torch.Tensor [#users=1] = placeholder[target=x]
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# %mul : [#users=1] = call_function[target=operator.mul](args = (%x, 2), kwargs = {})
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# %conv : [#users=1] = call_module[target=conv](args = (%mul,), kwargs = {})
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# return conv
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graph = tracer.trace(root=model, meta_args=input_sample)
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gm = GraphModule(model, graph, model.__class__.__name__)
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gm.recompile()
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# [x, mul, conv, output]
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nodes = [node for node in gm.graph.nodes]
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# strategies_for_input = [[R, R, R, R], [R, S0, R, R], [R, S1, R, R], [S0, R, R, R], [S0, S1, R, R], [S1, R, R, R], [S1, S0, R, R]]
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strategies_vector_for_input = StrategiesVector(node=nodes[0], in_nodes=[nodes[1], 2], strategies=strategies_for_input)
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setattr(nodes[1], 'strategies_vector', strategies_vector_for_input)
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strategies_vector = StrategiesVector(node=nodes[2], in_nodes=[nodes[1], ])
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conv_handler = ConvHandler(input_node=nodes[1], input_index=0, weight=dict(gm.named_modules())[nodes[2].name].weight, output_node=nodes[2],
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device_mesh=device_mesh, strategies_vector=strategies_vector, shape_consistency_manager=shape_consistency_manager)
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conv_handler.register_strategy_into_strategies_vector()
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for strategy in conv_handler.strategies_vector.strategies:
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print(f'{strategy.name}: compute_cost is {strategy.compute_cost}, communication_cost is {strategy.communication_cost}, memory_cost is {strategy.memory_cost}, resharding_costs is {strategy.resharding_costs}')
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|
Output:
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S0S1 = S0R x RS1: compute_cost is 8856576, communication_cost is 0, memory_cost is 492032.0, resharding_costs is {0: [0, 32769.001, 131074.2, 0, 32769.1, 131074.2, 98307.201]}
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S1S0 = S1R x RS0: compute_cost is 8856576, communication_cost is 0, memory_cost is 492032.0, resharding_costs is {0: [0, 131074.2, 32769.001, 131074.2, 98307.201, 0, 32769.1]}
|
||
|
|
S0R = S0S1 x S1R: compute_cost is 8856576, communication_cost is 984065.01, memory_cost is 984064.0, resharding_costs is {0: [0, 65538.002, 0, 0, 0, 65538.002, 196614.402]}
|
||
|
|
S1R = S1S0 x S0R: compute_cost is 8856576, communication_cost is 984065.01, memory_cost is 984064.0, resharding_costs is {0: [0, 0, 65538.002, 65538.002, 196614.402, 0, 0]}
|
||
|
|
RS1 = RS0 x S0S1: compute_cost is 8856576, communication_cost is 984065.01, memory_cost is 984064.0, resharding_costs is {0: [0, 0, 131074.2, 32769.001, 98307.201, 131074.2, 32769.1]}
|
||
|
|
RS0 = RS1 x S1S0: compute_cost is 8856576, communication_cost is 984065.01, memory_cost is 984064.0, resharding_costs is {0: [0, 131074.2, 0, 131074.2, 32769.1, 32769.001, 98307.201]}
|
||
|
|
RS0 = RR x RS0: compute_cost is 17713152, communication_cost is 0, memory_cost is 984064.0, resharding_costs is {0: [0, 65537.1, 65537.1, 65537.1, 131075.30000000002, 65537.1, 131075.30000000002]}
|
||
|
|
RS1 = RR x RS1: compute_cost is 17713152, communication_cost is 0, memory_cost is 984064.0, resharding_costs is {0: [0, 65537.1, 65537.1, 65537.1, 131075.30000000002, 65537.1, 131075.30000000002]}
|
||
|
|
RR = RR x RR: compute_cost is 35426304, communication_cost is 0, memory_cost is 1968128, resharding_costs is {0: [0, 65537.1, 65537.1, 65537.1, 131075.30000000002, 65537.1, 131075.30000000002]}
|
||
|
|
'''
|
||
|
|
# SS = SR x RS
|
||
|
|
self.split_input_batch_weight_out_channel(0, 1)
|
||
|
|
self.split_input_batch_weight_out_channel(1, 0)
|
||
|
|
|
||
|
|
# SR = SS x SR
|
||
|
|
self.split_input_both_dim_weight_in_channel(0, 1)
|
||
|
|
self.split_input_both_dim_weight_in_channel(1, 0)
|
||
|
|
|
||
|
|
# RS = RS x SS
|
||
|
|
self.split_input_in_channel_weight_both_channel(0, 1)
|
||
|
|
self.split_input_in_channel_weight_both_channel(1, 0)
|
||
|
|
|
||
|
|
# RS = RR x RS
|
||
|
|
self.split_weight_out_channel(0)
|
||
|
|
self.split_weight_out_channel(1)
|
||
|
|
|
||
|
|
# RR= RR x RR
|
||
|
|
self.non_split()
|