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203 lines
7.5 KiB
203 lines
7.5 KiB
import torch
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from torch.fx import symbolic_trace
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from torch.fx.node import Node
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from colossalai.fx.passes.split_module import split_module
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def pipe_split():
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pass
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def avgcompute_split_pass(gm: torch.fx.GraphModule, pp_size: int):
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"""
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In avgcompute_split_pass, we split module by the fwd flops.
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"""
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mod_graph = gm.graph
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# To use avgcompute_split_pass, we need run meta_info_prop interpreter first.
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# If nodes don't have meta info, this pass will fall back to normal balanced split pass.
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check_node = list(mod_graph.nodes)[0]
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if 'tensor_meta' not in check_node.meta:
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return balanced_split_pass(gm, pp_size)
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total_fwd_flop = 0
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for node in mod_graph.nodes:
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total_fwd_flop += node.fwd_flop
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partition_flop = total_fwd_flop // pp_size
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accumulate_fwd_flop = 0
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for node in mod_graph.nodes:
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if pp_size <= 1:
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break
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if 'pipe_split' in node.name:
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continue
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accumulate_fwd_flop += node.fwd_flop
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if accumulate_fwd_flop >= partition_flop:
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total_fwd_flop = total_fwd_flop - accumulate_fwd_flop
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accumulate_fwd_flop = 0
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pp_size -= 1
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partition_flop = total_fwd_flop // pp_size
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with mod_graph.inserting_after(node):
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split_node = mod_graph.create_node('call_function', pipe_split)
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gm.recompile()
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return gm
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def avgnode_split_pass(gm: torch.fx.GraphModule, pp_size: int):
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"""
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In avgnode_split_pass, simpliy split graph by node number.
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"""
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mod_graph = gm.graph
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avg_num_node = len(mod_graph.nodes) // pp_size
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accumulate_num_node = 0
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for node in mod_graph.nodes:
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if pp_size <= 1:
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break
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accumulate_num_node += 1
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if accumulate_num_node >= avg_num_node:
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accumulate_num_node = 0
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pp_size -= 1
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if node.next.op == 'output':
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with mod_graph.inserting_before(node):
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split_node = mod_graph.create_node('call_function', pipe_split)
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else:
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with mod_graph.inserting_after(node):
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split_node = mod_graph.create_node('call_function', pipe_split)
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gm.recompile()
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return gm
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def balanced_split_pass(gm: torch.fx.GraphModule, pp_size: int):
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"""
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In balanced_split_pass, we split module by the size of parameters(weights+bias).
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"""
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mod_graph = gm.graph
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total_param_amount = 0
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for param in mod_graph.owning_module.parameters():
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total_param_amount += param.numel()
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params_per_partition = total_param_amount // pp_size
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accumulate_param_amount = 0
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for node in mod_graph.nodes:
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if pp_size <= 1:
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break
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if node.op == "call_module":
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target_module = node.graph.owning_module.get_submodule(node.target)
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for param in target_module.parameters():
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accumulate_param_amount += param.numel()
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if accumulate_param_amount >= params_per_partition:
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accumulate_param_amount = 0
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pp_size -= 1
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# If the next node is output node, we will insert split annotation before
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# node to make sure there is at least one node in last partition.
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if node.next.op == 'output':
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with mod_graph.inserting_before(node):
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split_node = mod_graph.create_node('call_function', pipe_split)
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else:
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with mod_graph.inserting_after(node):
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split_node = mod_graph.create_node('call_function', pipe_split)
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if pp_size > 1:
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node_counter = 0
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for node in mod_graph.nodes:
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if pp_size <= 1:
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break
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if node.op == 'placeholder':
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continue
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elif node_counter == 0:
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node_counter += 1
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else:
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pp_size -= 1
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node_counter = 0
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with mod_graph.inserting_before(node):
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split_node = mod_graph.create_node('call_function', pipe_split)
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gm.recompile()
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return gm
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def balanced_split_pass_v2(gm: torch.fx.GraphModule, pp_size: int):
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"""
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In balanced_split_pass_v12, we split module by the size of nodes(weights+bias+outputs).
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"""
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mod_graph = gm.graph
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# To use balanced_split_pass_v2, we need run meta_info_prop interpreter first.
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# If nodes don't have meta info, this pass will fall back to normal balanced split pass.
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check_node = list(mod_graph.nodes)[0]
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if 'tensor_meta' not in check_node.meta:
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return balanced_split_pass(gm, pp_size)
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total_element_size = 0
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for node in mod_graph.nodes:
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total_element_size += node.node_size
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partition_size = total_element_size // pp_size
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accumulate_node_size = 0
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for node in mod_graph.nodes:
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if pp_size <= 1:
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break
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if 'pipe_split' in node.name:
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continue
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accumulate_node_size += node.node_size
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if accumulate_node_size >= partition_size:
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total_element_size = total_element_size - accumulate_node_size
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accumulate_node_size = 0
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pp_size -= 1
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partition_size = total_element_size // pp_size
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with mod_graph.inserting_after(node):
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split_node = mod_graph.create_node('call_function', pipe_split)
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gm.recompile()
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return gm
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def uniform_split_pass(gm: torch.fx.GraphModule, pp_size: int):
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mod_graph = gm.graph
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valid_children_size = 0
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valid_children = []
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for module in mod_graph.owning_module.children():
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valid_children_size += 1
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valid_children.append(module)
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if valid_children_size < pp_size:
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# If valid children is not enough to shard, we will use balanced policy instead of uniform policy.
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return balanced_split_pass(gm, pp_size)
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layers_per_partition = valid_children_size // pp_size
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accumulate_layer_amount = 0
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for node in mod_graph.nodes:
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if pp_size <= 1:
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break
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if node.op == "call_module":
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target_module = node.graph.owning_module.get_submodule(node.target)
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if target_module in valid_children:
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accumulate_layer_amount += 1
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if accumulate_layer_amount == layers_per_partition:
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accumulate_layer_amount = 0
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pp_size -= 1
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with mod_graph.inserting_after(node):
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split_node = mod_graph.create_node('call_function', pipe_split)
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gm.recompile()
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return gm
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def split_with_split_nodes_pass(annotated_gm: torch.fx.GraphModule, merge_output=False):
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# TODO(lyl): use partition IR to assign partition ID to each node.
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# Currently: analyzing graph -> annotate graph by inserting split node -> use split module pass to split graph
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# In future: graph to partitions -> analyzing partition IR -> recombining partitions to get best performance -> assign partition ID to each node
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part_idx = 0
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def split_callback(n: torch.fx.Node):
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nonlocal part_idx
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if (n.op, n.target) == ('call_function', pipe_split):
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part_idx += 1
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return part_idx
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split_mod = split_module(annotated_gm, None, split_callback, merge_output)
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split_submodules = []
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for name, submodule in split_mod.named_modules():
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if isinstance(submodule, torch.fx.GraphModule):
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for node in submodule.graph.nodes:
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if (node.op, node.target) == ('call_function', pipe_split):
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submodule.graph.erase_node(node)
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submodule.recompile()
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split_submodules.append(submodule)
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return split_mod, split_submodules
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