mirror of https://github.com/hpcaitech/ColossalAI
498 lines
20 KiB
Python
498 lines
20 KiB
Python
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from typing import Any, Callable, Dict, Iterable, List, Tuple
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import torch
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from torch.fx.graph import (
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CodeGen,
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PythonCode,
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_custom_builtins,
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_CustomBuiltin,
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_format_target,
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_is_from_torch,
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_Namespace,
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_origin_type_map,
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inplace_methods,
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magic_methods,
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)
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from torch.fx.node import Argument, Node, _get_qualified_name, _type_repr, map_arg
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import colossalai
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from .chunk_region_search import ChunkRegionSearch
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from .utils import delete_free_var_from_last_use, find_idx_by_name, get_node_shape
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CODEGEN_AVAILABLE = True
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__all__ = ["AutoChunkCodeGen"]
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def _gen_chunk_slice_dim(chunk_dim, chunk_idx_name, shape):
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new_shape = "["
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for idx, i in enumerate(shape):
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if idx == chunk_dim:
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new_shape += "%s:%s + chunk_size" % (chunk_idx_name, chunk_idx_name)
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else:
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new_shape += ":"
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new_shape += ", "
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new_shape = new_shape[:-2] + "]"
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return new_shape
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def _gen_loop_start(chunk_input, chunk_output, chunk_ouput_dim, chunk_size=2):
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input_node = chunk_input[0]
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out_shape = get_node_shape(chunk_output)
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out_str = str(list(out_shape))
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context = (
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"chunk_result = torch.empty(%s, dtype=%s.dtype, device=%s.device); chunk_size = %d\nfor chunk_idx in range"
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% (out_str, input_node.name, input_node.name, chunk_size)
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)
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context += "(0, %d, chunk_size):\n" % (out_shape[chunk_ouput_dim])
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return context
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def _gen_loop_end(
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chunk_inputs, chunk_non_compute_inputs, chunk_outputs, chunk_outputs_dim, node_list
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):
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chunk_outputs_name = chunk_outputs.name
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chunk_outputs_idx = find_idx_by_name(chunk_outputs_name, node_list)
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chunk_output_shape = chunk_outputs.meta["tensor_meta"].shape
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chunk_slice = _gen_chunk_slice_dim(
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chunk_outputs_dim, "chunk_idx", chunk_output_shape
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)
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context = " chunk_result%s = %s; %s = None\n" % (
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chunk_slice,
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chunk_outputs_name,
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chunk_outputs_name,
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)
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context += (
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chunk_outputs_name + " = chunk_result; chunk_result = None; chunk_size = None"
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)
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# determine if its the last use for chunk input
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for chunk_input in chunk_inputs + chunk_non_compute_inputs:
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if all(
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[
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find_idx_by_name(user.name, node_list) <= chunk_outputs_idx
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for user in chunk_input.users.keys()
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]
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):
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context += "; %s = None" % chunk_input.name
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context += "\n"
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return context
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def _replace_name(context, name_from, name_to):
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patterns = [(" ", " "), (" ", "."), (" ", ","), ("(", ")"), ("(", ","), (" ", ")")]
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for p in patterns:
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source = p[0] + name_from + p[1]
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target = p[0] + name_to + p[1]
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if source in context:
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context = context.replace(source, target)
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return context
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def _replace_reshape_size(context, node_name, reshape_size_dict):
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if node_name not in reshape_size_dict:
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return context
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for size_name, size_value in reshape_size_dict[node_name].items():
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context = context.replace(size_name, size_value)
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return context
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def emit_code_with_chunk(
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body,
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nodes,
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emit_node_func,
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delete_unused_value_func,
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chunk_region_search,
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chunk_infos,
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):
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"""Emit code with nested activation checkpoint
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When we detect some of the node.activation_checkpoint is a List, we will use
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this function to emit the activation checkpoint codes.
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Args:
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body: forward code
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ckpt_func: checkpoint functions code
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nodes: graph.nodes
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emit_node_func: function to emit node
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delete_unused_value_func: function to remove the unused value
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"""
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node_list = list(nodes)
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chunk_regions = [i["region"] for i in chunk_infos]
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chunk_starts = [i[0] for i in chunk_regions]
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chunk_ends = [i[1] for i in chunk_regions]
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chunk_inputs = [i["inputs"] for i in chunk_infos]
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chunk_inputs_non_chunk = [i["inputs_non_chunk"] for i in chunk_infos]
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chunk_inputs_dim = [i["inputs_dim"] for i in chunk_infos]
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chunk_inputs_names = [j.name for i in chunk_inputs for j in i] + [
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j.name for i in chunk_inputs_non_chunk for j in i
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]
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chunk_outputs = [i["outputs"][0] for i in chunk_infos]
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chunk_outputs_dim = [i["outputs_dim"] for i in chunk_infos]
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node_list = chunk_region_search.index_tracer.reorder_node_list(node_list)
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node_idx = 0
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region_idx = 0
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within_chunk_region = False
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while node_idx < len(node_list):
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node = node_list[node_idx]
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if node_idx in chunk_starts:
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within_chunk_region = True
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region_idx = chunk_starts.index(node_idx)
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body.append(
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_gen_loop_start(
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chunk_inputs[region_idx],
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chunk_outputs[region_idx],
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chunk_outputs_dim[region_idx],
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chunk_infos[region_idx]["chunk_size"],
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)
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)
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if within_chunk_region:
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emit_node_func(node, body)
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# replace input var with chunk var
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for input_node_idx, input_node in enumerate(chunk_inputs[region_idx]):
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for idx, dim in chunk_inputs_dim[region_idx][input_node_idx].items():
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if idx == node_idx:
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chunk_slice = _gen_chunk_slice_dim(
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dim[0], "chunk_idx", get_node_shape(input_node)
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)
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body[-1] = _replace_name(
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body[-1], input_node.name, input_node.name + chunk_slice
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)
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# ones like
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if "ones_like" in node.name:
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meta_node = chunk_region_search.index_tracer.node_list[node_idx]
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chunk_dim = chunk_infos[region_idx]["node_chunk_dim"][meta_node][
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"chunk_dim"
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]
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if get_node_shape(meta_node)[chunk_dim] != 1:
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source_node = meta_node.args[0].args[0]
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if (
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source_node not in chunk_infos[region_idx]["node_chunk_dim"]
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or chunk_infos[region_idx]["node_chunk_dim"][source_node][
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"chunk_dim"
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]
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is None
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):
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chunk_slice = _gen_chunk_slice_dim(
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chunk_dim, "chunk_idx", get_node_shape(node)
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)
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body[-1] = _replace_name(
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body[-1], node.args[0].name, node.args[0].name + chunk_slice
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)
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body[-1] = _replace_reshape_size(
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body[-1], node.name, chunk_infos[region_idx]["reshape_size"]
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)
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body[-1] = " " + body[-1]
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delete_unused_value_func(node, body, chunk_inputs_names)
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else:
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emit_node_func(node, body)
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if node_idx not in chunk_inputs:
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delete_unused_value_func(node, body, chunk_inputs_names)
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if node_idx in chunk_ends:
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body.append(
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_gen_loop_end(
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chunk_inputs[region_idx],
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chunk_inputs_non_chunk[region_idx],
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chunk_outputs[region_idx],
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chunk_outputs_dim[region_idx],
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node_list,
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)
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)
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within_chunk_region = False
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node_idx += 1
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if CODEGEN_AVAILABLE:
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class AutoChunkCodeGen(CodeGen):
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def __init__(self, meta_graph, max_memory=None):
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super().__init__()
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self.meta_graph = meta_graph
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self.max_memory = max_memory
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self.meta_node = list(meta_graph.graph.nodes)
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# find the chunk regions
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self.chunk_region_search = ChunkRegionSearch(meta_graph, max_memory)
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self.chunk_infos = self.chunk_region_search.search_region()
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def _gen_python_code(
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self, nodes, root_module: str, namespace: _Namespace
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) -> PythonCode:
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free_vars: List[str] = []
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body: List[str] = []
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globals_: Dict[str, Any] = {}
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wrapped_fns: Dict[str, None] = {}
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# Wrap string in list to pass by reference
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maybe_return_annotation: List[str] = [""]
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def add_global(name_hint: str, obj: Any):
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"""Add an obj to be tracked as a global.
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We call this for names that reference objects external to the
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Graph, like functions or types.
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Returns: the global name that should be used to reference 'obj' in generated source.
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"""
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if (
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_is_from_torch(obj) and obj != torch.device
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): # to support registering torch.device
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# HACK: workaround for how torch custom ops are registered. We
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# can't import them like normal modules so they must retain their
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# fully qualified name.
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return _get_qualified_name(obj)
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# normalize the name hint to get a proper identifier
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global_name = namespace.create_name(name_hint, obj)
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if global_name in globals_:
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assert globals_[global_name] is obj
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return global_name
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globals_[global_name] = obj
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return global_name
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# set _custom_builtins here so that we needn't import colossalai in forward
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_custom_builtins["colossalai"] = _CustomBuiltin(
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"import colossalai", colossalai
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)
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# Pre-fill the globals table with registered builtins.
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for name, (_, obj) in _custom_builtins.items():
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add_global(name, obj)
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def type_repr(o: Any):
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if o == ():
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# Empty tuple is used for empty tuple type annotation Tuple[()]
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return "()"
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typename = _type_repr(o)
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if hasattr(o, "__origin__"):
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# This is a generic type, e.g. typing.List[torch.Tensor]
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origin_type = _origin_type_map.get(o.__origin__, o.__origin__)
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origin_typename = add_global(_type_repr(origin_type), origin_type)
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if hasattr(o, "__args__"):
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# Assign global names for each of the inner type variables.
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args = [type_repr(arg) for arg in o.__args__]
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if len(args) == 0:
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# Bare type, such as `typing.Tuple` with no subscript
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# This code-path used in Python < 3.9
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return origin_typename
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return f'{origin_typename}[{",".join(args)}]'
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else:
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# Bare type, such as `typing.Tuple` with no subscript
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# This code-path used in Python 3.9+
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return origin_typename
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# Common case: this is a regular module name like 'foo.bar.baz'
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return add_global(typename, o)
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def _format_args(
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args: Tuple[Argument, ...], kwargs: Dict[str, Argument]
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) -> str:
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def _get_repr(arg):
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# Handle NamedTuples (if it has `_fields`) via add_global.
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if isinstance(arg, tuple) and hasattr(arg, "_fields"):
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qualified_name = _get_qualified_name(type(arg))
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global_name = add_global(qualified_name, type(arg))
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return f"{global_name}{repr(tuple(arg))}"
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return repr(arg)
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args_s = ", ".join(_get_repr(a) for a in args)
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kwargs_s = ", ".join(f"{k} = {_get_repr(v)}" for k, v in kwargs.items())
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if args_s and kwargs_s:
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return f"{args_s}, {kwargs_s}"
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return args_s or kwargs_s
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# Run through reverse nodes and record the first instance of a use
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# of a given node. This represents the *last* use of the node in the
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# execution order of the program, which we will use to free unused
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# values
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node_to_last_use: Dict[Node, Node] = {}
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user_to_last_uses: Dict[Node, List[Node]] = {}
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def register_last_uses(n: Node, user: Node):
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if n not in node_to_last_use:
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node_to_last_use[n] = user
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user_to_last_uses.setdefault(user, []).append(n)
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for node in reversed(nodes):
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map_arg(node.args, lambda n: register_last_uses(n, node))
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map_arg(node.kwargs, lambda n: register_last_uses(n, node))
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delete_free_var_from_last_use(user_to_last_uses)
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# NOTE: we add a variable to distinguish body and ckpt_func
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def delete_unused_values(user: Node, body, to_keep=[]):
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"""
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Delete values after their last use. This ensures that values that are
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not used in the remainder of the code are freed and the memory usage
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of the code is optimal.
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"""
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if user.op == "placeholder":
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return
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if user.op == "output":
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body.append("\n")
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return
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nodes_to_delete = user_to_last_uses.get(user, [])
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nodes_to_delete = [i for i in nodes_to_delete if i.name not in to_keep]
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if len(nodes_to_delete):
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to_delete_str = " = ".join(
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[repr(n) for n in nodes_to_delete] + ["None"]
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)
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body.append(f"; {to_delete_str}\n")
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else:
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|
|
body.append("\n")
|
||
|
|
|
||
|
|
# NOTE: we add a variable to distinguish body and ckpt_func
|
||
|
|
def emit_node(node: Node, body):
|
||
|
|
maybe_type_annotation = (
|
||
|
|
"" if node.type is None else f" : {type_repr(node.type)}"
|
||
|
|
)
|
||
|
|
if node.op == "placeholder":
|
||
|
|
assert isinstance(node.target, str)
|
||
|
|
maybe_default_arg = (
|
||
|
|
"" if not node.args else f" = {repr(node.args[0])}"
|
||
|
|
)
|
||
|
|
free_vars.append(
|
||
|
|
f"{node.target}{maybe_type_annotation}{maybe_default_arg}"
|
||
|
|
)
|
||
|
|
raw_name = node.target.replace("*", "")
|
||
|
|
if raw_name != repr(node):
|
||
|
|
body.append(f"{repr(node)} = {raw_name}\n")
|
||
|
|
return
|
||
|
|
elif node.op == "call_method":
|
||
|
|
assert isinstance(node.target, str)
|
||
|
|
body.append(
|
||
|
|
f"{repr(node)}{maybe_type_annotation} = {_format_target(repr(node.args[0]), node.target)}"
|
||
|
|
f"({_format_args(node.args[1:], node.kwargs)})"
|
||
|
|
)
|
||
|
|
return
|
||
|
|
elif node.op == "call_function":
|
||
|
|
assert callable(node.target)
|
||
|
|
# pretty print operators
|
||
|
|
if (
|
||
|
|
node.target.__module__ == "_operator"
|
||
|
|
and node.target.__name__ in magic_methods
|
||
|
|
):
|
||
|
|
assert isinstance(node.args, tuple)
|
||
|
|
body.append(
|
||
|
|
f"{repr(node)}{maybe_type_annotation} = "
|
||
|
|
f"{magic_methods[node.target.__name__].format(*(repr(a) for a in node.args))}"
|
||
|
|
)
|
||
|
|
return
|
||
|
|
|
||
|
|
# pretty print inplace operators; required for jit.script to work properly
|
||
|
|
# not currently supported in normal FX graphs, but generated by torchdynamo
|
||
|
|
if (
|
||
|
|
node.target.__module__ == "_operator"
|
||
|
|
and node.target.__name__ in inplace_methods
|
||
|
|
):
|
||
|
|
body.append(
|
||
|
|
f"{inplace_methods[node.target.__name__].format(*(repr(a) for a in node.args))}; "
|
||
|
|
f"{repr(node)}{maybe_type_annotation} = {repr(node.args[0])}"
|
||
|
|
)
|
||
|
|
return
|
||
|
|
|
||
|
|
qualified_name = _get_qualified_name(node.target)
|
||
|
|
global_name = add_global(qualified_name, node.target)
|
||
|
|
# special case for getattr: node.args could be 2-argument or 3-argument
|
||
|
|
# 2-argument: attribute access; 3-argument: fall through to attrib function call with default value
|
||
|
|
if (
|
||
|
|
global_name == "getattr"
|
||
|
|
and isinstance(node.args, tuple)
|
||
|
|
and isinstance(node.args[1], str)
|
||
|
|
and node.args[1].isidentifier()
|
||
|
|
and len(node.args) == 2
|
||
|
|
):
|
||
|
|
body.append(
|
||
|
|
f"{repr(node)}{maybe_type_annotation} = {_format_target(repr(node.args[0]), node.args[1])}"
|
||
|
|
)
|
||
|
|
return
|
||
|
|
body.append(
|
||
|
|
f"{repr(node)}{maybe_type_annotation} = {global_name}({_format_args(node.args, node.kwargs)})"
|
||
|
|
)
|
||
|
|
if node.meta.get("is_wrapped", False):
|
||
|
|
wrapped_fns.setdefault(global_name)
|
||
|
|
return
|
||
|
|
elif node.op == "call_module":
|
||
|
|
assert isinstance(node.target, str)
|
||
|
|
body.append(
|
||
|
|
f"{repr(node)}{maybe_type_annotation} = "
|
||
|
|
f"{_format_target(root_module, node.target)}({_format_args(node.args, node.kwargs)})"
|
||
|
|
)
|
||
|
|
return
|
||
|
|
elif node.op == "get_attr":
|
||
|
|
assert isinstance(node.target, str)
|
||
|
|
body.append(
|
||
|
|
f"{repr(node)}{maybe_type_annotation} = {_format_target(root_module, node.target)}"
|
||
|
|
)
|
||
|
|
return
|
||
|
|
elif node.op == "output":
|
||
|
|
if node.type is not None:
|
||
|
|
maybe_return_annotation[0] = f" -> {type_repr(node.type)}"
|
||
|
|
body.append(self.generate_output(node.args[0]))
|
||
|
|
return
|
||
|
|
raise NotImplementedError(f"node: {node.op} {node.target}")
|
||
|
|
|
||
|
|
# Modified for activation checkpointing
|
||
|
|
ckpt_func = []
|
||
|
|
|
||
|
|
# if any node has a list of labels for activation_checkpoint, we
|
||
|
|
# will use nested type of activation checkpoint codegen
|
||
|
|
emit_code_with_chunk(
|
||
|
|
body,
|
||
|
|
nodes,
|
||
|
|
emit_node,
|
||
|
|
delete_unused_values,
|
||
|
|
self.chunk_region_search,
|
||
|
|
self.chunk_infos,
|
||
|
|
)
|
||
|
|
|
||
|
|
if len(body) == 0:
|
||
|
|
# If the Graph has no non-placeholder nodes, no lines for the body
|
||
|
|
# have been emitted. To continue to have valid Python code, emit a
|
||
|
|
# single pass statement
|
||
|
|
body.append("pass\n")
|
||
|
|
|
||
|
|
if len(wrapped_fns) > 0:
|
||
|
|
wrap_name = add_global("wrap", torch.fx.wrap)
|
||
|
|
wrap_stmts = "\n".join(
|
||
|
|
[f'{wrap_name}("{name}")' for name in wrapped_fns]
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
wrap_stmts = ""
|
||
|
|
|
||
|
|
if self._body_transformer:
|
||
|
|
body = self._body_transformer(body)
|
||
|
|
|
||
|
|
for name, value in self.additional_globals():
|
||
|
|
add_global(name, value)
|
||
|
|
|
||
|
|
# as we need colossalai.utils.checkpoint, we need to import colossalai
|
||
|
|
# in forward function
|
||
|
|
prologue = self.gen_fn_def(free_vars, maybe_return_annotation[0])
|
||
|
|
prologue = "".join(ckpt_func) + prologue
|
||
|
|
prologue = prologue
|
||
|
|
|
||
|
|
code = "".join(body)
|
||
|
|
code = "\n".join(" " + line for line in code.split("\n"))
|
||
|
|
fn_code = f"""
|
||
|
|
{wrap_stmts}
|
||
|
|
|
||
|
|
{prologue}
|
||
|
|
{code}"""
|
||
|
|
# print(fn_code)
|
||
|
|
return PythonCode(fn_code, globals_)
|