# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import collections import re from enum import Enum from paddle.base.core import TracerEventType, TracerMemEventType from paddle.utils.flops import flops from .statistic_helper import ( intersection_ranges, merge_ranges, merge_self_ranges, sum_ranges, ) _AllTracerEventType = [ TracerEventType.Operator, TracerEventType.Dataloader, TracerEventType.ProfileStep, TracerEventType.CudaRuntime, TracerEventType.Kernel, TracerEventType.Memcpy, TracerEventType.Memset, TracerEventType.UserDefined, TracerEventType.OperatorInner, TracerEventType.Forward, TracerEventType.Backward, TracerEventType.Optimization, TracerEventType.Communication, TracerEventType.PythonOp, TracerEventType.PythonUserDefined, ] _CommunicationOpName = ['allreduce', 'broadcast', 'rpc'] class SortedKeys(Enum): r""" SortedKeys is used to specify how to sort items when printing ``paddle.profiler.Profiler.summary`` table. The meaning of each SortedKeys is as following - **SortedKeys.CPUTotal** : Sorted by CPU total time. - **SortedKeys.CPUAvg** : Sorted by CPU average time. - **SortedKeys.CPUMax** : Sorted by CPU max time. - **SortedKeys.CPUMin** : Sorted by CPU min time. - **SortedKeys.GPUTotal** : Sorted by GPU total time. - **SortedKeys.GPUAvg** : Sorted by GPU average time. - **SortedKeys.GPUMax** : Sorted by GPU max time. - **SortedKeys.GPUMin** : Sorted by GPU min time. """ CPUTotal = 0 CPUAvg = 1 CPUMax = 2 CPUMin = 3 GPUTotal = 4 GPUAvg = 5 GPUMax = 6 GPUMin = 7 def _nodename2opname(name): r''' convert static host node name to operator name ''' op_name = name.replace(' compute', '') op_name = op_name.replace(' dygraph', '') op_name = op_name.replace(' pybind_imperative_func', '') return op_name class HostStatisticNode: r''' Wrap original node for calculating statistic metrics. ''' def __init__(self, hostnode): self.hostnode = hostnode self.children_node = [] self.runtime_node = [] self.cpu_time = 0 self.self_cpu_time = 0 self.gpu_time = 0 # kernel time self.self_gpu_time = 0 self.general_gpu_time = 0 # besides kernel, include time of gpu events like memcpy and memset self.self_general_gpu_time = 0 self.flops = 0 def cal_flops(self): if self.hostnode.type == TracerEventType.Operator: if hasattr(self.hostnode, 'input_shapes'): op_name = _nodename2opname(self.hostnode.name) self.flops = flops( op_name, self.hostnode.input_shapes, self.hostnode.attributes, ) def cal_statistic(self): self.cpu_time = self.hostnode.end_ns - self.hostnode.start_ns self.self_cpu_time = self.cpu_time self.cal_flops() for child in self.children_node: child.cal_flops() child.cal_statistic() self.gpu_time += child.gpu_time self.general_gpu_time += child.general_gpu_time self.self_cpu_time -= child.end_ns - child.start_ns self.flops += child.flops for rt in self.runtime_node: rt.cal_statistic() self.self_cpu_time -= rt.end_ns - rt.start_ns self.gpu_time += rt.gpu_time self.self_gpu_time += rt.gpu_time self.general_gpu_time += rt.general_gpu_time self.self_general_gpu_time += rt.general_gpu_time for device in self.hostnode.device_node: if device.type == TracerEventType.Kernel: self.gpu_time += device.end_ns - device.start_ns self.self_gpu_time += device.end_ns - device.start_ns self.general_gpu_time += device.end_ns - device.start_ns self.self_general_gpu_time += device.end_ns - device.start_ns @property def end_ns(self): return self.hostnode.end_ns @property def start_ns(self): return self.hostnode.start_ns def __getattr__(self, name): return getattr(self.hostnode, name) def traverse_tree(nodetrees): results = collections.defaultdict(list) for thread_id, rootnode in nodetrees.items(): stack = [] stack.append(rootnode) threadlist = results[thread_id] while stack: current_node = stack.pop() threadlist.append(current_node) for childnode in current_node.children_node: stack.append(childnode) return results def get_device_nodes(hostnode): ''' Get all device nodes called in the time range of hostnode. ''' stack = [] device_nodes = [] stack.append(hostnode) while stack: current_node = stack.pop() for childnode in current_node.children_node: stack.append(childnode) for runtimenode in current_node.runtime_node: for devicenode in runtimenode.device_node: device_nodes.append(devicenode) return device_nodes def _build_layer_from_tree(nodetrees): def build_layer(node, depth=0): if "GradNode" in node.name: return [], 0 if node.type in [ TracerEventType.Backward, TracerEventType.Optimization, ]: return [], 0 if node.type == TracerEventType.Operator: stat_node = HostStatisticNode(node) stat_node.cal_statistic() return stat_node, stat_node.flops layer = [] nflops = 0 for c in node.children_node: l, f = build_layer(c, depth + 1) if l: nflops += f layer.append(l) if node.type == TracerEventType.Forward: stat_node = HostStatisticNode(node) stat_node.cal_statistic() stat_node.flops = nflops return [stat_node, layer], nflops return layer, nflops ret = [] for _, rootnode in nodetrees.items(): layer, _ = build_layer(rootnode) ret.append(layer) return ret def _format_large_number(n, precision=2): if n // 1e12 > 0: return f"{round(n / 1e12, precision)} T" if n // 1e9 > 0: return f"{round(n / 1e9, precision)} G" if n // 1e6 > 0: return f"{round(n / 1e6, precision)} M" if n // 1e3 > 0: return f"{round(n / 1e3, precision)} K" return f"{round(n, precision)}" def _format_time(n, precision=2): if n // 1e9 > 0: return f"{round(n / 1e9, precision)} s" if n // 1e6 > 0: return f"{round(n / 1e6, precision)} ms" if n // 1e3 > 0: return f"{round(n / 1e3, precision)} us" return f"{round(n, precision)} ns" def _gen_layer_flops(node, repeat=1): ret = [] offset = [] loop = [] def print_layer_tree(node, depth=0): if isinstance(node, list): for n in node: print_layer_tree(n, depth + 1) elif node.type in [TracerEventType.Forward, TracerEventType.Operator]: if len(offset) == 0: offset.append(depth) name = _nodename2opname(node.name) if ( depth == offset[-1] and len(ret) > 0 and ret[0].startswith(name) ): # repeat begin loop.append(1) if len(loop) >= repeat: return "".join(ret) align = " " * (depth - offset[-1]) tm = _format_time(node.cpu_time) flops_n = _format_large_number(node.flops) flops_s = _format_large_number(node.flops * 1e9 / node.cpu_time) ret.append( f"{align}{name} latency: {tm}, FLOPs: {flops_n}, FLOPS: {flops_s}\n" ) for n in node[1:]: print_layer_tree(n) return "".join(ret) def gen_layer_flops(nodetrees, repeat=1): r''' gen_layer_flops generate flops/runtime information depend on layer/operator. ''' layer_tree = _build_layer_from_tree(nodetrees) return _gen_layer_flops(layer_tree, repeat) def wrap_tree(nodetrees): ''' Using HostStatisticNode to wrap original profiler result tree, and calculate node statistic metrics. ''' node_statistic_tree = {} results = collections.defaultdict(list) newresults = collections.defaultdict(list) for thread_id, rootnode in nodetrees.items(): stack = [] stack.append(rootnode) root_statistic_node = HostStatisticNode(rootnode) newstack = [] newstack.append(root_statistic_node) node_statistic_tree[thread_id] = root_statistic_node threadlist = results[thread_id] newthreadlist = newresults[thread_id] while stack: current_node = stack.pop() threadlist.append(current_node) current_statistic_node = newstack.pop() newthreadlist.append(current_statistic_node) for childnode in current_node.children_node: stack.append(childnode) child_statistic_node = HostStatisticNode(childnode) current_statistic_node.children_node.append( child_statistic_node ) newstack.append(child_statistic_node) for runtimenode in current_node.runtime_node: runtime_statistic_node = HostStatisticNode(runtimenode) current_statistic_node.runtime_node.append( runtime_statistic_node ) # recursive calculate node statistic values for thread_id, root_statistic_node in node_statistic_tree.items(): root_statistic_node.cal_statistic() return node_statistic_tree, newresults class TimeRangeSummary: r""" Analyse time ranges for each TracerEventType, and summarize the time. """ def __init__(self): self.CPUTimeRange = collections.defaultdict(list) self.GPUTimeRange = collections.defaultdict( lambda: collections.defaultdict(list) ) # GPU events should be divided into different devices self.CPUTimeRangeSum = collections.defaultdict(int) self.GPUTimeRangeSum = collections.defaultdict( lambda: collections.defaultdict(int) ) self.call_times = collections.defaultdict(int) def parse(self, nodetrees): r""" Analysis node trees in profiler result, and get time range for different tracer event type. """ thread2hostnodes = traverse_tree(nodetrees) for threadid, hostnodes in thread2hostnodes.items(): CPUTimeRange = collections.defaultdict(list) GPUTimeRange = collections.defaultdict( lambda: collections.defaultdict( lambda: collections.defaultdict(list) ) ) # device_id/type/stream_id for hostnode in hostnodes[1:]: # skip root node CPUTimeRange[hostnode.type].append( (hostnode.start_ns, hostnode.end_ns) ) self.call_times[hostnode.type] += 1 for runtimenode in hostnode.runtime_node: CPUTimeRange[runtimenode.type].append( (runtimenode.start_ns, runtimenode.end_ns) ) self.call_times[runtimenode.type] += 1 for devicenode in runtimenode.device_node: GPUTimeRange[devicenode.device_id][devicenode.type][ devicenode.stream_id ].append((devicenode.start_ns, devicenode.end_ns)) self.call_times[devicenode.type] += 1 for event_type, time_ranges in CPUTimeRange.items(): time_ranges = merge_self_ranges(time_ranges, is_sorted=False) self.CPUTimeRange[event_type] = merge_ranges( self.CPUTimeRange[event_type], time_ranges, is_sorted=True ) for device_id, device_time_ranges in GPUTimeRange.items(): for event_type, event_time_ranges in device_time_ranges.items(): for stream_id, time_ranges in event_time_ranges.items(): time_ranges = merge_self_ranges( time_ranges, is_sorted=False ) self.GPUTimeRange[device_id][event_type] = merge_ranges( self.GPUTimeRange[device_id][event_type], time_ranges, is_sorted=True, ) for event_type, time_ranges in self.CPUTimeRange.items(): self.CPUTimeRangeSum[event_type] = sum_ranges(time_ranges) for device_id, device_time_ranges in self.GPUTimeRange.items(): for event_type, time_ranges in device_time_ranges.items(): self.GPUTimeRangeSum[device_id][event_type] = sum_ranges( time_ranges ) def get_gpu_devices(self): return self.GPUTimeRange.keys() def get_gpu_range_sum(self, device_id, event_type): return self.GPUTimeRangeSum[device_id][event_type] def get_cpu_range_sum(self, event_type): return self.CPUTimeRangeSum[event_type] class DistributedSummary: r""" Analysis communication and computation time range, and their overlap. The computation time is all kernel except kernels for communication like nccl. """ def __init__(self): self.cpu_communication_range = [] self.gpu_communication_range = [] self.communication_range = [] self.computation_range = [] self.overlap_range = [] self.cpu_calls = 0 self.gpu_calls = 0 def parse(self, nodetrees): ''' Collect all communication and computation time ranges. ''' thread2hostnodes = traverse_tree(nodetrees) for threadid, hostnodes in thread2hostnodes.items(): for hostnode in hostnodes[1:]: # skip root node # case 1: TracerEventType is Communication if hostnode.type == TracerEventType.Communication: self.cpu_communication_range.append( (hostnode.start_ns, hostnode.end_ns) ) device_nodes = get_device_nodes(hostnode) for device_node in device_nodes: if device_node.type == TracerEventType.Kernel: self.gpu_communication_range.append( (device_node.start_ns, device_node.end_ns) ) # case 2: TracerEventType is Operator but is communication op elif hostnode.type == TracerEventType.Operator and any( name in hostnode.name.lower() for name in _CommunicationOpName ): self.cpu_communication_range.append( (hostnode.start_ns, hostnode.end_ns) ) device_nodes = get_device_nodes(hostnode) for device_node in device_nodes: if device_node.type == TracerEventType.Kernel: self.gpu_communication_range.append( (device_node.start_ns, device_node.end_ns) ) # case 3: Others, filter kernels named with nccl else: for runtimenode in hostnode.runtime_node: for devicenode in runtimenode.device_node: if devicenode.type == TracerEventType.Kernel: kernel_name = devicenode.name.lower() if ( 'nccl' in kernel_name or 'xccl' in kernel_name ): self.gpu_communication_range.append( (devicenode.start_ns, devicenode.end_ns) ) else: self.computation_range.append( (devicenode.start_ns, devicenode.end_ns) ) self.cpu_calls = len(set(self.cpu_communication_range)) self.gpu_calls = len(set(self.gpu_communication_range)) self.cpu_communication_range = merge_self_ranges( self.cpu_communication_range, is_sorted=False ) self.gpu_communication_range = merge_self_ranges( self.gpu_communication_range, is_sorted=False ) self.communication_range = merge_ranges( self.cpu_communication_range, self.gpu_communication_range, is_sorted=True, ) self.computation_range = merge_self_ranges( self.computation_range, is_sorted=False ) self.overlap_range = intersection_ranges( self.communication_range, self.computation_range, is_sorted=True ) class EventSummary: r""" Analyse operator event in profiling data, correlate with its device event. """ class ItemBase: def __init__(self, name): self.name = name self.call = 0 self.cpu_time = 0 self.gpu_time = 0 self.max_cpu_time = 0 self.min_cpu_time = float('inf') self.max_gpu_time = 0 self.min_gpu_time = float('inf') self.devices = {} self.operator_inners = {} self.general_gpu_time = 0 self.min_general_gpu_time = float('inf') self.max_general_gpu_time = 0 self._flops = 0 @property def flops(self): return self._flops @property def avg_cpu_time(self): return self.cpu_time / self.call @property def avg_gpu_time(self): return self.gpu_time / self.call @property def avg_general_gpu_time(self): return self.general_gpu_time / self.call def add_cpu_time(self, time): if time > self.max_cpu_time: self.max_cpu_time = time if time < self.min_cpu_time: self.min_cpu_time = time self.cpu_time += time def add_gpu_time(self, time): if time > self.max_gpu_time: self.max_gpu_time = time if time < self.min_gpu_time: self.min_gpu_time = time self.gpu_time += time def add_general_gpu_time(self, time): if time > self.max_general_gpu_time: self.max_general_gpu_time = time if time < self.min_general_gpu_time: self.min_general_gpu_time = time self.general_gpu_time += time def add_call(self): self.call += 1 def add_flops(self, flops): self._flops += flops def add_item(self, node): raise NotImplementedError class DeviceItem(ItemBase): def add_item(self, node): self.call += 1 self.add_gpu_time(node.end_ns - node.start_ns) class OperatorItem(ItemBase): def add_item(self, node): self.add_call() self.add_cpu_time(node.cpu_time) self.add_gpu_time(node.gpu_time) self.add_general_gpu_time(node.general_gpu_time) self.add_flops(node.flops) for child in node.children_node: if child.type != TracerEventType.Operator: if child.name not in self.operator_inners: self.operator_inners[child.name] = ( EventSummary.OperatorItem(child.name) ) self.operator_inners[child.name].add_item(child) for runtimenode in node.runtime_node: for devicenode in runtimenode.device_node: name = devicenode.name if name not in self.devices: self.devices[name] = EventSummary.DeviceItem(name) self.devices[name].add_item(devicenode) class ForwardItem(ItemBase): def add_item(self, node): self.add_call() self.add_cpu_time(node.cpu_time) self.add_gpu_time(node.gpu_time) self.add_general_gpu_time(node.general_gpu_time) self.add_flops(node.flops) for child in node.children_node: if child.type != TracerEventType.Operator: if child.name not in self.operator_inners: self.operator_inners[child.name] = ( EventSummary.OperatorItem(child.name) ) self.operator_inners[child.name].add_item(child) class GeneralItem(ItemBase): def add_item(self, node): self.add_call() self.add_cpu_time(node.cpu_time) self.add_gpu_time(node.gpu_time) self.add_general_gpu_time(node.general_gpu_time) def __init__(self): self.items = {} # for operator summary self.thread_items = collections.defaultdict( dict ) # for operator summary self.userdefined_items = {} # for userdefined summary self.userdefined_thread_items = collections.defaultdict( dict ) # for userdefined summary self.model_perspective_items = {} # for model summary self.memory_manipulation_items = {} # for memory manipulation summary self.kernel_items = {} # for kernel summary def parse(self, nodetrees): r""" Analysis operator event in the nodetress. """ node_statistic_trees, thread2host_statistic_nodes = wrap_tree(nodetrees) for ( threadid, host_statistic_nodes, ) in thread2host_statistic_nodes.items(): for host_statistic_node in host_statistic_nodes[ 1: ]: # skip root node if host_statistic_node.type == TracerEventType.Operator: self.add_operator_item(host_statistic_node) if ( host_statistic_node.type == TracerEventType.UserDefined or host_statistic_node.type == TracerEventType.PythonUserDefined ): if ( 'memcpy' in host_statistic_node.name.lower() or 'memorycopy' in host_statistic_node.name.lower() or 'memset' in host_statistic_node.name.lower() ): self.add_memory_manipulation_item(host_statistic_node) else: if ( host_statistic_node.type == TracerEventType.PythonUserDefined ): self.add_userdefined_item(host_statistic_node) self.add_kernel_item(host_statistic_nodes[0]) for threadid, root_statistic_node in node_statistic_trees.items(): deque = collections.deque() deque.append(root_statistic_node) while deque: current_node = deque.popleft() for child in current_node.children_node: if ( child.type == TracerEventType.Forward or child.type == TracerEventType.Dataloader or child.type == TracerEventType.Backward or child.type == TracerEventType.Optimization ): self.add_model_perspective_item( child ) # find first model perspective node else: if child.type == TracerEventType.ProfileStep: self.add_model_perspective_item(child) deque.append(child) def add_forward_item(self, operator_node): pass def add_operator_item(self, operator_node): if operator_node.name not in self.items: self.items[operator_node.name] = EventSummary.OperatorItem( operator_node.name ) self.items[operator_node.name].add_item(operator_node) if operator_node.name not in self.thread_items[operator_node.thread_id]: self.thread_items[operator_node.thread_id][operator_node.name] = ( EventSummary.OperatorItem(operator_node.name) ) self.thread_items[operator_node.thread_id][operator_node.name].add_item( operator_node ) def add_userdefined_item(self, userdefined_node): if userdefined_node.name not in self.userdefined_items: self.userdefined_items[userdefined_node.name] = ( EventSummary.GeneralItem(userdefined_node.name) ) self.userdefined_items[userdefined_node.name].add_item(userdefined_node) if ( userdefined_node.name not in self.userdefined_thread_items[userdefined_node.thread_id] ): self.userdefined_thread_items[userdefined_node.thread_id][ userdefined_node.name ] = EventSummary.GeneralItem(userdefined_node.name) self.userdefined_thread_items[userdefined_node.thread_id][ userdefined_node.name ].add_item(userdefined_node) def add_memory_manipulation_item(self, memory_manipulation_node): if memory_manipulation_node.name not in self.memory_manipulation_items: self.memory_manipulation_items[memory_manipulation_node.name] = ( EventSummary.GeneralItem(memory_manipulation_node.name) ) self.memory_manipulation_items[memory_manipulation_node.name].add_item( memory_manipulation_node ) def add_model_perspective_item(self, model_perspective_node): if model_perspective_node.type == TracerEventType.Forward: name = 'Forward' elif model_perspective_node.type == TracerEventType.Backward: name = 'Backward' elif model_perspective_node.type == TracerEventType.Optimization: name = 'Optimization' elif model_perspective_node.type == TracerEventType.Dataloader: name = 'Dataloader' elif model_perspective_node.type == TracerEventType.ProfileStep: name = 'ProfileStep' else: return if name not in self.model_perspective_items: self.model_perspective_items[name] = EventSummary.GeneralItem(name) self.model_perspective_items[name].add_item(model_perspective_node) def add_kernel_item(self, root_node): device_nodes = get_device_nodes(root_node) for device_node in device_nodes: if device_node.type == TracerEventType.Kernel: name = device_node.name if name not in self.kernel_items: self.kernel_items[name] = EventSummary.DeviceItem(name) self.kernel_items[name].add_item(device_node) class MemorySummary: r""" Analyse memory events in profiling data. """ class MemoryItem: def __init__(self, event_name, place, memory_type='Allocated'): self.event_name = event_name self.place = place self.allocation_count = 0 self.free_count = 0 self.allocation_size = 0 self.free_size = 0 self.increase_size = 0 self.memory_type = memory_type def add_memory_record(self, size, allocation_type): if ( allocation_type == TracerMemEventType.Allocate or allocation_type == TracerMemEventType.ReservedAllocate ): self.allocation_count += 1 self.allocation_size += size elif ( allocation_type == TracerMemEventType.Free or allocation_type == TracerMemEventType.ReservedFree ): self.free_count += 1 self.free_size -= size # size is sign(-) when free. else: print("No corresponding type.") self.increase_size = self.allocation_size - self.free_size def __init__(self): self.allocated_items = collections.defaultdict( dict ) # for memory summary, device type: event self.reserved_items = collections.defaultdict( dict ) # for memory summary, device type: event self.peak_allocation_values = collections.defaultdict(int) self.peak_reserved_values = collections.defaultdict(int) def _analyse_node_memory(self, event_name, node): for memnode in node.mem_node: # self mem node if ( memnode.type == TracerMemEventType.Allocate or memnode.type == TracerMemEventType.Free ): if event_name not in self.allocated_items[memnode.place]: self.allocated_items[memnode.place][event_name] = ( MemorySummary.MemoryItem( event_name, memnode.place, 'Allocated' ) ) self.allocated_items[memnode.place][ event_name ].add_memory_record(memnode.increase_bytes, memnode.type) elif ( memnode.type == TracerMemEventType.ReservedAllocate or memnode.type == TracerMemEventType.ReservedFree ): if event_name not in self.reserved_items[memnode.place]: self.reserved_items[memnode.place][event_name] = ( MemorySummary.MemoryItem( event_name, memnode.place, 'Reserved' ) ) self.reserved_items[memnode.place][ event_name ].add_memory_record(memnode.increase_bytes, memnode.type) self.peak_allocation_values[memnode.place] = max( self.peak_allocation_values[memnode.place], memnode.peak_allocated, ) self.peak_reserved_values[memnode.place] = max( self.peak_reserved_values[memnode.place], memnode.peak_reserved ) def parse(self, nodetrees): r""" Analyse memory event in the nodetress. """ thread2hostnodes = traverse_tree(nodetrees) for threadid, host_nodes in thread2hostnodes.items(): for host_node in host_nodes[1:]: # skip root node if host_node.type == TracerEventType.OperatorInner: continue if host_node.type == TracerEventType.Operator: for child in host_node.children_node: self._analyse_node_memory(host_node.name, child) self._analyse_node_memory(host_node.name, host_node) class StatisticData: r""" Hold all analysed results. """ def __init__(self, node_trees, extra_info): self.node_trees = node_trees self.extra_info = extra_info self.time_range_summary = TimeRangeSummary() self.event_summary = EventSummary() self.distributed_summary = DistributedSummary() self.memory_summary = MemorySummary() self.time_range_summary.parse(node_trees) self.event_summary.parse(node_trees) self.distributed_summary.parse(node_trees) self.memory_summary.parse(node_trees) def _build_table( statistic_data, sorted_by=SortedKeys.CPUTotal, op_detail=True, thread_sep=False, time_unit='ms', row_limit=100, max_src_column_width=75, views=None, ): from .profiler import SummaryView """Prints a summary of events.""" # format table row SPACING_SIZE = 2 row_format_list = [""] header_sep_list = [""] line_length_list = [-SPACING_SIZE] def add_column(padding, text_dir='<'): row_format_list[0] += ( '{: ' + text_dir + str(padding) + '}' + (' ' * SPACING_SIZE) ) header_sep_list[0] += '-' * padding + (' ' * SPACING_SIZE) line_length_list[0] += padding + SPACING_SIZE def add_title(padding, text): left_length = padding - len(text) half = left_length // 2 return '-' * half + text + '-' * (left_length - half) result = [] def append(s): result.append(s) result.append('\n') def format_time(time, unit='ms', indent=0): r""" Transform time in ns to time in unit. """ if time == float('inf'): return '-' else: result = float(time) if unit == 's': result /= 1e9 elif unit == 'ms': result /= 1e6 elif unit == 'us': result /= 1e3 return '{}{:.2f}'.format(' ' * indent, result) def format_ratio(ratio, indent=0): r""" Transform ratio within [0, 1] to percentage presentation. """ return '{}{:.2f}'.format(' ' * indent, ratio * 100) total_time = statistic_data.time_range_summary.get_cpu_range_sum( TracerEventType.ProfileStep ) if views is None or SummaryView.DeviceView in views: # ----- Print Device Summary ----- # headers = ['Device', 'Utilization (%)'] name_column_width = 30 DEFAULT_COLUMN_WIDTH = 20 add_column(name_column_width) for _ in headers[1:]: add_column(DEFAULT_COLUMN_WIDTH) row_format = row_format_list[0] header_sep = header_sep_list[0] line_length = line_length_list[0] # construct table string append(add_title(line_length, "Device Summary")) append(header_sep) append(row_format.format(*headers)) append(header_sep) row_values = [ 'CPU(Process)', format_ratio( float(statistic_data.extra_info['Process Cpu Utilization']) ), ] append(row_format.format(*row_values)) row_values = [ 'CPU(System)', format_ratio( float(statistic_data.extra_info['System Cpu Utilization']) ), ] append(row_format.format(*row_values)) for gpu_name in statistic_data.time_range_summary.get_gpu_devices(): gpu_time = float( statistic_data.time_range_summary.get_gpu_range_sum( gpu_name, TracerEventType.Kernel ) ) utilization = gpu_time / total_time row_values = [f'GPU{gpu_name}', format_ratio(utilization)] append(row_format.format(*row_values)) append(header_sep) append( "Note:\nCPU(Process) Utilization = Current process CPU time over all cpu cores / elapsed time, so max utilization can be reached 100% * number of cpu cores.\n" "CPU(System) Utilization = All processes CPU time over all cpu cores(busy time) / (busy time + idle time).\n" "GPU Utilization = Current process GPU time / elapsed time." ) append('-' * line_length) append('') append('') if total_time == 0: return ''.join(result) if views is None or SummaryView.OverView in views: # ----- Print Overview Summary ----- # headers = ['Event Type', 'Calls', 'CPU Time', 'Ratio (%)'] row_format_list = [""] header_sep_list = [""] line_length_list = [-SPACING_SIZE] DEFAULT_COLUMN_WIDTH = 25 for _ in headers: add_column(DEFAULT_COLUMN_WIDTH) row_format = row_format_list[0] header_sep = header_sep_list[0] line_length = line_length_list[0] # construct table string append(add_title(line_length, "Overview Summary")) append(f'Time unit: {time_unit}') append(header_sep) append(row_format.format(*headers)) append(header_sep) cpu_type_time = collections.defaultdict(int) gpu_type_time = collections.defaultdict(int) cpu_call_times = collections.defaultdict(int) gpu_call_times = collections.defaultdict(int) cpu_call_times.update(statistic_data.time_range_summary.call_times) gpu_call_times.update(statistic_data.time_range_summary.call_times) for ( event_type, value, ) in statistic_data.time_range_summary.CPUTimeRangeSum.items(): if event_type != TracerEventType.Communication: cpu_type_time[event_type] = value if statistic_data.distributed_summary.cpu_communication_range: cpu_type_time[TracerEventType.Communication] = sum_ranges( statistic_data.distributed_summary.cpu_communication_range ) cpu_call_times[TracerEventType.Communication] = ( statistic_data.distributed_summary.cpu_calls ) for event_type in [ TracerEventType.Dataloader, TracerEventType.Forward, TracerEventType.Backward, TracerEventType.Optimization, ]: event_type_name = str(event_type).split('.')[1] if ( event_type in cpu_call_times and event_type_name in statistic_data.event_summary.model_perspective_items ): cpu_call_times[event_type] = ( statistic_data.event_summary.model_perspective_items[ event_type_name ].call ) cpu_type_time[event_type] = ( statistic_data.event_summary.model_perspective_items[ event_type_name ].cpu_time ) gpu_time_range = collections.defaultdict(list) for ( device_id, device_time_ranges, ) in statistic_data.time_range_summary.GPUTimeRange.items(): for event_type, time_range in device_time_ranges.items(): gpu_time_range[event_type] = merge_ranges( gpu_time_range[event_type], time_range, is_sorted=True ) for event_type, time_range in gpu_time_range.items(): gpu_type_time[event_type] = sum_ranges(time_range) if statistic_data.distributed_summary.gpu_communication_range: gpu_type_time[TracerEventType.Communication] = sum_ranges( statistic_data.distributed_summary.gpu_communication_range ) gpu_call_times[TracerEventType.Communication] = ( statistic_data.distributed_summary.gpu_calls ) sorted_items = sorted( cpu_type_time.items(), key=lambda x: x[1], reverse=True ) event_type, time = sorted_items[0] row_values = [ '{}'.format(str(event_type).split('.')[1]), cpu_call_times[event_type], format_time(time, unit=time_unit), format_ratio(float(time) / total_time), ] append(row_format.format(*row_values)) for event_type, time in sorted_items[1:]: row_values = [ ' {}'.format(str(event_type).split('.')[1]), cpu_call_times[event_type], format_time(time, unit=time_unit), format_ratio(float(time) / total_time), ] append(row_format.format(*row_values)) append(header_sep) headers = ['', 'Calls', 'GPU Time', 'Ratio (%)'] append(row_format.format(*headers)) append(header_sep) for event_type, time in gpu_type_time.items(): row_values = [ ' {}'.format(str(event_type).split('.')[1]), gpu_call_times[event_type], format_time(time, unit=time_unit), format_ratio(float(time) / total_time), ] append(row_format.format(*row_values)) append(header_sep) append( "Note:\nIn this table, We sum up all collected events in terms of event type.\n" "The time of events collected on host are presented as CPU Time, and as GPU Time if on device.\n" "Events with different types may overlap or inclusion, e.g. Operator includes OperatorInner, so the sum of ratios is not 100%.\n" "The time of events in the same type with overlap will not calculate twice, and all time is summed after merged.\n" "Example:\n" "Thread 1:\n" " Operator: |___________| |__________|\n" "Thread 2:\n" " Operator: |____________| |___|\n" "After merged:\n" " Result: |______________| |__________|\n" ) append('-' * line_length) append('') append('') if views is None or SummaryView.ModelView in views: # ----- Print Model Summary Report ----- # model_perspective_items = ( statistic_data.event_summary.model_perspective_items ) if len(model_perspective_items) > 1: all_row_values = [] accumulation_time = 0 gpu_accumulation_time = 0 gpu_total_time = ( statistic_data.event_summary.model_perspective_items[ 'ProfileStep' ].gpu_time ) for name in [ 'ProfileStep', 'Dataloader', 'Forward', 'Backward', 'Optimization', ]: if name in model_perspective_items: item = model_perspective_items[name] if gpu_total_time == 0: gpu_ratio = 0 else: gpu_ratio = float(item.gpu_time) / gpu_total_time name = f'{name}' if 'ProfileStep' in name else f' {name}' row_values = [ f'{name}', item.call, f'{format_time(item.cpu_time, unit=time_unit)} / {format_time(item.avg_cpu_time, unit=time_unit)} / {format_time(item.max_cpu_time, unit=time_unit)} / {format_time(item.min_cpu_time, unit=time_unit)} / {format_ratio(float(item.cpu_time) / total_time)}', f'{format_time(item.gpu_time, unit=time_unit)} / {format_time(item.avg_gpu_time, unit=time_unit)} / {format_time(item.max_gpu_time, unit=time_unit)} / {format_time(item.min_gpu_time, unit=time_unit)} / {format_ratio(gpu_ratio)}', ] all_row_values.append(row_values) if 'ProfileStep' not in name: accumulation_time += item.cpu_time gpu_accumulation_time += item.gpu_time other_time = total_time - accumulation_time other_gpu_time = gpu_total_time - gpu_accumulation_time if gpu_total_time == 0: gpu_ratio = 0 else: gpu_ratio = float(other_gpu_time) / gpu_total_time row_values = [ ' Others', '-', f'{format_time(other_time, unit=time_unit)} / - / - / - / {format_ratio(float(other_time) / total_time)}', f'{format_time(other_gpu_time, unit=time_unit)} / - / - / - / {format_ratio(gpu_ratio)}', ] all_row_values.append(row_values) # Calculate the column width calltime_width = 6 cpu_data_description_width = 40 gpu_data_description_width = 40 for row_values in all_row_values: if ( isinstance(row_values[1], int) and len(str(row_values[1])) > calltime_width ): calltime_width = len(str(row_values[1])) if len(row_values[2]) > cpu_data_description_width: cpu_data_description_width = len(row_values[2]) if len(row_values[3]) > gpu_data_description_width: gpu_data_description_width = len(row_values[3]) headers = [ 'Name', 'Calls', 'CPU Total / Avg / Max / Min / Ratio(%)', 'GPU Total / Avg / Max / Min / Ratio(%)', ] row_format_list = [""] header_sep_list = [""] line_length_list = [-SPACING_SIZE] name_column_width = 15 add_column(name_column_width) add_column(calltime_width) add_column(cpu_data_description_width) add_column(gpu_data_description_width) row_format = row_format_list[0] header_sep = header_sep_list[0] line_length = line_length_list[0] # construct table string append(add_title(line_length, "Model Summary")) append(f'Time unit: {time_unit}') append(header_sep) append(row_format.format(*headers)) append(header_sep) for row_values in all_row_values: append(row_format.format(*row_values)) append(header_sep) append( "Note:\nIn this table, GPU time is the sum of all device(GPU) events called in the phase.\n" "Unlike overview summary, if two device(GPU) events execute on different streams with overlap time, we sum them directly here.\n" ) append('-' * line_length) append('') append('') if views is None or SummaryView.DistributedView in views: # ----- Print Distribution Summary Report ----- # if statistic_data.distributed_summary.communication_range: headers = [ 'Name', 'Total Time', 'Ratio (%)', ] row_format_list = [""] header_sep_list = [""] line_length_list = [-SPACING_SIZE] DEFAULT_COLUMN_WIDTH = 25 for _ in headers: add_column(DEFAULT_COLUMN_WIDTH) row_format = row_format_list[0] header_sep = header_sep_list[0] line_length = line_length_list[0] # construct table string append(add_title(line_length, "Distribution Summary")) append(f'Time unit: {time_unit}') append(header_sep) append(row_format.format(*headers)) append(header_sep) communication_time = sum_ranges( statistic_data.distributed_summary.communication_range ) computation_time = sum_ranges( statistic_data.distributed_summary.computation_range ) overlap_time = sum_ranges( statistic_data.distributed_summary.overlap_range ) row_values = [ 'ProfileStep', format_time(total_time, unit=time_unit), format_ratio(float(total_time) / total_time), ] append(row_format.format(*row_values)) row_values = [ ' Communication', format_time(communication_time, unit=time_unit), format_ratio(float(communication_time) / total_time), ] append(row_format.format(*row_values)) row_values = [ ' Computation', format_time(computation_time, unit=time_unit), format_ratio(float(computation_time) / total_time), ] append(row_format.format(*row_values)) row_values = [ ' Overlap', format_time(overlap_time, unit=time_unit), format_ratio(float(overlap_time) / total_time), ] append(row_format.format(*row_values)) append(header_sep) append( "Note:\nCommunication time: Communication Event time, Communication Op time and its kernel time on gpu.\n" "Computation time: Kernel time, except kernels belong to communication(nccl kernels).\n" "Overlap time: Communication time intersects with computation time.\n" "Example:\n" "Communication:\n" " CPU: |_________________|\n" " GPU: |______________|\n" " Total: |_________________| |______________|\n" "Computation time(Kernel):\n" " GPU: |________________|\n" "Overlap time: |___________|\n" ) append('-' * line_length) append('') append('') if views is None or SummaryView.OperatorView in views: # ----- Print Operator Summary Report ----- # if statistic_data.event_summary.items: all_row_values = [] name_column_width = 52 if thread_sep: thread_items = statistic_data.event_summary.thread_items else: thread_items = { 'All threads merged': statistic_data.event_summary.items } for thread_id, items in thread_items.items(): all_row_values.append(f"Thread: {thread_id}") if sorted_by == SortedKeys.CPUTotal: sorted_items = sorted( items.items(), key=lambda x: x[1].cpu_time, reverse=True ) elif sorted_by == SortedKeys.CPUAvg: sorted_items = sorted( items.items(), key=lambda x: x[1].avg_cpu_time, reverse=True, ) elif sorted_by == SortedKeys.CPUMax: sorted_items = sorted( items.items(), key=lambda x: x[1].max_cpu_time, reverse=True, ) elif sorted_by == SortedKeys.CPUMin: sorted_items = sorted( items.items(), key=lambda x: x[1].min_cpu_time ) elif sorted_by == SortedKeys.GPUTotal: sorted_items = sorted( items.items(), key=lambda x: x[1].general_gpu_time, reverse=True, ) elif sorted_by == SortedKeys.GPUAvg: sorted_items = sorted( items.items(), key=lambda x: x[1].avg_general_gpu_time, reverse=True, ) elif sorted_by == SortedKeys.GPUMax: sorted_items = sorted( items.items(), key=lambda x: x[1].max_general_gpu_time, reverse=True, ) elif sorted_by == SortedKeys.GPUMin: sorted_items = sorted( items.items(), key=lambda x: x[1].min_general_gpu_time ) total_op_cpu_time = 0 total_op_gpu_time = 0 for name, item in sorted_items: total_op_cpu_time += item.cpu_time total_op_gpu_time += item.general_gpu_time for name, item in sorted_items: if total_op_cpu_time == 0: cpu_ratio = 0 else: cpu_ratio = float(item.cpu_time) / total_op_cpu_time if total_op_gpu_time == 0: gpu_ratio = 0 else: gpu_ratio = ( float(item.general_gpu_time) / total_op_gpu_time ) row_values = [ name, item.call, f'{format_time(item.cpu_time, unit=time_unit)} / {format_time(item.avg_cpu_time, unit=time_unit)} / {format_time(item.max_cpu_time, unit=time_unit)} / {format_time(item.min_cpu_time, unit=time_unit)} / {format_ratio(cpu_ratio)}', '{} / {} / {} / {} / {}'.format( format_time(item.general_gpu_time, unit=time_unit), format_time( item.avg_general_gpu_time, unit=time_unit ), format_time( item.max_general_gpu_time, unit=time_unit ), format_time( item.min_general_gpu_time, unit=time_unit ), format_ratio(gpu_ratio), ), item.flops, ] all_row_values.append(row_values) if op_detail: for ( innerop_name, innerop_node, ) in item.operator_inners.items(): if item.cpu_time == 0: cpu_ratio = 0 else: cpu_ratio = ( float(innerop_node.cpu_time) / item.cpu_time ) if item.general_gpu_time == 0: gpu_ratio = 0 else: gpu_ratio = ( float(innerop_node.general_gpu_time) / item.general_gpu_time ) if len(innerop_name) + 2 > name_column_width: innerop_name = innerop_name[ : name_column_width - 5 ] innerop_name += "..." row_values = [ f' {innerop_name}', innerop_node.call, '{} / {} / {} / {} / {}'.format( format_time( innerop_node.cpu_time, unit=time_unit ), format_time( innerop_node.avg_cpu_time, unit=time_unit, ), format_time( innerop_node.max_cpu_time, unit=time_unit, ), format_time( innerop_node.min_cpu_time, unit=time_unit, ), format_ratio(cpu_ratio), ), '{} / {} / {} / {} / {}'.format( format_time( innerop_node.general_gpu_time, unit=time_unit, ), format_time( innerop_node.avg_general_gpu_time, unit=time_unit, ), format_time( innerop_node.max_general_gpu_time, unit=time_unit, ), format_time( innerop_node.min_general_gpu_time, unit=time_unit, ), format_ratio(gpu_ratio), ), '-', ] all_row_values.append(row_values) for ( device_node_name, device_node, ) in innerop_node.devices.items(): if innerop_node.general_gpu_time == 0: gpu_ratio = 0 else: gpu_ratio = ( float(device_node.gpu_time) / innerop_node.general_gpu_time ) if ( len(device_node_name) + 4 > name_column_width ): device_node_name = device_node_name[ : name_column_width - 7 ] device_node_name += "..." row_values = [ f' {device_node_name}', device_node.call, '- / - / - / - / -', '{} / {} / {} / {} / {}'.format( format_time( device_node.gpu_time, unit=time_unit ), format_time( device_node.avg_gpu_time, unit=time_unit, ), format_time( device_node.max_gpu_time, unit=time_unit, ), format_time( device_node.min_gpu_time, unit=time_unit, ), format_ratio(gpu_ratio), ), '-', ] all_row_values.append(row_values) for ( device_node_name, device_node, ) in item.devices.items(): if item.general_gpu_time == 0: gpu_ratio = 0 else: gpu_ratio = ( float(device_node.gpu_time) / item.general_gpu_time ) if len(device_node_name) + 2 > name_column_width: device_node_name = device_node_name[ : name_column_width - 5 ] device_node_name += "..." row_values = [ f' {device_node_name}', device_node.call, '- / - / - / - / -', '{} / {} / {} / {} / {}'.format( format_time( device_node.gpu_time, unit=time_unit ), format_time( device_node.avg_gpu_time, unit=time_unit ), format_time( device_node.max_gpu_time, unit=time_unit ), format_time( device_node.min_gpu_time, unit=time_unit ), format_ratio(gpu_ratio), ), '-', ] all_row_values.append(row_values) # Calculate the column width calltime_width = 6 cpu_data_description_width = 40 gpu_data_description_width = 40 flops_width = 10 for row_values in all_row_values: if isinstance(row_values, str): continue if ( isinstance(row_values[1], int) and len(str(row_values[1])) > calltime_width ): calltime_width = len(str(row_values[1])) if len(row_values[2]) > cpu_data_description_width: cpu_data_description_width = len(row_values[2]) if len(row_values[3]) > gpu_data_description_width: gpu_data_description_width = len(row_values[3]) headers = [ 'Name', 'Calls', 'CPU Total / Avg / Max / Min / Ratio(%)', 'GPU Total / Avg / Max / Min / Ratio(%)', 'FLOPs', ] row_format_list = [""] header_sep_list = [""] line_length_list = [-SPACING_SIZE] add_column(name_column_width) add_column(calltime_width) add_column(cpu_data_description_width) add_column(gpu_data_description_width) add_column(flops_width) row_format = row_format_list[0] header_sep = header_sep_list[0] line_length = line_length_list[0] # construct table string append(add_title(line_length, "Operator Summary")) append(f'Time unit: {time_unit}') append(header_sep) append(row_format.format(*headers)) append(header_sep) for row_values in all_row_values: if isinstance(row_values, str): append(add_title(line_length, row_values)) else: append(row_format.format(*row_values)) append(header_sep) append('') append('') if views is None or SummaryView.KernelView in views: # ----- Print Kernel Summary Report ----- # if statistic_data.event_summary.kernel_items: all_row_values = [] kernel_items = statistic_data.event_summary.kernel_items if sorted_by == SortedKeys.GPUAvg: sorted_items = sorted( kernel_items.items(), key=lambda x: x[1].avg_gpu_time, reverse=True, ) elif sorted_by == SortedKeys.GPUMax: sorted_items = sorted( kernel_items.items(), key=lambda x: x[1].max_gpu_time, reverse=True, ) elif sorted_by == SortedKeys.GPUMin: sorted_items = sorted( kernel_items.items(), key=lambda x: x[1].min_gpu_time ) else: sorted_items = sorted( kernel_items.items(), key=lambda x: x[1].gpu_time, reverse=True, ) total_kernel_gpu_time = 0 for name, item in sorted_items: total_kernel_gpu_time += item.gpu_time for name, item in sorted_items: if total_kernel_gpu_time == 0: gpu_ratio = 0 else: gpu_ratio = float(item.gpu_time) / total_kernel_gpu_time row_values = [ name, item.call, f'{format_time(item.gpu_time, unit=time_unit)} / {format_time(item.avg_gpu_time, unit=time_unit)} / {format_time(item.max_gpu_time, unit=time_unit)} / {format_time(item.min_gpu_time, unit=time_unit)} / {format_ratio(gpu_ratio)}', ] all_row_values.append(row_values) headers = [ 'Name', 'Calls', 'GPU Total / Avg / Max / Min / Ratio(%)', ] # Calculate the column width name_column_width = 90 calltime_width = 6 gpu_data_description_width = 40 for row_values in all_row_values: if ( isinstance(row_values[1], int) and len(str(row_values[1])) > calltime_width ): calltime_width = len(str(row_values[1])) if len(row_values[2]) > gpu_data_description_width: gpu_data_description_width = len(row_values[2]) row_format_list = [""] header_sep_list = [""] line_length_list = [-SPACING_SIZE] add_column(name_column_width) add_column(calltime_width) add_column(gpu_data_description_width) row_format = row_format_list[0] header_sep = header_sep_list[0] line_length = line_length_list[0] # construct table string append(add_title(line_length, "Kernel Summary")) append(f'Time unit: {time_unit}') append(header_sep) append(row_format.format(*headers)) append(header_sep) kernel_name_pattern = re.compile(r'(.+?)(<.*>)(\(.*\))') for row_values in all_row_values: match = kernel_name_pattern.match(row_values[0]) if match: name = match.group(1) + match.group(2) else: name = row_values[0] if len(name) > name_column_width: row_values[0] = name[: name_column_width - 3] + '...' else: row_values[0] = name append(row_format.format(*row_values)) append(header_sep) append('') append('') if views is None or SummaryView.MemoryManipulationView in views: # ----- Print Memory Manipulation Summary Report ----- # if statistic_data.event_summary.memory_manipulation_items: all_row_values = [] memory_manipulation_items = ( statistic_data.event_summary.memory_manipulation_items ) gpu_total_time = ( statistic_data.event_summary.model_perspective_items[ 'ProfileStep' ].general_gpu_time ) for name, item in memory_manipulation_items.items(): if gpu_total_time == 0: gpu_ratio = 0 else: gpu_ratio = float(item.general_gpu_time) / gpu_total_time row_values = [ name, item.call, f'{format_time(item.cpu_time, unit=time_unit)} / {format_time(item.avg_cpu_time, unit=time_unit)} / {format_time(item.max_cpu_time, unit=time_unit)} / {format_time(item.min_cpu_time, unit=time_unit)} / {format_ratio(float(item.cpu_time) / total_time)}', f'{format_time(item.general_gpu_time, unit=time_unit)} / {format_time(item.avg_general_gpu_time, unit=time_unit)} / {format_time(item.max_general_gpu_time, unit=time_unit)} / {format_time(item.min_general_gpu_time, unit=time_unit)} / {format_ratio(gpu_ratio)}', ] all_row_values.append(row_values) headers = [ 'Name', 'Calls', 'CPU Total / Avg / Max / Min / Ratio(%)', 'GPU Total / Avg / Max / Min / Ratio(%)', ] # Calculate the column width name_column_width = 0 calltime_width = 6 cpu_data_description_width = 40 gpu_data_description_width = 40 for row_values in all_row_values: if len(row_values[0]) > name_column_width: name_column_width = len(row_values[0]) if ( isinstance(row_values[1], int) and len(str(row_values[1])) > calltime_width ): calltime_width = len(str(row_values[1])) if len(row_values[2]) > cpu_data_description_width: cpu_data_description_width = len(row_values[2]) if len(row_values[3]) > gpu_data_description_width: gpu_data_description_width = len(row_values[3]) row_format_list = [""] header_sep_list = [""] line_length_list = [-SPACING_SIZE] add_column(name_column_width) add_column(calltime_width) add_column(cpu_data_description_width) add_column(gpu_data_description_width) row_format = row_format_list[0] header_sep = header_sep_list[0] line_length = line_length_list[0] # construct table string append(add_title(line_length, "Memory Manipulation Summary")) append(f'Time unit: {time_unit}') append(header_sep) append(row_format.format(*headers)) append(header_sep) for row_values in all_row_values: append(row_format.format(*row_values)) append(header_sep) append('') append('') if views is None or SummaryView.UDFView in views: # ----- Print UserDefined Summary Report ----- # if statistic_data.event_summary.userdefined_items: all_row_values = [] gpu_total_time = ( statistic_data.event_summary.model_perspective_items[ 'ProfileStep' ].general_gpu_time ) if thread_sep: userdefined_thread_items = ( statistic_data.event_summary.userdefined_thread_items ) else: userdefined_thread_items = { 'All threads merged': statistic_data.event_summary.userdefined_items } for thread_id, items in userdefined_thread_items.items(): all_row_values.append(f"Thread: {thread_id}") if sorted_by == SortedKeys.CPUTotal: sorted_items = sorted( items.items(), key=lambda x: x[1].cpu_time, reverse=True ) elif sorted_by == SortedKeys.CPUAvg: sorted_items = sorted( items.items(), key=lambda x: x[1].avg_cpu_time, reverse=True, ) elif sorted_by == SortedKeys.CPUMax: sorted_items = sorted( items.items(), key=lambda x: x[1].max_cpu_time, reverse=True, ) elif sorted_by == SortedKeys.CPUMin: sorted_items = sorted( items.items(), key=lambda x: x[1].min_cpu_time ) elif sorted_by == SortedKeys.GPUTotal: sorted_items = sorted( items.items(), key=lambda x: x[1].general_gpu_time, reverse=True, ) elif sorted_by == SortedKeys.GPUAvg: sorted_items = sorted( items.items(), key=lambda x: x[1].avg_general_gpu_time, reverse=True, ) elif sorted_by == SortedKeys.GPUMax: sorted_items = sorted( items.items(), key=lambda x: x[1].max_general_gpu_time, reverse=True, ) elif sorted_by == SortedKeys.GPUMin: sorted_items = sorted( items.items(), key=lambda x: x[1].min_general_gpu_time ) for name, item in sorted_items: if gpu_total_time == 0: gpu_ratio = 0 else: gpu_ratio = ( float(item.general_gpu_time) / gpu_total_time ) row_values = [ name, item.call, f'{format_time(item.cpu_time, unit=time_unit)} / {format_time(item.avg_cpu_time, unit=time_unit)} / {format_time(item.max_cpu_time, unit=time_unit)} / {format_time(item.min_cpu_time, unit=time_unit)} / {format_ratio(float(item.cpu_time) / total_time)}', '{} / {} / {} / {} / {}'.format( format_time(item.general_gpu_time, unit=time_unit), format_time( item.avg_general_gpu_time, unit=time_unit ), format_time( item.max_general_gpu_time, unit=time_unit ), format_time( item.min_general_gpu_time, unit=time_unit ), format_ratio(gpu_ratio), ), ] all_row_values.append(row_values) # Calculate the column width name_column_width = 0 calltime_width = 6 cpu_data_description_width = 40 gpu_data_description_width = 40 for row_values in all_row_values: if isinstance(row_values, str): continue if len(row_values[0]) > name_column_width: name_column_width = len(row_values[0]) if ( isinstance(row_values[1], int) and len(str(row_values[1])) > calltime_width ): calltime_width = len(str(row_values[1])) if len(row_values[2]) > cpu_data_description_width: cpu_data_description_width = len(row_values[2]) if len(row_values[3]) > gpu_data_description_width: gpu_data_description_width = len(row_values[3]) headers = [ 'Name', 'Calls', 'CPU Total / Avg / Max / Min / Ratio(%)', 'GPU Total / Avg / Max / Min / Ratio(%)', ] row_format_list = [""] header_sep_list = [""] line_length_list = [-SPACING_SIZE] add_column(name_column_width) add_column(calltime_width) add_column(cpu_data_description_width) add_column(gpu_data_description_width) row_format = row_format_list[0] header_sep = header_sep_list[0] line_length = line_length_list[0] # construct table string append(add_title(line_length, "UserDefined Summary")) append(f'Time unit: {time_unit}') append(header_sep) append(row_format.format(*headers)) append(header_sep) for row_values in all_row_values: if isinstance(row_values, str): append(add_title(line_length, row_values)) else: append(row_format.format(*row_values)) append('') append('') if views is None or SummaryView.MemoryView in views: # ----- Print Memory Summary Report ----- # if ( statistic_data.memory_summary.allocated_items or statistic_data.memory_summary.reserved_items ): for ( device_type, memory_events, ) in statistic_data.memory_summary.allocated_items.items(): all_row_values = [] sorted_items = sorted( memory_events.items(), key=lambda x: x[1].increase_size, reverse=True, ) for event_name, item in sorted_items: row_values = [ event_name, item.memory_type, item.allocation_count, item.free_count, item.allocation_size, item.free_size, item.increase_size, ] all_row_values.append(row_values) sorted_reserved_items = sorted( statistic_data.memory_summary.reserved_items[ device_type ].items(), key=lambda x: x[1].increase_size, reverse=True, ) for event_name, item in sorted_reserved_items: row_values = [ event_name, item.memory_type, item.allocation_count, item.free_count, item.allocation_size, item.free_size, item.increase_size, ] all_row_values.append(row_values) # Calculate the column width headers = [ 'Name', 'Type', 'Allocation Count', 'Free Count', 'Allocation Size', 'Free Size', 'Increased Size', ] row_format_list = [""] header_sep_list = [""] line_length_list = [-SPACING_SIZE] name_column_width = 50 number_column_width = 15 add_column(name_column_width) add_column(12) add_column(number_column_width) add_column(number_column_width) add_column(number_column_width) add_column(number_column_width) add_column(number_column_width) row_format = row_format_list[0] header_sep = header_sep_list[0] line_length = line_length_list[0] # construct table string append( add_title(line_length, f"Memory Summary - {device_type}") ) append( 'Peak Allocated Memory: {}'.format( statistic_data.memory_summary.peak_allocation_values[ device_type ] ) ) append( 'Peak Reserved Memory: {}'.format( statistic_data.memory_summary.peak_reserved_values[ device_type ] ) ) append(header_sep) append(row_format.format(*headers)) append(header_sep) for row_values in all_row_values: if isinstance(row_values, str): append(add_title(line_length, row_values)) else: append(row_format.format(*row_values)) append('') append('') return ''.join(result)