663 lines
24 KiB
Python
663 lines
24 KiB
Python
import json
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import math
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import os
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import re
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from typing import Dict, List, Optional, Set
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import torch
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import torch.utils.benchmark as benchmark
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from torch._C._profiler import (
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_EventType,
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_ExtraFields_PyCall,
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_ExtraFields_PyCCall,
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_ExtraFields_TorchOp,
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_ProfilerEvent,
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)
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from torch.profiler import profile
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from torch.profiler._utils import index_of_first_match, traverse_bfs, traverse_dfs
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class Pattern:
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"""
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Base class for all patterns, subclass this class and implement match()
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to define custom patterns.
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In subclass, define description and skip property.
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"""
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def __init__(self, prof: profile, should_benchmark: bool = False):
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self.prof = prof
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self.should_benchmark = should_benchmark
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self.name = "Please specify a name for pattern"
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self.description = "Please specify a description for pattern"
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self.url = ""
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assert prof.profiler is not None and prof.profiler.kineto_results is not None
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self.event_tree = prof.profiler.kineto_results.experimental_event_tree()
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self.tid_root: Dict[int, List[_ProfilerEvent]] = {}
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for event in self.event_tree:
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self.tid_root.setdefault(event.start_tid, []).append(event)
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@property
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def skip(self):
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return False
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def report(self, event: _ProfilerEvent):
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msg = (
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f"{self.description}\n[Source Code Location] {source_code_location(event)}"
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)
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return msg
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def eventTreeTraversal(self):
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"""
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Traverse the event tree and yield all events.
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Override this method in subclass to customize the traversal.
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"""
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yield from traverse_dfs(self.event_tree)
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def summary(self, events: List[_ProfilerEvent]):
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default_summary = f"{self.name}: {len(events)} events matched."
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if self.should_benchmark:
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# If benchmark summary is not empty, use it.
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return (
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self.benchmark_summary(events)
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if hasattr(self, "benchmark") # type: ignore[attr-defined]
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else default_summary
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)
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return default_summary
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def benchmark_summary(self, events: List[_ProfilerEvent]):
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def format_time(time_ns: int):
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unit_lst = ["ns", "us", "ms"]
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for unit in unit_lst:
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if time_ns < 1000:
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return f"{time_ns:.2f} {unit}"
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time_ns //= 1000
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return f"{time_ns:.2f} s"
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assert hasattr(self, "benchmark"), "Please implement benchmark()"
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shapes_factor_map = self.benchmark(events) # type: ignore[attr-defined]
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original_time = sum(event.duration_time_ns for event in events)
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new_time = sum(
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shapes_factor_map[input_shapes(event)] * event.duration_time_ns
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for event in events
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)
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return (
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f"{self.name}: {len(events)} events matched. "
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f"Total Estimated Speedup: {format_time(original_time - new_time)} ({round(original_time/new_time, 2)}X)"
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)
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def match(self, event: _ProfilerEvent):
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"""
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Return True if the event matches the pattern.
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This method should be overriden in subclass.
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"""
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raise NotImplementedError
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def matched_events(self):
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if self.skip:
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return []
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matched_events = []
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for event in self.eventTreeTraversal():
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if self.match(event):
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matched_events.append(event)
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return matched_events
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def root_of(self, event: _ProfilerEvent):
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while event.parent:
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event = event.parent
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return event
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def siblings_of(self, event: _ProfilerEvent):
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if event.parent:
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children = event.parent.children
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else:
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children = self.tid_root[event.start_tid]
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index = children.index(event)
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return children[:index], children[index + 1 :]
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def next_of(self, event: _ProfilerEvent):
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_, next_events = self.siblings_of(event)
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return next_events[0] if next_events else None
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def prev_of(self, event: _ProfilerEvent):
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prev_events, _ = self.siblings_of(event)
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return prev_events[-1] if prev_events else None
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def go_up_until(self, event: _ProfilerEvent, predicate):
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if not event:
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return None
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while event.parent and not predicate(event):
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event = event.parent
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return event
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# Patterns
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class NamePattern(Pattern):
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def __init__(self, prof: profile, name: str, should_benchmark: bool = False):
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super().__init__(prof, should_benchmark)
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self.description = f"Matched Name Event: {name}"
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self.name = name
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def match(self, event: _ProfilerEvent):
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return re.search(self.name, event.name) is not None
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class ExtraCUDACopyPattern(Pattern):
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"""
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This pattern identifies if we creates a constant tensor on CPU and immediately moves it to GPU.
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example: torch.zeros((100, 100)).to("cuda")
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Pattern:
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build-in method |build-in method
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... | aten::to
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aten::fill_/aten::zero_ | aten::_to_copy
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Algorithm:
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We start at node aten::to, go parent events' previous events,
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and check if we have a aten::fill_/aten::zero_ as we keep going down the tree.
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We always select the last child in the children list when we go down the tree.
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If at any step we failed, it is not a match.
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"""
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def __init__(self, prof: profile, should_benchmark: bool = False):
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super().__init__(prof, should_benchmark)
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self.name = "Extra CUDA Copy Pattern"
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self.description = "Filled a CPU tensor and immediately moved it to GPU. Please initialize it on GPU."
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self.url = "https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html#create-tensors-directly-on-the-target-device"
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self.init_ops = {
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"aten::fill_",
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"aten::zero_",
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"aten::normal_",
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"aten::uniform_",
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}
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@property
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def skip(self):
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return not self.prof.with_stack or not self.prof.record_shapes
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def match(self, event):
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# TODO: We should also check tensor identities
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if event.name != "aten::to":
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return False
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to_event = event
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if not event.children:
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return False
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event = event.children[-1]
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if event.name != "aten::_to_copy":
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return False
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if not event.children:
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return False
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event = event.children[-1]
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if event.name != "aten::copy_":
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return False
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# aten::copy_ should have the first 2 args dtype the same
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dtypes = input_dtypes(event)
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if len(dtypes) < 2:
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return False
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if dtypes[0] is None or dtypes[0] != dtypes[1]:
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return False
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event = to_event
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# Up one level
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event = event.parent
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if event is None:
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return False
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# Check if we have a aten::fill_ in previous leaf
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event = self.prev_of(event)
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if event is None:
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return False
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while event.children:
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event = event.children[-1]
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# aten::zero_ is a special optimzation case where fill_ is not called
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if event.name in self.init_ops:
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return True
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return event.name in self.init_ops
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# TODO: Check if tensor is reused
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def benchmark(self, events: List[_ProfilerEvent]):
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shapes_factor_map = {input_shapes(event): 0.0 for event in events}
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for shape in shapes_factor_map:
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size = shape[0]
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to_timer = benchmark.Timer(
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stmt='torch.ones(size).to("cuda")', globals={"size": size}
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)
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de_timer = benchmark.Timer(
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stmt='torch.ones(size, device="cuda")', globals={"size": size}
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)
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to_time = to_timer.timeit(10).mean
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de_time = de_timer.timeit(10).mean
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shapes_factor_map[shape] = de_time / to_time
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return shapes_factor_map
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class ForLoopIndexingPattern(Pattern):
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"""
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This pattern identifies if we use a for loop to index a tensor that
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can be vectorized.
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example:
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tensor = torch.empty((100, 100))
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for i in range(100):
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tensor[i] = i
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Pattern:
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aten::select | ... | aten::select | ... (Repeat)
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Algorithm:
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We start at node aten::select, and we check if we can find this alternating patterns.
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We also keep a dictionary to avoid duplicate match in the for loop.
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"""
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def __init__(self, prof: profile, should_benchmark: bool = False):
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super().__init__(prof, should_benchmark)
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self.name = "For Loop Indexing Pattern"
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self.description = "For loop indexing detected. Vectorization recommended."
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self.visited: Set[int] = set()
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def eventTreeTraversal(self):
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"""
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We need to use BFS traversal order to avoid duplicate match.
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"""
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yield from traverse_bfs(self.event_tree)
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def match(self, event: _ProfilerEvent):
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if event.name != "aten::select":
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return False
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if event.id in self.visited:
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return False
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repeat_count = 1
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_, next = self.siblings_of(event)
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if len(next) <= 1:
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return False
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# Custom event list matching
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def same_ops(list1, list2):
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if len(list1) != len(list2):
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return False
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for op1, op2 in zip(list1, list2):
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if op1.name != op2.name:
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return False
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return True
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# Record the ops between two aten::select
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next_select_idx = index_of_first_match(next, lambda e: e.name == "aten::select")
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if next_select_idx is None:
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return False
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indexing_ops = [event] + next[:next_select_idx]
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next = next[len(indexing_ops) - 1 :]
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for i in range(0, len(next), len(indexing_ops)):
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if same_ops(indexing_ops, next[i : i + len(indexing_ops)]):
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repeat_count += 1
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self.visited.add(next[i].id)
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else:
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break
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return repeat_count >= 10
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class FP32MatMulPattern(Pattern):
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def __init__(self, prof: profile, should_benchmark: bool = False):
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super().__init__(prof, should_benchmark)
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self.name = "FP32 MatMul Pattern"
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self.description = (
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"You are currently using GPU that supports TF32. "
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"Please enable TF32 by setting 'torch.backends.cuda.matmul.allow_tf32 = True'"
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)
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self.url = "https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
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@property
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def skip(self):
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if torch.version.hip is not None:
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has_tf32 = False
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else:
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# Anything less than sm_80 is not Ampere which doesn't support TF32
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has_tf32 = all(int(arch[3:]) >= 80 for arch in torch.cuda.get_arch_list())
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return has_tf32 is False or super().skip or not self.prof.record_shapes
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def match(self, event: _ProfilerEvent):
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# If we saw this pattern once, we don't need to match it again
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if event.tag != _EventType.TorchOp:
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return False
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assert isinstance(event.extra_fields, _ExtraFields_TorchOp)
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if event.name == "aten::mm":
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if event.extra_fields.allow_tf32_cublas is False:
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return True
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return False
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def report(self, event: _ProfilerEvent):
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return self.description
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def benchmark(self, events: List[_ProfilerEvent]):
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shapes_factor_map = {input_shapes(event): 0.0 for event in events}
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for shape in shapes_factor_map:
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matrixA = torch.randn(shape[0], device="cuda", dtype=torch.float32)
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matrixB = torch.randn(shape[1], device="cuda", dtype=torch.float32)
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fp32_timer = benchmark.Timer(
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stmt="torch.mm(matrixA, matrixB)",
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globals={"matrixA": matrixA, "matrixB": matrixB},
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)
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tf32_timer = benchmark.Timer(
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stmt="torch.mm(matrixA, matrixB)",
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setup="torch.backends.cuda.matmul.allow_tf32 = True",
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globals={"matrixA": matrixA, "matrixB": matrixB},
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)
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torch.backends.cuda.matmul.allow_tf32 = False
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fp32_time = fp32_timer.timeit(10).mean
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tf32_time = tf32_timer.timeit(10).mean
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shapes_factor_map[shape] = tf32_time / fp32_time
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return shapes_factor_map
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class OptimizerSingleTensorPattern(Pattern):
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"""
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This pattern identifies if we are using the single-tensor version of an optimizer.
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example:
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optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
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By adding foreach=True to enable multi-tensor optimizer, we can gain speedup when
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the kernels are relatively small.
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Pattern:
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XXXXX: _single_tenser_<OPTIMIZER_NAME>
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Algorithm:
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String match
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"""
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def __init__(self, prof: profile, should_benchmark: bool = False):
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super().__init__(prof, should_benchmark)
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self.name = "Optimizer Single Tensor Pattern"
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self.optimizers_with_foreach = ["adam", "sgd", "adamw"]
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self.description = (
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"Deteced optimizer running with single tensor implementation. "
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"Please enable multi tensor implementation by passing 'foreach=True' into optimizer."
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)
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self.url = ""
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def match(self, event: _ProfilerEvent):
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for optimizer in self.optimizers_with_foreach:
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if event.name.endswith(f"_single_tensor_{optimizer}"):
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return True
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return False
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class SynchronizedDataLoaderPattern(Pattern):
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"""
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This pattern identifies if we are using num_workers=0 in DataLoader.
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example:
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torch.utils.data.DataLoader(dataset, batch_size=batch_size)
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Add num_workers=N to the arguments. N depends on system configuration.
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Pattern:
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dataloader.py(...): __iter__
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dataloader.py(...): _get_iterator
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NOT dataloader.py(...): check_worker_number_rationality
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Algorithm:
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If we don't see check_worker_number_rationality call in the dataloader __iter__,
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It is not an asynchronous dataloader.
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"""
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def __init__(self, prof: profile, should_benchmark: bool = False):
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super().__init__(prof, should_benchmark)
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self.name = "Synchronized DataLoader Pattern"
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self.description = (
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"Detected DataLoader running with synchronized implementation. "
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"Please enable asynchronous dataloading by setting num_workers > 0 when initializing DataLoader."
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)
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self.url = (
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"https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html"
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"#enable-async-data-loading-and-augmentation"
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)
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def match(self, event: _ProfilerEvent):
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def is_dataloader_function(name: str, function_name: str):
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return name.startswith(
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os.path.join("torch", "utils", "data", "dataloader.py")
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) and name.endswith(function_name)
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# TODO: fixme! Due to lifetime issues of the function name, this field might
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# actually point to an already freed string when the even is a PyCall.
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# Just silently skip this to unblock testing.
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try:
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event.name
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except UnicodeDecodeError:
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return False
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if not is_dataloader_function(event.name, "__iter__"):
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return False
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if not event.children:
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return False
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event = event.children[0]
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if not is_dataloader_function(event.name, "_get_iterator"):
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return False
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if not event.children:
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return False
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event = event.children[0]
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return not is_dataloader_function(event.name, "check_worker_number_rationality")
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# TODO: We should also check if the loader is bottleneck.
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class GradNotSetToNonePattern(Pattern):
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"""
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This pattern identifies if we are not setting grad to None in zero_grad.
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example:
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optimizer.zero_grad()
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By setting set_to_none=True, we can gain speedup
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Pattern:
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XXXXX: _zero_grad
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NOT aten::zeros
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aten::zero_
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aten::zero_ is called on each parameter in the model.
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We also want to make sure it is not called by aten::zeros.
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Algorithm:
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String match
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"""
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def __init__(self, prof: profile, should_benchmark: bool = False):
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super().__init__(prof, should_benchmark)
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self.name = "Gradient Set To Zero Instead of None Pattern"
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self.description = (
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"Detected gradient set to zero instead of None. "
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"Please add 'set_to_none=True' when calling zero_grad()."
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)
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self.url = (
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"https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html"
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"#disable-gradient-calculation-for-validation-or-inference"
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)
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def match(self, event: _ProfilerEvent):
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if not event.name.endswith(": zero_grad"):
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return False
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if not event.children:
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return False
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for sub_event in traverse_dfs(event.children):
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if (
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sub_event.name == "aten::zero_"
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and sub_event.parent.name != "aten::zeros"
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):
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return True
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# TODO: We should also check if the optimizer's numerical behavior will change.
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return False
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class Conv2dBiasFollowedByBatchNorm2dPattern(Pattern):
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"""
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This pattern identifies if we are enabling bias in Conv2d which is followed by BatchNorm2d.
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Bias doesn't do anything when followed by batchnorm.
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Pattern:
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nn.Module: Conv2d | nn.Module: BatchNorm2d
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...
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aten::conv2d AND dtype of third argument is not null
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The third argument is the bias
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Algorithm:
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String match
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"""
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def __init__(self, prof: profile, should_benchmark: bool = False):
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super().__init__(prof, should_benchmark)
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self.name = "Enabling Bias in Conv2d Followed By BatchNorm Pattern"
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self.description = "Detected bias enabled in Conv2d that is followed by BatchNorm2d. Please set 'bias=False' in Conv2d."
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self.url = (
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"https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html"
|
|
"#disable-bias-for-convolutions-directly-followed-by-a-batch-norm"
|
|
)
|
|
|
|
@property
|
|
def skip(self):
|
|
return self.prof.record_shapes is False or super().skip
|
|
|
|
def match(self, event: _ProfilerEvent):
|
|
if event.name != "aten::conv2d":
|
|
return False
|
|
if len(input_dtypes(event)) < 3 or input_dtypes(event)[2] is None:
|
|
return False
|
|
# This means bias=True
|
|
event = self.go_up_until(
|
|
event, lambda e: e.name.startswith("nn.Module: Conv2d")
|
|
)
|
|
if not event:
|
|
return False
|
|
event = self.next_of(event)
|
|
if not event:
|
|
return False
|
|
return event.name.startswith("nn.Module: BatchNorm2d")
|
|
|
|
|
|
class MatMulDimInFP16Pattern(Pattern):
|
|
def __init__(self, prof: profile, should_benchmark: bool = False):
|
|
super().__init__(prof, should_benchmark)
|
|
self.name = "Matrix Multiplication Dimension Not Aligned Pattern"
|
|
self.description = "Detected matmul with dimension not aligned. Please use matmul with aligned dimension."
|
|
self.url = "https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html#use-mixed-precision-and-amp"
|
|
|
|
@property
|
|
def skip(self):
|
|
return not self.prof.with_stack or not self.prof.record_shapes
|
|
|
|
def match(self, event: _ProfilerEvent):
|
|
def mutiple_of(shapes, multiple):
|
|
return all(dim % multiple == 0 for shape in shapes for dim in shape[-2:])
|
|
|
|
if event.name not in ("aten::mm", "aten::bmm", "aten::addmm"):
|
|
return False
|
|
if not input_dtypes(event):
|
|
return False
|
|
arg_dtype = input_dtypes(event)[0]
|
|
if arg_dtype in (torch.bfloat16, torch.half) and not mutiple_of(
|
|
input_shapes(event), 8
|
|
):
|
|
return True
|
|
return False
|
|
|
|
def benchmark(self, events: List[_ProfilerEvent]):
|
|
def closest_multiple(shapes, multiple):
|
|
return [multiple * math.ceil(shape / multiple) for shape in shapes]
|
|
|
|
shapes_factor_map = {input_shapes(event): 0.0 for event in events}
|
|
for shape in shapes_factor_map:
|
|
matrixA = torch.randn(shape[0], device="cuda", dtype=torch.float16)
|
|
matrixB = torch.randn(shape[1], device="cuda", dtype=torch.float16)
|
|
not_aligned_dim_timer = benchmark.Timer(
|
|
stmt="torch.mm(matrixA, matrixB)",
|
|
globals={"matrixA": matrixA, "matrixB": matrixB},
|
|
)
|
|
matrixA = torch.randn(
|
|
closest_multiple(shape[0], 8), device="cuda", dtype=torch.float16
|
|
)
|
|
matrixB = torch.randn(
|
|
closest_multiple(shape[1], 8), device="cuda", dtype=torch.float16
|
|
)
|
|
aligned_dim_timer = benchmark.Timer(
|
|
stmt="torch.mm(matrixA, matrixB)",
|
|
globals={"matrixA": matrixA, "matrixB": matrixB},
|
|
)
|
|
not_aligned_dim_time = not_aligned_dim_timer.timeit(10).mean
|
|
aligned_dim_time = aligned_dim_timer.timeit(10).mean
|
|
shapes_factor_map[shape] = aligned_dim_time / not_aligned_dim_time
|
|
return shapes_factor_map
|
|
|
|
|
|
def source_code_location(event: Optional[_ProfilerEvent]):
|
|
while event:
|
|
if event.tag == _EventType.PyCall or event.tag == _EventType.PyCCall:
|
|
assert isinstance(
|
|
event.extra_fields, (_ExtraFields_PyCall, _ExtraFields_PyCCall)
|
|
)
|
|
if not event.extra_fields.caller.file_name.startswith("torch" + os.sep):
|
|
return f"{event.extra_fields.caller.file_name}:{event.extra_fields.caller.line_number}"
|
|
event = event.parent
|
|
return "No source code location found"
|
|
|
|
|
|
def input_shapes(event: _ProfilerEvent):
|
|
assert isinstance(event.extra_fields, _ExtraFields_TorchOp)
|
|
return tuple(tuple(getattr(i, "sizes", ())) for i in event.extra_fields.inputs)
|
|
|
|
|
|
def input_dtypes(event: _ProfilerEvent):
|
|
assert isinstance(event.extra_fields, _ExtraFields_TorchOp)
|
|
return tuple(getattr(i, "dtype", None) for i in event.extra_fields.inputs)
|
|
|
|
|
|
def report_all_anti_patterns(
|
|
prof,
|
|
should_benchmark: bool = False,
|
|
print_enable: bool = True,
|
|
json_report_dir: Optional[str] = None,
|
|
):
|
|
report_dict: Dict = {}
|
|
anti_patterns = [
|
|
ExtraCUDACopyPattern(prof, should_benchmark),
|
|
# ForLoopIndexingPattern(prof, should_benchmark),
|
|
FP32MatMulPattern(prof, should_benchmark),
|
|
OptimizerSingleTensorPattern(prof, should_benchmark),
|
|
SynchronizedDataLoaderPattern(prof, should_benchmark),
|
|
GradNotSetToNonePattern(prof, should_benchmark),
|
|
Conv2dBiasFollowedByBatchNorm2dPattern(prof, should_benchmark),
|
|
MatMulDimInFP16Pattern(prof, should_benchmark),
|
|
]
|
|
reported = set()
|
|
summaries = []
|
|
message_list = [f"{'-'*40}TorchTidy Report{'-'*40}"]
|
|
message_list.append("Matched Events:")
|
|
|
|
for anti_pattern in anti_patterns:
|
|
matched_events = anti_pattern.matched_events()
|
|
if not matched_events:
|
|
continue
|
|
summaries.append(anti_pattern.summary(matched_events))
|
|
for event in matched_events:
|
|
report_msg = anti_pattern.report(event)
|
|
if report_msg not in reported:
|
|
message_list.append(report_msg)
|
|
reported.add(report_msg)
|
|
src_location, line_no = source_code_location(event).split(":")
|
|
report_dict.setdefault(src_location, []).append(
|
|
{
|
|
"line_number": int(line_no),
|
|
"name": anti_pattern.name,
|
|
"url": anti_pattern.url,
|
|
"message": anti_pattern.description,
|
|
}
|
|
)
|
|
|
|
if json_report_dir is not None:
|
|
json_report_path = os.path.join(json_report_dir, "torchtidy_report.json")
|
|
if os.path.exists(json_report_path):
|
|
with open(json_report_path) as f:
|
|
exisiting_report = json.load(f)
|
|
exisiting_report.update(report_dict)
|
|
report_dict = exisiting_report
|
|
with open(json_report_path, "w") as f:
|
|
json.dump(report_dict, f, indent=4)
|
|
|
|
message_list.append("Summary:")
|
|
message_list += summaries
|
|
message_list.append(f"{'-'*40}TorchTidy Report{'-'*40}")
|
|
if print_enable:
|
|
print("\n".join(message_list))
|