Upgrade Python to 3.10 and allow user to specify the number of warmup…

… iterations (facebookresearch#189)

Summary:

Meta production is on Python 3.10. PARAM lint is still on 3.8. I saw Lint reported some issues related to type hint syntax. To fix it, I upgrade python to 3.10.

ALso changed the option to allow user to specify the number of warmup iteration runs. Also print out the wall clock time for each iteration and the average wall clock time for the whole run.

Reviewed By: kingchc

Differential Revision: D66801912

et_replay/comm/backend/base_backend.py

@@ -5,7 +5,6 @@
 
 import logging
 from abc import ABC, abstractmethod
-from typing import Dict, List, Optional
 
 import torch
 

et_replay/comm/backend/pytorch_dist_backend.py

@@ -10,7 +10,6 @@
 
 from itertools import cycle
 from time import sleep
-from typing import Dict, List, Optional, Tuple
 
 import numpy as np
 import torch

et_replay/comm/commsTraceParser.py

@@ -4,7 +4,6 @@
 import json
 
 import logging
-from typing import List, Tuple
 
 from et_replay import ExecutionTrace
 from et_replay.comm import comms_utils

et_replay/comm/comms_utils.py

@@ -17,7 +17,7 @@
 from collections.abc import Callable
 from contextlib import ContextDecorator
 from io import StringIO
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any
 
 try:
     from param_bench.train.comms.pt.fb.internals import (

et_replay/execution_trace.py

@@ -9,7 +9,7 @@
 from collections.abc import Iterable
 from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Dict, List, Optional, Set, TextIO, Tuple
+from typing import Any, TextIO
 
 import pydot
 

et_replay/tools/comm_replay.py

@@ -11,7 +11,6 @@
 import logging
 import os
 import time
-from typing import Dict, List, Set, Tuple, Union
 
 import numpy as np
 import torch
@@ -114,7 +113,7 @@ def __init__(self):
         self.max_msg_cnt = 0  # 0 means no limit
         self.num_msg = 0
         self.is_blocking = False
-        self.do_warm_up = False
+        self.warmup_iter = 5
         self.reuse_tensors = False
 
         self.allowList = ""
@@ -215,10 +214,10 @@ def readArgs(self, parser: argparse.ArgumentParser) -> argparse.Namespace:
             help="Only replay first N operations (0 means no limit)",
         )
         parser.add_argument(
-            "--do-warm-up",
-            action="store_true",
-            default=self.do_warm_up,
-            help="Toggle to enable performing extra replaying for warm-up",
+            "--warmup-iter",
+            type=int,
+            default=self.warmup_iter,
+            help="Number of warmup iterations",
         )
         parser.add_argument(
             "--reuse-tensors",
@@ -275,7 +274,7 @@ def readArgs(self, parser: argparse.ArgumentParser) -> argparse.Namespace:
             "--profiler-num-replays-start",
             type=int,
             default=self.profiler_num_replays_start,
-            help=f"Replay iteration to start collecting profiler after warmup (if --do-warm-up is True). Default start from {self.profiler_num_replays_start} replay if --enables-profiler is  True",
+            help=f"Replay iteration to start collecting profiler after warmup runs. Default start from {self.profiler_num_replays_start} replay if --enables-profiler is  True",
         )
         parser.add_argument(
             "--profiler-num-replays",
@@ -393,12 +392,19 @@ def reportBenchTime(self):
         if not self.is_dry_run:
             print("\n{} Performance of replayed comms {}".format("=" * 20, "=" * 20))
             print(
-                "{}\n Total latency (us) of comms in trace {}: \n{}".format(
+                "{}\n Total latency (us) of comms in trace: {}. \n{}".format(
                     "-" * 50,
                     self.totalTraceLatency,
                     "-" * 50,
                 )
             )
+            print(
+                "{}\n Average latency (us) of comms in trace: {}. \n{}".format(
+                    "-" * 50,
+                    self.totalTraceLatency / self.num_replays,
+                    "-" * 50,
+                )
+            )
             for coll, lats in self.collLat.items():
                 if len(lats) == 0:
                     continue
@@ -1199,9 +1205,7 @@ def benchTime(self, commsParams: commsParamsHolderBase) -> None:
         """
         if commsParams.enable_profiler:
             # num of iterations to skip
-            numWarmupIters = (
-                1 if self.do_warm_up else 0
-            ) + self.profiler_num_replays_start
+            numWarmupIters = self.warmup_iter + self.profiler_num_replays_start
             # num of iterations to profile, at most num_replays iterations
             numProfileIters = (
                 self.profiler_num_replays
@@ -1215,29 +1219,17 @@ def benchTime(self, commsParams: commsParamsHolderBase) -> None:
                 numIters=numProfileIters,
             )
 
-        # warm-up
-        if self.do_warm_up:
-            if self.collectiveArgs.enable_profiler:
-                comms_utils.sampleProfiler()
-            self.replayIter = -1
-            self.replayTrace(commsParams=commsParams, warmup=True)
-        self.resetComms()
-
-        # sync everything before starting real runs
-        with paramProfile(description="# PARAM replay warmup post-replay global sync"):
-            self.backendFuncs.sync_barrier(self.collectiveArgs)
-
         if self.backendFuncs.get_global_rank() == 0:
             logger.info(
                 f"\n+ {self.max_msg_cnt} messages in the trace...replaying (if present) {list(self.allowList)}"
             )
             for coll, sizes in self.collInMsgBytes.items():
                 logger.info(f"\t{coll}: {len(sizes)}")
 
-        traceStartTime = time.monotonic_ns()
-        for i in range(self.num_replays):
-            if self.backendFuncs.get_global_rank() == 0:
-                logger.info(f"Replay #{i}")
+        traceStartTime = 0
+        for i in range(self.warmup_iter + self.num_replays):
+            if i == self.warmup_iter:
+                traceStartTime = time.monotonic_ns()
 
             if self.collectiveArgs.enable_profiler:
                 comms_utils.sampleProfiler()
@@ -1248,6 +1240,9 @@ def benchTime(self, commsParams: commsParamsHolderBase) -> None:
             except NameError:
                 pass
 
+            if self.backendFuncs.get_global_rank() == 0:
+                s = time.monotonic_ns()
+
             # replay comms trace
             self.replayIter = i
             self.replayTrace(commsParams=commsParams, warmup=False)
@@ -1259,6 +1254,10 @@ def benchTime(self, commsParams: commsParamsHolderBase) -> None:
             ):
                 self.backendFuncs.sync_barrier(self.collectiveArgs)
 
+            if self.backendFuncs.get_global_rank() == 0:
+                e = time.monotonic_ns()
+                logger.info(f"Replay #{i} took {(e-s)/1e3:.2f} us")
+
         # record how long it took for trace-replay to complete
         traceEndTime = time.monotonic_ns()
         self.totalTraceLatency = (traceEndTime - traceStartTime) / 1e3  # make it us
@@ -1457,7 +1456,7 @@ def initBench(
         self.shrink = args.auto_shrink
         self.max_msg_cnt = args.max_msg_cnt
         self.is_blocking = args.blocking
-        self.do_warm_up = args.do_warm_up
+        self.warmup_iter = args.warmup_iter
         self.reuse_tensors = args.reuse_tensors
         self.allowList = args.allow_ops
         if args.output_ranks == "all":

et_replay/tools/et_replay.py

@@ -7,7 +7,6 @@
 import time
 from collections import defaultdict
 from datetime import datetime
-from typing import Dict
 
 import numpy as np
 import torch

train/comms/pt/commsTraceParser.py

@@ -3,8 +3,6 @@
 
 import json
 
-from typing import List, Tuple
-
 from et_replay import ExecutionTrace
 
 from param_bench.train.comms.pt import comms_utils
@@ -188,7 +186,7 @@ def _parseKinetoUnitrace(in_trace: list, target_rank: int) -> list:
 
 def _getTensorInfoFromPyTorchETEntry(
     tensor_container: list, container_type: str
-) -> tuple[int, int, str]:
+) -> tuple[int, str]:
     """
     Extract message size, tensor count, type from PyTorch ET entry inputs/outputs field.
     NOTE: This format can be changed at anytime. TODO: When an extract/parsing tool is available in ATC, switch to it.