# Copyright The Lightning AI team.
#
# 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.
# Adapted from https://github.com/mosaicml/composer/blob/f2a2dc820/composer/callbacks/speed_monitor.py
from collections import deque
from typing import TYPE_CHECKING, Any, Callable, Deque, Dict, List, Optional, TypeVar, Union
import torch
from typing_extensions import override
from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_1
from lightning.fabric.utilities.rank_zero import rank_zero_only, rank_zero_warn
if TYPE_CHECKING:
from lightning.fabric import Fabric
from lightning.fabric.plugins import Precision
_THROUGHPUT_METRICS = Dict[str, Union[int, float]]
# The API design of this class follows `torchmetrics.Metric` but it doesn't need to be an actual Metric because there's
# no need for synchronization or reduction as it doesn't use Tensors at all.
class Throughput:
"""Computes throughput.
+------------------------+-------------------------------------------------------------------------------------+
| Key | Value |
+========================+=====================================================================================+
| batches_per_sec | Rolling average (over ``window_size`` most recent updates) of the number of batches |
| | processed per second |
+--------------------------+-----------------------------------------------------------------------------------+
| samples_per_sec | Rolling average (over ``window_size`` most recent updates) of the number of samples |
| | processed per second |
+--------------------------+-----------------------------------------------------------------------------------+
| items_per_sec | Rolling average (over ``window_size`` most recent updates) of the number of items |
| | processed per second |
+--------------------------+-----------------------------------------------------------------------------------+
| flpps_per_sec | Rolling average (over ``window_size`` most recent updates) of the number of flops |
| | processed per second |
+--------------------------+-----------------------------------------------------------------------------------+
| device/batches_per_sec | batches_per_sec divided by world size |
+--------------------------+-----------------------------------------------------------------------------------+
| device/samples_per_sec | samples_per_sec divided by world size |
+--------------------------+-----------------------------------------------------------------------------------+
| device/items_per_sec | items_per_sec divided by world size. This may include padding depending on the data |
+--------------------------+-----------------------------------------------------------------------------------+
| device/flops_per_sec | flops_per_sec divided by world size. |
+--------------------------+-----------------------------------------------------------------------------------+
| device/mfu | device/flops_per_sec divided by world size. |
+--------------------------+-----------------------------------------------------------------------------------+
| time | Total elapsed time |
+--------------------------+-----------------------------------------------------------------------------------+
| batches | Total batches seen |
+--------------------------+-----------------------------------------------------------------------------------+
| samples | Total samples seen |
+--------------------------+-----------------------------------------------------------------------------------+
| lengths | Total items seen |
+--------------------------+-----------------------------------------------------------------------------------+
Example::
throughput = Throughput()
t0 = time()
for i in range(1000):
do_work()
if torch.cuda.is_available(): torch.cuda.synchronize() # required or else time() won't be correct
throughput.update(time=time() - t0, samples=i)
if i % 10 == 0:
print(throughput.compute())
Notes:
- The implementation assumes that devices FLOPs are all the same as it normalizes by the world size and only
takes a single ``available_flops`` value.
- items_per_sec, flops_per_sec and MFU do not account for padding if present. We suggest using
samples_per_sec or batches_per_sec to measure throughput under this circumstance.
Args:
available_flops: Number of theoretical flops available for a single device.
world_size: Number of devices available across hosts. Global metrics are not included if the world size is 1.
window_size: Number of batches to use for a rolling average.
separator: Key separator to use when creating per-device and global metrics.
"""
def __init__(
self, available_flops: Optional[float] = None, world_size: int = 1, window_size: int = 100, separator: str = "/"
) -> None:
self.available_flops = available_flops
self.separator = separator
assert world_size > 0
self.world_size = world_size
# throughput is computed over a window of values. at least 2 is enforced since it looks at the difference
# between the first and last elements
assert window_size > 1
# custom class instead of `deque(maxlen=)` because it's easy for users to mess up their timer/counters and log
# values that do not increase monotonically. this class will raise an error if that happens.
self._time: _MonotonicWindow[float] = _MonotonicWindow(maxlen=window_size)
self._batches: _MonotonicWindow[int] = _MonotonicWindow(maxlen=window_size)
self._samples: _MonotonicWindow[int] = _MonotonicWindow(maxlen=window_size)
self._lengths: _MonotonicWindow[int] = _MonotonicWindow(maxlen=window_size)
self._flops: Deque[int] = deque(maxlen=window_size)
def update(
self,
*,
time: float,
batches: int,
samples: int,
lengths: Optional[int] = None,
flops: Optional[int] = None,
) -> None:
"""Update throughput metrics.
Args:
time: Total elapsed time in seconds. It should monotonically increase by the iteration time with each
call.
batches: Total batches seen per device. It should monotonically increase with each call.
samples: Total samples seen per device. It should monotonically increase by the batch size with each call.
lengths: Total length of the samples seen. It should monotonically increase by the lengths of a batch with
each call.
flops: Flops elapased per device since last ``update()`` call. You can easily compute this by using
:func:`measure_flops` and multiplying it by the number of batches that have been processed.
The value might be different in each device if the batch size is not the same.
"""
self._time.append(time)
if samples < batches:
raise ValueError(f"Expected samples ({samples}) to be greater or equal than batches ({batches})")
self._batches.append(batches)
self._samples.append(samples)
if lengths is not None:
if lengths < samples:
raise ValueError(f"Expected lengths ({lengths}) to be greater or equal than samples ({samples})")
self._lengths.append(lengths)
if len(self._samples) != len(self._lengths):
raise RuntimeError(
f"If lengths are passed ({len(self._lengths)}), there needs to be the same number of samples"
f" ({len(self._samples)})"
)
if flops is not None:
# sum of flops across ranks
self._flops.append(flops * self.world_size)
def compute(self) -> _THROUGHPUT_METRICS:
"""Compute throughput metrics."""
metrics = {
"time": self._time[-1],
"batches": self._batches[-1],
"samples": self._samples[-1],
}
if self._lengths:
metrics["lengths"] = self._lengths[-1]
add_global_metrics = self.world_size > 1
# a different but valid design choice would be to still compute all these metrics even if the window of values
# has not been filled
if len(self._time) == self._time.maxlen:
elapsed_time = self._time[-1] - self._time[0]
elapsed_batches = self._batches[-1] - self._batches[0]
elapsed_samples = self._samples[-1] - self._samples[0]
# we are safe from ZeroDivisionError thanks to `_MonotonicWindow`
dev_samples_per_sec = elapsed_samples / elapsed_time
dev_batches_per_sec = elapsed_batches / elapsed_time
metrics.update({
f"device{self.separator}batches_per_sec": elapsed_batches / elapsed_time,
f"device{self.separator}samples_per_sec": dev_samples_per_sec,
})
if add_global_metrics:
samples_per_sec = dev_batches_per_sec * self.world_size
metrics.update({
"batches_per_sec": samples_per_sec,
"samples_per_sec": dev_samples_per_sec * self.world_size,
})
if len(self._lengths) == self._lengths.maxlen:
elapsed_lengths = self._lengths[-1] - self._lengths[0]
dev_items_per_sec = elapsed_lengths / elapsed_time
metrics[f"device{self.separator}items_per_sec"] = dev_items_per_sec
if add_global_metrics:
items_per_sec = dev_items_per_sec * self.world_size
metrics["items_per_sec"] = items_per_sec
if len(self._flops) == self._flops.maxlen:
elapsed_flops = sum(self._flops) - self._flops[0]
elapsed_time = self._time[-1] - self._time[0]
flops_per_sec = elapsed_flops / elapsed_time
dev_flops_per_sec = flops_per_sec / self.world_size
if add_global_metrics:
metrics["flops_per_sec"] = flops_per_sec
metrics[f"device{self.separator}flops_per_sec"] = dev_flops_per_sec
if self.available_flops:
metrics[f"device{self.separator}mfu"] = dev_flops_per_sec / self.available_flops
return metrics
def reset(self) -> None:
self._time.clear()
self._batches.clear()
self._samples.clear()
self._lengths.clear()
self._flops.clear()
class ThroughputMonitor(Throughput):
r"""Computes throughput.
This class will automatically keep a count of the number of log calls (``step``). But that can be modified as
desired. For manual logging, using :class:`Throughput` directly might be desired.
Example::
logger = ...
fabric = Fabric(logger=logger)
throughput = ThroughputMonitor(fabric)
t0 = time()
for i in range(1, 100):
do_work()
if torch.cuda.is_available(): torch.cuda.synchronize() # required or else time() won't be correct
throughput.update(time=time() - t0, batches=i, samples=i)
if i % 10 == 0:
throughput.compute_and_log(step=i)
Args:
fabric: The Fabric object.
\**kwargs: See available parameters in :class:`Throughput`
"""
def __init__(self, fabric: "Fabric", **kwargs: Any) -> None:
fabric._validate_launched() # otherwise world_size might be incorrect
dtype = _plugin_to_compute_dtype(fabric.strategy.precision)
available_flops = get_available_flops(fabric.device, dtype)
super().__init__(available_flops=available_flops, world_size=fabric.world_size, **kwargs)
self._fabric = fabric
self.step = -1
self.update = rank_zero_only(self.update) # type: ignore[method-assign]
self.compute = rank_zero_only(self.compute, default={}) # type: ignore[method-assign]
self.compute_and_log = rank_zero_only(self.compute_and_log, default={}) # type: ignore[method-assign]
self.reset = rank_zero_only(self.reset) # type: ignore[method-assign]
def compute_and_log(self, step: Optional[int] = None, **kwargs: Any) -> _THROUGHPUT_METRICS:
r"""See :meth:`Throughput.compute`
Args:
step: Can be used to override the logging step.
\**kwargs: See available parameters in :meth:`Throughput.compute`
"""
self.step = (self.step + 1) if step is None else step
metrics = self.compute(**kwargs)
self._fabric.log_dict(metrics=metrics, step=self.step)
return metrics
[docs]def measure_flops(
model: torch.nn.Module,
forward_fn: Callable[[], torch.Tensor],
loss_fn: Optional[Callable[[torch.Tensor], torch.Tensor]] = None,
) -> int:
"""Utility to compute the total number of FLOPs used by a module during training or during inference.
It's recommended to create a meta-device model for this:
Example::
with torch.device("meta"):
model = MyModel()
x = torch.randn(2, 32)
model_fwd = lambda: model(x)
fwd_flops = measure_flops(model, model_fwd)
model_loss = lambda y: y.sum()
fwd_and_bwd_flops = measure_flops(model, model_fwd, model_loss)
Args:
model: The model whose FLOPs should be measured.
forward_fn: A function that runs ``forward`` on the model and returns the result.
loss_fn: A function that computes the loss given the ``forward_fn`` output. If provided, the loss and `backward`
FLOPs will be included in the result.
"""
if not _TORCH_GREATER_EQUAL_2_1:
raise ImportError("`measure_flops` requires PyTorch >= 2.1.")
from torch.utils.flop_counter import FlopCounterMode
flop_counter = FlopCounterMode(model, display=False)
with flop_counter:
if loss_fn is None:
forward_fn()
else:
loss_fn(forward_fn()).backward()
return flop_counter.get_total_flops()
_CUDA_FLOPS: Dict[str, Dict[Union[str, torch.dtype], float]] = {
# Hopper
# source: https://resources.nvidia.com/en-us-tensor-core
"h100 nvl": {
torch.float64: 67e12,
torch.float32: 133.8e12,
"tfloat32": 989.4e12,
torch.bfloat16: 1978.8e12,
torch.float16: 1978.8e12,
torch.int8: 3957.8e12,
},
"h100 sxm": {
torch.float64: 33.5e12,
torch.float32: 66.9e12,
"tfloat32": 494.7e12,
torch.bfloat16: 989.4e12,
torch.float16: 989.4e12,
torch.int8: 1978.9e12,
},
"h100 pcie": {
torch.float64: 25.6e12,
torch.float32: 51.2e12,
"tfloat32": 378e12,
torch.bfloat16: 756e12,
torch.float16: 756e12,
torch.int8: 1513e12,
},
# Ada
# source: https://images.nvidia.com/aem-dam/Solutions/Data-Center/l4/nvidia-ada-gpu-architecture-whitepaper-v2.1.pdf
"rtx 4090": {
torch.float32: 82.6e12,
"tfloat32": 82.6e12,
torch.bfloat16: 82.6e12,
torch.float16: 82.6e12,
torch.int8: 660.6e12,
"int4": 1321.2e12,
},
"rtx 4080": {
torch.float32: 48.7e12,
"tfloat32": 48.7e12,
torch.bfloat16: 48.7e12,
torch.float16: 48.7e12,
torch.int8: 389.9e12,
"int4": 779.8e12,
},
"l4": {
torch.float32: 30.3e12,
"tfloat32": 60e12,
torch.bfloat16: 121e12,
torch.float16: 121e12,
torch.int8: 242e12,
"int4": 484e12,
},
"l40": {
torch.float32: 90.5e12,
"tfloat32": 90.5e12,
torch.bfloat16: 181e12,
torch.float16: 181e12,
torch.int8: 362e12,
"int4": 724e12,
},
# Ampere
# source: https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf
# sxm and pcie have same flop counts
"a100": {
torch.float64: 9.7e12,
torch.float32: 19.5e12,
"tfloat32": 156e12,
torch.bfloat16: 312e12,
torch.float16: 312e12,
torch.int8: 624e12,
},
"a6000": {
torch.float32: 38.7e12,
"tfloat32": 77.4e12,
torch.bfloat16: 38.7e12,
torch.float16: 38.7e12,
torch.int8: 309.7e12,
"int4": 619.3e12,
},
"a40": {
torch.float32: 37.4e12,
"tfloat32": 74.8e12,
torch.bfloat16: 37.4e12,
torch.float16: 37.4e12,
torch.int8: 299.3e12,
"int4": 598.7e12,
},
# source: https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a10/pdf/a10-datasheet.pdf
"a10g": {
torch.float32: 31.2e12,
"tfloat32": 62.5e12,
torch.bfloat16: 125e12,
torch.float16: 125e12,
torch.int8: 250e12,
"int4": 500e12,
},
"rtx 3090 ti": {
torch.float32: 40e12,
"tfloat32": 40e12,
torch.bfloat16: 40e12,
torch.float16: 40e12,
torch.int8: 320e12,
"int4": 640e12,
},
"rtx 3090": {
torch.float32: 35.6e12,
"tfloat32": 35.6e12,
torch.bfloat16: 35.6e12,
torch.float16: 35.6e12,
torch.int8: 284e12,
"int4": 568e12,
},
"rtx 3080 ti": {
torch.float32: 34.1e12,
"tfloat32": 34.1e12,
torch.bfloat16: 34.1e12,
torch.float16: 34.1e12,
torch.int8: 272.8e12,
"int4": 546.6e12,
},
"rtx 3080": {
torch.float32: 29.8e12,
"tfloat32": 29.8e12,
torch.bfloat16: 29.8e12,
torch.float16: 29.8e12,
torch.int8: 238e12,
"int4": 476e12,
},
"rtx 3070": {
torch.float32: 20.3e12,
"tfloat32": 20.3e12,
torch.bfloat16: 20.3e12,
torch.float16: 20.3e12,
torch.int8: 162.6e12,
"int4": 325.2e12,
},
# Turing
# source: https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/tesla-t4/t4-tensor-core-datasheet-951643.pdf
# sxm and pcie have same flop counts
"t4": {
torch.float32: 8.1e12,
torch.float16: 65e12,
torch.int8: 130e12,
"int4": 260e12,
},
# https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/quadro-product-literature/quadro-rtx-5000-data-sheet-us-nvidia-704120-r4-web.pdf
"quadro rtx 5000": {
torch.float32: 11.2e12,
torch.float16: 89.2e12,
},
"rtx 2080 super": {
torch.float32: 11.2e12,
torch.float16: 22.3e12,
torch.int8: 178.4e12,
"int4": 356.8e12,
},
"rtx 2080 ti": {
torch.float32: 14.2e12,
torch.float16: 28.5e12,
torch.int8: 227.7e12,
"int4": 455.4e12,
},
"rtx 2080": {
torch.float32: 10.6e12,
torch.float16: 21.2e12,
torch.int8: 169.6e12,
"int4": 339.1e12,
},
# https://www.nvidia.com/content/PDF/nvidia-ampere-ga-102-gpu-architecture-whitepaper-v2.pdf
"rtx 2070 super": {
torch.float32: 9.1e12,
torch.float16: 18.1e12,
torch.int8: 145e12,
"int4": 290e12,
},
"titan rtx": {
torch.float32: 16.3e12,
torch.float16: 32.6e12,
torch.int8: 261e12,
"int4": 522e12,
},
# Volta
# source: https://images.nvidia.com/content/technologies/volta/pdf/volta-v100-datasheet-update-us-1165301-r5.pdf
"v100 sxm": {
torch.float64: 7.8e12,
torch.float32: 15.7e12,
torch.float16: 125e12,
},
"v100 pcie": {
torch.float64: 7e12,
torch.float32: 14e12,
torch.float16: 112e12,
},
"v100s pcie": {
torch.float64: 8.2e12,
torch.float32: 16.4e12,
torch.float16: 130e12,
},
}
_TPU_FLOPS = {
# flop count for each TPU generation is the same for all precisions
# since bfloat16 precision is always used for performing matrix operations
# for more info: https://cloud.google.com/tpu/docs/bfloat16#choosing_bfloat16
# source: https://arxiv.org/pdf/1907.10701.pdf
"v2": 45e12,
# source: https://cloud.google.com/tpu/docs/system-architecture-tpu-vm#tpu_v3
"v3": 123e12,
# source: https://cloud.google.com/tpu/docs/system-architecture-tpu-vm#tpu_v4
"v4": 275e12,
# source: https://cloud.google.com/tpu/docs/v5e-training
"v5litepod": 197e12,
}
def get_available_flops(device: torch.device, dtype: Union[torch.dtype, str]) -> Optional[int]:
"""Returns the available theoretical FLOPs.
This is an optimistic upper limit that could only be achievable if only thick matmuls were run in a benchmark
environment.
"""
if device.type == "cuda":
device_name = torch.cuda.get_device_name(device)
chip = device_name.lower()
if "h100" in chip:
if "hbm3" in chip:
chip = "h100 sxm"
elif "nvl" in chip:
chip = "h100 nvl"
elif "pcie" in chip or "hbm2e" in chip:
chip = "h100 pcie"
elif "l4" in chip:
chip = "l40" if "tesla" in chip else "l4"
elif "geforce rtx" in chip:
number = chip.split(" ")[3]
extra = ""
if "super" in chip:
extra = " super"
elif "ti" in chip:
extra = " ti"
chip = f"rtx {number}{extra}"
elif "a6000" in chip:
chip = "a6000"
elif "a100" in chip:
chip = "a100"
elif "a40" in chip:
chip = "a40"
elif "a10g" in chip:
chip = "a10g"
elif "t4" in chip:
chip = "t4"
elif "quadro rtx 5000" in chip:
chip = "quadro rtx 5000"
elif "titan rtx" in chip:
chip = "titan rtx"
elif "v100-sxm" in chip:
chip = "v100 sxm"
elif "v100-pcie" in chip:
chip = "v100 pcie"
elif "v100s-pcie" in chip:
chip = "v100s pcie"
else:
# the flops list is not exhaustive, return with a warning
rank_zero_warn(f"FLOPs not found for {device_name!r}")
return None
if chip not in _CUDA_FLOPS:
# parsing is implemented but we don't have the stats
rank_zero_warn(f"FLOPs not found for {device_name!r}, chip is {chip!r}")
return None
dtype_to_flops = _CUDA_FLOPS[chip]
if dtype is torch.float32:
from lightning.fabric.accelerators.cuda import _is_ampere_or_later
if _is_ampere_or_later() and torch.get_float32_matmul_precision() != "highest":
dtype = "tfloat32"
if dtype not in dtype_to_flops:
# for example, T4 doesn't support bfloat16. it might also be that we are missing this dtype from the list
rank_zero_warn(f"{device_name!r} does not support {dtype}")
return None
return int(dtype_to_flops[dtype])
if device.type == "xla":
from lightning.fabric.accelerators.xla import _XLA_GREATER_EQUAL_2_1
if _XLA_GREATER_EQUAL_2_1:
from torch_xla._internal import tpu
else:
from torch_xla.experimental import tpu
tpu_env = tpu.get_tpu_env()
# not all TPU generations define the "TYPE" envar. example: TYPE="V4", ACCELERATOR_TYPE="v4-8"
device_name = tpu_env.get("TYPE") or tpu_env["ACCELERATOR_TYPE"].split("-")[0]
chip = device_name.lower()
assert isinstance(device_name, str)
if chip not in _TPU_FLOPS:
rank_zero_warn(f"FLOPs not found for TPU {device_name!r} with {dtype}")
return None
return int(_TPU_FLOPS[chip])
def _plugin_to_compute_dtype(plugin: "Precision") -> torch.dtype:
# TODO: integrate this into the precision plugins
from lightning.fabric.plugins import (
BitsandbytesPrecision,
DeepSpeedPrecision,
DoublePrecision,
FSDPPrecision,
HalfPrecision,
MixedPrecision,
Precision,
TransformerEnginePrecision,
XLAPrecision,
)
if not isinstance(plugin, Precision):
raise RuntimeError(f"Expected a precision plugin, got {plugin}")
if isinstance(plugin, BitsandbytesPrecision):
return plugin.dtype
if isinstance(plugin, (HalfPrecision, MixedPrecision)):
return plugin._desired_input_dtype
if isinstance(plugin, DoublePrecision):
return torch.double
if isinstance(plugin, (XLAPrecision, DeepSpeedPrecision)):
return plugin._desired_dtype
if isinstance(plugin, TransformerEnginePrecision):
return torch.int8
if isinstance(plugin, FSDPPrecision):
return plugin.mixed_precision_config.reduce_dtype or torch.float32
if isinstance(plugin, Precision):
return torch.float32
raise NotImplementedError(plugin)
T = TypeVar("T", bound=float)
class _MonotonicWindow(List[T]):
"""Custom fixed size list that only supports right-append and ensures that all values increase monotonically."""
def __init__(self, maxlen: int) -> None:
super().__init__()
self.maxlen = maxlen
@property
def last(self) -> Optional[T]:
if len(self) > 0:
return self[-1]
return None
@override
def append(self, x: T) -> None:
last = self.last
if last is not None and last >= x:
raise ValueError(f"Expected the value to increase, last: {last}, current: {x}")
list.append(self, x)
# truncate excess
if len(self) > self.maxlen:
del self[0]
@override
def __setitem__(self, key: Any, value: Any) -> None:
# assigning is not implemented since we don't use it. it could be by checking all previous values
raise NotImplementedError("__setitem__ is not supported")
