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258f47fc0b
Fixes #10536
Reattempt of #61467. Thank you so much to @mskoh52 for your excellent work!
As I was trying to create a more efficient LLM data collator, I realized that `pad_sequence` only supports right padding, even though left padding is a very common format for LLMs, like Llama and Mistral.
The proposed alternative implementation was to use multiple flips, which tends to be 1.5x-2x slower. Instead we can add a [`padding_side` parameter as there is for for Hugging Face tokenizers](9d6c0641c4/src/transformers/tokenization_utils_base.py (L1565)), which requires only a very small change in the C++ code.
Here are the benchmarks of the new implementation!
`float32`:
`bool`:
Code:
```python
from __future__ import annotations
import random
import time
from typing import Literal
import numpy as np
import torch
def pad_sequence_with_flips(
sequences: list[torch.Tensor],
batch_first: bool = False,
padding_value: int | float | bool = 0.0,
padding_side: Literal["left", "right"] | str = "left",
) -> torch.Tensor:
if padding_side == 'right':
padded_sequence = torch._C._nn.pad_sequence([t.flatten() for t in sequences], batch_first=batch_first, padding_value=padding_value)
elif padding_side=='left':
padded_sequence = torch._C._nn.pad_sequence([t.flatten().flip(0) for t in sequences], batch_first=batch_first, padding_value=padding_value) # pyright: ignore[reportArgumentType]
padded_sequence = padded_sequence.flip(int(batch_first))
else:
raise ValueError(f"padding_side should be either 'right' or 'left', but got {padding_side}")
return padded_sequence
sequence_lengths: list[int] = []
flip_left_pad_times: list[float] = []
flip_left_pad_times_std: list[float] = []
left_pad_times: list[float] = []
left_pad_times_std: list[float] = []
RUNS_PER_LOOP: int = 100
for i in range(1, 7):
sequence_length = i * int(1e6) // 6
sequence_lengths.append(sequence_length)
sequences = [torch.randint(0, 2, (random.randint(1, sequence_length),), dtype=torch.bool) for _ in range(64)]
inner_left_pad_times: list[float] = []
inner_right_pad_times: list[float] = []
inner_flip_left_pad_times: list[float] = []
inner_flip_right_pad_times: list[float] = []
for _ in range(RUNS_PER_LOOP):
start = time.perf_counter()
torch._C._nn.pad_sequence(sequences, batch_first=True, padding_value=False, padding_side="left")
end = time.perf_counter()
inner_left_pad_times.append(end - start)
start = time.perf_counter()
pad_sequence_with_flips(sequences, batch_first=True, padding_value=False, padding_side="left")
end = time.perf_counter()
inner_flip_left_pad_times.append(end - start)
left_pad_times.append(sum(inner_left_pad_times) / len(inner_left_pad_times))
left_pad_times_std.append(np.std(inner_left_pad_times))
flip_left_pad_times.append(sum(inner_flip_left_pad_times) / len(inner_flip_left_pad_times))
flip_left_pad_times_std.append(np.std(inner_flip_left_pad_times))
print(f"Sequence Length: {sequence_length}, Left Pad Time: {left_pad_times[-1]}, Left with Flips Pad Time: {flip_left_pad_times[-1]}")
import matplotlib.pyplot as plt
plt.plot(sequence_lengths, left_pad_times, label="new pad_sequence left")
plt.scatter(sequence_lengths, left_pad_times)
plt.errorbar(sequence_lengths, left_pad_times, yerr=left_pad_times_std, linestyle='None', marker='^')
plt.plot(sequence_lengths, flip_left_pad_times, label="old pad_sequence left (2 flips)")
plt.scatter(sequence_lengths, flip_left_pad_times)
plt.errorbar(sequence_lengths, flip_left_pad_times, yerr=flip_left_pad_times_std, linestyle='None', marker='^')
plt.xlabel("Sequence Length")
plt.ylabel("Time (s)")
plt.legend(loc="upper right")
# Sequence Length: 166666, Left Pad Time: 0.06147645162009212, Left with Flips Pad Time: 0.09842291727001794
# Sequence Length: 333333, Left Pad Time: 0.08933195920990329, Left with Flips Pad Time: 0.15597836187991562
# Sequence Length: 500000, Left Pad Time: 0.08863158334006585, Left with Flips Pad Time: 0.15224887342999863
# Sequence Length: 666666, Left Pad Time: 0.10524682551997103, Left with Flips Pad Time: 0.18177212480995877
# Sequence Length: 833333, Left Pad Time: 0.11801802741003485, Left with Flips Pad Time: 0.20821274195001024
# Sequence Length: 1000000, Left Pad Time: 0.131894061660023, Left with Flips Pad Time: 0.23223503091008751
```
Co-authored-by: mskoh52 <mskoh52@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131884
Approved by: https://github.com/ezyang
C++ Frontend Tests
In this folder live the tests for PyTorch's C++ Frontend. They use the GoogleTest test framework.
CUDA Tests
To make a test runnable only on platforms with CUDA, you should suffix your
test with _CUDA, e.g.
TEST(MyTestSuite, MyTestCase_CUDA) { }
To make it runnable only on platforms with at least two CUDA machines, suffix
it with _MultiCUDA instead of _CUDA, e.g.
TEST(MyTestSuite, MyTestCase_MultiCUDA) { }
There is logic in main.cpp that detects the availability and number of CUDA
devices and supplies the appropriate negative filters to GoogleTest.
Integration Tests
Integration tests use the MNIST dataset. You must download it by running the following command from the PyTorch root folder:
$ python tools/download_mnist.py -d test/cpp/api/mnist
The required paths will be referenced as test/cpp/api/mnist/... in the test
code, so you must run the integration tests from the PyTorch root folder.