Note
Click here to download the full example code
(Beta) Implementing High-Performance Transformers with Scaled Dot Product Attention (SDPA)¶
Author: Driss Guessous
Summary¶
In this tutorial, we want to highlight a new torch.nn.functional
function
that can be helpful for implementing transformer architectures. The
function is named torch.nn.functional.scaled_dot_product_attention
.
For detailed description of the function, see the PyTorch documentation.
This function has already been incorporated into torch.nn.MultiheadAttention
and torch.nn.TransformerEncoderLayer
.
Overview¶
At a high level, this PyTorch function calculates the scaled dot product attention (SDPA) between query, key, and value according to the definition found in the paper Attention is all you need. While this function can be written in PyTorch using existing functions, a fused implementation can provide large performance benefits over a naive implementation.
Fused implementations¶
For CUDA tensor inputs, the function will dispatch into one of the following implementations:
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
A PyTorch implementation defined in C++
Note
This tutorial requires PyTorch 2.0.0 or later.
import torch
import torch.nn as nn
import torch.nn.functional as F
device = "cuda" if torch.cuda.is_available() else "cpu"
# Example Usage:
query, key, value = torch.randn(2, 3, 8, device=device), torch.randn(2, 3, 8, device=device), torch.randn(2, 3, 8, device=device)
F.scaled_dot_product_attention(query, key, value)
tensor([[[ 1.4489, -0.9517, 1.7322, -0.3158, 0.0061, -0.0160, -0.0440,
-0.5620],
[ 1.5587, -1.2447, 1.6347, -0.6292, 0.0829, 0.2622, 0.2162,
-0.9738],
[ 1.5695, -1.0620, 1.5871, -0.5774, 0.1050, 0.0583, 0.1481,
-0.7423]],
[[ 0.2101, 0.1745, -0.3384, 0.5186, 0.3884, 1.0978, 1.2457,
-0.8756],
[ 0.0208, -0.0619, 0.0872, 0.5350, 0.2143, 1.5087, 1.1684,
-0.1958],
[ 0.5467, 0.5180, -1.2721, 0.1513, 0.9687, 0.6173, 1.9405,
-2.0401]]], device='cuda:0')
Explicit Dispatcher Control¶
While the function will implicitly dispatch to one of the three implementations, the user can also explicitly control the dispatch via the use of a context manager. This context manager allows users to explicitly disable certain implementations. If a user wants to ensure the function is indeed using the fastest implementation for their specific inputs, the context manager can be used to sweep through measuring performance.
# Lets define a helpful benchmarking function:
import torch.utils.benchmark as benchmark
def benchmark_torch_function_in_microseconds(f, *args, **kwargs):
t0 = benchmark.Timer(
stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}
)
return t0.blocked_autorange().mean * 1e6
# Lets define the hyper-parameters of our input
batch_size = 32
max_sequence_len = 1024
num_heads = 32
embed_dimension = 32
dtype = torch.float16
query = torch.rand(batch_size, num_heads, max_sequence_len, embed_dimension, device=device, dtype=dtype)
key = torch.rand(batch_size, num_heads, max_sequence_len, embed_dimension, device=device, dtype=dtype)
value = torch.rand(batch_size, num_heads, max_sequence_len, embed_dimension, device=device, dtype=dtype)
print(f"The default implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds")
# Lets explore the speed of each of the 3 implementations
from torch.backends.cuda import sdp_kernel, SDPBackend
# Helpful arg mapper
backend_map = {
SDPBackend.MATH: {"enable_math": True, "enable_flash": False, "enable_mem_efficient": False},
SDPBackend.FLASH_ATTENTION: {"enable_math": False, "enable_flash": True, "enable_mem_efficient": False},
SDPBackend.EFFICIENT_ATTENTION: {
"enable_math": False, "enable_flash": False, "enable_mem_efficient": True}
}
with sdp_kernel(**backend_map[SDPBackend.MATH]):
print(f"The math implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds")
with sdp_kernel(**backend_map[SDPBackend.FLASH_ATTENTION]):
try:
print(f"The flash attention implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds")
except RuntimeError:
print("FlashAttention is not supported. See warnings for reasons.")
with sdp_kernel(**backend_map[SDPBackend.EFFICIENT_ATTENTION]):
try:
print(f"The memory efficient implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds")
except RuntimeError:
print("EfficientAttention is not supported. See warnings for reasons.")
The default implementation runs in 1779545.262 microseconds
The math implementation runs in 134760.071 microseconds
<timeit-src>:6: UserWarning:
Memory efficient kernel not used because: (Triggered internally at ../aten/src/ATen/native/transformers/cuda/sdp_utils.h:527.)
<timeit-src>:6: UserWarning:
Memory Efficient attention has been runtime disabled. (Triggered internally at ../aten/src/ATen/native/transformers/cuda/sdp_utils.h:338.)
<timeit-src>:6: UserWarning:
Flash attention kernel not used because: (Triggered internally at ../aten/src/ATen/native/transformers/cuda/sdp_utils.h:529.)
<timeit-src>:6: UserWarning:
Flash attention only supports sm75 and sm8x gpu architectures. Attempting to run on a sm 6.1 gpu. (Triggered internally at ../aten/src/ATen/native/transformers/cuda/sdp_utils.h:352.)
FlashAttention is not supported. See warnings for reasons.
The memory efficient implementation runs in 1779601.548 microseconds
Hardware dependence¶
Depending on what machine you ran the above cell on and what hardware is available, your results might be different. - If you don’t have a GPU and are running on CPU then the context manager will have no effect and all three runs should return similar timings. - Depending on what compute capability your graphics card supports flash attention or memory efficient might have failed.
Causal Self Attention¶
Below is an example implementation of a multi-headed causal self attention block inspired by Andrej Karpathy’s NanoGPT repository.
class CausalSelfAttention(nn.Module):
def __init__(self, num_heads: int, embed_dimension: int, bias: bool=False, is_causal: bool=False, dropout:float=0.0):
super().__init__()
assert embed_dimension % num_heads == 0
# key, query, value projections for all heads, but in a batch
self.c_attn = nn.Linear(embed_dimension, 3 * embed_dimension, bias=bias)
# output projection
self.c_proj = nn.Linear(embed_dimension, embed_dimension, bias=bias)
# regularization
self.dropout = dropout
self.resid_dropout = nn.Dropout(dropout)
self.num_heads = num_heads
self.embed_dimension = embed_dimension
# Perform causal masking
self.is_causal = is_causal
def forward(self, x):
# calculate query, key, values for all heads in batch and move head forward to be the batch dim
query_projected = self.c_attn(x)
batch_size = query_projected.size(0)
embed_dim = query_projected.size(2)
head_dim = embed_dim // (self.num_heads * 3)
query, key, value = query_projected.chunk(3, -1)
query = query.view(batch_size, -1, self.num_heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, self.num_heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, self.num_heads, head_dim).transpose(1, 2)
if self.training:
dropout = self.dropout
is_causal = self.is_causal
else:
dropout = 0.0
is_causal = False
y = F.scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=dropout, is_causal=is_causal)
y = y.transpose(1, 2).view(batch_size, -1, self.num_heads * head_dim)
y = self.resid_dropout(self.c_proj(y))
return y
num_heads = 8
heads_per_dim = 64
embed_dimension = num_heads * heads_per_dim
dtype = torch.float16
model = CausalSelfAttention(num_heads=num_heads, embed_dimension=embed_dimension, bias=False, is_causal=True, dropout=0.1).to("cuda").to(dtype).eval()
print(model)
CausalSelfAttention(
(c_attn): Linear(in_features=512, out_features=1536, bias=False)
(c_proj): Linear(in_features=512, out_features=512, bias=False)
(resid_dropout): Dropout(p=0.1, inplace=False)
)
NestedTensor and Dense tensor support¶
SDPA supports both NestedTensor and Dense tensor inputs. NestedTensors handle the case where the input is a batch of variable length sequences without needing to pad each sequence to the maximum length in the batch. For more information about NestedTensors see torch.nested and NestedTensors Tutorial.
import random
def generate_rand_batch(
batch_size,
max_sequence_len,
embed_dimension,
pad_percentage=None,
dtype=torch.float16,
device="cuda",
):
if not pad_percentage:
return (
torch.randn(
batch_size,
max_sequence_len,
embed_dimension,
dtype=dtype,
device=device,
),
None,
)
# Random sequence lengths
seq_len_list = [
int(max_sequence_len * (1 - random.gauss(pad_percentage, 0.01)))
for _ in range(batch_size)
]
# Make random entry in the batch have max sequence length
seq_len_list[random.randint(0, batch_size - 1)] = max_sequence_len
return (
torch.nested.nested_tensor(
[
torch.randn(seq_len, embed_dimension,
dtype=dtype, device=device)
for seq_len in seq_len_list
]
),
seq_len_list,
)
random_nt, _ = generate_rand_batch(32, 512, embed_dimension, pad_percentage=0.5, dtype=dtype, device=device)
random_dense, _ = generate_rand_batch(32, 512, embed_dimension, pad_percentage=None, dtype=dtype, device=device)
# Currently the fused implementations don't support NestedTensor for training
model.eval()
with sdp_kernel(**backend_map[SDPBackend.FLASH_ATTENTION]):
try:
print(f"Random NT runs in {benchmark_torch_function_in_microseconds(model, random_nt):.3f} microseconds")
print(f"Random Dense runs in {benchmark_torch_function_in_microseconds(model, random_dense):.3f} microseconds")
except RuntimeError:
print("FlashAttention is not supported. See warnings for reasons.")
/var/lib/jenkins/workspace/intermediate_source/scaled_dot_product_attention_tutorial.py:226: UserWarning:
The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:177.)
/var/lib/jenkins/workspace/intermediate_source/scaled_dot_product_attention_tutorial.py:174: UserWarning:
Memory efficient kernel not used because: (Triggered internally at ../aten/src/ATen/native/transformers/cuda/sdp_utils.h:527.)
/var/lib/jenkins/workspace/intermediate_source/scaled_dot_product_attention_tutorial.py:174: UserWarning:
Memory Efficient attention has been runtime disabled. (Triggered internally at ../aten/src/ATen/native/transformers/cuda/sdp_utils.h:338.)
/var/lib/jenkins/workspace/intermediate_source/scaled_dot_product_attention_tutorial.py:174: UserWarning:
Flash attention kernel not used because: (Triggered internally at ../aten/src/ATen/native/transformers/cuda/sdp_utils.h:529.)
/var/lib/jenkins/workspace/intermediate_source/scaled_dot_product_attention_tutorial.py:174: UserWarning:
Flash attention only supports sm75 and sm8x gpu architectures. Attempting to run on a sm 6.1 gpu. (Triggered internally at ../aten/src/ATen/native/transformers/cuda/sdp_utils.h:352.)
FlashAttention is not supported. See warnings for reasons.
Using SDPA with torch.compile¶
With the release of PyTorch 2.0, a new feature called
torch.compile()
has been introduced, which can provide
significant performance improvements over eager mode.
Scaled dot product attention is fully composable with torch.compile()
.
To demonstrate this, let’s compile the CausalSelfAttention module using
torch.compile()
and observe the resulting performance improvements.
batch_size = 32
max_sequence_len = 256
x = torch.rand(batch_size, max_sequence_len,
embed_dimension, device=device, dtype=dtype)
print(
f"The non compiled module runs in {benchmark_torch_function_in_microseconds(model, x):.3f} microseconds")
compiled_model = torch.compile(model)
# Let's compile it
compiled_model(x)
print(
f"The compiled module runs in {benchmark_torch_function_in_microseconds(compiled_model, x):.3f} microseconds")
The non compiled module runs in 42878.501 microseconds
The compiled module runs in 43182.843 microseconds
The exact execution time is dependent on machine, however the results for mine: The non compiled module runs in 166.616 microseconds The compiled module runs in 166.726 microseconds That is not what we were expecting. Let’s dig a little deeper. PyTorch comes with an amazing built-in profiler that you can use to inspect the performance characteristics of your code.
from torch.profiler import profile, record_function, ProfilerActivity
activities = [ProfilerActivity.CPU]
if device == 'cuda':
activities.append(ProfilerActivity.CUDA)
with profile(activities=activities, record_shapes=False) as prof:
with record_function(" Non-Compilied Causal Attention"):
for _ in range(25):
model(x)
print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
with profile(activities=activities, record_shapes=False) as prof:
with record_function("Compiled Causal Attention"):
for _ in range(25):
compiled_model(x)
print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
# For even more insights, you can export the trace and use ``chrome://tracing`` to view the results
# prof.export_chrome_trace("compiled_causal_attention_trace.json").
------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg Self CUDA Self CUDA % CUDA total CUDA time avg # of Calls
------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Non-Compilied Causal Attention 0.40% 4.347ms 1.51% 16.357ms 16.357ms 0.000us 0.00% 1.214s 1.214s 1
aten::_scaled_dot_product_efficient_attention 0.04% 397.000us 0.12% 1.246ms 49.840us 0.000us 0.00% 972.978ms 38.919ms 25
aten::_efficient_attention_forward 0.03% 275.000us 0.07% 775.000us 31.000us 972.978ms 90.40% 972.978ms 38.919ms 25
void attention_kernel_batched<AttentionKernel<cutlas... 0.00% 0.000us 0.00% 0.000us 0.000us 972.978ms 90.40% 972.978ms 38.919ms 25
aten::scaled_dot_product_attention 0.08% 896.000us 0.19% 2.099ms 83.960us 0.000us 0.00% 934.567ms 37.383ms 25
aten::matmul 0.07% 789.000us 0.70% 7.582ms 151.640us 0.000us 0.00% 240.834ms 4.817ms 50
aten::mm 0.38% 4.113ms 0.52% 5.598ms 111.960us 103.302ms 9.60% 240.834ms 4.817ms 50
aten::linear 0.03% 323.000us 0.74% 8.022ms 160.440us 0.000us 0.00% 234.893ms 4.698ms 50
cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFla... 0.09% 963.000us 0.09% 963.000us 5.503us 97.312ms 9.04% 97.312ms 556.069us 175
maxwell_fp16_sgemm_fp16_32x128_tn 0.00% 0.000us 0.00% 0.000us 0.000us 80.525ms 7.48% 80.525ms 3.221ms 25
------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 1.083s
Self CUDA time total: 1.076s
------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg Self CUDA Self CUDA % CUDA total CUDA time avg # of Calls
------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Compiled Causal Attention 0.30% 3.231ms 1.34% 14.471ms 14.471ms 0.000us 0.00% 1.364s 1.364s 1
CompiledFunction 0.66% 7.155ms 1.03% 11.145ms 445.800us 0.000us 0.00% 1.364s 54.567ms 25
aten::_scaled_dot_product_efficient_attention 0.03% 277.000us 0.10% 1.126ms 45.040us 0.000us 0.00% 1.053s 42.102ms 25
aten::_efficient_attention_forward 0.03% 290.000us 0.07% 771.000us 30.840us 974.325ms 90.42% 1.053s 42.102ms 25
void attention_kernel_batched<AttentionKernel<cutlas... 0.00% 0.000us 0.00% 0.000us 0.000us 974.325ms 90.42% 974.325ms 38.973ms 25
aten::mm 0.11% 1.219ms 0.17% 1.860ms 37.200us 103.275ms 9.58% 311.625ms 6.232ms 50
cudaLaunchKernel 0.07% 804.000us 0.07% 804.000us 10.720us 157.551ms 14.62% 157.551ms 2.101ms 75
cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFla... 0.00% 52.000us 0.00% 52.000us 0.297us 129.019ms 11.97% 129.019ms 737.251us 175
maxwell_fp16_sgemm_fp16_32x128_tn 0.00% 0.000us 0.00% 0.000us 0.000us 80.508ms 7.47% 80.508ms 3.220ms 25
hgemm_128x128x8_NT_vec 0.00% 0.000us 0.00% 0.000us 0.000us 22.767ms 2.11% 22.767ms 910.680us 25
------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 1.081s
Self CUDA time total: 1.078s
The previous code snippet generates a report of the top 10 PyTorch functions
that consumed the most GPU execution time, for both the compiled and non-compiled module.
The analysis reveals that the majority of time spent on the GPU is concentrated
on the same set of functions for both modules.
The reason for this here is that torch.compile
is very good at removing the
framework overhead associated with PyTorch. If your model is launching
large, efficient CUDA kernels, which in this case CausaulSelfAttention
is, then the overhead of PyTorch can be hidden.
In reality, your module does not normally consist of a singular
CausalSelfAttention block. When experimenting with Andrej Karpathy’s
NanoGPT repository, compiling
the module took the time per train step from: 6090.49ms
to
3273.17ms
! This was done on commit: ae3a8d5 of NanoGPT training on
the shakespeare dataset.
Conclusion¶
In this tutorial, we have demonstrated the basic usage of
torch.nn.functional.scaled_dot_product_attention
. We have shown how
the sdp_kernel
context manager can be used to assert a certain
implementation is used on GPU. As well, we built a simple
CausalSelfAttention module that works with NestedTensor and is torch
compilable. In the process we have shown how to the profiling tools can
be used to explore the performance characteristics of a user defined
module.
Total running time of the script: ( 0 minutes 15.821 seconds)