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pytorch/test/test_transformers.py
drisspg fb26b84390 Update fused kernels and call _safe_softmax from SDPA (#133882) 
# UPDATE:
This is  take 3 of https://github.com/pytorch/pytorch/pull/131863 which was landed via co dev but not applying correclty

# Summary
Changes the stance of SDPA on what to do for fully masked out rows

## Current Behavior
Several PyTorch users have expressed frustration over this issue:
- https://github.com/pytorch/pytorch/issues/41508
- https://github.com/pytorch/pytorch/issues/103749
- https://github.com/pytorch/pytorch/issues/103963

These are significant issues with extensive discussion but no satisfactory resolution. The PyTorch team's consensus, as stated here:
https://github.com/pytorch/pytorch/issues/24816#issuecomment-524415617

Can be paraphrased as follows:

When passing in fully masked out rows, attention becomes ambiguous. We have two main options:

1. Uniformly attend to all values:
   ```python
   scores[masked_out_rows] = 1 / len(row)
   out[masked_out_rows] = 1 / len(row) * value
   ```

2. Decide that attention between no queries (masked) and no keys (masked) is meaningless:
   ```python
   output[fully_masked_rows] = NaN
   ```

We went with option 2. Partially because it was easier to implement, but also people argued that users can slice the output to remove the NaNs:
``` Python
>fill_value = -float("inf")
>row0 = torch.randn(4)
>row1 = torch.tensor([(fill_value for _ in range(4)])
>matrix = torch.stack([row0, row1]).requires_grad_(True)
>out = torch.softmax(matrix, 1)
>out = out[0]
>print(out)
tensor([0.5377, 0.2729, 0.0692, 0.1201])
```
Cool, problem solved. But what happends when you call backwards..
```Python
>out.backward(torch.ones_like(out))
>print(matrix.grad)
tensor([[3.0957e-08, 1.4157e-08, 7.7802e-10, 1.3713e-08],
        [       nan,        nan,        nan,        nan]])
```
Those pesky NaNs are back!

## Why do we see NaNs today?

The core of the problem revolves around using softmax function in sdpa:

```python
> row = torch.tensor([(-float("inf")) for _ in range(4)])
> torch.softmax(row, 0)
tensor([nan, nan, nan, nan])
```

## Quick Aside: Masking in Attention

Attention itself doesn't have a concept of masking. The `sdpa` function has an argument called `attn_mask`, which would be more accurately named `attn_bias`. This is because we don't actually "mask" entries when computing attention. Instead, due to implementation details([performance](https://github.com/pytorch/pytorch/issues/25110#issuecomment-524519087)), we add a value to the masked-out query/key pairs.

We use a large negative number (typically -inf) to decrease the attention weight, as softmax assigns more weight to larger values.

## Alternative Approaches

If we use a very large negative number instead of -inf:

```python
> row = torch.tensor([(-1e6) for _ in range(4)])
> torch.softmax(row, 0)
tensor([0.2500, 0.2500, 0.2500, 0.2500])
```
However if users always remembered to "slice" out their outputs i.e.:
```Python
>fill_value = -1e6
>...
>out.backward(torch.ones_like(out))
>print(matrix.grad)
tensor([[-0.0563, -0.0564,  0.1613, -0.0486],
        [ 0.0000,  0.0000,  0.0000,  0.0000]])
```
This would bring us back into a better state.

## A Third Option

We don't necessarily need to alter the behavior of softmax for -inf or very large negative numbers. The fundamental goal is to exclude certain query/key pairs from attention, regardless of the underlying implementation.

This PR implements the new semantic for masking w/ attention in fully masked-out rows:
```python
out[masked_out_rows] = 0
```

**Important Note**: This idea isn't entirely new. The [MaskedTensor](https://pytorch.org/tutorials/prototype/maskedtensor_overview#safe-softmax) prototype, a tensor subclass, was designed to handle such cases. However, it remains a prototype feature and hasn't gained widespread adoption.

## Details
This PR stack does 3 things:
1. Adds a PRIVATE _safe_softmax op
2. Updates semantic for flash_cpu fused kernel
3. Updates semantic for efficient_cuda fused kernel

_safe_softmax is not supposed to be used generically and is only meant to be used within the context of SDPA. Due to this fact instead of decomposing softmax and checking for -inf rows we instead "cheat" and use nan_to_num.

Why I think this is okay? (please find a counter point if avail)
There are multiple ways NaNs can emerge. For the fully masked out rows case nan_to_num works. But what if there were other NaNs, wouldn't this silently remove them?

The only case that this can happen is if the input itself had a NaN or an Inf
For example:
```Python
a = torch.ones([4], requires_grad=False, dtype=torch.float16)
a[1] = torch.finfo(torch.float16).max
print(a.softmax(-1))
```
Will return
`tensor([0., 1., 0., 0.], dtype=torch.float16)`

Where
```Python
a = torch.ones([4], requires_grad=False, dtype=torch.float16)
a[1] = float("inf")
a.softmax(-1)
```
returns:
`tensor([nan, nan, nan, nan], dtype=torch.float16)`

If we dont want to even allow for the possibility of "inf" or "NaN" attention scores to be converted to 0 then we can implemented it something like this

```Python
max = torch.max(a, dim=-1, keepdim=True)
exp = torch.exp(a - max.values)
denom = torch.sum(exp, dim=-1, keepdim=True)
softmax = exp / denom
softmax = torch.where(max.values == float('-inf'), 0.0, softmax)
```
however we would be paying for this in math performance.

## Why Now
I think one point that has substantially changed where PyTorch should lie on this argument is the fact that we have fused implementations for SDPA now. And these fused implementations allow us to easily and performantly support this new semantic.

Differential Revision: [D61418679](https://our.internmc.facebook.com/intern/diff/D61418679)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133882
Approved by: https://github.com/soulitzer
2024-08-19 18:53:11 +00:00

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# Owner(s): ["module: multi-headed-attention"]
import contextlib
from functools import partial
from collections import namedtuple
import sys
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.functional import scaled_dot_product_attention
from torch.nn.attention import sdpa_kernel, SDPBackend
from torch.nn.attention.bias import CausalVariant, causal_lower_right, causal_upper_left
from torch.nn.parameter import Parameter
import unittest
from unittest.mock import patch, MagicMock, ANY
import math
import itertools
import torch.optim as optim
from torch.testing._internal.common_device_type import instantiate_device_type_tests, onlyCUDA, onlyCPU
from typing import List, Tuple, Optional, Dict
import torch.utils.cpp_extension
from torch.testing._internal.common_nn import NNTestCase
from torch.testing._internal.common_utils import (
IS_FBCODE,
TEST_WITH_ROCM,
skipIfRocm,
skipIfTorchDynamo,
TEST_FAIRSEQ,
run_tests,
parametrize,
freeze_rng_state,
TEST_WITH_CROSSREF,
slowTest,
set_default_dtype,
gradcheck,
make_tensor,
NOTEST_CPU,
IS_WINDOWS,
TEST_WITH_TORCHDYNAMO,
TEST_XPU,
)
from torch._dynamo.testing import CompileCounterWithBackend
from torch.testing._internal.common_methods_invocations import wrapper_set_seed
from torch.testing._internal.common_cuda import (
IS_JETSON, SM80OrLater, PLATFORM_SUPPORTS_FLASH_ATTENTION,
PLATFORM_SUPPORTS_MEM_EFF_ATTENTION,
PLATFORM_SUPPORTS_FUSED_ATTENTION,
PLATFORM_SUPPORTS_CUDNN_ATTENTION,
SM90OrLater,
tf32_on_and_off
)
if not IS_FBCODE:
from test_cpp_extensions_open_device_registration import (
remove_build_path,
generate_faked_module
)
if TEST_FAIRSEQ:
import fairseq.models.transformer as fairseq_transformer
SdpaShape = namedtuple('Sdpa_Shape', ['batch', 'num_heads', 'seq_len', 'head_dim'])
Tolerances = namedtuple('Tolerances', ['atol', 'rtol'])
@contextlib.contextmanager
def use_deterministic_algorithims(mode: bool, warn_only: bool):
r"""
This context manager can be used to temporarily enable or disable deterministic algorithms.
Upon exiting the context manager, the previous state of the flag will be restored.
"""
previous_mode: bool = torch.are_deterministic_algorithms_enabled()
previous_warn_only: bool = torch.is_deterministic_algorithms_warn_only_enabled()
try:
torch.use_deterministic_algorithms(mode, warn_only=warn_only)
yield {}
finally:
torch.use_deterministic_algorithms(previous_mode, warn_only=previous_warn_only)
# Found in torch/testing/_comparison.py
default_atol = {torch.float16: 1e-3, torch.bfloat16: 1e-3, torch.float32: 1e-5}
default_rtol = {torch.float16: 1e-3, torch.bfloat16: 1.6e-2, torch.float32: 1.3e-6}
isSM8XDevice = torch.cuda.is_available() and torch.cuda.get_device_capability() in [(8, 6), (8, 7), (8, 9)]
isSM90Device = torch.cuda.is_available() and torch.cuda.get_device_capability() == (9, 0)
isSM5xDevice = torch.cuda.is_available() and torch.cuda.get_device_capability()[0] == 5
isLessThanSM80Device = torch.cuda.is_available() and torch.cuda.get_device_capability()[0] < 8
def _check_equal(
golden: torch.Tensor,
reference: torch.Tensor,
test: torch.Tensor,
fudge_factor: float,
tensor_name: Optional[str] = None
) -> None:
"""
Compare test tensor against golden and reference tensors.
Golden is the highest precision possible serving as the "ground truth"
Refernce is the same precision as test and should also serve as less precisie ground truth.
We calcculate the "reference error" by comparing the golden to reference and use this as the
measruing stick for the test tensor.
Raises ValueError if compiled error exceeds threshold.
Args:
golden (torch.Tensor): The golden tensor to compare against.
reference (torch.Tensor): The reference tensor for error calculation.
test (torch.Tensor): The test tensor to be evaluated.
fudge_factor (float): A multiplier for the reference error to determine the threshold.
tensor_name (Optional[str], optional): Name of the tensor for error reporting. Defaults to None.
Raises:
ValueError: If the test tensor contains NaN values while the reference does not,
or if the test error exceeds the calculated threshold.
Notes:
- For nested tensors, the function recursively calls itself on each nested element.
- The error threshold is calculated as the maximum of a default tolerance for float32
and the product of the reference error and the fudge_factor.
- If the test error exceeds the threshold, a ValueError is raised with a detailed message.
"""
if golden.is_nested and reference.is_nested and test.is_nested:
for gold, ref, tst in zip(golden.unbind(), reference.unbind(), test.unbind()):
_check_equal(gold, ref, tst, fudge_factor, tensor_name)
return
# Compute error between golden
test_error = (golden - test).abs().max()
ref_error = (golden - reference).abs().max()
if torch.isnan(test_error).any() and not torch.isnan(ref_error).any():
raise ValueError("Output/Grad with NaN")
# Calculate the error threshold as the maximum of:
# 1. A predefined default tolerance for float32
# 2. The reference error multiplied by the fudge factor
threshold = max(default_atol[torch.float32], ref_error * fudge_factor)
if test_error > threshold:
name = tensor_name or ""
msg = f"{name} Test error {test_error} is greater than threshold {threshold}!"
raise ValueError(msg)
def check_out_and_grad(
out_tuple: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
grad_query_tuple: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
grad_key_tuple: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
grad_value_tuple: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
grad_attn_mask_tuple: Optional[Tuple[torch.Tensor, torch.Tensor, torch.Tensor]] = None,
fudge_factors: Optional[Dict[str, float]] = None
) -> None:
"""
Check output and gradients of attention mechanism tensors.
Compares compiled results against reference and low-precision reference tensors.
Args:
out_tuple: Tuple of (ref, lp_ref, compiled) for output tensor
grad_query_tuple: Tuple of (ref, lp_ref, compiled) for query gradient
grad_key_tuple: Tuple of (ref, lp_ref, compiled) for key gradient
grad_value_tuple: Tuple of (ref, lp_ref, compiled) for value gradient
grad_attn_mask_tuple: Optional tuple of (ref, lp_ref, compiled) for attention mask gradient
fudge_factors: Dictionary of fudge factors for each tensor type (default uses 5.0 for all)
"""
default_fudge_factor = 5.0
if fudge_factors is None:
fudge_factors = {}
out_ref, out_lp_ref, out = out_tuple
with torch.no_grad():
_check_equal(out_ref, out_lp_ref, out, fudge_factors.get('out', default_fudge_factor), "out")
grad_checks = [
(grad_query_tuple, "grad_query"),
(grad_key_tuple, "grad_key"),
(grad_value_tuple, "grad_value")
]
for grad_tuple, name in grad_checks:
ref_grad, lp_ref_grad, comp_grad = grad_tuple
_check_equal(ref_grad, lp_ref_grad, comp_grad, fudge_factors.get(name, default_fudge_factor), name)
if grad_attn_mask_tuple:
attn_mask_ref_grad, attn_mask_ref_lp_grad, attn_mask_grad = grad_attn_mask_tuple
_check_equal(
attn_mask_ref_grad,
attn_mask_ref_lp_grad,
attn_mask_grad,
fudge_factors.get("grad_attn_mask", default_fudge_factor),
"grad_attn_mask",
)
def query_key_value_clones(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, dtype: torch.dtype = None):
""" Clones the query, key, and value tensors and moves them to the specified dtype. """
if dtype is None:
dtype = query.dtype
query_ref = query.clone().detach().to(dtype).requires_grad_(query.requires_grad)
key_ref = key.clone().detach().to(dtype).requires_grad_(key.requires_grad)
value_ref = value.clone().detach().to(dtype).requires_grad_(value.requires_grad)
return query_ref, key_ref, value_ref
def get_platform_specific_sdpa():
ret = []
if PLATFORM_SUPPORTS_FLASH_ATTENTION:
ret.append(SDPBackend.FLASH_ATTENTION)
if PLATFORM_SUPPORTS_MEM_EFF_ATTENTION:
ret.append(SDPBackend.EFFICIENT_ATTENTION)
if PLATFORM_SUPPORTS_CUDNN_ATTENTION:
ret.append(SDPBackend.CUDNN_ATTENTION)
if not ret:
# Add a placeholder, an empty list causes "An empty arg_values was passed to @parametrize"
ret.append(SDPBackend.EFFICIENT_ATTENTION)
return ret
PLATFORM_SPECIFIC_SDPA = get_platform_specific_sdpa()
# Indicate the Efficient attention backend can support:
# 1. sequence longher than 512
# 2. head dimsion larger than 64
MEM_EFF_CAPABILITY_MATCHES_SM80 = SM80OrLater or TEST_WITH_ROCM
def rand_sdpa_tensor(shape: SdpaShape, device: str, dtype: torch.dtype, type: str,
requires_grad: bool = False, packed: bool = False) -> torch.Tensor:
"""Creates rand dense or nested tensor with given shape and type.
Args:
shape (Tuple[int]): Shape of Tensor to construct
device (str): which device to create tensor on
dtype (torch.dtype): Tensors' dtype
type (str): Nested or Dense
requires_grad (bool, optional): Tensors grad status. Defaults to False.
packed (bool, optional): Whether to create a single QKV packed or not. Defaults to False.
Returns:
torch.Tensor: A new tensor
"""
batch, num_heads, seq_len, head_dim = shape.batch, shape.num_heads, shape.seq_len, shape.head_dim
if type == "nested":
if isinstance(seq_len, list):
def _size(i):
return (seq_len[i], num_heads, head_dim) if not packed else (seq_len[i], 3 * num_heads * head_dim)
return torch.nested.nested_tensor([
torch.randn(_size(i), device=device, dtype=dtype, requires_grad=requires_grad)
for i in range(batch)])
else:
size = (seq_len, num_heads, head_dim) if not packed else (seq_len, 3 * num_heads * head_dim)
return torch.nested.nested_tensor([
torch.randn(size, device=device, dtype=dtype, requires_grad=requires_grad)
for _ in range(batch)])
else:
assert (isinstance(seq_len, int))
size = (batch, seq_len, num_heads, head_dim) if not packed else (batch, seq_len, 3 * num_heads * head_dim)
return torch.randn(size, device=device, dtype=dtype, requires_grad=requires_grad)
class TestTransformers(NNTestCase):
_do_cuda_memory_leak_check = True
_do_cuda_non_default_stream = True
@onlyCUDA
@unittest.skip("4D mask not supported yet - activate when 4D mask supported")
def test_self_attn_TxT_attn_mask(self, device):
embed_dim = 16
num_heads = 4
batch_size = 10
tgt_len = 16
query = torch.rand(batch_size, tgt_len, embed_dim, device=device) # [N, T, D]
attn_mask = torch.randint(0, 2, (tgt_len, tgt_len)).cuda().float() # [T, T]
attn_mask = attn_mask.masked_fill(attn_mask == 0, float('-inf')).masked_fill(attn_mask == 1, 0.0)
attn_mask_4d = attn_mask.expand(batch_size, num_heads, tgt_len, tgt_len)
mta_model = torch.nn.MultiheadAttention(embed_dim, num_heads, batch_first=True).cuda()
mta_model.eval()
# Generate 3D results
with torch.inference_mode():
output_mask_4d = mta_model(query, query, query, attn_mask=attn_mask_4d)[0]
output_mask_4d = output_mask_4d.transpose(0, 1) # [N, T, D]
output_mask_TxT = mta_model(query, query, query, attn_mask=attn_mask)[0]
output_mask_TxT = output_mask_TxT.transpose(0, 1) # [N, T, D]
self.assertEqual(output_mask_4d, output_mask_TxT)
@slowTest
def test_train_with_pad_and_catch_error(self, device):
iters = 100
pad_mask = torch.tensor([[1, 1, 0, 0]], dtype=torch.bool).to(device)
layer = nn.TransformerEncoderLayer(
d_model=2,
dim_feedforward=4,
nhead=2,
batch_first=True,
activation="gelu",
dropout=0,
)
criterion = nn.MSELoss()
encoder = nn.TransformerEncoder(layer, 2).to(device)
optimizer = optim.SGD(encoder.parameters(), lr=0.1, momentum=0.9)
encoder.train()
for i in range(iters):
encoder.train()
optimizer.zero_grad()
inputs = torch.cat([torch.randn(1, 2, 2), torch.zeros(1, 2, 2)], dim=1).to(device)
outputs = encoder(inputs, src_key_padding_mask=pad_mask)
loss = criterion(outputs[:, 0:2, :], inputs[:, 0:2, :])
loss.backward()
optimizer.step()
with torch.no_grad():
test = torch.cat([torch.randn(1, 2, 2), torch.zeros(1, 2, 2)], dim=1).to(device)
# Expect uint8 type not supported
ex = None
try:
test_train_uint8 = encoder(test, src_key_padding_mask=pad_mask.to(torch.uint8))
except AssertionError as e:
continue
self.assertFalse(e, "Failed to catch unsupported uint8 type exception") # noqa: F821
test_train_bool = encoder(test, src_key_padding_mask=pad_mask)
encoder.eval()
# Expect long type not supported
ex = None
try:
test_eval_uint8 = encoder(test, src_key_padding_mask=pad_mask.to(torch.int64))
except AssertionError as e:
continue
self.assertFalse(e, "Failed to catch unsupported Long type exception") # noqa: F821
test_eval_bool = encoder(test, src_key_padding_mask=pad_mask)
l1_bool = nn.L1Loss()(test_train_bool[:, 0:2, :], test_eval_bool[:, 0:2, :]).item()
self.assertTrue(l1_bool < 1e-4, "Eval/Train difference in pad_mask BOOL")
@tf32_on_and_off(0.001)
@parametrize("attn_mask_dim", [2, 3, None])
@parametrize("key_padding_mask_dim", [2, None])
@parametrize("mask_dtype", [torch.bool, torch.float32])
def test_multiheadattention_fastpath_attn_mask(self, device, attn_mask_dim, key_padding_mask_dim, mask_dtype):
# MHA converts all
with torch.no_grad():
B = 2
L = 4
D = 8
H = 4
if attn_mask_dim == 2:
attn_mask = make_tensor((L, L), dtype=mask_dtype, device=device)
elif attn_mask_dim == 3:
attn_mask = make_tensor((B, 1, L, L), dtype=mask_dtype, device=device).expand(B, H, L, L).reshape(B * H, L, L)
elif attn_mask_dim is None:
attn_mask = None
if key_padding_mask_dim == 2:
key_padding_mask = make_tensor((B, L), dtype=mask_dtype, device=device)
elif key_padding_mask_dim is None:
key_padding_mask = None
mha = nn.MultiheadAttention(D, H, batch_first=True, device=device)
X = torch.randn(B, L, D, device=device)
mha.train() # disable fast path
out, _ = mha(X, X, X, attn_mask=attn_mask, key_padding_mask=key_padding_mask, need_weights=False)
mha.eval() # enable fast path
out_fp, _ = mha(X, X, X, attn_mask=attn_mask, key_padding_mask=key_padding_mask, need_weights=False)
# The FP kernel will return NaNs while the sdpa kernel which is ran when the fast path is turned off returns 0 instead
# of NaNs for fully masked rows
torch.testing.assert_close(out, out_fp.nan_to_num())
@parametrize("nhead", [1, 4, 8])
def test_transformerencoderlayer_src_mask(self, device, nhead):
batch_size = 2
seqlen = 4
d_model = 8
dim_feedforward = 32
model = torch.nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=True).to(device)
src = torch.rand(batch_size, seqlen, d_model).to(device) # bs, seqlen, d_model
src_mask = torch.zeros(seqlen, seqlen).to(torch.bool).to(device)
model(src, src_mask=src_mask)
model.eval()
with torch.no_grad():
model(src, src_mask=src_mask)
@parametrize("nhead", [3, 4])
def test_transformerencoderlayer_no_fastpath_with_hooks(self, device, nhead):
batch_size = 2
seqlen = 4
d_model = 12
model = torch.nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=d_model,
batch_first=True).to(device).eval()
src = torch.rand(batch_size, seqlen, d_model).to(device) # bs, seqlen, d_model
cache = []
# forward hook to save output
def hook(module, inputs, output):
cache.append(output[0].detach())
# register hook to get the output of the self-attention layer
handle = model.self_attn.register_forward_hook(hook)
# forward pass
with torch.inference_mode():
model(src)
# output of the self-attention layer
assert len(cache) == 1, f"Expected 1 output, got {len(cache)}"
# remove hook
handle.remove()
@tf32_on_and_off(0.001)
@parametrize("use_torchscript", [False])
@parametrize("enable_nested_tensor", [True, False])
@parametrize("use_autocast", [True, False])
@parametrize("d_model", [12, 256])
def test_transformerencoder_fastpath(self, device, use_torchscript, enable_nested_tensor, use_autocast, d_model):
"""
Test TransformerEncoder fastpath output matches slowpath output
"""
torch.manual_seed(1234)
nhead = 4
dim_feedforward = d_model
batch_first = True
model = torch.nn.TransformerEncoder(
torch.nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=batch_first),
num_layers=2,
enable_nested_tensor=enable_nested_tensor
).to(device).eval()
if use_torchscript:
model = torch.jit.script(model)
# each input is (input, mask)
input_mask_pairs = [
(
torch.rand(3, 2, d_model),
[
[0, 1],
[0, 1],
[1, 1]
]
),
(
torch.rand(2, 100, d_model),
[
[0] * 98 + [1] * 2,
[0] * 90 + [1] * 10
]
),
# softmax.cu switches from fast->slowpath at masked seqlen 1024. test 1024.
(
torch.rand(2, 1024, d_model),
[
[0] * 1020 + [1] * 4,
[0] * 1024,
]
),
(
torch.rand(1, 1026, d_model),
[[0] * 1024 + [1] * 2]
),
# softmax.cu switches from fast->slowpath at masked seqlen 1024. test range of masks above 1024.
(
torch.rand(4, 1040, d_model),
[
[0] * 1024 + [1] * 16,
[0] * 1025 + [1] * 15,
[0] * 1031 + [1] * 9,
[0] * 1040,
]
)
]
input_mask_pairs = [
(
torch.tensor(pair[0], device=device, dtype=torch.get_default_dtype()), # float input
torch.tensor(pair[1], device=device, dtype=torch.bool) # bool mask
) for pair in input_mask_pairs
]
maybe_autocast = torch.autocast("cuda", dtype=torch.float16) if use_autocast else contextlib.nullcontext()
with maybe_autocast:
for input, src_key_padding_mask in input_mask_pairs:
with torch.no_grad():
fastpath_output = model(input, src_key_padding_mask=src_key_padding_mask)
slowpath_output = model(input, src_key_padding_mask=src_key_padding_mask) # reference
# Make sure fastpath_output is same shape as slowpath_output and mask.
# When enable_nested_tensor=true, fastpath_output may be smaller than input tensor.
# Eg if input bs=1, seqlen=6, and we mask out 2 tokens, fastpath_output will have bs=1, seqlen=4.
# Expand back to old size to match.
bs, true_seqlen, embed_dim = fastpath_output.shape
expanded_seqlen = src_key_padding_mask.shape[1]
fastpath_output_expanded = torch.zeros(bs, expanded_seqlen, embed_dim, device=device)
fastpath_output_expanded[:, :true_seqlen, :] = fastpath_output
# no garauntees on output corresponding to masked tokens, so they may vary between slow/fast path. set all to 0.
fastpath_output_expanded = fastpath_output_expanded.masked_fill(src_key_padding_mask.unsqueeze(-1), 0)
slowpath_output = slowpath_output.masked_fill(src_key_padding_mask.unsqueeze(-1), 0)
self.assertEqual(fastpath_output_expanded, slowpath_output)
@tf32_on_and_off(0.001)
@parametrize("with_no_grad", [True, False])
@parametrize("training", [True, False])
@parametrize("enable_nested_tensor", [False])
def test_transformerencoder_square_input(self, with_no_grad, training, enable_nested_tensor, device):
"""
Test for edge cases when input of shape (batch size, sequence length, embedding dimension) has
batch size == sequence length
"""
model = torch.nn.TransformerEncoder(
torch.nn.TransformerEncoderLayer(d_model=4, nhead=2, dim_feedforward=16, dropout=0.0, batch_first=True),
num_layers=2,
enable_nested_tensor=enable_nested_tensor
).to(device)
with torch.no_grad():
# set constant weights of the model
for idx, p in enumerate(model.parameters()):
x = p.data
sz = x.view(-1).size(0)
shape = x.shape
x = torch.cos(torch.arange(0, sz).float().view(shape))
p.data.copy_(x)
if training:
model = model.train()
else:
model = model.eval()
x = torch.arange(0, 16).reshape(2, 2, 4).to(torch.get_default_dtype()).to(device)
src_mask = torch.Tensor([[0, 1], [0, 0]]).to(torch.bool).to(device)
if with_no_grad:
cm = torch.no_grad()
else:
cm = contextlib.nullcontext()
with cm:
result = model(x, mask=src_mask)
ref_output = torch.Tensor([[[2.420306205749512, 0.017629241570830, -0.607857942581177, -0.085519507527351],
[2.420306205749512, 0.017629241570830, -0.607857942581177, -0.085519507527351]],
[[2.419836044311523, 0.017548924311996, -0.608187675476074, -0.085347734391689],
[2.419836044311523, 0.017548924311996, -0.608187675476074, -0.085347734391689]]]
).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
self.assertEqual(result, ref_output)
@parametrize("batch_first", [True, False])
@parametrize("training", [True, False])
@parametrize("enable_nested_tensor", [True, False])
def test_transformerencoder(self, batch_first, training, enable_nested_tensor, device):
def get_a_test_layer(activation, batch_first=False):
d_model = 4
nhead = 2
dim_feedforward = 16
dropout = 0.0
layer = nn.TransformerEncoderLayer(
d_model,
nhead,
dim_feedforward=dim_feedforward,
dropout=dropout,
activation=activation,
batch_first=batch_first,
).to(device)
with torch.no_grad():
# set constant weights of the model
for idx, p in enumerate(layer.parameters()):
x = p.data
sz = x.view(-1).size(0)
shape = x.shape
x = torch.cos(torch.arange(0, sz).float().view(shape))
p.data.copy_(x)
return layer
# this is a deterministic test for TransformerEncoder
activation = F.relu
def _test(batch_first, training, enable_nested_tensor):
def perm_fn(x):
return x.transpose(1, 0) if batch_first else x
encoder_layer = get_a_test_layer(activation=activation,
batch_first=batch_first)
model = nn.TransformerEncoder(
encoder_layer, 1, enable_nested_tensor=enable_nested_tensor
).to(device)
if not training:
model = model.eval()
# deterministic input
encoder_input = perm_fn(torch.tensor([[[0.7462, 0.6653, 0.5679, 0.4891],
[0.5387, 0.1655, 0.3565, 0.0471]],
[[0.8335, 0.2799, 0.5031, 0.2947],
[0.1402, 0.0318, 0.7636, 0.1346]],
[[0.6333, 0.9344, 0.1376, 0.9938],
[0.8924, 0.2872, 0.6692, 0.2944]],
[[0.9897, 0.6915, 0.3154, 0.1733],
[0.8645, 0.3513, 0.3064, 0.0767]],
[[0.8117, 0.2366, 0.4838, 0.7881],
[0.3718, 0.4945, 0.9511, 0.0864]]]
)).to(device)
result = model(encoder_input)
ref_output = perm_fn(torch.tensor([[[2.428589, 0.020835, -0.602055, -0.085249],
[2.427987, 0.021213, -0.602496, -0.084103]],
[[2.424689, 0.019155, -0.604793, -0.085672],
[2.413863, 0.022211, -0.612486, -0.072490]],
[[2.433774, 0.021598, -0.598343, -0.087548],
[2.425104, 0.019748, -0.604515, -0.084839]],
[[2.436185, 0.022682, -0.596625, -0.087261],
[2.433556, 0.021891, -0.598509, -0.086832]],
[[2.416246, 0.017512, -0.610712, -0.082961],
[2.422901, 0.024187, -0.606178, -0.074929]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
# all 0 src_mask
src_mask = torch.zeros([5, 5]).to(device) == 1
result = model(encoder_input, mask=src_mask)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
# all 0
mask = torch.zeros([2, 5]).to(device) == 1
result = model(encoder_input, src_key_padding_mask=mask)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
mask[0, 1] = 1
mask[1, 3] = 1
mask[1, 4] = 1
result = model(encoder_input, src_key_padding_mask=mask)
ref_output = perm_fn(torch.tensor([[[2.429026, 0.020793, -0.601741, -0.085642],
[2.428811, 0.021445, -0.601912, -0.084252]],
[[2.425009, 0.019155, -0.604566, -0.085899],
[2.415408, 0.02249, -0.611415, -0.073]],
[[2.434199, 0.021682, -0.598039, -0.087699],
[2.42598, 0.019941, -0.603896, -0.085091]],
[[2.436457, 0.022736, -0.59643, -0.08736],
[2.434021, 0.022093, -0.598179, -0.08679]],
[[2.416531, 0.017498, -0.610513, -0.083181],
[2.4242, 0.024653, -0.605266, -0.074959]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
# test case 2, multiple layers no norm
model = nn.TransformerEncoder(encoder_layer, 2, enable_nested_tensor=enable_nested_tensor).to(device)
if not training:
model = model.eval()
result = model(encoder_input, src_key_padding_mask=mask)
ref_output = perm_fn(torch.tensor([[[2.419051, 0.017446, -0.608738, -0.085003],
[2.419102, 0.017452, -0.608703, -0.085026]],
[[2.419043, 0.017445, -0.608744, -0.084999],
[2.419052, 0.017446, -0.608738, -0.085004]],
[[2.419067, 0.017448, -0.608727, -0.085010],
[2.419098, 0.017452, -0.608706, -0.085024]],
[[2.419072, 0.017449, -0.608724, -0.085012],
[2.419119, 0.017455, -0.608691, -0.085034]],
[[2.419019, 0.017442, -0.608761, -0.084989],
[2.419075, 0.017449, -0.608722, -0.085014]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
model = nn.TransformerEncoder(encoder_layer, 6, enable_nested_tensor=enable_nested_tensor).to(device)
if not training:
model = model.eval()
result = model(encoder_input, src_key_padding_mask=mask)
ref_output = perm_fn(torch.tensor([[[2.419101, 0.017453, -0.608703, -0.085025],
[2.419101, 0.017453, -0.608704, -0.085025]],
[[2.419101, 0.017453, -0.608703, -0.085025],
[2.419101, 0.017453, -0.608704, -0.085025]],
[[2.419101, 0.017453, -0.608703, -0.085025],
[2.419101, 0.017453, -0.608704, -0.085025]],
[[2.419101, 0.017453, -0.608703, -0.085025],
[2.419101, 0.017453, -0.608704, -0.085025]],
[[2.419101, 0.017453, -0.608703, -0.085025],
[2.419101, 0.017453, -0.608704, -0.085025]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
# test case 3, multiple layers with norm
# d_model = 4
norm = nn.LayerNorm(4)
model = nn.TransformerEncoder(encoder_layer, 2, norm=norm,
enable_nested_tensor=enable_nested_tensor).to(device)
if not training:
model = model.eval()
result = model(encoder_input, src_key_padding_mask=mask)
ref_output = perm_fn(torch.tensor([[[1.695949, -0.357635, -0.893077, -0.445238],
[1.695955, -0.357639, -0.893050, -0.445266]],
[[1.695948, -0.357634, -0.893082, -0.445233],
[1.695950, -0.357635, -0.893077, -0.445238]],
[[1.695951, -0.357636, -0.893069, -0.445246],
[1.695955, -0.357639, -0.893052, -0.445264]],
[[1.695952, -0.357636, -0.893066, -0.445249],
[1.695957, -0.357641, -0.893041, -0.445276]],
[[1.695946, -0.357632, -0.893095, -0.445220],
[1.695952, -0.357637, -0.893065, -0.445251]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
model = nn.TransformerEncoder(encoder_layer, 6, norm=norm,
enable_nested_tensor=enable_nested_tensor).to(device)
if not training:
model = model.eval()
result = model(encoder_input, src_key_padding_mask=mask)
ref_output = perm_fn(torch.tensor([[[1.695955, -0.357639, -0.893051, -0.445265],
[1.695955, -0.357639, -0.893051, -0.445265]],
[[1.695955, -0.357639, -0.893051, -0.445265],
[1.695955, -0.357639, -0.893051, -0.445265]],
[[1.695955, -0.357639, -0.893051, -0.445265],
[1.695955, -0.357639, -0.893051, -0.445265]],
[[1.695955, -0.357639, -0.893051, -0.445265],
[1.695955, -0.357639, -0.893051, -0.445265]],
[[1.695955, -0.357639, -0.893051, -0.445265],
[1.695955, -0.357639, -0.893051, -0.445265]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
# TODO: remove set default dtype to double by making ref_output more precise.
# Added because this test was copied from test_nn.py, which has default
# dtype double. If default dtype is float, tests will say tensors not close because
# ref output precision too low
with set_default_dtype(torch.double):
if training:
cm = contextlib.nullcontext()
else:
cm = torch.no_grad() # transformer fast path requires no grad
with cm:
_test(batch_first, training, enable_nested_tensor)
@unittest.skipIf(sys.version_info < (3, 11), "not supported on pre-3.11 Python")
def test_encoder_padding_and_src_mask_bool(self):
encoder_layer = nn.TransformerEncoderLayer(
d_model=16,
nhead=2,
dim_feedforward=32,
dropout=0.1,
activation='relu',
batch_first=True,
)
encoder_norm = nn.LayerNorm(16)
encoder = nn.TransformerEncoder(
encoder_layer, 2, encoder_norm
)
inputs = torch.randn(2, 3, 16)
src_mask = torch.ones(3, 3, dtype=torch.bool).triu_(diagonal=1)
input_seq_len = torch.tensor([3, 2])
padding_mask = (
torch.arange(3)[None, :].cpu() >= input_seq_len[:, None]
)
with (self.assertNoLogs(None) if not TEST_WITH_TORCHDYNAMO else contextlib.nullcontext()):
encoder(
inputs,
mask=src_mask,
src_key_padding_mask=padding_mask,
)
@unittest.skipIf(sys.version_info < (3, 11), "not supported on pre-3.11 Python")
def test_decoder_padding_and_src_mask_bool(self):
def transformer_decoder(inputs, input_seq_len, memory):
decoder_layer = nn.TransformerDecoderLayer(
d_model=16,
nhead=2,
dim_feedforward=32,
dropout=0.1,
activation='relu',
batch_first=True,
)
decoder_norm = nn.LayerNorm(16)
decoder = nn.TransformerDecoder(
decoder_layer, 2, decoder_norm
)
src_mask = torch.ones(
inputs.shape[1], inputs.shape[1], dtype=torch.bool
).triu_(diagonal=1)
padding_mask = (
torch.arange(inputs.shape[1])[None, :].cpu()
>= input_seq_len[:, None]
)
return decoder(
inputs,
memory,
tgt_mask=src_mask,
tgt_key_padding_mask=padding_mask,
memory_key_padding_mask=padding_mask,
)
inputs = torch.randn(2, 3, 16)
memory = torch.randn(2, 3, 16)
input_seq_len = torch.tensor([3, 2])
with self.assertNoLogs(None):
transformer_decoder(inputs, input_seq_len, memory)
def test_encoder_is_causal(self):
d_model = 3
layer = torch.nn.TransformerEncoderLayer(d_model, 1, 6, batch_first=True)
layer.eval()
x = torch.randn(1, 5, d_model)
unmasked_output = layer(x)
mask = torch.nn.Transformer.generate_square_subsequent_mask(x.size(1))
is_causal_output = layer(x, src_mask=mask, is_causal=True)
masked_output = layer(x, src_mask=mask)
self.assertEqual(masked_output, is_causal_output)
@onlyCUDA
@parametrize("nb_heads", [1, 8])
@parametrize("bias", [True, False])
def test_mha_native_args(self, nb_heads, bias):
B, L, F = 8, 100, 128
batch_first = True
fast_path = True
use_pad_mask = (bias % 2) == 1
mha = nn.MultiheadAttention(
embed_dim=F,
num_heads=nb_heads,
batch_first=batch_first,
bias=bias
).cuda()
mha.eval()
ctx = torch.no_grad if fast_path else contextlib.nullcontext
with ctx():
x = torch.randn(B, L, F).cuda()
if not batch_first:
x = x.transpose(0, 1)
pad_mask = None
if use_pad_mask:
pad_mask = torch.zeros((B, L), dtype=torch.bool).cuda()
mha(query=x, key=x, value=x, key_padding_mask=pad_mask)
def test_kpm_mask_trailing_column_with_nested_tensor(self, device):
encoder_layer = nn.TransformerEncoderLayer(
d_model=256,
nhead=4,
dim_feedforward=512,
activation='gelu',
norm_first=False,
batch_first=False,
)
transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=3, enable_nested_tensor=True).to(device)
x = torch.randn(10, 6, 256).to(device)
mask = torch.ones(6, 10)
mask[0, :] = 0 # here I masked 5 columns instead of just one
mask = mask.bool().to(device)
out = transformer_encoder(src=x, src_key_padding_mask=mask)
self.assertEqual(out.shape[1], 6)
# CPU unit test has_torch_functions in test environment,
# preventing successful completion
@onlyCUDA
def test_with_nested_tensor_input(self, device):
encoder_layer = nn.TransformerEncoderLayer(
d_model=256,
nhead=4,
dim_feedforward=512,
activation='gelu',
norm_first=False,
batch_first=True,
)
transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=3, enable_nested_tensor=True).to(device)
transformer_encoder.eval()
with torch.no_grad():
x = torch.randn(6, 10, 256).to(device)
mask = torch.ones(6, 10)
mask[0, 0:] = 0 # here I masked 5 columns instead of just one
mask[2, 2:] = 0 # here I masked 5 columns instead of just one
mask[4, 4:] = 0 # here I masked 5 columns instead of just one
mask[5, 8:] = 0 # here I masked 5 columns instead of just one
mask = mask.bool().to(device)
x = torch._nested_tensor_from_mask(x, mask.logical_not(), mask_check=False)
out = transformer_encoder(src=x, src_key_padding_mask=None)
self.assertEqual(out.is_nested, True)
def test_script_encoder_subclass(self, device):
class MyCustomLayer(nn.TransformerEncoderLayer):
pass
encoder = nn.TransformerEncoder(
MyCustomLayer(d_model=256, nhead=8), num_layers=6
).to(device=device)
torch.jit.script(encoder)
# brazenly adapted from test_transformerencoderlayer_src_mask to test execution of
# torchscripted transformerencoderlayer subclass
def test_transformerencoderlayer_subclass(self, device):
class MyCustomLayer(nn.TransformerEncoderLayer):
pass
nhead = 4
batch_size = 2
seqlen = 4
d_model = 8
dim_feedforward = 32
model = MyCustomLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=True).to(device)
script_model = torch.jit.script(model)
src = torch.rand(batch_size, seqlen, d_model).to(device) # bs, seqlen, d_model
src_mask = torch.zeros(seqlen, seqlen).to(torch.bool).to(device)
torch.manual_seed(42)
result = model(src, src_mask=src_mask)
torch.manual_seed(42)
scripted_result = script_model(src, src_mask=src_mask)
self.assertEqual(result, scripted_result)
model.eval()
script_model = torch.jit.script(model)
with torch.no_grad():
result = model(src, src_mask=src_mask)
scripted_result = script_model(src, src_mask=src_mask)
self.assertEqual(result, scripted_result)
def test_transformerencoderlayer_subclass_model(self, device):
class MyCustomLayer(nn.TransformerEncoderLayer):
pass
nhead = 4
batch_size = 2
seqlen = 4
d_model = 8
dim_feedforward = 32
layer = MyCustomLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=True)
model = nn.TransformerEncoder(
layer, num_layers=6
).to(device=device)
script_model = torch.jit.script(model)
src = torch.rand(batch_size, seqlen, d_model).to(device) # bs, seqlen, d_model
src_mask = torch.zeros(seqlen, seqlen).to(torch.bool).to(device)
torch.manual_seed(42)
result = model(src, mask=src_mask)
torch.manual_seed(42)
scripted_result = script_model(src, mask=src_mask)
self.assertEqual(result, scripted_result)
model.eval()
script_model = torch.jit.script(model)
with torch.no_grad():
result = model(src, mask=src_mask)
scripted_result = script_model(src, mask=src_mask)
self.assertEqual(result, scripted_result)
@onlyCUDA
@unittest.skipIf(not TEST_FAIRSEQ, "Fairseq not found")
def test_decoder_only_layer(self):
DEFAULT_PADDING_IDX = 0
class FairseqDecoder(torch.nn.Module):
def __init__(
self,
embed_dim,
attention_heads,
ffn_embed_dim,
num_layers,
embedding_layer, # torch.nn.Embedding. Must have a padding_idx field
dropout=0,
normalize_before=False,
torch_encoder=None, # torch encoder that you can map weights from
activation="relu",
):
super().__init__()
cfg = fairseq_transformer.TransformerConfig()
cfg.decoder.embed_dim = embed_dim
cfg.decoder.output_dim = embed_dim
cfg.decoder.attention_heads = attention_heads
cfg.decoder.ffn_embed_dim = ffn_embed_dim
cfg.dropout = dropout
cfg.decoder.normalize_before = normalize_before
cfg.decoder.layers = num_layers
# make embedding behavior same as other encoders
cfg.no_token_positional_embeddings = True
cfg.no_scale_embedding = True
cfg.activation_fn = activation
dictionary = {} # TODO: verify what this is
self.decoder = fairseq_transformer.TransformerDecoder(
cfg,
dictionary,
embedding_layer,
no_encoder_attn=True,
output_projection=None,
)
if torch_encoder is not None:
self.decoder = torch_to_fairseq(torch_encoder, self.decoder) # noqa: F821
self.decoder = self.decoder.eval().cuda().half()
def forward(
self,
tokens,
src_lengths=None,
with_triangle_mask=False,
incremental_state=None,
):
return self.decoder(
prev_output_tokens=tokens,
encoder_out=None,
incremental_state=incremental_state,
features_only=True,
full_context_alignment=not with_triangle_mask,
alignment_layer=None,
alignment_heads=None,
src_lengths=src_lengths,
return_all_hiddens=False,
)[0]
@tf32_on_and_off(0.003)
@parametrize("input_dim,attn_mask_dim,is_causal",
[(3, None, False), (3, 2, False), (3, 2, True), (3, 3, False), (3, 3, True),
(4, None, False), (4, 2, False), (4, 2, True), (4, 4, False), (4, 4, True)],
name_fn=lambda input_dim, attn_dim, is_causal: (
f"{input_dim}D_input_dim_" + (
f"{attn_dim}D_{'causal_' if is_causal else ''}attn_mask"
if attn_dim is not None else "no_attn_mask")))
@parametrize("dropout_p", [0.0, 0.2, 0.5])
@sdpa_kernel(backends=[SDPBackend.MATH])
def test_scaled_dot_product_attention(self, device, input_dim, attn_mask_dim, is_causal, dropout_p):
def sdp_ref(
q,
k,
v,
attn_mask=None,
dropout_p=0.0):
E = q.size(-1)
q = q / math.sqrt(E)
# (B, Nt, E) x (B, E, Ns) -> (B, Nt, Ns)
if attn_mask is not None:
attn = torch.baddbmm(attn_mask, q, k.transpose(-2, -1))
else:
attn = torch.bmm(q, k.transpose(-2, -1))
attn = torch.nn.functional.softmax(attn, dim=-1)
if dropout_p > 0.0:
attn = torch.nn.functional.dropout(attn, p=dropout_p)
# (B, Nt, Ns) x (B, Ns, E) -> (B, Nt, E)
output = torch.bmm(attn, v)
return output
# TODO: Support cross-device / dtype testing properly when instantiate_device_type_tests() is used.
dtypes = [torch.double, torch.float]
for dtype in dtypes:
def rand_tensor(*shape):
return torch.randn(shape, device=device, dtype=dtype)
# This test compares python and C++ implementations of SDP.
N, N_prime, L, S, E = 5, 2, 4, 3, 6
if input_dim == 3:
query = rand_tensor(N, L, E)
key = rand_tensor(N, S, E)
value = rand_tensor(N, S, E)
elif input_dim == 4:
query = rand_tensor(N, N_prime, L, E)
key = rand_tensor(N, N_prime, S, E)
value = rand_tensor(N, N_prime, S, E)
else:
self.fail(f'Invalid input_dim {input_dim} encountered in SDP test')
attn_mask = None
if attn_mask_dim is not None:
assert attn_mask_dim in [2, input_dim]
mask_size = (L, S) if attn_mask_dim == 2 else ((N, L, S) if input_dim == 3 else (N, N_prime, L, S))
attn_mask = (torch.ones(mask_size, device=device, dtype=torch.bool).tril() if is_causal
else torch.randint(0, 2, size=mask_size, device=device, dtype=torch.bool))
with freeze_rng_state():
# Python impl only supports float mask and 3D inputs.
attn_mask_float = attn_mask
if attn_mask_float is not None:
attn_mask_float = torch.zeros_like(attn_mask, dtype=query.dtype)
attn_mask_float.masked_fill_(attn_mask.logical_not(), float("-inf"))
q, k, v = query.view(-1, L, E), key.view(-1, S, E), value.view(-1, S, E)
a = attn_mask_float
if a is not None and attn_mask_dim > 3:
a = a.view(-1, L, S)
expected = sdp_ref(q, k, v, attn_mask=a, dropout_p=dropout_p)
if input_dim > 3:
expected = expected.view(-1, N_prime, L, E)
with freeze_rng_state():
if is_causal:
# NB: Don't pass attn_mask here
actual = torch.nn.functional.scaled_dot_product_attention(
query, key, value, None, dropout_p, is_causal)
# Error case: both explicit attn_mask and is_causal are set
with self.assertRaisesRegex(RuntimeError,
"Explicit attn_mask should not be set when is_causal=True"):
torch.nn.functional.scaled_dot_product_attention(
query, key, value, attn_mask, dropout_p, is_causal)
else:
actual = torch.nn.functional.scaled_dot_product_attention(
query, key, value, attn_mask, dropout_p, is_causal)
# This test the fully masked out rows case
if torch.isnan(expected).any():
row_sums = attn_mask.sum(dim=-1)
masked_out_rows = (row_sums == 0)
for _ in range((input_dim - attn_mask_dim) - 1):
masked_out_rows = masked_out_rows.unsqueeze(0)
masked_out_rows = masked_out_rows.expand(expected.shape[:-1])
# Slice out the fully masked rows from expected and actual
expected_masked_out = expected[masked_out_rows]
actual_masked_out = actual[masked_out_rows]
expected_all_nan = torch.isnan(expected_masked_out).all()
actual_all_zero = (actual_masked_out.abs().sum() == 0)
self.assertTrue(expected_all_nan)
self.assertTrue(actual_all_zero)
return
self.assertEqual(actual, expected)
if attn_mask_dim is None:
q = q.double().clone()
k = k.double().clone()
v = v.double().clone()
q.requires_grad_()
k.requires_grad_()
v.requires_grad_()
assert gradcheck(lambda *args, **kwargs: wrapper_set_seed(sdp_ref, *args, **kwargs),
(q, k, v, attn_mask, dropout_p))
assert gradcheck(lambda *args, **kwargs:
wrapper_set_seed(torch.nn.functional.scaled_dot_product_attention, *args, **kwargs),
(q, k, v, attn_mask, dropout_p))
def test_incompatible_mask(self, device):
def ones_tensor(*shape):
return torch.ones(shape, dtype=torch.float32)
S, L, E, H = 1, 2, 4, 1
qkv = ones_tensor(S, L, E)
mha = nn.MultiheadAttention(E, H)
mha.in_proj_weight = Parameter(torch.ones((E * 3, E)))
mha.out_proj.weight = Parameter(torch.ones((E, E)))
qkv = qkv.to(float)
kpm = ones_tensor(S, L) * float("-inf")
am = ones_tensor(L, L).to(bool)
def func():
return mha(qkv, qkv, qkv, need_weights=False, key_padding_mask=kpm, attn_mask=am)
self.assertRaises(RuntimeError, func)
@unittest.skipIf(TEST_WITH_CROSSREF, 'Fastpath not available with crossref')
@torch.no_grad()
def test_mask_check_fastpath(self):
"""
Test that fastpath is executed independently of the masks that are passed.
If the passed key padding mask is left aligned or mask_check=False, test that nested tensors are used
(sparsity fastpath), otherwise use fastpath with traditional tensors.
Also test that fast path is executed with both key padding mask and attention mask passed at the same time.
"""
x = torch.Tensor([[[1, 2], [3, 4], [5, 6]]]).to(torch.float)
def _test_fastpath(model, key_padding_mask, mock_return_value, attn_mask=None, nested_tensors=True):
with patch('torch._transformer_encoder_layer_fwd') as fastpath_mock:
fastpath_mock.return_value = mock_return_value
model(x, src_key_padding_mask=key_padding_mask, mask=attn_mask)
# If mock was called, fastpath was taken
self.assertTrue(fastpath_mock.called)
# If mock was called with nested tensors, sparsity fastpath was taken
for call_args, _ in fastpath_mock.call_args_list:
self.assertEqual(call_args[0].is_nested, nested_tensors)
encoder_layer = torch.nn.TransformerEncoderLayer(d_model=2, nhead=2, dim_feedforward=8, batch_first=True)
model = torch.nn.TransformerEncoder(encoder_layer, num_layers=2, enable_nested_tensor=True, mask_check=True)
model.eval()
aligned_key_padding_mask = torch.Tensor([[0, 0, 1]]).to(torch.bool)
not_aligned_key_padding_mask = torch.Tensor([[1, 0, 1]]).to(torch.bool)
attn_mask = torch.Tensor([[1, 0, 1], [0, 1, 0], [1, 0, 1]]).to(torch.bool)
nested_tensor_return_value = torch.nested.nested_tensor([torch.ones((2, 2), dtype=torch.float)])
tensor_return_value = torch.ones((1, 3, 2), dtype=torch.float)
# Left aligned mask results in sparsity fastpath
_test_fastpath(model, aligned_key_padding_mask, nested_tensor_return_value, nested_tensors=True)
# Not aligned mask results in fastpath
_test_fastpath(model, not_aligned_key_padding_mask, tensor_return_value, nested_tensors=False)
model = torch.nn.TransformerEncoder(encoder_layer, num_layers=2, enable_nested_tensor=False, mask_check=True)
model.eval()
# If nested tensor disabled, fastpath is always taken
_test_fastpath(model, aligned_key_padding_mask, tensor_return_value, nested_tensors=False)
_test_fastpath(model, not_aligned_key_padding_mask, tensor_return_value, nested_tensors=False)
# Fast path is taken if both attention mask and key padding mask are present
_test_fastpath(model, aligned_key_padding_mask, tensor_return_value, attn_mask=attn_mask, nested_tensors=False)
model = torch.nn.TransformerEncoder(encoder_layer, num_layers=2, enable_nested_tensor=True, mask_check=False)
model.eval()
# Mask check disabled results in sparisty fastpath, independently of the mask
_test_fastpath(model, aligned_key_padding_mask, nested_tensor_return_value, nested_tensors=True)
_test_fastpath(model, not_aligned_key_padding_mask, nested_tensor_return_value, nested_tensors=True)
# Test failing MHA when bias was NoneType
def test_bias_is_none(self):
x = torch.rand((1, 5, 10))
model = torch.nn.modules.activation.MultiheadAttention(10, 1, bias=False, batch_first=True)
model.eval()
model(x, x, x)
# completes without error
def test_transformer_bias_is_none(self, device):
batch_size = 2
seqlen = 3
d_model = 8
nhead = 4
encoder_layer = torch.nn.TransformerEncoderLayer(d_model, nhead, bias=False, batch_first=True, device=device)
encoder_layer.eval()
x = torch.randn(batch_size, seqlen, d_model, device=device)
# runs without error
encoder_layer(x)
with self.assertWarnsRegex(UserWarning, "encoder_layer.self_attn was passed bias=False"):
encoder = torch.nn.TransformerEncoder(encoder_layer, num_layers=1).eval()
encoder(x)
with self.assertWarnsRegex(UserWarning, "self_attn was passed bias=False"):
transformer = torch.nn.Transformer(
d_model=d_model, nhead=nhead, bias=False, batch_first=True, device=device
).eval()
transformer(x, x)
def test_train_with_is_causal(self, device):
# training with is_causal
S, L, E, H = 1, 2, 2, 1
layer = nn.TransformerEncoderLayer(
d_model=2,
dim_feedforward=4,
nhead=H,
batch_first=True,
activation="gelu",
dropout=0,
)
criterion = nn.MSELoss()
encoder = nn.TransformerEncoder(layer, 2).to(device)
optimizer = optim.SGD(encoder.parameters(), lr=0.1, momentum=0.9)
encoder.train()
encoder.train()
optimizer.zero_grad()
inputs = torch.randn(S, L, E).to(device)
mask = torch.nn.Transformer.generate_square_subsequent_mask(
inputs.size(1), device=device
)
outputs = encoder(inputs, mask=mask, is_causal=True)
loss = criterion(outputs[:, 0:2, :], inputs[:, 0:2, :])
loss.backward()
optimizer.step()
# inference with is_causal
t_qvk = torch.randn((S, L, E), device=device, dtype=torch.float32)
mha = nn.MultiheadAttention(E, H).to(device)
mask = torch.nn.Transformer.generate_square_subsequent_mask(
S, device=device
)
attn_out, _ = mha(t_qvk, t_qvk, t_qvk, attn_mask=mask, is_causal=True)
# Can't give only is_causal
attn_mask = torch.randint(0, 2, size=(L, L), device=device, dtype=torch.bool)
with self.assertRaises(RuntimeError):
_ = mha(t_qvk, t_qvk, t_qvk, is_causal=True)
# # Passing a causal mask sets is_causal to 1
causal_mask = torch.triu(
torch.ones(L, L, device=inputs.device) * float('-inf'), diagonal=1
).to(torch.bool)
mock_layer = MagicMock(torch.nn.MultiheadAttention(E, H), return_value=inputs)
encoder.layers[1] = mock_layer
outputs = encoder(inputs, mask=causal_mask)
mock_layer.assert_called_with(ANY, src_mask=ANY, is_causal=True, src_key_padding_mask=ANY)
# check expected numerical values with all kernels
self.is_causal_kernels([SDPBackend.MATH], device)
def is_causal_kernels(self, kernels, device):
def ones_tensor(*shape):
return torch.ones(shape, device=device, dtype=torch.float32).to(device)
S, L, E, H = 1, 2, 4, 1
qkv = ones_tensor(S, L, E)
mha = nn.MultiheadAttention(E, H).to(device)
mha.in_proj_weight = Parameter(torch.ones((E * 3, E), device=device))
mha.out_proj.weight = Parameter(torch.ones((E, E), device=device))
expected = torch.ones(size=(S, L, E)).to(device) * 16
mask = torch.nn.Transformer.generate_square_subsequent_mask(
qkv.size(1), device=device
)
for kernel in kernels:
with sdpa_kernel(backends=[kernel]):
actual, _ = mha(qkv, qkv, qkv, attn_mask=mask, need_weights=False, is_causal=True)
self.assertTrue(torch.equal(actual, expected))
if kernel != SDPBackend.MATH:
# fails with embedding size not multiple of 4
with self.assertRaisesRegex(RuntimeError, "No available kernel"):
qkv_f, mha_f = ones_tensor(S, L, 2), nn.MultiheadAttention(2, H).to(device)
mask = torch.nn.Transformer.generate_square_subsequent_mask(
qkv_f.size(1), device=device
)
_ = mha_f(qkv_f, qkv_f, qkv_f, attn_mask=mask, need_weights=False, is_causal=True)
torch.cuda.synchronize()
@skipIfRocm # Missing EFFICIENT_ATTENTION
@unittest.skipIf(
not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Platform does not supposrt fused SDPA or pre-SM80 hardware"
)
def test_is_causal_gpu(self):
device = 'cuda'
self.is_causal_kernels([SDPBackend.MATH, SDPBackend.EFFICIENT_ATTENTION], device)
def test_script_mha_in_proj_weight_none(self):
mha = torch.nn.MultiheadAttention(
embed_dim=128, num_heads=8, kdim=256, vdim=256
).eval()
torch.jit.script(mha)
@unittest.skipIf(TEST_WITH_CROSSREF, 'Fastpath not available with crossref')
@torch.no_grad()
def test_disable_fastpath(self, device):
def _test_te_fastpath_called(model, args, kwargs=None, return_value=None, is_called=True):
if kwargs is None:
kwargs = {}
with patch('torch._transformer_encoder_layer_fwd') as fastpath_mock:
fastpath_mock.return_value = return_value
output = model(*args, **kwargs)
self.assertTrue(fastpath_mock.called == is_called)
def _test_mha_fastpath_called(model, args, kwargs=None, return_value=None, is_called=True):
if kwargs is None:
kwargs = {}
with patch('torch._native_multi_head_attention') as fastpath_mock:
fastpath_mock.return_value = return_value
output = model(*args, **kwargs)
self.assertTrue(fastpath_mock.called == is_called)
inp = torch.tensor([[[1, 2], [3, 4], [5, 6]]], dtype=torch.float32, device=device)
aligned_key_padding_mask = torch.tensor([[0, 0, 1]], dtype=torch.bool, device=device)
src_key_padding_mask = torch.tensor([[1, 0, 1]], dtype=torch.bool, device=device)
attn_mask = torch.tensor([[1, 0, 1], [0, 1, 0], [1, 0, 1]], dtype=torch.bool, device=device)
te_return_value = torch.ones((1, 3, 2), dtype=torch.float32)
encoder_layer = torch.nn.TransformerEncoderLayer(d_model=2, nhead=2, dim_feedforward=8, batch_first=True)
te = torch.nn.TransformerEncoder(encoder_layer, num_layers=2, enable_nested_tensor=True, mask_check=True)
te = te.to(device).eval()
t = torch.nn.Transformer(d_model=2, nhead=2, batch_first=True, device=device).eval()
src = torch.tensor([[[0, 1], [2, 3], [4, 5]]], dtype=torch.float32, device=device)
tgt = torch.tensor([[[0, 1], [2, 3], [4, 5], [6, 7]]], dtype=torch.float32, device=device)
t_return_value = torch.ones((1, 3, 2), dtype=torch.float32, device=device)
mha = nn.MultiheadAttention(2, 2, batch_first=True, device=device).eval()
q = torch.tensor([[[0, 1], [2, 3]]], dtype=torch.float32, device=device)
mha_return_value = torch.ones((1, 3, 2), dtype=torch.float32, device=device)
_test_te_fastpath_called(
te, (inp,), kwargs={'src_key_padding_mask': src_key_padding_mask},
return_value=te_return_value, is_called=True
)
_test_te_fastpath_called(t, (src, tgt), return_value=t_return_value, is_called=True)
_test_mha_fastpath_called(mha, (q, q, q,), return_value=mha_return_value, is_called=True)
torch.backends.mha.set_fastpath_enabled(False)
_test_te_fastpath_called(
te, (inp,), kwargs={'src_key_padding_mask': src_key_padding_mask},
return_value=te_return_value, is_called=False
)
_test_te_fastpath_called(t, (src, tgt), return_value=t_return_value, is_called=False)
_test_mha_fastpath_called(mha, (q, q, q,), return_value=mha_return_value, is_called=False)
torch.backends.mha.set_fastpath_enabled(True)
_test_te_fastpath_called(
te, (inp,), kwargs={'src_key_padding_mask': src_key_padding_mask},
return_value=te_return_value, is_called=True
)
_test_te_fastpath_called(t, (src, tgt), return_value=t_return_value, is_called=True)
_test_mha_fastpath_called(mha, (q, q, q,), return_value=mha_return_value, is_called=True)
class TestSDPAFailureModes(NNTestCase):
""" Used to test the failure modes of scaled_dot_product_attention
"""
_do_cuda_memory_leak_check = True
_do_cuda_non_default_stream = True
@onlyCUDA
@unittest.skipIf(
not PLATFORM_SUPPORTS_FLASH_ATTENTION or not isSM8XDevice,
"Does not support fused SDPA or not SM86+ hardware",
)
@parametrize("head_dim", [193, 256])
@parametrize("dropout_p", [0.0, 0.2])
def test_flash_backward_failure_sm86plus(self, device, head_dim: int, dropout_p: float):
dtype = torch.float16
make_tensor = partial(torch.rand, device=device, dtype=dtype)
# See check_requires_grad_and_head_dim_gt192_constraints_on_sm86_89 in
# pytorch/aten/src/ATen/native/transformers/cuda/sdp_utils.h
size = (2, 2, 4, head_dim)
q, k, v = make_tensor(size), make_tensor(size), make_tensor(size)
with sdpa_kernel(backends=[SDPBackend.MATH]):
math_ref = torch.nn.functional.scaled_dot_product_attention(q, k, v, None, 0.0, False)
with sdpa_kernel(backends=[SDPBackend.FLASH_ATTENTION]):
# Should not fail because inputs don't require grad
flash_ref = torch.nn.functional.scaled_dot_product_attention(q, k, v, None, 0.0, False)
self.assertEqual(math_ref, flash_ref, atol=1e-3, rtol=1e-3)
# Should fail because inputs require grad
q = make_tensor(size, requires_grad=True)
k = make_tensor(size, requires_grad=True)
v = make_tensor(size, requires_grad=True)
if 192 < head_dim <= 224 or (head_dim > 224 and dropout_p != 0.0):
self.assertRaises(
RuntimeError,
lambda: torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, dropout_p, False
),
)
else:
flash_ref = torch.nn.functional.scaled_dot_product_attention(q, k, v, None, dropout_p, False)
@onlyCUDA
def test_dispatch_fails_no_backend(self, device):
dtype = torch.float16
with sdpa_kernel(backends=[SDPBackend.ERROR]):
size = (2, 3, 4)
q = torch.randn(size, device=device, dtype=dtype)
k = torch.randn(size, device=device, dtype=dtype)
v = torch.randn(size, device=device, dtype=dtype)
self.assertRaisesRegex(RuntimeError, "No viable backend for scaled_dot_product_attention was found.",
lambda: torch._fused_sdp_choice(q, k, v))
self.assertRaisesRegex(RuntimeError, "No viable backend for scaled_dot_product_attention was found.",
lambda: torch.nn.functional.scaled_dot_product_attention(q, k, v))
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FUSED_ATTENTION, "Does not support fused scaled dot product attention")
@parametrize(
"kernel",
PLATFORM_SPECIFIC_SDPA,
)
def test_invalid_fused_inputs_dim_3(self, device, kernel: SDPBackend):
with sdpa_kernel(backends=[kernel]):
# Dim is not 4
size = (2, 3, 8)
dtype = torch.float16
q = torch.randn(size, device=device, dtype=dtype)
k = torch.randn(size, device=device, dtype=dtype)
v = torch.randn(size, device=device, dtype=dtype)
with self.assertWarnsRegex(UserWarning, "All fused kernels requires query, key and value to be 4 dimensional"):
self.assertRaises(RuntimeError, lambda: torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, 0.0, False))
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FUSED_ATTENTION, "Does not support fused scaled dot product attention")
@parametrize(
"kernel",
PLATFORM_SPECIFIC_SDPA,
)
def test_invalid_fused_inputs_broadcast(self, device, kernel: SDPBackend):
with sdpa_kernel(backends=[kernel]):
# Fused Kernels don't support broadcasting for dense inputs
dtype = torch.float16
size = (2, 4, 3, 8)
size_broadcast = (1, 4, 3, 8)
q = torch.randn(size_broadcast, device=device, dtype=dtype)
k = torch.randn(size, device=device, dtype=dtype)
v = torch.randn(size, device=device, dtype=dtype)
self.assertRaises(RuntimeError, lambda: torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, 0.0, False))
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FUSED_ATTENTION, "Does not support fused scaled dot product attention")
@parametrize("kernel", PLATFORM_SPECIFIC_SDPA)
def test_invalid_sequence_lengths(self, device, kernel: SDPBackend):
with sdpa_kernel(backends=[kernel]):
# Passing in a q,k,v with 0 length sequences will error
dtype = torch.float16
make_tensor = partial(torch.rand, device=device, dtype=dtype)
size = SdpaShape(2, 2, 0, 8)
q, k, v = make_tensor(size), make_tensor(size), make_tensor(size)
with self.assertWarnsRegex(UserWarning, "All fused kernels do not support zero seq_len_q or seq_len_kv."):
self.assertRaises(RuntimeError, lambda: torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, 0.0, False))
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FUSED_ATTENTION, "Does not support fused scaled dot product attention")
@parametrize("kernel", PLATFORM_SPECIFIC_SDPA)
def test_invalid_last_dim_stride(self, device, kernel: SDPBackend):
with sdpa_kernel(backends=[kernel]):
# Passing in a q,k,v with last dim stride not equal to 1 will error
dtype = torch.float16
make_tensor = partial(torch.rand, device=device, dtype=dtype)
size = SdpaShape(2, 2, 8, 8)
q, k, v = make_tensor(size), make_tensor(size), make_tensor(size)
q.as_strided_(size, [2, 2, 2, 2])
with self.assertWarnsRegex(UserWarning, "All fused kernels require the last dimension of the input to have stride 1."):
self.assertRaises(RuntimeError, lambda: torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, 0.0, False))
@onlyCUDA
@skipIfRocm # Nested Tensor
@unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Does not support SDPA or pre-SM80 hardware")
@parametrize("fused_kernel", [SDPBackend.EFFICIENT_ATTENTION])
def test_invalid_sdpa_kernel_grouped_query_attention_cuda(self, device, fused_kernel):
rand_query = torch.rand(8, 8, 64, 64, device=device, dtype=torch.float16, requires_grad=True)
rand_key = torch.rand(8, 4, 64, 64, device=device, dtype=torch.float16, requires_grad=True)
rand_value = torch.rand(8, 4, 64, 64, device=device, dtype=torch.float16, requires_grad=True)
with sdpa_kernel(fused_kernel):
with self.assertRaisesRegex(RuntimeError, "No available kernel"):
with self.assertWarnsRegex(UserWarning, "For dense inputs, both fused kernels require query, "
"key and value to have"):
F.scaled_dot_product_attention(rand_query, rand_key, rand_value, dropout_p=0.0,
is_causal=False, enable_gqa=True)
@onlyCPU
@skipIfRocm # Nested Tensor
def test_invalid_sdpa_kernel_grouped_query_attention_cpu(self, device):
rand_query = torch.rand(8, 8, 64, 64, device=device, dtype=torch.float16, requires_grad=True)
rand_key = torch.rand(8, 4, 64, 64, device=device, dtype=torch.float16, requires_grad=True)
rand_value = torch.rand(8, 4, 64, 64, device=device, dtype=torch.float16, requires_grad=True)
with sdpa_kernel(backends=[SDPBackend.FLASH_ATTENTION]):
with self.assertRaisesRegex(RuntimeError, "No available kernel"):
with self.assertWarnsRegex(UserWarning, "For dense inputs, both fused kernels require query, "
"key and value to have"):
F.scaled_dot_product_attention(rand_query, rand_key, rand_value, dropout_p=0.0,
is_causal=False, enable_gqa=True)
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Does not flash_attention fused scaled dot product attention")
@parametrize("kernel", PLATFORM_SPECIFIC_SDPA)
def test_invalid_fused_inputs_head_dim(self, device, kernel: SDPBackend):
with sdpa_kernel(backends=[kernel]):
# The embed dim per head is not divisible by 8 for flash attention
dtype = torch.float16
make_tensor = partial(torch.rand, device=device, dtype=dtype)
size = SdpaShape(2, 2, 3, 9) if kernel == SDPBackend.EFFICIENT_ATTENTION else SdpaShape(2, 2, 3, 257)
q, k, v = make_tensor(size), make_tensor(size), make_tensor(size)
self.assertRaises(RuntimeError, lambda: torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, 0.0, False))
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FUSED_ATTENTION, "Does not support fused scaled dot product attention")
@parametrize(
"kernel",
PLATFORM_SPECIFIC_SDPA,
)
def test_invalid_fused_inputs_invalid_dtype(self, device, kernel: SDPBackend):
with sdpa_kernel(backends=[kernel]):
# Invalid dtype for both Flash Attention and Mem Efficient Attention
size = SdpaShape(2, 2, 3, 16)
make_tensor = partial(torch.rand, device=device, dtype=torch.float64)
q, k, v = make_tensor(size), make_tensor(size), make_tensor(size)
self.assertRaises(RuntimeError, lambda: torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, 0.0, False))
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Does not support flash attention")
@parametrize("kernel", [SDPBackend.FLASH_ATTENTION])
def test_invalid_fused_inputs_attn_mask_present(self, device, kernel: SDPBackend):
with sdpa_kernel(backends=[kernel]):
# Failures for unsupported SDP args
size = SdpaShape(2, 2, 3, 16)
make_tensor = partial(torch.rand, size, device=device, dtype=torch.float16)
q, k, v = make_tensor(), make_tensor(), make_tensor()
# Non-None attention mask
mask = torch.ones((2, 2, 3, 3), device=device, dtype=q.dtype)
self.assertRaises(RuntimeError, lambda: torch.nn.functional.scaled_dot_product_attention(
q, k, v, mask, 0.0, False))
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Does not support fused SDPA or pre-SM80 hardware")
def test_unaligned_tensors(self, device):
# The alignment is depdent on arch so we specifiy SM80OrLater
dtype = torch.float16
size = SdpaShape(2, 2, 8, 5)
make_tensor = partial(torch.rand, size, device=device, dtype=dtype)
q, k, v = make_tensor(), make_tensor(), make_tensor()
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
self.assertRaises(RuntimeError, lambda: torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, 0.0, False))
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Does not support fused SDPA or pre-SM80 hardware")
def test_flash_fail_fp32(self, device):
dtype = torch.float
size = SdpaShape(16, 16, 32, 32)
make_tensor = partial(torch.rand, size, device=device, dtype=dtype)
q, k, v = make_tensor(), make_tensor(), make_tensor()
with sdpa_kernel(backends=[SDPBackend.FLASH_ATTENTION]):
with self.assertWarnsRegex(UserWarning, "Expected query, key and value to all be of dtype: {Half, BFloat16}"):
self.assertRaises(RuntimeError, lambda: torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, 0.0, False))
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Does not support SDPA or pre-SM80 hardware")
def test_flash_autocast_fp32_float16(self, device):
dtype = torch.float
size = SdpaShape(16, 16, 32, 32)
make_tensor = partial(torch.rand, size, device=device, dtype=dtype)
q, k, v = make_tensor(), make_tensor(), make_tensor()
with torch.autocast(device_type='cuda', dtype=torch.float16):
with sdpa_kernel(backends=[SDPBackend.FLASH_ATTENTION]):
_ = torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, 0.0, False)
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Does not support SDPA or pre-SM80 hardware")
def test_flash_autocast_fp32_bfloat16(self, device):
dtype = torch.float
size = SdpaShape(16, 16, 32, 32)
make_tensor = partial(torch.rand, size, device=device, dtype=dtype)
q, k, v = make_tensor(), make_tensor(), make_tensor()
with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
with sdpa_kernel(backends=[SDPBackend.FLASH_ATTENTION]):
_ = torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, 0.0, False)
# Note: do not truncate the list according to platforms. These tests should always raise errors.
@parametrize("kernel", [SDPBackend.MATH, SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION])
def test_invalid_inputs_different_datatypes(self, device, kernel: SDPBackend):
with sdpa_kernel(backends=[kernel]):
# Different datatypes
shape = (1, 4, 8, 16)
query = torch.randn(shape, dtype=torch.float32, device=device)
key = torch.randn(shape, dtype=torch.float16, device=device)
value = torch.randn(shape, dtype=torch.float16, device=device)
self.assertRaises(RuntimeError, lambda: F.scaled_dot_product_attention(query, key, value))
@onlyCUDA
@parametrize("kernel", [SDPBackend.MATH, SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION])
def test_invalid_inputs_different_devices(self, device, kernel: SDPBackend):
# Different devices
shape = (1, 4, 8, 16)
query = torch.randn(shape, dtype=torch.float32, device=device)
key = torch.randn(shape, dtype=torch.float16, device='cpu')
value = torch.randn(shape, dtype=torch.float16, device='cpu')
self.assertRaises(RuntimeError, lambda: F.scaled_dot_product_attention(query, key, value))
@parametrize("kernel", [SDPBackend.MATH, SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION])
def test_invalid_inputs_1_dimensional_inputs(self, device, kernel: SDPBackend):
with sdpa_kernel(backends=[kernel]):
# 1 dimensional input
shape = (1, 4)
query = torch.randn(4, dtype=torch.float16, device=device)
key = torch.randn(shape, dtype=torch.float16, device=device)
value = torch.randn(shape, dtype=torch.float16, device=device)
self.assertRaises(RuntimeError, lambda: F.scaled_dot_product_attention(query, key, value))
@onlyCUDA
@skipIfRocm # Missing EFFICIENT_ATTENTION
@unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Fused SDPA was not built for this system")
def test_fused_kernels_nested_broadcasting_error_cases(self, device):
# one of k,v needs to be broadcasted and other has non consistent seq_len dim
rand_nested_tensor = partial(rand_sdpa_tensor, type="nested", device=device, dtype=torch.float32)
batch, num_heads, head_dim = 32, 8, 64
seq_lens_q = torch.randint(low=1, high=32, size=(batch,)).tolist()
seq_lens_v = torch.randint(low=1, high=32, size=(batch,)).tolist()
q_shape = SdpaShape(batch, num_heads, seq_lens_q, head_dim)
k_shape = SdpaShape(1, num_heads, 1, head_dim)
v_shape = SdpaShape(batch, num_heads, seq_lens_v, head_dim)
query = rand_nested_tensor(q_shape).transpose(1, 2)
key = rand_nested_tensor(k_shape).transpose(1, 2)
value = rand_nested_tensor(v_shape).transpose(1, 2)
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
with self.assertRaisesRegex(RuntimeError, "No available kernel"):
torch.nn.functional.scaled_dot_product_attention(
query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False)
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Fused SDPA was not built for this system")
def test_nested_fails_on_padding_head_dim(self, device):
dtype = torch.bfloat16
seq_len_list = [2, 4, 5, 6, 7]
shape = SdpaShape(5, 8, seq_len_list, 57)
make_tensor = partial(rand_sdpa_tensor, shape=shape, type="nested", device=device, dtype=dtype)
q, k, v = make_tensor().transpose(1, 2), make_tensor().transpose(1, 2), make_tensor().transpose(1, 2)
with sdpa_kernel(backends=[SDPBackend.FLASH_ATTENTION]):
with self.assertWarnsRegex(UserWarning, "For NestedTensor inputs, Flash attention requires"):
self.assertRaises(RuntimeError, lambda: torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, 0.0, False))
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FUSED_ATTENTION or not isLessThanSM80Device,
"Current platform does not support fused SDPA or is an SM80+ device.")
def test_mem_efficient_fail_bfloat16_less_than_sm80(self, device):
dtype = torch.bfloat16
size = SdpaShape(16, 16, 32, 32)
make_tensor = partial(torch.rand, size, device=device, dtype=dtype)
q, k, v = make_tensor(), make_tensor(), make_tensor()
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
with self.assertWarnsRegex(UserWarning, "Expected query, key and value to all be of dtype: {Half, Float}"):
self.assertRaises(RuntimeError, lambda: torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, 0.0, False))
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Does not support flash attention")
def test_flash_atteention_large_bf16_nan_values(self, device):
query = torch.full((1, 1, 1, 64), 133120.0, dtype=torch.bfloat16, device="cuda")
key = torch.full((1, 1, 1, 64), 133120.0, dtype=torch.bfloat16, device="cuda")
value = torch.full((1, 1, 1, 64), 133120.0, dtype=torch.bfloat16, device="cuda")
with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
out = torch.nn.functional.scaled_dot_product_attention(query, key, value)
self.assertFalse(torch.isnan(out).any(), "Output should not contain NaNs!")
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FUSED_ATTENTION, "Fused SDPA was not built for this system")
@parametrize("fused_kernel", [SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION] if
PLATFORM_SUPPORTS_FLASH_ATTENTION else [SDPBackend.EFFICIENT_ATTENTION])
def test_fused_kernels_seq_len_0_inputs(self, device, fused_kernel):
rand_nested_tensor = partial(rand_sdpa_tensor, type="nested", device=device, dtype=torch.float16)
batch, num_heads, head_dim = 32, 16, 64
seq_lens = torch.randint(low=1, high=32, size=(batch,))
# make sure some seq_lens are 0
num_zeros = 10
indices = torch.randint(low=0, high=batch, size=(num_zeros,))
seq_lens.scatter_(0, indices, 0)
shape = SdpaShape(batch, num_heads, seq_lens.tolist(), head_dim)
query = rand_nested_tensor(shape)
key = rand_nested_tensor(shape)
value = rand_nested_tensor(shape)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
with sdpa_kernel(backends=[fused_kernel]):
with self.assertRaisesRegex(RuntimeError, "No available kernel"):
torch.nn.functional.scaled_dot_product_attention(
query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False)
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Fused SDPA was not built for this system")
def test_fused_kernels_nested_broadcasting_requires_grad_failure(self, device):
rand_nested_tensor = partial(rand_sdpa_tensor, type="nested", device=device, dtype=torch.float16, requires_grad=True)
batch, num_heads, head_dim, head_dim_v = 32, 16, 64, 64
seq_lens = torch.randint(low=1, high=32, size=(batch,)).tolist()
q_shape = SdpaShape(1, num_heads, 1, head_dim)
k_shape = SdpaShape(batch, num_heads, seq_lens, head_dim)
v_shape = SdpaShape(batch, 1, seq_lens, head_dim_v)
# create a dense query
query = torch.randn(q_shape, device=device, dtype=torch.float16, requires_grad=True)
key = rand_nested_tensor(k_shape)
value = rand_nested_tensor(v_shape)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
with sdpa_kernel(backends=[SDPBackend.FLASH_ATTENTION]):
with self.assertWarnsRegex(UserWarning, "Both fused kernels do not support training with broadcasted NT inputs"):
with self.assertRaisesRegex(RuntimeError, "No available kernel"):
torch.nn.functional.scaled_dot_product_attention(
query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False)
@onlyCUDA
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Does not support flash attention")
def test_flash_attention_fail_with_non_square_causal_attention(self, device):
dtype = torch.bfloat16
q_shape = SdpaShape(1, 1, 8, 16)
kv_shape = SdpaShape(1, 1, 12, 16)
make_q = partial(torch.rand, q_shape, device=device, dtype=dtype)
make_kv = partial(torch.rand, kv_shape, device=device, dtype=dtype)
q, k, v = make_q(), make_kv(), make_kv()
warning_str = "Flash attention does not support the is_causal flag when seqlen_q != seqlen_k."
with sdpa_kernel(backends=[SDPBackend.FLASH_ATTENTION]):
with self.assertWarnsRegex(UserWarning, warning_str):
self.assertRaises(RuntimeError, lambda: torch.nn.functional.scaled_dot_product_attention(
q, k, v, None, 0.0, is_causal=True))
def _get_block_size_n(device, head_dim, is_dropout, is_causal):
# This should match the block sizes in the CUDA kernel
assert head_dim <= 256
major, minor = torch.cuda.get_device_capability(device)
is_sm8x = major == 8 and minor > 0 # Only include sm86 and sm89, exclude sm80 (A100)
is_sm80 = major == 8 and minor == 0
is_sm90 = major == 9 and minor == 0
if head_dim <= 32:
return 128
if head_dim <= 64:
return 128 if not is_dropout else 64
elif head_dim <= 96:
return 64
elif head_dim <= 128:
if is_sm8x:
return 64 if (not is_dropout and is_causal) else 32
else:
return 64 if not is_dropout else 32
elif head_dim <= 160:
if is_sm8x:
return 64
else:
return 32
elif head_dim <= 192:
return 64
elif head_dim <= 224:
return 64
elif head_dim <= 256:
return 64
def pad_last_dim(input_tensor, alignment_size, slice: bool = False):
last_dim_size = input_tensor.size(-1)
if (last_dim_size % alignment_size == 0):
return input_tensor, last_dim_size
pad_count = alignment_size - (last_dim_size % alignment_size)
padded_tensor = F.pad(input_tensor, (0, pad_count))
if slice:
return padded_tensor[..., :last_dim_size], last_dim_size
return padded_tensor, last_dim_size
class TestSDPA(NNTestCase):
""" Used to test generic functionality of scaled_dot_product_attention
Summary:
If you are adding a new test to this class, make sure that it runs
for both cpu and cuda. If you're test is only applicable to cuda,
add it to TestSDPACudaOnly.
"""
@parametrize("contiguous_inputs", [True, False])
def test_sdp_math_gradcheck(self, device, contiguous_inputs: bool):
batch_size, seq_len, num_heads, head_dim = 4, 4, 2, 16
shape = SdpaShape(batch_size, num_heads, seq_len, head_dim)
make_tensor = partial(rand_sdpa_tensor, type="dense", device=device,
dtype=torch.float64, requires_grad=True, packed=True)
qkv = make_tensor(shape)
query, key, value = qkv.chunk(3, dim=-1)
query = query.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
if contiguous_inputs:
query = query.contiguous()
key = key.contiguous()
value = value.contiguous()
with sdpa_kernel(backends=[SDPBackend.MATH]):
assert gradcheck(lambda *args, **kwargs:
wrapper_set_seed(torch.nn.functional.scaled_dot_product_attention, *args, **kwargs),
(query, key, value, None, 0.0, False)
)
@parametrize("kernel", [SDPBackend.MATH])
def test_scaled_dot_product_attention_math_with_negative_scale(self, device, kernel: SDPBackend):
# https://github.com/pytorch/pytorch/issues/105190.
def ref(x):
v1 = torch.matmul(x, x.transpose(-1, -2))
v2 = v1 / -0.0001
v3 = v2.softmax(dim=-1)
v4 = torch.matmul(v3, x)
return v4
x = torch.randn(1, 3, 64, 64, device=device)
ref_result = ref(x)
with sdpa_kernel(backends=[kernel]):
sdp_math = torch.nn.functional.scaled_dot_product_attention(x, x, x, scale=-1.0 / 0.0001)
self.assertEqual(ref_result, sdp_math)
class TestSDPACpuOnly(NNTestCase):
""" Used to test CPU only functionality of scaled_dot_product_attention """
@parametrize("type", ["dense", "nested"])
@parametrize("dropout", [0.0, 0.7])
@parametrize("dtype", [torch.float64, torch.float32, torch.bfloat16, torch.half])
@skipIfTorchDynamo()
def test_fused_sdp_choice_cpu(self, device, type: str, dropout: float, dtype: torch.dtype):
# Test that cpu and nestedtensor cpu return MATH backend
make_tensor = partial(rand_sdpa_tensor, type=type, device=device, dtype=dtype)
size = SdpaShape(2, 8, 128, 64)
q, k, v = make_tensor(size), make_tensor(size), make_tensor(size)
if type == "nested" \
or dropout > 0.0 \
or dtype not in [torch.float32, torch.float64, torch.bfloat16, torch.float16]:
assert torch._fused_sdp_choice(q, k, v, dropout_p=dropout) == SDPBackend.MATH.value
else:
assert torch._fused_sdp_choice(q, k, v, dropout_p=dropout) == SDPBackend.FLASH_ATTENTION.value
@parametrize("fused_kernel", [SDPBackend.FLASH_ATTENTION])
@parametrize("dtype", [torch.float64, torch.float32, torch.bfloat16, torch.float16])
@parametrize("batch_size", [2, 12])
@parametrize("q_seq_len", [514, 1030])
@parametrize("kv_seq_len", [514])
@parametrize("n_head", [1, 3])
@parametrize("head_dim", [8])
@parametrize("mask_dim", [2, 4])
@parametrize("bool_mask", [False, True])
@parametrize("train", [True, False])
@parametrize("casual", [True, False])
@parametrize("set_attn_mask", [True, False])
def test_scaled_dot_product_fused_attention_mask_vs_math_cpu(
self,
device,
fused_kernel,
dtype,
batch_size,
q_seq_len,
kv_seq_len,
n_head,
head_dim,
mask_dim,
bool_mask,
train,
casual,
set_attn_mask,
):
tol = Tolerances(1e-5, 5e-6)
if dtype is torch.bfloat16:
tol = Tolerances(5e-2, 5e-2)
if dtype is torch.float16:
tol = Tolerances(1e-2, 1e-2)
for mask_shape in itertools.product(
[q_seq_len, 1], [kv_seq_len, 1]
) if mask_dim == 2 else itertools.product(
[batch_size, 1], [n_head, 1], [q_seq_len, 1], [kv_seq_len, 1]
):
make_tensor = partial(rand_sdpa_tensor, type="dense", device=device, dtype=dtype, requires_grad=False)
q_shape = SdpaShape(batch_size, n_head, q_seq_len, head_dim)
kv_shape = SdpaShape(batch_size, n_head, kv_seq_len, head_dim)
q = make_tensor(q_shape)
k = make_tensor(kv_shape)
v = make_tensor(kv_shape)
q2, k2, v2 = q.clone(), k.clone(), v.clone()
if train:
q.requires_grad_(True)
k.requires_grad_(True)
v.requires_grad_(True)
q2.requires_grad_(True)
k2.requires_grad_(True)
v2.requires_grad_(True)
if dtype in [torch.bfloat16, torch.float16]:
q2, k2, v2 = q2.float(), k2.float(), v2.float()
# (B, nh, T, hs)
q = q.view(batch_size, q_seq_len, n_head, head_dim).transpose(1, 2)
k = k.view(batch_size, kv_seq_len, n_head, head_dim).transpose(1, 2)
v = v.view(batch_size, kv_seq_len, n_head, head_dim).transpose(1, 2)
if set_attn_mask and not casual:
if bool_mask:
attn_mask = torch.randint(0, 2, size=mask_shape, dtype=torch.bool, device=device)
else:
attn_mask = torch.randn(mask_shape, dtype=dtype, device=device)
else:
attn_mask = None
q2 = q2.view(batch_size, q_seq_len, n_head, head_dim).transpose(1, 2)
k2 = k2.view(batch_size, kv_seq_len, n_head, head_dim).transpose(1, 2)
v2 = v2.view(batch_size, kv_seq_len, n_head, head_dim).transpose(1, 2)
with sdpa_kernel(backends=[fused_kernel]):
actual = torch.nn.functional.scaled_dot_product_attention(
q, k, v, attn_mask=attn_mask, dropout_p=0.0, is_causal=casual)
with sdpa_kernel(backends=[SDPBackend.MATH]):
if not bool_mask and dtype in [torch.bfloat16, torch.float16] and attn_mask is not None:
attn_mask = attn_mask.float()
math_ref = torch.nn.functional.scaled_dot_product_attention(
q2, k2, v2, attn_mask=attn_mask, dropout_p=0.0, is_causal=casual)
if dtype in [torch.bfloat16, torch.float16]:
math_ref = math_ref.to(dtype)
self.assertFalse(torch.isnan(math_ref).any())
self.assertFalse(torch.isnan(actual).any())
self.assertEqual(actual, math_ref, atol=tol.atol, rtol=tol.rtol)
if train:
actual.sum().backward()
math_ref.sum().backward()
grad_q_actual, grad_k_actual, grad_v_actual = q.grad, k.grad, v.grad
grad_q_ref, grad_k_ref, grad_v_ref = q2.grad, k2.grad, v2.grad
self.assertEqual(grad_q_actual, grad_q_ref, atol=tol.atol, rtol=tol.rtol)
self.assertEqual(grad_k_actual, grad_k_ref, atol=tol.atol, rtol=tol.rtol)
self.assertEqual(grad_v_actual, grad_v_ref, atol=tol.atol, rtol=tol.rtol)
def test_scaled_dot_product_fused_attention_with_inf(self, device):
# https://github.com/pytorch/pytorch/issues/127055.
full = torch.full((600, 600), float("-inf"), device=device)
mask = torch.triu(full, diagonal=1) + torch.tril(full, diagonal=-10)
make_tensor = partial(rand_sdpa_tensor, type="dense", device=device, dtype=torch.float32, requires_grad=False)
input_shape = SdpaShape(1, 600, 2, 8)
q = make_tensor(input_shape)
k = make_tensor(input_shape)
v = make_tensor(input_shape)
with sdpa_kernel(backends=[SDPBackend.MATH]):
math_ref = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask)
with sdpa_kernel(backends=[SDPBackend.FLASH_ATTENTION]):
actual = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask)
self.assertEqual(math_ref, actual)
@unittest.skipIf(not PLATFORM_SUPPORTS_FUSED_ATTENTION, "Fused SDPA was not built for this system")
@parametrize("backend", [SDPBackend.EFFICIENT_ATTENTION, SDPBackend.FLASH_ATTENTION])
@parametrize("seq_len", [32, 64, 128])
@parametrize("head_dim", [16, 32])
@parametrize("dtype", [torch.float32, torch.float16])
def test_fully_masked_out_rows(self, backend, device, seq_len, head_dim, dtype):
def attention_inputs(seq_len, head_dim, device, dtype, mask_every_n_rows=4):
query = torch.rand(1, 1, seq_len, head_dim, requires_grad=True, device=device, dtype=dtype)
key = torch.rand(1, 1, seq_len, head_dim, requires_grad=True, device=device, dtype=dtype)
value = torch.rand(1, 1, seq_len, head_dim, requires_grad=True, device=device, dtype=dtype)
# Create a mask with deterministic row masking
mask = torch.ones(1, 1, seq_len, seq_len, dtype=torch.bool, device=device)
# Mask every nth row
mask[0, 0, ::mask_every_n_rows, :] = False
# Create a fixed pattern for element-wise masking
element_mask = torch.zeros(seq_len, seq_len, dtype=torch.bool, device=device)
element_mask[torch.arange(seq_len)[:, None] % 5 == torch.arange(seq_len) % 5] = True
# Combine row masking and element-wise masking
mask = mask & element_mask.unsqueeze(0).unsqueeze(0)
return query, key, value, mask
def compute_output_and_grads(query, key, value, mask, backend):
with sdpa_kernel(backend):
masked_out = scaled_dot_product_attention(query, key, value, attn_mask=mask)
loss = masked_out.sum()
grads = torch.autograd.grad(loss, [query, key, value])
return masked_out, grads
if backend == SDPBackend.FLASH_ATTENTION and "cuda" in str(device):
unittest.skip("FlashAttention does not support masks on cuda")
return
if backend == SDPBackend.EFFICIENT_ATTENTION and "cpu" in str(device):
unittest.skip("EfficientAttention does not support masks on cpu")
return
query, key, value, mask = attention_inputs(seq_len, head_dim, device, dtype)
# Compute results for the tested backend
backend_out, backend_grads = compute_output_and_grads(query, key, value, mask, backend)
# Compute results for the Math backend
math_out, math_grads = compute_output_and_grads(query, key, value, mask, SDPBackend.MATH)
# Compare outputs
torch.testing.assert_close(backend_out, math_out, atol=5e-3, rtol=0)
self.assertFalse(backend_out.isnan().any())
self.assertFalse(math_out.isnan().any())
# Compare gradients
for bg, mg in zip(backend_grads, math_grads):
torch.testing.assert_close(bg, mg, atol=3e-3, rtol=0)
self.assertFalse(bg.isnan().any())
self.assertFalse(mg.isnan().any())
# Check if masked rows are zero in output
mask_sum = mask.sum(dim=-1, keepdim=True)
masked_rows = (mask_sum == 0).expand_as(backend_out)
self.assertTrue((mask_sum == 0).sum() > 0, "No fully masked out rows found")
assert torch.all(backend_out[masked_rows] == 0), \
f"Non-zero values in fully masked rows for {backend=}"
# Check if gradients for masked rows are zero
grad_query = backend_grads[0]
assert torch.all(grad_query[masked_rows] == 0), f"Non-zero gradients in fully masked rows for {backend=}"
@parametrize("dtype", [torch.float32, torch.float16])
@parametrize("fill_val", [float("inf")])
def test_non_masked_rows_nan_props(self, device, dtype, fill_val):
query = torch.randn(1, 2, 4, 16, device=device, dtype=dtype)
# a single NaN in the query input
query[0, 1, 2, 3] = fill_val
query = query.detach().requires_grad_(True)
key = torch.randn(1, 2, 4, 16, device=device, dtype=dtype, requires_grad=True)
value = torch.randn(1, 2, 4, 16, device=device, dtype=dtype, requires_grad=True)
out = torch.nn.functional.scaled_dot_product_attention(query, key, value)
self.assertTrue(torch.isnan(out).any())
out.sum().backward()
self.assertTrue(torch.isnan(query.grad).any())
@parametrize("kernel", [SDPBackend.MATH])
def test_scaled_dot_product_attention_math_with_negative_scale(self, device, kernel: SDPBackend):
# https://github.com/pytorch/pytorch/issues/105190.
def ref(x):
v1 = torch.matmul(x, x.transpose(-1, -2))
v2 = v1 / -0.0001
v3 = v2.softmax(dim=-1)
v4 = torch.matmul(v3, x)
return v4
x = torch.randn(1, 3, 64, 64, device=device)
ref_result = ref(x)
with sdpa_kernel(backends=[kernel]):
sdp_math = torch.nn.functional.scaled_dot_product_attention(x, x, x, scale=-1.0 / 0.0001)
self.assertEqual(ref_result, sdp_math)
class TestSDPACudaOnly(NNTestCase):
""" Used to test CUDA only functionality of scaled_dot_product_attention
Quarks:
There is some trickiness with this function. Its runtime behavior
is dependent on the CUDA architecture you are testing it on. See
`PLATFORM_SUPPORTS_FUSED_ATTENTION` at the top of the file.
Summary:
Math: always supported
FlashAttention: Supported on sm80 or newer hardware
MemEfficientAttention: Supported on sm50 or newer hardware
"""
_do_cuda_memory_leak_check = True
_do_cuda_non_default_stream = True
# TODO USED FOR TESTING THE SCORES, e.g. testing ALIBI we don't need this now
def normalize_flash_attn_S(
self,
attn_unnorm,
q,
k,
v,
query_padding_mask=None,
key_padding_mask=None,
attn_bias=None,
is_dropout=False,
causal=False,
window_size=(-1, -1), # -1 means infinite window size
scale=None,
):
"""
Arguments:
q: (batch_size, seqlen_q, nheads, head_dim)
k, v: (batch_size, seqlen_k, nheads, head_dim)
key_padding_mask: (batch_size, seqlen_q)
attn_bias: broadcastable to (batch_size, nheads, seqlen_q, seqlen_k)
Output:
softmax_lse: (batch_size, nheads, seqlen_q)
softmax_max: (batch_size, nheads, seqlen_q)
"""
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
if causal:
window_size = (window_size[0], 0)
q, k, v = q.float(), k.float(), v.float()
_, seqlen_q, _, head_dim = q.shape
seqlen_k = k.shape[1]
b = q.shape[0]
from torch.nn.attention.bias import _calculate_scale
scale = _calculate_scale(head_dim, scale)
scores = torch.matmul(q.transpose(1, 2) * scale, k.permute(0, 2, 3, 1))
if key_padding_mask is not None:
scores.masked_fill_(~key_padding_mask.view(b, 1, 1, -1), float("-inf"))
if window_size[0] >= 0 or window_size[1] >= 0:
local_mask = self.construct_local_mask(
seqlen_q,
seqlen_k,
window_size,
query_padding_mask,
key_padding_mask,
q.device,
)
scores.masked_fill_(local_mask, float("-inf"))
if attn_bias is not None:
scores = scores + attn_bias.to(dtype=scores.dtype)
block_size_n = _get_block_size_n(scores.device, head_dim, is_dropout, causal)
scores_block = scores.split(block_size_n, dim=-1)
lse_block = torch.stack([torch.logsumexp(s, dim=-1) for s in scores_block], dim=-1)
lse = torch.logsumexp(lse_block, dim=-1)
# lse could be -inf (i.e. all values in scores are -inf), and we want to set those to inf
# so that when we do torch.exp(m - lse), we get 0.0 instead of NaN.
lse[lse == float("-inf")] = float("inf")
scores_max_block = torch.stack([torch.amax(s, dim=-1) for s in scores_block], dim=-1)
cummax_block = torch.cummax(scores_max_block.flip(-1), dim=-1).values.flip(-1).unbind(dim=-1)
attn_unnorm_block = attn_unnorm.split(block_size_n, dim=-1)
attn_norm = torch.cat(
[
a * (torch.exp(m - lse)).unsqueeze(-1)
for a, m in zip(attn_unnorm_block, cummax_block)
],
dim=-1,
)
if query_padding_mask is not None:
attn_norm.masked_fill_(~query_padding_mask.view(b, 1, -1, 1), 0.0)
# attn_norm.masked_fill_(rearrange(~query_padding_mask, "b s -> b 1 s 1"), 0.0)
return attn_norm.to(dtype=attn_unnorm.dtype)
def construct_local_mask(self, seqlen_q, seqlen_k, window_size, query_padding_mask, key_padding_mask, device):
# row_idx = rearrange(torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1")
row_idx = torch.arange(seqlen_q, device=device, dtype=torch.long).view(-1, 1)
col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long)
sk = (
seqlen_k
if key_padding_mask is None
else key_padding_mask.sum(-1).view(-1, 1, 1, 1)
# else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1")
)
sq = (
seqlen_q
if query_padding_mask is None
else query_padding_mask.sum(-1).view(-1, 1, 1, 1)
# else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1")
)
if window_size[0] < 0:
return col_idx > row_idx + sk - sq + window_size[1]
else:
sk = torch.full_like(col_idx, seqlen_k) if key_padding_mask is None else sk
return torch.logical_or(
col_idx > torch.minimum(row_idx + sk - sq + window_size[1], sk),
col_idx < row_idx + sk - sq - window_size[0],
)
def convert_flash_attn_S_to_softmax(
self,
S,
seqlen_q,
seqlen_k,
query_padding_mask,
key_padding_mask,
causal=False,
window_size=(-1, -1), # -1 means infinite window size
):
"""FlashAttention stores the S matrix in a different way.
Arguments:
S: (batch_size, nheads, seqlen_q, seqlen_k)
query_padding_mask: (batch_size, seqlen_q)
key_padding_mask: (batch_size, seqlen_k)
"""
if TEST_WITH_ROCM:
return S
b = S.shape[0]
if causal:
window_size = (window_size[0], 0)
seqlen_q_rounded, seqlen_k_rounded = S.shape[-2:]
S_converted = S
if window_size[0] >= 0 or window_size[1] >= 0:
local_mask = self.construct_local_mask(
seqlen_q,
seqlen_k,
window_size,
query_padding_mask,
key_padding_mask,
S.device,
)
local_mask = F.pad(
local_mask,
(0, seqlen_k_rounded - seqlen_k, 0, seqlen_q_rounded - seqlen_q),
value=True,
)
S_converted = S_converted.masked_fill(local_mask, 0.0)
# Need to zero out things not in attention_mask in case S was initialized with random values
# and some of those values aren't overwritten.
seqlen_q_og = (
query_padding_mask.shape[-1] if query_padding_mask is not None else seqlen_q_rounded
)
if query_padding_mask is not None:
query_padding_mask = F.pad(query_padding_mask, (0, seqlen_q_rounded - seqlen_q_og))
# S_converted = S_converted.masked_fill(rearrange(~query_padding_mask, "b s -> b 1 s 1"), 0.0)
S_converted = S_converted.masked_fill(~query_padding_mask.view(b, 1, -1, 1), 0.0)
seqlen_k_og = key_padding_mask.shape[-1] if key_padding_mask is not None else seqlen_k
if key_padding_mask is not None:
key_padding_mask = F.pad(key_padding_mask, (0, seqlen_k_rounded - seqlen_k_og))
S_converted = S_converted.masked_fill(~key_padding_mask.view(b, 1, 1, -1), 0.0)
# S_converted = S_converted.masked_fill(rearrange(~key_padding_mask, "b s -> b 1 1 s"), 0.0)
S_converted = F.pad(S_converted, (0, 0, 0, seqlen_q_og - seqlen_q_rounded))
S_converted = F.pad(S_converted, (0, seqlen_k_og - seqlen_k_rounded))
return S_converted[:, :, :seqlen_q, :seqlen_k]
@skipIfRocm # No cuDNN Attention
@unittest.skipIf(not PLATFORM_SUPPORTS_CUDNN_ATTENTION, "cuDNN Attention is not supported on this system")
def test_cudnn_attention_different_dk_dv(self, device):
dtype = torch.bfloat16
make_tensor = partial(torch.rand, device=device, dtype=dtype, requires_grad=True)
batch, num_heads, head_dim_k, head_dim_v = 32, 16, 128, 64
seq_len = 640
q_shape = SdpaShape(batch, num_heads, seq_len, head_dim_k)
k_shape = SdpaShape(batch, num_heads, seq_len, head_dim_k)
v_shape = SdpaShape(batch, num_heads, seq_len, head_dim_v)
query, key, value = make_tensor(q_shape), make_tensor(k_shape), make_tensor(v_shape)
with sdpa_kernel(backends=[SDPBackend.CUDNN_ATTENTION]):
actual = torch.nn.functional.scaled_dot_product_attention(
query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False)
with sdpa_kernel(backends=[SDPBackend.MATH]):
math_ref = torch.nn.functional.scaled_dot_product_attention(
query.contiguous().to(torch.float32),
key.contiguous().to(torch.float32),
value.contiguous().to(torch.float32),
attn_mask=None, dropout_p=0.0, is_causal=False)
self.assertEqual(actual.contiguous(), math_ref.contiguous().to(dtype), atol=1e-3, rtol=1e-2)
@skipIfRocm # No cuDNN Attention
@unittest.skipIf(not PLATFORM_SUPPORTS_CUDNN_ATTENTION, "cuDNN Attention is not supported on this system")
def test_cudnn_attention_fail_d128(self, device):
# Test that cuDNN attention dispatching correctly bails out on d > 128
b, h = 1, 2
s_q, s_kv = 128, 128
d_qk, d_v = 128, 144
q = torch.randn(b, h, s_q, d_qk, device=device, dtype=torch.bfloat16)
k = torch.randn(b, h, s_kv, d_qk, device=device, dtype=torch.bfloat16)
v = torch.randn(b, h, s_kv, d_v, device=device, dtype=torch.bfloat16)
with sdpa_kernel(backends=[SDPBackend.CUDNN_ATTENTION]):
with self.assertRaisesRegex(RuntimeError, "No available kernel."):
o = torch.nn.functional.scaled_dot_product_attention(q, k, v)
@unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Fused SDPA was not built for this system")
@parametrize("mask_dim", [1, 2, 3, 4])
def test_mem_efficient_attention_mask_variants(self, device, mask_dim: List[int]):
dtype = torch.float16
make_tensor = partial(torch.rand, device=device, dtype=dtype, requires_grad=True)
batch, num_heads, head_dim = 8, 8, 64
seq_len_q, seq_len_kv = 64, 15
query = make_tensor(SdpaShape(batch, num_heads, seq_len_q, head_dim))
kv_shape = SdpaShape(batch, num_heads, seq_len_kv, head_dim)
key, value = make_tensor(kv_shape), make_tensor(kv_shape)
if mask_dim == 1:
mask = torch.randn((seq_len_kv,), device=device, dtype=dtype)
elif mask_dim == 2:
mask = torch.randn((seq_len_q, seq_len_kv), device=device, dtype=dtype)
elif mask_dim == 3:
mask = torch.randn((num_heads, seq_len_q, seq_len_kv), device=device, dtype=dtype)
elif mask_dim == 4:
mask = torch.randn((batch, num_heads, seq_len_q, seq_len_kv), device=device, dtype=dtype)
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
out = F.scaled_dot_product_attention(query, key, value, mask)
out.sum().backward()
@unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Fused SDPA was not built for this system")
@parametrize("dtype", [torch.float, torch.float16])
def test_mem_eff_attention_non_contiguous_mask(self, device, dtype):
make_tensor = partial(torch.rand, device=device, dtype=dtype, requires_grad=True)
batch, num_heads, head_dim = 8, 8, 64
seq_len_q, seq_len_kv = 64, 16
query = make_tensor(SdpaShape(batch, num_heads, seq_len_q, head_dim))
kv_shape = SdpaShape(batch, num_heads, seq_len_kv, head_dim)
key, value = make_tensor(kv_shape), make_tensor(kv_shape)
mask = torch.randn((batch, num_heads, seq_len_q, seq_len_kv), device=device, dtype=dtype)
mask = torch.as_strided(mask, (batch, num_heads, seq_len_q, seq_len_kv), (0, 0, 0, 1))
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
out = F.scaled_dot_product_attention(query, key, value, mask)
out.sum().backward()
@unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Fused SDPA was not built for this system")
@parametrize("dtype", [torch.float, torch.float16])
def test_mem_eff_attention_long_sequence_mask(self, device, dtype):
if torch.cuda.get_device_properties('cuda').total_memory < 80 * 2**30:
unittest.skip("This test requires substatnial GPU memory.")
return
make_tensor = partial(torch.rand, device=device, dtype=dtype, requires_grad=True)
batch, num_heads, head_dim = 1, 32, 64
seq_len_q, seq_len_kv = 8192, 8192
query = make_tensor(SdpaShape(batch, num_heads, seq_len_q, head_dim))
kv_shape = SdpaShape(batch, num_heads, seq_len_kv, head_dim)
key, value = make_tensor(kv_shape), make_tensor(kv_shape)
mask = torch.randn((batch, num_heads, seq_len_q, seq_len_kv), device=device, dtype=dtype)
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
out = F.scaled_dot_product_attention(query, key, value, mask)
out.sum().backward()
@unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Fused SDPA was not built for this system")
def test_mem_eff_attention_non_contig_mask_bug(self, device):
# Without the fix this produces `AssertionError: assert 0.07352933287620544 < 1e-07`
# Shapes taken from repro
query_size = (3, 16, 1, 128)
query_strides = (2304, 128, 2048, 1)
key_size = (3, 16, 14, 128)
key_strides = (3584, 0, 256, 1)
value_size = (3, 16, 14, 128)
value_strides = (3584, 0, 256, 1)
attention_mask_size = (3, 1, 1, 14)
attn_mask_strides = (14, 14, 14, 1)
# Calculate the number of elements needed for each tensor
query_num_elements = max(size * stride for size, stride in zip(query_size, query_strides))
key_num_elements = max(size * stride for size, stride in zip(key_size, key_strides))
value_num_elements = max(size * stride for size, stride in zip(value_size, value_strides))
attention_mask_num_elements = max(size * stride for size, stride in zip(attention_mask_size, attn_mask_strides))
# Create the tensors with the specified sizes and strides
query = torch.randn(query_num_elements, device=device).as_strided(query_size, query_strides)
key = torch.randn(key_num_elements, device=device).as_strided(key_size, key_strides)
value = torch.randn(value_num_elements, device=device).as_strided(value_size, value_strides)
bias = torch.randn(attention_mask_num_elements, device=device).as_strided(attention_mask_size, attn_mask_strides)
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
out = F.scaled_dot_product_attention(query, key, value, bias)
out_contig = F.scaled_dot_product_attention(query, key, value, bias.contiguous())
max_diff = (out - out_contig).abs().mean()
self.assertTrue(max_diff.item() < 1e-7)
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Fused SDPA was not built for this system")
def test_singelton_head_dim_stride_ne_1(self, device):
query = torch.tensor([[[[1, 2]]]], dtype=torch.float16, device=device)
query = query.transpose(-1, -2)
key = torch.tensor([[[[1]]]], dtype=torch.float16, device=device)
value = torch.tensor([[[[1]]]], dtype=torch.float16, device=device)
with torch.backends.cuda.sdp_kernel(enable_math=False, enable_flash=True, enable_mem_efficient=False):
scaled_dot_product_attention(query, key, value)
@unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Fused SDPA was not built for this system")
@parametrize("type", ["dense", "nested"])
@parametrize("is_contiguous", [True, False])
def test_scaled_dot_product_attention_fused_kernels_packed(self, device, type: str, is_contiguous: bool):
if TEST_WITH_ROCM and type == 'nested':
self.skipTest("ROCM does not support efficient attention on nested tensors, for now")
make_tensor = partial(rand_sdpa_tensor, type=type, device=device, dtype=torch.float16, packed=True)
batch_size, seq_len, num_heads, head_dim = 32, 64, 16, 64
shape = SdpaShape(batch_size, num_heads, seq_len, head_dim)
# Test Packed
qkv = make_tensor(shape)
query, key, value = qkv.chunk(3, dim=-1)
query = query.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
if is_contiguous:
query = query.contiguous()
key = key.contiguous()
value = value.contiguous()
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
actual = torch.nn.functional.scaled_dot_product_attention(
query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False)
with sdpa_kernel(backends=[SDPBackend.MATH]):
math_ref = torch.nn.functional.scaled_dot_product_attention(
query.contiguous(), key.contiguous(), value.contiguous(),
attn_mask=None, dropout_p=0.0, is_causal=False)
self.assertEqual(actual.contiguous(), math_ref.contiguous(), atol=2e-3, rtol=1e-2)
@skipIfRocm # Missing nested and EFFICIENT_ATTENTION
@unittest.skipIf(not PLATFORM_SUPPORTS_FUSED_ATTENTION, "Fused SDPA was not built for this system")
@parametrize("type", ["dense", "nested"])
@parametrize("fused_kernel", [SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION] if
PLATFORM_SUPPORTS_FLASH_ATTENTION else [SDPBackend.EFFICIENT_ATTENTION])
def test_scaled_dot_product_attention_fused_kernels_packed_accuracy(self, device, type: str, fused_kernel: str):
def rand_nt(shape):
batch, seq_len, num_heads, head_dim = shape
tensors = [6 * torch.rand((seq_len, 3 * num_heads * head_dim), device=device, dtype=torch.float32) - 3
for _ in range(batch)]
return (torch.nested.nested_tensor(tensors, device=device, dtype=torch.float32),
torch.nested.nested_tensor(tensors, device=device, dtype=torch.float16))
def rand_tensor(shape):
batch, seq_len, num_heads, head_dim = shape
tensor = 6 * torch.rand((batch, seq_len, 3 * num_heads * head_dim), device=device, dtype=torch.float32) - 3
return tensor, tensor.to(dtype=torch.float16)
batch_size, seq_len, num_heads, head_dim = 16, 8, 4, 64
shape = (batch_size, seq_len, num_heads, head_dim)
# Test Packed
qkv, qkv_low_precision = rand_tensor(shape) if type == "dense" else rand_nt(shape)
query, key, value = qkv.chunk(3, dim=-1)
query_lp, key_lp, value_lp = qkv_low_precision.chunk(3, dim=-1)
query = query.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
query_lp = query_lp.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
key_lp = key_lp.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
value_lp = value_lp.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
with sdpa_kernel(backends=[fused_kernel]):
actual = torch.nn.functional.scaled_dot_product_attention(
query_lp, key_lp, value_lp, attn_mask=None, dropout_p=0.0, is_causal=False)
with sdpa_kernel(backends=[SDPBackend.MATH]):
math_ref_lp = torch.nn.functional.scaled_dot_product_attention(
query_lp.contiguous(), key_lp.contiguous(), value_lp.contiguous(),
attn_mask=None, dropout_p=0.0, is_causal=False)
math_query = query.contiguous()
math_key = key.contiguous()
math_value = value.contiguous()
math_ref = torch.nn.functional.scaled_dot_product_attention(
math_query, math_key, math_value, attn_mask=None, dropout_p=0.0, is_causal=False)
actual_test = actual
math_ref_test = math_ref
math_ref_lp_test = math_ref_lp
if actual_test.is_nested:
actual_test = torch.nested.to_padded_tensor(actual_test.contiguous(), padding=0.0)
math_ref_test = torch.nested.to_padded_tensor(math_ref_test, padding=0.0)
math_ref_lp_test = torch.nested.to_padded_tensor(math_ref_lp_test, padding=0.0)
actual_test = actual_test.to(dtype=torch.float32).contiguous()
math_ref_test = math_ref_test.to(dtype=torch.float32).contiguous()
math_ref_lp_test = math_ref_lp_test.to(dtype=torch.float32).contiguous()
self.assertEqual(math_ref_test, math_ref_lp_test, atol=7e-3, rtol=7e-3)
self.assertEqual(actual_test, math_ref_test, atol=7e-3, rtol=7e-3)
@unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Efficient Attention was not built for this system")
@parametrize("contiguous_inputs", [True, False])
@parametrize("is_causal", [True, False])
def test_sdp_mem_efficient_grad_against_math(self, device, contiguous_inputs: bool, is_causal: bool):
batch_size, seq_len, num_heads, head_dim = 4, 4, 2, 16
make_tensor = partial(rand_sdpa_tensor, type="dense", device=device,
dtype=torch.float64, requires_grad=True, packed=True)
qkv = make_tensor(SdpaShape(batch_size, num_heads, seq_len, head_dim))
qkv_lp = qkv.detach().clone().to(torch.float32).requires_grad_()
query, key, value = qkv.chunk(3, dim=-1)
query_lp, key_lp, value_lp = qkv_lp.chunk(3, dim=-1)
query = query.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
query_lp = query_lp.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
key_lp = key_lp.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
value_lp = value_lp.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
if contiguous_inputs:
query = query.contiguous()
key = key.contiguous()
value = value.contiguous()
query_lp = query_lp.contiguous()
key_lp = key_lp.contiguous()
value_lp = value_lp.contiguous()
with sdpa_kernel(backends=[SDPBackend.MATH]):
out = torch.nn.functional.scaled_dot_product_attention(query, key, value, None, 0.0, is_causal)
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
out_lp = torch.nn.functional.scaled_dot_product_attention(
query_lp, key_lp, value_lp, None, 0.0, is_causal)
rand_upward = torch.rand_like(out)
rand_upward_lp = rand_upward.to(torch.float32)
out.backward(rand_upward)
out_lp.backward(rand_upward_lp)
# Cast up and compare
self.assertEqual(qkv.grad, qkv_lp.grad.to(torch.float64), atol=1e-5, rtol=1e-5)
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Flash Attention was not built for this system")
@parametrize("contiguous_inputs", [True, False])
@parametrize("is_causal", [True, False])
@parametrize("dtype", [torch.float16, torch.bfloat16])
def test_sdp_flash_attention_grad_against_math(self, device, contiguous_inputs: bool, is_causal: bool, dtype: torch.dtype):
batch_size, seq_len, num_heads, head_dim = 4, 4, 2, 16
make_tensor = partial(rand_sdpa_tensor, type="dense", device=device,
dtype=torch.float64, requires_grad=True, packed=True)
qkv = make_tensor(SdpaShape(batch_size, num_heads, seq_len, head_dim))
qkv_lp = qkv.detach().clone().to(dtype).requires_grad_()
query, key, value = qkv.chunk(3, dim=-1)
query_lp, key_lp, value_lp = qkv_lp.chunk(3, dim=-1)
query = query.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
query_lp = query_lp.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
key_lp = key_lp.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
value_lp = value_lp.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
if contiguous_inputs:
query = query.contiguous()
key = key.contiguous()
value = value.contiguous()
query_lp = query_lp.contiguous()
key_lp = key_lp.contiguous()
value_lp = value_lp.contiguous()
with sdpa_kernel(backends=[SDPBackend.MATH]):
out = torch.nn.functional.scaled_dot_product_attention(query, key, value, None, 0.0, is_causal)
with sdpa_kernel(backends=[SDPBackend.FLASH_ATTENTION]):
out_lp = torch.nn.functional.scaled_dot_product_attention(
query_lp, key_lp, value_lp, None, 0.0, is_causal)
rand_upward = torch.rand_like(out)
rand_upward_lp = rand_upward.to(dtype)
out.backward(rand_upward)
out_lp.backward(rand_upward_lp)
# Cast up and compare
# Since we are doing the compute on fp16 we have to bump the tolerance
# Bump down the tolearnce for blfoat16
atol = 7e-4 if dtype == torch.float16 else 7e-3
rtol = 7e-4 if dtype == torch.float16 else 7e-3
if TEST_WITH_ROCM:
atol = 9e-4 if dtype == torch.float16 else 9e-3
self.assertEqual(qkv.grad, qkv_lp.grad.to(torch.float64), atol=atol, rtol=rtol)
@skipIfRocm # Missing nested and EFFICIENT_ATTENTION
@unittest.skipIf(not PLATFORM_SUPPORTS_FUSED_ATTENTION, "Platform does not support fused SDPA")
@parametrize("type", ["dense", "nested"])
def test_fused_sdp_choice(self, device, type: str):
batch_size, seq_len, num_heads, head_dim = 2, 128, 8, 64
shape = SdpaShape(batch_size, num_heads, seq_len, head_dim)
make_tensor = partial(rand_sdpa_tensor, device=device, dtype=torch.float16, packed=True, requires_grad=True)
qkv = make_tensor(shape, type=type)
query, key, value = qkv.chunk(3, dim=-1)
query = query.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
if type != "nested" and PLATFORM_SUPPORTS_CUDNN_ATTENTION and SM90OrLater:
self.assertEqual(torch._fused_sdp_choice(query, key, value), SDPBackend.CUDNN_ATTENTION.value)
elif PLATFORM_SUPPORTS_FLASH_ATTENTION:
self.assertEqual(torch._fused_sdp_choice(query, key, value), SDPBackend.FLASH_ATTENTION.value)
elif type != "nested" and PLATFORM_SUPPORTS_CUDNN_ATTENTION: # e.g., we're on Windows
self.assertEqual(torch._fused_sdp_choice(query, key, value), SDPBackend.CUDNN_ATTENTION.value)
else:
self.assertEqual(torch._fused_sdp_choice(query, key, value), SDPBackend.EFFICIENT_ATTENTION.value)
# Change dtype to float32 so that efficient attention should get chosen
make_tensor = partial(rand_sdpa_tensor, device=device, dtype=torch.float32, packed=True)
qkv = make_tensor(shape, type=type)
query, key, value = qkv.chunk(3, dim=-1)
query = query.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
assert torch._fused_sdp_choice(query, key, value) == SDPBackend.EFFICIENT_ATTENTION.value
@skipIfRocm # Missing triton.float32 ("triton" prefix is to locate skipped UTs), and deterministic algo
@unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Platform does not support fused SDPA")
@parametrize("warn_only", [True, False])
def test_sdp_choice_with_determinism(self, device, warn_only):
batch_size, seq_len, num_heads, head_dim = 1, 64, 8, 64
shape = SdpaShape(batch_size, num_heads, seq_len, head_dim)
make_tensor = partial(rand_sdpa_tensor, type="dense", device=device, dtype=torch.float32, packed=False)
query, key, value = make_tensor(shape), make_tensor(shape), make_tensor(shape)
with use_deterministic_algorithims(True, warn_only=warn_only):
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION, SDPBackend.MATH]):
assert torch._fused_sdp_choice(query, key, value) == SDPBackend.EFFICIENT_ATTENTION.value
@skipIfRocm # Missing deterministic algo
@unittest.skipIf(not PLATFORM_SUPPORTS_FUSED_ATTENTION, "Fused SDPA was not built for this system")
@parametrize("fused_kernel", PLATFORM_SPECIFIC_SDPA)
@parametrize("warn_only", [True, False])
def test_fused_backwards_throws_determinism_warning(self, device, warn_only, fused_kernel):
batch_size, seq_len, num_heads, head_dim = 1, 64, 8, 64
shape = SdpaShape(batch_size, num_heads, seq_len, head_dim)
make_tensor = partial(rand_sdpa_tensor, type="dense", device=device, dtype=torch.float16, packed=False, requires_grad=True)
query, key, value = make_tensor(shape), make_tensor(shape), make_tensor(shape)
kernel_name = "Memory Efficient attention" if fused_kernel == SDPBackend.EFFICIENT_ATTENTION else \
"Flash Attention" if fused_kernel == SDPBackend.FLASH_ATTENTION else "cuDNN Attention"
warning_context = (
self.assertWarnsRegex(
UserWarning,
f"{kernel_name} defaults to a non-deterministic algorithm.",
)
if warn_only
else contextlib.nullcontext()
)
with use_deterministic_algorithims(True, warn_only=warn_only):
with sdpa_kernel(backends=[fused_kernel]):
with warning_context:
if warn_only or fused_kernel != SDPBackend.CUDNN_ATTENTION:
torch.nn.functional.scaled_dot_product_attention(query, key, value).sum().backward()
else:
# cuDNN attention has no deterministic fallback
self.assertRaises(RuntimeError, lambda:
torch.nn.functional.scaled_dot_product_attention(query, key, value).sum().backward())
@unittest.skip("This test is not behaving deterministaclly non-deterministaclly on CI/CD")
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Platform does not support fused SDPA")
def test_mem_eff_backwards_determinism(self, device):
# Need big seq_len to ensure that num_splits > 1
dtype = torch.float32
batch_size, seq_len, n_heads, head_dim = 1, 1024, 8, 64
query = torch.rand(batch_size, n_heads, seq_len, head_dim,
device=device, dtype=dtype, requires_grad=True)
key = torch.rand(batch_size, n_heads, seq_len, head_dim, device=device,
dtype=dtype, requires_grad=True)
value = torch.rand(batch_size, n_heads, seq_len, head_dim,
device=device, dtype=dtype, requires_grad=True)
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
# Run once to establish baseline
out = F.scaled_dot_product_attention(query, key, value)
upward_grad = torch.rand_like(out)
out.backward(upward_grad)
intial_query_grad = query.grad
# Re-run the op with the same upward grad and check that the backward is
# not deterministic
diff_anwser_once = False
for _ in range(100):
query.grad = None
out = F.scaled_dot_product_attention(query, key, value)
out.backward(upward_grad)
if not torch.equal(intial_query_grad, query.grad):
diff_anwser_once = True
break
self.assertTrue(diff_anwser_once)
with use_deterministic_algorithims(True, warn_only=False):
query.grad = None
out = F.scaled_dot_product_attention(query, key, value)
upward_grad = torch.rand_like(out)
out.backward(upward_grad)
intial_query_grad = query.grad
# Re-run the op with the same upward grad and check that the backward is
# deterministic now that we have enforced it
diff_anwser_once = False
for _ in range(100):
query.grad = None
out = F.scaled_dot_product_attention(query, key, value)
out.backward(upward_grad)
if not torch.equal(intial_query_grad, query.grad):
diff_anwser_once = True
break
self.assertFalse(diff_anwser_once)
# verified passing successfully on H100
@unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Does not support SDPA")
@unittest.skipIf(IS_JETSON, "causing sigkill on Jetson")
@parametrize("batch_size", [1, 8])
@parametrize("seq_len_q", [8, 103, 1024, 2048] if MEM_EFF_CAPABILITY_MATCHES_SM80
else [4, 8, 256, 512])
@parametrize("seq_len_k", [8, 103, 1024, 2048] if MEM_EFF_CAPABILITY_MATCHES_SM80
else [4, 8, 256, 512])
@parametrize("head_dim", [8, 16, 96, 128] if MEM_EFF_CAPABILITY_MATCHES_SM80
else [8, 16, 32, 64])
@parametrize("is_causal", [False, True])
@parametrize("dropout_p", [0.0, 0.22])
@parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32] if MEM_EFF_CAPABILITY_MATCHES_SM80
else [torch.float16, torch.float32])
@parametrize("scale", [None, "l1"])
def test_mem_efficient_attention_vs_math_ref_grads(self, device, batch_size: int, seq_len_q: int, seq_len_k: int,
head_dim: int, is_causal: bool, dropout_p: float, dtype: torch.dtype,
scale: str):
def _get_mem_eff_drop_mask(batch_size, n_heads, q_len, kv_len, p, seed, offset, device=device):
mask = torch.empty((batch_size, n_heads, q_len, kv_len), device=device, dtype=torch.float32)
rand_uniform = torch._fill_mem_eff_dropout_mask_(mask, p, seed, offset)
mask = (rand_uniform > p).to(torch.float32)
return mask
if max(seq_len_q, seq_len_k) >= 2048 and torch.cuda.get_device_properties('cuda').total_memory < 40 * 2**30:
unittest.skip("Reference implementation OOM")
return
if TEST_WITH_ROCM and seq_len_q * seq_len_k * head_dim * batch_size > 1024 * 1024 * 128:
torch.cuda.empty_cache() # Prevent memory fragmentation
seed = 42
scale = scale if scale is None else (1 / head_dim)
n_heads = 4
query = torch.rand(batch_size, n_heads, seq_len_q, head_dim,
device=device, dtype=dtype, requires_grad=True)
key = torch.rand(batch_size, n_heads, seq_len_k, head_dim, device=device,
dtype=dtype, requires_grad=True)
value = torch.rand(batch_size, n_heads, seq_len_k, head_dim,
device=device, dtype=dtype, requires_grad=True)
higher_precision_dtype = torch.float64
query_ref, key_ref, value_ref = query_key_value_clones(query, key, value, dtype=higher_precision_dtype)
# Create real output
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
# Set the seed and run the kernel
torch.manual_seed(seed)
out = F.scaled_dot_product_attention(query, key, value, dropout_p=dropout_p, is_causal=is_causal, scale=scale)
if dropout_p == 0.0:
with sdpa_kernel(backends=[SDPBackend.MATH]):
# High Precision Math Reference
out_ref = F.scaled_dot_product_attention(query_ref, key_ref, value_ref,
dropout_p=dropout_p, is_causal=is_causal, scale=scale)
# Low Precision Math Reference
out_lp_ref = F.scaled_dot_product_attention(query, key, value,
dropout_p=dropout_p, is_causal=is_causal, scale=scale)
else:
if seq_len_q > 1024:
self.skipTest("Will call _fill_mem_eff_dropout_mask with too many threads!")
# Create the dropout_mask
torch.manual_seed(seed)
dropout_mask = _get_mem_eff_drop_mask(batch_size, n_heads, seq_len_q, seq_len_k, dropout_p, seed, 0, device=device)
# High Precision Math Reference
out_ref = torch.ops.aten._scaled_dot_product_attention_math(
query_ref, key_ref, value_ref, dropout_p=dropout_p, is_causal=is_causal, scale=scale, dropout_mask=dropout_mask)[0]
# Low Precision Math Reference
out_lp_ref = torch.ops.aten._scaled_dot_product_attention_math(
query, key, value, dropout_p=dropout_p, is_causal=is_causal, scale=scale,
dropout_mask=dropout_mask)[0]
upstream_grad = torch.rand_like(out, requires_grad=False)
grads = torch.autograd.grad(out, (query, key, value), upstream_grad)
grads_ref_lp = torch.autograd.grad(out_lp_ref, (query, key, value), upstream_grad)
grads_ref = torch.autograd.grad(out_ref, (query_ref, key_ref, value_ref), upstream_grad)
check_out_and_grad(
(out_ref, out_lp_ref, out),
*zip(grads_ref, grads_ref_lp, grads),
fudge_factors={
'out': 3.0 ,
'grad_query': 150.0 ,
'grad_key': 25.0,
'grad_value': 8.5,
}
)
@unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Does not support SDPA")
@unittest.skipIf(IS_JETSON, "causing sigkill on Jetson")
@parametrize("batch_size", [1, 8])
@parametrize("seq_len_q", [8, 312, 1024, 2048] if MEM_EFF_CAPABILITY_MATCHES_SM80
else [8, 152, 512])
@parametrize("seq_len_k", [8, 408, 1024, 2048] if MEM_EFF_CAPABILITY_MATCHES_SM80
else [8, 37, 512])
@parametrize("head_dim", [8, 16, 96, 128] if MEM_EFF_CAPABILITY_MATCHES_SM80
else [8, 16, 32, 64])
@parametrize("is_causal", [False])
@parametrize("dropout_p", [0.0, 0.22])
@parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32] if MEM_EFF_CAPABILITY_MATCHES_SM80
else [torch.float16, torch.float32])
@parametrize("scale", [None, "l1"])
def test_mem_efficient_attention_attn_mask_vs_math_ref_grads(self, device, batch_size: int, seq_len_q: int,
seq_len_k: int, head_dim: int, is_causal: bool,
dropout_p: float, dtype: torch.dtype,
scale: str):
def _get_mem_eff_drop_mask(batch_size, n_heads, q_len, kv_len, p, seed, offset, device=device):
mask = torch.empty((batch_size, n_heads, q_len, kv_len), device=device, dtype=torch.float32)
rand_uniform = torch._fill_mem_eff_dropout_mask_(mask, p, seed, offset)
mask = (rand_uniform > p).to(torch.float32)
return mask
if max(seq_len_q, seq_len_k) >= 2048 and torch.cuda.get_device_properties('cuda').total_memory < 40 * 2**30:
unittest.skip("Reference implementation OOM")
return
if TEST_WITH_ROCM and dtype == torch.float32:
unittest.skip("Skip fp32 attn_mask gradients on ROCM, for now.")
return
if TEST_WITH_ROCM and seq_len_q * seq_len_k * head_dim * batch_size > 1024 * 1024 * 128:
torch.cuda.empty_cache() # Prevent memory fragmentation
seed = 42
scale = scale if scale is None else (1 / head_dim)
n_heads = 4
query = torch.rand(batch_size, n_heads, seq_len_q, head_dim,
device=device, dtype=dtype, requires_grad=True)
key = torch.rand(batch_size, n_heads, seq_len_k, head_dim, device=device,
dtype=dtype, requires_grad=True)
value = torch.rand(batch_size, n_heads, seq_len_k, head_dim,
device=device, dtype=dtype, requires_grad=True)
attn_mask = torch.rand(seq_len_q, seq_len_k, device=device, dtype=dtype, requires_grad=True)
higher_precision_dtype = torch.float64 if dtype == torch.float32 else torch.float32
query_ref, key_ref, value_ref = query_key_value_clones(query, key, value, dtype=higher_precision_dtype)
attn_mask_ref = attn_mask.detach().to(higher_precision_dtype).requires_grad_(True)
# Create real output
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
# Set the seed and run the kernel
torch.manual_seed(seed)
out = F.scaled_dot_product_attention(query, key, value, attn_mask, dropout_p=dropout_p,
is_causal=is_causal, scale=scale)
if dropout_p == 0.0:
with sdpa_kernel(backends=[SDPBackend.MATH]):
# High Precision Math Reference
out_ref = F.scaled_dot_product_attention(query_ref, key_ref, value_ref, attn_mask_ref,
dropout_p=dropout_p, is_causal=is_causal, scale=scale)
# Low Precision Math Reference
out_lp_ref = F.scaled_dot_product_attention(query, key, value, attn_mask,
dropout_p=dropout_p, is_causal=is_causal, scale=scale)
else:
if seq_len_q > 1024:
self.skipTest("Will call _fill_mem_eff_dropout_mask with too many threads!")
# Create the dropout_mask
torch.manual_seed(seed)
dropout_mask = _get_mem_eff_drop_mask(batch_size, n_heads, seq_len_q,
seq_len_k, dropout_p, seed, 0, device=device)
# High Precision Math Reference
out_ref = torch.ops.aten._scaled_dot_product_attention_math(
query_ref, key_ref, value_ref, attn_mask_ref, dropout_p=dropout_p, is_causal=is_causal,
scale=scale, dropout_mask=dropout_mask)[0]
# Low Precision Math Reference
out_lp_ref = torch.ops.aten._scaled_dot_product_attention_math(
query, key, value, attn_mask,
dropout_p=dropout_p, is_causal=is_causal, scale=scale,
dropout_mask=dropout_mask)[0]
upstream_grad = torch.rand_like(out, requires_grad=False)
grads = torch.autograd.grad(out, (query, key, value, attn_mask), upstream_grad)
grads_ref_lp = torch.autograd.grad(out_lp_ref, (query, key, value, attn_mask), upstream_grad)
grads_ref = torch.autograd.grad(out_ref, (query_ref, key_ref, value_ref, attn_mask_ref), upstream_grad)
check_out_and_grad(
(out_ref, out_lp_ref, out),
*zip(grads_ref, grads_ref_lp, grads),
fudge_factors={
"out": 4,
"grad_query": 160.0,
"grad_key": 25.0,
"grad_value": 8.0,
"grad_attn_mask": 45.0,
},
)
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Does not support SDPA or pre-SM80 hardware")
@unittest.skipIf(IS_JETSON, "causing sigkill on Jetson")
@parametrize("batch_size", [1, 8])
@parametrize("seq_len_q", [4, 143, 2048])
@parametrize("seq_len_k", [4, 127, 579, 2048])
@parametrize("head_dim", [8, 203, 256])
@parametrize("is_causal", [True, False])
@parametrize("dropout_p", [0.0, 0.22, 0.48])
@parametrize("dtype", [torch.float16, torch.bfloat16])
@parametrize("scale", [None, "l1"])
@parametrize("enable_gqa", [True, False])
@parametrize("n_heads", [[16, 8], [10, 2]])
def test_flash_attention_vs_math_ref_grads(self, device, batch_size: int, seq_len_q: int, seq_len_k: int,
head_dim: int, is_causal: bool, dropout_p: float, dtype: torch.dtype,
scale: str, enable_gqa: bool, n_heads: List[int]):
if isSM8XDevice and head_dim in range(193, 256 + 1):
self.skipTest("Flash attention on sm86, sm87, and sm89 for headdim > 192 currently disabled")
if is_causal and seq_len_q != seq_len_k:
self.skipTest("Flash V2 does not accept is_casual when seq_len_q != seq_len_k")
if TEST_WITH_ROCM and seq_len_q >= 1024 and seq_len_k >= 1024 and batch_size > 1:
torch.cuda.empty_cache() # Prevent memory fragmentation
if max(seq_len_q, seq_len_k) >= 2048 and torch.cuda.get_device_properties('cuda').total_memory < 40 * 2**30:
unittest.skip("Reference implementation OOM")
return
scale = scale if scale is None else (1 / head_dim)
num_heads_q = num_heads_kv = 4
if enable_gqa:
num_heads_q = n_heads[0]
num_heads_kv = n_heads[1]
query = torch.rand(batch_size, num_heads_q, seq_len_q, head_dim,
device=device, dtype=dtype, requires_grad=True)
key = torch.rand(batch_size, num_heads_kv, seq_len_k, head_dim, device=device,
dtype=dtype, requires_grad=True)
value = torch.rand(batch_size, num_heads_kv, seq_len_k, head_dim,
device=device, dtype=dtype, requires_grad=True)
higher_precision_dtype = torch.float64 if dtype == torch.float32 else torch.float32
query_ref, key_ref, value_ref = query_key_value_clones(query, key, value, dtype=higher_precision_dtype)
is_dropout = dropout_p > 0.0
if not is_dropout:
with sdpa_kernel(backends=[SDPBackend.FLASH_ATTENTION]):
out = F.scaled_dot_product_attention(
query, key, value, dropout_p=dropout_p, is_causal=is_causal, scale=scale, enable_gqa=enable_gqa)
with sdpa_kernel(backends=[SDPBackend.MATH]):
# High Precision Math Reference
out_ref = F.scaled_dot_product_attention(
query_ref, key_ref, value_ref, is_causal=is_causal, scale=scale, enable_gqa=enable_gqa)
# Low Precision Math Reference
out_lp_ref = F.scaled_dot_product_attention(
query, key, value, is_causal=is_causal, scale=scale, enable_gqa=enable_gqa)
else:
# Problem: We pad sizes in the composite region of the top level SDPA. But we need the
# Debug mask when have dropout. So I am going to manualy pad up here when testing dropout
q_padded, q_og_size = pad_last_dim(query, 8)
k_padded, k_og_size = pad_last_dim(key, 8)
v_padded, v_og_size = pad_last_dim(value, 8)
# scale needs to be calculated on the og head_size
if scale is None:
scale = 1 / math.sqrt(q_og_size)
output_tuple = torch.ops.aten._scaled_dot_product_flash_attention(
q_padded, k_padded, v_padded, dropout_p=dropout_p, is_causal=is_causal, scale=scale, return_debug_mask=is_dropout)
out = output_tuple[0]
out = out[..., :v_og_size]
# Build dropout_mask
dbug_mask = output_tuple[-1]
query_padding_mask = torch.ones(
batch_size, seq_len_q, device=device, dtype=torch.bool)
key_padding_mask = torch.ones(
batch_size, seq_len_k, device=device, dtype=torch.bool)
softmax_mask = self.convert_flash_attn_S_to_softmax(
dbug_mask, seq_len_q, seq_len_k, query_padding_mask, key_padding_mask,
causal=is_causal)[:, :, :seq_len_q, :seq_len_k]
dropout_mask = softmax_mask >= 0
# High Precision Math Reference
out_ref = torch.ops.aten._scaled_dot_product_attention_math(
query_ref, key_ref, value_ref, dropout_p=dropout_p, is_causal=is_causal,
scale=scale, dropout_mask=dropout_mask, enable_gqa=enable_gqa)[0]
# Low Precision Math Reference
out_lp_ref = torch.ops.aten._scaled_dot_product_attention_math(
query, key, value, dropout_p=dropout_p, is_causal=is_causal, scale=scale,
dropout_mask=dropout_mask, enable_gqa=enable_gqa)[0]
upstream_grad = torch.rand_like(out, requires_grad=False)
# backward for flash attention on sm86, sm87, and sm89 for headdim >= 193 currently disabled
if isSM8XDevice and head_dim in range(193, 256):
self.assertRaises(RuntimeError, lambda: out.backward(upstream_grad))
return
grads = torch.autograd.grad(out, (query, key, value), upstream_grad)
grads_ref_lp = torch.autograd.grad(out_lp_ref, (query, key, value), upstream_grad)
grads_ref = torch.autograd.grad(out_ref, (query_ref, key_ref, value_ref), upstream_grad)
check_out_and_grad(
(out_ref, out_lp_ref, out),
*zip(grads_ref, grads_ref_lp, grads),
fudge_factors={
'out': 4,
'grad_query': 160.0,
'grad_key': 16,
'grad_value': 4,
}
)
@skipIfRocm # FIXME: "capturing stream has unjoined work"
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Does not support SDPA or pre-SM80 hardware")
@parametrize("batch_size", [1, 8])
@parametrize("seq_len_q", [256, 1024])
@parametrize("seq_len_k", [256, 1024])
@parametrize("head_dim", [32, 64])
@parametrize("is_causal", [True, False])
@parametrize("dropout_p", [0.0, 0.22])
@parametrize("dtype", [torch.float16])
@parametrize("scale", [None, "l1"])
@parametrize("fused_kernel", PLATFORM_SPECIFIC_SDPA)
def test_fused_attention_vs_math_ref_grads_cudagraph(self, device, batch_size: int,
seq_len_q: int, seq_len_k: int,
head_dim: int,
is_causal: bool,
dropout_p: float,
dtype: torch.dtype,
scale: str,
fused_kernel: SDPBackend):
def _get_mem_eff_drop_mask(batch_size, n_heads, q_len, kv_len, dropout_p, seed, offset, device=device):
mask = torch.empty((batch_size, n_heads, q_len, kv_len), device=device, dtype=torch.float32)
rand_uniform = torch._fill_mem_eff_dropout_mask_(mask, dropout_p, seed, offset)
mask = (rand_uniform > dropout_p).to(torch.float32)
return mask
def get_dropout_mask(output, fused_kernel, batch_size, n_heads, q_len, kv_len, dropout_p, device=device):
if fused_kernel == SDPBackend.EFFICIENT_ATTENTION:
output_seed, output_offset = output_tuple[2], output_tuple[3]
output_seed = output_seed.item()
output_offset = output_offset.item()
return _get_mem_eff_drop_mask(batch_size, n_heads, q_len, kv_len,
dropout_p, output_seed, output_offset, device=device)
else:
# Build dropout_mask
dbug_mask = output_tuple[-1]
query_padding_mask = torch.ones(
batch_size, seq_len_q, device=device, dtype=torch.bool)
key_padding_mask = torch.ones(
batch_size, seq_len_k, device=device, dtype=torch.bool)
softmax_mask = self.convert_flash_attn_S_to_softmax(
dbug_mask, seq_len_q, seq_len_k, query_padding_mask, key_padding_mask,
causal=is_causal)[:, :, :seq_len_q, :seq_len_k]
dropout_mask = softmax_mask >= 0
return dropout_mask
if fused_kernel == SDPBackend.FLASH_ATTENTION and is_causal and seq_len_q != seq_len_k:
self.skipTest("Flash V2 does not accept is_casual when seq_len_q != seq_len_k")
seed = 42
n_heads = 4
query = torch.rand(batch_size, n_heads, seq_len_q, head_dim,
device=device, dtype=dtype, requires_grad=True)
key = torch.rand(batch_size, n_heads, seq_len_k, head_dim, device=device,
dtype=dtype, requires_grad=True)
value = torch.rand(batch_size, n_heads, seq_len_k, head_dim,
device=device, dtype=dtype, requires_grad=True)
fused_op = (torch.ops.aten._scaled_dot_product_efficient_attention
if fused_kernel == SDPBackend.EFFICIENT_ATTENTION else torch.ops.aten._scaled_dot_product_flash_attention
if fused_kernel == SDPBackend.FLASH_ATTENTION else torch.ops.aten._scaled_dot_product_cudnn_attention)
higher_precision_dtype = torch.float64 if dtype == torch.float32 else torch.float32
query_ref, key_ref, value_ref = query_key_value_clones(query, key, value, dtype=higher_precision_dtype)
# warmup
s = torch.cuda.Stream()
s.wait_stream(torch.cuda.current_stream())
# Set the global seed before capture
torch.manual_seed(seed)
kwargs = {"dropout_p": dropout_p, "is_causal": is_causal}
if fused_kernel == SDPBackend.EFFICIENT_ATTENTION:
kwargs["compute_log_sumexp"] = True
kwargs["attn_bias"] = None
if fused_kernel == SDPBackend.FLASH_ATTENTION:
kwargs['return_debug_mask'] = dropout_p > 0.0
if fused_kernel == SDPBackend.CUDNN_ATTENTION:
kwargs["compute_log_sumexp"] = True
kwargs["attn_bias"] = None
if "return_debug_mask" in kwargs:
kwargs.pop("return_debug_mask")
with torch.cuda.stream(s):
# Create real output
output_tuple = fused_op(query, key, value, **kwargs)
torch.cuda.current_stream().wait_stream(s)
out = output_tuple[0]
upstream_grad = torch.rand_like(out, requires_grad=False)
s.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(s):
out.backward(upstream_grad)
for x in (query, key, value):
x.grad = None
g = torch.cuda.CUDAGraph()
# Create real output
with torch.cuda.graph(g):
tmp = torch.rand_like(query, device=query.device) # test non-zero intragraph offset
# Create real output
output_tuple = fused_op(query, key, value, **kwargs)
assert all(not isinstance(o, torch.Tensor) or o.is_cuda for o in output_tuple)
g.replay()
out_first = output_tuple[0].clone()
g.replay()
out = output_tuple[0]
if dropout_p == 0.0:
self.assertEqual(out_first, out, atol=0, rtol=0)
else:
# replays produce different results
self.assertNotEqual(out_first, out)
with sdpa_kernel(backends=[SDPBackend.MATH]):
if dropout_p == 0.0:
# High Precision Math Reference
out_ref = F.scaled_dot_product_attention(query_ref, key_ref, value_ref,
dropout_p=dropout_p, is_causal=is_causal)
# Low Precision Math Reference
out_lp_ref = F.scaled_dot_product_attention(query, key, value,
dropout_p=dropout_p, is_causal=is_causal)
# cuDNN attention doesn't support returning dropout mask
elif fused_kernel != SDPBackend.CUDNN_ATTENTION:
# Create the dropout_mask
dropout_mask = get_dropout_mask(output_tuple, fused_kernel, batch_size,
n_heads, seq_len_q, seq_len_k, dropout_p, device)
# High Precision Math Reference
out_ref = torch.ops.aten._scaled_dot_product_attention_math(
query_ref, key_ref, value_ref, dropout_p=dropout_p, is_causal=is_causal,
dropout_mask=dropout_mask)[0]
# Low Precision Math Reference
out_lp_ref = torch.ops.aten._scaled_dot_product_attention_math(
query, key, value, dropout_p=dropout_p, is_causal=is_causal,
dropout_mask=dropout_mask)[0]
g1 = torch.cuda.CUDAGraph()
with torch.cuda.graph(g1):
grads = torch.autograd.grad(out, (query, key, value), upstream_grad)
g1.replay()
if fused_kernel != SDPBackend.CUDNN_ATTENTION or dropout_p == 0.0:
grads_ref_lp = torch.autograd.grad(out_lp_ref, (query, key, value), upstream_grad)
grads_ref = torch.autograd.grad(out_ref, (query_ref, key_ref, value_ref), upstream_grad)
check_out_and_grad(
(out_ref, out_lp_ref, out),
*zip(grads_ref, grads_ref_lp, grads),
fudge_factors={
'out': 2.0,
'grad_query': 100.0,
'grad_key': 8.0,
'grad_value': 2.0,
}
)
@skipIfRocm # Nested Tensor
@unittest.skipIf(not PLATFORM_SUPPORTS_FUSED_ATTENTION, "Fused SDPA was not built for this system")
@parametrize("fused_kernel", [SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION] if
PLATFORM_SUPPORTS_FLASH_ATTENTION else [SDPBackend.EFFICIENT_ATTENTION])
def test_fused_kernels_seq_len_1_inputs(self, device, fused_kernel):
rand_nested_tensor = partial(rand_sdpa_tensor, type="nested", device=device, dtype=torch.float16)
batch, num_heads, head_dim = 32, 16, 64
seq_lens = torch.randint(low=1, high=32, size=(batch,))
# make sure some seq_lens are 1
num_ones = 10
indices = torch.randint(low=0, high=batch, size=(num_ones,))
seq_lens.scatter_(0, indices, 1)
shape = SdpaShape(batch, num_heads, seq_lens.tolist(), head_dim)
query = rand_nested_tensor(shape)
key = rand_nested_tensor(shape)
value = rand_nested_tensor(shape)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
with sdpa_kernel(backends=[fused_kernel]):
actual = torch.nn.functional.scaled_dot_product_attention(
query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False)
with sdpa_kernel(backends=[SDPBackend.MATH]):
math_ref = torch.nn.functional.scaled_dot_product_attention(
query.contiguous().to(torch.float32),
key.contiguous().to(torch.float32),
value.contiguous().to(torch.float32),
attn_mask=None, dropout_p=0.0, is_causal=False)
self.assertEqual(actual.contiguous(), math_ref.contiguous().to(torch.float16), atol=1e-3, rtol=1e-2)
@skipIfRocm # Nested tensor
@unittest.skipIf(not PLATFORM_SUPPORTS_FUSED_ATTENTION, "Fused SDPA was not built for this system")
@parametrize("kernel", [SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION] if
PLATFORM_SUPPORTS_FLASH_ATTENTION else [SDPBackend.EFFICIENT_ATTENTION])
@parametrize("expand_q_batch", [True, False])
@parametrize("expand_k_batch", [True, False])
@parametrize("expand_v_batch", [True, False])
@parametrize("expand_q_num_heads", [True, False])
@parametrize("expand_k_num_heads", [True, False])
@parametrize("expand_v_num_heads", [True, False])
def test_fused_kernels_nested_broadcasting(
self,
device,
kernel,
expand_q_batch,
expand_k_batch,
expand_v_batch,
expand_q_num_heads,
expand_k_num_heads,
expand_v_num_heads,
):
is_efficient = kernel == SDPBackend.EFFICIENT_ATTENTION
dtype = torch.float32 if is_efficient else torch.float16
rand_nested_tensor = partial(rand_sdpa_tensor, type="nested", device=device, dtype=dtype)
batch, num_heads, head_dim = 32, 8, 64
head_dim_v = 32 if is_efficient else head_dim
seq_lens_q = (torch.randint(low=1, high=5, size=(1,)).item()
if expand_q_batch
else torch.randint(low=1, high=32, size=(batch,)).tolist())
seq_lens_kv = (torch.randint(low=1, high=5, size=(1,)).item()
if (expand_k_batch or expand_v_batch)
else torch.randint(low=1, high=32, size=(batch,)).tolist())
batch_q = 1 if expand_q_batch else batch
batch_k = 1 if expand_k_batch else batch
batch_v = 1 if expand_v_batch else batch
# handle case where all batch_sizes are 1
batch = max(batch_q, batch_k, batch_v)
num_heads_q = 1 if expand_q_num_heads else num_heads
num_heads_k = 1 if expand_k_num_heads else num_heads
num_heads_v = 1 if expand_v_num_heads else num_heads
# handle case where all num_heads are 1
num_heads = max(num_heads_q, num_heads_k, num_heads_v)
q_shape = SdpaShape(batch_q, num_heads_q, seq_lens_q, head_dim)
k_shape = SdpaShape(batch_k, num_heads_k, seq_lens_kv, head_dim)
v_shape = SdpaShape(batch_v, num_heads_v, seq_lens_kv, head_dim_v)
query = rand_nested_tensor(q_shape)
key = rand_nested_tensor(k_shape)
value = rand_nested_tensor(v_shape)
def _broadcast(t, batch_broadcasted, num_heads_broadcasted):
if batch_broadcasted and num_heads_broadcasted:
# (1, seq_len, 1, head_dim) -> (batch, seq_len, num_heads, head_dim)
result = torch.nested.nested_tensor(
[t[0].expand(-1, num_heads, t.size(-1)) for _ in range(batch)], dtype=torch.float32)
elif batch_broadcasted:
# (1, seq_len, num_heads, head_dim) -> (batch, seq_len, num_heads, head_dim)
result = torch.nested.nested_tensor([t[0] for _ in range(batch)], dtype=torch.float32)
elif num_heads_broadcasted:
# (batch, seq_len, 1, head_dim) -> (batch, seq_len, num_heads, head_dim)
result = torch.nested.nested_tensor([x.expand(-1, num_heads, t.size(-1))
for x in t.unbind()], dtype=torch.float32)
else:
result = t.to(torch.float32)
return result
query_expanded = _broadcast(query, expand_q_batch, expand_q_num_heads).transpose(1, 2)
key_expanded = _broadcast(key, expand_k_batch, expand_k_num_heads).transpose(1, 2)
value_expanded = _broadcast(value, expand_v_batch, expand_v_num_heads).transpose(1, 2)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
with sdpa_kernel(backends=[kernel]):
actual = torch.nn.functional.scaled_dot_product_attention(
query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False)
with sdpa_kernel(backends=[SDPBackend.MATH]):
math_ref = torch.nn.functional.scaled_dot_product_attention(
query_expanded.contiguous(), key_expanded.contiguous(), value_expanded.contiguous(),
attn_mask=None, dropout_p=0.0, is_causal=False)
self.assertEqual(actual.contiguous(), math_ref.contiguous().to(dtype), atol=1.5e-3, rtol=1e-2)
@skipIfRocm # Nested tensor
@unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Fused SDPA was not built for this system")
def test_fused_kernels_nested_broadcasting_query_dense(self, device):
rand_nested_tensor = partial(rand_sdpa_tensor, type="nested", device=device, dtype=torch.float32)
batch, num_heads, head_dim, head_dim_v = 32, 16, 64, 96
seq_lens = torch.randint(low=1, high=32, size=(batch,)).tolist()
q_shape = (1, 1, num_heads, head_dim)
k_shape = SdpaShape(batch, num_heads, seq_lens, head_dim)
v_shape = SdpaShape(batch, 1, seq_lens, head_dim_v)
# create a dense query
query = torch.randn(q_shape, device=device, dtype=torch.float32)
key = rand_nested_tensor(k_shape)
value = rand_nested_tensor(v_shape)
# (1, 1, num_heads, head_dim) -> (batch, 1, num_heads, head_dim)
query_expanded = torch.nested.nested_tensor([query.squeeze(0) for _ in range(batch)]).transpose(1, 2)
# (batch, seq_lens, 1, head_dim) -> (batch, seq_lens, num_heads, head_dim)
value_expanded = torch.nested.nested_tensor(
[t.expand(-1, num_heads, head_dim_v) for t in value.unbind()]).transpose(1, 2)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
actual = torch.nn.functional.scaled_dot_product_attention(
query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False)
with sdpa_kernel(backends=[SDPBackend.MATH]):
math_ref = torch.nn.functional.scaled_dot_product_attention(
query_expanded.contiguous(), key.contiguous(), value_expanded.contiguous(),
attn_mask=None, dropout_p=0.0, is_causal=False)
self.assertEqual(actual.contiguous(), math_ref.contiguous(), atol=1e-3, rtol=1e-2)
@skipIfRocm # Nested tensor
@unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Does not support SDPA or pre-SM80 hardware")
@parametrize("batch_size", [8, 32])
@parametrize("max_seq_len_q", [32, 256])
@parametrize("max_seq_len_kv", [32, 256])
@parametrize("head_dim", [8, 64])
@parametrize("dropout_p", [0.0, 0.1])
@parametrize("dtype", [torch.float16])
@parametrize("scale", [None, "l1"])
@parametrize("is_causal", [True, False])
def test_flash_attention_vs_math_ref_grads_nestedtensor(self, device, batch_size: int, max_seq_len_q: int, max_seq_len_kv: int,
head_dim: int, dropout_p: float, dtype: torch.dtype,
scale: str, is_causal: bool):
if is_causal:
# TODO we should support this
self.assertRaisesRegex(RuntimeError, "Nested tensors for query / key are not supported when is_causal=True")
return
scale = scale if scale is None else (1 / head_dim)
n_heads = 4
seq_lens_q = torch.randint(low=1, high=max_seq_len_q, size=(batch_size,))
# Set one entry to max length
seq_lens_q[torch.randint(0, batch_size, size=(1,))] = max_seq_len_q
seq_lens_kv = torch.randint(low=1, high=max_seq_len_kv, size=(batch_size,))
seq_lens_kv[torch.randint(0, batch_size, size=(1,))] = max_seq_len_kv
def rand_nt(sequence_list, num_heads, head_dim):
tensors = [torch.rand((num_heads, seq_len, head_dim)) for seq_len in sequence_list]
return torch.nested.nested_tensor(tensors, requires_grad=True, device=device, dtype=dtype)
query = rand_nt(seq_lens_q, n_heads, head_dim)
key = rand_nt(seq_lens_kv, n_heads, head_dim)
value = rand_nt(seq_lens_kv, n_heads, head_dim)
# Run the math kernel on low precision references
query_ref_lp = query.clone().detach().requires_grad_(True)
key_ref_lp = key.clone().detach().requires_grad_(True)
value_ref_lp = value.clone().detach().requires_grad_(True)
query_ref = query.clone().detach().to(torch.float32).requires_grad_(True)
key_ref = key.clone().detach().to(torch.float32).requires_grad_(True)
value_ref = value.clone().detach().to(torch.float32).requires_grad_(True)
is_dropout = dropout_p > 0.0
if not is_dropout:
with sdpa_kernel(backends=[SDPBackend.FLASH_ATTENTION]):
out = F.scaled_dot_product_attention(query, key, value, dropout_p=dropout_p, is_causal=is_causal, scale=scale)
with sdpa_kernel(backends=[SDPBackend.MATH]):
# High Precision Math Reference
out_ref = F.scaled_dot_product_attention(
query_ref, key_ref, value_ref, is_causal=is_causal, scale=scale)
# Low Precision Math Reference
out_lp_ref = F.scaled_dot_product_attention(
query_ref_lp, key_ref_lp, value_ref_lp, is_causal=is_causal, scale=scale)
else:
# Create real output
output_tuple = torch.ops.aten._scaled_dot_product_flash_attention(
query, key, value, dropout_p=dropout_p, is_causal=is_causal,
scale=scale, return_debug_mask=is_dropout)
out = output_tuple[0]
dbug_mask = output_tuple[-1]
query_padding_mask = torch.arange(max_seq_len_q).unsqueeze(0).expand(
batch_size, max_seq_len_q
) < seq_lens_q.unsqueeze(-1)
query_padding_mask = query_padding_mask.to("cuda")
key_padding_mask = torch.arange(max_seq_len_kv).unsqueeze(0).expand(
batch_size, max_seq_len_kv
) < seq_lens_kv.unsqueeze(-1)
key_padding_mask = key_padding_mask.to("cuda")
softmax_mask = self.convert_flash_attn_S_to_softmax(
dbug_mask, max_seq_len_q, max_seq_len_kv, query_padding_mask, key_padding_mask, causal=is_causal)
dropout_mask = softmax_mask >= 0
nt_stack = []
for tensor_component in range(batch_size):
batch_stack = []
for head in range(n_heads):
batch_stack.append(dropout_mask[tensor_component, head,
0:seq_lens_q[tensor_component],
0:seq_lens_kv[tensor_component]].unsqueeze(0))
nt_stack.append(torch.cat(batch_stack))
nested_dropout_mask = torch.nested.nested_tensor(nt_stack)
# High Precision Math Reference
out_ref = torch.ops.aten._scaled_dot_product_attention_math(
query_ref, key_ref, value_ref, dropout_p=dropout_p,
is_causal=is_causal, scale=scale, dropout_mask=nested_dropout_mask)[0]
# Low Precision Math Reference
out_lp_ref = torch.ops.aten._scaled_dot_product_attention_math(
query_ref_lp, key_ref_lp, value_ref_lp, dropout_p=dropout_p, is_causal=is_causal, scale=scale,
dropout_mask=nested_dropout_mask)[0]
upstream_grad = out.detach().clone().contiguous()
out.backward(upstream_grad)
out_ref.backward(upstream_grad.to(out_ref.dtype))
out_lp_ref.backward(upstream_grad.to(out_lp_ref.dtype))
dropout_fudge_factor = 1.0 if dropout_p == 0.0 else 2.0
check_out_and_grad(
(out_ref, out_lp_ref, out),
(query_ref, query_ref_lp, query),
(key_ref, key_ref_lp, key),
(value_ref, value_ref_lp, value),
fudge_factors={
'out': 1.5 * dropout_fudge_factor,
'grad_query': 12.0 * dropout_fudge_factor,
'grad_key': 1.5 * dropout_fudge_factor,
'grad_value': 2.0 * dropout_fudge_factor,
}
)
class TestAttnBias(NNTestCase):
def run_test(
self,
device,
make_q,
make_kv,
attn_bias=None,
forw_tolerances: Optional[Tolerances] = None,
grad_tolerances: Optional[Tolerances] = None,
backend=None,
causal_variant=None,
):
if backend is not None:
torch._dynamo.reset()
query, key, value = make_q(), make_kv(), make_kv()
query_prototype, key_prototype, value_prototype = query_key_value_clones(query, key, value)
realized = attn_bias._materialize(device) if attn_bias is not None else None
pytorch_output = scaled_dot_product_attention(
query, key, value, attn_mask=realized, dropout_p=0.0, is_causal=False
)
sdpa_op = (
torch.compile(scaled_dot_product_attention, backend=backend)
if backend is not None
else scaled_dot_product_attention
)
sdpa_output = sdpa_op(
query_prototype,
key_prototype,
value_prototype,
attn_mask=attn_bias,
dropout_p=0.0,
is_causal=False,
scale=None,
)
dOut = torch.randn_like(pytorch_output)
pytorch_output.backward(dOut)
sdpa_output.backward(dOut)
# Use default assert_close tolerances for dtypes
if forw_tolerances is None:
forw_tolerances = Tolerances(atol=None, rtol=None)
if grad_tolerances is None:
grad_tolerances = Tolerances(atol=None, rtol=None)
torch.testing.assert_close(pytorch_output, sdpa_output, rtol=forw_tolerances.rtol, atol=forw_tolerances.atol)
torch.testing.assert_close(query.grad, query_prototype.grad, rtol=grad_tolerances.rtol, atol=grad_tolerances.atol)
torch.testing.assert_close(key.grad, key_prototype.grad, rtol=grad_tolerances.rtol, atol=grad_tolerances.atol)
torch.testing.assert_close(value.grad, value_prototype.grad, rtol=grad_tolerances.rtol, atol=grad_tolerances.atol)
@skipIfRocm # No support for the second variant for now
@parametrize("causal_variant", [CausalVariant.UPPER_LEFT, CausalVariant.LOWER_RIGHT])
@parametrize(
"shape",
[(16, 16, 128, 128, 16), (16, 16, 128, 256, 32), (16, 16, 256, 128, 32), (1, 1, 23, 56, 15)],
)
def test_causal_variants(self, device, causal_variant: CausalVariant, shape: List[Tuple[int]]):
make_tensor = partial(
torch.rand, device=device, dtype=torch.float16, requires_grad=True
)
bsz, num_heads, seq_len_q, seq_len_kv, head_dim = shape
make_q_tensor = partial(make_tensor, SdpaShape(bsz, num_heads, seq_len_q, head_dim))
make_kv_tensor = partial(make_tensor, SdpaShape(bsz, num_heads, seq_len_kv, head_dim))
if causal_variant == CausalVariant.LOWER_RIGHT and seq_len_q > seq_len_kv:
self.skipTest(
"Lower right causal mask will produce NaNs in the output when seq_len_q > seq_len_kv!"
)
forw_tol = Tolerances(1e-3, 1e-3)
grad_tol = Tolerances(5e-3, 5e-3)
if causal_variant == CausalVariant.UPPER_LEFT:
attn_bias = causal_upper_left(seq_len_q, seq_len_kv)
else:
attn_bias = causal_lower_right(seq_len_q, seq_len_kv)
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION,
SDPBackend.FLASH_ATTENTION,
SDPBackend.MATH,
SDPBackend.CUDNN_ATTENTION]):
self.run_test(device, make_q_tensor, make_kv_tensor, attn_bias, forw_tol, grad_tol, backend=None)
@skipIfRocm # CausalVariant
@parametrize("causal_variant", [CausalVariant.UPPER_LEFT, CausalVariant.LOWER_RIGHT])
@parametrize(
"shape",
[(16, 16, 128, 128, 16), (16, 16, 128, 256, 32), (16, 16, 256, 128, 32), (1, 1, 23, 56, 15)],
)
@unittest.skipIf(IS_WINDOWS, "torch.compile is not supported on windows")
@skipIfTorchDynamo("This function already calls torch.compile.")
def test_causal_variants_compile(self, device, causal_variant: CausalVariant, shape: List[Tuple[int]]):
cnts = CompileCounterWithBackend("aot_eager")
make_tensor = partial(
torch.rand, device=device, dtype=torch.float16, requires_grad=True
)
bsz, num_heads, seq_len_q, seq_len_kv, head_dim = shape
make_q_tensor = partial(make_tensor, SdpaShape(bsz, num_heads, seq_len_q, head_dim))
make_kv_tensor = partial(make_tensor, SdpaShape(bsz, num_heads, seq_len_kv, head_dim))
if causal_variant == CausalVariant.LOWER_RIGHT and seq_len_q > seq_len_kv:
self.skipTest(
"Lower right causal mask will produce NaNs in the output when seq_len_q > seq_len_kv!"
)
forw_tol = Tolerances(1e-3, 1e-3)
grad_tol = Tolerances(5e-3, 5e-3)
if causal_variant == CausalVariant.UPPER_LEFT:
attn_bias = causal_upper_left(seq_len_q, seq_len_kv)
else:
attn_bias = causal_lower_right(seq_len_q, seq_len_kv)
with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION,
SDPBackend.FLASH_ATTENTION,
SDPBackend.MATH,
SDPBackend.CUDNN_ATTENTION]):
self.run_test(device, make_q_tensor, make_kv_tensor, attn_bias, forw_tol, grad_tol, backend=cnts)
self.assertEqual(cnts.frame_count, 1, "Compiled graph should have 1 frame!")
@parametrize("shape", [(16, 16, 128, 128, 16), (16, 16, 128, 256, 32), (16, 16, 256, 128, 32), (1, 1, 23, 56, 15)])
def test_is_causal_equals_upper_left(self, device, shape: List[Tuple[int]]):
make_tensor = partial(
torch.rand, device=device, dtype=torch.float16, requires_grad=True
)
bsz, num_heads, seq_len_q, seq_len_kv, head_dim = shape
make_q_tensor = partial(make_tensor, SdpaShape(bsz, num_heads, seq_len_q, head_dim))
make_kv_tensor = partial(make_tensor, SdpaShape(bsz, num_heads, seq_len_kv, head_dim))
forw_tol = Tolerances(1e-3, 1e-3)
grad_tol = Tolerances(5e-3, 5e-3)
query = make_q_tensor()
key = make_kv_tensor()
value = make_kv_tensor()
attn_bias = causal_upper_left(seq_len_q, seq_len_kv)
out_attn_bias = scaled_dot_product_attention(query, key, value, attn_mask=attn_bias, dropout_p=0.0)
out_is_causal = scaled_dot_product_attention(query, key, value, is_causal=True, dropout_p=0.0)
torch.testing.assert_close(out_attn_bias, out_is_causal, rtol=forw_tol.rtol, atol=forw_tol.atol)
def test_is_causal_and_mask_fails(self, device):
make_tensor = partial(
torch.rand, device=device, dtype=torch.float16, requires_grad=True
)
make_q_tensor = partial(make_tensor, SdpaShape(16, 16, 128, 16))
make_kv_tensor = partial(make_tensor, SdpaShape(16, 16, 128, 16))
query = make_q_tensor()
key = make_kv_tensor()
value = make_kv_tensor()
attn_bias = causal_upper_left(128, 128)
with self.assertRaisesRegex(ValueError, "CausalBias should not be used with causal=True"):
scaled_dot_product_attention(query, key, value, attn_mask=attn_bias, is_causal=True, dropout_p=0.0)
@unittest.skipIf(TEST_XPU, "XPU does not support cppextension currently")
@unittest.skipIf(IS_FBCODE, "Ninja is required to load C++ extensions and it's not compatible with Buck ")
class TestSDPAPrivateUse1Only(NNTestCase):
@classmethod
def setUpClass(cls):
remove_build_path()
cls.module = torch.utils.cpp_extension.load(
name="custom_device_extension",
sources=[
f"{'test/' if not os.getcwd().endswith('test') else ''}cpp_extensions/open_registration_extension.cpp",
],
extra_include_paths=["cpp_extensions"],
extra_cflags=["-g"],
verbose=True,
)
# register torch.foo module and foo device to torch
torch.utils.rename_privateuse1_backend("foo")
torch.utils.generate_methods_for_privateuse1_backend(for_storage=True)
torch._register_device_module("foo", generate_faked_module())
@skipIfTorchDynamo()
def test_fused_sdp_choice_privateuseone(self):
batch_size, seq_len, num_heads, head_dim = 4, 256, 2, 128
make_tensor = partial(torch.rand, device="cpu", dtype=torch.float16)
shape = SdpaShape(batch_size, num_heads, seq_len, head_dim)
q_cpu, k_cpu, v_cpu = make_tensor(shape), make_tensor(shape), make_tensor(shape)
q_privateuse1 = q_cpu.to("foo")
k_privateuse1 = k_cpu.to("foo")
v_privateuse1 = v_cpu.to("foo")
assert torch._fused_sdp_choice(q_privateuse1, k_privateuse1, v_privateuse1) == SDPBackend.OVERRIDEABLE.value
def test_scaled_dot_product_fused_attention_overrideable(self):
batch_size, seq_len, num_heads, head_dim = 4, 256, 2, 128
make_tensor = partial(torch.rand, device="cpu", dtype=torch.float16)
shape = SdpaShape(batch_size, num_heads, seq_len, head_dim)
q_cpu, k_cpu, v_cpu = make_tensor(shape), make_tensor(shape), make_tensor(shape)
q_privateuse1 = q_cpu.to("foo")
k_privateuse1 = k_cpu.to("foo")
v_privateuse1 = v_cpu.to("foo")
actual = torch.nn.functional.scaled_dot_product_attention(
q_privateuse1, k_privateuse1, v_privateuse1, attn_mask=None, dropout_p=0.0)
def test_scaled_dot_product_fused_attention_overrideable_backward(self):
batch_size, seq_len, num_heads, head_dim = 4, 256, 2, 128
make_tensor = partial(torch.rand, device="cpu", dtype=torch.float16, requires_grad=True)
shape = (batch_size, num_heads, seq_len, head_dim)
q_cpu, k_cpu, v_cpu = make_tensor(shape), make_tensor(shape), make_tensor(shape)
attn_mask = make_tensor((batch_size, num_heads, seq_len, seq_len))
q_privateuse1 = q_cpu.to("foo")
k_privateuse1 = k_cpu.to("foo")
v_privateuse1 = v_cpu.to("foo")
attn_mask_privateuse1 = attn_mask.to("foo")
output, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, philox_seed, philox_offset, debug_attn_mask = \
torch.ops.aten._scaled_dot_product_fused_attention_overrideable(
q_privateuse1, k_privateuse1, v_privateuse1, attn_bias=attn_mask_privateuse1)
rand_upward = torch.rand(shape, device="cpu", dtype=torch.float16, requires_grad=False)
rand_upward_privateuse1 = rand_upward.to("foo")
grad_input_mask = [True, True, True, True]
grad_q, grad_k, grad_v, grad_attn_mask = torch.ops.aten._scaled_dot_product_fused_attention_overrideable_backward(
rand_upward_privateuse1, q_privateuse1, k_privateuse1, v_privateuse1, attn_mask_privateuse1,
grad_input_mask, output, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p=0.0,
is_causal=False, philox_seed=philox_seed, philox_offset=philox_offset)
if NOTEST_CPU:
device_types = ("cuda", )
else:
device_types = ("cpu", "cuda")
instantiate_device_type_tests(TestTransformers, globals(), only_for=device_types)
instantiate_device_type_tests(TestSDPAFailureModes, globals(), only_for=device_types)
instantiate_device_type_tests(TestSDPA, globals(), only_for=device_types)
instantiate_device_type_tests(TestSDPACudaOnly, globals(), only_for=("cuda"))
instantiate_device_type_tests(TestSDPACpuOnly, globals(), only_for=("cpu"))
instantiate_device_type_tests(TestAttnBias, globals(), only_for=device_types)
if __name__ == '__main__':
run_tests()
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