Attention Is All You Need!

The core idea behind Transformer models is the attention mechanism [1]. It identifies the correlation between words, selects the most important parts of the sentence to focus on, and captures meaningful patterns and dependencies in the data. Figure 1 shows a typical attention mechanism, where pre-softmax operations can be a combination of scaling, bias and masking while the post-softmax operation is often just dropout.

Figure 1: Dot product attention.

Transformer Engine supports the calculation of dot product attention in three frameworks, PyTorch, JAX and PaddlePaddle. The API for each framework is

• transformer_engine.pytorch.DotProductAttention

• transformer_engine.jax.flax.DotProductAttention

• transformer_engine.paddle.DotProductAttention

1. Attention Backends

Transformer Engine provides multiple attention backends for each supported framework. The framework-native backends provide a robust baseline, while the fused, GPU-optimized implementations offer more performance. For example, the flash-attention and cuDNN attention backends in PyTorch. The framework-native backends are often named with “unfused”, while the more optimized backends are “fused” or “flash”.

Framework	Backend (Module Name)	Module Location
PyTorch	cuDNN attention (FusedAttention)	transformer_engine.pytorch.attention
	flash-attention (FlashAttention)
	PyTorch-native attention (UnfusedDotProductAttention)
JAX	cuDNN attention (_FusedDotProductAttention)	transformer_engine.jax.flax.transformer
	JAX-native attention (_UnfusedDotProductAttention)
PaddlePaddle	cuDNN attention (_te_forward)	transformer_engine.paddle.layer.attention
	PaddlePaddle-native attention (_pd_forward)

1.1 Flash vs. Non-Flash

The attention calculation has quadratic computational and memory complexities to the sequence length. Its runtime and memory requirements quadruple, when the sequence length doubles. This presents a significant challenge to scale Transformer models up for longer contexts, in order to achieve higher model quality.

Compared to the standard, non-flash algorithm, the flash algorithm [2] was proposed to reduce the memory scaling to linear and improve the computational efficiency through optimized memory accesses. It employs the following two distinctive techniques.

• Tiling: The non-flash algorithm tries to process the query, key, value tensors in one single step, requiring large amounts of global memory and incurring high volumes of reads/writes between global memory and shared memory. The flash algorithm decomposes the input into several tiles, based on the available shared memory and register size, and it computes the softmax one tile at a time.

• Recomputation: The non-flash algorithm stores the softmax matrix (quadratic to sequence length) to global memory for the backward pass, while the flash algorithm only saves the softmax normalization factors (linear to sequence length). This reduces the amount of memory required as well as the bandwidth utilization between global memory and shared memory. Even though there is extra computation incurred in order to recalculate the attention in the backward pass, the bandwidth savings still provide significant improvement in efficiency.

Note:

Transformer Engine’s flash-attention backend, available in PyTorch, and cuDNN attention backend (sub-backends 1 and 2), available in PyTorch, JAX and PaddlePaddle, are both based on the flash algorithm.

1.2 flash-attention

The flash-attention backend, available only in PyTorch, is a module wrapped around the public flash-attn package [3].

The flash-attention backend supports flash-attn’s features as well as a few extra functionalities to facilitate the use of flash-attn, such as converting the attention_mask to cumulative sequence lengths cu_seqlens for padding mask use cases. Please see transformer_engine.pytorch.attention.FlashAttention for details.

The flash-attn dependency is regularly updated in Transformer Engine. As of v1.10, Transformer Engine supports flash-attn 2.0.6+ (see setup.py).

To understand flash-attn’s performance, please refer to their benchmarks here.

1.3 cuDNN Attention

The cuDNN attention backend, available in PyTorch, JAX and PaddlePaddle, offers another high-performance solution to the attention calculation. It requires cuDNN to run, and has several sub-backends to support the different precisions and sequence lengths.

Sub-Backend	Algorithm	Precision	Sequence Length	Architecture	Additional info
0	Non-Flash	BF16/FP16	≤512	sm80, 90	cuDNN
1	Flash	BF16/FP16	Any	sm80+	cuDNN, cudnn-frontend
2	Flash	FP8	cuDNN pre-9.0: ≤512	cuDNN pre-9.0: sm90
			cuDNN 9.0+: Any	cuDNN 9.0+: sm90+	cuDNN 9.0+: cudnn-frontend

The cuDNN attention backend and flash-attention backend have several notable differences. As of Transformer Engine 1.10, cuDNN 9.3 and flash-attn 2.4.2,

• flash-attention only supports the PyTorch framework while cuDNN attention supports PyTorch, JAX and PaddlePaddle.

• flash-attention supports BF16, FP16 precisions while cuDNN attention also supports FP8 (through its sub-backend 2).

• flash-attention supports bshd, thd input formats, without any transposes, and sbhd format, with transposes, while cuDNN attention supports all three formats without transposes (see Section 3.1 for more details).

• flash-attention does not support post_scale_bias, and cuDNN attention does.

• flash-attention supports KV-caching and paged attention, and cuDNN attention does not.

• flash-attention uses bottom right diagonal for causal mask in cross attention (see change log), and cuDNN attention supports both top left and bottom right.

• flash-attention outperforms cuDNN attention on Ampere architectures, and cuDNN attention has 20-50% advantages on Hopper architectures, based on our benchmarks for a number of commonly-used model configurations.

To compare cuDNN attention and flash-attention, users can modify the model_configs dictionary in benchmarks/attention/benchmark_attention.py to collect performance numbers. The script runs each entry in model_configs for num_iters times, each time with one forward pass and one backward pass. Both backends are tried, and if one backend does not have support for the specific user input, the runtimes and speedups in the final table would be 0.

model_configs = {
    #   test:             b,  h, hg,   d,   sq,  skv,   p,     mask,              bias
    "test_0": ModelConfig(2, 16, 16,  64,  512,  512, 0.0, "no_mask",         "no_bias"), # short seq
    "test_1": ModelConfig(2, 16, 16, 128, 2048, 2048, 0.0,  "causal",         "no_bias"), # longer seq, mask
    "test_2": ModelConfig(2, 16, 16, 128, 2048, 2048, 0.0,  "causal", "post_scale_bias"), # bias
    "test_3": ModelConfig(2, 32,  4, 128, 8192, 8192, 0.0,  "causal",         "no_bias"), # GQA
}

!cd ../../../benchmarks/attention/ && python benchmark_attention.py

Device 0: NVIDIA H100 80GB HBM3 GPU, sm90 compute capability, 79.1GB memory
Running test_0 with cuDNN attention and flash-attention...
Running test_1 with cuDNN attention and flash-attention...
Running test_2 with cuDNN attention...
Running test_3 with cuDNN attention and flash-attention...

        cuDNN fwd+bwd (ms)  flash-attn fwd+bwd (ms)  cuDNN vs flash speedup
test_0              0.0340                   0.0468                  1.3786
test_1              0.3664                   0.5850                  1.5968
test_2              0.9332                   0.0000                  0.0000
test_3              7.4875                  11.8879                  1.5877

2. Backend Selection

Given the various attention backends, Transformer Engine has a selection logic in place to choose the most appropriate backend for a particular set of user inputs and runtime environment. The selection logic is based on both backend availability and backend performance.

Backend availability is determined by factors such as model configuration, training hyper-parameters, software versions, and the GPU architecture in question. For example, some considerations are the sequence length, number of attention heads, head size, attention mask type, attention bias type, training or inference mode, self or cross attention, MHA or MQA/GQA, flash-attn/cuDNN library versions, and the compute capability of the GPU.

When there are multiple backends available, Transformer Engine makes backend selection based on performance. In general, there are a few rules being followed in our selection logic (see table below). As we monitor the performance of different backends, the selection logic may change.

Framework	Selection Order
PyTorch	sm90: cuDNN attention > flash-attention > PyTorch-native attention
	sm80: flash-attention > cuDNN attention > PyTorch-native attention
	cuDNN attention: sub-backend 1 > sub-backend 0
JAX	cuDNN attention > JAX-native attention
PaddlePaddle	cuDNN attention > PaddlePaddle-native attention

2.1 Debug Information

To find out which backend is being used during runtime, we have the following two debugging flags. Logging is done by using the logging package.

NVTE_DEBUG       = 0/1   # disables/enables debugging
NVTE_DEBUG_LEVEL = 0/1/2 # enables logging.WARNING/INFO/DEBUG-level messages

Note:

These flags are supported in PyTorch only as of Transformer Engine 1.10. JAX and PaddlePaddle support is expected to be added in the future.

The example script example_attention.py runs a very basic model with two attention backends, cuDNN attention and flash-attention. Here NVTE_DEBUG_LEVEL=1 allows us to find out which backend/sub-backend is used in runtime.

!NVTE_DEBUG=1 NVTE_DEBUG_LEVEL=1 python example_attention.py

Run cuDNN attention...
[INFO     | DotProductAttention]: Running with FusedAttention backend (sub-backend 1)

Run flash-attention...
[INFO     | DotProductAttention]: Running with FlashAttention backend

Test passed.

NVTE_DEBUG_LEVEL=2 allows us to find out more about the backend selection logic. Users are encouraged to double check the config and provide it to the Transformer Engine team if they would like to file a bug.

!NVTE_DEBUG=1 NVTE_DEBUG_LEVEL=2 python example_attention.py

Run cuDNN attention...
[DEBUG    | DotProductAttention]: Running with config={'transformer_engine_version': '1.10.0.dev0+ee85a91', 'compute_capability': 'sm90', 'flash_attn_version': <Version('2.4.2')>, 'cudnn_version': '9.3.0', 'qkv_type': <class 'torch.Tensor'>, 'qkv_dtype': torch.bfloat16, 'qkv_layout': 'bshd_bshd_bshd', 'batch_size': 2, 'num_heads': 16, 'num_gqa_groups': 16, 'max_seqlen_q': 512, 'max_seqlen_kv': 512, 'head_dim_qk': 64, 'head_dim_v': 64, 'attn_mask_type': 'no_mask', 'window_size': (-1, -1), 'alibi_slopes_shape': None, 'core_attention_bias_type': 'no_bias', 'core_attention_bias_shape': None, 'core_attention_bias_requires_grad': False, 'pad_between_seqs': False, 'attention_dropout': 0.0, 'context_parallel': False, 'deterministic': False, 'is_training': True, 'fp8': False, 'fp8_meta': {'fp8_checkpoint': False, 'fp8_group': None, 'recipe': margin=0, format=HYBRID, amax_history_len=1024, wgrad_override=False, fp8_dpa=False, fp8_mha=False}}
[DEBUG    | DotProductAttention]: Disabling FlashAttention due to NVTE_FLASH_ATTN=0
[DEBUG    | DotProductAttention]: Available backends = {FlashAttention=False, FusedAttention=True (sub-backend 1), UnfusedDotProductAttention=True}
[DEBUG    | DotProductAttention]: Selected backend = FusedAttention (sub-backend 1)
[INFO     | DotProductAttention]: Running with FusedAttention backend (sub-backend 1)

Run flash-attention...
[DEBUG    | DotProductAttention]: Running with config={'transformer_engine_version': '1.10.0.dev0+ee85a91', 'compute_capability': 'sm90', 'flash_attn_version': <Version('2.4.2')>, 'cudnn_version': '9.3.0', 'qkv_type': <class 'torch.Tensor'>, 'qkv_dtype': torch.bfloat16, 'qkv_layout': 'bshd_bshd_bshd', 'batch_size': 2, 'num_heads': 16, 'num_gqa_groups': 16, 'max_seqlen_q': 512, 'max_seqlen_kv': 512, 'head_dim_qk': 64, 'head_dim_v': 64, 'attn_mask_type': 'no_mask', 'window_size': (-1, -1), 'alibi_slopes_shape': None, 'core_attention_bias_type': 'no_bias', 'core_attention_bias_shape': None, 'core_attention_bias_requires_grad': False, 'pad_between_seqs': False, 'attention_dropout': 0.0, 'context_parallel': False, 'deterministic': False, 'is_training': True, 'fp8': False, 'fp8_meta': {'fp8_checkpoint': False, 'fp8_group': None, 'recipe': margin=0, format=HYBRID, amax_history_len=1024, wgrad_override=False, fp8_dpa=False, fp8_mha=False}}
[DEBUG    | DotProductAttention]: Disabling FusedAttention due to NVTE_FUSED_ATTN=0
[DEBUG    | DotProductAttention]: Available backends = {FlashAttention=True, FusedAttention=False, UnfusedDotProductAttention=True}
[DEBUG    | DotProductAttention]: Selected backend = FlashAttention
[INFO     | DotProductAttention]: Running with FlashAttention backend

Test passed.

2.2 User Control

Users usually do not need to worry about the backend selection. However, if there is a convergence or performance issue encountered, Transformer Engine provides a few other environment variables for users to experiment with different backends.

flash-attention or cuDNN attention: Users can enable/disable the flash-attention backend or cuDNN attention backend via the following two environment variables in PyTorch.

NVTE_FLASH_ATTN = 0 # disables flash-attention; default = 1
NVTE_FUSED_ATTN = 0 # disables cuDNN attention; default = 1

cuDNN attention sub-backends: This environment variable allows users to express their preference of cuDNN attention sub-backends. However, the elected sub-backend will only be used if it is eligible, i.e. if it has support for the provided inputs and runtime environment.

NVTE_FUSED_ATTN_BACKEND = 0/1/2 # user preference of cuDNN sub-backend

Execution paths of cuDNN sub-backend 1: cuDNN attention sub-backend 1 also offers two execution paths: workspace optimization path and non-workspace optimization path. The workspace optimization path requires a larger amount of global memory, provides determinism, and offers bias gradient support. Before cuDNN 9.0, it also has 20-30% performance advantage over the non-workspace optimization path. But after cuDNN 9.0, it is 20-30% slower than the non-workspace optimization path.

Users can experiment with these two paths through the following environment variable. However, please be aware of the possible Out-Of-Memory risks.

Before cuDNN 9.0:
    NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT = 0 # disables workspace optimization path
    NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT = 1 # enables workspace optimization path

After cuDNN 9.0:
    NVTE_ALLOW_NONDETERMINISTIC_ALGO = 1 # disables workspace optimization path
    NVTE_ALLOW_NONDETERMINISTIC_ALGO = 0 # enables workspace optimization path

Note

Environment variables NVTE_FLASH_ATTN, NVTE_FUSED_ATTN, NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT and NVTE_ALLOW_NONDETERMINISTIC_ALGO are only supported in PyTorch, and will be added to JAX and PaddlePaddle in the future.

2.3 Example Tests

Our unit tests demonstrate the use of Transformer Engine dot product attention APIs. Users are encouraged to use them as a template when integrating Transformer Engine to their ML workflows.

For example, in PyTorch, test_dot_product_attention offers a variety of use cases of pytorch.DotProductAttention, from data types, model configs, checkpointing, to QKV layouts.

3. Backend Support

Transformer Engine supports commonly-used features such as self and cross attention, FP16/BF16 precisions, dropout, and checkpointing. But it also offers a range of other features. As of v1.10, Transformer Engine’s attention backends have the following support matrix.

Attention Backend	Precision	Architecture	Sliding Window Attention	MQA/GQA	Multi-Latent Attention	Context Parallelism	Determinism Possible
cuDNN attention (all frameworks)	BF16, FP16, FP8 (PyTorch only)	sm80+	No	Yes	Yes	Yes (bshd,sbhd, thd)	Yes
flash-attention (PyTorch)	BF16, FP16	sm80+	Yes	Yes	No	Yes (bshd,thd)	Yes
Framework-native attention	BF16, FP16, FP32	Any	No, unless used as a mask	Yes	Yes (PyTorch only)	No	Yes

Some unit tests are provided to serve as a starting point for integrating such features into users’ models. For example, - sliding window attention: test_dpa_swa - MQA/GQA: test_te_layer_mqa_gqa - Multi-Latent Attention: test_dpa_mla - context parallelism: test_cp_with_fused_attention, test_cp_with_flash_attention

3.1 QKV Layout

Transformer Engine supports various layouts of the query q, key k, value v tensors. It has defined 15 QKV layouts, which are grouped into 3 QKV formats and 5 QKV layout groups to help with similar memory/computational operations across different layouts. The mapping relationships of these layouts and groups are,

qkv_layout	qkv_layout_group=3hd	h3d	hd_2hd	hd_h2d	hd_hd_hd
qkv_format=sbhd	sb3hd	sbh3d	sbhd_sb2hd	sbhd_sbh2d	sbhd_sbhd_sbhd
bshd	bs3hd	bsh3d	bshd_bs2hd	bshd_bsh2d	bshd_bshd_bshd
thd	t3hd	th3d	thd_t2hd	thd_th2d	thd_thd_thd

The notation system is that b stands for the batch size, s sequence length, h number of attention heads, d head dimension, and t the total number of tokens in the batch, i.e. t = sum(s_i) for i in 0,...,b-1. Here are a few examples of the layouts and their explanations to help clarify the definition.

qkv_layout=sb3hd: q, k, v are sequence first, i.e. s is the leading dimension in each tensor. They are different slices of one tensor qkv: q, k, v = [qkv[:,:,i,:,:] for i in range(3)]. They are interleaved at the h * d dimension.

qkv_layout=bshd_bsh2d: q, k, v are batch first, i.e. b is the leading dimension in each tensor. q is contiguous, and k, v are different slices of tensor kv: k, v = [kv[:,:,:,i,:] for i in range(2)]. k, v are interleaved at the d dimension.

The s and h in bsh2d are the max sequence length and number of heads for k, v, which can be different from the s and h in bshd for q. We denoted them as the same for brevity reasons. Transformer Engine does differentiate their values for actual execution.

qkv_layout=thd_thd_thd: q, k, v have variable sequence lengths in a batch. They are all contiguous and have no interleaving.

As of v1.10, Transformer Engine has the following support matrix.

Backend	Supported QKV Formats	Notes
flash-attention	bshd, sbhd, thd	PyTorch: 3 formats, i.e. 15 layouts
cuDNN attention	bshd, sbhd, thd	PyTorch: 3 formats, i.e. 15 layouts
		JAX, PaddlePaddle: bs3hd, bshd_bs2hd, bshd_bshd_bshd layouts
Framework-native attention	bshd, sbhd	PyTorch, JAX, PaddlePaddle: 2 formats, i.e. 10 layouts

Some example usage of the different layouts can be found at test_dpa_qkv_layout and test_dpa_qkv_layout_thd. Transformer Engine also provides a utility function transformer_engine.pytorch.attention.get_qkv_layout to help determine which layout a set of q, k, v tensors have (PyTorch only).

Note

When RoPE is employed, the qkv_layout may change in Transformer Engine PyTorch through get_qkv_layout. This is due to the in-place nature of our RoPE implementations. We convert q, k, v tensors from their initial layout to the corresponding hd_hd_hd layout. For example, from sbh3d in pytorch.MultiHeadAttention before RoPE, to sbhd_sbhd_sbhd in pytorch.DotProductAttention after RoPE.

3.2 Attention Mask

Transformer Engine supports 7 mask types, and all the masks are defined as True masking out the corresponding element and False including the corresponding element in attention calculation.

• no_mask, padding, causal, causal_bottom_right, padding_causal, padding_causal_bottom_right, arbitrary

Different backends offer different support for attention mask. As of Transformer Engine 1.10,

Backend	Supported Mask Types	Requires attention_mask
flash-attention	no_mask, causal (self-attention), padding, padding_causal (self-attention), causal_bottom_right, padding_causal_bottom_right	no_mask, causal causal_bottom_right: No padding, padding_causal, padding_causal_bottom_right: Yes if cu_seqlens not provided arbitrary: Yes
cuDNN attention	no_mask, causal, padding, padding_causal, causal_bottom_right, padding_causal_bottom_right
Framework-native attention	All (PyTorch) no_mask, causal, padding (Jax, PaddlePaddle)

Padding masks: For padding, padding_causal, padding_causal_bottom_right mask types, users need to provide sequence length information to help Transformer Engine figure out where each sequence ends in a batch. As of Transformer Engine 1.10, there are two options to do so in PyTorch and one in JAX and PaddlePaddle.

• PyTorch: When both options are provided by the user, cu_seqlens is preferred as there is no extra conversion needed.

  • cu_seqlens: Users can provide cumulative sequence length tensors cu_seqlens_q and cu_seqlens_kv for q and k/v to the flash-attention or cuDNN attention backend. An example of cu_seqlens is [0, 2, 6, 7] for a batch of 3 [aa000, bbbb0, c0000].

  • attention_mask: Users can also provide attention_mask as an alternative, which will then be converted to cu_seqlens. For self-attention, attention_mask should be one single tensor in shape [batch_size, 1, 1, seqlen_q], and for cross-attention, attention_mask should be a list of two tensors in shapes [batch_size, 1, 1, seqlen_q] and [batch_size, 1, 1, seqlen_kv], respectively.

• JAX and PaddlePaddle: Users should provide the attention_mask tensor in shape [batch_size, 1, seqlen_q, seqlen_kv].

qkv_format=thd: Transformer Engine extracts the max sequence length information from q, k, v if max_seqlen_q and max_seqlen_kv are not provided. This requires GPU-CPU copy and synchronization operations. For performance reasons, please set max_seqlen_q and max_seqlen_kv to their appropriate values for thd QKV format.

Arbitrary mask: cuDNN does not support Arbitrary mask type as of v9.3. However, users can convert the mask to a regular post_scale_bias bias and achieve the same functionality. An example script for this conversion is arbitrary_mask_to_post_scale_bias.py.

!NVTE_DEBUG=1 NVTE_DEBUG_LEVEL=1 python arbitrary_mask_to_post_scale_bias.py

Run with post_scale_bias:
[INFO     | DotProductAttention]: Running with FusedAttention backend (sub-backend 1)

Run with arbitrary mask:
[INFO     | DotProductAttention]: Running with UnfusedDotProductAttention backend

Test passed!

Some more examples of running Transformer Engine with different attention masks can be found at test_dpa_mask.

3.3 Attention Bias

Transformer Engine supports 4 attention bias types, no_bias, pre_scale_bias, post_scale_bias, and ALiBi (with/without custom slopes). As of Transformer Engine 1.10, their support matrix is as follows.

Backend	Bias Type	Bias Shape	Bias Data Type	Architecture
flash-attention	no_bias, ALiBi (with slopes)	N/A	ALiBi slopes: FP32	sm80+
cuDNN attention	PyTorch: no_bias, post_scale_bias, ALiBi (without slopes)	post_scale_bias: BHSS, 1HSS, B1SS, 11SS for forward, 1HSS for backward	post_scale_bias: same as QKV type	cuDNN 8.9.6+: sm90
	JAX, PaddlePaddle: no_bias, post_scale_bias		ALiBi slopes: FP32	cuDNN 9.0+: sm80+
Framework-native attention	no_bias, pre_scale_bias, post_scale_bias	post_scale_bias: BHSS, 1HSS, B1SS, 11SS	post_scale_bias: same as QKV type	sm80+

The flash-attention backend enables ALiBi by asking user to pass in an alibi_slopes tensor, which can be the default slopes of vanilla ALiBi, or user-defined slopes. On the other hand, cuDNN attention supports ALiBi by taking in a Boolean flag, and it only supports vanilla ALiBi as of cuDNN 9.0.

The framework-native backends do not explicitly support ALiBi, but users can convert ALiBi to a regular post_scale_bias bias to achieve the same effect. In PyTorch, this utility function, transformer_engine.pytorch.attention.get_alibi, can be used to help with the conversion.

More examples of how to use the various attention biases are at test_dpa_bias.

3.4 FP8 Attention

A unique feature of Transformer Engine is its FP8 support, not only for the Linear layers but also for dot product attention. Transformer Engine’s FP8 attention support is through its cuDNN attention sub-backend 2. Recall Figure 1: the two MatMul operations are performed in FP8 for computational efficiency, and the SoftMax operation is performed in FP32 for numerical accuracy.

Transformer Engine supports FP8 attention through its C APIs, and PyTorch API, as of v1.10. Its PyTorch API offers two options, both controlled through the FP8 recipe definition, transformer_engine.common.recipe.DelayedScaling.

• DelayedScaling.fp8_dpa=True (default=False): This enables the use of cuDNN attention sub-backend 2, when it does support the provided user inputs. The FusedAttention module for cuDNN attention takes FP16 or BF16 tensors as inputs, performs dot product attention in FP8, and returns attention logits in FP16 or BF16 (same as the input type). Casting operations are required to cast tensors to FP8 at the beginning, and back to FP16/BF16 at the end of the module.

• DelayedScaling.fp8_mha=True (default=False): This option, on top of fp8_dpa=True, removes the casting operations at the beginning and end of the FusedAttention module. This feature is experimental.

Examples of using the two features are available at test_dpa_fp8_vs_f16 and test_mha_fp8_vs_f16. To disable FP8 attention for backward and only use it for forward, users can also set NVTE_FP8_DPA_BWD=0 (default=1).