Set Float32 Precision for CONV/RNN

[zhuhaozhe]

RFC: Extend set fp32 precision API to support Convolution and RNN

Overview

This RFC proposes the addition of a user-controlled frontend API to configure the internal precision of float32 operations in convolutional (CONV) and recurrent neural networks (RNN) within PyTorch. Currently, PyTorch offers torch.set_float32_matmul_precision to configure the internal precision of float32 matrix multiplication. This RFC suggests extending this functionality to include convolution and recurrent neural network operations, providing torch.set_float32_conv_precision and torch.set_float32_rnn_precision. The proposed APIs will mimic the behavior of torch.set_float32_matmul_precision.

Frontend Changes

Frontend changes involve introducing new APIs:

• torch.set_float32_conv_precision, torch.get_float32_conv_precision
• torch.set_float32_rnn_precision, torch.get_float32_rnn_precision

These APIs will function similarly to torch.set_float32_matmul_precision and torch.get_float32_matmul_precision. Users can set the precision to highest, high, or medium, each with corresponding backend behavior:

• highest: Use the highest available precision, avoiding lower precision.
• high: Allow backends to use TensorFloat32 (TF32) or treat each float32 number as the sum of two bfloat16 numbers.
• medium: Allow backends to use BFloat 16 (BF16).

Backend Changes

Global flags float32_conv/rnn_precision will be introduced at this location in the PyTorch repository. This flag can be accessed and modified by the frontend APIs torch.get/set_float32_conv/rnn_precision. Backend-related operators will read this flag to control the internal computation data types. For example:

• For CuDNN backend, we should check float32_conv_precision in the CuDNN Conv kernel. We should also check float32_rnn_precision in the CuDNN RNN kernel. If not set to highest, the internal computation data type will be TF32.
• For OneDNN backend, we should check float32_conv_precision in OneDNN Conv kernel and check float32_rnn_precision in OneDNN RNN kernel. If set to medium, the internal data type will be BF16.

Flag Overrides

The existing CUDNN backend-specific flag torch.backends.cudnn.allow_tf32 will interact with the proposed backend-irrelevant flag torch.set_float32_conv/rnn_precision. These flags will override each other( we follow similar behavior between torch.backends.cuda.matmul.allow_tf32 and float32_matmul_precision):

• Turning on/off TF32 with torch.backends.cudnn.allow_tf32 will set float32_rnn/conv_precision to high (TF32 enabled) and highest (TF32 disabled).

  torch.backends.cudnn.alow_tf32=True
  print("float32_conv_precision", torch.get_float32_conv_precision)
  print("float32_rnn_precision", torch.get_float32_rnn_precision)
  # output:
  # float32_conv_precision, high
  # float32_rnn_precision, high
  torch.backends.cudnn.alow_tf32=False
  print("float32_conv_precision", torch.get_float32_conv_precision)
  print("float32_rnn_precision", torch.get_float32_rnn_precision)
  # output:
  # float32_conv_precision, highest
  # float32_rnn_precision, highest

• Setting both float32_rnn/conv_precision to high or medium will enable torch.backends.cudnn.allow_tf32, while setting one of it to highest will disable it.

  torch.backends.cudnn.alow_tf32=True
  torch.set_float32_conv_precision("highest")
  print("torch.backends.cudnn.alow_tf32", torch.backends.cudnn.alow_tf32)
  # output:
  # torch.backends.cudnn.alow_tf32, False
  torch.set_float32_rnn_precision("highest")
  print("torch.backends.cudnn.alow_tf32", torch.backends.cudnn.alow_tf32)
  # output:
  # torch.backends.cudnn.alow_tf32, False
  torch.set_float32_conv_precision("high")
  torch.set_float32_rnn_precision("high")
  print("torch.backends.cudnn.alow_tf32", torch.backends.cudnn.alow_tf32)
  # output:
  # torch.backends.cudnn.alow_tf32, True

Additional CuDNN Flag

We discussed how the existing CuDNN flag, torch.backends.cudnn.allow_tf32, interacts with torch.set_float32_conv/rnn_precision. However, we believe it is cleaner to use separate flags in CuDNN. We suggest deprecating torch.backends.cudnn.allow_tf32 in favor of torch.backends.cudnn.conv.allow_tf32 and torch.backends.cudnn.rnn.allow_tf32. Then, the CuDNN backend-specific flags and backend-irrelevant flags can have a one-to-one correspondence, such as torch.backends.cuda.matmul.allow_tf32 and torch.float32_matmul_precision

torch.backends.cudnn.conv.allow_tf32 <-> torch.float32_conv_precision
torch.backends.cudnn.rnn.allow_tf32 <-> torch.float32_rnn_precision
# below flags are already existing now
torch.backends.cuda.matmul.allow_tf32 <-> torch.float32_matmul_precision

Motivation

Lower-precision computation from different backends can significantly improve performance for deep learning workloads with minimal impact on precision. For example, TF32 from CUDA/CUDNN or implicit reduced precision arithmetic feature from oneDNN. By providing a user-controlled frontend API, users can easily configure the internal computation data type of convolutional and recurrent neural networks without knowing the detail of different backends. This allows them to leverage the performance benefits of lower precision while ensuring acceptable precision loss. Compared to Autocast, the proposed flags offer:

• Higher precision control as they only affect convolutional and recurrent neural network internal data types, unlike Autocast, which impacts more operators.
• Ease of use, as users do not need to modify their model scripts to enable autocasting.

Pitch

Introduce float32_conv/rnn_precision and enable users to control the internal data type for convolutional and recurrent neural networks by configuring the value of float32_conv/rnn_precision.

[leslie-fang-intel]

• For the feature title: USE the bfloat16 datatype (8 mantissa bits) for internal computations, refer to https://github.com/mgouicem/oneDNN/tree/mgouicem/rfcs/implicit_downconvert/rfcs/20210301-computation-datatype, oneDNN only take it as a hint which means BF16 or FP32 data type may be picked for internal computations.
• Here are the IPEX BF32 RFC: https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-cpu/issues/381

[leslie-fang-intel]

When the precision is high, the CUDA/CUDNN backend will be allowed to use TF32 as the internal computation data type. When the precision is medium, the MKLDNN backend will be allowed to use BF16 as the internal computation data type.

Refer to https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch-set-float32-matmul-precision, it should apply to all the backends?

[leslie-fang-intel]

For the 2 design options in Frontend API and Inductor linear packable, do we have any preferred option now? If so, we may talk about our preference for implementation.

[zhuhaozhe]

When the precision is high, the CUDA/CUDNN backend will be allowed to use TF32 as the internal computation data type. When the precision is medium, the MKLDNN backend will be allowed to use BF16 as the internal computation data type.

Refer to https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch-set-float32-matmul-precision, it should apply to all the backends?

Yes, I changed it to all backends instead of MKLDNN or CUDA

[zhuhaozhe]

• For the feature title: USE the bfloat16 datatype (8 mantissa bits) for internal computations, refer to https://github.com/mgouicem/oneDNN/tree/mgouicem/rfcs/implicit_downconvert/rfcs/20210301-computation-datatype, oneDNN only take it as a hint which means BF16 or FP32 data type may be picked for internal computations.
• Here are the IPEX BF32 RFC: [RFC] IPEX integration of oneDNN implicit reduced precision arithmetic feature intel-innersource/frameworks.ai.pytorch.ipex-cpu#381

Thanks, changed.

[jgong5]

Please add notes on how CUDA can support the new frontend APIs since it is general APIs that can be applied to all backends.

[zhuhaozhe]

Please add notes on how CUDA can support the new frontend APIs since it is general APIs that can be applied to all backends.

Thanks for advice, added.