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### Description This PR is to enable TF32 as fp32 internal precision for matmul/linear/conv in `mkldnn backend`. Since we have refined fp32 precision API in https://github.com/pytorch/pytorch/pull/125888, we can easily extend the API to support TF32 for `mkldnn backend`. ``` torch.backends.mkldnn.matmul.fp32_precision = 'tf32' torch.backends.mkldnn.conv.fp32_precision = "tf32" ``` Related kernel update and UTs update are done. And the wrapper `bf32_on_and _off` is updated to `reduced_f32_on_and_off`, and it can run tests 3 times, one is reduced_f32 OFF, the other two are reduced_f32 ON (including `bf32 ON` and `tf32 ON`). Pull Request resolved: https://github.com/pytorch/pytorch/pull/157520 Approved by: https://github.com/mingfeima, https://github.com/jansel
110 lines
3.9 KiB
ReStructuredText
110 lines
3.9 KiB
ReStructuredText
.. meta::
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:description: A guide to torch.backends.mkldnn, a PyTorch backend to run MKLDNN operations
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:keywords: optimize PyTorch, MKLDNN
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.. _mkldnn_backend:
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MKLDNN backend
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---------------------------------------------------
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MKLDNN is an open-source cross-platform performance library of basic building blocks
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for deep learning applications.
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.. code:: python
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# The flag below controls whether enable MKLDNN backend in Pytorch.
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torch.backends.mkldnn.enabled = True
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Users can disable MKLDNN backend by:
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.. code:: python
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torch.backends.mkldnn.enabled = False
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.. _bf16_on_mkldnn:
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Bfloat16 (BF16) on MKLDNN backend
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---------------------------------------------------
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Starting in PyTorch 2.9, there is a set of APIs to control the internal computation precision
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for `float32` operators.
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.. code:: python
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# The flag below controls the internal computation precision for mkldnn matmul. Default ieee is float32.
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torch.backends.mkldnn.matmul.fp32_precision = "ieee"
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# The flag below controls the internal computation precision for mkldnn conv. Default ieee is float32.
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torch.backends.mkldnn.conv.fp32_precision = "ieee"
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# The flag below controls the internal computation precision for mkldnn rnn. Default ieee is float32.
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torch.backends.mkldnn.rnn.fp32_precision = "ieee"
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Note that besides matmuls and convolutions themselves, functions and nn modules that internally uses
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matmuls or convolutions are also affected. These include :class:`torch.nn.Linear`, :class:`torch.nn._ConvNd`, :func:`torch.cdist`,
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:func:`torch.tensordot`, :func:`torch.nn.functional.affine_grid` and :func:`torch.nn.functional.grid_sample`,
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:class:`torch.nn.AdaptiveLogSoftmaxWithLoss`, :class:`torch.nn.GRU` and :class:`torch.nn.LSTM`.
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To get an idea of the precision and speed, see the example code and benchmark data (on SPR) below:
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.. code:: python
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torch.manual_seed(0)
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a_full = torch.randn(10240, 10240, dtype=torch.double)
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b_full = torch.randn(10240, 10240, dtype=torch.double)
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ab_full = a_full @ b_full
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mean = ab_full.abs().mean() # 80.7451
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a = a_full.float()
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b = b_full.float()
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# Do matmul at BF16 mode.
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torch.backends.mkldnn.matmul.fp32_precision = 'bf16'
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ab_bf16 = a @ b # expected speedup with BF16 dot-product acceleration
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error = (ab_bf16 - ab_full).abs().max() # 1.3704
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relative_error = error / mean # 0.0170
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print(error, relative_error)
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# Do matmul at TF32 mode.
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torch.backends.mkldnn.matmul.fp32_precision = 'tf32'
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ab_tf32 = a @ b # expected speedup with TF32 dot-product acceleration
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error = (ab_tf32 - ab_full).abs().max() # 0.0004
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relative_error = error / mean # 0.00000552
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print(error, relative_error)
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# Do matmul FP32 mode.
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torch.backends.mkldnn.matmul.fp32_precision = 'ieee'
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ab_fp32 = a @ b
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error = (ab_fp32 - ab_full).abs().max() # 0.0003
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relative_error = error / mean # 0.00000317
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print(error, relative_error)
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From the above example, we can see that with BF16, the speed is ~7x faster on SPR, and that
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relative error compared to double precision is approximately 2 orders of magnitude larger.
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If full FP32 precision is needed, users can disable BF16 by:
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.. code:: python
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torch.backends.mkldnn.matmul.fp32_precision = 'ieee'
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torch.backends.mkldnn.conv.fp32_precision = 'ieee'
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torch.backends.mkldnn.rnn.fp32_precision = 'ieee'
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To toggle the BF16 flags off in C++, you can do
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.. code:: C++
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at::globalContext().setFloat32Precision("ieee", "mkldnn", "matmul");
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at::globalContext().setFloat32Precision("ieee", "mkldnn", "conv");
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at::globalContext().setFloat32Precision("ieee", "mkldnn", "rnn");
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We can override a generic setting for a specific operator or backend if the fp32_precision is set to `ieee`.
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.. code:: python
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torch.backends.fp32_precision = "bf16"
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torch.backends.mkldnn.fp32_precision = "ieee"
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torch.backends.mkldnn.matmul.fp32_precision = "ieee"
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For such case, both `torch.backends.mkldnn.fp32_precision` and `torch.backends.mkldnn.matmul.fp32_precision`
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is overridden to bf16.
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