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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/48038 nn.ReLU works for both float and quantized input, we don't want to define an nn.quantized.ReLU that does the same thing as nn.ReLU, similarly for nn.quantized.functional.relu this also removes the numerical inconsistency for models quantizes nn.ReLU independently in qat mode Test Plan: Imported from OSS Imported from OSS Reviewed By: vkuzo Differential Revision: D25000462 fbshipit-source-id: e3609a3ae4a3476a42f61276619033054194a0d2
327 lines
16 KiB
ReStructuredText
327 lines
16 KiB
ReStructuredText
Quantization Operation coverage
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-------------------------------
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Quantized Tensors support a limited subset of data manipulation methods of the
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regular full-precision tensor. For NN operators included in PyTorch, we
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restrict support to:
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1. 8 bit weights (data\_type = qint8)
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2. 8 bit activations (data\_type = quint8)
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Note that operator implementations currently only
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support per channel quantization for weights of the **conv** and **linear**
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operators. Furthermore the minimum and the maximum of the input data is
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mapped linearly to the minimum and the maximum of the quantized data
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type such that zero is represented with no quantization error.
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Additional data types and quantization schemes can be implemented through
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the `custom operator mechanism <https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html>`_.
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Many operations for quantized tensors are available under the same API as full
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float version in ``torch`` or ``torch.nn``. Quantized version of NN modules that
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perform re-quantization are available in ``torch.nn.quantized``. Those
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operations explicitly take output quantization parameters (scale and zero\_point) in
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the operation signature.
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In addition, we also support fused versions corresponding to common fusion
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patterns that impact quantization at: `torch.nn.intrinsic.quantized`.
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For quantization aware training, we support modules prepared for quantization
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aware training at `torch.nn.qat` and `torch.nn.intrinsic.qat`
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.. end-of-part-included-in-quantization.rst
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The following operation list is sufficient to cover typical CNN and RNN models
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Quantized ``torch.Tensor`` operations
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Operations that are available from the ``torch`` namespace or as methods on
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Tensor for quantized tensors:
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* :func:`~torch.quantize_per_tensor` - Convert float tensor to quantized tensor
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with per-tensor scale and zero point
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* :func:`~torch.quantize_per_channel` - Convert float tensor to quantized
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tensor with per-channel scale and zero point
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* View-based operations like :meth:`~torch.Tensor.view`,
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:meth:`~torch.Tensor.as_strided`, :meth:`~torch.Tensor.expand`,
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:meth:`~torch.Tensor.flatten`, :meth:`~torch.Tensor.select`, python-style
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indexing, etc - work as on regular tensor (if quantization is not
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per-channel)
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* Comparators
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* :meth:`~torch.Tensor.ne` — Not equal
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* :meth:`~torch.Tensor.eq` — Equal
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* :meth:`~torch.Tensor.ge` — Greater or equal
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* :meth:`~torch.Tensor.le` — Less or equal
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* :meth:`~torch.Tensor.gt` — Greater
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* :meth:`~torch.Tensor.lt` — Less
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* :meth:`~torch.Tensor.copy_` — Copies src to self in-place
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* :meth:`~torch.Tensor.clone` — Returns a deep copy of the passed-in tensor
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* :meth:`~torch.Tensor.dequantize` — Convert quantized tensor to float tensor
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* :meth:`~torch.Tensor.equal` — Compares two tensors, returns true if
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quantization parameters and all integer elements are the same
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* :meth:`~torch.Tensor.int_repr` — Prints the underlying integer representation
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of the quantized tensor
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* :meth:`~torch.Tensor.max` — Returns the maximum value of the tensor (reduction only)
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* :meth:`~torch.Tensor.mean` — Mean function. Supported variants: reduction, dim, out
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* :meth:`~torch.Tensor.min` — Returns the minimum value of the tensor (reduction only)
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* :meth:`~torch.Tensor.q_scale` — Returns the scale of the per-tensor quantized tensor
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* :meth:`~torch.Tensor.q_zero_point` — Returns the zero_point of the per-tensor
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quantized zero point
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* :meth:`~torch.Tensor.q_per_channel_scales` — Returns the scales of the
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per-channel quantized tensor
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* :meth:`~torch.Tensor.q_per_channel_zero_points` — Returns the zero points of
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the per-channel quantized tensor
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* :meth:`~torch.Tensor.q_per_channel_axis` — Returns the channel axis of the
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per-channel quantized tensor
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* :meth:`~torch.Tensor.resize_` — In-place resize
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* :meth:`~torch.Tensor.sort` — Sorts the tensor
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* :meth:`~torch.Tensor.topk` — Returns k largest values of a tensor
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``torch.nn.functional``
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~~~~~~~~~~~~~~~~~~~~~~~
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Basic activations are supported.
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* :meth:`~torch.nn.functional.relu` — Rectified linear unit (copy)
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* :meth:`~torch.nn.functional.relu_` — Rectified linear unit (inplace)
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* :meth:`~torch.nn.functional.elu` - ELU
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* :meth:`~torch.nn.functional.max_pool2d` - Maximum pooling
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* :meth:`~torch.nn.functional.adaptive_avg_pool2d` - Adaptive average pooling
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* :meth:`~torch.nn.functional.avg_pool2d` - Average pooling
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* :meth:`~torch.nn.functional.interpolate` - Interpolation
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* :meth:`~torch.nn.functional.hardsigmoid` - Hardsigmoid
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* :meth:`~torch.nn.functional.hardswish` - Hardswish
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* :meth:`~torch.nn.functional.hardtanh` - Hardtanh
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* :meth:`~torch.nn.functional.upsample` - Upsampling
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* :meth:`~torch.nn.functional.upsample_bilinear` - Bilinear Upsampling
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* :meth:`~torch.nn.functional.upsample_nearest` - Upsampling Nearest
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``torch.nn.intrinsic``
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~~~~~~~~~~~~~~~~~~~~~~
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Fused modules are provided for common patterns in CNNs. Combining several
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operations together (like convolution and relu) allows for better quantization
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accuracy
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* `torch.nn.intrinsic` — float versions of the modules, can be swapped with
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quantized version 1 to 1:
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* :class:`~torch.nn.intrinsic.ConvBn1d` — Conv1d + BatchNorm1d
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* :class:`~torch.nn.intrinsic.ConvBn2d` — Conv2d + BatchNorm
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* :class:`~torch.nn.intrinsic.ConvBnReLU1d` — Conv1d + BatchNorm1d + ReLU
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* :class:`~torch.nn.intrinsic.ConvBnReLU2d` — Conv2d + BatchNorm + ReLU
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* :class:`~torch.nn.intrinsic.ConvReLU1d` — Conv1d + ReLU
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* :class:`~torch.nn.intrinsic.ConvReLU2d` — Conv2d + ReLU
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* :class:`~torch.nn.intrinsic.ConvReLU3d` — Conv3d + ReLU
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* :class:`~torch.nn.intrinsic.LinearReLU` — Linear + ReLU
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* `torch.nn.intrinsic.qat` — versions of layers for quantization-aware training:
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* :class:`~torch.nn.intrinsic.qat.ConvBn2d` — Conv2d + BatchNorm
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* :class:`~torch.nn.intrinsic.qat.ConvBnReLU2d` — Conv2d + BatchNorm + ReLU
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* :class:`~torch.nn.intrinsic.qat.ConvReLU2d` — Conv2d + ReLU
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* :class:`~torch.nn.intrinsic.qat.LinearReLU` — Linear + ReLU
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* `torch.nn.intrinsic.quantized` — quantized version of fused layers for
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inference (no BatchNorm variants as it's usually folded into convolution for
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inference):
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* :class:`~torch.nn.intrinsic.quantized.LinearReLU` — Linear + ReLU
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* :class:`~torch.nn.intrinsic.quantized.ConvReLU1d` — 1D Convolution + ReLU
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* :class:`~torch.nn.intrinsic.quantized.ConvReLU2d` — 2D Convolution + ReLU
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* :class:`~torch.nn.intrinsic.quantized.ConvReLU3d` — 3D Convolution + ReLU
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`torch.nn.qat`
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~~~~~~~~~~~~~~
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Layers for the quantization-aware training
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* :class:`~torch.nn.qat.Linear` — Linear (fully-connected) layer
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* :class:`~torch.nn.qat.Conv2d` — 2D convolution
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`torch.quantization`
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~~~~~~~~~~~~~~~~~~~~
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* Functions for eager mode quantization:
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* :func:`~torch.quantization.add_observer_` — Adds observer for the leaf
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modules (if quantization configuration is provided)
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* :func:`~torch.quantization.add_quant_dequant`— Wraps the leaf child module using :class:`~torch.quantization.QuantWrapper`
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* :func:`~torch.quantization.convert` — Converts float module with
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observers into its quantized counterpart. Must have quantization
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configuration
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* :func:`~torch.quantization.get_observer_dict` — Traverses the module
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children and collects all observers into a ``dict``
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* :func:`~torch.quantization.prepare` — Prepares a copy of a model for
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quantization
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* :func:`~torch.quantization.prepare_qat` — Prepares a copy of a model for
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quantization aware training
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* :func:`~torch.quantization.propagate_qconfig_` — Propagates quantization
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configurations through the module hierarchy and assign them to each leaf
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module
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* :func:`~torch.quantization.quantize` — Function for eager mode post training static quantization
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* :func:`~torch.quantization.quantize_dynamic` — Function for eager mode post training dynamic quantization
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* :func:`~torch.quantization.quantize_qat` — Function for eager mode quantization aware training function
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* :func:`~torch.quantization.swap_module` — Swaps the module with its
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quantized counterpart (if quantizable and if it has an observer)
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* :func:`~torch.quantization.default_eval_fn` — Default evaluation function
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used by the :func:`torch.quantization.quantize`
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* :func:`~torch.quantization.fuse_modules`
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* Functions for graph mode quantization:
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* :func:`~torch.quantization.quantize_jit` - Function for graph mode post training static quantization
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* :func:`~torch.quantization.quantize_dynamic_jit` - Function for graph mode post training dynamic quantization
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* Quantization configurations
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* :class:`~torch.quantization.QConfig` — Quantization configuration class
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* :attr:`~torch.quantization.default_qconfig` — Same as
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``QConfig(activation=default_observer, weight=default_weight_observer)``
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(See :class:`~torch.quantization.qconfig.QConfig`)
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* :attr:`~torch.quantization.default_qat_qconfig` — Same as
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``QConfig(activation=default_fake_quant,
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weight=default_weight_fake_quant)`` (See
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:class:`~torch.quantization.qconfig.QConfig`)
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* :attr:`~torch.quantization.default_dynamic_qconfig` — Same as
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``QConfigDynamic(weight=default_weight_observer)`` (See
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:class:`~torch.quantization.qconfig.QConfigDynamic`)
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* :attr:`~torch.quantization.float16_dynamic_qconfig` — Same as
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``QConfigDynamic(weight=NoopObserver.with_args(dtype=torch.float16))``
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(See :class:`~torch.quantization.qconfig.QConfigDynamic`)
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* Stubs
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* :class:`~torch.quantization.DeQuantStub` - placeholder module for
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dequantize() operation in float-valued models
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* :class:`~torch.quantization.QuantStub` - placeholder module for
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quantize() operation in float-valued models
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* :class:`~torch.quantization.QuantWrapper` — wraps the module to be
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quantized. Inserts the :class:`~torch.quantization.QuantStub` and
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* :class:`~torch.quantization.DeQuantStub`
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* Observers for computing the quantization parameters
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* Default Observers. The rest of observers are available from
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``torch.quantization.observer``:
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* :attr:`~torch.quantization.default_observer` — Same as ``MinMaxObserver.with_args(reduce_range=True)``
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* :attr:`~torch.quantization.default_weight_observer` — Same as ``MinMaxObserver.with_args(dtype=torch.qint8, qscheme=torch.per_tensor_symmetric)``
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* :class:`~torch.quantization.Observer` — Abstract base class for observers
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* :class:`~torch.quantization.MinMaxObserver` — Derives the quantization
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parameters from the running minimum and maximum of the observed tensor inputs
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(per tensor variant)
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* :class:`~torch.quantization.MovingAverageMinMaxObserver` — Derives the
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quantization parameters from the running averages of the minimums and
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maximums of the observed tensor inputs (per tensor variant)
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* :class:`~torch.quantization.PerChannelMinMaxObserver` — Derives the
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quantization parameters from the running minimum and maximum of the observed
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tensor inputs (per channel variant)
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* :class:`~torch.quantization.MovingAveragePerChannelMinMaxObserver` — Derives
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the quantization parameters from the running averages of the minimums and
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maximums of the observed tensor inputs (per channel variant)
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* :class:`~torch.quantization.HistogramObserver` — Derives the quantization
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parameters by creating a histogram of running minimums and maximums.
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* Observers that do not compute the quantization parameters:
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* :class:`~torch.quantization.RecordingObserver` — Records all incoming
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tensors. Used for debugging only.
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* :class:`~torch.quantization.NoopObserver` — Pass-through observer. Used
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for situation when there are no quantization parameters (i.e.
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quantization to ``float16``)
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* FakeQuantize module
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* :class:`~torch.quantization.FakeQuantize` — Module for simulating the
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quantization/dequantization at training time
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`torch.nn.quantized`
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~~~~~~~~~~~~~~~~~~~~
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Quantized version of standard NN layers.
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* :class:`~torch.nn.quantized.Quantize` — Quantization layer, used to
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automatically replace :class:`~torch.quantization.QuantStub`
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* :class:`~torch.nn.quantized.DeQuantize` — Dequantization layer, used to
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replace :class:`~torch.quantization.DeQuantStub`
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* :class:`~torch.nn.quantized.FloatFunctional` — Wrapper class to make
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stateless float operations stateful so that they can be replaced with
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quantized versions
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* :class:`~torch.nn.quantized.QFunctional` — Wrapper class for quantized
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versions of stateless operations like ``torch.add``
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* :class:`~torch.nn.quantized.Conv1d` — 1D convolution
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* :class:`~torch.nn.quantized.Conv2d` — 2D convolution
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* :class:`~torch.nn.quantized.Conv3d` — 3D convolution
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* :class:`~torch.nn.quantized.Linear` — Linear (fully-connected) layer
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* :class:`~torch.nn.MaxPool2d` — 2D max pooling
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* :class:`~torch.nn.quantized.ReLU6` — Rectified linear unit with cut-off at
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quantized representation of 6
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* :class:`~torch.nn.quantized.ELU` — ELU
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* :class:`~torch.nn.quantized.Hardswish` — Hardswish
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* :class:`~torch.nn.quantized.BatchNorm2d` — BatchNorm2d. *Note: this module is usually fused with Conv or Linear. Performance on ARM is not optimized*.
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* :class:`~torch.nn.quantized.BatchNorm3d` — BatchNorm3d. *Note: this module is usually fused with Conv or Linear. Performance on ARM is not optimized*.
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* :class:`~torch.nn.quantized.LayerNorm` — LayerNorm. *Note: performance on ARM is not optimized*.
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* :class:`~torch.nn.quantized.GroupNorm` — GroupNorm. *Note: performance on ARM is not optimized*.
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* :class:`~torch.nn.quantized.InstanceNorm1d` — InstanceNorm1d. *Note: performance on ARM is not optimized*.
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* :class:`~torch.nn.quantized.InstanceNorm2d` — InstanceNorm2d. *Note: performance on ARM is not optimized*.
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* :class:`~torch.nn.quantized.InstanceNorm3d` — InstanceNorm3d. *Note: performance on ARM is not optimized*.
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`torch.nn.quantized.dynamic`
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Layers used in dynamically quantized models (i.e. quantized only on weights)
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* :class:`~torch.nn.quantized.dynamic.Linear` — Linear (fully-connected) layer
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* :class:`~torch.nn.quantized.dynamic.LSTM` — Long-Short Term Memory RNN module
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* :class:`~torch.nn.quantized.dynamic.LSTMCell` — LSTM Cell
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* :class:`~torch.nn.quantized.dynamic.GRUCell` — GRU Cell
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* :class:`~torch.nn.quantized.dynamic.RNNCell` — RNN Cell
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`torch.nn.quantized.functional`
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Functional versions of quantized NN layers (many of them accept explicit
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quantization output parameters)
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* :func:`~torch.nn.quantized.functional.adaptive_avg_pool2d` — 2D adaptive average pooling
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* :func:`~torch.nn.quantized.functional.avg_pool2d` — 2D average pooling
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* :func:`~torch.nn.quantized.functional.avg_pool3d` — 3D average pooling
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* :func:`~torch.nn.quantized.functional.conv1d` — 1D convolution
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* :func:`~torch.nn.quantized.functional.conv2d` — 2D convolution
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* :func:`~torch.nn.quantized.functional.conv3d` — 3D convolution
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* :func:`~torch.nn.quantized.functional.interpolate` — Down-/up- sampler
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* :func:`~torch.nn.quantized.functional.linear` — Linear (fully-connected) op
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* :func:`~torch.nn.quantized.functional.max_pool2d` — 2D max pooling
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* :func:`~torch.nn.quantized.functional.elu` — ELU
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* :func:`~torch.nn.quantized.functional.hardsigmoid` — Hardsigmoid
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* :func:`~torch.nn.quantized.functional.hardswish` — Hardswish
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* :func:`~torch.nn.quantized.functional.hardtanh` — Hardtanh
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* :func:`~torch.nn.quantized.functional.upsample` — Upsampler. Will be
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deprecated in favor of :func:`~torch.nn.quantized.functional.interpolate`
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* :func:`~torch.nn.quantized.functional.upsample_bilinear` — Bilinear
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upsampler. Will be deprecated in favor of
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* :func:`~torch.nn.quantized.functional.interpolate`
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* :func:`~torch.nn.quantized.functional.upsample_nearest` — Nearest neighbor
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upsampler. Will be deprecated in favor of
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* :func:`~torch.nn.quantized.functional.interpolate`
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Quantized dtypes and quantization schemes
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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* :attr:`torch.qscheme` — Type to describe the quantization scheme of a tensor.
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Supported types:
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* :attr:`torch.per_tensor_affine` — per tensor, asymmetric
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* :attr:`torch.per_channel_affine` — per channel, asymmetric
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* :attr:`torch.per_tensor_symmetric` — per tensor, symmetric
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* :attr:`torch.per_channel_symmetric` — per tensor, symmetric
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* ``torch.dtype`` — Type to describe the data. Supported types:
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* :attr:`torch.quint8` — 8-bit unsigned integer
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* :attr:`torch.qint8` — 8-bit signed integer
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* :attr:`torch.qint32` — 32-bit signed integer
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