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<p class="caption" role="heading"><span class="caption-text">Developer Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notes/hip.html">HIP (ROCm) semantics</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notes/modules.html">Modules</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/mps.html">MPS backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/multiprocessing.html">Multiprocessing best practices</a></li>
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<section id="quantization-backend-configuration">
<h1>Quantization Backend Configuration<a class="headerlink" href="#quantization-backend-configuration" title="Permalink to this heading">¶</a></h1>
<p>FX Graph Mode Quantization allows the user to configure various
quantization behaviors of an op in order to match the expectation
of their backend.</p>
<p>In the future, this document will contain a detailed spec of
these configurations.</p>
<section id="default-values-for-native-configurations">
<h2>Default values for native configurations<a class="headerlink" href="#default-values-for-native-configurations" title="Permalink to this heading">¶</a></h2>
<p>Below is the output of the configuration for quantization of ops
in fbgemm and qnnpack (PyTorch’s default quantized backends).</p>
<p>Results:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>{
'pattern': <class 'torch.nn.modules.pooling.AdaptiveAvgPool1d'>,
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
},
],
'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
},
{
'pattern': <built-in method adaptive_avg_pool1d of type object at 0x7f870fa24980>,
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
},
],
'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
},
{
'pattern': <class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>,
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
},
],
'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
},
{
'pattern': <function adaptive_avg_pool2d at 0x7f8679535e60>,
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
},
],
'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
},
{
'pattern': <class 'torch.nn.modules.pooling.AdaptiveAvgPool3d'>,
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
},
],
'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
},
{
'pattern': <function adaptive_avg_pool3d at 0x7f8679535ef0>,
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
},
],
'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
},
{
'pattern': (<class 'torch.nn.modules.activation.ReLU'>, <built-in function add>),
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'bias_dtype': torch.float32,
},
],
'num_tensor_args_to_observation_type': {
0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
{
'pattern': (<function relu at 0x7f867953a3b0>, <built-in function add>),
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'bias_dtype': torch.float32,
},
],
'num_tensor_args_to_observation_type': {
0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
{
'pattern': (<built-in method relu of type object at 0x7f870fa24980>, <built-in function add>),
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'bias_dtype': torch.float32,
},
],
'num_tensor_args_to_observation_type': {
0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
{
'pattern': <built-in function add>,
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'bias_dtype': torch.float32,
},
],
'num_tensor_args_to_observation_type': {
0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
{
'pattern': (<class 'torch.nn.modules.activation.ReLU'>, <built-in method add of type object at 0x7f870fa24980>),
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'bias_dtype': torch.float32,
},
],
'num_tensor_args_to_observation_type': {
0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
{
'pattern': (<function relu at 0x7f867953a3b0>, <built-in method add of type object at 0x7f870fa24980>),
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'bias_dtype': torch.float32,
},
],
'num_tensor_args_to_observation_type': {
0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
{
'pattern': (<built-in method relu of type object at 0x7f870fa24980>, <built-in method add of type object at 0x7f870fa24980>),
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'bias_dtype': torch.float32,
},
],
'num_tensor_args_to_observation_type': {
0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
{
'pattern': <built-in method add of type object at 0x7f870fa24980>,
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'bias_dtype': torch.float32,
},
],
'num_tensor_args_to_observation_type': {
0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
{
'pattern': <class 'torch.nn.modules.pooling.AvgPool1d'>,
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
},
],
'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
},
{
'pattern': <built-in method avg_pool1d of type object at 0x7f870fa24980>,
'dtype_configs': [
{
'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None),
},
],
'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
},
{
'pattern': <class 'torch.nn.modules.pooling.AvgPool2d'>,
'dtype_configs': [
{
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