Quantization in PyTorch 2.0 Export Tutorial¶
Author: Leslie Fang, Weiwen Xia, Jiong Gong, Kimish Patel, Jerry Zhang
Note
Quantization in PyTorch 2.0 export is still a work in progress.
Today we have FX Graph Mode
Quantization
which uses symbolic_trace
to capture the model into a graph, and then
perform quantization transformations on top of the captured model. In a
similar way, for Quantization 2.0 flow, we will now use the PT2 Export
workflow to capture the model into a graph, and perform quantization
transformations on top of the ATen dialect graph. This approach is expected to
have significantly higher model coverage, better programmability, and
a simplified UX.
Prerequisites:¶
Introduction:¶
Previously in FX Graph Mode Quantization
we were using QConfigMapping
for users to specify how the model to be quantized
and BackendConfig
to specify the supported ways of quantization in their backend.
This API covers most use cases relatively well, but the main problem is that this API is not fully extensible
without involvement of the quantization team:
This API has limitation to support advanced quantization intention and complicated quantization operator patterns as in the discussion of Issue-96288 to support
conv add
fusion.This API uses
QConfigMapping
andBackendConfig
as separate object in quantization configuration which may cause confusion about incompatibilities between these two objects. Also these quantization configurations require too much quantization details users need to know which can be hidden from user interface to make it simpler.
To address these issues,
Quantizer
is introduced for quantization in PyTorch 2.0 export. Quantizer
is a class that users can use to
programmatically set the quantization specifications for input and output of each node in the model graph. It adds flexibility
to the quantization API and allows modeling users and backend developers to configure quantization programmatically.
This will allow users to express how they want an operator pattern to be observed in a more explicit
way by annotating the appropriate nodes.
Imagine a backend developer who wishes to integrate a third-party backend with PyTorch’s quantization 2.0 flow. To accomplish this, they would only need to define the backend specific quantizer. A backend specific quantizer inherited from base quantizer. The main method that need to be implemented for the backend specific quantizer is the annotate method which is used to annotate nodes in the graph with QuantizationAnnotation objects to convey the desired way of quantization.
The high level architecture of quantization 2.0 with quantizer could look like this:
float_model(Python) Input
\ /
\ /
—-------------------------------------------------------
| Dynamo Export |
—-------------------------------------------------------
|
FX Graph in ATen QNNPackQuantizer,
| or X86InductorQuantizer,
| or <Other Backend Quantizer>
| /
—--------------------------------------------------------
| prepare_pt2e_quantizer |
—--------------------------------------------------------
|
Calibrate/Train
|
—--------------------------------------------------------
| convert_pt2e |
—--------------------------------------------------------
|
Reference Quantized Model
|
—--------------------------------------------------------
| Lowering |
—--------------------------------------------------------
|
Executorch, or Inductor, or <Other Backends>
Note: prepare_pt2e_quantizer
will be updated to prepare_pt2e
soon.
An existing quantizer object defined for QNNPack/XNNPack is in QNNPackQuantizer. Taking QNNPackQuantizer as an example, the overall Quantization 2.0 flow could be:
import torch
import torch._dynamo as torchdynamo
from torch.ao.quantization._quantize_pt2e import convert_pt2e, prepare_pt2e
import torch.ao.quantization._pt2e.quantizer.qnnpack_quantizer as qq
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(5, 10)
def forward(self, x):
return self.linear(x)
example_inputs = (torch.randn(1, 5),)
model = M().eval()
# Step 1: Trace the model into an FX graph of flattened ATen operators
exported_graph_module, guards = torchdynamo.export(
model,
*copy.deepcopy(example_inputs),
aten_graph=True,
)
# Step 2: Insert observers or fake quantize modules
quantizer = qq.QNNPackQuantizer()
operator_config = qq.get_symmetric_quantization_config(is_per_channel=True)
quantizer.set_global(operator_config)
prepared_graph_module = prepare_pt2e_quantizer(exported_graph_module, quantizer)
# Step 3: Quantize the model
convered_graph_module = convert_pt2e(prepared_graph_module)
# Step 4: Lower Reference Quantized Model into the backend
Annotation API:¶
Quantizer
uses annotation API to convey quantization intent for different operators/patterns.
Annotation API mainly consists of
QuantizationSpec
and
QuantizationAnnotation.
QuantizationSpec
is used to convey intent of how a tensor will be quantized,
e.g. dtype, bitwidth, min, max values, symmetric vs. asymmetric etc.
Furthermore, QuantizationSpec
also allows quantizer to specify how a
tensor value should be observed, e.g. MinMaxObserver
, or HistogramObserver
, or some customized observer.
QuantizationAnnotation
composed of QuantizationSpec
objects is used to annotate input tensors
and output tensor of a pattern. Annotating input tensors is equivalent of annotating input edges,
while annotating output tensor is equivalent of annotating node. QuantizationAnnotation
is a dataclass
with several fields:
input_qspec_map
field is of classDict
to map each input tensor (as input edge) to aQuantizationSpec
.output_qspec
field expresses theQuantizationSpec
used to annotate the output tensor;_annotated
field indicates if this node has already been annotated by quantizer.
To conclude, annotation API requires quantizer to annotate edges (input tensors) or
nodes (output tensor) of the graph. Now, we will have a step-by-step tutorial for
how to use the annotation API with different types of QuantizationSpec
.
1. Annotate common operator patterns¶
In order to use the quantized pattern/operators, e.g. quantized add
,
backend developers will have intent to quantize (as expressed by QuantizationSpec
)
inputs, output of the pattern. Following is an example flow (take add
operator as example)
of how this intent is conveyed in the quantization workflow with annotation API.
Step 1: Identify the original floating point pattern in the FX graph. There are several ways to identify this pattern: Quantizer may use a pattern matcher to match the operator pattern; Quantizer may go through the nodes from start to the end and compare the node’s target type to match the operator pattern. In this example, we can use the get_source_partitions to match this pattern. The original floating point
add
pattern only contain a singleadd
node.
add_partitions = get_source_partitions(gm.graph, [operator.add, torch.add])
add_partitions = list(itertools.chain(*add_partitions.values()))
for add_partition in add_partitions:
add_node = add_partition.output_nodes[0]
Step 2: Define the
QuantizationSpec
for inputs and output of the pattern.QuantizationSpec
defines thedata type
,qscheme
, and other quantization parameters about users’ intent of how to observe or fake quantize a tensor.
act_quantization_spec = QuantizationSpec(
dtype=torch.int8,
quant_min=-128,
quant_max=127,
qscheme=torch.per_tensor_affine,
is_dynamic=False,
observer_or_fake_quant_ctr=HistogramObserver.with_args(eps=2**-12),
)
input_act_qspec = act_quantization_spec
output_act_qspec = act_quantization_spec
Step 3: Annotate the inputs and output of the pattern with
QuantizationAnnotation
. In this example, we will create theQuantizationAnnotation
object with theQuantizationSpec
created in above step 2 for two inputs and one output of theadd
node.
input_qspec_map = {}
input_act0 = add_node.args[0]
input_qspec_map[input_act0] = input_act_qspec
input_act1 = add_node.args[1]
input_qspec_map[input_act1] = input_act_qspec
add_node.meta["quantization_annotation"] = QuantizationAnnotation(
input_qspec_map=input_qspec_map,
output_qspec=output_act_qspec,
_annotated=True,
)
After we annotate the add
node like this, in the following up quantization flow, HistogramObserver
will
be inserted at its two input nodes and one output node in prepare phase. And HistogramObserver
will be substituted with
quantize
node and dequantize
node in the convert phase.
2. Annotate sharing qparams operators¶
It is natural that users want to annotate a quantized model where quantization parameters can be shared among some tensors explicitly. Two typical use cases are:
Example 1: One example is for
add
where having both inputs sharing quantization parameters makes operator implementation much easier. Without using of SharedQuantizationSpec, we must annotateadd
as example in above section 1, in which two inputs ofadd
has different quantization parameters.Example 2: Another example is that of sharing quantization parameters between inputs and output. This typically results from operators such as
maxpool
,average_pool
,concat
etc.
SharedQuantizationSpec
is designed for this use case to annotate tensors whose quantization
parameters are shared with other tensors. Input of SharedQuantizationSpec
is an EdgeOrNode
object which
can be an input edge or an output value.
Input edge is the connection between input node and the node consuming the input, so it’s a
Tuple[Node, Node]
.Output value is an FX
Node
.
Now, if we want to rewrite add
annotation example with SharedQuantizationSpec
to indicate
two input tensors as sharing quantization parameters. We can define its QuantizationAnnotation
as this:
Step 1: Identify the original floating point pattern in the FX graph. We can use the same methods introduced in
QuantizationSpec
example to identify theadd
pattern.Step 2: Annotate input_act0 of
add
withQuantizationSpec
.Step 3: Create a
SharedQuantizationSpec
object with input edge defined as(input_act0, add_node)
which means to share the observer used for this edge. Then, user can annotate input_act1 with thisSharedQuantizationSpec
object.
input_qspec_map = {}
share_qparams_with_input_act0_qspec = SharedQuantizationSpec((input_act0, add_node))
input_qspec_map = {input_act0: act_quantization_spec, input_act1: share_qparams_with_input_act0_qspec}
add_node.meta["quantization_annotation"] = QuantizationAnnotation(
input_qspec_map=input_qspec_map,
output_qspec=act_quantization_spec,
_annotated=True,
)
3. Annotate fixed qparams operators¶
Another typical use case to annotate a quantized model is for tensors whose
quantization parameters are known beforehand. For example, operator like sigmoid
, which has
predefined and fixed scale/zero_point at input and output tensors.
FixedQParamsQuantizationSpec
is designed for this use case. To use FixedQParamsQuantizationSpec
, users need to pass in parameters
of scale
and zero_point
explicitly.
Step 1: Identify the original floating point pattern in the FX graph. We can use the same methods introduced in
QuantizationSpec
example to identify thesigmoid
pattern.Step 2: Create
FixedQParamsQuantizationSpec
object with inputs of fixedscale
,zero_point
value. These values will be used to create thequantize
node anddequantize
node in the convert phase.Step 3: Annotate inputs and output to use this
FixedQParamsQuantizationSpec
object.
act_qspec = FixedQParamsQuantizationSpec(
dtype=torch.uint8,
quant_min=0,
quant_max=255,
qscheme=torch.per_tensor_affine,
scale=1.0 / 256.0,
zero_point=0,
)
sigmoid_node.meta["quantization_annotation"] = QuantizationAnnotation(
input_qspec_map={input_act: act_qspec},
output_qspec=act_qspec,
_annotated=True,
)
4. Annotate tensor with derived quantization parameters¶
Another use case is to define the constraint for tensors whose quantization parameters are derived from other tensors.
For example, if we want to annotate a convolution node, and define the scale
of its bias input tensor
as product of the activation tensor’s scale
and weight tensor’s scale
. We can use
DerivedQuantizationSpec
to annotate this conv node.
Step 1: Identify the original floating point pattern in the FX graph. We can use the same methods introduced in
QuantizationSpec
example to identify theconvolution
pattern.Step 2: Define
derive_qparams_fn
function, it accepts list ofObserverOrFakeQuantize
( ObserverBase or FakeQuantizeBase) as input. From eachObserverOrFakeQuantize
object, user can get thescale
,zero point
value. User can define its heuristic about how to derive newscale
,zero point
value based on the quantization parameters calculated from the observer or fake quant instances.Step 3: Define
DerivedQuantizationSpec
obejct, it accepts inputs of: list ofEdgeOrNode
objects. The observer corresponding to eachEdgeOrNode
object will be passed into thederive_qparams_fn
function;derive_qparams_fn
function; several other quantization parameters such asdtype
,qscheme
.Step 4: Annotate the inputs and output of this conv node with
QuantizationAnnotation
.
def derive_qparams_fn(obs_or_fqs: List[ObserverOrFakeQuantize]) -> Tuple[Tensor, Tensor]:
assert len(obs_or_fqs) == 2, \
"Expecting two obs/fqs, one for activation and one for weight, got: {}".format(len(obs_or_fq))
act_obs_or_fq = obs_or_fqs[0]
weight_obs_or_fq = obs_or_fqs[1]
act_scale, act_zp = act_obs_or_fq.calculate_qparams()
weight_scale, weight_zp = weight_obs_or_fq.calculate_qparams()
return torch.tensor([act_scale * weight_scale]).to(torch.float32), torch.tensor([0]).to(torch.int32)
bias_qspec = DerivedQuantizationSpec(
derived_from=[(input_act, node), (weight, node)],
derive_qparams_fn=derive_qparams_fn,
dtype=torch.int32,
quant_min=-2**31,
quant_max=2**31 - 1,
qscheme=torch.per_tensor_symmetric,
)
input_qspec_map = {input_act: act_quantization_spec, weight: weight_quantization_spec, bias: bias_qspec}
node.meta["quantization_annotation"] = QuantizationAnnotation(
input_qspec_map=input_qspec_map,
output_qspec=act_quantization_spec,
_annotated=True,
)
5. A Toy Example with Resnet18¶
After above annotation methods defined with QuantizationAnnotation API
, we can now put them together to construct a BackendQuantizer
and run a toy example
with Torchvision Resnet18
. To better understand the final example, here are the classes and utility
functions that are used in the example:
QuantizationConfig consists of
QuantizationSpec
for activation, weight, and bias separately.When annotating the model, get_input_act_qspec, get_output_act_qspec, get_weight_qspec, and get_bias_qspec can be used to get the
QuantizationSpec
fromQuantizationConfig
for a specific pattern.
Conclusion¶
With this tutorial, we introduce the new quantization path in PyTorch 2.0. Users can learn about
how to define a BackendQuantizer
with the QuantizationAnnotation API
and integrate it into the quantization 2.0 flow.
Examples of QuantizationSpec
, SharedQuantizationSpec
, FixedQParamsQuantizationSpec
, and DerivedQuantizationSpec
are given for specific annotation use case.