[quant][graphmode][fx] Support quantization for standalone module #44074
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Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
💊 CI failures summary and remediationsAs of commit 7dd90f4 (more details on the Dr. CI page): 💚 💚 Looks good so far! There are no failures yet. 💚 💚 This comment was automatically generated by Dr. CI (expand for details).Follow this link to opt-out of these comments for your Pull Requests.Please report bugs/suggestions on the GitHub issue tracker or post in the (internal) Dr. CI Users group. This comment has been revised 194 times. |
…ule" Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
…ule" Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
jerryzh168
added a commit
that referenced
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Sep 3, 2020
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: 731a64a128fb5851c9af779b77fc3426319a94ec Pull Request resolved: #44074
…ule" Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
jerryzh168
added a commit
that referenced
this pull request
Sep 8, 2020
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: afdeb1374c9bf0a528ce404616faadfd5f05f77f Pull Request resolved: #44074
…ule" Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642) [ghstack-poisoned]
…ule" Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642) [ghstack-poisoned]
jerryzh168
added a commit
that referenced
this pull request
Sep 9, 2020
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: 47dae57c936d9bae8e2f83485d7ac7c5ef31fb74 Pull Request resolved: #44074
Codecov Report
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… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
register_traceable_module_class(StandaloneModule)
m = ModelThatUsesStandaloneModule()
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
I think we can also put it in qconfig_dict, would that make sense? |
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
register_traceable_module_class(StandaloneModule)
m = ModelThatUsesStandaloneModule()
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
register_traceable_module_class(StandaloneModule)
m = ModelThatUsesStandaloneModule()
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
register_traceable_module_class(StandaloneModule)
m = ModelThatUsesStandaloneModule()
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
class ModelThatUsesStandaloneModule(...):
def __init__(self):
super().__init__()
self.standalone = StandaloneModule()
def forward(self, x):
return self.standalone(x)
m = ModelThatUsesStandaloneModule()
qconfig_dict = {"": qconfig, "standalone_module_name": ["standalone"]}
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
class ModelThatUsesStandaloneModule(...):
def __init__(self):
super().__init__()
self.standalone = StandaloneModule()
def forward(self, x):
return self.standalone(x)
m = ModelThatUsesStandaloneModule()
qconfig_dict = {"": qconfig, "standalone_module_name": ["standalone"]}
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
class ModelThatUsesStandaloneModule(...):
def __init__(self):
super().__init__()
self.standalone = StandaloneModule()
def forward(self, x):
return self.standalone(x)
m = ModelThatUsesStandaloneModule()
qconfig_dict = {"": qconfig, "standalone_module_name": ["standalone"]}
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
jerryzh168
commented
Sep 25, 2020
torch/fx/graph_module.py
Outdated
| @@ -194,10 +194,20 @@ def __reduce__(self): | |||
| def __deepcopy__(self, memo): | |||
| fake_mod = torch.nn.Module() | |||
| fake_mod.__dict__ = copy.deepcopy(self.__dict__) | |||
| return GraphModule(fake_mod, self.graph) | |||
| graph_module = GraphModule(fake_mod, self.graph) | |||
There was a problem hiding this comment.
these are changes from #45182, will rebase on master after it is landed.
vkuzo
approved these changes
Sep 25, 2020
torch/quantization/fx/quantize.py
Outdated
| @@ -199,7 +206,7 @@ def get_qconfig(module): | |||
| elif node.op == 'call_module': | |||
| self.qconfig_map[node.name] = get_qconfig(self.modules[node.target]) | |||
|
|
|||
| def _prepare(self, model, qconfig_dict, inplace, is_dynamic_quant): | |||
| def _prepare(self, model, qconfig_dict, inplace, is_dynamic_quant, is_child_module): | |||
There was a problem hiding this comment.
repeating docs is worth it in some cases, at least IMO. Up to you though.
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
class ModelThatUsesStandaloneModule(...):
def __init__(self):
super().__init__()
self.standalone = StandaloneModule()
def forward(self, x):
return self.standalone(x)
m = ModelThatUsesStandaloneModule()
qconfig_dict = {"": qconfig, "standalone_module_name": ["standalone"]}
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
class ModelThatUsesStandaloneModule(...):
def __init__(self):
super().__init__()
self.standalone = StandaloneModule()
def forward(self, x):
return self.standalone(x)
m = ModelThatUsesStandaloneModule()
qconfig_dict = {"": qconfig, "standalone_module_name": ["standalone"]}
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
class ModelThatUsesStandaloneModule(...):
def __init__(self):
super().__init__()
self.standalone = StandaloneModule()
def forward(self, x):
return self.standalone(x)
m = ModelThatUsesStandaloneModule()
qconfig_dict = {"": qconfig, "standalone_module_name": ["standalone"]}
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
jerryzh168
commented
Sep 29, 2020
| @@ -0,0 +1,27 @@ | |||
| from torch.fx import GraphModule | |||
|
|
|||
| class ObservedStandaloneGraphModule(GraphModule): | |||
There was a problem hiding this comment.
@jamesr66a @zdevito is this what we want?
There was a problem hiding this comment.
This looks fine to me in the absence of being able to directly customize what symbolic_trace returns. I've filed an issue to track that: #45534
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
class ModelThatUsesStandaloneModule(...):
def __init__(self):
super().__init__()
self.standalone = StandaloneModule()
def forward(self, x):
return self.standalone(x)
m = ModelThatUsesStandaloneModule()
qconfig_dict = {"": qconfig, "standalone_module_name": ["standalone"]}
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
class ModelThatUsesStandaloneModule(...):
def __init__(self):
super().__init__()
self.standalone = StandaloneModule()
def forward(self, x):
return self.standalone(x)
m = ModelThatUsesStandaloneModule()
qconfig_dict = {"": qconfig, "standalone_module_name": ["standalone"]}
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
class ModelThatUsesStandaloneModule(...):
def __init__(self):
super().__init__()
self.standalone = StandaloneModule()
def forward(self, x):
return self.standalone(x)
m = ModelThatUsesStandaloneModule()
qconfig_dict = {"": qconfig, "standalone_module_name": ["standalone"]}
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
class ModelThatUsesStandaloneModule(...):
def __init__(self):
super().__init__()
self.standalone = StandaloneModule()
def forward(self, x):
return self.standalone(x)
m = ModelThatUsesStandaloneModule()
qconfig_dict = {"": qconfig, "standalone_module_name": ["standalone"]}
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
… module"
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
```python
class StandaloneModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
class CustomTracer(Tracer):
def is_leaf_module(self, m, module_qualified_name):
return (m.__module__.startswith('torch.nn') and
not isinstance(m, torch.nn.Sequential)) or \
isinstance(m, StandaloneModule)
class ModelThatUsesStandaloneModule(...):
def __init__(self):
super().__init__()
self.standalone = StandaloneModule()
def forward(self, x):
return self.standalone(x)
m = ModelThatUsesStandaloneModule()
qconfig_dict = {"": qconfig, "standalone_module_name": ["standalone"]}
m = prepare_fx(m, qconfig_dict)
calibrate(m, data)
m = convert_fx(m)
m.standalone = lower_to_acclerator(m.standalone)
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D23580642](https://our.internmc.facebook.com/intern/diff/D23580642)
[ghstack-poisoned]
This was referenced Sep 29, 2020
|
This pull request has been merged in 5539066. |
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Stack from ghstack:
Summary:
Sometimes user need to quantize a submodule as one unit, and this submodule
will be lowered to a different backend like accelerator.
The submodule will be quantized with the same fx based graph mode quantization functions
and will be connected with the rest of the model automatically.
APIs:
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: D23580642