Add lift scalar to constant tensor pass#8313

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Add lift scalar to constant tensor pass #8313

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90 changes: 90 additions & 0 deletions backends/qualcomm/_passes/lift_constant_scalar_operands.py
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Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,90 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.

import torch
from torch import fx
from torch._subclasses import FakeTensor
from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix

COMPARE_SCALAR_OPS = {
torch.ops.aten.gt.Scalar: torch.ops.aten.gt.Tensor,
torch.ops.aten.lt.Scalar: torch.ops.aten.lt.Tensor,
torch.ops.aten.ge.Scalar: torch.ops.aten.ge.Tensor,
torch.ops.aten.le.Scalar: torch.ops.aten.le.Tensor,
torch.ops.aten.eq.Scalar: torch.ops.aten.eq.Tensor,
torch.ops.aten.ne.Scalar: torch.ops.aten.ne.Tensor,
}


def _not_float_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.float32


def _not_bool_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.bool


def lift_constant_scalar_operands(gm: torch.fx.GraphModule) -> None:
# If the node is add(input, constant) and constant is a scalar, we can lift the constant
# and the annotation for quantization will insert q/dq nodes around the lifted constant.
for n in gm.graph.nodes:
if n.op != "call_function" or n.target not in (
torch.ops.aten.add.Tensor,
torch.ops.aten.sub.Tensor,
torch.ops.aten.mul.Tensor,
torch.ops.aten.div.Tensor,
torch.ops.aten.rsub.Scalar,
torch.ops.aten.add_.Scalar,
) + tuple(COMPARE_SCALAR_OPS.keys()):
continue
const_arg = None
non_const_arg = None
for arg in n.args:
if isinstance(arg, torch.fx.Node):
non_const_arg = arg
else:
const_arg = arg
if non_const_arg is None or const_arg is None:
continue

if _not_float_tensor(n) and _not_bool_tensor(n):
continue

if not _not_float_tensor(n):
tensor_constant = torch.tensor(
[const_arg],
dtype=n.meta["val"].dtype,
device=n.meta["val"].device,
)
else:
tensor_constant = torch.tensor(
[const_arg],
dtype=n.args[0].meta["val"].dtype,
device=n.meta["val"].device,
)
tensor_constant_name = get_new_attr_name_with_prefix("_tensor_constant_")(gm)
gm.register_buffer(tensor_constant_name, tensor_constant)

fake_mode = n.meta["val"].fake_mode
with gm.graph.inserting_before(n):
get_attr_node = gm.graph.get_attr(tensor_constant_name)
get_attr_node.meta["val"] = fake_mode.from_tensor(tensor_constant)

if n.target == torch.ops.aten.rsub.Scalar:
n.args = (get_attr_node, non_const_arg) + n.args[2:]
n.target = torch.ops.aten.sub.Tensor
else:
n.args = (non_const_arg, get_attr_node) + n.args[2:]

if n.target == torch.ops.aten.add_.Scalar:
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = torch.ops.aten.add.Tensor

if n.target in tuple(COMPARE_SCALAR_OPS.keys()):
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = COMPARE_SCALAR_OPS[n.target]

gm.recompile()
4 changes: 4 additions & 0 deletions backends/qualcomm/quantizer/quantizer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -10,6 +10,9 @@
import torch
from executorch.backends.qualcomm._passes.decompose_einsum import DecomposeEinsum
from executorch.backends.qualcomm._passes.decompose_silu import DecomposeSilu
from executorch.backends.qualcomm._passes.lift_constant_scalar_operands import (
lift_constant_scalar_operands,
)
from executorch.backends.qualcomm._passes.recompose_pixel_unshuffle import (
RecomposePixelUnshuffle,
)
Expand DownExpand Up@@ -224,6 +227,7 @@ def transform_for_annotation(self, model: GraphModule) -> GraphModule:
model = DecomposeSilu()(model).graph_module
model = DecomposeEinsum()(model).graph_module
model = ReplaceInfBuffer()(model).graph_module
lift_constant_scalar_operands(model) # Turn scalar into tensor, such that we can annotate it for quantization
return model

def validate(self, model: GraphModule) -> None:
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all \u003cpre\u003e\u003ccode\u003e blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks"); } } catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); } })(); (function(){ try { var __m = "github.com"; var __re = new RegExp('^' + "github\\.com" + '
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Add lift scalar to constant tensor pass#8313

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Add lift scalar to constant tensor pass #8313

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90 changes: 90 additions & 0 deletions backends/qualcomm/_passes/lift_constant_scalar_operands.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,90 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.

import torch
from torch import fx
from torch._subclasses import FakeTensor
from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix

COMPARE_SCALAR_OPS = {
torch.ops.aten.gt.Scalar: torch.ops.aten.gt.Tensor,
torch.ops.aten.lt.Scalar: torch.ops.aten.lt.Tensor,
torch.ops.aten.ge.Scalar: torch.ops.aten.ge.Tensor,
torch.ops.aten.le.Scalar: torch.ops.aten.le.Tensor,
torch.ops.aten.eq.Scalar: torch.ops.aten.eq.Tensor,
torch.ops.aten.ne.Scalar: torch.ops.aten.ne.Tensor,
}


def _not_float_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.float32


def _not_bool_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.bool


def lift_constant_scalar_operands(gm: torch.fx.GraphModule) -> None:
# If the node is add(input, constant) and constant is a scalar, we can lift the constant
# and the annotation for quantization will insert q/dq nodes around the lifted constant.
for n in gm.graph.nodes:
if n.op != "call_function" or n.target not in (
torch.ops.aten.add.Tensor,
torch.ops.aten.sub.Tensor,
torch.ops.aten.mul.Tensor,
torch.ops.aten.div.Tensor,
torch.ops.aten.rsub.Scalar,
torch.ops.aten.add_.Scalar,
) + tuple(COMPARE_SCALAR_OPS.keys()):
continue
const_arg = None
non_const_arg = None
for arg in n.args:
if isinstance(arg, torch.fx.Node):
non_const_arg = arg
else:
const_arg = arg
if non_const_arg is None or const_arg is None:
continue

if _not_float_tensor(n) and _not_bool_tensor(n):
continue

if not _not_float_tensor(n):
tensor_constant = torch.tensor(
[const_arg],
dtype=n.meta["val"].dtype,
device=n.meta["val"].device,
)
else:
tensor_constant = torch.tensor(
[const_arg],
dtype=n.args[0].meta["val"].dtype,
device=n.meta["val"].device,
)
tensor_constant_name = get_new_attr_name_with_prefix("_tensor_constant_")(gm)
gm.register_buffer(tensor_constant_name, tensor_constant)

fake_mode = n.meta["val"].fake_mode
with gm.graph.inserting_before(n):
get_attr_node = gm.graph.get_attr(tensor_constant_name)
get_attr_node.meta["val"] = fake_mode.from_tensor(tensor_constant)

if n.target == torch.ops.aten.rsub.Scalar:
n.args = (get_attr_node, non_const_arg) + n.args[2:]
n.target = torch.ops.aten.sub.Tensor
else:
n.args = (non_const_arg, get_attr_node) + n.args[2:]

if n.target == torch.ops.aten.add_.Scalar:
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = torch.ops.aten.add.Tensor

if n.target in tuple(COMPARE_SCALAR_OPS.keys()):
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = COMPARE_SCALAR_OPS[n.target]

gm.recompile()
4 changes: 4 additions & 0 deletions backends/qualcomm/quantizer/quantizer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -10,6 +10,9 @@
import torch
from executorch.backends.qualcomm._passes.decompose_einsum import DecomposeEinsum
from executorch.backends.qualcomm._passes.decompose_silu import DecomposeSilu
from executorch.backends.qualcomm._passes.lift_constant_scalar_operands import (
lift_constant_scalar_operands,
)
from executorch.backends.qualcomm._passes.recompose_pixel_unshuffle import (
RecomposePixelUnshuffle,
)
Expand DownExpand Up@@ -224,6 +227,7 @@ def transform_for_annotation(self, model: GraphModule) -> GraphModule:
model = DecomposeSilu()(model).graph_module
model = DecomposeEinsum()(model).graph_module
model = ReplaceInfBuffer()(model).graph_module
lift_constant_scalar_operands(model) # Turn scalar into tensor, such that we can annotate it for quantization
return model

def validate(self, model: GraphModule) -> None:
Expand Down
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Add lift scalar to constant tensor pass#8313

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Add lift scalar to constant tensor pass #8313

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90 changes: 90 additions & 0 deletions backends/qualcomm/_passes/lift_constant_scalar_operands.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,90 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.

import torch
from torch import fx
from torch._subclasses import FakeTensor
from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix

COMPARE_SCALAR_OPS = {
torch.ops.aten.gt.Scalar: torch.ops.aten.gt.Tensor,
torch.ops.aten.lt.Scalar: torch.ops.aten.lt.Tensor,
torch.ops.aten.ge.Scalar: torch.ops.aten.ge.Tensor,
torch.ops.aten.le.Scalar: torch.ops.aten.le.Tensor,
torch.ops.aten.eq.Scalar: torch.ops.aten.eq.Tensor,
torch.ops.aten.ne.Scalar: torch.ops.aten.ne.Tensor,
}


def _not_float_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.float32


def _not_bool_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.bool


def lift_constant_scalar_operands(gm: torch.fx.GraphModule) -> None:
# If the node is add(input, constant) and constant is a scalar, we can lift the constant
# and the annotation for quantization will insert q/dq nodes around the lifted constant.
for n in gm.graph.nodes:
if n.op != "call_function" or n.target not in (
torch.ops.aten.add.Tensor,
torch.ops.aten.sub.Tensor,
torch.ops.aten.mul.Tensor,
torch.ops.aten.div.Tensor,
torch.ops.aten.rsub.Scalar,
torch.ops.aten.add_.Scalar,
) + tuple(COMPARE_SCALAR_OPS.keys()):
continue
const_arg = None
non_const_arg = None
for arg in n.args:
if isinstance(arg, torch.fx.Node):
non_const_arg = arg
else:
const_arg = arg
if non_const_arg is None or const_arg is None:
continue

if _not_float_tensor(n) and _not_bool_tensor(n):
continue

if not _not_float_tensor(n):
tensor_constant = torch.tensor(
[const_arg],
dtype=n.meta["val"].dtype,
device=n.meta["val"].device,
)
else:
tensor_constant = torch.tensor(
[const_arg],
dtype=n.args[0].meta["val"].dtype,
device=n.meta["val"].device,
)
tensor_constant_name = get_new_attr_name_with_prefix("_tensor_constant_")(gm)
gm.register_buffer(tensor_constant_name, tensor_constant)

fake_mode = n.meta["val"].fake_mode
with gm.graph.inserting_before(n):
get_attr_node = gm.graph.get_attr(tensor_constant_name)
get_attr_node.meta["val"] = fake_mode.from_tensor(tensor_constant)

if n.target == torch.ops.aten.rsub.Scalar:
n.args = (get_attr_node, non_const_arg) + n.args[2:]
n.target = torch.ops.aten.sub.Tensor
else:
n.args = (non_const_arg, get_attr_node) + n.args[2:]

if n.target == torch.ops.aten.add_.Scalar:
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = torch.ops.aten.add.Tensor

if n.target in tuple(COMPARE_SCALAR_OPS.keys()):
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = COMPARE_SCALAR_OPS[n.target]

gm.recompile()
4 changes: 4 additions & 0 deletions backends/qualcomm/quantizer/quantizer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -10,6 +10,9 @@
import torch
from executorch.backends.qualcomm._passes.decompose_einsum import DecomposeEinsum
from executorch.backends.qualcomm._passes.decompose_silu import DecomposeSilu
from executorch.backends.qualcomm._passes.lift_constant_scalar_operands import (
lift_constant_scalar_operands,
)
from executorch.backends.qualcomm._passes.recompose_pixel_unshuffle import (
RecomposePixelUnshuffle,
)
Expand DownExpand Up@@ -224,6 +227,7 @@ def transform_for_annotation(self, model: GraphModule) -> GraphModule:
model = DecomposeSilu()(model).graph_module
model = DecomposeEinsum()(model).graph_module
model = ReplaceInfBuffer()(model).graph_module
lift_constant_scalar_operands(model) # Turn scalar into tensor, such that we can annotate it for quantization
return model

def validate(self, model: GraphModule) -> None:
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length \u003e 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Add lift scalar to constant tensor pass#8313

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Add lift scalar to constant tensor pass #8313

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90 changes: 90 additions & 0 deletions backends/qualcomm/_passes/lift_constant_scalar_operands.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,90 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.

import torch
from torch import fx
from torch._subclasses import FakeTensor
from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix

COMPARE_SCALAR_OPS = {
torch.ops.aten.gt.Scalar: torch.ops.aten.gt.Tensor,
torch.ops.aten.lt.Scalar: torch.ops.aten.lt.Tensor,
torch.ops.aten.ge.Scalar: torch.ops.aten.ge.Tensor,
torch.ops.aten.le.Scalar: torch.ops.aten.le.Tensor,
torch.ops.aten.eq.Scalar: torch.ops.aten.eq.Tensor,
torch.ops.aten.ne.Scalar: torch.ops.aten.ne.Tensor,
}


def _not_float_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.float32


def _not_bool_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.bool


def lift_constant_scalar_operands(gm: torch.fx.GraphModule) -> None:
# If the node is add(input, constant) and constant is a scalar, we can lift the constant
# and the annotation for quantization will insert q/dq nodes around the lifted constant.
for n in gm.graph.nodes:
if n.op != "call_function" or n.target not in (
torch.ops.aten.add.Tensor,
torch.ops.aten.sub.Tensor,
torch.ops.aten.mul.Tensor,
torch.ops.aten.div.Tensor,
torch.ops.aten.rsub.Scalar,
torch.ops.aten.add_.Scalar,
) + tuple(COMPARE_SCALAR_OPS.keys()):
continue
const_arg = None
non_const_arg = None
for arg in n.args:
if isinstance(arg, torch.fx.Node):
non_const_arg = arg
else:
const_arg = arg
if non_const_arg is None or const_arg is None:
continue

if _not_float_tensor(n) and _not_bool_tensor(n):
continue

if not _not_float_tensor(n):
tensor_constant = torch.tensor(
[const_arg],
dtype=n.meta["val"].dtype,
device=n.meta["val"].device,
)
else:
tensor_constant = torch.tensor(
[const_arg],
dtype=n.args[0].meta["val"].dtype,
device=n.meta["val"].device,
)
tensor_constant_name = get_new_attr_name_with_prefix("_tensor_constant_")(gm)
gm.register_buffer(tensor_constant_name, tensor_constant)

fake_mode = n.meta["val"].fake_mode
with gm.graph.inserting_before(n):
get_attr_node = gm.graph.get_attr(tensor_constant_name)
get_attr_node.meta["val"] = fake_mode.from_tensor(tensor_constant)

if n.target == torch.ops.aten.rsub.Scalar:
n.args = (get_attr_node, non_const_arg) + n.args[2:]
n.target = torch.ops.aten.sub.Tensor
else:
n.args = (non_const_arg, get_attr_node) + n.args[2:]

if n.target == torch.ops.aten.add_.Scalar:
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = torch.ops.aten.add.Tensor

if n.target in tuple(COMPARE_SCALAR_OPS.keys()):
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = COMPARE_SCALAR_OPS[n.target]

gm.recompile()
4 changes: 4 additions & 0 deletions backends/qualcomm/quantizer/quantizer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -10,6 +10,9 @@
import torch
from executorch.backends.qualcomm._passes.decompose_einsum import DecomposeEinsum
from executorch.backends.qualcomm._passes.decompose_silu import DecomposeSilu
from executorch.backends.qualcomm._passes.lift_constant_scalar_operands import (
lift_constant_scalar_operands,
)
from executorch.backends.qualcomm._passes.recompose_pixel_unshuffle import (
RecomposePixelUnshuffle,
)
Expand DownExpand Up@@ -224,6 +227,7 @@ def transform_for_annotation(self, model: GraphModule) -> GraphModule:
model = DecomposeSilu()(model).graph_module
model = DecomposeEinsum()(model).graph_module
model = ReplaceInfBuffer()(model).graph_module
lift_constant_scalar_operands(model) # Turn scalar into tensor, such that we can annotate it for quantization
return model

def validate(self, model: GraphModule) -> None:
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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Add lift scalar to constant tensor pass#8313

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Add lift scalar to constant tensor pass #8313

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90 changes: 90 additions & 0 deletions backends/qualcomm/_passes/lift_constant_scalar_operands.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,90 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.

import torch
from torch import fx
from torch._subclasses import FakeTensor
from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix

COMPARE_SCALAR_OPS = {
torch.ops.aten.gt.Scalar: torch.ops.aten.gt.Tensor,
torch.ops.aten.lt.Scalar: torch.ops.aten.lt.Tensor,
torch.ops.aten.ge.Scalar: torch.ops.aten.ge.Tensor,
torch.ops.aten.le.Scalar: torch.ops.aten.le.Tensor,
torch.ops.aten.eq.Scalar: torch.ops.aten.eq.Tensor,
torch.ops.aten.ne.Scalar: torch.ops.aten.ne.Tensor,
}


def _not_float_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.float32


def _not_bool_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.bool


def lift_constant_scalar_operands(gm: torch.fx.GraphModule) -> None:
# If the node is add(input, constant) and constant is a scalar, we can lift the constant
# and the annotation for quantization will insert q/dq nodes around the lifted constant.
for n in gm.graph.nodes:
if n.op != "call_function" or n.target not in (
torch.ops.aten.add.Tensor,
torch.ops.aten.sub.Tensor,
torch.ops.aten.mul.Tensor,
torch.ops.aten.div.Tensor,
torch.ops.aten.rsub.Scalar,
torch.ops.aten.add_.Scalar,
) + tuple(COMPARE_SCALAR_OPS.keys()):
continue
const_arg = None
non_const_arg = None
for arg in n.args:
if isinstance(arg, torch.fx.Node):
non_const_arg = arg
else:
const_arg = arg
if non_const_arg is None or const_arg is None:
continue

if _not_float_tensor(n) and _not_bool_tensor(n):
continue

if not _not_float_tensor(n):
tensor_constant = torch.tensor(
[const_arg],
dtype=n.meta["val"].dtype,
device=n.meta["val"].device,
)
else:
tensor_constant = torch.tensor(
[const_arg],
dtype=n.args[0].meta["val"].dtype,
device=n.meta["val"].device,
)
tensor_constant_name = get_new_attr_name_with_prefix("_tensor_constant_")(gm)
gm.register_buffer(tensor_constant_name, tensor_constant)

fake_mode = n.meta["val"].fake_mode
with gm.graph.inserting_before(n):
get_attr_node = gm.graph.get_attr(tensor_constant_name)
get_attr_node.meta["val"] = fake_mode.from_tensor(tensor_constant)

if n.target == torch.ops.aten.rsub.Scalar:
n.args = (get_attr_node, non_const_arg) + n.args[2:]
n.target = torch.ops.aten.sub.Tensor
else:
n.args = (non_const_arg, get_attr_node) + n.args[2:]

if n.target == torch.ops.aten.add_.Scalar:
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = torch.ops.aten.add.Tensor

if n.target in tuple(COMPARE_SCALAR_OPS.keys()):
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = COMPARE_SCALAR_OPS[n.target]

gm.recompile()
4 changes: 4 additions & 0 deletions backends/qualcomm/quantizer/quantizer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -10,6 +10,9 @@
import torch
from executorch.backends.qualcomm._passes.decompose_einsum import DecomposeEinsum
from executorch.backends.qualcomm._passes.decompose_silu import DecomposeSilu
from executorch.backends.qualcomm._passes.lift_constant_scalar_operands import (
lift_constant_scalar_operands,
)
from executorch.backends.qualcomm._passes.recompose_pixel_unshuffle import (
RecomposePixelUnshuffle,
)
Expand DownExpand Up@@ -224,6 +227,7 @@ def transform_for_annotation(self, model: GraphModule) -> GraphModule:
model = DecomposeSilu()(model).graph_module
model = DecomposeEinsum()(model).graph_module
model = ReplaceInfBuffer()(model).graph_module
lift_constant_scalar_operands(model) # Turn scalar into tensor, such that we can annotate it for quantization
return model

def validate(self, model: GraphModule) -> None:
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Add lift scalar to constant tensor pass#8313

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Closed
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Add lift scalar to constant tensor pass #8313

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90 changes: 90 additions & 0 deletions backends/qualcomm/_passes/lift_constant_scalar_operands.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,90 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.

import torch
from torch import fx
from torch._subclasses import FakeTensor
from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix

COMPARE_SCALAR_OPS = {
torch.ops.aten.gt.Scalar: torch.ops.aten.gt.Tensor,
torch.ops.aten.lt.Scalar: torch.ops.aten.lt.Tensor,
torch.ops.aten.ge.Scalar: torch.ops.aten.ge.Tensor,
torch.ops.aten.le.Scalar: torch.ops.aten.le.Tensor,
torch.ops.aten.eq.Scalar: torch.ops.aten.eq.Tensor,
torch.ops.aten.ne.Scalar: torch.ops.aten.ne.Tensor,
}


def _not_float_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.float32


def _not_bool_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.bool


def lift_constant_scalar_operands(gm: torch.fx.GraphModule) -> None:
# If the node is add(input, constant) and constant is a scalar, we can lift the constant
# and the annotation for quantization will insert q/dq nodes around the lifted constant.
for n in gm.graph.nodes:
if n.op != "call_function" or n.target not in (
torch.ops.aten.add.Tensor,
torch.ops.aten.sub.Tensor,
torch.ops.aten.mul.Tensor,
torch.ops.aten.div.Tensor,
torch.ops.aten.rsub.Scalar,
torch.ops.aten.add_.Scalar,
) + tuple(COMPARE_SCALAR_OPS.keys()):
continue
const_arg = None
non_const_arg = None
for arg in n.args:
if isinstance(arg, torch.fx.Node):
non_const_arg = arg
else:
const_arg = arg
if non_const_arg is None or const_arg is None:
continue

if _not_float_tensor(n) and _not_bool_tensor(n):
continue

if not _not_float_tensor(n):
tensor_constant = torch.tensor(
[const_arg],
dtype=n.meta["val"].dtype,
device=n.meta["val"].device,
)
else:
tensor_constant = torch.tensor(
[const_arg],
dtype=n.args[0].meta["val"].dtype,
device=n.meta["val"].device,
)
tensor_constant_name = get_new_attr_name_with_prefix("_tensor_constant_")(gm)
gm.register_buffer(tensor_constant_name, tensor_constant)

fake_mode = n.meta["val"].fake_mode
with gm.graph.inserting_before(n):
get_attr_node = gm.graph.get_attr(tensor_constant_name)
get_attr_node.meta["val"] = fake_mode.from_tensor(tensor_constant)

if n.target == torch.ops.aten.rsub.Scalar:
n.args = (get_attr_node, non_const_arg) + n.args[2:]
n.target = torch.ops.aten.sub.Tensor
else:
n.args = (non_const_arg, get_attr_node) + n.args[2:]

if n.target == torch.ops.aten.add_.Scalar:
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = torch.ops.aten.add.Tensor

if n.target in tuple(COMPARE_SCALAR_OPS.keys()):
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = COMPARE_SCALAR_OPS[n.target]

gm.recompile()
4 changes: 4 additions & 0 deletions backends/qualcomm/quantizer/quantizer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -10,6 +10,9 @@
import torch
from executorch.backends.qualcomm._passes.decompose_einsum import DecomposeEinsum
from executorch.backends.qualcomm._passes.decompose_silu import DecomposeSilu
from executorch.backends.qualcomm._passes.lift_constant_scalar_operands import (
lift_constant_scalar_operands,
)
from executorch.backends.qualcomm._passes.recompose_pixel_unshuffle import (
RecomposePixelUnshuffle,
)
Expand DownExpand Up@@ -224,6 +227,7 @@ def transform_for_annotation(self, model: GraphModule) -> GraphModule:
model = DecomposeSilu()(model).graph_module
model = DecomposeEinsum()(model).graph_module
model = ReplaceInfBuffer()(model).graph_module
lift_constant_scalar_operands(model) # Turn scalar into tensor, such that we can annotate it for quantization
return model

def validate(self, model: GraphModule) -> None:
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Add lift scalar to constant tensor pass#8313

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Closed
cccclai wants to merge 1 commit into from
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Add lift scalar to constant tensor pass #8313

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90 changes: 90 additions & 0 deletions backends/qualcomm/_passes/lift_constant_scalar_operands.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,90 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.

import torch
from torch import fx
from torch._subclasses import FakeTensor
from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix

COMPARE_SCALAR_OPS = {
torch.ops.aten.gt.Scalar: torch.ops.aten.gt.Tensor,
torch.ops.aten.lt.Scalar: torch.ops.aten.lt.Tensor,
torch.ops.aten.ge.Scalar: torch.ops.aten.ge.Tensor,
torch.ops.aten.le.Scalar: torch.ops.aten.le.Tensor,
torch.ops.aten.eq.Scalar: torch.ops.aten.eq.Tensor,
torch.ops.aten.ne.Scalar: torch.ops.aten.ne.Tensor,
}


def _not_float_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.float32


def _not_bool_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.bool


def lift_constant_scalar_operands(gm: torch.fx.GraphModule) -> None:
# If the node is add(input, constant) and constant is a scalar, we can lift the constant
# and the annotation for quantization will insert q/dq nodes around the lifted constant.
for n in gm.graph.nodes:
if n.op != "call_function" or n.target not in (
torch.ops.aten.add.Tensor,
torch.ops.aten.sub.Tensor,
torch.ops.aten.mul.Tensor,
torch.ops.aten.div.Tensor,
torch.ops.aten.rsub.Scalar,
torch.ops.aten.add_.Scalar,
) + tuple(COMPARE_SCALAR_OPS.keys()):
continue
const_arg = None
non_const_arg = None
for arg in n.args:
if isinstance(arg, torch.fx.Node):
non_const_arg = arg
else:
const_arg = arg
if non_const_arg is None or const_arg is None:
continue

if _not_float_tensor(n) and _not_bool_tensor(n):
continue

if not _not_float_tensor(n):
tensor_constant = torch.tensor(
[const_arg],
dtype=n.meta["val"].dtype,
device=n.meta["val"].device,
)
else:
tensor_constant = torch.tensor(
[const_arg],
dtype=n.args[0].meta["val"].dtype,
device=n.meta["val"].device,
)
tensor_constant_name = get_new_attr_name_with_prefix("_tensor_constant_")(gm)
gm.register_buffer(tensor_constant_name, tensor_constant)

fake_mode = n.meta["val"].fake_mode
with gm.graph.inserting_before(n):
get_attr_node = gm.graph.get_attr(tensor_constant_name)
get_attr_node.meta["val"] = fake_mode.from_tensor(tensor_constant)

if n.target == torch.ops.aten.rsub.Scalar:
n.args = (get_attr_node, non_const_arg) + n.args[2:]
n.target = torch.ops.aten.sub.Tensor
else:
n.args = (non_const_arg, get_attr_node) + n.args[2:]

if n.target == torch.ops.aten.add_.Scalar:
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = torch.ops.aten.add.Tensor

if n.target in tuple(COMPARE_SCALAR_OPS.keys()):
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = COMPARE_SCALAR_OPS[n.target]

gm.recompile()
4 changes: 4 additions & 0 deletions backends/qualcomm/quantizer/quantizer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -10,6 +10,9 @@
import torch
from executorch.backends.qualcomm._passes.decompose_einsum import DecomposeEinsum
from executorch.backends.qualcomm._passes.decompose_silu import DecomposeSilu
from executorch.backends.qualcomm._passes.lift_constant_scalar_operands import (
lift_constant_scalar_operands,
)
from executorch.backends.qualcomm._passes.recompose_pixel_unshuffle import (
RecomposePixelUnshuffle,
)
Expand DownExpand Up@@ -224,6 +227,7 @@ def transform_for_annotation(self, model: GraphModule) -> GraphModule:
model = DecomposeSilu()(model).graph_module
model = DecomposeEinsum()(model).graph_module
model = ReplaceInfBuffer()(model).graph_module
lift_constant_scalar_operands(model) # Turn scalar into tensor, such that we can annotate it for quantization
return model

def validate(self, model: GraphModule) -> None:
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
Skip to content

Add lift scalar to constant tensor pass#8313

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Closed
cccclai wants to merge 1 commit into from
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Add lift scalar to constant tensor pass #8313

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90 changes: 90 additions & 0 deletions backends/qualcomm/_passes/lift_constant_scalar_operands.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,90 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.

import torch
from torch import fx
from torch._subclasses import FakeTensor
from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix

COMPARE_SCALAR_OPS = {
torch.ops.aten.gt.Scalar: torch.ops.aten.gt.Tensor,
torch.ops.aten.lt.Scalar: torch.ops.aten.lt.Tensor,
torch.ops.aten.ge.Scalar: torch.ops.aten.ge.Tensor,
torch.ops.aten.le.Scalar: torch.ops.aten.le.Tensor,
torch.ops.aten.eq.Scalar: torch.ops.aten.eq.Tensor,
torch.ops.aten.ne.Scalar: torch.ops.aten.ne.Tensor,
}


def _not_float_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.float32


def _not_bool_tensor(node: fx.Node) -> bool:
if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
return True
return node.meta["val"].dtype != torch.bool


def lift_constant_scalar_operands(gm: torch.fx.GraphModule) -> None:
# If the node is add(input, constant) and constant is a scalar, we can lift the constant
# and the annotation for quantization will insert q/dq nodes around the lifted constant.
for n in gm.graph.nodes:
if n.op != "call_function" or n.target not in (
torch.ops.aten.add.Tensor,
torch.ops.aten.sub.Tensor,
torch.ops.aten.mul.Tensor,
torch.ops.aten.div.Tensor,
torch.ops.aten.rsub.Scalar,
torch.ops.aten.add_.Scalar,
) + tuple(COMPARE_SCALAR_OPS.keys()):
continue
const_arg = None
non_const_arg = None
for arg in n.args:
if isinstance(arg, torch.fx.Node):
non_const_arg = arg
else:
const_arg = arg
if non_const_arg is None or const_arg is None:
continue

if _not_float_tensor(n) and _not_bool_tensor(n):
continue

if not _not_float_tensor(n):
tensor_constant = torch.tensor(
[const_arg],
dtype=n.meta["val"].dtype,
device=n.meta["val"].device,
)
else:
tensor_constant = torch.tensor(
[const_arg],
dtype=n.args[0].meta["val"].dtype,
device=n.meta["val"].device,
)
tensor_constant_name = get_new_attr_name_with_prefix("_tensor_constant_")(gm)
gm.register_buffer(tensor_constant_name, tensor_constant)

fake_mode = n.meta["val"].fake_mode
with gm.graph.inserting_before(n):
get_attr_node = gm.graph.get_attr(tensor_constant_name)
get_attr_node.meta["val"] = fake_mode.from_tensor(tensor_constant)

if n.target == torch.ops.aten.rsub.Scalar:
n.args = (get_attr_node, non_const_arg) + n.args[2:]
n.target = torch.ops.aten.sub.Tensor
else:
n.args = (non_const_arg, get_attr_node) + n.args[2:]

if n.target == torch.ops.aten.add_.Scalar:
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = torch.ops.aten.add.Tensor

if n.target in tuple(COMPARE_SCALAR_OPS.keys()):
n.args = (non_const_arg, get_attr_node) + n.args[2:]
n.target = COMPARE_SCALAR_OPS[n.target]

gm.recompile()
4 changes: 4 additions & 0 deletions backends/qualcomm/quantizer/quantizer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -10,6 +10,9 @@
import torch
from executorch.backends.qualcomm._passes.decompose_einsum import DecomposeEinsum
from executorch.backends.qualcomm._passes.decompose_silu import DecomposeSilu
from executorch.backends.qualcomm._passes.lift_constant_scalar_operands import (
lift_constant_scalar_operands,
)
from executorch.backends.qualcomm._passes.recompose_pixel_unshuffle import (
RecomposePixelUnshuffle,
)
Expand DownExpand Up@@ -224,6 +227,7 @@ def transform_for_annotation(self, model: GraphModule) -> GraphModule:
model = DecomposeSilu()(model).graph_module
model = DecomposeEinsum()(model).graph_module
model = ReplaceInfBuffer()(model).graph_module
lift_constant_scalar_operands(model) # Turn scalar into tensor, such that we can annotate it for quantization
return model

def validate(self, model: GraphModule) -> None:
Expand Down