Qualcomm AI Engine Direct - fix for pytorch uplevel#10769

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cccclai merged 3 commits into from
May 20, 2025
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Qualcomm AI Engine Direct - fix for pytorch uplevel #10769

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2e0535a
Qualcomm AI Engine Direct - fix for pytorch uplevel
haowhsu-quic May 5, 2025
9cdc89c
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 15, 2025
186ee40
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 19, 2025
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45 changes: 1 addition & 44 deletions backends/qualcomm/_passes/annotate_quant_attrs.py
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Expand Up@@ -7,22 +7,17 @@
from typing import Any, Dict

import torch
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.builders.utils import get_parameter
from executorch.backends.qualcomm.utils.constants import (
QCOM_AXIS,
QCOM_BLOCK_SIZE,
QCOM_DTYPE,
QCOM_ENCODING,
QCOM_QUANT_ATTRS,
QCOM_QUANT_MAX,
QCOM_QUANT_MIN,
QCOM_REQUANTIZE,
QCOM_SCALE,
QCOM_SCALES,
QCOM_ZERO_POINT,
QCOM_ZERO_POINTS,
)
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import dq_ops, get_quant_attrs, q_ops
Expand DownExpand Up@@ -101,43 +96,9 @@ def _annotate_requant(self, n):
n.args[0].meta.setdefault(QCOM_REQUANTIZE, {})
n.args[0].meta[QCOM_REQUANTIZE][user_node.name] = dq_attrs

# Dequant all the fold_quant parameters back to fp32.
# If an operation is not supported by QNN and got fallback, it will expect a fp32 param.
def _dequant_fold_params(self, n, quant_attrs, param):
if quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.quantized_decomposed.dequantize_per_channel.default
]:
dim, axis = param.dim(), quant_attrs[QCOM_AXIS]
scales = self._expand(quant_attrs[QCOM_SCALES], dim, axis)
offsets = self._expand(quant_attrs[QCOM_ZERO_POINTS], dim, axis)
param = param.sub(offsets).mul(scales).to(torch.float32).contiguous()
elif quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.pt2e_quant.dequantize_affine.default
]:
param = torch.ops.pt2e_quant.dequantize_affine(
param,
block_size=quant_attrs[QCOM_BLOCK_SIZE],
scale=quant_attrs[QCOM_SCALE],
zero_point=quant_attrs[QCOM_ZERO_POINT],
input_dtype=quant_attrs[QCOM_DTYPE],
quant_min=quant_attrs[QCOM_QUANT_MIN],
quant_max=quant_attrs[QCOM_QUANT_MAX],
output_dtype=torch.float32,
)
else:
scale = quant_attrs[QCOM_SCALE]
offset = quant_attrs[QCOM_ZERO_POINT]
param = param.sub(offset).mul(scale).to(torch.float32).contiguous()

set_parameter(param, n.args[0], self.edge_program)
n.args[0].meta["val"] = param

def _annotate_quant_attrs(
self, graph_module: torch.fx.GraphModule
) -> torch.fx.GraphModule:
# Keep track of const params that has been dequant, so it does not get
# dequant multiple times if the const param has more than 1 user
visited_const_param = set()
for n in graph_module.graph.nodes:
self._annotate_requant(n)
# With fold_quant enabled, check if the input of dq op is quantized param.
Expand All@@ -149,10 +110,6 @@ def _annotate_quant_attrs(
quant_attrs = get_quant_attrs(self.edge_program, n)
self._annotate_source_nodes(n, quant_attrs)

if param is not None and n.args[0] not in visited_const_param:
visited_const_param.add(n.args[0])
self._dequant_fold_params(n, quant_attrs, param)

return graph_module

def call(self, graph_module: torch.fx.GraphModule):
Expand Down
125 changes: 94 additions & 31 deletions backends/qualcomm/_passes/convert_conv1d_to_conv2d.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -5,10 +5,8 @@
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_REQUANTIZE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import copy_meta
Expand All@@ -23,16 +21,43 @@ class ConvertConv1dToConv2d(ExportPass):
def __init__(self, edge_program: torch.export.ExportedProgram):
super(ConvertConv1dToConv2d, self).__init__()
self.edge_program = edge_program
self.conv_op_map = {
torch.ops.aten.conv1d.default: torch.ops.aten.conv2d.default,
torch.ops.aten.conv_transpose1d.default: torch.ops.aten.conv_transpose2d.input,
}

def append_qdq(
self,
graph_module: torch.fx.GraphModule,
node: torch.fx.Node,
qdq_node: torch.fx.Node,
):
q_op = torch.ops.quantized_decomposed.quantize_per_tensor.default
dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor.default
if qdq_node.target not in {q_op, dq_op}:
return node

with graph_module.graph.inserting_after(node):
q_args = (node, *qdq_node.args[1:])
q_node = graph_module.graph.create_node("call_function", q_op, q_args)
q_node.meta = copy_meta(node.meta)
q_node.meta["val"] = q_node.meta["val"].to(q_args[-1])
with graph_module.graph.inserting_after(q_node):
dq_args = (q_node, *qdq_node.args[1:])
dq_node = graph_module.graph.create_node(
"call_function", dq_op, dq_args
)
dq_node.meta = copy_meta(node.meta)

return dq_node

def call(self, graph_module: torch.fx.GraphModule):
graph = graph_module.graph
conv_op = exir_ops.edge.aten.convolution.default
for node in graph.nodes:
if node.target == conv_op and node.meta["val"].dim() == 3:

if node.target in self.conv_op_map:
input_node = node.args[0]
with graph_module.graph.inserting_after(input_node):
unsqueeze_op = exir_ops.edge.aten.unsqueeze_copy.default
unsqueeze_op = torch.ops.aten.unsqueeze_copy.default
unsqueeze_node = graph.create_node(
"call_function",
unsqueeze_op,
Expand All@@ -44,52 +69,88 @@ def call(self, graph_module: torch.fx.GraphModule):
unsqueeze_node.meta = copy_meta(
input_node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_unsqueeze = self.append_qdq(
graph_module=graph_module,
node=unsqueeze_node,
qdq_node=input_node,
)

with graph_module.graph.inserting_after(unsqueeze_node):

filter_node = node.args[1]
with graph_module.graph.inserting_after(qdq_node_after_unsqueeze):
filter_arg = node.args[1]
filter_node = (
filter_arg
if filter_arg.op == "placeholder"
else node.args[1].args[0]
)
filter_node.meta["val"] = (
filter_node.meta["val"].unsqueeze(2).contiguous()
)
filter_tensor = get_parameter(filter_node, self.edge_program)
# Ensure tensor is nn.Parameter type, so program does not fail during edge_program._validate()
filter_tensor = nn.Parameter(filter_tensor.unsqueeze(2))
set_parameter(filter_tensor, filter_node, self.edge_program)
filter_tensor = get_parameter(
filter_node, self.edge_program
).unsqueeze(2)
set_parameter(
(
torch.nn.Parameter(filter_tensor)
if filter_tensor.dtype == torch.float
else filter_tensor
),
filter_node,
self.edge_program,
)

num_args = len(node.args)
bias_node = node.args[2]
stride = [1] + node.args[3]
padding = [0] + node.args[4]
dilation = [1] + node.args[5]
transpose = node.args[6]
output_padding = [0] + node.args[7]
groups = node.args[8]

conv2d_node = graph.create_node(
"call_function",
conv_op,
(
unsqueeze_node,
filter_node,
stride = [1] + node.args[3] if num_args > 3 else [1, 1]
padding = [0] + node.args[4] if num_args > 4 else [0, 0]
if node.target == torch.ops.aten.conv1d.default:
dilation = [1] + node.args[5] if num_args > 5 else [1, 1]
groups = node.args[6] if num_args > 5 else 1
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
dilation,
transpose,
groups,
)
else:
output_padding = (
[0] + node.args[5] if num_args > 5 else [0, 0]
)
groups = node.args[6] if num_args > 6 else 1
dilation = [1] + node.args[7] if num_args > 7 else [1, 1]
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
output_padding,
groups,
),
dilation,
)
conv2d_node = graph.create_node(
"call_function",
self.conv_op_map[node.target],
conv_args,
)
conv2d_node.meta = copy_meta(
node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_conv2d = self.append_qdq(
graph_module=graph_module,
node=conv2d_node,
qdq_node=list(node.users)[0],
)

with graph_module.graph.inserting_after(conv2d_node):
squeeze_op = exir_ops.edge.aten.squeeze_copy.dims
with graph_module.graph.inserting_after(qdq_node_after_conv2d):
squeeze_op = torch.ops.aten.squeeze_copy.dims
squeeze_node = graph.create_node(
"call_function",
squeeze_op,
(
conv2d_node,
qdq_node_after_conv2d,
[2],
),
)
Expand All@@ -102,8 +163,10 @@ def call(self, graph_module: torch.fx.GraphModule):
QCOM_REQUANTIZE
]
conv2d_node.meta.pop(QCOM_REQUANTIZE, None)

for user in node.users.copy():
user.replace_input_with(node, squeeze_node)

graph.eliminate_dead_code()
graph_module.recompile()
return PassResult(graph_module, True)
8 changes: 7 additions & 1 deletion backends/qualcomm/_passes/expand_broadcast_tensor_shape.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -9,6 +9,8 @@
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass

from .utils import dq_ops


class ExpandBroadcastTensorShape(ExportPass):
"""
Expand DownExpand Up@@ -45,9 +47,13 @@ def traverse_broadcast_node(self, graph_module: torch.fx.GraphModule):
exir_ops.edge.aten.view_copy.default,
(arg, tuple(new_rank)),
)
# try skip dq_ops to get correct param node if applicable
arg_meta = (
arg.args[0].meta if arg.target in dq_ops else arg.meta
)
# meta needs to be copied elementwisely for fake-tensor
# to be updated correctly and not affect meta of arg
for k, v in arg.meta.items():
for k, v in arg_meta.items():
reshape_node.meta[k] = v
reshape_node.meta["val"] = reshape_node.meta["val"].reshape(
new_rank
Expand Down
30 changes: 24 additions & 6 deletions backends/qualcomm/_passes/fold_qdq.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,6 +4,8 @@
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import torch
from executorch.backends.qualcomm.builders.utils import is_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_BYPASS_NODE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass
Expand All@@ -16,23 +18,38 @@ class FoldQDQ(ExportPass):
Erase QDQ pattern.
"""

def __init__(self):
def __init__(self, edge_program: torch.export.ExportedProgram, force_fold=False):
super(FoldQDQ, self).__init__()
self.edge_program = edge_program
self.force_fold = force_fold

def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
def _annotate_bypass(self, node):
node.meta[QCOM_BYPASS_NODE] = True
for arg in node.args:
if isinstance(arg, torch.fx.Node) and arg.op == "call_function":
self._annotate_bypass(arg)

def _fold_dq(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove dq
for n in graph_module.graph.nodes:
user_list = list(n.users.keys())
if n.target not in dq_ops:
continue
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

# skip parameters & buffers
if not self.force_fold and is_parameter(n.args[0], self.edge_program):
self._annotate_bypass(n)
else:
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

def _fold_q(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove q
for n in graph_module.graph.nodes:
if n.target not in q_ops:
continue

to_be_removed = [n]
source_n = n.args[0]

Expand All@@ -57,7 +74,8 @@ def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
graph_module.graph.erase_node(n)

def call(self, graph_module: torch.fx.GraphModule):
self._fold(graph_module)
self._fold_dq(graph_module)
self._fold_q(graph_module)
graph_module.recompile()
dead_code_elimination_pass(graph_module)
return PassResult(graph_module, True)
4 changes: 4 additions & 0 deletions backends/qualcomm/_passes/qnn_pass_manager.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -200,13 +200,17 @@ def transform_for_export_pipeline(self, exported_program: ExportedProgram):
self.add_pass(DecomposeScaledDotProductAttention())
self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True))
self.add_pass(DecomposeExpM1())
# this pass will rewrite state_dict, it needs to be accomplished before
# to_edge_transform_and_lower
self.add_pass(ConvertConv1dToConv2d(exported_program))
self.add_pass(ConvertSquareToPow())
self.add_pass(LiftConstantScalarOperands())
self._transform(exported_program.graph_module)
ep = lift_constant_tensor_pass(exported_program)
return ep

def transform_for_preprocess_pipeline(self, exported_program: ExportedProgram):
self.add_pass(FoldQDQ(exported_program, force_fold=True))
self.add_pass(InsertRequantize())
self.add_pass(InsertIOQDQ(exported_program))
self.add_pass(LayoutTransform(exported_program, insert_permute=True))
Expand Down
7 changes: 4 additions & 3 deletions backends/qualcomm/_passes/replace_index_put_input.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -33,7 +33,8 @@ def call(self, graph_module: torch.fx.GraphModule):
copy_node := list(node.users)[0]
) and copy_node.target == exir_ops.edge.aten.copy.default:
m_buffer_node = copy_node.args[0]
bad_frozen_node = node.args[0]
dq_node = node.args[0]
bad_frozen_node = dq_node.args[0]
if QCOM_QUANT_ATTRS in bad_frozen_node.meta:
m_buffer_node.meta[QCOM_QUANT_ATTRS] = bad_frozen_node.meta[
QCOM_QUANT_ATTRS
Expand All@@ -43,8 +44,8 @@ def call(self, graph_module: torch.fx.GraphModule):
m_buffer_node.meta[QCOM_QUANT_ATTRS][QCOM_ENCODING]
]
)
with graph.inserting_after(bad_frozen_node):
node.replace_input_with(bad_frozen_node, m_buffer_node)
with graph.inserting_after(dq_node):
node.replace_input_with(dq_node, m_buffer_node)
else:
continue

Expand Down
1 change: 0 additions & 1 deletion backends/qualcomm/_passes/utils.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -103,7 +103,6 @@ def get_passes_dependency_for_capture_program():
AnnotateStack: [RemoveRedundancy],
AnnotateUnbind: [RemoveRedundancy],
ConvertBmmToMatmul: [RecomposePixelUnshuffle],
ConvertConv1dToConv2d: [FoldQDQ],
ConvertUpsampleBicubicWithBilinear: [RemoveRedundancy],
DecomposeAny: [RemoveRedundancy],
DecomposeLinalgVectorNorm: [RemoveRedundancy],
Expand Down
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Qualcomm AI Engine Direct - fix for pytorch uplevel#10769

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Merged
cccclai merged 3 commits into from
May 20, 2025
Merged

Qualcomm AI Engine Direct - fix for pytorch uplevel #10769

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Commits
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3 commits
Select commit Hold shift + click to select a range
2e0535a
Qualcomm AI Engine Direct - fix for pytorch uplevel
haowhsu-quic May 5, 2025
9cdc89c
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 15, 2025
186ee40
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 19, 2025
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45 changes: 1 addition & 44 deletions backends/qualcomm/_passes/annotate_quant_attrs.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -7,22 +7,17 @@
from typing import Any, Dict

import torch
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.builders.utils import get_parameter
from executorch.backends.qualcomm.utils.constants import (
QCOM_AXIS,
QCOM_BLOCK_SIZE,
QCOM_DTYPE,
QCOM_ENCODING,
QCOM_QUANT_ATTRS,
QCOM_QUANT_MAX,
QCOM_QUANT_MIN,
QCOM_REQUANTIZE,
QCOM_SCALE,
QCOM_SCALES,
QCOM_ZERO_POINT,
QCOM_ZERO_POINTS,
)
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import dq_ops, get_quant_attrs, q_ops
Expand DownExpand Up@@ -101,43 +96,9 @@ def _annotate_requant(self, n):
n.args[0].meta.setdefault(QCOM_REQUANTIZE, {})
n.args[0].meta[QCOM_REQUANTIZE][user_node.name] = dq_attrs

# Dequant all the fold_quant parameters back to fp32.
# If an operation is not supported by QNN and got fallback, it will expect a fp32 param.
def _dequant_fold_params(self, n, quant_attrs, param):
if quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.quantized_decomposed.dequantize_per_channel.default
]:
dim, axis = param.dim(), quant_attrs[QCOM_AXIS]
scales = self._expand(quant_attrs[QCOM_SCALES], dim, axis)
offsets = self._expand(quant_attrs[QCOM_ZERO_POINTS], dim, axis)
param = param.sub(offsets).mul(scales).to(torch.float32).contiguous()
elif quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.pt2e_quant.dequantize_affine.default
]:
param = torch.ops.pt2e_quant.dequantize_affine(
param,
block_size=quant_attrs[QCOM_BLOCK_SIZE],
scale=quant_attrs[QCOM_SCALE],
zero_point=quant_attrs[QCOM_ZERO_POINT],
input_dtype=quant_attrs[QCOM_DTYPE],
quant_min=quant_attrs[QCOM_QUANT_MIN],
quant_max=quant_attrs[QCOM_QUANT_MAX],
output_dtype=torch.float32,
)
else:
scale = quant_attrs[QCOM_SCALE]
offset = quant_attrs[QCOM_ZERO_POINT]
param = param.sub(offset).mul(scale).to(torch.float32).contiguous()

set_parameter(param, n.args[0], self.edge_program)
n.args[0].meta["val"] = param

def _annotate_quant_attrs(
self, graph_module: torch.fx.GraphModule
) -> torch.fx.GraphModule:
# Keep track of const params that has been dequant, so it does not get
# dequant multiple times if the const param has more than 1 user
visited_const_param = set()
for n in graph_module.graph.nodes:
self._annotate_requant(n)
# With fold_quant enabled, check if the input of dq op is quantized param.
Expand All@@ -149,10 +110,6 @@ def _annotate_quant_attrs(
quant_attrs = get_quant_attrs(self.edge_program, n)
self._annotate_source_nodes(n, quant_attrs)

if param is not None and n.args[0] not in visited_const_param:
visited_const_param.add(n.args[0])
self._dequant_fold_params(n, quant_attrs, param)

return graph_module

def call(self, graph_module: torch.fx.GraphModule):
Expand Down
125 changes: 94 additions & 31 deletions backends/qualcomm/_passes/convert_conv1d_to_conv2d.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -5,10 +5,8 @@
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_REQUANTIZE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import copy_meta
Expand All@@ -23,16 +21,43 @@ class ConvertConv1dToConv2d(ExportPass):
def __init__(self, edge_program: torch.export.ExportedProgram):
super(ConvertConv1dToConv2d, self).__init__()
self.edge_program = edge_program
self.conv_op_map = {
torch.ops.aten.conv1d.default: torch.ops.aten.conv2d.default,
torch.ops.aten.conv_transpose1d.default: torch.ops.aten.conv_transpose2d.input,
}

def append_qdq(
self,
graph_module: torch.fx.GraphModule,
node: torch.fx.Node,
qdq_node: torch.fx.Node,
):
q_op = torch.ops.quantized_decomposed.quantize_per_tensor.default
dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor.default
if qdq_node.target not in {q_op, dq_op}:
return node

with graph_module.graph.inserting_after(node):
q_args = (node, *qdq_node.args[1:])
q_node = graph_module.graph.create_node("call_function", q_op, q_args)
q_node.meta = copy_meta(node.meta)
q_node.meta["val"] = q_node.meta["val"].to(q_args[-1])
with graph_module.graph.inserting_after(q_node):
dq_args = (q_node, *qdq_node.args[1:])
dq_node = graph_module.graph.create_node(
"call_function", dq_op, dq_args
)
dq_node.meta = copy_meta(node.meta)

return dq_node

def call(self, graph_module: torch.fx.GraphModule):
graph = graph_module.graph
conv_op = exir_ops.edge.aten.convolution.default
for node in graph.nodes:
if node.target == conv_op and node.meta["val"].dim() == 3:

if node.target in self.conv_op_map:
input_node = node.args[0]
with graph_module.graph.inserting_after(input_node):
unsqueeze_op = exir_ops.edge.aten.unsqueeze_copy.default
unsqueeze_op = torch.ops.aten.unsqueeze_copy.default
unsqueeze_node = graph.create_node(
"call_function",
unsqueeze_op,
Expand All@@ -44,52 +69,88 @@ def call(self, graph_module: torch.fx.GraphModule):
unsqueeze_node.meta = copy_meta(
input_node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_unsqueeze = self.append_qdq(
graph_module=graph_module,
node=unsqueeze_node,
qdq_node=input_node,
)

with graph_module.graph.inserting_after(unsqueeze_node):

filter_node = node.args[1]
with graph_module.graph.inserting_after(qdq_node_after_unsqueeze):
filter_arg = node.args[1]
filter_node = (
filter_arg
if filter_arg.op == "placeholder"
else node.args[1].args[0]
)
filter_node.meta["val"] = (
filter_node.meta["val"].unsqueeze(2).contiguous()
)
filter_tensor = get_parameter(filter_node, self.edge_program)
# Ensure tensor is nn.Parameter type, so program does not fail during edge_program._validate()
filter_tensor = nn.Parameter(filter_tensor.unsqueeze(2))
set_parameter(filter_tensor, filter_node, self.edge_program)
filter_tensor = get_parameter(
filter_node, self.edge_program
).unsqueeze(2)
set_parameter(
(
torch.nn.Parameter(filter_tensor)
if filter_tensor.dtype == torch.float
else filter_tensor
),
filter_node,
self.edge_program,
)

num_args = len(node.args)
bias_node = node.args[2]
stride = [1] + node.args[3]
padding = [0] + node.args[4]
dilation = [1] + node.args[5]
transpose = node.args[6]
output_padding = [0] + node.args[7]
groups = node.args[8]

conv2d_node = graph.create_node(
"call_function",
conv_op,
(
unsqueeze_node,
filter_node,
stride = [1] + node.args[3] if num_args > 3 else [1, 1]
padding = [0] + node.args[4] if num_args > 4 else [0, 0]
if node.target == torch.ops.aten.conv1d.default:
dilation = [1] + node.args[5] if num_args > 5 else [1, 1]
groups = node.args[6] if num_args > 5 else 1
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
dilation,
transpose,
groups,
)
else:
output_padding = (
[0] + node.args[5] if num_args > 5 else [0, 0]
)
groups = node.args[6] if num_args > 6 else 1
dilation = [1] + node.args[7] if num_args > 7 else [1, 1]
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
output_padding,
groups,
),
dilation,
)
conv2d_node = graph.create_node(
"call_function",
self.conv_op_map[node.target],
conv_args,
)
conv2d_node.meta = copy_meta(
node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_conv2d = self.append_qdq(
graph_module=graph_module,
node=conv2d_node,
qdq_node=list(node.users)[0],
)

with graph_module.graph.inserting_after(conv2d_node):
squeeze_op = exir_ops.edge.aten.squeeze_copy.dims
with graph_module.graph.inserting_after(qdq_node_after_conv2d):
squeeze_op = torch.ops.aten.squeeze_copy.dims
squeeze_node = graph.create_node(
"call_function",
squeeze_op,
(
conv2d_node,
qdq_node_after_conv2d,
[2],
),
)
Expand All@@ -102,8 +163,10 @@ def call(self, graph_module: torch.fx.GraphModule):
QCOM_REQUANTIZE
]
conv2d_node.meta.pop(QCOM_REQUANTIZE, None)

for user in node.users.copy():
user.replace_input_with(node, squeeze_node)

graph.eliminate_dead_code()
graph_module.recompile()
return PassResult(graph_module, True)
8 changes: 7 additions & 1 deletion backends/qualcomm/_passes/expand_broadcast_tensor_shape.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -9,6 +9,8 @@
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass

from .utils import dq_ops


class ExpandBroadcastTensorShape(ExportPass):
"""
Expand DownExpand Up@@ -45,9 +47,13 @@ def traverse_broadcast_node(self, graph_module: torch.fx.GraphModule):
exir_ops.edge.aten.view_copy.default,
(arg, tuple(new_rank)),
)
# try skip dq_ops to get correct param node if applicable
arg_meta = (
arg.args[0].meta if arg.target in dq_ops else arg.meta
)
# meta needs to be copied elementwisely for fake-tensor
# to be updated correctly and not affect meta of arg
for k, v in arg.meta.items():
for k, v in arg_meta.items():
reshape_node.meta[k] = v
reshape_node.meta["val"] = reshape_node.meta["val"].reshape(
new_rank
Expand Down
30 changes: 24 additions & 6 deletions backends/qualcomm/_passes/fold_qdq.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,6 +4,8 @@
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import torch
from executorch.backends.qualcomm.builders.utils import is_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_BYPASS_NODE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass
Expand All@@ -16,23 +18,38 @@ class FoldQDQ(ExportPass):
Erase QDQ pattern.
"""

def __init__(self):
def __init__(self, edge_program: torch.export.ExportedProgram, force_fold=False):
super(FoldQDQ, self).__init__()
self.edge_program = edge_program
self.force_fold = force_fold

def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
def _annotate_bypass(self, node):
node.meta[QCOM_BYPASS_NODE] = True
for arg in node.args:
if isinstance(arg, torch.fx.Node) and arg.op == "call_function":
self._annotate_bypass(arg)

def _fold_dq(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove dq
for n in graph_module.graph.nodes:
user_list = list(n.users.keys())
if n.target not in dq_ops:
continue
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

# skip parameters & buffers
if not self.force_fold and is_parameter(n.args[0], self.edge_program):
self._annotate_bypass(n)
else:
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

def _fold_q(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove q
for n in graph_module.graph.nodes:
if n.target not in q_ops:
continue

to_be_removed = [n]
source_n = n.args[0]

Expand All@@ -57,7 +74,8 @@ def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
graph_module.graph.erase_node(n)

def call(self, graph_module: torch.fx.GraphModule):
self._fold(graph_module)
self._fold_dq(graph_module)
self._fold_q(graph_module)
graph_module.recompile()
dead_code_elimination_pass(graph_module)
return PassResult(graph_module, True)
4 changes: 4 additions & 0 deletions backends/qualcomm/_passes/qnn_pass_manager.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -200,13 +200,17 @@ def transform_for_export_pipeline(self, exported_program: ExportedProgram):
self.add_pass(DecomposeScaledDotProductAttention())
self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True))
self.add_pass(DecomposeExpM1())
# this pass will rewrite state_dict, it needs to be accomplished before
# to_edge_transform_and_lower
self.add_pass(ConvertConv1dToConv2d(exported_program))
self.add_pass(ConvertSquareToPow())
self.add_pass(LiftConstantScalarOperands())
self._transform(exported_program.graph_module)
ep = lift_constant_tensor_pass(exported_program)
return ep

def transform_for_preprocess_pipeline(self, exported_program: ExportedProgram):
self.add_pass(FoldQDQ(exported_program, force_fold=True))
self.add_pass(InsertRequantize())
self.add_pass(InsertIOQDQ(exported_program))
self.add_pass(LayoutTransform(exported_program, insert_permute=True))
Expand Down
7 changes: 4 additions & 3 deletions backends/qualcomm/_passes/replace_index_put_input.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -33,7 +33,8 @@ def call(self, graph_module: torch.fx.GraphModule):
copy_node := list(node.users)[0]
) and copy_node.target == exir_ops.edge.aten.copy.default:
m_buffer_node = copy_node.args[0]
bad_frozen_node = node.args[0]
dq_node = node.args[0]
bad_frozen_node = dq_node.args[0]
if QCOM_QUANT_ATTRS in bad_frozen_node.meta:
m_buffer_node.meta[QCOM_QUANT_ATTRS] = bad_frozen_node.meta[
QCOM_QUANT_ATTRS
Expand All@@ -43,8 +44,8 @@ def call(self, graph_module: torch.fx.GraphModule):
m_buffer_node.meta[QCOM_QUANT_ATTRS][QCOM_ENCODING]
]
)
with graph.inserting_after(bad_frozen_node):
node.replace_input_with(bad_frozen_node, m_buffer_node)
with graph.inserting_after(dq_node):
node.replace_input_with(dq_node, m_buffer_node)
else:
continue

Expand Down
1 change: 0 additions & 1 deletion backends/qualcomm/_passes/utils.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -103,7 +103,6 @@ def get_passes_dependency_for_capture_program():
AnnotateStack: [RemoveRedundancy],
AnnotateUnbind: [RemoveRedundancy],
ConvertBmmToMatmul: [RecomposePixelUnshuffle],
ConvertConv1dToConv2d: [FoldQDQ],
ConvertUpsampleBicubicWithBilinear: [RemoveRedundancy],
DecomposeAny: [RemoveRedundancy],
DecomposeLinalgVectorNorm: [RemoveRedundancy],
Expand Down
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Qualcomm AI Engine Direct - fix for pytorch uplevel#10769

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Merged
cccclai merged 3 commits into from
May 20, 2025
Merged

Qualcomm AI Engine Direct - fix for pytorch uplevel #10769

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2e0535a
Qualcomm AI Engine Direct - fix for pytorch uplevel
haowhsu-quic May 5, 2025
9cdc89c
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 15, 2025
186ee40
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 19, 2025
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45 changes: 1 addition & 44 deletions backends/qualcomm/_passes/annotate_quant_attrs.py
View file
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Original file line numberDiff line numberDiff line change
Expand Up@@ -7,22 +7,17 @@
from typing import Any, Dict

import torch
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.builders.utils import get_parameter
from executorch.backends.qualcomm.utils.constants import (
QCOM_AXIS,
QCOM_BLOCK_SIZE,
QCOM_DTYPE,
QCOM_ENCODING,
QCOM_QUANT_ATTRS,
QCOM_QUANT_MAX,
QCOM_QUANT_MIN,
QCOM_REQUANTIZE,
QCOM_SCALE,
QCOM_SCALES,
QCOM_ZERO_POINT,
QCOM_ZERO_POINTS,
)
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import dq_ops, get_quant_attrs, q_ops
Expand DownExpand Up@@ -101,43 +96,9 @@ def _annotate_requant(self, n):
n.args[0].meta.setdefault(QCOM_REQUANTIZE, {})
n.args[0].meta[QCOM_REQUANTIZE][user_node.name] = dq_attrs

# Dequant all the fold_quant parameters back to fp32.
# If an operation is not supported by QNN and got fallback, it will expect a fp32 param.
def _dequant_fold_params(self, n, quant_attrs, param):
if quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.quantized_decomposed.dequantize_per_channel.default
]:
dim, axis = param.dim(), quant_attrs[QCOM_AXIS]
scales = self._expand(quant_attrs[QCOM_SCALES], dim, axis)
offsets = self._expand(quant_attrs[QCOM_ZERO_POINTS], dim, axis)
param = param.sub(offsets).mul(scales).to(torch.float32).contiguous()
elif quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.pt2e_quant.dequantize_affine.default
]:
param = torch.ops.pt2e_quant.dequantize_affine(
param,
block_size=quant_attrs[QCOM_BLOCK_SIZE],
scale=quant_attrs[QCOM_SCALE],
zero_point=quant_attrs[QCOM_ZERO_POINT],
input_dtype=quant_attrs[QCOM_DTYPE],
quant_min=quant_attrs[QCOM_QUANT_MIN],
quant_max=quant_attrs[QCOM_QUANT_MAX],
output_dtype=torch.float32,
)
else:
scale = quant_attrs[QCOM_SCALE]
offset = quant_attrs[QCOM_ZERO_POINT]
param = param.sub(offset).mul(scale).to(torch.float32).contiguous()

set_parameter(param, n.args[0], self.edge_program)
n.args[0].meta["val"] = param

def _annotate_quant_attrs(
self, graph_module: torch.fx.GraphModule
) -> torch.fx.GraphModule:
# Keep track of const params that has been dequant, so it does not get
# dequant multiple times if the const param has more than 1 user
visited_const_param = set()
for n in graph_module.graph.nodes:
self._annotate_requant(n)
# With fold_quant enabled, check if the input of dq op is quantized param.
Expand All@@ -149,10 +110,6 @@ def _annotate_quant_attrs(
quant_attrs = get_quant_attrs(self.edge_program, n)
self._annotate_source_nodes(n, quant_attrs)

if param is not None and n.args[0] not in visited_const_param:
visited_const_param.add(n.args[0])
self._dequant_fold_params(n, quant_attrs, param)

return graph_module

def call(self, graph_module: torch.fx.GraphModule):
Expand Down
125 changes: 94 additions & 31 deletions backends/qualcomm/_passes/convert_conv1d_to_conv2d.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -5,10 +5,8 @@
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_REQUANTIZE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import copy_meta
Expand All@@ -23,16 +21,43 @@ class ConvertConv1dToConv2d(ExportPass):
def __init__(self, edge_program: torch.export.ExportedProgram):
super(ConvertConv1dToConv2d, self).__init__()
self.edge_program = edge_program
self.conv_op_map = {
torch.ops.aten.conv1d.default: torch.ops.aten.conv2d.default,
torch.ops.aten.conv_transpose1d.default: torch.ops.aten.conv_transpose2d.input,
}

def append_qdq(
self,
graph_module: torch.fx.GraphModule,
node: torch.fx.Node,
qdq_node: torch.fx.Node,
):
q_op = torch.ops.quantized_decomposed.quantize_per_tensor.default
dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor.default
if qdq_node.target not in {q_op, dq_op}:
return node

with graph_module.graph.inserting_after(node):
q_args = (node, *qdq_node.args[1:])
q_node = graph_module.graph.create_node("call_function", q_op, q_args)
q_node.meta = copy_meta(node.meta)
q_node.meta["val"] = q_node.meta["val"].to(q_args[-1])
with graph_module.graph.inserting_after(q_node):
dq_args = (q_node, *qdq_node.args[1:])
dq_node = graph_module.graph.create_node(
"call_function", dq_op, dq_args
)
dq_node.meta = copy_meta(node.meta)

return dq_node

def call(self, graph_module: torch.fx.GraphModule):
graph = graph_module.graph
conv_op = exir_ops.edge.aten.convolution.default
for node in graph.nodes:
if node.target == conv_op and node.meta["val"].dim() == 3:

if node.target in self.conv_op_map:
input_node = node.args[0]
with graph_module.graph.inserting_after(input_node):
unsqueeze_op = exir_ops.edge.aten.unsqueeze_copy.default
unsqueeze_op = torch.ops.aten.unsqueeze_copy.default
unsqueeze_node = graph.create_node(
"call_function",
unsqueeze_op,
Expand All@@ -44,52 +69,88 @@ def call(self, graph_module: torch.fx.GraphModule):
unsqueeze_node.meta = copy_meta(
input_node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_unsqueeze = self.append_qdq(
graph_module=graph_module,
node=unsqueeze_node,
qdq_node=input_node,
)

with graph_module.graph.inserting_after(unsqueeze_node):

filter_node = node.args[1]
with graph_module.graph.inserting_after(qdq_node_after_unsqueeze):
filter_arg = node.args[1]
filter_node = (
filter_arg
if filter_arg.op == "placeholder"
else node.args[1].args[0]
)
filter_node.meta["val"] = (
filter_node.meta["val"].unsqueeze(2).contiguous()
)
filter_tensor = get_parameter(filter_node, self.edge_program)
# Ensure tensor is nn.Parameter type, so program does not fail during edge_program._validate()
filter_tensor = nn.Parameter(filter_tensor.unsqueeze(2))
set_parameter(filter_tensor, filter_node, self.edge_program)
filter_tensor = get_parameter(
filter_node, self.edge_program
).unsqueeze(2)
set_parameter(
(
torch.nn.Parameter(filter_tensor)
if filter_tensor.dtype == torch.float
else filter_tensor
),
filter_node,
self.edge_program,
)

num_args = len(node.args)
bias_node = node.args[2]
stride = [1] + node.args[3]
padding = [0] + node.args[4]
dilation = [1] + node.args[5]
transpose = node.args[6]
output_padding = [0] + node.args[7]
groups = node.args[8]

conv2d_node = graph.create_node(
"call_function",
conv_op,
(
unsqueeze_node,
filter_node,
stride = [1] + node.args[3] if num_args > 3 else [1, 1]
padding = [0] + node.args[4] if num_args > 4 else [0, 0]
if node.target == torch.ops.aten.conv1d.default:
dilation = [1] + node.args[5] if num_args > 5 else [1, 1]
groups = node.args[6] if num_args > 5 else 1
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
dilation,
transpose,
groups,
)
else:
output_padding = (
[0] + node.args[5] if num_args > 5 else [0, 0]
)
groups = node.args[6] if num_args > 6 else 1
dilation = [1] + node.args[7] if num_args > 7 else [1, 1]
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
output_padding,
groups,
),
dilation,
)
conv2d_node = graph.create_node(
"call_function",
self.conv_op_map[node.target],
conv_args,
)
conv2d_node.meta = copy_meta(
node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_conv2d = self.append_qdq(
graph_module=graph_module,
node=conv2d_node,
qdq_node=list(node.users)[0],
)

with graph_module.graph.inserting_after(conv2d_node):
squeeze_op = exir_ops.edge.aten.squeeze_copy.dims
with graph_module.graph.inserting_after(qdq_node_after_conv2d):
squeeze_op = torch.ops.aten.squeeze_copy.dims
squeeze_node = graph.create_node(
"call_function",
squeeze_op,
(
conv2d_node,
qdq_node_after_conv2d,
[2],
),
)
Expand All@@ -102,8 +163,10 @@ def call(self, graph_module: torch.fx.GraphModule):
QCOM_REQUANTIZE
]
conv2d_node.meta.pop(QCOM_REQUANTIZE, None)

for user in node.users.copy():
user.replace_input_with(node, squeeze_node)

graph.eliminate_dead_code()
graph_module.recompile()
return PassResult(graph_module, True)
8 changes: 7 additions & 1 deletion backends/qualcomm/_passes/expand_broadcast_tensor_shape.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -9,6 +9,8 @@
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass

from .utils import dq_ops


class ExpandBroadcastTensorShape(ExportPass):
"""
Expand DownExpand Up@@ -45,9 +47,13 @@ def traverse_broadcast_node(self, graph_module: torch.fx.GraphModule):
exir_ops.edge.aten.view_copy.default,
(arg, tuple(new_rank)),
)
# try skip dq_ops to get correct param node if applicable
arg_meta = (
arg.args[0].meta if arg.target in dq_ops else arg.meta
)
# meta needs to be copied elementwisely for fake-tensor
# to be updated correctly and not affect meta of arg
for k, v in arg.meta.items():
for k, v in arg_meta.items():
reshape_node.meta[k] = v
reshape_node.meta["val"] = reshape_node.meta["val"].reshape(
new_rank
Expand Down
30 changes: 24 additions & 6 deletions backends/qualcomm/_passes/fold_qdq.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -4,6 +4,8 @@
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import torch
from executorch.backends.qualcomm.builders.utils import is_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_BYPASS_NODE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass
Expand All@@ -16,23 +18,38 @@ class FoldQDQ(ExportPass):
Erase QDQ pattern.
"""

def __init__(self):
def __init__(self, edge_program: torch.export.ExportedProgram, force_fold=False):
super(FoldQDQ, self).__init__()
self.edge_program = edge_program
self.force_fold = force_fold

def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
def _annotate_bypass(self, node):
node.meta[QCOM_BYPASS_NODE] = True
for arg in node.args:
if isinstance(arg, torch.fx.Node) and arg.op == "call_function":
self._annotate_bypass(arg)

def _fold_dq(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove dq
for n in graph_module.graph.nodes:
user_list = list(n.users.keys())
if n.target not in dq_ops:
continue
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

# skip parameters & buffers
if not self.force_fold and is_parameter(n.args[0], self.edge_program):
self._annotate_bypass(n)
else:
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

def _fold_q(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove q
for n in graph_module.graph.nodes:
if n.target not in q_ops:
continue

to_be_removed = [n]
source_n = n.args[0]

Expand All@@ -57,7 +74,8 @@ def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
graph_module.graph.erase_node(n)

def call(self, graph_module: torch.fx.GraphModule):
self._fold(graph_module)
self._fold_dq(graph_module)
self._fold_q(graph_module)
graph_module.recompile()
dead_code_elimination_pass(graph_module)
return PassResult(graph_module, True)
4 changes: 4 additions & 0 deletions backends/qualcomm/_passes/qnn_pass_manager.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -200,13 +200,17 @@ def transform_for_export_pipeline(self, exported_program: ExportedProgram):
self.add_pass(DecomposeScaledDotProductAttention())
self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True))
self.add_pass(DecomposeExpM1())
# this pass will rewrite state_dict, it needs to be accomplished before
# to_edge_transform_and_lower
self.add_pass(ConvertConv1dToConv2d(exported_program))
self.add_pass(ConvertSquareToPow())
self.add_pass(LiftConstantScalarOperands())
self._transform(exported_program.graph_module)
ep = lift_constant_tensor_pass(exported_program)
return ep

def transform_for_preprocess_pipeline(self, exported_program: ExportedProgram):
self.add_pass(FoldQDQ(exported_program, force_fold=True))
self.add_pass(InsertRequantize())
self.add_pass(InsertIOQDQ(exported_program))
self.add_pass(LayoutTransform(exported_program, insert_permute=True))
Expand Down
7 changes: 4 additions & 3 deletions backends/qualcomm/_passes/replace_index_put_input.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -33,7 +33,8 @@ def call(self, graph_module: torch.fx.GraphModule):
copy_node := list(node.users)[0]
) and copy_node.target == exir_ops.edge.aten.copy.default:
m_buffer_node = copy_node.args[0]
bad_frozen_node = node.args[0]
dq_node = node.args[0]
bad_frozen_node = dq_node.args[0]
if QCOM_QUANT_ATTRS in bad_frozen_node.meta:
m_buffer_node.meta[QCOM_QUANT_ATTRS] = bad_frozen_node.meta[
QCOM_QUANT_ATTRS
Expand All@@ -43,8 +44,8 @@ def call(self, graph_module: torch.fx.GraphModule):
m_buffer_node.meta[QCOM_QUANT_ATTRS][QCOM_ENCODING]
]
)
with graph.inserting_after(bad_frozen_node):
node.replace_input_with(bad_frozen_node, m_buffer_node)
with graph.inserting_after(dq_node):
node.replace_input_with(dq_node, m_buffer_node)
else:
continue

Expand Down
1 change: 0 additions & 1 deletion backends/qualcomm/_passes/utils.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -103,7 +103,6 @@ def get_passes_dependency_for_capture_program():
AnnotateStack: [RemoveRedundancy],
AnnotateUnbind: [RemoveRedundancy],
ConvertBmmToMatmul: [RecomposePixelUnshuffle],
ConvertConv1dToConv2d: [FoldQDQ],
ConvertUpsampleBicubicWithBilinear: [RemoveRedundancy],
DecomposeAny: [RemoveRedundancy],
DecomposeLinalgVectorNorm: [RemoveRedundancy],
Expand Down
Loading
, '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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Qualcomm AI Engine Direct - fix for pytorch uplevel#10769

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Merged
cccclai merged 3 commits into from
May 20, 2025
Merged

Qualcomm AI Engine Direct - fix for pytorch uplevel #10769

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2e0535a
Qualcomm AI Engine Direct - fix for pytorch uplevel
haowhsu-quic May 5, 2025
9cdc89c
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 15, 2025
186ee40
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 19, 2025
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45 changes: 1 addition & 44 deletions backends/qualcomm/_passes/annotate_quant_attrs.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -7,22 +7,17 @@
from typing import Any, Dict

import torch
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.builders.utils import get_parameter
from executorch.backends.qualcomm.utils.constants import (
QCOM_AXIS,
QCOM_BLOCK_SIZE,
QCOM_DTYPE,
QCOM_ENCODING,
QCOM_QUANT_ATTRS,
QCOM_QUANT_MAX,
QCOM_QUANT_MIN,
QCOM_REQUANTIZE,
QCOM_SCALE,
QCOM_SCALES,
QCOM_ZERO_POINT,
QCOM_ZERO_POINTS,
)
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import dq_ops, get_quant_attrs, q_ops
Expand DownExpand Up@@ -101,43 +96,9 @@ def _annotate_requant(self, n):
n.args[0].meta.setdefault(QCOM_REQUANTIZE, {})
n.args[0].meta[QCOM_REQUANTIZE][user_node.name] = dq_attrs

# Dequant all the fold_quant parameters back to fp32.
# If an operation is not supported by QNN and got fallback, it will expect a fp32 param.
def _dequant_fold_params(self, n, quant_attrs, param):
if quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.quantized_decomposed.dequantize_per_channel.default
]:
dim, axis = param.dim(), quant_attrs[QCOM_AXIS]
scales = self._expand(quant_attrs[QCOM_SCALES], dim, axis)
offsets = self._expand(quant_attrs[QCOM_ZERO_POINTS], dim, axis)
param = param.sub(offsets).mul(scales).to(torch.float32).contiguous()
elif quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.pt2e_quant.dequantize_affine.default
]:
param = torch.ops.pt2e_quant.dequantize_affine(
param,
block_size=quant_attrs[QCOM_BLOCK_SIZE],
scale=quant_attrs[QCOM_SCALE],
zero_point=quant_attrs[QCOM_ZERO_POINT],
input_dtype=quant_attrs[QCOM_DTYPE],
quant_min=quant_attrs[QCOM_QUANT_MIN],
quant_max=quant_attrs[QCOM_QUANT_MAX],
output_dtype=torch.float32,
)
else:
scale = quant_attrs[QCOM_SCALE]
offset = quant_attrs[QCOM_ZERO_POINT]
param = param.sub(offset).mul(scale).to(torch.float32).contiguous()

set_parameter(param, n.args[0], self.edge_program)
n.args[0].meta["val"] = param

def _annotate_quant_attrs(
self, graph_module: torch.fx.GraphModule
) -> torch.fx.GraphModule:
# Keep track of const params that has been dequant, so it does not get
# dequant multiple times if the const param has more than 1 user
visited_const_param = set()
for n in graph_module.graph.nodes:
self._annotate_requant(n)
# With fold_quant enabled, check if the input of dq op is quantized param.
Expand All@@ -149,10 +110,6 @@ def _annotate_quant_attrs(
quant_attrs = get_quant_attrs(self.edge_program, n)
self._annotate_source_nodes(n, quant_attrs)

if param is not None and n.args[0] not in visited_const_param:
visited_const_param.add(n.args[0])
self._dequant_fold_params(n, quant_attrs, param)

return graph_module

def call(self, graph_module: torch.fx.GraphModule):
Expand Down
125 changes: 94 additions & 31 deletions backends/qualcomm/_passes/convert_conv1d_to_conv2d.py
View file
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Original file line numberDiff line numberDiff line change
Expand Up@@ -5,10 +5,8 @@
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_REQUANTIZE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import copy_meta
Expand All@@ -23,16 +21,43 @@ class ConvertConv1dToConv2d(ExportPass):
def __init__(self, edge_program: torch.export.ExportedProgram):
super(ConvertConv1dToConv2d, self).__init__()
self.edge_program = edge_program
self.conv_op_map = {
torch.ops.aten.conv1d.default: torch.ops.aten.conv2d.default,
torch.ops.aten.conv_transpose1d.default: torch.ops.aten.conv_transpose2d.input,
}

def append_qdq(
self,
graph_module: torch.fx.GraphModule,
node: torch.fx.Node,
qdq_node: torch.fx.Node,
):
q_op = torch.ops.quantized_decomposed.quantize_per_tensor.default
dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor.default
if qdq_node.target not in {q_op, dq_op}:
return node

with graph_module.graph.inserting_after(node):
q_args = (node, *qdq_node.args[1:])
q_node = graph_module.graph.create_node("call_function", q_op, q_args)
q_node.meta = copy_meta(node.meta)
q_node.meta["val"] = q_node.meta["val"].to(q_args[-1])
with graph_module.graph.inserting_after(q_node):
dq_args = (q_node, *qdq_node.args[1:])
dq_node = graph_module.graph.create_node(
"call_function", dq_op, dq_args
)
dq_node.meta = copy_meta(node.meta)

return dq_node

def call(self, graph_module: torch.fx.GraphModule):
graph = graph_module.graph
conv_op = exir_ops.edge.aten.convolution.default
for node in graph.nodes:
if node.target == conv_op and node.meta["val"].dim() == 3:

if node.target in self.conv_op_map:
input_node = node.args[0]
with graph_module.graph.inserting_after(input_node):
unsqueeze_op = exir_ops.edge.aten.unsqueeze_copy.default
unsqueeze_op = torch.ops.aten.unsqueeze_copy.default
unsqueeze_node = graph.create_node(
"call_function",
unsqueeze_op,
Expand All@@ -44,52 +69,88 @@ def call(self, graph_module: torch.fx.GraphModule):
unsqueeze_node.meta = copy_meta(
input_node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_unsqueeze = self.append_qdq(
graph_module=graph_module,
node=unsqueeze_node,
qdq_node=input_node,
)

with graph_module.graph.inserting_after(unsqueeze_node):

filter_node = node.args[1]
with graph_module.graph.inserting_after(qdq_node_after_unsqueeze):
filter_arg = node.args[1]
filter_node = (
filter_arg
if filter_arg.op == "placeholder"
else node.args[1].args[0]
)
filter_node.meta["val"] = (
filter_node.meta["val"].unsqueeze(2).contiguous()
)
filter_tensor = get_parameter(filter_node, self.edge_program)
# Ensure tensor is nn.Parameter type, so program does not fail during edge_program._validate()
filter_tensor = nn.Parameter(filter_tensor.unsqueeze(2))
set_parameter(filter_tensor, filter_node, self.edge_program)
filter_tensor = get_parameter(
filter_node, self.edge_program
).unsqueeze(2)
set_parameter(
(
torch.nn.Parameter(filter_tensor)
if filter_tensor.dtype == torch.float
else filter_tensor
),
filter_node,
self.edge_program,
)

num_args = len(node.args)
bias_node = node.args[2]
stride = [1] + node.args[3]
padding = [0] + node.args[4]
dilation = [1] + node.args[5]
transpose = node.args[6]
output_padding = [0] + node.args[7]
groups = node.args[8]

conv2d_node = graph.create_node(
"call_function",
conv_op,
(
unsqueeze_node,
filter_node,
stride = [1] + node.args[3] if num_args > 3 else [1, 1]
padding = [0] + node.args[4] if num_args > 4 else [0, 0]
if node.target == torch.ops.aten.conv1d.default:
dilation = [1] + node.args[5] if num_args > 5 else [1, 1]
groups = node.args[6] if num_args > 5 else 1
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
dilation,
transpose,
groups,
)
else:
output_padding = (
[0] + node.args[5] if num_args > 5 else [0, 0]
)
groups = node.args[6] if num_args > 6 else 1
dilation = [1] + node.args[7] if num_args > 7 else [1, 1]
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
output_padding,
groups,
),
dilation,
)
conv2d_node = graph.create_node(
"call_function",
self.conv_op_map[node.target],
conv_args,
)
conv2d_node.meta = copy_meta(
node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_conv2d = self.append_qdq(
graph_module=graph_module,
node=conv2d_node,
qdq_node=list(node.users)[0],
)

with graph_module.graph.inserting_after(conv2d_node):
squeeze_op = exir_ops.edge.aten.squeeze_copy.dims
with graph_module.graph.inserting_after(qdq_node_after_conv2d):
squeeze_op = torch.ops.aten.squeeze_copy.dims
squeeze_node = graph.create_node(
"call_function",
squeeze_op,
(
conv2d_node,
qdq_node_after_conv2d,
[2],
),
)
Expand All@@ -102,8 +163,10 @@ def call(self, graph_module: torch.fx.GraphModule):
QCOM_REQUANTIZE
]
conv2d_node.meta.pop(QCOM_REQUANTIZE, None)

for user in node.users.copy():
user.replace_input_with(node, squeeze_node)

graph.eliminate_dead_code()
graph_module.recompile()
return PassResult(graph_module, True)
8 changes: 7 additions & 1 deletion backends/qualcomm/_passes/expand_broadcast_tensor_shape.py
View file
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Original file line numberDiff line numberDiff line change
Expand Up@@ -9,6 +9,8 @@
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass

from .utils import dq_ops


class ExpandBroadcastTensorShape(ExportPass):
"""
Expand DownExpand Up@@ -45,9 +47,13 @@ def traverse_broadcast_node(self, graph_module: torch.fx.GraphModule):
exir_ops.edge.aten.view_copy.default,
(arg, tuple(new_rank)),
)
# try skip dq_ops to get correct param node if applicable
arg_meta = (
arg.args[0].meta if arg.target in dq_ops else arg.meta
)
# meta needs to be copied elementwisely for fake-tensor
# to be updated correctly and not affect meta of arg
for k, v in arg.meta.items():
for k, v in arg_meta.items():
reshape_node.meta[k] = v
reshape_node.meta["val"] = reshape_node.meta["val"].reshape(
new_rank
Expand Down
30 changes: 24 additions & 6 deletions backends/qualcomm/_passes/fold_qdq.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -4,6 +4,8 @@
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import torch
from executorch.backends.qualcomm.builders.utils import is_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_BYPASS_NODE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass
Expand All@@ -16,23 +18,38 @@ class FoldQDQ(ExportPass):
Erase QDQ pattern.
"""

def __init__(self):
def __init__(self, edge_program: torch.export.ExportedProgram, force_fold=False):
super(FoldQDQ, self).__init__()
self.edge_program = edge_program
self.force_fold = force_fold

def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
def _annotate_bypass(self, node):
node.meta[QCOM_BYPASS_NODE] = True
for arg in node.args:
if isinstance(arg, torch.fx.Node) and arg.op == "call_function":
self._annotate_bypass(arg)

def _fold_dq(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove dq
for n in graph_module.graph.nodes:
user_list = list(n.users.keys())
if n.target not in dq_ops:
continue
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

# skip parameters & buffers
if not self.force_fold and is_parameter(n.args[0], self.edge_program):
self._annotate_bypass(n)
else:
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

def _fold_q(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove q
for n in graph_module.graph.nodes:
if n.target not in q_ops:
continue

to_be_removed = [n]
source_n = n.args[0]

Expand All@@ -57,7 +74,8 @@ def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
graph_module.graph.erase_node(n)

def call(self, graph_module: torch.fx.GraphModule):
self._fold(graph_module)
self._fold_dq(graph_module)
self._fold_q(graph_module)
graph_module.recompile()
dead_code_elimination_pass(graph_module)
return PassResult(graph_module, True)
4 changes: 4 additions & 0 deletions backends/qualcomm/_passes/qnn_pass_manager.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -200,13 +200,17 @@ def transform_for_export_pipeline(self, exported_program: ExportedProgram):
self.add_pass(DecomposeScaledDotProductAttention())
self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True))
self.add_pass(DecomposeExpM1())
# this pass will rewrite state_dict, it needs to be accomplished before
# to_edge_transform_and_lower
self.add_pass(ConvertConv1dToConv2d(exported_program))
self.add_pass(ConvertSquareToPow())
self.add_pass(LiftConstantScalarOperands())
self._transform(exported_program.graph_module)
ep = lift_constant_tensor_pass(exported_program)
return ep

def transform_for_preprocess_pipeline(self, exported_program: ExportedProgram):
self.add_pass(FoldQDQ(exported_program, force_fold=True))
self.add_pass(InsertRequantize())
self.add_pass(InsertIOQDQ(exported_program))
self.add_pass(LayoutTransform(exported_program, insert_permute=True))
Expand Down
7 changes: 4 additions & 3 deletions backends/qualcomm/_passes/replace_index_put_input.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -33,7 +33,8 @@ def call(self, graph_module: torch.fx.GraphModule):
copy_node := list(node.users)[0]
) and copy_node.target == exir_ops.edge.aten.copy.default:
m_buffer_node = copy_node.args[0]
bad_frozen_node = node.args[0]
dq_node = node.args[0]
bad_frozen_node = dq_node.args[0]
if QCOM_QUANT_ATTRS in bad_frozen_node.meta:
m_buffer_node.meta[QCOM_QUANT_ATTRS] = bad_frozen_node.meta[
QCOM_QUANT_ATTRS
Expand All@@ -43,8 +44,8 @@ def call(self, graph_module: torch.fx.GraphModule):
m_buffer_node.meta[QCOM_QUANT_ATTRS][QCOM_ENCODING]
]
)
with graph.inserting_after(bad_frozen_node):
node.replace_input_with(bad_frozen_node, m_buffer_node)
with graph.inserting_after(dq_node):
node.replace_input_with(dq_node, m_buffer_node)
else:
continue

Expand Down
1 change: 0 additions & 1 deletion backends/qualcomm/_passes/utils.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -103,7 +103,6 @@ def get_passes_dependency_for_capture_program():
AnnotateStack: [RemoveRedundancy],
AnnotateUnbind: [RemoveRedundancy],
ConvertBmmToMatmul: [RecomposePixelUnshuffle],
ConvertConv1dToConv2d: [FoldQDQ],
ConvertUpsampleBicubicWithBilinear: [RemoveRedundancy],
DecomposeAny: [RemoveRedundancy],
DecomposeLinalgVectorNorm: [RemoveRedundancy],
Expand Down
Loading
, '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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Qualcomm AI Engine Direct - fix for pytorch uplevel#10769

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Merged
cccclai merged 3 commits into from
May 20, 2025
Merged

Qualcomm AI Engine Direct - fix for pytorch uplevel #10769

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2e0535a
Qualcomm AI Engine Direct - fix for pytorch uplevel
haowhsu-quic May 5, 2025
9cdc89c
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 15, 2025
186ee40
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 19, 2025
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45 changes: 1 addition & 44 deletions backends/qualcomm/_passes/annotate_quant_attrs.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -7,22 +7,17 @@
from typing import Any, Dict

import torch
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.builders.utils import get_parameter
from executorch.backends.qualcomm.utils.constants import (
QCOM_AXIS,
QCOM_BLOCK_SIZE,
QCOM_DTYPE,
QCOM_ENCODING,
QCOM_QUANT_ATTRS,
QCOM_QUANT_MAX,
QCOM_QUANT_MIN,
QCOM_REQUANTIZE,
QCOM_SCALE,
QCOM_SCALES,
QCOM_ZERO_POINT,
QCOM_ZERO_POINTS,
)
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import dq_ops, get_quant_attrs, q_ops
Expand DownExpand Up@@ -101,43 +96,9 @@ def _annotate_requant(self, n):
n.args[0].meta.setdefault(QCOM_REQUANTIZE, {})
n.args[0].meta[QCOM_REQUANTIZE][user_node.name] = dq_attrs

# Dequant all the fold_quant parameters back to fp32.
# If an operation is not supported by QNN and got fallback, it will expect a fp32 param.
def _dequant_fold_params(self, n, quant_attrs, param):
if quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.quantized_decomposed.dequantize_per_channel.default
]:
dim, axis = param.dim(), quant_attrs[QCOM_AXIS]
scales = self._expand(quant_attrs[QCOM_SCALES], dim, axis)
offsets = self._expand(quant_attrs[QCOM_ZERO_POINTS], dim, axis)
param = param.sub(offsets).mul(scales).to(torch.float32).contiguous()
elif quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.pt2e_quant.dequantize_affine.default
]:
param = torch.ops.pt2e_quant.dequantize_affine(
param,
block_size=quant_attrs[QCOM_BLOCK_SIZE],
scale=quant_attrs[QCOM_SCALE],
zero_point=quant_attrs[QCOM_ZERO_POINT],
input_dtype=quant_attrs[QCOM_DTYPE],
quant_min=quant_attrs[QCOM_QUANT_MIN],
quant_max=quant_attrs[QCOM_QUANT_MAX],
output_dtype=torch.float32,
)
else:
scale = quant_attrs[QCOM_SCALE]
offset = quant_attrs[QCOM_ZERO_POINT]
param = param.sub(offset).mul(scale).to(torch.float32).contiguous()

set_parameter(param, n.args[0], self.edge_program)
n.args[0].meta["val"] = param

def _annotate_quant_attrs(
self, graph_module: torch.fx.GraphModule
) -> torch.fx.GraphModule:
# Keep track of const params that has been dequant, so it does not get
# dequant multiple times if the const param has more than 1 user
visited_const_param = set()
for n in graph_module.graph.nodes:
self._annotate_requant(n)
# With fold_quant enabled, check if the input of dq op is quantized param.
Expand All@@ -149,10 +110,6 @@ def _annotate_quant_attrs(
quant_attrs = get_quant_attrs(self.edge_program, n)
self._annotate_source_nodes(n, quant_attrs)

if param is not None and n.args[0] not in visited_const_param:
visited_const_param.add(n.args[0])
self._dequant_fold_params(n, quant_attrs, param)

return graph_module

def call(self, graph_module: torch.fx.GraphModule):
Expand Down
125 changes: 94 additions & 31 deletions backends/qualcomm/_passes/convert_conv1d_to_conv2d.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -5,10 +5,8 @@
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_REQUANTIZE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import copy_meta
Expand All@@ -23,16 +21,43 @@ class ConvertConv1dToConv2d(ExportPass):
def __init__(self, edge_program: torch.export.ExportedProgram):
super(ConvertConv1dToConv2d, self).__init__()
self.edge_program = edge_program
self.conv_op_map = {
torch.ops.aten.conv1d.default: torch.ops.aten.conv2d.default,
torch.ops.aten.conv_transpose1d.default: torch.ops.aten.conv_transpose2d.input,
}

def append_qdq(
self,
graph_module: torch.fx.GraphModule,
node: torch.fx.Node,
qdq_node: torch.fx.Node,
):
q_op = torch.ops.quantized_decomposed.quantize_per_tensor.default
dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor.default
if qdq_node.target not in {q_op, dq_op}:
return node

with graph_module.graph.inserting_after(node):
q_args = (node, *qdq_node.args[1:])
q_node = graph_module.graph.create_node("call_function", q_op, q_args)
q_node.meta = copy_meta(node.meta)
q_node.meta["val"] = q_node.meta["val"].to(q_args[-1])
with graph_module.graph.inserting_after(q_node):
dq_args = (q_node, *qdq_node.args[1:])
dq_node = graph_module.graph.create_node(
"call_function", dq_op, dq_args
)
dq_node.meta = copy_meta(node.meta)

return dq_node

def call(self, graph_module: torch.fx.GraphModule):
graph = graph_module.graph
conv_op = exir_ops.edge.aten.convolution.default
for node in graph.nodes:
if node.target == conv_op and node.meta["val"].dim() == 3:

if node.target in self.conv_op_map:
input_node = node.args[0]
with graph_module.graph.inserting_after(input_node):
unsqueeze_op = exir_ops.edge.aten.unsqueeze_copy.default
unsqueeze_op = torch.ops.aten.unsqueeze_copy.default
unsqueeze_node = graph.create_node(
"call_function",
unsqueeze_op,
Expand All@@ -44,52 +69,88 @@ def call(self, graph_module: torch.fx.GraphModule):
unsqueeze_node.meta = copy_meta(
input_node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_unsqueeze = self.append_qdq(
graph_module=graph_module,
node=unsqueeze_node,
qdq_node=input_node,
)

with graph_module.graph.inserting_after(unsqueeze_node):

filter_node = node.args[1]
with graph_module.graph.inserting_after(qdq_node_after_unsqueeze):
filter_arg = node.args[1]
filter_node = (
filter_arg
if filter_arg.op == "placeholder"
else node.args[1].args[0]
)
filter_node.meta["val"] = (
filter_node.meta["val"].unsqueeze(2).contiguous()
)
filter_tensor = get_parameter(filter_node, self.edge_program)
# Ensure tensor is nn.Parameter type, so program does not fail during edge_program._validate()
filter_tensor = nn.Parameter(filter_tensor.unsqueeze(2))
set_parameter(filter_tensor, filter_node, self.edge_program)
filter_tensor = get_parameter(
filter_node, self.edge_program
).unsqueeze(2)
set_parameter(
(
torch.nn.Parameter(filter_tensor)
if filter_tensor.dtype == torch.float
else filter_tensor
),
filter_node,
self.edge_program,
)

num_args = len(node.args)
bias_node = node.args[2]
stride = [1] + node.args[3]
padding = [0] + node.args[4]
dilation = [1] + node.args[5]
transpose = node.args[6]
output_padding = [0] + node.args[7]
groups = node.args[8]

conv2d_node = graph.create_node(
"call_function",
conv_op,
(
unsqueeze_node,
filter_node,
stride = [1] + node.args[3] if num_args > 3 else [1, 1]
padding = [0] + node.args[4] if num_args > 4 else [0, 0]
if node.target == torch.ops.aten.conv1d.default:
dilation = [1] + node.args[5] if num_args > 5 else [1, 1]
groups = node.args[6] if num_args > 5 else 1
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
dilation,
transpose,
groups,
)
else:
output_padding = (
[0] + node.args[5] if num_args > 5 else [0, 0]
)
groups = node.args[6] if num_args > 6 else 1
dilation = [1] + node.args[7] if num_args > 7 else [1, 1]
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
output_padding,
groups,
),
dilation,
)
conv2d_node = graph.create_node(
"call_function",
self.conv_op_map[node.target],
conv_args,
)
conv2d_node.meta = copy_meta(
node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_conv2d = self.append_qdq(
graph_module=graph_module,
node=conv2d_node,
qdq_node=list(node.users)[0],
)

with graph_module.graph.inserting_after(conv2d_node):
squeeze_op = exir_ops.edge.aten.squeeze_copy.dims
with graph_module.graph.inserting_after(qdq_node_after_conv2d):
squeeze_op = torch.ops.aten.squeeze_copy.dims
squeeze_node = graph.create_node(
"call_function",
squeeze_op,
(
conv2d_node,
qdq_node_after_conv2d,
[2],
),
)
Expand All@@ -102,8 +163,10 @@ def call(self, graph_module: torch.fx.GraphModule):
QCOM_REQUANTIZE
]
conv2d_node.meta.pop(QCOM_REQUANTIZE, None)

for user in node.users.copy():
user.replace_input_with(node, squeeze_node)

graph.eliminate_dead_code()
graph_module.recompile()
return PassResult(graph_module, True)
8 changes: 7 additions & 1 deletion backends/qualcomm/_passes/expand_broadcast_tensor_shape.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -9,6 +9,8 @@
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass

from .utils import dq_ops


class ExpandBroadcastTensorShape(ExportPass):
"""
Expand DownExpand Up@@ -45,9 +47,13 @@ def traverse_broadcast_node(self, graph_module: torch.fx.GraphModule):
exir_ops.edge.aten.view_copy.default,
(arg, tuple(new_rank)),
)
# try skip dq_ops to get correct param node if applicable
arg_meta = (
arg.args[0].meta if arg.target in dq_ops else arg.meta
)
# meta needs to be copied elementwisely for fake-tensor
# to be updated correctly and not affect meta of arg
for k, v in arg.meta.items():
for k, v in arg_meta.items():
reshape_node.meta[k] = v
reshape_node.meta["val"] = reshape_node.meta["val"].reshape(
new_rank
Expand Down
30 changes: 24 additions & 6 deletions backends/qualcomm/_passes/fold_qdq.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -4,6 +4,8 @@
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import torch
from executorch.backends.qualcomm.builders.utils import is_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_BYPASS_NODE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass
Expand All@@ -16,23 +18,38 @@ class FoldQDQ(ExportPass):
Erase QDQ pattern.
"""

def __init__(self):
def __init__(self, edge_program: torch.export.ExportedProgram, force_fold=False):
super(FoldQDQ, self).__init__()
self.edge_program = edge_program
self.force_fold = force_fold

def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
def _annotate_bypass(self, node):
node.meta[QCOM_BYPASS_NODE] = True
for arg in node.args:
if isinstance(arg, torch.fx.Node) and arg.op == "call_function":
self._annotate_bypass(arg)

def _fold_dq(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove dq
for n in graph_module.graph.nodes:
user_list = list(n.users.keys())
if n.target not in dq_ops:
continue
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

# skip parameters & buffers
if not self.force_fold and is_parameter(n.args[0], self.edge_program):
self._annotate_bypass(n)
else:
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

def _fold_q(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove q
for n in graph_module.graph.nodes:
if n.target not in q_ops:
continue

to_be_removed = [n]
source_n = n.args[0]

Expand All@@ -57,7 +74,8 @@ def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
graph_module.graph.erase_node(n)

def call(self, graph_module: torch.fx.GraphModule):
self._fold(graph_module)
self._fold_dq(graph_module)
self._fold_q(graph_module)
graph_module.recompile()
dead_code_elimination_pass(graph_module)
return PassResult(graph_module, True)
4 changes: 4 additions & 0 deletions backends/qualcomm/_passes/qnn_pass_manager.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -200,13 +200,17 @@ def transform_for_export_pipeline(self, exported_program: ExportedProgram):
self.add_pass(DecomposeScaledDotProductAttention())
self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True))
self.add_pass(DecomposeExpM1())
# this pass will rewrite state_dict, it needs to be accomplished before
# to_edge_transform_and_lower
self.add_pass(ConvertConv1dToConv2d(exported_program))
self.add_pass(ConvertSquareToPow())
self.add_pass(LiftConstantScalarOperands())
self._transform(exported_program.graph_module)
ep = lift_constant_tensor_pass(exported_program)
return ep

def transform_for_preprocess_pipeline(self, exported_program: ExportedProgram):
self.add_pass(FoldQDQ(exported_program, force_fold=True))
self.add_pass(InsertRequantize())
self.add_pass(InsertIOQDQ(exported_program))
self.add_pass(LayoutTransform(exported_program, insert_permute=True))
Expand Down
7 changes: 4 additions & 3 deletions backends/qualcomm/_passes/replace_index_put_input.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -33,7 +33,8 @@ def call(self, graph_module: torch.fx.GraphModule):
copy_node := list(node.users)[0]
) and copy_node.target == exir_ops.edge.aten.copy.default:
m_buffer_node = copy_node.args[0]
bad_frozen_node = node.args[0]
dq_node = node.args[0]
bad_frozen_node = dq_node.args[0]
if QCOM_QUANT_ATTRS in bad_frozen_node.meta:
m_buffer_node.meta[QCOM_QUANT_ATTRS] = bad_frozen_node.meta[
QCOM_QUANT_ATTRS
Expand All@@ -43,8 +44,8 @@ def call(self, graph_module: torch.fx.GraphModule):
m_buffer_node.meta[QCOM_QUANT_ATTRS][QCOM_ENCODING]
]
)
with graph.inserting_after(bad_frozen_node):
node.replace_input_with(bad_frozen_node, m_buffer_node)
with graph.inserting_after(dq_node):
node.replace_input_with(dq_node, m_buffer_node)
else:
continue

Expand Down
1 change: 0 additions & 1 deletion backends/qualcomm/_passes/utils.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -103,7 +103,6 @@ def get_passes_dependency_for_capture_program():
AnnotateStack: [RemoveRedundancy],
AnnotateUnbind: [RemoveRedundancy],
ConvertBmmToMatmul: [RecomposePixelUnshuffle],
ConvertConv1dToConv2d: [FoldQDQ],
ConvertUpsampleBicubicWithBilinear: [RemoveRedundancy],
DecomposeAny: [RemoveRedundancy],
DecomposeLinalgVectorNorm: [RemoveRedundancy],
Expand Down
Loading
, '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

Qualcomm AI Engine Direct - fix for pytorch uplevel#10769

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Merged
cccclai merged 3 commits into from
May 20, 2025
Merged

Qualcomm AI Engine Direct - fix for pytorch uplevel #10769

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2e0535a
Qualcomm AI Engine Direct - fix for pytorch uplevel
haowhsu-quic May 5, 2025
9cdc89c
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 15, 2025
186ee40
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 19, 2025
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45 changes: 1 addition & 44 deletions backends/qualcomm/_passes/annotate_quant_attrs.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -7,22 +7,17 @@
from typing import Any, Dict

import torch
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.builders.utils import get_parameter
from executorch.backends.qualcomm.utils.constants import (
QCOM_AXIS,
QCOM_BLOCK_SIZE,
QCOM_DTYPE,
QCOM_ENCODING,
QCOM_QUANT_ATTRS,
QCOM_QUANT_MAX,
QCOM_QUANT_MIN,
QCOM_REQUANTIZE,
QCOM_SCALE,
QCOM_SCALES,
QCOM_ZERO_POINT,
QCOM_ZERO_POINTS,
)
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import dq_ops, get_quant_attrs, q_ops
Expand DownExpand Up@@ -101,43 +96,9 @@ def _annotate_requant(self, n):
n.args[0].meta.setdefault(QCOM_REQUANTIZE, {})
n.args[0].meta[QCOM_REQUANTIZE][user_node.name] = dq_attrs

# Dequant all the fold_quant parameters back to fp32.
# If an operation is not supported by QNN and got fallback, it will expect a fp32 param.
def _dequant_fold_params(self, n, quant_attrs, param):
if quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.quantized_decomposed.dequantize_per_channel.default
]:
dim, axis = param.dim(), quant_attrs[QCOM_AXIS]
scales = self._expand(quant_attrs[QCOM_SCALES], dim, axis)
offsets = self._expand(quant_attrs[QCOM_ZERO_POINTS], dim, axis)
param = param.sub(offsets).mul(scales).to(torch.float32).contiguous()
elif quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.pt2e_quant.dequantize_affine.default
]:
param = torch.ops.pt2e_quant.dequantize_affine(
param,
block_size=quant_attrs[QCOM_BLOCK_SIZE],
scale=quant_attrs[QCOM_SCALE],
zero_point=quant_attrs[QCOM_ZERO_POINT],
input_dtype=quant_attrs[QCOM_DTYPE],
quant_min=quant_attrs[QCOM_QUANT_MIN],
quant_max=quant_attrs[QCOM_QUANT_MAX],
output_dtype=torch.float32,
)
else:
scale = quant_attrs[QCOM_SCALE]
offset = quant_attrs[QCOM_ZERO_POINT]
param = param.sub(offset).mul(scale).to(torch.float32).contiguous()

set_parameter(param, n.args[0], self.edge_program)
n.args[0].meta["val"] = param

def _annotate_quant_attrs(
self, graph_module: torch.fx.GraphModule
) -> torch.fx.GraphModule:
# Keep track of const params that has been dequant, so it does not get
# dequant multiple times if the const param has more than 1 user
visited_const_param = set()
for n in graph_module.graph.nodes:
self._annotate_requant(n)
# With fold_quant enabled, check if the input of dq op is quantized param.
Expand All@@ -149,10 +110,6 @@ def _annotate_quant_attrs(
quant_attrs = get_quant_attrs(self.edge_program, n)
self._annotate_source_nodes(n, quant_attrs)

if param is not None and n.args[0] not in visited_const_param:
visited_const_param.add(n.args[0])
self._dequant_fold_params(n, quant_attrs, param)

return graph_module

def call(self, graph_module: torch.fx.GraphModule):
Expand Down
125 changes: 94 additions & 31 deletions backends/qualcomm/_passes/convert_conv1d_to_conv2d.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -5,10 +5,8 @@
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_REQUANTIZE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import copy_meta
Expand All@@ -23,16 +21,43 @@ class ConvertConv1dToConv2d(ExportPass):
def __init__(self, edge_program: torch.export.ExportedProgram):
super(ConvertConv1dToConv2d, self).__init__()
self.edge_program = edge_program
self.conv_op_map = {
torch.ops.aten.conv1d.default: torch.ops.aten.conv2d.default,
torch.ops.aten.conv_transpose1d.default: torch.ops.aten.conv_transpose2d.input,
}

def append_qdq(
self,
graph_module: torch.fx.GraphModule,
node: torch.fx.Node,
qdq_node: torch.fx.Node,
):
q_op = torch.ops.quantized_decomposed.quantize_per_tensor.default
dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor.default
if qdq_node.target not in {q_op, dq_op}:
return node

with graph_module.graph.inserting_after(node):
q_args = (node, *qdq_node.args[1:])
q_node = graph_module.graph.create_node("call_function", q_op, q_args)
q_node.meta = copy_meta(node.meta)
q_node.meta["val"] = q_node.meta["val"].to(q_args[-1])
with graph_module.graph.inserting_after(q_node):
dq_args = (q_node, *qdq_node.args[1:])
dq_node = graph_module.graph.create_node(
"call_function", dq_op, dq_args
)
dq_node.meta = copy_meta(node.meta)

return dq_node

def call(self, graph_module: torch.fx.GraphModule):
graph = graph_module.graph
conv_op = exir_ops.edge.aten.convolution.default
for node in graph.nodes:
if node.target == conv_op and node.meta["val"].dim() == 3:

if node.target in self.conv_op_map:
input_node = node.args[0]
with graph_module.graph.inserting_after(input_node):
unsqueeze_op = exir_ops.edge.aten.unsqueeze_copy.default
unsqueeze_op = torch.ops.aten.unsqueeze_copy.default
unsqueeze_node = graph.create_node(
"call_function",
unsqueeze_op,
Expand All@@ -44,52 +69,88 @@ def call(self, graph_module: torch.fx.GraphModule):
unsqueeze_node.meta = copy_meta(
input_node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_unsqueeze = self.append_qdq(
graph_module=graph_module,
node=unsqueeze_node,
qdq_node=input_node,
)

with graph_module.graph.inserting_after(unsqueeze_node):

filter_node = node.args[1]
with graph_module.graph.inserting_after(qdq_node_after_unsqueeze):
filter_arg = node.args[1]
filter_node = (
filter_arg
if filter_arg.op == "placeholder"
else node.args[1].args[0]
)
filter_node.meta["val"] = (
filter_node.meta["val"].unsqueeze(2).contiguous()
)
filter_tensor = get_parameter(filter_node, self.edge_program)
# Ensure tensor is nn.Parameter type, so program does not fail during edge_program._validate()
filter_tensor = nn.Parameter(filter_tensor.unsqueeze(2))
set_parameter(filter_tensor, filter_node, self.edge_program)
filter_tensor = get_parameter(
filter_node, self.edge_program
).unsqueeze(2)
set_parameter(
(
torch.nn.Parameter(filter_tensor)
if filter_tensor.dtype == torch.float
else filter_tensor
),
filter_node,
self.edge_program,
)

num_args = len(node.args)
bias_node = node.args[2]
stride = [1] + node.args[3]
padding = [0] + node.args[4]
dilation = [1] + node.args[5]
transpose = node.args[6]
output_padding = [0] + node.args[7]
groups = node.args[8]

conv2d_node = graph.create_node(
"call_function",
conv_op,
(
unsqueeze_node,
filter_node,
stride = [1] + node.args[3] if num_args > 3 else [1, 1]
padding = [0] + node.args[4] if num_args > 4 else [0, 0]
if node.target == torch.ops.aten.conv1d.default:
dilation = [1] + node.args[5] if num_args > 5 else [1, 1]
groups = node.args[6] if num_args > 5 else 1
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
dilation,
transpose,
groups,
)
else:
output_padding = (
[0] + node.args[5] if num_args > 5 else [0, 0]
)
groups = node.args[6] if num_args > 6 else 1
dilation = [1] + node.args[7] if num_args > 7 else [1, 1]
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
output_padding,
groups,
),
dilation,
)
conv2d_node = graph.create_node(
"call_function",
self.conv_op_map[node.target],
conv_args,
)
conv2d_node.meta = copy_meta(
node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_conv2d = self.append_qdq(
graph_module=graph_module,
node=conv2d_node,
qdq_node=list(node.users)[0],
)

with graph_module.graph.inserting_after(conv2d_node):
squeeze_op = exir_ops.edge.aten.squeeze_copy.dims
with graph_module.graph.inserting_after(qdq_node_after_conv2d):
squeeze_op = torch.ops.aten.squeeze_copy.dims
squeeze_node = graph.create_node(
"call_function",
squeeze_op,
(
conv2d_node,
qdq_node_after_conv2d,
[2],
),
)
Expand All@@ -102,8 +163,10 @@ def call(self, graph_module: torch.fx.GraphModule):
QCOM_REQUANTIZE
]
conv2d_node.meta.pop(QCOM_REQUANTIZE, None)

for user in node.users.copy():
user.replace_input_with(node, squeeze_node)

graph.eliminate_dead_code()
graph_module.recompile()
return PassResult(graph_module, True)
8 changes: 7 additions & 1 deletion backends/qualcomm/_passes/expand_broadcast_tensor_shape.py
View file
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Original file line numberDiff line numberDiff line change
Expand Up@@ -9,6 +9,8 @@
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass

from .utils import dq_ops


class ExpandBroadcastTensorShape(ExportPass):
"""
Expand DownExpand Up@@ -45,9 +47,13 @@ def traverse_broadcast_node(self, graph_module: torch.fx.GraphModule):
exir_ops.edge.aten.view_copy.default,
(arg, tuple(new_rank)),
)
# try skip dq_ops to get correct param node if applicable
arg_meta = (
arg.args[0].meta if arg.target in dq_ops else arg.meta
)
# meta needs to be copied elementwisely for fake-tensor
# to be updated correctly and not affect meta of arg
for k, v in arg.meta.items():
for k, v in arg_meta.items():
reshape_node.meta[k] = v
reshape_node.meta["val"] = reshape_node.meta["val"].reshape(
new_rank
Expand Down
30 changes: 24 additions & 6 deletions backends/qualcomm/_passes/fold_qdq.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -4,6 +4,8 @@
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import torch
from executorch.backends.qualcomm.builders.utils import is_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_BYPASS_NODE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass
Expand All@@ -16,23 +18,38 @@ class FoldQDQ(ExportPass):
Erase QDQ pattern.
"""

def __init__(self):
def __init__(self, edge_program: torch.export.ExportedProgram, force_fold=False):
super(FoldQDQ, self).__init__()
self.edge_program = edge_program
self.force_fold = force_fold

def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
def _annotate_bypass(self, node):
node.meta[QCOM_BYPASS_NODE] = True
for arg in node.args:
if isinstance(arg, torch.fx.Node) and arg.op == "call_function":
self._annotate_bypass(arg)

def _fold_dq(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove dq
for n in graph_module.graph.nodes:
user_list = list(n.users.keys())
if n.target not in dq_ops:
continue
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

# skip parameters & buffers
if not self.force_fold and is_parameter(n.args[0], self.edge_program):
self._annotate_bypass(n)
else:
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

def _fold_q(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove q
for n in graph_module.graph.nodes:
if n.target not in q_ops:
continue

to_be_removed = [n]
source_n = n.args[0]

Expand All@@ -57,7 +74,8 @@ def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
graph_module.graph.erase_node(n)

def call(self, graph_module: torch.fx.GraphModule):
self._fold(graph_module)
self._fold_dq(graph_module)
self._fold_q(graph_module)
graph_module.recompile()
dead_code_elimination_pass(graph_module)
return PassResult(graph_module, True)
4 changes: 4 additions & 0 deletions backends/qualcomm/_passes/qnn_pass_manager.py
View file
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Original file line numberDiff line numberDiff line change
Expand Up@@ -200,13 +200,17 @@ def transform_for_export_pipeline(self, exported_program: ExportedProgram):
self.add_pass(DecomposeScaledDotProductAttention())
self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True))
self.add_pass(DecomposeExpM1())
# this pass will rewrite state_dict, it needs to be accomplished before
# to_edge_transform_and_lower
self.add_pass(ConvertConv1dToConv2d(exported_program))
self.add_pass(ConvertSquareToPow())
self.add_pass(LiftConstantScalarOperands())
self._transform(exported_program.graph_module)
ep = lift_constant_tensor_pass(exported_program)
return ep

def transform_for_preprocess_pipeline(self, exported_program: ExportedProgram):
self.add_pass(FoldQDQ(exported_program, force_fold=True))
self.add_pass(InsertRequantize())
self.add_pass(InsertIOQDQ(exported_program))
self.add_pass(LayoutTransform(exported_program, insert_permute=True))
Expand Down
7 changes: 4 additions & 3 deletions backends/qualcomm/_passes/replace_index_put_input.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -33,7 +33,8 @@ def call(self, graph_module: torch.fx.GraphModule):
copy_node := list(node.users)[0]
) and copy_node.target == exir_ops.edge.aten.copy.default:
m_buffer_node = copy_node.args[0]
bad_frozen_node = node.args[0]
dq_node = node.args[0]
bad_frozen_node = dq_node.args[0]
if QCOM_QUANT_ATTRS in bad_frozen_node.meta:
m_buffer_node.meta[QCOM_QUANT_ATTRS] = bad_frozen_node.meta[
QCOM_QUANT_ATTRS
Expand All@@ -43,8 +44,8 @@ def call(self, graph_module: torch.fx.GraphModule):
m_buffer_node.meta[QCOM_QUANT_ATTRS][QCOM_ENCODING]
]
)
with graph.inserting_after(bad_frozen_node):
node.replace_input_with(bad_frozen_node, m_buffer_node)
with graph.inserting_after(dq_node):
node.replace_input_with(dq_node, m_buffer_node)
else:
continue

Expand Down
1 change: 0 additions & 1 deletion backends/qualcomm/_passes/utils.py
View file
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Original file line numberDiff line numberDiff line change
Expand Up@@ -103,7 +103,6 @@ def get_passes_dependency_for_capture_program():
AnnotateStack: [RemoveRedundancy],
AnnotateUnbind: [RemoveRedundancy],
ConvertBmmToMatmul: [RecomposePixelUnshuffle],
ConvertConv1dToConv2d: [FoldQDQ],
ConvertUpsampleBicubicWithBilinear: [RemoveRedundancy],
DecomposeAny: [RemoveRedundancy],
DecomposeLinalgVectorNorm: [RemoveRedundancy],
Expand Down
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Qualcomm AI Engine Direct - fix for pytorch uplevel#10769

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Merged
cccclai merged 3 commits into from
May 20, 2025
Merged

Qualcomm AI Engine Direct - fix for pytorch uplevel #10769

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2e0535a
Qualcomm AI Engine Direct - fix for pytorch uplevel
haowhsu-quic May 5, 2025
9cdc89c
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 15, 2025
186ee40
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 19, 2025
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45 changes: 1 addition & 44 deletions backends/qualcomm/_passes/annotate_quant_attrs.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -7,22 +7,17 @@
from typing import Any, Dict

import torch
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.builders.utils import get_parameter
from executorch.backends.qualcomm.utils.constants import (
QCOM_AXIS,
QCOM_BLOCK_SIZE,
QCOM_DTYPE,
QCOM_ENCODING,
QCOM_QUANT_ATTRS,
QCOM_QUANT_MAX,
QCOM_QUANT_MIN,
QCOM_REQUANTIZE,
QCOM_SCALE,
QCOM_SCALES,
QCOM_ZERO_POINT,
QCOM_ZERO_POINTS,
)
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import dq_ops, get_quant_attrs, q_ops
Expand DownExpand Up@@ -101,43 +96,9 @@ def _annotate_requant(self, n):
n.args[0].meta.setdefault(QCOM_REQUANTIZE, {})
n.args[0].meta[QCOM_REQUANTIZE][user_node.name] = dq_attrs

# Dequant all the fold_quant parameters back to fp32.
# If an operation is not supported by QNN and got fallback, it will expect a fp32 param.
def _dequant_fold_params(self, n, quant_attrs, param):
if quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.quantized_decomposed.dequantize_per_channel.default
]:
dim, axis = param.dim(), quant_attrs[QCOM_AXIS]
scales = self._expand(quant_attrs[QCOM_SCALES], dim, axis)
offsets = self._expand(quant_attrs[QCOM_ZERO_POINTS], dim, axis)
param = param.sub(offsets).mul(scales).to(torch.float32).contiguous()
elif quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.pt2e_quant.dequantize_affine.default
]:
param = torch.ops.pt2e_quant.dequantize_affine(
param,
block_size=quant_attrs[QCOM_BLOCK_SIZE],
scale=quant_attrs[QCOM_SCALE],
zero_point=quant_attrs[QCOM_ZERO_POINT],
input_dtype=quant_attrs[QCOM_DTYPE],
quant_min=quant_attrs[QCOM_QUANT_MIN],
quant_max=quant_attrs[QCOM_QUANT_MAX],
output_dtype=torch.float32,
)
else:
scale = quant_attrs[QCOM_SCALE]
offset = quant_attrs[QCOM_ZERO_POINT]
param = param.sub(offset).mul(scale).to(torch.float32).contiguous()

set_parameter(param, n.args[0], self.edge_program)
n.args[0].meta["val"] = param

def _annotate_quant_attrs(
self, graph_module: torch.fx.GraphModule
) -> torch.fx.GraphModule:
# Keep track of const params that has been dequant, so it does not get
# dequant multiple times if the const param has more than 1 user
visited_const_param = set()
for n in graph_module.graph.nodes:
self._annotate_requant(n)
# With fold_quant enabled, check if the input of dq op is quantized param.
Expand All@@ -149,10 +110,6 @@ def _annotate_quant_attrs(
quant_attrs = get_quant_attrs(self.edge_program, n)
self._annotate_source_nodes(n, quant_attrs)

if param is not None and n.args[0] not in visited_const_param:
visited_const_param.add(n.args[0])
self._dequant_fold_params(n, quant_attrs, param)

return graph_module

def call(self, graph_module: torch.fx.GraphModule):
Expand Down
125 changes: 94 additions & 31 deletions backends/qualcomm/_passes/convert_conv1d_to_conv2d.py
View file
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Original file line numberDiff line numberDiff line change
Expand Up@@ -5,10 +5,8 @@
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_REQUANTIZE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import copy_meta
Expand All@@ -23,16 +21,43 @@ class ConvertConv1dToConv2d(ExportPass):
def __init__(self, edge_program: torch.export.ExportedProgram):
super(ConvertConv1dToConv2d, self).__init__()
self.edge_program = edge_program
self.conv_op_map = {
torch.ops.aten.conv1d.default: torch.ops.aten.conv2d.default,
torch.ops.aten.conv_transpose1d.default: torch.ops.aten.conv_transpose2d.input,
}

def append_qdq(
self,
graph_module: torch.fx.GraphModule,
node: torch.fx.Node,
qdq_node: torch.fx.Node,
):
q_op = torch.ops.quantized_decomposed.quantize_per_tensor.default
dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor.default
if qdq_node.target not in {q_op, dq_op}:
return node

with graph_module.graph.inserting_after(node):
q_args = (node, *qdq_node.args[1:])
q_node = graph_module.graph.create_node("call_function", q_op, q_args)
q_node.meta = copy_meta(node.meta)
q_node.meta["val"] = q_node.meta["val"].to(q_args[-1])
with graph_module.graph.inserting_after(q_node):
dq_args = (q_node, *qdq_node.args[1:])
dq_node = graph_module.graph.create_node(
"call_function", dq_op, dq_args
)
dq_node.meta = copy_meta(node.meta)

return dq_node

def call(self, graph_module: torch.fx.GraphModule):
graph = graph_module.graph
conv_op = exir_ops.edge.aten.convolution.default
for node in graph.nodes:
if node.target == conv_op and node.meta["val"].dim() == 3:

if node.target in self.conv_op_map:
input_node = node.args[0]
with graph_module.graph.inserting_after(input_node):
unsqueeze_op = exir_ops.edge.aten.unsqueeze_copy.default
unsqueeze_op = torch.ops.aten.unsqueeze_copy.default
unsqueeze_node = graph.create_node(
"call_function",
unsqueeze_op,
Expand All@@ -44,52 +69,88 @@ def call(self, graph_module: torch.fx.GraphModule):
unsqueeze_node.meta = copy_meta(
input_node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_unsqueeze = self.append_qdq(
graph_module=graph_module,
node=unsqueeze_node,
qdq_node=input_node,
)

with graph_module.graph.inserting_after(unsqueeze_node):

filter_node = node.args[1]
with graph_module.graph.inserting_after(qdq_node_after_unsqueeze):
filter_arg = node.args[1]
filter_node = (
filter_arg
if filter_arg.op == "placeholder"
else node.args[1].args[0]
)
filter_node.meta["val"] = (
filter_node.meta["val"].unsqueeze(2).contiguous()
)
filter_tensor = get_parameter(filter_node, self.edge_program)
# Ensure tensor is nn.Parameter type, so program does not fail during edge_program._validate()
filter_tensor = nn.Parameter(filter_tensor.unsqueeze(2))
set_parameter(filter_tensor, filter_node, self.edge_program)
filter_tensor = get_parameter(
filter_node, self.edge_program
).unsqueeze(2)
set_parameter(
(
torch.nn.Parameter(filter_tensor)
if filter_tensor.dtype == torch.float
else filter_tensor
),
filter_node,
self.edge_program,
)

num_args = len(node.args)
bias_node = node.args[2]
stride = [1] + node.args[3]
padding = [0] + node.args[4]
dilation = [1] + node.args[5]
transpose = node.args[6]
output_padding = [0] + node.args[7]
groups = node.args[8]

conv2d_node = graph.create_node(
"call_function",
conv_op,
(
unsqueeze_node,
filter_node,
stride = [1] + node.args[3] if num_args > 3 else [1, 1]
padding = [0] + node.args[4] if num_args > 4 else [0, 0]
if node.target == torch.ops.aten.conv1d.default:
dilation = [1] + node.args[5] if num_args > 5 else [1, 1]
groups = node.args[6] if num_args > 5 else 1
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
dilation,
transpose,
groups,
)
else:
output_padding = (
[0] + node.args[5] if num_args > 5 else [0, 0]
)
groups = node.args[6] if num_args > 6 else 1
dilation = [1] + node.args[7] if num_args > 7 else [1, 1]
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
output_padding,
groups,
),
dilation,
)
conv2d_node = graph.create_node(
"call_function",
self.conv_op_map[node.target],
conv_args,
)
conv2d_node.meta = copy_meta(
node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_conv2d = self.append_qdq(
graph_module=graph_module,
node=conv2d_node,
qdq_node=list(node.users)[0],
)

with graph_module.graph.inserting_after(conv2d_node):
squeeze_op = exir_ops.edge.aten.squeeze_copy.dims
with graph_module.graph.inserting_after(qdq_node_after_conv2d):
squeeze_op = torch.ops.aten.squeeze_copy.dims
squeeze_node = graph.create_node(
"call_function",
squeeze_op,
(
conv2d_node,
qdq_node_after_conv2d,
[2],
),
)
Expand All@@ -102,8 +163,10 @@ def call(self, graph_module: torch.fx.GraphModule):
QCOM_REQUANTIZE
]
conv2d_node.meta.pop(QCOM_REQUANTIZE, None)

for user in node.users.copy():
user.replace_input_with(node, squeeze_node)

graph.eliminate_dead_code()
graph_module.recompile()
return PassResult(graph_module, True)
8 changes: 7 additions & 1 deletion backends/qualcomm/_passes/expand_broadcast_tensor_shape.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -9,6 +9,8 @@
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass

from .utils import dq_ops


class ExpandBroadcastTensorShape(ExportPass):
"""
Expand DownExpand Up@@ -45,9 +47,13 @@ def traverse_broadcast_node(self, graph_module: torch.fx.GraphModule):
exir_ops.edge.aten.view_copy.default,
(arg, tuple(new_rank)),
)
# try skip dq_ops to get correct param node if applicable
arg_meta = (
arg.args[0].meta if arg.target in dq_ops else arg.meta
)
# meta needs to be copied elementwisely for fake-tensor
# to be updated correctly and not affect meta of arg
for k, v in arg.meta.items():
for k, v in arg_meta.items():
reshape_node.meta[k] = v
reshape_node.meta["val"] = reshape_node.meta["val"].reshape(
new_rank
Expand Down
30 changes: 24 additions & 6 deletions backends/qualcomm/_passes/fold_qdq.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -4,6 +4,8 @@
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import torch
from executorch.backends.qualcomm.builders.utils import is_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_BYPASS_NODE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass
Expand All@@ -16,23 +18,38 @@ class FoldQDQ(ExportPass):
Erase QDQ pattern.
"""

def __init__(self):
def __init__(self, edge_program: torch.export.ExportedProgram, force_fold=False):
super(FoldQDQ, self).__init__()
self.edge_program = edge_program
self.force_fold = force_fold

def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
def _annotate_bypass(self, node):
node.meta[QCOM_BYPASS_NODE] = True
for arg in node.args:
if isinstance(arg, torch.fx.Node) and arg.op == "call_function":
self._annotate_bypass(arg)

def _fold_dq(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove dq
for n in graph_module.graph.nodes:
user_list = list(n.users.keys())
if n.target not in dq_ops:
continue
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

# skip parameters & buffers
if not self.force_fold and is_parameter(n.args[0], self.edge_program):
self._annotate_bypass(n)
else:
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

def _fold_q(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove q
for n in graph_module.graph.nodes:
if n.target not in q_ops:
continue

to_be_removed = [n]
source_n = n.args[0]

Expand All@@ -57,7 +74,8 @@ def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
graph_module.graph.erase_node(n)

def call(self, graph_module: torch.fx.GraphModule):
self._fold(graph_module)
self._fold_dq(graph_module)
self._fold_q(graph_module)
graph_module.recompile()
dead_code_elimination_pass(graph_module)
return PassResult(graph_module, True)
4 changes: 4 additions & 0 deletions backends/qualcomm/_passes/qnn_pass_manager.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -200,13 +200,17 @@ def transform_for_export_pipeline(self, exported_program: ExportedProgram):
self.add_pass(DecomposeScaledDotProductAttention())
self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True))
self.add_pass(DecomposeExpM1())
# this pass will rewrite state_dict, it needs to be accomplished before
# to_edge_transform_and_lower
self.add_pass(ConvertConv1dToConv2d(exported_program))
self.add_pass(ConvertSquareToPow())
self.add_pass(LiftConstantScalarOperands())
self._transform(exported_program.graph_module)
ep = lift_constant_tensor_pass(exported_program)
return ep

def transform_for_preprocess_pipeline(self, exported_program: ExportedProgram):
self.add_pass(FoldQDQ(exported_program, force_fold=True))
self.add_pass(InsertRequantize())
self.add_pass(InsertIOQDQ(exported_program))
self.add_pass(LayoutTransform(exported_program, insert_permute=True))
Expand Down
7 changes: 4 additions & 3 deletions backends/qualcomm/_passes/replace_index_put_input.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -33,7 +33,8 @@ def call(self, graph_module: torch.fx.GraphModule):
copy_node := list(node.users)[0]
) and copy_node.target == exir_ops.edge.aten.copy.default:
m_buffer_node = copy_node.args[0]
bad_frozen_node = node.args[0]
dq_node = node.args[0]
bad_frozen_node = dq_node.args[0]
if QCOM_QUANT_ATTRS in bad_frozen_node.meta:
m_buffer_node.meta[QCOM_QUANT_ATTRS] = bad_frozen_node.meta[
QCOM_QUANT_ATTRS
Expand All@@ -43,8 +44,8 @@ def call(self, graph_module: torch.fx.GraphModule):
m_buffer_node.meta[QCOM_QUANT_ATTRS][QCOM_ENCODING]
]
)
with graph.inserting_after(bad_frozen_node):
node.replace_input_with(bad_frozen_node, m_buffer_node)
with graph.inserting_after(dq_node):
node.replace_input_with(dq_node, m_buffer_node)
else:
continue

Expand Down
1 change: 0 additions & 1 deletion backends/qualcomm/_passes/utils.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -103,7 +103,6 @@ def get_passes_dependency_for_capture_program():
AnnotateStack: [RemoveRedundancy],
AnnotateUnbind: [RemoveRedundancy],
ConvertBmmToMatmul: [RecomposePixelUnshuffle],
ConvertConv1dToConv2d: [FoldQDQ],
ConvertUpsampleBicubicWithBilinear: [RemoveRedundancy],
DecomposeAny: [RemoveRedundancy],
DecomposeLinalgVectorNorm: [RemoveRedundancy],
Expand Down
Loading
, '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); } })(); })();
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Qualcomm AI Engine Direct - fix for pytorch uplevel#10769

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Merged
cccclai merged 3 commits into from
May 20, 2025
Merged

Qualcomm AI Engine Direct - fix for pytorch uplevel #10769

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2e0535a
Qualcomm AI Engine Direct - fix for pytorch uplevel
haowhsu-quic May 5, 2025
9cdc89c
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 15, 2025
186ee40
Merge branch 'main' into dev_fix_for_pytorch_uplevel
haowhsu-quic May 19, 2025
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45 changes: 1 addition & 44 deletions backends/qualcomm/_passes/annotate_quant_attrs.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -7,22 +7,17 @@
from typing import Any, Dict

import torch
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.builders.utils import get_parameter
from executorch.backends.qualcomm.utils.constants import (
QCOM_AXIS,
QCOM_BLOCK_SIZE,
QCOM_DTYPE,
QCOM_ENCODING,
QCOM_QUANT_ATTRS,
QCOM_QUANT_MAX,
QCOM_QUANT_MIN,
QCOM_REQUANTIZE,
QCOM_SCALE,
QCOM_SCALES,
QCOM_ZERO_POINT,
QCOM_ZERO_POINTS,
)
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import dq_ops, get_quant_attrs, q_ops
Expand DownExpand Up@@ -101,43 +96,9 @@ def _annotate_requant(self, n):
n.args[0].meta.setdefault(QCOM_REQUANTIZE, {})
n.args[0].meta[QCOM_REQUANTIZE][user_node.name] = dq_attrs

# Dequant all the fold_quant parameters back to fp32.
# If an operation is not supported by QNN and got fallback, it will expect a fp32 param.
def _dequant_fold_params(self, n, quant_attrs, param):
if quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.quantized_decomposed.dequantize_per_channel.default
]:
dim, axis = param.dim(), quant_attrs[QCOM_AXIS]
scales = self._expand(quant_attrs[QCOM_SCALES], dim, axis)
offsets = self._expand(quant_attrs[QCOM_ZERO_POINTS], dim, axis)
param = param.sub(offsets).mul(scales).to(torch.float32).contiguous()
elif quant_attrs[QCOM_ENCODING] in [
exir_ops.edge.pt2e_quant.dequantize_affine.default
]:
param = torch.ops.pt2e_quant.dequantize_affine(
param,
block_size=quant_attrs[QCOM_BLOCK_SIZE],
scale=quant_attrs[QCOM_SCALE],
zero_point=quant_attrs[QCOM_ZERO_POINT],
input_dtype=quant_attrs[QCOM_DTYPE],
quant_min=quant_attrs[QCOM_QUANT_MIN],
quant_max=quant_attrs[QCOM_QUANT_MAX],
output_dtype=torch.float32,
)
else:
scale = quant_attrs[QCOM_SCALE]
offset = quant_attrs[QCOM_ZERO_POINT]
param = param.sub(offset).mul(scale).to(torch.float32).contiguous()

set_parameter(param, n.args[0], self.edge_program)
n.args[0].meta["val"] = param

def _annotate_quant_attrs(
self, graph_module: torch.fx.GraphModule
) -> torch.fx.GraphModule:
# Keep track of const params that has been dequant, so it does not get
# dequant multiple times if the const param has more than 1 user
visited_const_param = set()
for n in graph_module.graph.nodes:
self._annotate_requant(n)
# With fold_quant enabled, check if the input of dq op is quantized param.
Expand All@@ -149,10 +110,6 @@ def _annotate_quant_attrs(
quant_attrs = get_quant_attrs(self.edge_program, n)
self._annotate_source_nodes(n, quant_attrs)

if param is not None and n.args[0] not in visited_const_param:
visited_const_param.add(n.args[0])
self._dequant_fold_params(n, quant_attrs, param)

return graph_module

def call(self, graph_module: torch.fx.GraphModule):
Expand Down
125 changes: 94 additions & 31 deletions backends/qualcomm/_passes/convert_conv1d_to_conv2d.py
View file
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Original file line numberDiff line numberDiff line change
Expand Up@@ -5,10 +5,8 @@
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_REQUANTIZE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult

from .utils import copy_meta
Expand All@@ -23,16 +21,43 @@ class ConvertConv1dToConv2d(ExportPass):
def __init__(self, edge_program: torch.export.ExportedProgram):
super(ConvertConv1dToConv2d, self).__init__()
self.edge_program = edge_program
self.conv_op_map = {
torch.ops.aten.conv1d.default: torch.ops.aten.conv2d.default,
torch.ops.aten.conv_transpose1d.default: torch.ops.aten.conv_transpose2d.input,
}

def append_qdq(
self,
graph_module: torch.fx.GraphModule,
node: torch.fx.Node,
qdq_node: torch.fx.Node,
):
q_op = torch.ops.quantized_decomposed.quantize_per_tensor.default
dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor.default
if qdq_node.target not in {q_op, dq_op}:
return node

with graph_module.graph.inserting_after(node):
q_args = (node, *qdq_node.args[1:])
q_node = graph_module.graph.create_node("call_function", q_op, q_args)
q_node.meta = copy_meta(node.meta)
q_node.meta["val"] = q_node.meta["val"].to(q_args[-1])
with graph_module.graph.inserting_after(q_node):
dq_args = (q_node, *qdq_node.args[1:])
dq_node = graph_module.graph.create_node(
"call_function", dq_op, dq_args
)
dq_node.meta = copy_meta(node.meta)

return dq_node

def call(self, graph_module: torch.fx.GraphModule):
graph = graph_module.graph
conv_op = exir_ops.edge.aten.convolution.default
for node in graph.nodes:
if node.target == conv_op and node.meta["val"].dim() == 3:

if node.target in self.conv_op_map:
input_node = node.args[0]
with graph_module.graph.inserting_after(input_node):
unsqueeze_op = exir_ops.edge.aten.unsqueeze_copy.default
unsqueeze_op = torch.ops.aten.unsqueeze_copy.default
unsqueeze_node = graph.create_node(
"call_function",
unsqueeze_op,
Expand All@@ -44,52 +69,88 @@ def call(self, graph_module: torch.fx.GraphModule):
unsqueeze_node.meta = copy_meta(
input_node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_unsqueeze = self.append_qdq(
graph_module=graph_module,
node=unsqueeze_node,
qdq_node=input_node,
)

with graph_module.graph.inserting_after(unsqueeze_node):

filter_node = node.args[1]
with graph_module.graph.inserting_after(qdq_node_after_unsqueeze):
filter_arg = node.args[1]
filter_node = (
filter_arg
if filter_arg.op == "placeholder"
else node.args[1].args[0]
)
filter_node.meta["val"] = (
filter_node.meta["val"].unsqueeze(2).contiguous()
)
filter_tensor = get_parameter(filter_node, self.edge_program)
# Ensure tensor is nn.Parameter type, so program does not fail during edge_program._validate()
filter_tensor = nn.Parameter(filter_tensor.unsqueeze(2))
set_parameter(filter_tensor, filter_node, self.edge_program)
filter_tensor = get_parameter(
filter_node, self.edge_program
).unsqueeze(2)
set_parameter(
(
torch.nn.Parameter(filter_tensor)
if filter_tensor.dtype == torch.float
else filter_tensor
),
filter_node,
self.edge_program,
)

num_args = len(node.args)
bias_node = node.args[2]
stride = [1] + node.args[3]
padding = [0] + node.args[4]
dilation = [1] + node.args[5]
transpose = node.args[6]
output_padding = [0] + node.args[7]
groups = node.args[8]

conv2d_node = graph.create_node(
"call_function",
conv_op,
(
unsqueeze_node,
filter_node,
stride = [1] + node.args[3] if num_args > 3 else [1, 1]
padding = [0] + node.args[4] if num_args > 4 else [0, 0]
if node.target == torch.ops.aten.conv1d.default:
dilation = [1] + node.args[5] if num_args > 5 else [1, 1]
groups = node.args[6] if num_args > 5 else 1
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
dilation,
transpose,
groups,
)
else:
output_padding = (
[0] + node.args[5] if num_args > 5 else [0, 0]
)
groups = node.args[6] if num_args > 6 else 1
dilation = [1] + node.args[7] if num_args > 7 else [1, 1]
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
output_padding,
groups,
),
dilation,
)
conv2d_node = graph.create_node(
"call_function",
self.conv_op_map[node.target],
conv_args,
)
conv2d_node.meta = copy_meta(
node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_conv2d = self.append_qdq(
graph_module=graph_module,
node=conv2d_node,
qdq_node=list(node.users)[0],
)

with graph_module.graph.inserting_after(conv2d_node):
squeeze_op = exir_ops.edge.aten.squeeze_copy.dims
with graph_module.graph.inserting_after(qdq_node_after_conv2d):
squeeze_op = torch.ops.aten.squeeze_copy.dims
squeeze_node = graph.create_node(
"call_function",
squeeze_op,
(
conv2d_node,
qdq_node_after_conv2d,
[2],
),
)
Expand All@@ -102,8 +163,10 @@ def call(self, graph_module: torch.fx.GraphModule):
QCOM_REQUANTIZE
]
conv2d_node.meta.pop(QCOM_REQUANTIZE, None)

for user in node.users.copy():
user.replace_input_with(node, squeeze_node)

graph.eliminate_dead_code()
graph_module.recompile()
return PassResult(graph_module, True)
8 changes: 7 additions & 1 deletion backends/qualcomm/_passes/expand_broadcast_tensor_shape.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -9,6 +9,8 @@
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass

from .utils import dq_ops


class ExpandBroadcastTensorShape(ExportPass):
"""
Expand DownExpand Up@@ -45,9 +47,13 @@ def traverse_broadcast_node(self, graph_module: torch.fx.GraphModule):
exir_ops.edge.aten.view_copy.default,
(arg, tuple(new_rank)),
)
# try skip dq_ops to get correct param node if applicable
arg_meta = (
arg.args[0].meta if arg.target in dq_ops else arg.meta
)
# meta needs to be copied elementwisely for fake-tensor
# to be updated correctly and not affect meta of arg
for k, v in arg.meta.items():
for k, v in arg_meta.items():
reshape_node.meta[k] = v
reshape_node.meta["val"] = reshape_node.meta["val"].reshape(
new_rank
Expand Down
30 changes: 24 additions & 6 deletions backends/qualcomm/_passes/fold_qdq.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -4,6 +4,8 @@
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import torch
from executorch.backends.qualcomm.builders.utils import is_parameter
from executorch.backends.qualcomm.utils.constants import QCOM_BYPASS_NODE
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass, PassResult
from executorch.exir.passes import dead_code_elimination_pass
Expand All@@ -16,23 +18,38 @@ class FoldQDQ(ExportPass):
Erase QDQ pattern.
"""

def __init__(self):
def __init__(self, edge_program: torch.export.ExportedProgram, force_fold=False):
super(FoldQDQ, self).__init__()
self.edge_program = edge_program
self.force_fold = force_fold

def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
def _annotate_bypass(self, node):
node.meta[QCOM_BYPASS_NODE] = True
for arg in node.args:
if isinstance(arg, torch.fx.Node) and arg.op == "call_function":
self._annotate_bypass(arg)

def _fold_dq(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove dq
for n in graph_module.graph.nodes:
user_list = list(n.users.keys())
if n.target not in dq_ops:
continue
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

# skip parameters & buffers
if not self.force_fold and is_parameter(n.args[0], self.edge_program):
self._annotate_bypass(n)
else:
for user_n in user_list:
user_n.replace_input_with(n, n.args[0])
graph_module.graph.erase_node(n)

def _fold_q(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
# remove q
for n in graph_module.graph.nodes:
if n.target not in q_ops:
continue

to_be_removed = [n]
source_n = n.args[0]

Expand All@@ -57,7 +74,8 @@ def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
graph_module.graph.erase_node(n)

def call(self, graph_module: torch.fx.GraphModule):
self._fold(graph_module)
self._fold_dq(graph_module)
self._fold_q(graph_module)
graph_module.recompile()
dead_code_elimination_pass(graph_module)
return PassResult(graph_module, True)
4 changes: 4 additions & 0 deletions backends/qualcomm/_passes/qnn_pass_manager.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -200,13 +200,17 @@ def transform_for_export_pipeline(self, exported_program: ExportedProgram):
self.add_pass(DecomposeScaledDotProductAttention())
self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True))
self.add_pass(DecomposeExpM1())
# this pass will rewrite state_dict, it needs to be accomplished before
# to_edge_transform_and_lower
self.add_pass(ConvertConv1dToConv2d(exported_program))
self.add_pass(ConvertSquareToPow())
self.add_pass(LiftConstantScalarOperands())
self._transform(exported_program.graph_module)
ep = lift_constant_tensor_pass(exported_program)
return ep

def transform_for_preprocess_pipeline(self, exported_program: ExportedProgram):
self.add_pass(FoldQDQ(exported_program, force_fold=True))
self.add_pass(InsertRequantize())
self.add_pass(InsertIOQDQ(exported_program))
self.add_pass(LayoutTransform(exported_program, insert_permute=True))
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7 changes: 4 additions & 3 deletions backends/qualcomm/_passes/replace_index_put_input.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -33,7 +33,8 @@ def call(self, graph_module: torch.fx.GraphModule):
copy_node := list(node.users)[0]
) and copy_node.target == exir_ops.edge.aten.copy.default:
m_buffer_node = copy_node.args[0]
bad_frozen_node = node.args[0]
dq_node = node.args[0]
bad_frozen_node = dq_node.args[0]
if QCOM_QUANT_ATTRS in bad_frozen_node.meta:
m_buffer_node.meta[QCOM_QUANT_ATTRS] = bad_frozen_node.meta[
QCOM_QUANT_ATTRS
Expand All@@ -43,8 +44,8 @@ def call(self, graph_module: torch.fx.GraphModule):
m_buffer_node.meta[QCOM_QUANT_ATTRS][QCOM_ENCODING]
]
)
with graph.inserting_after(bad_frozen_node):
node.replace_input_with(bad_frozen_node, m_buffer_node)
with graph.inserting_after(dq_node):
node.replace_input_with(dq_node, m_buffer_node)
else:
continue

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1 change: 0 additions & 1 deletion backends/qualcomm/_passes/utils.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -103,7 +103,6 @@ def get_passes_dependency_for_capture_program():
AnnotateStack: [RemoveRedundancy],
AnnotateUnbind: [RemoveRedundancy],
ConvertBmmToMatmul: [RecomposePixelUnshuffle],
ConvertConv1dToConv2d: [FoldQDQ],
ConvertUpsampleBicubicWithBilinear: [RemoveRedundancy],
DecomposeAny: [RemoveRedundancy],
DecomposeLinalgVectorNorm: [RemoveRedundancy],
Expand Down
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