File size: 7,650 Bytes
041c6b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | """Rewrites a constant-folded BiRefNet graph so every WebGPU shader fits in 8 storage buffers and nothing falls back to the CPU."""
import math
import sys
from collections import Counter, defaultdict
import numpy as np
import onnx
from onnx import TensorProto, helper, numpy_helper, shape_inference
FAN = 6
source, target = sys.argv[1], sys.argv[2]
model = shape_inference.infer_shapes(onnx.load(source))
graph = model.graph
shapes = {}
for value in list(graph.value_info) + list(graph.input) + list(graph.output):
t = value.type.tensor_type
shapes[value.name] = ([d.dim_value for d in t.shape.dim], t.elem_type)
inits = {i.name: i for i in graph.initializer}
for name, init in inits.items():
shapes[name] = (list(init.dims), init.data_type)
counter = Counter()
def fresh(base):
counter[base] += 1
return f"wonfy/{base}/{counter[base]}"
new_inits = []
def const(base, array):
name = fresh(base)
new_inits.append(numpy_helper.from_array(np.asarray(array), name))
return name
def attr(node, name, default=None):
for a in node.attribute:
if a.name == name:
return helper.get_attribute_value(a)
return default
consumers = defaultdict(list)
for n in graph.node:
for x in n.input:
consumers[x].append(n)
producers = {out: n for n in graph.node for out in n.output}
# The int64 index chain of each deformable-conv sample, done in float instead (the values are whole numbers).
cast_nodes = [n for n in graph.node if n.op_type == "Cast" and attr(n, "to") == TensorProto.INT64]
region = set()
frontier = [n.output[0] for n in cast_nodes]
while frontier:
name = frontier.pop()
if name in region:
continue
region.add(name)
for c in consumers[name]:
if c.op_type in ("Slice", "Add", "Clip", "Reshape"):
frontier.append(c.output[0])
elif c.op_type == "Concat":
region.add(c.output[0])
elif c.op_type != "GatherND":
raise SystemExit(f"unexpected consumer {c.op_type} of int64 tensor {name}")
cast_source = {n.output[0]: n.input[0] for n in cast_nodes}
drop = set(id(n) for n in cast_nodes)
renames = {}
for n in graph.node:
if n.op_type in ("Add", "Clip") and n.output[0] in region:
for i, x in enumerate(n.input):
if x in inits and inits[x].data_type == TensorProto.INT64:
n.input[i] = const("float_const", numpy_helper.to_array(inits[x]).astype(np.float32))
for n in graph.node:
for i, x in enumerate(n.input):
if x in cast_source:
n.input[i] = cast_source[x]
out_nodes = []
stats = Counter()
def emit(op, inputs, outputs, **attrs):
out_nodes.append(helper.make_node(op, inputs, outputs, name=fresh(op.lower()), **attrs))
def concat_tree(inputs, output, axis):
while len(inputs) > FAN:
grouped = []
for start in range(0, len(inputs), FAN):
group = inputs[start:start + FAN]
if len(group) == 1:
grouped.append(group[0])
continue
name = fresh("concat")
emit("Concat", group, [name], axis=axis)
grouped.append(name)
inputs = grouped
emit("Concat", inputs, [output], axis=axis)
def split_tree(source_name, sizes, outputs, axis):
if len(outputs) <= FAN:
emit("Split", [source_name, const("split_sizes", np.array(sizes, dtype=np.int64))], outputs, axis=axis)
return
size = math.ceil(len(outputs) / FAN)
parts, part_sizes = [], []
for start in range(0, len(outputs), size):
group_outputs, group_sizes = outputs[start:start + size], sizes[start:start + size]
name = group_outputs[0] if len(group_outputs) == 1 else fresh("split")
parts.append((name, group_sizes, group_outputs))
part_sizes.append(sum(group_sizes))
split_tree(source_name, part_sizes, [p[0] for p in parts], axis)
for name, group_sizes, group_outputs in parts:
if len(group_outputs) > 1:
split_tree(name, group_sizes, group_outputs, axis)
concat_inputs = {n.output[0]: list(n.input) for n in graph.node if n.op_type == "Concat" and n.output[0] in region}
for n in graph.node:
if id(n) in drop:
continue
if n.op_type == "Concat" and n.output[0] in region:
stats["index concat removed"] += 1
continue
if n.op_type == "Concat" and len(n.input) > FAN:
concat_tree(list(n.input), n.output[0], attr(n, "axis"))
stats["concat"] += 1
continue
if n.op_type == "Split" and len(n.output) > FAN:
sizes = numpy_helper.to_array(inits[n.input[1]]).tolist()
axis = attr(n, "axis", 0)
split_tree(n.input[0], sizes, list(n.output), axis)
stats["split"] += 1
continue
if n.op_type == "Sum":
total = n.input[0]
for i, x in enumerate(n.input[1:], start=1):
name = n.output[0] if i == len(n.input) - 1 else fresh("sum")
emit("Add", [total, x], [name])
total = name
stats["sum"] += 1
continue
if n.op_type == "GatherND":
assert attr(n, "batch_dims") == 2, "only batch_dims 2 is handled"
data, index = n.input
data_shape = shapes[data][0]
assert data_shape[:2] == [1, 1], data_shape
_, _, height, width, channels = data_shape
y, x = concat_inputs[index]
count = shapes[n.output[0]][0][2]
y32, x32, row, flat, flat1, table, picked = (fresh(k) for k in ("y32", "x32", "row", "flat", "flat1", "table", "picked"))
emit("Cast", [y], [y32], to=TensorProto.INT32)
emit("Cast", [x], [x32], to=TensorProto.INT32)
emit("Mul", [y32, const("width", np.array(width, dtype=np.int32))], [row])
emit("Add", [row, x32], [flat])
emit("Reshape", [flat, const("shape", np.array([count], dtype=np.int64))], [flat1])
emit("Reshape", [data, const("shape", np.array([height * width, channels], dtype=np.int64))], [table])
emit("Gather", [table, flat1], [picked], axis=0)
emit("Reshape", [picked, const("shape", np.array([1, 1, count, channels], dtype=np.int64))], [n.output[0]])
stats["gathernd"] += 1
continue
out_nodes.append(n)
del graph.node[:]
graph.node.extend(out_nodes)
graph.initializer.extend(new_inits)
del graph.value_info[:]
used = {x for n in graph.node for x in n.input}
kept = [i for i in graph.initializer if i.name in used]
del graph.initializer[:]
graph.initializer.extend(kept)
del model.opset_import[:]
model.opset_import.extend([helper.make_opsetid("", 17)])
model = shape_inference.infer_shapes(model, strict_mode=True)
onnx.checker.check_model(model)
onnx.save(model, target)
print("rewrote", dict(stats))
# Audit: buffers per node, and anything WebGPU would run on the CPU.
graph = model.graph
types = {v.name: v.type.tensor_type.elem_type for v in list(graph.value_info) + list(graph.output) + list(graph.input)}
cpu_inputs = {("Reshape", 1), ("Split", 1), ("Slice", 1), ("Slice", 2), ("Slice", 3), ("Slice", 4), ("Clip", 1), ("Clip", 2), ("Pad", 1), ("Pad", 2), ("Resize", 1), ("Resize", 2), ("Resize", 3)}
worst = Counter()
for n in graph.node:
buffers = sum(1 for i, x in enumerate(n.input) if x and (n.op_type, i) not in cpu_inputs) + len(n.output)
worst[n.op_type] = max(worst[n.op_type], buffers)
print("most buffers per op:", dict(sorted(worst.items(), key=lambda kv: -kv[1])))
int64 = Counter(n.op_type for n in graph.node if any(types.get(o) == TensorProto.INT64 for o in n.output))
print("ops still making int64:", dict(int64) or "none")
print("ops:", dict(Counter(n.op_type for n in graph.node).most_common()))
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