File size: 9,907 Bytes
cdf34e1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 | from __future__ import annotations
import argparse
import hashlib
import json
import re
from collections import defaultdict
from pathlib import Path
import numpy as np
import onnx
import onnxruntime as ort
from onnx import TensorProto, helper, numpy_helper
ROOT = Path(__file__).resolve().parent
BASELINE = ROOT / ".work/models/shared/decoder_unified_gather_qdq_int8.onnx"
DEFAULT_SOURCE = ROOT / ".work/models/model-opt/decoder_gather_before_dq_int8.onnx"
DEFAULT_DESTINATION = ROOT / ".work/models/model-opt/decoder_static_fp16_matmul.onnx"
DEFAULT_REPORT = ROOT / ".work/reports/model-fp16-matmul-optimization.json"
VISION_TOKENS = 256
KEEP_QDQ_MATMUL_NAMES = {"/lm_head/MatMul"}
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as source:
for chunk in iter(lambda: source.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def dequantize_to_fp16(
quantized: onnx.TensorProto,
scale: onnx.TensorProto,
zero_point: onnx.TensorProto,
axis: int,
) -> np.ndarray:
values = numpy_helper.to_array(quantized).astype(np.float32)
scales = numpy_helper.to_array(scale).astype(np.float32)
zeros = numpy_helper.to_array(zero_point).astype(np.float32)
broadcast_shape = [1] * values.ndim
broadcast_shape[axis] = scales.size
return ((values - zeros.reshape(broadcast_shape)) * scales.reshape(broadcast_shape)).astype(np.float16)
def rewrite_matmul_weights(model: onnx.ModelProto, selected_layers: set[int]) -> dict:
graph = model.graph
initializers = {value.name: value for value in graph.initializer}
consumers: dict[str, list[onnx.NodeProto]] = defaultdict(list)
for node in graph.node:
for name in node.input:
consumers[name].append(node)
dq_by_matmul_name: dict[str, onnx.NodeProto] = {}
fp16_initializers: list[onnx.TensorProto] = []
removable_initializers: set[str] = set()
quantized_elements = 0
for node in graph.node:
if node.op_type != "DequantizeLinear" or len(node.input) < 3:
continue
quantized = initializers.get(node.input[0])
scale = initializers.get(node.input[1])
zero_point = initializers.get(node.input[2])
if quantized is None or scale is None or zero_point is None:
continue
matches = [consumer for consumer in consumers[node.output[0]] if consumer.op_type == "MatMul"]
if not matches:
continue
if len(matches) != 1:
raise RuntimeError(f"Expected one MatMul consumer for {node.name}, found {len(matches)}")
matmul = matches[0]
if matmul.name in KEEP_QDQ_MATMUL_NAMES:
continue
layer_match = re.search(r"/decoder/layers\.(\d+)/", matmul.name)
if layer_match is None:
raise RuntimeError(f"Unexpected non-layer MatMul {matmul.name}")
if int(layer_match.group(1)) not in selected_layers:
continue
if matmul.input[1] != node.output[0]:
raise RuntimeError(f"Quantized weight is not RHS input for {matmul.name}")
axis = next((attribute.i for attribute in node.attribute if attribute.name == "axis"), 1)
fp16_name = f"{quantized.name}_static_fp16"
fp16 = dequantize_to_fp16(quantized, scale, zero_point, axis)
fp16_initializers.append(numpy_helper.from_array(fp16, fp16_name))
matmul.input[1] = fp16_name
dq_by_matmul_name[matmul.name] = node
removable_initializers.update(node.input[:3])
quantized_elements += fp16.size
expected_matmuls = 7 * len(selected_layers)
if len(dq_by_matmul_name) != expected_matmuls:
raise RuntimeError(f"Expected {expected_matmuls} selected MatMuls, found {len(dq_by_matmul_name)}")
rewritten: list[onnx.NodeProto] = []
removed_dq_names = {node.name for node in dq_by_matmul_name.values()}
for node in graph.node:
if node.name in removed_dq_names:
continue
if node.name not in dq_by_matmul_name:
rewritten.append(node)
continue
input_fp16 = f"{node.input[0]}__for_{node.name.replace('/', '_')}_fp16"
output_float = node.output[0]
output_fp16 = f"{output_float}__fp16"
rewritten.append(
helper.make_node(
"Cast",
[node.input[0]],
[input_fp16],
name=f"{node.name}/CastInputToFp16",
to=TensorProto.FLOAT16,
)
)
node.input[0] = input_fp16
node.output[0] = output_fp16
rewritten.append(node)
rewritten.append(
helper.make_node(
"Cast",
[output_fp16],
[output_float],
name=f"{node.name}/CastOutputToFp32",
to=TensorProto.FLOAT,
)
)
retained = [value for value in graph.initializer if value.name not in removable_initializers]
del graph.initializer[:]
graph.initializer.extend(retained)
graph.initializer.extend(fp16_initializers)
del graph.node[:]
graph.node.extend(rewritten)
return {
"matmuls_rewritten": len(dq_by_matmul_name),
"layers_rewritten": sorted(selected_layers),
"matmuls_kept_qdq": sorted(KEEP_QDQ_MATMUL_NAMES),
"weight_elements": quantized_elements,
"runtime_fp32_weight_bytes_avoided": quantized_elements * 4,
"static_fp16_weight_bytes": quantized_elements * 2,
"source_int8_weight_bytes_removed": quantized_elements,
"estimated_live_weight_bytes_avoided": quantized_elements * 3,
}
def feeds(session: ort.InferenceSession, past_length: int, *, prefill: bool) -> dict[str, np.ndarray]:
rng = np.random.default_rng(20260717 + past_length)
result: dict[str, np.ndarray] = {}
for value in session.get_inputs():
if value.name == "vision_embeds":
length = VISION_TOKENS if prefill else 0
result[value.name] = rng.normal(0, 0.2, [1, length, 512]).astype(np.float32)
elif value.name == "token_ids":
result[value.name] = np.array([[1 if prefill else 4]], dtype=np.int32)
elif value.name == "position_ids":
result[value.name] = (
np.arange(VISION_TOKENS + 1, dtype=np.int32)[None, :]
if prefill
else np.array([[past_length]], dtype=np.int32)
)
else:
result[value.name] = rng.normal(0, 0.02, [1, 2, past_length, 64]).astype(np.float32)
return result
def validate_cpu(baseline: Path, candidate: Path) -> dict:
baseline_session = ort.InferenceSession(str(baseline), providers=["CPUExecutionProvider"])
candidate_session = ort.InferenceSession(str(candidate), providers=["CPUExecutionProvider"])
checks = {}
for label, past_length, prefill in (
("prefill", 0, True),
("step_257", 257, False),
("step_383", 383, False),
):
model_feeds = feeds(baseline_session, past_length, prefill=prefill)
expected = baseline_session.run(None, model_feeds)
actual = candidate_session.run(None, model_feeds)
max_abs = [float(np.max(np.abs(left - right))) for left, right in zip(expected, actual)]
checks[label] = {
"logits_max_abs": max_abs[0],
"all_outputs_max_abs": max(max_abs),
"top_token_baseline": int(expected[0][0, -1].argmax()),
"top_token_candidate": int(actual[0][0, -1].argmax()),
}
if checks[label]["top_token_baseline"] != checks[label]["top_token_candidate"]:
raise RuntimeError(f"CPU top-token parity failed for {label}: {checks[label]}")
return checks
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--baseline", type=Path, default=BASELINE)
parser.add_argument("--source", type=Path, default=DEFAULT_SOURCE)
parser.add_argument("--destination", type=Path, default=DEFAULT_DESTINATION)
parser.add_argument("--report", type=Path, default=DEFAULT_REPORT)
parser.add_argument(
"--layers",
default="1,2,3",
help="Comma-separated decoder layers whose seven MatMuls execute in FP16",
)
arguments = parser.parse_args()
arguments.destination.parent.mkdir(parents=True, exist_ok=True)
arguments.report.parent.mkdir(parents=True, exist_ok=True)
selected_layers = {int(value) for value in arguments.layers.split(",") if value != ""}
if not selected_layers.issubset(set(range(6))):
raise ValueError(f"Invalid decoder layers {sorted(selected_layers)}")
model = onnx.load(arguments.source)
optimization = rewrite_matmul_weights(model, selected_layers)
model.producer_name = "vibe-manga-baberu-webgpu-static-fp16-matmul"
model.producer_version = "1"
onnx.checker.check_model(model)
onnx.save(model, arguments.destination)
parity = validate_cpu(arguments.baseline, arguments.destination)
report = {
"source": {
"path": str(arguments.source.relative_to(ROOT)),
"bytes": arguments.source.stat().st_size,
"sha256": sha256(arguments.source),
},
"optimized": {
"path": str(arguments.destination.relative_to(ROOT)),
"bytes": arguments.destination.stat().st_size,
"sha256": sha256(arguments.destination),
},
"capability": {
"layers": 6,
"hidden_size": 512,
"kv_heads": 2,
"vocabulary": 14630,
"max_new_tokens": 128,
"architecture_changed": False,
},
"static_fp16_matmul": optimization,
"cpu_validation": parity,
}
arguments.report.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
print(json.dumps(report, indent=2))
if __name__ == "__main__":
main()
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