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Use the isolated .venv-coreml environment. This is a component feasibility
benchmark, not a complete Core ML pipeline. Cache packages are reproducible.
"""
from __future__ import annotations
import argparse
import gc
import json
import platform
import statistics
import time
from pathlib import Path
import coremltools as ct
import numpy as np
import torch
from coremltools.converters.mil import Builder as mb
from coremltools.converters.mil.mil import types
from safetensors import safe_open
def weight_at(folder, key):
for p in sorted(Path(folder).glob("*.safetensors")):
with safe_open(p, framework="pt", device="cpu") as f:
if key in f.keys():
return f.get_tensor(key).float().numpy()
raise KeyError(key)
def devices(plan):
rows = []
def walk(block):
for op in block.operations:
usage = plan.get_compute_device_usage_for_mlprogram_operation(op)
cost = plan.get_estimated_cost_for_mlprogram_operation(op)
rows.append(
{
"operator": op.operator_name,
"preferred": type(usage.preferred_compute_device).__name__
if usage
else None,
"supported": [
type(d).__name__ for d in usage.supported_compute_devices
]
if usage
else [],
"estimated_cost": cost.weight if cost else None,
}
)
for child in op.blocks:
walk(child)
if plan.model_structure.program:
for fn in plan.model_structure.program.functions.values():
walk(fn.block)
return rows
def projection_program(shape, dtype, weight, conv):
@mb.program(
input_specs=[mb.TensorSpec(shape=shape, dtype=dtype)],
opset_version=ct.target.macOS15,
)
def program(x):
if conv:
return mb.conv(x=x, weight=weight[:, :, None, None], name="projection")
return mb.linear(x=x, weight=weight, name="projection")
return program
def main(a):
dest = Path(a.output)
dest.parent.mkdir(parents=True, exist_ok=True)
root = Path(a.packages)
root.mkdir(parents=True, exist_ok=True)
report = {
"platform": platform.platform(),
"coremltools": ct.__version__,
"repeats": a.repeats,
"scope": "Real checkpoint projection weights; synthetic inputs. Synchronous predict includes NumPy I/O. Component tests, not full image latency.",
"trials": [],
}
torch.set_num_threads(10)
cases = [
(
"dit-fc2",
Path(a.model) / "transformer",
"blocks.0.mlp.fc2.weight",
4181,
False,
),
(
"dit-fc2-conv256",
Path(a.model) / "transformer",
"blocks.0.mlp.fc2.weight",
256,
True,
),
]
if a.vae:
cases.extend(
[
(
"vae-fc2",
Path(a.vae),
"decoder.transformer_blocks.0.ff.w2.weight",
2088,
False,
),
(
"vae-fc2-conv261",
Path(a.vae),
"decoder.transformer_blocks.0.ff.w2.weight",
261,
True,
),
]
)
for name, folder, key, tokens, conv in cases:
if a.cases and name not in a.cases:
continue
w = weight_at(folder, key)
rng = np.random.default_rng(731)
# Round inputs to BF16 so the input precision matches the MLX study.
x = rng.standard_normal((tokens, w.shape[1]), dtype=np.float32)
u = x.view(np.uint32)
x = ((u + np.uint32(0x7FFF) + ((u >> 16) & 1)) & np.uint32(0xFFFF0000)).view(
np.float32
)
reference = (torch.from_numpy(x) @ torch.from_numpy(w).T).numpy()
if conv:
shape = (1, w.shape[1], 1, tokens)
input_array = x.T[None, :, None, :]
else:
shape = x.shape
input_array = x
for precision in ("fp16", "fp32"):
package = root / f"{name}-{precision}.mlpackage"
dtype = types.fp16 if precision == "fp16" else types.fp32
if not package.exists():
program = projection_program(
shape,
dtype,
w.astype(np.float16 if precision == "fp16" else np.float32),
conv,
)
model = ct.convert(
program,
convert_to="mlprogram",
compute_precision=ct.precision.FLOAT16
if precision == "fp16"
else ct.precision.FLOAT32,
minimum_deployment_target=ct.target.macOS15,
skip_model_load=True,
)
model.save(str(package))
del model, program
gc.collect()
for unit in (
ct.ComputeUnit.CPU_ONLY,
ct.ComputeUnit.CPU_AND_GPU,
ct.ComputeUnit.CPU_AND_NE,
ct.ComputeUnit.ALL,
):
row = {
"case": name,
"precision": precision,
"compute_units": unit.name,
"shape": list(shape),
"weight_shape": list(w.shape),
"package_bytes": sum(
p.stat().st_size for p in package.rglob("*") if p.is_file()
),
}
try:
tick = time.perf_counter()
model = ct.models.MLModel(
str(package),
compute_units=unit,
optimization_hints={
"specializationStrategy": ct.SpecializationStrategy.FastPrediction
},
)
row["load_compile_s"] = time.perf_counter() - tick
plan = ct.models.compute_plan.MLComputePlan.load_from_path(
model.get_compiled_model_path(), compute_units=unit
)
row["placement"] = devices(plan)
data = {
"x": input_array.astype(
np.float16 if precision == "fp16" else np.float32
)
}
tick = time.perf_counter()
result = model.predict(data)
row["first_predict_ms"] = 1000 * (time.perf_counter() - tick)
samples = []
for _ in range(a.repeats):
tick = time.perf_counter()
result = model.predict(data)
samples.append(1000 * (time.perf_counter() - tick))
y = next(iter(result.values())).astype(np.float32)
if conv:
y = y[0, :, 0, :].T
row.update(
samples_ms=samples,
median_ms=statistics.median(samples),
relative_rmse=float(
np.sqrt(
np.mean((reference - y) ** 2) / np.mean(reference**2)
)
),
max_abs=float(np.max(np.abs(reference - y))),
)
del result, model, plan, data
gc.collect()
except Exception as exc:
row["error"] = f"{type(exc).__name__}: {exc}"
report["trials"].append(row)
dest.write_text(json.dumps(report, indent=2) + "\n")
print(json.dumps(row), flush=True)
del w, x, reference
gc.collect()
if __name__ == "__main__":
p = argparse.ArgumentParser(description=__doc__)
p.add_argument("--model", required=True)
p.add_argument("--vae")
p.add_argument("--cases", nargs="+")
p.add_argument("--repeats", type=int, default=5)
p.add_argument("--packages", default="cache/coreml-v2")
p.add_argument("--output", default="reports/optimization-v2/coreml.json")
main(p.parse_args())
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