to deep emerald green glass. Preserve the pale engraved NIGHT TIDE label, exact lettering, bottle shape, cap, stone backdrop, composition, lighting and shadows. The glass should be visibly green with realistic emerald highlights and reflections.",
- "images": [
- "experiments/reference-bottle.png"
- ]
- }
-]
diff --git a/experiments/final_bf16.py b/experiments/final_bf16.py
deleted file mode 100644
index 8f87f5b44a44367c9e83e3ada740b80813bb2310..0000000000000000000000000000000000000000
--- a/experiments/final_bf16.py
+++ /dev/null
@@ -1,52 +0,0 @@
-"""Fresh end-to-end timings after the final decoder arithmetic audit."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import logging
-from pathlib import Path
-
-from h3_mlx.runtime import Pipeline
-
-
-def main():
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument("--text-encoder", required=True)
- a = p.parse_args()
- logging.basicConfig(
- level=logging.INFO, format="%(asctime)s %(message)s", datefmt="%H:%M:%S"
- )
- case = next(
- c
- for c in json.loads(Path("experiments/cases.json").read_text())
- if c["id"] == "product"
- )
- pipe = Pipeline(a.model, a.text_encoder)
- try:
- for name, trial, kernel, batch in [
- ("native", 2, "native", 1),
- ("native", 3, "native", 1),
- ("fused", 2, "metal-qk", 8),
- ]:
- pipe.dit_options.update(kernel=kernel)
- pipe.vae_options.update(tile_batch=batch)
- if pipe.model is not None:
- pipe.model.kernel = kernel
- if pipe.vae is not None:
- pipe.vae.tile_batch = batch
- pipe.generate(
- case["prompt"],
- width=2048,
- height=2048,
- seed=case["seed"],
- output=f"outputs/suite/product/{name}-{trial}.png",
- save_trace=True,
- )
- finally:
- pipe.clear()
-
-
-if __name__ == "__main__":
- main()
diff --git a/experiments/gallery_r3.py b/experiments/gallery_r3.py
deleted file mode 100644
index beeca281cef8673eaa1a02e5ba08bdff0e8eab7e..0000000000000000000000000000000000000000
--- a/experiments/gallery_r3.py
+++ /dev/null
@@ -1,91 +0,0 @@
-"""Assemble actual pipeline outputs without fabricated comparison imagery."""
-
-import hashlib
-import html
-import json
-from pathlib import Path
-
-from PIL import Image, ImageDraw, ImageFont
-
-
-def main():
- root = Path("comparisons/optimization-r3")
- root.mkdir(parents=True, exist_ok=True)
- cases = ["product", "portrait", "holdout-detail", "edit"]
- groups = [
- ("Exact Q/K fusion + FC2 tuning", "optimization-r3-qk", "qk-fc2-native-norm"),
- (
- "Lossless packed weights + exact tuning",
- "optimization-r3-compact",
- "qk-fc2-native-norm",
- ),
- (
- "Full-width fusion: exact pixels, memory tradeoff",
- "optimization-r3-deployment",
- "qk-fc2",
- ),
- ]
- parts = [
- 'MiniMax H3: measured comparisons',
- "MiniMax H3: measured image comparisons
Actual saved model outputs, matched prompts, seeds, canvas and reference inputs. Each linked gallery includes native-resolution crops, vertical stacks, and RGB differences amplified eight times.
Exact means no additional change relative to the MLX BF16 recipe on these cases. FP32-decoder and CUDA comparisons are linked separately below.
",
- ]
- records = []
- font = ImageFont.truetype("/System/Library/Fonts/Supplemental/Arial.ttf", 21)
- for title, folder, variant in groups:
- paths = [Path("comparisons") / folder / case / variant for case in cases]
- if not all(
- (p / "metrics.json").exists() and (p / "grid.png").exists() for p in paths
- ):
- raise FileNotFoundError(f"Incomplete final gallery: {folder}")
- parts.append(f"{html.escape(title)}
")
- images = []
- for case, path in zip(cases, paths, strict=True):
- rel = Path("..") / folder / case / variant
- metrics = json.loads((path / "metrics.json").read_text())
- records.append(
- {
- "group": title,
- "case": case,
- "gallery": str(path / "index.html"),
- "metrics_sha256": hashlib.sha256(
- (path / "metrics.json").read_bytes()
- ).hexdigest(),
- "grid_sha256": hashlib.sha256(
- (path / "grid.png").read_bytes()
- ).hexdigest(),
- "pixel_identical": all(
- r["pixel_identical"] for r in metrics["rows"]
- ),
- }
- )
- parts.append(
- f'{html.escape(case)}
Full gallery, detail crops and 8× difference · vertical stack
'
- )
- images.append((case, Image.open(path / "grid.png").convert("RGB")))
- width = max(im.width for _, im in images)
- canvas = Image.new(
- "RGB", (width, sum(im.height + 38 for _, im in images)), (16, 21, 29)
- )
- draw = ImageDraw.Draw(canvas)
- y = 0
- for case, im in images:
- draw.text((12, y + 6), case, font=font, fill="white")
- canvas.paste(im, (0, y + 38))
- y += im.height + 38
- stack = root / f"{folder}-all-cases.png"
- canvas.save(stack)
- parts.append(
- f'Download the four-case comparison stack
'
- )
- parts.append(
- 'Earlier precision-loss examples
These are earlier experiments, retained to show actual quality differences. Their timings are not pooled with this campaign.
'
- )
- (root / "index.html").write_text("\n".join(parts))
- (root / "gallery.json").write_text(json.dumps(records, indent=2) + "\n")
- if not all(r["pixel_identical"] for r in records):
- raise ValueError("An advertised exact gallery changes pixels")
- print(f"Wrote {len(records)} audited image comparisons and three four-case stacks")
-
-
-if __name__ == "__main__":
- main()
diff --git a/experiments/plot_r3.py b/experiments/plot_r3.py
deleted file mode 100644
index c7806b63fb4e2ce71a2941f6ef176d1314735297..0000000000000000000000000000000000000000
--- a/experiments/plot_r3.py
+++ /dev/null
@@ -1,78 +0,0 @@
-"""Plot paired raw samples without pooling campaigns or overlapping memory counters."""
-
-import json
-from pathlib import Path
-
-import matplotlib
-
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-
-
-def draw(path, variant, title, destination):
- data = json.loads(path.read_text())
- fig, axes = plt.subplots(1, 2, figsize=(10.2, 4.5), layout="constrained")
- colors = ["#62738A", "#087F8C"]
- labels = ["Paired current control", "Exact Q/K + FC2 tuning"]
- for j, case in enumerate(["product", "portrait"]):
- for k, (offset, name) in enumerate([(-0.18, "control"), (0.18, variant)]):
- row = data["cases"][case][name]
- x = j + offset
- med = row["median_s"]
- axes[0].bar(
- x, med, width=0.32, color=colors[k], label=labels[k] if j == 0 else None
- )
- axes[0].scatter(
- [x] * len(row["samples_s"]),
- row["samples_s"],
- s=15,
- color="black",
- zorder=3,
- )
- axes[0].text(x, med + 1.7, f"{med:.2f}", ha="center", fontsize=9)
- peak = row["peak_physical_bytes"] / 1024**3
- axes[1].bar(x, peak, width=0.32, color=colors[k])
- axes[1].text(x, peak + 0.5, f"{peak:.2f}", ha="center", fontsize=9)
- for ax in axes:
- ax.set_xticks([0, 1], ["Product 2048²", "Portrait 2048²"])
- ax.spines[["top", "right"]].set_visible(False)
- ax.set_axisbelow(True)
- ax.grid(axis="y", alpha=0.2)
- axes[0].set_ylabel("Complete warm generation (s)")
- axes[0].set_ylim(
- 0,
- max(
- r[name]["max_s"]
- for r in data["cases"].values()
- for name in ["control", variant]
- )
- + 8,
- )
- axes[0].set_title("Three paired repeats; dots are raw samples")
- axes[1].set_ylabel("Maximum sampled parent physical footprint (GiB)")
- axes[1].set_ylim(0, 50)
- axes[1].set_title("Includes Metal; 100 ms sampling; counters not summed")
- fig.legend(
- *axes[0].get_legend_handles_labels(),
- loc="outside lower center",
- ncol=2,
- fontsize=8,
- )
- fig.suptitle(title + " · M5 Pro / MLX 0.32.4", fontsize=12)
- fig.savefig(destination, dpi=170)
- plt.close(fig)
-
-
-if __name__ == "__main__":
- root = Path("reports/optimization-r3")
- for folder, title, output in [
- ("qk-deployment", "Native BF16 weight storage", "timing-memory.png"),
- (
- "compact-deployment",
- "Lossless packed BF16 weight storage",
- "compact-timing-memory.png",
- ),
- ]:
- path = root / folder / "summary.json"
- if path.exists():
- draw(path, "qk-fc2-native-norm", title, root / output)
diff --git a/experiments/r3/blocks.py b/experiments/r3/blocks.py
deleted file mode 100644
index a084197a0f2fb4c2b8b115099ccd3906cff7791f..0000000000000000000000000000000000000000
--- a/experiments/r3/blocks.py
+++ /dev/null
@@ -1,162 +0,0 @@
-"""Matched compiled-block validation of the operator finalists."""
-
-import argparse
-import copy
-import json
-import types
-from pathlib import Path
-
-import mlx.core as mx
-
-from experiments.r3.common import paired
-from experiments.r3.fusions import TunedFC2, qk_native_reduce
-from h3_mlx.benchmark import error
-from h3_mlx.config import SIGMAS
-from h3_mlx.dit import DiT
-from h3_mlx.layout import image_positions, rope_cache
-from h3_mlx.memory import FootprintMonitor
-from h3_mlx.ops import attention, modulate, rms_norm, rotate
-from h3_mlx.runtime import configure_memory, environment
-
-
-def main(a):
- configure_memory(wired_gb=14)
- report = {"environment": environment(), "trace": a.trace, "trials": []}
- with FootprintMonitor() as memory:
- data = mx.load(a.trace)
- model = DiT(a.model)
- model.prepare_modulation(SIGMAS)
- text = model.refine(data["conditioning"])
- x = mx.concatenate(
- (
- text,
- model._linear("video_patch_proj")(data["initial_rows"]).astype(
- mx.bfloat16
- ),
- ),
- axis=0,
- )
- rope = rope_cache(
- image_positions(len(text), 128, 128), model._constant("rope.inv_freq")
- )
- indices = mx.concatenate((data["text_tags"], mx.zeros(4096, mx.int32)))
- params = tuple(model.modulation["block.0"][0, :, j] for j in range(6))
- block = model._block("blocks.0")
- native_fc2 = block.fc2
- control = mx.compile(
- lambda x, params, indices, rope: block(
- x, params=params, indices=indices, rope=rope, mlp_chunk=0
- )
- )
- ref = control(x, params, indices, rope)
- mx.eval(ref)
- xx = modulate(x, block.n1, params[0], params[1], indices, block.config.norm_eps)
- qkv = block.qkv(xx).reshape(-1, 3, 56, 128)
- qref = tuple(
- rotate(rms_norm(qkv[None, :, j], w, block.config.qk_norm_eps), *rope)
- for j, w in ((0, block.qn), (1, block.kn))
- )
- mx.eval(qkv, qref)
- for head_major in (False, True):
-
- def fn(head_major=head_major):
- return qk_native_reduce(
- qkv,
- block.qn,
- block.kn,
- *rope,
- block.config.qk_norm_eps,
- head_major=head_major,
- )
-
- result = fn()
- if head_major:
- result = tuple(t.transpose(0, 2, 1, 3) for t in result)
- row = {
- "group": "qk_native_reduce",
- "head_major": head_major,
- **error(mx.stack(qref), mx.stack(result)),
- }
-
- def native():
- return tuple(
- rotate(
- rms_norm(qkv[None, :, j], w, block.config.qk_norm_eps), *rope
- )
- for j, w in ((0, block.qn), (1, block.kn))
- )
-
- row.update(paired(native, fn, a.repeats))
- report["trials"].append(row)
- print(json.dumps(row), flush=True)
- block.fc2 = TunedFC2(native_fc2)
- candidate = mx.compile(
- lambda x, params, indices, rope: block(
- x, params=params, indices=indices, rope=rope, mlp_chunk=0
- )
- )
- row = {
- "group": "fc2_compiled_block",
- **error(ref, candidate(x, params, indices, rope)),
- }
- row.update(
- paired(
- lambda: control(x, params, indices, rope),
- lambda: candidate(x, params, indices, rope),
- a.repeats,
- )
- )
- report["trials"].append(row)
- print(json.dumps(row), flush=True)
- # Separate block objects keep native compiled closures independent.
- for tuned_fc2 in (False, True):
- fused = copy.copy(block)
- fused.fc2 = TunedFC2(native_fc2) if tuned_fc2 else native_fc2
-
- def fused_attn(
- self, xx, rope, query_chunk, kernel="native", projection_weights=None
- ):
- qq = self.qkv(xx).reshape(-1, 3, 56, 128)
- q, k = qk_native_reduce(
- qq, self.qn, self.kn, *rope, self.config.qk_norm_eps
- )
- v = qq[None, :, 2]
- return self.out(
- attention(q, k, v, query_chunk=query_chunk).reshape(xx.shape[0], -1)
- )
-
- fused.attn = types.MethodType(fused_attn, fused)
- runner = mx.compile(
- lambda x, params, indices, rope, fused=fused: fused(
- x, params=params, indices=indices, rope=rope, mlp_chunk=0
- )
- )
- row = {
- "group": "qk_and_fc2_compiled_block"
- if tuned_fc2
- else "qk_compiled_block",
- **error(ref, runner(x, params, indices, rope)),
- }
- row.update(
- paired(
- lambda: control(x, params, indices, rope),
- lambda runner=runner: runner(x, params, indices, rope),
- a.repeats,
- )
- )
- report["trials"].append(row)
- print(json.dumps(row), flush=True)
- model.clear()
- report["memory"] = memory.result()
- Path(a.output).write_text(json.dumps(report, indent=2) + "\n")
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument(
- "--trace", default="outputs/suite/product/native-3.trace.safetensors"
- )
- p.add_argument("--repeats", type=int, default=10)
- p.add_argument("--output", default="reports/optimization-r3/blocks.json")
- main(p.parse_args())
diff --git a/experiments/r3/common.py b/experiments/r3/common.py
deleted file mode 100644
index 6d844c7af55bfb3f311718959b3ab4c085611fae..0000000000000000000000000000000000000000
--- a/experiments/r3/common.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""Counterbalanced timings and macOS physical footprint, without another GPU runtime."""
-
-from __future__ import annotations
-
-import statistics
-import time
-
-import mlx.core as mx
-
-
-def paired(control, candidate, repeats=6, warmup=2):
- for _ in range(warmup):
- mx.eval(control())
- mx.eval(candidate())
- samples = {"control": [], "candidate": []}
- order = []
- for i in range(repeats):
- names = ("control", "candidate") if i % 2 == 0 else ("candidate", "control")
- for name in names:
- fn = control if name == "control" else candidate
- mx.synchronize()
- start = time.perf_counter()
- value = fn()
- mx.eval(value)
- mx.synchronize()
- samples[name].append((time.perf_counter() - start) * 1000)
- order.append(name)
- del value
- result = {"order": order}
- for name, values in samples.items():
- result[name] = {
- "median_ms": statistics.median(values),
- "min_ms": min(values),
- "max_ms": max(values),
- "samples_ms": values,
- }
- result["reduction_pct"] = 100 * (
- 1 - result["candidate"]["median_ms"] / result["control"]["median_ms"]
- )
- return result
diff --git a/experiments/r3/embedding.py b/experiments/r3/embedding.py
deleted file mode 100644
index 6c902614723386f0ee2540be9cf1db928df3174e..0000000000000000000000000000000000000000
--- a/experiments/r3/embedding.py
+++ /dev/null
@@ -1,58 +0,0 @@
-"""Fresh-process embedding-row experiment; never initialize Torch/MPS."""
-
-import argparse
-import hashlib
-import json
-import time
-from pathlib import Path
-
-import mlx.core as mx
-import numpy as np
-
-from h3_mlx.memory import FootprintMonitor
-from h3_mlx.runtime import configure_memory, environment
-from h3_mlx.weights import WeightStore
-
-
-def main(a):
- configure_memory()
- store = WeightStore(a.encoder)
- key = "model.language_model.embed_tokens.weight"
- # Same number of padded/effective token rows as product, with duplicates,
- # padding, and vocabulary endpoints. No tokenizer/download dependency.
- ids = ([0, 1, 3, store.headers[key]["shape"][0] - 1, 3, 0, 100, 999] * 11)[:88]
- if max(ids) >= store.headers[key]["shape"][0]:
- raise ValueError("Unexpected vocabulary")
- tick = time.perf_counter()
- with FootprintMonitor(interval=0.001) as physical:
- if a.mode == "full":
- embedding = store.get(key)
- value = embedding[mx.array(ids, mx.int32)]
- mx.eval(value)
- del embedding
- else:
- value = store.get(key, rows=ids)
- mx.eval(value)
- elapsed = time.perf_counter() - tick
- bits = np.asarray(value.view(mx.uint16))
- row = {
- "mode": a.mode,
- "seconds": elapsed,
- "physical": physical.result(),
- "mlx_peak_bytes": mx.get_peak_memory(),
- "output_sha256": hashlib.sha256(bits.tobytes()).hexdigest(),
- "shape": list(value.shape),
- "environment": environment(),
- "scope": "Cold process, page-cache warm tensor read + gather only. Excludes tokenizer and full 50-layer encoder.",
- }
- dest = Path(a.output)
- dest.write_text(json.dumps(row, indent=2) + "\n")
- print(json.dumps(row), flush=True)
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--encoder", required=True)
- p.add_argument("--mode", choices=["full", "rows"], required=True)
- p.add_argument("--output", required=True)
- main(p.parse_args())
diff --git a/experiments/r3/embedding_campaign.py b/experiments/r3/embedding_campaign.py
deleted file mode 100644
index 2284a901722687c0a5da0845620634bf72969ae5..0000000000000000000000000000000000000000
--- a/experiments/r3/embedding_campaign.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""Fresh-process AB/BA comparison of full-vocabulary load vs exact row reads."""
-
-import argparse
-import json
-import statistics
-import subprocess
-import sys
-import time
-from pathlib import Path
-
-
-def main(a):
- root = Path(a.output)
- root.mkdir(parents=True, exist_ok=True)
- runs = []
-
- def run(mode, repeat, phase):
- output = root / f"{mode}-{phase}-{repeat}.json"
- command = [
- sys.executable,
- "-m",
- "experiments.r3.embedding",
- "--encoder",
- a.encoder,
- "--mode",
- mode,
- "--output",
- str(output),
- ]
- tick = time.perf_counter()
- with output.with_suffix(".log").open("w") as handle:
- subprocess.run(command, stdout=handle, stderr=subprocess.STDOUT, check=True)
- row = json.loads(output.read_text())
- row.update(
- outer_seconds=time.perf_counter() - tick,
- repeat=repeat,
- phase=phase,
- command=command,
- )
- output.write_text(json.dumps(row, indent=2) + "\n")
- runs.append(row)
-
- run("full", -1, "prepare-page-cache")
- for repeat in range(3):
- for mode in ("full", "rows") if repeat % 2 == 0 else ("rows", "full"):
- run(mode, repeat, "timed")
- if len({r["output_sha256"] for r in runs}) != 1:
- raise ValueError("Selected embedding payloads differ")
- summary = {
- "all_bits_identical": True,
- "scope": "Embedding read and gather only, fresh processes, page cache prepared. Not a complete encoder or generation speedup.",
- "variants": {},
- }
- for mode in ("full", "rows"):
- rr = [r for r in runs if r["mode"] == mode and r["phase"] == "timed"]
- summary["variants"][mode] = {
- "samples_s": [r["seconds"] for r in rr],
- "median_s": statistics.median(r["seconds"] for r in rr),
- "peak_physical_bytes": max(
- r["physical"]["sampled_peak_physical_bytes"] for r in rr
- ),
- "peak_mlx_bytes": max(r["mlx_peak_bytes"] for r in rr),
- }
- (root / "summary.json").write_text(json.dumps(summary, indent=2) + "\n")
- print(json.dumps(summary, indent=2))
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--encoder", required=True)
- p.add_argument("--output", default="reports/optimization-r3/embedding")
- main(p.parse_args())
diff --git a/experiments/r3/fc1.py b/experiments/r3/fc1.py
deleted file mode 100644
index 0c2a5c853c04c24df2809e69c697045dc7c8d15c..0000000000000000000000000000000000000000
--- a/experiments/r3/fc1.py
+++ /dev/null
@@ -1,136 +0,0 @@
-"""Metal 4 dual-accumulator FC1 + SwiGLU epilogue; operator screen only."""
-
-import argparse
-import itertools
-import json
-from functools import lru_cache
-from pathlib import Path
-
-import mlx.core as mx
-
-from experiments.r3.common import paired
-from h3_mlx.benchmark import error
-from h3_mlx.config import SIGMAS
-from h3_mlx.dit import DiT
-from h3_mlx.layout import image_positions, rope_cache
-from h3_mlx.ops import gated_add, modulate, swiglu
-from h3_mlx.runtime import configure_memory, environment
-
-
-@lru_cache(None)
-def kernel():
- return mx.fast.metal_kernel(
- name="h3_r3_mpp_fc1_swiglu",
- input_names=["x", "w"],
- output_names=["out"],
- header="#include \n#include \nusing namespace mpp::tensor_ops;",
- source=r"""
- const int M=x_shape[0], K=x_shape[1], N=w_shape[0]/2;
- int tm=(M+BM-1)/BM, tn=N/BN;
- int tile=threadgroup_position_in_grid.x;
- int group=tile/(8*tn), fm=group*8, sm=min(tm-fm,8);
- int local=tile-group*8*tn;
- int ti=fm+local%sm, tj=local/sm;
- auto A=tensor(const_cast(x),dextents(K,M),array{1,K});
- auto B=tensor(const_cast(w),dextents(K,N*2),array{1,K});
- auto D=tensor(out,dextents(N,M),array{1,N});
- constexpr auto desc=matmul2d_descriptor(BM,BN,BK,false,true,false,matmul2d_descriptor::mode::multiply_accumulate);
- matmul2d> op;
- auto a0=A.template slice(0,ti*BM);
- auto b0=B.template slice(0,tj*BN);
- auto ga=op.template get_destination_cooperative_tensor();
- auto ua=op.template get_destination_cooperative_tensor();
- #pragma unroll
- for (int e=0;e(k,ti*BM);
- auto bg=B.template slice(k,tj*BN);
- auto bu=B.template slice(k,N+tj*BN);
- op.run(a,bg,ga); op.run(a,bu,ua);
- }
- auto result=op.template get_destination_cooperative_tensor();
- #pragma unroll
- for (int e=0;e(tj*BN,ti*BM));
- """,
- )
-
-
-def fused(x, w, bm, bn, bk, sg):
- n = w.shape[0] // 2
- return kernel()(
- inputs=[x, w],
- template=[("T", x.dtype), ("BM", bm), ("BN", bn), ("BK", bk), ("SG", sg)],
- grid=(((x.shape[0] + bm - 1) // bm) * (n // bn) * sg * 32, 1, 1),
- threadgroup=(sg * 32, 1, 1),
- output_shapes=[(x.shape[0], n)],
- output_dtypes=[x.dtype],
- )[0]
-
-
-def main(a):
- configure_memory(wired_gb=14)
- data = mx.load(a.trace)
- model = DiT(a.model)
- model.prepare_modulation(SIGMAS)
- text = model.refine(data["conditioning"])
- x = mx.concatenate(
- (
- text,
- model._linear("video_patch_proj")(data["initial_rows"]).astype(mx.bfloat16),
- ),
- axis=0,
- )
- rope = rope_cache(
- image_positions(len(text), 128, 128), model._constant("rope.inv_freq")
- )
- ids = mx.concatenate((data["text_tags"], mx.zeros(4096, mx.int32)))
- params = tuple(model.modulation["block.0"][0, :, j] for j in range(6))
- block = model._block("blocks.0")
- h = modulate(x, block.n1, params[0], params[1], ids, block.config.norm_eps)
- x = gated_add(x, block.attn(h, rope, 0), params[2], ids)
- h = modulate(x, block.n2, params[3], params[4], ids, block.config.norm_eps)
- if block.fc1.bias is not None:
- raise ValueError("This probe assumes the actual bias-free FC1")
- mx.eval(h)
- native = mx.compile(lambda h: swiglu(block.fc1(h)))
- ref = native(h)
- mx.eval(ref)
- report = {
- "environment": environment(),
- "scope": "Real FC1 activations; fused dual GEMM + native precise sigmoid/BF16 boundaries. Compilation excluded, materialization included.",
- "trials": [],
- }
- for (bm, bn, sg), bk in itertools.product(
- ((64, 64, 4), (64, 128, 4), (64, 128, 8), (128, 128, 8)), (128, 256)
- ):
- row = {"tile": [bm, bn, bk], "simdgroups": sg}
- try:
-
- def fn(bm=bm, bn=bn, bk=bk, sg=sg):
- return fused(h, block.fc1.weight, bm, bn, bk, sg)
-
- row.update(error(ref, fn()), **paired(lambda: native(h), fn, a.repeats))
- except Exception as exc:
- row["error"] = f"{type(exc).__name__}: {exc}"
- report["trials"].append(row)
- Path(a.output).write_text(json.dumps(report, indent=2) + "\n")
- print(json.dumps(row), flush=True)
- model.clear()
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument(
- "--trace", default="outputs/suite/product/native-3.trace.safetensors"
- )
- p.add_argument("--repeats", type=int, default=8)
- p.add_argument("--output", default="reports/optimization-r3/fc1.json")
- main(p.parse_args())
diff --git a/experiments/r3/fusions.py b/experiments/r3/fusions.py
deleted file mode 100644
index a9a5e6f3f1c941e0caaaf62e705e0b881616953c..0000000000000000000000000000000000000000
--- a/experiments/r3/fusions.py
+++ /dev/null
@@ -1,167 +0,0 @@
-"""Research kernels. No default dispatch until full-output validation."""
-
-from functools import lru_cache
-
-import mlx.core as mx
-
-from experiments.v2_mpp import gemm
-
-
-class TunedFC2:
- def __init__(self, layer):
- self.original = layer
- self.bias = layer.bias
- self.weight = layer.weight
- self.zero = mx.zeros(self.weight.shape[0], self.weight.dtype)
-
- @property
- def nbytes(self):
- return self.original.nbytes
-
- def __call__(self, x):
- if x.ndim != 2 or x.dtype != mx.bfloat16 or self.weight.shape != (5376, 14336):
- return self.original(x)
- return gemm(
- x,
- self.weight,
- self.zero if self.bias is None else self.bias,
- 64,
- 128,
- 512,
- 4,
- 8,
- )
-
-
-@lru_cache(None)
-def _qk():
- return mx.fast.metal_kernel(
- name="h3_r3_qk_native_reduce",
- input_names=["qkv", "qw", "kw", "cosine", "sine", "epsilon"],
- output_names=["qout", "kout"],
- source=r"""
- uint lane = thread_position_in_threadgroup.x;
- uint row = threadgroup_position_in_grid.y;
- uint heads = qkv_shape[2], dim = qkv_shape[3];
- uint token = row / heads, head = row % heads;
- uint qb = token * 3 * heads * dim + head * dim;
- uint kb = qb + heads * dim;
- uint rot = cosine_shape[1];
- float qsum = 0, ksum = 0;
- #pragma unroll
- for (uint j = 0; j < 4; ++j) {
- uint c = lane * 4 + j;
- float q = float(qkv[qb+c]), k = float(qkv[kb+c]);
- qsum += q*q; ksum += k*k;
- }
- float qi = metal::precise::rsqrt(simd_sum(qsum)/float(dim)+epsilon[0]);
- float ki = metal::precise::rsqrt(simd_sum(ksum)/float(dim)+epsilon[0]);
- #pragma unroll
- for (uint j = 0; j < 4; ++j) {
- uint c = lane * 4 + j;
- T qn = T(float(qw[c])*(float(qkv[qb+c])*qi));
- T kn = T(float(kw[c])*(float(qkv[kb+c])*ki));
- if (c < rot) {
- uint partner = (c+rot/2)%rot;
- T qother = T(float(qw[partner])*(float(qkv[qb+partner])*qi));
- T kother = T(float(kw[partner])*(float(qkv[kb+partner])*ki));
- if (c < rot/2) { qother = -qother; kother = -kother; }
- T qcos = T(qn*cosine[token*rot+c]);
- T kcos = T(kn*cosine[token*rot+c]);
- T qsin = T(qother*sine[token*rot+c]);
- T ksin = T(kother*sine[token*rot+c]);
- qn = T(qcos+qsin); kn = T(kcos+ksin);
- }
- uint offset = HEAD_MAJOR ? (head*qkv_shape[0]+token)*dim+c : row*dim+c;
- qout[offset] = qn; kout[offset] = kn;
- }
- """,
- )
-
-
-def qk_native_reduce(qkv, qw, kw, cos, sin, eps=1e-5, *, head_major=False):
- n, _, heads, dim = qkv.shape
- if dim != 128:
- raise ValueError("Research kernel only supports D128")
- shape = (1, heads, n, dim) if head_major else (1, n, heads, dim)
- return _qk()(
- inputs=[
- qkv,
- qw,
- kw,
- cos.reshape(n, -1),
- sin.reshape(n, -1),
- mx.array([eps], mx.float32),
- ],
- template=[("T", qkv.dtype), ("HEAD_MAJOR", head_major)],
- grid=(32, n * heads, 1),
- threadgroup=(32, 1, 1),
- output_shapes=[shape, shape],
- output_dtypes=[qkv.dtype, qkv.dtype],
- )
-
-
-@lru_cache(None)
-def _norm():
- return mx.fast.metal_kernel(
- name="h3_r3_norm_mod_native_reduce",
- input_names=["x", "weight", "shift", "scale", "indices", "epsilon"],
- output_names=["out"],
- source=r"""
- uint tid = thread_position_in_threadgroup.x;
- uint row = threadgroup_position_in_grid.y;
- uint width = x_shape[1];
- float sum = 0;
- for (uint r = 0; r < width; r += TG*4) {
- #pragma unroll
- for (uint j = 0; j < 4; ++j) {
- uint c = r + tid*4+j;
- if (c < width) { float a = float(x[row*width+c]); sum += a*a; }
- }
- }
- sum = simd_sum(sum);
- threadgroup float partial[32];
- uint lane = tid % 32, warp = tid / 32;
- if (warp == 0) partial[lane] = 0;
- threadgroup_barrier(mem_flags::mem_threadgroup);
- if (lane == 0) partial[warp] = sum;
- threadgroup_barrier(mem_flags::mem_threadgroup);
- threadgroup float inverse;
- if (warp == 0) {
- float s = simd_sum(partial[lane]);
- if (lane == 0) inverse = metal::precise::rsqrt(s/float(width)+epsilon[0]);
- }
- threadgroup_barrier(mem_flags::mem_threadgroup);
- uint index = indices[row]*width;
- for (uint r = 0; r < width; r += TG*4) {
- #pragma unroll
- for (uint j = 0; j < 4; ++j) {
- uint c = r+tid*4+j;
- if (c < width) {
- T affine = T(float(weight[c])*(float(x[row*width+c])*inverse));
- T factor = T(T(1)+scale[index+c]);
- T product = T(affine*factor);
- out[row*width+c] = T(product+shift[index+c]);
- }
- }
- }
- """,
- )
-
-
-def norm_native_reduce(x, weight, shift, scale, indices, eps=1e-5, threads=1024):
- return _norm()(
- inputs=[
- x,
- weight,
- shift,
- scale,
- indices.astype(mx.uint32),
- mx.array([eps], mx.float32),
- ],
- template=[("T", x.dtype), ("TG", threads)],
- grid=(threads, x.shape[0], 1),
- threadgroup=(threads, 1, 1),
- output_shapes=[x.shape],
- output_dtypes=[x.dtype],
- )[0]
diff --git a/experiments/r3/images.py b/experiments/r3/images.py
deleted file mode 100644
index 6f4ff25f31821972c6dc9c411d6b57040365c694..0000000000000000000000000000000000000000
--- a/experiments/r3/images.py
+++ /dev/null
@@ -1,252 +0,0 @@
-"""Same-process, shared-weight, AB/BA full-image control/candidate validation."""
-
-from __future__ import annotations
-
-import argparse
-import copy
-import hashlib
-import json
-import logging
-import time
-import types
-from collections import OrderedDict
-from pathlib import Path
-
-import numpy as np
-
-from experiments.r3.fusions import TunedFC2, qk_native_reduce
-from experiments.r3.trace_audit import payloads
-from h3_mlx.compare import image_metrics, make_comparison
-from h3_mlx.memory import FootprintMonitor
-from h3_mlx.ops import attention
-from h3_mlx.runtime import Pipeline
-
-
-def canonical_pixels(image):
- return hashlib.sha256(np.asarray(image.convert("RGB")).tobytes()).hexdigest()
-
-
-def fused_attn(self, x, rope, query_chunk, kernel="native", projection_weights=None):
- if (
- rope is None
- or self.config.attention_head_dim != 128
- or projection_weights is not None
- ):
- return self._native_attn(x, rope, query_chunk, kernel, projection_weights)
- qkv = self.qkv(x).reshape(-1, 3, self.config.num_attention_heads, 128)
- q, k = qk_native_reduce(qkv, self.qn, self.kn, *rope, self.config.qk_norm_eps)
- return self.out(
- attention(q, k, qkv[None, :, 2], query_chunk=query_chunk).reshape(
- x.shape[0], -1
- )
- )
-
-
-def main(a):
- logging.basicConfig(
- level=logging.INFO, format="%(asctime)s %(message)s", datefmt="%H:%M:%S"
- )
- root = Path(a.output)
- if root.exists() and any(root.iterdir()):
- raise FileExistsError(
- f"Use a fresh output directory to preserve prior evidence: {root}"
- )
- root.mkdir(parents=True, exist_ok=True)
- # Retain the actual source bytes before inference, including research paths.
- source_manifest = {}
- for source in sorted(
- [
- *Path("h3_mlx").glob("*.py"),
- *Path("experiments/r3").glob("*.py"),
- Path("experiments/v2_mpp.py"),
- ]
- ):
- destination = root / "source" / source
- destination.parent.mkdir(parents=True, exist_ok=True)
- destination.write_bytes(source.read_bytes())
- source_manifest[str(source)] = hashlib.sha256(source.read_bytes()).hexdigest()
- (root / "source-manifest.json").write_text(
- json.dumps(source_manifest, indent=2) + "\n"
- )
- manifest = json.loads(Path(a.workload).read_text())
- (root / "workload.json").write_text(json.dumps(manifest, indent=2) + "\n")
- cases = {c["id"]: c for c in manifest["cases"]}
- config = {
- k: v
- for k, v in manifest["control_config"].items()
- if k not in ("rng", "six_original_steps")
- }
- pipeline = Pipeline(a.model, a.text_encoder, **config)
- states = {}
- rows = []
- quality = {}
-
- def run(case, variant, repeat, phase):
- dest = root / case["id"] / f"{variant}-{phase}-{repeat}.png"
- tick = time.perf_counter()
- with FootprintMonitor() as physical:
- image, record = pipeline.generate(
- case["prompt"],
- width=case["width"],
- height=case["height"],
- seed=case["seed"],
- images=case.get("images", ()),
- output=dest,
- save_trace=True,
- )
- record.update(
- physical_memory=physical.result(),
- outer_seconds=time.perf_counter() - tick,
- variant=variant,
- repeat=repeat,
- phase=phase,
- case=case["id"],
- canonical_pixel_sha256=canonical_pixels(image),
- )
- dest.with_suffix(".json").write_text(json.dumps(record, indent=2) + "\n")
- summary = {
- "case": case["id"],
- "variant": variant,
- "repeat": repeat,
- "phase": phase,
- "seconds": record["timings"]["total_s"],
- "peak_memory_bytes": record["physical_memory"][
- "sampled_peak_physical_bytes"
- ],
- "mlx_peak_bytes": round(record["memory"]["peak_gb"] * 1024**3),
- "output_sha256": record["canonical_pixel_sha256"],
- "output": str(dest),
- "nonfinite_values": len(record["nonfinite_steps"]),
- "step_seconds": [record["timings"][f"step_{i}_s"] for i in range(6)],
- }
- rows.append(summary)
- with (root / "runs.jsonl").open("a") as handle:
- handle.write(json.dumps(summary) + "\n")
- print(json.dumps(summary), flush=True)
- return image, dest
-
- def select(variant):
- model = pipeline.model
- if "control" not in states:
- states["control"] = (model.blocks, model.compiled_blocks)
- if variant not in states:
- blocks = OrderedDict()
- for key, original in states["control"][0].items():
- block = copy.copy(original)
- if "fc2" in variant:
- if a.deployment:
- from h3_mlx.tuning import TunedFC2 as DeployedFC2
-
- block.fc2 = DeployedFC2(block.fc2)
- else:
- block.fc2 = TunedFC2(block.fc2)
- if "qk" in variant and not a.deployment:
- block._native_attn = block.attn
- block.attn = types.MethodType(fused_attn, block)
- blocks[key] = block
- states[variant] = (blocks, {})
- model.blocks, model.compiled_blocks = states[variant]
- if a.deployment:
- model.kernel = (
- ("metal-qk-exact" if "native-norm" in variant else "metal-exact")
- if "qk" in variant
- else "native"
- )
- model.tune_matmul = "fc2" in variant
- pipeline.dit_options.update(
- kernel=model.kernel, tune_matmul=model.tune_matmul
- )
-
- try:
- # The first full image populates weights and native GPU/conditioning caches.
- first = cases[a.cases[0]]
- control_image, control_path = run(first, "control", -1, "cold")
- for variant in a.variants:
- select(variant)
- candidate_image, candidate_path = run(first, variant, -1, "warmup")
- metrics = image_metrics(control_image, candidate_image)
- quality[f"{first['id']}/{variant}"] = metrics
- make_comparison(
- control_path,
- [candidate_path],
- Path(a.comparisons) / first["id"] / variant,
- labels=["Current BF16 control", variant],
- )
- Path(root / "quality.json").write_text(json.dumps(quality, indent=2) + "\n")
- trace_equal = payloads(
- control_path.with_suffix(".trace.safetensors")
- ) == payloads(candidate_path.with_suffix(".trace.safetensors"))
- if a.stop_on_difference and (
- not metrics["pixel_identical"] or not trace_equal
- ):
- print(
- f"QUALITY STOP: {variant} changes pixels; comparison retained",
- flush=True,
- )
- return
- for case_id in a.cases:
- case = cases[case_id]
- if case_id != first["id"] and case_id in manifest["timing_cases"]:
- for variant in ("control", *a.variants):
- select(variant)
- run(case, variant, -1, "warmup")
- repeats = a.repeats if case_id in manifest["timing_cases"] else 1
- anchors = {}
- for repeat in range(repeats):
- order = ["control", *a.variants]
- if repeat % 2:
- order.reverse()
- for variant in order:
- select(variant)
- image, path = run(
- case,
- variant,
- repeat,
- "timed"
- if case_id in manifest["timing_cases"]
- else "validation",
- )
- anchors[variant] = (image, path)
- ref_image, ref_path = anchors["control"]
- for variant in a.variants:
- metrics = image_metrics(ref_image, anchors[variant][0])
- quality[f"{case_id}/{variant}/{repeat}"] = metrics
- if repeat == 0:
- make_comparison(
- ref_path,
- [anchors[variant][1]],
- Path(a.comparisons) / case_id / variant,
- labels=["Current BF16 control", variant],
- )
- Path(root / "quality.json").write_text(
- json.dumps(quality, indent=2) + "\n"
- )
- finally:
- states.clear()
- pipeline.clear()
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument("--text-encoder", required=True)
- p.add_argument(
- "--variants",
- nargs="+",
- choices=["qk", "fc2", "qk-fc2", "qk-fc2-native-norm"],
- default=["qk-fc2"],
- )
- p.add_argument(
- "--cases", nargs="+", default=["product", "portrait", "holdout-detail", "edit"]
- )
- p.add_argument("--repeats", type=int, default=3)
- p.add_argument("--workload", default="optimization/round-03/workload.json")
- p.add_argument("--stop-on-difference", action="store_true")
- p.add_argument(
- "--deployment",
- action="store_true",
- help="Use production dispatch and API implementation",
- )
- p.add_argument("--output", default="outputs/optimization-r3")
- p.add_argument("--comparisons", default="comparisons/optimization-r3")
- main(p.parse_args())
diff --git a/experiments/r3/norm.py b/experiments/r3/norm.py
deleted file mode 100644
index 3ef6d33f4ddc14d1e8d2c548fb3e83fb78306e83..0000000000000000000000000000000000000000
--- a/experiments/r3/norm.py
+++ /dev/null
@@ -1,120 +0,0 @@
-"""Test native reduction matching for full-width RMSNorm + AdaLN fusion."""
-
-import argparse
-import copy
-import json
-import types
-from pathlib import Path
-
-import mlx.core as mx
-
-from experiments.r3.common import paired
-from experiments.r3.fusions import norm_native_reduce
-from h3_mlx.benchmark import error
-from h3_mlx.config import SIGMAS
-from h3_mlx.dit import DiT
-from h3_mlx.layout import image_positions, rope_cache
-from h3_mlx.ops import gated_add, modulate
-from h3_mlx.runtime import configure_memory, environment
-
-
-def main(a):
- configure_memory(wired_gb=14)
- data = mx.load(a.trace)
- model = DiT(a.model, kernel="metal-exact", tune_matmul=True)
- model.prepare_modulation(SIGMAS)
- text = model.refine(data["conditioning"])
- x = mx.concatenate(
- (
- text,
- model._linear("video_patch_proj")(data["initial_rows"]).astype(mx.bfloat16),
- ),
- axis=0,
- )
- rope = rope_cache(
- image_positions(len(text), 128, 128), model._constant("rope.inv_freq")
- )
- indices = mx.concatenate((data["text_tags"], mx.zeros(4096, mx.int32)))
- params = tuple(model.modulation["block.0"][0, :, j] for j in range(6))
- block = model._block("blocks.0")
- report = {"environment": environment(), "trials": []}
- ref = modulate(x, block.n1, params[0], params[1], indices, block.config.norm_eps)
- mx.eval(ref)
- for threads in (256, 512, 1024):
-
- def fn(threads=threads):
- return norm_native_reduce(
- x,
- block.n1,
- params[0],
- params[1],
- indices,
- block.config.norm_eps,
- threads,
- )
-
- def native():
- return modulate(
- x, block.n1, params[0], params[1], indices, block.config.norm_eps
- )
-
- row = {
- "group": "norm",
- "threads": threads,
- **error(ref, fn()),
- **paired(native, fn, a.repeats),
- }
- report["trials"].append(row)
- print(json.dumps(row), flush=True)
- native = mx.compile(
- lambda x, params, indices, rope: block(
- x,
- params=params,
- indices=indices,
- rope=rope,
- mlp_chunk=0,
- kernel="metal-exact",
- )
- )
- reference = native(x, params, indices, rope)
- mx.eval(reference)
-
- def forward(self, xx, *, params, indices, rope, **kwargs):
- sa, ca, ga, sm, cm, gm = params
- h = norm_native_reduce(xx, self.n1, sa, ca, indices, self.config.norm_eps)
- xx = gated_add(xx, self.attn(h, rope, 0, "metal-exact"), ga, indices)
- h = norm_native_reduce(xx, self.n2, sm, cm, indices, self.config.norm_eps)
- return gated_add(xx, self.mlp(h, 0), gm, indices)
-
- # __call__ is looked up on the class, so use an explicit bound method.
- fused = copy.copy(block)
- fused.forward = types.MethodType(forward, fused)
- candidate = mx.compile(
- lambda x, params, indices, rope: fused.forward(
- x, params=params, indices=indices, rope=rope
- )
- )
- row = {
- "group": "block",
- **error(reference, candidate(x, params, indices, rope)),
- **paired(
- lambda: native(x, params, indices, rope),
- lambda: candidate(x, params, indices, rope),
- a.repeats,
- ),
- }
- report["trials"].append(row)
- print(json.dumps(row), flush=True)
- Path(a.output).write_text(json.dumps(report, indent=2) + "\n")
- model.clear()
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument(
- "--trace", default="outputs/suite/product/native-3.trace.safetensors"
- )
- p.add_argument("--repeats", type=int, default=10)
- p.add_argument("--output", default="reports/optimization-r3/norm.json")
- main(p.parse_args())
diff --git a/experiments/r3/panel.py b/experiments/r3/panel.py
deleted file mode 100644
index aeba0c41d54cabe99009155874e6496569b0d9ca..0000000000000000000000000000000000000000
--- a/experiments/r3/panel.py
+++ /dev/null
@@ -1,87 +0,0 @@
-"""Does a larger K panel improve the complete final fused block?"""
-
-import argparse
-import copy
-import json
-from pathlib import Path
-
-import mlx.core as mx
-
-from experiments.r3.common import paired
-from h3_mlx.benchmark import error
-from h3_mlx.config import SIGMAS
-from h3_mlx.dit import DiT
-from h3_mlx.layout import image_positions, rope_cache
-from h3_mlx.runtime import configure_memory, environment
-from h3_mlx.tuning import TunedFC2, _gemm
-
-
-class Panel2048(TunedFC2):
- def __call__(self, x):
- return _gemm(
- x,
- self.weight,
- self.zero if self.bias is None else self.bias,
- 64,
- 128,
- 2048,
- 4,
- 8,
- )
-
-
-def main(a):
- configure_memory(wired_gb=14)
- data = mx.load("outputs/suite/product/native-3.trace.safetensors")
- model = DiT(a.model, kernel="metal-exact", tune_matmul=True)
- model.prepare_modulation(SIGMAS)
- text = model.refine(data["conditioning"])
- x = mx.concatenate(
- (
- text,
- model._linear("video_patch_proj")(data["initial_rows"]).astype(mx.bfloat16),
- ),
- axis=0,
- )
- rope = rope_cache(
- image_positions(len(text), 128, 128), model._constant("rope.inv_freq")
- )
- ids = mx.concatenate((data["text_tags"], mx.zeros(4096, mx.int32)))
- params = tuple(model.modulation["block.0"][0, :, j] for j in range(6))
- block = model._block("blocks.0")
- candidate = copy.copy(block)
- candidate.fc2 = Panel2048(block.fc2.original)
- native = mx.compile(
- lambda x, p, i, r: block(
- x, params=p, indices=i, rope=r, kernel="metal-exact", mlp_chunk=0
- )
- )
- tuned = mx.compile(
- lambda x, p, i, r: candidate(
- x, params=p, indices=i, rope=r, kernel="metal-exact", mlp_chunk=0
- )
- )
- ref = native(x, params, ids, rope)
- mx.eval(ref)
- report = {
- "environment": environment(),
- "control": "All exact fusions + BK512 FC2",
- "candidate": "Same with BK2048 FC2",
- **error(ref, tuned(x, params, ids, rope)),
- **paired(
- lambda: native(x, params, ids, rope),
- lambda: tuned(x, params, ids, rope),
- a.repeats,
- ),
- }
- Path(a.output).write_text(json.dumps(report, indent=2) + "\n")
- print(json.dumps(report), flush=True)
- model.clear()
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument("--repeats", type=int, default=20)
- p.add_argument("--output", default="reports/optimization-r3/panel.json")
- main(p.parse_args())
diff --git a/experiments/r3/report.py b/experiments/r3/report.py
deleted file mode 100644
index 91109e119789110740034f7f15244ce99276a508..0000000000000000000000000000000000000000
--- a/experiments/r3/report.py
+++ /dev/null
@@ -1,231 +0,0 @@
-"""Reopen delivered pixels/traces and build skill receipts from actual runs."""
-
-import argparse
-import hashlib
-import json
-import statistics
-from pathlib import Path
-
-import numpy as np
-from PIL import Image
-
-from experiments.r3.trace_audit import payloads
-from h3_mlx.compare import _verify_identity, image_metrics
-
-
-def sha(path):
- return hashlib.sha256(Path(path).read_bytes()).hexdigest()
-
-
-def canonical(value):
- return hashlib.sha256(
- json.dumps(
- value, sort_keys=True, separators=(",", ":"), ensure_ascii=False
- ).encode()
- ).hexdigest()
-
-
-def main(a):
- root = Path(a.root)
- destination = Path(a.output)
- destination.mkdir(parents=True, exist_ok=True)
- contract = json.loads(Path(a.contract).read_text())
- manifest = json.loads(Path(a.workload).read_text())
- rows = [json.loads(line) for line in (root / "runs.jsonl").read_text().splitlines()]
- checks = []
- anchors = {}
- for case in contract["quality_cases"]:
- baseline = next(
- r for r in rows if r["case"] == case and r["variant"] == "control"
- )
- anchors[case] = baseline
- trace_anchors = {
- case: payloads(Path(row["output"]).with_suffix(".trace.safetensors"))
- for case, row in anchors.items()
- }
- for row in rows:
- path = Path(row["output"])
- image = Image.open(path).convert("RGB")
- actual = hashlib.sha256(np.asarray(image).tobytes()).hexdigest()
- if actual != row["output_sha256"]:
- raise ValueError(f"Output changed: {path}")
- reference = Path(anchors[row["case"]]["output"])
- _verify_identity(reference, path)
- metrics = image_metrics(Image.open(reference), image)
- trace = payloads(path.with_suffix(".trace.safetensors"))
- checks.append(
- {
- "output": str(path),
- "case": row["case"],
- "variant": row["variant"],
- "phase": row["phase"],
- "metrics": metrics,
- "trace_identical": trace == trace_anchors[row["case"]],
- "different_trace_tensors": [
- k
- for k in trace
- if trace.get(k) != trace_anchors[row["case"]].get(k)
- ],
- }
- )
- (destination / "quality-audit.json").write_text(json.dumps(checks, indent=2) + "\n")
- timed = [r for r in rows if r["phase"] == "timed"]
- summary = {
- "root": str(root),
- "cases": {},
- "all_pixel_identical": all(c["metrics"]["pixel_identical"] for c in checks),
- "all_trace_identical": all(c["trace_identical"] for c in checks),
- "scope": "Paired warm complete generation including encoding-cache checks, six heads, decode, trace/PNG save. Parent physical footprint sampled every 100ms; both compiled variants can be resident.",
- }
- for case in contract["timing_cases"]:
- summary["cases"][case] = {}
- for variant in ("control", a.variant):
- rr = [r for r in timed if r["case"] == case and r["variant"] == variant]
- values = [r["seconds"] for r in rr]
- summary["cases"][case][variant] = {
- "samples_s": values,
- "median_s": statistics.median(values),
- "min_s": min(values),
- "max_s": max(values),
- "peak_physical_bytes": max(r["peak_memory_bytes"] for r in rr),
- "peak_mlx_bytes": max(r["mlx_peak_bytes"] for r in rr),
- }
- control = summary["cases"][case]["control"]
- candidate = summary["cases"][case][a.variant]
- summary["cases"][case]["reduction_pct"] = 100 * (
- 1 - candidate["median_s"] / control["median_s"]
- )
- (destination / "summary.json").write_text(json.dumps(summary, indent=2) + "\n")
- if a.no_receipts:
- print(json.dumps(summary, indent=2))
- return
- context = {
- "hardware": "Apple M5 Pro, 16 GPU cores, 64 GiB unified memory",
- "os": "macOS 27.0.1 arm64",
- "software": "MLX/MLX Metal 0.32.4; Python 3.12.14; strict precision environment",
- "environment": "AC Automatic power; MLX_ENABLE_TF32=0, MLX_CONV_WINOGRAD=0; user background apps unchanged; no thermal warning recorded",
- "timing_boundary": "Pipeline.generate total_s: warm text cache, original CPU RNG, six heads, decode, trace and PNG save. Excludes interpreter startup and external quality scoring.",
- "memory_metric": "Parent macOS physical footprint sampled at 100ms, current-interval maximum; includes Metal, excludes compiler XPC. MLX allocator separately; do not sum. Both compiled variants may be resident.",
- "cache_state": "Resident shared native weights and VAE; shape warmup for each variant. Exact conditioning caches warmed outside timed population.",
- "execution_policy": "Single GPU benchmark process; per-case repeats alternate control/candidate then candidate/control then control/candidate. Warmup and first-load excluded from timed population.",
- }
- code_hash = sha(root / "source-manifest.json")
- for variant in ("control", a.variant):
- cfg = dict(manifest["control_config"])
- if variant != "control":
- cfg.update(
- kernel="metal-qk-exact" if "native-norm" in variant else "metal-exact",
- tune_matmul=True,
- )
- description = {
- "id": variant,
- "code_sha256": code_hash,
- "model_sha256": sha("provenance/sources.json"),
- "config_sha256": canonical(cfg),
- "command": [
- ".venv/bin/python",
- "-m",
- "experiments.r3.images",
- "--deployment",
- "--variants",
- a.variant,
- "--workload",
- a.workload,
- "--model",
- str(Path("../minimax-h3-image-opt/models/MiniMax-H3-Image").resolve()),
- "--text-encoder",
- str(
- Path(
- "../minimax-h3-image-opt/models/MiniMax-H3/text_encoder"
- ).resolve()
- ),
- "--output",
- str(root),
- ],
- }
- output_hashes = {}
- for case in contract["quality_cases"]:
- values = {
- r["output_sha256"]
- for r in rows
- if r["case"] == case and r["variant"] == variant
- }
- if len(values) != 1:
- raise ValueError(
- f"Missing or nondeterministic {case}/{variant}: {values}"
- )
- output_hashes[case] = values.pop()
- relevant = [c for c in checks if c["variant"] == variant]
- receipt = {
- "schema_version": 1,
- "id": f"r3-deployment-{variant}",
- "campaign_id": contract["campaign_id"],
- "contract_sha256": canonical(contract),
- "complete": True,
- "change_class": "control" if variant == "control" else "exact",
- "hypothesis": "Native reduction matching QK/RoPE fusion and shape-qualified Metal 4 FC2 tiling reduce full generation latency without changing a denoising tensor or pixel.",
- "context": context,
- "variant": description,
- "quality": {
- "variant_sha256": canonical(description),
- "reference_sha256": contract["reference_sha256"],
- "evaluator_sha256": contract["evaluator_sha256"],
- "outputs": output_hashes,
- "metrics": {
- "max_abs_error": max(
- c["metrics"]["max_error_8bit"] for c in relevant
- ),
- "nonfinite_values": sum(
- r["nonfinite_values"] for r in rows if r["variant"] == variant
- ),
- },
- },
- "runs": [
- {
- k: r[k]
- for k in (
- "case",
- "repeat",
- "seconds",
- "peak_memory_bytes",
- "output_sha256",
- )
- }
- for r in timed
- if r["variant"] == variant
- ],
- "evidence": [
- {"path": str(path), "sha256": sha(path)}
- for path in (
- root / "runs.jsonl",
- root / "source-manifest.json",
- destination / "quality-audit.json",
- destination / "summary.json",
- )
- ],
- "extensions": {
- "canonical_output": "SHA256 decoded RGB uint8 bytes in row-major order",
- "model_identity": "model_sha256 is the pinned model/source provenance manifest hash; no new full checkpoint rehash",
- "reference_scope": "Incremental identity to MLX BF16 recipe, not CUDA or unpublished FP32 training weights",
- "trace_identical": all(c["trace_identical"] for c in relevant),
- },
- }
- (destination / f"{variant}.receipt.json").write_text(
- json.dumps(receipt, indent=2) + "\n"
- )
- print(json.dumps(summary, indent=2))
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--root", default="outputs/optimization-r3-deployment")
- p.add_argument("--variant", default="qk-fc2")
- p.add_argument("--contract", default="optimization/round-03/contract.json")
- p.add_argument("--workload", default="optimization/round-03/workload.json")
- p.add_argument(
- "--no-receipts",
- action="store_true",
- help="Audit research outputs without assigning release source identity",
- )
- p.add_argument("--output", default="reports/optimization-r3/deployment")
- main(p.parse_args())
diff --git a/experiments/r3/screen.py b/experiments/r3/screen.py
deleted file mode 100644
index be6bb72552fe4aaa6a6d99a42afd83c1e620e02c..0000000000000000000000000000000000000000
--- a/experiments/r3/screen.py
+++ /dev/null
@@ -1,274 +0,0 @@
-"""Real first-block inputs: attention layout, Metal 4 FC2 tiling, lossless decode."""
-
-from __future__ import annotations
-
-import argparse
-import gc
-import itertools
-import json
-from functools import lru_cache
-from pathlib import Path
-
-import mlx.core as mx
-
-from experiments.r3.common import paired
-from experiments.v2_mpp import gemm
-from h3_mlx.benchmark import error
-from h3_mlx.config import SIGMAS
-from h3_mlx.dit import DiT
-from h3_mlx.layout import image_positions, rope_cache
-from h3_mlx.memory import FootprintMonitor
-from h3_mlx.ops import rms_norm, rotate
-from h3_mlx.packed import PackedBF16
-from h3_mlx.runtime import configure_memory, environment
-
-
-@lru_cache(None)
-def decoder(lanes):
- return mx.fast.metal_kernel(
- name=f"h3_unpack_bf16_palette12_vec{lanes}",
- input_names=["low", "nibbles", "escape_indices", "escape_values", "base"],
- output_names=["out"],
- source=r"""
- uint first = thread_position_in_grid.x * LANES;
- #pragma unroll
- for (uint lane = 0; lane < LANES; ++lane) {
- uint i = first + lane;
- if (i >= low_shape[0]) break;
- uint code = (nibbles[i / 2] >> ((i % 2) * 4)) & 15;
- uint count = escape_indices_shape[0];
- if (code == 0 && count != 0) {
- uint left = 0, right = count;
- while (left < right) {
- uint mid = (left + right) / 2;
- if (escape_indices[mid] < i) left = mid + 1;
- else right = mid;
- }
- out[i] = escape_values[left];
- } else {
- out[i] = ushort(low[i]) | ushort(((code & 7) + base[0]) << 8)
- | ushort((code & 8) << 12);
- }
- }
- """,
- )
-
-
-def unpack(packed, lanes, threads):
- raw = decoder(lanes)(
- inputs=packed.state(),
- template=[("LANES", lanes)],
- grid=((packed.low.size + lanes - 1) // lanes, 1, 1),
- threadgroup=(threads, 1, 1),
- output_shapes=[(packed.low.size,)],
- output_dtypes=[mx.uint16],
- )[0]
- return raw.view(mx.bfloat16).reshape(packed.shape)
-
-
-class Capture:
- def __init__(self, linear):
- self.linear = linear
-
- def __call__(self, x):
- self.input = x
- mx.eval(x)
- return self.linear(x)
-
-
-def main(a):
- configure_memory(wired_gb=14)
- dest = Path(a.output)
- dest.parent.mkdir(parents=True, exist_ok=True)
- report = {
- "environment": environment(),
- "scope": "Actual saved first-block activations, one GPU job, counterbalanced warmed calls including layout/decode costs. Operator evidence only.",
- "trials": [],
- }
-
- def record(row):
- report["trials"].append(row)
- dest.write_text(json.dumps(report, indent=2) + "\n")
- print(json.dumps(row), flush=True)
-
- with FootprintMonitor() as memory:
- data = mx.load(a.trace)
- model = DiT(a.model)
- model.prepare_modulation(SIGMAS)
- text = model.refine(data["conditioning"])
- x = mx.concatenate(
- (
- text,
- model._linear("video_patch_proj")(data["initial_rows"]).astype(
- mx.bfloat16
- ),
- ),
- axis=0,
- )
- rope = rope_cache(
- image_positions(len(text), 128, 128), model._constant("rope.inv_freq")
- )
- indices = mx.concatenate((data["text_tags"], mx.zeros(4096, mx.int32)))
- params = tuple(model.modulation["block.0"][0, :, j] for j in range(6))
- block = model._block("blocks.0")
- captures = {}
- for name in ("qkv", "out", "fc1", "fc2"):
- capture = Capture(getattr(block, name))
- captures[name] = capture
- setattr(block, name, capture)
- y = block(x, params=params, indices=indices, rope=rope, mlp_chunk=0)
- mx.eval(y)
- report["activation_ranges"] = {
- name: {
- "shape": list(v.input.shape),
- "max_abs": mx.max(mx.abs(v.input.astype(mx.float32))).item(),
- }
- for name, v in captures.items()
- }
- if "attention" in a.groups:
- qkv = (
- captures["qkv"]
- .linear(captures["qkv"].input)
- .reshape(1, x.shape[0], 3, 56, 128)
- )
- q, k = (
- rotate(
- rms_norm(qkv[:, :, j], weight, block.config.qk_norm_eps), *rope
- ).transpose(0, 2, 1, 3)
- for j, weight in ((0, block.qn), (1, block.kn))
- )
- v = qkv[:, :, 2].transpose(0, 2, 1, 3)
- mx.eval(q, k, v)
-
- def attention(layout="strided"):
- args = [
- mx.contiguous(t) if axis in layout else t
- for axis, t in zip("qkv", (q, k, v), strict=True)
- ]
- return mx.fast.scaled_dot_product_attention(*args, scale=128**-0.5)
-
- ref = attention()
- mx.eval(ref)
- for layout in ("q", "k", "v", "kv", "qk", "qkv"):
-
- def fn(layout=layout):
- return attention(layout)
-
- record(
- {
- "group": "attention",
- "variant": layout,
- **paired(attention, fn, a.repeats),
- **error(ref, fn()),
- }
- )
- del qkv, q, k, v, ref
- if "mpp" in a.groups:
- count = 0
- for name in ("fc2", "fc1"):
- cap = captures[name]
- layer, xx = cap.linear, cap.input
- w = layer.weight
- b = (
- layer.bias
- if layer.bias is not None
- else mx.zeros(w.shape[0], w.dtype)
- )
- ref = layer(xx)
- mx.eval(ref)
- tiles = itertools.product(
- (
- (64, 128, 4),
- (64, 128, 8),
- (128, 128, 4),
- (128, 128, 8),
- (128, 256, 8),
- (64, 256, 8),
- ),
- (128, 256, 512),
- (0, 8),
- )
- for (bm, bn, sg), bk, walk in tiles:
- if w.shape[1] % bk or w.shape[0] % bn:
- continue
- count += 1
- row = {
- "group": "mpp",
- "operator": name,
- "tile": [bm, bn, bk],
- "simdgroups": sg,
- "walk": walk,
- }
- try:
-
- def fn(bm=bm, bn=bn, bk=bk, sg=sg, walk=walk, xx=xx, w=w, b=b):
- return gemm(xx, w, b, bm, bn, bk, sg, walk)
-
- row.update(
- paired(lambda layer=layer, xx=xx: layer(xx), fn, a.repeats),
- **error(ref, fn()),
- )
- except Exception as exc:
- row["error"] = f"{type(exc).__name__}: {exc}"
- record(row)
- if count >= a.max_tiles:
- break
- if count >= a.max_tiles:
- break
- if "unpack" in a.groups:
- for name in ("qkv", "fc2"):
- cap = captures[name]
- layer, xx = cap.linear, cap.input
- packed = PackedBF16.pack(layer.weight)
- ref = packed.unpack()
- mx.eval(ref)
- for lanes, threads in itertools.product((2, 4, 8), (128, 256)):
-
- def fn(lanes=lanes, threads=threads, packed=packed):
- return unpack(packed, lanes, threads)
-
- candidate = fn()
- mx.eval(candidate)
- bit_equal = mx.array_equal(
- ref.view(mx.uint16), candidate.view(mx.uint16)
- ).item()
- row = {
- "group": "unpack",
- "operator": name,
- "lanes": lanes,
- "threads": threads,
- "bit_equal": bit_equal,
- "decode": paired(packed.unpack, fn, a.repeats),
- }
-
- def project(w, xx=xx, layer=layer):
- return (
- xx @ w.T
- if layer.bias is None
- else mx.addmm(layer.bias, xx, w.T)
- )
-
- row["with_gemm"] = paired(
- lambda project=project, packed=packed: project(packed.unpack()),
- lambda project=project, fn=fn: project(fn()),
- a.repeats,
- )
- record(row)
- del packed, ref
- model.clear()
- gc.collect()
- report["memory"] = memory.result()
- dest.write_text(json.dumps(report, indent=2) + "\n")
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument(
- "--trace", default="outputs/suite/product/native-3.trace.safetensors"
- )
- p.add_argument("--groups", nargs="+", default=["attention", "mpp", "unpack"])
- p.add_argument("--max-tiles", type=int, default=72)
- p.add_argument("--repeats", type=int, default=6)
- p.add_argument("--output", default="reports/optimization-r3/screen.json")
- main(p.parse_args())
diff --git a/experiments/r3/shapes.py b/experiments/r3/shapes.py
deleted file mode 100644
index 470e4ff36423f28cfd090f9f482dd776e2fcbedd..0000000000000000000000000000000000000000
--- a/experiments/r3/shapes.py
+++ /dev/null
@@ -1,134 +0,0 @@
-"""Real first-block FC2 activations across text, editing and 4K shapes."""
-
-import argparse
-import json
-from pathlib import Path
-
-import mlx.core as mx
-
-from experiments.r3.common import paired
-from experiments.r3.fusions import TunedFC2
-from experiments.r3.screen import Capture
-from h3_mlx.benchmark import error
-from h3_mlx.config import SIGMAS
-from h3_mlx.dit import DiT
-from h3_mlx.runtime import configure_memory, environment
-from h3_mlx.tuning import _gemm
-
-
-class Captured(Exception):
- pass
-
-
-def stop(*_):
- raise Captured
-
-
-def main(a):
- configure_memory(wired_gb=18)
- model = DiT(a.model)
- report = {
- "environment": environment(),
- "scope": "Real first-head block inputs, including original reference positions and modality/time selection for editing. Only operator timing; no 4K image latency claim.",
- "trials": [],
- }
- cases = [
- ("product", "outputs/suite/product/native-3.trace.safetensors"),
- ("edit", "outputs/suite/edit/native-0.trace.safetensors"),
- ("native-4k", "outputs/suite/native-4k/native-0.trace.safetensors"),
- ]
- for name, path in cases:
- if name not in a.cases:
- continue
- data = mx.load(path)
- model.prepare_modulation(SIGMAS, edit=name == "edit")
- text = model.refine(data["conditioning"])
- h, w = data["latent"].shape[-2:]
- references = ((64, 64),) if name == "edit" else ()
- block = model._block("blocks.0")
- original = block.fc2
- cap = Capture(original)
- block.fc2 = cap
- try:
- model.predict(
- data["initial_rows"],
- text,
- 0,
- h,
- w,
- references=references,
- text_tags=data["text_tags"],
- on_layer=stop,
- )
- except Captured:
- pass
- finally:
- block.fc2 = original
- x = cap.input
- tuned = TunedFC2(original)
- ref = original(x)
- mx.eval(ref)
- row = {
- "case": name,
- "shape": list(x.shape),
- "max_input_abs": mx.max(mx.abs(x.astype(mx.float32))).item(),
- **error(ref, tuned(x)),
- **paired(
- lambda original=original, x=x: original(x),
- lambda tuned=tuned, x=x: tuned(x),
- a.repeats,
- ),
- }
- report["trials"].append(row)
- Path(a.output).write_text(json.dumps(report, indent=2) + "\n")
- print(json.dumps(row), flush=True)
- if name == "product" and a.bk_sweep:
- bias = (
- original.bias
- if original.bias is not None
- else mx.zeros(5376, mx.bfloat16)
- )
- for bk, sg in ((1024, 4), (1024, 8), (2048, 4), (2048, 8)):
- row = {"case": name, "group": "extra-bk", "bk": bk, "simdgroups": sg}
- try:
-
- def fn(bk=bk, sg=sg, x=x, original=original, bias=bias):
- return _gemm(x, original.weight, bias, 64, 128, bk, sg, 8)
-
- row.update(
- error(ref, fn()),
- **paired(lambda tuned=tuned, x=x: tuned(x), fn, a.repeats),
- )
- except Exception as exc:
- row["error"] = f"{type(exc).__name__}: {exc}"
- report["trials"].append(row)
- Path(a.output).write_text(json.dumps(report, indent=2) + "\n")
- print(json.dumps(row), flush=True)
- if name == "edit" and a.boundary:
- boundary = mx.concatenate((x, x[: 8192 - x.shape[0]]))
- mx.eval(boundary)
- row = {
- "case": "boundary-8192-repeated-edit",
- "shape": list(boundary.shape),
- **error(original(boundary), tuned(boundary)),
- **paired(
- lambda original=original, boundary=boundary: original(boundary),
- lambda tuned=tuned, boundary=boundary: tuned(boundary),
- a.repeats,
- ),
- }
- report["trials"].append(row)
- Path(a.output).write_text(json.dumps(report, indent=2) + "\n")
- print(json.dumps(row), flush=True)
- model.clear()
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument("--repeats", type=int, default=8)
- p.add_argument("--cases", nargs="+", default=["product", "edit", "native-4k"])
- p.add_argument("--bk-sweep", action="store_true")
- p.add_argument("--boundary", action="store_true")
- p.add_argument("--output", default="reports/optimization-r3/shapes.json")
- main(p.parse_args())
diff --git a/experiments/r3/trace_audit.py b/experiments/r3/trace_audit.py
deleted file mode 100644
index fda95bd068c9db9c49231e3b688840dc9f17e635..0000000000000000000000000000000000000000
--- a/experiments/r3/trace_audit.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""CPU-only hashes of safetensors payloads, independent of MLX graph execution."""
-
-import argparse
-import hashlib
-import json
-import struct
-from pathlib import Path
-
-
-def payloads(path):
- result = {}
- with Path(path).open("rb") as handle:
- length = struct.unpack("
- #include
- using namespace mpp::tensor_ops;
- """,
- source=r"""
- const int N = q_shape[0], H = q_shape[1], D = 128;
- int sg = thread_position_in_threadgroup.x / 32;
- int row = (threadgroup_position_in_grid.x * SG + sg) * BQ;
- int head = threadgroup_position_in_grid.y;
- // SIMD groups own independent query tiles; the last group may be empty.
- auto Q = tensor(const_cast(q) + head*q_strides[1], dextents(D,N), array{1,int(q_strides[0])});
- auto K = tensor(const_cast(k) + head*k_strides[1], dextents(D,N), array{1,int(k_strides[0])});
- auto V = tensor(const_cast(v) + head*v_strides[1], dextents(D,N), array{1,int(v_strides[0])});
- auto O = tensor(out + head*D, dextents(D,N), array{1,H*D});
- if (row >= N) return;
- constexpr auto qkdesc = matmul2d_descriptor(BQ,BK,128,false,true,false);
- constexpr auto pvdesc = matmul2d_descriptor(BQ,128,BK,false,false,false,matmul2d_descriptor::mode::multiply_accumulate);
- matmul2d qkop;
- matmul2d pvop;
- auto qtile = Q.template slice<128,dynamic_extent>(0,row);
- auto ktile = K.template slice<128,dynamic_extent>(0,0);
- auto vt = V.template slice<128,dynamic_extent>(0,0);
- auto vtile = V.template slice<128,dynamic_extent>(0,0);
- auto qs = qkop.template get_left_input_cooperative_tensor();
- qs.load(qtile);
- auto scores = qkop.template get_destination_cooperative_tensor();
- auto maximum = qkop.template get_row_reduction_destination_cooperative_tensor();
- auto denom = maximum;
- auto tile_max = maximum;
- auto tile_sum = maximum;
- auto alpha = maximum;
- auto prob = pvop.template get_left_input_cooperative_tensor();
- auto p_shape = tensor(const_cast(q), extents(), array{1,BK});
- auto accum = pvop.template get_destination_cooperative_tensor();
- #pragma unroll
- for (int i=0;i(), array{1,BK});
- for (int start=0;start(0,start);
- qkop.run(qs,kk,scores);
- #pragma unroll
- for (auto it=scores.begin();it!=scores.end();++it) {
- auto coord = scores.get_multidimensional_index(it);
- *it = start+coord[0](0,start);
- pvop.run(prob,vv,accum);
- }
- #pragma unroll
- for (auto it=accum.begin();it!=accum.end();++it) *it /= *denom.map_iterator(it);
- #pragma unroll
- for (auto it=accum.begin();it!=accum.end();++it) {
- if (accum.is_valid_element(it)) {
- auto coord=accum.get_multidimensional_index(it);
- if (row+coord[1]> 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())
diff --git a/experiments/v2_coreml_image.py b/experiments/v2_coreml_image.py
deleted file mode 100644
index 8ee0b9295ca4e49eb38d9aef3d7942ad9642a441..0000000000000000000000000000000000000000
--- a/experiments/v2_coreml_image.py
+++ /dev/null
@@ -1,108 +0,0 @@
-"""One actual image for a Core ML device/batch, with NumPy bridge diagnostics."""
-
-from __future__ import annotations
-
-import argparse
-import hashlib
-import json
-import time
-from pathlib import Path
-
-import mlx.core as mx
-import numpy as np
-from PIL import Image
-
-from h3_mlx.compare import image_metrics, make_comparison
-from h3_mlx.coreml_vae import CoreMLVAE
-from h3_mlx.runtime import configure_memory
-from h3_mlx.vae import to_image
-
-
-def main(a):
- configure_memory(wired_gb=5)
- data = mx.load(a.trace)
- latent = data["latent"]
- mx.eval(latent)
- del data
- tick = time.perf_counter()
- model = CoreMLVAE(
- a.vae, package=a.package, tile_batch=a.batch, compute_units=a.units
- )
- load_s = time.perf_counter() - tick
- sample = (
- np.random.default_rng(731).normal(size=(a.batch, 24, 16, 16)).astype(np.float16)
- )
- raw = model.model.predict({"raw": sample})["pixels"]
- audit = {
- "output_shape": list(raw.shape),
- "output_strides": list(raw.strides),
- "output_c_contiguous": bool(raw.flags.c_contiguous),
- "original_mlx_numpy_import_exact": bool(
- np.array_equal(np.asarray(mx.array(raw)), raw, equal_nan=True)
- ),
- }
- source = json.loads(Path(a.reference).with_suffix(".json").read_text())
- tick = time.perf_counter()
- pixels = model.decode(latent)
- mx.eval(pixels)
- seconds = time.perf_counter() - tick
- image = to_image(pixels, *source["size"])
- root = Path(a.output)
- root.mkdir(parents=True, exist_ok=True)
- path = root / "image.png"
- image.save(path)
- record = {
- k: source[k]
- for k in (
- "prompt",
- "seed",
- "size",
- "canvas",
- "rng",
- "reference_images",
- "reference_sha256",
- "sigmas",
- "steps",
- )
- if k in source
- }
- record.update(
- validation_scope="Same final latent; decoder only",
- timing_scope="One complete decoder pass; model loading excluded",
- timings={"decode_s": seconds},
- load_s=load_s,
- vae_backend="coreml",
- vae={"batch": a.batch, "compute_units": a.units},
- source_trace=a.trace,
- source_trace_sha256=hashlib.sha256(Path(a.trace).read_bytes()).hexdigest(),
- output=str(path),
- png_sha256=hashlib.sha256(path.read_bytes()).hexdigest(),
- numpy_bridge=audit,
- **image_metrics(Image.open(a.reference), image),
- )
- path.with_suffix(".json").write_text(json.dumps(record, indent=2) + "\n")
- print(json.dumps(record), flush=True)
- make_comparison(
- a.reference,
- [path],
- root / "comparison",
- labels=[
- "Native BF16 decoder",
- f"Core ML {a.units}, batch {a.batch}, contiguous bridge",
- ],
- )
- model.clear()
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--vae", required=True)
- p.add_argument("--package", required=True)
- p.add_argument("--batch", type=int, default=8)
- p.add_argument("--units", default="CPU_AND_NE")
- p.add_argument(
- "--trace", default="outputs/suite/product/native-3.trace.safetensors"
- )
- p.add_argument("--reference", default="outputs/suite/product/native-3.png")
- p.add_argument("--output", required=True)
- main(p.parse_args())
diff --git a/experiments/v2_coreml_vae.py b/experiments/v2_coreml_vae.py
deleted file mode 100644
index 9336493f7ccdcab65443e36b407c4082c768422f..0000000000000000000000000000000000000000
--- a/experiments/v2_coreml_vae.py
+++ /dev/null
@@ -1,221 +0,0 @@
-"""Export complete native VAE tiles to Core ML and inspect real device placement.
-
-The source BF16 weights/input values are preserved through FP16 conversion.
-Internal FP16 rounding differs from the native BF16 decoder; compare images.
-Run export/benchmark in .venv-coreml, then use v2_decode.py for image evidence.
-"""
-
-from __future__ import annotations
-
-import argparse
-import gc
-import hashlib
-import json
-import math
-import statistics
-import time
-from pathlib import Path
-
-import coremltools as ct
-import numpy as np
-import torch
-import torch.nn.functional as F
-from safetensors import safe_open
-
-from experiments.v2_coreml import devices
-
-
-class TileDecoder(torch.nn.Module):
- def __init__(self, path, batch):
- super().__init__()
- self.batch = batch
- for p in sorted(Path(path).glob("*.safetensors")):
- with safe_open(p, framework="pt", device="cpu") as f:
- for key in f.keys():
- if key.startswith("decoder.") or key.startswith("post_quant_conv."):
- self.register_buffer(
- key.replace(".", "__"), f.get_tensor(key).bfloat16().half()
- )
- # Native BF16 grid arithmetic, including BF16 products in 2D RoPE.
- coords = 2 * ((torch.arange(16, dtype=torch.bfloat16) + 0.5) / 16) - 1
- yy, xx = torch.meshgrid(coords, coords, indexing="ij")
- pos = torch.stack((yy, xx), dim=-1).reshape(-1, 2)
- pos = torch.cat((pos, torch.zeros(5, 2, dtype=torch.bfloat16)))
- inv = 1 / (100 ** torch.arange(0, 1, 0.125, dtype=torch.float32))
- scaled = (2 * math.pi * pos.float()).bfloat16().float()
- angles = (scaled[:, :, None] * inv[None, None, :]).reshape(261, 16)
- angles = torch.cat((angles, angles), dim=-1)
- self.register_buffer("cosine", angles.cos().bfloat16().half()[None, :, None, :])
- self.register_buffer("sine", angles.sin().bfloat16().half()[None, :, None, :])
-
- def w(self, key):
- return getattr(self, key.replace(".", "__"))
-
- def linear(self, x, key):
- return F.linear(x, self.w(key + ".weight"), self.w(key + ".bias"))
-
- def norm(self, x, key=None):
- xf = x.float()
- y = xf * torch.rsqrt(torch.mean(xf * xf, dim=-1, keepdim=True) + 1e-5)
- if key is not None:
- y = y * self.w(key).float()
- return y.half()
-
- def rope(self, x):
- rotary, tail = x[..., :32], x[..., 32:]
- a, b = rotary.chunk(2, dim=-1)
- return torch.cat(
- (rotary * self.cosine + torch.cat((-b, a), dim=-1) * self.sine, tail),
- dim=-1,
- )
-
- def forward(self, raw):
- x = raw.permute(0, 2, 3, 1)
- x = F.linear(
- x,
- self.w("post_quant_conv.weight").reshape(24, 24),
- self.w("post_quant_conv.bias"),
- )
- x = self.linear(x, "decoder.x_embedder").reshape(self.batch, 256, 1024)
- x = torch.cat(
- (
- x,
- self.w("decoder.register_tokens").expand(self.batch, 4, 1024),
- torch.zeros(self.batch, 1, 1024, dtype=torch.float16),
- ),
- dim=1,
- )
- for i in range(24):
- p = f"decoder.transformer_blocks.{i}"
- h = self.norm(x, p + ".norm1.weight")
- qkv = self.linear(h, p + ".attn.to_qkv").reshape(self.batch, 261, 16, 3, 64)
- q, k, v = (qkv[:, :, :, j] for j in range(3))
- q, k = self.rope(self.norm(q)), self.rope(self.norm(k))
- attn = (
- F.scaled_dot_product_attention(
- q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
- )
- .transpose(1, 2)
- .reshape(self.batch, 261, 1024)
- )
- x = x + self.linear(attn, p + ".attn.to_out") * self.w(p + ".scale1")
- h = self.norm(x, p + ".norm2.weight")
- gate, up = self.linear(h, p + ".ff.w1").chunk(2, dim=-1)
- x = x + self.linear(F.silu(gate) * up, p + ".ff.w2") * self.w(p + ".scale2")
- x = F.layer_norm(
- x.float(),
- (1024,),
- self.w("decoder.norm_out.weight").float(),
- self.w("decoder.norm_out.bias").float(),
- 1e-5,
- ).half()
- x = self.linear(x, "decoder.proj_out")[:, :256]
- return (
- x.reshape(self.batch, 16, 16, 3, 16, 16)
- .permute(0, 3, 1, 4, 2, 5)
- .reshape(self.batch, 3, 256, 256)
- )
-
-
-def main(a):
- torch.set_num_threads(a.threads)
- package = Path(a.package)
- example = (
- np.random.default_rng(731).normal(size=(a.batch, 24, 16, 16)).astype(np.float16)
- )
- if not package.exists():
- model = TileDecoder(a.vae, a.batch).eval()
- with torch.inference_mode():
- traced = torch.jit.trace(model, torch.from_numpy(example))
- converted = ct.convert(
- traced,
- convert_to="mlprogram",
- inputs=[ct.TensorType(name="raw", shape=example.shape, dtype=np.float16)],
- outputs=[ct.TensorType(name="pixels", dtype=np.float16)],
- minimum_deployment_target=ct.target.macOS15,
- compute_precision=ct.precision.FLOAT16,
- skip_model_load=True,
- )
- files = sorted(Path(a.vae).glob("*.safetensors"))
- identity = [
- (str(p.resolve()), p.stat().st_size, p.stat().st_mtime_ns) for p in files
- ]
- converted.user_defined_metadata.update(
- {
- "h3.decoder.format": "h3-coreml-tile-v1",
- "h3.decoder.source": hashlib.sha256(
- json.dumps(identity).encode()
- ).hexdigest(),
- "h3.decoder.batch": str(a.batch),
- "h3.decoder.precision": "Source BF16 weights and inputs, FP16 internal arithmetic, BF16 tile assembly",
- }
- )
- package.parent.mkdir(parents=True, exist_ok=True)
- converted.save(str(package))
- del model, traced, converted
- gc.collect()
- report = {
- "coremltools": ct.__version__,
- "torch": torch.__version__,
- "package": str(package),
- "batch": a.batch,
- "scope": "Complete 24-layer tile decoder. FP16 internals, source weights rounded to BF16 before conversion. NumPy I/O included; tile assembly and normalization excluded.",
- "trials": [],
- }
- dest = Path(a.output)
- dest.parent.mkdir(parents=True, exist_ok=True)
- for unit in (getattr(ct.ComputeUnit, name) for name in a.units):
- row = {"compute_units": unit.name}
- 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)
- tick = time.perf_counter()
- y = model.predict({"raw": example})["pixels"]
- row["first_predict_ms"] = 1000 * (time.perf_counter() - tick)
- samples = []
- for _ in range(a.repeats):
- tick = time.perf_counter()
- y = model.predict({"raw": example})["pixels"]
- samples.append(1000 * (time.perf_counter() - tick))
- row.update(
- samples_ms=samples,
- median_ms=statistics.median(samples),
- finite=bool(np.isfinite(y).all()),
- )
- del model, plan, y
- 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({k: v for k, v in row.items() if k != "placement"}), flush=True
- )
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--vae", required=True)
- p.add_argument("--batch", type=int, default=8)
- p.add_argument("--threads", type=int, default=10)
- p.add_argument(
- "--units",
- nargs="+",
- choices=("CPU_ONLY", "CPU_AND_GPU", "CPU_AND_NE", "ALL"),
- default=["CPU_ONLY", "CPU_AND_GPU", "CPU_AND_NE", "ALL"],
- )
- p.add_argument("--repeats", type=int, default=3)
- p.add_argument("--package", default="cache/coreml-v2/vae-batch8.mlpackage")
- p.add_argument("--output", default="reports/optimization-v2/coreml-vae.json")
- main(p.parse_args())
diff --git a/experiments/v2_decode.py b/experiments/v2_decode.py
deleted file mode 100644
index 09865fc843a30ed98c12c494915c2de6f4a4a2b6..0000000000000000000000000000000000000000
--- a/experiments/v2_decode.py
+++ /dev/null
@@ -1,140 +0,0 @@
-"""Compare native, compiled and Core ML decoders on the exact same final latent."""
-
-from __future__ import annotations
-
-import argparse
-import gc
-import hashlib
-import json
-from pathlib import Path
-
-import mlx.core as mx
-
-from h3_mlx.benchmark import measure
-from h3_mlx.compare import image_metrics, make_comparison
-from h3_mlx.coreml_vae import CoreMLVAE
-from h3_mlx.runtime import configure_memory, environment
-from h3_mlx.vae import VAE, to_image
-
-
-def main(a):
- configure_memory(wired_gb=5)
- root = Path(a.output)
- root.mkdir(parents=True, exist_ok=True)
- data = mx.load(a.trace)
- latent = data["latent"]
- mx.eval(latent)
- del data
- source = json.loads(Path(a.record).read_text())
- width, height = source["size"]
- results = []
- images = []
- labels = []
- reference = None
- variants = [
- ("native-b1", VAE, {"tile_batch": 1}),
- ("native-b4", VAE, {"tile_batch": 4}),
- ("compiled-b4", VAE, {"tile_batch": 4, "compile_blocks": True}),
- ("native-b8", VAE, {"tile_batch": 8}),
- ("compiled-b8", VAE, {"tile_batch": 8, "compile_blocks": True}),
- ]
- if a.include_fp32:
- variants.insert(
- 0,
- (
- "fp32-b4-compiled",
- VAE,
- {"tile_batch": 4, "compile_blocks": True, "dtype": "float32"},
- ),
- )
- if a.package:
- variants.extend(
- (
- "coreml-" + unit,
- CoreMLVAE,
- {
- "tile_batch": a.coreml_batch,
- "package": a.package,
- "compute_units": unit,
- },
- )
- for unit in a.units
- )
- for name, cls, options in variants:
- model = cls(a.vae, **options)
- timer = measure(
- lambda model=model: model.decode(latent), warmup=1, repeats=a.repeats
- )
- pixels = model.decode(latent)
- image = to_image(pixels, width, height)
- if reference is None:
- reference = image
- path = root / f"{name}.png"
- image.save(path)
- record = {
- k: source[k]
- for k in (
- "prompt",
- "seed",
- "size",
- "canvas",
- "rng",
- "reference_images",
- "reference_sha256",
- "sigmas",
- "steps",
- )
- if k in source
- }
- record.update(
- vae=options,
- vae_backend="coreml" if cls is CoreMLVAE else "mlx",
- environment=environment(),
- validation_scope="Same final latent; decoder only",
- timing_scope="Warm decoder only; model loading excluded",
- timings={"decode_s": timer["median_ms"] / 1000},
- source_trace=a.trace,
- source_trace_sha256=hashlib.sha256(Path(a.trace).read_bytes()).hexdigest(),
- output=str(path),
- png_sha256=hashlib.sha256(path.read_bytes()).hexdigest(),
- )
- path.with_suffix(".json").write_text(json.dumps(record, indent=2) + "\n")
- row = {"variant": name, **timer, **image_metrics(reference, image)}
- results.append(row)
- images.append(path)
- labels.append(name)
- print(json.dumps(row), flush=True)
- (root / "trials.json").write_text(
- json.dumps(
- {
- "source_trace": a.trace,
- "scope": "Same final latent, decoder-only hot latency",
- "trials": results,
- },
- indent=2,
- )
- + "\n"
- )
- model.clear()
- del model
- gc.collect()
- mx.clear_cache()
- make_comparison(images[0], images[1:], root / "comparison", labels=labels)
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--vae", required=True)
- p.add_argument("--trace", required=True)
- p.add_argument("--record", required=True)
- p.add_argument("--package")
- p.add_argument(
- "--include-fp32",
- action="store_true",
- help="Use the original FP32 decoder as the same-latent reference",
- )
- p.add_argument("--coreml-batch", type=int, default=8)
- p.add_argument("--units", nargs="+", default=["CPU_AND_GPU"])
- p.add_argument("--repeats", type=int, default=3)
- p.add_argument("--output", default="comparisons/optimization-v2/decoder-product")
- main(p.parse_args())
diff --git a/experiments/v2_images.py b/experiments/v2_images.py
deleted file mode 100644
index 407bab16a220d117118c29753c6f701734705586..0000000000000000000000000000000000000000
--- a/experiments/v2_images.py
+++ /dev/null
@@ -1,203 +0,0 @@
-"""Matched full-pipeline optimization trials, with load and warm runs separate."""
-
-from __future__ import annotations
-
-import argparse
-import gc
-import json
-import logging
-from pathlib import Path
-
-from h3_mlx.runtime import Pipeline
-from h3_mlx.weights import Linear
-
-
-class HalfProjection(Linear):
- """Experimental FP16 GEMM, with BF16 rounding at every native boundary."""
-
- def __call__(self, x):
- import mlx.core as mx
-
- return super().__call__(x).astype(mx.bfloat16)
-
-
-PROFILES = {
- "quiet-baseline": {"wired_gb": 42, "cache_refined_text": False},
- "quiet-native": {
- "wired_gb": 42,
- "compile_blocks": True,
- "eval_every": 0,
- "prefetch_blocks": 4,
- "vae_batch": 4,
- "vae_compile": True,
- },
- "quiet-packed": {
- "wired_gb": 32,
- "compile_blocks": True,
- "eval_every": 0,
- "prefetch_blocks": 4,
- "weight_storage": "packed12",
- "vae_batch": 4,
- "vae_compile": True,
- },
- "speed": {
- "wired_gb": 42,
- "compile_blocks": True,
- "eval_every": 0,
- "vae_batch": 4,
- "vae_compile": True,
- },
- "prefetch": {
- "wired_gb": 42,
- "compile_blocks": True,
- "eval_every": 0,
- "prefetch_blocks": 4,
- "vae_batch": 4,
- "vae_compile": True,
- },
- "compact": {
- "wired_gb": 32,
- "compile_blocks": True,
- "eval_every": 0,
- "prefetch_blocks": 4,
- "weight_storage": "packed12",
- "vae_batch": 4,
- "vae_compile": True,
- },
- "baseline": {},
- "compiled": {"compile_blocks": True},
- "compiled-batched": {
- "compile_blocks": True,
- "eval_every": 0,
- "keep_refiners": True,
- },
- "compiled-fast": {
- "compile_blocks": True,
- "eval_every": 0,
- "keep_refiners": True,
- "vae_batch": 8,
- },
- "fused-compiled": {
- "compile_blocks": True,
- "eval_every": 0,
- "keep_refiners": True,
- "vae_batch": 8,
- "kernel": "metal-qk",
- },
- "batched": {"eval_every": 0, "keep_refiners": True},
- "wired-baseline": {"wired_gb": 42},
- "wired-compiled": {
- "wired_gb": 42,
- "compile_blocks": True,
- "eval_every": 0,
- "keep_refiners": True,
- },
- "wired-fast": {
- "wired_gb": 42,
- "compile_blocks": True,
- "eval_every": 0,
- "keep_refiners": True,
- "vae_batch": 8,
- },
- "packed12": {
- "wired_gb": 34,
- "compile_blocks": True,
- "eval_every": 0,
- "keep_refiners": True,
- "weight_storage": "packed12",
- },
- "packed12-fast": {
- "wired_gb": 34,
- "compile_blocks": True,
- "eval_every": 0,
- "keep_refiners": True,
- "weight_storage": "packed12",
- "vae_batch": 8,
- },
- "five-pass": {
- "wired_gb": 42,
- "compile_blocks": True,
- "eval_every": 0,
- "keep_refiners": True,
- "vae_batch": 8,
- },
- "fp16": {
- "allow_nonfinite": True,
- "wired_gb": 42,
- "compile_blocks": True,
- "eval_every": 0,
- "keep_refiners": True,
- "vae_batch": 8,
- },
-}
-
-
-def main(a):
- logging.basicConfig(
- level=logging.INFO, format="%(asctime)s %(message)s", datefmt="%H:%M:%S"
- )
- root = Path(a.output)
- root.mkdir(parents=True, exist_ok=True)
- cases = [
- c
- for c in json.loads(Path("experiments/cases.json").read_text())
- if c["id"] in a.cases
- ]
- for name in a.profiles:
- original_load = Linear.load.__func__
- if name == "fp16":
-
- def half_load(cls, store, prefix, original_load=original_load, **kwargs):
- import mlx.core as mx
-
- layer = original_load(cls, store, prefix, **kwargs)
- if prefix.startswith("blocks."):
- layer = HalfProjection(
- layer.weight.astype(mx.float16),
- None if layer.bias is None else layer.bias.astype(mx.float16),
- )
- return layer
-
- Linear.load = classmethod(half_load)
- pipeline = Pipeline(a.model, a.text_encoder, **PROFILES[name])
- try:
- for case in cases:
- for trial in range(a.repeat):
- _, row = pipeline.generate(
- case["prompt"],
- width=case["width"],
- height=case["height"],
- seed=case["seed"],
- images=case.get("images", ()),
- save_trace=True,
- steps=[0, 2, 3, 4, 5] if name == "five-pass" else None,
- output=root / case["id"] / f"{name}-{trial}.png",
- )
- row.update(profile=name, case=case["id"], trial=trial)
- if name == "fp16":
- row["experimental_precision"] = (
- "Main projection GEMMs FP16, native BF16 output rounding; norms, attention and other components retain the original policy"
- )
- Path(row["output"]).with_suffix(".json").write_text(
- json.dumps(row, indent=2) + "\n"
- )
- with (root / "trials.jsonl").open("a") as f:
- f.write(json.dumps(row) + "\n")
- finally:
- pipeline.clear()
- del pipeline
- gc.collect()
- Linear.load = classmethod(original_load)
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument("--text-encoder", required=True)
- p.add_argument(
- "--profiles", nargs="+", choices=PROFILES, default=["compiled-batched"]
- )
- p.add_argument("--cases", nargs="+", default=["product"])
- p.add_argument("--repeat", type=int, default=3)
- p.add_argument("--output", default="outputs/optimization-v2")
- main(p.parse_args())
diff --git a/experiments/v2_loading.py b/experiments/v2_loading.py
deleted file mode 100644
index 4ef643c7b8d07c95142dbdb526abb394fdc6e176..0000000000000000000000000000000000000000
--- a/experiments/v2_loading.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""Compare the original tensor reader with lazy native MLX safetensors I/O."""
-
-from __future__ import annotations
-
-import argparse
-import gc
-import hashlib
-import json
-import statistics
-import time
-from pathlib import Path
-
-import mlx.core as mx
-import numpy as np
-
-from h3_mlx.runtime import configure_memory, environment
-from h3_mlx.weights import WeightStore
-
-
-def main(a):
- configure_memory(wired_gb=4)
- store = WeightStore(Path(a.model) / "transformer", reader="memmap")
- report = {
- "environment": environment(),
- "scope": "One original tensor at a time. OS page cache is not flushed; includes read/copy and Metal synchronization.",
- "trials": [],
- }
- for key in (
- "blocks.0.attn.qkv_proj.weight",
- "blocks.25.mlp.fc1.weight",
- "blocks.49.mlp.fc2.weight",
- ):
- native = mx.load(str(store.index[key]))
- for mode in ("memmap_copy", "mlx_lazy_load"):
- samples = []
- for _ in range(a.repeats):
- gc.collect()
- mx.clear_cache()
- mx.synchronize()
- tick = time.perf_counter()
- if mode == "memmap_copy":
- value = store.get(key)
- else:
- # Reload dictionary so every sample has an unevaluated Load node.
- value = mx.load(str(store.index[key]))[key]
- mx.eval(value)
- mx.synchronize()
- samples.append(time.perf_counter() - tick)
- bits = hashlib.sha256(
- np.asarray(value.view(mx.uint16)).tobytes()
- ).hexdigest()
- del value
- report["trials"].append(
- {
- "key": key,
- "mode": mode,
- "median_s": statistics.median(samples),
- "samples_s": samples,
- "uint16_sha256": bits,
- }
- )
- print(json.dumps(report["trials"][-1]), flush=True)
- del native
- dest = Path(a.output)
- dest.parent.mkdir(parents=True, exist_ok=True)
- dest.write_text(json.dumps(report, indent=2) + "\n")
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument("--repeats", type=int, default=3)
- p.add_argument("--output", default="reports/optimization-v2/loading.json")
- main(p.parse_args())
diff --git a/experiments/v2_mpp.py b/experiments/v2_mpp.py
deleted file mode 100644
index d1e89d3e2da018e69ca7451db5547e240d9ed30c..0000000000000000000000000000000000000000
--- a/experiments/v2_mpp.py
+++ /dev/null
@@ -1,179 +0,0 @@
-"""Shape-specific Metal 4 TensorOps GEMM tile, K barrier and walk-order search."""
-
-from __future__ import annotations
-
-import argparse
-import gc
-import json
-from functools import lru_cache
-from pathlib import Path
-
-import mlx.core as mx
-
-from h3_mlx.benchmark import error, measure
-from h3_mlx.runtime import configure_memory, environment
-from h3_mlx.weights import Linear, WeightStore
-
-
-@lru_cache(maxsize=None)
-def kernel(bm, bn, bk, groups, walk):
- header = """
- #include
- #include
- using namespace mpp::tensor_ops;
- """
- source = r"""
- const int M = x_shape[0], K = x_shape[1], N = w_shape[0];
- int tiles_m = (M + BM - 1) / BM, tiles_n = N / BN;
- int tile = threadgroup_position_in_grid.x;
- int ti, tj;
- if (WALK == 0) {
- ti = tile / tiles_n; tj = tile % tiles_n;
- } else {
- // Group adjacent M rows, interleaving N tiles for L2 reuse.
- int group = tile / (WALK * tiles_n);
- int first_m = group * WALK;
- int size_m = min(tiles_m - first_m, WALK);
- int local = tile - group * WALK * tiles_n;
- ti = first_m + local % size_m;
- tj = local / size_m;
- }
- auto A = tensor(const_cast(x), dextents(K, M), array{1, K});
- auto B = tensor(const_cast(w), dextents(K, N), array{1, K});
- auto D = tensor(out, dextents(N, M), array{1, N});
- constexpr auto mode = matmul2d_descriptor::mode::multiply_accumulate;
- constexpr auto desc = matmul2d_descriptor(BM, BN, BK, false, true, false, mode);
- matmul2d> op;
- auto a0 = A.template slice(0, ti * BM);
- auto b0 = B.template slice(0, tj * BN);
- auto accum = op.template get_destination_cooperative_tensor();
- for (int e = 0; e < accum.get_capacity(); ++e) accum[e] = 0.0f;
- for (int k = 0; k < K; k += BK) {
- threadgroup_barrier(mem_flags::mem_none);
- auto a = A.template slice(k, ti * BM);
- auto b = B.template slice(k, tj * BN);
- op.run(a, b, accum);
- }
- auto bias_tensor = tensor(const_cast(bias), dextents(N, M), array{1, 0});
- auto bias_tile = bias_tensor.template slice(tj * BN, ti * BM);
- auto bias_regs = op.template get_destination_cooperative_tensor();
- bias_regs.load(bias_tile);
- auto result = op.template get_destination_cooperative_tensor();
- for (int e = 0; e < accum.get_capacity(); ++e) result[e] = T(accum[e] + float(bias_regs[e]));
- result.store(D.template slice(tj * BN, ti * BM));
- """
- return mx.fast.metal_kernel(
- name=f"h3_mpp_{bm}_{bn}_{bk}_{groups}_{walk}",
- input_names=["x", "w", "bias"],
- output_names=["out"],
- source=source,
- header=header,
- )
-
-
-def gemm(x, w, bias, bm, bn, bk, groups, walk):
- m, k = x.shape
- n = w.shape[0]
- if k % bk or n % bn:
- raise ValueError("This experimental kernel requires aligned K and N")
- return kernel(bm, bn, bk, groups, walk)(
- inputs=[x, w, bias],
- template=[
- ("T", x.dtype),
- ("BM", bm),
- ("BN", bn),
- ("BK", bk),
- ("SG", groups),
- ("WALK", walk),
- ],
- grid=(((m + bm - 1) // bm) * (n // bn) * groups * 32, 1, 1),
- threadgroup=(groups * 32, 1, 1),
- output_shapes=[(m, n)],
- output_dtypes=[x.dtype],
- )[0]
-
-
-def main(a):
- configure_memory()
- mx.random.seed(731)
- dest = Path(a.output)
- dest.parent.mkdir(parents=True, exist_ok=True)
- report = {
- "environment": environment(),
- "warmup": 2,
- "repeats": a.repeats,
- "scope": "Original projection weights, synthetic BF16 inputs. Custom Metal 4 TensorOps with FP32 accumulation and BF16 bias epilogue.",
- "trials": [],
- }
- store = WeightStore(Path(a.model) / "transformer")
- for name in a.operators:
- layer = Linear.load(
- store,
- "blocks.0." + name,
- grouped_heads=(56, 128) if name == "attn.qkv_proj" else None,
- )
- w = layer.weight
- b = layer.bias if layer.bias is not None else mx.zeros(w.shape[0], w.dtype)
- x = mx.random.normal((a.tokens, w.shape[1])).astype(w.dtype)
- ref = layer(x)
- mx.eval(x, w, b, ref)
- variants = [(0, 0, 0, 0, 0)]
- variants += [
- (bm, bn, bk, sg, walk)
- for bm, bn, sg in (
- (32, 64, 2),
- (64, 64, 4),
- (64, 128, 4),
- (128, 64, 4),
- (128, 128, 4),
- (128, 128, 8),
- (64, 128, 8),
- )
- for bk in (128, 256, 512)
- if w.shape[1] % bk == 0
- for walk in (0, 8)
- ]
- for bm, bn, bk, sg, walk in variants:
- row = {
- "operator": name,
- "tokens": a.tokens,
- "tile": [bm, bn, bk],
- "simdgroups": sg,
- "walk_m": walk,
- }
- try:
- fn = (
- (lambda layer=layer, x=x: layer(x))
- if bm == 0
- else (
- lambda x=x, w=w, b=b, bm=bm, bn=bn, bk=bk, sg=sg, walk=walk: (
- gemm(x, w, b, bm, bn, bk, sg, walk)
- )
- )
- )
- row.update(measure(fn, repeats=a.repeats), **error(ref, fn()))
- 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)
- if "error" in row and bm == 32:
- # A syntax or SDK incompatibility must be fixed before a sweep.
- return
- del layer, w, b, x, ref
- gc.collect()
- mx.clear_cache()
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument("--tokens", type=int, default=4181)
- p.add_argument(
- "--operators",
- nargs="+",
- default=["attn.qkv_proj", "attn.out_proj", "mlp.fc1", "mlp.fc2"],
- )
- p.add_argument("--repeats", type=int, default=5)
- p.add_argument("--output", default="reports/optimization-v2/mpp.json")
- main(p.parse_args())
diff --git a/experiments/v2_mps.swift b/experiments/v2_mps.swift
deleted file mode 100644
index a78e938c6cea9b75b11312634c3c5fdf03701ec9..0000000000000000000000000000000000000000
--- a/experiments/v2_mps.swift
+++ /dev/null
@@ -1,51 +0,0 @@
-// Synchronized MPSMatrix baseline, including CPU submission and shared buffers.
-// Build: swiftc -O experiments/v2_mps.swift -o cache/v2-mps
-import Foundation
-import Metal
-import MetalPerformanceShaders
-
-let device = MTLCreateSystemDefaultDevice()!
-let queue = device.makeCommandQueue()!
-let repetitions = 7
-let tokens = Int(CommandLine.arguments.dropFirst().first ?? "4181")!
-var results: [[String: Any]] = []
-for (k,n) in [(5376,21504),(7168,5376),(5376,28672),(14336,5376)] {
- for fp16 in [true,false] {
- let bytes = fp16 ? 2 : 4
- let type: MPSDataType = fp16 ? .float16 : .float32
- let ab = device.makeBuffer(length: tokens*k*bytes, options:.storageModeShared)!
- let bb = device.makeBuffer(length: n*k*bytes, options:.storageModeShared)!
- let cb = device.makeBuffer(length: tokens*n*bytes, options:.storageModeShared)!
- if fp16 {
- ab.contents().bindMemory(to:Float16.self, capacity:tokens*k).initialize(repeating:Float16(1.0/64),count:tokens*k)
- bb.contents().bindMemory(to:Float16.self, capacity:n*k).initialize(repeating:Float16(1.0/64),count:n*k)
- } else {
- ab.contents().bindMemory(to:Float.self, capacity:tokens*k).initialize(repeating:1.0/64,count:tokens*k)
- bb.contents().bindMemory(to:Float.self, capacity:n*k).initialize(repeating:1.0/64,count:n*k)
- }
- let a = MPSMatrix(buffer:ab,descriptor:MPSMatrixDescriptor(rows:tokens,columns:k,rowBytes:k*bytes,dataType:type))
- let b = MPSMatrix(buffer:bb,descriptor:MPSMatrixDescriptor(rows:n,columns:k,rowBytes:k*bytes,dataType:type))
- let c = MPSMatrix(buffer:cb,descriptor:MPSMatrixDescriptor(rows:tokens,columns:n,rowBytes:n*bytes,dataType:type))
- let mul = MPSMatrixMultiplication(device:device,transposeLeft:false,transposeRight:true,resultRows:tokens,resultColumns:n,interiorColumns:k,alpha:1,beta:0)
- var wall: [Double] = [], gpu: [Double] = []
- for trial in 0..<(repetitions+2) {
- let start = ProcessInfo.processInfo.systemUptime
- let command = queue.makeCommandBuffer()!
- mul.encode(commandBuffer:command,leftMatrix:a,rightMatrix:b,resultMatrix:c)
- command.commit()
- command.waitUntilCompleted()
- precondition(command.status == .completed, "MPS command failed")
- if trial>=2 {
- wall.append(1000*(ProcessInfo.processInfo.systemUptime-start))
- gpu.append(1000*(command.gpuEndTime-command.gpuStartTime))
- }
- }
- let y = fp16 ? Float(cb.contents().bindMemory(to:Float16.self,capacity:tokens*n)[0]) : cb.contents().bindMemory(to:Float.self,capacity:tokens*n)[0]
- precondition(y == Float(k)/4096, "Projection correctness check failed")
- let row: [String:Any] = ["backend":"MPSMatrixMultiplication","dtype":fp16 ? "float16":"float32","shape":[tokens,k,n],"median_ms":wall.sorted()[wall.count/2],"gpu_median_ms":gpu.sorted()[gpu.count/2],"samples_ms":wall,"gpu_samples_ms":gpu,"first_element":y,"expected":Float(k)/4096]
- results.append(row)
- let encoded = try! JSONSerialization.data(withJSONObject:row,options:[.sortedKeys])
- print(String(data:encoded,encoding:.utf8)!)
- fflush(stdout)
- }
-}
diff --git a/experiments/v2_operators.py b/experiments/v2_operators.py
deleted file mode 100644
index aa5d7953102617770f79e80db40697d5316684d1..0000000000000000000000000000000000000000
--- a/experiments/v2_operators.py
+++ /dev/null
@@ -1,165 +0,0 @@
-"""Actual H3 projection dimensions, M5 layouts, and new quantization formats.
-
-Run as a module from the repository root. Each result is persisted immediately.
-The inputs are synthetic; weights come from the original checkpoint.
-"""
-
-from __future__ import annotations
-
-import argparse
-import gc
-import json
-from pathlib import Path
-
-import mlx.core as mx
-
-from h3_mlx.benchmark import error, measure
-from h3_mlx.ops import swiglu
-from h3_mlx.runtime import configure_memory, environment
-from h3_mlx.weights import Linear, WeightStore
-
-
-def run(args):
- configure_memory()
- mx.random.seed(731)
- dest = Path(args.output)
- dest.parent.mkdir(parents=True, exist_ok=True)
- report = {
- "environment": environment(),
- "warmup": 2,
- "repeats": args.repeats,
- "scope": "Original block-0 weights; synthetic BF16 inputs. Operator latency only, excludes conversion and loading.",
- "trials": [],
- }
-
- def record(op, variant, fn, ref, **extra):
- try:
- timing = measure(fn, repeats=args.repeats)
- row = {
- "operator": op,
- "variant": variant,
- **extra,
- **timing,
- **error(ref, fn()),
- }
- except Exception as exc:
- row = {
- "operator": op,
- "variant": variant,
- **extra,
- "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)
-
- store = WeightStore(Path(args.model) / "transformer")
- for name in ("attn.qkv_proj", "attn.out_proj", "mlp.fc1", "mlp.fc2"):
- layer = Linear.load(
- store,
- "blocks.0." + name,
- grouped_heads=(56, 128) if name == "attn.qkv_proj" else None,
- )
- w, bias = layer.weight, layer.bias
- x = mx.random.normal((args.tokens, w.shape[1])).astype(mx.bfloat16)
- mx.eval(x, w)
- ref = layer(x)
- mx.eval(ref)
- shape = [args.tokens, w.shape[1], w.shape[0]]
- record(name, "bf16_nt", lambda layer=layer, x=x: layer(x), ref, shape=shape)
- wt = mx.contiguous(w.T)
- mx.eval(wt)
- fn = (
- (lambda x=x, wt=wt: x @ wt)
- if bias is None
- else (lambda bias=bias, x=x, wt=wt: mx.addmm(bias, x, wt))
- )
- record(name, "bf16_nn", fn, ref, shape=shape)
- del wt, fn
- for pad in (64, 128, 256):
- n = ((args.tokens + pad - 1) // pad) * pad
- padded = mx.pad(x, ((0, n - args.tokens), (0, 0)))
- mx.eval(padded)
- record(
- name,
- f"bf16_pad{pad}",
- lambda layer=layer, padded=padded: layer(padded)[: args.tokens],
- ref,
- shape=shape,
- padded_tokens=n,
- )
- del padded
- half, halfw = x.astype(mx.float16), w.astype(mx.float16)
- halfb = None if bias is None else bias.astype(mx.float16)
- mx.eval(half, halfw, halfb)
- fn = (
- (lambda half=half, halfw=halfw: (half @ halfw.T).astype(mx.bfloat16))
- if bias is None
- else (
- lambda halfb=halfb, half=half, halfw=halfw: mx.addmm(
- halfb, half, halfw.T
- ).astype(mx.bfloat16)
- )
- )
- record(name, "fp16_round_bf16", fn, ref, shape=shape)
- del half, halfw, halfb, fn
- for mode, bits, group in (
- ("mxfp8", 8, 32),
- ("mxfp4", 4, 32),
- ("nvfp4", 4, 16),
- ("affine", 8, 32),
- ("affine", 6, 32),
- ("affine", 4, 32),
- ):
- try:
- packed = mx.quantize(w, mode=mode, bits=bits, group_size=group)
- mx.eval(packed)
-
- def quant(
- x=x, packed=packed, mode=mode, bits=bits, group=group, bias=bias
- ):
- y = mx.quantized_matmul(
- x, *packed, mode=mode, bits=bits, group_size=group
- )
- return y if bias is None else y + bias
-
- record(
- name,
- f"{mode}{bits}_g{group}",
- quant,
- ref,
- shape=shape,
- weight_gib=sum(v.nbytes for v in packed) / 1024**3,
- )
- del packed, quant
- except Exception as exc:
- record(
- name,
- f"{mode}{bits}_g{group}",
- lambda exc=exc: (_ for _ in ()).throw(exc),
- ref,
- )
- del x, w, bias, layer, ref
- gc.collect()
- mx.clear_cache()
- a = Linear.load(store, "blocks.0.mlp.fc1")
- b = Linear.load(store, "blocks.0.mlp.fc2")
- x = mx.random.normal((args.tokens, 5376)).astype(mx.bfloat16)
-
- def mlp(x):
- return b(swiglu(a(x)))
-
- ref = mlp(x)
- mx.eval(ref)
- record("mlp", "eager", lambda: mlp(x), ref)
- compiled = mx.compile(mlp)
- record("mlp", "compiled", lambda: compiled(x), ref)
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument("--tokens", type=int, default=4181)
- p.add_argument("--repeats", type=int, default=5)
- p.add_argument("--output", default="reports/optimization-v2/operators.json")
- run(p.parse_args())
diff --git a/experiments/v2_report.py b/experiments/v2_report.py
deleted file mode 100644
index 08919fa30af3cdbf4c7dc4c213c9a29715e1416a..0000000000000000000000000000000000000000
--- a/experiments/v2_report.py
+++ /dev/null
@@ -1,203 +0,0 @@
-"""Build the second optimization report from recorded timings and real PNGs."""
-
-from __future__ import annotations
-
-import json
-import statistics
-from pathlib import Path
-
-from h3_mlx.compare import image_metrics, make_comparison
-
-ROOT = Path("reports/optimization-v2")
-OUTPUT = Path("outputs/optimization-v2")
-GALLERIES = Path("comparisons/optimization-v2")
-NAMES = {
- "quiet-baseline": "BF16 baseline (packing still active)",
- "quiet-native": "Exact optimized BF16, final quiet session",
- "quiet-packed": "Exact optimized packed BF16, final quiet session",
- "wired-baseline": "BF16 baseline",
- "wired-compiled": "Compiled BF16, retained refiners",
- "packed12": "Lossless packed, retained refiners",
- "prefetch": "Compiled BF16 + exact caches + prefetch",
- "compact": "Lossless packed + exact caches + prefetch",
- "fp16": "FP16 projection failure (non-finite)",
- "five-pass": "Five original passes (skip head 1)",
-}
-
-
-def latest(case, profile):
- files = sorted(
- (OUTPUT / case).glob(f"{profile}-*.png"),
- key=lambda p: int(p.stem.rsplit("-", 1)[1]),
- )
- return files[-1] if files else None
-
-
-def main():
- from PIL import Image
-
- ROOT.mkdir(parents=True, exist_ok=True)
- ledger = [
- json.loads(line) for line in (OUTPUT / "trials.jsonl").read_text().splitlines()
- ]
- summary = []
- for case in ("product", "portrait", "native-4k"):
- reference = latest(case, "quiet-baseline") or latest(case, "wired-baseline")
- if reference is None:
- old = list((Path("outputs/suite") / case).glob("native-*.png"))
- reference = sorted(old)[-1] if old else None
- if reference is None:
- continue
- ref = Image.open(reference)
- for profile in NAMES:
- path = latest(case, profile)
- if path is None:
- continue
- group = [r for r in ledger if r["case"] == case and r["profile"] == profile]
- # If a suite was rerun with the same filenames, keep its latest row.
- group = list({r["output"]: r for r in group}.values())
- hot = [r for r in group if r["dit_was_resident"]]
- import numpy as np
- from safetensors import safe_open
-
- with safe_open(
- path.with_suffix(".trace.safetensors"), framework="numpy"
- ) as trace:
- finite = bool(np.isfinite(trace.get_tensor("latent")).all())
- row = {
- "valid_denoising": finite,
- "case": case,
- "profile": profile,
- "label": NAMES[profile],
- "image": str(path),
- "reference": str(reference),
- "resident_samples": len(hot),
- "load_included_s": [
- r["timings"]["total_s"] for r in group if not r["dit_was_resident"]
- ],
- "resident_total_s": [r["timings"]["total_s"] for r in hot],
- "resident_dit_s": [
- sum(v for k, v in r["timings"].items() if k.startswith("step_"))
- for r in hot
- ],
- "peak_mlx_gib": max(r["memory"]["peak_gb"] for r in group),
- **image_metrics(ref, Image.open(path)),
- }
- row["resident_median_s"] = (
- statistics.median(row["resident_total_s"]) if hot else None
- )
- row["resident_dit_median_s"] = (
- statistics.median(row["resident_dit_s"]) if hot else None
- )
- summary.append(row)
- if case in ("product", "portrait"):
- candidates = []
- labels = ["Native six-pass BF16"]
- for profile in ("prefetch", "compact", "fp16", "five-pass"):
- selected = {"prefetch": "quiet-native", "compact": "quiet-packed"}.get(
- profile, profile
- )
- path = latest(case, selected) or latest(case, profile)
- if path:
- candidates.append(path)
- labels.append(NAMES[profile])
- for profile, label in (
- ("q8", "Affine Q8 (earlier trial)"),
- ("q4", "Affine Q4 (earlier trial)"),
- ):
- paths = sorted((Path("outputs/suite") / case).glob(f"{profile}-*.png"))
- if paths:
- candidates.append(paths[-1])
- labels.append(label)
- make_comparison(reference, candidates, GALLERIES / case, labels=labels)
- elif latest(case, "compact"):
- make_comparison(
- reference,
- [latest(case, "compact")],
- GALLERIES / case,
- labels=["Native 4K six-pass BF16", "Optimized 4K lossless packed BF16"],
- )
- (ROOT / "summary.json").write_text(json.dumps(summary, indent=2) + "\n")
- lines = [
- "| Case | Configuration | Warm samples | Warm median (s) | Peak MLX GiB | SSIM | Exact pixels |",
- "|---|---|---:|---:|---:|---:|---|",
- ]
- for r in summary:
- seconds = (
- "—" if r["resident_median_s"] is None else f"{r['resident_median_s']:.2f}"
- )
- if not r["valid_denoising"]:
- seconds = "invalid"
- similarity = f"{r['ssim_rgb']:.6f}" if r["valid_denoising"] else "invalid state"
- exact = (
- ("yes" if r["pixel_identical"] else "no")
- if r["valid_denoising"]
- else "non-finite"
- )
- lines.append(
- f"| {r['case']} | {r['label']} | {r['resident_samples']} | {seconds} | {r['peak_mlx_gib']:.2f} | {similarity} | {exact} |"
- )
- (ROOT / "table.md").write_text("\n".join(lines) + "\n")
- target = ROOT / "RESULTS.md"
- if target.exists():
- text = target.read_text()
- start = ""
- end = ""
- if start in text and end in text:
- text = (
- text.split(start)[0]
- + start
- + "\n\n"
- + "\n".join(lines)
- + "\n\n"
- + end
- + text.split(end)[1]
- )
- target.write_text(text)
- try:
- import matplotlib
-
- matplotlib.use("Agg")
- import matplotlib.pyplot as plt
-
- rows = [
- r
- for r in summary
- if r["case"] == "product"
- and r["resident_median_s"] is not None
- and r["profile"] in ("quiet-native", "quiet-packed")
- ]
- fig, ax = plt.subplots(figsize=(10, 6), layout="constrained")
- for r in rows:
- ax.scatter(
- r["resident_median_s"],
- r["peak_mlx_gib"],
- s=90,
- c=[r["ssim_rgb"]],
- vmin=0.6,
- vmax=1,
- cmap="viridis",
- edgecolor="black",
- )
- ax.annotate(
- r["profile"],
- (r["resident_median_s"], r["peak_mlx_gib"]),
- xytext=(5, 6),
- textcoords="offset points",
- fontsize=9,
- )
- ax.set(
- xlabel="Recorded warm end-to-end seconds (lower is faster)",
- ylabel="Peak MLX allocator GiB (lower is smaller)",
- title="M5 Pro 64 GB · 2048² product · exact and approximate options",
- )
- ax.grid(alpha=0.2)
- fig.savefig(ROOT / "tradeoffs.png", dpi=170)
- plt.close(fig)
- except ImportError:
- pass
- print(json.dumps(summary, indent=2))
-
-
-if __name__ == "__main__":
- main()
diff --git a/experiments/v2_storage.py b/experiments/v2_storage.py
deleted file mode 100644
index 5400ba32033df41d4447f63cf19097dc0cfd1738..0000000000000000000000000000000000000000
--- a/experiments/v2_storage.py
+++ /dev/null
@@ -1,124 +0,0 @@
-"""Measure exact palette12 packing versus quantized and decoded-weight GEMM."""
-
-from __future__ import annotations
-
-import argparse
-import gc
-import json
-import time
-from pathlib import Path
-
-import mlx.core as mx
-
-from h3_mlx.benchmark import error, measure
-from h3_mlx.packed import PackedLinear
-from h3_mlx.runtime import configure_memory, environment
-from h3_mlx.weights import Linear, WeightStore
-
-
-def main(a):
- configure_memory(wired_gb=8)
- dest = Path(a.output)
- dest.parent.mkdir(parents=True, exist_ok=True)
- report = {
- "environment": environment(),
- "warmup": 2,
- "repeats": a.repeats,
- "scope": "Original block-0 weights; synthetic BF16 activations. Includes per-call GPU weight reconstruction, excludes initial packing.",
- "trials": [],
- }
- store = WeightStore(Path(a.model) / "transformer")
- mx.random.seed(731)
- for name in ("attn.qkv_proj", "attn.out_proj", "mlp.fc1", "mlp.fc2"):
- layer = Linear.load(
- store,
- "blocks.0." + name,
- grouped_heads=(56, 128) if name == "attn.qkv_proj" else None,
- )
- w = layer.weight
- x = mx.random.normal((a.tokens, w.shape[1])).astype(mx.bfloat16)
- ref = layer(x)
- mx.eval(ref, x)
- tick = time.perf_counter()
- packed = PackedLinear.from_linear(layer)
- pack_s = time.perf_counter() - tick
- exact = mx.all(
- packed.weight.unpack().view(mx.uint16) == w.view(mx.uint16)
- ).item()
- if not exact:
- raise AssertionError("Lossless weight reconstruction failed")
- functions = [
- ("bf16", lambda layer=layer, x=x: layer(x), {}),
- (
- "packed12",
- lambda packed=packed, x=x: packed(x),
- {
- "pack_s": pack_s,
- "weight_bytes": packed.nbytes,
- "ratio": packed.nbytes / layer.nbytes,
- "weight_bits_identical": exact,
- "escape_fraction": packed.weight.escape_indices.size / w.size,
- },
- ),
- ]
- # Explicit state inputs prevent compile from retaining a dense constant.
- def packed_forward(x, state, packed=packed):
- return packed(x, state=state)
- fn = mx.compile(packed_forward)
- state = packed.weight.state()
- functions.append(("packed12_compiled", lambda fn=fn, x=x, state=state: fn(x, state), {}))
- for variant, call, extra in functions:
- row = {
- "operator": name,
- "variant": variant,
- "shape": [a.tokens, w.shape[1], w.shape[0]],
- **extra,
- **measure(call, repeats=a.repeats),
- **error(ref, call()),
- }
- report["trials"].append(row)
- dest.write_text(json.dumps(report, indent=2) + "\n")
- print(json.dumps(row), flush=True)
- del functions, fn, packed
- for bits in (8, 6, 4):
- q, s, z = mx.quantize(w, bits=bits, group_size=32)
- mx.eval(q, s, z)
-
- def decoded(q=q, s=s, z=z, bits=bits, x=x, layer=layer):
- d = mx.dequantize(q, s, z, bits=bits, group_size=32)
- return x @ d.T if layer.bias is None else mx.addmm(layer.bias, x, d.T)
-
- def fused(q=q, s=s, z=z, bits=bits, x=x, layer=layer):
- y = mx.quantized_matmul(x, q, s, z, bits=bits, group_size=32)
- return y if layer.bias is None else y + layer.bias
-
- for variant, call in (
- (
- f"q{bits}_fused",
- fused,
- ),
- (f"q{bits}_dequant_gemm", decoded),
- ):
- row = {
- "operator": name,
- "variant": variant,
- "shape": [a.tokens, w.shape[1], w.shape[0]],
- **measure(call, repeats=a.repeats),
- **error(ref, call()),
- }
- report["trials"].append(row)
- dest.write_text(json.dumps(report, indent=2) + "\n")
- print(json.dumps(row), flush=True)
- del q, s, z, decoded
- del layer, w, x, ref
- gc.collect()
- mx.clear_cache()
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument("--tokens", type=int, default=4181)
- p.add_argument("--repeats", type=int, default=7)
- p.add_argument("--output", default="reports/optimization-v2/storage.json")
- main(p.parse_args())
diff --git a/experiments/v2_torch.py b/experiments/v2_torch.py
deleted file mode 100644
index 44ba4e8111b80ad5fc0fc2fd9eabd291844f30bf..0000000000000000000000000000000000000000
--- a/experiments/v2_torch.py
+++ /dev/null
@@ -1,133 +0,0 @@
-"""Apple GPU/CPU alternatives through PyTorch, including MPS Inductor fusion."""
-
-from __future__ import annotations
-
-import argparse
-import gc
-import json
-import platform
-import statistics
-import time
-from pathlib import Path
-
-import torch
-import torch.nn.functional as F
-from safetensors import safe_open
-
-
-def load(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)
- raise KeyError(key)
-
-
-def measure(fn, device, repeats):
- def sync():
- if device == "mps":
- torch.mps.synchronize()
-
- for _ in range(2):
- fn()
- sync()
- samples = []
- for _ in range(repeats):
- sync()
- tick = time.perf_counter()
- fn()
- sync()
- samples.append(1000 * (time.perf_counter() - tick))
- return {"median_ms": statistics.median(samples), "samples_ms": samples}
-
-
-def main(a):
- torch.set_num_threads(10)
- torch.manual_seed(731)
- root = Path(a.model) / "transformer"
- out = Path(a.output)
- out.parent.mkdir(parents=True, exist_ok=True)
- report = {
- "platform": platform.platform(),
- "torch": torch.__version__,
- "threads": 10,
- "repeats": a.repeats,
- "warmup": 2,
- "scope": "Actual H3 block-0 weights, synthetic activations; model loading and dtype conversion excluded.",
- "trials": [],
- }
- with torch.inference_mode():
- for name in ("attn.qkv_proj", "attn.out_proj", "mlp.fc1", "mlp.fc2"):
- w = load(root, "blocks.0." + name + ".weight")
- x = torch.randn(a.tokens, w.shape[1]).bfloat16()
- # A single FP32 CPU reference distinguishes accumulation precision.
- ref = F.linear(x.float(), w.float())
- for device, dtype in (
- ("cpu", torch.bfloat16),
- ("cpu", torch.float32),
- ("mps", torch.bfloat16),
- ("mps", torch.float16),
- ("mps", torch.float32),
- ):
- row = {
- "operator": name,
- "device": device,
- "dtype": str(dtype),
- "shape": [a.tokens, w.shape[1], w.shape[0]],
- }
- try:
- xx, ww = x.to(device, dtype), w.to(device, dtype)
- row.update(
- measure(
- lambda xx=xx, ww=ww: F.linear(xx, ww), device, a.repeats
- )
- )
- y = F.linear(xx, ww).cpu().float()
- row["relative_rmse"] = torch.sqrt(
- torch.mean((ref - y) ** 2) / torch.mean(ref**2)
- ).item()
- del xx, ww, y
- if device == "mps":
- torch.mps.empty_cache()
- except Exception as exc:
- row["error"] = f"{type(exc).__name__}: {exc}"
- report["trials"].append(row)
- out.write_text(json.dumps(report, indent=2) + "\n")
- print(json.dumps(row), flush=True)
- del x, w, ref
- gc.collect()
- w1 = load(root, "blocks.0.mlp.fc1.weight").to("mps")
- w2 = load(root, "blocks.0.mlp.fc2.weight").to("mps")
- x = torch.randn(a.tokens, 5376, device="mps").bfloat16()
-
- def mlp(x):
- gate, up = F.linear(x, w1).chunk(2, dim=-1)
- return F.linear(F.silu(gate) * up, w2)
-
- reference = mlp(x)
- for compiled in (False, True):
- row = {
- "operator": "mlp",
- "compiled": compiled,
- "device": "mps",
- "dtype": "torch.bfloat16",
- }
- try:
- fn = torch.compile(mlp) if compiled else mlp
- row.update(measure(lambda fn=fn: fn(x), "mps", a.repeats))
- y = fn(x)
- row["equal_fraction"] = (reference == y).float().mean().item()
- except Exception as exc:
- row["error"] = f"{type(exc).__name__}: {exc}"
- report["trials"].append(row)
- out.write_text(json.dumps(report, indent=2) + "\n")
- print(json.dumps(row), flush=True)
-
-
-if __name__ == "__main__":
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument("--tokens", type=int, default=4181)
- p.add_argument("--repeats", type=int, default=5)
- p.add_argument("--output", default="reports/optimization-v2/torch.json")
- main(p.parse_args())
diff --git a/experiments/wheel_verify_r3.py b/experiments/wheel_verify_r3.py
deleted file mode 100644
index 2e1c9068849019150c0a6a6a5c056607dec11aaf..0000000000000000000000000000000000000000
--- a/experiments/wheel_verify_r3.py
+++ /dev/null
@@ -1,126 +0,0 @@
-"""Run a complete 2K CLI generation from the actual wheel, outside the checkout."""
-
-import hashlib
-import json
-import os
-import subprocess
-import sys
-import time
-from pathlib import Path
-
-from experiments.r3.trace_audit import payloads
-
-
-def main():
- root = Path.cwd()
- wheel = (root / "dist/minimax_h3_image_mlx-0.3.0-py3-none-any.whl").resolve()
- case = next(
- c
- for c in json.loads((root / "experiments/cases.json").read_text())
- if c["id"] == "product"
- )
- output = root / "outputs/optimization-r3-wheel/product.png"
- output.parent.mkdir(parents=True, exist_ok=True)
- script = """import os, h3_mlx
-if not h3_mlx.__file__.startswith(os.environ['H3_R3_WHEEL_PATH']):
- raise RuntimeError('The checkout shadowed the wheel')
-print('Loaded runtime from', h3_mlx.__file__, flush=True)
-from h3_mlx.cli import main
-main()
-"""
- command = [
- sys.executable,
- "-c",
- script,
- "generate",
- "--model",
- str((root / "../minimax-h3-image-opt/models/MiniMax-H3-Image").resolve()),
- "--text-encoder",
- str(
- (root / "../minimax-h3-image-opt/models/MiniMax-H3/text_encoder").resolve()
- ),
- "--cache-dir",
- str(root / "cache"),
- "--compile-blocks",
- "--eval-every",
- "0",
- "--prefetch-blocks",
- "4",
- "--wired-gb",
- "42",
- "--compile-vae",
- "--vae-batch",
- "4",
- "--kernel",
- "metal-qk-exact",
- "--tune-matmul",
- "--seed",
- str(case["seed"]),
- "--width",
- "2048",
- "--height",
- "2048",
- "--save-trace",
- "--output",
- str(output),
- case["prompt"],
- ]
- task_env = dict(os.environ, PYTHONPATH=str(wheel), H3_R3_WHEEL_PATH=str(wheel))
- tick = time.perf_counter()
- with (root / "reports/optimization-r3/wheel-cli.log").open("w") as handle:
- subprocess.run(
- command,
- cwd="/tmp",
- env=task_env,
- stdout=handle,
- stderr=subprocess.STDOUT,
- check=True,
- )
- elapsed = time.perf_counter() - tick
- import numpy as np
- from PIL import Image
-
- reference = root / "outputs/suite/product/native-3.png"
- pixels = np.asarray(Image.open(output).convert("RGB"))
- expected = np.asarray(Image.open(reference).convert("RGB"))
- trace_equal = payloads(output.with_suffix(".trace.safetensors")) == payloads(
- reference.with_suffix(".trace.safetensors")
- )
- if not np.array_equal(pixels, expected) or not trace_equal:
- raise ValueError("Wheel CLI changed delivered pixels or trajectory")
- result = {
- "wheel": str(wheel),
- "wheel_sha256": hashlib.sha256(wheel.read_bytes()).hexdigest(),
- "command": command,
- "outer_seconds": elapsed,
- "cwd": "/tmp",
- "loaded_from_wheel": True,
- "pixel_identical": True,
- "trace_identical": True,
- "reference": str(reference),
- "output": str(output),
- "png_sha256": hashlib.sha256(output.read_bytes()).hexdigest(),
- "record": json.loads(output.with_suffix(".json").read_text()),
- }
- (root / "reports/optimization-r3/wheel-cli.json").write_text(
- json.dumps(result, indent=2) + "\n"
- )
- print(
- json.dumps(
- {
- k: result[k]
- for k in [
- "outer_seconds",
- "loaded_from_wheel",
- "pixel_identical",
- "trace_identical",
- "output",
- ]
- },
- indent=2,
- )
- )
-
-
-if __name__ == "__main__":
- main()
diff --git a/files.json b/files.json
index d16f17390d8a8f60f7b30fb2ea0ca038167259d7..17ca75cda47cb226061bc58b9a636765561afdc9 100644
--- a/files.json
+++ b/files.json
@@ -1,13 +1,25 @@
{
"format": "h3-mlx-files-v1",
"files": {
+ ".gitattributes": {
+ "bytes": 1519,
+ "sha256": "11ad7efa24975ee4b0c3c3a38ed18737f0658a5f75a0a96787b576a78a023361"
+ },
".gitignore": {
- "bytes": 144,
- "sha256": "17b671f22b45ce39468ce71da3b145dc8e35198f286584945bdbf4eeabc30d34"
+ "bytes": 183,
+ "sha256": "48747f523cccbc7cc541126d7624096ebc0b4e6770ce846e654a67edd29f1c9b"
},
"BENCHMARKS.md": {
- "bytes": 4018,
- "sha256": "501db54fcc9bc67d4afe01855820fe1d2e21c9b1f7ed34c75f2ad0deb8254137"
+ "bytes": 6627,
+ "sha256": "8cdd3e2333c4208136a57c2332234abc2fbacd399e98c2e368f6bc3ddc06928c"
+ },
+ "CHANGELOG.md": {
+ "bytes": 970,
+ "sha256": "6832b3ad1ec75940af057aba874e15406318172fe6ce31a7620481542db38203"
+ },
+ "CONTRIBUTING.md": {
+ "bytes": 1569,
+ "sha256": "4de5e6a3107cdc5f9c92689750ad8a8fbef935faf93447c393c698e69a8b9068"
},
"LICENSE": {
"bytes": 11346,
@@ -21,565 +33,145 @@
"bytes": 17604,
"sha256": "59b99642b95ea21630e311198ddbfffbfe05aadba0c2f5d884cbdf4efcc90f44"
},
- "MODEL_INDEX.md": {
- "bytes": 2842,
- "sha256": "46fc7146a9ef5842539dc859987c2d98c1dcc399150cd113b9ded16dc4292a5e"
+ "MANIFEST.in": {
+ "bytes": 411,
+ "sha256": "ae7aa19afa61cce60cbabd6894a841366bc484030840a0fd6b903469166181bf"
},
"NOTICE": {
"bytes": 772,
"sha256": "3c38183c655099f4587694631ff0c203df3cc27824085236081faf0b6abc091c"
},
"README.md": {
- "bytes": 8787,
- "sha256": "507551bd6e9630e5e54ad9570b7d7467245d3d3141ddadcbe14e5b9b592e77c0"
+ "bytes": 9660,
+ "sha256": "adcb992597335bf80e8ddb2a97a07e34ca5662849a8ed0f7b1ed3464560ec8a7"
},
"README.zh-CN.md": {
- "bytes": 2667,
- "sha256": "2b27c462341efe798b4a05cb00a1be4cd8e18df60662d191d12ad65df0a12218"
- },
- "RELEASE.md": {
- "bytes": 2590,
- "sha256": "26b70cd4567e59bc6597fd4730c04027e4f546bef6b5cefaf2eddc74ac6ff093"
+ "bytes": 3963,
+ "sha256": "ee7ac77fc989ed64e606cfc605fc7fbf2a3222c1618b0c270dc7aaa53f53199f"
},
"USAGE.md": {
- "bytes": 4522,
- "sha256": "ffc7e12bb12c588cec302c4aa99096bc98face0672b7d735e2210c3ce5cc5b4a"
- },
- "comparisons/README.md": {
- "bytes": 3809,
- "sha256": "c5010d12a6b5f6d4f3773da046a55352d13c2983ddd7193b038d664e3a603963"
- },
- "comparisons/cuda-reference/native-4k.json": {
- "bytes": 2009,
- "sha256": "4e119c5f05547e3eae6ca641dc2ad15652173c684100bd661c66cc0e57cedd98"
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+ "bytes": 2736,
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+ },
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+ "bytes": 1872,
+ "sha256": "3e3a6c0cc6ce4d3bbddbc432335a490bfb6c98a70dad15c27b0b0c0cca54861a"
},
"tests/test_refiner_cache.py": {
"bytes": 1688,
"sha256": "3d975c227d0a7b796123eddda80eb0d59f2b626aed1be85555f1bfed2dc153d8"
},
- "tests/test_release_guards.py": {
- "bytes": 1462,
- "sha256": "6cd89e47699bee2caf0daabc2bec086ecdc7b28b45029fe9e9531045e5d73ac4"
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"tests/test_runtime_guard.py": {
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"tests/test_tuning.py": {
"bytes": 3039,
@@ -1321,13 +301,13 @@
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"sha256": "8f9d9b858325a91f6169bf7b4ca417f4e9f17ad8c1621a303256ecfb00d72886"
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- "sha256": "c0f408dfe07239a042184a360edfbe00398defff4231d19220319952cf7ad296"
+ "tools/check_release.py": {
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+ "sha256": "887614778a317ea8546bf6441c06be3fcbfdce2d5e62dd1876049ad5b6a9027c"
},
- "config.json": {
- "bytes": 358,
- "sha256": "295e1d9074a372ba6c62c4a0002e57ea719b6d14f8480cc68e04a1a72ffd6f06"
+ "tools/plot_benchmarks.py": {
+ "bytes": 3840,
+ "sha256": "5201da7ee124aa087706a853b90e484ade5b42e562954942e688f6afbbc459a6"
}
}
}
diff --git a/h3_mlx/__init__.py b/h3_mlx/__init__.py
index 04a4cb6d8495507dada8c5648813a627e3eefec8..7e5ae86d532de1f1b3e216503709411775e1f7b3 100644
--- a/h3_mlx/__init__.py
+++ b/h3_mlx/__init__.py
@@ -8,4 +8,4 @@ import os
os.environ.setdefault("MLX_ENABLE_TF32", "0")
os.environ.setdefault("MLX_CONV_WINOGRAD", "0")
-__version__ = "0.4.0"
+__version__ = "0.5.0"
diff --git a/h3_mlx/benchmark.py b/h3_mlx/benchmark.py
index 39fcfaabd49b9980274e484be3a24ac2ab533118..a7601ea9070df7771d0891397863f4cfa5bc5516 100644
--- a/h3_mlx/benchmark.py
+++ b/h3_mlx/benchmark.py
@@ -1,202 +1,105 @@
-"""Synchronized operator experiments on real H3 dimensions, with numerical error."""
+"""Measure one inference profile with explicit warmups and resident repeats."""
from __future__ import annotations
import argparse
-import gc
import json
+import logging
import statistics
import time
from pathlib import Path
-import mlx.core as mx
-from .kernels import norm_modulate, qk_norm_rope
-from .ops import attention, modulate, rms_norm, rotate
-from .runtime import configure_memory, environment
-
-
-def measure(fn, *, warmup=2, repeats=7):
- for _ in range(warmup):
- mx.eval(fn())
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--model", required=True)
+ parser.add_argument("--text-encoder", required=True)
+ parser.add_argument("--prompt", required=True)
+ parser.add_argument("--seed", type=int, default=731)
+ parser.add_argument("--width", type=int, default=2048)
+ parser.add_argument("--height", type=int, default=2048)
+ parser.add_argument("--profile", choices=("control", "tuned"), default="tuned")
+ parser.add_argument("--warmup", type=int, default=1)
+ parser.add_argument("--repeats", type=int, default=3)
+ parser.add_argument("--image", action="append", default=[])
+ parser.add_argument("--cache-dir", default="cache")
+ parser.add_argument("--wired-gb", type=float)
+ parser.add_argument("--output", required=True, help="New directory for this run")
+ args = parser.parse_args(argv)
+ if args.warmup < 1 or args.repeats < 1:
+ parser.error("At least one warmup and one measured repeat are required")
+ if args.width < 1 or args.height < 1:
+ parser.error("Width and height must be positive")
+ root = Path(args.output)
+ root.mkdir(parents=True, exist_ok=False)
+
+ import mlx.core as mx
+
+ from .runtime import Pipeline, environment
+
+ logging.basicConfig(level=logging.INFO, format="%(message)s")
+ pipe = Pipeline(
+ args.model,
+ args.text_encoder,
+ cache_dir=args.cache_dir,
+ compile_blocks=True,
+ eval_every=0,
+ prefetch_blocks=4,
+ vae_compile=True,
+ vae_batch=4,
+ kernel="metal-qk-exact" if args.profile == "tuned" else "native",
+ tune_matmul=args.profile == "tuned",
+ wired_gb=args.wired_gb,
+ )
samples = []
- for _ in range(repeats):
- mx.synchronize()
- tick = time.perf_counter()
- mx.eval(fn())
- mx.synchronize()
- samples.append((time.perf_counter() - tick) * 1000)
- return {
- "median_ms": statistics.median(samples),
- "min_ms": min(samples),
- "max_ms": max(samples),
- "samples_ms": samples,
- }
-
-
-def error(reference, candidate):
- a, b = reference.astype(mx.float32), candidate.astype(mx.float32)
- diff = a - b
- return {
- "max_abs": mx.max(mx.abs(diff)).item(),
- "relative_rmse": mx.sqrt(
- mx.mean(diff * diff) / (mx.mean(a * a) + 1e-30)
- ).item(),
- "equal_fraction": mx.mean((a == b).astype(mx.float32)).item(),
- }
-
-
-def run(output, *, tokens=(256, 4096), repeats=7):
- configure_memory()
- mx.random.seed(731)
- result = {
- "environment": environment(),
- "warmup": 2,
- "repeats": repeats,
- "scope": "Synthetic operator microbenchmarks; excludes loading/quantization. Not image latency.",
- "trials": [],
- }
-
- def record(group, variant, shape, fn, reference):
- data = {
- "operator": group,
- "variant": variant,
- "shape": shape,
- **measure(fn, repeats=repeats),
- **error(reference, fn()),
- }
- result["trials"].append(data)
- print(json.dumps(data), flush=True)
- # Persist each completed trial so interrupted sweeps retain evidence.
- Path(output).parent.mkdir(parents=True, exist_ok=True)
- Path(output).write_text(json.dumps(result, indent=2) + "\n")
-
- for n in tokens:
- x = mx.random.normal((n, 5376)).astype(mx.bfloat16)
- w = mx.random.normal((5376,)).astype(mx.bfloat16)
- shift = (mx.random.normal((3, 5376)) * 0.1).astype(mx.bfloat16)
- scale = (mx.random.normal((3, 5376)) * 0.1).astype(mx.bfloat16)
- ids = mx.arange(n, dtype=mx.int32) % 3
- mx.eval(x, w, shift, scale, ids)
- ref = modulate(x, w, shift, scale, ids)
- mx.eval(ref)
- record(
- "rms_adaln",
- "native_rounding",
- [n, 5376],
- lambda x=x, w=w, shift=shift, scale=scale, ids=ids: modulate(
- x, w, shift, scale, ids
- ),
- ref,
- )
- for threads in (128, 256, 512):
- record(
- "rms_adaln",
- f"metal_tg{threads}",
- [n, 5376],
- lambda x=x, w=w, shift=shift, scale=scale, ids=ids, threads=threads: (
- norm_modulate(x, w, shift, scale, ids, threads=threads)
- ),
- ref,
- )
- qkv = mx.random.normal((n, 3, 56, 128)).astype(mx.bfloat16)
- qw = mx.random.normal((128,)).astype(mx.bfloat16)
- angles = mx.random.normal((1, n, 1, 48))
- angles = mx.concatenate((angles, angles), axis=-1)
- cos, sin = (
- mx.cos(angles).astype(mx.bfloat16),
- mx.sin(angles).astype(mx.bfloat16),
- )
-
- def native_qk(qkv=qkv, qw=qw, cos=cos, sin=sin):
- return tuple(
- rotate(rms_norm(qkv[None, :, i], qw), cos, sin) for i in (0, 1)
- )
-
- def fused_qk(qkv=qkv, qw=qw, cos=cos, sin=sin):
- return qk_norm_rope(qkv, qw, qw, cos, sin)
-
- qr, kr = native_qk()
- both = mx.concatenate((qr, kr), axis=0)
- mx.eval(both)
- record(
- "qk_norm_rope",
- "native",
- [n, 56, 128],
- lambda fn=native_qk: mx.concatenate(fn(), axis=0),
- both,
- )
- record(
- "qk_norm_rope",
- "metal",
- [n, 56, 128],
- lambda fn=fused_qk: mx.concatenate(fn(), axis=0),
- both,
- )
- del qkv, qw, angles, cos, sin, qr, kr, both, native_qk, fused_qk
- mx.clear_cache()
- for k, out in ((5376, 21504), (5376, 28672), (14336, 5376)):
- a = mx.random.normal((n, k)).astype(mx.bfloat16)
- weight = (mx.random.normal((out, k)) * 0.02).astype(mx.bfloat16)
- mx.eval(a, weight)
- baseline = a @ weight.T
- mx.eval(baseline)
- record(
- "gemm",
- "bf16",
- [n, k, out],
- lambda a=a, weight=weight: a @ weight.T,
- baseline,
+ try:
+ for i in range(args.warmup + args.repeats):
+ phase = "warmup" if i < args.warmup else "measured"
+ index = i if phase == "warmup" else i - args.warmup
+ mx.synchronize()
+ start = time.perf_counter()
+ _, record = pipe.generate(
+ args.prompt,
+ seed=args.seed,
+ width=args.width,
+ height=args.height,
+ images=args.image,
+ save_trace=True,
+ output=root / f"{phase}-{index}.png",
)
- for bits in (8, 4):
- for size in (32, 64, 128):
- q, s, z = mx.quantize(weight, bits=bits, group_size=size)
- mx.eval(q, s, z)
- record(
- "gemm",
- f"q{bits}_g{size}",
- [n, k, out],
- lambda a=a, q=q, s=s, z=z, size=size, bits=bits: (
- mx.quantized_matmul(
- a, q, s, z, transpose=True, group_size=size, bits=bits
- )
- ),
- baseline,
- )
- del a, weight, baseline, q, s, z
- gc.collect()
- mx.clear_cache()
- q = mx.random.normal((1, n, 56, 128)).astype(mx.bfloat16)
- k = mx.random.normal(q.shape).astype(mx.bfloat16)
- v = mx.random.normal(q.shape).astype(mx.bfloat16)
- mx.eval(q, k, v)
- baseline = attention(q, k, v)
- mx.eval(baseline)
- for chunk in (0, 128, 512, 1024):
- record(
- "sdpa",
- f"query_chunk_{chunk}",
- list(q.shape),
- lambda q=q, k=k, v=v, chunk=chunk: attention(
- q, k, v, query_chunk=chunk
- ),
- baseline,
+ mx.synchronize()
+ samples.append(
+ {
+ "phase": phase,
+ "index": index,
+ "wall_seconds": time.perf_counter() - start,
+ "record": f"{phase}-{index}.json",
+ "png_sha256": record["png_sha256"],
+ "conditioning_cache_hit": record["conditioning_cache_hit"],
+ "dit_was_resident": record["dit_was_resident"],
+ }
)
- del q, k, v, baseline, x, w, shift, scale, ids, ref
- gc.collect()
- mx.clear_cache()
- return result
-
-
-def main():
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--output", default="reports/operators.json")
- p.add_argument("--tokens", type=int, nargs="+", default=[256, 4096])
- p.add_argument("--repeats", type=int, default=7)
- a = p.parse_args()
- if a.repeats < 1 or any(n < 1 for n in a.tokens):
- p.error("tokens and repeats must be positive")
- run(a.output, tokens=a.tokens, repeats=a.repeats)
+ finally:
+ pipe.clear()
+ times = [s["wall_seconds"] for s in samples if s["phase"] == "measured"]
+ summary = {
+ "format": "h3-mlx-benchmark-v1",
+ "arguments": vars(args),
+ "environment": environment(),
+ "samples": samples,
+ "median_seconds": statistics.median(times),
+ "min_seconds": min(times),
+ "max_seconds": max(times),
+ "scope": (
+ "Single-profile resident repeats after warmup; synchronized outer wall time "
+ "includes generation, trace, PNG and JSON save. Downloads, imports and "
+ "pipeline construction excluded. This is not the historical alternating "
+ "paired campaign. Per-request MLX peaks are in each inference record; "
+ "process lifetime high water is not a sampled per-request physical peak."
+ ),
+ }
+ (root / "summary.json").write_text(json.dumps(summary, indent=2) + "\n")
+ print(json.dumps(summary, indent=2))
if __name__ == "__main__":
diff --git a/h3_mlx/cli.py b/h3_mlx/cli.py
index b613509c365fe306f3a3f2f8ec4038e77989a64a..67419e2c55e5b0fee12029e2ededff6197458f23 100644
--- a/h3_mlx/cli.py
+++ b/h3_mlx/cli.py
@@ -66,14 +66,6 @@ def main(argv=None):
help="Ordered edit reference; repeat up to sixteen times",
)
generate.add_argument("--dit-cache-gb", type=float, default=38)
- generate.add_argument(
- "--quantize",
- type=int,
- choices=(4, 8),
- help="Experimental weight-only DiT quantization; AdaLN/FP32 islands stay native",
- )
- generate.add_argument("--encoder-quantize", type=int, choices=(4, 8))
- generate.add_argument("--group-size", type=int, choices=(32, 64, 128), default=64)
generate.add_argument("--mlp-chunk", type=int, default=0)
generate.add_argument(
"--attention-chunk",
@@ -97,16 +89,6 @@ def main(argv=None):
)
generate.add_argument("--vae-batch", type=int, default=1)
generate.add_argument("--compile-vae", action="store_true")
- generate.add_argument("--vae-backend", choices=("mlx", "coreml"), default="mlx")
- generate.add_argument(
- "--coreml-package",
- help="Fixed-tile decoder exported by experiments.v2_coreml_vae",
- )
- generate.add_argument(
- "--coreml-compute-units",
- choices=("CPU_ONLY", "CPU_AND_GPU", "CPU_AND_NE", "ALL"),
- default="CPU_AND_GPU",
- )
generate.add_argument(
"--vae-dtype", choices=("bfloat16", "float32"), default="bfloat16"
)
@@ -155,10 +137,7 @@ def main(argv=None):
"--weight-storage",
choices=("bf16", "packed12"),
default="bf16",
- help="packed12 caches exact BF16 weights in a smaller representation",
- )
- generate.add_argument(
- "--quantized-core", help="Preconverted DiT core from python -m h3_mlx.convert"
+ help="Storage format, automatically selected by downloaded variants",
)
generate.add_argument(
"--offload-after-denoise",
@@ -220,9 +199,6 @@ def main(argv=None):
args.text_encoder,
cache_dir=args.cache_dir,
dit_cache_gb=args.dit_cache_gb,
- bits=args.quantize,
- encoder_bits=args.encoder_quantize,
- group_size=args.group_size,
mlp_chunk=args.mlp_chunk,
query_chunk=args.attention_chunk,
kernel=args.kernel,
@@ -230,16 +206,12 @@ def main(argv=None):
vae_batch=args.vae_batch,
vae_dtype=args.vae_dtype,
keep_resident=not args.offload_after_denoise,
- quantized_path=args.quantized_core,
compile_blocks=args.compile_blocks,
eval_every=args.eval_every,
keep_refiners=args.keep_refiners,
wired_gb=args.wired_gb,
weight_storage=args.weight_storage,
vae_compile=args.compile_vae,
- vae_backend=args.vae_backend,
- coreml_package=args.coreml_package,
- coreml_compute_units=args.coreml_compute_units,
async_blocks=args.async_blocks,
cache_refined_text=not args.no_refiner_cache,
prefetch_blocks=args.prefetch_blocks,
diff --git a/h3_mlx/convert.py b/h3_mlx/convert.py
deleted file mode 100644
index 32d4cfd99e63f84200cd019097e405802061cad3..0000000000000000000000000000000000000000
--- a/h3_mlx/convert.py
+++ /dev/null
@@ -1,113 +0,0 @@
-"""Convert attention/MLP matrices one block at a time, retaining native precision islands."""
-
-from __future__ import annotations
-
-import argparse
-import hashlib
-import json
-import logging
-from pathlib import Path
-
-import mlx.core as mx
-
-from .config import DiTConfig
-from .runtime import configure_memory
-from .weights import Linear, WeightStore
-
-log = logging.getLogger(__name__)
-
-
-def convert(model_path, destination, *, bits=8, group_size=64):
- configure_memory()
- model_path, destination = Path(model_path), Path(destination)
- cfg = DiTConfig.load(model_path / "transformer/config.json")
- source = WeightStore(model_path / "transformer")
- if destination.exists() and any(destination.iterdir()):
- raise FileExistsError(f"Conversion destination must be empty: {destination}")
- destination.mkdir(parents=True, exist_ok=True)
- prefixes = [f"blocks.{i}" for i in range(cfg.num_layers)]
- prefixes += [
- f"token_refiner.blocks.{i}" for i in range(cfg.token_refiner_num_layers)
- ]
- metadata = {
- "format": "h3-mlx-core-v1",
- "bits": bits,
- "group_size": group_size,
- "qkv_layout": "qkv-head-dim",
- "source_fingerprint": source.fingerprint(),
- "source_config_sha256": hashlib.sha256(
- (model_path / "transformer/config.json").read_bytes()
- ).hexdigest(),
- "precision_policy": "Only BF16 attention/MLP matrices quantized; AdaLN and FP32 islands remain in the original checkpoint",
- }
- files = []
- for i, prefix in enumerate(prefixes):
- tensors = {}
- for name in (
- "norm1.weight",
- "norm2.weight",
- "attn.q_norm.weight",
- "attn.k_norm.weight",
- ):
- tensors[f"{prefix}.{name}"] = source.get(f"{prefix}.{name}")
- for name in ("attn.qkv_proj", "attn.out_proj", "mlp.fc1", "mlp.fc2"):
- grouped = (
- (cfg.num_attention_heads, cfg.attention_head_dim)
- if name == "attn.qkv_proj"
- else None
- )
- layer = Linear.load(
- source,
- f"{prefix}.{name}",
- bits=bits,
- group_size=group_size,
- grouped_heads=grouped,
- )
- for suffix, value in (
- ("weight", layer.weight),
- ("scales", layer.scales),
- ("biases", layer.qbiases),
- ("bias", layer.bias),
- ):
- if value is not None:
- tensors[f"{prefix}.{name}.{suffix}"] = value
- del layer
- mx.eval(tensors)
- file = destination / f"core-{i:03d}.safetensors"
- tmp = file.with_suffix(".tmp.safetensors")
- mx.save_safetensors(str(tmp), tensors)
- tmp.replace(file)
- digest = hashlib.sha256()
- with file.open("rb") as handle:
- for data in iter(lambda: handle.read(8 * 1024**2), b""):
- digest.update(data)
- files.append(
- {
- "file": file.name,
- "bytes": file.stat().st_size,
- "sha256": digest.hexdigest(),
- }
- )
- del tensors
- mx.clear_cache()
- log.info("Converted block %d/%d", i + 1, len(prefixes))
- metadata["files"] = files
- (destination / "quantization.json").write_text(
- json.dumps(metadata, indent=2) + "\n"
- )
- return metadata
-
-
-def main():
- p = argparse.ArgumentParser(description=__doc__)
- p.add_argument("--model", required=True)
- p.add_argument("--output", required=True)
- p.add_argument("--bits", type=int, choices=(4, 8), default=8)
- p.add_argument("--group-size", type=int, choices=(32, 64, 128), default=64)
- a = p.parse_args()
- logging.basicConfig(level=logging.INFO)
- convert(a.model, a.output, bits=a.bits, group_size=a.group_size)
-
-
-if __name__ == "__main__":
- main()
diff --git a/h3_mlx/coreml_vae.py b/h3_mlx/coreml_vae.py
deleted file mode 100644
index cb7363c74127e837a1a2113531abf3d8144dfdde..0000000000000000000000000000000000000000
--- a/h3_mlx/coreml_vae.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""Optional fixed-tile Core ML decoder with the native MLX tile assembly.
-
-Core ML uses FP16 internally. This is an explicitly approximate alternative to
-the native BF16 decoder; use the saved-latent image comparisons before choosing.
-"""
-
-from __future__ import annotations
-
-from pathlib import Path
-
-import mlx.core as mx
-import numpy as np
-
-from .vae import VAE
-
-
-class CoreMLVAE(VAE):
- @staticmethod
- def validate_metadata(meta, source_fingerprint, tile_batch):
- if meta.get("h3.decoder.format") != "h3-coreml-tile-v1":
- raise ValueError("This ML Package lacks H3 decoder provenance metadata")
- if meta.get("h3.decoder.source") != source_fingerprint:
- raise ValueError(
- "Core ML decoder does not match this source VAE checkpoint"
- )
- fixed_batch = int(meta["h3.decoder.batch"])
- if tile_batch != fixed_batch:
- raise ValueError(f"ML Package was exported for --vae-batch {fixed_batch}")
- return fixed_batch
-
- def __init__(
- self,
- path,
- *,
- package,
- compute_units="CPU_AND_GPU",
- tile_batch=8,
- dtype="bfloat16",
- compile_blocks=False,
- ):
- if dtype != "bfloat16":
- raise ValueError(
- "Core ML tile assembly currently requires the native BF16 policy"
- )
- import coremltools as ct
-
- super().__init__(path, tile_batch=tile_batch, dtype=dtype)
- self.package = Path(package)
- self.model = ct.models.MLModel(
- str(self.package),
- compute_units=getattr(ct.ComputeUnit, compute_units),
- optimization_hints={
- "specializationStrategy": ct.SpecializationStrategy.FastPrediction
- },
- )
- meta = self.model.user_defined_metadata
- self.fixed_batch = self.validate_metadata(
- meta, self.store.fingerprint(), tile_batch
- )
-
- def decode_tiles(self, raw):
- if raw.shape[1:] != (24, 16, 16) or raw.shape[0] > self.fixed_batch:
- raise ValueError(
- "Core ML export requires native 256px tiles and its fixed batch size"
- )
- count = raw.shape[0]
- # BF16 source values are exactly representable by FP16 in the ordinary
- # decoder range. Preserve the native input rounding before conversion.
- x = raw.astype(mx.bfloat16).astype(mx.float16)
- if count < self.fixed_batch:
- x = mx.pad(x, ((0, self.fixed_batch - count), (0, 0), (0, 0), (0, 0)))
- pixels = self.model.predict({"raw": np.ascontiguousarray(np.asarray(x))})[
- "pixels"
- ]
- if not np.isfinite(pixels).all():
- raise FloatingPointError("Core ML decoder produced non-finite pixels")
- return mx.array(np.ascontiguousarray(pixels[:count])).astype(mx.bfloat16)
-
- def clear(self):
- self.model = None
- super().clear()
-
-
-def source_metadata(path, batch):
- """Metadata shared with the exporter, without importing the converter."""
- from .weights import WeightStore
-
- return {
- "h3.decoder.format": "h3-coreml-tile-v1",
- "h3.decoder.source": WeightStore(path).fingerprint(),
- "h3.decoder.batch": str(batch),
- "h3.decoder.precision": "Source BF16 weights and inputs, FP16 internal arithmetic, BF16 tile assembly",
- }
diff --git a/h3_mlx/dit.py b/h3_mlx/dit.py
index 5587701c95831deee24b0a8ef05157c09b8644d5..6cfc776611dde0ba1a6ee886d81059dff4da821a 100644
--- a/h3_mlx/dit.py
+++ b/h3_mlx/dit.py
@@ -24,9 +24,7 @@ log = logging.getLogger(__name__)
class DiTBlock:
- def __init__(
- self, store, prefix, config, *, bits=None, group_size=64, packed_cache=None
- ):
+ def __init__(self, store, prefix, config, *, packed_cache=None):
self.config = config
self.n1 = store.get(prefix + ".norm1.weight")
self.n2 = store.get(prefix + ".norm2.weight")
@@ -38,59 +36,29 @@ class DiTBlock:
cache_path = Path(packed_cache) / f"{prefix}.safetensors"
names = ("attn.qkv_proj", "attn.out_proj", "mlp.fc1", "mlp.fc2")
attrs = ("qkv", "out", "fc1", "fc2")
- if cache_path.exists():
- data, metadata = mx.load(str(cache_path), return_metadata=True)
- if metadata.get("format") != "h3-palette12-v1":
- raise ValueError("Unsupported packed checkpoint cache")
- shapes = json.loads(metadata["shapes"])
- for attr, name in zip(attrs, names, strict=True):
- packed = PackedBF16.from_tensors(data, name, shapes[name])
- setattr(self, attr, PackedLinear(packed, data.get(name + ".bias")))
- mx.eval(data)
- return
+ if not cache_path.is_file():
+ raise FileNotFoundError(
+ f"Missing packed block: {cache_path}. "
+ "Download the preconverted packed12 variant with h3-mlx download."
+ )
+ data, metadata = mx.load(str(cache_path), return_metadata=True)
+ if metadata.get("format") != "h3-palette12-v1":
+ raise ValueError("Unsupported packed checkpoint format")
+ shapes = json.loads(metadata["shapes"])
+ for attr, name in zip(attrs, names, strict=True):
+ packed = PackedBF16.from_tensors(data, name, shapes[name])
+ setattr(self, attr, PackedLinear(packed, data.get(name + ".bias")))
+ mx.eval(data)
+ return
grouped = (config.num_attention_heads, config.attention_head_dim)
self.qkv = Linear.load(
store,
prefix + ".attn.qkv_proj",
- bits=bits,
- group_size=group_size,
grouped_heads=grouped,
)
- self.out = Linear.load(
- store, prefix + ".attn.out_proj", bits=bits, group_size=group_size
- )
- self.fc1 = Linear.load(
- store, prefix + ".mlp.fc1", bits=bits, group_size=group_size
- )
- self.fc2 = Linear.load(
- store, prefix + ".mlp.fc2", bits=bits, group_size=group_size
- )
- if packed_cache is not None:
- data, shapes = {}, {}
- for attr, name in zip(attrs, names, strict=True):
- linear = getattr(self, attr)
- packed = PackedLinear.from_linear(linear)
- setattr(self, attr, packed)
- shapes[name] = packed.weight.shape
- data.update(packed.weight.tensors(name))
- if packed.bias is not None:
- data[name + ".bias"] = packed.bias
- cache_path.parent.mkdir(parents=True, exist_ok=True)
- temporary = cache_path.with_name(
- f".{cache_path.stem}-{uuid.uuid4().hex}.safetensors"
- )
- try:
- mx.save_safetensors(
- str(temporary),
- data,
- metadata={
- "format": "h3-palette12-v1",
- "shapes": json.dumps(shapes),
- },
- )
- temporary.replace(cache_path)
- finally:
- temporary.unlink(missing_ok=True)
+ self.out = Linear.load(store, prefix + ".attn.out_proj")
+ self.fc1 = Linear.load(store, prefix + ".mlp.fc1")
+ self.fc2 = Linear.load(store, prefix + ".mlp.fc2")
@property
def nbytes(self):
@@ -211,13 +179,10 @@ class DiT:
*,
cache_dir="cache",
cache_gb=38,
- bits=None,
- group_size=64,
mlp_chunk=0,
query_chunk=0,
kernel="native",
threads=256,
- quantized_path=None,
compile_blocks=False,
eval_every=1,
keep_refiners=False,
@@ -232,15 +197,12 @@ class DiT:
self.store = WeightStore(self.path / "transformer")
self.core_store = self.store
self.release = None
+ bits, group_size = None, 64
release_path = self.path / "release.json"
if release_path.exists():
from .hub import load_release
self.release = load_release(self.path)
- if quantized_path is not None or bits is not None:
- raise ValueError(
- "Published variants already select their weight precision"
- )
core_format = self.release["core_format"]
if core_format == "quantized":
meta = json.loads((self.path / "core/quantization.json").read_text())
@@ -260,20 +222,6 @@ class DiT:
weight_storage = "packed12"
if core_format != "packed12" and weight_storage == "packed12":
raise ValueError("Select the packed12 download to use packed storage")
- if quantized_path is not None:
- meta = json.loads((Path(quantized_path) / "quantization.json").read_text())
- if meta.get("format") != "h3-mlx-core-v1":
- raise ValueError("Unsupported quantized checkpoint format")
- if meta["source_fingerprint"] != self.store.fingerprint():
- raise ValueError(
- "Quantized core does not match this original checkpoint"
- )
- if bits is not None and bits != meta["bits"]:
- raise ValueError(
- "--quantize conflicts with the converted core precision"
- )
- bits, group_size = meta["bits"], meta["group_size"]
- self.core_store = WeightStore(quantized_path)
self.heads = WeightStore(self.path / "pdd_fused_heads.safetensors")
self.cache_dir = Path(cache_dir)
safe_limit = max(
@@ -311,22 +259,18 @@ class DiT:
self.tune_matmul = tune_matmul
if weight_storage not in ("bf16", "packed12"):
raise ValueError("weight_storage must be bf16 or packed12")
- if weight_storage == "packed12" and (
- bits is not None or quantized_path is not None
- ):
- raise ValueError("Lossless packing cannot be combined with quantization")
self.weight_storage = weight_storage
self.packed_cache = None
if weight_storage == "packed12":
- key = hashlib.sha256(
- (self.store.fingerprint() + "palette12-v1").encode()
- ).hexdigest()[:24]
- self.packed_cache = self.cache_dir / f"packed12-{key}"
- if self.release is not None:
- self.packed_cache = self.path / "core"
- for i in range(self.config.num_layers):
- if not (self.packed_cache / f"blocks.{i}.safetensors").is_file():
- raise FileNotFoundError(f"Missing published packed block {i}")
+ if self.release is None:
+ raise ValueError(
+ "Packed inference requires the published packed12 variant; "
+ "model conversion is not included in this runtime."
+ )
+ self.packed_cache = self.path / "core"
+ for i in range(self.config.num_layers):
+ if not (self.packed_cache / f"blocks.{i}.safetensors").is_file():
+ raise FileNotFoundError(f"Missing published packed block {i}")
def _constant(self, key):
if key not in self.constants:
@@ -350,8 +294,6 @@ class DiT:
self.core_store,
prefix,
self.config,
- bits=self.bits,
- group_size=self.group_size,
packed_cache=self.packed_cache if prefix.startswith("blocks.") else None,
)
if self.tune_matmul and prefix.startswith("blocks."):
diff --git a/h3_mlx/encoder.py b/h3_mlx/encoder.py
index ed08d354c3888b3e1b1b29fb69771ef69a5eb105..1edcc374cd5d62fee73f3fef6417c594c91066a1 100644
--- a/h3_mlx/encoder.py
+++ b/h3_mlx/encoder.py
@@ -24,9 +24,7 @@ class TextEncoder:
presentation and precision policy; it is independent of diffusion seed.
"""
- def __init__(
- self, path, *, cache_dir="cache", bits=None, group_size=64, query_chunk=0
- ):
+ def __init__(self, path, *, cache_dir="cache", query_chunk=0):
self.path = Path(path)
self.config = json.loads((self.path / "config.json").read_text())
self.text_config = self.config["text_config"]
@@ -35,7 +33,7 @@ class TextEncoder:
str(self.path), local_files_only=True
)
self.cache_dir = Path(cache_dir)
- self.bits, self.group_size, self.query_chunk = bits, group_size, query_chunk
+ self.query_chunk = query_chunk
def encode(self, prompt, *, images=(), on_layer=None):
self.cache_hit = False
@@ -60,8 +58,8 @@ class TextEncoder:
for p in ("mlx", "numpy", "Pillow", "transformers")
},
"ids": ids,
- "bits": self.bits,
- "group_size": self.group_size,
+ "bits": None,
+ "group_size": 64,
"query_chunk": self.query_chunk,
"tap": 50,
"tf32": os.environ.get("MLX_ENABLE_TF32"),
@@ -129,8 +127,6 @@ class TextEncoder:
return Linear.load(
self.store,
layer + "." + suffix,
- bits=self.bits,
- group_size=self.group_size,
)
qproj, kproj, vproj = (
diff --git a/h3_mlx/packed.py b/h3_mlx/packed.py
index 1e582db0c113c1005a24ab868c14499a7c855c3d..654888405869ec632fc1d01264eeea48be98c03b 100644
--- a/h3_mlx/packed.py
+++ b/h3_mlx/packed.py
@@ -52,34 +52,6 @@ class PackedBF16:
escape_values: mx.array
base: mx.array
- @classmethod
- def pack(cls, value):
- if value.dtype != mx.bfloat16:
- raise ValueError("Lossless palette12 stores BF16 tensors only")
- raw = np.asarray(mx.contiguous(value).view(mx.uint16)).reshape(-1)
- high = ((raw >> 8) & 127).astype(np.uint8)
- base = max(0, int(high.max(initial=0)) - 7)
- sign = ((raw >> 15) << 3).astype(np.uint8)
- codes = np.clip(high.astype(np.int16) - base, 0, 7).astype(np.uint8) | sign
- # Nibble zero is the escape code. When base==0 the whole representable
- # high-byte range fits and zero can retain its ordinary interpretation.
- mask = high < base
- if base:
- mask |= (high == base) & (sign == 0)
- indices = np.flatnonzero(mask).astype(np.uint32)
- codes[indices] = 0
- if codes.size % 2:
- codes = np.pad(codes, (0, 1))
- values = [
- mx.array(raw.astype(np.uint8)),
- mx.array(codes[::2] | (codes[1::2] << 4)),
- mx.array(indices),
- mx.array(raw[indices]),
- mx.array([base], mx.uint32),
- ]
- mx.eval(values)
- return cls(tuple(value.shape), *values)
-
@property
def nbytes(self):
return sum(
@@ -112,12 +84,6 @@ class PackedBF16:
)[0]
return raw.view(mx.bfloat16).reshape(self.shape)
- def tensors(self, prefix):
- return {
- f"{prefix}.{name}": getattr(self, name)
- for name in ("low", "nibbles", "escape_indices", "escape_values", "base")
- }
-
@classmethod
def from_tensors(cls, tensors, prefix, shape):
result = cls(
@@ -145,12 +111,6 @@ class PackedLinear:
def __init__(self, weight, bias=None):
self.weight, self.bias = weight, bias
- @classmethod
- def from_linear(cls, linear):
- if linear.scales is not None:
- raise ValueError("Lossless packing cannot be combined with quantization")
- return cls(PackedBF16.pack(linear.weight), linear.bias)
-
@property
def nbytes(self):
return self.weight.nbytes + (0 if self.bias is None else self.bias.nbytes)
diff --git a/h3_mlx/runtime.py b/h3_mlx/runtime.py
index 981d046989292b83a93fecc7ad6214d9bc377c92..191145c176ef503e71822c6e2e9d4355dac9fc63 100644
--- a/h3_mlx/runtime.py
+++ b/h3_mlx/runtime.py
@@ -80,7 +80,7 @@ def initial_noise(seed, h, w, *, rng="torch"):
import torch
except ImportError as error:
raise RuntimeError(
- "Install the audit extra for native CPU noise, or use --rng mlx"
+ "Install the native extra for original CPU noise, or use --rng mlx"
) from error
generator = torch.Generator(device="cpu").manual_seed(seed)
return mx.array(
@@ -99,9 +99,6 @@ class Pipeline:
*,
cache_dir="cache",
dit_cache_gb=38,
- bits=None,
- encoder_bits=None,
- group_size=64,
mlp_chunk=0,
query_chunk=0,
kernel="native",
@@ -109,16 +106,12 @@ class Pipeline:
vae_batch=1,
vae_dtype="bfloat16",
keep_resident=True,
- quantized_path=None,
compile_blocks=False,
eval_every=1,
keep_refiners=False,
wired_gb=None,
weight_storage="bf16",
vae_compile=False,
- vae_backend="mlx",
- coreml_package=None,
- coreml_compute_units="CPU_AND_GPU",
async_blocks=False,
cache_refined_text=True,
prefetch_blocks=0,
@@ -126,17 +119,15 @@ class Pipeline:
tune_matmul=False,
):
self.model_path, self.encoder_path = Path(model_path), Path(encoder_path)
+ cache_dir = str(cache_dir)
self.cache_dir = Path(cache_dir)
self.dit_options = dict(
cache_dir=cache_dir,
cache_gb=dit_cache_gb,
- bits=bits,
- group_size=group_size,
mlp_chunk=mlp_chunk,
query_chunk=query_chunk,
kernel=kernel,
threads=threads,
- quantized_path=quantized_path,
compile_blocks=compile_blocks,
eval_every=eval_every,
keep_refiners=keep_refiners,
@@ -148,38 +139,11 @@ class Pipeline:
)
self.encoder_options = dict(
cache_dir=cache_dir,
- bits=encoder_bits,
- group_size=group_size,
query_chunk=query_chunk,
)
- if vae_backend not in ("mlx", "coreml"):
- raise ValueError("vae_backend must be mlx or coreml")
- if vae_backend == "coreml" and (
- coreml_package is None or not Path(coreml_package).is_dir()
- ):
- raise ValueError("Core ML decoding requires an exported --coreml-package")
- self.vae_backend = vae_backend
self.vae_options = dict(
tile_batch=vae_batch, dtype=vae_dtype, compile_blocks=vae_compile
)
- if vae_backend == "coreml":
- if vae_dtype != "bfloat16":
- raise ValueError("Core ML tile assembly requires --vae-dtype bfloat16")
- import coremltools as ct
-
- from .coreml_vae import CoreMLVAE
- from .weights import WeightStore
-
- metadata = ct.models.MLModel(
- str(coreml_package), skip_model_load=True
- ).user_defined_metadata
- CoreMLVAE.validate_metadata(
- metadata, WeightStore(self.model_path / "vae").fingerprint(), vae_batch
- )
- self.vae_options["compile_blocks"] = False
- self.vae_options.update(
- package=str(coreml_package), compute_units=coreml_compute_units
- )
self.keep_resident = keep_resident
self.wired_gb = wired_gb
self.allow_nonfinite = allow_nonfinite
@@ -243,7 +207,7 @@ class Pipeline:
"dit": self.dit_options,
"encoder": self.encoder_options,
"vae": self.vae_options,
- "vae_backend": self.vae_backend,
+ "vae_backend": "mlx",
"environment": environment(),
"wired_gb": self.wired_gb,
"timings": {},
@@ -360,12 +324,7 @@ class Pipeline:
mx.clear_cache()
decode_start = time.perf_counter()
if self.vae is None:
- if self.vae_backend == "coreml":
- from .coreml_vae import CoreMLVAE
-
- self.vae = CoreMLVAE(self.model_path / "vae", **self.vae_options)
- else:
- self.vae = VAE(self.model_path / "vae", **self.vae_options)
+ self.vae = VAE(self.model_path / "vae", **self.vae_options)
vae = self.vae
last_log = [time.perf_counter()]
@@ -385,9 +344,7 @@ class Pipeline:
record["timings"]["total_s"] = time.perf_counter() - start
record["memory"] = memory_stats()
record["memory"]["scope"] = (
- "active/peak/cache: MLX allocator, Core ML excluded; process_*: macOS parent process including Metal/Core ML, lifetime peak includes earlier requests; overlapping counters, do not sum"
- if self.vae_backend == "coreml"
- else "active/peak/cache: MLX allocator; process_*: macOS parent process including Metal, lifetime peak includes earlier requests; overlapping counters, do not sum"
+ "active/peak/cache: MLX allocator; process_*: macOS parent process including Metal, lifetime peak includes earlier requests; overlapping counters, do not sum"
)
record["output"] = str(output)
record["png_sha256"] = hashlib.sha256(output.read_bytes()).hexdigest()
diff --git a/h3_mlx/weights.py b/h3_mlx/weights.py
index ed26138b0e5cbd49fd91ac112e22d62f1b97e3d7..f5863215b8f6704c5eb910cbb5323b90ef80bb80 100644
--- a/h3_mlx/weights.py
+++ b/h3_mlx/weights.py
@@ -145,7 +145,7 @@ class Linear:
self.group_size = group_size
@classmethod
- def load(cls, store, prefix, *, bits=None, group_size=64, grouped_heads=None):
+ def load(cls, store, prefix, *, grouped_heads=None):
# Converted MLX quantized tensors use .scales/.biases, independently of bias.
if prefix + ".scales" in store:
meta = json.loads((store.path / "quantization.json").read_text())
@@ -165,10 +165,6 @@ class Linear:
w = w.reshape(heads, 3, dim, -1).transpose(1, 0, 2, 3).reshape(w.shape)
if b is not None:
b = b.reshape(heads, 3, dim).transpose(1, 0, 2).reshape(b.shape)
- if bits is not None and w.dtype != mx.float32:
- q, s, z = mx.quantize(w, bits=bits, group_size=group_size)
- mx.eval(q, s, z)
- return cls(q, b, s, z, bits, group_size)
mx.eval(w)
return cls(w, b)
diff --git a/optimization/lessons/minimax-h3-r3-exact-qk-fc2.json b/optimization/lessons/minimax-h3-r3-exact-qk-fc2.json
deleted file mode 100644
index 45ff813f2e243766b719b06fd5d2d8ada97b66f2..0000000000000000000000000000000000000000
--- a/optimization/lessons/minimax-h3-r3-exact-qk-fc2.json
+++ /dev/null
@@ -1,72 +0,0 @@
-{
- "schema_version": 1,
- "id": "minimax-h3-r3-exact-qk-fc2",
- "title": "Native reduction matching gives exact M5 QK fusion; whole-image memory still decides",
- "tags": [
- "rmsnorm",
- "rope",
- "bf16",
- "m5",
- "gemm",
- "physical-footprint",
- "operator-to-pipeline"
- ],
- "decision": "accepted",
- "evidence_level": "observed",
- "mechanism": "Four contiguous BF16 reads per SIMD lane and precise rsqrt reproduce installed MLX RMSNorm before the affine/partial-RoPE BF16 boundaries. M5 FC2 benefits from a 64x128x512 TensorOps tile with four SIMD groups and an eight-row tile walk.",
- "recommendation": "Use the measured M5 Pro/MLX 0.32.4 QK producer and shape-qualified FC2 tile, with native full-width norms and native fallback outside the qualified scope. Require bitwise trajectories and paired full-image receipts; do not infer a deployment win from a kernel ratio.",
- "counterexample": "The 1024-thread full-width fusion was exact and faster in a block, but the complete timed population used 1.04094% more physical memory and missed its 1% gate. BK2048 improved an operator but regressed the block by 0.19%. A 512MiB allocator cache was 1.65% slower in the block screen and did not reduce sampled physical peak.",
- "retest_when": "Retest after MLX reduction/dispatch changes, a new GPU/Metal driver, different projection shapes, or changes to opaque-kernel buffer lifetimes. Wider norm fusion and larger K panels may deserve new pipeline tests under those changed mechanisms.",
- "scope": {
- "projects": [
- "minimax-h3-image-mlx"
- ],
- "hardware": [
- "Apple M5 Pro, 16 GPU cores, 64GiB unified memory"
- ],
- "runtime": [
- "MLX/MLX Metal 0.32.4; macOS 27.0.1; AC Automatic power"
- ],
- "workloads": [
- "Two 2048-square timing cases, three paired repeats each; four exact full-image/trajectory quality cases including a detail prompt and one edit. Dense BF16 storage in this lesson; not a new packed-storage qualification."
- ]
- },
- "evidence": [
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/qk-deployment/comparison.json",
- "sha256": "346409df254b31a692f4d031c1363efd35ba9638db494e4bf5f13afe3b6d31ad"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/qk-deployment/quality-audit.json",
- "sha256": "3d242d63e283fa6dc645adee2c3a1dfda59b5d9ae4ac56cf04e15a5486816a2e"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/deployment/comparison.json",
- "sha256": "5438ce89ced53d19913534af51cb1e20d6d11e98794a5b7a986ba07480a393d8"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/norm.json",
- "sha256": "473e30d475788737cf7e45349ef94c0330f527a5011d9a8d3c8590822a87e3e7"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/panel.json",
- "sha256": "7c1d64f439912eccdf0b959b3c3e460046b5737aec04959642a9ec90a76c9ce8"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/cache-screen.json",
- "sha256": "568b51938cf5f02a94c14d47298fa225d98b5ae384200dac0e4b55a1e91c6009"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/embedding/summary.json",
- "sha256": "dabdba5b4d3b9cebbccfc43671014971ba26216c96e52d50054e050c2ff676d9"
- }
- ],
- "supersedes": []
-}
diff --git a/optimization/lessons/minimax-h3-r3-packed-exact-tuning.json b/optimization/lessons/minimax-h3-r3-packed-exact-tuning.json
deleted file mode 100644
index 0dee2c9df07ac33e9125862a824e7c49e94befc8..0000000000000000000000000000000000000000
--- a/optimization/lessons/minimax-h3-r3-packed-exact-tuning.json
+++ /dev/null
@@ -1,56 +0,0 @@
-{
- "schema_version": 1,
- "id": "minimax-h3-r3-packed-exact-tuning",
- "title": "Exact QK/FC2 tuning survives lossless BF16 storage on M5 Pro",
- "tags": [
- "packed-weights",
- "bf16",
- "memory",
- "m5",
- "dynamic-inputs",
- "gemm"
- ],
- "decision": "accepted",
- "evidence_level": "observed",
- "mechanism": "Compressed arrays remain explicit dynamic inputs to each compiled block, then reconstruct the original BF16 bits before native projections or the selected FC2 TensorOps kernel. Exact QK fusion operates on the same reconstructed projection outputs.",
- "recommendation": "Use existing packed12 storage when lower physical footprint is needed, together with the qualified QK/FC2 mode. Compare against its own packed control and retain native dense storage for the lowest observed latency. Reuse the source-bound cache rather than making another model copy.",
- "counterexample": "Faster vector reconstruction alone did not establish a useful decode-plus-GEMM gain, and is not shipped. Dense and packed timings from different campaigns are not a paired storage-speed comparison.",
- "retest_when": "Retest after codec, MLX compiler constant-folding, Metal matrix dispatch, hardware or token-range changes. Inspect full physical footprint as well as output equality; a retained dense constant can erase the storage benefit.",
- "scope": {
- "projects": [
- "minimax-h3-image-mlx"
- ],
- "hardware": [
- "Apple M5 Pro, 16 GPU cores, 64GiB unified memory"
- ],
- "runtime": [
- "MLX/MLX Metal 0.32.4; macOS27.0.1"
- ],
- "workloads": [
- "Four reused full-image/trajectory quality cases; product and portrait three paired warm repeats each. 3.5667% latency reduction vs packed control; 31.83GiB maximum timed physical peak, 32.56GiB across all candidate cases."
- ]
- },
- "evidence": [
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/compact-deployment/comparison.json",
- "sha256": "f04c312015382fdfd7cd021df6168d7729344594ee7c4ac2143b18f4b503c66c"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/compact-deployment/quality-audit.json",
- "sha256": "57ae4aef9579058528381de211636fe9ff02ad9b2f793f65b7b217e91c6e7f16"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/wheel-cli.json",
- "sha256": "813384da8643af0ea86432df03aaaab840fbac9f1133e84e9d9497116ef95fd5"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/screen.json",
- "sha256": "58675fb0df998d08b52c9fe4258cedf708ce638c914f628eb0b417ee27993c8f"
- }
- ],
- "supersedes": []
-}
diff --git a/optimization/round-03-compact/contract.json b/optimization/round-03-compact/contract.json
deleted file mode 100644
index 7d19576df721fd9863c8c7ad2d06436232899643..0000000000000000000000000000000000000000
--- a/optimization/round-03-compact/contract.json
+++ /dev/null
@@ -1,35 +0,0 @@
-{
- "schema_version": 1,
- "campaign_id": "h3-m5pro-round-03-compact",
- "objective": "latency",
- "quality_cases": [
- "product",
- "portrait",
- "edit",
- "holdout-detail"
- ],
- "timing_cases": [
- "product",
- "portrait"
- ],
- "quality_gates": {
- "max_abs_error": {
- "direction": "max",
- "limit": 0
- },
- "nonfinite_values": {
- "direction": "max",
- "limit": 0
- }
- },
- "workload_manifest_sha256": "8d14bb9873982547f69bbbd13e2a1e645f97454f14428220aa78001d4f843b13",
- "reference_sha256": "5fccbc8664a6efb3f9ff7e14b764c1aae876785fc28fd492dbb6c24b1b773a8b",
- "evaluator_sha256": "4db0c5e8606ffc011bf85dbadabc482468a2e49cd5a1757a774620d493eba7ee",
- "min_repeats": 3,
- "min_latency_reduction_pct": 2,
- "min_memory_reduction_pct": 3,
- "max_latency_regression_pct": 0,
- "max_memory_regression_pct": 5,
- "peak_memory_limit_bytes": 42949672960,
- "search_budget": "At most 40 minutes and two complete configurations: current packed12 champion vs same storage plus exact fusions/FC2. Same 4 quality and 2 timing cases, three paired warm repeats, <=1.5 GiB additional disk. One GPU job at a time. Start after dense campaign, global experiment deadline 10:02 UTC. Reused validation cases, not fresh holdouts."
-}
diff --git a/optimization/round-03-compact/evaluator.json b/optimization/round-03-compact/evaluator.json
deleted file mode 100644
index 1d15be1d32e53a6826fafff5b184a62e2deb7244..0000000000000000000000000000000000000000
--- a/optimization/round-03-compact/evaluator.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
- "files": {
- "h3_mlx/compare.py": "78a48ea24967c7a6349ed1e50cc1dab690f4c90e65939624d82bb0b146126095",
- "h3_mlx/reference.py": "7a08f60e3d21c708f71112a2f012356faff45042e43cc0a0627693421d9d4e3e",
- "tests/test_numerics.py": "373eb2f050ae101316dfd3a5f8a86eb74ceda98e180690b5c22094b4aad106dc"
- },
- "exact_metric": "maximum absolute decoded uint8 pixel error over all cases; nonfinite trace values must be zero",
- "numerical_screen": "Only if separately classified: per-case SSIM >=0.9995, MAE <=0.1/255, max RGB error <=4; visual crop review still required. No promotion from metrics alone."
-}
diff --git a/optimization/round-03-compact/lesson.json b/optimization/round-03-compact/lesson.json
deleted file mode 100644
index 0dee2c9df07ac33e9125862a824e7c49e94befc8..0000000000000000000000000000000000000000
--- a/optimization/round-03-compact/lesson.json
+++ /dev/null
@@ -1,56 +0,0 @@
-{
- "schema_version": 1,
- "id": "minimax-h3-r3-packed-exact-tuning",
- "title": "Exact QK/FC2 tuning survives lossless BF16 storage on M5 Pro",
- "tags": [
- "packed-weights",
- "bf16",
- "memory",
- "m5",
- "dynamic-inputs",
- "gemm"
- ],
- "decision": "accepted",
- "evidence_level": "observed",
- "mechanism": "Compressed arrays remain explicit dynamic inputs to each compiled block, then reconstruct the original BF16 bits before native projections or the selected FC2 TensorOps kernel. Exact QK fusion operates on the same reconstructed projection outputs.",
- "recommendation": "Use existing packed12 storage when lower physical footprint is needed, together with the qualified QK/FC2 mode. Compare against its own packed control and retain native dense storage for the lowest observed latency. Reuse the source-bound cache rather than making another model copy.",
- "counterexample": "Faster vector reconstruction alone did not establish a useful decode-plus-GEMM gain, and is not shipped. Dense and packed timings from different campaigns are not a paired storage-speed comparison.",
- "retest_when": "Retest after codec, MLX compiler constant-folding, Metal matrix dispatch, hardware or token-range changes. Inspect full physical footprint as well as output equality; a retained dense constant can erase the storage benefit.",
- "scope": {
- "projects": [
- "minimax-h3-image-mlx"
- ],
- "hardware": [
- "Apple M5 Pro, 16 GPU cores, 64GiB unified memory"
- ],
- "runtime": [
- "MLX/MLX Metal 0.32.4; macOS27.0.1"
- ],
- "workloads": [
- "Four reused full-image/trajectory quality cases; product and portrait three paired warm repeats each. 3.5667% latency reduction vs packed control; 31.83GiB maximum timed physical peak, 32.56GiB across all candidate cases."
- ]
- },
- "evidence": [
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/compact-deployment/comparison.json",
- "sha256": "f04c312015382fdfd7cd021df6168d7729344594ee7c4ac2143b18f4b503c66c"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/compact-deployment/quality-audit.json",
- "sha256": "57ae4aef9579058528381de211636fe9ff02ad9b2f793f65b7b217e91c6e7f16"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/wheel-cli.json",
- "sha256": "813384da8643af0ea86432df03aaaab840fbac9f1133e84e9d9497116ef95fd5"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/screen.json",
- "sha256": "58675fb0df998d08b52c9fe4258cedf708ce638c914f628eb0b417ee27993c8f"
- }
- ],
- "supersedes": []
-}
diff --git a/optimization/round-03-compact/reference.json b/optimization/round-03-compact/reference.json
deleted file mode 100644
index 89f64ffc952c88c3d144294b5f97d4e0fcc9b3da..0000000000000000000000000000000000000000
--- a/optimization/round-03-compact/reference.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
- "anchors": {
- "product": {
- "path": "outputs/optimization-r3/product/control-timed-2.png",
- "png_sha256": "63684ef8ae148a35ddef02650c6aa30b4843b47c99b06cec2ea3708ef3fc2293",
- "trace_sha256": "40440f8bf2c8f2ab3cbb454ccc9416062b9d69718aa6a0ef9baf1268037b9a29"
- },
- "portrait": {
- "path": "outputs/optimization-r3/portrait/control-warmup--1.png",
- "png_sha256": "32af5e3b95919e79e0019058ec396824c5861f9a43a4fa50c7b20348e8d81933",
- "trace_sha256": "5808ccec8c5049ce2761d0b5f41638e28107b287c9e63fe6bef175e600c9c6c8"
- },
- "holdout-detail": {
- "path": "outputs/optimization-r3/holdout-detail/control-warmup--1.png",
- "png_sha256": "ddbfc06c4556c08669d35a4618d380a99998e39a0226e7688d149993ab183038",
- "trace_sha256": "ec09215c7aceba45e8174d197b83240ac42daf2538330c33b58591695bfd059d"
- },
- "edit": {
- "path": "outputs/optimization-r3/edit/control-warmup--1.png",
- "png_sha256": "9df3f80efa5ef9f078c9acda81febdbc2d5d706f3c85c574fdfd9fd274b71304",
- "trace_sha256": "ab1f86da280c5b355627ea5384dac3ea27b2a3ce23204f5ea66910d83639073c"
- }
- },
- "foundation_manifest": "provenance/sources.json",
- "foundation_sha256": "3274a3e5fe0d2ea0d63f66b020beb193276a0a9d8de7ea466b312fbae9a2746f",
- "scope": "Incremental identity to original MLX BF16 recipe, not CUDA/FP32 equality."
-}
diff --git a/optimization/round-03-compact/workload.json b/optimization/round-03-compact/workload.json
deleted file mode 100644
index 3dc994974c57e767cbb44e3bfc596b7ac4e0fc30..0000000000000000000000000000000000000000
--- a/optimization/round-03-compact/workload.json
+++ /dev/null
@@ -1,54 +0,0 @@
-{
- "cases": [
- {
- "id": "product",
- "width": 2048,
- "height": 2048,
- "seed": 731,
- "prompt": "Square editorial studio photograph of a dark cobalt glass perfume bottle on pale travertine. The centered cream label reads exactly 'NIGHT TIDE' in large engraved serif letters and 'EAU DE PARFUM' in small capitals beneath. Dappled blue caustics, crisp glass edges, fine pores in the stone, dark reflections through the translucent bottle, restrained palette and generous negative space. No other text."
- },
- {
- "id": "portrait",
- "width": 2048,
- "height": 2048,
- "seed": 1234,
- "prompt": "An intimate natural-light portrait of an elderly woman in a botanical conservatory, reading a small red clothbound book. Warm brown skin with visible freckles and fine wrinkles, silver curls lit from behind, intricate indigo knit cardigan. Soft fern fronds out of focus in the foreground; detailed hands and realistic skin texture. A candid 85mm color photograph with gentle shadows and no lettering."
- },
- {
- "id": "edit",
- "width": 1024,
- "height": 1024,
- "seed": 42,
- "prompt": "Change the cobalt blue glass perfume bottle in to deep emerald green glass. Preserve the pale engraved NIGHT TIDE label, exact lettering, bottle shape, cap, stone backdrop, composition, lighting and shadows. The glass should be visibly green with realistic emerald highlights and reflections.",
- "images": [
- "experiments/reference-bottle.png"
- ]
- },
- {
- "id": "holdout-detail",
- "width": 2048,
- "height": 2048,
- "seed": 928417,
- "prompt": "A meticulous natural light still life on a wooden watchmaker's bench: a silver mechanical wristwatch with a clear glass back, brass gears and hair-thin springs, a folded cream linen glove, a small matte ceramic bowl and a card reading 'SECOND LIGHT'. In the background a woman with warm brown freckled skin holds a magnifying lens, her hands realistically detailed. Fine brushed metal, subtle skin pores, smooth dark-to-light blue gradients and soft window reflections, editorial macro photography."
- }
- ],
- "timing_cases": [
- "product",
- "portrait"
- ],
- "control_config": {
- "wired_gb": 32,
- "compile_blocks": true,
- "eval_every": 0,
- "prefetch_blocks": 4,
- "vae_batch": 4,
- "vae_compile": true,
- "cache_refined_text": true,
- "weight_storage": "packed12",
- "rng": "torch",
- "six_original_steps": true,
- "kernel": "native"
- },
- "holdout_rule": "Same fixed suite as round 03; these are reused validation cases and not fresh blind holdouts.",
- "shape_fallback_validation": "native-4k uses existing reference and no tile outside measured shape gets auto dispatch"
-}
diff --git a/optimization/round-03/abi-check.json b/optimization/round-03/abi-check.json
deleted file mode 100644
index b35bd90d1917e7d80b48f0c11acf33ec89433e00..0000000000000000000000000000000000000000
--- a/optimization/round-03/abi-check.json
+++ /dev/null
@@ -1,14 +0,0 @@
-{
- "sdk_header": "sys/resource.h, struct rusage_info_v4",
- "ctypes_bytes": 296,
- "sdk_sizeof_bytes": 296,
- "ctypes_offsets": {
- "ri_phys_footprint": 72,
- "ri_lifetime_max_phys_footprint": 240
- },
- "sdk_offsets": {
- "ri_phys_footprint": 72,
- "ri_lifetime_max_phys_footprint": 240
- },
- "method": "Compiled C sizeof/offsetof with xcrun clang and compared to ctypes; tool output retained in session. These overlapping macOS ledger counters must not be added to MLX memory."
-}
diff --git a/optimization/round-03/baseline-code.json b/optimization/round-03/baseline-code.json
deleted file mode 100644
index e74438141b32eb3e5ffafb707c05a23685c0195c..0000000000000000000000000000000000000000
--- a/optimization/round-03/baseline-code.json
+++ /dev/null
@@ -1,21 +0,0 @@
-{
- "h3_mlx/__init__.py": "d0997d9e18dfbe918635e49d6390ff892aeb96c02484841f6c5d0ff8cc17f226",
- "h3_mlx/__main__.py": "6d8b7d7846a845059d7a3107143f11131f63c5511d669b44085b15ec5e3d2279",
- "h3_mlx/benchmark.py": "702f72cf7dedf966a2033e7a3209cea5aca64e3063007f8e6da6c79ac7fc37c6",
- "h3_mlx/cli.py": "1e53be2de3e67013c1ac597dfc16bb976196b623d492176ef4b31f71c1772ac0",
- "h3_mlx/compare.py": "78a48ea24967c7a6349ed1e50cc1dab690f4c90e65939624d82bb0b146126095",
- "h3_mlx/config.py": "8927453a127b4c392d7d3024e2472e50b4fb9070e504f66859ad327a1f85d168",
- "h3_mlx/convert.py": "2e087f136a3a6e6f52cb5dbab48c1d7e9073b30353e22e566da32437ad231db0",
- "h3_mlx/coreml_vae.py": "d8139a6604d57339c56cf055bf2e5c355d4b71e1c05994bdcb9d49192d1aedf2",
- "h3_mlx/dit.py": "d988d48a38e06622a3b55f22103a88b6501d37cec70ffa0bf4e3ce294d412c6a",
- "h3_mlx/encoder.py": "386bbde46357c52c5dc32de26127f5b6714e6d14a6ad39802e1004f115ec62ad",
- "h3_mlx/kernels.py": "48b5e19366b7282f4dc41690e9d07cd0b8867e5ac2b8af6729095b7f8545f10d",
- "h3_mlx/layout.py": "a925693e8fc6abf51c02be91d0af6751a9b116e40dada01602d1b52e052cf2b1",
- "h3_mlx/ops.py": "d8f00859df35b8df4e7f881f21bc964d2f9091bdd1f6ebdf8211f2d32878a221",
- "h3_mlx/packed.py": "ec2dda08eb1642186e11463c1324b35bd23735ae8bfe0452f7a8e55bee0e9dde",
- "h3_mlx/reference.py": "7a08f60e3d21c708f71112a2f012356faff45042e43cc0a0627693421d9d4e3e",
- "h3_mlx/runtime.py": "588fdf5918dc5d70438e07de814869c8ca62ed6411282e0acb23626e1b52e759",
- "h3_mlx/vae.py": "73d48984dde57bd01a31f1ac51e1e7efb359024da69c5de34a8a0117ddca9cc0",
- "h3_mlx/vision.py": "d4141e3e14d3294ea42f096d72e4cb036a3f2805ec5d814718ed4f8eeff4d8ea",
- "h3_mlx/weights.py": "62f1348249599c0bc5ae6b2f1231ee521ebe852889800d99e1e17f1d94abcfbb"
-}
diff --git a/optimization/round-03/cache-candidate.json b/optimization/round-03/cache-candidate.json
deleted file mode 100644
index 5684879e6bd7e43df579770e5c65c1824acbbbaa..0000000000000000000000000000000000000000
--- a/optimization/round-03/cache-candidate.json
+++ /dev/null
@@ -1,23 +0,0 @@
-{
- "id": "qk-fc2-cache",
- "change_class": "exact",
- "hypothesis": "A 512 MiB free-allocator cache can remove the small physical-footprint regression of the fused path while retaining its speed. It cannot reduce live model memory.",
- "candidate_config": {
- "kernel": "metal-exact",
- "tune_matmul": true,
- "allocator_cache_gb": 0.5
- },
- "control_allocator_cache_gb": 1,
- "reject_if": "Full declared quality/timing/memory contract fails; do not change gates or pool samples from other campaigns.",
- "screen_before_full": "Real compiled block at both cache limits; no GPU concurrency.",
- "bounded_search": "One additional full-image finalist at most, within original 120-minute and 5GiB global budget. No inference-code edits until current benchmark completes.",
- "result": {
- "decision": "rejected-at-block-screen",
- "evidence": "reports/optimization-r3/cache-screen.json",
- "slowdown_pct": 1.6453551990850412,
- "cache_1_peak_physical_bytes": 2387051720,
- "cache_05_peak_physical_bytes": 2581791944,
- "accuracy": "all BF16 block outputs identical",
- "scope": "Isolated real first block; does not establish whole-image behavior. No allocator-limit change shipped."
- }
-}
diff --git a/optimization/round-03/contract.json b/optimization/round-03/contract.json
deleted file mode 100644
index f367d5b48f0c218af0094b32216aac56f30ef0f2..0000000000000000000000000000000000000000
--- a/optimization/round-03/contract.json
+++ /dev/null
@@ -1,35 +0,0 @@
-{
- "schema_version": 1,
- "campaign_id": "h3-m5pro-round-03",
- "objective": "latency",
- "quality_cases": [
- "product",
- "portrait",
- "edit",
- "holdout-detail"
- ],
- "timing_cases": [
- "product",
- "portrait"
- ],
- "quality_gates": {
- "max_abs_error": {
- "direction": "max",
- "limit": 0
- },
- "nonfinite_values": {
- "direction": "max",
- "limit": 0
- }
- },
- "workload_manifest_sha256": "5dfe34824190abd241185fd37214e3de5be425d82f0a6f556d0d23f0dec68042",
- "reference_sha256": "201be3382041aec890cbd766c2f88a43799f7e2d33807e9a1504f7e29e422065",
- "evaluator_sha256": "4db0c5e8606ffc011bf85dbadabc482468a2e49cd5a1757a774620d493eba7ee",
- "min_repeats": 3,
- "min_latency_reduction_pct": 2,
- "min_memory_reduction_pct": 3,
- "max_latency_regression_pct": 0,
- "max_memory_regression_pct": 1,
- "peak_memory_limit_bytes": 53687091200,
- "search_budget": "120 minutes from 08:02 UTC, at most 72 MPP GEMM tiles, 6 attention layouts and 12 unpack settings, three full-image finalists, one GPU job at a time, <=5 GiB additional disk. Exact track first. Early reject on no gain. Parent process physical footprint sampled at 100ms; allocator peak reported separately. Warm paired product/portrait repeats, alternate AB/BA; first-load separately. No background user applications changed."
-}
diff --git a/optimization/round-03/evaluator.json b/optimization/round-03/evaluator.json
deleted file mode 100644
index 1d15be1d32e53a6826fafff5b184a62e2deb7244..0000000000000000000000000000000000000000
--- a/optimization/round-03/evaluator.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
- "files": {
- "h3_mlx/compare.py": "78a48ea24967c7a6349ed1e50cc1dab690f4c90e65939624d82bb0b146126095",
- "h3_mlx/reference.py": "7a08f60e3d21c708f71112a2f012356faff45042e43cc0a0627693421d9d4e3e",
- "tests/test_numerics.py": "373eb2f050ae101316dfd3a5f8a86eb74ceda98e180690b5c22094b4aad106dc"
- },
- "exact_metric": "maximum absolute decoded uint8 pixel error over all cases; nonfinite trace values must be zero",
- "numerical_screen": "Only if separately classified: per-case SSIM >=0.9995, MAE <=0.1/255, max RGB error <=4; visual crop review still required. No promotion from metrics alone."
-}
diff --git a/optimization/round-03/hypotheses.json b/optimization/round-03/hypotheses.json
deleted file mode 100644
index fa9f49dbd1194ceca9f6800723b18008115c7682..0000000000000000000000000000000000000000
--- a/optimization/round-03/hypotheses.json
+++ /dev/null
@@ -1,43 +0,0 @@
-{
- "queue": [
- {
- "id": "head-layout",
- "class": "exact-or-numerical",
- "mechanism": "Contiguous per-head Q/K/V might remove strided memory access; count copy costs. Reject no operator/block win or quality gate."
- },
- {
- "id": "m5-fc2-tiles",
- "class": "exact-or-numerical",
- "mechanism": "Native FC2 K=14336 selects BK64; test MPP BK128/256/512 and cache walk. FP32 accumulation, BF16 epilogue. Full trajectory validates reduction changes."
- },
- {
- "id": "vector-unpack",
- "class": "exact",
- "mechanism": "Reconstruct 2/4/8 lossless BF16 lanes per thread, fewer redundant nibble loads. Decode+GEMM and full packed block must improve."
- },
- {
- "id": "sparse-embedding",
- "class": "exact",
- "mechanism": "Read only actual token rows, avoiding loading 1.45GiB vocabulary on cold encoding; measure phase-specific peak."
- },
- {
- "id": "exact-qk-rope",
- "class": "numerical-until-proven",
- "mechanism": "Replicate native RMS reduction and cast order in one head-major producer. Retain native fallback."
- }
- ],
- "deprioritized": [
- {
- "mechanism": "CoreML CPU/GPU/ANE/all VAE",
- "reason": "Already measured complete images in v2. All-GPU ~MLX speed, ANE slower and b8 corrupted tiles; VAE <=3% total latency limits payoff."
- },
- {
- "mechanism": "Unscaled FP16 GEMM, skipped flow heads, approximate Q4/Q8",
- "reason": "Actual full trajectories previously failed finite or visual accuracy gates."
- },
- {
- "mechanism": "MPS/CPU dense, whole DiT CoreML export",
- "reason": "Prior real operator tests did not identify a throughput advantage; huge export/compile and memory cost. Revisit only if a new operator mechanism qualifies."
- }
- ]
-}
diff --git a/optimization/round-03/lesson.json b/optimization/round-03/lesson.json
deleted file mode 100644
index 45ff813f2e243766b719b06fd5d2d8ada97b66f2..0000000000000000000000000000000000000000
--- a/optimization/round-03/lesson.json
+++ /dev/null
@@ -1,72 +0,0 @@
-{
- "schema_version": 1,
- "id": "minimax-h3-r3-exact-qk-fc2",
- "title": "Native reduction matching gives exact M5 QK fusion; whole-image memory still decides",
- "tags": [
- "rmsnorm",
- "rope",
- "bf16",
- "m5",
- "gemm",
- "physical-footprint",
- "operator-to-pipeline"
- ],
- "decision": "accepted",
- "evidence_level": "observed",
- "mechanism": "Four contiguous BF16 reads per SIMD lane and precise rsqrt reproduce installed MLX RMSNorm before the affine/partial-RoPE BF16 boundaries. M5 FC2 benefits from a 64x128x512 TensorOps tile with four SIMD groups and an eight-row tile walk.",
- "recommendation": "Use the measured M5 Pro/MLX 0.32.4 QK producer and shape-qualified FC2 tile, with native full-width norms and native fallback outside the qualified scope. Require bitwise trajectories and paired full-image receipts; do not infer a deployment win from a kernel ratio.",
- "counterexample": "The 1024-thread full-width fusion was exact and faster in a block, but the complete timed population used 1.04094% more physical memory and missed its 1% gate. BK2048 improved an operator but regressed the block by 0.19%. A 512MiB allocator cache was 1.65% slower in the block screen and did not reduce sampled physical peak.",
- "retest_when": "Retest after MLX reduction/dispatch changes, a new GPU/Metal driver, different projection shapes, or changes to opaque-kernel buffer lifetimes. Wider norm fusion and larger K panels may deserve new pipeline tests under those changed mechanisms.",
- "scope": {
- "projects": [
- "minimax-h3-image-mlx"
- ],
- "hardware": [
- "Apple M5 Pro, 16 GPU cores, 64GiB unified memory"
- ],
- "runtime": [
- "MLX/MLX Metal 0.32.4; macOS 27.0.1; AC Automatic power"
- ],
- "workloads": [
- "Two 2048-square timing cases, three paired repeats each; four exact full-image/trajectory quality cases including a detail prompt and one edit. Dense BF16 storage in this lesson; not a new packed-storage qualification."
- ]
- },
- "evidence": [
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/qk-deployment/comparison.json",
- "sha256": "346409df254b31a692f4d031c1363efd35ba9638db494e4bf5f13afe3b6d31ad"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/qk-deployment/quality-audit.json",
- "sha256": "3d242d63e283fa6dc645adee2c3a1dfda59b5d9ae4ac56cf04e15a5486816a2e"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/deployment/comparison.json",
- "sha256": "5438ce89ced53d19913534af51cb1e20d6d11e98794a5b7a986ba07480a393d8"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/norm.json",
- "sha256": "473e30d475788737cf7e45349ef94c0330f527a5011d9a8d3c8590822a87e3e7"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/panel.json",
- "sha256": "7c1d64f439912eccdf0b959b3c3e460046b5737aec04959642a9ec90a76c9ce8"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/cache-screen.json",
- "sha256": "568b51938cf5f02a94c14d47298fa225d98b5ae384200dac0e4b55a1e91c6009"
- },
- {
- "project": "minimax-h3-image-mlx",
- "path": "reports/optimization-r3/embedding/summary.json",
- "sha256": "dabdba5b4d3b9cebbccfc43671014971ba26216c96e52d50054e050c2ff676d9"
- }
- ],
- "supersedes": []
-}
diff --git a/optimization/round-03/receipt.json b/optimization/round-03/receipt.json
deleted file mode 100644
index 14ffde49f58151ac18af1f5945a293f0e3207216..0000000000000000000000000000000000000000
--- a/optimization/round-03/receipt.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
- "schema_version": 1,
- "id": "r3-deployment-qk-fc2-native-norm",
- "campaign_id": "h3-m5pro-round-03",
- "contract_sha256": "cbd4ce596524b59629216716299ee8677b10f49140de962384153aaaa2e55dec",
- "complete": true,
- "change_class": "exact",
- "hypothesis": "Native reduction matching QK/RoPE fusion and shape-qualified Metal 4 FC2 tiling reduce full generation latency without changing a denoising tensor or pixel.",
- "context": {
- "hardware": "Apple M5 Pro, 16 GPU cores, 64 GiB unified memory",
- "os": "macOS 27.0.1 arm64",
- "software": "MLX/MLX Metal 0.32.4; Python 3.12.14; strict precision environment",
- "environment": "AC Automatic power; MLX_ENABLE_TF32=0, MLX_CONV_WINOGRAD=0; user background apps unchanged; no thermal warning recorded",
- "timing_boundary": "Pipeline.generate total_s: warm text cache, original CPU RNG, six heads, decode, trace and PNG save. Excludes interpreter startup and external quality scoring.",
- "memory_metric": "Parent macOS physical footprint sampled at 100ms, current-interval maximum; includes Metal, excludes compiler XPC. MLX allocator separately; do not sum. Both compiled variants may be resident.",
- "cache_state": "Resident shared native weights and VAE; shape warmup for each variant. Exact conditioning caches warmed outside timed population.",
- "execution_policy": "Single GPU benchmark process; per-case repeats alternate control/candidate then candidate/control then control/candidate. Warmup and first-load excluded from timed population."
- },
- "variant": {
- "id": "qk-fc2-native-norm",
- "code_sha256": "8121aef031c28e2af6aa47e2492040619695b465eca661413576fcc00e6e58ea",
- "model_sha256": "3274a3e5fe0d2ea0d63f66b020beb193276a0a9d8de7ea466b312fbae9a2746f",
- "config_sha256": "d8ede93ee4766952aa052d0f550a675508f271dcd04c064747713fdf8c4cc24e",
- "command": [
- ".venv/bin/python",
- "-m",
- "experiments.r3.images",
- "--deployment",
- "--variants",
- "qk-fc2-native-norm",
- "--workload",
- "optimization/round-03/workload.json",
- "--model",
- "/Users/devin/Desktop/minimax-h3-image-opt/models/MiniMax-H3-Image",
- "--text-encoder",
- "/Users/devin/Desktop/minimax-h3-image-opt/models/MiniMax-H3/text_encoder",
- "--output",
- "outputs/optimization-r3-qk"
- ]
- },
- "quality": {
- "variant_sha256": "11f7dd242ba190be2c0fd67530140cd7da6154960cdd902126b7ebb3a56f3ec6",
- "reference_sha256": "201be3382041aec890cbd766c2f88a43799f7e2d33807e9a1504f7e29e422065",
- "evaluator_sha256": "4db0c5e8606ffc011bf85dbadabc482468a2e49cd5a1757a774620d493eba7ee",
- "outputs": {
- "product": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f",
- "portrait": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47",
- "edit": "b4b5875fea6451b9c7c24efc7903945a454ae7885ed3016bfc361b4799b2508e",
- "holdout-detail": "561b18aa708d61a5accbfd12f2280e8f7e303505324c43471ba06d60fe0351d5"
- },
- "metrics": {
- "max_abs_error": 0,
- "nonfinite_values": 0
- }
- },
- "runs": [
- {
- "case": "product",
- "repeat": 0,
- "seconds": 63.90098704188131,
- "peak_memory_bytes": 42450606784,
- "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f"
- },
- {
- "case": "product",
- "repeat": 1,
- "seconds": 64.23788916692138,
- "peak_memory_bytes": 42492910320,
- "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f"
- },
- {
- "case": "product",
- "repeat": 2,
- "seconds": 61.27363566705026,
- "peak_memory_bytes": 42598718264,
- "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f"
- },
- {
- "case": "portrait",
- "repeat": 0,
- "seconds": 60.65490966709331,
- "peak_memory_bytes": 42673544112,
- "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47"
- },
- {
- "case": "portrait",
- "repeat": 1,
- "seconds": 61.10937987500802,
- "peak_memory_bytes": 42671692696,
- "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47"
- },
- {
- "case": "portrait",
- "repeat": 2,
- "seconds": 62.00676687504165,
- "peak_memory_bytes": 42665237448,
- "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47"
- }
- ],
- "evidence": [
- {
- "path": "outputs/optimization-r3-qk/runs.jsonl",
- "sha256": "ec303dd7f11fa79c1539a424d04ff9dc9f427a3d4181a7957199b7dc6ee6b9c8"
- },
- {
- "path": "outputs/optimization-r3-qk/source-manifest.json",
- "sha256": "8121aef031c28e2af6aa47e2492040619695b465eca661413576fcc00e6e58ea"
- },
- {
- "path": "reports/optimization-r3/qk-deployment/quality-audit.json",
- "sha256": "3d242d63e283fa6dc645adee2c3a1dfda59b5d9ae4ac56cf04e15a5486816a2e"
- },
- {
- "path": "reports/optimization-r3/qk-deployment/summary.json",
- "sha256": "4dcff0c7944ac8d80a9c9961a1a90507e465ee1b610f86441a45804e02cc3cbe"
- }
- ],
- "extensions": {
- "canonical_output": "SHA256 decoded RGB uint8 bytes in row-major order",
- "model_identity": "model_sha256 is the pinned model/source provenance manifest hash; no new full checkpoint rehash",
- "reference_scope": "Incremental identity to MLX BF16 recipe, not CUDA or unpublished FP32 training weights",
- "trace_identical": true
- }
-}
diff --git a/optimization/round-03/reference.json b/optimization/round-03/reference.json
deleted file mode 100644
index 90e840b287f7198725ea1d836ad8a56b8ea3a376..0000000000000000000000000000000000000000
--- a/optimization/round-03/reference.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
- "incremental_anchors": {
- "product": {
- "path": "outputs/suite/product/native-3.png",
- "sha256": "63684ef8ae148a35ddef02650c6aa30b4843b47c99b06cec2ea3708ef3fc2293"
- },
- "portrait": {
- "path": "outputs/optimization-v2/portrait/wired-baseline-2.png",
- "sha256": "32af5e3b95919e79e0019058ec396824c5861f9a43a4fa50c7b20348e8d81933"
- }
- },
- "precision_oracle": {
- "source_manifest": "provenance/sources.json",
- "sha256": "3274a3e5fe0d2ea0d63f66b020beb193276a0a9d8de7ea466b312fbae9a2746f",
- "description": "Previously audited official BF16 model recipe and independent ATen primitive checks; this round tests incremental losslessness against immutable outputs, not an independent full original GPU reproduction."
- },
- "missing_cases": "Fresh control outputs for edit/holdout are generated before candidate validation and cannot be used for tuning."
-}
diff --git a/optimization/round-03/workload.json b/optimization/round-03/workload.json
deleted file mode 100644
index e68cca65ddec17624e6cef7abebbda5d377a2289..0000000000000000000000000000000000000000
--- a/optimization/round-03/workload.json
+++ /dev/null
@@ -1,54 +0,0 @@
-{
- "cases": [
- {
- "id": "product",
- "width": 2048,
- "height": 2048,
- "seed": 731,
- "prompt": "Square editorial studio photograph of a dark cobalt glass perfume bottle on pale travertine. The centered cream label reads exactly 'NIGHT TIDE' in large engraved serif letters and 'EAU DE PARFUM' in small capitals beneath. Dappled blue caustics, crisp glass edges, fine pores in the stone, dark reflections through the translucent bottle, restrained palette and generous negative space. No other text."
- },
- {
- "id": "portrait",
- "width": 2048,
- "height": 2048,
- "seed": 1234,
- "prompt": "An intimate natural-light portrait of an elderly woman in a botanical conservatory, reading a small red clothbound book. Warm brown skin with visible freckles and fine wrinkles, silver curls lit from behind, intricate indigo knit cardigan. Soft fern fronds out of focus in the foreground; detailed hands and realistic skin texture. A candid 85mm color photograph with gentle shadows and no lettering."
- },
- {
- "id": "edit",
- "width": 1024,
- "height": 1024,
- "seed": 42,
- "prompt": "Change the cobalt blue glass perfume bottle in to deep emerald green glass. Preserve the pale engraved NIGHT TIDE label, exact lettering, bottle shape, cap, stone backdrop, composition, lighting and shadows. The glass should be visibly green with realistic emerald highlights and reflections.",
- "images": [
- "experiments/reference-bottle.png"
- ]
- },
- {
- "id": "holdout-detail",
- "width": 2048,
- "height": 2048,
- "seed": 928417,
- "prompt": "A meticulous natural light still life on a wooden watchmaker's bench: a silver mechanical wristwatch with a clear glass back, brass gears and hair-thin springs, a folded cream linen glove, a small matte ceramic bowl and a card reading 'SECOND LIGHT'. In the background a woman with warm brown freckled skin holds a magnifying lens, her hands realistically detailed. Fine brushed metal, subtle skin pores, smooth dark-to-light blue gradients and soft window reflections, editorial macro photography."
- }
- ],
- "timing_cases": [
- "product",
- "portrait"
- ],
- "control_config": {
- "wired_gb": 42,
- "compile_blocks": true,
- "eval_every": 0,
- "prefetch_blocks": 4,
- "vae_batch": 4,
- "vae_compile": true,
- "cache_refined_text": true,
- "weight_storage": "bf16",
- "rng": "torch",
- "six_original_steps": true,
- "kernel": "native"
- },
- "holdout_rule": "holdout-detail and edit are validation only; do not tune against them",
- "shape_fallback_validation": "native-4k uses existing reference and no tile outside measured shape gets auto dispatch"
-}
diff --git a/provenance/implementation-sha256.json b/provenance/implementation-sha256.json
deleted file mode 100644
index 443d4dac8af644c60b71f19b8a7b1c4999f377bc..0000000000000000000000000000000000000000
--- a/provenance/implementation-sha256.json
+++ /dev/null
@@ -1,29 +0,0 @@
-{
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- "h3_mlx/reference.py": "7a08f60e3d21c708f71112a2f012356faff45042e43cc0a0627693421d9d4e3e",
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- "h3_mlx/vision.py": "d4141e3e14d3294ea42f096d72e4cb036a3f2805ec5d814718ed4f8eeff4d8ea",
- "h3_mlx/weights.py": "3fd490a6aeb910a7b8c426f034e07f448178619ed6430e00d2d2e1ce15e232f6",
- "pyproject.toml": "c09b616b3ff91dbe99804db08398cd23669b1212a035b25fca06e4d6db0e36ef",
- "tests/test_conversion.py": "411b571336dbab4d6c46074a67f1894318d5ccadebbef49c84c4b0d778169a87",
- "tests/test_numerics.py": "373eb2f050ae101316dfd3a5f8a86eb74ceda98e180690b5c22094b4aad106dc",
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diff --git a/provenance/optimization-r3.json b/provenance/optimization-r3.json
deleted file mode 100644
index 4a4ea962ef0f8201e2eb402be487f051e63b0c78..0000000000000000000000000000000000000000
--- a/provenance/optimization-r3.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
- "skill": {
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- "sha256": "5850817b8b7eaa534414c5bcb67d03a30b5a94c8537dcfe327226ccdf170e2b5"
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- "sha256": "b2e04e377fdad1d645581f9beeaf9cbb06d1ad32926161e06cbc15240caf12bf"
- },
- {
- "url": "https://raw.githubusercontent.com/ml-explore/mlx/v0.32.4/mlx/backend/metal/normalization.cpp",
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- "sha256": "a6c2c824e855078cd092fd9592457616f995755f0e865475c3f006a3f5d6bf1d"
- },
- {
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- "sha256": "af710925f066b029f67289a0bbe4edd0ba6c5e7f51c9df609abb111216fa963f"
- }
- ],
- "native_source_cache": "cache/mlx-source-0324 (ignored; preceding campaign pinned v0.32.4)",
- "historical_skill_sources": "reports/optimization-r3/skill-source-audit.json; unavailable historical artifacts were treated as hypotheses, not current performance evidence.",
- "scope": "Local M5 Pro 16-GPU 64GiB, MLX 0.32.4, macOS27.0.1. No current CoreML/MPS/CPU retiming; earlier complete tests remain separate."
-}
diff --git a/provenance/weights-manifest.json b/provenance/weights-manifest.json
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- "5c72a170d2a4a1a3bc5adad2e689ae28138a9700e5b8c96c0266331e86c0acce"
- ],
- [
- "MiniMax-H3",
- "text_encoder/chat_template.json",
- 5499,
- "5c72a170d2a4a1a3bc5adad2e689ae28138a9700e5b8c96c0266331e86c0acce"
- ],
- [
- "MiniMax-H3-Image",
- "vae/config.json",
- 1826,
- "7fcc21bec549804ff0a04d8246e7f90e679d4f8d4b0a27a252bbd18c30e3e4bb"
- ],
- [
- "MiniMax-H3",
- "text_encoder/config.json",
- 1474,
- "d2dd0c60d01b9e195d9447c52da61c7302d28828524914c044d9c6e1b81d0427"
- ],
- [
- "MiniMax-H3-Image",
- "model_index.json",
- 891,
- "b3d478fb7d5fcb432d377cae77fd125b876eb4cbc9834c86e6fb8f5d2613af68"
- ],
- [
- "MiniMax-H3-Image",
- "transformer/config.json",
- 630,
- "67ceb1d7752942ee984405c26c1187ff58c073b698839ea0ca289e34b8dc61a9"
- ],
- [
- "MiniMax-H3-Image",
- "processor/preprocessor_config.json",
- 390,
- "27225450ac9c6529872ee1924fcb0962ff5634834f817040f444118116f4e516"
- ],
- [
- "MiniMax-H3",
- "text_encoder/preprocessor_config.json",
- 390,
- "27225450ac9c6529872ee1924fcb0962ff5634834f817040f444118116f4e516"
- ],
- [
- "MiniMax-H3-Image",
- "processor/video_preprocessor_config.json",
- 385,
- "7768af27c1fafa9cc9011c1dc20067e03f8915e03b63504550e11d5066986d13"
- ],
- [
- "MiniMax-H3",
- "text_encoder/video_preprocessor_config.json",
- 385,
- "7768af27c1fafa9cc9011c1dc20067e03f8915e03b63504550e11d5066986d13"
- ],
- [
- "MiniMax-H3-Image",
- "pdd_config.json",
- 174,
- "cbc0cf1fbb7a7f9525987db453c1bf8218b95ebc686c9c5b74cbb5bbc4852e0b"
- ]
-]
diff --git a/pyproject.toml b/pyproject.toml
index 4c198db79e5fe652644e884333f96c640560ad20..3792772556d249a9b287bade92a682071a90d184 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,12 +1,23 @@
[build-system]
-requires = ["setuptools>=69"]
+requires = ["setuptools>=77"]
build-backend = "setuptools.build_meta"
[project]
name = "minimax-h3-image-mlx"
-version = "0.4.1"
-description = "Native MLX inference and precision audits for MiniMax H3 Image on Apple silicon"
+version = "0.5.0"
+description = "Local MiniMax H3 image generation and editing on Apple silicon with MLX"
+readme = "README.md"
+license = "Apache-2.0"
+license-files = ["LICENSE", "NOTICE", "LICENSE-model", "LICENSE-text-encoder"]
requires-python = ">=3.10"
+authors = [{name = "Devin Lai"}]
+keywords = ["mlx", "apple-silicon", "minimax", "image-generation", "image-editing"]
+classifiers = [
+ "Development Status :: 4 - Beta",
+ "Programming Language :: Python :: 3",
+ "Operating System :: MacOS :: MacOS X",
+ "Topic :: Scientific/Engineering :: Artificial Intelligence",
+]
dependencies = [
"mlx>=0.32.4,<0.33; platform_system == 'Darwin' and platform_machine == 'arm64'",
"numpy>=1.26", "Pillow>=10", "safetensors>=0.5", "transformers>=4.57,<5",
@@ -14,14 +25,14 @@ dependencies = [
]
[project.optional-dependencies]
-audit = ["torch>=2.6", "scikit-image>=0.24", "pytest>=8", "matplotlib>=3.8"]
native = ["torch>=2.6"]
-coreml = ["coremltools>=9,<10"]
-release = ["PyYAML>=6", "build>=1"]
+audit = ["torch>=2.6", "scikit-image>=0.24", "pytest>=8"]
+dev = ["torch>=2.6", "scikit-image>=0.24", "pytest>=8", "ruff>=0.17", "build>=1"]
[project.urls]
-Homepage = "https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx"
-Issues = "https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx/discussions"
+Homepage = "https://github.com/devin-lai/MiniMax-H3-Image-MLX"
+Issues = "https://github.com/devin-lai/MiniMax-H3-Image-MLX/issues"
+Models = "https://huggingface.co/collections/devin-lai/minimax-h3-image-apple-silicon-mlx"
[project.scripts]
h3-mlx = "h3_mlx.cli:main"
diff --git a/reports/RESULTS.md b/reports/RESULTS.md
deleted file mode 100644
index db975f6eaabb654489bbf1272617d7fa6e4a7d28..0000000000000000000000000000000000000000
--- a/reports/RESULTS.md
+++ /dev/null
@@ -1,231 +0,0 @@
-# MLX implementation and experiment report
-
-Measured on 11 November 2026: **Apple M5 Pro, 16 GPU cores, 64 GB unified
-memory, macOS 27.0.1, MLX/MLX Metal 0.32.4**. The port runs actual original
-MiniMax-H3-Image weights locally. No remote inference or substituted image
-model is used.
-
-## What the default preserves
-
-The default keeps the DiT's BF16 matrices, the original six sigma intervals
-and six individual PDD output heads, dense attention, FP32 Euler state and
-FP32 input/timestep/output projections. It reads the first fifty Qwen3-VL
-language layers without the final normalization. The image VAE uses the
-native BF16 decoder, 256 px tiles, at least 64 px overlap and the released
-vertical-then-horizontal blending order. The checkpoint stores VAE weights
-in FP32; this decoder casting policy follows the inspected SGLang image path.
-
-Specific precision fixes are covered by differential tests:
-
-* Original DiT QKV weights are head-interleaved, and must be reordered before
- a conventional Q/K/V split. The image VAE retains its own interleaved layout.
-* ATen DiT/VAE RMSNorm and LayerNorm apply their affine in FP32 before the BF16 cast. Qwen RMSNorm
- explicitly casts before its affine. Using one MLX normalization convention
- for both introduced many incorrect rounded values.
-* BF16 biased linear layers use an `addmm` epilogue. Separately rounding the
- matrix product and then adding bias changes results.
-* `MLX_ENABLE_TF32=0` retains the FP32 precision islands on M5.
- `MLX_CONV_WINOGRAD=0` also protects the FP32 reference-image encoder.
-* RoPE coordinates, partial rotation, BF16 products and SiLU rounding follow
- each component's original recipe. CPU torch noise follows the original
- generator family; `--rng mlx` explicitly uses a different stream.
-
-This preserves storage precision and the model's mathematical operations.
-It does **not** promise CUDA-identical pixels. Metal/CPU/CUDA reductions and
-attention kernels can cross BF16 rounding boundaries. An evolving diffusion
-trajectory can amplify these differences.
-
-The source checkpoint revisions and inspected implementation hashes are in
-[sources.json](../provenance/sources.json). These are a port of the pinned
-SGLang inference contracts, not a benchmark of a pristine official
-MiniMax/Diffusers application.
-
-## Fitting the unquantized model
-
-Each DiT block has a large modulation projection that depends on the sampling
-timestep, not the image. For the fixed schedule we compute its small outputs
-once, cache them, and release approximately **26 GB** of original modulation
-weights. This is direct evaluation of the learned matrices: no curve fitting,
-low-rank approximation, pruning or interpolation is involved.
-
-The remaining BF16 transformer core occupies about **36 GiB** of active MLX
-memory. A persistent `Pipeline` retains it across all steps and requests.
-The conditioning encoder streams one layer at a time, and the VAE can coexist
-with the core on this Mac. A fixed resident subset avoids the pathological
-all-miss behavior of a rotating LRU when the whole core does not fit.
-
-The [actual-weight modulation audit](modulation-audit.json) checks layers 0,
-25 and 49 against individual PyTorch CPU projections. Relative RMSE is
-1.06e-4–1.63e-4, with 98.6–99.9% of BF16 values identical. Individual MLX GEMVs
-and batched MLX projection also round differently despite identical source
-weights. These results support retaining the original matrices and FP32
-islands, while avoiding any claim of bit identity across backends.
-
-## Matched 2K product trials
-
-All rows below use the same detailed product prompt, seed 731, CPU torch noise
-and 2048×2048 working/output size. Each profile has one initial request and
-one subsequent request reusing resident weights and cached conditioning.
-The table uses one recorded resident DiT sample per profile, not a statistically
-established latency median. A final decoder LayerNorm audit re-rendered the
-saved latents without rerunning diffusion. These decoder-only changes leave
-the recorded DiT timings applicable. **Fresh end-to-end** values are shown
-only for requests measured again with the final corrected decoder. Other rows
-report denoising time, with their separate decode records retained in JSON.
-Within each profile, its two original repetitions re-render to identical pixels.
-
-
-
-| Profile | Passes | DiT only | Fresh end-to-end | Recorded peak MLX | RGB SSIM | RGB MAE / 255 |
-|---|---:|---:|---:|---:|---:|---:|
-| native | 6 | 67.87 s | 72.78 s | 38.31 GiB | 1.0000 | 0.000 |
-| tuned | 6 | 65.02 s | — | 38.33 GiB | 0.9757 | 2.339 |
-| fused | 6 | 63.47 s | 66.35 s | 38.31 GiB | 0.9769 | 2.252 |
-| q8 | 6 | 72.28 s | — | 21.48 GiB | 0.9318 | 5.793 |
-| q4 | 6 | 67.71 s | — | 12.51 GiB | 0.7799 | 22.925 |
-| preview | 3 | 32.30 s | — | 38.33 GiB | 0.6781 | 25.687 |
-
-
-
-[Recorded latency, memory and similarity tradeoffs](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/reports/tradeoffs.png)
-
-The final BF16 native and tuned requests took **72.78 s** and **66.35 s**,
-respectively: an observed **8.8% reduction**. The final
-measurements appear above; there is only one resident timing sample per
-profile. The three-pass preview took about half the denoising time and changes the image much
-more. Q8 is useful for memory capacity here, **not a speed improvement**.
-Q4 substantially changes the trajectory and fine details. Neither is the
-default near-original-precision path.
-
-In the final timing pass, a native request took **118.59 s** with a fresh
-conditioning encode, a cached modulation table and model loading. Its subsequent
-resident request took **72.78 s**. This distinction is critical: a new CLI process
-does not inherit the previous process's resident weights. Building the native
-modulation table also takes additional time on a fresh installation. Initial
-quantized-request totals are not a controlled cold-start comparison.
-
-Memory figures are peak **MLX allocations**, not whole-system RAM, RSS or
-swap. The tests do not establish capacity/performance on every Mac.
-
-Inspect the actual evidence:
-
-* [Product gallery](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/product/index.html)
-* [Vertical full-frame stack](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/product/stack.png)
-* [Identical 1:1 detail crops](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/product/details.png)
-* [Absolute RGB differences amplified eight times](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/product/difference-x8.png)
-* [Full-resolution scores and source PNG hashes](../comparisons/product/metrics.json)
-* [All exported inference records](image-trials.json)
-
-## Operator tuning
-
-The [62 operator trials](operators.json) use actual H3 dimensions, synthetic
-BF16 inputs, two warmups and seven synchronized repetitions per setting.
-They measure Python/MLX dispatch plus GPU execution. They exclude weight
-loading and quantization, and are not image-generation latency measurements.
-
-Selected medians at 4096 tokens:
-
-| Operation | Native | Experiment | Result |
-|---|---:|---:|---|
-| RMSNorm + indexed AdaLN, width 5376 | 3.558 ms | 0.543 ms, 256 threads | ~6.6× operator speedup; relative RMSE ~1e-5 |
-| Q/K RMSNorm + partial RoPE, 56×128 heads | 10.961 ms | 2.057 ms | ~5.3× operator speedup; relative RMSE ~9.6e-6 |
-| QKV GEMM, 5376→21504 | 37.589 ms | Q8/g64: 43.336 ms | Quantization is slower at this shape |
-| Fused gate/up GEMM, 5376→28672 | 50.184 ms | Q8/g64: 57.969 ms | Same finding |
-| Down GEMM, 14336→5376 | 29.900 ms | Q4/g64: 29.812 ms | Essentially unchanged; quantization changes values |
-| Dense SDPA, 56 heads, head dim 128 | 24.146 ms | 128-query chunks: 41.219 ms | Full memory-efficient SDPA is faster |
-
-The sweep also tests normalization threadgroups 128/256/512, Q4 and Q8 groups
-32/64/128, and attention query chunks 0/128/512/1024. Query chunking retained
-identical outputs in these measured attention trials, but increased latency.
-Smaller quantization groups slightly reduced numerical error without making
-large-batch Q8 GEMM faster than BF16 on this device.
-
-The custom kernels preserve explicit BF16 operation boundaries, but their
-parallel reduction order can change a few values. Their large isolated
-speedups translate to a much smaller image-generation gain. The earlier
-combined-kernel trials had very similar denoising times; these few samples
-do not establish a reliable end-to-end advantage over AdaLN fusion alone.
-
-## Decoder-only batching experiment
-
-The [VAE trials](vae/trials.json) decode the **same saved final latent**, so
-diffusion drift cannot obscure decoder differences. Loaded weights and one
-warmup are excluded; each number is the median of two measured decodes.
-
-| Tile batch | Decode time | Pixel identity to batch 1 | RGB SSIM | Max 8-bit error |
-|---|---:|---|---:|---:|
-| 1 | 2.972 s | Yes | 1.0000 | 0 |
-| 4 | 2.014 s | Yes | 1.0000 | 0 |
-| 8 | 1.931 s | No | 0.99890 | 4 |
-| 16 | 1.898 s | No | 0.99889 | 4 |
-
-Batch 4 is an attractive opt-in for this tested latent: **32.3% faster decode
-with identical pixels**. This is a stage-specific, one-input result, not an
-end-to-end 32.3% speedup or a universal byte-identity guarantee. The default
-retains the native batch of one. Larger batches remain explicit tradeoffs.
-Inspect the [decoder-only comparison stack](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/vae/index.html).
-
-## Interpreting precision comparisons
-
-Scores use full-resolution 8-bit RGB. SSIM is scikit-image's per-channel
-structural similarity with its default 7×7 window; PSNR uses RGB MSE and a
-255 data range. These differ from the neighboring repository's luma/window
-metrics and should not be numerically conflated. Neither metric measures
-prompt adherence or overall visual quality. All crops use exactly the same
-source coordinates within a case.
-
-The [separate CUDA comparison](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/product-cuda/index.html) uses
-the saved, hash-verified original BF16 RTX 5090 `single-bf16-stream` image at
-the same prompt/seed/resolution. Its upstream `quality=lossless` VAE path
-differs from this port's native tile recipe. The MLX default scores **0.9196
-RGB SSIM**, PSNR **21.15 dB**, MAE **7.448/255** against that image. This is
-useful visual context, but does not isolate backend error or establish
-CUDA-identical output. Timings from different hardware/quality settings are
-not used as a port speedup claim.
-
-## Additional functional and quality checks
-
-* **Portrait, 2048×2048:** native BF16 and Q8 both complete. Q8 scores 0.8880
- RGB SSIM and 10.525/255 MAE against native. The [matched face crops](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/portrait/index.html)
- make the changed skin detail visible. This is a second prompt/seed, not a
- broad perceptual-quality evaluation.
-* **Native 4096×4096:** all six native passes complete, producing the direct
- 16 MP image. Denoising took 397.76 s and recorded peak MLX allocation was
- 41.29 GiB. The separately revalidated final decoder took 12.11 s. The
- [CUDA comparison](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/native-4k-cuda/index.html) scores 0.8437 RGB
- SSIM; hardware and upstream VAE quality differ, as explained above.
-* **Editing:** the [reference/output example](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/edit-example/before-after.png)
- changes cobalt glass to emerald glass, retaining the broad scene and main
- label. Small lettering remains imperfect. The 1024×1024 output uses the
- native 2048×2048 minimum generation canvas. A fresh end-to-end run took
- 157.74 s. This is a one-reference functional check, not an editing-quality
- equivalence audit or validation of all sixteen-reference workloads.
-
-The separate [portrait CUDA comparison](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/portrait-cuda/index.html)
-scores 0.9382 RGB SSIM. All input identities and source hashes remain in the
-individual gallery records.
-
-## Validation and next optimization opportunities
-
-All **20 numerical tests pass** on the development Mac, and the distributable
-wheel builds successfully. The suite checks native/Qwen
-normalization semantics, BF16 bias epilogues and SiLU, full/partial RoPE,
-dense causal GQA including query chunks, original QKV layout, BF16 binary
-loading, quantized checkpoint round trips, patch ordering, native tiling and
-blending, reflection padding, image output quantization and rejection of
-unmatched comparison metadata.
-
-The most useful next work is shape-specific BF16 GEMM tuning and profiling of
-the M5 matrix-unit dispatch at the **actual** packed sequence lengths. Matrix
-projections dominate the remaining work; replacing them with slower Q8
-operators does not help this 64 GB Mac. More efficient original-weight loading
-and persistent-service reuse can improve startup without changing arithmetic.
-The preconversion tool already removes online Q8/Q4 conversion from future
-quantized startups while retaining native modulation/FP32 islands.
-
-Further precision work should expand same-prompt multi-seed tests, preserve
-exported original noise/conditioning tensors across backends, and compare
-against an identical upstream VAE quality setting. New custom attention or
-matrix kernels should be evaluated with both tensor errors and complete
-seeded image stacks. Sparse attention, residual reuse and timestep skipping
-would change the model and should remain separate experiments.
diff --git a/reports/image-trials.json b/reports/image-trials.json
deleted file mode 100644
index ecd20ff8650f7a4099dc233f83a4e0c4a4d5be0a..0000000000000000000000000000000000000000
--- a/reports/image-trials.json
+++ /dev/null
@@ -1,2371 +0,0 @@
-[
- {
- "prompt": "Change the cobalt blue glass perfume bottle in to deep emerald green glass. Preserve the pale engraved NIGHT TIDE label, exact lettering, bottle shape, cap, stone backdrop, composition, lighting and shadows. The glass should be visibly green with realistic emerald highlights and reflections.",
- "seed": 42,
- "size": [
- 1024,
- 1024
- ],
- "canvas": [
- 2048,
- 2048
- ],
- "rng": "torch",
- "sigmas": [
- 1.0,
- 0.9675236940383911,
- 0.8875485062599182,
- 0.75,
- 0.5327739119529724,
- 0.23200887441635132,
- 0.0
- ],
- "steps": [
- 0,
- 1,
- 2,
- 3,
- 4,
- 5
- ],
- "dit": {
- "cache_dir": "cache",
- "cache_gb": 38,
- "bits": null,
- "group_size": 64,
- "mlp_chunk": 0,
- "query_chunk": 0,
- "kernel": "native",
- "threads": 256,
- "quantized_path": null
- },
- "encoder": {
- "cache_dir": "cache",
- "bits": null,
- "group_size": 64,
- "query_chunk": 0
- },
- "vae": {
- "tile_batch": 1,
- "dtype": "bfloat16"
- },
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "timings": {
- "encode_s": 28.711510707857087,
- "dit_setup_s": 0.3510471251793206,
- "step_0_s": 32.27381366607733,
- "step_1_s": 18.178750790888444,
- "step_2_s": 17.952110541053116,
- "step_3_s": 17.822316124802455,
- "step_4_s": 17.744772125035524,
- "step_5_s": 17.770629958016798,
- "decode_s": 4.267031416995451,
- "total_s": 157.7393269999884
- },
- "reference_images": [
- "experiments/reference-bottle.png"
- ],
- "reference_sha256": [
- "96d23a5fb97d8dbff46990ec6c13a8caf8573f2b4d41820c9cae0efc3d0b872c"
- ],
- "conditioning_cache_hit": false,
- "dit_effective": {
- "bits": null,
- "group_size": 64,
- "cache_limit_gb": 38.0,
- "checkpoint_fingerprint": "d92ff1655d366a65a0964dc770fb50a3db623de2f490371e82776a10e2d5d900"
- },
- "dit_was_resident": false,
- "dit_metrics": {
- "block_load_s": 16.658172828843817,
- "block_compute_s": 105.10201758518815,
- "block_loads": 52
- },
- "memory_before_decode": {
- "active_gb": 36.05878337472677,
- "peak_gb": 37.71541484631598,
- "cache_gb": 0.9982056273147464
- },
- "memory": {
- "active_gb": 36.81037116795778,
- "peak_gb": 37.71541484631598,
- "cache_gb": 0.31941457837820053
- },
- "output": "outputs/suite/edit/native-0.png",
- "png_sha256": "9df3f80efa5ef9f078c9acda81febdbc2d5d706f3c85c574fdfd9fd274b71304",
- "case": "edit",
- "profile": "native",
- "trial": 0
- },
- {
- "prompt": "An overhead editorial photograph of an oceanographer's desk: a coiled orange rope, silver calipers, a pale-blue sea chart with fine contour lines, a black-and-white photograph of a lighthouse, a few tiny shells and a weathered brass compass. On a pale notecard write 'NORTH STAR' in neat bold navy capitals. One shaft of late afternoon sunlight, precise paper fibers and metal scratches, realistic soft shadows, a carefully balanced square composition.",
- "seed": 802,
- "size": [
- 4096,
- 4096
- ],
- "canvas": [
- 4096,
- 4096
- ],
- "rng": "torch",
- "sigmas": [
- 1.0,
- 0.9675236940383911,
- 0.8875485062599182,
- 0.75,
- 0.5327739119529724,
- 0.23200887441635132,
- 0.0
- ],
- "steps": [
- 0,
- 1,
- 2,
- 3,
- 4,
- 5
- ],
- "dit": {
- "cache_dir": "cache",
- "cache_gb": 38,
- "bits": null,
- "group_size": 64,
- "mlp_chunk": 0,
- "query_chunk": 0,
- "kernel": "native",
- "threads": 256,
- "quantized_path": null
- },
- "encoder": {
- "cache_dir": "cache",
- "bits": null,
- "group_size": 64,
- "query_chunk": 0
- },
- "vae": {
- "tile_batch": 1,
- "dtype": "bfloat16"
- },
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
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diff --git a/reports/inference/edit/native-0.json b/reports/inference/edit/native-0.json
deleted file mode 100644
index a3721f75a6e087375aeb59d5aaa17bc88c5aa703..0000000000000000000000000000000000000000
--- a/reports/inference/edit/native-0.json
+++ /dev/null
@@ -1,120 +0,0 @@
-{
- "prompt": "Change the cobalt blue glass perfume bottle in to deep emerald green glass. Preserve the pale engraved NIGHT TIDE label, exact lettering, bottle shape, cap, stone backdrop, composition, lighting and shadows. The glass should be visibly green with realistic emerald highlights and reflections.",
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diff --git a/reports/inference/native-4k/native-0.json b/reports/inference/native-4k/native-0.json
deleted file mode 100644
index 228ff9fb78882b895751ccbb38738e65d6e2d4d6..0000000000000000000000000000000000000000
--- a/reports/inference/native-4k/native-0.json
+++ /dev/null
@@ -1,131 +0,0 @@
-{
- "prompt": "An overhead editorial photograph of an oceanographer's desk: a coiled orange rope, silver calipers, a pale-blue sea chart with fine contour lines, a black-and-white photograph of a lighthouse, a few tiny shells and a weathered brass compass. On a pale notecard write 'NORTH STAR' in neat bold navy capitals. One shaft of late afternoon sunlight, precise paper fibers and metal scratches, realistic soft shadows, a carefully balanced square composition.",
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diff --git a/reports/inference/portrait/native-0.json b/reports/inference/portrait/native-0.json
deleted file mode 100644
index f9439413b6ccd5b2395879793ba9ab2f3367819f..0000000000000000000000000000000000000000
--- a/reports/inference/portrait/native-0.json
+++ /dev/null
@@ -1,131 +0,0 @@
-{
- "prompt": "An intimate natural-light portrait of an elderly woman in a botanical conservatory, reading a small red clothbound book. Warm brown skin with visible freckles and fine wrinkles, silver curls lit from behind, intricate indigo knit cardigan. Soft fern fronds out of focus in the foreground; detailed hands and realistic skin texture. A candid 85mm color photograph with gentle shadows and no lettering.",
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diff --git a/reports/inference/portrait/q8-0.json b/reports/inference/portrait/q8-0.json
deleted file mode 100644
index 5ca2e63c0f51cbb6f6f4d3d561182992486cbeac..0000000000000000000000000000000000000000
--- a/reports/inference/portrait/q8-0.json
+++ /dev/null
@@ -1,116 +0,0 @@
-{
- "prompt": "An intimate natural-light portrait of an elderly woman in a botanical conservatory, reading a small red clothbound book. Warm brown skin with visible freckles and fine wrinkles, silver curls lit from behind, intricate indigo knit cardigan. Soft fern fronds out of focus in the foreground; detailed hands and realistic skin texture. A candid 85mm color photograph with gentle shadows and no lettering.",
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diff --git a/reports/inference/product/fused-0.json b/reports/inference/product/fused-0.json
deleted file mode 100644
index 7cf1b80b058fe7c5f87c112ac00a05b88ccc03b2..0000000000000000000000000000000000000000
--- a/reports/inference/product/fused-0.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
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diff --git a/reports/inference/product/fused-1.json b/reports/inference/product/fused-1.json
deleted file mode 100644
index 951eddc33ffd01e3b23cbd582b3818db87597543..0000000000000000000000000000000000000000
--- a/reports/inference/product/fused-1.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
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diff --git a/reports/inference/product/fused-2.json b/reports/inference/product/fused-2.json
deleted file mode 100644
index ba5b6f7dc898bb2caaa453501caa3e890f85e3b9..0000000000000000000000000000000000000000
--- a/reports/inference/product/fused-2.json
+++ /dev/null
@@ -1,116 +0,0 @@
-{
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diff --git a/reports/inference/product/native-0.json b/reports/inference/product/native-0.json
deleted file mode 100644
index 3b0e0860f145c2c53e2e0b67029545216d73d956..0000000000000000000000000000000000000000
--- a/reports/inference/product/native-0.json
+++ /dev/null
@@ -1,123 +0,0 @@
-{
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diff --git a/reports/inference/product/native-1.json b/reports/inference/product/native-1.json
deleted file mode 100644
index 7ffbee5ad0c216e058c14fe7126af2b0745b2ef2..0000000000000000000000000000000000000000
--- a/reports/inference/product/native-1.json
+++ /dev/null
@@ -1,123 +0,0 @@
-{
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diff --git a/reports/inference/product/native-2.json b/reports/inference/product/native-2.json
deleted file mode 100644
index 740609c706445878ac9c5445b99133267c5e12a8..0000000000000000000000000000000000000000
--- a/reports/inference/product/native-2.json
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-{
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diff --git a/reports/inference/product/native-3.json b/reports/inference/product/native-3.json
deleted file mode 100644
index 4860c8f36ceceea1938cccd7ee98e43f16b32a55..0000000000000000000000000000000000000000
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-{
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diff --git a/reports/inference/product/preview-0.json b/reports/inference/product/preview-0.json
deleted file mode 100644
index 3610b831742ba9452b49c487ddc8f706639ef2ff..0000000000000000000000000000000000000000
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-{
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diff --git a/reports/inference/product/preview-1.json b/reports/inference/product/preview-1.json
deleted file mode 100644
index 93502fda6a3a8e986ce9f73e1e8331daccd52f18..0000000000000000000000000000000000000000
--- a/reports/inference/product/preview-1.json
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-{
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diff --git a/reports/inference/product/q4-0.json b/reports/inference/product/q4-0.json
deleted file mode 100644
index 78736fed793c6154f32150c069437c1608320c25..0000000000000000000000000000000000000000
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-{
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diff --git a/reports/inference/product/q4-1.json b/reports/inference/product/q4-1.json
deleted file mode 100644
index c94d7856ff3be2896e5154050a234045988931a7..0000000000000000000000000000000000000000
--- a/reports/inference/product/q4-1.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
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diff --git a/reports/inference/product/q8-0.json b/reports/inference/product/q8-0.json
deleted file mode 100644
index 958c1f05637446606e4d67179b76ad5f138669e7..0000000000000000000000000000000000000000
--- a/reports/inference/product/q8-0.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
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diff --git a/reports/inference/product/q8-1.json b/reports/inference/product/q8-1.json
deleted file mode 100644
index e4370a44312a853ca6ca40d8baf8b203fccdc254..0000000000000000000000000000000000000000
--- a/reports/inference/product/q8-1.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
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diff --git a/reports/inference/product/tuned-0.json b/reports/inference/product/tuned-0.json
deleted file mode 100644
index b20dfa0199671f250750951901b95dc87bd96b66..0000000000000000000000000000000000000000
--- a/reports/inference/product/tuned-0.json
+++ /dev/null
@@ -1,123 +0,0 @@
-{
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diff --git a/reports/inference/product/tuned-1.json b/reports/inference/product/tuned-1.json
deleted file mode 100644
index 94d820b6354249aa5d99a8b18008cf2a626667a2..0000000000000000000000000000000000000000
--- a/reports/inference/product/tuned-1.json
+++ /dev/null
@@ -1,123 +0,0 @@
-{
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diff --git a/reports/modulation-audit.json b/reports/modulation-audit.json
deleted file mode 100644
index 07bfcc0f5cc568ac295f04181e58de0b25cca430..0000000000000000000000000000000000000000
--- a/reports/modulation-audit.json
+++ /dev/null
@@ -1,45 +0,0 @@
-{
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diff --git a/reports/operators.json b/reports/operators.json
deleted file mode 100644
index 16be6cbda3d2c081168e876db923f85c835d322d..0000000000000000000000000000000000000000
--- a/reports/operators.json
+++ /dev/null
@@ -1,1519 +0,0 @@
-{
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- 23.585624992847443,
- 23.93633290193975,
- 24.196083890274167,
- 24.06216599047184,
- 24.146165931597352,
- 24.465749971568584,
- 24.197667138651013
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- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
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- {
- "operator": "sdpa",
- "variant": "query_chunk_128",
- "shape": [
- 1,
- 4096,
- 56,
- 128
- ],
- "median_ms": 41.21925006620586,
- "min_ms": 41.048833169043064,
- "max_ms": 41.29937500692904,
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- 41.06008284725249,
- 41.21925006620586,
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- 41.09108285047114
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- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "operator": "sdpa",
- "variant": "query_chunk_512",
- "shape": [
- 1,
- 4096,
- 56,
- 128
- ],
- "median_ms": 26.095834095031023,
- "min_ms": 25.888917036354542,
- "max_ms": 26.268708053976297,
- "samples_ms": [
- 25.888917036354542,
- 26.00116585381329,
- 25.939542101696134,
- 26.095834095031023,
- 26.154958875849843,
- 26.268708053976297,
- 26.120957918465137
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- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
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- {
- "operator": "sdpa",
- "variant": "query_chunk_1024",
- "shape": [
- 1,
- 4096,
- 56,
- 128
- ],
- "median_ms": 25.337791070342064,
- "min_ms": 25.203832890838385,
- "max_ms": 25.563500123098493,
- "samples_ms": [
- 25.29804199002683,
- 25.253874948248267,
- 25.337791070342064,
- 25.203832890838385,
- 25.563500123098493,
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- 25.39620897732675
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- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- }
- ]
-}
diff --git a/reports/optimization-r3/METHOD.md b/reports/optimization-r3/METHOD.md
deleted file mode 100644
index 8259bf787395d7b8403ad74a167c2a53fa116a72..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/METHOD.md
+++ /dev/null
@@ -1,36 +0,0 @@
-# M5 Pro optimization round 3
-
-This campaign applies `tmp/mlx-skills/SKILL.md` to MiniMax-H3-Image. The contract is frozen in `optimization/round-03/contract.json`. The objective is latency, with zero additional decoded-pixel error and zero nonfinite states. Saved trajectory payloads are checked independently of PNG conversion. The production source is snapshotted before inference under `outputs/optimization-r3-deployment/source/`.
-
-The independent precision foundation remains the inspected official implementation and ATen primitive checks in `provenance/sources.json` and `tests/test_numerics.py`. This round establishes incremental identity to the existing MLX BF16 recipe. It does not establish CUDA byte identity or recover unpublished FP32 weights. The earlier same-latent BF16/FP32 decoder and cross-backend CUDA comparison galleries remain separate references.
-
-## Workload and scope
-
-- Hardware: local Apple M5 Pro, 16 GPU cores, 64 GiB unified memory, macOS 27.0.1, MLX/MLX Metal 0.32.4.
-- AC Automatic power. User applications remain running. No system power, swap, or background-process settings are changed.
-- Timing cases: 2048-square product and portrait, three warm repeats each, with control/candidate order AB, BA, AB. Both configurations receive one complete warmup per timing shape. Loading and first-image results are retained separately.
-- Quality cases: the same product and portrait, a detail-heavy prompt/seed fixed before search, and a 1024 reference-image edit on the original 2048 generation canvas. The detail case and edit are not used to select operator parameters. They are validation cases rather than a broad perceptual-quality dataset.
-- Timing boundary: `Pipeline.generate` total time, including original CPU Torch RNG, conditioning-cache checks, all six original heads, native decode, trace and PNG save. Interpreter startup and external image scoring are excluded.
-- Physical memory: parent-process macOS `ri_phys_footprint`, sampled every 100 ms over each generation. Includes Metal, excludes compiler XPC services; short peaks may be missed. The kernel's lifetime high-water counter is also retained but includes earlier requests. The ctypes ABI was verified against a compiled SDK `sizeof/offsetof` probe.
-- MLX active/peak/cache counters are reported independently. They overlap physical footprint and must not be added. Both compiled variants can remain resident in the paired process, sharing the same native weights.
-
-## Search sequence
-
-1. Actual first-block activations, including FC2 values above 4,000, rather than small normally distributed test inputs.
-2. Six attention copy/layout settings; 60 Metal 4 FC1/FC2 tiles; 12 lossless unpack settings (two real projection weights); native-order Q/K producers; complete compiled blocks.
-3. Three RMSNorm/AdaLN threadgroup layouts. Only the 1,024-thread native loop/reduction layout matched the actual BF16 operator exactly.
-4. Eight dual-accumulator FC1/SwiGLU epilogues, then four larger FC2 K-panel settings. Combined with step 2, this is 72 distinct matrix settings. Compiler/harness failures are archived separately and are not successful operator trials.
-5. Real 6,213-token edit and 16,478-token 4K FC2 activations. An additional 8,192-row boundary probe repeats actual edit activations and is explicitly an operator test, not a new complete-image validation.
-6. Complete research outputs and then a separate production-entry-point campaign. No timing is pooled across those campaigns or with v2.
-
-All selected work preserves the original weight bits, six sigma intervals, RNG, attention semantics and BF16/FP32 boundaries. Unsupported devices, MLX versions and shapes retain native execution. Q4/Q8, fewer heads, differing reductions and Core ML FP16 exports do not meet the exact campaign's quality contract.
-
-## Other backends
-
-The earlier `reports/optimization-v2/RESULTS.md` contains actual complete VAE ML Packages on CPU_ONLY, CPU_AND_GPU, CPU_AND_NE and ALL, plus Torch CPU/MPS and direct Metal/MPSMatrix probes. Those are historical evidence, not retimings in this campaign. The Core ML work is a complete 24-block VAE export, not a 33B Core ML denoiser. GPU/ALL did not establish a generation-throughput advantage, and ANE showed real-image accuracy failures. No new full-denoiser ANE export or universal backend limit is claimed here.
-
-The skill's historical source artifacts were unavailable at their registered workspace paths; the audit is retained as `skill-source-audit.json`. Its workflows and hypotheses were applied, but its previous project numbers were not used as current controls. New source-derived reductions were checked against the installed runtime and complete model outputs.
-
-## Compact campaign
-
-A separate frozen contract in `optimization/round-03-compact` compares the previous packed12 champion (native kernels, wired 32 GiB) with the same storage plus exact QK/FC2 tuning. It uses the identical four cases and three paired timing repeats. Its timings are not pooled with the dense campaigns. All underlying BF16 weight bits and output tensors remain unchanged.
diff --git a/reports/optimization-r3/RESULTS.md b/reports/optimization-r3/RESULTS.md
deleted file mode 100644
index 9f949e8f48499a8815852547f2add89662089e1f..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/RESULTS.md
+++ /dev/null
@@ -1,81 +0,0 @@
-# Exact M5 optimization, round 3
-
-On the local **M5 Pro, 16 GPU cores, 64 GiB**, the selected Q/K fusion and FC2 tile reduce warm geometric-mean latency from **65.83 s to 62.49 s (5.07%)**. Maximum sampled parent physical footprint over the timing population decreases from **40.12 to 39.74 GiB**. All four declared cases retain identical RGB pixels, conditioning, six velocities, six Euler states and final latents.
-
-This is an incremental comparison with the existing MLX BF16 recipe. It does not claim CUDA byte identity or identity to the separate FP32 decoder. The [image gallery](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/optimization-r3/index.html) links actual matched grids, vertical stacks, native-resolution crops and amplified differences, plus earlier precision-loss examples.
-
-## Paired complete-image results
-
-macOS 27.0.1, MLX/MLX Metal 0.32.4, original CPU Torch RNG, all six heads, native compiled VAE batch four, cached conditioning and resident shared weights. Each timing shape receives a full warmup per variant. Repeats alternate AB, BA, AB; black dots in the chart are individual samples. First-load samples are retained separately. The timing includes trace/PNG save and excludes interpreter startup and external image scoring.
-
-| 2048-square case | Native samples (s) | Native median | Tuned samples (s) | Tuned median |
-|---|---|---:|---|---:|
-| product | 67.02, 66.80, 64.56 | 66.80 | 63.90, 64.24, 61.27 | 63.90 |
-| portrait | 64.23, 64.86, 65.15 | 64.86 | 60.65, 61.11, 62.01 | 61.11 |
-
-The frozen helper reports **`measured_improvement`**: all quality gates pass, both timing-case ranges are separated, and the memory constraint passes. This is a descriptive range screen with three repeats, not a statistical confidence interval. [Comparison receipt](qk-deployment/comparison.json), [reopened pixel/tensor audit](qk-deployment/quality-audit.json), [timing and memory chart](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/reports/optimization-r3/timing-memory.png).
-
-Physical footprint is sampled every 100 ms for the parent process, including Metal and excluding compiler XPC services. Short peaks can be missed. MLX allocator peaks and process lifetime high water are separate overlapping counters; they are not added. Both compiled variants can remain resident. The [method and frozen scope](METHOD.md) explain the boundary, environment and references. The two extra quality cases are a detail prompt fixed before search and a reference-image edit; subsequent configurations reuse these cases rather than introducing fresh blind holdouts.
-
-## Lossless packed configuration
-
-The existing `packed12` BF16 codec combines successfully with the selected exact tuning. Its own paired campaign reduces warm geometric-mean latency from **66.42 to 64.05 s (3.57%)**, with timing-population physical peak decreasing from **32.04 to 31.83 GiB**. All four image and trajectory cases are exact. The helper returns `measured_improvement`. [Receipt](compact-deployment/comparison.json), [full audit](compact-deployment/quality-audit.json), [chart](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/reports/optimization-r3/compact-timing-memory.png).
-
-| Case | Packed control median (s) | Packed tuned median (s) | Tuned samples (s) |
-|---|---:|---:|---|
-| product | 66.74 | 64.86 | 64.86, 65.30, 63.02 |
-| portrait | 66.11 | 63.26 | 63.44, 62.98, 63.26 |
-
-Across **all** observed candidate outputs, including the edit validation rather than just timed cases, maximum sampled physical footprint is **40.50 GiB dense / 32.56 GiB packed**. These are separate campaigns: timings are compared against each profile's own paired control, not pooled into a storage speedup. The packing format was implemented in v2; this round validates the new operator tuning with it. Original BF16 payloads are reconstructed exactly before GEMM. No quantization is introduced.
-
-Add `--weight-storage packed12 --wired-gb 32` to the dense command below. The existing 50-block, 27.23 GiB cache was reused; no second model cache was created. Compressed arrays remain explicit dynamic inputs to compiled blocks, preventing retained dense-weight constant folding. The separate frozen compact contract allows up to 5% memory regression before measurement; the measured peak actually decreases. Its validation cases are reused, not fresh blind holdouts.
-
-## Implemented changes
-
-- **Q/K normalization and RoPE:** four adjacent BF16 reads per SIMD lane, the installed MLX reduction order, precise reciprocal square root, FP32 affine and explicit BF16 product/add boundaries. The earlier differing fusion did not preserve this arithmetic. The new producer matches complete deployed trajectories.
-- **M5 FC2:** Metal 4 TensorOps, 64×128×512 tile, four SIMD groups, eight-row tile walk, FP32 accumulation and one BF16 epilogue. Dispatch is restricted to the tested M5 Pro/MLX version and 4,096–8,192 joint tokens. The real 6,213-token edit operator improves about 13%; the 16,478-token 4K operator improves under 1%, so 4K retains native execution. The 8,192-row boundary probe repeats real edit activations and is an operator test, not a full 8K-token image qualification.
-- **Vocabulary row reads:** cold encoding reads only requested safetensors rows with original BF16 payloads, preserving order, duplicates and padding. Three fresh-process pairs on real weights reduce this phase from 107.8 ms to 4.95 ms and its sampled physical peak from 1.52 GiB to about 31 MiB. This is an embedding-read result, not a whole-model speedup or peak reduction.
-- **Memory accounting:** JSON now separates process physical memory from MLX active/peak/cache. The SDK struct layout was checked with a compiled C ABI probe.
-
-The released implementation is in [tuning.py](../../h3_mlx/tuning.py), [ops.py](../../h3_mlx/ops.py), [dit.py](../../h3_mlx/dit.py), [weights.py](../../h3_mlx/weights.py) and [memory.py](../../h3_mlx/memory.py). Unsupported devices, versions and shapes keep native execution. Default precision, schedule and native fallback remain available.
-
-## Rejected and conditional results
-
-The full-width RMSNorm/AdaLN fusion is exact on all four cases. It improves an already tuned first block by 4.34%, but its complete-image campaign has 5.24% lower latency and **1.04094% higher** timing-population physical peak. That narrowly exceeds the predeclared 1% allowance, so the helper returns `no_qualified_improvement`. It remains an optional `--kernel metal-exact` tradeoff. Those samples are not pooled with the selected campaign, and their closeness does not establish an extra whole-model advantage. [Receipt](deployment/comparison.json).
-
-Other retained experiments include:
-
-| Mechanism | Observed result | Decision |
-|---|---|---|
-| Six explicit attention-copy layouts | Exact, no useful gain; copying all Q/K/V is slower | Native SDPA layout retained |
-| Twelve vector-unpack settings | Decode alone about 20% faster; gain mostly disappears with GEMM | Existing scalar lossless codec retained |
-| Eight FC1/SwiGLU dual-accumulator epilogues | Exact; best is still about 0.7% slower | Rejected |
-| FC2 K panel 2048 | Operator improves 4.43% vs panel 512, complete block regresses 0.19% | Panel 512 retained |
-| RMSNorm threadgroups 256/512 | Tiny BF16 differences; only 1024 reproduces the native loop | Differing operators rejected |
-| Free-allocator cache 512 MiB | Block about 1.65% slower, no sampled physical-peak reduction | No allocator-limit change shipped |
-
-There are 72 distinct matrix settings, six attention layouts and twelve unpack settings, plus reduction/layout, block and shape checks. Compiler/harness failures are archived separately and are not successful trials. [Decision ledger](screen-decisions.json), [source provenance](../../provenance/optimization-r3.json), [observed project-local lesson](../../optimization/lessons/minimax-h3-r3-exact-qk-fc2.json).
-
-## Other backends and precision
-
-The [previous backend report](../optimization-v2/RESULTS.md) contains real Torch CPU/MPS, direct Metal/MPSMatrix, and complete 24-block VAE ML Packages on CPU_ONLY, CPU_AND_GPU, CPU_AND_NE and ALL. These are historical measurements, not current controls. The full 33B denoiser remains MLX; no complete ANE/Core ML denoiser export is claimed. ANE VAE quality failures, FP16 projection overflow, quantization and reduced-head losses have real [comparison grids](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/optimization-v2/product/index.html) and a [same-latent device grid](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/optimization-v2/decoder-all-product/comparison/index.html).
-
-The [same-latent FP32 decoder comparison](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/optimization-v2/fp32-reference/comparison/index.html) isolates the existing BF16 decoder choice. Select `--vae-dtype float32` when that separate precision/performance tradeoff is preferred. New exact tuning adds no loss relative to the stated MLX reference; that does not erase preexisting cross-backend differences.
-
-## Reproduce
-
-Use the pinned environment in `requirements-macos.lock` and the original local checkpoints. The recommended dense configuration is:
-
-```sh
-h3-mlx generate --model /path/to/MiniMax-H3-Image \
- --text-encoder /path/to/MiniMax-H3/text_encoder \
- --compile-blocks --eval-every 0 --prefetch-blocks 4 --wired-gb 42 \
- --compile-vae --vae-batch 4 --kernel metal-qk-exact --tune-matmul \
- "A detailed image description" --output outputs/tuned.png
-```
-
-The benchmark entry point is `python -m experiments.r3.images --deployment --variants qk-fc2-native-norm`, with model/encoder paths and a **fresh** output/comparison directory. Reopen and audit with `experiments.r3.report`, then validate and compare its receipts with `tmp/mlx-skills/scripts/mlx_skill.py`. Raw commands and configurations are retained in receipts and output JSON. The wheel is `dist/minimax_h3_image_mlx-0.3.0-py3-none-any.whl`; runtime source bytes are verified in `wheel.json`.
-
-## Deployment verification
-
-Version 0.3.0 builds as a 55,953-byte wheel. All 21 runtime modules match the repository source bytes. A complete 2048-square generation ran through the CLI from the actual wheel in `/tmp`, with an assertion preventing checkout imports. The delivered pixels and every saved trajectory tensor match the immutable reference exactly. Its fresh-process outer wall time was 67.35 s, including imports and loading; it is not pooled with warm timings. [Wheel/CLI verification](wheel-cli.json), [source-byte verification](wheel.json). All 39 tests pass, project lint passes, and changed-code formatting checks pass.
diff --git a/reports/optimization-r3/STATUS.json b/reports/optimization-r3/STATUS.json
deleted file mode 100644
index 419cfbe1c83d20bbe71f5f8d196b3a0cfc0cc250..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/STATUS.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
- "state": "complete",
- "active_session": null,
- "current_log": null,
- "runtime_frozen": "h3_mlx source matches outputs/optimization-r3-qk/source-manifest.json",
- "complete": [
- "72 matrix settings, 6 attention layouts, 12 unpack settings, native-order norm/QK block tests",
- "24 research images all tensors/pixels exact",
- "20 full-norm production images all tensors/pixels exact, +5.2357% latency reduction, +1.04094% physical peak => strict memory gate failed",
- "39 tests passed",
- "512MiB cache screen rejected: slower and no footprint saving",
- "Dense QK+FC2 production: measured_improvement, 5.0685% latency reduction, 0.9451% physical peak reduction, all four cases tensor/pixel exact.",
- "Packed: measured_improvement, 3.5667% latency reduction, all four cases tensor/pixel exact, 32.56GiB maximum across candidate cases.",
- "Actual 0.3.0 wheel CLI from /tmp: complete 2K pixels and every trajectory tensor identical to immutable reference.",
- "12 case galleries, three four-case stacks, README and results complete; 39 tests and project lint pass."
- ],
- "next": [],
- "global_gpu_deadline_utc": "2026-11-12 10:02:00",
- "remaining_budget_note": "One GPU job at a time; retain <=5GiB new artifacts; no user background apps changed.",
- "finished_utc": "2026-11-12T09:59:25.739751+00:00",
- "artifacts": [
- "reports/optimization-r3/RESULTS.md",
- "comparisons/optimization-r3/index.html",
- "dist/minimax_h3_image_mlx-0.3.0-py3-none-any.whl",
- "reports/optimization-r3/artifact-manifest.json"
- ]
-}
diff --git a/reports/optimization-r3/SUMMARY.json b/reports/optimization-r3/SUMMARY.json
deleted file mode 100644
index afec331bffafea3f025315353dce2eeb9a267c0b..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/SUMMARY.json
+++ /dev/null
@@ -1,189 +0,0 @@
-{
- "schema_version": 1,
- "dense": {
- "schema_version": 1,
- "baseline": "r3-deployment-control",
- "candidate": "r3-deployment-qk-fc2-native-norm",
- "decision": "measured_improvement",
- "quality_pass": true,
- "quality_checks": {
- "max_abs_error": true,
- "nonfinite_values": true
- },
- "feasible": true,
- "objective": "latency",
- "latency_reduction_pct": 5.068505095942822,
- "speedup": 1.053391185939559,
- "peak_memory_reduction_pct": 0.9451447766078203,
- "latency_range_screen_pass": true,
- "baseline_summary": {
- "cases": {
- "product": {
- "median_seconds": 66.80325495894067,
- "min_seconds": 64.55974454106763,
- "max_seconds": 67.02427170914598,
- "repeats": 3,
- "max_peak_memory_bytes": 42924694280
- },
- "portrait": {
- "median_seconds": 64.86302212486044,
- "min_seconds": 64.2339057920035,
- "max_seconds": 65.15299695800059,
- "repeats": 3,
- "max_peak_memory_bytes": 43080719280
- }
- },
- "geomean_seconds": 65.82599034131171,
- "max_peak_memory_bytes": 43080719280
- },
- "candidate_summary": {
- "cases": {
- "product": {
- "median_seconds": 63.90098704188131,
- "min_seconds": 61.27363566705026,
- "max_seconds": 64.23788916692138,
- "repeats": 3,
- "max_peak_memory_bytes": 42598718264
- },
- "portrait": {
- "median_seconds": 61.10937987500802,
- "min_seconds": 60.65490966709331,
- "max_seconds": 62.00676687504165,
- "repeats": 3,
- "max_peak_memory_bytes": 42673544112
- }
- },
- "geomean_seconds": 62.48959666640749,
- "max_peak_memory_bytes": 42673544112
- },
- "scope": "Equal-weight geometric mean of declared timing-case medians; maximum observed memory. Range screen is descriptive, not statistical confidence; no automatic deployment."
- },
- "packed": {
- "schema_version": 1,
- "baseline": "r3-deployment-control",
- "candidate": "r3-deployment-qk-fc2-native-norm",
- "decision": "measured_improvement",
- "quality_pass": true,
- "quality_checks": {
- "max_abs_error": true,
- "nonfinite_values": true
- },
- "feasible": true,
- "objective": "latency",
- "latency_reduction_pct": 3.566731220528463,
- "speedup": 1.0369865220340613,
- "peak_memory_reduction_pct": 0.651693102293327,
- "latency_range_screen_pass": true,
- "baseline_summary": {
- "cases": {
- "product": {
- "median_seconds": 66.73659237497486,
- "min_seconds": 66.51806045905687,
- "max_seconds": 67.69735000003129,
- "repeats": 3,
- "max_peak_memory_bytes": 34269748624
- },
- "portrait": {
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- "min_seconds": 65.95004508295096,
- "max_seconds": 66.77446975000203,
- "repeats": 3,
- "max_peak_memory_bytes": 34397462120
- }
- },
- "geomean_seconds": 66.42395530811467,
- "max_peak_memory_bytes": 34397462120
- },
- "candidate_summary": {
- "cases": {
- "product": {
- "median_seconds": 64.8621540828608,
- "min_seconds": 63.02173458295874,
- "max_seconds": 65.30018041608855,
- "repeats": 3,
- "max_peak_memory_bytes": 34089393624
- },
- "portrait": {
- "median_seconds": 63.25747816590592,
- "min_seconds": 62.976549667073414,
- "max_seconds": 63.443048041081056,
- "repeats": 3,
- "max_peak_memory_bytes": 34173296232
- }
- },
- "geomean_seconds": 64.05479135623027,
- "max_peak_memory_bytes": 34173296232
- },
- "scope": "Equal-weight geometric mean of declared timing-case medians; maximum observed memory. Range screen is descriptive, not statistical confidence; no automatic deployment."
- },
- "full_norm_tradeoff": {
- "schema_version": 1,
- "baseline": "r3-deployment-control",
- "candidate": "r3-deployment-qk-fc2",
- "decision": "no_qualified_improvement",
- "quality_pass": true,
- "quality_checks": {
- "max_abs_error": true,
- "nonfinite_values": true
- },
- "feasible": false,
- "objective": "latency",
- "latency_reduction_pct": 5.23567719335637,
- "speedup": 1.0552494550511293,
- "peak_memory_reduction_pct": -1.040944261192811,
- "latency_range_screen_pass": true,
- "baseline_summary": {
- "cases": {
- "product": {
- "median_seconds": 65.85317666619085,
- "min_seconds": 64.79533495893702,
- "max_seconds": 68.77809108304791,
- "repeats": 3,
- "max_peak_memory_bytes": 42928544448
- },
- "portrait": {
- "median_seconds": 64.66372808301821,
- "min_seconds": 64.64417616720311,
- "max_seconds": 64.91441170801409,
- "repeats": 3,
- "max_peak_memory_bytes": 43071298312
- }
- },
- "geomean_seconds": 65.25574234767025,
- "max_peak_memory_bytes": 43071298312
- },
- "candidate_summary": {
- "cases": {
- "product": {
- "median_seconds": 62.625355625059456,
- "min_seconds": 61.922258959151804,
- "max_seconds": 63.23245245893486,
- "repeats": 3,
- "max_peak_memory_bytes": 43414117080
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diff --git a/reports/optimization-r3/artifact-manifest.sha256 b/reports/optimization-r3/artifact-manifest.sha256
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index 31b759ddbee61e13361910c66b9580c080689c2b..0000000000000000000000000000000000000000
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diff --git a/reports/optimization-r3/blocks-finalists.log b/reports/optimization-r3/blocks-finalists.log
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index 5ba36aebcba4c544a63cf83569e249fe5529a771..0000000000000000000000000000000000000000
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diff --git a/reports/optimization-r3/blocks-first.json b/reports/optimization-r3/blocks-first.json
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diff --git a/reports/optimization-r3/blocks.json b/reports/optimization-r3/blocks.json
deleted file mode 100644
index 44eef8905b39ee6b4940b729b390a58aca4f6933..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/blocks.json
+++ /dev/null
@@ -1,355 +0,0 @@
-{
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
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- "resource_limit": 499000
- },
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- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "trace": "outputs/suite/product/native-3.trace.safetensors",
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- {
- "group": "qk_native_reduce",
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- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 9.447520948015153,
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- },
- "candidate": {
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- "group": "qk_native_reduce",
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- "control",
- "control",
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- "group": "qk_and_fc2_compiled_block",
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diff --git a/reports/optimization-r3/blocks.log b/reports/optimization-r3/blocks.log
deleted file mode 100644
index d2790d31aa88d00e9e671d7e9724febf092e32b2..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/blocks.log
+++ /dev/null
@@ -1,3 +0,0 @@
-{"group": "qk_native_reduce", "head_major": false, "max_abs": 0.0, "relative_rmse": 0.0, "equal_fraction": 1.0, "order": ["control", "candidate", "candidate", "control", "control", "candidate", "candidate", "control", "control", "candidate", "candidate", "control", "control", "candidate", "candidate", "control", "control", "candidate", "candidate", "control"], "control": {"median_ms": 9.35591699089855, "min_ms": 9.21908300369978, "max_ms": 10.320875095203519, "samples_ms": [10.320875095203519, 9.416084038093686, 9.4499581027776, 9.21908300369978, 9.381124982610345, 9.304291103035212, 9.221042040735483, 9.390667080879211, 9.330708999186754, 9.25099989399314]}, "candidate": {"median_ms": 1.136083505116403, "min_ms": 1.108833122998476, "max_ms": 1.1643751058727503, "samples_ms": [1.1643751058727503, 1.1221659369766712, 1.1629578657448292, 1.1406249832361937, 1.1251671239733696, 1.135583035647869, 1.108833122998476, 1.1329168919473886, 1.1530828196555376, 1.136583974584937]}, "reduction_pct": 87.8570587338303}
-{"group": "qk_native_reduce", "head_major": true, "max_abs": 0.0, "relative_rmse": 0.0, "equal_fraction": 1.0, "order": ["control", "candidate", "candidate", "control", "control", "candidate", "candidate", "control", "control", "candidate", "candidate", "control", "control", "candidate", "candidate", "control", "control", "candidate", "candidate", "control"], "control": {"median_ms": 9.307708591222763, "min_ms": 9.221249958500266, "max_ms": 9.424749994650483, "samples_ms": [9.32566705159843, 9.424749994650483, 9.402042021974921, 9.289750130847096, 9.419791167601943, 9.259665850549936, 9.280375204980373, 9.286541026085615, 9.221249958500266, 9.354749927297235]}, "candidate": {"median_ms": 1.1880624806508422, "min_ms": 1.1598749551922083, "max_ms": 1.287125051021576, "samples_ms": [1.19629199616611, 1.19012501090765, 1.227125059813261, 1.287125051021576, 1.1761249043047428, 1.1859999503940344, 1.1777919717133045, 1.1598749551922083, 1.1925841681659222, 1.1800408829003572]}, "reduction_pct": 87.2357146873808}
-{"group": "fc2_compiled_block", "max_abs": 0.0, "relative_rmse": 0.0, "equal_fraction": 1.0, "order": ["control", "candidate", "candidate", "control", "control", "candidate", "candidate", "control", "control", "candidate", "candidate", "control", "control", "candidate", "candidate", "control", "control", "candidate", "candidate", "control"], "control": {"median_ms": 178.9687710115686, "min_ms": 177.46241693384945, "max_ms": 179.53375005163252, "samples_ms": [178.8127920590341, 177.46241693384945, 178.88116696849465, 178.74775012023747, 179.53375005163252, 179.08595805056393, 179.05637505464256, 179.51779207214713, 177.8062500525266, 179.50733401812613]}, "candidate": {"median_ms": 173.98847895674407, "min_ms": 172.66458296217024, "max_ms": 174.95650006458163, "samples_ms": [173.14049997366965, 172.66458296217024, 174.0382919088006, 173.26654097996652, 174.76550000719726, 174.06474985182285, 173.93866600468755, 173.82433405146003, 174.95650006458163, 174.41516695544124]}, "reduction_pct": 2.7827715565541866}
diff --git a/reports/optimization-r3/cache-screen.json b/reports/optimization-r3/cache-screen.json
deleted file mode 100644
index e6cd331f5fd5061b8f84c5f5ec68db1f9c0001a7..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/cache-screen.json
+++ /dev/null
@@ -1,245 +0,0 @@
-{
- "environment": {
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- "machine": "arm64",
- "metal": {
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- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "scope": "Real complete first block, not full-image performance. Cache is free allocations only; same compiled graph and original weights.",
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- },
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diff --git a/reports/optimization-r3/cache-screen.log b/reports/optimization-r3/cache-screen.log
deleted file mode 100644
index 42052ca41328279c16f50189e886440da0de8b9a..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/cache-screen.log
+++ /dev/null
@@ -1,12 +0,0 @@
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diff --git a/reports/optimization-r3/compact-audit.log b/reports/optimization-r3/compact-audit.log
deleted file mode 100644
index 9e89231a8533e8af4f625c8b81d7f4856e38d1bc..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/compact-audit.log
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "root": "outputs/optimization-r3-compact",
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diff --git a/reports/optimization-r3/compact-deployment/control.receipt.json b/reports/optimization-r3/compact-deployment/control.receipt.json
deleted file mode 100644
index 063104feb0c483022579860b6c8fd539fba77c60..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/compact-deployment/control.receipt.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
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- "trace_identical": true
- }
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diff --git a/reports/optimization-r3/compact-deployment/qk-fc2-native-norm.receipt.json b/reports/optimization-r3/compact-deployment/qk-fc2-native-norm.receipt.json
deleted file mode 100644
index 341813e318329335d733738151b40371c3905ff4..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/compact-deployment/qk-fc2-native-norm.receipt.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
- "schema_version": 1,
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- "sha256": "fdd30c091a2f2eaf01e5b2facc32d146796152d560701c472b37c9b226dfe7e3"
- },
- {
- "path": "outputs/optimization-r3-compact/source-manifest.json",
- "sha256": "5be81c114b17d671881c9d0323d8c305e10fa450ad228a36efd4a264892e6d3d"
- },
- {
- "path": "reports/optimization-r3/compact-deployment/quality-audit.json",
- "sha256": "57ae4aef9579058528381de211636fe9ff02ad9b2f793f65b7b217e91c6e7f16"
- },
- {
- "path": "reports/optimization-r3/compact-deployment/summary.json",
- "sha256": "020ad62fe1e4ad92331b2e86fb63caa18462adad65e12a86ebadf5b751fa607d"
- }
- ],
- "extensions": {
- "canonical_output": "SHA256 decoded RGB uint8 bytes in row-major order",
- "model_identity": "model_sha256 is the pinned model/source provenance manifest hash; no new full checkpoint rehash",
- "reference_scope": "Incremental identity to MLX BF16 recipe, not CUDA or unpublished FP32 training weights",
- "trace_identical": true
- }
-}
diff --git a/reports/optimization-r3/compact-images.log b/reports/optimization-r3/compact-images.log
deleted file mode 100644
index 3268b6165a2f46c6798019a49eb1dcceba9b07e8..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/compact-images.log
+++ /dev/null
@@ -1,204 +0,0 @@
-04:31:18 Conditioning cache hit: 85 effective tokens
-04:31:18 Loading cached native AdaLN schedule cache/adaln-4dda935938d45a975c179220.safetensors
-04:31:29 DiT step 1/6: 11.156s, 27.30 GiB active
-04:31:39 DiT step 2/6: 10.092s, 27.30 GiB active
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-04:32:22 DiT step 6/6: 10.800s, 27.32 GiB active
-04:32:24 VAE tiles 121/121
-04:32:24 Saved outputs/optimization-r3-compact/product/control-cold--1.png in 66.563s
-{"case": "product", "variant": "control", "repeat": -1, "phase": "cold", "seconds": 66.56290258304216, "peak_memory_bytes": 32360290864, "mlx_peak_bytes": 31294559724, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-compact/product/control-cold--1.png", "nonfinite_values": 0, "step_seconds": [11.155969333136454, 10.091942500090227, 10.362390333088115, 10.569547584047541, 10.826430707937106, 10.799633791903034]}
-04:32:25 Conditioning cache hit: 85 effective tokens
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-04:33:29 VAE tiles 121/121
-04:33:29 Saved outputs/optimization-r3-compact/product/qk-fc2-native-norm-warmup--1.png in 64.283s
-{"case": "product", "variant": "qk-fc2-native-norm", "repeat": -1, "phase": "warmup", "seconds": 64.28288616612554, "peak_memory_bytes": 33057774392, "mlx_peak_bytes": 32104519860, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-compact/product/qk-fc2-native-norm-warmup--1.png", "nonfinite_values": 0, "step_seconds": [10.220672250026837, 10.41268050018698, 10.374119875021279, 10.41737558413297, 10.304355666972697, 10.378587416838855]}
-04:33:32 Conditioning cache hit: 85 effective tokens
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-04:33:53 DiT step 2/6: 10.518s, 28.06 GiB active
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-04:34:36 DiT step 6/6: 10.853s, 28.07 GiB active
-04:34:38 VAE tiles 121/121
-04:34:38 Saved outputs/optimization-r3-compact/product/control-timed-0.png in 66.737s
-{"case": "product", "variant": "control", "repeat": 0, "phase": "timed", "seconds": 66.73659237497486, "peak_memory_bytes": 34106662192, "mlx_peak_bytes": 32102341812, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-compact/product/control-timed-0.png", "nonfinite_values": 0, "step_seconds": [10.524454500060529, 10.518413708079606, 10.792438166914508, 10.938075749902055, 10.803379165939987, 10.852768250042573]}
-04:34:38 Conditioning cache hit: 85 effective tokens
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-04:35:43 Saved outputs/optimization-r3-compact/product/qk-fc2-native-norm-timed-0.png in 64.862s
-{"case": "product", "variant": "qk-fc2-native-norm", "repeat": 0, "phase": "timed", "seconds": 64.8621540828608, "peak_memory_bytes": 33937792328, "mlx_peak_bytes": 32159138384, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-compact/product/qk-fc2-native-norm-timed-0.png", "nonfinite_values": 0, "step_seconds": [10.171907834010199, 10.420595958130434, 10.435337207978591, 10.569814499933273, 10.503622999880463, 10.588133125100285]}
-04:35:46 Conditioning cache hit: 85 effective tokens
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-04:36:51 Saved outputs/optimization-r3-compact/product/qk-fc2-native-norm-timed-1.png in 65.300s
-{"case": "product", "variant": "qk-fc2-native-norm", "repeat": 1, "phase": "timed", "seconds": 65.30018041608855, "peak_memory_bytes": 33980079456, "mlx_peak_bytes": 32119505460, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-compact/product/qk-fc2-native-norm-timed-1.png", "nonfinite_values": 0, "step_seconds": [9.622593499952927, 10.695367791922763, 10.674400207819417, 10.561083749867976, 10.58698095800355, 10.6969631658867]}
-04:36:51 Conditioning cache hit: 85 effective tokens
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-04:37:58 Saved outputs/optimization-r3-compact/product/control-timed-1.png in 67.697s
-{"case": "product", "variant": "control", "repeat": 1, "phase": "timed", "seconds": 67.69735000003129, "peak_memory_bytes": 34232442256, "mlx_peak_bytes": 32102341812, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-compact/product/control-timed-1.png", "nonfinite_values": 0, "step_seconds": [11.051171125145629, 11.139043250121176, 10.98283537500538, 10.889101000037044, 10.829879583092406, 10.752662291983142]}
-04:37:59 Conditioning cache hit: 85 effective tokens
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-04:39:06 Saved outputs/optimization-r3-compact/product/control-timed-2.png in 66.518s
-{"case": "product", "variant": "control", "repeat": 2, "phase": "timed", "seconds": 66.51806045905687, "peak_memory_bytes": 34269748624, "mlx_peak_bytes": 32102341812, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-compact/product/control-timed-2.png", "nonfinite_values": 0, "step_seconds": [10.491216375026852, 10.836496958043426, 10.815235082991421, 10.784631582908332, 10.82166066719219, 10.725950000109151]}
-04:39:06 Conditioning cache hit: 85 effective tokens
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-04:40:09 Saved outputs/optimization-r3-compact/product/qk-fc2-native-norm-timed-2.png in 63.022s
-{"case": "product", "variant": "qk-fc2-native-norm", "repeat": 2, "phase": "timed", "seconds": 63.02173458295874, "peak_memory_bytes": 34089393624, "mlx_peak_bytes": 32159138472, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-compact/product/qk-fc2-native-norm-timed-2.png", "nonfinite_values": 0, "step_seconds": [10.073159834137186, 10.141389958094805, 10.191435000160709, 10.194627792108804, 10.164645916083828, 10.1853685840033]}
-04:40:10 Conditioning cache hit: 82 effective tokens
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-04:41:16 Saved outputs/optimization-r3-compact/portrait/control-warmup--1.png in 66.682s
-{"case": "portrait", "variant": "control", "repeat": -1, "phase": "warmup", "seconds": 66.68211762490682, "peak_memory_bytes": 34360909320, "mlx_peak_bytes": 32101358860, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-compact/portrait/control-warmup--1.png", "nonfinite_values": 0, "step_seconds": [10.69750374997966, 10.77719487505965, 10.656269041122869, 10.785387499956414, 10.852885874919593, 10.872083165915683]}
-04:41:16 Conditioning cache hit: 82 effective tokens
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-04:42:20 Saved outputs/optimization-r3-compact/portrait/qk-fc2-native-norm-warmup--1.png in 63.945s
-{"case": "portrait", "variant": "qk-fc2-native-norm", "repeat": -1, "phase": "warmup", "seconds": 63.944655042141676, "peak_memory_bytes": 34187468344, "mlx_peak_bytes": 32103570176, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-compact/portrait/qk-fc2-native-norm-warmup--1.png", "nonfinite_values": 0, "step_seconds": [10.141530999913812, 10.25759816612117, 10.529667666880414, 10.268967832904309, 10.369134375127032, 10.359984292183071]}
-04:42:20 Conditioning cache hit: 82 effective tokens
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-04:43:27 Saved outputs/optimization-r3-compact/portrait/control-timed-0.png in 66.774s
-{"case": "portrait", "variant": "control", "repeat": 0, "phase": "timed", "seconds": 66.77446975000203, "peak_memory_bytes": 34397462120, "mlx_peak_bytes": 32101359360, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-compact/portrait/control-timed-0.png", "nonfinite_values": 0, "step_seconds": [10.599762792000547, 10.859696834115312, 10.841980583034456, 10.818649291992188, 10.812689499929547, 10.815967541886494]}
-04:43:27 Conditioning cache hit: 82 effective tokens
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-04:44:30 Saved outputs/optimization-r3-compact/portrait/qk-fc2-native-norm-timed-0.png in 63.443s
-{"case": "portrait", "variant": "qk-fc2-native-norm", "repeat": 0, "phase": "timed", "seconds": 63.443048041081056, "peak_memory_bytes": 34173296232, "mlx_peak_bytes": 32103570176, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-compact/portrait/qk-fc2-native-norm-timed-0.png", "nonfinite_values": 0, "step_seconds": [10.038613541051745, 10.278275415999815, 10.234170750016347, 10.295444874791428, 10.20847308402881, 10.264521708944812]}
-04:44:33 Conditioning cache hit: 82 effective tokens
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-04:45:36 Saved outputs/optimization-r3-compact/portrait/qk-fc2-native-norm-timed-1.png in 62.977s
-{"case": "portrait", "variant": "qk-fc2-native-norm", "repeat": 1, "phase": "timed", "seconds": 62.976549667073414, "peak_memory_bytes": 34171510376, "mlx_peak_bytes": 32103570176, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-compact/portrait/qk-fc2-native-norm-timed-1.png", "nonfinite_values": 0, "step_seconds": [9.948953832965344, 9.86027512489818, 10.12343249982223, 10.28167499997653, 10.216354915872216, 10.20913299988024]}
-04:45:36 Conditioning cache hit: 82 effective tokens
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-04:46:42 Saved outputs/optimization-r3-compact/portrait/control-timed-1.png in 66.113s
-{"case": "portrait", "variant": "control", "repeat": 1, "phase": "timed", "seconds": 66.11278283409774, "peak_memory_bytes": 34380471936, "mlx_peak_bytes": 32101359360, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-compact/portrait/control-timed-1.png", "nonfinite_values": 0, "step_seconds": [10.713293666951358, 10.622679209103808, 10.62054287479259, 10.692101916996762, 10.695621291873977, 10.675941500114277]}
-04:46:43 Conditioning cache hit: 82 effective tokens
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-04:47:49 Saved outputs/optimization-r3-compact/portrait/control-timed-2.png in 65.950s
-{"case": "portrait", "variant": "control", "repeat": 2, "phase": "timed", "seconds": 65.95004508295096, "peak_memory_bytes": 34352488040, "mlx_peak_bytes": 32101359360, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-compact/portrait/control-timed-2.png", "nonfinite_values": 0, "step_seconds": [10.589294624980539, 10.54889929201454, 10.693320791004226, 10.65848904196173, 10.653912290930748, 10.724682250060141]}
-04:47:49 Conditioning cache hit: 82 effective tokens
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-04:48:52 Saved outputs/optimization-r3-compact/portrait/qk-fc2-native-norm-timed-2.png in 63.257s
-{"case": "portrait", "variant": "qk-fc2-native-norm", "repeat": 2, "phase": "timed", "seconds": 63.25747816590592, "peak_memory_bytes": 34164809368, "mlx_peak_bytes": 32103570176, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-compact/portrait/qk-fc2-native-norm-timed-2.png", "nonfinite_values": 0, "step_seconds": [10.064300541998819, 10.16261374996975, 10.265114292036742, 10.26566179189831, 10.19391308305785, 10.203351042000577]}
-04:48:53 Conditioning cache hit: 98 effective tokens
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-04:49:58 Saved outputs/optimization-r3-compact/holdout-detail/control-validation-0.png in 65.621s
-{"case": "holdout-detail", "variant": "control", "repeat": 0, "phase": "validation", "seconds": 65.62054104101844, "peak_memory_bytes": 34385337984, "mlx_peak_bytes": 32106749860, "output_sha256": "561b18aa708d61a5accbfd12f2280e8f7e303505324c43471ba06d60fe0351d5", "output": "outputs/optimization-r3-compact/holdout-detail/control-validation-0.png", "nonfinite_values": 0, "step_seconds": [10.586685917107388, 10.475943374913186, 10.536071791080758, 10.609299124917015, 10.648755833040923, 10.659710499923676]}
-04:49:58 Conditioning cache hit: 98 effective tokens
-04:50:08 DiT step 1/6: 10.020s, 28.05 GiB active
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diff --git a/reports/optimization-r3/compact-trace-audit.json b/reports/optimization-r3/compact-trace-audit.json
deleted file mode 100644
index a268710a244957f58ce3dc4dccb01a1cbc52d2fa..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/compact-trace-audit.json
+++ /dev/null
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diff --git a/reports/optimization-r3/deployment-audit.log b/reports/optimization-r3/deployment-audit.log
deleted file mode 100644
index 07a4df73abdacf9342d8daa7f656319901539bfd..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/deployment-audit.log
+++ /dev/null
@@ -1,62 +0,0 @@
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- },
- "reduction_pct": 5.568638486762323
- }
- },
- "all_pixel_identical": true,
- "all_trace_identical": true,
- "scope": "Paired warm complete generation including encoding-cache checks, six heads, decode, trace/PNG save. Parent physical footprint sampled every 100ms; both compiled variants can be resident."
-}
diff --git a/reports/optimization-r3/deployment-images.log b/reports/optimization-r3/deployment-images.log
deleted file mode 100644
index 0b2f17ab7237674b00594dbf7d8fe7f2caa36cf2..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/deployment-images.log
+++ /dev/null
@@ -1,204 +0,0 @@
-03:41:31 Conditioning cache hit: 85 effective tokens
-03:41:31 Loading cached native AdaLN schedule cache/adaln-4dda935938d45a975c179220.safetensors
-03:41:47 DiT step 1/6: 15.460s, 35.95 GiB active
-03:41:58 DiT step 2/6: 10.785s, 35.96 GiB active
-03:42:09 DiT step 3/6: 11.218s, 35.96 GiB active
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-03:42:31 DiT step 5/6: 11.013s, 35.97 GiB active
-03:42:42 DiT step 6/6: 11.161s, 35.97 GiB active
-03:42:44 VAE tiles 121/121
-03:42:45 Saved outputs/optimization-r3-deployment/product/control-cold--1.png in 73.420s
-{"case": "product", "variant": "control", "repeat": -1, "phase": "cold", "seconds": 73.42016170895658, "peak_memory_bytes": 41042515304, "mlx_peak_bytes": 40352740044, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-deployment/product/control-cold--1.png", "nonfinite_values": 0, "step_seconds": [15.459630792029202, 10.785492290975526, 11.217789333080873, 11.185308292042464, 11.012755416100845, 11.16101283300668]}
-03:42:45 Conditioning cache hit: 85 effective tokens
-03:42:55 DiT step 1/6: 10.349s, 36.71 GiB active
-03:43:06 DiT step 2/6: 10.599s, 36.71 GiB active
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-03:43:49 DiT step 6/6: 10.629s, 36.72 GiB active
-03:43:50 VAE tiles 121/121
-03:43:51 Saved outputs/optimization-r3-deployment/product/qk-fc2-warmup--1.png in 65.867s
-{"case": "product", "variant": "qk-fc2", "repeat": -1, "phase": "warmup", "seconds": 65.86725029186346, "peak_memory_bytes": 42375206848, "mlx_peak_bytes": 41121027764, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-deployment/product/qk-fc2-warmup--1.png", "nonfinite_values": 0, "step_seconds": [10.348964083008468, 10.59941374999471, 10.660093375016004, 10.695137459086254, 10.708188417134807, 10.628758375067264]}
-03:43:54 Conditioning cache hit: 85 effective tokens
-03:44:04 DiT step 1/6: 10.516s, 36.71 GiB active
-03:44:15 DiT step 2/6: 11.313s, 36.71 GiB active
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-03:44:49 DiT step 5/6: 11.155s, 36.72 GiB active
-03:45:00 DiT step 6/6: 11.049s, 36.72 GiB active
-03:45:02 VAE tiles 121/121
-03:45:02 Saved outputs/optimization-r3-deployment/product/control-timed-0.png in 68.778s
-{"case": "product", "variant": "control", "repeat": 0, "phase": "timed", "seconds": 68.77809108304791, "peak_memory_bytes": 42843920992, "mlx_peak_bytes": 40836142612, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-deployment/product/control-timed-0.png", "nonfinite_values": 0, "step_seconds": [10.51596958306618, 11.313274249900132, 11.170203125104308, 11.17257241695188, 11.15542795881629, 11.049065208993852]}
-03:45:02 Conditioning cache hit: 85 effective tokens
-03:45:12 DiT step 1/6: 10.126s, 36.71 GiB active
-03:45:23 DiT step 2/6: 10.258s, 36.71 GiB active
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-03:46:05 VAE tiles 121/121
-03:46:05 Saved outputs/optimization-r3-deployment/product/qk-fc2-timed-0.png in 63.232s
-{"case": "product", "variant": "qk-fc2", "repeat": 0, "phase": "timed", "seconds": 63.23245245893486, "peak_memory_bytes": 43257453152, "mlx_peak_bytes": 41171441196, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-deployment/product/qk-fc2-timed-0.png", "nonfinite_values": 0, "step_seconds": [10.12600183300674, 10.25785204092972, 10.31819008407183, 10.244670124957338, 10.107232958078384, 10.029554083012044]}
-03:46:08 Conditioning cache hit: 85 effective tokens
-03:46:18 DiT step 1/6: 9.108s, 36.71 GiB active
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-03:47:10 VAE tiles 121/121
-03:47:10 Saved outputs/optimization-r3-deployment/product/qk-fc2-timed-1.png in 62.625s
-{"case": "product", "variant": "qk-fc2", "repeat": 1, "phase": "timed", "seconds": 62.625355625059456, "peak_memory_bytes": 43304770168, "mlx_peak_bytes": 41121027772, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-deployment/product/qk-fc2-timed-1.png", "nonfinite_values": 0, "step_seconds": [9.10803550016135, 10.214009959017858, 10.184275583131239, 10.159577749902382, 10.139954125043005, 10.18216724996455]}
-03:47:10 Conditioning cache hit: 85 effective tokens
-03:47:21 DiT step 1/6: 10.631s, 36.71 GiB active
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-03:48:16 VAE tiles 121/121
-03:48:16 Saved outputs/optimization-r3-deployment/product/control-timed-1.png in 65.853s
-{"case": "product", "variant": "control", "repeat": 1, "phase": "timed", "seconds": 65.85317666619085, "peak_memory_bytes": 42928544448, "mlx_peak_bytes": 40796608084, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-deployment/product/control-timed-1.png", "nonfinite_values": 0, "step_seconds": [10.63060558307916, 10.641592833912, 10.74713350017555, 10.96550845890306, 10.4751707080286, 10.339393000118434]}
-03:48:17 Conditioning cache hit: 85 effective tokens
-03:48:27 DiT step 1/6: 10.116s, 36.71 GiB active
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-03:49:22 VAE tiles 121/121
-03:49:22 Saved outputs/optimization-r3-deployment/product/control-timed-2.png in 64.795s
-{"case": "product", "variant": "control", "repeat": 2, "phase": "timed", "seconds": 64.79533495893702, "peak_memory_bytes": 42819443440, "mlx_peak_bytes": 40826607164, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-deployment/product/control-timed-2.png", "nonfinite_values": 0, "step_seconds": [10.115730124991387, 10.395829000044614, 10.450229041976854, 10.589849167037755, 10.53937704092823, 10.645488375099376]}
-03:49:22 Conditioning cache hit: 85 effective tokens
-03:49:32 DiT step 1/6: 9.674s, 36.71 GiB active
-03:49:42 DiT step 2/6: 9.989s, 36.71 GiB active
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-03:50:23 VAE tiles 121/121
-03:50:24 Saved outputs/optimization-r3-deployment/product/qk-fc2-timed-2.png in 61.922s
-{"case": "product", "variant": "qk-fc2", "repeat": 2, "phase": "timed", "seconds": 61.922258959151804, "peak_memory_bytes": 43414117080, "mlx_peak_bytes": 41177798156, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-deployment/product/qk-fc2-timed-2.png", "nonfinite_values": 0, "step_seconds": [9.673605000134557, 9.988852082984522, 9.940544875105843, 10.32847441593185, 9.995755833107978, 9.889043750008568]}
-03:50:24 Conditioning cache hit: 82 effective tokens
-03:50:35 DiT step 1/6: 10.223s, 36.71 GiB active
-03:50:46 DiT step 2/6: 10.746s, 36.71 GiB active
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-03:51:29 VAE tiles 121/121
-03:51:29 Saved outputs/optimization-r3-deployment/portrait/control-warmup--1.png in 65.022s
-{"case": "portrait", "variant": "control", "repeat": -1, "phase": "warmup", "seconds": 65.02161391708069, "peak_memory_bytes": 43045591840, "mlx_peak_bytes": 40760121108, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-deployment/portrait/control-warmup--1.png", "nonfinite_values": 0, "step_seconds": [10.223438958171755, 10.746248750016093, 10.422004875028506, 10.444954250007868, 10.489227459067479, 10.591024375054985]}
-03:51:29 Conditioning cache hit: 82 effective tokens
-03:51:39 DiT step 1/6: 9.659s, 36.71 GiB active
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-03:52:30 VAE tiles 121/121
-03:52:30 Saved outputs/optimization-r3-deployment/portrait/qk-fc2-warmup--1.png in 61.093s
-{"case": "portrait", "variant": "qk-fc2", "repeat": -1, "phase": "warmup", "seconds": 61.0925478329882, "peak_memory_bytes": 43499969312, "mlx_peak_bytes": 41119701556, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-deployment/portrait/qk-fc2-warmup--1.png", "nonfinite_values": 0, "step_seconds": [9.659394290996715, 9.83391104103066, 9.865367457969114, 9.875216708052903, 9.90178812504746, 9.89776345808059]}
-03:52:31 Conditioning cache hit: 82 effective tokens
-03:52:41 DiT step 1/6: 10.344s, 36.71 GiB active
-03:52:52 DiT step 2/6: 10.484s, 36.71 GiB active
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-03:53:35 VAE tiles 121/121
-03:53:35 Saved outputs/optimization-r3-deployment/portrait/control-timed-0.png in 64.644s
-{"case": "portrait", "variant": "control", "repeat": 0, "phase": "timed", "seconds": 64.64417616720311, "peak_memory_bytes": 43071298312, "mlx_peak_bytes": 40760121908, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-deployment/portrait/control-timed-0.png", "nonfinite_values": 0, "step_seconds": [10.343989124987274, 10.484140957938507, 10.492746208095923, 10.494633583119139, 10.36117170797661, 10.398765416117385]}
-03:53:35 Conditioning cache hit: 82 effective tokens
-03:53:45 DiT step 1/6: 9.701s, 36.71 GiB active
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-03:54:36 VAE tiles 121/121
-03:54:36 Saved outputs/optimization-r3-deployment/portrait/qk-fc2-timed-0.png in 60.624s
-{"case": "portrait", "variant": "qk-fc2", "repeat": 0, "phase": "timed", "seconds": 60.62431841692887, "peak_memory_bytes": 43481553720, "mlx_peak_bytes": 41179601316, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-deployment/portrait/qk-fc2-timed-0.png", "nonfinite_values": 0, "step_seconds": [9.701315457932651, 9.964592250064015, 9.679471249924973, 9.77542870794423, 9.722767374943942, 9.726279291789979]}
-03:54:39 Conditioning cache hit: 82 effective tokens
-03:54:48 DiT step 1/6: 9.074s, 36.71 GiB active
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-03:55:39 VAE tiles 121/121
-03:55:39 Saved outputs/optimization-r3-deployment/portrait/qk-fc2-timed-1.png in 61.063s
-{"case": "portrait", "variant": "qk-fc2", "repeat": 1, "phase": "timed", "seconds": 61.06283883401193, "peak_memory_bytes": 43519646520, "mlx_peak_bytes": 41119701556, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-deployment/portrait/qk-fc2-timed-1.png", "nonfinite_values": 0, "step_seconds": [9.073689167154953, 9.6807163329795, 9.89693420799449, 9.90687695890665, 9.962622791994363, 9.991009167162701]}
-03:55:39 Conditioning cache hit: 82 effective tokens
-03:55:50 DiT step 1/6: 10.558s, 36.71 GiB active
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-03:56:44 VAE tiles 121/121
-03:56:44 Saved outputs/optimization-r3-deployment/portrait/control-timed-1.png in 64.914s
-{"case": "portrait", "variant": "control", "repeat": 1, "phase": "timed", "seconds": 64.91441170801409, "peak_memory_bytes": 42932624232, "mlx_peak_bytes": 40835029428, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-deployment/portrait/control-timed-1.png", "nonfinite_values": 0, "step_seconds": [10.558383875060827, 10.542425042018294, 10.432395208859816, 10.452659833012149, 10.422242624917999, 10.458984000142664]}
-03:56:45 Conditioning cache hit: 82 effective tokens
-03:56:55 DiT step 1/6: 10.113s, 36.71 GiB active
-03:57:06 DiT step 2/6: 10.445s, 36.71 GiB active
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-03:57:49 VAE tiles 121/121
-03:57:50 Saved outputs/optimization-r3-deployment/portrait/control-timed-2.png in 64.664s
-{"case": "portrait", "variant": "control", "repeat": 2, "phase": "timed", "seconds": 64.66372808301821, "peak_memory_bytes": 43044674408, "mlx_peak_bytes": 40903547416, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-deployment/portrait/control-timed-2.png", "nonfinite_values": 0, "step_seconds": [10.112921707797796, 10.445210125064477, 10.440206999890506, 10.410174916032702, 10.556556208990514, 10.62265295884572]}
-03:57:50 Conditioning cache hit: 82 effective tokens
-03:58:00 DiT step 1/6: 9.737s, 36.71 GiB active
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-03:58:51 VAE tiles 121/121
-03:58:51 Saved outputs/optimization-r3-deployment/portrait/qk-fc2-timed-2.png in 61.680s
-{"case": "portrait", "variant": "qk-fc2", "repeat": 2, "phase": "timed", "seconds": 61.68039979180321, "peak_memory_bytes": 43479440208, "mlx_peak_bytes": 41119701556, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-deployment/portrait/qk-fc2-timed-2.png", "nonfinite_values": 0, "step_seconds": [9.73736875015311, 9.856456334004179, 9.973663166165352, 9.926210709149018, 10.001038458198309, 10.09989216690883]}
-03:58:52 Conditioning cache hit: 98 effective tokens
-03:59:03 DiT step 1/6: 10.426s, 36.71 GiB active
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-03:59:58 VAE tiles 121/121
-03:59:58 Saved outputs/optimization-r3-deployment/holdout-detail/control-validation-0.png in 66.156s
-{"case": "holdout-detail", "variant": "control", "repeat": 0, "phase": "validation", "seconds": 66.1562814170029, "peak_memory_bytes": 42931149672, "mlx_peak_bytes": 40765758168, "output_sha256": "561b18aa708d61a5accbfd12f2280e8f7e303505324c43471ba06d60fe0351d5", "output": "outputs/optimization-r3-deployment/holdout-detail/control-validation-0.png", "nonfinite_values": 0, "step_seconds": [10.42564162495546, 10.77243841602467, 10.619838000042364, 10.630091832950711, 10.930967458989471, 10.638426082907245]}
-03:59:58 Conditioning cache hit: 98 effective tokens
-04:00:08 DiT step 1/6: 9.876s, 36.71 GiB active
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-04:00:59 VAE tiles 121/121
-04:00:59 Saved outputs/optimization-r3-deployment/holdout-detail/qk-fc2-validation-0.png in 60.607s
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diff --git a/reports/optimization-r3/deployment-trace-audit.json b/reports/optimization-r3/deployment-trace-audit.json
deleted file mode 100644
index 52bd3d8fb319a340b8adec724a55cc2bec5b87e6..0000000000000000000000000000000000000000
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diff --git a/reports/optimization-r3/deployment/comparison.json b/reports/optimization-r3/deployment/comparison.json
deleted file mode 100644
index 76b236d7cd7de882608e09a415c9096feb923e13..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/deployment/comparison.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
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diff --git a/reports/optimization-r3/deployment/control.receipt.json b/reports/optimization-r3/deployment/control.receipt.json
deleted file mode 100644
index 6319ed7798098c717c0b445a26b52dacbe301e4a..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/deployment/control.receipt.json
+++ /dev/null
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-{
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diff --git a/reports/optimization-r3/deployment/qk-fc2.receipt.json b/reports/optimization-r3/deployment/qk-fc2.receipt.json
deleted file mode 100644
index 9612379b0bdb1f0700af83d8c152896d2a129ac1..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/deployment/qk-fc2.receipt.json
+++ /dev/null
@@ -1,122 +0,0 @@
-{
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diff --git a/reports/optimization-r3/deployment/quality-audit.json b/reports/optimization-r3/deployment/quality-audit.json
deleted file mode 100644
index 08fadf4f873b4b3baaffdd0869e5bd742c56905f..0000000000000000000000000000000000000000
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diff --git a/reports/optimization-r3/deployment/summary.json b/reports/optimization-r3/deployment/summary.json
deleted file mode 100644
index 07a4df73abdacf9342d8daa7f656319901539bfd..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/deployment/summary.json
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@@ -1,62 +0,0 @@
-{
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diff --git a/reports/optimization-r3/embedding-campaign.log b/reports/optimization-r3/embedding-campaign.log
deleted file mode 100644
index 882710451e0a6edc9ead153b6fa9270030d95e9f..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding-campaign.log
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-{
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diff --git a/reports/optimization-r3/embedding-tests.log b/reports/optimization-r3/embedding-tests.log
deleted file mode 100644
index f8c7546d629387e85371aa4eb61141528fe0c60d..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding-tests.log
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-.... [100%]
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diff --git a/reports/optimization-r3/embedding/full-prepare-page-cache--1.json b/reports/optimization-r3/embedding/full-prepare-page-cache--1.json
deleted file mode 100644
index af5c54e77d37c1ba7c907795237e22d568c4678d..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/full-prepare-page-cache--1.json
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-{
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deleted file mode 100644
index d0bbfd9bde29025eaf946e8c56b10c662f64a21b..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/full-prepare-page-cache--1.log
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diff --git a/reports/optimization-r3/embedding/full-timed-0.json b/reports/optimization-r3/embedding/full-timed-0.json
deleted file mode 100644
index bc366884f1ff38cbdbc114a6d1f6293ad658ec05..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/full-timed-0.json
+++ /dev/null
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-{
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- "experiments.r3.embedding",
- "--encoder",
- "/Users/devin/Desktop/minimax-h3-image-opt/models/MiniMax-H3/text_encoder",
- "--mode",
- "full",
- "--output",
- "reports/optimization-r3/embedding/full-timed-0.json"
- ]
-}
diff --git a/reports/optimization-r3/embedding/full-timed-0.log b/reports/optimization-r3/embedding/full-timed-0.log
deleted file mode 100644
index eeb300fb4bc03739006557c239aadd09322db64e..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/full-timed-0.log
+++ /dev/null
@@ -1 +0,0 @@
-{"mode": "full", "seconds": 0.10779520799405873, "physical": {"sampled_peak_physical_bytes": 1635764000, "lifetime_peak_physical_bytes": 1635764000, "sample_interval_s": 0.001, "sample_count": 72, "scope": "Parent process only; includes Metal; excludes compiler XPC services. Counters overlap; do not sum."}, "mlx_peak_bytes": 1556726112, "output_sha256": "4acd76ed7d093f0a9c07c88a77295f93d7d20199b11a9a932707e321d2afbdbf", "shape": [88, 5120], "environment": {"platform": "macOS-27.0.1-arm64-arm-64bit", "machine": "arm64", "metal": {"device_name": "Apple M5 Pro", "max_recommended_working_set_size": 55662788608, "memory_size": 68719476736, "architecture": "applegpu_g17s", "max_buffer_length": 41747087360, "resource_limit": 499000}, "versions": {"mlx": "0.32.4", "mlx-metal": "0.32.4", "numpy": "2.5.4", "safetensors": "0.8.0", "transformers": "4.57.6", "torch": "2.15.0", "Pillow": "13.0.0", "scikit-image": "0.26.0"}, "precision_environment": {"MLX_ENABLE_TF32": "0", "MLX_CONV_WINOGRAD": "0"}}, "scope": "Cold process, page-cache warm tensor read + gather only. Excludes tokenizer and full 50-layer encoder."}
diff --git a/reports/optimization-r3/embedding/full-timed-1.json b/reports/optimization-r3/embedding/full-timed-1.json
deleted file mode 100644
index 845ff5e937061031e9d956036b5514a4fe005b8e..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/full-timed-1.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "mode": "full",
- "seconds": 0.10787329101003706,
- "physical": {
- "sampled_peak_physical_bytes": 1635731232,
- "lifetime_peak_physical_bytes": 1635731232,
- "sample_interval_s": 0.001,
- "sample_count": 71,
- "scope": "Parent process only; includes Metal; excludes compiler XPC services. Counters overlap; do not sum."
- },
- "mlx_peak_bytes": 1556726112,
- "output_sha256": "4acd76ed7d093f0a9c07c88a77295f93d7d20199b11a9a932707e321d2afbdbf",
- "shape": [
- 88,
- 5120
- ],
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "scope": "Cold process, page-cache warm tensor read + gather only. Excludes tokenizer and full 50-layer encoder.",
- "outer_seconds": 0.20390770817175508,
- "repeat": 1,
- "phase": "timed",
- "command": [
- "/Users/devin/Desktop/MiniMax-H3-Image-mlx/.venv/bin/python",
- "-m",
- "experiments.r3.embedding",
- "--encoder",
- "/Users/devin/Desktop/minimax-h3-image-opt/models/MiniMax-H3/text_encoder",
- "--mode",
- "full",
- "--output",
- "reports/optimization-r3/embedding/full-timed-1.json"
- ]
-}
diff --git a/reports/optimization-r3/embedding/full-timed-1.log b/reports/optimization-r3/embedding/full-timed-1.log
deleted file mode 100644
index 732a18a3ed78570575c45a048162c8e122004878..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/full-timed-1.log
+++ /dev/null
@@ -1 +0,0 @@
-{"mode": "full", "seconds": 0.10787329101003706, "physical": {"sampled_peak_physical_bytes": 1635731232, "lifetime_peak_physical_bytes": 1635731232, "sample_interval_s": 0.001, "sample_count": 71, "scope": "Parent process only; includes Metal; excludes compiler XPC services. Counters overlap; do not sum."}, "mlx_peak_bytes": 1556726112, "output_sha256": "4acd76ed7d093f0a9c07c88a77295f93d7d20199b11a9a932707e321d2afbdbf", "shape": [88, 5120], "environment": {"platform": "macOS-27.0.1-arm64-arm-64bit", "machine": "arm64", "metal": {"device_name": "Apple M5 Pro", "max_recommended_working_set_size": 55662788608, "memory_size": 68719476736, "architecture": "applegpu_g17s", "max_buffer_length": 41747087360, "resource_limit": 499000}, "versions": {"mlx": "0.32.4", "mlx-metal": "0.32.4", "numpy": "2.5.4", "safetensors": "0.8.0", "transformers": "4.57.6", "torch": "2.15.0", "Pillow": "13.0.0", "scikit-image": "0.26.0"}, "precision_environment": {"MLX_ENABLE_TF32": "0", "MLX_CONV_WINOGRAD": "0"}}, "scope": "Cold process, page-cache warm tensor read + gather only. Excludes tokenizer and full 50-layer encoder."}
diff --git a/reports/optimization-r3/embedding/full-timed-2.json b/reports/optimization-r3/embedding/full-timed-2.json
deleted file mode 100644
index 76872e374aa97966898b125d5a4a5bdf8d25a569..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/full-timed-2.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "mode": "full",
- "seconds": 0.10731062502600253,
- "physical": {
- "sampled_peak_physical_bytes": 1635845968,
- "lifetime_peak_physical_bytes": 1635878736,
- "sample_interval_s": 0.001,
- "sample_count": 71,
- "scope": "Parent process only; includes Metal; excludes compiler XPC services. Counters overlap; do not sum."
- },
- "mlx_peak_bytes": 1556726112,
- "output_sha256": "4acd76ed7d093f0a9c07c88a77295f93d7d20199b11a9a932707e321d2afbdbf",
- "shape": [
- 88,
- 5120
- ],
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "scope": "Cold process, page-cache warm tensor read + gather only. Excludes tokenizer and full 50-layer encoder.",
- "outer_seconds": 0.20484258304350078,
- "repeat": 2,
- "phase": "timed",
- "command": [
- "/Users/devin/Desktop/MiniMax-H3-Image-mlx/.venv/bin/python",
- "-m",
- "experiments.r3.embedding",
- "--encoder",
- "/Users/devin/Desktop/minimax-h3-image-opt/models/MiniMax-H3/text_encoder",
- "--mode",
- "full",
- "--output",
- "reports/optimization-r3/embedding/full-timed-2.json"
- ]
-}
diff --git a/reports/optimization-r3/embedding/full-timed-2.log b/reports/optimization-r3/embedding/full-timed-2.log
deleted file mode 100644
index 40a01aa852367e7c928fa6b67b5e3af86107d545..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/full-timed-2.log
+++ /dev/null
@@ -1 +0,0 @@
-{"mode": "full", "seconds": 0.10731062502600253, "physical": {"sampled_peak_physical_bytes": 1635845968, "lifetime_peak_physical_bytes": 1635878736, "sample_interval_s": 0.001, "sample_count": 71, "scope": "Parent process only; includes Metal; excludes compiler XPC services. Counters overlap; do not sum."}, "mlx_peak_bytes": 1556726112, "output_sha256": "4acd76ed7d093f0a9c07c88a77295f93d7d20199b11a9a932707e321d2afbdbf", "shape": [88, 5120], "environment": {"platform": "macOS-27.0.1-arm64-arm-64bit", "machine": "arm64", "metal": {"device_name": "Apple M5 Pro", "max_recommended_working_set_size": 55662788608, "memory_size": 68719476736, "architecture": "applegpu_g17s", "max_buffer_length": 41747087360, "resource_limit": 499000}, "versions": {"mlx": "0.32.4", "mlx-metal": "0.32.4", "numpy": "2.5.4", "safetensors": "0.8.0", "transformers": "4.57.6", "torch": "2.15.0", "Pillow": "13.0.0", "scikit-image": "0.26.0"}, "precision_environment": {"MLX_ENABLE_TF32": "0", "MLX_CONV_WINOGRAD": "0"}}, "scope": "Cold process, page-cache warm tensor read + gather only. Excludes tokenizer and full 50-layer encoder."}
diff --git a/reports/optimization-r3/embedding/rows-timed-0.json b/reports/optimization-r3/embedding/rows-timed-0.json
deleted file mode 100644
index 119d0f9073cce36f79d4b708752936a3910a5820..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/rows-timed-0.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "mode": "rows",
- "seconds": 0.0038837501779198647,
- "physical": {
- "sampled_peak_physical_bytes": 32178872,
- "lifetime_peak_physical_bytes": 32178872,
- "sample_interval_s": 0.001,
- "sample_count": 3,
- "scope": "Parent process only; includes Metal; excludes compiler XPC services. Counters overlap; do not sum."
- },
- "mlx_peak_bytes": 901120,
- "output_sha256": "4acd76ed7d093f0a9c07c88a77295f93d7d20199b11a9a932707e321d2afbdbf",
- "shape": [
- 88,
- 5120
- ],
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "scope": "Cold process, page-cache warm tensor read + gather only. Excludes tokenizer and full 50-layer encoder.",
- "outer_seconds": 0.0941844170447439,
- "repeat": 0,
- "phase": "timed",
- "command": [
- "/Users/devin/Desktop/MiniMax-H3-Image-mlx/.venv/bin/python",
- "-m",
- "experiments.r3.embedding",
- "--encoder",
- "/Users/devin/Desktop/minimax-h3-image-opt/models/MiniMax-H3/text_encoder",
- "--mode",
- "rows",
- "--output",
- "reports/optimization-r3/embedding/rows-timed-0.json"
- ]
-}
diff --git a/reports/optimization-r3/embedding/rows-timed-0.log b/reports/optimization-r3/embedding/rows-timed-0.log
deleted file mode 100644
index dcfcca2e4347536923ae794e8de2880cab301e82..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/rows-timed-0.log
+++ /dev/null
@@ -1 +0,0 @@
-{"mode": "rows", "seconds": 0.0038837501779198647, "physical": {"sampled_peak_physical_bytes": 32178872, "lifetime_peak_physical_bytes": 32178872, "sample_interval_s": 0.001, "sample_count": 3, "scope": "Parent process only; includes Metal; excludes compiler XPC services. Counters overlap; do not sum."}, "mlx_peak_bytes": 901120, "output_sha256": "4acd76ed7d093f0a9c07c88a77295f93d7d20199b11a9a932707e321d2afbdbf", "shape": [88, 5120], "environment": {"platform": "macOS-27.0.1-arm64-arm-64bit", "machine": "arm64", "metal": {"device_name": "Apple M5 Pro", "max_recommended_working_set_size": 55662788608, "memory_size": 68719476736, "architecture": "applegpu_g17s", "max_buffer_length": 41747087360, "resource_limit": 499000}, "versions": {"mlx": "0.32.4", "mlx-metal": "0.32.4", "numpy": "2.5.4", "safetensors": "0.8.0", "transformers": "4.57.6", "torch": "2.15.0", "Pillow": "13.0.0", "scikit-image": "0.26.0"}, "precision_environment": {"MLX_ENABLE_TF32": "0", "MLX_CONV_WINOGRAD": "0"}}, "scope": "Cold process, page-cache warm tensor read + gather only. Excludes tokenizer and full 50-layer encoder."}
diff --git a/reports/optimization-r3/embedding/rows-timed-1.json b/reports/optimization-r3/embedding/rows-timed-1.json
deleted file mode 100644
index 9d20338a66b61f990b17d67ec0e49eb6f702cecd..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/rows-timed-1.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "mode": "rows",
- "seconds": 0.004945333115756512,
- "physical": {
- "sampled_peak_physical_bytes": 32080568,
- "lifetime_peak_physical_bytes": 32080568,
- "sample_interval_s": 0.001,
- "sample_count": 3,
- "scope": "Parent process only; includes Metal; excludes compiler XPC services. Counters overlap; do not sum."
- },
- "mlx_peak_bytes": 901120,
- "output_sha256": "4acd76ed7d093f0a9c07c88a77295f93d7d20199b11a9a932707e321d2afbdbf",
- "shape": [
- 88,
- 5120
- ],
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "scope": "Cold process, page-cache warm tensor read + gather only. Excludes tokenizer and full 50-layer encoder.",
- "outer_seconds": 0.09497620817273855,
- "repeat": 1,
- "phase": "timed",
- "command": [
- "/Users/devin/Desktop/MiniMax-H3-Image-mlx/.venv/bin/python",
- "-m",
- "experiments.r3.embedding",
- "--encoder",
- "/Users/devin/Desktop/minimax-h3-image-opt/models/MiniMax-H3/text_encoder",
- "--mode",
- "rows",
- "--output",
- "reports/optimization-r3/embedding/rows-timed-1.json"
- ]
-}
diff --git a/reports/optimization-r3/embedding/rows-timed-1.log b/reports/optimization-r3/embedding/rows-timed-1.log
deleted file mode 100644
index 1f4c69d8ae0aae9603183d71dd91270379234f6b..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/rows-timed-1.log
+++ /dev/null
@@ -1 +0,0 @@
-{"mode": "rows", "seconds": 0.004945333115756512, "physical": {"sampled_peak_physical_bytes": 32080568, "lifetime_peak_physical_bytes": 32080568, "sample_interval_s": 0.001, "sample_count": 3, "scope": "Parent process only; includes Metal; excludes compiler XPC services. Counters overlap; do not sum."}, "mlx_peak_bytes": 901120, "output_sha256": "4acd76ed7d093f0a9c07c88a77295f93d7d20199b11a9a932707e321d2afbdbf", "shape": [88, 5120], "environment": {"platform": "macOS-27.0.1-arm64-arm-64bit", "machine": "arm64", "metal": {"device_name": "Apple M5 Pro", "max_recommended_working_set_size": 55662788608, "memory_size": 68719476736, "architecture": "applegpu_g17s", "max_buffer_length": 41747087360, "resource_limit": 499000}, "versions": {"mlx": "0.32.4", "mlx-metal": "0.32.4", "numpy": "2.5.4", "safetensors": "0.8.0", "transformers": "4.57.6", "torch": "2.15.0", "Pillow": "13.0.0", "scikit-image": "0.26.0"}, "precision_environment": {"MLX_ENABLE_TF32": "0", "MLX_CONV_WINOGRAD": "0"}}, "scope": "Cold process, page-cache warm tensor read + gather only. Excludes tokenizer and full 50-layer encoder."}
diff --git a/reports/optimization-r3/embedding/rows-timed-2.json b/reports/optimization-r3/embedding/rows-timed-2.json
deleted file mode 100644
index f4dd6f57b0ff55348eb6725d1d25757cd4a33f6b..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/rows-timed-2.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "mode": "rows",
- "seconds": 0.005294707836583257,
- "physical": {
- "sampled_peak_physical_bytes": 32211640,
- "lifetime_peak_physical_bytes": 32244408,
- "sample_interval_s": 0.001,
- "sample_count": 3,
- "scope": "Parent process only; includes Metal; excludes compiler XPC services. Counters overlap; do not sum."
- },
- "mlx_peak_bytes": 901120,
- "output_sha256": "4acd76ed7d093f0a9c07c88a77295f93d7d20199b11a9a932707e321d2afbdbf",
- "shape": [
- 88,
- 5120
- ],
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "scope": "Cold process, page-cache warm tensor read + gather only. Excludes tokenizer and full 50-layer encoder.",
- "outer_seconds": 0.09905358287505805,
- "repeat": 2,
- "phase": "timed",
- "command": [
- "/Users/devin/Desktop/MiniMax-H3-Image-mlx/.venv/bin/python",
- "-m",
- "experiments.r3.embedding",
- "--encoder",
- "/Users/devin/Desktop/minimax-h3-image-opt/models/MiniMax-H3/text_encoder",
- "--mode",
- "rows",
- "--output",
- "reports/optimization-r3/embedding/rows-timed-2.json"
- ]
-}
diff --git a/reports/optimization-r3/embedding/rows-timed-2.log b/reports/optimization-r3/embedding/rows-timed-2.log
deleted file mode 100644
index 89b91b3ffe7a2390397f7823d3a8f610b0d658db..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/rows-timed-2.log
+++ /dev/null
@@ -1 +0,0 @@
-{"mode": "rows", "seconds": 0.005294707836583257, "physical": {"sampled_peak_physical_bytes": 32211640, "lifetime_peak_physical_bytes": 32244408, "sample_interval_s": 0.001, "sample_count": 3, "scope": "Parent process only; includes Metal; excludes compiler XPC services. Counters overlap; do not sum."}, "mlx_peak_bytes": 901120, "output_sha256": "4acd76ed7d093f0a9c07c88a77295f93d7d20199b11a9a932707e321d2afbdbf", "shape": [88, 5120], "environment": {"platform": "macOS-27.0.1-arm64-arm-64bit", "machine": "arm64", "metal": {"device_name": "Apple M5 Pro", "max_recommended_working_set_size": 55662788608, "memory_size": 68719476736, "architecture": "applegpu_g17s", "max_buffer_length": 41747087360, "resource_limit": 499000}, "versions": {"mlx": "0.32.4", "mlx-metal": "0.32.4", "numpy": "2.5.4", "safetensors": "0.8.0", "transformers": "4.57.6", "torch": "2.15.0", "Pillow": "13.0.0", "scikit-image": "0.26.0"}, "precision_environment": {"MLX_ENABLE_TF32": "0", "MLX_CONV_WINOGRAD": "0"}}, "scope": "Cold process, page-cache warm tensor read + gather only. Excludes tokenizer and full 50-layer encoder."}
diff --git a/reports/optimization-r3/embedding/summary.json b/reports/optimization-r3/embedding/summary.json
deleted file mode 100644
index 882710451e0a6edc9ead153b6fa9270030d95e9f..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/embedding/summary.json
+++ /dev/null
@@ -1,26 +0,0 @@
-{
- "all_bits_identical": true,
- "scope": "Embedding read and gather only, fresh processes, page cache prepared. Not a complete encoder or generation speedup.",
- "variants": {
- "full": {
- "samples_s": [
- 0.10779520799405873,
- 0.10787329101003706,
- 0.10731062502600253
- ],
- "median_s": 0.10779520799405873,
- "peak_physical_bytes": 1635845968,
- "peak_mlx_bytes": 1556726112
- },
- "rows": {
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- "peak_physical_bytes": 32211640,
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- }
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-}
diff --git a/reports/optimization-r3/fc1-compile-failure.json b/reports/optimization-r3/fc1-compile-failure.json
deleted file mode 100644
index e877e18240861bf4ae05c6f838c7eae139ef81f3..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/fc1-compile-failure.json
+++ /dev/null
@@ -1,103 +0,0 @@
-{
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "scope": "Real FC1 activations; fused dual GEMM + native precise sigmoid/BF16 boundaries. Compilation excluded, materialization included.",
- "trials": [
- {
- "tile": [
- 64,
- 64,
- 128
- ],
- "simdgroups": 4,
- "error": "RuntimeError: [metal::Device] Unable to build metal library from source\nmlx/backend/metal/kernels/utils.h:565:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:565:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\n\n"
- },
- {
- "tile": [
- 64,
- 64,
- 256
- ],
- "simdgroups": 4,
- "error": "RuntimeError: [metal::Device] Unable to build metal library from source\nmlx/backend/metal/kernels/utils.h:565:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:565:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\n\n"
- },
- {
- "tile": [
- 64,
- 128,
- 128
- ],
- "simdgroups": 4,
- "error": "RuntimeError: [metal::Device] Unable to build metal library from source\nmlx/backend/metal/kernels/utils.h:565:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:565:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\n\n"
- },
- {
- "tile": [
- 64,
- 128,
- 256
- ],
- "simdgroups": 4,
- "error": "RuntimeError: [metal::Device] Unable to build metal library from source\nmlx/backend/metal/kernels/utils.h:565:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:565:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\n\n"
- },
- {
- "tile": [
- 64,
- 128,
- 128
- ],
- "simdgroups": 8,
- "error": "RuntimeError: [metal::Device] Unable to build metal library from source\nmlx/backend/metal/kernels/utils.h:565:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:565:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\n\n"
- },
- {
- "tile": [
- 64,
- 128,
- 256
- ],
- "simdgroups": 8,
- "error": "RuntimeError: [metal::Device] Unable to build metal library from source\nmlx/backend/metal/kernels/utils.h:565:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:565:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\n\n"
- },
- {
- "tile": [
- 128,
- 128,
- 128
- ],
- "simdgroups": 8,
- "error": "RuntimeError: [metal::Device] Unable to build metal library from source\nmlx/backend/metal/kernels/utils.h:565:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:565:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\n\n"
- },
- {
- "tile": [
- 128,
- 128,
- 256
- ],
- "simdgroups": 8,
- "error": "RuntimeError: [metal::Device] Unable to build metal library from source\nmlx/backend/metal/kernels/utils.h:565:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:565:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: use of undeclared identifier 'full'; did you mean 'all'?\n #pragma unroll full\n ^~~~\n all\n/System/Library/PrivateFrameworks/GPUCompiler.framework/Versions/32023/Libraries/lib/clang/32023/include/metal/metal_relational:734:17: note: 'all' declared here\nMETAL_FUNC bool all(bool x)\n ^\nmlx/backend/metal/kernels/utils.h:575:24: error: invalid argument of type 'bool (bool)'; expected an integer type\n #pragma unroll full\n ^\n\n"
- }
- ]
-}
diff --git a/reports/optimization-r3/fc1.json b/reports/optimization-r3/fc1.json
deleted file mode 100644
index cdb01b3f8b554d38379c8f3931413bf6f622b577..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/fc1.json
+++ /dev/null
@@ -1,511 +0,0 @@
-{
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "scope": "Real FC1 activations; fused dual GEMM + native precise sigmoid/BF16 boundaries. Compilation excluded, materialization included.",
- "trials": [
- {
- "tile": [
- 64,
- 64,
- 128
- ],
- "simdgroups": 4,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0,
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- "control",
- "candidate",
- "candidate",
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- "candidate",
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- "control",
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- "candidate",
- "control"
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- "control": {
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- "min_ms": 53.88062517158687,
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- ]
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- "candidate": {
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diff --git a/reports/optimization-r3/fc1.log b/reports/optimization-r3/fc1.log
deleted file mode 100644
index 3e9c2bae1a25c3a8ad520feb303ef049a1bef956..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/fc1.log
+++ /dev/null
@@ -1,8 +0,0 @@
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diff --git a/reports/optimization-r3/images.log b/reports/optimization-r3/images.log
deleted file mode 100644
index 17bb7e0a8aba6da68b1d1f4e468cb34017e715c5..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/images.log
+++ /dev/null
@@ -1,250 +0,0 @@
-03:04:06 Conditioning cache hit: 85 effective tokens
-03:04:06 Loading cached native AdaLN schedule cache/adaln-4dda935938d45a975c179220.safetensors
-03:04:22 DiT step 1/6: 15.381s, 35.95 GiB active
-03:04:32 DiT step 2/6: 10.495s, 35.96 GiB active
-03:04:43 DiT step 3/6: 10.931s, 35.96 GiB active
-03:04:54 DiT step 4/6: 10.801s, 35.96 GiB active
-03:05:04 DiT step 5/6: 10.701s, 35.97 GiB active
-03:05:15 DiT step 6/6: 10.931s, 35.97 GiB active
-03:05:18 VAE tiles 121/121
-03:05:18 Saved outputs/optimization-r3/product/control-cold--1.png in 71.881s
-{"case": "product", "variant": "control", "repeat": -1, "phase": "cold", "seconds": 71.88108945800923, "peak_memory_bytes": 41023231816, "mlx_peak_bytes": 40352740044, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3/product/control-cold--1.png", "nonfinite_values": 0, "step_seconds": [15.38127875002101, 10.495420958148316, 10.931075125001371, 10.80113216699101, 10.700902125099674, 10.93087162496522]}
-03:05:18 Conditioning cache hit: 85 effective tokens
-03:05:28 DiT step 1/6: 10.279s, 36.71 GiB active
-03:05:39 DiT step 2/6: 10.563s, 36.71 GiB active
-03:05:50 DiT step 3/6: 10.621s, 36.71 GiB active
-03:06:00 DiT step 4/6: 10.538s, 36.71 GiB active
-03:06:11 DiT step 5/6: 10.626s, 36.72 GiB active
-03:06:21 DiT step 6/6: 10.609s, 36.72 GiB active
-03:06:23 VAE tiles 121/121
-03:06:23 Saved outputs/optimization-r3/product/qk-fc2-warmup--1.png in 65.456s
-{"case": "product", "variant": "qk-fc2", "repeat": -1, "phase": "warmup", "seconds": 65.45604600012302, "peak_memory_bytes": 41405339560, "mlx_peak_bytes": 40731251916, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3/product/qk-fc2-warmup--1.png", "nonfinite_values": 0, "step_seconds": [10.27856070897542, 10.562935125082731, 10.621015165932477, 10.538033208111301, 10.62600470916368, 10.609485917026177]}
-03:06:26 Conditioning cache hit: 85 effective tokens
-03:06:37 DiT step 1/6: 10.232s, 36.71 GiB active
-03:06:48 DiT step 2/6: 11.155s, 36.71 GiB active
-03:06:59 DiT step 3/6: 11.182s, 36.71 GiB active
-03:07:10 DiT step 4/6: 11.191s, 36.72 GiB active
-03:07:22 DiT step 5/6: 11.266s, 36.72 GiB active
-03:07:33 DiT step 6/6: 11.148s, 36.72 GiB active
-03:07:34 VAE tiles 121/121
-03:07:35 Saved outputs/optimization-r3/product/control-timed-0.png in 68.599s
-{"case": "product", "variant": "control", "repeat": 0, "phase": "timed", "seconds": 68.59933650004677, "peak_memory_bytes": 42686339608, "mlx_peak_bytes": 40836142240, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3/product/control-timed-0.png", "nonfinite_values": 0, "step_seconds": [10.232307167025283, 11.155369125073776, 11.181794625008479, 11.19105491694063, 11.266168375033885, 11.14753987500444]}
-03:07:35 Conditioning cache hit: 85 effective tokens
-03:07:45 DiT step 1/6: 10.573s, 36.71 GiB active
-03:07:56 DiT step 2/6: 10.765s, 36.71 GiB active
-03:08:07 DiT step 3/6: 10.744s, 36.71 GiB active
-03:08:18 DiT step 4/6: 10.554s, 36.72 GiB active
-03:08:28 DiT step 5/6: 10.528s, 36.72 GiB active
-03:08:39 DiT step 6/6: 10.615s, 36.72 GiB active
-03:08:40 VAE tiles 121/121
-03:08:41 Saved outputs/optimization-r3/product/qk-fc2-timed-0.png in 65.933s
-{"case": "product", "variant": "qk-fc2", "repeat": 0, "phase": "timed", "seconds": 65.93319679214619, "peak_memory_bytes": 42287602224, "mlx_peak_bytes": 40731251916, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3/product/qk-fc2-timed-0.png", "nonfinite_values": 0, "step_seconds": [10.573326792102307, 10.764806583058089, 10.743913582991809, 10.55372745799832, 10.52767491596751, 10.614938125014305]}
-03:08:44 Conditioning cache hit: 85 effective tokens
-03:08:53 DiT step 1/6: 9.328s, 36.71 GiB active
-03:09:04 DiT step 2/6: 10.417s, 36.71 GiB active
-03:09:14 DiT step 3/6: 10.334s, 36.71 GiB active
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-03:09:35 DiT step 5/6: 10.444s, 36.72 GiB active
-03:09:45 DiT step 6/6: 10.416s, 36.72 GiB active
-03:09:47 VAE tiles 121/121
-03:09:47 Saved outputs/optimization-r3/product/qk-fc2-timed-1.png in 64.017s
-{"case": "product", "variant": "qk-fc2", "repeat": 1, "phase": "timed", "seconds": 64.01737074996345, "peak_memory_bytes": 42329938480, "mlx_peak_bytes": 40731251916, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3/product/qk-fc2-timed-1.png", "nonfinite_values": 0, "step_seconds": [9.327726167161018, 10.41661983402446, 10.333738416898996, 10.428783542010933, 10.443926375126466, 10.415708124870434]}
-03:09:47 Conditioning cache hit: 85 effective tokens
-03:09:58 DiT step 1/6: 10.867s, 36.71 GiB active
-03:10:09 DiT step 2/6: 11.098s, 36.71 GiB active
-03:10:20 DiT step 3/6: 10.960s, 36.71 GiB active
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-03:10:42 DiT step 5/6: 10.916s, 36.72 GiB active
-03:10:53 DiT step 6/6: 10.796s, 36.72 GiB active
-03:10:55 VAE tiles 121/121
-03:10:55 Saved outputs/optimization-r3/product/control-timed-1.png in 67.678s
-{"case": "product", "variant": "control", "repeat": 1, "phase": "timed", "seconds": 67.6780596668832, "peak_memory_bytes": 42925644384, "mlx_peak_bytes": 40761185480, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3/product/control-timed-1.png", "nonfinite_values": 0, "step_seconds": [10.867017999989912, 11.097974750213325, 10.9604552090168, 10.893143624998629, 10.916360415983945, 10.795650250045583]}
-03:10:56 Conditioning cache hit: 85 effective tokens
-03:11:06 DiT step 1/6: 10.350s, 36.71 GiB active
-03:11:17 DiT step 2/6: 10.759s, 36.71 GiB active
-03:11:28 DiT step 3/6: 10.788s, 36.71 GiB active
-03:11:38 DiT step 4/6: 10.724s, 36.71 GiB active
-03:11:49 DiT step 5/6: 10.768s, 36.72 GiB active
-03:12:00 DiT step 6/6: 10.648s, 36.72 GiB active
-03:12:01 VAE tiles 121/121
-03:12:02 Saved outputs/optimization-r3/product/control-timed-2.png in 66.156s
-{"case": "product", "variant": "control", "repeat": 2, "phase": "timed", "seconds": 66.15550529095344, "peak_memory_bytes": 42964867704, "mlx_peak_bytes": 40836142216, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3/product/control-timed-2.png", "nonfinite_values": 0, "step_seconds": [10.350283042062074, 10.759192750090733, 10.788403084035963, 10.724042082903907, 10.76825154083781, 10.648499625036493]}
-03:12:02 Conditioning cache hit: 85 effective tokens
-03:12:12 DiT step 1/6: 10.049s, 36.71 GiB active
-03:12:22 DiT step 2/6: 10.214s, 36.71 GiB active
-03:12:32 DiT step 3/6: 10.270s, 36.71 GiB active
-03:12:43 DiT step 4/6: 10.387s, 36.72 GiB active
-03:12:53 DiT step 5/6: 10.474s, 36.72 GiB active
-03:13:04 DiT step 6/6: 10.462s, 36.72 GiB active
-03:13:05 VAE tiles 121/121
-03:13:06 Saved outputs/optimization-r3/product/qk-fc2-timed-2.png in 63.998s
-{"case": "product", "variant": "qk-fc2", "repeat": 2, "phase": "timed", "seconds": 63.998171125072986, "peak_memory_bytes": 42437024448, "mlx_peak_bytes": 40731251916, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3/product/qk-fc2-timed-2.png", "nonfinite_values": 0, "step_seconds": [10.048932957928628, 10.214330125134438, 10.270042832940817, 10.386969999875873, 10.473770290846005, 10.46236433298327]}
-03:13:07 Conditioning cache hit: 82 effective tokens
-03:13:17 DiT step 1/6: 10.414s, 36.71 GiB active
-03:13:28 DiT step 2/6: 10.767s, 36.71 GiB active
-03:13:39 DiT step 3/6: 10.739s, 36.71 GiB active
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-03:14:00 DiT step 5/6: 10.839s, 36.72 GiB active
-03:14:11 DiT step 6/6: 10.818s, 36.72 GiB active
-03:14:13 VAE tiles 121/121
-03:14:13 Saved outputs/optimization-r3/portrait/control-warmup--1.png in 66.501s
-{"case": "portrait", "variant": "control", "repeat": -1, "phase": "warmup", "seconds": 66.50067662494257, "peak_memory_bytes": 42928413400, "mlx_peak_bytes": 40760120608, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3/portrait/control-warmup--1.png", "nonfinite_values": 0, "step_seconds": [10.414454083191231, 10.767468499951065, 10.738615582929924, 10.799391249893233, 10.839179374976084, 10.817673500161618]}
-03:14:13 Conditioning cache hit: 82 effective tokens
-03:14:23 DiT step 1/6: 10.169s, 36.71 GiB active
-03:14:34 DiT step 2/6: 10.406s, 36.71 GiB active
-03:14:44 DiT step 3/6: 10.413s, 36.71 GiB active
-03:14:54 DiT step 4/6: 10.333s, 36.71 GiB active
-03:15:05 DiT step 5/6: 10.290s, 36.72 GiB active
-03:15:15 DiT step 6/6: 10.427s, 36.72 GiB active
-03:15:17 VAE tiles 121/121
-03:15:17 Saved outputs/optimization-r3/portrait/qk-fc2-warmup--1.png in 64.191s
-{"case": "portrait", "variant": "qk-fc2", "repeat": -1, "phase": "warmup", "seconds": 64.1909431249369, "peak_memory_bytes": 42697366304, "mlx_peak_bytes": 40903562708, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3/portrait/qk-fc2-warmup--1.png", "nonfinite_values": 0, "step_seconds": [10.168502958957106, 10.405522250104696, 10.41272783302702, 10.332954499870539, 10.289986541960388, 10.426924583967775]}
-03:15:17 Conditioning cache hit: 82 effective tokens
-03:15:28 DiT step 1/6: 10.886s, 36.71 GiB active
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-03:16:25 Saved outputs/optimization-r3/portrait/control-timed-0.png in 68.022s
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-03:16:25 Conditioning cache hit: 82 effective tokens
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-03:17:28 VAE tiles 121/121
-03:17:28 Saved outputs/optimization-r3/portrait/qk-fc2-timed-0.png in 63.147s
-{"case": "portrait", "variant": "qk-fc2", "repeat": 0, "phase": "timed", "seconds": 63.14736045780592, "peak_memory_bytes": 42692877208, "mlx_peak_bytes": 40730220112, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3/portrait/qk-fc2-timed-0.png", "nonfinite_values": 0, "step_seconds": [10.343852832913399, 10.311937124934047, 10.07040312513709, 10.080784375080839, 10.095044541871175, 10.125651291804388]}
-03:17:31 Conditioning cache hit: 82 effective tokens
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-03:18:34 VAE tiles 121/121
-03:18:34 Saved outputs/optimization-r3/portrait/qk-fc2-timed-1.png in 63.367s
-{"case": "portrait", "variant": "qk-fc2", "repeat": 1, "phase": "timed", "seconds": 63.36688454193063, "peak_memory_bytes": 42690911152, "mlx_peak_bytes": 40900384316, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3/portrait/qk-fc2-timed-1.png", "nonfinite_values": 0, "step_seconds": [9.240180917084217, 10.322078374912962, 10.228034707950428, 10.350710666039959, 10.351933625061065, 10.238879750017077]}
-03:18:34 Conditioning cache hit: 82 effective tokens
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-03:18:56 DiT step 2/6: 10.694s, 36.71 GiB active
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-03:19:40 VAE tiles 121/121
-03:19:40 Saved outputs/optimization-r3/portrait/control-timed-1.png in 66.101s
-{"case": "portrait", "variant": "control", "repeat": 1, "phase": "timed", "seconds": 66.10143391694874, "peak_memory_bytes": 43080047536, "mlx_peak_bytes": 40819135904, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3/portrait/control-timed-1.png", "nonfinite_values": 0, "step_seconds": [10.560056417016312, 10.694475375115871, 10.76400970807299, 10.616949083050713, 10.67808008310385, 10.68667491688393]}
-03:19:41 Conditioning cache hit: 82 effective tokens
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-03:20:47 VAE tiles 121/121
-03:20:47 Saved outputs/optimization-r3/portrait/control-timed-2.png in 66.395s
-{"case": "portrait", "variant": "control", "repeat": 2, "phase": "timed", "seconds": 66.39527866686694, "peak_memory_bytes": 42926791600, "mlx_peak_bytes": 40835028428, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3/portrait/control-timed-2.png", "nonfinite_values": 0, "step_seconds": [10.53064745804295, 11.14463270897977, 10.842613791115582, 10.565500583034009, 10.513344791950658, 10.671420915983617]}
-03:20:48 Conditioning cache hit: 82 effective tokens
-03:20:58 DiT step 1/6: 9.933s, 36.71 GiB active
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-03:21:50 VAE tiles 121/121
-03:21:51 Saved outputs/optimization-r3/portrait/qk-fc2-timed-2.png in 63.249s
-{"case": "portrait", "variant": "qk-fc2", "repeat": 2, "phase": "timed", "seconds": 63.248574083903804, "peak_memory_bytes": 42674756528, "mlx_peak_bytes": 40730220112, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3/portrait/qk-fc2-timed-2.png", "nonfinite_values": 0, "step_seconds": [9.932632416021079, 10.11093954113312, 10.21475570788607, 10.205349916126579, 10.297738917171955, 10.362275833962485]}
-03:21:54 Text encoder 10/50
-03:21:56 Text encoder 20/50
-03:21:58 Text encoder 30/50
-03:22:00 Text encoder 40/50
-03:22:02 Text encoder 50/50
-03:22:12 DiT step 1/6: 9.417s, 36.76 GiB active
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-03:23:08 VAE tiles 121/121
-03:23:08 Saved outputs/optimization-r3/holdout-detail/control-warmup--1.png in 76.453s
-{"case": "holdout-detail", "variant": "control", "repeat": -1, "phase": "warmup", "seconds": 76.45314999995753, "peak_memory_bytes": 43234991504, "mlx_peak_bytes": 41048584124, "output_sha256": "561b18aa708d61a5accbfd12f2280e8f7e303505324c43471ba06d60fe0351d5", "output": "outputs/optimization-r3/holdout-detail/control-warmup--1.png", "nonfinite_values": 0, "step_seconds": [9.416530583053827, 9.788487208075821, 10.603374124970287, 11.22392320795916, 11.056711124954745, 11.022258040960878]}
-03:23:08 Conditioning cache hit: 98 effective tokens
-03:23:18 DiT step 1/6: 10.296s, 36.76 GiB active
-03:23:29 DiT step 2/6: 10.309s, 36.76 GiB active
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-03:24:11 VAE tiles 121/121
-03:24:12 Saved outputs/optimization-r3/holdout-detail/qk-fc2-warmup--1.png in 63.806s
-{"case": "holdout-detail", "variant": "qk-fc2", "repeat": -1, "phase": "warmup", "seconds": 63.80645116697997, "peak_memory_bytes": 42756955128, "mlx_peak_bytes": 40790813728, "output_sha256": "561b18aa708d61a5accbfd12f2280e8f7e303505324c43471ba06d60fe0351d5", "output": "outputs/optimization-r3/holdout-detail/qk-fc2-warmup--1.png", "nonfinite_values": 0, "step_seconds": [10.295682708034292, 10.309457333059981, 10.143778291996568, 10.246140707982704, 10.29201937513426, 10.354217084124684]}
-03:24:12 Conditioning cache hit: 98 effective tokens
-03:24:22 DiT step 1/6: 10.619s, 36.76 GiB active
-03:24:33 DiT step 2/6: 10.636s, 36.76 GiB active
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-03:25:16 DiT step 6/6: 10.629s, 36.77 GiB active
-03:25:17 VAE tiles 121/121
-03:25:18 Saved outputs/optimization-r3/holdout-detail/control-validation-0.png in 65.847s
-{"case": "holdout-detail", "variant": "control", "repeat": 0, "phase": "validation", "seconds": 65.84704962512478, "peak_memory_bytes": 43195964408, "mlx_peak_bytes": 40886446984, "output_sha256": "561b18aa708d61a5accbfd12f2280e8f7e303505324c43471ba06d60fe0351d5", "output": "outputs/optimization-r3/holdout-detail/control-validation-0.png", "nonfinite_values": 0, "step_seconds": [10.619318875018507, 10.635955666890368, 10.632296457886696, 10.644406750099733, 10.566011917078868, 10.629142459016293]}
-03:25:18 Conditioning cache hit: 98 effective tokens
-03:25:28 DiT step 1/6: 9.848s, 36.76 GiB active
-03:25:38 DiT step 2/6: 9.983s, 36.76 GiB active
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-03:26:20 VAE tiles 121/121
-03:26:20 Saved outputs/optimization-r3/holdout-detail/qk-fc2-validation-0.png in 62.687s
-{"case": "holdout-detail", "variant": "qk-fc2", "repeat": 0, "phase": "validation", "seconds": 62.68734062509611, "peak_memory_bytes": 42753088432, "mlx_peak_bytes": 40964811720, "output_sha256": "561b18aa708d61a5accbfd12f2280e8f7e303505324c43471ba06d60fe0351d5", "output": "outputs/optimization-r3/holdout-detail/qk-fc2-validation-0.png", "nonfinite_values": 0, "step_seconds": [9.848138374974951, 9.98282212507911, 10.09999533300288, 10.163251249818131, 10.270917958114296, 10.198511166963726]}
-03:26:23 Conditioning cache hit: 1093 effective tokens
-03:26:26 Encoded edit reference 1/1
-03:26:26 Loading cached native AdaLN schedule cache/adaln-5be858fe25a798948bdcf804.safetensors
-03:26:42 DiT step 1/6: 16.423s, 36.79 GiB active
-03:27:00 DiT step 2/6: 17.473s, 36.79 GiB active
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-03:28:12 VAE tiles 121/121
-03:28:12 Saved outputs/optimization-r3/edit/control-warmup--1.png in 108.990s
-{"case": "edit", "variant": "control", "repeat": -1, "phase": "warmup", "seconds": 108.99002920882776, "peak_memory_bytes": 43859008576, "mlx_peak_bytes": 41508039384, "output_sha256": "b4b5875fea6451b9c7c24efc7903945a454ae7885ed3016bfc361b4799b2508e", "output": "outputs/optimization-r3/edit/control-warmup--1.png", "nonfinite_values": 0, "step_seconds": [16.422514125006273, 17.473146083997563, 17.559966416098177, 17.518435042118654, 17.190047999843955, 17.674221999943256]}
-03:28:12 Conditioning cache hit: 1093 effective tokens
-03:28:14 Encoded edit reference 1/1
-03:28:30 DiT step 1/6: 16.292s, 36.79 GiB active
-03:28:47 DiT step 2/6: 16.649s, 36.79 GiB active
-03:29:03 DiT step 3/6: 16.421s, 36.80 GiB active
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-03:29:36 DiT step 5/6: 16.444s, 36.81 GiB active
-03:29:53 DiT step 6/6: 16.546s, 36.81 GiB active
-03:29:55 VAE tiles 121/121
-03:29:55 Saved outputs/optimization-r3/edit/qk-fc2-warmup--1.png in 103.104s
-{"case": "edit", "variant": "qk-fc2", "repeat": -1, "phase": "warmup", "seconds": 103.10370362503454, "peak_memory_bytes": 43303574520, "mlx_peak_bytes": 41370729656, "output_sha256": "b4b5875fea6451b9c7c24efc7903945a454ae7885ed3016bfc361b4799b2508e", "output": "outputs/optimization-r3/edit/qk-fc2-warmup--1.png", "nonfinite_values": 0, "step_seconds": [16.292056374950334, 16.648515042150393, 16.42146575008519, 16.660248917061836, 16.44410966616124, 16.54576624999754]}
-03:29:55 Conditioning cache hit: 1093 effective tokens
-03:29:57 Encoded edit reference 1/1
-03:30:15 DiT step 1/6: 17.436s, 36.79 GiB active
-03:30:33 DiT step 2/6: 17.916s, 36.79 GiB active
-03:30:50 DiT step 3/6: 17.715s, 36.80 GiB active
-03:31:08 DiT step 4/6: 17.545s, 36.80 GiB active
-03:31:25 DiT step 5/6: 17.438s, 36.81 GiB active
-03:31:43 DiT step 6/6: 17.364s, 36.81 GiB active
-03:31:44 VAE tiles 121/121
-03:31:44 Saved outputs/optimization-r3/edit/control-validation-0.png in 109.678s
-{"case": "edit", "variant": "control", "repeat": 0, "phase": "validation", "seconds": 109.67779112490825, "peak_memory_bytes": 43836054520, "mlx_peak_bytes": 41416383425, "output_sha256": "b4b5875fea6451b9c7c24efc7903945a454ae7885ed3016bfc361b4799b2508e", "output": "outputs/optimization-r3/edit/control-validation-0.png", "nonfinite_values": 0, "step_seconds": [17.43612916697748, 17.915810333099216, 17.71518375002779, 17.54526187502779, 17.438321667024866, 17.364450709195808]}
-03:31:45 Conditioning cache hit: 1093 effective tokens
-03:31:47 Encoded edit reference 1/1
-03:32:03 DiT step 1/6: 15.942s, 36.79 GiB active
-03:32:19 DiT step 2/6: 16.155s, 36.79 GiB active
-03:32:35 DiT step 3/6: 16.165s, 36.80 GiB active
-03:32:51 DiT step 4/6: 16.264s, 36.80 GiB active
-03:33:07 DiT step 5/6: 16.198s, 36.81 GiB active
-03:33:24 DiT step 6/6: 16.065s, 36.81 GiB active
-03:33:25 VAE tiles 121/121
-03:33:25 Saved outputs/optimization-r3/edit/qk-fc2-validation-0.png in 100.865s
-{"case": "edit", "variant": "qk-fc2", "repeat": 0, "phase": "validation", "seconds": 100.86470816703513, "peak_memory_bytes": 43313486864, "mlx_peak_bytes": 41452262753, "output_sha256": "b4b5875fea6451b9c7c24efc7903945a454ae7885ed3016bfc361b4799b2508e", "output": "outputs/optimization-r3/edit/qk-fc2-validation-0.png", "nonfinite_values": 0, "step_seconds": [15.941608458058909, 16.155159290879965, 16.165109500056133, 16.26405395916663, 16.198108582990244, 16.064961167052388]}
diff --git a/reports/optimization-r3/lint-draft.log b/reports/optimization-r3/lint-draft.log
deleted file mode 100644
index add456ff308eab3aa63c421384dae45953eaa738..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/lint-draft.log
+++ /dev/null
@@ -1,15 +0,0 @@
-experiments/r3/blocks.py:44:110: B023 Function definition does not bind loop variable `head_major`
-experiments/r3/blocks.py:74:82: B023 Function definition does not bind loop variable `runner`
-experiments/r3/screen.py:139:41: B023 Function definition does not bind loop variable `xx`
-experiments/r3/screen.py:139:45: B023 Function definition does not bind loop variable `w`
-experiments/r3/screen.py:139:48: B023 Function definition does not bind loop variable `b`
-experiments/r3/screen.py:140:51: B023 Function definition does not bind loop variable `layer`
-experiments/r3/screen.py:140:57: B023 Function definition does not bind loop variable `xx`
-experiments/r3/screen.py:157:39: B023 Function definition does not bind loop variable `packed`
-experiments/r3/screen.py:162:42: B023 Function definition does not bind loop variable `xx`
-experiments/r3/screen.py:162:99: B023 Function definition does not bind loop variable `layer`
-experiments/r3/screen.py:162:111: B023 Function definition does not bind loop variable `xx`
-experiments/r3/screen.py:163:55: B023 Function definition does not bind loop variable `project`
-experiments/r3/screen.py:163:63: B023 Function definition does not bind loop variable `packed`
-experiments/r3/screen.py:163:89: B023 Function definition does not bind loop variable `project`
-Found 28 errors (14 fixed, 14 remaining).
diff --git a/reports/optimization-r3/norm.json b/reports/optimization-r3/norm.json
deleted file mode 100644
index b4f0fb1c9c45f813dfa1330ab4e87f664d8e6dcf..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/norm.json
+++ /dev/null
@@ -1,285 +0,0 @@
-{
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "trials": [
- {
- "group": "norm",
- "threads": 256,
- "max_abs": 0.0078125,
- "relative_rmse": 7.710310455877334e-06,
- "equal_fraction": 0.9999983906745911,
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 7.1304794400930405,
- "min_ms": 6.400833837687969,
- "max_ms": 8.965583052486181,
- "samples_ms": [
- 8.965583052486181,
- 8.636000100523233,
- 7.390500046312809,
- 8.6834158282727,
- 7.523125037550926,
- 6.400833837687969,
- 6.5052921418100595,
- 6.870458833873272,
- 6.462374934926629,
- 6.410582922399044
- ]
- },
- "candidate": {
- "median_ms": 1.1129380436614156,
- "min_ms": 1.0103341192007065,
- "max_ms": 1.903250114992261,
- "samples_ms": [
- 1.4779160264879465,
- 1.903250114992261,
- 1.444583060219884,
- 1.5383749268949032,
- 1.1386671103537083,
- 1.037792069837451,
- 1.0872089769691229,
- 1.0422500781714916,
- 1.084415940567851,
- 1.0103341192007065
- ]
- },
- "reduction_pct": 84.39182031149797
- },
- {
- "group": "norm",
- "threads": 512,
- "max_abs": 0.001953125,
- "relative_rmse": 3.359136826475151e-06,
- "equal_fraction": 0.9999990463256836,
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 6.538687623105943,
- "min_ms": 5.488791968673468,
- "max_ms": 6.9223749451339245,
- "samples_ms": [
- 6.9223749451339245,
- 6.678625009953976,
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diff --git a/reports/optimization-r3/norm.log b/reports/optimization-r3/norm.log
deleted file mode 100644
index ffcb2996da60d99c20d9f49a28950bc2c279723d..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/norm.log
+++ /dev/null
@@ -1,4 +0,0 @@
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diff --git a/reports/optimization-r3/panel.json b/reports/optimization-r3/panel.json
deleted file mode 100644
index 0f4a642cd7607b6533e70bbcdbf405a7899b38ad..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/panel.json
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-{
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diff --git a/reports/optimization-r3/panel.log b/reports/optimization-r3/panel.log
deleted file mode 100644
index bda067dc4006289e0b95b9b0424ef42084c23849..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/panel.log
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diff --git a/reports/optimization-r3/qk-audit.log b/reports/optimization-r3/qk-audit.log
deleted file mode 100644
index 3e619f354e501edd71b2218f47282244fbf5447a..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/qk-audit.log
+++ /dev/null
@@ -1,62 +0,0 @@
-{
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diff --git a/reports/optimization-r3/qk-deployment/control.receipt.json b/reports/optimization-r3/qk-deployment/control.receipt.json
deleted file mode 100644
index 37c803ad128e3da2eee5a921cc2877bb66a8e625..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/qk-deployment/control.receipt.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
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diff --git a/reports/optimization-r3/qk-deployment/qk-fc2-native-norm.receipt.json b/reports/optimization-r3/qk-deployment/qk-fc2-native-norm.receipt.json
deleted file mode 100644
index 14ffde49f58151ac18af1f5945a293f0e3207216..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/qk-deployment/qk-fc2-native-norm.receipt.json
+++ /dev/null
@@ -1,124 +0,0 @@
-{
- "schema_version": 1,
- "id": "r3-deployment-qk-fc2-native-norm",
- "campaign_id": "h3-m5pro-round-03",
- "contract_sha256": "cbd4ce596524b59629216716299ee8677b10f49140de962384153aaaa2e55dec",
- "complete": true,
- "change_class": "exact",
- "hypothesis": "Native reduction matching QK/RoPE fusion and shape-qualified Metal 4 FC2 tiling reduce full generation latency without changing a denoising tensor or pixel.",
- "context": {
- "hardware": "Apple M5 Pro, 16 GPU cores, 64 GiB unified memory",
- "os": "macOS 27.0.1 arm64",
- "software": "MLX/MLX Metal 0.32.4; Python 3.12.14; strict precision environment",
- "environment": "AC Automatic power; MLX_ENABLE_TF32=0, MLX_CONV_WINOGRAD=0; user background apps unchanged; no thermal warning recorded",
- "timing_boundary": "Pipeline.generate total_s: warm text cache, original CPU RNG, six heads, decode, trace and PNG save. Excludes interpreter startup and external quality scoring.",
- "memory_metric": "Parent macOS physical footprint sampled at 100ms, current-interval maximum; includes Metal, excludes compiler XPC. MLX allocator separately; do not sum. Both compiled variants may be resident.",
- "cache_state": "Resident shared native weights and VAE; shape warmup for each variant. Exact conditioning caches warmed outside timed population.",
- "execution_policy": "Single GPU benchmark process; per-case repeats alternate control/candidate then candidate/control then control/candidate. Warmup and first-load excluded from timed population."
- },
- "variant": {
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- "command": [
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- "experiments.r3.images",
- "--deployment",
- "--variants",
- "qk-fc2-native-norm",
- "--workload",
- "optimization/round-03/workload.json",
- "--model",
- "/Users/devin/Desktop/minimax-h3-image-opt/models/MiniMax-H3-Image",
- "--text-encoder",
- "/Users/devin/Desktop/minimax-h3-image-opt/models/MiniMax-H3/text_encoder",
- "--output",
- "outputs/optimization-r3-qk"
- ]
- },
- "quality": {
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- "holdout-detail": "561b18aa708d61a5accbfd12f2280e8f7e303505324c43471ba06d60fe0351d5"
- },
- "metrics": {
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- {
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- "trace_identical": true
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diff --git a/reports/optimization-r3/qk-images.log b/reports/optimization-r3/qk-images.log
deleted file mode 100644
index 4c06cd131e7970a47433a7df2177916b972be9c0..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/qk-images.log
+++ /dev/null
@@ -1,204 +0,0 @@
-04:07:57 Conditioning cache hit: 85 effective tokens
-04:07:57 Loading cached native AdaLN schedule cache/adaln-4dda935938d45a975c179220.safetensors
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-04:08:33 DiT step 3/6: 10.888s, 35.96 GiB active
-04:08:44 DiT step 4/6: 10.992s, 35.96 GiB active
-04:08:55 DiT step 5/6: 10.873s, 35.97 GiB active
-04:09:06 DiT step 6/6: 10.648s, 35.97 GiB active
-04:09:08 VAE tiles 121/121
-04:09:08 Saved outputs/optimization-r3-qk/product/control-cold--1.png in 71.585s
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-04:09:08 Conditioning cache hit: 85 effective tokens
-04:09:18 DiT step 1/6: 9.830s, 36.71 GiB active
-04:09:28 DiT step 2/6: 10.125s, 36.71 GiB active
-04:09:38 DiT step 3/6: 10.319s, 36.71 GiB active
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-04:10:10 DiT step 6/6: 10.491s, 36.72 GiB active
-04:10:12 VAE tiles 121/121
-04:10:12 Saved outputs/optimization-r3-qk/product/qk-fc2-native-norm-warmup--1.png in 63.789s
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-04:10:15 Conditioning cache hit: 85 effective tokens
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-04:10:36 DiT step 2/6: 11.024s, 36.71 GiB active
-04:10:47 DiT step 3/6: 10.975s, 36.71 GiB active
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-04:11:09 DiT step 5/6: 10.889s, 36.72 GiB active
-04:11:20 DiT step 6/6: 10.934s, 36.72 GiB active
-04:11:21 VAE tiles 121/121
-04:11:22 Saved outputs/optimization-r3-qk/product/control-timed-0.png in 67.024s
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-04:11:22 Conditioning cache hit: 85 effective tokens
-04:11:32 DiT step 1/6: 10.229s, 36.71 GiB active
-04:11:42 DiT step 2/6: 10.410s, 36.71 GiB active
-04:11:53 DiT step 3/6: 10.289s, 36.71 GiB active
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-04:12:25 VAE tiles 121/121
-04:12:25 Saved outputs/optimization-r3-qk/product/qk-fc2-native-norm-timed-0.png in 63.901s
-{"case": "product", "variant": "qk-fc2-native-norm", "repeat": 0, "phase": "timed", "seconds": 63.90098704188131, "peak_memory_bytes": 42450606784, "mlx_peak_bytes": 40911082900, "output_sha256": "eaed6c290ea3f7843ed4a789614cf3ad384d5acf3d0e0cc40a569fdcb850390f", "output": "outputs/optimization-r3-qk/product/qk-fc2-native-norm-timed-0.png", "nonfinite_values": 0, "step_seconds": [10.22877712501213, 10.409983040997759, 10.289188583847135, 10.299693416105583, 10.278323167003691, 10.27706433297135]}
-04:12:28 Conditioning cache hit: 85 effective tokens
-04:12:38 DiT step 1/6: 9.401s, 36.71 GiB active
-04:12:48 DiT step 2/6: 10.537s, 36.71 GiB active
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-04:13:32 VAE tiles 121/121
-04:13:32 Saved outputs/optimization-r3-qk/product/qk-fc2-native-norm-timed-1.png in 64.238s
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-04:13:32 Conditioning cache hit: 85 effective tokens
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-04:14:39 VAE tiles 121/121
-04:14:39 Saved outputs/optimization-r3-qk/product/control-timed-1.png in 66.803s
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-04:14:40 Conditioning cache hit: 85 effective tokens
-04:14:50 DiT step 1/6: 10.246s, 36.71 GiB active
-04:15:00 DiT step 2/6: 10.346s, 36.71 GiB active
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-04:15:44 VAE tiles 121/121
-04:15:44 Saved outputs/optimization-r3-qk/product/control-timed-2.png in 64.560s
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-04:15:54 DiT step 1/6: 9.653s, 36.71 GiB active
-04:16:04 DiT step 2/6: 9.891s, 36.71 GiB active
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-04:16:45 VAE tiles 121/121
-04:16:46 Saved outputs/optimization-r3-qk/product/qk-fc2-native-norm-timed-2.png in 61.274s
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-04:17:50 Saved outputs/optimization-r3-qk/portrait/control-warmup--1.png in 63.665s
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-04:17:50 Conditioning cache hit: 82 effective tokens
-04:18:00 DiT step 1/6: 9.711s, 36.71 GiB active
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-04:18:51 VAE tiles 121/121
-04:18:52 Saved outputs/optimization-r3-qk/portrait/qk-fc2-native-norm-warmup--1.png in 61.693s
-{"case": "portrait", "variant": "qk-fc2-native-norm", "repeat": -1, "phase": "warmup", "seconds": 61.69332629116252, "peak_memory_bytes": 42689043352, "mlx_peak_bytes": 40909920220, "output_sha256": "4226310bd1d79a0d0d4c0b572e2e7729541f5f171260cfeefd5e73b0a4196a47", "output": "outputs/optimization-r3-qk/portrait/qk-fc2-native-norm-warmup--1.png", "nonfinite_values": 0, "step_seconds": [9.710975375026464, 9.927052166080102, 9.86899883300066, 9.911723082885146, 10.16768925008364, 10.056352417217568]}
-04:18:52 Conditioning cache hit: 82 effective tokens
-04:19:02 DiT step 1/6: 10.410s, 36.71 GiB active
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-04:19:56 Saved outputs/optimization-r3-qk/portrait/control-timed-0.png in 64.234s
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-04:23:05 VAE tiles 121/121
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-04:25:13 VAE tiles 121/121
-04:25:13 Saved outputs/optimization-r3-qk/portrait/qk-fc2-native-norm-timed-2.png in 62.007s
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-04:25:45 DiT step 3/6: 10.388s, 36.71 GiB active
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-04:26:16 DiT step 6/6: 10.426s, 36.72 GiB active
-04:26:18 VAE tiles 121/121
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-04:26:28 DiT step 1/6: 9.826s, 36.71 GiB active
-04:26:38 DiT step 2/6: 10.011s, 36.71 GiB active
-04:26:48 DiT step 3/6: 10.023s, 36.71 GiB active
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-04:27:18 DiT step 6/6: 10.093s, 36.72 GiB active
-04:27:20 VAE tiles 121/121
-04:27:20 Saved outputs/optimization-r3-qk/holdout-detail/qk-fc2-native-norm-validation-0.png in 61.965s
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-04:27:23 Conditioning cache hit: 1093 effective tokens
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-04:28:16 DiT step 3/6: 17.102s, 36.75 GiB active
-04:28:33 DiT step 4/6: 17.527s, 36.75 GiB active
-04:28:50 DiT step 5/6: 17.179s, 36.76 GiB active
-04:29:08 DiT step 6/6: 17.103s, 36.76 GiB active
-04:29:09 VAE tiles 121/121
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-04:30:16 DiT step 4/6: 16.131s, 36.75 GiB active
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-04:30:48 DiT step 6/6: 16.001s, 36.76 GiB active
-04:30:49 VAE tiles 121/121
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diff --git a/reports/optimization-r3/qk-trace-audit.json b/reports/optimization-r3/qk-trace-audit.json
deleted file mode 100644
index 50aa25ee84971cd2e5d7b115332c195a6a431e7d..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/qk-trace-audit.json
+++ /dev/null
@@ -1,562 +0,0 @@
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diff --git a/reports/optimization-r3/research-audit.log b/reports/optimization-r3/research-audit.log
deleted file mode 100644
index 583a5be2833a15c3cb0fa66a4e878102f61cce74..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/research-audit.log
+++ /dev/null
@@ -1,62 +0,0 @@
-{
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diff --git a/reports/optimization-r3/research/quality-audit.json b/reports/optimization-r3/research/quality-audit.json
deleted file mode 100644
index 0eafa0927a1ee032d7b2fb19a8ec67417c142515..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/research/quality-audit.json
+++ /dev/null
@@ -1,362 +0,0 @@
-[
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- "different_trace_tensors": []
- },
- {
- "output": "outputs/optimization-r3/edit/qk-fc2-validation-0.png",
- "case": "edit",
- "variant": "qk-fc2",
- "phase": "validation",
- "metrics": {
- "pixel_identical": true,
- "mae_8bit": 0.0,
- "max_error_8bit": 0,
- "psnr_db": null,
- "ssim_rgb": 1.0
- },
- "trace_identical": true,
- "different_trace_tensors": []
- }
-]
diff --git a/reports/optimization-r3/research/summary.json b/reports/optimization-r3/research/summary.json
deleted file mode 100644
index 583a5be2833a15c3cb0fa66a4e878102f61cce74..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/research/summary.json
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "root": "outputs/optimization-r3",
- "cases": {
- "product": {
- "control": {
- "samples_s": [
- 68.59933650004677,
- 67.6780596668832,
- 66.15550529095344
- ],
- "median_s": 67.6780596668832,
- "min_s": 66.15550529095344,
- "max_s": 68.59933650004677,
- "peak_physical_bytes": 42964867704,
- "peak_mlx_bytes": 40836142240
- },
- "qk-fc2": {
- "samples_s": [
- 65.93319679214619,
- 64.01737074996345,
- 63.998171125072986
- ],
- "median_s": 64.01737074996345,
- "min_s": 63.998171125072986,
- "max_s": 65.93319679214619,
- "peak_physical_bytes": 42437024448,
- "peak_mlx_bytes": 40731251916
- },
- "reduction_pct": 5.4089743927913325
- },
- "portrait": {
- "control": {
- "samples_s": [
- 68.0219049169682,
- 66.10143391694874,
- 66.39527866686694
- ],
- "median_s": 66.39527866686694,
- "min_s": 66.10143391694874,
- "max_s": 68.0219049169682,
- "peak_physical_bytes": 43090402152,
- "peak_mlx_bytes": 40835028428
- },
- "qk-fc2": {
- "samples_s": [
- 63.14736045780592,
- 63.36688454193063,
- 63.248574083903804
- ],
- "median_s": 63.248574083903804,
- "min_s": 63.14736045780592,
- "max_s": 63.36688454193063,
- "peak_physical_bytes": 42692877208,
- "peak_mlx_bytes": 40900384316
- },
- "reduction_pct": 4.739349914850843
- }
- },
- "all_pixel_identical": true,
- "all_trace_identical": true,
- "scope": "Paired warm complete generation including encoding-cache checks, six heads, decode, trace/PNG save. Parent physical footprint sampled every 100ms; both compiled variants can be resident."
-}
diff --git a/reports/optimization-r3/screen-decisions.json b/reports/optimization-r3/screen-decisions.json
deleted file mode 100644
index 06832fa32c9474cb7efc2e3ea07a9d92184f8bb1..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/screen-decisions.json
+++ /dev/null
@@ -1,67 +0,0 @@
-{
- "distinct_mpp_tiles": 72,
- "attention_layout_settings": 6,
- "unpack_settings": 12,
- "decisions": [
- {
- "id": "attention-layout",
- "decision": "rejected",
- "evidence": "screen.json",
- "reason": "All six preserve bits. Best layout gain below 0.5%; full QKV copying slower; no dispatch change."
- },
- {
- "id": "vector-unpack",
- "decision": "rejected-for-throughput",
- "evidence": "screen.json",
- "reason": "Decode alone improves about 20%, but decode+GEMM benefits are small or negative. Scalar codec remains the deployed implementation."
- },
- {
- "id": "dual-fc1-swiglu",
- "decision": "rejected",
- "evidence": "fc1.json",
- "reason": "Eight tested tiles preserve bits, all slower than compiled native FC1+SwiGLU. Best reduction_pct=-0.6940844599806928"
- },
- {
- "id": "bk2048",
- "decision": "rejected",
- "evidence": "panel.json",
- "reason": "Operator improvement did not survive the complete exact fused block; reduction_pct=-0.1895493265225756"
- },
- {
- "id": "fc2-range",
- "decision": "qualified-operator",
- "evidence": [
- "shapes.json",
- "shapes-extra.json"
- ],
- "reason": "Positive exact gains for real product and edit activations and repeated real edit activations at 8192. Under 1% at 16478, so native 4K fallback retained."
- },
- {
- "id": "norm-tg256-512",
- "decision": "rejected-quality",
- "evidence": "norm.json",
- "reason": "Changed BF16 values even with native rsqrt and four-lane reads. No deployment or full-image speed claim; previous differing-normalization image stacks remain in v2."
- },
- {
- "id": "norm-tg1024",
- "decision": "qualified-operator-and-block",
- "evidence": "norm.json",
- "reason": "Native loop/reduction order and BF16 boundaries match on actual first-block operator and complete block; full production trajectory validation required."
- },
- {
- "id": "qk-native-reduction",
- "decision": "qualified-full-research-suite",
- "evidence": [
- "blocks.json",
- "research/quality-audit.json"
- ],
- "reason": "Four contiguous reads per lane + precise rsqrt correct the earlier differing QK fusion; exact full tensors and pixels on four cases in research runner."
- },
- {
- "id": "selected-embedding-rows",
- "decision": "accepted",
- "evidence": "embedding/summary.json",
- "reason": "Native selected BF16 payload bits match across fresh-process AB/BA trials, padding/duplicates/endpoints covered; phase-specific improvement only."
- }
- ]
-}
diff --git a/reports/optimization-r3/screen-initial.json b/reports/optimization-r3/screen-initial.json
deleted file mode 100644
index 8670c7dbca911dd123046b959c993ca215370a67..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/screen-initial.json
+++ /dev/null
@@ -1,1069 +0,0 @@
-{
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "scope": "Actual saved first-block activations, one GPU job, counterbalanced warmed calls including layout/decode costs. Operator evidence only.",
- "trials": [
- {
- "group": "attention",
- "variant": "q",
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 25.29854152817279,
- "min_ms": 24.697290966287255,
- "max_ms": 26.445457944646478,
- "samples_ms": [
- 25.25966614484787,
- 25.221708929166198,
- 24.697290966287255,
- 25.337416911497712,
- 26.445457944646478,
- 25.767792016267776
- ]
- },
- "candidate": {
- "median_ms": 25.85845801513642,
- "min_ms": 25.21937503479421,
- "max_ms": 26.969833998009562,
- "samples_ms": [
- 25.5732499063015,
- 26.969833998009562,
- 25.21937503479421,
- 25.925040943548083,
- 25.791875086724758,
- 26.10474987886846
- ]
- },
- "reduction_pct": -2.2132362307925924,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "attention",
- "variant": "k",
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 26.20543702505529,
- "min_ms": 25.752374902367592,
- "max_ms": 26.57158300280571,
- "samples_ms": [
- 25.782624958083034,
- 26.211207965388894,
- 26.57158300280571,
- 26.279167039319873,
- 26.199666084721684,
- 25.752374902367592
- ]
- },
- "candidate": {
- "median_ms": 26.317708427086473,
- "min_ms": 25.898332940414548,
- "max_ms": 26.895249960944057,
- "samples_ms": [
- 25.898332940414548,
- 26.31920902058482,
- 26.239249855279922,
- 26.895249960944057,
- 26.78008400835097,
- 26.316207833588123
- ]
- },
- "reduction_pct": -0.42842789427186556,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "attention",
- "variant": "v",
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 26.858124998398125,
- "min_ms": 26.17204189300537,
- "max_ms": 27.15720795094967,
- "samples_ms": [
- 27.006583055481315,
- 26.709666941314936,
- 26.31966699846089,
- 26.17204189300537,
- 27.15720795094967,
- 27.045499999076128
- ]
- },
- "candidate": {
- "median_ms": 26.744915987364948,
- "min_ms": 26.116249850019813,
- "max_ms": 27.01420779339969,
- "samples_ms": [
- 26.773415971547365,
- 26.116249850019813,
- 26.71641600318253,
- 26.796292047947645,
- 26.38900000602007,
- 27.01420779339969
- ]
- },
- "reduction_pct": 0.4215074992760304,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "attention",
- "variant": "kv",
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 26.695103966630995,
- "min_ms": 26.05529106222093,
- "max_ms": 27.2064171731472,
- "samples_ms": [
- 26.333791902288795,
- 26.05529106222093,
- 26.562999933958054,
- 27.10829209536314,
- 26.827207999303937,
- 27.2064171731472
- ]
- },
- "candidate": {
- "median_ms": 28.822416556067765,
- "min_ms": 27.573708212003112,
- "max_ms": 29.26095901057124,
- "samples_ms": [
- 28.851791052147746,
- 28.79554219543934,
- 29.26095901057124,
- 27.573708212003112,
- 28.84929091669619,
- 28.213000157848
- ]
- },
- "reduction_pct": -7.9689241596358595,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "attention",
- "variant": "qk",
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 27.338978950865567,
- "min_ms": 26.72087517566979,
- "max_ms": 27.7062498498708,
- "samples_ms": [
- 26.72087517566979,
- 27.375249890610576,
- 27.7062498498708,
- 27.465333929285407,
- 27.302708011120558,
- 26.962917065247893
- ]
- },
- "candidate": {
- "median_ms": 27.81647944357246,
- "min_ms": 27.56862505339086,
- "max_ms": 28.574875090271235,
- "samples_ms": [
- 27.77000004425645,
- 27.756375027820468,
- 27.862958842888474,
- 27.56862505339086,
- 28.066290775313973,
- 28.574875090271235
- ]
- },
- "reduction_pct": -1.7465922687349478,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "attention",
- "variant": "qkv",
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 27.105562505312264,
- "min_ms": 26.939583010971546,
- "max_ms": 27.794708963483572,
- "samples_ms": [
- 26.9512920640409,
- 26.965250028297305,
- 26.939583010971546,
- 27.470084140077233,
- 27.245874982327223,
- 27.794708963483572
- ]
- },
- "candidate": {
- "median_ms": 29.57308362238109,
- "min_ms": 29.383666114881635,
- "max_ms": 29.900791123509407,
- "samples_ms": [
- 29.388041933998466,
- 29.383666114881635,
- 29.839250026270747,
- 29.67100008390844,
- 29.900791123509407,
- 29.475167160853744
- ]
- },
- "reduction_pct": -9.103375429250837,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 128
- ],
- "simdgroups": 4,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 128
- ],
- "simdgroups": 4,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 256
- ],
- "simdgroups": 4,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 256
- ],
- "simdgroups": 4,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 512
- ],
- "simdgroups": 4,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 512
- ],
- "simdgroups": 4,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 128
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 128
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 256
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 256
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 512
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 512
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 128,
- 128
- ],
- "simdgroups": 4,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 128,
- 128
- ],
- "simdgroups": 4,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 128,
- 256
- ],
- "simdgroups": 4,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 128,
- 256
- ],
- "simdgroups": 4,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 128,
- 512
- ],
- "simdgroups": 4,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 128,
- 512
- ],
- "simdgroups": 4,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 128,
- 128
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 128,
- 128
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 128,
- 256
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 128,
- 256
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 128,
- 512
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 128,
- 512
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 256,
- 128
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 256,
- 128
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 256,
- 256
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 256,
- 256
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 256,
- 512
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 128,
- 256,
- 512
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 256,
- 128
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 256,
- 128
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 256,
- 256
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 256,
- 256
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 256,
- 512
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 256,
- 512
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 64,
- 128,
- 128
- ],
- "simdgroups": 4,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 64,
- 128,
- 128
- ],
- "simdgroups": 4,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 64,
- 128,
- 256
- ],
- "simdgroups": 4,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 64,
- 128,
- 256
- ],
- "simdgroups": 4,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 64,
- 128,
- 128
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 64,
- 128,
- 128
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 64,
- 128,
- 256
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 64,
- 128,
- 256
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 128,
- 128,
- 128
- ],
- "simdgroups": 4,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 128,
- 128,
- 128
- ],
- "simdgroups": 4,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 128,
- 128,
- 256
- ],
- "simdgroups": 4,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 128,
- 128,
- 256
- ],
- "simdgroups": 4,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 128,
- 128,
- 128
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 128,
- 128,
- 128
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 128,
- 128,
- 256
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 128,
- 128,
- 256
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 128,
- 256,
- 128
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 128,
- 256,
- 128
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 128,
- 256,
- 256
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 128,
- 256,
- 256
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 64,
- 256,
- 128
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 64,
- 256,
- 128
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 64,
- 256,
- 256
- ],
- "simdgroups": 8,
- "walk": 0,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- },
- {
- "group": "mpp",
- "operator": "fc1",
- "tile": [
- 64,
- 256,
- 256
- ],
- "simdgroups": 8,
- "walk": 8,
- "error": "TypeError: __call__(): incompatible function arguments. The following argument types are supported:\n 1. __call__(self, *, inputs: list[scalar | array], output_shapes: list[Sequence[int]], output_dtypes: list[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: list[tuple[str, bool | int | Dtype]] | None = None, init_value: float | None = None, verbose: bool = false, stream: StreamOrDevice = None)\n\nInvoked with types: kwargs = { inputs: list, template: list, grid: tuple, threadgroup: tuple, output_shapes: list, output_dtypes: list }"
- }
- ],
- "activation_ranges": {
- "qkv": {
- "shape": [
- 4181,
- 5376
- ],
- "max_abs": 10.0625
- },
- "out": {
- "shape": [
- 4181,
- 7168
- ],
- "max_abs": 38.75
- },
- "fc1": {
- "shape": [
- 4181,
- 5376
- ],
- "max_abs": 3.21875
- },
- "fc2": {
- "shape": [
- 4181,
- 14336
- ],
- "max_abs": 4640.0
- }
- }
-}
diff --git a/reports/optimization-r3/screen.json b/reports/optimization-r3/screen.json
deleted file mode 100644
index 6f17a7aecdf68a073c8ee34d8d2769f489848abd..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/screen.json
+++ /dev/null
@@ -1,4772 +0,0 @@
-{
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "scope": "Actual saved first-block activations, one GPU job, counterbalanced warmed calls including layout/decode costs. Operator evidence only.",
- "trials": [
- {
- "group": "attention",
- "variant": "q",
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 25.229458115063608,
- "min_ms": 25.031917029991746,
- "max_ms": 25.60575003735721,
- "samples_ms": [
- 25.268583092838526,
- 25.075667072087526,
- 25.19033313728869,
- 25.031917029991746,
- 25.60575003735721,
- 25.284667033702135
- ]
- },
- "candidate": {
- "median_ms": 25.469625601544976,
- "min_ms": 25.04358277656138,
- "max_ms": 27.149833040311933,
- "samples_ms": [
- 25.305458111688495,
- 25.851749815046787,
- 25.04358277656138,
- 25.56595904752612,
- 25.37329215556383,
- 27.149833040311933
- ]
- },
- "reduction_pct": -0.9519327977083103,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "attention",
- "variant": "k",
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 25.859437533654273,
- "min_ms": 25.340416003018618,
- "max_ms": 26.08179091475904,
- "samples_ms": [
- 25.340416003018618,
- 25.849125115200877,
- 25.9084589779377,
- 25.79154190607369,
- 25.869749952107668,
- 26.08179091475904
- ]
- },
- "candidate": {
- "median_ms": 26.141000096686184,
- "min_ms": 25.78525012359023,
- "max_ms": 26.738915825262666,
- "samples_ms": [
- 25.89174988679588,
- 25.78525012359023,
- 26.06958313845098,
- 26.21241705492139,
- 26.308625005185604,
- 26.738915825262666
- ]
- },
- "reduction_pct": -1.0888193630100407,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "attention",
- "variant": "v",
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 26.5235414262861,
- "min_ms": 26.06820804066956,
- "max_ms": 26.9916660618037,
- "samples_ms": [
- 26.572000002488494,
- 26.06820804066956,
- 26.71158406883478,
- 26.47508285008371,
- 26.14770899526775,
- 26.9916660618037
- ]
- },
- "candidate": {
- "median_ms": 26.45568735897541,
- "min_ms": 25.472500128671527,
- "max_ms": 26.980499969795346,
- "samples_ms": [
- 25.472500128671527,
- 26.262166909873486,
- 26.980499969795346,
- 26.169166900217533,
- 26.649207808077335,
- 26.651000138372183
- ]
- },
- "reduction_pct": 0.25582581986371977,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "attention",
- "variant": "kv",
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 26.858228840865195,
- "min_ms": 26.246291119605303,
- "max_ms": 27.52129198051989,
- "samples_ms": [
- 27.52129198051989,
- 26.246291119605303,
- 26.920874835923314,
- 26.890749810263515,
- 26.825707871466875,
- 26.35725005529821
- ]
- },
- "candidate": {
- "median_ms": 27.170770918019116,
- "min_ms": 26.904917089268565,
- "max_ms": 27.513458160683513,
- "samples_ms": [
- 27.103166794404387,
- 27.238375041633844,
- 27.05774991773069,
- 26.904917089268565,
- 27.513458160683513,
- 27.28683315217495
- ]
- },
- "reduction_pct": -1.163673446248925,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "attention",
- "variant": "qk",
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 27.14068756904453,
- "min_ms": 26.804166147485375,
- "max_ms": 27.53179194405675,
- "samples_ms": [
- 27.08041714504361,
- 27.53179194405675,
- 27.262166142463684,
- 26.89216611906886,
- 27.20095799304545,
- 26.804166147485375
- ]
- },
- "candidate": {
- "median_ms": 27.800749870948493,
- "min_ms": 27.702665887773037,
- "max_ms": 28.138082940131426,
- "samples_ms": [
- 27.702665887773037,
- 27.784874895587564,
- 27.734083821997046,
- 27.816624846309423,
- 28.138082940131426,
- 28.064000187441707
- ]
- },
- "reduction_pct": -2.4320028747422118,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "attention",
- "variant": "qkv",
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 27.442208491265774,
- "min_ms": 26.71104110777378,
- "max_ms": 27.863250114023685,
- "samples_ms": [
- 27.492749970406294,
- 27.391667012125254,
- 27.72591612301767,
- 27.863250114023685,
- 27.305457973852754,
- 26.71104110777378
- ]
- },
- "candidate": {
- "median_ms": 28.753166552633047,
- "min_ms": 28.096041874960065,
- "max_ms": 29.45591602474451,
- "samples_ms": [
- 28.803500114008784,
- 28.70283299125731,
- 28.096041874960065,
- 28.122124960646033,
- 29.45591602474451,
- 28.994416119530797
- ]
- },
- "reduction_pct": -4.7771594687232355,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 128
- ],
- "simdgroups": 4,
- "walk": 0,
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 32.69812499638647,
- "min_ms": 32.49645885080099,
- "max_ms": 33.13204203732312,
- "samples_ms": [
- 32.848249888047576,
- 32.50425006262958,
- 32.49645885080099,
- 32.93229197151959,
- 33.13204203732312,
- 32.54800010472536
- ]
- },
- "candidate": {
- "median_ms": 46.483063022606075,
- "min_ms": 44.436374912038445,
- "max_ms": 46.96791688911617,
- "samples_ms": [
- 46.82158399373293,
- 44.436374912038445,
- 46.14454205147922,
- 45.50975002348423,
- 46.96791688911617,
- 46.862542163580656
- ]
- },
- "reduction_pct": -42.15819111261887,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 128
- ],
- "simdgroups": 4,
- "walk": 8,
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control"
- ],
- "control": {
- "median_ms": 33.702207962051034,
- "min_ms": 33.23649987578392,
- "max_ms": 34.3878751154989,
- "samples_ms": [
- 33.23649987578392,
- 33.68545789271593,
- 33.96387514658272,
- 34.3878751154989,
- 33.64429180510342,
- 33.71895803138614
- ]
- },
- "candidate": {
- "median_ms": 30.430083512328565,
- "min_ms": 29.547041980549693,
- "max_ms": 32.16029214672744,
- "samples_ms": [
- 29.918708838522434,
- 30.43100005015731,
- 32.16029214672744,
- 29.547041980549693,
- 30.42916697449982,
- 30.4317909758538
- ]
- },
- "reduction_pct": 9.708931988690207,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0
- },
- {
- "group": "mpp",
- "operator": "fc2",
- "tile": [
- 64,
- 128,
- 256
- ],
- "simdgroups": 4,
- "walk": 0,
- "order": [
- "control",
- "candidate",
- "candidate",
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diff --git a/reports/optimization-r3/screen.log b/reports/optimization-r3/screen.log
deleted file mode 100644
index 5630e26201600105c7df22b90ab4a8a736e11958..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/screen.log
+++ /dev/null
@@ -1,78 +0,0 @@
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diff --git a/reports/optimization-r3/shapes-extra.json b/reports/optimization-r3/shapes-extra.json
deleted file mode 100644
index e5f9cd54c8c8af276f48aae736626c3fc35eaa4e..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/shapes-extra.json
+++ /dev/null
@@ -1,442 +0,0 @@
-{
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "scope": "Real first-head block inputs, including original reference positions and modality/time selection for editing. Only operator timing; no 4K image latency claim.",
- "trials": [
- {
- "case": "product",
- "shape": [
- 4181,
- 14336
- ],
- "max_input_abs": 4640.0,
- "max_abs": 0.0,
- "relative_rmse": 0.0,
- "equal_fraction": 1.0,
- "order": [
- "control",
- "candidate",
- "candidate",
- "control",
- "control",
- "candidate",
- "candidate",
- "control",
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diff --git a/reports/optimization-r3/shapes-extra.log b/reports/optimization-r3/shapes-extra.log
deleted file mode 100644
index 238b2b359e8673f624b4faa591e031c0a712b95e..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/shapes-extra.log
+++ /dev/null
@@ -1,7 +0,0 @@
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diff --git a/reports/optimization-r3/shapes.json b/reports/optimization-r3/shapes.json
deleted file mode 100644
index d0ff8c1b2a803db891be694b5441f23cacad2dad..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/shapes.json
+++ /dev/null
@@ -1,211 +0,0 @@
-{
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diff --git a/reports/optimization-r3/wheel-build.log b/reports/optimization-r3/wheel-build.log
deleted file mode 100644
index 1f9742bae1c21d9b2246ab6b7bdfc5c9775d1019..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/wheel-build.log
+++ /dev/null
@@ -1,94 +0,0 @@
-Building wheel...
-running egg_info
-writing minimax_h3_image_mlx.egg-info/PKG-INFO
-writing dependency_links to minimax_h3_image_mlx.egg-info/dependency_links.txt
-writing entry points to minimax_h3_image_mlx.egg-info/entry_points.txt
-writing requirements to minimax_h3_image_mlx.egg-info/requires.txt
-writing top-level names to minimax_h3_image_mlx.egg-info/top_level.txt
-reading manifest file 'minimax_h3_image_mlx.egg-info/SOURCES.txt'
-adding license file 'LICENSE'
-adding license file 'NOTICE'
-writing manifest file 'minimax_h3_image_mlx.egg-info/SOURCES.txt'
-running bdist_wheel
-running build
-running build_py
-copying h3_mlx/memory.py -> build/lib/h3_mlx
-copying h3_mlx/__init__.py -> build/lib/h3_mlx
-copying h3_mlx/encoder.py -> build/lib/h3_mlx
-copying h3_mlx/runtime.py -> build/lib/h3_mlx
-copying h3_mlx/ops.py -> build/lib/h3_mlx
-copying h3_mlx/cli.py -> build/lib/h3_mlx
-copying h3_mlx/tuning.py -> build/lib/h3_mlx
-copying h3_mlx/weights.py -> build/lib/h3_mlx
-copying h3_mlx/dit.py -> build/lib/h3_mlx
-running egg_info
-writing minimax_h3_image_mlx.egg-info/PKG-INFO
-writing dependency_links to minimax_h3_image_mlx.egg-info/dependency_links.txt
-writing entry points to minimax_h3_image_mlx.egg-info/entry_points.txt
-writing requirements to minimax_h3_image_mlx.egg-info/requires.txt
-writing top-level names to minimax_h3_image_mlx.egg-info/top_level.txt
-reading manifest file 'minimax_h3_image_mlx.egg-info/SOURCES.txt'
-adding license file 'LICENSE'
-adding license file 'NOTICE'
-writing manifest file 'minimax_h3_image_mlx.egg-info/SOURCES.txt'
-installing to build/bdist.macosx-11.0-arm64/wheel
-running install
-running install_lib
-creating build/bdist.macosx-11.0-arm64/wheel
-creating build/bdist.macosx-11.0-arm64/wheel/h3_mlx
-copying build/lib/h3_mlx/packed.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/config.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/benchmark.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/vision.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/memory.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/layout.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/convert.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/__init__.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/encoder.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/runtime.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/ops.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/cli.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/kernels.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/reference.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/tuning.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/weights.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/dit.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/compare.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/coreml_vae.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/__main__.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-copying build/lib/h3_mlx/vae.py -> build/bdist.macosx-11.0-arm64/wheel/./h3_mlx
-running install_egg_info
-Copying minimax_h3_image_mlx.egg-info to build/bdist.macosx-11.0-arm64/wheel/./minimax_h3_image_mlx-0.3.0-py3.12.egg-info
-running install_scripts
-creating build/bdist.macosx-11.0-arm64/wheel/minimax_h3_image_mlx-0.3.0.dist-info/WHEEL
-creating '/Users/devin/Desktop/MiniMax-H3-Image-mlx/dist/.tmp-p6up3bru/minimax_h3_image_mlx-0.3.0-py3-none-any.whl' and adding 'build/bdist.macosx-11.0-arm64/wheel' to it
-adding 'h3_mlx/__init__.py'
-adding 'h3_mlx/__main__.py'
-adding 'h3_mlx/benchmark.py'
-adding 'h3_mlx/cli.py'
-adding 'h3_mlx/compare.py'
-adding 'h3_mlx/config.py'
-adding 'h3_mlx/convert.py'
-adding 'h3_mlx/coreml_vae.py'
-adding 'h3_mlx/dit.py'
-adding 'h3_mlx/encoder.py'
-adding 'h3_mlx/kernels.py'
-adding 'h3_mlx/layout.py'
-adding 'h3_mlx/memory.py'
-adding 'h3_mlx/ops.py'
-adding 'h3_mlx/packed.py'
-adding 'h3_mlx/reference.py'
-adding 'h3_mlx/runtime.py'
-adding 'h3_mlx/tuning.py'
-adding 'h3_mlx/vae.py'
-adding 'h3_mlx/vision.py'
-adding 'h3_mlx/weights.py'
-adding 'minimax_h3_image_mlx-0.3.0.dist-info/licenses/LICENSE'
-adding 'minimax_h3_image_mlx-0.3.0.dist-info/licenses/NOTICE'
-adding 'minimax_h3_image_mlx-0.3.0.dist-info/METADATA'
-adding 'minimax_h3_image_mlx-0.3.0.dist-info/WHEEL'
-adding 'minimax_h3_image_mlx-0.3.0.dist-info/entry_points.txt'
-adding 'minimax_h3_image_mlx-0.3.0.dist-info/top_level.txt'
-adding 'minimax_h3_image_mlx-0.3.0.dist-info/RECORD'
-removing build/bdist.macosx-11.0-arm64/wheel
-Successfully built dist/minimax_h3_image_mlx-0.3.0-py3-none-any.whl
diff --git a/reports/optimization-r3/wheel-cli.json b/reports/optimization-r3/wheel-cli.json
deleted file mode 100644
index 1646777ac0359d3a32e9a5867f7610a13131d7b7..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/wheel-cli.json
+++ /dev/null
@@ -1,186 +0,0 @@
-{
- "wheel": "/Users/devin/Desktop/MiniMax-H3-Image-mlx/dist/minimax_h3_image_mlx-0.3.0-py3-none-any.whl",
- "wheel_sha256": "80ea81fb0cc8efd598ca20d1fa1a1133c037d5495464800a980dd9c94ac566b2",
- "command": [
- "/Users/devin/Desktop/MiniMax-H3-Image-mlx/.venv/bin/python",
- "-c",
- "import os, h3_mlx\nif not h3_mlx.__file__.startswith(os.environ['H3_R3_WHEEL_PATH']):\n raise RuntimeError('The checkout shadowed the wheel')\nprint('Loaded runtime from', h3_mlx.__file__, flush=True)\nfrom h3_mlx.cli import main\nmain()\n",
- "generate",
- "--model",
- "/Users/devin/Desktop/minimax-h3-image-opt/models/MiniMax-H3-Image",
- "--text-encoder",
- "/Users/devin/Desktop/minimax-h3-image-opt/models/MiniMax-H3/text_encoder",
- "--cache-dir",
- "/Users/devin/Desktop/MiniMax-H3-Image-mlx/cache",
- "--compile-blocks",
- "--eval-every",
- "0",
- "--prefetch-blocks",
- "4",
- "--wired-gb",
- "42",
- "--compile-vae",
- "--vae-batch",
- "4",
- "--kernel",
- "metal-qk-exact",
- "--tune-matmul",
- "--seed",
- "731",
- "--width",
- "2048",
- "--height",
- "2048",
- "--save-trace",
- "--output",
- "/Users/devin/Desktop/MiniMax-H3-Image-mlx/outputs/optimization-r3-wheel/product.png",
- "Square editorial studio photograph of a dark cobalt glass perfume bottle on pale travertine. The centered cream label reads exactly 'NIGHT TIDE' in large engraved serif letters and 'EAU DE PARFUM' in small capitals beneath. Dappled blue caustics, crisp glass edges, fine pores in the stone, dark reflections through the translucent bottle, restrained palette and generous negative space. No other text."
- ],
- "outer_seconds": 67.35480091697536,
- "cwd": "/tmp",
- "loaded_from_wheel": true,
- "pixel_identical": true,
- "trace_identical": true,
- "reference": "/Users/devin/Desktop/MiniMax-H3-Image-mlx/outputs/suite/product/native-3.png",
- "output": "/Users/devin/Desktop/MiniMax-H3-Image-mlx/outputs/optimization-r3-wheel/product.png",
- "png_sha256": "63684ef8ae148a35ddef02650c6aa30b4843b47c99b06cec2ea3708ef3fc2293",
- "record": {
- "started_utc": "2026-11-12T09:55:13.034874+00:00",
- "prompt": "Square editorial studio photograph of a dark cobalt glass perfume bottle on pale travertine. The centered cream label reads exactly 'NIGHT TIDE' in large engraved serif letters and 'EAU DE PARFUM' in small capitals beneath. Dappled blue caustics, crisp glass edges, fine pores in the stone, dark reflections through the translucent bottle, restrained palette and generous negative space. No other text.",
- "seed": 731,
- "size": [
- 2048,
- 2048
- ],
- "canvas": [
- 2048,
- 2048
- ],
- "rng": "torch",
- "sigmas": [
- 1.0,
- 0.9675236940383911,
- 0.8875485062599182,
- 0.75,
- 0.5327739119529724,
- 0.23200887441635132,
- 0.0
- ],
- "steps": [
- 0,
- 1,
- 2,
- 3,
- 4,
- 5
- ],
- "dit": {
- "cache_dir": "/Users/devin/Desktop/MiniMax-H3-Image-mlx/cache",
- "cache_gb": 38,
- "bits": null,
- "group_size": 64,
- "mlp_chunk": 0,
- "query_chunk": 0,
- "kernel": "metal-qk-exact",
- "threads": 256,
- "quantized_path": null,
- "compile_blocks": true,
- "eval_every": 0,
- "keep_refiners": false,
- "weight_storage": "bf16",
- "async_blocks": false,
- "cache_refined_text": true,
- "prefetch_blocks": 4,
- "tune_matmul": true
- },
- "encoder": {
- "cache_dir": "/Users/devin/Desktop/MiniMax-H3-Image-mlx/cache",
- "bits": null,
- "group_size": 64,
- "query_chunk": 0
- },
- "vae": {
- "tile_batch": 4,
- "dtype": "bfloat16",
- "compile_blocks": true
- },
- "vae_backend": "mlx",
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "wired_gb": 42.0,
- "timings": {
- "encode_s": 0.09430462517775595,
- "dit_setup_s": 0.012480250094085932,
- "step_0_s": 15.092059707967564,
- "step_1_s": 8.915590958902612,
- "step_2_s": 9.443030667025596,
- "step_3_s": 9.649657709058374,
- "step_4_s": 9.697865832829848,
- "step_5_s": 9.593672332819551,
- "decode_s": 2.4450902498792857,
- "total_s": 65.08694416703656
- },
- "nonfinite_steps": [],
- "allow_nonfinite_diagnostic": false,
- "reference_images": [],
- "reference_sha256": [],
- "conditioning_cache_hit": true,
- "dit_effective": {
- "bits": null,
- "group_size": 64,
- "cache_limit_gb": 38.0,
- "checkpoint_fingerprint": "d92ff1655d366a65a0964dc770fb50a3db623de2f490371e82776a10e2d5d900"
- },
- "dit_was_resident": false,
- "refiner_cache_hit": true,
- "dit_metrics": {
- "block_load_s": 11.097363171400502,
- "block_loads": 50,
- "block_wall_s": 62.285294874571264,
- "timing_note": "Weight loading can overlap GPU execution. block_wall_s and step timings measure elapsed time; block_load_s is CPU load time and is not additive."
- },
- "memory_before_decode": {
- "active_gb": 35.969786152243614,
- "peak_gb": 37.34910457953811,
- "cache_gb": 0.9806220140308142,
- "process_physical_gb": 37.33862812817097,
- "process_lifetime_peak_physical_gb": 37.87251789867878
- },
- "valid_denoising": true,
- "memory": {
- "active_gb": 36.74952659010887,
- "peak_gb": 37.581412971019745,
- "cache_gb": 1.0329973511397839,
- "process_physical_gb": 38.205953381955624,
- "process_lifetime_peak_physical_gb": 38.26527953147888,
- "scope": "active/peak/cache: MLX allocator; process_*: macOS parent process including Metal, lifetime peak includes earlier requests; overlapping counters, do not sum"
- },
- "output": "/Users/devin/Desktop/MiniMax-H3-Image-mlx/outputs/optimization-r3-wheel/product.png",
- "png_sha256": "63684ef8ae148a35ddef02650c6aa30b4843b47c99b06cec2ea3708ef3fc2293",
- "finished_utc": "2026-11-12T09:56:18.143983+00:00"
- }
-}
diff --git a/reports/optimization-r3/wheel-cli.log b/reports/optimization-r3/wheel-cli.log
deleted file mode 100644
index e87deb131d98ae11404a1c4b7488127190efc74c..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/wheel-cli.log
+++ /dev/null
@@ -1,34 +0,0 @@
-Loaded runtime from /Users/devin/Desktop/MiniMax-H3-Image-mlx/dist/minimax_h3_image_mlx-0.3.0-py3-none-any.whl/h3_mlx/__init__.py
-04:55:13 Conditioning cache hit: 85 effective tokens
-04:55:13 Loading cached native AdaLN schedule /Users/devin/Desktop/MiniMax-H3-Image-mlx/cache/adaln-4dda935938d45a975c179220.safetensors
-04:55:28 DiT step 1/6: 15.092s, 35.95 GiB active
-04:55:37 DiT step 2/6: 8.916s, 35.96 GiB active
-04:55:46 DiT step 3/6: 9.443s, 35.96 GiB active
-04:55:56 DiT step 4/6: 9.650s, 35.96 GiB active
-04:56:06 DiT step 5/6: 9.698s, 35.97 GiB active
-04:56:15 DiT step 6/6: 9.594s, 35.97 GiB active
-04:56:17 VAE tiles 121/121
-04:56:18 Saved /Users/devin/Desktop/MiniMax-H3-Image-mlx/outputs/optimization-r3-wheel/product.png in 65.087s
-{
- "output": "/Users/devin/Desktop/MiniMax-H3-Image-mlx/outputs/optimization-r3-wheel/product.png",
- "timings": {
- "encode_s": 0.09430462517775595,
- "dit_setup_s": 0.012480250094085932,
- "step_0_s": 15.092059707967564,
- "step_1_s": 8.915590958902612,
- "step_2_s": 9.443030667025596,
- "step_3_s": 9.649657709058374,
- "step_4_s": 9.697865832829848,
- "step_5_s": 9.593672332819551,
- "decode_s": 2.4450902498792857,
- "total_s": 65.08694416703656
- },
- "memory": {
- "active_gb": 36.74952659010887,
- "peak_gb": 37.581412971019745,
- "cache_gb": 1.0329973511397839,
- "process_physical_gb": 38.205953381955624,
- "process_lifetime_peak_physical_gb": 38.26527953147888,
- "scope": "active/peak/cache: MLX allocator; process_*: macOS parent process including Metal, lifetime peak includes earlier requests; overlapping counters, do not sum"
- }
-}
diff --git a/reports/optimization-r3/wheel-verification.log b/reports/optimization-r3/wheel-verification.log
deleted file mode 100644
index 57b058df3ab773af2de2a4cae89bfada72f296f2..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/wheel-verification.log
+++ /dev/null
@@ -1,7 +0,0 @@
-{
- "outer_seconds": 67.35480091697536,
- "loaded_from_wheel": true,
- "pixel_identical": true,
- "trace_identical": true,
- "output": "/Users/devin/Desktop/MiniMax-H3-Image-mlx/outputs/optimization-r3-wheel/product.png"
-}
diff --git a/reports/optimization-r3/wheel.json b/reports/optimization-r3/wheel.json
deleted file mode 100644
index a94a4b08b10a10e9668305a56eb567368f6e377f..0000000000000000000000000000000000000000
--- a/reports/optimization-r3/wheel.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
- "wheel": "dist/minimax_h3_image_mlx-0.3.0-py3-none-any.whl",
- "sha256": "80ea81fb0cc8efd598ca20d1fa1a1133c037d5495464800a980dd9c94ac566b2",
- "bytes": 55953,
- "all_runtime_source_bytes_match": true,
- "module_sha256": {
- "h3_mlx/__init__.py": "aed249298f9d0dc0df85ea4ac6904302c725b3432e389c01078908bbfae554f2",
- "h3_mlx/__main__.py": "6d8b7d7846a845059d7a3107143f11131f63c5511d669b44085b15ec5e3d2279",
- "h3_mlx/benchmark.py": "702f72cf7dedf966a2033e7a3209cea5aca64e3063007f8e6da6c79ac7fc37c6",
- "h3_mlx/cli.py": "087168864ff0df22dc83536650483fc8c09a6ef526a77e2adc61dae86cf9441d",
- "h3_mlx/compare.py": "78a48ea24967c7a6349ed1e50cc1dab690f4c90e65939624d82bb0b146126095",
- "h3_mlx/config.py": "8927453a127b4c392d7d3024e2472e50b4fb9070e504f66859ad327a1f85d168",
- "h3_mlx/convert.py": "2e087f136a3a6e6f52cb5dbab48c1d7e9073b30353e22e566da32437ad231db0",
- "h3_mlx/coreml_vae.py": "d8139a6604d57339c56cf055bf2e5c355d4b71e1c05994bdcb9d49192d1aedf2",
- "h3_mlx/dit.py": "6a2d3318f813ef88e366845fb38f86971e6cacfd414a5340f68a5bb20beb9a10",
- "h3_mlx/encoder.py": "24d68a2e00e334c10b6d7b1ea9dde50074edecbb30236a2c238c14ffb80e64c2",
- "h3_mlx/kernels.py": "48b5e19366b7282f4dc41690e9d07cd0b8867e5ac2b8af6729095b7f8545f10d",
- "h3_mlx/layout.py": "a925693e8fc6abf51c02be91d0af6751a9b116e40dada01602d1b52e052cf2b1",
- "h3_mlx/memory.py": "53dc36a7e08391523f9f19f0c27e2059bdc87219282a9bb7f23f92d92cb0049d",
- "h3_mlx/ops.py": "111b3ee956ee4090f356d68644edc936ad2fe4bde394a699830fd0e8ae8ba273",
- "h3_mlx/packed.py": "ec2dda08eb1642186e11463c1324b35bd23735ae8bfe0452f7a8e55bee0e9dde",
- "h3_mlx/reference.py": "7a08f60e3d21c708f71112a2f012356faff45042e43cc0a0627693421d9d4e3e",
- "h3_mlx/runtime.py": "654a4d07b79e48d015c7f1bcf3084c8b92c875b2f957b5afb81a8900a9162a0d",
- "h3_mlx/tuning.py": "d26a892d8b45b0a688eeb85274b6a7706e4ace97baaaab885e978a785b9cf71e",
- "h3_mlx/vae.py": "73d48984dde57bd01a31f1ac51e1e7efb359024da69c5de34a8a0117ddca9cc0",
- "h3_mlx/vision.py": "d4141e3e14d3294ea42f096d72e4cb036a3f2805ec5d814718ed4f8eeff4d8ea",
- "h3_mlx/weights.py": "8f81139011d1a813e6245d3a97681aa17285ace90fe32f5831e327e5781fcdd1"
- },
- "full_wheel_cli_generation": "passed: reports/optimization-r3/wheel-cli.json"
-}
diff --git a/reports/optimization-v2/RESULTS.md b/reports/optimization-v2/RESULTS.md
deleted file mode 100644
index 329a3a813ef09c904c45cafe31b0f7f79f443923..0000000000000000000000000000000000000000
--- a/reports/optimization-v2/RESULTS.md
+++ /dev/null
@@ -1,318 +0,0 @@
-# Further Mac optimization experiments
-
-These experiments run the original MiniMax-H3-Image checkpoint on the local
-64 GB M5 Pro Mac mini. The preferred path remains MLX with BF16 weights,
-FP32 precision islands, dense attention and all six original PDD heads.
-The new exact optimizations are block compilation, compiled four-tile VAE
-batches, cached refined conditioning, native lazy safetensors loading and
-bounded overlap of weight reads with GPU execution. `packed12` adds an exact
-weight-storage option when memory matters more than a small decoding overhead.
-
-The measured optimum is specific to this Mac and these prompts. This is a
-practical search across runtimes and operators, not proof of a hardware ceiling.
-
-The final product samples measured **67.30 s median at 38.03 GiB peak MLX**
-for exact optimized BF16 (two warm samples, 65.14–69.45 s), and **72.23 s at
-29.90 GiB** for exact packed BF16 (one warm sample). Product and portrait pixels
-matched their BF16 references. The packed 4K trial also matched every pixel,
-at 31.20 GiB peak and 446.75 s including loading; its background load was not
-controlled, so it is evidence of memory/equality rather than a 4K speed win.
-
-## Run the measured configurations
-
-The following memory settings were tested on **this 64 GB machine**. The runtime
-rejects wired limits above Metal's recommended working set and caps block
-residency below that set. `--wired-gb` affects this process; it does not change
-the system's wired-memory setting.
-
-```bash
-# Full six-head BF16 generation, with exact optimizations.
-h3-mlx generate --model /path/to/MiniMax-H3-Image \
- --text-encoder /path/to/MiniMax-H3/text_encoder \
- --compile-blocks --eval-every 0 --prefetch-blocks 4 --wired-gb 42 \
- --compile-vae --vae-batch 4 \
- "A detailed image description" --output outputs/optimized.png
-
-# Same arithmetic, smaller resident weights. The first use builds a ~27 GiB cache.
-h3-mlx generate --model /path/to/MiniMax-H3-Image \
- --text-encoder /path/to/MiniMax-H3/text_encoder \
- --weight-storage packed12 --compile-blocks --eval-every 0 \
- --prefetch-blocks 4 --wired-gb 32 --compile-vae --vae-batch 4 \
- "A detailed image description" --output outputs/compact.png
-```
-
-Keep one `Pipeline` alive for warm requests. The CLI starts a new process for
-each invocation, so it includes loading. The refined-text cache is enabled by
-default and is keyed by the exact BF16 conditioning bytes, checkpoint identity,
-configuration, quantization/chunk policy, MLX version and TF32 policy.
-`--no-refiner-cache` bypasses it. A cache hit avoids loading the two refiner
-blocks and their projection, without approximating their output.
-
-## Full-image measurements
-
-The generated table comes from the raw ledger and complete PNG outputs. A warm
-sample reuses resident DiT weights; it may still compile a new sequence shape.
-Peak memory is the **MLX allocator peak**, not total system RAM. The images and
-metric definitions are linked below. A single load-inclusive 4K sample is not
-reported as a warm benchmark.
-
-
-
-| Case | Configuration | Warm samples | Warm median (s) | Peak MLX GiB | SSIM | Exact pixels |
-|---|---|---:|---:|---:|---:|---|
-| product | BF16 baseline (packing still active) | 1 | 80.67 | 38.29 | 1.000000 | yes |
-| product | Exact optimized BF16, final quiet session | 2 | 67.30 | 38.03 | 1.000000 | yes |
-| product | Exact optimized packed BF16, final quiet session | 1 | 72.23 | 29.90 | 1.000000 | yes |
-| product | BF16 baseline | 2 | 78.21 | 38.31 | 1.000000 | yes |
-| product | Compiled BF16, retained refiners | 2 | 65.34 | 39.52 | 1.000000 | yes |
-| product | Lossless packed, retained refiners | 1 | 70.62 | 31.38 | 1.000000 | yes |
-| product | Compiled BF16 + exact caches + prefetch | 1 | 68.04 | 38.08 | 1.000000 | yes |
-| product | Lossless packed + exact caches + prefetch | 1 | 73.86 | 29.90 | 1.000000 | yes |
-| product | FP16 projection failure (non-finite) | 0 | invalid | 37.58 | invalid state | non-finite |
-| product | Five original passes (skip head 1) | 0 | — | 37.35 | 0.796451 | no |
-| portrait | BF16 baseline | 3 | 72.06 | 38.30 | 1.000000 | yes |
-| portrait | Compiled BF16, retained refiners | 3 | 67.38 | 39.59 | 1.000000 | yes |
-| portrait | Lossless packed, retained refiners | 2 | 73.56 | 31.38 | 1.000000 | yes |
-| portrait | Compiled BF16 + exact caches + prefetch | 2 | 69.54 | 38.22 | 1.000000 | yes |
-| portrait | Lossless packed + exact caches + prefetch | 2 | 74.44 | 29.90 | 1.000000 | yes |
-| portrait | FP16 projection failure (non-finite) | 1 | invalid | 38.33 | invalid state | non-finite |
-| portrait | Five original passes (skip head 1) | 1 | 68.43 | 38.09 | 0.694111 | no |
-| native-4k | Lossless packed + exact caches + prefetch | 0 | — | 31.20 | 1.000000 | yes |
-
-
-
-The original current-arithmetic product baseline was 72.78 s end to end,
-including 67.87 s of denoising, at 38.31 GiB peak MLX memory. The retained-refiner
-compiled trials subsequently reached 65.00–65.68 s on that prompt, with a
-39.52 GiB peak. The later exact-cache/prefetch configuration frees the refiner
-weights, so its speed and memory should be assessed from its own table row.
-
-The baseline rows labeled with active packing are not clean speedup
-denominators. Three background Git maintenance jobs were eventually identified;
-initially only one had been paused. All three were paused for the final
-`quiet-native` warm samples and `quiet-packed` samples, starting at 03:12:36 UTC,
-and automatically resumed at 03:24:36 UTC. Other user applications remained
-open. Our full-image GPU jobs ran sequentially. The 4K run and earlier trials
-had mixed CPU background load; their memory and equality results remain valid,
-but no controlled 4K speedup is claimed. The OS file cache was never flushed.
-
-Hardware: Mac mini, M5 Pro, 15 CPU cores, 16 GPU cores, 64 GB; macOS 27.0.1,
-MLX/Metal 0.32.4, Python 3.12.14, PyTorch 2.15.0 and Core ML Tools 9.0.
-AC power mode remained Automatic. High Power Mode is supported, but changing it
-required unavailable administrator credentials; no High Power measurements are
-claimed. `MLX_ENABLE_TF32=0` and `MLX_CONV_WINOGRAD=0` retain the original
-precision policy.
-
-## Look at the actual losses
-
-- [Product: complete-generation grid](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/optimization-v2/product/grid.png)
- and [native detail crops](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/optimization-v2/product/details-grid.png).
-- [Portrait: complete-generation grid](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/optimization-v2/portrait/grid.png)
- and [native detail crops](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/optimization-v2/portrait/details-grid.png).
-- [4K comparison](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/optimization-v2/native-4k/index.html).
-- [All Core ML devices on the same product latent](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/optimization-v2/decoder-all-product/comparison/index.html).
-- [Decoder alternatives on the same portrait latent](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/optimization-v2/decoder-portrait/comparison/index.html).
-
-Every gallery has side-by-side frames, identical native-resolution crops,
-vertical stacks, RGB differences amplified eight times, full-resolution
-PSNR/SSIM/MAE and source PNG hashes. The tool verifies prompt, seed, dimensions,
-canvas, noise source, schedule and reference inputs. Fewer-head experiments are
-explicitly labeled. Pixel similarity is not a general measure of visual quality
-or prompt adherence. Decoder-only tests use the *identical saved final latent*.
-No comparison imagery is synthesized or retouched.
-
-Complete-generation equality is measured against the original **MLX BF16
-recipe**, not byte identity with CUDA or the FP32 lossless decoder. The
-[same-latent FP32 decoder comparison](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/optimization-v2/fp32-reference/comparison/index.html)
-isolates that separate precision choice. Cross-backend CUDA comparisons remain
-in [the initial report](../RESULTS.md); different decoder quality modes must not be mistaken for
-optimization-induced loss.
-
-On the product latent, native BF16 versus the original FP32 decoder measured
-SSIM 0.996629 and MAE 0.559/255. The FP32 compiled decoder took 5.21 s in its
-single recorded sample. Use `--vae-dtype float32 --compile-vae --vae-batch 4`
-to select that precision; weight packing and decoder precision are independent.
-
-## Lossless 12-bit storage
-
-The codec stores each BF16 low byte verbatim and uses a nibble for its sign and
-one of eight adjacent high-byte exponent values. Rare values outside that
-window use sorted uint32 indices plus their exact uint16 payload. The GPU
-reconstructs the original BF16 bits immediately before an ordinary BF16 GEMM.
-It performs no quantization, multiplication by a scale, pruning or low-rank fit.
-
-Block-0 projection storage was 75.3–76.2% of its original size. Seven-repeat
-operator medians, including per-call reconstruction, were:
-
-| Projection, actual 4181-token shape | BF16 ms | Packed ms | Output error |
-|---|---:|---:|---|
-| QKV, 5376 → 21504 | 39.11 | 41.56 | zero |
-| Attention output, 7168 → 5376 | 13.41 | 14.00 | zero |
-| MLP input, 5376 → 28672 | 52.17 | 54.94 | zero |
-| MLP output, 14336 → 5376 | 32.67 | 34.37 | zero |
-
-The tests reconstruct all 65,536 BF16 bit patterns, including signed zero,
-subnormals, infinities and NaN payloads. Real projection outputs are bit-exact.
-Compiled blocks receive compressed arrays as explicit dynamic inputs so the
-compiler cannot fold decoding into a permanently retained dense matrix. Cache
-shards are written atomically and separated by source-checkpoint fingerprint.
-The tradeoff is extra disk space and a small reconstruction cost, not accuracy.
-
-## Compilation, scheduling and loading
-
-The actual first DiT block, using saved H3 conditioning and noise, took a
-193.87 ms median eagerly and 181.86 ms compiled, with identical BF16 output.
-MLP chunks of 512/1024/2048 were slower on this machine. Compilation also
-reduced the complete product VAE from about 2.87 s at batch one to about 1.56 s
-at batch four. The batch-four image was identical; batch eight introduced small
-rounding changes (product SSIM 0.998905, MAE 0.125/255).
-
-Native lazy `mx.load` was 2.1–2.3× faster than NumPy memmap/copy in three
-page-cache-warm tensor-read tests. Full tensor SHA256s matched. `WeightStore`
-discards each lazy shard dictionary immediately, so evaluated tensors cannot
-bypass its explicit residency budget. The memmap reader remains available for
-audits. These read tests do not measure an uncached whole-checkpoint load.
-
-Prefetching asynchronously dispatches a block while loading the next weights,
-synchronizing after a bounded number of blocks. Once all core blocks are
-resident, it returns to the requested warm evaluation schedule. It preserves
-all operations and dependency ordering. Load time may overlap GPU execution;
-the raw records therefore expose block wall time and per-step wall time rather
-than adding CPU load time to a supposed GPU compute time.
-
-## Core ML / ML Package: CPU, GPU, ANE and all
-
-The repository exports the **complete 24-block VAE tile decoder** as a fixed-batch
-ML Package and supports using it behind the MLX denoiser. This is not a complete
-33B Core ML denoiser port. Source weights and inputs are rounded to native BF16
-values before conversion; Core ML's internal arithmetic is FP16 and changes
-rounding. Package metadata binds the decoder to its original VAE and batch.
-
-`MLComputePlan` was inspected for actual preferred devices. Selecting `ALL`
-alone is not evidence of ANE execution, as described in Apple's
-[compute-plan utilities](https://apple.github.io/coremltools/docs-guides/source/mlmodel-utilities.html).
-In the eight-tile test:
-
-| Compute units | Preferred placement | Median tile-batch ms | Same-latent product SSIM |
-|---|---|---:|---:|
-| CPU_ONLY | CPU | 655.40 | 0.995786 |
-| CPU_AND_GPU | GPU | 85.27 | 0.998319 |
-| CPU_AND_NE | ANE, with two CPU operations | 459.67 | **0.492298** |
-| ALL | GPU | 85.20 | 0.998319 |
-
-The ANE export produces conspicuous tile/reconstruction artifacts on real
-latents despite finite outputs on random test data. Its cause is not established;
-this is a failed correctness result, not proof that ANE inevitably loses this
-much precision. It is slower as well. Treat this batch-eight ANE export as a
-diagnostic, not a quality-preserving generation option. Its recorded model-load
-wall time includes a deliberately paused client and is not an isolated compiler
-benchmark. Prediction samples were taken after resuming.
-
-The batch-one ANE export also completed, at 251.73 ms per tile. Its full-image
-check reduced the batch-eight artifacts but still changed pixels: SSIM 0.988974,
-MAE 1.179/255, max error 43/255, and 30.63 s decoder-only latency. Its
-[image grid](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples/blob/v0.4.0/comparisons/optimization-v2/contiguous-b1-CPU_AND_NE/comparison/index.html)
-and raw NumPy-bridge audit are preserved. The output was contiguous and MLX
-imported its values exactly, so that test did not reveal an output-import error.
-A repeated batch-eight cold probe with explicit contiguous I/O did not complete
-within 150 s. These are bounded feasibility probes, not comparable compilation
-benchmarks. Smaller batches are not an ANE speed win here.
-
-Core ML GPU decoded the full product in 1.56 s in the first three-repeat trial;
-compiled MLX took 1.56 s with closer or identical pixels. A later run after CPU
-tests was slower. The MLX decoder is preferred on this Mac. The package remains
-available for reproducible device experiments and other hardware.
-
-```bash
-# Isolated conversion environment; no MLX dependency is required by the exporter.
-uv venv --python 3.12 .venv-coreml
-uv pip install --python .venv-coreml/bin/python coremltools==9.0 torch safetensors numpy
-.venv-coreml/bin/python -m experiments.v2_coreml_vae \
- --vae /path/to/MiniMax-H3-Image/vae --batch 8 \
- --package cache/coreml-v2/vae-batch8.mlpackage
-
-# Optional Core ML GPU decoder in the real MLX generation pipeline.
-pip install -e '.[coreml]'
-h3-mlx generate --model /path/to/MiniMax-H3-Image \
- --text-encoder /path/to/MiniMax-H3/text_encoder \
- --compile-blocks --eval-every 0 --prefetch-blocks 4 --wired-gb 42 \
- --vae-backend coreml --vae-batch 8 \
- --coreml-package cache/coreml-v2/vae-batch8.mlpackage \
- --coreml-compute-units CPU_AND_GPU \
- "A detailed image description" --output outputs/coreml-decoder.png
-```
-
-Core ML allocations are excluded from MLX allocator counters, so those counters
-must not be used to claim a hybrid pipeline's total memory footprint. The
-experimental converter completed successfully with the recorded PyTorch version;
-Core ML Tools warns that this PyTorch release is outside its tested range.
-
-## Other operator and runtime experiments
-
-- **FP16 projection compute failed:** both prompts produced entirely non-finite
- velocity/state tensors at the first head. The saved black images are invalid
- diagnostic artifacts, not valid low-quality generations. This was missed by
- normalized synthetic GEMM inputs. The runtime now rejects non-finite states;
- the research profile explicitly opts into saving failure diagnostics.
-- **Five-head sampling:** skip original head 1 and advance head 0 across the
- larger interval. The five-head images are compared directly; this is a sampler
- approximation, not a precision-preserving speedup. Product/portrait SSIM was
- 0.79645/0.69411, too large a change for the minimal-loss recommendation.
-- **Affine Q8/Q6/Q4, MXFP8/MXFP4/NVFP4:** all four real block-0 projections were
- measured at the actual 4181-token shape. Quantization did not provide a clear
- overall compute advantage. Separate dequantize-then-GEMM tests occasionally
- improved over fused quantized matmul but did not justify replacing the exact
- path. Quantization remains useful for smaller weight storage; earlier full
- Q8/Q4 images are included in the grids.
-- **FP16 versus BF16, row padding, transposed weights:** padding and transposing
- preserved tested outputs, but did not beat the native layout. FP16 changes
- rounding and was not consistently faster.
-- **Metal 4 TensorOps GEMM:** threadgroup shapes, SIMD counts, K barriers and
- locality-preserving walk orders were swept on the actual QKV shape. The best
- custom kernel was about 39.65 ms versus 39.12 ms native, with exact output.
- It was not selected. MLX already uses M5 GPU neural acceleration here.
-- **Custom online-softmax attention:** query/key tile sizes and SIMD counts were
- varied. The correct prototype was much slower than native SDPA and introduced
- small numeric differences. It remains an isolated operator experiment, with
- no full-image quality or speed claim.
-- **PyTorch MPS / Inductor and MPSMatrix:** real H3 matrix shapes were benchmarked
- across CPU, MPS BF16/FP16/FP32 and raw MPSMatrix. MPS was competitive; CPU was
- much slower. The compiled MPS MLP reached 83.18 ms and preserved its eager
- output. This is an operator result, not a measured complete H3 MPS pipeline.
-- **Memory residency:** default MLX wired memory is zero. An explicit, bounded
- per-process limit prevented paging from becoming an uncontrolled variable in
- resident-weight trials. Retaining refiners saves some load time for new
- conditioning, but the exact refined-text cache is preferable for repeats.
-
-The custom attention result is compared numerically to native SDPA. It was
-rejected before full-image deployment because it was substantially slower;
-operator-level error is not presented as an image-quality measurement.
-
-## Reproduction and remaining opportunities
-
-All scripts are under `experiments/v2_*.py`; raw operator samples are adjacent
-JSON files. The image ledger and full traces are in the ignored local
-`outputs/optimization-v2` directory. `v2_report.py` rebuilds the table, PNG grids,
-hashes and chart from completed outputs. The normal test suite covers precision
-boundaries, exact packed reconstruction, cached conditioning, conversion and
-image comparison provenance.
-
-Remaining opportunities include native mixed-format Metal tensor kernels,
-vectorized lossless reconstruction, smaller ANE batches/layout rewrites with
-intermediate tensor audits, and overlapping an ANE decoder with the next
-request's GPU denoising for throughput. None should be advertised as a speed or
-quality win without full-image checks. Further pruning, token merging, low-rank
-compression or learned timestep reduction would need calibration or training;
-they are not silently applied to the original checkpoint.
-
-The runtime distinctions and operator design follow Apple's
-[Core ML compute-unit documentation](https://apple.github.io/coremltools/docs-guides/source/model-prediction.html),
-[compute-plan utilities](https://apple.github.io/coremltools/docs-guides/source/mlmodel-utilities.html),
-[typed execution](https://apple.github.io/coremltools/docs-guides/source/typed-execution.html),
-[Metal TensorOps guide](https://developer.apple.com/download/files/Metal-Performance-Primitives-Programming-Guide.pdf),
-and [M5 GPU tensor operations talk](https://developer.apple.com/videos/play/wwdc2026/330/).
-The M5 GPU shader-core neural accelerators are distinct from the ANE selected by
-Core ML. The MLX implementation examined was
-[v0.32.4](https://github.com/ml-explore/mlx/tree/v0.32.4), including its NAX GEMM
-and SDPA routing. All performance and image-quality numbers above come from
-local experiments, not vendor performance claims.
diff --git a/reports/optimization-v2/approximation-quality.json b/reports/optimization-v2/approximation-quality.json
deleted file mode 100644
index d5ca9c1bc34882bdcaf188f3a277b5bf3f724f35..0000000000000000000000000000000000000000
--- a/reports/optimization-v2/approximation-quality.json
+++ /dev/null
@@ -1,38 +0,0 @@
-[
- {
- "case": "product",
- "profile": "fp16",
- "pixel_identical": false,
- "mae_8bit": 154.0353469053904,
- "max_error_8bit": 255,
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- },
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- "case": "product",
- "profile": "five-pass",
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- "max_error_8bit": 255,
- "psnr_db": 16.798797459226876,
- "ssim_rgb": 0.7964514877131589
- },
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- "case": "portrait",
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- "pixel_identical": false,
- "mae_8bit": 104.15469749768575,
- "max_error_8bit": 255,
- "psnr_db": 5.980561568481523,
- "ssim_rgb": 0.02374704438244847
- },
- {
- "case": "portrait",
- "profile": "five-pass",
- "pixel_identical": false,
- "mae_8bit": 25.05439265569051,
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- "ssim_rgb": 0.6941111421384556
- }
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diff --git a/reports/optimization-v2/attention-debug.json b/reports/optimization-v2/attention-debug.json
deleted file mode 100644
index 569dfe5c66fd817402940871bb8febb85b53b635..0000000000000000000000000000000000000000
--- a/reports/optimization-v2/attention-debug.json
+++ /dev/null
@@ -1,241 +0,0 @@
-{
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diff --git a/reports/optimization-v2/attention.json b/reports/optimization-v2/attention.json
deleted file mode 100644
index 33102185d0e3f8fd93736a89d751a1b774cd8fa1..0000000000000000000000000000000000000000
--- a/reports/optimization-v2/attention.json
+++ /dev/null
@@ -1,527 +0,0 @@
-{
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diff --git a/reports/optimization-v2/coreml-vae.json b/reports/optimization-v2/coreml-vae.json
deleted file mode 100644
index e81b52fd743c219f759c7c90f2d0071e02f7d7ce..0000000000000000000000000000000000000000
--- a/reports/optimization-v2/coreml-vae.json
+++ /dev/null
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diff --git a/reports/optimization-v2/coreml.json b/reports/optimization-v2/coreml.json
deleted file mode 100644
index 4378d9bdddd041f7c6369a7850c32e4cd61cd6fd..0000000000000000000000000000000000000000
--- a/reports/optimization-v2/coreml.json
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diff --git a/reports/optimization-v2/mpp.json b/reports/optimization-v2/mpp.json
deleted file mode 100644
index 3487fd5b5bb725ab74117ebc29a5f753dceb7dbe..0000000000000000000000000000000000000000
--- a/reports/optimization-v2/mpp.json
+++ /dev/null
@@ -1,729 +0,0 @@
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diff --git a/reports/optimization-v2/mps.jsonl b/reports/optimization-v2/mps.jsonl
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index 866228cc2c9fa5b7de726f00bed7bda6bc77561a..0000000000000000000000000000000000000000
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diff --git a/reports/optimization-v2/storage.json b/reports/optimization-v2/storage.json
deleted file mode 100644
index 2f692d39f8eefc628698f02ead388f089325c0cc..0000000000000000000000000000000000000000
--- a/reports/optimization-v2/storage.json
+++ /dev/null
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diff --git a/reports/optimization-v2/summary.json b/reports/optimization-v2/summary.json
deleted file mode 100644
index f6d7dd5477f063b8dbb46cbe7fc92eb8fb7e309a..0000000000000000000000000000000000000000
--- a/reports/optimization-v2/summary.json
+++ /dev/null
@@ -1,464 +0,0 @@
-[
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diff --git a/reports/optimization-v2/table.md b/reports/optimization-v2/table.md
deleted file mode 100644
index 974be11dd3899f46b988c50554bf1f76fe9a1783..0000000000000000000000000000000000000000
--- a/reports/optimization-v2/table.md
+++ /dev/null
@@ -1,20 +0,0 @@
-| Case | Configuration | Warm samples | Warm median (s) | Peak MLX GiB | SSIM | Exact pixels |
-|---|---|---:|---:|---:|---:|---|
-| product | BF16 baseline (packing still active) | 1 | 80.67 | 38.29 | 1.000000 | yes |
-| product | Exact optimized BF16, final quiet session | 2 | 67.30 | 38.03 | 1.000000 | yes |
-| product | Exact optimized packed BF16, final quiet session | 1 | 72.23 | 29.90 | 1.000000 | yes |
-| product | BF16 baseline | 2 | 78.21 | 38.31 | 1.000000 | yes |
-| product | Compiled BF16, retained refiners | 2 | 65.34 | 39.52 | 1.000000 | yes |
-| product | Lossless packed, retained refiners | 1 | 70.62 | 31.38 | 1.000000 | yes |
-| product | Compiled BF16 + exact caches + prefetch | 1 | 68.04 | 38.08 | 1.000000 | yes |
-| product | Lossless packed + exact caches + prefetch | 1 | 73.86 | 29.90 | 1.000000 | yes |
-| product | FP16 projection failure (non-finite) | 0 | invalid | 37.58 | invalid state | non-finite |
-| product | Five original passes (skip head 1) | 0 | — | 37.35 | 0.796451 | no |
-| portrait | BF16 baseline | 3 | 72.06 | 38.30 | 1.000000 | yes |
-| portrait | Compiled BF16, retained refiners | 3 | 67.38 | 39.59 | 1.000000 | yes |
-| portrait | Lossless packed, retained refiners | 2 | 73.56 | 31.38 | 1.000000 | yes |
-| portrait | Compiled BF16 + exact caches + prefetch | 2 | 69.54 | 38.22 | 1.000000 | yes |
-| portrait | Lossless packed + exact caches + prefetch | 2 | 74.44 | 29.90 | 1.000000 | yes |
-| portrait | FP16 projection failure (non-finite) | 1 | invalid | 38.33 | invalid state | non-finite |
-| portrait | Five original passes (skip head 1) | 1 | 68.43 | 38.09 | 0.694111 | no |
-| native-4k | Lossless packed + exact caches + prefetch | 0 | — | 31.20 | 1.000000 | yes |
diff --git a/reports/optimization-v2/torch.json b/reports/optimization-v2/torch.json
deleted file mode 100644
index 5d7879e8f00396b787055b843f9e38376ec2a19a..0000000000000000000000000000000000000000
--- a/reports/optimization-v2/torch.json
+++ /dev/null
@@ -1,420 +0,0 @@
-{
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index 7d0e1e02ae5defa91eab0c0bb9ceffab2e9b7e7a..0000000000000000000000000000000000000000
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-{
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diff --git a/reports/vae/batch-4.json b/reports/vae/batch-4.json
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index d6d7e0745ea1493253feb0f487725c4cffa9f9ae..0000000000000000000000000000000000000000
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- },
- "output": "reports/vae/batch-4.png",
- "png_sha256": "63684ef8ae148a35ddef02650c6aa30b4843b47c99b06cec2ea3708ef3fc2293",
- "validation_scope": "Decoder only: identical final latent, BF16 decoder; diffusion is not rerun",
- "input_trace": "outputs/suite/product/native-3.trace.safetensors"
-}
diff --git a/reports/vae/batch-8.json b/reports/vae/batch-8.json
deleted file mode 100644
index bc683a457e17741784aae55a12cfc5f577faeaab..0000000000000000000000000000000000000000
--- a/reports/vae/batch-8.json
+++ /dev/null
@@ -1,109 +0,0 @@
-{
- "prompt": "Square editorial studio photograph of a dark cobalt glass perfume bottle on pale travertine. The centered cream label reads exactly 'NIGHT TIDE' in large engraved serif letters and 'EAU DE PARFUM' in small capitals beneath. Dappled blue caustics, crisp glass edges, fine pores in the stone, dark reflections through the translucent bottle, restrained palette and generous negative space. No other text.",
- "seed": 731,
- "size": [
- 2048,
- 2048
- ],
- "canvas": [
- 2048,
- 2048
- ],
- "rng": "torch",
- "sigmas": [
- 1.0,
- 0.9675236940383911,
- 0.8875485062599182,
- 0.75,
- 0.5327739119529724,
- 0.23200887441635132,
- 0.0
- ],
- "steps": [
- 0,
- 1,
- 2,
- 3,
- 4,
- 5
- ],
- "dit": {
- "cache_dir": "cache",
- "cache_gb": 38,
- "bits": null,
- "group_size": 64,
- "mlp_chunk": 0,
- "query_chunk": 0,
- "kernel": "native",
- "threads": 256,
- "quantized_path": null
- },
- "encoder": {
- "cache_dir": "cache",
- "bits": null,
- "group_size": 64,
- "query_chunk": 0
- },
- "vae": {
- "dtype": "bfloat16",
- "tile_batch": 8
- },
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "timings": {
- "total_s": 1.9313540210714564
- },
- "reference_images": [],
- "reference_sha256": [],
- "conditioning_cache_hit": true,
- "dit_effective": {
- "bits": null,
- "group_size": 64,
- "cache_limit_gb": 38.0,
- "checkpoint_fingerprint": "d92ff1655d366a65a0964dc770fb50a3db623de2f490371e82776a10e2d5d900"
- },
- "dit_was_resident": true,
- "dit_metrics": {
- "block_load_s": 1.119593916228041,
- "block_compute_s": 67.66628884081729,
- "block_loads": 2
- },
- "memory_before_decode": {
- "active_gb": 36.7733853533864,
- "peak_gb": 38.30599339865148,
- "cache_gb": 0.994278697296977
- },
- "memory": {
- "active_gb": 36.80082065612078,
- "peak_gb": 38.30599339865148,
- "cache_gb": 0.2694286108016968
- },
- "output": "reports/vae/batch-8.png",
- "png_sha256": "57a85569ad45d469b191470541e8cfff8d576fed6cc6bb8bd54db16e9bd2d81f",
- "validation_scope": "Decoder only: identical final latent, BF16 decoder; diffusion is not rerun",
- "input_trace": "outputs/suite/product/native-3.trace.safetensors"
-}
diff --git a/reports/vae/trials.json b/reports/vae/trials.json
deleted file mode 100644
index 1d6191679194328dab6aa896f52e274d22370042..0000000000000000000000000000000000000000
--- a/reports/vae/trials.json
+++ /dev/null
@@ -1,96 +0,0 @@
-{
- "environment": {
- "platform": "macOS-27.0.1-arm64-arm-64bit",
- "machine": "arm64",
- "metal": {
- "device_name": "Apple M5 Pro",
- "max_recommended_working_set_size": 55662788608,
- "memory_size": 68719476736,
- "architecture": "applegpu_g17s",
- "max_buffer_length": 41747087360,
- "resource_limit": 499000
- },
- "versions": {
- "mlx": "0.32.4",
- "mlx-metal": "0.32.4",
- "numpy": "2.5.4",
- "safetensors": "0.8.0",
- "transformers": "4.57.6",
- "torch": "2.15.0",
- "Pillow": "13.0.0",
- "scikit-image": "0.26.0"
- },
- "precision_environment": {
- "MLX_ENABLE_TF32": "0",
- "MLX_CONV_WINOGRAD": "0"
- }
- },
- "source_trace": "outputs/suite/product/native-3.trace.safetensors",
- "scope": "Same final latent; decoder only, loaded weights and one warmup excluded",
- "trials": [
- {
- "batch": 1,
- "median_ms": 2972.3427920835093,
- "min_ms": 2965.7569171395153,
- "max_ms": 2978.9286670275033,
- "samples_ms": [
- 2978.9286670275033,
- 2965.7569171395153
- ],
- "pixel_identical": true,
- "mae_8bit": 0.0,
- "max_error_8bit": 0,
- "psnr_db": null,
- "ssim_rgb": 1.0,
- "max_float_error": 0.0
- },
- {
- "batch": 4,
- "median_ms": 2013.5446878848597,
- "min_ms": 2011.7882918566465,
- "max_ms": 2015.301083913073,
- "samples_ms": [
- 2011.7882918566465,
- 2015.301083913073
- ],
- "pixel_identical": true,
- "mae_8bit": 0.0,
- "max_error_8bit": 0,
- "psnr_db": null,
- "ssim_rgb": 1.0,
- "max_float_error": 0.0
- },
- {
- "batch": 8,
- "median_ms": 1931.3540210714564,
- "min_ms": 1928.6854171659797,
- "max_ms": 1934.022624976933,
- "samples_ms": [
- 1928.6854171659797,
- 1934.022624976933
- ],
- "pixel_identical": false,
- "mae_8bit": 0.1251803239186605,
- "max_error_8bit": 4,
- "psnr_db": 56.9881575908215,
- "ssim_rgb": 0.9989046120633382,
- "max_float_error": 0.015625
- },
- {
- "batch": 16,
- "median_ms": 1898.3302710112184,
- "min_ms": 1892.1958750579506,
- "max_ms": 1904.4646669644862,
- "samples_ms": [
- 1892.1958750579506,
- 1904.4646669644862
- ],
- "pixel_identical": false,
- "mae_8bit": 0.12682731946309408,
- "max_error_8bit": 4,
- "psnr_db": 56.93080322502411,
- "ssim_rgb": 0.9988915771568488,
- "max_float_error": 0.015625
- }
- ]
-}
diff --git a/scripts/prepare_hf_release.py b/scripts/prepare_hf_release.py
deleted file mode 100644
index 0b4d51bc40dd5e746f884694da41113b67cd306c..0000000000000000000000000000000000000000
--- a/scripts/prepare_hf_release.py
+++ /dev/null
@@ -1,427 +0,0 @@
-"""Build reviewable Hugging Face upload folders without changing Hub state.
-
-Run from the source checkout with its tested MLX environment. Existing native
-and packed weights are linked, never duplicated or removed. Quantized outputs
-are generated only when explicitly selected with --quantize.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-import shutil
-import sys
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-sys.path.insert(0, str(ROOT))
-
-from h3_mlx.hub import read_json, sha256 # noqa: E402
-
-IMAGE_REVISION = "6534ea1c979a5f8cb00c2bd1025d5ca4ee8d8901"
-ENCODER_REVISION = "0919fb684102ef115126612f3fbb84d1027e7317"
-
-
-def write_json(path, value):
- path.parent.mkdir(parents=True, exist_ok=True)
- path.write_text(json.dumps(value, indent=2) + "\n")
-
-
-def component_config(component, catalog):
- data = {
- "format": "h3-mlx-hub-component-v1",
- "component": component,
- "library_name": "mlx",
- "runtime_repository": "devin-lai/MiniMax-H3-Image-mlx",
- "base_model": catalog["source"]["repo_id"],
- "source_revision": catalog["source"]["revision"],
- "manifest": "files.json",
- }
- if component in catalog["variants"]:
- entry = catalog["variants"][component]
- data.update(
- core_format=entry["core_format"],
- bits=entry.get("bits"),
- group_size=entry.get("group_size"),
- )
- elif component == "runtime":
- data["variants"] = sorted(catalog["variants"])
- return data
-
-
-def link(source, destination):
- source = Path(source).resolve(strict=True)
- destination.parent.mkdir(parents=True, exist_ok=True)
- if destination.is_symlink() and destination.resolve() == source:
- return
- if destination.exists() or destination.is_symlink():
- raise FileExistsError(destination)
- destination.symlink_to(source)
-
-
-def model_metadata(variant=None):
- import yaml
-
- data = {
- "library_name": "mlx",
- "pipeline_tag": "text-to-image",
- "license": "other",
- "license_name": "minimax-h3-image-community-license-agreement",
- "license_link": "LICENSE",
- "base_model": "MiniMaxAI/MiniMax-H3-Image",
- "language": ["en"],
- "inference": False,
- "tags": [
- "mlx",
- "apple-silicon",
- "macos",
- "minimax-h3",
- "image-generation",
- "image-editing",
- "image-to-image",
- "safetensors",
- ],
- "extra_gated_heading": "Request access under the MiniMax license",
- "extra_gated_description": "Manual review. Read LICENSE and the territorial, commercial and acceptable-use conditions before requesting access.",
- "extra_gated_fields": {
- "Country of use": "country",
- "I accept the MiniMax-H3-Image Community License Agreement including Exhibit A": "checkbox",
- "I am authorized to receive and use these materials in my territory": "checkbox",
- },
- "extra_gated_eu_disallowed": True,
- }
- if variant in ("q8", "q4"):
- data["base_model_relation"] = "quantized"
- data["tags"].append("quantized")
- return (
- "---\n" + yaml.safe_dump(data, sort_keys=False, allow_unicode=True) + "---\n\n"
- )
-
-
-def licenses(destination):
- import requests
-
- for repo, revision, filename in (
- ("MiniMaxAI/MiniMax-H3-Image", IMAGE_REVISION, "LICENSE"),
- ("MiniMaxAI/MiniMax-H3", ENCODER_REVISION, "LICENSE-text-encoder"),
- ):
- response = requests.get(
- f"https://huggingface.co/{repo}/raw/{revision}/LICENSE", timeout=30
- )
- response.raise_for_status()
- (destination / filename).write_text(response.text)
- notice = (
- "MiniMax-H3-Image is licensed under the MiniMax-H3-Image Community License Agreement, "
- "Copyright © 2026 MiniMax. All Rights Reserved.\n\n"
- "Community MLX adaptation and packaging by Devin Lai. This is an independent release.\n"
- "Modified files: MLX affine Q8/Q4 core shards, lossless palette12 shards, "
- "repacked support tensors and evaluated six-step modulation tables. "
- "No training or fine-tuning was performed. See MODIFICATIONS.md.\n"
- "The separate text encoder retains its MiniMax H3 license.\n"
- )
- (destination / "NOTICE").write_text(notice)
- shutil.copy2(ROOT / "docs/MODIFICATIONS.md", destination / "MODIFICATIONS.md")
-
-
-def manifest(destination, cache):
- files = {}
- for file in sorted(destination.rglob("*")):
- if not file.is_file() or file.name == "files.json" or ".cache" in file.parts:
- continue
- stat = file.stat()
- key = f"{file.resolve()}:{stat.st_size}:{stat.st_mtime_ns}"
- if key not in cache:
- print("Hashing", file.relative_to(destination), flush=True)
- cache[key] = sha256(file)
- files[file.relative_to(destination).as_posix()] = {
- "bytes": stat.st_size,
- "sha256": cache[key],
- }
- write_json(
- destination / "files.json", {"format": "h3-mlx-files-v1", "files": files}
- )
- return sum(
- spec["bytes"] for name, spec in files.items() if name.startswith("model/")
- )
-
-
-def shared_components(model, packed, destination):
- import mlx.core as mx
-
- from h3_mlx.config import load_sigmas
- from h3_mlx.dit import DiT
- from h3_mlx.runtime import configure_memory
- from h3_mlx.weights import WeightStore
-
- configure_memory()
- target = destination / "model"
- (target / "transformer").mkdir(parents=True, exist_ok=True)
- support = target / "transformer/support.safetensors"
- if not support.exists():
- store = WeightStore(model / "transformer")
- names = []
- for key in store.index:
- if key.startswith("blocks."):
- if key.endswith(
- (
- "norm1.weight",
- "norm2.weight",
- "attn.q_norm.weight",
- "attn.k_norm.weight",
- )
- ):
- names.append(key)
- elif ".adaln_proj." not in key:
- names.append(key)
- print("Exporting native support tensors", flush=True)
- tensors = {key: store.get(key) for key in names}
- mx.save_safetensors(
- str(support),
- tensors,
- metadata={
- "modification": "Repacked native support tensors for the community MLX port by Devin Lai; original tensor values retained",
- "source": f"MiniMaxAI/MiniMax-H3-Image@{IMAGE_REVISION}",
- },
- )
- del tensors, store
- mx.clear_cache()
- link(model / "transformer/config.json", target / "transformer/config.json")
- for name in ("pdd_config.json", "pdd_fused_heads.safetensors", "model_index.json"):
- link(model / name, target / name)
- for file in (model / "vae").iterdir():
- if file.is_file() and not file.name.startswith("."):
- link(file, target / "vae" / file.name)
- modulation = target / "modulation"
- modulation.mkdir(exist_ok=True)
- if not all(
- (modulation / f"{name}.safetensors").exists()
- for name in ("generation", "editing")
- ):
- dit = DiT(model, cache_dir=ROOT / "cache", cache_gb=0)
- for name, edit in (("generation", False), ("editing", True)):
- dit.prepare_modulation(load_sigmas(model), edit=edit)
- data, metadata = mx.load(
- str(ROOT / "cache" / f"adaln-{dit.modulation_key}.safetensors"),
- return_metadata=True,
- )
- metadata["modification"] = (
- "Original timestep projections evaluated for the six released PDD steps by Devin Lai; no interpolation or quantization"
- )
- mx.save_safetensors(
- str(modulation / f"{name}.safetensors"), data, metadata=metadata
- )
- del data
- dit.clear()
- if len(list(packed.glob("blocks.*.safetensors"))) != 50:
- raise ValueError("Expected the validated 50-block packed12 cache")
-
-
-def stage_main(destination):
- for name in (
- "README.md",
- "README.zh-CN.md",
- "USAGE.md",
- "MODEL_INDEX.md",
- "BENCHMARKS.md",
- "RELEASE.md",
- "LICENSE",
- "NOTICE",
- "pyproject.toml",
- "requirements-macos.lock",
- ".gitignore",
- ):
- shutil.copy2(ROOT / name, destination / name)
- for name in (
- "h3_mlx",
- "tests",
- "docs",
- "assets",
- "examples",
- "scripts",
- "provenance",
- "reports",
- "comparisons",
- "experiments",
- "optimization",
- ):
- shutil.copytree(
- ROOT / name,
- destination / name,
- dirs_exist_ok=True,
- ignore=shutil.ignore_patterns("__pycache__", "*.pyc", ".DS_Store"),
- )
- shutil.copy2(ROOT / "LICENSE-model", destination / "LICENSE-model")
- shutil.copy2(ROOT / "LICENSE-text-encoder", destination / "LICENSE-text-encoder")
- write_json(
- destination / "config.json",
- component_config("runtime", read_json(ROOT / "h3_mlx/model_index.json")),
- )
-
-
-def stage_gallery(main_folder, destination, cache):
- """Keep generated images and visual comparisons under manual access review."""
- destination.mkdir(parents=True, exist_ok=True)
- licenses(destination)
- write_json(
- destination / "config.json",
- component_config("examples", read_json(ROOT / "h3_mlx/model_index.json")),
- )
- for name in ("examples/README.md", "comparisons/README.md"):
- source = main_folder / name
- target = destination / name
- target.parent.mkdir(parents=True, exist_ok=True)
- shutil.copy2(source, target)
- for source in (main_folder / "examples").glob("*.json"):
- shutil.copy2(source, destination / "examples" / source.name)
- comparison_index = destination / "comparisons/README.md"
- comparison_index.write_text(
- comparison_index.read_text().replace(
- "../reports/RESULTS.md",
- "https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx/blob/v0.4.0/reports/RESULTS.md",
- )
- )
- moved = set()
- for source in list(main_folder.rglob("*")):
- if source.is_file() and source.suffix.lower() in (".png", ".jpg", ".jpeg", ".html"):
- relative = source.relative_to(main_folder)
- target = destination / relative
- target.parent.mkdir(parents=True, exist_ok=True)
- shutil.move(str(source), str(target))
- moved.add(relative.as_posix())
-
- gallery_url = "https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx-examples"
- pattern = re.compile(r"(!?)\[([^\]]*)\]\(([^)\n]+)\)")
- for document in main_folder.rglob("*.md"):
- text = document.read_text()
- text = re.sub(r"^thumbnail: .*\n", "", text, flags=re.MULTILINE)
-
- def replace_link(match, base=document.parent):
- target = match.group(3)
- if "://" in target or target.startswith("#"):
- return match.group(0)
- try:
- relative = (base / target).resolve().relative_to(
- main_folder.resolve()
- ).as_posix()
- except ValueError:
- return match.group(0)
- if relative not in moved:
- return match.group(0)
- return f"[{match.group(2)}]({gallery_url}/blob/v0.4.0/{relative})"
-
- text = pattern.sub(replace_link, text)
- document.write_text(text)
- (destination / "README.md").write_text(
- model_metadata()
- + "# MiniMax H3 Image MLX · example gallery\n\n"
- "Generated images and visual comparisons from the community MLX port by Devin Lai. "
- "Powered by MiniMax-H3-Image. These images are AI-generated.\n\n"
- "Examples are subject to the MiniMax-H3-Image Community License Agreement, "
- "including its territorial and acceptable-use restrictions. Request access "
- "and review the license before viewing or using the files. The standard grant "
- "excludes the EU, UK, Republic of Korea and US.\n\n"
- "- [Showcase](assets/showcase.jpg)\n"
- "- [Generated examples, prompts and seeds](examples/README.md)\n"
- "- [Visual precision comparisons](comparisons/README.md)\n"
- "- [Runtime and installation](https://huggingface.co/devin-lai/MiniMax-H3-Image-mlx)\n\n"
- "See [LICENSE](LICENSE), [NOTICE](NOTICE) and [modifications](MODIFICATIONS.md). "
- "Access review does not grant additional rights.\n"
- )
- manifest(destination, cache)
-
-
-def main():
- parser = argparse.ArgumentParser(description=__doc__)
- parser.add_argument("--model", required=True, type=Path)
- parser.add_argument("--packed-cache", required=True, type=Path)
- parser.add_argument("--output", type=Path, default=ROOT / ".release/huggingface")
- parser.add_argument("--quantize", nargs="*", choices=("q8", "q4"), default=[])
- args = parser.parse_args()
- model, packed = args.model.resolve(), args.packed_cache.resolve()
- output = args.output.resolve()
- output.mkdir(parents=True, exist_ok=True)
- catalog = read_json(ROOT / "h3_mlx/model_index.json")
- provenance = read_json(ROOT / "provenance/sources.json")
- if (
- sha256(model / "transformer/config.json")
- != provenance["files"]["models/MiniMax-H3-Image/transformer/config.json"]
- ):
- raise ValueError("Image model configuration differs from the pinned source")
- cache_path = output.parent / "hash-cache.json"
- cache = read_json(cache_path) if cache_path.exists() else {}
- summary = {"status": "prepared", "publication": "not started", "repositories": {}}
- shared = output / "MiniMax-H3-Image-mlx-shared"
- shared.mkdir(exist_ok=True)
- licenses(shared)
- write_json(shared / "config.json", component_config("shared", catalog))
- shared_components(model, packed, shared)
- (ROOT / "LICENSE-model").write_text((shared / "LICENSE").read_text())
- (ROOT / "LICENSE-text-encoder").write_text(
- (shared / "LICENSE-text-encoder").read_text()
- )
- (shared / "README.md").write_text(
- model_metadata() + (ROOT / "docs/cards/shared.md").read_text()
- )
- summary["repositories"][shared.name] = {
- "ready": True,
- "model_bytes": manifest(shared, cache),
- }
- write_json(cache_path, cache)
- for variant, entry in catalog["variants"].items():
- destination = output / entry["repo_id"].split("/")[-1]
- destination.mkdir(exist_ok=True)
- licenses(destination)
- write_json(destination / "config.json", component_config(variant, catalog))
- write_json(
- destination / "variant.json",
- {"variant": variant, **entry, "source": catalog["source"]},
- )
- (destination / "README.md").write_text(
- model_metadata(variant) + (ROOT / f"docs/cards/{variant}.md").read_text()
- )
- if variant == "bf16":
- for file in (model / "transformer").iterdir():
- if file.is_file() and not file.name.startswith("."):
- link(file, destination / "model/transformer" / file.name)
- elif variant == "packed12":
- for file in sorted(packed.glob("blocks.*.safetensors")):
- link(file, destination / "model/core" / file.name)
- elif variant in args.quantize:
- from h3_mlx.convert import convert
-
- core = destination / "model/core"
- if not (core / "quantization.json").exists():
- convert(model, core, bits=entry["bits"], group_size=64)
- ready = (destination / "model").is_dir() and (
- entry["core_format"] != "quantized"
- or (destination / "model/core/quantization.json").is_file()
- )
- if ready and entry["core_format"] == "quantized":
- metadata = read_json(destination / "model/core/quantization.json")
- ready = len(metadata["files"]) == 52 and all(
- (destination / "model/core" / file["file"]).is_file()
- and (destination / "model/core" / file["file"]).stat().st_size
- == file["bytes"]
- for file in metadata["files"]
- )
- size = manifest(destination, cache)
- summary["repositories"][destination.name] = {
- "ready": ready,
- "model_bytes": size,
- }
- write_json(cache_path, cache)
- main_folder = output / "MiniMax-H3-Image-mlx"
- main_folder.mkdir(exist_ok=True)
- stage_main(main_folder)
- gallery = output / "MiniMax-H3-Image-mlx-examples"
- stage_gallery(main_folder, gallery, cache)
- summary["repositories"][gallery.name] = {"ready": True}
- write_json(cache_path, cache)
- summary["repositories"][main_folder.name] = {"ready": True}
- write_json(output.parent / "preparation.json", summary)
- print(json.dumps(summary, indent=2))
-
-
-if __name__ == "__main__":
- main()
diff --git a/scripts/publish_hf_release.py b/scripts/publish_hf_release.py
deleted file mode 100644
index 9c0089a17d9e476524e3881427712262d9789983..0000000000000000000000000000000000000000
--- a/scripts/publish_hf_release.py
+++ /dev/null
@@ -1,305 +0,0 @@
-"""Publish verified release components after redistribution rights are established.
-
-This command changes Hugging Face state. It never accepts model access requests
-or sends announcements. Components may be uploaded one at a time to conserve
-scratch space. The main repository is finalized only after every component.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import sys
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-sys.path.insert(0, str(ROOT))
-
-from h3_mlx.hub import read_json, sha256 # noqa: E402
-
-PROJECT = "devin-lai/MiniMax-H3-Image-mlx"
-RELEASE = "v0.4.0"
-COMPONENTS = ("shared", "bf16", "packed12", "q8", "q4", "examples")
-
-
-def write_json(path, value):
- path.write_text(json.dumps(value, indent=2) + "\n")
-
-
-def verify_local(folder):
- manifest = read_json(folder / "files.json")
- if manifest.get("format") != "h3-mlx-files-v1":
- raise ValueError("Unknown file manifest")
- for name, spec in manifest["files"].items():
- from h3_mlx.hub import relative_path
-
- file = folder / relative_path(name)
- if not file.is_file() or file.stat().st_size != spec["bytes"]:
- raise ValueError(f"Missing or truncated local file: {file}")
- if sha256(file) != spec["sha256"]:
- raise ValueError(f"Local SHA-256 mismatch: {file}")
- quantization = folder / "model/core/quantization.json"
- if quantization.exists():
- files = read_json(quantization)["files"]
- if len(files) != 52:
- raise ValueError("Expected all fifty main blocks and two token refiners")
- for file in files:
- expected = manifest["files"].get("model/core/" + file["file"])
- if expected != {"bytes": file["bytes"], "sha256": file["sha256"]}:
- raise ValueError(f"Quantized export/manifest mismatch: {file['file']}")
- return manifest
-
-
-def verify_remote(api, repo_id, manifest, *, repo_type="model"):
- from huggingface_hub import hf_hub_download
-
- info = api.repo_info(repo_id, repo_type=repo_type, files_metadata=True)
- files = {file.rfilename: file for file in info.siblings}
- for name, spec in manifest["files"].items():
- remote = files.get(name)
- if remote is None or remote.size != spec["bytes"]:
- raise ValueError(f"Remote file missing or wrong size: {repo_id}/{name}")
- lfs = getattr(remote, "lfs", None)
- digest = getattr(lfs, "sha256", None) if lfs is not None else None
- if isinstance(lfs, dict):
- digest = lfs.get("sha256")
- if digest is None:
- if remote.size > 10 * 1024**2:
- raise ValueError(
- f"Cannot verify remote weight digest: {repo_id}/{name}"
- )
- digest = sha256(
- hf_hub_download(repo_id, name, revision=info.sha, repo_type=repo_type)
- )
- if digest != spec["sha256"]:
- raise ValueError(f"Remote SHA-256 mismatch: {repo_id}/{name}")
- return info.sha
-
-
-def ensure_owned_repository(api, repo_id, folder, *, gated):
- from huggingface_hub import hf_hub_download
-
- marker = {"project": PROJECT, "release": RELEASE}
- if api.repo_exists(repo_id):
- try:
- existing = read_json(hf_hub_download(repo_id, "release-owner.json"))
- except Exception as error:
- raise RuntimeError(
- f"Existing repository has no matching release marker: {repo_id}"
- ) from error
- if existing != marker:
- raise RuntimeError(f"Refusing to modify unrelated repository: {repo_id}")
- else:
- api.create_repo(repo_id, private=True)
- # Establish ownership immediately so an interrupted upload is resumable.
- api.upload_file(
- repo_id=repo_id,
- path_in_repo="release-owner.json",
- path_or_fileobj=(json.dumps(marker) + "\n").encode(),
- commit_message=f"Initialize {RELEASE} release staging",
- )
- write_json(folder / "release-owner.json", marker)
- if gated:
- api.update_repo_settings(repo_id, gated="manual")
-
-
-def refresh_small_file(folder, name):
- manifest = read_json(folder / "files.json")
- file = folder / name
- manifest["files"][name] = {"bytes": file.stat().st_size, "sha256": sha256(file)}
- write_json(folder / "files.json", manifest)
-
-
-def publish_component(api, component, folder):
- repo_id = f"{PROJECT}-{component}"
- if component != "examples" and not (folder / "model").is_dir():
- raise ValueError(f"Model files are not prepared for {component}")
- if (
- component in ("q8", "q4")
- and not (folder / "model/core/quantization.json").is_file()
- ):
- raise ValueError(f"Quantized export incomplete: {component}")
- if (
- component == "packed12"
- and len(list((folder / "model/core").glob("blocks.*.safetensors"))) != 50
- ):
- raise ValueError("Expected all fifty packed blocks")
- card = (
- (folder / "README.md")
- .read_text()
- .replace(
- "**Preparation status:** this model repository is staged locally; publication\n"
- "and immutable commit IDs are pending confirmation of redistribution rights.",
- f"**Release:** {RELEASE}. Access requests are reviewed manually under the retained license.",
- )
- .replace(
- "**Preparation status:** staged locally; publication is pending confirmation\n"
- "of redistribution rights.",
- f"**Release:** {RELEASE}. Access requests are reviewed manually.",
- )
- )
- (folder / "README.md").write_text(card)
- refresh_small_file(folder, "README.md")
- manifest = verify_local(folder)
- ensure_owned_repository(api, repo_id, folder, gated=True)
- api.upload_large_folder(repo_id, folder, repo_type="model", num_workers=4)
- revision = verify_remote(api, repo_id, manifest)
- api.create_tag(repo_id, tag=RELEASE, revision=revision, exist_ok=True)
- if api.model_info(repo_id, revision=RELEASE).sha != revision:
- raise ValueError(f"Existing release tag points at another commit: {repo_id}")
- api.update_repo_settings(repo_id, gated="manual", private=False)
- info = api.model_info(repo_id)
- if info.private or info.gated != "manual":
- raise RuntimeError(f"Unexpected publication/access state: {repo_id}")
- return {
- "repo_id": repo_id,
- "revision": revision,
- "gated": "manual",
- "verified": True,
- }
-
-
-def finalize_main(api, output, state):
- missing = [name for name in COMPONENTS if not state.get(name, {}).get("verified")]
- if missing:
- raise ValueError(
- f"Publish and verify these components first: {', '.join(missing)}"
- )
- from huggingface_hub import ModelCard
-
- catalog = read_json(ROOT / "h3_mlx/model_index.json")
- catalog["status"] = "published"
- catalog["shared"]["revision"] = state["shared"]["revision"]
- for name in catalog["variants"]:
- catalog["variants"][name]["revision"] = state[name]["revision"]
- main_folder = output / PROJECT.split("/")[-1]
- write_json(main_folder / "h3_mlx/model_index.json", catalog)
- collection = api.create_collection(
- "MiniMax H3 Image · Apple silicon / MLX",
- namespace="devin-lai",
- private=True,
- description="Local image generation and editing. BF16, lossless packed BF16, Q8 and Q4; selective downloads, shared components and measured comparisons.",
- exists_ok=True,
- )
- readme = (main_folder / "README.md").read_text()
- readme = readme.replace(
- "The current preparation has **not yet been\npublished** while redistribution rights are clarified. The commands below are\nthe prepared release workflow.",
- f"Release **{RELEASE}** is available with manual access review for model components.",
- )
- if f"[Collection]({collection.url})" not in readme:
- readme = readme.replace(
- "[Repository map](MODEL_INDEX.md)",
- f"[Collection]({collection.url}) · [Repository map](MODEL_INDEX.md)",
- )
- for name in ("q8", "q4"):
- files = read_json(output / f"MiniMax-H3-Image-mlx-{name}/files.json")["files"]
- size = (
- sum(
- spec["bytes"]
- for path, spec in files.items()
- if path.startswith("model/")
- )
- / 1024**3
- )
- readme = readme.replace(
- "About 20 GiB²" if name == "q8" else "About 11 GiB²", f"{size:.2f} GiB"
- )
- readme = readme.replace(
- " ² Estimates until the prepared export manifest is finalized.", ""
- )
- readme = readme.replace(
- " ² Q8 is an estimate until its export manifest is finalized.", ""
- )
- (main_folder / "README.md").write_text(readme)
- chinese = (
- (main_folder / "README.zh-CN.md")
- .read_text()
- .replace(
- "**发布状态：目前仅本地准备，尚未上传。**",
- f"**发布版本：{RELEASE}，模型组件采用人工审核访问。**",
- )
- .replace(
- "在确认发布方具有所需权限之前暂停公开发布。", "使用前请确认具备所需权限。"
- )
- )
- (main_folder / "README.zh-CN.md").write_text(chinese)
- release_file = main_folder / "RELEASE.md"
- release_notes = release_file.read_text()
- if "" in release_notes:
- release_notes = release_notes.replace(
- "", f"[Browse the collection]({collection.url})."
- )
- elif collection.url not in release_notes:
- release_notes += f"\n[Browse the collection]({collection.url}).\n"
- release_file.write_text(release_notes)
- ModelCard.load(main_folder / "README.md").validate()
- ensure_owned_repository(api, PROJECT, main_folder, gated=False)
- from scripts.prepare_hf_release import manifest as build_manifest
-
- build_manifest(main_folder, {})
- main_manifest = read_json(main_folder / "files.json")
- commit = api.upload_folder(
- repo_id=PROJECT,
- folder_path=main_folder,
- commit_message=f"Release {RELEASE}: selective MLX model downloads",
- )
- if verify_remote(api, PROJECT, main_manifest) != commit.oid:
- raise RuntimeError("Main repository changed during publication verification")
- api.create_tag(PROJECT, tag=RELEASE, revision=commit.oid, exist_ok=True)
- if api.model_info(PROJECT, revision=RELEASE).sha != commit.oid:
- raise ValueError("Main release tag points at another commit")
- api.update_repo_settings(PROJECT, private=False)
- notes = {
- "main": "Start here: runtime, quick start, model selection, authentic outputs and measured results.",
- "packed12": "Recommended exact storage choice: lossless BF16 packing, not quantization.",
- "bf16": "Unchanged original transformer files: native BF16/FP32 storage control.",
- "q8": "Smaller affine Q8 core; image changes and no established generation-speed advantage.",
- "q4": "Smallest affine Q4 core; stronger detail and lettering changes.",
- "shared": "VAE, native support, PDD heads, configurations and frozen six-step tables; reused across variants.",
- "examples": "Generated examples and visual comparisons, with access review under the retained license.",
- }
- for name in ("main", "packed12", "bf16", "q8", "q4", "shared", "examples"):
- repo_id = PROJECT if name == "main" else state[name]["repo_id"]
- api.add_collection_item(
- collection.slug, repo_id, "model", note=notes[name], exists_ok=True
- )
- api.update_collection_metadata(collection.slug, private=False, position=0)
- write_json(ROOT / "h3_mlx/model_index.json", catalog)
- return {
- "repo_id": PROJECT,
- "revision": commit.oid,
- "collection": collection.url,
- "verified": True,
- }
-
-
-def main():
- parser = argparse.ArgumentParser(description=__doc__)
- parser.add_argument("--output", type=Path, default=ROOT / ".release/huggingface")
- parser.add_argument("--component", choices=(*COMPONENTS, "main"), required=True)
- parser.add_argument(
- "--rights-confirmed",
- action="store_true",
- help="Publisher has established the required redistribution rights",
- )
- args = parser.parse_args()
- if not args.rights_confirmed:
- parser.error("Establish redistribution rights first; no Hub changes were made")
- from huggingface_hub import HfApi
-
- api = HfApi()
- if api.whoami()["name"] != "devin-lai":
- raise RuntimeError("Expected the devin-lai publishing account")
- state_path = args.output.resolve().parent / "publication.json"
- state = read_json(state_path) if state_path.exists() else {}
- if args.component == "main":
- state["main"] = finalize_main(api, args.output.resolve(), state)
- else:
- folder = args.output / f"MiniMax-H3-Image-mlx-{args.component}"
- state[args.component] = publish_component(api, args.component, folder)
- write_json(state_path, state)
- print(json.dumps(state[args.component], indent=2))
-
-
-if __name__ == "__main__":
- main()
diff --git a/scripts/publish_hf_stats_v041.py b/scripts/publish_hf_stats_v041.py
deleted file mode 100644
index 1994a6985c177e99196ce946434c16d5b1a2334b..0000000000000000000000000000000000000000
--- a/scripts/publish_hf_stats_v041.py
+++ /dev/null
@@ -1,149 +0,0 @@
-"""Publish the v0.4.1 Hub download-statistics metadata patch.
-
-This changes only small metadata/documentation files. It keeps the v0.4.0
-commits and tags intact and records each new immutable revision locally.
-"""
-
-from __future__ import annotations
-
-import hashlib
-import json
-import sys
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-sys.path.insert(0, str(ROOT))
-
-from huggingface_hub import CommitOperationAdd, HfApi, hf_hub_download # noqa: E402
-
-from h3_mlx.hub import read_json # noqa: E402
-from scripts.prepare_hf_release import component_config # noqa: E402
-from scripts.publish_hf_release import verify_remote # noqa: E402
-
-PROJECT = "devin-lai/MiniMax-H3-Image-mlx"
-RELEASE = "v0.4.1"
-COMPONENTS = ("shared", "bf16", "packed12", "q8", "q4", "examples")
-MAIN_FILES = (
- "README.md",
- "README.zh-CN.md",
- "USAGE.md",
- "MODEL_INDEX.md",
- "RELEASE.md",
- "docs/VALIDATION.md",
- "pyproject.toml",
- "tests/test_hub.py",
- "scripts/prepare_hf_release.py",
- "scripts/publish_hf_stats_v041.py",
- "h3_mlx/model_index.json",
- "config.json",
-)
-
-
-def json_bytes(value):
- return (json.dumps(value, indent=2, ensure_ascii=False) + "\n").encode()
-
-
-def add_to_manifest(manifest, name, content):
- manifest["files"][name] = {
- "bytes": len(content),
- "sha256": hashlib.sha256(content).hexdigest(),
- }
-
-
-def save_state(path, state):
- path.write_bytes(json_bytes(state))
-
-
-def publish_patch(api, repo_id, old_revision, changes):
- current = api.model_info(repo_id)
- if current.sha != old_revision:
- raise RuntimeError(f"Repository changed since v0.4.0: {repo_id}")
- manifest = read_json(hf_hub_download(repo_id, "files.json", revision=old_revision))
- if manifest.get("format") != "h3-mlx-files-v1":
- raise ValueError(f"Unexpected manifest format: {repo_id}")
- for name, content in changes.items():
- add_to_manifest(manifest, name, content)
- changes = {**changes, "files.json": json_bytes(manifest)}
- operations = [
- CommitOperationAdd(path_in_repo=name, path_or_fileobj=content)
- for name, content in changes.items()
- ]
- commit = api.create_commit(
- repo_id=repo_id,
- operations=operations,
- parent_commit=old_revision,
- commit_message=f"{RELEASE}: enable Hub download statistics",
- )
- revision = verify_remote(api, repo_id, manifest)
- if revision != commit.oid:
- raise RuntimeError(f"Repository changed during verification: {repo_id}")
- api.create_tag(repo_id, tag=RELEASE, revision=revision, exist_ok=True)
- if api.model_info(repo_id, revision=RELEASE).sha != revision:
- raise RuntimeError(f"Release tag points to a different commit: {repo_id}")
- if api.model_info(repo_id, revision="v0.4.0").sha != old_revision:
- raise RuntimeError(f"Original v0.4.0 tag changed: {repo_id}")
- return {"repo_id": repo_id, "old_revision": old_revision, "revision": revision}
-
-
-def main():
- api = HfApi()
- if api.whoami()["name"] != "devin-lai":
- raise RuntimeError("Expected the devin-lai publishing account")
- old_state = read_json(ROOT / ".release/publication.json")
- state_path = ROOT / ".release/stats-v0.4.1.json"
- state = read_json(state_path) if state_path.exists() else {}
- catalog = read_json(ROOT / "h3_mlx/model_index.json")
-
- # Check every untouched repository before making the first remote change.
- for name in (*COMPONENTS, "main"):
- if name in state:
- continue
- info = api.model_info(old_state[name]["repo_id"])
- if info.sha != old_state[name]["revision"]:
- raise RuntimeError(f"Repository changed since v0.4.0: {info.id}")
- if name != "main" and (info.private or info.gated != "manual"):
- raise RuntimeError(f"Unexpected access settings: {info.id}")
- if name == "main" and (info.private or info.gated):
- raise RuntimeError(f"Unexpected access settings: {info.id}")
- if any(tag.name == RELEASE for tag in api.list_repo_refs(info.id).tags):
- raise RuntimeError(f"Release tag already exists: {info.id}")
-
- for name in COMPONENTS:
- if name in state:
- continue
- entry = old_state[name]
- config = json_bytes(component_config(name, catalog))
- changes = {"config.json": config}
- if name != "examples":
- old_card = Path(
- hf_hub_download(entry["repo_id"], "README.md", revision=entry["revision"])
- ).read_text()
- new_card = old_card.replace(
- "**Release:** v0.4.0.",
- "**Release:** v0.4.1 (v0.4.0 weights with download-count metadata).",
- )
- if new_card == old_card:
- raise RuntimeError(f"Unexpected model card: {entry['repo_id']}")
- changes["README.md"] = new_card.encode()
- state[name] = publish_patch(api, entry["repo_id"], entry["revision"], changes)
- save_state(state_path, state)
- print(f"Verified {name}: {state[name]['revision']}", flush=True)
-
- catalog["release"] = RELEASE
- catalog["shared"]["revision"] = state["shared"]["revision"]
- for name in catalog["variants"]:
- catalog["variants"][name]["revision"] = state[name]["revision"]
- (ROOT / "h3_mlx/model_index.json").write_bytes(json_bytes(catalog))
- (ROOT / "config.json").write_bytes(json_bytes(component_config("runtime", catalog)))
-
- if "main" not in state:
- changes = {name: (ROOT / name).read_bytes() for name in MAIN_FILES}
- entry = old_state["main"]
- state["main"] = publish_patch(api, entry["repo_id"], entry["revision"], changes)
- save_state(state_path, state)
- print(f"Verified main: {state['main']['revision']}", flush=True)
- print(json.dumps(state, indent=2))
-
-
-if __name__ == "__main__":
- main()
diff --git a/tests/test_benchmark.py b/tests/test_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..102952cd676748aaad56f801cd34168539f72673
--- /dev/null
+++ b/tests/test_benchmark.py
@@ -0,0 +1,65 @@
+import json
+from types import SimpleNamespace
+
+import mlx.core as mx
+
+from h3_mlx import benchmark, runtime
+
+
+def test_summary_excludes_warmup_and_retains_every_resident_sample(
+ tmp_path, monkeypatch
+):
+ calls = []
+ cleared = []
+
+ class FakePipeline:
+ def __init__(self, *args, **kwargs):
+ assert kwargs["kernel"] == "metal-qk-exact"
+ assert kwargs["tune_matmul"] is True
+
+ def generate(self, prompt, **kwargs):
+ calls.append(kwargs)
+ return None, {
+ "png_sha256": "a" * 64,
+ "conditioning_cache_hit": len(calls) > 1,
+ "dit_was_resident": len(calls) > 1,
+ }
+
+ def clear(self):
+ cleared.append(True)
+
+ clock = iter([0.0, 50.0, 50.0, 55.0, 55.0, 62.0])
+ monkeypatch.setattr(
+ benchmark, "time", SimpleNamespace(perf_counter=lambda: next(clock))
+ )
+ monkeypatch.setattr(runtime, "Pipeline", FakePipeline)
+ monkeypatch.setattr(runtime, "environment", lambda: {"machine": "test"})
+ monkeypatch.setattr(mx, "synchronize", lambda: None)
+ output = tmp_path / "run"
+ benchmark.main(
+ [
+ "--model",
+ "model",
+ "--text-encoder",
+ "encoder",
+ "--prompt",
+ "test",
+ "--warmup",
+ "1",
+ "--repeats",
+ "2",
+ "--output",
+ str(output),
+ ]
+ )
+ summary = json.loads((output / "summary.json").read_text())
+ assert summary["median_seconds"] == 6.0
+ assert [s["wall_seconds"] for s in summary["samples"]] == [50.0, 5.0, 7.0]
+ assert [s["phase"] for s in summary["samples"]] == [
+ "warmup",
+ "measured",
+ "measured",
+ ]
+ assert all(s["dit_was_resident"] for s in summary["samples"][1:])
+ assert all(c["save_trace"] for c in calls)
+ assert cleared == [True]
diff --git a/tests/test_compilation.py b/tests/test_compilation.py
deleted file mode 100644
index c85632803345d7ff5b98c473d586a8b69d473686..0000000000000000000000000000000000000000
--- a/tests/test_compilation.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import mlx.core as mx
-import numpy as np
-
-from h3_mlx.config import DiTConfig
-from h3_mlx.dit import DiTBlock
-from h3_mlx.weights import WeightStore
-
-
-def test_packed_block_cache_and_compiled_bf16_boundaries(tmp_path):
- mx.random.seed(729)
- c = DiTConfig(
- hidden_size=32, num_attention_heads=2, attention_head_dim=16, ffn_hidden_size=64
- )
- tensors = {}
- for name, shape in {
- "norm1": (32,),
- "norm2": (32,),
- "attn.q_norm": (16,),
- "attn.k_norm": (16,),
- "attn.qkv_proj": (96, 32),
- "attn.out_proj": (32, 32),
- "mlp.fc1": (128, 32),
- "mlp.fc2": (32, 64),
- }.items():
- tensors[f"blocks.0.{name}.weight"] = (mx.random.normal(shape) * 0.02).astype(
- mx.bfloat16
- )
- if len(shape) == 2:
- tensors[f"blocks.0.{name}.bias"] = (
- mx.random.normal((shape[0],)) * 0.01
- ).astype(mx.bfloat16)
- original = tmp_path / "original"
- original.mkdir()
- mx.save_safetensors(str(original / "model.safetensors"), tensors)
- store = WeightStore(original)
- native = DiTBlock(store, "blocks.0", c)
- packed = DiTBlock(store, "blocks.0", c, packed_cache=tmp_path / "packed")
- restored = DiTBlock(store, "blocks.0", c, packed_cache=tmp_path / "packed")
- x = mx.random.normal((17, 32)).astype(mx.bfloat16)
- params = tuple(
- (mx.random.normal((3, 32)) * 0.1).astype(mx.bfloat16) for _ in range(6)
- )
- ids = mx.arange(17) % 3
- expected = native(x, params=params, indices=ids, mlp_chunk=0)
- state = tuple(
- p.weight.state()
- for p in (restored.qkv, restored.out, restored.fc1, restored.fc2)
- )
-
- def forward(x, params, ids, state):
- return restored(
- x, params=params, indices=ids, mlp_chunk=0, projection_weights=state
- )
-
- compiled = mx.compile(forward)
- for actual in (
- packed(x, params=params, indices=ids, mlp_chunk=0),
- restored(x, params=params, indices=ids, mlp_chunk=0),
- compiled(x, params, ids, state),
- ):
- np.testing.assert_array_equal(
- np.asarray(actual.view(mx.uint16)), np.asarray(expected.view(mx.uint16))
- )
diff --git a/tests/test_conversion.py b/tests/test_conversion.py
deleted file mode 100644
index 0ee2db08c83bdbacbda369715a932beba87512ad..0000000000000000000000000000000000000000
--- a/tests/test_conversion.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import json
-
-import mlx.core as mx
-import numpy as np
-import pytest
-import torch
-from safetensors.torch import save_file
-
-from h3_mlx.convert import convert
-from h3_mlx.weights import Linear, WeightStore
-
-
-def test_quantized_shards_roundtrip_and_preserve_qkv_layout(tmp_path):
- model = tmp_path / "original"
- transformer = model / "transformer"
- transformer.mkdir(parents=True)
- config = {
- "hidden_size": 128,
- "num_layers": 1,
- "token_refiner_num_layers": 0,
- "num_attention_heads": 2,
- "attention_head_dim": 128,
- "ffn_hidden_size": 128,
- "audio_enabled": False,
- }
- (transformer / "config.json").write_text(json.dumps(config))
- torch.manual_seed(77)
- tensors = {}
- for name in (
- "norm1.weight",
- "norm2.weight",
- "attn.q_norm.weight",
- "attn.k_norm.weight",
- ):
- tensors["blocks.0." + name] = torch.randn(128).bfloat16()
- for name, shape in (
- ("attn.qkv_proj", (768, 128)),
- ("attn.out_proj", (128, 256)),
- ("mlp.fc1", (256, 128)),
- ("mlp.fc2", (128, 128)),
- ):
- tensors[f"blocks.0.{name}.weight"] = torch.randn(*shape).bfloat16()
- save_file(tensors, str(transformer / "model.safetensors"))
- output = tmp_path / "converted"
- meta = convert(model, output, bits=8, group_size=64)
- assert meta["format"] == "h3-mlx-core-v1" and len(meta["files"]) == 1
- original = WeightStore(transformer)
- converted = WeightStore(output)
- a = Linear.load(original, "blocks.0.attn.qkv_proj", bits=8, grouped_heads=(2, 128))
- b = Linear.load(converted, "blocks.0.attn.qkv_proj", grouped_heads=(2, 128))
- for x, y in ((a.weight, b.weight), (a.scales, b.scales), (a.qbiases, b.qbiases)):
- np.testing.assert_array_equal(
- np.asarray(x.view(mx.uint16) if x.dtype == mx.bfloat16 else x),
- np.asarray(y.view(mx.uint16) if y.dtype == mx.bfloat16 else y),
- )
- with pytest.raises(FileExistsError):
- convert(model, output)
diff --git a/tests/test_hub.py b/tests/test_hub.py
index 289123211b85f1229ffdffc4562faab9e8c9d6bb..57e67106caae969d43557f03eb182309460a341b 100644
--- a/tests/test_hub.py
+++ b/tests/test_hub.py
@@ -172,7 +172,7 @@ def test_quantized_release_moves_without_original_checkpoint(tmp_path):
import hashlib
import shutil
- import mlx.core as mx
+ mx = pytest.importorskip("mlx.core")
from h3_mlx.config import DiTConfig
from h3_mlx.dit import DiT
@@ -218,7 +218,7 @@ def test_quantized_release_moves_without_original_checkpoint(tmp_path):
shutil.move(str(model), moved)
loaded = DiT(moved, cache_dir=tmp_path / "cache")
assert loaded.bits == 4 and loaded.core_store.path == moved / "core"
- with pytest.raises(ValueError, match="already select"):
+ with pytest.raises(TypeError):
DiT(moved, bits=8)
metadata = hub.read_json(moved / "core/quantization.json")
metadata["source_config_sha256"] = "wrong"
diff --git a/tests/test_inference_only.py b/tests/test_inference_only.py
new file mode 100644
index 0000000000000000000000000000000000000000..bca085d393048ac5c896ad63cd4c815f2b5f0cc3
--- /dev/null
+++ b/tests/test_inference_only.py
@@ -0,0 +1,47 @@
+import importlib.util
+from pathlib import Path
+
+import pytest
+
+from h3_mlx.cli import main
+
+
+def test_conversion_module_is_not_discoverable():
+ assert importlib.util.find_spec("h3_mlx.convert") is None
+
+
+@pytest.mark.parametrize(
+ "option", ["--quantize", "--encoder-quantize", "--quantized-core"]
+)
+def test_cli_rejects_conversion_options(option, tmp_path):
+ with pytest.raises(SystemExit) as error:
+ main(
+ [
+ "generate",
+ "--model",
+ str(tmp_path),
+ "--text-encoder",
+ str(tmp_path),
+ "prompt",
+ option,
+ "8",
+ ]
+ )
+ assert error.value.code == 2
+
+
+def test_help_works_without_loading_metal(capsys):
+ with pytest.raises(SystemExit) as error:
+ main(["--help"])
+ assert error.value.code == 0
+ assert "download" in capsys.readouterr().out
+
+
+def test_release_audit_accepts_public_runtime():
+ path = Path(__file__).resolve().parents[1] / "tools/check_release.py"
+ spec = importlib.util.spec_from_file_location("release_audit", path)
+ audit = importlib.util.module_from_spec(spec)
+ spec.loader.exec_module(audit)
+ audit.check_source(path.parents[1])
+ with pytest.raises(ValueError, match="Conversion operation"):
+ audit.check_python("bad.py", "import mlx.core as mx\nmx.quantize(weights)")
diff --git a/tests/test_packed.py b/tests/test_packed.py
index c307cec5c834e2b1dc78a4add7addadd7cc1d096..858eae7383f5fdc31c6f4a2df4549945ba77d423 100644
--- a/tests/test_packed.py
+++ b/tests/test_packed.py
@@ -1,43 +1,81 @@
import mlx.core as mx
import numpy as np
+import pytest
from h3_mlx.packed import PackedBF16, PackedLinear
from h3_mlx.weights import Linear
-def test_palette12_preserves_all_bf16_bit_patterns():
- # Includes signs, subnormals, signed zero, Inf and every NaN payload.
+def test_decoder_preserves_all_bf16_payloads_in_an_all_escape_fixture():
+ # A deliberately uncompressed synthetic fixture: no packing algorithm.
raw = mx.arange(65536, dtype=mx.uint16)
- packed = PackedBF16.pack(raw.view(mx.bfloat16))
+ packed = PackedBF16(
+ (65536,),
+ mx.zeros(65536, mx.uint8),
+ mx.zeros(32768, mx.uint8),
+ mx.arange(65536, dtype=mx.uint32),
+ raw,
+ mx.array([56], mx.uint32),
+ )
np.testing.assert_array_equal(
np.asarray(packed.unpack().view(mx.uint16)), np.asarray(raw)
)
-def test_palette12_odd_length_zero_and_wide_exponents():
- for raw in (
- [0, 0, 0],
- [0, 0x8000, 1, 0x8001, 0x3F80, 0x7F80, 0xFFFF],
- [0x3800, 0x3B01, 0xBAFF, 0x0080, 0x8000],
- ):
- x = mx.array(raw, mx.uint16)
- packed = PackedBF16.pack(x.view(mx.bfloat16))
- np.testing.assert_array_equal(np.asarray(packed.unpack().view(mx.uint16)), raw)
-
-
-def test_palette12_realistic_linear_is_exact_and_smaller():
- mx.random.seed(731)
- w = (mx.random.normal((256, 512)) * 0.02).astype(mx.bfloat16)
- b = mx.random.normal((256,)).astype(mx.bfloat16)
- x = mx.random.normal((71, 512)).astype(mx.bfloat16)
- original = Linear(w, b)
- packed = PackedLinear.from_linear(original)
- assert packed.nbytes < original.nbytes * 0.76
+def test_decoder_common_path_escapes_signs_and_odd_length():
+ packed = PackedBF16(
+ (7,),
+ mx.array([128, 128, 1, 0, 128, 255, 0], mx.uint8),
+ mx.array([0xF7, 0, 0, 0], mx.uint8),
+ mx.array([2, 3, 4, 5, 6], mx.uint32),
+ mx.array([0x3801, 0x8000, 0x7F80, 0xFFFF, 0], mx.uint16),
+ mx.array([56], mx.uint32),
+ )
np.testing.assert_array_equal(
- np.asarray(packed(x).view(mx.uint16)), np.asarray(original(x).view(mx.uint16))
+ np.asarray(packed.unpack().view(mx.uint16)),
+ [0x3F80, 0xBF80, 0x3801, 0x8000, 0x7F80, 0xFFFF, 0],
+ )
+ zero_base = PackedBF16(
+ (3,),
+ mx.array([0, 0, 1], mx.uint8),
+ mx.array([0x80, 1], mx.uint8),
+ mx.array([], mx.uint32),
+ mx.array([], mx.uint16),
+ mx.array([0], mx.uint32),
)
- tensors = packed.weight.tensors("weight")
- restored = PackedBF16.from_tensors(tensors, "weight", w.shape)
np.testing.assert_array_equal(
- np.asarray(restored.unpack().view(mx.uint16)), np.asarray(w.view(mx.uint16))
+ np.asarray(zero_base.unpack().view(mx.uint16)), [0, 0x8000, 0x0101]
+ )
+
+
+def test_prepacked_linear_preserves_bias_epilogue_and_compiled_inputs():
+ # Known encoding of a 4x8 matrix of BF16 ones.
+ packed = PackedBF16(
+ (4, 8),
+ mx.full((32,), 128, mx.uint8),
+ mx.full((16,), 0x77, mx.uint8),
+ mx.array([], mx.uint32),
+ mx.array([], mx.uint16),
+ mx.array([56], mx.uint32),
)
+ bias = mx.array([0.25, -0.5, 1, 0], mx.bfloat16)
+ x = mx.arange(24).reshape(3, 8).astype(mx.bfloat16)
+ layer = PackedLinear(packed, bias)
+ reference = Linear(mx.ones((4, 8), mx.bfloat16), bias)(x)
+ compiled = mx.compile(lambda x, state: layer(x, state=state))
+ for actual in (layer(x), compiled(x, packed.state())):
+ np.testing.assert_array_equal(
+ np.asarray(actual.view(mx.uint16)), np.asarray(reference.view(mx.uint16))
+ )
+
+
+def test_packed_shape_validation():
+ tensors = {
+ "w.low": mx.zeros(3, mx.uint8),
+ "w.nibbles": mx.zeros(1, mx.uint8),
+ "w.escape_indices": mx.array([], mx.uint32),
+ "w.escape_values": mx.array([], mx.uint16),
+ "w.base": mx.array([0], mx.uint32),
+ }
+ with pytest.raises(ValueError, match="dimensions"):
+ PackedBF16.from_tensors(tensors, "w", (3,))
diff --git a/tests/test_preconverted.py b/tests/test_preconverted.py
new file mode 100644
index 0000000000000000000000000000000000000000..12398d052fff45b73a562af63695798bd0ef9c65
--- /dev/null
+++ b/tests/test_preconverted.py
@@ -0,0 +1,53 @@
+import json
+
+import mlx.core as mx
+import numpy as np
+import pytest
+
+from h3_mlx.config import DiTConfig
+from h3_mlx.dit import DiTBlock
+from h3_mlx.weights import Linear, WeightStore
+
+
+@pytest.mark.parametrize("bits,word", [(4, 0x11111111), (8, 0x01010101)])
+def test_preconverted_quantized_linear_loads_without_online_quantization(
+ tmp_path, monkeypatch, bits, word
+):
+ # Fixed synthetic quantized matrix: each decoded weight is 0.5.
+ tensors = {
+ "proj.weight": mx.full((4, 64 // (32 // bits)), word, mx.uint32),
+ "proj.scales": mx.full((4, 1), 0.5, mx.bfloat16),
+ "proj.biases": mx.zeros((4, 1), mx.bfloat16),
+ "proj.bias": mx.array([0, 0.25, 0.5, 1], mx.bfloat16),
+ }
+ mx.save_safetensors(str(tmp_path / "core.safetensors"), tensors)
+ (tmp_path / "quantization.json").write_text(
+ json.dumps({"bits": bits, "group_size": 64})
+ )
+
+ def forbidden(*args, **kwargs):
+ pytest.fail("Inference must never quantize weights")
+
+ monkeypatch.setattr(mx, "quantize", forbidden)
+ layer = Linear.load(WeightStore(tmp_path), "proj")
+ x = mx.ones((3, 64), mx.bfloat16)
+ expected = mx.array([[32, 32.25, 32.5, 33]] * 3, mx.bfloat16)
+ np.testing.assert_array_equal(
+ np.asarray(layer(x).view(mx.uint16)), np.asarray(expected.view(mx.uint16))
+ )
+
+
+def test_missing_packed_block_cannot_trigger_conversion(tmp_path):
+ class NormStore:
+ def get(self, key):
+ assert (
+ key.endswith("norm1.weight")
+ or key.endswith("norm2.weight")
+ or key.endswith("_norm.weight")
+ )
+ return mx.ones(32, mx.bfloat16)
+
+ missing = tmp_path / "missing"
+ with pytest.raises(FileNotFoundError, match="preconverted"):
+ DiTBlock(NormStore(), "blocks.0", DiTConfig(), packed_cache=missing)
+ assert not missing.exists()
diff --git a/tests/test_release_guards.py b/tests/test_release_guards.py
deleted file mode 100644
index e618d3db9fa23f7a0118bd5e9593d72b3ac18592..0000000000000000000000000000000000000000
--- a/tests/test_release_guards.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import json
-
-import pytest
-
-from h3_mlx.hub import sha256
-from scripts.publish_hf_release import publish_component, verify_local
-
-
-def test_partial_quantized_export_cannot_be_published(tmp_path):
- core = tmp_path / "model/core"
- core.mkdir(parents=True)
- file = core / "quantization.json"
- file.write_text(
- json.dumps(
- {
- "files": [
- {
- "file": f"core-{i:03d}.safetensors",
- "bytes": 1,
- "sha256": "a" * 64,
- }
- for i in range(52)
- ]
- }
- )
- )
- (tmp_path / "files.json").write_text(
- json.dumps(
- {
- "format": "h3-mlx-files-v1",
- "files": {
- "model/core/quantization.json": {
- "bytes": file.stat().st_size,
- "sha256": sha256(file),
- },
- },
- }
- )
- )
- with pytest.raises(ValueError, match="export/manifest mismatch"):
- verify_local(tmp_path)
-
-
-def test_partial_packed_export_cannot_create_or_modify_a_repository(tmp_path):
- (tmp_path / "model/core").mkdir(parents=True)
- # No API method exists: an attempted remote mutation would fail this test.
- with pytest.raises(ValueError, match="fifty packed blocks"):
- publish_component(object(), "packed12", tmp_path)
diff --git a/tests/test_runtime_guard.py b/tests/test_runtime_guard.py
index c0f86a808c6075f16acce7a661854767b2e0e416..2be9f930e67a8306f3481ada4889d88233b734e0 100644
--- a/tests/test_runtime_guard.py
+++ b/tests/test_runtime_guard.py
@@ -8,6 +8,14 @@ from h3_mlx.config import SIGMAS
from h3_mlx.runtime import Pipeline
+def test_path_cache_arguments_can_be_saved_in_inference_json(tmp_path):
+ pipe = Pipeline(tmp_path, tmp_path, cache_dir=tmp_path / "cache")
+ record = {"dit": pipe.dit_options, "encoder": pipe.encoder_options}
+ saved = json.loads(json.dumps(record))
+ assert saved["dit"]["cache_dir"] == str(tmp_path / "cache")
+ assert saved["encoder"]["cache_dir"] == str(tmp_path / "cache")
+
+
def test_nonfinite_prediction_is_rejected_before_image_write(tmp_path, monkeypatch):
from h3_mlx import encoder
diff --git a/tools/check_release.py b/tools/check_release.py
new file mode 100644
index 0000000000000000000000000000000000000000..be648676bd89014978f61fd3c216d0be8541b72d
--- /dev/null
+++ b/tools/check_release.py
@@ -0,0 +1,102 @@
+"""Audit the public source and distributions for the inference-only boundary."""
+
+from __future__ import annotations
+
+import argparse
+import ast
+import tarfile
+import zipfile
+from pathlib import Path
+
+RUNTIME_MODULES = {
+ "__init__",
+ "__main__",
+ "benchmark",
+ "cli",
+ "compare",
+ "config",
+ "dit",
+ "encoder",
+ "hub",
+ "kernels",
+ "layout",
+ "memory",
+ "ops",
+ "packed",
+ "reference",
+ "runtime",
+ "tuning",
+ "vae",
+ "vision",
+ "weights",
+}
+
+
+def check_python(name, source):
+ tree = ast.parse(source, filename=name)
+ for node in ast.walk(tree):
+ if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
+ if node.name in {"pack", "from_linear", "convert", "export_model"}:
+ raise ValueError(f"Conversion entry point in {name}: {node.name}")
+ if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute):
+ if node.func.attr in {"quantize", "save_model", "export_model"}:
+ raise ValueError(f"Conversion operation in {name}: {node.func.attr}")
+ if isinstance(node, (ast.Import, ast.ImportFrom)):
+ names = [a.name for a in node.names]
+ if isinstance(node, ast.ImportFrom) and node.module:
+ names.append(node.module)
+ if any(
+ n.split(".")[0] in {"experiments", "scripts"} or n.endswith(".convert")
+ for n in names
+ ):
+ raise ValueError(f"Private tooling import in {name}")
+
+
+def check_entry(name, data):
+ path = Path(name)
+ if path.suffix in {".safetensors", ".gguf", ".npz", ".npy"}:
+ raise ValueError(f"Model or tensor artifact in public distribution: {name}")
+ if path.suffix == ".py" and "h3_mlx" in path.parts:
+ if path.stem not in RUNTIME_MODULES:
+ raise ValueError(f"Unreviewed runtime module: {name}")
+ check_python(name, data)
+
+
+def check_source(root):
+ for path in (root / "h3_mlx").glob("*.py"):
+ check_entry(str(path), path.read_bytes())
+ for name in ("experiments", "optimization", ".release"):
+ if (root / name).exists():
+ raise ValueError(
+ f"Private development directory in public checkout: {name}"
+ )
+
+
+def check_artifact(path):
+ if path.suffix == ".whl":
+ with zipfile.ZipFile(path) as archive:
+ for name in archive.namelist():
+ check_entry(name, archive.read(name))
+ elif path.name.endswith(".tar.gz"):
+ with tarfile.open(path) as archive:
+ for member in archive.getmembers():
+ if member.isfile():
+ check_entry(member.name, archive.extractfile(member).read())
+ else:
+ raise ValueError(f"Unsupported distribution: {path}")
+
+
+def main():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("artifacts", nargs="*", type=Path)
+ args = parser.parse_args()
+ check_source(Path(__file__).resolve().parents[1])
+ for path in args.artifacts:
+ check_artifact(path)
+ print(
+ f"Inference-only audit passed: source and {len(args.artifacts)} distributions"
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tools/plot_benchmarks.py b/tools/plot_benchmarks.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dd7c87ce1e3a4ddfd1bb207df1bce15a000292f
--- /dev/null
+++ b/tools/plot_benchmarks.py
@@ -0,0 +1,106 @@
+"""Render recorded sample timings; requires matplotlib (no model weights)."""
+
+import json
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def main():
+ root = Path(__file__).resolve().parents[1]
+ plt.rcParams.update(
+ {
+ "font.family": "DejaVu Sans",
+ "font.size": 11,
+ "svg.fonttype": "none",
+ "axes.spines.top": False,
+ "axes.spines.right": False,
+ }
+ )
+ fig, axes = plt.subplots(1, 2, figsize=(11.6, 4.5), sharey=True)
+ fig.patch.set_facecolor("#f7fafc")
+ colors = ["#8296ac", "#137f82"]
+ for ax, storage, title in zip(
+ axes, ["bf16", "packed12"], ["BF16", "Lossless packed BF16"], strict=True
+ ):
+ ax.set_facecolor("#f7fafc")
+ summary = json.loads(
+ (root / f"benchmarks/data/{storage}/summary.json").read_text()
+ )
+ comparison = json.loads(
+ (root / f"benchmarks/data/{storage}/comparison.json").read_text()
+ )
+ for case_index, case in enumerate(["product", "portrait"]):
+ for index, (variant, label) in enumerate(
+ [("control", "Control"), ("qk-fc2-native-norm", "Tuned")]
+ ):
+ values = summary["cases"][case][variant]["samples_s"]
+ x = case_index + (-0.16 if index == 0 else 0.16)
+ ax.scatter(
+ x + np.array([-0.045, 0, 0.045]),
+ values,
+ color=colors[index],
+ s=35,
+ zorder=3,
+ label=label if case_index == 0 else None,
+ )
+ median = float(np.median(values))
+ ax.plot(
+ [x - 0.11, x + 0.11], [median, median], color=colors[index], lw=2.3
+ )
+ ax.annotate(
+ f"{median:.2f}",
+ (x, median),
+ xytext=(0, -17 if index else 12),
+ textcoords="offset points",
+ ha="center",
+ color=colors[index],
+ fontsize=10,
+ fontweight="bold",
+ )
+ before = comparison["baseline_summary"]["geomean_seconds"]
+ after = comparison["candidate_summary"]["geomean_seconds"]
+ ax.set_title(
+ f"{title}\n{before:.2f} s → {after:.2f} s (−{comparison['latency_reduction_pct']:.2f}%)",
+ loc="left",
+ fontsize=13,
+ fontweight="bold",
+ pad=18,
+ )
+ ax.set_xticks([0, 1], ["Product · seed 731", "Portrait · seed 1234"])
+ ax.set_xlim(-0.48, 1.48)
+ ax.set_ylim(58.5, 69)
+ ax.grid(axis="y", color="#dce5ed", lw=0.8, zorder=0)
+ ax.legend(frameon=False, loc="lower left", ncols=2)
+ ax.spines["left"].set_color("#b7c8d6")
+ ax.spines["bottom"].set_color("#b7c8d6")
+ axes[0].set_ylabel("Warm complete generation · seconds")
+ fig.suptitle(
+ "2048 × 2048 · six steps · M5 Pro / 64 GiB · MLX 0.32.4",
+ fontsize=14,
+ fontweight="bold",
+ y=0.98,
+ )
+ fig.text(
+ 0.5,
+ 0.025,
+ "Dots: all three repeats. Lines: medians. Cached conditioning and resident weights; separate paired campaigns.\nHeadline values: geometric mean of case medians. Startup/downloads excluded; trace and PNG save included.",
+ ha="center",
+ fontsize=9,
+ color="#465d70",
+ )
+ fig.tight_layout(rect=(0, 0.12, 1, 0.9), w_pad=3)
+ target = root / "assets/benchmarks.svg"
+ fig.savefig(target, facecolor=fig.get_facecolor())
+ fig.savefig(
+ "/tmp/minimax-benchmarks-preview.png", dpi=140, facecolor=fig.get_facecolor()
+ )
+ print(target)
+
+
+if __name__ == "__main__":
+ main()
+
+ 
