File size: 8,926 Bytes
2f48fc1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | """CPU regression checks against the pinned native ComfyUI consumer."""
import ast
from contextlib import contextmanager
import importlib.util
import json
import math
from pathlib import Path
import sys
import tempfile
from types import SimpleNamespace
import unittest
import torch
from safetensors import safe_open
from safetensors.torch import load_file, save_file
HERE = Path(__file__).parent
spec = importlib.util.spec_from_file_location("omniref_converter", HERE / "convert_omniref_comfy.py")
conv = importlib.util.module_from_spec(spec)
sys.modules[spec.name] = conv
spec.loader.exec_module(conv)
def native_namespace():
# Execute the actual pinned architecture, with ordinary CPU Linear/RMSNorm
# and an explicit activation implementation in place of CUDA kitchen ops.
@contextmanager
def cast_weights(head, h, offloadable=True):
yield head.weight.to(h), head.bias.to(h)
def swiglu(linear, packed, activation):
assert activation == "swiglu"
gate, value = packed.chunk(2, dim=-1)
return linear(torch.nn.functional.silu(gate) * value)
namespace = {"torch": torch, "nn": torch.nn, "math": math,
"ComfyAttention": lambda: None,
"comfy": SimpleNamespace(ops=SimpleNamespace(
CastBiasWeightContext=cast_weights, linear_input_act=swiglu))}
tree = ast.parse((HERE / "upstream/comfy_model.py").read_text())
body = [node for node in tree.body if isinstance(node, (ast.FunctionDef, ast.ClassDef))]
exec(compile(ast.Module(body=body, type_ignores=[]), "pinned-ComfyUI-model", "exec"), namespace)
detection = ast.parse((HERE / "upstream/comfy_model_detection.py").read_text())
funcs = [n for n in detection.body if isinstance(n, ast.FunctionDef)
and n.name in {"count_blocks", "detect_unet_config", "any_suffix_in"}]
namespace["json"] = json
exec(compile(ast.Module(body=funcs, type_ignores=[]), "pinned-ComfyUI-detection", "exec"), namespace)
return namespace
def fixture(path):
config = {"hidden_size": 4, "num_attention_heads": 2, "attention_head_dim": 2,
"ffn_dim": 6, "time_embed_dim": 3, "pdd_steps": 4, "in_channels": 2,
"audio_in_channels": 2, "patch_size": [1, 2, 2], "text_dim": 5,
"time_embed_hidden_dim": 4, "freq_dim": 4, "num_layers": 2,
"num_refiner_layers": 1, "rope_theta": 10000.0, "rope_freq_dim": 2,
"norm_eps": 1e-5, "qk_norm_eps": 1e-5, "final_norm_eps": 1e-5}
g = torch.Generator().manual_seed(24)
tensors = {k: (torch.randn(shape, generator=g) * 0.1).bfloat16()
for k, shape in conv.expected_shapes(config).items()}
names = sorted(tensors)
mapping = {}
for j in range(2):
selected = {k: tensors[k] for k in names[j::2]}
filename = f"part-{j}.safetensors"
save_file(selected, str(path / filename))
mapping.update({k: filename for k in selected})
(path / "config.json").write_text(json.dumps(config))
(path / "diffusion_pytorch_model.safetensors.index.json").write_text(json.dumps({"weight_map": mapping}))
return config, tensors
class ConversionTests(unittest.TestCase):
@classmethod
def setUpClass(cls):
torch.set_num_threads(2)
cls.native = native_namespace()
def convert(self, path, rank=0):
c, original = fixture(path)
_, source = conv.load_source(path)
scratch = path / "scratch"
scratch.mkdir()
output, curve = conv.make_plan(c, source, scratch, rank=rank, grid=1025)
contract = {"pdd_steps": 4, "grid_max_t": 0.999,
"video_scheduler_shift": 12.0, "audio_scheduler_shift": 3.0}
metadata = conv.metadata_for(c, contract, rank)
target = path / "export.safetensors"
receipt = conv.write_file(target, output, metadata)
return c, original, load_file(str(target)), metadata, curve, receipt
def test_native_detection_and_strict_dense_state_dict_load(self):
with tempfile.TemporaryDirectory() as d:
c, source, target, metadata, _, _ = self.convert(Path(d))
detected = self.native["detect_unet_config"](target, "", metadata=metadata)
self.assertEqual(detected["pdd_steps"], 4)
self.assertTrue(detected["gate_compress"])
self.assertEqual(detected["latents_dim"], 2)
model = self.native["MiniMaxH3Model"](**detected, device="cpu", dtype=torch.float32, operations=torch.nn)
model.load_state_dict(target, strict=True)
qkv = model.blocks[0].attn.qkv_proj.weight
expected = torch.cat([source[f"transformer_blocks.0.attn.to_{p}.weight"].float() for p in ("q", "k", "v")])
self.assertTrue(torch.equal(qkv, expected))
def test_swiglu_native_forward_matches_diffusers_value_first(self):
with tempfile.TemporaryDirectory() as d:
c, source, target, _, _, _ = self.convert(Path(d))
mlp = self.native["MLP"](c["hidden_size"], c["ffn_dim"], operations=torch.nn, dtype=torch.float32)
mlp.load_state_dict({"fc1.weight": target["blocks.0.mlp.fc1.weight"],
"fc2.weight": target["blocks.0.mlp.fc2.weight"]}, strict=True)
x = torch.randn(7, c["hidden_size"])
value, gate = torch.nn.functional.linear(x, source["transformer_blocks.0.ff.net.0.proj.weight"].float()).chunk(2, dim=-1)
expected = torch.nn.functional.linear(value * torch.nn.functional.silu(gate),
source["transformer_blocks.0.ff.net.2.weight"].float())
torch.testing.assert_close(mlp(x), expected, rtol=0, atol=0)
def test_pdd_native_consumer_recovers_weighted_full_heads(self):
with tempfile.TemporaryDirectory() as d:
c, source, target, _, _, _ = self.convert(Path(d))
h = torch.randn(3, c["hidden_size"])
for stream, key in [("video", "proj_out"), ("audio", "audio_proj_out")]:
weight = target[f"final_layer.{stream}_out.weight"]
bias = target[f"final_layer.{stream}_out.bias"]
head = SimpleNamespace(weight=weight, bias=bias)
original_w = source[key + ".weight"].float().reshape(4, -1, c["hidden_size"])
original_b = source[key + ".bias"].float().reshape(4, -1)
for start, stop in [(0, 1), (0, 2), (1, 3), (3, 4)]:
for shift in [3.0, 12.0]:
grid = torch.linspace(1.0, 0.0, 5, dtype=torch.float64)
dt = (1 - shift * grid / (1 + (shift - 1) * grid)).diff()[start:stop]
normalized = (dt / dt.sum()).float()
expected = torch.nn.functional.linear(h,
torch.einsum("n,noi->oi", normalized, original_w[start:stop]),
torch.einsum("n,no->o", normalized, original_b[start:stop]))
actual = self.native["_pdd_head"](head, h, 4, start, stop, shift)
torch.testing.assert_close(actual, expected, rtol=1e-5, atol=1e-6)
def test_curve_export_native_strict_load_and_holdout_error(self):
with tempfile.TemporaryDirectory() as d:
c, source, target, metadata, curve, _ = self.convert(Path(d), rank=3)
detected = self.native["detect_unet_config"](target, "", metadata=metadata)
self.assertEqual(detected["adaln_curve_grid"], 1025)
self.assertEqual(detected["time_embed_dim"], 3)
model = self.native["MiniMaxH3Model"](**detected, device="cpu", dtype=torch.float32, operations=torch.nn)
model.load_state_dict(target, strict=True)
self.assertFalse(any(k.startswith("time_embedder.") for k in target))
self.assertTrue(curve["approximation"])
self.assertLess(max(e["relative_l2"] for e in curve["holdout_modulation_samples"]), 1e-5)
def test_reject_unexpected_tensor_before_writing(self):
with tempfile.TemporaryDirectory() as d:
path = Path(d)
fixture(path)
c, source = conv.load_source(path)
source["optimizer.unknown"] = source["proj_in.weight"]
with self.assertRaisesRegex(ValueError, "Unexpected source keys"):
conv.make_plan(c, source, path)
def test_output_hash_and_refuse_overwrite(self):
import hashlib
with tempfile.TemporaryDirectory() as d:
path = Path(d)
_, _, _, _, _, receipt = self.convert(path)
self.assertEqual(receipt["sha256"], hashlib.sha256((path / "export.safetensors").read_bytes()).hexdigest())
with self.assertRaises(FileExistsError):
conv.write_file(path / "export.safetensors", {}, {})
if __name__ == "__main__":
unittest.main(verbosity=2)
|