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from __future__ import annotations
import json
from pathlib import Path
from typing import Any
import torch
import torch.nn as nn
from safetensors import safe_open
from packed_artifact import (
assign_tensor_by_name,
instantiate_mage_transformer_on_meta,
materialize_mage_rope_tensor_attributes,
set_child_module,
unregistered_meta_tensor_attribute_names,
)
from standard_transformer import (
_apply_runtime_defaults,
_quantized_keys,
_target_specs_for_config,
fail,
)
from torch_ops_native import (
PackedNvfp4LinearNativeOp,
initialize_native_sm120_op,
)
def parse_metadata_json(
metadata: dict[str, str],
key: str,
) -> dict[str, Any]:
try:
value = json.loads(metadata[key])
except (KeyError, json.JSONDecodeError) as exc:
fail(f"single-file checkpoint has invalid {key} metadata: {exc}")
if not isinstance(value, dict):
fail(f"single-file checkpoint metadata {key} is not an object")
return value
def load_single_file_native_transformer(
checkpoint_path: str | Path,
*,
support_root: str | Path,
device: torch.device,
) -> tuple[nn.Module, dict[str, Any]]:
checkpoint_path = Path(checkpoint_path).resolve()
support_root = Path(support_root).resolve()
if not checkpoint_path.is_file():
fail(f"single-file checkpoint is missing: {checkpoint_path}")
if device.type != "cuda":
fail("the native resident transformer requires a CUDA destination")
with safe_open(
checkpoint_path,
framework="pt",
device="cpu",
) as handle:
metadata = handle.metadata() or {}
if metadata.get("mage_flow.component") != "transformer":
fail("single-file checkpoint is not a Mage-Flow transformer")
supported_variants = {
"turbo-balanced-v2-fused-qkv",
"edit-turbo-xpo3-v1-fused-qkv",
"edit-xpo3-v1-fused-qkv-direct-hnd-steps7-29",
}
if metadata.get("mage_flow.variant") not in supported_variants:
fail(
"unsupported Mage-Flow single-file variant: "
f"{metadata.get('mage_flow.variant')!r}"
)
config = parse_metadata_json(
metadata,
"mage_flow.transformer_config",
)
quant_config = config.get("quantization_config")
if not isinstance(quant_config, dict):
fail("single-file transformer config has no quantization_config")
if quant_config.get("quant_method") not in {
"mage_flow_nvfp4",
"xpo3_nvfp4",
}:
fail(
"single-file transformer does not declare a supported "
"Mage-Flow/XPO3 NVFP4 method"
)
if quant_config.get("quant_algo") != "NVFP4":
fail("single-file transformer does not declare NVFP4")
nvfp4_metadata = parse_metadata_json(
metadata,
"mage_flow.nvfp4_metadata",
)
attention_metadata = parse_metadata_json(
metadata,
"mage_flow.attention_nvfp4",
)
runtime_defaults = _apply_runtime_defaults(quant_config)
if not initialize_native_sm120_op(
allow_python_schema_fallback=False
):
fail("compiled native SM120 torch op did not load")
depth = int(config.get("depth", 0))
target_specs = _target_specs_for_config(depth, quant_config)
expected_targets = [spec.module_key for spec in target_specs]
recorded_targets = nvfp4_metadata.get("targets")
if not isinstance(recorded_targets, list):
fail("single-file NVFP4 metadata has no target list")
if [
entry.get("module_key")
for entry in recorded_targets
if isinstance(entry, dict)
] != expected_targets:
fail("single-file MLP targets do not match transformer config")
non_target_keys = nvfp4_metadata.get("non_target_keys")
if not isinstance(non_target_keys, list) or not all(
isinstance(key, str) for key in non_target_keys
):
fail("single-file NVFP4 metadata has no non-target key list")
attention_groups = attention_metadata.get("groups")
if (
attention_metadata.get("mode") != "fused_qkv"
or not isinstance(attention_groups, list)
or len(attention_groups) != 24
):
fail("single-file attention metadata is not 24 fused QKV groups")
attention_source_keys = {
key
for group in attention_groups
if isinstance(group, dict)
for field in ("source_weight_keys", "source_bias_keys")
for key in group.get(field, [])
if isinstance(key, str)
}
fused_attention_keys = {
f"{group['module_key']}.{suffix}"
for group in attention_groups
if isinstance(group, dict)
for suffix in (
"packed_weight",
"weight_scales",
"weight_scale",
"bias",
)
}
quantized_mlp_keys = {
key
for spec in target_specs
for key in _quantized_keys(spec.module_key).values()
}
expected_keys = (
(set(non_target_keys) - attention_source_keys)
| quantized_mlp_keys
| fused_attention_keys
)
actual_keys = set(handle.keys())
if actual_keys != expected_keys:
missing = sorted(expected_keys - actual_keys)
unexpected = sorted(actual_keys - expected_keys)
fail(
"single-file tensor coverage mismatch; "
f"missing={missing[:1]}, unexpected={unexpected[:1]}"
)
model = instantiate_mage_transformer_on_meta(support_root)
def tensor(key: str) -> torch.Tensor:
if key not in actual_keys:
fail(f"tensor is absent from single-file checkpoint: {key}")
return handle.get_tensor(key)
for spec in target_specs:
original = model.get_submodule(spec.module_key)
if not isinstance(original, nn.Linear):
fail(
f"expected target {spec.module_key} to be nn.Linear, "
f"found {type(original).__name__}"
)
keys = _quantized_keys(spec.module_key)
replacement = PackedNvfp4LinearNativeOp(
in_features=int(original.in_features),
out_features=int(original.out_features),
packed_weight=tensor(keys["packed_weight"]).to(device),
weight_scales=tensor(keys["weight_scales"]).to(device),
weight_scale=tensor(keys["weight_scale"]).to(device),
bias=tensor(keys["bias"]).to(device),
)
set_child_module(model, spec.module_key, replacement)
installed_attention: list[str] = []
for group in attention_groups:
if not isinstance(group, dict):
fail("single-file attention group is malformed")
module_key = str(group.get("module_key", ""))
parts = module_key.split(".")
if (
len(parts) != 4
or parts[0] != "transformer_blocks"
or parts[2] != "attn"
or parts[3] not in {"to_qkv", "add_qkv_proj"}
):
fail(f"invalid fused attention module key: {module_key}")
block_index = int(parts[1])
attention = model.transformer_blocks[block_index].attn
replacement = PackedNvfp4LinearNativeOp(
in_features=int(group["in_features"]),
out_features=int(group["out_features"]),
packed_weight=tensor(
f"{module_key}.packed_weight"
).to(device),
weight_scales=tensor(
f"{module_key}.weight_scales"
).to(device),
weight_scale=tensor(
f"{module_key}.weight_scale"
).to(device),
bias=tensor(f"{module_key}.bias").to(device),
)
setattr(attention, parts[3], replacement)
source_names = (
("to_q", "to_k", "to_v")
if parts[3] == "to_qkv"
else ("add_q_proj", "add_k_proj", "add_v_proj")
)
for source_name in source_names:
setattr(attention, source_name, None)
installed_attention.append(module_key)
retained_keys = sorted(
set(non_target_keys) - attention_source_keys
)
for key in retained_keys:
assign_tensor_by_name(model, key, tensor(key).to(device))
materialized = materialize_mage_rope_tensor_attributes(model)
meta_parameters = [
name for name, value in model.named_parameters() if value.is_meta
]
meta_buffers = [
name for name, value in model.named_buffers() if value.is_meta
]
unregistered_meta = unregistered_meta_tensor_attribute_names(model)
if meta_parameters or meta_buffers or unregistered_meta:
fail(
"single-file loader left unresolved meta tensors: "
f"{(meta_parameters + meta_buffers + unregistered_meta)[:4]}"
)
return model.eval().requires_grad_(False), {
"layout": "comfyui_single_file_fused_qkv",
"checkpoint": str(checkpoint_path),
"checkpoint_tensor_count": len(actual_keys),
"loaded_non_target_tensor_count": len(retained_keys),
"loaded_quantized_mlp_projection_count": len(target_specs),
"loaded_fused_attention_projection_count": len(
installed_attention
),
"bf16_attention_weight_reads": 0,
"bf16_mlp_target_weight_reads": 0,
"materialized_unregistered_tensor_attribute_names": materialized,
"runtime_defaults_applied": runtime_defaults,
}
__all__ = ["load_single_file_native_transformer"]
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