Run InternVLA synthetic policy with Radeon Cosmos handoff
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tzhouam committed
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commit
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21 files changed
+2020
-16
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@@ -0,0 +1,93 @@
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#!/usr/bin/env python3
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"""Export the unchanged InternVLA Cosmos encoder as a fixed-shape graph."""
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from __future__ import annotations
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import argparse
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import hashlib
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import importlib.util
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import json
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import sys
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import time
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import types
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from pathlib import Path
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def sha256(path: Path) -> str:
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digest = hashlib.sha256()
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with path.open("rb") as source:
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for block in iter(lambda: source.read(8 << 20), b""):
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digest.update(block)
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return digest.hexdigest()
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def main() -> None:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--encoder", type=Path, required=True)
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parser.add_argument("--output", type=Path, required=True)
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parser.add_argument("--report", type=Path, required=True)
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parser.add_argument("--batch-size", type=int, default=6)
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args = parser.parse_args()
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import torch
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source_dir = Path(__file__).resolve().parents[3] / "vllm_omni" / "diffusion" / "models" / "internvla_a1"
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package = types.ModuleType("internvla_cosmos_export")
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package.__path__ = [str(source_dir)]
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sys.modules[package.__name__] = package
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spec = importlib.util.spec_from_file_location(f"{package.__name__}.model_cosmos", source_dir / "model_cosmos.py")
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if spec is None or spec.loader is None:
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raise RuntimeError("cannot load pinned Cosmos source")
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module = importlib.util.module_from_spec(spec)
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sys.modules[spec.name] = module
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spec.loader.exec_module(module)
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encoder_path = args.encoder.resolve(strict=True)
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encoder_model = module.load_cosmos_component(encoder_path, component="encoder", device="cpu")
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class LatentOnly(torch.nn.Module):
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def __init__(self, inner: torch.nn.Module) -> None:
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super().__init__()
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self.inner = inner
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def forward(self, pixels: torch.Tensor) -> torch.Tensor:
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result = self.inner(pixels)
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return result if isinstance(result, torch.Tensor) else result[0]
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model = LatentOnly(encoder_model).eval()
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image = torch.linspace(-1, 1, args.batch_size * 3 * 256 * 256, dtype=torch.float32).view(
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args.batch_size, 3, 256, 256
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)
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started = time.perf_counter()
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with torch.no_grad():
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reference = model(image)
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exported = torch.export.export(model, (image,), strict=False)
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actual = exported.module()(image)
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export_s = time.perf_counter() - started
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reference_tensor = reference
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actual_tensor = actual
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if not torch.allclose(reference_tensor, actual_tensor, atol=1e-5, rtol=1e-5):
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raise RuntimeError("exported CPU graph does not match source module")
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args.output.parent.mkdir(parents=True, exist_ok=True)
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torch.export.save(exported, args.output)
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report = {
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"status": "passed",
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"scope": "fixed-shape Cosmos CI8x8 encoder graph; no InternVLA action-policy claim",
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"encoder_sha256": sha256(encoder_path),
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"source_sha256": {name: sha256(source_dir / name) for name in ("model_cosmos.py", "cosmos_ci_torch.py")},
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"torch": torch.__version__,
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"input_shape": list(image.shape),
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"input_dtype": str(image.dtype),
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"output_shape": list(reference_tensor.shape),
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"max_abs_export_error": float((reference_tensor - actual_tensor).abs().max()),
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"export_s": export_s,
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"artifact_sha256": sha256(args.output),
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"artifact_bytes": args.output.stat().st_size,
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"artifact_path": str(args.output.resolve()),
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}
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args.report.parent.mkdir(parents=True, exist_ok=True)
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args.report.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
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print(json.dumps(report, indent=2))
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if __name__ == "__main__":
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main()
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@@ -54,11 +54,13 @@ def main() -> None:
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parser.add_argument("--encoder", type=Path, required=True)
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parser.add_argument("--output", type=Path, required=True)
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parser.add_argument("--dml-index", type=int, default=1)
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parser.add_argument("--batch-size", type=int, default=1)
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parser.add_argument("--model-dtype", choices=("float32", "bfloat16"), default="float32")
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parser.add_argument("--warmups", type=int, default=1)
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parser.add_argument("--repeats", type=int, default=20)
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args = parser.parse_args()
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if args.warmups < 0 or args.repeats < 1:
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parser.error("invalid warmup or measured count")
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if args.warmups < 0 or args.repeats < 1 or args.batch_size < 1:
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parser.error("invalid batch size, warmup or measured count")
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import numpy as np
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import psutil
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@@ -83,8 +85,8 @@ def main() -> None:
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"device_name": device_name,
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"torch": torch.__version__,
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"torch_directml": __import__("importlib.metadata", fromlist=["version"]).version("torch-directml"),
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"input_shape": [1, 3, 256, 256],
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"input_dtype": "float32",
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"input_shape": [args.batch_size, 3, 256, 256],
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"input_dtype": args.model_dtype,
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"warmups": args.warmups,
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"repeats": args.repeats,
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}
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@@ -94,9 +96,12 @@ def main() -> None:
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image[:, 0, 48:208, 48:208] = 1
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image[:, 1, 48:208, 48:208] = -1
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image[:, 2, :, :] = torch.linspace(-1, 1, 256)[None, None, :]
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image = image.repeat(args.batch_size, 1, 1, 1)
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model_dtype = getattr(torch, args.model_dtype)
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image = image.to(model_dtype)
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started = time.perf_counter()
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cpu_model = source.load_cosmos_component(encoder, component="encoder", device="cpu")
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cpu_model = source.load_cosmos_component(encoder, component="encoder", device="cpu").to(model_dtype)
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report["cpu_load_s"] = time.perf_counter() - started
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with safe_open(str(encoder), framework="pt", device="cpu") as checkpoint:
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checkpoint_keys = set(checkpoint.keys())
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@@ -125,7 +130,7 @@ def main() -> None:
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set(report["checkpoint_unexpected_keys"]) != set(checkpoint_buffers)):
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raise RuntimeError("Cosmos encoder checkpoint/state keys do not match")
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started = time.perf_counter()
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dml_model = source.load_cosmos_component(encoder, component="encoder", device="cpu").to(dml)
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dml_model = source.load_cosmos_component(encoder, component="encoder", device="cpu").to(dtype=model_dtype, device=dml)
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report["dml_load_and_transfer_s"] = time.perf_counter() - started
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if {parameter.device.type for parameter in cpu_model.parameters()} != {"cpu"}:
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raise RuntimeError("CPU reference encoder placement differs")
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@@ -0,0 +1,225 @@
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#!/usr/bin/env python3
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"""Compare complete InternVLA actions on CPU with a Radeon Cosmos handoff.
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The policy and action sampler remain on CPU. Only its Cosmos encoder is
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replaced by Omni's existing external torch-DirectML graph worker. The test
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uses synthetic observations and does not establish robot-task quality.
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"""
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from __future__ import annotations
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import argparse
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import hashlib
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import json
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import math
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import os
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import time
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import traceback
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from pathlib import Path
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def sha256(path: Path) -> str:
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digest = hashlib.sha256()
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with path.open("rb") as source:
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for block in iter(lambda: source.read(8 << 20), b""):
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digest.update(block)
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return digest.hexdigest()
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def rank(values: list[float], fraction: float) -> float:
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return sorted(values)[math.ceil(len(values) * fraction) - 1]
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def main() -> None:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--model-dir", type=Path, required=True)
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parser.add_argument("--cosmos-dir", type=Path, required=True)
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parser.add_argument("--processor-dir", type=Path, required=True)
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parser.add_argument("--graph", type=Path, required=True)
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parser.add_argument("--export-report", type=Path, required=True)
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parser.add_argument("--output", type=Path, required=True)
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parser.add_argument("--warmups", type=int, default=1)
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parser.add_argument("--repeats", type=int, default=20)
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args = parser.parse_args()
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if args.warmups < 0 or args.repeats < 1:
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parser.error("warmups must be nonnegative and repeats must be positive")
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os.environ["INTERNVLA_A1_COSMOS_DIR"] = str(args.cosmos_dir.resolve(strict=True))
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os.environ["INTERNVLA_A1_PROCESSOR_DIR"] = str(args.processor_dir.resolve(strict=True))
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os.environ["HF_HUB_OFFLINE"] = "1"
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import numpy as np
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import psutil
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import torch
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from vllm_omni.diffusion.data import OmniDiffusionConfig
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from vllm_omni.diffusion.registry import initialize_model
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from vllm_omni.diffusion.request import OmniDiffusionRequest
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from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
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from vllm_omni.edge.local.external.client import ExternalWorker
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from vllm_omni.edge.local.external.launch import ROUTE_TORCH_DML, resolve
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from vllm_omni.inputs.data import OmniDiffusionSamplingParams
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graph = args.graph.resolve(strict=True)
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export_report = json.loads(args.export_report.read_text(encoding="utf-8"))
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if export_report["artifact_sha256"] != sha256(graph):
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raise RuntimeError("exported graph hash differs from the pinned manifest")
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if export_report["input_shape"] != [6, 3, 256, 256] or export_report["input_dtype"] != "torch.float32":
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raise RuntimeError("wrong Cosmos export shape or precision")
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model_dir = args.model_dir.resolve(strict=True)
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checkpoint = model_dir / "model.safetensors"
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if not checkpoint.is_file():
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raise FileNotFoundError(checkpoint)
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report = {
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"scope": "InternVLA real Place_Markpen checkpoint, synthetic zero camera/state observations and zero diffusion noise; CPU BF16 policy with FP32 Radeon Cosmos encoder; no robot-task quality claim",
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"model_checkpoint_sha256": sha256(checkpoint),
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"graph_sha256": export_report["artifact_sha256"],
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"graph_export": export_report,
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"policy_torch": torch.__version__,
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"warmups": args.warmups,
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"repeats": args.repeats,
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"status": "running",
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}
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worker = None
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try:
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config = OmniDiffusionConfig(
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model=str(model_dir),
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model_class_name="InternVLAA1Pipeline",
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dtype=torch.bfloat16,
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custom_pipeline_args={
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"device": "cpu", "dtype": "bfloat16", "compile_model": False,
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"enable_regional_compile": False, "enable_warmup": False,
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"strict_load": True, "processor_model_name": str(args.processor_dir.resolve()),
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},
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)
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started = time.perf_counter()
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pipeline = initialize_model(config)
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report["policy_load_s"] = time.perf_counter() - started
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if pipeline.runtime_mode() != "real_checkpoint_loaded":
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raise RuntimeError(f"unexpected policy mode: {pipeline.runtime_mode()}")
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if {p.device.type for p in pipeline.policy.parameters()} != {"cpu"}:
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raise RuntimeError("policy did not load wholly on CPU")
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report["policy_parameters_device"] = "cpu"
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inputs = pipeline._build_fake_batch_inputs()
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noise = torch.zeros((1, pipeline.config.chunk_size, pipeline.config.max_action_dim), dtype=torch.float32)
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route = resolve(ROUTE_TORCH_DML)
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if not route.available:
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raise RuntimeError(f"DirectML route unavailable: {route.reason}")
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worker = ExternalWorker(route)
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started = time.perf_counter()
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hello = worker.start()
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load_report = worker.load(graph, example_inputs={"pixels": np.zeros((6, 3, 256, 256), dtype=np.float32)})
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report["worker_start_and_graph_load_s"] = time.perf_counter() - started
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report["worker_hello"] = {key: hello.get(key) for key in ("torch", "torch_directml", "available_providers", "executable")}
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report["worker_load"] = load_report.to_dict()
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if (load_report.device_name != "AMD Radeon(TM) 890M Graphics" or
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load_report.fraction_on_target != 1.0 or
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load_report.outputs[0]["shape"] != [6, 16, 32, 32]):
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raise RuntimeError("Radeon placement or output shape not verified")
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class ExternalCosmosEncoder(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.calls = 0
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self.timings = []
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def forward(self, pixels: torch.Tensor) -> torch.Tensor:
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if pixels.device.type != "cpu" or tuple(pixels.shape) != (6, 3, 256, 256):
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raise RuntimeError(f"unexpected encoder input: {pixels.device}, {tuple(pixels.shape)}")
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# DirectML BF16 aborts this runtime; the FP32 graph is an
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# explicit artifact change, then latents return as policy BF16.
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array = pixels.detach().float().contiguous().numpy()
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output, timing = worker.run({"pixels": array})
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self.calls += 1
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self.timings.append(timing.to_dict())
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latent = output["out_0"]
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if latent.shape != (6, 16, 32, 32) or not np.isfinite(latent).all():
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raise RuntimeError("bad DirectML Cosmos latent")
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return torch.from_numpy(latent).to(pixels.dtype)
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external_encoder = ExternalCosmosEncoder()
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cosmos = pipeline.policy.model.cosmos
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cpu_encoder = cosmos._enc_model
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def actions(encoder: torch.nn.Module) -> np.ndarray:
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cosmos._enc_model = encoder
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result = pipeline.forward(DiffusionRequestBatch(requests=[
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OmniDiffusionRequest(
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prompt="",
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sampling_params=OmniDiffusionSamplingParams(extra_args={
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"batch_inputs": inputs, "noise": noise, "decode_image": False,
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}),
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request_id="internvla-hybrid-synthetic-observation",
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)
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]))
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if result.error:
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raise RuntimeError(result.error)
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tensor = result.output["payload"]["actions"]
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if tensor.device.type != "cpu" or tuple(tensor.shape) != (1, 50, 32):
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raise RuntimeError(f"bad policy action shape/device: {tensor.shape} {tensor.device}")
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if not torch.isfinite(tensor).all():
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raise RuntimeError("policy actions nonfinite")
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return tensor.detach().float().cpu().contiguous().numpy()
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cpu_wall = []
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hybrid_wall = []
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errors = []
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reference = actual = None
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for index in range(args.warmups + args.repeats):
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started = time.perf_counter()
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reference = actions(cpu_encoder)
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cpu_s = time.perf_counter() - started
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started = time.perf_counter()
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actual = actions(external_encoder)
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hybrid_s = time.perf_counter() - started
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if index >= args.warmups:
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cpu_wall.append(cpu_s)
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hybrid_wall.append(hybrid_s)
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errors.append({
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"max_abs": float(np.max(np.abs(reference - actual))),
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"relative_l2": float(np.linalg.norm(reference - actual) / np.linalg.norm(reference)),
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"cosine": float(np.dot(reference.ravel(), actual.ravel()) / (
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np.linalg.norm(reference) * np.linalg.norm(actual)
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)),
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})
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assert reference is not None and actual is not None
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cosmos._enc_model = cpu_encoder
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report.update({
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"status": "passed",
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"action_shape": list(actual.shape),
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"cpu_action_sha256_last": hashlib.sha256(reference.tobytes()).hexdigest(),
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"hybrid_action_sha256_last": hashlib.sha256(actual.tobytes()).hexdigest(),
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"cpu_wall_s": cpu_wall,
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"hybrid_wall_s": hybrid_wall,
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"per_request_action_error": errors,
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"max_action_abs_error": max(e["max_abs"] for e in errors),
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"max_action_relative_l2_error": max(e["relative_l2"] for e in errors),
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"min_action_cosine": min(e["cosine"] for e in errors),
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"cpu_p50_wall_s": rank(cpu_wall, .5),
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"cpu_p95_wall_s": rank(cpu_wall, .95),
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"hybrid_p50_wall_s": rank(hybrid_wall, .5),
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"hybrid_p95_wall_s": rank(hybrid_wall, .95),
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"external_encoder_calls": external_encoder.calls,
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"external_encoder_timings": external_encoder.timings,
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"worker_stats": worker.stats(),
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"parent_rss_bytes_after": psutil.Process().memory_info().rss,
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"wsl_available_bytes_after": psutil.virtual_memory().available,
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})
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except BaseException as exc:
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report["status"] = "failed"
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report["error"] = f"{type(exc).__name__}: {exc}"
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report["traceback"] = traceback.format_exc()
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raise
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finally:
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if worker is not None:
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worker.close()
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args.output.parent.mkdir(parents=True, exist_ok=True)
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args.output.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
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print(json.dumps({key: value for key, value in report.items() if key not in (
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"cpu_wall_s", "hybrid_wall_s", "per_request_action_error", "external_encoder_timings"
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)}, indent=2))
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if __name__ == "__main__":
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main()
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@@ -5,6 +5,7 @@ This extends the [60-cell device × model matrix](../e2e_profiling_20260922/evid
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| Device / model | Verified depth | Observed output and time | Remaining gate |
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|---|---|---|---|
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| Native Windows HX370 Radeon 890M / InternVLA-A1 Cosmos CI8x8 encoder | Component C; unchanged Omni encoder source in separate DirectML/PyTorch 2.4 environment, not an Omni full-policy stage | One warmup + 20 serial patterned 256×256 image encodes, including upload/readback: Radeon p50/p95 26.56/27.49 ms versus CPU 156.49/158.71 ms; relative L2 error 4.73e-07. Encoder weights and output device checked. | Complete policy/action and real-observation parity; policy/controller runtime compatibility, bounded handoff, shared-RAM admission and end-to-end benefit. [Raw evidence](evidence/internvla_cosmos_radeon890m/README.md). |
|
||||
| WSL HX370 CPU policy + Radeon 890M DirectML Cosmos / InternVLA-A1 Place_Markpen | Complete synthetic action-policy path through Omni diffusion and its external graph worker; FP32 encoder to BF16 policy, not robot-task qualification | One warmup pair + 20 alternating measured CPU/hybrid complete requests returned finite `[1,50,32]` actions. Hybrid wall p50/p95 2.952/3.322 s versus CPU 3.178/3.266 s; 16/20 paired faster but the hybrid tail regressed. Action relative L2 difference 0.9007%, cosine 0.999959. The selected Radeon adapter and graph hash were checked. | Real observations/reference actions, units/timestamps, integrated admission/cancellation, loading peak and sustained tail/power; [raw evidence](evidence/internvla_cosmos_radeon890m/README.md). |
|
||||
| WSL HX370 CPU / Qwen3-TTS 0.6B CustomVoice BF16 | Scoped complete-request Omni resident CPU whole-session stage (P for two named texts; not playable streaming) | One warmup + 20 serial requests returned identical 3.44 s WAVs; nearest-rank complete-request wall p50/p95 8.27/8.47 s (RTF 2.40/2.46), excluding 19.82 s startup. One pinned Whisper tiny.en transcription matched the reference (WER 0); public `AsyncOmni` audio, 10 GiB admission, 4 GiB refusal and cancellation drain passed. The WSL PCM differs from the native Windows output for the same checkpoint/settings. | Cross-OS numerical/quality investigation, playable streaming, post-cancel restart, loading peak, concurrency and sustained power; [raw evidence](evidence/qwen_tts_omni_wsl_cpu/README.md). |
|
||||
| WSL HX370 CPU / MiniCPM-o 4.5 Q4_K_M/F16 GGUF | Scoped complete-request Omni C++ whole-session graph stage (P for one synthetic combined spoken-question+image input; CPU-only model execution) | All 20 measured requests after one warmup answered the red-square question correctly with complete nonzero 24 kHz speech. Nearest-rank complete-request wall p50/p95 22.67/24.83 s; audio duration 1.60–2.92 s, maximum sampled worker RSS 13.70 GB. One WAV independently transcribed exactly (Whisper tiny.en WER 0). 24 GiB admission, 8 GiB refusal and cancellation with zero remaining ledger passed. | Real multimodal suites, playable streaming, speech alignment, post-cancel restart, loading peak, concurrency and sustained power; [raw evidence](evidence/minicpmo_omni_wsl_cpu/README.md). |
|
||||
| Native Windows HX370 CPU / MiniCPM-o 4.5 Q4_K_M/F16 GGUF | Scoped complete-request Omni C++ whole-session graph stage (P for one synthetic combined audio+image input) | Correct red-square answer, 2.24 s 24 kHz WAV; one cold request 24.34 s, sampled RSS 13.11 GB. 24 GiB admission, 8 GiB refusal and in-flight cancellation with zero remaining reservation passed. | Real multimodal suites, playable streaming, repeated latency, loading peak, post-cancel restart and sustained power; [raw evidence](evidence/minicpmo_omni_cpp/README.md). |
|
||||
|
||||
+92
-2
@@ -1,6 +1,6 @@
|
||||
# InternVLA Cosmos image-encoder component on Radeon 890M
|
||||
# InternVLA Cosmos encoder and synthetic hybrid policy on Radeon 890M
|
||||
|
||||
This is a **component (C) pass**, not an InternVLA action-policy (P) pass. The unchanged Omni `model_cosmos.py` and `cosmos_ci_torch.py` loaded the pinned Cosmos-Tokenizer-CI8x8 encoder and executed one patterned `[1,3,256,256]` float32 image in `[-1,1]` on CPU and DirectML Radeon 890M. The pattern is synthetic; no camera, language, proprioception, diffusion action loop, unit/timestamp or robot-task quality is covered.
|
||||
The first experiment below is a **component (C) pass**. The unchanged Omni `model_cosmos.py` and `cosmos_ci_torch.py` loaded the pinned Cosmos-Tokenizer-CI8x8 encoder and executed one patterned `[1,3,256,256]` float32 image in `[-1,1]` on CPU and DirectML Radeon 890M. The pattern is synthetic; this component result alone covers no camera, language, proprioception, diffusion action loop, unit/timestamp or robot-task quality. A later section records a separate complete synthetic policy path.
|
||||
|
||||
The host was a Ryzen AI 9 HX 370 on native Windows 11 build 26200, AMD Radeon 890M driver 32.0.22018.6001 (2026-08-12), PyTorch 2.4.1+cpu and torch-directml 0.2.5.dev240914. The separate DirectML environment is not the installed Omni/vLLM 0.29 policy environment. Explicit DirectML device index 1 resolved to `AMD Radeon(TM) 890M Graphics`; all encoder parameters and the pre-readback output were checked on `privateuseone`. The encoder checkpoint SHA256 is `520a3b878e72ee872faccb1211b5d622d194c0fdd177f2f6df097586b8fa98e5`. Source SHA256 values are in [profile20.json](profile20.json). The 88 persistent encoder state tensors all had checkpoint keys. Two nonpersistent patcher buffers were also present in the checkpoint and ignored by the existing model loader; the wavelet buffer differs by at most `7.55e-05` because the checkpoint uses BF16 while the source constructs FP32. The same source-generated buffer was used for CPU and DirectML.
|
||||
|
||||
@@ -19,3 +19,93 @@ $env:PYTHONUTF8 = '1'
|
||||
--output "$root\vllm-omni-edge\benchmarks\edge_harness\results\e2e_expansion_20260923\evidence\internvla_cosmos_radeon890m\profile20.json" `
|
||||
--warmups 1 --repeats 20 --dml-index 1
|
||||
```
|
||||
|
||||
## Fixed six-frame component and complete synthetic policy
|
||||
|
||||
The policy's three-camera × two-history fixture sends **six** frames through
|
||||
Cosmos in one call. A separate [six-frame component profile](profile_batch6_20.json)
|
||||
used one warmup plus 20 serial patterned calls: Radeon p50/p95
|
||||
133.82/144.88 ms including upload/readback, CPU FP32 835.17/851.48 ms,
|
||||
relative L2 error `5.14e-07`. This is a more relevant shape than the first
|
||||
single-image component test, but still not a whole-policy measurement.
|
||||
|
||||
DirectML in the tested PyTorch 2.4 worker **aborts on BF16**; the
|
||||
[fatal log](bf16_batch6_probe.log) is retained. The existing InternVLA policy
|
||||
uses BF16 Cosmos on CPU. Rather than silently cast it, the hybrid experiment
|
||||
uses an explicitly FP32 fixed-shape [`.pt2` export](export_batch6.json) of the
|
||||
same checkpoint, then converts the returned latent to the policy's BF16.
|
||||
`torch.export(..., strict=False)` produced a CPU output identical to the
|
||||
unchanged source on the export sample. The 129,872,312-byte graph is stored
|
||||
outside Git at the report's artifact path; its SHA256 is
|
||||
`9111b3841893acaaa11cf45715d246da3ac9cdd03c7b33fb3c7c20fe38d74704`.
|
||||
The experiment refuses a hash, shape or dtype mismatch. Recreate it with
|
||||
[`export_internvla_cosmos.py`](../../../../experiments/export_internvla_cosmos.py).
|
||||
|
||||
The [hybrid policy probe](../../../../experiments/probe_internvla_hybrid_actions.py)
|
||||
loaded the real Place_Markpen checkpoint strictly in the WSL CPU Omni
|
||||
diffusion pipeline, kept the Qwen3-VL/flow action policy on CPU and used
|
||||
Omni's existing external `torch-dml` worker for only the Cosmos encoder.
|
||||
The worker reported the selected adapter as `AMD Radeon(TM) 890M Graphics`;
|
||||
its output remained on DirectML before readback. Its graph placement report
|
||||
counts one **output tensor**, not every internal operation; no per-operator
|
||||
attribution was captured. The controller used PyTorch 2.13.0+cpu, vLLM
|
||||
0.28.0 and current Omni 0.29 source (a version mismatch warning remains);
|
||||
the worker used PyTorch 2.4.1+cu121 over WSL GPU-PV. The underlying host was
|
||||
the same Windows 11 build 26200 HX370/Radeon with driver 32.0.22018.6001.
|
||||
Both policies received identical synthetic zero camera/state observations,
|
||||
empty task and zero diffusion noise; output was a finite `[1,50,32]` action
|
||||
tensor. This is a **synthetic complete policy path**, not a real robot-task
|
||||
E2E result. The checkpoint's `train_config.json` says `action_mode=delta`,
|
||||
but physical units, joint order, observation timestamps, action horizon and
|
||||
reference action quality were not validated.
|
||||
|
||||
The [paired 20-request result](hybrid_profile20.json) alternated CPU-only and
|
||||
CPU+Radeon complete policy calls after one warmup pair, with one loaded policy
|
||||
and persistent worker. Nearest-rank complete-request wall p50/p95 was
|
||||
**3.178/3.266 s** on CPU and **2.952/3.322 s** on the hybrid. The paired
|
||||
median improvement was 0.207 s (16/20 hybrid calls faster), but the hybrid
|
||||
p95 regressed because several DirectML calls took about 0.5 s instead of
|
||||
about 0.15 s. The largest action max-absolute difference was `0.00715`,
|
||||
relative L2 `0.9007%`, cosine `0.999959`; the difference was repeatable on
|
||||
this one fixture. No task tolerance is established, and the FP32/BF16 change
|
||||
needs real-observation reference actions before deployment. Policy load was
|
||||
32.96 s, worker start+graph load 3.34 s; these costs are excluded from the
|
||||
request percentiles. Parent RSS after profiling was 9.30 GB and worker peak
|
||||
RSS 0.91 GB; these are not shared-iGPU allocation or loading-peak bounds.
|
||||
Power and thermal state were not recorded.
|
||||
|
||||
The [first hybrid smoke](hybrid_smoke.json) and the later
|
||||
[selected-adapter check](hybrid_selected_adapter.json) are retained. The
|
||||
candidate still lacks an Omni graph-stage deployment plan with combined
|
||||
shared-RAM admission, cancellation/restart validation, observation/action
|
||||
metadata and real input quality. A single cold request loses the worker
|
||||
startup cost, and the latency tail currently prevents a general speedup
|
||||
claim. Keep CPU as the qualified baseline until those gates pass.
|
||||
|
||||
Recreate the graph with the recorded Windows PyTorch 2.4 environment. Use the
|
||||
freshly generated report alongside its graph, since serialization may produce
|
||||
a different byte hash on another run:
|
||||
|
||||
```powershell
|
||||
$root = '\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer'
|
||||
$env:PYTHONUTF8 = '1'
|
||||
& 'C:\Users\zhout\w2\qwen_dml_venv\Scripts\python.exe' `
|
||||
"$root\vllm-omni-edge\benchmarks\edge_harness\experiments\export_internvla_cosmos.py" `
|
||||
--encoder "$root\models\Cosmos-Tokenizer-CI8x8\encoder.safetensors" `
|
||||
--output "$root\models\InternVLA-cosmos-encoder-batch6.pt2" `
|
||||
--report "$root\models\InternVLA-cosmos-encoder-batch6.export.json" --batch-size 6
|
||||
```
|
||||
|
||||
Reproduce the complete synthetic policy probe from this repository root on
|
||||
WSL, using the matching local export report:
|
||||
|
||||
```bash
|
||||
PYTHONPATH="$PWD" OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 TOKENIZERS_PARALLELISM=false \
|
||||
../.venvs/omni-cpu/bin/python benchmarks/edge_harness/experiments/probe_internvla_hybrid_actions.py \
|
||||
--model-dir ../models/InternVLA-A1-3B-FT-Place_Markpen \
|
||||
--cosmos-dir ../models/Cosmos-Tokenizer-CI8x8 \
|
||||
--processor-dir ../models/Qwen3-VL-2B-Instruct-processor \
|
||||
--graph ../models/InternVLA-cosmos-encoder-batch6.pt2 \
|
||||
--export-report ../models/InternVLA-cosmos-encoder-batch6.export.json \
|
||||
--output /tmp/internvla-hybrid-profile.json --warmups 1 --repeats 20
|
||||
```
|
||||
+1
@@ -0,0 +1 @@
|
||||
[F924 00:53:57.000000000 dml_util.cc:118] Invalid or unsupported data type BFloat16.
|
||||
+28
@@ -0,0 +1,28 @@
|
||||
{
|
||||
"status": "passed",
|
||||
"scope": "fixed-shape Cosmos CI8x8 encoder graph; no InternVLA action-policy claim",
|
||||
"encoder_sha256": "520a3b878e72ee872faccb1211b5d622d194c0fdd177f2f6df097586b8fa98e5",
|
||||
"source_sha256": {
|
||||
"model_cosmos.py": "81f523767d356f16856fa35521bc637ac5df9fc8383ca11fc9a58875eb4c97c7",
|
||||
"cosmos_ci_torch.py": "a88015b3854b8da730b89c6a35eb3b23721608797208847d9b4cb9a8b8d60f5e"
|
||||
},
|
||||
"torch": "2.4.1+cpu",
|
||||
"input_shape": [
|
||||
6,
|
||||
3,
|
||||
256,
|
||||
256
|
||||
],
|
||||
"input_dtype": "torch.float32",
|
||||
"output_shape": [
|
||||
6,
|
||||
16,
|
||||
32,
|
||||
32
|
||||
],
|
||||
"max_abs_export_error": 0.0,
|
||||
"export_s": 3.224664800101891,
|
||||
"artifact_sha256": "9111b3841893acaaa11cf45715d246da3ac9cdd03c7b33fb3c7c20fe38d74704",
|
||||
"artifact_bytes": 129872312,
|
||||
"artifact_path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\InternVLA-cosmos-encoder-batch6.pt2"
|
||||
}
|
||||
+28
@@ -0,0 +1,28 @@
|
||||
{
|
||||
"status": "passed",
|
||||
"scope": "fixed-shape Cosmos CI8x8 encoder graph; no InternVLA action-policy claim",
|
||||
"encoder_sha256": "520a3b878e72ee872faccb1211b5d622d194c0fdd177f2f6df097586b8fa98e5",
|
||||
"source_sha256": {
|
||||
"model_cosmos.py": "81f523767d356f16856fa35521bc637ac5df9fc8383ca11fc9a58875eb4c97c7",
|
||||
"cosmos_ci_torch.py": "a88015b3854b8da730b89c6a35eb3b23721608797208847d9b4cb9a8b8d60f5e"
|
||||
},
|
||||
"torch": "2.4.1+cpu",
|
||||
"input_shape": [
|
||||
6,
|
||||
3,
|
||||
256,
|
||||
256
|
||||
],
|
||||
"input_dtype": "torch.float32",
|
||||
"output_shape": [
|
||||
6,
|
||||
16,
|
||||
32,
|
||||
32
|
||||
],
|
||||
"max_abs_export_error": 0.0,
|
||||
"export_s": 3.224664800101891,
|
||||
"artifact_sha256": "9111b3841893acaaa11cf45715d246da3ac9cdd03c7b33fb3c7c20fe38d74704",
|
||||
"artifact_bytes": 129872312,
|
||||
"artifact_path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\InternVLA-cosmos-encoder-batch6.pt2"
|
||||
}
|
||||
+416
@@ -0,0 +1,416 @@
|
||||
{
|
||||
"scope": "InternVLA real Place_Markpen checkpoint, synthetic zero camera/state observations and zero diffusion noise; CPU BF16 policy with FP32 Radeon Cosmos encoder; no robot-task quality claim",
|
||||
"model_checkpoint_sha256": "586799539888c69c35f84d083e559cc5f0ba887801d000e70e65f58d3f85c313",
|
||||
"graph_sha256": "9111b3841893acaaa11cf45715d246da3ac9cdd03c7b33fb3c7c20fe38d74704",
|
||||
"graph_export": {
|
||||
"status": "passed",
|
||||
"scope": "fixed-shape Cosmos CI8x8 encoder graph; no InternVLA action-policy claim",
|
||||
"encoder_sha256": "520a3b878e72ee872faccb1211b5d622d194c0fdd177f2f6df097586b8fa98e5",
|
||||
"source_sha256": {
|
||||
"model_cosmos.py": "81f523767d356f16856fa35521bc637ac5df9fc8383ca11fc9a58875eb4c97c7",
|
||||
"cosmos_ci_torch.py": "a88015b3854b8da730b89c6a35eb3b23721608797208847d9b4cb9a8b8d60f5e"
|
||||
},
|
||||
"torch": "2.4.1+cpu",
|
||||
"input_shape": [
|
||||
6,
|
||||
3,
|
||||
256,
|
||||
256
|
||||
],
|
||||
"input_dtype": "torch.float32",
|
||||
"output_shape": [
|
||||
6,
|
||||
16,
|
||||
32,
|
||||
32
|
||||
],
|
||||
"max_abs_export_error": 0.0,
|
||||
"export_s": 3.224664800101891,
|
||||
"artifact_sha256": "9111b3841893acaaa11cf45715d246da3ac9cdd03c7b33fb3c7c20fe38d74704",
|
||||
"artifact_bytes": 129872312,
|
||||
"artifact_path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\InternVLA-cosmos-encoder-batch6.pt2"
|
||||
},
|
||||
"policy_torch": "2.13.0+cpu",
|
||||
"warmups": 1,
|
||||
"repeats": 20,
|
||||
"status": "passed",
|
||||
"policy_load_s": 32.959274790016934,
|
||||
"policy_parameters_device": "cpu",
|
||||
"worker_start_and_graph_load_s": 3.3366938079707325,
|
||||
"worker_hello": {
|
||||
"torch": "2.4.1+cu121",
|
||||
"torch_directml": "unknown",
|
||||
"available_providers": [
|
||||
"NVIDIA GeForce RTX 5090 Laptop GPU\u0000",
|
||||
"AMD Radeon(TM) 890M Graphics\u0000"
|
||||
],
|
||||
"executable": "/home/zhout/project/edge_infer/.venvs/dml/bin/python"
|
||||
},
|
||||
"worker_load": {
|
||||
"ep": "dml",
|
||||
"graph_path": "/home/zhout/project/edge_infer/models/InternVLA-cosmos-encoder-batch6.pt2",
|
||||
"session_providers": [
|
||||
"torch-directml"
|
||||
],
|
||||
"available_providers": [
|
||||
"torch-directml"
|
||||
],
|
||||
"node_counts": {
|
||||
"dml": 1,
|
||||
"cpu": 0
|
||||
},
|
||||
"total_nodes": 1,
|
||||
"target_nodes": 1,
|
||||
"fraction_on_target": 1.0,
|
||||
"session_create_s": 0.8180774669745006,
|
||||
"warmup_s": 0.677739959966857,
|
||||
"rss_bytes": 696750080,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "pixels",
|
||||
"shape": [
|
||||
6,
|
||||
3,
|
||||
256,
|
||||
256
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "out_0",
|
||||
"shape": [
|
||||
6,
|
||||
16,
|
||||
32,
|
||||
32
|
||||
]
|
||||
}
|
||||
],
|
||||
"profile_path": "",
|
||||
"ep_directory": "",
|
||||
"requested_provider_missing": "",
|
||||
"fallback_note": "",
|
||||
"onnxruntime": "",
|
||||
"note": ""
|
||||
},
|
||||
"action_shape": [
|
||||
1,
|
||||
50,
|
||||
32
|
||||
],
|
||||
"cpu_action_sha256_last": "db60d6590e242694948f7dcdf873b2fff44819d31b0b178fdad5ec9608d46140",
|
||||
"hybrid_action_sha256_last": "256b244d3b6a8745391690ff88182a5f0f6f9325d279de1e9c5287a00c463798",
|
||||
"cpu_wall_s": [
|
||||
3.266188815992791,
|
||||
3.2130069519625977,
|
||||
3.200305638019927,
|
||||
3.182389290013816,
|
||||
3.2533019420225173,
|
||||
3.163670579029713,
|
||||
3.1292421899852343,
|
||||
3.1379489160026424,
|
||||
3.258549672027584,
|
||||
3.2664072579937056,
|
||||
3.162602616008371,
|
||||
3.1880405800184235,
|
||||
3.1626134979887865,
|
||||
3.1519040080020204,
|
||||
3.178045471024234,
|
||||
3.110473181004636,
|
||||
3.1697323330445215,
|
||||
3.1663852329947986,
|
||||
3.2292595140170306,
|
||||
3.2074096809956245
|
||||
],
|
||||
"hybrid_wall_s": [
|
||||
3.1687436809879728,
|
||||
2.9065867449971847,
|
||||
2.9316007799934596,
|
||||
3.405398574017454,
|
||||
2.9016875599627383,
|
||||
2.891013133979868,
|
||||
2.9670994469779544,
|
||||
3.3216434589703567,
|
||||
2.911582596017979,
|
||||
3.267322317988146,
|
||||
2.9339384569902904,
|
||||
2.8921132480027154,
|
||||
3.0768511260394007,
|
||||
3.2951800349983387,
|
||||
2.9927860139869153,
|
||||
3.008475508017,
|
||||
2.9399544459884055,
|
||||
3.071420267049689,
|
||||
2.8709001630195417,
|
||||
2.952331782958936
|
||||
],
|
||||
"per_request_action_error": [
|
||||
{
|
||||
"max_abs": 0.007147848606109619,
|
||||
"relative_l2": 0.009007224813103676,
|
||||
"cosine": 0.9999594688415527
|
||||
},
|
||||
{
|
||||
"max_abs": 0.007147848606109619,
|
||||
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/home/zhout/project/edge_infer/vllm-omni-edge/vllm_omni/version.py:55: RuntimeWarning: vLLM and vLLM-Omni appear to have mismatched major/minor versions:
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||||
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||||
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||||
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||||
W0924 01:10:49.657000 3705598 torch/utils/_pytree.py:630] <enum 'KernelPreference'> is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release.
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W0924 01:10:49.680000 3705598 torch/utils/_pytree.py:630] <enum 'ScaleCalculationMode'> is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release.
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INFO 09-24 01:10:50 [importing.py:53] Triton is installed but 0 active driver(s) found (expected 1). Disabling Triton to prevent runtime errors.
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WARNING 09-24 01:10:50 [importing.py:65] Triton is installed, but doesn't include CPU backend. Disabling Triton.
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INFO 09-24 01:10:51 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
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INFO 09-24 01:10:55 [pipeline_internvla_a1.py:164] Loading InternVLA-A1 weights from /home/zhout/project/edge_infer/models/InternVLA-A1-3B-FT-Place_Markpen
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@@ -0,0 +1,178 @@
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/home/zhout/project/edge_infer/vllm-omni-edge/vllm_omni/version.py:55: RuntimeWarning: vLLM and vLLM-Omni appear to have mismatched major/minor versions:
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||||
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--> vLLM version 0.28.0
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||||
warn_if_misaligned_vllm_version()
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||||
W0924 01:00:26.790000 3704813 torch/utils/_pytree.py:630] <enum 'KernelPreference'> is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release.
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W0924 01:00:26.813000 3704813 torch/utils/_pytree.py:630] <enum 'ScaleCalculationMode'> is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release.
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INFO 09-24 01:00:28 [importing.py:53] Triton is installed but 0 active driver(s) found (expected 1). Disabling Triton to prevent runtime errors.
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||||
WARNING 09-24 01:00:28 [importing.py:65] Triton is installed, but doesn't include CPU backend. Disabling Triton.
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INFO 09-24 01:00:28 [importing.py:88] Triton not installed or not compatible; certain GPU-related functions will not be available.
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||||
INFO 09-24 01:00:28 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
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INFO 09-24 01:00:28 [patch.py:510] inductor factorable-divisibility patch: installed.
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INFO 09-24 01:00:28 [patch.py:646] CPU AOT compile-cache load shim installed: compiled models are reused across engine starts instead of being recompiled.
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INFO 09-24 01:00:29 [patch.py:756] CPU weight-load memory release installed (set VLLM_OMNI_CPU_RELEASE_MEMORY=0 to disable).
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||||
INFO 09-24 01:00:35 [pipeline_internvla_a1.py:164] Loading InternVLA-A1 weights from /home/zhout/project/edge_infer/models/InternVLA-A1-3B-FT-Place_Markpen
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|
||||
0.13055210001766682,
|
||||
0.12943259999155998,
|
||||
0.13381510006729513,
|
||||
0.13458940002601594,
|
||||
0.13404289993923157,
|
||||
0.1437174000311643,
|
||||
0.12850900006014854,
|
||||
0.12948820006567985,
|
||||
0.12899790005758405,
|
||||
0.1348303999984637,
|
||||
0.12745120003819466,
|
||||
0.13730459997896105,
|
||||
0.14488479995634407,
|
||||
0.1366466999752447,
|
||||
0.12663509999401867,
|
||||
0.13856200000736862,
|
||||
0.1294181999983266
|
||||
],
|
||||
"process_rss_bytes_after": 2996514816
|
||||
}
|
||||
+70
@@ -0,0 +1,70 @@
|
||||
{
|
||||
"scope": "InternVLA real Cosmos CI8x8 image encoder component; synthetic patterned 256x256 input; no full policy or robot-task claim",
|
||||
"source_files_sha256": {
|
||||
"model_cosmos.py": "81f523767d356f16856fa35521bc637ac5df9fc8383ca11fc9a58875eb4c97c7",
|
||||
"cosmos_ci_torch.py": "a88015b3854b8da730b89c6a35eb3b23721608797208847d9b4cb9a8b8d60f5e"
|
||||
},
|
||||
"encoder_sha256": "520a3b878e72ee872faccb1211b5d622d194c0fdd177f2f6df097586b8fa98e5",
|
||||
"device_name": "AMD Radeon(TM) 890M Graphics",
|
||||
"torch": "2.4.1+cpu",
|
||||
"torch_directml": "0.2.5.dev240914",
|
||||
"input_shape": [
|
||||
6,
|
||||
3,
|
||||
256,
|
||||
256
|
||||
],
|
||||
"input_dtype": "float32",
|
||||
"warmups": 1,
|
||||
"repeats": 20,
|
||||
"cpu_load_s": 0.5824802999850363,
|
||||
"checkpoint_tensor_count": 90,
|
||||
"encoder_state_tensor_count": 88,
|
||||
"checkpoint_missing_keys": [],
|
||||
"checkpoint_unexpected_keys": [
|
||||
"encoder.patcher._arange",
|
||||
"encoder.patcher.wavelets"
|
||||
],
|
||||
"nonpersistent_buffer_comparison": {
|
||||
"encoder.patcher._arange": {
|
||||
"checkpoint_shape": [
|
||||
2
|
||||
],
|
||||
"model_shape": [
|
||||
2
|
||||
],
|
||||
"checkpoint_dtype": "torch.int64",
|
||||
"model_dtype": "torch.int64",
|
||||
"max_abs_error": 0.0
|
||||
},
|
||||
"encoder.patcher.wavelets": {
|
||||
"checkpoint_shape": [
|
||||
2
|
||||
],
|
||||
"model_shape": [
|
||||
2
|
||||
],
|
||||
"checkpoint_dtype": "torch.bfloat16",
|
||||
"model_dtype": "torch.float32",
|
||||
"max_abs_error": 7.551908493041992e-05
|
||||
}
|
||||
},
|
||||
"dml_load_and_transfer_s": 0.65743709995877,
|
||||
"status": "passed",
|
||||
"output_shape": [
|
||||
6,
|
||||
16,
|
||||
32,
|
||||
32
|
||||
],
|
||||
"cpu_output_sha256_float32": "e9e6cf789cbefb1c2f288cf17066f257a89de5548d528e66d2e984ddfddfbe79",
|
||||
"dml_output_sha256_float32": "f371d1faaf6dfe00759a15d4b238844b6c75aba7757d10683a6b72a7c7c95df0",
|
||||
"max_abs_error": 1.049041748046875e-05,
|
||||
"relative_l2_error": 5.140985308571544e-07,
|
||||
"cosine_similarity": 0.9999999403953552,
|
||||
"cpu_nearest_rank_p50_wall_s": 0.8351699999766424,
|
||||
"cpu_nearest_rank_p95_wall_s": 0.8514761000405997,
|
||||
"dml_nearest_rank_p50_wall_s": 0.13381510006729513,
|
||||
"dml_nearest_rank_p95_wall_s": 0.14488479995634407,
|
||||
"process_rss_bytes_after": 2996514816
|
||||
}
|
||||
@@ -2,7 +2,7 @@
|
||||
|
||||
All 60 pairings have a disposition. The timing tables below remain the 2026-09-22 measurements. The configuration matrix and affected per-cell reasons incorporate the [2026-09-23 scoped recovery runs](../../../e2e_recovery_20260923/README.md) and [WSL CPU/native Windows expansion](../../../e2e_expansion_20260923/README.md). The expansion adds separate 20-request serial MiniCPM-o text-to-speech profiles on WSL CPU and native Windows GPU, a Spark output-head component profile on the HX370 AMD NPU, complete Spark text-request profiles through standalone llama.cpp and a bounded Omni whole-session stage on native Windows CPU and Radeon 890M, native Windows CPU Qwen3-TTS text-to-WAV profiles through both standalone PyTorch and a bounded Omni whole-session stage, and a hybrid CPU+Radeon 890M Qwen3-TTS profile likewise repeated through a bounded Omni whole-session stage. A pinned GGUF MiniCPM-o route adds combined image+audio to text+speech runs on WSL CPU, native Windows CPU and native Windows Radeon 890M+CPU. All three now pass a bounded complete request through an Omni graph stage; the WSL CPU path additionally passed one warmup plus 20 serial measured requests. These are scoped synthetic cases, not full release qualification.
|
||||
|
||||
The Radeon 890M InternVLA cell now has a separate 20-call [Cosmos image-encoder component profile](../../../e2e_expansion_20260923/evidence/internvla_cosmos_radeon890m/README.md). It remains **NOT E2E** because no complete action-policy workload ran on that placement.
|
||||
The Radeon 890M InternVLA cell now has a [Cosmos image-encoder component profile and a separate CPU+Radeon complete synthetic action-policy profile](../../../e2e_expansion_20260923/evidence/internvla_cosmos_radeon890m/README.md). It remains short of robot-task E2E qualification because real observations, reference actions, units/timestamps, complete admission and lifecycle are unverified.
|
||||
|
||||
## Configuration × model
|
||||
|
||||
@@ -20,7 +20,7 @@ NOT E2E means no complete device/model workload was verified. “Scoped E2E” i
|
||||
| PC HX370: CPU / native Windows | scoped E2E: Omni Spark text | scoped E2E: Omni CPU text → complete WAV | NOT E2E | scoped E2E: Omni GGUF image+audio → text+speech | synthetic policy pass |
|
||||
| PC HX370 + RTX 5090 Laptop / WSL | profile completed | profile completed | scoped E2E: text + image | scoped E2E: text/image/audio → speech | synthetic policy pass |
|
||||
| PC HX370 + RTX 5090 Laptop / Windows | profile completed | profile completed | NOT E2E | scoped E2E: text/image/audio → speech | synthetic policy pass |
|
||||
| PC HX370 + Radeon 890M / Windows worker | scoped E2E: Omni Spark text | scoped E2E: Omni Radeon talker/codec + CPU predictor | NOT E2E | scoped E2E: Omni Radeon+CPU image+audio → text+speech | NOT E2E (Cosmos encoder component passed) |
|
||||
| PC HX370 + Radeon 890M / Windows worker | scoped E2E: Omni Spark text | scoped E2E: Omni Radeon talker/codec + CPU predictor | NOT E2E | scoped E2E: Omni Radeon+CPU image+audio → text+speech | synthetic policy pass: CPU policy + Radeon Cosmos |
|
||||
| PC HX370 + AMD NPU / Windows worker | NOT E2E (output-head component passed) | NOT E2E | NOT E2E | NOT E2E | NOT E2E |
|
||||
| PC CPU+iGPU+NPU +/- discrete GPU, joint model execution | NOT E2E | NOT E2E | NOT E2E | NOT E2E | NOT E2E |
|
||||
| PC Snapdragon X Elite CRD / AI Hub | NOT E2E | NOT E2E | NOT E2E | NOT E2E | NOT E2E |
|
||||
@@ -57,7 +57,7 @@ NOT E2E means no complete device/model workload was verified. “Scoped E2E” i
|
||||
| PC HX370 + Radeon 890M / Windows worker | Qwen3-TTS 0.6B CustomVoice | SCOPED_E2E_OMNI_COMPLETE_REQUEST_HYBRID | The pinned Q8_0 CustomVoice talker, F16 codec and punctuation GGUFs completed two named text-to-WAV requests through an Omni whole-session CrispASR stage on native Windows. The runtime log identifies Radeon 890M Vulkan0 for talker/codec and the 432 MB code predictor pinned to CPU FP32; this is not all-Radeon or NPU execution. Both PCM hashes exactly match the earlier standalone outputs. Whisper tiny.en transcribed the first Omni WAV and both standalone WAVs exactly (WER 0 as an intelligibility proxy). One warmup plus 20 serial complete Omni requests for 2.56 s of audio had identical PCM and nearest-rank wall p50/p95 3.298/3.336 s (RTF 1.29/1.30). A public AsyncOmni audio request passed; cancellation drained the worker without stale output or remaining reservation. The earlier standalone profile was 3.310/3.345 s. The default Vulkan configuration previously fell back to CPU, all-Vulkan ran away to a 360-frame cap, and DirectML failed waveform quality. No playable stream, post-cancel restart, loading peak, broad speech quality or sustained power/thermal run is claimed. | Validate playable streaming, restart/recovery, broader voice quality, shared-RAM/GPU peak, concurrency and sustained real-time behavior before wider qualification; [Omni raw evidence](../../../e2e_expansion_20260923/evidence/qwen_tts_omni_hybrid/README.md), [standalone evidence](../../../e2e_expansion_20260923/evidence/qwen_tts_radeon890m_joint/README.md), [failed DirectML route](../../../e2e_expansion_20260923/evidence/qwen_tts_radeon890m_directml/README.md). |
|
||||
| PC HX370 + Radeon 890M / Windows worker | Qwen3.8-27B | NOT_E2E_PROFILEABLE | FP32 448x448 vision tower: DirectML 85/113 nodes; normalized max error 9.095e-06 versus torch CPU. Historical component path, not full 27B or current graph-stage image-to-text integration. | Recover the hashed tower bundle and qualify its current Omni graph handoff with the language stage. |
|
||||
| PC HX370 + Radeon 890M / Windows worker | MiniCPM-o 4.5 | SCOPED_E2E_OMNI_COMPLETE_REQUEST_HYBRID | The same pinned GGUF set ran one combined synthetic spoken-question+JPEG → text+speech request through a bounded Omni whole-session graph stage. Logs verify `GGML_VK_VISIBLE_DEVICES=1`, Radeon 890M Vulkan0 language (37/37 layers) and vision, with CPU TTS/Token2Wav/vocoder. It answered “The square in the image is red.” and produced 2.60 s of 24 kHz speech in 18.27 s complete-request wall time (one cold sample, zero warmups); sampled worker RSS peaked at 15.57 GB. The stage verified artifact and binary hashes, reserved 24 GiB shared RAM, emitted a terminal audio event, and in-flight cancellation left no stale output or reservation. The prior standalone prompted run took 18.63 s and its output transcribed with WER 0. This is CPU+iGPU joint execution, not all-Radeon or NPU execution; neither run qualifies general quality or sustained performance. | Validate real multimodal quality, playable streaming, post-cancel restart, loading peak, repeated/concurrent latency and sustained thermal behavior. [Omni raw evidence](../../../e2e_expansion_20260923/evidence/minicpmo_omni_cpp/README.md), [standalone prompted evidence](../../../e2e_expansion_20260923/evidence/minicpmo_cpp_radeon890m_prompted/README.md). |
|
||||
| PC HX370 + Radeon 890M / Windows worker | InternVLA-A1 | COMPONENT_PASS; NOT_E2E_PROFILEABLE | The pinned Cosmos CI8x8 image encoder ran on Radeon 890M through DirectML with weights and pre-readback output checked on that device. One warmup plus 20 serial patterned `[1,3,256,256]` component requests, including upload/readback, gave nearest-rank wall p50/p95 26.56/27.49 ms versus 156.49/158.71 ms for the same source on CPU; relative L2 output error was 4.73e-07. This is the real vision-tokenizer component only. The separate DirectML/PyTorch 2.4 environment cannot host the installed Omni/vLLM 0.29 policy directly; no complete Radeon or Radeon+CPU action-policy path, real-observation quality, state/admission or handoff benefit is verified. | Build and validate a bounded component-to-policy handoff in the current Omni controller only if whole-request gain exceeds transfer, startup and shared-RAM costs; check action parity on real observations, units/timestamps and cancellation. [Raw component evidence](../../../e2e_expansion_20260923/evidence/internvla_cosmos_radeon890m/README.md). |
|
||||
| PC HX370 + Radeon 890M / Windows worker | InternVLA-A1 | SYNTHETIC_POLICY_PASS_HYBRID; NOT_TASK_E2E | The real Place_Markpen checkpoint strict-loaded in WSL CPU Omni diffusion. Its Cosmos encoder ran as a pinned FP32 fixed-six-frame export in Omni's external DirectML worker; the selected device was verified as Radeon 890M, and the remainder of the BF16 policy/action loop stayed on CPU. One warmup plus 20 alternating identical synthetic zero-observation/noise requests produced finite `[1,50,32]` actions. Complete-request wall p50/p95 was 2.952/3.322 s hybrid versus 3.178/3.266 s CPU; 16/20 paired calls were faster, but the hybrid tail regressed. Action relative L2 difference was 0.9007% and cosine 0.999959, without a task tolerance. DirectML BF16 aborted, so FP32 encoder is an explicit precision change. Worker startup+graph load took 3.34 s, excluded from warm timing. This proves a scoped synthetic CPU+iGPU policy path, not real-observation quality, action units/time, complete shared-RAM admission, cancellation or sustained benefit. | Validate real observation/reference-action quality and metadata, integrate an admitted bounded graph-stage plan with cancellation/restart, and resolve latency-tail and startup amortization before deployment. [Raw hybrid and component evidence](../../../e2e_expansion_20260923/evidence/internvla_cosmos_radeon890m/README.md). |
|
||||
| PC HX370 + AMD NPU / Windows worker | Spark-X2.5 | COMPONENT_PASS; NOT_E2E_PROFILEABLE | The real 1.7B output head (RMS norm and full 131,072-logit projection) ran through Omni `external.graph.v1` with VitisAI: one fused NPU partition and seven CPU nodes per request. The A16W8 graph matched top-1 on four captured activations (full-logit SNR 55.43–56.81 dB). One warmup plus 20 serial repeated-input component requests gave p50/p95 10.21/12.44 ms, versus 17.97/20.29 ms for the original FP32 output head on the same Omni CPU stage. NPU startup was 18.21 s versus 4.48 s for FP32 CPU, and NPU worker peak RSS was 3.52 GiB versus 2.06 GiB. No full prefill/decode/KV/sampling, real handoff, power, or sustained run was verified. | Measure an actual vLLM-to-output-head handoff and whole-generation cost, state/cancellation, memory admission and quality before considering a split plan; [component evidence](../../../e2e_expansion_20260923/evidence/spark_amd_npu_output_head/README.md). |
|
||||
| PC HX370 + AMD NPU / Windows worker | Qwen3-TTS 0.6B CustomVoice | NOT_E2E_PROFILEABLE | No placement- and quality-verified whole-model graph bundle/stateful adapter for this model on this AMD route. Small backend graphs do not establish model support. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
|
||||
| PC HX370 + AMD NPU / Windows worker | Qwen3.8-27B | NOT_E2E_PROFILEABLE | The tested A16W8 whole-vision-tower export failed NPU placement. This conclusion is specific to that export, not all future Qwen/NPU combinations. | Validate a supported export or smaller useful component, including same-checkpoint numerical quality. |
|
||||
|
||||
@@ -33,9 +33,15 @@ and output were checked on DirectML, with one warmup and 20 measured patterned
|
||||
256×256 encodes at 26.56/27.49 ms p50/p95 including transfer and readback,
|
||||
versus 156.49/158.71 ms for the same CPU source. Relative L2 error was
|
||||
4.73e-07. The separate older DirectML runtime cannot host the current Omni
|
||||
policy directly. M4 remains open for complete actions, real observations,
|
||||
time/units, state/admission and a measured full-request handoff benefit; no
|
||||
joint CPU+iGPU InternVLA E2E status is inferred from this component timing.
|
||||
policy directly. A follow-up fixed-six-frame FP32 export ran in the existing
|
||||
Omni external DirectML worker while the real-weight BF16 action policy stayed
|
||||
on WSL CPU. Twenty paired synthetic complete-policy requests after one warmup
|
||||
gave hybrid p50/p95 2.952/3.322 s versus CPU 3.178/3.266 s, with 16/20 paired
|
||||
calls faster but a worse hybrid tail. Action relative L2 difference was 0.9007%
|
||||
without a task tolerance. This advances M4 to a synthetic CPU+iGPU policy
|
||||
path, while real observations/reference actions, units/time, shared-RAM
|
||||
admission, cancellation and sustained benefit remain open. It does not turn
|
||||
the joint CPU+iGPU+NPU matrix cell into a pass.
|
||||
|
||||
The WSL RTX MiniCPM-o image-to-text+speech path now passes for one 448×448
|
||||
synthetic red-square image under an explicit 0.61 thinker GPU budget. The
|
||||
|
||||
+14
@@ -152,6 +152,20 @@ def test_verify_placement_accepts_a_real_split(a16w8_graph, npu_device):
|
||||
assert verify_placement(plan, report) is None
|
||||
|
||||
|
||||
def test_load_report_preserves_selected_adapter_name():
|
||||
from vllm_omni.edge.local.external.client import LoadReport
|
||||
|
||||
report = LoadReport.from_body(
|
||||
{"device_name": "AMD Radeon(TM) 890M Graphics\x00", "fraction_on_target": 1.0,
|
||||
"placement_granularity": "output_device", "target_nodes": 1, "total_nodes": 1},
|
||||
ep="dml", graph_path="cosmos.pt2",
|
||||
)
|
||||
assert report.device_name == "AMD Radeon(TM) 890M Graphics"
|
||||
assert report.placement_granularity == "output_device"
|
||||
assert "1/1 outputs" in report.summary()
|
||||
assert report.to_dict()["device_name"] == report.device_name
|
||||
|
||||
|
||||
def test_refusing_a_plan_cannot_be_opened(a16w8_graph, npu_device):
|
||||
plan = plan_external_stage(_artifact(a16w8_graph, fmt=FORMAT_ONNX_FP16), [npu_device])
|
||||
with pytest.raises(PlacementRefused):
|
||||
|
||||
+8
-2
@@ -64,6 +64,8 @@ class LoadReport:
|
||||
|
||||
ep: str
|
||||
graph_path: str
|
||||
device_name: str = ""
|
||||
placement_granularity: str = "graph_nodes"
|
||||
session_providers: tuple[str, ...] = ()
|
||||
available_providers: tuple[str, ...] = ()
|
||||
node_counts: dict[str, int] = field(default_factory=dict)
|
||||
@@ -89,6 +91,8 @@ class LoadReport:
|
||||
return cls(
|
||||
ep=ep,
|
||||
graph_path=graph_path,
|
||||
device_name=str(body.get("device_name") or "").rstrip("\x00"),
|
||||
placement_granularity=str(body.get("placement_granularity") or "graph_nodes"),
|
||||
session_providers=tuple(body.get("session_providers") or ()),
|
||||
available_providers=tuple(body.get("available_providers") or ()),
|
||||
node_counts=dict(body.get("node_counts") or {}),
|
||||
@@ -116,9 +120,11 @@ class LoadReport:
|
||||
placement = f"placement unverified ({self.note or 'no profiled run'})"
|
||||
else:
|
||||
split = ", ".join(f"{k}={v}" for k, v in sorted(self.node_counts.items()))
|
||||
placement = f"{self.target_nodes}/{self.total_nodes} nodes on {self.ep} ({split})"
|
||||
counted = "outputs" if self.placement_granularity == "output_device" else "nodes"
|
||||
placement = f"{self.target_nodes}/{self.total_nodes} {counted} on {self.ep} ({split})"
|
||||
device_suffix = f" ({self.device_name})" if self.device_name else ""
|
||||
return (
|
||||
f"{Path(self.graph_path).name} on {self.ep}: {placement}; "
|
||||
f"{Path(self.graph_path).name} on {self.ep}{device_suffix}: {placement}; "
|
||||
f"session {self.session_create_s:.3f}s, warmup {self.warmup_s:.3f}s, "
|
||||
f"worker rss {self.rss_bytes / 2**20:.0f} MiB"
|
||||
)
|
||||
|
||||
+1
@@ -150,6 +150,7 @@ def _load(state: _Session, body: dict[str, Any], tensors: dict[str, np.ndarray])
|
||||
info: dict[str, Any] = {
|
||||
"ep": "dml",
|
||||
"device_name": device_name,
|
||||
"placement_granularity": "output_device",
|
||||
"torch": torch.__version__,
|
||||
"session_create_s": create_s,
|
||||
"session_providers": ["torch-directml"],
|
||||
|
||||
Reference in new issue
Block a user