Profile InternVLA Cosmos encoder on Radeon 890M and update matrix
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#!/usr/bin/env python3
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"""Probe InternVLA's real Cosmos image encoder on CPU and one DirectML GPU.
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This loads the unchanged Omni model source as an isolated experiment because
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the installed DirectML PyTorch environment is separate from the vLLM runtime.
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It tests one model component, not the full action policy or 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 importlib.util
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import json
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import math
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import sys
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import time
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import traceback
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import types
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from pathlib import Path
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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 _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 _load_omni_cosmos_module(source_dir: Path):
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# Keep the model implementation unchanged without importing the unrelated
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# vLLM engine in a separate, older Torch/DirectML experiment environment.
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package_name = "internvla_cosmos_probe"
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package = types.ModuleType(package_name)
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package.__path__ = [str(source_dir)]
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sys.modules[package_name] = package
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name = f"{package_name}.model_cosmos"
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spec = importlib.util.spec_from_file_location(name, source_dir / "model_cosmos.py")
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if spec is None or spec.loader is None:
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raise RuntimeError("could not load unchanged InternVLA Cosmos source")
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module = importlib.util.module_from_spec(spec)
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sys.modules[name] = module
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spec.loader.exec_module(module)
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return module
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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("--dml-index", type=int, default=1)
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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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import numpy as np
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import psutil
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import torch
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import torch_directml
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from safetensors import safe_open
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source_dir = Path(__file__).resolve().parents[3] / "vllm_omni" / "diffusion" / "models" / "internvla_a1"
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source = _load_omni_cosmos_module(source_dir)
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device_name = torch_directml.device_name(args.dml_index).rstrip("\x00")
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if device_name != "AMD Radeon(TM) 890M Graphics":
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raise RuntimeError(f"refusing unverified DirectML device: {device_name!r}")
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dml = torch_directml.device(args.dml_index)
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encoder = args.encoder.resolve(strict=True)
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report = {
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"scope": "InternVLA real Cosmos CI8x8 image encoder component; synthetic patterned 256x256 input; no full policy or robot-task claim",
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"source_files_sha256": {
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name: _sha256(source_dir / name)
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for name in ("model_cosmos.py", "cosmos_ci_torch.py")
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},
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"encoder_sha256": _sha256(encoder),
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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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"warmups": args.warmups,
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"repeats": args.repeats,
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}
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try:
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torch.manual_seed(42)
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image = torch.zeros((1, 3, 256, 256), dtype=torch.float32)
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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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started = time.perf_counter()
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cpu_model = source.load_cosmos_component(encoder, component="encoder", device="cpu")
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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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checkpoint_buffers = {
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key: checkpoint.get_tensor(key)
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for key in checkpoint_keys
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if key in {"encoder.patcher._arange", "encoder.patcher.wavelets"}
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}
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model_keys = set(cpu_model.state_dict())
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report["checkpoint_tensor_count"] = len(checkpoint_keys)
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report["encoder_state_tensor_count"] = len(model_keys)
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report["checkpoint_missing_keys"] = sorted(model_keys - checkpoint_keys)
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report["checkpoint_unexpected_keys"] = sorted(checkpoint_keys - model_keys)
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model_buffers = dict(cpu_model.named_buffers())
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report["nonpersistent_buffer_comparison"] = {
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key: {
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"checkpoint_shape": list(value.shape),
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"model_shape": list(model_buffers[key].shape),
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"checkpoint_dtype": str(value.dtype),
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"model_dtype": str(model_buffers[key].dtype),
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"max_abs_error": float((value.float() - model_buffers[key].float()).abs().max()),
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}
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for key, value in checkpoint_buffers.items()
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}
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if (report["checkpoint_missing_keys"] or
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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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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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if {parameter.device.type for parameter in dml_model.parameters()} != {"privateuseone"}:
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raise RuntimeError("DirectML encoder weights were not on the declared GPU")
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def execute(model, input_tensor, expected_device_type):
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with torch.no_grad():
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output = model(input_tensor)
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if not isinstance(output, torch.Tensor):
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output = output[0]
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if output.device.type != expected_device_type:
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raise RuntimeError(f"encoder output on {output.device}, expected {expected_device_type}")
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return output.cpu().float().contiguous()
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cpu_times: list[float] = []
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dml_times: list[float] = []
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cpu_output = dml_output = 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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cpu_output = execute(cpu_model, image, "cpu")
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cpu_wall = time.perf_counter() - started
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started = time.perf_counter()
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dml_output = execute(dml_model, image.to(dml), "privateuseone")
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dml_wall = time.perf_counter() - started
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if index >= args.warmups:
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cpu_times.append(cpu_wall)
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dml_times.append(dml_wall)
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assert cpu_output is not None and dml_output is not None
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if cpu_output.shape != dml_output.shape or not torch.isfinite(dml_output).all():
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raise RuntimeError("DirectML encoder returned wrong shape or non-finite output")
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reference = cpu_output.numpy()
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actual = dml_output.numpy()
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difference = reference - actual
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report.update({
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"status": "passed",
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"output_shape": list(reference.shape),
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"cpu_output_sha256_float32": hashlib.sha256(reference.tobytes()).hexdigest(),
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"dml_output_sha256_float32": hashlib.sha256(actual.tobytes()).hexdigest(),
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"max_abs_error": float(np.max(np.abs(difference))),
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"relative_l2_error": float(np.linalg.norm(difference) / np.linalg.norm(reference)),
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"cosine_similarity": 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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"cpu_nearest_rank_p50_wall_s": _rank(cpu_times, 0.5),
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"cpu_nearest_rank_p95_wall_s": _rank(cpu_times, 0.95),
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"dml_nearest_rank_p50_wall_s": _rank(dml_times, 0.5),
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"dml_nearest_rank_p95_wall_s": _rank(dml_times, 0.95),
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"cpu_times_s": cpu_times,
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"dml_times_s": dml_times,
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"process_rss_bytes_after": psutil.Process().memory_info().rss,
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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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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 not key.endswith("times_s")}, indent=2))
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if __name__ == "__main__":
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main()
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@@ -4,6 +4,7 @@ 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). |
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| 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). |
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| 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). |
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| 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). |
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+21
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# InternVLA Cosmos image-encoder component on Radeon 890M
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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.
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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.
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One warmup followed by 20 serial measured calls produced `[1,16,32,32]` outputs. The complete per-call timing includes host-to-DirectML upload, encoder forward, DirectML-to-host readback/synchronization and float32 conversion. Nearest-rank wall p50/p95 were **26.56/27.49 ms** on Radeon and **156.49/158.71 ms** on CPU. The DirectML result versus this CPU source had max absolute error `1.168e-05`, relative L2 error `4.732e-07`, and cosine similarity `0.99999994`. Load plus weight transfer took 0.71 s after CPU construction. These are one-shape, warmed component observations, without external device-utilization trace, process isolation, peak shared-RAM, concurrent load, power or sustained thermal measurement. They cannot be added to prior whole-policy times to claim a hybrid speedup; the full handoff, co-residency, policy output and action quality must be measured together.
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The [first failed report](first_report.json) and [log](first_driver.log) preserve a DirectML `torch.inference_mode()` version-counter failure in the first experiment. Using `torch.no_grad()` in the probe resolved that runtime incompatibility without changing model operations. The final [profile report](profile20.json) records checkpoint-key coverage, the two nonpersistent buffers and the successful run. The [single-call pass](second_report.json) preceded the 20-request measurement.
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Reproduce in PowerShell from this checkout, with `PYTHONUTF8=1` and the recorded DirectML environment:
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```powershell
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$root = '\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer'
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$env:PYTHONUTF8 = '1'
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& 'C:\Users\zhout\w2\qwen_dml_venv\Scripts\python.exe' `
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"$root\vllm-omni-edge\benchmarks\edge_harness\experiments\probe_internvla_cosmos_directml.py" `
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--encoder "$root\models\Cosmos-Tokenizer-CI8x8\encoder.safetensors" `
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--output "$root\vllm-omni-edge\benchmarks\edge_harness\results\e2e_expansion_20260923\evidence\internvla_cosmos_radeon890m\profile20.json" `
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--warmups 1 --repeats 20 --dml-index 1
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```
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+65
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{
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Traceback (most recent call last):
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"scope": "InternVLA real Cosmos CI8x8 image encoder component; synthetic patterned 256x256 input; no full policy or robot-task claim",
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"source_files_sha256": {
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"model_cosmos.py": "81f523767d356f16856fa35521bc637ac5df9fc8383ca11fc9a58875eb4c97c7",
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"cosmos_ci_torch.py": "a88015b3854b8da730b89c6a35eb3b23721608797208847d9b4cb9a8b8d60f5e"
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},
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"encoder_sha256": "520a3b878e72ee872faccb1211b5d622d194c0fdd177f2f6df097586b8fa98e5",
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"device_name": "AMD Radeon(TM) 890M Graphics",
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"torch": "2.4.1+cpu",
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"torch_directml": "0.2.5.dev240914",
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"input_shape": [
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1,
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3,
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256,
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256
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],
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"input_dtype": "float32",
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"warmups": 0,
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"repeats": 1,
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"cpu_load_s": 0.5681545999832451,
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"dml_load_and_transfer_s": 1.1489207999547943,
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"status": "failed",
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"error": "RuntimeError: Cannot set version_counter for inference tensor",
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File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\benchmarks\edge_harness\experiments\probe_internvla_cosmos_directml.py", line 165, in <module>
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"traceback": "Traceback (most recent call last):\n File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\experiments\\probe_internvla_cosmos_directml.py\", line 124, in main\n dml_output = execute(dml_model, image.to(dml))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\experiments\\probe_internvla_cosmos_directml.py\", line 111, in execute\n output = model(input_tensor)\n ^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1553, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1562, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\container.py\", line 219, in forward\n input = module(input)\n ^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1553, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1562, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\diffusion\\models\\internvla_a1\\cosmos_ci_torch.py\", line 270, in forward\n hs = [self.conv_in(x)]\n ^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1553, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1562, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\conv.py\", line 458, in forward\n return self._conv_forward(input, self.weight, self.bias)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\conv.py\", line 454, in _conv_forward\n return F.conv2d(input, weight, bias, self.stride,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nRuntimeError: Cannot set version_counter for inference tensor\n"
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}
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main()
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File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\benchmarks\edge_harness\experiments\probe_internvla_cosmos_directml.py", line 124, in main
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dml_output = execute(dml_model, image.to(dml))
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\benchmarks\edge_harness\experiments\probe_internvla_cosmos_directml.py", line 111, in execute
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output = model(input_tensor)
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^^^^^^^^^^^^^^^^^^^
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File "C:\Users\zhout\w2\qwen_dml_venv\Lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
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return self._call_impl(*args, **kwargs)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
File "C:\Users\zhout\w2\qwen_dml_venv\Lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
|
||||
return forward_call(*args, **kwargs)
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
File "C:\Users\zhout\w2\qwen_dml_venv\Lib\site-packages\torch\nn\modules\container.py", line 219, in forward
|
||||
input = module(input)
|
||||
^^^^^^^^^^^^^
|
||||
File "C:\Users\zhout\w2\qwen_dml_venv\Lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
|
||||
return self._call_impl(*args, **kwargs)
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
File "C:\Users\zhout\w2\qwen_dml_venv\Lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
|
||||
return forward_call(*args, **kwargs)
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\diffusion\models\internvla_a1\cosmos_ci_torch.py", line 270, in forward
|
||||
hs = [self.conv_in(x)]
|
||||
^^^^^^^^^^^^^^^
|
||||
File "C:\Users\zhout\w2\qwen_dml_venv\Lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
|
||||
return self._call_impl(*args, **kwargs)
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
File "C:\Users\zhout\w2\qwen_dml_venv\Lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
|
||||
return forward_call(*args, **kwargs)
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
File "C:\Users\zhout\w2\qwen_dml_venv\Lib\site-packages\torch\nn\modules\conv.py", line 458, in forward
|
||||
return self._conv_forward(input, self.weight, self.bias)
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
File "C:\Users\zhout\w2\qwen_dml_venv\Lib\site-packages\torch\nn\modules\conv.py", line 454, in _conv_forward
|
||||
return F.conv2d(input, weight, bias, self.stride,
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
RuntimeError: Cannot set version_counter for inference tensor
|
||||
+25
@@ -0,0 +1,25 @@
|
||||
{
|
||||
"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": [
|
||||
1,
|
||||
3,
|
||||
256,
|
||||
256
|
||||
],
|
||||
"input_dtype": "float32",
|
||||
"warmups": 0,
|
||||
"repeats": 1,
|
||||
"cpu_load_s": 0.5681545999832451,
|
||||
"dml_load_and_transfer_s": 1.1489207999547943,
|
||||
"status": "failed",
|
||||
"error": "RuntimeError: Cannot set version_counter for inference tensor",
|
||||
"traceback": "Traceback (most recent call last):\n File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\experiments\\probe_internvla_cosmos_directml.py\", line 124, in main\n dml_output = execute(dml_model, image.to(dml))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\experiments\\probe_internvla_cosmos_directml.py\", line 111, in execute\n output = model(input_tensor)\n ^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1553, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1562, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\container.py\", line 219, in forward\n input = module(input)\n ^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1553, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1562, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\diffusion\\models\\internvla_a1\\cosmos_ci_torch.py\", line 270, in forward\n hs = [self.conv_in(x)]\n ^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1553, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1562, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\conv.py\", line 458, in forward\n return self._conv_forward(input, self.weight, self.bias)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\zhout\\w2\\qwen_dml_venv\\Lib\\site-packages\\torch\\nn\\modules\\conv.py\", line 454, in _conv_forward\n return F.conv2d(input, weight, bias, self.stride,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nRuntimeError: Cannot set version_counter for inference tensor\n"
|
||||
}
|
||||
+114
@@ -0,0 +1,114 @@
|
||||
{
|
||||
"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": [
|
||||
1,
|
||||
3,
|
||||
256,
|
||||
256
|
||||
],
|
||||
"input_dtype": "float32",
|
||||
"warmups": 1,
|
||||
"repeats": 20,
|
||||
"cpu_load_s": 0.5784786000149325,
|
||||
"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": 0.6941631999798119,
|
||||
"status": "passed",
|
||||
"output_shape": [
|
||||
1,
|
||||
16,
|
||||
32,
|
||||
32
|
||||
],
|
||||
"cpu_output_sha256_float32": "75b0c7cc33cc2a9845242030080cf901e4b05bdd5bdd8a65a0a3da253361dc28",
|
||||
"dml_output_sha256_float32": "f80d2c94d6ccac5c4975b22f5731018be2ae94dbd603abfa37a4a783ec4e6196",
|
||||
"max_abs_error": 1.1682510375976562e-05,
|
||||
"relative_l2_error": 4.732218030767399e-07,
|
||||
"cosine_similarity": 0.9999999403953552,
|
||||
"cpu_nearest_rank_p50_wall_s": 0.15648690005764365,
|
||||
"cpu_nearest_rank_p95_wall_s": 0.158714700024575,
|
||||
"dml_nearest_rank_p50_wall_s": 0.02656210004352033,
|
||||
"dml_nearest_rank_p95_wall_s": 0.027489100000821054,
|
||||
"cpu_times_s": [
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
0.15519369998946786,
|
||||
0.15620780002791435,
|
||||
0.15926450002007186,
|
||||
0.15724650002084672,
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||||
0.1561526000732556,
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||||
0.15597209997940809,
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0.158714700024575,
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0.15829010005109012,
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0.1574720999924466,
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0.15677859995048493,
|
||||
0.15707050007767975,
|
||||
0.15649339999072254
|
||||
],
|
||||
"dml_times_s": [
|
||||
0.027489100000821054,
|
||||
0.026306899962946773,
|
||||
0.02755960007198155,
|
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||||
0.026282600010745227,
|
||||
0.024926300044171512,
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||||
0.0269368999870494,
|
||||
0.02647149993572384,
|
||||
0.026945200050249696,
|
||||
0.026124599971808493,
|
||||
0.02645699994172901,
|
||||
0.026391200022771955,
|
||||
0.026137599954381585,
|
||||
0.026738699991256,
|
||||
0.02733730000909418,
|
||||
0.027434600051492453,
|
||||
0.026773600024171174,
|
||||
0.02701279998291284,
|
||||
0.026166999945417047,
|
||||
0.026771700009703636
|
||||
],
|
||||
"process_rss_bytes_after": 1406013440
|
||||
}
|
||||
+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": [
|
||||
1,
|
||||
3,
|
||||
256,
|
||||
256
|
||||
],
|
||||
"input_dtype": "float32",
|
||||
"warmups": 1,
|
||||
"repeats": 20,
|
||||
"cpu_load_s": 0.5784786000149325,
|
||||
"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": 0.6941631999798119,
|
||||
"status": "passed",
|
||||
"output_shape": [
|
||||
1,
|
||||
16,
|
||||
32,
|
||||
32
|
||||
],
|
||||
"cpu_output_sha256_float32": "75b0c7cc33cc2a9845242030080cf901e4b05bdd5bdd8a65a0a3da253361dc28",
|
||||
"dml_output_sha256_float32": "f80d2c94d6ccac5c4975b22f5731018be2ae94dbd603abfa37a4a783ec4e6196",
|
||||
"max_abs_error": 1.1682510375976562e-05,
|
||||
"relative_l2_error": 4.732218030767399e-07,
|
||||
"cosine_similarity": 0.9999999403953552,
|
||||
"cpu_nearest_rank_p50_wall_s": 0.15648690005764365,
|
||||
"cpu_nearest_rank_p95_wall_s": 0.158714700024575,
|
||||
"dml_nearest_rank_p50_wall_s": 0.02656210004352033,
|
||||
"dml_nearest_rank_p95_wall_s": 0.027489100000821054,
|
||||
"process_rss_bytes_after": 1406013440
|
||||
}
|
||||
+39
@@ -0,0 +1,39 @@
|
||||
{
|
||||
"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": [
|
||||
1,
|
||||
3,
|
||||
256,
|
||||
256
|
||||
],
|
||||
"input_dtype": "float32",
|
||||
"warmups": 0,
|
||||
"repeats": 1,
|
||||
"cpu_load_s": 0.558287899941206,
|
||||
"dml_load_and_transfer_s": 1.2977324000094086,
|
||||
"status": "passed",
|
||||
"output_shape": [
|
||||
1,
|
||||
16,
|
||||
32,
|
||||
32
|
||||
],
|
||||
"cpu_output_sha256_float32": "75b0c7cc33cc2a9845242030080cf901e4b05bdd5bdd8a65a0a3da253361dc28",
|
||||
"dml_output_sha256_float32": "f80d2c94d6ccac5c4975b22f5731018be2ae94dbd603abfa37a4a783ec4e6196",
|
||||
"max_abs_error": 1.1682510375976562e-05,
|
||||
"relative_l2_error": 4.732218030767399e-07,
|
||||
"cosine_similarity": 0.9999999403953552,
|
||||
"cpu_nearest_rank_p50_wall_s": 0.17620460002217442,
|
||||
"cpu_nearest_rank_p95_wall_s": 0.17620460002217442,
|
||||
"dml_nearest_rank_p50_wall_s": 0.8288762000156567,
|
||||
"dml_nearest_rank_p95_wall_s": 0.8288762000156567,
|
||||
"process_rss_bytes_after": 1006407680
|
||||
}
|
||||
+45
@@ -0,0 +1,45 @@
|
||||
{
|
||||
"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": [
|
||||
1,
|
||||
3,
|
||||
256,
|
||||
256
|
||||
],
|
||||
"input_dtype": "float32",
|
||||
"warmups": 0,
|
||||
"repeats": 1,
|
||||
"cpu_load_s": 0.558287899941206,
|
||||
"dml_load_and_transfer_s": 1.2977324000094086,
|
||||
"status": "passed",
|
||||
"output_shape": [
|
||||
1,
|
||||
16,
|
||||
32,
|
||||
32
|
||||
],
|
||||
"cpu_output_sha256_float32": "75b0c7cc33cc2a9845242030080cf901e4b05bdd5bdd8a65a0a3da253361dc28",
|
||||
"dml_output_sha256_float32": "f80d2c94d6ccac5c4975b22f5731018be2ae94dbd603abfa37a4a783ec4e6196",
|
||||
"max_abs_error": 1.1682510375976562e-05,
|
||||
"relative_l2_error": 4.732218030767399e-07,
|
||||
"cosine_similarity": 0.9999999403953552,
|
||||
"cpu_nearest_rank_p50_wall_s": 0.17620460002217442,
|
||||
"cpu_nearest_rank_p95_wall_s": 0.17620460002217442,
|
||||
"dml_nearest_rank_p50_wall_s": 0.8288762000156567,
|
||||
"dml_nearest_rank_p95_wall_s": 0.8288762000156567,
|
||||
"cpu_times_s": [
|
||||
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|
||||
],
|
||||
"dml_times_s": [
|
||||
0.8288762000156567
|
||||
],
|
||||
"process_rss_bytes_after": 1006407680
|
||||
}
|
||||
@@ -1,6 +1,8 @@
|
||||
# E2E check and profiling
|
||||
|
||||
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.
|
||||
|
||||
## Configuration × model
|
||||
|
||||
@@ -18,7 +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 |
|
||||
| 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 + 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 |
|
||||
@@ -55,7 +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 | 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 + 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 + 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. |
|
||||
|
||||
@@ -27,6 +27,16 @@ use reached 11.17 GB. Native Windows InternVLA direct-policy runs separately
|
||||
passed on CPU and RTX 5090 Laptop with synthetic inputs; AMD and mobile cells
|
||||
do not inherit these passes.
|
||||
|
||||
A [native Windows Radeon 890M InternVLA Cosmos encoder probe](../../benchmarks/edge_harness/results/e2e_expansion_20260923/evidence/internvla_cosmos_radeon890m/README.md)
|
||||
now establishes only component depth C: the real CI8x8 image encoder weights
|
||||
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.
|
||||
|
||||
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
|
||||
previous 0.58 budget failed the 2,048-token KV admission gate; the successful
|
||||
|
||||
Reference in new issue
Block a user