fix(uv): 2.x conflict groups silently drop Torch transitive dependencies

## Motivation

With stock `aspect_rules_py` **2.0.0-alpha.6**, a native uv project containing mutually exclusive CPU/CUDA Torch dependency groups produces an incomplete configured dependency graph. The correct top-level Torch wheel is selected, but required runtime dependencies disappear. All configured queries still exit successfully.

`uv.lock` contains the missing packages and dependency edges, and uv's frozen exports select them correctly. This blocks using the CPU/CUDA group feature without custom lockfile processing or importer patches. We want to stay on the 2.x line and use native uv plus documented rules_py configuration.

## Scope

Importing uv conflict-group / environment-marker metadata and preserving the transitive package graph in the 2.x uv extension. No patches, overrides, manually edited lockfiles, remote cache, or remote executor are involved in this reproducer.

## Reproduction

Verified on 2026-08-31 inside an Ubuntu 24.04.3 Linux x86-64 container (glibc 2.39), with Python 3.12.13, uv 0.11.16 and Bazel 8.7.0. The rules_py release resolves to commit `7a679659d78f940b1cbc831cc51429b9eb6b1c45`.

Create an empty directory containing these files. The exact uv-generated lockfile used in the test is included below; it was not hand-edited.

### MODULE.bazel

~~~starlark
module(name = "native_torch_probe")
bazel_dep(name = "aspect_rules_py", version = "2.0.0-alpha.6")
bazel_dep(name = "platforms", version = "1.0.0")

interpreters = use_extension("@aspect_rules_py//py:extensions.bzl", "python_interpreters")
interpreters.configure(releases = ["20260610"])
interpreters.toolchain(python_version = "3.12")
use_repo(interpreters, "python_interpreters")
register_toolchains("@python_interpreters//:all")

uv = use_extension("@aspect_rules_py//uv:extensions.bzl", "uv")
uv.declare_hub(hub_name = "pypi")
uv.project(hub_name = "pypi", pyproject = "//:pyproject.toml", lock = "//:uv.lock")
use_repo(uv, "pypi")
~~~

### BUILD.bazel

~~~starlark
platform(
name = "linux_x86_64",
constraint_values = ["@platforms//os:linux", "@platforms//cpu:x86_64"],
flags = [
"--@aspect_rules_py//uv/private/constraints/platform:platform_libc=glibc",
"--@aspect_rules_py//uv/private/constraints/platform:platform_version=2.39",
],
)

exports_files(["pyproject.toml", "uv.lock"])
~~~

### pyproject.toml

~~~toml
[project]
name = "native-torch-probe"
version = "0.0.0"
requires-python = ">=3.12,<3.13"

[dependency-groups]
cpu = ["torch==2.12.0"]
cuda = ["torch==2.12.0; sys_platform == 'linux'"]

[tool.uv]
package = false
environments = [
"sys_platform == 'linux' and platform_machine == 'x86_64'",
"sys_platform == 'darwin' and platform_machine == 'arm64'",
]
conflicts = [[{group = "cpu"}, {group = "cuda"}]]

[tool.uv.sources]
torch = [{index = "pytorch-cpu", group = "cpu", marker = "sys_platform == 'linux'"}]

[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
explicit = true
~~~

### Commands

Use the Bazel 8.7.0 binary as `bazel`. Run in the new directory:

~~~bash
uvx --from uv==0.11.16 uv lock --python 3.12
uvx --from uv==0.11.16 uv export --frozen --only-group cpu --no-hashes --no-emit-project > uv-cpu.txt
uvx --from uv==0.11.16 uv export --frozen --only-group cuda --no-hashes --no-emit-project > uv-cuda.txt

probe_bazel_root=$(mktemp -d)
for group in cpu cuda; do
bazel --ignore_all_rc_files --output_user_root="$probe_bazel_root" \
cquery 'deps(@pypi//torch)' \
--platforms=//:linux_x86_64 \
"--@pypi//dep_group=$group" \
--@aspect_rules_py//py:python_version=3.12 \
> "bazel-$group-graph.txt" 2> "bazel-$group.log"
done

# Inspect the installed-package targets, not just the top-level wheel.
grep -E 'whl_install__native_torch_probe__.*//:install ' bazel-cpu-graph.txt
grep -E 'whl_install__native_torch_probe__.*//:install ' bazel-cuda-graph.txt
~~~

### Expected and observed

Package counts below include Torch itself and evaluate uv's export markers for Linux x86-64 / Python 3.12.

| Configuration | uv-selected packages | Configured `whl_install` targets | Result |
| --- | ---: | ---: | --- |
| CPU in the mixed CPU/CUDA project | 10 | 8 | `markupsafe` and `mpmath` missing |
| CUDA in the mixed CPU/CUDA project | 29 | 1 | Only Torch; all 28 transitive packages missing |
| Plain PyPI Torch control, without CPU group/conflicts/source override | 29 | 29 | Complete package-name closure |

Both mixed configurations choose the intended wheel:

- CPU: `torch-2.12.0%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl`
- CUDA: `torch-2.12.0-cp312-cp312-manylinux_2_28_x86_64.whl`

The CPU configured graph contains exactly:

~~~text
torch 2.12.0+cpu
sympy 1.14.0
jinja2 3.1.6
typing-extensions 4.16.0
setuptools 81.0.0
filelock 3.32.4
fsspec 2026.7.0
networkx 3.6.1
~~~

But the lockfile explicitly includes `sympy -> mpmath` and `jinja2 -> markupsafe`. Those edges have compound markers involving Linux/macOS and uv's synthetic group extras, such as `extra == 'group-18-native-torch-probe-cpu'` and `extra == 'group-18-native-torch-probe-cuda'`.

For the CUDA case, the expected 29 packages are all present in uv's own frozen export below, while the configured install closure contains only Torch.

All three `cquery` invocations exit 0 and report `Build completed successfully, 0 total actions`. This report is dependency-analysis evidence, **not** a runtime import test, GPU test, or performance measurement.

### Positive control

In a separate empty workspace, keep the project name and Linux platform, but use only this pyproject (same rules_py pin/toolchain; the control MODULE used `module(name = "native_torch_control")` and `platforms` 1.1.0):

~~~toml
[project]
name = "native-torch-probe"
version = "0.0.0"
requires-python = ">=3.12,<3.13"

[dependency-groups]
cuda = ["torch==2.12.0; sys_platform == 'linux'"]

[tool.uv]
package = false
environments = [
"sys_platform == 'linux' and platform_machine == 'x86_64'",
"sys_platform == 'darwin' and platform_machine == 'arm64'",
]
~~~

Generate its lock with the same uv command, then run the CUDA export and query above. The uv export and configured install closure both contain 29 packages, including the expected Torch wheel. This control isolates the mixed conflict-group configuration rather than a missing general dependency declaration.

### Exact mixed-project lockfile

uv.lock generated by uv 0.11.16 (unchanged)

~~~toml
version = 1
revision = 3
requires-python = "==3.12.*"
resolution-markers = [
"platform_machine == 'x86_64' and sys_platform == 'linux'",
"platform_machine == 'arm64' and sys_platform == 'darwin'",
]
supported-markers = [
"platform_machine == 'x86_64' and sys_platform == 'linux'",
"platform_machine == 'arm64' and sys_platform == 'darwin'",
]
conflicts = [[
{ package = "native-torch-probe", group = "cpu" },
{ package = "native-torch-probe", group = "cuda" },
]]

[[package]]
name = "cuda-bindings"
version = "13.3.1"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "cuda-pathfinder", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'group-18-native-torch-probe-cuda') or (platform_machine != 'x86_64' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform != 'linux' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda')" },
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]

[package.optional-dependencies]
cudart = [
{ name = "nvidia-cuda-runtime", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'group-18-native-torch-probe-cuda') or (platform_machine != 'x86_64' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform != 'linux' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda')" },
]
cufft = [
{ name = "nvidia-cufft", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'group-18-native-torch-probe-cuda') or (platform_machine != 'x86_64' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform != 'linux' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda')" },
]
cufile = [
{ name = "nvidia-cufile", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'group-18-native-torch-probe-cuda') or (platform_machine != 'x86_64' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform != 'linux' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda')" },
]
cupti = [
{ name = "nvidia-cuda-cupti", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'group-18-native-torch-probe-cuda') or (platform_machine != 'x86_64' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform != 'linux' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda')" },
]
curand = [
{ name = "nvidia-curand", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'group-18-native-torch-probe-cuda') or (platform_machine != 'x86_64' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform != 'linux' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda')" },
]
cusolver = [
{ name = "nvidia-cusolver", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'group-18-native-torch-probe-cuda') or (platform_machine != 'x86_64' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform != 'linux' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda')" },
]
cusparse = [
{ name = "nvidia-cusparse", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'group-18-native-torch-probe-cuda') or (platform_machine != 'x86_64' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform != 'linux' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda')" },
]
nvjitlink = [
{ name = "nvidia-nvjitlink", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'group-18-native-torch-probe-cuda') or (platform_machine != 'x86_64' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform != 'linux' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda')" },
]
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]
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[[package]]
name = "native-torch-probe"
version = "0.0.0"
source = { virtual = "." }

[package.dev-dependencies]
cpu = [
{ name = "torch", version = "2.12.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin' and extra == 'group-18-native-torch-probe-cpu') or (platform_machine != 'arm64' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform != 'darwin' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda')" },
{ name = "torch", version = "2.12.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'group-18-native-torch-probe-cpu') or (platform_machine != 'x86_64' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform != 'linux' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda')" },
]
cuda = [
{ name = "torch", version = "2.12.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
]

[package.metadata]

[package.metadata.requires-dev]
cpu = [
{ name = "torch", marker = "sys_platform != 'linux'", specifier = "==2.12.0" },
{ name = "torch", marker = "sys_platform == 'linux'", specifier = "==2.12.0", index = "https://download.pytorch.org/whl/cpu", conflict = { package = "native-torch-probe", group = "cpu" } },
]
cuda = [{ name = "torch", marker = "sys_platform == 'linux'", specifier = "==2.12.0" }]

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{ name = "triton", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'group-18-native-torch-probe-cuda') or (platform_machine != 'x86_64' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform != 'linux' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda')" },
{ name = "typing-extensions", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin' and extra == 'group-18-native-torch-probe-cpu') or (platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform == 'darwin' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda') or (sys_platform == 'linux' and extra == 'group-18-native-torch-probe-cpu' and extra == 'group-18-native-torch-probe-cuda')" },
]
wheels = [
{ url = "https://files.pythonhosted.org/packages/ef/bb/285d643f254731294c9b595a007eac39db4600a98682d7bca688f42ca164/torch-2.12.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:b41339df93d491435e790ff8bcbae1c0ce777175889bfd1281d119862793e6a2", size = 88010197, upload-time = "2026-05-13T14:55:35.414Z" },
{ url = "https://files.pythonhosted.org/packages/de/f0/80026028b603c4650ff270fc3785bdef4bd6738765a9cc5a0f5a637d65a2/torch-2.12.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:4b4f64c2c2b11f7510d93dd6412b87025ff6eddd6bb61c3b5a3d892ea20c4756", size = 532261691, upload-time = "2026-05-13T14:52:54.453Z" },
]

[[package]]
name = "torch"
version = "2.12.0+cpu"
source = { registry = "https://download.pytorch.org/whl/cpu" }
resolution-markers = [
"platform_machine == 'x86_64' and sys_platform == 'linux'",
]
dependencies = [
{ name = "filelock", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
{ name = "fsspec", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
{ name = "jinja2", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
{ name = "networkx", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
{ name = "setuptools", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
{ name = "sympy", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
{ name = "typing-extensions", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
]
wheels = [
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.12.0%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:5e3dc83725581fa38b7b2e45c58692e30b2a3cde19191af54b675ffcac3840a6", upload-time = "2026-05-12T23:16:48Z" },
]

[[package]]
name = "triton"
version = "3.7.0"
source = { registry = "https://pypi.org/simple" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/62/7b/468a576e35beef1426e0828e28e9ba9e65f5474d496f16ee126c15646324/triton-3.7.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8f111161d49bf903c0eaedde3962353a3d841c08a836839b7cc1025b8426efcf", size = 201457567, upload-time = "2026-05-07T18:46:13.505Z" },
]

[[package]]
name = "typing-extensions"
version = "4.16.0"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/f6/cc/6253133b5bb138fc3306cebfbda2c520f545d36b5be2c7255cc528bb45d6/typing_extensions-4.16.0.tar.gz", hash = "sha256:dc983d19a509c94dba722ee6abd33940f7c05a89e243c47e907eb4db6f1a43e5", size = 113555, upload-time = "2026-07-02T08:40:05.92Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/49/d3/b8441a820a491ddfc024b0b0cf0393375b75ea13866d9c66727e54c2fc80/typing_extensions-4.16.0-py3-none-any.whl", hash = "sha256:481caa481374e813c1b176ada14e97f1f67a4539ce9cfeb3f350d78d6370c2e8", size = 45571, upload-time = "2026-07-02T08:40:04.659Z" },
]
~~~

uv frozen CPU export

~~~text
# This file was autogenerated by uv via the following command:
# uv export --frozen --only-group cpu --no-hashes --no-emit-project
filelock==3.32.4 ; (platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux')
# via torch
fsspec==2026.7.0 ; (platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux')
# via torch
jinja2==3.1.6 ; (platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux')
# via torch
markupsafe==3.0.3 ; (platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux')
# via jinja2
mpmath==1.3.0 ; (platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux')
# via sympy
networkx==3.6.1 ; (platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux')
# via torch
setuptools==81.0.0 ; (platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux')
# via torch
sympy==1.14.0 ; (platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux')
# via torch
torch==2.12.0 ; platform_machine == 'arm64' and sys_platform == 'darwin'
torch==2.12.0+cpu ; platform_machine == 'x86_64' and sys_platform == 'linux'
typing-extensions==4.16.0 ; (platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux')
# via torch
~~~

uv frozen CUDA export

~~~text
# This file was autogenerated by uv via the following command:
# uv export --frozen --only-group cuda --no-hashes --no-emit-project
cuda-bindings==13.3.1 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
cuda-pathfinder==1.8.0 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via cuda-bindings
cuda-toolkit==13.0.2 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
filelock==3.32.4 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
fsspec==2026.7.0 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
jinja2==3.1.6 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
markupsafe==3.0.3 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via jinja2
mpmath==1.3.0 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via sympy
networkx==3.6.1 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
nvidia-cublas==13.1.1.3 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via
# nvidia-cudnn-cu13
# nvidia-cusolver
# torch
nvidia-cuda-cupti==13.0.85 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via cuda-toolkit
nvidia-cuda-nvrtc==13.0.88 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via
# cuda-toolkit
# nvidia-cublas
nvidia-cuda-runtime==13.0.96 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via cuda-toolkit
nvidia-cudnn-cu13==9.20.0.48 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
nvidia-cufft==12.0.0.61 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via cuda-toolkit
nvidia-cufile==1.15.1.6 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via cuda-toolkit
nvidia-curand==10.4.0.35 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via cuda-toolkit
nvidia-cusolver==12.0.4.66 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via cuda-toolkit
nvidia-cusparse==12.6.3.3 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via
# cuda-toolkit
# nvidia-cusolver
nvidia-cusparselt-cu13==0.8.1 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
nvidia-nccl-cu13==2.29.7 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
nvidia-nvjitlink==13.0.88 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via
# cuda-toolkit
# nvidia-cufft
# nvidia-cusolver
# nvidia-cusparse
nvidia-nvshmem-cu13==3.4.5 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
nvidia-nvtx==13.0.85 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via cuda-toolkit
setuptools==81.0.0 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
sympy==1.14.0 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
torch==2.12.0 ; platform_machine == 'x86_64' and sys_platform == 'linux'
triton==3.7.0 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
typing-extensions==4.16.0 ; platform_machine == 'x86_64' and sys_platform == 'linux'
# via torch
~~~

## Acceptance criteria

- On the 2.x line, the configured dependency closure agrees with uv's own selection for both groups, with the correct Torch variant and no CUDA-only packages in the Linux CPU selection.
- Keep the lockfile uv-owned and consume the native group/source/index configuration without downstream parsing or importer patches.
- Add regression coverage for compound conflict-group + platform markers, including the two-hop `torch -> sympy -> mpmath` and `torch -> jinja2 -> markupsafe` paths.

If a configuration is unsupported, a clear diagnostic would be preferable to a successfully analyzed but incomplete graph.

## Related work / open questions

- #817: earlier dependency-group conflict handling.
- #994: Torch platform / resolution-marker selection.
- #1473: marker-gated group versions and transitive condition propagation, merged to 1.x.
- #1503: transitive dependency marker preservation proposed for main / 2.x.

This reproducer has **not** been tested against #1503 or the 1.x fixes; linking them does not claim they resolve these compound conflict-group markers. Is this case covered by the planned 2.x fixes, or does it need separate conflict-marker handling?

## Out of scope

Downgrading the consumer to 1.x, hand-editing uv.lock, manually listing missing transitive dependencies, or maintaining a consumer-side importer fork.

## Agentic issue trace

**Authorship:** Codex desktop harness 0.150.0-alpha.8 · OpenAI/gpt-5.6-sol · frontier/proprietary · effort xhigh · for @henridwyer · multi-turn, human read final text: no, verified at 7a679659d78f940b1cbc831cc51429b9eb6b1c45

| Field | Value |
| --- | --- |
| User-stated | Report the 2.x failure; stay on 2.x; use native uv and rules_py configuration without custom parsing or importer patches. |
| Agent-inferred | Include the exact standalone lockfile, both group closures and a plain-Torch positive control; relate the result to upstream marker work without claiming an untested fix. |
| Verified against repo | Pinned upstream importer source; standalone MODULE.bazel, BUILD.bazel, pyproject.toml and uv.lock; Linux-container uv lock/export and Bazel configured queries. |
| Residual gaps | Runtime imports and candidate 2.x fixes were not tested; exact relationship to #1503 remains unverified. |

Reproduce the issue from MODULE.bazel, BUILD.bazel, pyproject.toml, and uv.lock using the listed uv export and Bazel cquery commands. Start at the uv extension entry point @aspect_rules_py//uv:extensions.bzl and compare the CPU and CUDA configured graphs with uv's frozen exports. Done means both conflict-group configurations preserve the complete transitive package closure and the positive control remains unchanged.

Written by the indexing model from the issue text.