fix deprecated paddle compat api usage#11
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SigureMo merged 1 commit intoApr 12, 2026
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- flashinfer/prefill.py: convert workspace_size (tensor scalar from numel()*element_size()) to Python int via .item() before passing to the tvm_ffi C++ kernel, which expects int but receives ffi.Tensor under paddle (doc item PFCCLab#11) - tests/conftest.py: revert paddle.enable_compat() to global scope so that `import torch` at conftest module level (outside flashinfer scope) also resolves via the proxy
BingooYang
added a commit
to BingooYang/flashinfer
that referenced
this pull request
May 13, 2026
- enable paddle torch proxy in conftest via paddle.enable_compat(scope={"flashinfer"})
- in tests/attention/test_attention_sink_blackwell.py: prepend paddle.enable_compat(),
replace torch.manual_seed with paddle.seed, replace torch.testing.assert_close with
numpy.testing.assert_allclose, parametrize to a minimal shape for quick verification
- flashinfer/utils.py: access TorchVersion via torch.torch_version proxy with fallback
for paddle compat where paddle.torch_version is not exposed
- flashinfer/cute_dsl/fp4_common.py: add "from __future__ import annotations" to
defer evaluation of "int | torch.device | str | None" annotation which fails under
paddle proxy (torch.device is a CallableProxyModule, not a type)
adapt prefill trtllm paged attention for paddle compat
- flashinfer/prefill.py: convert workspace_size (tensor scalar from numel()*element_size())
to Python int via .item() before passing to the tvm_ffi C++ kernel, which expects int
but receives ffi.Tensor under paddle (doc item PFCCLab#11)
- tests/conftest.py: revert paddle.enable_compat() to global scope so that `import torch`
at conftest module level (outside flashinfer scope) also resolves via the proxy
paddle compat: decode workspace_size .item(), moe fp8 index via int8 view, autotuner shape tuple, moe test
support allreduce fusion
dist.group.WORLD compat
modify readme
modify format
fix env issue
fix some issue
paddle compat: fix dtype.itemsize + expand trtllm_allreduce_fusion test
- flashinfer/comm/trtllm_ar.py: paddle.dtype has no `itemsize`; add
_DTYPE_SIZE_MAP + _dtype_itemsize() fallback used in _should_use_oneshot
(fixes AttributeError when use_oneshot=None triggers the heuristic).
- tests/comm/test_trtllm_allreduce_fusion.py: restore full parametrize
scope (patterns/layouts/pdls/oneshots/trigger/fp32_acc); drop leftover
[DBG] prints; guard `if __name__ == "__main__"` block so mp-spawn
children do not re-enter it under pytest (was double-initializing
paddle TCPStore and SIGABRT in libuv).
Verified: pytest tests/comm/test_trtllm_allreduce_fusion.py::test_trtllm_allreduce_fusion[True-1024-dtype0-2] and [False-1024-dtype0-2] both pass on 2xGPU.
add adaptation paddle skill
paddle compat: revert over-adaptation in test_trtllm_gen_fused_moe
`torch.cuda.get_device_capability`, `tensor.device`, and `tensor.to(device)`
are fully aligned under `paddle.enable_compat()`. Revert the earlier
paddle-specific detours (`torch.device.cuda.get_device_capability`,
`paddle.device(x.place)`, `paddle.get_device()`) back to plain torch APIs.
Also record the finding in adaptation-paddle skill (§10, items 31-34) as a
"do-not-over-adapt" reference for future MoE test reviews.
Verified: `pytest tests/moe/test_trtllm_gen_fused_moe.py -k test_moe_quantization_classes`
passes (1 passed).
paddle compat: restore test_trtllm_gen_fused_moe to upstream + minimal patches
The previous adaptation commented out / trimmed ~1800 lines from upstream,
making future rebases painful and dropping valid test coverage. Reset the
file to exact upstream content (github.com/flashinfer-ai/flashinfer main)
and keep only the minimum compat patches needed to run on paddle:
test file patches:
- add `import paddle; paddle.enable_compat()` at top
- `block.aminmax()` -> `block.float().aminmax()` (paddle missing bf16 kernel)
- fp8 slice assign via `.view(torch.int8)` on both sides (paddle missing fp8 set_value kernel)
- `expertLogits.cpu()` -> `.cpu().float()` (paddle missing cpu-bf16 topk)
- `torch.random.manual_seed` -> `torch.manual_seed` (paddle.random lacks manual_seed)
- `torch.device(device="cuda")` -> `torch.device("cuda")` (paddle Device rejects kwarg)
same `torch.device(...)` kwarg fix in tests/moe/utils.py.
library patch (flashinfer/autotuner.py):
- `torch.cuda.OutOfMemoryError` missing under paddle. Use a sentinel placeholder
class (NOT `RuntimeError` - that would silently swallow real kernel errors).
Verified: `pytest test_trtllm_gen_fused_moe.py::test_fp8_block_scale_routed_activation_type_relu2_smoke`
passes. Larger parametrized cases still need library-side fixes (e.g.
`core.py::_init_packed_topk_ids` bitwise_or dtype mismatch).
Docs (skills/adaptation-paddle): record new patches 31-36 and the
"do-not-trim-upstream" lesson.
paddle compat: fix bitwise_or dtype mismatch in _init_packed_topk_ids
torch implicitly promotes int16->int32 in `(expert_ids << 16) | expert_weights`.
Paddle's bitwise_or does not, so it raises
ValueError: The type of data we are trying to retrieve (int16) does not
match the type of data (int32)
Explicitly .to(torch.int32) after .view(torch.int16). Works on both backends.
With this fix, routing-family tests (renormalize/sigmoid/deepseekv3/topk/
llama4/dyn_block/tier_1024/deepseek_ngroup1/routing_dtype_flexibility) all
progress past the dtype check. Remaining failures on this machine are
infrastructure (cubin artifactory unreachable), not paddle-compat.
modify skill
fix some issues
paddle compat: test_fused_rmsnorm_silu zero-patch adaptation
tests/norm/test_fused_rmsnorm_silu.py runs under paddle.enable_compat()
with no source changes (conftest.py already enables compat). Full run:
102 passed, 50 skipped (all skips due to torch.float4_e2m1fn_x2 missing
from paddle torch-proxy, not a kernel adaptation issue).
- adp_test.md: add row 18 recording PASS 102/152
- adaptation_exp.md: add section XI (flashinfer-ai#37-39) documenting zero-patch
result, rationale, reproduction command, and the methodology
recommendation (bare-run first, consult adaptation table only on
failure).
fix format
BingooYang
added a commit
to BingooYang/flashinfer
that referenced
this pull request
May 13, 2026
- enable paddle torch proxy in conftest via paddle.enable_compat(scope={"flashinfer"})
- in tests/attention/test_attention_sink_blackwell.py: prepend paddle.enable_compat(),
replace torch.manual_seed with paddle.seed, replace torch.testing.assert_close with
numpy.testing.assert_allclose, parametrize to a minimal shape for quick verification
- flashinfer/utils.py: access TorchVersion via torch.torch_version proxy with fallback
for paddle compat where paddle.torch_version is not exposed
- flashinfer/cute_dsl/fp4_common.py: add "from __future__ import annotations" to
defer evaluation of "int | torch.device | str | None" annotation which fails under
paddle proxy (torch.device is a CallableProxyModule, not a type)
adapt prefill trtllm paged attention for paddle compat
- flashinfer/prefill.py: convert workspace_size (tensor scalar from numel()*element_size())
to Python int via .item() before passing to the tvm_ffi C++ kernel, which expects int
but receives ffi.Tensor under paddle (doc item PFCCLab#11)
- tests/conftest.py: revert paddle.enable_compat() to global scope so that `import torch`
at conftest module level (outside flashinfer scope) also resolves via the proxy
paddle compat: decode workspace_size .item(), moe fp8 index via int8 view, autotuner shape tuple, moe test
support allreduce fusion
dist.group.WORLD compat
modify readme
modify format
fix env issue
fix some issue
paddle compat: fix dtype.itemsize + expand trtllm_allreduce_fusion test
- flashinfer/comm/trtllm_ar.py: paddle.dtype has no `itemsize`; add
_DTYPE_SIZE_MAP + _dtype_itemsize() fallback used in _should_use_oneshot
(fixes AttributeError when use_oneshot=None triggers the heuristic).
- tests/comm/test_trtllm_allreduce_fusion.py: restore full parametrize
scope (patterns/layouts/pdls/oneshots/trigger/fp32_acc); drop leftover
[DBG] prints; guard `if __name__ == "__main__"` block so mp-spawn
children do not re-enter it under pytest (was double-initializing
paddle TCPStore and SIGABRT in libuv).
Verified: pytest tests/comm/test_trtllm_allreduce_fusion.py::test_trtllm_allreduce_fusion[True-1024-dtype0-2] and [False-1024-dtype0-2] both pass on 2xGPU.
add adaptation paddle skill
paddle compat: revert over-adaptation in test_trtllm_gen_fused_moe
`torch.cuda.get_device_capability`, `tensor.device`, and `tensor.to(device)`
are fully aligned under `paddle.enable_compat()`. Revert the earlier
paddle-specific detours (`torch.device.cuda.get_device_capability`,
`paddle.device(x.place)`, `paddle.get_device()`) back to plain torch APIs.
Also record the finding in adaptation-paddle skill (§10, items 31-34) as a
"do-not-over-adapt" reference for future MoE test reviews.
Verified: `pytest tests/moe/test_trtllm_gen_fused_moe.py -k test_moe_quantization_classes`
passes (1 passed).
paddle compat: restore test_trtllm_gen_fused_moe to upstream + minimal patches
The previous adaptation commented out / trimmed ~1800 lines from upstream,
making future rebases painful and dropping valid test coverage. Reset the
file to exact upstream content (github.com/flashinfer-ai/flashinfer main)
and keep only the minimum compat patches needed to run on paddle:
test file patches:
- add `import paddle; paddle.enable_compat()` at top
- `block.aminmax()` -> `block.float().aminmax()` (paddle missing bf16 kernel)
- fp8 slice assign via `.view(torch.int8)` on both sides (paddle missing fp8 set_value kernel)
- `expertLogits.cpu()` -> `.cpu().float()` (paddle missing cpu-bf16 topk)
- `torch.random.manual_seed` -> `torch.manual_seed` (paddle.random lacks manual_seed)
- `torch.device(device="cuda")` -> `torch.device("cuda")` (paddle Device rejects kwarg)
same `torch.device(...)` kwarg fix in tests/moe/utils.py.
library patch (flashinfer/autotuner.py):
- `torch.cuda.OutOfMemoryError` missing under paddle. Use a sentinel placeholder
class (NOT `RuntimeError` - that would silently swallow real kernel errors).
Verified: `pytest test_trtllm_gen_fused_moe.py::test_fp8_block_scale_routed_activation_type_relu2_smoke`
passes. Larger parametrized cases still need library-side fixes (e.g.
`core.py::_init_packed_topk_ids` bitwise_or dtype mismatch).
Docs (skills/adaptation-paddle): record new patches 31-36 and the
"do-not-trim-upstream" lesson.
paddle compat: fix bitwise_or dtype mismatch in _init_packed_topk_ids
torch implicitly promotes int16->int32 in `(expert_ids << 16) | expert_weights`.
Paddle's bitwise_or does not, so it raises
ValueError: The type of data we are trying to retrieve (int16) does not
match the type of data (int32)
Explicitly .to(torch.int32) after .view(torch.int16). Works on both backends.
With this fix, routing-family tests (renormalize/sigmoid/deepseekv3/topk/
llama4/dyn_block/tier_1024/deepseek_ngroup1/routing_dtype_flexibility) all
progress past the dtype check. Remaining failures on this machine are
infrastructure (cubin artifactory unreachable), not paddle-compat.
modify skill
fix some issues
paddle compat: test_fused_rmsnorm_silu zero-patch adaptation
tests/norm/test_fused_rmsnorm_silu.py runs under paddle.enable_compat()
with no source changes (conftest.py already enables compat). Full run:
102 passed, 50 skipped (all skips due to torch.float4_e2m1fn_x2 missing
from paddle torch-proxy, not a kernel adaptation issue).
- adp_test.md: add row 18 recording PASS 102/152
- adaptation_exp.md: add section XI (flashinfer-ai#37-39) documenting zero-patch
result, rationale, reproduction command, and the methodology
recommendation (bare-run first, consult adaptation table only on
failure).
fix format
fix some issue
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Summary
paddle.compat.enable_torch_proxy(scope={"flashinfer"})inREADME.mdwithpaddle.enable_compat(scope={"flashinfer"})tests/conftest.py,tests/moe/test_trtllm_gen_fused_moe.py, andtests/attention/test_attention_sink_blackwell.pywithpaddle.enable_compat()tests/comm/test_trtllm_allreduce_fusion_paddle.pyVerification
grep -R -nE "enable_torch_proxy|disable_torch_proxy" .python3 -m py_compile tests/conftest.py tests/moe/test_trtllm_gen_fused_moe.py tests/attention/test_attention_sink_blackwell.py tests/comm/test_trtllm_allreduce_fusion_paddle.py