- vendor/deep_ep: EPBuffer dispatch/combine with torch.distributed uneven
all_to_all (host-synced split sizes), EngramBuffer sharded lookup,
BucketBuffer all_gather (CP-only reduce paths stubbed); verified against
the DeepEP-Ascend references at world size 1/2/4.
- tilelang_ops/neuron sampling: random_sampling (asc two-level fixed-point
sampler, Philox), fused_sample (logprobs / do_sample), sampler metadata,
sum_smaller_probs, structured-output bitmask; DSpark/MTP/varlen kernels
raise NotImplementedError. All 42 asc *_tl factories now have a Neuron
counterpart.
- vendor/flash_attn: SDPA varlen stand-in for the ViT (mirrors npu_ops).
- transfer_to_neuron: keep `with torch.device(...)` working on patched
factories; fix manual_seed recursion in host mode.
- engram: gather the packed ue8m0 sf as bytes on Neuron.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Run DeepInfer on the native torch-neuronx device (PrivateUse1 `neuron`,
torch-neuronx 2.14, no torch-xla), following the Ascend port's structure.
- deep_cluster: Neuron discovery (one DeepInfer device = one logical
NeuronCore, NEURON_RT_VISIBLE_CORES, NUMA from neuron-ls).
- PlatformNeuron: takes the Ascend (is_npu) kernel layouts, adds is_neuron;
DEEPINFER_NEURON_DEVICE=cpu runs the same code on the host for
correctness work.
- neuron_ops/transfer_to_neuron: map "cuda" devices, torch.device and
torch.cuda.* onto neuron:0 (counterpart of torch_npu transfer_to_npu).
- C++ extension: -DWITH_NEURON host-only build; torch-allocated buffers and
tensor-view slot copies instead of raw device-pointer copies.
- Pure-torch drop-in vendor packages (Ascend API semantics): deep_gemm,
flash_mla, deep_select, tile_kernels, tilelang (constants only).
- tilelang_ops/neuron backend: KV cache / metadata / norm / compress kernels;
CP, ZOS and VL entry points raise NotImplementedError.
- Basic mode defaults on Neuron: eager, no DSpark/MTP drafting, no
zero-overhead scheduling; MoE avoids the GPU-only mega kernel; mHC
prenorm GEMM uses the Ascend single-split reduction.
Not yet included: deep_ep, the sampling kernels, end-to-end harness.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
