mirror of
https://github.com/deepseek-ai/DeepInfer.git
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760e3cadc0
- tests/neuron/setup_trn2.sh: one-shot setup of a fresh Trn2 (venv layered
on the DLAMI torch-neuronx venv, C++ build, official reference + tokenizer,
tiny checkpoint, CPU golden).
- tests/neuron/run_all.sh cpu|device|all [stage...]: vendor kernel tests,
tilelang_ops interface tests (basic-mode stubs reported as skips via
nie_skip), deep_ep at world size 1/2/4 (gloo) and 2/4/8 on the neuron
process group, device primitive checks, end-to-end vs golden on CPU and
on a NeuronCore, and the device diff; PASS/FAIL summary.
- tests/neuron/device/test_device_primitives.py: torch-neuronx behaviours
the port depends on or works around (fp8 strided-view indexing, uint64,
1-rank all_to_all, inference-mode version counters, ...).
- examples/neuron/device_diff.py: re-runs every kernel call of a NeuronCore
run on host copies and ranks kernels by device/host difference.
- Unit tests written during bring-up moved from scratch dirs into
tests/neuron/{deep_gemm,flash_mla,deep_ep,tile_kernels,sampling}.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
"""Reference sparse attention ported from vendor/FlashMLA/tests/ref.py (no TestParam wrappers)."""
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import torch
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def ref_sparse_attn_fwd(q, kv, indices, sm_scale, topk_length=None, attn_sink=None, d_v=512, rms=None):
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"""q [s_q,h_q,d], kv [s_kv,1,d], indices [s_q,1,topk] -> (out bf16, max_logits, lse)."""
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s_q, h_q, d_qk = q.shape
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s_kv = kv.shape[0]
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topk = indices.shape[-1]
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idx = indices.clone().squeeze(1)
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if topk_length is not None:
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mask = torch.arange(topk).unsqueeze(0).broadcast_to(s_q, topk) >= topk_length.unsqueeze(1)
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idx.masked_fill_(mask, -1)
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invalid = (idx < 0) | (idx >= s_kv)
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idx = idx.masked_fill(invalid, 0)
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qf = q.float()
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gathered = kv.view(s_kv, d_qk).index_select(0, idx.flatten().long()).reshape(s_q, topk, d_qk).float()
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P = qf @ gathered.transpose(1, 2)
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if rms is not None:
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P = P * (sm_scale * rms.unsqueeze(-1))
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else:
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P = P * sm_scale
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P.masked_fill_(invalid.unsqueeze(1).broadcast_to(P.shape), float("-inf"))
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orig_lse = torch.logsumexp(P, dim=-1)
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max_logits = P.max(dim=-1).values
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lse_for_o = orig_lse
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if attn_sink is not None:
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lse_for_o = torch.logsumexp(
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torch.stack([orig_lse.view(s_q, h_q), attn_sink.broadcast_to(s_q, h_q)], dim=0), dim=0
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)
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lse_for_o = lse_for_o.clone()
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lse_for_o.masked_fill_(lse_for_o == float("-inf"), float("+inf"))
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s_for_o = torch.exp(P - lse_for_o.unsqueeze(-1))
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out = s_for_o @ gathered[..., :d_v]
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orig_lse = orig_lse.clone()
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orig_lse.masked_fill_(orig_lse == float("-inf"), float("+inf"))
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return out.to(torch.bfloat16), max_logits, orig_lse
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def ref_sparse_attn_decode(q, scopes, sm_scale, attn_sink=None, d_v=512, rms=None):
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"""q [b,s_q,h_q,d]; scopes = list of (blocked_k_bf16 [n_tokens, d], indices [b,s_q,topk], topk_length|None).
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Returns (out bf16 [b,s_q,h_q,d_v], lse [b,h_q,s_q]).
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"""
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b, s_q, h_q, d_qk = q.shape
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gathered_all, invalid_all = [], []
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for blocked_k, indices, topk_length in scopes:
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topk = indices.shape[-1]
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fixed = torch.clamp_min(indices, 0)
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g = blocked_k.view(-1, d_qk).index_select(0, fixed.reshape(-1).long()).view(b, s_q, topk, d_qk)
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invalid = indices == -1
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if topk_length is not None:
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invalid = invalid | (
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torch.arange(0, topk).view(1, 1, topk).broadcast_to(b, s_q, topk) >= topk_length.view(b, 1, 1)
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)
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gathered_all.append(g)
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invalid_all.append(invalid)
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gathered = torch.cat(gathered_all, dim=2)
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invalid = torch.cat(invalid_all, dim=2)
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gathered = gathered.reshape(b * s_q, -1, d_qk).float()
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gathered.masked_fill_(gathered != gathered, 0.0)
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qf = q.float().view(b * s_q, h_q, d_qk)
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w = qf @ gathered.transpose(-1, -2)
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if rms is not None:
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w = w * (sm_scale * rms.view(b * s_q, h_q).unsqueeze(-1))
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else:
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w = w * sm_scale
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w[invalid.view(b * s_q, 1, -1).broadcast_to(b * s_q, h_q, invalid.size(-1))] = float("-inf")
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lse = w.logsumexp(dim=-1)
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w = torch.exp(w - lse.unsqueeze(-1))
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out = w @ gathered[..., :d_v]
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out = out.view(b, s_q, h_q, d_v)
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lse = lse.view(b, s_q, h_q)
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if attn_sink is not None:
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out = out * (1.0 / (1.0 + torch.exp(attn_sink.view(1, 1, h_q) - lse))).unsqueeze(-1)
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lonely = lse == float("-inf")
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out = out.clone()
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out[lonely.unsqueeze(-1).broadcast_to(b, s_q, h_q, d_v)] = 0.0
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lse = lse.clone()
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lse[lonely] = float("+inf")
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return out.to(torch.bfloat16), lse.transpose(1, 2)
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