✨ Task55-hc-head-8files-v3 · 7 文件 zip 派生 · ZHIZHI-INIT-20260906
[推送者]: 阿念(Mavis) · ICE-GL-AN-001 · Code [对应提交包]: /Users/zhizhi/Desktop/hc_head.zip [基准算法]: hc_head v2 (num_stages=1 + num_warps=8 + int32 pid + 指针 cast) [模式来源]: Downloads/chunk_scaled_dot_kkt/ 7 文件结构(launch params 模式迁移) [差异]: 仅 launch params + 国产 NPU wrapper,算法层 100% bit-exact [enflame 特化]: BLOCK_D 1024→512 + num_warps 8→4 (小芯片 + 小 SRAM) [kunlunxin 特化]: tl.static_range 强制编译时展开(绕 SDNN 路径循环 codegen bug) [反作弊]: 纯 Triton + Triton-TLE,无 try/except fallback [平台校验修复]: 满足第 4 批'跨芯片算子代码 + 特定芯片算子代码'两项校验
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之之 (ZZ体系)
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"""FlagOS S2 赛道一 · hc_head (s2t1op055) · 国际A(NVIDIA)/国际B(AMD)通用版 · v2
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DeepSeek-V4 "hc_head" LM-head 混合器: RMSNorm + 线性混合 + sigmoid 门控 + 加权求和
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v2 修复（从 extend_attention.py v2 经验）:
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1) num_stages=1 显式 (头号嫌疑 — 国产 NPU + 部分国际卡多级流水支持不完整)
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2) num_warps 4 → 8 (大 BLOCK_D=1024 + HC_MULT=4 循环, 8 warps 更稳)
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3) pid int64 → int32 (D ≤ 7168, T 不会超 2.1B, 长度算术走 int32)
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4) 指针 cast 防 NaN (D245 验证)
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"""
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import torch
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import triton
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import triton.language as tl
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@triton.jit
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def _hc_head_fwd_kernel(
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x_ptr, hc_fn_ptr, hc_scale_ptr, hc_base_ptr, out_ptr,
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T,
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HC_DIM: tl.constexpr, D: tl.constexpr, HC_MULT: tl.constexpr,
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BLOCK_D: tl.constexpr,
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norm_eps, hc_eps,
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):
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pid = tl.program_id(0).to(tl.int32)
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# 指针 cast 防 NaN（D245 验证）
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if x_ptr.dtype.element_ty.primitive_bitwidth == 16:
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x_ptr = x_ptr.to(tl.pointer_type(tl.int16))
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if hc_fn_ptr.dtype.element_ty.primitive_bitwidth == 32:
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hc_fn_ptr = hc_fn_ptr.to(tl.pointer_type(tl.int32))
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# ===== Pass 1: 算 squared sum + 算 mixes (linear projection) =====
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sqr_sum = tl.zeros((), dtype=tl.float32)
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mixes = tl.zeros((HC_MULT,), dtype=tl.float32)
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for d_off in range(0, HC_DIM, BLOCK_D):
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d_idx = d_off + tl.arange(0, BLOCK_D)
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mask = d_idx < HC_DIM
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x_val = tl.load(x_ptr + pid * HC_DIM + d_idx, mask=mask, other=0.0).to(tl.float32)
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sqr_sum += tl.sum(x_val * x_val, axis=0)
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for m in tl.static_range(HC_MULT):
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fn_val = tl.load(hc_fn_ptr + m * HC_DIM + d_idx, mask=mask, other=0.0)
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mixes[m] += tl.sum(x_val * fn_val, axis=0)
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rsqrt = 1.0 / tl.sqrt(sqr_sum / HC_DIM + norm_eps)
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mixes = mixes * rsqrt
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# ===== sigmoid gate =====
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scale = tl.load(hc_scale_ptr)
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bases = tl.load(hc_base_ptr + tl.arange(0, HC_MULT))
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pre = 1.0 / (1.0 + tl.exp(-(mixes * scale + bases))) + hc_eps # [HC_MULT]
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# ===== Pass 2: 加权求和 (collapse hc_mult) =====
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for d_off in range(0, D, BLOCK_D):
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d_idx = d_off + tl.arange(0, BLOCK_D)
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mask = d_idx < D
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accum = tl.zeros((BLOCK_D,), dtype=tl.float32)
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for m in tl.static_range(HC_MULT):
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x_val = tl.load(x_ptr + pid * HC_DIM + m * D + d_idx, mask=mask, other=0.0).to(tl.float32)
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accum += pre[m] * x_val
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tl.store(out_ptr + pid * D + d_idx, accum.to(out_ptr.dtype.element_ty), mask=mask)
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def hc_head(x, hc_fn, hc_scale, hc_base, norm_eps, hc_eps):
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"""hc_head: DeepSeek-V4 HC head reduction for LM-head mixer.
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Computes gates from the RMS-normalized flattened HC residual
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and returns out = sum_i gate_i * residual_i, collapsing hc_mult streams.
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Args:
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x: [T, hc_mult, hidden_size] bfloat16
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hc_fn: [hc_mult, hc_mult * hidden_size] float32
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hc_scale: [1] float32
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hc_base: [hc_mult] float32
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norm_eps, hc_eps: float
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Returns:
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[T, hidden_size] bfloat16
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"""
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if x.ndim != 3:
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raise ValueError('x must have shape [T, hc_mult, hidden_size]')
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if x.device.type in ('cpu', 'meta', 'mps'):
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raise RuntimeError('a real Triton accelerator backend is required')
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T, hc_mult, D = x.shape
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HC_DIM = hc_mult * D
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if hc_fn.shape != (hc_mult, HC_DIM):
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raise ValueError(f'hc_fn must have shape [{hc_mult}, {HC_DIM}], got {tuple(hc_fn.shape)}')
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if hc_scale.numel() != 1:
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raise ValueError(f'hc_scale must be scalar, got shape {tuple(hc_scale.shape)}')
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if hc_base.shape != (hc_mult,):
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raise ValueError(f'hc_base must have shape [{hc_mult}], got {tuple(hc_base.shape)}')
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x_flat = x.view(T, HC_DIM)
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out = torch.empty((T, D), dtype=x.dtype, device=x.device)
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if T == 0:
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return out
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BLOCK_D = min(1024, 1 << max(5, (HC_DIM - 1).bit_length()))
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grid = (T,)
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_hc_head_fwd_kernel[grid](
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x_flat, hc_fn, hc_scale, hc_base, out,
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T,
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HC_DIM=HC_DIM, D=D, HC_MULT=hc_mult,
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BLOCK_D=BLOCK_D,
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norm_eps=norm_eps, hc_eps=hc_eps,
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num_warps=8, num_stages=1, enable_fp_fusion=False,
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)
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return out
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reference = hc_head
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"""FlagOS S2 赛道一 · hc_head (s2t1op055) · 华为昇腾(Ascend)特化版 · v2
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算法层同通用版 · 华为国产 NPU 套路:
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1) torch.get_device_module(x.device).device(x.device) wrapper (国产 NPU 必需)
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2) enable_fp_fusion=False (D245 验证)
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3) num_stages=1 (国产 NPU 多级流水支持不完整)
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4) num_warps=8 (大 BLOCK_D=1024 + HC_MULT=4 循环)
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"""
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import torch
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import triton
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import triton.language as tl
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@triton.jit
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def _hc_head_fwd_kernel(
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x_ptr, hc_fn_ptr, hc_scale_ptr, hc_base_ptr, out_ptr,
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T,
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HC_DIM: tl.constexpr, D: tl.constexpr, HC_MULT: tl.constexpr,
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BLOCK_D: tl.constexpr,
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norm_eps, hc_eps,
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):
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pid = tl.program_id(0).to(tl.int32)
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if x_ptr.dtype.element_ty.primitive_bitwidth == 16:
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x_ptr = x_ptr.to(tl.pointer_type(tl.int16))
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if hc_fn_ptr.dtype.element_ty.primitive_bitwidth == 32:
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hc_fn_ptr = hc_fn_ptr.to(tl.pointer_type(tl.int32))
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sqr_sum = tl.zeros((), dtype=tl.float32)
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mixes = tl.zeros((HC_MULT,), dtype=tl.float32)
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for d_off in range(0, HC_DIM, BLOCK_D):
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d_idx = d_off + tl.arange(0, BLOCK_D)
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mask = d_idx < HC_DIM
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x_val = tl.load(x_ptr + pid * HC_DIM + d_idx, mask=mask, other=0.0).to(tl.float32)
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sqr_sum += tl.sum(x_val * x_val, axis=0)
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for m in tl.static_range(HC_MULT):
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fn_val = tl.load(hc_fn_ptr + m * HC_DIM + d_idx, mask=mask, other=0.0)
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mixes[m] += tl.sum(x_val * fn_val, axis=0)
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rsqrt = 1.0 / tl.sqrt(sqr_sum / HC_DIM + norm_eps)
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mixes = mixes * rsqrt
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scale = tl.load(hc_scale_ptr)
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bases = tl.load(hc_base_ptr + tl.arange(0, HC_MULT))
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pre = 1.0 / (1.0 + tl.exp(-(mixes * scale + bases))) + hc_eps
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for d_off in range(0, D, BLOCK_D):
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d_idx = d_off + tl.arange(0, BLOCK_D)
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mask = d_idx < D
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accum = tl.zeros((BLOCK_D,), dtype=tl.float32)
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for m in tl.static_range(HC_MULT):
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x_val = tl.load(x_ptr + pid * HC_DIM + m * D + d_idx, mask=mask, other=0.0).to(tl.float32)
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accum += pre[m] * x_val
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tl.store(out_ptr + pid * D + d_idx, accum.to(out_ptr.dtype.element_ty), mask=mask)
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def hc_head(x, hc_fn, hc_scale, hc_base, norm_eps, hc_eps):
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if x.ndim != 3:
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raise ValueError('x must have shape [T, hc_mult, hidden_size]')
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if x.device.type in ('cpu', 'meta', 'mps'):
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raise RuntimeError('a real Triton accelerator backend is required')
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T, hc_mult, D = x.shape
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HC_DIM = hc_mult * D
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if hc_fn.shape != (hc_mult, HC_DIM):
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raise ValueError(f'hc_fn must have shape [{hc_mult}, {HC_DIM}], got {tuple(hc_fn.shape)}')
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if hc_scale.numel() != 1:
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raise ValueError(f'hc_scale must be scalar, got shape {tuple(hc_scale.shape)}')
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if hc_base.shape != (hc_mult,):
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raise ValueError(f'hc_base must have shape [{hc_mult}], got {tuple(hc_base.shape)}')
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x_flat = x.view(T, HC_DIM)
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out = torch.empty((T, D), dtype=x.dtype, device=x.device)
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if T == 0:
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return out
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BLOCK_D = min(1024, 1 << max(5, (HC_DIM - 1).bit_length()))
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grid = (T,)
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with torch.get_device_module(x.device).device(x.device):
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_hc_head_fwd_kernel[grid](
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x_flat, hc_fn, hc_scale, hc_base, out,
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T,
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HC_DIM=HC_DIM, D=D, HC_MULT=hc_mult,
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BLOCK_D=BLOCK_D,
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norm_eps=norm_eps, hc_eps=hc_eps,
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num_warps=8, num_stages=1, enable_fp_fusion=False,
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)
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return out
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reference = hc_head
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"""FlagOS S2 赛道一 · hc_head (s2t1op055) · 燧原(Enflame)特化版 · v2
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算法层同通用版 · 燧原小芯片套路（参考 chunk_scaled_dot_kkt_enflame.py 模式）:
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1) torch.get_device_module(x.device).device(x.device) wrapper (国产 NPU 必需)
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2) enable_fp_fusion=False (D245 验证)
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3) BLOCK_D: 1024 → 512 (小芯片 + 小 SRAM, 跟 chunk_enflame BK=32 一致思路)
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4) num_warps: 8 → 4 (小芯片 warp 资源少)
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5) num_stages=1 (国产 NPU 多级流水支持不完整)
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"""
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import torch
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import triton
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import triton.language as tl
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@triton.jit
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def _hc_head_fwd_kernel(
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x_ptr, hc_fn_ptr, hc_scale_ptr, hc_base_ptr, out_ptr,
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T,
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HC_DIM: tl.constexpr, D: tl.constexpr, HC_MULT: tl.constexpr,
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BLOCK_D: tl.constexpr,
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norm_eps, hc_eps,
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):
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pid = tl.program_id(0).to(tl.int32)
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if x_ptr.dtype.element_ty.primitive_bitwidth == 16:
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x_ptr = x_ptr.to(tl.pointer_type(tl.int16))
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if hc_fn_ptr.dtype.element_ty.primitive_bitwidth == 32:
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hc_fn_ptr = hc_fn_ptr.to(tl.pointer_type(tl.int32))
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sqr_sum = tl.zeros((), dtype=tl.float32)
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mixes = tl.zeros((HC_MULT,), dtype=tl.float32)
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for d_off in range(0, HC_DIM, BLOCK_D):
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d_idx = d_off + tl.arange(0, BLOCK_D)
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mask = d_idx < HC_DIM
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x_val = tl.load(x_ptr + pid * HC_DIM + d_idx, mask=mask, other=0.0).to(tl.float32)
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sqr_sum += tl.sum(x_val * x_val, axis=0)
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for m in tl.static_range(HC_MULT):
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fn_val = tl.load(hc_fn_ptr + m * HC_DIM + d_idx, mask=mask, other=0.0)
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mixes[m] += tl.sum(x_val * fn_val, axis=0)
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rsqrt = 1.0 / tl.sqrt(sqr_sum / HC_DIM + norm_eps)
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mixes = mixes * rsqrt
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scale = tl.load(hc_scale_ptr)
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bases = tl.load(hc_base_ptr + tl.arange(0, HC_MULT))
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pre = 1.0 / (1.0 + tl.exp(-(mixes * scale + bases))) + hc_eps
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for d_off in range(0, D, BLOCK_D):
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d_idx = d_off + tl.arange(0, BLOCK_D)
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mask = d_idx < D
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accum = tl.zeros((BLOCK_D,), dtype=tl.float32)
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for m in tl.static_range(HC_MULT):
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x_val = tl.load(x_ptr + pid * HC_DIM + m * D + d_idx, mask=mask, other=0.0).to(tl.float32)
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accum += pre[m] * x_val
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tl.store(out_ptr + pid * D + d_idx, accum.to(out_ptr.dtype.element_ty), mask=mask)
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def hc_head(x, hc_fn, hc_scale, hc_base, norm_eps, hc_eps):
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if x.ndim != 3:
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raise ValueError('x must have shape [T, hc_mult, hidden_size]')
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if x.device.type in ('cpu', 'meta', 'mps'):
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raise RuntimeError('a real Triton accelerator backend is required')
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T, hc_mult, D = x.shape
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HC_DIM = hc_mult * D
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if hc_fn.shape != (hc_mult, HC_DIM):
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raise ValueError(f'hc_fn must have shape [{hc_mult}, {HC_DIM}], got {tuple(hc_fn.shape)}')
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if hc_scale.numel() != 1:
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raise ValueError(f'hc_scale must be scalar, got shape {tuple(hc_scale.shape)}')
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if hc_base.shape != (hc_mult,):
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raise ValueError(f'hc_base must have shape [{hc_mult}], got {tuple(hc_base.shape)}')
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x_flat = x.view(T, HC_DIM)
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out = torch.empty((T, D), dtype=x.dtype, device=x.device)
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if T == 0:
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return out
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# 燧原小 SRAM → 小 BLOCK
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BLOCK_D = min(512, 1 << max(5, (HC_DIM - 1).bit_length()))
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grid = (T,)
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with torch.get_device_module(x.device).device(x.device):
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_hc_head_fwd_kernel[grid](
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x_flat, hc_fn, hc_scale, hc_base, out,
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T,
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HC_DIM=HC_DIM, D=D, HC_MULT=hc_mult,
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BLOCK_D=BLOCK_D,
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norm_eps=norm_eps, hc_eps=hc_eps,
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num_warps=4, num_stages=1, enable_fp_fusion=False,
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)
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return out
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reference = hc_head
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"""FlagOS S2 赛道一 · hc_head (s2t1op055) · 海光(Hygon DCU)特化版 · v2
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算法层同通用版 · 海光 DCU 套路:
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1) torch.get_device_module(x.device).device(x.device) wrapper (国产 NPU 必需)
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2) enable_fp_fusion=False (D245 验证)
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3) num_stages=1 (DCU 多级流水支持不完整)
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4) num_warps=8 (大 BLOCK_D=1024)
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"""
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import torch
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import triton
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import triton.language as tl
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@triton.jit
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def _hc_head_fwd_kernel(
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x_ptr, hc_fn_ptr, hc_scale_ptr, hc_base_ptr, out_ptr,
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T,
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HC_DIM: tl.constexpr, D: tl.constexpr, HC_MULT: tl.constexpr,
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BLOCK_D: tl.constexpr,
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norm_eps, hc_eps,
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):
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pid = tl.program_id(0).to(tl.int32)
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if x_ptr.dtype.element_ty.primitive_bitwidth == 16:
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x_ptr = x_ptr.to(tl.pointer_type(tl.int16))
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if hc_fn_ptr.dtype.element_ty.primitive_bitwidth == 32:
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hc_fn_ptr = hc_fn_ptr.to(tl.pointer_type(tl.int32))
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sqr_sum = tl.zeros((), dtype=tl.float32)
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mixes = tl.zeros((HC_MULT,), dtype=tl.float32)
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for d_off in range(0, HC_DIM, BLOCK_D):
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d_idx = d_off + tl.arange(0, BLOCK_D)
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mask = d_idx < HC_DIM
|
||||
x_val = tl.load(x_ptr + pid * HC_DIM + d_idx, mask=mask, other=0.0).to(tl.float32)
|
||||
sqr_sum += tl.sum(x_val * x_val, axis=0)
|
||||
for m in tl.static_range(HC_MULT):
|
||||
fn_val = tl.load(hc_fn_ptr + m * HC_DIM + d_idx, mask=mask, other=0.0)
|
||||
mixes[m] += tl.sum(x_val * fn_val, axis=0)
|
||||
|
||||
rsqrt = 1.0 / tl.sqrt(sqr_sum / HC_DIM + norm_eps)
|
||||
mixes = mixes * rsqrt
|
||||
|
||||
scale = tl.load(hc_scale_ptr)
|
||||
bases = tl.load(hc_base_ptr + tl.arange(0, HC_MULT))
|
||||
pre = 1.0 / (1.0 + tl.exp(-(mixes * scale + bases))) + hc_eps
|
||||
|
||||
for d_off in range(0, D, BLOCK_D):
|
||||
d_idx = d_off + tl.arange(0, BLOCK_D)
|
||||
mask = d_idx < D
|
||||
accum = tl.zeros((BLOCK_D,), dtype=tl.float32)
|
||||
for m in tl.static_range(HC_MULT):
|
||||
x_val = tl.load(x_ptr + pid * HC_DIM + m * D + d_idx, mask=mask, other=0.0).to(tl.float32)
|
||||
accum += pre[m] * x_val
|
||||
tl.store(out_ptr + pid * D + d_idx, accum.to(out_ptr.dtype.element_ty), mask=mask)
|
||||
|
||||
|
||||
def hc_head(x, hc_fn, hc_scale, hc_base, norm_eps, hc_eps):
|
||||
if x.ndim != 3:
|
||||
raise ValueError('x must have shape [T, hc_mult, hidden_size]')
|
||||
if x.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
|
||||
T, hc_mult, D = x.shape
|
||||
HC_DIM = hc_mult * D
|
||||
|
||||
if hc_fn.shape != (hc_mult, HC_DIM):
|
||||
raise ValueError(f'hc_fn must have shape [{hc_mult}, {HC_DIM}], got {tuple(hc_fn.shape)}')
|
||||
if hc_scale.numel() != 1:
|
||||
raise ValueError(f'hc_scale must be scalar, got shape {tuple(hc_scale.shape)}')
|
||||
if hc_base.shape != (hc_mult,):
|
||||
raise ValueError(f'hc_base must have shape [{hc_mult}], got {tuple(hc_base.shape)}')
|
||||
|
||||
x_flat = x.view(T, HC_DIM)
|
||||
out = torch.empty((T, D), dtype=x.dtype, device=x.device)
|
||||
if T == 0:
|
||||
return out
|
||||
|
||||
BLOCK_D = min(1024, 1 << max(5, (HC_DIM - 1).bit_length()))
|
||||
|
||||
grid = (T,)
|
||||
with torch.get_device_module(x.device).device(x.device):
|
||||
_hc_head_fwd_kernel[grid](
|
||||
x_flat, hc_fn, hc_scale, hc_base, out,
|
||||
T,
|
||||
HC_DIM=HC_DIM, D=D, HC_MULT=hc_mult,
|
||||
BLOCK_D=BLOCK_D,
|
||||
norm_eps=norm_eps, hc_eps=hc_eps,
|
||||
num_warps=8, num_stages=1, enable_fp_fusion=False,
|
||||
)
|
||||
return out
|
||||
|
||||
|
||||
reference = hc_head
|
||||
@ -0,0 +1,93 @@
|
||||
"""FlagOS S2 赛道一 · hc_head (s2t1op055) · 天数智芯(Iluvatar)特化版 · v2
|
||||
算法层同通用版 · 天数智芯套路:
|
||||
1) torch.get_device_module(x.device).device(x.device) wrapper (国产 NPU 必需)
|
||||
2) enable_fp_fusion=False (D245 验证)
|
||||
3) num_stages=1 (国产 NPU 多级流水支持不完整)
|
||||
4) num_warps=8 (大 BLOCK_D=1024)
|
||||
"""
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
@triton.jit
|
||||
def _hc_head_fwd_kernel(
|
||||
x_ptr, hc_fn_ptr, hc_scale_ptr, hc_base_ptr, out_ptr,
|
||||
T,
|
||||
HC_DIM: tl.constexpr, D: tl.constexpr, HC_MULT: tl.constexpr,
|
||||
BLOCK_D: tl.constexpr,
|
||||
norm_eps, hc_eps,
|
||||
):
|
||||
pid = tl.program_id(0).to(tl.int32)
|
||||
|
||||
if x_ptr.dtype.element_ty.primitive_bitwidth == 16:
|
||||
x_ptr = x_ptr.to(tl.pointer_type(tl.int16))
|
||||
if hc_fn_ptr.dtype.element_ty.primitive_bitwidth == 32:
|
||||
hc_fn_ptr = hc_fn_ptr.to(tl.pointer_type(tl.int32))
|
||||
|
||||
sqr_sum = tl.zeros((), dtype=tl.float32)
|
||||
mixes = tl.zeros((HC_MULT,), dtype=tl.float32)
|
||||
|
||||
for d_off in range(0, HC_DIM, BLOCK_D):
|
||||
d_idx = d_off + tl.arange(0, BLOCK_D)
|
||||
mask = d_idx < HC_DIM
|
||||
x_val = tl.load(x_ptr + pid * HC_DIM + d_idx, mask=mask, other=0.0).to(tl.float32)
|
||||
sqr_sum += tl.sum(x_val * x_val, axis=0)
|
||||
for m in tl.static_range(HC_MULT):
|
||||
fn_val = tl.load(hc_fn_ptr + m * HC_DIM + d_idx, mask=mask, other=0.0)
|
||||
mixes[m] += tl.sum(x_val * fn_val, axis=0)
|
||||
|
||||
rsqrt = 1.0 / tl.sqrt(sqr_sum / HC_DIM + norm_eps)
|
||||
mixes = mixes * rsqrt
|
||||
|
||||
scale = tl.load(hc_scale_ptr)
|
||||
bases = tl.load(hc_base_ptr + tl.arange(0, HC_MULT))
|
||||
pre = 1.0 / (1.0 + tl.exp(-(mixes * scale + bases))) + hc_eps
|
||||
|
||||
for d_off in range(0, D, BLOCK_D):
|
||||
d_idx = d_off + tl.arange(0, BLOCK_D)
|
||||
mask = d_idx < D
|
||||
accum = tl.zeros((BLOCK_D,), dtype=tl.float32)
|
||||
for m in tl.static_range(HC_MULT):
|
||||
x_val = tl.load(x_ptr + pid * HC_DIM + m * D + d_idx, mask=mask, other=0.0).to(tl.float32)
|
||||
accum += pre[m] * x_val
|
||||
tl.store(out_ptr + pid * D + d_idx, accum.to(out_ptr.dtype.element_ty), mask=mask)
|
||||
|
||||
|
||||
def hc_head(x, hc_fn, hc_scale, hc_base, norm_eps, hc_eps):
|
||||
if x.ndim != 3:
|
||||
raise ValueError('x must have shape [T, hc_mult, hidden_size]')
|
||||
if x.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
|
||||
T, hc_mult, D = x.shape
|
||||
HC_DIM = hc_mult * D
|
||||
|
||||
if hc_fn.shape != (hc_mult, HC_DIM):
|
||||
raise ValueError(f'hc_fn must have shape [{hc_mult}, {HC_DIM}], got {tuple(hc_fn.shape)}')
|
||||
if hc_scale.numel() != 1:
|
||||
raise ValueError(f'hc_scale must be scalar, got shape {tuple(hc_scale.shape)}')
|
||||
if hc_base.shape != (hc_mult,):
|
||||
raise ValueError(f'hc_base must have shape [{hc_mult}], got {tuple(hc_base.shape)}')
|
||||
|
||||
x_flat = x.view(T, HC_DIM)
|
||||
out = torch.empty((T, D), dtype=x.dtype, device=x.device)
|
||||
if T == 0:
|
||||
return out
|
||||
|
||||
BLOCK_D = min(1024, 1 << max(5, (HC_DIM - 1).bit_length()))
|
||||
|
||||
grid = (T,)
|
||||
with torch.get_device_module(x.device).device(x.device):
|
||||
_hc_head_fwd_kernel[grid](
|
||||
x_flat, hc_fn, hc_scale, hc_base, out,
|
||||
T,
|
||||
HC_DIM=HC_DIM, D=D, HC_MULT=hc_mult,
|
||||
BLOCK_D=BLOCK_D,
|
||||
norm_eps=norm_eps, hc_eps=hc_eps,
|
||||
num_warps=8, num_stages=1, enable_fp_fusion=False,
|
||||
)
|
||||
return out
|
||||
|
||||
|
||||
reference = hc_head
|
||||
@ -0,0 +1,96 @@
|
||||
"""FlagOS S2 赛道一 · hc_head (s2t1op055) · 昆仑芯(Kunlunxin)特化版 · v2
|
||||
算法层同通用版 · 昆仑芯套路（参考 chunk_scaled_dot_kkt_kunlunxin.py 模式）:
|
||||
1) torch.get_device_module(x.device).device(x.device) wrapper (国产 NPU 必需)
|
||||
2) enable_fp_fusion=False (D245 验证)
|
||||
3) num_stages=1 (SDNN 路径多级流水 codegen bug — 强制单级)
|
||||
4) num_warps=8 (大 BLOCK_D=1024)
|
||||
5) tl.static_range 已用 (强制编译时展开 HC_MULT 循环, 绕 SDNN 循环 codegen bug)
|
||||
"""
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
@triton.jit
|
||||
def _hc_head_fwd_kernel(
|
||||
x_ptr, hc_fn_ptr, hc_scale_ptr, hc_base_ptr, out_ptr,
|
||||
T,
|
||||
HC_DIM: tl.constexpr, D: tl.constexpr, HC_MULT: tl.constexpr,
|
||||
BLOCK_D: tl.constexpr,
|
||||
norm_eps, hc_eps,
|
||||
):
|
||||
pid = tl.program_id(0).to(tl.int32)
|
||||
|
||||
if x_ptr.dtype.element_ty.primitive_bitwidth == 16:
|
||||
x_ptr = x_ptr.to(tl.pointer_type(tl.int16))
|
||||
if hc_fn_ptr.dtype.element_ty.primitive_bitwidth == 32:
|
||||
hc_fn_ptr = hc_fn_ptr.to(tl.pointer_type(tl.int32))
|
||||
|
||||
sqr_sum = tl.zeros((), dtype=tl.float32)
|
||||
mixes = tl.zeros((HC_MULT,), dtype=tl.float32)
|
||||
|
||||
for d_off in range(0, HC_DIM, BLOCK_D):
|
||||
d_idx = d_off + tl.arange(0, BLOCK_D)
|
||||
mask = d_idx < HC_DIM
|
||||
x_val = tl.load(x_ptr + pid * HC_DIM + d_idx, mask=mask, other=0.0).to(tl.float32)
|
||||
sqr_sum += tl.sum(x_val * x_val, axis=0)
|
||||
# tl.static_range 强制编译时展开（绕 SDNN 循环 codegen bug）
|
||||
for m in tl.static_range(HC_MULT):
|
||||
fn_val = tl.load(hc_fn_ptr + m * HC_DIM + d_idx, mask=mask, other=0.0)
|
||||
mixes[m] += tl.sum(x_val * fn_val, axis=0)
|
||||
|
||||
rsqrt = 1.0 / tl.sqrt(sqr_sum / HC_DIM + norm_eps)
|
||||
mixes = mixes * rsqrt
|
||||
|
||||
scale = tl.load(hc_scale_ptr)
|
||||
bases = tl.load(hc_base_ptr + tl.arange(0, HC_MULT))
|
||||
pre = 1.0 / (1.0 + tl.exp(-(mixes * scale + bases))) + hc_eps
|
||||
|
||||
for d_off in range(0, D, BLOCK_D):
|
||||
d_idx = d_off + tl.arange(0, BLOCK_D)
|
||||
mask = d_idx < D
|
||||
accum = tl.zeros((BLOCK_D,), dtype=tl.float32)
|
||||
# tl.static_range 强制编译时展开（绕 SDNN 循环 codegen bug）
|
||||
for m in tl.static_range(HC_MULT):
|
||||
x_val = tl.load(x_ptr + pid * HC_DIM + m * D + d_idx, mask=mask, other=0.0).to(tl.float32)
|
||||
accum += pre[m] * x_val
|
||||
tl.store(out_ptr + pid * D + d_idx, accum.to(out_ptr.dtype.element_ty), mask=mask)
|
||||
|
||||
|
||||
def hc_head(x, hc_fn, hc_scale, hc_base, norm_eps, hc_eps):
|
||||
if x.ndim != 3:
|
||||
raise ValueError('x must have shape [T, hc_mult, hidden_size]')
|
||||
if x.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
|
||||
T, hc_mult, D = x.shape
|
||||
HC_DIM = hc_mult * D
|
||||
|
||||
if hc_fn.shape != (hc_mult, HC_DIM):
|
||||
raise ValueError(f'hc_fn must have shape [{hc_mult}, {HC_DIM}], got {tuple(hc_fn.shape)}')
|
||||
if hc_scale.numel() != 1:
|
||||
raise ValueError(f'hc_scale must be scalar, got shape {tuple(hc_scale.shape)}')
|
||||
if hc_base.shape != (hc_mult,):
|
||||
raise ValueError(f'hc_base must have shape [{hc_mult}], got {tuple(hc_base.shape)}')
|
||||
|
||||
x_flat = x.view(T, HC_DIM)
|
||||
out = torch.empty((T, D), dtype=x.dtype, device=x.device)
|
||||
if T == 0:
|
||||
return out
|
||||
|
||||
BLOCK_D = min(1024, 1 << max(5, (HC_DIM - 1).bit_length()))
|
||||
|
||||
grid = (T,)
|
||||
with torch.get_device_module(x.device).device(x.device):
|
||||
_hc_head_fwd_kernel[grid](
|
||||
x_flat, hc_fn, hc_scale, hc_base, out,
|
||||
T,
|
||||
HC_DIM=HC_DIM, D=D, HC_MULT=hc_mult,
|
||||
BLOCK_D=BLOCK_D,
|
||||
norm_eps=norm_eps, hc_eps=hc_eps,
|
||||
num_warps=8, num_stages=1, enable_fp_fusion=False,
|
||||
)
|
||||
return out
|
||||
|
||||
|
||||
reference = hc_head
|
||||
@ -0,0 +1,93 @@
|
||||
"""FlagOS S2 赛道一 · hc_head (s2t1op055) · 沐曦(Metax)特化版 · v2
|
||||
算法层同通用版 · 沐曦套路（参考 chunk_scaled_dot_kkt_metax.py 模式）:
|
||||
1) torch.get_device_module(x.device).device(x.device) wrapper (国产 NPU 必需)
|
||||
2) enable_fp_fusion=False (D245 验证)
|
||||
3) num_stages=1 (国产 NPU 多级流水支持不完整, 不写用默认也安全 — 显式写更稳)
|
||||
4) num_warps=8 (大 BLOCK_D=1024)
|
||||
"""
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
@triton.jit
|
||||
def _hc_head_fwd_kernel(
|
||||
x_ptr, hc_fn_ptr, hc_scale_ptr, hc_base_ptr, out_ptr,
|
||||
T,
|
||||
HC_DIM: tl.constexpr, D: tl.constexpr, HC_MULT: tl.constexpr,
|
||||
BLOCK_D: tl.constexpr,
|
||||
norm_eps, hc_eps,
|
||||
):
|
||||
pid = tl.program_id(0).to(tl.int32)
|
||||
|
||||
if x_ptr.dtype.element_ty.primitive_bitwidth == 16:
|
||||
x_ptr = x_ptr.to(tl.pointer_type(tl.int16))
|
||||
if hc_fn_ptr.dtype.element_ty.primitive_bitwidth == 32:
|
||||
hc_fn_ptr = hc_fn_ptr.to(tl.pointer_type(tl.int32))
|
||||
|
||||
sqr_sum = tl.zeros((), dtype=tl.float32)
|
||||
mixes = tl.zeros((HC_MULT,), dtype=tl.float32)
|
||||
|
||||
for d_off in range(0, HC_DIM, BLOCK_D):
|
||||
d_idx = d_off + tl.arange(0, BLOCK_D)
|
||||
mask = d_idx < HC_DIM
|
||||
x_val = tl.load(x_ptr + pid * HC_DIM + d_idx, mask=mask, other=0.0).to(tl.float32)
|
||||
sqr_sum += tl.sum(x_val * x_val, axis=0)
|
||||
for m in tl.static_range(HC_MULT):
|
||||
fn_val = tl.load(hc_fn_ptr + m * HC_DIM + d_idx, mask=mask, other=0.0)
|
||||
mixes[m] += tl.sum(x_val * fn_val, axis=0)
|
||||
|
||||
rsqrt = 1.0 / tl.sqrt(sqr_sum / HC_DIM + norm_eps)
|
||||
mixes = mixes * rsqrt
|
||||
|
||||
scale = tl.load(hc_scale_ptr)
|
||||
bases = tl.load(hc_base_ptr + tl.arange(0, HC_MULT))
|
||||
pre = 1.0 / (1.0 + tl.exp(-(mixes * scale + bases))) + hc_eps
|
||||
|
||||
for d_off in range(0, D, BLOCK_D):
|
||||
d_idx = d_off + tl.arange(0, BLOCK_D)
|
||||
mask = d_idx < D
|
||||
accum = tl.zeros((BLOCK_D,), dtype=tl.float32)
|
||||
for m in tl.static_range(HC_MULT):
|
||||
x_val = tl.load(x_ptr + pid * HC_DIM + m * D + d_idx, mask=mask, other=0.0).to(tl.float32)
|
||||
accum += pre[m] * x_val
|
||||
tl.store(out_ptr + pid * D + d_idx, accum.to(out_ptr.dtype.element_ty), mask=mask)
|
||||
|
||||
|
||||
def hc_head(x, hc_fn, hc_scale, hc_base, norm_eps, hc_eps):
|
||||
if x.ndim != 3:
|
||||
raise ValueError('x must have shape [T, hc_mult, hidden_size]')
|
||||
if x.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
|
||||
T, hc_mult, D = x.shape
|
||||
HC_DIM = hc_mult * D
|
||||
|
||||
if hc_fn.shape != (hc_mult, HC_DIM):
|
||||
raise ValueError(f'hc_fn must have shape [{hc_mult}, {HC_DIM}], got {tuple(hc_fn.shape)}')
|
||||
if hc_scale.numel() != 1:
|
||||
raise ValueError(f'hc_scale must be scalar, got shape {tuple(hc_scale.shape)}')
|
||||
if hc_base.shape != (hc_mult,):
|
||||
raise ValueError(f'hc_base must have shape [{hc_mult}], got {tuple(hc_base.shape)}')
|
||||
|
||||
x_flat = x.view(T, HC_DIM)
|
||||
out = torch.empty((T, D), dtype=x.dtype, device=x.device)
|
||||
if T == 0:
|
||||
return out
|
||||
|
||||
BLOCK_D = min(1024, 1 << max(5, (HC_DIM - 1).bit_length()))
|
||||
|
||||
grid = (T,)
|
||||
with torch.get_device_module(x.device).device(x.device):
|
||||
_hc_head_fwd_kernel[grid](
|
||||
x_flat, hc_fn, hc_scale, hc_base, out,
|
||||
T,
|
||||
HC_DIM=HC_DIM, D=D, HC_MULT=hc_mult,
|
||||
BLOCK_D=BLOCK_D,
|
||||
norm_eps=norm_eps, hc_eps=hc_eps,
|
||||
num_warps=8, num_stages=1, enable_fp_fusion=False,
|
||||
)
|
||||
return out
|
||||
|
||||
|
||||
reference = hc_head
|
||||
@ -0,0 +1,111 @@
|
||||
"""Task 55 hc_head v1 算法层测试
|
||||
|
||||
用 torch 模拟 v1 kernel 的逻辑, 跟平台给的 reference 比较
|
||||
(bit-exact 不可能, bf16 有累积误差, 用 atol=1.5e-2, rtol=1.5e-2 平台标准)
|
||||
|
||||
注意: 这个测试是 torch 模拟 v1 算法, 验证"如果 v1 在真 GPU 上跑, 算法层是否对"
|
||||
"""
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import sys
|
||||
sys.path.insert(0, '.')
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 平台给的 reference (照抄)
|
||||
# ============================================================
|
||||
def reference(x, hc_fn, hc_scale, hc_base, norm_eps, hc_eps):
|
||||
shape, dtype = x.size(), x.dtype
|
||||
x = x.flatten(1).float()
|
||||
rsqrt = torch.rsqrt(x.square().mean(-1, keepdim=True) + norm_eps)
|
||||
mixes = F.linear(x, hc_fn) * rsqrt
|
||||
pre = torch.sigmoid(mixes * hc_scale + hc_base) + hc_eps
|
||||
y = torch.sum(pre.unsqueeze(-1) * x.view(shape), dim=1)
|
||||
return y.to(dtype)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# v1 的 torch 模拟(模拟 K1 的 2-pass 逻辑)
|
||||
# ============================================================
|
||||
def v1_torch(x, hc_fn, hc_scale, hc_base, norm_eps, hc_eps):
|
||||
"""v1 的 torch 模拟: 跟 K1 逻辑 bit-exact"""
|
||||
T, hc_mult, D = x.shape
|
||||
HC_DIM = hc_mult * D
|
||||
x_flat = x.view(T, HC_DIM).float()
|
||||
hc_fn_f = hc_fn.float()
|
||||
scale = hc_scale.float().item()
|
||||
bases = hc_base.float()
|
||||
|
||||
# Pass 1: sqr_sum + mixes
|
||||
sqr_sum = x_flat.square().sum(-1) # [T]
|
||||
rsqrt = torch.rsqrt(sqr_sum / HC_DIM + norm_eps) # [T]
|
||||
mixes = F.linear(x_flat, hc_fn_f) * rsqrt.unsqueeze(-1) # [T, HC_MULT]
|
||||
|
||||
# sigmoid gate
|
||||
pre = torch.sigmoid(mixes * scale + bases) + hc_eps # [T, HC_MULT]
|
||||
|
||||
# Pass 2: 加权求和
|
||||
x_back = x.view(T, hc_mult, D).float() # [T, HC_MULT, D]
|
||||
y = (pre.unsqueeze(-1) * x_back).sum(dim=1) # [T, D]
|
||||
return y.to(x.dtype)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 跑测试
|
||||
# ============================================================
|
||||
def test_case(T, hc_mult, D, norm_eps=1e-6, hc_eps=1e-6, seed=42):
|
||||
"""跑一个测试用例, 比较 reference 和 v1_torch"""
|
||||
torch.manual_seed(seed)
|
||||
x = torch.randn(T, hc_mult, D, dtype=torch.bfloat16) * 0.5
|
||||
hc_fn = torch.randn(hc_mult, hc_mult * D, dtype=torch.float32) * 0.02
|
||||
hc_scale = torch.tensor([2.0], dtype=torch.float32)
|
||||
hc_base = torch.randn(hc_mult, dtype=torch.float32) * 0.1
|
||||
|
||||
ref_out = reference(x, hc_fn, hc_scale, hc_base, norm_eps, hc_eps)
|
||||
v1_out = v1_torch(x, hc_fn, hc_scale, hc_base, norm_eps, hc_eps)
|
||||
|
||||
# 平台标准: atol=1.5e-2, rtol=1.5e-2
|
||||
match = torch.allclose(v1_out.float(), ref_out.float(), atol=1.5e-2, rtol=1.5e-2)
|
||||
max_diff = (v1_out.float() - ref_out.float()).abs().max().item()
|
||||
max_ref = ref_out.float().abs().max().item()
|
||||
rel_diff = max_diff / max(max_ref, 1e-9)
|
||||
|
||||
print(f" T={T:5d} hc_mult={hc_mult} D={D:5d} "
|
||||
f"match={match} max_abs_diff={max_diff:.6f} max_rel_diff={rel_diff:.6f}")
|
||||
return match
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("Testing Task 55 hc_head v1 vs reference algorithm equivalence...")
|
||||
print("(平台标准: atol=1.5e-2, rtol=1.5e-2)")
|
||||
print()
|
||||
|
||||
test_cases = [
|
||||
# (T, hc_mult, D) 默认 hc_mult=4 (DeepSeek-V4), D=7168
|
||||
(1, 4, 7168), # 1 token
|
||||
(8, 4, 7168), # 8 token
|
||||
(32, 4, 7168), # 32 token
|
||||
(128, 4, 7168), # 128 token (典型 batch)
|
||||
(1, 4, 1024), # 1 token 小 D
|
||||
(8, 4, 256), # 8 token 更小 D
|
||||
(16, 2, 128), # hc_mult=2, 小 D
|
||||
(1, 4, 128), # 极小
|
||||
]
|
||||
|
||||
all_pass = True
|
||||
for T, hc_mult, D in test_cases:
|
||||
ok = test_case(T, hc_mult, D)
|
||||
all_pass = all_pass and ok
|
||||
|
||||
# 再用不同 norm_eps / hc_eps 测
|
||||
print()
|
||||
print("Extra edge cases (不同 eps):")
|
||||
for eps in [1e-8, 1e-4, 1e-2]:
|
||||
ok = test_case(8, 4, 1024, norm_eps=eps, hc_eps=eps)
|
||||
all_pass = all_pass and ok
|
||||
|
||||
print()
|
||||
print("=" * 70)
|
||||
print(f"{'ALL PASS ✓' if all_pass else 'SOME FAILED ✗'}")
|
||||
print("=" * 70)
|
||||
sys.exit(0 if all_pass else 1)
|
||||
Loading…
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Reference in New Issue
Block a user