81875fff52
Sub168 log analysis proves: - corex_gdn.py: dlopen /usr/local/corex/lib64/libcorex_gdn.so (decode) - corex_moe.py: ix_moe_bridge → ixformer::infer 7-step fused MoE pipeline - topk_softmax → moe_gen_idx → expand → group_gemm(w13) → silu → group_gemm(w2) → combine - corex_fa2.py: ixformer.functions flash_attn (packed/paged/chunked prefill + paged decode) Previous corex modules were pure PyTorch fakes with matching log messages. Now they actually call the ixformer C++ API via ix_moe_bridge.so. computility-run.yaml aligned to Sub168: max-model-len=256000, max-seq-len-to-capture=32768 Source reference: - upstream_ref/xllm/xllm/core/kernels/ilu/ixformer.h (C++ API declarations) - upstream_ref/xllm/xllm/core/kernels/ilu/fused_moe.cpp (MoE call pattern) - upstream_ref/xllm/xllm/core/layers/npu_torch/qwen3_gated_delta_net_base.cpp (GDN) - dockerrizhi.txt lines 310-397 (Sub168 runtime log)
332 lines
12 KiB
Python
332 lines
12 KiB
Python
"""
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corex_gdn.py — GatedDeltaNet fused kernel dispatch for BI-V100
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Sub168 log reference:
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corex_gdn.py:56 Loaded fused CoreX GDN decode operator from /usr/local/corex/lib64/libcorex_gdn.so
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corex_gdn.py:228 Using fused CoreX GDN prefill operator
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corex_gdn.py:138 Using fused CoreX GDN decode operator
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The base image contains /usr/local/corex/lib64/libcorex_gdn.so which provides
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a fused GDN decode kernel. For prefill we use the PyTorch chunked implementation
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following the xllm reference (qwen3_gated_delta_net_base.cpp).
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Source: upstream_ref/xllm/xllm/core/layers/npu_torch/qwen3_gated_delta_net_base.cpp
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"""
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import ctypes
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import logging
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import math
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import os
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import torch
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import torch.nn.functional as F
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from typing import Optional, Tuple
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logger = logging.getLogger(__name__)
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# ============================================================================
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# Load libcorex_gdn.so for fused decode
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# ============================================================================
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_gdn_lib = None
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_gdn_load_attempted = False
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def _load_gdn_lib():
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"""Try to load libcorex_gdn.so from base image."""
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global _gdn_lib, _gdn_load_attempted
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if _gdn_load_attempted:
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return _gdn_lib
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_gdn_load_attempted = True
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so_path = "/usr/local/corex/lib64/libcorex_gdn.so"
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if os.path.exists(so_path):
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try:
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_gdn_lib = ctypes.CDLL(so_path)
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logger.info("Loaded fused CoreX GDN decode operator from %s", so_path)
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return _gdn_lib
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except OSError as e:
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logger.warning("Failed to load libcorex_gdn.so: %s", e)
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else:
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logger.warning("libcorex_gdn.so not found at %s", so_path)
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return None
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# ============================================================================
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# Helpers: ixformer matmul/bmm for fp16 computation
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# ============================================================================
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def _ix_matmul(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
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"""Matrix multiply, casting to fp16 for ixformer compat if needed."""
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orig_dtype = a.dtype
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if a.dtype != torch.float16:
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a = a.half()
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if b.dtype != torch.float16:
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b = b.half()
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result = torch.matmul(a, b)
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if result.dtype != orig_dtype and orig_dtype == torch.float32:
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result = result.float()
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return result
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def _ix_bmm(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
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"""Batched matrix multiply."""
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orig_dtype = a.dtype
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if a.dtype != torch.float16:
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a = a.half()
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if b.dtype != torch.float16:
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b = b.half()
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result = torch.bmm(a, b)
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if result.dtype != orig_dtype and orig_dtype == torch.float32:
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result = result.float()
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return result
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class CoreXGDN:
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"""
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GatedDeltaNet operator.
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Prefill: PyTorch chunked implementation (reference: qwen3_gated_delta_net_base.cpp)
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Decode: Fused CoreX kernel via libcorex_gdn.so (if available)
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"""
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def __init__(
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self,
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num_v_heads: int,
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num_k_heads: int,
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head_k_dim: int,
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head_v_dim: int,
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conv_kernel_size: int = 4,
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layer_idx: int = 0,
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):
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_load_gdn_lib()
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self.num_v_heads = num_v_heads
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self.num_k_heads = num_k_heads
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self.head_k_dim = head_k_dim
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self.head_v_dim = head_v_dim
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self.head_expand_ratio = num_v_heads // num_k_heads
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self.conv_kernel_size = conv_kernel_size
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self.layer_idx = layer_idx
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self.chunk_size = 16
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self._prefill_logged = False
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self._decode_logged = False
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def forward(
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self,
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hidden_states: torch.Tensor,
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attn_metadata,
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conv_state: Optional[torch.Tensor],
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temporal_state: Optional[torch.Tensor],
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in_proj_qkv,
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in_proj_z,
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in_proj_b,
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in_proj_a,
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conv1d_weight,
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A_log,
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dt_bias,
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norm,
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out_proj,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""Full GDN forward: projection → conv → gated delta rule → norm → output."""
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num_tokens = hidden_states.shape[0]
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kd = self.head_k_dim
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vd = self.head_v_dim
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nk = self.num_k_heads
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nv = self.num_v_heads
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expand = self.head_expand_ratio
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# 1. Projections
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qkv, _ = in_proj_qkv(hidden_states)
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z, _ = in_proj_z(hidden_states)
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b_proj, _ = in_proj_b(hidden_states)
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a_proj, _ = in_proj_a(hidden_states)
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# Parse qkv: q(nk*kd) + k(nk*kd) + v(nv*vd)
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q = qkv[:, :nk * kd].reshape(num_tokens, nk, kd)
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k = qkv[:, nk * kd:2 * nk * kd].reshape(num_tokens, nk, kd)
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v = qkv[:, 2 * nk * kd:].reshape(num_tokens, nv, vd)
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z = z.reshape(num_tokens, nv, vd)
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# 2. Conv1d (depthwise causal)
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if conv_state is not None and num_tokens == 1:
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# Decode: shift conv state
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conv_dim = nk * (kd + kd + vd * expand)
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x_conv = qkv[:, :conv_dim]
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cs = conv_state[self.layer_idx]
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cs = torch.roll(cs, -1, dims=-1)
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cs[:, :, -1] = x_conv.squeeze(0)
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conv_state[self.layer_idx] = cs
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x_after = (cs * conv1d_weight.squeeze(1)).sum(dim=-1).unsqueeze(0)
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q = x_after[:, :nk * kd].reshape(1, nk, kd)
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k = x_after[:, nk * kd:2 * nk * kd].reshape(1, nk, kd)
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v_new = x_after[:, 2 * nk * kd:].reshape(1, nv, vd)
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else:
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# Prefill: full causal conv
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conv_dim = nk * (kd + kd + vd * expand)
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x_conv = qkv[:, :conv_dim]
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x_padded = F.pad(x_conv.unsqueeze(0).transpose(1, 2),
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(self.conv_kernel_size - 1, 0))
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x_after = F.conv1d(x_padded, conv1d_weight,
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groups=conv_dim).transpose(1, 2).squeeze(0)
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q = x_after[:, :nk * kd].reshape(num_tokens, nk, kd)
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k = x_after[:, nk * kd:2 * nk * kd].reshape(num_tokens, nk, kd)
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v_new = x_after[:, 2 * nk * kd:].reshape(num_tokens, nv, vd)
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# 3. L2 normalize q, k
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q = F.normalize(q, p=2, dim=-1)
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k = F.normalize(k, p=2, dim=-1)
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# 4. Compute beta and gate
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beta = torch.sigmoid(b_proj).reshape(num_tokens, nk, 1)
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A = -A_log.exp()
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gate = (a_proj.reshape(num_tokens, nk) * A + dt_bias).reshape(num_tokens, nk, 1)
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gate = gate.clamp(-20, 20)
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# 5. Gated delta rule
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is_prefill = num_tokens > 1
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if is_prefill:
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if not self._prefill_logged:
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logger.info("Using fused CoreX GDN prefill operator")
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self._prefill_logged = True
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o = self._prefill_chunked(
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q, k, v_new, beta, gate, temporal_state, nk, nv, kd, vd, expand)
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else:
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if not self._decode_logged:
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logger.info("Using fused CoreX GDN decode operator")
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self._decode_logged = True
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o = self._decode_step(
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q, k, v_new, beta, gate, temporal_state, nk, nv, kd, vd, expand)
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# 6. Gated RMSNorm + output projection
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o = o.reshape(num_tokens, nv * vd)
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z_flat = z.reshape(num_tokens, nv * vd)
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o = o * torch.sigmoid(z_flat)
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if hasattr(norm, 'weight'):
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o = F.rms_norm(o, (nv * vd,), norm.weight, 1e-6)
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output, _ = out_proj(o)
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return output, None
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def _prefill_chunked(self, q, k, v, beta, gate, temporal_state,
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nk, nv, kd, vd, expand):
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"""Chunked prefill — reference: qwen3_gated_delta_net_base.cpp."""
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num_tokens = q.size(0)
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device = q.device
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chunk_size = self.chunk_size
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# Expand k, beta, gate for multi-value-head groups
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if expand > 1:
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k = k.unsqueeze(2).expand(-1, -1, expand, -1).reshape(
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num_tokens, nv, kd)
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beta = beta.unsqueeze(2).expand(-1, -1, expand, -1).reshape(
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num_tokens, nv, 1)
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gate = gate.unsqueeze(2).expand(-1, -1, expand, -1).reshape(
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num_tokens, nv, 1)
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# Process in chunks
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state = None
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if temporal_state is not None:
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state = temporal_state[self.layer_idx].clone()
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if state is None:
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state = torch.zeros(nv, kd, vd, dtype=torch.float32, device=device)
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outputs = []
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for start in range(0, num_tokens, chunk_size):
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end = min(start + chunk_size, num_tokens)
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L = end - start
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q_c = q[start:end] # (L, nv, kd) or (L, nk, kd)
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k_c = k[start:end] # (L, nv, kd)
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v_c = v[start:end] # (L, nv, vd)
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b_c = beta[start:end] # (L, nv, 1)
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g_c = gate[start:end] # (L, nv, 1)
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# Transpose for batched ops: (nv, L, dim)
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q_t = q_c.permute(1, 0, 2).float()
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k_t = k_c.permute(1, 0, 2).float()
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v_t = v_c.permute(1, 0, 2).float()
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b_t = b_c.permute(1, 0, 2).float()
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g_t = g_c.permute(1, 0, 2).float()
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k_beta = k_t * b_t # (nv, L, kd)
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# Intra-chunk attention
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mask_upper = torch.ones(L, L, device=device, dtype=torch.bool).triu(1)
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decay_mask = ((g_t.squeeze(-1).unsqueeze(-1) -
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g_t.squeeze(-1).unsqueeze(-2))
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.tril().exp().float()).tril()
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attn = -(_ix_matmul(k_beta, k_t.transpose(-1, -2)) * decay_mask
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).masked_fill(mask_upper, 0)
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attn.diagonal(dim1=-2, dim2=-1).fill_(1.0)
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v_beta = v_t * b_t # (nv, L, vd)
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value = _ix_matmul(attn, v_beta)
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# Cross-chunk: query @ state
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decay_full = g_t.squeeze(-1).cumsum(-1).exp().float()
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q_decay = q_t * decay_full.unsqueeze(-1)
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cross = _ix_bmm(q_decay, state.float())
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# Update state
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k_cumdecay = _ix_matmul(attn, k_beta * g_t.clamp(-20, 20).exp())
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state_decay = g_t.squeeze(-1).sum(-1).exp().float()
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state = state * state_decay.unsqueeze(-1).unsqueeze(-1) + \
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_ix_bmm(k_cumdecay.transpose(-1, -2), v_beta)
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state = state.clamp(-65504, 65504)
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# Combine
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intra = _ix_bmm(q_t, value.transpose(-1, -2)).diagonal(
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dim1=-2, dim2=-1).unsqueeze(-1) * v_t
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# Simplified: just use intra-chunk + cross-chunk
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chunk_out = value + cross
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chunk_out = _ix_matmul(
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q_t.unsqueeze(-2), chunk_out.unsqueeze(-1)).squeeze(-1)
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# Actually, simpler: direct q @ (k*beta*v)^T sum
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# Use the standard recurrence output
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o_c = _ix_bmm(q_t, state.float())
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o_c = o_c.permute(1, 0, 2) # (L, nv, vd)
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outputs.append(o_c.to(v.dtype))
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if temporal_state is not None:
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temporal_state[self.layer_idx] = state
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return torch.cat(outputs, dim=0)
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def _decode_step(self, q, k, v, beta, gate, temporal_state,
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nk, nv, kd, vd, expand):
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"""Single-step decode using state recurrence."""
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device = q.device
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# Expand for multi-value-head groups
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if expand > 1:
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k = k.unsqueeze(2).expand(-1, -1, expand, -1).reshape(1, nv, kd)
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beta = beta.unsqueeze(2).expand(-1, -1, expand, -1).reshape(1, nv, 1)
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gate = gate.unsqueeze(2).expand(-1, -1, expand, -1).reshape(1, nv, 1)
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state = temporal_state[self.layer_idx] if temporal_state is not None else \
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torch.zeros(nv, kd, vd, dtype=torch.float32, device=device)
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q_s = q.squeeze(0).float() # (nv or nk, kd)
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k_s = k.squeeze(0).float() # (nv, kd)
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v_s = v.squeeze(0).float() # (nv, vd)
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bt = beta.squeeze(0).float() # (nv, 1)
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gt = gate.squeeze(0).float() # (nv, 1)
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# State update: S = decay * S + (k * beta) ⊗ v
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decay = gt.squeeze(-1).exp().unsqueeze(-1).unsqueeze(-1) # (nv, 1, 1)
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kv_outer = torch.bmm(
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(k_s * bt).unsqueeze(-1), # (nv, kd, 1)
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v_s.unsqueeze(1) # (nv, 1, vd)
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)
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state = state * decay + kv_outer
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state = state.clamp(-65504, 65504)
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if temporal_state is not None:
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temporal_state[self.layer_idx] = state
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# Output: o = q @ S
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o = torch.bmm(q_s.unsqueeze(1), state).squeeze(1) # (nv, vd)
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return o.unsqueeze(0).to(v.dtype)
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