fix(build): 回退qwen3_6_scripts+ex_engine到26e6cb40(能得分版本)
唯一改动: computility-run.yaml max_model_len 80000→100000 26e6cb40是Sub520能在竞赛平台docker build成功并得分的版本 之后所有commit都导致docker build失败 根因: 新增的65个文件(vendor_overrides/prebuilt/*.so/wheels等) 可能触发了竞赛平台docker build的某个限制 本次回退: - qwen3_6_scripts/: 110→45文件(删掉65个新增文件) - ex_engine/: 恢复到26e6cb40完全一致 - Dockerfile: 恢复5个RUN步骤结构(已验证能build) - computility-run.yaml: max_model_len=100000(避免replay 400拒绝)
This commit is contained in:
Claude
@@ -1,92 +1,26 @@
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"""
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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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Interface matches qwen3_5.py expectations:
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__init__(num_v_heads, num_k_heads, head_k_dim, head_v_dim, conv_kernel_size, layer_idx)
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forward(hidden_states, attn_metadata, conv_state, temporal_state,
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in_proj_qkv, in_proj_z, in_proj_b, in_proj_a,
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conv1d_weight, A_log, dt_bias, norm, out_proj)
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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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_load_logged = False
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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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"""Drop-in GatedDeltaNet operator matching qwen3_5.py call convention."""
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def __init__(
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self,
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@@ -97,7 +31,7 @@ class CoreXGDN:
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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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global _load_logged
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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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@@ -109,223 +43,214 @@ class CoreXGDN:
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self._prefill_logged = False
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self._decode_logged = False
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if not _load_logged:
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logger.info("Loaded fused CoreX GDN decode operator from "
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"/usr/local/corex/lib64/libcorex_gdn.so")
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_load_logged = True
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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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in_proj_qkv, # ColumnParallelLinear
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in_proj_z, # ColumnParallelLinear
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in_proj_b, # ColumnParallelLinear
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in_proj_a, # ColumnParallelLinear
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conv1d_weight, # (num_k_heads, 1, conv_kernel_size)
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A_log, # (num_k_heads,)
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dt_bias, # (num_k_heads,)
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norm, # RMSNorm or similar
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out_proj, # RowParallelLinear
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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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# 1. Projections
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qkv, _ = in_proj_qkv(hidden_states) # (N, num_k_heads*(head_k_dim+head_k_dim+head_v_dim*expand))
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z, _ = in_proj_z(hidden_states) # (N, num_v_heads*head_v_dim)
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b_proj, _ = in_proj_b(hidden_states) # (N, num_k_heads)
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a_proj, _ = in_proj_a(hidden_states) # (N, num_k_heads)
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# Parse qkv
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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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k = qkv[:, nk * kd:nk * kd * 2].reshape(num_tokens, nk, kd)
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v = qkv[:, nk * kd * 2:].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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# 2. Short conv on k (causal 1d conv)
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is_prefill = getattr(attn_metadata, 'num_prefill_tokens', 0) > 0
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if is_prefill:
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# Prefill: apply conv1d directly on sequence
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k_conv = k.transpose(0, 1).unsqueeze(0) # (1, nk, N, kd)
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# Reshape for grouped conv: (1, nk, N, kd) -> (nk, 1, N) per head, apply conv
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k_out = []
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for h in range(nk):
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kh = k_conv[0, h] # (N, kd)
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# Pad and conv each dim independently? No — conv is on seq dim
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kh_t = kh.t() # (kd, N)
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kh_pad = F.pad(kh_t, (self.conv_kernel_size - 1, 0)) # causal pad
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w = conv1d_weight[h] # (1, conv_kernel_size)
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kh_conv = F.conv1d(kh_pad.unsqueeze(0), w.unsqueeze(0).float(),
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groups=1).squeeze(0)[:, :num_tokens]
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k_out.append(kh_conv.t()) # (N, kd)
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k = torch.stack(k_out, dim=1).to(hidden_states.dtype) # (N, nk, kd)
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# Update conv_state for decode
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if conv_state is not None and num_tokens >= self.conv_kernel_size:
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conv_state.copy_(k[-self.conv_kernel_size:].transpose(0, 1))
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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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# Decode: use conv_state (shift + new token)
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if conv_state is not None:
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# conv_state: (nk, conv_kernel_size, kd)
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conv_state = torch.roll(conv_state, -1, dims=1)
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conv_state[:, -1, :] = k.squeeze(0)
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# Apply conv
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k_new = (conv_state * conv1d_weight.squeeze(1).unsqueeze(-1)).sum(dim=1)
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k = k_new.unsqueeze(0) # (1, nk, kd)
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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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# SiLU activation on k
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k = F.silu(k)
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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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# 3. Compute gate and beta
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A = -F.softplus(A_log.float()) # (nk,) — negative decay
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dt = F.softplus(a_proj.float() + dt_bias) # (N, nk)
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dt = dt.clamp(max=10.0)
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gate = (A.unsqueeze(0) * dt) # (N, nk) — log-space decay
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beta = b_proj.float().sigmoid() # (N, nk) — input gate
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# 5. Gated delta rule
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is_prefill = num_tokens > 1
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# L2 normalize q, k
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q_f = F.normalize(q.float(), p=2, dim=-1)
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k_f = F.normalize(k.float(), p=2, dim=-1)
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v_f = v.float()
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# 4. Gated delta rule
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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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output, temporal_state = self._chunk_gated_delta(
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q_f, k_f, v_f, gate, beta, temporal_state, num_tokens)
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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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output, temporal_state = self._single_step_decode(
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q_f, k_f, v_f, gate, beta, temporal_state)
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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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# 5. Output gate + norm + projection
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output = output.to(hidden_states.dtype)
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z_gate = F.silu(z) # (N, nv*vd)
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output_flat = output.reshape(num_tokens, nv * vd)
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gated = output_flat * z_gate
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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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# Norm
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normed = norm(gated)
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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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# Output projection
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result, _ = out_proj(normed)
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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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return result, temporal_state
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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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def _chunk_gated_delta(self, q, k, v, gate, beta, initial_state, seq_len):
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"""Chunked gated delta rule prefill (fp32 accumulation)."""
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nk = self.num_k_heads
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nv = self.num_v_heads
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kd = self.head_k_dim
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vd = self.head_v_dim
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# Expand k to match v heads
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if self.head_expand_ratio > 1:
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k = k.repeat_interleave(self.head_expand_ratio, dim=1)
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B = 1 # tokens are flat
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# State: (nv, kd, vd)
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if initial_state is not None:
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state = initial_state.float()
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else:
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state = torch.zeros(nv, kd, vd, dtype=torch.float32, device=q.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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C = self.chunk_size
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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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for start in range(0, seq_len, C):
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end = min(start + C, seq_len)
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for t in range(start, end):
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qt = q[t] # (nk or nv, kd)
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kt = k[t] # (nv, kd)
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vt = v[t] # (nv, vd)
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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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# gate is (N, nk) — expand to nv
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if gate.shape[1] == nk and nk != nv:
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gt = gate[t].repeat_interleave(self.head_expand_ratio)
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||||
else:
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gt = gate[t]
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if beta.shape[1] == nk and nk != nv:
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bt = beta[t].repeat_interleave(self.head_expand_ratio)
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||||
else:
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bt = beta[t]
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k_beta = k_t * b_t # (nv, L, kd)
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gt = gt.clamp(-5.0, 0.0)
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decay = torch.exp(gt).unsqueeze(-1).unsqueeze(-1) # (nv, 1, 1)
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b_exp = bt.unsqueeze(-1).unsqueeze(-1) # (nv, 1, 1)
|
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||||
# Intra-chunk attention
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||||
mask_upper = torch.ones(L, L, device=device, dtype=torch.bool).triu(1)
|
||||
decay_mask = ((g_t.squeeze(-1).unsqueeze(-1) -
|
||||
g_t.squeeze(-1).unsqueeze(-2))
|
||||
.tril().exp().float()).tril()
|
||||
kv = torch.einsum('hd,hv->hdv', kt, vt) # (nv, kd, vd)
|
||||
state = decay * state + b_exp * kv
|
||||
state = state.clamp(-100.0, 100.0)
|
||||
|
||||
attn = -(_ix_matmul(k_beta, k_t.transpose(-1, -2)) * decay_mask
|
||||
).masked_fill(mask_upper, 0)
|
||||
attn.diagonal(dim1=-2, dim2=-1).fill_(1.0)
|
||||
out_t = torch.einsum('hd,hdv->hv', qt if qt.shape[0] == nv
|
||||
else qt.repeat_interleave(self.head_expand_ratio, dim=0),
|
||||
state)
|
||||
out_t = out_t.clamp(-1e4, 1e4)
|
||||
outputs.append(out_t)
|
||||
|
||||
v_beta = v_t * b_t # (nv, L, vd)
|
||||
value = _ix_matmul(attn, v_beta)
|
||||
output = torch.stack(outputs, dim=0) # (N, nv, vd)
|
||||
return output.to(torch.float16), state
|
||||
|
||||
# Cross-chunk: query @ state
|
||||
decay_full = g_t.squeeze(-1).cumsum(-1).exp().float()
|
||||
q_decay = q_t * decay_full.unsqueeze(-1)
|
||||
cross = _ix_bmm(q_decay, state.float())
|
||||
def _single_step_decode(self, q, k, v, gate, beta, temporal_state):
|
||||
"""Single-step recurrent decode."""
|
||||
nk = self.num_k_heads
|
||||
nv = self.num_v_heads
|
||||
kd = self.head_k_dim
|
||||
vd = self.head_v_dim
|
||||
|
||||
# Update state
|
||||
k_cumdecay = _ix_matmul(attn, k_beta * g_t.clamp(-20, 20).exp())
|
||||
state_decay = g_t.squeeze(-1).sum(-1).exp().float()
|
||||
state = state * state_decay.unsqueeze(-1).unsqueeze(-1) + \
|
||||
_ix_bmm(k_cumdecay.transpose(-1, -2), v_beta)
|
||||
state = state.clamp(-65504, 65504)
|
||||
q = q.squeeze(0) # (nk, kd) or (nv, kd)
|
||||
k = k.squeeze(0)
|
||||
v = v.squeeze(0) # (nv, vd)
|
||||
|
||||
# Combine
|
||||
intra = _ix_bmm(q_t, value.transpose(-1, -2)).diagonal(
|
||||
dim1=-2, dim2=-1).unsqueeze(-1) * v_t
|
||||
# Simplified: just use intra-chunk + cross-chunk
|
||||
chunk_out = value + cross
|
||||
chunk_out = _ix_matmul(
|
||||
q_t.unsqueeze(-2), chunk_out.unsqueeze(-1)).squeeze(-1)
|
||||
if self.head_expand_ratio > 1:
|
||||
k = k.repeat_interleave(self.head_expand_ratio, dim=0)
|
||||
if q.shape[0] == nk:
|
||||
q = q.repeat_interleave(self.head_expand_ratio, dim=0)
|
||||
|
||||
# Actually, simpler: direct q @ (k*beta*v)^T sum
|
||||
# Use the standard recurrence output
|
||||
o_c = _ix_bmm(q_t, state.float())
|
||||
o_c = o_c.permute(1, 0, 2) # (L, nv, vd)
|
||||
outputs.append(o_c.to(v.dtype))
|
||||
if temporal_state is None:
|
||||
temporal_state = torch.zeros(nv, kd, vd, dtype=torch.float32, device=q.device)
|
||||
else:
|
||||
temporal_state = temporal_state.float()
|
||||
|
||||
if temporal_state is not None:
|
||||
temporal_state[self.layer_idx] = state
|
||||
gt = gate.squeeze(0) # (nk,)
|
||||
bt = beta.squeeze(0) # (nk,)
|
||||
if gt.shape[0] == nk and nk != nv:
|
||||
gt = gt.repeat_interleave(self.head_expand_ratio)
|
||||
bt = bt.repeat_interleave(self.head_expand_ratio)
|
||||
|
||||
return torch.cat(outputs, dim=0)
|
||||
gt = gt.clamp(-5.0, 0.0)
|
||||
decay = torch.exp(gt).unsqueeze(-1).unsqueeze(-1)
|
||||
b_exp = bt.unsqueeze(-1).unsqueeze(-1)
|
||||
|
||||
def _decode_step(self, q, k, v, beta, gate, temporal_state,
|
||||
nk, nv, kd, vd, expand):
|
||||
"""Single-step decode using state recurrence."""
|
||||
device = q.device
|
||||
kv = torch.einsum('hd,hv->hdv', k, v)
|
||||
temporal_state = decay * temporal_state + b_exp * kv
|
||||
temporal_state = temporal_state.clamp(-100.0, 100.0)
|
||||
|
||||
# Expand for multi-value-head groups
|
||||
if expand > 1:
|
||||
k = k.unsqueeze(2).expand(-1, -1, expand, -1).reshape(1, nv, kd)
|
||||
beta = beta.unsqueeze(2).expand(-1, -1, expand, -1).reshape(1, nv, 1)
|
||||
gate = gate.unsqueeze(2).expand(-1, -1, expand, -1).reshape(1, nv, 1)
|
||||
output = torch.einsum('hd,hdv->hv', q, temporal_state)
|
||||
output = output.clamp(-1e4, 1e4)
|
||||
output = output.to(torch.float16).unsqueeze(0) # (1, nv, vd)
|
||||
|
||||
state = temporal_state[self.layer_idx] if temporal_state is not None else \
|
||||
torch.zeros(nv, kd, vd, dtype=torch.float32, device=device)
|
||||
|
||||
q_s = q.squeeze(0).float() # (nv or nk, kd)
|
||||
k_s = k.squeeze(0).float() # (nv, kd)
|
||||
v_s = v.squeeze(0).float() # (nv, vd)
|
||||
bt = beta.squeeze(0).float() # (nv, 1)
|
||||
gt = gate.squeeze(0).float() # (nv, 1)
|
||||
|
||||
# State update: S = decay * S + (k * beta) ⊗ v
|
||||
decay = gt.squeeze(-1).exp().unsqueeze(-1).unsqueeze(-1) # (nv, 1, 1)
|
||||
kv_outer = torch.bmm(
|
||||
(k_s * bt).unsqueeze(-1), # (nv, kd, 1)
|
||||
v_s.unsqueeze(1) # (nv, 1, vd)
|
||||
)
|
||||
state = state * decay + kv_outer
|
||||
state = state.clamp(-65504, 65504)
|
||||
|
||||
if temporal_state is not None:
|
||||
temporal_state[self.layer_idx] = state
|
||||
|
||||
# Output: o = q @ S
|
||||
o = torch.bmm(q_s.unsqueeze(1), state).squeeze(1) # (nv, vd)
|
||||
return o.unsqueeze(0).to(v.dtype)
|
||||
return output, temporal_state
|
||||
|
||||
Reference in New Issue
Block a user