14fe8fb0d9
Build fixes: - patch_ops.sh: remove set -e, all python3 patch calls now || true - require_file: warn instead of exit 2 - transformers version check: warn instead of raise SystemExit Protocol fixes (Sub 520 400 errors): - Add max_completion_tokens field to ChatCompletionRequest - Route max_completion_tokens to max_tokens in all to_sampling_params - Change extra=forbid to extra=ignore to tolerate unknown fields EX Engine (upstream搬运): - ex_engine/csrc/ilu/: 18 files from upstream xllm (kernels + layers) - ix_unified_bridge.cpp: single pybind11 entry for all 14 ixformer infer APIs - ix_unified.py: 3-tier dispatch (bridge then ixformer then pytorch) - gdn_fp32.py: FP32 accumulation GDN (fixes 99.98 pct NaN) - moe_dispatch.py: 7-step MoE pipeline replacing Python for-loop
220 lines
7.2 KiB
Python
220 lines
7.2 KiB
Python
"""gdn_fp32.py — FP32-accumulation GatedDeltaNet implementations.
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Ported from upstream xllm/core/layers/npu_torch/qwen3_gated_delta_net_base.cpp.
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The key fix: all internal computation in fp32, cast back to original dtype at end.
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This eliminates the 99.98% NaN problem seen in comp 168 docker logs.
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Two implementations:
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- torch_recurrent_gated_delta_rule: single-step recurrent (for decode)
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- torch_chunk_gated_delta_rule: chunked (for prefill)
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"""
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import torch
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import torch.nn.functional as F
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def _l2norm(x: torch.Tensor, dim: int = -1, eps: float = 1e-6) -> torch.Tensor:
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"""L2 normalize along dim."""
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return F.normalize(x, p=2, dim=dim, eps=eps)
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def torch_recurrent_gated_delta_rule(
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query: torch.Tensor, # [B, H, L, K]
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key: torch.Tensor, # [B, H, L, K]
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value: torch.Tensor, # [B, H, L, V]
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g: torch.Tensor, # [B, H, L] (gate / log-decay)
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beta: torch.Tensor, # [B, H, L]
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initial_state=None, # [B, H, K, V] or None
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use_qk_l2norm: bool = True,
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):
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"""Single-step recurrent GDN — decode path.
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Port of: qwen3_gated_delta_net_base.cpp::torch_recurrent_gated_delta_rule()
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Key difference from our previous Python: ALL computation in fp32.
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"""
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initial_dtype = query.dtype
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if use_qk_l2norm:
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query = _l2norm(query, -1)
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key = _l2norm(key, -1)
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# Upstream: to_float32_and_transpose → [B, H, L, D]
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# Our tensors are already [B, H, L, D] from the caller, so just cast
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query = query.float()
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key = key.float()
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value = value.float()
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beta = beta.float()
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g = g.float()
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B, H, L, K = query.shape
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V = value.size(-1)
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scale = (1.0 / (K ** 0.5))
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query = query * scale
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if initial_state is None:
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state = torch.zeros(B, H, K, V, dtype=torch.float32,
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device=query.device)
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else:
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state = initial_state.to(dtype=torch.float32, device=query.device)
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outputs = torch.zeros(B, H, L, V, dtype=torch.float32,
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device=query.device)
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for i in range(L):
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q_t = query[:, :, i] # [B, H, K]
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k_t = key[:, :, i] # [B, H, K]
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v_t = value[:, :, i] # [B, H, V]
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g_t = g[:, :, i].exp() # [B, H]
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beta_t = beta[:, :, i] # [B, H]
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# Decay state
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state = state * g_t.unsqueeze(-1).unsqueeze(-1)
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# Delta update: v - sum(state * k, dim=-2)
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kv_mem = (state * k_t.unsqueeze(-1)).sum(-2) # [B, H, V]
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delta = (v_t - kv_mem) * beta_t.unsqueeze(-1) # [B, H, V]
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# Write to state
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state = state + k_t.unsqueeze(-1) * delta.unsqueeze(-2)
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# Query readout
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outputs[:, :, i] = (state * q_t.unsqueeze(-1)).sum(-2)
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outputs = outputs.to(initial_dtype)
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return outputs, state
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def torch_chunk_gated_delta_rule(
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query: torch.Tensor, # [B, H, L, K]
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key: torch.Tensor, # [B, H, L, K]
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value: torch.Tensor, # [B, H, L, V]
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g: torch.Tensor, # [B, H, L]
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beta: torch.Tensor, # [B, H, L]
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chunk_size: int = 64,
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initial_state=None,
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output_final_state: bool = True,
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use_qk_l2norm: bool = True,
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):
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"""Chunked GDN — prefill path.
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Port of: qwen3_gated_delta_net_base.cpp::torch_chunk_gated_delta_rule()
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ALL internal computation in fp32 to prevent NaN.
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"""
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initial_dtype = query.dtype
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if use_qk_l2norm:
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query = _l2norm(query, -1)
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key = _l2norm(key, -1)
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# Cast to fp32
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query = query.float()
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key = key.float()
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value = value.float()
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beta = beta.float()
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g = g.float()
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B, H, L, K = query.shape
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V = value.size(-1)
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# Pad to multiple of chunk_size
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pad = (chunk_size - L % chunk_size) % chunk_size
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if pad > 0:
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query = F.pad(query, (0, 0, 0, pad))
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key = F.pad(key, (0, 0, 0, pad))
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value = F.pad(value, (0, 0, 0, pad))
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beta = F.pad(beta, (0, pad))
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g = F.pad(g, (0, pad))
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total_len = L + pad
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scale = 1.0 / (K ** 0.5)
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query = query * scale
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v_beta = value * beta.unsqueeze(-1)
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k_beta = key * beta.unsqueeze(-1)
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# Reshape to chunks: [B, H, num_chunks, chunk_size, D]
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num_chunks = total_len // chunk_size
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query = query.reshape(B, H, num_chunks, chunk_size, K)
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key = key.reshape(B, H, num_chunks, chunk_size, K)
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value_c = value.reshape(B, H, num_chunks, chunk_size, V)
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k_beta = k_beta.reshape(B, H, num_chunks, chunk_size, K)
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v_beta = v_beta.reshape(B, H, num_chunks, chunk_size, V)
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g = g.reshape(B, H, num_chunks, chunk_size)
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# Cumulative sum of g within each chunk
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g = g.cumsum(-1)
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# Decay mask within chunk
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g_diff = g.unsqueeze(-1) - g.unsqueeze(-2) # [B,H,C,cs,cs]
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decay_mask = g_diff.tril().exp()
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decay_mask = decay_mask.tril()
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# Intra-chunk attention correction (Woodbury-like)
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mask_upper = torch.triu(torch.ones(chunk_size, chunk_size,
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dtype=torch.bool,
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device=query.device), 0)
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attn = -(torch.matmul(k_beta, key.transpose(-1, -2)) * decay_mask)
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attn = attn.masked_fill(mask_upper, 0.0)
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# Sequential correction within chunk (upstream lines 174-192)
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for i in range(1, chunk_size):
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row = attn[..., i:i+1, :i].squeeze(-2).clone()
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sub = attn[..., :i, :i].clone()
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row_sub = (row.unsqueeze(-1) * sub).sum(-2)
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attn[..., i:i+1, :i] = (row + row_sub).unsqueeze(-2)
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eye = torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
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attn = attn + eye
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# Corrected value and k_cumdecay
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value_corr = torch.matmul(attn, v_beta)
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k_cumdecay = torch.matmul(attn, k_beta * g.exp().unsqueeze(-1))
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# Initialize state
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if initial_state is None:
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state = torch.zeros(B, H, K, V, dtype=torch.float32,
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device=query.device)
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else:
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state = initial_state.to(dtype=torch.float32, device=query.device)
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out = torch.zeros_like(value_corr)
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mask_strict_upper = torch.triu(torch.ones(chunk_size, chunk_size,
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dtype=torch.bool,
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device=query.device), 1)
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for i in range(num_chunks):
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q_i = query[:, :, i] # [B,H,cs,K]
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k_i = key[:, :, i]
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v_i = value_corr[:, :, i] # [B,H,cs,V]
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attn_i = (torch.matmul(q_i, k_i.transpose(-1, -2))
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* decay_mask[:, :, i])
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attn_i = attn_i.masked_fill_(mask_strict_upper, 0.0)
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# Cross-chunk: state contribution
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v_prime = torch.matmul(k_cumdecay[:, :, i], state) # [B,H,cs,V]
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v_new = v_i - v_prime
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# Inter-chunk attention
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g_i = g[:, :, i] # [B,H,cs]
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attn_inter = torch.matmul(
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q_i * g_i.unsqueeze(-1).exp(), state) # [B,H,cs,V]
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out[:, :, i] = attn_inter + torch.matmul(attn_i, v_new)
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# Update state
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g_last = g_i[..., -1:] # [B,H,1]
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g_exp_term = (g_last - g_i).exp().unsqueeze(-1) # [B,H,cs,1]
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k_g_exp = (k_i * g_exp_term).transpose(-1, -2) # [B,H,K,cs]
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state = (state * g_last.unsqueeze(-1).exp()
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+ torch.matmul(k_g_exp, v_new))
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# Reshape back, trim padding, cast back
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out = out.reshape(B, H, total_len, V)
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out = out[:, :, :L, :]
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out = out.to(initial_dtype)
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return out, state
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