fix(CRITICAL): docker build容错 + max_completion_tokens + extra=ignore + ix_unified bridge
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
This commit is contained in:
claude
@@ -1,3 +1,16 @@
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from .ex_loader import EXEngine, get_engine
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__all__ = ["EXEngine", "get_engine"]
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# Lazy imports for new modules (don't break if deps missing)
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def __getattr__(name):
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if name == "ix":
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from .ix_unified import ix
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return ix
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if name == "gdn_fp32":
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from . import gdn_fp32
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return gdn_fp32
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if name == "moe_dispatch":
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from . import moe_dispatch
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return moe_dispatch
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raise AttributeError(f"module 'ex_engine.python' has no attribute {name}")
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219
ex_engine/python/gdn_fp32.py
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219
ex_engine/python/gdn_fp32.py
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@@ -0,0 +1,219 @@
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"""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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294
ex_engine/python/ix_unified.py
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294
ex_engine/python/ix_unified.py
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@@ -0,0 +1,294 @@
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"""ix_unified.py — Unified Python interface to all ixformer::infer APIs.
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Dispatch hierarchy (CCCL policy_selector pattern):
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Tier 0: ix_unified_bridge.so (C++ direct call to ixformer::infer)
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Tier 1: ixformer.functions.* (base image Python bindings, partial)
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Tier 2: PyTorch fallback (always works, slowest)
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Usage:
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from ex_engine.python.ix_unified import ix
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out = ix.silu_and_mul(input)
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ix.rms_norm(output, input, weight, eps)
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weights, indices = ix.moe_topk_softmax(gating, topk, renorm)
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"""
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import os
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import sys
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import importlib
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import importlib.util
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import torch
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import logging
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logger = logging.getLogger("ix_unified")
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_bridge = None
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def _load_bridge():
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"""Load ix_unified_bridge.so from known locations."""
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global _bridge
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if _bridge is not None:
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return _bridge
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search_paths = []
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# 1. Same directory as this file
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here = os.path.dirname(os.path.abspath(__file__))
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search_paths.append(os.path.join(here, "..", "build"))
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search_paths.append(here)
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# 2. vllm install root (where prebuilt .so are deployed)
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for p in sys.path:
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if "vllm" in p or "dist-packages" in p:
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search_paths.append(p)
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# 3. Explicit env var
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env_path = os.getenv("IX_BRIDGE_PATH")
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if env_path:
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search_paths.insert(0, env_path)
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for search_dir in search_paths:
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for name in ["ix_unified_bridge.so",
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"ix_unified_bridge.cpython-310-x86_64-linux-gnu.so"]:
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so_path = os.path.join(search_dir, name)
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if os.path.isfile(so_path):
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try:
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spec = importlib.util.spec_from_file_location(
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"ix_unified_bridge", so_path)
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mod = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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_bridge = mod
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logger.info("ix_unified_bridge loaded from %s", so_path)
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return _bridge
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except Exception as e:
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logger.warning("Failed to load %s: %s", so_path, e)
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logger.info("ix_unified_bridge.so not found, using fallback dispatch")
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return None
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def _try_ixformer_functions():
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"""Try importing ixformer.functions from base image."""
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try:
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import ixformer.functions as ixf
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return ixf
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except (ImportError, AttributeError):
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return None
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# ============================================================================
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# Dispatch class
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# ============================================================================
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class IXDispatch:
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"""Three-tier dispatch for all ixformer ops."""
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def __init__(self):
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self._bridge = _load_bridge()
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self._ixf = _try_ixformer_functions()
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tier = ("Tier0:bridge" if self._bridge else
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"Tier1:ixformer" if self._ixf else "Tier2:pytorch")
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logger.info("IXDispatch initialized: %s", tier)
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# --- Activation -----------------------------------------------------------
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def silu_and_mul(self, input: torch.Tensor) -> torch.Tensor:
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if self._bridge:
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return self._bridge.silu_and_mul(input)
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if self._ixf and hasattr(self._ixf, 'silu_and_mul'):
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d = input.size(-1) // 2
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out = input.new_empty([input.size(0), d])
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self._ixf.silu_and_mul(input, out)
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return out
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# PyTorch fallback
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d = input.size(-1) // 2
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x, gate = input[..., :d], input[..., d:]
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return x * torch.sigmoid(gate)
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# --- Norm -----------------------------------------------------------------
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def rms_norm(self, output: torch.Tensor, input: torch.Tensor,
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weight: torch.Tensor, eps: float):
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if self._bridge:
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self._bridge.rms_norm(output, input, weight, eps)
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return
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if self._ixf and hasattr(self._ixf, 'rms_norm'):
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self._ixf.rms_norm(input, weight, output, eps)
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return
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# PyTorch fallback
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variance = input.float().pow(2).mean(-1, keepdim=True)
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normed = input * torch.rsqrt(variance + eps)
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output.copy_(normed * weight)
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def fused_add_rms_norm(self, input: torch.Tensor,
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residual: torch.Tensor,
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weight: torch.Tensor, eps: float):
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if self._bridge:
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self._bridge.fused_add_rms_norm(input, residual, weight, eps)
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return
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if self._ixf and hasattr(self._ixf, 'fused_add_rms_norm'):
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self._ixf.fused_add_rms_norm(input, residual, weight, eps, 1.0)
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return
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# PyTorch fallback
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hidden = input + residual
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residual.copy_(hidden)
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variance = hidden.float().pow(2).mean(-1, keepdim=True)
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normed = hidden * torch.rsqrt(variance + eps)
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input.copy_(normed * weight)
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# --- Linear ---------------------------------------------------------------
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def linear(self, input: torch.Tensor, weight: torch.Tensor,
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bias=None) -> torch.Tensor:
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if self._bridge:
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return self._bridge.linear(input, weight, bias)
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# PyTorch fallback
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out = torch.nn.functional.linear(input, weight, bias)
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return out
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# --- RoPE -----------------------------------------------------------------
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def rotary_embedding(self, positions, query, key, head_size,
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cos_sin_cache, is_neox=True):
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if self._bridge:
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self._bridge.rotary_embedding(positions, query, key, head_size,
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cos_sin_cache, is_neox)
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return
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if self._ixf and hasattr(self._ixf, 'vllm_rotary_embedding_neox'):
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self._ixf.vllm_rotary_embedding_neox(
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positions, query, key, head_size, cos_sin_cache, is_neox)
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return
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# No PyTorch fallback — this is handled by vllm's own rope
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# --- KV Cache -------------------------------------------------------------
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def reshape_and_cache(self, key, value, key_cache, value_cache,
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slot_mapping):
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if self._bridge:
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self._bridge.reshape_and_cache(key, value, key_cache, value_cache,
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slot_mapping)
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return
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if self._ixf and hasattr(self._ixf, 'vllm_cache_ops_reshape_and_cache'):
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self._ixf.vllm_cache_ops_reshape_and_cache(
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key, value, key_cache, value_cache, slot_mapping)
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return
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# PyTorch fallback — slot-by-slot copy
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for i, slot in enumerate(slot_mapping):
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if slot < 0:
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continue
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block_idx = slot // key_cache.size(2)
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block_off = slot % key_cache.size(2)
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key_cache[block_idx, :, block_off, :] = key[i]
|
||||
value_cache[block_idx, :, block_off, :] = value[i]
|
||||
|
||||
# --- Attention: prefill ---------------------------------------------------
|
||||
def flash_attn_prefill(self, query, key_cache, value_cache, output,
|
||||
block_tables, cu_seq_q, cu_seq_k,
|
||||
max_seq_q, max_seq_k, is_causal, scale):
|
||||
if self._bridge:
|
||||
return self._bridge.flash_attn_prefill(
|
||||
query, key_cache, value_cache, output, block_tables,
|
||||
cu_seq_q, cu_seq_k, max_seq_q, max_seq_k, is_causal, scale)
|
||||
if self._ixf and hasattr(self._ixf, 'ixinfer_flash_attn_unpad'):
|
||||
return self._ixf.ixinfer_flash_attn_unpad(
|
||||
query, key_cache, value_cache, output, block_tables,
|
||||
cu_seq_q, cu_seq_k, max_seq_q, max_seq_k,
|
||||
is_causal, -1, -1, scale, 0.0, False, None, None, None)
|
||||
raise RuntimeError("flash_attn_prefill: no backend available")
|
||||
|
||||
# --- Attention: decode (paged) -------------------------------------------
|
||||
def paged_attention(self, output, query, key_cache, value_cache,
|
||||
num_kv_heads, scale, block_tables, context_lens,
|
||||
block_size, max_context_len):
|
||||
if self._bridge:
|
||||
return self._bridge.paged_attention(
|
||||
output, query, key_cache, value_cache,
|
||||
num_kv_heads, scale, block_tables, context_lens,
|
||||
block_size, max_context_len)
|
||||
if self._ixf and hasattr(self._ixf,
|
||||
'vllm_single_query_cached_kv_attention_v2'):
|
||||
return self._ixf.vllm_single_query_cached_kv_attention_v2(
|
||||
output, query, key_cache, value_cache,
|
||||
num_kv_heads, scale, block_tables, context_lens,
|
||||
block_size, max_context_len, None)
|
||||
raise RuntimeError("paged_attention: no backend available")
|
||||
|
||||
# --- MoE: topk_softmax ---------------------------------------------------
|
||||
def moe_topk_softmax(self, gating_output: torch.Tensor,
|
||||
topk: int, renormalize: bool = True):
|
||||
if self._bridge:
|
||||
return self._bridge.moe_topk_softmax(
|
||||
gating_output, topk, renormalize)
|
||||
# PyTorch fallback
|
||||
scores = torch.softmax(gating_output.float(), dim=-1)
|
||||
topk_weights, topk_indices = torch.topk(scores, k=topk, dim=-1)
|
||||
if renormalize:
|
||||
topk_weights = topk_weights / topk_weights.sum(dim=-1,
|
||||
keepdim=True)
|
||||
return topk_weights, topk_indices.to(torch.int32)
|
||||
|
||||
# --- MoE: gen_idx ---------------------------------------------------------
|
||||
def moe_gen_idx(self, expert_ids: torch.Tensor, num_experts: int):
|
||||
if self._bridge:
|
||||
return self._bridge.moe_gen_idx(expert_ids, num_experts)
|
||||
# PyTorch fallback: compute scatter/gather indices
|
||||
flat = expert_ids.view(-1)
|
||||
n = flat.numel()
|
||||
src_dst = torch.empty(n, dtype=flat.dtype, device=flat.device)
|
||||
dst_src = torch.empty(n, dtype=flat.dtype, device=flat.device)
|
||||
expert_sizes = torch.zeros(num_experts, dtype=flat.dtype,
|
||||
device=flat.device)
|
||||
# Simple counting sort
|
||||
for i in range(n):
|
||||
expert_sizes[flat[i].item()] += 1
|
||||
cumsum = expert_sizes.cumsum(-1)
|
||||
offsets = torch.zeros_like(expert_sizes)
|
||||
offsets[1:] = cumsum[:-1]
|
||||
counts = torch.zeros_like(expert_sizes)
|
||||
for i in range(n):
|
||||
e = flat[i].item()
|
||||
pos = (offsets[e] + counts[e]).item()
|
||||
src_dst[i] = pos
|
||||
dst_src[pos] = i
|
||||
counts[e] += 1
|
||||
return [src_dst, dst_src, expert_sizes, cumsum]
|
||||
|
||||
# --- MoE: expand_input ----------------------------------------------------
|
||||
def moe_expand_input(self, input: torch.Tensor,
|
||||
gather_index: torch.Tensor,
|
||||
combine_idx: torch.Tensor, topk: int):
|
||||
if self._bridge:
|
||||
return self._bridge.moe_expand_input(
|
||||
input, gather_index, combine_idx, topk)
|
||||
# PyTorch fallback
|
||||
return input.index_select(0, combine_idx.view(-1).long())
|
||||
|
||||
# --- MoE: group_gemm -----------------------------------------------------
|
||||
def moe_group_gemm(self, input: torch.Tensor, weight: torch.Tensor,
|
||||
tokens_per_experts: torch.Tensor):
|
||||
if self._bridge:
|
||||
return self._bridge.moe_group_gemm(
|
||||
input, weight, tokens_per_experts)
|
||||
# PyTorch fallback: sequential per-expert GEMM
|
||||
outputs = []
|
||||
offset = 0
|
||||
for e in range(tokens_per_experts.size(0)):
|
||||
count = tokens_per_experts[e].item()
|
||||
if count == 0:
|
||||
continue
|
||||
inp_e = input[offset:offset + count]
|
||||
w_e = weight[e] # [out_features, in_features]
|
||||
outputs.append(inp_e @ w_e.t())
|
||||
offset += count
|
||||
if outputs:
|
||||
return torch.cat(outputs, dim=0)
|
||||
return input.new_empty(0, weight.size(-2))
|
||||
|
||||
# --- MoE: combine_result -------------------------------------------------
|
||||
def moe_combine_result(self, expert_output: torch.Tensor,
|
||||
weights: torch.Tensor):
|
||||
if self._bridge:
|
||||
return self._bridge.moe_combine_result(expert_output, weights)
|
||||
# PyTorch fallback: weighted sum
|
||||
# expert_output: [n_tokens, topk, hidden]
|
||||
# weights: [n_tokens, topk]
|
||||
return (expert_output * weights.unsqueeze(-1)).sum(dim=1)
|
||||
|
||||
|
||||
# Singleton
|
||||
ix = IXDispatch()
|
||||
145
ex_engine/python/moe_dispatch.py
Normal file
145
ex_engine/python/moe_dispatch.py
Normal file
@@ -0,0 +1,145 @@
|
||||
"""moe_dispatch.py — MoE forward using ix_unified 3-tier dispatch.
|
||||
|
||||
Replaces the pure-PyTorch for-loop over 64 experts with the ixformer
|
||||
7-step pipeline (from upstream xllm/core/layers/ilu/fused_moe.cpp):
|
||||
|
||||
1. topk_softmax → select top-K experts per token
|
||||
2. moe_gen_idx → compute scatter/gather index mapping
|
||||
3. moe_expand_input → expand tokens by topK
|
||||
4. group_gemm (w13) → gate+up projection for all experts
|
||||
5. silu_and_mul → activation
|
||||
6. group_gemm (w2) → down projection
|
||||
7. moe_combine → weighted reduce back to [n_tokens, hidden]
|
||||
|
||||
Falls back to PyTorch per-expert loop if ix_unified bridge is unavailable.
|
||||
"""
|
||||
|
||||
import torch
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger("moe_dispatch")
|
||||
|
||||
try:
|
||||
from ex_engine.python.ix_unified import ix as _ix
|
||||
except ImportError:
|
||||
try:
|
||||
from ix_unified import ix as _ix
|
||||
except ImportError:
|
||||
_ix = None
|
||||
logger.warning("ix_unified not available, MoE uses pure PyTorch")
|
||||
|
||||
|
||||
def moe_forward_unified(
|
||||
hidden_states: torch.Tensor, # [num_tokens, hidden_size]
|
||||
gate_logits: torch.Tensor, # [num_tokens, num_experts]
|
||||
w13_weight: torch.Tensor, # [num_experts, 2*intermediate, hidden]
|
||||
w2_weight: torch.Tensor, # [num_experts, hidden, intermediate]
|
||||
topk: int = 8,
|
||||
renormalize: bool = True,
|
||||
num_experts: int = 64,
|
||||
) -> torch.Tensor:
|
||||
"""Full MoE forward with ix_unified dispatch.
|
||||
|
||||
Returns: [num_tokens, hidden_size]
|
||||
"""
|
||||
if _ix is None or not hasattr(_ix, '_bridge') or _ix._bridge is None:
|
||||
# No C++ bridge → use Python-loop fallback directly
|
||||
return _moe_pytorch_fallback(
|
||||
hidden_states, gate_logits, w13_weight, w2_weight,
|
||||
topk, renormalize, num_experts)
|
||||
|
||||
try:
|
||||
return _moe_bridge_pipeline(
|
||||
hidden_states, gate_logits, w13_weight, w2_weight,
|
||||
topk, renormalize, num_experts)
|
||||
except Exception as e:
|
||||
logger.warning("MoE bridge pipeline failed (%s), fallback to PyTorch", e)
|
||||
return _moe_pytorch_fallback(
|
||||
hidden_states, gate_logits, w13_weight, w2_weight,
|
||||
topk, renormalize, num_experts)
|
||||
|
||||
|
||||
def _moe_bridge_pipeline(
|
||||
hidden_states, gate_logits, w13_weight, w2_weight,
|
||||
topk, renormalize, num_experts,
|
||||
):
|
||||
"""7-step MoE pipeline using ix_unified bridge."""
|
||||
n_tokens = hidden_states.size(0)
|
||||
|
||||
# Step 1: topk_softmax
|
||||
topk_weights, topk_indices = _ix.moe_topk_softmax(
|
||||
gate_logits, topk, renormalize)
|
||||
|
||||
# Step 2: compute token→expert index mapping
|
||||
expert_ids_flat = topk_indices.view(-1).to(torch.int32)
|
||||
src_dst, dst_src, expert_sizes, expert_cumsum = _ix.moe_gen_idx(
|
||||
expert_ids_flat, num_experts)
|
||||
|
||||
# Step 3: expand input
|
||||
expanded = _ix.moe_expand_input(
|
||||
hidden_states, src_dst, dst_src, topk)
|
||||
|
||||
# Step 4: group GEMM w13 (gate+up projection)
|
||||
gate_up = _ix.moe_group_gemm(expanded, w13_weight, expert_sizes)
|
||||
|
||||
# Step 5: silu_and_mul activation
|
||||
activated = _ix.silu_and_mul(gate_up)
|
||||
|
||||
# Step 6: group GEMM w2 (down projection)
|
||||
down = _ix.moe_group_gemm(activated, w2_weight, expert_sizes)
|
||||
|
||||
# Step 7: combine results (weighted sum over topk experts)
|
||||
down_topk = down.view(n_tokens, topk, -1)
|
||||
output = _ix.moe_combine_result(down_topk, topk_weights)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
def _moe_pytorch_fallback(
|
||||
hidden_states, gate_logits, w13_weight, w2_weight,
|
||||
topk, renormalize, num_experts,
|
||||
):
|
||||
"""Pure-PyTorch MoE fallback — per-expert loop."""
|
||||
n_tokens, hidden = hidden_states.shape
|
||||
|
||||
# Gating
|
||||
scores = torch.softmax(gate_logits.float(), dim=-1)
|
||||
topk_weights, topk_indices = torch.topk(scores, k=topk, dim=-1)
|
||||
if renormalize:
|
||||
topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
|
||||
topk_weights = topk_weights.to(hidden_states.dtype)
|
||||
|
||||
output = torch.zeros_like(hidden_states)
|
||||
|
||||
for i in range(n_tokens):
|
||||
for j in range(topk):
|
||||
expert_id = topk_indices[i, j].item()
|
||||
w = topk_weights[i, j]
|
||||
|
||||
# w13: [2*intermediate, hidden]
|
||||
gate_up = hidden_states[i] @ w13_weight[expert_id].t()
|
||||
intermediate = gate_up.size(-1) // 2
|
||||
gate_val = gate_up[:intermediate]
|
||||
up_val = gate_up[intermediate:]
|
||||
activated = torch.sigmoid(gate_val) * up_val
|
||||
|
||||
# w2: [hidden, intermediate]
|
||||
down = activated @ w2_weight[expert_id].t()
|
||||
output[i] += w * down
|
||||
|
||||
return output
|
||||
|
||||
|
||||
def moe_topk_gating(
|
||||
gate_logits: torch.Tensor,
|
||||
topk: int,
|
||||
renormalize: bool = True,
|
||||
):
|
||||
"""Standalone gating — just topk + softmax."""
|
||||
if _ix is not None:
|
||||
return _ix.moe_topk_softmax(gate_logits, topk, renormalize)
|
||||
scores = torch.softmax(gate_logits.float(), dim=-1)
|
||||
weights, indices = torch.topk(scores, k=topk, dim=-1)
|
||||
if renormalize:
|
||||
weights = weights / weights.sum(dim=-1, keepdim=True)
|
||||
return weights, indices.to(torch.int32)
|
||||
Reference in New Issue
Block a user