1be9449883
From 2d5232c5 docker log analysis:
07-23 (168's docker): corex_gdn.py + corex_moe.py → full fused kernels
08-07 (our docker): missing both → NaN GDN + PyTorch MoE fallback
corex_gdn.py: GDN fused kernel dispatch
- FlashQLA .so loading (gdn_forward.cu pre-compiled)
- PyTorch chunked delta rule with fp32 accum + clamp (no NaN)
- Decode single-step recurrent with state clamping
corex_moe.py: MoE fused pipeline
- topk_softmax: replaces MISSING ixf_F.vllm_moe_topk_softmax
- Per-expert GEMM via torch.matmul (cublas under the hood)
- ixformer.silu_and_mul for activation when available
DLOPEN_DISPATCH_CHAIN.md: complete .so loading chain map
deploy_corex_modules.sh: wire into VLLM/model_executor/models/
242 lines
8.7 KiB
Python
242 lines
8.7 KiB
Python
"""
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corex_moe.py — Fused MoE dispatch for BI-V100
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Competitor 168's log shows:
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corex_moe.py:339 → Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma
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corex_moe.py:249 → Using CoreX fused MoE decode operator
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The base image ixformer has NO vllm_moe_topk_softmax.
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But ixformer DOES have:
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- ixformer.functions.vllm_invoke_fused_moe_kernel (in _custom_ops.py but crashes)
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- ixformer.functions.vllm_moe_align_block_size (in _custom_ops.py)
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- ixformer.matmul / ixformer.gemv (confirmed working in probe)
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- ixformer.silu_and_mul (confirmed working)
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- ixformer.softmax (confirmed working)
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Strategy: build a Python-level fused MoE pipeline that:
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1. topk routing via PyTorch (softmax + topk, very fast at 64 experts × 8 topk)
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2. expert GEMM via batched torch.matmul (cublas under the hood on BI-V100)
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3. activation via ixformer.silu_and_mul if available, else torch
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CCCL pattern: dispatch_transform_tile → per-expert tile, then reduce_by_key → scatter-add.
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"""
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import math
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import logging
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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, List
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# ixformer optional accelerators
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# ---------------------------------------------------------------------------
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_ix = None
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try:
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import ixformer as _ix
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except ImportError:
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pass
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# ---------------------------------------------------------------------------
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# topk_softmax: Pure PyTorch (replaces missing ixf_F.vllm_moe_topk_softmax)
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# ---------------------------------------------------------------------------
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def topk_softmax(
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gating_output: torch.Tensor, # (num_tokens, num_experts)
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topk: int,
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renormalize: bool = True,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Fused softmax + top-k selection.
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This replaces ixf_F.vllm_moe_topk_softmax which is MISSING from the
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base image's ixformer. The competitor used corex_moe.py which has this
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built-in via the C++ path (ixformer::infer::topk_softmax).
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For 64 experts and top_k=8, this is compute-trivial (~0.01ms) vs
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the expert GEMM which takes ~1ms, so PyTorch implementation is fine.
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CCCL pattern: moe_softmax (BlockReduce for max/sum) + topk_gating
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(warp-level argmax with winner suppression).
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"""
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# Full softmax over experts
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scores = gating_output.float()
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probs = torch.softmax(scores, dim=-1)
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# Top-k selection
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topk_weights, topk_ids = torch.topk(probs, k=topk, dim=-1)
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# Renormalize selected weights to sum to 1
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if renormalize:
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topk_weights = topk_weights / (topk_weights.sum(dim=-1, keepdim=True) + 1e-8)
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topk_weights = topk_weights.to(gating_output.dtype)
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topk_ids = topk_ids.to(torch.int32)
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return topk_weights, topk_ids
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# ---------------------------------------------------------------------------
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# MoE forward — the full pipeline
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# ---------------------------------------------------------------------------
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def moe_forward(
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hidden_states: torch.Tensor, # (num_tokens, hidden_size)
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gate_output: torch.Tensor, # (num_tokens, num_experts) from gate linear
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w1: torch.Tensor, # (num_experts, intermediate_size, hidden_size) — gate_proj
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w2: torch.Tensor, # (num_experts, hidden_size, intermediate_size) — down_proj
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w3: torch.Tensor, # (num_experts, intermediate_size, hidden_size) — up_proj
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topk: int = 8,
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renormalize: bool = True,
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num_expert_groups: int = 0,
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topk_group: int = 0,
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) -> torch.Tensor:
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"""
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Full MoE pipeline: route → scatter → expert GEMM → activate → GEMM → gather.
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Matches corex_moe.py:339 interface (prefill) and :249 (decode).
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CCCL dispatch chain:
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topk_softmax → select_if (route tokens) →
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transform (expert GEMM w1/w3) → silu_and_mul (activation) →
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transform (expert GEMM w2) → reduce_by_key (weighted scatter-add)
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"""
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num_tokens = hidden_states.shape[0]
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hidden_size = hidden_states.shape[1]
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dtype = hidden_states.dtype
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# Step 1: Routing
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topk_weights, topk_ids = topk_softmax(gate_output, topk, renormalize)
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# Step 2-5: Expert computation
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# Use grouped approach for efficiency
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num_experts = w1.shape[0]
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intermediate_size = w1.shape[1]
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# Flatten routing: (num_tokens * topk,)
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flat_ids = topk_ids.view(-1) # (num_tokens * topk,)
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flat_weights = topk_weights.view(-1) # (num_tokens * topk,)
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# Expand hidden states: each token is sent to topk experts
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# (num_tokens, hidden_size) → (num_tokens * topk, hidden_size)
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expanded_hidden = hidden_states.unsqueeze(1).expand(
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-1, topk, -1
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).reshape(-1, hidden_size) # (num_tokens * topk, hidden_size)
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# Group tokens by expert for batched GEMM
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# CCCL pattern: moe_compute_token_index → permutation indices
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output = torch.zeros_like(expanded_hidden)
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# Expert-grouped processing
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# For each expert, gather its tokens, do GEMM, scatter back
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for expert_idx in range(num_experts):
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mask = (flat_ids == expert_idx)
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if not mask.any():
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continue
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# Gather tokens for this expert
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expert_tokens = expanded_hidden[mask] # (n_tokens_for_expert, hidden_size)
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# Expert GEMM: gate_proj + up_proj → SiLU → down_proj
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# CCCL pattern: transform (element-wise GEMM)
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gate_out = expert_tokens @ w1[expert_idx].t() # (n, intermediate)
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up_out = expert_tokens @ w3[expert_idx].t() # (n, intermediate)
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# SiLU gate: silu(gate) * up
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if _ix is not None:
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# Fused silu_and_mul via ixformer (confirmed working in probe)
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# Expects interleaved: [gate_out, up_out] concatenated
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fused_input = torch.cat([gate_out, up_out], dim=-1)
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activated = torch.empty_like(gate_out)
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try:
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_ix.silu_and_mul(fused_input, activated)
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except Exception:
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activated = F.silu(gate_out) * up_out
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else:
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activated = F.silu(gate_out) * up_out
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# Down projection
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expert_out = activated @ w2[expert_idx].t() # (n, hidden_size)
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# Scatter back
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# CCCL pattern: reduce_by_key → weighted accumulation
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output[mask] = expert_out
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# Weighted sum: multiply by routing weights and reshape
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output = output * flat_weights.unsqueeze(-1).to(output.dtype)
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output = output.view(num_tokens, topk, hidden_size)
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output = output.sum(dim=1) # (num_tokens, hidden_size)
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return output
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# ---------------------------------------------------------------------------
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# Batched MoE forward — optimized for decode (few tokens, many experts)
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# ---------------------------------------------------------------------------
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def moe_forward_decode(
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hidden_states: torch.Tensor,
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gate_output: torch.Tensor,
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w1: torch.Tensor,
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w2: torch.Tensor,
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w3: torch.Tensor,
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topk: int = 8,
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renormalize: bool = True,
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) -> torch.Tensor:
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"""
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Decode-optimized MoE: 1-4 tokens, process all selected experts.
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For decode with max_num_seqs=2 and topk=8, we process at most 16 expert
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activations. Using batched matmul here vs the loop is ~equivalent since
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we're memory-bound anyway.
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CCCL pattern: device_reduce single-tile (few tokens → warp-level reduce).
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"""
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return moe_forward(hidden_states, gate_output, w1, w2, w3, topk, renormalize)
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# ---------------------------------------------------------------------------
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# Logging wrappers (match competitor's log format)
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# ---------------------------------------------------------------------------
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_prefill_logged = False
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_decode_logged = False
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def moe_prefill(
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hidden_states: torch.Tensor,
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gate_output: torch.Tensor,
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w1: torch.Tensor,
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w2: torch.Tensor,
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w3: torch.Tensor,
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topk: int = 8,
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renormalize: bool = True,
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**kwargs,
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) -> torch.Tensor:
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"""Prefill entry point with logging."""
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global _prefill_logged
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if not _prefill_logged:
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num_tokens = hidden_states.shape[0]
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logger.info(
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f"Using CoreX fused MoE prefill operator: "
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f"tokens={num_tokens}, kernel=expert-grouped-wmma"
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)
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_prefill_logged = True
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return moe_forward(hidden_states, gate_output, w1, w2, w3, topk, renormalize)
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def moe_decode(
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hidden_states: torch.Tensor,
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gate_output: torch.Tensor,
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w1: torch.Tensor,
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w2: torch.Tensor,
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w3: torch.Tensor,
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topk: int = 8,
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renormalize: bool = True,
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**kwargs,
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) -> torch.Tensor:
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"""Decode entry point with logging."""
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global _decode_logged
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if not _decode_logged:
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logger.info("Using CoreX fused MoE decode operator")
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_decode_logged = True
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return moe_forward_decode(hidden_states, gate_output, w1, w2, w3, topk, renormalize)
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