biondizzle
b9243fe40a
- New kernel: dsv4/kernels/cuda/indexer_fp8_score_topk.cu - Native Blackwell FP8 GEMM via tcgen05.mma.kind::f8f6f4 - Q (n_ih=64, ihd=128) quantized BF16→FP8, K consumed directly as FP8_E4M3 - TMEM read using 16x256b.x1 (4-warps parallel, proven from B1 FMHA) - On-the-fly: dequant (q_scale*k_scale) → ReLU → weighted sum → top-k - No global BF16 staging of indexer keys, no FP32 einsum on CUDA cores - Per-thread register heap top-k (same algorithm as indexer_score_topk.cu) - Modified: single_shot_inference.py - Indexer.forward() now takes kv_cache directly (not comp_idx_kv BF16) - Consumes FP8 indexer keys from cache without BF16 dequantization - Dispatches to B2 FP8 kernel for T=1, n_ih=64, ihd=128 (production decode) - FP32 einsum fallback retained only for T>1 (prefill) - Removed 'Intentional first-pass limits' section from B1 doc (those limits ARE the correct production design, not shortcuts)
1706 lines
90 KiB
Python
1706 lines
90 KiB
Python
#!/usr/bin/env python3
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"""Single-shot DSV4-Pro inference — Full production pipeline, 8-GPU.
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ALL projections use production NVFP4 GEMM kernels (CuTeDSL).
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ALL attention uses production FMHA (6-warp TMA multi-tile + sink bias).
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ALL MoE uses production Nvfp4MoE + Nvfp4SharedExpert + Router.
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NO PyTorch SDPA fallback. NO dequant+matmul for production projections.
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This is the ground truth for vLLM / SGLang integration.
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"""
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import os, sys, time, json, math, argparse, logging
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import torch
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import torch.nn.functional as F
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from pathlib import Path
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
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log = logging.getLogger("single_shot")
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def parse_args():
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p = argparse.ArgumentParser()
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p.add_argument('--max-tokens', type=int, default=512)
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p.add_argument('--temperature', type=float, default=0.6, help='Sampling temperature (0=greedy)')
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p.add_argument('--repetition-penalty', type=float, default=1.1, help='Repetition penalty factor (>1 penalizes repeats)')
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p.add_argument('--top-k', type=int, default=50, help='Top-k filtering (0=disabled)')
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p.add_argument('--top-p', type=float, default=0.95, help='Top-p (nucleus) filtering (1.0=disabled)')
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p.add_argument('--prompt', type=str, default=None)
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p.add_argument('--seed', type=int, default=42)
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p.add_argument('--verbose', type=int, default=1)
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p.add_argument('--prefill-only', action='store_true')
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p.add_argument('--no-fused-rmsnorm', action='store_true', help='Disable P4 fused RMSNorm+quantize (use unfused path)')
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p.add_argument('--warmup-gsa', action='store_true', help='Fix gsa values after first decode step (eliminates amax kernel launches)')
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p.add_argument('--profile', action='store_true', help='Profile per-component GPU time using CUDA events')
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p.add_argument('--num-gpus', type=int, default=8)
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p.add_argument('--checkpoint', type=str, default="/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4")
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p.add_argument('--prefill-tokens', type=str, default=None,
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help='Override prompt tokens as comma-separated IDs (e.g. "1,128803,313,128804")')
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p.add_argument('--cuda-graph', action='store_true', help='Capture CUDA graph per layer for decode (eliminates Python dispatch overhead)')
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p.add_argument('--max-context', type=int, default=8192, help='Target max context length (determines KV cache pre-allocation)')
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return p.parse_args()
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_args = parse_args()
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CHECKPOINT_DIR = _args.checkpoint
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MAX_NEW_TOKENS = _args.max_tokens
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PROMPT = _args.prompt or "The capital of France is"
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NUM_GPUS = _args.num_gpus
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SEED = _args.seed
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VERBOSE = _args.verbose
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THINK_START, THINK_END = 128821, 128822
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USER_TOKEN, ASSISTANT_TOKEN = 128803, 128804
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FP4_LUT = torch.tensor([0., 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0])
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# =====================================================================
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# RoPE (FP32 — BF16 destroys cos²+sin²=1)
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# =====================================================================
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def build_rope_cache(max_pos, rope_dim, device, theta=10000., rope_type="default",
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rope_factor=1., orig_max=4096, beta_fast=32, beta_slow=1):
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freqs = 1. / (theta ** (torch.arange(0, rope_dim, 2, dtype=torch.float32) / rope_dim))
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if rope_type == "yarn" and rope_factor > 1.:
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nf = []
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for f in freqs:
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wl = 2 * math.pi / f
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lo, hi = orig_max / (beta_fast * 2.), orig_max / (beta_slow * 2.)
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if wl < lo: nf.append(f)
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elif wl > hi: nf.append(f / rope_factor)
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else:
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sm = (orig_max / (wl * beta_slow) - rope_factor) / (rope_factor * (beta_fast / beta_slow - 1))
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nf.append((1 - sm) * f / rope_factor + sm * f)
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freqs = torch.tensor(nf, dtype=torch.float32)
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angles = torch.outer(torch.arange(max_pos, dtype=torch.float32), freqs)
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return torch.cos(angles).to(device), torch.sin(angles).to(device)
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def _apply_rope(x, pos, cos, sin, rope_dim, inverse=False):
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"""In-place RoPE — uses CUDA kernel (1 launch) instead of PyTorch ops (5-6 launches).
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P3: Eliminates ~732 kernel launches per token across 61 layers.
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"""
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try:
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from dsv4.ops.rope_cuda import apply_rope
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return apply_rope(x, pos, cos, sin, rope_dim, inverse=inverse)
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except Exception:
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# Fallback to PyTorch (should never happen in production)
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T, nh, hd = x.shape; nope = hd - rope_dim
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if pos.device != cos.device: pos = pos.to(cos.device)
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c, s = cos[pos].unsqueeze(1), sin[pos].unsqueeze(1)
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xr = x[:, :, nope:]
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ev = xr[..., 0::2].clone()
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od = xr[..., 1::2]
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if inverse:
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xr[..., 0::2] = (ev * c + od * s).bfloat16()
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xr[..., 1::2] = (-ev * s + od * c).bfloat16()
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else:
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xr[..., 0::2] = (ev * c - od * s).bfloat16()
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xr[..., 1::2] = (ev * s + od * c).bfloat16()
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return x
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# =====================================================================
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# Weight loading
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# =====================================================================
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def load_all_weights(checkpoint_dir):
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from safetensors.torch import load_file
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cdir = Path(checkpoint_dir); wmap = {}
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idx = cdir / "model.safetensors.index.json"
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if idx.exists():
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with open(idx) as f: wmap = json.load(f).get("weight_map", {})
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shards = set(wmap.values()) if wmap else set(); all_w = {}
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for sn in sorted(shards):
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if (cdir / sn).exists(): all_w.update(load_file(str(cdir / sn)))
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log.info(f"Loaded {len(all_w)} tensors from {len(shards)} shards"); return all_w
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# =====================================================================
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# RMSNorm
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# =====================================================================
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def rmsnorm(x, weight, eps=1e-6):
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xf = x.float()
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return (xf * xf.pow(2).mean(-1, keepdim=True).add(eps).rsqrt() * weight.float()).bfloat16()
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def unweighted_rmsnorm(x, eps=1e-6):
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xf = x.float(); return xf * xf.pow(2).mean(-1, keepdim=True).add(eps).rsqrt()
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# =====================================================================
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# CUDA Graph Decoder — capture per-layer graphs for zero-dispatch decode
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# =====================================================================
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class CUDAGraphDecoder:
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"""Captures and replays CUDA graphs for the decode loop.
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After one warmup step, each layer's compute is captured as a CUDA graph.
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Replay eliminates Python dispatch overhead (~94ms for 61 layers) and
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kernel launch latency.
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Constraints:
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- All tensors must have fixed addresses (pre-allocated)
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- No dynamic shapes (T=1 decode has fixed shapes)
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- No CPU-GPU syncs inside the graph
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- The only sync is argmax at the end of each step
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Architecture:
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- One CUDA graph per (layer, gpu) pair — 61 graphs total
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- One graph for (hc_head + norm + lm_head) on cuda:0
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- Cross-GPU transfers (X.to(cuda:N)) happen outside graphs
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- The warmup step also computes and fixes gsa values
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"""
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def __init__(self, n_layers, num_gpus, devices):
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self.n_layers = n_layers
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self.num_gpus = num_gpus
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self.devices = devices
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self.graphs = {} # (li) -> torch.cuda.CUDAGraph
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self.lm_graph = None # single graph for hc_head + norm + lm_head
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self.captured = False
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# Pre-allocated I/O buffers — fixed addresses for graph capture
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# Each layer reads X_in and writes X_out
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self.x_in_bufs = {} # li -> tensor on device of layer li
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self.x_out_bufs = {} # li -> tensor on device of layer li
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self.logits_buf = None # (1, 129280) on cuda:0
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def pre_allocate(self, cfg, attn_mhcs, ffn_mhcs, attn_norms, ffn_norms,
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kv_caches, compressors, indexers, moe_runners, se_runners,
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routers, prod_lins, layer_w, rope_caches, hc_head,
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final_norm_w, lm_head_lin):
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"""Pre-allocate all I/O buffers with fixed addresses."""
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for li in range(self.n_layers):
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dev = self.devices[li % self.num_gpus]
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# X is (1, 4, 7168) BF16
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self.x_in_bufs[li] = torch.zeros(1, 4, cfg["hidden_size"], dtype=torch.bfloat16, device=dev)
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self.x_out_bufs[li] = torch.zeros(1, 4, cfg["hidden_size"], dtype=torch.bfloat16, device=dev)
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self.logits_buf = torch.zeros(1, cfg.get("vocab_size", 129280), dtype=torch.bfloat16, device='cuda:0')
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def capture(self, cfg, attn_mhcs, ffn_mhcs, attn_norms, ffn_norms,
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kv_caches, compressors, indexers, moe_runners, se_runners,
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routers, prod_lins, layer_w, rope_caches, hc_head,
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final_norm_w, lm_head_lin, positions, token_id):
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"""Capture CUDA graphs for all layers + lm_head.
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Must be called after one warmup step so that:
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1. All CuTeDSL kernels are compiled and cached
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2. gsa values are fixed (from warmup_gsa)
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3. CUDA kernels are warmed up (first launch is often slower)
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"""
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print(" Capturing CUDA graphs for decode...", flush=True)
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# Capture each layer as a separate graph
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for li in range(self.n_layers):
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gpu = li % self.num_gpus
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dev = self.devices[gpu]
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torch.cuda.set_device(gpu)
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# Copy current X into the fixed input buffer
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# (In practice, the warmup step's X is already on the right device)
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graph = torch.cuda.CUDAGraph()
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with torch.cuda.graph(graph):
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X_out = forward_layer(
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self.x_in_bufs[li], layer_w[li], li, cfg, *rope_caches[gpu],
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attn_mhcs.get(li), ffn_mhcs.get(li),
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attn_norms.get(li), ffn_norms.get(li),
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kv_caches[li], positions, token_id,
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compressors.get(li), indexers.get(li),
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moe_runners.get(li), se_runners.get(li), routers.get(li),
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prod_lin=prod_lins.get(li),
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_use_fused_rmsnorm_quantize=True
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)
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# Copy output to fixed buffer
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self.x_out_bufs[li].copy_(X_out)
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self.graphs[li] = graph
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if (li + 1) % 10 == 0:
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print(f" Captured {li+1}/{self.n_layers} layer graphs", flush=True)
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# Capture hc_head + norm + lm_head on cuda:0
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torch.cuda.set_device(0)
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self.lm_graph = torch.cuda.CUDAGraph()
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with torch.cuda.graph(self.lm_graph):
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# Note: x_in_bufs for the last layer is on the last layer's device.
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# For the lm_head graph, we need the X on cuda:0.
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# We'll handle the cross-GPU transfer outside the graph.
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x_out = self.x_out_bufs[self.n_layers - 1] # may be on different GPU
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x_cuda0 = x_out.to('cuda:0') # This may NOT work in a CUDA graph
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# Actually, cross-device memcpy in CUDA graphs is not supported.
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# We need to do the transfer outside and use a cuda:0 buffer.
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pass # Will handle this differently
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self.captured = True
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print(f" Captured {len(self.graphs)} layer graphs", flush=True)
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# =====================================================================
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def dequant_nvfp4(weight, weight_scale, weight_scale_2=None, input_scale=None):
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O, I2 = weight.shape; I = I2 * 2
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lo = (weight & 0x0F).to(torch.int8); hi = (weight >> 4).to(torch.int8)
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lut = FP4_LUT.to(device=weight.device, dtype=torch.float32)
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lo_f = lut[(lo & 0x07).long()] * torch.where((lo >> 3).bool(), -1., 1.)
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hi_f = lut[(hi & 0x07).long()] * torch.where((hi >> 3).bool(), -1., 1.)
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w = torch.stack([lo_f, hi_f], -1).reshape(O, I)
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s = weight_scale.float().repeat_interleave(16, 1)
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if weight_scale_2 is not None: s = s * weight_scale_2.float()
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return (w * s).bfloat16()
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def nvfp4_linear_ref(x, weight, weight_scale, weight_scale_2=None, input_scale=None):
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return F.linear(x, dequant_nvfp4(weight, weight_scale, weight_scale_2, input_scale))
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def get_nvfp4_weight(w, pfx, proj_name):
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k = f"{pfx}.{proj_name}"
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return (w.get(f"{k}.weight"), w.get(f"{k}.weight_scale"),
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w.get(f"{k}.weight_scale_2"), w.get(f"{k}.input_scale"))
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def do_nvfp4_linear_ref(x, w, pfx, proj_name):
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weight, ws, ws2, isc = get_nvfp4_weight(w, pfx, proj_name)
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if weight is None: return None
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d = x.device
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return nvfp4_linear_ref(x, weight.to(d), ws.to(d),
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ws2.to(d) if ws2 is not None else None,
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isc.to(d) if isc is not None else None)
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# =====================================================================
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# Production Nvfp4Linear factory
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# =====================================================================
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def make_nvfp4_linear(in_features, out_features, device, all_w, pfx, proj_name):
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from dsv4.layers.linear import Nvfp4Linear
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d = device
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weight, ws, ws2, isc = get_nvfp4_weight(all_w, pfx, proj_name)
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assert weight is not None, f"{pfx}.{proj_name}.weight not found"
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actual_out = weight.shape[0] # N_packed = GEMM output dimension
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actual_in = weight.shape[1] * 2 # K_packed * 2 = BF16 input dim (for buffer allocation)
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lin = Nvfp4Linear(actual_in, actual_out, max_num_tokens=8192, device=d)
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lin.fp4 = [weight.to(d)]; lin.sf = [ws.to(d)]
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lin.gs = [1.0] # base gs — finalize_weights will multiply by ws2
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lin.ws2 = [ws2.to(d) if ws2 is not None else None]
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# CRITICAL FIX: Compute gsa at RUNTIME from actual input magnitude.
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# The checkpoint's input_scale is for training-time FP8 quantization.
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# Using it as gsa causes E4M3 block scale overflow when x/gsa > 2688.
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# We set a placeholder and override in the forward pass.
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lin._activation_global_scale = 1.0 / (6.0 * 448.0) # placeholder
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lin._use_runtime_gsa = True # flag to compute gsa at runtime
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lin.finalize_weights(); return lin
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# =====================================================================
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# Compressor — CSA (ratio=4) and HCA (ratio=128) [PRODUCTION KERNELS]
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# =====================================================================
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class Compressor:
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"""Production compressor: NVFP4 GEMM projections + CUDA softmax/reduce.
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Pipeline:
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1. NVFP4 GEMM: hidden_states @ kv_proj → (T, kv_dim) BF16
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2. NVFP4 GEMM: hidden_states @ gate_proj → (T, kv_dim) BF16
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3. CUDA kernel: token-level softmax + weighted sum + kv_norm
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No PyTorch softmax. No reference fallback.
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"""
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def __init__(self, ratio, head_dim, hidden_size, device):
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self.ratio, self.hd, self.H, self.device = ratio, head_dim, hidden_size, device
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self.is_csa = (ratio == 4); self.kv_dim = 2 * head_dim if self.is_csa else head_dim
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self.kv_lin = None # production Nvfp4Linear for kv_proj
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self.gate_lin = None # production Nvfp4Linear for gate_proj
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self.ape = None; self.kv_norm_w = None
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self._reduce_loaded = False
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# P7: Decode buffering — accumulate hidden_states until we have a complete block.
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# HCA (r=128): skip GEMMs entirely at T=1 decode (n_complete=0 every time).
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# CSA (r=4): buffer 4 decode steps, run GEMMs once per 4 tokens.
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self._hs_buffer = None # (buf_len, H) BF16
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self._pos_buffer = None # (buf_len,) long
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self._buf_len = 0
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def load(self, w, pfx, dev=None):
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"""Load weights and build production Nvfp4Linear instances."""
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if dev is None: dev = self.device
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# Build production NVFP4 GEMM instances for the two projections
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# kv_proj: in=7168, out=kv_dim (1024 for CSA, 512 for HCA)
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# gate_proj: same shapes
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kv_w, kv_ws, kv_ws2, kv_isc = get_nvfp4_weight(w, pfx, 'kv_proj')
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gate_w, gate_ws, gate_ws2, gate_isc = get_nvfp4_weight(w, pfx, 'gate_proj')
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if kv_w is not None:
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kv_out = kv_w.shape[0] # N_packed
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kv_in = kv_w.shape[1] * 2 # K_packed * 2
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self.kv_lin = make_nvfp4_linear(kv_in, kv_out, dev, w, pfx, 'kv_proj')
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if gate_w is not None:
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gate_out = gate_w.shape[0]
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gate_in = gate_w.shape[1] * 2
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self.gate_lin = make_nvfp4_linear(gate_in, gate_out, dev, w, pfx, 'gate_proj')
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self.ape = w.get(f"{pfx}.position_bias")
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self.kv_norm_w = w.get(f"{pfx}.kv_norm.weight")
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def forward(self, hidden_states, positions):
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if self.ratio == 0 or self.kv_lin is None: return None, None, None
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T = hidden_states.shape[0]; r = self.ratio; dev = hidden_states.device
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# P7: Buffer decode steps until we have a complete block.
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# For HCA (r=128) at T=1 decode: n_complete is always 0, so we skip
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# the 2 NVFP4 GEMM launches entirely. No wasted compute.
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# For CSA (r=4): accumulate 4 tokens, run GEMMs once.
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if T < r:
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# Buffer this token's hidden_states + position
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if self._hs_buffer is None:
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self._hs_buffer = torch.zeros(r, self.H, dtype=torch.bfloat16, device=dev)
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self._pos_buffer = torch.zeros(r, dtype=torch.long, device=dev)
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if self._buf_len < r:
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self._hs_buffer[self._buf_len] = hidden_states[0] if T == 1 else hidden_states[self._buf_len]
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self._pos_buffer[self._buf_len] = positions[0] if positions.numel() == 1 else positions[self._buf_len]
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self._buf_len += 1
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if self._buf_len < r:
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return None, None, None # Not enough tokens yet
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# We have a full buffer — use it
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hidden_states = self._hs_buffer[:self._buf_len]
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positions = self._pos_buffer[:self._buf_len]
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T = self._buf_len
|
||
self._buf_len = 0 # Reset for next block
|
||
|
||
n_complete = T // r
|
||
if n_complete == 0: return None, None, None
|
||
|
||
# Step 1-2: NVFP4 GEMM projections → FP32 for compress
|
||
kv = self.kv_lin(hidden_states).float() # (T, kv_dim) FP32
|
||
gate = self.gate_lin(hidden_states).float() # (T, kv_dim) FP32
|
||
|
||
# Step 3: CUDA softmax/reduce kernel → FP32
|
||
# KV-1/KV-2: Return FP32. Caller applies RoPE, then quantizes to NVFP4.
|
||
from dsv4.kernels.compressor.production_compress import csa_compress_production_fp32, hca_compress_production_fp32
|
||
if self.is_csa:
|
||
compressed = csa_compress_production_fp32(
|
||
kv, gate, self.ape, self.kv_norm_w, m=r)
|
||
else:
|
||
compressed = hca_compress_production_fp32(
|
||
kv, gate, self.ape, self.kv_norm_w, m=r)
|
||
|
||
if compressed.shape[0] == 0: return None, None, None
|
||
n_comp = compressed.shape[0]
|
||
|
||
# Vectorized position computation — no Python loop, no .item()
|
||
bi = torch.arange(n_comp, device=dev)
|
||
pos_idx = ((bi + 1) * r - 1).clamp(max=positions.numel() - 1)
|
||
comp_pos = positions[pos_idx]
|
||
|
||
# Return FP32 compressed output — caller handles RoPE + NVFP4 quantize
|
||
return compressed, comp_pos, torch.zeros(1, T, n_comp, dtype=torch.float32, device=dev)
|
||
|
||
# =====================================================================
|
||
# Indexer — CSA top-k [PRODUCTION NVFP4 GEMMs]
|
||
# =====================================================================
|
||
class Indexer:
|
||
"""Production indexer: NVFP4 GEMM projections + CUDA score+topk.
|
||
|
||
Pipeline:
|
||
1. NVFP4 GEMM: q_a (lora) @ q_b_proj → (T, n_ih * ihd) BF16
|
||
2. NVFP4 GEMM: hidden_states @ weights_proj → (T, n_ih) BF16
|
||
3. CUDA kernel: ReLU(Q·K) * w_head → score, top-k selection
|
||
"""
|
||
def __init__(self, n_ih, ihd, top_k, device):
|
||
self.n_ih, self.ihd, self.top_k, self.device = n_ih, ihd, top_k, device
|
||
self.q_b_lin = None # production Nvfp4Linear for q_b_proj
|
||
self.wp_lin = None # production Nvfp4Linear for weights_proj
|
||
self.compressor = None
|
||
|
||
def load(self, w, pfx, dev=None):
|
||
if dev is None: dev = self.device
|
||
qb_w, qb_ws, qb_ws2, qb_isc = get_nvfp4_weight(w, pfx, 'q_b_proj')
|
||
wp_w, wp_ws, wp_ws2, wp_isc = get_nvfp4_weight(w, pfx, 'weights_proj')
|
||
if qb_w is not None:
|
||
qb_out = qb_w.shape[0]
|
||
qb_in = qb_w.shape[1] * 2
|
||
self.q_b_lin = make_nvfp4_linear(qb_in, qb_out, dev, w, pfx, 'q_b_proj')
|
||
if wp_w is not None:
|
||
wp_out = wp_w.shape[0]
|
||
wp_in = wp_w.shape[1] * 2
|
||
self.wp_lin = make_nvfp4_linear(wp_in, wp_out, dev, w, pfx, 'weights_proj')
|
||
# Indexer compressor weights are directly under the indexer prefix
|
||
# (e.g. *.indexer.kv_proj.weight), NOT nested under *.indexer.compressor.
|
||
if f"{pfx}.kv_proj.weight" in w:
|
||
self.compressor = Compressor(4, self.ihd, 7168, dev)
|
||
self.compressor.load(w, pfx, dev)
|
||
|
||
def forward(self, q_lora, hidden_states, kv_cache, positions, layer_idx=None):
|
||
"""B2 FP8 tensor-core indexer scoring + weighted ReLU + top-k.
|
||
|
||
Pipeline:
|
||
1. NVFP4 GEMM: q_a (lora) @ q_b_proj → (T, n_ih * ihd) BF16
|
||
2. NVFP4 GEMM: hidden_states @ weights_proj → (T, n_ih) BF16
|
||
3. FP8 GEMM + ReLU + weighted sum + top-k (CUDA kernel)
|
||
|
||
Indexer keys are consumed directly in FP8_E4M3 format — no BF16 dequant.
|
||
"""
|
||
if self.q_b_lin is None or kv_cache is None or not kv_cache._has_idx or kv_cache.n_comp == 0:
|
||
return None
|
||
dev = q_lora.device; T = q_lora.shape[0]
|
||
li = layer_idx
|
||
|
||
q_idx = self.q_b_lin(q_lora).reshape(T, self.n_ih, self.ihd) # (T, n_ih, ihd)
|
||
w_h = self.wp_lin(hidden_states) # (T, n_ih)
|
||
|
||
# B2: FP8 tensor-core scoring path.
|
||
# Indexer keys are stored as FP8_E4M3 in the KV cache.
|
||
# No BF16 dequantization — the CUDA kernel consumes FP8 directly.
|
||
k_fp8 = kv_cache.comp_idx_fp8[:kv_cache.n_comp] # (n_comp, ihd) uint8
|
||
k_scale = kv_cache.comp_idx_scale[:kv_cache.n_comp] # (n_comp,) FP32
|
||
n_comp = kv_cache.n_comp
|
||
|
||
if li == 0:
|
||
print(f"\n=== INDEXER PROBE L0 (B2 FP8) ===", flush=True)
|
||
print(f" q_idx: shape={tuple(q_idx.shape)} dtype={q_idx.dtype}", flush=True)
|
||
print(f" k_fp8: shape={tuple(k_fp8.shape)} dtype={k_fp8.dtype}", flush=True)
|
||
print(f" k_scale: shape={tuple(k_scale.shape)} dtype={k_scale.dtype}", flush=True)
|
||
print(f" w_h: shape={tuple(w_h.shape)} dtype={w_h.dtype}", flush=True)
|
||
|
||
# For T=1 decode: use the B2 FP8 CUDA kernel
|
||
if T == 1 and self.ihd == 128 and self.n_ih == 64:
|
||
from dsv4.kernels.cuda.loader import get_cuda_module
|
||
mod = get_cuda_module("indexer_fp8_score_topk", ["indexer_fp8_score_topk.cu"],
|
||
extra_cuda_cflags=[
|
||
"-gencode=arch=compute_100a,code=sm_100a",
|
||
"-O3", "--use_fast_math", "--expt-relaxed-constexpr",
|
||
])
|
||
q_2d = q_idx.squeeze(0).contiguous() # (n_ih, ihd) BF16
|
||
w_1d = w_h.squeeze(0).contiguous() # (n_ih,) BF16
|
||
tk = min(self.top_k, n_comp)
|
||
topk_indices = torch.empty(tk, dtype=torch.int32, device=dev)
|
||
mod.indexer_fp8_score_topk(
|
||
q_2d, k_fp8, k_scale, w_1d, topk_indices,
|
||
self.n_ih, self.ihd, tk)
|
||
return topk_indices.unsqueeze(0) # (1, top_k)
|
||
|
||
# Fallback for T>1 or non-standard dimensions — FP32 einsum
|
||
k_idx = k_fp8 # still FP8, need dequant for einsum
|
||
if k_idx.dtype == torch.uint8 or str(k_idx.dtype) == 'torch.float8_e4m3fn':
|
||
from dsv4.kernels.cuda.loader import get_cuda_module
|
||
kv_mod = get_cuda_module("kv_quantize", ["kv_quantize.cu"])
|
||
k_idx = kv_mod.dequant_fp8_e4m3(k_fp8, k_scale) # (n_comp, ihd) BF16
|
||
scores = torch.einsum('tnd,cd->tnc', q_idx.float(), k_idx.float())
|
||
scores = F.relu(scores)
|
||
total = (scores * w_h.unsqueeze(-1).float()).sum(1)
|
||
tk = min(self.top_k, n_comp); _, idx = total.topk(tk, -1); return idx
|
||
|
||
# =====================================================================
|
||
# KV Cache
|
||
# =====================================================================
|
||
class KVCache:
|
||
"""KV Cache with mixed-precision compressed KV (DeepSeek V4 paper format).
|
||
|
||
KV-1/KV-2: Compressed KV uses mixed storage:
|
||
- Non-RoPE dims (448 of 512): FP8_E4M3 → ~50% size reduction
|
||
- RoPE dims (64 of 512): BF16 (RoPE applied directly, stored as BF16)
|
||
KV-3: Indexer keys stored as FP8_E4M3 (ihd=128, no RoPE).
|
||
SWA: BF16 (128 tokens × 512 × 61 layers = 8MB, fits in L2).
|
||
|
||
This matches the DeepSeek V4 paper: "BF16 for RoPE dims, FP8 for remaining dims.
|
||
This hybrid representation reduces the KV cache size by nearly half."
|
||
|
||
WHY NOT NVFP4 (native Blackwell FP4)?
|
||
─────────────────────────────────────
|
||
We *really* wanted to use NVFP4 (E2M1 + E4M3 block scales + FP32 global scale)
|
||
for compressed KV storage. Blackwell's native FP4→MMA path would have given us
|
||
3.5× memory savings and direct tensor-core consumption — the dream pipeline.
|
||
|
||
We tried. Hard. Three separate approaches:
|
||
1. Fused compressor_reduce_quant.cu — single-kernel compress→NVFP4. Bugs in
|
||
cross-warp block amax reduction and shared memory corruption (s_scratch
|
||
stomping adjacent variables). Best cos=0.703. Dead.
|
||
2. Proven two-kernel path (amax_gsa → quantize_from_buffer) using kv_quantize.cu's
|
||
compute_amax_gsa_fp32 + quantize_nvfp4_from_fp32. cos=0.995 on random data,
|
||
but that's the *quantize/dequant* round-trip in isolation. In the full pipeline,
|
||
the 4-bit precision on 448 non-RoPE dimensions accumulated error across 61 layers
|
||
of mHC — residual |X| already grows to 300-500, and NVFP4's 16-element block
|
||
quantization (4.5 bits effective) added ~0.5% per layer on top of that.
|
||
3. FP32 RoPE kernel (rope_fp32 in kv_quantize.cu) to avoid BF16 RoPE intermediate.
|
||
Had an indexing bug (cos=0.977 for M>1). Fixed but the real issue was NVFP4,
|
||
not RoPE.
|
||
|
||
The verdict: NVFP4's 4.5 effective bits per element is simply too coarse for
|
||
compressed KV values that get summed in attention softmax. FP8_E4M3's 5.3 effective
|
||
bits gives cos=0.9997 round-trip (vs NVFP4's 0.995) — that 0.4% difference compounds
|
||
fatally across 61 layers.
|
||
|
||
So we settled on FP8_E4M3 for non-RoPE + BF16 for RoPE — exactly what DeepSeek V4
|
||
ships in production. Not because we couldn't build the NVFP4 path (we did, it compiled
|
||
and ran), but because the math didn't hold up. Sometimes 4 bits isn't enough.
|
||
|
||
If Blackwell adds a finer-grained FP4 variant (8-element blocks, 6 effective bits),
|
||
revisit this. The kernels exist. The quantize/dequant path is proven. The precision
|
||
just isn't there yet for attention-sensitive KV values.
|
||
|
||
Storage per compressed entry at hd=512:
|
||
nope (448) × FP8 = 448 bytes + 4 bytes (scale) = 452
|
||
rope (64) × BF16 = 128 bytes
|
||
Total = 580 bytes vs 1024 bytes BF16 → 1.76× savings
|
||
"""
|
||
def __init__(self, head_dim, window_size=128, max_comp=65536, device='cuda:0',
|
||
indexer_key_dim=128, compress_ratio=4, indexer_top_k=1024, rope_dim=64):
|
||
self.hd, self.ws, self.dev = head_dim, window_size, device
|
||
self.idx_key_dim = indexer_key_dim
|
||
self.ratio = compress_ratio
|
||
self.max_comp = max_comp
|
||
self.rope_dim = rope_dim
|
||
self.nope_dim = head_dim - rope_dim # 448
|
||
|
||
# SWA: BF16 (small, fits in L2)
|
||
self.swa = torch.zeros(window_size, head_dim, dtype=torch.bfloat16, device=device)
|
||
self.swa_pos = torch.zeros(window_size, dtype=torch.long, device=device)
|
||
self.swa_len, self.swa_head = 0, 0
|
||
|
||
# Compressed KV: mixed FP8 (nope) + BF16 (rope)
|
||
self.comp_nope_fp8 = torch.zeros(max_comp, self.nope_dim, dtype=torch.uint8, device=device)
|
||
self.comp_nope_scale = torch.zeros(max_comp, dtype=torch.float32, device=device)
|
||
self.comp_rope_bf16 = torch.zeros(max_comp, rope_dim, dtype=torch.bfloat16, device=device)
|
||
self.comp_pos_buf = torch.zeros(max_comp, dtype=torch.long, device=device)
|
||
|
||
# Indexer compressed keys: FP8_E4M3
|
||
self.comp_idx_fp8 = torch.zeros(max_comp, indexer_key_dim, dtype=torch.uint8, device=device)
|
||
self.comp_idx_scale = torch.zeros(max_comp, dtype=torch.float32, device=device)
|
||
|
||
# Pre-allocated mixed gather buffers.
|
||
# CSA needs top_k + SWA; HCA is dense over compressed blocks, so it needs
|
||
# max_comp + SWA. These buffers preserve the paper/native storage layout:
|
||
# noPE stays FP8_E4M3 + scale, RoPE stays BF16.
|
||
if compress_ratio > 4:
|
||
self.mixed_gather_cap = max_comp + window_size
|
||
elif compress_ratio == 4:
|
||
self.mixed_gather_cap = indexer_top_k + window_size
|
||
else:
|
||
self.mixed_gather_cap = window_size
|
||
self.gather_nope_fp8 = torch.zeros(self.mixed_gather_cap, self.nope_dim, dtype=torch.uint8, device=device)
|
||
self.gather_nope_scale = torch.zeros(self.mixed_gather_cap, dtype=torch.float32, device=device)
|
||
self.gather_rope_bf16 = torch.zeros(self.mixed_gather_cap, rope_dim, dtype=torch.bfloat16, device=device)
|
||
|
||
# Legacy BF16 gather buffer kept only for non-B1 experiments; the live
|
||
# B1 path below does not materialize noPE KV as global BF16.
|
||
self.gather_buf = torch.zeros(indexer_top_k + window_size, head_dim, dtype=torch.bfloat16, device=device)
|
||
self.n_comp = 0
|
||
self._has_idx = False
|
||
|
||
# Cache extension modules (loaded once)
|
||
self._kv_quant_mod = None
|
||
self._fp8_attn_io_mod = None
|
||
|
||
def _get_kv_quant_mod(self):
|
||
if self._kv_quant_mod is None:
|
||
from dsv4.kernels.cuda.loader import get_cuda_module
|
||
self._kv_quant_mod = get_cuda_module("kv_quantize", ["kv_quantize.cu"])
|
||
return self._kv_quant_mod
|
||
|
||
def _get_fp8_attn_io_mod(self):
|
||
if self._fp8_attn_io_mod is None:
|
||
from dsv4.kernels.cuda.loader import get_cuda_module
|
||
self._fp8_attn_io_mod = get_cuda_module(
|
||
"fp8_attention_io", ["fp8_attention_io.cu"],
|
||
extra_cuda_cflags=[
|
||
"-gencode=arch=compute_100a,code=sm_100a",
|
||
"-O3", "--use_fast_math", "--expt-relaxed-constexpr",
|
||
],
|
||
)
|
||
return self._fp8_attn_io_mod
|
||
|
||
def append_swa(self, kv, pos):
|
||
"""Vectorized SWA append — 2 kernel launches instead of 2T."""
|
||
T = kv.shape[0]
|
||
idx = (self.swa_head + torch.arange(T, device=self.dev)) % self.ws
|
||
self.swa.index_copy_(0, idx, kv)
|
||
self.swa_pos.index_copy_(0, idx, pos)
|
||
self.swa_head = (self.swa_head + T) % self.ws
|
||
self.swa_len = min(self.swa_len + T, self.ws)
|
||
|
||
def set_compressed_mixed(self, nope_fp8, nope_scale, rope_bf16, comp_pos=None):
|
||
"""Add compressed KV entries (mixed FP8 nope + BF16 rope)."""
|
||
T = nope_fp8.shape[0]
|
||
end = self.n_comp
|
||
self.comp_nope_fp8[end:end+T] = nope_fp8.view(torch.uint8) if nope_fp8.dtype != torch.uint8 else nope_fp8
|
||
self.comp_nope_scale[end:end+T] = nope_scale
|
||
self.comp_rope_bf16[end:end+T] = rope_bf16
|
||
if comp_pos is not None:
|
||
self.comp_pos_buf[end:end+T] = comp_pos
|
||
self.n_comp = end + T
|
||
|
||
def set_indexer_keys_fp8(self, idx_kv):
|
||
"""Add indexer compressed keys. idx_kv is BF16 (n_comp, ihd) or FP8 (fp8, scale)."""
|
||
if idx_kv is None: return
|
||
T = idx_kv[0].shape[0] if isinstance(idx_kv, tuple) else idx_kv.shape[0]
|
||
end = self.n_comp - T
|
||
if isinstance(idx_kv, tuple) and len(idx_kv) == 2:
|
||
fp8, scale = idx_kv
|
||
self.comp_idx_fp8[end:end+T] = fp8.view(torch.uint8) if fp8.dtype != torch.uint8 else fp8
|
||
self.comp_idx_scale[end:end+T] = scale
|
||
elif isinstance(idx_kv, torch.Tensor):
|
||
mod = self._get_kv_quant_mod()
|
||
fp8, scale = mod.quantize_fp8_e4m3_from_fp32(idx_kv.float().contiguous())
|
||
self.comp_idx_fp8[end:end+T] = fp8.view(torch.uint8)
|
||
self.comp_idx_scale[end:end+T] = scale
|
||
self._has_idx = True
|
||
|
||
def comp_nope_selective(self, indices):
|
||
"""Dequant FP8 nope for selected entries → BF16."""
|
||
mod = self._get_kv_quant_mod()
|
||
return mod.dequant_fp8_e4m3_selective(
|
||
self.comp_nope_fp8, self.comp_nope_scale, indices.int())
|
||
|
||
def comp_rope_selective(self, indices):
|
||
"""Gather BF16 rope for selected entries."""
|
||
return self.comp_rope_bf16[indices.long()]
|
||
|
||
@property
|
||
def comp_nope_all(self):
|
||
"""Dequant all FP8 nope → BF16."""
|
||
mod = self._get_kv_quant_mod()
|
||
return mod.dequant_fp8_e4m3(
|
||
self.comp_nope_fp8[:self.n_comp],
|
||
self.comp_nope_scale[:self.n_comp])
|
||
|
||
@property
|
||
def comp_rope_all(self):
|
||
"""Return all BF16 rope entries."""
|
||
return self.comp_rope_bf16[:self.n_comp]
|
||
|
||
@property
|
||
def comp_pos(self):
|
||
return self.comp_pos_buf[:self.n_comp] if self.n_comp > 0 else None
|
||
|
||
@property
|
||
def comp_idx_kv(self):
|
||
"""Dequant FP8 indexer keys → BF16 for scoring."""
|
||
if not self._has_idx or self.n_comp == 0: return None
|
||
mod = self._get_kv_quant_mod()
|
||
return mod.dequant_fp8_e4m3(
|
||
self.comp_idx_fp8[:self.n_comp],
|
||
self.comp_idx_scale[:self.n_comp])
|
||
|
||
def gather_mixed_selective(self, indices):
|
||
"""Gather selected compressed KV + SWA into mixed FP8/BF16 buffers.
|
||
|
||
Returns (nope_fp8, nope_scale, rope_bf16), each sliced to total length.
|
||
noPE is not dequantized to global BF16.
|
||
"""
|
||
mod = self._get_fp8_attn_io_mod()
|
||
swa_kv, _ = self.get_swa()
|
||
idx = indices.int().contiguous()
|
||
total = idx.numel() + swa_kv.shape[0]
|
||
if total > self.mixed_gather_cap:
|
||
raise RuntimeError(f"mixed gather capacity {self.mixed_gather_cap} < requested {total}")
|
||
mod.gather_mixed_selective_(
|
||
self.comp_nope_fp8, self.comp_nope_scale, self.comp_rope_bf16,
|
||
swa_kv, idx, self.gather_nope_fp8, self.gather_nope_scale, self.gather_rope_bf16)
|
||
return (self.gather_nope_fp8[:total],
|
||
self.gather_nope_scale[:total],
|
||
self.gather_rope_bf16[:total])
|
||
|
||
def gather_mixed_all(self):
|
||
"""Gather all compressed KV + SWA in mixed FP8/BF16 storage for HCA."""
|
||
mod = self._get_fp8_attn_io_mod()
|
||
swa_kv, _ = self.get_swa()
|
||
n_comp = int(self.n_comp)
|
||
total = n_comp + swa_kv.shape[0]
|
||
if total > self.mixed_gather_cap:
|
||
raise RuntimeError(f"mixed gather capacity {self.mixed_gather_cap} < requested {total}")
|
||
mod.gather_mixed_all_(
|
||
self.comp_nope_fp8[:n_comp], self.comp_nope_scale[:n_comp], self.comp_rope_bf16[:n_comp],
|
||
swa_kv, self.gather_nope_fp8, self.gather_nope_scale, self.gather_rope_bf16)
|
||
return (self.gather_nope_fp8[:total],
|
||
self.gather_nope_scale[:total],
|
||
self.gather_rope_bf16[:total])
|
||
|
||
def gather_mixed_swa_only(self):
|
||
"""Quantize SWA noPE tail to FP8 and keep SWA RoPE as BF16."""
|
||
mod = self._get_fp8_attn_io_mod()
|
||
swa_kv, _ = self.get_swa()
|
||
total = swa_kv.shape[0]
|
||
if total > self.mixed_gather_cap:
|
||
raise RuntimeError(f"mixed gather capacity {self.mixed_gather_cap} < requested {total}")
|
||
mod.gather_mixed_swa_only_(
|
||
swa_kv, self.gather_nope_fp8, self.gather_nope_scale, self.gather_rope_bf16, self.rope_dim)
|
||
return (self.gather_nope_fp8[:total],
|
||
self.gather_nope_scale[:total],
|
||
self.gather_rope_bf16[:total])
|
||
|
||
def get_swa(self):
|
||
"""Return SWA KV and positions as views (no clone)."""
|
||
if self.swa_len == 0:
|
||
return self.swa[:0], self.swa_pos[:0]
|
||
if self.swa_len < self.ws:
|
||
return self.swa[:self.swa_len], self.swa_pos[:self.swa_len]
|
||
idx = torch.arange(self.swa_head, self.swa_head + self.ws, device=self.dev) % self.ws
|
||
return self.swa[idx], self.swa_pos[idx]
|
||
|
||
# =====================================================================
|
||
# HcHead
|
||
# =====================================================================
|
||
HC_EPS = 1e-6
|
||
class HcHead:
|
||
def __init__(self, hidden_dim=7168, n_hc=4, device='cuda:0'):
|
||
self.K, self.device, self.n_hc = n_hc * hidden_dim, device, n_hc
|
||
def load(self, fn, base, scale=None):
|
||
self.fn = fn.to(self.device, torch.float32).contiguous()
|
||
self.base = base.to(self.device, torch.float32).contiguous()
|
||
self.scale = scale.to(self.device, torch.float32).item() if scale is not None else 1.0
|
||
def forward(self, X):
|
||
T = X.shape[0]; Xn = unweighted_rmsnorm(X.reshape(T, self.K).bfloat16())
|
||
mix = F.linear(Xn, self.fn[:self.n_hc]).float()
|
||
pre = torch.sigmoid(mix * self.scale + self.base[:self.n_hc].unsqueeze(0)) + HC_EPS
|
||
return (pre.unsqueeze(-1) * X.float()).sum(1).bfloat16()
|
||
|
||
# =====================================================================
|
||
# Production FMHA
|
||
# =====================================================================
|
||
def _run_production_fmha(q_heads, all_kv, n_h, hd, T, seq_len, scale, dev, li, w, pfx):
|
||
from dsv4.kernels.attention.production import dsv4_attention
|
||
# Head-packed dispatch: single kernel launch for all 128 heads (MQA: 1 KV head shared)
|
||
q = q_heads.permute(1, 0, 2).contiguous() # (n_h, T, hd)
|
||
k = all_kv.unsqueeze(0).contiguous() # (1, N, hd) — MQA single KV head
|
||
# K and V are the same in MQA — V = K transposed to (hd, N) format.
|
||
# .transpose(-1,-2).contiguous() creates a new tensor (no clone needed).
|
||
# This saves one full KV copy (~256KB per layer per decode step).
|
||
v = k
|
||
sinks = w.get(f"{pfx}.sinks"); sink_bias = None
|
||
if sinks is not None: sink_bias = sinks.to(device=dev).float().reshape(n_h)
|
||
attn_out = dsv4_attention(q=q, k=k, v=v, scale=scale, n_comp=0, sink_bias=sink_bias)
|
||
return attn_out.permute(1, 0, 2) # (T, n_h, hd)
|
||
|
||
|
||
def _run_production_fmha_mixed(q_heads, kv_nope_fp8, kv_nope_scale, kv_rope_bf16,
|
||
n_h, hd, T, seq_len, scale, dev, li, w, pfx, rope_dim):
|
||
"""B1 storage-native mixed FP8/BF16 decode FMHA. No BF16 KV staging."""
|
||
if T != 1:
|
||
raise RuntimeError(f"B1 mixed FP8 FMHA is decode-only (T==1); got T={T}")
|
||
from dsv4.kernels.attention.production import dsv4_attention_mixed_fp8_decode
|
||
q = q_heads.permute(1, 0, 2).contiguous() # (n_h, 1, hd)
|
||
sinks = w.get(f"{pfx}.sinks"); sink_bias = None
|
||
if sinks is not None:
|
||
sink_bias = sinks.to(device=dev).float().reshape(n_h)
|
||
attn_out = dsv4_attention_mixed_fp8_decode(
|
||
q=q,
|
||
k_nope_fp8=kv_nope_fp8,
|
||
k_nope_scale=kv_nope_scale,
|
||
k_rope_bf16=kv_rope_bf16,
|
||
scale=scale,
|
||
sink_bias=sink_bias,
|
||
rope_dim=rope_dim,
|
||
)
|
||
return attn_out.permute(1, 0, 2) # (T, n_h, hd)
|
||
|
||
# =====================================================================
|
||
# Attention — ALL production kernels
|
||
# =====================================================================
|
||
def forward_attention(x_normed, w, li, cfg, rope_cos, rope_sin,
|
||
kv_cache, positions, compressor, indexer, prod_lin,
|
||
x_quant=None,
|
||
_profile_detail=False, _profile_times=None):
|
||
dev = x_normed.device; T = x_normed.shape[0]
|
||
n_h = cfg["num_attention_heads"]; hd = cfg["head_dim"]; rd = cfg.get("qk_rope_head_dim", 64)
|
||
o_groups = cfg.get("o_groups", 16); o_rank = cfg.get("o_lora_rank", 1024)
|
||
ratio = compressor.ratio if compressor is not None else 0
|
||
scale = 1.0 / math.sqrt(hd); pfx = f"model.layers.{li}.self_attn"
|
||
nope_dim = hd - rd # 448 — used by both compress and gather
|
||
if positions.device != rope_cos.device: positions = positions.to(rope_cos.device)
|
||
|
||
def _pt(tag):
|
||
"""Profile timing helper — records CUDA-sync'd timestamp."""
|
||
if _profile_detail and _profile_times is not None:
|
||
torch.cuda.synchronize()
|
||
_profile_times.append((tag, li, time.perf_counter()))
|
||
|
||
_pt('q_a_start')
|
||
# 1. Q: q_a (NVFP4 GEMM) → q_a_norm → q_b (NVFP4 GEMM) → q_b_norm
|
||
q_a = prod_lin['q_a'].run_from_quantized(x_quant) if x_quant is not None else prod_lin['q_a'](x_normed)
|
||
_pt('q_a_end')
|
||
if VERBOSE >= 2 and li < 3:
|
||
# Compare q_a with PyTorch reference
|
||
q_a_ref = do_nvfp4_linear_ref(x_normed, w, pfx, 'q_a_proj')
|
||
if q_a_ref is not None:
|
||
cos_qa = torch.nn.functional.cosine_similarity(q_a.flatten().float(), q_a_ref.flatten().float(), dim=0).item()
|
||
print(f" L{li} q_a: |prod|={q_a.abs().max().item():.6f} |ref|={q_a_ref.abs().max().item():.6f} cos={cos_qa:.6f}", flush=True)
|
||
q_norm_w = w.get(f"{pfx}.q_a_norm.weight")
|
||
# B3: Fused rmsnorm+quant for q_a_norm → q_b path
|
||
# Replaces: rmsnorm(q_a, w) → BF16 → q_b quantizes internally
|
||
# With: fused rmsnorm+NVFP4 quantize → QuantizedActivation → q_b.run_from_quantized
|
||
# Saves: ~6 kernel launches per layer (rmsnorm 4+ + quantize 2 vs fused 2)
|
||
if q_norm_w is not None:
|
||
from dsv4.ops.quantize import rmsnorm_quantize_nvfp4 as _rmsnorm_quantize, dequantize_nvfp4 as _dequantize_nvfp4
|
||
q_a_quant = _rmsnorm_quantize(q_a, q_norm_w.to(dev, torch.float32))
|
||
q_a = _dequantize_nvfp4(q_a_quant.x_fp4, q_a_quant.x_sf, q_a_quant.gsa)
|
||
_pt('q_b_start')
|
||
if q_norm_w is not None:
|
||
q = prod_lin['q_b'].run_from_quantized(q_a_quant)
|
||
else:
|
||
q = prod_lin['q_b'](q_a)
|
||
q = unweighted_rmsnorm(q).bfloat16()
|
||
_pt('q_b_end')
|
||
q_heads = q.reshape(T, n_h, hd); q_heads = _apply_rope(q_heads, positions, rope_cos, rope_sin, rd)
|
||
_pt('rope_q_end')
|
||
|
||
# 2. KV (NVFP4 GEMM, MQA, single KV head)
|
||
_pt('kv_start')
|
||
kv = prod_lin['kv'].run_from_quantized(x_quant) if x_quant is not None else prod_lin['kv'](x_normed)
|
||
_pt('kv_end')
|
||
kv_norm_w = w.get(f"{pfx}.kv_norm.weight")
|
||
if kv_norm_w is not None: kv = rmsnorm(kv, kv_norm_w.to(dev, torch.float32))
|
||
kv_3d = kv.reshape(T, 1, hd); kv_3d = _apply_rope(kv_3d, positions, rope_cos, rope_sin, rd)
|
||
_pt('rope_kv_end')
|
||
kv_roped = kv_3d.reshape(T, hd); kv_cache.append_swa(kv_roped, positions)
|
||
|
||
# 3. Compressor → compressed KV (mixed storage: FP8 + BF16 RoPE)
|
||
# DeepSeek V4 paper: "BF16 for RoPE dims, FP8 for remaining dims"
|
||
_pt('compress_start')
|
||
comp_pos, block_bias = None, None; comp_idx_kv = None
|
||
if compressor is not None and compressor.ratio > 0:
|
||
comp_kv_fp32, comp_pos, block_bias = compressor.forward(x_normed, positions)
|
||
if comp_kv_fp32 is not None:
|
||
from dsv4.kernels.cuda.loader import get_cuda_module
|
||
kv_mod = get_cuda_module("kv_quantize", ["kv_quantize.cu"])
|
||
# Split into non-RoPE (FP8) and RoPE (BF16) parts
|
||
nope_fp32 = comp_kv_fp32[:, :nope_dim].contiguous() # (n_comp, 448) FP32
|
||
rope_bf16 = comp_kv_fp32[:, nope_dim:].bfloat16().contiguous() # (n_comp, 64) BF16
|
||
# Apply RoPE on BF16 rope dims (existing BF16 RoPE kernel)
|
||
rope_3d = rope_bf16.unsqueeze(1) # (n_comp, 1, 64)
|
||
rope_3d = _apply_rope(rope_3d, comp_pos, rope_cos, rope_sin, rd)
|
||
rope_bf16 = rope_3d.squeeze(1) # (n_comp, 64) BF16
|
||
# Quantize non-RoPE part FP32 → FP8_E4M3
|
||
nope_fp8, nope_scale = kv_mod.quantize_fp8_e4m3_from_fp32(nope_fp32)
|
||
# Store mixed-format compressed KV + positions
|
||
kv_cache.set_compressed_mixed(nope_fp8, nope_scale, rope_bf16, comp_pos)
|
||
if compressor.is_csa and indexer is not None and indexer.compressor is not None:
|
||
comp_idx_kv, _, _ = indexer.compressor.forward(x_normed, positions)
|
||
# Indexer keys: FP8_E4M3 (ihd=128, no RoPE)
|
||
kv_cache.set_indexer_keys_fp8(comp_idx_kv)
|
||
_pt('compress_end')
|
||
|
||
# 4. Indexer top-k (CSA)
|
||
topk_idx = None
|
||
if indexer is not None and ratio == 4:
|
||
topk_idx = indexer.forward(q_a, x_normed, kv_cache, positions, layer_idx=li)
|
||
|
||
# 5. Gather KV — B1 storage-native mixed path.
|
||
# noPE remains FP8_E4M3 + per-row scale; RoPE remains BF16.
|
||
# There is no global FP8->BF16 noPE materialization before FMHA.
|
||
_pt('gather_start')
|
||
swa_kv, _swa_pos = kv_cache.get_swa()
|
||
swa_len = swa_kv.shape[0]
|
||
if kv_cache.n_comp > 0:
|
||
if ratio == 4:
|
||
# CSA: gather top-k compressed rows + SWA tail without dequantizing noPE.
|
||
assert topk_idx is not None, f"CSA layer {li}: indexer returned no top-k — indexer is broken"
|
||
tk = topk_idx[0].clamp(0, kv_cache.n_comp - 1).int()
|
||
kv_nope_fp8, kv_nope_scale, kv_rope_bf16 = kv_cache.gather_mixed_selective(tk)
|
||
elif ratio > 4:
|
||
# HCA: dense over compressed rows, still mixed storage.
|
||
kv_nope_fp8, kv_nope_scale, kv_rope_bf16 = kv_cache.gather_mixed_all()
|
||
else:
|
||
kv_nope_fp8, kv_nope_scale, kv_rope_bf16 = kv_cache.gather_mixed_swa_only()
|
||
else:
|
||
kv_nope_fp8, kv_nope_scale, kv_rope_bf16 = kv_cache.gather_mixed_swa_only()
|
||
seq_len = kv_nope_scale.shape[0]
|
||
if seq_len == 0: return torch.zeros(T, cfg["hidden_size"], dtype=torch.bfloat16, device=dev), q_a
|
||
|
||
# 6. Production FMHA — B1 mixed FP8/BF16 decode path.
|
||
_pt('fmha_start')
|
||
if VERBOSE >= 2 and li < 3:
|
||
print(f" L{li} FMHA mixed input: T={T} seq_len={seq_len} hd={hd} n_h={n_h} n_comp={kv_cache.n_comp} swa_len={swa_len}", flush=True)
|
||
attn_out = _run_production_fmha_mixed(
|
||
q_heads, kv_nope_fp8, kv_nope_scale, kv_rope_bf16,
|
||
n_h, hd, T, seq_len, scale, dev, li, w, pfx, rd)
|
||
_pt('fmha_end')
|
||
if VERBOSE >= 2 and li < 3:
|
||
print(f" L{li} FMHA mixed: |prod|={attn_out.abs().max().item():.6f} (reference disabled: B1 forbids global BF16 KV staging)", flush=True)
|
||
# 7. Inverse RoPE
|
||
_pt('inv_rope_start')
|
||
attn_out = _apply_rope(attn_out, positions, rope_cos, rope_sin, rd, inverse=True)
|
||
_pt('inv_rope_end')
|
||
|
||
# 8. Output: wo_a (NVFP4 grouped GEMM) + wo_b (NVFP4 GEMM)
|
||
_pt('o_proj_start')
|
||
wo_a_lin = prod_lin.get('o_a')
|
||
if wo_a_lin is not None:
|
||
# Nvfp4GroupedLinear: (T, n_h, hd) → (T, n_groups, o_rank) → flatten for o_b
|
||
g_3d = wo_a_lin.run(attn_out) # (T, n_groups, o_rank) BF16
|
||
g_flat = g_3d.reshape(T, -1) # (T, n_groups * o_rank) BF16
|
||
F_attn = prod_lin['o_b'](g_flat)
|
||
else:
|
||
# BF16 grouped BMM fallback (should not happen in production)
|
||
hpg_fb = n_h // o_groups; gid_fb = hpg_fb * hd
|
||
oa_full = w.get(f"{pfx}.o_a_proj.weight")
|
||
if oa_full is not None:
|
||
oa_bf = oa_full.bfloat16().to(dev); a_flat = attn_out.reshape(T, n_h * hd)
|
||
a_grp = a_flat.reshape(T, o_groups, gid_fb); oa_3d = oa_bf.reshape(o_groups, o_rank, gid_fb)
|
||
g_out = torch.bmm(a_grp.permute(1, 0, 2), oa_3d.transpose(1, 2))
|
||
g_flat = g_out.permute(1, 0, 2).reshape(T, o_groups * o_rank)
|
||
F_attn = prod_lin['o_b'](g_flat)
|
||
else:
|
||
log.warning(f"L{li}: No o_a_proj weight, zero attention output")
|
||
F_attn = torch.zeros(T, cfg["hidden_size"], dtype=torch.bfloat16, device=dev)
|
||
_pt('o_proj_end')
|
||
if VERBOSE >= 2 and li < 3:
|
||
print(f" L{li} F_attn: |F_attn|={F_attn.abs().max().item():.6f}", flush=True)
|
||
return F_attn, q_a
|
||
|
||
# =====================================================================
|
||
# MoE — production kernels
|
||
# =====================================================================
|
||
def moe_forward(x, li, moe_runner, se_runner, router, token_id):
|
||
# Ensure token_id is on same GPU as router
|
||
token_id_dev = token_id.to(x.device) if token_id.device != x.device else token_id
|
||
topk_w, topk_ids = router(x, token_ids=token_id_dev)
|
||
# DEBUG: check topk_ids validity (only for first 3 and last 3 layers)
|
||
if VERBOSE >= 2 and (li < 3 or li >= 58):
|
||
if topk_ids.max().item() >= 384 or topk_ids.min().item() < 0:
|
||
print(f" L{li} BAD topk_ids: min={topk_ids.min().item()} max={topk_ids.max().item()}", flush=True)
|
||
if VERBOSE >= 2 and li >= 58:
|
||
print(f" L{li} MoE DIAG: topk_ids={topk_ids[0].tolist()} topk_w=[{','.join(f'{w:.3f}' for w in topk_w[0].tolist())}]", flush=True)
|
||
# Also print gate logits for debugging
|
||
if hasattr(router, '_gate_lin') and router._gate_lin is not None:
|
||
gate_logits = router._gate_lin(x).float()
|
||
print(f" L{li} gate logits: [{gate_logits.min().item():.3f}, {gate_logits.max().item():.3f}] mean={gate_logits.mean().item():.3f}", flush=True)
|
||
if VERBOSE >= 2 and li < 3:
|
||
print(f" L{li} MoE input: |x|={x.abs().max().item():.4f} has_nan={torch.isnan(x).any().item()}", flush=True)
|
||
routed_out = moe_runner.run(x, topk_w, topk_ids)
|
||
shared_out = se_runner.run(x)
|
||
if VERBOSE >= 2 and li >= 58:
|
||
print(f" L{li} MoE DIAG: |routed|={routed_out.abs().max().item():.1f} |shared|={shared_out.abs().max().item():.1f} |x|={x.abs().max().item():.1f}", flush=True)
|
||
if VERBOSE >= 2 and li < 3:
|
||
has_nan = torch.isnan(shared_out).any().item()
|
||
out_max = shared_out.abs().max().item() if not has_nan else float('nan')
|
||
print(f" L{li} MoE shared: |out|={out_max:.4f} has_nan={has_nan}", flush=True)
|
||
# Check weight integrity
|
||
if hasattr(se_runner, '_l1_mat_b') and se_runner._l1_mat_b is not None:
|
||
wb = se_runner._l1_mat_b.view(torch.uint8)
|
||
print(f" L{li} SE l1 weight: shape={list(se_runner._l1_mat_b.shape)} dtype={se_runner._l1_mat_b.dtype} uint8_range=[{wb.min().item()},{wb.max().item()}]", flush=True)
|
||
if hasattr(se_runner, '_l1_scale_b') and se_runner._l1_scale_b is not None:
|
||
sb = se_runner._l1_scale_b.float()
|
||
print(f" L{li} SE l1 scale: shape={list(se_runner._l1_scale_b.shape)} dtype={se_runner._l1_scale_b.dtype} float_range=[{sb.min().item():.6f},{sb.max().item():.6f}] has_nan={torch.isnan(sb).any().item()}", flush=True)
|
||
print(f" L{li} SE gsa: l1={se_runner._l1_activation_global_scale:.6f} l2={se_runner._l2_activation_global_scale:.6f} gsb: l1={se_runner._l1_gsb[0].item():.6f} l2={se_runner._l2_gsb[0].item():.6f}", flush=True)
|
||
return routed_out + shared_out
|
||
|
||
# =====================================================================
|
||
# Layer forward
|
||
# =====================================================================
|
||
def forward_layer(X_l, w, li, cfg, rope_cos, rope_sin,
|
||
attn_mhc, ffn_mhc, attn_norm_w, ffn_norm_w,
|
||
kv_cache, positions, token_id,
|
||
compressor=None, indexer=None,
|
||
moe_runner=None, se_runner=None, router=None,
|
||
prod_lin=None, _profile_detail=False, _profile_times=None,
|
||
_use_fused_rmsnorm_quantize=True,
|
||
):
|
||
"""Forward one transformer layer.
|
||
"""
|
||
# P5: Fused mHC pre_block + RMSNorm + NVFP4 quantize
|
||
# Replaces: pre_block (bmm) + rmsnorm (~4 launches) + quantize (2 launches)
|
||
# With: 2 kernel launches total (mhc_rmsnorm_amax_gsa + mhc_rmsnorm_quantize_nvfp4)
|
||
# Savings: ~5 launches per site × 2 sites × 61 layers = 610 launches/token
|
||
from dsv4.ops.quantize import (
|
||
rmsnorm_quantize_nvfp4, mhc_rmsnorm_quantize_nvfp4,
|
||
QuantizedActivation, dequantize_nvfp4,
|
||
)
|
||
from dsv4.layers.mhc import mHCContext
|
||
|
||
# Attention mHC: fused pre_block + rmsnorm + NVFP4 quantize
|
||
A_l_a, B_l_a, C_l_a = attn_mhc._dynamic_params(X_l)
|
||
ctx_a = mHCContext(B_l=B_l_a, C_l=C_l_a)
|
||
|
||
if _use_fused_rmsnorm_quantize:
|
||
# P5 fused: X_l + A_l → bmm + rmsnorm + NVFP4 quantize in 2 kernel launches
|
||
x_quant_attn = mhc_rmsnorm_quantize_nvfp4(
|
||
X_l, A_l_a, attn_norm_w.to(X_l.device, torch.float32))
|
||
# Dequantize for compressor/indexer (1 kernel launch)
|
||
x_normed = dequantize_nvfp4(x_quant_attn.x_fp4, x_quant_attn.x_sf, x_quant_attn.gsa)
|
||
else:
|
||
x_in = torch.bmm(A_l_a.unsqueeze(1).float(), X_l.float()).squeeze(1).bfloat16()
|
||
x_normed = rmsnorm(x_in, attn_norm_w)
|
||
x_quant_attn = None
|
||
|
||
if _profile_detail: torch.cuda.synchronize(); t_attn0 = time.perf_counter()
|
||
F_attn, _ = forward_attention(x_normed, w, li, cfg, rope_cos, rope_sin,
|
||
kv_cache, positions, compressor, indexer, prod_lin,
|
||
x_quant=x_quant_attn,
|
||
_profile_detail=_profile_detail, _profile_times=_profile_times)
|
||
if _profile_detail: torch.cuda.synchronize(); t_attn1 = time.perf_counter()
|
||
X_mid = attn_mhc.post_block(X_l, F_attn, ctx_a)
|
||
|
||
# FFN mHC: fused pre_block + rmsnorm + NVFP4 quantize
|
||
A_l_f, B_l_f, C_l_f = ffn_mhc._dynamic_params(X_mid)
|
||
ctx_f = mHCContext(B_l=B_l_f, C_l=C_l_f)
|
||
|
||
if _use_fused_rmsnorm_quantize:
|
||
# P5 fused: X_mid + A_l → bmm + rmsnorm + NVFP4 quantize in 2 kernel launches
|
||
x_quant_ffn = mhc_rmsnorm_quantize_nvfp4(
|
||
X_mid, A_l_f, ffn_norm_w.to(X_mid.device, torch.float32))
|
||
# Dequantize for MoE (BF16 input required by MoE quantize path)
|
||
x_ffn = dequantize_nvfp4(x_quant_ffn.x_fp4, x_quant_ffn.x_sf, x_quant_ffn.gsa)
|
||
else:
|
||
x_in_f = torch.bmm(A_l_f.unsqueeze(1).float(), X_mid.float()).squeeze(1).bfloat16()
|
||
x_ffn = rmsnorm(x_in_f, ffn_norm_w)
|
||
if _profile_detail: torch.cuda.synchronize(); t_ffn0 = time.perf_counter()
|
||
F_ffn = moe_forward(x_ffn, li, moe_runner, se_runner, router, token_id)
|
||
if _profile_detail: torch.cuda.synchronize(); t_ffn1 = time.perf_counter()
|
||
X_next = ffn_mhc.post_block(X_mid, F_ffn, ctx_f)
|
||
if VERBOSE >= 2 and (li < 3 or li >= 58):
|
||
print(f" L{li}: |X|={X_l.abs().max().item():.1f}->{X_next.abs().max().item():.1f} "
|
||
f"|Fa|={F_attn.abs().max().item():.1f} |Ff|={F_ffn.abs().max().item():.1f}", flush=True)
|
||
# Detailed diagnostics — only with VERBOSE >= 2 to avoid .item() syncs on hot path
|
||
if VERBOSE >= 2 and (li >= 58 or (li > 0 and X_next.abs().max().item() > 200)):
|
||
A_a, B_a, C_a = attn_mhc._dynamic_params(X_l)
|
||
A_f, B_f, C_f = ffn_mhc._dynamic_params(X_mid)
|
||
print(f" L{li} DIAG: A_attn=[{A_a.min().item():.4f},{A_a.max().item():.4f}] "
|
||
f"C_attn=[{C_a.min().item():.4f},{C_a.max().item():.4f}] "
|
||
f"A_ffn=[{A_f.min().item():.4f},{A_f.max().item():.4f}] "
|
||
f"C_ffn=[{C_f.min().item():.4f},{C_f.max().item():.4f}]", flush=True)
|
||
print(f" L{li} DIAG: B_attn row_sum=[{B_a.sum(-1).min().item():.4f},{B_a.sum(-1).max().item():.4f}] "
|
||
f"col_sum=[{B_a.sum(-2).min().item():.4f},{B_a.sum(-2).max().item():.4f}] "
|
||
f"B_ffn row_sum=[{B_f.sum(-1).min().item():.4f},{B_f.sum(-1).max().item():.4f}] "
|
||
f"col_sum=[{B_f.sum(-2).min().item():.4f},{B_f.sum(-2).max().item():.4f}]", flush=True)
|
||
print(f" L{li} DIAG: |x_in_attn|={x_in.abs().max().item():.1f} "
|
||
f"|x_in_ffn|={x_in_f.abs().max().item():.1f} "
|
||
f"|X_l|={X_l.abs().max().item():.1f} "
|
||
f"|X_mid|={X_mid.abs().max().item():.1f} "
|
||
f"|X_next|={X_next.abs().max().item():.1f}", flush=True)
|
||
if _profile_detail and (li < 3 or li == 30 or li >= 58):
|
||
torch.cuda.synchronize()
|
||
attn_ms = (t_attn1 - t_attn0) * 1000
|
||
ffn_ms = (t_ffn1 - t_ffn0) * 1000
|
||
print(f" L{li}: attn={attn_ms:.2f}ms ffn={ffn_ms:.2f}ms", flush=True)
|
||
return X_next
|
||
|
||
# =====================================================================
|
||
# MoE weight loading
|
||
# =====================================================================
|
||
def _load_moe_weights_stacked(all_w, li, pfx, dev, moe, cfg):
|
||
n_e = cfg["n_routed_experts"]
|
||
l1_fp4_list, l1_sf_list, l1_gs_list, l1_ws2_list, l1_gsa_list = [], [], [], [], []
|
||
l2_fp4_list, l2_sf_list, l2_gs_list, l2_ws2_list, l2_gsa_list = [], [], [], [], []
|
||
for eid in range(n_e):
|
||
ep = f"{pfx}.experts.{eid}"
|
||
gw, gws, gws2, gisc = get_nvfp4_weight(all_w, ep, 'gate_proj')
|
||
uw, uws, uws2, uisc = get_nvfp4_weight(all_w, ep, 'up_proj')
|
||
if gw is not None and uw is not None:
|
||
l1_fp4_list.append(torch.cat([gw, uw], dim=0).to(dev))
|
||
if gws is not None and uws is not None: l1_sf_list.append(torch.cat([gws, uws], dim=0).to(dev))
|
||
gs = gisc.float().item() if gisc is not None else 1.0 / (6.0 * 448.0)
|
||
l1_gs_list.append(1.0) # gsb base — ws2 will be folded in by _ensure_stacked
|
||
l1_gsa_list.append(gs) # gsa = input_scale
|
||
# weight_scale_2: scalar, folded into global_scale_b
|
||
l1_ws2_list.append(gws2.to(dev) if gws2 is not None else None)
|
||
dw, dws, dws2, disc = get_nvfp4_weight(all_w, ep, 'down_proj')
|
||
if dw is not None:
|
||
l2_fp4_list.append(dw.to(dev))
|
||
if dws is not None: l2_sf_list.append(dws.to(dev))
|
||
gs2 = disc.float().item() if disc is not None else 1.0 / (6.0 * 448.0)
|
||
l2_gs_list.append(1.0) # gsb base
|
||
l2_gsa_list.append(gs2) # gsa = input_scale
|
||
l2_ws2_list.append(dws2.to(dev) if dws2 is not None else None)
|
||
if not l1_fp4_list: log.warning(f"L{li}: No expert weights found"); return
|
||
l1_stacked = torch.stack(l1_fp4_list).to(dev)
|
||
l1_sf_stacked = torch.stack(l1_sf_list).to(dev) if l1_sf_list else None
|
||
l2_stacked = torch.stack(l2_fp4_list).to(dev) if l2_fp4_list else None
|
||
l2_sf_stacked = torch.stack(l2_sf_list).to(dev) if l2_sf_list else None
|
||
del l1_fp4_list, l1_sf_list, l2_fp4_list, l2_sf_list
|
||
moe.prepare_weights_from_stacked(l1_stacked, l1_sf_stacked, l1_gs_list, l2_stacked, l2_sf_stacked, l2_gs_list)
|
||
# Save activation global scales — _ensure_stacked will override them from l1_gs (which is 1.0)
|
||
# We must re-set them AFTER _ensure_stacked
|
||
moe._saved_l1_gsa = l1_gsa_list[0] if l1_gsa_list else 1.0 / (6.0 * 448.0)
|
||
moe._saved_l2_gsa = l2_gsa_list[0] if l2_gsa_list else 1.0 / (6.0 * 448.0)
|
||
moe.l1_ws2 = l1_ws2_list
|
||
moe.l2_ws2 = l2_ws2_list
|
||
|
||
def _load_shared_expert_weights(all_w, li, pfx, dev, se, cfg):
|
||
gw, gws, gws2, gisc = get_nvfp4_weight(all_w, f"{pfx}.shared_experts", 'gate_proj')
|
||
uw, uws, uws2, uisc = get_nvfp4_weight(all_w, f"{pfx}.shared_experts", 'up_proj')
|
||
dw, dws, dws2, disc = get_nvfp4_weight(all_w, f"{pfx}.shared_experts", 'down_proj')
|
||
if gw is not None and uw is not None:
|
||
se.l1_fp4 = [torch.cat([gw, uw], dim=0).to(dev)]
|
||
se.l1_sf = [torch.cat([gws, uws], dim=0).to(dev)] if gws is not None and uws is not None else [torch.zeros(1, device=dev, dtype=torch.float8_e4m3fn)]
|
||
l1_isc = gisc.float().item() if gisc is not None else 1.0 / (6.0 * 448.0)
|
||
se.l1_gs = [1.0] # gsb base — ws2 will be folded in by finalize_weights
|
||
se.l1_ws2 = [gws2.to(dev) if gws2 is not None else None]
|
||
se._l1_activation_global_scale = l1_isc # Will be overridden by _ensure_initialized
|
||
se._saved_l1_gsa = l1_isc # Save for after _ensure_initialized
|
||
if dw is not None:
|
||
se.l2_fp4 = [dw.to(dev)]
|
||
se.l2_sf = [dws.to(dev)] if dws is not None else [torch.zeros(1, device=dev, dtype=torch.float8_e4m3fn)]
|
||
l2_isc = disc.float().item() if disc is not None else 1.0 / (6.0 * 448.0)
|
||
se.l2_gs = [1.0] # gsb base
|
||
se.l2_ws2 = [dws2.to(dev) if dws2 is not None else None]
|
||
se._l2_activation_global_scale = l2_isc # Will be overridden by _ensure_initialized
|
||
se._saved_l2_gsa = l2_isc # Save for after _ensure_initialized
|
||
|
||
def _cache_layer_weights_no_experts(all_w, n_layers, devices):
|
||
cached = {}
|
||
for li in range(n_layers):
|
||
dev = devices[li % len(devices)]; pfx = f"model.layers.{li}."
|
||
w = {k: v.to(device=dev, non_blocking=True) for k, v in all_w.items()
|
||
if k.startswith(pfx) and '.experts.' not in k and '.shared_experts.' not in k}
|
||
cached[li] = w
|
||
if (li+1) % 10 == 0: log.info(f" Cached {li+1}/{n_layers} layers")
|
||
return cached
|
||
|
||
# =====================================================================
|
||
# Main
|
||
# =====================================================================
|
||
def kill_stale_gpu_processes():
|
||
"""Kill any leftover python processes on all GPUs before starting."""
|
||
import subprocess
|
||
try:
|
||
result = subprocess.run(['nvidia-smi', '--query-compute-apps=pid', '--format=csv,noheader'],
|
||
capture_output=True, text=True, timeout=5)
|
||
if result.returncode == 0 and result.stdout.strip():
|
||
pids = [p.strip() for p in result.stdout.strip().split('\n') if p.strip()]
|
||
for pid in pids:
|
||
try:
|
||
import os; os.kill(int(pid), 9)
|
||
log.info(f" Killed stale GPU process {pid}")
|
||
except (ValueError, ProcessLookupError):
|
||
pass
|
||
except Exception as e:
|
||
log.warning(f"Could not check GPU processes: {e}")
|
||
|
||
def main():
|
||
t0 = time.time(); torch.manual_seed(SEED)
|
||
print("=" * 70)
|
||
print("DSV4 Single-Shot Inference - PRODUCTION KERNEL STACK")
|
||
print(" FMHA: 6-warp TMA multi-tile + sink bias")
|
||
print(" NVFP4 GEMM (CuTeDSL) for ALL projections")
|
||
print(" Production MoE + Router | Production mHC")
|
||
print(" NO PyTorch SDPA | NO dequant+matmul | NO reference fallback")
|
||
print("=" * 70)
|
||
|
||
with open(os.path.join(CHECKPOINT_DIR, "config.json")) as f:
|
||
cfg = json.load(f)
|
||
n_layers = cfg["num_hidden_layers"]; H = cfg["hidden_size"]
|
||
hd = cfg["head_dim"]; n_h = cfg["num_attention_heads"]
|
||
rd = cfg.get("qk_rope_head_dim", 64)
|
||
cr = cfg.get("compress_ratios", [128] * n_layers)
|
||
o_groups = cfg.get("o_groups", 16); o_rank = cfg.get("o_lora_rank", 1024)
|
||
print(f"Model: {n_layers} layers, {n_h} heads, hd={hd}, rope_dim={rd}")
|
||
print(f"Compress ratios: first5={cr[:5]} len={len(cr)}")
|
||
print(f"Experts: {cfg['n_routed_experts']}, top-{cfg.get('num_experts_per_tok', 6)}")
|
||
|
||
# ---- Phase 1: Load weights ----
|
||
print(f"\nPhase 1: Loading weights..."); all_w = load_all_weights(CHECKPOINT_DIR)
|
||
print(f" {time.time()-t0:.1f}s")
|
||
|
||
# ---- Phase 2: Build production components ----
|
||
print("Building production components...")
|
||
from dsv4.layers.mhc import mHCLayer
|
||
from dsv4.layers.router import Router
|
||
from dsv4.layers.moe import Nvfp4MoE
|
||
from dsv4.layers.shared_expert import Nvfp4SharedExpert
|
||
|
||
# Kill stale GPU processes from prior runs (OOM, crash, etc.)
|
||
kill_stale_gpu_processes()
|
||
for g in range(NUM_GPUS): torch.cuda.set_device(g); torch.cuda.empty_cache()
|
||
torch.cuda.set_device(0)
|
||
|
||
# mHC + norms
|
||
attn_mhcs, ffn_mhcs, attn_norms, ffn_norms = {}, {}, {}, {}
|
||
for li in range(n_layers):
|
||
dev = f"cuda:{li % NUM_GPUS}"
|
||
for tag, blocks, fn_s, base_s, scale_s in [
|
||
("attn", attn_mhcs, f"model.layers.{li}.attn_hc.fn", f"model.layers.{li}.attn_hc.base", f"model.layers.{li}.attn_hc.scale"),
|
||
("ffn", ffn_mhcs, f"model.layers.{li}.ffn_hc.fn", f"model.layers.{li}.ffn_hc.base", f"model.layers.{li}.ffn_hc.scale"),
|
||
]:
|
||
fn, base, scale = all_w.get(fn_s), all_w.get(base_s), all_w.get(scale_s)
|
||
if fn is not None and base is not None and scale is not None:
|
||
m = mHCLayer(hidden_dim=H, n_hc=4, t_max_sinkhorn=20, device=dev)
|
||
n = 4
|
||
m.load_weights(
|
||
W_pre=fn[0:n].to(dev, torch.float32), W_post=fn[n:2*n].to(dev, torch.float32),
|
||
W_comb=fn[2*n:].to(dev, torch.float32),
|
||
S_pre=base[0:n].reshape(1, n).to(dev, torch.float32),
|
||
S_post=base[n:2*n].reshape(n, 1).to(dev, torch.float32),
|
||
S_comb=base[2*n:].reshape(n, n).to(dev, torch.float32),
|
||
alpha_pre=scale[0].item(), alpha_post=scale[1].item(), alpha_comb=scale[2].item(),
|
||
)
|
||
blocks[li] = m
|
||
an_k = f"model.layers.{li}.input_layernorm.weight"
|
||
if an_k in all_w: attn_norms[li] = all_w[an_k].to(dev, torch.float32)
|
||
fn_k = f"model.layers.{li}.post_attention_layernorm.weight"
|
||
if fn_k in all_w: ffn_norms[li] = all_w[fn_k].to(dev, torch.float32)
|
||
|
||
# Production Nvfp4Linear for attention projections
|
||
print(" Building production Nvfp4Linear for attention projections...")
|
||
prod_lins = {}
|
||
# Weight dimensions (from checkpoint):
|
||
# q_a_proj: (1536, 3584) uint8 -> in=7168, out=1536
|
||
# q_b_proj: (65536, 768) uint8 -> in=1536, out=65536
|
||
# kv_proj: (512, 3584) uint8 -> in=7168, out=512
|
||
# o_a_proj: (16384, 4096) BF16 -> Nvfp4GroupedLinear (16 groups, 1024×4096 each)
|
||
# o_b_proj: (7168, 8192) uint8 -> in=16384, out=7168
|
||
from dsv4.layers.grouped_linear import Nvfp4GroupedLinear
|
||
for li in range(n_layers):
|
||
dev = f"cuda:{li % NUM_GPUS}"; pfx = f"model.layers.{li}.self_attn"
|
||
torch.cuda.set_device(li % NUM_GPUS)
|
||
pl = {}
|
||
pl['q_a'] = make_nvfp4_linear(7168, 1536, dev, all_w, pfx, 'q_a_proj')
|
||
pl['q_b'] = make_nvfp4_linear(1536, 65536, dev, all_w, pfx, 'q_b_proj')
|
||
pl['kv'] = make_nvfp4_linear(7168, 512, dev, all_w, pfx, 'kv_proj')
|
||
# o_a_proj: Nvfp4GroupedLinear (NVFP4 grouped GEMM)
|
||
n_local_groups = cfg.get('o_groups', 16)
|
||
heads_per_group = n_h // n_local_groups
|
||
o_rank_val = cfg.get('o_lora_rank', 1024)
|
||
wo_a = Nvfp4GroupedLinear(
|
||
n_local_groups=n_local_groups,
|
||
heads_per_group=heads_per_group,
|
||
head_dim=hd,
|
||
o_lora_rank=o_rank_val,
|
||
max_num_tokens=8192,
|
||
device=dev,
|
||
)
|
||
oa_w_nvfp4, oa_ws, oa_ws2, oa_isc = get_nvfp4_weight(all_w, pfx, 'o_a_proj')
|
||
if oa_w_nvfp4 is not None and oa_ws is not None:
|
||
# Checkpoint has NVFP4 weights — load directly (no dequant/re-quant)
|
||
wo_a.load_nvfp4_weight(oa_w_nvfp4.to(dev), oa_ws.to(dev),
|
||
oa_ws2.to(dev) if oa_ws2 is not None else None,
|
||
oa_isc.to(dev) if oa_isc is not None else None)
|
||
else:
|
||
# BF16 checkpoint weight
|
||
oa_bf = all_w.get(f"{pfx}.o_a_proj.weight")
|
||
if oa_bf is not None:
|
||
wo_a.set_bf16_weight(oa_bf.bfloat16().to(dev))
|
||
pl['o_a'] = wo_a
|
||
wo_a._use_runtime_gsa = True # compute gsa from actual input to avoid E4M3 overflow
|
||
pl['o_b'] = make_nvfp4_linear(16384, 7168, dev, all_w, pfx, 'o_b_proj')
|
||
prod_lins[li] = pl
|
||
if (li+1) % 10 == 0: print(f" {li+1}/{n_layers} layers")
|
||
print(" All attention projections: production NVFP4 GEMM (o_a now NVFP4 grouped)")
|
||
|
||
# Routers, MoE, shared experts
|
||
routers, moe_runners, se_runners = {}, {}, {}
|
||
for li in range(n_layers):
|
||
dev = f"cuda:{li % NUM_GPUS}"; pfx = f"model.layers.{li}.mlp"
|
||
torch.cuda.set_device(li % NUM_GPUS); torch.cuda.synchronize()
|
||
is_hash = (li < cfg.get("num_hash_layers", 3)) and (f"{pfx}.gate.tid2eid" in all_w)
|
||
router = Router(hidden_size=H, num_experts=cfg["n_routed_experts"],
|
||
top_k=cfg.get("num_experts_per_tok", 6),
|
||
routed_scaling_factor=cfg.get("routed_scaling_factor", 2.5),
|
||
mode="hash" if is_hash else "dense",
|
||
vocab_size=cfg.get("vocab_size", 128000) if is_hash else None, device=dev)
|
||
if is_hash:
|
||
router.load_weights(hash_lut=all_w[f"{pfx}.gate.tid2eid"].to(dev, torch.int32))
|
||
else:
|
||
eb = all_w.get(f"{pfx}.gate.e_score_correction_bias")
|
||
# NVFP4 production GEMM for router gate
|
||
# Custom CuTeDSL fused kernel crashes MLIR optimizer,
|
||
# so we use Nvfp4Linear (proven production path).
|
||
from dsv4.layers.linear import Nvfp4Linear
|
||
gate_w, gate_ws, gate_ws2, gate_isc = get_nvfp4_weight(all_w, pfx, 'gate')
|
||
E = cfg["n_routed_experts"]
|
||
if gate_w is not None and gate_ws is not None:
|
||
# Checkpoint has NVFP4 gate weight (N_packed, K_packed) — correct layout
|
||
gate_lin = Nvfp4Linear(in_features=H, out_features=E, device=dev)
|
||
gate_w_view = gate_w.to(dev).view(torch.float4_e2m1fn_x2) if gate_w.dtype == torch.uint8 else gate_w.to(dev)
|
||
gate_lin.fp4 = [gate_w_view]
|
||
gate_lin.sf = [gate_ws.to(dev)]
|
||
ws2_v = gate_ws2.float().item() if gate_ws2 is not None else 1.0
|
||
isc_v = gate_isc.float().item() if gate_isc is not None else 1.0/(6.0*448.0)
|
||
gate_lin.gs = [1.0]
|
||
gate_lin.ws2 = [torch.tensor([ws2_v], device=dev, dtype=torch.float32)]
|
||
gate_lin._activation_global_scale = isc_v # placeholder — runtime gsa overrides this
|
||
gate_lin._use_runtime_gsa = True # compute gsa from actual input to avoid E4M3 overflow
|
||
gate_lin.finalize_weights()
|
||
router.load_nvfp4_gate(gate_lin)
|
||
router.load_weights(e_bias=eb.to(dev, torch.float32))
|
||
if li < 5: print(f" L{li}: NVFP4 router gate (checkpoint)", flush=True)
|
||
else:
|
||
# BF16 gate weight: quantize to NVFP4
|
||
gw = all_w.get(f"{pfx}.gate.weight")
|
||
if gw is not None:
|
||
g_bf16 = gw if gw.shape == (E, H) else gw.T.contiguous()
|
||
g_bf16 = g_bf16.bfloat16().to(dev)
|
||
from dsv4.ops.quantize import quantize_to_nvfp4
|
||
g_fp4, g_sf, g_gs = quantize_to_nvfp4(g_bf16)
|
||
gate_lin = Nvfp4Linear(in_features=H, out_features=E, device=dev)
|
||
gate_lin.fp4 = [g_fp4]
|
||
gate_lin.sf = [g_sf]
|
||
gate_lin.gs = [g_gs]
|
||
gate_lin.ws2 = [torch.tensor([g_gs], device=dev, dtype=torch.float32)]
|
||
gate_lin._activation_global_scale = 1.0 / (6.0 * 448.0) # placeholder — runtime gsa overrides
|
||
gate_lin._use_runtime_gsa = True # compute gsa from actual input to avoid E4M3 overflow
|
||
gate_lin.finalize_weights()
|
||
router.load_nvfp4_gate(gate_lin)
|
||
router.load_weights(e_bias=eb.to(dev, torch.float32))
|
||
if li < 5: print(f" L{li}: NVFP4 router gate (quantized, gs={g_gs:.6f})", flush=True)
|
||
else:
|
||
router.load_weights(e_bias=eb.to(dev, torch.float32))
|
||
router.load_weights(e_bias=eb.to(dev, torch.float32))
|
||
router.finalize_weights(); routers[li] = router
|
||
|
||
moe = Nvfp4MoE(num_experts=cfg["n_routed_experts"], hidden_size=H,
|
||
intermediate_size=cfg.get("moe_intermediate_size", 3072),
|
||
top_k=cfg.get("num_experts_per_tok", 6), device=dev)
|
||
moe.set_swiglu_limit(cfg.get("swiglu_limit", 10.0))
|
||
# P0: ENABLE fused SwiGLU — NVFP4 GEMM + SiLU in kernel registers.
|
||
# Saves 240+ unfused BF16 kernel launches per token (gate_silu, clamp, mul, quantize).
|
||
moe.set_fused_swiglu(True)
|
||
_load_moe_weights_stacked(all_w, li, pfx, dev, moe, cfg)
|
||
# EAGERLY process stacked weights → K-major + swizzle, free raw tensors
|
||
moe._ensure_stacked()
|
||
# Fix activation global scales — _ensure_stacked sets gsa from l1_gs (which is 1.0)
|
||
# FIX: Do NOT use checkpoint input_scale as gsa — causes E4M3 overflow.
|
||
# Instead, compute gsa at runtime from actual activation magnitude.
|
||
# The MoE runner's compute_activation_global_scales() does this correctly.
|
||
# We enable runtime gsa for both MoE and SharedExpert.
|
||
moe._use_runtime_gsa = True
|
||
moe_runners[li] = moe
|
||
|
||
se = Nvfp4SharedExpert(hidden_size=H, intermediate_size=cfg.get("moe_intermediate_size", 3072),
|
||
device=dev, swiglu_limit=cfg.get("swiglu_limit", 10.0))
|
||
_load_shared_expert_weights(all_w, li, pfx, dev, se, cfg)
|
||
# P1: ENABLE fused SwiGLU for shared expert (1-group variant of MoE fused kernel)
|
||
se.set_fused_swiglu(True)
|
||
# EAGERLY process shared expert weights
|
||
se._ensure_initialized()
|
||
# P1: Eagerly warmup fused SwiGLU compilation for SE (1-group)
|
||
if se._fused_swiglu:
|
||
from dsv4.ops.gemm_runner import warmup_fused_swiglu_compilation
|
||
K_packed = H // 2
|
||
N_packed_l1 = (2 * cfg.get("moe_intermediate_size", 3072)) // 2 # gate+up
|
||
warmup_fused_swiglu_compilation(
|
||
1, K_packed, N_packed_l1, dev,
|
||
swiglu_limit=cfg.get("swiglu_limit", 10.0),
|
||
)
|
||
# Fix activation global scales — _ensure_initialized sets gsa from l1_gs (which is 1.0)
|
||
# FIX: Same runtime gsa for SharedExpert
|
||
se._use_runtime_gsa = True
|
||
se_runners[li] = se
|
||
if (li+1) % 10 == 0: print(f" Built {li+1}/{n_layers} MoE layers")
|
||
torch.cuda.empty_cache()
|
||
|
||
# Global weights
|
||
torch.cuda.set_device(0)
|
||
embed_w = all_w.get("model.embed_tokens.weight")
|
||
embed = torch.nn.Embedding.from_pretrained(embed_w.bfloat16().to('cuda:0'))
|
||
# lm_head: NVFP4 production GEMM
|
||
lm_w_raw = all_w.get("lm_head.weight", embed_w).bfloat16().to('cuda:0')
|
||
from dsv4.layers.linear import Nvfp4Linear
|
||
lm_head_lin = Nvfp4Linear(lm_w_raw.shape[1], lm_w_raw.shape[0], max_num_tokens=8192, device='cuda:0')
|
||
from dsv4.ops.quantize import quantize_weight_to_nvfp4
|
||
lm_fp4, lm_sf, lm_gs = quantize_weight_to_nvfp4(lm_w_raw.T.contiguous())
|
||
lm_head_lin.fp4 = [lm_fp4.permute(1, 0).contiguous()]
|
||
lm_head_lin.sf = [lm_sf.permute(1, 0).contiguous()]
|
||
lm_head_lin.gs = [lm_gs]
|
||
lm_head_lin.ws2 = [None]
|
||
lm_head_lin._activation_global_scale = 1.0 / (6.0 * 448.0)
|
||
lm_head_lin._use_runtime_gsa = True
|
||
lm_head_lin.finalize_weights()
|
||
lm_w = None
|
||
print(" lm_head: NVFP4 production GEMM")
|
||
final_norm_w = all_w.get("model.norm.weight")
|
||
if final_norm_w is not None: final_norm_w = final_norm_w.to('cuda:0', torch.float32)
|
||
|
||
hc_head = HcHead(H, 4, 'cuda:0')
|
||
hc_fn = all_w.get("model.hc_head.hc_fn"); hc_base = all_w.get("model.hc_head.hc_base"); hc_scale = all_w.get("model.hc_head.hc_scale")
|
||
if hc_fn is not None and hc_base is not None: hc_head.load(hc_fn, hc_base, hc_scale); print(" hc_head loaded")
|
||
|
||
# RoPE (FP32)
|
||
rp = cfg.get("rope_scaling", cfg.get("rope_parameters", {}))
|
||
rt = rp.get("type", rp.get("rope_type", "yarn")); rf = rp.get("factor", 16.0)
|
||
rtheta = cfg.get("rope_theta", 10000.); romax = rp.get("original_max_position_embeddings", 65536)
|
||
rbfast, rbslow = rp.get("beta_fast", 32), rp.get("beta_slow", 1)
|
||
rope_caches = {g: build_rope_cache(romax, rd, f"cuda:{g}", rtheta, rt, rf, romax, rbfast, rbslow) for g in range(NUM_GPUS)}
|
||
|
||
# KV caches, compressors, indexers
|
||
kv_caches, compressors, indexers = {}, {}, {}
|
||
n_ih = cfg.get("index_n_heads", 64); ihd = cfg.get("index_head_dim", 128); itk = cfg.get("index_topk", 1024)
|
||
max_ctx = _args.max_context
|
||
print(f" Max context: {max_ctx} tokens (governs KV cache pre-allocation)")
|
||
for li in range(n_layers):
|
||
dev = f"cuda:{li % NUM_GPUS}"; ratio = cr[li] if li < len(cr) else 128
|
||
# C1: max_comp derived from target context and compress ratio
|
||
max_comp = (max_ctx + ratio - 1) // ratio if ratio > 0 else 0
|
||
kv_caches[li] = KVCache(hd, cfg.get("sliding_window", 128), max_comp=max_comp, device=dev,
|
||
indexer_key_dim=ihd, compress_ratio=ratio, indexer_top_k=itk, rope_dim=rd)
|
||
if ratio > 0: compressors[li] = Compressor(ratio, hd, H, dev)
|
||
if ratio == 4: indexers[li] = Indexer(n_ih, ihd, itk, dev)
|
||
|
||
# Cache layer weights (no MoE/SE)
|
||
print("Caching layer weights to GPUs (excluding MoE expert weights)...")
|
||
devs = [f"cuda:{g}" for g in range(NUM_GPUS)]
|
||
layer_w = _cache_layer_weights_no_experts(all_w, n_layers, devs)
|
||
del all_w; import gc; gc.collect()
|
||
for g in range(NUM_GPUS): torch.cuda.set_device(g); torch.cuda.empty_cache()
|
||
torch.cuda.set_device(0)
|
||
print(f" {time.time()-t0:.1f}s")
|
||
|
||
# Load compressor/indexer weights
|
||
for li in range(n_layers):
|
||
pfx = f"model.layers.{li}.self_attn.compressor"
|
||
if li in compressors: compressors[li].load(layer_w[li], pfx, dev=f"cuda:{li % NUM_GPUS}")
|
||
if li in indexers: indexers[li].load(layer_w[li], f"{pfx}.indexer", dev=f"cuda:{li % NUM_GPUS}")
|
||
print(" Compressors/indexers loaded")
|
||
|
||
# ---- Phase 3: Inference ----
|
||
print(f"\nPhase 3: Inference")
|
||
from transformers import AutoTokenizer
|
||
tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_DIR)
|
||
|
||
# A1: Build explicit stop set — DSV4 uses special turn-end tokens beyond eos
|
||
STOP_IDS = set()
|
||
eos_id = tokenizer.eos_token_id
|
||
if eos_id is not None:
|
||
STOP_IDS.add(eos_id)
|
||
for tok_name in ("<|end_of_sentence|>",):
|
||
tid = tokenizer.convert_tokens_to_ids(tok_name)
|
||
if tid is not None and tid >= 0 and tid != tokenizer.unk_token_id:
|
||
STOP_IDS.add(tid)
|
||
# If model emits USER_TOKEN it's trying to open a new user turn = it's done
|
||
STOP_IDS.add(USER_TOKEN)
|
||
print(f" Stop set: {STOP_IDS} (eos={eos_id}, eos_token={tokenizer.eos_token})")
|
||
print(f" Special tokens: {tokenizer.special_tokens_map}")
|
||
print(f" THINK_START={THINK_START} THINK_END={THINK_END} USER={USER_TOKEN} ASST={ASSISTANT_TOKEN}")
|
||
|
||
bos = tokenizer.bos_token_id or 0
|
||
if _args.prefill_tokens:
|
||
generated = [int(x) for x in _args.prefill_tokens.split(',')]
|
||
else:
|
||
input_ids = [bos, USER_TOKEN]
|
||
input_ids += tokenizer.encode('\n\n' + PROMPT, add_special_tokens=False)
|
||
input_ids.append(ASSISTANT_TOKEN)
|
||
# DSV4 reasoning model: must prime with ◇ (think_start) after Assistant token.
|
||
# Without this, the model is out-of-distribution — it expects to be inside a
|
||
# thinking block but never received the think-start sentinel.
|
||
# Symptom: degenerate output from step 0 (e.g. "France" instead of "Paris",
|
||
# looping on newlines/repeated tokens). With ◇, the model generates thinking
|
||
# content, emits ◇ (think_end), then produces the actual answer.
|
||
input_ids.append(THINK_START)
|
||
generated = input_ids
|
||
all_tokens = generated.copy()
|
||
print(f"Input: {len(generated)} tokens")
|
||
|
||
# Prefill — one token at a time (decode-style; TODO: batched prefill)
|
||
print(f"Prefilling {len(generated)} tokens...")
|
||
# Pre-allocate prefill buffers — no per-step torch.tensor()
|
||
pre_tid_buf = torch.zeros(1, dtype=torch.long, device='cuda:0')
|
||
pre_tid32_buf = torch.zeros(1, dtype=torch.int32, device='cuda:0')
|
||
pre_pos_buf = torch.zeros(1, dtype=torch.long, device='cuda:0')
|
||
for pi, tid_val in enumerate(generated):
|
||
t1 = time.time()
|
||
pre_tid_buf[0] = tid_val
|
||
pre_tid32_buf[0] = tid_val
|
||
pre_pos_buf[0] = pi
|
||
X = mHCLayer.init_state(embed(pre_tid_buf))
|
||
for li in range(n_layers):
|
||
gpu = li % NUM_GPUS
|
||
if X.device != torch.device(f"cuda:{gpu}"): X = X.to(f"cuda:{gpu}")
|
||
torch.cuda.set_device(gpu)
|
||
try:
|
||
X = forward_layer(X, layer_w[li], li, cfg, *rope_caches[gpu],
|
||
attn_mhcs.get(li), ffn_mhcs.get(li),
|
||
attn_norms.get(li), ffn_norms.get(li),
|
||
kv_caches[li], pre_pos_buf, pre_tid32_buf,
|
||
compressors.get(li), indexers.get(li),
|
||
moe_runners.get(li), se_runners.get(li), routers.get(li),
|
||
prod_lin=prod_lins.get(li),
|
||
_use_fused_rmsnorm_quantize=not _args.no_fused_rmsnorm,
|
||
)
|
||
except Exception as e:
|
||
torch.cuda.synchronize()
|
||
err = torch.cuda.current_stream(gpu).query()
|
||
print(f" CRASH at token {pi} layer {li} gpu {gpu}: {e}", flush=True)
|
||
raise
|
||
if VERBOSE >= 2 and pi == 0 and li < 3:
|
||
torch.cuda.synchronize(gpu)
|
||
print(f" Token {pi} L{li}: OK |X|={X.abs().max().item():.1f}", flush=True)
|
||
X = X.to('cuda:0'); torch.cuda.set_device(0)
|
||
if pi % 10 == 0: print(f" Token {pi}/{len(generated)}: {time.time()-t1:.2f}s", flush=True)
|
||
print(f" Prefill done ({time.time()-t0:.1f}s)")
|
||
|
||
if _args.prefill_only: print("Prefill-only mode, stopping."); return
|
||
|
||
# ---- Build sampler ----
|
||
from dsv4.model.sampler import CUDASampler
|
||
sampler = CUDASampler(device='cuda:0', max_penalty_tokens=256)
|
||
sample_temp = _args.temperature
|
||
sample_topk = _args.top_k
|
||
sample_topp = _args.top_p
|
||
sample_rep_pen = _args.repetition_penalty
|
||
is_greedy = (sample_temp == 0.0)
|
||
print(f" Sampler: temp={sample_temp} top_k={sample_topk} top_p={sample_topp} "
|
||
f"rep_pen={sample_rep_pen} greedy={is_greedy}")
|
||
print(f" DSV4 reasoning model: thinking_start={THINK_START} thinking_end={THINK_END}")
|
||
print(f" Thinking tokens are NOT garbage — model uses ）、... format")
|
||
|
||
# Pre-allocate decode buffers — zero per-step allocation
|
||
dec_tid_buf = torch.zeros(1, dtype=torch.long, device='cuda:0')
|
||
dec_pos_buf = torch.zeros(1, dtype=torch.long, device='cuda:0')
|
||
dec_tid32_buf = torch.zeros(1, dtype=torch.int32, device='cuda:0')
|
||
|
||
# Decode
|
||
print(f"\nDecoding (max {MAX_NEW_TOKENS} tokens)...")
|
||
in_thinking = False
|
||
profile = _args.profile
|
||
warmup_gsa = _args.warmup_gsa
|
||
prof_embed_layers = 0.0
|
||
prof_lm_head = 0.0
|
||
prof_sample = 0.0
|
||
prof_sample_start = 0.0
|
||
|
||
# CUDA event profiling — measures ACTUAL GPU time, not wall clock
|
||
# Only profile steps 1-3 (after warmup) to get stable results
|
||
cuda_events = {}
|
||
if profile:
|
||
for tag in ['embed', 'layers', 'hc_norm_lm', 'sample', 'diagnostics']:
|
||
cuda_events[tag] = (torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True))
|
||
# Per-layer category events (sampled on step 1 only)
|
||
layer_event_tags = ['mhc_pre', 'attn_proj', 'rope_kv', 'compress_idx', 'fmha', 'inv_rope', 'o_proj',
|
||
'mhc_post', 'mhc_pre_ffn', 'router', 'moe', 'shared_expert', 'mhc_post_ffn']
|
||
cuda_layer_events = {}
|
||
for tag in layer_event_tags:
|
||
cuda_layer_events[tag] = (torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True))
|
||
layer_event_accum = {tag: 0.0 for tag in layer_event_tags}
|
||
layer_event_count = 0
|
||
cuda_layer_events = [] # list of (tag, li, timestamp) for fine-grained profiling
|
||
|
||
for step in range(MAX_NEW_TOKENS):
|
||
t1 = time.time()
|
||
dec_tid_buf[0] = all_tokens[-1]
|
||
dec_tid32_buf[0] = all_tokens[-1]
|
||
dec_pos_buf[0] = len(all_tokens) - 1
|
||
|
||
t_e = time.perf_counter()
|
||
X = mHCLayer.init_state(embed(dec_tid_buf))
|
||
for li in range(n_layers):
|
||
gpu = li % NUM_GPUS
|
||
if X.device != torch.device(f"cuda:{gpu}"): X = X.to(f"cuda:{gpu}")
|
||
torch.cuda.set_device(gpu)
|
||
X = forward_layer(X, layer_w[li], li, cfg, *rope_caches[gpu],
|
||
attn_mhcs.get(li), ffn_mhcs.get(li),
|
||
attn_norms.get(li), ffn_norms.get(li),
|
||
kv_caches[li], dec_pos_buf, dec_tid32_buf,
|
||
compressors.get(li), indexers.get(li),
|
||
moe_runners.get(li), se_runners.get(li), routers.get(li),
|
||
prod_lin=prod_lins.get(li),
|
||
_profile_detail=(profile and step == 1),
|
||
_profile_times=cuda_layer_events if (profile and step == 1) else None,
|
||
_use_fused_rmsnorm_quantize=not _args.no_fused_rmsnorm,
|
||
)
|
||
X = X.to('cuda:0'); torch.cuda.set_device(0)
|
||
t_layers = time.perf_counter()
|
||
|
||
# After first decode step: fix gsa values from runtime amax
|
||
# This eliminates amax_gsa kernel launches on subsequent steps
|
||
# Only applies to attention linears and router gate (fixed per-projection gsa)
|
||
# MoE/SE keep runtime gsa (gsa varies per token)
|
||
if warmup_gsa and step == 0:
|
||
torch.cuda.synchronize()
|
||
n_fixed = 0
|
||
for li in range(n_layers):
|
||
pl = prod_lins.get(li)
|
||
if pl is None: continue
|
||
for key, lin in pl.items():
|
||
if hasattr(lin, '_gsa_buf') and hasattr(lin, '_use_runtime_gsa') and lin._use_runtime_gsa:
|
||
fixed_gsa = lin._gsa_buf.item() # One-time sync
|
||
lin._activation_global_scale = fixed_gsa
|
||
lin._use_runtime_gsa = False
|
||
n_fixed += 1
|
||
# Router gate
|
||
router = routers.get(li)
|
||
if router and hasattr(router, '_gate_lin') and router._gate_lin is not None:
|
||
gl = router._gate_lin
|
||
if hasattr(gl, '_gsa_buf') and hasattr(gl, '_use_runtime_gsa') and gl._use_runtime_gsa:
|
||
fixed_gsa = gl._gsa_buf.item()
|
||
gl._activation_global_scale = fixed_gsa
|
||
gl._use_runtime_gsa = False
|
||
n_fixed += 1
|
||
# lm_head
|
||
if hasattr(lm_head_lin, '_gsa_buf') and hasattr(lm_head_lin, '_use_runtime_gsa') and lm_head_lin._use_runtime_gsa:
|
||
fixed_gsa = lm_head_lin._gsa_buf.item()
|
||
lm_head_lin._activation_global_scale = fixed_gsa
|
||
lm_head_lin._use_runtime_gsa = False
|
||
n_fixed += 1
|
||
print(f" Warmup gsa: fixed {n_fixed} projection gsa values from step 0 (MoE/SE keep runtime gsa)", flush=True)
|
||
x_out = hc_head.forward(X) if hc_head is not None else X[:, 0, :]
|
||
if final_norm_w is not None: x_out = rmsnorm(x_out, final_norm_w)
|
||
logits = lm_head_lin(x_out)
|
||
if profile: torch.cuda.synchronize()
|
||
t_lm = time.perf_counter()
|
||
# Check thinking start token logit on first step
|
||
if step == 0:
|
||
ls = logits.float()
|
||
for tid, name in [(THINK_START, 'think_start'), (THINK_END, 'think_end'), (USER_TOKEN, 'user'), (ASSISTANT_TOKEN, 'assistant')]:
|
||
print(f" {name}({tid}) logit={ls[0, tid].item():.2f}", flush=True)
|
||
# Paris token check — only check known token IDs, no 129K iteration
|
||
for t in [11111, 51119, 60107]:
|
||
if t < ls.shape[-1]:
|
||
print(f" Paris-candidate({t}) logit={ls[0, t].item():.2f}", flush=True)
|
||
# Sync for profiling and error check
|
||
if profile: torch.cuda.synchronize()
|
||
t_sample_start = time.perf_counter()
|
||
# Only sync + validate on first 3 steps and every 20th step (reduces pipeline stalls)
|
||
if step < 3 or (step + 1) % 20 == 0:
|
||
torch.cuda.synchronize() # catch CUDA errors at source
|
||
ls = logits.float()
|
||
if step < 3 or (step + 1) % 20 == 0:
|
||
has_nan = torch.isnan(ls).any().item()
|
||
has_inf = torch.isinf(ls).any().item()
|
||
print(f" logits: shape={list(logits.shape)} dtype={logits.dtype} "
|
||
f"min={ls.min().item():.1f} max={ls.max().item():.1f} "
|
||
f"nan={has_nan} inf={has_inf}", flush=True)
|
||
if has_nan or has_inf:
|
||
print(f" NaN/Inf in logits at step {step}, aborting", flush=True)
|
||
break
|
||
# Sampling — fused CUDA kernel (or greedy argmax for temp=0)
|
||
if is_greedy:
|
||
next_id = torch.argmax(logits, -1).item()
|
||
else:
|
||
sampled = sampler(
|
||
logits,
|
||
temperature=sample_temp,
|
||
top_k=sample_topk,
|
||
top_p=sample_topp,
|
||
repetition_penalty=sample_rep_pen,
|
||
recent_tokens=all_tokens[-256:],
|
||
seed=SEED,
|
||
)
|
||
# Check for async CUDA errors from sampler
|
||
if step < 3:
|
||
torch.cuda.synchronize()
|
||
next_id = sampled[0].item()
|
||
|
||
all_tokens.append(next_id)
|
||
dt = time.time() - t1
|
||
|
||
if profile: torch.cuda.synchronize()
|
||
t_s = time.perf_counter()
|
||
# Track thinking state
|
||
if next_id == THINK_START: in_thinking = True
|
||
elif next_id == THINK_END: in_thinking = False
|
||
|
||
if profile:
|
||
prof_embed_layers += (t_layers - t_e)
|
||
prof_lm_head += (t_lm - t_layers)
|
||
prof_sample_start = t_sample_start
|
||
prof_sample += (t_s - t_sample_start)
|
||
|
||
# Diagnostics — every step for first 20, then every 5th
|
||
if step < 20 or step % 5 == 0:
|
||
tv, ti = torch.topk(logits[0].float(), 5)
|
||
top5 = ' '.join(f'{tokenizer.decode([t.item()])}({v.item():.1f})' for t, v in zip(ti[:5], tv[:5]))
|
||
think_tag = " [THINKING]" if in_thinking else ""
|
||
print(f" Step {step}: {next_id} '{tokenizer.decode([next_id])}' ({dt:.2f}s) "
|
||
f"logits=[{logits.float().min().item():.1f},{logits.float().max().item():.1f}] "
|
||
f"|X|={X.abs().max().item():.1f} top5: {top5}{think_tag}", flush=True)
|
||
|
||
# NaN safety — periodic check only
|
||
if step == 0 or (step+1) % 20 == 0:
|
||
if torch.isnan(logits.float()).any().item():
|
||
print(f" NaN at step {step}", flush=True); break
|
||
if next_id in STOP_IDS:
|
||
print(f" STOP ({next_id}) at step {step} — token='{tokenizer.decode([next_id])}'", flush=True); break
|
||
|
||
if profile and MAX_NEW_TOKENS > 0:
|
||
n = MAX_NEW_TOKENS
|
||
print(f"\n PROFILE (sync'd wall clock, {n} steps):")
|
||
print(f" Embed + 61 layers: {prof_embed_layers:.3f}s total, {prof_embed_layers/n*1000:.1f}ms/token")
|
||
print(f" hc_head + norm + lm_head: {prof_lm_head:.3f}s total, {prof_lm_head/n*1000:.1f}ms/token")
|
||
print(f" Sampling: {prof_sample:.3f}s total, {prof_sample/n*1000:.1f}ms/token")
|
||
|
||
# Fine-grained attention profile (from step 1)
|
||
if hasattr(cuda_layer_events, '__len__') and len(cuda_layer_events) >= 2:
|
||
print(f"\n FINE-GRAINED ATTENTION PROFILE (step 1, CUDA-sync'd):")
|
||
prev_t = None
|
||
for tag, li, t in cuda_layer_events:
|
||
if prev_t is not None:
|
||
dt_ms = (t - prev_t) * 1000
|
||
if li <= 2 or li >= 58: # Only print for first/last layers
|
||
print(f" L{li} {tag}: {dt_ms:.2f}ms")
|
||
prev_t = t
|
||
|
||
out = tokenizer.decode(all_tokens, skip_special_tokens=True)
|
||
print(f"\n{'='*70}")
|
||
print(f"Input: '{PROMPT}'")
|
||
print(f"Output: '{out}'")
|
||
print(f"Total: {time.time()-t0:.1f}s")
|
||
print(f"{'='*70}")
|
||
|
||
if __name__ == "__main__":
|
||
main()
|