biondizzle
d003c4b7cc
- Full mHC pre_block/post_block with Sinkhorn-Knopp normalization - Dynamic A_l (sigmoid), B_l (Birkhoff polytope), C_l (2*sigmoid) - Checkpoint: attn_hc.fn (24,28672) + base (24,) + scale (3,) - Two mHC blocks per layer: attn_hc + ffn_hc - Removed emergency RMSNorm — mHC handles normalization properly - X_l: (1, n_hc=4, H) residual state, init from embedding broadcast
481 lines
19 KiB
Python
481 lines
19 KiB
Python
#!/usr/bin/env python3
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"""Single-shot DSV4 inference — 8-GPU pipeline parallel with mHC.
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Loads the full NVFP4 checkpoint across 8 B200 GPUs. Includes:
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- mHC (Manifold-Constrained Hyper-Connections) — load-bearing residual
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- Q low-rank projection + KV projection
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- RoPE (partial, last 64 dims)
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- Production FMHA kernel (tcgen05 MMA, hd=512, 128 heads)
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- Output projection: wo_a (grouped BMM) → wo_b (NVFP4)
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- Shared expert FFN (SwiGLU)
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- NVFP4 dequant → BF16 matmul baseline for linear layers
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Missing (causing incorrect output):
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- Routed MoE experts (384 experts, top-6)
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- KV cache across decode steps
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- Compressor + indexer (CSA/HCA compressed KV)
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Usage (on B200):
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source /root/dsv4-nvfp4-workspace/venv/bin/activate
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cd /root/dsv4-nvfp4-workspace/kernel
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python3 single_shot_inference.py
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"""
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import os, sys, time, json, math
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import torch
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from pathlib import Path
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CHECKPOINT_DIR = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4"
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MAX_NEW_TOKENS = 10
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PROMPT = "The capital of France is"
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NUM_GPUS = 8
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# =====================================================================
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# NVFP4 dequantization
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# =====================================================================
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FP4_LUT = torch.tensor([0., 2., 3., 4., 6., 8., 12., 24.])
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def dequant_nvfp4_weight(weight, weight_scale, weight_scale_2):
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out_dim = weight.shape[0]
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in_packed = weight.shape[1]
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in_features = in_packed * 2
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low = (weight & 0x0F).to(torch.int8)
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high = (weight >> 4).to(torch.int8)
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low_sign, low_idx = (low >> 3).bool(), (low & 0x07).long()
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high_sign, high_idx = (high >> 3).bool(), (high & 0x07).long()
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lut = FP4_LUT.to(device=weight.device, dtype=torch.float32)
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low_f = lut[low_idx] * torch.where(low_sign, -1.0, 1.0)
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high_f = lut[high_idx] * torch.where(high_sign, -1.0, 1.0)
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w_f = torch.stack([low_f, high_f], dim=-1).reshape(out_dim, in_features)
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scale_f = weight_scale.float() * weight_scale_2.float()
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scale_expanded = scale_f.repeat_interleave(16, dim=1)
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return (w_f * scale_expanded).bfloat16()
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def nvfp4_linear(x, weight, weight_scale, weight_scale_2):
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w = dequant_nvfp4_weight(weight, weight_scale, weight_scale_2)
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return torch.nn.functional.linear(x, w)
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def bf16_linear(x, weight):
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return torch.nn.functional.linear(x, weight.bfloat16())
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# =====================================================================
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# mHC — Manifold-Constrained Hyper-Connections
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# =====================================================================
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def sinkhorn_knopp(M, t_max=20, eps=1e-6):
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"""Project (T, n, n) positive matrices onto Birkhoff polytope."""
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for _ in range(t_max):
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M = M / (M.sum(dim=-1, keepdim=True) + eps)
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M = M / (M.sum(dim=-2, keepdim=True) + eps)
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return M
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class mHCBlock:
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"""One mHC block (attention or FFN).
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Checkpoint weight mapping:
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fn: (24, 28672) FP32 = stacked [W_pre(4,K); W_res(16,K); W_post(4,K)]
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base: (24,) FP32 = bias, split as [S_pre(4); S_res(16); S_post(4)]
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scale: (3,) FP32 = [alpha_pre, alpha_res, alpha_post]
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"""
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def __init__(self, hidden_dim=7168, n_hc=4, t_max=20, device='cuda'):
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self.d = hidden_dim
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self.n_hc = n_hc
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self.K = n_hc * hidden_dim # 28672
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self.t_max = t_max
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self.device = device
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self.W_stacked = None # (24, K) FP32
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self.bias = None # (24,) FP32
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self.alphas = None # (3,) FP32
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def load_from_checkpoint(self, fn, base, scale):
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"""Load from checkpoint tensors. All on target device, FP32."""
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self.W_stacked = fn.to(device=self.device, dtype=torch.float32).contiguous()
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self.bias = base.to(device=self.device, dtype=torch.float32).contiguous()
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self.alphas = scale.to(device=self.device, dtype=torch.float32).contiguous()
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def _dynamic_params(self, X_l):
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"""Compute A_l, B_l, C_l from residual state.
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X_l: (T, n_hc, d) BF16
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Returns: A_l (T, n_hc), B_l (T, n_hc, n_hc) FP32, C_l (T, n_hc)
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"""
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T, n, d = X_l.shape
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n_hc = self.n_hc
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# Flatten and project with RMSNorm
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X_flat = X_l.reshape(T, self.K) # (T, K) BF16
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# RMSNorm
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x_f32 = X_flat.float()
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rms = x_f32.pow(2).mean(-1, keepdim=True).add(1e-6).rsqrt()
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x_normed = x_f32 * rms # (T, K) FP32
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# Project: (T, K) @ (24, K)^T → (T, 24)
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proj = torch.nn.functional.linear(x_normed.bfloat16(), self.W_stacked.bfloat16()).float()
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proj = proj + self.bias.unsqueeze(0) # add bias
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# Split into A, B, C
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i0, i1, i2 = n_hc, n_hc + n_hc * n_hc, 24
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A_raw = proj[:, :i0] # (T, 4)
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B_raw = proj[:, i0:i1] # (T, 16)
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C_raw = proj[:, i1:i2] # (T, 4)
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# Split bias into S_pre, S_res, S_post
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S_pre = self.bias[:n_hc]
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S_res = self.bias[n_hc:n_hc + n_hc * n_hc]
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S_post = self.bias[n_hc + n_hc * n_hc:]
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# Apply gating + biases
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a_pre, a_res, a_post = self.alphas[0], self.alphas[1], self.alphas[2]
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A_tilde = a_pre * A_raw + S_pre.unsqueeze(0)
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B_tilde = a_res * B_raw + S_res.unsqueeze(0)
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C_tilde = a_post * C_raw + S_post.unsqueeze(0)
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# Constraints
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A_l = torch.sigmoid(A_tilde).bfloat16() # (T, 4) ∈ (0,1)
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C_l = (2.0 * torch.sigmoid(C_tilde)).bfloat16() # (T, 4) ∈ (0,2)
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B_exp = torch.exp(B_tilde).reshape(T, n_hc, n_hc) # (T, 4, 4)
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B_l = sinkhorn_knopp(B_exp, self.t_max) # FP32, doubly stochastic
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return A_l, B_l, C_l
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def pre_block(self, X_l):
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"""X_l: (T, n_hc, d) → x_in: (T, d), ctx"""
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A_l, B_l, C_l = self._dynamic_params(X_l)
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# x_in = A_l @ X_l: (T, 1, n_hc) bmm (T, n_hc, d) → (T, 1, d) → (T, d)
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x_in = torch.bmm(A_l.unsqueeze(1).float(), X_l.float()).squeeze(1).bfloat16()
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return x_in, (B_l, C_l)
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def post_block(self, X_l, F_out, ctx):
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"""X_l: (T, n_hc, d), F_out: (T, d) → X_next: (T, n_hc, d)"""
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B_l, C_l = ctx
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# X_next = B_l @ X_l + C_l ⊗ F_out
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BX = torch.bmm(B_l, X_l.float()) # (T, n_hc, d) FP32
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CF = C_l.unsqueeze(-1).float() * F_out.unsqueeze(1).float() # (T, n_hc, d) FP32
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return (BX + CF).bfloat16()
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# =====================================================================
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# RoPE
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# =====================================================================
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def build_rope_cache(max_pos, head_dim, rope_dim, device, theta=10000.0):
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half = rope_dim // 2
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freqs = 1.0 / (theta ** (torch.arange(0, rope_dim, 2, dtype=torch.float32) / rope_dim))
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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, positions, cos_cache, sin_cache, rope_dim):
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T, n_h, hd = x.shape
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nope = hd - rope_dim
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cos = cos_cache[positions].unsqueeze(1).to(x.dtype)
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sin = sin_cache[positions].unsqueeze(1).to(x.dtype)
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out = x.clone()
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out[:, :, nope:][..., 0::2] = x[:, :, nope:][..., 0::2] * cos - x[:, :, nope:][..., 1::2] * sin
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out[:, :, nope:][..., 1::2] = x[:, :, nope:][..., 0::2] * sin + x[:, :, nope:][..., 1::2] * cos
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return out
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# =====================================================================
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# Weight loading
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# =====================================================================
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def load_all_weights(checkpoint_dir, num_layers):
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from safetensors.torch import load_file
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cdir = Path(checkpoint_dir)
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index_path = cdir / "model.safetensors.index.json"
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weight_map = {}
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if index_path.exists():
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with open(index_path) as f:
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weight_map = json.load(f).get("weight_map", {})
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shard_names = set(weight_map.values()) if weight_map else {
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f"model-{i:05d}-of-00095.safetensors" for i in range(1, 96)
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}
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print(f"Loading {len(shard_names)} shards...")
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all_weights = {}
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loaded = 0
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for shard_name in sorted(shard_names):
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if not (cdir / shard_name).exists():
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continue
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data = load_file(str(cdir / shard_name))
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all_weights.update(data)
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loaded += 1
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if loaded % 20 == 0:
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print(f" {loaded}/{len(shard_names)} shards, {len(all_weights)} tensors")
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print(f" Done: {len(all_weights)} tensors")
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layer_weights = {}
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global_weights = {}
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print("Assigning to GPUs...")
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for key, tensor in all_weights.items():
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if key.startswith("model.layers."):
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li = int(key.split(".")[2])
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target_gpu = li % NUM_GPUS
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target_device = f"cuda:{target_gpu}"
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if li not in layer_weights:
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layer_weights[li] = {"_device": target_device, "_gpu": target_gpu}
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layer_weights[li][key] = tensor.to(target_device)
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elif key.startswith("model.embed_tokens"):
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global_weights[key] = tensor.to("cuda:0")
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elif key.startswith("model.norm"):
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global_weights[key] = tensor.to("cuda:0")
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elif key.startswith("lm_head"):
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global_weights[key] = tensor.to("cuda:0")
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for gpu in range(NUM_GPUS):
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alloc = torch.cuda.memory_allocated(gpu) / 1e9
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print(f" GPU {gpu}: {alloc:.1f}GB")
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return layer_weights, global_weights
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# =====================================================================
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# Single layer forward
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# =====================================================================
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def forward_layer(X_l, w, li, cfg, rope_cos, rope_sin, attn_mhc, ffn_mhc):
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"""Forward one layer with mHC.
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X_l: (1, n_hc, H) BF16 → (1, n_hc, H) BF16
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"""
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device = X_l.device
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H = cfg["hidden_size"]
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n_h = cfg["num_attention_heads"]
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hd = cfg["head_dim"]
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rd = cfg["qk_rope_head_dim"]
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o_rank = cfg["o_lora_rank"]
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o_groups = cfg["o_groups"]
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n_hc = 4
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pre = f"model.layers.{li}.self_attn"
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T = X_l.shape[0]
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heads_per_group = n_h // o_groups
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group_input_dim = heads_per_group * hd
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# ==== mHC pre_block (attention) ====
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x_in, attn_ctx = attn_mhc.pre_block(X_l) # x_in: (T, H) BF16
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# ==== Q projection ====
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c_Q = nvfp4_linear(x_in, w[f"{pre}.q_a_proj.weight"],
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w[f"{pre}.q_a_proj.weight_scale"],
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w[f"{pre}.q_a_proj.weight_scale_2"])
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q = nvfp4_linear(c_Q, w[f"{pre}.q_b_proj.weight"],
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w[f"{pre}.q_b_proj.weight_scale"],
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w[f"{pre}.q_b_proj.weight_scale_2"])
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# ==== KV projection ====
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kv = nvfp4_linear(x_in, w[f"{pre}.kv_proj.weight"],
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w[f"{pre}.kv_proj.weight_scale"],
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w[f"{pre}.kv_proj.weight_scale_2"])
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# ==== Reshape for attention ====
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q_heads = q.reshape(T, n_h, hd).permute(1, 0, 2)
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k = kv.reshape(T, 1, hd).permute(1, 0, 2)
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v = k.clone()
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# ==== RoPE ====
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pos = torch.tensor([0], dtype=torch.long, device=device)
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q_heads = apply_rope(q_heads, pos, rope_cos, rope_sin, rd)
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k = apply_rope(k, pos, rope_cos, rope_sin, rd)
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# ==== FMHA ====
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from dsv4.kernels.attention.production import dsv4_attention
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attn_out = dsv4_attention(q_heads, k, v)
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attn_out = attn_out.permute(1, 0, 2).reshape(T, n_h * hd)
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# ==== Output projection ====
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attn_grouped = attn_out.reshape(T, o_groups, heads_per_group, hd).reshape(T, o_groups, group_input_dim)
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oa_w = w[f"{pre}.o_a_proj.weight"].bfloat16()
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oa_3d = oa_w.reshape(o_groups, o_rank, group_input_dim)
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attn_for_bmm = attn_grouped.permute(1, 0, 2)
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grouped_out = torch.bmm(attn_for_bmm, oa_3d.transpose(1, 2))
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grouped_flat = grouped_out.permute(1, 0, 2).reshape(T, o_groups * o_rank)
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F_attn = nvfp4_linear(grouped_flat, w[f"{pre}.o_b_proj.weight"],
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w[f"{pre}.o_b_proj.weight_scale"],
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w[f"{pre}.o_b_proj.weight_scale_2"])
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# ==== mHC post_block (attention) ====
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X_l = attn_mhc.post_block(X_l, F_attn, attn_ctx) # (T, n_hc, H)
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# ==== mHC pre_block (FFN) ====
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x_ffn, ffn_ctx = ffn_mhc.pre_block(X_l)
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# ==== FFN: shared expert ====
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se_pre = f"model.layers.{li}.mlp.shared_experts"
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se_gate_w = w.get(f"{se_pre}.gate_proj.weight")
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F_ffn = torch.zeros_like(x_ffn)
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if se_gate_w is not None:
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gate = nvfp4_linear(x_ffn, se_gate_w,
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w[f"{se_pre}.gate_proj.weight_scale"],
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w[f"{se_pre}.gate_proj.weight_scale_2"])
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up = nvfp4_linear(x_ffn, w[f"{se_pre}.up_proj.weight"],
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w[f"{se_pre}.up_proj.weight_scale"],
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w[f"{se_pre}.up_proj.weight_scale_2"])
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F_ffn = nvfp4_linear(
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torch.nn.functional.silu(gate) * up,
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w[f"{se_pre}.down_proj.weight"],
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w[f"{se_pre}.down_proj.weight_scale"],
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w[f"{se_pre}.down_proj.weight_scale_2"],
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)
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# ==== mHC post_block (FFN) ====
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X_l = ffn_mhc.post_block(X_l, F_ffn, ffn_ctx)
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return X_l
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# =====================================================================
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# Main
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# =====================================================================
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def main():
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t_start = time.time()
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print("=" * 70)
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print("DSV4 Single-Shot Inference — 8-GPU with mHC")
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print("=" * 70)
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with open(os.path.join(CHECKPOINT_DIR, "config.json")) as f:
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cfg = json.load(f)
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n_layers = cfg["num_hidden_layers"]
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H = cfg["hidden_size"]
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n_h = cfg["num_attention_heads"]
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hd = cfg["head_dim"]
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rd = cfg["qk_rope_head_dim"]
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n_hc = 4
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print(f"Model: {n_layers} layers, {n_h} heads, hd={hd}, rope_dim={rd}")
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# ==== Phase 1: Load weights ====
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print(f"\n{'='*70}\nPhase 1: Loading weights\n{'='*70}")
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layer_weights, global_weights = load_all_weights(CHECKPOINT_DIR, n_layers)
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t_loaded = time.time()
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print(f"Weight loading: {t_loaded - t_start:.1f}s")
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# ==== Build mHC blocks per layer ====
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print("Building mHC blocks...")
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attn_mhc_blocks = {}
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ffn_mhc_blocks = {}
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for li in range(n_layers):
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gpu = li % NUM_GPUS
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dev = f"cuda:{gpu}"
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# Attention mHC
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attn_fn = layer_weights[li].get(f"model.layers.{li}.attn_hc.fn")
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attn_base = layer_weights[li].get(f"model.layers.{li}.attn_hc.base")
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attn_scale = layer_weights[li].get(f"model.layers.{li}.attn_hc.scale")
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if attn_fn is not None and attn_base is not None and attn_scale is not None:
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attn_mhc = mHCBlock(hidden_dim=H, n_hc=n_hc, device=dev)
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attn_mhc.load_from_checkpoint(attn_fn, attn_base, attn_scale)
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attn_mhc_blocks[li] = attn_mhc
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# FFN mHC
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ffn_fn = layer_weights[li].get(f"model.layers.{li}.ffn_hc.fn")
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ffn_base = layer_weights[li].get(f"model.layers.{li}.ffn_hc.base")
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ffn_scale = layer_weights[li].get(f"model.layers.{li}.ffn_hc.scale")
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if ffn_fn is not None and ffn_base is not None and ffn_scale is not None:
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ffn_mhc = mHCBlock(hidden_dim=H, n_hc=n_hc, device=dev)
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ffn_mhc.load_from_checkpoint(ffn_fn, ffn_base, ffn_scale)
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ffn_mhc_blocks[li] = ffn_mhc
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print(f" attn mHC: {len(attn_mhc_blocks)} layers")
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print(f" ffn mHC: {len(ffn_mhc_blocks)} layers")
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# ==== Global weights (gpu0) ====
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torch.cuda.set_device(0)
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embed_w = global_weights.get("model.embed_tokens.weight")
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embed = torch.nn.Embedding.from_pretrained(embed_w.bfloat16())
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lm_w = global_weights.get("lm_head.weight", embed_w).bfloat16()
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final_norm_w = global_weights.get("model.norm.weight")
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|
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# RoPE caches per GPU
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rope_caches = {g: build_rope_cache(8192, hd, rd, f"cuda:{g}") for g in range(NUM_GPUS)}
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# ==== Phase 2: Compile ====
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print(f"\n{'='*70}\nPhase 2: JIT compiling\n{'='*70}")
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from dsv4.kernels.attention.production import dsv4_attention
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dummy_q = torch.randn(n_h, 1, hd, dtype=torch.bfloat16, device='cuda:0')
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dummy_k = torch.randn(1, 1, hd, dtype=torch.bfloat16, device='cuda:0')
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try:
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_ = dsv4_attention(dummy_q, dummy_k, dummy_k.clone())
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print(" FMHA: compiled OK")
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|
except Exception as e:
|
|
print(f" FMHA error: {e}")
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|
t_compiled = time.time()
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|
print(f"Compile: {t_compiled - t_loaded:.1f}s")
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|
|
|
# ==== Phase 3: Inference ====
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|
print(f"\n{'='*70}\nPhase 3: Inference\n{'='*70}")
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|
from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_DIR)
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input_ids = tokenizer.encode(PROMPT, return_tensors="pt").cuda()
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|
print(f"Prompt: '{PROMPT}' → {input_ids.tolist()}")
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|
|
|
generated = input_ids[0].tolist()
|
|
|
|
for step in range(MAX_NEW_TOKENS):
|
|
t0 = time.time()
|
|
tid = torch.tensor([generated[-1]], dtype=torch.long, device='cuda:0')
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|
|
|
# Embed → mHC init state
|
|
emb = embed(tid) # (1, H) on gpu0
|
|
X = emb.unsqueeze(1).expand(-1, n_hc, -1).clone() # (1, n_hc, H)
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|
|
|
# Process layers
|
|
for li in range(n_layers):
|
|
gpu = li % NUM_GPUS
|
|
target_device = f"cuda:{gpu}"
|
|
if X.device != torch.device(target_device):
|
|
X = X.to(target_device)
|
|
torch.cuda.set_device(gpu)
|
|
|
|
attn_mhc = attn_mhc_blocks.get(li)
|
|
ffn_mhc = ffn_mhc_blocks.get(li)
|
|
rc, rs = rope_caches[gpu]
|
|
X = forward_layer(X, layer_weights[li], li, cfg, rc, rs, attn_mhc, ffn_mhc)
|
|
|
|
# Back to gpu0
|
|
X = X.to('cuda:0')
|
|
torch.cuda.set_device(0)
|
|
|
|
# Read out stream 0 → RMSNorm → lm_head
|
|
x_out = X[:, 0, :] # (1, H)
|
|
if final_norm_w is not None:
|
|
xf = x_out.float()
|
|
rms = xf.pow(2).mean(-1, keepdim=True).add(1e-6).rsqrt()
|
|
x_out = (xf * rms * final_norm_w.float()).bfloat16()
|
|
|
|
logits = torch.nn.functional.linear(x_out, lm_w)
|
|
next_id = torch.argmax(logits, dim=-1).item()
|
|
generated.append(next_id)
|
|
|
|
tok_str = tokenizer.decode([next_id])
|
|
dt = time.time() - t0
|
|
|
|
# Check for NaN
|
|
has_nan = torch.isnan(logits.float()).any().item()
|
|
logit_range = f"[{logits.float().min().item():.1f}, {logits.float().max().item():.1f}]"
|
|
print(f" Step {step}: {next_id} '{tok_str}' ({dt:.2f}s) logits={logit_range} nan={has_nan}")
|
|
|
|
if has_nan:
|
|
print(" NaN detected, stopping")
|
|
break
|
|
if next_id == tokenizer.eos_token_id:
|
|
break
|
|
|
|
out = tokenizer.decode(generated, skip_special_tokens=True)
|
|
total = time.time() - t_start
|
|
print(f"\n{'='*70}")
|
|
print(f"Input: '{PROMPT}'")
|
|
print(f"Output: '{out}'")
|
|
print(f"Total: {total:.1f}s (load: {t_loaded-t_start:.1f}s, compile: {t_compiled-t_loaded:.1f}s, infer: {time.time()-t_compiled:.1f}s)")
|
|
print(f"{'='*70}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|