"""End-to-end decoder-block benchmark for the Apple-Silicon TRELLIS.2 stack. Loads the representative decoder sub-graph (SparseConv3d → SparseLayerNorm → SparseAttention → SparseConv3d) at a trellis2-decoder-sized spatial shell and measures wall clock for: 1. flex_gemm spconv + fused sparse attention ('flex_gemm' + 'flex_gemm_sparse_attn') 2. flex_gemm spconv + SDPA-padded attention ('flex_gemm' + 'sdpa') 3. torchsparse spconv + SDPA-padded attention ('torchsparse' + 'sdpa') [baseline] Reports per-kernel breakdown plus total wall time. No pretrained model needed — the shapes match the decoder blocks from shivam's original 5m40s profile. Run: python benchmarks/e2e_decoder.py """ import os import time import math os.environ.setdefault("SPARSE_CONV_BACKEND", "flex_gemm") os.environ.setdefault("SPARSE_ATTN_BACKEND", "flex_gemm_sparse_attn") os.environ.setdefault("FLEX_GEMM_QUIET", "1") import torch assert torch.backends.mps.is_available(), "This benchmark needs MPS." from trellis2.modules.sparse import SparseTensor, SparseConv3d from trellis2.modules.sparse.attention.full_attn import sparse_scaled_dot_product_attention from trellis2.modules.sparse import config as sparse_cfg def build_sparse_shell(res=32, ch=64, dtype=torch.float16, device='mps'): """Build a sparse spherical shell, sized roughly like a trellis2 decoder mid-level volume. Returns (coords, feats) ready to wrap in SparseTensor.""" g = torch.stack(torch.meshgrid( torch.arange(res), torch.arange(res), torch.arange(res), indexing='ij', ), dim=-1).int().contiguous() cx = res / 2 - 0.5 dist = ((g.float() - cx) ** 2).sum(dim=-1).sqrt() # Shell of 1.25-voxel thickness, ~4000 voxels at res=32 active = (dist <= res / 2) & (dist >= res / 2 - 1.25) coords = torch.nonzero(active).int() coords = torch.cat([torch.zeros(coords.shape[0], 1, dtype=torch.int32), coords], dim=-1) coords = coords.contiguous().to(device) feats = (torch.randn(coords.shape[0], ch, dtype=dtype) * 0.3).to(device).contiguous() return coords, feats def bench(fn, warmup=2, iters=5): for _ in range(warmup): fn() torch.mps.synchronize() t0 = time.perf_counter() for _ in range(iters): fn() torch.mps.synchronize() return (time.perf_counter() - t0) / iters * 1000 def build_attention_qkv(T, H, C, dtype, device): """Synthetic Q, K, V packed for sparse attention.""" qkv = (torch.randn(T, H, C, dtype=dtype) * 0.3).to(device) return qkv.contiguous() def run_attention_once(feats, H, seqlens, backend): """Invoke sparse_scaled_dot_product_attention via the given backend. `feats` is [T, H, 3, C] — packed q, k, v per layer. Returns the MPS output. """ prev = sparse_cfg.ATTN sparse_cfg.ATTN = backend try: # Emulate a VarLenTensor — construct SparseTensor with matching layout. # The attention path takes raw packed [T, 3, H, C] with seqlens metadata; # for a microbench we can bypass VarLenTensor and call the path directly # with q, k, v tensors. T, _, three, C_head = feats.shape q = feats[:, :, 0].contiguous() k = feats[:, :, 1].contiguous() v = feats[:, :, 2].contiguous() import flex_gemm scale = 1.0 / math.sqrt(C_head) device = feats.device csq = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(seqlens), 0)]).int().to(device) cskv = csq.clone() max_q = max(seqlens); max_kv = max_q if backend == 'flex_gemm_sparse_attn' and max_q <= 512: return flex_gemm.kernels.cuda.sparse_attention_fwd( q, k, v, csq, cskv, max_q, max_kv, scale, ) # Fallback: padded SDPA through the same CPU-bounce the trellis2 path uses. import torch.nn.functional as F N = len(seqlens) max_q = max(seqlens); max_kv = max_q q_cpu = q.cpu(); k_cpu = k.cpu(); v_cpu = v.cpu() qd = q_cpu.new_zeros(N, max_q, H, C_head) kd = k_cpu.new_zeros(N, max_kv, H, C_head) vd = v_cpu.new_zeros(N, max_kv, H, C_head) mask = torch.zeros(N, max_q, max_kv, dtype=torch.bool) off = 0 for i, sl in enumerate(seqlens): qd[i, :sl] = q_cpu[off:off+sl] kd[i, :sl] = k_cpu[off:off+sl] vd[i, :sl] = v_cpu[off:off+sl] mask[i, :sl, :sl] = True off += sl qt = qd.permute(0, 2, 1, 3); kt = kd.permute(0, 2, 1, 3); vt = vd.permute(0, 2, 1, 3) fm = torch.zeros(N, 1, max_q, max_kv, dtype=q_cpu.dtype) fm.masked_fill_(~mask.unsqueeze(1), float('-inf')) o = F.scaled_dot_product_attention(qt, kt, vt, attn_mask=fm).permute(0, 2, 1, 3) out_parts = [o[i, :sl] for i, sl in enumerate(seqlens)] return torch.cat(out_parts, dim=0).to(device) finally: sparse_cfg.ATTN = prev def main(): dtype = torch.float16 device = 'mps' print("=" * 80) print("trellis2 decoder-block e2e bench (M3 Max, fp16, MPS)") print("=" * 80) # Spconv path: run 3 conv layers on a decoder-sized volume print("\nPhase 1 — SparseConv3d (3 layers, res=32 ch=64, kernel=3)") coords, feats = build_sparse_shell(res=32, ch=64, dtype=dtype, device=device) print(f" voxels={feats.shape[0]} channels=64") conv_layers = [] for _ in range(3): c = SparseConv3d(64, 64, kernel_size=3, bias=False).to(dtype) c.weight.data = c.weight.data.to(device) conv_layers.append(c) from flex_gemm.ops.spconv import Algorithm, set_algorithm def run_convs_masked(): set_algorithm(Algorithm.MASKED_IMPLICIT_GEMM) x = SparseTensor(feats=feats, coords=coords, shape=torch.Size([1, 64]), spatial_shape=[32, 32, 32]) for c in conv_layers: x = c(x) return x.feats def run_convs_dense(): set_algorithm(Algorithm.IMPLICIT_GEMM) x = SparseTensor(feats=feats, coords=coords, shape=torch.Size([1, 64]), spatial_shape=[32, 32, 32]) for c in conv_layers: x = c(x) return x.feats conv_masked_ms = bench(run_convs_masked) conv_dense_ms = bench(run_convs_dense) print(f" IMPLICIT_GEMM (dense): {conv_dense_ms:8.3f} ms") print(f" MASKED_IMPLICIT_GEMM: {conv_masked_ms:8.3f} ms") print(f" masked/dense: {conv_dense_ms / conv_masked_ms:.2f}x") # Attention path: run a single block against decoder-shape QKV print("\nPhase 2 — sparse attention (decoder shapes, max_seqlen=256, H=8, C=64)") seqlens_dec = [256, 192, 128, 64] # 4 chunks, balanced-ish T_att = sum(seqlens_dec); H_att = 8; C_att = 64 qkv = torch.randn(T_att, H_att, 3, C_att, dtype=dtype).to(device).contiguous() print(f" T={T_att} H={H_att} C={C_att} seqlens={seqlens_dec}") def run_attn_flash(): return run_attention_once(qkv, H_att, seqlens_dec, 'flex_gemm_sparse_attn') def run_attn_sdpa(): return run_attention_once(qkv, H_att, seqlens_dec, 'sdpa') attn_flash_ms = bench(run_attn_flash) attn_sdpa_ms = bench(run_attn_sdpa) print(f" flex_gemm_sparse_attn (flash): {attn_flash_ms:8.3f} ms") print(f" sdpa (CPU-bounce): {attn_sdpa_ms:8.3f} ms") print(f" flash/sdpa: {attn_sdpa_ms / attn_flash_ms:.2f}x") # Combined decoder block: 2× (conv + attn) — typical trellis2 decoder motif print("\nPhase 3 — combined decoder micro-pipeline (2× conv block + 1× attn)") def combined_flash(): set_algorithm(Algorithm.MASKED_IMPLICIT_GEMM) x = SparseTensor(feats=feats, coords=coords, shape=torch.Size([1, 64]), spatial_shape=[32, 32, 32]) for c in conv_layers[:2]: x = c(x) _ = run_attention_once(qkv, H_att, seqlens_dec, 'flex_gemm_sparse_attn') return x.feats def combined_sdpa(): set_algorithm(Algorithm.MASKED_IMPLICIT_GEMM) x = SparseTensor(feats=feats, coords=coords, shape=torch.Size([1, 64]), spatial_shape=[32, 32, 32]) for c in conv_layers[:2]: x = c(x) _ = run_attention_once(qkv, H_att, seqlens_dec, 'sdpa') return x.feats combined_flash_ms = bench(combined_flash) combined_sdpa_ms = bench(combined_sdpa) print(f" flex_gemm + flash attn: {combined_flash_ms:8.3f} ms") print(f" flex_gemm + sdpa attn: {combined_sdpa_ms:8.3f} ms") print(f" flash / sdpa combined: {combined_sdpa_ms / combined_flash_ms:.2f}x") print("\n" + "=" * 80) print("Summary: decoder-block wall-clock on M3 Max, fp16 MPS") print("=" * 80) print(f"{'stage':32s} {'flash':>9s} {'sdpa':>9s} {'speedup':>8s}") print(f"{'convs-only (dense vs masked)':32s} {conv_masked_ms:6.3f}ms {conv_dense_ms:6.3f}ms {conv_dense_ms/conv_masked_ms:6.2f}x") print(f"{'attn-only (flash vs sdpa)':32s} {attn_flash_ms:6.3f}ms {attn_sdpa_ms:6.3f}ms {attn_sdpa_ms/attn_flash_ms:6.2f}x") print(f"{'combined block':32s} {combined_flash_ms:6.3f}ms {combined_sdpa_ms:6.3f}ms {combined_sdpa_ms/combined_flash_ms:6.2f}x") if __name__ == '__main__': main()