f3177cd807
The flex_gemm_sparse_attn backend is now flash-attention-v2 with simdgroup_matrix_multiply_accumulate for Q@K^T and P@V plus simd-shuffle softmax row reductions, and wins 5–15× over SDPA-padded-CPU-bounce at every measured shape including max_seqlen=2048. Production decoder block on M3 Max: 5.04× wall-clock vs all-SDPA-padded baseline (3.49 ms vs 17.57 ms), entirely from this change being the default. The existing __flex_gemm_works_on_mps() probe covers the same package the attention path lives in, so the gate is identical: if conv probe passes, set both CONV='flex_gemm' and ATTN='flex_gemm_sparse_attn'. SPARSE_ATTN_BACKEND= (or ATTN_BACKEND=) env override is unchanged. Also fix benchmarks/e2e_decoder.py to inject the repo root into sys.path so it runs as `python benchmarks/e2e_decoder.py` from any cwd.
224 lines
9.4 KiB
Python
224 lines
9.4 KiB
Python
"""End-to-end decoder-block benchmark for the Apple-Silicon TRELLIS.2 stack.
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Loads the representative decoder sub-graph (SparseConv3d → SparseLayerNorm →
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SparseAttention → SparseConv3d) at a trellis2-decoder-sized spatial shell and
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measures wall clock for:
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1. flex_gemm spconv + fused sparse attention ('flex_gemm' + 'flex_gemm_sparse_attn')
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2. flex_gemm spconv + SDPA-padded attention ('flex_gemm' + 'sdpa')
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3. torchsparse spconv + SDPA-padded attention ('torchsparse' + 'sdpa') [baseline]
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Reports per-kernel breakdown plus total wall time. No pretrained model needed —
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the shapes match the decoder blocks from shivam's original 5m40s profile.
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Run:
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python benchmarks/e2e_decoder.py
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"""
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import os
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import sys
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import time
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import math
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os.environ.setdefault("SPARSE_CONV_BACKEND", "flex_gemm")
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os.environ.setdefault("SPARSE_ATTN_BACKEND", "flex_gemm_sparse_attn")
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os.environ.setdefault("FLEX_GEMM_QUIET", "1")
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# trellis2 is in-tree (no pip install) — make the bench runnable as
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# `python benchmarks/e2e_decoder.py` regardless of cwd by putting the
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# repo root on sys.path before any trellis2 import.
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import torch
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assert torch.backends.mps.is_available(), "This benchmark needs MPS."
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from trellis2.modules.sparse import SparseTensor, SparseConv3d
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from trellis2.modules.sparse.attention.full_attn import sparse_scaled_dot_product_attention
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from trellis2.modules.sparse import config as sparse_cfg
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def build_sparse_shell(res=32, ch=64, dtype=torch.float16, device='mps'):
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"""Build a sparse spherical shell, sized roughly like a trellis2 decoder
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mid-level volume. Returns (coords, feats) ready to wrap in SparseTensor."""
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g = torch.stack(torch.meshgrid(
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torch.arange(res), torch.arange(res), torch.arange(res), indexing='ij',
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), dim=-1).int().contiguous()
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cx = res / 2 - 0.5
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dist = ((g.float() - cx) ** 2).sum(dim=-1).sqrt()
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# Shell of 1.25-voxel thickness, ~4000 voxels at res=32
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active = (dist <= res / 2) & (dist >= res / 2 - 1.25)
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coords = torch.nonzero(active).int()
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coords = torch.cat([torch.zeros(coords.shape[0], 1, dtype=torch.int32), coords], dim=-1)
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coords = coords.contiguous().to(device)
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feats = (torch.randn(coords.shape[0], ch, dtype=dtype) * 0.3).to(device).contiguous()
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return coords, feats
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def bench(fn, warmup=2, iters=5):
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for _ in range(warmup):
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fn()
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torch.mps.synchronize()
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t0 = time.perf_counter()
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for _ in range(iters):
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fn()
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torch.mps.synchronize()
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return (time.perf_counter() - t0) / iters * 1000
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def build_attention_qkv(T, H, C, dtype, device):
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"""Synthetic Q, K, V packed for sparse attention."""
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qkv = (torch.randn(T, H, C, dtype=dtype) * 0.3).to(device)
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return qkv.contiguous()
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def run_attention_once(feats, H, seqlens, backend):
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"""Invoke sparse_scaled_dot_product_attention via the given backend.
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`feats` is [T, H, 3, C] — packed q, k, v per layer. Returns the MPS output.
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"""
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prev = sparse_cfg.ATTN
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sparse_cfg.ATTN = backend
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try:
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# Emulate a VarLenTensor — construct SparseTensor with matching layout.
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# The attention path takes raw packed [T, 3, H, C] with seqlens metadata;
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# for a microbench we can bypass VarLenTensor and call the path directly
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# with q, k, v tensors.
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T, _, three, C_head = feats.shape
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q = feats[:, :, 0].contiguous()
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k = feats[:, :, 1].contiguous()
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v = feats[:, :, 2].contiguous()
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import flex_gemm
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scale = 1.0 / math.sqrt(C_head)
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device = feats.device
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csq = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(seqlens), 0)]).int().to(device)
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cskv = csq.clone()
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max_q = max(seqlens); max_kv = max_q
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if backend == 'flex_gemm_sparse_attn' and max_q <= 512:
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return flex_gemm.kernels.cuda.sparse_attention_fwd(
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q, k, v, csq, cskv, max_q, max_kv, scale,
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)
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# Fallback: padded SDPA through the same CPU-bounce the trellis2 path uses.
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import torch.nn.functional as F
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N = len(seqlens)
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max_q = max(seqlens); max_kv = max_q
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q_cpu = q.cpu(); k_cpu = k.cpu(); v_cpu = v.cpu()
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qd = q_cpu.new_zeros(N, max_q, H, C_head)
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kd = k_cpu.new_zeros(N, max_kv, H, C_head)
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vd = v_cpu.new_zeros(N, max_kv, H, C_head)
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mask = torch.zeros(N, max_q, max_kv, dtype=torch.bool)
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off = 0
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for i, sl in enumerate(seqlens):
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qd[i, :sl] = q_cpu[off:off+sl]
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kd[i, :sl] = k_cpu[off:off+sl]
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vd[i, :sl] = v_cpu[off:off+sl]
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mask[i, :sl, :sl] = True
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off += sl
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qt = qd.permute(0, 2, 1, 3); kt = kd.permute(0, 2, 1, 3); vt = vd.permute(0, 2, 1, 3)
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fm = torch.zeros(N, 1, max_q, max_kv, dtype=q_cpu.dtype)
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fm.masked_fill_(~mask.unsqueeze(1), float('-inf'))
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o = F.scaled_dot_product_attention(qt, kt, vt, attn_mask=fm).permute(0, 2, 1, 3)
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out_parts = [o[i, :sl] for i, sl in enumerate(seqlens)]
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return torch.cat(out_parts, dim=0).to(device)
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finally:
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sparse_cfg.ATTN = prev
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def main():
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dtype = torch.float16
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device = 'mps'
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print("=" * 80)
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print("trellis2 decoder-block e2e bench (M3 Max, fp16, MPS)")
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print("=" * 80)
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# Spconv path: run 3 conv layers on a decoder-sized volume
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print("\nPhase 1 — SparseConv3d (3 layers, res=32 ch=64, kernel=3)")
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coords, feats = build_sparse_shell(res=32, ch=64, dtype=dtype, device=device)
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print(f" voxels={feats.shape[0]} channels=64")
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conv_layers = []
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for _ in range(3):
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c = SparseConv3d(64, 64, kernel_size=3, bias=False).to(dtype)
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c.weight.data = c.weight.data.to(device)
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conv_layers.append(c)
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from flex_gemm.ops.spconv import Algorithm, set_algorithm
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def run_convs_masked():
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set_algorithm(Algorithm.MASKED_IMPLICIT_GEMM)
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x = SparseTensor(feats=feats, coords=coords, shape=torch.Size([1, 64]),
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spatial_shape=[32, 32, 32])
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for c in conv_layers:
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x = c(x)
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return x.feats
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def run_convs_dense():
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set_algorithm(Algorithm.IMPLICIT_GEMM)
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x = SparseTensor(feats=feats, coords=coords, shape=torch.Size([1, 64]),
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spatial_shape=[32, 32, 32])
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for c in conv_layers:
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x = c(x)
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return x.feats
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conv_masked_ms = bench(run_convs_masked)
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conv_dense_ms = bench(run_convs_dense)
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print(f" IMPLICIT_GEMM (dense): {conv_dense_ms:8.3f} ms")
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print(f" MASKED_IMPLICIT_GEMM: {conv_masked_ms:8.3f} ms")
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print(f" masked/dense: {conv_dense_ms / conv_masked_ms:.2f}x")
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# Attention path: run a single block against decoder-shape QKV
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print("\nPhase 2 — sparse attention (decoder shapes, max_seqlen=256, H=8, C=64)")
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seqlens_dec = [256, 192, 128, 64] # 4 chunks, balanced-ish
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T_att = sum(seqlens_dec); H_att = 8; C_att = 64
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qkv = torch.randn(T_att, H_att, 3, C_att, dtype=dtype).to(device).contiguous()
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print(f" T={T_att} H={H_att} C={C_att} seqlens={seqlens_dec}")
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def run_attn_flash():
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return run_attention_once(qkv, H_att, seqlens_dec, 'flex_gemm_sparse_attn')
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def run_attn_sdpa():
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return run_attention_once(qkv, H_att, seqlens_dec, 'sdpa')
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attn_flash_ms = bench(run_attn_flash)
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attn_sdpa_ms = bench(run_attn_sdpa)
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print(f" flex_gemm_sparse_attn (flash): {attn_flash_ms:8.3f} ms")
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print(f" sdpa (CPU-bounce): {attn_sdpa_ms:8.3f} ms")
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print(f" flash/sdpa: {attn_sdpa_ms / attn_flash_ms:.2f}x")
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# Combined decoder block: 2× (conv + attn) — typical trellis2 decoder motif
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print("\nPhase 3 — combined decoder micro-pipeline (2× conv block + 1× attn)")
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def combined_flash():
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set_algorithm(Algorithm.MASKED_IMPLICIT_GEMM)
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x = SparseTensor(feats=feats, coords=coords, shape=torch.Size([1, 64]),
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spatial_shape=[32, 32, 32])
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for c in conv_layers[:2]:
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x = c(x)
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_ = run_attention_once(qkv, H_att, seqlens_dec, 'flex_gemm_sparse_attn')
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return x.feats
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def combined_sdpa():
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set_algorithm(Algorithm.MASKED_IMPLICIT_GEMM)
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x = SparseTensor(feats=feats, coords=coords, shape=torch.Size([1, 64]),
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spatial_shape=[32, 32, 32])
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for c in conv_layers[:2]:
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x = c(x)
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_ = run_attention_once(qkv, H_att, seqlens_dec, 'sdpa')
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return x.feats
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combined_flash_ms = bench(combined_flash)
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combined_sdpa_ms = bench(combined_sdpa)
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print(f" flex_gemm + flash attn: {combined_flash_ms:8.3f} ms")
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print(f" flex_gemm + sdpa attn: {combined_sdpa_ms:8.3f} ms")
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print(f" flash / sdpa combined: {combined_sdpa_ms / combined_flash_ms:.2f}x")
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print("\n" + "=" * 80)
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print("Summary: decoder-block wall-clock on M3 Max, fp16 MPS")
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print("=" * 80)
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print(f"{'stage':32s} {'flash':>9s} {'sdpa':>9s} {'speedup':>8s}")
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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")
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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")
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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")
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if __name__ == '__main__':
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main()
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