import random import torch from typing import Tuple import deep_gemm from deep_gemm.testing import bench_kineto, calc_diff, count_bytes from deep_gemm.utils import ceil_div, per_custom_dims_cast_to_fp8 from generators import get_arch_major, generate_normal, get_ue8m0_usage, get_kernel_types, MajorTypeAB def apply_skip_head_mid(d: torch.Tensor, head_splits: Tuple[int, int, int]): left, mid, right = head_splits m, n = d.shape assert n % (left + right) == 0 num_heads = n // (left + right) # Split and insert padding tensor d = d.view(m, num_heads, -1) d_left = d[:, :, :left] d_right = d[:, :, -right:] d_mid = torch.zeros((m, num_heads, mid), dtype=d.dtype, device=d.device) return torch.cat([d_left, d_mid, d_right], dim=2).view(m, -1) def test_gemm_skip_head_mid() -> None: print('Testing GEMM skip head mid:') head_splits = (128, 64, 128) major_a, major_b = MajorTypeAB.KMajor, MajorTypeAB.KMajor out_dtype, accumulate = torch.bfloat16, False for kernel_type in get_kernel_types(dtype=torch.float8_e4m3fn): for m in (128, 4096): for n, k in [(32768, 512), (8192, 512)]: kernel_opt = f'1D1D' if kernel_type.is_1d1d() else '1D2D' use_ue8m0 = get_ue8m0_usage(kernel_type) disable_ue8m0_cast = not use_ue8m0 a, b, _, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, kernel_type, use_ue8m0=use_ue8m0) d = apply_skip_head_mid(d, head_splits) ref_d = apply_skip_head_mid(ref_d, head_splits) deep_gemm.fp8_gemm_nt_skip_head_mid(a, b, d, head_splits, disable_ue8m0_cast=disable_ue8m0_cast) diff = calc_diff(d, ref_d) assert diff < 0.001, f'{m=}, {n=}, {k=}, {kernel_opt}, {diff:.5f}' t = bench_kineto(lambda: deep_gemm.fp8_gemm_nt_skip_head_mid(a, b, d, head_splits, disable_ue8m0_cast=disable_ue8m0_cast), 'fp8_gemm', suppress_kineto_output=True) print(f' > Perf (m={m:5}, n={n:5}, k={k:5}, {kernel_opt}): ' f'{t * 1e6:4.0f} us | ' f'{2 * m * n * k / t / 1e12:4.0f} TFLOPS | ' f'{(count_bytes(a, b, d)) / 1e9 / t:4.0f} GB/s') print() def kv_cache_cast_to_fp8(x: torch.Tensor) -> torch.Tensor: num_blocks, block_size, num_heads, head_dim = x.shape assert num_heads == 1 x_amax = x.abs().float().amax(dim=3, keepdim=True).clamp(1e-4) sf = x_amax / 448.0 x_scaled = (x * (1.0 / sf)).to(torch.float8_e4m3fn) x_fp8 = torch.empty((num_blocks, block_size * (head_dim + 4)), device=x.device, dtype=torch.uint8) x_fp8[ :, : block_size * head_dim] = x_scaled.view(num_blocks, block_size * head_dim).view(dtype=torch.uint8) x_fp8[ :, block_size * head_dim :] = sf.view(num_blocks, block_size).view(dtype=torch.uint8) return x_fp8.view(num_blocks, block_size, num_heads, head_dim + 4) def generate_cp_test_data(seq_len, seq_len_kv): assert seq_len_kv % seq_len == 0 and seq_len % 2 == 0 chunk_size = seq_len // 2 cp_size = seq_len_kv // seq_len # Select an arbitrary CP rank cp_id = cp_size // 3 ks = torch.zeros(seq_len, dtype=torch.int, device='cuda') ke = torch.zeros(seq_len, dtype=torch.int, device='cuda') for i in range(chunk_size): ke[i] = cp_id * chunk_size + i ke[i + chunk_size] = (cp_size * 2 - 1 - cp_id) * chunk_size + i return ks, ke def ref_fp8_mqa_logits(q: torch.Tensor, kv: torch.Tensor, weights: torch.Tensor, cu_seqlen_ks: torch.Tensor, cu_seqlen_ke: torch.Tensor, cost_only: bool = False): seq_len_kv = kv.shape[0] if cost_only: start = cu_seqlen_ks.clamp(min=0, max=seq_len_kv) end = cu_seqlen_ke.clamp(min=0, max=seq_len_kv) count_ones_per_row = (end - start).clamp(min=0) return count_ones_per_row.sum() k = kv q = q.float() k = k.float() mask_lo = torch.arange(0, seq_len_kv, device='cuda')[None, :] >= cu_seqlen_ks[:, None] mask_hi = torch.arange(0, seq_len_kv, device='cuda')[None, :] < cu_seqlen_ke[:, None] mask = mask_lo & mask_hi score = torch.einsum('mhd,nd->hmn', q, k) logits = (score.relu() * weights.unsqueeze(-1).transpose(0, 1)).sum(dim=0) logits = logits.masked_fill(~mask, float('-inf')) cost = mask.sum() return logits, cost def test_mqa_logits(): print('Testing FP8 MQA Logits:') num_heads, head_dim = 64, 128 for seq_len in (2048, 4096): for seq_len_kv in (4096, 8192, 16384, 32768, 65536, 131072): for disable_cp in (False, True): q = torch.randn(seq_len, num_heads, head_dim, device='cuda', dtype=torch.bfloat16) kv = torch.randn(seq_len_kv, head_dim, device='cuda', dtype=torch.bfloat16) weights = torch.randn(seq_len, num_heads, device='cuda', dtype=torch.float32) if disable_cp: ks = torch.zeros(seq_len, dtype=torch.int, device='cuda') ke = torch.arange(seq_len, dtype=torch.int, device='cuda') + (seq_len_kv - seq_len) else: ks, ke = generate_cp_test_data(seq_len, seq_len_kv) q_fp8 = q.to(torch.float8_e4m3fn) kv_fp8 = per_custom_dims_cast_to_fp8(kv, (0, ), False) logits = deep_gemm.fp8_mqa_logits(q_fp8, kv_fp8, weights, ks, ke) do_check = (seq_len_kv < 32768) if do_check: ref_logits, ref_cost = ref_fp8_mqa_logits(q=q, kv=kv, weights=weights, cu_seqlen_ks=ks, cu_seqlen_ke=ke) ref_neginf_mask = (ref_logits == float('-inf')) neginf_mask = (logits == float('-inf')) assert torch.equal(neginf_mask, ref_neginf_mask) ref_logits = ref_logits.masked_fill(ref_neginf_mask, 0) logits = logits.masked_fill(neginf_mask, 0) diff = calc_diff(logits, ref_logits) assert diff < 1e-3, f"{diff=}" else: ref_cost = ref_fp8_mqa_logits(q=q, kv=kv, weights=weights, cu_seqlen_ks=ks, cu_seqlen_ke=ke, cost_only=True) tflops = 2 * ref_cost * num_heads * head_dim / 1e12 t, clean_t = bench_kineto(lambda: deep_gemm.fp8_mqa_logits(q_fp8, kv_fp8, weights, ks, ke), ('fp8_mqa_logits', 'clean_logits')) clean_bytes = (seq_len * seq_len_kv - ref_cost) * 4 + count_bytes(ks, ke) print(f' > S={seq_len:4}, SKV={seq_len_kv:6}, H={num_heads:3}, D={head_dim:3}, CP={0 if disable_cp else 1}: ' f'{tflops / t:4.0f} TFLOPS, {t * 1e6:4.0f} us, ' f'{(count_bytes(q_fp8, kv_fp8, weights, ks, ke) + ref_cost * 4) / t / 1e9:4.0f} GB/s | ' f'clean: {clean_t * 1e6:3.0f} us, {clean_bytes / clean_t / 1e9:4.0f} GB/s') print() def ref_fp8_paged_mqa_logits(q: torch.Tensor, kv_cache: torch.Tensor, weights: torch.Tensor, context_lens: torch.Tensor, block_tables: torch.Tensor, max_model_len: int): batch_size, next_n, heads, dim = q.size() num_block, block_size, _, dim = kv_cache.size() logits = torch.full([batch_size * next_n, max_model_len], float('-inf'), device=q.device, dtype=torch.float32) context_lens = context_lens.tolist() for i in range(batch_size): context_len = context_lens[i] q_offsets = torch.arange(context_len - next_n, context_len, device='cuda') weight_slice = weights[i * next_n:(i + 1) * next_n, :].transpose(0, 1).contiguous() for block_rk in range(ceil_div(context_len, block_size)): block_idx = block_tables[i][block_rk] qx, kx = q[i], kv_cache[block_idx] k_offsets = torch.arange(block_rk * block_size, (block_rk + 1) * block_size, device='cuda') mask = (k_offsets[None, :] < context_len) & (k_offsets[None, :] <= q_offsets[:, None]) s = torch.where(mask[None, :, :], (qx.transpose(0, 1) @ kx.transpose(0, 1).transpose(1, 2)).to(logits.dtype), float('-inf')) s = torch.relu(s) * weight_slice[..., None] s = s.sum(dim=0) logits[i * next_n:(i + 1) * next_n, block_rk * block_size: (block_rk + 1) * block_size] = torch.where(k_offsets[None, :] <= q_offsets[:, None], s, float('-inf')) return logits def test_paged_mqa_logits(): print('Testing FP8 Paged MQA Logits:') max_model_len = 111 * 1000 for batch_size, next_n in [(64, 1), (64, 2), (128, 1)]: for heads, index_dim in [(64, 128)]: for avg_kv in (8192, 32768): num_blocks, blocksize = max_model_len * 3, 64 q = torch.randn((batch_size, next_n, heads, index_dim), device='cuda', dtype=torch.bfloat16) kv_cache = torch.randn((num_blocks, blocksize, 1, index_dim), device='cuda', dtype=torch.bfloat16) weights = torch.randn((batch_size * next_n, heads), device='cuda', dtype=torch.float32) context_lens = torch.randint(int(0.7 * avg_kv), int(1.3 * avg_kv), (batch_size, )).cuda().to(torch.int32) max_block_len = (context_lens.max().item() + blocksize - 1) // blocksize * blocksize block_tables = torch.zeros((batch_size, max_block_len), device='cuda', dtype=torch.int32) counter = 0 block_idx_pool = list(range(num_blocks)) random.shuffle(block_idx_pool) for i in range(batch_size): ctx_len = context_lens[i].item() for j in range(ceil_div(ctx_len, blocksize)): block_tables[i][j] = block_idx_pool[counter] counter += 1 q_fp8 = q.to(torch.float8_e4m3fn) kv_cache_fp8 = kv_cache_cast_to_fp8(kv_cache) schedule_metadata = deep_gemm.get_paged_mqa_logits_metadata(context_lens, blocksize, deep_gemm.get_num_sms()) logits = deep_gemm.fp8_paged_mqa_logits(q_fp8, kv_cache_fp8, weights, context_lens, block_tables, schedule_metadata, max_model_len, clean_logits=True) ref_logits = ref_fp8_paged_mqa_logits(q, kv_cache, weights, context_lens, block_tables, max_model_len) positions = torch.arange(max_model_len, device='cuda').unsqueeze(0).expand(batch_size * next_n, -1) row_indices = torch.arange(batch_size * next_n, device='cuda') // next_n next_n_offset = torch.arange(batch_size * next_n, device='cuda') % next_n ref_neginf_mask = ~(positions <= (context_lens[row_indices] - next_n + next_n_offset).unsqueeze(1)) neginf_mask = (logits == float('-inf')) assert torch.equal(neginf_mask, ref_neginf_mask) logits = logits.masked_fill(neginf_mask, 0) ref_logits = ref_logits.masked_fill(ref_neginf_mask, 0) diff = calc_diff(logits, ref_logits) assert diff < 1e-3, f"{diff=}" sum_lens = sum(context_lens.to(torch.int64)) tflops = 2 * sum_lens * next_n * heads * index_dim / 1e12 input_bytes = count_bytes(q_fp8, weights, context_lens) + sum_lens * (index_dim + 4) + (sum_lens / blocksize) * 4 output_bytes = sum_lens * next_n * 4 t, clean_t = bench_kineto(lambda: deep_gemm.fp8_paged_mqa_logits(q_fp8, kv_cache_fp8, weights, context_lens, block_tables, schedule_metadata, max_model_len, clean_logits=True), ('fp8_paged_mqa_logits', 'clean_logits')) clean_bytes = (batch_size * next_n * max_model_len - neginf_mask.sum().item()) * 4 + count_bytes(context_lens) print(f' > BSZ={batch_size:3}, NextN={next_n:1}, H={heads:2}, D={index_dim:2}, L={avg_kv:6}: ' f'{tflops / t:4.0f} TFLOPS, {t * 1e6:3.0f} us, ' f'{(input_bytes + output_bytes) / t / 1e9:4.0f} GB/s | ' f'clean: {clean_t * 1e6:3.0f} us, {clean_bytes / clean_t / 1e9:4.0f} GB/s') print() if __name__ == '__main__': torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True torch.manual_seed(0) random.seed(0) test_gemm_skip_head_mid() test_mqa_logits() test_paged_mqa_logits()