mirror of
https://github.com/deepseek-ai/DeepGEMM
synced 2025-05-05 17:34:22 +00:00
238 lines
11 KiB
Python
238 lines
11 KiB
Python
import math
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import torch
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from functools import lru_cache
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from typing import Tuple
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from .tuner import jit_tuner
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from .utils import get_num_sms, ceil_div, get_col_major_tma_aligned_tensor, get_m_alignment_for_contiguous_layout
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# C++ code templates
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includes = ('"deep_gemm/fp8_gemm.cuh"', )
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template = """
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using namespace deep_gemm;
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// Templated args from Python JIT call
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constexpr auto N = {N}, K = {K};
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constexpr auto BLOCK_M = {BLOCK_M};
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constexpr auto BLOCK_N = {BLOCK_N};
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constexpr auto BLOCK_K = 128;
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constexpr auto BLOCK_N_PADDING = {BLOCK_N_PADDING};
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constexpr auto kSwizzleDMode = {SWIZZLE_D_MODE};
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constexpr auto kNumGroups = 1;
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constexpr auto kNumStages = {NUM_STAGES};
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constexpr auto kNumTMAMulticast = {NUM_TMA_MULTICAST};
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constexpr auto kIsTMAMulticastOnA = {IS_TMA_MULTICAST_ON_A};
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// Make a templated GEMM
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using gemm_t = Gemm<N, K, BLOCK_M, BLOCK_N, BLOCK_K, BLOCK_N_PADDING, kSwizzleDMode, kNumGroups, kNumStages, kNumTMAMulticast, kIsTMAMulticastOnA, GemmType::Normal>;
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// Launch kernel
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auto tma_a_desc = gemm_t::make_2d_tma_a_desc(lhs, m);
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auto tma_b_desc = gemm_t::make_2d_tma_b_desc(rhs);
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auto tma_scales_a_desc = gemm_t::make_2d_tma_scales_a_desc(lhs_scales, m);
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auto tma_d_desc = gemm_t::make_2d_tma_d_desc(out, m);
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gemm_t::run(out, rhs_scales, nullptr,
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m,
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tma_a_desc, tma_b_desc, tma_scales_a_desc, tma_d_desc,
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stream, num_sms, smem_size);
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"""
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def is_tma_multicast_legal(shape_dim: int, block_dim: int, num_tma_multicast: int, num_sms: int) -> bool:
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if num_tma_multicast == 1:
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return True
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return (shape_dim % (block_dim * num_tma_multicast) == 0) and num_sms % num_tma_multicast == 0
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def get_swizzle_mode(block_n: int) -> int:
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# TODO: remove some candidates if slow
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elem_size = 2
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for mode_bytes in (128, 64, 32):
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if (block_n * elem_size) % mode_bytes == 0:
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return mode_bytes
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return 0
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def get_block_n_padding_for_smem_d(block_n: int) -> int:
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# NOTES: padding is for solving bank conflicts, but wastes shared memory space
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elem_size, requirement = 2, (4, 8)
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bank_stride = (block_n * elem_size) // 4
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padding = (requirement[0] - bank_stride) % requirement[1]
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return (((padding + requirement[1]) if padding < 0 else padding) * 4) // elem_size
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def get_smem_config(num_stages: int, k: int, block_m: int, block_n: int, block_k: int = 128) -> Tuple[int, int, int]:
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# Try swizzle first, as it does not waste shared memory
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swizzle_mode = get_swizzle_mode(block_n)
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block_n_padding = get_block_n_padding_for_smem_d(block_n) if swizzle_mode == 0 else 0
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smem_d = block_m * (block_n + block_n_padding) * 2
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smem_a_per_stage = block_m * block_k
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smem_scales_a_per_stage = block_m * 4
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smem_b_per_stage = block_n * block_k
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smem_scales_b = ceil_div(k, block_k) * 4
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smem_barrier = num_stages * 8 * 2
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smem_size = 0
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smem_size += smem_d
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smem_size += num_stages * smem_a_per_stage
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smem_size += num_stages * smem_scales_a_per_stage
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smem_size += num_stages * smem_b_per_stage
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smem_size += ceil_div(smem_scales_b * (1 if block_k % block_n == 0 else 2), 8) * 8
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smem_size += smem_barrier
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# Swizzle and padding are not compatible
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assert int(swizzle_mode > 0) + int(block_n_padding > 0) <= 1
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return smem_size, swizzle_mode, block_n_padding
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@lru_cache(maxsize=None)
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def get_best_configs(m: int, n: int, k: int, num_groups: int, num_sms: int,
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is_grouped_contiguous: bool = False, is_grouped_masked: bool = False) -> \
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Tuple[int, int, int, int, Tuple[int, bool], Tuple[int, int, int]]:
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if not is_grouped_contiguous:
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block_ms = (64, 128, 256)
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else:
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block_ms = (get_m_alignment_for_contiguous_layout(), )
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block_ns = tuple(range(16, 129, 8)) + (144, 160, )
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fix_wave_saturate = lambda x: num_sms if x == 0 else x
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get_num_waves = lambda bm, bn: (ceil_div(ceil_div(m, bm) * ceil_div(n, bn) * num_groups, num_sms) if bm else None)
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get_last_wave_util = lambda bm, bn: fix_wave_saturate((ceil_div(m, bm) * ceil_div(n, bn) * num_groups) % num_sms)
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# Decide block sizes by waves
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best_block_m, best_block_n = None, None
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for block_m in block_ms:
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# NOTES: the block sizes can not be too large, so at least one dim less than 128
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for block_n in filter(lambda bn: block_m <= 128 or bn <= 128, block_ns):
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success = False
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num_waves, best_num_waves = get_num_waves(block_m, block_n), get_num_waves(best_block_m, best_block_n)
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if best_block_m is None or best_block_n is None:
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success = True
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elif num_waves < best_num_waves:
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success = True
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elif num_waves == best_num_waves:
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# Check last wave utilization
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util = get_last_wave_util(block_m, block_n)
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best_util = get_last_wave_util(best_block_m, best_block_n)
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success = util > best_util
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if util == best_util:
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# Case 1: same `block_m`, smaller `block_n` (wasted)
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success |= block_m == best_block_m and block_n < best_block_n
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# Case 2: same `block_n`, smaller `block_m` (wasted)
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success |= block_n == best_block_n and block_m < best_block_m
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# Case 3: different for both `block_m` and `block_n`, `block_n` larger is better
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success |= block_m != best_block_m and block_n > best_block_n
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best_block_m, best_block_n = (block_m, block_n) if success else (best_block_m, best_block_n)
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assert best_block_m is not None and best_block_n is not None
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# Always pick the longest one
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# NOTES: for double B scales, the best number of stages may be reduced
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best_num_stages, best_smem_config, sm90_capacity = None, None, 232448
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stage_candidates = (8, 7, 6, 5, 4, 3)
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if 128 % best_block_n != 0 and 128 // math.gcd(128, best_block_n) <= 4:
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# Unrolling both stages and `num_former_iters` will cause large code size
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stage_candidates = (4, 3)
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for num_stages in stage_candidates:
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best_smem_config = get_smem_config(num_stages, k, best_block_m, best_block_n)
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if best_smem_config[0] <= sm90_capacity:
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best_num_stages = num_stages
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break
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assert best_smem_config is not None
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assert best_num_stages is not None
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# Decide the number of TMA multicast and whether broadcast on A
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best_tma_multicast_config = (1, True)
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# Try to multicast on the larger block side first
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is_dense_gemm = (not is_grouped_contiguous) and (not is_grouped_masked)
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is_multicast_legal = {
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'A': is_tma_multicast_legal(n, best_block_n, 2, num_sms),
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'B': is_tma_multicast_legal(m, best_block_m, 2, num_sms) and is_dense_gemm,
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}
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for i in ('A', 'B') if best_block_m > best_block_n else ('B', 'A'):
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if m >= 512 and is_multicast_legal[i]:
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best_tma_multicast_config = (2, i == 'A')
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break
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# Recompute the minimal number of SMs required
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# NOTES: less L2 cache usage and less GPU frequency drop
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num_waves = get_num_waves(best_block_m, best_block_n)
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num_min_sms = ceil_div(ceil_div(m, best_block_m) * ceil_div(n, best_block_n) * num_groups, num_waves)
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num_min_sms = ceil_div(num_min_sms, best_tma_multicast_config[0]) * best_tma_multicast_config[0]
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assert num_min_sms <= num_sms
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return num_min_sms, best_block_m, best_block_n, best_num_stages, best_tma_multicast_config, best_smem_config
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def gemm_fp8_fp8_bf16_nt(lhs: Tuple[torch.Tensor, torch.Tensor],
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rhs: Tuple[torch.Tensor, torch.Tensor],
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out: torch.Tensor) -> None:
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"""
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Do a normal GEMM with FP8 inputs and BF16 output, with 1x128 LHS scaling and 128x128 RHS scaling.
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LHS, RHS, RHS scaling factors, and output tensors must be in contiguous format.
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RHS and RHS scaling factors are required to be transposed.
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The LHS scaling tensor requires TMA-aligned transposed format, if your input does not match the requirement,
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this function will do a transposing with a set of slow PyTorch operations.
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Arguments:
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lhs: the first element is an FP8 tensor (typed `torch.float8_e4m3fn`) of shape `[m, k]`,
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the second element is an FP32 1x128 scaling tensor for LHS of shape `[m, ⌈k / 128⌉]`.
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rhs: the first element is an FP8 tensor (typed `torch.float8_e4m3fn`) of shape `[n, k]`.
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the second element is an FP32 128x128 scaling tensor for RHS of shape `[⌈n / 128⌉, ⌈k / 128⌉]`.
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out: the BF16 output tensor of shape `[m, n]`, representing the result.
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"""
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lhs, lhs_scales = lhs
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rhs, rhs_scales = rhs
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m, k = lhs.shape
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n, k_ = rhs.shape
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m_, n_ = out.shape
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assert n % 64 == 0 and k % 128 == 0
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# Type and shape checks
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assert m == m_ and n == n_ and k == k_
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assert n > 0 and k > 0
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assert lhs_scales.shape == (m, (k + 127) // 128)
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assert rhs_scales.shape == ((n + 127) // 128, (k + 127) // 128)
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assert lhs.dtype == torch.float8_e4m3fn and lhs_scales.dtype == torch.float32
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assert rhs.dtype == torch.float8_e4m3fn and rhs_scales.dtype == torch.float32
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assert out.dtype == torch.bfloat16
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assert lhs.is_contiguous() and rhs.is_contiguous() and out.is_contiguous()
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# LHS scales must be transposed for TMA load, but not for RHS scales
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# NOTES: `get_tma_aligned_lhs_scales` may launch a kernel if not processed by previous kernels
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lhs_scales = get_col_major_tma_aligned_tensor(lhs_scales)
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assert rhs_scales.is_contiguous()
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# Do nothing if `m` is zero
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if m == 0:
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return
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# Auto-tuning with compilation
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global includes, template
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num_sms = get_num_sms()
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num_sms, block_m, block_n, num_stages, tma_multicast_config, smem_config = get_best_configs(m, n, k, 1, num_sms)
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args = (lhs, lhs_scales, rhs, rhs_scales, out, m, torch.cuda.current_stream(), num_sms, smem_config[0])
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runtime = jit_tuner.compile_and_tune(
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name='gemm_fp8_fp8_bf16_nt',
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keys={'N': n, 'K': k, 'BLOCK_M': block_m, 'BLOCK_N': block_n,
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'SWIZZLE_D_MODE': smem_config[1],
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'BLOCK_N_PADDING': smem_config[2],
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'NUM_STAGES': num_stages,
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'NUM_TMA_MULTICAST': tma_multicast_config[0],
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'IS_TMA_MULTICAST_ON_A': tma_multicast_config[1]},
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space=(),
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includes=includes,
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arg_defs=(('lhs', torch.float8_e4m3fn), ('lhs_scales', torch.float),
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('rhs', torch.float8_e4m3fn), ('rhs_scales', torch.float),
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('out', torch.bfloat16), ('m', int),
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('stream', torch.cuda.Stream), ('num_sms', int), ('smem_size', int)),
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template=template,
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args=args
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)
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# Run the kernel
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runtime(*args)
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