mirror of
https://github.com/ROCm/ROCm.git
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1af893d8a2
* [FRONTEND] Add input dtypes to autotuning key (#2534) * Fix conflict in 06-fused-attention * Fix get_best_config in FA-transV.py * Fix leftover get_best_config() --------- Co-authored-by: Adnan Akhundov <adnan.akhundov@gmail.com>
376 lines
16 KiB
Python
376 lines
16 KiB
Python
"""
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Matrix Multiplication Tuning Scripts, Changed from the tutorial example "python/tutorials/03-matrix-multiplication.py"
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"""
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import torch
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import triton
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import triton.language as tl
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import argparse
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import sys
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import yaml
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import os
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import subprocess
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# global flag to indicate whether using the full tuing space
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tuning_full_space = True
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# pruned some unreasonable config
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def prune_configs(configs, named_args):
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# call only for full tuning space
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if not tuning_full_space:
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return configs
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SIZE_M = named_args["a_ptr"].shape[0]
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SIZE_N = named_args["b_ptr"].shape[1]
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SIZE_K = named_args["a_ptr"].shape[1]
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pruned_configs = []
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for config in configs:
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kw = config.kwargs
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BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K =\
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kw["BLOCK_SIZE_M"], kw["BLOCK_SIZE_N"], kw["BLOCK_SIZE_K"]
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SPLIT_K = kw["SPLIT_K"]
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if SIZE_M <=32 and BLOCK_SIZE_M != 32:
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continue
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if SIZE_N <=32 and BLOCK_SIZE_N != 32:
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continue
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# skip large split_k when not necessary
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if SPLIT_K != 1 and not need_split_k(SIZE_M, SIZE_N, SIZE_K):
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continue
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pruned_configs.append(config)
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return pruned_configs
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def get_full_tuning_space(use_split_k):
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configs = []
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if not tuning_full_space:
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return configs
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block_mn_range = [32, 64, 128]
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block_k_range = [32, 64]
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split_k_range = [1, 2, 4, 5, 8, 10]
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num_warps_range = [1, 2, 4, 8]
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group_m_range = [1, 4, 8]
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# For now we see better perf with num_stages=0 for all gemm configs we care
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# But keep this explicit so that we do not forget we may need to set it to
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# other values in the future
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num_stage_range = [0]
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for block_m in block_mn_range:
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for block_n in block_mn_range:
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for block_k in block_k_range:
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for num_warps in num_warps_range:
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for group_m in group_m_range:
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for split_k in split_k_range:
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for num_stages in num_stage_range:
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configs.append(triton.Config({'BLOCK_SIZE_M': block_m, 'BLOCK_SIZE_N': block_n, 'BLOCK_SIZE_K': block_k, 'GROUP_SIZE_M': group_m, 'SPLIT_K': split_k}, num_stages=num_stages, num_warps=num_warps))
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return configs
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# `triton.jit`'ed functions can be auto-tuned by using the `triton.autotune` decorator, which consumes:
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# - A list of `triton.Config` objects that define different configurations of
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# meta-parameters (e.g., `BLOCK_SIZE_M`) and compilation options (e.g., `num_warps`) to try
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# - An auto-tuning *key* whose change in values will trigger evaluation of all the
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# provided configs
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@triton.autotune(
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configs= get_full_tuning_space(True) if tuning_full_space else [
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8, 'SPLIT_K': 1}, num_stages=1, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8, 'SPLIT_K': 1}, num_stages=1, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8, 'SPLIT_K': 1}, num_stages=1, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8, 'SPLIT_K': 1}, num_stages=1, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8, 'SPLIT_K': 1}, num_stages=1, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8, 'SPLIT_K': 1}, num_stages=1, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8, 'SPLIT_K': 1}, num_stages=1, num_warps=2),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8, 'SPLIT_K': 1}, num_stages=1, num_warps=2),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 1, 'SPLIT_K': 8}, num_stages=1, num_warps=2),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 1, 'SPLIT_K': 10}, num_stages=1, num_warps=2),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 1, 'SPLIT_K': 8}, num_stages=1, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 1, 'SPLIT_K': 10}, num_stages=1, num_warps=1),
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],
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key=['M', 'N', 'K'],
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prune_configs_by={
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'early_config_prune': prune_configs,
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'perf_model': None,
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"top_k": None
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},
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)
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@triton.heuristics({
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'EVEN_K': lambda args: args['K'] % (args['BLOCK_SIZE_K'] * args['SPLIT_K']) == 0,
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})
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@triton.jit
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def matmul_kernel_splitK(
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# Pointers to matrices
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a_ptr, b_ptr, c_ptr,
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# Matrix dimensions
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M, N, K,
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# The stride variables represent how much to increase the ptr by when moving by 1
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# element in a particular dimension. E.g. `stride_am` is how much to increase `a_ptr`
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# by to get the element one row down (A has M rows).
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stride_am, stride_ak,
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stride_bk, stride_bn,
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stride_cm, stride_cn,
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# Meta-parameters
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BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
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SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
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GROUP_SIZE_M: tl.constexpr,
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ACTIVATION: tl.constexpr,
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):
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"""Kernel for computing the matmul C = A x B.
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A has shape (M, K), B has shape (K, N) and C has shape (M, N)
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"""
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# -----------------------------------------------------------
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# Map program ids `pid` to the block of C it should compute.
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# This is done in a grouped ordering to promote L2 data reuse.
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# See above `L2 Cache Optimizations` section for details.
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pid = tl.program_id(axis=0)
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pid_z = tl.program_id(1)
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num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
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num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
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num_pid_in_group = GROUP_SIZE_M * num_pid_n
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group_id = pid // num_pid_in_group
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first_pid_m = group_id * GROUP_SIZE_M
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group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
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pid_m = first_pid_m + (pid % group_size_m)
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pid_n = (pid % num_pid_in_group) // group_size_m
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# ----------------------------------------------------------
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# Create pointers for the first blocks of A and B.
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# We will advance this pointer as we move in the K direction
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# and accumulate
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# `a_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
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# `b_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers
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# See above `Pointer Arithmetics` section for details
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if SPLIT_K == 1:
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offs_k = tl.arange(0, BLOCK_SIZE_K)
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else:
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offs_k = pid_z * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
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if torch.version.hip is None:
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offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
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offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
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a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
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b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
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else:
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offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M))
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offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N))
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a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
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b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn
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# -----------------------------------------------------------
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# Iterate to compute a block of the C matrix.
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# We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
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# of fp32 values for higher accuracy.
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# `accumulator` will be converted back to fp16 after the loop.
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accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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for k in range(0, tl.cdiv(K, BLOCK_SIZE_K * SPLIT_K)):
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# Load the next block of A and B, generate a mask by checking the K dimension.
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# If it is out of bounds, set it to 0.
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if EVEN_K:
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a = tl.load(a_ptrs)
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b = tl.load(b_ptrs)
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else:
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k_remaining = K - k * (BLOCK_SIZE_K * SPLIT_K)
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a = tl.load(a_ptrs, mask=offs_k[None, :] < k_remaining, other=0.0)
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b = tl.load(b_ptrs, mask=offs_k[:, None] < k_remaining, other=0.0)
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# We accumulate along the K dimension.
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accumulator += tl.dot(a, b)
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# Advance the ptrs to the next K block.
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a_ptrs += BLOCK_SIZE_K * SPLIT_K * stride_ak
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b_ptrs += BLOCK_SIZE_K * SPLIT_K * stride_bk
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# You can fuse arbitrary activation functions here
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# while the accumulator is still in FP32!
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if ACTIVATION == "leaky_relu":
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accumulator = leaky_relu(accumulator)
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c = accumulator.to(tl.float16)
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# -----------------------------------------------------------
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# Write back the block of the output matrix C with masks.
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offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
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c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
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c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
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if SPLIT_K == 1:
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tl.store(c_ptrs, c, mask=c_mask)
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else:
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tl.atomic_add(c_ptrs, c, mask=c_mask)
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# We can fuse `leaky_relu` by providing it as an `ACTIVATION` meta-parameter in `_matmul`.
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@triton.jit
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def leaky_relu(x):
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x = x + 1
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return tl.where(x >= 0, x, 0.01 * x)
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def need_split_k(SIZE_M, SIZE_N, SIZE_K):
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return (SIZE_M < 64 or SIZE_N < 64) and SIZE_K > 1024
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def matmul(a, b, activation=""):
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# Check constraints.
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assert a.shape[1] == b.shape[0], "Incompatible dimensions"
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assert a.is_contiguous(), "Matrix A must be contiguous"
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assert b.is_contiguous(), "Matrix B must be contiguous"
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M, K = a.shape
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K, N = b.shape
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# Allocates output.
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c = torch.empty((M, N), device=a.device, dtype=a.dtype)
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# 1D launch kernel where each block gets its own program.
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grid_splitK = lambda META: (
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triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),
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META['SPLIT_K']
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)
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matmul_kernel_splitK[grid_splitK](
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a, b, c,
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M, N, K,
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a.stride(0), a.stride(1),
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b.stride(0), b.stride(1),
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c.stride(0), c.stride(1),
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ACTIVATION=activation
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)
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return c
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def test_correctness(M, N, K, datatype = torch.float16):
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torch.manual_seed(0)
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a = torch.randn((M, K), device='cuda', dtype=datatype)
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b = torch.randn((K, N), device='cuda', dtype=datatype)
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triton_output = matmul(a, b)
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torch_output = torch.matmul(a, b)
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print(f"triton_output={triton_output}")
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print(f"torch_output={torch_output}")
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rtol = 0 if torch.version.hip is None else 1e-2
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size_str = f'size, (M: {M}, N: {N}, K: {K})'
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if torch.allclose(triton_output, torch_output, atol=1e-2, rtol=rtol):
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print(f'✅ Triton and Torch match for {size_str}')
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else:
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print(f'❌ Triton and Torch differ for {size_str}')
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def run_speed(M, N, K, datatype, use_rocprof, provider):
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a = torch.randn((M, K), device='cuda', dtype=datatype)
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b = torch.randn((K, N), device='cuda', dtype=datatype)
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quantiles = [0.5, 0.2, 0.8]
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if provider == 'pytorch':
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ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a, b), quantiles=quantiles)
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if provider == 'triton':
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ms, min_ms, max_ms = triton.testing.do_bench(lambda: matmul(a, b), quantiles=quantiles)
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return min_ms
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def run_bash_command(commandstring):
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#print( commandstring )
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proc = subprocess.run(commandstring, shell=True, check=True, executable='/bin/bash', stdout = subprocess.PIPE)
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return proc.stdout.splitlines()
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def parse_args():
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parser = argparse.ArgumentParser(
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prog="tune a specific gemm size",
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allow_abbrev=False,
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)
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parser.add_argument("-m", type=int, default=0)
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parser.add_argument("-n", type=int, default=0)
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parser.add_argument("-k", type=int, default=0)
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parser.add_argument("-dtype", type=str, default='fp16', help="Input data type, default is fp16")
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parser.add_argument("--specify_type", action='store_true', default=False, help="Whether user specify data type, default false")
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parser.add_argument("--specify_size", action='store_true', default=False, help="Whether user specify input matrix size, default false")
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parser.add_argument("--compare", action='store_true', default=False, help="Whether check result correctness")
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parser.add_argument("--gemm_size_file", type=str, default="", help='yaml file to indicate matrix size')
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parser.add_argument("--rocprof", action='store_true', default=False, help='Use rocprof to measure kernel time, default uses do_bench()!')
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parser.add_argument("-v", action='store_true', default=False, help="Print out the best tuning config")
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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dtype = torch.float16
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if args.specify_type:
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if args.dtype == 'fp16':
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dtype = torch.float16
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elif args.dtype == 'fp32':
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dtype = torch.float32
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elif args.dtype == 'bf16':
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dtype = torch.bfloat16
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else:
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print(f"Unsupported datatype {args.dtype}")
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sys.exit(1)
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use_rocprof = args.rocprof
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verbose = args.v
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mnks = []
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if args.specify_size:
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M = args.m
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N = args.n
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K = args.k
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if M == 0 or N == 0 or K == 0:
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print(f"Input matrix size: (M {M}, N {N}, K {K}) contains dim size 0!")
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mnks = [(M, N, K)]
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else:
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matrix_size_file = args.gemm_size_file
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if matrix_size_file == "" or not os.path.isfile(matrix_size_file):
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print(f"Matrix size file: {matrix_size_file} does not exist!")
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sys.exit(1)
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with open(matrix_size_file) as file:
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matrix_sizes = yaml.safe_load(file)
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for sizes in matrix_sizes:
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M = sizes['M']
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N = sizes['N']
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K = sizes['K']
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mnks.append((M, N, K))
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for (m, n, k) in mnks:
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min_ms = run_speed(m, n, k, dtype, use_rocprof, 'triton')
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# function to compute flops
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perf_flops = lambda ms: 2 * m * n * k * 1e-12 / (ms * 1e-3)
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if args.compare:
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test_correctness(m, n, k, dtype)
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best_config = matmul_kernel_splitK.get_best_config()
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if use_rocprof:
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dtype_str = 'fp16' if (not args.specify_type) else args.dtype
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block_m = best_config.kwargs['BLOCK_SIZE_M']
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block_n = best_config.kwargs['BLOCK_SIZE_N']
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block_k = best_config.kwargs['BLOCK_SIZE_K']
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group_m = best_config.kwargs['GROUP_SIZE_M']
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split_k = best_config.kwargs['SPLIT_K']
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# num_warps = best_config['num_warps']
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num_warps = best_config.num_warps
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driver = 'rocprof_gemm.py'
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TRITON_DIR = os.getenv('TRITON_DIR')
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if TRITON_DIR is not None:
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driver = os.path.join(TRITON_DIR, 'scripts/amd/gemm', driver)
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run_cmd = f'python {driver} -m {m} -n {n} -k {k} \
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-block_m {block_m} -block_n {block_n} -block_k {block_k} \
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-group_m {group_m} -split_k {split_k} -num_warps {num_warps} \
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-dtype {dtype_str}'
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prof_cmd = f'rocprof --stats {run_cmd}'
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run_bash_command(prof_cmd)
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parse_result_cmd = f'sed -n \'/matmul_kernel/p\' results.stats.csv | awk -F \',\' \'{{print $4}}\''
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parse_outputs = run_bash_command(parse_result_cmd)
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min_ms = int(parse_outputs[0]) / 1000000
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out_str = f'SIZE: {m},{n},{k} '
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# print best config
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if verbose:
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out_str += f' best_config: ({best_config}), '
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out_str += f'TFLOPS: {perf_flops(min_ms)} time(ns): {min_ms * 1000000}'
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print(out_str)
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if __name__ == '__main__':
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sys.exit(main())
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