gaspersic/triton
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

the matmul example

Browse Source
This commit is contained in:
Yan Da
2022-04-07 20:27:18 +08:00
parent 62f772123c
commit 0864b253bb
2 changed files with 222 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

127
rewrite-test/jit/matmul/matmul.mlir Normal file
View File

@@ -0,0 +1,127 @@
module {
func @matmul_kernel(%arg0: !triton.ptr<f16>, %arg1: !triton.ptr<f16>, %arg2: !triton.ptr<f16>, %arg3: i32, %arg4: i32, %arg5: i32, %arg6: i32, %arg7: i32, %arg8: i32) {
%0 = triton.get_program_id {axis = 0 : i32} : i32
%c64_i32 = arith.constant 64 : i32
%1 = arith.addi %arg3, %c64_i32 : i32
%c1_i32 = arith.constant 1 : i32
%2 = arith.subi %1, %c1_i32 : i32
%c64_i32_0 = arith.constant 64 : i32
%3 = arith.divsi %2, %c64_i32_0 : i32
%c64_i32_1 = arith.constant 64 : i32
%4 = arith.addi %arg4, %c64_i32_1 : i32
%c1_i32_2 = arith.constant 1 : i32
%5 = arith.subi %4, %c1_i32_2 : i32
%c64_i32_3 = arith.constant 64 : i32
%6 = arith.divsi %5, %c64_i32_3 : i32
%c8_i32 = arith.constant 8 : i32
%7 = arith.muli %6, %c8_i32 : i32
%8 = arith.divsi %0, %7 : i32
%c8_i32_4 = arith.constant 8 : i32
%9 = arith.muli %8, %c8_i32_4 : i32
%10 = arith.subi %3, %9 : i32
%c8_i32_5 = arith.constant 8 : i32
%11 = arith.cmpi slt, %10, %c8_i32_5 : i32
%c8_i32_6 = arith.constant 8 : i32
%12 = select %11, %10, %c8_i32_6 : i32
%13 = arith.remsi %0, %12 : i32
%14 = arith.addi %9, %13 : i32
%15 = arith.remsi %0, %7 : i32
%16 = arith.divsi %15, %12 : i32
%c64_i32_7 = arith.constant 64 : i32
%17 = arith.muli %14, %c64_i32_7 : i32
%18 = triton.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
%19 = "triton.broadcast"(%17) : (i32) -> tensor<64xi32>
%20 = arith.addi %19, %18 : tensor<64xi32>
%c64_i32_8 = arith.constant 64 : i32
%21 = arith.muli %16, %c64_i32_8 : i32
%22 = triton.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
%23 = "triton.broadcast"(%21) : (i32) -> tensor<64xi32>
%24 = arith.addi %23, %22 : tensor<64xi32>
%25 = triton.make_range {end = 32 : i32, start = 0 : i32} : tensor<32xi32>
%26 = "triton.reshape"(%20) {shape = [64, 1]} : (tensor<64xi32>) -> tensor<64x1xi32>
%27 = "triton.broadcast"(%arg6) : (i32) -> tensor<64x1xi32>
%28 = arith.muli %26, %27 : tensor<64x1xi32>
%29 = "triton.reshape"(%25) {shape = [1, 32]} : (tensor<32xi32>) -> tensor<1x32xi32>
%c1_i32_9 = arith.constant 1 : i32
%30 = "triton.broadcast"(%c1_i32_9) : (i32) -> tensor<1x32xi32>
%31 = arith.muli %29, %30 : tensor<1x32xi32>
%32 = "triton.broadcast"(%28) : (tensor<64x1xi32>) -> tensor<64x32xi32>
%33 = "triton.broadcast"(%31) : (tensor<1x32xi32>) -> tensor<64x32xi32>
%34 = arith.addi %32, %33 : tensor<64x32xi32>
%35 = "triton.broadcast"(%arg0) : (!triton.ptr<f16>) -> tensor<64x32x!triton.ptr<f16>>
%36 = "triton.getelementptr"(%35, %34) : (tensor<64x32x!triton.ptr<f16>>, tensor<64x32xi32>) -> tensor<64x32x!triton.ptr<f16>>
%37 = "triton.reshape"(%25) {shape = [32, 1]} : (tensor<32xi32>) -> tensor<32x1xi32>
%38 = "triton.broadcast"(%arg7) : (i32) -> tensor<32x1xi32>
%39 = arith.muli %37, %38 : tensor<32x1xi32>
%40 = "triton.reshape"(%24) {shape = [1, 64]} : (tensor<64xi32>) -> tensor<1x64xi32>
%c1_i32_10 = arith.constant 1 : i32
%41 = "triton.broadcast"(%c1_i32_10) : (i32) -> tensor<1x64xi32>
%42 = arith.muli %40, %41 : tensor<1x64xi32>
%43 = "triton.broadcast"(%39) : (tensor<32x1xi32>) -> tensor<32x64xi32>
%44 = "triton.broadcast"(%42) : (tensor<1x64xi32>) -> tensor<32x64xi32>
%45 = arith.addi %43, %44 : tensor<32x64xi32>
%46 = "triton.broadcast"(%arg1) : (!triton.ptr<f16>) -> tensor<32x64x!triton.ptr<f16>>
%47 = "triton.getelementptr"(%46, %45) : (tensor<32x64x!triton.ptr<f16>>, tensor<32x64xi32>) -> tensor<32x64x!triton.ptr<f16>>
%cst = arith.constant 0.000000e+00 : f32
%48 = "triton.broadcast"(%cst) : (f32) -> tensor<64x64xf32>
%c0_i32 = arith.constant 0 : i32
%c32_i32 = arith.constant 32 : i32
%49 = arith.index_cast %c0_i32 : i32 to index
%50 = arith.index_cast %arg5 : i32 to index
%51 = arith.index_cast %c32_i32 : i32 to index
%52:3 = scf.for %arg9 = %49 to %50 step %51 iter_args(%arg10 = %48, %arg11 = %36, %arg12 = %47) -> (tensor<64x64xf32>, tensor<64x32x!triton.ptr<f16>>, tensor<32x64x!triton.ptr<f16>>) {
%cst_14 = arith.constant dense<true> : tensor<64x32xi1>
%cst_15 = arith.constant dense<0.000000e+00> : tensor<64x32xf16>
%82 = "triton.load"(%arg11, %cst_14, %cst_15) {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : (tensor<64x32x!triton.ptr<f16>>, tensor<64x32xi1>, tensor<64x32xf16>) -> tensor<64x32xf16>
%cst_16 = arith.constant dense<true> : tensor<32x64xi1>
%cst_17 = arith.constant dense<0.000000e+00> : tensor<32x64xf16>
%83 = "triton.load"(%arg12, %cst_16, %cst_17) {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : (tensor<32x64x!triton.ptr<f16>>, tensor<32x64xi1>, tensor<32x64xf16>) -> tensor<32x64xf16>
%cst_18 = arith.constant 0.000000e+00 : f32
%84 = "triton.broadcast"(%cst_18) : (f32) -> tensor<64x64xf32>
%85 = "triton.dot"(%82, %83, %84) {allowTF32 = true} : (tensor<64x32xf16>, tensor<32x64xf16>, tensor<64x64xf32>) -> tensor<64x64xf32>
%86 = arith.addf %arg10, %85 : tensor<64x64xf32>
%c32_i32_19 = arith.constant 32 : i32
%87 = "triton.broadcast"(%c32_i32_19) : (i32) -> tensor<64x32xi32>
%88 = "triton.getelementptr"(%arg11, %87) : (tensor<64x32x!triton.ptr<f16>>, tensor<64x32xi32>) -> tensor<64x32x!triton.ptr<f16>>
%c32_i32_20 = arith.constant 32 : i32
%89 = arith.muli %arg7, %c32_i32_20 : i32
%90 = "triton.broadcast"(%89) : (i32) -> tensor<32x64xi32>
%91 = "triton.getelementptr"(%arg12, %90) : (tensor<32x64x!triton.ptr<f16>>, tensor<32x64xi32>) -> tensor<32x64x!triton.ptr<f16>>
scf.yield %86, %88, %91 : tensor<64x64xf32>, tensor<64x32x!triton.ptr<f16>>, tensor<32x64x!triton.ptr<f16>>
}
%53 = arith.truncf %52#0 : tensor<64x64xf32> to tensor<64x64xf16>
%c64_i32_11 = arith.constant 64 : i32
%54 = arith.muli %14, %c64_i32_11 : i32
%55 = triton.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
%56 = "triton.broadcast"(%54) : (i32) -> tensor<64xi32>
%57 = arith.addi %56, %55 : tensor<64xi32>
%c64_i32_12 = arith.constant 64 : i32
%58 = arith.muli %16, %c64_i32_12 : i32
%59 = triton.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
%60 = "triton.broadcast"(%58) : (i32) -> tensor<64xi32>
%61 = arith.addi %60, %59 : tensor<64xi32>
%62 = "triton.reshape"(%57) {shape = [64, 1]} : (tensor<64xi32>) -> tensor<64x1xi32>
%63 = "triton.broadcast"(%arg8) : (i32) -> tensor<64x1xi32>
%64 = arith.muli %63, %62 : tensor<64x1xi32>
%65 = "triton.broadcast"(%arg2) : (!triton.ptr<f16>) -> tensor<64x1x!triton.ptr<f16>>
%66 = "triton.getelementptr"(%65, %64) : (tensor<64x1x!triton.ptr<f16>>, tensor<64x1xi32>) -> tensor<64x1x!triton.ptr<f16>>
%67 = "triton.reshape"(%61) {shape = [1, 64]} : (tensor<64xi32>) -> tensor<1x64xi32>
%c1_i32_13 = arith.constant 1 : i32
%68 = "triton.broadcast"(%c1_i32_13) : (i32) -> tensor<1x64xi32>
%69 = arith.muli %67, %68 : tensor<1x64xi32>
%70 = "triton.broadcast"(%66) : (tensor<64x1x!triton.ptr<f16>>) -> tensor<64x64x!triton.ptr<f16>>
%71 = "triton.broadcast"(%69) : (tensor<1x64xi32>) -> tensor<64x64xi32>
%72 = "triton.getelementptr"(%70, %71) : (tensor<64x64x!triton.ptr<f16>>, tensor<64x64xi32>) -> tensor<64x64x!triton.ptr<f16>>
%73 = "triton.reshape"(%57) {shape = [64, 1]} : (tensor<64xi32>) -> tensor<64x1xi32>
%74 = "triton.broadcast"(%arg3) : (i32) -> tensor<64x1xi32>
%75 = arith.cmpi slt, %73, %74 : tensor<64x1xi32>
%76 = "triton.reshape"(%61) {shape = [1, 64]} : (tensor<64xi32>) -> tensor<1x64xi32>
%77 = "triton.broadcast"(%arg4) : (i32) -> tensor<1x64xi32>
%78 = arith.cmpi slt, %76, %77 : tensor<1x64xi32>
%79 = "triton.broadcast"(%75) : (tensor<64x1xi1>) -> tensor<64x64xi1>
%80 = "triton.broadcast"(%78) : (tensor<1x64xi1>) -> tensor<64x64xi1>
%81 = arith.andi %79, %80 : tensor<64x64xi1>
"triton.store"(%72, %53, %81) : (tensor<64x64x!triton.ptr<f16>>, tensor<64x64xf16>, tensor<64x64xi1>) -> ()
return
}
}

95
rewrite-test/jit/matmul/matmul.py Normal file
View File

@@ -0,0 +1,95 @@
import triton
import triton.language as tl
import torch
@triton.jit
def matmul_kernel(
# Pointers to matrices
a_ptr, b_ptr, c_ptr,
# Matrix dimensions
M, N, K,
# The stride variables represent how much to increase the ptr by when moving by 1
# element in a particular dimension. E.g. stride_am is how much to increase a_ptr
# by to get the element one row down (A has M rows)
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
# Meta-parameters
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
GROUP_SIZE_M: tl.constexpr,
):
"""Kernel for computing the matmul C = A x B.
A has shape (M, K), B has shape (K, N) and C has shape (M, N)
"""
# -----------------------------------------------------------
# Map program ids `pid` to the block of C it should compute.
# This is done in a grouped ordering to promote L2 data reuse
# See above `L2 Cache Optimizations` section for details
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
num_pid_in_group = GROUP_SIZE_M * num_pid_n
group_id = pid // num_pid_in_group
first_pid_m = group_id * GROUP_SIZE_M
group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
pid_m = first_pid_m + (pid % group_size_m)
pid_n = (pid % num_pid_in_group) // group_size_m
# ----------------------------------------------------------
# Create pointers for the first blocks of A and B.
# We will advance this pointer as we move in the K direction
# and accumulate
# a_ptrs is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
# b_ptrs is a block of [BLOCK_SIZE_K, BLOCK_SIZE_n] pointers
# see above `Pointer Arithmetics` section for details
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
# -----------------------------------------------------------
# Iterate to compute a block of the C matrix
# We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
# of fp32 values for higher accuracy.
# `accumulator` will be converted back to fp16 after the loop
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, K, BLOCK_SIZE_K):
# Note that for simplicity, we don't apply a mask here.
# This means that if K is not a multiple of BLOCK_SIZE_K,
# this will access out-of-bounds memory and produce an
# error or (worse!) incorrect results.
a = tl.load(a_ptrs)
b = tl.load(b_ptrs)
# We accumulate along the K dimension
accumulator += tl.dot(a, b)
# Advance the ptrs to the next K block
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
c = accumulator.to(tl.float16)
# -----------------------------------------------------------
# Write back the block of the output matrix C
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
tl.store(c_ptrs, c, mask=c_mask)
a = torch.randn((512, 512), device='cuda', dtype=torch.float16)
b = torch.randn((512, 512), device='cuda', dtype=torch.float16)
c = torch.empty((512, 512), device='cuda', dtype=torch.float16)
mod, ctx = matmul_kernel.compile_to_ttir(
a, b, c,
512, 512, 512,
a.stride(0), a.stride(1),
b.stride(0), b.stride(1),
c.stride(0), c.stride(1),
64, 64, 32,
8, grid=(2,)
)
mod.dump()
mod.verify()