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

[GENERAL] Various improvements:

Browse Source 
* Sparse einsum in triton.ops.einsum
* Hacky support for fixed-tile-size atomic-add
* Various bugfixes in parser
This commit is contained in:
Philippe Tillet
2020-10-25 11:55:58 -07:00
parent 444907589d
commit 049ab989b5
16 changed files with 574 additions and 331 deletions
Download Patch File Download Diff File Expand all files Collapse all files

18
python/examples/tutorials/mat_mul.py
View File

@@ -56,8 +56,8 @@ class _dot(torch.autograd.Function):
TYPE c[TM, TN] = acc;
// epilogue
int rxm[TM] = get_program_id(0) * TM + 0 ... TM;
int rxn[TN] = get_program_id(1) * TN + 0 ... TN;
int rxm[TM] = ridx * TM + 0 ... TM;
int rxn[TN] = ridy * TN + 0 ... TN;
int offc[TM, TN] = rxm[:, newaxis] * ldc + rxn[newaxis, :];
TYPE* pc[TM, TN] = C + offc;
bool checkc[TM, TN] = (rxm[:, newaxis] < M) && (rxn[newaxis, :] < N);
@@ -95,7 +95,7 @@ class _dot(torch.autograd.Function):
if dtype not in _dot.kernel:
defines = {
'TYPE' : dtype,
'STRIDE_AM': '1', 'STRIDE_AK': 'lda',
'STRIDE_AM': 'lda', 'STRIDE_AK': '1',
'STRIDE_BN': '1', 'STRIDE_BK': 'ldb',
'TM' : [64, 128],
'TN' : [64, 128],
@@ -107,14 +107,12 @@ class _dot(torch.autograd.Function):
# allocate output
M, K = a.shape
K, N = b.shape
c = triton.empty([M,N], dtype=dtype)
c = torch.empty([M,N], dtype=dtype, device=a.device)
# enqueue
grid = lambda opt: [triton.cdiv(M, opt.d('TM')),
triton.cdiv(N, opt.d('TN'))]
time = kernel(a, b, c, 1., M, N, K,
a.stride(0), b.stride(0), c.stride(0),
grid=grid, bench=100)
print(2*M*N*K/(time*1e-6)*1e-9)
a.stride(0), b.stride(0), c.stride(0), grid=grid)
return c
@@ -126,8 +124,10 @@ M, N, K = 2048, 2048, 2048
a = torch.rand((M, K)).cuda()
b = torch.rand((K, N)).cuda()
#a[:] = 1
#b[:] = 1
#zc = torch.matmul(a,b)
zc = torch.matmul(a,b)
zc_ = dot(a,b)
#print(torch.allclose(zc, zc_))
print(torch.allclose(zc, zc_))