[EXAMPLES] Added conv2d example
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Philippe Tillet
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Philippe Tillet
Philippe Tillet
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python/examples/tutorials/conv2d.py
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python/examples/tutorials/conv2d.py
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import torch
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import triton
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class _conv(torch.autograd.Function):
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src = """
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__global__ void conv(TYPE *A __noalias __readonly __aligned(16),
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TYPE *B __noalias __readonly __aligned(16),
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TYPE *C __noalias __aligned(16),
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float alpha,
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// equivalent matmul
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int M, int N, int K,
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// convolution properties
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int pad_h, int pad_w, int stride_h, int stride_w,
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// pointer increment
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int *ADELTA,
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// memory strides
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int lda_z __multipleof(8), int lda_ci __multipleof(8), int lda_h __multipleof(8), int lda_w __multipleof(8),
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int ldb_ci __multipleof(8), int ldb_r __multipleof(8), int ldb_s __multipleof(8), int ldb_co __multipleof(8),
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int ldc_z __multipleof(8), int ldc_co __multipleof(8), int ldc_p __multipleof(8), int ldc_q __multipleof(8)) {
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// prologue
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int ridx = get_program_id(0);
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int ridy = get_program_id(1);
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int ridz = get_program_id(2);
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/*
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int gridx = M / TM;
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int gridy = N / TN;
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int rid = ridx + ridy * gridx;
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ridx = rid / gridy;
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ridy = rid % gridy;
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*/
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int rm[TM] = ridx * TM + 0 ... TM;
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int rn[TN] = ridy * TN + 0 ... TN;
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// reduction splitting
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K = K / TZ;
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int rk[TK] = ridz * K + 0 ... TK;
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// unpack aggregate rows
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// m = (z, p, q)
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int rq[TM] = rm % QQ;
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int rzp[TM] = rm / QQ;
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int rp[TM] = rzp % PP;
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int rz[TM] = rzp / PP;
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// unpack aggregate reduction
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// k = (ci, r, s)
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int rs [TK] = rk % SS;
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int rcir[TK] = rk / SS;
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int rr [TK] = rcir % RR;
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int rci [TK] = rcir / RR;
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// padding / striding
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int rh_0[TM] = rp * stride_h - pad_h;
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int rw_0[TM] = rq * stride_w - pad_w;
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int rh[TM, TK] = rh_0[:, newaxis] + rr[newaxis, :];
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int rw[TM, TK] = rw_0[:, newaxis] + rs[newaxis, :];
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// pointers to lhs
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int offa[TM, TK] = rz [:, newaxis] * lda_z +
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rci[newaxis, :] * lda_ci +
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rh * lda_h +
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rw * 1;
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TYPE* pa[TM, TK] = A + offa;
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int* padelta[TK] = ADELTA + rk;
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// pointers to rhs
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int offb[TK, TN] = rci[:, newaxis] * ldb_ci +
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rr [:, newaxis] * ldb_r +
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rs [:, newaxis] * ldb_s +
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rn [newaxis, :] * 1;
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TYPE* pb[TK, TN] = B + offb;
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// prefetches operands
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bool checka[TM, TK] = rh >= 0 && rh < HH && rw >= 0 && rw < WW;
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bool checkb[TK, TN] = rk[:, newaxis] < K;
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TYPE a[TM, TK] = checka ? *pa : 0;
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TYPE b[TK, TN] = checkb ? *pb : 0;
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// reduction loop
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float acc[TM, TN] = 0;
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for(int k = K; k > 0; k -= TK){
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acc += a @ b;
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// increment A
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int adelta[TK] = *padelta;
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padelta += TK;
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pa += adelta[newaxis, :];
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// increment B
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pb += TK * ldb_s;
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// bounds-checking A
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rk += TK;
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rs = rk % SS;
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rcir = rk / SS;
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rr = rcir % RR;
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rh = rh_0[:, newaxis] + rr[newaxis, :];
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rw = rw_0[:, newaxis] + rs[newaxis, :];
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bool checka[TM, TK] = rh >= 0 && rh < HH && rw >= 0 && rw < WW;
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// bounds-checking B
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bool checkb[TK, TN] = k > TK;
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a = checka ? *pa : 0;
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b = *?(checkb)pb;
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}
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acc = acc * alpha;
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TYPE c[TM, TN] = acc;
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// epilogue
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rm = ridx * TM + 0 ... TM;
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rn = ridy * TN + 0 ... TN;
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rq = rm % QQ;
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rzp = rm / QQ;
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rp = rzp % PP;
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rz = rzp / PP;
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int offc[TM, TN] = rz [:, newaxis] * ldc_z +
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rn [newaxis, :] * ldc_co+
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rp [:, newaxis] * ldc_p +
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rq [:, newaxis] * 1;
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TYPE* pc[TM, TN] = C + offc;
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bool checkc[TM, TN] = rm[:, newaxis] < M && rn[newaxis, :] < N;
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#if (TZ==1)
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*?(checkc) pc = c;
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#else
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// accumulate partial result using spin-locks
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int *plock = locks + rid;
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int *pcount = plock + get_num_programs(0) * get_num_programs(1);
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for(int repeat = 1; repeat == 1; repeat = atomic_cas(plock, 0, 1));
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int count = *pcount;
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if(count == 0)
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*?(checkc) pc = c;
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else
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*?(checkc) pc = c + *?(checkc)pc;
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atomic_xchg(pcount, (count + 1) % TZ);
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atomic_xchg(plock, 0);
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#endif
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}
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"""
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kernel = dict()
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@staticmethod
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def unpack(IDX, CI, R, S):
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s = IDX % S
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cr = IDX // S
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r = cr % R
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ci = cr // R
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return ci, r, s
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@staticmethod
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def forward(ctx, a, b, pad, stride, time):
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# create kernel if necessary
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dtype = a.dtype
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# shapes
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Z, CI, H, W = a.shape
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_, R, S, CO = b.shape
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P = (H + 2*pad[0] - R)//stride[0] + 1
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Q = (W + 2*pad[1] - S)//stride[1] + 1
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# compile kernel
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if dtype not in _conv.kernel:
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defines = {
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'TYPE' : dtype,
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'TM' : [64, 128],
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'TN' : [64, 128],
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'TK' : [8],
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'TZ' : [1],
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'LUTSIZE' : 4*CI*R*S,
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'HH': H, 'WW': W, 'PP': P, 'QQ': Q, 'SS': S, 'RR': R,
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}
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idx = torch.arange(CI*R*S)
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ci, r, s = _conv.unpack(idx, CI, R, S)
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nci, nr, ns = _conv.unpack(idx + 8, CI, R, S)
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delta = (nci - ci)*a.stride(1) + (nr - r)*a.stride(2) + (ns - s)*a.stride(3)
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delta = delta.type(torch.int32).cuda()
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_conv.kernel[dtype] = (delta, triton.kernel(_conv.src, num_warps=[2, 4], defines=defines))
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delta, kernel = _conv.kernel[dtype]
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# allocate output
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c = triton.empty([Z, CO, P, Q], dtype=dtype)
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# enqueue
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grid = lambda opt: [triton.cdiv(Z*P*Q, opt.d('TM')),
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triton.cdiv(CO, opt.d('TN'))]
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time[0] = kernel(a, b, c, 1., Z*P*Q, CO, CI*R*S,
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pad[0], pad[1], stride[0], stride[1],
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delta,
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a.stride(0), a.stride(1), a.stride(2), a.stride(3),
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b.stride(0), b.stride(1), b.stride(2), b.stride(3),
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c.stride(0), c.stride(1), c.stride(2), c.stride(3),
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grid=grid, bench=100)
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return c
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conv = _conv.apply
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torch.manual_seed(0)
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Z, H, W, CI, CO, R, S = 1, 32, 64, 256, 2048, 3, 3
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pad = (1, 1)
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stride = (1, 1)
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a = torch.rand((Z, CI, H, W)).cuda()
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b = torch.rand((CI, R, S, CO)).cuda()
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time = [None]
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cc = torch.nn.functional.conv2d(a, b.permute(3,0,1,2), None, stride, pad, [1, 1])
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c = conv(a, b, pad, stride, time)
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print((cc - c).abs().max() / max(cc.max(), c.max()))
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print(time[0], 2*Z*H*W*CI*CO*R*S/(time[0]*1e-9)*1e-12)
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#zc = torch.matmul(a,b)
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#zc_ = dot(a,b)
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