f278d9741a
improvements and bugfixes: * Added preliminary support for extended Einstein summation in PyTriton * Significant performance improvement on FP32 kernels containing matrix multiplication * Added re-coalescing pass for FP16 kernels containing matrix multiplication * Various bugfixes
126 lines
4.4 KiB
Python
126 lines
4.4 KiB
Python
import triton
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class _dot(triton.function):
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src = """
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void dot(TYPE * A __noalias __readonly __aligned(16),
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TYPE * B __noalias __readonly __aligned(16),
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TYPE * C,
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float alpha,
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int M, int N, int K,
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int lda __multipleof(8),
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int ldb __multipleof(8),
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int ldc) {
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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 rm[TM] = ridx * TM + 0 ... TM;
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int rn[TN] = ridy * TN + 0 ... TN;
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int rk[TK] = 0 ... TK;
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// pointers to operands
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TYPE* pa[SHAPE_A] = A + rk[BROADCAST_AK] * STRIDE_AK + rm[BROADCAST_AM] * STRIDE_AM;
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TYPE* pb[SHAPE_B] = B + rk[BROADCAST_BK] * STRIDE_BK + rn[BROADCAST_BN] * STRIDE_BN;
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// prefetches operands
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bool checka[SHAPE_A] = rk[BROADCAST_AK] < K;
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bool checkb[SHAPE_B] = rk[BROADCAST_BK] < K;
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TYPE a[SHAPE_A] = checka ? *pa : 0;
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TYPE b[SHAPE_B] = checkb ? *pb : 0;
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// reduction loop
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float c[TM, TN] = 0;
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for(int k = K; k > 0; k -= TK){
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c += USE_A @ USE_B;
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bool checka[SHAPE_A] = k > TK;
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bool checkb[SHAPE_B] = k > TK;
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pa += TK * STRIDE_AK;
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pb += TK * STRIDE_BK;
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a = *?(checka)pa;
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b = *?(checkb)pb;
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}
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//c = c * alpha;
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// epilogue
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int rxm[TM] = get_program_id(0) * TM + 0 ... TM;
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int rxn[TN] = get_program_id(1) * TN + 0 ... TN;
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TYPE* pc[TM, TN] = C + rxm[:, newaxis] * ldc + rxn[newaxis, :];
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bool checkc[TM, TN] = (rxm[:, newaxis] < M) && (rxn[newaxis, :] < N);
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*?(checkc)pc = (TYPE[TM, TN])c;
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}
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"""
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kernel = triton.kernel(src, ['C'])
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@staticmethod
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def _call(a, b, transpose_a, transpose_b, bench):
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# extract shapes
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shape_a = triton.shape(a)
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shape_b = triton.shape(b)
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M, Ka = shape_a[0], shape_a[1]
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Kb, N = shape_b[0], shape_b[1]
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# transpose shapes
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if transpose_a:
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M, Ka = Ka, M
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if transpose_b:
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Kb, N = N, Kb
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# contiguous dimensions
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lda = M if transpose_a else Ka
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ldb = Kb if transpose_b else N
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ldc = N
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# data-type
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dtype = a.dtype
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# allocate output
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c = triton.empty([M, N], dtype = dtype)
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# compute
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grid = lambda opt: [triton.cdiv(M, opt.d('TM')), triton.cdiv(N, opt.d('TN'))]
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# macros -- not necessary but makes kernel source-code simpler
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macros = {# handle A transposition
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'USE_A' : '^a' if transpose_a else 'a',
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'STRIDE_AK' : 'lda' if transpose_a else '1',
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'STRIDE_AM' : '1' if transpose_a else 'lda',
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'BROADCAST_AK': ':, newaxis' if transpose_a else 'newaxis, :',
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'BROADCAST_AM': 'newaxis, :' if transpose_a else ':, newaxis',
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'SHAPE_A' : 'TK, TM' if transpose_a else 'TM, TK',
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# handle B transposition
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'USE_B' : '^b' if transpose_b else 'b',
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'STRIDE_BK' : '1' if transpose_b else 'ldb',
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'STRIDE_BN' : 'ldb' if transpose_b else '1',
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'BROADCAST_BK': 'newaxis, :' if transpose_b else ':, newaxis',
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'BROADCAST_BN': ':, newaxis' if transpose_b else 'newaxis, :',
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'SHAPE_B' : 'TN, TK' if transpose_b else 'TK, TN'}
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_dot.kernel(a, b, c, 1., M, N, Ka, lda, ldb, ldc,
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grid, bench=bench,
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AT = transpose_a, BT = transpose_b, TYPE = dtype,
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TM = [64], TN = [128], TK = [8], **macros)
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return c
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@staticmethod
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def forward(ctx, a, b, transpose_a = False, transpose_b = False, bench = 0):
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ctx.save_for_backward(a, b)
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ctx.t_a = transpose_a
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ctx.t_b = transpose_b
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ctx.bench = bench
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return _dot._call(a, b, transpose_a, transpose_b, bench)
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@staticmethod
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def backward(ctx, dy):
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a, b = ctx.saved_tensors
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t_a, t_b = ctx.t_a, ctx.t_b
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bench = ctx.bench
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if not t_a and not t_b:
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da = _dot._call(dy, b, False, True, bench)
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db = _dot._call(a, dy, True, False, bench)
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elif not t_a and t_b:
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da = _dot._call(dy, b, False, False, bench)
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db = _dot._call(dy, a, True, False, bench)
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elif t_a and not t_b:
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da = _dot._call(b, dy, False, True, bench)
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db = _dot._call(a, dy, False, False, bench)
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elif t_a and t_b:
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da = _dot._call(b, dy, True, True, bench)
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db = _dot._call(dy, a, True, True, bench)
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else:
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assert False
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return da, db, None, None, None
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dot = _dot.apply |