84 lines
2.3 KiB
Python
84 lines
2.3 KiB
Python
import triton
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import tensorflow as tf
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import numpy as np
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src = """
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const tunable int TM = {128};
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const tunable int TN = {128};
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const tunable int TK = {32};
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void matmul(restrict read_only align(16) half *A,
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restrict read_only align(16) half *B,
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restrict read_only align(16) half *C,
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int M, int N, int K,
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multiple_of(8) int lda, multiple_of(8) int ldb, int ldc) {
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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 rxa[TM] = ridx * TM + (0 ... TM);
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int ryb[TN] = ridy * TN + (0 ... TN);
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int rka[TK] = 0 ... TK;
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int rkb[TK] = 0 ... TK;
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float xc[TM, TN] = 0;
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half* pa[TM, TK] = A + rka[newaxis, :]*lda + rxa[:, newaxis];
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half* pb[TN, TK] = B + rkb[newaxis, :]*ldb + ryb[:, newaxis];
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half a[TM, TK] = *pa;
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half b[TN, TK] = *pb;
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for(int k = K; k > 0; k = k - TK){
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xc = dot(a, trans(b), xc);
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pa = pa + TK*lda;
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pb = pb + TK*ldb;
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a = *pa;
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b = *pb;
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}
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int rxc[TM] = ridx * TM + (0 ... TM);
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int ryc[TN] = ridy * TN + (0 ... TN);
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half* pc[TM, TN] = C + ryc[newaxis, :] + rxc[:, newaxis]*ldc;
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half c[TM, TN] = xc;
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bool checkc0[TM] = rxc < M;
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bool checkc1[TN] = ryc < N;
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bool checkc[TM, TN] = checkc0[:, newaxis] && checkc1[newaxis, :];
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@checkc *pc = c;
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}
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"""
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class dot:
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def __init__(self):
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self.matmul = triton.make_tensorflow_op(src, ['C'], ['(M + #TM - 1)/#TM', '(N + #TN - 1)/#TN'])
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def __call__(self, a, b):
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shape_a = tf.shape(a)
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shape_b = tf.shape(b)
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M = shape_a[0]
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K = shape_a[1]
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N = shape_b[0]
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lda = M
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ldb = K
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ldc = N
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c = triton.empty([M, N])
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return self.matmul.matmul(a, b, c, M, N, K, lda, ldb, ldc)
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dot_tn = dot()
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def run_dot():
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M, N, K = 128, 128, 128
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a = tf.placeholder(tf.float16, shape=[M, K])
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b = tf.placeholder(tf.float16, shape=[N, K])
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# c = tf.matmul(a, b, transpose_a=True)
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c = dot_tn(a, b)
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# Reference
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ha = np.random.rand(M, K).astype(np.float16)
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hb = np.random.rand(N, K).astype(np.float16)
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# Run
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sess = tf.InteractiveSession()
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sess.run(tf.global_variables_initializer())
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result = sess.run([c], feed_dict = {a: ha,
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b: hb})[0]
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# Test
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hresult = np.dot(ha.T, hb)
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dif = np.abs(result - hresult)
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np.savetxt('dif.dat', dif, '%2.4f')
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print(hresult)
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print(result)
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print("dif: %f" % np.max(dif))
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run_dot() |