[feature] basic tensor core utilization works
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@@ -54,26 +54,11 @@ void matmul(restrict read_only fp16 *A, restrict read_only fp16 *B,
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}
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int32 rxc[TM] = get_global_range[TM](0);
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int32 ryc[TN] = get_global_range[TN](1);
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int32 ridx = get_range_id(0);
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int32 ridy = get_range_id(1);
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fp32* pc[TM, TN] = C + ryc[newaxis, :]*ldc + rxc[:, newaxis];
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int32 *plock = locks + ridx + ridy*grid0;
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while(__atomic_cas(plock, 0, 1));
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int32 *pcount = plock + grid0*grid1;
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int32 count = *pcount;
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int32 countp1 = select(count == GZ - 1, 0, count + 1);
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fp32* pc[TM, TN] = C + ryc[newaxis, :]*ldc + rxc[:, newaxis];
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int1 checkc0[TM] = rxc < M;
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int1 checkc1[TN] = ryc < N;
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int1 checkc[TM, TN] = checkc0[:, newaxis] && checkc1[newaxis, :];
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if(count == 0) {
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@checkc *pc = c;
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*pcount = countp1;
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}
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else {
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@checkc *pc = c + *pc;
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*pcount = countp1;
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}
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__atomic_cas(plock, 1, 0);
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@checkc *pc = c;
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}
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)";
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@@ -6,14 +6,14 @@ data_files_path = tf.resource_loader.get_data_files_path()
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library_dir = '/home/philippe/development/triton/build/examples/python/tensorflow'
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module = tf.load_op_library(os.path.join(library_dir, 'libtf_blocksparse.so'))
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M, N, K = 16, 16, 16
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M, N, K = 256, 256, 256
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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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locks = tf.placeholder(tf.int32, shape=[4096])
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c = module.dot(a, b, locks)
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# Reference
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ha = np.ones((M, K)).astype(np.float16)
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hb = np.ones((N, K)).astype(np.float16)
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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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hresult = np.dot(hb.T, ha)
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# Run
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@@ -22,4 +22,7 @@ sess.run(tf.global_variables_initializer())
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result = sess.run([c], feed_dict = {locks: np.zeros(4096),
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a: ha,
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b: hb})
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print(result - hresult)
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print(result)
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print(hresult)
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#print(result - hresult)
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print(np.max(np.abs(result - hresult)))
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