reminisce commented on a change in pull request #7226: Extending the GPU dot 
operator
URL: https://github.com/apache/incubator-mxnet/pull/7226#discussion_r132054842
 
 

 ##########
 File path: benchmark/python/dot.py
 ##########
 @@ -0,0 +1,265 @@
+import ctypes
+
+from mxnet.test_utils import *
+import scipy.sparse as sp
+import os
+import time
+import argparse
+
+from mxnet.base import check_call, _LIB
+from util import get_data, estimate_density
+
+parser = argparse.ArgumentParser(description="Benchmark sparse operators",
+                                 
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument('--num-omp-threads', type=int, default=1, help='number of 
omp threads to set in MXNet')
+args = parser.parse_args()
+
+# some data information
+kdda = {
+    'data_mini': 'kdda.t.mini',
+    'data_name': 'kdda.t',
+    'data_origin_name': 'kdda.t.bz2',
+    'url': 
"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/kdda.t.bz2";,
+    'feature_dim': 20216830,
+    'm': 200,
+    'batch_size': [64]
+}
+
+avazu = {
+    'data_mini': 'avazu-app.t.mini',
+    'data_name': 'avazu-app.t',
+    'data_origin_name': 'avazu-app.t.bz2',
+    'url': 
"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/avazu-app.t.bz2";,
+    'feature_dim': 1000000,
+    'm': 500,
+    'batch_size': [64, 128]
+}
+
+
+def measure_cost(wait, repeat, f, *args, **kwargs):
+    start = time.time()
+    if wait:
+        for i in range(repeat):
+            (f(*args, **kwargs)).wait_to_read()
+    else:
+        for i in range(repeat):
+            f(*args, **kwargs)
+    end = time.time()
+    diff = end - start
+    return diff / repeat
+
+
+def test_dot_real(data_dict):
+    def get_iter(path, data_shape, batch_size):
+        data_train = mx.io.LibSVMIter(data_libsvm=path,
+                                      data_shape=data_shape,
+                                      batch_size=batch_size)
+        data_iter = iter(data_train)
+        return data_iter
+
+    data_dir = os.path.join(os.getcwd(), 'data')
+
+    path = os.path.join(data_dir, data_dict['data_name'])
+    if not os.path.exists(path):
+        get_data(
+            data_dir,
+            data_dict['data_name'],
+            data_dict['url'],
+            data_dict['data_origin_name']
+        )
+        assert os.path.exists(path)
+    
+    k = data_dict['feature_dim']
+    m = data_dict['m']
+    density = estimate_density(path, data_dict['feature_dim'])
+
+    mini_path = os.path.join(data_dir, data_dict['data_mini'])
+    if not os.path.exists(mini_path):
+        os.system("head -n 2000 %r > %r" % (path, mini_path))
+        assert os.path.exists(mini_path)
+    
+    print "Running Benchmarking on %r data" % data_dict['data_mini']
+    for batch_size in data_dict['batch_size']:  # iterator through different 
batch size of choice
+        print "batch_size is %d" % batch_size
+        # model
+        data_shape = (k, )
+        train_iter = get_iter(mini_path, data_shape, batch_size)
+        weight = mx.nd.random_uniform(low=0, high=1, shape=(k, m))
+
+        csr_data = []
+        dns_data = []
+        num_batch = 0
+        for batch in train_iter:
+            data = train_iter.getdata()
+            csr_data.append(data)
+            dns_data.append(data.todense())
+            num_batch += 1
+        bag_of_data = [csr_data, dns_data]
+        num_repeat = 5
+        costs = []
+        for d in bag_of_data:
+            weight.wait_to_read()
+            cost = 0.
+            count = 0
+            for d_batch in d:
+                d_batch.wait_to_read()
+                cost += measure_cost(True, num_repeat, mx.nd.dot, d_batch, 
weight)
+                count += 1
+            costs.append(cost/count)
+        t_sparse = costs[0]
+        t_dense = costs[1]
+        ratio = t_dense / t_sparse
+        print('density(%)\tn\tm\tk\tt_dense/t_sparse\tt_dense\tt_sparse')
+        fmt = "%0.4f\t\t%d\t%d\t%d\t%0.2f\t\t\t%0.4f\t%0.6f"
+        print(fmt % (density * 100, batch_size, m, k, ratio, t_dense, 
t_sparse))
+
+
+def test_dot_synthetic():
+    """benchmark sparse mxnet dot and scipy dot operator with matrices of 
given density.
+    `t_sparse` is the runtime of the invoked sparse dot operator in ms, while 
`t_dense` is the 
+    runtime of dot(dns, dns), with the same matrices except that they are in 
default storage type.
+    """
+    # Benchmark MXNet's sparse dot operator
+    def bench_mx_dot(lhs_shape, rhs_shape, lhs_stype, rhs_stype, lhs_den, 
rhs_den, trans_lhs, ctx, repeat):
+        set_default_context(ctx)
+        # Create matrix instances
+        lhs_nd = rand_ndarray(lhs_shape, lhs_stype, density=lhs_den)
+        rhs_nd = rand_ndarray(rhs_shape, rhs_stype, density=rhs_den)
+        lhs_dns = lhs_nd if lhs_stype == 'default' else lhs_nd.todense()
+        rhs_dns = rhs_nd if rhs_stype == 'default' else rhs_nd.todense()
+        # One warm up run, verify correctness
+        out = mx.nd.dot(lhs_nd, rhs_dns, trans_lhs)
+        out_expected = mx.nd.dot(lhs_dns, rhs_dns, trans_lhs)
+        assert_almost_equal(out.asnumpy(), out_expected.asnumpy(), rtol=1e-2, 
atol=1e-3)
+        # Start benchmarking
+        lhs_nd.wait_to_read()
+        rhs_nd.wait_to_read()
+        sparse_cost = measure_cost(True, repeat, mx.nd.dot, lhs_nd, rhs_nd, 
trans_lhs)
+        dense_cost = measure_cost(True, repeat, mx.nd.dot, lhs_dns, rhs_dns, 
trans_lhs)
+        speedup = dense_cost / sparse_cost
+        # Print results
+        m = lhs_shape[0]
+        k = lhs_shape[1]
+        n = rhs_shape[1]
+        results = '{:15.1f} {:15.1f} {:>10} {:8d} {:8d} {:8d} {:13.2f} 
{:13.2f} {:8.2f}'.format(lhs_den*100, rhs_den*100, str(ctx), m, k, n, 
sparse_cost*1000, dense_cost*1000, speedup)
+        print(results)
+
+    # Benchmark Scipy's sparse dot operator
+    def bench_sp_dot(lhs_shape, rhs_shape, lhs_stype, rhs_stype, lhs_den, 
rhs_den, trans_lhs, ctx, repeat):
+        set_default_context(ctx)
+        assert default_context().device_type is 'cpu'
+        assert lhs_stype is 'csr'
+        assert rhs_stype is 'default'
+        # Create matrix instances
+        lhs_nd = rand_ndarray(lhs_shape, lhs_stype, density=lhs_den)
+        rhs_nd = rand_ndarray(rhs_shape, rhs_stype, density=rhs_den)
+        lhs_nd.wait_to_read()
+        rhs_nd.wait_to_read()
+        lhs_dns_np = np.transpose(lhs_nd.asnumpy()) if trans_lhs else 
lhs_nd.asnumpy()
+        rhs_dns_np = rhs_nd.asnumpy()
+        lhs_csr_sp = sp.spmatrix.transpose(sp.csr_matrix(lhs_nd.asnumpy())) if 
trans_lhs else sp.csr_matrix(lhs_nd.asnumpy())
+        # One warm up run
+        out = sp.spmatrix.dot(lhs_csr_sp, rhs_dns_np)
+        # Start benchmarking
+        sparse_cost = measure_cost(False, repeat, sp.spmatrix.dot, lhs_csr_sp, 
rhs_dns_np)
+        dense_cost = measure_cost(False, repeat, np.dot, lhs_dns_np, 
rhs_dns_np)
+        speedup = dense_cost / sparse_cost
+        # Print results
+        m = lhs_shape[0]
+        k = lhs_shape[1]
+        n = rhs_shape[1]
+        results = '{:15.1f} {:15.1f} {:>10} {:8d} {:8d} {:8d} {:13.2f} 
{:13.2f} {:8.2f}'.format(lhs_den*100, rhs_den*100, str(ctx), m, k, n, 
sparse_cost*1000, dense_cost*1000, speedup)
+        print(results)
+
+    check_call(_LIB.MXSetNumOMPThreads(ctypes.c_int(args.num_omp_threads)))
+    # TODO(haibin): make these runtime options
+    # params
+    # m, n, k        rows and columns of lhs and rhs matrix
+    #                forward  pass:  m x k    * k x n = m x n
+    #                backward pass: (m x k)^T * m x n = k x n
+    # density_lhs    density of the left-hand side matrix
+    # density_rhs    density of the right-hand side matrix, if applicable
+    # num_repeat     number of benchmark runs to average over
+    # context        mx.cpu(), mx.gpu()
+    #                note: benchmark different contexts separately; to 
benchmark cpu, compile without CUDA
+    # mx_benchmarks  csr_dns, csr.T_dns, csr_rsp
+    # sp_benchmarks  csr_dns, csr.T_dns
+    #                note: scipy benchmarks are only conducted if context is 
mx.cpu()
+    m = 512
+    k = [50000, 100000]
+    n = [64, 128]
+    density_lhs = [0.64, 0.32, 0.16, 0.08, 0.04, 0.02, 0.01]
+    density_rhs = [0.64, 0.32, 0.16, 0.08, 0.04, 0.02, 0.01]
+    num_repeat = 10
+    context = mx.cpu()
+    mx_benchmarks = ["csr_dns", "csr.T_dns", "csr_rsp"]
+    sp_benchmarks = ["csr_dns", "csr.T_dns"]
+
+    headline = '{:>15} {:>15} {:>10} {:>8} {:>8} {:>8} {:>13} {:>13} 
{:>8}'.format('lhs_density(%)', 'rhs_density(%)', 'context', 'm', 'k', 'n', 
't_sparse(ms)', 't_dense(ms)', 'speedup')
+    if "csr_dns" in mx_benchmarks:
+        print("==================================================")
+        print("  mxnet sparse dot benchmark: dot(csr, dns) = dns ")
+        print("  (matrix multiplication: m x k * k x n = m x n)  ")
+        print("==================================================")
+        print(headline)
+        transpose_lhs = False
+        for i in range(len(n)):
+            for d_lhs in density_lhs:
+                bench_mx_dot((m, k[i]), (k[i], n[i]), 'csr', 'default', d_lhs, 
1, transpose_lhs, context, num_repeat)
+            print ""
+
+    if "csr_dns" in sp_benchmarks and mx.cpu() == context:
+        print("==================================================")
+        print("  scipy sparse dot benchmark: dot(csr, dns) = dns ")
+        print("  (matrix multiplication: m x k * k x n = m x n)  ")
 
 Review comment:
   Better to put parentheses to here for clearer view: `(m x k) * (k x n) = m x 
n`.
 
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