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Posted to commits@mxnet.apache.org by gi...@git.apache.org on 2017/08/08 00:40:54 UTC
[GitHub] eric-haibin-lin commented on a change in pull request #7226: Extending the GPU dot operator
eric-haibin-lin commented on a change in pull request #7226: Extending the GPU dot operator
URL: https://github.com/apache/incubator-mxnet/pull/7226#discussion_r131786617
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File path: benchmark/python/dot.py
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@@ -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) ")
+ print("==================================================")
+ print(headline)
+ transpose_lhs = False
+ for i in range(len(n)):
+ for d_lhs in density_lhs:
+ bench_sp_dot((m, k[i]), (k[i], n[i]), 'csr', 'default', d_lhs, 1, transpose_lhs, context, num_repeat)
+ print ""
+
+ if "csr.T_dns" in mx_benchmarks:
+ print("==================================================")
+ print(" mxnet sparse dot benchmark: dot(csr.T, dns) = rsp")
+ print("(matrix multiplication: (m x k)^T * m x n = k x n)")
+ print("==================================================")
+ print(headline)
+ transpose_lhs = True
+ for i in range(len(n)):
+ for d_lhs in density_lhs:
+ bench_mx_dot((m, k[i]), (m, n[i]), 'csr', 'default', d_lhs, 1, transpose_lhs, context, num_repeat)
+ print ""
+
+ if "csr.T_dns" in sp_benchmarks and mx.cpu() == context:
+ print("==================================================")
+ print(" scipy sparse dot benchmark: dot(csr.T, dns) = dns")
+ print("(matrix multiplication: (m x k)^T * m x n = k x n)")
+ print("==================================================")
+ print(headline)
+ transpose_lhs = True
+ for i in range(len(n)):
+ for d_lhs in density_lhs:
+ bench_sp_dot((m, k[i]), (m, n[i]), 'csr', 'default', d_lhs, 1, transpose_lhs, context, num_repeat)
+ print ""
+
+ if "csr_rsp" in mx_benchmarks:
+ print("==================================================")
+ print(" mxnet sparse dot benchmark: dot(csr, rsp) = dns ")
Review comment:
I'm interested in a workload where:
(row_sparse) rhs has the rows exactly needed by (csr) lhs.
e.g. if lhs.indices = [1,2,4,1,2], then we perform dot with rhs where rhs.indices=[1,2,4].
The motivation for `dot(csr, row_sparse)` is that if rhs is too large to fit into GPU memory for `dot(csr, dns)`, then we have to use `sparse_retain` to send generate a `row_sparse` rhs with all the rows needed and copy it over to gpu
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