[GitHub] haojin2 commented on issue #10371: [MXNET-263] Support for dot(dns, csr) = dns and dot(dns, csr.T) = dns on GPU

[GitBox]

haojin2 commented on issue #10371: [MXNET-263] Support for dot(dns, csr) = dns 
and dot(dns, csr.T) = dns on GPU
URL: https://github.com/apache/incubator-mxnet/pull/10371#issuecomment-384114344
 
 
   benchmark script:
   `import mxnet as mx
   import numpy as np
   from mxnet.test_utils import assert_almost_equal
   import time
   
   def measure_cost(repeat, f, *args, **kwargs):
       # start bench
       start = time.time()
       results = []
       for i in range(repeat):
           results.append(f(*args, **kwargs))
       for result in results:
           result.wait_to_read()
       end = time.time()
       diff = end - start
       return diff / repeat
   
   def measure_fallback(repeat, a):
       # start bench
       start = time.time()
       results = []
       for i in range(repeat):
           results.append(a.tostype('default'))
       for result in results:
           result.wait_to_read()
       end = time.time()
       diff = end - start
       return diff / repeat
   
   def main():
       shape_lhs = (256, 30000)
       shape_rhs = (30000, 30000)
       dns = np.random.uniform(size=shape_lhs)
       mx_dns = mx.nd.array(dns, ctx=mx.gpu())
       mx_dns_cpy = mx_dns.copy()
       for density in [0.01, 0.005, 0.001, 0.0005, 0.0001]:
           csr = scipy.sparse.random(shape_rhs[0], shape_rhs[1], 
density=density, format = 'csr', dtype=np.float32)
           mx_csr = mx.nd.sparse.csr_matrix((csr.data, csr.indices, 
csr.indptr), shape=shape_rhs, ctx=mx.gpu())
           mx_csr_dns = mx_csr.tostype('default')
           sparse_cost = 0.0
           dns_cost = 0.0
           mx.nd.waitall()
           #warmup
           check = mx.nd.dot(mx_dns, mx_csr, forward_stype='default')
           check_np = np.dot(dns, mx_csr_dns.asnumpy())
           assert_almost_equal(check.asnumpy(), check_np, atol=1e-5, rtol=1e-4)
           print(check.shape)
           mx.nd.waitall()
           for i in range(50):
               sparse_cost += measure_cost(1, mx.nd.dot, mx_dns, mx_csr, 
forward_stype='default')
               dns_cost += measure_fallback(1, mx_csr)
               dns_cost += measure_cost(1, mx.nd.dot, mx_dns, mx_csr_dns)
           print("%.2f %%" % (density*100), dns_cost / sparse_cost)
   
           sparse_cost = 0.0
           dns_cost = 0.0
           check = mx.nd.dot(mx_dns, mx_csr, transpose_b=True, 
forward_stype='default')
           check_np = np.dot(dns, mx_csr_dns.asnumpy().T)
           assert_almost_equal(check.asnumpy(), check_np, atol=1e-5, rtol=1e-4)
           print(check.shape)
           mx.nd.waitall()
           for i in range(50):
               sparse_cost += measure_cost(1, mx.nd.dot, mx_dns, mx_csr, 
transpose_b=True, forward_stype='default')
               dns_cost += measure_fallback(1, mx_csr)
               dns_cost += measure_cost(1, mx.nd.dot, mx_dns, mx_csr_dns, 
transpose_b=True)
           print("%.2f %%" % (density*100), dns_cost / sparse_cost)
   
   
   if __name__ == "__main__":
       main()`

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