[GitHub] haojin2 commented on issue #11330: [MXNET-537] add_n(dense, csr, dense) = dense and add_n([dense, csr, rsp]*, dense, [dense, csr, rsp]*) = dense on CPU & GPU

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haojin2 commented on issue #11330: [MXNET-537] add_n(dense, csr, dense) = dense 
and add_n([dense, csr, rsp]*, dense, [dense, csr, rsp]*) = dense on CPU & GPU
URL: https://github.com/apache/incubator-mxnet/pull/11330#issuecomment-398581824
 
 
   Benchmark result for add_n(dense, csr, dense) = dense:
   ([density%] [speedup])
   CPU:
   1.00% 1.1282194997074237
   0.50% 1.1686160529139418
   0.10% 1.1909255730224886
   0.05% 1.1970586102280831
   0.01% 1.202483677804412
   GPU:
   1.00% 1.1627124767202126
   0.50% 1.2392510678426578
   0.10% 1.3169708612264934
   0.05% 1.3275811285384644
   0.01 % 1.3358768672033845
   benchmark script:
   ```python
   import mxnet as mx
   import sys
   import os
   import scipy
   import numpy as np
   from mxnet.test_utils import rand_ndarray, assert_almost_equal
   import time
   
   def measure_cost(repeat, a, b, c, out=None):
       # start bench
       start = time.time()
       results = []
       for i in range(repeat):
           results.append(mx.nd.sparse.add_n(a, b, c, out=out))
       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 = (128, 1000000)
       dns = np.random.uniform(size=shape)
       # context = mx.gpu(0)
       context = mx.cpu()
       mx_dns1 = mx.nd.array(dns, ctx=context)
       mx_dns2 = mx.nd.array(dns, ctx=context)
       for density in [0.01, 0.005, 0.001, 0.0005, 0.0001]:
           mx_csr = rand_ndarray(shape=shape, stype='csr', 
density=density).as_in_context(context)
           mx_csr_dns = mx_csr.tostype('default')
           sparse_cost = 0.0
           dns_cost = 0.0
           mx.nd.waitall()
           #warmup
           check = mx.nd.sparse.add_n(mx_dns1, mx_csr, mx_dns2)
           dns1 = dns + mx_csr_dns.asnumpy() + dns
           assert_almost_equal(check.asnumpy(), dns1, atol=1e-5, rtol=1e-4)
           mx.nd.waitall()
           for i in range(20):
               sparse_cost += measure_cost(5, mx_dns1, mx_csr, mx_dns2)
               dns_cost += measure_cost(5, mx_dns1, mx_csr_dns, mx_dns2)
           print("%.2f %%" % (density*100), dns_cost / sparse_cost)
   
   
   if __name__ == "__main__":
       main()
   ```
   

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