[math] Support taichi customized op with metal cpu backend

brainpy/_src/math/event/tests/event_csrmv_taichi_VS_event_csrmv_grad.py

@@ -0,0 +1,271 @@
+# from jax_taichi import jax_taichi_call
+
+import time
+from functools import partial
+import os
+
+import brainpy as bp
+import brainpy.math as bm
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+import taichi as ti
+
+bm.set_platform('cpu')
+
+s = [1000, 5000, 10000, 20000, 25000, 30000]
+p = [0.1, 0.2, 0.3, 0.4, 0.5]
+
+shape = [
+        1000, 
+        2500, 
+        5000, 
+        10000, 
+        25000, 
+        37500, 
+        50000
+]
+
+
+
+values_type = [
+               'homo', 
+               'heter'
+               ]
+events_type = [
+               'bool', 
+               'float',
+               ]
+transpose = [
+             True, 
+             False
+            ]
+
+ITERATION = 100
+if bm.get_platform() == 'cpu':
+  ITERATION = 10
+
+print(bm.get_platform())
+
+def sum_op(op):
+  def func(*args, **kwargs):
+    r = op(*args, **kwargs)
+    return r.sum()
+
+  return func
+
+
+def sum_op2(op):
+  def func(*args, **kwargs):
+    r = op(*args, **kwargs)[0]
+    return r.sum()
+
+  return func
+
+@partial(jax.jit, static_argnums=(4, 5))
+def event_csrmv_taichi_grad(weight, indices, indptr, vector, shape, transpose):
+  r = 0
+  for i in range(ITERATION):
+    r += jax.grad(sum_op2(bm.event.csrmv_taichi), argnums=3)(
+      weight, indices, indptr, vector.astype(float), shape=shape, transpose=transpose)
+  return r
+
+@partial(jax.jit, static_argnums=(4, 5))
+def event_csrmv_grad(weight, indices, indptr, vector, shape, transpose):
+  r = 0
+  for i in range(ITERATION):
+    r += jax.grad(sum_op(bm.event.csrmv), argnums=3)(
+      weight, indices, indptr, vector.astype(float), shape=shape, transpose=transpose)
+  return r
+
+
+def test_event_csrmv(shape, values_type, events_type, transpose):
+  rng = bm.random.RandomState(seed=1234)
+  indices, indptr = bp.conn.FixedProb(0.05, seed=1234, allow_multi_conn=True)(*shape).require('pre2post')
+  vector = rng.random(shape[0] if transpose else shape[1]) < 0.1
+  weight = 1.
+
+
+  if events_type == 'float':
+    vector = vector.astype(bm.float32)
+  if values_type == 'heter':
+    heter_data = bm.ones(indices.shape) * weight
+    weight = heter_data
+
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+
+  time0 = time.time()
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time1 = time.time()
+
+  time2 = time.time()
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time3 = time.time()
+
+  time4 = time.time()
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time5 = time.time()
+
+  time6 = time.time()
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time7 = time.time()
+
+  time8 = time.time()
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time9 = time.time()
+
+  time10 = time.time()
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time11 = time.time()
+
+  time12 = time.time()
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time13 = time.time()
+
+  time14 = time.time()
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time15 = time.time()
+
+  time16 = time.time()
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time17 = time.time()
+
+  time18 = time.time()
+  result = jax.block_until_ready(event_csrmv_taichi_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time19 = time.time()
+
+
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+
+  time20 = time.time()
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time21 = time.time()
+
+  time22 = time.time()
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time23 = time.time()
+
+  time24 = time.time()
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time25 = time.time()
+
+  time26 = time.time()
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time27 = time.time()
+
+  time28 = time.time()
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time29 = time.time()
+
+  time30 = time.time()
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time31 = time.time()
+
+  time32 = time.time()
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time33 = time.time()
+
+  time34 = time.time()
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time35 = time.time()
+
+  time36 = time.time()
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time37 = time.time()
+
+  time38 = time.time()
+  result = jax.block_until_ready(event_csrmv_grad(weight, indices, indptr, vector, shape=shape, transpose=transpose))
+  time39 = time.time()
+
+  taichi_aot_time1 = (time1 - time0) * 1000
+  taichi_aot_time2 = (time3 - time2) * 1000
+  taichi_aot_time3 = (time5 - time4) * 1000
+  taichi_aot_time4 = (time7 - time6) * 1000
+  taichi_aot_time5 = (time9 - time8) * 1000
+  taichi_aot_time6 = (time11 - time10) * 1000
+  taichi_aot_time7 = (time13 - time12) * 1000
+  taichi_aot_time8 = (time15 - time14) * 1000
+  taichi_aot_time9 = (time17 - time16) * 1000
+  taichi_aot_time10 = (time19 - time18) * 1000
+  brainpy_time1 = (time21 - time20) * 1000
+  brainpy_time2 = (time23 - time22) * 1000
+  brainpy_time3 = (time25 - time24) * 1000
+  brainpy_time4 = (time27 - time26) * 1000
+  brainpy_time5 = (time29 - time28) * 1000
+  brainpy_time6 = (time31 - time30) * 1000
+  brainpy_time7 = (time33 - time32) * 1000
+  brainpy_time8 = (time35 - time34) * 1000
+  brainpy_time9 = (time37 - time36) * 1000
+  brainpy_time10 = (time39 - time38) * 1000
+  print('shape: ', shape, 'values_type: ', values_type, 'events_type: ', events_type, 'transpose: ', transpose)
+  print('taichi_aot_1: ', taichi_aot_time1, 'ms')
+  print('taichi_aot_3: ', taichi_aot_time3, 'ms')
+  print('taichi_aot_5: ', taichi_aot_time5, 'ms')
+  print('taichi_aot_7: ', taichi_aot_time7, 'ms')
+  print('taichi_aot_9: ', taichi_aot_time9, 'ms')
+  print('brainpylib_1: ', brainpy_time1, 'ms')
+  print('brainpylib_3: ', brainpy_time3, 'ms')
+  print('brainpylib_5: ', brainpy_time5, 'ms')
+  print('brainpylib_7: ', brainpy_time7, 'ms')
+  print('brainpylib_9: ', brainpy_time9, 'ms')
+
+
+  return taichi_aot_time1, taichi_aot_time2, taichi_aot_time3, taichi_aot_time4, taichi_aot_time5,\
+      taichi_aot_time6, taichi_aot_time7, taichi_aot_time8, taichi_aot_time9, taichi_aot_time10,\
+      brainpy_time1, brainpy_time2, brainpy_time3, brainpy_time4, brainpy_time5, \
+      brainpy_time6, brainpy_time7, brainpy_time8, brainpy_time9, brainpy_time10
+
+PATH = os.path.dirname(os.path.abspath(__file__))
+
+# init dataframe
+df = pd.DataFrame(columns=['s', 'p', 'shape[0]', 'shape[1]', 'backend', 'values type', 'events type', 'transpose',
+                           'taichi aot time1(ms)', 'taichi aot time2(ms)', 'taichi aot time3(ms)', 'taichi aot time4(ms)', 'taichi aot time5(ms)',
+                           'taichi aot time6(ms)', 'taichi aot time7(ms)', 'taichi aot time8(ms)', 'taichi aot time9(ms)', 'taichi aot time10(ms)',
+                           'brainpy time1(ms)', 'brainpy time2(ms)', 'brainpy time3(ms)', 'brainpy time4(ms)', 'brainpy time5(ms)',
+                           'brainpy time6(ms)', 'brainpy time7(ms)', 'brainpy time8(ms)', 'brainpy time9(ms)', 'brainpy time10(ms)'])
+
+### RECTANGULAR MATRIX
+if (bm.get_platform() == 'cpu'):
+  for shape1 in shape:
+    for shape2 in shape:
+      for _values_type in values_type:
+          for _events_type in events_type:
+            for _transpose in transpose:
+              taichi_aot_time_1, taichi_aot_time_2, taichi_aot_time_3, taichi_aot_time_4, taichi_aot_time_5,\
+                taichi_aot_time_6, taichi_aot_time_7, taichi_aot_time_8, taichi_aot_time_9, taichi_aot_time_10,\
+                  brainpy_time_1, brainpy_time_2, brainpy_time_3, brainpy_time_4, brainpy_time_5, \
+                  brainpy_time_6, brainpy_time_7, brainpy_time_8, brainpy_time_9, brainpy_time_10 = test_event_csrmv((shape1, shape2), _values_type, _events_type, _transpose)
+              # append to dataframe
+              df.loc[df.shape[0]] = [(shape1, shape2), 0.5 , shape1, shape2, 'cpu', _values_type, _events_type, _transpose,
+                                    taichi_aot_time_1, taichi_aot_time_2, taichi_aot_time_3, taichi_aot_time_4, taichi_aot_time_5,
+                                    taichi_aot_time_6, taichi_aot_time_7, taichi_aot_time_8, taichi_aot_time_9, taichi_aot_time_10,
+                                    brainpy_time_1, brainpy_time_2, brainpy_time_3, brainpy_time_4, brainpy_time_5, 
+                                    brainpy_time_6, brainpy_time_7, brainpy_time_8, brainpy_time_9, brainpy_time_10]
+  df.to_csv(f'{PATH}/event_csrmv_grad_cpu.csv', index=False)
+
+if (bm.get_platform() == 'gpu'):
+  for shape1 in shape:
+    for shape2 in shape:
+      for _values_type in values_type:
+          for _events_type in events_type:
+            for _transpose in transpose:
+              taichi_aot_time_1, taichi_aot_time_2, taichi_aot_time_3, taichi_aot_time_4, taichi_aot_time_5,\
+                taichi_aot_time_6, taichi_aot_time_7, taichi_aot_time_8, taichi_aot_time_9, taichi_aot_time_10,\
+                  brainpy_time_1, brainpy_time_2, brainpy_time_3, brainpy_time_4, brainpy_time_5, \
+                  brainpy_time_6, brainpy_time_7, brainpy_time_8, brainpy_time_9, brainpy_time_10 = test_event_csrmv((shape1, shape2), _values_type, _events_type, _transpose)
+              # append to dataframe
+              df.loc[df.shape[0]] = [(shape1, shape2), 0.5 , shape1, shape2, 'gpu', _values_type, _events_type, _transpose,
+                                    taichi_aot_time_1, taichi_aot_time_2, taichi_aot_time_3, taichi_aot_time_4, taichi_aot_time_5,
+                                    taichi_aot_time_6, taichi_aot_time_7, taichi_aot_time_8, taichi_aot_time_9, taichi_aot_time_10,
+                                    brainpy_time_1, brainpy_time_2, brainpy_time_3, brainpy_time_4, brainpy_time_5, 
+                                    brainpy_time_6, brainpy_time_7, brainpy_time_8, brainpy_time_9, brainpy_time_10]
+  df.to_csv(f'{PATH}/event_csrmv_grad_gpu.csv', index=False)