def shutdown():
"""Finalizes CUDA global state.
This function is automatically called by :mod:`atexit`. Multiple calls are
allowed, so user can manually call this function if necessary.
"""
global _contexts, _cublas_handles, _pid, _pools
pid = os.getpid()
if _pid != pid: # not initialized
return
for cublas_handle in six.itervalues(_cublas_handles):
cublas.cublasDestroy(cublas_handle)
_cublas_handles = {}
cumisc.shutdown()
_pools = {}
for ctx in six.itervalues(_contexts):
ctx.detach()
_contexts = {}
_pid = None # mark as uninitialized
def classify(image_names, model_file_name, output_names):
"""
Classify a set of images using the given model.
Parameters
----------
image_names : iterable of strings
names of the input images
model_file_name : string
name of the file containing the model
output_names : iterable of strings
names of the output images
Notes
-----
image_names and output_names should have the same length and indices match. i.e. image_names[idx] -> output_names[idx]
"""
handle = cublas.cublasCreate()
model = serial.load(model_file_name)
outputs = []
for image_name, output_name in zip(image_names, output_names):
image = load_image(image_name)
output = classify_image(image, model, handle)
save_image(np.int32(np.round(output*255)), output_name)
cublas.cublasDestroy(handle)
def shutdown():
"""Finalizes CUDA global state.
This function is automatically called by :mod:`atexit`. Multiple calls are
allowed, so user can manually call this function if necessary.
"""
global _contexts, _cublas_handles, _pid, _pools
pid = os.getpid()
if _pid != pid: # not initialized
return
for cublas_handle in _cublas_handles.itervalues():
cublas.cublasDestroy(cublas_handle)
_cublas_handles = {}
cumisc.shutdown()
_pools = {}
for ctx in _contexts.itervalues():
ctx.detach()
_contexts = {}
_pid = None # mark as uninitialized
def transpose(a):
'''
https://github.com/lebedov/scikit-cuda/issues/33
pip install --upgrade --no-deps git+https://github.com/lebedov/scikits.cuda.git
:return:
'''
import time
import numpy as np
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
import scikits.cuda.cublas as cublas
handle = cublas.cublasCreate()
# N = 1000
# a = np.random.rand(N, N)
R = a.shape[0]
C = a.shape[1]
a_gpu = gpuarray.to_gpu(a)
a_trans_gpu = gpuarray.zeros((C, R), dtype=np.double)
alpha = 1.0
beta = 0.0
start = time.time()
cublas.cublasDgeam(handle, 't', 'n', R, R,
alpha, a_gpu.gpudata, R,
beta, a_gpu.gpudata, R,
a_trans_gpu.gpudata, R)
print time.time()-start
# assert np.allclose(a_trans_gpu.get(), a.T)
cublas.cublasDestroy(handle)
return a_trans_gpu
def calc_x(Kp1, C, Cm1, rp1, lm2, Am1, A, Ap1, lm1_s, lm1_si, r_s, r_si, Vsh):
handle = cb.cublasCreate()
if not rp1 is None:
rp1 = garr.to_gpu(sp.asarray(rp1))
if not lm2 is None:
lm2 = garr.to_gpu(sp.asarray(lm2))
lm1_s = garr.to_gpu(sp.asarray(lm1_s))
lm1_si = garr.to_gpu(sp.asarray(lm1_si))
r_s = garr.to_gpu(sp.asarray(r_s))
r_si = garr.to_gpu(sp.asarray(r_si))
A = list(map(garr.to_gpu, A))
if not Am1 is None:
Am1 = list(map(garr.to_gpu, Am1))
if not Ap1 is None:
Ap1 = list(map(garr.to_gpu, Ap1))
Vsh = list(map(garr.to_gpu, Vsh))
if not Cm1 is None:
Cm1 = [[garr.to_gpu(Cm1[t, s]) for t in range(Cm1.shape[1])] for s in range(Cm1.shape[0])]
if not (C is None and Kp1 is None):
C = [[garr.to_gpu(C[s, t]) for t in range(C.shape[1])] for s in range(C.shape[0])]
Kp1 = garr.to_gpu(Kp1)
x = calc_x_G(Kp1, C, Cm1, rp1, lm2, Am1, A, Ap1, lm1_s, lm1_si, r_s, r_si, Vsh, handle=handle)
cb.cublasDestroy(handle)
return x.get()
def classify(image_names, model_file_name, output_names):
"""
Classify a set of images using the given model.
Parameters
----------
image_names : iterable of strings
names of the input images
model_file_name : string
name of the file containing the model
output_names : iterable of strings
names of the output images
Notes
-----
image_names and output_names should have the same length and indices match. i.e. image_names[idx] -> output_names[idx]
This network copies the weights to the gpu once to classify all the images as it should. This can be used as a model
to make the same change to the fully connected network.
"""
handle = cublas.cublasCreate()
model = serial.load(model_file_name)
layers = model.layers
convs = layers[:-1]; softmax = layers[-1];
convs = map(lambda layer: layer.get_params(), convs)
kernels = map(lambda layer: np.array(layer[0].eval()), convs)
#This can be simplified
kernels = map(lambda kernel: np.ascontiguousarray(np.rollaxis(kernel, 0, 3)), kernels)
kdims = map(lambda kernel: kernel.shape, kernels)
kernels = map(lambda layer: layer[0].dimshuffle(3, 0, 1, 2).eval(), convs)
kernels = map(lambda kernel, kdim: kernel.reshape(kdim), kernels, kdims)
biases = map(lambda layer: np.array(layer[1].eval()), convs)
bias_dims = map(lambda bias: bias.shape, biases)
max_sizes = map(lambda layer: layer.pool_shape + [layer.num_pieces], layers[:-1])
weights = softmax.get_params()[1]; bias = softmax.get_params()[0];
soft_weights = softmax.get_params()[1].reshape((3, 3, 32, 2)).dimshuffle(3, 2, 0, 1).eval()
soft_weights = np.ascontiguousarray(np.reshape(soft_weights, (2, 288)).transpose())
soft_bias = softmax.get_params()[0].get_value()[::1]
window = layers[0].input_space.shape
outputs = []
for image_name, output_name in zip(image_names, output_names):
image = load_image(image_name)
output = classify_image(image, model, kernels, biases, max_sizes, soft_weights, soft_bias, window, handle)
save_image(np.int8(np.round(output*255)), output_name)
cublas.cublasDestroy(handle)
def calc_x(Kp1, C, Cm1, rp1, lm2, Am1, A, Ap1, lm1_s, lm1_si, r_s, r_si, Vsh):
handle = cb.cublasCreate()
if not rp1 is None:
rp1 = garr.to_gpu(sp.asarray(rp1))
if not lm2 is None:
lm2 = garr.to_gpu(sp.asarray(lm2))
lm1_s = garr.to_gpu(sp.asarray(lm1_s))
lm1_si = garr.to_gpu(sp.asarray(lm1_si))
r_s = garr.to_gpu(sp.asarray(r_s))
r_si = garr.to_gpu(sp.asarray(r_si))
A = list(map(garr.to_gpu, A))
if not Am1 is None:
Am1 = list(map(garr.to_gpu, Am1))
if not Ap1 is None:
Ap1 = list(map(garr.to_gpu, Ap1))
Vsh = list(map(garr.to_gpu, Vsh))
if not Cm1 is None:
Cm1 = [[garr.to_gpu(Cm1[t, s]) for t in range(Cm1.shape[1])]
for s in range(Cm1.shape[0])]
if not (C is None and Kp1 is None):
C = [[garr.to_gpu(C[s, t]) for t in range(C.shape[1])]
for s in range(C.shape[0])]
Kp1 = garr.to_gpu(Kp1)
x = calc_x_G(Kp1,
C,
Cm1,
rp1,
lm2,
Am1,
A,
Ap1,
lm1_s,
lm1_si,
r_s,
r_si,
Vsh,
handle=handle)
cb.cublasDestroy(handle)
return x.get()
def main():
"""
For testing and timing.
"""
handle = cublas.cublasCreate()
image = np.float32((np.random.rand(1024, 1024) - .5) * 2)
model = serial.load(model_file_name)
layers = model.layers
patch_dims = (39, 39)
#There is a bug that occurs if running with too long a batch_rows_l
#Most likely a memory allocation issue that is not being reported correctly
batch_rows_l = [8]
batchsizes = map(lambda x: x*(1024-39+1), batch_rows_l)
pixels = [(x, y) for x in range(1024-39+1) for y in range(1024-39+1)]
#Uncomment to use pylearn2 to classify to check result
p_output = pylearn2_computation(model, image, patch_dims, batchsizes[0], pixels)
p_output = np.transpose(p_output)
num_trials = 1
for batchsize, batch_rows in zip(batchsizes, batch_rows_l):
st = time.time()
for trial in range(num_trials):
output = gpu_computation(image, patch_dims, batchsize, batch_rows, layers, pixels, handle)
output = output.get()
tot = time.time()-st
print "Batchsize {0}".format(batchsize)
print "Total time: {0:.4e} seconds".format(tot)
print "Time per pixel: {0:.4e} seconds".format(tot/len(pixels*num_trials))
print "Pixels per second: {0:.4e}".format(len(pixels*num_trials)/tot)
for end in time_ends:
end.synchronize()
sgemm_times = map(lambda start, end: end.time_since(start)/1000, time_starts, time_ends)
tot_sgemm_time = sum(sgemm_times)
print "Total sgemm time: {0:.4e} seconds\nTotal gflop: {1:.4e}\nGflops: {2:.4e}".format(tot_sgemm_time, sgemm_gflop, sgemm_gflop/tot_sgemm_time)
#Uncomment to compare results of gpu and pylearn2 classifications
#output = output.reshape(1024-39, 1024-39)
print output, p_output
print np.allclose(p_output[0], output, rtol=1e-04, atol=1e-07)
cublas.cublasDestroy(handle)
return
def main():
m = 64; k = 512; n = 400;
#m = 2; k = 3; n = 4;
handle = cublas.cublasCreate()
_, narrays, batchsize = sys.argv
narrays = int(narrays); batchsize = int(batchsize);
cols = []; kernels = []; biases = [];
pcols = []; pkernels = []; pbiases= []; #lists to stores pointers to gpu arrays
kernel = np.float32((np.random.rand(m, k) -.5) * 2)
kernel = np.float32(np.reshape(np.arange(0, m*k, 1), [m, k]))
for i in range(narrays):
col = np.float32((np.random.rand(k, n) - .5) * 2)
#col = np.float32(np.reshape(np.arange(0, k*n, 1), [k, n]))
bias = np.float32(np.zeros((m, n)))
col_d = gpu.to_gpu(col)
kernel_d = gpu.to_gpu(kernel)
bias_d = gpu.to_gpu(bias)
cols.append(col_d); kernels.append(kernel_d); biases.append(bias_d);
pcols.append(col_d.ptr); pkernels.append(kernel_d.ptr); pbiases.append(bias_d.ptr);
pcols = np.array(pcols); pkernels = np.array(pkernels); pbiases = np.array(pbiases);
pcols_d = gpu.to_gpu(pcols); pkernels_d = gpu.to_gpu(pkernels); pbiases_d = gpu.to_gpu(pbiases);
for i in range(narrays):
compute_sgemm(cols[i], kernels[i], biases[i], 0, handle);
#zero out arrays for checking results
#for i in range(narrays):
#print biases[i]
# biases[i] -= biases[i]
print "\n\n"
for i in range((narrays+batchsize-1)/batchsize):
start = i*batchsize
compute_sgemm_batched(pcols_d[start:start+batchsize], pkernels_d[start:start+batchsize], pbiases_d[start:start+batchsize], m, k, n, 0, handle)
#for i in range(narrays):
# print biases[i]
cublas.cublasDestroy(handle)
ng.dot(devA1,
devB1,
devC1,
alpha=alpha,
beta=beta,
repeat=repeat)
cublas_dot(devA2,
devB2,
devC2,
alpha=alpha,
beta=beta,
repeat=repeat)
partial1 = ng.empty((devC1.shape[0], 1), dtype=np.float32)
partial2 = partial1[0:1, 0:1]
diff = ng.max(abs(devC2 - devC1),
partial=partial1,
out=partial2).get()[0, 0]
mean = ng.mean(abs(devC2), partial=partial1,
out=partial2).get()[0, 0]
#if diff > .1:
print "Error: %.3f%%" % (100 * diff / mean)
print "--------------------------------------------------------------------------------"
cublas.cublasDestroy(handle)
def calc_Bs(N, A, l, l_s, l_si, r, r_s, r_si, C, K, Vsh):
GA = []
for An in A:
if An is None:
GA.append(None)
else:
GAn = []
for Ans in An:
GAn.append(garr.to_gpu(Ans))
GA.append(GAn)
GA.append(None)
Gl = []
Gl_s = []
Gl_si = []
for n in range(len(l)):
if l[n] is None:
Gl.append(None)
Gl_s.append(None)
Gl_si.append(None)
else:
Gl.append(garr.to_gpu(sp.asarray(
l[n]))) #TODO: Support special types...
Gl_s.append(garr.to_gpu(sp.asarray(l_s[n])))
Gl_si.append(garr.to_gpu(sp.asarray(l_si[n])))
Gl.append(None)
Gl_s.append(None)
Gl_si.append(None)
Gr = []
Gr_s = []
Gr_si = []
for n in range(len(r)):
if r[n] is None:
Gr.append(None)
Gr_s.append(None)
Gr_si.append(None)
else:
Gr.append(garr.to_gpu(sp.asarray(
r[n]))) #TODO: Support special types...
Gr_s.append(garr.to_gpu(sp.asarray(r_s[n])))
Gr_si.append(garr.to_gpu(sp.asarray(r_si[n])))
Gr.append(None)
Gr_s.append(None)
Gr_si.append(None)
GK = []
for n in range(len(K)):
if K[n] is None:
GK.append(None)
else:
GK.append(garr.to_gpu(sp.asarray(K[n])))
GK.append(None)
GVsh = []
for n in range(len(Vsh)):
if Vsh[n] is None:
GVsh.append(None)
else:
GVshn = []
for s in range(Vsh[n].shape[0]):
GVshn.append(garr.to_gpu(Vsh[n][s]))
GVsh.append(GVshn)
GC = []
for n in range(len(C)):
if C[n] is None:
GC.append(None)
else:
GCn = []
for s in range(C[n].shape[0]):
GCns = []
for t in range(C[n].shape[1]):
GCns.append(garr.to_gpu(C[n][s, t]))
GCn.append(GCns)
GC.append(GCn)
GC.append(None)
GCts = []
for n in range(len(GC)):
if GC[n] is None:
GCts.append(None)
else:
GCtsn = []
for t in range(len(GC[n])):
GCtsns = []
for s in range(len(GC[n][0])):
GCtsns.append(GC[n][s][t])
GCtsn.append(GCtsns)
GCts.append(GCtsn)
hdl = cb.cublasCreate()
num_strms = 10
curr_stream = cb.cublasGetStream(hdl)
sites_per_strm = max((N) // num_strms, 1)
#print "sites_per_stream = ", sites_per_strm
strms = []
for i in range(N // sites_per_strm):
strms.append(cd.Stream())
GB = [None]
for n in range(1, N + 1):
if (n - 1) % sites_per_strm == 0:
#print n
#print "strm = ", (n - 1) // sites_per_strm
cb.cublasSetStream(hdl, strms[(n - 1) // sites_per_strm].handle)
if not Vsh[n] is None:
if n > 1:
Glm2 = Gl[n - 2]
else:
Glm2 = None
Gx = calc_x_G(GK[n + 1],
GC[n],
GCts[n - 1],
Gr[n + 1],
Glm2,
GA[n - 1],
GA[n],
GA[n + 1],
Gl_s[n - 1],
Gl_si[n - 1],
Gr_s[n],
Gr_si[n],
GVsh[n],
handle=hdl)
GBn = []
for s in range(A[n].shape[0]):
GBns = cla.dot(Gl_si[n - 1], Gx, handle=hdl)
GBns = cla.dot(GBns, GVsh[n][s], transb='C', handle=hdl)
GBns = cla.dot(GBns, Gr_si[n], handle=hdl)
GBn.append(GBns)
GB.append(GBn)
else:
GB.append(None)
cb.cublasSetStream(hdl, curr_stream)
cb.cublasDestroy(hdl)
B = [None]
for n in range(1, N + 1):
if GB[n] is None:
B.append(None)
else:
Bn = sp.empty_like(A[n])
for s in range(A[n].shape[0]):
Bn[s] = GB[n][s].get()
B.append(Bn)
return B
def close_cuda(self):
if not self.hdl is None:
cb.cublasDestroy(self.hdl)
self.hdl = None
glops = max(glops16, glops32, glops64, glops128)
if glops16 == glops:
fastest = 16
elif glops32 == glops:
fastest = 32
elif glops64 == glops:
fastest = 64
else:
fastest = 128
glopsref = cublas_dot(devA2, devB2, devC2, repeat=repeat)
partial1 = ng.empty((devC1.shape[0],1), dtype=np.float32)
partial2 = partial1[0:1,0:1]
diff = ng.max(abs(devC2 - devC1), partial=partial1, out=partial2).get()[0,0]
mean = ng.mean(abs(devC2), partial=partial1, out=partial2).get()[0,0]
flops_diff = glops - glopsref
note = "**************" if flops_diff <= 0 else ""
print "Faster: %.0f gflops Choice: %d Error: %.3f%%%s" % (flops_diff, fastest, 100 * diff / mean, note)
print "--------------------------------------------------------------------------------"
cublas.cublasDestroy(handle)
def gpu_computation(image, kernels, biases, max_sizes, soft_weights, soft_bias, batches, window_sizes):
nbatches = len(batches)
batchsize = len(batches[0])
npixels = nbatches*batchsize
layers = len(kernels)
handle = cublas.cublasCreate()
results = []
result_ps = []
pad = 0; stride = 1;
full_image_d = gpu.to_gpu(image)
image_dims, col_dims, kernel_dims, bias_dims, sgemm_dims, out_dims, ksizes, kchannels_s = compute_dims(image, kernels, biases, max_sizes, batchsize, window_sizes, pad, stride)
b_result = [];
b_offsets_d = [];
kernels_d = [];
cols = []; col_ps = [];
biases_d = [];
sgemm_biases = []; sgemm_biases_ps = [];
outputs = [];
for layer_n, (bias, kernel, sgemm_dim, im_dim, out_dim, max_ksize, ksize, kchannels) in enumerate(zip(biases, kernels, sgemm_dims, image_dims, out_dims, max_sizes, ksizes, kchannels_s)):
col = gpu.empty((batchsize, sgemm_dim[1], sgemm_dim[2]), np.float32)
cols.append(col)
col_ps.append([col[idx, :, :].ptr for idx in range(batchsize)])
#reuse the same kernels for every pixel
kernel_d = gpu.to_gpu(kernel)
kernel_d = kernel_d.reshape(kchannels, ksize*ksize*im_dim[2])
kernels_d.append(kernel_d)
#contain the actual data of the biases
bias = bias.reshape(1, bias.shape[2], bias.shape[0]*bias.shape[1])
batch_bias = np.tile(bias, (batchsize, 1, 1))
batch_bias_d = gpu.to_gpu(batch_bias)
biases_d.append(batch_bias_d)
#scratch space to copy biases to and then write output of sgemm to
sgemm_bias = gpu.empty(batch_bias.shape, np.float32)
sgemm_biases.append(sgemm_bias)
sgemm_biases_ps.append([sgemm_bias[idx, :, :].ptr for idx in range(batchsize)])
#space for output of maxpool
output = gpu.empty((batchsize, out_dim[2], out_dim[0], out_dim[1]), np.float32)
outputs.append(output)
#space for final output
classes = gpu.empty(npixels, np.float32)
soft_weights_d = gpu.to_gpu(soft_weights)
soft_bias = soft_bias.reshape(1, soft_bias.shape[0])
soft_bias_d = gpu.to_gpu(np.ascontiguousarray(np.reshape(np.tile(soft_bias, (batchsize, 1)), (2, batchsize))))
soft_bias_scratch = gpu.empty((soft_bias_d.shape[0], soft_bias_d.shape[1]), np.float32)
col_ps_d = gpu.to_gpu(np.array(col_ps))
kernel_ps = map(lambda x: [x.ptr]*batchsize, kernels_d)
kernel_ps_d = gpu.to_gpu(np.array(kernel_ps))
sgemm_biases_ps_d = gpu.to_gpu(np.array(sgemm_biases_ps))
for batch in batches:
offsets = comp_offsets(batch, full_image_d)
offsets_d = gpu.to_gpu(np.int32(np.array(offsets)))
b_offsets_d.append(offsets_d);
#space to hold final result of each layer
result = gpu.empty((out_dims[layers-1][2], out_dims[layers-1][0], out_dims[layers-1][1]), np.float32)
b_result.append(result)
for batchn, (batch, offsets_d, result) in enumerate(zip(batches, b_offsets_d, b_result)):
image_d = full_image_d
for layer_n, (im_dim, col_dim, kdim, bias_dim, sgemm_dim, out_dim, ksize, kchannels, max_size) in enumerate(zip(image_dims, col_dims, kernel_dims, bias_dims, sgemm_dims, out_dims, ksizes, kchannels_s, max_sizes)):
sgemm_bias = sgemm_biases[layer_n]
cu.memcpy_dtod(sgemm_bias.ptr, biases_d[layer_n].ptr, sgemm_bias.nbytes)
im2col_gpu.compute_im2col_batched(image_d, im_dim[0], im_dim[1], im_dim[2], np.int32(ksize), np.int32(pad), np.int32(stride), offsets_d, layer_n, batchsize, cols[layer_n])
compute_sgemm_batched(col_ps_d[layer_n], kernel_ps_d[layer_n], sgemm_biases_ps_d[layer_n], handle, sgemm_dim[0], sgemm_dim[1], sgemm_dim[2])
sgemm_bias = sgemm_bias.reshape(np.int32(batchsize), np.int32(kchannels), col_dim[0], col_dim[1])
maxpool_gpu.compute_max_batched(sgemm_bias, outputs[layer_n], np.int32(max_size))
image_d = outputs[layer_n]
result = outputs[layers-1]
result = result.reshape(result.shape[0], result.shape[1]*result.shape[2]*result.shape[3])
cu.memcpy_dtod(soft_bias_scratch.ptr, soft_bias_d.ptr, soft_bias_d.nbytes)
np_soft_weights = soft_weights_d.get()
np_result = result.get()
compute_sgemm(soft_weights_d, result, soft_bias_scratch, handle)
offset = batchn*batchsize
soft_max_in = soft_bias_scratch
soft_max.compute_soft_max(soft_max_in, classes, offset)
result_ps.append(result)
cublas.cublasDestroy(handle)
return classes
def destroy_cublas():
cublas.cublasDestroy(handle)
def destroy(self):
if self.handle is not None:
cublas.cublasDestroy(self.handle)
def tearDown(self):
cublas.cublasDestroy(self.cublas_handle)
def close_cuda(self):
if not self.hdl is None:
cb.cublasDestroy(self.hdl)
self.hdl = None
def calc_Bs(N, A, l, l_s, l_si, r, r_s, r_si, C, K, Vsh):
GA = []
for An in A:
if An is None:
GA.append(None)
else:
GAn = []
for Ans in An:
GAn.append(garr.to_gpu(Ans))
GA.append(GAn)
GA.append(None)
Gl = []
Gl_s = []
Gl_si = []
for n in range(len(l)):
if l[n] is None:
Gl.append(None)
Gl_s.append(None)
Gl_si.append(None)
else:
Gl.append(garr.to_gpu(sp.asarray(l[n]))) #TODO: Support special types...
Gl_s.append(garr.to_gpu(sp.asarray(l_s[n])))
Gl_si.append(garr.to_gpu(sp.asarray(l_si[n])))
Gl.append(None)
Gl_s.append(None)
Gl_si.append(None)
Gr = []
Gr_s = []
Gr_si = []
for n in range(len(r)):
if r[n] is None:
Gr.append(None)
Gr_s.append(None)
Gr_si.append(None)
else:
Gr.append(garr.to_gpu(sp.asarray(r[n]))) #TODO: Support special types...
Gr_s.append(garr.to_gpu(sp.asarray(r_s[n])))
Gr_si.append(garr.to_gpu(sp.asarray(r_si[n])))
Gr.append(None)
Gr_s.append(None)
Gr_si.append(None)
GK = []
for n in range(len(K)):
if K[n] is None:
GK.append(None)
else:
GK.append(garr.to_gpu(sp.asarray(K[n])))
GK.append(None)
GVsh = []
for n in range(len(Vsh)):
if Vsh[n] is None:
GVsh.append(None)
else:
GVshn = []
for s in range(Vsh[n].shape[0]):
GVshn.append(garr.to_gpu(Vsh[n][s]))
GVsh.append(GVshn)
GC = []
for n in range(len(C)):
if C[n] is None:
GC.append(None)
else:
GCn = []
for s in range(C[n].shape[0]):
GCns = []
for t in range(C[n].shape[1]):
GCns.append(garr.to_gpu(C[n][s, t]))
GCn.append(GCns)
GC.append(GCn)
GC.append(None)
GCts = []
for n in range(len(GC)):
if GC[n] is None:
GCts.append(None)
else:
GCtsn = []
for t in range(len(GC[n])):
GCtsns = []
for s in range(len(GC[n][0])):
GCtsns.append(GC[n][s][t])
GCtsn.append(GCtsns)
GCts.append(GCtsn)
hdl = cb.cublasCreate()
num_strms = 10
curr_stream = cb.cublasGetStream(hdl)
sites_per_strm = max((N) // num_strms, 1)
#print "sites_per_stream = ", sites_per_strm
strms = []
for i in range(N // sites_per_strm):
strms.append(cd.Stream())
GB = [None]
for n in range(1, N + 1):
if (n - 1) % sites_per_strm == 0:
#print n
#print "strm = ", (n - 1) // sites_per_strm
cb.cublasSetStream(hdl, strms[(n - 1) // sites_per_strm].handle)
if not Vsh[n] is None:
if n > 1:
Glm2 = Gl[n - 2]
else:
Glm2 = None
Gx = calc_x_G(GK[n + 1], GC[n], GCts[n - 1], Gr[n + 1], Glm2, GA[n - 1], GA[n],
GA[n + 1], Gl_s[n - 1], Gl_si[n - 1], Gr_s[n], Gr_si[n], GVsh[n], handle=hdl)
GBn = []
for s in range(A[n].shape[0]):
GBns = cla.dot(Gl_si[n - 1], Gx, handle=hdl)
GBns = cla.dot(GBns, GVsh[n][s], transb='C', handle=hdl)
GBns = cla.dot(GBns, Gr_si[n], handle=hdl)
GBn.append(GBns)
GB.append(GBn)
else:
GB.append(None)
cb.cublasSetStream(hdl, curr_stream)
cb.cublasDestroy(hdl)
B = [None]
for n in range(1, N + 1):
if GB[n] is None:
B.append(None)
else:
Bn = sp.empty_like(A[n])
for s in range(A[n].shape[0]):
Bn[s] = GB[n][s].get()
B.append(Bn)
return B
def destroy(self):
if self.handle is not None:
cublas.cublasDestroy(self.handle)
def tearDown(self):
cublas.cublasDestroy(self.cublas_handle)
