def test_multiInstrv1(int_range, m, k, n, add_bias=False):
print ("test_multiInstrv1: %d %d %d %d" % (int_range, m, k, n))
A = np.random.randint(low=-int_range, high=int_range, size=(m, k), dtype=np.int16)
B = np.random.randint(low=-int_range, high=int_range, size=(k, n), dtype=np.int16)
C = np.zeros ((m, n), dtype=np.int16);
D = np.random.randint(low=-int_range, high=int_range, size=(m, k), dtype=np.int16)
E = np.zeros ((m, n), dtype=np.int16);
b0 = np.zeros ((m, n), dtype=np.int32);
b1 = np.zeros ((m, n), dtype=np.int32);
if add_bias == True:
b0 = np.random.randint(low=-int_range, high=int_range, size=(m, n), dtype=np.int32)
b1 = np.random.randint(low=-int_range, high=int_range, size=(m, n), dtype=np.int32)
gemx.sendMat(A)
gemx.sendMat(B)
gemx.sendMat(b0)
gemx.sendMat(C)
gemx.sendMat(D)
gemx.sendMat(E)
gemx.sendMat(b1)
gemx.addFCNOp(A, B, C, b0, 1, 13, 307, 10)
gemx.addFCNOp(D, C, E, b1, 1, 18, 307, 10)
gemx.execute()
gemx.getMat(C)
gemx.getMat(E)
print("test C")
test.multiply_and_cmp(C, A, B, b0, m, n, [1, 0])
print("test E")
test.multiply_and_cmp(E, D, C, b1, m, n, [1, 0])
def test_basic(self,
PE,
mat_A,
mat_B,
bias,
post_scale=[1, 1],
RELU_scale=[1, 0]):
m = mat_A.shape[0]
k = mat_A.shape[1]
n = mat_B.shape[1]
print("test Fcn")
print("test_basic: %d %d %d %d %d" %
(m, k, n, post_scale[0], post_scale[1]))
print("A: ", np.amax(mat_A), np.amin(mat_A), np.average(mat_A))
print("B: ", np.amax(mat_B), np.amin(mat_B), np.average(mat_B))
print("bias: ", np.amax(bias), np.amin(bias), np.average(bias))
C_fpga = np.zeros((m, n), dtype=np.int16, order='C')
gemx.sendMat(mat_A, PE)
gemx.sendMat(mat_B, PE)
gemx.sendMat(C_fpga, PE)
gemx.sendMat(bias, PE)
gemx.addFCNOp(mat_A, mat_B, C_fpga, bias, post_scale[0], post_scale[1],
RELU_scale[0], RELU_scale[1], PE)
gemx.execute(PE)
gemx.clearInstrBuf(PE)
gemx.getMat(C_fpga, PE)
self.multiply_and_cmp(C_fpga, mat_A, mat_B, bias, m, n, post_scale,
RELU_scale)
def test_perf_fcn(A_range, B_range, bias_range, m, k, n, post_scale):
mat_A = np.random.randint(low=-A_range, high=A_range, size=(m, k), dtype=np.int16)
mat_B = np.random.randint(low=-B_range, high=B_range, size=(k, n), dtype=np.int16)
bias = []
if bias_range != 0:
bias = np.random.randint(low=-bias_range, high=bias_range, size=(m, n), dtype=np.int32)
else:
bias = np.zeros ((m, n), dtype=np.int32, order='C');
C_fpga = np.zeros( (m, n), dtype=np.int16)
timePointKernel = []
timePointKernel.append(time.time()) # current time
gemx.sendMat(mat_A)
gemx.sendMat(mat_B)
gemx.sendMat(C_fpga)
gemx.sendMat(bias)
gemx.addFCNOp ( mat_A, mat_B, C_fpga, bias, post_scale[0], post_scale[1],1,0)
timePointKernel.append(time.time()) # send to FPGA
gemx.execute()
timePointKernel.append(time.time()) # call kernel
gemx.getMat(C_fpga)
timePointKernel.append(time.time()) # copy from FPGA
total_operations = 2 * m * n * k + m * n * 3
total_parallel_operations = 2 * m * n * k
freq = gemx.getFreq()
test.test_perf(timePointKernel,total_operations,total_parallel_operations,freq,m,k,n)
if m > 4096 and n > 4096 and k > 4096:
print("Skip golden comparision because large matrix size")
else:
test.multiply_and_cmp(C_fpga, mat_A, mat_B, bias, m, n, post_scale)
def loadInstr(self):
gemx.clearInstrBuf()
for i,l in enumerate(self.kmodel.layers):
act = l.get_config()['activation']
if act == 'relu':
gemx.addFCNOp( self.fpga_buf[i], self._qw[i], self.fpga_buf[i+1], self._qb[i], self.post_scale[i][0], self.post_scale[i][1], 0, 0)
else:
gemx.addGEMMOp( self.fpga_buf[i], self._qw[i], self.fpga_buf[i+1], self._qb[i], self.post_scale[i][0], self.post_scale[i][1])
def test_perf_fcn(m,
k,
n,
xclbin_opts,
post_scale=[1, 0],
A_range=32764,
B_range=32764,
bias_range=32764):
ddrWidth = int(xclbin_opts["GEMX_ddrWidth"])
m = test.get_padded_size(m,
int(xclbin_opts["GEMX_gemmMBlocks"]) * ddrWidth)
k = test.get_padded_size(k,
int(xclbin_opts["GEMX_gemmKBlocks"]) * ddrWidth)
n = test.get_padded_size(n,
int(xclbin_opts["GEMX_gemmNBlocks"]) * ddrWidth)
if xclbin_opts["GEMX_dataType"] == "short":
mat_A = np.random.randint(low=-A_range,
high=A_range,
size=(m, k),
dtype=np.int16)
mat_B = np.random.randint(low=-B_range,
high=B_range,
size=(k, n),
dtype=np.int16)
bias = []
if bias_range != 0:
bias = np.random.randint(low=-bias_range,
high=bias_range,
size=(m, n),
dtype=np.int32)
else:
bias = np.zeros((m, n), dtype=np.int32, order='C')
C_fpga = np.zeros((m, n), dtype=np.int16)
else:
mat_A = np.random.uniform(low=-128, high=128,
size=(m, k)).astype(np.float32)
mat_B = np.random.uniform(low=-128, high=128,
size=(k, n)).astype(np.float32)
bias = np.zeros((m, n), dtype=np.float32, order='C')
C_fpga = np.zeros((m, n), dtype=np.float32)
start_time = time.time()
gemx.sendMat(mat_A)
gemx.sendMat(mat_B)
gemx.sendMat(C_fpga)
gemx.sendMat(bias)
gemx.addFCNOp(mat_A, mat_B, C_fpga, bias, post_scale[0], post_scale[1], 1,
0)
gemx.execute()
gemx.clearInstrBuf()
gemx.getMat(C_fpga)
end_time = time.time()
total_operations = 2 * m * n * k + m * n * 3
test.test_perf(end_time - start_time, total_operations, m, k, n, ddrWidth)
test.multiply_and_cmp(C_fpga, mat_A, mat_B, bias, m, n, post_scale)
def test_textfiles(self, path_to_a, path_to_b, path_to_bias,post_scale): mat_A = np.loadtxt(path_to_a, dtype=np.int16) mat_B = np.loadtxt(path_to_b, dtype=np.int16) bias = np.loadtxt(path_to_bias, dtype=np.int32) m = mat_A.shape[0] n = mat_B.shape[1] C_fpga = np.zeros((m, n), dtype=np.int16, order='C') gemx.sendMat(mat_A) gemx.sendMat(mat_B) gemx.sendMat(C_fpga) gemx.sendMat(bias) gemx.addFCNOp (mat_A, mat_B, C_fpga, bias, post_scale[0], post_scale[1], 1, 0) gemx.execute() gemx.clearInstrBuf() gemx.getMat(C_fpga) self.multiply_and_cmp(C_fpga, mat_A, mat_B, bias, m, n, post_scale)
def predict ( self, inp, in_scale, post_scale):
row_padded, col_padded = self.get_padded_shape( inp.shape, self.min_m, self.min_k)
padded_arr = np.zeros ( (row_padded, col_padded), dtype=inp.dtype, order='C')
padded_arr[0:inp.shape[0], 0:inp.shape[1]] = inp
print ("input shape", padded_arr.shape)
np.copyto(self.fpga_buf[0], np.int16( padded_arr * in_scale ), casting='same_kind', where=True)
gemx.sendMat(self.fpga_buf[0])
for i,l in enumerate(self.kmodel.layers):
act = l.get_config()['activation']
if act == 'relu':
gemx.addFCNOp( self.fpga_buf[i], self.w[i], self.fpga_buf[i+1], self.b[i], post_scale[i][0], post_scale[i][1], 0, 0)
else:
gemx.addGEMMOp( self.fpga_buf[i], self.w[i], self.fpga_buf[i+1], self.b[i], post_scale[i][0], post_scale[i][1])
gemx.execute()
gemx.getMat (self.fpga_buf[-1])
return self.fpga_buf[-1][:self.out_dim[0],:self.out_dim[1]]
def test_multi_fcn(ins_count,
m_size,
k_size,
n_size,
post_scale=[1, 0],
A_range=32764,
B_range=32764):
mat_A = []
mat_C = []
mat_bias = []
ddrWidth = int(xclbin_opts["GEMX_ddrWidth"])
for i in range(ins_count):
m_size[i] = test.get_padded_size(
m_size[i],
int(xclbin_opts["GEMX_gemmMBlocks"]) * ddrWidth)
k_size[i] = test.get_padded_size(
k_size[i],
int(xclbin_opts["GEMX_gemmKBlocks"]) * ddrWidth)
n_size[i] = test.get_padded_size(
n_size[i],
int(xclbin_opts["GEMX_gemmNBlocks"]) * ddrWidth)
mat_A.append(
np.random.randint(low=-A_range,
high=A_range,
size=(m_size[i], k_size[i]),
dtype=np.int16))
mat_bias.append(np.zeros((m_size[i], n_size[i]), dtype=np.int32))
mat_C.append(
np.zeros((m_size[i], n_size[i]), dtype=np.int16, order='C'))
mat_B0 = np.random.randint(low=-B_range,
high=B_range,
size=(k_size[0], n_size[0]),
dtype=np.int16)
for i in range(ins_count):
gemx.sendMat(mat_A[i])
gemx.sendMat(mat_C[i])
gemx.sendMat(mat_bias[i])
gemx.sendMat(mat_B0)
gemx.addFCNOp(mat_A[0], mat_B0, mat_C[0], mat_bias[0], post_scale[0],
post_scale[1], 1, 0)
gemx.addFCNOp(mat_A[1], mat_C[0], mat_C[1], mat_bias[1], post_scale[0],
post_scale[1], 1, 0)
gemx.addFCNOp(mat_A[2], mat_C[1], mat_C[2], mat_bias[2], post_scale[0],
post_scale[1], 1, 0)
gemx.addFCNOp(mat_A[3], mat_C[2], mat_C[3], mat_bias[3], post_scale[0],
post_scale[1], 1, 0)
gemx.execute()
gemx.clearInstrBuf()
gemx.getMat(mat_C[0])
gemx.getMat(mat_C[1])
gemx.getMat(mat_C[2])
gemx.getMat(mat_C[3])
test.multiply_and_cmp(mat_C[3], mat_A[3], mat_C[2], mat_bias[3], m_size[3],
n_size[3], post_scale)
def test_perf_multi_fcn(ins_count, m_size, k_size, n_size, A_range, B_range, post_scale):
total_operations = 0
total_parallel_operations = 0
mat_A=[]
mat_C=[]
mat_bias=[]
for i in range(ins_count):
total_operations += 2 * m_size[i] * n_size[i] * k_size[i] + m_size[i] * n_size[i] * 3
total_parallel_operations += 2 * m_size[i] * n_size[i] * k_size[i]
mat_A.append(np.random.randint(low=-A_range, high=A_range, size=(m_size[i], k_size[i]), dtype=np.int16))
mat_bias.append(np.zeros ((m_size[i], n_size[i]), dtype=np.int32))
mat_C.append(np.zeros((m_size[i], n_size[i]), dtype=np.int16, order='C'))
mat_B0 = np.random.randint(low=-B_range, high=B_range, size=(k_size[0], n_size[0]), dtype=np.int16)
timePointKernel = []
timePointKernel.append(time.time()) # current time
for i in range(ins_count):
gemx.sendMat(mat_A[i])
gemx.sendMat(mat_C[i])
gemx.sendMat(mat_bias[i])
gemx.sendMat(mat_B0)
gemx.addFCNOp (mat_A[0], mat_B0, mat_C[0], mat_bias[0], post_scale[0], post_scale[1],1,0)
gemx.addFCNOp (mat_A[1], mat_C[0], mat_C[1], mat_bias[1], post_scale[0], post_scale[1],1,0)
gemx.addFCNOp (mat_A[2], mat_C[1], mat_C[2], mat_bias[2], post_scale[0], post_scale[1],1,0)
gemx.addFCNOp (mat_A[3], mat_C[2], mat_C[3], mat_bias[3], post_scale[0], post_scale[1],1,0)
timePointKernel.append(time.time()) # send to FPGA
gemx.execute()
timePointKernel.append(time.time()) # call kernel
gemx.getMat(mat_C[0])
gemx.getMat(mat_C[1])
gemx.getMat(mat_C[2])
gemx.getMat(mat_C[3])
timePointKernel.append(time.time()) # copy from FPGA
freq = gemx.getFreq()
test.test_perf(timePointKernel,total_operations,total_parallel_operations,freq,0,0,0)
if np.max(m_size) > 4096 and np.max(k_size) > 4096 and np.max(n_size) > 4096:
print("Skip golden comparision because large matrix size")
else:
test.multiply_and_cmp(mat_C[3], mat_A[3], mat_C[2], mat_bias[3], m_size[3], n_size[3], post_scale)
bias.fill(1)
bias_buf.append(bias)
B_buf[0].fill(1) #fill vetor B
C_buf.insert(0, B_buf[0])
gemx.sendMat(C_buf[0])
for i in range(num_matrix):
if args.engine == 'spmv':
gemx.sendMat(C_buf[i + 1])
gemx.addSPMVOp(A_buf[i], C_buf[i], C_buf[i + 1], nnz_size[i],
xclbin_opt, True)
else:
gemx.sendMat(C_buf[i + 1])
gemx.sendMat(bias_buf[i])
gemx.addFCNOp(A_buf[i], C_buf[i], C_buf[i + 1], bias_buf[i], 1, 0, 0,
0)
time.sleep(2)
total_time = 0
for k in range(args.numiter): #interations
start_time = time.time()
for j in range(args.vectors):
gemx.sendMat(C_buf[0])
for i in range(num_matrix):
gemx.sendMat(C_buf[i + 1])
gemx.execute()
gemx.getMat(C_buf[-1])
total_time += time.time() - start_time
print("Average FPGA exec time(python): ", (total_time / args.numiter) * 1000,
" ms")
