Spaces:
Runtime error
Runtime error
File size: 7,968 Bytes
4bdb245 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 |
Python Examples
========
This section contains simple and advanced examples using the Python Kompute class. For an overview of the module check `Python Package Overview <python-package.html>`_, for a deep dive into functions check the `Python Class Reference Section <python-reference.html>`_.
You will be able to run the examples below by installing the dependencies in `python/test/requirements-dev.txt`
Python Example (Simple)
^^^^^
Then you can interact with it from your interpreter. Below is the same sample as above "Your First Kompute (Simple Version)" but in Python:
.. code-block:: python
:linenos:
from kp import Manager, Tensor, OpTensorSyncDevice, OpTensorSyncLocal, OpAlgoDispatch
from pyshader import python2shader, ivec3, f32, Array
mgr = Manager()
# Can be initialized with List[] or np.Array
tensor_in_a = mgr.tensor([2, 2, 2])
tensor_in_b = mgr.tensor([1, 2, 3])
tensor_out = mgr.tensor([0, 0, 0])
sq = mgr.sequence()
sq.eval(OpTensorSyncDevice([tensor_in_a, tensor_in_b, tensor_out]))
# Define the function via PyShader or directly as glsl string or spirv bytes
@python2shader
def compute_shader_multiply(index=("input", "GlobalInvocationId", ivec3),
data1=("buffer", 0, Array(f32)),
data2=("buffer", 1, Array(f32)),
data3=("buffer", 2, Array(f32))):
i = index.x
data3[i] = data1[i] * data2[i]
algo = mgr.algorithm([tensor_in_a, tensor_in_b, tensor_out], compute_shader_multiply.to_spirv())
# Run shader operation synchronously
sq.eval(OpAlgoDispatch(algo))
sq.eval(OpTensorSyncLocal([tensor_out]))
assert tensor_out.data().tolist() == [2.0, 4.0, 6.0]
Python Example (Extended)
^^^^^
Similarly you can find the same extended example as above:
.. code-block:: python
:linenos:
from kp import Manager, Tensor
import kp
from pyshader import python2shader, ivec3, f32, Array
mgr = Manager(0, [2])
# Can be initialized with List[] or np.Array
tensor_in_a = mgr.tensor([2, 2, 2])
tensor_in_b = mgr.tensor([1, 2, 3])
tensor_out = mgr.tensor([0, 0, 0])
seq = mgr.sequence()
seq.eval(kp.OpTensorSyncDevice([tensor_in_a, tensor_in_b, tensor_out]))
# Define the function via PyShader or directly as glsl string or spirv bytes
@python2shader
def compute_shader_multiply(index=("input", "GlobalInvocationId", ivec3),
data1=("buffer", 0, Array(f32)),
data2=("buffer", 1, Array(f32)),
data3=("buffer", 2, Array(f32))):
i = index.x
data3[i] = data1[i] * data2[i]
algo = mgr.algorithm([tensor_in_a, tensor_in_b, tensor_out], compute_shader_multiply.to_spirv())
# Run shader operation asynchronously and then await
seq.eval_async(kp.OpAlgoDispatch(algo))
seq.eval_await()
seq.record(kp.OpTensorSyncLocal([tensor_in_a]))
seq.record(kp.OpTensorSyncLocal([tensor_in_b]))
seq.record(kp.OpTensorSyncLocal([tensor_out]))
seq.eval()
assert tensor_out.data().tolist() == [2.0, 4.0, 6.0]
Kompute Operation Capabilities
^^^^^
Handling multiple capabilites of processing can be done by compute shaders being loaded into separate sequences. The example below shows how this can be done:
.. code-block:: python
:linenos:
from kp import Manager
import kp
# We'll assume we have the shader data available
from my_spv_shader_data import mult_shader, sum_shader
mgr = Manager()
t1 = mgr.tensor([2,2,2])
t2 = mgr.tensor([1,2,3])
t3 = mgr.tensor([1,2,3])
mgr.sequence().eval(kp.OpTensorSyncLocal([t1, t3]))
# Create multiple separate sequences
sq_mult = mgr.sequence()
sq_sum = mgr.sequence()
sq_sync = mgr.sequence()
sq_mult.record(kp.OpAlgoDispatch(mgr.algorithm([t1, t2, t3], add_shader))
sq_sum.record(kp.OpAlgoDispatch(mgr.algorithm([t3, t2, t1], sum_shader))
sq_sync.record(kp.OpTensorSyncLocal([t1, t3]))
# Run multiple iterations
for i in range(10):
sq_mult.eval()
sq_sum.eval()
sq_sync.eval()
print(t1.data(), t2.data(), t3.data())
Machine Learning Logistic Regression Implementation
^^^^^^
Similar to the logistic regression implementation in the C++ examples section, below you can find the Python implementation of the Logistic Regression algorithm.
.. code-block:: python
:linenos:
from kp import Manager, Tensor
import kp
from pyshader import python2shader, ivec3, f32, Array
@python2shader
def compute_shader(
index = ("input", "GlobalInvocationId", ivec3),
x_i = ("buffer", 0, Array(f32)),
x_j = ("buffer", 1, Array(f32)),
y = ("buffer", 2, Array(f32)),
w_in = ("buffer", 3, Array(f32)),
w_out_i = ("buffer", 4, Array(f32)),
w_out_j = ("buffer", 5, Array(f32)),
b_in = ("buffer", 6, Array(f32)),
b_out = ("buffer", 7, Array(f32)),
l_out = ("buffer", 8, Array(f32)),
M = ("buffer", 9, Array(f32))):
i = index.x
m = M[0]
w_curr = vec2(w_in[0], w_in[1])
b_curr = b_in[0]
x_curr = vec2(x_i[i], x_j[i])
y_curr = y[i]
z_dot = w_curr @ x_curr
z = z_dot + b_curr
y_hat = 1.0 / (1.0 + exp(-z))
d_z = y_hat - y_curr
d_w = (1.0 / m) * x_curr * d_z
d_b = (1.0 / m) * d_z
loss = -((y_curr * log(y_hat)) + ((1.0 + y_curr) * log(1.0 - y_hat)))
w_out_i[i] = d_w.x
w_out_j[i] = d_w.y
b_out[i] = d_b
l_out[i] = loss
mgr = Manager()
# First we create input and ouput tensors for shader
tensor_x_i = mgr.tensor([0.0, 1.0, 1.0, 1.0, 1.0])
tensor_x_j = mgr.tensor([0.0, 0.0, 0.0, 1.0, 1.0])
tensor_y = mgr.tensor([0.0, 0.0, 0.0, 1.0, 1.0])
tensor_w_in = mgr.tensor([0.001, 0.001])
tensor_w_out_i = mgr.tensor([0.0, 0.0, 0.0, 0.0, 0.0])
tensor_w_out_j = mgr.tensor([0.0, 0.0, 0.0, 0.0, 0.0])
tensor_b_in = mgr.tensor([0.0])
tensor_b_out = mgr.tensor([0.0, 0.0, 0.0, 0.0, 0.0])
tensor_l_out = mgr.tensor([0.0, 0.0, 0.0, 0.0, 0.0])
tensor_m = mgr.tensor([ 5.0 ])
# We store them in an array for easier interaction
params = [tensor_x_i, tensor_x_j, tensor_y, tensor_w_in, tensor_w_out_i,
tensor_w_out_j, tensor_b_in, tensor_b_out, tensor_l_out, tensor_m]
sq.sequence().eval(kp.OpTensorSyncDevice(params))
# Record commands for efficient evaluation
sq = mgr.sequence()
sq.record(kp.OpTensorSyncDevice([tensor_w_in, tensor_b_in]))
sq.record(kp.OpAlgoDispatch(mgr.algorithm(params, compute_shader.to_spirv())))
sq.record(kp.OpTensorSyncLocal([tensor_w_out_i, tensor_w_out_j, tensor_b_out, tensor_l_out]))
ITERATIONS = 100
learning_rate = 0.1
# Perform machine learning training and inference across all input X and Y
for i_iter in range(ITERATIONS):
sq.eval()
# Calculate the parameters based on the respective derivatives calculated
w_in_i_val = tensor_w_in.data()[0]
w_in_j_val = tensor_w_in.data()[1]
b_in_val = tensor_b_in.data()[0]
for j_iter in range(tensor_b_out.size()):
w_in_i_val -= learning_rate * tensor_w_out_i.data()[j_iter]
w_in_j_val -= learning_rate * tensor_w_out_j.data()[j_iter]
b_in_val -= learning_rate * tensor_b_out.data()[j_iter]
# Update the parameters to process inference again
tensor_w_in.set_data([w_in_i_val, w_in_j_val])
tensor_b_in.set_data([b_in_val])
assert tensor_w_in.data()[0] < 0.01
assert tensor_w_in.data()[0] > 0.0
assert tensor_w_in.data()[1] > 1.5
assert tensor_b_in.data()[0] < 0.7
# Print outputs
print(tensor_w_in.data())
print(tensor_b_in.data())
|