mirror of
https://github.com/nomic-ai/kompute.git
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229 lines
6.7 KiB
Python
229 lines
6.7 KiB
Python
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from pyshader import python2shader, f32, ivec3, Array
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from pyshader.stdlib import exp, log
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from kp import Tensor, Manager, Sequence
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def test_opmult():
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"""
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Test basic OpMult operation
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"""
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tensor_in_a = Tensor([2, 2, 2])
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tensor_in_b = Tensor([1, 2, 3])
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tensor_out = Tensor([0, 0, 0])
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mgr = Manager()
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mgr.eval_tensor_create_def([tensor_in_a, tensor_in_b, tensor_out])
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mgr.eval_algo_mult_def([tensor_in_a, tensor_in_b, tensor_out])
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mgr.eval_tensor_sync_local_def([tensor_out])
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assert tensor_out.data() == [2.0, 4.0, 6.0]
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def test_opalgobase_data():
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"""
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Test basic OpAlgoBase operation
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"""
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tensor_in_a = Tensor([2, 2, 2])
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tensor_in_b = Tensor([1, 2, 3])
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tensor_out = Tensor([0, 0, 0])
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mgr = Manager()
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shaderData = """
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#version 450
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layout (local_size_x = 1) in;
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// The input tensors bind index is relative to index in parameter passed
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layout(set = 0, binding = 0) buffer bina { float tina[]; };
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layout(set = 0, binding = 1) buffer binb { float tinb[]; };
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layout(set = 0, binding = 2) buffer bout { float tout[]; };
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void main() {
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uint index = gl_GlobalInvocationID.x;
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tout[index] = tina[index] * tinb[index];
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}
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"""
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mgr.eval_tensor_create_def([tensor_in_a, tensor_in_b, tensor_out])
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mgr.eval_algo_str_def([tensor_in_a, tensor_in_b, tensor_out], list(shaderData))
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mgr.eval_tensor_sync_local_def([tensor_out])
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assert tensor_out.data() == [2.0, 4.0, 6.0]
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def test_opalgobase_file():
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"""
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Test basic OpAlgoBase operation
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"""
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tensor_in_a = Tensor([2, 2, 2])
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tensor_in_b = Tensor([1, 2, 3])
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tensor_out = Tensor([0, 0, 0])
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mgr = Manager()
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shaderFilePath = "../../shaders/glsl/opmult.comp"
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mgr.eval_tensor_create_def([tensor_in_a, tensor_in_b, tensor_out])
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mgr.eval_algo_file_def([tensor_in_a, tensor_in_b, tensor_out], shaderFilePath)
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mgr.eval_tensor_sync_local_def([tensor_out])
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assert tensor_out.data() == [2.0, 4.0, 6.0]
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def test_sequence():
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"""
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Test basic OpAlgoBase operation
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"""
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mgr = Manager(0, [2])
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tensor_in_a = Tensor([2, 2, 2])
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tensor_in_b = Tensor([1, 2, 3])
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tensor_out = Tensor([0, 0, 0])
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mgr.eval_tensor_create_def([tensor_in_a, tensor_in_b, tensor_out])
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seq = mgr.create_sequence("op")
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shaderFilePath = "../../shaders/glsl/opmult.comp"
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mgr.eval_async_algo_file_def([tensor_in_a, tensor_in_b, tensor_out], shaderFilePath)
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mgr.eval_await_def()
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seq.begin()
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seq.record_tensor_sync_local([tensor_in_a])
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seq.record_tensor_sync_local([tensor_in_b])
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seq.record_tensor_sync_local([tensor_out])
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seq.end()
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seq.eval()
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assert tensor_out.data() == [2.0, 4.0, 6.0]
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def test_pyshader_pyshader():
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@python2shader
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def compute_shader_multiply(index=("input", "GlobalInvocationId", ivec3),
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data1=("buffer", 0, Array(f32)),
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data2=("buffer", 1, Array(f32)),
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data3=("buffer", 2, Array(f32))):
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i = index.x
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data3[i] = data1[i] * data2[i]
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tensor_in_a = Tensor([2, 2, 2])
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tensor_in_b = Tensor([1, 2, 3])
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tensor_out = Tensor([0, 0, 0])
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mgr = Manager()
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mgr.eval_tensor_create_def([tensor_in_a, tensor_in_b, tensor_out])
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mgr.eval_algo_data_def([tensor_in_a, tensor_in_b, tensor_out], compute_shader_multiply.to_spirv())
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mgr.eval_tensor_sync_local_def([tensor_out])
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assert tensor_out.data() == [2.0, 4.0, 6.0]
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def test_logistic_regression_pyshader():
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@python2shader
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def compute_shader(
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index = ("input", "GlobalInvocationId", ivec3),
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x_i = ("buffer", 0, Array(f32)),
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x_j = ("buffer", 1, Array(f32)),
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y = ("buffer", 2, Array(f32)),
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w_in = ("buffer", 3, Array(f32)),
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w_out_i = ("buffer", 4, Array(f32)),
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w_out_j = ("buffer", 5, Array(f32)),
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b_in = ("buffer", 6, Array(f32)),
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b_out = ("buffer", 7, Array(f32)),
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l_out = ("buffer", 8, Array(f32)),
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M = ("buffer", 9, Array(f32))):
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i = index.x
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m = M[0]
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w_curr = vec2(w_in[0], w_in[1])
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b_curr = b_in[0]
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x_curr = vec2(x_i[i], x_j[i])
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y_curr = y[i]
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z_dot = w_curr @ x_curr
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z = z_dot + b_curr
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y_hat = 1.0 / (1.0 + exp(-z))
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d_z = y_hat - y_curr
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d_w = (1.0 / m) * x_curr * d_z
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d_b = (1.0 / m) * d_z
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loss = -((y_curr * log(y_hat)) + ((1.0 + y_curr) * log(1.0 - y_hat)))
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w_out_i[i] = d_w.x
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w_out_j[i] = d_w.y
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b_out[i] = d_b
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l_out[i] = loss
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# First we create input and ouput tensors for shader
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tensor_x_i = Tensor([0.0, 1.0, 1.0, 1.0, 1.0])
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tensor_x_j = Tensor([0.0, 0.0, 0.0, 1.0, 1.0])
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tensor_y = Tensor([0.0, 0.0, 0.0, 1.0, 1.0])
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tensor_w_in = Tensor([0.001, 0.001])
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tensor_w_out_i = Tensor([0.0, 0.0, 0.0, 0.0, 0.0])
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tensor_w_out_j = Tensor([0.0, 0.0, 0.0, 0.0, 0.0])
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tensor_b_in = Tensor([0.0])
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tensor_b_out = Tensor([0.0, 0.0, 0.0, 0.0, 0.0])
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tensor_l_out = Tensor([0.0, 0.0, 0.0, 0.0, 0.0])
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tensor_m = Tensor([ 5.0 ])
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# We store them in an array for easier interaction
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params = [tensor_x_i, tensor_x_j, tensor_y, tensor_w_in, tensor_w_out_i,
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tensor_w_out_j, tensor_b_in, tensor_b_out, tensor_l_out, tensor_m]
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mgr = Manager()
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mgr.eval_tensor_create_def(params)
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# Record commands for efficient evaluation
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sq = mgr.create_sequence()
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sq.begin()
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sq.record_tensor_sync_device([tensor_w_in, tensor_b_in])
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sq.record_algo_data(params, compute_shader.to_spirv())
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sq.record_tensor_sync_local([tensor_w_out_i, tensor_w_out_j, tensor_b_out, tensor_l_out])
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sq.end()
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ITERATIONS = 100
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learning_rate = 0.1
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# Perform machine learning training and inference across all input X and Y
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for i_iter in range(ITERATIONS):
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sq.eval()
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# Calculate the parameters based on the respective derivatives calculated
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w_in_i_val = tensor_w_in.data()[0]
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w_in_j_val = tensor_w_in.data()[1]
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b_in_val = tensor_b_in.data()[0]
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for j_iter in range(tensor_b_out.size()):
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w_in_i_val -= learning_rate * tensor_w_out_i.data()[j_iter]
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w_in_j_val -= learning_rate * tensor_w_out_j.data()[j_iter]
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b_in_val -= learning_rate * tensor_b_out.data()[j_iter]
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# Update the parameters to process inference again
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tensor_w_in.set_data([w_in_i_val, w_in_j_val])
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tensor_b_in.set_data([b_in_val])
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assert tensor_w_in.data()[0] < 0.01
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assert tensor_w_in.data()[0] > 0.0
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assert tensor_w_in.data()[1] > 1.5
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assert tensor_b_in.data()[0] < 0.7
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if __name__ == "__main__":
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test_sequence()
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