mirror of
https://github.com/nomic-ai/kompute.git
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169 lines
4.5 KiB
Python
169 lines
4.5 KiB
Python
import os
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import kp
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import numpy as np
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import logging
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import pyshader as ps
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DIRNAME = os.path.dirname(os.path.abspath(__file__))
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def test_opalgobase_file():
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"""
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Test basic OpMult operation
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"""
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tensor_in_a = kp.Tensor([2, 2, 2])
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tensor_in_b = kp.Tensor([1, 2, 3])
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tensor_out = kp.Tensor([0, 0, 0])
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mgr = kp.Manager()
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mgr.rebuild([tensor_in_a, tensor_in_b, tensor_out])
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shader_path = os.path.join(DIRNAME, "../../shaders/glsl/opmult.comp.spv")
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mgr.eval_algo_file_def([tensor_in_a, tensor_in_b, tensor_out], shader_path)
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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_shader_str():
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"""
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Test basic OpAlgoBase operation
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"""
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shader = """
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#version 450
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layout(set = 0, binding = 0) buffer tensorLhs {float valuesLhs[];};
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layout(set = 0, binding = 1) buffer tensorRhs {float valuesRhs[];};
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layout(set = 0, binding = 2) buffer tensorOutput { float valuesOutput[];};
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layout (local_size_x = 1, local_size_y = 1, local_size_z = 1) in;
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void main()
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{
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uint index = gl_GlobalInvocationID.x;
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valuesOutput[index] = valuesLhs[index] * valuesRhs[index];
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}
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"""
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tensor_in_a = kp.Tensor([2, 2, 2])
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tensor_in_b = kp.Tensor([1, 2, 3])
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tensor_out = kp.Tensor([0, 0, 0])
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mgr = kp.Manager()
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mgr.rebuild([tensor_in_a, tensor_in_b, tensor_out])
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spirv = kp.Shader.compile_source(shader)
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mgr.eval_algo_data_def([tensor_in_a, tensor_in_b, tensor_out], 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_sequence():
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"""
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Test basic OpAlgoBase operation
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"""
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mgr = kp.Manager(0, [2])
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tensor_in_a = kp.Tensor([2, 2, 2])
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tensor_in_b = kp.Tensor([1, 2, 3])
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tensor_out = kp.Tensor([0, 0, 0])
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mgr.rebuild([tensor_in_a, tensor_in_b, tensor_out])
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shader_path = os.path.abspath(os.path.join(DIRNAME, "../../shaders/glsl/opmult.comp.spv"))
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mgr.eval_async_algo_file_def([tensor_in_a, tensor_in_b, tensor_out], shader_path)
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mgr.eval_await_def()
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seq = mgr.sequence("op")
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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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mgr.destroy("op")
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assert seq.is_init() == False
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assert tensor_out.data() == [2.0, 4.0, 6.0]
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assert np.all(tensor_out.numpy() == [2.0, 4.0, 6.0])
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mgr.destroy(tensor_in_a)
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mgr.destroy([tensor_in_b, tensor_out])
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assert tensor_in_a.is_init() == False
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assert tensor_in_b.is_init() == False
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assert tensor_out.is_init() == False
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def test_workgroup():
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mgr = kp.Manager(0)
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tensor_a = kp.Tensor(np.zeros([16,8]))
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tensor_b = kp.Tensor(np.zeros([16,8]))
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mgr.rebuild([tensor_a, tensor_b])
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@ps.python2shader
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def compute_shader_wg(gl_idx=("input", "GlobalInvocationId", ps.ivec3),
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gl_wg_id=("input", "WorkgroupId", ps.ivec3),
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gl_wg_num=("input", "NumWorkgroups", ps.ivec3),
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data1=("buffer", 0, ps.Array(ps.f32)),
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data2=("buffer", 1, ps.Array(ps.f32))):
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i = gl_wg_id.x * gl_wg_num.y + gl_wg_id.y
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data1[i] = f32(gl_idx.x)
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data2[i] = f32(gl_idx.y)
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seq = mgr.sequence("new")
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seq.begin()
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seq.record_algo_data([tensor_a, tensor_b], compute_shader_wg.to_spirv(), workgroup=(16,8,1))
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seq.end()
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seq.eval()
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mgr.destroy(seq)
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assert seq.is_init() == False
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mgr.eval_tensor_sync_local_def([tensor_a, tensor_b])
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print(tensor_a.numpy())
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print(tensor_b.numpy())
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assert np.all(tensor_a.numpy() == np.stack([np.arange(16)]*8, axis=1).ravel())
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assert np.all(tensor_b.numpy() == np.stack([np.arange(8)]*16, axis=0).ravel())
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mgr.destroy([tensor_a, tensor_b])
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assert tensor_a.is_init() == False
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assert tensor_b.is_init() == False
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def test_tensor_rebuild_backwards_compat():
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"""
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Test basic OpMult operation
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"""
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tensor_in_a = kp.Tensor([2, 2, 2])
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tensor_in_b = kp.Tensor([1, 2, 3])
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tensor_out = kp.Tensor([0, 0, 0])
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mgr = kp.Manager()
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mgr.eval_tensor_create_def([tensor_in_a, tensor_in_b, tensor_out])
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shader_path = os.path.abspath(os.path.join(DIRNAME, "../../shaders/glsl/opmult.comp.spv"))
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mgr.eval_async_algo_file_def([tensor_in_a, tensor_in_b, tensor_out], shader_path)
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mgr.eval_await_def()
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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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assert np.all(tensor_out.numpy() == [2.0, 4.0, 6.0])
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