mirror of https://github.com/commaai/tinygrad.git
100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
# An example to compile a small Tensorflow model to extremely portable C code
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import os, sys
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os.environ["CLANG"] = '1'
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import numpy as np
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import subprocess
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import tensorflow as tf
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import tf2onnx
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from extra.onnx import get_run_onnx
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from tinygrad.tensor import Tensor
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from extra.export_model import export_model_clang, compile_net, jit_model
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def get_uncompiled_model2(dataset_size=32, output_size=4):
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inputs = tf.keras.Input(shape=(dataset_size,), name="inputs")
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x = tf.keras.layers.Dense(16, activation="relu", name="dense_1")(inputs)
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x = tf.keras.layers.BatchNormalization()(x)
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x = tf.keras.layers.Dense(32, activation="relu", name="dense_2")(x)
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outputs = tf.keras.layers.Dense(output_size, activation="sigmoid", name="predictions")(x)
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model = tf.keras.Model(inputs=inputs, outputs=outputs)
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return model
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class TinyOnnx:
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def __init__(self, keras_model):
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input_signature = [tf.TensorSpec([1,32], tf.float32, name='x')]
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onnx_model, _ = tf2onnx.convert.from_keras(keras_model, input_signature, opset=13)
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self.run_onnx = get_run_onnx(onnx_model)
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def forward(self, x):
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return self.run_onnx({"x": x}, debug=False)['predictions']
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def compile_onnx_model(onnx_model):
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tinyonnx = TinyOnnx(onnx_model)
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the_input = Tensor.randn(1,32)
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run, special_names = jit_model(tinyonnx, the_input)
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functions, statements, bufs, bufs_to_save = compile_net(run, special_names)
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prg = export_model_clang(functions, statements, bufs, {}, ["input0"], ["output0"])
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the_output = run(the_input)
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cprog = ["#include <string.h>", "#include <stdio.h>", "#include <stdlib.h>"]
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cprog.append(prg)
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# weights
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cprog.append("void initialize(float *weights) {")
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weights = bytes()
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for name,cl in bufs_to_save.items():
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cprog.append(f"memcpy({name}, weights + {len(weights)//4}, {len(cl._buf)*4});")
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weights += bytes(cl._buf)
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cprog.append("}")
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# write the weights to disk
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with open("/tmp/tf_weights", "wb") as f:
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f.write(weights)
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# test program
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cprog.append(f"""int main(int argc, char *argv[]) {{
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// read in the weights from disk
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FILE *f = fopen("/tmp/tf_weights", "rb");
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float *weights = (float *)malloc({len(weights)});
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fread(weights, 1, {len(weights)}, f);
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fclose(f);
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// init the net
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initialize(weights);
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// test run
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float input[32];
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float outputs[4];
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for (int i = 0; i < 32; i++) scanf("%f", &input[i]);
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net(input, outputs);
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printf("%f %f %f %f\\n", outputs[0], outputs[1], outputs[2], outputs[3]);
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}}""")
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# ready the program
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prg = '\n'.join(cprog)
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print(prg)
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# add test weights
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subprocess.check_output(['clang', '-O2', '-lm', '-fPIC', '-x', 'c', '-', '-o', "/tmp/tf_test"], input=prg.encode('utf-8'))
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tinygrad_output = the_output[0].numpy()[0].tolist()
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print("tinygrad:", tinygrad_output, file=sys.stderr)
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c_input = ' '.join(["%f" % x for x in the_input[0].numpy()])+"\n"
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c_output = [float(x) for x in subprocess.check_output(["/tmp/tf_test"], input=c_input.encode('utf-8')).decode('utf-8').strip().split(" ")]
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print("compiled:", c_output, file=sys.stderr)
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np.testing.assert_allclose(tinygrad_output, c_output, atol=1e-5, rtol=1e-5)
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return the_input.numpy(), c_output
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if __name__ == "__main__":
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keras_model = get_uncompiled_model2()
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test_input, test_output = compile_onnx_model(keras_model)
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tf_output = keras_model(test_input).numpy()[0]
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print("keras: ", tf_output, file=sys.stderr)
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np.testing.assert_allclose(tf_output, test_output, atol=1e-5, rtol=1e-5)
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