deleted legacy code

tf-addOp/build.sh

@@ -1,15 +0,0 @@
-#!/bin/bash
-
-if [ "$TF_CFLAGS" == "" ]; then
-  export TF_CFLAGS=( $(python3 -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))') )
-fi
-if [ "$TF_LFLAGS" == "" ]; then
-  export TF_LFLAGS=( $(python3 -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))') )
-fi
-
-g++ -std=c++11 -shared zero_out.cc -o zero_out.so -fPIC ${TF_CFLAGS[@]} ${TF_LFLAGS[@]} -O2 -v
-
-#g++ -g -std=c++11 -shared zero_out.cc -o zero_out.so -fPIC -I/usr/local/lib/python3.6/dist-packages/tensorflow_core/include -D_GLIBCXX_USE_CXX11_ABI=0 -L/usr/local/lib/python3.6/dist-packages/tensorflow_core -l:libtensorflow_framework.so.2 -O2
-
-#g++ -g -std=c++11 -shared zero_out.cc -o zero_out.so -fPIC ${TF_CFLAGS[@]} ${TF_LFLAGS[@]} -O2 -Wall -Wl,-z,defs
-

tf-addOp/test.py

@@ -1,88 +0,0 @@
-import tensorflow as tf
-import tensorflow.keras as keras
-from keras import layers
-from keras.layers import Input, Embedding, LSTM, Dense, Dropout, Flatten, MaxPooling2D, Conv2D
-from keras.models import Model, Sequential
-from keras.datasets import mnist
-from keras.utils import plot_model, to_categorical
-
-import numpy as np
-from IPython import embed
-
-
-batch_size = 128
-num_classes = 10
-epochs = 1 # 12
-
-# input image dimensions
-img_rows, img_cols = 28, 28
-
-# the data, split between train and test sets
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-
-x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
-x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
-input_shape = (img_rows, img_cols, 1)
-
-x_train = x_train.astype('float32')
-x_test = x_test.astype('float32')
-x_train /= 255
-x_test /= 255
-print('x_train shape:', x_train.shape)
-print(x_train.shape[0], 'train samples')
-print(x_test.shape[0], 'test samples')
-
-# convert class vectors to binary class matrices
-y_train = to_categorical(y_train, num_classes)
-y_test = to_categorical(y_test, num_classes)
-
-
-class Linear(layers.Layer):
-
-  def __init__(self, units=32, input_dim=32):
-    super(Linear, self).__init__()
-    w_init = tf.random_normal_initializer()
-    self.w = tf.Variable(initial_value=w_init(shape=(input_dim, units),
-                                              dtype='float32'),
-                         trainable=True)
-    b_init = tf.zeros_initializer()
-    self.b = tf.Variable(initial_value=b_init(shape=(units,),
-                                              dtype='float32'),
-                         trainable=True)
-
-  def call(self, inputs):
-    print(inputs)
-    embed()
-    return tf.matmul(inputs, self.w) + self.b
-
-
-model = Sequential()
-model.add(Conv2D(32, kernel_size=(3, 3),
-                 activation='relu',
-                 input_shape=input_shape))
-model.add(Conv2D(64, (3, 3), activation='relu'))
-
-model.add(MaxPooling2D(pool_size=(2, 2)))
-model.add(Dropout(0.25))
-model.add(Flatten())
-model.add(Dense(128, activation='relu'))
-model.add(Dropout(0.5))
-model.add(Dense(num_classes, activation='softmax'))
-
-model.add(Linear(10,10))
-
-
-model.compile(loss=keras.losses.categorical_crossentropy,
-              optimizer=keras.optimizers.Adadelta(),
-              metrics=['accuracy'])
-
-model.fit(x_train, y_train,
-          batch_size=batch_size,
-          epochs=epochs,
-          verbose=1,
-          validation_data=(x_test, y_test))
-score = model.evaluate(x_test, y_test, verbose=0)
-print('Test loss:', score[0])
-print('Test accuracy:', score[1])
-
-plot_model(model, to_file='model.png', expand_nested=True, show_shapes=True)

tf-addOp/train.py

@@ -1,72 +0,0 @@
-import tensorflow as tf
-import tensorflow.keras as keras
-from tensorflow.keras import layers
-from tensorflow.keras.layers import Input, Embedding, LSTM, Dense, Dropout, Flatten, MaxPooling2D, Conv2D
-from tensorflow.keras.models import Model, Sequential
-from tensorflow.keras.datasets import mnist
-from tensorflow.keras.utils import plot_model, to_categorical
-
-import numpy as np
-from IPython import embed
-
-zero_out_module = tf.load_op_library('./zero_out.so')
-
-batch_size = 128
-num_classes = 10
-epochs = 1 # 12
-
-# input image dimensions
-img_rows, img_cols = 28, 28
-
-# the data, split between train and test sets
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-
-x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
-x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
-input_shape = (img_rows, img_cols, 1)
-
-x_train = x_train.astype('float32')
-x_test = x_test.astype('float32')
-x_train /= 255
-x_test /= 255
-print('x_train shape:', x_train.shape)
-print(x_train.shape[0], 'train samples')
-print(x_test.shape[0], 'test samples')
-
-# convert class vectors to binary class matrices
-y_train = to_categorical(y_train, num_classes)
-y_test = to_categorical(y_test, num_classes)
-
-class Linear(layers.Layer):
-
-  def __init__(self, units=32, input_dim=32):
-    super(Linear, self).__init__()
-  def call(self, inputs):
-    ints = tf.dtypes.cast(inputs, dtype=tf.int32)
-    print(ints)
-    outs = zero_out_module.zero_out(ints)
-    return tf.dtypes.cast(outs, dtype=tf.float32)
-
-model = Sequential()
-model.add(Flatten())
-model.add(Dense(128, activation='relu'))
-model.add(Dropout(0.5))
-model.add(Dense(num_classes, activation='softmax'))
-
-model.add(Linear())
-
-model.compile(loss=keras.losses.categorical_crossentropy,
-              optimizer=keras.optimizers.Adadelta(),
-              metrics=['accuracy'])
-              
-model.fit(x_train, y_train,
-          batch_size=batch_size,
-          epochs=epochs,
-          verbose=1,
-          validation_data=(x_test, y_test))
-
-score = model.evaluate(x_test, y_test, verbose=0)
-print('Test loss:', score[0])
-print('Test accuracy:', score[1])
-
-plot_model(model, to_file='model.png', expand_nested=True, show_shapes=True)

tf-addOp/zero_out.cc

@@ -1,49 +0,0 @@
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/shape_inference.h"
-#include "tensorflow/core/framework/function.h"
-
-using namespace tensorflow;
-
-REGISTER_OP("ZeroOut")
-    .Input("to_zero: int32")
-    .Output("zeroed: int32")
-    .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
-      c->set_output(0, c->input(0));
-      return Status::OK();
-    });
-
-#include "tensorflow/core/framework/op_kernel.h"
-
-using namespace tensorflow;
-
-class ZeroOutOp : public OpKernel {
- public:
-  explicit ZeroOutOp(OpKernelConstruction* context) : OpKernel(context) {}
-
-  void Compute(OpKernelContext* context) override {
-    // Grab the input tensor
-    const Tensor& input_tensor = context->input(0);
-    auto input = input_tensor.flat<int32>();
-
-    printf("call n: %d\n", n++);
-
-    // Create an output tensor
-    Tensor* output_tensor = NULL;
-    OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(),
-                                                     &output_tensor));
-    auto output_flat = output_tensor->flat<int32>();
-
-    // Set all but the first element of the output tensor to 0.
-    const int N = input.size();
-    
-    for (int i = 1; i < N; i++) {
-      output_flat(i) = 0;
-    }
-    // Preserve the first input value if possible.
-    if (N > 0) output_flat(0) = input(0);
-  }
-
-  int n = 0;
-};
-
-REGISTER_KERNEL_BUILDER(Name("ZeroOut").Device(DEVICE_CPU), ZeroOutOp);

tf-addOp/zero_out.py

@@ -1,7 +0,0 @@
-import tensorflow as tf
-zero_out_module = tf.load_op_library('./zero_out.so')
-
-print(zero_out_module.zero_out([[1, 2], [3, 4]]))
-
-# Prints
-# array([[1, 0], [0, 0]], dtype=int32)

tf-matMulOp/build.sh

@@ -1,10 +0,0 @@
-#!/bin/bash
-
-if [ "$TF_CFLAGS" == "" ]; then
-  export TF_CFLAGS=( $(python3 -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))' 2>/dev/null) )
-fi
-if [ "$TF_LFLAGS" == "" ]; then
-  export TF_LFLAGS=( $(python3 -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))' 2>/dev/null) )
-fi
-
-g++ -g -std=c++11 -shared matMul.cc -o matMul.so -fPIC ${TF_CFLAGS[@]} ${TF_LFLAGS[@]} -O2 -Wall

tf-matMulOp/makefile

@@ -1,29 +0,0 @@
-CXX=/usr/bin/g++
-
-FLAGS = -g -Wall -pthread -std=c++11
-
-TF_CFLAGS=$(shell python3 -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))' 2>/dev/null)
-TF_LFLAGS=$(shell python3 -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))' 2>/dev/null)
-
-SRC_DIR=.
-INC_DIR=.
-BUILD_DIR=.
-
-SRCS=$(wildcard $(SRC_DIR)/*.cpp)
-OBJS=$(patsubst $(SRC_DIR)/%.cpp,$(BUILD_DIR)/%.o,$(SRCS))
-
-EXECUTABLE=matMul.so
-
-all: dir $(BUILD_DIR)/$(EXECUTABLE)
-
-dir:
-	mkdir -p $(BUILD_DIR)
-
-$(BUILD_DIR)/$(EXECUTABLE): $(OBJS)
-	$(CXX) -shared $(TF_LFLAGS) -Wall -o $@ $^
-
-$(OBJS): $(BUILD_DIR)/%.o : $(SRC_DIR)/%.cpp $(INC_DIR)/%.hpp
-	$(CXX) $(FLAGS) -fPIC -c $(TF_CFLAGS) -I$(INC_DIR) -o $@ $< -O2
-
-clean:
-	rm -f $(BUILD_DIR)/*.o $(BUILD_DIR)/$(EXECUTABLE)

tf-matMulOp/matMul.cc

@@ -1,165 +0,0 @@
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/shape_inference.h"
-#include "tensorflow/core/framework/function.h"
-
-#include "tensorflow/core/lib/math/math_util.h"
-
-using namespace tensorflow;
-typedef FunctionDefHelper FDH;
-
-REGISTER_OP("MyMatMul")
-    .Input("to_zero: int32")
-    .Output("zeroed: int32")
-    .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
-      c->set_output(0, c->input(0));
-      return Status::OK();
-    });
-
-REGISTER_OP("MyConv2D")
-    .Input("input: int32")
-    .Input("filter: int32")
-    .Output("output: int32")
-    .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
-      c->set_output(0, c->input(0));
-      return Status::OK();
-    });
-    
-#include "tensorflow/core/framework/op_kernel.h"
-
-using namespace tensorflow;
-/*
-class Conv2DOp : public OpKernel {
- public:
-  explicit Conv2DOp(OpKernelConstruction* context) : OpKernel(context) {}
-
-  void Compute(OpKernelContext* context) override {
-    // Grab the input tensor
-    const Tensor& input_tensor = context->input(0);
-    auto input = input_tensor.flat<int32>();
-
-    printf("call n: %d\n", n++);
-
-    // Create an output tensor
-    Tensor* output_tensor = NULL;
-    OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(),
-                                                     &output_tensor));
-    auto output_flat = output_tensor->flat<int32>();
-
-    // Set all but the first element of the output tensor to 0.
-    const int N = input.size();
-    
-    for (int i = 1; i < N; i++) {
-      output_flat(i) = 0;
-    }
-    // Preserve the first input value if possible.
-    if (N > 0) output_flat(0) = input(0);
-  }
-
-  int n = 0;
-};
-*/
-
-
-class Conv2DOp : public OpKernel {
- public:
-  explicit Conv2DOp(OpKernelConstruction* context) : OpKernel(context) {
-  }
-
-  void Compute(OpKernelContext* context) override {
-    // Input tensor is of the following dimensions:
-    // [ batch, in_rows, in_cols, in_depth ]
-    const Tensor& input = context->input(0);
-
-    // Input filter is of the following dimensions:
-    // [ filter_rows, filter_cols, in_depth, out_depth]
-    const Tensor& filter = context->input(1);
-
-    TensorShape out_shape = input.shape();
-
-    // Output tensor is of the following dimensions:
-    // [ in_batch, out_rows, out_cols, out_depth ]
-    Tensor* output = nullptr;
-    OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output));
-
-    std::cout << "Conv2D" << std::endl;
-
-    // If there is nothing to compute, return.
-    if (out_shape.num_elements() == 0) {
-      return;
-    }
-
-    
-  }
-
- private:
-  //LaunchConv2DOp<Device, T> launcher_;
-
-  TF_DISALLOW_COPY_AND_ASSIGN(Conv2DOp);
-};
-
-
-REGISTER_KERNEL_BUILDER(Name("MyConv2D").Device(DEVICE_CPU), Conv2DOp);
-
-static Status MatMulGradHelper(FunctionDef* g, const string& opname,
-                               const string& attr_adj_x,
-                               const string& attr_adj_y, const string& x0,
-                               bool ax0, const string& x1, bool ax1,
-                               const string& y0, bool ay0, const string& y1,
-                               bool ay1) {
-  // The final outputs are "dx" and "dy". If we're broadcasting compute
-  // intermediate nodes for now.
-  std::vector<FDH::Node> nodes = {
-      {{("dx")},
-       opname,
-       {x0, x1},
-       {{"T", "$T"}, {attr_adj_x, ax0}, {attr_adj_y, ax1}}},
-      {{("dy")},
-       opname,
-       {y0, y1},
-       {{"T", "$T"}, {attr_adj_x, ay0}, {attr_adj_y, ay1}}},
-  };
-
-  *g = FDH::Define(
-      // Arg defs
-      {"x: T", "y: T", "dz: T"},
-      // Ret val defs
-      {"dx: T", "dy: T"},
-      // Attr defs
-      {{"T: {half, float, double}"}},
-      // Nodes
-      nodes);
-  return Status::OK();
-}
-Status MatMulGrad(const AttrSlice& attrs, FunctionDef* g) {
-  const string opname = "MyMatMul";
-  const string attr_adj_x = "transpose_a";
-  const string attr_adj_y = "transpose_b";
-  DataType T;
-  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "T", &T));
-  if (T == DT_COMPLEX64 || T == DT_COMPLEX128) {
-    return errors::Unimplemented(
-        "MatMul gradient for complex is not supported yet.");
-  }
-  bool ta;
-  bool tb;
-  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, attr_adj_x, &ta));
-  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, attr_adj_y, &tb));
-
-  if (!ta && !tb) {
-    return MatMulGradHelper(g, opname, attr_adj_x, attr_adj_y, "dz", false, "y",
-                            true, "x", true, "dz", false);
-  }
-  if (!ta && tb) {
-    return MatMulGradHelper(g, opname, attr_adj_x, attr_adj_y, "dz", false, "y",
-                            false, "dz", true, "x", false);
-  }
-  if (ta && !tb) {
-    return MatMulGradHelper(g, opname, attr_adj_x, attr_adj_y, "y", false, "dz",
-                            true, "x", false, "dz", false);
-  }
-  CHECK(ta && tb);
-  return MatMulGradHelper(g, opname, attr_adj_x, attr_adj_y, "y", true, "dz",
-                          true, "dz", true, "x", true);
-}
-
-REGISTER_OP_GRADIENT("MyConv2D", MatMulGrad);

tf-matMulOp/train.py

@@ -1,70 +0,0 @@
-import tensorflow as tf
-import tensorflow.keras as keras
-from tensorflow.keras import layers
-from tensorflow.keras.layers import Input, Embedding, LSTM, Dense, Dropout, Flatten, MaxPooling2D, Conv2D
-from tensorflow.keras.models import Model, Sequential
-from tensorflow.keras.datasets import mnist
-from tensorflow.keras.utils import plot_model, to_categorical
-
-import numpy as np
-from IPython import embed
-
-my_matmul_module = tf.load_op_library('./matMul.so')
-
-batch_size = 128
-num_classes = 10
-epochs = 1 # 12
-
-# input image dimensions
-img_rows, img_cols = 28, 28
-
-# the data, split between train and test sets
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-
-x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
-x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
-input_shape = (img_rows, img_cols, 1)
-
-x_train = x_train.astype('float32')
-x_test = x_test.astype('float32')
-x_train /= 255
-x_test /= 255
-print('x_train shape:', x_train.shape)
-print(x_train.shape[0], 'train samples')
-print(x_test.shape[0], 'test samples')
-
-# convert class vectors to binary class matrices
-y_train = to_categorical(y_train, num_classes)
-y_test = to_categorical(y_test, num_classes)
-
-class Conv2DFPGA(layers.Layer):
-  def __init__(self, kernel):
-    super(Conv2DFPGA, self).__init__()
-    self.kernel = kernel
-  def call(self, inputs):
-    ints = tf.dtypes.cast(inputs, dtype=tf.int32)
-    outs = my_matmul_module.MyConv2D(input=ints, filter=ints)
-    return tf.dtypes.cast(outs, dtype=tf.float32)
-
-model = Sequential()
-model.add(Conv2DFPGA([0,0]))
-model.add(Flatten())
-model.add(Dense(128, activation='relu'))
-model.add(Dropout(0.5))
-model.add(Dense(num_classes, activation='softmax'))
-
-model.compile(loss=keras.losses.categorical_crossentropy,
-              optimizer=keras.optimizers.Adadelta(),
-              metrics=['accuracy'])
-              
-model.fit(x_train, y_train,
-          batch_size=batch_size,
-          epochs=epochs,
-          verbose=1,
-          validation_data=(x_test, y_test))
-
-score = model.evaluate(x_test, y_test, verbose=0)
-print('Test loss:', score[0])
-print('Test accuracy:', score[1])
-
-plot_model(model, to_file='model.png', expand_nested=True, show_shapes=True)