yzhliu

import os

import numpy as np

import nnvm.testing
import nnvm.compiler
import tvm
from tvm import autotvm
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
from tvm.contrib.util import tempdir

yzhliu

diff --git a/nnvm/src/top/nn/nn.cc b/nnvm/src/top/nn/nn.cc
index 322d77b6..12146cca 100644
--- a/nnvm/src/top/nn/nn.cc
+++ b/nnvm/src/top/nn/nn.cc
@@ -641,6 +641,31 @@ inline bool LayoutTransformInferShape(const NodeAttrs& attrs,
   return true;
 }
 
+inline Array<Expr> layout_transform_func(const std::string& src, const std::string& dst, const Array<Var>& dst_indices) {
+  Layout src_layout(src);

yzhliu

208 [main] INFO MXNetJVM - Try loading mxnet-scala from native path.
209 [main] INFO MXNetJVM - Try loading mxnet-scala-osx-x86_64-gpu from native path.
209 [main] INFO MXNetJVM - Try loading mxnet-scala-osx-x86_64-cpu from native path.
210 [main] WARN MXNetJVM - MXNet Scala native library not found in path. Copying native library from the archive. Consider installing the library somewhere in the path (for Windows: PATH, for Linux: LD_LIBRARY_PATH), or specifying by Java cmd option -Djava.library.path=[lib path].
224 [main] INFO org.apache.mxnet.util.NativeLibraryLoader - Replaced .dylib with .jnilib
228 [main] INFO org.apache.mxnet.util.NativeLibraryLoader - Loading libmxnet-scala.jnilib from /lib/native/ copying to mxnet-scala
661 [main] INFO org.apache.mxnet.WarnIfNotDisposed - Leaked object tracing is enabled (property mxnet.traceLeakedObjects is set)
[22:30:36] src/io/iter_mnist.cc:110: MNISTIter: load 60000 images, shuffle=1, shape=(128,784)
[22:30:37] src/io/iter_mnist.cc:110: MNISTIter: load 10000 ima

yzhliu

import tvm

def compute_conv2d(A, W, stride, padding):
    batch_size, in_channel, height, width = A.shape
    out_channel, _ = W.shape

    kh = 1
    kw = 1

    out_height = (height + 2 * padding - kh) // stride + 1

yzhliu

import numpy as np
import tvm
import topi
from tvm.contrib.pickle_memoize import memoize
from topi.util import get_const_tuple
from topi.nn.conv2d import SpatialPack, Im2ColPack, _WORKLOADS
from topi.nn.conv2d import _get_workload
from topi.nn.util import infer_pad, infer_stride
from topi import tag
from topi.nn import pad

yzhliu

import numpy as np
import tvm
import topi
from tvm.contrib.pickle_memoize import memoize
from topi.util import get_const_tuple
from topi import tag

def schedule_conv2d(outs):
    print('Run in x86 sch ...')
    """Create schedule for tensors"""

yzhliu

// attr [data_vec] storage_scope = "global"
allocate data_vec[float32 * 1 * 56 * 8 * 64 * 3 * 9]
// attr [kernel_vec] storage_scope = "global"
allocate kernel_vec[float32 * 16 * 64 * 3 * 3 * 4]
produce data_vec {
  // attr [iter_var(h.outer, )] pragma_scope = "parallel_launch_point"
  // attr [iter_var(h.outer, )] pragma_scope = "parallel_barrier_when_finish"
  for (h.outer, 0, 28) {
    // attr [iter_var(h.inner, )] pragma_scope = "parallel_stride_pattern"
    parallel (h.inner, 0, 2) {

yzhliu

import tvm                      
import numpy  
import time   
              
# The size of the square matrix
N = 100000000 
# The default tensor type in tvm
dtype = "float32"
target = "llvm"
#target = "llvm -mcpu=skylake-avx512"

yzhliu

@generic.schedule_conv2d_nchw.register(["cpu"])
def schedule_conv2d(outs):
    print('Run in x86 sch ...')
    """Create schedule for tensors"""
    s = tvm.create_schedule([x.op for x in outs])

    def traverse(op):
        """Traverse operators from computation graph"""
        # inline all one-to-one-mapping operators except the last stage (output)
        if tag.is_broadcast(op.tag):

yzhliu

@generic.schedule_conv2d_nchw.register(["cpu"])
def schedule_conv2d(outs):
    print('Run in x86 sch ...')
    """Create schedule for tensors"""
    s = tvm.create_schedule([x.op for x in outs])

    def traverse(op):
        """Traverse operators from computation graph"""
        # inline all one-to-one-mapping operators except the last stage (output)
        if tag.is_broadcast(op.tag):