ajtulloch

/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 *  * Redistributions of source code must retain the above copyright
 *    notice, this list of conditions and the following disclaimer.
 *  * Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in the
 *    documentation and/or other materials provided with the distribution.

ajtulloch

ajtulloch

ajtulloch

from tvm import relay
from mxnet.gluon import nn
import mxnet as mx

class TestBlock(nn.HybridBlock):
    def __init__(self):
        super(TestBlock, self).__init__()
        self.conv = nn.Conv2D(8, 3, 1, 1, use_bias=False)
        self.a000 = nn.Activation("relu")
        self.a0_0 = nn.MaxPool2D(pool_size=2, strides=2)

ajtulloch

diff --git a/src/relay/pass/quantize.cc b/src/relay/pass/quantize.cc
index 3a2e54c8..4059dc3a 100644
--- a/src/relay/pass/quantize.cc
+++ b/src/relay/pass/quantize.cc
@@ -340,18 +340,9 @@ Expr MulRealize(const Call& ref_call,
     const auto* rhs = new_args[1].as<QRealizeIntExprNode>();
     Expr ldata = lhs->data;
     Expr rdata = rhs->data;
-
     DataType dtype = cfg->dtype_activation;

ajtulloch

ajtulloch

ajtulloch

diff --git a/tutorials/optimize/opt_gemm.py b/tutorials/optimize/opt_gemm.py
index 44ee53a7..c9785cbf 100644
--- a/tutorials/optimize/opt_gemm.py
+++ b/tutorials/optimize/opt_gemm.py
@@ -44,24 +44,24 @@ import timeit
 # The size of the matrix
 # (M, K) x (K, N)
 # You are free to try out different shapes, sometimes TVM optimization outperforms numpy with MKL.
-M = 1024
-K = 1024

ajtulloch

diff --git a/tutorials/optimize/opt_gemm.py b/tutorials/optimize/opt_gemm.py
index 44ee53a7..c9785cbf 100644
--- a/tutorials/optimize/opt_gemm.py
+++ b/tutorials/optimize/opt_gemm.py
@@ -44,24 +44,24 @@ import timeit
 # The size of the matrix
 # (M, K) x (K, N)
 # You are free to try out different shapes, sometimes TVM optimization outperforms numpy with MKL.
-M = 1024
-K = 1024

ajtulloch

#! /usr/bin/env python

import pexpect
import pexpect.replwrap

repl = pexpect.replwrap.REPLWrapper("lua", u"> ", None, u"> ")

output = repl.run_command("= 1 + 1", timeout=1).splitlines()[1:]

assert(int(output[0]) == 2)