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Tutorials fix with set_all_parameters #113

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Tutorials fix with set_all_parameters #113

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45fcec4
Tutorials fix with set_all_parameters
gmuraru May 12, 2020
b112ab1
Change load/save to save/load from party
gmuraru May 13, 2020
f0b847b
Remove checkpoints
gmuraru May 13, 2020
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13 changes: 4 additions & 9 deletions test/test_crypten.py
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Original file line number Diff line number Diff line change
Expand Up @@ -283,12 +283,8 @@ def forward(self, input):
return out

def set_all_parameters(self, value):
self.fc1.weight.data.fill_(value)
self.fc1.bias.data.fill_(value)
self.fc2.weight.data.fill_(value)
self.fc2.bias.data.fill_(value)
self.fc3.weight.data.fill_(value)
self.fc3.bias.data.fill_(value)
for p in self.parameters():
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@gmuraru gmuraru May 12, 2020 •
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Q: Is there any reason why data.fill_ is used?

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@knottb knottb May 12, 2020 •
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As far as I'm aware, torch.nn.init.constant_(param, value) is the same as param.data.fill_(value).

constant_ doc
fill_ doc

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They should do the same thing but changed it to constant_ because of an issue indicated in this PR

torch.nn.init.constant_(p, value)


class NestedTestModule(nn.Module):
Expand All @@ -302,9 +298,8 @@ def forward(self, input):
out = self.nested(out)

def set_all_parameters(self, value):
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Q: Is there any plan to have a method like this added directly in PyTorch for nn.Module?

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I don't believe there is any plan to do this. This function isn't defined in CrypTen either - it is defined in a unit test as a helper function. In general, I think it is left to the user to initialize (or otherwise set) parameter values.

self.fc1.weight.data.fill_(value)
self.fc1.bias.data.fill_(value)
self.nested.set_all_parameters(value)
for p in self.parameters():
torch.nn.init.constant_(p, value)


# This code only runs when executing the file outside the test harness
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2 changes: 1 addition & 1 deletion tutorials/Introduction.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -77,7 +77,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
"version": "3.7.6"
}
},
"nbformat": 4,
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137 changes: 15 additions & 122 deletions tutorials/Tutorial_1_Basics_of_CrypTen_Tensors.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -39,18 +39,9 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([1., 2., 3.])\n",
"tensor([4., 5., 6.])\n"
]
}
],
"outputs": [],
"source": [
"# Create torch tensor\n",
"x = torch.tensor([1.0, 2.0, 3.0])\n",
Expand Down Expand Up @@ -87,28 +78,9 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Public addition: tensor([3., 4., 5.])\n",
"Private addition: tensor([3., 4., 5.])\n",
"\n",
"Public subtraction: tensor([-1., 0., 1.])\n",
"Private subtraction: tensor([-1., 0., 1.])\n",
"\n",
"Public multiplication: tensor([2., 4., 6.])\n",
"Private multiplication: tensor([2., 4., 6.])\n",
"\n",
"Public division: tensor([0.5000, 1.0000, 1.5000])\n",
"Private division: tensor([0.5000, 1.0000, 1.5000])\n"
]
}
],
"outputs": [],
"source": [
"#Arithmetic operations between CrypTensors and plaintext tensors\n",
"x_enc = crypten.cryptensor([1.0, 2.0, 3.0])\n",
Expand Down Expand Up @@ -153,36 +125,9 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x: tensor([1., 2., 3., 4., 5.])\n",
"y: tensor([5., 4., 3., 2., 1.])\n",
"\n",
"Public (x < y) : tensor([1., 1., 0., 0., 0.])\n",
"Private (x < y) : tensor([1., 1., 0., 0., 0.])\n",
"\n",
"Public (x <= y): tensor([1., 1., 1., 0., 0.])\n",
"Private (x <= y): tensor([1., 1., 1., 0., 0.])\n",
"\n",
"Public (x > y) : tensor([0., 0., 0., 1., 1.])\n",
"Private (x > y) : tensor([0., 0., 0., 1., 1.])\n",
"\n",
"Public (x >= y): tensor([0., 0., 1., 1., 1.])\n",
"Private (x >= y): tensor([0., 0., 1., 1., 1.])\n",
"\n",
"Public (x == y): tensor([0., 0., 1., 0., 0.])\n",
"Private (x == y): tensor([0., 0., 1., 0., 0.])\n",
"\n",
"Public (x != y): tensor([1., 1., 0., 1., 1.])\n",
"Private (x != y): tensor([1., 1., 0., 1., 1.])\n"
]
}
],
"outputs": [],
"source": [
"#Construct two example CrypTensors\n",
"x_enc = crypten.cryptensor([1.0, 2.0, 3.0, 4.0, 5.0])\n",
Expand Down Expand Up @@ -243,31 +188,9 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Public reciprocal: tensor([10.0000, 3.3333, 2.0000, 1.0000, 0.6667, 0.5000, 0.4000])\n",
"Private reciprocal: tensor([10.0009, 3.3335, 2.0000, 1.0000, 0.6667, 0.5000, 0.4000])\n",
"\n",
"Public logarithm: tensor([-2.3026, -1.2040, -0.6931, 0.0000, 0.4055, 0.6931, 0.9163])\n",
"Private logarithm: tensor([-2.3181, -1.2110, -0.6997, 0.0004, 0.4038, 0.6918, 0.9150])\n",
"\n",
"Public exponential: tensor([ 1.1052, 1.3499, 1.6487, 2.7183, 4.4817, 7.3891, 12.1825])\n",
"Private exponential: tensor([ 1.1021, 1.3440, 1.6468, 2.7121, 4.4574, 7.3280, 12.0188])\n",
"\n",
"Public square root: tensor([0.3162, 0.5477, 0.7071, 1.0000, 1.2247, 1.4142, 1.5811])\n",
"Private square root: tensor([0.3135, 0.5445, 0.7051, 1.0000, 1.2195, 1.4080, 1.5762])\n",
"\n",
"Public tanh: tensor([0.0997, 0.2913, 0.4621, 0.7616, 0.9051, 0.9640, 0.9866])\n",
"Private tanh: tensor([0.1019, 0.2892, 0.4613, 0.7642, 0.9081, 0.9689, 0.9922])\n"
]
}
],
"outputs": [],
"source": [
"torch.set_printoptions(sci_mode=False)\n",
"\n",
Expand Down Expand Up @@ -321,20 +244,9 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RuntimeError caught: \"Cannot evaluate MPCTensors to boolean values\"\n",
"\n",
"z: tensor(2.)\n",
"z: tensor(2.)\n"
]
}
],
"outputs": [],
"source": [
"x_enc = crypten.cryptensor(2.0)\n",
"y_enc = crypten.cryptensor(4.0)\n",
Expand Down Expand Up @@ -372,28 +284,9 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Indexing:\n",
" tensor([1., 2.])\n",
"\n",
"Concatenation:\n",
" tensor([1., 2., 3., 4., 5., 6.])\n",
"\n",
"Stacking:\n",
" tensor([[1., 2., 3.],\n",
" [4., 5., 6.]])\n",
"\n",
"Reshaping:\n",
" tensor([[1., 2., 3., 4., 5., 6.]])\n"
]
}
],
"outputs": [],
"source": [
"x_enc = crypten.cryptensor([1.0, 2.0, 3.0])\n",
"y_enc = crypten.cryptensor([4.0, 5.0, 6.0])\n",
Expand All @@ -411,7 +304,7 @@
"print('\\nStacking:\\n', z_enc.get_plain_text())\n",
"\n",
"# Reshaping\n",
"w_enc = z_enc.reshape(-1, 6)\n",
"w_enc = z_enc.reshape((-1, 6))\n",
"print('\\nReshaping:\\n', w_enc.get_plain_text())\n"
]
},
Expand Down Expand Up @@ -446,7 +339,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
"version": "3.7.6"
}
},
"nbformat": 4,
Expand Down
99 changes: 12 additions & 87 deletions tutorials/Tutorial_2_Inside_CrypTensors.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -49,18 +49,9 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_enc internal type: ptype.arithmetic\n",
"y_enc internal type: ptype.binary\n"
]
}
],
"outputs": [],
"source": [
"#Constructing CrypTensors with ptype attribute\n",
"\n",
Expand Down Expand Up @@ -90,28 +81,9 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rank 0:\n",
" MPCTensor(\n",
"\t_tensor=tensor([1559458991263271971, 464556655590487085, 3264140948701540070])\n",
"\tplain_text=HIDDEN\n",
"\tptype=ptype.arithmetic\n",
")Rank 1:\n",
" MPCTensor(\n",
"\t_tensor=tensor([-1559458991263206435, -464556655590356013, -3264140948701343462])\n",
"\tplain_text=HIDDEN\n",
"\tptype=ptype.arithmetic\n",
")\n",
"\n"
]
}
],
"outputs": [],
"source": [
"import crypten.mpc as mpc\n",
"import crypten.communicator as comm \n",
Expand Down Expand Up @@ -142,28 +114,9 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rank 0:\n",
" MPCTensor(\n",
"\t_tensor=tensor([ 125494978816570963, 7944957613546045877])\n",
"\tplain_text=HIDDEN\n",
"\tptype=ptype.binary\n",
")Rank 1:\n",
" MPCTensor(\n",
"\t_tensor=tensor([ 125494978816570961, 7944957613546045878])\n",
"\tplain_text=HIDDEN\n",
"\tptype=ptype.binary\n",
")\n",
"\n"
]
}
],
"outputs": [],
"source": [
"@mpc.run_multiprocess(world_size=2)\n",
"def examine_binary_shares():\n",
Expand All @@ -187,24 +140,9 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"to(crypten.binary):\n",
" ptype: ptype.binary\n",
" plaintext: tensor([1., 2., 3.])\n",
"\n",
"to(crypten.arithmetic):\n",
" ptype: ptype.arithmetic\n",
" plaintext: tensor([1., 2., 3.])\n",
"\n"
]
}
],
"outputs": [],
"source": [
"from crypten.mpc import MPCTensor\n",
"\n",
Expand Down Expand Up @@ -253,22 +191,9 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rank 0: 0\n",
"Rank 2: 2\n",
"Rank 1: 1\n",
"Source 0: 0.0\n",
"Source 1: 1.0\n",
"Source 2: 2.0\n"
]
}
],
"outputs": [],
"source": [
"@mpc.run_multiprocess(world_size=3)\n",
"def examine_sources():\n",
Expand Down Expand Up @@ -306,7 +231,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
"version": "3.7.6"
}
},
"nbformat": 4,
Expand Down
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