{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "academic-surname",
"metadata": {},
"outputs": [],
"source": [
"import paddle\n",
"from paddle import nn"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "fundamental-treasure",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/workspace/DeepSpeech-2.x/tools/venv-dev/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
" and should_run_async(code)\n"
]
}
],
"source": [
"L = nn.Linear(256, 2048)\n",
"L2 = nn.Linear(2048, 256)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "consolidated-elephant",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import torch\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "moderate-noise",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"float64\n",
"Tensor(shape=[2, 51, 256], dtype=float32, place=CUDAPlace(0), stop_gradient=True,\n",
" [[[-1.54171216, -2.61531472, -1.79881978, ..., -0.31395876, 0.56513089, -0.44516513],\n",
" [-0.79492962, 1.91157901, 0.66567147, ..., 0.54825783, -1.01471853, -0.84924090],\n",
" [-1.22556651, -0.36225814, 0.65063190, ..., 0.65726501, 0.05563191, 0.09009409],\n",
" ...,\n",
" [ 0.38615900, -0.77905393, 0.99732304, ..., -1.38463700, -3.32365036, -1.31089687],\n",
" [ 0.05579993, 0.06885809, -1.66662002, ..., -0.23346378, -3.29372883, 1.30561364],\n",
" [ 1.90676069, 1.95093191, -0.28849599, ..., -0.06860496, 0.95347673, 1.00475824]],\n",
"\n",
" [[-0.91453546, 0.55298805, -1.06146812, ..., -0.86378336, 1.00454640, 1.26062179],\n",
" [ 0.10223761, 0.81301165, 2.36865163, ..., 0.16821407, 0.29240361, 1.05408621],\n",
" [-1.33196676, 1.94433689, 0.01934209, ..., 0.48036841, 0.51585966, 1.22893548],\n",
" ...,\n",
" [-0.19558455, -0.47075930, 0.90796155, ..., -1.28598249, -0.24321797, 0.17734711],\n",
" [ 0.89819717, -1.39516675, 0.17138045, ..., 2.39761519, 1.76364994, -0.52177650],\n",
" [ 0.94122332, -0.18581429, 1.36099780, ..., 0.67647684, -0.04699665, 1.51205540]]])\n",
"tensor([[[-1.5417, -2.6153, -1.7988, ..., -0.3140, 0.5651, -0.4452],\n",
" [-0.7949, 1.9116, 0.6657, ..., 0.5483, -1.0147, -0.8492],\n",
" [-1.2256, -0.3623, 0.6506, ..., 0.6573, 0.0556, 0.0901],\n",
" ...,\n",
" [ 0.3862, -0.7791, 0.9973, ..., -1.3846, -3.3237, -1.3109],\n",
" [ 0.0558, 0.0689, -1.6666, ..., -0.2335, -3.2937, 1.3056],\n",
" [ 1.9068, 1.9509, -0.2885, ..., -0.0686, 0.9535, 1.0048]],\n",
"\n",
" [[-0.9145, 0.5530, -1.0615, ..., -0.8638, 1.0045, 1.2606],\n",
" [ 0.1022, 0.8130, 2.3687, ..., 0.1682, 0.2924, 1.0541],\n",
" [-1.3320, 1.9443, 0.0193, ..., 0.4804, 0.5159, 1.2289],\n",
" ...,\n",
" [-0.1956, -0.4708, 0.9080, ..., -1.2860, -0.2432, 0.1773],\n",
" [ 0.8982, -1.3952, 0.1714, ..., 2.3976, 1.7636, -0.5218],\n",
" [ 0.9412, -0.1858, 1.3610, ..., 0.6765, -0.0470, 1.5121]]])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/workspace/DeepSpeech-2.x/tools/venv-dev/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
" and should_run_async(code)\n"
]
}
],
"source": [
"x = np.random.randn(2, 51, 256)\n",
"print(x.dtype)\n",
"px = paddle.to_tensor(x, dtype='float32')\n",
"tx = torch.tensor(x, dtype=torch.float32)\n",
"print(px)\n",
"print(tx)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cooked-progressive",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"id": "mechanical-prisoner",
"metadata": {},
"outputs": [],
"source": [
"data = np.load('enc_0_ff_out.npz', allow_pickle=True)\n",
"t_norm_ff = data['norm_ff']\n",
"t_ff_out = data['ff_out']\n",
"t_ff_l_x = data['ff_l_x']\n",
"t_ff_l_a_x = data['ff_l_a_x']\n",
"t_ff_l_a_l_x = data['ff_l_a_l_x']\n",
"t_ps = data['ps']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "indie-marriage",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"id": "assured-zambia",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"True\n",
"True\n",
"True\n"
]
}
],
"source": [
"L.set_state_dict({'weight': t_ps[0].T, 'bias': t_ps[1]})\n",
"L2.set_state_dict({'weight': t_ps[2].T, 'bias': t_ps[3]})\n",
"\n",
"ps = []\n",
"for n, p in L.named_parameters():\n",
" ps.append(p)\n",
"\n",
"for n, p in L2.state_dict().items():\n",
" ps.append(p)\n",
" \n",
"for p, tp in zip(ps, t_ps):\n",
" print(np.allclose(p.numpy(), tp.T))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "committed-jacob",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "extreme-traffic",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "optimum-milwaukee",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 7,
"id": "viral-indian",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"True\n",
"True\n",
"True\n"
]
}
],
"source": [
"# data = np.load('enc_0_ff_out.npz', allow_pickle=True)\n",
"# t_norm_ff = data['norm_ff']\n",
"# t_ff_out = data['ff_out']\n",
"# t_ff_l_x = data['ff_l_x']\n",
"# t_ff_l_a_x = data['ff_l_a_x']\n",
"# t_ff_l_a_l_x = data['ff_l_a_l_x']\n",
"# t_ps = data['ps']\n",
"TL = torch.nn.Linear(256, 2048)\n",
"TL2 = torch.nn.Linear(2048, 256)\n",
"TL.load_state_dict({'weight': torch.tensor(t_ps[0]), 'bias': torch.tensor(t_ps[1])})\n",
"TL2.load_state_dict({'weight': torch.tensor(t_ps[2]), 'bias': torch.tensor(t_ps[3])})\n",
"\n",
"# for n, p in TL.named_parameters():\n",
"# print(n, p)\n",
"# for n, p in TL2.named_parameters():\n",
"# print(n, p)\n",
"\n",
"ps = []\n",
"for n, p in TL.state_dict().items():\n",
" ps.append(p.data.numpy())\n",
" \n",
"for n, p in TL2.state_dict().items():\n",
" ps.append(p.data.numpy())\n",
" \n",
"for p, tp in zip(ps, t_ps):\n",
" print(np.allclose(p, tp))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "skilled-vietnamese",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[ 0.67277956 0.08313607 -0.62761104 ... -0.17480263 0.42718208\n",
" -0.5787626 ]\n",
" [ 0.91516656 0.5393416 1.7159258 ... 0.06144593 0.06486575\n",
" -0.03350811]\n",
" [ 0.438351 0.6227843 0.24096036 ... 1.0912522 -0.90929437\n",
" -1.012989 ]\n",
" ...\n",
" [ 0.68631977 0.14240924 0.10763275 ... -0.11513516 0.48065388\n",
" 0.04070369]\n",
" [-0.9525228 0.23197874 0.31264272 ... 0.5312439 0.18773697\n",
" -0.8450228 ]\n",
" [ 0.42024016 -0.04561988 0.54541194 ... -0.41933843 -0.00436018\n",
" -0.06663495]]\n",
"\n",
" [[-0.11638781 -0.33566502 -0.20887226 ... 0.17423287 -0.9195841\n",
" -0.8161046 ]\n",
" [-0.3469874 0.88269687 -0.11887559 ... -0.15566081 0.16357468\n",
" -0.20766167]\n",
" [-0.3847657 0.3984318 -0.06963477 ... -0.00360622 1.2360432\n",
" -0.26811332]\n",
" ...\n",
" [ 0.08230796 -0.46158582 0.54582864 ... 0.15747628 -0.44790155\n",
" 0.06020184]\n",
" [-0.8095085 0.43163058 -0.42837143 ... 0.8627463 0.90656304\n",
" 0.15847842]\n",
" [-1.485811 -0.18216592 -0.8882585 ... 0.32596245 0.7822631\n",
" -0.6460344 ]]]\n",
"[[[ 0.67278004 0.08313602 -0.6276114 ... -0.17480245 0.42718196\n",
" -0.5787625 ]\n",
" [ 0.91516703 0.5393413 1.7159253 ... 0.06144581 0.06486579\n",
" -0.03350812]\n",
" [ 0.43835106 0.62278455 0.24096027 ... 1.0912521 -0.9092943\n",
" -1.0129892 ]\n",
" ...\n",
" [ 0.6863195 0.14240888 0.10763284 ... -0.11513527 0.48065376\n",
" 0.04070365]\n",
" [-0.9525231 0.23197863 0.31264275 ... 0.53124386 0.18773702\n",
" -0.84502304]\n",
" [ 0.42024007 -0.04561983 0.545412 ... -0.41933888 -0.00436005\n",
" -0.066635 ]]\n",
"\n",
" [[-0.11638767 -0.33566508 -0.20887226 ... 0.17423296 -0.9195838\n",
" -0.8161046 ]\n",
" [-0.34698725 0.88269705 -0.11887549 ... -0.15566081 0.16357464\n",
" -0.20766166]\n",
" [-0.3847657 0.3984319 -0.06963488 ... -0.00360619 1.2360426\n",
" -0.26811326]\n",
" ...\n",
" [ 0.08230786 -0.4615857 0.5458287 ... 0.15747619 -0.44790167\n",
" 0.06020182]\n",
" [-0.8095083 0.4316307 -0.42837155 ... 0.862746 0.9065631\n",
" 0.15847899]\n",
" [-1.485811 -0.18216613 -0.8882584 ... 0.32596254 0.7822631\n",
" -0.6460344 ]]]\n",
"True\n",
"False\n"
]
}
],
"source": [
"y = L(px)\n",
"print(y.numpy())\n",
"\n",
"ty = TL(tx)\n",
"print(ty.data.numpy())\n",
"print(np.allclose(px.numpy(), tx.detach().numpy()))\n",
"print(np.allclose(y.numpy(), ty.detach().numpy()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "incorrect-allah",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "prostate-cameroon",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"id": "governmental-surge",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.04476918 0.554463 -0.3027508 ... -0.49600336 0.3751858\n",
" 0.8254095 ]\n",
" [ 0.95594174 -0.29528382 -1.2899452 ... 0.43718258 0.05584608\n",
" -0.06974669]]\n",
"[[ 0.04476918 0.5544631 -0.3027507 ... -0.49600336 0.37518573\n",
" 0.8254096 ]\n",
" [ 0.95594174 -0.29528376 -1.2899454 ... 0.4371827 0.05584623\n",
" -0.0697467 ]]\n",
"True\n",
"False\n",
"True\n"
]
}
],
"source": [
"x = np.random.randn(2, 256)\n",
"px = paddle.to_tensor(x, dtype='float32')\n",
"tx = torch.tensor(x, dtype=torch.float32)\n",
"y = L(px)\n",
"print(y.numpy())\n",
"ty = TL(tx)\n",
"print(ty.data.numpy())\n",
"print(np.allclose(px.numpy(), tx.detach().numpy()))\n",
"print(np.allclose(y.numpy(), ty.detach().numpy()))\n",
"print(np.allclose(y.numpy(), ty.detach().numpy(), atol=1e-5))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "confidential-jacket",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"id": "improved-civilization",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5e7e7c9fde8350084abf1898cf52651cfc84b17a\n"
]
}
],
"source": [
"print(paddle.version.commit)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d1e2d3b4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['__builtins__',\n",
" '__cached__',\n",
" '__doc__',\n",
" '__file__',\n",
" '__loader__',\n",
" '__name__',\n",
" '__package__',\n",
" '__spec__',\n",
" 'commit',\n",
" 'full_version',\n",
" 'istaged',\n",
" 'major',\n",
" 'minor',\n",
" 'mkl',\n",
" 'patch',\n",
" 'rc',\n",
" 'show',\n",
" 'with_mkl']"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(paddle.version)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "c880c719",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.1.0\n"
]
}
],
"source": [
"print(paddle.version.full_version)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "f26977bf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"commit: 5e7e7c9fde8350084abf1898cf52651cfc84b17a\n",
"None\n"
]
}
],
"source": [
"print(paddle.version.show())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "04ad47f6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.6.0\n"
]
}
],
"source": [
"print(torch.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e1e03830",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['__builtins__',\n",
" '__cached__',\n",
" '__doc__',\n",
" '__file__',\n",
" '__loader__',\n",
" '__name__',\n",
" '__package__',\n",
" '__spec__',\n",
" '__version__',\n",
" 'cuda',\n",
" 'debug',\n",
" 'git_version',\n",
" 'hip']"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(torch.version)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "4ad0389b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'b31f58de6fa8bbda5353b3c77d9be4914399724d'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.version.git_version"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "7870ea10",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'10.2'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.version.cuda"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "db8ee5a7",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "6321ec2a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}