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Commit b11386e1 authored by Victor SANH's avatar Victor SANH
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less prints in saving prunebert

parent 8b5d4003
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Showing with 16 additions and 1270 deletions
examples/movement-pruning/Saving_PruneBERT.ipynb
View file @ b11386e1
......@@ -63,532 +63,7 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BertForQuestionAnswering(\n",
" (bert): BertModel(\n",
" (embeddings): BertEmbeddings(\n",
" (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
" (position_embeddings): Embedding(512, 768)\n",
" (token_type_embeddings): Embedding(2, 768)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (encoder): BertEncoder(\n",
" (layer): ModuleList(\n",
" (0): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (key): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (value): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=3072\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" )\n",
" (output): BertOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=3072, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (1): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (key): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (value): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=3072\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" )\n",
" (output): BertOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=3072, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (2): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (key): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (value): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=3072\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" )\n",
" (output): BertOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=3072, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (3): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (key): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (value): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=3072\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" )\n",
" (output): BertOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=3072, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (4): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (key): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (value): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=3072\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" )\n",
" (output): BertOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=3072, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (5): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (key): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (value): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=3072\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" )\n",
" (output): BertOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=3072, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (6): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (key): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (value): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=3072\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" )\n",
" (output): BertOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=3072, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (7): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (key): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (value): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=3072\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" )\n",
" (output): BertOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=3072, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (8): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (key): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (value): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=3072\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" )\n",
" (output): BertOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=3072, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (9): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (key): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (value): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=3072\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" )\n",
" (output): BertOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=3072, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (10): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (key): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (value): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=3072\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" )\n",
" (output): BertOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=3072, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (11): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (key): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (value): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=3072\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" )\n",
" (output): BertOutput(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=3072, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (pooler): BertPooler(\n",
" (dense): DynamicQuantizedLinear(\n",
" in_features=768, out_features=768\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
" (activation): Tanh()\n",
" )\n",
" )\n",
" (qa_outputs): DynamicQuantizedLinear(\n",
" in_features=768, out_features=2\n",
" (_packed_params): LinearPackedParams()\n",
" )\n",
")\n"
]
}
],
"outputs": [],
"source": [
"# Load fine-pruned model and quantize the model\n",
"\n",
......@@ -605,7 +80,7 @@
" },\n",
" dtype=torch.qint8,\n",
" )\n",
"print(quantized_model)\n",
"# print(quantized_model)\n",
"\n",
"qtz_st = quantized_model.state_dict()"
]
......@@ -910,745 +385,7 @@
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Unpack bert.encoder.layer.0.attention.output.dense._packed_params.weight.int_repr.shape\n",
"Unpack bert.encoder.layer.0.attention.output.dense._packed_params.weight.scale\n",
"Unpack bert.encoder.layer.0.attention.output.dense._packed_params.weight.zero_point\n",
"Unpack bert.encoder.layer.0.attention.output.dense.scale\n",
"Unpack bert.encoder.layer.0.attention.output.dense.zero_point\n",
"Unpack bert.encoder.layer.0.attention.self.key._packed_params.weight.int_repr.shape\n",
"Unpack bert.encoder.layer.0.attention.self.key._packed_params.weight.scale\n",
"Unpack bert.encoder.layer.0.attention.self.key._packed_params.weight.zero_point\n",
"Unpack bert.encoder.layer.0.attention.self.key.scale\n",
"Unpack bert.encoder.layer.0.attention.self.key.zero_point\n",
"Unpack bert.encoder.layer.0.attention.self.query._packed_params.weight.int_repr.shape\n",
"Unpack bert.encoder.layer.0.attention.self.query._packed_params.weight.scale\n",
"Unpack bert.encoder.layer.0.attention.self.query._packed_params.weight.zero_point\n",
"Unpack bert.encoder.layer.0.attention.self.query.scale\n",
"Unpack bert.encoder.layer.0.attention.self.query.zero_point\n",
"Unpack bert.encoder.layer.0.attention.self.value._packed_params.weight.int_repr.shape\n",
"Unpack bert.encoder.layer.0.attention.self.value._packed_params.weight.scale\n",
"Unpack bert.encoder.layer.0.attention.self.value._packed_params.weight.zero_point\n",
"Unpack bert.encoder.layer.0.attention.self.value.scale\n",
"Unpack bert.encoder.layer.0.attention.self.value.zero_point\n",
"Unpack bert.encoder.layer.0.intermediate.dense._packed_params.weight.int_repr.shape\n",
"Unpack bert.encoder.layer.0.intermediate.dense._packed_params.weight.scale\n",
"Unpack bert.encoder.layer.0.intermediate.dense._packed_params.weight.zero_point\n",
"Unpack bert.encoder.layer.0.intermediate.dense.scale\n",
"Unpack bert.encoder.layer.0.intermediate.dense.zero_point\n",
"Unpack bert.encoder.layer.0.output.dense._packed_params.weight.int_repr.shape\n",
"Unpack bert.encoder.layer.0.output.dense._packed_params.weight.scale\n",
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{
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]
},
{
"name": "stdout",
"output_type": "stream",
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]
}
],
"outputs": [],
"source": [
"# Reconstruct the elementary state dict\n",
"\n",
......@@ -1670,7 +407,7 @@
" else:\n",
" attr_param = torch.tensor(attr_param)\n",
" reconstructed_elementary_qtz_st[attr_name] = attr_param\n",
" print(f\"Unpack {attr_name}\")\n",
" # print(f\"Unpack {attr_name}\")\n",
" \n",
"# Get the tensors/arrays\n",
"for data_name, data_param in hf.items():\n",
......@@ -1683,7 +420,7 @@
" if \"indices\" in data_name:\n",
" data_param = np.array(data_param, dtype=np.int32)\n",
" reconstructed_elementary_qtz_st[data_name] = data_param\n",
" print(f\"Unpack {data_name}\")\n",
" # print(f\"Unpack {data_name}\")\n",
" \n",
"\n",
"hf.close()"
......@@ -1818,7 +555,15 @@
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sanity check passed\n"
]
}
],
"source": [
"# Sanity checks on the infernce\n",
"\n",
......@@ -1831,7 +576,8 @@
" y_reconstructed = reconstructed_qtz_model(input_ids=inputs, attention_mask=mask)[0]\n",
" y = quantized_model(input_ids=inputs, attention_mask=mask)[0]\n",
" \n",
" assert torch.all(torch.eq(y, y_reconstructed))"
" assert torch.all(torch.eq(y, y_reconstructed))\n",
"print(\"Sanity check passed\")"
]
},
{
......
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