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VictorSanh 473808da0d update mvmt-pruning/saving_prunebert (updating torch to 1.5)

2020-06-11 19:42:45 +00:00

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Saving PruneBERT

This notebook aims at showcasing how we can leverage standard tools to save (and load) an extremely sparse model fine-pruned with movement pruning (or any other unstructured pruning mehtod).

In this example, we used BERT (base-uncased, but the procedure described here is not specific to BERT and can be applied to a large variety of models.

We first obtain an extremely sparse model by fine-pruning with movement pruning on SQuAD v1.1. We then used the following combination of standard tools:

We reduce the precision of the model with Int8 dynamic quantization using PyTorch implementation. We only quantized the Fully Connected Layers.
Sparse quantized matrices are converted into the Compressed Sparse Row format.
We use HDF5 with gzip compression to store the weights.

We experiment with a question answering model with only 6% of total remaining weights in the encoder (previously obtained with movement pruning). We are able to reduce the memory size of the encoder from 340MB (original dense BERT) to 11MB, which fits on a 91' floppy disk!

Note: this notebook is compatible with torch>=1.5.0 If you are using, torch==1.4.0, please refer to this previous version of the notebook.

In [1]:

# Includes

import h5py
import os
import json
from collections import OrderedDict

from scipy import sparse
import numpy as np

import torch
from torch import nn

from transformers import *

os.chdir('../../')

Saving

Dynamic quantization induces little or no loss of performance while significantly reducing the memory footprint.

In [2]:

# Load fine-pruned model and quantize the model

model = BertForQuestionAnswering.from_pretrained("huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad")
model.to('cpu')

quantized_model = torch.quantization.quantize_dynamic(
                    model=model,
                    qconfig_spec = {
                        torch.nn.Linear : torch.quantization.default_dynamic_qconfig,
                    },
                    dtype=torch.qint8,
                )
# print(quantized_model)

qtz_st = quantized_model.state_dict()

In [3]:

# Saving the original (encoder + classifier) in the standard torch.save format

dense_st = {name: param for name, param in model.state_dict().items() 
                            if "embedding" not in name and "pooler" not in name}
torch.save(dense_st, 'dbg/dense_squad.pt',)
dense_mb_size = os.path.getsize("dbg/dense_squad.pt")

In [4]:

# Elementary representation: we decompose the quantized tensors into (scale, zero_point, int_repr).
# See https://pytorch.org/docs/stable/quantization.html

# We further leverage the fact that int_repr is sparse matrix to optimize the storage: we decompose int_repr into
# its CSR representation (data, indptr, indices).

elementary_qtz_st = {}
for name, param in qtz_st.items():
    if "dtype" not in name and param.is_quantized:
        print("Decompose quantization for", name)
        # We need to extract the scale, the zero_point and the int_repr for the quantized tensor and modules
        scale = param.q_scale()                                # torch.tensor(1,) - float32
        zero_point = param.q_zero_point()                      # torch.tensor(1,) - int32
        elementary_qtz_st[f"{name}.scale"] = scale
        elementary_qtz_st[f"{name}.zero_point"] = zero_point

        # We assume the int_repr is sparse and compute its CSR representation
        # Only the FCs in the encoder are actually sparse
        int_repr = param.int_repr()                         # torch.tensor(nb_rows, nb_columns) - int8
        int_repr_cs = sparse.csr_matrix(int_repr)           # scipy.sparse.csr.csr_matrix

        elementary_qtz_st[f"{name}.int_repr.data"] = int_repr_cs.data                  # np.array int8
        elementary_qtz_st[f"{name}.int_repr.indptr"] = int_repr_cs.indptr              # np.array int32
        assert max(int_repr_cs.indices) < 65535 # If not, we shall fall back to int32
        elementary_qtz_st[f"{name}.int_repr.indices"] = np.uint16(int_repr_cs.indices) # np.array uint16
        elementary_qtz_st[f"{name}.int_repr.shape"] = int_repr_cs.shape                # tuple(int, int)
    else:
        elementary_qtz_st[name] = param

Decompose quantization for bert.encoder.layer.0.attention.self.query._packed_params.weight
Decompose quantization for bert.encoder.layer.0.attention.self.key._packed_params.weight
Decompose quantization for bert.encoder.layer.0.attention.self.value._packed_params.weight
Decompose quantization for bert.encoder.layer.0.attention.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.0.intermediate.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.0.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.1.attention.self.query._packed_params.weight
Decompose quantization for bert.encoder.layer.1.attention.self.key._packed_params.weight
Decompose quantization for bert.encoder.layer.1.attention.self.value._packed_params.weight
Decompose quantization for bert.encoder.layer.1.attention.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.1.intermediate.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.1.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.2.attention.self.query._packed_params.weight
Decompose quantization for bert.encoder.layer.2.attention.self.key._packed_params.weight
Decompose quantization for bert.encoder.layer.2.attention.self.value._packed_params.weight
Decompose quantization for bert.encoder.layer.2.attention.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.2.intermediate.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.2.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.3.attention.self.query._packed_params.weight
Decompose quantization for bert.encoder.layer.3.attention.self.key._packed_params.weight
Decompose quantization for bert.encoder.layer.3.attention.self.value._packed_params.weight
Decompose quantization for bert.encoder.layer.3.attention.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.3.intermediate.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.3.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.4.attention.self.query._packed_params.weight
Decompose quantization for bert.encoder.layer.4.attention.self.key._packed_params.weight
Decompose quantization for bert.encoder.layer.4.attention.self.value._packed_params.weight
Decompose quantization for bert.encoder.layer.4.attention.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.4.intermediate.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.4.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.5.attention.self.query._packed_params.weight
Decompose quantization for bert.encoder.layer.5.attention.self.key._packed_params.weight
Decompose quantization for bert.encoder.layer.5.attention.self.value._packed_params.weight
Decompose quantization for bert.encoder.layer.5.attention.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.5.intermediate.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.5.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.6.attention.self.query._packed_params.weight
Decompose quantization for bert.encoder.layer.6.attention.self.key._packed_params.weight
Decompose quantization for bert.encoder.layer.6.attention.self.value._packed_params.weight
Decompose quantization for bert.encoder.layer.6.attention.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.6.intermediate.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.6.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.7.attention.self.query._packed_params.weight
Decompose quantization for bert.encoder.layer.7.attention.self.key._packed_params.weight
Decompose quantization for bert.encoder.layer.7.attention.self.value._packed_params.weight
Decompose quantization for bert.encoder.layer.7.attention.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.7.intermediate.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.7.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.8.attention.self.query._packed_params.weight
Decompose quantization for bert.encoder.layer.8.attention.self.key._packed_params.weight
Decompose quantization for bert.encoder.layer.8.attention.self.value._packed_params.weight
Decompose quantization for bert.encoder.layer.8.attention.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.8.intermediate.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.8.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.9.attention.self.query._packed_params.weight
Decompose quantization for bert.encoder.layer.9.attention.self.key._packed_params.weight
Decompose quantization for bert.encoder.layer.9.attention.self.value._packed_params.weight
Decompose quantization for bert.encoder.layer.9.attention.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.9.intermediate.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.9.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.10.attention.self.query._packed_params.weight
Decompose quantization for bert.encoder.layer.10.attention.self.key._packed_params.weight
Decompose quantization for bert.encoder.layer.10.attention.self.value._packed_params.weight
Decompose quantization for bert.encoder.layer.10.attention.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.10.intermediate.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.10.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.11.attention.self.query._packed_params.weight
Decompose quantization for bert.encoder.layer.11.attention.self.key._packed_params.weight
Decompose quantization for bert.encoder.layer.11.attention.self.value._packed_params.weight
Decompose quantization for bert.encoder.layer.11.attention.output.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.11.intermediate.dense._packed_params.weight
Decompose quantization for bert.encoder.layer.11.output.dense._packed_params.weight
Decompose quantization for bert.pooler.dense._packed_params.weight
Decompose quantization for qa_outputs._packed_params.weight

In [5]:

# Create mapping from torch.dtype to string description (we could also used an int8 instead of string)
str_2_dtype = {"qint8": torch.qint8}
dtype_2_str = {torch.qint8: "qint8"}

In [6]:

# Saving the pruned (encoder + classifier) in the standard torch.save format

dense_optimized_st = {name: param for name, param in elementary_qtz_st.items() 
                                    if "embedding" not in name and "pooler" not in name}
torch.save(dense_optimized_st, 'dbg/dense_squad_optimized.pt',)
print("Encoder Size (MB) - Sparse & Quantized - `torch.save`:",
      round(os.path.getsize("dbg/dense_squad_optimized.pt")/1e6, 2))

Encoder Size (MB) - Sparse & Quantized - `torch.save`: 21.29

In [7]:

# Save the decomposed state_dict with an HDF5 file
# Saving only the encoder + QA Head

with h5py.File('dbg/squad_sparse.h5','w') as hf:
    for name, param in elementary_qtz_st.items():
        if "embedding" in name:
            print(f"Skip {name}")
            continue

        if "pooler" in name:
            print(f"Skip {name}")
            continue

        if type(param) == torch.Tensor:
            if param.numel() == 1:
                # module scale
                # module zero_point
                hf.attrs[name] = param
                continue

            if param.requires_grad:
                # LayerNorm
                param = param.detach().numpy()
            hf.create_dataset(name, data=param, compression="gzip", compression_opts=9)

        elif type(param) == float or type(param) == int or type(param) == tuple:
            # float - tensor _packed_params.weight.scale
            # int   - tensor _packed_params.weight.zero_point
            # tuple - tensor _packed_params.weight.shape
            hf.attrs[name] = param

        elif type(param) == torch.dtype:
            # dtype - tensor _packed_params.dtype
            hf.attrs[name] = dtype_2_str[param]
            
        else:
            hf.create_dataset(name, data=param, compression="gzip", compression_opts=9)


with open('dbg/metadata.json', 'w') as f:
    f.write(json.dumps(qtz_st._metadata))  

size = os.path.getsize("dbg/squad_sparse.h5") + os.path.getsize("dbg/metadata.json")
print("")
print("Encoder Size (MB) - Dense:             ", round(dense_mb_size/1e6, 2))
print("Encoder Size (MB) - Sparse & Quantized:", round(size/1e6, 2))

Skip bert.embeddings.word_embeddings.weight
Skip bert.embeddings.position_embeddings.weight
Skip bert.embeddings.token_type_embeddings.weight
Skip bert.embeddings.LayerNorm.weight
Skip bert.embeddings.LayerNorm.bias
Skip bert.pooler.dense.scale
Skip bert.pooler.dense.zero_point
Skip bert.pooler.dense._packed_params.weight.scale
Skip bert.pooler.dense._packed_params.weight.zero_point
Skip bert.pooler.dense._packed_params.weight.int_repr.data
Skip bert.pooler.dense._packed_params.weight.int_repr.indptr
Skip bert.pooler.dense._packed_params.weight.int_repr.indices
Skip bert.pooler.dense._packed_params.weight.int_repr.shape
Skip bert.pooler.dense._packed_params.bias
Skip bert.pooler.dense._packed_params.dtype

Encoder Size (MB) - Dense:              340.26
Encoder Size (MB) - Sparse & Quantized: 11.28

In [8]:

# Save the decomposed state_dict to HDF5 storage
# Save everything in the architecutre (embedding + encoder + QA Head)

with h5py.File('dbg/squad_sparse_with_embs.h5','w') as hf:
    for name, param in elementary_qtz_st.items():
#         if "embedding" in name:
#             print(f"Skip {name}")
#             continue

#         if "pooler" in name:
#             print(f"Skip {name}")
#             continue

        if type(param) == torch.Tensor:
            if param.numel() == 1:
                # module scale
                # module zero_point
                hf.attrs[name] = param
                continue

            if param.requires_grad:
                # LayerNorm
                param = param.detach().numpy()
            hf.create_dataset(name, data=param, compression="gzip", compression_opts=9)

        elif type(param) == float or type(param) == int or type(param) == tuple:
            # float - tensor _packed_params.weight.scale
            # int   - tensor _packed_params.weight.zero_point
            # tuple - tensor _packed_params.weight.shape
            hf.attrs[name] = param

        elif type(param) == torch.dtype:
            # dtype - tensor _packed_params.dtype
            hf.attrs[name] = dtype_2_str[param]
            
        else:
            hf.create_dataset(name, data=param, compression="gzip", compression_opts=9)



with open('dbg/metadata.json', 'w') as f:
    f.write(json.dumps(qtz_st._metadata))   

size = os.path.getsize("dbg/squad_sparse_with_embs.h5") + os.path.getsize("dbg/metadata.json")
print('\nSize (MB):', round(size/1e6, 2))

Size (MB): 99.41

Loading

In [9]:

# Reconstruct the elementary state dict

reconstructed_elementary_qtz_st = {}

hf = h5py.File('dbg/squad_sparse_with_embs.h5','r')

for attr_name, attr_param in hf.attrs.items():
    if 'shape' in attr_name:
        attr_param = tuple(attr_param)
    elif ".scale" in attr_name:
        if "_packed_params" in attr_name:
            attr_param = float(attr_param)
        else:
            attr_param = torch.tensor(attr_param)
    elif ".zero_point" in attr_name:
        if "_packed_params" in attr_name:
            attr_param = int(attr_param)
        else:
            attr_param = torch.tensor(attr_param)
    elif ".dtype" in attr_name:
        attr_param = str_2_dtype[attr_param]
    reconstructed_elementary_qtz_st[attr_name] = attr_param
    # print(f"Unpack {attr_name}")
    
# Get the tensors/arrays
for data_name, data_param in hf.items():
    if "LayerNorm" in data_name or "_packed_params.bias" in data_name:
        reconstructed_elementary_qtz_st[data_name] = torch.from_numpy(np.array(data_param))
    elif "embedding" in data_name:
        reconstructed_elementary_qtz_st[data_name] = torch.from_numpy(np.array(data_param))
    else: # _packed_params.weight.int_repr.data, _packed_params.weight.int_repr.indices and _packed_params.weight.int_repr.indptr
        data_param = np.array(data_param)
        if "indices" in data_name:
            data_param = np.array(data_param, dtype=np.int32)
        reconstructed_elementary_qtz_st[data_name] = data_param
    # print(f"Unpack {data_name}")
    

hf.close()

In [10]:

# Sanity checks

for name, param in reconstructed_elementary_qtz_st.items():
    assert name in elementary_qtz_st
for name, param in elementary_qtz_st.items():
    assert name in reconstructed_elementary_qtz_st, name

for name, param in reconstructed_elementary_qtz_st.items():
    assert type(param) == type(elementary_qtz_st[name]), name
    if type(param) == torch.Tensor:
        assert torch.all(torch.eq(param, elementary_qtz_st[name])), name
    elif type(param) == np.ndarray:
        assert (param == elementary_qtz_st[name]).all(), name
    else:
        assert param == elementary_qtz_st[name], name

In [11]:

# Re-assemble the sparse int_repr from the CSR format

reconstructed_qtz_st = {}

for name, param in reconstructed_elementary_qtz_st.items():
    if "weight.int_repr.indptr" in name:
        prefix_ = name[:-16]
        data    = reconstructed_elementary_qtz_st[f"{prefix_}.int_repr.data"]
        indptr  = reconstructed_elementary_qtz_st[f"{prefix_}.int_repr.indptr"]
        indices = reconstructed_elementary_qtz_st[f"{prefix_}.int_repr.indices"]
        shape   = reconstructed_elementary_qtz_st[f"{prefix_}.int_repr.shape"]

        int_repr = sparse.csr_matrix(arg1=(data, indices, indptr),
                                     shape=shape)
        int_repr = torch.tensor(int_repr.todense())

        scale = reconstructed_elementary_qtz_st[f"{prefix_}.scale"]
        zero_point = reconstructed_elementary_qtz_st[f"{prefix_}.zero_point"]
        weight = torch._make_per_tensor_quantized_tensor(int_repr,
                                                         scale,
                                                         zero_point)

        reconstructed_qtz_st[f"{prefix_}"] = weight
    elif "int_repr.data" in name or "int_repr.shape" in name or "int_repr.indices" in name or \
         "weight.scale" in name or "weight.zero_point" in name:
        continue
    else:
        reconstructed_qtz_st[name] = param

In [12]:

# Sanity checks

for name, param in reconstructed_qtz_st.items():
    assert name in qtz_st
for name, param in qtz_st.items():
    assert name in reconstructed_qtz_st, name

for name, param in reconstructed_qtz_st.items():
    assert type(param) == type(qtz_st[name]), name
    if type(param) == torch.Tensor:
        assert torch.all(torch.eq(param, qtz_st[name])), name
    elif type(param) == np.ndarray:
        assert (param == qtz_st[name]).all(), name
    else:
        assert param == qtz_st[name], name

Sanity checks

In [13]:

# Load the re-constructed state dict into a model

dummy_model = BertForQuestionAnswering.from_pretrained('bert-base-uncased')
dummy_model.to('cpu')

reconstructed_qtz_model = torch.quantization.quantize_dynamic(
                            model=dummy_model,
                            qconfig_spec = None,
                            dtype=torch.qint8,
                          )

reconstructed_qtz_st = OrderedDict(reconstructed_qtz_st)
with open('dbg/metadata.json', 'r') as read_file:
    metadata = json.loads(read_file.read())
reconstructed_qtz_st._metadata = metadata

reconstructed_qtz_model.load_state_dict(reconstructed_qtz_st)

Out [13]:

<All keys matched successfully>

In [14]:

# Sanity checks on the infernce

N = 32

for _ in range(25):
    inputs = torch.randint(low=0, high=30000, size=(N, 128))
    mask = torch.ones(size=(N, 128))

    y_reconstructed = reconstructed_qtz_model(input_ids=inputs, attention_mask=mask)[0]
    y               = quantized_model(input_ids=inputs, attention_mask=mask)[0]
    
    assert torch.all(torch.eq(y, y_reconstructed))
print("Sanity check passed")

Sanity check passed

In [ ]:

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Saving PruneBERT

Saving

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Sanity checks

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