You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/07/02 00:40:49 UTC
[GitHub] [incubator-tvm] binarybana commented on a change in pull request #5975: [Tutorial] Demo showing how to run a pruned 🤗 model.
binarybana commented on a change in pull request #5975:
URL: https://github.com/apache/incubator-tvm/pull/5975#discussion_r448652687
##########
File path: tutorials/frontend/deploy_sparse.py
##########
@@ -0,0 +1,326 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Deploy a Hugging Face Pruned Model on CPU
+=========================================
+**Author**: `Josh Fromm <https://github.com/jwfromm>`_
+
+This tutorial demonstrates how to take a state of the art pruned model,
+in this case PruneBert from Hugging Face, and use TVM to leverage the model's
Review comment:
Can you link to the model here?
##########
File path: tutorials/frontend/deploy_sparse.py
##########
@@ -0,0 +1,326 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Deploy a Hugging Face Pruned Model on CPU
+=========================================
+**Author**: `Josh Fromm <https://github.com/jwfromm>`_
+
+This tutorial demonstrates how to take a state of the art pruned model,
+in this case PruneBert from Hugging Face, and use TVM to leverage the model's
+sparsity to produce real speedups. Although the primary purpose of this
Review comment:
```suggestion
sparsity support to produce real speedups. Although the primary purpose of this
```
##########
File path: tutorials/frontend/deploy_sparse.py
##########
@@ -0,0 +1,326 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Deploy a Hugging Face Pruned Model on CPU
+=========================================
+**Author**: `Josh Fromm <https://github.com/jwfromm>`_
+
+This tutorial demonstrates how to take a state of the art pruned model,
+in this case PruneBert from Hugging Face, and use TVM to leverage the model's
+sparsity to produce real speedups. Although the primary purpose of this
+tutorial is to show speedups on already pruned models, it may be useful
+to estimate how fast a model would be *if* it were pruned. To this end,
+we also provide a function that takes an unpruned model and replaces its weights
+with random and pruned weights at a specified sparsity. This may be a useful
+feature when trying to decide if a model is worth pruning or not.
+
+Before we get into the code, it's useful to discuss sparsity and pruning
+and dig into the two
+different types of sparsity: **structured** and **unstructured**.
+
+Pruning is a technique primarily used to reduce the parameter size of a model
+by replacing weights with 0s. Although many methods exist for choosing which
+weights should be set to 0, the most straight forward is by picking the
+weights with the smallest value. Typically, weights are pruned to a desired
+sparsity percentage. For example, a 95% sparse model would have only 5% of
+its weights non-zero. Pruning to very high sparsities often requires
+finetuning or full retraining as it tends to be a lossy approximation.
+Although parameter size benefits are quite easy to obtain from a pruned model
+through simple compression, leveraging sparsity to yield runtime speedups
+is more complicated.
+
+In structured sparsity weights are pruned with the goal of clustering
+pruned weights together. In other words, they are pruned using both their
+value and location. The benefit of bunching up pruned weights is that it allows
+an algorithm such as matrix multiplication to skip entire blocks. It turns out
+that some degree of *block sparsity* is very important to realizing significant
+speedups. This is because when loading memory in most CPUs or GPUs, it's not
+possible to load a single value, instead an entire chunk or tile is read in and
+executed using something like vectorized instructions.
+
+Unstructured sparse weights are those that are pruned only on the value of
+the original weights. They may appear to be scattered randomly throughout
+a tensor rather than in chunks like we'd see in block sparse weights.
+At low sparsities, unstructured pruning techniques are difficult to
+accelerate. However, at high sparsities many blocks of all 0 values
+will naturally appear, making it possible to accelerate.
+
+This tutorial interacts with both structured and unstructured sparsity.
+Hugging Face's PruneBert model is unstructured but 95% sparse, allowing us
+to apply TVM's block sparse optimizations to it, even if not optimally.
+When generating random sparse weights for an unpruned model, we do so structured
+sparsity. A fun exercise is comparing the real speed of PruneBert with the block
+sparse speed using fake weights to see the benefit of structured sparsity.
+"""
+
+###############################################################################
+# Load Required Modules
+# ---------------------
+# Other than TVM, scipy, the latest transformers, and
+# tensorflow 2.2+ are required.
+import os
+import tvm
+import time
+import itertools
+import numpy as np
+import tensorflow as tf
+from tvm import relay
+from tvm.contrib import graph_runtime
+from tvm.relay import data_dep_optimization as ddo
+from tensorflow.python.framework.convert_to_constants import (
+ convert_variables_to_constants_v2,
+)
+import scipy.sparse as sp
+
+
+###############################################################################
+# Configure Settings
+# ------------------
+# Let's start by defining some parameters that define the type of model
+# and sparsity to run.
+# Args:
+# name (str):
+# The name of the transformer model to download and run.
+# batch_size (int):
+# The number of batches in an input.
+# seq_len (int):
+# The length of each input sequence.
+# target (str):
+# TVM platform identifier. Although cuda is also supported, it requires
+# tuning that is outside the scope of this tutorial. Note that best
+# cpu performance can be achieved by setting -mcpu appropriately for
+# your specific machine.
+# ctx (context):
+# Which device to run on. Should be one of tvm.cpu() or tvm.gpu().
+# measure_sparse (bool):
+# If true, then a sparse variant of the network will be run and
+# benchmarked.
+# bs_r (int):
+# The block size of structured sparsity to convert weight tensors
+# into. Changing this parameter may yield speedups for some platforms.
+# sparsity (float):
+# For models besides PruneBert (which is 95% sparse), this parameter
+# determines how sparse the generated weights should be. The higher
+# the sparsity, the faster the result.
+name = "huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad"
+batch_size = 1
+seq_len = 128
+target = "llvm"
+ctx = tvm.cpu()
+measure_sparse = True
+bs_r = 1
+sparsity = 0.85
+
+
+###############################################################################
+# Download and Convert Transformers Model
+# ---------------------------------------
+# Now we'll grab a model from the transformers module, download it,
+# convert it into a graphdef, and finally convert that graphdef into
Review comment:
```suggestion
# convert it into a TensorFlow graphdef, in preparation for converting that graphdef into
```
##########
File path: tutorials/frontend/deploy_sparse.py
##########
@@ -0,0 +1,326 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Deploy a Hugging Face Pruned Model on CPU
+=========================================
+**Author**: `Josh Fromm <https://github.com/jwfromm>`_
+
+This tutorial demonstrates how to take a state of the art pruned model,
+in this case PruneBert from Hugging Face, and use TVM to leverage the model's
+sparsity to produce real speedups. Although the primary purpose of this
+tutorial is to show speedups on already pruned models, it may be useful
+to estimate how fast a model would be *if* it were pruned. To this end,
+we also provide a function that takes an unpruned model and replaces its weights
+with random and pruned weights at a specified sparsity. This may be a useful
+feature when trying to decide if a model is worth pruning or not.
+
+Before we get into the code, it's useful to discuss sparsity and pruning
+and dig into the two
+different types of sparsity: **structured** and **unstructured**.
+
+Pruning is a technique primarily used to reduce the parameter size of a model
+by replacing weights with 0s. Although many methods exist for choosing which
Review comment:
```suggestion
by replacing weight values with 0s. Although many methods exist for choosing which
```
##########
File path: tutorials/frontend/deploy_sparse.py
##########
@@ -0,0 +1,326 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Deploy a Hugging Face Pruned Model on CPU
+=========================================
+**Author**: `Josh Fromm <https://github.com/jwfromm>`_
+
+This tutorial demonstrates how to take a state of the art pruned model,
+in this case PruneBert from Hugging Face, and use TVM to leverage the model's
+sparsity to produce real speedups. Although the primary purpose of this
+tutorial is to show speedups on already pruned models, it may be useful
+to estimate how fast a model would be *if* it were pruned. To this end,
+we also provide a function that takes an unpruned model and replaces its weights
+with random and pruned weights at a specified sparsity. This may be a useful
+feature when trying to decide if a model is worth pruning or not.
+
+Before we get into the code, it's useful to discuss sparsity and pruning
+and dig into the two
+different types of sparsity: **structured** and **unstructured**.
+
+Pruning is a technique primarily used to reduce the parameter size of a model
+by replacing weights with 0s. Although many methods exist for choosing which
+weights should be set to 0, the most straight forward is by picking the
+weights with the smallest value. Typically, weights are pruned to a desired
+sparsity percentage. For example, a 95% sparse model would have only 5% of
+its weights non-zero. Pruning to very high sparsities often requires
+finetuning or full retraining as it tends to be a lossy approximation.
+Although parameter size benefits are quite easy to obtain from a pruned model
+through simple compression, leveraging sparsity to yield runtime speedups
+is more complicated.
+
+In structured sparsity weights are pruned with the goal of clustering
+pruned weights together. In other words, they are pruned using both their
+value and location. The benefit of bunching up pruned weights is that it allows
+an algorithm such as matrix multiplication to skip entire blocks. It turns out
+that some degree of *block sparsity* is very important to realizing significant
+speedups. This is because when loading memory in most CPUs or GPUs, it's not
+possible to load a single value, instead an entire chunk or tile is read in and
+executed using something like vectorized instructions.
+
+Unstructured sparse weights are those that are pruned only on the value of
+the original weights. They may appear to be scattered randomly throughout
+a tensor rather than in chunks like we'd see in block sparse weights.
+At low sparsities, unstructured pruning techniques are difficult to
+accelerate. However, at high sparsities many blocks of all 0 values
+will naturally appear, making it possible to accelerate.
+
+This tutorial interacts with both structured and unstructured sparsity.
+Hugging Face's PruneBert model is unstructured but 95% sparse, allowing us
+to apply TVM's block sparse optimizations to it, even if not optimally.
+When generating random sparse weights for an unpruned model, we do so structured
+sparsity. A fun exercise is comparing the real speed of PruneBert with the block
+sparse speed using fake weights to see the benefit of structured sparsity.
+"""
+
+###############################################################################
+# Load Required Modules
+# ---------------------
+# Other than TVM, scipy, the latest transformers, and
+# tensorflow 2.2+ are required.
+import os
+import tvm
+import time
+import itertools
+import numpy as np
+import tensorflow as tf
+from tvm import relay
+from tvm.contrib import graph_runtime
+from tvm.relay import data_dep_optimization as ddo
+from tensorflow.python.framework.convert_to_constants import (
+ convert_variables_to_constants_v2,
+)
+import scipy.sparse as sp
+
+
+###############################################################################
+# Configure Settings
+# ------------------
+# Let's start by defining some parameters that define the type of model
+# and sparsity to run.
+# Args:
+# name (str):
+# The name of the transformer model to download and run.
+# batch_size (int):
+# The number of batches in an input.
+# seq_len (int):
+# The length of each input sequence.
+# target (str):
+# TVM platform identifier. Although cuda is also supported, it requires
+# tuning that is outside the scope of this tutorial. Note that best
+# cpu performance can be achieved by setting -mcpu appropriately for
+# your specific machine.
+# ctx (context):
+# Which device to run on. Should be one of tvm.cpu() or tvm.gpu().
+# measure_sparse (bool):
+# If true, then a sparse variant of the network will be run and
+# benchmarked.
+# bs_r (int):
+# The block size of structured sparsity to convert weight tensors
+# into. Changing this parameter may yield speedups for some platforms.
+# sparsity (float):
+# For models besides PruneBert (which is 95% sparse), this parameter
+# determines how sparse the generated weights should be. The higher
+# the sparsity, the faster the result.
+name = "huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad"
+batch_size = 1
+seq_len = 128
+target = "llvm"
+ctx = tvm.cpu()
+measure_sparse = True
+bs_r = 1
+sparsity = 0.85
+
+
+###############################################################################
+# Download and Convert Transformers Model
+# ---------------------------------------
+# Now we'll grab a model from the transformers module, download it,
+# convert it into a graphdef, and finally convert that graphdef into
+# a relay graph that we can optimize and deploy.
+def load_keras_model(module, name, seq_len, batch_size, report_runtime=True):
+ model = module.from_pretrained(name)
+ dummy_input = tf.keras.Input(shape=[seq_len], batch_size=batch_size, dtype="int32")
+ dummy_out = model(dummy_input) # Propagate shapes through the keras model.
+ if report_runtime:
+ np_input = np.random.uniform(
+ size=[batch_size, seq_len], low=0, high=seq_len
+ ).astype("int32")
+ start = time.time()
+ repeats = 50
+ for i in range(repeats):
+ np_out = model(np_input)
+ end = time.time()
+ print("Keras Runtime: %f ms." % (1000 * ((end - start) / repeats)))
+ return model
+
+
+def convert_to_graphdef(model, batch_size, seq_len):
+ model_func = tf.function(lambda x: model(x))
+ input_dict = model._saved_model_inputs_spec
+ input_spec = input_dict[list(input_dict.keys())[0]]
+ model_func = model_func.get_concrete_function(
+ tf.TensorSpec([batch_size, seq_len], input_spec.dtype)
+ )
+ frozen_func = convert_variables_to_constants_v2(model_func)
+ return frozen_func.graph.as_graph_def()
+
+
+def download_model(name, batch_size, seq_len):
+ import transformers
+ module = getattr(transformers, "TFBertForSequenceClassification")
+ model = load_keras_model(module, name=name, batch_size=batch_size, seq_len=seq_len)
+ return convert_to_graphdef(model, batch_size, seq_len)
+
+
+###############################################################################
+# Convert to Relay Graph
+# ----------------------
+# We now have all the tooling to get a transformers model in the right format
+# for relay conversion. Let's import it! In the following function we
+# save the imported graph in relay's json format so that we dont have
+# to reimport from tensorflow each time this script is run.
+def import_graphdef(
+ name,
+ batch_size,
+ seq_len,
+ save_relay=True,
+ relay_file="model.json",
+ relay_params="model.params",
+):
+ abs_path = os.path.dirname(os.path.abspath(__file__))
+ shape_dict = {"input_1": (batch_size, seq_len)}
+ relay_file = ("%s_%d_%d_%s" % (name, batch_size, seq_len, relay_file)).replace(
+ "/", "_"
+ )
+ relay_params = ("%s_%d_%d_%s" % (name, batch_size, seq_len, relay_params)).replace(
+ "/", "_"
+ )
+ if os.path.exists(os.path.join(abs_path, relay_file)) and os.path.exists(
+ os.path.join(abs_path, relay_params)
+ ):
+ with open(os.path.join(abs_path, relay_file), "r") as fi:
+ mod = tvm.ir.load_json(fi.read())
+ with open(os.path.join(abs_path, relay_params), "rb") as fi:
+ params = relay.load_param_dict(fi.read())
+ else:
+ graph_def = download_model(name, batch_size, seq_len)
+
+ mod, params = relay.frontend.from_tensorflow(graph_def, shape=shape_dict)
+
+ if save_relay:
+ with open(os.path.join(abs_path, relay_file), "w") as fo:
+ fo.write(tvm.ir.save_json(mod))
+ with open(os.path.join(abs_path, relay_params), "wb") as fo:
+ fo.write(relay.save_param_dict(params))
+
+ return mod, params, shape_dict
+
+
+###############################################################################
+# Run the Dense Graph
+# -------------------
+# Let's run the default version of the imported model. Note that even if
+# the weights are sparse, we won't see any speedup because we are using
+# regular matrix multiplies instead of fast sparse kernels.
Review comment:
```suggestion
# regular dense matrix multiplications on these dense (but mostly zero) tensors instead of sparse aware kernels.
```
##########
File path: tutorials/frontend/deploy_sparse.py
##########
@@ -0,0 +1,326 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Deploy a Hugging Face Pruned Model on CPU
+=========================================
+**Author**: `Josh Fromm <https://github.com/jwfromm>`_
+
+This tutorial demonstrates how to take a state of the art pruned model,
+in this case PruneBert from Hugging Face, and use TVM to leverage the model's
+sparsity to produce real speedups. Although the primary purpose of this
+tutorial is to show speedups on already pruned models, it may be useful
Review comment:
```suggestion
tutorial is to realize speedups on already pruned models, it may also be useful
```
##########
File path: tutorials/frontend/deploy_sparse.py
##########
@@ -0,0 +1,326 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Deploy a Hugging Face Pruned Model on CPU
+=========================================
+**Author**: `Josh Fromm <https://github.com/jwfromm>`_
+
+This tutorial demonstrates how to take a state of the art pruned model,
Review comment:
```suggestion
This tutorial demonstrates how to take any pruned model,
```
##########
File path: tutorials/frontend/deploy_sparse.py
##########
@@ -0,0 +1,326 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Deploy a Hugging Face Pruned Model on CPU
+=========================================
+**Author**: `Josh Fromm <https://github.com/jwfromm>`_
+
+This tutorial demonstrates how to take a state of the art pruned model,
+in this case PruneBert from Hugging Face, and use TVM to leverage the model's
+sparsity to produce real speedups. Although the primary purpose of this
+tutorial is to show speedups on already pruned models, it may be useful
+to estimate how fast a model would be *if* it were pruned. To this end,
+we also provide a function that takes an unpruned model and replaces its weights
+with random and pruned weights at a specified sparsity. This may be a useful
+feature when trying to decide if a model is worth pruning or not.
+
+Before we get into the code, it's useful to discuss sparsity and pruning
+and dig into the two
+different types of sparsity: **structured** and **unstructured**.
+
+Pruning is a technique primarily used to reduce the parameter size of a model
+by replacing weights with 0s. Although many methods exist for choosing which
+weights should be set to 0, the most straight forward is by picking the
+weights with the smallest value. Typically, weights are pruned to a desired
+sparsity percentage. For example, a 95% sparse model would have only 5% of
+its weights non-zero. Pruning to very high sparsities often requires
+finetuning or full retraining as it tends to be a lossy approximation.
+Although parameter size benefits are quite easy to obtain from a pruned model
+through simple compression, leveraging sparsity to yield runtime speedups
+is more complicated.
+
+In structured sparsity weights are pruned with the goal of clustering
+pruned weights together. In other words, they are pruned using both their
+value and location. The benefit of bunching up pruned weights is that it allows
+an algorithm such as matrix multiplication to skip entire blocks. It turns out
+that some degree of *block sparsity* is very important to realizing significant
+speedups. This is because when loading memory in most CPUs or GPUs, it's not
Review comment:
```suggestion
speedups on most hardware available today. This is because when loading memory in most CPUs or GPUs, it doesn't
```
##########
File path: tutorials/frontend/deploy_sparse.py
##########
@@ -0,0 +1,326 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Deploy a Hugging Face Pruned Model on CPU
+=========================================
+**Author**: `Josh Fromm <https://github.com/jwfromm>`_
+
+This tutorial demonstrates how to take a state of the art pruned model,
+in this case PruneBert from Hugging Face, and use TVM to leverage the model's
+sparsity to produce real speedups. Although the primary purpose of this
+tutorial is to show speedups on already pruned models, it may be useful
+to estimate how fast a model would be *if* it were pruned. To this end,
+we also provide a function that takes an unpruned model and replaces its weights
+with random and pruned weights at a specified sparsity. This may be a useful
+feature when trying to decide if a model is worth pruning or not.
+
+Before we get into the code, it's useful to discuss sparsity and pruning
+and dig into the two
+different types of sparsity: **structured** and **unstructured**.
+
+Pruning is a technique primarily used to reduce the parameter size of a model
+by replacing weights with 0s. Although many methods exist for choosing which
+weights should be set to 0, the most straight forward is by picking the
+weights with the smallest value. Typically, weights are pruned to a desired
+sparsity percentage. For example, a 95% sparse model would have only 5% of
+its weights non-zero. Pruning to very high sparsities often requires
+finetuning or full retraining as it tends to be a lossy approximation.
+Although parameter size benefits are quite easy to obtain from a pruned model
+through simple compression, leveraging sparsity to yield runtime speedups
+is more complicated.
+
+In structured sparsity weights are pruned with the goal of clustering
+pruned weights together. In other words, they are pruned using both their
+value and location. The benefit of bunching up pruned weights is that it allows
+an algorithm such as matrix multiplication to skip entire blocks. It turns out
+that some degree of *block sparsity* is very important to realizing significant
+speedups. This is because when loading memory in most CPUs or GPUs, it's not
+possible to load a single value, instead an entire chunk or tile is read in and
Review comment:
```suggestion
save any work to skip reading a single value at a time, instead an entire chunk or tile is read in and
```
##########
File path: tutorials/frontend/deploy_sparse.py
##########
@@ -0,0 +1,326 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Deploy a Hugging Face Pruned Model on CPU
+=========================================
+**Author**: `Josh Fromm <https://github.com/jwfromm>`_
+
+This tutorial demonstrates how to take a state of the art pruned model,
+in this case PruneBert from Hugging Face, and use TVM to leverage the model's
+sparsity to produce real speedups. Although the primary purpose of this
+tutorial is to show speedups on already pruned models, it may be useful
+to estimate how fast a model would be *if* it were pruned. To this end,
+we also provide a function that takes an unpruned model and replaces its weights
+with random and pruned weights at a specified sparsity. This may be a useful
+feature when trying to decide if a model is worth pruning or not.
+
+Before we get into the code, it's useful to discuss sparsity and pruning
+and dig into the two
+different types of sparsity: **structured** and **unstructured**.
+
+Pruning is a technique primarily used to reduce the parameter size of a model
+by replacing weights with 0s. Although many methods exist for choosing which
+weights should be set to 0, the most straight forward is by picking the
+weights with the smallest value. Typically, weights are pruned to a desired
+sparsity percentage. For example, a 95% sparse model would have only 5% of
+its weights non-zero. Pruning to very high sparsities often requires
+finetuning or full retraining as it tends to be a lossy approximation.
+Although parameter size benefits are quite easy to obtain from a pruned model
+through simple compression, leveraging sparsity to yield runtime speedups
+is more complicated.
+
+In structured sparsity weights are pruned with the goal of clustering
+pruned weights together. In other words, they are pruned using both their
+value and location. The benefit of bunching up pruned weights is that it allows
+an algorithm such as matrix multiplication to skip entire blocks. It turns out
+that some degree of *block sparsity* is very important to realizing significant
+speedups. This is because when loading memory in most CPUs or GPUs, it's not
+possible to load a single value, instead an entire chunk or tile is read in and
+executed using something like vectorized instructions.
+
+Unstructured sparse weights are those that are pruned only on the value of
+the original weights. They may appear to be scattered randomly throughout
+a tensor rather than in chunks like we'd see in block sparse weights.
+At low sparsities, unstructured pruning techniques are difficult to
+accelerate. However, at high sparsities many blocks of all 0 values
+will naturally appear, making it possible to accelerate.
+
+This tutorial interacts with both structured and unstructured sparsity.
+Hugging Face's PruneBert model is unstructured but 95% sparse, allowing us
+to apply TVM's block sparse optimizations to it, even if not optimally.
+When generating random sparse weights for an unpruned model, we do so structured
Review comment:
```suggestion
When generating random sparse weights for an unpruned model, we do so with structured
```
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org