From 0efd6352fc1d3acedbea467d94ec45776a838d15 Mon Sep 17 00:00:00 2001 From: Josh Fromm Date: Wed, 1 Jul 2020 14:43:16 -0700 Subject: [PATCH 1/5] Added tutorial showing how to run a sparse transformer model. --- tutorials/frontend/deploy_sparse.py | 326 ++++++++++++++++++++++++++++ 1 file changed, 326 insertions(+) create mode 100644 tutorials/frontend/deploy_sparse.py diff --git a/tutorials/frontend/deploy_sparse.py b/tutorials/frontend/deploy_sparse.py new file mode 100644 index 000000000000..23a1967aca2b --- /dev/null +++ b/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 `_ + +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 +import transformers +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): + 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. +def run_relay_graph(mod, params, shape_dict, target, ctx): + with relay.build_config(opt_level=3): + graph, lib, params = relay.build(mod, target=target, params=params) + input_shape = shape_dict["input_1"] + dummy_data = np.random.uniform(size=input_shape, low=0, high=input_shape[1]).astype( + "int32" + ) + + m = graph_runtime.create(graph, lib, ctx) + m.set_input(0, dummy_data) + m.set_input(**params) + m.run() + tvm_output = m.get_output(0) + + ftimer = m.module.time_evaluator("run", ctx, repeat=5, number=5) + prof_res = np.array(ftimer().results) * 1000 + print( + "%-20s %-19s (%s)" + % ("Runtime:", "%.2f ms" % np.mean(prof_res), "%.2f ms" % np.std(prof_res)) + ) + return tvm_output + + +def run_dense(mod, params, shape_dict, target, ctx): + print("Dense Model Benchmark:") + return run_relay_graph(mod, params, shape_dict, target, ctx) + + +############################################################################### +# Run the Sparse Graph +# ------------------- +# Next we'll convert the graph into a sparse representation and generate +# fake sparse weights if needed. Then we'll use the same benchmarking +# script as dense to see how much faster we go! We apply a few relay passes +# to the graph to get it leveraging sparsity. First we use +# `simplify_fc_transpose` to use transposes on the weights of dense layers +# into the parameters. This makes it easier to convert to matrix multiplies +# to sparse versions. Next we apply `bsr_dense.convert` to identify all +# weight matrices that can be sparse, and automatically replace them. +def random_bsr_matrix(M, N, BS_R, BS_C, density, dtype="float32"): + Y = np.zeros((M, N), dtype=dtype) + assert M % BS_R == 0 + assert N % BS_C == 0 + nnz = int(density * M * N) + num_blocks = int(nnz / (BS_R * BS_C)) + 1 + candidate_blocks = np.asarray( + list(itertools.product(range(0, M, BS_R), range(0, N, BS_C))) + ) + assert candidate_blocks.shape[0] == M // BS_R * N // BS_C + chosen_blocks = candidate_blocks[ + np.random.choice(candidate_blocks.shape[0], size=num_blocks, replace=False) + ] + for i in range(len(chosen_blocks)): + r, c = chosen_blocks[i] + Y[r : r + BS_R, c : c + BS_C] = np.random.uniform(-0.1, 0.1, (BS_R, BS_C)) + s = sp.bsr_matrix(Y, blocksize=(BS_R, BS_C)) + assert s.data.shape == (num_blocks, BS_R, BS_C) + assert s.data.size >= nnz + assert s.indices.shape == (num_blocks,) + assert s.indptr.shape == (M // BS_R + 1,) + return s.todense() + + +def random_sparse_bert_params(func, params, density, BS_R, BS_C): + def deepcopy(param_dic): + ret = {} + for k, v in param_dic.items(): + ret[k] = tvm.nd.array(v.asnumpy()) + return ret + + new_params = deepcopy(params) + dense_weight_names = relay.analysis.sparse_dense._search_dense_op_weight(func) + for item in dense_weight_names: + name = str(item) + shape = new_params[name].shape + if shape[0] % BS_R == 0 and shape[1] % BS_C == 0: + new_w = random_bsr_matrix(shape[0], shape[1], BS_R, BS_C, density) + new_params[name] = tvm.nd.array(new_w) + return new_params + + +def run_sparse(mod, params, shape_dict, target, ctx, bs_r, sparsity, gen_weights): + mod, params = ddo.simplify_fc_transpose.convert(mod["main"], params) + if gen_weights: + params = random_sparse_bert_params( + mod, params, BS_R=bs_r, BS_C=1, density=1 - sparsity + ) + mod, params = ddo.bsr_dense.convert(mod, params, (bs_r, 1), sparsity_threshold=0.8) + print("Block Sparse Model with {blocksize}x1 blocks:".format(blocksize=bs_r)) + return run_relay_graph(mod, params, shape_dict, target, ctx) + + +############################################################################### +# Run All the Code! +# ----------------- +# And that's it! Now we'll simply call all the needed function to benchmark +# the model according to the set parameters. Note that to run this code +# you'll need to uncomment the last line first. +def benchmark(): + mod, params, shape_dict = import_graphdef(name, batch_size, seq_len) + run_dense(mod, params, shape_dict, target, ctx) + if measure_sparse: + gen_weights = "prune" not in name + run_sparse(mod, params, shape_dict, target, ctx, bs_r, sparsity, gen_weights) + + +# benchmark() From 6de48861748a6abee0ec00c4e6537247eabfe6dc Mon Sep 17 00:00:00 2001 From: Josh Fromm Date: Wed, 1 Jul 2020 14:53:07 -0700 Subject: [PATCH 2/5] Move transformers import to avoid dependency during doc generation. --- tutorials/frontend/deploy_sparse.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/frontend/deploy_sparse.py b/tutorials/frontend/deploy_sparse.py index 23a1967aca2b..e375b2b16fe6 100644 --- a/tutorials/frontend/deploy_sparse.py +++ b/tutorials/frontend/deploy_sparse.py @@ -78,7 +78,6 @@ import itertools import numpy as np import tensorflow as tf -import transformers from tvm import relay from tvm.contrib import graph_runtime from tvm.relay import data_dep_optimization as ddo @@ -162,6 +161,7 @@ def convert_to_graphdef(model, batch_size, seq_len): 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) From 08676a80a5ce57b23c4c25cf2da833063c7853ed Mon Sep 17 00:00:00 2001 From: Josh Fromm Date: Wed, 1 Jul 2020 19:30:56 -0700 Subject: [PATCH 3/5] Fixed formatting bugs. --- tutorials/frontend/deploy_sparse.py | 42 ++++++++++++----------------- 1 file changed, 17 insertions(+), 25 deletions(-) diff --git a/tutorials/frontend/deploy_sparse.py b/tutorials/frontend/deploy_sparse.py index e375b2b16fe6..6ee6f00287f5 100644 --- a/tutorials/frontend/deploy_sparse.py +++ b/tutorials/frontend/deploy_sparse.py @@ -92,37 +92,29 @@ # ------------------ # 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. + +# The name of the transformer model to download and run. name = "huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad" +# The number of batches in an input. batch_size = 1 +# The length of each input sequence. seq_len = 128 +# 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. target = "llvm" +# Which device to run on. Should be one of tvm.cpu() or tvm.gpu(). ctx = tvm.cpu() +# If true, then a sparse variant of the network will be run and +# benchmarked. measure_sparse = True +# The block size of structured sparsity to convert weight tensors +# into. Changing this parameter may yield speedups for some platforms. bs_r = 1 +# 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. sparsity = 0.85 @@ -247,7 +239,7 @@ def run_dense(mod, params, shape_dict, target, ctx): ############################################################################### # Run the Sparse Graph -# ------------------- +# -------------------- # Next we'll convert the graph into a sparse representation and generate # fake sparse weights if needed. Then we'll use the same benchmarking # script as dense to see how much faster we go! We apply a few relay passes From 72ef919986e0d604499bcd80f68d4194d7e2979f Mon Sep 17 00:00:00 2001 From: Josh Fromm Date: Thu, 2 Jul 2020 09:52:45 -0700 Subject: [PATCH 4/5] Jason edits added. --- tutorials/frontend/deploy_sparse.py | 42 ++++++++++++++++++++--------- 1 file changed, 29 insertions(+), 13 deletions(-) diff --git a/tutorials/frontend/deploy_sparse.py b/tutorials/frontend/deploy_sparse.py index 6ee6f00287f5..c34ab5e9bffc 100644 --- a/tutorials/frontend/deploy_sparse.py +++ b/tutorials/frontend/deploy_sparse.py @@ -19,12 +19,14 @@ ========================================= **Author**: `Josh Fromm `_ -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 +This tutorial demonstrates how to take any pruned model, in this case `PruneBert +from Hugging Face +`_, +and use TVM to leverage the model's sparsity support to produce real speedups. Although +the primary purpose of this tutorial is to realize speedups on already pruned +models, it may also 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. @@ -33,7 +35,7 @@ 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 +by replacing weight values 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 @@ -48,9 +50,10 @@ 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. +speedups on most hardware available today. +This is because when loading memory in most CPUs or GPUs, +it doesn't save any work to skip reading a single value at a time, 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 @@ -62,7 +65,7 @@ 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 +When generating random sparse weights for an unpruned model, we do so with 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. """ @@ -122,7 +125,7 @@ # 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 +# convert it into a TensorFlow graphdef in preperation for converting 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) @@ -154,6 +157,7 @@ def convert_to_graphdef(model, batch_size, seq_len): 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) @@ -208,7 +212,8 @@ def import_graphdef( # ------------------- # 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. +# regular dense matrix multiplications on these dense (but mostly zero) +# tensors instead of sparse aware kernels. def run_relay_graph(mod, params, shape_dict, target, ctx): with relay.build_config(opt_level=3): graph, lib, params = relay.build(mod, target=target, params=params) @@ -248,6 +253,17 @@ def run_dense(mod, params, shape_dict, target, ctx): # into the parameters. This makes it easier to convert to matrix multiplies # to sparse versions. Next we apply `bsr_dense.convert` to identify all # weight matrices that can be sparse, and automatically replace them. +# +# The `bsr_dense.convert` call below is doing the heavy lifting of identifying +# which weights in the model can be made sparse by checking if they are +# at least `sparsity_threshold` percent sparse. If so, it converts those +# weights into *Block Compressed Row Format (BSR)*. BSR is essentially +# a representation that indexes into the nonzero chunks of the tensor, +# making it easy for an algorithm to load those non-zero chunks and ignore +# the rest of the tensor. Once the sparse weights are in BSR format, +# `relay.transform.DenseToSparse` is applied to actually replace +# `relay.dense` operations with `relay.sparse_dense` calls that can be +# run faster. def random_bsr_matrix(M, N, BS_R, BS_C, density, dtype="float32"): Y = np.zeros((M, N), dtype=dtype) assert M % BS_R == 0 From c62f8edba02e8160792dbc62389868216c099b97 Mon Sep 17 00:00:00 2001 From: Josh Fromm Date: Thu, 2 Jul 2020 10:01:23 -0700 Subject: [PATCH 5/5] Added sample output. --- tutorials/frontend/deploy_sparse.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/tutorials/frontend/deploy_sparse.py b/tutorials/frontend/deploy_sparse.py index c34ab5e9bffc..87d5e45dd30a 100644 --- a/tutorials/frontend/deploy_sparse.py +++ b/tutorials/frontend/deploy_sparse.py @@ -332,3 +332,21 @@ def benchmark(): # benchmark() + +############################################################################### +# Sample Output +# ------------- +# For reference, below is the output of the script when run on an AMD CPU +# and shows about a 2.5X speedup from using sparsity. + +# Dense Model Benchmark: +# Cannot find config for target=llvm, workload=('dense_nopack.x86', ('TENSOR', (1, 768), 'float32'), ('TENSOR', (2, 768), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression. +# Cannot find config for target=llvm, workload=('dense_nopack.x86', ('TENSOR', (1, 768), 'float32'), ('TENSOR', (768, 768), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression. +# Cannot find config for target=llvm, workload=('dense_nopack.x86', ('TENSOR', (128, 3072), 'float32'), ('TENSOR', (768, 3072), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression. +# Cannot find config for target=llvm, workload=('dense_nopack.x86', ('TENSOR', (128, 768), 'float32'), ('TENSOR', (3072, 768), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression. +# Cannot find config for target=llvm, workload=('dense_nopack.x86', ('TENSOR', (128, 768), 'float32'), ('TENSOR', (768, 768), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression. +# Cannot find config for target=llvm, workload=('batch_matmul.x86', ('TENSOR', (12, 128, 128), 'float32'), ('TENSOR', (12, 64, 128), 'float32')). A fallback configuration is used, which may bring great performance regression. +# Cannot find config for target=llvm, workload=('batch_matmul.x86', ('TENSOR', (12, 128, 64), 'float32'), ('TENSOR', (12, 128, 64), 'float32')). A fallback configuration is used, which may bring great performance regression. +# Runtime: 165.26 ms (12.83 ms) +# Block Sparse Model with 1x1 blocks: +# Runtime: 67.75 ms (8.83 ms)