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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/09/16 02:46:02 UTC
[GitHub] [incubator-tvm] tmoreau89 commented on a change in pull request #6488: [TUTORIAL][ANSOR] Using the template-free auto-scheduler on CPU
tmoreau89 commented on a change in pull request #6488:
URL: https://github.com/apache/incubator-tvm/pull/6488#discussion_r489127689
##########
File path: tutorials/auto_scheduler/tune_matmul_x86.py
##########
@@ -0,0 +1,136 @@
+# 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.
+"""
+Using the template-free auto-scheduler on CPU
+=============================================
+**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, \
+ `Chengfan Jia <https://github.com/jcf94/>`_
+
+Different from the exiting :ref:`autotvm <tutorials-autotvm-sec>` which relies on
+manual templates to define the search space, the auto-scheduler does not require any templates.
+The auto-scheduler is template-free, so users only need to write the computation declaration without
+any schedule commands or templates.
+The auto-scheduler can automatically generate a large
+search space and find a good schedule in the space.
+
+We use matrix multiplication as an example in this tutorial.
+"""
+
+import numpy as np
+import tvm
+from tvm import te, testing, auto_scheduler
+
+######################################################################
+# Define the computation
+# ^^^^^^^^^^^^^^^^^^^^^^
+# To begin with, we define the computation of a matmul with bias add.
+# The function should return the list of input/output tensors.
+# From these tensors, the auto-scheduler can get the whole computational graph.
+
+
+@auto_scheduler.register_workload
+def matmul_add(N, L, M, dtype):
+ A = te.placeholder((N, L), name="A", dtype=dtype)
+ B = te.placeholder((L, M), name="B", dtype=dtype)
+ C = te.placeholder((N, M), name="C", dtype=dtype)
+
+ k = te.reduce_axis((0, L), name="k")
+ matmul = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name="matmul")
+ out = te.compute((N, M), lambda i, j: matmul[i, j] + C[i, j], name="out")
+
+ return [A, B, C, out]
+
+
+######################################################################
+# Create the search task
+# ^^^^^^^^^^^^^^^^^^^^^^
+# We then create the a search task with N=L=M=128 and dtype="float32"
Review comment:
remove the/a
##########
File path: tutorials/auto_scheduler/tune_matmul_x86.py
##########
@@ -0,0 +1,136 @@
+# 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.
+"""
+Using the template-free auto-scheduler on CPU
+=============================================
+**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, \
+ `Chengfan Jia <https://github.com/jcf94/>`_
+
+Different from the exiting :ref:`autotvm <tutorials-autotvm-sec>` which relies on
+manual templates to define the search space, the auto-scheduler does not require any templates.
+The auto-scheduler is template-free, so users only need to write the computation declaration without
+any schedule commands or templates.
+The auto-scheduler can automatically generate a large
+search space and find a good schedule in the space.
+
+We use matrix multiplication as an example in this tutorial.
+"""
+
+import numpy as np
+import tvm
+from tvm import te, testing, auto_scheduler
+
+######################################################################
+# Define the computation
+# ^^^^^^^^^^^^^^^^^^^^^^
+# To begin with, we define the computation of a matmul with bias add.
+# The function should return the list of input/output tensors.
+# From these tensors, the auto-scheduler can get the whole computational graph.
+
+
+@auto_scheduler.register_workload
+def matmul_add(N, L, M, dtype):
+ A = te.placeholder((N, L), name="A", dtype=dtype)
+ B = te.placeholder((L, M), name="B", dtype=dtype)
+ C = te.placeholder((N, M), name="C", dtype=dtype)
+
+ k = te.reduce_axis((0, L), name="k")
+ matmul = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name="matmul")
+ out = te.compute((N, M), lambda i, j: matmul[i, j] + C[i, j], name="out")
+
+ return [A, B, C, out]
+
+
+######################################################################
+# Create the search task
+# ^^^^^^^^^^^^^^^^^^^^^^
+# We then create the a search task with N=L=M=128 and dtype="float32"
+
+target = tvm.target.Target("llvm")
+task = auto_scheduler.create_task(matmul_add, (128, 128, 128, "float32"), target)
+
+# Inspect the computational graph
+print(task.compute_dag)
+
+######################################################################
+# Next, we set parameters for the auto-scheduler.
+#
+# * `num_measure_trials` is the number of measurement trials we can use during the search.
+# We only make 10 trials in this tutorial for a fast demonstration. In practice, 1000 is a
+# good value for the search to converge. You can do more trials according to your time budget.
+# * In addition, we use `RecordToFile` to dump measurement records into a file `matmul.json`.
+# The measurement records can be used to query the history best, resume the search,
+# or do more analysis later.
+# * see :any:`auto_schedule.TuningOptions`: for more parameters
+
+tune_option = auto_scheduler.TuningOptions(
+ num_measure_trials=10, measure_callbacks=[auto_scheduler.RecordToFile("matmul.json")]
+)
+
+######################################################################
+# Run the search
+# ^^^^^^^^^^^^^^
+# Now we get all inputs ready. Pretty simple, isn't it?
+# We can kick off the search and let the auto-scheduler do its magic.
+# After some measurement trials, it will return the best schedule it founds.
+
+sch, args = auto_scheduler.auto_schedule(task, tuning_options=tune_option)
+
+######################################################################
+# We can lower schedule to see the IR after auto-scheduling.
Review comment:
lower the schedule
##########
File path: tutorials/auto_scheduler/tune_matmul_x86.py
##########
@@ -0,0 +1,136 @@
+# 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.
+"""
+Using the template-free auto-scheduler on CPU
+=============================================
+**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, \
+ `Chengfan Jia <https://github.com/jcf94/>`_
+
+Different from the exiting :ref:`autotvm <tutorials-autotvm-sec>` which relies on
+manual templates to define the search space, the auto-scheduler does not require any templates.
+The auto-scheduler is template-free, so users only need to write the computation declaration without
+any schedule commands or templates.
+The auto-scheduler can automatically generate a large
+search space and find a good schedule in the space.
+
+We use matrix multiplication as an example in this tutorial.
+"""
+
+import numpy as np
+import tvm
+from tvm import te, testing, auto_scheduler
+
+######################################################################
+# Define the computation
+# ^^^^^^^^^^^^^^^^^^^^^^
+# To begin with, we define the computation of a matmul with bias add.
+# The function should return the list of input/output tensors.
+# From these tensors, the auto-scheduler can get the whole computational graph.
+
+
+@auto_scheduler.register_workload
+def matmul_add(N, L, M, dtype):
+ A = te.placeholder((N, L), name="A", dtype=dtype)
+ B = te.placeholder((L, M), name="B", dtype=dtype)
+ C = te.placeholder((N, M), name="C", dtype=dtype)
+
+ k = te.reduce_axis((0, L), name="k")
+ matmul = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name="matmul")
+ out = te.compute((N, M), lambda i, j: matmul[i, j] + C[i, j], name="out")
+
+ return [A, B, C, out]
+
+
+######################################################################
+# Create the search task
+# ^^^^^^^^^^^^^^^^^^^^^^
+# We then create the a search task with N=L=M=128 and dtype="float32"
+
+target = tvm.target.Target("llvm")
+task = auto_scheduler.create_task(matmul_add, (128, 128, 128, "float32"), target)
+
+# Inspect the computational graph
+print(task.compute_dag)
+
+######################################################################
+# Next, we set parameters for the auto-scheduler.
+#
+# * `num_measure_trials` is the number of measurement trials we can use during the search.
+# We only make 10 trials in this tutorial for a fast demonstration. In practice, 1000 is a
+# good value for the search to converge. You can do more trials according to your time budget.
+# * In addition, we use `RecordToFile` to dump measurement records into a file `matmul.json`.
+# The measurement records can be used to query the history best, resume the search,
+# or do more analysis later.
+# * see :any:`auto_schedule.TuningOptions`: for more parameters
+
+tune_option = auto_scheduler.TuningOptions(
+ num_measure_trials=10, measure_callbacks=[auto_scheduler.RecordToFile("matmul.json")]
+)
+
+######################################################################
+# Run the search
+# ^^^^^^^^^^^^^^
+# Now we get all inputs ready. Pretty simple, isn't it?
+# We can kick off the search and let the auto-scheduler do its magic.
+# After some measurement trials, it will return the best schedule it founds.
+
+sch, args = auto_scheduler.auto_schedule(task, tuning_options=tune_option)
+
+######################################################################
+# We can lower schedule to see the IR after auto-scheduling.
+# The auto-scheduler correctly performs optimizations including multi-level tiling,
+# parallelization, vectorization, unrolling and fusion.
+
+print(tvm.lower(sch, args, simple_mode=True))
+
+######################################################################
+# Check correctness
+# ^^^^^^^^^^^^^^^^^
+# We build the binary and check its correctness
+
+func = tvm.build(sch, args)
+a_np = np.random.uniform(size=(128, 128)).astype(np.float32)
+b_np = np.random.uniform(size=(128, 128)).astype(np.float32)
+c_np = np.random.uniform(size=(128, 128)).astype(np.float32)
+d_np = a_np.dot(b_np) + c_np
+
+d_tvm = tvm.nd.empty(d_np.shape)
+func(tvm.nd.array(a_np), tvm.nd.array(b_np), tvm.nd.array(c_np), d_tvm)
+
+tvm.testing.assert_allclose(d_np, d_tvm.asnumpy(), rtol=1e-3)
+
+######################################################################
+# Using the record file
+# ^^^^^^^^^^^^^^^^^^^^^
+# During the search, all measuremnt records are dumpped into the record
+# file "matmul.json". The measurement records can be used to resume the
+# search, re-apply search results and other analysis.
+#
+# Here we show an example where we load the best schedule from a file,
+# print the equivalent python schedule API, and build the binary again.
+
+# Load the measuremnt record for the best schedule
+inp, res = auto_scheduler.load_best("matmul.json", task.workload_key)
+
+# Print equivalent python schedule API. This can be used for debugging and
+# learning the behavior of auto-scheduler.
Review comment:
"of the autoscheduler"
##########
File path: tutorials/auto_scheduler/tune_matmul_x86.py
##########
@@ -0,0 +1,136 @@
+# 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.
+"""
+Using the template-free auto-scheduler on CPU
+=============================================
+**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, \
+ `Chengfan Jia <https://github.com/jcf94/>`_
+
+Different from the exiting :ref:`autotvm <tutorials-autotvm-sec>` which relies on
+manual templates to define the search space, the auto-scheduler does not require any templates.
+The auto-scheduler is template-free, so users only need to write the computation declaration without
+any schedule commands or templates.
+The auto-scheduler can automatically generate a large
+search space and find a good schedule in the space.
+
+We use matrix multiplication as an example in this tutorial.
+"""
+
+import numpy as np
+import tvm
+from tvm import te, testing, auto_scheduler
+
+######################################################################
+# Define the computation
+# ^^^^^^^^^^^^^^^^^^^^^^
+# To begin with, we define the computation of a matmul with bias add.
+# The function should return the list of input/output tensors.
+# From these tensors, the auto-scheduler can get the whole computational graph.
+
+
+@auto_scheduler.register_workload
+def matmul_add(N, L, M, dtype):
+ A = te.placeholder((N, L), name="A", dtype=dtype)
+ B = te.placeholder((L, M), name="B", dtype=dtype)
+ C = te.placeholder((N, M), name="C", dtype=dtype)
+
+ k = te.reduce_axis((0, L), name="k")
+ matmul = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name="matmul")
+ out = te.compute((N, M), lambda i, j: matmul[i, j] + C[i, j], name="out")
+
+ return [A, B, C, out]
+
+
+######################################################################
+# Create the search task
+# ^^^^^^^^^^^^^^^^^^^^^^
+# We then create the a search task with N=L=M=128 and dtype="float32"
+
+target = tvm.target.Target("llvm")
+task = auto_scheduler.create_task(matmul_add, (128, 128, 128, "float32"), target)
+
+# Inspect the computational graph
+print(task.compute_dag)
+
+######################################################################
+# Next, we set parameters for the auto-scheduler.
+#
+# * `num_measure_trials` is the number of measurement trials we can use during the search.
+# We only make 10 trials in this tutorial for a fast demonstration. In practice, 1000 is a
+# good value for the search to converge. You can do more trials according to your time budget.
+# * In addition, we use `RecordToFile` to dump measurement records into a file `matmul.json`.
+# The measurement records can be used to query the history best, resume the search,
+# or do more analysis later.
+# * see :any:`auto_schedule.TuningOptions`: for more parameters
+
+tune_option = auto_scheduler.TuningOptions(
+ num_measure_trials=10, measure_callbacks=[auto_scheduler.RecordToFile("matmul.json")]
+)
+
+######################################################################
+# Run the search
+# ^^^^^^^^^^^^^^
+# Now we get all inputs ready. Pretty simple, isn't it?
+# We can kick off the search and let the auto-scheduler do its magic.
+# After some measurement trials, it will return the best schedule it founds.
+
+sch, args = auto_scheduler.auto_schedule(task, tuning_options=tune_option)
+
+######################################################################
+# We can lower schedule to see the IR after auto-scheduling.
+# The auto-scheduler correctly performs optimizations including multi-level tiling,
+# parallelization, vectorization, unrolling and fusion.
+
+print(tvm.lower(sch, args, simple_mode=True))
+
+######################################################################
+# Check correctness
+# ^^^^^^^^^^^^^^^^^
+# We build the binary and check its correctness
+
+func = tvm.build(sch, args)
+a_np = np.random.uniform(size=(128, 128)).astype(np.float32)
+b_np = np.random.uniform(size=(128, 128)).astype(np.float32)
+c_np = np.random.uniform(size=(128, 128)).astype(np.float32)
+d_np = a_np.dot(b_np) + c_np
+
+d_tvm = tvm.nd.empty(d_np.shape)
+func(tvm.nd.array(a_np), tvm.nd.array(b_np), tvm.nd.array(c_np), d_tvm)
+
+tvm.testing.assert_allclose(d_np, d_tvm.asnumpy(), rtol=1e-3)
+
+######################################################################
+# Using the record file
+# ^^^^^^^^^^^^^^^^^^^^^
+# During the search, all measuremnt records are dumpped into the record
+# file "matmul.json". The measurement records can be used to resume the
+# search, re-apply search results and other analysis.
Review comment:
and perform other analyses.
##########
File path: tutorials/auto_scheduler/tune_matmul_x86.py
##########
@@ -0,0 +1,136 @@
+# 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.
+"""
+Using the template-free auto-scheduler on CPU
+=============================================
+**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, \
+ `Chengfan Jia <https://github.com/jcf94/>`_
+
+Different from the exiting :ref:`autotvm <tutorials-autotvm-sec>` which relies on
+manual templates to define the search space, the auto-scheduler does not require any templates.
+The auto-scheduler is template-free, so users only need to write the computation declaration without
+any schedule commands or templates.
+The auto-scheduler can automatically generate a large
+search space and find a good schedule in the space.
+
+We use matrix multiplication as an example in this tutorial.
+"""
+
+import numpy as np
+import tvm
+from tvm import te, testing, auto_scheduler
+
+######################################################################
+# Define the computation
+# ^^^^^^^^^^^^^^^^^^^^^^
+# To begin with, we define the computation of a matmul with bias add.
+# The function should return the list of input/output tensors.
+# From these tensors, the auto-scheduler can get the whole computational graph.
+
+
+@auto_scheduler.register_workload
+def matmul_add(N, L, M, dtype):
+ A = te.placeholder((N, L), name="A", dtype=dtype)
+ B = te.placeholder((L, M), name="B", dtype=dtype)
+ C = te.placeholder((N, M), name="C", dtype=dtype)
+
+ k = te.reduce_axis((0, L), name="k")
+ matmul = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name="matmul")
+ out = te.compute((N, M), lambda i, j: matmul[i, j] + C[i, j], name="out")
+
+ return [A, B, C, out]
+
+
+######################################################################
+# Create the search task
+# ^^^^^^^^^^^^^^^^^^^^^^
+# We then create the a search task with N=L=M=128 and dtype="float32"
+
+target = tvm.target.Target("llvm")
+task = auto_scheduler.create_task(matmul_add, (128, 128, 128, "float32"), target)
+
+# Inspect the computational graph
+print(task.compute_dag)
+
+######################################################################
+# Next, we set parameters for the auto-scheduler.
+#
+# * `num_measure_trials` is the number of measurement trials we can use during the search.
+# We only make 10 trials in this tutorial for a fast demonstration. In practice, 1000 is a
+# good value for the search to converge. You can do more trials according to your time budget.
+# * In addition, we use `RecordToFile` to dump measurement records into a file `matmul.json`.
+# The measurement records can be used to query the history best, resume the search,
+# or do more analysis later.
+# * see :any:`auto_schedule.TuningOptions`: for more parameters
+
+tune_option = auto_scheduler.TuningOptions(
+ num_measure_trials=10, measure_callbacks=[auto_scheduler.RecordToFile("matmul.json")]
+)
+
+######################################################################
+# Run the search
+# ^^^^^^^^^^^^^^
+# Now we get all inputs ready. Pretty simple, isn't it?
+# We can kick off the search and let the auto-scheduler do its magic.
+# After some measurement trials, it will return the best schedule it founds.
Review comment:
it found
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