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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/11/08 17:24:32 UTC
[GitHub] [tvm] mehrdadh opened a new pull request, #13324: [microTVM][PyTorch][Tutorial]Adding a PyTorch tutorial for microTVM with CRT
mehrdadh opened a new pull request, #13324:
URL: https://github.com/apache/tvm/pull/13324
This PR adds a tutorial to compile/run a PyTorch model using microTVM CRT.
Do not merge before https://github.com/apache/tvm/pull/13313
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[GitHub] [tvm] gromero commented on a diff in pull request #13324: [microTVM][PyTorch][Tutorial]Adding a PyTorch tutorial for microTVM with CRT
Posted by GitBox <gi...@apache.org>.
gromero commented on code in PR #13324:
URL: https://github.com/apache/tvm/pull/13324#discussion_r1017216300
##########
src/runtime/crt/host/microtvm_api_server.py:
##########
@@ -35,6 +37,18 @@
IS_TEMPLATE = not os.path.exists(os.path.join(PROJECT_DIR, MODEL_LIBRARY_FORMAT_RELPATH))
+MEMORY_SIZE_BYTES = 2 * 1024 * 1024
Review Comment:
@mehrdadh I think that's actually interesting / important info: the constant is determined experimentally, it's really good to have comment about it as @alanmacd requested, I don't even consider it a nit :-)
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[GitHub] [tvm] alanmacd commented on a diff in pull request #13324: [microTVM][PyTorch][Tutorial]Adding a PyTorch tutorial for microTVM with CRT
Posted by GitBox <gi...@apache.org>.
alanmacd commented on code in PR #13324:
URL: https://github.com/apache/tvm/pull/13324#discussion_r1016952380
##########
src/runtime/crt/host/microtvm_api_server.py:
##########
@@ -35,6 +37,18 @@
IS_TEMPLATE = not os.path.exists(os.path.join(PROJECT_DIR, MODEL_LIBRARY_FORMAT_RELPATH))
+MEMORY_SIZE_BYTES = 2 * 1024 * 1024
Review Comment:
nit: maybe add comment as to why this is default memory size
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[GitHub] [tvm] mehrdadh commented on pull request #13324: [microTVM][PyTorch][Tutorial]Adding a PyTorch tutorial for microTVM with CRT
Posted by GitBox <gi...@apache.org>.
mehrdadh commented on PR #13324:
URL: https://github.com/apache/tvm/pull/13324#issuecomment-1307992585
@gromero that's a good point. I added that compiler flag.
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[GitHub] [tvm] mehrdadh commented on a diff in pull request #13324: [microTVM][PyTorch][Tutorial]Adding a PyTorch tutorial for microTVM with CRT
Posted by GitBox <gi...@apache.org>.
mehrdadh commented on code in PR #13324:
URL: https://github.com/apache/tvm/pull/13324#discussion_r1016956677
##########
src/runtime/crt/host/microtvm_api_server.py:
##########
@@ -35,6 +37,18 @@
IS_TEMPLATE = not os.path.exists(os.path.join(PROJECT_DIR, MODEL_LIBRARY_FORMAT_RELPATH))
+MEMORY_SIZE_BYTES = 2 * 1024 * 1024
Review Comment:
added, it's not an interesting reason. It's basically chosen to pass CRT tests in TVM
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[GitHub] [tvm] mehrdadh commented on a diff in pull request #13324: [microTVM][PyTorch][Tutorial]Adding a PyTorch tutorial for microTVM with CRT
Posted by GitBox <gi...@apache.org>.
mehrdadh commented on code in PR #13324:
URL: https://github.com/apache/tvm/pull/13324#discussion_r1017222970
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
+#
+# To begin with, load pre-trained MobileNetV2 from torchvision. Then,
+# download a cat image and preprocess it to use as the model input.
+#
+
+model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+model = model.eval()
+
+input_shape = [1, 3, 224, 224]
+input_data = torch.randn(input_shape)
+scripted_model = torch.jit.trace(model, input_data).eval()
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_path = download_testdata(img_url, "cat.png", module="data")
+img = Image.open(img_path).resize((224, 224))
+
+# Preprocess the image and convert to tensor
+my_preprocess = transforms.Compose(
+ [
+ transforms.Resize(256),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+img = my_preprocess(img)
+img = np.expand_dims(img, 0)
+
+input_name = "input0"
+shape_list = [(input_name, input_shape)]
+relay_mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
+
+#################################
+# Define Target, Runtime and Executor
+# -------------------------------
+#
+# In this tutorial we use AOT host driven executor. To compile the model
Review Comment:
done
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
Review Comment:
done
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,205 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+
+# sphinx_gallery_start_ignore
+from tvm import testing
+
+testing.utils.install_request_hook(depth=3)
+# sphinx_gallery_end_ignore
+
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
+#
+# To begin with, load pre-trained MobileNetV2 from torchvision. Then,
+# download a cat image and preprocess it to use as the model input.
+#
+
+model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+model = model.eval()
+
+input_shape = [1, 3, 224, 224]
+input_data = torch.randn(input_shape)
+scripted_model = torch.jit.trace(model, input_data).eval()
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_path = download_testdata(img_url, "cat.png", module="data")
+img = Image.open(img_path).resize((224, 224))
+
+# Preprocess the image and convert to tensor
+my_preprocess = transforms.Compose(
+ [
+ transforms.Resize(256),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+img = my_preprocess(img)
+img = np.expand_dims(img, 0)
+
+input_name = "input0"
+shape_list = [(input_name, input_shape)]
+relay_mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
+
+#################################
+# Define Target, Runtime and Executor
+# -------------------------------
+#
+# In this tutorial we use AOT host driven executor. To compile the model
+# for an emulated embedded environment on an X86 machine we use C runtime (CRT)
+# and we use `host` micro target. Using this setup, TVM compiles the model
+# for C runtime which can run on a X86 CPU machine with the same flow that
+# would run on a physical microcontroller.
+#
+
+
+# Simulate a microcontroller on the host machine. Uses the main() from `src/runtime/crt/host/main.cc`
+# To use physical hardware, replace "host" with something matching your hardware.
Review Comment:
done
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
+#
+# To begin with, load pre-trained MobileNetV2 from torchvision. Then,
+# download a cat image and preprocess it to use as the model input.
+#
+
+model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+model = model.eval()
+
+input_shape = [1, 3, 224, 224]
+input_data = torch.randn(input_shape)
+scripted_model = torch.jit.trace(model, input_data).eval()
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_path = download_testdata(img_url, "cat.png", module="data")
+img = Image.open(img_path).resize((224, 224))
+
+# Preprocess the image and convert to tensor
+my_preprocess = transforms.Compose(
+ [
+ transforms.Resize(256),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+img = my_preprocess(img)
+img = np.expand_dims(img, 0)
+
+input_name = "input0"
+shape_list = [(input_name, input_shape)]
+relay_mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
+
+#################################
+# Define Target, Runtime and Executor
+# -------------------------------
Review Comment:
done
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
+#
+# To begin with, load pre-trained MobileNetV2 from torchvision. Then,
+# download a cat image and preprocess it to use as the model input.
+#
+
+model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+model = model.eval()
+
+input_shape = [1, 3, 224, 224]
+input_data = torch.randn(input_shape)
+scripted_model = torch.jit.trace(model, input_data).eval()
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_path = download_testdata(img_url, "cat.png", module="data")
+img = Image.open(img_path).resize((224, 224))
+
+# Preprocess the image and convert to tensor
+my_preprocess = transforms.Compose(
+ [
+ transforms.Resize(256),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+img = my_preprocess(img)
+img = np.expand_dims(img, 0)
+
+input_name = "input0"
+shape_list = [(input_name, input_shape)]
+relay_mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
+
+#################################
Review Comment:
done
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
Review Comment:
done
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
+#
+# To begin with, load pre-trained MobileNetV2 from torchvision. Then,
+# download a cat image and preprocess it to use as the model input.
+#
+
+model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+model = model.eval()
+
+input_shape = [1, 3, 224, 224]
+input_data = torch.randn(input_shape)
+scripted_model = torch.jit.trace(model, input_data).eval()
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_path = download_testdata(img_url, "cat.png", module="data")
+img = Image.open(img_path).resize((224, 224))
+
+# Preprocess the image and convert to tensor
+my_preprocess = transforms.Compose(
+ [
+ transforms.Resize(256),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+img = my_preprocess(img)
+img = np.expand_dims(img, 0)
+
+input_name = "input0"
+shape_list = [(input_name, input_shape)]
+relay_mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
+
+#################################
+# Define Target, Runtime and Executor
+# -------------------------------
+#
+# In this tutorial we use AOT host driven executor. To compile the model
+# for an emulated embedded environment on an X86 machine we use C runtime (CRT)
Review Comment:
done
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
+#
+# To begin with, load pre-trained MobileNetV2 from torchvision. Then,
+# download a cat image and preprocess it to use as the model input.
+#
+
+model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+model = model.eval()
+
+input_shape = [1, 3, 224, 224]
+input_data = torch.randn(input_shape)
+scripted_model = torch.jit.trace(model, input_data).eval()
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_path = download_testdata(img_url, "cat.png", module="data")
+img = Image.open(img_path).resize((224, 224))
+
+# Preprocess the image and convert to tensor
+my_preprocess = transforms.Compose(
+ [
+ transforms.Resize(256),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+img = my_preprocess(img)
+img = np.expand_dims(img, 0)
+
+input_name = "input0"
+shape_list = [(input_name, input_shape)]
+relay_mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
+
+#################################
+# Define Target, Runtime and Executor
+# -------------------------------
+#
+# In this tutorial we use AOT host driven executor. To compile the model
+# for an emulated embedded environment on an X86 machine we use C runtime (CRT)
+# and we use `host` micro target. Using this setup, TVM compiles the model
+# for C runtime which can run on a X86 CPU machine with the same flow that
Review Comment:
done
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[GitHub] [tvm] gromero merged pull request #13324: [microTVM][PyTorch][Tutorial]Adding a PyTorch tutorial for microTVM with CRT
Posted by GitBox <gi...@apache.org>.
gromero merged PR #13324:
URL: https://github.com/apache/tvm/pull/13324
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[GitHub] [tvm] tvm-bot commented on pull request #13324: [microTVM][PyTorch][Tutorial]Adding a PyTorch tutorial for microTVM with CRT
Posted by GitBox <gi...@apache.org>.
tvm-bot commented on PR #13324:
URL: https://github.com/apache/tvm/pull/13324#issuecomment-1307570041
<!---bot-comment-->
Thanks for contributing to TVM! Please refer to the contributing guidelines https://tvm.apache.org/docs/contribute/ for useful information and tips. Please request code reviews from [Reviewers](https://github.com/apache/incubator-tvm/blob/master/CONTRIBUTORS.md#reviewers) by @-ing them in a comment.
<!--bot-comment-ccs-start-->
* cc @alanmacd, @gromero, @yelite <sub>See [#10317](https://github.com/apache/tvm/issues/10317) for details</sub><!--bot-comment-ccs-end-->
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[GitHub] [tvm] gromero commented on a diff in pull request #13324: [microTVM][PyTorch][Tutorial]Adding a PyTorch tutorial for microTVM with CRT
Posted by GitBox <gi...@apache.org>.
gromero commented on code in PR #13324:
URL: https://github.com/apache/tvm/pull/13324#discussion_r1017158971
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
+#
+# To begin with, load pre-trained MobileNetV2 from torchvision. Then,
+# download a cat image and preprocess it to use as the model input.
+#
+
+model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+model = model.eval()
+
+input_shape = [1, 3, 224, 224]
+input_data = torch.randn(input_shape)
+scripted_model = torch.jit.trace(model, input_data).eval()
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_path = download_testdata(img_url, "cat.png", module="data")
+img = Image.open(img_path).resize((224, 224))
+
+# Preprocess the image and convert to tensor
+my_preprocess = transforms.Compose(
+ [
+ transforms.Resize(256),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+img = my_preprocess(img)
+img = np.expand_dims(img, 0)
+
+input_name = "input0"
+shape_list = [(input_name, input_shape)]
+relay_mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
+
+#################################
Review Comment:
nit: add more `#` chars to "cover" end of line below?
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
+#
+# To begin with, load pre-trained MobileNetV2 from torchvision. Then,
+# download a cat image and preprocess it to use as the model input.
+#
+
+model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+model = model.eval()
+
+input_shape = [1, 3, 224, 224]
+input_data = torch.randn(input_shape)
+scripted_model = torch.jit.trace(model, input_data).eval()
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_path = download_testdata(img_url, "cat.png", module="data")
+img = Image.open(img_path).resize((224, 224))
+
+# Preprocess the image and convert to tensor
+my_preprocess = transforms.Compose(
+ [
+ transforms.Resize(256),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+img = my_preprocess(img)
+img = np.expand_dims(img, 0)
+
+input_name = "input0"
+shape_list = [(input_name, input_shape)]
+relay_mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
+
+#################################
+# Define Target, Runtime and Executor
+# -------------------------------
+#
+# In this tutorial we use AOT host driven executor. To compile the model
+# for an emulated embedded environment on an X86 machine we use C runtime (CRT)
Review Comment:
s/X86/x86/
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
Review Comment:
nit: add one more `-` to match end of line above?
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
+#
+# To begin with, load pre-trained MobileNetV2 from torchvision. Then,
+# download a cat image and preprocess it to use as the model input.
+#
+
+model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+model = model.eval()
+
+input_shape = [1, 3, 224, 224]
+input_data = torch.randn(input_shape)
+scripted_model = torch.jit.trace(model, input_data).eval()
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_path = download_testdata(img_url, "cat.png", module="data")
+img = Image.open(img_path).resize((224, 224))
+
+# Preprocess the image and convert to tensor
+my_preprocess = transforms.Compose(
+ [
+ transforms.Resize(256),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+img = my_preprocess(img)
+img = np.expand_dims(img, 0)
+
+input_name = "input0"
+shape_list = [(input_name, input_shape)]
+relay_mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
+
+#################################
+# Define Target, Runtime and Executor
+# -------------------------------
+#
+# In this tutorial we use AOT host driven executor. To compile the model
+# for an emulated embedded environment on an X86 machine we use C runtime (CRT)
+# and we use `host` micro target. Using this setup, TVM compiles the model
+# for C runtime which can run on a X86 CPU machine with the same flow that
Review Comment:
same here: x86 instead of X86
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
+#
+# To begin with, load pre-trained MobileNetV2 from torchvision. Then,
+# download a cat image and preprocess it to use as the model input.
+#
+
+model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+model = model.eval()
+
+input_shape = [1, 3, 224, 224]
+input_data = torch.randn(input_shape)
+scripted_model = torch.jit.trace(model, input_data).eval()
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_path = download_testdata(img_url, "cat.png", module="data")
+img = Image.open(img_path).resize((224, 224))
+
+# Preprocess the image and convert to tensor
+my_preprocess = transforms.Compose(
+ [
+ transforms.Resize(256),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+img = my_preprocess(img)
+img = np.expand_dims(img, 0)
+
+input_name = "input0"
+shape_list = [(input_name, input_shape)]
+relay_mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
+
+#################################
+# Define Target, Runtime and Executor
+# -------------------------------
Review Comment:
nit: add enough `-` chars to align this to the end of line above?
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
Review Comment:
nit: add one more `#` to "cover" the line below?
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,198 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
+#
+# To begin with, load pre-trained MobileNetV2 from torchvision. Then,
+# download a cat image and preprocess it to use as the model input.
+#
+
+model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+model = model.eval()
+
+input_shape = [1, 3, 224, 224]
+input_data = torch.randn(input_shape)
+scripted_model = torch.jit.trace(model, input_data).eval()
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_path = download_testdata(img_url, "cat.png", module="data")
+img = Image.open(img_path).resize((224, 224))
+
+# Preprocess the image and convert to tensor
+my_preprocess = transforms.Compose(
+ [
+ transforms.Resize(256),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+img = my_preprocess(img)
+img = np.expand_dims(img, 0)
+
+input_name = "input0"
+shape_list = [(input_name, input_shape)]
+relay_mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
+
+#################################
+# Define Target, Runtime and Executor
+# -------------------------------
+#
+# In this tutorial we use AOT host driven executor. To compile the model
Review Comment:
nit: host-driven?
##########
gallery/how_to/work_with_microtvm/micro_pytorch.py:
##########
@@ -0,0 +1,205 @@
+# 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.
+"""
+.. _tutorial-micro-Pytorch:
+
+microTVM PyTorch Tutorial
+===========================
+**Authors**:
+`Mehrdad Hessar <https://github.com/mehrdadh>`_
+
+This tutorial is showcasing microTVM host-driven AoT compilation with
+a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).
+
+**Note:** This tutorial only runs on x86 CPU using CRT and does not run on Zephyr
+since the model would not fit on our current supported Zephyr boards.
+"""
+
+# sphinx_gallery_start_ignore
+from tvm import testing
+
+testing.utils.install_request_hook(depth=3)
+# sphinx_gallery_end_ignore
+
+import pathlib
+
+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.backend import Executor
+
+#################################
+# Load a pre-trained PyTorch model
+# -------------------------------
+#
+# To begin with, load pre-trained MobileNetV2 from torchvision. Then,
+# download a cat image and preprocess it to use as the model input.
+#
+
+model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+model = model.eval()
+
+input_shape = [1, 3, 224, 224]
+input_data = torch.randn(input_shape)
+scripted_model = torch.jit.trace(model, input_data).eval()
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_path = download_testdata(img_url, "cat.png", module="data")
+img = Image.open(img_path).resize((224, 224))
+
+# Preprocess the image and convert to tensor
+my_preprocess = transforms.Compose(
+ [
+ transforms.Resize(256),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+img = my_preprocess(img)
+img = np.expand_dims(img, 0)
+
+input_name = "input0"
+shape_list = [(input_name, input_shape)]
+relay_mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
+
+#################################
+# Define Target, Runtime and Executor
+# -------------------------------
+#
+# In this tutorial we use AOT host driven executor. To compile the model
+# for an emulated embedded environment on an X86 machine we use C runtime (CRT)
+# and we use `host` micro target. Using this setup, TVM compiles the model
+# for C runtime which can run on a X86 CPU machine with the same flow that
+# would run on a physical microcontroller.
+#
+
+
+# Simulate a microcontroller on the host machine. Uses the main() from `src/runtime/crt/host/main.cc`
+# To use physical hardware, replace "host" with something matching your hardware.
Review Comment:
How about instead of using "something'" say "replace 'hosts' with another physical micro target, e.g. 'nrf52840' or 'mps2_an521' -- see more more target examples in micro_train.py and micro_tflite.py tutorials "?
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[GitHub] [tvm] tvm-bot commented on pull request #13324: [microTVM][PyTorch][Tutorial]Adding a PyTorch tutorial for microTVM with CRT
Posted by GitBox <gi...@apache.org>.
tvm-bot commented on PR #13324:
URL: https://github.com/apache/tvm/pull/13324#issuecomment-1307570038
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[GitHub] [tvm] gromero commented on a diff in pull request #13324: [microTVM][PyTorch][Tutorial]Adding a PyTorch tutorial for microTVM with CRT
Posted by GitBox <gi...@apache.org>.
gromero commented on code in PR #13324:
URL: https://github.com/apache/tvm/pull/13324#discussion_r1017216300
##########
src/runtime/crt/host/microtvm_api_server.py:
##########
@@ -35,6 +37,18 @@
IS_TEMPLATE = not os.path.exists(os.path.join(PROJECT_DIR, MODEL_LIBRARY_FORMAT_RELPATH))
+MEMORY_SIZE_BYTES = 2 * 1024 * 1024
Review Comment:
@mehrdadh I think that's actually interesting / important info: the constant is determined experimentally, it really good to have comment about it as @alanmacd requested, I don't even consider it a nit :-)
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