Caffe2 docs

caffe2/python/convnet_benchmarks.py

@@ -59,11 +59,12 @@
 """
 
 import argparse
+import os
 
-from caffe2.python import workspace, brew, model_helper
+from caffe2.python import workspace, brew, model_helper, core
+from caffe2.proto import caffe2_pb2
 
-
-def MLP(order, cudnn_ws):
+def MLP(order, cudnn_ws, model_path=""):
     model = model_helper.ModelHelper(name="MLP")
     d = 256
     depth = 20
@@ -98,7 +99,7 @@ def MLP(order, cudnn_ws):
     return model, d
 
 
-def AlexNet(order, cudnn_ws):
+def AlexNet(order, cudnn_ws, model_path=""):
     my_arg_scope = {
         'order': order,
         'use_cudnn': True,
@@ -191,7 +192,7 @@ def AlexNet(order, cudnn_ws):
     return model, 224
 
 
-def OverFeat(order, cudnn_ws):
+def OverFeat(order, cudnn_ws, model_path=""):
     my_arg_scope = {
         'order': order,
         'use_cudnn': True,
@@ -277,7 +278,7 @@ def OverFeat(order, cudnn_ws):
     return model, 231
 
 
-def VGGA(order, cudnn_ws):
+def VGGA(order, cudnn_ws, model_path=""):
     my_arg_scope = {
         'order': order,
         'use_cudnn': True,
@@ -475,7 +476,7 @@ def _InceptionModule(
     return output
 
 
-def Inception(order, cudnn_ws):
+def Inception(order, cudnn_ws, model_path=""):
     my_arg_scope = {
         'order': order,
         'use_cudnn': True,
@@ -562,6 +563,84 @@ def Inception(order, cudnn_ws):
     model.net.AveragedLoss(xent, "loss")
     return model, 224
 
+def Resnet50(order, cudnn_ws, model_path=""):
+    if model_path == "":
+        print("ERROR: please specify paths to init_net and predict_net protobufs for Resnet50")
+        exit(1)
+    device_opts = caffe2_pb2.DeviceOption()
+    device_opts.device_type = caffe2_pb2.HIP
+    device_opts.hip_gpu_id = 0
+
+    INIT_NET_PB = os.path.join(model_path, "init_net.pb")
+    PREDICT_NET_PB = os.path.join(model_path, "predict_net.pb")
+    init_def = caffe2_pb2.NetDef()
+    with open(INIT_NET_PB, 'rb') as f:
+        init_def.ParseFromString(f.read())
+        init_def.device_option.CopyFrom(device_opts)
+
+    net_def = caffe2_pb2.NetDef()
+    with open(PREDICT_NET_PB, 'rb') as f:
+        net_def.ParseFromString(f.read())
+        net_def.device_option.CopyFrom(device_opts)
+
+    init_net = core.Net(init_def)
+    predict_net = core.Net(net_def)
+    for op in init_net.Proto().op:
+        op.device_option.CopyFrom(device_opts)
+    for op in predict_net.Proto().op:
+        op.device_option.CopyFrom(device_opts)
+    my_arg_scope = {
+        'order': order,
+    }
+    model = model_helper.ModelHelper(
+        name="resnet50",
+        arg_scope=my_arg_scope,
+    )
+
+    model.param_init_net = init_net
+    model.net = predict_net
+    xent = model.net.LabelCrossEntropy(["gpu_0/softmax", "label"], "xent")
+    model.net.AveragedLoss(xent, "loss")
+    return model, 224
+
+def Inception_v2(order, cudnn_ws, model_path=""):
+    if model_path == "":
+        print("ERROR: please specify paths to init_net and predict_net protobufs for Inception_v2")
+        exit(1)
+    device_opts = caffe2_pb2.DeviceOption()
+    device_opts.device_type = caffe2_pb2.HIP
+    device_opts.hip_gpu_id = 0
+
+    INIT_NET_PB = os.path.join(model_path, "init_net.pb")
+    PREDICT_NET_PB = os.path.join(model_path, "predict_net.pb")
+    init_def = caffe2_pb2.NetDef()
+    with open(INIT_NET_PB, 'rb') as f:
+        init_def.ParseFromString(f.read())
+        init_def.device_option.CopyFrom(device_opts)
+
+    net_def = caffe2_pb2.NetDef()
+    with open(PREDICT_NET_PB, 'rb') as f:
+        net_def.ParseFromString(f.read())
+        net_def.device_option.CopyFrom(device_opts)
+
+    init_net = core.Net(init_def)
+    predict_net = core.Net(net_def)
+
+    my_arg_scope = {
+        'order': order,
+    }
+
+    model = model_helper.ModelHelper(
+        name="GoogleNet",
+        arg_scope=my_arg_scope,
+    )
+
+    model.param_init_net = init_net
+    model.net = predict_net
+    xent = model.net.LabelCrossEntropy(["prob", "label"], "xent")
+    model.net.AveragedLoss(xent, "loss")
+    return model, 224
+
 
 def AddParameterUpdate(model):
     """ Simple plain SGD update -- not tuned to actually train the models """
@@ -575,7 +654,7 @@ def AddParameterUpdate(model):
 
 
 def Benchmark(model_gen, arg):
-    model, input_size = model_gen(arg.order, arg.cudnn_ws)
+    model, input_size = model_gen(arg.order, arg.cudnn_ws, arg.model_path)
     model.Proto().type = arg.net_type
     model.Proto().num_workers = arg.num_workers
 
@@ -590,7 +669,7 @@ def Benchmark(model_gen, arg):
 
     model.param_init_net.GaussianFill(
         [],
-        "data",
+        "gpu_0/data" if arg.model == "Resnet50" else "data",
         shape=input_shape,
         mean=0.0,
         std=1.0
@@ -701,6 +780,7 @@ def GetArgumentParser():
     parser.add_argument("--num_workers", type=int, default=2)
     parser.add_argument("--use-nvtx", default=False, action='store_true')
     parser.add_argument("--htrace_span_log_path", type=str)
+    parser.add_argument("--model_path", type=str, default="", help="set path to init net and predict_net protobufs")
     return parser
 
 
@@ -723,5 +803,8 @@ def GetArgumentParser():
             'VGGA': VGGA,
             'Inception': Inception,
             'MLP': MLP,
+            'Resnet50': Resnet50,
+            'Inception_v2':Inception_v2
+
         }
         Benchmark(model_map[args.model], args)

caffe2/python/core.py

@@ -85,6 +85,7 @@ def IsOperatorWithEngine(op_type, engine):
 def DeviceOption(
     device_type,
     cuda_gpu_id=0,
+    hip_gpu_id=0,
     random_seed=None,
     node_name=None,
     numa_node_id=None,
@@ -93,6 +94,7 @@ def DeviceOption(
     option = caffe2_pb2.DeviceOption()
     option.device_type = device_type
     option.cuda_gpu_id = cuda_gpu_id
+    option.hip_gpu_id = hip_gpu_id
     if node_name is not None:
         option.node_name = node_name
     if random_seed is not None:
@@ -2022,8 +2024,9 @@ def DeduplicateGradientSlices(self, g, aggregator='sum'):
     def RunAllOnGPU(self, gpu_id=0, use_cudnn=False):
         """A convenient function to run everything on the GPU."""
         device_option = caffe2_pb2.DeviceOption()
-        device_option.device_type = caffe2_pb2.CUDA
+        device_option.device_type = caffe2_pb2.CUDA if workspace.has_gpu_support else caffe2_pb2.HIP
         device_option.cuda_gpu_id = gpu_id
+        device_option.hip_gpu_id = gpu_id
         self._net.device_option.CopyFrom(device_option)
         if use_cudnn:
             for op in self._net.op:

rocm-docs/caffe2-build.md

@@ -0,0 +1,103 @@
+# Caffe2: Building From Source on ROCm Platform
+
+## Intro
+This instruction provides a starting point to build caffe2 on AMD GPUs (Caffe2 ROCm port) from source.
+*Note*: it is recommended to start with a clean Ubuntu 16.04 system
+
+## Install docker
+
+ If your machine doesn't have docker installed, follow the steps [here](https://docs.docker.com/install/linux/docker-ce/ubuntu/#install-docker-ce) to install docker.
+
+## Install ROCm
+
+Install ROCm stack following steps at [link](https://github.com/RadeonOpenCompute/ROCm/blob/master/README.md) if your machine doesn't have ROCm already.
+
+Once the machine is ready with ROCm stack, there are two ways to use caffe2 
+* Run the docker container with caffe2 installed in it.
+
+* Build caffe2 from source inside a docker with all the dependencies.
+
+## Launch docker container with caffe2 pre-installed
+```
+docker run -it --network=host --device=/dev/kfd --device=/dev/dri --group-add video rocm/caffe2:rocm1.8.2
+```
+
+To run benchmarks, skip directly to benchmarks section of the document.
+
+## Build Caffe2 from source
+### Pull the docker image
+```
+docker pull rocm/caffe2:unbuilt-rocm1.8.2
+```
+This docker image has all the dependencies for caffe2 pre-installed.
+
+### Pull the latest caffe2 source:
+* Using https 
+```
+git clone --recurse-submodules https://github.com/ROCmSoftwarePlatform/pytorch.git
+```
+* Using ssh
+```
+git clone --recurse-submodules [email protected]:ROCmSoftwarePlatform/pytorch.git
+```
+Navigate to repo directory
+```
+cd pytorch
+```
+
+### Launch the docker container
+```	
+docker run -it --network=host --device=/dev/kfd --device=/dev/dri --group-add video -v $PWD:/pytorch rocm/caffe2:unbuilt-rocm1.8.2
+``` 
+Navigate to pytorch directory `cd /pytorch` inside the container.
+
+### Build caffe2 Project from source
+
+* Run the command  
+
+	`.jenkins/caffe2/build.sh`
+
+
+* Test the rocm-caffe2 Installation 
+
+	Before running the tests, make sure that the required environment variables are set:
+	``` 
+	export LD_LIBRARY_PATH=/usr/local/caffe2/lib:$LD_LIBRARY_PATH
+	export PYTHONPATH=/usr/local/caffe2/lib/python2.7/dist-packages:$PYTHONPATH
+	```
+
+	Run the binaries under `/pytorch/build_caffe2/bin`
+
+## Run benchmarks
+
+Navigate to build directory, `cd /pytorch/build_caffe2` to run benchmarks.
+
+Caffe2 benchmarking script supports the following networks.
+1. MLP
+2. AlexNet
+3. OverFeat
+4. VGGA
+5. Inception
+6. Inception_v2
+7. Resnet50 
+
+*Special case:* Inception_v2 and Resnet50 will need their corresponding protobuf files to run the benchmarks. Protobufs can be downloaded from caffe2 model zoo using the below command. Substitute model_name with `inception_v2` or `resnet50`
+
+```
+python caffe2/python/models/download.py <model_name>
+```
+This will download the protobufs to current working directory.
+
+To run benchmarks for networks MLP, AlexNet, OverFeat, VGGA, Inception, run the command replacing `<name_of_the_netwrok>` with one of the networks. 
+
+```
+python caffe2/python/convnet_benchmarks.py --batch_size 64 --model <name_of_the_network> --engine MIOPEN --layer_wise_benchmark True --net_type simple
+
+```
+To run Inception_v2 or Resnet50, please add additional argument `--model_path` to the above command which should point to the model directories downloaded above.
+
+```
+python caffe2/python/convnet_benchmarks.py --batch_size 64 --model <name_of_the_network> --engine MIOPEN --layer_wise_benchmark True --net_type simple --model_path <path_to_model_protobufs>
+
+```
+