[RUNTIME] Improve memory usage for RPC (#1741)

apps/benchmark/README.md

@@ -31,8 +31,10 @@ python3 gpu_imagenet_bench.py --model titanx
 ```
 
 ### ARM CPU & Mali GPU
-For embedded deivces, we use RPC infrastructure in TVM to make the management easy.
-So you need to use it for reproducing benchmark results.
+For embedded devices, we use RPC infrastructure in TVM to make the management easy.
+You need to use it for reproducing benchmark results.
+
+**Note**: We use llvm-4.0 in our tuning environment. Mismatch of the LLVM version during tuning and deployment can influence the performance, so you have to use a same version for reproduction.
 
 0. Build TVM with LLVM enabled. [Help](https://docs.tvm.ai/install/from_source.html)
 
@@ -87,6 +89,10 @@ python3 -m tvm.exec.rpc_tracker
   python3 arm_cpu_imagenet_bench.py --model mate10pro --rpc-key mate10pro  
 
   # Mali GPU
+  # NOTE: To make the test environment more stable, we close GUI and lock the frequency
+  sudo /etc/init.d/lightdm stop
+  sudo -i
+  echo performance > /sys/class/misc/mali0/device/devfreq/ff9a0000.gpu/governor
   python3 mobile_gpu_imagenet_bench.py --model rk3399 --rpc-key rk3399
   ```
 

apps/benchmark/arm_cpu_imagenet_bench.py

@@ -41,15 +41,12 @@ def evaluate_network(network, target, target_host, number):
     print_progress("%-20s uploading..." % network)
     ctx = remote.context(str(target), 0)
     remote.upload(tmp.relpath(filename))
-    rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
 
     rlib = remote.load_module(filename)
     module = runtime.create(graph, rlib, ctx)
     data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
     module.set_input('data', data_tvm)
-    module.set_input(**rparams)
-
-    del rparams
+    module.set_input(**params)
 
     # evaluate
     print_progress("%-20s evaluating..." % network)

apps/benchmark/mobile_gpu_imagenet_bench.py

@@ -46,9 +46,7 @@ def evaluate_network(network, target, target_host, number):
     module = runtime.create(graph, rlib, ctx)
     data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
     module.set_input('data', data_tvm)
-    module.set_input(**rparams)
-
-    del rparams
+    module.set_input(**params)
 
     # evaluate
     print_progress("%-20s evaluating..." % network)
@@ -87,4 +85,4 @@ if __name__ == "__main__":
     print("--------------------------------------------------")
 
     for network in networks:
-        evaluate_network(network, target, target_host, args.number)
\ No newline at end of file
+        evaluate_network(network, target, target_host, args.number)

nnvm/python/nnvm/testing/tf.py

@@ -8,12 +8,13 @@ import re
 import os.path
 import collections
 import numpy as np
-from tvm.contrib import util
 
 # Tensorflow imports
 import tensorflow as tf
 from tensorflow.core.framework import graph_pb2
 
+from tvm.contrib import util
+
 ######################################################################
 # Some helper functions
 # ---------------------

python/tvm/contrib/graph_runtime.py

 """Minimum graph runtime that executes graph containing TVM PackedFunc."""
+import numpy as np
+
 from .._ffi.base import string_types
 from .._ffi.function import get_global_func
 from ..rpc import base as rpc_base
@@ -97,9 +99,13 @@ class GraphModule(object):
         """
         if key:
             self._set_input(key, nd.array(value, ctx=self.ctx))
-        for k, v in params.items():
-            self._set_input(k, nd.array(v, ctx=self.ctx))
-        return self
+
+        if params:
+            # upload big arrays first to avoid memory issue in rpc mode
+            keys = list(params.keys())
+            keys.sort(key=lambda x: -np.prod(params[x].shape))
+            for k in keys:
+                self._set_input(k, nd.array(params[k], ctx=self.ctx))
 
     def run(self, **input_dict):
         """Run forward execution of the graph

src/common/ring_buffer.h

@@ -36,19 +36,31 @@ class RingBuffer {
    * \param n The size of capacity.
    */
   void Reserve(size_t n) {
-    if (ring_.size() >= n) return;
-    size_t old_size = ring_.size();
-    size_t new_size = ring_.size();
-    while (new_size < n) {
-      new_size *= 2;
-    }
-    ring_.resize(new_size);
-    if (head_ptr_ + bytes_available_ > old_size) {
-      // copy the ring overflow part into the tail.
-      size_t ncopy = head_ptr_ + bytes_available_ - old_size;
-      memcpy(&ring_[0] + old_size, &ring_[0], ncopy);
+    if (ring_.size() < n) {
+        size_t old_size = ring_.size();
+        size_t new_size = static_cast<size_t>(n * 1.2);
+        ring_.resize(new_size);
+        if (head_ptr_ + bytes_available_ > old_size) {
+          // copy the ring overflow part into the tail.
+          size_t ncopy = head_ptr_ + bytes_available_ - old_size;
+          memcpy(&ring_[0] + old_size, &ring_[0], ncopy);
+        }
+    } else if (ring_.size() > n * 8 && ring_.size() > kInitCapacity) {
+        // shrink too large temporary buffer to avoid out of memory on some embedded devices
+        size_t old_bytes = bytes_available_;
+
+        std::vector<char> tmp(old_bytes);
+
+        Read(&tmp[0], old_bytes);
+        ring_.resize(kInitCapacity);
+        ring_.shrink_to_fit();
+
+        memcpy(&ring_[0], &tmp[0], old_bytes);
+        head_ptr_ = 0;
+        bytes_available_ = old_bytes;
     }
   }
+
   /*!
    * \brief Peform a non-blocking read from buffer
    *  size must be smaller than this->bytes_available()

tutorials/autotvm/tune_nnvm_arm.py

@@ -327,11 +327,10 @@ def tune_and_evaluate(tuning_opt):
 
         # upload parameters to device
         ctx = remote.context(str(target), 0)
-        rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
-        data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
         module = runtime.create(graph, rlib, ctx)
+        data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
         module.set_input('data', data_tvm)
-        module.set_input(**rparams)
+        module.set_input(**params)
 
         # evaluate
         print("Evaluate inference time cost...")

tutorials/autotvm/tune_nnvm_cuda.py

@@ -229,11 +229,10 @@ def tune_and_evaluate(tuning_opt):
 
         # load parameters
         ctx = tvm.context(str(target), 0)
-        params_tvm = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
-        data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
         module = runtime.create(graph, lib, ctx)
+        data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
         module.set_input('data', data_tvm)
-        module.set_input(**params_tvm)
+        module.set_input(**params)
 
         # evaluate
         print("Evaluate inference time cost...")

tutorials/autotvm/tune_nnvm_mobile_gpu.py

@@ -328,11 +328,10 @@ def tune_and_evaluate(tuning_opt):
 
         # upload parameters to device
         ctx = remote.context(str(target), 0)
-        rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
-        data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
         module = runtime.create(graph, rlib, ctx)
+        data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
         module.set_input('data', data_tvm)
-        module.set_input(**rparams)
+        module.set_input(**params)
 
         # evaluate
         print("Evaluate inference time cost...")
@@ -357,9 +356,28 @@ def tune_and_evaluate(tuning_opt):
 #
 #    Extract tasks...
 #    Tuning...
-#    [Task  1/17]  Current/Best:   12.22/  36.05 GFLOPS | Progress: (32/1000) | 42.12 s
+#    [Task  1/17]  Current/Best:   25.30/  39.12 GFLOPS | Progress: (992/1000) | 751.22 s Done.
+#    [Task  2/17]  Current/Best:   40.70/  45.50 GFLOPS | Progress: (736/1000) | 545.46 s Done.
+#    [Task  3/17]  Current/Best:   38.83/  42.35 GFLOPS | Progress: (992/1000) | 1549.85 s Done.
+#    [Task  4/17]  Current/Best:   23.31/  31.02 GFLOPS | Progress: (640/1000) | 1059.31 s Done.
+#    [Task  5/17]  Current/Best:    0.06/   2.34 GFLOPS | Progress: (544/1000) | 305.45 s Done.
+#    [Task  6/17]  Current/Best:   10.97/  17.20 GFLOPS | Progress: (992/1000) | 1050.00 s Done.
+#    [Task  7/17]  Current/Best:    8.98/  10.94 GFLOPS | Progress: (928/1000) | 421.36 s Done.
+#    [Task  8/17]  Current/Best:    4.48/  14.86 GFLOPS | Progress: (704/1000) | 582.60 s Done.
+#    [Task  9/17]  Current/Best:   10.30/  25.99 GFLOPS | Progress: (864/1000) | 899.85 s Done.
+#    [Task 10/17]  Current/Best:   11.73/  12.52 GFLOPS | Progress: (608/1000) | 304.85 s Done.
+#    [Task 11/17]  Current/Best:   15.26/  18.68 GFLOPS | Progress: (800/1000) | 747.52 s Done.
+#    [Task 12/17]  Current/Best:   17.48/  26.71 GFLOPS | Progress: (1000/1000) | 1166.40 s Done.
+#    [Task 13/17]  Current/Best:    0.96/  11.43 GFLOPS | Progress: (960/1000) | 611.65 s Done.
+#    [Task 14/17]  Current/Best:   17.88/  20.22 GFLOPS | Progress: (672/1000) | 670.29 s Done.
+#    [Task 15/17]  Current/Best:   11.62/  13.98 GFLOPS | Progress: (736/1000) | 449.25 s Done.
+#    [Task 16/17]  Current/Best:   19.90/  23.83 GFLOPS | Progress: (608/1000) | 708.64 s Done.
+#    [Task 17/17]  Current/Best:   17.98/  22.75 GFLOPS | Progress: (736/1000) | 1122.60 s Done.
+#    Compile...
+#    Upload...
+#    Evaluate inference time cost...
+#    Mean inference time (std dev): 128.05 ms (7.74 ms)
 #
-#    (The following part is running, will update it later).
 
 ######################################################################
 #

tutorials/nnvm/deploy_model_on_mali_gpu.py

@@ -132,7 +132,6 @@ batch_size = 1
 num_classes = 1000
 image_shape = (3, 224, 224)
 data_shape = (batch_size,) + image_shape
-out_shape = (batch_size, num_classes)
 
 ######################################################################
 # Compile The Graph
@@ -197,20 +196,17 @@ else:
 remote.upload(lib_fname)
 rlib = remote.load_module('net.tar')
 
-ctx = remote.cpu(0) if local_demo else remote.cl(0)
-# upload the parameter
-rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
-
 # create the remote runtime module
+ctx = remote.cl(0) if not local_demo else remote.cpu(0)
 module = runtime.create(graph, rlib, ctx)
-# set parameter
-module.set_input(**rparams)
+# set parameter (upload params to the remote device. This may take a while)
+module.set_input(**params)
 # set input data
 module.set_input('data', tvm.nd.array(x.astype('float32')))
 # run
 module.run()
 # get output
-out = module.get_output(0, tvm.nd.empty(out_shape, ctx=ctx))
+out = module.get_output(0)
 # get top1 result
 top1 = np.argmax(out.asnumpy())
 print('TVM prediction top-1: {}'.format(synset[top1]))

tutorials/nnvm/deploy_model_on_rasp.py

@@ -128,7 +128,6 @@ batch_size = 1
 num_classes = 1000
 image_shape = (3, 224, 224)
 data_shape = (batch_size,) + image_shape
-out_shape = (batch_size, num_classes)
 
 ######################################################################
 # Compile The Graph
@@ -188,20 +187,17 @@ else:
 remote.upload(lib_fname)
 rlib = remote.load_module('net.tar')
 
-# upload the parameter (this may take a while)
-ctx = remote.cpu(0)
-rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
-
 # create the remote runtime module
+ctx = remote.cpu(0)
 module = runtime.create(graph, rlib, ctx)
-# set parameter
-module.set_input(**rparams)
+# set parameter (upload params to the remote device. This may take a while)
+module.set_input(**params)
 # set input data
 module.set_input('data', tvm.nd.array(x.astype('float32')))
 # run
 module.run()
 # get output
-out = module.get_output(0, tvm.nd.empty(out_shape, ctx=ctx))
+out = module.get_output(0)
 # get top1 result
 top1 = np.argmax(out.asnumpy())
 print('TVM prediction top-1: {}'.format(synset[top1]))