Merge branch 'tuning-default' into 'master'

Fix converter bug and add winograd parameters selection.

See merge request !604

mace/python/tools/converter.py

@@ -112,7 +112,7 @@ def main(unused_args):
             option = cvt.ConverterOption(FLAGS.transformers.split(','))
         else:
             option = cvt.ConverterOption()
-        option.winograd_enabled = bool(FLAGS.winograd)
+        option.winograd = FLAGS.winograd
 
         input_node_names = FLAGS.input_node.split(',')
         input_node_shapes = FLAGS.input_shape.split(':')
@@ -146,6 +146,17 @@ def main(unused_args):
         print("Transform model to one that can better run on device")
         if FLAGS.runtime == 'cpu+gpu':
             cpu_graph_def = copy.deepcopy(output_graph_def)
+
+            option.device = cvt.DeviceType.GPU.value
+            option.data_type = parse_data_type(
+                FLAGS.data_type, cvt.DeviceType.GPU.value)
+            mace_gpu_transformer = transformer.Transformer(
+                option, output_graph_def)
+            output_graph_def = mace_gpu_transformer.run()
+            print "start optimize gpu memory."
+            memory_optimizer.optimize_gpu_memory(output_graph_def)
+            print "GPU memory optimization done."
+
             option.device = cvt.DeviceType.CPU.value
             option.data_type = parse_data_type(
                 FLAGS.data_type, cvt.DeviceType.CPU.value)
@@ -157,17 +168,6 @@ def main(unused_args):
             memory_optimizer.optimize_cpu_memory(cpu_graph_def)
             print "CPU memory optimization done."
 
-            option.device = cvt.DeviceType.GPU.value
-            option.data_type = parse_data_type(
-                FLAGS.data_type, cvt.DeviceType.GPU.value)
-            option.enable_transpose_filters()
-            mace_gpu_transformer = transformer.Transformer(
-                option, output_graph_def)
-            output_gpu_graph_def = mace_gpu_transformer.run()
-            print "start optimize gpu memory."
-            memory_optimizer.optimize_gpu_memory(output_gpu_graph_def)
-            print "GPU memory optimization done."
-
             print "Merge cpu and gpu ops together"
             output_graph_def.op.extend(cpu_graph_def.op)
             output_graph_def.mem_arena.mem_block.extend(
@@ -261,11 +261,9 @@ def parse_args():
         help="model tag for generated function and namespace")
     parser.add_argument(
         "--winograd",
-        type=str2bool,
-        nargs='?',
-        const=False,
-        default=False,
-        help="open winograd convolution or not")
+        type=int,
+        default=0,
+        help="Which version of winograd convolution to use. [2 | 4]")
     parser.add_argument(
         "--dsp_mode", type=int, default=0, help="dsp run mode, defalut=0")
     parser.add_argument(

mace/python/tools/converter_tool/base_converter.py

@@ -158,6 +158,7 @@ class MaceKeyword(object):
     mace_shrink_axis_mask_str = 'shrink_axis_mask'
     mace_transpose_a_str = 'transpose_a'
     mace_transpose_b_str = 'transpose_b'
+    mace_op_data_type_str = 'T'
 
 
 class TransformerRule(Enum):
@@ -182,6 +183,7 @@ class TransformerRule(Enum):
     SORT_BY_EXECUTION = 19
     ADD_IN_OUT_TENSOR_INFO = 20
     ADD_MACE_INPUT_AND_OUTPUT_NODES = 21
+    UPDATE_FLOAT_OP_DATA_TYPE = 22
 
 
 class ConverterInterface(object):
@@ -226,7 +228,7 @@ class ConverterOption(object):
         self._output_nodes = {}
         self._data_type = mace_pb2.DT_FLOAT
         self._device = DeviceType.CPU.value
-        self._winograd_enabled = False
+        self._winograd = 0
         if transformers:
             self._transformer_option = [TransformerRule[transformer]
                                         for transformer in transformers]
@@ -251,6 +253,7 @@ class ConverterOption(object):
                 TransformerRule.RESHAPE_FC_WEIGHT,
                 TransformerRule.TRANSFORM_BUFFER_IMAGE,
                 TransformerRule.ADD_DEVICE,
+                TransformerRule.UPDATE_FLOAT_OP_DATA_TYPE,
                 TransformerRule.ADD_MACE_INPUT_AND_OUTPUT_NODES,
                 TransformerRule.SORT_BY_EXECUTION,
             ]
@@ -272,8 +275,8 @@ class ConverterOption(object):
         return self._device
 
     @property
-    def winograd_enabled(self):
-        return self._winograd_enabled
+    def winograd(self):
+        return self._winograd
 
     @property
     def transformer_option(self):
@@ -303,9 +306,9 @@ class ConverterOption(object):
     def device(self, device):
         self._device = device
 
-    @winograd_enabled.setter
-    def winograd_enabled(self, winograd_enabled):
-        self._winograd_enabled = winograd_enabled
+    @winograd.setter
+    def winograd(self, winograd):
+        self._winograd = winograd
 
     def disable_transpose_filters(self):
         if TransformerRule.TRANSPOSE_FILTERS in self._transformer_option:

mace/python/tools/converter_tool/transformer.py

@@ -31,7 +31,6 @@ from mace.python.tools.converter_tool.base_converter import TransformerRule
 from mace.python.tools.convert_util import mace_check
 
 OPENCL_IMAGE_MAX_SIZE = 16384
-DEFAULT_GPU_WINO_BLK_SIZE = 4
 
 
 class OpenCLBufferType(enum.Enum):
@@ -53,6 +52,8 @@ class Transformer(base_converter.ConverterInterface):
     """
 
     def __init__(self, option, model):
+        # Dependencies
+        # (TRANSFORM_MATMUL_TO_FC, TRANSFORM_GLOBAL_CONV_TO_FC) -> RESHAPE_FC_WEIGHT  # noqa
         self._registered_transformers = {
             TransformerRule.REMOVE_IDENTITY_OP: self.remove_identity_op,
             TransformerRule.TRANSFORM_GLOBAL_POOLING:
@@ -83,6 +84,8 @@ class Transformer(base_converter.ConverterInterface):
                 self.transform_buffer_image,
             TransformerRule.ADD_DEVICE:
                 self.add_device,
+            TransformerRule.UPDATE_FLOAT_OP_DATA_TYPE:
+                self.update_float_op_data_type,
             TransformerRule.ADD_MACE_INPUT_AND_OUTPUT_NODES:
                 self.add_mace_input_and_output_nodes,
             TransformerRule.SORT_BY_EXECUTION: self.sort_by_execution,
@@ -90,7 +93,7 @@ class Transformer(base_converter.ConverterInterface):
 
         self._option = option
         self._model = model
-        self._gpu_wino_blk = DEFAULT_GPU_WINO_BLK_SIZE
+        self._gpu_wino_blk = self._option.winograd
 
         self._ops = {}
         self._consts = {}
@@ -442,7 +445,7 @@ class Transformer(base_converter.ConverterInterface):
         return filter_height, filter_width, in_channels, out_channels
 
     def check_if_gpu_use_winograd_conv(self, op):
-        if not self._option.winograd_enabled:
+        if not self._option.winograd:
             return False
         if op.type != MaceOp.Conv2D.name:
             return False
@@ -464,7 +467,6 @@ class Transformer(base_converter.ConverterInterface):
         if filter_height != 3 or filter_width != 3 or strides[0] > 1 \
                 or strides[1] > 1 or dilations[0] > 1 or dilations[1] > 1:
             return False
-        self._gpu_wino_blk = DEFAULT_GPU_WINO_BLK_SIZE
         block_size = self._gpu_wino_blk
         blk_sqr = (block_size + 2) * (block_size + 2)
         width =\
@@ -479,9 +481,9 @@ class Transformer(base_converter.ConverterInterface):
             width = \
                 batch * ((out_height + block_size - 1) / block_size) * \
                 ((out_width + block_size - 1) / block_size)
-        return (blk_sqr * in_channels <= OPENCL_IMAGE_MAX_SIZE) and \
-               (blk_sqr * out_channels <= OPENCL_IMAGE_MAX_SIZE) and \
-               (width <= OPENCL_IMAGE_MAX_SIZE)
+        return (blk_sqr * in_channels < OPENCL_IMAGE_MAX_SIZE) and \
+               (blk_sqr * out_channels < OPENCL_IMAGE_MAX_SIZE) and \
+               (width < OPENCL_IMAGE_MAX_SIZE)
 
     def transform_gpu_winograd(self):
         """Only gpu needs winograd transform."""
@@ -577,17 +579,6 @@ class Transformer(base_converter.ConverterInterface):
                     blk_size_arg.i = block_size
                     ConverterUtil.add_data_format_arg(iwt_op, data_format)
 
-                    filter_data = np.array(filter.float_data).reshape(
-                        filter.dims)
-
-                    weight_tensor_value = filter_data
-                    if filter_format == FilterFormat.HWIO:
-                        weight_tensor_value = filter_data.transpose(3, 2, 0, 1)
-                    elif filter_format == FilterFormat.HWOI:
-                        weight_tensor_value = filter_data.transpose(2, 3, 0, 1)
-                    filter.float_data[:] = weight_tensor_value.flat[:]
-                    filter.dims[:] = weight_tensor_value.shape[:]
-
                     self.safe_remove_node(op, iwt_op)
 
         return False
@@ -608,12 +599,13 @@ class Transformer(base_converter.ConverterInterface):
     def fold_biasadd(self):
         net = self._model
         for op in net.op:
-            if ((op.type == MaceOp.Conv2D.name
-                 or op.type == MaceOp.Deconv2D.name
-                 or op.type == MaceOp.DepthwiseConv2d.name
-                 or op.type == MaceOp.FullyConnected.name
-                 or op.type == MaceOp.WinogradInverseTransform.name)
-                and len(op.input) == 2) \
+            if (((op.type == MaceOp.Conv2D.name
+                  or op.type == MaceOp.Deconv2D.name
+                  or op.type == MaceOp.DepthwiseConv2d.name
+                  or op.type == MaceOp.FullyConnected.name)
+                 and len(op.input) == 2)
+                or (op.type == MaceOp.WinogradInverseTransform.name
+                    and len(op.input) == 1)) \
                     and len(self._consumers.get(op.output[0], [])) == 1:
                 consumer_op = self._consumers[op.output[0]][0]
                 if consumer_op.type == MaceOp.BiasAdd.name:
@@ -893,25 +885,24 @@ class Transformer(base_converter.ConverterInterface):
                 if op.type == MaceOp.Conv2D.name \
                         or op.type == MaceOp.Deconv2D.name \
                         or op.type == MaceOp.DepthwiseConv2d.name:
-                    if ConverterUtil.get_arg(
-                            op, MaceKeyword.mace_winograd_filter_transformed) \
-                            is None:
-                        filter = self._consts[op.input[1]]
-                        filter_data = np.array(filter.float_data).reshape(
-                            filter.dims)
-                        if op.type == MaceOp.Deconv2D.name:
-                            filter_data = filter_data.transpose(2, 3, 0, 1)
-                        else:
-                            filter_data = filter_data.transpose(3, 2, 0, 1)
-                        filter.float_data[:] = filter_data.flat
-                        filter.dims[:] = filter_data.shape
-                if op.type == MaceOp.FullyConnected.name:
-                    weight = self._consts[op.input[1]]
-                    weight_data = np.array(weight.float_data).reshape(
-                        weight.dims)
-                    weight_data = weight_data.transpose(1, 0)
-                    weight.float_data[:] = weight_data.flat
-                    weight.dims[:] = weight_data.shape
+                    filter = self._consts[op.input[1]]
+                    filter_data = np.array(filter.float_data).reshape(
+                        filter.dims)
+                    if op.type == MaceOp.Deconv2D.name:
+                        filter_data = filter_data.transpose(2, 3, 0, 1)
+                    else:
+                        filter_data = filter_data.transpose(3, 2, 0, 1)
+                    filter.float_data[:] = filter_data.flat
+                    filter.dims[:] = filter_data.shape
+                if (op.type == MaceOp.MatMul.name and
+                            ConverterUtil.get_arg(op, MaceKeyword.mace_winograd_filter_transformed) is not None):  # noqa
+                    filter = self._consts[op.input[0]]
+                    filter_data = np.array(filter.float_data).reshape(
+                        filter.dims)
+                    filter_data = filter_data.transpose(3, 2, 0, 1)
+                    filter.float_data[:] = filter_data.flat
+                    filter.dims[:] = filter_data.shape
+
             self.set_filter_format(FilterFormat.OIHW)
 
         return False
@@ -1104,6 +1095,11 @@ class Transformer(base_converter.ConverterInterface):
                         weight = self._consts[op.input[1]]
                         if len(weight.dims) == 2:
                             op.type = MaceOp.FullyConnected.name
+                            weight_data = np.array(weight.float_data).reshape(
+                                weight.dims)
+                            weight_data = weight_data.transpose(1, 0)
+                            weight.float_data[:] = weight_data.flat
+                            weight.dims[:] = weight_data.shape
 
         return False
 
@@ -1156,6 +1152,22 @@ class Transformer(base_converter.ConverterInterface):
 
         return False
 
+    def update_float_op_data_type(self):
+        print("update op with float data type")
+        net = self._model
+        for op in net.op:
+            data_type_arg = ConverterUtil.get_arg(
+                op, MaceKeyword.mace_op_data_type_str)
+            if not data_type_arg:
+                data_type_arg = op.arg.add()
+                data_type_arg.name = MaceKeyword.mace_op_data_type_str
+                data_type_arg.i = self._option.data_type
+            elif data_type_arg.i != self._option.data_type \
+                    and data_type_arg.i == mace_pb2.DT_FLOAT:
+                data_type_arg.i = self._option.data_type
+
+        return False
+
     def sort_dfs(self, op, visited, sorted_nodes):
         visited.update([op.name])
         if len(op.input) > 0:

tools/converter.py

@@ -112,6 +112,8 @@ DSPDataTypeStrs = [
 DSPDataType = Enum('DSPDataType', [(ele, ele) for ele in DSPDataTypeStrs],
                    type=str)
 
+WinogradParameters = [0, 2, 4]
+
 
 class DefaultValues(object):
     omp_num_threads = -1,
@@ -408,6 +410,12 @@ def format_model_config(flags):
             else:
                 subgraph[YAMLKeyword.validation_inputs_data] = \
                     validation_inputs_data
+            input_ranges = subgraph.get(
+                YAMLKeyword.input_ranges, [])
+            if not isinstance(input_ranges, list):
+                subgraph[YAMLKeyword.input_ranges] = [input_ranges]
+            else:
+                subgraph[YAMLKeyword.input_ranges] = input_ranges
 
         for key in [YAMLKeyword.limit_opencl_kernel_time,
                     YAMLKeyword.nnlib_graph_mode,
@@ -417,6 +425,12 @@ def format_model_config(flags):
             if value == "":
                 model_config[key] = 0
 
+        mace_check(model_config[YAMLKeyword.winograd] in WinogradParameters,
+                   ModuleName.YAML_CONFIG,
+                   "'winograd' parameters must be in "
+                   + str(WinogradParameters) +
+                   ". 0 for disable winograd convolution")
+
         weight_file_path = model_config.get(YAMLKeyword.weight_file_path, "")
         model_config[YAMLKeyword.weight_file_path] = weight_file_path
 
@@ -511,7 +525,7 @@ def print_configuration(flags, configs):
                  configs[YAMLKeyword.embed_model_data]])
     data.append([YAMLKeyword.linkshared,
                  configs[YAMLKeyword.linkshared]])
-    data.append(["Tuning", flags.tuning])
+    data.append(["Tuning", flags.disable_tuning])
     MaceLogger.summary(StringFormatter.table(header, data, title))
 
 
@@ -736,7 +750,7 @@ def build_specific_lib(target_abi, target_soc, serial_num,
                 subgraphs[0][YAMLKeyword.input_tensors],
                 subgraphs[0][YAMLKeyword.input_shapes],
                 subgraphs[0][YAMLKeyword.validation_inputs_data],
-                input_ranges=subgraphs[0].get(YAMLKeyword.input_ranges, None))
+                input_ranges=subgraphs[0][YAMLKeyword.input_ranges])
 
             device_type = parse_device_type(RuntimeType.gpu)
             sh_commands.tuning_run(
@@ -869,8 +883,8 @@ def build_library(flags):
 
     convert_model(configs)
 
-    generate_library(configs, flags.tuning,
-                     flags.enable_openmp, flags.address_sanitizer)
+    generate_library(configs, flags.disable_tuning,
+                     flags.disable_openmp, flags.address_sanitizer)
 
     print_library_summary(configs)
 
@@ -980,7 +994,7 @@ def run_specific_target(flags, configs, target_abi,
             subgraphs[0][YAMLKeyword.input_tensors],
             subgraphs[0][YAMLKeyword.input_shapes],
             subgraphs[0][YAMLKeyword.validation_inputs_data],
-            input_ranges=subgraphs[0].get(YAMLKeyword.input_ranges, None))
+            input_ranges=subgraphs[0][YAMLKeyword.input_ranges])
         runtime_list = []
         if target_abi == ABIType.host:
             runtime_list.extend([RuntimeType.cpu])
@@ -1129,7 +1143,7 @@ def bm_specific_target(flags, configs, target_abi, target_soc, serial_num):
             subgraphs[0][YAMLKeyword.input_tensors],
             subgraphs[0][YAMLKeyword.input_shapes],
             subgraphs[0][YAMLKeyword.validation_inputs_data],
-            input_ranges=subgraphs[0].get(YAMLKeyword.input_ranges, None))
+            input_ranges=subgraphs[0][YAMLKeyword.input_ranges])
         runtime_list = []
         if target_abi == ABIType.host:
             runtime_list.extend([RuntimeType.cpu])
@@ -1262,13 +1276,13 @@ def parse_args():
         help='build model library and test tools')
     build.set_defaults(func=build_library)
     build.add_argument(
-        '--tuning',
-        action="store_true",
-        help="whether tuning the parameters for the GPU of specified SoC.")
+        '--disable_tuning',
+        action="store_false",
+        help="Disable tuning the parameters for the GPU of specified SoC.")
     build.add_argument(
-        "--enable_openmp",
+        "--disable_openmp",
         action="store_false",
-        help="Enable openmp for multiple thread.")
+        help="Disable openmp for multiple thread.")
     run = subparsers.add_parser(
         'run',
         parents=[all_type_parent_parser, run_bm_parent_parser,

tools/sh_commands.py

@@ -486,7 +486,7 @@ def gen_model_code(model_codegen_dir,
                    input_shapes,
                    dsp_mode,
                    embed_model_data,
-                   fast_conv,
+                   winograd,
                    obfuscate,
                    model_build_type,
                    data_type,
@@ -512,7 +512,7 @@ def gen_model_code(model_codegen_dir,
               "--input_shape=%s" % input_shapes,
               "--dsp_mode=%s" % dsp_mode,
               "--embed_model_data=%s" % embed_model_data,
-              "--winograd=%s" % fast_conv,
+              "--winograd=%s" % winograd,
               "--obfuscate=%s" % obfuscate,
               "--output_dir=%s" % model_codegen_dir,
               "--model_build_type=%s" % model_build_type,
@@ -525,8 +525,8 @@ def gen_random_input(model_output_dir,
                      input_nodes,
                      input_shapes,
                      input_files,
-                     input_file_name="model_input",
-                     input_ranges=None):
+                     input_ranges,
+                     input_file_name="model_input"):
     for input_name in input_nodes:
         formatted_name = common.formatted_file_name(
             input_file_name, input_name)
@@ -534,10 +534,7 @@ def gen_random_input(model_output_dir,
             sh.rm("%s/%s" % (model_output_dir, formatted_name))
     input_nodes_str = ",".join(input_nodes)
     input_shapes_str = ":".join(input_shapes)
-    if input_ranges:
-        input_ranges_str = ":".join(input_ranges)
-    else:
-        input_ranges_str = None
+    input_ranges_str = ":".join(input_ranges)
     generate_input_data("%s/%s" % (model_output_dir, input_file_name),
                         input_nodes_str,
                         input_shapes_str,