Adding support for accepting pytorch model config as input.

requirements.txt

@@ -5,3 +5,4 @@ numpy
 pandas
 cython
 setuptools
+torch

scalesim/scale.py

@@ -1,6 +1,12 @@
 import argparse
+import os
+
+import torch
+import torch.nn as nn
 
 from scalesim.scale_sim import scalesim
+from torch_to_topo import create_conv_topo
+from torchvision import models, datasets
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
@@ -20,13 +26,35 @@
                         default="conv",
                         help="Type of input topology, gemm: MNK, conv: conv"
                         )
+    # Changes from torch_to_topo files
+    parser.add_argument('-m', metavar='PyTorch module name', type=str,
+                        default='',
+                        help="Class name of PyTorch module to turn into SCALESim topology"
+                        )
+    parser.add_argument('-l', metavar='Include linear layers?', type=bool,
+                        default=False,
+                        help="Boolean deciding whether or not to include nn.Linear layers in topology"
+                        )    
 
     args = parser.parse_args()
     topology = args.t
     config = args.c
     logpath = args.p
     inp_type = args.i
 
+    if args.m != '':
+        # Changes from torch_to_topo files
+        path = '../topologies/custom/'
+        if not os.path.exists(path):
+            os.makedirs(path)
+        include_lin = args.l
+        model_name = args.m
+        model = getattr(models, model_name)(pretrained=True)
+        file_name = model_name + '.csv'
+        create_conv_topo(model, path=path, filename=file_name, include_lin=include_lin)
+
+        topology = path + file_name
+
     gemm_input = False
     if inp_type == 'gemm':
         gemm_input = True

scalesim/torch_to_topo.py

@@ -0,0 +1,108 @@
+
+import argparse
+import os
+
+import torch
+import torch.nn as nn
+
+from torchvision import models, datasets
+from torchvision.transforms import ToTensor
+from torchinfo import summary
+
+from scalesim import topology_utils
+
+
+base_dir = '../topologies/custom'
+
+
+# creates topology file from PyTorch module
+def create_conv_topo(model, path=base_dir, filename='', input_dim=(2, 3, 224, 224), include_lin=False):
+    if filename == '':
+        filename = model.__class__.__name__+'_custom.csv'
+
+    topo = topology_utils.topologies()
+    prev_arr, curr_arr = [], []
+    last = 0
+
+    model_stats = summary(model, input_size=input_dim, col_names=('input_size','output_size','num_params','kernel_size'), verbose=1)
+    idx = 1
+    for layer in model_stats.summary_list:
+        if layer.class_name == 'Conv2d':
+            if prev_arr:
+                prev_arr[6] = last
+                topo.topo_arrays.append(prev_arr)
+                idx += 1
+            curr_arr.append(layer.class_name+'_{}'.format(idx))  # layer name
+            curr_arr.append(layer.input_size[2])  # IFMAP height
+            curr_arr.append(layer.input_size[3])  # IFMAP width
+            curr_arr.append(layer.kernel_size[0])  # filter height
+            curr_arr.append(layer.kernel_size[1])  # filter width
+            curr_arr.append(layer.input_size[1])  # channels
+            curr_arr.append(0)  # output channels (num filters)
+            curr_arr.append(layer.module.stride[0])  # stride
+            prev_arr = curr_arr
+            curr_arr = []
+        elif layer.class_name == 'Linear' and include_lin:
+            if prev_arr:
+                prev_arr[6] = last
+                topo.topo_arrays.append(prev_arr)
+                idx += 1
+            curr_arr.append(layer.class_name+'_{}'.format(idx))  # layer name
+            print(layer.input_size)
+            curr_arr.append(1)  # IFMAP height
+            curr_arr.append(1)  # IFMAP width
+            curr_arr.append(1)  # filter height
+            curr_arr.append(1)  # filter width
+            curr_arr.append(layer.input_size[1])  # channels
+            curr_arr.append(0)  # output channels (num filters)
+            curr_arr.append(1)  # stride
+            prev_arr = curr_arr
+            curr_arr = []
+        last = layer.input_size[1]
+    # last layer
+    if prev_arr:
+        prev_arr[6] = last
+        topo.topo_arrays.append(prev_arr)
+
+    topo.topo_load_flag = True
+    topo.write_topo_file(path=path, filename=filename)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-m', metavar='PyTorch module name', type=str,
+                        default='alexnet',
+                        help="Class name of PyTorch module to turn into SCALESim topology"
+                        )
+    parser.add_argument('-p', metavar='Path to directory', type=str,
+                        default='../topologies/custom',
+                        help="Directory at which to save the created topology file"
+                        )
+    parser.add_argument('-f', metavar='File name', type=str,
+                        default='alexnet_custom.csv',
+                        help="File name for SCALESim topology"
+                        )
+    parser.add_argument('-l', metavar='Include linear layers?', type=bool,
+                        default=False,
+                        help="Boolean deciding whether or not to include nn.Linear layers in topology"
+                        )
+
+
+    args = parser.parse_args()
+    if not os.path.exists(base_dir):
+        os.mkdir(base_dir)
+
+    model_name = args.m
+    print(model_name)
+    print(args.p)
+    try:
+        model = getattr(models, model_name)(pretrained=True)
+    except:
+        print('PyTorch module does not exist')
+        exit()
+
+    path = args.p
+    file_name = args.f
+    include_lin = args.l
+
+    create_conv_topo(model, path=path, filename=file_name, include_lin=include_lin)