mnist

test/test_test_mnist.py

@@ -57,6 +57,7 @@ def __init__(self):
     self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5).to(xm.xla_device())
     self.bn2 = torch.nn.BatchNorm2d(20).to(xm.xla_device())
     self.fc1 = torch.nn.Linear(500, 50).to(xm.xla_device())
+    self.fc2 = torch.nn.Linear(50, 10).to(xm.xla_device())
     # self.fc1 = torch.nn.Linear(320, 50).to(xm.xla_device())
     # self.linear = torch.nn.Linear(10, 20).to(xm.xla_device())
     # self.linear2 = torch.nn.Linear(20, 30).to(xm.xla_device())
@@ -74,7 +75,9 @@ def forward(self, x):
     x = self.bn2(x)
     x = torch.flatten(x, 1)
     x = F.relu(self.fc1(x))
-    return x
+    x = self.fc2(x)
+    return F.log_softmax(x, dim=1)
+    # return x
 
 class SimpleWithLinear(torch.nn.Module):
   def __init__(self):
@@ -328,7 +331,9 @@ def body_fn(upper, lower, one_value, x, input_value, output_value, *args):
   # output_value = torch.zeros([16,10,28,28], dtype=torch.float32, device=device) # conv2d
   # output_value = torch.zeros([16,10,14,14], dtype=torch.float32, device=device) # conv2d+mnist-treat # conv1 + bn1
   # output_value = torch.zeros([16,20,5,5], dtype=torch.float32, device=device) # conv1 + bn1 + conv2
-  output_value = torch.zeros([16,500], dtype=torch.float32, device=device) # conv1 + bn1 + conv2 + bn2 + flatten1
+  # output_value = torch.zeros([16,500], dtype=torch.float32, device=device) # conv1 + bn1 + conv2 + bn2 + flatten1
+  # output_value = torch.zeros([16,50], dtype=torch.float32, device=device) # conv1 + bn1 + conv2 + bn2 + flatten1 + fc1
+  output_value = torch.zeros([16,10], dtype=torch.float32, device=device) # conv1 + bn1 + conv2 + bn2 + flatten1 + fc1
 
 
   additional_inputs = []
@@ -377,8 +382,11 @@ def body_fn(upper, lower, one_value, x, input_value, output_value, *args):
   ##### conv1 + bn1 + conv2
   # upper__, lower__, one_value__, torch_add_res__, input_value__, weight1__, bias1__, bw1, bw11, bb1, bb11, p1, p2, output_value_real__, = _xla_while_loop(
   ##### conv1 + bn1 + conv2 + bn2
-  upper__, lower__, one_value__, torch_add_res__, input_value__, weight1__, bias1__, bw1, bw11, bb1, bb11, p1, p2, p3, p4, p5, p6, output_value_real__, = _xla_while_loop(
-
+  # upper__, lower__, one_value__, torch_add_res__, input_value__, weight1__, bias1__, bw1, bw11, bb1, bb11, p1, p2, p3, p4, p5, p6, output_value_real__, = _xla_while_loop(
+  ##### conv1 + bn1 + conv2 + bn2 + fc1
+  # upper__, lower__, one_value__, torch_add_res__, input_value__, weight1__, bias1__, bw1, bw11, bb1, bb11, p1, p2, p3, p4, p5, p6, p7, p8, output_value_real__, = _xla_while_loop(
+  ##### conv1 + bn1 + conv2 + bn2 + fc1 + fc2 + softmax
+  upper__, lower__, one_value__, torch_add_res__, input_value__, weight1__, bias1__, bw1, bw11, bb1, bb11, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, output_value_real__, = _xla_while_loop(
       cond_fn, body_fn,
       (upper, lower, one_value, init_val, l_in_0, output_value), tuple(additional_inputs))
       # (upper, lower, one_value, init_val, l_in_0, output_value), ())

torch_xla/csrc/init_python_bindings.cpp

@@ -941,7 +941,8 @@ class PyLoweringContext {
       // int64_t parameter_idx = 7; // conv2d
       // int64_t parameter_idx = 11; // conv2d+mnist-treat // conv1 + bn1
       // int64_t parameter_idx = 13; // conv1 + bn1 + conv2
-      int64_t parameter_idx = 19; // conv1 + bn1 + conv2 + bn2
+      // int64_t parameter_idx = 19; // conv1 + bn1 + conv2 + bn2
+      int64_t parameter_idx = 21; // conv1 + bn1 + conv2 + bn2
       // int64_t parameter_idx = 9; // linear
       // int64_t parameter_idx = tensors.size();
       for (auto& additional_input_tensor : additional_inputs_list) {