support unet metainfo prop (#2544)

colossalai/fx/_meta_registrations.py

@@ -164,18 +164,9 @@ def meta_conv(
 
 
 @register_meta(aten._convolution.default)
-def meta_conv_1(
-    input_tensor: torch.Tensor,
-    weight: torch.Tensor,
-    bias: torch.Tensor,
-    stride: List[int],
-    padding: List[int],
-    dilation: List[int],
-    is_transposed: bool,
-    output_padding: List[int],
-    groups: int,
-    *extra_args
-):
+def meta_conv_1(input_tensor: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, stride: List[int],
+                padding: List[int], dilation: List[int], is_transposed: bool, output_padding: List[int], groups: int,
+                *extra_args):
     out = meta_conv(input_tensor, weight, bias, stride, padding, dilation, is_transposed, output_padding, groups)
     return out
 
@@ -233,11 +224,8 @@ def meta_cuda_rnn(
     if is_input_packed:
         out_shape = [batch_sizes_sum, out_size * num_directions]
     else:
-        out_shape = (
-            [mini_batch, seq_length, out_size * num_directions]
-            if batch_first
-            else [seq_length, mini_batch, out_size * num_directions]
-        )
+        out_shape = ([mini_batch, seq_length, out_size *
+                      num_directions] if batch_first else [seq_length, mini_batch, out_size * num_directions])
     output = input.new_empty(out_shape)
 
     cell_shape = [num_layers * num_directions, mini_batch, hidden_size]
@@ -372,6 +360,15 @@ def meta_ln_backward(dY: torch.Tensor, input: torch.Tensor, normalized_shape, me
     return dX, dgamma, dbeta
 
 
+# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/group_norm.cpp
+@register_meta(aten.native_group_norm_backward.default)
+def meta_gn_backward(dY: torch.Tensor, input: torch.Tensor, mean, rstd, gamma, N, C, HxW, group, grad_input_mask):
+    dX = torch.empty_like(input)
+    dgamma = torch.empty_like(gamma)
+    dbeta = torch.empty_like(gamma)
+    return dX, dgamma, dbeta
+
+
 # ================================== Misc ==========================================
 # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml
 @register_meta(aten.roll.default)

colossalai/fx/profiler/opcount.py

@@ -70,6 +70,19 @@ def bmm_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
     return flops
 
 
+def baddbmm_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
+    """
+    Count flops for the baddbmm(batch add and batch matmul) operation.
+    """
+    # Inputs = [input, batch1, batch2]
+    # out = input + batch1 x batch2
+    assert len(inputs) == 3, len(inputs)
+    n, c, t = inputs[1].shape
+    d = inputs[2].shape[-1]
+    flops = n * c * t * d
+    return flops
+
+
 def conv_flop_count(
     x_shape: List[int],
     w_shape: List[int],
@@ -196,6 +209,7 @@ if version.parse(torch.__version__) >= version.parse('1.12.0'):
         aten.matmul.default: matmul_flop_jit,
         aten.addmm.default: addmm_flop_jit,
         aten.bmm.default: bmm_flop_jit,
+        aten.baddbmm.default: baddbmm_flop_jit,
 
     # convolution
         aten.convolution.default: conv_flop_jit,
@@ -209,6 +223,8 @@ if version.parse(torch.__version__) >= version.parse('1.12.0'):
         aten.cudnn_batch_norm_backward.default: partial(batchnorm_flop_jit, training=True),
         aten.native_layer_norm.default: norm_flop_counter(2, 0),
         aten.native_layer_norm_backward.default: norm_flop_counter(2, 0),
+        aten.native_group_norm.default: norm_flop_counter(2, 0),
+        aten.native_group_norm_backward.default: norm_flop_counter(2, 0),
 
     # pooling
         aten.avg_pool1d.default: elementwise_flop_counter(1, 0),
@@ -230,6 +246,8 @@ if version.parse(torch.__version__) >= version.parse('1.12.0'):
         aten._adaptive_avg_pool3d_backward.default: elementwise_flop_counter(0, 1),
         aten.embedding_dense_backward.default: elementwise_flop_counter(0, 1),
         aten.embedding.default: elementwise_flop_counter(1, 0),
+        aten.upsample_nearest2d.vec: elementwise_flop_counter(0, 1),
+        aten.upsample_nearest2d_backward.vec: elementwise_flop_counter(0, 1),
     }
 
     elementwise_flop_aten = [
@@ -251,6 +269,9 @@ if version.parse(torch.__version__) >= version.parse('1.12.0'):
         aten.mean.dim,
         aten.sub.Tensor,
         aten.sub_.Tensor,
+        aten.exp.default,
+        aten.sin.default,
+        aten.cos.default,
 
     # activation op
         aten.hardswish.default,