Fix LayerNorm fp16 precision

Author: HolyWu

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -134,10 +134,6 @@ def aten_ops_batch_norm_legit_no_training(
     capability_validator=one_user_validator,
     supports_dynamic_shapes=True,
 )
-@dynamo_tensorrt_converter(
-    torch.ops.aten.layer_norm.default, supports_dynamic_shapes=True
-)
-@dynamo_tensorrt_converter(torch.ops.aten.layer_norm, supports_dynamic_shapes=True)
 @enforce_tensor_types(
     {
         0: (TRTTensor,),
@@ -157,11 +153,9 @@ def aten_ops_layer_norm(
         name,
         input=args[0],
         normalized_shape=args[1],
-        weight=args_bounds_check(args, 2, 1.0),
-        bias=args_bounds_check(args, 3, 0.0),
-        eps=args_bounds_check(args, 4, 1e-05),
-        cudnn_enable=args_bounds_check(args, 5, True),
-        return_mean_rstd=(target == torch.ops.aten.native_layer_norm.default),
+        weight=args_bounds_check(args, 2),
+        bias=args_bounds_check(args, 3),
+        eps=args[4],
     )
 
 

py/torch_tensorrt/dynamo/conversion/impl/normalization/ops.py

@@ -159,49 +159,42 @@ def layer_norm(
     name: str,
     input: TRTTensor,
     normalized_shape: List[int],
-    weight: Optional[Union[torch.Tensor, np.ndarray]],
-    bias: Optional[Union[torch.Tensor, np.ndarray]],
+    weight: Optional[Union[TRTTensor, torch.Tensor, np.ndarray]],
+    bias: Optional[Union[TRTTensor, torch.Tensor, np.ndarray]],
     eps: float,
-    cudnn_enable: bool,
-    return_mean_rstd: bool,
-) -> Union[TRTTensor, Tuple[TRTTensor, torch.Tensor, torch.Tensor]]:
+) -> Tuple[TRTTensor, torch.Tensor, torch.Tensor]:
     dims = list(range(len(input.shape) - len(normalized_shape), len(input.shape)))
     axes = get_axes_for_reduce_op(dims)
-    weight = get_trt_tensor(ctx, weight, f"{name}_weight")
-    bias = get_trt_tensor(ctx, bias, f"{name}_bias")
+
+    weight = get_trt_tensor(
+        ctx, weight if weight is not None else 1.0, f"{name}_weight"
+    )
+    bias = get_trt_tensor(ctx, bias if bias is not None else 0.0, f"{name}_bias")
+
     # Cast weight and bias to have same dtype as input
     weight = cast_trt_tensor(
         ctx, weight, input.dtype, f"{name}_weight_cast", target, source_ir
     )
     bias = cast_trt_tensor(
         ctx, bias, input.dtype, f"{name}_bias_cast", target, source_ir
     )
+
     if tuple(input.shape) != tuple(weight.shape):
         weight = impl.slice.expand(
             ctx, target, source_ir, f"{name}_expand_weight", weight, input.shape
         )
+
     if tuple(input.shape) != tuple(bias.shape):
         bias = impl.slice.expand(
             ctx, target, source_ir, f"{name}_expand_bias", bias, input.shape
         )
-    strongly_typed_network = False
-    if ctx.net.get_flag(trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED):
-        weight = cast_trt_tensor(ctx, weight, input.dtype, name)
-        bias = cast_trt_tensor(ctx, bias, input.dtype, name)
-        strongly_typed_network = True
-
-    layer_norm = ctx.net.add_normalization(input, weight, bias, axes)
-    layer_norm.epsilon = eps
-    # compute_precision ignored for strongly typed network.
-    if not strongly_typed_network:
-        layer_norm.compute_precision = input.dtype
-    set_layer_name(layer_norm, target, f"{name}_layer_norm", source_ir)
 
-    if return_mean_rstd:
-        # return fake mean and rstd for now
-        return layer_norm.get_output(0), None, None
+    layer = ctx.net.add_normalization(input, weight, bias, axes)
+    layer.epsilon = eps
+    set_layer_name(layer, target, name, source_ir)
 
-    return layer_norm.get_output(0)
+    # return fake mean and rstd for now
+    return layer.get_output(0), None, None
 
 
 def native_group_norm(

tests/py/dynamo/conversion/test_layer_norm_aten.py

@@ -6,78 +6,51 @@
 from .harness import DispatchTestCase
 
 
-class TestLayerNormConverter(DispatchTestCase):
+class TestNativeLayerNormConverter(DispatchTestCase):
     @parameterized.expand(
         [
-            (
-                (5, 3, 2, 4),
-                [
-                    4,
-                ],
-            ),
-            ((5, 3, 2, 4), [2, 4]),
-            ((5, 3, 2, 4), [3, 2, 4]),
-            ((5, 3, 2, 4), [5, 3, 2, 4]),
+            ((2, 4, 6), [6]),
+            ((2, 4, 6), [4, 6]),
+            ((2, 4, 6), [2, 4, 6]),
         ]
     )
-    def test_layer_norm(self, input_shape, normalized_shape, eps=1e-05):
+    def test_layer_norm_1d(self, input_shape, normalized_shape):
         class LayerNorm(torch.nn.Module):
             def forward(self, x):
-                return torch.ops.aten.layer_norm.default(
-                    x,
-                    normalized_shape,
-                    torch.randn(normalized_shape),
-                    torch.randn(normalized_shape),
-                    eps,
-                )
+                return torch.ops.aten.native_layer_norm.default(
+                    x, normalized_shape, None, None, 1e-05
+                )[0]
 
         inputs = [torch.randn(input_shape)]
-        self.run_test(
-            LayerNorm(),
-            inputs,
-        )
-
+        self.run_test(LayerNorm(), inputs, use_dynamo_tracer=True)
 
-class TestNativeLayerNormConverter(DispatchTestCase):
     @parameterized.expand(
         [
-            (
-                (5, 3, 2, 4),
-                [
-                    4,
-                ],
-            ),
+            ((5, 3, 2, 4), [4]),
             ((5, 3, 2, 4), [2, 4]),
             ((5, 3, 2, 4), [3, 2, 4]),
             ((5, 3, 2, 4), [5, 3, 2, 4]),
         ]
     )
-    def test_layer_norm(self, input_shape, normalized_shape, eps=1e-05):
+    def test_layer_norm_2d(self, input_shape, normalized_shape):
         class LayerNorm(torch.nn.Module):
-            def forward(self, x):
+            def forward(self, x, weight, bias):
                 return torch.ops.aten.native_layer_norm.default(
-                    x,
-                    normalized_shape,
-                    torch.randn(normalized_shape),
-                    torch.randn(normalized_shape),
-                    eps,
+                    x, normalized_shape, weight, bias, 1e-05
                 )[0]
 
-        inputs = [torch.randn(input_shape)]
-        self.run_test(
-            LayerNorm(),
-            inputs,
-        )
+        inputs = [
+            torch.randn(input_shape),
+            torch.randn(normalized_shape),
+            torch.randn(normalized_shape),
+        ]
+        self.run_test(LayerNorm(), inputs, use_dynamo_tracer=True)
 
     def test_layernorm_with_dynamic_shape(self):
         class LayerNorm(torch.nn.Module):
-            def forward(self, x):
+            def forward(self, x, weight, bias):
                 return torch.ops.aten.native_layer_norm.default(
-                    x,
-                    torch.tensor([3, 224, 224]),
-                    torch.ones((3, 224, 224)),
-                    torch.zeros((3, 224, 224)),
-                    1e-05,
+                    x, [3, 224, 224], weight, bias, 1e-05
                 )[0]
 
         input_specs = [
@@ -87,22 +60,19 @@ def forward(self, x):
                 opt_shape=(5, 3, 224, 224),
                 max_shape=(10, 3, 224, 224),
             ),
+            Input(dtype=torch.float32, shape=(3, 224, 224)),
+            Input(dtype=torch.float32, shape=(3, 224, 224)),
         ]
 
         self.run_test_with_dynamic_shape(
-            LayerNorm(),
-            input_specs,
+            LayerNorm(), input_specs, use_dynamo_tracer=True
         )
 
     def test_layernorm_with_dynamic_shape_1(self):
         class LayerNorm(torch.nn.Module):
-            def forward(self, x):
+            def forward(self, x, weight, bias):
                 return torch.ops.aten.native_layer_norm.default(
-                    x,
-                    torch.tensor([3]),
-                    torch.ones((3)),
-                    torch.zeros((3)),
-                    1e-05,
+                    x, [3], weight, bias, 1e-05
                 )[0]
 
         input_specs = [
@@ -112,29 +82,12 @@ def forward(self, x):
                 opt_shape=(3, 3, 3),
                 max_shape=(4, 5, 3),
             ),
+            Input(dtype=torch.float32, shape=(3,)),
+            Input(dtype=torch.float32, shape=(3,)),
         ]
 
         self.run_test_with_dynamic_shape(
-            LayerNorm(),
-            input_specs,
-        )
-
-    @parameterized.expand([((5, 3, 2, 4), [2, 4])])
-    def test_layer_norm_without_Scaling(self, input_shape, normalized_shape, eps=1e-05):
-        class LayerNorm(torch.nn.Module):
-            def forward(self, x):
-                return torch.ops.aten.native_layer_norm.default(
-                    x,
-                    normalized_shape,
-                    None,
-                    None,
-                    eps,
-                )[0]
-
-        inputs = [torch.randn(input_shape)]
-        self.run_test(
-            LayerNorm(),
-            inputs,
+            LayerNorm(), input_specs, use_dynamo_tracer=True
         )