#13127: Remove shape_without_padding() pybinding and usage

tenstorrent · Oct 2, 2024 · 9ddd6e1 · 9ddd6e1
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commit 9ddd6e1
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Showing 9 changed files with 24 additions and 34 deletions.
diff --git a/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_large_new_conv_api.py b/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_large_new_conv_api.py
@@ -703,7 +703,7 @@ def first_run(self, input_tensor, device, batch_size, ops_parallel_config) -> tt
         print(f"=================================== layer: 4, module: 3")
         x, x_height, x_width = self.layer4_module3(x, device, batch_size, x_height, x_width, conv_op_cache)
 
-        unpadded_shape = x.shape_without_padding()
+        unpadded_shape = x.shape
         x = ttnn.untilize_with_unpadding(
             x,
             output_tensor_end=(
@@ -796,7 +796,7 @@ def first_run(self, input_tensor, device, batch_size, ops_parallel_config) -> tt
         )
 
         x = self.fc(x)
-        desired_shape = list(x.shape_without_padding())
+        desired_shape = list(x.shape)
         desired_shape[-1] = 1000
         x = ttnn.untilize_with_unpadding(
             x,
@@ -934,7 +934,7 @@ def optimized_run(self, input_tensor, device, batch_size, ops_parallel_config, c
         print(f"=================================== layer: 4, module: 3")
         x, x_height, x_width = self.layer4_module3(x, device, batch_size, x_height, x_width, conv_op_cache)
 
-        unpadded_shape = x.shape_without_padding()
+        unpadded_shape = x.shape
         x = ttnn.untilize_with_unpadding(
             x,
             output_tensor_end=(
@@ -1028,7 +1028,7 @@ def optimized_run(self, input_tensor, device, batch_size, ops_parallel_config, c
         )
 
         x = self.fc(x)
-        desired_shape = list(x.shape_without_padding())
+        desired_shape = list(x.shape)
         desired_shape[-1] = 1000
         x = ttnn.untilize_with_unpadding(
             x,

diff --git a/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_new_conv_api.py b/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_new_conv_api.py
@@ -1023,7 +1023,7 @@ def run(self, input_tensor, device, ops_parallel_config, conv_op_cache={}) -> tt
         )
 
         if is_wormhole_b0() and self.batch_size == 16:
-            xshape = x.shape_without_padding()
+            xshape = x.shape
             x = ttnn.slice(x, [0, 0, 0, 0], [xshape[0], xshape[1], xshape[2], xshape[3]])
 
         layer4_module1_input_shape = ttnn.Shape(x.shape.with_tile_padding())
@@ -1119,7 +1119,7 @@ def run(self, input_tensor, device, ops_parallel_config, conv_op_cache={}) -> tt
         )
         x = ttnn.to_memory_config(x, width_sharded_mem_config)
 
-        unpadded_shape = x.shape_without_padding()
+        unpadded_shape = x.shape
         x = ttnn.untilize_with_unpadding(
             x,
             output_tensor_end=(
@@ -1195,7 +1195,7 @@ def run(self, input_tensor, device, ops_parallel_config, conv_op_cache={}) -> tt
         )
 
         x = self.fc(x)
-        desired_shape = list(x.shape_without_padding())
+        desired_shape = list(x.shape)
         desired_shape[-1] = 1000
         x = ttnn.untilize_with_unpadding(
             x,

diff --git a/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_xlarge_new_conv_api.py b/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_xlarge_new_conv_api.py
@@ -699,7 +699,7 @@ def first_run(self, input_tensor, device, batch_size, ops_parallel_config) -> tt
         print(f"=================================== layer: 4, module: 3")
         x, x_height, x_width = self.layer4_module3(x, device, batch_size, x_height, x_width, conv_op_cache)
 
-        unpadded_shape = x.shape_without_padding()
+        unpadded_shape = x.shape
         x = ttnn.untilize_with_unpadding(
             x,
             output_tensor_end=(
@@ -792,7 +792,7 @@ def first_run(self, input_tensor, device, batch_size, ops_parallel_config) -> tt
         )
 
         x = self.fc(x)
-        desired_shape = list(x.shape_without_padding())
+        desired_shape = list(x.shape)
         desired_shape[-1] = 1000
         x = ttnn.untilize_with_unpadding(
             x,
@@ -916,7 +916,7 @@ def optimized_run(self, input_tensor, device, batch_size, ops_parallel_config, c
         x, x_height, x_width = self.layer4_module2(x, device, batch_size, x_height, x_width, conv_op_cache)
         x, x_height, x_width = self.layer4_module3(x, device, batch_size, x_height, x_width, conv_op_cache)
 
-        unpadded_shape = x.shape_without_padding()
+        unpadded_shape = x.shape
         x = ttnn.untilize_with_unpadding(
             x,
             output_tensor_end=(
@@ -1010,7 +1010,7 @@ def optimized_run(self, input_tensor, device, batch_size, ops_parallel_config, c
         )
 
         x = self.fc(x)
-        desired_shape = list(x.shape_without_padding())
+        desired_shape = list(x.shape)
         desired_shape[-1] = 1000
         x = ttnn.untilize_with_unpadding(
             x,

diff --git a/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_xlarge_new_conv_api_24.py b/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_xlarge_new_conv_api_24.py
@@ -686,7 +686,7 @@ def first_run(self, input_tensor, device, batch_size, ops_parallel_config) -> tt
             eltwise_binary_out_in_place=True,
         )
 
-        unpadded_shape = x.shape_without_padding()
+        unpadded_shape = x.shape
         x = ttnn.slice(
             x,
             (0, 0, 0, 0),
@@ -728,7 +728,7 @@ def first_run(self, input_tensor, device, batch_size, ops_parallel_config) -> tt
         print(f"=================================== layer: 4, module: 3")
         x, x_height, x_width = self.layer4_module3(x, device, batch_size, x_height, x_width, conv_op_cache)
 
-        unpadded_shape = x.shape_without_padding()
+        unpadded_shape = x.shape
         x = ttnn.untilize_with_unpadding(
             x,
             output_tensor_end=(
@@ -821,7 +821,7 @@ def first_run(self, input_tensor, device, batch_size, ops_parallel_config) -> tt
         )
 
         x = self.fc(x)
-        desired_shape = list(x.shape_without_padding())
+        desired_shape = list(x.shape)
         desired_shape[-1] = 1000
         x = ttnn.untilize_with_unpadding(
             x,
@@ -941,7 +941,7 @@ def optimized_run(self, input_tensor, device, batch_size, ops_parallel_config, c
         x, x_height, x_width = self.layer4_module2(x, device, batch_size, x_height, x_width, conv_op_cache)
         x, x_height, x_width = self.layer4_module3(x, device, batch_size, x_height, x_width, conv_op_cache)
 
-        unpadded_shape = x.shape_without_padding()
+        unpadded_shape = x.shape
         x = ttnn.untilize_with_unpadding(
             x,
             output_tensor_end=(
@@ -1035,7 +1035,7 @@ def optimized_run(self, input_tensor, device, batch_size, ops_parallel_config, c
         )
 
         x = self.fc(x)
-        desired_shape = list(x.shape_without_padding())
+        desired_shape = list(x.shape)
         desired_shape[-1] = 1000
         x = ttnn.untilize_with_unpadding(
             x,

diff --git a/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_xxlarge_new_conv_api.py b/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_xxlarge_new_conv_api.py
@@ -787,7 +787,7 @@ def first_run(self, input_tensor, device, batch_size, ops_parallel_config) -> tt
         print(f"=================================== layer: 4, module: 3")
         x, x_height, x_width = self.layer4_module3(x, device, batch_size, x_height, x_width, conv_op_cache)
 
-        unpadded_shape = x.shape_without_padding()
+        unpadded_shape = x.shape
         x = ttnn.untilize_with_unpadding(
             x,
             output_tensor_end=(
@@ -882,7 +882,7 @@ def first_run(self, input_tensor, device, batch_size, ops_parallel_config) -> tt
         )
 
         x = self.fc(x)
-        desired_shape = list(x.shape_without_padding())
+        desired_shape = list(x.shape)
         desired_shape[-1] = 1000
         x = ttnn.untilize_with_unpadding(
             x,
@@ -1016,7 +1016,7 @@ def optimized_run(self, input_tensor, device, batch_size, ops_parallel_config, c
         x, x_height, x_width = self.layer4_module2(x, device, batch_size, x_height, x_width, conv_op_cache)
         x, x_height, x_width = self.layer4_module3(x, device, batch_size, x_height, x_width, conv_op_cache)
 
-        unpadded_shape = x.shape_without_padding()
+        unpadded_shape = x.shape
         x = ttnn.untilize_with_unpadding(
             x,
             output_tensor_end=(
@@ -1112,7 +1112,7 @@ def optimized_run(self, input_tensor, device, batch_size, ops_parallel_config, c
         )
 
         x = self.fc(x)
-        desired_shape = list(x.shape_without_padding())
+        desired_shape = list(x.shape)
         desired_shape[-1] = 1000
         x = ttnn.untilize_with_unpadding(
             x,

diff --git a/models/experimental/vgg/vgg_utils.py b/models/experimental/vgg/vgg_utils.py
@@ -22,7 +22,7 @@ def format_tensor(x, target_layout, device, output_mem_config, pad_value=0.0):
         else:
             return ttnn.tilize(x, memory_config=output_mem_config, use_multicore=True)
     elif x.get_layout() == ttnn.TILE_LAYOUT and target_layout == ttnn.ROW_MAJOR_LAYOUT:
-        if x.shape.with_tile_padding() != x.shape_without_padding():
+        if x.shape.with_tile_padding() != x.shape:
             return ttnn.format_output_tensor(x, x.shape_without_padding(), device, target_layout, output_mem_config)
         else:
             return ttnn.untilize(x, memory_config=output_mem_config, use_multicore=True)

diff --git a/tests/tt_eager/python_api_testing/unit_testing/misc/test_downsample.py b/tests/tt_eager/python_api_testing/unit_testing/misc/test_downsample.py
@@ -147,8 +147,8 @@ def test_run_downsample(
     out_shape = [1, 1, _nearest_y(batch_size * output_height * output_width, 32), input_channels]
     assert out_shape == list(out.shape.with_tile_padding())
     out_shape_unpadded = [1, 1, batch_size * output_height * output_width, input_channels]
-    assert out_shape_unpadded == list(out.shape_without_padding())
-    out = ttnn.format_output_tensor(out, out.shape_without_padding(), device, ttnn.ROW_MAJOR_LAYOUT)
+    assert out_shape_unpadded == list(out.shape)
+    out = ttnn.format_output_tensor(out, out.shape, device, ttnn.ROW_MAJOR_LAYOUT)
     out = out.cpu()
 
     out_debug = out

diff --git a/ttnn/cpp/pybind11/pytensor.cpp b/ttnn/cpp/pybind11/pytensor.cpp
@@ -1591,16 +1591,6 @@ void pytensor_module(py::module &m_tensor) {
 
                 dtype = tt_tensor.get_dtype()
         )doc")
-        .def(
-            "shape_without_padding",
-            [](const Tensor &self) { return Shape{self.get_legacy_shape().without_padding()}; },
-            R"doc(
-            Get shape without padding of TT Tensor.
-
-            .. code-block:: python
-
-                dtype = tt_tensor.shape_without_padding()
-        )doc")
         .def(
             "reshape",
             [](Tensor &self, int N, int C, int H, int W) { return self.reshape(N, C, H, W); },

diff --git a/ttnn/tt_lib/fused_ops/max_pool.py b/ttnn/tt_lib/fused_ops/max_pool.py
@@ -28,7 +28,7 @@ def run_max_pool_on_device_wrapper(
     reshape_2d=False,
 ):
     def max_pool_2d(x):
-        # x_shape_nopad = x.shape_without_padding()
+        # x_shape_nopad = x.shape
         # out_shape_nopad = compute_max_pool_shape(kernel_size, stride, padding, x_shape_nopad)
         # if reshape_2d and channels_last:
         #     x = x.reshape(x_shape_nopad[0], 1, x_shape_nopad[1] * x_shape_nopad[2], x_shape_nopad[3])