Revert "Update samples"

This reverts commit 7a8697e.

Author: ccummingsNV

examples/README.md

@@ -21,7 +21,7 @@ Welcome to SlangPy examples! Here you'll find well written and documented exampl
 |-------------|-------------|
 | [first_function](https://slangpy.shader-slang.org/en/latest/src/basics/firstfunctions.html) | Most basic use of SlangPy to call a single function. |
 | [return_type](https://slangpy.shader-slang.org/en/latest/src/basics/returntype.html) | Use a different return type for a function. |
-| [buffers](https://slangpy.shader-slang.org/en/latest/src/basics/buffers.html) | Tensor creation/use. |
+| [buffers](https://slangpy.shader-slang.org/en/latest/src/basics/buffers.html) | NDBuffer creation/use. |
 | [textures](https://slangpy.shader-slang.org/en/latest/src/basics/textures.html) | Simple manipulation of texture data using SlangPy. |
 | [nested](https://slangpy.shader-slang.org/en/latest/src/basics/nested.html) | Shows how to use Python dictionaries to pass nested data in SOA form. |
 | [type_methods](https://slangpy.shader-slang.org/en/latest/src/basics/typemethods.html) | Use of InstanceList, InstanceBuffer and get_this to invoke methods of a type. |

examples/autodiff/main.py

@@ -28,7 +28,7 @@
 
 # Attach gradients to the result, and set them to 1 for the backward pass
 result = result.with_grads()
-result.grad.copy_from_numpy(np.array([1, 1, 1, 1], dtype=np.float32))
+result.grad.storage.copy_from_numpy(np.array([1, 1, 1, 1], dtype=np.float32))
 
 # Call the backwards version of module.polynomial
 # This will read the grads from _result, and write the grads to x

examples/buffers/main.py

@@ -18,11 +18,11 @@
 # Load module
 module = spy.Module.load_from_file(device, "example.slang")
 
-# Create a couple of 2D 16x16 tensors
-image_1 = spy.Tensor.empty(device,shape=(16, 16), dtype=module.Pixel)
-image_2 = spy.Tensor.empty(device,shape=(16, 16), dtype=module.Pixel)
+# Create a couple of 2D 16x16 buffers
+image_1 = spy.NDBuffer(device, dtype=module.Pixel, shape=(16, 16))
+image_2 = spy.NDBuffer(device, dtype=module.Pixel, shape=(16, 16))
 
-# Use a cursor to fill the first tensor with readable structured data.
+# Use a cursor to fill the first buffer with readable structured data.
 cursor_1 = image_1.cursor()
 for x in range(16):
     for y in range(16):
@@ -35,8 +35,8 @@
         )
 cursor_1.apply()
 
-# Use the fact that we know the tensors are just 16x16 grids of 3 floats
-# to just populate the 2nd tensor straight from random numpy array
+# Use the fact that we know the buffers are just 16x16 grids of 3 floats
+# to just populate the 2nd buffer straight from random numpy array
 image_2.copy_from_numpy(0.1 * np.random.rand(16 * 16 * 3).astype(np.float32))
 
 # Call the module's add function

examples/sdf-match/README.md

@@ -75,7 +75,7 @@ input, input_data = generate_input(samplesSize)
 params, params_grad, params_data, params_grad_data = generate_init_params(network_shape)
 
 # Initialize Adam optimizer state
-adam_state = spy.Tensor.empty(app.device, dtype=module.AdamState, shape=(param_size,))
+adam_state = spy.NDBuffer(app.device, dtype=module.AdamState, shape=(param_size,))
 ```
 
 ### 2. Main Loop

examples/sdf-match/main.py

@@ -49,8 +49,8 @@ def generate_init_params(network_shape: list[int]):
 
     params_grad_data = np.zeros((params_size,), dtype=np.float32)
 
-    params = spy.Tensor.from_numpy(app.device, params_data)
-    params_grad = spy.Tensor.from_numpy(app.device, params_grad_data)
+    params = spy.NDBuffer.from_numpy(app.device, params_data)
+    params_grad = spy.NDBuffer.from_numpy(app.device, params_grad_data)
     return params, params_grad, params_data, params_grad_data
 
 
@@ -63,8 +63,8 @@ def generate_input(samplesSize: int, input_size: int):
 
 
 # construct the network, the network is a tiny MLP with 1 hidden layer
-def construct_network(params: spy.Tensor, params_grad: spy.Tensor):
-    tiny_mlp = spy.Tensor.empty(app.device, dtype=module.TinyMLP_Params, shape=(1,))
+def construct_network(params: spy.NDBuffer, params_grad: spy.NDBuffer):
+    tiny_mlp = spy.NDBuffer(app.device, dtype=module.TinyMLP_Params, shape=(1,))
     tiny_mlp_cursor = tiny_mlp.cursor()
     tiny_mlp_cursor[0].write(
         {
@@ -78,8 +78,8 @@ def construct_network(params: spy.Tensor, params_grad: spy.Tensor):
 
 def trainMLP(
     iter: int,
-    tiny_mlp_params: spy.Tensor,
-    adam_state: spy.Tensor,
+    tiny_mlp_params: spy.NDBuffer,
+    adam_state: spy.NDBuffer,
     input: spy.Tensor,
     batch_size: int,
 ):
@@ -113,7 +113,7 @@ def trainMLP(
 # Initialize Adam state, the adam state is per-parameter state, so the size is the same as the params.
 # Call slang function `clearAdamState` to clear the adam state.
 param_size = get_weight_size(network_shape) + get_bias_size(network_shape)
-adam_state = spy.Tensor.empty(app.device, dtype=module.AdamState, shape=(param_size,))
+adam_state = spy.NDBuffer(app.device, dtype=module.AdamState, shape=(param_size,))
 module.clearAdamState(adam_state)
 
 # Main loop

examples/soft-rasterizer/rasterizer2d.slang

@@ -212,15 +212,15 @@ void generate_gradients(Camera camera, in Tensor<float2, 1> primal, AtomicTensor
 {
     // Wrap the vertices in a diffPair so we can use autodiff to get the
     // partial derivatives and calculate gradients
-    float2 vertices[3] = { primal.load(0), primal.load(1), primal.load(2) };
+    float2 vertices[3] = { primal.get({0}), primal.get({1}), primal.get({2}) };
     var dVertices = diffPair(vertices);
 
     bwd_diff(loss)(camera, dVertices, pixelCoord, reference, 1.0);
 
     // Accumulate (add) gradients for this point into the gradients tensor.
-    grad.add(0, dVertices.d[0]);
-    grad.add(1, dVertices.d[1]);
-    grad.add(2, dVertices.d[2]);
+    grad.set({0}, dVertices.d[0]);
+    grad.set({1}, dVertices.d[1]);
+    grad.set({2}, dVertices.d[2]);
 }
 
 

examples/toy-restir/main.py

@@ -59,14 +59,14 @@
 
 # Initialize the random generators.
 np.random.seed(0)
-pathRandom = spy.Tensor.empty(device,shape=(imageHeight, imageWidth), dtype=module.RandomStream)
-risRandom = spy.Tensor.empty(device,shape=(imageHeight, imageWidth), dtype=module.RandomStream)
+pathRandom = spy.NDBuffer(device, dtype=module.RandomStream, shape=(imageHeight, imageWidth))
+risRandom = spy.NDBuffer(device, dtype=module.RandomStream, shape=(imageHeight, imageWidth))
 risRandom.copy_from_numpy(np.random.randint(2**32, dtype=np.uint32, size=(imageHeight, imageWidth)))
 
 # Create reservoirs.
-initialOutput = spy.Tensor.empty(device,shape=(imageHeight, imageWidth), dtype=module.Reservoir)
-temporalOutput = spy.Tensor.empty(device,shape=(imageHeight, imageWidth), dtype=module.Reservoir)
-spatialOutput = spy.Tensor.empty(device,shape=(imageHeight, imageWidth), dtype=module.Reservoir)
+initialOutput = spy.NDBuffer(device, dtype=module.Reservoir, shape=(imageHeight, imageWidth))
+temporalOutput = spy.NDBuffer(device, dtype=module.Reservoir, shape=(imageHeight, imageWidth))
+spatialOutput = spy.NDBuffer(device, dtype=module.Reservoir, shape=(imageHeight, imageWidth))
 
 # Prepare the image.
 tex = device.create_texture(

examples/toy-restir/toy-restir.slang

@@ -360,7 +360,7 @@ void sampleAndMergeInitialCandidate(int2 pixel, int frame, int2 imageSize, int s
     reservoir.c = 1.0f;
 }
 
-void performTemporalReuse(int2 pixel, int frame, inout Reservoir outReservoir, in RWTensor<Reservoir, 2> initialSampleReservoirs, in RWTensor<Reservoir, 2> temporalReservoirs, int2 imageSize,
+void performTemporalReuse(int2 pixel, int frame, inout Reservoir outReservoir, in RWNDBuffer<Reservoir, 2> initialSampleReservoirs, in RWNDBuffer<Reservoir, 2> temporalReservoirs, int2 imageSize,
                          inout RandomStream risRandom)
 {
     float t = (float)frame;
@@ -405,7 +405,7 @@ void performTemporalReuse(int2 pixel, int frame, inout Reservoir outReservoir, i
     outReservoir = result;
 }
 
-void performSpatialReuse(int2 pixel, inout Reservoir outReservoir, in RWTensor<Reservoir, 2> reservoirs, int2 imageSize, inout RandomStream risRandom)
+void performSpatialReuse(int2 pixel, inout Reservoir outReservoir, in RWNDBuffer<Reservoir, 2> reservoirs, int2 imageSize, inout RandomStream risRandom)
 {
     // Get current pixel's reservoir.
     let currentReservoir = reservoirs[pixel];

examples/type_methods/extend_instancelists.py

@@ -25,8 +25,8 @@ class MyParticles(spy.InstanceList):
     def __init__(self, name: str, count: int):
         super().__init__(module.Particle.as_struct())
         self.name = name
-        self.position = spy.Tensor.empty(device,shape=(count,), dtype=module.float3)
-        self.velocity = spy.Tensor.empty(device,shape=(count,), dtype=module.float3)
+        self.position = spy.NDBuffer(device, dtype=module.float3, shape=(count,))
+        self.velocity = spy.NDBuffer(device, dtype=module.float3, shape=(count,))
 
     def print_particles(self):
         print(self.name)

examples/type_methods/main.py

@@ -17,14 +17,14 @@
 module = spy.Module.load_from_file(device, "example.slang")
 
 # Create buffer of particles (.as_struct is used to make python typing happy!)
-particles = spy.InstanceTensor(struct=module.Particle.as_struct(), shape=(10,))
+particles = spy.InstanceBuffer(struct=module.Particle.as_struct(), shape=(10,))
 
 # Construct every particle with position of 0, and use slangpy's rand_float
 # functionality to supply a different rand vector for each one.
 particles.construct(p=spy.float3(0), v=spy.rand_float(-1, 1, 3))
 
 # Print all the particles by breaking them down into groups of 6 floats
-result_cursor = particles.tensor.cursor()
+result_cursor = particles.buffer.cursor()
 
 for i in range(particles.shape[0]):
     instance = result_cursor[i].read()
@@ -33,7 +33,7 @@
 # Update the particles
 particles.update(0.1)
 
-result_cursor = particles.tensor.cursor()
+result_cursor = particles.buffer.cursor()
 # Print all the particles by breaking them down into groups of 6 floats
 for i in range(particles.shape[0]):
     instance = result_cursor[i].read()