Add decomposition for Grid_Sample and Floor op

python/tvm/relay/op/contrib/forge/forge_passes.py

@@ -1891,7 +1891,12 @@ def callback(self, pre, post, node_map):
         act = node_map[self.act][0]
         axis = post.attrs.axis
         input_shape = [int(dim) for dim in pre.args[0].checked_type.shape]
-        adjusted_axes = [(ax - len(input_shape)) if ax >= 0 else ax for ax in axis]
+        if axis is None:
+            # PyTorch's `squeeze_()` removes all dimensions of size 1.
+            # Adjust all size-1 dimensions into negative axis indices.
+            adjusted_axes = [i - len(input_shape) for i, dim in enumerate(input_shape) if dim == 1]
+        else:
+            adjusted_axes = [(ax - len(input_shape)) if ax >= 0 else ax for ax in axis]
         assert all(ax < 0 for ax in adjusted_axes), "Invalid squeeze dimension: all axes must be negative."
         for ax in sorted(adjusted_axes):
             act = tvm.relay.squeeze(act, axis=[ax])
@@ -3953,7 +3958,203 @@ def callback(self, pre, post, node_map):
                 num_newaxis-=1
             return act
         return post
+
+class DecomposeGridSample(DFPatternCallback):
+    """Given an input and a flow-field grid, computes the output using input
+    values and pixel locations from grid. The grid_sample operation is typically used for sampling an input tensor (such as a feature map) 
+    at specified coordinates, which are provided by a grid tensor. This operation is widely used in tasks 
+    such as image warping, spatial transformers, and differentiable image resizing.
+    
+    Args:
+        im (torch.Tensor): Input feature map, shape (N, C, H, W)
+        grid (torch.Tensor): Point coordinates, shape (N, Hg, Wg, 2)
+        align_corners (bool): If set to True, the extrema (-1 and 1) are
+            considered as referring to the center points of the input’s
+            corner pixels. If set to False, they are instead considered as
+            referring to the corner points of the input’s corner pixels,
+            making the sampling more resolution agnostic.
+    
+    This decomposition handles the following main cases:
+    1. Image and grid split across batch: The input image and grid are split per batch, ensuring each sample is processed independently.
+    2. Grid coordinates handling: The grid coordinates (x, y) are extracted from the flow-field grid and mapped to pixel indices, considering 
+       the `align_corners` flag to adjust for how the extrema are interpreted.
+    3. Boundary handling: The image is padded to handle boundary conditions, ensuring that when grid coordinates point outside the valid image range,
+       they are clamped to the image bounds.
+    4. Bilinear interpolation: For each pixel in the grid, the surrounding pixels are retrieved using bilinear interpolation, which computes the 
+       weighted average of the nearest four pixels based on the grid's fractional coordinates.
+    5. Efficient computation: The image is reshaped and flattened for efficient indexing, and bilinear interpolation is computed using the 
+       precomputed weights and indices.
+    6. Results reconstruction: The interpolated values are computed for each channel and combined back into the final result, which is concatenated 
+       along the batch dimension.
+    
+    Returns:
+        torch.Tensor: A tensor with sampled points, shape (N, C, Hg, Wg)
+    This decomposition is adapted from the implementation in 
+    MMCV: https://mmcv.readthedocs.io/en/latest/_modules/mmcv/ops/point_sample.html
+    """
+
+    def __init__(self):
+        super().__init__(rewrite_once=True, require_type=True)
+
+        self.data = wildcard()
+        self.grid = wildcard()
+        self.grid_sample = is_op("image.grid_sample")(self.data, self.grid)
+        self.pattern = self.grid_sample
+
+    def callback(self, pre, post, node_map):
+        from tvm.relay.frontend.common import infer_shape
+
+        # Attributes extraction
+        data = node_map[self.data][0]  
+        grid = node_map[self.grid][0].args[0]  
+        mode = post.attrs["method"]
+        align_corners = post.attrs["align_corners"]
+
+        # Split the image and grid tensors across the batch size 
+        batch_size = infer_shape(data)[0]
+        split_im = tvm.relay.split(data, indices_or_sections=batch_size, axis=0)
+        split_grid = tvm.relay.split(grid, indices_or_sections=batch_size, axis=0)
+        results = []
+        for batch_idx in range(batch_size):
+            data = split_im[batch_idx]
+            grid = split_grid[batch_idx]
+            n, c, h, w = infer_shape(data)
+            gn, gh, gw, _ = infer_shape(grid)
+            assert len(infer_shape(data)) == 4 and len(infer_shape(grid)) == 4, "Length of data and grid shapes should be 4."
+
+            # Extract x and y components from the grid
+            x = tvm.relay.strided_slice(grid, begin=[0, 0, 0, 0], end=[gn, gh, gw, 1], strides=[1, 1, 1, 1])
+            x = tvm.relay.squeeze(x, axis=[3]) 
+            y = tvm.relay.strided_slice(grid, begin=[0, 0, 0, 1], end=[gn, gh, gw, 2], strides=[1, 1, 1, 1])
+            y = tvm.relay.squeeze(y, axis=[3])
+            # Map grid coordinates to image pixel indices
+            if align_corners:
+                x = ((x + tvm.relay.const(1.0)) / tvm.relay.const(2.0)) * tvm.relay.const(float(w - 1))
+                y = ((y + tvm.relay.const(1.0)) / tvm.relay.const(2.0)) * tvm.relay.const(float(h - 1))
+            else:
+                x = ((x + tvm.relay.const(1.0)) * tvm.relay.const(float(w)) - tvm.relay.const(1.0)) / tvm.relay.const(2.0)
+                y = ((y + tvm.relay.const(1.0)) * tvm.relay.const(float(h)) - tvm.relay.const(1.0)) / tvm.relay.const(2.0)
+
+            # Compute integer pixel indices for bilinear interpolation
+            if mode == 'bilinear':
+                x0 = tvm.relay.floor(x).astype("int32")
+                y0 = tvm.relay.floor(y).astype("int32")
+            else:
+                assert False, f"Unsupported mode: {mode}. Only 'bilinear' is supported."
+
+            # Compute next indices for interpolation
+            x1 = x0 + tvm.relay.const(1, dtype="int32")
+            y1 = y0 + tvm.relay.const(1, dtype="int32")
+
+            # Compute interpolation weights
+            if mode == 'bilinear':
+                wa = tvm.relay.expand_dims((tvm.relay.cast(x1, "float32") - x) * (tvm.relay.cast(y1, "float32") - y), axis=1)
+                wb = tvm.relay.expand_dims((tvm.relay.cast(x1, "float32") - x) * (y - tvm.relay.cast(y0, "float32")), axis=1)
+                wc = tvm.relay.expand_dims((x - tvm.relay.cast(x0, "float32")) * (tvm.relay.cast(y1, "float32") - y), axis=1)
+                wd = tvm.relay.expand_dims((x - tvm.relay.cast(x0, "float32")) * (y - tvm.relay.cast(y0, "float32")), axis=1)
+
+            # Add padding to the input image to handle boundary conditions
+            im_padded = tvm.relay.nn.pad(data, pad_width=((0, 0), (0, 0), (1, 1), (1, 1)))
+            padded_h = h + 2
+            padded_w = w + 2
+            temp_w = padded_w - 1
+            temp_h = padded_h - 1
+
+            # Adjust indices to include padding and clip within valid image bounds
+            pad_const = tvm.relay.const(1, dtype="int32")
+            x0 = tvm.relay.clip(x0 + pad_const, a_min=0, a_max=temp_w)
+            x1 = tvm.relay.clip(x1 + pad_const, a_min=0, a_max=temp_w)
+            y0 = tvm.relay.clip(y0 + pad_const, a_min=0, a_max=temp_h)
+            y1 = tvm.relay.clip(y1 + pad_const, a_min=0, a_max=temp_h)
+            # Precompute constants and reshape padded image for flattened indexing
+            padded_w_const = tvm.relay.const(padded_w, dtype="int32")
+            im_padded = tvm.relay.reshape(im_padded, (n, c, -1))
+
+            # Compute flattened indices for each corner in a compact way
+            def compute_flattened_indices(x, y):
+                return tvm.relay.reshape(x + y * padded_w_const, newshape=[-1])
+
+            x0_y0_flattened = compute_flattened_indices(x0, y0)
+            x0_y1_flattened = compute_flattened_indices(x0, y1)
+            x1_y0_flattened = compute_flattened_indices(x1, y0)
+            x1_y1_flattened = compute_flattened_indices(x1, y1)
+
+            num_splits = infer_shape(im_padded)[1]
+            split_im_padded = tvm.relay.split(im_padded, indices_or_sections=num_splits, axis=1)
+            x0_y0_parts = []
+            x0_y1_parts = []
+            x1_y0_parts = []
+            x1_y1_parts = []
+            x0_y0 = x0 + y0 * padded_w_const
+            t1, t2 = infer_shape(tvm.relay.squeeze(x0_y0, axis=[0]))
+            flatten_shape = [t1, t2]
+
+            # Perform bilinear interpolation for each channel
+            for i in range(num_splits):
+                im_part = split_im_padded[i]
+                im_part = tvm.relay.squeeze(im_part, axis=[0])
+                im_part = tvm.relay.transpose(im_part, [1, 0])  
+                im_part = tvm.relay.expand_dims(im_part, axis=1)
+
+                x0_y0 = tvm.relay.expand_dims(tvm.relay.reshape(tvm.relay.take(im_part, x0_y0_flattened, axis=0),newshape=flatten_shape),axis=0)
+                x0_y0_parts.append(x0_y0)
+
+                x0_y1 = tvm.relay.expand_dims(tvm.relay.reshape(tvm.relay.take(im_part, x0_y1_flattened, axis=0),newshape=flatten_shape),axis=0)
+                x0_y1_parts.append(x0_y1)  
+
+                x1_y0 = tvm.relay.expand_dims(tvm.relay.reshape(tvm.relay.take(im_part, x1_y0_flattened, axis=0),newshape=flatten_shape),axis=0)
+                x1_y0_parts.append(x1_y0)  
+
+                x1_y1 = tvm.relay.expand_dims(tvm.relay.reshape(tvm.relay.take(im_part, x1_y1_flattened, axis=0),newshape=flatten_shape),axis=0)
+                x1_y1_parts.append(x1_y1)  
+
+            # Concatenate parts back for all channels
+            x0_y0 = tvm.relay.concatenate(x0_y0_parts, axis=0)  
+            x0_y1 = tvm.relay.concatenate(x0_y1_parts, axis=0)  
+            x1_y0 = tvm.relay.concatenate(x1_y0_parts, axis=0)  
+            x1_y1 = tvm.relay.concatenate(x1_y1_parts, axis=0)  
+
+            # Expand dimensions to align with batch/channel format
+            x0_y0, x0_y1, x1_y0, x1_y1 = [tvm.relay.expand_dims(x, axis=1) for x in [x0_y0, x0_y1, x1_y0, x1_y1]]
+
+            # Compute the final output using bilinear weights
+            if mode == 'bilinear':
+                output = (x0_y0 * wa + x0_y1 * wb + x1_y0 * wc + x1_y1 * wd)
+                output = tvm.relay.transpose(output, [1, 0, 2, 3])
+            else:
+                assert False, f"Unsupported mode: {mode}. Only 'bilinear' is supported."
+
+            results.append(output)
+
+        # Concatenate results along the batch dimension
+        final_output = tvm.relay.concatenate(results, axis=0)
+        return final_output
 
+
+class DecomposeFloor(DFPatternCallback):
+    def __init__(self):
+        super().__init__(rewrite_once=True, require_type=True)
+
+        self.data = wildcard()
+        self.floor = is_op("floor")(self.data)
+
+        self.pattern = self.floor
+
+    def callback(self, pre, post, node_map):
+        pre_node_map = construct_pre_node_map(self.pattern, pre)
+        data = node_map[self.data][0]
+
+        # Decompose floor operation using the formula:
+        # floor(x) = int(x) - 1 if x < 0 and x != int(x)
+        int_part = tvm.relay.cast(data, "int32") 
+        int_part = tvm.relay.cast(int_part, "float32") 
+        negative_mask = tvm.relay.less(data, tvm.relay.const(0.0, "float32")) 
+        not_equal_mask = tvm.relay.not_equal(data, int_part) 
+
+        # Compute the adjustment: subtract 1 for negative non-integer values
+        adjustment = tvm.relay.where(negative_mask, tvm.relay.cast(not_equal_mask, "float32"), tvm.relay.const(0.0, "float32"))
+        floor_result = tvm.relay.subtract(int_part, adjustment) # Subtract adjustment to get the floor value
+        return floor_result
 
 def _get_callback_name(callback):
     if isinstance(callback, DFPatternCallback):
@@ -4002,6 +4203,8 @@ def run_forge_compile_passes(relay_module, params=None, inputs=None, target=None
     return run_pattern_callbacks(
         relay_module,
         [
+            DecomposeGridSample(),
+            DecomposeFloor(),
             ExpandMultipleDims(),
             DecomposeReverse(),
             ConvertLayout(),