handle static ints and floats (#140)

Summary:
Pull Request resolved: #140

In order to support MV3 which has decomposed hardswish and hardsigmoid

Decomp rules for both:
### Hardswish
https://www.internalfb.com/code/fbsource/[9368f8417bd843ee8c91e24ac616ed7f4b194ed8]/xplat/caffe2/torch/_decomp/decompositions.py?lines=182-185

### Hardsigmoid
https://www.internalfb.com/code/fbsource/[9368f8417bd843ee8c91e24ac616ed7f4b194ed8]/xplat/caffe2/torch/_decomp/decompositions.py?lines=159-162

### Fixing Zero-Dim tensors
Both of these decompositions produce zero-dim tensors in the graph ( The + 3 and the / 6). This breaks for XNNPACK because it does not have zero-dim tensors. Instead if the static data is zero dim, then we will interpret it as [1].

#### Fixing torch.int64 static data
In the decomposition 3 is converted via to_copy(torch.float32). However 6 remains as an int64. XNNPACK does not handle non-quantized integers, so we also cast all static data that is not quantized to float32 values.

Reviewed By: digantdesai

Differential Revision: D48667679

fbshipit-source-id: 0b20363bde480a98349e65ae8f569c7c95c95ef6

Author: mcr229

backends/xnnpack/operators/node_visitor.py

@@ -230,6 +230,7 @@ def define_tensor(
         # Get new xnn id for tensor value
         ext_id, id_out, flag = self.gen_ids_and_flags(tensor, xnn_graph, quant_params)
         dims = get_shape(tensor)
+        dims = [1] if len(dims) == 0 else dims
 
         # constant values serialize data
         buffer_idx = self.get_serialized_buffer(
@@ -336,6 +337,10 @@ def get_serialized_buffer(
         # Quantize buffer if static data is indeed quantized
         if quant_params is not None and not quant_params.is_dynamic:
             const_val = quant_params.quantize_tensor(const_val).contiguous()
+        else:
+            # ensure that the const is fp32
+            const_val = const_val.to(dtype=torch.float32).contiguous()
+
         if swap_nc_for_depthwise_weights:
             const_val = const_val.permute(
                 dims=((1, 0) + tuple(range(2, const_val.dim())))

backends/xnnpack/partition/configs.py

@@ -37,6 +37,9 @@
 
 SUPPORTED_MODULES = [
     torch.nn.Conv1d,
+    # TODO(T161981984) recomposed hardswish into a single node
+    torch.nn.Hardswish,
+    torch.nn.Hardsigmoid,
     torch.nn.Conv2d,
     torch.nn.ReLU,
     torch.nn.Sigmoid,