Add op support table

Author: GregoryComer

.gitignore

@@ -62,7 +62,6 @@ xcuserdata/
 /include/
 /share/
 /version.py
-*.csv
 *_etdump
 
 # Android

backends/xnnpack/README.md

@@ -134,4 +134,4 @@ create an issue on [github](https://www.github.com/pytorch/executorch/issues).
 ## See Also
 For more information about the XNNPACK Backend, please check out the following resources:
 - [XNNPACK Backend](https://pytorch.org/executorch/main/backends-xnnpack)
-- [XNNPACK Backend Internals](https://pytorch.org/executorch/main/backend-delegates-xnnpack-reference)
+- [XNNPACK Backend Internals](https://pytorch.org/executorch/main/backends/xnnpack/backend-delegates-xnnpack-reference)

docs/source/backends/xnnpack/reference/op-support.csv

@@ -0,0 +1,47 @@
+Operator,Compute DType,Quantization,Constraints
+_to_dim_order_copy,"fp16, fp32",,no dtype conversion
+abs,"fp16, fp32",,
+add,"fp16, fp32",PT2E: static int8,alpha=1
+avg_pool2d,"fp16, fp32",PT2E: static int8,"ceil_mode=False, count_include_pad=False, divisor_override=pooling_region"
+bmm,"fp16, fp32",,
+cat,"fp16, fp32",PT2E: static int8,
+ceil,"fp16, fp32",,
+clamp,"fp16, fp32",,
+constant_pad_nd,"fp16, fp32",,no negative padding values
+conv1d,"fp16, fp32","PT2E: static or dynamic int8 activations
+8-bit weights, symmetric per-tensor or per-channel",constant weights
+conv2d,"fp16, fp32","PT2E: static or dynamic int8 activations
+8-bit weights, symmetric per-tensor or per-channel",constant weights
+dequantize_per_tensor,"fp16, fp32",,
+div,"fp16, fp32",,
+elu,"fp16, fp32",,
+exp,"fp16, fp32",,
+floor,"fp16, fp32",,
+gelu,"fp16, fp32",,
+hardswish,"fp16, fp32",,
+hardtanh,"fp16, fp32",,
+leaky_relu,"fp16, fp32",,
+linear,"fp16, fp32","PT2E: static or dynamic int8 activations
+8-bit weights, symmetric per-tensor or per-channel
+
+quantize\_: 8-bit dynamic activations
+4-bit groupwise weights",constant weights
+log,"fp16, fp32",,
+max_pool2d,"fp16, fp32",,"stride ≤ kernel_size, ceil_mode only for static shapes"
+maximum,"fp16, fp32",,
+mean,"fp16, fp32",,"4D tensors only; dims=[-2,-1] or [-1,-2]"
+minimum,"fp16, fp32",,
+mul,"fp16, fp32",PT2E: static int8,
+neg,"fp16, fp32",,
+permute_copy,"fp16, fp32",,
+pow,"fp16, fp32",,power=2 only
+quantize_per_tensor,"fp16, fp32",,
+relu,"fp16, fp32",,
+rsqrt,"fp16, fp32",,
+sigmoid,"fp16, fp32",,
+slice_copy,"fp16, fp32",,"no zero-dim tensors, no dynamic shapes"
+softmax,"fp16, fp32",,dim must be last dimension
+sqrt,"fp16, fp32",,
+sub,"fp16, fp32",,alpha=1
+tanh,"fp16, fp32",,
+upsample_bilinear2d,"fp16, fp32",,no dynamic output sizes

docs/source/backends/xnnpack/reference/xnnpack-reference-op-support.rst

@@ -0,0 +1,13 @@
+================
+Operator Support
+================
+
+This page lists the operators supported by the XNNPACK backend. Operators are the building blocks of the ML model. See `IRs <https://docs.pytorch.org/docs/stable/torch.compiler_ir.html>`_ for more information on the PyTorch operator set.
+
+All operators support dynamic input shapes unless otherwise noted.
+
+.. csv-table:: Operator Support
+   :file: op-support.csv
+   :header-rows: 1
+   :widths: 20 20 30 30
+   :align: center

docs/source/backends/xnnpack/reference/xnnpack-reference.md

@@ -6,6 +6,8 @@
 
 **→{doc}`xnnpack-reference-quantization` — Supported quantization schemes.**
 
+**→{doc}`xnnpack-reference-op-support` — Supported operators.**
+
 ## Internals
 
 **→{doc}`xnnpack-reference-arch-internals` — XNNPACK backend internals.**
@@ -14,6 +16,7 @@
 :hidden:
 :maxdepth: 1
 
-xnnpack-reference-arch-internals
 xnnpack-reference-partitioner
 xnnpack-reference-quantization
+xnnpack-reference-op-support
+xnnpack-reference-arch-internals