Enable per-row/per-col grouping in CoreML LUT ops (#13674)

This PR enables per-row grouping in CoreML LUT ops.

Author: metascroy

backends/apple/coreml/compiler/torch_ops.py

@@ -175,11 +175,22 @@ def dequantize_codebook(context, node):
 
     # Assert codebook is as expected.  codebook.dim() = codes.dim() + 2
     assert len(codebook.shape) == 4, "Only rank 4 inputs are supported for codebook"
-    assert codebook.shape[0] == 1, "Only grouped_channel granularity is supported"
-    n_luts = codebook.shape[1]
-    assert (
-        codes.shape[1] % n_luts == 0
-    ), "codes.shape[1] must be divisible by codebook.shape[1]"
+    assert (codebook.shape[0] == 1) or (
+        codebook.shape[1] == 1
+    ), "Only grouped_channel granularity is supported"
+    if codebook.shape[0] == 1:
+        # LUT is per column group
+        n_luts = codebook.shape[1]
+        assert (
+            codes.shape[1] % n_luts == 0
+        ), "codes.shape[1] must be divisible by codebook.shape[1]"
+    else:
+        # LUT is per row group
+        n_luts = codebook.shape[0]
+        assert (
+            codes.shape[0] % n_luts == 0
+        ), "codes.shape[0] must be divisible by codebook.shape[0]"
+
     assert codebook.shape[2] == 2**nbits
     assert codebook.shape[3] == 1, "Only scalar look up values are supported"
 

backends/apple/coreml/test/test_torch_ops.py

@@ -35,7 +35,7 @@ def _coreml_partitioner(self):
 
     def _get_test_model(self):
         model = torch.nn.Sequential(
-            torch.nn.Embedding(64, 128), torch.nn.Linear(128, 128), torch.nn.ReLU()
+            torch.nn.Embedding(64, 128), torch.nn.Linear(128, 256), torch.nn.ReLU()
         )
         example_inputs = (torch.LongTensor([0]),)
         return model, example_inputs
@@ -158,7 +158,7 @@ def test_dequantize_affine_c8w_embedding_b4w_linear(self):
         et_prog = delegated_program.to_executorch()
         self._compare_outputs(et_prog, model, example_inputs)
 
-    def test_dequantize_codebook_linear(self):
+    def test_dequantize_codebook_linear_per_grouped_col(self):
         model, example_inputs = self._get_test_model()
         quantize_(
             model,
@@ -185,7 +185,34 @@ def test_dequantize_codebook_linear(self):
         et_prog = delegated_program.to_executorch()
         self._compare_outputs(et_prog, model, example_inputs)
 
-    def test_dequantize_codebook_embedding(self):
+    def test_dequantize_codebook_linear_per_grouped_row(self):
+        model, example_inputs = self._get_test_model()
+        quantize_(
+            model,
+            CodebookWeightOnlyConfig(dtype=torch.uint2, block_size=[16, -1]),
+        )
+        ep = torch.export.export(model, example_inputs)
+        assert "torch.ops.quant.dequantize_codebook.default" in ep.graph_module.code
+        delegated_program = executorch.exir.to_edge_transform_and_lower(
+            ep,
+            partitioner=[self._coreml_partitioner()],
+        )
+        for node in delegated_program.exported_program().graph.nodes:
+            if node.op == "call_function":
+                assert node.target.__name__ in [
+                    "executorch_call_delegate",
+                    "getitem",
+                ], f"Got unexpected node target after delegation: {node.target.__name__}"
+
+        assert (
+            "executorch.exir.dialects.edge._ops.quant.dequantize_codebook.default"
+            in format_delegated_graph(delegated_program.exported_program().graph_module)
+        )
+
+        et_prog = delegated_program.to_executorch()
+        self._compare_outputs(et_prog, model, example_inputs)
+
+    def test_dequantize_codebook_embedding_per_grouped_col(self):
         model, example_inputs = self._get_test_model()
         quantize_(
             model,
@@ -213,6 +240,34 @@ def test_dequantize_codebook_embedding(self):
         et_prog = delegated_program.to_executorch()
         self._compare_outputs(et_prog, model, example_inputs)
 
+    def test_dequantize_codebook_embedding_per_grouped_row(self):
+        model, example_inputs = self._get_test_model()
+        quantize_(
+            model,
+            CodebookWeightOnlyConfig(dtype=torch.uint3, block_size=[16, -1]),
+            lambda m, fqn: isinstance(m, torch.nn.Embedding),
+        )
+        ep = torch.export.export(model, example_inputs)
+        assert "torch.ops.quant.dequantize_codebook.default" in ep.graph_module.code
+        delegated_program = executorch.exir.to_edge_transform_and_lower(
+            ep,
+            partitioner=[self._coreml_partitioner()],
+        )
+        for node in delegated_program.exported_program().graph.nodes:
+            if node.op == "call_function":
+                assert node.target.__name__ in [
+                    "executorch_call_delegate",
+                    "getitem",
+                ], f"Got unexpected node target after delegation: {node.target.__name__}"
+
+        assert (
+            "executorch.exir.dialects.edge._ops.quant.dequantize_codebook.default"
+            in format_delegated_graph(delegated_program.exported_program().graph_module)
+        )
+
+        et_prog = delegated_program.to_executorch()
+        self._compare_outputs(et_prog, model, example_inputs)
+
 
 if __name__ == "__main__":
     test_runner = TestTorchOps()
@@ -221,5 +276,7 @@ def test_dequantize_codebook_embedding(self):
     test_runner.test_dequantize_affine_c4w_embedding()
     test_runner.test_dequantize_affine_c4w_linear()
     test_runner.test_dequantize_affine_c8w_embedding_b4w_linear()
-    test_runner.test_dequantize_codebook_linear()
-    test_runner.test_dequantize_codebook_embedding()
+    test_runner.test_dequantize_codebook_linear_per_grouped_col()
+    test_runner.test_dequantize_codebook_linear_per_grouped_row()
+    test_runner.test_dequantize_codebook_embedding_per_grouped_col()
+    test_runner.test_dequantize_codebook_embedding_per_grouped_row()