Merge branch 'main' into fix-warnings

Author: digantdesai

.ci/docker/ci_commit_pins/pytorch.txt

@@ -1 +1 @@
-08434df1f2f88c9770e59246caa2ff9c6f613270
+295f2ed4d103017f7e19a7b8263ece606cd629db

.ci/docker/conda-env-ci.txt

@@ -1,4 +1,4 @@
-cmake=3.22.1
+cmake=3.26.4
 ninja=1.10.2
 libuv
 llvm-openmp

.github/workflows/android-perf.yml

@@ -2,7 +2,7 @@ name: android-perf
 
 on:
   schedule:
-    - cron: 0 0 * * *
+    - cron: 0 0,8,16 * * *
   pull_request:
     paths:
       - .github/workflows/android-perf.yml

backends/arm/_passes/insert_rescales_pass.py

@@ -38,17 +38,17 @@ def rescale_fake(
     """Casts the input tensor to dtype `dtype` to produce the correct tensor meta for a _rescale op.
     Additionally validates TOSA constraints of a RESCALE op.
     """
-    if not (dtype == torch.int32 or dtype == torch.int8):
+    if dtype not in (torch.int32, torch.int8, torch.int16):
         raise NotImplementedError(
-            "tosa::rescale currently only supports int32 and int8."
+            f"tosa::rescale currently only supports int32, int16 and int8, not {dtype}"
         )
-    if dtype == torch.int32 and out_zp != 0:
+    if dtype in (torch.int32, torch.int16) and out_zp != 0:
         raise ValueError(
-            "TOSA requires output_zp to be zero when the output dtype is int32."
+            f"TOSA requires output_zp to be zero when the output dtype is {dtype}."
         )
-    if x.dtype == torch.int32 and in_zp != 0:
+    if x.dtype in (torch.int32, torch.int16) and in_zp != 0:
         raise ValueError(
-            "TOSA requires input_zp to be zero when the input dtype is int32."
+            f"TOSA requires input_zp to be zero when the input dtype is {dtype}"
         )
     if x.dtype == torch.int8 and not -128 <= in_zp <= 127:
         raise ValueError(f"{in_zp=} outside valid range (-128,127) for int8.")

backends/arm/_passes/insert_table_ops.py

@@ -1,5 +1,4 @@
 # Copyright 2024-2025 Arm Limited and/or its affiliates.
-# All rights reserved.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -18,6 +17,7 @@
 
 from executorch.exir.pass_base import ExportPass, PassResult
 from torch.fx import GraphModule
+
 from torch.library import impl, Library
 
 lib = Library("tosa", "DEF")
@@ -26,7 +26,10 @@
 
 @impl(lib, "_table")
 def _table_impl(*args, **kwargs):  # pyre-ignore
-    return args[0]
+    in_dtype = args[0].dtype
+    if in_dtype == torch.int8:
+        return args[0]
+    return args[0].to(dtype=torch.int32)
 
 
 class InsertTableOpsPass(ExportPass):
@@ -59,29 +62,105 @@ def register_buffer(self, buffer_name: str, buffer: torch.Tensor) -> None:
         """
         self.exported_program.state_dict[buffer_name] = buffer
 
-    def generate_table_values(
+    def generate_8bit_table_values(
         self,
         torch_op: Callable[[torch.Tensor], torch.Tensor],
         in_quantargs: QuantArgs,
         out_quantargs: QuantArgs,
-    ) -> torch.Tensor:
+    ) -> tuple[torch.Tensor, int]:
+        """Compute LUT values for a INT8 TOSA.TABLE. Also returns 0 since no shifting is required after 8bit table.
+        The INT8 table is a simple 256 value 1-1 LUT.
+        """
+
         def f(x: torch.Tensor) -> torch.Tensor:
             x = in_quantargs.dequantize_value(x)
             x = torch_op(x)
             return out_quantargs.quantize_value(x)
 
-        input_dtype = in_quantargs.dtype
-        steps = in_quantargs.qmax - in_quantargs.qmin + 1
-        return f(
+        return (
+            f(
+                torch.linspace(
+                    start=in_quantargs.qmin,
+                    end=in_quantargs.qmax,
+                    steps=256,
+                    # use torch.int64 to avoid overflow when dequantizing (subtracting zp).
+                    # e.g. torch.tensor(-50, dtype=torch.int8) - 100 == torch.tensor(106, dtype=torch.int8)
+                    dtype=torch.int64,
+                )
+            ).to(dtype=torch.int8),
+            0,
+        )
+
+    def generate_16_bit_table_values(
+        self,
+        torch_op: Callable[[torch.Tensor], torch.Tensor],
+        in_quantargs: QuantArgs,
+        out_quantargs: QuantArgs,
+    ) -> tuple[torch.Tensor, int]:
+        """Compute LUT values for a INT16 TOSA.TABLE with 32 bit output.
+        In practice the output is 23 bits that should be interpreted as 16 'whole' bits and 7 fractional bits, see
+        the specification: https://www.mlplatform.org/tosa/tosa_spec.html#_table. This means that the output
+        will interpreted as 2**7=128 times too large unless accounted for by rescaling down the table output.
+
+        Quantization can be either int16 or int32 which means that the op output could be larger than the 23 bits from
+        the TOSA.TABLE output. In that case, we need to rescale up the output.
+
+        To handle this we need to:
+        1) Make sure that our table values fit within 16 bits.
+        2) Insert a rescale after the table to handle the x128 from the fractional bits and match the quantization.
+
+        The function returns rescale_lshift which says how much to rescale after the table. This value can negative.
+        """
+
+        def f(x: torch.Tensor) -> torch.Tensor:
+            # Dont use the 7 LSBs.
+            x = in_quantargs.dequantize_value((x & ~0x7F))
+            x = torch_op(x)
+            return out_quantargs.quantize_value(x)
+
+        lut_values = f(
             torch.linspace(
                 start=in_quantargs.qmin,
-                end=in_quantargs.qmax,
-                steps=steps,
+                end=in_quantargs.qmax + 1,
+                steps=513,
                 # use torch.int64 to avoid overflow when dequantizing (subtracting zp).
                 # e.g. torch.tensor(-50, dtype=torch.int8) - 100 == torch.tensor(106, dtype=torch.int8)
                 dtype=torch.int64,
             )
-        ).to(dtype=input_dtype)
+        )
+        # Calculate how much we need to shift table values to fit in 16 signed bits
+        # ceil(log2(max absolute table value)) + 1 bit for signedness - 16
+        # Example:
+        #       Max value in the table is 70 000. We want to fit it in 16 signed bits.
+        #       70 000=0b10001000101110000 (17 digits) has ceil(log2(70 000)) = ceil(16.095) = 17 bits.
+        #       If we shift it 17-16=1 bit, we do get 16 bits (0b1000100010111000),
+        #       but due to signedness this is a negative number! So we need to shift it one more bit.
+        # Note: for out_quantargs.dtype=torch.int16, rshift == 0 and rescale_lshift = -7.
+        rshift = int(torch.ceil(torch.log2(lut_values.abs().max()))) + 1 - 16
+        # The 7 fractional bits are equivalent to a lshift of 7, so subtract 7 from the lshift we do.
+        rescale_lshift = rshift - 7
+        lut_values = lut_values >> rshift
+        return lut_values.to(dtype=torch.int16), rescale_lshift
+
+    def generate_table_values(
+        self,
+        torch_op: Callable[[torch.Tensor], torch.Tensor],
+        in_quantargs: QuantArgs,
+        out_quantargs: QuantArgs,
+    ) -> tuple[torch.Tensor, int]:
+        match out_quantargs.dtype:
+            case torch.int8:
+                return self.generate_8bit_table_values(
+                    torch_op, in_quantargs, out_quantargs
+                )
+            case torch.int16 | torch.int32:
+                return self.generate_16_bit_table_values(
+                    torch_op, in_quantargs, out_quantargs
+                )
+            case _:
+                raise ValueError(
+                    f"Unsupported output dtype for table: {out_quantargs.dtype}"
+                )
 
     def call(self, graph_module: GraphModule) -> PassResult:
         modified = False
@@ -100,10 +179,12 @@ def call(self, graph_module: GraphModule) -> PassResult:
                     op_target=torch.ops.tosa._table.default,
                     args=(node.args[0],),
                 )
+                output_node = table_node
                 assert len(input_qparams) == 1
                 assert len(output_qparams) == 1
-                # Generate table buffer
-                buffer = self.generate_table_values(
+
+                # Generate table buffer and how much to lshift the table output.
+                buffer, lshift = self.generate_table_values(
                     torch_op=self.table_ops[node.target],
                     in_quantargs=input_qparams[0],
                     out_quantargs=output_qparams[0],
@@ -114,10 +195,20 @@ def call(self, graph_module: GraphModule) -> PassResult:
                 self.register_buffer(
                     buffer_name=table_node.name.replace("_default", ""), buffer=buffer
                 )
-                node.replace_all_uses_with(table_node)
+
+                if lshift != 0:
+                    scale = 2.0**lshift
+                    rescale_node = create_node(
+                        graph=graph_module.graph,
+                        op_target=torch.ops.tosa._rescale.default,
+                        args=(table_node, output_qparams[0].dtype, scale, 0, 0),
+                    )
+                    output_node = rescale_node
+
+                node.replace_all_uses_with(output_node)
             graph_module.graph.erase_node(node)
-            table_node.meta["input_qparams"] = input_qparams
-            table_node.meta["output_qparams"] = output_qparams
+            output_node.meta["input_qparams"] = input_qparams
+            output_node.meta["output_qparams"] = output_qparams
             modified = True
 
         if modified:

backends/arm/operators/node_visitor.py

@@ -30,7 +30,7 @@ class NodeVisitor:
     ]
 
     def __init__(self, exported_program: ExportedProgram, tosa_spec: TosaSpecification):
-        self._exported_program = exported_program or None
+        self._exported_program = exported_program
         self.tosa_spec = tosa_spec
 
     def define_node(

backends/arm/operators/op_bmm.py

@@ -80,7 +80,7 @@ def define_node(
 
             build_rescale(
                 tosa_fb=tosa_graph,
-                scale=final_output_scale,
+                scale=[final_output_scale],
                 # pyre-ignore[61]: Uninitialized local [61]: Local variable `bmm_result` is undefined, or not always defined.
                 input_node=bmm_result,  # type: ignore[possibly-undefined]
                 output_name=output.name,

backends/arm/operators/op_conv2d.py

@@ -176,8 +176,8 @@ def define_node(
                 conv2d_res,  # type: ignore[possibly-undefined]
                 output.name,
                 output.dtype,
-                input_scale,
-                weight_scale,
-                output_qargs[0].scale,
+                [input_scale],
+                [weight_scale],
+                [output_qargs[0].scale],
                 output_qargs[0].zp,
             )

backends/arm/operators/op_mul.py

@@ -63,13 +63,13 @@ def define_node(
             tosa_graph,
             input_A,
             input_A_qargs.zp,
-            rescale_scale=1.0,
+            [1.0],
         )
         input_B_rescaled = tqutils.build_rescale_to_int32(
             tosa_graph,
             input_B,
             input_B_qargs.zp,
-            rescale_scale=1.0,
+            [1.0],
         )
 
         output_shape = tutils.tosa_shape(output.shape, output.dim_order)

backends/arm/operators/op_rescale.py

@@ -38,7 +38,6 @@ def define_node(
         input_zp = cast(int, node.args[3])
         output_zp = cast(int, node.args[4])
 
-        # Skip int16 cases for now.
         if input_dtype != map_dtype(torch.int8) and input_zp != 0:
             raise ValueError(
                 f"If input dtype is not int8, input_zp must be 0. Got input_dtype{ts.DTypeNames[input_dtype]}, {input_zp=}"
@@ -48,17 +47,20 @@ def define_node(
                 f"If output dtype is not int8, output_zp must be 0. Got {output_dtype=}, {output_zp=}"
             )
 
-        scale_width = 32 if output_dtype == torch.int32 else 16
+        # scale32 gives higher accuracy but for a higher HW cost.
+        # For now, always go for scale32.
+        scale_32 = True
+        scale_width = 32 if scale_32 else 16
         multiplier, shift = tosa_quant_utils.compute_multiplier_and_shift(
-            scale, scale_width
+            [scale], scale_width
         )
         attr_rescale = ts.TosaSerializerAttribute()
         attr_rescale.RescaleAttribute(
             input_zp=input_zp,
             output_zp=output_zp,
-            multiplier=[multiplier],
-            shift=[shift],
-            scale32=output_dtype == torch.int32,
+            multiplier=multiplier,
+            shift=shift,
+            scale32=scale_32,
             double_round=False,
             per_channel=False,
             input_unsigned=False,

backends/arm/operators/op_table.py

@@ -30,11 +30,24 @@ def define_node(
         inputs: List[TosaArg],
         output: TosaArg,
     ) -> None:
-        assert node.name in self._exported_program.state_dict.keys()  # type: ignore[union-attr]
-        assert inputs[0].dtype == output.dtype == ts.DType.INT8
+        if node.name not in self._exported_program.state_dict.keys():  # type: ignore[union-attr]
+            raise RuntimeError(
+                f"Did not find key {node.name} in state_dict {self._exported_program.state_dict.keys()}."
+            )
+        if inputs[0].dtype == ts.DType.INT8 and output.dtype != ts.DType.INT8:
+            raise ValueError(f"Int8 tables need int8 output, got {output.dtype=}.")
+        if inputs[0].dtype == ts.DType.INT16 and output.dtype != ts.DType.INT32:
+            raise ValueError(f"Int16 tables need int32 output, got {output.dtype=}.")
+
+        if inputs[0].dtype not in (ts.DType.INT8, ts.DType.INT16):
+            raise ValueError(
+                f"TOSA.TABLE only supports int8 or int16 inputs, got {ts.DTypeNames[inputs[0]]}"
+            )
+
         table = self._exported_program.state_dict[node.name]  # type: ignore[union-attr]
         table_attr = ts.TosaSerializerAttribute()
         table_attr.TableAttribute(np.array(table))
+
         tosa_graph.addOperator(
             TosaOp.Op().TABLE, [inputs[0].name], [output.name], table_attr
         )

backends/arm/quantizer/arm_quantizer.py

@@ -191,14 +191,16 @@ def _get_module_type_filter(tp: Callable) -> NodeFilterType:
     True  # the node is from the submodule `Sub` (same for `Block` and `Linear` as well)
     """
 
+    tp_str = tp.__module__ + "." + tp.__qualname__
+
     def module_type_filter(n: Node) -> bool:
         # node_stack example: {
         #     'L__self___sub': ("L['self'].sub", <class '....Sub'>),
         #     'L__self___sub_linear': ("L['self'].sub.linear", <class 'torch.nn.modules.linear.Linear'>)
         # }
         nn_module_stack = n.meta.get("nn_module_stack", {})
         types = [t for _, t in nn_module_stack.values()]
-        return tp in types
+        return tp_str in types
 
     return module_type_filter