Quantized Softmax Kernel (#14096)

Summary:
Pull Request resolved: #14096

Generic implementation of quantized softmax, dummy implementation of DLA_V130 implementation for now

NOTE: Mask parameter is nop

Reviewed By: mcremon-meta

Differential Revision: D78716203

Author: Eashan Garg

backends/cadence/aot/ops_registrations.py

@@ -300,6 +300,19 @@
     "rope.out(Tensor input, Tensor sin_tensor, Tensor cos_tensor, Tensor? pos, *, Tensor(a!) out) -> Tensor(a!)"
 )
 
+lib.define(
+    "quantized_softmax(Tensor input, Tensor mask, int dim, Tensor in_scale, Tensor in_zero_point, Tensor out_scale, Tensor out_zero_point) -> (Tensor out)"
+)
+lib.define(
+    "quantized_softmax.per_tensor(Tensor input, Tensor mask, int dim, float in_scale, int in_zero_point, float out_scale, int out_zero_point) -> (Tensor out)"
+)
+lib.define(
+    "quantized_softmax.out(Tensor input, Tensor mask, int dim, Tensor in_scale, Tensor in_zero_point, Tensor out_scale, Tensor out_zero_point, *, Tensor(a!) out) -> Tensor (a!)"
+)
+lib.define(
+    "quantized_softmax.per_tensor_out(Tensor input, Tensor mask, int dim, float in_scale, int in_zero_point, float out_scale, int out_zero_point, *, Tensor(a!) out) -> Tensor (a!)"
+)
+
 # Load/store with iDMA. These only exist before memory planning.
 # Post memory planning, we check that outputs/inputs for the load/store are in
 # DTCM and replace idma_load/idma_store with idma_copy.
@@ -2097,3 +2110,29 @@ def softmax_f32_f32_meta(
     half_to_float: Optional[bool] = None,
 ) -> torch.Tensor:
     return self.new_empty(self.size(), dtype=self.dtype)
+
+
+@register_fake("cadence::quantized_softmax")
+def quantized_softmax_meta(
+    input: torch.Tensor,
+    mask: torch.Tensor,
+    dim: int,
+    in_scale: torch.Tensor,
+    in_zero_point: torch.Tensor,
+    out_scale: torch.Tensor,
+    out_zero_point: torch.Tensor,
+) -> torch.Tensor:
+    return input.new_empty(input.size(), dtype=input.dtype)
+
+
+@register_fake("cadence::quantized_softmax.per_tensor")
+def quantized_softmax_per_tensor_meta(
+    input: torch.Tensor,
+    mask: torch.Tensor,
+    dim: int,
+    in_scale: float,
+    in_zero_point: int,
+    out_scale: float,
+    out_zero_point: int,
+) -> torch.Tensor:
+    return input.new_empty(input.size(), dtype=input.dtype)

backends/cadence/aot/quantizer/fusion_pass.py

@@ -6,9 +6,10 @@
 
 # pyre-strict
 
-from typing import Any, Dict, List, Tuple
+from typing import Any, cast, Dict, List, Tuple
 
 import torch
+from executorch.backends.cadence.aot.compiler_utils import get_shape
 from executorch.backends.cadence.aot.quantizer.patterns import (
     AddmmPattern,
     AddPattern,
@@ -21,6 +22,7 @@
     MatmulPattern,
     ReluPattern0,
     ReluPattern1,
+    SoftmaxPattern,
 )
 from executorch.backends.cadence.aot.quantizer.utils import (
     create_zero_bias_int32,
@@ -376,6 +378,73 @@ def get_args_and_kwargs_relu(
     return args, kwargs
 
 
+def get_args_and_kwargs_softmax(
+    graph_module: GraphModule,
+    inputs_inputs: List[fx.Node],
+    dequants_inputs: List[fx.Node],
+    quant_node: fx.Node,
+    op_node: fx.Node,
+) -> Tuple[Tuple[ArgsType, ...], Dict[str, ArgsType]]:
+    # Make a dummy mask tensor
+    mask_shape = get_shape(graph_module, cast(fx.Node, quant_node.args[0]))
+    mask_shape = list(mask_shape) if mask_shape else []
+    mask_shape[-1] = mask_shape[-1] // 16
+    mask_tensor = graph_module.graph.call_function(
+        torch.ops.aten.full.default,
+        (
+            mask_shape,
+            0.0,
+        ),
+        {"dtype": torch.int32},
+    )
+    # Make the scale and zero_point tensors
+    in_scale_tensor = graph_module.graph.call_function(
+        torch.ops.aten.full.default,
+        (
+            [1],
+            dequants_inputs[0].args[1],
+        ),
+        {"dtype": torch.float32},
+    )
+    in_zero_point_tensor = graph_module.graph.call_function(
+        torch.ops.aten.full.default,
+        (
+            [1],
+            dequants_inputs[0].args[2],
+        ),
+        {"dtype": torch.int32},
+    )
+    out_scale_tensor = graph_module.graph.call_function(
+        torch.ops.aten.full.default,
+        (
+            [1],
+            quant_node.args[1],
+        ),
+        {"dtype": torch.float32},
+    )
+    out_zero_point_tensor = graph_module.graph.call_function(
+        torch.ops.aten.full.default,
+        (
+            [1],
+            quant_node.args[2],
+        ),
+        {"dtype": torch.int32},
+    )
+
+    # Make the args and kwargs for the replacement op
+    args = (
+        inputs_inputs[0],
+        mask_tensor,
+        op_node.args[1],
+        in_scale_tensor,
+        in_zero_point_tensor,
+        out_scale_tensor,
+        out_zero_point_tensor,
+    )
+    kwargs = {}
+    return args, kwargs
+
+
 class QuantFusion(ExportPass):
     # pyre-ignore[2]: Parameter `patterns` has no type specified
     def __init__(self, patterns) -> None:
@@ -511,6 +580,14 @@ def call(self, graph_module: fx.GraphModule) -> PassResult:  # noqa: C901
                             dequants_inputs,
                             quant_node,
                         )
+                    elif isinstance(pattern, SoftmaxPattern):
+                        args, kwargs = get_args_and_kwargs_softmax(
+                            graph_module,
+                            inputs_inputs,
+                            dequants_inputs,
+                            quant_node,
+                            op_node,
+                        )
                     fused = graph_module.graph.call_function(
                         pattern.replacement_op(),
                         args,

backends/cadence/aot/quantizer/patterns.py

@@ -417,3 +417,25 @@ def partition_types(self) -> List[OpOverload]:
 class ReluPattern1(ReluBasePattern):
     def partition_types(self) -> List[OpOverload]:
         return [torch.ops.aten.relu_.default]
+
+
+class SoftmaxPattern(QuantizationPattern):
+
+    def partition_types(self) -> List[OpOverload]:
+        return [torch.ops.aten._softmax.default]
+
+    def get_anchors(
+        self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule]
+    ) -> PartitionAnchors:
+        # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorBase.__ge...
+        softmax_node = fused_partition[0].nodes[-1]
+
+        return PartitionAnchors(
+            inputs=[(softmax_node, 0)],
+            weights=[],
+            biases=[],
+            output=[(softmax_node,)],
+        )
+
+    def replacement_op(self) -> OpOverload:
+        return torch.ops.cadence.quantized_softmax.default

backends/cadence/aot/quantizer/quantizer.py

@@ -23,6 +23,7 @@
     QuantizationPattern,
     ReluPattern0,
     ReluPattern1,
+    SoftmaxPattern,
 )
 from executorch.backends.cadence.aot.quantizer.utils import (
     find_sequential_partitions_aten,
@@ -54,6 +55,15 @@
     observer_or_fake_quant_ctr=HistogramObserver.with_args(eps=2**-12),
 )
 
+act_qspec_asym16s = QuantizationSpec(
+    dtype=torch.int16,
+    quant_min=-32768,
+    quant_max=32767,
+    qscheme=torch.per_tensor_affine,
+    is_dynamic=False,
+    observer_or_fake_quant_ctr=HistogramObserver.with_args(eps=2**-12),
+)
+
 wgt_qspec_asym8s = QuantizationSpec(
     dtype=torch.int8,
     quant_min=-128,
@@ -88,6 +98,13 @@
     None,
 )
 
+qconfig_A16 = QuantizationConfig(
+    act_qspec_asym16s,
+    act_qspec_asym16s,
+    wgt_qspec_asym8s,
+    None,
+)
+
 
 class CadenceAtenQuantizer(Quantizer):
     def __init__(
@@ -260,3 +277,15 @@ def __init__(self, quantizers: Optional[list[Quantizer]] = None) -> None:
         quantizers.append(CadenceAtenQuantizer(AddPattern(), qconfig_A8W8))
         quantizers.append(CadenceAtenQuantizer(CatPattern(), qconfig_A8W8))
         super().__init__(quantizers)
+
+
+class CadenceWithSoftmaxQuantizer(CadenceQuantizer):
+    """
+    Quantizer including A16 softmax
+    """
+
+    def __init__(self, quantizers: Optional[list[Quantizer]] = None) -> None:
+        if quantizers is None:
+            quantizers = get_cadence_default_quantizers()
+        quantizers.append(CadenceAtenQuantizer(SoftmaxPattern(), qconfig_A16))
+        super().__init__(quantizers)