fix/feat: Add and repair multiple converters

- Focus on SD-performance-accelerating converters
- Add test cases for converters to avoid regressions
- Add prims sum converter

Author: gs-olive

py/torch_tensorrt/dynamo/conversion/__init__.py

@@ -2,5 +2,6 @@
 from ._TRTInterpreter import *  # noqa: F403
 from .aten_ops_converters import *  # noqa: F403
 from .conversion import *  # noqa: F403
-from .op_evaluators import *  # noqa: F403
+from .ops_evaluators import *  # noqa: F403
+from .prims_ops_converters import *  # noqa: F403
 from .truncate_long_and_double import repair_long_or_double_inputs

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -27,6 +27,24 @@ def args_bounds_check(
     return args[i] if len(args) > i else replacement
 
 
+def get_ir(target: Target) -> SourceIR:
+    target_module = getattr(target, "__module__", "None")
+    if any(
+        target_module.startswith(prefix)
+        for prefix in ("torch.ops.prims", "torch._ops.prims")
+    ):
+        return SourceIR.ATEN
+    elif any(
+        target_module.startswith(prefix)
+        for prefix in ("torch.ops.prims", "torch._ops.prims")
+    ):
+        return SourceIR.PRIM
+    elif target_module.startswith("torch.nn"):
+        return SourceIR.NN
+
+    return SourceIR.UNKNOWN
+
+
 @dynamo_tensorrt_converter(torch.ops.aten.batch_norm)  # type: ignore[misc]
 def aten_ops_batch_norm(
     ctx: ConversionContext,
@@ -651,23 +669,37 @@ def aten_ops_amax(
 
 @dynamo_tensorrt_converter(torch.ops.aten.sum.default)  # type: ignore[misc]
 @dynamo_tensorrt_converter(torch.ops.aten.sum.dim_IntList)  # type: ignore[misc]
+@dynamo_tensorrt_converter(torch.ops.prims.sum.default)  # type: ignore[misc]
 def aten_ops_sum(
     ctx: ConversionContext,
     target: Target,
     args: Tuple[Argument, ...],
     kwargs: Dict[str, Argument],
     name: str,
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    return impl.reduce.sum(
+    sum_ = impl.reduce.sum(
         ctx,
         target,
-        SourceIR.ATEN,
+        get_ir(target),
         name,
         args[0],
         args_bounds_check(args, 1, replacement=None),
         args_bounds_check(args, 2, replacement=False),
     )
 
+    if kwargs.get("output_dtype", None) is not None:
+        return impl.cast.to_copy(
+            ctx,
+            target,
+            SourceIR.ATEN,
+            name,
+            sum_,
+            kwargs["output_dtype"],
+            force_layer=False,
+        )
+    else:
+        return sum_
+
 
 @dynamo_tensorrt_converter(torch.ops.aten.exp.default)  # type: ignore[misc]
 def aten_ops_exp(
@@ -1166,6 +1198,7 @@ def aten_ops_sub(
 @dynamo_tensorrt_converter(torch.ops.aten.div.Tensor_mode)  # type: ignore[misc]
 @dynamo_tensorrt_converter(torch.ops.aten.div.Scalar)  # type: ignore[misc]
 @dynamo_tensorrt_converter(torch.ops.aten.div.Scalar_mode)  # type: ignore[misc]
+@dynamo_tensorrt_converter(torch.ops.prims.div.default)  # type: ignore[misc]
 def aten_ops_div(
     ctx: ConversionContext,
     target: Target,
@@ -1179,7 +1212,7 @@ def aten_ops_div(
         return impl.elementwise.div(
             ctx,
             target,
-            SourceIR.ATEN,
+            get_ir(target),
             name,
             args[0],
             args[1],
@@ -1188,7 +1221,7 @@ def aten_ops_div(
         return impl.elementwise.floor_divide(
             ctx,
             target,
-            SourceIR.ATEN,
+            get_ir(target),
             name,
             args[0],
             args[1],
@@ -1197,7 +1230,7 @@ def aten_ops_div(
         return impl.elementwise.trunc_div(
             ctx,
             target,
-            SourceIR.ATEN,
+            get_ir(target),
             name,
             args[0],
             args[1],

py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py

@@ -1,5 +1,6 @@
 from typing import Optional
 
+import numpy as np
 import tensorrt as trt
 import torch
 from torch.fx.node import Target
@@ -23,16 +24,6 @@ def where(
     other: TRTTensor,
     condition: TRTTensor,
 ) -> TRTTensor:
-    input_dim = len(tuple(input.shape))
-    other_dim = len(tuple(other.shape))
-    condition_dim = len(tuple(condition.shape))
-
-    if type(input) != TRTTensor:
-        assert type(input) is torch.Tensor, f"value {input} is not torch.Tensor!"
-
-    if type(other) != TRTTensor:
-        assert type(other) is torch.Tensor, f"value {other} is not torch.Tensor!"
-
     if not (broadcastable(input, other)):
         assert "The two torch tensors should be broadcastable"
 
@@ -49,33 +40,37 @@ def where(
     x_shape = list(input.shape)
     y_shape = list(other.shape)
     condition_shape = list(condition.shape)
+
     output_shape = list(torch.broadcast_shapes(condition_shape, x_shape, y_shape))
 
     # expand shape
-    if type(condition) != TRTTensor:
-        assert condition.dtype == torch.bool, "condition dtype is not bool"
+    if not isinstance(condition, TRTTensor):
+        assert condition.dtype in (torch.bool, np.bool_), "condition dtype is not bool"
         if condition_shape != output_shape:
-            condition.expand(output_shape)
-        condition = condition.to(torch.int32)
-        condition_const = get_trt_tensor(ctx, condition, f"{name}_condition")
-        condition_layer = ctx.net.add_identity(condition_const)
-        condition_layer.set_output_type(0, trt.bool)
-        set_layer_name(condition_layer, target, f"{name}_condition")
-        condition_val = condition_layer.get_output(0)
+            condition = (
+                condition.expand(output_shape)
+                if isinstance(condition, torch.Tensor)
+                else np.broadcast_to(condition, output_shape)
+            )
+        condition_val = get_trt_tensor(ctx, condition, f"{name}_condition")
     else:
         assert condition.dtype == trt.bool, "mask dtype is not bool!"
-        if len(condition_shape) != condition_dim:
+        if condition_shape != output_shape:
             condition_val = expand(
                 ctx, target, source_ir, f"{name}_expand", condition, output_shape
             )
         else:
             condition_val = condition
 
-    if type(input) != TRTTensor:
+    if not isinstance(input, TRTTensor):
         if x_shape != output_shape:
             # special case where 1 element in input
             if len(input.shape) == 0:
-                input = input.unsqueeze(0)
+                input = (
+                    input.unsqueeze(0)
+                    if isinstance(input, torch.Tensor)
+                    else np.expand_dims(input, axis=0)
+                )
             input = input.expand(output_shape)
         x_val = get_trt_tensor(ctx, input, f"{name}_x")
     else:
@@ -85,11 +80,15 @@ def where(
                 ctx, target, source_ir, f"{name}_x_expand", input, output_shape
             )
 
-    if type(other) != TRTTensor:
+    if not isinstance(other, TRTTensor):
         if y_shape != output_shape:
             # special case where 1 element in other
             if len(other.shape) == 0:
-                other = other.unsqueeze(0)
+                other = (
+                    other.unsqueeze(0)
+                    if isinstance(other, torch.Tensor)
+                    else np.expand_dims(other, axis=0)
+                )
             other = other.expand(output_shape)
         y_val = get_trt_tensor(ctx, other, f"{name}_y")
     else:

py/torch_tensorrt/dynamo/conversion/impl/reduce.py

@@ -51,7 +51,7 @@ def sum(
     ):
         input_val = cast_trt_tensor(ctx, input_val, trt.float32, name)
 
-    if dim is None:
+    if dim is None or (isinstance(dim, (tuple, list)) and len(dim) == 0):
         dim = tuple(range(len(input_val.shape)))
     layer = ctx.net.add_reduce(
         input_val,

py/torch_tensorrt/dynamo/conversion/impl/unsqueeze.py

@@ -1,4 +1,4 @@
-from typing import Optional, cast
+from typing import List, Optional, Sequence, cast
 
 from torch.fx.node import Target
 from torch_tensorrt.dynamo._SourceIR import SourceIR
@@ -49,3 +49,42 @@ def unsqueeze(
     )
     set_layer_name(layer, target, name, source_ir)
     return layer.get_output(0)
+
+
+def broadcast_in_dim(
+    ctx: ConversionContext,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input_t: TRTTensor,
+    shape: Sequence[int],
+    broadcast_dimensions: Sequence[int],
+) -> TRTTensor:
+    augmented_shape_list: List[Optional[int]] = list(shape)
+
+    # For each dimension being broadcasted, set the augmented shape to None
+    for broadcast_dim in broadcast_dimensions:
+        augmented_shape_list[broadcast_dim] = None
+
+    # TODO: Expand support to arbitrary broadcasts
+    assert all(
+        dim in (1, None) for dim in augmented_shape_list
+    ), "broadcast_in_dim currently only supports unsqueeze broadcasting"
+
+    # Unsqueeze the shape repeatedly to broadcast
+    output = input_t
+    for idx, x in enumerate(augmented_shape_list):
+        # If the value is not None, that dimension is to be broadcasted
+        if x is not None:
+            output = unsqueeze(
+                ctx,
+                target,
+                source_ir,
+                name + f"_unsqueeze_for_broadcast_{idx}",
+                output,
+                idx,
+            )
+
+    assert tuple(output.shape) == tuple(shape), "broadcast_in_dim shapes don't match"
+
+    return output

py/torch_tensorrt/dynamo/conversion/op_evaluators.py renamed to py/torch_tensorrt/dynamo/conversion/ops_evaluators.py

@@ -19,7 +19,7 @@ def getitem_validator(getitem_node: Node) -> bool:
 
 
 # TODO: Subsequent evaluators should be registered here with their own validators
-@dynamo_tensorrt_converter(operator.getitem, capability_validator=getitem_validator)
+@dynamo_tensorrt_converter(operator.getitem, capability_validator=getitem_validator)  # type: ignore[misc]
 def generic_evaluator(
     ctx: ConversionContext,
     target: Target,

py/torch_tensorrt/dynamo/conversion/prims_ops_converters.py

@@ -0,0 +1,44 @@
+import logging
+from typing import Dict, Sequence, Tuple, Union
+
+import torch
+from torch.fx.node import Argument, Target
+from torch_tensorrt.dynamo._SourceIR import SourceIR
+from torch_tensorrt.dynamo.conversion import impl
+from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
+from torch_tensorrt.fx.types import TRTTensor
+
+from .converter_registry import dynamo_tensorrt_converter
+
+_LOGGER: logging.Logger = logging.getLogger(__name__)
+
+
+# TODO: expand the scope of this converter with aten.expand implementation
+def broadcast_checker(broadcast_node: torch.fx.Node) -> bool:
+    # The current implementation of broadcast_in_dim can only handle unsqueeze
+    return all(
+        broadcast_node.args[1][i] == 1
+        for i in range(len(broadcast_node.args[1]))
+        if i not in broadcast_node.args[2]
+    )
+
+
+@dynamo_tensorrt_converter(
+    torch.ops.prims.broadcast_in_dim.default, capability_validator=broadcast_checker
+)  # type: ignore[misc]
+def aten_ops_broadcast_in_dim(
+    ctx: ConversionContext,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.unsqueeze.broadcast_in_dim(
+        ctx,
+        target,
+        SourceIR.PRIM,
+        name,
+        args[0],
+        args[1],
+        args[2],
+    )

tests/py/dynamo/conversion/test_div_aten.py

@@ -82,6 +82,24 @@ def forward(self, lhs_val):
             expected_ops={torch.ops.aten.div.Tensor_mode},
         )
 
+    @parameterized.expand(
+        [
+            ("2d", (2, 1)),
+            ("3d", (2, 1, 2)),
+        ]
+    )
+    def test_prims_div_tensor(self, _, shape):
+        class div(nn.Module):
+            def forward(self, lhs_val, rhs_val):
+                return torch.ops.prims.div.default(lhs_val, rhs_val)
+
+        inputs = [torch.randn(shape), torch.randn(shape)]
+        self.run_test(
+            div(),
+            inputs,
+            expected_ops={torch.ops.prims.div.default},
+        )
+
 
 if __name__ == "__main__":
     run_tests()

tests/py/dynamo/conversion/test_sum_aten.py

@@ -113,5 +113,27 @@ def forward(self, x):
         )
 
 
+class TestPrimsSumConverter(DispatchTestCase):
+    @parameterized.expand(
+        [
+            ((3, 2, 4), [1]),
+            ((2, 1, 4, 5), [1, 2]),
+            ((2, 3, 4, 5), [0, 1, 2, 3]),
+            ((6, 7, 5, 4, 5), [1, 3, 4]),
+        ]
+    )
+    def test_sum_dim_sequence(self, input_shape, dim):
+        class Sum(nn.Module):
+            def forward(self, x):
+                return torch.ops.prims.sum.default(x, dim)
+
+        inputs = [torch.randn(*input_shape)]
+        self.run_test(
+            Sum(),
+            inputs,
+            expected_ops={torch.ops.prims.sum.default},
+        )
+
+
 if __name__ == "__main__":
     run_tests()