Add jagged_sum operator for padded nested tensors to TritonBench (#2305)

Summary:
Pull Request resolved: #2305

Add a `jagged_sum` reduction operator for padded nested tensors, based on the PyTorch `sum` operator, to TritonBench. This diff uses the PyTorch function [`torch.ops.aten._jagged_to_padded_dense_forward`](https://www.internalfb.com/code/fbsource/[92c2a067ab04e3eebc999254fed4ae2fbea6def3]/fbcode/deeplearning/fbgemm/fbgemm_gpu/fb/inductor_lowerings/elementwise_ops.py?lines=26), hosted at this [GitHub pull request](pytorch/pytorch#125968), to pad each 2-dimensional tensor in a nested tensor of shape `(B, *, M)`, then reduce across the `N`-th dimension (`dim == 1`) to a `(B, M)` output tensor.

Measure accuracy of padded implementation against unpadded baseline implementation via `accuracy` TritonBench metric.

Reviewed By: davidberard98

Differential Revision: D58423489

fbshipit-source-id: d2f6095f8af1cb188bb979e2f5605ad80db50a46

Author: jananisriram

torchbenchmark/operators/jagged_sum/operator.py

@@ -1,4 +1,5 @@
 import argparse
+import itertools
 import math
 import random
 from typing import Callable, Generator, List, Optional, Tuple
@@ -14,19 +15,21 @@
     register_metric,
 )
 
-random.seed(16)
-torch.manual_seed(16)
+seed = 16
+random.seed(seed)
+torch.manual_seed(seed)
 
 GIGABYTES_PER_BYTE = 1e-6
 RANDOM_CHOICE_MARGIN = 0.3
+ABSOLUTE_TOLERANCE = 1e-3
 
 
 def parse_op_args(args: List[str]):
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "--seqlen",
         type=int,
-        default=100,
+        default=500,
         help="Maximum sequence length on ragged dimension (integer)",
     )
     parser.add_argument(
@@ -40,6 +43,9 @@ def parse_op_args(args: List[str]):
 
 class Operator(BenchmarkOperator):
 
+    DEFAULT_METRICS = ["latency", "accuracy"]
+    use_cuda_graphs = False  # enables GPU/CPU sync (for methods like NestedTensor unbind)
+
     def __init__(self, mode: str, device: str, extra_args: Optional[List[str]] = None):
         super().__init__(mode=mode, device=device, extra_args=extra_args)
         self.sizes = range(4, 10, 2)
@@ -58,6 +64,17 @@ def torch_jagged_sum_no_pad(self, x: torch.Tensor):
             dtype=self.dtype,
         )
 
+    @register_benchmark()
+    def torch_jagged_sum_pad(self, x: torch.Tensor):
+        return lambda: torch.sum(
+            torch.ops.aten._jagged_to_padded_dense_forward(
+                x.values(),
+                [x.offsets()],  # pyre-ignore: Undefined attribute [16]: `torch._tensor.Tensor` has no attribute `offsets`.
+                max_lengths=[self.seqlen],  # max length of ragged dimension
+            ),
+            dim=1,
+        )  # sum along ragged dimension (dim == 1)
+
     def get_x_val(self, example_inputs):
         return len(example_inputs[0])
 
@@ -90,50 +107,52 @@ def get_input_iter(self) -> Generator:
         """
 
         B_vals, M_vals = self.get_x_vals()
-
-        for B in B_vals:
-            for M in M_vals:
-                tensors = []
-
-                # greater sparsity --> shorter sequence lengths on ragged dimension
-                seqlen_avg = math.floor(
-                    self.seqlen * (1 - self.sparsity)
-                )  # average sequence length across all tensors in nested tensor
-                seqlen_margin = math.floor(
-                    self.seqlen * RANDOM_CHOICE_MARGIN
-                )  # use margin to constrain sequence lengths to range [seqlen_avg - seqlen_margin, seqlen_avg + seqlen_margin] to approximate an average sequence length, which correlates with sparsity
-
-                for _ in range(B):
-                    seqlen_randint = random.randint(
-                        max(seqlen_avg - seqlen_margin, 1),
-                        min(seqlen_avg + seqlen_margin, self.seqlen),
-                    )
-                    tensor_2d = torch.randn(
-                        (seqlen_randint, M), device=self.device, dtype=self.dtype
-                    )
-                    tensors.append(tensor_2d)
-
-                nt = torch.nested.nested_tensor(
-                    tensors,
-                    layout=torch.jagged,
-                    device=self.device,
-                    dtype=self.dtype,
+        B_M_vals = itertools.product(B_vals, M_vals)
+
+        for B, M in B_M_vals:
+            tensors = []
+
+            # greater sparsity --> shorter sequence lengths on ragged dimension
+            seqlen_avg = math.floor(
+                self.seqlen * (1 - self.sparsity)
+            )  # average sequence length across all tensors in nested tensor
+            seqlen_margin = math.floor(
+                self.seqlen * RANDOM_CHOICE_MARGIN
+            )  # use margin to constrain sequence lengths to range [seqlen_avg - seqlen_margin, seqlen_avg + seqlen_margin] to approximate an average sequence length, which correlates with sparsity
+
+            for _ in range(B):
+                seqlen_randint = random.randint(
+                    max(
+                        seqlen_avg - seqlen_margin, 1
+                    ),  # seqlen_randint must be at least 1
+                    min(
+                        seqlen_avg + seqlen_margin, self.seqlen
+                    ),  # seqlen_randint must not exceed self.seqlen
                 )
+                tensor_2d = torch.randn(
+                    (seqlen_randint, M), device=self.device, dtype=self.dtype
+                )
+                tensors.append(tensor_2d)
 
-                yield (nt,)
+            nt = torch.nested.nested_tensor(
+                tensors,
+                layout=torch.jagged,
+                device=self.device,
+                dtype=self.dtype,
+            )
 
-    @register_metric()
-    def B_M(self, fn_name: str, example_inputs, metrics: BenchmarkOperatorMetrics):
-        return tuple([(ex.size(0), ex.size(2)) for ex in example_inputs])[
-            0
-        ]  # return (B, M) for each example input
+            yield (nt,)
+
+    def _get_accuracy(self, fn: Callable, baseline_fn: Callable) -> bool:
+        output = fn()
+        baseline_output = baseline_fn()
+        return torch.allclose(output, baseline_output, atol=ABSOLUTE_TOLERANCE)
 
     @register_metric()
     def gbps(self, fn_name, example_inputs, metrics: BenchmarkOperatorMetrics):
-        gbps = (
-            lambda ms: example_inputs[0].element_size()
+        return (
+            example_inputs[0].element_size()
             * example_inputs[0].numel()
-            / ms
+            / metrics.latency
             * GIGABYTES_PER_BYTE
         )
-        return list(map(gbps, metrics.latency if metrics.latency else [0]))