feat: Add support for is_causal argument in attention (#2780)

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -2357,8 +2357,14 @@ def aten_ops_max_pool(
     )
 
 
+def attention_validator(node: Node) -> bool:
+    # Currently, `attn_mask` is not supported
+    return args_bounds_check(node.args, 3) is None
+
+
 @dynamo_tensorrt_converter(
     torch.nn.functional.scaled_dot_product_attention,
+    capability_validator=attention_validator,
 )
 def tensorrt_scaled_dot_product_attention(
     ctx: ConversionContext,
@@ -2375,6 +2381,7 @@ def tensorrt_scaled_dot_product_attention(
         args[0],
         args[1],
         args[2],
+        args_bounds_check(args, 5, False),
         kwargs.get("scale", None),
     )
 

py/torch_tensorrt/dynamo/conversion/impl/attention.py

@@ -1,11 +1,13 @@
 import math
 from typing import Optional, Union
 
+import numpy as np
 import tensorrt as trt
 from torch.fx.node import Target
+from torch_tensorrt._enums import dtype
 from torch_tensorrt.dynamo.conversion import impl
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
-from torch_tensorrt.dynamo.conversion.converter_utils import SourceIR
+from torch_tensorrt.dynamo.conversion.converter_utils import SourceIR, get_trt_tensor
 from torch_tensorrt.fx.types import TRTTensor
 
 
@@ -17,8 +19,11 @@ def scaled_dot_product_attention(
     query: TRTTensor,
     key: TRTTensor,
     value: TRTTensor,
+    is_causal: bool,
     scale: Optional[float],
 ) -> TRTTensor:
+    L, S = query.shape[-2], key.shape[-2]
+
     mm = impl.matmul.matrix_multiply(
         ctx,
         target,
@@ -46,6 +51,17 @@ def scaled_dot_product_attention(
             mm,
             scale,
         )
+
+    if is_causal:
+        attn_bias = np.zeros((L, S), dtype=dtype._from(query.dtype).to(np.dtype))
+        temp_mask = np.logical_not(np.tril(np.ones((L, S), dtype=np.bool_), k=0))
+        attn_bias = np.ma.array(attn_bias, mask=temp_mask).filled(float("-inf"))
+        attn_bias = get_trt_tensor(ctx, attn_bias, name + "_attn_bias")
+
+        scaled = impl.elementwise.add(
+            ctx, target, source_ir, name + "_attn_bias_add", scaled, attn_bias
+        )
+
     softmax = impl.normalization.softmax(
         ctx, target, source_ir, name + "_softmax", scaled, -1
     )

py/torch_tensorrt/dynamo/lowering/passes/lower_scaled_dot_product_attention.py

@@ -3,6 +3,7 @@
 from typing import Callable, Sequence, Tuple
 
 import torch
+from torch_tensorrt.dynamo.conversion.aten_ops_converters import args_bounds_check
 from torch_tensorrt.dynamo.lowering.passes.pass_utils import (
     clean_up_graph_after_modifications,
 )
@@ -34,6 +35,7 @@ def lower_scaled_dot_product_attention(
 
     if replaced_nodes:
         # Repair instances which use the kwargs field (specifically the "scale" kwarg)
+        # Also repair instances which specified the is_causal or attn_bias fields
         for match in replaced_nodes:
             attention_node_replaced = None
             # Seek the attention operator being replaced
@@ -43,17 +45,52 @@ def lower_scaled_dot_product_attention(
                     break
 
             assert attention_node_replaced is not None
+            assert len(match.replacements) == 1
+
+            new_attention_node = match.replacements[0]
+
+            assert (
+                new_attention_node.target
+                == torch.nn.functional.scaled_dot_product_attention
+            )
 
             # If the attention operator had keyword-args, copy them to the new node
             if attention_node_replaced.kwargs:
-                assert len(match.replacements) == 1
-                new_attention_node = match.replacements[0]
-                assert (
-                    new_attention_node.target
-                    == torch.nn.functional.scaled_dot_product_attention
-                )
                 new_attention_node.kwargs = {**attention_node_replaced.kwargs}
 
+            # Set default args in new node:
+            # Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False
+            new_attention_node.args = new_attention_node.args + (None, 0.0, False)
+
+            # The `is_causal` argument was specified
+            if (
+                (
+                    attention_node_replaced.target
+                    == torch.ops.aten._scaled_dot_product_flash_attention.default
+                )
+                and args_bounds_check(attention_node_replaced.args, 4, False)
+            ) or (
+                (
+                    attention_node_replaced.target
+                    == torch.ops.aten._scaled_dot_product_efficient_attention.default
+                )
+                and args_bounds_check(attention_node_replaced.args, 6, False)
+            ):
+                new_attention_node.args = (
+                    new_attention_node.args[:5] + (True,) + new_attention_node.args[6:]
+                )
+
+            # The `attn_bias` argument was specified
+            if (
+                attention_node_replaced.target
+                == torch.ops.aten._scaled_dot_product_efficient_attention.default
+            ) and args_bounds_check(attention_node_replaced.args, 3) is not None:
+                new_attention_node.args = (
+                    new_attention_node.args[:3]
+                    + attention_node_replaced.args[3]
+                    + new_attention_node.args[4:]
+                )
+
         gm = clean_up_graph_after_modifications(gm)
         logger.debug(f"Graph after lowering scaled dot product attention:\n{gm.graph}")
 

tests/py/dynamo/conversion/test_attention.py

@@ -0,0 +1,112 @@
+import unittest
+
+import torch
+import torch.nn as nn
+from parameterized import parameterized
+from torch.testing._internal.common_utils import run_tests
+
+from ..testing_utilities import DECIMALS_OF_AGREEMENT
+from .harness import DispatchTestCase
+
+
+class TestScaledDotProductAttention(DispatchTestCase):
+    @parameterized.expand([((32, 8, 128, 64), (32, 8, 128, 64))])
+    def test_sdpa_no_causal(self, query_shape, key_shape):
+        class SDPA(nn.Module):
+            def forward(self, query, key, value):
+                return torch.nn.functional.scaled_dot_product_attention(
+                    query, key, value, None, 0.0, False, scale=None
+                )
+
+        inputs = []
+        query = torch.randn(query_shape, dtype=torch.float16)
+        key = torch.rand(key_shape, dtype=torch.float16)
+        value = torch.rand(key_shape, dtype=torch.float16)
+        inputs.extend([query, key, value])
+        self.run_test(SDPA(), inputs, rtol=1e-2, atol=1e-2, precision=torch.float16)
+
+    @parameterized.expand([((32, 8, 128, 64), (32, 8, 128, 64))])
+    def test_sdpa_causal(self, query_shape, key_shape):
+        class SDPA(nn.Module):
+            def forward(self, query, key, value):
+                return torch.nn.functional.scaled_dot_product_attention(
+                    query, key, value, None, 0.0, True, scale=None
+                )
+
+        inputs = []
+        query = torch.randn(query_shape, dtype=torch.float16)
+        key = torch.rand(key_shape, dtype=torch.float16)
+        value = torch.rand(key_shape, dtype=torch.float16)
+        inputs.extend([query, key, value])
+        self.run_test(SDPA(), inputs, rtol=1e-2, atol=1e-2, precision=torch.float16)
+
+
+@unittest.skipIf(
+    torch.cuda.get_device_properties(torch.cuda.current_device()).major < 8,
+    "GPU compute capability is too low to run flash attention, need Ampere (8.0) or greater",
+)
+class TestFlashAttention(DispatchTestCase):
+    @parameterized.expand([((32, 8, 128, 64), (32, 8, 128, 64))])
+    def test_sdpa_causal(self, query_shape, key_shape):
+        class SDPA(nn.Module):
+            def forward(self, query, key, value):
+                attn = torch.ops.aten._scaled_dot_product_flash_attention.default(
+                    query,
+                    key,
+                    value,
+                    0,
+                    True,  # is_causal
+                    False,
+                    scale=0.25,
+                )
+                return attn[0]
+
+        inputs = []
+        query = torch.randn(query_shape, dtype=torch.float16)
+        key = torch.rand(key_shape, dtype=torch.float16)
+        value = torch.rand(key_shape, dtype=torch.float16)
+        inputs.extend([query, key, value])
+        self.run_test(
+            SDPA(),
+            inputs,
+            rtol=1e-2,
+            atol=1e-2,
+            precision=torch.float16,
+            enable_passes=True,
+        )
+
+
+class TestEfficientAttention(DispatchTestCase):
+    @parameterized.expand([((32, 8, 128, 64), (32, 8, 128, 64))])
+    def test_sdpa_causal(self, query_shape, key_shape):
+        class SDPA(nn.Module):
+            def forward(self, query, key, value):
+                attn = torch.ops.aten._scaled_dot_product_efficient_attention.default(
+                    query,
+                    key,
+                    value,
+                    None,
+                    False,
+                    0,
+                    True,  # is_causal
+                    scale=0.5,
+                )
+                return attn[0]
+
+        inputs = []
+        query = torch.randn(query_shape, dtype=torch.float16)
+        key = torch.rand(key_shape, dtype=torch.float16)
+        value = torch.rand(key_shape, dtype=torch.float16)
+        inputs.extend([query, key, value])
+        self.run_test(
+            SDPA(),
+            inputs,
+            rtol=1e-2,
+            atol=1e-2,
+            precision=torch.float16,
+            enable_passes=True,
+        )
+
+
+if __name__ == "__main__":
+    run_tests()

tests/py/dynamo/lowering/test_aten_lowering_passes.py

@@ -1,9 +1,10 @@
 import unittest
 
 import torch
-import torch_tensorrt
 from torch.testing._internal.common_utils import TestCase, run_tests
 
+import torch_tensorrt
+
 from ..testing_utilities import DECIMALS_OF_AGREEMENT, lower_graph_testing