2025-02-27 nightly release (4f0bb6f)

Author: pytorchbot

.github/workflows/build-test-linux.yml

@@ -140,6 +140,8 @@ jobs:
         python -m pip install -r requirements.txt
         cd dynamo
         python -m pytest -ra --junitxml=${RUNNER_TEST_RESULTS_DIR}/dynamo_converters_test_results.xml -n 4 conversion/
+        python -m pytest -ra --junitxml=${RUNNER_TEST_RESULTS_DIR}/dynamo_converters_test_results.xml automatic_plugin/test_automatic_plugin.py
+        python -m pytest -ra --junitxml=${RUNNER_TEST_RESULTS_DIR}/dynamo_converters_test_results.xml automatic_plugin/test_automatic_plugin_with_attrs.py
         popd
 
   tests-py-dynamo-fe:

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -2722,40 +2722,10 @@ def aten_ops_max_pool(
     )
 
 
-def attention_validator(
-    node: Node, settings: Optional[CompilationSettings] = None
-) -> bool:
-    # Currently, `attn_mask` is not supported
-    return args_bounds_check(node.args, 3) is None
-
-
+@dynamo_tensorrt_converter(torch.ops.aten.reshape.default, supports_dynamic_shapes=True)
 @dynamo_tensorrt_converter(
-    torch.nn.functional.scaled_dot_product_attention,
-    capability_validator=attention_validator,
-    supports_dynamic_shapes=True,
+    torch.ops.aten._reshape_copy.default, supports_dynamic_shapes=True
 )
-def tensorrt_scaled_dot_product_attention(
-    ctx: ConversionContext,
-    target: Target,
-    args: Tuple[Argument, ...],
-    kwargs: Dict[str, Argument],
-    name: str,
-) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    return impl.attention.scaled_dot_product_attention(
-        ctx,
-        target,
-        SourceIR.TORCHTRT_LOWERED,
-        name,
-        args[0],
-        args[1],
-        args[2],
-        args_bounds_check(args, 5, False),
-        kwargs.get("scale", None),
-    )
-
-
-@dynamo_tensorrt_converter(torch.ops.aten.reshape.default, supports_dynamic_shapes=True)
-@dynamo_tensorrt_converter(torch.ops.aten.view.default, supports_dynamic_shapes=True)
 @enforce_tensor_types(
     {
         0: (TRTTensor,),

py/torch_tensorrt/dynamo/conversion/impl/__init__.py

@@ -2,7 +2,6 @@
     activation,
     addmm,
     arange,
-    attention,
     cast,
     cat,
     condition,

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

@@ -1,6 +1,6 @@
 import logging
 from enum import Enum, auto
-from typing import Any, Callable, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional, Tuple
 
 import torch
 from torch._decomp import register_decomposition
@@ -435,6 +435,137 @@ def full_like_decomposition(*args, **kwargs) -> torch.Tensor:
     return torch.full(shape, fill_value, dtype=kwargs["dtype"], device=kwargs["device"])
 
 
+@register_torch_trt_decomposition(aten.view.default, registry=TORCH_TRT_DECOMPOSITIONS)
+def view_decomposition(x: torch.Tensor, size: List[torch.SymInt]) -> torch.Tensor:
+    return aten._reshape_copy.default(x, size)
+
+
+@register_torch_trt_decomposition(
+    aten.scaled_dot_product_attention, registry=TORCH_TRT_DECOMPOSITIONS
+)
+def scaled_dot_product_attention_decomposition(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_mask: Optional[torch.Tensor] = None,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    *,
+    scale: Optional[float] = None,
+    enable_gqa: bool = False,
+) -> torch.Tensor:
+    L, S = query.size(-2), key.size(-2)
+    device = query.device
+    attn_bias = torch.zeros(L, S, dtype=query.dtype, device=device)
+
+    if is_causal:
+        assert attn_mask is None, "attn_mask must be None when is_causal=True"
+        temp_mask = torch.ones(L, S, dtype=torch.bool, device=device).tril(diagonal=0)
+        attn_bias = attn_bias.masked_fill(temp_mask.logical_not(), float("-inf"))
+
+    if attn_mask is not None:
+        if attn_mask.dtype == torch.bool:
+            attn_bias = attn_bias.masked_fill(attn_mask.logical_not(), float("-inf"))
+        else:
+            attn_bias = attn_mask + attn_bias
+
+    if enable_gqa:
+        key = key.repeat_interleave(query.size(-3) // key.size(-3), -3)
+        value = value.repeat_interleave(query.size(-3) // value.size(-3), -3)
+
+    attn_weight = query @ key.transpose(-2, -1)
+
+    if scale is None:
+        scale = torch.sqrt(torch.scalar_tensor(query.size(-1), dtype=torch.int))
+        attn_weight = attn_weight / scale
+    else:
+        attn_weight = attn_weight * scale
+
+    attn_weight = attn_weight + attn_bias
+    attn_weight = torch.softmax(attn_weight, dim=-1)
+    return attn_weight @ value
+
+
+@register_torch_trt_decomposition(
+    aten._scaled_dot_product_flash_attention, registry=TORCH_TRT_DECOMPOSITIONS
+)
+def scaled_dot_product_flash_attention_decomposition(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    return_debug_mask: bool = False,
+    *,
+    scale: Optional[float] = None,
+) -> Tuple[
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.SymInt,
+    torch.SymInt,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+]:
+    attn = scaled_dot_product_attention_decomposition(
+        query, key, value, None, dropout_p, is_causal, scale=scale
+    )
+    return attn, None, None, None, 0, 0, None, None, None
+
+
+@register_torch_trt_decomposition(
+    aten._scaled_dot_product_efficient_attention, registry=TORCH_TRT_DECOMPOSITIONS
+)
+def scaled_dot_product_efficient_attention_decomposition(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_bias: Optional[torch.Tensor],
+    compute_log_sumexp: bool,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    *,
+    scale: Optional[float] = None,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    attn = scaled_dot_product_attention_decomposition(
+        query, key, value, attn_bias, dropout_p, is_causal, scale=scale
+    )
+    return attn, None, None, None
+
+
+@register_torch_trt_decomposition(
+    aten._scaled_dot_product_cudnn_attention, registry=TORCH_TRT_DECOMPOSITIONS
+)
+def scaled_dot_product_cudnn_attention_decomposition(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_bias: Optional[torch.Tensor],
+    compute_log_sumexp: bool,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    return_debug_mask: bool = False,
+    *,
+    scale: Optional[float] = None,
+) -> Tuple[
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.SymInt,
+    torch.SymInt,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+]:
+    attn = scaled_dot_product_attention_decomposition(
+        query, key, value, attn_bias, dropout_p, is_causal, scale=scale
+    )
+    return attn, None, None, None, 0, 0, None, None, None
+
+
 def get_decompositions(
     enable_experimental_decompositions: bool = False,
 ) -> Dict[OpOverload, Callable[[Any], Any]]:

py/torch_tensorrt/dynamo/lowering/_decompositions.py

@@ -8,14 +8,12 @@
 from .constant_folding import constant_fold
 from .fuse_distributed_ops import fuse_distributed_ops
 from .fuse_prims_broadcast import fuse_prims_broadcast
-from .lower_scaled_dot_product_attention import lower_scaled_dot_product_attention
 from .pass_manager import DynamoPassManager
 from .remove_assert_nodes import remove_assert_nodes
 from .remove_detach import remove_detach
 from .remove_input_alias_fixing_clones import remove_input_alias_fixing_clones
 from .repair_input_as_output import repair_input_as_output
 from .replace_max_pool_with_indices import replace_max_pool_with_indices
-from .view_to_reshape import view_to_reshape
 
 ATEN_POST_LOWERING_PASSES = DynamoPassManager.build_from_passlist(
     [
@@ -25,8 +23,6 @@
         fuse_prims_broadcast,
         fuse_distributed_ops,
         replace_max_pool_with_indices,
-        lower_scaled_dot_product_attention,
-        view_to_reshape,
         remove_assert_nodes,
         accumulate_fp32_matmul,
     ]