feat: Add support for Groot N1.5 model

docsrc/user_guide/mixed_precision.rst

@@ -1,15 +1,18 @@
 .. _mixed_precision:
 
 Compile Mixed Precision models with Torch-TensorRT
-====================================
+===================================================
 .. currentmodule:: torch_tensorrt.dynamo
 
 .. automodule:: torch_tensorrt.dynamo
    :members:
    :undoc-members:
    :show-inheritance:
 
-Consider the following Pytorch model which explicitly casts intermediate layer to run in FP16. 
+Explicit Typing
+---------------
+
+Consider the following PyTorch model which explicitly casts intermediate layer to run in FP16. 
 
 .. code-block:: python
 
@@ -54,6 +57,7 @@ the compilation setting ``use_explicit_typing=True``. Compiling with this option
 
 .. note:: If you enable ``use_explicit_typing=True``, only torch.float32 is supported in the enabled_precisions.
 
+
 .. code-block:: python
 
     inputs = [torch.randn((1, 10), dtype=torch.float32).cuda()]
@@ -62,7 +66,7 @@ the compilation setting ``use_explicit_typing=True``. Compiling with this option
     with torch_tensorrt.logging.debug():
         trt_gm = torch_tensorrt.dynamo.compile(ep, 
                                             inputs=inputs, 
-                                            use_explicit_typing=True
+                                            use_explicit_typing=True,
                                             debug=True)
 
     # Debug log info
@@ -71,4 +75,36 @@ the compilation setting ``use_explicit_typing=True``. Compiling with this option
     # Name: __myl_ResMulSumAddCas_myl0_1, LayerType: kgen, Inputs: [ { Name: __mye127_dconst, Dimensions: [10,30], Format/Datatype: Half }, { Name: linear2/addmm_1_constant_0 _ linear2/addmm_1_add_broadcast_to_same_shape_lhs_broadcast_constantHalf, Dimensions: [1,30], Format/Datatype: Half }, { Name: __myln_k_arg__bb1_2, Dimensions: [1,10], Format/Datatype: Half }], Outputs: [ { Name: __myln_k_arg__bb1_3, Dimensions: [1,30], Format/Datatype: Float }], TacticName: __myl_ResMulSumAddCas_0x5a3b318b5a1c97b7d5110c0291481337, StreamId: 0, Metadata: 
     # Name: __myl_ResMulSumAdd_myl0_2, LayerType: kgen, Inputs: [ { Name: __mye142_dconst, Dimensions: [30,40], Format/Datatype: Float }, { Name: linear3/addmm_2_constant_0 _ linear3/addmm_2_add_broadcast_to_same_shape_lhs_broadcast_constantFloat, Dimensions: [1,40], Format/Datatype: Float }, { Name: __myln_k_arg__bb1_3, Dimensions: [1,30], Format/Datatype: Float }], Outputs: [ { Name: output0, Dimensions: [1,40], Format/Datatype: Float }], TacticName: __myl_ResMulSumAdd_0x3fad91127c640fd6db771aa9cde67db0, StreamId: 0, Metadata: 
 
-Now the ``linear2`` layer runs in FP16 as shown in the above logs. 
+Now the ``linear2`` layer runs in FP16 as shown in the above logs. 
+
+
+
+FP32 Accumulation
+-----------------
+
+When ``use_fp32_acc=True`` is set, Torch-TensorRT will attempt to use FP32 accumulation for matmul layers, even if the input and output tensors are in FP16. This is particularly useful for models that are sensitive to numerical errors introduced by lower-precision accumulation.
+
+.. important::
+
+    When enabling ``use_fp32_acc=True``, **explicit typing must be enabled** by setting ``use_explicit_typing=True``. Without ``use_explicit_typing=True``, the accumulation type may not be properly respected, and you may not see the intended numerical benefits.
+
+.. code-block:: python
+
+    inputs = [torch.randn((1, 10), dtype=torch.float16).cuda()]
+    mod = MyModule().eval().cuda()
+    ep = torch.export.export(mod, tuple(inputs))
+    with torch_tensorrt.logging.debug():
+        trt_gm = torch_tensorrt.dynamo.compile(
+            ep,
+            inputs=inputs,
+            use_fp32_acc=True,
+            use_explicit_typing=True,  # Explicit typing must be enabled
+            debug=True
+        )
+
+    # Debug log info
+    # Layers:
+    # Name: __myl_MulSumAddCas_myl0_0, LayerType: kgen, Inputs: [ ... ], Outputs: [ ... ], Format/Datatype: Half, Accumulation: Float
+    # ...
+
+For more information on these settings, see the explicit typing examples above.

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -393,7 +393,9 @@ def index_dtype_validator(
 
 
 @dynamo_tensorrt_converter(
-    torch.ops.aten.index.Tensor, capability_validator=index_dtype_validator
+    torch.ops.aten.index.Tensor,
+    capability_validator=index_dtype_validator,
+    supports_dynamic_shapes=True,
 )
 @enforce_tensor_types(
     {

py/torch_tensorrt/dynamo/conversion/converter_utils.py

@@ -24,14 +24,15 @@
 from torch.fx.node import Argument, Target
 from torch.fx.passes.shape_prop import TensorMetadata
 from torch_tensorrt import _enums
+from torch_tensorrt._utils import is_tensorrt_version_supported
 from torch_tensorrt.dynamo._settings import CompilationSettings
 from torch_tensorrt.dynamo._SourceIR import SourceIR
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
 from torch_tensorrt.dynamo.conversion._ConverterRegistry import (
     ConverterRegistry,
     DynamoConverterImplSignature,
 )
-from torch_tensorrt._utils import is_tensorrt_version_supported
+
 from ..types import Shape, TRTDataType, TRTLayer, TRTTensor
 
 _LOGGER: logging.Logger = logging.getLogger(__name__)
@@ -72,6 +73,9 @@ def format_tensor_metadata(metadata: Union[Any, Sequence[Any]]) -> str:
         # If the provided data is a scalar, return it as is
         elif isinstance(metadata, (int, float, bool)):
             return f"{metadata}@Python-{type(metadata)}"
+        # If the provided data is a SymInt, return it as is
+        elif isinstance(metadata, (torch.SymInt)):
+            return f"{metadata}@SymInt"
         # If the provided data is a sequence, recursively parse it
         elif isinstance(metadata, collections.abc.Sequence):
             formatted_str = "("

py/torch_tensorrt/dynamo/conversion/impl/matmul.py

@@ -48,17 +48,25 @@ def matrix_multiply(
     input, other = broadcast(
         ctx, input, other, f"{name}_input", f"{name}_other", preset_diff
     )
-    if ctx.net.get_flag(trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED):
-        promoted_type = _enums.dtype._from(
-            torch.promote_types(
-                _enums.dtype._from(input.dtype).to(torch.dtype),
-                _enums.dtype._from(other.dtype).to(torch.dtype),
-            )
+    if (
+        ctx.net.get_flag(trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED)
+        and ctx.compilation_settings.use_fp32_acc
+    ):
+        input = cast_trt_tensor(ctx, input, torch.float32, f"{name}_input_casted")
+        other = cast_trt_tensor(ctx, other, torch.float32, f"{name}_other_casted")
+
+    matmul_layer = ctx.net.add_matrix_multiply(
+        input, input_matrix_op, other, other_matrix_op
+    )
+    matmul_output = matmul_layer.get_output(0)
+
+    if (
+        ctx.net.get_flag(trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED)
+        and ctx.compilation_settings.use_fp32_acc
+    ):
+        matmul_output = cast_trt_tensor(
+            ctx, matmul_output, torch.float16, f"{name}_output_casted"
         )
-        trt_promoted_type = promoted_type.to(trt.DataType)
-        input = cast_trt_tensor(ctx, input, trt_promoted_type, f"{name}_input_casted")
-        other = cast_trt_tensor(ctx, other, trt_promoted_type, f"{name}_other_casted")
 
-    layer = ctx.net.add_matrix_multiply(input, input_matrix_op, other, other_matrix_op)
-    set_layer_name(layer, target, name, source_ir)
-    return layer.get_output(0)
+    set_layer_name(matmul_layer, target, name, source_ir)
+    return matmul_output

py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py

@@ -5,7 +5,6 @@
 from torch_tensorrt.dynamo._settings import CompilationSettings
 from torch_tensorrt.dynamo.utils import is_tegra_platform
 
-from .accumulate_fp32_matmul import accumulate_fp32_matmul
 from .complex_graph_rewrite import complex_graph_detection
 from .constant_folding import constant_fold
 from .fuse_prims_broadcast import fuse_prims_broadcast
@@ -24,7 +23,6 @@
     fuse_prims_broadcast,
     replace_max_pool_with_indices,
     remove_assert_nodes,
-    accumulate_fp32_matmul,
     remove_num_users_is_0_nodes,
     complex_graph_detection,
 ]

py/torch_tensorrt/dynamo/runtime/_MutableTorchTensorRTModule.py

@@ -367,7 +367,8 @@ def compile(self) -> None:
             enabled_precisions=self.enabled_precisions,
             **self.additional_settings,
         )
-        deallocate_module(self.original_model, delete_module=False)
+        if self.additional_settings.get("offload_module_to_cpu", False):
+            deallocate_module(self.original_model, delete_module=False)
         if self.enable_weight_streaming:
             self.set_weight_streaming_ctx(self.weight_streaming_budget)