Fix the coverage report issues (#3874)

Author: lanluo-nvidia

docker/Dockerfile

@@ -16,8 +16,7 @@ ENV PYTHON_VERSION=${PYTHON_VERSION}
 ENV DEBIAN_FRONTEND=noninteractive
 
 # Install basic dependencies
-RUN apt-get update
-RUN apt install -y build-essential manpages-dev wget zlib1g software-properties-common git libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget ca-certificates curl llvm libncurses5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev mecab-ipadic-utf8
+RUN apt-get update && apt-get install -y build-essential manpages-dev wget zlib1g software-properties-common git libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget ca-certificates curl llvm libncurses5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev mecab-ipadic-utf8
 
 # Install PyEnv and desired Python version
 ENV HOME="/root"
@@ -34,8 +33,7 @@ RUN pyenv global ${PYTHON_VERSION}
 # Install TensorRT + dependencies
 RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub
 RUN add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /"
-RUN apt-get update
-RUN TENSORRT_MAJOR_VERSION=`echo ${TENSORRT_VERSION} | cut -d '.' -f 1` && \
+RUN apt-get update && TENSORRT_MAJOR_VERSION=`echo ${TENSORRT_VERSION} | cut -d '.' -f 1` && \
     apt-get install -y libnvinfer${TENSORRT_MAJOR_VERSION}=${TENSORRT_VERSION}.* \
                        libnvinfer-plugin${TENSORRT_MAJOR_VERSION}=${TENSORRT_VERSION}.* \
                        libnvinfer-dev=${TENSORRT_VERSION}.* \
@@ -55,9 +53,9 @@ FROM base as torch-tensorrt-builder-base
 ARG ARCH="x86_64"
 ARG TARGETARCH="amd64"
 
-RUN apt-get update
-RUN apt-get install -y python3-setuptools
-RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub
+RUN apt-get update && \
+    apt-get install -y python3-setuptools && \
+    apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub
 
 RUN apt-get update &&\
     apt-get install -y --no-install-recommends locales ninja-build &&\

py/torch_tensorrt/dynamo/conversion/converter_utils.py

@@ -446,53 +446,47 @@ def create_constant(
         else:
             shape = list(torch_value.shape)
 
-        if torch_value is not None:
-
-            if torch_value.dtype == torch.uint8:
-                if is_tensorrt_version_supported("10.8.0"):
-                    if (
-                        target_quantized_type is None
-                        or target_quantized_type != trt.DataType.FP4
-                    ):
-                        # Iconstant layer does not support Uint8, it only support that FP4 data packed in uint8
-                        raise ValueError(
-                            "Currently supported target_quantized_type for uint8 is FP4, got {target_quantized_type=}"
-                        )
-                    shape[-1] = shape[-1] * 2
-                    weights = to_trt_weights(
-                        ctx,
-                        torch_value,
-                        name,
-                        "CONSTANT",
-                        "CONSTANT",
-                        dtype=trt.DataType.FP4,
-                        count=torch_value.numel() * 2,
-                    )
-                    constant = ctx.net.add_constant(
-                        shape,
-                        weights,
-                    )
-                    constant.name = name
-                    return constant.get_output(0)
-                else:
+        if torch_value.dtype == torch.uint8:
+            if is_tensorrt_version_supported("10.8.0"):
+                if (
+                    target_quantized_type is None
+                    or target_quantized_type != trt.DataType.FP4
+                ):
+                    # Iconstant layer does not support Uint8, it only support that FP4 data packed in uint8
                     raise ValueError(
-                        "Currently FP4 is only supported in TensorRT 10.8.0 and above"
+                        "Currently supported target_quantized_type for uint8 is FP4, got {target_quantized_type=}"
                     )
-            # Record the weight in ctx for refit and cpu memory reference
+                shape[-1] = shape[-1] * 2
+                weights = to_trt_weights(
+                    ctx,
+                    torch_value,
+                    name,
+                    "CONSTANT",
+                    "CONSTANT",
+                    dtype=trt.DataType.FP4,
+                    count=torch_value.numel() * 2,
+                )
+                constant = ctx.net.add_constant(
+                    shape,
+                    weights,
+                )
+                constant.name = name
+                return constant.get_output(0)
+            else:
+                raise ValueError(
+                    "Currently FP4 is only supported in TensorRT 10.8.0 and above"
+                )
+        # Record the weight in ctx for refit and cpu memory reference
 
-            # Convert the torch.Tensor to a trt.Weights object
-            trt_weights = to_trt_weights(ctx, torch_value, name, "CONSTANT", "CONSTANT")
-            constant = ctx.net.add_constant(
-                shape,
-                trt_weights,
-            )
-            constant.name = name
+        # Convert the torch.Tensor to a trt.Weights object
+        trt_weights = to_trt_weights(ctx, torch_value, name, "CONSTANT", "CONSTANT")
+        constant = ctx.net.add_constant(
+            shape,
+            trt_weights,
+        )
+        constant.name = name
 
-            return constant.get_output(0)
-        else:
-            raise ValueError(
-                f"Cannot convert tensor '{name}' to a TensorRT constant because its value is None."
-            )
+        return constant.get_output(0)
 
 
 def get_trt_tensor(

py/torch_tensorrt/dynamo/conversion/impl/deconv.py

@@ -109,13 +109,16 @@ def deconvNd(
     assert len(kernel_shape) > 0, "Deconvolution kernel shape must be non-empty"
 
     # add deconv layer
+    if groups is not None:
+        num_output_maps = num_output_maps * groups
     deconv_layer = ctx.net.add_deconvolution_nd(
         input=input,
-        num_output_maps=num_output_maps * groups,
+        num_output_maps=num_output_maps,
         kernel_shape=kernel_shape,
         kernel=trt.Weights() if isinstance(weight, TRTTensor) else weight,
         bias=trt.Weights() if isinstance(bias, TRTTensor) else bias,
     )
+    assert deconv_layer is not None, "Deconvolution layer is None"
     set_layer_name(deconv_layer, target, name, source_ir)
 
     # If the weight is a TRTTensor, set it as an input of the layer
@@ -145,7 +148,6 @@ def deconvNd(
             if output_padding is not None
             else output_padding
         )
-
     # Set relevant attributes of deconvolution layer
     if padding is not None:
         deconv_layer.padding_nd = padding
@@ -156,19 +158,20 @@ def deconvNd(
     if groups is not None:
         deconv_layer.num_groups = groups
 
-    ndims = len(padding)
-    pre_padding_values = []
-    post_padding_values = []
+    if padding is not None:
+        ndims = len(padding)
+        pre_padding_values = []
+        post_padding_values = []
 
-    for dim in range(ndims):
-        pre_padding = padding[dim]
-        post_padding = padding[dim] - output_padding[dim]
+        for dim in range(ndims):
+            pre_padding = padding[dim]
+            post_padding = padding[dim] - output_padding[dim]
 
-        pre_padding_values.append(pre_padding)
-        post_padding_values.append(post_padding)
+            pre_padding_values.append(pre_padding)
+            post_padding_values.append(post_padding)
 
-    deconv_layer.pre_padding = tuple(pre_padding_values)
-    deconv_layer.post_padding = tuple(post_padding_values)
+        deconv_layer.pre_padding = tuple(pre_padding_values)
+        deconv_layer.post_padding = tuple(post_padding_values)
 
     result = deconv_layer.get_output(0)
 

py/torch_tensorrt/dynamo/lowering/_decompositions.py

@@ -331,7 +331,7 @@ def reduce_operation_with_scatter(
             scatter_tensor = initial_tensor
         else:
             # This case would not be encountered from torch itself
-            print("Invalid Operation for Reduce op!!")
+            raise ValueError(f"Invalid Operation for Reduce op: {self}")
 
         operation_rhs = torch.scatter(scatter_tensor, dim, index_tensor, src_tensor)
         device = to_torch_device(scatter_tensor.device)

py/torch_tensorrt/dynamo/utils.py

@@ -826,13 +826,13 @@ def get_output_metadata(
     return [node.meta for node in nodes]
 
 
-def get_output_dtypes(output: Any, truncate_doulbe: bool = False) -> List[dtype]:
+def get_output_dtypes(output: Any, truncate_double: bool = False) -> List[dtype]:
     output_dtypes = []
     if isinstance(output, torch.fx.node.Node):
         if "val" in output.meta:
             output_meta = output.meta["val"]
             if isinstance(output_meta, (FakeTensor, torch.Tensor)):
-                if truncate_doulbe and output_meta.dtype == torch.float64:
+                if truncate_double and output_meta.dtype == torch.float64:
                     output_dtypes.append(dtype.float32)
                 else:
                     output_dtypes.append(dtype._from(output_meta.dtype))

py/torch_tensorrt/fx/converters/converter_utils.py

@@ -173,7 +173,10 @@ def to_numpy(
     """
     output = None
 
-    if value is None or isinstance(value, np.ndarray):
+    if value is None:
+        return None
+
+    elif isinstance(value, np.ndarray):
         output = value
 
     elif isinstance(value, torch.Tensor):

py/torch_tensorrt/fx/tools/timing_cache_utils.py

@@ -28,12 +28,12 @@ def get_timing_cache_trt(self, timing_cache_file: str) -> bytearray:
             return None
 
     def update_timing_cache(
-        self, timing_cache_file: str, serilized_cache: bytearray
+        self, timing_cache_file: str, serialized_cache: bytearray
     ) -> None:
         if not self.save_timing_cache:
             return
         timing_cache_file = self.get_file_full_name(timing_cache_file)
         with open(timing_cache_file, "wb") as local_cache:
             local_cache.seek(0)
-            local_cache.write(serilized_cache)
+            local_cache.write(serialized_cache)
             local_cache.truncate()