chore: Modify circle ci to reduce tests

Signed-off-by: Dheeraj Peri <[email protected]>

Author: peri044

.circleci/config.yml

@@ -727,6 +727,24 @@ commands:
       - store_artifacts:
           path: /tmp/testlogs
 
+  test-dynamo-torch_compile:
+    description: "Test the Dynamo torch_compile path"
+    steps:
+      - run:
+          name: Run Dynamo torch_compile E2E tests
+          command: |
+            cd py/torch_tensorrt/dynamo/
+            pushd test/
+            pip3 install timm
+            pip3 install transformers
+            pytest --junitxml=/tmp/artifacts/test_results/dynamo/test_results.xml --ir torch_compile
+            popd
+
+      - store_test_results:
+          path: /tmp/artifacts
+      - store_artifacts:
+          path: /tmp/testlogs
+
   test-dynamo-fx_ts:
     description: "Test the dynamo backend"
     steps:
@@ -947,6 +965,7 @@ jobs:
           command: pip3 install --pre /tmp/dist/x86_64-linux/*cp39-cp39*.whl
       # We install torch after torch-trt because pip automatically enforces the version constraint otherwise
       - dump-test-env
+      - test-dynamo-torch_compile
       - test-dynamo-fx_ts
 
   package-x86_64-linux:

py/torch_tensorrt/_Input.py

@@ -367,10 +367,10 @@ def example_tensor(self, optimization_profile_field: str = None) -> torch.Tensor
             )
 
         if self.shape_mode == Input._ShapeMode.STATIC:
-            return torch.randn(self.shape).to(
+            return torch.rand(self.shape).to(
                 dtype=self.dtype if not self.torch_dtype else self.torch_dtype
             )
         else:
-            return torch.randn(self.shape[optimization_profile_field]).to(
+            return torch.rand(self.shape[optimization_profile_field]).to(
                 dtype=self.dtype if not self.torch_dtype else self.torch_dtype
             )

py/torch_tensorrt/__init__.py

@@ -94,6 +94,7 @@ def _find_lib(name, paths):
 
 from torch_tensorrt import fx
 from torch_tensorrt import dynamo
+from torch_tensorrt.dynamo import torch_compile
 
 
 def _register_with_torch():

py/torch_tensorrt/_compile.py

@@ -16,6 +16,7 @@ class _IRType(Enum):
     ts = 0
     fx = 1
     fx_ts_compat = 2
+    torch_compile = 3
 
 
 class _ModuleType(Enum):
@@ -46,6 +47,7 @@ def _get_target_ir(module_type: _ModuleType, ir: str) -> _IRType:
 
     ir_targets_torchscript = any([ir == opt for opt in ["torchscript", "ts"]])
     ir_targets_fx = ir == "fx"
+    ir_targets_torch_compile = ir == "torch_compile"
     ir_targets_fx_ts_compat = ir == "fx_ts_compat"
 
     if module_is_tsable and ir_targets_torchscript:
@@ -54,6 +56,8 @@ def _get_target_ir(module_type: _ModuleType, ir: str) -> _IRType:
         return _IRType.fx
     elif module_is_fxable and ir_targets_fx_ts_compat:
         return _IRType.fx_ts_compat
+    elif module_is_fxable and ir_targets_torch_compile:
+        return _IRType.torch_compile
     else:
         if ir == "default":
             # Options are listed in order of preference
@@ -152,6 +156,10 @@ def compile(
             dynamic_batch=False,
             **kwargs,
         )
+    elif target_ir == _IRType.torch_compile:
+        return torch_tensorrt.dynamo.torch_compile(
+            module, inputs=inputs, enabled_precisions=enabled_precisions, **kwargs
+        )
     elif target_ir == _IRType.fx_ts_compat:
         return torch_tensorrt.dynamo.fx_ts_compat.compile(
             module, inputs=inputs, enabled_precisions=enabled_precisions, **kwargs

py/torch_tensorrt/dynamo/__init__.py

@@ -1 +1,2 @@
 from torch_tensorrt.dynamo import fx_ts_compat
+from .torch_compile import compile as torch_compile

py/torch_tensorrt/dynamo/test/conftest.py

@@ -0,0 +1,18 @@
+import pytest
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--ir",
+        metavar="Internal Representation",
+        nargs=1,
+        type=str,
+        required=True,
+        help="IR to compile with",
+        choices=["torch_compile", "fx_ts_compat"],
+    )
+
+
+@pytest.fixture
+def ir(request):
+    return request.config.getoption("--ir")[0]

py/torch_tensorrt/dynamo/test/test_dynamo_backend.py

@@ -0,0 +1,144 @@
+import torch
+import timm
+import pytest
+
+import torch_tensorrt as torchtrt
+import torchvision.models as models
+
+from transformers import BertModel
+
+from utils import COSINE_THRESHOLD, cosine_similarity
+
+
+@pytest.mark.unit
+def test_resnet18(ir):
+    model = models.resnet18(pretrained=True).eval().to("cuda")
+    input = torch.randn((1, 3, 224, 224)).to("cuda")
+
+    compile_spec = {
+        "inputs": [
+            torchtrt.Input(
+                input.shape, dtype=torch.float, format=torch.contiguous_format
+            )
+        ],
+        "device": torchtrt.Device("cuda:0"),
+        "enabled_precisions": {torch.float},
+        "ir": ir,
+    }
+
+    trt_mod = torchtrt.compile(model, **compile_spec)
+    cos_sim = cosine_similarity(model(input), trt_mod(input))
+    assert (
+        cos_sim > COSINE_THRESHOLD,
+        f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+    )
+
+
+@pytest.mark.unit
+def test_mobilenet_v2(ir):
+    model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
+    input = torch.randn((1, 3, 224, 224)).to("cuda")
+
+    compile_spec = {
+        "inputs": [
+            torchtrt.Input(
+                input.shape, dtype=torch.float, format=torch.contiguous_format
+            )
+        ],
+        "device": torchtrt.Device("cuda:0"),
+        "enabled_precisions": {torch.float},
+        "ir": ir,
+    }
+
+    trt_mod = torchtrt.compile(model, **compile_spec)
+    cos_sim = cosine_similarity(model(input), trt_mod(input))
+    assert (
+        cos_sim > COSINE_THRESHOLD,
+        f"Mobilenet v2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+    )
+
+
+@pytest.mark.unit
+def test_efficientnet_b0(ir):
+    model = timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+    input = torch.randn((1, 3, 224, 224)).to("cuda")
+
+    compile_spec = {
+        "inputs": [
+            torchtrt.Input(
+                input.shape, dtype=torch.float, format=torch.contiguous_format
+            )
+        ],
+        "device": torchtrt.Device("cuda:0"),
+        "enabled_precisions": {torch.float},
+        "ir": ir,
+    }
+
+    trt_mod = torchtrt.compile(model, **compile_spec)
+    cos_sim = cosine_similarity(model(input), trt_mod(input))
+    assert (
+        cos_sim > COSINE_THRESHOLD,
+        f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+    )
+
+
+@pytest.mark.unit
+def test_bert_base_uncased(ir):
+    model = BertModel.from_pretrained("bert-base-uncased").cuda().eval()
+    input = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
+    input2 = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
+
+    compile_spec = {
+        "inputs": [
+            torchtrt.Input(
+                input.shape,
+                dtype=input.dtype,
+                format=torch.contiguous_format,
+            ),
+            torchtrt.Input(
+                input.shape,
+                dtype=input.dtype,
+                format=torch.contiguous_format,
+            ),
+        ],
+        "device": torchtrt.Device("cuda:0"),
+        "enabled_precisions": {torch.float},
+        "truncate_long_and_double": True,
+        "debug": True,
+        "ir": ir,
+    }
+    trt_mod = torchtrt.compile(model, **compile_spec)
+
+    model_outputs = model(input, input2)
+    trt_model_outputs = trt_mod(input, input2)
+    for key in model_outputs.keys():
+        out, trt_out = model_outputs[key], trt_model_outputs[key]
+        cos_sim = cosine_similarity(out, trt_out)
+        assert (
+            cos_sim > COSINE_THRESHOLD,
+            f"HF BERT base-uncased TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+
+@pytest.mark.unit
+def test_resnet18_half(ir):
+    model = models.resnet18(pretrained=True).eval().to("cuda").half()
+    input = torch.randn((1, 3, 224, 224)).to("cuda").half()
+
+    compile_spec = {
+        "inputs": [
+            torchtrt.Input(
+                input.shape, dtype=torch.half, format=torch.contiguous_format
+            )
+        ],
+        "device": torchtrt.Device("cuda:0"),
+        "enabled_precisions": {torch.half},
+        "ir": ir,
+    }
+
+    trt_mod = torchtrt.compile(model, **compile_spec)
+    cos_sim = cosine_similarity(model(input), trt_mod(input))
+    assert (
+        cos_sim > COSINE_THRESHOLD,
+        f"Resnet50 Half TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+    )

py/torch_tensorrt/dynamo/test/utils.py

@@ -0,0 +1,54 @@
+import torch
+
+COSINE_THRESHOLD = 0.99
+
+
+def cosine_similarity(gt_tensor, pred_tensor):
+    gt_tensor = gt_tensor.flatten().to(torch.float32)
+    pred_tensor = pred_tensor.flatten().to(torch.float32)
+    if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
+        if torch.allclose(gt_tensor, pred_tensor, atol=1e-4, rtol=1e-4, equal_nan=True):
+            return 1.0
+    res = torch.nn.functional.cosine_similarity(gt_tensor, pred_tensor, dim=0, eps=1e-6)
+    res = res.cpu().detach().item()
+
+    return res
+
+
+def same_output_format(trt_output, torch_output):
+    # For each encountered collection type, ensure the torch and trt outputs agree
+    # on type and size, checking recursively through all member elements.
+    if isinstance(trt_output, tuple):
+        return (
+            isinstance(torch_output, tuple)
+            and (len(trt_output) == len(torch_output))
+            and all(
+                same_output_format(trt_entry, torch_entry)
+                for trt_entry, torch_entry in zip(trt_output, torch_output)
+            )
+        )
+    elif isinstance(trt_output, list):
+        return (
+            isinstance(torch_output, list)
+            and (len(trt_output) == len(torch_output))
+            and all(
+                same_output_format(trt_entry, torch_entry)
+                for trt_entry, torch_entry in zip(trt_output, torch_output)
+            )
+        )
+    elif isinstance(trt_output, dict):
+        return (
+            isinstance(torch_output, dict)
+            and (len(trt_output) == len(torch_output))
+            and (trt_output.keys() == torch_output.keys())
+            and all(
+                same_output_format(trt_output[key], torch_output[key])
+                for key in trt_output.keys()
+            )
+        )
+    elif isinstance(trt_output, set) or isinstance(trt_output, frozenset):
+        raise AssertionError(
+            "Unsupported output type 'set' encountered in output format check."
+        )
+    else:
+        return type(trt_output) is type(torch_output)