cherry pick refit error 3170 from main to release/2.5 branch (#3236)

Co-authored-by: cehongwang <[email protected]>
Co-authored-by: Evan Li <[email protected]>

Author: 3 people

examples/dynamo/refit_engine_example.py

@@ -70,6 +70,7 @@
     min_block_size=min_block_size,
     torch_executed_ops=torch_executed_ops,
     make_refittable=True,
+    reuse_cached_engines=False,
 )  # Output is a torch.fx.GraphModule
 
 # Save the graph module as an exported program

py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py

@@ -476,12 +476,18 @@ def _save_weight_mapping(self) -> None:
                     # Retrieve each weight name(s) in state_dict
                     if layer_type == "CONSTANT":
                         if "embedding" in suffix:
-                            sd_weight_name = f"{sd_weight_name}.{torch_attr[0]}"
+                            sd_weight_name = f"{sd_weight_name}.weight"
                         elif "weight" in suffix or "mm_other" in suffix:
                             # Linear layer weight
-                            sd_weight_name = f"{sd_weight_name}.{torch_attr[0]}"
+                            sd_weight_name = f"{sd_weight_name}.weight"
+                        elif "running_mean" in suffix:
+                            # Linear layer weight
+                            sd_weight_name = f"{sd_weight_name}.running_mean"
+                        elif "running_var" in suffix:
+                            # Linear layer weight
+                            sd_weight_name = f"{sd_weight_name}.running_var"
                         else:
-                            sd_weight_name = f"{sd_weight_name}.{torch_attr[1]}"
+                            sd_weight_name = f"{sd_weight_name}.bias"
                     elif layer_type == "SCALE":
                         # Batch norm needs all weights to calculate scale and shift
                         sd_weight_name = [f"{sd_weight_name}.{n}" for n in torch_attr]

py/torch_tensorrt/dynamo/conversion/impl/normalization/ops.py

@@ -50,14 +50,27 @@ def batch_norm(
     # Save the original output shape for later use
     output_shape = input.shape
 
-    if weight is None:
-        weight = get_trt_tensor(ctx, 1.0, f"{name}_weight")
-    if bias is None:
-        bias = get_trt_tensor(ctx, 0.0, f"{name}_bias")
-    if running_mean is None:
-        running_mean = get_trt_tensor(ctx, 0.0, f"{name}_running_mean")
-    if running_var is None:
-        running_var = get_trt_tensor(ctx, 1.0, f"{name}_running_var")
+    # We name the weight here according to the state_dict name
+    weight = (
+        get_trt_tensor(ctx, 1.0, f"{name}_weight")
+        if weight is None
+        else get_trt_tensor(ctx, weight, f"{name}_weight")
+    )
+    bias = (
+        get_trt_tensor(ctx, 0.0, f"{name}_bias")
+        if bias is None
+        else get_trt_tensor(ctx, bias, f"{name}_bias")
+    )
+    running_mean = (
+        get_trt_tensor(ctx, 0.0, f"{name}_running_mean")
+        if running_mean is None
+        else get_trt_tensor(ctx, running_mean, f"{name}_running_mean")
+    )
+    running_var = (
+        get_trt_tensor(ctx, 1.0, f"{name}_running_var")
+        if running_var is None
+        else get_trt_tensor(ctx, running_var, f"{name}_running_var")
+    )
 
     # eps_tensor for numerical stability
     eps_tensor = get_trt_tensor(ctx, eps, f"{name}_eps")

tests/py/dynamo/models/test_model_refit.py

@@ -35,8 +35,8 @@
 @pytest.mark.unit
 def test_mapping():
 
-    model = models.resnet18(pretrained=True).eval().to("cuda")
-    model2 = models.resnet18(pretrained=False).eval().to("cuda")
+    model = models.resnet18(pretrained=False).eval().to("cuda")
+    model2 = models.resnet18(pretrained=True).eval().to("cuda")
     inputs = [torch.randn((1, 3, 224, 224)).to("cuda")]
     trt_input = [
         torchtrt.Input(i.shape, dtype=torch.float, format=torch.contiguous_format)
@@ -58,6 +58,7 @@ def test_mapping():
         debug=debug,
         min_block_size=min_block_size,
         make_refittable=True,
+        reuse_cached_engines=False,
     )
     settings = trt_gm._run_on_acc_0.settings
     runtime = trt.Runtime(TRT_LOGGER)
@@ -110,6 +111,7 @@ def test_refit_one_engine_with_weightmap():
         debug=debug,
         min_block_size=min_block_size,
         make_refittable=True,
+        reuse_cached_engines=False,
     )
 
     new_trt_gm = refit_module_weights(
@@ -141,8 +143,8 @@ def test_refit_one_engine_with_weightmap():
 @pytest.mark.unit
 def test_refit_one_engine_no_map_with_weightmap():
 
-    model = models.resnet18(pretrained=True).eval().to("cuda")
-    model2 = models.resnet18(pretrained=False).eval().to("cuda")
+    model = models.resnet18(pretrained=False).eval().to("cuda")
+    model2 = models.resnet18(pretrained=True).eval().to("cuda")
     inputs = [torch.randn((1, 3, 224, 224)).to("cuda")]
     enabled_precisions = {torch.float}
     debug = False
@@ -160,6 +162,7 @@ def test_refit_one_engine_no_map_with_weightmap():
         debug=debug,
         min_block_size=min_block_size,
         make_refittable=True,
+        reuse_cached_engines=False,
     )
 
     trt_gm._run_on_acc_0.weight_name_map = None
@@ -192,8 +195,8 @@ def test_refit_one_engine_no_map_with_weightmap():
 @pytest.mark.unit
 def test_refit_one_engine_with_wrong_weightmap():
 
-    model = models.resnet18(pretrained=True).eval().to("cuda")
-    model2 = models.resnet18(pretrained=False).eval().to("cuda")
+    model = models.resnet18(pretrained=False).eval().to("cuda")
+    model2 = models.resnet18(pretrained=True).eval().to("cuda")
     inputs = [torch.randn((1, 3, 224, 224)).to("cuda")]
     enabled_precisions = {torch.float}
     debug = False
@@ -211,6 +214,7 @@ def test_refit_one_engine_with_wrong_weightmap():
         debug=debug,
         min_block_size=min_block_size,
         make_refittable=True,
+        reuse_cached_engines=False,
     )
     # Manually Deleted all batch norm layer. This suppose to fail the fast refit
     trt_gm._run_on_acc_0.weight_name_map = {
@@ -268,6 +272,7 @@ def test_refit_one_engine_bert_with_weightmap():
         debug=debug,
         min_block_size=min_block_size,
         make_refittable=True,
+        reuse_cached_engines=False,
     )
 
     new_trt_gm = refit_module_weights(
@@ -302,8 +307,8 @@ def test_refit_one_engine_bert_with_weightmap():
 @pytest.mark.unit
 def test_refit_one_engine_inline_runtime__with_weightmap():
     trt_ep_path = os.path.join(tempfile.gettempdir(), "compiled.ep")
-    model = models.resnet18(pretrained=True).eval().to("cuda")
-    model2 = models.resnet18(pretrained=False).eval().to("cuda")
+    model = models.resnet18(pretrained=False).eval().to("cuda")
+    model2 = models.resnet18(pretrained=True).eval().to("cuda")
     inputs = [torch.randn((1, 3, 224, 224)).to("cuda")]
     enabled_precisions = {torch.float}
     debug = False
@@ -321,6 +326,7 @@ def test_refit_one_engine_inline_runtime__with_weightmap():
         debug=debug,
         min_block_size=min_block_size,
         make_refittable=True,
+        reuse_cached_engines=False,
     )
     torchtrt.save(trt_gm, trt_ep_path, inputs=inputs)
     trt_gm = torch.export.load(trt_ep_path)
@@ -348,8 +354,8 @@ def test_refit_one_engine_inline_runtime__with_weightmap():
 @pytest.mark.unit
 def test_refit_one_engine_python_runtime_with_weightmap():
 
-    model = models.resnet18(pretrained=True).eval().to("cuda")
-    model2 = models.resnet18(pretrained=False).eval().to("cuda")
+    model = models.resnet18(pretrained=False).eval().to("cuda")
+    model2 = models.resnet18(pretrained=True).eval().to("cuda")
     inputs = [torch.randn((1, 3, 224, 224)).to("cuda")]
     enabled_precisions = {torch.float}
     debug = False
@@ -367,6 +373,7 @@ def test_refit_one_engine_python_runtime_with_weightmap():
         debug=debug,
         min_block_size=min_block_size,
         make_refittable=True,
+        reuse_cached_engines=False,
     )
 
     new_trt_gm = refit_module_weights(
@@ -438,6 +445,7 @@ def forward(self, x):
         min_block_size=min_block_size,
         make_refittable=True,
         torch_executed_ops=torch_executed_ops,
+        reuse_cached_engines=False,
     )
 
     new_trt_gm = refit_module_weights(
@@ -487,6 +495,7 @@ def test_refit_one_engine_without_weightmap():
         debug=debug,
         min_block_size=min_block_size,
         make_refittable=True,
+        reuse_cached_engines=False,
     )
 
     new_trt_gm = refit_module_weights(
@@ -538,6 +547,7 @@ def test_refit_one_engine_bert_without_weightmap():
         debug=debug,
         min_block_size=min_block_size,
         make_refittable=True,
+        reuse_cached_engines=False,
     )
 
     new_trt_gm = refit_module_weights(
@@ -591,6 +601,7 @@ def test_refit_one_engine_inline_runtime_without_weightmap():
         debug=debug,
         min_block_size=min_block_size,
         make_refittable=True,
+        reuse_cached_engines=False,
     )
     torchtrt.save(trt_gm, trt_ep_path, inputs=inputs)
     trt_gm = torch.export.load(trt_ep_path)
@@ -637,6 +648,7 @@ def test_refit_one_engine_python_runtime_without_weightmap():
         debug=debug,
         min_block_size=min_block_size,
         make_refittable=True,
+        reuse_cached_engines=False,
     )
 
     new_trt_gm = refit_module_weights(
@@ -708,6 +720,7 @@ def forward(self, x):
         min_block_size=min_block_size,
         make_refittable=True,
         torch_executed_ops=torch_executed_ops,
+        reuse_cached_engines=False,
     )
 
     new_trt_gm = refit_module_weights(