Adds an example for resnet50 in half precision

examples/resnet/resnet-50-fp16.py

@@ -0,0 +1,85 @@
+from torchvision.models import resnet50, ResNet50_Weights
+import torch
+import numpy as np
+from shark_turbine.aot import *
+import iree.runtime as rt
+
+# Loading feature extractor and pretrained model from huggingface
+# extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-18")
+model = resnet50(weights="DEFAULT")
+float_model = model.eval().float()
+model = model.eval().half()
+
+
+# define a function to do inference
+# this will get passed to the compiled module as a jittable function
+def vision_forward(pixel_values_tensor: torch.Tensor):
+    with torch.no_grad():
+        logits = model.forward(pixel_values_tensor)
+    predicted_id = torch.argmax(logits, -1)
+    return predicted_id
+
+
+def vision_forward_float(pixel_values_tensor: torch.Tensor):
+    with torch.no_grad():
+        logits = float_model.forward(pixel_values_tensor)
+    predicted_id = torch.argmax(logits, -1)
+    return predicted_id
+
+
+# a dynamic module for doing inference
+# this will be compiled AOT to a memory buffer
+class Resnet50_f16(CompiledModule):
+    params = export_parameters(model)
+
+    def forward(self, x=AbstractTensor(None, 3, 224, 224, dtype=torch.float16)):
+        # set a constraint for the dynamic number of batches
+        # interestingly enough, it doesn't seem to limit BATCH_SIZE
+        const = [x.dynamic_dim(0) < 16]
+        return jittable(vision_forward)(x, constraints=const)
+
+
+# build an mlir module with 1-shot exporter
+exported = export(Resnet50_f16)
+# compile exported module to a memory buffer
+compiled_binary = exported.compile(save_to=None)
+
+
+# return type is rt.array_interop.DeviceArray
+# np.array of outputs can be accessed via to_host() method
+def shark_infer(x):
+    config = rt.Config("local-task")
+    vmm = rt.load_vm_module(
+        rt.VmModule.wrap_buffer(config.vm_instance, compiled_binary.map_memory()),
+        config,
+    )
+    y = vmm.forward(x)
+    return y
+
+
+# prints the text corresponding to output label codes
+def print_labels(class_id):
+    weights = ResNet50_Weights.DEFAULT
+    for l in class_id:
+        print(weights.meta["categories"][l])
+
+
+# finds discrepancies between id0 and id1
+def largest_error(array1, array2):
+    absolute_diff = np.abs(array1 - array2)
+    max_error = np.max(absolute_diff)
+    return max_error
+
+
+# load some examples and check for discrepancies between
+# compiled module and standard inference (forward function)
+
+x = torch.randn((10, 3, 224, 224), dtype=torch.float16)
+x_float = torch.randn((10, 3, 224, 224), dtype=torch.float32)
+y0 = shark_infer(x).to_host()
+float_model = float_model.float()
+y1 = np.asarray(vision_forward_float(x_float))
+print_labels(y0)
+print(
+    f"Largest error between turbine (fp16) and pytorch (fp32) baseline is {largest_error(y0,y1)}"
+)