initial implementation for logistic_regression

PIMbench/layer-normalization/baselines/layer_norm.py

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+import argparse
+import torch
+import torch.nn as nn
+import time
+
+# Function to perform layer normalization
+def perform_layer_norm(input_tensor, norm_layer, device):
+    input_tensor = input_tensor.to(device)
+    norm_layer = norm_layer.to(device)
+
+    start_time = time.time()
+    output = norm_layer(input_tensor)
+    if device.type == 'cuda':
+        torch.cuda.synchronize()  # Wait for GPU ops to complete
+    end_time = time.time()
+
+    elapsed_time = end_time - start_time
+    return elapsed_time
+
+# Main function
+def main(args):
+    # Set device
+    device = torch.device('cuda' if args.cuda and torch.cuda.is_available() else 'cpu')
+    print(f"[INFO] Using device: {device}")
+
+    # Input tensor: [B, C, H, W]
+    input_tensor = torch.randn(args.batch_size, args.input_channels, args.input_height, args.input_width)
+
+    # LayerNorm normalized over [C, H, W] for each sample
+    normalized_shape = [args.input_channels, args.input_height, args.input_width]
+    norm_layer = nn.LayerNorm(normalized_shape, eps=args.epsilon)
+
+    # Run layer normalization
+    time_taken = perform_layer_norm(input_tensor, norm_layer, device)
+    print(f"[INFO] Time taken for layer normalization: {time_taken * 1000:.6f} ms")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="CNN-style Layer Normalization on CPU/GPU")
+
+    parser.add_argument("-b", "--batch_size", type=int, default=64, help="Batch size")
+    parser.add_argument("-c", "--input_channels", type=int, default=64, help="Number of input channels")
+    parser.add_argument("-r", "--input_height", type=int, default=32, help="Input height")
+    parser.add_argument("-w", "--input_width", type=int, default=32, help="Input width")
+    parser.add_argument("-eps", "--epsilon", type=float, default=1e-5, help="Epsilon for LayerNorm")
+    parser.add_argument("-cuda", "--cuda", action='store_true', help="Use CUDA if available")
+
+    args = parser.parse_args()
+    main(args)

PIMbench/logistic-regression/Makefile

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+# Makefile: C++ version of logistic regression
+# Copyright (c) 2024 University of Virginia
+# This file is licensed under the MIT License.
+# See the LICENSE file in the root of this repository for more details.
+
+SUBDIRS := PIM
+
+.PHONY: debug perf dramsim3_integ clean $(SUBDIRS)
+.DEFAULT_GOAL := perf
+
+USE_OPENMP ?= 0
+
+debug perf dramsim3_integ clean: $(SUBDIRS)
+
+$(SUBDIRS):
+	$(MAKE) -C $@ $(MAKECMDGOALS) USE_OPENMP=$(USE_OPENMP)

PIMbench/logistic-regression/PIM/Makefile

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+# Makefile: C++ version of logistic regression
+# Copyright (c) 2024 University of Virginia
+# This file is licensed under the MIT License.
+# See the LICENSE file in the root of this repository for more details.
+
+PROJ_ROOT = ../../..
+include ${PROJ_ROOT}/Makefile.common
+
+# make USE_OPENMP=1
+USE_OPENMP ?= 0
+ifeq ($(USE_OPENMP),1)
+	CXXFLAGS += -fopenmp
+endif
+
+EXEC := lr.out
+SRC := lr.cpp
+
+debug perf dramsim3_integ: $(EXEC)
+
+$(EXEC): $(SRC) $(DEPS)
+	$(CXX) $< $(CXXFLAGS) -o $@
+
+clean:
+	rm -rf $(EXEC) *.dSYM