From 1d8581747781148143f1d40f795711c2e42e5914 Mon Sep 17 00:00:00 2001
From: Plutonium-239 <plutonium.239.811@gmail.com>
Date: Thu, 25 Jul 2024 18:30:25 +0530
Subject: [PATCH] add bn eval option in demo instead of hardcoding

(cherry picked from commit 164732d55efc76194e018ff2982a89031a7c3994)
---
 experiments/paper_demo.py | 121 ++++++++++++++++++++------------------
 1 file changed, 65 insertions(+), 56 deletions(-)

diff --git a/experiments/paper_demo.py b/experiments/paper_demo.py
index 27b9b87..0312850 100644
--- a/experiments/paper_demo.py
+++ b/experiments/paper_demo.py
@@ -14,7 +14,7 @@
 from experiments.util.models import prefix_in_pairs
 
 estimators = ["time", "memory"]
-estimators = ["memory"]
+# estimators = ["memory"]
 # estimators = ["time"]
 
 # improvements can be either speedups or savings based on context
@@ -27,70 +27,68 @@
 
 # repeat the experiment multiple times (generates multiple files to be aggregated by `get_best_results`)
 n_repeat = 5
+batchnorm_eval = True # BatchNorm in eval mode
 
-# CONV
+# ============== CONV CONFIG ==============
 # Valid choices for models are in models.conv_model_fns
+# models = [
+#     "deepmodel",
+#     "resnet101",
+#     "resnet18",
+#     "vgg16",  # "convnext_base",
+#     "fasterrcnn_resnet50_fpn_v2",
+#     "ssdlite320_mobilenet_v3_large",  # "retinanet_resnet50_fpn_v2",
+#     "deeplabv3_resnet101",
+#     "fcn_resnet101",
+#     "efficientnet_v2_l",
+#     "mobilenet_v3_large",
+#     "resnext101_64x4d",
+# ]
+# models = prefix_in_pairs("memsave_", models)
+# # models = ["resnet101", "memsave_resnet101_conv", "memsave_resnet101_conv+relu+bn", "memsave_resnet101_conv_full"]
+# batch_size = 64
+# input_channels = 3
+# input_HW = 224
+# num_classes = 1000
+# device = "cuda"
+# architecture = "conv"
+# cases = collect_results.select_cases(['All', 'Input', 'Conv', 'Norm', 'SurgicalFirst', 'SurgicalLast'])
+
+# ============== TRANSFORMER CONFIG ==============
+# Valid choices for models are in models.transformer_model_fns
 models = [
-    "deepmodel",
-    "resnet101",
-    "resnet18",
-    "vgg16",  # "convnext_base",
-    "fasterrcnn_resnet50_fpn_v2",
-    "ssdlite320_mobilenet_v3_large",  # "retinanet_resnet50_fpn_v2",
-    "deeplabv3_resnet101",
-    "fcn_resnet101",
-    "efficientnet_v2_l",
-    "mobilenet_v3_large",
-    "resnext101_64x4d",
+    "transformer",
+    "gpt2",
+    "bert",
+    "bart",
+    "roberta",
+    "t5",
+    "flan-t5",
+    # "xlm-roberta",
+    "mistral-7b",
+    "llama3-8b",
+    "phi3-4b",
 ]
-
-# models = ["resnet101", "memsave_resnet101_conv", "memsave_resnet101_conv+relu+bn", "memsave_resnet101_conv_full"]
-# models = ["resnet101", "memsave_resnet101_conv_full"]
-
 models = prefix_in_pairs("memsave_", models)
-# models = ["memsave_resnet101"]
 batch_size = 64
-input_channels = 3
-input_HW = 224
-num_classes = 1000
+input_channels = 2048
+input_HW = 256
+num_classes = 5000
 device = "cuda"
-architecture = "conv"
+architecture = "transformer"
+cases = collect_results.select_cases(["All", "Input", "Norm"])
 
-# LINEAR
+# ============== LINEAR CONFIG ==============
 # Valid choices for models are in models.linear_model_fns
-# models = ['deeplinearmodel']
+# models = ["deeplinearmodel"]
 # models += [f"memsave_{m}" for m in models]  # add memsave versions for each model
-# batch_size = 32768
-# input_channels = 3
-# input_HW = 64
+# batch_size = 1024
+# input_channels = 3  # ignored
+# input_HW = 64  # square of this is passed in estimate.py
 # num_classes = 1000
-# device = 'cuda'
-# architecture = 'linear' # use high batch size
-
-cases = [
-    None,  # ALL
-    [  # INPUT
-        "grad_input",
-        "no_grad_conv_weights",
-        "no_grad_conv_bias",
-        "no_grad_linear_weights",
-        "no_grad_linear_bias",
-        "no_grad_norm_weights",
-        "no_grad_norm_bias",
-    ],
-    [  # CONV
-        "no_grad_linear_weights",
-        "no_grad_linear_bias",
-        "no_grad_norm_weights",
-        "no_grad_norm_bias",
-    ],
-    [  # NORM
-        "no_grad_conv_weights",
-        "no_grad_conv_bias",
-        "no_grad_linear_weights",
-        "no_grad_linear_bias",
-    ],
-]
+# device = "cuda"
+# architecture = "linear"  # use high batch size
+# cases = collect_results.select_cases(["All", "Input", "Linear"])
 
 
 if __name__ == "__main__":
@@ -108,19 +106,30 @@
             cases,
             "results",
         )
+        bn_eval_str = "--bn_eval" if batchnorm_eval else ""
 
         for model in models:
+            B = batch_size
+            if model in prefix_in_pairs("memsave_", ["flan-t5"]):
+                B = 56
+            if model in prefix_in_pairs("memsave_", ["mistral-7b", "phi3-4b"]):
+                B = 16
+            if model in prefix_in_pairs("memsave_", ["llama3-8b"]):
+                B = 8
             for estimate in estimators:
                 outputs = []
 
                 collector.clear_file(estimate)
                 for case in cases:
                     pbar.update()
-                    pbar.set_description(f"{model} {estimate} case {case}")
+                    case_display = collect_results.case_inv_mapping[
+                        collect_results.make_case_str(case)
+                    ]
                     case_str = f"--case {' '.join(case)}" if case is not None else ""
+                    pbar.set_description(f"{model} {estimate} case {case_display}")
                     cmd = (
                         f"python experiments/util/estimate.py --architecture {architecture} --model {model} --estimate {estimate} {case_str} "
-                        + f"--device {device} -B {batch_size} -C_in {input_channels} -HW {input_HW} -n_class {num_classes}"
+                        + f"--device {device} -B {B} -C_in {input_channels} -HW {input_HW} -n_class {num_classes} {bn_eval_str}"
                     )
                     proc = subprocess.run(shlex.split(cmd), capture_output=True)
                     assert proc.stderr in [