Update app.py

app.py

@@ -1,47 +1,64 @@
 import gradio as gr
 import matplotlib.pyplot as plt
 
+import matplotlib.pyplot as plt
+
 
 def plot_forecast(num_param, batch_size, precision, seq_len):
     # Convert number (input as B)
     num_param = float(num_param) * 1e9
-    
+
     # Convert precision to bytes
     precision = {"float32": 4, "float16": 2, "bfloat16": 2}[precision]
 
     # Model Parameters: N×precision
-    y1 = num_param * precision / (1024**3)
-
-    # Activations: B×Sequence Length×K×precision
-    K = 4.6894e-04 * num_param + 1.8494e06
-    y2 = batch_size * seq_len * K * precision / (1024**3)
+    y1 = num_param * precision / (1000**3)
 
     # Optimizer States: 2×N×precision
-    y3 = 2 * num_param * precision / (1024**3)
+    y2 = 2 * num_param * precision / (1000**3)
+
+    # Activations: B×Sequence Length×K×precision
+    K = 4.6894e-4 * num_param + 1.8494e6
+    print(K)
+    y3 = batch_size * seq_len * K * precision / (1000**3)
 
     # Gradients: N×precision
-    y4 = num_param * 4 / (1024**3)
+    y4 = num_param * precision / (1000**3)
+
+    # Optimizer intermediates: N×precision
+    y5 = num_param * precision / (1000**3)
+
+    # Calculate total memory
+    total_memory = y1 + y2 + max(y3, y4 + y5)
 
     fig = plt.figure(figsize=(4, 4))
     ax = fig.add_subplot(111)
 
     # Create stacked bars
-    ax.bar(0, y1, color="r")
-    ax.bar(0, y2, bottom=y1, color="b")
-    ax.bar(0, y3, bottom=y1 + y2, color="g")
-    ax.bar(0, y4, bottom=y1 + y2 + y3, color="y")
+    bar_width = 0.5
+    ax.bar(0, y1, width=bar_width, color="r")
+    ax.bar(0, y2, bottom=y1, width=bar_width, color="b")
+    ax.bar(-bar_width / 4, y3, bottom=y1 + y2, width=bar_width / 2, color="g")
+    ax.bar(bar_width / 4, y4, bottom=y1 + y2, width=bar_width / 2, color="y")
+    ax.bar(bar_width / 4, y5, bottom=y1 + y2 + y4, width=bar_width / 2, color="c")
 
     # Add text labels inside the bars
-    ax.text(0, y1 / 2, "Model Parameters", ha="center", va="center", color="white", fontweight="bold")
-    ax.text(0, y1 + y2 / 2,"Activations", ha="center", va="center", color="white", fontweight="bold")
-    ax.text(0, y1 + y2 + y3 / 2, "Optimizer States", ha="center", va="center", color="white", fontweight="bold")
-    ax.text(0, y1 + y2 + y3 + y4 / 2, "Gradients", ha="center", va="center", color="white", fontweight="bold")
+    ax.text(0, y1 / 2, f"Model Parameters ({y1:.1f} GB)", ha="center", va="center", color="white", fontweight="bold")
+    ax.text(0, y1 + y2 / 2, f"Optimizer States ({y2:.1f} GB)", ha="center", va="center", color="white", fontweight="bold")
+    ax.text(-bar_width / 4, y1 + y2 + y3 / 2, f"Activations\n({y3:.1f} GB)", ha="center", va="center", color="white", fontweight="bold")
+    ax.text(bar_width / 4, y1 + y2 + y4 / 2, f"Gradients\n({y4:.1f} GB)", ha="center", va="center", color="white", fontweight="bold")
+    ax.text(bar_width / 4, y1 + y2 + y4 + y5 / 2, f"Optimizer\nintermediates\n({y5:.1f} GB)", ha="center", va="center", color="white", fontweight="bold")
 
-    # remove x axis
+    # Or as title
+    ax.set_title(f"Total Memory: {total_memory:.1f} GB", fontweight="bold")
+
+    # Remove x-axis
     ax.xaxis.set_visible(False)
 
     # Set GB as the unit for the y-axis
     ax.set_ylabel("Memory (GB)")
+
+    # Adjust layout
     fig.tight_layout()
     return fig
 
@@ -52,7 +69,7 @@ demo = gr.Interface(
         gr.Number(7, label="Number of parameters (B)"),
         gr.Radio([1, 2, 4, 8, 16, 32, 64, 128], value=8, label="Batch size"),
         gr.Radio(["float32", "float16", "bfloat16"], value="float32", label="Precision"),
-        gr.Slider(1, 1024, label="Sequence Length", step=1, value=128),
+        gr.Slider(1, 1000, label="Sequence Length", step=1, value=128),
     ],
     gr.Plot(label="forecast", format="png"),
 )