fix time plot

examples/benchmarking/plot_csv_file.py

@@ -1,7 +1,7 @@
 import csv
 from collections import defaultdict
 from dataclasses import dataclass, field
-from typing import Optional
+from typing import List, Optional
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -10,6 +10,10 @@ from matplotlib.ticker import ScalarFormatter
 from transformers import HfArgumentParser
 
 
+def list_field(default=None, metadata=None):
+    return field(default_factory=lambda: default, metadata=metadata)
+
+
 @dataclass
 class PlotArguments:
     """
@@ -37,6 +41,25 @@ class PlotArguments:
     figure_png_file: Optional[str] = field(
         default=None, metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."},
     )
+    short_model_names: Optional[List[str]] = list_field(
+        default=None, metadata={"help": "List of model names that are used instead of the ones in the csv file."}
+    )
+
+
+def can_convert_to_int(string):
+    try:
+        int(string)
+        return True
+    except ValueError:
+        return False
+
+
+def can_convert_to_float(string):
+    try:
+        float(string)
+        return True
+    except ValueError:
+        return False
 
 
 class Plot:
@@ -50,9 +73,16 @@ class Plot:
                 model_name = row["model"]
                 self.result_dict[model_name]["bsz"].append(int(row["batch_size"]))
                 self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]))
-                self.result_dict[model_name]["result"][(int(row["batch_size"]), int(row["sequence_length"]))] = row[
-                    "result"
-                ]
+                if can_convert_to_int(row["result"]):
+                    # value is not None
+                    self.result_dict[model_name]["result"][
+                        (int(row["batch_size"]), int(row["sequence_length"]))
+                    ] = int(row["result"])
+                elif can_convert_to_float(row["result"]):
+                    # value is not None
+                    self.result_dict[model_name]["result"][
+                        (int(row["batch_size"]), int(row["sequence_length"]))
+                    ] = float(row["result"])
 
     def plot(self):
         fig, ax = plt.subplots()
@@ -67,7 +97,7 @@ class Plot:
         for axis in [ax.xaxis, ax.yaxis]:
             axis.set_major_formatter(ScalarFormatter())
 
-        for model_name in self.result_dict.keys():
+        for model_name_idx, model_name in enumerate(self.result_dict.keys()):
             batch_sizes = sorted(list(set(self.result_dict[model_name]["bsz"])))
             sequence_lengths = sorted(list(set(self.result_dict[model_name]["seq_len"])))
             results = self.result_dict[model_name]["result"]
@@ -76,23 +106,33 @@ class Plot:
                 (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
             )
 
+            label_model_name = (
+                model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
+            )
+
             for inner_loop_value in inner_loop_array:
                 if self.args.plot_along_batch:
-                    y_axis_array = np.asarray([results[(x, inner_loop_value)] for x in x_axis_array], dtype=np.int)
+                    y_axis_array = np.asarray(
+                        [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results],
+                        dtype=np.int,
+                    )
                 else:
-                    y_axis_array = np.asarray([results[(inner_loop_value, x)] for x in x_axis_array], dtype=np.float32)
+                    y_axis_array = np.asarray(
+                        [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results],
+                        dtype=np.float32,
+                    )
 
                 (x_axis_label, inner_loop_label) = (
-                    ("batch_size", "sequence_length in #tokens")
-                    if self.args.plot_along_batch
-                    else ("sequence_length in #tokens", "batch_size")
+                    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                 )
 
-                x_axis_array = np.asarray(x_axis_array, np.int)
-                plt.scatter(x_axis_array, y_axis_array, label=f"{model_name} - {inner_loop_label}: {inner_loop_value}")
+                x_axis_array = np.asarray(x_axis_array, np.int)[: len(y_axis_array)]
+                plt.scatter(
+                    x_axis_array, y_axis_array, label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"
+                )
                 plt.plot(x_axis_array, y_axis_array, "--")
 
-            title_str += f" {model_name} vs."
+            title_str += f" {label_model_name} vs."
 
         title_str = title_str[:-4]
         y_axis_label = "Time in s" if self.args.is_time else "Memory in MB"

examples/benchmarking/time_xla_1.csv

@@ -0,0 +1,4 @@
+model,batch_size,sequence_length,result
+aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,8,512,0.2032
+aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,64,512,1.5279
+aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,256,512,6.1837

src/transformers/benchmark/benchmark_args_utils.py

@@ -74,12 +74,6 @@ class BenchmarkArguments:
             "help": "Don't use multiprocessing for memory and speed measurement. It is highly recommended to use multiprocessing for accurate CPU and GPU memory measurements. This option should only be used for debugging / testing and on TPU."
         },
     )
-    with_lm_head: bool = field(
-        default=False,
-        metadata={
-            "help": "Use model with its language model head (MODEL_WITH_LM_HEAD_MAPPING instead of MODEL_MAPPING)"
-        },
-    )
     inference_time_csv_file: str = field(
         default=f"inference_time_{round(time())}.csv",
         metadata={"help": "CSV filename used if saving time results to csv."},