Add tensorflow GEMM benchmark script (#1000)

* Modelled closely after the existing torch benchmark script but just adapted slightly for tensorflow

extra/gemm/tf_gemm.py

@@ -0,0 +1,33 @@
+import time
+import tensorflow as tf
+
+gpus = tf.config.list_physical_devices('GPU')
+if gpus:
+  try:
+    # Currently, memory growth needs to be the same across GPUs
+    for gpu in gpus:
+      tf.config.experimental.set_memory_growth(gpu, True)
+    logical_gpus = tf.config.list_logical_devices('GPU')
+    print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
+  except RuntimeError as e:
+    # Memory growth must be set before GPUs have been initialized
+    print(e)
+
+for dtype in [tf.float16, tf.float32]:
+  for N in [256, 512, 1024, 2048, 4096, 8192]:
+    FLOPS = N*N*N*2
+
+    b = tf.random.uniform((N, N), dtype=dtype)
+    c = tf.random.uniform((N, N), dtype=dtype)
+
+    b = tf.Variable(b)
+    c = tf.Variable(c)
+
+    def tf_prog(b, c):
+      st = time.perf_counter()
+      a = tf.matmul(b, c)
+      tf.debugging.check_numerics(a, "Nan or Inf in result") # Ensures that the calculation is done.
+      return time.perf_counter() - st
+
+    tm = min([tf_prog(b, c) for _ in range(20)])
+    print(f"{N*N:10d} {tm*1e6:9.2f} us, would be {FLOPS*1e-9/tm:9.2f} GFLOPS {N:4d}x{N:4d}x{N:4d} matmul in {dtype}")