[PYTHON] Added automated benchmark script (#63)

This adds a bench functionality to the setup.py that can be used to run the benchmark suite and generates a bunch of csv files (and optionally plots)

python setup.py bench
python setup.py bench --with-plots
python setup.py bench --filter=cross_entropy

python/bench/bench_blocksparse.py

@@ -0,0 +1,87 @@
+import torch
+import triton
+
+# -------------------------------
+# Matrix Multiplication
+# -------------------------------
+
+nt = {False: 'n', True: 't'}
+square_confs = [
+    triton.testing.Benchmark(
+              x_names = ['M', 'N', 'K'],
+              x_vals  = [128, 256, 512, 1024, 2048, 3072, 4096, 6144],
+              y_name  = 'block',
+              y_vals  = [16, 32, 64],
+              y_lines = ['Block16', 'Block32', 'Block64'],
+              ylabel  = 'TFLOPS',
+              loglog  = False,
+              plot_name = f'{op_mode}-{layout_mode}-square-{nt[AT]}{nt[BT]}',
+              args = {'layout_mode': layout_mode, 'op_mode': op_mode,
+                      'AT': AT, 'BT': BT, 'dtype': torch.float16, 'provider': 'triton'}
+    )\
+    for AT in [False] for BT in [False] \
+    for op_mode in ['sdd', 'dsd', 'dds']  for layout_mode in ['tril', 'dense']
+]
+
+@triton.testing.perf_report(square_confs)
+def bench_matmul(M, N, K, block, layout_mode, op_mode, AT, BT, dtype, provider, warmup=5, rep=5):
+    Z, H = 1, 1
+    make_layout = {
+        'tril': lambda H, M, N: torch.tril(torch.ones((H, M, N), dtype=torch.int64)),\
+        'dense': lambda H, M, N: torch.ones(H, M, N, dtype=torch.int64),
+    }[layout_mode]
+    # create layout
+    shape = {'sdd': (M, N), 'dsd': (K, M) if AT else (M, K), 'dds': (N, K) if BT else (K, N)}[op_mode]
+    layout = make_layout(H, shape[0] // block, shape[1] // block)
+    # creat inputs
+    a = torch.randn((Z, H, K, M) if AT else (Z, H, M, K), dtype=dtype, device='cuda')
+    b = torch.randn((Z, H, N, K) if BT else (Z, H, K, N), dtype=dtype, device='cuda')
+    # create op
+    if provider == 'triton':
+        op = triton.ops.blocksparse.matmul(layout, block, op_mode, trans_a=AT, trans_b=BT)
+        # inputs
+        a = triton.testing.sparsify_tensor(a, layout, block) if op_mode == 'dsd' else a
+        b = triton.testing.sparsify_tensor(b, layout, block) if op_mode == 'dds' else b
+        ms = triton.testing.do_bench(lambda: op(a, b), warmup=warmup, rep=rep)
+        num_flops = {
+            'sdd': 2 * Z * K * float(layout.sum()) * block * block,\
+            'dsd': 2 * Z * N * float(layout.sum()) * block * block,\
+            'dds': 2 * Z * M * float(layout.sum()) * block * block
+        }[op_mode]*1e-12
+        triton_tflops = num_flops / ms * 1e3
+        return triton_tflops
+
+# -------------------------------
+# Softmax
+# -------------------------------
+
+square_confs = [
+    triton.testing.Benchmark(
+              x_names = ['M', 'N'],
+              x_vals  = [128, 256, 512, 1024, 2048, 3072, 4096, 6144],
+              y_name  = 'block',
+              y_vals  = [16, 32, 64],
+              y_lines = ['Block16', 'Block32', 'Block64'],
+              ylabel  = 'GBPS',
+              loglog  = False,
+              plot_name = f'{layout_mode}-square',
+              args = {'layout_mode': layout_mode, 'dtype': torch.float16, 'provider': 'triton'}
+    )\
+    for layout_mode in ['dense', 'tril']
+]
+
+@triton.testing.perf_report(square_confs)
+def bench_softmax(M, N, block, layout_mode, dtype, provider, warmup=10, rep=50):
+    Z, H = 1, 1
+    make_layout = {
+        'tril': lambda H, M, N: torch.tril(torch.ones((H, M, N), dtype=torch.int64)),
+        'dense': lambda H, M, N: torch.ones(H, M, N, dtype=torch.int64),
+    }[layout_mode]
+    layout = make_layout(H, M // block, N // block)
+    a = torch.randn((Z, H, M, N), dtype=dtype, device='cuda')
+    if provider == 'triton':
+        a = triton.testing.sparsify_tensor(a, layout, block)
+        op = triton.ops.blocksparse.softmax(layout, block)
+        ms = triton.testing.do_bench(lambda: op(a), warmup=warmup, rep=rep)
+        gbps = (2 * a.numel() * a.element_size() * 1e-9) / (ms * 1e-3)
+        return gbps