Updates for 3.2 release (#1065)

test/python/gemm/gemm_batched.py

@@ -0,0 +1,139 @@
+#################################################################################################
+#
+# Copyright (c) 2023 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+#################################################################################################
+
+"""
+High-level tests for running batched GEMMs
+"""
+
+from functools import partial
+from math import prod
+
+import cutlass
+import logging
+import torch
+import unittest
+
+from cutlass.backend.test.utils import LayoutCombination, add_test_gemm
+from cutlass.backend.utils.device import device_cc
+
+cutlass.set_log_level(logging.WARNING)
+
+torch.manual_seed(2023)
+
+
+def pytorch_reference(A, B, C, alpha, beta):
+    # Get the batch count. Assume that any of A, B, and C
+    # with a batch dimension ahve matching batch count. Thus,
+    # we break out of the loop once we have found the first
+    # tensor containing a batch dimension.
+    batch_count = (1,)
+    for tensor in [A, B, C]:
+        if len(tensor.shape) > 2:
+            batch_count = tensor.shape[:-2]
+            break
+
+    int_batch_count = prod(batch_count)
+
+    def add_batch(tensor):
+        if len(tensor.shape) == 2:
+            return tensor.unsqueeze(0).repeat(int_batch_count, 1, 1)
+        else:
+            return tensor.reshape(-1, tensor.size(-2), tensor.size(-1))
+
+    # Reshape tensors to have batch dimension
+    A = add_batch(A)
+    B = add_batch(B)
+    C = add_batch(C)
+
+    ret = (torch.bmm(A, B) * alpha) + (C * beta)
+    reshape_vals = batch_count + C.shape[-2:]
+    return ret.reshape(*reshape_vals)
+
+
+def initialize(rows, cols, batch):
+    tensor = torch.randint(-3, 3, size=(rows*cols*prod(batch),), device='cuda').half()
+    if len(batch) > 0 and prod(batch) > 1:
+        reshape_vals = batch + (rows, cols)
+        return tensor.reshape(*reshape_vals)
+    else:
+        return tensor.reshape(rows, cols)
+
+
+class GemmF16Batched(unittest.TestCase):
+    def run_batched(self, batch_count: tuple, batch_A: bool, batch_B: bool, batch_C: bool):
+        M = 512
+        N = 256
+        K = 128
+        alpha = 1.
+        beta = 2.
+
+        A = initialize(M, K, batch_count if batch_A else (1,))
+        B = initialize(K, N, batch_count if batch_B else (1,))
+        C = initialize(M, N, batch_count if batch_C else (1,))
+        D = initialize(M, N, batch_count)
+
+        plan = cutlass.op.Gemm(A=A, B=B, C=C, D=D, element_accumulator=cutlass.DataType.f32)
+        plan.run(A, B, C, D, alpha, beta)
+        reference = pytorch_reference(A, B, C, alpha, beta)
+        assert reference.equal(D)
+
+    def test_batched_ABC(self):
+        self.run_batched((3,), True, True, True)
+        self.run_batched((2, 3), True, True, True)
+
+    def test_batched_AB(self):
+        self.run_batched((3,), True, True, False)
+        self.run_batched((2, 3), True, True, False)
+
+    def test_batched_AC(self):
+        self.run_batched((3,), True, False, True)
+        self.run_batched((2, 3), True, False, True)
+
+    def test_batched_BC(self):
+        self.run_batched((3,), False, True, True)
+        self.run_batched((2, 3), False, True, True)
+
+    def test_batched_A(self):
+        self.run_batched((3,), True, False, False)
+        self.run_batched((2, 3), True, False, False)
+
+    def test_batched_B(self):
+        self.run_batched((3,), False, True, False)
+        self.run_batched((2, 3), False, True, False)
+
+    def test_batched_C(self):
+        self.run_batched((3,), False, False, True)
+        self.run_batched((2, 3), False, False, True)
+
+
+if __name__ == '__main__':
+    unittest.main()