Sync v2.0 version of code to github repo

test/operators/test_cutlass_scaled_mm.py

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+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" UT for air_topp_sampling kernel """
+
+import subprocess
+import unittest
+
+import numpy as np
+import paddle
+
+from fastdeploy.model_executor.layers.quantization.ops import (
+    cutlass_scaled_mm, scaled_fp8_quant)
+
+
+class Test(unittest.TestCase):
+
+    def setUp(self):
+        """
+        Initialize.
+        """
+        paddle.seed(2024)
+        np.random.seed(42)
+        self.prop = paddle.device.cuda.get_device_properties()
+        self.sm_version = self.prop.major * 10 + self.prop.minor
+        print(self.prop)
+        print(paddle.__git_commit__)
+        nvcc_output = subprocess.check_output(["nvcc", "--version"],
+                                              universal_newlines=True)
+        output = nvcc_output.split()
+        release_idx = output.index("release") + 1
+        self.nvcc_cuda_version = float(output[release_idx].split(",")[0])
+
+    def test_cutlass_scaled_mm_fp8(self):
+        """
+        Check cutlass_scaled_mm output.
+        """
+        if self.sm_version < 89:
+            self.skipTest(
+                "cutlass_scaled_mm with fp8 input only support sm89+")
+        M = 32
+        N = 1024
+        K = 1024
+        a = paddle.rand([M, K], dtype=paddle.bfloat16)
+        b = paddle.rand([N, K], dtype=paddle.bfloat16)
+        b_q, b_scales = scaled_fp8_quant(b, use_per_token_if_dynamic=False)
+        a_q, a_scales = scaled_fp8_quant(a, use_per_token_if_dynamic=True)
+
+        # Ensure quantized tensors and scales are valid
+        assert a_q.numel() > 0, "Quantized tensor 'a_q' must not be empty"
+        assert b_q.numel() > 0, "Quantized tensor 'b_q' must not be empty"
+        assert a_scales.numel(
+        ) > 0, "Scale tensor 'a_scales' must not be empty"
+        assert b_scales.numel(
+        ) > 0, "Scale tensor 'b_scales' must not be empty"
+
+        bias = paddle.rand([N], dtype=paddle.bfloat16)
+        baseline = paddle.matmul(a, b, transpose_x=False, transpose_y=True)
+        if bias is not None:
+            baseline = paddle.add(baseline, bias)
+        out_type = a.dtype
+        c = cutlass_scaled_mm(a_q, b_q, a_scales, b_scales, out_type, bias)
+        euqal = np.allclose(baseline.numpy(), c.numpy(), rtol=1e-2, atol=1e-2)
+        print(euqal)  #
+
+    def test_cutlass_scaled_mm_int8(self):
+        """
+        Check cutlass_scaled_mm output.
+        """
+        M = 32
+        N = 1024
+        K = 512
+        a = paddle.rand([M, K], dtype=paddle.bfloat16)
+        b = paddle.rand([N, K], dtype=paddle.bfloat16)
+        a_scales = (a.cast(paddle.float32).abs().max(axis=-1) / 127)[:, None]
+        a_q = paddle.clip(a / a_scales, -127, 127).cast(paddle.int8)
+        b_scales = (b.cast(paddle.float32).abs().max(axis=-1) / 127)[:, None]
+        b_q = paddle.clip(b / b_scales, -127, 127).cast(paddle.int8)
+
+        bias = paddle.rand([N], dtype=paddle.bfloat16)
+        baseline = paddle.matmul(a, b, transpose_x=False, transpose_y=True)
+        if bias is not None:
+            baseline = paddle.add(baseline, bias)
+        out_type = a.dtype
+        c = cutlass_scaled_mm(a_q, b_q, a_scales, b_scales, out_type, bias)
+        euqal = np.allclose(baseline.numpy(), c.numpy(), rtol=1e-2, atol=1e-2)
+        print(euqal)  #
+
+
+if __name__ == "__main__":
+    unittest.main()