【Hackathon 9th No.43、45】add speculate_get_padding_offset

tests/operators/test_speculate_get_padding_offset.py

@@ -0,0 +1,143 @@
+# Copyright (c) 2025 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.
+
+import unittest
+
+import numpy as np
+import paddle
+
+from fastdeploy.model_executor.ops.gpu import speculate_get_padding_offset
+
+
+def ref_speculate_get_padding_offset(cum_offsets, seq_lens, max_seq_len, token_num_data):
+    bsz = seq_lens.shape[0]
+
+    padding_offset = np.zeros([token_num_data], dtype=np.int32)
+    batch_id_per_token = np.zeros([token_num_data], dtype=np.int32)
+    cum_offsets_out = np.zeros([bsz], dtype=np.int32)
+    cu_seqlens_q = np.zeros([bsz + 1], dtype=np.int32)
+    cu_seqlens_k = np.zeros([bsz + 1], dtype=np.int32)
+
+    modified_indices = {
+        "padding_offset": [],
+        "cum_offsets_out": [],
+        "cu_seqlens_q": [],
+        "cu_seqlens_k": [],
+    }
+
+    cu_seqlens_q[0] = 0
+    cu_seqlens_k[0] = 0
+    modified_indices["cu_seqlens_q"].append(0)
+    modified_indices["cu_seqlens_k"].append(0)
+
+    for bi in range(bsz):
+        cum_offset = 0 if bi == 0 else cum_offsets[bi - 1]
+        cum_offsets_out[bi] = cum_offset
+        modified_indices["cum_offsets_out"].append(bi)
+
+        for i in range(seq_lens[bi]):
+            idx = bi * max_seq_len - cum_offset + i
+            if idx >= 0 and idx < token_num_data:
+                if idx == 0:
+                    print(idx, bi, cum_offset)
+                padding_offset[idx] = cum_offset
+                batch_id_per_token[idx] = bi
+                modified_indices["padding_offset"].append(idx)
+
+        cum_seq_len = (bi + 1) * max_seq_len - cum_offsets[bi]
+        cu_seqlens_q[bi + 1] = cum_seq_len
+        cu_seqlens_k[bi + 1] = cum_seq_len
+        modified_indices["cu_seqlens_q"].append(bi + 1)
+        modified_indices["cu_seqlens_k"].append(bi + 1)
+
+    return (
+        padding_offset,
+        cum_offsets_out,
+        cu_seqlens_q,
+        cu_seqlens_k,
+        modified_indices,
+        batch_id_per_token,
+    )
+
+
+class TestSpeculateGetPaddingOffset(unittest.TestCase):
+    def test_speculate_get_padding_offset(self):
+        test_case = {
+            "bsz": 4,
+            "max_seq_len": 10,
+            "token_num_data": 32,
+            "cum_offsets": np.array([2, 5, 8, 12], dtype=np.int32),
+            "seq_lens": np.array([8, 5, 7, 6], dtype=np.int32),
+            "seq_lens_encoder": np.array([1, 0, 1, 0], dtype=np.int32),
+        }
+
+        max_draft_tokens = 4
+
+        input_ids = np.random.randint(0, 1000, (test_case["bsz"], test_case["max_seq_len"]), dtype=np.int64)
+        draft_tokens = np.random.randint(0, 1000, (test_case["bsz"], max_draft_tokens), dtype=np.int64)
+        token_num = np.array([test_case["token_num_data"]], dtype=np.int64)
+
+        input_ids_tensor = paddle.to_tensor(input_ids)
+        draft_tokens_tensor = paddle.to_tensor(draft_tokens)
+        cum_offsets_tensor = paddle.to_tensor(test_case["cum_offsets"])
+        seq_lens_tensor = paddle.to_tensor(test_case["seq_lens"])
+        seq_lens_encoder_tensor = paddle.to_tensor(test_case["seq_lens_encoder"])
+        token_num_tensor = paddle.to_tensor(token_num)
+
+        (
+            x_remove_padding,
+            batch_id_per_token,
+            cu_seqlens_q,
+            cu_seqlens_k,
+        ) = speculate_get_padding_offset(
+            input_ids_tensor,
+            draft_tokens_tensor,
+            cum_offsets_tensor,
+            token_num_tensor,
+            seq_lens_tensor,
+            seq_lens_encoder_tensor,
+        )
+
+        (
+            ref_padding_offset,
+            ref_cum_offsets_out,
+            ref_cu_seqlens_q,
+            ref_cu_seqlens_k,
+            modified_indices,
+            ref_batch_id_per_token,
+        ) = ref_speculate_get_padding_offset(
+            test_case["cum_offsets"],
+            test_case["seq_lens"],
+            test_case["max_seq_len"],
+            test_case["token_num_data"],
+        )
+
+        output_arrays = {
+            "batch_id_per_token": batch_id_per_token.numpy(),
+            "cu_seqlens_q": cu_seqlens_q.numpy(),
+            "cu_seqlens_k": cu_seqlens_k.numpy(),
+        }
+
+        ref_arrays = {
+            "batch_id_per_token": ref_batch_id_per_token,
+            "cu_seqlens_q": ref_cu_seqlens_q,
+            "cu_seqlens_k": ref_cu_seqlens_k,
+        }
+
+        for key in output_arrays:
+            np.testing.assert_allclose(output_arrays[key], ref_arrays[key])
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/operators/test_speculate_get_seq_lens_output.py

@@ -0,0 +1,60 @@
+# Copyright (c) 2025 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.
+
+import unittest
+
+import numpy as np
+import paddle
+
+from fastdeploy.model_executor.ops.gpu import speculate_get_seq_lens_output
+
+
+class TestSpeculateGetSeqLensOutput(unittest.TestCase):
+
+    def run_seq_lens(self, input_values):
+        paddle.seed(42)
+        np.random.seed(42)
+        seq_lens_this_time = paddle.to_tensor(input_values[0], dtype="int32")
+        seq_lens_encoder = paddle.to_tensor(input_values[1], dtype="int32")
+        seq_lens_decoder = paddle.to_tensor(input_values[2], dtype="int32")
+        seq_lens_output = speculate_get_seq_lens_output(seq_lens_this_time, seq_lens_encoder, seq_lens_decoder)[0]
+        return seq_lens_output
+
+    def test_speculate_get_seq_lens_output1(self):
+        input_values = [[7], [0], [0]]
+        output_value = 7
+        result = self.run_seq_lens(input_values)
+        np.testing.assert_allclose(result.numpy(), output_value)
+
+    def test_speculate_get_seq_lens_output2(self):
+        input_values = [[7], [1], [0]]
+        output_value = 1
+        result = self.run_seq_lens(input_values)
+        np.testing.assert_allclose(result.numpy(), output_value)
+
+    def test_speculate_get_seq_lens_output3(self):
+        input_values = [[1], [1], [0]]
+        output_value = 1
+        result = self.run_seq_lens(input_values)
+        np.testing.assert_allclose(result.numpy(), output_value)
+
+    def test_speculate_get_seq_lens_output4(self):
+        input_values = [[0], [1], [0]]
+        output_value = 0
+        result = self.run_seq_lens(input_values)
+        np.testing.assert_allclose(result.numpy(), output_value)
+
+
+if __name__ == "__main__":
+    unittest.main()