[utils] Revisit pytree_utils

test/modules/model/LlamaAttentionWithDynamicCache_transformers500/__init__.py

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+# DO NOT REMOVE THIS FILE

test/modules/model/LlamaAttentionWithDynamicCache_transformers500/model.py

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+# Copyright (c) 2025 Samsung Electronics Co., Ltd. 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 torch
+from tico.utils.pytree_utils import register_dynamic_cache, register_dynamic_layer
+from transformers.cache_utils import DynamicCache
+from transformers.models.llama.modeling_llama import LlamaAttention, LlamaConfig
+
+from test.modules.base import TestModuleBase
+
+
+class LlamaAttentionWithKVCache_transformers500(TestModuleBase):
+    def __init__(self):
+        super().__init__()
+
+        self.config = LlamaConfig(use_cache=True, attn_implementation="sdpa")
+        self.model = LlamaAttention(config=self.config, layer_idx=0).to("cpu")
+        self.rtol = 1e-4
+        self.atol = 1e-4
+
+    def forward(self, *args, **kwargs):
+        return self.model(*args, **kwargs)
+
+    def get_example_inputs(self):
+        seq_len = 1  # Assume token generation
+        hidden_size = self.config.hidden_size
+        head_dim = self.config.head_dim
+        num_heads = self.config.num_attention_heads
+
+        hidden_states = torch.randn(1, seq_len, hidden_size)
+        position_embeddings = (
+            torch.randn(1, seq_len, head_dim),
+            torch.randn(1, seq_len, head_dim),
+        )
+        attention_mask = torch.Tensor([[[[0.0]] * seq_len]])  # shape: 1, 1, seq_len, 1
+        # This attention_mask will become a causal_mask of shape: (batch_size, 1, query_length, key_value_length)
+        prev_seq_len = 4
+        past_key_values = DynamicCache()
+        register_dynamic_cache()
+        register_dynamic_layer()
+
+        past_key_values.update(
+            torch.randn(1, num_heads, prev_seq_len, head_dim),
+            torch.randn(1, num_heads, prev_seq_len, head_dim),
+            0,
+        )
+        return (
+            hidden_states,
+            position_embeddings,
+            attention_mask,
+        ), {"past_key_values": past_key_values}

test/modules/model/LlamaAttentionWithDynamicCache_transformers500/requirements.txt

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+numpy==1.24.1
+transformers==5.0.0