[V1] Allocate kv_cache with stride order for V1 (#18775)

Signed-off-by: nicklucche <nlucches@redhat.com>

tests/v1/worker/test_gpu_model_runner.py

 # SPDX-License-Identifier: Apache-2.0
 
+import random
+
 import pytest
 
+from vllm.attention import Attention
 from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
                          SchedulerConfig, VllmConfig)
 from vllm.sampling_params import SamplingParams
@@ -13,27 +16,30 @@ from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.worker.gpu_input_batch import InputBatch
 from vllm.v1.worker.gpu_model_runner import GPUModelRunner
 
+BLOCK_SIZE = 16
+NUM_BLOCKS = 10
+
 
 def initialize_kv_cache(runner: GPUModelRunner):
     """
     Only perform necessary steps in GPUModelRunner.initialize_kv_cache()
     """
-    kv_cache_config = KVCacheConfig(
-        num_blocks=10,
-        tensors={
-            "layer.0": KVCacheTensor(size=1024),
-        },
-        kv_cache_groups=[
-            KVCacheGroupSpec(
-                layer_names=["layer.0"],
-                kv_cache_spec=FullAttentionSpec(
-                    block_size=16,
+    attn_spec = FullAttentionSpec(
+        block_size=BLOCK_SIZE,
         num_kv_heads=runner.model_config.get_num_kv_heads(
             runner.parallel_config),
         head_size=runner.model_config.get_head_size(),
         dtype=runner.kv_cache_dtype,
         use_mla=False,
-                ))
+    )
+    tensor_size = attn_spec.page_size_bytes * NUM_BLOCKS
+    kv_cache_config = KVCacheConfig(
+        num_blocks=NUM_BLOCKS,
+        tensors={
+            "layer.0": KVCacheTensor(size=tensor_size),
+        },
+        kv_cache_groups=[
+            KVCacheGroupSpec(layer_names=["layer.0"], kv_cache_spec=attn_spec)
         ])
     runner.kv_cache_config = kv_cache_config
     runner.input_batch = InputBatch(
@@ -65,7 +71,7 @@ def model_runner():
         seed=42,
     )
     cache_config = CacheConfig(
-        block_size=16,
+        block_size=BLOCK_SIZE,
         gpu_memory_utilization=0.9,
         swap_space=0,
         cache_dtype="auto",
@@ -77,6 +83,10 @@ def model_runner():
         scheduler_config=scheduler_config,
         parallel_config=parallel_config,
     )
+    num_heads = model_config.get_num_kv_heads(parallel_config)
+    head_size = model_config.get_head_size()
+    vllm_config.compilation_config.static_forward_context[
+        "layer.0"] = Attention(num_heads, head_size, 0.1)
 
     device = "cuda"
     runner = GPUModelRunner(vllm_config, device)
@@ -321,3 +331,38 @@ def test_update_states_request_unscheduled(model_runner):
 
     assert _is_req_added(model_runner, req_ids[1])
     assert not _is_req_scheduled(model_runner, req_ids[1])
+
+
+def test_kv_cache_stride_order(monkeypatch, model_runner):
+    # This test checks if GPUModelRunner initializes correctly when an attention
+    # backend enforces a non-default KV cache stride order.
+    n_heads = model_runner.model_config.get_num_kv_heads(
+        model_runner.parallel_config)
+    expected_kv_cache_shape = [
+        2, NUM_BLOCKS, BLOCK_SIZE, n_heads,
+        model_runner.model_config.get_head_size()
+    ]
+    # TODO mla test
+    default_stride = list(range(5))
+    # Permutation that gets you back to expected kv shape
+    rnd_stride = tuple(random.sample(default_stride, len(default_stride)))
+
+    def rnd_stride_order():
+        return rnd_stride
+
+    # Patch the attention backend class and re-trigger the KV cache creation.
+    for attn_backend in model_runner.attn_backends:
+        monkeypatch.setattr(attn_backend, "get_kv_cache_stride_order",
+                            rnd_stride_order)
+
+    model_runner.attn_backends = []
+    model_runner.attn_metadata_builders = []
+    model_runner.initialize_kv_cache(model_runner.kv_cache_config)
+
+    # Shape is unchanged, but layout may differ
+    kv_cache_shape = model_runner.kv_caches[0].shape
+    assert list(kv_cache_shape) == expected_kv_cache_shape
+    if default_stride == rnd_stride:
+        assert all(kv.is_contiguous() for kv in model_runner.kv_caches)
+    else:
+        assert all(not kv.is_contiguous() for kv in model_runner.kv_caches)

vllm/v1/worker/gpu_model_runner.py

@@ -2033,9 +2033,29 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                         num_blocks, kv_cache_spec.block_size,
                         kv_cache_spec.num_kv_heads, kv_cache_spec.head_size)
                     dtype = kv_cache_spec.dtype
-                    kv_caches[layer_name] = torch.zeros(kv_cache_shape,
-                                                        dtype=dtype,
-                                                        device=self.device)
+                    try:
+                        kv_cache_stride_order = self.attn_backends[
+                            i].get_kv_cache_stride_order()
+                        assert len(kv_cache_stride_order) == len(
+                            kv_cache_shape)
+                    except (AttributeError, NotImplementedError):
+                        kv_cache_stride_order = tuple(
+                            range(len(kv_cache_shape)))
+                    # The allocation respects the backend-defined stride order
+                    # to ensure the semantic remains consistent for each
+                    # backend. We first obtain the generic kv cache shape and
+                    # then permute it according to the stride order which could
+                    # result in a non-contiguous tensor.
+                    kv_cache_shape = tuple(kv_cache_shape[i]
+                                           for i in kv_cache_stride_order)
+                    # Maintain original KV shape view.
+                    inv_order = [
+                        kv_cache_stride_order.index(i)
+                        for i in range(len(kv_cache_stride_order))
+                    ]
+                    kv_caches[layer_name] = torch.zeros(
+                        kv_cache_shape, dtype=dtype,
+                        device=self.device).permute(*inv_order)
                 else:
                     # TODO: add new branches when introducing more types of
                     # KV cache specs.