Sync from v0.13

tests/models/language/pooling/test_splade_sparse_pooler.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import types
+
+import pytest
+import torch
+import torch.nn as nn
+
+from vllm.model_executor.models.bert import (
+    BertMLMHead,
+    SPLADESparsePooler,
+)
+
+# ---------------------------------------------------------------------
+# Functional test: SPLADE formula correctness (no HF download needed)
+# ---------------------------------------------------------------------
+
+
+@pytest.mark.parametrize("B,T,H,V", [(2, 3, 5, 7)])
+@torch.inference_mode
+def test_splade_pooler_matches_reference_formula(B, T, H, V):
+    """Ensure SPLADESparsePooler forward() matches the mathematical formula:
+    log1p(relu(logits)) -> max over sequence length (after masking)."""
+    torch.manual_seed(0)
+
+    # Prepare [B] sequences of shape [T, H]
+    hs_list = [torch.randn(T, H) for _ in range(B)]
+    hs_tenser = torch.cat(hs_list)
+
+    # Simulate PoolingMetadata (only required fields)
+    prompt_lens = [T, T - 1]
+    prompt_lens_tenser = torch.tensor(prompt_lens, dtype=torch.int32)
+    token_ids = torch.tensor(
+        [
+            [101, 5, 102],  # Batch 0: [CLS], token, [SEP]
+            [101, 6, 6],  # Batch 1: [CLS], token, token (last token ignored)
+        ],
+        dtype=torch.long,
+    )
+    meta = types.SimpleNamespace(
+        prompt_lens=prompt_lens_tenser, prompt_token_ids=token_ids
+    )
+
+    # MLM head (prefer BertMLMHead, fallback to Linear if unavailable)
+    try:
+        mlm_head = BertMLMHead(hidden_size=H, vocab_size=V, layer_norm_eps=1e-12)
+    except Exception:
+        mlm_head = nn.Linear(H, V, bias=True)
+
+    # Forward pass through SPLADE pooler
+    pooler = SPLADESparsePooler(mlm_head=mlm_head, pooling="max", remove_cls_sep=True)
+    pooled = pooler(hidden_states=hs_tenser, pooling_metadata=meta)  # list of [V]
+
+    # Basic output checks
+    assert isinstance(pooled, torch.Tensor) and len(pooled) == B
+    for vec in pooled:
+        assert vec.shape == (V,)
+        assert torch.isfinite(vec).all()
+        assert (vec >= 0).all(), "SPLADE outputs must be non-negative."
+
+    # Reference implementation for comparison
+    def ref_one(hs: torch.Tensor, L: int, tid_row: torch.Tensor) -> torch.Tensor:
+        keep = torch.ones(L, dtype=torch.bool)
+        if L > 0 and tid_row[0].item() == 101:  # remove CLS
+            keep[0] = False
+        if L > 0 and tid_row[L - 1].item() == 102:  # remove SEP
+            keep[L - 1] = False
+
+        valid = hs[:L][keep[:L]]
+        if valid.numel() == 0:
+            return torch.zeros(V, dtype=torch.float32)
+
+        logits = mlm_head(valid)  # [L', V]
+        scores = torch.log1p(torch.relu(logits))  # [L', V]
+        return scores.max(dim=0).values.to(torch.float32)
+
+    torch.testing.assert_close(
+        pooled[0],
+        ref_one(hs_list[0], prompt_lens[0], token_ids[0]),
+        rtol=1e-4,
+        atol=1e-4,
+    )
+    torch.testing.assert_close(
+        pooled[1],
+        ref_one(hs_list[1], prompt_lens[1], token_ids[1]),
+        rtol=1e-4,
+        atol=1e-4,
+    )