masahi commented on code in PR #15837:
URL: https://github.com/apache/tvm/pull/15837#discussion_r1344611811
##########
tests/python/relax/test_codegen_cutlass.py:
##########
@@ -1992,5 +1992,107 @@ def main(
tvm.testing.assert_allclose(out, ref, rtol=1e-2, atol=1e-2)
+def test_batched_var_len_attention():
+ if not tvm.get_global_func("tvm.contrib.thrust.sum_scan", True):
+ return
+
+ @I.ir_module
+ class Module:
+ @R.function
+ def main(
+ queries: R.Tensor(("num_tokens", 4096), dtype="float16"),
+ keys: R.Tensor(("num_tokens", 4096), dtype="float16"),
+ values: R.Tensor(("num_tokens", 4096), dtype="float16"),
+ seq_lens: R.Tensor(("num_seq",), dtype="int32"),
+ ) -> R.Tensor(("num_tokens", 4096), dtype="float16"):
+ cls = Module
+ num_tokens = T.int64()
+ num_seq = T.int64()
+
+ with R.dataflow():
+ # TODO(masahi): Workaround for the broken Relax cumsum op on
GPU.
+ # https://github.com/apache/tvm/issues/15851
+ cumsum = R.call_dps_packed(
+ "tvm.contrib.thrust.sum_scan", seq_lens,
out_sinfo=seq_lens.struct_info
+ )
+ max_seqlen_q = R.max(seq_lens)
+ seqstart_q = R.concat([R.zeros((1,), "int32"), cumsum])
+ q = R.reshape(queries, R.shape([1, num_tokens, 128, 32]))
+ k = R.reshape(keys, R.shape([1, num_tokens, 128, 32]))
+ v = R.reshape(values, R.shape([1, num_tokens, 128, 32]))
+ attn_out = R.nn.attention_var_len(
+ q,
+ k,
+ v,
+ seqstart_q,
+ max_seqlen_q,
+ causal_mask="BottomRight",
+ )
+ out = R.reshape(attn_out, R.shape([num_tokens, 4096]))
+ R.output(out)
+ return out
+
+ seq_lens = [5, 3, 8]
+ num_head = 128
+ head_size = 32
+ hidden_size = num_head * head_size
+
+ batched_queries = []
+ batched_keys = []
+ batched_values = []
+ batched_refs = []
+
+ for s in seq_lens:
+ q, k, v, _, ref = get_numpy_attention_ref(
+ 1, s, s, num_head, head_size, head_size, "none", "none",
"BottomRight", "float16"
+ )
+ batched_queries.append(np.reshape(q, [-1, hidden_size]))
+ batched_keys.append(np.reshape(k, [-1, hidden_size]))
+ batched_values.append(np.reshape(v, [-1, hidden_size]))
+ batched_refs.append(np.reshape(ref, [-1, hidden_size]))
+
+ batched_queries = np.vstack(batched_queries)
+ batched_keys = np.vstack(batched_keys)
+ batched_values = np.vstack(batched_values)
+ ref = np.vstack(batched_refs)
+
+ mod = partition_for_cutlass(Module)
+ codegen_pass = relax.transform.RunCodegen({"cutlass": {"sm": 80}})
+ mod = codegen_pass(mod)
+
+ with tvm.target.Target("cuda"):
+ mod = relax.transform.LegalizeOps()(mod)
+ mod = tvm.tir.transform.DefaultGPUSchedule()(mod)
+
+ out = build_and_run(
+ mod,
+ [
+ batched_queries,
+ batched_keys,
+ batched_values,
+ np.array(seq_lens, dtype="int32"),
+ ],
+ "cuda",
+ )
+
+ tvm.testing.assert_allclose(out, ref, rtol=1e-2, atol=1e-2)
+
+ ############# xformer reference for verification #############
+
+ # attn_bias = BlockDiagonalCausalMask.from_seqlens(seq_lens)
Review Comment:
yes, it depends on torch and xformer.
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