yongwww commented on code in PR #15910: URL: https://github.com/apache/tvm/pull/15910#discussion_r1357544161
########## tests/python/relax/test_runtime_builtin_paged_attention_kv_cache.py: ########## @@ -0,0 +1,420 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. +from typing import List + +import numpy as np +import tvm +import tvm.testing +from tvm.script import tir as T + + +reserved_nseq = 2 +total_seq_len = 128 +page_size = 8 +nlayer = 4 +nhead = 16 +nfeat = 32 +dtype = "float16" + + +fcreate = tvm.get_global_func("vm.builtin.paged_attention_kv_cache_create") +fadd_sequence = tvm.get_global_func("vm.builtin.paged_attention_kv_cache_add_sequence") +freserve = tvm.get_global_func( + "vm.builtin.paged_attention_kv_cache_reserve_extra_length_for_append" +) +fappend = tvm.get_global_func("vm.builtin.paged_attention_kv_cache_append") +freset_append_length = tvm.get_global_func( + "vm.builtin.paged_attention_kv_cache_reset_append_lengths" +) +fsync = tvm.get_global_func("vm.builtin.paged_attention_kv_cache_sync_aux_array_to_device") +fremove = tvm.get_global_func("vm.builtin.paged_attention_kv_cache_remove") +fpopn = tvm.get_global_func("vm.builtin.paged_attention_kv_cache_popn") +fclear = tvm.get_global_func("vm.builtin.paged_attention_kv_cache_clear") + +# fmt: off [email protected]_func +def transpose_append( + var_pages: T.handle, + var_k_data: T.handle, + var_v_data: T.handle, + var_page_table_indptr: T.handle, + var_page_table_values: T.handle, + var_last_page_offset: T.handle, + var_append_length_indptr: T.handle, + var_pos2seqidx: T.handle, + layer_id: T.int32, +): + nseq = T.int32() + ntoken = T.int32() + nhead = T.int32() + nfeat = T.int32() + nlayer = T.int32() + npage = T.int32() + page_size = T.int32() + num_pages = T.int32() + + pages = T.match_buffer(var_pages, (num_pages, nlayer, 2, nhead, page_size, nfeat), "float16") + k_data = T.match_buffer(var_k_data, (ntoken, nhead, nfeat), "float16") + v_data = T.match_buffer(var_v_data, (ntoken, nhead, nfeat), "float16") + last_page_offset = T.match_buffer(var_last_page_offset, (nseq,), "int32") + page_table_indptr = T.match_buffer(var_page_table_indptr, (nseq + 1,), "int32") + page_table_values = T.match_buffer(var_page_table_values, (npage,), "int32") + append_length_indptr = T.match_buffer(var_append_length_indptr, (nseq + 1,), "int32") + pos2seqidx = T.match_buffer(var_pos2seqidx, (ntoken,), "int32") + + for global_pos, h, f in T.grid(ntoken, nhead, nfeat): + with T.block("k_transpose_append"): + vgpos, vh, vf = T.axis.remap("SSS", [global_pos, h, f]) + seq_idx = pos2seqidx[vgpos] + seqlen: T.int32 = (page_table_indptr[seq_idx + 1] - page_table_indptr[seq_idx] - 1) * page_size + last_page_offset[seq_idx] + pages[ + page_table_values[page_table_indptr[seq_idx] + T.floordiv(seqlen - (append_length_indptr[seq_idx + 1] - vgpos), page_size)], + layer_id, + 0, + vh, + T.floormod(seqlen - (append_length_indptr[seq_idx + 1] - vgpos), page_size), + vf, + ] = k_data[vgpos, vh, vf] + with T.block("v_transpose_append"): + vgpos, vh, vf = T.axis.remap("SSS", [global_pos, h, f]) + seq_idx = pos2seqidx[vgpos] + seqlen: T.int32 = (page_table_indptr[seq_idx + 1] - page_table_indptr[seq_idx] - 1) * page_size + last_page_offset[seq_idx] + pages[ + page_table_values[page_table_indptr[seq_idx] + T.floordiv(seqlen - (append_length_indptr[seq_idx + 1] - vgpos), page_size)], + layer_id, + 1, + vh, + T.floormod(seqlen - (append_length_indptr[seq_idx + 1] - vgpos), page_size), + vf, + ] = v_data[vgpos, vh, vf] + + [email protected]_func +def copy_cache( + var_pages: T.handle, + var_page_table_indptr: T.handle, + var_page_table_values: T.handle, + var_values: T.handle, + seq_id: T.int32, +): + nhead = T.int32() + nfeat = T.int32() + nlayer = T.int32() + seqlen = T.int32() + npage = T.int32() + page_size = T.int32() + num_pages = T.int32() + num_total_seqs_plus_1 = T.int32() + + pages = T.match_buffer(var_pages, (num_pages, nlayer, 2, nhead, page_size, nfeat), "float16") + page_table_indptr = T.match_buffer(var_page_table_indptr, (num_total_seqs_plus_1,), "int32") + page_table_values = T.match_buffer(var_page_table_values, (npage,), "int32") + values = T.match_buffer(var_values, (nlayer, 2, nhead, seqlen, nfeat), "float16") + + for l, kv_idx, h, pos, f in T.grid(nlayer, 2, nhead, seqlen, nfeat): + with T.block("view"): + vl, vi, vh, vp, vf = T.axis.remap("SSSSS", [l, kv_idx, h, pos, f]) + values[vl, vi, vh, vp, vf] = pages[ + page_table_values[page_table_indptr[seq_id] + T.floordiv(vp, page_size)], + vl, + vi, + vh, + T.floormod(vp, page_size), + vf, + ] +# fmt: on + + +def verify_cached_values(cache, expected, f_copy_cache): + fview = tvm.get_global_func("vm.builtin.paged_attention_kv_cache_debug_get_kv") + + actual = fview(cache, f_copy_cache) + assert len(actual) == len(expected) + for seq_actual, seq_expected in zip(actual, expected): + tvm.testing.assert_allclose(np.transpose(seq_actual.numpy(), [0, 1, 3, 2, 4]), seq_expected) + + +def build_tir_func(tir_funcs: List[tvm.tir.PrimFunc], target="llvm"): + return [tvm.build(tir_func, target=target).entry_func for tir_func in tir_funcs] + + +def test_paged_attention_kv_cache_append_prefill(): + f_transpose_append, f_copy_cache = build_tir_func([transpose_append, copy_cache]) + cache = fcreate( + tvm.runtime.ShapeTuple([reserved_nseq, total_seq_len, page_size]), + nlayer, + nhead, + nfeat, + tvm.nd.empty((), dtype), + ) + + operation_seq = [[(0, 6)], [(1, 8)], [(2, 11)], [(3, 16)], [(4, 19), (5, 20)]] + operation_seq += [[(6, 21), (7, 24)], [(2, 5), (4, 7), (8, 24)]] + operation_seq += [[(6, 13)], [(8, 19)], [(0, 1)], [(1, 3), (3, 8), (5, 12), (7, 11)]] + + current_nseq = 0 + cached_values = [] + for batch in operation_seq: + for seq_id, _ in batch: + if seq_id >= current_nseq: + seq_id_in_cache = fadd_sequence(cache) + assert seq_id_in_cache == seq_id + assert seq_id == current_nseq + current_nseq += 1 + + freset_append_length(cache) + for seq_id, append_length in batch: + freserve(cache, seq_id, append_length) + fsync(cache) + + global_new_kv = np.zeros((nlayer, 2, 0, nhead, nfeat), dtype) + for seq_id, new_len in batch: + if seq_id >= len(cached_values): + assert seq_id == len(cached_values) + cached_values.append(np.zeros((nlayer, 2, 0, nhead, nfeat), dtype)) + + new_kv = np.random.rand(nlayer, 2, new_len, nhead, nfeat).astype(dtype) + cached_values[seq_id] = np.concatenate([cached_values[seq_id], new_kv], axis=2) + global_new_kv = np.concatenate([global_new_kv, new_kv], axis=2) + for layer_id in range(nlayer): + keys = tvm.nd.array(np.expand_dims(global_new_kv[layer_id, 0], axis=0)) + values = tvm.nd.array(np.expand_dims(global_new_kv[layer_id, 1], axis=0)) + fappend(cache, f_transpose_append, keys, values, layer_id) + + # Verify + verify_cached_values(cache, cached_values, f_copy_cache) + + +def test_paged_attention_kv_cache_append_decode(): + f_transpose_append, f_copy_cache = build_tir_func([transpose_append, copy_cache]) + cache = fcreate( + tvm.runtime.ShapeTuple([reserved_nseq, total_seq_len, page_size]), + nlayer, + nhead, + nfeat, + tvm.nd.empty((), dtype), + ) + + cached_values = [] + initial_lengths = [31, 21, 16, 3, 8, 7, 3] + nseq = len(initial_lengths) + + # Initial prefill + freset_append_length(cache) + for seq_id, append_length in enumerate(initial_lengths): + seq_id_in_cache = fadd_sequence(cache) + assert seq_id_in_cache == seq_id + freserve(cache, seq_id, append_length) + fsync(cache) + + global_new_kv = np.zeros((nlayer, 2, 0, nhead, nfeat), dtype) + for length in initial_lengths: + new_kv = np.random.rand(nlayer, 2, length, nhead, nfeat).astype(dtype) + cached_values.append(new_kv) + global_new_kv = np.concatenate([global_new_kv, new_kv], axis=2) + for layer_id in range(nlayer): + keys = tvm.nd.array(np.expand_dims(global_new_kv[layer_id, 0], axis=0)) + values = tvm.nd.array(np.expand_dims(global_new_kv[layer_id, 1], axis=0)) + fappend(cache, f_transpose_append, keys, values, layer_id) + + verify_cached_values(cache, cached_values, f_copy_cache) + + # Decode + for _ in range(16): + decode_new_kv = np.random.rand(nlayer, 2, nseq, 1, nhead, nfeat).astype(dtype) + freset_append_length(cache) + for seq_id in range(nseq): + freserve(cache, seq_id, 1) + fsync(cache) + for seq_id in range(nseq): + cached_values[seq_id] = np.concatenate( + [cached_values[seq_id], decode_new_kv[:, :, seq_id, ...]], axis=2 + ) + for layer_id in range(nlayer): + keys = tvm.nd.array(decode_new_kv[layer_id, 0]) + values = tvm.nd.array(decode_new_kv[layer_id, 1]) + fappend(cache, f_transpose_append, keys, values, layer_id) + + verify_cached_values(cache, cached_values, f_copy_cache) + + +def test_paged_attention_kv_cache_remove(): + f_transpose_append, f_copy_cache = build_tir_func([transpose_append, copy_cache]) + cache = fcreate( + tvm.runtime.ShapeTuple([reserved_nseq, total_seq_len, page_size]), + nlayer, + nhead, + nfeat, + tvm.nd.empty((), dtype), + ) + + cached_values = [] + initial_lengths = [31, 21, 16, 3, 8, 7, 3] + + # Initial prefill + freset_append_length(cache) + for seq_id, append_length in enumerate(initial_lengths): + seq_id_in_cache = fadd_sequence(cache) + assert seq_id_in_cache == seq_id + freserve(cache, seq_id, append_length) + fsync(cache) + + global_new_kv = np.zeros((nlayer, 2, 0, nhead, nfeat), dtype) + for length in initial_lengths: + new_kv = np.random.rand(nlayer, 2, length, nhead, nfeat).astype(dtype) + cached_values.append(new_kv) + global_new_kv = np.concatenate([global_new_kv, new_kv], axis=2) + for layer_id in range(nlayer): + keys = tvm.nd.array(np.expand_dims(global_new_kv[layer_id, 0], axis=0)) + values = tvm.nd.array(np.expand_dims(global_new_kv[layer_id, 1], axis=0)) + fappend(cache, f_transpose_append, keys, values, layer_id) + + verify_cached_values(cache, cached_values, f_copy_cache) + + # Remove + while len(cached_values) > 2: + seq_id = np.random.randint(0, len(cached_values)) + fremove(cache, seq_id) + cached_values.pop(seq_id) + fsync(cache) + verify_cached_values(cache, cached_values, f_copy_cache) + + # Append after removal + seq_id = 2 + new_len = 29 + seq_id_in_cache = fadd_sequence(cache) + assert seq_id_in_cache == seq_id + freset_append_length(cache) + freserve(cache, seq_id, new_len) + fsync(cache) + new_kv = np.random.rand(nlayer, 2, new_len, nhead, nfeat).astype(dtype) + cached_values.append(new_kv) + for layer_id in range(nlayer): + keys = tvm.nd.array(np.expand_dims(new_kv[layer_id, 0], axis=0)) + values = tvm.nd.array(np.expand_dims(new_kv[layer_id, 1], axis=0)) + fappend(cache, f_transpose_append, keys, values, layer_id) + + verify_cached_values(cache, cached_values, f_copy_cache) + + +def test_paged_attention_kv_cache_popn(): + f_transpose_append, f_copy_cache = build_tir_func([transpose_append, copy_cache]) + cache = fcreate( + tvm.runtime.ShapeTuple([reserved_nseq, total_seq_len, page_size]), + nlayer, + nhead, + nfeat, + tvm.nd.empty((), dtype), + ) + + cached_values = [] + initial_lengths = [20, 24, 26, 27] + nseq = len(initial_lengths) + + # Initial prefill + freset_append_length(cache) + for seq_id, append_length in enumerate(initial_lengths): + seq_id_in_cache = fadd_sequence(cache) + assert seq_id_in_cache == seq_id + freserve(cache, seq_id, append_length) + fsync(cache) + + global_new_kv = np.zeros((nlayer, 2, 0, nhead, nfeat), dtype) + for length in initial_lengths: + new_kv = np.random.rand(nlayer, 2, length, nhead, nfeat).astype(dtype) + cached_values.append(new_kv) + global_new_kv = np.concatenate([global_new_kv, new_kv], axis=2) + for layer_id in range(nlayer): + keys = tvm.nd.array(np.expand_dims(global_new_kv[layer_id, 0], axis=0)) + values = tvm.nd.array(np.expand_dims(global_new_kv[layer_id, 1], axis=0)) + fappend(cache, f_transpose_append, keys, values, layer_id) + + verify_cached_values(cache, cached_values, f_copy_cache) + + # Pop n + for pop_length in [3, 13]: + for seq_id in range(nseq): + fpopn(cache, seq_id, pop_length) + cached_values[seq_id] = cached_values[seq_id][:, :, :-pop_length, ...] + fsync(cache) + verify_cached_values(cache, cached_values, f_copy_cache) + + # Decode after pop n + for _ in range(5): + decode_new_kv = np.random.rand(nlayer, 2, nseq, 1, nhead, nfeat).astype(dtype) + freset_append_length(cache) + for seq_id in range(nseq): + freserve(cache, seq_id, 1) + fsync(cache) + + for seq_id in range(nseq): + cached_values[seq_id] = np.concatenate( + [cached_values[seq_id], decode_new_kv[:, :, seq_id, ...]], axis=2 + ) + for layer_id in range(nlayer): + keys = tvm.nd.array(decode_new_kv[layer_id, 0]) + values = tvm.nd.array(decode_new_kv[layer_id, 1]) + fappend(cache, f_transpose_append, keys, values, layer_id) + + verify_cached_values(cache, cached_values, f_copy_cache) + + +def test_paged_attention_kv_cache_clear(): + f_transpose_append, f_copy_cache = build_tir_func([transpose_append, copy_cache]) + cache = fcreate( + tvm.runtime.ShapeTuple([reserved_nseq, total_seq_len, page_size]), + nlayer, + nhead, + nfeat, + tvm.nd.empty((), dtype), + ) + + cached_values = [] + initial_lengths = [20, 24, 26, 27] + + # Initial prefill + freset_append_length(cache) + for seq_id, append_length in enumerate(initial_lengths): + seq_id_in_cache = fadd_sequence(cache) + assert seq_id_in_cache == seq_id + freserve(cache, seq_id, append_length) + fsync(cache) + + global_new_kv = np.zeros((nlayer, 2, 0, nhead, nfeat), dtype) + for length in initial_lengths: + new_kv = np.random.rand(nlayer, 2, length, nhead, nfeat).astype(dtype) + cached_values.append(new_kv) + global_new_kv = np.concatenate([global_new_kv, new_kv], axis=2) + for layer_id in range(nlayer): + keys = tvm.nd.array(np.expand_dims(global_new_kv[layer_id, 0], axis=0)) + values = tvm.nd.array(np.expand_dims(global_new_kv[layer_id, 1], axis=0)) + fappend(cache, f_transpose_append, keys, values, layer_id) + + verify_cached_values(cache, cached_values, f_copy_cache) + + # Clear + fclear(cache) + verify_cached_values(cache, [], f_copy_cache) + + +if __name__ == "__main__": + test_paged_attention_kv_cache_append_prefill() + test_paged_attention_kv_cache_append_decode() + test_paged_attention_kv_cache_remove() + test_paged_attention_kv_cache_popn() + test_paged_attention_kv_cache_clear() + # tvm.testing.main() Review Comment: how about uncomment this test. BTW, looks the test case for "vm.builtin.paged_attention_kv_cache_attention" is missing -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
