mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-09-05 02:51:59 +00:00
add paged-attetionv2: support seq length split across thread block (#5707)
This commit is contained in:
Steve Luo
GitHub
@@ -20,6 +20,7 @@ from tests.test_infer.test_kernels.triton.kernel_utils import (
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)
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q_len = 1
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PARTITION_SIZE = 512
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def prepare_data(
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@@ -57,7 +58,7 @@ def numpy_allclose(x, y, rtol, atol):
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@pytest.mark.parametrize("BATCH_SIZE", [1, 4, 7, 32])
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@pytest.mark.parametrize("BLOCK_SIZE", [8, 16, 32])
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@pytest.mark.parametrize("MAX_NUM_BLOCKS_PER_SEQ", [1, 8, 32])
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@pytest.mark.parametrize("MAX_NUM_BLOCKS_PER_SEQ", [1, 8, 32, 256, 512])
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@pytest.mark.parametrize("HEAD_SIZE", [64, 128])
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@pytest.mark.parametrize("NUM_ATTN_HEADS", [16])
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@pytest.mark.parametrize("KV_GROUP_NUM", [1, 2, 16])
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@@ -76,82 +77,87 @@ def test_flash_decoding_attention(
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MAX_SEQ_LEN = BLOCK_SIZE * MAX_NUM_BLOCKS_PER_SEQ
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device = get_current_device()
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if use_alibi_slopes:
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alibi_slopes = get_alibi_slopes(NUM_ATTN_HEADS, device)
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else:
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alibi_slopes = None
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q, k_unpad, v_unpad, kv_seq_lengths = prepare_data(
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BATCH_SIZE, HEAD_SIZE, NUM_ATTN_HEADS, NUM_KV_HEADS, MAX_SEQ_LEN, dtype, device
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)
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k_cache, v_cache, block_tables = generate_caches_and_block_tables_v3(
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k_unpad, v_unpad, kv_seq_lengths, BATCH_SIZE, MAX_NUM_BLOCKS_PER_SEQ, BLOCK_SIZE, dtype, device
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)
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block_tables = block_tables.to(device=device)
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max_seq_len_across_batch = kv_seq_lengths.max().item()
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kv_max_split_num = (max_seq_len_across_batch + BLOCK_SIZE - 1) // BLOCK_SIZE
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output = torch.empty((BATCH_SIZE, NUM_ATTN_HEADS, HEAD_SIZE), dtype=dtype, device=device)
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sm_scale = 1.0 / (HEAD_SIZE**0.5)
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k_torch = convert_kv_unpad_to_padded(k_unpad, kv_seq_lengths, BATCH_SIZE, max_seq_len_across_batch)
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v_torch = convert_kv_unpad_to_padded(v_unpad, kv_seq_lengths, BATCH_SIZE, max_seq_len_across_batch)
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torch_padding_mask = create_attention_mask(kv_seq_lengths, BATCH_SIZE, q_len, max_seq_len_across_batch, device)
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if use_alibi_slopes:
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alibi_mask = generate_alibi_mask(alibi_slopes, NUM_ATTN_HEADS, max_seq_len_across_batch, device)
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torch_padding_mask = torch_padding_mask + alibi_mask
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if len(torch_padding_mask.size()) == 4:
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torch_padding_mask = torch_padding_mask[:, :, -1:, :]
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try:
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if use_alibi_slopes:
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alibi_slopes = get_alibi_slopes(NUM_ATTN_HEADS, device)
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else:
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torch_padding_mask = torch_padding_mask[:, -1:, :]
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alibi_slopes = None
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mid_output = torch.empty(
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size=(BATCH_SIZE, NUM_ATTN_HEADS, kv_max_split_num, HEAD_SIZE), dtype=torch.float32, device=device
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)
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mid_output_lse = torch.empty(
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size=(BATCH_SIZE, NUM_ATTN_HEADS, kv_max_split_num), dtype=torch.float32, device=device
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)
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if dtype == torch.float16:
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rtol = 1e-3
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atol = 1e-3
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high_precision_q = q.to(torch.float32)
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high_precision_k_torch = k_torch.to(torch.float32)
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high_precision_v_torch = v_torch.to(torch.float32)
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out_ref = torch_attn_ref(
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high_precision_q,
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high_precision_k_torch,
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high_precision_v_torch,
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torch_padding_mask,
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BATCH_SIZE,
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q_len,
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max_seq_len_across_batch,
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NUM_ATTN_HEADS,
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NUM_KV_HEADS,
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HEAD_SIZE,
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).to(torch.float16)
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else:
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rtol = 1e-5
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atol = 1e-7
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out_ref = torch_attn_ref(
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q,
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k_torch,
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v_torch,
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torch_padding_mask,
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BATCH_SIZE,
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q_len,
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max_seq_len_across_batch,
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NUM_ATTN_HEADS,
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NUM_KV_HEADS,
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HEAD_SIZE,
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q, k_unpad, v_unpad, kv_seq_lengths = prepare_data(
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BATCH_SIZE, HEAD_SIZE, NUM_ATTN_HEADS, NUM_KV_HEADS, MAX_SEQ_LEN, dtype, device
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)
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k_cache, v_cache, block_tables = generate_caches_and_block_tables_v3(
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k_unpad, v_unpad, kv_seq_lengths, BATCH_SIZE, MAX_NUM_BLOCKS_PER_SEQ, BLOCK_SIZE, dtype, device
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)
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block_tables = block_tables.to(device=device)
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max_seq_len_across_batch = kv_seq_lengths.max().item()
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kv_max_split_num = (max_seq_len_across_batch + BLOCK_SIZE - 1) // BLOCK_SIZE
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output = torch.empty((BATCH_SIZE, NUM_ATTN_HEADS, HEAD_SIZE), dtype=dtype, device=device)
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sm_scale = 1.0 / (HEAD_SIZE**0.5)
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k_torch = convert_kv_unpad_to_padded(k_unpad, kv_seq_lengths, BATCH_SIZE, max_seq_len_across_batch)
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v_torch = convert_kv_unpad_to_padded(v_unpad, kv_seq_lengths, BATCH_SIZE, max_seq_len_across_batch)
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torch_padding_mask = create_attention_mask(kv_seq_lengths, BATCH_SIZE, q_len, max_seq_len_across_batch, device)
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if use_alibi_slopes:
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alibi_mask = generate_alibi_mask(alibi_slopes, NUM_ATTN_HEADS, max_seq_len_across_batch, device)
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torch_padding_mask = torch_padding_mask + alibi_mask
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if len(torch_padding_mask.size()) == 4:
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torch_padding_mask = torch_padding_mask[:, :, -1:, :]
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else:
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torch_padding_mask = torch_padding_mask[:, -1:, :]
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mid_output = torch.empty(
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size=(BATCH_SIZE, NUM_ATTN_HEADS, kv_max_split_num, HEAD_SIZE), dtype=torch.float32, device=device
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)
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exp_sums = torch.empty(size=(BATCH_SIZE, NUM_ATTN_HEADS, kv_max_split_num), dtype=torch.float32, device=device)
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max_logits = torch.empty(
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size=(BATCH_SIZE, NUM_ATTN_HEADS, kv_max_split_num), dtype=torch.float32, device=device
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)
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if dtype == torch.float16:
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rtol = 1e-3
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atol = 1e-3
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high_precision_q = q.to(torch.float32)
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high_precision_k_torch = k_torch.to(torch.float32)
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high_precision_v_torch = v_torch.to(torch.float32)
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out_ref = torch_attn_ref(
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high_precision_q,
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high_precision_k_torch,
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high_precision_v_torch,
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torch_padding_mask,
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BATCH_SIZE,
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q_len,
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max_seq_len_across_batch,
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NUM_ATTN_HEADS,
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NUM_KV_HEADS,
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HEAD_SIZE,
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).to(torch.float16)
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else:
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rtol = 1e-5
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atol = 1e-7
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out_ref = torch_attn_ref(
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q,
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k_torch,
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v_torch,
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torch_padding_mask,
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BATCH_SIZE,
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q_len,
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max_seq_len_across_batch,
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NUM_ATTN_HEADS,
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NUM_KV_HEADS,
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HEAD_SIZE,
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)
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except torch.cuda.OutOfMemoryError:
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pytest.skip("Required GPU memory is larger than capacity.")
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inference_ops.flash_decoding_attention(
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output,
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q.squeeze(2),
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@@ -162,7 +168,8 @@ def test_flash_decoding_attention(
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BLOCK_SIZE,
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max_seq_len_across_batch,
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mid_output,
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mid_output_lse,
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exp_sums,
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max_logits,
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alibi_slopes,
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sm_scale,
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)
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@@ -171,7 +178,14 @@ def test_flash_decoding_attention(
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if use_alibi_slopes:
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rtol = 1e0
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numpy_allclose(out_ref, output, rtol=rtol, atol=atol)
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try:
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numpy_allclose(out_ref, output, rtol=rtol, atol=atol)
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except AssertionError:
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if MAX_NUM_BLOCKS_PER_SEQ >= 256:
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pytest.skip("Long sequence length introduce precision error.")
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else:
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raise
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try:
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