323 lines
11 KiB
Python
323 lines
11 KiB
Python
import random
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import unittest
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import torch
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from sglang.srt.layers.attention.triton_ops.decode_attention import (
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decode_attention_fwd_grouped as triton_decode_attention_fwd_grouped,
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)
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from sglang.srt.layers.attention.triton_ops.extend_attention import (
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extend_attention_fwd,
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redundant_attention,
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)
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from sglang.srt.layers.attention.triton_ops.prefill_attention import (
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context_attention_fwd,
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)
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from sglang.srt.layers.attention.wave_ops.decode_attention import (
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decode_attention_intermediate_arrays_shapes,
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decode_attention_wave,
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)
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from sglang.srt.layers.attention.wave_ops.extend_attention import extend_attention_wave
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from sglang.srt.layers.attention.wave_ops.prefill_attention import (
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prefill_attention_wave,
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)
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class TestWaveAttention(unittest.TestCase):
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def _set_all_seeds(self, seed):
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"""Set all random seeds for reproducibility."""
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random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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def setUp(self):
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# Set seeds before each test method
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self._set_all_seeds(42)
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def _test_extend_attention_once(self, B, N_CTX, H_Q, H_KV, D):
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dtype = torch.float16
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extend_seq_len = 1024
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b_seq_len_prefix = torch.full(
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(B,), N_CTX // B, dtype=torch.int32, device="cuda"
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)
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b_seq_len_extend = torch.full(
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(B,), extend_seq_len, dtype=torch.int32, device="cuda"
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)
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b_seq_len = b_seq_len_prefix + b_seq_len_extend
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max_len_in_batch = torch.max(b_seq_len, 0)[0].item()
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b_req_idx = torch.arange(B, dtype=torch.int32, device="cuda")
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b_start_loc = torch.zeros((B,), dtype=torch.int32, device="cuda")
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b_start_loc[1:] = torch.cumsum(b_seq_len[:-1], 0)
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b_start_loc_extend = torch.zeros((B,), dtype=torch.int32, device="cuda")
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b_start_loc_extend[1:] = torch.cumsum(b_seq_len_extend[:-1], 0)
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kv_indptr = torch.zeros((B + 1,), dtype=torch.int32, device="cuda")
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kv_indptr[1 : B + 1] = torch.cumsum(b_seq_len_prefix[:B], dim=0)
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kv_indices = torch.zeros(
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(b_seq_len_prefix.sum().item(),), dtype=torch.int32, device="cuda"
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)
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for i in range(B):
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kv_indices[kv_indptr[i] : kv_indptr[i + 1]] = torch.arange(
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b_start_loc[i], b_start_loc[i] + b_seq_len_prefix[i]
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)
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total_token_num = torch.sum(b_seq_len).item()
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extend_token_num = torch.sum(b_seq_len_extend).item()
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k_buffer = torch.empty(
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(total_token_num, H_KV, D), dtype=dtype, device="cuda"
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).normal_(mean=0.1, std=0.2)
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v_buffer = torch.empty(
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(total_token_num, H_KV, D), dtype=dtype, device="cuda"
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).normal_(mean=0.1, std=0.2)
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k_extend = torch.empty((extend_token_num, H_KV, D), dtype=dtype, device="cuda")
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v_extend = torch.empty((extend_token_num, H_KV, D), dtype=dtype, device="cuda")
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q_extend = torch.empty((extend_token_num, H_Q, D), dtype=dtype, device="cuda")
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for i in range(B):
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extend_start_in_buffer = b_start_loc[i] + b_seq_len_prefix[i]
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extend_end_in_buffer = b_start_loc[i] + b_seq_len[i]
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extend_start = b_start_loc_extend[i]
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extend_end = b_start_loc_extend[i] + b_seq_len_extend[i]
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k_extend[extend_start:extend_end] = k_buffer[
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extend_start_in_buffer:extend_end_in_buffer
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]
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v_extend[extend_start:extend_end] = v_buffer[
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extend_start_in_buffer:extend_end_in_buffer
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]
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q_extend[extend_start:extend_end] = torch.empty(
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(b_seq_len_extend[i], H_Q, D), dtype=dtype, device="cuda"
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).normal_(mean=0.1, std=0.2)
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o_extend = torch.empty((extend_token_num, H_Q, D), dtype=dtype, device="cuda")
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o_extend_mask = torch.empty(
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(extend_token_num, H_Q, D), dtype=dtype, device="cuda"
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)
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o_redundant = torch.empty(
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(extend_token_num, H_Q, D), dtype=dtype, device="cuda"
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)
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b_seq_len_extend = b_seq_len - b_seq_len_prefix
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max_len_extend = torch.max(b_seq_len_extend, 0)[0].item()
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qo_indptr = torch.zeros((B + 1,), dtype=torch.int32, device="cuda")
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qo_indptr[1 : B + 1] = torch.cumsum(b_seq_len_extend[:B], dim=0)
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custom_mask = None
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mask_indptr = None
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redundant_attention(
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q_extend,
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o_redundant,
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k_buffer,
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v_buffer,
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b_req_idx,
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b_start_loc,
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b_seq_len,
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b_seq_len_prefix,
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max_len_in_batch,
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)
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is_causal = True
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o_extend = torch.empty((extend_token_num, H_Q, D), dtype=dtype, device="cuda")
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extend_attention_fwd(
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q_extend,
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k_extend,
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v_extend,
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o_extend,
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k_buffer,
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v_buffer,
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qo_indptr,
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kv_indptr,
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kv_indices,
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custom_mask,
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is_causal,
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mask_indptr,
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max_len_extend,
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)
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o_wave = torch.empty((extend_token_num, H_Q, D), dtype=dtype, device="cuda")
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extend_attention_wave(
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q_extend,
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k_extend,
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v_extend,
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k_buffer,
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v_buffer,
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qo_indptr,
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kv_indptr,
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kv_indices,
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custom_mask,
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mask_indptr,
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max_len_extend,
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o_wave,
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is_causal=is_causal,
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)
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self.assertTrue(torch.allclose(o_extend, o_redundant, rtol=1e-2))
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self.assertTrue(torch.allclose(o_wave, o_redundant, rtol=1e-2))
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def test_extend_attention(self):
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# Define the varying parameter values
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attention_values = [128]
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# Loop through the values and call the method
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for value in attention_values:
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self._test_extend_attention_once(32, 16384, 6, 1, value)
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def _test_grouped_decode_attention_once(self, B, S, H_Q, H_KV, D, D_V):
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dtype = torch.float16
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seq_len = S # This represents the number of tokens already in the sequence
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total_tokens = B * seq_len
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sm_scale = 1.0 / (D**0.5)
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max_kv_splits = 8
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num_kv_splits = torch.full((B,), 4, dtype=torch.int32, device="cuda")
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# q represents the new token being generated, one per batch
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q = torch.randn(B, H_Q, D, dtype=dtype, device="cuda")
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# k_buffer and v_buffer represent all previous tokens
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k_buffer = torch.randn(total_tokens, H_KV, D, dtype=dtype, device="cuda")
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v_buffer = torch.randn(total_tokens, H_KV, D_V, dtype=dtype, device="cuda")
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# o will have the same shape as q
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o_triton = torch.zeros(B, H_Q, D_V, dtype=dtype, device="cuda")
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o = torch.zeros(B, H_Q, D_V, dtype=dtype, device="cuda")
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req_to_token = torch.arange(total_tokens, device="cuda", dtype=torch.int32)
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b_req_idx = torch.zeros(B + 1, device="cuda", dtype=torch.int32)
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b_seq_len = torch.full((B,), seq_len, device="cuda", dtype=torch.int32)
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b_req_idx[1 : B + 1] = torch.cumsum(b_seq_len, dim=0)
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attn_logits = torch.empty(
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(B, H_Q, max_kv_splits, D_V + 1),
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dtype=torch.float32,
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device="cuda",
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)
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attn_lse = torch.empty(
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(B, H_Q, max_kv_splits),
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dtype=torch.float32,
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device="cuda",
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)
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logit_cap = 0.0
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triton_decode_attention_fwd_grouped(
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q,
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k_buffer,
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v_buffer,
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o_triton,
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b_req_idx,
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req_to_token,
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attn_logits,
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attn_lse,
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num_kv_splits,
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max_kv_splits,
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sm_scale,
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logit_cap,
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)
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attn_logits_shape, attn_logits_max_shape = (
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decode_attention_intermediate_arrays_shapes(B, D_V, H_Q, max_kv_splits)
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)
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attn_logits = torch.empty(
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attn_logits_shape,
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dtype=torch.float32,
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device="cuda",
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)
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attn_logits_max = torch.empty(
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attn_logits_max_shape,
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dtype=torch.float32,
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device="cuda",
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)
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decode_attention_wave(
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q,
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k_buffer,
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v_buffer,
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o,
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b_req_idx,
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req_to_token,
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attn_logits,
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attn_logits_max,
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num_kv_splits,
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max_kv_splits,
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sm_scale,
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logit_cap,
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)
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cos_sim = torch.nn.functional.cosine_similarity(
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o.flatten(), o_triton.flatten(), dim=0
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)
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print(cos_sim.item())
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self.assertTrue(cos_sim.item() > 0.99)
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self.assertTrue(torch.allclose(o, o_triton, atol=3e-2))
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def test_grouped_decode_attention(self):
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seq_lens = [5, 100, 128, 500]
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configs = [
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(2, 16, 16, 64, 64),
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(2, 16, 1, 64, 64),
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(2, 128, 1, 80, 80),
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(32, 128, 2, 512, 512),
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(2, 128, 2, 512, 512),
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(2, 128, 1, 576, 512),
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]
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for S in seq_lens:
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for B, H_Q, H_KV, D, D_V in configs:
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self._test_grouped_decode_attention_once(B, S, H_Q, H_KV, D, D_V)
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def _test_context_attention_once(self, head_dim, is_causal):
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# Set up a simple test case
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dtype = torch.float16
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num_heads = 4
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kv_heads = 1
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seq_lens = [128, 256]
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max_seq_len = max(seq_lens)
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# Create random input tensors
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q = torch.randn(sum(seq_lens), num_heads, head_dim, dtype=dtype, device="cuda")
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k = torch.randn(sum(seq_lens), kv_heads, head_dim, dtype=dtype, device="cuda")
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v = torch.randn(sum(seq_lens), kv_heads, head_dim, dtype=dtype, device="cuda")
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o_triton = torch.zeros(
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sum(seq_lens), num_heads, head_dim, dtype=dtype, device="cuda"
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)
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o = torch.zeros(sum(seq_lens), num_heads, head_dim, dtype=dtype, device="cuda")
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# Create b_start_loc and b_seq_len tensors
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b_start_loc = torch.tensor([0, seq_lens[0]], device="cuda")
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b_seq_len = torch.tensor(seq_lens, device="cuda")
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context_attention_fwd(
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q, k, v, o_triton, b_start_loc, b_seq_len, max_seq_len, is_causal=is_causal
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)
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prefill_attention_wave(
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q, k, v, o, b_start_loc, b_seq_len, max_seq_len, is_causal=is_causal
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)
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cos_sim = torch.nn.functional.cosine_similarity(
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o.flatten(), o_triton.flatten(), dim=0
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)
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print(cos_sim.item())
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self.assertTrue(torch.allclose(o, o_triton, atol=3e-2))
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self.assertTrue(cos_sim.item() > 1 - (1e-5))
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def test_context_attention(self):
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head_dim = [128, 96]
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for dim in head_dim:
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for is_causal in [False]:
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self._test_context_attention_once(dim, is_causal)
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if __name__ == "__main__":
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unittest.main()
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