207 lines
7.4 KiB
Python
207 lines
7.4 KiB
Python
import unittest
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import torch
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import torch.nn.functional as F
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from utils import precision
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from sglang.test.test_utils import CustomTestCase
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torch.manual_seed(1234)
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def l2norm(x: torch.FloatTensor, dim: int = -1, eps: float = 1e-6):
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"""This function is intended to align with the l2norm implementation in the FLA library."""
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inv_norm = torch.rsqrt((x * x).sum(dim=dim, keepdim=True) + eps)
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return x * inv_norm
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def torch_chunk_gated_delta_rule(
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query,
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key,
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value,
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g,
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beta,
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chunk_size=64,
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initial_state=None,
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output_final_state=False,
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use_qk_l2norm_in_kernel=False,
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):
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initial_dtype = query.dtype
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if use_qk_l2norm_in_kernel:
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query = l2norm(query, dim=-1, eps=1e-6)
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key = l2norm(key, dim=-1, eps=1e-6)
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query, key, value, beta, g = [
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x.transpose(1, 2).contiguous().to(torch.float32)
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for x in (query, key, value, beta, g)
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]
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batch_size, sequence_length, num_heads, k_head_dim = key.shape
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v_head_dim = value.shape[-1]
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pad_size = (chunk_size - num_heads % chunk_size) % chunk_size
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query = F.pad(query, (0, 0, 0, pad_size))
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key = F.pad(key, (0, 0, 0, pad_size))
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value = F.pad(value, (0, 0, 0, pad_size))
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beta = F.pad(beta, (0, pad_size))
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g = F.pad(g, (0, pad_size))
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tot_heads = num_heads + pad_size
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scale = 1 / (query.shape[-1] ** 0.5)
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query = query * scale
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v_beta = value * beta.unsqueeze(-1)
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k_beta = key * beta.unsqueeze(-1)
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# reshape to chunks
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query, key, value, k_beta, v_beta = [
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x.reshape(x.shape[0], x.shape[1], -1, chunk_size, x.shape[-1])
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for x in (query, key, value, k_beta, v_beta)
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]
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g = g.reshape(g.shape[0], g.shape[1], -1, chunk_size)
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mask = torch.triu(
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torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device),
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diagonal=0,
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)
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# chunk decay
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g = g.cumsum(dim=-1)
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decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril()
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attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask, 0)
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for i in range(1, chunk_size):
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row = attn[..., i, :i].clone()
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sub = attn[..., :i, :i].clone()
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attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
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attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
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value = attn @ v_beta
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k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
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last_recurrent_state = (
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torch.zeros(batch_size, sequence_length, k_head_dim, v_head_dim).to(value)
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if initial_state is None
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else initial_state.to(value)
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)
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core_attn_out = torch.zeros_like(value)
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mask = torch.triu(
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torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device),
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diagonal=1,
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)
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# for each chunk
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for i in range(0, tot_heads // chunk_size):
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q_i, k_i, v_i = query[:, :, i], key[:, :, i], value[:, :, i]
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attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
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v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
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v_new = v_i - v_prime
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attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
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core_attn_out[:, :, i] = attn_inter + attn @ v_new
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last_recurrent_state = (
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last_recurrent_state * g[:, :, i, -1, None, None].exp()
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+ (k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]).transpose(
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-1, -2
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)
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@ v_new
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)
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if not output_final_state:
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last_recurrent_state = None
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core_attn_out = core_attn_out.reshape(
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core_attn_out.shape[0], core_attn_out.shape[1], -1, core_attn_out.shape[-1]
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)
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core_attn_out = core_attn_out[:, :, :num_heads]
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core_attn_out = core_attn_out.transpose(1, 2).contiguous().to(initial_dtype)
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return core_attn_out, last_recurrent_state
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def chunk_gated_delta_rule_update(
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query, # [B, T, HK, K]
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key, # [B, T, HK, K]
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value, # [B, T, HV, V]
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g, # [B, T, HV]
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beta, # [B, T, HV]
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cu_seqlens, # [N+1]
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initial_state, # [N, HV, K, V]
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use_qk_l2norm_in_kernel, # True
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):
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num_heads = query.shape[2]
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num_value_heads = value.shape[2]
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batch_size = initial_state.shape[0]
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if num_value_heads // num_heads > 1:
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query = query.repeat_interleave(num_value_heads // num_heads, dim=2)
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key = key.repeat_interleave(num_value_heads // num_heads, dim=2)
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output = torch.empty_like(value)
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final_state = torch.empty_like(initial_state)
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start_q = 0
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for i in range(batch_size):
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end_q = cu_seqlens[i + 1]
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core_attn_outi, last_recurrent_state = torch_chunk_gated_delta_rule(
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query=query[:, start_q:end_q, :, :],
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key=key[:, start_q:end_q, :, :],
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value=value[:, start_q:end_q, :, :],
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g=g[:, start_q:end_q, :],
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beta=beta[:, start_q:end_q, :],
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initial_state=initial_state[i],
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output_final_state=True,
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use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel,
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)
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output[:, start_q:end_q, :, :] = core_attn_outi
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final_state[i] = last_recurrent_state
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start_q = end_q
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return output, final_state
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class TestMambaAttention(CustomTestCase):
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def test_chunk_gated_delta_rule(self):
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B, L, HK, HV, EK, EV, N = 1, 100, 3, 6, 64, 64, 4
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seqlens = torch.randint(1, L, (N + 1,))
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seqlens[0] = 0
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cu_seqlens_ = torch.cumsum(seqlens, dim=0).to(torch.int32)
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T = cu_seqlens_[-1].item()
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query_ = torch.rand((B, T, HK, EK), dtype=torch.bfloat16) * 0.05
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key_ = torch.rand((B, T, HK, EK), dtype=torch.bfloat16) * 0.05
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value_ = torch.rand((B, T, HV, EV), dtype=torch.bfloat16) * 0.05
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g_ = torch.rand((B, T, HV), dtype=torch.float32) * 0.05
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beta_ = torch.rand((B, T, HV), dtype=torch.bfloat16) * 0.05
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initial_state_ = torch.rand((N, HV, EK, EV), dtype=torch.float32) * 0.05
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for use_qk_l2norm_in_kernel in [True, False]:
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core_attn_out_ref, last_recurrent_state_ref = chunk_gated_delta_rule_update(
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query=query_,
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key=key_,
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value=value_,
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g=g_,
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beta=beta_,
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cu_seqlens=cu_seqlens_,
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initial_state=initial_state_,
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use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel,
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)
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query = query_.clone()
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key = key_.clone()
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value = value_.clone()
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g = g_.clone()
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beta = beta_.clone()
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cu_seqlens = cu_seqlens_.clone()
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initial_state = initial_state_.clone()
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core_attn_out, last_recurrent_state = (
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torch.ops.sgl_kernel.chunk_gated_delta_rule_cpu(
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query=query,
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key=key,
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value=value,
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g=g,
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beta=beta,
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initial_state=initial_state,
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output_final_state=True,
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cu_seqlens=cu_seqlens,
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head_first=False,
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use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel,
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)
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)
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atol = rtol = precision[core_attn_out.dtype]
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torch.testing.assert_close(
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core_attn_out, core_attn_out_ref, atol=atol, rtol=rtol
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)
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torch.testing.assert_close(
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last_recurrent_state, last_recurrent_state_ref, atol=atol, rtol=rtol
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)
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if __name__ == "__main__":
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unittest.main()
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