Phi3 support compresskv (#11733)
* phi3 support compresskv * fix phi3 mtl error * fix conflict with quant kv * fix abnormal on mtl * fix style * use slide windows size to compress kv * support sliding window * fix style * fix style * temp: partial support quant kv * support quant kv with compress kv, todo: model check * temp * fix style * fix style * remove prepare * address comment * default -> 1.8k
This commit is contained in:
Yina Chen
GitHub
parent
d8808cc2e3
commit
dd46c141bd
3 changed files with 146 additions and 82 deletions
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@ -155,62 +155,71 @@ def compress_kv(attn_config, key_states, query_states, value_states, attention_m
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if q_len <= attn_config.max_capacity_prompt:
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return key_states, value_states
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else:
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key_states_expand = repeat_kv(key_states, num_key_value_groups).to(key_states.device)
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attn_weights = torch.matmul(query_states[..., -attn_config.window_size:, :],
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key_states_expand.transpose(2, 3)) / math.sqrt(head_dim)
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mask = torch.full((attn_config.window_size, attn_config.window_size),
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torch.finfo(attn_weights.dtype).min,
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device=attn_weights.device)
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mask_cond = torch.arange(mask.size(-1), device=attn_weights.device)
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mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
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mask = mask.to(attn_weights.device)
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attention_mask = mask[None, None, :, :]
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attn_weights[:, :, -attn_config.window_size:, -attn_config.window_size:] += attention_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1,
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dtype=torch.float32).to(query_states.dtype)
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attn_weights_sum = attn_weights[:, :, -attn_config.window_size:,
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:-attn_config.window_size].sum(dim=-2)
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if attn_config.pooling == 'avgpool':
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if num_key_value_groups > 1:
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attn_cache = F.avg_pool2d(attn_weights_sum, kernel_size=(num_key_value_groups,
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attn_config.kernel_size),
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padding=(0, attn_config.kernel_size//2),
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stride=(num_key_value_groups, 1))
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else:
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attn_cache = F.avg_pool1d(attn_weights_sum, kernel_size=attn_config.kernel_size,
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padding=attn_config.kernel_size//2, stride=1)
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elif attn_config.pooling == 'maxpool':
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if num_key_value_groups > 1:
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attn_cache = F.max_pool2d(attn_weights_sum,
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kernel_size=(num_key_value_groups,
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attn_config.kernel_size),
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padding=(0, attn_config.kernel_size//2),
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stride=(num_key_value_groups, 1))
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else:
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attn_cache = F.max_pool1d(attn_weights_sum, kernel_size=attn_config.kernel_size,
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padding=attn_config.kernel_size//2, stride=1)
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sliding_window_size = getattr(attn_config, "sliding_window", None)
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if sliding_window_size is not None and sliding_window_size <= 2500:
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return key_states[:, :, -sliding_window_size:, :], \
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value_states[:, :, -sliding_window_size:, :]
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else:
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invalidInputError(False, 'Pooling method not supported')
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indices = attn_cache.topk(attn_config.max_capacity_prompt - attn_config.window_size,
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dim=-1).indices
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indices = indices.unsqueeze(-1).expand(-1, -1, -1, head_dim)
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k_past_compress = key_states[:, :, :-attn_config.window_size, :].gather(dim=2,
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index=indices)
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v_past_compress = value_states[:, :, :-attn_config.window_size, :].gather(dim=2,
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index=indices)
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k_cur = key_states[:, :, -attn_config.window_size:, :]
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v_cur = value_states[:, :, -attn_config.window_size:, :]
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key_states = torch.cat([k_past_compress, k_cur], dim=2)
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value_states = torch.cat([v_past_compress, v_cur], dim=2)
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return key_states, value_states
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key_states_expand = repeat_kv(key_states, num_key_value_groups).to(key_states.device)
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attn_weights = torch.matmul(query_states[..., -attn_config.window_size:, :],
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key_states_expand.transpose(2, 3)) / math.sqrt(head_dim)
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mask = torch.full((attn_config.window_size, attn_config.window_size),
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torch.finfo(attn_weights.dtype).min,
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device=attn_weights.device)
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mask_cond = torch.arange(mask.size(-1), device=attn_weights.device)
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mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
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mask = mask.to(attn_weights.device)
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attention_mask = mask[None, None, :, :]
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attn_weights[:, :, -attn_config.window_size:,
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-attn_config.window_size:] += attention_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1,
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dtype=torch.float32).to(query_states.dtype)
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attn_weights_sum = attn_weights[:, :, -attn_config.window_size:,
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:-attn_config.window_size].sum(dim=-2)
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if attn_config.pooling == 'avgpool':
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if num_key_value_groups > 1:
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attn_cache = F.avg_pool2d(attn_weights_sum,
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kernel_size=(num_key_value_groups,
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attn_config.kernel_size),
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padding=(0, attn_config.kernel_size//2),
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stride=(num_key_value_groups, 1))
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else:
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attn_cache = F.avg_pool1d(attn_weights_sum, kernel_size=attn_config.kernel_size,
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padding=attn_config.kernel_size//2, stride=1)
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elif attn_config.pooling == 'maxpool':
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if num_key_value_groups > 1:
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attn_cache = F.max_pool2d(attn_weights_sum,
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kernel_size=(num_key_value_groups,
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attn_config.kernel_size),
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padding=(0, attn_config.kernel_size//2),
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stride=(num_key_value_groups, 1))
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else:
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attn_cache = F.max_pool1d(attn_weights_sum, kernel_size=attn_config.kernel_size,
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padding=attn_config.kernel_size//2, stride=1)
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else:
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invalidInputError(False, 'Pooling method not supported')
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indices = attn_cache.topk(attn_config.max_capacity_prompt - attn_config.window_size,
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dim=-1).indices
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indices = indices.unsqueeze(-1).expand(-1, -1, -1, head_dim)
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k_past_compress = key_states[:, :, :-attn_config.window_size, :]\
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.gather(dim=2, index=indices)
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v_past_compress = value_states[:, :, :-attn_config.window_size, :]\
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.gather(dim=2, index=indices)
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k_cur = key_states[:, :, -attn_config.window_size:, :]
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v_cur = value_states[:, :, -attn_config.window_size:, :]
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key_states = torch.cat([k_past_compress, k_cur], dim=2)
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value_states = torch.cat([v_past_compress, v_cur], dim=2)
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return key_states, value_states
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class DynamicCompressCache(DynamicCache):
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def __init__(self, *args, **kwargs):
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def __init__(self, quant_kv=False, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.real_kv_len = 0
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self.quant_kv = quant_kv
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self.append_kv_func = append_fp8_kv_cache if quant_kv else append_kv_cache
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def update_seen_tokens(self, layer_idx, q_len):
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if layer_idx == 0:
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@ -260,49 +269,62 @@ class DynamicCompressCache(DynamicCache):
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self.key_cache.append(key_states_compress)
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self.value_cache.append(value_states_compress)
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k_cache_compressed, v_cache_compressed = init_kv_cache(
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bsz, num_heads, head_dim,
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0, key_states_compress.size(2) + KV_CACHE_ALLOC_BLOCK_LENGTH,
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key_states.dtype, key_states.device
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)
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k_cache_compressed, v_cache_compressed = append_kv_cache(
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if not self.quant_kv:
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k_cache_compressed, v_cache_compressed = init_kv_cache(
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bsz, num_heads, head_dim,
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0, key_states_compress.size(2) + KV_CACHE_ALLOC_BLOCK_LENGTH,
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key_states.dtype, key_states.device
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)
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else:
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k_cache_compressed, v_cache_compressed = init_fp8_kv_cache(
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bsz, num_heads, seq_len, head_dim,
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device=key_states.device,
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)
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k_cache_compressed, v_cache_compressed = self.append_kv_func(
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k_cache_compressed, v_cache_compressed,
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key_states_compress, value_states_compress)
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self.key_cache[layer_idx] = k_cache_compressed
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self.value_cache[layer_idx] = v_cache_compressed
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if key_states.stride(2) != head_dim:
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k_cache, v_cache = init_kv_cache(
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bsz, num_heads, head_dim,
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0, key_states.size(2),
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key_states.dtype, key_states.device
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)
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k_cache, v_cache = append_kv_cache(k_cache, v_cache, key_states, value_states)
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if not self.quant_kv:
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k_cache, v_cache = init_kv_cache(
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bsz, num_heads, head_dim,
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0, key_states.size(2),
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key_states.dtype, key_states.device
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)
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else:
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k_cache, v_cache = init_fp8_kv_cache(
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bsz, num_heads, 0, head_dim, key_states.device
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)
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k_cache, v_cache = self.append_kv_func(k_cache, v_cache,
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key_states, value_states)
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return k_cache, v_cache
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else:
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return key_states, value_states
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else:
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cache_k = self.key_cache[layer_idx]
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cache_v = self.value_cache[layer_idx]
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if not enough_kv_room:
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if not enough_kv_room and not self.quant_kv:
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# allocate new
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new_c_k, new_c_v = extend_kv_cache(bsz,
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num_heads, # Support GQA
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head_dim,
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cache_k.size(2),
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cache_k.size(2) + KV_CACHE_ALLOC_BLOCK_LENGTH,
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dtype=cache_k.dtype,
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device=query_states.device)
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new_c_k, new_c_v = extend_kv_cache(
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bsz,
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num_heads, # Support GQA
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head_dim,
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cache_k.size(2),
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cache_k.size(2) + KV_CACHE_ALLOC_BLOCK_LENGTH,
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dtype=cache_k.dtype,
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device=query_states.device)
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new_c_k[:] = cache_k
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new_c_v[:] = cache_v
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cache_k = new_c_k
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cache_v = new_c_v
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key_states, value_states = append_kv_cache(cache_k,
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cache_v,
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key_states,
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value_states)
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key_states, value_states = self.append_kv_func(cache_k,
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cache_v,
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key_states,
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value_states)
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# update past_key_value
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self.key_cache[layer_idx] = key_states
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@ -316,3 +338,14 @@ class DynamicCompressCache(DynamicCache):
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if len(self.key_cache) <= layer_idx:
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return 0
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return self.real_kv_len
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@classmethod
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def from_legacy_cache(cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
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quantize_kv: Optional[bool] = False) -> "DynamicCache":
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"""Converts a cache in the legacy cache format into an equivalent `DynamicCache`."""
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cache = cls(quantize_kv)
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if past_key_values is not None:
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for layer_idx in range(len(past_key_values)):
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key_states, value_states = past_key_values[layer_idx]
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cache.update(key_states, value_states, layer_idx)
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return cache
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@ -31,6 +31,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import math
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import torch
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import warnings
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@ -40,11 +41,13 @@ from ipex_llm.transformers.models.utils import should_use_fuse_rope, rotate_half
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from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU
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from ipex_llm.transformers.models.utils import use_sdp, use_sdp_causal
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
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from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache
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from ipex_llm.transformers.models.utils import should_use_compresskv, is_enough_kv_cache_room_4_36
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from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache, DynamicCompressCache
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from typing import Optional, Tuple, List
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from transformers.models.phi.modeling_phi import repeat_kv
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from transformers.cache_utils import Cache
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KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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@ -94,6 +97,9 @@ def attention_forward(
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bsz, q_len, _ = hidden_states.size()
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# [CompressKV]
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use_compresskv = isinstance(past_key_value, DynamicCompressCache)
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qkv = self.qkv_proj(hidden_states)
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qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
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qkv = qkv.transpose(1, 2)
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@ -127,12 +133,26 @@ def attention_forward(
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cos, sin, position_ids)
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if past_key_value is not None:
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key_states, value_states = past_key_value.update(key_states, value_states,
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self.layer_idx, None)
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# [CompressKV]
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if use_compresskv:
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enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
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key_states, value_states = past_key_value.update(
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key_states, value_states, self.layer_idx,
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query_states, attention_mask, self.num_key_value_groups,
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self.config, enough_kv_room, KV_CACHE_ALLOC_BLOCK_LENGTH)
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else:
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key_states, value_states = past_key_value.update(key_states, value_states,
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self.layer_idx, None)
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if use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
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# [CompressKV]
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if use_compresskv:
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# print(attention_mask.shape)
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context_len = key_states.size(2)
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attention_mask = attention_mask[:, :, :, -context_len:]
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import xe_addons
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if isinstance(past_key_value, DynamicFp8Cache):
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if isinstance(past_key_value,
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DynamicFp8Cache) or (use_compresskv and past_key_value.quant_kv):
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attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
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attention_mask)
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else:
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@ -148,7 +168,8 @@ def attention_forward(
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# attn_output = xe_addons.sdp_causal(query_states, key_states,
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# value_states, attention_mask)
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else:
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if isinstance(past_key_value, DynamicFp8Cache):
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if isinstance(past_key_value,
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DynamicFp8Cache) or (use_compresskv and past_key_value.quant_kv):
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key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
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query_states.dtype)
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# repeat k/v heads if n_kv_heads < n_heads
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@ -235,10 +256,20 @@ def phi3_model_forward_wrapper(origin_model_forward):
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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input = input_ids if input_ids is not None else inputs_embeds
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use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.down_proj, input)
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use_compress_kv = should_use_compresskv(input, input.shape[-1])
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if use_cache:
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if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
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if use_compress_kv and not isinstance(past_key_values,
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DynamicCompressCache):
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past_key_values = DynamicCompressCache.\
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from_legacy_cache(past_key_values,
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quantize_kv=use_quantize_kv)
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if use_quantize_kv and not isinstance(past_key_values,
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(DynamicFp8Cache, DynamicCompressCache)):
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past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
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if not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache):
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if not use_quantize_kv and not use_compress_kv and not isinstance(past_key_values,
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(DynamicNormalCache,
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DynamicCompressCache
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)):
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past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
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return origin_model_forward(
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self=self,
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@ -490,7 +490,7 @@ def should_use_compresskv(x: torch.Tensor, prompt_len: int):
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if use_compress_kv is None:
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return (
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get_xpu_device_type(x) == "mtl"
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and prompt_len >= 2500
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and prompt_len >= 1800
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and prompt_len <= 4500
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)
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else:
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