diff --git a/python/llm/src/bigdl/llm/transformers/models/baichuan.py b/python/llm/src/bigdl/llm/transformers/models/baichuan.py index c20ed3d2..44a10d56 100644 --- a/python/llm/src/bigdl/llm/transformers/models/baichuan.py +++ b/python/llm/src/bigdl/llm/transformers/models/baichuan.py @@ -28,6 +28,8 @@ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from bigdl.llm.utils.common import invalidInputError from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, \ append_kv_cache, is_enough_kv_cache_room_4_31 +from bigdl.llm.transformers.models.utils import init_fp8_kv_cache, append_fp8_kv_cache, \ + restore_fp8_kv_cache, use_quantize_kv_cache from bigdl.llm.transformers.models.utils import rotate_half, apply_rotary_pos_emb from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu @@ -42,6 +44,160 @@ def baichuan_attention_forward_7b( past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: bool = False, use_cache: bool = False, +) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if use_quantize_kv_cache(self.W_pack, hidden_states): + forward_function = baichuan_attention_forward_7b_quantized + else: + forward_function = baichuan_attention_forward_7b_origin + return forward_function( + self=self, + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache + ) + + +def baichuan_attention_forward_7b_quantized( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, +) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + device = hidden_states.device + + proj = self.W_pack(hidden_states) + proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2) + # batch_size x source_len x hidden_size + query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + # batch_size x target_len x head_size + key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + # batch_size x source_len x hidden_size + value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + if query_states.device.type == "xpu" and not (self.training and query_states.requires_grad): + query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states, + key_states, + position_ids, + "baichuan") + else: + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, + cos, sin, position_ids, "baichuan") + # [bsz, nh, t, hd] + + if past_key_value is None: + attn_weights = torch.matmul(query_states, + key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + invalidInputError( + False, + f"Attention weights should be of size " + f"{(bsz, self.num_heads, q_len, kv_seq_len)}" + f", but is {attn_weights.size()}" + ) + + if attention_mask is not None: + invalidInputError( + attention_mask.size() == (bsz, 1, q_len, kv_seq_len), + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, " + f"but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + attn_weights = torch.max(attn_weights, + torch.tensor(torch.finfo(attn_weights.dtype).min)) + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, + dtype=torch.float32).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + kv_seq_len = key_states.shape[-2] + if use_cache: + k_cache, v_cache = init_fp8_kv_cache( + bsz, self.num_heads, kv_seq_len, self.head_dim, + device=device + ) + key_states, value_states = append_kv_cache(k_cache, v_cache, key_states, value_states) + past_key_value = (key_states, value_states) + else: + k_cache, v_cache = past_key_value + key_states, value_states = append_fp8_kv_cache(k_cache, v_cache, + key_states, value_states) + kv_seq_len = key_states.shape[-2] + past_key_value = (key_states, value_states) + if query_states.size(2) != 1 or query_states.device.type != 'xpu': + key_states, value_states = restore_fp8_kv_cache(key_states, value_states, + query_states.dtype) + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) + else: + import linear_q4_0 + attn_weights = linear_q4_0.query_key_fp8_matmul(query_states, key_states) + + attn_weights = attn_weights / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + invalidInputError( + False, + f"Attention weights should be of size " + f"{(bsz, self.num_heads, q_len, kv_seq_len)}" + f", but is {attn_weights.size()}" + ) + + if attention_mask is not None: + invalidInputError( + attention_mask.size() == (bsz, 1, q_len, kv_seq_len), + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, " + f"but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + attn_weights = torch.max(attn_weights, + torch.tensor(torch.finfo(attn_weights.dtype).min)) + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, + dtype=torch.float32).to(query_states.dtype) + if query_states.size(2) != 1 or query_states.device.type != 'xpu': + attn_output = torch.matmul(attn_weights, value_states) + else: + import linear_q4_0 + attn_output = linear_q4_0.attn_value_fp8_matmul(attn_weights, + value_states.transpose(-1, -2)) + + invalidInputError( + attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim), + f"`attn_output` should be of size " + f"{(bsz, self.num_heads, q_len, self.head_dim)}," + f"but is {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +def baichuan_attention_forward_7b_origin( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() device = hidden_states.device @@ -155,6 +311,119 @@ def baichuan_attention_forward_13b( output_attentions: bool = False, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if use_quantize_kv_cache(self.W_pack, hidden_states): + forward_function = baichuan_attention_forward_13b_quantized + else: + forward_function = baichuan_attention_forward_13b_origin + return forward_function( + self=self, + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache + ) + + +def baichuan_attention_forward_13b_quantized( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, +) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + + bsz, q_len, _ = hidden_states.size() + device = hidden_states.device + + proj = self.W_pack(hidden_states) + proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2) + query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + + if past_key_value is None: + attn_weights = torch.matmul(query_states, + key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + if attention_mask is not None: + if q_len == 1: # inference with cache + if len(attention_mask.size()) == 4: + attention_mask = attention_mask[:, :, -1:, :] + else: + attention_mask = attention_mask[:, -1:, :] + attn_weights = attn_weights + attention_mask + attn_weights = torch.max(attn_weights, + torch.tensor(torch.finfo(attn_weights.dtype).min)) + + attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1) + + attn_output = torch.matmul(attn_weights, value_states) + kv_seq_len = key_states.shape[-2] + if use_cache: + k_cache, v_cache = init_fp8_kv_cache( + bsz, self.num_heads, kv_seq_len, self.head_dim, + device=device + ) + key_states, value_states = append_fp8_kv_cache(k_cache, v_cache, + key_states, value_states) + past_key_value = (key_states, value_states) + else: + k_cache, v_cache = past_key_value + key_states, value_states = append_fp8_kv_cache(k_cache, v_cache, + key_states, value_states) + kv_seq_len = key_states.shape[-2] + past_key_value = (key_states, value_states) + if query_states.size(2) != 1 or query_states.device.type != 'xpu': + key_states, value_states = restore_fp8_kv_cache(key_states, value_states, + query_states.dtype) + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) + else: + import linear_q4_0 + attn_weights = linear_q4_0.query_key_fp8_matmul(query_states, key_states) + + attn_weights = attn_weights / math.sqrt(self.head_dim) + + if attention_mask is not None: + if q_len == 1: # inference with cache + if len(attention_mask.size()) == 4: + attention_mask = attention_mask[:, :, -1:, :] + else: + attention_mask = attention_mask[:, -1:, :] + attn_weights = attn_weights + attention_mask + attn_weights = torch.max(attn_weights, + torch.tensor(torch.finfo(attn_weights.dtype).min)) + + attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1) + if query_states.size(2) != 1 or query_states.device.type != 'xpu': + attn_output = torch.matmul(attn_weights, value_states) + else: + import linear_q4_0 + attn_output = linear_q4_0.attn_value_fp8_matmul(attn_weights, + value_states.transpose(-1, -2)) + + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +def baichuan_attention_forward_13b_origin( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, +) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() device = hidden_states.device