diff --git a/python/llm/src/bigdl/llm/transformers/convert.py b/python/llm/src/bigdl/llm/transformers/convert.py index 0717523d..ff34951b 100644 --- a/python/llm/src/bigdl/llm/transformers/convert.py +++ b/python/llm/src/bigdl/llm/transformers/convert.py @@ -1196,13 +1196,14 @@ def _optimize_post(model, lightweight_bmm=False): modeling_module_name = model.__class__.__module__ module = importlib.import_module(modeling_module_name) from bigdl.llm.transformers.models.yuan import yuan_attention_forward - from bigdl.llm.transformers.models.yuan import yuan_mlp_forward + # from bigdl.llm.transformers.models.yuan import yuan_mlp_forward convert_forward(model, module.YuanAttention, yuan_attention_forward ) - convert_forward(model, - module.YuanMLP, - yuan_mlp_forward - ) + # disable able mlp_forward for quantize_kv on mtl. + # convert_forward(model, + # module.YuanMLP, + # yuan_mlp_forward + # ) return model diff --git a/python/llm/src/bigdl/llm/transformers/models/yuan.py b/python/llm/src/bigdl/llm/transformers/models/yuan.py index 5c07b9a7..abd09442 100644 --- a/python/llm/src/bigdl/llm/transformers/models/yuan.py +++ b/python/llm/src/bigdl/llm/transformers/models/yuan.py @@ -32,6 +32,8 @@ from bigdl.llm.utils.common import invalidInputError from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb, \ apply_rotary_pos_emb_cache_freq_xpu, mlp_fusion_check, fp16_fusion_check from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache +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 is_enough_kv_cache_room_4_31 from bigdl.llm.transformers.low_bit_linear import SYM_INT4, FP8E5 @@ -144,6 +146,182 @@ def yuan_attention_forward( 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.merged_qk_proj, hidden_states): + forward_function = yuan_attention_forward_quantized + else: + forward_function = yuan_attention_forward_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 yuan_attention_forward_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 + before_hidden_states = None + is_first_step = False + + use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids) + + invalidInputError(use_cache, "use_cache=True is needed") + invalidInputError(not self.use_shareqk, "use_shareqk is not supported for now") + + if past_key_value is None: + is_first_step = True + if q_len >= 2: + before_hidden_states = hidden_states[:, -2:, :].transpose(0, 1).half() + else: + before_hidden_states = torch.zeros(2, bsz, self.hidden_size, + dtype=torch.half, device=hidden_states.device) + before_hidden_states[-1:, :, :] = hidden_states[:, -1:, :].transpose(0, 1) + else: + before_hidden_states = past_key_value[2] + this_hidden_states = torch.cat([ + before_hidden_states, + hidden_states.transpose(0, 1).half(), + ], dim=0) + before_hidden_states = this_hidden_states[-2:, :, ] + + value_states = \ + self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + if is_first_step: + hidden_states = yuan_localized_filtering_forward(self.lf_gate, hidden_states, + None, hidden_states.dtype) + else: + hidden_states = yuan_localized_filtering_forward(self.lf_gate, hidden_states, + this_hidden_states, hidden_states.dtype) + qk_states = self.merged_qk_proj(hidden_states) + (query_states, key_states) = torch.chunk(qk_states, 2, dim=-1) + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.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] + + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + if use_fuse_rope: + query_states, key_states = apply_rotary_pos_emb_cache_freq_xpu(query_states, + key_states, + sin, cos, + "yuan", + position_ids) + else: + query_states, key_states = apply_rotary_pos_emb(query_states, + key_states, + cos, sin, + position_ids, + "yuan") + + if past_key_value is None: + # should use origin attn here + attn_weights = torch.matmul(query_states, + key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len), + "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) + + 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, before_hidden_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) + past_key_value = (key_states, value_states, before_hidden_states) + + # torch.matmul + if query_states.size(2) != 1 or 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) + + invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len), + "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 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), + "`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 yuan_attention_forward_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]]]: use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids) bsz, q_len, _ = hidden_states.size()