refactor to simplify following upgrade 2 (#12685)
This commit is contained in:
Yishuo Wang
GitHub
parent
2673792de6
commit
68857494a5
6 changed files with 33 additions and 376 deletions
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@ -1590,6 +1590,9 @@ def _optimize_post(model):
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convert_forward(model,
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module.Qwen2ForCausalLM,
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qwen2_causal_lm_forward)
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convert_forward(model,
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module.Qwen2Model,
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qwen2_model_forward)
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convert_forward(model,
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module.Qwen2RMSNorm,
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rms_norm_forward)
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@ -1602,12 +1605,6 @@ def _optimize_post(model):
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convert_forward(model,
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module.Qwen2SdpaAttention,
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qwen2_attention_forward)
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if version.parse(trans_version) >= version.parse("4.42"):
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from ipex_llm.transformers.models.qwen2 import qwen2_model_forward_4_42
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convert_forward(model, module.Qwen2Model, qwen2_model_forward_4_42)
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else:
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from ipex_llm.transformers.models.qwen2 import qwen2_model_forward
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convert_forward(model, module.Qwen2Model, qwen2_model_forward)
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elif model.config.model_type == "qwen2_moe":
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# for Qwen1.5-MOE-A2.7B
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modeling_module_name = model.__class__.__module__
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@ -1819,9 +1816,7 @@ def _optimize_post(model):
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from ipex_llm.transformers.models.phi3 import attention_forward
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convert_forward(model, module.Phi3Attention, attention_forward)
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convert_forward(model, module.Phi3SdpaAttention, attention_forward)
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from ipex_llm.transformers.models.phi3 import mlp_forward
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convert_forward(model, module.Phi3MLP, mlp_forward)
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from ipex_llm.transformers.models.common import rms_norm_forward
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convert_forward(model, module.Phi3MLP, mlp_silu_forward)
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convert_forward(model, module.Phi3RMSNorm, rms_norm_forward)
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if model.config.model_type == "phi3":
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from ipex_llm.transformers.models.phi3 import phi3_model_forward_wrapper
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@ -30,8 +30,7 @@ from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp
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from ipex_llm.transformers.models.utils import update_past_key_value
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from ipex_llm.transformers.models.utils import should_use_fuse_rope
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from ipex_llm.transformers.models.utils import use_sdp
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from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, SILU
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from ipex_llm.transformers.models.utils import mlp_fusion_check
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from ipex_llm.transformers.models.utils import apply_rotary_pos_emb
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from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36
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from ipex_llm.transformers.kv import DynamicCompressFp8Cache, DynamicCompressCache
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import warnings
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@ -113,21 +113,6 @@ def internlm_attention_forward(
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return attn_output, attn_weights, past_key_value
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
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The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to
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(batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads,
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n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def internlm2_attention_forward(
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self,
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hidden_states: torch.Tensor,
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@ -39,7 +39,6 @@ import warnings
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from ipex_llm.transformers.models.common import attention_softmax
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from ipex_llm.transformers.models.common import scaled_dot_product_attention
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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.models.utils import should_use_compresskv, is_enough_kv_cache_room_4_36
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@ -213,24 +212,8 @@ def split_mlp(module: torch.nn.Module):
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del module.gate_up_proj
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def mlp_forward(
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self,
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hidden_states: torch.FloatTensor
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) -> torch.FloatTensor:
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x_2d = hidden_states.view(-1, hidden_states.shape[-1])
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qtype = getattr(self.gate_proj, "qtype", None)
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if mlp_fusion_check(x_2d, qtype, self.training):
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x_2d = x_2d.contiguous()
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import xe_linear
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return self.down_proj(xe_linear.mlp_forward_xpu(
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x_2d, self.gate_proj.weight.data, self.up_proj.weight.data,
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x_2d.shape[0], x_2d.shape[1], self.gate_proj.out_features,
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SILU, qtype
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))
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return self.down_proj(
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self.activation_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states)
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)
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# rename activation function
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module.act_fn = module.activation_fn
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def phi3_model_forward_wrapper(origin_model_forward):
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@ -51,16 +51,11 @@ from ipex_llm.transformers.models.utils import use_quantize_kv_cache, \
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should_use_compresskv, is_enough_kv_cache_room_4_36
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from ipex_llm.transformers.kv import DynamicFp8Cache, DynamicNormalCache, \
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DynamicCompressCache, DynamicCompressFp8Cache
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from ipex_llm.utils.common import invalidInputError
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from transformers.models.qwen2.modeling_qwen2 import Qwen2Attention, Qwen2MLP
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from transformers.models.qwen2.modeling_qwen2 import Qwen2Model, Qwen2Attention, Qwen2MLP
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from transformers.models.qwen2.modeling_qwen2 import apply_rotary_pos_emb
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from transformers.cache_utils import Cache
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from transformers import logging
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logger = logging.get_logger(__name__)
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def qwen2_model_forward(
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@ -74,50 +69,18 @@ def qwen2_model_forward(
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None, # for transformers >= 4.42
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cache_position: Optional[torch.LongTensor] = None,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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output_attentions = (
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output_attentions if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# retrieve input_ids and inputs_embeds
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if input_ids is not None and inputs_embeds is not None:
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invalidInputError(False,
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"You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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batch_size, seq_length = input_ids.shape
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elif inputs_embeds is not None:
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batch_size, seq_length, _ = inputs_embeds.shape
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else:
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invalidInputError(False,
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"You have to specify either decoder_input_ids or decoder_inputs_embeds")
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. "
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"Setting `use_cache=False`..."
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)
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use_cache = False
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past_key_values_length = 0
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# ipex-llm changes start
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# IPEX-LLM OPT: kv cache and quantize kv cache
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# IPEX-LLM OPT start: kv cache and quantize kv cache
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inputs = input_ids if input_ids is not None else inputs_embeds
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num_heads, num_kv_heads = self.config.num_attention_heads, self.config.num_key_value_heads
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use_quantize_kv = (
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self.config.hidden_size != 3584 # disable quantize kv in specific model
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and use_quantize_kv_cache(self.layers[0].mlp.up_proj, inputs, num_heads, num_kv_heads)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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use_cache = True if inputs.device.type == "xpu" else use_cache
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use_quantize_kv = self.config.hidden_size != 3584 and use_quantize_kv_cache(
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self.layers[0].mlp.down_proj, inputs,
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self.config.num_attention_heads, self.config.num_key_value_heads
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)
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use_compress_kv = should_use_compresskv(inputs, inputs.shape[1]) or \
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isinstance(past_key_values, DynamicCompressCache)
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@ -133,274 +96,26 @@ def qwen2_model_forward(
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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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past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
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past_key_values_length = past_key_values.get_usable_length(seq_length)
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# ipex-llm changes end
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if position_ids is None:
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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position_ids = torch.arange(
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past_key_values_length, seq_length + past_key_values_length,
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dtype=torch.long, device=device
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)
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position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
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# `cache_position` is required after transformers 4.42
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if cache_position is not None:
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kwargs = {"cache_position": cache_position}
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else:
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position_ids = position_ids.view(-1, seq_length).long()
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kwargs = {}
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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flash_attn_2 = self._attn_implementation == "flash_attention_2"
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if attention_mask is not None and flash_attn_2 and use_cache:
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is_padding_right = attention_mask[:, -1].sum().item() != batch_size
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if is_padding_right:
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invalidInputError(
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False,
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"You are attempting to perform batched generation with padding_side='right'"
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" this may lead to unexpected behaviour for Flash Attention version of Qwen2."
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" Make sure to call `tokenizer.padding_side = 'left'` before tokenizing "
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"the input. "
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)
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from transformers.models.qwen2.modeling_qwen2 import _prepare_4d_causal_attention_mask_for_sdpa
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from transformers.models.qwen2.modeling_qwen2 import _prepare_4d_causal_attention_mask
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# ipex-llm changes start: don't generate `attention_mask` in decode phase
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if seq_length == 1:
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attention_mask = None
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# ipex-llm changes end
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elif self._attn_implementation == "flash_attention_2":
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# 2d mask is passed through the layers
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attention_mask = attention_mask if (attention_mask is not None and
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0 in attention_mask) else None
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elif self._attn_implementation == "sdpa" and not output_attentions:
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# output_attentions=True can not be supported when using SDPA, and we fall back on
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# the manual implementation that requires a 4D causal mask in all cases.
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attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
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attention_mask,
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(batch_size, seq_length),
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inputs_embeds,
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past_key_values_length,
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)
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else:
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# 4d mask is passed through the layers
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attention_mask = _prepare_4d_causal_attention_mask(
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attention_mask,
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(batch_size, seq_length),
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inputs_embeds,
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past_key_values_length,
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sliding_window=self.config.sliding_window,
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)
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hidden_states = inputs_embeds
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = None
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for decoder_layer in self.layers:
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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if self.gradient_checkpointing and self.training:
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layer_outputs = self._gradient_checkpointing_func(
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decoder_layer.__call__,
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hidden_states,
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attention_mask,
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position_ids,
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past_key_values,
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output_attentions,
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use_cache,
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)
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else:
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# ipex-llm changes
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curr_device = decoder_layer.input_layernorm.weight.device
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if attention_mask is not None:
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attention_mask = attention_mask.to(curr_device)
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if position_ids is not None:
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position_ids = position_ids.to(curr_device)
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# ipex-llm changes end
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layer_outputs = decoder_layer(
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hidden_states,
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return Qwen2Model.forward(
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self=self,
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_values,
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output_attentions=output_attentions,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache = layer_outputs[2 if output_attentions else 1]
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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hidden_states = self.norm(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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# ipex-llm changes start: remove `to_legacy_cache`
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next_cache = None
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if use_cache:
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next_cache = next_decoder_cache
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# ipex-llm changes end
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if not return_dict:
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return tuple(v for v in [hidden_states, next_cache,
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all_hidden_states, all_self_attns] if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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def qwen2_model_forward_4_42(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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output_attentions = (
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output_attentions if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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invalidInputError(
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(input_ids is None) ^ (inputs_embeds is None),
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"You cannot specify both input_ids and inputs_embeds at the same time, "
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"and must specify either one"
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)
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. "
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"Setting `use_cache=False`..."
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)
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use_cache = False
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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# ipex-llm changes start
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# IPEX-LLM OPT: kv cache and quantize kv cache
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num_heads, num_kv_heads = self.config.num_attention_heads, self.config.num_key_value_heads
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use_quantize_kv = (
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self.config.hidden_size != 3584 # disable quantize kv in specific model
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and use_quantize_kv_cache(self.layers[0].mlp.up_proj, inputs_embeds,
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num_heads, num_kv_heads)
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)
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use_compress_kv = should_use_compresskv(inputs_embeds, inputs_embeds.shape[1]) or \
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isinstance(past_key_values, DynamicCompressCache)
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if use_cache:
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if use_compress_kv and not isinstance(past_key_values, DynamicCompressCache):
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if use_quantize_kv:
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past_key_values = DynamicCompressFp8Cache.from_legacy_cache(past_key_values)
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else:
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past_key_values = DynamicCompressCache.from_legacy_cache(past_key_values)
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elif use_quantize_kv and not use_compress_kv and not isinstance(past_key_values,
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DynamicFp8Cache):
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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 use_compress_kv and not isinstance(past_key_values,
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DynamicNormalCache):
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past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
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# ipex-llm changes end
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if cache_position is None:
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0)
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causal_mask = self._update_causal_mask(
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attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
||||
)
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
# decoder layers
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
next_decoder_cache = None
|
||||
|
||||
for decoder_layer in self.layers:
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
layer_outputs = self._gradient_checkpointing_func(
|
||||
decoder_layer.__call__,
|
||||
hidden_states,
|
||||
causal_mask,
|
||||
position_ids,
|
||||
past_key_values,
|
||||
output_attentions,
|
||||
use_cache,
|
||||
cache_position,
|
||||
)
|
||||
else:
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=causal_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_values,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if use_cache:
|
||||
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
||||
|
||||
if output_attentions:
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
|
||||
hidden_states = self.norm(hidden_states)
|
||||
|
||||
# add hidden states from the last decoder layer
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
# ipex-llm changes start: remove `to_legacy_cache`
|
||||
next_cache = None
|
||||
if use_cache:
|
||||
next_cache = next_decoder_cache
|
||||
# ipex-llm changes end
|
||||
|
||||
if not return_dict:
|
||||
return tuple(v for v in [hidden_states, next_cache,
|
||||
all_hidden_states, all_self_attns] if v is not None)
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=next_cache,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attns,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -272,26 +272,6 @@ def use_xmx(x: torch.Tensor, qtype: int):
|
|||
)
|
||||
|
||||
|
||||
def fp16_fusion_check(proj, x, training):
|
||||
# only use fp16 fusion on PVC inference
|
||||
if proj is None:
|
||||
return False
|
||||
if not hasattr(proj, "qtype"):
|
||||
return False
|
||||
if proj.qtype != ggml_tensor_qtype["fp16"]:
|
||||
return False
|
||||
if proj.weight_type != 2:
|
||||
return False
|
||||
if training:
|
||||
return False
|
||||
if x.requires_grad:
|
||||
return False
|
||||
device_type = get_xpu_device_name(x.device)
|
||||
if device_type != "pvc":
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
||||
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
||||
if n_rep == 1:
|
||||
|
|
|
|||
Loading…
Reference in a new issue