Model patcher

optimum/exporters/ipex/__init__.py

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+from .model_patcher import export_model

optimum/exporters/ipex/llama_functions.py

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+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+from intel_extension_for_pytorch.llm.modules import linear2SiluMul, linearAdd
+from torch import nn
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.models.llama.modeling_llama import repeat_kv
+
+
+def llama_layer_norm_forward(self, hidden_states):
+    return torch.ops.torch_ipex.rmsnorm(hidden_states, self.weight, self.variance_epsilon)
+
+
+def llama_attn_forward(
+    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,
+    **kwargs,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    bsz, q_len, _ = hidden_states.size()
+
+    query = self.q_proj(hidden_states)
+    key = self.k_proj(hidden_states)
+    value = self.v_proj(hidden_states)
+
+    kv_seq_len = q_len + past_key_value[0].size(-2) if past_key_value is not None else q_len
+
+    query = query.view(bsz, q_len, self.num_heads, self.head_dim)
+    key = key.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
+    value = value.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
+    # Use ipex op to rotary position embedding more efficient.
+    key = self.ipex_rope(
+        key,
+        position_ids,
+        self.num_key_value_heads,
+        self.head_dim,
+        self.head_dim // 2,
+        self.head_dim,
+        kv_seq_len,
+    )
+    query = self.ipex_rope(
+        query,
+        position_ids,
+        self.num_heads,
+        self.head_dim,
+        self.head_dim // 2,
+        self.head_dim,
+        kv_seq_len,
+    )
+
+    if use_cache:
+        # This ipex op pre-allocates buffers for past_key_values and use beam index history
+        # which to decide which beam should be used to make attention scale dot more efficient.
+        (attn_output, attn_weights, past_key_value) = self.ipex_scale_dot_product(
+            query,
+            key,
+            value,
+            math.sqrt(self.head_dim),
+            past_key_value,
+            None,
+            attention_mask,
+        )
+    else:
+        value_states = value.transpose(1, 2)
+        query_states = query.transpose(1, 2)
+        key_states = key.transpose(1, 2)
+        kv_seq_len = key_states.shape[-2]
+
+        past_key_value = None
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:
+            attn_weights = torch.tensor(attn_weights) + torch.tensor(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)
+
+    attn_output = attn_output.transpose(1, 2)
+    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+    if not output_attentions:
+        attn_weights = None
+
+    return attn_output, attn_weights, past_key_value
+
+
+def prepare_inputs_for_generation(
+    self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+):
+    if past_key_values is not None:
+        past_length = past_key_values[0][0].shape[2]
+
+        # Some generation methods already pass only the last input ID
+        if input_ids.shape[1] > past_length:
+            remove_prefix_length = past_length
+        else:
+            # Default to old behavior: keep only final ID
+            remove_prefix_length = input_ids.shape[1] - 1
+
+        input_ids = input_ids[:, remove_prefix_length:]
+
+    position_ids = kwargs.get("position_ids", None)
+    if attention_mask is not None and position_ids is None:
+        # create position_ids on the fly for batch generation
+        position_ids = attention_mask.long().cumsum(-1) - 1
+        position_ids.masked_fill_(attention_mask == 0, 1)
+        if past_key_values:
+            position_ids = position_ids[:, -input_ids.shape[1] :]
+
+    # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+    if inputs_embeds is not None and past_key_values is None:
+        model_inputs = {"inputs_embeds": inputs_embeds}
+    else:
+        model_inputs = {"input_ids": input_ids}
+
+    model_inputs.update(
+        {
+            "position_ids": position_ids,
+            "past_key_values": past_key_values,
+            "use_cache": kwargs.get("use_cache"),
+            "attention_mask": attention_mask,
+        }
+    )
+    return model_inputs
+
+
+def llama_model_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    **kwargs,
+) -> Union[Tuple, BaseModelOutputWithPast]:
+    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+    output_hidden_states = (
+        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+    )
+    use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+    # retrieve input_ids and inputs_embeds
+    if input_ids is not None and inputs_embeds is not None:
+        raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+    elif input_ids is not None:
+        batch_size, seq_length = input_ids.shape[:2]
+    elif inputs_embeds is not None:
+        batch_size, seq_length = inputs_embeds.shape[:2]
+    else:
+        raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+    past_key_values_length = 0
+    if past_key_values is not None:
+        past_key_values_length = past_key_values[0][0].shape[2]
+
+    if position_ids is None:
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+        position_ids = torch.arange(
+            past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+        )
+        position_ids = position_ids.unsqueeze(0)
+
+    if inputs_embeds is None:
+        inputs_embeds = self.embed_tokens(input_ids)
+
+    if getattr(self.config, "_flash_attn_2_enabled", False):
+        # 2d mask is passed through the layers
+        attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+    else:
+        # 4d mask is passed through the layers
+        attention_mask = _prepare_4d_causal_attention_mask(
+            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+        )
+
+    # embed positions
+    hidden_states = inputs_embeds
+
+    if self.gradient_checkpointing and self.training:
+        use_cache = False
+
+    # decoder layers
+    all_hidden_states = () if output_hidden_states else None
+    all_self_attns = () if output_attentions else None
+    next_decoder_cache = () if use_cache else None
+
+    for idx, decoder_layer in enumerate(self.layers):
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+        if self.gradient_checkpointing and self.training:
+            layer_outputs = self._gradient_checkpointing_func(
+                decoder_layer.__call__,
+                hidden_states,
+                attention_mask,
+                position_ids,
+                past_key_value,
+                output_attentions,
+                use_cache,
+            )
+        else:
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        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,)
+
+    next_cache = next_decoder_cache if use_cache else None
+    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,
+    )
+
+
+class _IPEXLlamaDecoderLayerRef(nn.Module):
+    def __init__(self, module, config, distributed=False):
+        super().__init__()
+        for k, v in module.__dict__.items():
+            setattr(self, k, v)
+        for k, v in module.__class__.__dict__.items():
+            if k.startswith("__") or k.startswith("forward"):
+                continue
+            setattr(self.__class__, k, getattr(module.__class__, k))
+        self.distributed = distributed
+        if not self.distributed:
+            self.mha_linear_add = linearAdd(module.self_attn.o_proj)
+            self.mlp_linear_add = linearAdd(module.mlp.down_proj)
+            del self.__dict__["_modules"]["self_attn"].o_proj
+            del self.__dict__["_modules"]["mlp"].down_proj
+        self.linear_silu_mul = linear2SiluMul(module.mlp.gate_proj, module.mlp.up_proj)
+        del self.__dict__["_modules"]["mlp"].gate_proj
+        del self.__dict__["_modules"]["mlp"].up_proj
+
+    def forward(
+        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: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            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,
+        )
+        if not self.distributed:
+            hidden_states = self.mha_linear_add(hidden_states, residual)
+        else:
+            hidden_states = self.self_attn.o_proj(hidden_states)
+            hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        mlp_gate = self.linear_silu_mul(hidden_states)
+
+        if not self.distributed:
+            hidden_states = self.mlp_linear_add(mlp_gate, residual)
+        else:
+            hidden_states = self.mlp.down_proj(mlp_gate)
+            hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs