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modeling_decicoder.py

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+# coding=utf-8
+# Copyright and license here
+""" PyTorch DeciCoder model."""
+import math
+from typing import Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from transformers.models.llama.modeling_llama import LlamaMLP, LlamaRMSNorm, LlamaAttention, apply_rotary_pos_emb, \
+    repeat_kv, LlamaPreTrainedModel, LLAMA_START_DOCSTRING, LlamaDecoderLayer, LlamaForCausalLM, LlamaModel
+from transformers.utils import add_start_docstrings
+
+from .configuration_decicoder import DeciCoderConfig
+
+_CONFIG_FOR_DOC = "DeciCoderConfig"
+
+
+class DeciCoderAttention(LlamaAttention):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: DeciCoderConfig):
+        nn.Module.__init__(self)
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.pretraining_tp = config.pretraining_tp
+        self.max_position_embeddings = config.max_position_embeddings
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.naive_attention_prefill = config.naive_attention_prefill
+        self.naive_attention_decode_batched = config.naive_attention_decode_batched
+        self.naive_attention_decode_single = config.naive_attention_decode_single
+        self._init_rope()
+
+    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: bool = False,
+            use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+        if past_key_value is None:
+            is_decode = False
+        else:
+            is_decode = True
+        if self.pretraining_tp > 1:
+            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp
+            query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.pretraining_tp, dim=0)
+            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+            query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)]
+            query_states = torch.cat(query_states, dim=-1)
+
+            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)]
+            key_states = torch.cat(key_states, dim=-1)
+
+            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)]
+            value_states = torch.cat(value_states, dim=-1)
+
+        else:
+            query_states = self.q_proj(hidden_states)
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
+
+        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_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_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)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        if is_decode:
+            query_states = query_states.view(bsz, self.num_key_value_heads, self.num_key_value_groups, self.head_dim)
+            if self.naive_attention_decode_batched and bsz > 1 or self.naive_attention_decode_single and bsz == 1:
+                attn_weights = (query_states @ key_states.transpose(-2, -1)) / math.sqrt(key_states.size(-1))
+                attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+                if attention_mask is not None:
+                    if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                        raise ValueError(
+                            f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                        )
+                    attn_weights = attn_weights + attention_mask
+
+                attn_output = torch.matmul(attn_weights, value_states)
+            else:
+                attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, is_causal=False,
+                                                             dropout_p=0.0)
+            attn_output = attn_output.contiguous().view(bsz, q_len, self.hidden_size)
+
+        else:
+            key_states = repeat_kv(key_states, self.num_key_value_groups)
+            value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+            if not self.naive_attention_prefill:
+                attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, is_causal=True,
+                                                             dropout_p=0.0)
+            else:
+                attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+                # attn_weights = (query_states @ key_states.transpose(-2, -1)) / math.sqrt(key_states.size(-1))
+                if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+                    raise ValueError(
+                        f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                        f" {attn_weights.size()}"
+                    )
+
+                if attention_mask is not None:
+                    if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                        raise ValueError(
+                            f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                        )
+                    attn_weights = attn_weights + attention_mask
+
+                # 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 attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+                raise ValueError(
+                    f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                    f" {attn_output.size()}"
+                )
+
+            attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, q_len, self.hidden_size)
+            # attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        if self.pretraining_tp > 1:
+            attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
+            o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.pretraining_tp, dim=1)
+            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.pretraining_tp)])
+        else:
+            attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class DeciCoderDecoderLayer(LlamaDecoderLayer):
+    def __init__(self, config: DeciCoderConfig):
+        nn.Module.__init__(self)
+        self.hidden_size = config.hidden_size
+        self.self_attn = DeciCoderAttention(config=config)
+        self.mlp = LlamaMLP(config)
+        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+
+@add_start_docstrings(
+    "The bare DeciCoder Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class DeciCoderPreTrainedModel(LlamaPreTrainedModel):
+    config_class = DeciCoderConfig
+    _no_split_modules = ["DeciCoderDecoderLayer"]
+    _keys_to_ignore_on_load_missing = ["self_attn.rotary_emb.inv_freq"]
+
+
+@add_start_docstrings(
+    "The bare DeciCoder Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class DeciCoderModel(LlamaModel, DeciCoderPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeciCoderDecoderLayer`]
+
+    Args:
+        config: DeciCoderConfig
+    """
+
+    def __init__(self, config: DeciCoderConfig):
+        DeciCoderPreTrainedModel.__init__(self, config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList([DeciCoderDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        self._validate_config_supports_attention_mask(attention_mask, input_shape, past_key_values_length)
+        return LlamaModel._prepare_decoder_attention_mask(
+            self, attention_mask, input_shape, inputs_embeds, past_key_values_length)
+
+    def _validate_config_supports_attention_mask(self, attention_mask, input_shape, past_key_values_length):
+        is_decode = past_key_values_length > 0
+        if not torch.all(torch.eq(attention_mask, 1)).item():
+            if is_decode:
+                if input_shape[0] == 1 and not self.config.naive_attention_decode_single:
+                    raise ValueError(
+                        "For support of custom attention masks please set naive_attention_decode_single to True in the "
+                        "config")
+                elif input_shape[0] > 1 and not self.config.naive_attention_decode_batched:
+                    raise ValueError(
+                        "For support of custom attention masks please set naive_attention_decode_batched to True in the"
+                        "config")
+            else:
+                if not self.config.naive_attention_prefill:
+                    raise ValueError("For support of custom attention masks please set naive_attention_prefill to "
+                                     "True in the config")
+
+
+class DeciCoderForCausalLM(LlamaForCausalLM, DeciCoderPreTrainedModel):
+    def __init__(self, config):
+        DeciCoderPreTrainedModel.__init__(self, config)
+        self.model = DeciCoderModel(config)
+        self.pretraining_tp = config.pretraining_tp
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()