import math from typing import List, Union from typing import Optional, Tuple import torch import torch.utils.checkpoint import torch.utils.checkpoint from einops import rearrange from torch import nn from torch.nn import CrossEntropyLoss from transformers.activations import ACT2FN from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from transformers.modeling_utils import PreTrainedModel from transformers.utils import logging from .configuration_InternLM_XComposer import InternLMXComposerConfig from .modeling_utils import LoRALinear logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "InternLMXComposerConfig" def rotary_embed(x1, x2, cos, sin, conj): x1, x2 = x1.float(), x2.float() if conj: x1, x2 = x1 * cos + x2 * sin, x1 * sin + x2 * cos else: x1, x2 = x1 * cos - x2 * sin, x1 * sin + x2 * cos return x1, x2 class LegacyApplyRotaryEmbQKV_(torch.autograd.Function): @staticmethod def forward(ctx, qkv, cos, sin, cos_k=None, sin_k=None, interleaved=False): """ qkv: (batch_size, seqlen, 3, nheads, headdim) cos, sin: (seqlen, rotary_dim / 2) cos_k, sin_k: (seqlen, rotary_dim / 2), optional interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead of 1st half and 2nd half (GPT-NeoX style). rotary_dim must be <= headdim Apply rotary embedding *inplace* to the first rotary_dim of q and k. """ batch, seqlen, three, nheads, headdim = qkv.shape assert three == 3 rotary_seqlen, rotary_dim = cos.shape rotary_dim *= 2 assert rotary_dim <= headdim assert seqlen <= rotary_seqlen cos_k = cos if cos_k is None else cos_k sin_k = sin if sin_k is None else sin_k assert sin.shape == cos_k.shape == sin_k.shape == (rotary_seqlen, rotary_dim // 2) q_ro = qkv[:, :, 0, :, :rotary_dim] q1, q2 = q_ro.chunk(2, dim=-1) if not interleaved else (q_ro[..., ::2], q_ro[..., 1::2]) # rotary_emb.apply_rotary(q1, q2, rearrange(cos[:seqlen], 's d -> s 1 d'), # rearrange(sin[:seqlen], 's d -> s 1 d'), q1, q2, False) q1, q2 = rotary_embed(q1, q2, rearrange(cos[:seqlen], 's d -> s 1 d'), rearrange(sin[:seqlen], 's d -> s 1 d'), False) qkv[:, :, 0, :, :rotary_dim] = torch.cat([q1, q2], dim=-1) k_ro = qkv[:, :, 1, :, :rotary_dim] k1, k2 = k_ro.chunk(2, dim=-1) if not interleaved else (k_ro[..., ::2], k_ro[..., 1::2]) # rotary_emb.apply_rotary(k1, k2, rearrange(cos_k[:seqlen], 's d -> s 1 d'), # rearrange(sin_k[:seqlen], 's d -> s 1 d'), k1, k2, False) k1, k2 = rotary_embed(k1, k2, rearrange(cos_k[:seqlen], 's d -> s 1 d'), rearrange(sin_k[:seqlen], 's d -> s 1 d'), False) qkv[:, :, 1, :, :rotary_dim] = torch.cat([k1, k2], dim=-1) ctx.save_for_backward(cos, sin, cos_k, sin_k) ctx.interleaved = interleaved return qkv @staticmethod def backward(ctx, dqkv): cos, sin, cos_k, sin_k = ctx.saved_tensors _, seqlen, _, _, headdim = dqkv.shape rotary_dim = cos.shape[-1] rotary_dim *= 2 dq_ro = dqkv[:, :, 0, :, :rotary_dim] dq1, dq2 = (dq_ro.chunk(2, dim=-1) if not ctx.interleaved else (dq_ro[..., ::2], dq_ro[..., 1::2])) rotary_emb.apply_rotary(dq1, dq2, rearrange(cos[:seqlen], 's d -> s 1 d'), rearrange(sin[:seqlen], 's d -> s 1 d'), dq1, dq2, True) dk_ro = dqkv[:, :, 1, :, :rotary_dim] dk1, dk2 = (dk_ro.chunk(2, dim=-1) if not ctx.interleaved else (dk_ro[..., ::2], dk_ro[..., 1::2])) rotary_emb.apply_rotary(dk1, dk2, rearrange(cos_k[:seqlen], 's d -> s 1 d'), rearrange(sin_k[:seqlen], 's d -> s 1 d'), dk1, dk2, True) return dqkv, None, None, None, None, None class ConvertedInternLMRotaryEmbedding(torch.nn.Module): def __init__(self, dim: int, base=10000, scale_base=0, device=None): """ """ super().__init__() # Generate and save the inverse frequency buffer (non trainable) inv_freq = 1.0 / (base**( torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)) self.register_buffer("inv_freq", inv_freq) self.scale_base = scale_base scale = ((torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim) if scale_base > 0 else None) self.register_buffer("scale", scale) self._seq_len_cached = 0 self._cos_cached = None self._sin_cached = None self._cos_k_cached = None self._sin_k_cached = None def _update_cos_sin_cache(self, x, indexes): """x: (batch, seqlen, nheads, headdim) or (batch, seqlen, 3, nheads, headdim)""" if not isinstance(indexes, int): seqlen = indexes.max().item() + 1 else: seqlen = indexes + 1 # eval_forward # Reset the tables if the sequence length has changed, # or if we're on a new device (possibly due to tracing for instance) if seqlen > self._seq_len_cached or self._cos_cached.device != x.device or self._cos_cached.dtype != x.dtype: self._seq_len_cached = seqlen t = torch.arange(seqlen, device=x.device, dtype=self.inv_freq.dtype) # Don't do einsum, it converts fp32 to fp16 # freqs = torch.einsum("i,j->ij", t, self.inv_freq) freqs = torch.outer(t, self.inv_freq.to(device=t.device)) if self.scale is None: self._cos_cached = torch.cos(freqs).to(x.dtype) self._sin_cached = torch.sin(freqs).to(x.dtype) else: power = (torch.arange( seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2) / self.scale_base scale = self.scale.to(device=power.device)**rearrange( power, "s -> s 1") # We want the multiplication by scale to happen in fp32 self._cos_cached = (torch.cos(freqs) * scale).to(x.dtype) self._sin_cached = (torch.sin(freqs) * scale).to(x.dtype) self._cos_k_cached = (torch.cos(freqs) / scale).to(x.dtype) self._sin_k_cached = (torch.sin(freqs) / scale).to(x.dtype) def eval_forward(self, qkv, seqlen_offset=0): """ seqlen_offset: can be used in generation where the qkv being passed in is only the last token in the batch. """ self._update_cos_sin_cache(qkv, seqlen_offset + qkv.shape[1]) if self.scale is None: return legacy_apply_rotary_embed_qkv( qkv, self._cos_cached[seqlen_offset:], self._sin_cached[seqlen_offset:]) else: return legacy_apply_rotary_embed_qkv( qkv, self._cos_cached[seqlen_offset:], self._sin_cached[seqlen_offset:], self._cos_k_cached[seqlen_offset:], self._sin_k_cached[seqlen_offset:], ) legacy_apply_rotary_embed_qkv = LegacyApplyRotaryEmbQKV_.apply class InternConvertedInternLMAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: InternLMXComposerConfig): super().__init__() 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.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}).") if config.lora_cfg is None: self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.kqvo_bias) self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.kqvo_bias) self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.kqvo_bias) self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.kqvo_bias) else: lora_cfg = config.lora_cfg if 'q' in lora_cfg['learn_param']: self.q_proj = LoRALinear(self.hidden_size, self.num_heads * self.head_dim, bias=config.kqvo_bias, **lora_cfg) else: self.q_proj = nn.Linear( self.hidden_size, self.num_heads * self.head_dim, bias=config.kqvo_bias, ) if 'k' in lora_cfg['learn_param']: self.k_proj = LoRALinear(self.hidden_size, self.num_heads * self.head_dim, bias=config.kqvo_bias, **lora_cfg) else: self.k_proj = nn.Linear( self.hidden_size, self.num_heads * self.head_dim, bias=config.kqvo_bias, ) if 'v' in lora_cfg['learn_param']: self.v_proj = LoRALinear(self.hidden_size, self.num_heads * self.head_dim, bias=config.kqvo_bias, **lora_cfg) else: self.v_proj = nn.Linear( self.hidden_size, self.num_heads * self.head_dim, bias=config.kqvo_bias, ) if 'o' in lora_cfg['learn_param']: self.o_proj = LoRALinear(self.num_heads * self.head_dim, self.hidden_size, bias=config.kqvo_bias, **lora_cfg) else: self.o_proj = nn.Linear( self.num_heads * self.head_dim, self.hidden_size, bias=config.kqvo_bias, ) self.rotary_emb = ConvertedInternLMRotaryEmbedding(self.head_dim) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() 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() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) q = query_states k = key_states v = value_states qkv = torch.cat([q, k, v], dim=2).contiguous() qkv = qkv.view(bsz, q_len, -1) qkv = rearrange(qkv, "b s (three h d) -> b s three h d", three=3, d=self.head_dim) if past_key_value is not None: qkv = self.rotary_emb.eval_forward( qkv, seqlen_offset=past_key_value[0].shape[2]) else: qkv = self.rotary_emb.eval_forward(qkv) query_states, key_states, value_states = qkv.unbind(2) query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.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) # [bsz, nh, t, hd] 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 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): 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 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 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) 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 class ConvertedLoRALinear(nn.Linear): def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None, lora_r=8, lora_alpha=16, lora_dropout=0.05, **kwargs) -> None: super().__init__(in_features, out_features, bias, device, dtype) self.lora_r = lora_r self.lora_alpha = lora_alpha if lora_dropout > 0.: self.lora_dropout = nn.Dropout(p=lora_dropout) else: self.lora_dropout = lambda x: x self.lora_scaling = self.lora_alpha / self.lora_r self.lora_A = nn.Linear(in_features, self.lora_r, bias=False, device=device, dtype=dtype) self.lora_B = nn.Linear(self.lora_r, out_features, bias=False, device=device, dtype=dtype) self.reset_parameters() def reset_parameters(self): if hasattr(self, 'lora_A'): # initialize A the same way as the default for nn.Linear and B to zero nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5)) nn.init.zeros_(self.lora_B.weight) # print ("lora weight init {} {}".format(torch.mean(self.lora_A.weight), torch.mean(self.lora_B.weight))) def forward(self, x): orig_type = x.dtype res = super().forward(x) dim = int(res.shape[-1] // 2) r1 = res[..., :dim] r2 = res[..., dim:] r1 = r1.float() r2 = r2.float() x_ = x.float() tmp = self.lora_B(self.lora_A( self.lora_dropout(x_))) * self.lora_scaling tmp1 = tmp[..., ::2] tmp2 = tmp[..., 1::2] r1 += tmp1 r2 += tmp2 r1 = r1.to(orig_type) r2 = r2.to(orig_type) res = torch.cat([r1, r2], -1) # res += self.lora_B(self.lora_A( # self.lora_dropout(x))) * self.lora_scaling return res # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) if past_key_values_length > 0: mask = torch.cat([ torch.zeros( tgt_len, past_key_values_length, dtype=dtype, device=device), mask ], dim=-1) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) class InternLMRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ InternLMRMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) # convert into half-precision if necessary if self.weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(self.weight.dtype) return self.weight * hidden_states class InternLMRotaryEmbedding(torch.nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() inv_freq = 1.0 / (base **(torch.arange(0, dim, 2).float().to(device) / dim)) self.register_buffer("inv_freq", inv_freq) # Build here to make `torch.jit.trace` work. self.max_seq_len_cached = max_position_embeddings t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False) self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case. if seq_len > self.max_seq_len_cached: self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1).to(x.device) self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False) self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) return ( self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), ) def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., :x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2:] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids): gather_indices = position_ids[:, None, :, None] # [bs, 1, seq_len, 1] gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3]) cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices) sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class InternLMMLP(nn.Module): def __init__(self, hidden_size: int, intermediate_size: int, hidden_act: str, config: InternLMXComposerConfig): super().__init__() self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False) if config.lora_cfg is not None and 'ffn' in config.lora_cfg[ 'learn_param']: lora_cfg = config.lora_cfg self.down_proj = LoRALinear(intermediate_size, hidden_size, bias=False, **lora_cfg) self.up_proj = LoRALinear(hidden_size, intermediate_size, bias=False, **lora_cfg) else: self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False) self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False) self.act_fn = ACT2FN[hidden_act] def forward(self, x): return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) class InternLMAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: InternLMXComposerConfig): super().__init__() 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.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}).") if config.lora_cfg is None: 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_heads * self.head_dim, bias=False) self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) else: lora_cfg = config.lora_cfg if 'q' in lora_cfg['learn_param']: self.q_proj = LoRALinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, **lora_cfg) else: self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) if 'k' in lora_cfg['learn_param']: self.k_proj = LoRALinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, **lora_cfg) else: self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) if 'v' in lora_cfg['learn_param']: self.v_proj = LoRALinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, **lora_cfg) else: self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) if 'o' in lora_cfg['learn_param']: self.o_proj = LoRALinear(self.num_heads * self.head_dim, self.hidden_size, bias=False, **lora_cfg) else: self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) self.rotary_emb = InternLMRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() 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() query_states = self.q_proj(hidden_states).view( bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = self.k_proj(hidden_states).view( bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) value_states = self.v_proj(hidden_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) query_states, key_states = apply_rotary_pos_emb( query_states, key_states, cos, sin, position_ids) # [bsz, nh, t, hd] 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 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): 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 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 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) 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 class InternLMDecoderLayer(nn.Module): def __init__(self, config: InternLMXComposerConfig): super().__init__() self.hidden_size = config.hidden_size if hasattr(config, 'intern_converted_llm') and config.intern_converted_llm: self.self_attn = InternConvertedInternLMAttention(config=config) else: self.self_attn = InternLMAttention(config=config) self.mlp = InternLMMLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, config=config, ) self.input_layernorm = InternLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = InternLMRMSNorm( config.hidden_size, eps=config.rms_norm_eps) 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, ) -> 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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. 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, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states, ) if output_attentions: outputs += (self_attn_weights, ) if use_cache: outputs += (present_key_value, ) return outputs class InternLMPreTrainedModel(PreTrainedModel): config_class = InternLMXComposerConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["InternLMDecoderLayer"] _keys_to_ignore_on_load_unexpected = [r"decoder\.version"] def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, InternLMModel): module.gradient_checkpointing = value class InternLMModel(InternLMPreTrainedModel): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLMDecoderLayer`] Args: config: InternLMXComposerConfig """ def __init__(self, config: InternLMXComposerConfig): super().__init__(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([ InternLMDecoderLayer(config) for _ in range(config.num_hidden_layers) ]) self.norm = InternLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, device=inputs_embeds.device, past_key_values_length=past_key_values_length, ) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( inputs_embeds.device) combined_attention_mask = (expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask) return combined_attention_mask def 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, query_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, ) -> 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 decoder_input_ids and decoder_inputs_embeds at the same time" ) elif input_ids is not None: batch_size, seq_length = input_ids.shape elif inputs_embeds is not None: batch_size, seq_length, _ = inputs_embeds.shape else: raise ValueError( "You have to specify either decoder_input_ids or decoder_inputs_embeds" ) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if query_embeds is not None: inputs_embeds = torch.cat([query_embeds, inputs_embeds], dim=1) batch_size, seq_length, _ = inputs_embeds.shape seq_length_with_past = seq_length past_key_values_length = 0 if past_key_values is not None: past_key_values_length = past_key_values[0][0].shape[2] seq_length_with_past = seq_length_with_past + past_key_values_length 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).view(-1, seq_length) else: position_ids = position_ids.view(-1, seq_length).long() # embed positions if attention_mask is None: attention_mask = torch.ones((batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device) attention_mask = self._prepare_decoder_attention_mask( attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length) hidden_states = inputs_embeds if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) 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: def create_custom_forward(module): def custom_forward(*inputs): # None for past_key_value return module(*inputs, output_attentions, None) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(decoder_layer), hidden_states, attention_mask, position_ids, None, ) 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 InternLMForCausalLM(InternLMPreTrainedModel): lora_cfg = None # init in MiniGPT4 def __init__(self, config): super().__init__(config) # TODO: find a way to explicitly initialize InternLM setattr(config, 'lora_cfg', self.lora_cfg) if hasattr(config, 'kqvo_bias'): setattr(config, 'kqvo_bias', config.kqvo_bias) else: setattr(config, 'kqvo_bias', False) self.model = InternLMModel(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) if hasattr(config, 'ex_size'): self.ex_size = config.ex_size else: self.ex_size = 0 if hasattr(config, 'sp_id'): self.sp_id = config.sp_id else: self.sp_id = -1 # Initialize weights and apply final processing self.post_init() @classmethod def from_pretrained(cls, pretrained_model_name_or_path, llm_cfg=None, *model_args, **kwargs): if llm_cfg: if 'torch_dtype' in kwargs: llm_cfg.torch_dtype = kwargs['torch_dtype'] if 'load_in_8bit' in kwargs: llm_cfg.load_in_8bit = kwargs['load_in_8bit'] if 'device_map' in kwargs: llm_cfg.device_map = kwargs['device_map'] return cls._from_config(llm_cfg) else: return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): self.model = decoder def get_decoder(self): return self.model def 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, query_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: Example: ```python >>> from transformers import AutoTokenizer, InternLMForCausalLM >>> model = InternLMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) >>> prompt = "Hey, are you consciours? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." ```""" 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) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, query_embeds=query_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) loss = None if labels is not None: # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss(reduce=False) loss_reduce = CrossEntropyLoss() shift_logits = shift_logits.view(-1, self.config.vocab_size) shift_labels = shift_labels.view(-1) shift_labels = shift_labels.to(shift_logits.device) ### if self.sp_id >= 0: ori_mask = (shift_labels != self.sp_id).float() ori_mask = ori_mask * (shift_labels >= 0).float() local_mask = (shift_labels == self.sp_id).float() else: ori_mask = (shift_labels < self.config.vocab_size - self.ex_size).float() ori_mask = ori_mask * (shift_labels >= 0).float() local_mask = (shift_labels >= self.config.vocab_size - self.ex_size).float() # Enable model parallelism loss = loss_reduce(shift_logits, shift_labels) loss_all = loss_fct(shift_logits, shift_labels) loss_o = (loss_all * ori_mask).sum() / ori_mask.sum() if torch.sum(local_mask) == 0: loss_l = loss_o * 0 else: loss_l = (loss_all * local_mask).sum() / local_mask.sum() if not return_dict: output = (logits, ) + outputs[1:] return (loss, ) + output if loss is not None else output if (self.ex_size > 0 or self.sp_id >= 0) and labels is not None: return loss, loss_o, loss_l return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation(self, input_ids, query_embeds=None, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs): if past_key_values: input_ids = input_ids[:, -1:] 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[:, -1].unsqueeze(-1) query_embeds = None # 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, "query_embeds": query_embeds, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, }) return model_inputs @staticmethod def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += (tuple( past_state.index_select(0, beam_idx) for past_state in layer_past), ) return reordered_past