Crystalcareai
commited on
Commit
•
ec7309a
1
Parent(s):
54b48ed
Update modeling_gemmoe.py
Browse files- modeling_gemmoe.py +753 -603
modeling_gemmoe.py
CHANGED
@@ -26,11 +26,14 @@ from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.activations import ACT2FN
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from transformers.cache_utils import Cache, DynamicCache
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from transformers.modeling_attn_mask_utils import (
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_prepare_4d_causal_attention_mask,
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)
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from transformers.modeling_outputs import SequenceClassifierOutputWithPast,
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from transformers.modeling_utils import PreTrainedModel
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from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
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from transformers.utils import (
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_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "GemmoeConfig"
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max_seqlen_in_batch,
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)
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class GemmoeRMSNorm(nn.Module):
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def __init__(self,
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super().__init__()
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self.
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self.
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self,
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ALL_LAYERNORM_LAYERS.append(GemmoeRMSNorm)
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class GemmoeRotaryEmbedding(nn.Module):
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
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super().__init__()
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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self.
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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)
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self.
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self.register_buffer("sin_cached", sin, persistent=False)
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def forward(self, x, position_ids=None, seq_len=None):
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if seq_len is None:
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seq_len = x.size(2)
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if seq_len > self.max_seq_len_cached:
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self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
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return (
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self.cos_cached[:seq_len],
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self.sin_cached[:seq_len],
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)
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=
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"""Applies Rotary Position Embedding to the query and key tensors.
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, 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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class GemmoeAttention(nn.Module):
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"""
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Multi-headed attention module for Gemmoe model.
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Args:
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config (GemmoeConfig): The configuration object for the Gemmoe model.
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layer_idx (Optional[int]): The index of the layer. Default is None.
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"""
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def __init__(self, config: GemmoeConfig, layer_idx: Optional[int] = None):
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super().__init__()
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self.layer_idx = layer_idx
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if layer_idx is None:
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logger.warning_once(
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f"Instantiating {self.__class__.__name__} without passing
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"
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"when creating this class."
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)
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self.attention_dropout = config.attention_dropout
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim =
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self.num_key_value_heads = config.num_key_value_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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self.is_causal = True
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if self.
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raise ValueError(
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads})."
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)
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=
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self.
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def forward(
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self,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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""
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hidden_states (torch.Tensor): The input hidden states.
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attention_mask (Optional[torch.Tensor]): The attention mask. Default is None.
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position_ids (Optional[torch.LongTensor]): The position IDs. Default is None.
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past_key_value (Optional[Cache]): The past key-value cache. Default is None.
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output_attentions (bool): Whether to output the attention weights. Default is False.
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use_cache (bool): Whether to use caching. Default is False.
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cache_position (Optional[torch.LongTensor]): The cache position. Default is None.
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**kwargs: Additional keyword arguments.
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Returns:
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Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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- The output hidden states.
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- The attention weights (if `output_attentions=True`).
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- The past key-value cache (if `use_cache=True`).
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"""
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bsz, q_len, _ = hidden_states.size()
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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if past_key_value is not None:
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.view(bsz, q_len, -1)
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attn_output = self.
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if not output_attentions:
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attn_weights = None
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untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
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flash attention and deal with padding tokens in case the input contains any of them.
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
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def forward(
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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output_attentions = False
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bsz, q_len, _ = hidden_states.size()
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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past_key_value = getattr(self, "past_key_value", past_key_value)
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if past_key_value is not None:
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
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# cast them back in the correct dtype just to be sure everything works as expected.
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# This might slowdown training & inference so it is recommended to not cast the LayerNorms
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# in fp32. (GemmoeRMSNorm handles it correctly)
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input_dtype = query_states.dtype
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if input_dtype == torch.float32:
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if torch.is_autocast_enabled():
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target_dtype = torch.get_autocast_gpu_dtype()
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# Handle the case where the model is quantized
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target_dtype = self.config._pre_quantization_dtype
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else:
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target_dtype = self.q_proj.weight.dtype
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f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
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f" {target_dtype}."
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)
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query_states = query_states.to(target_dtype)
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key_states = key_states.to(target_dtype)
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value_states = value_states.to(target_dtype)
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query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
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)
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attn_output = attn_output.reshape(bsz, q_len,
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
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query_states, key_states, value_states, attention_mask, query_length
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)
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cu_seqlens_q, cu_seqlens_k = cu_seq_lens
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max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
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softmax_scale=softmax_scale,
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causal=causal,
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)
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attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
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else:
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attn_output = flash_attn_func(
|
@@ -509,15 +820,14 @@ class GemmoeFlashAttention2(GemmoeAttention):
|
|
509 |
|
510 |
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
511 |
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
512 |
-
|
513 |
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
|
|
514 |
key_layer = index_first_axis(
|
515 |
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
516 |
)
|
517 |
value_layer = index_first_axis(
|
518 |
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
519 |
)
|
520 |
-
|
521 |
if query_length == kv_seq_len:
|
522 |
query_layer = index_first_axis(
|
523 |
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
@@ -549,21 +859,11 @@ class GemmoeFlashAttention2(GemmoeAttention):
|
|
549 |
class GemmoeSdpaAttention(GemmoeAttention):
|
550 |
"""
|
551 |
Gemmoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
552 |
-
GemmoeAttention as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
553 |
SDPA API.
|
554 |
"""
|
555 |
|
556 |
-
|
557 |
-
"""
|
558 |
-
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
559 |
-
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
560 |
-
"""
|
561 |
-
batch, num_key_value_heads, slen, head_dim = x.shape
|
562 |
-
if n_rep == 1:
|
563 |
-
return x
|
564 |
-
x = x[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
565 |
-
return x.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
566 |
-
|
567 |
def forward(
|
568 |
self,
|
569 |
hidden_states: torch.Tensor,
|
@@ -572,15 +872,13 @@ class GemmoeSdpaAttention(GemmoeAttention):
|
|
572 |
past_key_value: Optional[Cache] = None,
|
573 |
output_attentions: bool = False,
|
574 |
use_cache: bool = False,
|
575 |
-
cache_position: Optional[torch.LongTensor] = None,
|
576 |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
577 |
if output_attentions:
|
578 |
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
579 |
-
|
580 |
-
|
581 |
-
|
582 |
-
|
583 |
-
|
584 |
return super().forward(
|
585 |
hidden_states=hidden_states,
|
586 |
attention_mask=attention_mask,
|
@@ -588,9 +886,8 @@ class GemmoeSdpaAttention(GemmoeAttention):
|
|
588 |
past_key_value=past_key_value,
|
589 |
output_attentions=output_attentions,
|
590 |
use_cache=use_cache,
|
591 |
-
cache_position=cache_position,
|
592 |
)
|
593 |
-
|
594 |
bsz, q_len, _ = hidden_states.size()
|
595 |
|
596 |
query_states = self.q_proj(hidden_states)
|
@@ -601,48 +898,46 @@ class GemmoeSdpaAttention(GemmoeAttention):
|
|
601 |
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
602 |
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
603 |
|
604 |
-
|
605 |
-
|
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|
|
|
|
|
|
606 |
|
607 |
-
past_key_value = getattr(self, "past_key_value", past_key_value)
|
608 |
if past_key_value is not None:
|
609 |
-
|
610 |
-
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
611 |
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
612 |
|
613 |
-
key_states =
|
614 |
-
value_states =
|
615 |
|
616 |
-
|
617 |
-
|
618 |
-
|
619 |
-
|
620 |
-
|
621 |
-
common_dtype = query_states.dtype
|
622 |
-
key_states = key_states.to(dtype=common_dtype)
|
623 |
-
value_states = value_states.to(dtype=common_dtype)
|
624 |
|
625 |
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
626 |
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
627 |
-
if query_states.device.type == "cuda" and
|
628 |
query_states = query_states.contiguous()
|
629 |
key_states = key_states.contiguous()
|
630 |
value_states = value_states.contiguous()
|
631 |
|
632 |
-
# Cast causal_mask to the same dtype as query_states
|
633 |
-
if causal_mask is not None:
|
634 |
-
causal_mask = causal_mask.to(dtype=query_states.dtype)
|
635 |
-
|
636 |
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
637 |
query_states,
|
638 |
key_states,
|
639 |
value_states,
|
640 |
-
attn_mask=
|
641 |
dropout_p=self.attention_dropout if self.training else 0.0,
|
|
|
|
|
642 |
)
|
643 |
|
644 |
attn_output = attn_output.transpose(1, 2).contiguous()
|
645 |
-
attn_output = attn_output.
|
|
|
646 |
attn_output = self.o_proj(attn_output)
|
647 |
|
648 |
return attn_output, None, past_key_value
|
@@ -653,74 +948,17 @@ GEMMOE_ATTENTION_CLASSES = {
|
|
653 |
"sdpa": GemmoeSdpaAttention,
|
654 |
}
|
655 |
|
656 |
-
class GemmoeBlockSparseTop2MLP(nn.Module):
|
657 |
-
def __init__(self, config: GemmoeConfig):
|
658 |
-
super().__init__()
|
659 |
-
self.ffn_dim = config.intermediate_size
|
660 |
-
self.hidden_dim = config.hidden_size
|
661 |
-
|
662 |
-
self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
|
663 |
-
self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
|
664 |
-
self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
|
665 |
-
|
666 |
-
self.act_fn = approx_gelu
|
667 |
-
|
668 |
-
def forward(self, hidden_states):
|
669 |
-
current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
|
670 |
-
current_hidden_states = self.w2(current_hidden_states)
|
671 |
-
return current_hidden_states
|
672 |
-
|
673 |
-
|
674 |
-
class GemmoeSparseMoeBlock(nn.Module):
|
675 |
-
def __init__(self, config):
|
676 |
-
super().__init__()
|
677 |
-
self.hidden_dim = config.hidden_size
|
678 |
-
self.ffn_dim = config.intermediate_size
|
679 |
-
self.num_experts = config.num_local_experts
|
680 |
-
self.top_k = 2
|
681 |
-
|
682 |
-
# gating
|
683 |
-
self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
|
684 |
-
|
685 |
-
self.experts = nn.ModuleList([GemmoeBlockSparseTop2MLP(config) for _ in range(self.num_experts)])
|
686 |
-
|
687 |
-
def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
688 |
-
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
689 |
-
hidden_states = hidden_states.view(-1, hidden_dim)
|
690 |
-
|
691 |
-
# router_logits: (batch * sequence_length, n_experts)
|
692 |
-
router_logits = self.gate(hidden_states)
|
693 |
-
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
|
694 |
-
topk_weight, topk_idx = torch.topk(routing_weights, self.top_k, dim=-1, sorted=False)
|
695 |
-
topk_weight /= topk_weight.sum(dim=-1, keepdim=True)
|
696 |
-
|
697 |
-
# we cast back to the input dtype
|
698 |
-
topk_weight = topk_weight.to(hidden_states.dtype)
|
699 |
|
700 |
-
hidden_states = hidden_states.repeat_interleave(self.top_k, dim=0)
|
701 |
-
|
702 |
-
y = torch.empty_like(hidden_states)
|
703 |
-
|
704 |
-
flat_topk_idx = topk_idx.view(-1)
|
705 |
-
for i in range(self.num_experts):
|
706 |
-
expert = self.experts[i]
|
707 |
-
expert_output = expert(hidden_states[flat_topk_idx == i])
|
708 |
-
y[flat_topk_idx == i] = expert_output.to(y.dtype) # Cast expert_output to the same dtype as y
|
709 |
-
|
710 |
-
y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
|
711 |
-
|
712 |
-
final_hidden_states = y.reshape(batch_size, sequence_length, hidden_dim)
|
713 |
-
return final_hidden_states, router_logits
|
714 |
-
|
715 |
-
|
716 |
class GemmoeDecoderLayer(nn.Module):
|
717 |
def __init__(self, config: GemmoeConfig, layer_idx: int):
|
718 |
super().__init__()
|
719 |
self.hidden_size = config.hidden_size
|
720 |
|
721 |
-
self.self_attn = GEMMOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
|
722 |
|
723 |
-
self.
|
|
|
|
|
724 |
self.input_layernorm = GemmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
725 |
self.post_attention_layernorm = GemmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
726 |
|
@@ -731,9 +969,7 @@ class GemmoeDecoderLayer(nn.Module):
|
|
731 |
position_ids: Optional[torch.LongTensor] = None,
|
732 |
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
733 |
output_attentions: Optional[bool] = False,
|
734 |
-
output_router_logits: Optional[bool] = False,
|
735 |
use_cache: Optional[bool] = False,
|
736 |
-
cache_position: Optional[torch.LongTensor] = None,
|
737 |
**kwargs,
|
738 |
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
739 |
"""
|
@@ -749,16 +985,13 @@ class GemmoeDecoderLayer(nn.Module):
|
|
749 |
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
750 |
(see `past_key_values`).
|
751 |
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
752 |
-
output_router_logits (`bool`, *optional*):
|
753 |
-
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
|
754 |
-
should not be returned during inference.
|
755 |
"""
|
756 |
if "padding_mask" in kwargs:
|
757 |
warnings.warn(
|
758 |
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
759 |
)
|
760 |
-
|
761 |
residual = hidden_states
|
|
|
762 |
hidden_states = self.input_layernorm(hidden_states)
|
763 |
|
764 |
# Self Attention
|
@@ -769,7 +1002,6 @@ class GemmoeDecoderLayer(nn.Module):
|
|
769 |
past_key_value=past_key_value,
|
770 |
output_attentions=output_attentions,
|
771 |
use_cache=use_cache,
|
772 |
-
cache_position=cache_position,
|
773 |
**kwargs,
|
774 |
)
|
775 |
hidden_states = residual + hidden_states
|
@@ -777,9 +1009,8 @@ class GemmoeDecoderLayer(nn.Module):
|
|
777 |
# Fully Connected
|
778 |
residual = hidden_states
|
779 |
hidden_states = self.post_attention_layernorm(hidden_states)
|
780 |
-
hidden_states
|
781 |
hidden_states = residual + hidden_states
|
782 |
-
|
783 |
|
784 |
outputs = (hidden_states,)
|
785 |
|
@@ -789,15 +1020,23 @@ class GemmoeDecoderLayer(nn.Module):
|
|
789 |
if use_cache:
|
790 |
outputs += (present_key_value,)
|
791 |
|
792 |
-
if output_router_logits:
|
793 |
-
outputs += (router_logits,)
|
794 |
-
|
795 |
return outputs
|
796 |
|
|
|
797 |
GEMMOE_START_DOCSTRING = r"""
|
798 |
-
This model inherits from [PreTrainedModel]. Check the superclass documentation for the generic methods the
|
799 |
-
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
800 |
-
etc.)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
801 |
"""
|
802 |
|
803 |
@add_start_docstrings(
|
@@ -806,52 +1045,94 @@ GEMMOE_START_DOCSTRING,
|
|
806 |
)
|
807 |
|
808 |
class GemmoePreTrainedModel(PreTrainedModel):
|
809 |
-
|
810 |
-
|
811 |
-
|
812 |
-
|
813 |
-
|
814 |
-
|
815 |
-
|
816 |
-
|
817 |
-
|
818 |
-
|
819 |
-
|
820 |
-
|
821 |
-
|
822 |
-
|
823 |
-
|
824 |
-
|
825 |
-
|
826 |
-
|
827 |
-
|
828 |
-
|
829 |
-
|
830 |
-
|
831 |
-
|
832 |
-
|
833 |
-
|
834 |
-
|
835 |
-
|
836 |
-
|
837 |
-
|
838 |
-
|
839 |
-
|
840 |
-
|
841 |
-
|
842 |
-
|
843 |
-
|
844 |
-
|
845 |
-
|
846 |
-
|
847 |
-
|
848 |
-
|
849 |
-
|
850 |
-
|
851 |
-
|
852 |
-
|
853 |
-
|
854 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
855 |
"""
|
856 |
|
857 |
@add_start_docstrings(
|
@@ -860,263 +1141,168 @@ GEMMOE_START_DOCSTRING,
|
|
860 |
)
|
861 |
|
862 |
class GemmoeModel(GemmoePreTrainedModel):
|
863 |
-
|
864 |
-
|
865 |
-
config: GemmoeConfig
|
866 |
-
"""
|
867 |
-
|
868 |
-
|
869 |
-
def __init__(self, config: GemmoeConfig):
|
870 |
-
super().__init__(config)
|
871 |
-
self.padding_idx = config.pad_token_id
|
872 |
-
self.vocab_size = config.vocab_size
|
873 |
-
|
874 |
-
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
875 |
-
self.layers = nn.ModuleList(
|
876 |
-
[GemmoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
877 |
-
)
|
878 |
-
|
879 |
-
self.norm = GemmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
880 |
-
|
881 |
-
self.gradient_checkpointing = False
|
882 |
-
|
883 |
-
# Register a causal mask to separate causal and padding mask creation. Merging happens in the attention class.
|
884 |
-
# NOTE: This is not friendly with TorchScript, ONNX, ExportedProgram serialization for very large `max_position_embeddings`.
|
885 |
-
causal_mask = torch.full(
|
886 |
-
(config.max_position_embeddings, config.max_position_embeddings), fill_value=True, dtype=torch.bool
|
887 |
-
)
|
888 |
-
self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)
|
889 |
-
|
890 |
-
# Initialize weights and apply final processing
|
891 |
-
self.post_init()
|
892 |
-
|
893 |
-
def get_input_embeddings(self):
|
894 |
-
return self.embed_tokens
|
895 |
-
|
896 |
-
def set_input_embeddings(self, value):
|
897 |
-
self.embed_tokens = value
|
898 |
-
|
899 |
-
@add_start_docstrings_to_model_forward(GEMMOE_INPUTS_DOCSTRING)
|
900 |
-
@replace_return_docstrings(output_type=MoeModelOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
901 |
-
def forward(
|
902 |
-
self,
|
903 |
-
input_ids: torch.LongTensor = None,
|
904 |
-
attention_mask: Optional[torch.Tensor] = None,
|
905 |
-
position_ids: Optional[torch.LongTensor] = None,
|
906 |
-
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
907 |
-
inputs_embeds: Optional[torch.FloatTensor] = None,
|
908 |
-
use_cache: Optional[bool] = None,
|
909 |
-
output_attentions: Optional[bool] = None,
|
910 |
-
output_hidden_states: Optional[bool] = None,
|
911 |
-
output_router_logits: Optional[bool] = None,
|
912 |
-
return_dict: Optional[bool] = None,
|
913 |
-
cache_position: Optional[torch.LongTensor] = None,
|
914 |
-
) -> Union[Tuple, MoeModelOutputWithPast]:
|
915 |
-
"""
|
916 |
-
Forward pass of the sequence classification model.
|
917 |
-
|
918 |
-
Args:
|
919 |
-
input_ids: Input token IDs.
|
920 |
-
attention_mask: Attention mask.
|
921 |
-
position_ids: Position IDs.
|
922 |
-
past_key_values: Past key-value pairs.
|
923 |
-
inputs_embeds: Input embeddings.
|
924 |
-
labels: Labels for sequence classification.
|
925 |
-
use_cache: Whether to use cache.
|
926 |
-
output_attentions: Whether to output attentions.
|
927 |
-
output_hidden_states: Whether to output hidden states.
|
928 |
-
return_dict: Whether to return a dictionary or tuple.
|
929 |
-
|
930 |
-
Returns:
|
931 |
-
Output of the sequence classification model.
|
932 |
-
"""
|
933 |
-
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
934 |
-
output_hidden_states = (
|
935 |
-
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
936 |
-
)
|
937 |
-
output_router_logits = (
|
938 |
-
output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
939 |
-
)
|
940 |
-
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
941 |
-
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
942 |
-
|
943 |
-
if (input_ids is None) ^ (inputs_embeds is not None):
|
944 |
-
raise ValueError(
|
945 |
-
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
|
946 |
-
)
|
947 |
-
|
948 |
-
if self.gradient_checkpointing and self.training and use_cache:
|
949 |
-
logger.warning_once(
|
950 |
-
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
951 |
-
)
|
952 |
-
use_cache = False
|
953 |
-
|
954 |
-
if inputs_embeds is None:
|
955 |
-
inputs_embeds = self.embed_tokens(input_ids)
|
956 |
-
|
957 |
-
past_seen_tokens = 0
|
958 |
-
if use_cache: # kept for BC (cache positions)
|
959 |
-
if not isinstance(past_key_values, StaticCache):
|
960 |
-
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
961 |
-
past_seen_tokens = past_key_values.get_seq_length()
|
962 |
-
|
963 |
-
if cache_position is None:
|
964 |
-
cache_position = torch.arange(
|
965 |
-
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
966 |
-
)
|
967 |
-
|
968 |
-
if position_ids is None:
|
969 |
-
position_ids = cache_position.unsqueeze(0)
|
970 |
-
|
971 |
-
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds)
|
972 |
-
|
973 |
-
hidden_states = inputs_embeds
|
974 |
-
|
975 |
-
# Normalize
|
976 |
-
scale_factor = torch.tensor(math_sqrt(self.config.hidden_size), dtype=hidden_states.dtype)
|
977 |
-
hidden_states = hidden_states * scale_factor
|
978 |
-
# Decoder layers
|
979 |
-
all_hidden_states = () if output_hidden_states else None
|
980 |
-
all_self_attns = () if output_attentions else None
|
981 |
-
all_router_logits = () if output_router_logits else None
|
982 |
-
next_decoder_cache = None
|
983 |
-
|
984 |
-
for decoder_layer in self.layers:
|
985 |
-
if output_hidden_states:
|
986 |
-
all_hidden_states += (hidden_states,)
|
987 |
-
|
988 |
-
if self.gradient_checkpointing and self.training:
|
989 |
-
layer_outputs = self._gradient_checkpointing_func(
|
990 |
-
decoder_layer.__call__,
|
991 |
-
hidden_states,
|
992 |
-
causal_mask,
|
993 |
-
position_ids,
|
994 |
-
past_key_values,
|
995 |
-
output_attentions,
|
996 |
-
output_router_logits,
|
997 |
-
use_cache,
|
998 |
-
cache_position,
|
999 |
-
)
|
1000 |
-
else:
|
1001 |
-
layer_outputs = decoder_layer(
|
1002 |
-
hidden_states,
|
1003 |
-
attention_mask=causal_mask,
|
1004 |
-
position_ids=position_ids,
|
1005 |
-
past_key_value=past_key_values,
|
1006 |
-
output_attentions=output_attentions,
|
1007 |
-
output_router_logits=output_router_logits,
|
1008 |
-
use_cache=use_cache,
|
1009 |
-
cache_position=cache_position,
|
1010 |
-
)
|
1011 |
-
|
1012 |
-
hidden_states = layer_outputs[0]
|
1013 |
-
if use_cache:
|
1014 |
-
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
1015 |
-
if output_attentions:
|
1016 |
-
all_self_attns += (layer_outputs[1],)
|
1017 |
-
if output_router_logits:
|
1018 |
-
all_router_logits += (layer_outputs[-1],)
|
1019 |
-
|
1020 |
-
hidden_states = self.norm(hidden_states)
|
1021 |
-
|
1022 |
-
# Add hidden states from the last decoder layer
|
1023 |
-
if output_hidden_states:
|
1024 |
-
all_hidden_states += (hidden_states,)
|
1025 |
-
|
1026 |
-
next_cache = None
|
1027 |
-
if use_cache:
|
1028 |
-
next_cache = (
|
1029 |
-
next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache
|
1030 |
-
)
|
1031 |
-
|
1032 |
-
if not return_dict:
|
1033 |
-
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits] if v is not None)
|
1034 |
-
|
1035 |
-
return MoeModelOutputWithPast(
|
1036 |
-
last_hidden_state=hidden_states,
|
1037 |
-
past_key_values=next_cache,
|
1038 |
-
hidden_states=all_hidden_states,
|
1039 |
-
attentions=all_self_attns,
|
1040 |
-
router_logits=all_router_logits
|
1041 |
-
)
|
1042 |
-
|
1043 |
-
def _update_causal_mask(self, attention_mask, input_tensor):
|
1044 |
-
"""
|
1045 |
-
Update the causal mask based on the attention mask and input tensor.
|
1046 |
-
|
1047 |
-
Args:
|
1048 |
-
attention_mask (torch.Tensor): The attention mask.
|
1049 |
-
input_tensor (torch.Tensor): The input tensor.
|
1050 |
-
|
1051 |
-
Returns:
|
1052 |
-
torch.Tensor: The updated causal mask.
|
1053 |
-
"""
|
1054 |
-
|
1055 |
-
if self.config._attn_implementation == "flash_attention_2":
|
1056 |
-
if attention_mask is not None and 0.0 in attention_mask:
|
1057 |
-
return attention_mask
|
1058 |
-
return None
|
1059 |
-
|
1060 |
-
batch_size, seq_length = input_tensor.shape[:2]
|
1061 |
-
dtype = input_tensor.dtype
|
1062 |
-
device = input_tensor.device
|
1063 |
-
|
1064 |
-
# support going beyond cached `max_position_embedding`
|
1065 |
-
if seq_length > self.causal_mask.shape[-1]:
|
1066 |
-
logger.info(f"Resizing causal mask buffer from {self.causal_mask.shape[-1]} to {2 * self.causal_mask.shape[-1]}")
|
1067 |
-
causal_mask = torch.full((2 * self.causal_mask.shape[-1], 2 * self.causal_mask.shape[-1]), fill_value=1)
|
1068 |
-
self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)
|
1069 |
-
|
1070 |
-
# We use the current dtype to avoid any overflows
|
1071 |
-
min_dtype = torch.finfo(dtype).min
|
1072 |
-
causal_mask = self.causal_mask[None, None, :, :].repeat(batch_size, 1, 1, 1).to(dtype) * min_dtype
|
1073 |
-
causal_mask = causal_mask.to(dtype=dtype, device=device)
|
1074 |
-
|
1075 |
-
if attention_mask is not None and attention_mask.dim() == 2:
|
1076 |
-
mask_length = attention_mask.shape[-1]
|
1077 |
-
padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
|
1078 |
-
causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
|
1079 |
-
|
1080 |
-
if self.config._attn_implementation == "sdpa" and attention_mask is not None:
|
1081 |
-
# TODO: For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
|
1082 |
-
is_tracing = (
|
1083 |
-
torch.jit.is_tracing()
|
1084 |
-
or isinstance(input_tensor, torch.fx.Proxy)
|
1085 |
-
or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
|
1086 |
-
)
|
1087 |
-
|
1088 |
-
if not is_tracing and torch.any(attention_mask != 1):
|
1089 |
-
# Attend to all tokens in masked rows from the causal_mask, for example the relevant first rows when
|
1090 |
-
# using left padding. This is required by
|
1091 |
-
# F.scaled_dot_product_attention memory-efficient attention path.
|
1092 |
-
# Details: https://github.com/pytorch/pytorch/issues/110213
|
1093 |
-
causal_mask = causal_mask.mul(~torch.all(causal_mask == min_dtype, dim=-1, keepdim=True)).to(dtype)
|
1094 |
-
|
1095 |
-
return causal_mask
|
1096 |
-
|
1097 |
-
class GemmoeForCausalLM(GemmoePreTrainedModel):
|
1098 |
-
r"""
|
1099 |
-
The Gemmoe Model transformer with a language modeling head on top for causal language modeling (CLM).
|
1100 |
|
1101 |
Args:
|
1102 |
-
config
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1103 |
|
1104 |
-
|
1105 |
-
|
1106 |
-
|
1107 |
|
1108 |
-
|
1109 |
-
|
1110 |
|
1111 |
-
|
1112 |
-
|
1113 |
|
1114 |
-
|
1115 |
-
|
1116 |
-
|
1117 |
-
|
1118 |
-
|
1119 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1120 |
_tied_weights_keys = ["lm_head.weight"]
|
1121 |
|
1122 |
def __init__(self, config):
|
@@ -1124,9 +1310,6 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
|
|
1124 |
self.model = GemmoeModel(config)
|
1125 |
self.vocab_size = config.vocab_size
|
1126 |
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
1127 |
-
self.router_aux_loss_coef = config.router_aux_loss_coef
|
1128 |
-
self.num_experts = 8
|
1129 |
-
self.num_experts_per_tok = config.num_experts_per_tok
|
1130 |
|
1131 |
# Initialize weights and apply final processing
|
1132 |
self.post_init()
|
@@ -1149,8 +1332,8 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
|
|
1149 |
def get_decoder(self):
|
1150 |
return self.model
|
1151 |
|
1152 |
-
@add_start_docstrings_to_model_forward(
|
1153 |
-
@replace_return_docstrings(output_type=
|
1154 |
def forward(
|
1155 |
self,
|
1156 |
input_ids: torch.LongTensor = None,
|
@@ -1162,16 +1345,14 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
|
|
1162 |
use_cache: Optional[bool] = None,
|
1163 |
output_attentions: Optional[bool] = None,
|
1164 |
output_hidden_states: Optional[bool] = None,
|
1165 |
-
output_router_logits: Optional[bool] = None,
|
1166 |
return_dict: Optional[bool] = None,
|
1167 |
-
|
1168 |
-
) -> Union[Tuple, MoeCausalLMOutputWithPast]:
|
1169 |
r"""
|
1170 |
Args:
|
1171 |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
1172 |
-
Labels for computing the masked language modeling loss. Indices should either be in `[0,
|
1173 |
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
1174 |
-
(masked), the loss is only computed for the tokens with labels in `[0,
|
1175 |
|
1176 |
Returns:
|
1177 |
|
@@ -1180,26 +1361,24 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
|
|
1180 |
```python
|
1181 |
>>> from transformers import AutoTokenizer, GemmoeForCausalLM
|
1182 |
|
1183 |
-
>>> model = GemmoeForCausalLM.from_pretrained(
|
1184 |
-
>>> tokenizer = AutoTokenizer.from_pretrained(
|
1185 |
|
1186 |
-
>>> prompt = "
|
1187 |
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
1188 |
|
1189 |
>>> # Generate
|
1190 |
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
1191 |
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
1192 |
-
"
|
1193 |
```"""
|
1194 |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
1195 |
-
output_router_logits = (
|
1196 |
-
output_router_logits if output_router_logits is not None else getattr(self.config, "output_router_logits", False)
|
1197 |
-
)
|
1198 |
output_hidden_states = (
|
1199 |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
1200 |
)
|
1201 |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1202 |
|
|
|
1203 |
outputs = self.model(
|
1204 |
input_ids=input_ids,
|
1205 |
attention_mask=attention_mask,
|
@@ -1209,61 +1388,46 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
|
|
1209 |
use_cache=use_cache,
|
1210 |
output_attentions=output_attentions,
|
1211 |
output_hidden_states=output_hidden_states,
|
1212 |
-
output_router_logits=output_router_logits,
|
1213 |
return_dict=return_dict,
|
1214 |
-
cache_position=cache_position,
|
1215 |
)
|
1216 |
|
1217 |
hidden_states = outputs[0]
|
1218 |
-
|
1219 |
-
|
1220 |
-
|
1221 |
-
|
1222 |
-
|
|
|
|
|
1223 |
|
1224 |
loss = None
|
1225 |
if labels is not None:
|
|
|
1226 |
shift_logits = logits[..., :-1, :].contiguous()
|
1227 |
shift_labels = labels[..., 1:].contiguous()
|
|
|
1228 |
loss_fct = CrossEntropyLoss()
|
1229 |
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
1230 |
shift_labels = shift_labels.view(-1)
|
|
|
1231 |
shift_labels = shift_labels.to(shift_logits.device)
|
1232 |
loss = loss_fct(shift_logits, shift_labels)
|
1233 |
|
1234 |
-
aux_loss = None
|
1235 |
-
if output_router_logits:
|
1236 |
-
router_logits = outputs.router_logits if return_dict else outputs[-1]
|
1237 |
-
if router_logits is not None:
|
1238 |
-
aux_loss = load_balancing_loss_func(
|
1239 |
-
router_logits,
|
1240 |
-
self.num_experts,
|
1241 |
-
self.num_experts_per_tok,
|
1242 |
-
attention_mask,
|
1243 |
-
)
|
1244 |
-
if labels is not None:
|
1245 |
-
loss += self.router_aux_loss_coef * aux_loss.to(loss.device)
|
1246 |
-
|
1247 |
if not return_dict:
|
1248 |
output = (logits,) + outputs[1:]
|
1249 |
-
if aux_loss is not None:
|
1250 |
-
output = (aux_loss,) + output
|
1251 |
return (loss,) + output if loss is not None else output
|
1252 |
|
1253 |
-
return
|
1254 |
loss=loss,
|
1255 |
-
aux_loss=aux_loss,
|
1256 |
logits=logits,
|
1257 |
past_key_values=outputs.past_key_values,
|
1258 |
hidden_states=outputs.hidden_states,
|
1259 |
attentions=outputs.attentions,
|
1260 |
-
router_logits=outputs.router_logits,
|
1261 |
)
|
1262 |
|
1263 |
def prepare_inputs_for_generation(
|
1264 |
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
1265 |
):
|
1266 |
-
past_length = 0
|
1267 |
if past_key_values is not None:
|
1268 |
if isinstance(past_key_values, Cache):
|
1269 |
cache_length = past_key_values.get_seq_length()
|
@@ -1273,11 +1437,19 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
|
|
1273 |
cache_length = past_length = past_key_values[0][0].shape[2]
|
1274 |
max_cache_length = None
|
1275 |
|
|
|
|
|
|
|
|
|
1276 |
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
1277 |
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
|
|
|
|
1278 |
elif past_length < input_ids.shape[1]:
|
1279 |
input_ids = input_ids[:, past_length:]
|
1280 |
-
|
|
|
|
|
1281 |
if (
|
1282 |
max_cache_length is not None
|
1283 |
and attention_mask is not None
|
@@ -1287,37 +1459,26 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
|
|
1287 |
|
1288 |
position_ids = kwargs.get("position_ids", None)
|
1289 |
if attention_mask is not None and position_ids is None:
|
|
|
1290 |
position_ids = attention_mask.long().cumsum(-1) - 1
|
1291 |
position_ids.masked_fill_(attention_mask == 0, 1)
|
1292 |
if past_key_values:
|
1293 |
position_ids = position_ids[:, -input_ids.shape[1] :]
|
1294 |
|
1295 |
-
if
|
1296 |
-
cache_position = kwargs.get("cache_position", None)
|
1297 |
-
if cache_position is None:
|
1298 |
-
past_length = 0
|
1299 |
-
else:
|
1300 |
-
past_length = cache_position[-1] + 1
|
1301 |
-
input_ids = input_ids[:, -1].unsqueeze(-1)
|
1302 |
-
position_ids = position_ids[:, -1].unsqueeze(-1)
|
1303 |
-
|
1304 |
-
cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device)
|
1305 |
-
|
1306 |
if inputs_embeds is not None and past_key_values is None:
|
1307 |
model_inputs = {"inputs_embeds": inputs_embeds}
|
1308 |
else:
|
1309 |
-
model_inputs = {"input_ids": input_ids
|
1310 |
|
1311 |
model_inputs.update(
|
1312 |
{
|
1313 |
-
"position_ids": position_ids
|
1314 |
-
"cache_position": cache_position,
|
1315 |
"past_key_values": past_key_values,
|
1316 |
"use_cache": kwargs.get("use_cache"),
|
1317 |
"attention_mask": attention_mask,
|
1318 |
}
|
1319 |
)
|
1320 |
-
|
1321 |
return model_inputs
|
1322 |
|
1323 |
@staticmethod
|
@@ -1350,6 +1511,7 @@ class GemmoeForSequenceClassification(GemmoePreTrainedModel):
|
|
1350 |
self.num_labels = config.num_labels
|
1351 |
self.model = GemmoeModel(config)
|
1352 |
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
|
|
1353 |
# Initialize weights and apply final processing
|
1354 |
self.post_init()
|
1355 |
|
@@ -1359,8 +1521,7 @@ class GemmoeForSequenceClassification(GemmoePreTrainedModel):
|
|
1359 |
def set_input_embeddings(self, value):
|
1360 |
self.model.embed_tokens = value
|
1361 |
|
1362 |
-
@add_start_docstrings_to_model_forward(
|
1363 |
-
@replace_return_docstrings(output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
1364 |
def forward(
|
1365 |
self,
|
1366 |
input_ids: torch.LongTensor = None,
|
@@ -1374,25 +1535,14 @@ class GemmoeForSequenceClassification(GemmoePreTrainedModel):
|
|
1374 |
output_hidden_states: Optional[bool] = None,
|
1375 |
return_dict: Optional[bool] = None,
|
1376 |
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
1377 |
-
"""
|
1378 |
-
|
1379 |
-
|
1380 |
-
|
1381 |
-
|
1382 |
-
attention_mask (torch.Tensor, optional): Attention mask.
|
1383 |
-
position_ids (torch.LongTensor, optional): Position IDs.
|
1384 |
-
past_key_values (List[torch.FloatTensor], optional): Past key-value pairs.
|
1385 |
-
inputs_embeds (torch.FloatTensor, optional): Input embeddings.
|
1386 |
-
labels (torch.LongTensor, optional): Labels for sequence classification.
|
1387 |
-
use_cache (bool, optional): Whether to use cache.
|
1388 |
-
output_attentions (bool, optional): Whether to output attentions.
|
1389 |
-
output_hidden_states (bool, optional): Whether to output hidden states.
|
1390 |
-
return_dict (bool, optional): Whether to return a dictionary or tuple.
|
1391 |
-
|
1392 |
-
Returns:
|
1393 |
-
Union[Tuple, SequenceClassifierOutputWithPast]: Output of the sequence classification model.
|
1394 |
"""
|
1395 |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
1396 |
transformer_outputs = self.model(
|
1397 |
input_ids,
|
1398 |
attention_mask=attention_mask,
|
@@ -1418,8 +1568,9 @@ class GemmoeForSequenceClassification(GemmoePreTrainedModel):
|
|
1418 |
sequence_lengths = -1
|
1419 |
else:
|
1420 |
if input_ids is not None:
|
1421 |
-
sequence_lengths = torch.
|
1422 |
-
|
|
|
1423 |
else:
|
1424 |
sequence_lengths = -1
|
1425 |
|
@@ -1448,7 +1599,6 @@ class GemmoeForSequenceClassification(GemmoePreTrainedModel):
|
|
1448 |
elif self.config.problem_type == "multi_label_classification":
|
1449 |
loss_fct = BCEWithLogitsLoss()
|
1450 |
loss = loss_fct(pooled_logits, labels)
|
1451 |
-
|
1452 |
if not return_dict:
|
1453 |
output = (pooled_logits,) + transformer_outputs[1:]
|
1454 |
return ((loss,) + output) if loss is not None else output
|
|
|
26 |
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
27 |
|
28 |
from transformers.activations import ACT2FN
|
29 |
+
from transformers.cache_utils import Cache, DynamicCache
|
30 |
from transformers.modeling_attn_mask_utils import (
|
31 |
+
AttentionMaskConverter,
|
32 |
+
_prepare_4d_attention_mask,
|
33 |
_prepare_4d_causal_attention_mask,
|
34 |
+
_prepare_4d_causal_attention_mask_for_sdpa,
|
35 |
)
|
36 |
+
from transformers.modeling_outputs import SequenceClassifierOutputWithPast, BaseModelOutputWithPast, CausalLMOutputWithPast
|
37 |
from transformers.modeling_utils import PreTrainedModel
|
38 |
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
|
39 |
from transformers.utils import (
|
|
|
63 |
|
64 |
_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
|
65 |
|
|
|
66 |
logger = logging.get_logger(__name__)
|
67 |
|
68 |
_CONFIG_FOR_DOC = "GemmoeConfig"
|
|
|
158 |
max_seqlen_in_batch,
|
159 |
)
|
160 |
|
161 |
+
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
162 |
+
warnings.warn(
|
163 |
+
"Calling `transformers.models.Gemmoe.modeling_Gemmoe._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
|
164 |
+
)
|
165 |
+
return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
|
166 |
+
|
167 |
+
def _make_causal_mask(
|
168 |
+
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
|
169 |
+
):
|
170 |
+
warnings.warn(
|
171 |
+
"Calling `transformers.models.Gemmoe.modeling_Gemmoe._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.Gemmoe.modeling_Gemmoe.AttentionMaskConverter._make_causal_mask"
|
172 |
+
)
|
173 |
+
return AttentionMaskConverter._make_causal_mask(
|
174 |
+
input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
|
175 |
+
)
|
176 |
+
|
177 |
|
178 |
|
179 |
class GemmoeRMSNorm(nn.Module):
|
180 |
+
def __init__(self, hidden_size, eps=1e-6):
|
181 |
+
"""
|
182 |
+
GemmoeRMSNorm is equivalent to T5LayerNorm
|
183 |
+
"""
|
184 |
super().__init__()
|
185 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
186 |
+
self.variance_epsilon = eps
|
|
|
|
|
|
|
187 |
|
188 |
+
def forward(self, hidden_states):
|
189 |
+
input_dtype = hidden_states.dtype
|
190 |
+
hidden_states = hidden_states.to(torch.float32)
|
191 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
192 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
193 |
+
return self.weight * hidden_states.to(input_dtype)
|
194 |
|
195 |
ALL_LAYERNORM_LAYERS.append(GemmoeRMSNorm)
|
196 |
|
197 |
class GemmoeRotaryEmbedding(nn.Module):
|
198 |
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
199 |
super().__init__()
|
200 |
+
|
201 |
self.dim = dim
|
202 |
self.max_position_embeddings = max_position_embeddings
|
203 |
self.base = base
|
204 |
+
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
205 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
206 |
+
|
207 |
+
# Build here to make `torch.jit.trace` work.
|
208 |
+
self._set_cos_sin_cache(
|
209 |
+
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
|
210 |
+
)
|
211 |
+
self.max_seq_len_cached = None
|
212 |
+
|
213 |
|
214 |
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
215 |
self.max_seq_len_cached = seq_len
|
216 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
217 |
+
|
218 |
+
freqs = torch.outer(t, self.inv_freq.to(t.device))
|
219 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
220 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
221 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
222 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
223 |
+
|
224 |
+
def forward(self, x, seq_len=None):
|
225 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
226 |
+
if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached:
|
|
|
|
|
|
|
|
|
|
|
|
|
227 |
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
|
228 |
+
|
229 |
return (
|
230 |
+
self.cos_cached[:seq_len].to(dtype=x.dtype),
|
231 |
+
self.sin_cached[:seq_len].to(dtype=x.dtype),
|
232 |
)
|
233 |
+
|
234 |
+
class GemmoeLinearScalingRotaryEmbedding(GemmoeRotaryEmbedding):
|
235 |
+
"""GemmoeRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
236 |
+
|
237 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
238 |
+
self.scaling_factor = scaling_factor
|
239 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
240 |
+
|
241 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
242 |
+
self.max_seq_len_cached = seq_len
|
243 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
244 |
+
t = t / self.scaling_factor
|
245 |
+
|
246 |
+
freqs = torch.outer(t, self.inv_freq)
|
247 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
248 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
249 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
250 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
251 |
+
|
252 |
+
class GemmoeDynamicNTKScalingRotaryEmbedding(GemmoeRotaryEmbedding):
|
253 |
+
"""GemmoeRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
254 |
+
|
255 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
256 |
+
self.scaling_factor = scaling_factor
|
257 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
258 |
+
|
259 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
260 |
+
self.max_seq_len_cached = seq_len
|
261 |
+
|
262 |
+
if seq_len > self.max_position_embeddings:
|
263 |
+
base = self.base * (
|
264 |
+
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
|
265 |
+
) ** (self.dim / (self.dim - 2))
|
266 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
267 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
268 |
+
|
269 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
270 |
+
|
271 |
+
freqs = torch.outer(t, self.inv_freq)
|
272 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
273 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
274 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
275 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
276 |
|
277 |
def rotate_half(x):
|
278 |
"""Rotates half the hidden dims of the input."""
|
|
|
280 |
x2 = x[..., x.shape[-1] // 2 :]
|
281 |
return torch.cat((-x2, x1), dim=-1)
|
282 |
|
283 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
|
284 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
285 |
+
|
286 |
+
Args:
|
287 |
+
q (`torch.Tensor`): The query tensor.
|
288 |
+
k (`torch.Tensor`): The key tensor.
|
289 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
290 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
291 |
+
position_ids (`torch.Tensor`):
|
292 |
+
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
|
293 |
+
used to pass offsetted position ids when working with a KV-cache.
|
294 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
295 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
296 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
297 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
298 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
299 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
300 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
301 |
+
Returns:
|
302 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
303 |
+
"""
|
304 |
+
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
|
305 |
+
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
|
306 |
q_embed = (q * cos) + (rotate_half(q) * sin)
|
307 |
k_embed = (k * cos) + (rotate_half(k) * sin)
|
308 |
return q_embed, k_embed
|
309 |
|
310 |
+
class GemmoeMLP(nn.Module):
|
311 |
+
def __init__(self, config, hidden_size = None, intermediate_size = None):
|
312 |
+
super().__init__()
|
313 |
+
self.config = config
|
314 |
+
self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
|
315 |
+
self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size
|
316 |
+
|
317 |
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
318 |
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
319 |
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
320 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
321 |
+
|
322 |
+
def forward(self, x):
|
323 |
+
if self.config.pretraining_tp > 1:
|
324 |
+
slice = self.intermediate_size // self.config.pretraining_tp
|
325 |
+
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
|
326 |
+
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
|
327 |
+
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
|
328 |
+
|
329 |
+
gate_proj = torch.cat(
|
330 |
+
[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
|
331 |
+
)
|
332 |
+
up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
|
333 |
+
|
334 |
+
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
|
335 |
+
down_proj = [
|
336 |
+
F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
|
337 |
+
]
|
338 |
+
down_proj = sum(down_proj)
|
339 |
+
else:
|
340 |
+
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
341 |
+
|
342 |
+
return down_proj
|
343 |
+
|
344 |
+
class MoEGate(nn.Module):
|
345 |
+
def __init__(self, config):
|
346 |
+
super().__init__()
|
347 |
+
self.config = config
|
348 |
+
self.top_k = config.num_experts_per_tok
|
349 |
+
self.n_routed_experts = config.n_routed_experts
|
350 |
+
|
351 |
+
self.scoring_func = config.scoring_func
|
352 |
+
self.alpha = config.aux_loss_alpha
|
353 |
+
self.seq_aux = config.seq_aux
|
354 |
+
|
355 |
+
# topk selection algorithm
|
356 |
+
self.norm_topk_prob = config.norm_topk_prob
|
357 |
+
self.gating_dim = config.hidden_size
|
358 |
+
self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
|
359 |
+
self.reset_parameters()
|
360 |
+
|
361 |
+
def reset_parameters(self) -> None:
|
362 |
+
import torch.nn.init as init
|
363 |
+
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
|
364 |
+
|
365 |
+
def forward(self, hidden_states):
|
366 |
+
bsz, seq_len, h = hidden_states.shape
|
367 |
+
### compute gating score
|
368 |
+
hidden_states = hidden_states.view(-1, h)
|
369 |
+
logits = F.linear(hidden_states, self.weight, None)
|
370 |
+
if self.scoring_func == 'softmax':
|
371 |
+
scores = logits.softmax(dim=-1)
|
372 |
+
else:
|
373 |
+
raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
|
374 |
+
|
375 |
+
### select top-k experts
|
376 |
+
topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
|
377 |
+
|
378 |
+
### norm gate to sum 1
|
379 |
+
if self.top_k > 1 and self.norm_topk_prob:
|
380 |
+
denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
|
381 |
+
topk_weight = topk_weight / denominator
|
382 |
+
|
383 |
+
### expert-level computation auxiliary loss
|
384 |
+
if self.training and self.alpha > 0.0:
|
385 |
+
scores_for_aux = scores
|
386 |
+
aux_topk = self.top_k
|
387 |
+
# always compute aux loss based on the naive greedy topk method
|
388 |
+
topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
|
389 |
+
if self.seq_aux:
|
390 |
+
scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
|
391 |
+
ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
|
392 |
+
ce.scatter_add_(1, topk_idx_for_aux_loss, torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(seq_len * aux_topk / self.n_routed_experts)
|
393 |
+
aux_loss = (ce * scores_for_seq_aux.mean(dim = 1)).sum(dim = 1).mean() * self.alpha
|
394 |
+
else:
|
395 |
+
mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
|
396 |
+
ce = mask_ce.float().mean(0)
|
397 |
+
Pi = scores_for_aux.mean(0)
|
398 |
+
fi = ce * self.n_routed_experts
|
399 |
+
aux_loss = (Pi * fi).sum() * self.alpha
|
400 |
+
else:
|
401 |
+
aux_loss = None
|
402 |
+
return topk_idx, topk_weight, aux_loss
|
403 |
+
|
404 |
+
class AddAuxiliaryLoss(torch.autograd.Function):
|
405 |
+
"""
|
406 |
+
The trick function of adding auxiliary (aux) loss,
|
407 |
+
which includes the gradient of the aux loss during backpropagation.
|
408 |
+
"""
|
409 |
+
@staticmethod
|
410 |
+
def forward(ctx, x, loss):
|
411 |
+
assert loss.numel() == 1
|
412 |
+
ctx.dtype = loss.dtype
|
413 |
+
ctx.required_aux_loss = loss.requires_grad
|
414 |
+
return x
|
415 |
+
|
416 |
+
@staticmethod
|
417 |
+
def backward(ctx, grad_output):
|
418 |
+
grad_loss = None
|
419 |
+
if ctx.required_aux_loss:
|
420 |
+
grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device)
|
421 |
+
return grad_output, grad_loss
|
422 |
+
|
423 |
+
class GemMoE(nn.Module):
|
424 |
+
"""
|
425 |
+
A mixed expert module containing shared experts.
|
426 |
+
"""
|
427 |
+
def __init__(self, config):
|
428 |
+
super().__init__()
|
429 |
+
self.config = config
|
430 |
+
self.num_experts_per_tok = config.num_experts_per_tok
|
431 |
+
self.experts = nn.ModuleList([GemmoeMLP(config, intermediate_size = config.moe_intermediate_size) for i in range(config.n_routed_experts)])
|
432 |
+
self.gate = MoEGate(config)
|
433 |
+
if config.n_shared_experts is not None:
|
434 |
+
intermediate_size = config.moe_intermediate_size * config.n_shared_experts
|
435 |
+
self.shared_experts = GemmoeMLP(config=config, intermediate_size = intermediate_size)
|
436 |
+
|
437 |
+
def forward(self, hidden_states):
|
438 |
+
identity = hidden_states
|
439 |
+
orig_shape = hidden_states.shape
|
440 |
+
topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
|
441 |
+
hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
|
442 |
+
flat_topk_idx = topk_idx.view(-1)
|
443 |
+
if self.training:
|
444 |
+
hidden_states = hidden_states.repeat_interleave(self.num_experts_per_tok, dim=0)
|
445 |
+
y = torch.empty_like(hidden_states)
|
446 |
+
for i, expert in enumerate(self.experts):
|
447 |
+
y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
|
448 |
+
y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
|
449 |
+
y = y.view(*orig_shape)
|
450 |
+
y = AddAuxiliaryLoss.apply(y, aux_loss)
|
451 |
+
else:
|
452 |
+
y = self.moe_infer(hidden_states, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
|
453 |
+
if self.config.n_shared_experts is not None:
|
454 |
+
y = y + self.shared_experts(identity)
|
455 |
+
return y
|
456 |
+
|
457 |
+
@torch.no_grad()
|
458 |
+
def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
|
459 |
+
expert_cache = torch.zeros_like(x)
|
460 |
+
idxs = flat_expert_indices.argsort()
|
461 |
+
tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
|
462 |
+
token_idxs = idxs // self.num_experts_per_tok
|
463 |
+
for i, end_idx in enumerate(tokens_per_expert):
|
464 |
+
start_idx = 0 if i == 0 else tokens_per_expert[i-1]
|
465 |
+
if start_idx == end_idx:
|
466 |
+
continue
|
467 |
+
expert = self.experts[i]
|
468 |
+
exp_token_idx = token_idxs[start_idx:end_idx]
|
469 |
+
expert_tokens = x[exp_token_idx]
|
470 |
+
expert_out = expert(expert_tokens)
|
471 |
+
expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
|
472 |
+
expert_cache.scatter_reduce_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out, reduce='sum')
|
473 |
+
return expert_cache
|
474 |
+
|
475 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
476 |
"""
|
477 |
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
478 |
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
|
|
482 |
return hidden_states
|
483 |
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
484 |
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
485 |
+
|
486 |
|
487 |
class GemmoeAttention(nn.Module):
|
488 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
|
|
|
|
|
|
|
|
|
|
|
|
489 |
|
490 |
def __init__(self, config: GemmoeConfig, layer_idx: Optional[int] = None):
|
491 |
super().__init__()
|
|
|
493 |
self.layer_idx = layer_idx
|
494 |
if layer_idx is None:
|
495 |
logger.warning_once(
|
496 |
+
f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
|
497 |
+
"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
|
498 |
"when creating this class."
|
499 |
)
|
500 |
+
|
501 |
self.attention_dropout = config.attention_dropout
|
502 |
self.hidden_size = config.hidden_size
|
503 |
self.num_heads = config.num_attention_heads
|
504 |
+
self.head_dim = self.hidden_size // self.num_heads
|
505 |
self.num_key_value_heads = config.num_key_value_heads
|
506 |
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
507 |
self.max_position_embeddings = config.max_position_embeddings
|
508 |
self.rope_theta = config.rope_theta
|
509 |
self.is_causal = True
|
510 |
|
511 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
512 |
raise ValueError(
|
513 |
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
514 |
f" and `num_heads`: {self.num_heads})."
|
515 |
)
|
516 |
+
|
517 |
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
|
518 |
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
519 |
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
520 |
+
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
|
521 |
+
self._init_rope()
|
522 |
+
|
523 |
+
def _init_rope(self):
|
524 |
+
if self.config.rope_scaling is None:
|
525 |
+
self.rotary_emb = GemmoeRotaryEmbedding(
|
526 |
+
self.head_dim,
|
527 |
+
max_position_embeddings=self.max_position_embeddings,
|
528 |
+
base=self.rope_theta,
|
529 |
+
)
|
530 |
+
else:
|
531 |
+
scaling_type = self.config.rope_scaling["type"]
|
532 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
533 |
+
if scaling_type == "linear":
|
534 |
+
self.rotary_emb = GemmoeLinearScalingRotaryEmbedding(
|
535 |
+
self.head_dim,
|
536 |
+
max_position_embeddings=self.max_position_embeddings,
|
537 |
+
scaling_factor=scaling_factor,
|
538 |
+
base=self.rope_theta,
|
539 |
+
)
|
540 |
+
elif scaling_type == "dynamic":
|
541 |
+
self.rotary_emb = GemmoeDynamicNTKScalingRotaryEmbedding(
|
542 |
+
self.head_dim,
|
543 |
+
max_position_embeddings=self.max_position_embeddings,
|
544 |
+
scaling_factor=scaling_factor,
|
545 |
+
base=self.rope_theta,
|
546 |
+
)
|
547 |
+
else:
|
548 |
+
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
549 |
+
|
550 |
+
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
551 |
+
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
552 |
|
553 |
def forward(
|
554 |
self,
|
|
|
558 |
past_key_value: Optional[Cache] = None,
|
559 |
output_attentions: bool = False,
|
560 |
use_cache: bool = False,
|
|
|
561 |
**kwargs,
|
562 |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
563 |
+
if "padding_mask" in kwargs:
|
564 |
+
warnings.warn(
|
565 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
566 |
+
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
567 |
|
|
|
|
|
|
|
|
|
|
|
|
|
568 |
bsz, q_len, _ = hidden_states.size()
|
569 |
|
570 |
+
if self.config.pretraining_tp > 1:
|
571 |
+
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
|
572 |
+
query_slices = self.q_proj.weight.split(
|
573 |
+
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
|
574 |
+
)
|
575 |
+
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
|
576 |
+
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
|
577 |
+
|
578 |
+
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
|
579 |
+
query_states = torch.cat(query_states, dim=-1)
|
580 |
+
|
581 |
+
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
|
582 |
+
key_states = torch.cat(key_states, dim=-1)
|
583 |
+
|
584 |
+
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
|
585 |
+
value_states = torch.cat(value_states, dim=-1)
|
586 |
+
|
587 |
+
else:
|
588 |
+
query_states = self.q_proj(hidden_states)
|
589 |
+
key_states = self.k_proj(hidden_states)
|
590 |
+
value_states = self.v_proj(hidden_states)
|
591 |
|
592 |
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
593 |
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
594 |
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
595 |
|
596 |
+
kv_seq_len = key_states.shape[-2]
|
597 |
+
if past_key_value is not None:
|
598 |
+
if self.layer_idx is None:
|
599 |
+
raise ValueError(
|
600 |
+
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
601 |
+
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
602 |
+
"with a layer index."
|
603 |
+
)
|
604 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
605 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
606 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
607 |
|
608 |
if past_key_value is not None:
|
609 |
+
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
|
|
610 |
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
611 |
|
612 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
613 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
614 |
|
615 |
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
616 |
|
617 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
618 |
+
raise ValueError(
|
619 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
620 |
+
f" {attn_weights.size()}"
|
621 |
+
)
|
622 |
+
|
623 |
+
if attention_mask is not None:
|
624 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
625 |
+
raise ValueError(
|
626 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
627 |
+
)
|
628 |
+
attn_weights = attn_weights + attention_mask
|
629 |
|
630 |
# upcast attention to fp32
|
631 |
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
632 |
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
|
|
633 |
attn_output = torch.matmul(attn_weights, value_states)
|
634 |
|
635 |
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
|
|
639 |
)
|
640 |
|
641 |
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
|
642 |
|
643 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
644 |
+
|
645 |
+
if self.config.pretraining_tp > 1:
|
646 |
+
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
|
647 |
+
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
|
648 |
+
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
|
649 |
+
else:
|
650 |
+
attn_output = self.o_proj(attn_output)
|
651 |
|
652 |
if not output_attentions:
|
653 |
attn_weights = None
|
|
|
660 |
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
661 |
flash attention and deal with padding tokens in case the input contains any of them.
|
662 |
"""
|
663 |
+
|
664 |
def __init__(self, *args, **kwargs):
|
665 |
super().__init__(*args, **kwargs)
|
666 |
+
|
667 |
+
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
668 |
+
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
669 |
+
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
|
670 |
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
671 |
|
672 |
def forward(
|
|
|
677 |
past_key_value: Optional[Cache] = None,
|
678 |
output_attentions: bool = False,
|
679 |
use_cache: bool = False,
|
|
|
680 |
**kwargs,
|
681 |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
682 |
+
# GemmoeFlashAttention2 attention does not support output_attentions
|
683 |
+
if "padding_mask" in kwargs:
|
684 |
+
warnings.warn(
|
685 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
686 |
+
)
|
687 |
+
|
688 |
+
# overwrite attention_mask with padding_mask
|
689 |
+
attention_mask = kwargs.pop("padding_mask")
|
690 |
+
|
691 |
output_attentions = False
|
692 |
|
693 |
bsz, q_len, _ = hidden_states.size()
|
|
|
703 |
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
704 |
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
705 |
|
706 |
+
kv_seq_len = key_states.shape[-2]
|
707 |
+
if past_key_value is not None:
|
708 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
709 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
710 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
711 |
|
|
|
712 |
if past_key_value is not None:
|
713 |
+
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
|
|
714 |
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
715 |
|
716 |
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
|
|
726 |
# cast them back in the correct dtype just to be sure everything works as expected.
|
727 |
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
728 |
# in fp32. (GemmoeRMSNorm handles it correctly)
|
729 |
+
|
730 |
input_dtype = query_states.dtype
|
731 |
if input_dtype == torch.float32:
|
|
|
|
|
732 |
# Handle the case where the model is quantized
|
733 |
+
if hasattr(self.config, "_pre_quantization_dtype"):
|
734 |
target_dtype = self.config._pre_quantization_dtype
|
735 |
+
elif torch.is_autocast_enabled():
|
736 |
+
target_dtype = torch.get_autocast_gpu_dtype()
|
737 |
else:
|
738 |
target_dtype = self.q_proj.weight.dtype
|
739 |
|
|
|
742 |
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
743 |
f" {target_dtype}."
|
744 |
)
|
745 |
+
|
746 |
query_states = query_states.to(target_dtype)
|
747 |
key_states = key_states.to(target_dtype)
|
748 |
value_states = value_states.to(target_dtype)
|
|
|
751 |
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
|
752 |
)
|
753 |
|
754 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
755 |
attn_output = self.o_proj(attn_output)
|
756 |
|
757 |
if not output_attentions:
|
|
|
793 |
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
794 |
query_states, key_states, value_states, attention_mask, query_length
|
795 |
)
|
796 |
+
|
797 |
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
798 |
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
799 |
|
|
|
809 |
softmax_scale=softmax_scale,
|
810 |
causal=causal,
|
811 |
)
|
812 |
+
|
813 |
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
814 |
else:
|
815 |
attn_output = flash_attn_func(
|
|
|
820 |
|
821 |
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
822 |
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
|
|
823 |
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
824 |
+
|
825 |
key_layer = index_first_axis(
|
826 |
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
827 |
)
|
828 |
value_layer = index_first_axis(
|
829 |
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
830 |
)
|
|
|
831 |
if query_length == kv_seq_len:
|
832 |
query_layer = index_first_axis(
|
833 |
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
|
|
859 |
class GemmoeSdpaAttention(GemmoeAttention):
|
860 |
"""
|
861 |
Gemmoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
862 |
+
`GemmoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
863 |
SDPA API.
|
864 |
"""
|
865 |
|
866 |
+
# Adapted from GemmoeAttention.forward
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
867 |
def forward(
|
868 |
self,
|
869 |
hidden_states: torch.Tensor,
|
|
|
872 |
past_key_value: Optional[Cache] = None,
|
873 |
output_attentions: bool = False,
|
874 |
use_cache: bool = False,
|
|
|
875 |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
876 |
if output_attentions:
|
877 |
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
878 |
+
logger.warning_once(
|
879 |
+
"GemmoeModel is using GemmoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
|
880 |
+
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
881 |
+
)
|
|
|
882 |
return super().forward(
|
883 |
hidden_states=hidden_states,
|
884 |
attention_mask=attention_mask,
|
|
|
886 |
past_key_value=past_key_value,
|
887 |
output_attentions=output_attentions,
|
888 |
use_cache=use_cache,
|
|
|
889 |
)
|
890 |
+
|
891 |
bsz, q_len, _ = hidden_states.size()
|
892 |
|
893 |
query_states = self.q_proj(hidden_states)
|
|
|
898 |
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
899 |
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
900 |
|
901 |
+
kv_seq_len = key_states.shape[-2]
|
902 |
+
if past_key_value is not None:
|
903 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
904 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
905 |
+
|
906 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
907 |
|
|
|
908 |
if past_key_value is not None:
|
909 |
+
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
|
|
910 |
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
911 |
|
912 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
913 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
914 |
|
915 |
+
if attention_mask is not None:
|
916 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
917 |
+
raise ValueError(
|
918 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
919 |
+
)
|
|
|
|
|
|
|
920 |
|
921 |
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
922 |
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
923 |
+
if query_states.device.type == "cuda" and attention_mask is not None:
|
924 |
query_states = query_states.contiguous()
|
925 |
key_states = key_states.contiguous()
|
926 |
value_states = value_states.contiguous()
|
927 |
|
|
|
|
|
|
|
|
|
928 |
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
929 |
query_states,
|
930 |
key_states,
|
931 |
value_states,
|
932 |
+
attn_mask=attention_mask,
|
933 |
dropout_p=self.attention_dropout if self.training else 0.0,
|
934 |
+
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
|
935 |
+
is_causal=self.is_causal and attention_mask is None and q_len > 1,
|
936 |
)
|
937 |
|
938 |
attn_output = attn_output.transpose(1, 2).contiguous()
|
939 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
940 |
+
|
941 |
attn_output = self.o_proj(attn_output)
|
942 |
|
943 |
return attn_output, None, past_key_value
|
|
|
948 |
"sdpa": GemmoeSdpaAttention,
|
949 |
}
|
950 |
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
951 |
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|
|
952 |
class GemmoeDecoderLayer(nn.Module):
|
953 |
def __init__(self, config: GemmoeConfig, layer_idx: int):
|
954 |
super().__init__()
|
955 |
self.hidden_size = config.hidden_size
|
956 |
|
957 |
+
self.self_attn = GEMMOE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
|
958 |
|
959 |
+
self.mlp = GemMoE(config) if (config.n_routed_experts is not None and \
|
960 |
+
layer_idx >= config.first_k_dense_replace and layer_idx % config.moe_layer_freq == 0) \
|
961 |
+
else GemmoeMLP(config)
|
962 |
self.input_layernorm = GemmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
963 |
self.post_attention_layernorm = GemmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
964 |
|
|
|
969 |
position_ids: Optional[torch.LongTensor] = None,
|
970 |
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
971 |
output_attentions: Optional[bool] = False,
|
|
|
972 |
use_cache: Optional[bool] = False,
|
|
|
973 |
**kwargs,
|
974 |
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
975 |
"""
|
|
|
985 |
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
986 |
(see `past_key_values`).
|
987 |
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
|
|
|
|
|
|
988 |
"""
|
989 |
if "padding_mask" in kwargs:
|
990 |
warnings.warn(
|
991 |
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
992 |
)
|
|
|
993 |
residual = hidden_states
|
994 |
+
|
995 |
hidden_states = self.input_layernorm(hidden_states)
|
996 |
|
997 |
# Self Attention
|
|
|
1002 |
past_key_value=past_key_value,
|
1003 |
output_attentions=output_attentions,
|
1004 |
use_cache=use_cache,
|
|
|
1005 |
**kwargs,
|
1006 |
)
|
1007 |
hidden_states = residual + hidden_states
|
|
|
1009 |
# Fully Connected
|
1010 |
residual = hidden_states
|
1011 |
hidden_states = self.post_attention_layernorm(hidden_states)
|
1012 |
+
hidden_states = self.mlp(hidden_states)
|
1013 |
hidden_states = residual + hidden_states
|
|
|
1014 |
|
1015 |
outputs = (hidden_states,)
|
1016 |
|
|
|
1020 |
if use_cache:
|
1021 |
outputs += (present_key_value,)
|
1022 |
|
|
|
|
|
|
|
1023 |
return outputs
|
1024 |
|
1025 |
+
|
1026 |
GEMMOE_START_DOCSTRING = r"""
|
1027 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
1028 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
1029 |
+
etc.)
|
1030 |
+
|
1031 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
1032 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
1033 |
+
and behavior.
|
1034 |
+
|
1035 |
+
Parameters:
|
1036 |
+
config ([`GemmoeConfig`]):
|
1037 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
1038 |
+
load the weights associated with the model, only the configuration. Check out the
|
1039 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
1040 |
"""
|
1041 |
|
1042 |
@add_start_docstrings(
|
|
|
1045 |
)
|
1046 |
|
1047 |
class GemmoePreTrainedModel(PreTrainedModel):
|
1048 |
+
config_class = GemmoeConfig
|
1049 |
+
base_model_prefix = "model"
|
1050 |
+
supports_gradient_checkpointing = True
|
1051 |
+
_no_split_modules = ["GemmoeDecoderLayer"]
|
1052 |
+
_skip_keys_device_placement = "past_key_values"
|
1053 |
+
_supports_flash_attn_2 = True
|
1054 |
+
_supports_sdpa = True
|
1055 |
+
_supports_cache_class = True
|
1056 |
+
|
1057 |
+
def _init_weights(self, module):
|
1058 |
+
std = self.config.initializer_range
|
1059 |
+
if isinstance(module, nn.Linear):
|
1060 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
1061 |
+
if module.bias is not None:
|
1062 |
+
module.bias.data.zero_()
|
1063 |
+
elif isinstance(module, nn.Embedding):
|
1064 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
1065 |
+
if module.padding_idx is not None:
|
1066 |
+
module.weight.data[module.padding_idx].zero_()
|
1067 |
+
|
1068 |
+
|
1069 |
+
Gemmoe_INPUTS_DOCSTRING = r"""
|
1070 |
+
Args:
|
1071 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
1072 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
1073 |
+
it.
|
1074 |
+
|
1075 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
1076 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
1077 |
+
|
1078 |
+
[What are input IDs?](../glossary#input-ids)
|
1079 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
1080 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
1081 |
+
|
1082 |
+
- 1 for tokens that are **not masked**,
|
1083 |
+
- 0 for tokens that are **masked**.
|
1084 |
+
|
1085 |
+
[What are attention masks?](../glossary#attention-mask)
|
1086 |
+
|
1087 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
1088 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
1089 |
+
|
1090 |
+
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
1091 |
+
`past_key_values`).
|
1092 |
+
|
1093 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
1094 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
1095 |
+
information on the default strategy.
|
1096 |
+
|
1097 |
+
- 1 indicates the head is **not masked**,
|
1098 |
+
- 0 indicates the head is **masked**.
|
1099 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
1100 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
1101 |
+
config.n_positions - 1]`.
|
1102 |
+
|
1103 |
+
[What are position IDs?](../glossary#position-ids)
|
1104 |
+
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
1105 |
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
1106 |
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
1107 |
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
1108 |
+
|
1109 |
+
Two formats are allowed:
|
1110 |
+
- a [`~cache_utils.Cache`] instance;
|
1111 |
+
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
1112 |
+
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
1113 |
+
cache format.
|
1114 |
+
|
1115 |
+
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
1116 |
+
legacy cache format will be returned.
|
1117 |
+
|
1118 |
+
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
1119 |
+
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
1120 |
+
of shape `(batch_size, sequence_length)`.
|
1121 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
1122 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
1123 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
1124 |
+
model's internal embedding lookup matrix.
|
1125 |
+
use_cache (`bool`, *optional*):
|
1126 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
1127 |
+
`past_key_values`).
|
1128 |
+
output_attentions (`bool`, *optional*):
|
1129 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
1130 |
+
tensors for more detail.
|
1131 |
+
output_hidden_states (`bool`, *optional*):
|
1132 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
1133 |
+
more detail.
|
1134 |
+
return_dict (`bool`, *optional*):
|
1135 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
1136 |
"""
|
1137 |
|
1138 |
@add_start_docstrings(
|
|
|
1141 |
)
|
1142 |
|
1143 |
class GemmoeModel(GemmoePreTrainedModel):
|
1144 |
+
"""
|
1145 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GemmoeDecoderLayer`]
|
|
|
|
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|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
1146 |
|
1147 |
Args:
|
1148 |
+
config: GemmoeConfig
|
1149 |
+
"""
|
1150 |
+
|
1151 |
+
def __init__(self, config: GemmoeConfig):
|
1152 |
+
super().__init__(config)
|
1153 |
+
self.padding_idx = config.pad_token_id
|
1154 |
+
self.vocab_size = config.vocab_size
|
1155 |
+
|
1156 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
1157 |
+
self.layers = nn.ModuleList(
|
1158 |
+
[GemmoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
1159 |
+
)
|
1160 |
+
self._use_sdpa = config._attn_implementation == "sdpa"
|
1161 |
+
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
1162 |
+
self.norm = GemmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
1163 |
|
1164 |
+
self.gradient_checkpointing = False
|
1165 |
+
# Initialize weights and apply final processing
|
1166 |
+
self.post_init()
|
1167 |
|
1168 |
+
def get_input_embeddings(self):
|
1169 |
+
return self.embed_tokens
|
1170 |
|
1171 |
+
def set_input_embeddings(self, value):
|
1172 |
+
self.embed_tokens = value
|
1173 |
|
1174 |
+
@add_start_docstrings_to_model_forward(Gemmoe_INPUTS_DOCSTRING)
|
1175 |
+
def forward(
|
1176 |
+
self,
|
1177 |
+
input_ids: torch.LongTensor = None,
|
1178 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1179 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1180 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1181 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1182 |
+
use_cache: Optional[bool] = None,
|
1183 |
+
output_attentions: Optional[bool] = None,
|
1184 |
+
output_hidden_states: Optional[bool] = None,
|
1185 |
+
return_dict: Optional[bool] = None,
|
1186 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
1187 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
1188 |
+
output_hidden_states = (
|
1189 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
1190 |
+
)
|
1191 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
1192 |
+
|
1193 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1194 |
+
|
1195 |
+
# retrieve input_ids and inputs_embeds
|
1196 |
+
if input_ids is not None and inputs_embeds is not None:
|
1197 |
+
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
1198 |
+
elif input_ids is not None:
|
1199 |
+
batch_size, seq_length = input_ids.shape[:2]
|
1200 |
+
elif inputs_embeds is not None:
|
1201 |
+
batch_size, seq_length = inputs_embeds.shape[:2]
|
1202 |
+
else:
|
1203 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
1204 |
+
|
1205 |
+
if self.gradient_checkpointing and self.training:
|
1206 |
+
if use_cache:
|
1207 |
+
logger.warning_once(
|
1208 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`transformers."
|
1209 |
+
)
|
1210 |
+
use_cache = False
|
1211 |
+
|
1212 |
+
past_key_values_length = 0
|
1213 |
+
if use_cache:
|
1214 |
+
use_legacy_cache = not isinstance(past_key_values, Cache)
|
1215 |
+
if use_legacy_cache:
|
1216 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
1217 |
+
past_key_values_length = past_key_values.get_usable_length(seq_length)
|
1218 |
+
|
1219 |
+
if position_ids is None:
|
1220 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
1221 |
+
position_ids = torch.arange(
|
1222 |
+
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
1223 |
+
)
|
1224 |
+
position_ids = position_ids.unsqueeze(0)
|
1225 |
+
|
1226 |
+
if inputs_embeds is None:
|
1227 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
1228 |
+
|
1229 |
+
if self._use_flash_attention_2:
|
1230 |
+
# 2d mask is passed through the layers
|
1231 |
+
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
|
1232 |
+
elif self._use_sdpa and not output_attentions:
|
1233 |
+
# output_attentions=True can not be supported when using SDPA, and we fall back on
|
1234 |
+
# the manual implementation that requires a 4D causal mask in all cases.
|
1235 |
+
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
|
1236 |
+
attention_mask,
|
1237 |
+
(batch_size, seq_length),
|
1238 |
+
inputs_embeds,
|
1239 |
+
past_key_values_length,
|
1240 |
+
)
|
1241 |
+
else:
|
1242 |
+
# 4d mask is passed through the layers
|
1243 |
+
attention_mask = _prepare_4d_causal_attention_mask(
|
1244 |
+
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
1245 |
+
)
|
1246 |
+
|
1247 |
+
# embed positions
|
1248 |
+
hidden_states = inputs_embeds
|
1249 |
+
|
1250 |
+
# decoder layers
|
1251 |
+
all_hidden_states = () if output_hidden_states else None
|
1252 |
+
all_self_attns = () if output_attentions else None
|
1253 |
+
next_decoder_cache = None
|
1254 |
+
|
1255 |
+
for decoder_layer in self.layers:
|
1256 |
+
if output_hidden_states:
|
1257 |
+
all_hidden_states += (hidden_states,)
|
1258 |
+
|
1259 |
+
if self.gradient_checkpointing and self.training:
|
1260 |
+
layer_outputs = self._gradient_checkpointing_func(
|
1261 |
+
decoder_layer.__call__,
|
1262 |
+
hidden_states,
|
1263 |
+
attention_mask,
|
1264 |
+
position_ids,
|
1265 |
+
past_key_values,
|
1266 |
+
output_attentions,
|
1267 |
+
use_cache,
|
1268 |
+
)
|
1269 |
+
else:
|
1270 |
+
layer_outputs = decoder_layer(
|
1271 |
+
hidden_states,
|
1272 |
+
attention_mask=attention_mask,
|
1273 |
+
position_ids=position_ids,
|
1274 |
+
past_key_value=past_key_values,
|
1275 |
+
output_attentions=output_attentions,
|
1276 |
+
use_cache=use_cache,
|
1277 |
+
)
|
1278 |
+
|
1279 |
+
hidden_states = layer_outputs[0]
|
1280 |
+
|
1281 |
+
if use_cache:
|
1282 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
1283 |
+
|
1284 |
+
if output_attentions:
|
1285 |
+
all_self_attns += (layer_outputs[1],)
|
1286 |
+
|
1287 |
+
hidden_states = self.norm(hidden_states)
|
1288 |
+
|
1289 |
+
# add hidden states from the last decoder layer
|
1290 |
+
if output_hidden_states:
|
1291 |
+
all_hidden_states += (hidden_states,)
|
1292 |
+
|
1293 |
+
next_cache = None
|
1294 |
+
if use_cache:
|
1295 |
+
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
|
1296 |
+
if not return_dict:
|
1297 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
1298 |
+
return BaseModelOutputWithPast(
|
1299 |
+
last_hidden_state=hidden_states,
|
1300 |
+
past_key_values=next_cache,
|
1301 |
+
hidden_states=all_hidden_states,
|
1302 |
+
attentions=all_self_attns,
|
1303 |
+
)
|
1304 |
+
|
1305 |
+
class GemmoeForCausalLM(GemmoePreTrainedModel):
|
1306 |
_tied_weights_keys = ["lm_head.weight"]
|
1307 |
|
1308 |
def __init__(self, config):
|
|
|
1310 |
self.model = GemmoeModel(config)
|
1311 |
self.vocab_size = config.vocab_size
|
1312 |
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
|
|
|
1313 |
|
1314 |
# Initialize weights and apply final processing
|
1315 |
self.post_init()
|
|
|
1332 |
def get_decoder(self):
|
1333 |
return self.model
|
1334 |
|
1335 |
+
@add_start_docstrings_to_model_forward(Gemmoe_INPUTS_DOCSTRING)
|
1336 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
1337 |
def forward(
|
1338 |
self,
|
1339 |
input_ids: torch.LongTensor = None,
|
|
|
1345 |
use_cache: Optional[bool] = None,
|
1346 |
output_attentions: Optional[bool] = None,
|
1347 |
output_hidden_states: Optional[bool] = None,
|
|
|
1348 |
return_dict: Optional[bool] = None,
|
1349 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
|
|
1350 |
r"""
|
1351 |
Args:
|
1352 |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
1353 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
|
1354 |
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
1355 |
+
(masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
|
1356 |
|
1357 |
Returns:
|
1358 |
|
|
|
1361 |
```python
|
1362 |
>>> from transformers import AutoTokenizer, GemmoeForCausalLM
|
1363 |
|
1364 |
+
>>> model = GemmoeForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
1365 |
+
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
1366 |
|
1367 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
1368 |
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
1369 |
|
1370 |
>>> # Generate
|
1371 |
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
1372 |
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
1373 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
1374 |
```"""
|
1375 |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
|
|
|
|
|
1376 |
output_hidden_states = (
|
1377 |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
1378 |
)
|
1379 |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1380 |
|
1381 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
1382 |
outputs = self.model(
|
1383 |
input_ids=input_ids,
|
1384 |
attention_mask=attention_mask,
|
|
|
1388 |
use_cache=use_cache,
|
1389 |
output_attentions=output_attentions,
|
1390 |
output_hidden_states=output_hidden_states,
|
|
|
1391 |
return_dict=return_dict,
|
|
|
1392 |
)
|
1393 |
|
1394 |
hidden_states = outputs[0]
|
1395 |
+
if self.config.pretraining_tp > 1:
|
1396 |
+
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
|
1397 |
+
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
|
1398 |
+
logits = torch.cat(logits, dim=-1)
|
1399 |
+
else:
|
1400 |
+
logits = self.lm_head(hidden_states)
|
1401 |
+
logits = logits.float()
|
1402 |
|
1403 |
loss = None
|
1404 |
if labels is not None:
|
1405 |
+
# Shift so that tokens < n predict n
|
1406 |
shift_logits = logits[..., :-1, :].contiguous()
|
1407 |
shift_labels = labels[..., 1:].contiguous()
|
1408 |
+
# Flatten the tokens
|
1409 |
loss_fct = CrossEntropyLoss()
|
1410 |
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
1411 |
shift_labels = shift_labels.view(-1)
|
1412 |
+
# Enable model parallelism
|
1413 |
shift_labels = shift_labels.to(shift_logits.device)
|
1414 |
loss = loss_fct(shift_logits, shift_labels)
|
1415 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1416 |
if not return_dict:
|
1417 |
output = (logits,) + outputs[1:]
|
|
|
|
|
1418 |
return (loss,) + output if loss is not None else output
|
1419 |
|
1420 |
+
return CausalLMOutputWithPast(
|
1421 |
loss=loss,
|
|
|
1422 |
logits=logits,
|
1423 |
past_key_values=outputs.past_key_values,
|
1424 |
hidden_states=outputs.hidden_states,
|
1425 |
attentions=outputs.attentions,
|
|
|
1426 |
)
|
1427 |
|
1428 |
def prepare_inputs_for_generation(
|
1429 |
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
1430 |
):
|
|
|
1431 |
if past_key_values is not None:
|
1432 |
if isinstance(past_key_values, Cache):
|
1433 |
cache_length = past_key_values.get_seq_length()
|
|
|
1437 |
cache_length = past_length = past_key_values[0][0].shape[2]
|
1438 |
max_cache_length = None
|
1439 |
|
1440 |
+
# Keep only the unprocessed tokens:
|
1441 |
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
1442 |
+
# some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
|
1443 |
+
# input)
|
1444 |
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
1445 |
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
1446 |
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
1447 |
+
# input_ids based on the past_length.
|
1448 |
elif past_length < input_ids.shape[1]:
|
1449 |
input_ids = input_ids[:, past_length:]
|
1450 |
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
1451 |
+
|
1452 |
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
1453 |
if (
|
1454 |
max_cache_length is not None
|
1455 |
and attention_mask is not None
|
|
|
1459 |
|
1460 |
position_ids = kwargs.get("position_ids", None)
|
1461 |
if attention_mask is not None and position_ids is None:
|
1462 |
+
# create position_ids on the fly for batch generation
|
1463 |
position_ids = attention_mask.long().cumsum(-1) - 1
|
1464 |
position_ids.masked_fill_(attention_mask == 0, 1)
|
1465 |
if past_key_values:
|
1466 |
position_ids = position_ids[:, -input_ids.shape[1] :]
|
1467 |
|
1468 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1469 |
if inputs_embeds is not None and past_key_values is None:
|
1470 |
model_inputs = {"inputs_embeds": inputs_embeds}
|
1471 |
else:
|
1472 |
+
model_inputs = {"input_ids": input_ids}
|
1473 |
|
1474 |
model_inputs.update(
|
1475 |
{
|
1476 |
+
"position_ids": position_ids,
|
|
|
1477 |
"past_key_values": past_key_values,
|
1478 |
"use_cache": kwargs.get("use_cache"),
|
1479 |
"attention_mask": attention_mask,
|
1480 |
}
|
1481 |
)
|
|
|
1482 |
return model_inputs
|
1483 |
|
1484 |
@staticmethod
|
|
|
1511 |
self.num_labels = config.num_labels
|
1512 |
self.model = GemmoeModel(config)
|
1513 |
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
1514 |
+
|
1515 |
# Initialize weights and apply final processing
|
1516 |
self.post_init()
|
1517 |
|
|
|
1521 |
def set_input_embeddings(self, value):
|
1522 |
self.model.embed_tokens = value
|
1523 |
|
1524 |
+
@add_start_docstrings_to_model_forward(Gemmoe_INPUTS_DOCSTRING)
|
|
|
1525 |
def forward(
|
1526 |
self,
|
1527 |
input_ids: torch.LongTensor = None,
|
|
|
1535 |
output_hidden_states: Optional[bool] = None,
|
1536 |
return_dict: Optional[bool] = None,
|
1537 |
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
1538 |
+
r"""
|
1539 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1540 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, transformers.,
|
1541 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1542 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1543 |
"""
|
1544 |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1545 |
+
|
1546 |
transformer_outputs = self.model(
|
1547 |
input_ids,
|
1548 |
attention_mask=attention_mask,
|
|
|
1568 |
sequence_lengths = -1
|
1569 |
else:
|
1570 |
if input_ids is not None:
|
1571 |
+
sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
|
1572 |
+
logits.device
|
1573 |
+
)
|
1574 |
else:
|
1575 |
sequence_lengths = -1
|
1576 |
|
|
|
1599 |
elif self.config.problem_type == "multi_label_classification":
|
1600 |
loss_fct = BCEWithLogitsLoss()
|
1601 |
loss = loss_fct(pooled_logits, labels)
|
|
|
1602 |
if not return_dict:
|
1603 |
output = (pooled_logits,) + transformer_outputs[1:]
|
1604 |
return ((loss,) + output) if loss is not None else output
|