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""" PyTorch Mistral model.""" |
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import inspect |
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import math |
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from typing import List, Optional, Tuple, Union |
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import torch |
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import torch.nn.functional as F |
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import torch.utils.checkpoint |
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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.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.utils import ( |
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add_start_docstrings, |
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add_start_docstrings_to_model_forward, |
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is_flash_attn_2_available, |
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logging, |
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replace_return_docstrings, |
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) |
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from .configuration_mistral import MistralConfig |
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if is_flash_attn_2_available(): |
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from flash_attn import flash_attn_func, flash_attn_varlen_func |
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input |
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_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) |
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logger = logging.get_logger(__name__) |
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_CONFIG_FOR_DOC = "MistralConfig" |
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def _get_unpad_data(padding_mask): |
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seqlens_in_batch = padding_mask.sum(dim=-1, dtype=torch.int32) |
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indices = torch.nonzero(padding_mask.flatten(), as_tuple=False).flatten() |
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max_seqlen_in_batch = seqlens_in_batch.max().item() |
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cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) |
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return ( |
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indices, |
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cu_seqlens, |
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max_seqlen_in_batch, |
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) |
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def _make_causal_mask( |
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input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 |
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): |
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""" |
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Make causal mask used for bi-directional self-attention. |
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""" |
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bsz, tgt_len = input_ids_shape |
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mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) |
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mask_cond = torch.arange(mask.size(-1), device=device) |
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mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) |
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mask = mask.to(dtype) |
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if past_key_values_length > 0: |
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mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) |
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return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) |
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def _make_sliding_window_causal_mask( |
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input_ids_shape: torch.Size, |
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dtype: torch.dtype, |
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device: torch.device, |
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past_key_values_length: int = 0, |
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sliding_window: int = 4096, |
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): |
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""" |
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Make causal mask used for sliding window attention |
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""" |
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bsz, tgt_len = input_ids_shape |
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tensor = torch.full( |
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(tgt_len, tgt_len), |
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fill_value=1, |
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device=device, |
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) |
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mask = torch.tril(tensor, diagonal=0) |
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mask = torch.triu(mask, diagonal=-sliding_window) |
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mask = torch.log(mask).to(dtype) |
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if past_key_values_length > 0: |
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mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) |
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return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) |
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def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): |
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""" |
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Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
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""" |
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bsz, src_len = mask.size() |
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tgt_len = tgt_len if tgt_len is not None else src_len |
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expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) |
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inverted_mask = 1.0 - expanded_mask |
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return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) |
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def _yarn_find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048): |
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return (dim * math.log(max_position_embeddings/(num_rotations * 2 * math.pi)))/(2 * math.log(base)) |
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def _yarn_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048): |
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low = math.floor(_yarn_find_correction_dim( |
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low_rot, dim, base, max_position_embeddings)) |
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high = math.ceil(_yarn_find_correction_dim( |
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high_rot, dim, base, max_position_embeddings)) |
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return max(low, 0), min(high, dim-1) |
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def _yarn_linear_ramp_mask(min, max, dim): |
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if min == max: |
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max += 0.001 |
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linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min) |
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ramp_func = torch.clamp(linear_func, 0, 1) |
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return ramp_func |
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def _yarn_get_mscale(scale=1): |
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if scale <= 1: |
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return 1.0 |
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return 0.07 * math.log(scale) + 1.0 |
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class MistralRMSNorm(nn.Module): |
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def __init__(self, hidden_size, eps=1e-6): |
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""" |
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MistralRMSNorm is equivalent to T5LayerNorm |
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""" |
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super().__init__() |
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self.weight = nn.Parameter(torch.ones(hidden_size)) |
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self.variance_epsilon = eps |
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def forward(self, hidden_states): |
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input_dtype = hidden_states.dtype |
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hidden_states = hidden_states.to(torch.float32) |
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variance = hidden_states.pow(2).mean(-1, keepdim=True) |
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) |
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return self.weight * hidden_states.to(input_dtype) |
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class MistralRotaryEmbedding(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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inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) |
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self.register_buffer("inv_freq", inv_freq, persistent=False) |
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self._set_cos_sin_cache( |
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seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() |
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) |
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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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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) |
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freqs = torch.einsum("i,j->ij", t, self.inv_freq) |
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emb = torch.cat((freqs, freqs), dim=-1) |
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) |
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) |
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def forward(self, x, seq_len=None): |
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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].to(dtype=x.dtype), |
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self.sin_cached[:seq_len].to(dtype=x.dtype), |
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) |
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class MistralLinearScalingRotaryEmbedding(MistralRotaryEmbedding): |
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"""MistralRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" |
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): |
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self.scaling_factor = scaling_factor |
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super().__init__(dim, max_position_embeddings, base, device) |
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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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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) |
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t = t / self.scaling_factor |
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freqs = torch.einsum("i,j->ij", t, self.inv_freq) |
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emb = torch.cat((freqs, freqs), dim=-1) |
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) |
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self.register_buffer("sin_cached", emb.cos().to(dtype), persistent=False) |
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class MistralDynamicNTKScalingRotaryEmbedding(MistralRotaryEmbedding): |
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"""MistralRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" |
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): |
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self.scaling_factor = scaling_factor |
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super().__init__(dim, max_position_embeddings, base, device) |
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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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if seq_len > self.max_position_embeddings: |
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base = self.base * ( |
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(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) |
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) ** (self.dim / (self.dim - 2)) |
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inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) |
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self.register_buffer("inv_freq", inv_freq, persistent=False) |
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) |
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freqs = torch.einsum("i,j->ij", t, self.inv_freq) |
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emb = torch.cat((freqs, freqs), dim=-1) |
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) |
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) |
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class MistralYaRNScaledRotaryEmbedding(torch.nn.Module): |
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"""MistralRotaryEmbedding extended with YaRN. See: https://arxiv.org/abs/2309.00071""" |
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def __init__(self, dim, max_position_embeddings=2048, base=10000, scale=1, original_max_position_embeddings=2048, |
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extrapolation_factor=1, attn_factor=1, beta_fast=128, beta_slow=2, finetuned=False, 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.scale = scale |
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self.original_max_position_embeddings = original_max_position_embeddings |
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self.extrapolation_factor = extrapolation_factor |
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self.attn_factor = attn_factor |
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self.beta_fast = beta_fast |
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self.beta_slow = beta_slow |
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self.yarn(device) |
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self.max_seq_len_cached = max_position_embeddings |
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t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype) |
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freqs = torch.einsum("i,j->ij", t, self.inv_freq) |
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emb = torch.cat((freqs, freqs), dim=-1) |
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dtype = torch.get_default_dtype() |
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self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(dtype), persistent=False) |
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self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(dtype), persistent=False) |
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def forward(self, x, seq_len=None): |
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if seq_len > self.max_seq_len_cached: |
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self.max_seq_len_cached = seq_len |
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t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype) |
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freqs = torch.einsum("i,j->ij", t, self.inv_freq) |
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emb = torch.cat((freqs, freqs), dim=-1).to(x.device) |
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self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(x.dtype), persistent=False) |
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self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(x.dtype), persistent=False) |
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return ( |
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self.cos_cached[:seq_len].to(dtype=x.dtype), |
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self.sin_cached[:seq_len].to(dtype=x.dtype), |
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) |
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def yarn(self, device): |
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pos_freqs = self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim) |
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inv_freq_extrapolation = 1.0 / pos_freqs |
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inv_freq_interpolation = 1.0 / (self.scale * pos_freqs) |
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low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.original_max_position_embeddings) |
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inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor |
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inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask |
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self.register_buffer("inv_freq", inv_freq, persistent=False) |
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self.mscale = float(_yarn_get_mscale(self.scale) * self.attn_factor) |
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class MistralDynamicYaRNScaledRotaryEmbedding(torch.nn.Module): |
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"""MistralRotaryEmbedding extended with Dynamic YaRN. See: https://arxiv.org/abs/2309.00071""" |
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def __init__(self, dim, max_position_embeddings=2048, base=10000, original_max_position_embeddings=2048, |
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extrapolation_factor=1, attn_factor=1, beta_fast=128, beta_slow=2, finetuned=False, 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.original_max_position_embeddings = original_max_position_embeddings |
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self.extrapolation_factor = extrapolation_factor |
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self.attn_factor = attn_factor |
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self.beta_fast = beta_fast |
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self.beta_slow = beta_slow |
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if finetuned: |
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self.yarn(self.max_position_embeddings / self.original_max_position_embeddings, device) |
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else: |
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inv_freq = 1.0 / \ |
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(base ** (torch.arange(0, dim, 2).float().to(device) / dim)) |
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self.register_buffer("inv_freq", inv_freq, persistent=False) |
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self.mscale = 1 |
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self.max_seq_len_cached = max_position_embeddings |
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t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32) |
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freqs = torch.einsum("i,j->ij", t, self.inv_freq) |
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emb = torch.cat((freqs, freqs), dim=-1) |
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dtype = torch.get_default_dtype() |
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self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(dtype), persistent=False) |
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self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(dtype), persistent=False) |
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def forward(self, x, seq_len=None): |
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if seq_len > self.max_seq_len_cached: |
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self.max_seq_len_cached = seq_len |
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self.yarn(seq_len / self.max_position_embeddings, x.device) |
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t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype) |
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freqs = torch.einsum("i,j->ij", t, self.inv_freq) |
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emb = torch.cat((freqs, freqs), dim=-1).to(x.device) |
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self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(x.dtype), persistent=False) |
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self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(x.dtype), persistent=False) |
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return ( |
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self.cos_cached[:seq_len].to(dtype=x.dtype), |
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self.sin_cached[:seq_len].to(dtype=x.dtype), |
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) |
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def yarn(self, scale, device): |
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pos_freqs = self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim) |
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inv_freq_extrapolation = 1.0 / pos_freqs |
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inv_freq_interpolation = 1.0 / (scale * pos_freqs) |
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low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.original_max_position_embeddings) |
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inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor |
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inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask |
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self.register_buffer("inv_freq", inv_freq, persistent=False) |
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self.mscale = float(_yarn_get_mscale(scale) * self.attn_factor) |
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def rotate_half(x): |
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"""Rotates half the hidden dims of the input.""" |
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x1 = x[..., : x.shape[-1] // 2] |
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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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cos = cos[position_ids].unsqueeze(1) |
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sin = sin[position_ids].unsqueeze(1) |
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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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class MistralMLP(nn.Module): |
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def __init__(self, config): |
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super().__init__() |
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self.config = config |
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self.hidden_size = config.hidden_size |
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self.intermediate_size = config.intermediate_size |
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) |
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) |
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) |
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self.act_fn = ACT2FN[config.hidden_act] |
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|
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def forward(self, x): |
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) |
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|
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: |
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""" |
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). 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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""" |
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape |
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if n_rep == 1: |
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return hidden_states |
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, 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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|
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class MistralAttention(nn.Module): |
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""" |
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Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer |
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and "Generating Long Sequences with Sparse Transformers". |
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""" |
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|
|
def __init__(self, config: MistralConfig): |
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super().__init__() |
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self.config = config |
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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 = self.hidden_size // self.num_heads |
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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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|
|
if (self.head_dim * self.num_heads) != self.hidden_size: |
|
raise ValueError( |
|
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" |
|
f" and `num_heads`: {self.num_heads})." |
|
) |
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) |
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) |
|
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) |
|
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) |
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|
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self._init_rope() |
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|
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): |
|
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() |
|
|
|
def _init_rope(self): |
|
if self.config.rope_scaling is None: |
|
self.rotary_emb = MistralRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta) |
|
else: |
|
scaling_type = self.config.rope_scaling["type"] |
|
scaling_factor = self.config.rope_scaling["factor"] |
|
if scaling_type == "linear": |
|
self.rotary_emb = MistralLinearScalingRotaryEmbedding( |
|
self.head_dim, max_position_embeddings=self.max_position_embeddings, |
|
scaling_factor=scaling_factor, base=self.rope_theta, |
|
) |
|
elif scaling_type == "dynamic": |
|
self.rotary_emb = MistralDynamicNTKScalingRotaryEmbedding( |
|
self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, |
|
base=self.rope_theta, |
|
) |
|
elif scaling_type == "yarn": |
|
original_max_position_embeddings = self.config.rope_scaling["original_max_position_embeddings"] |
|
self.rotary_emb = MistralYaRNScaledRotaryEmbedding( |
|
self.head_dim, max_position_embeddings=self.max_position_embeddings, scale=scaling_factor, |
|
original_max_position_embeddings=original_max_position_embeddings, base=self.rope_theta, |
|
) |
|
elif scaling_type == "dynamic-yarn": |
|
original_max_position_embeddings = self.config.rope_scaling["original_max_position_embeddings"] |
|
self.rotary_emb = MistralDynamicYaRNScaledRotaryEmbedding( |
|
self.head_dim, max_position_embeddings=self.max_position_embeddings, |
|
original_max_position_embeddings=original_max_position_embeddings, base=self.rope_theta, |
|
) |
|
else: |
|
raise ValueError(f"Unknown RoPE scaling type {scaling_type}") |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_value: Optional[Tuple[torch.Tensor]] = None, |
|
output_attentions: bool = False, |
|
use_cache: bool = False, |
|
padding_mask: Optional[torch.Tensor] = None, |
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
|
bsz, q_len, _ = hidden_states.size() |
|
|
|
query_states = self.q_proj(hidden_states) |
|
key_states = self.k_proj(hidden_states) |
|
value_states = self.v_proj(hidden_states) |
|
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
|
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
|
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
|
|
|
kv_seq_len = key_states.shape[-2] |
|
if past_key_value is not None: |
|
kv_seq_len += past_key_value[0].shape[-2] |
|
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) |
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) |
|
|
|
if past_key_value is not None: |
|
|
|
key_states = torch.cat([past_key_value[0], key_states], dim=2) |
|
value_states = torch.cat([past_key_value[1], value_states], dim=2) |
|
|
|
past_key_value = (key_states, value_states) if use_cache else None |
|
|
|
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups) |
|
value_states = repeat_kv(value_states, self.num_key_value_groups) |
|
|
|
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) |
|
|
|
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): |
|
raise ValueError( |
|
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" |
|
f" {attn_weights.size()}" |
|
) |
|
|
|
if attention_mask is not None: |
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): |
|
raise ValueError( |
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" |
|
) |
|
|
|
attn_weights = attn_weights + attention_mask |
|
|
|
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) |
|
attn_output = torch.matmul(attn_weights, value_states) |
|
|
|
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): |
|
raise ValueError( |
|
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" |
|
f" {attn_output.size()}" |
|
) |
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous() |
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) |
|
|
|
attn_output = self.o_proj(attn_output) |
|
|
|
if not output_attentions: |
|
attn_weights = None |
|
|
|
return attn_output, attn_weights, past_key_value |
|
|
|
|
|
class MistralFlashAttention2(MistralAttention): |
|
""" |
|
Mistral flash attention module. This module inherits from `MistralAttention` as the weights of the module stays |
|
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of |
|
flash attention and deal with padding tokens in case the input contains any of them. |
|
""" |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_value: Optional[Tuple[torch.Tensor]] = None, |
|
output_attentions: bool = False, |
|
use_cache: bool = False, |
|
padding_mask: Optional[torch.LongTensor] = None, |
|
): |
|
bsz, q_len, _ = hidden_states.size() |
|
|
|
query_states = self.q_proj(hidden_states) |
|
key_states = self.k_proj(hidden_states) |
|
value_states = self.v_proj(hidden_states) |
|
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
|
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
|
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
|
|
|
kv_seq_len = key_states.shape[-2] |
|
if past_key_value is not None: |
|
kv_seq_len += past_key_value[0].shape[-2] |
|
|
|
|
|
rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1 |
|
cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len) |
|
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) |
|
|
|
use_sliding_windows = ( |
|
_flash_supports_window_size |
|
and hasattr(self.config, "sliding_window") |
|
and kv_seq_len > self.config.sliding_window |
|
) |
|
|
|
if not _flash_supports_window_size: |
|
logger.warning_once( |
|
"The current flash attention version does not support sliding window attention, for a more memory efficient implementation" |
|
" make sure to upgrade flash-attn library." |
|
) |
|
|
|
if past_key_value is not None: |
|
|
|
if hasattr(self.config, "sliding_window") and kv_seq_len > self.config.sliding_window: |
|
slicing_tokens = kv_seq_len - self.config.sliding_window |
|
|
|
past_key = past_key_value[0] |
|
past_value = past_key_value[1] |
|
|
|
past_key = past_key[:, :, slicing_tokens:, :].contiguous() |
|
past_value = past_value[:, :, slicing_tokens:, :].contiguous() |
|
|
|
if past_key.shape[-2] != self.config.sliding_window - 1: |
|
raise ValueError( |
|
f"past key much have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got" |
|
f" {past_key.shape}" |
|
) |
|
|
|
past_key_value = (past_key, past_value) |
|
|
|
if padding_mask is not None: |
|
padding_mask = padding_mask[:, slicing_tokens:] |
|
padding_mask = torch.cat([padding_mask, torch.ones_like(padding_mask[:, -1:])], dim=-1) |
|
|
|
key_states = torch.cat([past_key_value[0], key_states], dim=2) |
|
value_states = torch.cat([past_key_value[1], value_states], dim=2) |
|
|
|
past_key_value = (key_states, value_states) if use_cache else None |
|
|
|
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups) |
|
value_states = repeat_kv(value_states, self.num_key_value_groups) |
|
|
|
|
|
|
|
|
|
dropout_rate = 0.0 |
|
|
|
|
|
|
|
|
|
input_dtype = query_states.dtype |
|
if input_dtype == torch.float32: |
|
logger.warning_once( |
|
"The input hidden states seems to be silently casted in float32, this might be related to" |
|
" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" |
|
" float16." |
|
) |
|
|
|
query_states = query_states.to(torch.float16) |
|
key_states = key_states.to(torch.float16) |
|
value_states = value_states.to(torch.float16) |
|
|
|
|
|
query_states = query_states.transpose(1, 2) |
|
key_states = key_states.transpose(1, 2) |
|
value_states = value_states.transpose(1, 2) |
|
|
|
attn_output = self._flash_attention_forward( |
|
query_states, |
|
key_states, |
|
value_states, |
|
padding_mask, |
|
q_len, |
|
dropout=dropout_rate, |
|
use_sliding_windows=use_sliding_windows, |
|
) |
|
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() |
|
attn_output = self.o_proj(attn_output) |
|
|
|
if not output_attentions: |
|
attn_weights = None |
|
|
|
return attn_output, attn_weights, past_key_value |
|
|
|
def _flash_attention_forward( |
|
self, |
|
query_states, |
|
key_states, |
|
value_states, |
|
padding_mask, |
|
query_length, |
|
dropout=0.0, |
|
softmax_scale=None, |
|
use_sliding_windows=False, |
|
): |
|
""" |
|
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token |
|
first unpad the input, then computes the attention scores and pad the final attention scores. |
|
|
|
Args: |
|
query_states (`torch.Tensor`): |
|
Input query states to be passed to Flash Attention API |
|
key_states (`torch.Tensor`): |
|
Input key states to be passed to Flash Attention API |
|
value_states (`torch.Tensor`): |
|
Input value states to be passed to Flash Attention API |
|
padding_mask (`torch.Tensor`): |
|
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the |
|
position of padding tokens and 1 for the position of non-padding tokens. |
|
dropout (`int`, *optional*): |
|
Attention dropout |
|
softmax_scale (`float`, *optional*): |
|
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) |
|
use_sliding_windows (`bool`, *optional*): |
|
Whether to activate sliding window attention. |
|
""" |
|
|
|
if padding_mask is not None: |
|
batch_size = query_states.shape[0] |
|
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( |
|
query_states, key_states, value_states, padding_mask, query_length |
|
) |
|
|
|
cu_seqlens_q, cu_seqlens_k = cu_seq_lens |
|
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens |
|
|
|
if not use_sliding_windows: |
|
attn_output_unpad = flash_attn_varlen_func( |
|
query_states, |
|
key_states, |
|
value_states, |
|
cu_seqlens_q=cu_seqlens_q, |
|
cu_seqlens_k=cu_seqlens_k, |
|
max_seqlen_q=max_seqlen_in_batch_q, |
|
max_seqlen_k=max_seqlen_in_batch_k, |
|
dropout_p=dropout, |
|
softmax_scale=softmax_scale, |
|
causal=True, |
|
) |
|
else: |
|
attn_output_unpad = flash_attn_varlen_func( |
|
query_states, |
|
key_states, |
|
value_states, |
|
cu_seqlens_q=cu_seqlens_q, |
|
cu_seqlens_k=cu_seqlens_k, |
|
max_seqlen_q=max_seqlen_in_batch_q, |
|
max_seqlen_k=max_seqlen_in_batch_k, |
|
dropout_p=dropout, |
|
softmax_scale=softmax_scale, |
|
causal=True, |
|
window_size=(self.config.sliding_window, self.config.sliding_window), |
|
) |
|
|
|
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) |
|
else: |
|
if not use_sliding_windows: |
|
attn_output = flash_attn_func( |
|
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=True |
|
) |
|
else: |
|
attn_output = flash_attn_func( |
|
query_states, |
|
key_states, |
|
value_states, |
|
dropout, |
|
softmax_scale=softmax_scale, |
|
causal=True, |
|
window_size=(self.config.sliding_window, self.config.sliding_window), |
|
) |
|
|
|
return attn_output |
|
|
|
def _upad_input(self, query_layer, key_layer, value_layer, padding_mask, query_length): |
|
batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape |
|
|
|
|
|
|
|
if kv_seq_len != padding_mask.shape[-1]: |
|
padding_mask_num_tokens = padding_mask.shape[-1] |
|
padding_mask = padding_mask[:, padding_mask_num_tokens - kv_seq_len :] |
|
|
|
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(padding_mask) |
|
|
|
key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) |
|
value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) |
|
|
|
if query_length == kv_seq_len: |
|
query_layer = index_first_axis( |
|
query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k |
|
) |
|
cu_seqlens_q = cu_seqlens_k |
|
max_seqlen_in_batch_q = max_seqlen_in_batch_k |
|
indices_q = indices_k |
|
elif query_length == 1: |
|
max_seqlen_in_batch_q = 1 |
|
cu_seqlens_q = torch.arange( |
|
batch_size + 1, dtype=torch.int32, device=query_layer.device |
|
) |
|
indices_q = cu_seqlens_q[:-1] |
|
query_layer = query_layer.squeeze(1) |
|
else: |
|
|
|
padding_mask = padding_mask[:, -query_length:] |
|
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, padding_mask) |
|
|
|
return ( |
|
query_layer, |
|
key_layer, |
|
value_layer, |
|
indices_q, |
|
(cu_seqlens_q, cu_seqlens_k), |
|
(max_seqlen_in_batch_q, max_seqlen_in_batch_k), |
|
) |
|
|
|
|
|
class MistralDecoderLayer(nn.Module): |
|
def __init__(self, config: MistralConfig): |
|
super().__init__() |
|
self.hidden_size = config.hidden_size |
|
self.self_attn = ( |
|
MistralAttention(config=config) |
|
if not getattr(config, "_flash_attn_2_enabled", False) |
|
else MistralFlashAttention2(config) |
|
) |
|
self.mlp = MistralMLP(config) |
|
self.input_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
|
self.post_attention_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_value: Optional[Tuple[torch.Tensor]] = None, |
|
output_attentions: Optional[bool] = False, |
|
use_cache: Optional[bool] = False, |
|
padding_mask: Optional[torch.Tensor] = None, |
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: |
|
""" |
|
Args: |
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` |
|
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size |
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under |
|
returned tensors for more detail. |
|
use_cache (`bool`, *optional*): |
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding |
|
(see `past_key_values`). |
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states |
|
""" |
|
|
|
residual = hidden_states |
|
|
|
hidden_states = self.input_layernorm(hidden_states) |
|
|
|
|
|
hidden_states, self_attn_weights, present_key_value = self.self_attn( |
|
hidden_states=hidden_states, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
past_key_value=past_key_value, |
|
output_attentions=output_attentions, |
|
use_cache=use_cache, |
|
padding_mask=padding_mask, |
|
) |
|
hidden_states = residual + hidden_states |
|
|
|
|
|
residual = hidden_states |
|
hidden_states = self.post_attention_layernorm(hidden_states) |
|
hidden_states = self.mlp(hidden_states) |
|
hidden_states = residual + hidden_states |
|
|
|
outputs = (hidden_states,) |
|
|
|
if output_attentions: |
|
outputs += (self_attn_weights,) |
|
|
|
if use_cache: |
|
outputs += (present_key_value,) |
|
|
|
return outputs |
|
|
|
|
|
MISTRAL_START_DOCSTRING = r""" |
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the |
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads |
|
etc.) |
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. |
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage |
|
and behavior. |
|
|
|
Parameters: |
|
config ([`MistralConfig`]): |
|
Model configuration class with all the parameters of the model. Initializing with a config file does not |
|
load the weights associated with the model, only the configuration. Check out the |
|
[`~PreTrainedModel.from_pretrained`] method to load the model weights. |
|
""" |
|
|
|
|
|
@add_start_docstrings( |
|
"The bare Mistral Model outputting raw hidden-states without any specific head on top.", |
|
MISTRAL_START_DOCSTRING, |
|
) |
|
class MistralPreTrainedModel(PreTrainedModel): |
|
config_class = MistralConfig |
|
base_model_prefix = "model" |
|
supports_gradient_checkpointing = True |
|
_no_split_modules = ["MistralDecoderLayer"] |
|
_skip_keys_device_placement = "past_key_values" |
|
_supports_flash_attn_2 = True |
|
|
|
def _init_weights(self, module): |
|
std = self.config.initializer_range |
|
if isinstance(module, nn.Linear): |
|
module.weight.data.normal_(mean=0.0, std=std) |
|
if module.bias is not None: |
|
module.bias.data.zero_() |
|
elif isinstance(module, nn.Embedding): |
|
module.weight.data.normal_(mean=0.0, std=std) |
|
if module.padding_idx is not None: |
|
module.weight.data[module.padding_idx].zero_() |
|
|
|
def _set_gradient_checkpointing(self, module, value=False): |
|
if isinstance(module, MistralModel): |
|
module.gradient_checkpointing = value |
|
|
|
|
|
MISTRAL_INPUTS_DOCSTRING = r""" |
|
Args: |
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): |
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide |
|
it. |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
[What are input IDs?](../glossary#input-ids) |
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: |
|
|
|
- 1 for tokens that are **not masked**, |
|
- 0 for tokens that are **masked**. |
|
|
|
[What are attention masks?](../glossary#attention-mask) |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see |
|
`past_key_values`). |
|
|
|
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] |
|
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more |
|
information on the default strategy. |
|
|
|
- 1 indicates the head is **not masked**, |
|
- 0 indicates the head is **masked**. |
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, |
|
config.n_positions - 1]`. |
|
|
|
[What are position IDs?](../glossary#position-ids) |
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): |
|
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape |
|
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape |
|
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. |
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention |
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. |
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that |
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all |
|
`decoder_input_ids` of shape `(batch_size, sequence_length)`. |
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): |
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This |
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the |
|
model's internal embedding lookup matrix. |
|
use_cache (`bool`, *optional*): |
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see |
|
`past_key_values`). |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned |
|
tensors for more detail. |
|
output_hidden_states (`bool`, *optional*): |
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for |
|
more detail. |
|
return_dict (`bool`, *optional*): |
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. |
|
""" |
|
|
|
|
|
@add_start_docstrings( |
|
"The bare Mistral Model outputting raw hidden-states without any specific head on top.", |
|
MISTRAL_START_DOCSTRING, |
|
) |
|
class MistralModel(MistralPreTrainedModel): |
|
""" |
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`] |
|
|
|
Args: |
|
config: MistralConfig |
|
""" |
|
|
|
def __init__(self, config: MistralConfig): |
|
super().__init__(config) |
|
self.padding_idx = config.pad_token_id |
|
self.vocab_size = config.vocab_size |
|
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) |
|
self.layers = nn.ModuleList([MistralDecoderLayer(config) for _ in range(config.num_hidden_layers)]) |
|
self.norm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
|
|
|
self.gradient_checkpointing = False |
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self): |
|
return self.embed_tokens |
|
|
|
def set_input_embeddings(self, value): |
|
self.embed_tokens = value |
|
|
|
def _prepare_decoder_attention_mask( |
|
self, attention_mask, input_shape, inputs_embeds, past_key_values_length, sliding_window |
|
): |
|
|
|
|
|
combined_attention_mask = None |
|
if input_shape[-1] > 1: |
|
if sliding_window is not None: |
|
combined_attention_mask = _make_sliding_window_causal_mask( |
|
input_shape, |
|
inputs_embeds.dtype, |
|
device=inputs_embeds.device, |
|
past_key_values_length=past_key_values_length, |
|
sliding_window=sliding_window, |
|
) |
|
else: |
|
combined_attention_mask = _make_causal_mask( |
|
input_shape, |
|
inputs_embeds.dtype, |
|
device=inputs_embeds.device, |
|
past_key_values_length=past_key_values_length, |
|
) |
|
|
|
if attention_mask is not None: |
|
|
|
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( |
|
inputs_embeds.device |
|
) |
|
combined_attention_mask = ( |
|
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask |
|
) |
|
|
|
return combined_attention_mask |
|
|
|
@add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING) |
|
def forward( |
|
self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[List[torch.FloatTensor]] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, BaseModelOutputWithPast]: |
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
|
output_hidden_states = ( |
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
|
) |
|
use_cache = use_cache if use_cache is not None else self.config.use_cache |
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
|
|
if input_ids is not None and inputs_embeds is not None: |
|
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") |
|
elif input_ids is not None: |
|
batch_size, seq_length = input_ids.shape |
|
elif inputs_embeds is not None: |
|
batch_size, seq_length, _ = inputs_embeds.shape |
|
else: |
|
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") |
|
|
|
seq_length_with_past = seq_length |
|
past_key_values_length = 0 |
|
|
|
if past_key_values is not None: |
|
past_key_values_length = past_key_values[0][0].shape[2] |
|
seq_length_with_past = seq_length_with_past + past_key_values_length |
|
|
|
if position_ids is None: |
|
device = input_ids.device if input_ids is not None else inputs_embeds.device |
|
position_ids = torch.arange( |
|
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device |
|
) |
|
position_ids = position_ids.unsqueeze(0).view(-1, seq_length) |
|
else: |
|
position_ids = position_ids.view(-1, seq_length).long() |
|
|
|
if inputs_embeds is None: |
|
inputs_embeds = self.embed_tokens(input_ids) |
|
|
|
padding_mask = None |
|
|
|
|
|
if attention_mask is None: |
|
attention_mask = torch.ones( |
|
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device |
|
) |
|
elif 0 in attention_mask: |
|
padding_mask = attention_mask |
|
|
|
if ( |
|
padding_mask is not None |
|
and hasattr(self.config, "_flash_attn_2_enabled") |
|
and self.config._flash_attn_2_enabled |
|
): |
|
is_padding_right = padding_mask[:, -1].sum().item() != batch_size |
|
if is_padding_right: |
|
raise ValueError( |
|
"You are attempting to perform batched generation with padding_side='right'" |
|
" this may lead to unexpected behaviour for Flash Attention version of Mistral. Make sure to " |
|
" call `tokenizer.padding_side = 'left'` before tokenizing the input. " |
|
) |
|
|
|
attention_mask = self._prepare_decoder_attention_mask( |
|
attention_mask, |
|
(batch_size, seq_length), |
|
inputs_embeds, |
|
past_key_values_length, |
|
sliding_window=self.config.sliding_window if hasattr(self.config, "sliding_window") else None, |
|
) |
|
|
|
hidden_states = inputs_embeds |
|
|
|
if self.gradient_checkpointing and self.training: |
|
if use_cache: |
|
logger.warning_once( |
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." |
|
) |
|
use_cache = False |
|
|
|
|
|
all_hidden_states = () if output_hidden_states else None |
|
all_self_attns = () if output_attentions else None |
|
next_decoder_cache = () if use_cache else None |
|
|
|
for idx, decoder_layer in enumerate(self.layers): |
|
if output_hidden_states: |
|
all_hidden_states += (hidden_states,) |
|
|
|
past_key_value = past_key_values[idx] if past_key_values is not None else None |
|
|
|
if self.gradient_checkpointing and self.training: |
|
|
|
def create_custom_forward(module): |
|
def custom_forward(*inputs): |
|
|
|
return module(*inputs, past_key_value, output_attentions, padding_mask=padding_mask) |
|
|
|
return custom_forward |
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint( |
|
create_custom_forward(decoder_layer), |
|
hidden_states, |
|
attention_mask, |
|
position_ids, |
|
) |
|
else: |
|
layer_outputs = decoder_layer( |
|
hidden_states, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
past_key_value=past_key_value, |
|
output_attentions=output_attentions, |
|
use_cache=use_cache, |
|
padding_mask=padding_mask, |
|
) |
|
|
|
hidden_states = layer_outputs[0] |
|
|
|
if use_cache: |
|
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) |
|
|
|
if output_attentions: |
|
all_self_attns += (layer_outputs[1],) |
|
|
|
hidden_states = self.norm(hidden_states) |
|
|
|
|
|
if output_hidden_states: |
|
all_hidden_states += (hidden_states,) |
|
|
|
next_cache = next_decoder_cache if use_cache else None |
|
if not return_dict: |
|
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) |
|
return BaseModelOutputWithPast( |
|
last_hidden_state=hidden_states, |
|
past_key_values=next_cache, |
|
hidden_states=all_hidden_states, |
|
attentions=all_self_attns, |
|
) |
|
|
|
|
|
class MistralForCausalLM(MistralPreTrainedModel): |
|
_tied_weights_keys = ["lm_head.weight"] |
|
|
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.model = MistralModel(config) |
|
self.vocab_size = config.vocab_size |
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) |
|
|
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self): |
|
return self.model.embed_tokens |
|
|
|
def set_input_embeddings(self, value): |
|
self.model.embed_tokens = value |
|
|
|
def get_output_embeddings(self): |
|
return self.lm_head |
|
|
|
def set_output_embeddings(self, new_embeddings): |
|
self.lm_head = new_embeddings |
|
|
|
def set_decoder(self, decoder): |
|
self.model = decoder |
|
|
|
def get_decoder(self): |
|
return self.model |
|
|
|
@add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING) |
|
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) |
|
def forward( |
|
self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[List[torch.FloatTensor]] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
labels: Optional[torch.LongTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, CausalLMOutputWithPast]: |
|
r""" |
|
Args: |
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., |
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored |
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. |
|
|
|
Returns: |
|
|
|
Example: |
|
|
|
```python |
|
>>> from transformers import AutoTokenizer, MistralForCausalLM |
|
|
|
>>> model = MistralForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) |
|
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) |
|
|
|
>>> prompt = "Hey, are you conscious? Can you talk to me?" |
|
>>> inputs = tokenizer(prompt, return_tensors="pt") |
|
|
|
>>> # Generate |
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30) |
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] |
|
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." |
|
```""" |
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
|
output_hidden_states = ( |
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
|
) |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
|
|
outputs = self.model( |
|
input_ids=input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
past_key_values=past_key_values, |
|
inputs_embeds=inputs_embeds, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
hidden_states = outputs[0] |
|
logits = self.lm_head(hidden_states) |
|
logits = logits.float() |
|
|
|
loss = None |
|
if labels is not None: |
|
|
|
shift_logits = logits[..., :-1, :].contiguous() |
|
shift_labels = labels[..., 1:].contiguous() |
|
|
|
loss_fct = CrossEntropyLoss() |
|
shift_logits = shift_logits.view(-1, self.config.vocab_size) |
|
shift_labels = shift_labels.view(-1) |
|
|
|
shift_labels = shift_labels.to(shift_logits.device) |
|
loss = loss_fct(shift_logits, shift_labels) |
|
|
|
if not return_dict: |
|
output = (logits,) + outputs[1:] |
|
return (loss,) + output if loss is not None else output |
|
|
|
return CausalLMOutputWithPast( |
|
loss=loss, |
|
logits=logits, |
|
past_key_values=outputs.past_key_values, |
|
hidden_states=outputs.hidden_states, |
|
attentions=outputs.attentions, |
|
) |
|
|
|
def prepare_inputs_for_generation( |
|
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs |
|
): |
|
if past_key_values: |
|
input_ids = input_ids[:, -1:] |
|
|
|
position_ids = kwargs.get("position_ids", None) |
|
if attention_mask is not None and position_ids is None: |
|
|
|
position_ids = attention_mask.long().cumsum(-1) - 1 |
|
position_ids.masked_fill_(attention_mask == 0, 1) |
|
if past_key_values: |
|
position_ids = position_ids[:, -1].unsqueeze(-1) |
|
|
|
|
|
if inputs_embeds is not None and past_key_values is None: |
|
model_inputs = {"inputs_embeds": inputs_embeds} |
|
else: |
|
model_inputs = {"input_ids": input_ids} |
|
|
|
model_inputs.update( |
|
{ |
|
"position_ids": position_ids, |
|
"past_key_values": past_key_values, |
|
"use_cache": kwargs.get("use_cache"), |
|
"attention_mask": attention_mask, |
|
} |
|
) |
|
return model_inputs |
|
|
|
@staticmethod |
|
def _reorder_cache(past_key_values, beam_idx): |
|
reordered_past = () |
|
for layer_past in past_key_values: |
|
reordered_past += ( |
|
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), |
|
) |
|
return reordered_past |
|
|
|
|
|
@add_start_docstrings( |
|
""" |
|
The Mistral Model transformer with a sequence classification head on top (linear layer). |
|
|
|
[`MistralForSequenceClassification`] uses the last token in order to do the classification, as other causal models |
|
(e.g. GPT-2) do. |
|
|
|
Since it does classification on the last token, it requires to know the position of the last token. If a |
|
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If |
|
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the |
|
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in |
|
each row of the batch). |
|
""", |
|
MISTRAL_START_DOCSTRING, |
|
) |
|
|
|
class MistralForSequenceClassification(MistralPreTrainedModel): |
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.num_labels = config.num_labels |
|
self.model = MistralModel(config) |
|
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) |
|
|
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self): |
|
return self.model.embed_tokens |
|
|
|
def set_input_embeddings(self, value): |
|
self.model.embed_tokens = value |
|
|
|
@add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING) |
|
def forward( |
|
self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[List[torch.FloatTensor]] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
labels: Optional[torch.LongTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, SequenceClassifierOutputWithPast]: |
|
r""" |
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): |
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., |
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If |
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy). |
|
""" |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
transformer_outputs = self.model( |
|
input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
past_key_values=past_key_values, |
|
inputs_embeds=inputs_embeds, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
hidden_states = transformer_outputs[0] |
|
logits = self.score(hidden_states) |
|
|
|
if input_ids is not None: |
|
batch_size = input_ids.shape[0] |
|
else: |
|
batch_size = inputs_embeds.shape[0] |
|
|
|
if self.config.pad_token_id is None and batch_size != 1: |
|
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") |
|
if self.config.pad_token_id is None: |
|
sequence_lengths = -1 |
|
else: |
|
if input_ids is not None: |
|
sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to( |
|
logits.device |
|
) |
|
else: |
|
sequence_lengths = -1 |
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] |
|
|
|
loss = None |
|
if labels is not None: |
|
labels = labels.to(logits.device) |
|
if self.config.problem_type is None: |
|
if self.num_labels == 1: |
|
self.config.problem_type = "regression" |
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): |
|
self.config.problem_type = "single_label_classification" |
|
else: |
|
self.config.problem_type = "multi_label_classification" |
|
|
|
if self.config.problem_type == "regression": |
|
loss_fct = MSELoss() |
|
if self.num_labels == 1: |
|
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) |
|
else: |
|
loss = loss_fct(pooled_logits, labels) |
|
elif self.config.problem_type == "single_label_classification": |
|
loss_fct = CrossEntropyLoss() |
|
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) |
|
elif self.config.problem_type == "multi_label_classification": |
|
loss_fct = BCEWithLogitsLoss() |
|
loss = loss_fct(pooled_logits, labels) |
|
if not return_dict: |
|
output = (pooled_logits,) + transformer_outputs[1:] |
|
return ((loss,) + output) if loss is not None else output |
|
|
|
return SequenceClassifierOutputWithPast( |
|
loss=loss, |
|
logits=pooled_logits, |
|
past_key_values=transformer_outputs.past_key_values, |
|
hidden_states=transformer_outputs.hidden_states, |
|
attentions=transformer_outputs.attentions, |
|
) |
|
|