text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
return x_int * scaling_factor, scaling_factor | 9,206 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
class IntSoftmax(nn.Module):
"""
Quantized version of `torch.nn.Softmax`. Adds quantization-specific arguments on top of `torch.nn.Softmax`.
Args:
output_bit (`int`):
Bitwidth for the layer output activation.
quant_mode (`bool`, *optional*, defaults to `False`):
Whet... | 9,207 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
self.act = QuantAct(16, quant_mode=self.quant_mode)
self.x0 = -0.6931 # -ln2
self.const = 30 # dummy integer constant
self.coef = [0.35815147, 0.96963238, 1.0] # ax**2 + bx + c
self.coef[1] /= self.coef[0]
self.coef[2] /= self.coef[0]
def int_polynomial(self, x_int, scali... | 9,207 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
q = floor_ste.apply(x_int / x0_int)
r = x_int - x0_int * q
exp_int, exp_scaling_factor = self.int_polynomial(r, scaling_factor)
exp_int = torch.clamp(floor_ste.apply(exp_int * 2 ** (self.const - q)), min=0)
scaling_factor = exp_scaling_factor / 2**self.const
return exp_int, scali... | 9,207 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
exp_int_sum = exp_int.sum(dim=-1, keepdim=True)
factor = floor_ste.apply(2**self.max_bit / exp_int_sum)
exp_int = floor_ste.apply(exp_int * factor / 2 ** (self.max_bit - self.output_bit))
scaling_factor = 1 / 2**self.output_bit
return exp_int * scaling_factor, scaling_factor | 9,207 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
class IntLayerNorm(nn.Module):
"""
Quantized version of `torch.nn.LayerNorm`. Adds quantization-specific arguments on top of `torch.nn.LayerNorm`.
Args:
output_bit (`int`, *optional*, defaults to `8`):
Bitwidth for the layer output activation.
quant_mode (`bool`, *optional*, def... | 9,208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
self.quant_mode = quant_mode
if force_dequant in ["nonlinear", "layernorm"]:
logger.info("Force dequantize layernorm")
self.quant_mode = False
self.register_buffer("shift", torch.zeros(1))
self.output_bit = output_bit
self.max_bit = 32
self.dim_sqrt = Non... | 9,208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
def overflow_fallback(self, y_int):
"""
This fallback function is called when overflow is detected during training time, and adjusts the `self.shift`
to avoid overflow in the subsequent runs.
"""
self.set_shift(y_int) # adjusts `self.shift`
y_int_shifted = floor_ste.appl... | 9,208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
# Normalization: computes mean and variance(std)
x_int = x / scaling_factor
mean_int = round_ste.apply(x_int.mean(axis=2, keepdim=True))
y_int = x_int - mean_int
y_int_shifted = floor_ste.apply(y_int / 2**self.shift)
y_sq_int = y_int_shifted**2
var_int = torch.sum(y_sq_in... | 9,208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
# To be replaced with integer-sqrt kernel that produces the same output
std_int = floor_ste.apply(torch.sqrt(var_int)) * 2**self.shift
factor = floor_ste.apply(2**31 / std_int)
y_int = floor_ste.apply(y_int * factor / 2)
scaling_factor = self.dim_sqrt / 2**30
# scaling and shift... | 9,208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
class SymmetricQuantFunction(Function):
"""
Class to quantize the given floating-point values using symmetric quantization with given range and bitwidth.
"""
@staticmethod
def forward(ctx, x, k, percentile_mode, scale):
"""
Args:
x (`torch.Tensor`):
Float... | 9,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
n = 2 ** (k - 1) - 1
new_quant_x = linear_quantize(x, scale, zero_point, inplace=False)
new_quant_x = torch.clamp(new_quant_x, -n, n - 1)
ctx.scale = scale
return new_quant_x
@staticmethod
def backward(ctx, grad_output):
scale = ctx.scale
if len(grad_output.shap... | 9,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
class floor_ste(Function):
"""
Straight-through Estimator(STE) for torch.floor()
"""
@staticmethod
def forward(ctx, x):
return torch.floor(x)
@staticmethod
def backward(ctx, grad_output):
return grad_output.clone() | 9,210 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
class round_ste(Function):
"""
Straight-through Estimator(STE) for torch.round()
"""
@staticmethod
def forward(ctx, x):
return torch.round(x)
@staticmethod
def backward(ctx, grad_output):
return grad_output.clone() | 9,211 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
class FixedPointMul(Function):
"""
Function to perform fixed-point arithmetic that can match integer arithmetic on hardware.
Args:
pre_act (`torch.Tensor`):
Input tensor.
pre_act_scaling_factor (`torch.Tensor`):
Scaling factor of the input tensor *pre_act*.
b... | 9,212 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
@staticmethod
def forward(
ctx,
pre_act,
pre_act_scaling_factor,
bit_num,
z_scaling_factor,
identity=None,
identity_scaling_factor=None,
):
if len(pre_act_scaling_factor.shape) == 3:
reshape = lambda x: x # noqa: E731
else:
... | 9,212 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
m, e = batch_frexp(new_scale)
output = z_int.type(torch.double) * m.type(torch.double)
output = torch.round(output / (2.0**e))
if identity is not None:
# needs addition of identity activation
wx_int = torch.round(identity / identity_scaling_factor)
... | 9,212 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
@staticmethod
def backward(ctx, grad_output):
identity_grad = None
if ctx.identity is not None:
identity_grad = grad_output.clone() / ctx.z_scaling_factor
return grad_output.clone() / ctx.z_scaling_factor, None, None, None, None, identity_grad, None | 9,212 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
class IBertConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`IBertModel`]. It is used to instantiate a I-BERT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar co... | 9,213 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/configuration_ibert.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the I-BERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`IBertModel`]
hidden_size (`int`, *optional*, defaults to 768):
Dimens... | 9,213 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/configuration_ibert.py |
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings,... | 9,213 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/configuration_ibert.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Typ... | 9,213 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/configuration_ibert.py |
force_dequant (`str`, *optional*, defaults to `"none"`):
Force dequantize specific nonlinear layer. Dequatized layers are then executed with full precision.
`"none"`, `"gelu"`, `"softmax"`, `"layernorm"` and `"nonlinear"` are supported. As deafult, it is set as
`"none"`, which does n... | 9,213 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/configuration_ibert.py |
model_type = "ibert"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_positio... | 9,213 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/configuration_ibert.py |
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
... | 9,213 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/configuration_ibert.py |
class IBertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | 9,214 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/configuration_ibert.py |
class GlmMLP(Phi3MLP):
pass | 9,215 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modular_glm.py |
class GlmAttention(LlamaAttention):
def __init__(self, config: GlmConfig, layer_idx: Optional[int] = None):
super().__init__(config, layer_idx)
self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False) | 9,216 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modular_glm.py |
class GlmForCausalLM(LlamaForCausalLM):
pass | 9,217 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modular_glm.py |
class GlmForSequenceClassification(LlamaForSequenceClassification):
pass | 9,218 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modular_glm.py |
class GlmForTokenClassification(LlamaForTokenClassification):
pass | 9,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modular_glm.py |
class GlmConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GlmModel`]. It is used to instantiate an Glm
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configu... | 9,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/configuration_glm.py |
intermediate_size (`int`, *optional*, defaults to 13696):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 40):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number o... | 9,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/configuration_glm.py |
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
partial_rotary_factor (`float`, *optional*, defaults to 0.5): The factor of the partial rotary position.
head_dim (`int`, *optional*, defaults to 128):
The attention head ... | 9,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/configuration_glm.py |
rms_norm_eps (`float`, *optional*, defaults to 1.5625e-07):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `... | 9,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/configuration_glm.py |
Whether to use a bias in the query, key, value and output projection layers during self-attention.
```python
>>> from transformers import GlmModel, GlmConfig
>>> # Initializing a Glm glm-4-9b-chat style configuration
>>> configuration = GlmConfig()
>>> # Initializing a model from the glm-4-9b-chat s... | 9,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/configuration_glm.py |
model_type = "glm"
keys_to_ignore_at_inference = ["past_key_values"] | 9,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/configuration_glm.py |
def __init__(
self,
vocab_size=151552,
hidden_size=4096,
intermediate_size=13696,
num_hidden_layers=40,
num_attention_heads=32,
num_key_value_heads=2,
partial_rotary_factor=0.5,
head_dim=128,
hidden_act="silu",
attention_dropout=0.0... | 9,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/configuration_glm.py |
self.partial_rotary_factor = partial_rotary_factor
self.head_dim = head_dim
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope... | 9,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/configuration_glm.py |
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
) | 9,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/configuration_glm.py |
class GlmMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
self.... | 9,221 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
class GlmAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GlmConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim... | 9,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
self.q_proj = nn.Linear(
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
... | 9,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttenti... | 9,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, ca... | 9,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
... | 9,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
class GlmRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
GlmRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
in... | 9,223 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
class GlmRotaryEmbedding(nn.Module):
def __init__(self, config: GlmConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config... | 9,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
def _dynamic_frequency_update(self, position_ids, device):
"""
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with ... | 9,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
# This .to() is needed if the model has been moved to a device after being initialized (because
# the buffer is automatically moved, but not the original copy)
self.original_inv_f... | 9,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
device_type = x.device.type
device... | 9,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
class GlmDecoderLayer(nn.Module):
def __init__(self, config: GlmConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = GlmAttention(config=config, layer_idx=layer_idx)
self.mlp = GlmMLP(config)
self.input_layernorm = GlmRMSNorm(co... | 9,225 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
... | 9,225 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
# Self Attention
hidden_states, self_attn_weights = 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_cac... | 9,225 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
class GlmPreTrainedModel(PreTrainedModel):
config_class = GlmConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["GlmDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_f... | 9,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
class GlmModel(GlmPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GlmDecoderLayer`]
Args:
config: GlmConfig
"""
def __init__(self, config: GlmConfig):
super().__init__(config)
self.padding_idx = config.pad_token_i... | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
@add_start_docstrings_to_model_forward(GLM_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[Cache] = None,
inputs_embeds: ... | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
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 | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient check... | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states,... | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
... | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
... | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask... | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
... | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
dev... | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows whe... | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
... | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
... | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(... | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
) | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
return causal_mask | 9,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | 9,228 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
class GlmForCausalLM(GlmPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = GlmModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.L... | 9,229 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
@add_start_docstrings_to_model_forward(GLM_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[t... | 9,229 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
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
... | 9,229 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, whic... | 9,229 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
>>> # 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."
```"""
... | 9,229 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
... | 9,229 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
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_sta... | 9,229 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
class GlmForSequenceClassification(GlmPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = GlmModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and app... | 9,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
@add_start_docstrings_to_model_forward(GLM_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.Floa... | 9,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
`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 | 9,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
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,
o... | 9,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
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:
# if no pad token foun... | 9,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
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_va... | 9,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
class GlmForTokenClassification(GlmPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = GlmModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.classifier_d... | 9,231 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
@add_start_docstrings_to_model_forward(GLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attent... | 9,231 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
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).
"""
... | 9,231 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
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... | 9,231 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glm/modeling_glm.py |
class TFFlaubertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FlaubertConfig
base_model_prefix = "transformer"
@property
def dummy_inputs(self):
... | 9,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_tf_flaubert.py |
class TFFlaubertModel(TFFlaubertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFFlaubertMainLayer(config, name="transformer") | 9,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_tf_flaubert.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(FLAUBERT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: np.ndarray | tf.Tensor | None = ... | 9,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_tf_flaubert.py |
outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_emb... | 9,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_tf_flaubert.py |
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None) | 9,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_tf_flaubert.py |
class TFFlaubertMultiHeadAttention(keras.layers.Layer):
NEW_ID = itertools.count()
def __init__(self, n_heads, dim, config, **kwargs):
super().__init__(**kwargs)
self.layer_id = next(TFFlaubertMultiHeadAttention.NEW_ID)
self.dim = dim
self.n_heads = n_heads
self.output_a... | 9,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_tf_flaubert.py |
def prune_heads(self, heads):
raise NotImplementedError
def call(self, input, mask, kv, cache, head_mask, output_attentions, training=False):
"""
Self-attention (if kv is None) or attention over source sentence (provided by kv).
"""
# Input is (bs, qlen, dim)
# Mask ... | 9,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_tf_flaubert.py |
def unshape(x):
"""compute context"""
return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))
q = shape(self.q_lin(input)) # (bs, n_heads, qlen, dim_per_head)
if kv is None:
k = shape(self.k_lin(input)) # (bs, n_heads, qlen, di... | 9,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_tf_flaubert.py |
if cache is not None:
if self.layer_id in cache:
if kv is None:
k_, v_ = cache[self.layer_id]
k = tf.concat([k_, k], axis=2) # (bs, n_heads, klen, dim_per_head)
v = tf.concat([v_, v], axis=2) # (bs, n_heads, klen, dim_per_head)
... | 9,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_tf_flaubert.py |
f_dim_per_head = tf.cast(dim_per_head, dtype=q.dtype)
q = tf.multiply(q, tf.math.rsqrt(f_dim_per_head)) # (bs, n_heads, qlen, dim_per_head)
k = tf.cast(k, dtype=q.dtype)
scores = tf.matmul(q, k, transpose_b=True) # (bs, n_heads, qlen, klen)
mask = tf.reshape(mask, mask_reshape) # (bs,... | 9,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_tf_flaubert.py |
if output_attentions:
outputs = outputs + (weights,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "q_lin", None) is not None:
with tf.name_scope(self.q_lin.name):
self.... | 9,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_tf_flaubert.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.