# coding=utf-8 # Copyright 2022 HuggingFace Inc. team and BigScience workshop. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch BLOOM model.""" import math import warnings from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss, LayerNorm from torch.nn import functional as F from transformers.file_utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, ) from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions from m4.models import DecoupledEmbedding, DecoupledLinear from m4.models.common import ( expand_inputs_for_generation, prepare_inputs_for_generation, update_model_kwargs_for_generation, ) from m4.models.custom_modules import VLOOMPreTrainedModelBase from m4.models.perceiver.perceiver import PerceiverResampler from m4.models.vbloom.configuration_vbloom import VBloomConfig from m4.training.utils import ( compute_perceiver_tflops_per_batch_per_gpu, compute_tflops_per_batch_per_gpu, freeze_model, ) from m4.utils import logging logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "bigscience/bloom-560m" _CONFIG_FOR_DOC = "VBloomConfig" _TOKENIZER_FOR_DOC = "BloomTokenizerFast" BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST = [ "bigscience/bigscience-small-testing", "bigscience/bloom-560m", "bigscience/bloom-1b1", "bigscience/bloom-1b7", "bigscience/bloom-3b", "bigscience/bloom-7b1", "bigscience/bloom", ] def _make_causal_mask( input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int ) -> torch.BoolTensor: """ Make causal mask used for self-attention. """ batch_size, target_length = input_ids_shape mask = torch.empty((target_length, target_length + past_key_values_length), dtype=torch.bool, device=device) # ONNX doesn't support `torch.Tensor.triu` properly, thus we use this workaround seq_ids = torch.arange(target_length, device=device) mask[:, past_key_values_length:] = seq_ids[:, None] < seq_ids[None, :] if past_key_values_length > 0: mask[:, :past_key_values_length] = False expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length) return expanded_mask def _expand_mask(mask: torch.Tensor, tgt_length: int) -> torch.BoolTensor: """ Expands attention_mask from `[batch_size, src_length]` to `[batch_size, 1, tgt_length, src_length]`. """ batch_size, src_length = mask.shape tgt_length = tgt_length if tgt_length is not None else src_length expanded_mask = ~(mask[:, None, None, :].to(torch.bool)) return expanded_mask.expand(batch_size, 1, tgt_length, src_length) def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor: """ Link to paper: https://arxiv.org/abs/2108.12409 Alibi tensor is not causal as the original paper mentions, it relies on a translation invariance of softmax for quick implementation: with l being a tensor, and a fixed value `softmax(l+a) = softmax(l)`. Based on https://github.com/ofirpress/attention_with_linear_biases/blob/a35aaca144e0eb6b789dfcb46784c4b8e31b7983/fairseq/models/transformer.py#L742 TODO @thomasw21 this doesn't work as nicely due to the masking strategy, and so masking varies slightly. Args: Returns tensor shaped (batch_size * num_heads, 1, max_seq_len) attention_mask (`torch.Tensor`): Token-wise attention mask, this should be of shape (batch_size, max_seq_len). num_heads (`int`, *required*): number of heads dtype (`torch.dtype`, *optional*, default=`torch.bfloat16`): dtype of the output tensor """ batch_size, seq_length = attention_mask.shape closest_power_of_2 = 2 ** math.floor(math.log2(num_heads)) base = torch.tensor( 2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32 ) powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32) slopes = torch.pow(base, powers) if closest_power_of_2 != num_heads: extra_base = torch.tensor( 2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32 ) num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2) extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32) slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0) # Note: alibi will added to the attention bias that will be applied to the query, key product of attention # => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length) # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length) # => the query_length dimension will then be broadcasted correctly # This is more or less identical to T5's relative position bias: # https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527 arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :] alibi = slopes[..., None] * arange_tensor return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype) def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor: """ Dropout add function Args: x (`torch.tensor`, *required*): input tensor residual (`torch.tensor`, *required*): esidual tensor prob (`float`, *required*): dropout probability training (`bool`, *required*): training mode """ out = F.dropout(x, p=prob, training=training) out = residual + out return out def bloom_gelu_forward(x: torch.Tensor) -> torch.Tensor: """ Custom bias GELU function. Adapted from Megatron-DeepSpeed code. Here we use a simple implementation (inference) to make the model jitable. Args: x (`torch.tensor`, *required*): input hidden states """ return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))) def bloom_gelu_back(g: torch.Tensor, x: torch.Tensor) -> torch.Tensor: """ gradient of tanh approximation of gelu gradient of actual gelu is: 0.5 * (1. + torch.erf(x * 0.70710678)) + 0.3989423 * x * torch.exp(-0.5 * x * x) Args: g (`torch.tensor`, *required*): gradient output tensor x (`torch.tensor`, *required*): input tensor """ x = x[0] # x is a tuple of 1 element, needs to unpack it first tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)) # sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243 ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * (1 + tanh_out) return ff * g class GeLUFunction(torch.autograd.Function): @staticmethod def forward(ctx, input: torch.Tensor) -> torch.Tensor: ctx.save_for_backward(input) return bloom_gelu_forward(input) @staticmethod def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor: input = ctx.saved_tensors tmp = bloom_gelu_back(grad_output, input) return tmp class BloomGelu(nn.Module): """ BloomBiasGelu wrapper function that make use of the simple function on inference mode to make the model torchscriptable and use the autograd function in training mode to get the accurate results of the gradients Partly copied from Megatron-DeepSpeed code and adapted for our needs See here why autograd functions are not torchscriptable: https://github.com/pytorch/pytorch/issues/22329 """ def __init__(self): super().__init__() def forward(self, x: torch.Tensor) -> torch.Tensor: if self.training: return GeLUFunction.apply(x) else: return bloom_gelu_forward(x) class BloomAttention(nn.Module): def __init__(self, config: VBloomConfig, is_cross_attention=False): super().__init__() self.pretraining_tp = config.pretraining_tp self.slow_but_exact = config.slow_but_exact self.hidden_size = config.hidden_size self.num_heads = config.n_head self.head_dim = self.hidden_size // self.num_heads self.split_size = self.hidden_size self.hidden_dropout = config.hidden_dropout 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} and `num_heads`:" f" {self.num_heads})." ) # Layer-wise attention scaling self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim) self.beta = 1.0 self.is_cross_attention = is_cross_attention if self.is_cross_attention: self.query = nn.Linear(self.hidden_size, 1 * self.hidden_size, bias=True) kv_input_dim = self.hidden_size if not hasattr(config, "vision_embed_dim") else config.vision_embed_dim self.key_value = nn.Linear(kv_input_dim, 2 * self.hidden_size, bias=True) else: self.query_key_value = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=True) self.dense = nn.Linear(self.hidden_size, self.hidden_size) self.attention_dropout = nn.Dropout(config.attention_dropout) if self.is_cross_attention: # The alpha stuff self.act = nn.Tanh() if config.alpha_initializer == "zeros": if config.alpha_type == "vector": self.alpha_cross_attn = nn.Parameter(torch.zeros(1, 1, self.hidden_size)) elif config.alpha_type == "float": self.alpha_cross_attn = nn.Parameter(torch.zeros(1)) else: raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})") elif config.alpha_initializer == "ones": if config.alpha_type == "vector": self.alpha_cross_attn = nn.Parameter(torch.ones(1, 1, self.hidden_size)) elif config.alpha_type == "float": self.alpha_cross_attn = nn.Parameter(torch.ones(1)) else: raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})") elif config.alpha_initializer in {"normal", "gaussian", "random"}: if config.alpha_type == "vector": self.alpha_cross_attn = nn.Parameter( torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.hidden_size)) ) elif config.alpha_type == "float": self.alpha_cross_attn = nn.Parameter( torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1)) ) else: raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})") else: raise NotImplementedError( f"Alpha initialization scheme {config.alpha_initializer} not yet implemented!" ) def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Split the last dimension into (num_heads, head_dim) without making any copies, results share same memory storage as `fused_qkv` Args: fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim] Returns: query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim] value: [batch_size, seq_length, num_heads, head_dim] """ batch_size, seq_length, n_times_hidden_size = fused_qkv.shape n = int(n_times_hidden_size / self.hidden_size) fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, n, self.head_dim) outputs = () for i in range(n): outputs += (fused_qkv[..., i, :],) return outputs def _merge_heads(self, x: torch.Tensor) -> torch.Tensor: """ Merge heads together over the last dimenstion Args: x: (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim] Returns: torch.tensor: [batch_size, seq_length, num_heads * head_dim] """ # What we want to achieve is: # batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim batch_size_and_num_heads, seq_length, _ = x.shape batch_size = batch_size_and_num_heads // self.num_heads # First view to decompose the batch size # batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim x = x.view(batch_size, self.num_heads, seq_length, self.head_dim) # batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim x = x.permute(0, 2, 1, 3) # batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim) def forward( self, hidden_states: torch.Tensor, residual: torch.Tensor, alibi: torch.Tensor, attention_mask: torch.Tensor, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, head_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, use_cache: bool = False, output_attentions: bool = False, ): if not self.is_cross_attention: fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size] # 3 x [batch_size, seq_length, num_heads, head_dim] (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv) else: if encoder_hidden_states is not None: attention_mask = encoder_attention_mask q = self.query(hidden_states) kv = self.key_value(encoder_hidden_states) query_layer = self._split_heads(q)[0] key_layer, value_layer = self._split_heads(kv) batch_size, q_length, _, _ = query_layer.shape _, kv_length, _, _ = key_layer.shape query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim) key_layer = key_layer.permute(0, 2, 3, 1).reshape(batch_size * self.num_heads, self.head_dim, kv_length) value_layer = value_layer.transpose(1, 2).reshape(batch_size * self.num_heads, kv_length, self.head_dim) if layer_past is not None: past_key, past_value = layer_past # concatenate along seq_length dimension: # - key: [batch_size * self.num_heads, head_dim, kv_length] # - value: [batch_size * self.num_heads, kv_length, head_dim] key_layer = torch.cat((past_key, key_layer), dim=2) value_layer = torch.cat((past_value, value_layer), dim=1) _, _, kv_length = key_layer.shape if use_cache is True: present = (key_layer, value_layer) else: present = None # [batch_size * num_heads, q_length, kv_length] # we use `torch.Tensor.baddbmm` instead of `torch.baddbmm` as the latter isn't supported by TorchScript v1.11 if alibi is None: alibi = torch.empty( batch_size * self.num_heads, q_length, kv_length, dtype=query_layer.dtype, device=query_layer.device ) matmul_result = alibi.baddbmm( batch1=query_layer, batch2=key_layer, beta=0.0 if self.is_cross_attention else self.beta, alpha=self.inv_norm_factor, ) # change view to [batch_size, num_heads, q_length, kv_length] attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length) # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length] input_dtype = attention_scores.dtype # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38` if input_dtype == torch.float16: attention_scores = attention_scores.to(torch.float) attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min) attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(input_dtype) # [batch_size, num_heads, q_length, kv_length] attention_probs = self.attention_dropout(attention_probs) if head_mask is not None: attention_probs = attention_probs * head_mask # change view [batch_size x num_heads, q_length, kv_length] attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, kv_length) # matmul: [batch_size * num_heads, q_length, head_dim] context_layer = torch.bmm(attention_probs_reshaped, value_layer) # change view [batch_size, num_heads, q_length, head_dim] context_layer = self._merge_heads(context_layer) # aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232 if self.pretraining_tp > 1 and self.slow_but_exact: slices = self.hidden_size / self.pretraining_tp output_tensor = torch.zeros_like(context_layer) for i in range(self.pretraining_tp): output_tensor = output_tensor + F.linear( context_layer[:, :, int(i * slices) : int((i + 1) * slices)], self.dense.weight[:, int(i * slices) : int((i + 1) * slices)], ) else: output_tensor = self.dense(context_layer) if not self.is_cross_attention: output_tensor = dropout_add(output_tensor, residual, self.hidden_dropout, self.training) else: output_tensor = dropout_add( self.act(self.alpha_cross_attn) * output_tensor, residual, self.hidden_dropout, self.training ) outputs = (output_tensor, present) if output_attentions: outputs += (attention_probs,) return outputs class BloomMLP(nn.Module): def __init__(self, config: VBloomConfig, is_gated=False): super().__init__() hidden_size = config.hidden_size self.pretraining_tp = config.pretraining_tp self.slow_but_exact = config.slow_but_exact self.dense_h_to_4h = nn.Linear(hidden_size, 4 * hidden_size) self.gelu_impl = BloomGelu() self.dense_4h_to_h = nn.Linear(4 * hidden_size, hidden_size) self.hidden_dropout = config.hidden_dropout # The alpha stuff self.is_gated = is_gated if is_gated: self.act = nn.Tanh() if config.alpha_initializer == "zeros": if config.alpha_type == "vector": self.alpha_dense = nn.Parameter(torch.zeros(1, 1, hidden_size)) elif config.alpha_type == "float": self.alpha_dense = nn.Parameter(torch.zeros(1)) else: raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})") elif config.alpha_initializer == "ones": if config.alpha_type == "vector": self.alpha_dense = nn.Parameter(torch.ones(1, 1, hidden_size)) elif config.alpha_type == "float": self.alpha_dense = nn.Parameter(torch.ones(1)) else: raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})") elif config.alpha_initializer in {"normal", "gaussian", "random"}: if config.alpha_type == "vector": self.alpha_dense = nn.Parameter( torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, hidden_size)) ) elif config.alpha_type == "float": self.alpha_dense = nn.Parameter( torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1)) ) else: raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})") else: raise NotImplementedError( f"Alpha initialization scheme {config.alpha_initializer} not yet implemented!" ) def forward(self, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Tensor: hidden_states = self.gelu_impl(self.dense_h_to_4h(hidden_states)) if self.pretraining_tp > 1 and self.slow_but_exact: intermediate_output = torch.zeros_like(residual) slices = self.dense_4h_to_h.weight.shape[-1] / self.pretraining_tp for i in range(self.pretraining_tp): intermediate_output = intermediate_output + F.linear( hidden_states[:, :, int(i * slices) : int((i + 1) * slices)], self.dense_4h_to_h.weight[:, int(i * slices) : int((i + 1) * slices)], ) else: intermediate_output = self.dense_4h_to_h(hidden_states) if not self.is_gated: output = dropout_add(intermediate_output, residual, self.hidden_dropout, self.training) else: output = dropout_add( self.act(self.alpha_dense) * intermediate_output, residual, self.hidden_dropout, self.training ) return output class BloomBlock(nn.Module): def __init__(self, config: VBloomConfig): super().__init__() hidden_size = config.hidden_size self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.num_heads = config.n_head self.self_attention = BloomAttention(config) self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.mlp = BloomMLP(config) self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm self.hidden_dropout = config.hidden_dropout def forward( self, hidden_states: torch.Tensor, alibi: torch.Tensor, attention_mask: torch.Tensor, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, ): # hidden_states: [batch_size, seq_length, hidden_size] # Layer norm at the beginning of the transformer layer. layernorm_output = self.input_layernorm(hidden_states) # Layer norm post the self attention. if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = hidden_states # Self attention. attn_outputs = self.self_attention( layernorm_output, residual, layer_past=layer_past, attention_mask=attention_mask, alibi=alibi, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, ) attention_output = attn_outputs[0] outputs = attn_outputs[1:] layernorm_output = self.post_attention_layernorm(attention_output) # Get residual if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = attention_output # MLP. output = self.mlp(layernorm_output, residual) if use_cache: outputs = (output,) + outputs else: outputs = (output,) + outputs[1:] return outputs # hidden_states, present, attentions class VBloomGatedCrossAttentionBlock(nn.Module): def __init__(self, config): super().__init__() hidden_size = config.hidden_size self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.num_heads = config.n_head self.cross_attention = BloomAttention(config, is_cross_attention=True) self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.gated_mlp = BloomMLP(config, is_gated=True) self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm self.hidden_dropout = config.hidden_dropout def forward( self, hidden_states: Optional[Tuple[torch.FloatTensor]], layer_past: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, image_hidden_states: Optional[torch.Tensor] = None, image_attention_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]: # hidden_states: [batch_size, seq_length, hidden_size] # Layer norm at the beginning of the transformer layer. layernorm_output = self.input_layernorm(hidden_states) # Layer norm post the self attention. if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = hidden_states # Self attention. attn_outputs = self.cross_attention( layernorm_output, residual, alibi=None, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=image_hidden_states, encoder_attention_mask=image_attention_mask, use_cache=use_cache, output_attentions=output_attentions, ) attention_output = attn_outputs[0] outputs = attn_outputs[1:] layernorm_output = self.post_attention_layernorm(attention_output) # Get residual if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = attention_output # MLP. output = self.gated_mlp(layernorm_output, residual) if use_cache: outputs = (output,) + outputs else: outputs = (output,) + outputs[1:] return outputs # hidden_states, present, attentions class VBloomPreTrainedModel(VLOOMPreTrainedModelBase): _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"] """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = VBloomConfig base_model_prefix = "transformer" supports_gradient_checkpointing = True _no_split_modules = ["BloomBlock"] def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) def _init_weights(self, module: nn.Module): """Initialize the weights.""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False): if isinstance(module, VBloomModel): module.gradient_checkpointing = value @classmethod def override_vision_model_wrapper(cls, model, config, vision_model_name, vision_model_params, torch_dtype): # this can be called via from_pretrained from a class w/ head or w/o head so we extract the beheaded model version beheaded_model = model.transformer if hasattr(model, "transformer") else model cls.override_vision_model(beheaded_model, vision_model_name, vision_model_params, torch_dtype) beheaded_model.freeze_relevant_params(config) BLOOM_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 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 ([`BloomConfig`]): 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. """ BLOOM_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`): `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0][0].shape[2]` (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary. If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as `input_ids`. Indices can be obtained using [`BloomTokenizerFast`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.n_layers`): Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have their past given to this model should not be passed as `input_ids` as they have already been computed. Each element of `past_key_values` is a tuple (past_key, past_value): - past_key: [batch_size * num_heads, head_dim, kv_length] - past_value: [batch_size * num_heads, kv_length, head_dim] attention_mask (`torch.FloatTensor` 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) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. 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. If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see `past_key_values`). 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 [`~file_utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare Bloom Model transformer outputting raw hidden-states without any specific head on top.", BLOOM_START_DOCSTRING, ) class VBloomModel(VBloomPreTrainedModel): def __init__(self, config: VBloomConfig, vision_model=None): super().__init__(config) self.embed_dim = config.hidden_size self.num_heads = config.n_head # Embedding + LN Embedding self.word_embeddings = DecoupledEmbedding( num_embeddings=config.vocab_size, num_additional_embeddings=config.additional_vocab_size, embedding_dim=self.embed_dim, partially_freeze=config.freeze_text_layers, ) self.word_embeddings_layernorm = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) # Transformer blocks self.h = nn.ModuleList([BloomBlock(config) for _ in range(config.num_hidden_layers)]) # Final Layer Norm self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) self.cross_layer_interval = config.cross_layer_interval num_cross_layers = config.num_hidden_layers // self.cross_layer_interval self.gated_cross_attn_layers = nn.ModuleList( [VBloomGatedCrossAttentionBlock(config) for i in range(num_cross_layers)] ) # Perceiver Resampler if config.use_resampler: self.perceiver_resampler = PerceiverResampler( self.config, self.config.vision_embed_dim, config.resampler_depth, config.resampler_n_heads, config.resampler_head_dim, config.resampler_n_latents, ) self.gradient_checkpointing = False # Load an uninitialized model and later in from_pretrained will load the pre-trained model - # this solves the losing of weights in `from_pretrained` on the main model self.vision_model = vision_model # Initialize weights and apply final processing self.post_init() self.freeze_relevant_params(config) def freeze_relevant_params(self, config=None): if config is None: config = self.config if config.freeze_text_layers: self.freeze_text_layers() if config.freeze_vision_layers: freeze_model(self.vision_model) def freeze_text_layers(self): for module in [self.word_embeddings_layernorm, self.h, self.ln_f]: freeze_model(module) def get_input_embeddings(self): return self.word_embeddings def _prepare_attn_mask( self, attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int ) -> torch.BoolTensor: # create causal mask # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length] combined_attention_mask = None device = attention_mask.device _, src_length = input_shape if src_length > 1: combined_attention_mask = _make_causal_mask( input_shape, device=device, past_key_values_length=past_key_values_length ) # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length] expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask ) return combined_attention_mask def set_input_embeddings(self, new_embeddings: torch.Tensor): self.word_embeddings = new_embeddings @add_start_docstrings_to_model_forward(BLOOM_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, image_embeddings: Optional[torch.FloatTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, crossblock_head_mask: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **deprecated_arguments, ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: if deprecated_arguments.pop("position_ids", False) is not False: # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` warnings.warn( ( "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely" " ignore passing `position_ids`." ), FutureWarning, ) if len(deprecated_arguments) > 0: raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") 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 input_ids and 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 input_ids or inputs_embeds") if past_key_values is None: past_key_values = tuple([None] * len(self.h)) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape batch_size x num_heads x N x N # head_mask has shape n_layer x batch x num_heads x N x N head_mask = self.get_head_mask(head_mask, self.config.n_layer) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) hidden_states = self.word_embeddings_layernorm(inputs_embeds) presents = () if use_cache else None all_self_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None # Compute alibi tensor: check build_alibi_tensor documentation seq_length_with_past = seq_length past_key_values_length = 0 if past_key_values[0] 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 attention_mask is None: attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device) else: attention_mask = attention_mask.to(hidden_states.device) alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype) causal_mask = self._prepare_attn_mask( attention_mask, input_shape=(batch_size, seq_length), past_key_values_length=past_key_values_length, ) if pixel_values is not None and image_embeddings is not None: raise ValueError("You cannot specify both pixel_values and image_embeddings at the same time") elif pixel_values is not None: pixel_values = pixel_values.to(dtype=self.dtype, device=input_ids.device) # fp16 compatibility batch_size, num_images = pixel_values.size(0), pixel_values.size(1) pixel_values = pixel_values.contiguous().view(batch_size * num_images, *pixel_values.shape[2:]) # Get sequence from the vision encoder image_hidden_states = self.vision_model(pixel_values=pixel_values).last_hidden_state elif image_embeddings is not None: batch_size, num_images, image_seq_len, image_hidden_size = image_embeddings.size() image_hidden_states = image_embeddings.to(dtype=self.dtype, device=input_ids.device) image_hidden_states = image_hidden_states.view(batch_size * num_images, image_seq_len, image_hidden_size) if self.config.use_resampler: image_hidden_states = self.perceiver_resampler(image_hidden_states) image_seq_len, image_hidden_size = image_hidden_states.size(1), image_hidden_states.size(2) image_hidden_states = image_hidden_states.view(batch_size, num_images * image_seq_len, image_hidden_size) # Make image_attention_mask compatible with hidden states text_seq_len = image_attention_mask.size(1) image_attention_mask = image_attention_mask.unsqueeze( -1 ) # TODO: something i don't understand here. why are the few last tokens not attending when there is just a single image? image_attention_mask = image_attention_mask.repeat(1, 1, 1, image_seq_len) image_attention_mask = image_attention_mask.view(batch_size, text_seq_len, num_images * image_seq_len) if image_hidden_states is not None: image_batch_size, image_sequence_length, _ = image_hidden_states.size() image_hidden_shape = (image_batch_size, image_sequence_length) if image_attention_mask is None: image_attention_mask = torch.ones(image_hidden_shape, device=hidden_states.device) # image_attention_mask = self.invert_attention_mask(image_attention_mask) image_attention_mask = image_attention_mask.to(torch.bool) image_attention_mask = image_attention_mask[:, None, :, :] else: image_attention_mask = None for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) def vblock( main_block, hidden_states, alibi, layer_past, attention_mask, layer_head_mask, use_cache, output_attentions, image_hidden_states, image_attention_mask, layer_idx, cross_layer_interval, gated_cross_attn_layers, ): if layer_idx % cross_layer_interval == 0: xblock = gated_cross_attn_layers[layer_idx // cross_layer_interval] outputs = xblock( hidden_states, attention_mask=attention_mask, image_hidden_states=image_hidden_states, image_attention_mask=image_attention_mask, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = outputs[0] outputs = main_block( hidden_states, alibi=alibi, layer_past=layer_past, attention_mask=attention_mask, head_mask=layer_head_mask, use_cache=use_cache, output_attentions=output_attentions, ) return outputs if self.gradient_checkpointing and self.training: layer_past = None if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False outputs = torch.utils.checkpoint.checkpoint( vblock, block, hidden_states, alibi, layer_past, causal_mask, head_mask[i], use_cache, output_attentions, image_hidden_states, image_attention_mask, i, self.cross_layer_interval, self.gated_cross_attn_layers, ) else: outputs = vblock( block, hidden_states, alibi=alibi, layer_past=layer_past, attention_mask=causal_mask, layer_head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, image_hidden_states=image_hidden_states, image_attention_mask=image_attention_mask, layer_idx=i, cross_layer_interval=self.cross_layer_interval, gated_cross_attn_layers=self.gated_cross_attn_layers, ) hidden_states = outputs[0] if use_cache is True: presents = presents + (outputs[1],) if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) # Add last hidden state hidden_states = self.ln_f(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions, ) @add_start_docstrings( """ The Bloom Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, BLOOM_START_DOCSTRING, ) class VBloomForCausalLM(VBloomPreTrainedModel): _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"] def __init__(self, config: VBloomConfig, vision_model=None): super().__init__(config) self.transformer = VBloomModel(config, vision_model=vision_model) self.lm_head = DecoupledLinear( in_features=config.hidden_size, out_features=config.vocab_size, out_additional_features=config.additional_vocab_size, bias=False, partially_freeze=config.freeze_lm_head, ) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings: torch.Tensor): self.lm_head = new_embeddings def tie_weights(self): """ Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of DecoupledLinear and DecoupledEmbedding. """ output_embeddings = self.get_output_embeddings() input_embeddings = self.get_input_embeddings() if getattr(self.config, "tie_word_embeddings", True): output_embeddings.weight = input_embeddings.weight if input_embeddings.num_additional_embeddings > 0: assert output_embeddings.out_additional_features == input_embeddings.num_additional_embeddings output_embeddings.additional_fc.weight = input_embeddings.additional_embedding.weight if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"): output_embeddings.out_features = input_embeddings.num_embeddings if hasattr(output_embeddings, "out_additional_features") and hasattr( input_embeddings, "num_additional_embeddings" ): output_embeddings.out_additional_features = input_embeddings.num_additional_embeddings def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs): inputs = prepare_inputs_for_generation(input_ids, past=past, **kwargs) unwanted_kwargs = ["position_ids", "token_type_ids"] for kwarg in unwanted_kwargs: inputs.pop(kwarg, None) return inputs @staticmethod def _expand_inputs_for_generation( *args, **model_kwargs, ): return expand_inputs_for_generation(*args, **model_kwargs) @staticmethod def _update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False): return update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=is_encoder_decoder) @add_start_docstrings_to_model_forward(BLOOM_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, pixel_values: Optional[torch.FloatTensor] = None, image_embeddings: Optional[torch.FloatTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, crossblock_head_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **deprecated_arguments, ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` """ if deprecated_arguments.pop("position_ids", False) is not False: # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` warnings.warn( ( "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely" " ignore passing `position_ids`." ), FutureWarning, ) if len(deprecated_arguments) > 0: raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, pixel_values=pixel_values, image_embeddings=image_embeddings, image_attention_mask=image_attention_mask, crossblock_head_mask=crossblock_head_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] lm_logits = self.lm_head(hidden_states) loss = None if labels is not None: # Shift so that tokens < n predict n if attention_mask is not None: shift_attention_mask = attention_mask[..., 1:] shift_logits = lm_logits[..., :-1, :][shift_attention_mask != 0].contiguous() shift_labels = labels[..., 1:][shift_attention_mask != 0].contiguous() else: shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) if not return_dict: output = (lm_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return CausalLMOutputWithCrossAttentions( loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @staticmethod def _reorder_cache( past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: """ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct beam_idx at every generation step. Output shares the same memory storage as `past`. """ batch_size_times_num_heads, head_dim, seq_length = past[0][0].shape batch_size = len(beam_idx) num_heads = batch_size_times_num_heads // batch_size # Get a copy of `beam_idx` on all the devices where we need those indices. device_to_beam_idx = { past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past } # key: layer_past[0] [batch_size * num_heads, head_dim, seq_length] # value: layer_past[1] [batch_size * num_heads, seq_length, head_dim] return tuple( ( layer_past[0] .view(batch_size, num_heads, head_dim, seq_length) .index_select(0, device_to_beam_idx[layer_past[0].device]) .view(batch_size_times_num_heads, head_dim, seq_length), layer_past[1] .view(batch_size, num_heads, seq_length, head_dim) .index_select(0, device_to_beam_idx[layer_past[0].device]) .view(batch_size_times_num_heads, seq_length, head_dim), ) for layer_past in past ) def get_model_tflops_per_batch_per_gpu(self, hparams, data_param, tokenizer, max_num_images): config_vl_model = self.config language_embed_size = config_vl_model.hidden_size vision_config = self.transformer.vision_model.config num_language_layers = config_vl_model.n_layer ffn_inner_size = 4 * config_vl_model.hidden_size # Get vision model blocks infos vision_patch_size = vision_config.patch_size vision_hidden_size = vision_config.hidden_size num_vision_layers = vision_config.num_hidden_layers # The +1 is for the CLS token single_image_seq_len = (vision_config.image_size // vision_patch_size) ** 2 + 1 vision_exp_factor = vision_config.intermediate_size // vision_hidden_size # Get language and cross-att blocks infos num_cross_attn_layers = num_language_layers // config_vl_model.cross_layer_interval language_seq_len = data_param.max_seq_len language_exp_factor = (ffn_inner_size // language_embed_size) if ffn_inner_size is not None else 4 cross_att_exp_factor = (ffn_inner_size // language_embed_size) if ffn_inner_size is not None else 4 k_v_cross_attn_seq_len = ( (self.config.resampler_n_latents * max_num_images) if self.config.use_resampler else (single_image_seq_len * max_num_images) ) language_tflops_per_batch_per_gpu = compute_tflops_per_batch_per_gpu( num_layers=num_language_layers, batch_size=hparams.batch_size_per_gpu, q_seq_len=language_seq_len, k_seq_len=language_seq_len, hidden_size=language_embed_size, kv_in_dim=language_embed_size, ff_exp_factor=language_exp_factor, grad_acc_size=hparams.grad_acc_size, swiglu=False, vocab_size=tokenizer.vocab_size, count_backward=True, # Always True regardless of freezing, because gradients are computed for cross-attentions use_grad_checkpointing=hparams.gradient_checkpointing, ) cross_attention_tflops_per_batch_per_gpu = compute_tflops_per_batch_per_gpu( num_layers=num_cross_attn_layers, batch_size=hparams.batch_size_per_gpu, q_seq_len=language_seq_len, k_seq_len=k_v_cross_attn_seq_len, hidden_size=language_embed_size, kv_in_dim=vision_hidden_size, ff_exp_factor=cross_att_exp_factor, grad_acc_size=hparams.grad_acc_size, swiglu=False, vocab_size=None, count_backward=True, use_grad_checkpointing=hparams.gradient_checkpointing, ) vision_tflops_per_batch_per_gpu = compute_tflops_per_batch_per_gpu( num_layers=num_vision_layers, batch_size=hparams.batch_size_per_gpu * max_num_images, q_seq_len=single_image_seq_len, k_seq_len=single_image_seq_len, hidden_size=vision_hidden_size, kv_in_dim=vision_hidden_size, ff_exp_factor=vision_exp_factor, grad_acc_size=hparams.grad_acc_size, swiglu=False, vocab_size=None, count_backward=not hparams.model_params["freeze_vision_layers"], use_grad_checkpointing=hparams.gradient_checkpointing, ) if self.config.use_resampler: perceiver_tflops_per_batch_per_gpu = compute_perceiver_tflops_per_batch_per_gpu( num_layers=self.config.resampler_depth, batch_size=hparams.batch_size_per_gpu * max_num_images, q_seq_len=self.config.resampler_n_latents, vision_embed_seq_len=single_image_seq_len, q_k_v_input_dim=vision_hidden_size, attention_hidden_size=self.config.resampler_n_heads * self.config.resampler_head_dim, ff_exp_factor=cross_att_exp_factor, count_backward=True, use_grad_checkpointing=hparams.gradient_checkpointing, ) flop_count = ( language_tflops_per_batch_per_gpu + cross_attention_tflops_per_batch_per_gpu + vision_tflops_per_batch_per_gpu + perceiver_tflops_per_batch_per_gpu ) else: flop_count = ( language_tflops_per_batch_per_gpu + cross_attention_tflops_per_batch_per_gpu + vision_tflops_per_batch_per_gpu ) return flop_count