adopt transformers bloom model

Co-authored-by: Thomwolf <thomwolf@gmail.com>
Co-authored-by: Thomas Wolf <thomas@huggingface.co>
Co-authored-by: thomasw21 <24695242+thomasw21@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: sIncerass <sheng.s@berkeley.edu>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Niklas Muennighoff <n.muennighoff@gmail.com>
Co-authored-by: Nicolas Patry <Narsil@users.noreply.github.com>
Co-authored-by: thomasw21 <thomasw21@users.noreply.github.com>
Co-authored-by: sgugger <sgugger@users.noreply.github.com>
Co-authored-by: patrickvonplaten <patrickvonplaten@users.noreply.github.com>
Co-authored-by: LysandreJik <LysandreJik@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: justheuristic <justheuristic@gmail.com>
Co-authored-by: Stas Bekman <stas@stason.org>

src/layer.py

@@ -0,0 +1,273 @@
+"""
+Bloom intermediate layer
+Based on https://github.com/huggingface/transformers/commit/ca2a55e9dfb245527b5e1c954fec6ffbb7aef07b
+"""
+import math
+
+import torch
+from torch import nn
+from torch.nn import LayerNorm
+
+from src.ops import BloomScaledSoftmax, attention_mask_func, pre_process_alibi_for_pad, split_tensor_along_last_dim, \
+    dropout_add, BloomGelu
+
+
+class BloomAttention(nn.Module):
+    def __init__(self, config, layer_number=None):
+        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.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
+        self.masked_softmax_fusion = config.masked_softmax_fusion
+        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.layer_number = max(1, layer_number)
+        self.norm_factor = math.sqrt(self.head_dim) * self.layer_number
+
+        # Scaled Softmax
+        self.scale_mask_softmax = BloomScaledSoftmax(
+            self.masked_softmax_fusion,
+            attention_mask_func,
+            self.attention_softmax_in_fp32,
+            self.layer_number,
+        )
+
+        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)
+
+    def forward(
+        self,
+        hidden_states,
+        residual,
+        layer_past=None,
+        attention_mask=None,
+        alibi=None,
+        head_mask=None,
+        use_cache=False,
+        output_attentions=False,
+    ):
+        # hidden_states: [batch_size, seq_length, hidden_size]
+        # repeat alibi tensor with the batch size
+        alibi = alibi.repeat(hidden_states.shape[0], 1, 1).to(hidden_states.device)
+
+        # apply preprocessing if the input is padded
+        if attention_mask is not None and 0 in attention_mask:
+            alibi = pre_process_alibi_for_pad(alibi, attention_mask, self.num_heads)
+
+        mixed_x_layer = self.query_key_value(hidden_states)
+
+        # [batch_size, seq_length, 3 x hidden_size] --> [batch_size, seq_length, num_heads, 3 x head_dim]
+        new_tensor_shape = mixed_x_layer.size()[:-1] + (self.num_heads, 3 * self.head_dim)
+        mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
+
+        # [batch_size, seq_length, num_heads, 3 x head_dim] --> 3  [batch_size, seq_length, num_heads, head_dim]
+        (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
+
+        if layer_past is not None:
+            past_key, past_value = layer_past
+            key_layer = torch.cat((past_key.type_as(key_layer), key_layer), dim=1)
+            value_layer = torch.cat((past_value.type_as(value_layer), value_layer), dim=1)
+
+        if use_cache is True:
+            present = (key_layer, value_layer)
+        else:
+            present = None
+
+        # [batch_size, head_dim, q_length, k_length]
+        output_size = (query_layer.size(0), query_layer.size(2), query_layer.size(1), key_layer.size(1))
+
+        # [batch_size, q_length, num_heads, head_dim] -> [q_length, batch_size * num_heads, head_dim]
+        query_layer = query_layer.transpose(1, 0).reshape(output_size[2], output_size[0] * output_size[1], -1)
+
+        # [batch_size, k_length, num_heads, head_dim] -> [k_length, batch_size * num_heads, head_dim]
+        key_layer = key_layer.transpose(1, 0).reshape(output_size[3], output_size[0] * output_size[1], -1)
+
+        # slice alibi tensor until the query length
+        sliced_alibi = alibi[: output_size[0] * output_size[1], :, : output_size[3]]
+
+        # Raw attention scores. [batch_size * num_heads, q_length, k_length]
+        beta = 1.0 / self.layer_number
+
+        matmul_result = torch.baddbmm(
+            sliced_alibi,
+            query_layer.transpose(1, 0),
+            key_layer.transpose(1, 0).transpose(1, 2),
+            beta=beta,
+            alpha=(1.0 / self.norm_factor),
+        )
+
+        # change view to [batch_size, num_heads, q_length, k_length]
+        attention_scores = matmul_result.view(*output_size)
+
+        # attention scores and attention mask [b, np, sq, sk]
+        max_positions = max(attention_scores.shape[-1], attention_scores.shape[-2])
+        attention_probs = self.scale_mask_softmax(attention_scores, attention_mask, max_positions).to(
+            value_layer.dtype
+        )
+        attention_probs = self.attention_dropout(attention_probs)
+
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        # context layer shape: [batch_size, num_heads, q_length, head_dim]
+        output_size = (value_layer.size(0), value_layer.size(2), query_layer.size(0), value_layer.size(3))
+
+        # change view [k_length, batch_size x num_heads, head_dim]
+        value_layer = value_layer.transpose(1, 0).reshape(value_layer.size(1), output_size[0] * output_size[1], -1)
+
+        # change view [batch_size x num_heads, q_length, k_length]
+        attention_probs_reshaped = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
+
+        # matmul: [batch_size * num_heads, q_length, head_dim]
+        context_layer = torch.bmm(attention_probs_reshaped, value_layer.transpose(0, 1))
+
+        # change view [batch_size, num_heads, q_length, head_dim]
+        context_layer = context_layer.view(*output_size)
+
+        # [batchs_size, num_heads, q_length, head_dim] --> [q_length, batch_size, num_heads, head_dim]
+        context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
+
+        # [q_length, batch_size, num_heads, head_dim] --> [q_length, batch_size, hidden_size]
+        new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size,)
+
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        # Output. [q_length, batch_size, hidden_size]
+
+        # 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 = context_layer.shape[-1] / self.pretraining_tp
+            output_tensor = torch.zeros_like(context_layer)
+            for i in range(self.pretraining_tp):
+                output_tensor = output_tensor + nn.functional.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)
+
+        output = output_tensor.transpose(1, 0)
+
+        output = dropout_add(output, residual, self.hidden_dropout, self.training)
+
+        outputs = (output, present)
+        if output_attentions:
+            outputs += (attention_probs,)
+
+        return outputs
+
+
+class BloomMLP(nn.Module):
+    def __init__(self, config):
+        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.dense_4h_to_h = nn.Linear(4 * hidden_size, hidden_size)
+        self.hidden_dropout = config.hidden_dropout
+        self.gelu_impl = BloomGelu()
+
+    def forward(self, hidden_states, residual):
+        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 + nn.functional.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)
+
+        output = dropout_add(intermediate_output, residual, self.hidden_dropout, self.training)
+
+        return output
+
+
+class BloomBlock(nn.Module):
+    def __init__(self, config, layer_number=None):
+        super().__init__()
+        hidden_size = config.hidden_size
+
+        self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.n_head = config.n_head
+        self.self_attention = BloomAttention(config, layer_number=layer_number)
+        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,
+        layer_past=None,
+        attention_mask=None,
+        head_mask=None,
+        use_cache=False,
+        output_attentions=False,
+        alibi=None,
+    ):
+        # 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

src/model.py

@@ -0,0 +1,418 @@
+"""PyTorch BLOOM model ."""
+
+from typing import Tuple
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss, LayerNorm
+
+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 transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from transformers.models.bloom.configuration_bloom import BloomConfig
+
+from src.layer import BloomBlock
+from src.ops import build_alibi_tensor
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "bigscience/Bloom"
+_CONFIG_FOR_DOC = "BloomConfig"
+_TOKENIZER_FOR_DOC = "BloomTokenizer"
+
+
+class BloomPreTrainedModel(PreTrainedModel):
+    _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 = BloomConfig
+    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):
+        """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, value=False):
+        if isinstance(module, BloomModel):
+            module.gradient_checkpointing = value
+
+
+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 [`BloomTokenizer`]. 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.
+        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)
+        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.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        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 BloomModel(BloomPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.embed_dim = config.hidden_size
+        self.n_head = config.n_head
+
+        # Embedding + LN Embedding
+        self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
+
+        self.word_embeddings_layernorm = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+        # Transformer blocks
+        self.h = nn.ModuleList([BloomBlock(config, layer_number=i) for i in range(config.num_hidden_layers)])
+
+        # Final Layer Norm
+        self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.word_embeddings
+
+    def set_input_embeddings(self, new_embeddings):
+        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=None,
+        past_key_values=None,
+        attention_mask=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        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:
+            input_shape = input_ids.size()
+            input_ids = input_ids.view(-1, input_shape[-1])
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        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 bsz x n_head x N x N
+        # head_mask has shape n_layer x batch x n_head 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)
+
+        output_shape = input_shape + (hidden_states.size(-1),)
+
+        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
+        current_sequence_length = hidden_states.shape[1]
+        if past_key_values[0] is not None:
+            current_sequence_length += past_key_values[0][0].shape[1]
+        alibi = build_alibi_tensor(current_sequence_length, self.n_head, hidden_states.dtype)
+
+        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,)
+
+            if self.gradient_checkpointing and self.training:
+
+                if use_cache:
+                    logger.warning(
+                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                    )
+                    use_cache = False
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, use_cache, output_attentions, alibi)
+
+                    return custom_forward
+
+                outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    None,
+                    attention_mask,
+                    head_mask[i],
+                )
+            else:
+                outputs = block(
+                    hidden_states,
+                    layer_past=layer_past,
+                    attention_mask=attention_mask,
+                    head_mask=head_mask[i],
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                    alibi=alibi,
+                )
+
+            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,)
+
+        hidden_states = hidden_states.view(output_shape)
+
+        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 BloomForCausalLM(BloomPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = BloomModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # 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):
+        self.lm_head = new_embeddings
+
+    def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
+        # only last token for inputs_ids if past is defined in kwargs
+        if past:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past,
+            "use_cache": kwargs.get("use_cache"),
+            "position_ids": position_ids,
+            "attention_mask": attention_mask,
+        }
+
+    @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=None,
+        past_key_values=None,
+        attention_mask=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        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]`
+        """
+        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,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            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]
+
+        lm_logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            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]], beam_idx: torch.Tensor) -> Tuple[Tuple[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.
+        """
+        return tuple(
+            tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
+            for layer_past in past
+        )

src/ops.py

@@ -0,0 +1,268 @@
+"""
+Utility operations used in the the BLOOM model
+Based on https://github.com/huggingface/transformers/commit/ca2a55e9dfb245527b5e1c954fec6ffbb7aef07b
+"""
+import math
+
+import torch
+import torch.autograd
+from torch import nn
+
+
+def split_tensor_along_last_dim(tensor, num_partitions, contiguous_split_chunks=False):
+    """Split a tensor along its last dimension.
+
+    Args:
+        tensor: ([`torch.tensor`], *required*):
+            input tensor to split
+        num_partitions ([`int`], *required*):
+            number of partitions to split the tensor
+        contiguous_split_chunks ([`bool`], *optional*, default=`False`)::
+            If True, make each chunk contiguous in memory.
+    """
+    # Get the size and dimension.
+    last_dim = tensor.dim() - 1
+    numerator, denominator = tensor.size()[last_dim], num_partitions
+    if not (numerator % denominator == 0):
+        raise ValueError(f"{numerator} is not divisible by {denominator}")
+    last_dim_size = numerator // denominator
+    # Split.
+    tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
+    # Note: torch.split does not create contiguous tensors by default.
+    if contiguous_split_chunks:
+        return tuple(chunk.contiguous() for chunk in tensor_list)
+
+    return tensor_list
+
+
+def attention_mask_func(attention_scores, attention_mask, causal_mask):
+    if attention_mask.dtype == torch.bool:
+        attention_mask_bool = ~attention_mask
+    else:
+        attention_mask_bool = (1 - attention_mask).bool()
+
+    query_length, key_length, n_heads = attention_scores.size(2), attention_scores.size(3), attention_scores.size(1)
+    padded_causal_mask = (
+        attention_mask_bool[:, None, key_length - query_length : key_length, None]
+        + ~causal_mask[:, :, key_length - query_length : key_length, :key_length]
+    ).bool()
+    padded_causal_mask = padded_causal_mask + attention_mask_bool[:, None, None, :key_length].bool()
+    # Make use of floats
+    return (
+        attention_scores.masked_fill_(padded_causal_mask.expand(-1, n_heads, -1, -1), -10000.0),
+        padded_causal_mask,
+    )
+
+
+def build_alibi_tensor(max_seq_len, n_head, dtype=torch.bfloat16):
+    """
+    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
+
+    Args:
+    Returns tensor shaped (n_head, 1, max_seq_len)
+        max_seq_len: (`int`, *required*):
+            max sequence length
+        n_head: (`int`, *required*):
+            number of heads
+        dtype: (`torch.dtype`, *optional*, default=`torch.bfloat16`):
+            dtype of the output tensor
+    """
+
+    def get_slopes(n):
+        def get_slopes_power_of_2(n):
+            start = 2 ** (-(2 ** -(math.log2(n) - 3)))
+            ratio = start
+            return [start * ratio**i for i in range(n)]
+
+        if math.log2(n).is_integer():
+            return get_slopes_power_of_2(n)
+        else:
+            closest_power_of_2 = 2 ** math.floor(math.log2(n))
+            return (
+                get_slopes_power_of_2(closest_power_of_2)
+                + get_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
+            )
+
+    slopes = torch.Tensor(get_slopes(n_head)).unsqueeze(1).unsqueeze(1)
+    arange_tensor = torch.arange(max_seq_len).unsqueeze(0).unsqueeze(0)
+    alibi = slopes * arange_tensor.expand(n_head, -1, -1)
+
+    alibi = alibi.to(dtype)
+
+    return alibi
+
+
+def pre_process_alibi_for_pad(alibi, attention_mask, num_heads):
+    """
+    Args:
+    Pre-process the alibi tensor for padding.
+        alibi: ([`torch.tensor`], *required*):
+            alibi tensor to pre-process
+        attention_mask: ([`torch.tensor`], *required*):
+            attention mask to pre-process"""
+
+    # Sanity check if we are not inferring less tokens than the total sequence length
+    # This usually happens when the inference is done with past_key_values
+    # In this case we re-create the alibi tensor with the correct sequence length
+    if attention_mask.shape[-1] != alibi.shape[-1]:
+        alibi = build_alibi_tensor(attention_mask.shape[-1], num_heads, alibi.dtype).repeat(
+            attention_mask.shape[0], 1, 1
+        )
+    # Get the indexes of the padding tokens
+    index_x0, index_y0 = torch.where(attention_mask == 0.0)
+    index_x1, index_y1 = torch.where(attention_mask == 1.0)
+
+    # Clone the embeddings  - we can detach because the embeddings are not learned
+    # Get a refence tensor
+    slice_reference_alibi = build_alibi_tensor(alibi.shape[-1], num_heads, alibi.dtype)
+
+    # Loop over the batch where the padding is and replace the alibi tensor by the reference tensor
+    # Only where you do not have padding. Replace padding tokens by zeros
+    # This operation can be seen as a shifting operation.
+    for i, index in enumerate(torch.unique(index_x0)):
+        slice_to_modify = torch.zeros_like(slice_reference_alibi)
+        index_shift = index_y1[index_x1 == index]
+        shift_value = len(index_shift)
+        slice_to_modify[:, :, index_shift] = slice_reference_alibi[:, :, :shift_value]
+        alibi[index * num_heads : (index + 1) * num_heads] = slice_to_modify
+    return alibi
+
+
+def dropout_add(x, residual, prob, training):
+    """
+    Dropout add function
+
+    Args:
+        x (`torch.tensor`, *required*):
+            input tensor
+        residual (`torch.tensor`, *rquired*):
+            esidual tensor
+        prob (`float`, *required*):
+            dropout probability
+        training (`bool`, *required*):
+            training mode
+    """
+    out = nn.functional.dropout(x, p=prob, training=training)
+    out = residual + out
+    return out
+
+
+def bloom_gelu_forward(x):
+    """
+    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, x):
+    """
+    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):
+        ctx.save_for_backward(input)
+        return bloom_gelu_forward(input)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        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):
+        if self.training:
+            return GeLUFunction.apply(x)
+        else:
+            return bloom_gelu_forward(x)
+
+
+class BloomScaledSoftmax(nn.Module):
+    """
+    fused operation: scaling + mask + softmax
+
+    Args:
+        input_in_fp16 (`bool`, *required*):
+            flag to indicate if input in fp16 data format.
+        input_in_bf16 (`bool`, *required*):
+            flag to indicate if input in bf16 data format.
+        scaled_masked_softmax_fusion (`bool`, *required*):
+            flag to indicate user want to use softmax fusion
+        mask_func (`function`, *required*):
+            mask function to be applied.
+        softmax_in_fp32 (`bool`, *required*):
+            if true, softmax in performed at fp32 precision.
+        scale (`float`, *required*):
+            scaling factor used in input tensor scaling.
+    """
+
+    def __init__(self, scaled_masked_softmax_fusion, mask_func, softmax_in_fp32, scale):
+        super().__init__()
+        self.scaled_masked_softmax_fusion = scaled_masked_softmax_fusion
+        self.mask_func = mask_func
+        self.softmax_in_fp32 = softmax_in_fp32
+        self.scale = scale
+
+        if not (self.scale is None or softmax_in_fp32):
+            raise ValueError("softmax should be in fp32 when scaled")
+
+    def forward(self, input, mask, max_positions):
+        input_dtype = input.dtype
+        input_in_16bit = input_dtype in [torch.float16, torch.bfloat16]
+        softmax_dtype = torch.float32 if self.softmax_in_fp32 else input_dtype
+
+        if self.scale is not None:
+            input = input * self.scale
+
+        if mask is not None:
+            mask = mask.to(input.device)
+            causal_mask = (
+                torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool))
+                .view(1, 1, max_positions, max_positions)
+                .to(input.device)
+            )
+            mask_output, padded_causal_mask = self.mask_func(input, mask, causal_mask)
+            probs = nn.functional.softmax(mask_output, dim=-1, dtype=softmax_dtype) * (~padded_causal_mask)
+        else:
+            probs = nn.functional.softmax(input, dim=-1, dtype=softmax_dtype)
+
+        if input_in_16bit and self.softmax_in_fp32:
+            probs = probs.to(dtype=input_dtype)
+
+        return probs