megatron_patch/model/mistral/language_model.py [31:360]:
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def parallel_lm_logits(input_, word_embeddings_weight, parallel_output,
                       bias=None):
    """LM logits using word embedding weights."""
    args = get_args()
    # Parallel logits.
    if args.async_tensor_model_parallel_allreduce or\
            args.sequence_parallel:
        input_parallel = input_
        model_parallel = mpu.get_tensor_model_parallel_world_size() > 1
        async_grad_allreduce = args.async_tensor_model_parallel_allreduce and \
            model_parallel and not args.sequence_parallel
    else:
        input_parallel = tensor_parallel.copy_to_tensor_model_parallel_region(input_)
        async_grad_allreduce = False

    # Matrix multiply.
    logits_parallel = tensor_parallel.linear_with_grad_accumulation_and_async_allreduce(
        input=input_parallel,
        weight=word_embeddings_weight,
        bias=bias,
        gradient_accumulation_fusion=args.gradient_accumulation_fusion,
        async_grad_allreduce=async_grad_allreduce,
        sequence_parallel=args.sequence_parallel)
    # Gather if needed.

    if parallel_output:
        return logits_parallel

    return tensor_parallel.gather_from_tensor_model_parallel_region(logits_parallel)


def get_language_model(config, num_tokentypes, add_pooler,
                       encoder_attn_mask_type,
                       add_encoder=True,
                       add_decoder=False,
                       decoder_attn_mask_type=AttnMaskType.causal,
                       pre_process=True, post_process=True):
    """Build language model and return along with the key to save."""
    args = get_args()
    if config.init_method is None:
        config.init_method = init_method_normal(config.init_method_std)

    if config.output_layer_init_method is None:
        config.output_layer_init_method = scaled_init_method_normal(config.init_method_std,
                                                                    config.num_layers)

    # Language model.
    language_model = TransformerLanguageModel(
        config,
        encoder_attn_mask_type,
        num_tokentypes=num_tokentypes,
        add_encoder=add_encoder,
        add_decoder=add_decoder,
        decoder_attn_mask_type=decoder_attn_mask_type,
        add_pooler=add_pooler,
        pre_process=pre_process,
        post_process=post_process
    )
    # key used for checkpoints.
    language_model_key = 'language_model'

    return language_model, language_model_key


class Pooler(MegatronModule):
    """Pooler layer.

    Pool hidden states of a specific token (for example start of the
    sequence) and add a linear transformation followed by a tanh.

    Arguments:
        hidden_size: hidden size
        init_method: weight initialization method for the linear layer.
            bias is set to zero.
    """

    def __init__(self, hidden_size, init_method):
        super(Pooler, self).__init__()
        args = get_args()
        self.dense = get_linear_layer(hidden_size, hidden_size, init_method)
        self.sequence_parallel = args.sequence_parallel


    def forward(self, hidden_states, sequence_index=0):
        # hidden_states: [s, b, h]
        # sequence_index: index of the token to pool.

        # gather data along sequence dimensions
        # same pooler is run on all tensor parallel nodes
        if self.sequence_parallel:
            hidden_states = tensor_parallel.gather_from_sequence_parallel_region(
                hidden_states,
                tensor_parallel_output_grad=False)

        pooled = hidden_states[sequence_index, :, :]
        pooled = self.dense(pooled)
        pooled = torch.tanh(pooled)
        return pooled


class Embedding(MegatronModule):
    """Language model embeddings.

    Arguments:
        hidden_size: hidden size
        vocab_size: vocabulary size
        max_sequence_length: maximum size of sequence. This
                             is used for positional embedding
        embedding_dropout_prob: dropout probability for embeddings
        init_method: weight initialization method
        num_tokentypes: size of the token-type embeddings. 0 value
                        will ignore this embedding
    """

    def __init__(self,
                 hidden_size,
                 vocab_size,
                 max_sequence_length,
                 embedding_dropout_prob,
                 config,
                 num_tokentypes=0):
        super(Embedding, self).__init__()

        self.hidden_size = hidden_size
        self.init_method = config.init_method
        self.num_tokentypes = num_tokentypes

        args = get_args()

        # Word embeddings (parallel).
        self.params_dtype = args.params_dtype
        self.word_embeddings = tensor_parallel.VocabParallelEmbedding(
            vocab_size, self.hidden_size, config=config, init_method=config.init_method)
        self._word_embeddings_key = 'word_embeddings'

        # Position embedding (serial).
        self.add_position_embedding = args.position_embedding_type == 'learned_absolute'
        if self.add_position_embedding:
            self.position_embeddings = torch.nn.Embedding(
                max_sequence_length, self.hidden_size)
            self._position_embeddings_key = 'position_embeddings'
            # Initialize the position embeddings.
            if args.perform_initialization:
                self.init_method(self.position_embeddings.weight)

        # Token type embedding.
        # Add this as an optional field that can be added through
        # method call so we can load a pretrain model without
        # token types and add them as needed.
        self._tokentype_embeddings_key = 'tokentype_embeddings'
        if self.num_tokentypes > 0:
            self.tokentype_embeddings = torch.nn.Embedding(self.num_tokentypes,
                                                           self.hidden_size)
            # Initialize the token-type embeddings.
            if args.perform_initialization:
                self.init_method(self.tokentype_embeddings.weight)
        else:
            self.tokentype_embeddings = None

        self.fp32_residual_connection = args.fp32_residual_connection
        self.sequence_parallel = args.sequence_parallel
        # Embeddings dropout
        self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob)

    def zero_parameters(self):
        """Zero out all parameters in embedding."""
        self.word_embeddings.weight.data.fill_(0)
        self.word_embeddings.weight.shared = True
        if self.add_position_embedding:
            self.position_embeddings.weight.data.fill_(0)
            self.position_embeddings.weight.shared = True
        if self.num_tokentypes > 0:
            self.tokentype_embeddings.weight.data.fill_(0)
            self.tokentype_embeddings.weight.shared = True

    def add_tokentype_embeddings(self, num_tokentypes):
        """Add token-type embedding. This function is provided so we can add
        token-type embeddings in case the pretrained model does not have it.
        This allows us to load the model normally and then add this embedding.
        """
        if self.tokentype_embeddings is not None:
            raise Exception('tokentype embeddings is already initialized')
        if torch.distributed.get_rank() == 0:
            print('adding embedding for {} tokentypes'.format(num_tokentypes),
                  flush=True)
        self.num_tokentypes = num_tokentypes
        self.tokentype_embeddings = torch.nn.Embedding(num_tokentypes,
                                                       self.hidden_size)
        # Initialize the token-type embeddings.
        args = get_args()
        self.init_method(self.tokentype_embeddings.weight)

    def forward(self, input_ids, position_ids, tokentype_ids=None):
        # Embeddings.
        words_embeddings = self.word_embeddings(input_ids)
        if self.add_position_embedding:
            position_embeddings = self.position_embeddings(position_ids)
            embeddings = words_embeddings + position_embeddings
        else:
            embeddings = words_embeddings

        if tokentype_ids is not None:
            assert self.tokentype_embeddings is not None
            embeddings = embeddings + self.tokentype_embeddings(tokentype_ids)
        else:
            assert self.tokentype_embeddings is None

        # Data format change to avoid explicit tranposes : [b s h] --> [s b h].
        embeddings = embeddings.transpose(0, 1).contiguous()

        # If the input flag for fp32 residual connection is set, convert for float.
        if self.fp32_residual_connection:
            embeddings = embeddings.float()

        # Dropout.
        if self.sequence_parallel:
            embeddings = tensor_parallel.scatter_to_sequence_parallel_region(embeddings)
            with tensor_parallel.get_cuda_rng_tracker().fork():
                embeddings = self.embedding_dropout(embeddings)
        else:
            embeddings = self.embedding_dropout(embeddings)

        return embeddings

    def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False):
        """For easy load."""

        state_dict_ = {}
        state_dict_[self._word_embeddings_key] \
            = self.word_embeddings.state_dict(prefix=prefix,
                                              keep_vars=keep_vars)
        if self.add_position_embedding:
            state_dict_[self._position_embeddings_key] \
                = self.position_embeddings.state_dict(prefix=prefix,
                                                  keep_vars=keep_vars)
        if self.num_tokentypes > 0:
            state_dict_[self._tokentype_embeddings_key] \
                = self.tokentype_embeddings.state_dict(prefix=prefix,
                                                       keep_vars=keep_vars)

        return state_dict_

    def load_state_dict(self, state_dict, strict=True):
        """Customized load."""

        # Word embedding.
        if self._word_embeddings_key in state_dict:
            state_dict_ = state_dict[self._word_embeddings_key]
        else:
            # for backward compatibility.
            state_dict_ = {}
            for key in state_dict.keys():
                if 'word_embeddings' in key:
                    state_dict_[key.split('word_embeddings.')[1]] \
                        = state_dict[key]
        self.word_embeddings.load_state_dict(state_dict_, strict=strict)

        # Position embedding.
        if self.add_position_embedding:
            if self._position_embeddings_key in state_dict:
                state_dict_ = state_dict[self._position_embeddings_key]
            else:
                # for backward compatibility.
                state_dict_ = {}
                for key in state_dict.keys():
                    if 'position_embeddings' in key:
                        state_dict_[key.split('position_embeddings.')[1]] \
                            = state_dict[key]
            self.position_embeddings.load_state_dict(state_dict_, strict=strict)

        # Tokentype embedding.
        if self.num_tokentypes > 0:
            state_dict_ = {}
            if self._tokentype_embeddings_key in state_dict:
                state_dict_ = state_dict[self._tokentype_embeddings_key]
            else:
                # for backward compatibility.
                for key in state_dict.keys():
                    if 'tokentype_embeddings' in key:
                        state_dict_[key.split('tokentype_embeddings.')[1]] \
                            = state_dict[key]
            if len(state_dict_.keys()) > 0:
                self.tokentype_embeddings.load_state_dict(state_dict_,
                                                          strict=strict)
            else:
                print('***WARNING*** expected tokentype embeddings in the '
                      'checkpoint but could not find it', flush=True)


class TransformerLanguageModel(MegatronModule):
    """Transformer language model.

    Arguments:
        transformer_hparams: transformer hyperparameters
        vocab_size: vocabulary size
        max_sequence_length: maximum size of sequence. This
                             is used for positional embedding
        embedding_dropout_prob: dropout probability for embeddings
        num_tokentypes: size of the token-type embeddings. 0 value
                        will ignore this embedding
    """

    def __init__(self,
                 config,
                 encoder_attn_mask_type,
                 num_tokentypes=0,
                 add_encoder=True,
                 add_decoder=False,
                 decoder_attn_mask_type=AttnMaskType.causal,
                 add_pooler=False,
                 pre_process=True,
                 post_process=True):
        args = get_args()
        # TODO: passing share_embeddings_and_output_weights=False will not work correctly for T5 and embeddings will not be synced. Fix later for T5.
        if args.untie_embeddings_and_output_weights: assert not add_decoder
        super(TransformerLanguageModel, self).__init__(share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights)

        self.pre_process = pre_process
        self.post_process = post_process
        self.hidden_size = config.hidden_size
        self.num_tokentypes = num_tokentypes
        self.init_method = config.init_method
        self.add_encoder = add_encoder
        self.encoder_attn_mask_type = encoder_attn_mask_type
        self.add_decoder = add_decoder
        self.decoder_attn_mask_type = decoder_attn_mask_type
        self.add_pooler = add_pooler
        self.encoder_hidden_state = None
        self.add_retriever = args.retro_add_retriever
        self.untie_embeddings_and_output_weights = args.untie_embeddings_and_output_weights
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megatron_patch/model/qwen1_5_megablocks/language_model.py [29:358]:
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def parallel_lm_logits(input_, word_embeddings_weight, parallel_output,
                       bias=None):
    """LM logits using word embedding weights."""
    args = get_args()
    # Parallel logits.
    if args.async_tensor_model_parallel_allreduce or\
            args.sequence_parallel:
        input_parallel = input_
        model_parallel = mpu.get_tensor_model_parallel_world_size() > 1
        async_grad_allreduce = args.async_tensor_model_parallel_allreduce and \
            model_parallel and not args.sequence_parallel
    else:
        input_parallel = tensor_parallel.copy_to_tensor_model_parallel_region(input_)
        async_grad_allreduce = False

    # Matrix multiply.
    logits_parallel = tensor_parallel.linear_with_grad_accumulation_and_async_allreduce(
        input=input_parallel,
        weight=word_embeddings_weight,
        bias=bias,
        gradient_accumulation_fusion=args.gradient_accumulation_fusion,
        async_grad_allreduce=async_grad_allreduce,
        sequence_parallel=args.sequence_parallel)
    # Gather if needed.

    if parallel_output:
        return logits_parallel

    return tensor_parallel.gather_from_tensor_model_parallel_region(logits_parallel)


def get_language_model(config, num_tokentypes, add_pooler,
                       encoder_attn_mask_type,
                       add_encoder=True,
                       add_decoder=False,
                       decoder_attn_mask_type=AttnMaskType.causal,
                       pre_process=True, post_process=True):
    """Build language model and return along with the key to save."""
    args = get_args()
    if config.init_method is None:
        config.init_method = init_method_normal(config.init_method_std)

    if config.output_layer_init_method is None:
        config.output_layer_init_method = scaled_init_method_normal(config.init_method_std,
                                                                    config.num_layers)

    # Language model.
    language_model = TransformerLanguageModel(
        config,
        encoder_attn_mask_type,
        num_tokentypes=num_tokentypes,
        add_encoder=add_encoder,
        add_decoder=add_decoder,
        decoder_attn_mask_type=decoder_attn_mask_type,
        add_pooler=add_pooler,
        pre_process=pre_process,
        post_process=post_process
    )
    # key used for checkpoints.
    language_model_key = 'language_model'

    return language_model, language_model_key


class Pooler(MegatronModule):
    """Pooler layer.

    Pool hidden states of a specific token (for example start of the
    sequence) and add a linear transformation followed by a tanh.

    Arguments:
        hidden_size: hidden size
        init_method: weight initialization method for the linear layer.
            bias is set to zero.
    """

    def __init__(self, hidden_size, init_method):
        super(Pooler, self).__init__()
        args = get_args()
        self.dense = get_linear_layer(hidden_size, hidden_size, init_method)
        self.sequence_parallel = args.sequence_parallel


    def forward(self, hidden_states, sequence_index=0):
        # hidden_states: [s, b, h]
        # sequence_index: index of the token to pool.

        # gather data along sequence dimensions
        # same pooler is run on all tensor parallel nodes
        if self.sequence_parallel:
            hidden_states = tensor_parallel.gather_from_sequence_parallel_region(
                hidden_states,
                tensor_parallel_output_grad=False)

        pooled = hidden_states[sequence_index, :, :]
        pooled = self.dense(pooled)
        pooled = torch.tanh(pooled)
        return pooled


class Embedding(MegatronModule):
    """Language model embeddings.

    Arguments:
        hidden_size: hidden size
        vocab_size: vocabulary size
        max_sequence_length: maximum size of sequence. This
                             is used for positional embedding
        embedding_dropout_prob: dropout probability for embeddings
        init_method: weight initialization method
        num_tokentypes: size of the token-type embeddings. 0 value
                        will ignore this embedding
    """

    def __init__(self,
                 hidden_size,
                 vocab_size,
                 max_sequence_length,
                 embedding_dropout_prob,
                 config,
                 num_tokentypes=0):
        super(Embedding, self).__init__()

        self.hidden_size = hidden_size
        self.init_method = config.init_method
        self.num_tokentypes = num_tokentypes

        args = get_args()

        # Word embeddings (parallel).
        self.params_dtype = args.params_dtype
        self.word_embeddings = tensor_parallel.VocabParallelEmbedding(
            vocab_size, self.hidden_size, config=config, init_method=config.init_method)
        self._word_embeddings_key = 'word_embeddings'

        # Position embedding (serial).
        self.add_position_embedding = args.position_embedding_type == 'learned_absolute'
        if self.add_position_embedding:
            self.position_embeddings = torch.nn.Embedding(
                max_sequence_length, self.hidden_size)
            self._position_embeddings_key = 'position_embeddings'
            # Initialize the position embeddings.
            if args.perform_initialization:
                self.init_method(self.position_embeddings.weight)

        # Token type embedding.
        # Add this as an optional field that can be added through
        # method call so we can load a pretrain model without
        # token types and add them as needed.
        self._tokentype_embeddings_key = 'tokentype_embeddings'
        if self.num_tokentypes > 0:
            self.tokentype_embeddings = torch.nn.Embedding(self.num_tokentypes,
                                                           self.hidden_size)
            # Initialize the token-type embeddings.
            if args.perform_initialization:
                self.init_method(self.tokentype_embeddings.weight)
        else:
            self.tokentype_embeddings = None

        self.fp32_residual_connection = args.fp32_residual_connection
        self.sequence_parallel = args.sequence_parallel
        # Embeddings dropout
        self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob)

    def zero_parameters(self):
        """Zero out all parameters in embedding."""
        self.word_embeddings.weight.data.fill_(0)
        self.word_embeddings.weight.shared = True
        if self.add_position_embedding:
            self.position_embeddings.weight.data.fill_(0)
            self.position_embeddings.weight.shared = True
        if self.num_tokentypes > 0:
            self.tokentype_embeddings.weight.data.fill_(0)
            self.tokentype_embeddings.weight.shared = True

    def add_tokentype_embeddings(self, num_tokentypes):
        """Add token-type embedding. This function is provided so we can add
        token-type embeddings in case the pretrained model does not have it.
        This allows us to load the model normally and then add this embedding.
        """
        if self.tokentype_embeddings is not None:
            raise Exception('tokentype embeddings is already initialized')
        if torch.distributed.get_rank() == 0:
            print('adding embedding for {} tokentypes'.format(num_tokentypes),
                  flush=True)
        self.num_tokentypes = num_tokentypes
        self.tokentype_embeddings = torch.nn.Embedding(num_tokentypes,
                                                       self.hidden_size)
        # Initialize the token-type embeddings.
        args = get_args()
        self.init_method(self.tokentype_embeddings.weight)

    def forward(self, input_ids, position_ids, tokentype_ids=None):
        # Embeddings.
        words_embeddings = self.word_embeddings(input_ids)
        if self.add_position_embedding:
            position_embeddings = self.position_embeddings(position_ids)
            embeddings = words_embeddings + position_embeddings
        else:
            embeddings = words_embeddings

        if tokentype_ids is not None:
            assert self.tokentype_embeddings is not None
            embeddings = embeddings + self.tokentype_embeddings(tokentype_ids)
        else:
            assert self.tokentype_embeddings is None

        # Data format change to avoid explicit tranposes : [b s h] --> [s b h].
        embeddings = embeddings.transpose(0, 1).contiguous()

        # If the input flag for fp32 residual connection is set, convert for float.
        if self.fp32_residual_connection:
            embeddings = embeddings.float()

        # Dropout.
        if self.sequence_parallel:
            embeddings = tensor_parallel.scatter_to_sequence_parallel_region(embeddings)
            with tensor_parallel.get_cuda_rng_tracker().fork():
                embeddings = self.embedding_dropout(embeddings)
        else:
            embeddings = self.embedding_dropout(embeddings)

        return embeddings

    def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False):
        """For easy load."""

        state_dict_ = {}
        state_dict_[self._word_embeddings_key] \
            = self.word_embeddings.state_dict(prefix=prefix,
                                              keep_vars=keep_vars)
        if self.add_position_embedding:
            state_dict_[self._position_embeddings_key] \
                = self.position_embeddings.state_dict(prefix=prefix,
                                                  keep_vars=keep_vars)
        if self.num_tokentypes > 0:
            state_dict_[self._tokentype_embeddings_key] \
                = self.tokentype_embeddings.state_dict(prefix=prefix,
                                                       keep_vars=keep_vars)

        return state_dict_

    def load_state_dict(self, state_dict, strict=True):
        """Customized load."""

        # Word embedding.
        if self._word_embeddings_key in state_dict:
            state_dict_ = state_dict[self._word_embeddings_key]
        else:
            # for backward compatibility.
            state_dict_ = {}
            for key in state_dict.keys():
                if 'word_embeddings' in key:
                    state_dict_[key.split('word_embeddings.')[1]] \
                        = state_dict[key]
        self.word_embeddings.load_state_dict(state_dict_, strict=strict)

        # Position embedding.
        if self.add_position_embedding:
            if self._position_embeddings_key in state_dict:
                state_dict_ = state_dict[self._position_embeddings_key]
            else:
                # for backward compatibility.
                state_dict_ = {}
                for key in state_dict.keys():
                    if 'position_embeddings' in key:
                        state_dict_[key.split('position_embeddings.')[1]] \
                            = state_dict[key]
            self.position_embeddings.load_state_dict(state_dict_, strict=strict)

        # Tokentype embedding.
        if self.num_tokentypes > 0:
            state_dict_ = {}
            if self._tokentype_embeddings_key in state_dict:
                state_dict_ = state_dict[self._tokentype_embeddings_key]
            else:
                # for backward compatibility.
                for key in state_dict.keys():
                    if 'tokentype_embeddings' in key:
                        state_dict_[key.split('tokentype_embeddings.')[1]] \
                            = state_dict[key]
            if len(state_dict_.keys()) > 0:
                self.tokentype_embeddings.load_state_dict(state_dict_,
                                                          strict=strict)
            else:
                print('***WARNING*** expected tokentype embeddings in the '
                      'checkpoint but could not find it', flush=True)


class TransformerLanguageModel(MegatronModule):
    """Transformer language model.

    Arguments:
        transformer_hparams: transformer hyperparameters
        vocab_size: vocabulary size
        max_sequence_length: maximum size of sequence. This
                             is used for positional embedding
        embedding_dropout_prob: dropout probability for embeddings
        num_tokentypes: size of the token-type embeddings. 0 value
                        will ignore this embedding
    """

    def __init__(self,
                 config,
                 encoder_attn_mask_type,
                 num_tokentypes=0,
                 add_encoder=True,
                 add_decoder=False,
                 decoder_attn_mask_type=AttnMaskType.causal,
                 add_pooler=False,
                 pre_process=True,
                 post_process=True):
        args = get_args()
        # TODO: passing share_embeddings_and_output_weights=False will not work correctly for T5 and embeddings will not be synced. Fix later for T5.
        if args.untie_embeddings_and_output_weights: assert not add_decoder
        super(TransformerLanguageModel, self).__init__(share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights)

        self.pre_process = pre_process
        self.post_process = post_process
        self.hidden_size = config.hidden_size
        self.num_tokentypes = num_tokentypes
        self.init_method = config.init_method
        self.add_encoder = add_encoder
        self.encoder_attn_mask_type = encoder_attn_mask_type
        self.add_decoder = add_decoder
        self.decoder_attn_mask_type = decoder_attn_mask_type
        self.add_pooler = add_pooler
        self.encoder_hidden_state = None
        self.add_retriever = args.retro_add_retriever
        self.untie_embeddings_and_output_weights = args.untie_embeddings_and_output_weights
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