import math import torch import torch.nn as nn import torch.nn.functional as F from typing import Optional, Tuple, Union, List from transformers import PreTrainedModel from transformers.modeling_outputs import ( CausalLMOutputWithPast, BaseModelOutputWithPast, ) from transformers.cache_utils import Cache, DynamicCache from transformers.modeling_attn_mask_utils import ( _prepare_4d_causal_attention_mask, ) from .configuration_spark import SparkConfig def rotate_half(x): x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class SparkLayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6, use_bias=True): super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.use_bias = use_bias if use_bias: self.bias = nn.Parameter(torch.zeros(hidden_size)) else: self.register_parameter('bias', None) self.eps = eps self.normalized_shape = (hidden_size,) def forward(self, hidden_states): return F.layer_norm(hidden_states, self.normalized_shape, self.weight, self.bias, self.eps) class SparkMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.hidden_size = config.hidden_size self.ffn_hidden_size = config.ffn_hidden_size self.dense_h_to_4h = nn.Linear(self.hidden_size, self.ffn_hidden_size * 2, bias=True) self.dense_4h_to_h = nn.Linear(self.ffn_hidden_size, self.hidden_size, bias=True) if config.hidden_act == "fast_gelu": self.activation_func = lambda x: F.gelu(x, approximate="tanh") else: self.activation_func = F.gelu def forward(self, hidden_states): intermediate = self.dense_h_to_4h(hidden_states) hshape = intermediate.shape[:-1] intermediate = intermediate.view(hshape + (-1, 2)) intermediate_parallel1, intermediate_parallel2 = torch.chunk(intermediate, 2, dim=-1) intermediate_parallel1 = intermediate_parallel1.squeeze(-1) intermediate_parallel2 = intermediate_parallel2.squeeze(-1) intermediate_parallel1 = self.activation_func(intermediate_parallel1) intermediate = intermediate_parallel1 * intermediate_parallel2 output = self.dense_4h_to_h(intermediate) return output class SparkAttention(nn.Module): def __init__(self, config: SparkConfig, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx self.hidden_size = config.hidden_size self.num_heads = config.num_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.head_dim = self.hidden_size // self.num_heads self.use_bias = config.use_bias self.query_key_value = nn.Linear( self.hidden_size, self.num_heads * self.head_dim + 2 * self.num_key_value_heads * self.head_dim, bias=self.use_bias ) self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=self.use_bias) self.attention_dropout = config.attention_dropout def forward(self, hidden_states, attention_mask=None, position_ids=None, past_key_value=None, output_attentions=False, use_cache=False, cache_position=None, position_embeddings=None): bsz, q_len, _ = hidden_states.size() qkv = self.query_key_value(hidden_states) query_pos = self.num_heads * self.head_dim key_value_pos = query_pos + self.num_key_value_heads * self.head_dim query_states = qkv[..., :query_pos] key_states = qkv[..., query_pos:key_value_pos] value_states = qkv[..., key_value_pos:] query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: if isinstance(past_key_value, Cache): cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) else: past_key, past_value = past_key_value key_states = torch.cat([past_key, key_states], dim=2) value_states = torch.cat([past_value, value_states], dim=2) if past_key_value is None or not isinstance(past_key_value, Cache): cached_key_states = key_states cached_value_states = value_states else: cached_key_states = None cached_value_states = None key_states = self.repeat_kv(key_states, self.num_key_value_groups) value_states = self.repeat_kv(value_states, self.num_key_value_groups) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attention_mask is not None: causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) attn_output = self.dense(attn_output) if use_cache: present_key_value = past_key_value if isinstance(past_key_value, Cache) else (cached_key_states, cached_value_states) else: present_key_value = None return attn_output, attn_weights if output_attentions else None, present_key_value def repeat_kv(self, hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) class SparkDecoderLayer(nn.Module): def __init__(self, config: SparkConfig, layer_idx: int): super().__init__() self.input_layernorm = SparkLayerNorm(config.hidden_size, eps=config.layernorm_epsilon) self.self_attn = SparkAttention(config, layer_idx) self.post_attention_layernorm = SparkLayerNorm(config.hidden_size, eps=config.layernorm_epsilon) self.mlp = SparkMLP(config) def forward(self, hidden_states, attention_mask=None, position_ids=None, past_key_value=None, output_attentions=False, use_cache=False, cache_position=None, position_embeddings=None): residual = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs class SparkPreTrainedModel(PreTrainedModel): config_class = SparkConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["SparkDecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_sdpa = True _supports_cache_class = True def _init_weights(self, module): std = self.config.init_std if hasattr(self.config, "init_std") else 0.02 if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() class SparkModel(SparkPreTrainedModel): def __init__(self, config: SparkConfig): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.layers = nn.ModuleList([SparkDecoderLayer(config, layer_idx) for layer_idx in range(config.num_layers)]) self.norm = SparkLayerNorm(config.hidden_size, eps=config.layernorm_epsilon) self.rope_theta = config.rope_theta self.rotary_emb = SparkRotaryEmbedding( config.hidden_size // config.num_heads, max_position_embeddings=config.max_position_embeddings, base=self.rope_theta, ) self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def forward(self, input_ids=None, attention_mask=None, position_ids=None, past_key_values=None, inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, cache_position=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: batch_size, seq_length = input_ids.shape else: batch_size, seq_length, _ = inputs_embeds.shape if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) past_key_values_length = 0 if past_key_values is not None: past_key_values_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length() if cache_position is None: cache_position = torch.arange(past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = _prepare_4d_causal_attention_mask(attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length) hidden_states = inputs_embeds all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = None position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) for layer_idx, decoder_layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) layer_past_key_value = past_key_values if isinstance(past_key_values, Cache) else (past_key_values[layer_idx] if past_key_values is not None else None) layer_outputs = decoder_layer( hidden_states, attention_mask=causal_mask, position_ids=position_ids, past_key_value=layer_past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, ) hidden_states = layer_outputs[0] if use_cache: layer_present_key_value = layer_outputs[2 if output_attentions else 1] if next_decoder_cache is None: next_decoder_cache = layer_present_key_value if isinstance(layer_present_key_value, Cache) else [] if not isinstance(next_decoder_cache, Cache): next_decoder_cache.append(layer_present_key_value) if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast(last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns) class SparkForCausalLM(SparkPreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.model = SparkModel(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.post_init() def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def forward(self, input_ids=None, attention_mask=None, position_ids=None, past_key_values=None, inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, cache_position=None): return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.model(input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict, cache_position) hidden_states = outputs[0] logits = self.lm_head(hidden_states).float() loss = None if labels is not None: shift_logits = logits[..., :-1, :].contiguous().view(-1, self.config.vocab_size) shift_labels = labels[..., 1:].contiguous().view(-1).to(shift_logits.device) loss = nn.CrossEntropyLoss()(shift_logits, shift_labels) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions) def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs): if input_ids is not None: input_ids = input_ids.long() if attention_mask is not None: attention_mask = attention_mask.long() cache_position = kwargs.get("cache_position", None) if past_key_values is not None: if isinstance(past_key_values, Cache): if hasattr(past_key_values, 'cache_position') and past_key_values.cache_position is not None and past_key_values.cache_position.numel() > 0: past_length = past_key_values.cache_position.max().item() + 1 else: past_length = getattr(past_key_values, 'seen_tokens', 0) else: past_length = past_key_values[0][0].shape[2] else: past_length = 0 if past_key_values is not None and input_ids is not None: if cache_position is not None: cache_position = cache_position.long() input_ids = input_ids[:, cache_position] if cache_position.max() < input_ids.shape[1] else input_ids[:, past_length:] else: input_ids = input_ids[:, past_length:] position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values is not None and past_length > 0: position_ids = position_ids[:, past_length:] if cache_position is None and input_ids is not None: cache_position = torch.arange(past_length, past_length + input_ids.shape[1], device=input_ids.device, dtype=torch.long) model_inputs = {"use_cache": kwargs.get("use_cache", True)} if inputs_embeds is not None and past_key_values is None: model_inputs["inputs_embeds"] = inputs_embeds elif input_ids is not None: model_inputs["input_ids"] = input_ids if position_ids is not None: model_inputs["position_ids"] = position_ids if cache_position is not None: model_inputs["cache_position"] = cache_position if past_key_values is not None: model_inputs["past_key_values"] = past_key_values if attention_mask is not None: model_inputs["attention_mask"] = attention_mask return model_inputs class SparkRotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=32768, base=1000000.0, device=None): super().__init__() self.dim, self.max_position_embeddings, self.base = dim, max_position_embeddings, base inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) self._set_cos_sin_cache(seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) freqs = torch.outer(t, self.inv_freq) emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, position_ids=None, seq_len=None): if position_ids is not None: max_position = position_ids.max().item() + 1 actual_seq_len = max(max_position, position_ids.shape[-1]) else: actual_seq_len = seq_len if isinstance(seq_len, int) else (seq_len.item() if seq_len is not None else x.shape[2]) if actual_seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=actual_seq_len, device=x.device, dtype=x.dtype) cos = self.cos_cached[:actual_seq_len].to(dtype=x.dtype) sin = self.sin_cached[:actual_seq_len].to(dtype=x.dtype) if position_ids is not None: cos, sin = cos[position_ids], sin[position_ids] return cos, sin