# coding=utf-8 # Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved. # # # 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 Gemma2 model.""" import math from functools import partial from typing import List, Optional, Tuple, Union import torch import torch.nn.functional as F from transformers.cache_utils import Cache, DynamicCache, StaticCache from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS from transformers.models.gemma2.modeling_gemma2 import ( Gemma2Attention, Gemma2Config, Gemma2DecoderLayer, Gemma2ForCausalLM, Gemma2MLP, Gemma2Model, apply_rotary_pos_emb, ) from transformers.utils import logging from ...modeling_attn_mask_utils import _gaudi_prepare_4d_causal_attention_mask try: from habana_frameworks.torch.hpex.kernels import RotaryPosEmbeddingHelperV2 as FusedRoPE has_fused_rope = True except ImportError: has_fused_rope = False print("Not using HPU fused kernel for apply_rotary_pos_emb") try: from habana_frameworks.torch.hpex.kernels import FusedSDPA except ImportError: print("Not using HPU fused scaled dot-product attention kernel.") FusedSDPA = None try: from habana_frameworks.torch.hpex.normalization import FusedRMSNorm as FusedRMSNorm has_fused_rms_norm = True except ImportError: has_fused_rms_norm = False print("Not using HPU fused kernel for RMSNorm") import habana_frameworks.torch.core as htcore logger = logging.get_logger(__name__) class GaudiGemma2RotaryEmbedding(torch.nn.Module): def __init__( self, dim=None, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, rope_type="default", config: Optional[Gemma2Config] = None, ): super().__init__() # TODO (joao): remove the `if` below, only used for BC self.rope_kwargs = {} if config is None: logger.warning_once( "`LlamaRotaryEmbedding` can now be fully parameterized by passing the model config through the " "`config` argument. All other arguments will be removed in v4.45" ) self.rope_kwargs = { "rope_type": rope_type, "factor": scaling_factor, "dim": dim, "base": base, "max_position_embeddings": max_position_embeddings, } self.rope_type = rope_type self.max_seq_len_cached = max_position_embeddings self.original_max_seq_len = max_position_embeddings else: # BC: "rope_type" was originally "type" if hasattr(config, "rope_scaling") and config.rope_scaling is not None: self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) else: self.rope_type = "default" self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs) self.register_buffer("inv_freq", inv_freq, persistent=False) self.original_inv_freq = self.inv_freq # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=self.max_seq_len_cached, 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=self.inv_freq.dtype) freqs = torch.outer(t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation 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 _dynamic_frequency_update(self, seq_len, device): """ dynamic RoPE layers should recompute `inv_freq` in the following situations: 1 - growing beyond the cached sequence length (allow scaling) 2 - the current sequence length is in the original scale (avoid losing precision with small sequences) """ # seq_len = torch.max(position_ids) + 1 if seq_len > self.max_seq_len_cached: # growth inv_freq, self.attention_scaling = self.rope_init_fn( self.config, device, seq_len=seq_len, **self.rope_kwargs ) self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation self.max_seq_len_cached = seq_len if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset # This .to() is needed if the model has been moved to a device after being initialized (because # the buffer is automatically moved, but not the original copy) self.original_inv_freq = self.original_inv_freq.to(device) self.register_buffer("inv_freq", self.original_inv_freq, persistent=False) self.max_seq_len_cached = self.original_max_seq_len @torch.no_grad() def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if "dynamic" in self.rope_type: self._dynamic_frequency_update(seq_len, device=x.device) if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) if self.attention_scaling == 1.0: return ( self._cos_cached[:seq_len].to(dtype=x.dtype), self._sin_cached[:seq_len].to(dtype=x.dtype), ) else: return ( self._cos_cached[:seq_len].to(dtype=x.dtype) * self.attention_scaling, self._sin_cached[:seq_len].to(dtype=x.dtype) * self.attention_scaling, ) def gaudi_gemma2_repeat_kv( query_states: torch.Tensor, key_states: torch.Tensor, value_states: torch.Tensor, attention_mask: torch.Tensor, n_rep: int, ): batch, num_key_value_heads, kv_len, head_dim = key_states.shape if n_rep == 1 or num_key_value_heads == 1: return query_states, key_states, value_states, attention_mask new_kv_shape = (batch, num_key_value_heads, 1, kv_len, head_dim) key_states = key_states.reshape(new_kv_shape) value_states = value_states.reshape(new_kv_shape) batch, _, q_len, head_dim = query_states.shape new_q_shape = (batch, num_key_value_heads, n_rep, q_len, head_dim) query_states = query_states.reshape(new_q_shape) if attention_mask is not None: # Add groups dim and set to 1 attention_mask = attention_mask.unsqueeze(1) return query_states, key_states, value_states, attention_mask class Matmul(torch.nn.Module): def __init__(self): super().__init__() def forward(self, x, y): return torch.matmul(x, y) class KVCache(torch.nn.Module): def __init__(self): super(KVCache, self).__init__() self.cache = None self.inp_seq_len = -1 def allocate(self, inp_seq_len, dtype, device, shape): if self.cache is None or self.cache.shape != shape: self.inp_seq_len = inp_seq_len self.cache = torch.zeros(shape, dtype=dtype, device=device) else: assert self.inp_seq_len == inp_seq_len, ( f"inp_seq_len must be the same. self.inp_seq_len:{self.inp_seq_len} inp_seq_len:{inp_seq_len}" ) self.cache.fill_(0) def update(self, prev, cur, dim, idx, inp_seq_len): orig_cur = cur if prev.shape == cur.shape: prev.copy_(cur) return orig_cur if cur.shape[2] > 1 and cur.shape[2] <= prev.shape[2]: # Initialize prev[:, :, :inp_seq_len, :].copy_(cur) return orig_cur assert cur.shape[2] == 1, f"Cannot update kv-cache. Unsupported shapes. prev:{prev.shape} cur:{cur.shape}" if idx is not None: prev.index_copy_(dim, idx - 1, cur) return prev else: return torch.cat((prev, cur), dim=dim) def get_shape(self): if self.cache is None: return None return self.cache.shape def forward(self, cur, dim, idx): return self.update(self.cache, cur, dim, idx, self.inp_seq_len) def gaudi_eager_attention_forward( module: torch.nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor], dropout: float = 0.0, scaling: Optional[float] = None, softcap: Optional[float] = None, **kwargs, ) -> Tuple[torch.Tensor, torch.Tensor]: bsz, q_len = kwargs["input_shape"] if scaling is None: scaling = module.head_dim**-0.5 query_states, key_states, value_states, attention_mask = gaudi_gemma2_repeat_kv( query, key, value, attention_mask, module.num_key_value_groups ) attn_weights = module.matmul_qk(query_states, key_states.transpose(-2, -1)) * scaling if softcap is not None: attn_weights = attn_weights / softcap attn_weights = torch.tanh(attn_weights) attn_weights = attn_weights * softcap if attention_mask is not None: # no matter the length, we just slice it causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask # upcast attention to fp32 attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = torch.nn.functional.dropout(attn_weights, p=dropout, training=module.training) attn_output = module.matmul_av(attn_weights, value_states) attn_output = attn_output.reshape(bsz, -1, q_len, module.head_dim) return attn_output, attn_weights class GaudiGemma2Attention(Gemma2Attention): def __init__(self, config: Gemma2Config, layer_idx: Optional[int] = None): super().__init__(config, layer_idx) self.rotary_emb = GaudiGemma2RotaryEmbedding( self.head_dim, max_position_embeddings=config.max_position_embeddings, base=config.rope_theta, ) self.matmul_qk = Matmul() self.matmul_av = Matmul() self.k_cache = KVCache() self.v_cache = KVCache() self.inp_seq_len = -1 self.block_size = 4096 def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len): cache_shape = (batch_size, self.num_key_value_heads, max_seq_len, self.head_dim) device = self.k_proj.weight.device dtype = self.config.torch_dtype self.k_cache.allocate(inp_seq_len, dtype, device, cache_shape) self.v_cache.allocate(inp_seq_len, dtype, device, cache_shape) def update_sincos_cache(self, seq_len): # Call rotary emb forward() to update cos/sin cache when infering more than self.max_position_embeddings # This helps in avoiding creation of these caches during actual model forward pass and # reduce memory consumption and improve performance. if seq_len > self.max_position_embeddings: self.max_position_embeddings = seq_len _, _ = self.rotary_emb(self.k_proj.weight, seq_len=seq_len) def reorder(self, tensor, beam_idx, dim_a, dim_b): updated = tensor.index_select(0, beam_idx) tensor.copy_(updated) def reorder_kv_cache(self, beam_idx: torch.LongTensor): if self.k_cache.cache is None: return (None, None) head_dim = self.k_cache.cache.size(-1) seq_length = self.k_cache.cache.size(-2) self.reorder(self.k_cache.cache, beam_idx, seq_length, head_dim) self.reorder(self.v_cache.cache, beam_idx, seq_length, head_dim) return (self.k_cache.cache.shape, self.v_cache.cache.shape) def gaudi_flash_attn_v1(self, query_layer, key_layer, value_layer, attention_mask, dropout_rate, q_block_size): """ Gaudi version of Flash Attention V1 to support long sequence at prompt phase Causal mask is not supported in this optimization """ q_len = query_layer.size(-2) q_tiles = (q_len // q_block_size) if (q_len % q_block_size == 0) else math.ceil(q_len / q_block_size) q_padding = q_tiles * q_block_size - q_len query_layer = F.pad(query_layer, (0, 0, 0, q_padding), "constant", 0) if attention_mask is not None: attention_mask = F.pad(attention_mask, (0, 0, 0, q_padding), "constant", -10000.0) row_o_list = [] for i in range(q_tiles): s, e = i * q_block_size, (i + 1) * q_block_size row_q = query_layer[:, :, s:e, :] row_mask = attention_mask[:, :, s:e, :] attn_output_partial = FusedSDPA.apply(row_q, key_layer, value_layer, row_mask, dropout_rate, False, None) row_o_list.append(attn_output_partial) attn_output = torch.cat(row_o_list, dim=-2) if q_padding != 0: attn_output = attn_output[:, :, :-q_padding, :] return attn_output def pre_attn_forward( self, hidden_states: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, token_idx: Optional[torch.Tensor] = None, attn_softmax_bf16: Optional[bool] = False, reuse_cache: Optional[bool] = False, use_flash_attention: Optional[bool] = False, flash_attention_recompute: Optional[bool] = False, flash_attention_causal_mask: Optional[bool] = False, flash_attention_fast_softmax: Optional[bool] = False, cache_idx: int = None, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """ The only differences are: - add new args token_idx - optimize KV cache - add new args attn_softmax_bf16 - add new args reuse_cache - add new args use_flash_attention - add new arg flash_attention_recompute """ input_shape = hidden_states.shape[:-1] q_len = input_shape[1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) kv_seq_len = key_states.shape[-2] if past_key_value is not None: if token_idx is None: if hasattr(past_key_value, "get_usable_length"): kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) else: kv_seq_len += past_key_value[0].shape[-2] else: if reuse_cache and not isinstance(past_key_value[0], torch.Tensor): kv_seq_len = past_key_value[0][-2] else: kv_seq_len = past_key_value[0].shape[-2] cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) query_states, key_states = apply_customized_rope(query_states, key_states, cos, sin, kwargs["position_ids"]) if use_cache: # reuse k, v, self_attention if reuse_cache: key_states = self.k_cache(key_states, 2, token_idx) value_states = self.v_cache(value_states, 2, token_idx) past_key_value = (self.k_cache.get_shape(), self.v_cache.get_shape()) else: if past_key_value is None: past_key = torch.zeros(key_states.shape, dtype=self.k_proj.weight.dtype, device=key_states.device) past_value = torch.zeros( key_states.shape, dtype=self.k_proj.weight.dtype, device=key_states.device ) past_key_value = (past_key, past_value) key_states = self.k_cache.update(past_key_value[0], key_states, 2, token_idx, self.inp_seq_len) value_states = self.v_cache.update(past_key_value[1], value_states, 2, token_idx, self.inp_seq_len) if token_idx is None: past_key_value = (key_states, value_states) if cache_idx is not None and q_len == 1: key_states = key_states[:, :, :cache_idx, :] value_states = value_states[:, :, :cache_idx, :] if attention_mask is not None: attention_mask = attention_mask[:, :, :, :cache_idx] kv_seq_len = key_states.shape[-2] else: past_key_value = None if use_flash_attention and FusedSDPA: attn_weights = None import habana_frameworks.torch.hpu as ht softmax_mode = "fast" if flash_attention_fast_softmax else "None" if q_len == 1: # next token with ht.sdp_kernel(enable_recompute=False): attn_output = FusedSDPA.apply( query_states, key_states, value_states, attention_mask, 0.0, False, None, "None" ) else: # first token if flash_attention_causal_mask: # causal masking on first token requires inputs to be of the same length with ht.sdp_kernel(enable_recompute=flash_attention_recompute): attn_output = FusedSDPA.apply(query_states, key_states, value_states, None, 0.0, True, None) else: with ht.sdp_kernel(enable_recompute=flash_attention_recompute): if q_len > 16384: attn_output = self.gaudi_flash_attn_v1( query_states, key_states, value_states, attention_mask, 0.0, self.block_size ) htcore.mark_step() else: attn_output = FusedSDPA.apply( query_states, key_states, value_states, attention_mask, 0.0, False, None, softmax_mode ) else: attn_output, attn_weights = gaudi_eager_attention_forward( self, query_states, key_states, value_states, attention_mask, dropout=self.attention_dropout if self.training else 0.0, scaling=self.scaling, sliding_window=self.sliding_window, softcap=self.attn_logit_softcapping, input_shape=input_shape, ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) if not reuse_cache and token_idx is not None and cache_idx is not None and q_len == 1: # Return only past key value shapes and not the tensors during decode phase (q len is 1) # to avoid making past key values as persistent output tensors of HPU graphs. past_key_value = (past_key_value[0].shape, past_key_value[1].shape) return attn_output, attn_weights, past_key_value def attention_all_reduce(self, attn_output): if hasattr(self.o_proj, "all_reduce"): self.o_proj.all_reduce(attn_output) def post_attn_forward(self, attn_output): if hasattr(self.o_proj, "post_all_reduce"): self.o_proj.post_all_reduce(attn_output) return attn_output class GaudiGemma2MLP(Gemma2MLP): def pre_mlp_forward(self, x): inputs = self.act_fn(self.gate_proj(x)) * self.up_proj(x) output = self.down_proj(inputs) return output def mlp_all_reduce(self, x): if hasattr(self.down_proj, "all_reduce"): self.down_proj.all_reduce(x) def post_mlp_forward(self, x): if hasattr(self.down_proj, "post_all_reduce"): return self.down_proj.post_all_reduce(x) return x class GaudiGemma2DecoderLayer(Gemma2DecoderLayer): def __init__(self, config: Gemma2Config, layer_idx: int): super().__init__(config, layer_idx) self.self_attn = GaudiGemma2Attention(config, layer_idx) self.mlp = GaudiGemma2MLP(config) def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len): self.self_attn.allocate_kv_cache(batch_size, max_seq_len, inp_seq_len) def reorder_kv_cache(self, beam_idx: torch.LongTensor): return self.self_attn.reorder_kv_cache(beam_idx) def update_sincos_cache(self, seq_len): self.self_attn.update_sincos_cache(seq_len) def pre_attn( self, hidden_states: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, token_idx: Optional[torch.Tensor] = None, attn_softmax_bf16: Optional[bool] = False, reuse_cache: Optional[bool] = False, use_flash_attention: Optional[bool] = False, flash_attention_recompute: Optional[bool] = False, flash_attention_causal_mask: Optional[bool] = False, flash_attention_fast_softmax: Optional[bool] = False, cache_idx: int = None, **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: hidden_states = self.input_layernorm(hidden_states) hidden_states, attn_weights, present_key_value = self.self_attn.pre_attn_forward( hidden_states=hidden_states, position_embeddings=position_embeddings, 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, token_idx=token_idx, attn_softmax_bf16=attn_softmax_bf16, reuse_cache=reuse_cache, use_flash_attention=use_flash_attention, flash_attention_recompute=flash_attention_recompute, flash_attention_causal_mask=flash_attention_causal_mask, flash_attention_fast_softmax=flash_attention_fast_softmax, cache_idx=cache_idx, **kwargs, ) return hidden_states, attn_weights, present_key_value def forward( self, hidden_states: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, last_cache_position: int = 0, token_idx: Optional[torch.Tensor] = None, attn_softmax_bf16: Optional[bool] = False, reuse_cache: Optional[bool] = False, use_flash_attention: Optional[bool] = False, flash_attention_recompute: Optional[bool] = False, flash_attention_causal_mask: Optional[bool] = False, flash_attention_fast_softmax: Optional[bool] = False, cache_idx: int = None, **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Copied from GemmaDecoderLayer.forward: https://github.com/huggingface/transformers/blob/v4.38.1/src/transformers/models/gemma/modeling_gemma.py The only differences are: - add new args token_idx """ residual = hidden_states hidden_states, self_attn_weights, present_key_value = self.pre_attn( hidden_states, position_embeddings, attention_mask, position_ids, past_key_value, output_attentions, use_cache, cache_position, token_idx, attn_softmax_bf16, reuse_cache, use_flash_attention=use_flash_attention, flash_attention_recompute=flash_attention_recompute, flash_attention_causal_mask=flash_attention_causal_mask, flash_attention_fast_softmax=flash_attention_fast_softmax, cache_idx=cache_idx, **kwargs, ) self.self_attn.attention_all_reduce(hidden_states) hidden_states, residual = self.post_attn_pre_mlp(hidden_states, residual) self.mlp.mlp_all_reduce(hidden_states) hidden_states = self.post_mlp(hidden_states, residual) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs def post_attn_pre_mlp(self, hidden_states, residual): hidden_states = self.self_attn.post_attn_forward(hidden_states) hidden_states = self.post_attention_layernorm(hidden_states) if self.training: hidden_states = hidden_states + residual residual = hidden_states else: residual.add_(hidden_states) hidden_states = residual residual = hidden_states hidden_states = self.pre_feedforward_layernorm(hidden_states) hidden_states = self.mlp.pre_mlp_forward(hidden_states) return hidden_states, residual def post_mlp(self, hidden_states, residual): hidden_states = self.mlp.post_mlp_forward(hidden_states) hidden_states = self.post_feedforward_layernorm(hidden_states) if self.training: hidden_states = hidden_states + residual else: residual.add_(hidden_states) hidden_states = residual return hidden_states class GaudiGemma2Model(Gemma2Model): def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len): for layer in self.layers: layer.allocate_kv_cache(batch_size, max_seq_len, inp_seq_len) def reorder_kv_cache(self, beam_idx: torch.LongTensor): return tuple(layer.reorder_kv_cache(beam_idx) for layer in self.layers) def update_sincos_cache(self, seq_len): for layer in self.layers: layer.update_sincos_cache(seq_len) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, last_cache_position: Optional[int] = None, token_idx: Optional[torch.Tensor] = None, attn_softmax_bf16: Optional[bool] = False, reuse_cache: Optional[bool] = False, use_flash_attention: Optional[bool] = False, flash_attention_recompute: Optional[bool] = False, flash_attention_causal_mask: Optional[bool] = False, flash_attention_fast_softmax: Optional[bool] = False, cache_idx: int = None, lazy_mode: Optional[bool] = True, **kwargs, ) -> BaseModelOutputWithPast: """ Copied from GemmaModel.forward: https://github.com/huggingface/transformers/blob/v4.38.1/src/transformers/models/gemma/modeling_gemma.py The only differences are: - add new args token_idx - replace _update_causal_mask with _gaudi_prepare_4d_causal_attention_mask """ 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 self._attn_implementation = "eager" if input_ids is not None and inputs_embeds is not None: raise ValueError("You must specify exactly one of input_ids or inputs_embeds") elif input_ids is not None: batch_size, seq_length = input_ids.shape[:2] elif inputs_embeds is not None: batch_size, seq_length = inputs_embeds.shape[:2] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." ) use_cache = False if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) ignore_cache_position = True # Ignoring cache position for HPU use_new_cache = False # Ignoring new Cache path for HPU past_seen_tokens = 0 if past_key_values is not None and use_cache: # kept for BC (cache positions) if reuse_cache: if isinstance(past_key_values[0][0], torch.Tensor): past_seen_tokens = past_key_values[0][0].shape[2] else: past_seen_tokens = past_key_values[0][0][2] else: if use_new_cache: if not isinstance(past_key_values, StaticCache): past_key_values = DynamicCache.from_legacy_cache(past_key_values) past_seen_tokens = past_key_values.get_seq_length() else: past_seen_tokens = past_key_values[0][0].shape[2] if ignore_cache_position is False: if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) if position_ids is None and cache_position: position_ids = cache_position.unsqueeze(0) else: if position_ids is None: position_ids = torch.arange( past_seen_tokens, seq_length + past_seen_tokens, dtype=torch.long, device=inputs_embeds.device ) position_ids = position_ids.unsqueeze(0) cache_position = None # HPU specific mask generation if ignore_cache_position: causal_mask = _gaudi_prepare_4d_causal_attention_mask( attention_mask, input_ids.shape if input_ids is not None else (batch_size, seq_length), inputs_embeds, past_seen_tokens, ) else: causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_seen_tokens) # embed positions hidden_states = inputs_embeds normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype, device=inputs_embeds.device) hidden_states = hidden_states * normalizer # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = () if not use_new_cache else None if lazy_mode: htcore.mark_step() for layer_idx, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]): if ( lazy_mode and not self.training and (torch.distributed.is_initialized() is False or torch.distributed.get_world_size() == 1) ): htcore.mark_step() if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( partial(decoder_layer.__call__, **kwargs), hidden_states, causal_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, last_cache_position, None, attn_softmax_bf16, False, use_flash_attention, flash_attention_recompute, flash_attention_causal_mask, flash_attention_fast_softmax, None, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=causal_mask, position_ids=position_ids, past_key_value=None if past_key_values is None else past_key_values[layer_idx], output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, last_cache_position=last_cache_position, token_idx=token_idx, attn_softmax_bf16=attn_softmax_bf16, reuse_cache=reuse_cache, use_flash_attention=use_flash_attention, flash_attention_recompute=flash_attention_recompute, flash_attention_causal_mask=flash_attention_causal_mask, flash_attention_fast_softmax=flash_attention_fast_softmax, cache_idx=cache_idx, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = None if use_cache: next_cache = ( next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) class GaudiGemma2ForCausalLM(Gemma2ForCausalLM): def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len): self.model.allocate_kv_cache(batch_size, max_seq_len, inp_seq_len) def reorder_kv_cache(self, beam_idx: torch.LongTensor): return self.model.reorder_kv_cache(beam_idx) def update_sincos_cache(self, seq_len): self.model.update_sincos_cache(seq_len) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, token_idx: Optional[torch.Tensor] = None, trim_logits: Optional[bool] = False, attn_softmax_bf16: Optional[bool] = False, reuse_cache: Optional[bool] = False, use_flash_attention: Optional[bool] = False, flash_attention_recompute: Optional[bool] = False, flash_attention_causal_mask: Optional[bool] = False, flash_attention_fast_softmax: Optional[bool] = False, cache_idx: int = None, lazy_mode: Optional[bool] = True, **loss_kwargs, ) -> CausalLMOutputWithPast: """ Inherits from GemmaForCausalLM: https://github.com/huggingface/transformers/blob/v4.38.1/src/transformers/models/gemma/modeling_gemma.py The only differences are: - add new args token_idx """ 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 ) # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs: BaseModelOutputWithPast = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, cache_position=cache_position, token_idx=token_idx, attn_softmax_bf16=attn_softmax_bf16, reuse_cache=reuse_cache, use_flash_attention=use_flash_attention, flash_attention_recompute=flash_attention_recompute, flash_attention_causal_mask=flash_attention_causal_mask, flash_attention_fast_softmax=flash_attention_fast_softmax, cache_idx=cache_idx, lazy_mode=lazy_mode, **loss_kwargs, ) hidden_states = outputs.last_hidden_state _, seq_len, _ = hidden_states.shape if seq_len > 1 and trim_logits and not self.training: if token_idx is not None: hidden_states = hidden_states.index_select(1, token_idx - 1) else: hidden_states = hidden_states[:, -1, :] slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.lm_head(hidden_states[:, slice_indices, :]).float() if self.config.final_logit_softcapping is not None: logits = logits / self.config.final_logit_softcapping logits = torch.tanh(logits) logits = logits * self.config.final_logit_softcapping loss = None if labels is not None: loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs) 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, cache_position=None, position_ids=None, use_cache=True, **kwargs, ): """ Inherits from GemmaForCausalLM: https://github.com/huggingface/transformers/blob/v4.38.1/src/transformers/models/gemma/modeling_gemma.py The only differences are: - add new args token_idx - add token_idx into model_inputs - from step2 when enable KV cache, slice next_input_ids from input_ids base on the token_idx - from step2 when enable KV cache, slice next_position_ids from position_ids base on the token_idx """ reuse_cache = kwargs.get("reuse_cache") bucket_internal = kwargs.get("bucket_internal") token_idx = kwargs.get("token_idx", None) if past_key_values is not None: if token_idx is None: if inputs_embeds is not None: # Exception 1 input_ids = input_ids[:, -cache_position.shape[0] :] elif ( input_ids.shape[1] != cache_position.shape[0] ): # Default case (the "else", a no op, is Exception 2) input_ids = input_ids[:, cache_position] else: # past_length += token_idx input_ids = torch.index_select(input_ids, 1, token_idx - 1) elif (reuse_cache or bucket_internal) and token_idx is not None: # KV cache is pre allocated with reuse cache or will be padded with bucket internal # hence for the 1st token we can slice the inputs till token idx for the fwd pass. input_ids = input_ids[:, :token_idx] attention_mask = attention_mask[:, :token_idx] 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_key_values: if token_idx is not None: position_ids = torch.index_select(position_ids, 1, token_idx - 1) else: position_ids = position_ids[:, -input_ids.shape[1] :] if token_idx is None: if past_key_value := getattr(self.model.layers[0].self_attn, "past_key_value", None): # generation with static cache past_length = past_key_value.get_seq_length() input_ids = input_ids[:, past_length:] position_ids = position_ids[:, past_length:] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids.contiguous()} model_inputs.update( { "position_ids": position_ids, "cache_position": cache_position, "past_key_values": past_key_values, "use_cache": use_cache, "attention_mask": attention_mask, "token_idx": token_idx, "trim_logits": kwargs.get("trim_logits"), "attn_softmax_bf16": kwargs.get("attn_softmax_bf16"), "reuse_cache": reuse_cache, "use_flash_attention": kwargs.get("use_flash_attention"), "flash_attention_recompute": kwargs.get("flash_attention_recompute"), "flash_attention_causal_mask": kwargs.get("flash_attention_causal_mask"), "flash_attention_fast_softmax": kwargs.get("flash_attention_fast_softmax"), "cache_idx": kwargs.get("cache_idx"), "lazy_mode": kwargs.get("lazy_mode"), } ) return model_inputs def apply_customized_rope(q, k, cos, sin, position_ids): if q.device.type == "hpu" and has_fused_rope: # TODO: remove `.clone()` when it is fixed in SynapseAI if k.dtype == torch.bfloat16: return FusedRoPE.apply( q, cos.unsqueeze(0).unsqueeze(0).clone(), sin.unsqueeze(0).unsqueeze(0).clone(), position_ids ), FusedRoPE.apply( k, cos.unsqueeze(0).unsqueeze(0).clone().to(torch.bfloat16), sin.unsqueeze(0).unsqueeze(0).clone().to(torch.bfloat16), position_ids, ) return FusedRoPE.apply( q, cos.unsqueeze(0).unsqueeze(0).clone(), sin.unsqueeze(0).unsqueeze(0).clone(), position_ids ), FusedRoPE.apply( k, cos.unsqueeze(0).unsqueeze(0).clone(), sin.unsqueeze(0).unsqueeze(0).clone(), position_ids ) else: # keep the same implementation as Transformers v4.37.2 return apply_rotary_pos_emb(q, k, cos[position_ids], sin[position_ids])