From 139b754f29cc0b57f0c1e055c3d4a8fe606838d5 Mon Sep 17 00:00:00 2001 From: YWMditto <862779238@qq.com> Date: Fri, 3 Nov 2023 16:34:35 +0800 Subject: [PATCH] support dynamic ntk in transformers --- .../internlm_model/modeling_internlm.py | 108 ++++++++++++++++-- 1 file changed, 96 insertions(+), 12 deletions(-) diff --git a/tools/transformers/internlm_model/modeling_internlm.py b/tools/transformers/internlm_model/modeling_internlm.py index 269cdd2..7a28a9b 100644 --- a/tools/transformers/internlm_model/modeling_internlm.py +++ b/tools/transformers/internlm_model/modeling_internlm.py @@ -124,6 +124,65 @@ class InternLMRotaryEmbedding(torch.nn.Module): ) +class InternLMDynamicNTKScalingRotaryEmbedding(torch.nn.Module): + """实现dynamic ntk rope； + + 需要保证： + 1. 长度小于 seq len 时能够不断地复用； + 2. 长度超过 seq len 时，每一个 新的token，都需要一个新的base； + + Args: + InternLMRotaryEmbedding (_type_): _description_ + """ + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + super().__init__() + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim)) + self.register_buffer("inv_freq", inv_freq) + self.dim = dim + self.base = base + self.scaling_factor = scaling_factor + + # Build here to make `torch.jit.trace` work. + self.max_position_embeddings = max_position_embeddings + self.max_seq_len_cached = max_position_embeddings + t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype) + freqs = torch.einsum("i,j->ij", 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()[None, None, :, :], persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) + + def _update_cached(self, x, seq_len=None): + self.max_seq_len_cached = max(seq_len, self.max_position_embeddings) + if seq_len > self.max_position_embeddings: + base = self.base * ( + (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(x.device) / self.dim)) + else: + inv_freq = self.inv_freq + t = torch.arange(self.max_seq_len_cached, device=inv_freq.device, dtype=inv_freq.dtype) + freqs = torch.einsum("i,j->ij", t, inv_freq) + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) + + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case. + if seq_len <= self.max_position_embeddings: + # Reset the tables if the sequence length has changed, + if self.max_seq_len_cached > self.max_position_embeddings: + self._update_cached(x, seq_len) + else: + self._update_cached(x, seq_len) + + return ( + self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + ) + def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] @@ -135,10 +194,18 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids): # The first two dimensions of cos and sin are always 1, so we can `squeeze` them. cos = cos.squeeze(1).squeeze(0) # [seq_len, dim] sin = sin.squeeze(1).squeeze(0) # [seq_len, dim] - cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] - sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] - q_embed = (q * cos) + (rotate_half(q) * sin) - k_embed = (k * cos) + (rotate_half(k) * sin) + cos = cos.unsqueeze(0).unsqueeze(0).expand(len(position_ids), -1, -1, -1) + sin = sin.unsqueeze(0).unsqueeze(0).expand(len(position_ids), -1, -1, -1) + if q.size(2) == 1: + q_embed = (q * cos[:, :, -1, :]) + (rotate_half(q) * sin[:, :, -1, :]) + else: + q_embed = (q * cos) + (rotate_half(q) * sin) + + if k.size(2) == 1: + k_embed = (k * cos[:, :, -1, :]) + (rotate_half(k) * sin[:, :, -1, :]) + else: + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed @@ -179,7 +246,26 @@ class InternLMAttention(nn.Module): self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias) self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias) self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias) - self.rotary_emb = InternLMRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings) + self.rotary_emb = self._init_rope() + + def _init_rope(self): + if self.config.rotary["type"] == "origin": + self.rotary_emb = InternLMRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.config.rotary["base"], + ) + elif self.config.rotary["type"] == "dynamic": + self.rotary_emb = InternLMDynamicNTKScalingRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.config.rotary["base"], + scaling_factor=self.config.rotary.get("scaling_factor", 1.0) + ) + else: + raise ValueError("Currently we only support rotary embedding's type being one of ('origin', 'dynamic').") + + return self.rotary_emb def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() @@ -199,20 +285,18 @@ class InternLMAttention(nn.Module): key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) - kv_seq_len = key_states.shape[-2] - if past_key_value is not None: - 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_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) - # [bsz, nh, t, hd] - if past_key_value is not None: # reuse k, v, self_attention key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) + # print(use_cache) past_key_value = (key_states, value_states) if use_cache else None + kv_seq_len = key_states.shape[-2] + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):