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Add TPU gemma
This commit is contained in:
Woosuk Kwon
parent
563c1d7ec5
commit
52a1e908e4
0
vllm/model_executor/models/tpu/__init__.py
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0
vllm/model_executor/models/tpu/__init__.py
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vllm/model_executor/models/tpu/gemma.py
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vllm/model_executor/models/tpu/gemma.py
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"""Inference-only Gemma model compatible with HF weights.
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NOTE(woosuk): This is a temporary workaround to run the Gemma model using
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PyTorch XLA. This should be merged into the main Gemma model implementation
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once the custom ops are refactored and the model becomes torch.compile-able.
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"""
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from typing import List
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import torch
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import torch.nn.functional as F
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from torch import nn
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from transformers import GemmaConfig, PreTrainedModel
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from vllm.attention import Attention, AttentionMetadata
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class Linear(nn.Module):
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"""PyTorch Linear layer without parameter initialization."""
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def __init__(
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self,
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in_features: int,
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out_features: int,
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bias: bool = True,
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):
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super().__init__()
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self.weight = nn.Parameter(torch.empty((out_features, in_features)))
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if bias:
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self.bias = nn.Parameter(torch.empty(out_features))
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else:
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self.bias = None
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return F.linear(x, self.weight, self.bias)
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def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
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"""Precomputes the frequency cis."""
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freqs = 1.0 / (theta**(torch.arange(0, dim, 2)[:(dim // 2)].float() / dim))
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t = torch.arange(end, device=freqs.device) # type: ignore
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freqs = torch.outer(t, freqs).float() # type: ignore
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freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
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return freqs_cis
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def apply_rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
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"""Applies the rotary embedding to the query and key tensors."""
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x_ = torch.view_as_complex(
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torch.stack(torch.chunk(x.transpose(1, 2).float(), 2, dim=-1), dim=-1))
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x_out = torch.view_as_real(x_ * freqs_cis).type_as(x)
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x_out = torch.cat(torch.chunk(x_out, 2, dim=-1), dim=-2)
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x_out = x_out.reshape(x_out.shape[0], x_out.shape[1], x_out.shape[2],
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-1).transpose(1, 2)
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return x_out
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class RMSNorm(torch.nn.Module):
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def __init__(
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self,
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dim: int,
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eps: float = 1e-6,
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):
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super().__init__()
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(dim))
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def _norm(self, x: torch.Tensor) -> torch.Tensor:
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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orig_dtype = x.dtype
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x = self._norm(x.float())
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output = x * (1 + self.weight.float())
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return output.to(orig_dtype)
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class GemmaMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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):
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super().__init__()
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.gate_proj = Linear(
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hidden_size,
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intermediate_size,
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bias=False,
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)
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self.up_proj = Linear(
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hidden_size,
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intermediate_size,
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bias=False,
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)
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self.down_proj = Linear(
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intermediate_size,
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hidden_size,
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bias=False,
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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gate = self.gate_proj(x)
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gate = F.gelu(gate, approximate="tanh")
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up = self.up_proj(x)
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fuse = gate * up
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outputs = self.down_proj(fuse)
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return outputs
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class GemmaAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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head_dim: int,
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):
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super().__init__()
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self.num_heads = num_heads
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self.num_kv_heads = num_kv_heads
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assert self.num_heads % self.num_kv_heads == 0
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self.num_queries_per_kv = self.num_heads // self.num_kv_heads
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self.hidden_size = hidden_size
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self.head_dim = head_dim
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self.scaling = self.head_dim**-0.5
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self.q_proj = Linear(self.hidden_size,
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self.num_heads * self.head_dim,
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bias=False)
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self.k_proj = Linear(self.hidden_size,
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self.num_kv_heads * self.head_dim,
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bias=False)
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self.v_proj = Linear(self.hidden_size,
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self.num_kv_heads * self.head_dim,
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bias=False)
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self.o_proj = Linear(self.num_heads * self.head_dim,
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self.hidden_size,
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bias=False)
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads)
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def forward(
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self,
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hidden_states: torch.Tensor,
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freqs_cis: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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batch_size, seq_len, _ = hidden_states.shape
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q = self.q_proj(hidden_states)
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k = self.k_proj(hidden_states)
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v = self.v_proj(hidden_states)
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q = q.view(batch_size, seq_len, self.num_heads, self.head_dim)
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q = apply_rotary_emb(q, freqs_cis)
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k = k.view(batch_size, seq_len, self.num_kv_heads, self.head_dim)
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k = apply_rotary_emb(k, freqs_cis)
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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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o = self.o_proj(attn_output)
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return o
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class GemmaDecoderLayer(nn.Module):
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def __init__(
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self,
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config: GemmaConfig,
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):
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super().__init__()
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self.self_attn = GemmaAttention(
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hidden_size=config.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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head_dim=config.head_dim,
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)
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self.mlp = GemmaMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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freqs_cis: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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# Self Attention
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states = self.self_attn(
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hidden_states=hidden_states,
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freqs_cis=freqs_cis,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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hidden_states = residual + hidden_states
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# MLP
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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class GemmaModel(nn.Module):
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def __init__(
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self,
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config: GemmaConfig,
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):
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super().__init__()
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self.config = config
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self.vocab_size = config.vocab_size
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self.embed_tokens = nn.Embedding(
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config.vocab_size,
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config.hidden_size,
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)
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self.layers = nn.ModuleList()
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for _ in range(config.num_hidden_layers):
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self.layers.append(GemmaDecoderLayer(config))
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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input_ids: torch.Tensor,
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freqs_cis: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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hidden_states = self.embed_tokens(input_ids)
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# Gemma normalizes the embedding by sqrt(hidden_size).
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# FIXME(woosuk): Downcast the normalizer.
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hidden_states = hidden_states * (self.config.hidden_size**0.5)
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for i in range(len(self.layers)):
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layer = self.layers[i]
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hidden_states = layer(
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hidden_states=hidden_states,
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freqs_cis=freqs_cis,
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kv_cache=kv_caches[i],
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attn_metadata=attn_metadata,
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)
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hidden_states = self.norm(hidden_states)
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return hidden_states
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class GemmaForCausalLM(PreTrainedModel):
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def __init__(
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self,
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config: GemmaConfig,
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):
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super().__init__(config)
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self.config = config
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self.model = GemmaModel(config)
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rope_theta = getattr(config, 'rope_theta', 10000)
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# [head_dim * 2, ] -> complex -> two dim (real, imaginary) implicitly
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freqs_cis = precompute_freqs_cis(config.head_dim,
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config.max_position_embeddings * 2,
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theta=rope_theta)
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self.register_buffer('freqs_cis', freqs_cis)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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freqs_cis = self.freqs_cis.index_select(0, positions)
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hidden_states = self.model(
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input_ids=input_ids,
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freqs_cis=freqs_cis,
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kv_caches=kv_caches,
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attn_metadata=attn_metadata,
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)
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return hidden_states
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