wehub-resource-sync
200 lines
7.1 KiB
Python
200 lines
7.1 KiB
Python
# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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from typing import Iterable, Optional, Tuple
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import torch
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import deepspeed.comm as dist
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from ...allocator import empty_from
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from ...inference_utils import ActivationType, DtypeEnum
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from .. import *
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from ...modules.configs import *
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from ...modules.interfaces import *
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from ...ragged import RaggedBatchWrapper
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from .containers import PhiNonTransformerContainer, PhiTransformerContainer
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class PhiInferenceModel(DSTransformerModelBase):
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"""
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Inference model implementation for ragged batching for Llama-2 models.
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"""
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_non_transformer: Optional[PhiNonTransformerContainer]
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"""
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Embed + unembed container. Specializing the type annotation.
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"""
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_transformer: Optional[Iterable[PhiTransformerContainer]]
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"""
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Per-layer transformer container. Specializing the type annotation.
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"""
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"""
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Properties inherited from `DSInferenceModelBase`
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"""
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@property
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def max_sequence_length(self) -> int:
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return self._config.max_seq_length
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"""
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Properties inherited from `DSTransformerModelBase`
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"""
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@property
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def num_layers(self) -> int:
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return self._config.num_hidden_layers
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@property
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def model_dim(self) -> int:
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return self._config.hidden_size
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@property
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def vocab_size(self) -> int:
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return self._config.vocab_size
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@property
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def head_size(self) -> int:
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return self.model_dim // self.n_heads
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@property
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def n_heads(self) -> int:
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return self._config.num_attention_heads
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@property
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def intermediate_dim(self) -> int:
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return self._config.intermediate_size
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@property
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def n_heads_kv(self) -> int:
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return self._config.num_key_value_heads
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@property
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def activation_dtype(self) -> DtypeEnum:
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if self._config.torch_dtype == torch.float16:
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return DtypeEnum.fp16
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elif self._config.torch_dtype == torch.bfloat16:
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return DtypeEnum.bf16
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else:
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raise NotImplementedError("Only fp16 and bf16 are supported")
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@property
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def mlp_activation_fn(self) -> ActivationType:
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return ActivationType.GELU
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@property
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def norm_type(self) -> NormTypeEnum:
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return NormTypeEnum.LayerNorm
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@property
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def positional_embedding_type(self) -> PositionalEmbeddingType:
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return PositionalEmbeddingType.rotate_half
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@property
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def positional_embedding_config(self) -> Optional[RotateHalfConfig]:
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rotary_dim = int(self._config.partial_rotary_factor * self.head_size)
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return RotateHalfConfig(rotate_dim=rotary_dim, theta_base=self._config.rope_theta)
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"""
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Forward implementations
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"""
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def _forward_embed(self, ragged_batch: RaggedBatchWrapper) -> torch.Tensor:
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"""
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Performs the embedding lookup prior to running the transformer of the model.
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Arguments:
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ragged_batch (RaggedBatchWrapper): The batch to embed.
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Returns:
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torch.Tensor: The embedded batch.
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"""
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embed = self.embed(ragged_batch, self._non_transformer.word_emb)
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if embed.shape[-1] != self.model_dim:
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raise ValueError(f"Embedding output shape {embed.shape} does not match model_dim {self.model_dim}")
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return embed
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def _forward_transformer_layer(self, layer_idx: int, residual: torch.Tensor, hidden_states: torch.Tensor,
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ragged_batch_info: RaggedBatchWrapper) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Executes one (slightly offset) layer of the transformer. This implementation does a peak-ahead
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optimization to fuse the layer norm of the next layer into the current layer.
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Arguments:
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layer_idx (int): The index of the layer to execute.
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residual (torch.Tensor): The residual tensor from the previous layer.
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hidden_states (torch.Tensor): The hidden states from the previous layer. This is the
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hidden states after pre normalization.
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ragged_batch_info (RaggedBatchWrapper): The batch metadata.
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"""
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cur_params = self._transformer[layer_idx]
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kv_cache = self.state_manager.get_cache(layer_idx)
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attn_ln_out = hidden_states
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attn_hidden_state = self.qkv(attn_ln_out, cur_params.qkv_w, b=cur_params.qkv_b)
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attn_hidden_state = self.attn(attn_hidden_state, kv_cache, ragged_batch_info)
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attention_output = self.attn_out(attn_hidden_state, cur_params.attn_out_w, b=cur_params.attn_out_b)
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mlp_ln_out = hidden_states
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mlp_hidden_state = self.mlp_1(mlp_ln_out, cur_params.mlp_1_w, b=cur_params.mlp_1_b)
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mlp_output = self.mlp_2(mlp_hidden_state, cur_params.mlp_2_w, b=cur_params.mlp_2_b)
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mlp_output.add_(attention_output)
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if self.tp_size > 1:
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dist.all_reduce(mlp_output, group=self._base_mp_group)
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if layer_idx != self.num_layers - 1:
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next_params = self._transformer[layer_idx + 1]
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residual, mlp_output = self.norm(residual, mlp_output, next_params.ln_gamma, beta=next_params.ln_beta)
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else:
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# On last layer, we just need to perform the residual add. Adding into the residual
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# here is safe.
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residual.add_(mlp_output)
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return residual, mlp_output
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def _forward_unembed(self, hidden_states: torch.Tensor, ragged_batch_info: RaggedBatchWrapper) -> torch.Tensor:
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"""
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Performs unembedding of the hidden states to logits. This will only sample the final
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token of each sequence.
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"""
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logits = self.unembed(hidden_states,
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self._non_transformer.word_unembed_w,
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ragged_batch_info,
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bias=self._non_transformer.word_unembed_b,
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gamma=self._non_transformer.final_norm_gamma,
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beta=self._non_transformer.final_norm_beta)
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if self.tp_size > 1:
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comm_buffer = empty_from(self._comm_logits, (self.tp_size, logits.shape[0], logits.shape[1]))
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full_logits = empty_from(self._return_logits, (logits.shape[0], self.vocab_size))
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dist.all_gather_into_tensor(comm_buffer, logits, group=self._base_mp_group)
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full_logits.copy_(comm_buffer.permute(1, 0, 2).reshape(logits.shape[0], self.vocab_size))
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return full_logits
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else:
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return logits
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def forward(self, wrapped_batch: RaggedBatchWrapper) -> torch.Tensor:
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residual = self._forward_embed(wrapped_batch)
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residual, hidden_states = self.norm(residual,
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None,
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gamma=self._transformer[0].ln_gamma,
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beta=self._transformer[0].ln_beta)
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for layer_idx in range(self.num_layers):
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residual, hidden_states = self._forward_transformer_layer(layer_idx, residual, hidden_states,
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wrapped_batch)
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return self._forward_unembed(residual, wrapped_batch)
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