mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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317 lines
12 KiB
Python
317 lines
12 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional
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import torch
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from .._utils import set_obj_attrs
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from ..functional import Tensor, allgather, cast, concat, matmul, rg_lru, shape
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from ..mapping import Mapping
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from ..module import Module
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from ..parameter import Parameter
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from .linear import ColumnLinear, RowLinear
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from .ssm import MambaConv1d
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class GroupedLinear(Module):
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def __init__(self,
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in_features,
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out_features,
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num_blocks,
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bias=True,
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dtype=None,
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use_fp8=False,
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tp_group=None,
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tp_size=1,
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gather_output=True,
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strict_dtype=False,
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fuse_bias=False):
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super().__init__()
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assert in_features % num_blocks == 0 and out_features % num_blocks == 0
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assert num_blocks % tp_size == 0
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assert not (gather_output and fuse_bias)
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self.in_features = in_features // tp_size
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self.out_features = out_features // tp_size
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self.num_blocks = num_blocks // tp_size
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self.dtype = dtype
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self.use_fp8 = use_fp8
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self.fuse_bias = fuse_bias
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self.weight = Parameter(shape=(self.num_blocks,
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self.in_features // self.num_blocks,
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self.out_features // self.num_blocks),
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dtype=('fp8' if use_fp8 else dtype))
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set_obj_attrs(self.weight, {
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"weight_loader": self.weight_loader,
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})
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self.tp_size = tp_size
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self.tp_group = tp_group
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self.gather_output = gather_output
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self.strict_dtype = self.dtype if strict_dtype else None
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if bias:
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self.bias = Parameter(shape=(self.num_blocks,
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self.out_features // self.num_blocks),
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dtype=dtype)
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set_obj_attrs(self.bias, {
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"weight_loader": self.weight_loader,
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})
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else:
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self.register_parameter('bias', None)
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def multiply_gather(self, x, weight):
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grouped_shape = []
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out_shape = []
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ndim = x.ndim()
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for i in range(x.ndim() - 1):
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grouped_shape.append(shape(x, i))
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out_shape.append(shape(x, i))
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grouped_shape.extend(
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[self.num_blocks, self.in_features // self.num_blocks])
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out_shape.append(self.out_features)
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x = x.view(concat(grouped_shape)).permute([i for i in range(ndim - 2)] +
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[-2, -3, -1])
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x = matmul(x, weight)
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x = x.permute([i for i in range(ndim - 2)] + [-2, -3, -1])
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if self.bias is not None and not self.fuse_bias:
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bias = cast(self.bias.value, x.dtype)
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x = x + bias
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x = x.view(concat(out_shape))
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if self.gather_output and self.tp_size > 1 and self.tp_group is not None:
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# [dim0, local_dim] -> [dim0 * tp_size, local_dim] --> [dim0, local_dim * tp_size]
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x = allgather(x, self.tp_group, gather_dim=-1)
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return x
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def forward(self, x):
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return self.multiply_gather(x, self.weight.value)
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def weight_loader(self, mapping: Mapping, param: Parameter,
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loaded_weight: torch.Tensor):
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tp_rank = mapping.tp_rank
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output_dim = 0
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shard_size = param._shape[output_dim]
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start_idx = tp_rank * shard_size
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loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)
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param.value = loaded_weight
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class RgLru(Module):
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def __init__(self,
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lru_width,
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num_heads=1,
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dtype=None,
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tp_group=None,
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tp_size=1):
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super().__init__()
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self.lru_width = lru_width
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self.dtype = dtype
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self.num_heads = num_heads
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self.tp_group = tp_group
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self.tp_size = tp_size
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self.recurrent_param = Parameter(shape=(self.lru_width //
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self.tp_size, ),
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dtype=self.dtype)
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self.input_gate = GroupedLinear(self.lru_width,
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self.lru_width,
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self.num_heads,
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dtype=self.dtype,
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tp_group=self.tp_group,
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tp_size=self.tp_size,
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gather_output=False,
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fuse_bias=True)
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self.recurrent_gate = GroupedLinear(self.lru_width,
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self.lru_width,
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self.num_heads,
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dtype=self.dtype,
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tp_group=self.tp_group,
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tp_size=self.tp_size,
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gather_output=False,
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fuse_bias=True)
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def forward(self,
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x: Tensor,
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y: Tensor,
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y_bias: Tensor,
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lru_state: Tensor,
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host_request_types: Tensor,
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last_token_ids: Tensor,
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slot_mapping: Optional[Tensor] = None):
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gate_x = self.input_gate(x)
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gate_a = self.recurrent_gate(x)
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out, lru_state = rg_lru(input=x,
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gate_x=gate_x,
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gate_x_bias=self.input_gate.bias.value,
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gate_a=gate_a,
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gate_a_bias=self.recurrent_gate.bias.value,
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y=y,
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y_bias=y_bias,
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state_or_ptr=lru_state,
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A=self.recurrent_param.value,
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host_request_types=host_request_types,
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last_token_ids=last_token_ids,
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dim=self.lru_width // self.tp_size,
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dtype=self.dtype,
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slot_mapping=slot_mapping)
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return out, lru_state
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class FusedRgLru(Module):
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def __init__(self,
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lru_width,
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num_heads=1,
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dtype=None,
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tp_group=None,
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tp_size=1):
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super().__init__()
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self.lru_width = lru_width
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self.tp_size = tp_size
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self.dtype = dtype
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self.dim = self.lru_width // self.tp_size
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self.block_size = self.lru_width // num_heads
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self.recurrent_param = Parameter(shape=(self.lru_width // tp_size, ),
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dtype=dtype)
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self.gate = GroupedLinear(self.lru_width,
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self.lru_width * 2,
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num_heads,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size,
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gather_output=False,
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fuse_bias=True)
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def forward(self,
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x: Tensor,
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y: Tensor,
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y_bias: Tensor,
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lru_state: Tensor,
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host_request_types: Tensor,
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last_token_ids: Tensor,
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slot_mapping: Optional[Tensor] = None):
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gate = self.gate(x)
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out, lru_state = rg_lru(input=x,
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gate=gate,
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gate_bias=self.gate.bias.value,
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block_size=self.block_size,
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y=y,
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y_bias=y_bias,
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state_or_ptr=lru_state,
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A=self.recurrent_param.value,
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host_request_types=host_request_types,
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last_token_ids=last_token_ids,
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dim=self.dim,
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dtype=self.dtype,
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slot_mapping=slot_mapping)
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return out, lru_state
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class Recurrent(Module):
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def __init__(
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self,
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width,
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lru_width,
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d_conv=4,
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num_heads=1,
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dtype=None,
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tp_group=None,
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tp_size=1,
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):
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super().__init__()
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self.width = width
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self.lru_width = lru_width
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self.d_conv = d_conv
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self.dtype = dtype
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self.linear_x = ColumnLinear(self.width,
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self.lru_width,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size,
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gather_output=False)
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self.linear_y = ColumnLinear(self.width,
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self.lru_width,
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bias=False,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size,
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gather_output=False)
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self.y_bias = Parameter(shape=(self.lru_width // tp_size, ),
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dtype=dtype)
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self.conv1d = MambaConv1d(self.lru_width // tp_size,
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self.d_conv,
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dtype=self.dtype,
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apply_silu=False)
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self.rg_lru = RgLru(self.lru_width,
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num_heads=num_heads,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size)
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self.linear_out = RowLinear(self.lru_width,
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self.width,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size)
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def forward(self,
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hidden_states: Tensor,
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conv_state: Tensor,
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lru_state: Tensor,
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host_request_types: Tensor,
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last_token_ids: Tensor,
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host_context_lengths: Optional[Tensor] = None,
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slot_mapping: Optional[Tensor] = None,
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conv_indices: Optional[Tensor] = None):
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'''
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Parameters:
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hidden_states: [B, L, D] or [T, D]
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conv_state: [B, W, D] or [1] of type int64 for paged state
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lru_state: [B, N] or [1] of type int64 for paged state
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host_request_types: [B]
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last_token_ids: [B]
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host_context_lengths: [B]
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slot_mapping: [B]
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conv_indices: [B]
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'''
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# y branch
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y = self.linear_y(hidden_states)
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# x branch
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x = self.linear_x(hidden_states)
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x_conv, conv_state = self.conv1d(x, conv_state, host_request_types,
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last_token_ids, host_context_lengths,
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slot_mapping, conv_indices)
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# rg-lru
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out, lru_state = self.rg_lru(x_conv, y, self.y_bias.value, lru_state,
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host_request_types, last_token_ids,
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slot_mapping)
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# linear out
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out = self.linear_out(out)
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return out, conv_state, lru_state
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