tensorrt_llm.layers.mlp — TensorRT-LLM (original) (raw)
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from typing import Optional
import tensorrt as trt
from .._common import default_net from ..functional import (ACT2FN, AllReduceParams, cast, chunk, concat, gemm_swiglu, is_gated_activation, low_latency_gemm_swiglu) from ..mapping import Mapping from ..module import Module from ..quantization import QuantMode from ..quantization.functional import quantize from ..quantization.layers import FP8Linear, FP8RowLinear from .linear import ColumnLinear, RowLinear from .lora import LoraRuntimeParams from .normalization import LayerNorm
[docs] def fc_gate_lora(hidden_states, lora, fused_gate_up_lora, lora_layer_params): if lora_layer_params is not None: mlp_fc_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_h_to_4h") mlp_gate_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_gate") mlp_gate_up_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_gate_up")
if mlp_gate_up_lora_params is not None:
assert fused_gate_up_lora is not None
mlp_gate_up_lora = fused_gate_up_lora(hidden_states,
mlp_gate_up_lora_params)
return mlp_gate_up_lora
elif mlp_fc_lora_params is not None and mlp_gate_lora_params is not None:
mlp_in_lora_params = LoraRuntimeParams(
lora_ranks=[
mlp_fc_lora_params.lora_ranks[0],
mlp_gate_lora_params.lora_ranks[0]
],
lora_weights_pointers=[
mlp_fc_lora_params.lora_weights_pointers[0],
mlp_gate_lora_params.lora_weights_pointers[0]
],
host_request_types=mlp_fc_lora_params.host_request_types,
host_context_lengths=mlp_fc_lora_params.host_context_lengths)
mlp_fc_lora, mlp_gate_lora = lora(hidden_states, mlp_in_lora_params)
mlp_in_result = concat([mlp_gate_lora, mlp_fc_lora],
dim=mlp_fc_lora.rank() - 1)
return mlp_in_result
return None[docs] def fc_gate_dora(hidden_states, dora, fused_gate_up_dora, lora_layer_params): if lora_layer_params is not None: mlp_fc_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_h_to_4h") mlp_gate_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_gate") mlp_gate_up_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_gate_up")
if mlp_gate_up_lora_params is not None:
assert fused_gate_up_dora is not None
return fused_gate_up_dora(hidden_states, mlp_gate_up_lora_params)
if mlp_fc_lora_params is not None and mlp_gate_lora_params is not None:
mlp_in_lora_params = LoraRuntimeParams(
lora_ranks=[
mlp_fc_lora_params.lora_ranks[0],
mlp_gate_lora_params.lora_ranks[0]
],
lora_weights_pointers=[
mlp_fc_lora_params.lora_weights_pointers[0],
mlp_gate_lora_params.lora_weights_pointers[0]
],
host_request_types=mlp_fc_lora_params.host_request_types,
host_context_lengths=mlp_fc_lora_params.host_context_lengths)
return dora(hidden_states, mlp_in_lora_params)
return None[docs] class MLP(Module):
def __init__(
self,
hidden_size,
ffn_hidden_size,
hidden_act,
bias=True,
dtype=None,
tp_group=None,
tp_size=1,
quant_mode=QuantMode(0),
inner_layernorm=False,
eps=1e-05,
is_expert=False,
):
super().__init__()
if hidden_act not in ACT2FN:
raise ValueError(
'unsupported activation function: {}'.format(hidden_act))
fc_output_size = 2 * ffn_hidden_size if hidden_act in [
'swiglu', 'gegelu'
] else ffn_hidden_size
self.inner_layernorm = LayerNorm(ffn_hidden_size, dtype=dtype,
eps=eps) if inner_layernorm else None
self.fc = ColumnLinear(hidden_size,
fc_output_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
gather_output=False)
self.proj = RowLinear(ffn_hidden_size,
hidden_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
is_expert=is_expert)
self.hidden_size = hidden_size
self.ffn_hidden_size = ffn_hidden_size
self.hidden_act = hidden_act
self.dtype = dtype
self.bias = bias
self.tp_group = tp_group
self.tp_size = tp_size
self.quant_mode = quant_mode
self.eps = eps
self.is_expert = is_expert
# see optimize_model's add_lora for LoRA initialization
self.lora = None
self.dora = None[docs] def forward(self, hidden_states, lora_layer_params=None, gegelu_limit=None): if lora_layer_params is not None: assert lora_layer_params.get_runtime_params( 0, "mlp_gate_up" ) is None, f"LoRA module 'mlp_gate_up' is not supported in {self}" if is_gated_activation(self.hidden_act): inter = self.fc(hidden_states) lora_result = fc_gate_lora(hidden_states, self.lora, None, lora_layer_params) if lora_result is not None: inter = inter + lora_result if self.dora is not None: inter = fc_gate_dora(inter, self.dora, self.fused_gate_up_dora, lora_layer_params) else: mlp_fc_lora_params = None if lora_layer_params is not None: mlp_fc_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_h_to_4h") inter = self.fc(hidden_states, mlp_fc_lora_params)
mlp_proj_lora_params = None
if lora_layer_params is not None:
mlp_proj_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_4h_to_h")
if self.hidden_act == 'gegelu':
inter = ACT2FN[self.hidden_act](inter, gegelu_limit)
else:
inter = ACT2FN[self.hidden_act](inter)
if self.inner_layernorm is not None:
inter = self.inner_layernorm(inter)
output = self.proj(inter, lora_runtime_params=mlp_proj_lora_params)
return output[docs] class GatedMLP(MLP):
def __init__(
self,
hidden_size,
ffn_hidden_size,
hidden_act,
bias=True,
dtype=None,
tp_group=None,
tp_size=1,
quant_mode=QuantMode(0),
inner_layernorm=False,
eps=1e-05,
is_expert=False,
):
super().__init__(hidden_size,
ffn_hidden_size,
hidden_act,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=quant_mode,
inner_layernorm=inner_layernorm,
eps=eps,
is_expert=is_expert)
self.hidden_size = hidden_size
self.ffn_hidden_size = ffn_hidden_size
self.tp_group = tp_group
self.tp_size = tp_size
self.gate = ColumnLinear(hidden_size,
ffn_hidden_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
gather_output=False)[docs] def forward(self, hidden_states, lora_layer_params=None, all_reduce_params: Optional[AllReduceParams] = None): if lora_layer_params is not None: assert lora_layer_params.get_runtime_params( 0, "mlp_gate_up" ) is None, f"LoRA module 'mlp_gate_up' is not supported in {self}"
mlp_fc_lora_params = None
if lora_layer_params is not None:
mlp_fc_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_h_to_4h")
mlp_gate_lora_params = None
if lora_layer_params is not None:
mlp_gate_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_gate")
mlp_proj_lora_params = None
if lora_layer_params is not None:
mlp_proj_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_4h_to_h")
inter = self.fc(hidden_states, mlp_fc_lora_params)
inter = ACT2FN[self.hidden_act](inter)
gate = self.gate(hidden_states, mlp_gate_lora_params)
intermediate = inter * gate
if self.inner_layernorm is not None:
intermediate = self.inner_layernorm(intermediate)
output = self.proj(intermediate,
lora_runtime_params=mlp_proj_lora_params,
all_reduce_params=all_reduce_params)
return output[docs] class FusedGatedMLP(Module):
def __init__(
self,
hidden_size,
ffn_hidden_size,
hidden_act,
bias=True,
dtype=None,
tp_group=None,
tp_size=1,
quant_mode=QuantMode(0),
inner_layernorm=False,
eps=1e-05,
is_expert=False,
):
super().__init__()
self.hidden_size = hidden_size
self.ffn_hidden_size = ffn_hidden_size
self.hidden_act = hidden_act
self.bias = bias
self.dtype = dtype
self.tp_group = tp_group
self.tp_size = tp_size
self.quant_mode = quant_mode
self.fused_fc = ColumnLinear(
self.hidden_size,
self.ffn_hidden_size * 2,
bias=self.bias,
dtype=self.dtype,
tp_group=self.tp_group,
tp_size=self.tp_size,
gather_output=False,
)
self.inner_layernorm = LayerNorm(ffn_hidden_size, dtype=dtype,
eps=eps) if inner_layernorm else None
self.proj = RowLinear(ffn_hidden_size,
hidden_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
is_expert=is_expert)
# see optimize_model's add_lora for LoRA initialization
self.lora = None # used for split up and gate proj
self.fused_gate_up_lora = None # used for merged up_gate proj
self.dora = None
self.fused_gate_up_dora = None[docs] def fc_gate_plugin(self, hidden_states, lora_layer_params=None): # Combine the following pattern # # SiLU(FC(x)) * Gate(x) # # into: # # SwiGLU(FusedFC(x)) if default_net( ).plugin_config.low_latency_gemm_swiglu_plugin is not None: p_dtype = default_net().plugin_config.low_latency_gemm_swiglu_plugin else: p_dtype = default_net().plugin_config.gemm_swiglu_plugin use_fp8 = p_dtype == 'fp8' assert use_fp8, "gemm_swiglu_plugin and low_latency_gemm_swiglu_plugin only supports fp8 now"
if lora_layer_params is not None:
mlp_fc_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_h_to_4h")
mlp_gate_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_gate")
if mlp_fc_lora_params is not None or mlp_gate_lora_params is not None:
raise NotImplementedError(
f"LoRA of splitting fc and gate is not yet implemented for gemm_swiglu_plugin"
)
if self.hidden_act != 'silu':
raise NotImplementedError(
f"Activation {self.hidden_act} not yet implemented for gemm_swiglu_plugin"
)
if self.bias:
raise NotImplementedError(
f"bias not yet implemented for gemm_swiglu_plugin fp8")
assert isinstance(
self.fused_fc,
FP8Linear), "fp8 gemm_swiglu only supports fp8 weights"
assert isinstance(
self.proj,
FP8RowLinear), "fp8 gemm_swiglu only supports fp8 weights"
assert self.fused_fc.weight.shape == (
self.hidden_size, self.ffn_hidden_size * 2 //
self.tp_size), "fp8 gemm_swiglu only supports (k, n) weights"
scale_d0 = (self.fused_fc.weights_scaling_factor.raw_value.item() *
self.fused_fc.activation_scaling_factor.raw_value.item())
scale_d1 = scale_d0
scale_output = 1.0 / self.proj.activation_scaling_factor.raw_value.item(
)
activation_scaling_factor = cast(
self.fused_fc.activation_scaling_factor.value, self.dtype)
if hidden_states.dtype != trt.fp8:
hidden_states = quantize(hidden_states, activation_scaling_factor,
'fp8')
if default_net(
).plugin_config.low_latency_gemm_swiglu_plugin is not None:
inter = low_latency_gemm_swiglu(hidden_states,
self.fused_fc.weight.value,
scale_d0, scale_d1, scale_output)
else:
inter = gemm_swiglu(hidden_states, self.fused_fc.weight.value, None,
scale_d0, scale_d1, scale_output)
lora_result = fc_gate_lora(hidden_states, self.lora,
self.fused_gate_up_lora, lora_layer_params)
if lora_result is not None:
inter = inter + lora_result
return inter[docs] def fc_gate(self, hidden_states, lora_layer_params=None): # Combine the following pattern # # SiLU(FC(x)) * Gate(x) # # into: # # SwiGLU(FusedFC(x)) # # Upside is we don't need to modify 4 different weight loading paths just to concat weights
inter = self.fused_fc(hidden_states)
lora_result = fc_gate_lora(hidden_states, self.lora,
self.fused_gate_up_lora, lora_layer_params)
if lora_result is not None:
inter = inter + lora_result
if self.dora is not None:
inter = fc_gate_dora(inter, self.dora, self.fused_gate_up_lora,
lora_layer_params)
if self.hidden_act == 'silu':
inter = ACT2FN['swiglu'](inter)
elif self.hidden_act == 'gelu':
inter = ACT2FN['geglu'](inter)
else:
raise NotImplementedError(
f"Activation {self.hidden_act} not yet implemented for {self.__class__.__name__}."
)
return inter[docs] def forward(self, hidden_states, lora_layer_params=None, all_reduce_params: Optional[AllReduceParams] = None): if default_net().plugin_config.gemm_swiglu_plugin or default_net( ).plugin_config.low_latency_gemm_swiglu_plugin: inter = self.fc_gate_plugin(hidden_states, lora_layer_params) else: inter = self.fc_gate(hidden_states, lora_layer_params)
if self.inner_layernorm is not None:
inter = self.inner_layernorm(inter)
mlp_proj_lora_params = None
if lora_layer_params is not None:
mlp_proj_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_4h_to_h")
output = self.proj(inter,
lora_runtime_params=mlp_proj_lora_params,
all_reduce_params=all_reduce_params)
return output[docs] class LinearGELU(Module):
def __init__(self,
dim_in: int,
dim_out: int,
approximate: str = 'tanh',
bias: bool = True,
mapping=Mapping(),
dtype=None):
super().__init__()
self.proj = ColumnLinear(dim_in,
dim_out,
bias=bias,
dtype=dtype,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size)
if approximate != 'tanh':
raise NotImplementedError('GELU only support tanh now.')[docs] def forward(self, hidden_states): hidden_states = self.proj(hidden_states) hidden_states = ACT2FN'gelu_pytorch_tanh' return hidden_states
[docs] class LinearGEGLU(Module):
def __init__(self,
dim_in: int,
dim_out: int,
approximate: str = 'tanh',
bias: bool = True,
mapping=Mapping(),
dtype=None):
super().__init__()
self.proj = ColumnLinear(dim_in,
dim_out * 2,
bias=bias,
dtype=dtype,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size)
if approximate != 'tanh':
raise NotImplementedError('GELU only support tanh now.')[docs] def forward(self, hidden_states): hidden_states = self.proj(hidden_states) hidden_states, gate = chunk(hidden_states, 2, dim=(hidden_states.ndim() - 1)) return hidden_states * ACT2FN'gelu_pytorch_tanh'
[docs] class LinearApproximateGELU(Module):
def __init__(self,
dim_in: int,
dim_out: int,
bias: bool = True,
mapping=Mapping(),
dtype=None):
super().__init__()
self.proj = ColumnLinear(dim_in,
dim_out,
bias=bias,
dtype=dtype,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size)[docs] def forward(self, x): x = self.proj(x) return x * ACT2FN['sigmoid'](1.702 * x)
[docs] class LinearSwiGLU(Module):
def __init__(self,
dim_in: int,
dim_out: int,
bias: bool = True,
mapping=Mapping(),
dtype=None):
super().__init__()
self.proj = ColumnLinear(dim_in,
dim_out * 2,
bias=bias,
dtype=dtype,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size)
self.hidden_act = 'silu'[docs] def forward(self, hidden_states): hidden_states = self.proj(hidden_states) hidden_states, gate = chunk(hidden_states, 2, dim=(hidden_states.ndim() - 1)) return hidden_states * ACT2FNself.hidden_act
[docs] class LinearActivation(Module):
def __init__(self,
dim_in: int,
dim_out: int,
bias: bool = True,
activation: str = "silu",
mapping=Mapping(),
dtype=None):
super().__init__()
self.proj = ColumnLinear(dim_in,
dim_out,
bias=bias,
dtype=dtype,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size)
self.hidden_act = activation[docs] def forward(self, hidden_states): hidden_states = self.proj(hidden_states) return ACT2FNself.activation