284 lines
12 KiB
Python
284 lines
12 KiB
Python
# Copyright 2024 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from ..activations import ACT2FN
|
|
from ..utils import is_accelerate_available, is_fbgemm_gpu_available, is_torch_available, logging
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
from torch import nn
|
|
|
|
if is_accelerate_available():
|
|
from accelerate import init_empty_weights
|
|
|
|
if is_fbgemm_gpu_available():
|
|
import fbgemm_gpu.experimental.gen_ai # noqa: F401
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
class FbgemmFp8Linear(torch.nn.Linear):
|
|
def __init__(self, in_features, out_features, bias, weight_dtype=torch.float32):
|
|
super().__init__(in_features, out_features, bias)
|
|
self.in_features = in_features
|
|
self.out_features = out_features
|
|
|
|
self.weight = torch.nn.Parameter(torch.zeros((out_features, in_features), dtype=torch.float8_e4m3fn))
|
|
self.weight_scale = torch.nn.Parameter(torch.zeros((out_features, 1), dtype=weight_dtype))
|
|
self.register_buffer("input_scale_ub", torch.zeros([1], dtype=torch.float), persistent=False)
|
|
|
|
if bias:
|
|
self.bias = torch.nn.Parameter(torch.zeros((self.out_features), dtype=weight_dtype))
|
|
else:
|
|
self.bias = None
|
|
|
|
def forward(self, x):
|
|
# quantize_fp8_per_row will squash the leading dimensions, so save the desired shape here
|
|
output_shape = (*x.shape[:-1], -1)
|
|
# x_quantized and x_scale are not necessarily on the same device as x, this is an issue.
|
|
# https://github.com/pytorch/FBGEMM/blob/e08af8539c391437f447173863df0f3f6f6f1855/fbgemm_gpu/experimental/gen_ai/src/quantize/quantize.cu#L1237C3-L1237C45
|
|
x_quantized, x_scale = torch.ops.fbgemm.quantize_fp8_per_row(
|
|
x.view(-1, x.shape[-1]).contiguous(), scale_ub=self.input_scale_ub
|
|
)
|
|
# moving x_quantized, x_scale here creates glibberish output ... However, if we move the output, it works
|
|
# x_quantized, x_scale = x_quantized.to(x.device), x_scale.to(x.device)
|
|
|
|
# The computation still happens on the device where self.weight is even if x_quantized is not on the same device as self.weight
|
|
weight_scale_float32 = self.weight_scale.to(torch.float32)
|
|
output = torch.ops.fbgemm.f8f8bf16_rowwise(
|
|
x_quantized, self.weight, x_scale, weight_scale_float32, use_fast_accum=True
|
|
)
|
|
output = output + self.bias if self.bias is not None else output
|
|
# Hacky for now, we have the output to the device of x
|
|
output = output.to(x.device)
|
|
output = output.reshape(output_shape)
|
|
del x_quantized, x_scale
|
|
return output
|
|
|
|
|
|
class FbgemmFp8Llama4TextExperts(nn.Module):
|
|
def __init__(self, config, dtype=torch.float32):
|
|
super().__init__()
|
|
self.num_experts = config.num_local_experts
|
|
self.intermediate_size = config.intermediate_size
|
|
self.hidden_size = config.hidden_size
|
|
self.expert_dim = self.intermediate_size
|
|
self.act_fn = ACT2FN[config.hidden_act]
|
|
# Register FP8 buffers for gate_up_proj
|
|
self.gate_up_proj = torch.nn.Parameter(
|
|
torch.zeros((self.num_experts, self.hidden_size, 2 * self.expert_dim), dtype=torch.float8_e4m3fn)
|
|
)
|
|
self.gate_up_proj_scale = torch.nn.Parameter(
|
|
torch.zeros((self.num_experts, 1, self.expert_dim * 2), dtype=torch.float32)
|
|
)
|
|
# Register FP8 buffers for down_proj
|
|
self.down_proj = torch.nn.Parameter(
|
|
torch.zeros((self.num_experts, self.expert_dim, self.hidden_size), dtype=torch.float8_e4m3fn)
|
|
)
|
|
self.down_proj_scale = torch.nn.Parameter(
|
|
torch.zeros((self.num_experts, self.hidden_size, 1), dtype=torch.float32)
|
|
)
|
|
# Register input scale upper bound
|
|
self.register_buffer("input_scale_ub", torch.zeros([1], dtype=torch.float), persistent=False)
|
|
|
|
def forward(self, hidden_states):
|
|
"""
|
|
Args:
|
|
hidden_states (torch.Tensor): (batch_size * token_num, hidden_size)
|
|
Returns:
|
|
torch.Tensor: (batch_size * token_num, hidden_size)
|
|
"""
|
|
# Reshape hidden states for expert computation
|
|
hidden_states = hidden_states.view(self.num_experts, -1, self.hidden_size)
|
|
num_tokens = None
|
|
|
|
# Pre-allocate tensor for all expert outputs with same shape as hidden_states
|
|
next_states = torch.empty_like(hidden_states)
|
|
|
|
for i in range(self.num_experts):
|
|
# Extract expert's hidden states
|
|
expert_hidden = hidden_states[i]
|
|
expert_hidden_reshaped = expert_hidden.reshape(-1, self.hidden_size)
|
|
# Quantize for this expert
|
|
expert_quantized, expert_scale = torch.ops.fbgemm.quantize_fp8_per_row(
|
|
expert_hidden_reshaped, num_tokens, self.input_scale_ub
|
|
)
|
|
sharded_expert_dim = self.gate_up_proj.shape[-1] // 2
|
|
gate_up_proj_scale_float32 = self.gate_up_proj_scale.to(torch.float32)
|
|
|
|
gate = torch.ops.fbgemm.f8f8bf16_rowwise(
|
|
expert_quantized,
|
|
self.gate_up_proj[i].transpose(0, 1)[:sharded_expert_dim].contiguous(),
|
|
expert_scale,
|
|
gate_up_proj_scale_float32[i][0][:sharded_expert_dim].view(-1, 1).contiguous(),
|
|
use_fast_accum=True,
|
|
)
|
|
|
|
up = torch.ops.fbgemm.f8f8bf16_rowwise(
|
|
expert_quantized,
|
|
self.gate_up_proj[i].transpose(0, 1)[sharded_expert_dim:].contiguous(),
|
|
expert_scale,
|
|
gate_up_proj_scale_float32[i][0][sharded_expert_dim:].view(-1, 1).contiguous(),
|
|
use_fast_accum=True,
|
|
)
|
|
|
|
activated = up * self.act_fn(gate)
|
|
|
|
activated_quantized, activated_scale = torch.ops.fbgemm.quantize_fp8_per_row(
|
|
activated, num_tokens, self.input_scale_ub
|
|
)
|
|
|
|
down_proj_scale_float32 = self.down_proj_scale.to(torch.float32)
|
|
expert_output = torch.ops.fbgemm.f8f8bf16_rowwise(
|
|
activated_quantized,
|
|
self.down_proj[i].transpose(0, 1).contiguous(),
|
|
activated_scale,
|
|
down_proj_scale_float32[i].view(-1, 1).contiguous(),
|
|
use_fast_accum=True,
|
|
)
|
|
|
|
next_states[i] = expert_output
|
|
next_states = next_states.to(hidden_states.device)
|
|
return next_states.view(-1, self.hidden_size)
|
|
|
|
|
|
def _replace_with_fbgemm_fp8_linear(
|
|
model,
|
|
modules_to_not_convert=None,
|
|
current_key_name=None,
|
|
quantization_config=None,
|
|
has_been_replaced=False,
|
|
pre_quantized=False,
|
|
config=None,
|
|
tp_plan=None,
|
|
):
|
|
"""
|
|
Private method that wraps the recursion for module replacement.
|
|
|
|
Returns the converted model and a boolean that indicates if the conversion has been successful or not.
|
|
"""
|
|
|
|
import re
|
|
|
|
if current_key_name is None:
|
|
current_key_name = []
|
|
|
|
for name, module in model.named_children():
|
|
current_key_name.append(name)
|
|
|
|
if (isinstance(module, nn.Linear)) and name not in modules_to_not_convert:
|
|
# Check if the current key is not in the `modules_to_not_convert`
|
|
current_key_name_str = ".".join(current_key_name)
|
|
if not any(
|
|
(key + "." in current_key_name_str) or (key == current_key_name_str) for key in modules_to_not_convert
|
|
):
|
|
with init_empty_weights(include_buffers=True):
|
|
in_features = module.in_features
|
|
out_features = module.out_features
|
|
model._modules[name] = FbgemmFp8Linear(
|
|
in_features,
|
|
out_features,
|
|
module.bias is not None,
|
|
)
|
|
has_been_replaced = True
|
|
|
|
# Force requires grad to False to avoid unexpected errors
|
|
model._modules[name].requires_grad_(False)
|
|
# set non persistent buffer outside of init_empty_weights
|
|
model._modules[name].input_scale_ub = torch.tensor(
|
|
[quantization_config.activation_scale_ub],
|
|
dtype=torch.float,
|
|
)
|
|
if module.__class__.__name__ == "Llama4TextExperts" and name not in modules_to_not_convert:
|
|
current_key_name_str = ".".join(current_key_name)
|
|
if not any(
|
|
(key + "." in current_key_name_str) or (key == current_key_name_str) for key in modules_to_not_convert
|
|
):
|
|
with init_empty_weights(include_buffers=True):
|
|
tp_plan[re.sub(r"\d+", "*", current_key_name_str + ".down_proj_scale")] = None
|
|
model._modules[name] = FbgemmFp8Llama4TextExperts(
|
|
config.text_config,
|
|
)
|
|
model._modules[name].input_scale_ub = torch.tensor(
|
|
[quantization_config.activation_scale_ub], dtype=torch.float
|
|
)
|
|
|
|
if len(list(module.children())) > 0:
|
|
_, has_been_replaced = _replace_with_fbgemm_fp8_linear(
|
|
module,
|
|
modules_to_not_convert,
|
|
current_key_name,
|
|
quantization_config,
|
|
has_been_replaced=has_been_replaced,
|
|
pre_quantized=pre_quantized,
|
|
config=config,
|
|
tp_plan=tp_plan,
|
|
)
|
|
# Remove the last key for recursion
|
|
current_key_name.pop(-1)
|
|
return model, has_been_replaced
|
|
|
|
|
|
def replace_with_fbgemm_fp8_linear(
|
|
model,
|
|
modules_to_not_convert=None,
|
|
current_key_name=None,
|
|
quantization_config=None,
|
|
pre_quantized=False,
|
|
config=None,
|
|
tp_plan=None,
|
|
):
|
|
"""
|
|
A helper function to replace all `torch.nn.Linear` modules by `FbgemmFp8Linear` modules.
|
|
This will enable running your models using high performance fp8 kernel from FBGEMM library.
|
|
|
|
The function will be run recursively and replace all `torch.nn.Linear` modules except for the `lm_head` that should
|
|
be kept as a `torch.nn.Linear` module. The replacement is done under `init_empty_weights` context manager so no
|
|
CPU/GPU memory is required to run this function. Each weight will be quantized along the channel.
|
|
|
|
Parameters:
|
|
model (`torch.nn.Module`):
|
|
Input model or `torch.nn.Module` as the function is run recursively.
|
|
modules_to_not_convert (`list[`str`]`, *optional*, defaults to `["lm_head"]`):
|
|
Names of the modules to not convert in `FP8Linear`. In practice we keep the `lm_head` in full precision
|
|
for numerical stability reasons.
|
|
current_key_name (`list[`str`]`, *optional*):
|
|
An array to track the current key of the recursion. This is used to check whether the current key (part of
|
|
it) is not in the list of modules to not convert (for instances modules that are offloaded to `cpu` or
|
|
`disk`).
|
|
"""
|
|
|
|
modules_to_not_convert = ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
|
|
|
|
if quantization_config.modules_to_not_convert is not None:
|
|
modules_to_not_convert.extend(quantization_config.modules_to_not_convert)
|
|
modules_to_not_convert = list(set(modules_to_not_convert))
|
|
model, has_been_replaced = _replace_with_fbgemm_fp8_linear(
|
|
model,
|
|
modules_to_not_convert,
|
|
current_key_name,
|
|
quantization_config,
|
|
pre_quantized=pre_quantized,
|
|
config=config,
|
|
tp_plan=tp_plan,
|
|
)
|
|
if not has_been_replaced:
|
|
logger.warning(
|
|
"You are loading your model using FP8 quantization but no linear modules were found in your model."
|
|
" Please double check your model architecture, or submit an issue on github if you think this is"
|
|
" a bug."
|
|
)
|
|
|
|
return model
|