Improving mamba runtime by using updates (#1552)
- Move float16 to bfloat16, which has less imprecisions (load test are failing with the update kernels + f16, all working under bf16). Another note, is that we are not respecting the layer norm in f32 defined in the configuration (this is OK in my book, but that could impact the f16 precision) - Moved to update kernels. Triton overhead is super high, removed by switching to cuda graphs works great (update cuda graph is available in TRT-LLM if needed, seems *exactly* like the regular ssm kernel. - Moved inference_params struct in order to make only 2 tensors, to reduce the overhead of copying back and forth to the cuda graphs. - Left over overhead seems entirely in the tokenization bit. (Still 4 copies are paid before launching the graph) # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil -->
This commit is contained in:
Nicolas Patry
GitHub
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|
||||
"logprob": -12.8203125,
|
||||
"logprob": -13.25,
|
||||
"text": " Deep"
|
||||
},
|
||||
{
|
||||
"id": 20727,
|
||||
"logprob": -2.9902344,
|
||||
"logprob": -2.828125,
|
||||
"text": " Learning"
|
||||
},
|
||||
{
|
||||
"id": 32,
|
||||
"logprob": -1.1523438,
|
||||
"logprob": -1.1953125,
|
||||
"text": "?"
|
||||
}
|
||||
],
|
||||
|
|
@ -332,61 +332,61 @@
|
|||
"tokens": [
|
||||
{
|
||||
"id": 187,
|
||||
"logprob": -0.35351562,
|
||||
"logprob": -0.296875,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 187,
|
||||
"logprob": -0.38256836,
|
||||
"logprob": -0.3359375,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 30763,
|
||||
"logprob": -1.1269531,
|
||||
"logprob": -1.2578125,
|
||||
"special": false,
|
||||
"text": "Deep"
|
||||
},
|
||||
{
|
||||
"id": 4715,
|
||||
"logprob": -0.54541016,
|
||||
"logprob": -0.5546875,
|
||||
"special": false,
|
||||
"text": " learning"
|
||||
},
|
||||
{
|
||||
"id": 310,
|
||||
"logprob": -0.59765625,
|
||||
"logprob": -0.62890625,
|
||||
"special": false,
|
||||
"text": " is"
|
||||
},
|
||||
{
|
||||
"id": 247,
|
||||
"logprob": -0.7001953,
|
||||
"logprob": -0.64453125,
|
||||
"special": false,
|
||||
"text": " a"
|
||||
},
|
||||
{
|
||||
"id": 747,
|
||||
"logprob": -2.0585938,
|
||||
"logprob": -2.078125,
|
||||
"special": false,
|
||||
"text": " new"
|
||||
},
|
||||
{
|
||||
"id": 1511,
|
||||
"logprob": -2.3789062,
|
||||
"logprob": -2.28125,
|
||||
"special": false,
|
||||
"text": " type"
|
||||
},
|
||||
{
|
||||
"id": 273,
|
||||
"logprob": -0.0027446747,
|
||||
"logprob": -0.0030670166,
|
||||
"special": false,
|
||||
"text": " of"
|
||||
},
|
||||
{
|
||||
"id": 5145,
|
||||
"logprob": -1.2851562,
|
||||
"logprob": -1.3125,
|
||||
"special": false,
|
||||
"text": " machine"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -47,14 +47,14 @@ async def test_mamba_all_params(fused_kernel_mamba, response_snapshot):
|
|||
assert response.details.generated_tokens == 10
|
||||
assert (
|
||||
response.generated_text
|
||||
== "blue, red, yellow, \nand orange (in the order they appear in"
|
||||
== "blue, red, yellow, \nand blue colors. A number of different color"
|
||||
)
|
||||
assert response == response_snapshot
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.private
|
||||
async def test_mamba_load(fused_kernel_mamba, generate_load, response_snapshot):
|
||||
async def test_mamba_load(fused_kernel_mamba, generate_load, generous_response_snapshot):
|
||||
responses = await generate_load(
|
||||
fused_kernel_mamba, "What is Deep Learning?", max_new_tokens=10, n=4
|
||||
)
|
||||
|
|
@ -63,4 +63,4 @@ async def test_mamba_load(fused_kernel_mamba, generate_load, response_snapshot):
|
|||
assert all([r.generated_text == responses[0].generated_text for r in responses])
|
||||
assert responses[0].generated_text == "\n\nDeep learning is a new type of machine"
|
||||
|
||||
assert responses == response_snapshot
|
||||
assert responses == generous_response_snapshot
|
||||
|
|
|
|||
|
|
@ -3,7 +3,6 @@ import torch.distributed
|
|||
|
||||
from mamba_ssm.ops.triton.selective_state_update import selective_state_update
|
||||
from mamba_ssm.ops.selective_scan_interface import selective_scan_fn
|
||||
from mamba_ssm.utils.generation import InferenceParams
|
||||
from torch import nn
|
||||
from typing import Optional, Tuple, Any
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
|
|
@ -18,6 +17,17 @@ from text_generation_server.utils.layers import (
|
|||
from einops import rearrange
|
||||
from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
|
||||
import math
|
||||
from dataclasses import dataclass
|
||||
|
||||
@dataclass
|
||||
class InferenceParams:
|
||||
"""Inference parameters that are passed to the main model in order
|
||||
to efficienly calculate and store the context during inference."""
|
||||
max_seqlen: int
|
||||
max_batch_size: int
|
||||
conv_states: torch.Tensor
|
||||
ssm_states: torch.Tensor
|
||||
seqlen_offset: int
|
||||
|
||||
|
||||
class MambaConfig(PretrainedConfig):
|
||||
|
|
@ -56,9 +66,9 @@ class MambaConfig(PretrainedConfig):
|
|||
|
||||
|
||||
class MambaBlock(nn.Module):
|
||||
def __init__(self, prefix, config, weights):
|
||||
def __init__(self, prefix, config, weights, layer_id):
|
||||
super().__init__()
|
||||
self.layer_idx = int(prefix.split(".")[2])
|
||||
self.layer_id = layer_id
|
||||
self.in_proj = FastLinear.load(config, f"{prefix}.in_proj", weights, bias=False)
|
||||
self.x_proj = FastLinear.load(config, f"{prefix}.x_proj", weights, bias=False)
|
||||
self.dt_proj = FastLinear.load(config, f"{prefix}.dt_proj", weights, bias=True)
|
||||
|
|
@ -79,21 +89,20 @@ class MambaBlock(nn.Module):
|
|||
|
||||
# inference_params
|
||||
def forward(self, hidden_states: torch.Tensor, inference_params=None):
|
||||
_, seqlen, _ = hidden_states.shape
|
||||
conv_state, ssm_state = inference_params.key_value_memory_dict[self.layer_idx]
|
||||
|
||||
if inference_params.seqlen_offset > 0:
|
||||
conv_state = inference_params.conv_states[self.layer_id]
|
||||
ssm_state = inference_params.ssm_states[self.layer_id]
|
||||
out, conv_state, ssm_state = self.step(hidden_states, conv_state, ssm_state)
|
||||
return out, conv_state, ssm_state
|
||||
|
||||
_, seqlen, _ = hidden_states.shape
|
||||
projected_states = self.in_proj(hidden_states).transpose(1, 2)
|
||||
# assert projected_states.shape == [batch_size, 2 * dstate, seqlen], f"{projected_states.shape} [{batch_size}, {dstate}, {seqlen}]"
|
||||
x, z = projected_states.chunk(2, dim=1)
|
||||
conv_state = F.pad(x, (self.d_conv - seqlen, 0))
|
||||
x = causal_conv1d_fn(
|
||||
x=x,
|
||||
weight=self.conv1d.weight.view(
|
||||
self.conv1d.weight.size(0), self.conv1d.weight.size(2)
|
||||
),
|
||||
weight=self.conv1d.weight.squeeze(1),
|
||||
bias=self.conv1d.bias,
|
||||
activation=self.activation,
|
||||
)
|
||||
|
|
@ -126,56 +135,28 @@ class MambaBlock(nn.Module):
|
|||
return attn_outputs, conv_state, last_state
|
||||
|
||||
def step(self, hidden_states, conv_state, ssm_state):
|
||||
_xz = self.in_proj(hidden_states)
|
||||
_x, _z = _xz.chunk(2, dim=-1) # (B D)
|
||||
conv_state_new = torch.cat([conv_state, _x.transpose(1, 2)], dim=-1)
|
||||
conv_out = causal_conv1d_fn(
|
||||
x=conv_state_new,
|
||||
weight=self.conv1d.weight.view(
|
||||
self.conv1d.weight.size(0), self.conv1d.weight.size(2)
|
||||
),
|
||||
bias=self.conv1d.bias,
|
||||
activation=self.activation,
|
||||
xz = self.in_proj(hidden_states.squeeze(1))
|
||||
x, z = xz.chunk(2, dim=-1) # (B D)
|
||||
x = causal_conv1d_update(x, conv_state, self.conv1d.weight.squeeze(1), self.conv1d.bias, self.activation)
|
||||
x_db = self.x_proj(x) # (B dt_rank+2*d_state)
|
||||
dt, B, C = torch.split(x_db, [self.dt_rank, self.d_state, self.d_state], dim=-1)
|
||||
dt = F.linear(dt, self.dt_proj.weight)
|
||||
A = self.negA
|
||||
y = selective_state_update(
|
||||
ssm_state, x, dt, A, B, C, self.D, z=z, dt_bias=self.dt_proj.bias, dt_softplus=True
|
||||
)
|
||||
conv_state = conv_state_new[:, :, 1:]
|
||||
bsz, seqlen, dim = hidden_states.shape
|
||||
output_tensor = torch.zeros(
|
||||
(bsz, seqlen, dim), device=hidden_states.device, dtype=hidden_states.dtype
|
||||
)
|
||||
for i in range(0, bsz):
|
||||
x = conv_out[i : i + 1, :, -1]
|
||||
z = _z[i : i + 1, -1, :]
|
||||
x_db = self.x_proj(x)
|
||||
dt, B, C = torch.split(
|
||||
x_db, [self.dt_rank, self.d_state, self.d_state], dim=-1
|
||||
)
|
||||
dt = F.linear(dt, self.dt_proj.weight)
|
||||
y = selective_state_update(
|
||||
ssm_state[i : i + 1, :, :],
|
||||
x,
|
||||
dt,
|
||||
self.negA,
|
||||
B,
|
||||
C,
|
||||
self.D,
|
||||
z=z,
|
||||
dt_bias=self.dt_proj.bias,
|
||||
dt_softplus=True,
|
||||
)
|
||||
out = self.out_proj(y)
|
||||
output_tensor[i] = out
|
||||
|
||||
return output_tensor, conv_state, ssm_state
|
||||
out = self.out_proj(y)
|
||||
return out.unsqueeze(1), conv_state.clone(), ssm_state.clone()
|
||||
|
||||
|
||||
class ResidualBlock(nn.Module):
|
||||
def __init__(self, layer_id, config, weights):
|
||||
def __init__(self, prefix, config, weights, layer_id):
|
||||
super().__init__()
|
||||
self.mamba_block = MambaBlock(
|
||||
prefix=f"{layer_id}.mixer", config=config, weights=weights
|
||||
prefix=f"{prefix}.mixer", config=config, weights=weights, layer_id=layer_id
|
||||
)
|
||||
self.layer_norm = FastRMSNorm.load(
|
||||
prefix=f"{layer_id}.norm", weights=weights, eps=config.layer_norm_epsilon
|
||||
prefix=f"{prefix}.norm", weights=weights, eps=config.layer_norm_epsilon
|
||||
)
|
||||
|
||||
def forward(
|
||||
|
|
@ -200,7 +181,7 @@ class MambaModel(nn.Module):
|
|||
self.embed_tokens = TensorParallelEmbedding(f"{prefix}.embedding", weights)
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
ResidualBlock(f"{prefix}.layers.{i}", config, weights)
|
||||
ResidualBlock(f"{prefix}.layers.{i}", config, weights, layer_id=i)
|
||||
for i in range(config.n_layer)
|
||||
]
|
||||
)
|
||||
|
|
@ -216,14 +197,12 @@ class MambaModel(nn.Module):
|
|||
self, input_ids: torch.Tensor, inference_params=None, residual=None
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, InferenceParams]:
|
||||
hidden_states = self.embed_tokens(input_ids)
|
||||
for block in self.blocks:
|
||||
for i, block in enumerate(self.blocks):
|
||||
hidden_states, residual, conv_state, ssm_state = block(
|
||||
hidden_states, residual, inference_params
|
||||
)
|
||||
inference_params.key_value_memory_dict[block.mamba_block.layer_idx] = (
|
||||
conv_state,
|
||||
ssm_state,
|
||||
)
|
||||
inference_params.conv_states[i].copy_(conv_state)
|
||||
inference_params.ssm_states[i].copy_(ssm_state)
|
||||
|
||||
hidden_states = (
|
||||
hidden_states + residual if residual is not None else hidden_states
|
||||
|
|
@ -234,4 +213,4 @@ class MambaModel(nn.Module):
|
|||
|
||||
# update the offset for the next inference using these params
|
||||
inference_params.seqlen_offset += input_ids.size(1)
|
||||
return logits, input_ids, inference_params
|
||||
return logits
|
||||
|
|
|
|||
|
|
@ -28,12 +28,12 @@ from text_generation_server.models.cache_manager import (
|
|||
BLOCK_SIZE,
|
||||
)
|
||||
from text_generation_server.pb import generate_pb2
|
||||
from text_generation_server.models.globals import MEM_POOL
|
||||
from text_generation_server.utils import StoppingCriteria, HeterogeneousNextTokenChooser
|
||||
from text_generation_server.utils.dist import MEMORY_FRACTION
|
||||
|
||||
tracer = trace.get_tracer(__name__)
|
||||
|
||||
MEM_POOL = torch.cuda.graph_pool_handle()
|
||||
|
||||
|
||||
@dataclass
|
||||
|
|
|
|||
|
|
@ -0,0 +1,3 @@
|
|||
import torch
|
||||
|
||||
MEM_POOL = torch.cuda.graph_pool_handle()
|
||||
|
|
@ -2,17 +2,20 @@ import torch
|
|||
import torch.distributed
|
||||
from transformers import AutoTokenizer, PreTrainedTokenizerBase
|
||||
from typing import Optional
|
||||
import os
|
||||
from text_generation_server.models.custom_modeling.mamba_modeling import (
|
||||
MambaConfig,
|
||||
)
|
||||
from loguru import logger
|
||||
from text_generation_server.pb import generate_pb2
|
||||
from text_generation_server.utils import (
|
||||
initialize_torch_distributed,
|
||||
weight_files,
|
||||
Weights,
|
||||
)
|
||||
from text_generation_server.models.globals import MEM_POOL
|
||||
import time
|
||||
from text_generation_server.models.custom_modeling.mamba_modeling import MambaModel
|
||||
from text_generation_server.models.custom_modeling.mamba_modeling import MambaModel, InferenceParams
|
||||
from text_generation_server.models import Model
|
||||
from typing import Any, List, Optional, Tuple, Type, Dict
|
||||
from text_generation_server.models.types import (
|
||||
|
|
@ -24,7 +27,34 @@ from text_generation_server.models.types import (
|
|||
from text_generation_server.utils.tokens import batch_top_tokens, Sampling
|
||||
from dataclasses import dataclass
|
||||
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
|
||||
from mamba_ssm.utils.generation import InferenceParams
|
||||
|
||||
def new_inference_params(n_blocks: int, batch_size: int, d_inner: int, d_conv: int, d_state: int, seqlen_offset: int, dtype: torch.dtype, device: torch.device):
|
||||
max_seqlen = 0
|
||||
conv_states = torch.zeros(
|
||||
(n_blocks,
|
||||
batch_size,
|
||||
d_inner,
|
||||
d_conv,),
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
)
|
||||
ssm_states = torch.zeros(
|
||||
(n_blocks,
|
||||
batch_size,
|
||||
d_inner,
|
||||
d_state,),
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
)
|
||||
inference_params = InferenceParams(
|
||||
max_seqlen=max_seqlen,
|
||||
max_batch_size=batch_size,
|
||||
seqlen_offset=seqlen_offset,
|
||||
conv_states=conv_states,
|
||||
ssm_states=ssm_states,
|
||||
|
||||
)
|
||||
return inference_params
|
||||
|
||||
|
||||
@dataclass
|
||||
|
|
@ -221,14 +251,8 @@ class MambaBatch(Batch):
|
|||
|
||||
# TODO
|
||||
# Kept it simple by just updating the state, maybe updating the other CPU values is necessary.
|
||||
key_value_memory_dict = {}
|
||||
for i, (
|
||||
conv_state,
|
||||
ssm_state,
|
||||
) in self.inference_params.key_value_memory_dict.items():
|
||||
key_value_memory_dict[i] = (conv_state[indices], ssm_state[indices])
|
||||
self.inference_params.key_value_memory_dict = key_value_memory_dict
|
||||
|
||||
self.inference_params.conv_states = self.inference_params.conv_states[:, indices]
|
||||
self.inference_params.ssm_states = self.inference_params.ssm_states[:, indices]
|
||||
return self
|
||||
|
||||
@classmethod
|
||||
|
|
@ -254,9 +278,16 @@ class MambaBatch(Batch):
|
|||
top_n_tokens = []
|
||||
max_tokens = 0
|
||||
max_seqlen = 0
|
||||
batch_size = 0
|
||||
seqlen_offset = 0
|
||||
|
||||
(n_blocks, _, d_inner, d_conv) = (
|
||||
batches[0].inference_params.conv_states.shape
|
||||
)
|
||||
(_, _, _, d_state) = batches[0].inference_params.ssm_states.shape
|
||||
dtype = batches[0].inference_params.conv_states.dtype
|
||||
device = batches[0].inference_params.conv_states.device
|
||||
inference_params = new_inference_params(n_blocks=n_blocks, batch_size=total_batch_size, d_state=d_state, d_conv=d_conv, d_inner=d_inner, seqlen_offset=seqlen_offset, device=device, dtype=dtype)
|
||||
|
||||
# Batch tensors
|
||||
input_ids = None
|
||||
top_n_tokens_tensor = None
|
||||
|
|
@ -303,63 +334,16 @@ class MambaBatch(Batch):
|
|||
max_input_length - batch.max_input_length
|
||||
) * len(batch)
|
||||
|
||||
max_seqlen = max(max_seqlen, batch.inference_params.max_seqlen)
|
||||
seqlen_offset = max(seqlen_offset, batch.inference_params.seqlen_offset)
|
||||
batch_size += batch.inference_params.max_batch_size
|
||||
inference_params.max_seqlen = max(inference_params.max_seqlen, batch.inference_params.max_seqlen)
|
||||
assert batch.inference_params.seqlen_offset != 0, "Invalid seqlen offset"
|
||||
inference_params.seqlen_offset = max(inference_params.seqlen_offset, batch.inference_params.seqlen_offset)
|
||||
|
||||
|
||||
inference_params.conv_states[:, start_index:end_index] = batch.inference_params.conv_states
|
||||
inference_params.ssm_states[:, start_index:end_index] = batch.inference_params.ssm_states
|
||||
|
||||
start_index = end_index
|
||||
|
||||
(_, d_model, d_conv) = (
|
||||
batches[0].inference_params.key_value_memory_dict[0][0].shape
|
||||
)
|
||||
(_, _, d_state) = batches[0].inference_params.key_value_memory_dict[0][1].shape
|
||||
n_blocks = len(batches[0].inference_params.key_value_memory_dict)
|
||||
dtype = batches[0].inference_params.key_value_memory_dict[0][0].dtype
|
||||
device = batches[0].inference_params.key_value_memory_dict[0][0].device
|
||||
|
||||
key_value_memory_dict = {}
|
||||
for i in range(n_blocks):
|
||||
conv_state = torch.zeros(
|
||||
batch_size,
|
||||
d_model,
|
||||
d_conv,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
)
|
||||
ssm_state = torch.zeros(
|
||||
batch_size,
|
||||
d_model,
|
||||
d_state,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
)
|
||||
key_value_memory_dict[i] = (conv_state, ssm_state)
|
||||
lengths_per_sample = torch.zeros(batch_size, dtype=torch.int32, device=device)
|
||||
|
||||
inference_params = InferenceParams(
|
||||
max_seqlen=max_seqlen,
|
||||
max_batch_size=batch_size,
|
||||
seqlen_offset=seqlen_offset,
|
||||
key_value_memory_dict=key_value_memory_dict,
|
||||
lengths_per_sample=lengths_per_sample,
|
||||
)
|
||||
|
||||
current_batch = 0
|
||||
for batch in batches:
|
||||
for i in range(n_blocks):
|
||||
conv_state, ssm_state = batch.inference_params.key_value_memory_dict[i]
|
||||
batch_size = batch.inference_params.max_batch_size
|
||||
inference_params.key_value_memory_dict[i][0][
|
||||
current_batch : current_batch + batch_size
|
||||
] = conv_state
|
||||
inference_params.key_value_memory_dict[i][1][
|
||||
current_batch : current_batch + batch_size
|
||||
] = ssm_state
|
||||
inference_params.lengths_per_sample[
|
||||
current_batch : current_batch + batch_size
|
||||
] = batch.inference_params.lengths_per_sample
|
||||
current_batch += batch_size
|
||||
|
||||
return cls(
|
||||
batch_id=batches[0].batch_id,
|
||||
requests=requests,
|
||||
|
|
@ -394,9 +378,13 @@ class Mamba(Model):
|
|||
trust_remote_code: bool = False,
|
||||
):
|
||||
self.process_group, _rank, _world_size = initialize_torch_distributed()
|
||||
self.cuda_graphs = {}
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda")
|
||||
dtype = torch.float16 if dtype is None else dtype
|
||||
# Bf16 is important. In f16 accumulations in the matmul are causing
|
||||
# differences while the server is under load.
|
||||
# This is detectable by the integration load test
|
||||
dtype = torch.bfloat16 if dtype is None else dtype
|
||||
else:
|
||||
if quantize:
|
||||
raise ValueError("quantization is not available on CPU")
|
||||
|
|
@ -439,17 +427,93 @@ class Mamba(Model):
|
|||
|
||||
def warmup(self, batch) -> Optional[int]:
|
||||
# TODO: implement warmup for Mamba if needed
|
||||
if os.getenv("ENABLE_CUDA_GRAPHS", "False") == "True":
|
||||
if self.speculate is None or self.speculate == 0:
|
||||
try:
|
||||
logger.info("Experimental support for Cuda Graphs is enabled")
|
||||
# Warmup cuda graphs
|
||||
for bs in [1, 2, 4] + [8 * i for i in range(1, 9)]:
|
||||
self.cuda_graph_warmup(bs)
|
||||
except Exception:
|
||||
logger.exception(f"Decode cuda graph warmup failed")
|
||||
|
||||
return None
|
||||
|
||||
def cuda_graph_warmup(self, batch_size: int):
|
||||
input_ids = torch.zeros((batch_size, 1), dtype=torch.int64, device=self.device)
|
||||
n_blocks = len(self.model.blocks)
|
||||
|
||||
d_state = self.model.config.d_state
|
||||
d_conv = self.model.config.d_conv
|
||||
# Inner takes the expand multiplication
|
||||
d_inner = self.model.config.d_inner
|
||||
|
||||
# Important seqlen_offset to go through the update mecanism with the state
|
||||
seqlen_offset = 1
|
||||
inference_params = new_inference_params(n_blocks=n_blocks, batch_size=batch_size, d_state=d_state, d_conv=d_conv, d_inner=d_inner, seqlen_offset=seqlen_offset, device=self.device, dtype=self.dtype)
|
||||
|
||||
graph = torch.cuda.CUDAGraph()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
# Run once outside to warmup
|
||||
self.model.forward(
|
||||
input_ids=input_ids,
|
||||
inference_params=inference_params
|
||||
)
|
||||
torch.cuda.synchronize()
|
||||
|
||||
with torch.cuda.graph(graph, pool=MEM_POOL):
|
||||
logits = self.model.forward(
|
||||
input_ids=input_ids,
|
||||
inference_params=inference_params
|
||||
)
|
||||
torch.cuda.synchronize()
|
||||
graph_dict = {
|
||||
"input_ids": input_ids,
|
||||
"inference_params": inference_params,
|
||||
"graph": graph,
|
||||
"logits": logits
|
||||
}
|
||||
self.cuda_graphs[batch_size] = graph_dict
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
past: Optional[List[torch.Tensor]] = None,
|
||||
inference_params: Any
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
return self.model(
|
||||
input_ids,
|
||||
past=past,
|
||||
)
|
||||
bs = input_ids.shape[0]
|
||||
padded_bs = bs
|
||||
if bs == 3:
|
||||
padded_bs = 4
|
||||
elif 3 < bs <= 8:
|
||||
padded_bs = 8
|
||||
elif bs > 8:
|
||||
padded_bs = (bs + 7) // 8 * 8
|
||||
|
||||
# Try to find an associated cuda graph
|
||||
cuda_graph = self.cuda_graphs.get(padded_bs, None)
|
||||
is_prefill = inference_params is None or inference_params.seqlen_offset == 0
|
||||
|
||||
if is_prefill or cuda_graph is None:
|
||||
return self.model(
|
||||
input_ids,
|
||||
inference_params=inference_params,
|
||||
)
|
||||
|
||||
# Copy inputs to the static inputs of the cuda graph
|
||||
# Static inputs are potentially padded
|
||||
cuda_graph["input_ids"][: bs] = input_ids
|
||||
cuda_graph["inference_params"].conv_states[:, : bs] = inference_params.conv_states
|
||||
cuda_graph["inference_params"].ssm_states[:, : bs] = inference_params.ssm_states
|
||||
|
||||
# Replay the graph
|
||||
cuda_graph["graph"].replay()
|
||||
|
||||
inference_params.conv_states.copy_(cuda_graph["inference_params"].conv_states[:, :bs])
|
||||
inference_params.ssm_states.copy_(cuda_graph["inference_params"].ssm_states[:, :bs])
|
||||
|
||||
# Slice output to the correct shape
|
||||
return cuda_graph["logits"][:bs]
|
||||
|
||||
def generate_token(self, batch) -> Tuple[List[Any], Optional[Any], Tuple[int, int]]:
|
||||
start = time.time_ns()
|
||||
|
|
@ -457,56 +521,26 @@ class Mamba(Model):
|
|||
batch.input_ids
|
||||
) # batch.past_input_ids if batch.past_input_ids is not None else batch.input_ids
|
||||
|
||||
batch_size = input_ids.shape[0]
|
||||
max_seqlen = input_ids.shape[1]
|
||||
dtype = input_ids.dtype
|
||||
|
||||
batch_size, max_seqlen = input_ids.shape
|
||||
# Inference params
|
||||
seqlen_og = 0
|
||||
inf_cache = {}
|
||||
lengths_per_sample = (
|
||||
torch.ones(batch_size, dtype=torch.int32, device=input_ids.device)
|
||||
* max_seqlen
|
||||
)
|
||||
|
||||
if batch.inference_params is None:
|
||||
inference_params = InferenceParams(
|
||||
max_seqlen=max_seqlen,
|
||||
max_batch_size=batch_size,
|
||||
seqlen_offset=seqlen_og,
|
||||
key_value_memory_dict=inf_cache,
|
||||
lengths_per_sample=lengths_per_sample,
|
||||
)
|
||||
|
||||
# Allocate inference cache
|
||||
for res_block in self.model.blocks:
|
||||
block = res_block.mamba_block
|
||||
conv_state = torch.zeros(
|
||||
batch_size,
|
||||
self.model.config.d_model * self.model.config.expand,
|
||||
self.model.config.d_conv,
|
||||
device=block.conv1d.weight.device,
|
||||
dtype=block.conv1d.weight.dtype,
|
||||
)
|
||||
ssm_state = torch.zeros(
|
||||
batch_size,
|
||||
self.model.config.d_model * self.model.config.expand,
|
||||
self.model.config.d_state,
|
||||
device=block.dt_proj.weight.device,
|
||||
dtype=block.dt_proj.weight.dtype,
|
||||
)
|
||||
inference_params.key_value_memory_dict[block.layer_idx] = (
|
||||
conv_state,
|
||||
ssm_state,
|
||||
)
|
||||
# 0 is important here
|
||||
seqlen_offset = 0
|
||||
n_blocks = len(self.model.blocks)
|
||||
d_state = self.model.config.d_state
|
||||
d_conv = self.model.config.d_conv
|
||||
d_inner = self.model.config.d_inner
|
||||
inference_params = new_inference_params(n_blocks=n_blocks, batch_size=batch_size, d_state=d_state, d_conv=d_conv, d_inner=d_inner, seqlen_offset=seqlen_offset, device=self.device, dtype=self.dtype)
|
||||
batch.inference_params = inference_params
|
||||
|
||||
# Forward pass
|
||||
logits, past_input_ids, new_inference_params = self.model(
|
||||
input_ids, batch.inference_params
|
||||
logits = self.forward(
|
||||
input_ids, inference_params=batch.inference_params
|
||||
)
|
||||
|
||||
batch.inference_params = new_inference_params
|
||||
|
||||
# batch.inference_params = new_inference_params
|
||||
# Results
|
||||
generations: List[Generation] = []
|
||||
stopped = True
|
||||
|
|
|
|||
Loading…
Reference in New Issue