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# SPDX-License-Identifier: Apache-2.0
import os
import pytest
import torch
from loguru import logger
import ttnn
from models.common.utility_functions import comp_allclose, comp_pcc
from models.tt_transformers.tests.test_utils import get_ref_model_dype
from models.tt_transformers.tt.ccl import TT_CCL
from models.tt_transformers.tt.common import Mode, PagedAttentionConfig, get_rot_transformation_mat, precompute_freqs
from models.tt_transformers.tt.decoder import TransformerBlock
from models.tt_transformers.tt.model_config import ModelArgs
from models.tt_transformers.tt.prefetcher import Prefetcher
from models.tt_transformers.tt.rope import get_rot_mats, get_rot_mats_hf
@torch.no_grad()
@pytest.mark.parametrize(
"mesh_device",
[
{"N150": (1, 1), "N300": (1, 2), "T3K": (1, 8), "TG": (8, 4)}.get(
os.environ.get("MESH_DEVICE"), len(ttnn.get_device_ids())
)
],
indirect=True,
)
@pytest.mark.parametrize(
"paged_attention",
(
True,
# False
),
ids=(
"paged_attention",
# "default_attention"
),
)
@pytest.mark.parametrize(
"page_params",
[{"page_block_size": 32, "page_max_num_blocks": 1024}],
)
@pytest.mark.parametrize(
"max_seq_len",
(
4096,
128,
),
)
@pytest.mark.parametrize(
"use_prefetcher",
([False]),
)
@pytest.mark.parametrize("device_params", [{"fabric_config": True}], indirect=True)
def test_decoder_inference(
max_seq_len,
paged_attention,
page_params,
mesh_device,
reset_seeds,
ensure_gc,
use_prefetcher,
):
model_name_env = os.getenv("HF_MODEL")
if max_seq_len > 256 and model_name_env and "Mistral-7B" in model_name_env:
pytest.skip(
"Mistral-7B models do not support max_seq_len > 256. See issue: https://github.com/tenstorrent/tt-metal/issues/19806"
)
dtype = ttnn.bfloat8_b
batch_size = 1 # For prefill we only support batch_size = 1
num_tensors = 0
prefetcher = Prefetcher(mesh_device, num_tensors=num_tensors, num_layers=1) if use_prefetcher else None
if use_prefetcher:
prefetcher.init(mode=Mode.PREFILL)
model_args = ModelArgs(
mesh_device,
max_batch_size=batch_size,
max_seq_len=max_seq_len,
cache_hf=True,
prefetcher=prefetcher,
use_hf_rope=False,
)
model_args.n_layers = 1
state_dict = model_args.load_state_dict()
reference_model = model_args.reference_decoder(load_checkpoint=True)
generation_start_pos = 0
generation_length = 1
all_tests_pass = True
rot_mats_fn = get_rot_mats_hf if model_args.use_hf_rope else get_rot_mats
# pre-compute the rotational embedding matrix and send to device
rot_mats = rot_mats_fn(
head_dim=model_args.head_dim,
device=mesh_device,
seq_len=max_seq_len,
theta=model_args.rope_theta,
rope_scaling=model_args.rope_scaling,
)
if model_args.rope_theta_local is not None:
rot_mats_local = get_rot_mats(
head_dim=model_args.head_dim,
device=mesh_device,
seq_len=max_seq_len,
theta=model_args.rope_theta_local,
rope_scaling=None,
)
else:
rot_mats_local = None
transformation_mats = {}
if not model_args.use_hf_rope:
transformation_mat_torch = get_rot_transformation_mat(model_args.head_dim)
transformation_mats_prefill = ttnn.as_tensor(
transformation_mat_torch,
dtype=ttnn.bfloat16,
layout=ttnn.TILE_LAYOUT,
device=mesh_device,
memory_config=ttnn.DRAM_MEMORY_CONFIG,
mesh_mapper=ttnn.ReplicateTensorToMesh(mesh_device),
)
transformation_mats = {"prefill": transformation_mats_prefill}
# Setup page table
page_table_tt = None
paged_attention_config = None
if paged_attention:
paged_attention_config = PagedAttentionConfig(
block_size=page_params["page_block_size"],
max_num_blocks=page_params["page_max_num_blocks"],
)
# Implied shuffling of blocks
permutation = torch.randperm(paged_attention_config.max_num_blocks)
# Page table which maps virtual blocks to physical
reverse_permutation = torch.argsort(permutation)
page_table = reverse_permutation.reshape(
model_args.max_batch_size, paged_attention_config.max_num_blocks // model_args.max_batch_size
)
page_table_tt = ttnn.from_torch(
page_table,
device=mesh_device,
dtype=ttnn.int32,
layout=ttnn.ROW_MAJOR_LAYOUT,
mesh_mapper=ttnn.ReplicateTensorToMesh(mesh_device),
)
# Initialize TT model
tt_ccl = TT_CCL(mesh_device)
tt_model = TransformerBlock(
mesh_device=mesh_device,
tt_ccl=tt_ccl,
state_dict=state_dict,
weight_cache_path=model_args.weight_cache_path(dtype),
layer_num=0,
dtype=dtype,
transformation_mats=transformation_mats,
args=model_args,
paged_attention_config=paged_attention_config,
prefetcher=prefetcher,
)
for i in range(generation_length):
logger.info(f"[Decoder] Generating token {i}")
pt_decode_input = (
torch.rand(
batch_size,
max_seq_len,
model_args.dim,
dtype=get_ref_model_dype(reference_model, model_args.model_name),
)
* 2
) - 1
tt_decode_input = pt_decode_input.clone()
decode_input = model_args.prepare_residual_tensor_prefill(
tt_decode_input,
)
positions = torch.LongTensor(range(max_seq_len))
cos, sin = precompute_freqs(
model_args.head_dim,
model_args.max_seq_len * 2,
model_args.rope_theta,
model_args.rope_scaling.factor if model_args.rope_scaling else None,
model_args.rope_scaling.original_max_position_embeddings if model_args.rope_scaling else None,
model_args.rope_scaling.rope_type.value if model_args.rope_scaling else "llama3",
)
freqs_cis_i = torch.complex(cos, sin)[positions]
# Reference model
attn_mask = torch.triu(torch.full((max_seq_len, max_seq_len), torch.finfo(torch.float32).min), diagonal=1)
if (
model_args.sliding_window is not None
and model_args.sliding_window > 0
and (
hasattr(reference_model.decoder, "attention_type")
and reference_model.decoder.attention_type == "sliding_attention"
)
):
attn_mask += torch.tril(
torch.full((max_seq_len, max_seq_len), -float("inf")), diagonal=-model_args.sliding_window
)
ref_output = reference_model(
pt_decode_input,
positions[0],
freqs_cis_i,
mask=attn_mask,
)
if ref_output.dim() == 2:
ref_output = ref_output.unsqueeze(0)
# Run TT model
tt_out = tt_model(
decode_input,
None,
rot_mats_global=rot_mats,
rot_mats_local=rot_mats_local,
user_id=0,
mode=Mode.PREFILL,
page_table=page_table_tt,
)
tt_out = ttnn.to_torch(
tt_out,
mesh_composer=ttnn.ConcatMesh2dToTensor(mesh_device, dims=(1, 3), mesh_shape=model_args.cluster_shape),
)
tt_output_torch = tt_out[:, 0:1, :, : model_args.dim].view(
batch_size, max_seq_len, -1
) # [ batch_size, seq, hidden_dim]
passing, pcc_message = comp_pcc(ref_output, tt_output_torch)
logger.info(comp_allclose(ref_output, tt_output_torch))
logger.info(f"PCC: {pcc_message}")
if passing:
logger.info("Decoder Block Passed!")
else:
logger.warning("Decoder Block Failed!")
all_tests_pass = False
if all_tests_pass:
logger.info(f"All decode iterations Passed!")
else:
logger.warning("One or more iterations of decode Failed!")
assert all_tests_pass, f"PCC value is lower than {0.99} for some of the outputs. Check Warnings!"
|