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Introduce CausalLMModel intefrace and add IREE numerics test for Llama 3.1 8B FP16 TP8 #375

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Introduce CausalLMModel intefrace and add IREE numerics test for Llama 3.1 8B FP16 TP8 #375

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9f93f87
Introduce CausalLMModel intefrace and add IREE numerics test for Llam…
sogartar Oct 25, 2024
9c9defd
Make PathPrefixTestBase inherit TempDirTestBase
sogartar Oct 30, 2024
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140 changes: 71 additions & 69 deletions sharktank/sharktank/examples/export_paged_llm_v1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,16 +13,20 @@

from sharktank.layers import *
from sharktank.types import *
from sharktank.utils.math import ceildiv

# TODO: Should be using a base class with the protocol supported.
from ..models.llama.llama import LlamaModelConfig, PagedLlamaModelV1
from ..models.llama.sharding import shard_theta
from ..models.mixtral.mixtral import *
from ..models.grok.grok import *
from .. import ops


def main():
def dtype_from_str(s: str) -> torch.dtype:
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Use a map rather than string manipulations. It is as simple as

{
  "fp8":torch.fp8,
  "f32":torch.f32,
....
}

parts = s.split(".", maxsplit=1)
assert parts != 2 or parts[0] != "torch"
return getattr(torch, parts[1])


def main(raw_args: list[str] | None = None):
from ..utils import cli

parser = cli.create_parser()
Expand All @@ -43,6 +47,12 @@ def main():
type=lambda arg: [int(bs) for bs in arg.split(",")],
default="4",
)
parser.add_argument(
"--block-seq-stride",
help="Block sequence stride for a paged KV cache.",
type=int,
default=LlamaModelConfig.default_block_seq_stride,
)
parser.add_argument(
"--verbose",
help="Include verbose logging",
Expand All @@ -59,8 +69,18 @@ def main():
default="decomposed",
choices=["decomposed", "torch"],
)
parser.add_argument(
"--attention-dtype",
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Can you provide a justification for adding these in the first place? They should be inferred from the data types of the functions

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There is one test that uses fp32.

type=str,
default=str(LlamaModelConfig.default_attention_dtype),
)
parser.add_argument(
"--activation-dtype",
type=str,
default=str(LlamaModelConfig.default_activation_dtype),
)

args = cli.parse(parser)
args = cli.parse(parser, args=raw_args)
dataset_type = cli.get_input_data_files(args)
dataset_type = "irpa" if "irpa" in dataset_type else "gguf"
dataset = cli.get_input_dataset(args)
Expand All @@ -73,11 +93,14 @@ def main():
)
llama_config = LlamaModelConfig(
hp,
block_seq_stride=args.block_seq_stride,
tensor_parallelism_size=tensor_parallelism_size,
use_hf=False,
static_tables=False, # Rely on the compiler for hoisting tables.
kv_cache_type="direct" if args.bs == [1] else "paged",
attention_kernel=args.attention_kernel,
attention_dtype=dtype_from_str(args.attention_dtype),
activation_dtype=dtype_from_str(args.activation_dtype),
)

if llama_config.hp.expert_count:
Expand Down Expand Up @@ -110,7 +133,7 @@ def generate_params_json(hp, prefill_bs: list[int], decode_bs: list[int]):

fxb = FxProgramsBuilder(model)

def setup_cache(model, shard_count):
def setup_cache(model):
if model.config.kv_cache_type == "paged":
cache_state = model.cache.allocate(
page_count=hp.context_length // llama_config.block_seq_stride
Expand Down Expand Up @@ -152,16 +175,21 @@ def repack_cache(cache, shard_dim):
return [SplitPrimitiveTensor(ts=c, shard_dim=shard_dim) for c in cache]

def generate_batch_prefill(bs: int):
tokens = torch.empty(bs, 64, dtype=torch.int64)
seq_lens = torch.empty(bs, dtype=torch.int64)
seq_block_ids = torch.empty(bs, 4, dtype=torch.int64)
block_dim = torch.export.Dim(
"block", max=(hp.context_length - 1) // llama_config.block_seq_stride
)
# torch.export.Dim would make min at least 2
block_dim_min = 2
block_dim_max = ceildiv(hp.context_length, llama_config.block_seq_stride) - 1
block_dim = torch.export.Dim("block", min=block_dim_min, max=block_dim_max)
sl_dim = llama_config.block_seq_stride * block_dim
seq_block_ids = torch.empty(bs, block_dim_min, dtype=torch.int64)
tokens = torch.empty(
bs,
seq_block_ids.shape[1] * llama_config.block_seq_stride,
dtype=torch.int64,
)
seq_lens = torch.empty(bs, dtype=torch.int64)

cache, cache_shard_dim, cache_dynamic_shapes, arg_affinities = setup_cache(
model, llama_config.tensor_parallelism_size
model
)

# We need to offset the indices for the cache
Expand All @@ -174,7 +202,7 @@ def generate_batch_prefill(bs: int):
"tokens": {1: sl_dim},
"seq_lens": {},
"seq_block_ids": {1: block_dim},
"cs": cache_dynamic_shapes,
"cache_state": cache_dynamic_shapes,
}

print(f"Exporting prefill_bs{bs}")
Expand All @@ -186,55 +214,42 @@ def generate_batch_prefill(bs: int):
strict=args.strict,
arg_device=arg_affinities,
)
def _(model, tokens, seq_lens, seq_block_ids, cs):
def _(model, tokens, seq_lens, seq_block_ids, cache_state):
if (
model.config.tensor_parallelism_size == 1
and model.config.kv_cache_type == "direct"
):
cache_tensors = torch.unbind(cs)
else:
cache_tensors = cs

sl = tokens.shape[1]
input_mask = model.input_mask(seq_lens, sl)
attention_mask = model.attention_mask(input_mask)

if llama_config.tensor_parallelism_size != 1:
shard_count = llama_config.tensor_parallelism_size

tokens = ops.replicate(tokens, count=shard_count)
attention_mask = ops.replicate(attention_mask, count=shard_count)
seq_block_ids = ops.replicate(seq_block_ids, count=shard_count)

cache_tensors = repack_cache(cs, cache_shard_dim)
cache_state = torch.unbind(cache_state)
if model.config.tensor_parallelism_size != 1:
cache_state = repack_cache(cache_state, cache_shard_dim)

logits = model.prefill(
tokens,
attention_mask=attention_mask,
return model.prefill_from_seq_lens(
tokens=tokens,
seq_lens=seq_lens,
seq_block_ids=seq_block_ids,
cache_state=cache_tensors,
cache_state=cache_state,
)

if llama_config.tensor_parallelism_size != 1:
logits = ops.unshard(logits)

return logits

def generate_batch_decode(bs: int):
tokens = torch.ones(bs, 1, dtype=torch.int64)
seq_lens = torch.ones(bs, dtype=torch.int64)
start_positions = torch.ones(bs, dtype=torch.int64)
seq_block_ids = torch.zeros(bs, 4, dtype=torch.int64)
block_dim = torch.export.Dim(
"block", max=(hp.context_length - 1) // llama_config.block_seq_stride
# torch.export.Dim would make min at least 2
block_dim_min = 2
block_dim_max = ceildiv(hp.context_length, llama_config.block_seq_stride) - 1
block_dim = torch.export.Dim("block", min=block_dim_min, max=block_dim_max)
tokens = torch.empty(
bs,
1,
dtype=torch.int64,
)
seq_lens = torch.empty(bs, dtype=torch.int64)
start_positions = torch.ones(bs, dtype=torch.int64)
seq_block_ids = torch.empty(bs, block_dim_min, dtype=torch.int64)

(
cache_state,
cache_shard_dim,
cache_dynamic_shapes,
arg_affinities,
) = setup_cache(model, llama_config.tensor_parallelism_size)
) = setup_cache(model)

# We need to offset the indices for the cache
arg_affinities = {key + 4: arg_affinities[key] for key in arg_affinities}
Expand Down Expand Up @@ -274,34 +289,21 @@ def _(
seq_block_ids,
cache_state,
):
input_mask = model.input_mask(
seq_lens, seq_block_ids.shape[1] * model.cache.block_seq_stride
)
attention_mask = model.decode_attention_mask(input_mask)

if llama_config.tensor_parallelism_size != 1:
shard_count = llama_config.tensor_parallelism_size

tokens = ops.replicate(tokens, count=shard_count)
attention_mask = ops.replicate(attention_mask, count=shard_count)
start_positions = ops.replicate(start_positions, count=shard_count)
seq_block_ids = ops.replicate(seq_block_ids, count=shard_count)

if (
model.config.tensor_parallelism_size == 1
and model.config.kv_cache_type == "direct"
):
cache_state = torch.unbind(cache_state)
if model.config.tensor_parallelism_size != 1:
cache_state = repack_cache(cache_state, cache_shard_dim)

logits = model.decode(
tokens,
attention_mask=attention_mask,
return model.decode_from_seq_lens(
tokens=tokens,
seq_lens=seq_lens,
start_positions=start_positions,
seq_block_ids=seq_block_ids,
cache_state=cache_state,
)

if llama_config.tensor_parallelism_size != 1:
logits = ops.unshard(logits)

return logits

bsizes = []
for bs in args.bs:
generate_batch_prefill(bs)
Expand Down
29 changes: 10 additions & 19 deletions sharktank/sharktank/examples/paged_llm_v1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@ class TorchGenerator:

def __init__(
self,
model: PagedLlamaModelV1,
model: CausalLMModel,
tokenizer: InferenceTokenizer,
page_cache_size: int = 128,
# Need to look at the model more for this.
Expand Down Expand Up @@ -162,17 +162,14 @@ def compute_prefill_logits(

def prefill(self):
model = self.parent.model
attention_mask = model.attention_mask(
model.input_mask(self.seq_lens, self.token_ids.shape[1])
)
seq_block_ids_tensor = self.pad_block_ids()
print(f":: Invoke prefill:")
trace_tensor("prefill.token_ids", self.token_ids)
trace_tensor("prefill.seq_lens", self.seq_lens)
trace_tensor("prefill.seq_block_ids", seq_block_ids_tensor)
trace_tensor("prefill.attention_mask", attention_mask)
logits = model.prefill(
self.logits = model.prefill_from_seq_lens(
self.token_ids,
attention_mask=attention_mask,
seq_lens=self.seq_lens,
seq_block_ids=seq_block_ids_tensor,
cache_state=self.cache_state,
)
Expand All @@ -181,7 +178,7 @@ def prefill(self):
# TODO: Normalize the output of extract_tokens_from_logits into
# tensor [bs, 1].
tokens = torch.tensor(
model.extract_tokens_from_logits(logits, self.seq_lens)
model.extract_tokens_from_logits(self.logits, self.seq_lens)
).unsqueeze(1)
print(f":: Prefill results:\n{tokens.tolist()}")
self.add_result_token(tokens)
Expand All @@ -194,28 +191,22 @@ def decode(self):
self.allocate_seq_block_ids()
# TODO: Allocate more blocks on overflow.
seq_block_ids_tensor = self.pad_block_ids()
decode_attention_mask = model.decode_attention_mask(
model.input_mask(
self.seq_lens,
seq_block_ids_tensor.shape[1] * self.parent.block_seq_stride,
)
)
trace_tensor("decode.token_ids", self.next_tokens)
trace_tensor("decode.seq_lens", self.seq_lens)
trace_tensor("decode.start_positions", start_positions)
trace_tensor("decode.seq_block_ids", seq_block_ids_tensor)
trace_tensor("decode.attention_mask", decode_attention_mask)
logits = model.decode(
self.logits = model.decode_from_seq_lens(
self.next_tokens,
attention_mask=decode_attention_mask,
seq_lens=self.seq_lens,
start_positions=start_positions,
seq_block_ids=seq_block_ids_tensor,
cache_state=self.cache_state,
)
trace_tensor("decode.logits", logits)
trace_tensor("decode.logits", self.logits)
# TODO: Normalize the output of extract_tokens_from_logits into
# tensor [bs, 1].
tokens = torch.tensor(
model.extract_tokens_from_logits(logits, [1] * self.bs),
model.extract_tokens_from_logits(self.logits, [1] * self.bs),
device=self.parent.model.device,
).unsqueeze(1)
self.add_result_token(tokens)
Expand Down
5 changes: 4 additions & 1 deletion sharktank/sharktank/layers/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,10 @@
from .base import BaseLayer, ThetaLayer
from .conv import Conv2DLayer
from .kv_cache import BaseKVCache, DirectKVCache, PagedKVCache
from .causal_llm import BaseCausalLMModel
from .causal_llm import (
CausalLMModel,
BaseCausalLMModel,
)
from .linear import LinearLayer
from .norm import RMSNormLayer
from .rotary_embedding import RotaryEmbeddingLayer
Expand Down
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