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Add prefill/decode from seq lens in BaseCausalLMModel #383

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Add prefill/decode from seq lens in BaseCausalLMModel #383

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1b0f2f0
Introduce CausalLMModelABC
sogartar Oct 30, 2024
d0a9466
Remove CausalLMModelABC
sogartar Nov 7, 2024
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67 changes: 18 additions & 49 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 @@ -174,7 +174,7 @@ 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 @@ -186,40 +186,22 @@ 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)
Expand Down Expand Up @@ -274,34 +256,21 @@ 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: CausalLMModelABC,
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 (
CausalLMModelABC,
BaseCausalLMModel,
)
from .linear import LinearLayer
from .norm import RMSNormLayer
from .rotary_embedding import RotaryEmbeddingLayer
Expand Down
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