Phi3 support (#1797)

# 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 2024-04-23 18:40:05 +02:00 committed by GitHub
parent 9be1db3101
commit 986b4044d1
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
4 changed files with 49 additions and 14 deletions

View File

@ -327,7 +327,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
elif model_type == "llama" or model_type == "baichuan":
elif model_type == "llama" or model_type == "baichuan" or model_type == "phi3":
if FLASH_ATTENTION:
return FlashLlama(
model_id,

View File

@ -101,6 +101,13 @@ def load_attention(config, prefix, weights):
weights=weights,
bias=False,
)
elif config.model_type == "phi3":
return TensorParallelColumnLinear.load_qkv(
config,
prefix=f"{prefix}.qkv_proj",
weights=weights,
bias=False,
)
else:
return TensorParallelColumnLinear.load_multi(
config,
@ -257,13 +264,21 @@ class LlamaMLP(nn.Module):
)
)
# Fuse gate and up proj
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
weights=weights,
dim=0,
bias=False,
)
if config.model_type == "phi3":
self.gate_up_proj = TensorParallelColumnLinear.load_gate_up(
config,
prefix=f"{prefix}.gate_up_proj",
weights=weights,
bias=False,
)
else:
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
weights=weights,
dim=0,
bias=False,
)
self.down_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.down_proj",

View File

@ -696,6 +696,19 @@ class TensorParallelHead(SuperLayer):
class TensorParallelColumnLinear(SuperLayer):
@classmethod
def load_gate_up(cls, config, prefix: str, weights, bias: bool):
"""Specific method when the QKV was joined after the fact"""
weight = weights.get_weights_col_packed_gate_up(
prefix, quantize=config.quantize
)
if bias:
raise NotImplementedError("packed_gate_up only implemented without bias")
else:
bias = None
linear = get_linear(weight, bias, config.quantize)
return cls(linear)
@classmethod
def load_qkv(cls, config, prefix: str, weights, bias: bool):
"""Specific method when the QKV was joined after the fact"""

View File

@ -141,6 +141,12 @@ class Weights:
return weight
def get_weights_col_packed_qkv(self, prefix: str, quantize: str):
return self.get_weights_col_packed(prefix, quantize, 3)
def get_weights_col_packed_gate_up(self, prefix: str, quantize: str):
return self.get_weights_col_packed(prefix, quantize, 2)
def get_weights_col_packed(self, prefix: str, quantize: str, blocks: int):
"""
Highly specific when the underlying tensor is a simple cat of Q,K,V instead of being
already alternating Q,K,V within the main tensor
@ -181,8 +187,8 @@ class Weights:
else:
slice_ = self._get_slice(f"{prefix}.weight")
total_size = slice_.get_shape()[0]
assert total_size % 3 == 0, "Prepacked qkv is not divisible by 3"
single_size = total_size // 3
assert total_size % blocks == 0, f"Prepacked is not divisible by {blocks}"
single_size = total_size // blocks
world_size = self.process_group.size()
rank = self.process_group.rank()
@ -192,10 +198,11 @@ class Weights:
block_size = single_size // world_size
start = rank * block_size
stop = (rank + 1) * block_size
q = slice_[start:stop]
k = slice_[start + single_size : stop + single_size]
v = slice_[start + 2 * single_size : stop + 2 * single_size]
weight = torch.cat([q, k, v], dim=0)
tensors = []
for i in range(blocks):
tensor = slice_[start + i * single_size : stop + i * single_size]
tensors.append(tensor)
weight = torch.cat(tensors, dim=0)
weight = weight.to(device=self.device)
weight = weight.to(dtype=self.dtype)
return weight