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[KV Cache] support kv cache int8 per channel quantization #1663

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[KV Cache] support kv cache int8 per channel quantization #1663

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20 changes: 18 additions & 2 deletions src/llmcompressor/modifiers/quantization/cache.py
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Original file line number Diff line number Diff line change
Expand Up @@ -94,6 +94,14 @@ def update(
_pad_and_append_at_idx_(self.k_observers, layer_idx, k_observer)
_pad_and_append_at_idx_(self.v_observers, layer_idx, v_observer)

# reshape for per channel scenario
num_heads = key_states.shape[1]
head_dim = key_states.shape[-1]
# from [batch_size, num_heads, seq_len - residual_length, head_dim]
# to [batch_size, seq_len - residual_length, num_heads * head_dim]
key_states = key_states.transpose(1, 2).flatten(2)
value_states = value_states.transpose(1, 2).flatten(2)
Comment on lines +97 to +103
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high

The reshape logic is specific to per-channel quantization but is currently applied unconditionally. This can cause incorrect behavior for other quantization strategies. Consider adding a conditional check to apply this logic only when self.quantization_args.strategy == QuantizationStrategy.CHANNEL.

Suggested change
# reshape for per channel scenario
num_heads = key_states.shape[1]
head_dim = key_states.shape[-1]
# from [batch_size, num_heads, seq_len - residual_length, head_dim]
# to [batch_size, seq_len - residual_length, num_heads * head_dim]
key_states = key_states.transpose(1, 2).flatten(2)
value_states = value_states.transpose(1, 2).flatten(2)
# reshape for per channel scenario
num_heads = key_states.shape[1]
head_dim = key_states.shape[-1]
if self.quantization_args.strategy == QuantizationStrategy.CHANNEL:
# from [batch_size, num_heads, seq_len - residual_length, head_dim]
# to [batch_size, seq_len - residual_length, num_heads * head_dim]
key_states = key_states.transpose(1, 2).flatten(2)
value_states = value_states.transpose(1, 2).flatten(2)

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per tensor is same


q_key_states = self._quantize(
key_states.contiguous(), KVCacheScaleType.KEY, layer_idx
)
Expand All @@ -106,6 +114,14 @@ def update(
q_value_states, KVCacheScaleType.VALUE, layer_idx
)

# reshape for per channel scenario
# from [batch_size, seq_len - residual_length, num_heads * head_dim]
# to [batch_size, num_heads, seq_len - residual_length, head_dim]
qdq_key_states = qdq_key_states.view(
qdq_key_states.shape[0], qdq_key_states.shape[1], num_heads, head_dim).transpose(1, 2)
qdq_value_states = qdq_value_states.view(
qdq_value_states.shape[0], qdq_value_states.shape[1], num_heads, head_dim).transpose(1, 2)
Comment on lines +117 to +123
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high

This reverse reshape operation is specific to the per-channel scenario and should be executed conditionally. Applying this to tensors from other quantization strategies will result in shape mismatches. Wrap this in a condition that checks for the per-channel strategy: if self.quantization_args.strategy == QuantizationStrategy.CHANNEL:.

Suggested change
# reshape for per channel scenario
# from [batch_size, seq_len - residual_length, num_heads * head_dim]
# to [batch_size, num_heads, seq_len - residual_length, head_dim]
qdq_key_states = qdq_key_states.view(
qdq_key_states.shape[0], qdq_key_states.shape[1], num_heads, head_dim).transpose(1, 2)
qdq_value_states = qdq_value_states.view(
qdq_value_states.shape[0], qdq_value_states.shape[1], num_heads, head_dim).transpose(1, 2)
if self.quantization_args.strategy == QuantizationStrategy.CHANNEL:
# reshape for per channel scenario
# from [batch_size, seq_len - residual_length, num_heads * head_dim]
# to [batch_size, num_heads, seq_len - residual_length, head_dim]
qdq_key_states = qdq_key_states.view(
qdq_key_states.shape[0], qdq_key_states.shape[1], num_heads, head_dim).transpose(1, 2)
qdq_value_states = qdq_value_states.view(
qdq_value_states.shape[0], qdq_value_states.shape[1], num_heads, head_dim).transpose(1, 2)

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per tensor is same


keys_to_return, values_to_return = qdq_key_states, qdq_value_states

return keys_to_return, values_to_return
Expand Down Expand Up @@ -155,8 +171,8 @@ def _quantize(self, tensor, kv_type, layer_idx):
zps = self.v_zps

scale, zp = observer(tensor)
_pad_and_append_at_idx_(scales, layer_idx, scale)
_pad_and_append_at_idx_(zps, layer_idx, zp)
_pad_and_append_at_idx_(scales, layer_idx, scale.squeeze())
_pad_and_append_at_idx_(zps, layer_idx, zp.squeeze())

q_tensor = quantize(
x=tensor,
Expand Down
7 changes: 5 additions & 2 deletions src/llmcompressor/observers/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -181,8 +181,11 @@ def get_qparams(
self._zero_point[:, group_index] = zero_point.squeeze(1)

elif self.quantization_args.strategy == QuantizationStrategy.CHANNEL:
# assume observed is transposed, because its the output, hence use dim 0
self._scale, self._zero_point = self.get_qparams_along_dim(observed, 0)
# 1. dim=2 scenario: in kv cache quant scenario which is
# [batch_size, seq_len - residual_length, num_heads * head_dim]
# 2. dim=0 scenario: assume observed is transposed, because its the output, hence use dim 0
dim = 2 if observed.dim() == 3 else 0
self._scale, self._zero_point = self.get_qparams_along_dim(observed, dim)

elif self.quantization_args.strategy == QuantizationStrategy.TOKEN:
# use dim 1, assume the obsersed.shape = [batch, token, hidden]
Expand Down
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