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Updating Chunking method #398

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Merged
merged 1 commit into from
May 9, 2025
Merged

Updating Chunking method #398

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2 changes: 1 addition & 1 deletion QEfficient/transformers/models/gemma3/modeling_gemma3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -608,7 +608,7 @@ def get_specializations(
vision_seq_len = compiler_options.pop("vision_seq_len", None)
if vision_seq_len is None:
# TODO: Check properly for Gemma3, Not verified yet.
vision_seq_len = 2560 # for Gemma3 Vision feature shape is (1, 4096, 1152) --> 1152 is hidden size)
vision_seq_len = 512 # for Gemma3 Vision feature shape is (1, 4096, 1152) --> 1152 is hidden size)

prefill_seq_len = prefill_seq_len if prefill_seq_len else 32
ctx_len = ctx_len if ctx_len else constants.INTERN_CTX_LEN
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22 changes: 15 additions & 7 deletions QEfficient/transformers/models/modeling_auto.py
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Expand Up @@ -751,8 +751,8 @@ def kv_offload_generate(
input_len = inputs["attention_mask"].sum(1, keepdims=True)
input_ids_length = inputs["input_ids"].shape[1]
num_chunks = -(input_ids_length // -prefill_seq_len) # ceil divide without float
padded_len = num_chunks * prefill_seq_len # Convert to a multiple of prompt_len

# padded_len = num_chunks * prefill_seq_len # Convert to a multiple of prompt_len
padded_len = vision_session.bindings[vision_session.binding_index_map["input_ids"]].dims[1]
if generation_len is None:
generation_len = ctx_len - input_len.max()
assert generation_len > 0, "generation length should be greater than zero"
Expand Down Expand Up @@ -783,28 +783,36 @@ def kv_offload_generate(
}

vision_inputs["pixel_values"] = vision_inputs["pixel_values"].astype("float16")
vision_inputs["input_ids"] = inputs["input_ids"]
vision_start = perf_counter()
vision_outputs = vision_session.run(vision_inputs)
vision_end = perf_counter()

lang_inputs = {k: v for k, v in inputs.items() if k not in vision_inputs}
lang_inputs["input_ids"] = inputs["input_ids"]
lang_inputs["position_ids"] = np.where(
lang_inputs.pop("attention_mask"), np.arange(padded_len), -1
) # Need to use -1 as position_ids for invalid tokens

vision_session.deactivate()
lang_session.activate()

lang_session.set_buffers(vision_outputs)

lang_inputs["vision_embeds"] = vision_outputs["vision_embeds"]
# lang_session.set_buffers(vision_outputs)
prefill_start = perf_counter()
# Run prefill
for i in range(num_chunks):
chunk_inputs = lang_inputs.copy()
chunk_inputs["input_ids"] = lang_inputs["input_ids"][:, i * prefill_seq_len : (i + 1) * prefill_seq_len]
chunk_inputs["position_ids"] = lang_inputs["position_ids"][
:, i * prefill_seq_len : (i + 1) * prefill_seq_len
]
chunk_inputs["vision_embeds"] = lang_inputs["vision_embeds"][
:, i * prefill_seq_len : (i + 1) * prefill_seq_len
]
outputs = lang_session.run(chunk_inputs)

prefill_time = perf_counter() - prefill_start
prefill_time = perf_counter() - prefill_start + vision_end - vision_start
lang_inputs["vision_embeds"] = lang_inputs["vision_embeds"][:, :prefill_seq_len]
# Skip inputs/outputs again
lang_session.skip_buffers(
[x for x in lang_session.input_names + lang_session.output_names if x.startswith("past_")]
Expand Down Expand Up @@ -838,7 +846,7 @@ def kv_offload_generate(
streamer.end()

decode_perf = (num_token - 1) / (decode_end - decode_start)
total_time = decode_end - prefill_start
total_time = decode_end - decode_start + prefill_time
total_perf = num_token / total_time

return CloudAI100ExecInfoNew(
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