Peacemann commited on
Commit
5b8d9fc
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1 Parent(s): d8b0028

Add lmul-attention version of deepseek-ai/DeepSeek-R1-0528-Qwen3-8B

Browse files
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
chat_template.jinja ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='', is_first_sp=true, is_last_user=false) %}{%- for message in messages %}{%- if message['role'] == 'system' %}{%- if ns.is_first_sp %}{% set ns.system_prompt = ns.system_prompt + message['content'] %}{% set ns.is_first_sp = false %}{%- else %}{% set ns.system_prompt = ns.system_prompt + '
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+
3
+ ' + message['content'] %}{%- endif %}{%- endif %}{%- endfor %}{{ bos_token }}{{ ns.system_prompt }}{%- for message in messages %}{% set content = message['content'] %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{%- set ns.is_first = false -%}{%- set ns.is_last_user = true -%}{{'<|User|>' + content + '<|Assistant|>'}}{%- endif %}{%- if message['role'] == 'assistant' %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{% endif %}{%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<|tool▁outputs▁end|>'}}{%- endif %}{%- set ns.is_first = false %}{%- set ns.is_tool = false -%}{%- set ns.is_output_first = true %}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{%- if content is none %}{{'<|tool▁calls▁begin|><|tool▁call▁begin|>' + tool['type'] + '<|tool▁sep|>' + tool['function']['name'] + '
4
+ ' + '```json' + '
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+ ' + tool['function']['arguments'] + '
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+ ' + '```' + '<|tool▁call▁end|>'}}{%- else %}{{content + '<|tool▁calls▁begin|><|tool▁call▁begin|>' + tool['type'] + '<|tool▁sep|>' + tool['function']['name'] + '
7
+ ' + '```json' + '
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+ ' + tool['function']['arguments'] + '
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+ ' + '```' + '<|tool▁call▁end|>'}}{%- endif %}{%- set ns.is_first = true -%}{%- else %}{{'
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+ ' + '<|tool▁call▁begin|>' + tool['type'] + '<|tool▁sep|>' + tool['function']['name'] + '
11
+ ' + '```json' + '
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+ ' + tool['function']['arguments'] + '
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+ ' + '```' + '<|tool▁call▁end|>'}}{%- endif %}{%- endfor %}{{'<|tool▁calls▁end|><|end▁of▁sentence|>'}}{%- endif %}{%- if message['role'] == 'assistant' and (message['tool_calls'] is not defined or message['tool_calls'] is none)%}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<|tool▁outputs▁end|>' + content + '<|end▁of▁sentence|>'}}{%- set ns.is_tool = false -%}{%- else %}{{content + '<|end▁of▁sentence|>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_last_user = false -%}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<|tool▁outputs▁begin|><|tool▁output▁begin|>' + content + '<|tool▁output▁end|>'}}{%- set ns.is_output_first = false %}{%- else %}{{'
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+ <|tool▁output▁begin|>' + content + '<|tool▁output▁end|>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<|tool▁outputs▁end|>'}}{% endif %}{% if add_generation_prompt and not ns.is_last_user and not ns.is_tool %}{{'<|Assistant|>'}}{% endif %}
config.json ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "architectures": [
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+ "Qwen3ForCausalLM"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 151643,
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+ "eos_token_id": 151645,
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+ "head_dim": 128,
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 12288,
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+ "max_position_embeddings": 131072,
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+ "max_window_layers": 36,
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+ "model_type": "qwen3",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 36,
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+ "num_key_value_heads": 8,
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+ "rms_norm_eps": 1e-06,
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+ "rope_scaling": {
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+ "attn_factor": 0.8782488562869419,
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+ "factor": 4.0,
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+ "original_max_position_embeddings": 32768,
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+ "rope_type": "yarn"
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+ },
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+ "rope_theta": 1000000,
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+ "sliding_window": null,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.52.4",
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+ "use_cache": true,
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+ "use_sliding_window": false,
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+ "vocab_size": 151936
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+ }
lmul.py ADDED
@@ -0,0 +1,88 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ """
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+ PyTorch-native implementation of the L-Mul algorithm.
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+ """
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+ from __future__ import annotations
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+
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+ import torch
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+
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+ __all__ = [
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+ "l_mul_tensor",
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+ "l_mul_attention",
11
+ ]
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+
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+ def l_mul_tensor(
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+ x: torch.Tensor, y: torch.Tensor, *, offset: float = 1.0 / 16.0
15
+ ) -> torch.Tensor:
16
+ """
17
+ Approximates `x * y` element-wise using the L-Mul algorithm.
18
+ """
19
+ sign = torch.sign(x) * torch.sign(y)
20
+
21
+ x_abs = torch.abs(x)
22
+ y_abs = torch.abs(y)
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+
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+ # Decompose tensors into mantissa and exponent.
25
+ # torch.frexp gives mantissa in [0.5, 1.0)
26
+ mx, ex = torch.frexp(x_abs)
27
+ my, ey = torch.frexp(y_abs)
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+
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+ # The paper's logic implies a mantissa in [1.0, 2.0).
30
+ # We reconstruct this by multiplying the mantissa by 2 and adjusting the exponent.
31
+ mant_a = mx * 2.0
32
+ mant_b = my * 2.0
33
+ exp_a = ex - 1
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+ exp_b = ey - 1
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+
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+ # Approximate multiplication using the L-Mul formula
37
+ result_mant = (mant_a - 1.0) + (mant_b - 1.0) + 1.0 + offset
38
+ result_exp = exp_a + exp_b
39
+
40
+ # Reconstruct the final number
41
+ result = torch.ldexp(result_mant, result_exp)
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+
43
+ # Apply the correct sign and handle zero inputs
44
+ final_result = sign * result
45
+ final_result[x == 0] = 0
46
+ final_result[y == 0] = 0
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+
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+ return final_result
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+
50
+ def l_mul_attention(
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+ query: torch.Tensor,
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+ key: torch.Tensor,
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+ value: torch.Tensor,
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+ mask: torch.Tensor | None = None,
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+ dropout: torch.nn.Module | None = None
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+ ) -> tuple[torch.Tensor, torch.Tensor]:
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+ """
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+ Scaled dot-product attention where matrix multiplications are replaced
59
+ by the L-Mul approximation for performance.
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+ """
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+ d_k = query.size(-1)
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+
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+ # Approximate Q @ K.T using l_mul_tensor.
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+ # This requires broadcasting and summing to perform the matrix multiplication.
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+ scores = l_mul_tensor(
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+ query.unsqueeze(-1),
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+ key.transpose(-2, -1).unsqueeze(-3)
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+ ).sum(-2)
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+
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+ scores = scores / (d_k ** 0.5)
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+
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+ if mask is not None:
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+ scores = scores.masked_fill(mask == 0, -1e9)
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+
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+ attn_probs = torch.nn.functional.softmax(scores, dim=-1)
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+ if dropout is not None:
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+ attn_probs = dropout(attn_probs)
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+
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+ # Approximate Attn @ V using l_mul_tensor
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+ output = l_mul_tensor(
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+ attn_probs.unsqueeze(-1),
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+ value.unsqueeze(-3)
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+ ).sum(-2)
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+
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+ return output, attn_probs
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+
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+
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+
special_tokens_map.json ADDED
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tokenizer.json ADDED
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