Upload Qwen2ForCausalLM

README.md

@@ -1,113 +1,3 @@
----
-license: apache-2.0
----
-
-# How to use this model
-
-```python
-tl_methods = [
-    'PropagateNan', 'TRITON_MAX_TENSOR_NUMEL', 'abs', 'advance', 'arange',
-    'argmax', 'argmin', 'associative_scan', 'atomic_add', 'atomic_and',
-    'atomic_cas', 'atomic_max', 'atomic_min', 'atomic_or', 'atomic_xchg',
-    'atomic_xor', 'bfloat16', 'block_type', 'broadcast', 'broadcast_to',
-    'cast', 'cat', 'cdiv', 'ceil', 'clamp', 'const', 'const_pointer_type',
-    'constexpr', 'cos', 'cumprod', 'cumsum', 'debug_barrier', 'device_assert',
-    'device_print', 'div_rn', 'dot', 'dtype', 'erf', 'exp', 'exp2',
-    'expand_dims', 'fdiv', 'flip', 'float16', 'float32', 'float64',
-    'float8e4b15', 'float8e4b8', 'float8e4nv', 'float8e5', 'float8e5b16',
-    'floor', 'fma', 'full', 'function_type', 'histogram',
-    'inline_asm_elementwise', 'int1', 'int16', 'int32', 'int64', 'int8',
-    'interleave', 'join', 'load', 'log', 'log2', 'make_block_ptr', 'max',
-    'max_constancy', 'max_contiguous', 'maximum', 'min', 'minimum',
-    'multiple_of', 'num_programs', 'pair_uniform_to_normal', 'permute',
-    'philox', 'pi32_t', 'pointer_type', 'program_id', 'rand', 'rand4x',
-    'randint', 'randint4x', 'randn', 'randn4x', 'range', 'ravel', 'reduce',
-    'reshape', 'rsqrt', 'sigmoid', 'sin', 'softmax', 'sort', 'split', 'sqrt',
-    'sqrt_rn', 'static_assert', 'static_print', 'static_range', 'store',
-    'str_to_ty', 'sum', 'swizzle2d', 'tensor', 'trans', 'uint16', 'uint32',
-    'uint64', 'uint8', 'uint_to_uniform_float', 'umulhi', 'view', 'void',
-    'where', 'xor_sum', 'zeros', 'zeros_like'
-]
-
-
-def get_user_prompt(name, pytorch_impl):
-    prompt = f"""Convert this PyTorch module implementation into an equivalent Triton kernel:
-
-<torch_code>
-{pytorch_impl}
-</torch_code>
-
-The Triton kernel should:
-1. Import torch, triton, and triton.language as tl and other necessary modules
-2. Use @triton.jit decorator on the kernel implementation (not the entrypoint function)
-3. Have proper grid and block sizes
-4. Use a mask in the load/store operations
-5. Use typed constants (tl.constexpr)
-6. Handle tensor dimensions correctly
-7. Return output matching PyTorch's implementation
-8. Do not include any test code in your response, only the Triton kernel implementation and entrypoint function
-
-The triton.language (tl) module supports the following methods: {", ".join(tl_methods)}
-
-The entrypoint function must be named: {name}_triton
-The Triton kernel implementation (called by the entrypoint) must be named: {name}_kernel
-
-No computation logic should be done within the entrypoint function. All computation logic should be done within the Triton kernel implementation.
-
-The final generated code in the response must start with <triton_code> and end with </triton_code> tags."""
-
-    return prompt
-
-
-SYSTEM_PROMPT = """You are a helpful assistant that converts PyTorch code into Triton kernels."""
-
-messages = [
-    {"role": "system", "content": SYSTEM_PROMPT},
-    {"role": "user", "content": get_user_prompt(name, code)},
-]
-
-...
-```
-
-Example PyTorch code (from Kernelbench):
-
-```python
-import torch
-import torch.nn as nn
-
-class Model(nn.Module):
-    """
-    Simple model that performs a LeakyReLU activation.
-    """
-    def __init__(self, negative_slope: float = 0.01):
-        """
-        Initializes the LeakyReLU module.
-
-        Args:
-            negative_slope (float, optional): The negative slope of the activation function. Defaults to 0.01.
-        """
-        super(Model, self).__init__()
-        self.negative_slope = negative_slope
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Applies LeakyReLU activation to the input tensor.
-
-        Args:
-            x (torch.Tensor): Input tensor of any shape.
-
-        Returns:
-            torch.Tensor: Output tensor with LeakyReLU applied, same shape as input.
-        """
-        return torch.nn.functional.leaky_relu(x, negative_slope=self.negative_slope)
-
-batch_size = 16
-dim = 16384
-
-def get_inputs():
-    x = torch.randn(batch_size, dim)
-    return [x]
-
-def get_init_inputs():
-    return []  # No special initialization inputs needed
-```
+---
+license: apache-2.0
+---

config.json

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+{
+  "_name_or_path": "predibase/qwen-32b-coder-kernelbench-sft_2",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 27648,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 64,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 64,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.48.1"
+}

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