跳到内容

vllm_gaudi.ops.hpu_compressed_tensors

SUPPORTED_STRATEGIES 模块属性 

SUPPORTED_STRATEGIES = [CHANNEL, TENSOR]

logger 模块属性 

logger = init_logger(__name__)

HPUCompressedTensorsLinearMethod

Bases: CompressedTensorsLinearMethod

Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
@CustomOp.register_oot(name='CompressedTensorsLinearMethod')
class HPUCompressedTensorsLinearMethod(OrigCompressedTensorsLinearMethod):

    def __init__(self, quantization_config: CompressedTensorsConfig):
        super().__init__(quantization_config)
        torch.hpu.synchronize()

    def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int, output_partition_sizes: list[int],
                       input_size: int, output_size: int, params_dtype: torch.dtype, **extra_weight_attrs):
        """
        Use the CompressedTensorsScheme associated with each layer to create
        the necessary parameters for the layer. See LinearMethodBase for param
        details
        """
        weight_loader = extra_weight_attrs.get("weight_loader")

        # Explicitly override scheme since register_oot and monkey-patching not working
        layer.scheme = self.get_hpu_scheme(layer)
        layer.scheme.create_weights(layer=layer,
                                    input_size=input_size,
                                    input_size_per_partition=input_size_per_partition,
                                    output_partition_sizes=output_partition_sizes,
                                    output_size=output_size,
                                    params_dtype=params_dtype,
                                    weight_loader=weight_loader)

    def get_hpu_scheme(self, layer: torch.nn.Module):
        scheme = layer.scheme
        if scheme is None:
            raise ValueError("A scheme must be defined for each layer")
        scheme_classname = scheme.__class__.__name__
        if (scheme_classname in ("CompressedTensorsW8A8Fp8", "CompressedTensorsW8A16Fp8")):
            hpu_scheme = HPUCompressedTensorsW8A8Fp8(scheme.strategy, scheme.is_static_input_scheme)
        elif (scheme_classname == "CompressedTensorsWNA16"):
            matched_target = find_matched_target(layer_name=layer.prefix,
                                                 module=layer,
                                                 targets=self.quantization_config.target_scheme_map.keys(),
                                                 fused_mapping=self.quantization_config.packed_modules_mapping)

            scheme_dict = self.quantization_config.target_scheme_map[matched_target]
            weight_quant = scheme_dict.get("weights")

            hpu_scheme = HPUCompressedTensorsWNA16(num_bits=weight_quant.num_bits,
                                                   strategy=scheme.strategy,
                                                   symmetric=scheme.symmetric,
                                                   group_size=scheme.group_size,
                                                   actorder=weight_quant.actorder)
        else:
            raise ValueError(f"{scheme_classname} compressed format is not supported on HPU")
        return hpu_scheme

__init__ 

__init__(quantization_config: CompressedTensorsConfig)
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
43
44
45
def __init__(self, quantization_config: CompressedTensorsConfig):
    super().__init__(quantization_config)
    torch.hpu.synchronize()

create_weights 

create_weights(
    layer: Module,
    input_size_per_partition: int,
    output_partition_sizes: list[int],
    input_size: int,
    output_size: int,
    params_dtype: dtype,
    **extra_weight_attrs,
)

Use the CompressedTensorsScheme associated with each layer to create the necessary parameters for the layer. See LinearMethodBase for param details

Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int, output_partition_sizes: list[int],
                   input_size: int, output_size: int, params_dtype: torch.dtype, **extra_weight_attrs):
    """
    Use the CompressedTensorsScheme associated with each layer to create
    the necessary parameters for the layer. See LinearMethodBase for param
    details
    """
    weight_loader = extra_weight_attrs.get("weight_loader")

    # Explicitly override scheme since register_oot and monkey-patching not working
    layer.scheme = self.get_hpu_scheme(layer)
    layer.scheme.create_weights(layer=layer,
                                input_size=input_size,
                                input_size_per_partition=input_size_per_partition,
                                output_partition_sizes=output_partition_sizes,
                                output_size=output_size,
                                params_dtype=params_dtype,
                                weight_loader=weight_loader)

get_hpu_scheme 

get_hpu_scheme(layer: Module)
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
def get_hpu_scheme(self, layer: torch.nn.Module):
    scheme = layer.scheme
    if scheme is None:
        raise ValueError("A scheme must be defined for each layer")
    scheme_classname = scheme.__class__.__name__
    if (scheme_classname in ("CompressedTensorsW8A8Fp8", "CompressedTensorsW8A16Fp8")):
        hpu_scheme = HPUCompressedTensorsW8A8Fp8(scheme.strategy, scheme.is_static_input_scheme)
    elif (scheme_classname == "CompressedTensorsWNA16"):
        matched_target = find_matched_target(layer_name=layer.prefix,
                                             module=layer,
                                             targets=self.quantization_config.target_scheme_map.keys(),
                                             fused_mapping=self.quantization_config.packed_modules_mapping)

        scheme_dict = self.quantization_config.target_scheme_map[matched_target]
        weight_quant = scheme_dict.get("weights")

        hpu_scheme = HPUCompressedTensorsWNA16(num_bits=weight_quant.num_bits,
                                               strategy=scheme.strategy,
                                               symmetric=scheme.symmetric,
                                               group_size=scheme.group_size,
                                               actorder=weight_quant.actorder)
    else:
        raise ValueError(f"{scheme_classname} compressed format is not supported on HPU")
    return hpu_scheme

HPUCompressedTensorsW8A8Fp8

Bases: CompressedTensorsScheme

Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
@CustomOp.register_oot(name='CompressedTensorsW8A16Fp8')
class HPUCompressedTensorsW8A8Fp8(CompressedTensorsScheme):

    def __init__(self, strategy: str, is_static_input_scheme: bool):
        self.strategy = strategy
        self.is_static_input_scheme = is_static_input_scheme

    @classmethod
    def get_min_capability(cls) -> int:
        return -1

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        if layer.scheme.strategy == QuantizationStrategy.TENSOR:
            ws_channelwise = convert_to_channelwise(layer.weight_scale, layer.logical_widths)
            layer.weight_scale = torch.nn.Parameter(ws_channelwise, requires_grad=False)
        else:
            # required by torch.compile to be torch.nn.Parameter
            layer.weight_scale = torch.nn.Parameter(layer.weight_scale.data, requires_grad=False)

        # Weights must be transposed for marlin
        layer.weight = torch.nn.Parameter(layer.weight.t(), requires_grad=False)

        if layer.scheme.is_static_input_scheme:
            # required by torch.compile to be torch.nn.Parameter
            layer.input_scale = torch.nn.Parameter(layer.input_scale.data, requires_grad=False)

    def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int, output_partition_sizes: list[int],
                       input_size: int, output_size: int, params_dtype: torch.dtype, **extra_weight_attrs):
        """
        Use the CompressedTensorsScheme associated with each layer to create
        the necessary parameters for the layer. See LinearMethodBase for param
        details
        """
        weight_loader = extra_weight_attrs.get("weight_loader")
        if hpu_ops.is_hpu_gaudi2:
            weight_loader = hpu_ops.gaudi_weight_wrapper(weight_loader)
        output_size_per_partition = sum(output_partition_sizes)
        layer.logical_widths = output_partition_sizes
        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition
        layer.orig_dtype = params_dtype
        layer.weight_block_size = None

        # WEIGHT
        weight = ModelWeightParameter(data=torch.empty(output_size_per_partition,
                                                       input_size_per_partition,
                                                       dtype=torch.float8_e4m3fn),
                                      input_dim=1,
                                      output_dim=0,
                                      weight_loader=weight_loader)
        layer.register_parameter("weight", weight)

        # WEIGHT SCALE
        if layer.scheme.strategy == QuantizationStrategy.CHANNEL:
            weight_scale = ChannelQuantScaleParameter(data=torch.empty((sum(output_partition_sizes), 1),
                                                                       dtype=torch.float32),
                                                      output_dim=0,
                                                      weight_loader=weight_loader)
        elif layer.scheme.strategy == QuantizationStrategy.TENSOR:
            weight_scale = PerTensorScaleParameter(data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
                                                   weight_loader=weight_loader)
        else:
            raise ValueError(f"Unsupported weight strategy={layer.scheme.strategy}, "
                             f"supported strategies are {SUPPORTED_STRATEGIES}")

        weight_scale[:] = torch.finfo(torch.float32).min
        layer.register_parameter("weight_scale", weight_scale)

        # INPUT SCALE (to deal with converted checkpoints)
        if layer.scheme.is_static_input_scheme:
            input_scale = PerTensorScaleParameter(data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
                                                  weight_loader=weight_loader)
            layer.register_parameter("input_scale", input_scale)

    def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None):
        weight_scale = layer.weight_scale.transpose(0, 1) if layer.weight_scale.dim() > 1 else layer.weight_scale
        input_scale = getattr(layer, 'input_scale', None)
        return hpu_ops.apply_fp8_linear_hpu(input=x,
                                            weight=layer.weight,
                                            weight_scale=weight_scale,
                                            input_scale=input_scale,
                                            bias=bias,
                                            trans_B=False)

is_static_input_scheme 实例属性 

is_static_input_scheme = is_static_input_scheme

strategy 实例属性 

strategy = strategy

__init__ 

__init__(strategy: str, is_static_input_scheme: bool)
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
95
96
97
def __init__(self, strategy: str, is_static_input_scheme: bool):
    self.strategy = strategy
    self.is_static_input_scheme = is_static_input_scheme

apply_weights 

apply_weights(
    layer: Module, x: Tensor, bias: Optional[Tensor] = None
)
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
166
167
168
169
170
171
172
173
174
def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None):
    weight_scale = layer.weight_scale.transpose(0, 1) if layer.weight_scale.dim() > 1 else layer.weight_scale
    input_scale = getattr(layer, 'input_scale', None)
    return hpu_ops.apply_fp8_linear_hpu(input=x,
                                        weight=layer.weight,
                                        weight_scale=weight_scale,
                                        input_scale=input_scale,
                                        bias=bias,
                                        trans_B=False)

create_weights 

create_weights(
    layer: Module,
    input_size_per_partition: int,
    output_partition_sizes: list[int],
    input_size: int,
    output_size: int,
    params_dtype: dtype,
    **extra_weight_attrs,
)

Use the CompressedTensorsScheme associated with each layer to create the necessary parameters for the layer. See LinearMethodBase for param details

Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int, output_partition_sizes: list[int],
                   input_size: int, output_size: int, params_dtype: torch.dtype, **extra_weight_attrs):
    """
    Use the CompressedTensorsScheme associated with each layer to create
    the necessary parameters for the layer. See LinearMethodBase for param
    details
    """
    weight_loader = extra_weight_attrs.get("weight_loader")
    if hpu_ops.is_hpu_gaudi2:
        weight_loader = hpu_ops.gaudi_weight_wrapper(weight_loader)
    output_size_per_partition = sum(output_partition_sizes)
    layer.logical_widths = output_partition_sizes
    layer.input_size_per_partition = input_size_per_partition
    layer.output_size_per_partition = output_size_per_partition
    layer.orig_dtype = params_dtype
    layer.weight_block_size = None

    # WEIGHT
    weight = ModelWeightParameter(data=torch.empty(output_size_per_partition,
                                                   input_size_per_partition,
                                                   dtype=torch.float8_e4m3fn),
                                  input_dim=1,
                                  output_dim=0,
                                  weight_loader=weight_loader)
    layer.register_parameter("weight", weight)

    # WEIGHT SCALE
    if layer.scheme.strategy == QuantizationStrategy.CHANNEL:
        weight_scale = ChannelQuantScaleParameter(data=torch.empty((sum(output_partition_sizes), 1),
                                                                   dtype=torch.float32),
                                                  output_dim=0,
                                                  weight_loader=weight_loader)
    elif layer.scheme.strategy == QuantizationStrategy.TENSOR:
        weight_scale = PerTensorScaleParameter(data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
                                               weight_loader=weight_loader)
    else:
        raise ValueError(f"Unsupported weight strategy={layer.scheme.strategy}, "
                         f"supported strategies are {SUPPORTED_STRATEGIES}")

    weight_scale[:] = torch.finfo(torch.float32).min
    layer.register_parameter("weight_scale", weight_scale)

    # INPUT SCALE (to deal with converted checkpoints)
    if layer.scheme.is_static_input_scheme:
        input_scale = PerTensorScaleParameter(data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
                                              weight_loader=weight_loader)
        layer.register_parameter("input_scale", input_scale)

get_min_capability 类方法 

get_min_capability() -> int
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
 99
100
101
@classmethod
def get_min_capability(cls) -> int:
    return -1

process_weights_after_loading 

process_weights_after_loading(layer: Module) -> None
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
103
104
105
106
107
108
109
110
111
112
113
114
115
116
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
    if layer.scheme.strategy == QuantizationStrategy.TENSOR:
        ws_channelwise = convert_to_channelwise(layer.weight_scale, layer.logical_widths)
        layer.weight_scale = torch.nn.Parameter(ws_channelwise, requires_grad=False)
    else:
        # required by torch.compile to be torch.nn.Parameter
        layer.weight_scale = torch.nn.Parameter(layer.weight_scale.data, requires_grad=False)

    # Weights must be transposed for marlin
    layer.weight = torch.nn.Parameter(layer.weight.t(), requires_grad=False)

    if layer.scheme.is_static_input_scheme:
        # required by torch.compile to be torch.nn.Parameter
        layer.input_scale = torch.nn.Parameter(layer.input_scale.data, requires_grad=False)

HPUCompressedTensorsW8A8Fp8MoEMethod

Bases: CompressedTensorsW8A8Fp8MoEMethod

MoE method without quantization.

Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
@CustomOp.register_oot(name='CompressedTensorsW8A8Fp8MoEMethod')
class HPUCompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsW8A8Fp8MoEMethod):
    """MoE method without quantization."""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        torch.hpu.synchronize()

    def create_weights(self, *args, **kwargs) -> None:
        if hpu_ops.is_hpu_gaudi2:
            kwargs['weight_loader'] = hpu_ops.gaudi_weight_wrapper(kwargs.get('weight_loader'))
        super().create_weights(*args, **kwargs)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        #NOTE: This method is called after the weights are loaded.
        # super().process_weights_after_loading(layer)
        # custom handling for HPU
        num_experts = layer.local_num_experts
        ep_shift = layer.ep_rank * num_experts

        experts_min, experts_max = ep_shift, num_experts + ep_shift - 1
        layer.moe_op = VllmMixtureOfExpertsOpFP8PerChannel(
            num_experts,
            experts_min,
            experts_max,
        )

        layer = hpu_ops.fp8_channel_moe_prepare_weights(layer)
        return

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        **kwargs,
    ) -> torch.Tensor:
        input_shape = x.shape
        x = x.view(-1, x.shape[-1])
        if use_grouped_topk or custom_routing_function is not None:
            topk_weights, topk_ids, zero_expert_result = FusedMoE.select_experts(
                hidden_states=x,
                router_logits=router_logits,
                use_grouped_topk=use_grouped_topk,
                top_k=top_k,
                renormalize=renormalize,
                topk_group=topk_group,
                num_expert_group=num_expert_group,
                custom_routing_function=custom_routing_function,
                scoring_func=scoring_func,
                e_score_correction_bias=e_score_correction_bias)
        else:
            import torch.nn.functional as F
            topk_weights = F.softmax(router_logits, dim=1, dtype=torch.float32)
            topk_weights, topk_ids = torch.topk(topk_weights, top_k, dim=-1)
            topk_weights /= topk_weights.sum(dim=-1, keepdim=True)
            topk_weights = topk_weights.to(x.dtype)
        topk_ids = topk_ids.view(*x.shape[:-1], -1)
        topk_weights = topk_weights.view(*x.shape[:-1], -1)
        output = layer.moe_op(
            x,
            topk_ids.to(torch.int64),
            topk_weights.to(x.dtype),
            permuted_weights=True,
            activation=activation,
        )
        return output.view(*input_shape)

__init__ 

__init__(*args, **kwargs)
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
181
182
183
def __init__(self, *args, **kwargs):
    super().__init__(*args, **kwargs)
    torch.hpu.synchronize()

apply 

apply(
    layer: Module,
    x: Tensor,
    router_logits: Tensor,
    top_k: int,
    renormalize: bool,
    use_grouped_topk: bool = False,
    topk_group: Optional[int] = None,
    num_expert_group: Optional[int] = None,
    global_num_experts: int = -1,
    expert_map: Optional[Tensor] = None,
    custom_routing_function: Optional[Callable] = None,
    scoring_func: str = "softmax",
    e_score_correction_bias: Optional[Tensor] = None,
    apply_router_weight_on_input: bool = False,
    activation: str = "silu",
    **kwargs,
) -> Tensor
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
def apply(
    self,
    layer: torch.nn.Module,
    x: torch.Tensor,
    router_logits: torch.Tensor,
    top_k: int,
    renormalize: bool,
    use_grouped_topk: bool = False,
    topk_group: Optional[int] = None,
    num_expert_group: Optional[int] = None,
    global_num_experts: int = -1,
    expert_map: Optional[torch.Tensor] = None,
    custom_routing_function: Optional[Callable] = None,
    scoring_func: str = "softmax",
    e_score_correction_bias: Optional[torch.Tensor] = None,
    apply_router_weight_on_input: bool = False,
    activation: str = "silu",
    **kwargs,
) -> torch.Tensor:
    input_shape = x.shape
    x = x.view(-1, x.shape[-1])
    if use_grouped_topk or custom_routing_function is not None:
        topk_weights, topk_ids, zero_expert_result = FusedMoE.select_experts(
            hidden_states=x,
            router_logits=router_logits,
            use_grouped_topk=use_grouped_topk,
            top_k=top_k,
            renormalize=renormalize,
            topk_group=topk_group,
            num_expert_group=num_expert_group,
            custom_routing_function=custom_routing_function,
            scoring_func=scoring_func,
            e_score_correction_bias=e_score_correction_bias)
    else:
        import torch.nn.functional as F
        topk_weights = F.softmax(router_logits, dim=1, dtype=torch.float32)
        topk_weights, topk_ids = torch.topk(topk_weights, top_k, dim=-1)
        topk_weights /= topk_weights.sum(dim=-1, keepdim=True)
        topk_weights = topk_weights.to(x.dtype)
    topk_ids = topk_ids.view(*x.shape[:-1], -1)
    topk_weights = topk_weights.view(*x.shape[:-1], -1)
    output = layer.moe_op(
        x,
        topk_ids.to(torch.int64),
        topk_weights.to(x.dtype),
        permuted_weights=True,
        activation=activation,
    )
    return output.view(*input_shape)

create_weights 

create_weights(*args, **kwargs) -> None
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
185
186
187
188
def create_weights(self, *args, **kwargs) -> None:
    if hpu_ops.is_hpu_gaudi2:
        kwargs['weight_loader'] = hpu_ops.gaudi_weight_wrapper(kwargs.get('weight_loader'))
    super().create_weights(*args, **kwargs)

process_weights_after_loading 

process_weights_after_loading(layer: Module) -> None
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
    #NOTE: This method is called after the weights are loaded.
    # super().process_weights_after_loading(layer)
    # custom handling for HPU
    num_experts = layer.local_num_experts
    ep_shift = layer.ep_rank * num_experts

    experts_min, experts_max = ep_shift, num_experts + ep_shift - 1
    layer.moe_op = VllmMixtureOfExpertsOpFP8PerChannel(
        num_experts,
        experts_min,
        experts_max,
    )

    layer = hpu_ops.fp8_channel_moe_prepare_weights(layer)
    return

HPUCompressedTensorsWNA16

Bases: CompressedTensorsWNA16

Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
class HPUCompressedTensorsWNA16(CompressedTensorsWNA16):
    _kernel_backends_being_used: set[str] = set()

    @classmethod
    def get_min_capability(cls) -> int:
        return -1

    def create_weights(self, layer: torch.nn.Module, output_size: int, input_size: int,
                       output_partition_sizes: list[int], input_size_per_partition: int, params_dtype: torch.dtype,
                       weight_loader: Callable, **kwargs):
        output_size_per_partition = sum(output_partition_sizes)

        mp_linear_kernel_config = MPLinearLayerConfig(
            full_weight_shape=(input_size, output_size),
            partition_weight_shape=\
                (input_size_per_partition, output_size_per_partition),
            weight_type=self.quant_type,
            act_type=params_dtype,
            group_size=self.group_size,
            zero_points=not self.symmetric,
            has_g_idx=self.has_g_idx
        )

        kernel_type = HPUMPLinearKernel

        if kernel_type.__name__ not in self._kernel_backends_being_used:
            logger.info("Using %s for CompressedTensorsWNA16", kernel_type.__name__)
            self._kernel_backends_being_used.add(kernel_type.__name__)

        # If group_size is -1, we are in channelwise case.
        group_size = self.group_size if self.group_size != -1 else input_size
        row_parallel = (input_size != input_size_per_partition)
        partition_scales = not marlin_repeat_scales_on_all_ranks(self.has_g_idx, self.group_size, row_parallel)

        scales_and_zp_size = input_size // group_size

        if partition_scales:
            assert input_size_per_partition % group_size == 0
            scales_and_zp_size = input_size_per_partition // group_size

        weight = PackedvLLMParameter(input_dim=1,
                                     output_dim=0,
                                     weight_loader=weight_loader,
                                     packed_factor=self.pack_factor,
                                     packed_dim=1,
                                     data=torch.empty(
                                         output_size_per_partition,
                                         input_size_per_partition // self.pack_factor,
                                         dtype=torch.int32,
                                     ))

        weight_scale_args = {
            "weight_loader": weight_loader,
            "data": torch.empty(
                output_size_per_partition,
                scales_and_zp_size,
                dtype=params_dtype,
            )
        }

        zeros_args = {
            "weight_loader": weight_loader,
            "data": torch.zeros(
                output_size_per_partition // self.pack_factor,
                scales_and_zp_size,
                dtype=torch.int32,
            )
        }

        if not partition_scales:
            weight_scale = ChannelQuantScaleParameter(output_dim=0, **weight_scale_args)

            if not self.symmetric:
                qzeros = PackedColumnParameter(output_dim=0, packed_dim=0, packed_factor=self.pack_factor, **zeros_args)
        else:
            weight_scale = GroupQuantScaleParameter(output_dim=0, input_dim=1, **weight_scale_args)
            if not self.symmetric:
                qzeros = PackedvLLMParameter(input_dim=1,
                                             output_dim=0,
                                             packed_dim=0,
                                             packed_factor=self.pack_factor,
                                             **zeros_args)

        # A 2D array defining the original shape of the weights
        # before packing
        weight_shape = BasevLLMParameter(data=torch.empty(2, dtype=torch.int64), weight_loader=weight_loader)

        layer.register_parameter("weight_packed", weight)
        layer.register_parameter("weight_scale", weight_scale)
        layer.register_parameter("weight_shape", weight_shape)

        if not self.symmetric:
            layer.register_parameter("weight_zero_point", qzeros)

        # group index (for activation reordering)
        if self.has_g_idx:
            weight_g_idx = RowvLLMParameter(data=torch.empty(
                input_size_per_partition,
                dtype=torch.int32,
            ),
                                            input_dim=0,
                                            weight_loader=weight_loader)
            layer.register_parameter("weight_g_idx", weight_g_idx)

        self.kernel = kernel_type(mp_linear_kernel_config,
                                  w_q_param_name="weight_packed",
                                  w_s_param_name="weight_scale",
                                  w_zp_param_name="weight_zero_point",
                                  w_gidx_param_name="weight_g_idx")

_kernel_backends_being_used 类属性 实例属性 

_kernel_backends_being_used: set[str] = set()

create_weights 

create_weights(
    layer: Module,
    output_size: int,
    input_size: int,
    output_partition_sizes: list[int],
    input_size_per_partition: int,
    params_dtype: dtype,
    weight_loader: Callable,
    **kwargs,
)
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
def create_weights(self, layer: torch.nn.Module, output_size: int, input_size: int,
                   output_partition_sizes: list[int], input_size_per_partition: int, params_dtype: torch.dtype,
                   weight_loader: Callable, **kwargs):
    output_size_per_partition = sum(output_partition_sizes)

    mp_linear_kernel_config = MPLinearLayerConfig(
        full_weight_shape=(input_size, output_size),
        partition_weight_shape=\
            (input_size_per_partition, output_size_per_partition),
        weight_type=self.quant_type,
        act_type=params_dtype,
        group_size=self.group_size,
        zero_points=not self.symmetric,
        has_g_idx=self.has_g_idx
    )

    kernel_type = HPUMPLinearKernel

    if kernel_type.__name__ not in self._kernel_backends_being_used:
        logger.info("Using %s for CompressedTensorsWNA16", kernel_type.__name__)
        self._kernel_backends_being_used.add(kernel_type.__name__)

    # If group_size is -1, we are in channelwise case.
    group_size = self.group_size if self.group_size != -1 else input_size
    row_parallel = (input_size != input_size_per_partition)
    partition_scales = not marlin_repeat_scales_on_all_ranks(self.has_g_idx, self.group_size, row_parallel)

    scales_and_zp_size = input_size // group_size

    if partition_scales:
        assert input_size_per_partition % group_size == 0
        scales_and_zp_size = input_size_per_partition // group_size

    weight = PackedvLLMParameter(input_dim=1,
                                 output_dim=0,
                                 weight_loader=weight_loader,
                                 packed_factor=self.pack_factor,
                                 packed_dim=1,
                                 data=torch.empty(
                                     output_size_per_partition,
                                     input_size_per_partition // self.pack_factor,
                                     dtype=torch.int32,
                                 ))

    weight_scale_args = {
        "weight_loader": weight_loader,
        "data": torch.empty(
            output_size_per_partition,
            scales_and_zp_size,
            dtype=params_dtype,
        )
    }

    zeros_args = {
        "weight_loader": weight_loader,
        "data": torch.zeros(
            output_size_per_partition // self.pack_factor,
            scales_and_zp_size,
            dtype=torch.int32,
        )
    }

    if not partition_scales:
        weight_scale = ChannelQuantScaleParameter(output_dim=0, **weight_scale_args)

        if not self.symmetric:
            qzeros = PackedColumnParameter(output_dim=0, packed_dim=0, packed_factor=self.pack_factor, **zeros_args)
    else:
        weight_scale = GroupQuantScaleParameter(output_dim=0, input_dim=1, **weight_scale_args)
        if not self.symmetric:
            qzeros = PackedvLLMParameter(input_dim=1,
                                         output_dim=0,
                                         packed_dim=0,
                                         packed_factor=self.pack_factor,
                                         **zeros_args)

    # A 2D array defining the original shape of the weights
    # before packing
    weight_shape = BasevLLMParameter(data=torch.empty(2, dtype=torch.int64), weight_loader=weight_loader)

    layer.register_parameter("weight_packed", weight)
    layer.register_parameter("weight_scale", weight_scale)
    layer.register_parameter("weight_shape", weight_shape)

    if not self.symmetric:
        layer.register_parameter("weight_zero_point", qzeros)

    # group index (for activation reordering)
    if self.has_g_idx:
        weight_g_idx = RowvLLMParameter(data=torch.empty(
            input_size_per_partition,
            dtype=torch.int32,
        ),
                                        input_dim=0,
                                        weight_loader=weight_loader)
        layer.register_parameter("weight_g_idx", weight_g_idx)

    self.kernel = kernel_type(mp_linear_kernel_config,
                              w_q_param_name="weight_packed",
                              w_s_param_name="weight_scale",
                              w_zp_param_name="weight_zero_point",
                              w_gidx_param_name="weight_g_idx")

get_min_capability 类方法 

get_min_capability() -> int
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
261
262
263
@classmethod
def get_min_capability(cls) -> int:
    return -1

HPUCompressedTensorsWNA16MoEMethod

Bases: CompressedTensorsWNA16MarlinMoEMethod

Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
@CustomOp.register_oot(name='CompressedTensorsWNA16MarlinMoEMethod')
class HPUCompressedTensorsWNA16MoEMethod(CompressedTensorsWNA16MarlinMoEMethod):

    def __init__(
        self,
        quant_config: "CompressedTensorsConfig",  # type: ignore # noqa E501
        moe: FusedMoEConfig,
    ):
        super().__init__(quant_config, moe)

        HPU_WNA16_SUPPORTED_BITS = [4]
        if not (self.quant_config.quant_format == CompressionFormat.pack_quantized.value
                and self.num_bits in HPU_WNA16_SUPPORTED_BITS):
            raise ValueError("For Fused MoE layers, only ", f"{CompressionFormat.pack_quantized.value} ",
                             "is supported for the following bits: ", f"{HPU_WNA16_SUPPORTED_BITS}")
        self.quant_type = WNA16_SUPPORTED_TYPES_MAP[self.num_bits]

    def create_weights(self, layer: torch.nn.Module, num_experts: int, hidden_size: int,
                       intermediate_size_per_partition: int, params_dtype: torch.dtype, **extra_weight_attrs):
        extra_weight_attrs["intermediate_size_full"] = intermediate_size_per_partition * layer.tp_size

        # Will transpose the loaded weight along the
        # intermediate and hidden dim sizes. Will
        # shard for TP along the transposed dims
        extra_weight_attrs.update({"is_transposed": False, "quant_method": self.strategy})
        w13_weight = torch.nn.Parameter(torch.empty(num_experts,
                                                    2 * intermediate_size_per_partition,
                                                    hidden_size // self.packed_factor,
                                                    dtype=torch.int32),
                                        requires_grad=False)
        layer.register_parameter("w13_weight_packed", w13_weight)
        set_weight_attrs(w13_weight, extra_weight_attrs)

        w2_weight = torch.nn.Parameter(torch.empty(num_experts,
                                                   hidden_size,
                                                   intermediate_size_per_partition // self.packed_factor,
                                                   dtype=torch.int32),
                                       requires_grad=False)
        layer.register_parameter("w2_weight_packed", w2_weight)
        set_weight_attrs(w2_weight, extra_weight_attrs)

        w2_scales_size = intermediate_size_per_partition

        if self.strategy == "channel":
            num_groups_w2 = num_groups_w13 = 1
            self.group_size = -1
        else:
            num_groups_w2 = w2_scales_size // self.group_size
            num_groups_w13 = hidden_size // self.group_size

        w13_scale = torch.nn.Parameter(torch.ones(num_experts,
                                                  2 * intermediate_size_per_partition,
                                                  num_groups_w13,
                                                  dtype=params_dtype),
                                       requires_grad=False)
        layer.register_parameter("w13_weight_scale", w13_scale)
        set_weight_attrs(w13_scale, extra_weight_attrs)

        w2_scale = torch.nn.Parameter(torch.ones(num_experts, hidden_size, num_groups_w2, dtype=params_dtype),
                                      requires_grad=False)
        layer.register_parameter("w2_weight_scale", w2_scale)
        set_weight_attrs(w2_scale, extra_weight_attrs)
        set_weight_attrs(w2_scale, {"load_full_w2": False})

        w2_weight_shape = torch.nn.Parameter(torch.empty(num_experts, 2), requires_grad=False)
        layer.register_parameter("w2_weight_shape", w2_weight_shape)
        set_weight_attrs(w2_weight_shape, extra_weight_attrs)
        w13_weight_shape = torch.nn.Parameter(torch.empty(num_experts, 2), requires_grad=False)

        layer.register_parameter("w13_weight_shape", w13_weight_shape)
        set_weight_attrs(w13_weight_shape, extra_weight_attrs)

        w13_g_idx = torch.nn.Parameter(
            torch.empty(
                num_experts,
                hidden_size,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_weight_g_idx", w13_g_idx)
        set_weight_attrs(w13_g_idx, extra_weight_attrs)

        w2_g_idx = torch.nn.Parameter(
            torch.empty(
                num_experts,
                intermediate_size_per_partition,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_weight_g_idx", w2_g_idx)
        set_weight_attrs(w2_g_idx, extra_weight_attrs)

        w13_g_idx_sort_indices = torch.nn.Parameter(
            torch.empty(
                num_experts,
                hidden_size,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_g_idx_sort_indices", w13_g_idx_sort_indices)
        set_weight_attrs(w13_g_idx_sort_indices, extra_weight_attrs)

        w2_g_idx_sort_indices = torch.nn.Parameter(
            torch.empty(
                num_experts,
                intermediate_size_per_partition,
                dtype=torch.int32,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_g_idx_sort_indices", w2_g_idx_sort_indices)
        set_weight_attrs(w2_g_idx_sort_indices, extra_weight_attrs)

        layer.a13_scale = None
        layer.a2_scale = None

        # Shared zero points for converting symmetric weights on HPU
        if self.strategy == "channel":
            num_groups_w2 = num_groups_w13 = 1
            self.group_size = -1
        else:
            w2_scales_size = intermediate_size_per_partition
            num_groups_w2 = w2_scales_size // self.group_size
            num_groups_w13 = hidden_size // self.group_size

        w13_zeros = torch.full((num_groups_w13, 2 * intermediate_size_per_partition),
                               self.quant_type.bias,
                               dtype=torch.int32)
        w13_zeros = pack_quantized_values_into_int32(w13_zeros, self.quant_type, packed_dim=1)
        layer.register_parameter("w13_zero_point", torch.nn.Parameter(w13_zeros, requires_grad=False))
        w2_zeros = torch.full((num_groups_w2, hidden_size), self.quant_type.bias, dtype=torch.int32)
        w2_zeros = pack_quantized_values_into_int32(w2_zeros, self.quant_type, packed_dim=1)
        layer.register_parameter("w2_zero_point", torch.nn.Parameter(w2_zeros, requires_grad=False))

        layer.a13_scale = None
        layer.a2_scale = None

    def gptq_hpu_moe_repack(self, b_q_weight: torch.Tensor) -> torch.Tensor:
        num_experts = b_q_weight.shape[0]
        outputs = []
        for e in range(num_experts):
            weight = unpack_quantized_values_into_int32(b_q_weight[e].data.contiguous().transpose(0, 1),
                                                        self.quant_type,
                                                        packed_dim=0)
            q_weight = pack_quantized_values_into_int32(weight, self.quant_type, packed_dim=1)
            outputs.append(q_weight)

        return torch.stack(outputs, dim=0)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        # Reconfigure packed weights and scales to match moe_wna16 format
        w13_weight_packed = self.gptq_hpu_moe_repack(layer.w13_weight_packed)
        w2_weight_packed = self.gptq_hpu_moe_repack(layer.w2_weight_packed)

        # for torch.compile
        layer.w13_weight_packed = torch.nn.Parameter(w13_weight_packed, requires_grad=False)
        layer.w2_weight_packed = torch.nn.Parameter(w2_weight_packed, requires_grad=False)
        layer.w13_weight_scale = torch.nn.Parameter(layer.w13_weight_scale.data.transpose(1, 2).contiguous(),
                                                    requires_grad=False)
        layer.w2_weight_scale = torch.nn.Parameter(layer.w2_weight_scale.data.transpose(1, 2).contiguous(),
                                                   requires_grad=False)

        # Initialize HPU MoE op
        num_experts = layer.local_num_experts
        ep_shift = layer.ep_rank * num_experts

        experts_min, experts_max = ep_shift, num_experts + ep_shift - 1
        layer.moe_op = VllmMixtureOfExpertsOpWNA16(
            num_experts,
            experts_min,
            experts_max,
        )
        for expert_id in range(layer.local_num_experts):
            layer.moe_op.w13_list[expert_id].set_weight_packed(layer.w13_weight_packed.data[expert_id])
            layer.moe_op.w2_list[expert_id].set_weight_packed(layer.w2_weight_packed.data[expert_id])
            layer.moe_op.w13_list[expert_id].set_weight_scale(layer.w13_weight_scale.data[expert_id])
            layer.moe_op.w2_list[expert_id].set_weight_scale(layer.w2_weight_scale.data[expert_id])
            layer.moe_op.w13_list[expert_id].set_zero_point(layer.w13_zero_point.data)
            layer.moe_op.w2_list[expert_id].set_zero_point(layer.w2_zero_point.data)

            if self.actorder == "group":
                layer.moe_op.w13_list[expert_id].set_g_idx(layer.w13_weight_g_idx.data[expert_id])
                layer.moe_op.w2_list[expert_id].set_g_idx(layer.w2_weight_g_idx.data[expert_id])

        htorch.core.mark_step()

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        routed_scaling_factor: float = 1.0,
        e_score_correction_bias: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        enable_eplb: bool = False,
        expert_load_view: Optional[torch.Tensor] = None,
        logical_to_physical_map: Optional[torch.Tensor] = None,
        logical_replica_count: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:

        if enable_eplb:
            raise NotImplementedError("EPLB not supported for "
                                      "`CompressedTensorsWNA16MoEMethod` yet.")

        input_shape = x.shape
        x = x.view(-1, x.shape[-1])

        if use_grouped_topk or custom_routing_function is not None:
            topk_weights, topk_ids, zero_expert_result = FusedMoE.select_experts(
                hidden_states=x,
                router_logits=router_logits,
                use_grouped_topk=use_grouped_topk,
                top_k=top_k,
                renormalize=renormalize,
                topk_group=topk_group,
                num_expert_group=num_expert_group,
                custom_routing_function=custom_routing_function,
                scoring_func=scoring_func,
                routed_scaling_factor=routed_scaling_factor,
                e_score_correction_bias=e_score_correction_bias,
                indices_type=self.topk_indices_dtype)
        else:
            import torch.nn.functional as F
            topk_weights = F.softmax(router_logits, dim=1, dtype=torch.float32)
            topk_weights, topk_ids = torch.topk(topk_weights, top_k, dim=-1)
            topk_weights /= topk_weights.sum(dim=-1, keepdim=True)
            topk_weights = topk_weights.to(x.dtype)
        topk_ids = topk_ids.view(*x.shape[:-1], -1)
        topk_weights = topk_weights.view(*x.shape[:-1], -1)
        output = layer.moe_op(
            x,
            topk_ids.to(torch.int64),
            topk_weights.to(x.dtype),
            permuted_weights=False,
            activation=activation,
        )
        return output.view(*input_shape)

quant_type 实例属性 

quant_type = WNA16_SUPPORTED_TYPES_MAP[num_bits]

__init__ 

__init__(
    quant_config: CompressedTensorsConfig,
    moe: FusedMoEConfig,
)
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
448
449
450
451
452
453
454
455
456
457
458
459
460
def __init__(
    self,
    quant_config: "CompressedTensorsConfig",  # type: ignore # noqa E501
    moe: FusedMoEConfig,
):
    super().__init__(quant_config, moe)

    HPU_WNA16_SUPPORTED_BITS = [4]
    if not (self.quant_config.quant_format == CompressionFormat.pack_quantized.value
            and self.num_bits in HPU_WNA16_SUPPORTED_BITS):
        raise ValueError("For Fused MoE layers, only ", f"{CompressionFormat.pack_quantized.value} ",
                         "is supported for the following bits: ", f"{HPU_WNA16_SUPPORTED_BITS}")
    self.quant_type = WNA16_SUPPORTED_TYPES_MAP[self.num_bits]

apply 

apply(
    layer: Module,
    x: Tensor,
    router_logits: Tensor,
    top_k: int,
    renormalize: bool,
    use_grouped_topk: bool = False,
    topk_group: Optional[int] = None,
    num_expert_group: Optional[int] = None,
    global_num_experts: int = -1,
    expert_map: Optional[Tensor] = None,
    custom_routing_function: Optional[Callable] = None,
    scoring_func: str = "softmax",
    routed_scaling_factor: float = 1.0,
    e_score_correction_bias: Optional[Tensor] = None,
    apply_router_weight_on_input: bool = False,
    activation: str = "silu",
    enable_eplb: bool = False,
    expert_load_view: Optional[Tensor] = None,
    logical_to_physical_map: Optional[Tensor] = None,
    logical_replica_count: Optional[Tensor] = None,
) -> Union[Tensor, tuple[Tensor, Tensor]]
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
def apply(
    self,
    layer: torch.nn.Module,
    x: torch.Tensor,
    router_logits: torch.Tensor,
    top_k: int,
    renormalize: bool,
    use_grouped_topk: bool = False,
    topk_group: Optional[int] = None,
    num_expert_group: Optional[int] = None,
    global_num_experts: int = -1,
    expert_map: Optional[torch.Tensor] = None,
    custom_routing_function: Optional[Callable] = None,
    scoring_func: str = "softmax",
    routed_scaling_factor: float = 1.0,
    e_score_correction_bias: Optional[torch.Tensor] = None,
    apply_router_weight_on_input: bool = False,
    activation: str = "silu",
    enable_eplb: bool = False,
    expert_load_view: Optional[torch.Tensor] = None,
    logical_to_physical_map: Optional[torch.Tensor] = None,
    logical_replica_count: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:

    if enable_eplb:
        raise NotImplementedError("EPLB not supported for "
                                  "`CompressedTensorsWNA16MoEMethod` yet.")

    input_shape = x.shape
    x = x.view(-1, x.shape[-1])

    if use_grouped_topk or custom_routing_function is not None:
        topk_weights, topk_ids, zero_expert_result = FusedMoE.select_experts(
            hidden_states=x,
            router_logits=router_logits,
            use_grouped_topk=use_grouped_topk,
            top_k=top_k,
            renormalize=renormalize,
            topk_group=topk_group,
            num_expert_group=num_expert_group,
            custom_routing_function=custom_routing_function,
            scoring_func=scoring_func,
            routed_scaling_factor=routed_scaling_factor,
            e_score_correction_bias=e_score_correction_bias,
            indices_type=self.topk_indices_dtype)
    else:
        import torch.nn.functional as F
        topk_weights = F.softmax(router_logits, dim=1, dtype=torch.float32)
        topk_weights, topk_ids = torch.topk(topk_weights, top_k, dim=-1)
        topk_weights /= topk_weights.sum(dim=-1, keepdim=True)
        topk_weights = topk_weights.to(x.dtype)
    topk_ids = topk_ids.view(*x.shape[:-1], -1)
    topk_weights = topk_weights.view(*x.shape[:-1], -1)
    output = layer.moe_op(
        x,
        topk_ids.to(torch.int64),
        topk_weights.to(x.dtype),
        permuted_weights=False,
        activation=activation,
    )
    return output.view(*input_shape)

create_weights 

create_weights(
    layer: Module,
    num_experts: int,
    hidden_size: int,
    intermediate_size_per_partition: int,
    params_dtype: dtype,
    **extra_weight_attrs,
)
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
def create_weights(self, layer: torch.nn.Module, num_experts: int, hidden_size: int,
                   intermediate_size_per_partition: int, params_dtype: torch.dtype, **extra_weight_attrs):
    extra_weight_attrs["intermediate_size_full"] = intermediate_size_per_partition * layer.tp_size

    # Will transpose the loaded weight along the
    # intermediate and hidden dim sizes. Will
    # shard for TP along the transposed dims
    extra_weight_attrs.update({"is_transposed": False, "quant_method": self.strategy})
    w13_weight = torch.nn.Parameter(torch.empty(num_experts,
                                                2 * intermediate_size_per_partition,
                                                hidden_size // self.packed_factor,
                                                dtype=torch.int32),
                                    requires_grad=False)
    layer.register_parameter("w13_weight_packed", w13_weight)
    set_weight_attrs(w13_weight, extra_weight_attrs)

    w2_weight = torch.nn.Parameter(torch.empty(num_experts,
                                               hidden_size,
                                               intermediate_size_per_partition // self.packed_factor,
                                               dtype=torch.int32),
                                   requires_grad=False)
    layer.register_parameter("w2_weight_packed", w2_weight)
    set_weight_attrs(w2_weight, extra_weight_attrs)

    w2_scales_size = intermediate_size_per_partition

    if self.strategy == "channel":
        num_groups_w2 = num_groups_w13 = 1
        self.group_size = -1
    else:
        num_groups_w2 = w2_scales_size // self.group_size
        num_groups_w13 = hidden_size // self.group_size

    w13_scale = torch.nn.Parameter(torch.ones(num_experts,
                                              2 * intermediate_size_per_partition,
                                              num_groups_w13,
                                              dtype=params_dtype),
                                   requires_grad=False)
    layer.register_parameter("w13_weight_scale", w13_scale)
    set_weight_attrs(w13_scale, extra_weight_attrs)

    w2_scale = torch.nn.Parameter(torch.ones(num_experts, hidden_size, num_groups_w2, dtype=params_dtype),
                                  requires_grad=False)
    layer.register_parameter("w2_weight_scale", w2_scale)
    set_weight_attrs(w2_scale, extra_weight_attrs)
    set_weight_attrs(w2_scale, {"load_full_w2": False})

    w2_weight_shape = torch.nn.Parameter(torch.empty(num_experts, 2), requires_grad=False)
    layer.register_parameter("w2_weight_shape", w2_weight_shape)
    set_weight_attrs(w2_weight_shape, extra_weight_attrs)
    w13_weight_shape = torch.nn.Parameter(torch.empty(num_experts, 2), requires_grad=False)

    layer.register_parameter("w13_weight_shape", w13_weight_shape)
    set_weight_attrs(w13_weight_shape, extra_weight_attrs)

    w13_g_idx = torch.nn.Parameter(
        torch.empty(
            num_experts,
            hidden_size,
            dtype=torch.int32,
        ),
        requires_grad=False,
    )
    layer.register_parameter("w13_weight_g_idx", w13_g_idx)
    set_weight_attrs(w13_g_idx, extra_weight_attrs)

    w2_g_idx = torch.nn.Parameter(
        torch.empty(
            num_experts,
            intermediate_size_per_partition,
            dtype=torch.int32,
        ),
        requires_grad=False,
    )
    layer.register_parameter("w2_weight_g_idx", w2_g_idx)
    set_weight_attrs(w2_g_idx, extra_weight_attrs)

    w13_g_idx_sort_indices = torch.nn.Parameter(
        torch.empty(
            num_experts,
            hidden_size,
            dtype=torch.int32,
        ),
        requires_grad=False,
    )
    layer.register_parameter("w13_g_idx_sort_indices", w13_g_idx_sort_indices)
    set_weight_attrs(w13_g_idx_sort_indices, extra_weight_attrs)

    w2_g_idx_sort_indices = torch.nn.Parameter(
        torch.empty(
            num_experts,
            intermediate_size_per_partition,
            dtype=torch.int32,
        ),
        requires_grad=False,
    )
    layer.register_parameter("w2_g_idx_sort_indices", w2_g_idx_sort_indices)
    set_weight_attrs(w2_g_idx_sort_indices, extra_weight_attrs)

    layer.a13_scale = None
    layer.a2_scale = None

    # Shared zero points for converting symmetric weights on HPU
    if self.strategy == "channel":
        num_groups_w2 = num_groups_w13 = 1
        self.group_size = -1
    else:
        w2_scales_size = intermediate_size_per_partition
        num_groups_w2 = w2_scales_size // self.group_size
        num_groups_w13 = hidden_size // self.group_size

    w13_zeros = torch.full((num_groups_w13, 2 * intermediate_size_per_partition),
                           self.quant_type.bias,
                           dtype=torch.int32)
    w13_zeros = pack_quantized_values_into_int32(w13_zeros, self.quant_type, packed_dim=1)
    layer.register_parameter("w13_zero_point", torch.nn.Parameter(w13_zeros, requires_grad=False))
    w2_zeros = torch.full((num_groups_w2, hidden_size), self.quant_type.bias, dtype=torch.int32)
    w2_zeros = pack_quantized_values_into_int32(w2_zeros, self.quant_type, packed_dim=1)
    layer.register_parameter("w2_zero_point", torch.nn.Parameter(w2_zeros, requires_grad=False))

    layer.a13_scale = None
    layer.a2_scale = None

gptq_hpu_moe_repack 

gptq_hpu_moe_repack(b_q_weight: Tensor) -> Tensor
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
585
586
587
588
589
590
591
592
593
594
595
def gptq_hpu_moe_repack(self, b_q_weight: torch.Tensor) -> torch.Tensor:
    num_experts = b_q_weight.shape[0]
    outputs = []
    for e in range(num_experts):
        weight = unpack_quantized_values_into_int32(b_q_weight[e].data.contiguous().transpose(0, 1),
                                                    self.quant_type,
                                                    packed_dim=0)
        q_weight = pack_quantized_values_into_int32(weight, self.quant_type, packed_dim=1)
        outputs.append(q_weight)

    return torch.stack(outputs, dim=0)

process_weights_after_loading 

process_weights_after_loading(layer: Module) -> None
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
    # Reconfigure packed weights and scales to match moe_wna16 format
    w13_weight_packed = self.gptq_hpu_moe_repack(layer.w13_weight_packed)
    w2_weight_packed = self.gptq_hpu_moe_repack(layer.w2_weight_packed)

    # for torch.compile
    layer.w13_weight_packed = torch.nn.Parameter(w13_weight_packed, requires_grad=False)
    layer.w2_weight_packed = torch.nn.Parameter(w2_weight_packed, requires_grad=False)
    layer.w13_weight_scale = torch.nn.Parameter(layer.w13_weight_scale.data.transpose(1, 2).contiguous(),
                                                requires_grad=False)
    layer.w2_weight_scale = torch.nn.Parameter(layer.w2_weight_scale.data.transpose(1, 2).contiguous(),
                                               requires_grad=False)

    # Initialize HPU MoE op
    num_experts = layer.local_num_experts
    ep_shift = layer.ep_rank * num_experts

    experts_min, experts_max = ep_shift, num_experts + ep_shift - 1
    layer.moe_op = VllmMixtureOfExpertsOpWNA16(
        num_experts,
        experts_min,
        experts_max,
    )
    for expert_id in range(layer.local_num_experts):
        layer.moe_op.w13_list[expert_id].set_weight_packed(layer.w13_weight_packed.data[expert_id])
        layer.moe_op.w2_list[expert_id].set_weight_packed(layer.w2_weight_packed.data[expert_id])
        layer.moe_op.w13_list[expert_id].set_weight_scale(layer.w13_weight_scale.data[expert_id])
        layer.moe_op.w2_list[expert_id].set_weight_scale(layer.w2_weight_scale.data[expert_id])
        layer.moe_op.w13_list[expert_id].set_zero_point(layer.w13_zero_point.data)
        layer.moe_op.w2_list[expert_id].set_zero_point(layer.w2_zero_point.data)

        if self.actorder == "group":
            layer.moe_op.w13_list[expert_id].set_g_idx(layer.w13_weight_g_idx.data[expert_id])
            layer.moe_op.w2_list[expert_id].set_g_idx(layer.w2_weight_g_idx.data[expert_id])

    htorch.core.mark_step()

HPUMPLinearKernel

Bases: MPLinearKernel

Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
class HPUMPLinearKernel(MPLinearKernel):

    @classmethod
    def get_min_capability(cls) -> int:
        return -1

    @classmethod
    def can_implement(cls, c: MPLinearLayerConfig) -> tuple[bool, Optional[str]]:
        bits = c.weight_type.size_bits
        assert bits == 4, f"w{bits}a16 not yet supported on HPU"
        return True, None

    # note assumes that
    #  `weight_packed` is: {input_dim = 0, output_dim = 1, packed_dim = 0}
    #  `weight_scale` is: {input_dim = 0, output_dim = 1}
    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        c = self.config

        def transform_w_q(x):
            assert isinstance(x, BasevLLMParameter)
            qweight = unpack_quantized_values_into_int32(x.data.transpose(0, 1), c.weight_type, packed_dim=0)
            x.data = pack_quantized_values_into_int32(qweight, c.weight_type, packed_dim=1)

            return x

        def transform_w_s(x):
            assert isinstance(x, BasevLLMParameter)
            x.data = x.data.transpose(0, 1).contiguous()
            return x

        def transform_w_zp(x):
            x.data = x.data.transpose(0, 1).contiguous()
            return x

        if c.zero_points:
            self._transform_param(layer, self.w_zp_name, transform_w_zp)
        else:
            self.w_zp_name: str = "qzeros"
            device = getattr(layer, self.w_q_name).device
            # use groups=1 for channelwise quantization
            groups = (c.partition_weight_shape[0] // c.group_size) if c.group_size > 0 else 1
            out_features = c.partition_weight_shape[1]

            if c.weight_type.has_bias():
                # if the type has a bias we have to create a zeros tensor that
                # contains the bias values repeated for each group
                # Documentation of the bug can be found here:
                #  https://garden.danieldk.eu/GPTQ-Checkpoint-Format
                zeros = torch.full((groups, out_features), c.weight_type.bias, dtype=torch.int32, device=device)
            else:
                raise NotImplementedError("A 0 zero-point is not supported on HPU compressed wNa16 format")
            zeros = pack_quantized_values_into_int32(zeros, c.weight_type, packed_dim=1)
            setattr(layer, self.w_zp_name, torch.nn.Parameter(zeros, requires_grad=False))

        self._transform_param(layer, self.w_q_name, transform_w_q)
        self._transform_param(layer, self.w_s_name, transform_w_s)

    def apply_weights(self,
                      layer: torch.nn.Module,
                      x: torch.Tensor,
                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
        c = self.config
        w_q, w_s, w_zp, w_gidx = self._get_weight_params(layer)

        reshaped_x = x.reshape(-1, x.shape[-1])
        out_shape = x.shape[:-1] + (c.partition_weight_shape[1], )

        weight = torch.ops.hpu.convert_from_uint4(w_q, w_s, w_zp, x.dtype, w_gidx)
        output = torch.matmul(reshaped_x, weight)

        if bias is not None:
            output.add_(bias)  # In-place add

        return output.reshape(out_shape)

apply_weights 

apply_weights(
    layer: Module, x: Tensor, bias: Optional[Tensor] = None
) -> Tensor
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
def apply_weights(self,
                  layer: torch.nn.Module,
                  x: torch.Tensor,
                  bias: Optional[torch.Tensor] = None) -> torch.Tensor:
    c = self.config
    w_q, w_s, w_zp, w_gidx = self._get_weight_params(layer)

    reshaped_x = x.reshape(-1, x.shape[-1])
    out_shape = x.shape[:-1] + (c.partition_weight_shape[1], )

    weight = torch.ops.hpu.convert_from_uint4(w_q, w_s, w_zp, x.dtype, w_gidx)
    output = torch.matmul(reshaped_x, weight)

    if bias is not None:
        output.add_(bias)  # In-place add

    return output.reshape(out_shape)

can_implement 类方法 

can_implement(
    c: MPLinearLayerConfig,
) -> tuple[bool, Optional[str]]
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
375
376
377
378
379
@classmethod
def can_implement(cls, c: MPLinearLayerConfig) -> tuple[bool, Optional[str]]:
    bits = c.weight_type.size_bits
    assert bits == 4, f"w{bits}a16 not yet supported on HPU"
    return True, None

get_min_capability 类方法 

get_min_capability() -> int
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
371
372
373
@classmethod
def get_min_capability(cls) -> int:
    return -1

process_weights_after_loading 

process_weights_after_loading(layer: Module) -> None
Source code in vllm_gaudi/ops/hpu_compressed_tensors.py 
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
    c = self.config

    def transform_w_q(x):
        assert isinstance(x, BasevLLMParameter)
        qweight = unpack_quantized_values_into_int32(x.data.transpose(0, 1), c.weight_type, packed_dim=0)
        x.data = pack_quantized_values_into_int32(qweight, c.weight_type, packed_dim=1)

        return x

    def transform_w_s(x):
        assert isinstance(x, BasevLLMParameter)
        x.data = x.data.transpose(0, 1).contiguous()
        return x

    def transform_w_zp(x):
        x.data = x.data.transpose(0, 1).contiguous()
        return x

    if c.zero_points:
        self._transform_param(layer, self.w_zp_name, transform_w_zp)
    else:
        self.w_zp_name: str = "qzeros"
        device = getattr(layer, self.w_q_name).device
        # use groups=1 for channelwise quantization
        groups = (c.partition_weight_shape[0] // c.group_size) if c.group_size > 0 else 1
        out_features = c.partition_weight_shape[1]

        if c.weight_type.has_bias():
            # if the type has a bias we have to create a zeros tensor that
            # contains the bias values repeated for each group
            # Documentation of the bug can be found here:
            #  https://garden.danieldk.eu/GPTQ-Checkpoint-Format
            zeros = torch.full((groups, out_features), c.weight_type.bias, dtype=torch.int32, device=device)
        else:
            raise NotImplementedError("A 0 zero-point is not supported on HPU compressed wNa16 format")
        zeros = pack_quantized_values_into_int32(zeros, c.weight_type, packed_dim=1)
        setattr(layer, self.w_zp_name, torch.nn.Parameter(zeros, requires_grad=False))

    self._transform_param(layer, self.w_q_name, transform_w_q)
    self._transform_param(layer, self.w_s_name, transform_w_s)